In the era dominated by language models and machine learning, the importance of structured data is growing rapidly: social networks, biological relationships, and business connections. This data is represented in the form of graphs, which are often not homogeneous: they contain nodes of different types (e.g., people, products, companies) and different types of edges (e.g., “purchased”, “recommended”, “works at”). Processing such heterogeneous graphs requires specialized methods. What are heterogeneous graphs? A heterogeneous graph is a structure in which: ...

The accelerating crisis of antimicrobial resistance (AMR) demands new computational methods to stay ahead of evolving pathogens. ApexOracle is a unified ML platform designed to both predict the activity of candidate compounds against specific bacterial strains and generate novel molecules de novo, proactively targeting future superbugs. Motivation and Scope Global Impact: AMR contributes to nearly 5 million deaths annually. Traditional Challenges: Standard drug discovery pipelines are slow, resource-intensive, and reactive. ApexOracle Goal: Integrate genomic context and molecular design into one end-to-end framework. ApexOracle Architecture Layman’s Explanation: Imagine you have three sets of clues: the code of the bacteria (its genome), a simple description of its behaviors (like a basic fact sheet), and the building blocks of a potential drug (a molecular recipe). ApexOracle acts like a super-smart detective that reads all three clues at once. It combines them, figures out which molecules might work best, and even drafts entirely new molecular recipes that could stop the bacteria in its tracks. ...

Modern emotion-recognition systems increasingly leverage data from multiple sources—ranging from physiological signals (e.g., heart rate, skin conductance) to facial video. The goal is to capture the richness of human feelings, where multiple emotions often co-occur. Traditional approaches, however, focused on single-label classification (e.g., “happy” or “sad”). The paper “HeLo: Heterogeneous Multi-Modal Fusion with Label Correlation for Emotion Distribution Learning” introduces an entirely new paradigm: emotion distribution learning, where the model predicts the probability of each basic emotion being present. ...

Associative memory is the ability to store patterns and retrieve them when presented with partial or noisy inputs. Inspired by how the human brain recalls memories, associative memory models are recurrent neural networks that converge to stored patterns over time. The tutorial ‘Modern Methods in Associative Memory’ by Krotov et al. offers an accessible overview for newcomers and a rigorous mathematical treatment for experts, bridging classical ideas with cutting-edge developments in deep learning. ...

Imagine you track your morning commute times by recording 50 real-world trips with your GPS-enabled phone. You also run a traffic simulator to generate 5,000 possible commute scenarios. You want a reliable estimate of the 95th percentile of commute time—the duration you won’t exceed 95% of the days. Using only your 50 recorded trips yields a wide confidence interval. Using only the simulator risks systematic biases: it might ignore sudden road closures or special events. ...

The text-to-SQL task involves converting natural language questions into executable SQL queries on a relational database. While modern large language models (LLMs) excel at many generative tasks, generating correct and complex SQL queries remains challenging. In the paper RetrySQL: text-to-SQL training with retry data for self-correcting query generation, the authors introduce a training paradigm that teaches the model to self-monitor and correct its reasoning steps during generation, rather than relying solely on post-processing modules. ...

Diagnosing mental disorders such as autism, depression, or schizophrenia goes beyond taking simple brain images. Resting-state fMRI (rs-fMRI) observes brain activity while at rest, revealing which regions activate simultaneously. This forms the basis for functional connectivity. Traditional studies have used graphs and neural networks, but they mostly focus on pairwise interactions — asking “do regions A and B co-activate?” But what about higher-order relationships, like among regions A, B, and C all at once? ...

Reinforcement Learning (RL) enables agents to learn behaviors through reward signals. However, in tasks requiring long chains of reasoning, two main challenges arise: The near-miss problem – a single mistake at the end can invalidate the entire reasoning chain. Exploration stagnation – the agent repeatedly follows known paths without discovering new strategies. The paper StepHint: Multi-level Stepwise Hints Enhance Reinforcement Learning to Reason introduces StepHint, a method that provides agents with multi-level hints to support both beginners and advanced users. ...

Can we predict when and where people will rent electric scooters? Yes — and with impressive accuracy. A recent publication shows how advanced algorithms like XGBoost can revolutionize the management of micromobility in cities. 🌍 Context: Micromobility and Demand In many cities, dockless electric scooters have become a daily transport option. But for operators, a crucial question remains: Where and when will people want to rent a scooter? Too many vehicles in one location is wasteful. Too few — lost revenue and frustrated users. That’s why accurately predicting demand is so important. ...