Between cartel threats and U.S. airstrikes, Ecuador fishers have nowhere to hide Fishers unload their fresh catch near a market in Manta, Ecuador, on June 4. JARAMIJO/SAN MATEO, ECUADOR - At first light on Ecuador's Pacific coast, small fishing boats fan out from the villages of San Mateo and Jaramijo, their bows pointed toward deeper waters and the promise of a good catch. Other boats come in from the rolling surf to battered coves, where crews unload baskets of tuna and swordfish and traders rush to inspect the day's haul. The scene belies the dangers some fishers face pushing out into these waters. Onshore, drug cartels have turned the small ports of Ecuador's Manabi province into a cocaine-trafficking hub, extorting local fishers who are prized for their navigation skills. The cartels present an impossible choice: earn more than a year's legal income smuggling drugs, or face extortion, threats and violence.

Veterinarians successfully removed the debris from the juvenile Salvin's albatross. Breakthroughs, discoveries, and DIY tips sent every weekday. A rare seabird is recovering from a successful and life-saving surgery. A fisherman from Anconcito, Ecuador, found the juvenile Salvin's albatross after he noticed that it appeared unwell. The bird had ingested four large fishing hooks and some fishing line and was brought to Puerto Lopez for rehabilitation and care.

Vision-Language Models (VLMs) are increasingly deployed in diverse cultural contexts, yet their internal biases remain poorly understood. In this work, we propose a novel framework to systematically evaluate how VLMs encode cultural differences and biases related to race, gender, and physical traits across countries. We introduce three retrieval-based tasks: (1) Race to Country retrieval, which examines the association between individuals from specific racial groups (East Asian, White, Middle Eastern, Latino, South Asian, and Black) and different countries; (2) Personal Traits to Country retrieval, where images are paired with trait-based prompts (e.g., Smart, Honest, Criminal, Violent) to investigate potential stereotypical associations; and (3) Physical Characteristics to Country retrieval, focusing on visual attributes like skinny, young, obese, and old to explore how physical appearances are culturally linked to nations. Our findings reveal persistent biases in VLMs, highlighting how visual representations may inadvertently reinforce societal stereotypes.

Video games have been simulating football since the 1970s, but they have rarely ever thought about simulating fandom. You can play a whole international tournament in the Fifa titles, but what they never show is the way the competition seeps into the everyday lives of supporters, how whole towns are overtaken, how a World Cup can become a national obsession. The way most of us experience the really big matches is through stolen moments of vicarious glory on televisions and giant pub screens, surrounded by friends and family and the sounds and images of real life. This is the territory of Despelote, a beautiful, utterly transportive game about childhood and memory, set during Ecuador's historic 2002 World Cup qualifying campaign. Football-mad eight-year-old Julián – a semi-autobiographical version of the game's co-designer Julián Cordero – has just watched the team beat Peru, but now four more matches stand between Ecuador and the World Cup finals in Japan and Korea.

This paper presents Killkan, the first dataset for automatic speech recognition (ASR) in the Kichwa language, an indigenous language of Ecuador. Kichwa is an extremely low-resource endangered language, and there have been no resources before Killkan for Kichwa to be incorporated in applications of natural language processing. The dataset contains approximately 4 hours of audio with transcription, translation into Spanish, and morphosyntactic annotation in the format of Universal Dependencies. The audio data was retrieved from a publicly available radio program in Kichwa. This paper also provides corpus-linguistic analyses of the dataset with a special focus on the agglutinative morphology of Kichwa and frequent code-switching with Spanish. The experiments show that the dataset makes it possible to develop the first ASR system for Kichwa with reliable quality despite its small dataset size. This dataset, the ASR model, and the code used to develop them will be publicly available.

Current methods to identify and classify racist language in text rely on small-n qualitative approaches or large-n approaches focusing exclusively on overt forms of racist discourse. This article provides a step-by-step generalizable guideline to identify and classify different forms of racist discourse in large corpora. In our approach, we start by conceptualizing racism and its different manifestations. We then contextualize these racist manifestations to the time and place of interest, which allows researchers to identify their discursive form. Finally, we apply XLM-RoBERTa (XLM-R), a cross-lingual model for supervised text classification with a cutting-edge contextual understanding of text. We show that XLM-R and XLM-R-Racismo, our pretrained model, outperform other state-of-the-art approaches in classifying racism in large corpora. We illustrate our approach using a corpus of tweets relating to the Ecuadorian ind\'igena community between 2018 and 2021.

