#Maxent

[dropsofsciencenews]

OpenStreetMap to Stop Cheetah Trafficking

ITA version ESP version

Behind the glamorous social media photos of cheetah cubs lies a dark reality. With fewer than 7,000 individuals in the wild, the cheetah (Acinonyx jubatus) is one of the world’s most endangered species, and the illegal trade of live cubs, especially in the Horn of Africa, is devastating its populations. Poachers take cubs from their mothers while they hunt, selling them into the luxury pet market at prices reaching $50,000 per cub. Between 2020 and 2023, this trade increased by 50%, posing a serious threat to the species' survival.

However, a new methodology developed by a research team could finally help monitor and disrupt illegal cheetah trafficking through a three-tiered approach. Scientists are mapping not only the trafficking routes but also previously undocumented cheetah populations, combining prey species distribution models, habitat suitability, and a trafficking network based on OpenStreetMap data.

The first step involves locating cheetah prey, such as gazelles and antelopes, which are essential for their survival. The team used Maxent, a machine learning method, to cross-reference prey presence data with environmental variables like climate and land cover, thus mapping the most suitable areas for cheetah survival. This step identifies key conservation areas and, unfortunately, those most vulnerable to poaching.

Based on these data, researchers developed a Habitat Suitability Index (HSI) specific to cheetahs, highlighting areas where their survival is most probable. This model identifies regions with optimal conditions for stable populations and locates areas at risk of trafficking, enabling targeted interventions.

Finally, a model was created to understand how traffickers move cheetahs from remote origin areas to Arabian Peninsula markets. By integrating OpenStreetMap data on roads, ports, and border crossings, researchers identified the most likely land and sea routes and main transit points. Route optimization tools mapped out trafficking “hotspots,” allowing law enforcement to plan strategic interventions at critical crossing points, such as less monitored ports and borders.

This model is a breakthrough in conservation, as it not only enables mapping and monitoring of trafficking routes but also reveals previously undocumented cheetahs, giving scientists and conservationists a more complete picture of the situation. With this discovery, we finally have a system to protect one of Africa’s most iconic species and provide local communities with a tool to defend their natural resources.

See You Soon and Good Science!

source

[i-am-l-ananas]

it's too cold in the house for my sourdough to rise properly and so I'm stuck with a bowl of dough in my lap trying to keep it warm like a broody hen

[captain-price-unofficially]

Wrecked Light Tank Mk VIC on a road between Huppy and Saint-Maxent, Picardy, France. 29 May 1940

[max1461]

So Yoruba vowel harmony, huh. You can model it with maxent. But should you? We shall see.

[botanyone]

AI Spots Plant Changes from Space Using Citizen Science

AI Spots Plant Changes from Space Using Citizen Science https://ift.tt/ajD9Vvy Gillespie and colleagues have created an AI tool called Deepbiosphere to track plant biodiversity. Using satellite images and data from citizen scientists, this deep learning model has mapped over 2,000 plant species across California. It outperforms traditional methods, spotting both towering redwoods and wildflowers with high accuracy. Deepbiosphere could revolutionise global efforts to monitor plant life, helping us understand how climate change and human activity are reshaping ecosystems. The authors claim that Deepbiosphere outperforms traditional species distribution models, achieving higher accuracy. It can map species at up to a few metres resolution and accurately identify plant communities. The model detected both mature and young regrowth in redwood forests, showing the lasting effects of deforestation. It can also identify rapid changes in plant communities after events like wildfires, demonstrating its potential for monitoring biodiversity changes over time. Gillespie and colleagues developed a deep learning model, Deepbiosphere, using a  modified convolutional neural network architecture to fed with combined aerial imagery from the National Agriculture Imagery Program with over 650,000 plant observations from citizen scientists across California. The model was trained to predict the presence of 2,221 plant species. Its performance was compared to traditional modeling approaches like MaxEnt and Random Forest. Plant biodiversity is changing rapidly due to habitat destruction and climate change. Traditional methods lack the spatial and temporal resolution to detect these rapid changes for individual species. Deepbiosphere’s approach, combining deep learning with remote sensing, offers new possibilities for high-resolution biodiversity monitoring. Ultimately, we envision a paradigm shift toward open-source foundation models that are continuously trained and improved with new remote sensing data, citizen science observations, and data modalities as they become available. Achieving this from public airborne or satellite imagery and growing citizen science observations will make biodiversity monitoring more accessible, thus advancing local and global nature conservation goals. Gillespie, L. E., Ruffley, M., & Exposito-Alonso, M. (2024). Deep learning models map rapid plant species changes from citizen science and remote sensing data. Proceedings of the National Academy of Sciences, 121(37), e2318296121. https://doi.org/10.1073/pnas.2318296121 (OA) Cross-posted to Bluesky, Mastodon & Threads. The post AI Spots Plant Changes from Space Using Citizen Science appeared first on Botany One. via Botany One https://botany.one/ September 17, 2024 at 12:30PM

