OpenRaiser/Intern-Atlas huggingface dataset:
eegFloss: A Python package for refining sleep EEG recordings using machine learning models
Venue: arXiv cs.LG 2025
Year: 2025

Electroencephalography (EEG) allows monitoring of brain activity, providing insights into the functional dynamics of various brain regions and their roles in cognitive processes. EEG is a cornerstone in sleep research, serving as the primary modality of polysomnography, the gold standard in the field. However, EEG signals are prone to artifacts caused by both internal (device-specific) factors and external (environmental) interferences. As sleep studies are becoming larger, most rely on automatic sleep staging, a process highly susceptible to artifacts, leading to erroneous sleep scores. This paper addresses this challenge by introducing eegFloss, an open-source Python package to utilize eegUsability, a novel machine learning (ML) model designed to detect segments with artifacts in sleep EEG recordings. eegUsability has been trained and evaluated on manually artifact-labeled EEG data collected from 15 participants over 127 nights using the Zmax headband. It demonstrates solid overall classification performance (F1-score is approximately 0.85, Cohens kappa is 0.78), achieving a high recall rate of approximately 94% in identifying channel-wise usable EEG data, and extends beyond Zmax. Additionally, eegFloss offers features such as automatic time-in-bed detection using another ML model named eegMobility, filtering out certain artifacts, and generating hypnograms and sleep statistics. By addressing a fundamental challenge faced by most sleep studies, eegFloss can enhance the precision and rigor of their analysis as well as the accuracy and reliability of their outcomes.
Deprecating Benchmarks: Criteria and Framework
Venue: arXiv cs.CY 2025
Year: 2025

As frontier artificial intelligence (AI) models rapidly advance, benchmarks are integral to comparing different models and measuring their progress in different task-specific domains. However, there is a lack of guidance on when and how benchmarks should be deprecated once they cease to effectively perform their purpose. This risks benchmark scores over-valuing model capabilities, or worse, obscuring capabilities and safety-washing. Based on a review of benchmarking practices, we propose criteria to decide when to fully or partially deprecate benchmarks, and a framework for deprecating benchmarks. Our work aims to advance the state of benchmarking towards rigorous and quality evaluations, especially for frontier models, and our recommendations are aimed to benefit benchmark developers, benchmark users, AI governance actors (across governments, academia, and industry panels), and policy makers.
THOR: Thermal-guided Hand-Object Reasoning via Adaptive Vision Sampling
Venue: arXiv cs.CV 2025
Year: 2025

Wearable cameras are increasingly used as an observational and interventional tool for human behaviors by providing detailed visual data of hand-related activities. This data can be leveraged to facilitate memory recall for logging of behavior or timely interventions aimed at improving health. However, continuous processing of RGB images from these cameras consumes significant power impacting battery lifetime, generates a large volume of unnecessary video data for post-processing, raises privacy concerns, and requires substantial computational resources for real-time analysis. We introduce THOR, a real-time adaptive spatio-temporal RGB frame sampling method that leverages thermal sensing to capture hand-object patches and classify them in real-time. We use low-resolution thermal camera data to identify moments when a person switches from one hand-related activity to another, and adjust the RGB frame sampling rate by increasing it during activity transitions and reducing it during periods of sustained activity. Additionally, we use the thermal cues from the hand to localize the region of interest (i.e., the hand-object interaction) in each RGB frame, allowing the system to crop and process only the necessary part of the image for activity recognition. We develop a wearable device to validate our method through an in-the-wild study with 14 participants and over 30 activities, and further evaluate it on Ego4D (923 participants across 9 countries, totaling 3,670 hours of video). Our results show that using only 3% of the original RGB video data, our method captures all the activity segments, and achieves hand-related activity recognition F1-score (95%) comparable to using the entire RGB video (94%). Our work provides a more practical path for the longitudinal use of wearable cameras to monitor hand-related activities and health-risk behaviors in real time.
Perception-Aware Policy Optimization for Multimodal Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Reinforcement Learning with Verifiable Rewards (RLVR) has proven to be a highly effective strategy for endowing Large Language Models (LLMs) with robust multi-step reasoning abilities. However, its design and optimizations remain tailored to purely textual domains, resulting in suboptimal performance when applied to multimodal reasoning tasks. In particular, we observe that a major source of error in current multimodal reasoning lies in the perception of visual inputs. To address this bottleneck, we propose PAPO, a novel policy gradient algorithm that encourages the model to learn to perceive while learning to reason. Specifically, we introduce the Implicit Perception Loss in the form of a KL divergence term, which can be seamlessly plugged into mainstream RLVR algorithms such as GRPO and DAPO. Notably, PAPO does not rely on additional data curation, reward models, or stronger teacher models. To further enhance the training stability of PAPO, we introduce the Double Entropy Loss, which effectively regularizes the new KL objective without compromising performance. Despite its simplicity, PAPO yields significant overall improvements of 4.4%-17.5% on diverse multimodal benchmarks. The improvements are more pronounced, approaching 8.0%-19.1%, on tasks with high vision dependency. We also observe a substantial reduction of 30.5% in perception errors, indicating improved perceptual capabilities with PAPO. Overall, our work introduces a deeper integration of perception-aware supervision into core learning objectives and lays the groundwork for a new RL framework that encourages visually grounded reasoning. Code and data will be made publicly available for research purposes. Project page: https://mikewangwzhl.github.io/PAPO.
gigiProfiler: Diagnosing Performance Issues by Uncovering Application Resource Bottlenecks
Venue: arXiv cs.PF 2025
Year: 2025

