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. ...

When we train deep neural networks, they often get stuck — not in a bad result, but in a “flat region” of the loss landscape. The authors of this paper introduce ghost nodes: extra, fake output nodes that aren’t real classes, but help the model explore better paths during training. Imagine you’re rolling a ball into a valley. Sometimes the valley floor is flat and the ball slows down. Ghost nodes are like adding new dimensions to the terrain — giving the ball more freedom to move and find a better path. ...

Large‑scale epidemic modeling is a key tool for public health—but it often requires sensitive data (e.g., hospital admissions, financial records, mobility). A recent paper, “A Framework for Multi‑source Privacy Preserving Epidemic Analysis” (June 27, 2025), introduces a hybrid neural‑mechanistic model that respects Differential Privacy (DP). This means we can use private data without compromising individuals’ privacy. 🌍 Why It Matters 🚑 Accurate predictions help allocate resources (like vaccines, ICU beds). 🕵️‍♂️ But using private data poses a privacy risk. 🔐 Differential Privacy (DP) adds controlled randomness—protecting individuals at a formal, mathematical level. 🧠 Inside the Framework: Neural + Mechanistic The model is a hybrid system combining: ...

Introduction Unmanned Aerial Vehicles (UAVs) play pivotal roles today in photography, deliveries, rescue missions, border surveillance, and military operations. However, the growing availability of signal disruption tools (GPS spoofing, gyroscope jamming, magnetometer manipulation) poses significant threats to autonomous systems. Even a slight navigational drift can turn a mission into a disaster. Why Physical-Attack Robustness Matters Traditional safe RL methods or adversarial trainings rely on known attack scenarios. In practice, it’s impossible to anticipate every possible manipulation—an adversary could employ novel jamming or optical disruption techniques. Iterative adversarial training is computationally expensive and often poorly generalizes to unseen scenarios. ...