[codeshive]

LIN570: HW10 – maxent solved

1. Q1 (55 points): Create a MaxEnt POS tagger, maxent tagger.sh. • The command line is: maxent tagger.sh train file test file rare thres feat thres output dir • The train file and test file have the format (e.g., test.word pos): w1/t1 w2/t2 … wn/tn • rare thres is an integer: any words (in the train file and test file) that appear LESS THAN raw thres times in the train file are treated as rare…

[lettucefather]

Pray for me in my MAXENT adventures (it's been reading the environmental file for 6 hours and it's still 0%)

[geckophysics]

Born to MAXENT, Forced to Equal a Priori Probabilities.

[444names]

Names generated from the full list of Moon selenographical features and French names

Aford Afrey Ageasbeas Agnetinz Albeauzeau Aldis Alement Alley Alpoune Alvenois Amauberis Amblamist Amene Amons Aneau Apent Arbin Ardan Aseidger Asheille Assen Astine Auxure Aviconne Avien Ayermonna...

Bacharyn Bacquer Baire Bancitat Bannyson Bardimer Barte Batillesse Bayeaudeck Bazine Beaur Beauval Beaux Belley Belmir Berno Biert Binglacour Bisoner Bizée Bjesse Blanque Blault Boischaël Bolzeau Bommon Bonius Boragore Borber Borene Borélodil Bosselle Bouay Boulta Bound Bounsuels Bouron Boynisell Bozon Brach Bradin Brantion Brodour Brosimirs Broux Brussau Bujobile Bunoister Burche Burcouray Burier Bustelin Cadebon Callard Capances Caphéopey Caplourie Caravalet Carna Casque Casse Chard Chazo Cheect Cheland Cheliandre Chene Choamy Choeurne Clois Clorectoue Cloux Coderthwev Coison Colaford Cotte Coudeau Couis Coupur Coute Couth Crard Crocher Croterre Custivalps Cécierise Célin Dabeau Dabing Daner Daramorva Darmy Darvilemie Davoyeux Deate Deaulis Decon Dejousts Delenter Dellanger Dellegrass Derafors Desmarrise Desquer Dessamas Desse Desset Dettegis Doine Donton Dorcad Dornanne Dorte Doufore Doviergeog Drablante Drailau Drivet Dubertain Dublet Dubrice Dubue Dubuilance Duchefand Ducierthin Ducrèchay Dudecurin Duernien Dullin Dunne Dupina Dupornaud Dupra Durea Délinoël Emessaveux Erdefoux Ertalps Exhinck Fabiernis Fabontorne Fallard Fauchomarl Faville Fayet Febrier Fecoutais Feneve Flecov Flemi Flordin Fordine Forochau Fraist Freau Fretteline Frier Frobin Galen Galvegerd Ganhord Garks Genion Gidosteen Gimon Glard Goigni Gotaudes Gouille Grache Grade Graninne Graste Grefed Grich Grier Groquiront Guilia Gärtionoy Hasset Hathe Heaudi Hiboix Hiche Hotterun Houchard Hugan Humeng Hunet Héniatrun Jacieux Jacom Jacquer Jaloquel Jambon Janarobir Janbron Janneks Jearmán Jeaux Jesjasher Joldukev Josylvale Julamo Krachard Laclandris Lacolzeaux Lafain Lafolze Lagaisegir Lagriault Laharoury Lallooks Lamilet Lanchee Lanissago Lapen Laper Larbeley Lardoise Larolia Lathing Lavergay Lavide Laïsen Leauzier Lecre Lemette Leminette Lerroux Leton Leuregate Lexan Liedolson Lierlev Livigne Lormedis Lorétatte