#ft. defne

[primpk]

❛ isn’t it a little cliche to get engaged on christmas? ❜

‘ un poco. ’ responde, con la mirada aún fija en la pareja que se encuentra a un par de mesas donde ellas están sentadas. son el centro de atención del lugar, todos le han felicitado después de que el varón se arrodillara. ‘ hubiese sido más cliché si sacaba el anillo de la hamburguesa — ¡o del postre! ’ la simple idea le hace reír, volviendo su atención por fin a su acompañante. ‘ ¿de qué manera no te gustaría que te propusieran matrimonio? ’ / @navruzxd .

[scftlightz]

ੈ♡˳┊closed starter for @gcldenhcurs

⠀⠀⠀⠀the job was simple, but labeled under special. this bounty owed a debt to an unhappy collector and jupiter was hired to due any means to get the owed debt. thanks to the state of the apartments, his breaking and entering was successful. he spent sometime in the home of the bounty in silence as he was mostly building up the intensity of the situation. after some convincing, he finally received the debt but it wasn't clean. what felt like hours of cleaning after his job, he exited the apartment with a scent of obvious bleach leaving him. adjusting his bag as he turned the corner but stopped short when he almost collided with a another figure. " are the hallways always this fucking small? are you alright? you scared the hell out of me. "

[ghcstlyhearts]

closed starter for @faiirytalesx ( safieh & defne ! ) location: safieh's home

A soft hum escaped the elder fae's lips as she darted around her kitchen with ease, making what she knew to be Defne's favourite dish. It had been a while since she'd last seen the other girl, so naturally, it made sense for Safieh to welcome her home with a dish that tasted as such. "So, as much as I love you popping in," Safieh began softly, leaning across her counter to smile at the other. "What brings you to my neck of the woods. Is everything good?"

[ccmpletemess]

closed starter for @prcttyvxnom ( feyza & defne )

"You owe me dinner," Fey mused, leaning against her sisters desk with a smile. "The phone call lasted about... ten minutes and he spent at least nine of them complaining about how his life was so unfair that Iva was here." She shook her head, arching an eyebrow as her phone chimed. "Wanna bet again? I'm almost certain that's Iva texting to complain about Cey being... well, Ceyhan."

[patriciers]

for  tobias  ,  the  main  problem  he  has  with  being  at  court  is  making  small  talk  .  his  duties  as  a  sworn  shield  grant  him  an  excuse  to  disengage  without  coming  across  too  rude  ,  but  that  could  never  be  the  case  when  he  attends  an  event  where  he  is  meant  to  be  a  guest  first  and  foremost  .  still  ,  he  tends  to  try  and  blend  in  ,  avoiding  conversation  altogether  ,  and  it  does  startle  him  whenever  someone  outside  his  family  or  small  circle  of  friends  approaches  him  .  so  when  he  sees  another  standing  before  him ,  expression  seeming  to  indicate  that  they’re  awaiting  a  reply  ,  his  brows  raise  slightly  as  he  looks  around  ,  “  …  were  you  speaking  to  me  ,  @dieluminis​  ?  ”  his  voice  is  level  ,  giving  away  no  indication  of  his  surprise  ,  though  he  has  to  wrestle  with  the  hesitant  ‘  um  ’  that  wants  to  spill  out  as  he  continues  ,  “  pardon  me  ,  i  was  lost  in  thought  .  ”  he  winces  internally  at  the  uncharacteristically  formal  air  to  his  words  .

[luredhearts]

⸻ ︰ ⧽ 𝑴𝑨𝑫𝑫𝑰𝑬 ; 𝐂𝐋𝐎𝐒𝐄𝐃 𝐒𝐓𝐀𝐑𝐓𝐄𝐑. ⸻ ︰ ⧽ defne aksoy ; @svgarbcbies !

there is a sleepiness to her features, like after a long night of no sleep, but yet she still slept last night. like someone who didn't appreciate the early mornings, but yet she did. she wasn't tired, she was relaxed, the fatigue that crossed her features came with the feeling of content that settled in her bones. she was home. her fingers curled in the soft ends her the others hair, content in the quiet alone. ❛ have you decided yet ? ❜

[misfitxmagic]

* 𝐢𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐢𝐧𝐠 𝐚𝐳𝐫𝐚 𝐳𝐨𝐫𝐥𝐮...

