#Deep Sehgal

[ilyricshub]

Vailpuna Lyrics - Jordan Sandhu | From Je Jatt Vigarh Gaya

Vailpuna Lyrics - Jordan Sandhu | From Je Jatt Vigarh Gaya #Vailpuna #JordanSandhu #PunjabiMusic #JayyRandhawa #DeepSehgal #Preeta #DesiCrew #JeJattVigarhGaya

[mahisblog]

•BANJARE KO GHAR•

A couple was shown arguing with eachother and we could hear yelling and it was increasing with each second

"I wish I never met you" The boy angrily said and leaves the house smacking the door hard

"I wish I never met you" This words roamed in the girl's head and a tear escaped from her eyes

"Na jaane humari bhul kya thi, unse pyaar karna ya unki parwah karna"

She tried to calm herself but that was not enough, tears were falling down her cheeks without any hurdles

She remembered the time she met him for the first time and found her self falling for him

“Tumne jo dard diye the usske sahare jeete hai

Ye dard ko hum yun Haas ke sehte hai

Na jaane tumse ishq karna sahi tha ya galat

Tumse pyaar karke bhi tumse sab chipate hai”

It was first day of college and ofcourse she was worried about it, soon she reached the college gate and took a deep breath and walked in with a smile

"Bela, you here in Mumbai?" a shocked voice called her

"Hii Vish, yeah I got admission here" she replied with a small smile

"Wow!! I'm so excited, you know I missed you so muchh" as soon as the words were left, Bela was caged in a tight, breath taking, hug from her childhood bestie

"Vish, I- I can't breath and I missed you equally"

"Hehe, sorry"

"Btw how is uncle and aunty?"

"They are great and where are you living?"

"At my place, dad bought it some year ago and after his dead this place was transferred to my name"

There was a long but comfortable silence between them, Vish side-hugged her and squeezed her shoulder lightly

They both got to know that they have only 2 lectures with each other, first and second and after that they have all different lectures

Bela was in the classroom, sitting in the corner just when the lecture was about to start, a boy came and sat beside her

"Hi, I'm Mahir, Mahir Sehgal" and forwarded his hand

Bela shaked hand with him and replied, "I'm Bela Sharma"

Both nodded at each other and focused on the lecture

Few months passed like this and now Bela and Mahir was close friends, in the meantime they got to know that Vish is in relationship with Mahir's younger brother, Yuvraj Sehgal

She had 2 more friends, Darshan Raval and Anu Mittal they both lives with each other as they are childhood sweethearts

They were happy for YuVish, they were a perfect couple as Yuvi can be reckless sometimes but only Vish can make him responsible man, meanwhile Bela was also feeling something for Mahir but choosed to ignore the feeling coz it can affect her friendship with him

"Darte reh gaye hum kuch

keh na paaye,

Unke aache dost banke reh gaye, Ishq na ban paaye,

Mohobbat ki khata bhi aasa na thi, naa to hum unke the, nahi unko hum apna bana paaye,

Unko na paane ke aas mein aur khone ke daar mein hi hum ek tarfa ashiqui kar paaye"

Few weeks later they got to know that there is singing competition between college and nobody knows that Bela loves to sing expect Mahir and right now he is requesting her to take part in it

"Pls Albela take part na, you loves to sing then why you aren't participating?"

"Mahi I'm not good enough"

"Nonsense, you are talented enough to be a professional singer"

And they continued to argue about this for half an hour and finally Bela said, "Okay baba I'll take part"

Mahir hugged her tightly and smiled

Soon the day came when the competition was held on, Bela practiced song written by herself

Not even Mahir, YuVish or DarshAnu knew the name or the lyrics because Bela said that will be surprise for them

It was Bela's turn to sing,

"Umm... Hey! I'm Bela Sharma from XYZ college and I'm going to sing a song written and composed by me only, hope you'll like it"

"What is the title?" one of the judge asked

"Ohh it's called Banjare Ko Ghar"

"Okay you may start now"

Bela nodded at them and closed her eyes and opened them, looking at Mahir and the gang and smiled at them

"Jise zindagi dhoondh rahi hai

Kya ye woh makaam mera hai

Yahaan chain se bas ruk jaaun

Kyun dil ye mujhe kehta hai

Jazbaat naye se mile hain

Jaane kya asar ye huaa hai

Ik aas mili phir mujhko

Jo qubool kisi ne kiya hai"

She remembers the day she meet him and found unknown peace in his presence

The times when she was close to breaking down and Mahir was the one to console her

"Haan...

Kisi shaayar ki ghazal

Jo de rooh ko sukoon ke pal

Koi mujhko yun mila hai

Jaise banjaare ko ghar

Naye mausam ki sehar

Yaa sard mein dopahar

Koi mujhko yun mila hai

Jaise banjare ko ghar

Hmm..."

He was a beautiful sin for her, she knew he wasn't going to be hers yet she didn't let her hopes down and wished for him to be hers

"Jaise koi kinaara

Deta ho sahaara

Mujhe wo mila kisi mod par

Koi raat ka taara

Karta ho ujaala

Waise hi roshan

kare woh shehar"

He was the Sunshine she wants to protect, she remembered the time when an party's decoration was going on and Mahir was helping them without taking rest and doing almost work alone

"Dard mere woh

bhula hi gayaa

Kuch aisa asar huaa

Jeena mujhe phir se

woh sikha raha"

She forgot her pain when she was with him

There was a time when she decided to commit suicide but now she feels like there is a reason to live and that is Mahir

"Hmm.. Jaise

baarish kar de tar

Yaa marham dard par

Koi mujhko yun mila hai

Jaise banjare ko ghar

Naye mausam ki sehar

Yaa sard mein dopahar

Koi mujhko yun mila hai

Jaise banjaare ko ghar"

She remembered his pranks on her and him pacifying her

"Muskaata yeh chehra

Deta hai jo pehraa

Jaane chhupata kya

dil ka samandar

Auron ko toh har

dum saaya deta hai

Woh dhoop mein

hai khada khud magar"

Now Mahir remembers her selfless works, even if she was in hurry she never let anyone down who asked her for any

type of help

She is the mystery he wants to solve

"Chot lagi hai usey,

Phir kyun mehsoos

mujhe ho raha hai

Dil tu bata de kya

hai iraada tera"

Even if Mahir was the one who get hurts but Bela was the only who feel pains

"Main parinda besabar

Tha uda jo darbadar

Koi mujhko yun mila hai

Jaise banjarey ko ghar

Naye mausam ki sehar

Yaa sard mein dopahar

Koi mujhko yun mila hai

Jaise banjarey ko ghar"

She longed for love, not friends' love but a lover's

She wanted someone to rely on, someone on whom she can trust with her life

"Koi mujhko yun mila hai

Jaise banjare ko ghar

Jaise banjare ko ghar..

Jaise banjare ko ghar...

Jaise banjare ko ghar...."

Bela opened her eyes and looked at the audiance, her group were the one cheering most, she made an eye contact with Mahir and smiled, she had decided to confess to Mahir through this Song as after meeting Mahir, Bela really thought she found home

But guess what? Mahir had a surprise for her, introducing her to his girlfriend, Bani Singhania

Bela compared herself to her and found that Bani is above her in each category and she will be best for Mahri though she didn't let this affect her friendship with Mahir and was as close as before

But someone was not happy with their bonding, She wanted them to be separate, she tried to create misunderstanding between them but nothing worked

Few days later, Bela found Mahir drunk at her doorstep

She made him sit inside and was about to go when Mahir held her hand and made her turn towards him and hugged her, resting his head on her chest

He was crying, and murmuring things like, “Why she did that?” “Wasn't I enough for her?” “She wanted to separate you from me, separate me from my albela”

Bela was shocked but got to know that Bani cheated on him and she was the one who tried to create misunderstanding between them

Bela called Darsh and Yuvi and asked them to take him home carefully

Next day when she goes to him home, she found YuVish and DarshAnu there sitting with sad face, she asked them what happened and they told her that Mahir went to London for forever without informing anyone just leaving a note

She was so sad that he went without saying anything, she was again turned into the quiet girl

She remembered why they were arguing

"I love Ruhi" Mahir said those words looking directly into her eyes

"Why?" that was only question that was able to come out of her

"She treated me like I was somebody" he whispered

"But will she love you when you will be nobody?" she silently asked him

"No one loved me when I was nobody" he yelled

"I did" she almost screamed

Mahir was taken back by it and stood stunned

"Before the money and before the fame, before the success and before everything" Bela couldn't hide it anymore, he needs to know

"I loved you Mahir, when you were just yourself not any arrogant CEO, when you used to be careful and you used to come to me when there was any problem not drinking so much that you don't know what happened last night, when you used to be a gentleman who respect women, not a Playboy who use woman as a tissue!

You don't know how much it hurts to see you with someone else, how difficult it is to behave that we are just friends when my feelings for you are more than just friends"

Mahir didn't said anything for good 5 mins because he was so shocked to say something

"You.... You are lying, right?" He stammers

"Why would I Mahir? you became my world, you were the one I thought I would rely on, the one who would never judge me, and then you also disappeared!"

Bela was not stopping she was saying all the things she wished to say him earlier

"What was my fault Mahir? Where was I wrong? I did everything to make you happy from saving you from professors to being there whenever you were down! But you didn't hesitate to leave me, you never care Mahir, never!"

Now she was a crying mess, she was letting her feelings flow which she hided for years but not now

He needs to know what she suffered when he left her all alone by herself

Here Mahir was getting irritated because of her crying because it somehows still effects him, so to make her stop he said something which he knew will broke her completely

"I wish I never meet you"

Back to Present

"Tum bhi ye galti na kar na jis galti ke liye humein nazar

andaaz karte ho,

Kisi din aake ye na keh dena ki ab tum bhi humse pyaar karte ho"

Bela stopped crying and goes to washroom to wash her face, her eyes fells on something and she smiled sarcastically, she picked it up and it was razor blades

She puts her selves up and turn on that, she again looked at it and cuts her arm, she felt satisfied after seeing the blood oozing from the their

There were faded scars on their arms, she made 4-5 more cuts and puts the razor blades down, there were no sign of hurt or anything like that on her face, it was just pure satisfied smile

She has changed very much, it was the first thought of Mahir who came back to Bela's apartment after knowing what he meant for her and what he had done, it brokes his heart to see her in this condition, he can't see his love hurting herself

Yeah his love, she was her first love which he didn't realised earlier, he realised his love and missed her when he was in London, yes earlier whatever he said in the argument he was forced to do that

He is now a businessman and he have many enemies behind him who can hurt her, he didn't want her to get hurt that's why he wanted her to hate him

And it wasn't like his wanted to leave her at first place, he was forced by Bani to leave her or else she will leak some pictures of Bela taken from the wrong angles and some photoshopped pics of her kissing different different boys and then Bela's reputation will be gone

Earlier when Mahir stromed out of the apartment, he got call from Darshan

“Hello Mahir did you met Bela?”

“Yes, I met her”

“Don't tell me you hurted her again?”

“.....”

“Means you hurted her, didn't you? Are you fool Mahir??? Can't you see that stupid loves you more than herself, you don't know what she went through without you”

“Then tell me what did I did???”

“You left her when she needed you most! She was suffering, she lost her last living family after 2 months you left her, left us. She was already in the state is going to depression because of you and when she learnt that her grandfather left her for forever she was broken beyond repair”

Mahir was speechless, he can't imagine what pain she went through

“After 3 months we found out that she is in depression and started to hurt herself and had attempted suicide 2 times, we somehow managed to made her love herself again”

Now Mahir had no words left, he is ready to fight any enemy and anyone for her. Non can hurt what is his and Bela was born to be His

She is his angel and now he have to save her and win her trust again and prove himself worthy of her

He goes inside with the first aid and holds her hand and make her sit on the table

She looked at him as if he is just her imagination while he was doing her bandage, a tear escape from his eyes and he looked at him

“Khayal nahi rakh sakti apna? Ye kya soch ke Kiya tumne haan?!”

(“Can't you take care of yourself? What did you thought before doing this!?”)

“Why you cares? You left me, go I won't mind you going away again”

And she pushed him and gets up to go but Mahir holds her wrists

“Leave me”

“.…......”

“I SAID LEAVE ME! GO AWAY, GO TO YOUR RUHI WHOM YOU LOVE AND YOU HATE ME NA, THEN WHY ARE YOU HERE??”

“SHUT UP!”

Bela stood stunned and immediately shuts up, then Mahir calms himself down and said

“I never said that I hates you, I don't love Ruhi, Bela…..

I love you, only you, this sher loves his albela only”

And he continues to say why he left without any msg and all

Bela just hugged him tightly and let herself lost in his arms, Mahir holds her tight, close to himself

1 years later

“Bela fast come na”

Mahir was ready in a formal suit and was waiting for Bela to come with him somewhere, throughout the year Mahir worked hard to gain Bela's trust back and with the help of his friends he managed to get Bani arrested for blackmailing him

Bela who forgived him long ago, but she was still hurt by him so she wanted to make him pay for that

“Coming”

And when she came, she was the most beautiful women he had ever seen

He smiled at her hold out his hands for her, Bela smiled back and took his hand in hers and walks wherever he was leading her to

Few moments later,

Bela had blindfold on her eyes and Mahir was taking her to a beautifully decorated place, it was simple yet elegant

“You can remove that blindfold now”

Bela romoves her blindfold and got shocked, she covers her mouth with her hands and her eyes were widen, she can't believe this was happening

It was like her dreams coming true

Mahir was on his knees, with a open ring box with a beautiful ring inside it

"Bela I know I'm not the perfect one and the one who deserves you but trust me in all this year there was not a single moment I didn't thought about you

Your pain became mine, I started to feel your pain and you became my home

I swear I'll never let you go away from me, I'll protect you with my life

I Love You So Much Bela Sharma, more than anything else”

Bela was a crying mess now but with happy tears, he continued

“So miss Sharma will you marry me and make me the happiest man in the world, I'll never control you and force you for anything”

Bela was in so much happiness that she was unable to answer and Mahir started to think that he did wrong step by proposing her and should have waited for few more months

“Okay, I understood your answer, it was my mistake but as I said I'll never force you for anything, take you time”

He said in disappointed tone and stood up and turned around to leave, when he found Bela holding him from behind, back hugging him,

“Are you going to leave me again?”