Loutartion Loyothie Luaillan Luclanill Lunetiss Mahin Maile Maimo Malme Mangen Manne Manstrain Mantio Marchalp Mardille Mardinet Marics Marivas Marle Marmon Marnes Martanifte Mashilie Mathélous Matiaquert Maudin Maxelle Maxente Mayohanne Maëll Medefau Medier Mellierond Menacke Menan Mierdonc Misimax Mobin Mocho Monne Monovskill Moroit Motte Moure Mukeve Nange Narder Nelleguis Nelogette Nesarrim Niline Nistin Nolia Nonser Ochamp Océcil Offeaulin Ohneautrev Olfrable Oreyoe Oucyrenne Palen Pandive Paque Pardé Paste Patte Payere Peliphone Pettette Phabon Phienne Phome Phorree Plageollin Plope Poldrau Polet Popper Poqueriver Poutridon Prett Pricouis Proch Rabriquese Rainien Rallet Rance Rante Raument Rence Retion Reurn Ricle Rigok Risson Ristry Robeauc Robeyriff Roier Romonse Rompoisle Romsdebon Rondoux Roodev Rotiedge Rourity Ruglia Runierisem Rélémy Sabarge Sardes Schoux Selegous Seline Sellagard Shaine Sichenato Siolau Slitz Smane Soncy Sophaléo Sopponne Soutre Spard Spatrum Spenesieu Spuyeau Stion Stivilloë Streher Stéon Stéont Suille Suprin Suriste Sylvy Talins Tatier Telaux Tervier Tesmytoux Theeperse Thenarraim Theushard Thiree Tinke Tonneau Treauzie Trulx Trumnerey Tudemined Ungeau Ussette Ustus Vadejeau Venyu Verry Verte Vetterry Villanie Villemes Voites Vopoix Wanusionis Whartouche Whoside Wiche Wikisevre Wikoe Witte Worasser Wriau Yalmodnard Yamescasim Zemadne Éliandry Élinette Élène

[malcolmgg]

GGBet - legalne kasyno i bukmacher z licencją MGA

Czy zastanawiałeś się, czy GGBet to legalne kasyno i bukmacher? Jeśli tak, mamy dla Ciebie dobrą wiadomość - tak jest! Platforma GGBet działa w oparciu o międzynarodową licencję MGA/B2C/210/2011 wydaną przez Malta Gaming Authority. W tym artykule omówimy kluczowe informacje na temat licencji GGBet oraz sposobu działania kasyna i bukmachera.

GGBet jest własnością spółki Maxent Limited, zarejestrowanej zgodnie z prawem Unii Europejskiej i Malty. Numer rejestracyjny spółki to C47261, a adres siedziby to JPR Buildings, Level 2, Triq Taz-Zwejt, San Gwann, SGN3000, MALTA. Wszystkie te informacje świadczą o legalności działalności GGBet.

GGBet działa w oparciu o międzynarodową licencję wydaną przez Malta Gaming Authority. Licencja ta, o numerze MGA/B2C/210/2011, została wydana 1 sierpnia 2018 roku i jest zgodna z Gaming Authorisations Regulations (L.N. 243 of 2018) oraz prawem maltańskim. Dzięki tej licencji GGBet może oferować swoje usługi w wielu krajach europejskich.

Działalność ggbets-pl.com to nie tylko kasyno, ale również bukmacherka. Aby rozpocząć grę na platformie, należy założyć konto przez stronę internetową lub aplikację mobilną. Po założeniu konta i wpłaceniu depozytu, gracz może wybierać spośród wielu zakładów bukmacherskich i gier kasynowych online. Pełna oferta platformy jest dostępna dla pełnoletnich użytkowników z kontem na stronie.