[ Ayça Ayşin Turan | she/her ] A new face takes refuge under Dark Skies. AZRA ZORLU, a 29 year old BANSHEE, is one of those from the PRESENT learning to navigate this changed world. People say behind their back that they’re UNSTABLE but the truth is that they’re really KIND HEARTED. Their style can best be described as BLUE OCEANS, PENDING TRAGEDIES, OLD OAK TREE, FRESH LINEN, and we’ll see how that helps them fit in.

𝐛𝐞𝐥𝐨𝐰 𝐭𝐡𝐞 𝐜𝐮𝐭:

*𝐛𝐚𝐬𝐢𝐜 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 *𝐛𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 *𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐭𝐲 *𝐡𝐞𝐚𝐝𝐜𝐚𝐧𝐨𝐧𝐬

𝐛𝐚𝐬𝐢𝐜 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧

nicknames: az, lu birthday: may 4th, 2000 born: turkey, ankara parents: davut & defne zorlu siblings: two older brothers moved to: new orleans, aged 14 occupation: forensic blood splatter analyst for nola pd eye colour: blue hair colour: brunette weight: 53kg height: 5 ft 4 inches tattoos: right ankle (here) + left wrist (here) piercings: many on her ears (here) + belly button scars: 7 to 8" diagonal tear across the back of her right shoulder

𝐛𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝 𝐢𝐧𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧

born into a seemingly happy family, azra was the third and final child, the only girl and the apple of her father's eye.

from a young age it was clear to the zorlu's that azra was no ordinary girl. nightmares, sleepwalking, hearing and seeing things that nobody else could. luckily for her, the family was no stranger to what they considered an affliction. azra's grandmother was a banshee, and whilst the gene had skipped her mother, it was clear from a young age that it hadn't skipped her.

her "symptoms" were manageable until she hit her teenage years and required more help than phone calls from her grandmother could provide. with a lot of careful thought and consideration, the family uprooted from turkey and moved to new orleans { where grandmother lives }. her parents have since returned to their homeland three years ago, her two brothers chose to remain in the city with her.

worked in various bars while she completed her studies to qualify as a forensic scientist specialising in blood splatter. her placement was with nola pd who then hired her after she qualified. she's been working there full-time for 5 years. it's not the job people typically expect her to be in but she loves the morbidity of it. it feels fitting given her banshee status that her job is also surrounded by death.

azra has a good handle on her abilities as a banshee thanks to her grandmother's teachings but she isn't always in control. occasionally, she'll find herself standing in the middle of something awful with no idea how she got there. she still gets nightmares and visions she doesn't understand. she does her best not to let it show, keeping her supernatural label a secret to the best of her ability.

𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐭𝐲 & 𝐡𝐞𝐚𝐝𝐜𝐚𝐧𝐨𝐧𝐬

on the outside, she can be described as sunsets, a cool breeze on a summer day or perhaps even a lone star in the night sky

she's kind and caring with a depth to her that stems from years of being haunted by death

looks like like a cinnamon roll, will bite you with her blunt human teeth to display dominance { that's a joke }

but for real, she won't allow anyone to walk all over her, despite looking like a petite sweetheart, she knows how to kick back

tends to enjoy the company of animals more than people but is still quite sociable despite this

has a fennec fox named tiki that she rescued as a baby 2 years ago. she's perfectly legal and due to being hand-reared, could not be released back into the wild

she does a lot for wildlife rescues, taking in and helping injured and abandoned creatures wherever she can

despite the fact her job can get nitty + gritty, she always makes sure she has a perfect manicure, a different colour every 2 weeks

will run in terror at the sight of clowns

likes to binge watch horror movies or scary series as if her real life doesn't have enough of that

obsessed with mint choc chip ice cream and british chocolate

{ will probably add to this as time goes on }

[aykutiltertr]