Mahir turned and hugged her tightly

“Never again”

“So why were you leaving before listeningy answer, so Mr. Mahir Sehgal I'll marry you”

Mahir looked at her with smile and teary eyes

“I'll never allow you to leave me, you are mine & only mine and in this lifetime you are stuck with me”

Mahir looked into her eyes and slowly leaned towards her, asking her permission he kissed her passionately, pouring all love he had for her

[my-music-1460]

Rohanpreet Singh: The Journey of a Musical Prodigy

Rohanpreet Singh, the celebrated singer and rising star of the Indian music industry, has captivated millions with his melodious voice and soulful renditions. Known for his ability to blend traditional Punjabi rhythms with contemporary sounds, Rohanpreet Singh has created a niche for himself. His journey from a small-town boy with big dreams to becoming a household name is a testament to his talent, hard work, and unwavering passion for music. As we delve deeper into his life, let’s explore how this gifted artist carved his path to success and became an icon in Punjabi and Indian music.

Early Life and Inspiration

Born on December 1, 1994, in Patiala, Punjab, Rohanpreet Singh grew up in a family where music was more than just a passion — it was a way of life. His father, Gurinder Pal Singh, who is also a skilled musician, played a pivotal role in shaping Rohanpreet’s early interest in music. From a young age, Rohanpreet was encouraged to sing and was enrolled in classical music training under the guidance of Professor Gurmukh Singh Sehgal.

Even as a child, Rohanpreet Singh demonstrated exceptional talent, participating in school competitions and local events. His mesmerizing voice and ability to convey deep emotions through song made him a standout performer among his peers.

Reality Shows: A Launchpad for Stardom

Rohanpreet’s big break came when he participated in the popular singing competition Sa Re Ga Ma Pa L’il Champs in 2007. As one of the finalists, his performances were lauded by judges and viewers alike. The show not only boosted his confidence but also introduced him to a wider audience.

Years later, his participation in Rising Star India Season 2 further cemented his place in the music industry. He finished as the first runner-up, gaining recognition for his versatility and stage presence. These reality shows served as the perfect platforms for him to showcase his skills and build a loyal fanbase.

Musical Style and Popular Hits

Rohanpreet Singh’s music is a seamless blend of modern beats and Punjabi folk influences. His unique ability to experiment with different styles has led to a discography filled with memorable tracks.

Breakthrough Singles: Songs like “Taqleef” and “Pehli Mulakat” showcased his ability to deliver heartfelt lyrics with emotional depth.

Chartbusters: Tracks such as “Ex Calling,” “Baarish Mein Tum,” and “Nehu Da Vyah” became instant hits, amassing millions of views on YouTube and streaming platforms.

Romantic Ballads: Rohanpreet’s romantic songs have become fan favorites, resonating with listeners of all ages.

Collaboration with Neha Kakkar

One of the turning points in Rohanpreet Singh’s career was his collaboration with Neha Kakkar, a celebrated singer in the Indian music industry. Their first project together, “Nehu Da Vyah,” quickly went viral. While working on this song, the duo fell in love and eventually tied the knot in October 2020.

Their union has been celebrated by fans, who regard them as a power couple. Together, they have released several songs, blending their unique styles and creating musical magic.

Staying True to His Roots

Despite his fame, Rohanpreet Singh remains deeply connected to his Punjabi heritage. He often incorporates traditional instruments and themes into his music, ensuring that his cultural roots are celebrated in a modern context. This dedication to authenticity has earned him respect within the music community and among his fans.

Global Appeal

Rohanpreet Singh’s music has transcended borders, appealing to Punjabi music enthusiasts worldwide. His ability to combine relatable lyrics with infectious beats has made him a favorite among the diaspora.

Conclusion

Rohanpreet Singh’s rise to stardom is a story of determination, talent, and staying true to one’s passion. From his early days in Patiala to becoming one of the most beloved singers in India, Rohanpreet Singh has continually pushed boundaries and inspired millions. His journey is a reminder that with perseverance and hard work, dreams can become reality. As Rohanpreet Singh continues to enchant listeners with his soulful voice and compelling music, there is no doubt that he is destined for even greater heights.

[fastestrank]

OYO alumni have gone on to launch over 50 startups!!

here is a list of some notable startups founded by OYO alumni: - Zolostays: Founded by Abhishek Kumar and Sandeep Saxena, Zolostays is a hospitality platform that provides managed housing solutions for students and young professionals. - Nestaway: Founded by Amitava Saha and Smruti Juneja, Nestaway is a furniture rental startup offering managed housing solutions. - Stanza Living: Founded by Anindya Dutta and Vineet Kumar, Stanza Living is a student housing startup that provides fully furnished apartments with amenities like housekeeping and laundry services. - OYO Life: Founded by Ritesh Agarwal, OYO Life is a co-living startup that provides shared living spaces for young professionals. - Revv: Founded by Kunal Kumar and Karan Jain, Revv is a car rental startup that offers self-drive car rentals in multiple cities in India. - Ola Electric: Founded by Bhavish Aggarwal, Ola Electric is an electric vehicle company that manufactures and sells electric scooters and is working on developing electric cars. - Urban Company: Founded by Abhiraj Bhal, Raghav Chandra, and Varun Khaitan, Urban Company is a home services platform that provides services like cleaning, plumbing, and pest control. - MyGlamm: Founded by Darpan Sanghvi, Priyanka Gill, and Sanjaya Sehgal, MyGlamm is a beauty brand that sells cosmetics and personal care products online and through its stores. - Pepperfry: Founded by Ambareesh Murthy and Ashish Shah, Pepperfry is an online furniture and home decor marketplace. - Policybazaar: Founded by Yashish Dahiya and Alok Bansal, Policybazaar is an online insurance marketplace that allows users to compare and buy insurance policies.

launch over 50 startups!! This is just a small sample of the many startups that OYO alumni have founded. Many other successful ventures are out there, and the list continues to grow.

What could be the reason that OYO alumni have gone on to launch over 50 startups!!

There are several possible reasons why OYO alumni have launched over 50 startups: Exposure and experience: Working at OYO, a high-growth startup, would have exposed its employees to various aspects of running a business, from product development and marketing to fundraising and operations. This firsthand experience could equip them with the skills and knowledge needed to launch their ventures. Network and support: OYO has built a strong network of investors, mentors, and partners. Alumni might leverage these connections to secure funding, advice, and support for their startups. Additionally, the alumni network itself could serve as a valuable source of collaboration and support. Entrepreneurial culture: OYO's fast-paced, dynamic environment might have fostered an entrepreneurial spirit among its employees. They might have been encouraged to take risks, innovate, and think outside the box, qualities that are crucial for entrepreneurs. Industry understanding: Many OYO alumni launch startups within the hospitality or related industries. Their experience at OYO would have given them a deep understanding of the market, its challenges, and opportunities, which could be valuable assets for their businesses. Financial incentives: OYO offered ESOPs (Employee Stock Ownership Plans) to its employees, which could have provided them with financial resources to start their ventures upon leaving the company. Market opportunity: The Indian startup ecosystem is booming, with increasing access to funding and support. This favorable environment could make launching a startup more attractive to OYO alumni. It's important to note that these are just some potential reasons, and the specific motivations of each OYO alumnus who launched a startup would vary. Here are some additional points to consider: - The number of startups launched by OYO alumni is impressive, but it's worth noting that not all of them will be successful. - Launching a startup is a risky endeavor, and many factors contribute to success or failure. - While OYO's experience may have played a role in the success of some alumni startups, it's not the only factor. Overall, the high number of startups launched by OYO alumni is a testament to the company's impact on its employees and the broader Indian startup ecosystem. Read the full article

[jcmarchi]

Google at NeurIPS 2023

New Post has been published on https://thedigitalinsider.com/google-at-neurips-2023/

Google at NeurIPS 2023

This week the 37th annual Conference on Neural Information Processing Systems (NeurIPS 2023), the biggest machine learning conference of the year, kicks off in New Orleans, LA. Google is proud to be a Diamond Level sponsor of NeurIPS this year and will have a strong presence with >170 accepted papers, two keynote talks, and additional contributions to the broader research community through organizational support and involvement in >20 workshops and tutorials. Google is also proud to be a Platinum Sponsor for both the Women in Machine Learning and LatinX in AI workshops. We look forward to sharing some of our extensive ML research and expanding our partnership with the broader ML research community.

Attending for NeurIPS 2023 in person? Come visit the Google Research booth to learn more about the exciting work we’re doing to solve some of the field’s most interesting challenges. Visit the @GoogleAI X (Twitter) account to find out about Google booth activities (e.g., demos and Q&A sessions).

You can learn more about our latest cutting edge work being presented at the conference in the list below (Google affiliations highlighted in bold). And see Google DeepMind’s blog to learn more about their participation at NeurIPS 2023.

Anonymous Learning via Look-Alike Clustering: A Precise Analysis of Model Generalization Adel Javanmard, Vahab Mirrokni

Better Private Linear Regression Through Better Private Feature Selection Travis Dick, Jennifer Gillenwater*, Matthew Joseph

Binarized Neural Machine Translation Yichi Zhang, Ankush Garg, Yuan Cao, Łukasz Lew, Behrooz Ghorbani*, Zhiru Zhang, Orhan Firat

BoardgameQA: A Dataset for Natural Language Reasoning with Contradictory Information Mehran Kazemi, Quan Yuan, Deepti Bhatia, Najoung Kim, Xin Xu, Vaiva Imbrasaite, Deepak Ramachandran

Boosting with Tempered Exponential Measures Richard Nock, Ehsan Amid, Manfred Warmuth

Concept Algebra for (Score-Based) Text-Controlled Generative Models Zihao Wang, Lin Gui, Jeffrey Negrea, Victor Veitch

Deep Contract Design via Discontinuous Networks Tonghan Wang, Paul Dütting, Dmitry Ivanov, Inbal Talgam-Cohen, David C. Parkes

Diffusion-SS3D: Diffusion Model for Semi-supervised 3D Object Detection Cheng-Ju Ho, Chen-Hsuan Tai, Yen-Yu Lin, Ming-Hsuan Yang, Yi-Hsuan Tsai

Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Matthew Walter

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy Anastasia Koloskova*, Ryan McKenna, Zachary Charles, J Keith Rush, Hugh Brendan McMahan

Hardness of Low Rank Approximation of Entrywise Transformed Matrix Products Tamas Sarlos, Xingyou Song, David P. Woodruff, Qiuyi (Richard) Zhang

Module-wise Adaptive Distillation for Multimodality Foundation Models

Chen Liang, Jiahui Yu, Ming-Hsuan Yang, Matthew Brown, Yin Cui, Tuo Zhao, Boqing Gong, Tianyi Zhou

Multi-Swap k-Means++ Lorenzo Beretta, Vincent Cohen-Addad, Silvio Lattanzi, Nikos Parotsidis

OpenMask3D: Open-Vocabulary 3D Instance Segmentation Ayça Takmaz, Elisabetta Fedele, Robert Sumner, Marc Pollefeys, Federico Tombari, Francis Engelmann

Order Matters in the Presence of Dataset Imbalance for Multilingual Learning Dami Choi*, Derrick Xin, Hamid Dadkhahi, Justin Gilmer, Ankush Garg, Orhan Firat, Chih-Kuan Yeh, Andrew M. Dai, Behrooz Ghorbani

PopSign ASL v1.0: An Isolated American Sign Language Dataset Collected via Smartphones Thad Starner, Sean Forbes, Matthew So, David Martin, Rohit Sridhar, Gururaj Deshpande, Sam Sepah, Sahir Shahryar, Khushi Bhardwaj, Tyler Kwok, Daksh Sehgal, Saad Hassan, Bill Neubauer, Sofia Vempala, Alec Tan, Jocelyn Heath, Unnathi Kumar, Priyanka Mosur, Tavenner Hall, Rajandeep Singh, Christopher Cui, Glenn Cameron, Sohier Dane, Garrett Tanzer

Semi-Implicit Denoising Diffusion Models (SIDDMs) Yanwu Xu*, Mingming Gong, Shaoan Xie, Wei Wei, Matthias Grundmann, Kayhan Batmanghelich, Tingbo Hou

State2Explanation: Concept-Based Explanations to Benefit Agent Learning and User Understanding Devleena Das, Sonia Chernova, Been Kim

StoryBench: A Multifaceted Benchmark for Continuous Story Visualization Emanuele Bugliarello*, Hernan Moraldo, Ruben Villegas, Mohammad Babaeizadeh, Mohammad Taghi Saffar, Han Zhang, Dumitru Erhan, Vittorio Ferrari, Pieter-Jan Kindermans, Paul Voigtlaender

Subject-driven Text-to-Image Generation via Apprenticeship Learning Wenhu Chen, Hexiang Hu, Yandong Li, Nataniel Ruiz, Xuhui Jia, Ming-Wei Chang, William W. Cohen

TpuGraphs: A Performance Prediction Dataset on Large Tensor Computational Graphs Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Kaidi Cao*, Bahare Fatemi, Mike Burrows, Charith Mendis*, Bryan Perozzi

Training Chain-of-Thought via Latent-Variable Inference Du Phan, Matthew D. Hoffman, David Dohan*, Sholto Douglas, Tuan Anh Le, Aaron Parisi, Pavel Sountsov, Charles Sutton, Sharad Vikram, Rif A. Saurous

Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints Jayadev Acharya, Clement L. Canonne, Ziteng Sun, Himanshu Tyagi

What You See is What You Read? Improving Text-Image Alignment Evaluation Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig, Oran Lang, Eran Ofek, Idan Szpektor

When Does Confidence-Based Cascade Deferral Suffice? Wittawat Jitkrittum, Neha Gupta, Aditya Krishna Menon, Harikrishna Narasimhan, Ankit Singh Rawat, Sanjiv Kumar

Accelerating Molecular Graph Neural Networks via Knowledge Distillation Filip Ekström Kelvinius, Dimitar Georgiev, Artur Petrov Toshev, Johannes Gasteiger

AVIS: Autonomous Visual Information Seeking with Large Language Model Agent Ziniu Hu*, Ahmet Iscen, Chen Sun, Kai-Wei Chang, Yizhou Sun, David Ross, Cordelia Schmid, Alireza Fathi

Beyond Invariance: Test-Time Label-Shift Adaptation for Addressing “Spurious” Correlations Qingyao Sun, Kevin Patrick Murphy, Sayna Ebrahimi, Alexander D’Amour

