A unique mating call led biologists to this newly discovered pint-sized amphibian. 'Brachycephalus lulai' is a tiny pumpkin toadlet measuring less than 14 millimeters in length. It is sitting on a pencil tip for scale. Breakthroughs, discoveries, and DIY tips sent every weekday. A new pumpkin toadlet species was recently discovered in the mountains of southern Brazil. is just over one centimeter (only 0.39 inches) long and the size of a pencil tip.

Monitoring species distribution is vital for conservation efforts, enabling the assessment of environmental impacts and the development of effective preservation strategies. Traditional data collection methods, including citizen science, offer valuable insights but remain limited in coverage and completeness. Species Distribution Modelling (SDM) helps address these gaps by using occurrence data and environmental variables to predict species presence across large regions. In this study, we enhance SDM accuracy for frogs (Anura) by applying deep learning and data imputation techniques using data from the "EY - 2022 Biodiversity Challenge." Our experiments show that data balancing significantly improved model performance, reducing the Mean Absolute Error (MAE) from 189 to 29 in frog counting tasks. Feature selection identified key environmental factors influencing occurrence, optimizing inputs while maintaining predictive accuracy. The multimodal ensemble model, integrating land cover, NDVI, and other environmental inputs, outperformed individual models and showed robust generalization across unseen regions. The fusion of image and tabular data improved both frog counting and habitat classification, achieving 84.9% accuracy with an AUC of 0.90. This study highlights the potential of multimodal learning and data preprocessing techniques such as balancing and imputation to improve predictive ecological modeling when data are sparse or incomplete, contributing to more precise and scalable biodiversity monitoring.

Alzheimer's disease is a progressive neurodegenerative disorder that remains challenging to predict due to its multifactorial etiology and the complexity of multimodal clinical data. Accurate forecasting of clinically relevant biomarkers, including diagnostic and quantitative measures, is essential for effective monitoring of disease progression. This work introduces L2C-TabPFN, a method that integrates a longitudinal-to-cross-sectional (L2C) transformation with a pre-trained Tabular Foundation Model (TabPFN) to predict Alzheimer's disease outcomes using the TADPOLE dataset. L2C-TabPFN converts sequential patient records into fixed-length feature vectors, enabling robust prediction of diagnosis, cognitive scores, and ventricular volume. Experimental results demonstrate that, while L2C-TabPFN achieves competitive performance on diagnostic and cognitive outcomes, it provides state-of-the-art results in ventricular volume prediction. This key imaging biomarker reflects neurodegeneration and progression in Alzheimer's disease. These findings highlight the potential of tabular foundational models for advancing longitudinal prediction of clinically relevant imaging markers in Alzheimer's disease.

Breakthroughs, discoveries, and DIY tips sent every weekday. Poison dart frogs are hard to miss. They're bright, agile, and as their name suggests, toxic. But at least a few of these showy amphibians have gone under the radar, until now. Scientists surveying a difficult to reach area of the Brazilian Amazon report two new species in a set of recent papers. The first, published in April in the journal ZooKeys, describes the teal and black Ranitomeya aquamarina.

RL game playing agents are traditionally initialized with zero pre-existing knowledge about a specific game environment and learn to play the game through millions of interactions with the environment. Significant time and compute is often spent exploring states that will not be experienced during high scoring policies. Exploration is particularly challenging in environments that require long horizon action sequences and provide sparse rewards, such as the Atari games and real-world robotics challenges where the state space is too large to effectively sample through free-form exploration. In this paper we will explore whether pretrained general RL agents like reasoning LLMs can play Atari games and investigate ways to leverage pretrained RL agents to reduce the training samples for training smaller agents from scratch. We first explore whether the contextual under-1 Stanford University.

Due to the convenience of mobile devices, the online games have become an important part for user entertainments in reality, creating a demand for friend recommendation in online games. However, none of existing approaches can effectively incorporate the multi-modal user features (e.g., images and texts) with the structural information in the friendship graph, due to the following limitations: (1) some of them ignore the high-order structural proximity between users, (2) some fail to learn the pairwise relevance between users at modality-specific level, and (3) some cannot capture both the local and global user preferences on different modalities. By addressing these issues, in this paper, we propose an end-to-end model FROG that better models the user preferences on potential friends. Comprehensive experiments on both offline evaluation and online deployment at Tencent have demonstrated the superiority of FROG over existing approaches.

As compliance with privacy regulations becomes increasingly critical, the growing demand for data privacy has highlighted the significance of machine unlearning in many real world applications, such as social network and recommender systems, many of which can be represented as graph-structured data. However, existing graph unlearning algorithms indiscriminately modify edges or nodes from well-trained models without considering the potential impact of such structural modifications on fairness. For example, forgetting links between nodes with different genders in a social network may exacerbate group disparities, leading to significant fairness concerns. To address these challenges, we propose a novel approach that jointly optimizes the graph structure and the corresponding model for fair unlearning tasks. Specifically,our approach rewires the graph to enhance unlearning efficiency by removing redundant edges that hinder forgetting while preserving fairness through targeted edge augmentation. Additionally, we introduce a worst-case evaluation mechanism to assess the reliability of fair unlearning performance. Extensive experiments on real-world datasets demonstrate the effectiveness of the proposed approach in achieving superior unlearning outcomes.

Axios is expanding its local newsletter presence from 30 to 34 cities. In its continued pretense of benefiting newsrooms, OpenAI has partnered with Axios in a three-year deal to cover Pittsburgh, Pennsylvania; Kansas City, Missouri; Boulder, Colorado; and Huntsville, Alabama. What does OpenAI get in exchange for its funding? Oh, just the ability to use Axios content to answer users' questions. Like the close to 20 newsrooms that OpenAI has already partnered with, Axios seems to have forgotten that the scorpion did end up stinging the frog.

Some of the best tech we see at CES feels pulled straight from sci-fi. Yesterday at CES 2025, I tested out Neural Lab's AirTouch technology, which lets you interact with a display using hand gestures alone, exactly what movies like Minority Report and Iron Man promised. Of course, plenty of companies have delivered on varying forms of gesture control. Microsoft's Kinect is an early example while the Apple Watch's double tap feature and Vision Pro's pinch gestures are just two of many current iterations. But I was impressed with how well AirTouch delivered and, unlike most gesture technology out there, it requires no special equipment -- just a standard webcam -- and works with a wide range of devices.