Tutuklu - Sezen Aksu ✩ Ritim Karaoke Orijinal Trafik (Türkçe Pop)  ⭐ Video'yu beğenmeyi ve Abone olmayı unutmayın  👍 Zile basarak bildirimleri açabilirsiniz 🔔 ✩ KATIL'dan Ritim Karaoke Ekibine Destek Olun (Join this channel to enjoy privileges.) ✩ ╰┈➤ https://www.youtube.com/channel/UCqm-5vmc2L6oFZ1vo2Fz3JQ/join ✩ ORİJİNAL VERSİYONU Linkten Dinleyip Canlı Enstrüman Çalıp Söyleyerek Çalışabilirsiniz. ⭐ 🎧 ╰┈➤ https://youtu.be/fEdZGZpAZZk ✩ (MAKE A LIVE INSTRUMENT ACCOMPANIMENT ON RHYTHM IN EVERY TONE) ✩ Aykut ilter Ritim Karaoke Ekibini Sosyal Medya Kanallarından Takip Edebilirsiniz. ✩ İNSTAGRAM https://www.instagram.com/rhythmkaraoke/ ✩ TİK TOK https://www.tiktok.com/@rhythmkaraoke ✩ DAILYMOTION https://www.dailymotion.com/RhythmKaraoke ⭐ Tutuklu - Sezen Aksu ✩ Ritim Karaoke Orijinal Trafik (Türkçe Pop) Söz - Beste: Sezen Aksu Bm               G    Bm Ne senden öncesi                      F#m Ne senden sonrası Bm               G    Bm Ne senden öncesi                    F#m Ne senden sonrası Bm Ayrılık aman A Ölümden yaman G                               F#m Geçmiyor zaman geçmiyor Bm Ne anam, babam A Ne hoş hatıram G                              F#m Yetmiyor canım yetmiyor                                    Bm Ben sende tutuklu kaldım                                    A Kendi hayatımdan çaldım                           G Yedi cihan dolandım                      A   F#m   Bana mısın demiyor Bm                 Sakladım gözlerimi A Sustum hep sözlerimi Bm                                G Yandım yar közlerimi ah                            F#m Savur savur bitmiyor Nakarat Sezen Aksu tarafından yazılmış şarkılar listesi Madde Tartışma Oku Bekleyen değişiklikler Değiştir Kaynağı değiştir Geçmişi gör Araçlar Vikipedi, özgür ansiklopedi Bu liste genel olarak Sezen Aksu'nun söylediği değil yazdığı veya bestelediği şarkıların listesidir. Sezen Aksu'nun bugüne kadar 400'den fazla şiir ve bestesi vardır. Bu şiirlerden 197 tanesi Eksik Şiir kitabında yayınlanmıştır. A Şarkı Söz Müzik Seslendiren Albüm Albüm yılı "Abanoz'daki Emine" Necati Cumalı Levent Yüksel, Sezen Aksu Levent Yüksel Levent Yüksel'in 2.CD'si 1996 "Abidin" Sibel Algan Sezen Aksu Defne Samyeli Abidin (Single) 2022 "Ablam Aşktan Öldü" Yıldırım Türker Sezen Aksu Sezen Aksu Şarkı Söylemek Lazım (albüm) 2002 "Acıtmışım Canını Sevdikçe" Yıldırım Türker Sezen Aksu Sezen Aksu Öptüm 2011 "Adamların Adamı" Sezen Aksu Sezen Aksu Nükhet Duru Nükhet Duru (albüm) 1994 "Adem Olan Anlar" Sezen