Collaborative Score Distillation for Consistent Visual Editing Subin Kim, Kyungmin Lee, June Suk Choi, Jongheon Jeong, Kihyuk Sohn, Jinwoo Shin

CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graphs Guangyao Zhai, Evin Pınar Örnek, Shun-Cheng Wu, Yan Di, Federico Tombari, Nassir Navab, Benjamin Busam

Computational Complexity of Learning Neural Networks: Smoothness and Degeneracy Amit Daniely, Nathan Srebro, Gal Vardi

A Computationally Efficient Sparsified Online Newton Method Fnu Devvrit*, Sai Surya Duvvuri, Rohan Anil, Vineet Gupta, Cho-Jui Hsieh, Inderjit S Dhillon

DDF-HO: Hand-Held Object Reconstruction via Conditional Directed Distance Field Chenyangguang Zhang, Yan Di, Ruida Zhang, Guangyao Zhai, Fabian Manhardt, Federico Tombari, Xiangyang Ji

Double Auctions with Two-sided Bandit Feedback Soumya Basu, Abishek Sankararaman

Grammar Prompting for Domain-Specific Language Generation with Large Language Models Bailin Wang, Zi Wang, Xuezhi Wang, Yuan Cao, Rif A. Saurous, Yoon Kim

Inconsistency, Instability, and Generalization Gap of Deep Neural Network Training Rie Johnson, Tong Zhang*

Large Graph Property Prediction via Graph Segment Training Kaidi Cao*, Phitchaya Mangpo Phothilimthana, Sami Abu-El-Haija, Dustin Zelle, Yanqi Zhou, Charith Mendis*, Jure Leskovec, Bryan Perozzi

On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Adam Sealfon

On Student-teacher Deviations in Distillation: Does it Pay to Disobey? Vaishnavh Nagarajan, Aditya Krishna Menon, Srinadh Bhojanapalli, Hossein Mobahi, Sanjiv Kumar

Optimal Cross-learning for Contextual Bandits with Unknown Context Distributions Jon Schneider, Julian Zimmert

Near-Optimal k-Clustering in the Sliding Window Model David Woodruff, Peilin Zhong, Samson Zhou

Post Hoc Explanations of Language Models Can Improve Language Models Satyapriya Krishna, Jiaqi Ma, Dylan Z Slack, Asma Ghandeharioun, Sameer Singh, Himabindu Lakkaraju

Recommender Systems with Generative Retrieval Shashank Rajput*, Nikhil Mehta, Anima Singh, Raghunandan Hulikal Keshavan, Trung Vu, Lukasz Heldt, Lichan Hong, Yi Tay, Vinh Q. Tran, Jonah Samost, Maciej Kula, Ed H. Chi, Maheswaran Sathiamoorthy

Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models Ying Fan, Olivia Watkins, Yuqing Du, Hao Liu, Moonkyung Ryu, Craig Boutilier, Pieter Abbeel, Mohammad Ghavamzadeh*, Kangwook Lee, Kimin Lee*

Replicable Clustering Hossein Esfandiari, Amin Karbasi, Vahab Mirrokni, Grigoris Velegkas, Felix Zhou

Replicability in Reinforcement Learning Amin Karbasi, Grigoris Velegkas, Lin Yang, Felix Zhou

Riemannian Projection-free Online Learning Zihao Hu, Guanghui Wang, Jacob Abernethy

Sharpness-Aware Minimization Leads to Low-Rank Features Maksym Andriushchenko, Dara Bahri, Hossein Mobahi, Nicolas Flammarion

What is the Inductive Bias of Flatness Regularization? A Study of Deep Matrix Factorization Models Khashayar Gatmiry, Zhiyuan Li, Ching-Yao Chuang, Sashank Reddi, Tengyu Ma, Stefanie Jegelka

Block Low-Rank Preconditioner with Shared Basis for Stochastic Optimization Jui-Nan Yen, Sai Surya Duvvuri, Inderjit S Dhillon, Cho-Jui Hsieh

Blocked Collaborative Bandits: Online Collaborative Filtering with Per-Item Budget Constraints Soumyabrata Pal, Arun Sai Suggala, Karthikeyan Shanmugam, Prateek Jain

Boundary Guided Learning-Free Semantic Control with Diffusion Models Ye Zhu, Yu Wu, Zhiwei Deng, Olga Russakovsky, Yan Yan

Conditional Adapters: Parameter-efficient Transfer Learning with Fast Inference Tao Lei, Junwen Bai, Siddhartha Brahma, Joshua Ainslie, Kenton Lee, Yanqi Zhou, Nan Du*, Vincent Y. Zhao, Yuexin Wu, Bo Li, Yu Zhang, Ming-Wei Chang

Conformal Prediction for Time Series with Modern Hopfield Networks Andreas Auer, Martin Gauch, Daniel Klotz, Sepp Hochreiter

Does Visual Pretraining Help End-to-End Reasoning? Chen Sun, Calvin Luo, Xingyi Zhou, Anurag Arnab, Cordelia Schmid

Effective Robustness Against Natural Distribution Shifts for Models with Different Training Data Zhouxing Shi*, Nicholas Carlini, Ananth Balashankar, Ludwig Schmidt, Cho-Jui Hsieh, Alex Beutel*, Yao Qin

Improving Neural Network Representations Using Human Similarity Judgments Lukas Muttenthaler*, Lorenz Linhardt, Jonas Dippel, Robert A. Vandermeulen, Katherine Hermann, Andrew K. Lampinen, Simon Kornblith

Label Robust and Differentially Private Linear Regression: Computational and Statistical Efficiency Xiyang Liu, Prateek Jain, Weihao Kong, Sewoong Oh, Arun Sai Suggala

Mnemosyne: Learning to Train Transformers with Transformers Deepali Jain, Krzysztof Choromanski, Avinava Dubey, Sumeet Singh, Vikas Sindhwani, Tingnan Zhang, Jie Tan

Nash Regret Guarantees for Linear Bandits Ayush Sawarni, Soumyabrata Pal, Siddharth Barman

A Near-Linear Time Algorithm for the Chamfer Distance Ainesh Bakshi, Piotr Indyk, Rajesh Jayaram, Sandeep Silwal, Erik Waingarten.

On Differentially Private Sampling from Gaussian and Product Distributions Badih Ghazi, Xiao Hu*, Ravi Kumar, Pasin Manurangsi

On Dynamic Programming Decompositions of Static Risk Measures in Markov Decision Processes Jia Lin Hau, Erick Delage, Mohammad Ghavamzadeh*, Marek Petrik

ResMem: Learn What You Can and Memorize the Rest Zitong Yang, Michal Lukasik, Vaishnavh Nagarajan, Zonglin Li, Ankit Singh Rawat, Manzil Zaheer, Aditya Krishna Menon, Sanjiv Kumar

Responsible AI (RAI) Games and Ensembles Yash Gupta, Runtian Zhai, Arun Suggala, Pradeep Ravikumar

RoboCLIP: One Demonstration Is Enough to Learn Robot Policies Sumedh A Sontakke, Jesse Zhang, Sébastien M. R. Arnold, Karl Pertsch, Erdem Biyik, Dorsa Sadigh, Chelsea Finn, Laurent Itti

Robust Concept Erasure via Kernelized Rate-Distortion Maximization Somnath Basu Roy Chowdhury, Nicholas Monath, Kumar Avinava Dubey, Amr Ahmed, Snigdha Chaturvedi

Robust Multi-Agent Reinforcement Learning via Adversarial Regularization: Theoretical Foundation and Stable Algorithms Alexander Bukharin, Yan Li, Yue Yu, Qingru Zhang, Zhehui Chen, Simiao Zuo, Chao Zhang, Songan Zhang, Tuo Zhao

Simplicity Bias in 1-Hidden Layer Neural Networks Depen Morwani*, Jatin Batra, Prateek Jain, Praneeth Netrapalli

SLaM: Student-Label Mixing for Distillation with Unlabeled Examples Vasilis Kontonis, Fotis Iliopoulos, Khoa Trinh, Cenk Baykal, Gaurav Menghani, Erik Vee

SNAP: Self-Supervised Neural Maps for Visual Positioning and Semantic Understanding Paul-Edouard Sarlin*, Eduard Trulls, Marc Pollefeys, Jan Hosang, Simon Lynen

SOAR: Improved Indexing for Approximate Nearest Neighbor Search Philip Sun, David Simcha, Dave Dopson, Ruiqi Guo, Sanjiv Kumar

StyleDrop: Text-to-Image Synthesis of Any Style Kihyuk Sohn, Lu Jiang, Jarred Barber, Kimin Lee*, Nataniel Ruiz, Dilip Krishnan, Huiwen Chang*, Yuanzhen Li, Irfan Essa, Michael Rubinstein, Yuan Hao, Glenn Entis, Irina Blok, Daniel Castro Chin

Three Towers: Flexible Contrastive Learning with Pretrained Image Models Jannik Kossen*, Mark Collier, Basil Mustafa, Xiao Wang, Xiaohua Zhai, Lucas Beyer, Andreas Steiner, Jesse Berent, Rodolphe Jenatton, Efi Kokiopoulou

Two-Stage Learning to Defer with Multiple Experts Anqi Mao, Christopher Mohri, Mehryar Mohri, Yutao Zhong

AdANNS: A Framework for Adaptive Semantic Search Aniket Rege, Aditya Kusupati, Sharan Ranjit S, Alan Fan, Qingqing Cao, Sham Kakade, Prateek Jain, Ali Farhadi

Cappy: Outperforming and Boosting Large Multi-Task LMs with a Small Scorer Bowen Tan*, Yun Zhu, Lijuan Liu, Eric Xing, Zhiting Hu, Jindong Chen

Causal-structure Driven Augmentations for Text OOD Generalization Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David Blei

Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel Valerii Likhosherstov, Krzysztof Choromanski, Avinava Dubey, Frederick Liu, Tamas Sarlos, Adrian Weller

Diffusion Hyperfeatures: Searching Through Time and Space for Semantic Correspondence Grace Luo, Lisa Dunlap, Dong Huk Park, Aleksander Holynski, Trevor Darrell

Diffusion Self-Guidance for Controllable Image Generation Dave Epstein, Allan Jabri, Ben Poole, Alexei A Efros, Aleksander Holynski

Fully Dynamic k-Clustering in Õ(k) Update Time Sayan Bhattacharya, Martin Nicolas Costa, Silvio Lattanzi, Nikos Parotsidis

Improving CLIP Training with Language Rewrites Lijie Fan, Dilip Krishnan, Phillip Isola, Dina Katabi, Yonglong Tian

<!–k-Means Clustering with Distance-Based Privacy Alessandro Epasto, Vahab Mirrokni, Shyam Narayanan, Peilin Zhong

–>

LayoutGPT: Compositional Visual Planning and Generation with Large Language Models Weixi Feng, Wanrong Zhu, Tsu-Jui Fu, Varun Jampani, Arjun Reddy Akula, Xuehai He, Sugato Basu, Xin Eric Wang, William Yang Wang

Offline Reinforcement Learning for Mixture-of-Expert Dialogue Management Dhawal Gupta*, Yinlam Chow, Azamat Tulepbergenov, Mohammad Ghavamzadeh*, Craig Boutilier

Optimal Unbiased Randomizers for Regression with Label Differential Privacy Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath, Ravi Kumar, Ethan Jacob Leeman, Pasin Manurangsi, Avinash V Varadarajan, Chiyuan Zhang

Paraphrasing Evades Detectors of AI-generated Text, but Retrieval Is an Effective Defense Kalpesh Krishna, Yixiao Song, Marzena Karpinska, John Wieting, Mohit Iyyer

ReMaX: Relaxing for Better Training on Efficient Panoptic Segmentation Shuyang Sun*, Weijun Wang, Qihang Yu*, Andrew Howard, Philip Torr, Liang-Chieh Chen*

Robust and Actively Secure Serverless Collaborative Learning Nicholas Franzese, Adam Dziedzic, Christopher A. Choquette-Choo, Mark R. Thomas, Muhammad Ahmad Kaleem, Stephan Rabanser, Congyu Fang, Somesh Jha, Nicolas Papernot, Xiao Wang

SpecTr: Fast Speculative Decoding via Optimal Transport Ziteng Sun, Ananda Theertha Suresh, Jae Hun Ro, Ahmad Beirami, Himanshu Jain, Felix Yu

Structured Prediction with Stronger Consistency Guarantees Anqi Mao, Mehryar Mohri, Yutao Zhong

Affinity-Aware Graph Networks Ameya Velingker, Ali Kemal Sinop, Ira Ktena, Petar Veličković, Sreenivas Gollapudi

ARTIC3D: Learning Robust Articulated 3D Shapes from Noisy Web Image Collections Chun-Han Yao*, Amit Raj, Wei-Chih Hung, Yuanzhen Li, Michael Rubinstein, Ming-Hsuan Yang, Varun Jampani

Black-Box Differential Privacy for Interactive ML Haim Kaplan, Yishay Mansour, Shay Moran, Kobbi Nissim, Uri Stemmer

Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits Haolin Liu, Chen-Yu Wei, Julian Zimmert

DaTaSeg: Taming a Universal Multi-Dataset Multi-Task Segmentation Model

Xiuye Gu, Yin Cui*, Jonathan Huang, Abdullah Rashwan, Xuan Yang, Xingyi Zhou, Golnaz Ghiasi, Weicheng Kuo, Huizhong Chen, Liang-Chieh Chen*, David Ross

Easy Learning from Label Proportions Robert Busa-Fekete, Heejin Choi*, Travis Dick, Claudio Gentile, Andres Munoz Medina

Efficient Data Subset Selection to Generalize Training Across Models: Transductive and Inductive Networks Eeshaan Jain, Tushar Nandy, Gaurav Aggarwal, Ashish Tendulkar, Rishabh Iyer, Abir De

Faster Differentially Private Convex Optimization via Second-Order Methods Arun Ganesh, Mahdi Haghifam*, Thomas Steinke, Abhradeep Guha Thakurta

Finding Safe Zones of Markov Decision Processes Policies Lee Cohen, Yishay Mansour, Michal Moshkovitz

Focused Transformer: Contrastive Training for Context Scaling Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek, Yuhuai Wu*, Henryk Michalewski, Piotr Miłoś

