The Swampy Black Iguana is the oldest specimen living at the Iguana Station scientific station, where they have a breeding and conservation project for black spiny-tailed iguanas. This species, endemic to Utila, is in danger of extinction. The Utila Iguana Conservation Project seeks to ensure the survival of this species. Breakthroughs, discoveries, and DIY tips sent every weekday. With the turning of the calendar comes a new year and new vulnerable endangered plant and animal species to keep a watchful eye on.

Prime Minister Sanae Takaichi attends a summit with five Central Asian nations in Tokyo on Saturday. Japan and five Central Asian nations adopted a joint declaration at their first summit, held in Tokyo for two days through Saturday. The declaration identifies transportation infrastructure development, decarbonization and people-to-people exchanges as three priority areas. The current rapidly changing environment surrounding Central Asia, due to recent changes in the international situation, is making regional and global cooperation more important, Prime Minister Sanae Takaichi said at the summit. The summit was also attended by the leaders of Kazakhstan, Uzbekistan, Turkmenistan, Kyrgyzstan and Tajikistan.

We present a transfer-learning generative downscaling framework to reconstruct fine resolution satellite images from coarse scale inputs. Our approach combines a lightweight U-Net transfer encoder with a diffusion-based generative model. The simpler U-Net is first pretrained on a long time series of coarse resolution data to learn spatiotemporal representations; its encoder is then frozen and transferred to a larger downscaling model as physically meaningful latent features. Our application uses NASA's MERRA-2 reanalysis as the low resolution source domain (50 km) and the GEOS-5 Nature Run (G5NR) as the high resolution target (7 km). Our study area included a large area in Asia, which was made computationally tractable by splitting into two subregions and four seasons. We conducted domain similarity analysis using Wasserstein distances confirmed minimal distributional shift between MERRA-2 and G5NR, validating the safety of parameter frozen transfer. Across seasonal regional splits, our model achieved excellent performance (R2 = 0.65 to 0.94), outperforming comparison models including deterministic U-Nets, variational autoencoders, and prior transfer learning baselines. Out of data evaluations using semivariograms, ACF/PACF, and lag-based RMSE/R2 demonstrated that the predicted downscaled images preserved physically consistent spatial variability and temporal autocorrelation, enabling stable autoregressive reconstruction beyond the G5NR record. These results show that transfer enhanced diffusion models provide a robust and physically coherent solution for downscaling a long time series of coarse resolution images with limited training periods. This advancement has significant implications for improving environmental exposure assessment and long term environmental monitoring.

Kyrgyz remains a low-resource language with limited foundational NLP tools. To address this gap, we introduce KyrgyzBERT, the first publicly available monolingual BERT-based language model for Kyrgyz. The model has 35.9M parameters and uses a custom tokenizer designed for the language's morphological structure. To evaluate performance, we create kyrgyz-sst2, a sentiment analysis benchmark built by translating the Stanford Sentiment Treebank and manually annotating the full test set. KyrgyzBERT fine-tuned on this dataset achieves an F1-score of 0.8280, competitive with a fine-tuned mBERT model five times larger. All models, data, and code are released to support future research in Kyrgyz NLP.