Aksu Sezen Aksu, Uzay Heparı Sezen Aksu Deli Kızın Türküsü 1993 Seda Yavuz & Funky C Uzay Heparı - Sonsuza 2008 What Da Funk? ft. Simge WDF1 2017 Ceylan Ertem Adem Olan Anlar (single) 2020 "Adı Bende Saklı" Sezen Aksu, Meral Okay Yannis Karalis Sezen Aksu Adı Bende Saklı 1998 "Adı Menekşe" Sezen Aksu, Meral Okay Sezen Aksu, Aşkın Arsunan Levent Yüksel Adı Menekşe 1998 Sezen Aksu Adı Bende Saklı (albüm) "Affet" Sezen Aksu Sezen Aksu Sezen Aksu Affet (single) 2021 "Ağır Abim" Sezen Aksu, Meral Okay Sezen Aksu Ayşegül Aldinç Nefes (Ayşegül Aldinç albümü) 2000 "Ağla" Sezen Aksu Sezen Aksu Ayşegül Aldinç O Kız 2010 "Ağla Ağla" Sezen Aksu Sezen Aksu Defne Samyeli Ağla Ağla (single) 2019 "Ağlama Anne" Sezen Aksu Kostas Mountakis Ajda Pekkan Ajda 93 1993 Gönül Akkor Dönüş 1994 Selda Bağcan Çifte Çiftetelli 1997 40 Yılın 40 Şarkısı 2015 Nilüfer Yeniden Yeni Yine 2016 "Ağlamak Güzeldir" Sezen Aksu Sezen Aksu Sezen Aksu Ağlamak Güzeldir 1981 Göksel Mektubumu Buldun mu? 2009 Işıl Yücesoy Zamansız 2016 "Ağlayamam" Sezen Aksu Sezen Aksu Onurr Bir Kahramanlık Hikâyesi 2017 "Ahbap Çavuşlar" Sezen Aksu Sezen Aksu Cihan Okan Cihan Okan (albüm) 2010 Eşref Vakti Beyaz Sayfa 2013 Bekir Ünlüataer Ahbap Çavuşlar (single) 2015 Sezen Aksu Demo 2018 "Ahdım Olsun" Sezen Aksu Sezen Aksu Ebru Gündeş Ahdım Olsun (album) 2001 Sezen Aksu Bahane (albüm) 2005 "Aha" Sezen Aksu Sezen Aksu Yonca Evcimik Aha 2016 "Ah Be Güzelim" Sezen Aksu Sezen Aksu Hülya Avşar Hayat Böyle 1998 "Ah Felek Yordun Beni" Sezen Aksu Sezen Aksu Sezen Aksu Öptüm 2011 "Ahmet" Sezen Aksu Sezen Aksu Deniz Seki Hiç Kimse Değilim 1997 "Ah Yıllar" Sezen Aksu Sezen Aksu Ferhat Göçer Biz Aşkımıza Bakalım 2010 "Ah Yıllar" Sezen Aksu Sezen Aksu Mustafa Ceceli Kalpten 2014 "Alaturka" Sezen Aksu Fahir Atakoğlu Sezen Aksu Işık Doğudan Yükselir 1995 Tarkan İz (Fahir Atakoğlu albümü) 2008 "Aldatıldık" Sezen Aksu Sezen Aksu Rengin Rengin (albüm) 1996 Zeliha Sunal Çok Ayıp 2014 Bergüzar Korel Aykut Gürel Presents 2016 Ercüment Vural Ercüment Vural Sunar 2016 Sezen Aksu Demo 2018 "Aldatma" Sezen Aksu Cristiano Malgiaglio, G.P. Felisatti Ajda Pekkan Cool Kadın 2006 "Aldırma Deli Gönlüm" Sezen Aksu Sezen Aksu Sertab Erener Sakin Ol! 1992 Hülya Avşar Dost Musun Düşman Mı? 1993 Sinan Özen Ölürüm Yoluna Ceyda Mazalto Türkiye Popstar 2003 Ebru Gündeş Beyaz (albüm) 2011 Burak Yeter & Sertab Erener Blue (albüm) 2012 Mehmet Erdem Hiç Konuşmadan 2013 "Al Götür Beni" Sezen Aksu Anthony James & Yorgos Bellapaisiotis