There's much, much more to the rainforest than meets the eye. Even a highly trained observer can struggle to pick out individual animals in the tangle of plant life--animals that are often specifically adapted to hide from their enemies. Listen to the music of the forest, though, and you can get a decent idea of the species by their chirps, croaks, and grunts. This is why scientists are increasingly bugging rainforests with microphones--a burgeoning field known as bioacoustics--and using AI to automatically parse sounds to identify species. Writing today in the journal Nature Communications, researchers describe a proof-of-concept project in the lowland Chocó region of Ecuador that shows the potential power of bioacoustics in conserving forests.

Paris – Native Americans and Polynesians bridged vast expanses of open ocean around the year 1200 and mingled, leaving incontrovertible proof of their encounter in the DNA of present-day populations, scientists revealed Wednesday. Whether peoples from what is today Colombia or Ecuador drifted thousands of kilometers to tiny islands in the middle of the Pacific, or whether seafaring Polynesians sailed upwind to South America and then back again is still unknown. But what is certain, according to a study in Nature, is that the hook up took place hundreds of years before Europeans set foot in either region, and left individuals scattered across French Polynesia with signature traces of the New World in their DNA. "These findings change our understanding of one of the most unknown chapters in the history of our species' great continental expansions," senior author Andreas Moreno-Estrada, principal investigator at Mexico's National Laboratory of Genomics for biodiversity, said. Archeologists and historians have tussled for decades over whether Oceana islanders and native Americans crossed paths during the Middle Ages, and how, if they did, that contact might have unfolded.

This is part 5 of a Guide in 6 parts about Artificial Intelligence. The guide covers some of its basic concepts, history and present applications, possible developments in the future, and also its challenges as opportunities. Reviewing some case studies helps to bring artificial intelligence to life, and to understand how it is used. Here we will review the field of entertainment, where the company Magic Leap has made great strides with the use of artificial intelligence. Magic Leap is a start up company located in the USA.

One noninvasive way to study frog communities is by analyzing long-term samples of acoustic material containing calls. This immense task has been optimized by the development of Machine Learning tools to extract ecological information. We explored a likelihood-ratio audio detector based on Gaussian mixture model classification of 10 frog species, and applied it to estimate presence-absence in audio recordings from an actual amphibian monitoring performed at Yasun ı National Park in the Ecuadorian Amazonia. A modified filter-bank was used to extract 20 cepstral features that model the spectral content of frog calls. Experiments were carried out to investigate the hyperparameters and the minimum frog-call time needed to train an accurate GMM classifier. With 64 Gaussians and 12 seconds of training time, the classifier achieved an average weighted error rate of 0.9% on the 10-fold cross-validation for nine species classification, as compared to 3% with MFCC and 1.8% with PLP features. For testing, 10 GMMs were trained using all the available training-validation dataset to study 23.5 hours in 141, 10-minute long samples of unidentified real-world audio recorded at two frog communities in 2001 with analog equipment. To evaluate automatic presence-absence estimation, we characterized the audio samples with 10 binary variables each corresponding to a frog species, and manually labeled a subset of 18 samples using headphones. The one-vs-all Receiver Operating Characteristics curves were used to tune the likelihood-ratio detector per class in order to set operating points that minimize false positives while still allowing moderately noisy calls to be detected. A recall of 87.5% and precision of 100% with average accuracy of 96.66% suggests good generalization ability of the algorithm, and provides evidence of the validity of this approach Finally, we applied the algorithm to the available corpus, and show its potentiality to gain insights into the temporal reproductive behavior of frogs. Introduction In long term ecological studies, it is important to quantify changes that occur on biodiversity and the ecosystem as a whole. Large scale temporal and spatial studies to understand the natural and anthropogenic induced population dynamics are demanded by the scientific community. In addition, recent anuran population declines around the world have motivated studies to gain an understanding of the phenomenon [1].