OpenRaiser/Intern-Atlas huggingface dataset:
SARGes: Semantically Aligned Reliable Gesture Generation via Intent Chain
Venue: arXiv cs.CL 2025
Year: 2025

Co-speech gesture generation enhances human-computer interaction realism through speech-synchronized gesture synthesis. However, generating semantically meaningful gestures remains a challenging problem. We propose SARGes, a novel framework that leverages large language models (LLMs) to parse speech content and generate reliable semantic gesture labels, which subsequently guide the synthesis of meaningful co-speech gestures.First, we constructed a comprehensive co-speech gesture ethogram and developed an LLM-based intent chain reasoning mechanism that systematically parses and decomposes gesture semantics into structured inference steps following ethogram criteria, effectively guiding LLMs to generate context-aware gesture labels. Subsequently, we constructed an intent chain-annotated text-to-gesture label dataset and trained a lightweight gesture label generation model, which then guides the generation of credible and semantically coherent co-speech gestures. Experimental results demonstrate that SARGes achieves highly semantically-aligned gesture labeling (50.2% accuracy) with efficient single-pass inference (0.4 seconds). The proposed method provides an interpretable intent reasoning pathway for semantic gesture synthesis.
BEAR: A Video Dataset For Fine-grained Behaviors Recognition Oriented with Action and Environment Factors
Venue: arXiv cs.CV 2025
Year: 2025

Behavior recognition is an important task in video representation learning. An essential aspect pertains to effective feature learning conducive to behavior recognition. Recently, researchers have started to study fine-grained behavior recognition, which provides similar behaviors and encourages the model to concern with more details of behaviors with effective features for distinction. However, previous fine-grained behaviors limited themselves to controlling partial information to be similar, leading to an unfair and not comprehensive evaluation of existing works. In this work, we develop a new video fine-grained behavior dataset, named BEAR, which provides fine-grained (i.e. similar) behaviors that uniquely focus on two primary factors defining behavior: Environment and Action. It includes two fine-grained behavior protocols including Fine-grained Behavior with Similar Environments and Fine-grained Behavior with Similar Actions as well as multiple sub-protocols as different scenarios. Furthermore, with this new dataset, we conduct multiple experiments with different behavior recognition models. Our research primarily explores the impact of input modality, a critical element in studying the environmental and action-based aspects of behavior recognition. Our experimental results yield intriguing insights that have substantial implications for further research endeavors.
TC-GS: Tri-plane based compression for 3D Gaussian Splatting
Venue: arXiv cs.CV 2025
Year: 2025

Recently, 3D Gaussian Splatting (3DGS) has emerged as a prominent framework for novel view synthesis, providing high fidelity and rapid rendering speed. However, the substantial data volume of 3DGS and its attributes impede its practical utility, requiring compression techniques for reducing memory cost. Nevertheless, the unorganized shape of 3DGS leads to difficulties in compression. To formulate unstructured attributes into normative distribution, we propose a well-structured tri-plane to encode Gaussian attributes, leveraging the distribution of attributes for compression. To exploit the correlations among adjacent Gaussians, K-Nearest Neighbors (KNN) is used when decoding Gaussian distribution from the Tri-plane. We also introduce Gaussian position information as a prior of the position-sensitive decoder. Additionally, we incorporate an adaptive wavelet loss, aiming to focus on the high-frequency details as iterations increase. Our approach has achieved results that are comparable to or surpass that of SOTA 3D Gaussians Splatting compression work in extensive experiments across multiple datasets. The codes are released at https://github.com/timwang2001/TC-GS.
Solving 2-D Helmholtz equation in the rectangular, circular, and elliptical domains using neural networks
Venue: arXiv cs.LG 2025
Year: 2025