[wxnhvs]

°✧｡× :   @pulchramflo​   ➳ 𝒅𝒆𝒇𝒏𝒆 & 𝒂𝒛𝒎𝒊  .

        “  𝒂ş𝒌ı𝒎,  ”  𝒕𝒉𝒆𝒓𝒆’𝒔 𝒂𝒅𝒐𝒓𝒂𝒕𝒊𝒐𝒏 𝒊𝒏 her hues as she greets the male who was standing in their new kitchen looking way too attractive with his messy hair this early in the morning.  “  günaydın,  ”  she greets, running her fingers through her dark locks. the two had just moved into their new apartment a week or two ago and had yet to unpack the rest of their belongings or decorate the place. defne’s lips curve into a smile as she slips her arms around his torso from behind, pressing her cheek against his back.  “  when did you get up ?  ”  she asks, lips brushing against his skin as she leaves a soft kiss against skin. 

[sciforce]

Robust image classification with a small data set

One of the biggest myths about AI is that you need to have a large amount of data to obtain sufficient accuracy — and the rapid development of Big Data analytics seems to prove this intuition. It is true, that deep learning methods require model training on a huge number of labeled images. However, in image classification even a small collection of training images may produce a reasonable accuracy rate (90–100%) if using new machine learning techniques, that either make use of previously collected data to adjacent domains or modify the classification process completely, working on similarity of images.

Knowledge Cross-Utilization

Similar to human capability to apply knowledge obtained in one sphere to related spheres, machine learning and deep learning algorithms can also utilize knowledge acquired for one task to sole adjacent problems.

Even though traditionally ML/DL algorithms are designed to work in isolation to address specific tasks, the methods of transfer knowledge and domain adaptation are aimed to overcome the isolated learning paradigm to develop models which would be closer to human way of learning.

Transfer learning

Transfer learning is the method that generalizes knowledge, including features and weights, from previously learned tasks and applies them to newer, related ones that lack data. In computer vision, for instance, certain low-level features, such as edges, shapes, corners and intensity, can be shared across multiple tasks.

To understand how it works, we can use the framework presented in the paper, A Survey on Transfer Learning (Pan & Yang 2010) where they use domain, task, and marginal probabilities:

A domain D consists of two components: a feature space X and a marginal probability distribution P(x), where x ∈ X . As a rule if two domains are different, they may have different feature spaces or different marginal probability distributions.

Similarly, a task T consists of two components: a label space Y and a predictive function f(·), i.e., a mapping from the feature space to the label space. From a probabilistic viewpoint, f(x) can also be written as the conditional distribution P(y|x). Based on these representations, transfer knowledge can be defined as follows: given a source domain Ds and learning task Ts, a target domain Dt and learning task Tt, transfer learning aims to help improve the learning of the target predictive function fT (·) in DT using the knowledge in DS and TS, where DS≠ DT , or TS≠ TT, T = {Y, f(·)}.(Pan & Yang 2010) In most cases, it is assumed that there is a limited number of labeled target examples, which is exponentially smaller than the number of labeled source examples are available.

To explain how transfer learning can be used in a real life, let’s look at one particular application which is learning from simulation. Simulation is the preferred tool for gathering data and training a model rather than collecting data in the real world. While learning from a simulation and applying the acquired knowledge to the real world, the model uses the same feature spaces between source and target domain (both generally rely on pixels). However, the marginal probability distributions between simulation and reality are different, so objects in the simulation and the source look different, although this difference diminishes as simulations become more realistic.

Further reading

SJ Pan, Q Yang (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering 22 (10), 1345–1359 [PDF]

Domain adaptation

Usually considered a subfield of transfer knowledge, domain adaptation refers to the method of fighting the so-called domain shift challenge: since the distribution of data in the target domain is different than in the source domain and there exists the similar gap between the marginal probabilities between the source and target domains, such as P(Xs) ≠ P(Xt), there is a need to devise models that can cope with this shift.

To achieve successful unsupervised domain adaptation we need to cover three main aspects:

domain-agnostic feature extraction: the distributions of features extracted from both domains should be indistinguishable as judged by an adversarial discriminator network;

domain-specific reconstruction: embeddings should be decoded back to the source and target domains;

cycle consistency: to ensure that the mappings are learned correctly, we should be able to get back where we started.

The simplest approach to unsupervised domain adaptation is building a network to extract features that remain the same across the domains by making them indistinguishable for a separate part of the network, a discriminator. But at the same time, these features should be representative for the source domain so the network will be able to classify objects. As the approach is unsupervised, we don’t have to have any labels for the target domain, only for the source domain, and in many cases, for synthetic data.

Alternatively, domain adaptation can map the source data distribution to the target distribution. Both domains X and Y could be mapped into a shared domain Z where the distributions are aligned. This embedding must be domain-agnostic, hence we want to maximize the similarity between the distributions of embedded source and target images.

Further reading

Murez, Zak & Kolouri, Soheil & Kriegman, David & Ramamoorthi, Ravi & Kim, Kyungnam. (2017). Image to Image Translation for Domain Adaptation. [PDF]

Pinheiro, Pedro H. O.( 2018). Unsupervised Domain Adaptation with Similarity Learning. IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018): 8004–8013. [PDF]

Similarity-based approaches

An alternative to direct classifying of an input image to any of the output classes is measuring the similarity between images by learning a similarity function.