Front-door Adjustment Beyond Markov Equivalence with Limited Graph Knowledge Abhin Shah, Karthikeyan Shanmugam, Murat Kocaoglu

H-Consistency Bounds: Characterization and Extensions Anqi Mao, Mehryar Mohri, Yutao Zhong

Inverse Dynamics Pretraining Learns Good Representations for Multitask Imitation David Brandfonbrener, Ofir Nachum, Joan Bruna

Most Neural Networks Are Almost Learnable Amit Daniely, Nathan Srebro, Gal Vardi

Multiclass Boosting: Simple and Intuitive Weak Learning Criteria Nataly Brukhim, Amit Daniely, Yishay Mansour, Shay Moran

NeRF Revisited: Fixing Quadrature Instability in Volume Rendering Mikaela Angelina Uy, Kiyohiro Nakayama, Guandao Yang, Rahul Krishna Thomas, Leonidas Guibas, Ke Li

Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation Wei-Ning Chen, Dan Song, Ayfer Ozgur, Peter Kairouz

Private Federated Frequency Estimation: Adapting to the Hardness of the Instance Jingfeng Wu*, Wennan Zhu, Peter Kairouz, Vladimir Braverman

RETVec: Resilient and Efficient Text Vectorizer Elie Bursztein, Marina Zhang, Owen Skipper Vallis, Xinyu Jia, Alexey Kurakin

Symbolic Discovery of Optimization Algorithms Xiangning Chen*, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, Yifeng Lu, Quoc V. Le

A Tale of Two Features: Stable Diffusion Complements DINO for Zero-Shot Semantic Correspondence Junyi Zhang, Charles Herrmann, Junhwa Hur, Luisa F. Polania, Varun Jampani, Deqing Sun, Ming-Hsuan Yang

A Trichotomy for Transductive Online Learning Steve Hanneke, Shay Moran, Jonathan Shafer

A Unified Fast Gradient Clipping Framework for DP-SGD William Kong, Andres Munoz Medina

Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla, Galen Andrew, Peter Kairouz, H. Brendan McMahan, Alina Oprea, Sewoong Oh

(Amplified) Banded Matrix Factorization: A unified approach to private training Christopher A Choquette-Choo, Arun Ganesh, Ryan McKenna, H Brendan McMahan, Keith Rush, Abhradeep Guha Thakurta, Zheng Xu

Adversarial Resilience in Sequential Prediction via Abstention Surbhi Goel, Steve Hanneke, Shay Moran, Abhishek Shetty

Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception Hassan Akbari, Dan Kondratyuk, Yin Cui, Rachel Hornung, Huisheng Wang, Hartwig Adam

Android in the Wild: A Large-Scale Dataset for Android Device Control Christopher Rawles, Alice Li, Daniel Rodriguez, Oriana Riva, Timothy Lillicrap

Benchmarking Robustness to Adversarial Image Obfuscations Florian Stimberg, Ayan Chakrabarti, Chun-Ta Lu, Hussein Hazimeh, Otilia Stretcu, Wei Qiao, Yintao Liu, Merve Kaya, Cyrus Rashtchian, Ariel Fuxman, Mehmet Tek, Sven Gowal

Building Socio-culturally Inclusive Stereotype Resources with Community Engagement Sunipa Dev, Jaya Goyal, Dinesh Tewari, Shachi Dave, Vinodkumar Prabhakaran

Consensus and Subjectivity of Skin Tone Annotation for ML Fairness Candice Schumann, Gbolahan O Olanubi, Auriel Wright, Ellis Monk Jr*, Courtney Heldreth, Susanna Ricco

Counting Distinct Elements Under Person-Level Differential Privacy Alexander Knop, Thomas Steinke

DICES Dataset: Diversity in Conversational AI Evaluation for Safety Lora Aroyo, Alex S. Taylor, Mark Diaz, Christopher M. Homan, Alicia Parrish, Greg Serapio-García, Vinodkumar Prabhakaran, Ding Wang

Does Progress on ImageNet Transfer to Real-world Datasets? Alex Fang, Simon Kornblith, Ludwig Schmidt

Estimating Generic 3D Room Structures from 2D Annotations Denys Rozumnyi*, Stefan Popov, Kevis-kokitsi Maninis, Matthias Nießner, Vittorio Ferrari

Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias Yue Yu, Yuchen Zhuang, Jieyu Zhang, Yu Meng, Alexander Ratner, Ranjay Krishna, Jiaming Shen, Chao Zhang

MADLAD-400: A Multilingual And Document-Level Large Audited Dataset Sneha Kudugunta, Isaac Caswell, Biao Zhang, Xavier Garcia, Derrick Xin, Aditya Kusupati, Romi Stella, Ankur Bapna, Orhan Firat

Mechanic: A Learning Rate Tuner Ashok Cutkosky, Aaron Defazio, Harsh Mehta

NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations Varun Jampani, Kevis-kokitsi Maninis, Andreas Engelhardt, Arjun Karpur, Karen Truong, Kyle Sargent, Stefan Popov, Andre Araujo, Ricardo Martin Brualla, Kaushal Patel, Daniel Vlasic, Vittorio Ferrari, Ameesh Makadia, Ce Liu*, Yuanzhen Li, Howard Zhou

Neural Ideal Large Eddy Simulation: Modeling Turbulence with Neural Stochastic Differential Equations Anudhyan Boral, Zhong Yi Wan, Leonardo Zepeda-Nunez, James Lottes, Qing Wang, Yi-Fan Chen, John Roberts Anderson, Fei Sha

Restart Sampling for Improving Generative Processes Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi Jaakkola

Rethinking Incentives in Recommender Systems: Are Monotone Rewards Always Beneficial? Fan Yao, Chuanhao Li, Karthik Abinav Sankararaman, Yiming Liao, Yan Zhu, Qifan Wang, Hongning Wang, Haifeng Xu

Revisiting Evaluation Metrics for Semantic Segmentation: Optimization and Evaluation of Fine-grained Intersection over Union Zifu Wang, Maxim Berman, Amal Rannen-Triki, Philip Torr, Devis Tuia, Tinne Tuytelaars, Luc Van Gool, Jiaqian Yu, Matthew B. Blaschko

RoboHive: A Unified Framework for Robot Learning Vikash Kumar, Rutav Shah, Gaoyue Zhou, Vincent Moens, Vittorio Caggiano, Abhishek Gupta, Aravind Rajeswaran

SatBird: Bird Species Distribution Modeling with Remote Sensing and Citizen Science Data Mélisande Teng, Amna Elmustafa, Benjamin Akera, Yoshua Bengio, Hager Radi, Hugo Larochelle, David Rolnick

Sparsity-Preserving Differentially Private Training of Large Embedding Models Badih Ghazi, Yangsibo Huang*, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chiyuan Zhang

StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners Yonglong Tian, Lijie Fan, Phillip Isola, Huiwen Chang, Dilip Krishnan

Towards Federated Foundation Models: Scalable Dataset Pipelines for Group-Structured Learning Zachary Charles, Nicole Mitchell, Krishna Pillutla, Michael Reneer, Zachary Garrett

Universality and Limitations of Prompt Tuning Yihan Wang, Jatin Chauhan, Wei Wang, Cho-Jui Hsieh

Unsupervised Semantic Correspondence Using Stable Diffusion Eric Hedlin, Gopal Sharma, Shweta Mahajan, Hossam Isack, Abhishek Kar, Andrea Tagliasacchi, Kwang Moo Yi

YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus Dave Uthus, Garrett Tanzer, Manfred Georg

The Noise Level in Linear Regression with Dependent Data Ingvar Ziemann, Stephen Tu, George J. Pappas, Nikolai Matni

[kamalsehgalaus]

Just How a Realty Agent can make a difference in your Australian Home Journey

The Australian property market is a dynamic as well as affordable landscape, where purchasing or selling a property entails a myriad of ins and outs. For both seasoned investors and also novice buyers, the procedure can be overwhelming and full of obstacles. Nevertheless, employing the solutions of an expert property agent can prove to be a game-changer. Here's why employing a realty agent in Australia is of vital value:

Kamal Sehgal

Expert Guidance via the Market:

The Australian building market is diverse as well as ever-changing, differing dramatically from one city or region to another. Real estate representatives have a deep understanding of neighborhood markets, including fads, building values, as well as area characteristics. They can offer important assistance on which locations line up with your preferences as well as spending plan, making your residential property search more targeted and efficient.

Matching Demands with Qualities:

Whether you're looking for your desire residence or a financial investment possibility, a property agent can match your specific requirements and also preferences with ideal homes. They have access to an extensive database of listings, including off-market properties, allowing you to explore a wider series of options.

Experienced Settlement Strategies:

Working out the most effective bargain is a crucial aspect of any kind of property transaction. Realty representatives have superb settlement skills honed via experience. They can discuss in your place to safeguard the best cost for your purchase or achieve the greatest worth for your residential property sale.

Documentation as well as Legalities Made Easy:

The paperwork associated with home purchases can be elaborate and time-consuming. From contracts to compliance certificates as well as title actions, property representatives are well-versed in taking care of the paperwork efficiently as well as properly. This makes sure that your transaction proceeds smoothly and also remains in compliance with all legal demands.

Access to Networks as well as Resources:

Realty agents have considerable networks within the sector, including home mortgage brokers, conveyancers, as well as house assessors. They can attach you with trusted professionals, conserving you the effort of finding them individually. This network of sources streamlines the whole process, making it more convenient for you.

Expert Insights on Home Valuations:

Figuring out the proper worth of a home is crucial, whether you're acquiring or selling. Property agents have accessibility to historical information, current sales, and also market analytics, enabling them to give precise residential or commercial property assessments. This understanding ensures that you're not paying too much as a customer or underselling as a vendor.

Peace of Mind and Support:

Navigating the property market can be difficult as well as emotionally billed. A property agent gives emotional support and satisfaction throughout the process. They resolve your worries, address your concerns, as well as serve as a reliable source of information and guidance.

Finally, employing a realty agent in Australia is a critical move that can favorably influence your residential property journey. From their market knowledge and arrangement expertise to accessibility to exclusive listings as well as support services, realty agents bring substantial value to the table.

[all-about-news24x7]

International Yoga Day observed at NTPC Kaniha |

Bureau,Odishabarta                Talcher:International Yoga Day was observed  at NTPC Kaniha on June 21. In the morning, employees and members of their families enthusiastically participated  a special yoga session  organized at Recreation Centre, Deep Tarang. Shri K. S. Sundaram, ED, Talcher Kaniha, Shri A. K. Sehgal, GM(O&M), Shri Ch. Satya Ramakrishna, GM(ADM), Shri B. K. Pandey, AGM(HR),…

[typingtess]

NCIS: Los Angeles Season Twelve Rewatch:  “Signs of Change”

The basics:  When secret military technology is stolen, Kensi brings in a woman who is not able to serve her country.

Written by:  Indira Gibson Wilson and Jordana Lewis Jaffe

Indira Gibson Wilson and Jordana Lewis Jaffe co-wrote “The Frogman’s Daughter” (Wilson’s first episode with the program).  

Jordana Lewis Jaffe wrote or co-wrote “Honor”, “Patriot Acts”, “Dead Body Politic”, “Paper Soldiers”, “Unwritten Rule”, “Big Brother”, “Iron Curtain Rising”, “Exposure”, “Savior Faire”, “Beacon”, “Defectors”, “Exchange Rate”, “Black Market”, “Payback”, “Battle Scars”, “Mountebank”, “Vendetta”, “Where Everybody Knows Your Name”, “Pro Se” “Heist”, “Born to Run”, “Provenance”, “Commitment Issues”, “Knock Out” and “War Crimes”.

Directed by:  Dennis Smith directed “Fame”, “Standoff”, “Rocket Man”, “Cyberthreat”, “Exit Strategy”, “Patriot Acts”, “Out of the Past” part one, “The Livelong Day”, Between the Lines”, “Deep Trouble” part two, “Black Budget", “Black Wind”, “Blame it On Rio”, “Defectors”, “Matryoshka” part one, “Granger, O”, “The Queen’s Gambit”, “Hot Water”, “From Havana With Love”, “Plain Sight”, the lighthearted “Monster”, “Superhuman”, “One of Us”, “Smokescreen” part one, "Decoy”, “Mother” (episode 250), “Alsiyadun”, “The Bear” and “Angry Karen”.

Guest stars of note:  Gerald McRaney as Retired Navy Admiral Hollace Kilbride and Duncan Campbell as NCIS Special Agent Castor are back from “The Noble Maidens”.  Other guest stars: Raquel McPeek Rodriguez as Sienna Marchione, Matt Bush as Owen Winnick, Mo Sehgal as Randall/Man #1, Maurizio Rasti as Ehsan Rahman and Abraham Justice as Carl/Driver.

Our heroes:  Save the day and make a friend.

What important things did we learn about:

Callen:  Provided a Sam anecdote for Kam’s graduation speech. Sam:  Was made a Chief when Michelle was eight months pregnant with Kam. Kensi:   Really wanted Marchione as part of the team. Deeks: Thinks Kensi needed time with Sienna Marchione and gave it to her. Eric:  Absent. Nell:   A hard no on a cooperating witness. Fatima:   Plays lost local very well. Roundtree:  See Eric. Hetty:  Does not support using cooperating witnesses.

What not so important things did we learn about:

Callen:  Calls for an ambulance against Sam’s wishes. Sam:  Said a gunshot wound only stings a little. Kensi: Grandfather fought in WWII. Deeks: Cooler than a polar bear’s toenails.   Eric:  See Roundtree. Nell:  Doing things the Nell way. Fatima:   Knows sign language. Roundtree: Absent. Hetty:  Hides valuables with her scotch.

Who's down with OTP:  Deeks is still making plans about their future family and Kensi finally can tell him why she thinks that’s a mistake.  

Who's down with BrOTP:  Sam and Uncle Callen are flying to Kam’s graduation.  It doesn’t get better than that.

Any pressing need for a cranky retired Admiral?  He’s getting his cranky on today.  Also realizes that the institution he loves is doing something that hurts other and hurts the institution.  

Who is running the team this week?  Nell with the “WWHD” – What Would Hetty Do – playbook losing favor.