Ważne jest, aby grać odpowiedzialnie i korzystać z najlepszych metod ochrony danych i finansów, co zapewnia GGBet. Platforma oferuje także akcje promocyjne, które umożliwiają graczom zdobycie dodatkowych środków do gry.

GGBet to legalne kasyno i bukmacher, działające na podstawie międzynarodowej licencji MGA/B2C/210/2011 wydanej przez Malta Gaming Authority. Dzięki tej licencji platforma może oferować swoje usługi w wielu krajach europejskich. Gra na GGBet jest bezpieczna, a platforma korzysta z najlepszych metod ochrony danych i finansów. Odpowiedzialna gra i korzystanie z oferowanych akcji promocyjnych mogą przynieść graczom wysokie wygrane.

[dropsofsciencenews]

OpenStreetMap per fermare il traffico di ghepardi

ENG version ESP version

Dietro le foto patinate dei cuccioli di ghepardo sui social si nasconde un mondo oscuro. Con meno di 7.000 esemplari in natura, il ghepardo (Acinonyx jubatus) è una delle specie più minacciate al mondo, e il commercio illegale di cuccioli vivi, specialmente nel Corno d'Africa, ne sta decimando le popolazioni. I bracconieri sottraggono i piccoli alle madri mentre queste sono a caccia e li destinano al mercato del lusso come animali da compagnia esotici, a un costo che può arrivare fino a 50.000 dollari per esemplare. Tra il 2020 e il 2023, questo commercio è aumentato del 50%, rappresentando una minaccia concreta alla sopravvivenza della specie.

Ora, però, una nuova metodologia sviluppata da un team di ricerca potrebbe finalmente aiutare a monitorare e interrompere il traffico illegale di ghepardi, con un approccio innovativo a tre livelli. Gli scienziati stanno mappando non solo le rotte di traffico, ma anche le popolazioni di ghepardi non ancora documentate, combinando i modelli di distribuzione delle specie preda, l’idoneità dell’habitat e una rete di traffico basata sui dati di OpenStreetMap.

Il primo passo consiste nel localizzare le prede dei ghepardi, come gazzelle e antilopi, essenziali per la loro sopravvivenza. Per farlo, il team ha utilizzato Maxent, un metodo di apprendimento automatico, per incrociare i dati di presenza delle specie preda con variabili ambientali come clima e copertura del suolo, mappando così le aree più adatte a sostenere i ghepardi. Questo passaggio consente di identificare le zone chiave per la conservazione e, purtroppo, anche quelle più vulnerabili al bracconaggio.

Sulla base di questi dati, i ricercatori hanno sviluppato un Indice di Idoneità dell'Habitat (HSI) specifico per i ghepardi, che evidenzia le aree dove la loro sopravvivenza è maggiormente probabile. Questo modello permette di individuare le aree con le condizioni migliori per ospitare popolazioni stabili e di identificare i luoghi a rischio di traffico, favorendo interventi mirati.

Infine, è stato costruito un modello per comprendere come i trafficanti trasportano i ghepardi dalle aree di origine, spesso remote, fino ai mercati della Penisola Arabica. Integrando i dati di OpenStreetMap su strade, porti e punti di confine, i ricercatori hanno individuato le rotte terrestri e marittime più probabili e i principali punti di transito. Grazie agli strumenti di ottimizzazione delle rotte, sono stati mappati i percorsi più caldi del traffico, permettendo alle forze dell’ordine di pianificare interventi strategici nei punti di passaggio critici, come porti e confini meno sorvegliati.

Questo modello rappresenta una svolta per la conservazione, non solo perché consente di mappare e monitorare le rotte di traffico, ma anche perché ha permesso di identificare ghepardi che finora sfuggivano alle ricerche, dando a scienziati e conservazionisti un quadro più completo della situazione. Grazie a questa scoperta, abbiamo finalmente in mano un sistema che permetterà di proteggere una delle specie più iconiche dell’Africa e di offrire alle comunità locali uno strumento per difendere le proprie risorse naturali.