Few-shot learning

Few-shot learning is an object categorization problem mostly in computer vision. In contrast to other ML-based algorithms, few-shot learning aims to learn information about object categories from a single (the so-called one-shot learning) or a few training images. In addition to the input image, it also takes a reference image of a specific object as input and produces a similarity score denoting the chances that the two input images belong to the same object.

In its simplest form, one-shot learning method computes a distance-weighted combination of support set labels. The distance metric can be defined using a Siamese network that uses two identical fully connected CNNs with same weights and accepting two different images. The last layers of the two networks are then fed to a contrastive loss function, which calculates the similarity between the two images.

The first network outputs the encoding / vector of the image being queried and the second network, correspondingly, — the encoding / vector of the reference image from the dataset. Afterwards the two encodings are compared to check whether there is a similarity between the images. The networks are optimized based on the loss between their outputs by using the triplet loss or the contrastive lost functions.

The triplet loss function is used to calculate gradients and is represented as follows:

where a represents the anchor image (or the reference image from the data set), p represents a positive image and n represents a negative image. We know that the dissimilarity between a and p should be less than the dissimilarity between a and n. Another variable called margin is added as a hyperparameter to defne how far away the dissimilarities should be, i.e if margin = 0.2 and d(a,p) = 0.5 then d(a,n) should at least be equal to 0.7.

The contrastive loss function is given as follows:

where Dw is the Euclidean distance between the outputs of the sister Siamese networks. Mathematically the Euclidean distance is represented as follows:

where Gw is the output of one of the sister networks. X1 and X2 is the input data pair

The loss functions calculate the gradients that are used to update the weights and biases of the Siamese network. Loss will be smaller, if the image are similar and will be further apart when images are not similar.

A development of the approach can be seen in the method by Santoro et al. (2016) using Memory-Augmented Neural Network (MANN). In their model, a neural network extended with an external memory module so that the model is differentiable and can be trained end-to-end. Thanks to their training procedure, they forced the network to learn general knowledge whereas the quick memory access allowed to rapidly bind this general knowledge to new data.

Further reading

Li Fei-Fei, Rob Fergus, and Pietro Perona (2006). One-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(4):594–611. [PDF]

Koch, Gregory, Richard Zemel, and Ruslan Salakhutdinov.Siamese neural networks for one-shot image recognition.2015. [PDF]

Santoro, Adam, Bartunov, Sergey, Botvinick, Matthew, Wierstra, Daan and Lillicrap, Timothy P. (2016). One-shot Learning with Memory-Augmented Neural Networks.” CoRR abs/1605.06065. [PDF]

[read365days]

Idaho: A Novel

Emily Ruskovich

absolutely beautiful, heartbreaking, lonely, mysterious - she is a lovely, lovely, and very young, writer. Defn want to read more of her work

A stunning debut novel about love and forgiveness, about the violence of memory and the equal violence of its loss—from O. Henry Prize–winning author Emily Ruskovich

Ann and Wade have carved out a life for themselves from a rugged landscape in northern Idaho, where they are bound together by more than love. With her husband’s memory fading, Ann attempts to piece together the truth of what happened to Wade’s first wife, Jenny, and to their daughters. In a story written in exquisite prose and told from multiple perspectives—including Ann, Wade, and Jenny, now in prison—we gradually learn of the mysterious and shocking act that fractured Wade and Jenny's lives, of the love and compassion that brought Ann and Wade together, and of the memories that reverberate through the lives of every character in* Idaho*.

In a wild emotional and physical landscape, Wade’s past becomes the center of Ann’s imagination, as Ann becomes determined to understand the family she never knew—and to take responsibility for them, reassembling their lives, and her own.