Fashion review:  Medium blue button down shirt for Callen.  Sam starts the day in a long sleeve grey tee-shirt before changing into this post-shooting long sleeve red tee-shirt.  Kensi is wearing a long-sleeve pink sweater.  Deeks is in a dark blue sweatshirt.  Nell has on a brown/beige/gold plaid dress.  Fatima is wearing a grey blouse with a snakeskin pattern over a black top with a Nehru collar.

Music:  The acoustic version of “Unfolding” by Luca Fogale is playing at the end.

Any notable cut scene:  Three of them!

Scene one:  Before interviewing Sienna Marchione, Kensi and Deeks talk about how well she can handle herself based on the security footage.  She is the only member of the engineering team to survive.  Deeks figures she has to be a foreign agent or working for a rival government’s security agency with her levels of skill.  Kensi is suspicious about a woman who can fight like Marchione working as an engineer.  Deeks can’t wait for “Captain Marvel” to talk her way out of trouble.

Scene two:  Callen is questioning “Randall” who answers every question with the word “lawyer”.  Callen admires his neck tattoo but thinks it is a little feminine.

Scene three:  Deeks arrives in Ops offering help.  Nell is looking through the different companies at the tech exhibition.  A competitor could have killed the boss, Brandon Lindemore.  Nell wants to whittle down the list.  Deeks didn’t get much from the suspects at the boat shed but he knows they are pros.  He doubts some start-up could afford pros.  Deep-dive the big dogs.  Deeks talks about big dogs including his dog Lenny that was so big, he ate the couch.

Quote:  Kensi:  “So... I didn't know how to communicate this this morning, but, um... after meeting Sienna and hearing her story, it just made me realize that we can do everything right to start a family.  You know, I can take all my shots and you can read all the best books, we can go to the best fertility doctors in the city... But... ...just sometimes stuff doesn't work out. You know? Even if we have a plan. And, um... there's nothing we can do about it.” Deeks:  “Okay. Um... Well, we will cross that bridge if and when we get to it. And we'll do it... we'll do it together.”

Anything else:   In an office break room, a woman is making a cup of coffee.  Leaving with her coffee, the woman sees a dead uniformed security office on the floor.  There are more bodies all around the office.  Three men dressed all in black are starting at really high-tech computer stations downloading data.  As one of the men takes out his weapon, the woman races to him and is able to easily disarm him.  The two fight and she is amazing – finds a pen and stabs him in the neck.  She ends the fight with a fire extinguisher to the head.

As a second intruder runs toward the woman, they have a short struggle until she knocks him out with a sneaker to the head.  Going to a computer terminal, she sees the download has been completed.  The third man is gone.  Pulling out an ear-piece, the woman attaches it to her phone and calls 911.

A smiling Deeks with Kensi walk into the armory.  Kensi tells Deeks to be cool.  He’s cooler than a polar bear’s toenails.  The “would-be” nursery will be cornflower blue, a symbol of hope.  Kensi reminds Deeks she closed her eyes and pointed at a color wheel at Lowe’s.  

Moving past paint, Deeks is worried about the logistics of two bad-ass NCIS warriors raising a little child.  Kensi isn’t interested in that conversation until they need to have it.  Deeks is thinking proactive.  Kensi gives him a kiss a promises Deeks that everything will be fine.

Walking into her/Hetty’s office, Nell hears noise in the back.  She finds the “big guy” – Sam – in the back area looking around and asking for where Hetty is.  He needs to speak to her immediately with a question that needs answering and only Hetty can do it.  Nell nods her head and notes “it kinda sucks, doesn’t it.”  Sam thinks Nell isn’t helping him.  She’d like to – what does Sam need and how can she help.  

Sam gave his medals and ribbons from his Navy career to Hetty to keep in a safe place.  Going to Kam’s graduation the following day, Sam wants to give her a medal he earned when maked a chief with the SEALs.  Eight months pregnant with Kam at the time, Michelle could barely move but she was able to pin the anchors on Sam’s collar.  Michelle told him that as a chief, he had the hardest job in the Navy.  Sam disagreed – Michelle had the hardest job – a Navy wife, Mom.  The anchors reflect everything about Sam, Michelle and how Kam is never alone.  Nell is getting emotional and it isn’t even 9AM.  Thinking “WWHD” – what would Hetty do – Nell was going to start the search.

Asking for Sam, Callen joins Fatima in Ops.  Fatima says Sam is looking for a box and Callen thinks that’s very Sam.  On the big screen is the website for Heptagon Labs.  Fatima tells Callen nearly everyone working at the lab being killed..   The company is working with the Defense Department to come up with the next generation of exoskeleton suits.  When Callen asks what does the next-gen tech do, Fatima says the new suits make wearers invisible.  “Invisible, invisible?” Callen asks.  Fatima gives Callen a demonstration with a small prototype sent to the NCIS offices.  This is “pretty awesome” but now someone else now has the tech too.

Callen asks about the survivor – Sienna Marchione, an engineer with the company.  Marchione has a clean record and no suspicious bank activity.  She is also the lead engineer on the project.  Kensi and Deeks are on their way to speak to Marchione at the lab.  Callen asks about the thieves.  Fatima explains one escaped, two are in custody.  Surprised, Callen thinks LAPD found the intruders quickly.  Fatima tells Callen of Marchione’s actions and how the men never left the building.  Callen wants to talk to the suspects in the boat shed.

Walking into the Heptagon Labs computer room, Kensi wants to speak to Marchione, who has her back to Kensi and Deeks.  She’s working on her computer.  Kensi keeps talking, Marchione keeps typing.  When Deeks moves into Marchione’s sightline, she jumps and moves her hand to her pocket.  Deeks pulls his gun and asks why was Marchione ignoring Kensi.  Slowly pulling out her earpiece, Marchione said she couldn’t hear Kensi.

Saying they saw the security footage, Kensi and Deeks asks Marchione about her night.  It was awful.  She thought if she didn’t go for coffee, she could have saved everyone on her team.  Deeks mentions she also could have saved the technology.  He finds her timing “uncanny” how in the short time Marchione was gone, someone stole the schematics for the exoskeleton suit.  Marchione says coincidence, Deeks thinks collusion, especially since she so easily took down two much larger men like it was child’s play.  Marchione says “Thank you.”  

Kensi is interested in how Marchione learned to fight.  Marchione has been training her entire life to serve her country.  Deeks asks “this country” and Marchione confirms “this country.”  She turns to Kensi and ask if Deeks is always so “warm and cuddly.”  Deeks answers, saying he’s having a hard time connecting the dots.  Marchione comes from a proud Navy family.  It was her dream to serve.  Being deaf means she cannot serve.  So if she can’t serve, Marchione is going to protect everyone who does.  It is both the least and most Marchione can do until someone finally changes the rules.  Deeks thanks her for telling them that before Marchione makes it clear, she would never betray her country.  Deeks apologizes.

Asking for Marchione’s version of what happened, she tells Kensi that she wasn’t gone for very long and the men who broke in were very fast.  There had to be inside help.  Deeks asks if Marchione thinks one of her team members is involved.  She mentions an engineer who stayed home sick on the day of the break-in.  Her boss, Brandon Lindemore, was also going through a pricey divorce.  Marchione asks for a favor – find the technology and “nail these guys to the wall.”

As they are leaving, Kensi asks Deeks if he trusts her.  Of course he does.  As Kensi leaves him, Deeks says he trusts her but now is really worried.  Kensi isn’t.  Finding Marchione, Kensi asks for her help – “hell yes.”

In the boat shed, Callen and Sam are getting nothing from the two men Marchione took down.  Just requests for lawyers.  Fatima ran the men’s IDs and Sam’s guy Doug died in 2017, Callen’s guy Randall died in 2011.  Callen and Sam have no idea who they are interrogating.

Arriving in Ops, Nell asks Fatima for an update.  Fatima provide the info on the men in the boat shed as well as Marchione’s boss Lindemore was one of the dead men in the office and mentioned the engineer calling in sick – Owen Winnick.  Nell wonders if someone inside of Heptagon Labs got the intruders into the building and paid for that move with their life.

When Kensi returns to Deeks with Marchione in tow, Deeks needs to talk to Kensi.  While Marchione seems like a “perfectly lovely person with a perfectly nasty roundhouse”, Kensi can’t invite people to work with them.  As they discuss Deeks’s objection, Marchione mentions she can read lips.  Kensi wants to register Marchione as a cooperating witness.  Deeks says the first lesson in FLETC is the three ways you can ruin your career: drug and alcohol abuse, interoffice liaisons and cooperating witnesses.  Since they’re already doing the interoffice liaison, Deeks thinks there will be fallout.  Kensi calls Nell because she knows Nell will say yes.

Nell does not say yes.  In fact, it is a “hard pass”.  It isn’t going to happen.  Admiral Kilbride is impressed with Nell’s harsh tone. When Nell gives him a polite greeting, he’s announces she ruined things by being nice.  Nell tries to explain she’s dealing with something and he wants her to deal with it harshly.   When Nell explains Marchione’s situation – wanting to serve her country and being denied – Kilbride doesn’t think they are Make-A-Wish.   Trying to take the phone from Nell, the Admiral is denied.  Nell is dealing with the call.  That’s fine, the Admiral just wants Kensi to know that Marchione can work the case.  

Making their way to the bullpen, Sam thanks Callen for covering him for Kam’s graduation.  Callen shares that Kam wanted a funny Sam anecdote for her speech and was happy to oblige.  Sam got Kam a weighted tactical training vest.  If she is not going to Annapolis, she still needs to train.  Callen believes that is every young girl’s dream graduation gift.  “It’s pink,” Sam tells Callen.  And there are the tickets to Italy.   Michelle left behind a gift as well – Sam thinks Michelle somehow knew she wasn’t going to be there for Kam’s graduation.  “A CIA Agent never knows if their next mission is their last,” Callen says.  Sam lists all the milestones Michelle is going to miss – Kam’s college graduation, her wedding.  Callen says Kam is lucky to have Sam and Aiden.  And Uncle G.  But nobody will replace Michelle for Kam, Sam says with real sadness in his voice. Callen thinks it is their job to make sure Kam knows how much she meant to Michelle.

While Marchione looks all around the boat shed, Deeks wants to interrogate “thing one and thing two”.  As Deeks goes on about his interrogation techniques, Kensi walks over to Marchione.  The two look at some of the historic Navy photos hanging on the back wall of the boat shed.  Marchione picks out a Douglas SBD-3 Dauntless Bomber.  Her grandfather was a lieutenant on the USS Hornet.  Kensi is impressed – her grandfather fought in WWII as well.  According to Marchione, there is no greater honor than to serve your country.  Kensi agrees, telling Marchione they are happy to have her working with the team.  It is Marchione’s dream come true.

Kensi gives Marchione a basic review of the office set-up but Marchione knows how NCIS works.  She thought of a career with the agency.  Deeks is impressed that Marchione knows more about NCIS than he did on his first day.  Nell pops up on the screen.  The third member intruder – the one who got away – is named Steve Tricks.  Kaleidoscope is looking for him.  Marchione’s boss’s financials, as well as the company’s were in good shape.   The soon to be ex-wife said the success of the exoskeleton suit was part of the divorce – she wanted him to live so she could get her fair share.  The only thing on Owen Winnick’s record was he attended a recent conference.  That makes sense to Marchione since Winnick is not rules breaker.  Kensi wants to take Marchione to see Winnick.  Nell wants to send Fatima as well as a precaution.  Meanwhile Deeks is going to use the golden rule of interrogation to get info from the two men in custody – listening.

When Fatima meets Marchione, the two exchange greetings in sign language.  Fatima apologizes if she is rusty but Marchione thinks Fatima is doing well.  Kensi want to talk to Winnick.  Asking if Winnick is friend, Marchione says yes – Winnick was helpful when she joined the company.  Kensi has to knocks on the door a few times before Winnick answers.  When Kensi announces federal agents, Winnick suddenly closes the door.  He’s not running, he’s vomiting.  “Never eat grocery store sushi after 5PM.”  This is Kensi and Fatima’s first questioning of a vomiting possible witness.  

Relaxing in his rather spacious outdoor gathering area, Winnick is asked by Marchione if he needs anything.  Marchione leaves to get Winnick some water.  Winnick calls Marchione “an angel” as she walks away.  They had a date when they first started working together but the exoskeleton project took over their lives. When Marchione returns, Kensi asks about the dead boss.  Winnick calls himself to Wozniak to Lindemore’s Jobs.  After Marchione, Lindemore was the nicest person Winnick ever met.

Marchione asks if Lindemore met with anyone at the tech conference.  Lindemore met with a lot of people – even some private meetings.  Their technology was so revolutionary, everyone was interested.  Asked about the private meetings, Winnick doesn’t know much.  When Kensi notes that Winnick said they were close, Winnick starts to cry.  Putting his hand on Marchione’s knee, he says it is “just you and me now.”  Kensi and Fatima share a look.

As Deeks calls for “no whammys”, Nell has a hit on Steve Tricks’s location.  As she sends it to Callen and Sam, Deeks is going back to the list of tech companies at the conference.  Listening to Deeks’s suggestion, Nell narrowed down the tech companies that she can check out.  Nell asks if Deeks is going to join Kensi and Marchione.  He’s not.  Deeks thinks Kensi needs some time with Marchione.  Nell doesn’t know what that means.  Deeks really doesn’t either but being with Kensi for so long, that he just knows.  “Couples goals,” Nell says as Deeks leaves to talk to the suspects again.  

In their full tactical gear, Callen and Sam approach Tricks’s SUV outside of a long closed club off Cahuenga.  The club could be a hideout.  Approaching the SUV, there are some firearms magazines in the back seat but not Tricks.  That is until the door to the club opens and Tricks is firing at Callen and Sam, hitting Sam in the arm.  Nell hears all this through the earwigs.

Saying it just “stings a little”, Sam is still in the gunfight.  Callen and Nell are worried.  As Callen goes to the other side of the vehicle to distract Tricks, Sam is flat on the ground looking at Trick’s feet.  When Sam shoots Tricks around the ankles, a second SUV arrives.  More shooting as Tricks drags himself into the vehicle.  Against Sam’s wishes, Callen tells Nell to send an ambulance.