Diagnosing performance bottlenecks in modern software is essential yet challenging, particularly as applications become more complex and rely on custom resource management policies. While traditional profilers effectively identify execution bottlenecks by tracing system-level metrics, they fall short when it comes to application-level resource contention caused by waiting for application-level events. In this work, we introduce OmniResource Profiling, a performance analysis approach that integrates system-level and application-level resource tracing to diagnose resource bottlenecks comprehensively. gigiProfiler, our realization of OmniResource Profiling, uses a hybrid LLM-static analysis approach to identify application-defined resources offline and analyze their impact on performance during buggy executions to uncover the performance bottleneck. gigiProfiler then samples and records critical variables related to these bottleneck resources during buggy execution and compares their value with those from normal executions to identify the root causes. We evaluated gigiProfiler on 12 real-world performance issues across five applications. gigiProfiler accurately identified performance bottlenecks in all cases. gigiProfiler also successfully diagnosed the root causes of two newly emerged, previously undiagnosed problems, with the findings confirmed by developers.
Fast Collection Operations from Indexed Stream Fusion
Venue: arXiv cs.PL 2025
Year: 2025

We present a system of efficient methods for traversing and combining associative collection data structures. A distinguishing feature of the system is that, like traditional sequential iterator libraries, it does not require specialized compiler infrastructure or staged compilation for efficiency and composability. By using a representation based on indexed streams, the library can express complex joins over input collections while using no intermediate allocations. We implement the library for the Lean, Morphic, and Rust programming languages and provide a mechanized proof of functional correctness in Lean.
A Systematic Analysis of Hybrid Linear Attention
Venue: arXiv cs.CL 2025
Year: 2025

Transformers face quadratic complexity and memory issues with long sequences, prompting the adoption of linear attention mechanisms using fixed-size hidden states. However, linear models often suffer from limited recall performance, leading to hybrid architectures that combine linear and full attention layers. Despite extensive hybrid architecture research, the choice of linear attention component has not been deeply explored. We systematically evaluate various linear attention models across generations - vector recurrences to advanced gating mechanisms - both standalone and hybridized. To enable this comprehensive analysis, we trained and open-sourced 72 models: 36 at 340M parameters (20B tokens) and 36 at 1.3B parameters (100B tokens), covering six linear attention variants across five hybridization ratios. Benchmarking on standard language modeling and recall tasks reveals that superior standalone linear models do not necessarily excel in hybrids. While language modeling remains stable across linear-to-full attention ratios, recall significantly improves with increased full attention layers, particularly below a 3:1 ratio. Our study highlights selective gating, hierarchical recurrence, and controlled forgetting as critical for effective hybrid models. We recommend architectures such as HGRN-2 or GatedDeltaNet with a linear-to-full ratio between 3:1 and 6:1 to achieve Transformer-level recall efficiently. Our models are open-sourced at https://huggingface.co/collections/m-a-p/hybrid-linear-attention-research-686c488a63d609d2f20e2b1e.
EA: An Event Autoencoder for High-Speed Vision Sensing
Venue: arXiv cs.CV 2025
Year: 2025

High-speed vision sensing is essential for real-time perception in applications such as robotics, autonomous vehicles, and industrial automation. Traditional frame-based vision systems suffer from motion blur, high latency, and redundant data processing, limiting their performance in dynamic environments. Event cameras, which capture asynchronous brightness changes at the pixel level, offer a promising alternative but pose challenges in object detection due to sparse and noisy event streams. To address this, we propose an event autoencoder architecture that efficiently compresses and reconstructs event data while preserving critical spatial and temporal features. The proposed model employs convolutional encoding and incorporates adaptive threshold selection and a lightweight classifier to enhance recognition accuracy while reducing computational complexity. Experimental results on the existing Smart Event Face Dataset (SEFD) demonstrate that our approach achieves comparable accuracy to the YOLO-v4 model while utilizing up to $35.5\times$ fewer parameters. Implementations on embedded platforms, including Raspberry Pi 4B and NVIDIA Jetson Nano, show high frame rates ranging from 8 FPS up to 44.8 FPS. The proposed classifier exhibits up to 87.84x better FPS than the state-of-the-art and significantly improves event-based vision performance, making it ideal for low-power, high-speed applications in real-time edge computing.
Stochastic Alignments: Matching an Observed Trace to Stochastic Process Models
Venue: arXiv cs.FL 2025
Year: 2025