Physics-informed neural networks offered an alternate way to solve several differential equations that govern complicated physics. However, their success in predicting the acoustic field is limited by the vanishing-gradient problem that occurs when solving the Helmholtz equation. In this paper, a formulation is presented that addresses this difficulty. The problem of solving the two-dimensional Helmholtz equation with the prescribed boundary conditions is posed as an unconstrained optimization problem using trial solution method. According to this method, a trial neural network that satisfies the given boundary conditions prior to the training process is constructed using the technique of transfinite interpolation and the theory of R-functions. This ansatz is initially applied to the rectangular domain and later extended to the circular and elliptical domains. The acoustic field predicted from the proposed formulation is compared with that obtained from the two-dimensional finite element methods. Good agreement is observed in all three domains considered. Minor limitations associated with the proposed formulation and their remedies are also discussed.
Automated UI Interface Generation via Diffusion Models: Enhancing Personalization and Efficiency
Venue: arXiv cs.HC 2025
Year: 2025

This study proposes a UI interface generation method based on a diffusion model, aiming to achieve high-quality, diversified, and personalized interface design through generative artificial intelligence technology. The diffusion model is based on its step-by-step denoising generation process. By combining the conditional generation mechanism, design optimization module, and user feedback mechanism, the model can generate a UI interface that meets the requirements based on multimodal inputs such as text descriptions and sketches provided by users. In the study, a complete experimental evaluation framework was designed, and mainstream generation models (such as GAN, VAE, DALL E, etc.) were selected for comparative experiments. The generation results were quantitatively analyzed from indicators such as PSNR, SSIM, and FID. The results show that the model proposed in this study is superior to other models in terms of generation quality and user satisfaction, especially in terms of logical clarity of information transmission and visual aesthetics. The ablation experiment further verifies the key role of conditional generation and design optimization modules in improving interface quality. This study provides a new technical path for UI design automation and lays the foundation for the intelligent and personalized development of human-computer interaction interfaces. In the future, the application potential of the model in virtual reality, game design, and other fields will be further explored.
Dynamic Learning and Productivity for Data Analysts: A Bayesian Hidden Markov Model Perspective
Venue: arXiv cs.SI 2025
Year: 2025

Data analysts are essential in organizations, transforming raw data into insights that drive decision-making and strategy. This study explores how analysts' productivity evolves on a collaborative platform, focusing on two key learning activities: writing queries and viewing peer queries. While traditional research often assumes static models, where performance improves steadily with cumulative learning, such models fail to capture the dynamic nature of real-world learning. To address this, we propose a Hidden Markov Model (HMM) that tracks how analysts transition between distinct learning states based on their participation in these activities. Using an industry dataset with 2,001 analysts and 79,797 queries, this study identifies three learning states: novice, intermediate, and advanced. Productivity increases as analysts advance to higher states, reflecting the cumulative benefits of learning. Writing queries benefits analysts across all states, with the largest gains observed for novices. Viewing peer queries supports novices but may hinder analysts in higher states due to cognitive overload or inefficiencies. Transitions between states are also uneven, with progression from intermediate to advanced being particularly challenging. This study advances understanding of into dynamic learning behavior of knowledge worker and offers practical implications for designing systems, optimizing training, enabling personalized learning, and fostering effective knowledge sharing.
ESSR: An 8K@30FPS Super-Resolution Accelerator With Edge Selective Network
Venue: arXiv cs.AR 2025
Year: 2025

Deep learning-based super-resolution (SR) is challenging to implement in resource-constrained edge devices for resolutions beyond full HD due to its high computational complexity and memory bandwidth requirements. This paper introduces an 8K@30FPS SR accelerator with edge-selective dynamic input processing. Dynamic processing chooses the appropriate subnets for different patches based on simple input edge criteria, achieving a 50\% MAC reduction with only a 0.1dB PSNR decrease. The quality of reconstruction images is guaranteed and maximized its potential with \textit{resource adaptive model switching} even under resource constraints. In conjunction with hardware-specific refinements, the model size is reduced by 84\% to 51K, but with a decrease of less than 0.6dB PSNR. Additionally, to support dynamic processing with high utilization, this design incorporates a \textit{configurable group of layer mapping} that synergizes with the \textit{structure-friendly fusion block}, resulting in 77\% hardware utilization and up to 79\% reduction in feature SRAM access. The implementation, using the TSMC 28nm process, can achieve 8K@30FPS throughput at 800MHz with a gate count of 2749K, 0.2075W power consumption, and 4797Mpixels/J energy efficiency, exceeding previous work.
Fair and efficient allocation of indivisible items under category constraints
Venue: arXiv cs.GT 2025
Year: 2025