In the bullpen,  Callen has Sam’s pain meds but Sam isn’t doing pain meds.  Callen offers to put them in applesauce.  Sam laughs and he’s not wearing a sling.  Kilbride arrives, pleased to see Sam up on his feet, not pleased with the team’s “lackluster performance” so far.  The SecNav called about the technology that the Navy invested a whole “crapload” of money in is still in the wind.  Callen thinks there is more to the technology than the SecNav has shared – does the Admiral know?  While the SecNav doesn’t owe any of them any more information, the technology not only makes people invisible to the naked eye, they are also invisible on radar and countersurveillance devices.  Planes, tanks, subs all unseeable, untraceable would change modern warfare  

The Admiral starts to leave, asking if Sam is up to the job.  How long has it been since he felt the “hot lead of hostile fire penetrate his flesh.”  An angry Sam stands up and says “two years, ten months, three weeks and a day.”  Sam finished his work that day and he’s finishing his work today. “Outstanding Agent Hanna, that is the first good answer I’ve heard today.”  Callen thinks Sam’s timeline sounds like a country song.

Leaving Winnick’s, Kensi is amazing how talkative he is but seems nice.  Marchione agrees – he’s like a brother to her.  Fatima says Winnick doesn’t see her as a sister and mentions the date.  They had one date but it felt wrong, according to Marchione.  His house smells like her grandmother’s house and the grandmother she didn’t like.  

Knowing about Tricks’s escape, Marchione believes he’s part of a bigger operation.  The question isn’t the size of the operation, it is who out there would kill an office full of people for the technology.  When Marchione says “my grandmother,” Kensi and Fatima are stunned.  Remembering when she was sick visiting her grandmother, Marchione was forced to drink this awful syrup – Ipecac.  It made her throw up.  She smelled the same thing in Winnick’s house.  Winnick may not be sick at all.

Leaving Ops, Nell sees the Admiral and tries to return to Ops.  She’s called into his office instead.  The Admiral is impressed by Marchione’s impact on the investigation, he’d like to know Nell’s thoughts, especially since she was against Marchione working the team.  Nell didn’t doubt Marchione’s abilities, Hetty was just…  Before Nell could finish her sentence, Kilbride tells Nell that Hetty isn’t here.  And while he has no idea where goes on in Hetty’s mind, he’s pretty sure he did not put Nell in charge just to have her do what Hetty would on every tough call.  Nell talks about protecting the team and putting Marchione in harm’s way.  She’d be negligent not to worry about breaking Hetty’s precedents.  Worrying about what Hetty would do will leave Nell “a day late and a dollar short,” according to the Admiral.

Returning to the boat shed, Kensi is talking about snacks while Marchione looks at her shoes.  There is blood on the shoelaces.   When Kensi asks if Marchione is alright, she’s more worried about Fatima watching Winnick.  Kensi assures Marchione that Fatima will share everything.  Asking what happens if Winnick is involved, Kensi explains that in a case like this, everyone is a suspect.  “Everyone’s gone,” Marchione says.  What is she supposed to do.  Kensi tells Marchione that things will be hard for a while but they have a responsibility to her dead coworkers.  Fatima calls in – Winnick is on the move.  Fatima is going to stay with Winnick “like a bad perm.”

Icing his sore arm, Sam asks Nell if there is any news on Trick’s back-up crew.  Nell doesn’t have that information but she knows who owns the abandoned club – Tabalah-Gulf Holdings, a Saudi Arabian commercial real estate company.  With no obvious connection to Tricks, Nell is looking into company CEO Eshan Rahman, a Saudi billionaire with a private “Super Model Island”.  Tabalah-Gulf Holdings could be reopening the club for his model/pop star/movie starlet pals or he could be using the club to hide criminal activity.  Sam and Nell believe it to be the latter.  As Sam leaves, Nell asks him to wear the sling.

Watching Winnick at a coffee shop, he’s meeting with Eshan Rahman.  Fatima sends some photos and Nell confirms it is Rahman.  An alert comes from Winnick’s bank – he just made $3 million.  As Fatima is about to approach the two, Kensi asks Marchione if there is a digital key to open the downloaded files.  There is.  It’s likely they downloaded the encrypted files and Rahman needs Winnick to unlock the files.  Sending Callen and Sam, Nell wants Fatima to slow Winnick and Rahman down.

Knocking on the window of Rahman’s SUV, Fatima tells Rahman’s driver that she left her phone in an Uber and the Uber driver left her in the wrong neighborhood.  Rahman’s driver can’t help.  Fatima flirts a little with the driver and then with an arriving Rahman to stall.  That doesn’t work when Winnick passes by and asks why Fatima is there.  Rahman orders his driver to go.  Winnick tries to escape but Fatima has him.  When he struggles, she punches him in the stomach and we have more vomiting.

Cutting off Nell in the hallway, the Admiral had another call from Washington, this time from the Secretary of Defense.  The Admiral said they’d have the case wrapped up by the end of the day.  Nell thinks that he shouldn’t make promises he can’t keep.  He will heed the advice if the case is settled today.  Nell has confirmation that Marchione’s boss met with the biotech firm Quantumgencis, which as Rahman as a lead investor.   Quantumgencis is working with a Saudi prince for a “City of the Future” plan.  Nell gets an alert.  Rahman is moving west to LaBrea.  She updates Callen and Sam.  

Chasing Rahman, Callen and Sam think he’s on his way to the airport.  The Admiral disagrees – there is no way Rahman is flying commercial, he has a jet ready at a moment’s notice.  At a private airstrip, Rahman and his driver are just at the property’s gate.  Hey break thought the security gate with Callen and Sam right behind them.  As they race along the runway, Kensi is near the plane in the Audi.  She is able to cut Rahman’s vehicle off.  Rahman and his driver are arrested.

As the sun goes down, Sam is packing his gear.  Nell found the medals where Hetty keeps her prized possessions – in a compartment in the back of her liquor cabinet.   If Sam wasn’t in a hurry, he’d hug Nell but he’s already missed his flight.  He has a long drive ahead.  No he doesn’t.  Nell made a special transportation arrangement for Sam – a private jet.  Rahman’s getaway plan gave Nell an idea.  When Sam calls the private jet a Hetty move, Nell thinks it is a high compliment but tells Sam it is actually a Nell move.  Callen arrives – he’s going too.  

In the boat shed, Agent Castor is learning sign language with Marchione.  They are both laughing and having a good time.  Kensi arrives and Marchione gives her a big hug.  She was so grateful for the opportunity to show what she could do. Kensi asks what’s next for Marchione.  Advocacy and awareness.  She is one of many people who want to serve but are being kept from serving.  Kensi thinks the team would do well to have someone like Marchione.  “Someday,” Marchione signs and shows Kensi the same.

The Admiral joins Nell in Nell/Hetty’s office.  Sitting and thinking, the Admiral shares that he is proud that he devotes his life to the US military.  99 times out of 100, he thinks they get things right.  Noticing his tie is loose, Nell wonders if she should break out the gin.  She’s not sure how to read the situation.  He’d like her to let and old, tired man talk.  The Admiral says they don’t always get things right.  Sienna Marchione was really something.

When Kensi walks into the Armory, Deeks asks about Marchione.  Kensi and Marchione are going hiking next Sunday.  Deeks starts talking about hiking trails but that’s not what Kensi wants to talk about.  Admitting she didn’t know how to tells Deeks this morning, Kensi realized after meeting Marchione that they can do everything right with their fertility treatments and planning for their lives as parents and things may not work out.  Deeks thinks they’ll cross that bridge if and when they get to it.  Kensi isn’t up to any logistics talks and Deeks agrees.  After Deeks says he was being a big, dumb jerk, Kensi disagrees.  Her worry is that all the logistics talk is making a bet that everything is going to work out.  Deeks hugs her.  He apologizes and they share I love yous.

What head canon can be formed from here:   This was a really good season 12 episode.  

The cuts scenes helped explain Deeks’s attitude with Sienna Marchione in the start of the episode and Nell’s gratitude to Deeks for coming up with a way to sift through the companies.  I’m not sure what you cut to get those scenes in but they would have helped.

Episode number:  This is episode 16 of season 12, the 278th episode overall.

[thespiralstaircaselitmag]

The Spiral Staircase: Our Literary Magazine’s Namesake

Going up the spiral staircase was the Shady Side Academy Middle School experience. Students from decades past have left their footprints on the staircase, each showcasing a different yet similar story; once, they were like us. Some of us moved quicker than others, some lingering on a specific step. We all moved at a different pace, stumbling backwards when hardships arose, climbing back up after our achievements and solutions. We would be able to see our friends on the spiral staircase with us, as well as strangers that we would befriend along the way. One step at a time, ascending and descending, we were improving, we were changing. Everything was changing, from expectations to relationships and friendships. Yet, all of us started from the bottom, identical yet individual strangers to this new and thrilling journey. The railings would help us along the way, our teachers, friends, and parents supporting us. Throughout our years here, we would all ascend to the pinnacle, able to reminisce about the memories and experiences. We all ascended the spiral staircase of Shady Side Academy Middle School. We all graduated from Shady Side Academy Middle School at the top of the spiral staircase. -- Audrey Jiang

The spiral staircase feels like it could be in an old house, which Shady Side once was, or a castle, like one where the royal ascend to the ball. It would most likely look good in an entrance room, with a cozy nook under it, or a sitting room right before you walk up. It feels like something you would find in an old manor, like maybe you walk up and enter a magical library. It could also be part of a magical garden, if it was made of large leaves as the stairs, and real vines and flowers as the railings and balusters.-- Chloe Will

The wood of the staircase is as rough and deep as the feelings that cascade through it every day. The intricate details represent the wealth of feelings, joys, and frustrations that have been experienced from the many years of school at Shady Side Academy. Crushes, breakups, academic triumphs and failures, drama and excitement, all happening on the old wood. All types of people, young or old, experienced or new, coming together to walk their destinies. --Suhani Sehgal 

[fmdtaeyongarchive]

headcanon #043: interest in performance art

word count: 1,688 words.

triggers: mentions of violence (including gun violence), assault.

ash is notoriously talented in the musical arts and terribly untalented in traditional visual art like drawing or painting, but performance art is something ash has developed a unique interest in over the years. he’s a dancer, a performer by nature, but it’s situation art pieces, so-called “happenings” that step outside the bounds of a simple enjoyable story performance that intrigue him the most. 

ash hasn’t gotten the opportunity to experience much situation art in person, as it’s generally meant to be experienced, but he loves reading about it and watching videos of it or on it. since the beginning of the year, he’s made a point to read more, and in addition to topics like philosophy, he’s delved deeper into research on performance art as well.

he’s most interested in art the explores topics of human relationships, the dynamic between audience and artist, the nature of art, and the human experience as a whole. that’s part of why he’s so drawn to situational performance art. though he’s one of many, he’s in particular a fan of marina abramović‘s works.

two of marina abramović‘s works he’s most interested in are breathing in, breathing out (which marina abramović did with ulay) and rhythm 0.

in breathing in, breathing out, the two blocked their nostrils and pressed their mouths to one another so that they could only breathe the air the other breathed out until eventually, they were each only breathing in carbon dioxide and deprived of oxygen up to the point of passing out. ash views it as a commentary on human connection and relationships and the danger of dependence in relationships — to rely too much on another is to deprive and ultimately poison oneself. while ash considers this to be a commentary on all relationships, be they romantic, friendly, or familial, it resonates with him in particular because of his own tendency to depend too much on romantic partners in the past.

violence tw, assault tw, gun tw // his other favorite abramović piece, and possibly his favorite performance art piece, is the famous rhythm 0 where abramović stood in a room with seventy-two objects, varying from feathers and food to a scalpel and a loaded gun, laid out on a table. she left instructions on the table stating that the others in the room could do whatever they wanted to her for six hours and she would take responsibility for whatever they did, absolving them of any responsibility of their own for their actions. by the end of the six hours, she’d had her clothes cut off of her body, had been cut with various sharp objects, someone had attempted to suck blood from one of her cuts, and someone had pressed the gun to her head and put her own finger on the trigger before someone had torn it away. when she moved after the six hour mark had passed, the audience had all scattered and the observers-turned-participants left at the closure of the piece, not wanting to talk to her. abramović stated she realized in the midst of the piece that the participants might very well kill her if given the right time and circumstances. there are obvious feminist implications to this piece that ash acknowledges, but wouldn’t feel qualified to talk on himself as a man, but the part that resonates with him the most is how it speaks to the relationship between artist and audience. art builds a fourth wall, even despite all attempts to tear it down, that makes an audience feel ownership and emotional detachment from the artist. the pedestal a performer is put on grants the audience the ability to do or say anything they’d like, even if it’s demeaning, violent, or sexual, without the fear of consequences — things they’d never do or say if faced with the face-to-face humanity of the artist. the piece really struck ash the first time he read about it and saw pictures of it, and he’d like to do a song based on the piece one day, or even an entire album inspired by marina abramović‘s works and his own interpretations of them in the context of human relationships and the relationship between artist and audience.

gun tw // the death of the artist by abel azcona is another piece that stuck with ash for similar reasons as rhythm 0. the artist had previously done performance art pieces on topics like religious institutions, politics, and sexuality that angered several organizations and groups that had caused controversy and caused him to receive death threats, and for this piece, he wrote letters to all of the organizations and people who had threatened him inviting them to a gallery where he stood on a raised platform facing a loaded gun on open display nearby on a platform. this is more of a commentary on persecution and attempted censorships of artists by powerful entities, something ash can’t really relate to since he doesn’t do anything provocative enough to earn him death threats from anyone other than edgy teenagers on twitter. nevertheless, it’s a piece he thinks about a lot in relation to the nature of art and an artist’s place in society.

other pieces ash likes to ponder are:

tehching hsieh thirteen year plan, where he declared that he would make art in private without showing it publicly for a span of thirteen years. at the end of the thirteen years, he concluded the project with the statement “i kept myself alive. i passed the december 31st, 1999.” it makes ash consider his relationship to his own art and whether he’s doing it for others or doing it to keep himself alive.

marina abramović and ulay’s lovers (another piece he’d like to use as inspiration for a song), where the pair embarked on a dramatic spiritual journey to end their romance. they started walking from opposite ends of the great wall of china and met in the middle to officially end their relationship with an embrace, a final farewell, and the promise to never meet again afterward. it’s the sort of poignant closure ash thinks every relationship could have in an ideal world, and it speaks to the depth of bond romance can root in two people.