Process mining leverages event data extracted from IT systems to generate insights into the business processes of organizations. Such insights benefit from explicitly considering the frequency of behavior in business processes, which is captured by stochastic process models. Given an observed trace and a stochastic process model, conventional alignment-based conformance checking techniques face a fundamental limitation: They prioritize matching the trace to a model path with minimal deviations, which may, however, lead to selecting an unlikely path. In this paper, we study the problem of matching an observed trace to a stochastic process model by identifying a likely model path with a low edit distance to the trace. We phrase this as an optimization problem and develop a heuristic-guided path-finding algorithm to solve it. Our open-source implementation demonstrates the feasibility of the approach and shows that it can provide new, useful diagnostic insights for analysts.
TELSAFE: Security Gap Quantitative Risk Assessment Framework
Venue: arXiv cs.CR 2025
Year: 2025

Gaps between established security standards and their practical implementation have the potential to introduce vulnerabilities, possibly exposing them to security risks. To effectively address and mitigate these security and compliance challenges, security risk management strategies are essential. However, it must adhere to well-established strategies and industry standards to ensure consistency, reliability, and compatibility both within and across organizations. In this paper, we introduce a new hybrid risk assessment framework called TELSAFE, which employs probabilistic modeling for quantitative risk assessment and eliminates the influence of expert opinion bias. The framework encompasses both qualitative and quantitative assessment phases, facilitating effective risk management strategies tailored to the unique requirements of organizations. A specific use case utilizing Common Vulnerabilities and Exposures (CVE)-related data demonstrates the framework's applicability and implementation in real-world scenarios, such as in the telecommunications industry.
MoFE-Time: Mixture of Frequency Domain Experts for Time-Series Forecasting Models
Venue: arXiv cs.LG 2025
Year: 2025

As a prominent data modality task, time series forecasting plays a pivotal role in diverse applications. With the remarkable advancements in Large Language Models (LLMs), the adoption of LLMs as the foundational architecture for time series modeling has gained significant attention. Although existing models achieve some success, they rarely both model time and frequency characteristics in a pretraining-finetuning paradigm leading to suboptimal performance in predictions of complex time series, which requires both modeling periodicity and prior pattern knowledge of signals. We propose MoFE-Time, an innovative time series forecasting model that integrates time and frequency domain features within a Mixture of Experts (MoE) network. Moreover, we use the pretraining-finetuning paradigm as our training framework to effectively transfer prior pattern knowledge across pretraining and finetuning datasets with different periodicity distributions. Our method introduces both frequency and time cells as experts after attention modules and leverages the MoE routing mechanism to construct multidimensional sparse representations of input signals. In experiments on six public benchmarks, MoFE-Time has achieved new state-of-the-art performance, reducing MSE and MAE by 6.95% and 6.02% compared to the representative methods Time-MoE. Beyond the existing evaluation benchmarks, we have developed a proprietary dataset, NEV-sales, derived from real-world business scenarios. Our method achieves outstanding results on this dataset, underscoring the effectiveness of the MoFE-Time model in practical commercial applications.
Concept Unlearning by Modeling Key Steps of Diffusion Process
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image diffusion models (T2I DMs), represented by Stable Diffusion, which generate highly realistic images based on textual input, have been widely used, but their flexibility also makes them prone to misuse for producing harmful or unsafe content. Concept unlearning has been used to prevent text-to-image diffusion models from being misused to generate undesirable visual content. However, existing methods struggle to trade off unlearning effectiveness with the preservation of generation quality. To address this limitation, we propose Key Step Concept Unlearning (KSCU), which selectively fine-tunes the model at key steps to the target concept. KSCU is inspired by the fact that different diffusion denoising steps contribute unequally to the final generation. Compared to previous approaches, which treat all denoising steps uniformly, KSCU avoids over-optimization of unnecessary steps for higher effectiveness and reduces the number of parameter updates for higher efficiency. For example, on the I2P dataset, KSCU outperforms ESD by 8.3% in nudity unlearning accuracy while improving FID by 8.4%, and achieves a high overall score of 0.92, substantially surpassing all other SOTA methods.
Speak2Sign3D: A Multi-modal Pipeline for English Speech to American Sign Language Animation
Venue: arXiv cs.CV 2025
Year: 2025