We study the problem of fairly allocating indivisible items under category constraints. Specifically, there are $n$ agents and $m$ indivisible items which are partitioned into categories with associated capacities. An allocation is considered feasible if each bundle satisfies the capacity constraints of its respective categories. For the case of two agents, Shoshan et al. (2023) recently developed a polynomial-time algorithm to find a Pareto-optimal allocation satisfying a relaxed version of envy-freeness, called EF$[1,1]$. In this paper, we extend the result of Shoshan et al. to $n$ agents, proving the existence of a Pareto-optimal allocation where each agent can be made envy-free by reallocating at most ${n(n-1)}$ items. Furthermore, we present a polynomial-time algorithm to compute such an allocation when the number $n$ of agents is constant.
TileLink: Generating Efficient Compute-Communication Overlapping Kernels using Tile-Centric Primitives
Venue: arXiv cs.DC 2025
Year: 2025

Large deep learning models have achieved state-of-the-art performance in a wide range of tasks. These models often necessitate distributed systems for efficient training and inference. The fundamental building blocks for distributed model execution are intra-layer parallel operators. The most effective approach to enhancing the performance of intra-layer parallel operators involves overlapping computation with communication. The overlapping can be achieved through either operator decomposition or kernel fusion. While decomposing operators is straightforward to implement, it often results in suboptimal performance. On the other hand, fusing communication kernels with compute kernels demands significant expertise and is error-prone. In this paper, we propose TileLink to enable efficient compilation and generation of overlapped compute-communication kernels. TileLink is composed of frontend and backend. In the frontend, TileLink decouples the design space of communication and computation, linking these two parts via tile-centric primitives. In the backend, TileLink translates these primitives into low-level communication instructions, integrating the communication and computation components to achieve overlapped execution. In experiments, TileLink achieves from $1.17\times$ to $20.76\times$ speedup to non-overlapping baseline and achieves performance comparable to state-of-the-art overlapping libraries on GPUs.
Wasserstein Distributionally Robust Bayesian Optimization with Continuous Context
Venue: arXiv cs.LG 2025
Year: 2025

We address the challenge of sequential data-driven decision-making under context distributional uncertainty. This problem arises in numerous real-world scenarios where the learner optimizes black-box objective functions in the presence of uncontrollable contextual variables. We consider the setting where the context distribution is uncertain but known to lie within an ambiguity set defined as a ball in the Wasserstein distance. We propose a novel algorithm for Wasserstein Distributionally Robust Bayesian Optimization that can handle continuous context distributions while maintaining computational tractability. Our theoretical analysis combines recent results in self-normalized concentration in Hilbert spaces and finite-sample bounds for distributionally robust optimization to establish sublinear regret bounds that match state-of-the-art results. Through extensive comparisons with existing approaches on both synthetic and real-world problems, we demonstrate the simplicity, effectiveness, and practical applicability of our proposed method.
GeoNimbus: A serverless framework to build earth observation and environmental services
Venue: arXiv cs.DC 2025
Year: 2025