Praise for Idaho

“You know you’re in masterly hands here. [Emily] Ruskovich’s language is itself a consolation, as she subtly posits the troubling thought that only decency can save us. . . . Ruskovich’s novel will remind many readers of the great Idaho novel, Marilynne Robinson’s Housekeeping. . . . * [A] wrenching and beautiful book.”***—The *New York Times Book Review***

“Sensuous, exquisitely crafted.”—The Wall Street Journal

“The first thing you should know about Idaho, *the shatteringly original debut by O. Henry Prize winner Emily Ruskovich, is that it upturns everything you think you know about story. . . . You could read *Idaho just for the sheer beauty of the prose, the expert way Ruskovich makes everything strange and yet absolutely familiar.”—San Francisco Chronicle

“Mesmerizing . . . [an] eerie story about what the heart is capable of fathoming and what the hand is capable of executing.”—Marie Claire

“Idaho is a wonderful debut. Ruskovich knows how to build a page-turner from the opening paragraph.”**—Ft. Worth* Star-Telegram***

“Ruskovich’s debut is haunting, a portrait of an unusual family and a state that becomes a foreboding figure in her vivid depiction.”—The Huffington Post

“Idaho is both a place and an emotional dimension. Haunted, haunting, Ruskovich’s novel winds through time, braiding events and their consequences in the most unexpected and moving ways.”—Andrea Barrett

“Ruskovich digs deeply into everyday moments, and shows that it is there, in our quietest thoughts and experiences, where we find and create our true selves.”—Hannah Tinti, author of The Good Thief

“[Idaho] caught and held me absolutely.”**—Leslie Jamison, author of* The Empathy Exams***

“Ruskovich has written a poem in prose, a beautiful and intricate homage to place, and a celebration of the defeats and triumphs of love. Beautifully crafted, emotionally evocative, and psychologically astute, Idaho is one of the best books I have read in a long time.”—Chinelo Okparanta, author of Under the Udala Trees

“Ruskovich has intricately entwined a terrifying human story with an austere and impervious setting. The result—something bigger than either—is beautiful, brutal, and incandescent.”—Deirdre McNamer, author of Red Rover

[scftlightz]

ੈ♡˳┊closed starter for @gcldenhcurs

it's been a few days since the collected debt and breaking the vase that belonged to his neighbor. he's been in and out the office for the past few days due to the long hours of staking out and hunting down the bounty. thankfully the one that's been leading them on a wild chase through out town, made a pit stop thanks to an anonymous caller. by the time they got them processed, it was time for the shift change so he spent most of his time in the office doing paperwork instead of leaving.

as he flipped through papers he jotted down notes ands time stamps, he stopped when he heard a knock on the door of his office. " come in. " he spoke from his chair, leaning his back against it as he dropped the pen. thinking it could be a co-worker coming to ask him the same question for the 100th time. when eh realized it was a completely different face he sat up, " defne. hey. "

[ccmpletemess]

closed starter for @prcttyvxnom ft. ceyhan yazici & defne yazici

"Do you know who I saw the other day?" Ceyhan's eyes were narrowed, arms crossed over his chest as he huffed in annoyance. "Ivana. Clearly she didn't ruin my life enough the first time, when she, you know, brought someone to our engagement party." Glancing over in his baby sister's direction, he arched an eyebrow. "What's that face for?"

[patriciers]

as  footsteps  approach  the  bench  she  had  chosen  as  her  roost  ,  serena  doesn’t  think  to  look  up  ,  anticipating  rickard  or  another  family  member  .  “  i  haven’t  moved  since  you  checked  on  me  last  —  ”  she  starts  ,  though  as  her  gaze  trails  upwards  ,  she  cuts  herself  off  ,  breathing  out  somewhat  forcefully  in  surprise  .  the  figure  before  her  is  not  who  she  had  expected  at  all  .  “  —  ah  ,  my  apologies  ,  @dieluminis​  .  i’ve  mistaken  you  for  someone  else  .  ”  her  attention  darts  away  ,  momentarily  ,  before  meeting  the  other’s  eyes  again  with  a  sheepish  grin  .

[scftlightz]

ੈ♡˳┊closed starter for @cigvrettedvet

⠀⠀it was tradition to have an after party after a concert but with morgan's band, it started to feel like part of the schedule. the expensive villa was filled with fans and a mixture of their contacts to celebrate the last gig of the north american leg. sitting in front of his control board, morgan was in the middle of playing a demo for those who lounged or was actually interested in new music. he turned when he heard the chair next to him move, " i was wondering when you'll get here. im glad i didn't get a fuck off. "