The Others: Naturally Isolating Out-of-Distribution Samples for Robust Open-Set Semi-Supervised Learning

Open-Set Semi-Supervised Learning (OSSL) tackles the practical challenge of learning from unlabeled data that may include both in-distribution (ID) and unknown out-of-distribution (OOD) classes. However, existing OSSL methods form suboptimal feature spaces by either excluding OOD samples, interfering with them, or overtrusting their information during training. In this work, we introduce MagMatch, a novel framework that naturally isolates OOD samples through a prototype-based contrastive learning paradigm. Unlike conventional methods, MagMatch does not assign any prototypes to OOD samples; instead, it selectively aligns ID samples with class prototypes using an ID-Selective Magnetic (ISM) module, while allowing OOD samples - the "others" - to remain unaligned in the feature space. To support this process, we propose Selective Magnetic Alignment (SMA) loss for unlabeled data, which dynamically adjusts alignment based on sample confidence. Extensive experiments on diverse datasets demonstrate that MagMatch significantly outperforms existing methods in both closed-set classification accuracy and OOD detection AUROC, especially in generalizing to unseen OOD data.

Effective Heterogeneous Federated Learning via Efficient Hypernetwork-based Weight Generation

While federated learning leverages distributed client resources, it faces challenges due to heterogeneous client capabilities. This necessitates allocating models suited to clients' resources and careful parameter aggregation to accommodate this heterogeneity. We propose HypeMeFed, a novel federated learning framework for supporting client heterogeneity by combining a multi-exit network architecture with hypernetwork-based model weight generation. This approach aligns the feature spaces of heterogeneous model layers and resolves per-layer information disparity during weight aggregation. To practically realize HypeMeFed, we also propose a low-rank factorization approach to minimize computation and memory overhead associated with hypernetworks. Our evaluations on a real-world heterogeneous device testbed indicate that HypeMeFed enhances accuracy by 5.12% over FedAvg, reduces the hypernetwork memory requirements by 98.22%, and accelerates its operations by 1.86 times compared to a naive hypernetwork approach. These results demonstrate HypeMeFed's effectiveness in leveraging and engaging heterogeneous clients for federated learning.

SlAction: Non-intrusive, Lightweight Obstructive Sleep Apnea Detection using Infrared Video

Obstructive sleep apnea (OSA) is a prevalent sleep disorder affecting approximately one billion people world-wide. The current gold standard for diagnosing OSA, Polysomnography (PSG), involves an overnight hospital stay with multiple attached sensors, leading to potential inaccuracies due to the first-night effect. To address this, we present SlAction, a non-intrusive OSA detection system for daily sleep environments using infrared videos. Recognizing that sleep videos exhibit minimal motion, this work investigates the fundamental question: "Are respiratory events adequately reflected in human motions during sleep?" Analyzing the largest sleep video dataset of 5,098 hours, we establish correlations between OSA events and human motions during sleep. Our approach uses a low frame rate (2.5 FPS), a large size (60 seconds) and step (30 seconds) for sliding window analysis to capture slow and long-term motions related to OSA. Furthermore, we utilize a lightweight deep neural network for resource-constrained devices, ensuring all video streams are processed locally without compromising privacy. Evaluations show that SlAction achieves an average F1 score of 87.6% in detecting OSA across various environments. Implementing SlAction on NVIDIA Jetson Nano enables real-time inference (~3 seconds for a 60-second video clip), highlighting its potential for early detection and personalized treatment of OSA.