marina abramović and ulay’s rest energy, where the pair balanced a drawn bow and arrow between them for four minutes, with the arrow aimed directly at marina abramović’s heart. this is again a piece with feminist implications of the societal power men hold over women, but it also speaks to the vulnerability of love to ash and the kind of unwavering trust he’s not sure he’s ever had with anyone.

yoko ono’s cut piece, where she invited audience members to cut pieces of  clothing and remove it. ash likes this one for similar reasons to why he likes rhythm 0.

roi vaara’s artist’s dilemma, a video piece where a sign saying “art” points in one direction, while another stating “life” points the other in a frozen, barren landscape. vaara deliberates between the two for the duration of the video before it ends with him standing in the middle, still not having chose one direction or the other. to ash, it’s unclear whether it’s saying one must choose between one or the other, that an artist exists between two different worlds, or that performance art lays in between the balance of life and art, and he likes that he can interpret it in so many ways without any explanations feeling hollow.

tino sehgal’s kiss, a choreographed piece involving two dancers slowly acting out a passionate embrace on the floor of a museum or gallery. among original choreography and poses, they reenact famous kissing scenes from other artworks such as rodin’s kiss and brancusi’s kiss. the piece to society’s simultaneous discomfort with publicized intimacy and fascination with other’s love lives, both historically through art and socially through gossip and rumors. it also speaks to the difficulty of recreating intimacy through art. as someone who spends a lot of his time trying to recreate love and intimacy through music, and has had plenty of people both obsessed with and shaming him for their own perception of his love life, this piece stands out to him on a personal level as well.

ash’s interest in performance art comes mostly from how directly it can invite the audience of the art into the work. it’s part of why he’s come to love performing (solo) concerts over spending every day of his life in front of a camera. (jay z himself has argued concerts can be akin to performance art in an alternative venue, and ash would like it if one day he could hold a concert that plays with that idea more.) performance art allows for statements to be made that can’t be made as resoundingly through musical art or the fine arts. in performance art, the artists themselves are the art, and oftentimes the audience becomes a part of the art, too, and it can be outside of the commodification and greediness of other forms of art because of that if the artist so chooses. videos, pictures, and books on performance art can be sold, but often the piece itself cannot be and lives within the minds and memories of the artist and the audience. situational art can’t as easily be censored, controlled, or shaped by rich collectors or executives at labels, and that’s a part of the appeal to ash, who has often felt his music has been compromised by the business processes of the music industry. it reminds him of why he used to love dance so much, how a live performance lives in the moment.

ash himself doesn’t think he could ever be a performance artist, though he’d one day like to have the chance to sit down and talk to performance artists about art through their eyes. he believes performance artists are greatly underrated and too often written off as fake deep try hards when the legends of the form have just as much to say about the human experience as the greatest artists of any other art form.

[morethanonepage]

i’m angery

i know i’m mostly a star wars killjoy on tumblr dot com lately but allow me to SCREAM about my latest nemesis, a book called “American Dirt”:

Lydia Quixano Pérez lives in the Mexican city of Acapulco. She runs a bookstore. She has a son, Luca, the love of her life, and a wonderful husband who is a journalist. And while there are cracks beginning to show in Acapulco because of the drug cartels, her life is, by and large, fairly comfortable.

Even though she knows they’ll never sell, Lydia stocks some of her all-time favorite books in her store. And then one day a man enters the shop to browse and comes up to the register with a few books he would like to buy―two of them her favorites. Javier is erudite. He is charming. And, unbeknownst to Lydia, he is the jefe of the newest drug cartel that has gruesomely taken over the city. When Lydia’s husband’s tell-all profile of Javier is published, none of their lives will ever be the same.

Forced to flee, Lydia and eight-year-old Luca soon find themselves miles and worlds away from their comfortable middle-class existence. Instantly transformed into migrants, Lydia and Luca ride la bestia―trains that make their way north toward the United States, which is the only place Javier’s reach doesn’t extend. As they join the countless people trying to reach el norte, Lydia soon sees that everyone is running from something. But what exactly are they running to?

Written by one Jeanine Cummins, who has Puerto Rican ancestry but grew up in the MD suburbs (as I did, tbf) and in 2015 considered herself white (”I am white...in every practical way, my family is mostly white.”  [cw for sexual assault and murder at the link]), everything I read about this book has begun to drive me to madness.

Recommended by the Mary Sue book club (the source of the above summary), it has since been retracted bc a) its sucks and b) THEY DIDN’T READ IT BEFORE PUTTING IT ON THE BOOK CLUB LIST (”I try to read most, if not all, of the books I recommend for the Book Club because I truly do love reading, and I want to make sure that if I suggest someone grab something, it’s something I can say I liked. When I was looking up two books to fill out the list, one of them was American Dirt. I saw that it had received a lot of positive press from Stephen King, Rumaan Alam, Don Winslow, Sandra Cisneros, and other literary news outlets including Oprah’s Book Club. It seemed like the type of literary fiction that’s always good for a book club read. I was mistaken.“)

Myriam Gurba, at Tropics of Meta, describes being asked to review it for a feminist magazine, and then being told her review was too negative to publish. It included gems such as:

Cummins bombards with clichés from the get-go. Chapter One starts with assassins opening fire on a quinceañera, a fifteenth birthday party, a scene one can easily imagine President Donald Trump breathlessly conjuring at a Midwestern rally, and while Cummins’ executioners are certainly animated, their humanity remains shallow. By categorizing these characters as “the modern bogeymen of urban Mexico,” she flattens them. By invoking monsters with English names and European lineages, Cummins reveals the color of her intended audience: white. Mexicans don’t fear the bogeyman. We fear his very distant cousin, el cucuy.

[...]

With their family annihilated by narcotraffickers, mother and son embark on a refugees’ journey. They head north, or, as Cummins’ often writes, to “el norte,” and italicized Spanish words like carajo, mijo, and amigo litter the prose, yielding the same effect as store-bought taco seasoning.

[...] Lydia’s husband, a journalist, describes her as one of the “smartest” women he’s ever known. Nonetheless, she behaves in gallingly naïve and stupid ways. Despite being an intellectually engaged woman, and the wife of a reporter whose beat is narcotrafficking, Lydia experiences shock after shock when confronted with the realities of México, realities that would not shock a Mexican.

It shocks Lydia to learn that the mysterious and wealthy patron who frequents her bookstore flanked by “[thuggish]” bodyguards is the capo of the local drug cartel! It shocks Lydia to learn that some central Americans migrate to the United States by foot! It shocks Lydia to learn that men rape female migrants en route to the United States! It shocks Lydia to learn that Mexico City has an ice-skating rink! (This “surprise” gave me a good chuckle: I learned to ice skate in México.) That Lydia is so shocked by her own country’s day-to-day realities, realities that I’m intimate with as a Chicana living en el norte, gives the impression that Lydia might not be…a credible Mexican. In fact, she perceives her own country through the eyes of a pearl-clutching American tourist.

Parul Sehgal, at the NYT, digs into the fact that while the motives of this book may be unimpeachable (tho: are they??), the writing itself is...perhaps less so:

I found myself flinching as I read, not from the perils the characters face, but from the mauling the English language receives. Lydia’s expression “is one Luca has never seen before, and he fears it might be permanent. It’s as if seven fishermen have cast their hooks into her from different directions and they’re all pulling at once. One from the eyebrow, one from the lip, another at the nose, one from the cheek.” Yes, of course. That expression.

Sehgal also highlights my favorite line I’ve heard about in this book: “when Lydia finds she is unable to pray, ‘she believes it’s a divine kindness. Like a government furlough, God has deferred her nonessential agencies.’” The Raised in the DMV Suburbs just JUMPED OUT, didn’t it, Jeanine? But like legit, why on earth would a Mexican bookstore lady’s frame of reference ever be A GOVERNMENT FURLOUGH and NONESSENTIAL AGENCIES. followers, i just about died. 

David J. Schmidt, at The Blue Nib, calls out other inaccuracies and stereotypes:

It is worth dwelling on the character of Javier for a moment. A “drinking game” could be created based on all the Latin American stereotypes he personifies. Javier is dapper, yet dangerous. He is charming, yet mysterious. He wears a white guayabera, a shirt the author describes as “more suitable for Sunday Mass than a regular workday.” (Untrue—this is a casual garment, more suitable for a love affair in a Fabio-bedecked romance novel.)

This quintessential “Latin lover” shows up at Lydia’s bookstore and speaks to her in a tone significantly different from the other characters of American Dirt. I  must emphasise, Javier’s dialogue does not reflect the normal speech patterns of Mexico, but perfectly reflects U.S. stereotypes. The only way to properly read Javier’s lines is through the most gross of caricatures.

One should imagine the husky voice of Antonio Banderas, speaking at his most sensual and Spanishy. Any character he has played in English will do, although it is clear that Javier was ideally written for the voice of Puss in Boots. When Lydia asks if Javier reads English, the dapper narco responds:

“I try, yes […] My English isn’t fluent, but it’s close. And this story is so delicate.”

[...]

The cultural inaccuracies of American Dirt run deep, right down to the language. Throughout her book, Cummins shows confusion regarding the grammatical genders in Spanish. Most notably, she baptises the drug kingpin Javier with the nickname La Lechuza. It is difficult to imagine a macho, womanizing capo using a feminine-gendered noun as his moniker. Would a hardened mafia boss call himself “The Princess of Compton” or “The Belle of Belfast”?

Cummins got a seven figure advance for this. A SEVEN FIGURE ADVANCE. She “wished someone slightly browner than me would write it,” but she did it,  and her team is throwing around the fact that her husband’s previously undocumented status as some sort of justification without mentioning that he’s white & Irish. 

Also, there’s this news:

Imperative Entertainment, the production banner behind the Clint Eastwood hit The Mule, has acquired the rights to American Dirt, the Mexican migrant drama novel by Jeanine Cummins.

Charles Leavitt, the scribe who penned the Leonardo DiCaprio drama Blood Diamond, has been tapped to write the adaptation, which will be produced by Imperative’s Dan Friedkin and Bradley Thomas.

Charles Leavitt is a white guy who, most recently, wrote the Warcraft movie. So, that’s going to end well. 

I’ll leave you with this other gem from Gurba (from her essay about it, “Pendeja, You Ain’t Steinbeck “):

Susan Sontag wrote that “[a] sensibility (as distinct from an idea) is one of the hardest things to talk about” and with this challenge in mind, I assert that American Dirt fails to convey any Mexican sensibility. It aspires to be Día de los Muertos but it, instead, embodies Halloween. The proof rests in the novel’s painful humorlessness. Mexicans have over a hundred nicknames for death, most of them are playful because death is our favorite playmate, and Octavio Paz explained our unique relationship with la muerte when he wrote, “The Mexican…is familiar with death. [He] jokes about it, caresses it, sleeps with it, celebrates it. It is one of his favorite toys and his most steadfast love.” Cummins’ failure to approach death with appropriate curiosity, and humility, is what makes American Dirt a perfect read for your local self-righteous gringa book club.

so idk, The Mary Sue, maybe it should stay on your Book Club list after all. (Oh wait: as of this writing, it still is.) 

[ilyricshub]

Daler Lyrics - Varinder Brar | From Je Jatt Vigarh Gaya

Daler Lyrics Varinder Brar | From Je Jatt Vigarh Gaya #Daler #VarinderBrar #JeJattVigarhGaya #JayyRandhawa #DeepSehgal #GillSaab #DilsherSingh #KhushpalSingh

[art-now-india]

The end of an era - V, Sripriya Mozumdar

This series of charcoal portraits offers my homage to maestros who are no longer with us. Each one here is a stalwart in his/her own field, a supreme artist or performer. For generations like my own, the sitar has been synonymous with Pandit Ravi Shankar, just as pop has meant Michael Jackson. When we have lost each one of these maestros; I have felt a deep vacuum, felt an era come to a close. I offer these paintings as my humble tribute to each master here. May they rest in peace. This one captures the essence of Zohra Sehgal - the grand old lady of Indian theatre and cinema

https://www.saatchiart.com/art/Drawing-The-end-of-an-era-V/391949/2633826/view

[authorstalker]

Jenny Offill with Rumaan Alam at Books Are Magic

I sure did devour Parul Sehgal’s profile of Jenny Offill, so you’d think I’d know better than to show up five minutes late to the launch of Weather. Yes, there were certain famous-to-me bloggers in Books Are Magic’s back room, but I missed out on Jenny and Rumaan’s facial expressions, including during the Q&A when a man said to Jenny, “I’d like to read you a passage from Dept. of Speculation.”   Oh and I also missed a great Jenny quote about how she wishes she could live like an art monster/coder and stay locked up for weeks with only cereal and Mountain Dew for company. ANYWAY, here’s what I wrote down:

On the subject of autofiction: 

Everything I write is emotionally autobiographical.

On writing about a librarian:

I would have liked to be a librarian....I tried not to have a lot of fake librarian scenes because that would’ve just been too hard on the real librarians.

A helpful tip for the end of the world:

When society collapses, you should definitely have gum. It’s good for morale....something in your brain says, “Well it can’t be too bad, I’m chewing gum!”

What happens when you do a climate change deep dive:

It turned me into a person I maybe would’ve made fun of...once you’ve looked at something, it’s hard to unlook.

One of the first things I did was start talking to people about the weather. Not even climate change, just the weather.

That’s what writing this book meant: you can’t go back to thinking, “Oh what a lovely day!”

From writing to activism:

I don’t exactly think of the book as hopeful or not hopeful...I was prone to a kind of fatalism; it started to feel to me that that was a different kind of denial. That’s when I had to get out of my bookish head and do more activism. What was I going to tell my daughter, that I just didn’t like the [climate change] movement aesthetically?

I remember with a chill seeing the “No Politics” sign in the bakery. It becomes a question of conscience. Do you stand by and say, “Well, it’s not about me”? — a lot of people don’t do that.

The climate research that most affected her:

Learning when our cities won’t have recognizable climates; little things like it will be hard for the Hudson Valley to grow apples because that requires a frost; how frightened the scientists were.

The process of creating the “word constellations” that make up Weather:

My friends who work in the visual arts have ways to get their minds out of the expected patterns. I started to make the boards [pictured in the NYT profile] — it was not very productive but it was very pleasing to do. It was great to see it outside of my head and to see which lines still struck me. So much of writing is about intuition as well as the analytical side.