A Presto e Buona Scienza! fonte

[i-am-l-ananas]

born to find joy in catching and looking at bugs; forced to do math about it

[vividmaps]

What is the largest country in the world based on the most suitable land...

[jacob-cs]

Natural Language Processing | Dan Jurafsky, Christopher Manning 7강

7 1 What is Sentiment Analysis https://youtu.be/vy0HC5H-484

7 2 Sentiment Analysis A baseline algorithm https://youtu.be/Dgqt62RQMaY

negation을 처리하는 한가지 예이다. 

P(cj)는 특정 class의 문서가 출현할 확률이다. P(wi | cj)는 특정 class 문서내에서 단어 w가 출현할 확률이다. 밑의 공식은 add one smoothing을 적용한 것이다.

sentiment작업에서는 단어출현 유무는 중요하나 출현횟수를 중요하지 않을때가 많으므로 출현유무만을 가지고 계산한다. 이런 형태를 binarized (boolean feature) multinomial naive bayes라고 한다.

7 3 Sentiment Lexicons https://youtu.be/wBE0FE_2ddE

이미 연구자들이 단어들을 다양한 기준을 통해 classify 한 자료들이 많이 있는데 아래에서 확인 할수 있다.

P(w | c)를 P(w)로 나눠줌으로써 다른 단어와 비교가능하게 할수 있다. 이를 scaled likelihood라고 한다. 

위 그림을 통해 no, not, never등의 negation 단어들이 negative 문자에 보다 자주 사용된것을 알수 있다. 

7 4 Learning Sentiment Lexicons https://youtu.be/Z7RxBcpyN1U

여기서는 lexicon을 직접 만드는 과정을 보여준다.

Hatzivassiloglou and McKeown 이 개발한 방법을 여기서는 예제 방법으로 사용한다. 기본 단어와 and, but으로 연결된 새로운 단어들을 추가로 정리해 가는 것이 기본 원리이다.

플러스는 positive, 녹색은 and로 엮여진 경우, 굵은 선은 많이 엮어진 경우. 적색 점선은 but으로 연결되었던 단어들이다.

turney algorithm은 연속된 phrase를 이용하는 방법이다. 

우선 단어들을 phrase로 뽑아내고 이 phrase가 positive 단어중의 하나인 excellent와 얼마나 자주 출현하는지 PMI 값을 확인한다. 또 negative 단어와의 PMI값을 구한다. 

이 두값들의 차가 Polarity 값이 된다. polarity 값은 phrase 가 positive에 가까운지 negative에 가까운지를 말해준다. 문서안의 phrase들의 polarity값을 평규내면 문서가 positive 인지 negative인지 알수 있다. 

첫줄 해설. jj (형용사) 와 nn(명사), nns(복수명사)가 연결된 경우 세번째 단어와는 무관하게 모두 phrase로 추출한다. 

P(x, y)는 동시에 출현하는 확률, P(x)P(y)는 두 단어가 독립이라고 보고 출현하는 확률이다. 즉 완전 독립된 단어라고 본경우에 비해 얼마나 동시에 출현하는지를 나타내는 비율이다. 

하나의 문서에 존재하는 다양한 phrase들의 polarity값들을 평균내는 과정이다.

7 5 Other Sentiment Tasks https://youtu.be/3Eo--0_ocIk

어떤 항목 (aspects)에 관한 sentiment인지를 확인해 가는 작업

문서에서 가장 자주 등장하는 단어가 aspect일 가능성이 있다. sentiment 형용사 뒤에 자주 등장하는 단어가 aspect일 가능성이 있다.

aspect를 위위에서 언급한 방법으로 알수 없는 경우. 손수 labeling하는 경우도 있다. 

data가 불균형한경우 일부로 맞춰주는 경우도 있을수 있다. 갯수를 낮추어 맞추기도 한다.

[avecvues]

Louise, Audrey - 07.05.18 à Maxent