Yige Yang

Yige Yang, Man Zhang, Tao Yue

Test optimization contains test case selection and minimization, which is an important challenge in software testing and has been addressed with search-based approaches intensively in the past. Inspired by the recent advancement of using quantum optimization solutions for addressing test optimization problems, we looked into Coherent Ising Machines (CIM), which offer potential for solving combinatorial optimization problems, but have not yet been exploited in test optimization. Hence, in this paper, we present IsingTester, an open-source, Python-based command-line tool that provides an end-to-end pipeline for solving test optimization problems that are formulated as Ising models. With IsingTester, we reformulate test selection and minimization as Ising spin configurations, encode multiple optimization strategies into Ising Hamiltonians, and implement solvers including CIM simulation and brute-force search. Given a user-provided dataset and solver configuration, IsingTester automatically performs problem encoding, optimization, and spin decoding, returning selected test cases back to the user. Along with IsingTester, we also present the accompanying IsingBench for evaluating and comparing optimization techniques across Ising-based paradigms against baseline approaches. A screencast demonstrating the tool is available at: https://github.com/WSE-Lab/IsingBench.

Pengfei Zhao, Qigong Sun, Xiaolin Tian et al.

There has been growing interest in facial video-based remote photoplethysmography (rPPG) measurement recently, with a focus on assessing various vital signs such as heart rate and heart rate variability. Despite previous efforts on static datasets, their approaches have been hindered by inaccurate region of interest (ROI) localization and motion issues, and have shown limited generalization in real-world scenarios. To address these challenges, we propose a novel masked attention regularization (MAR-rPPG) framework that mitigates the impact of ROI localization and complex motion artifacts. Specifically, our approach first integrates a masked attention regularization mechanism into the rPPG field to capture the visual semantic consistency of facial clips, while it also employs a masking technique to prevent the model from overfitting on inaccurate ROIs and subsequently degrading its performance. Furthermore, we propose an enhanced rPPG expert aggregation (EREA) network as the backbone to obtain rPPG signals and attention maps simultaneously. Our EREA network is capable of discriminating divergent attentions from different facial areas and retaining the consistency of spatiotemporal attention maps. For motion robustness, a simple open source detector MediaPipe for data preprocessing is sufficient for our framework due to its superior capability of rPPG signal extraction and attention regularization. Exhaustive experiments on three benchmark datasets (UBFC-rPPG, PURE, and MMPD) substantiate the superiority of our proposed method, outperforming recent state-of-the-art works by a considerable margin.

Xianheng Wang, Yige Yang, Damien Coyle

Large EEG Foundation Models (FMs) have shown great potential for decoding EEG signals across diverse cognitive tasks. However, existing EEG-FM studies exhibit three critical limitations: opaque supervised baseline tuning, unverified contributions of complex learning paradigms, and a lack of transparency in model decision-making. To address these, we propose EEG-FM-Audit, a comprehensive evaluation and analysis pipeline designed to systematize the assessment of EEG-FMs. EEG-FM-Audit consists of three primary components: (1) an ASHA-driven benchmarking protocol that ensures fair comparisons by transparently optimizing supervised baselines; (2) paradigm-level ablation studies to evaluate the effectiveness of learning paradigms in FMs; and (3) a neurophysiological probing (NPP) framework, which explores whether FMs leverage valid temporal, spatial, and spectral EEG properties. We apply EEG-FM-Audit to four state-of-the-art EEG-FMs and five representative supervised models across three public datasets. Our results reveal that properly tuned supervised baselines can match or outperform advanced FMs, despite requiring significantly fewer parameters. Furthermore, we find that the effectiveness of learning paradigms of FMs is highly dependent on dataset scale and architecture. Finally, NPP analysis demonstrates how FMs rely on specific physiological features, establishing a framework for more interpretable neural decoding.