When men ask all the questions:

A guy in the audience said, “I’d like to read you a passage from Dept. of Speculation” and the whole room braced itself, oh god! But then it was actually kind of funny, here’s the part he read:

“What are you reading about?” the husband asks her from across the room. “Weather,” she tells him.

So the guy wanted to know if Jenny Offill had planned to write a book called Weather back when she wrote Dept. of Speculation and the answer was: nope.

No, that was a reference to the fog of adultery, how everything the old person does is annoying and everything the new person does is bathed in gold. 

A theater girl/a drama girl:

Another guy, but maybe the same guy—this is the downside of the back room—asked about her experience recording the Dept. of Speculation audiobook.

It was really fun. I have a theater girl/drama girl past, and the humor in this has to be deadpan. But it turns out I couldn’t pronounce the names of anything in the book. It’s sort of a stamina thing to be able to read a book for that long. 

[longlistshort]

This month there are a lot of excellent exhibitions on view in Chelsea.

At David Zwirner is God Made My Face: A Collective Portrait of James Baldwin, a group show curated by writer Hilton Als. The works are varied and include portraits by Richard Avedon (shown above), a friend of Baldwin’s who also attended De Witt Clinton High School with him, as well work by Njideka Akunyili Crosby (seen below), Kara Walker, James Welling, Beuford Delaney, Glenn Ligon and many more.

At Marianne Boesky Gallery is Pure, Very, New, Paul Stephen Benjamin’s first solo exhibition in New York. The exhibition includes paintings, photographs, sculpture, and single and multi-channel video installations, as well as a new site-specific black light installation in the internal passageway between the two spaces.

From the press release–

Benjamin’s practice is rooted in a vigorous meditation on blackness, considering: “What is the color black?” “What does black sound like?” “Is it an adjective, a verb, an essence, or all of these components mixed to create a nuanced whole?” For his large-scale monochromatic paintings, Benjamin thickly coats the canvas in varying shades of black, producing a sensation of boundless depth. This is further accentuated by Benjamin’s application of the particular tonality’s name within the field of color—the words appearing to float and dissipate within the richness of the paint itself. The development of these paintings followed an ordinary visit to a hardware store, where Benjamin was confronted with the many permutations of commercial black paint. Shades of black came with emotive titles like “Totally Black,” “New Black,” and “Pure Black,” among numerous others. For Benjamin, this sparked a multi-layered investigation of the color and whether it could be distilled or understood differently within the context of a painting or the color itself.

… Benjamin’s practice also extends into a conceptual investigation of sound, and how “black” can be conveyed and experienced aurally. In these works, he often uses single and multi-channel video installations to loop portions of particular historic and cultural footage to isolate fragments of collective memories or internalized narratives. With Black is the Color (2015), which will be included in the exhibition, Benjamin arranges a towering cluster of antiquated televisions, forming a glowing grid that endlessly repeats a segment of Nina Simone’s 1959 performance of “Black is the Color of My True Love’s Hair.” Here, Benjamin appropriates only the words “Black is the Color,” creating an abstraction of the song that reveals the contradictions and parallels between the notion of black being the color and it being a color. Moving fluidly from sound installation to painting to photography and sculpture, Benjamin’s practice is driven by the idea that blackness, whether explored as a matter of conceptual inquiry or identity, cannot be captured in a single action, emotion, or language.

At Yancey Richardson is Blue Sweep, an exhibition of Andrew Moore’s beautiful photographs, taken in Alabama and Mississippi over the course of three years.

At Bryce Wolkowitz Gallery is Oliver Jeffers' charming painting exhibition For All We Know. If his work looks familiar it may be because Jeffers is also the author of several critically acclaimed picture books.

From the press release-

This series of paintings illuminate a dream-like nocturnal world populated by astronauts, deep-sea divers, sinking ships, floating pianos, and burning matches. Omnipresent throughout are the night sky and the ocean - the two great and unknown frontiers - glittered with the imaginary lines that create constellations, serving in this case as a mysterious key to unlock our world.

Expanding on years of observation, from the history of his upbringing in Belfast, to contemporary New York City, Jeffers' evokes the precarious state of our home and its inhabitants. Inspired by Buckminster Fuller's seminal book Operating Manual for Spaceship Earth, he presents pianos as dubious flotation devices and our planet presented as a cumbersome motor vehicle, overheating as we argue over what to play on the radio. From researching astronaut's descriptions of looking at Earth from the distance of the Moon, Jeffers noticed certain recognizable patterns to the way in which he discussed the politics of his hometown from a vantage point of across the Atlantic Ocean. In finding that few people outside of Northern Ireland knew or cared of the intricate conflict there, a great waste of time was revealed: a divided population identical to each other in every way save for the flags they flew and the stories they told. Tragically, each side's identity are still firmly rooted to the existence of the other, and therefore locked into a spiral of repeated patterns.

At both of Jack Shainman's locations are a series of impressive paintings by Lynette Yiadom-Boakye.

For a new kind of exhibition experience, Asad Raza has organized the group show Life to Come, at Metro Pictures which "brings together works that meditate on the creation of new worlds and new models for living." There are no labels or listings for the works included in the show. Instead there is a guided tour by hosts who take you around the various works to help you draw connections between the objects. Adding to the uniqueness of the experience, at one point the host pauses while talking and partially in motion, recreating a work by artist Tino Sehgal, and at another they show you that they have changed their eye color, a work by Rirkrit Tiravanija.

From the press release-

Experiencing these works together incites intellectual, physical, and spiritual understandings of what it means to make an entirely new world, one in which reality is made from fiction. Raza asserts that “by re-immersing ourselves in the strangeness and fecundity of attempts to create worlds that have gone before, our imagination of a world beyond the present may be renewed.” The uncertainty about what new paradigm awaits us is unsettling in the wake of the modernist 20th century, but it links us to previous generations who experienced radical reinventions of biological and social life.

All of these exhibitions close 2/16/19.

[jujhdapunjab]

Devinder Sharma dwells deep to explain the farm crisis, the wheat export ban, the need to make farming economically viable and profitable.

If Flight, Cab and Train Fares are Dynamic, why Can’t Farm MSP Also Change Anytime?

Agri expert Devinder Sharma says today, as in the past, commodity trading, massive speculation and unfair terms of trade are behind rising food prices.

As India battles food price inflation, Devinder Sharma, a well-known trade and agriculture expert, explains why Indian policymakers should boost farm incomes to revive the economy. “If farmers, comprising 50% of the country’s population, were to receive a higher income through a guaranteed MSP, it would create a vast rural demand, nothing short of a ‘rocket dose’ for the economy,” he says. Excerpts from an interview with Rashme Sehgal.

There are apprehensions that India’s food security is at risk. Wheat procurement is half what it was last year. Is that why the government has banned wheat exports?

These are testing times. India will have to be doubly cautious with the Russia-Ukraine conflict showing no signs of ending soon and a scramble to buy and stock wheat globally given the supply shortfalls. Perhaps, for this reason, the government has imposed a ban on wheat exports. And rightly so. Imagine where we would have gotten wheat supplies from if the domestic stocks in future were not enough to address food security concerns.

Let’s first try to understand what has led to this situation. A swirling heatwave has reduced wheat production within the country, with procurement not likely to touch even 20 million tonnes against the target of 44.4 million tonnes. There is a lot of trade interest in allowing unhindered exports, with some private companies claiming that even 21 million tonnes can be exported, but my understanding, despite the euphoria being exhibited, is that there should be restrictions on unbridled wheat exports.

We have to be extra watchful, knowing well that with any climatic aberrations, like the sudden heatwave that struck in early March, production estimates anytime can go awry. The heatwave has reduced wheat productivity in Punjab and Haryana, the food bowl, by five quintals per acre. This has pulled down the production estimate by six million tonnes, which may turn out to be still higher when the final production statistics come in. Given that the next wheat harvest is a year away, in April 2023, we cannot take any chances. Food security cannot be put at risk.

This is because India cannot afford to repeat the wheat blunder of 2005-06 when it was forced to import 7.1 million tonnes of wheat in 2005-06 and 2006-07 at double the prices we had paid to our farmers. A policy shift favouring private companies, allowing them to purchase wheat directly from farmers, resulted in short supplies for the public distribution requirements. There was no shortfall in production at the time, and yet India had to resort to massive wheat imports. Luckily, these imports came in before the global food crisis erupted in 2007-08, which resulted in food riots in 37 countries. But this time, if India needs to import, there would be no supplies available off-the-shelf.

On the other hand, edible oil prices have also skyrocketed. Can you talk about “self-sufficiency” in edible oil? How can this situation be reversed?

The Russia-Ukraine region provides for 70% of India’s sunflower imports (14% of India’s edible oil imports). India already imports roughly 55% of its edible oil needs, the value of imports increasing to Rs 1.17-lakh crore in 2020-21. There has been a growing dependence on imports to meet domestic requirements. Further, with Indonesia banning crude palm oil export, edible oil availability is under increased stress. Therefore, market prices of edible oils have skyrocketed.

India would not have reached such a dire situation if it had not abandoned the Yellow Revolution, which would have made it almost self-sufficient in edible oil production. In around 1993-94, nearly 97% of domestic edible oil requirements were being met within the country. This was when the bound import duty on edible oils under the World Trade Organisation (WTO) obligations was in the 300% range. We had enough policy space to provide an enabling environment to increase oilseed production and enhance processing capacity. But for some strange reason, the government gradually lowered the import tariffs (reaching almost zero per cent), as a result of which imports began to swell.

The ongoing Russian-Ukraine war has again focused on the need to become atmanirbhar in edible oils apart from food. I see no reason why India cannot pay our own farmers a higher price to increase domestic production of oilseed rather than pay farmers in other countries. Sunflower production can be easily increased if farmers are provided with assured marketing and guaranteed prices. Take mustard; with market prices high for over a year, touching Rs 7,000 per quintal, farmers have already sown the crop in an area targeted for 2024. Give farmers a higher price, and they will make the country self-reliant.

Does not the high food inflation precede the Russia-Ukraine crisis? What other factors have brought wholesale and retail inflation to 15% and 7%, respectively?

Even earlier, food prices globally were at a high. According to the UN Food and Agricultural Organisation (FAO), food prices in February exceeded prices that prevailed during a severe worldwide food crisis in 2007-08. That was when 37 countries faced food riots. In other words, the world was hurtling into a food crisis before the war, which made it still worse, with prices jumping another 22%. As a result, an additional 193 million people have slid below the hunger line.

Globally, prices were increasing before the war erupted. Although the justification being given is supply chain disruptions during the pandemic, commodity trading and speculation are believed to be the reason behind the rising prices. Studies have shown several investment funds and firms have increased their stakes in commodity trading, and speculation was rife. Like the 2007-08 world food crisis, when speculation is believed to have been responsible for at least 75% of the spike in prices, as agribusiness companies literally earned fortunes in commodity trading, the recent trends, too, are pointing in the same direction. Among the largest food companies, Cargill is known to have already made a record profit this year, exceeding its earlier best in 2021. Also, other prominent grain [and oilseed] handlers, Bunge and ADM [Archer-Daniels-Midland Co] have recorded increased profits. In any case, it is now widely acknowledged that profits of big business have multiplied, which is being passed on to consumers as high inflation.

The Russia-Ukraine crisis has also adversely impacted fuel and fertiliser prices? How are farmers and others coping?

Estimates point to the fertiliser subsidy bill increasing to Rs two lakh crores in the current financial year, as the government is likely to keep fertiliser prices affordable for the farming community. It has already allayed fears over any shortage of supplies for the Kharif crop season.

As far as fuel prices are concerned, the rise in prices at periodic intervals increases the cost of production for farmers. Soon after the state Assembly elections were over, we saw fuel prices being raised. This year, we have seen the cost going up on several fronts, including the rising cost of operation for tractors and combine harvesters, increased farm wages, etc., which has not been reflected in the Minimum Support Price (MSP) that the farmers have finally received.

Since the MSP was announced on 8 September, much before farmers sowed the crop, I think the time has come when government should revise the final MSP price (just before the harvesting begins) whenever higher prices hit farm cultivation. If there can be dynamic pricing for airlines, trains and also for cab aggregators, I see no reason why we can’t have dynamic prices for agricultural commodities whereby a revised MSP be paid to farmers. In the digital age, this shouldn’t be difficult.

Are farmers very disappointed with the policies of the central government and especially their continued attempts to undermine MSP?

The iconic farm protest at the borders of New Delhi had drawn attention to it. Not only in India, but farmers worldwide have been demanding a guaranteed price for their crops. In India, farmers are asking for guaranteed MSP for the 23 crops for which these prices are announced. In the United States, farmers have been asking for a parity price. Both mean the same thing. This essentially tells us that farmers worldwide have been denied a living income all these years. That’s why agrarian distress continues to worsen everywhere.

This essentially is because, for neo-liberal economists, agriculture serves only two purposes—it provides cheaper raw materials and provides for cheaper labour in the cities. Unless the food prices are deliberately kept low, the chances of providing a cheaper workforce become distant. Agriculture has therefore been kept deliberately impoverished. In the name of “competitive” prices, farm prices have been kept lower than the cost of production. As I have often said, when a farmer undertakes crop cultivation, he does not realise that he is actually cultivating losses. With low farm prices and, in addition, reducing private sector investment in agriculture over the years, you cannot expect a miracle. Yet, despite such low prices, farmers continue to produce a record harvest year after year. This means they continue to produce economic wealth for the country. We must realise that India stands next to China when it comes to the gross value of agricultural production at current prices. According to the FAO, the gross value of agricultural production stands at $418,541,343 million. In other words, look at the enormous economic wealth our farmers produce. Isn’t it time to compensate our farmers accordingly? After all, they too, are wealth creators?

Further, the Russia-Ukraine war has shown that the focus now needs to shift to atmanirbharta, reducing the dependence on food imports. Which means that instead of depending on “competitive” global supply chains, the focus should shift to food self-sufficiency. This has the added advantage of creating gainful employment for the masses.

The governments must realise that if farmers get a higher income in their hands, they will spend it in the markets. Remember, when the 7th Pay Commission was announced, benefiting some 4-5% of the population, economists termed it a booster dose for the economy. If farmers, comprising 50% of the country’s population, were to receive a higher income through a guaranteed MSP, it would create a vast rural demand, which would be nothing short of a “rocket dose” for the economy.

(Rashme Sehgal is a freelance journalist.)