Helping deaf and hard-of-hearing people communicate more easily is the main goal of Automatic Sign Language Translation. Although most past research has focused on turning sign language into text, doing the reverse, turning spoken English into sign language animations, has been largely overlooked. That's because it involves multiple steps, such as understanding speech, translating it into sign-friendly grammar, and generating natural human motion. In this work, we introduce a complete pipeline that converts English speech into smooth, realistic 3D sign language animations. Our system starts with Whisper to translate spoken English into text. Then, we use a MarianMT machine translation model to translate that text into American Sign Language (ASL) gloss, a simplified version of sign language that captures meaning without grammar. This model performs well, reaching BLEU scores of 0.7714 and 0.8923. To make the gloss translation more accurate, we also use word embeddings such as Word2Vec and FastText to understand word meanings. Finally, we animate the translated gloss using a 3D keypoint-based motion system trained on Sign3D-WLASL, a dataset we created by extracting body, hand, and face key points from real ASL videos in the WLASL dataset. To support the gloss translation stage, we also built a new dataset called BookGlossCorpus-CG, which turns everyday English sentences from the BookCorpus dataset into ASL gloss using grammar rules. Our system stitches everything together by smoothly interpolating between signs to create natural, continuous animations. Unlike previous works like How2Sign and Phoenix-2014T that focus on recognition or use only one type of data, our pipeline brings together audio, text, and motion in a single framework that goes all the way from spoken English to lifelike 3D sign language animation.
A model-agnostic active learning approach for animal detection from camera traps
Venue: arXiv cs.CV 2025
Year: 2025

Smart data selection is becoming increasingly important in data-driven machine learning. Active learning offers a promising solution by allowing machine learning models to be effectively trained with optimal data including the most informative samples from large datasets. Wildlife data captured by camera traps are excessive in volume, requiring tremendous effort in data labelling and animal detection models training. Therefore, applying active learning to optimise the amount of labelled data would be a great aid in enabling automated wildlife monitoring and conservation. However, existing active learning techniques require that a machine learning model (i.e., an object detector) be fully accessible, limiting the applicability of the techniques. In this paper, we propose a model-agnostic active learning approach for detection of animals captured by camera traps. Our approach integrates uncertainty and diversity quantities of samples at both the object-based and image-based levels into the active learning sample selection process. We validate our approach in a benchmark animal dataset. Experimental results demonstrate that, using only 30% of the training data selected by our approach, a state-of-the-art animal detector can achieve a performance of equal or greater than that with the use of the complete training dataset.
Graph-based Fake Account Detection: A Survey
Venue: arXiv cs.SI 2025
Year: 2025

In recent years, there has been a growing effort to develop effective and efficient algorithms for fake account detection in online social networks. This survey comprehensively reviews existing methods, with a focus on graph-based techniques that utilise topological features of social graphs (in addition to account information, such as their shared contents and profile data) to distinguish between fake and real accounts. We provide several categorisations of these methods (for example, based on techniques used, input data, and detection time), discuss their strengths and limitations, and explain how these methods connect in the broader context. We also investigate the available datasets, including both real-world data and synthesised models. We conclude the paper by proposing several potential avenues for future research.
Omni-Fusion of Spatial and Spectral for Hyperspectral Image Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Medical Hyperspectral Imaging (MHSI) has emerged as a promising tool for enhanced disease diagnosis, particularly in computational pathology, offering rich spectral information that aids in identifying subtle biochemical properties of tissues. Despite these advantages, effectively fusing both spatial-dimensional and spectral-dimensional information from MHSIs remains challenging due to its high dimensionality and spectral redundancy inherent characteristics. To solve the above challenges, we propose a novel spatial-spectral omni-fusion network for hyperspectral image segmentation, named as Omni-Fuse. Here, we introduce abundant cross-dimensional feature fusion operations, including a cross-dimensional enhancement module that refines both spatial and spectral features through bidirectional attention mechanisms, a spectral-guided spatial query selection to select the most spectral-related spatial feature as the query, and a two-stage cross-dimensional decoder which dynamically guide the model to focus on the selected spatial query. Despite of numerous attention blocks, Omni-Fuse remains efficient in execution. Experiments on two microscopic hyperspectral image datasets show that our approach can significantly improve the segmentation performance compared with the state-of-the-art methods, with over 5.73 percent improvement in DSC. Code available at: https://github.com/DeepMed-Lab-ECNU/Omni-Fuse.