King Charles'never understood' Meghan Markle but Queen Camilla saw through her'performance' - as royal expert reveals what really happened at Castle of Mey in 2018 Epstein lawyer ALAN DERSHOWITZ: I've seen the secret files and their damning contents. Here's the inconvenient truth they don't want you to know Gavin Newsom forced to revoke thousands of driver's licenses for illegal migrants after being'caught red-handed' Ariana Grande in crisis: Fan attack triggers'PTSD spiral' as sick new details of targeted plot are revealed... and insiders warn of'worst case scenario' years after concert bombing Michael Jackson's daughter Paris looks downcast after losing legal battle with his estate amid ongoing fight Chinese labs' race to discover the secret of immortality: After Xi and Putin were caught discussing how to cheat death, the communist nation is driving to stop ageing - with'living to 150 realistic' Amy Schumer's marriage on the BRINK as star sheds pounds and sells off homes amid'difficult time' Friends' haunting text messages to fashion designer moments before she was found dead on Hamptons yacht as owner explains why he was naked Sydney Sweeney wows in a low-cut black velvet gown as she joins glamorous Hailey Bieber, Olivia Rodrigo and Becky G on the red carpet at GQ's Men Of The Year 2025 awards Mutant meat enters Canada's food supply... and shocked Americans get a nasty surprise Why you've stopped losing weight on Mounjaro - and how to fix it: These are the sleep, alcohol and diet issues standing in your way... and the harsh truth about'microdosing' Two-time Super Bowl champion L'Jarius Sneed caught driving Lamborghini at center of alleged shooting I shed 14.5 stone after ditching my junk food habit - my secret weapon was grapes that you can get from any supermarket Grim truth about'catastrophic' diarrhea incident at Gwyneth Paltrow's house: One year later, insiders dare to tell full REAL story that will'forever haunt' her Bizarre VERY different stories I'm told about the deleted Harry and Meghan photos. The Sussex insiders are spinning one way... the Kardashians' another. Read both... and judge who you believe: ALISON BOSHOFF Vogue accused of Facetuning Amal Clooney: 'I thought it was someone else' Nutritionist influencer Diana Areas, 39, dies after'falling from top of building' The app that lets you speak with your deceased loved ones: Creepy AI creates interactive avatars of the dead - but sceptics call it'demonic, dishonest, and dehumanizing' Former Disney star Calum Worthy has been blasted for his app that uses artificial intelligence ( AI) to create avatars of dead loved ones. In a post on X, Mr Worthy, 34, shared a disturbing advert for the app, writing: 'What if the loved ones we've lost could be part of our future?'

This systematic review explores the application of Large Language Models (LLMs) in Combinatorial Optimization (CO). We report our findings using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We conduct a literature search via Scopus and Google Scholar, examining over 2,000 publications. We assess publications against four inclusion and four exclusion criteria related to their language, research focus, publication year, and type. Eventually, we select 103 studies. We classify these studies into semantic categories and topics to provide a comprehensive overview of the field, including the tasks performed by LLMs, the architectures of LLMs, the existing datasets specifically designed for evaluating LLMs in CO, and the field of application. Finally, we identify future directions for leveraging LLMs in this field.

Recent advances in self-supervised learning have led to the development of foundation models that have significantly advanced performance in various computer vision tasks. However, despite their potential, these models often overlook the crucial role of high-resolution digital surface models (DSMs) in understanding urban environments, particularly for building-level analysis, which is essential for applications like digital twins. To address this gap, we introduce HiRes-FusedMIM, a novel pre-trained model specifically designed to leverage the rich information contained within high-resolution RGB and DSM data. HiRes-FusedMIM utilizes a dual-encoder simple masked image modeling (SimMIM) architecture with a multi-objective loss function that combines reconstruction and contrastive objectives, enabling it to learn powerful, joint representations from both modalities. We conducted a comprehensive evaluation of HiRes-FusedMIM on a diverse set of downstream tasks, including classification, semantic segmentation, and instance segmentation. Our results demonstrate that: 1) HiRes-FusedMIM outperforms previous state-of-the-art geospatial methods on several building-related datasets, including WHU Aerial and LoveDA, demonstrating its effectiveness in capturing and leveraging fine-grained building information; 2) Incorporating DSMs during pre-training consistently improves performance compared to using RGB data alone, highlighting the value of elevation information for building-level analysis; 3) The dual-encoder architecture of HiRes-FusedMIM, with separate encoders for RGB and DSM data, significantly outperforms a single-encoder model on the Vaihingen segmentation task, indicating the benefits of learning specialized representations for each modality. To facilitate further research and applications in this direction, we will publicly release the trained model weights.

Training state-of-the-art large language models requires vast amounts of clean and diverse textual data. However, building suitable multilingual datasets remains a challenge. In this work, we present HPLT v2, a collection of high-quality multilingual monolingual and parallel corpora. The monolingual portion of the data contains 8T tokens covering 193 languages, while the parallel data contains 380M sentence pairs covering 51 languages. We document the entire data pipeline and release the code to reproduce it. We provide extensive analysis of the quality and characteristics of our data. Finally, we evaluate the performance of language models and machine translation systems trained on HPLT v2, demonstrating its value.