Cloud computing has become a popular solution for organizations implementing Earth Observation Systems (EOS). However, this produces a dependency on provider resources. Moreover, managing and executing tasks and data in these environments are challenges that commonly arise when building an EOS. This paper presents GeoNimbus, a serverless framework for composing and deploying spatio-temporal EOS on multiple infrastructures, e.g., on-premise resources and public or private clouds. This framework organizes EOS tasks as functions and automatically manages their deployment, invocation, scalability, and monitoring in the cloud. GeoNimbus framework enables organizations to reuse and share available functions to compose multiple EOS. We use this framework to implement EOS as a service for conducting a case study focused on measuring water resource changes in a lake in the south of Mexico. The experimental evaluation revealed the feasibility and efficiency of using GeoNimbus to build different earth observation studies.
RSRWKV: A Linear-Complexity 2D Attention Mechanism for Efficient Remote Sensing Vision Task
Venue: arXiv cs.CV 2025
Year: 2025

High-resolution remote sensing analysis faces challenges in global context modeling due to scene complexity and scale diversity. While CNNs excel at local feature extraction via parameter sharing, their fixed receptive fields fundamentally restrict long-range dependency modeling. Vision Transformers (ViTs) effectively capture global semantic relationships through self-attention mechanisms but suffer from quadratic computational complexity relative to image resolution, creating critical efficiency bottlenecks for high-resolution imagery. The RWKV model's linear-complexity sequence modeling achieves breakthroughs in NLP but exhibits anisotropic limitations in vision tasks due to its 1D scanning mechanism. To address these challenges, we propose RSRWKV, featuring a novel 2D-WKV scanning mechanism that bridges sequential processing and 2D spatial reasoning while maintaining linear complexity. This enables isotropic context aggregation across multiple directions. The MVC-Shift module enhances multi-scale receptive field coverage, while the ECA module strengthens cross-channel feature interaction and semantic saliency modeling. Experimental results demonstrate RSRWKV's superior performance over CNN and Transformer baselines in classification, detection, and segmentation tasks on NWPU RESISC45, VHR-10.v2, and GLH-Water datasets, offering a scalable solution for high-resolution remote sensing analysis.
FastFT: Accelerating Reinforced Feature Transformation via Advanced Exploration Strategies
Venue: arXiv cs.LG 2025
Year: 2025

Feature Transformation is crucial for classic machine learning that aims to generate feature combinations to enhance the performance of downstream tasks from a data-centric perspective. Current methodologies, such as manual expert-driven processes, iterative-feedback techniques, and exploration-generative tactics, have shown promise in automating such data engineering workflow by minimizing human involvement. However, three challenges remain in those frameworks: (1) It predominantly depends on downstream task performance metrics, as assessment is time-consuming, especially for large datasets. (2) The diversity of feature combinations will hardly be guaranteed after random exploration ends. (3) Rare significant transformations lead to sparse valuable feedback that hinders the learning processes or leads to less effective results. In response to these challenges, we introduce FastFT, an innovative framework that leverages a trio of advanced strategies.We first decouple the feature transformation evaluation from the outcomes of the generated datasets via the performance predictor. To address the issue of reward sparsity, we developed a method to evaluate the novelty of generated transformation sequences. Incorporating this novelty into the reward function accelerates the model's exploration of effective transformations, thereby improving the search productivity. Additionally, we combine novelty and performance to create a prioritized memory buffer, ensuring that essential experiences are effectively revisited during exploration. Our extensive experimental evaluations validate the performance, efficiency, and traceability of our proposed framework, showcasing its superiority in handling complex feature transformation tasks.
Siformer: Feature-isolated Transformer for Efficient Skeleton-based Sign Language Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Sign language recognition (SLR) refers to interpreting sign language glosses from given videos automatically. This research area presents a complex challenge in computer vision because of the rapid and intricate movements inherent in sign languages, which encompass hand gestures, body postures, and even facial expressions. Recently, skeleton-based action recognition has attracted increasing attention due to its ability to handle variations in subjects and backgrounds independently. However, current skeleton-based SLR methods exhibit three limitations: 1) they often neglect the importance of realistic hand poses, where most studies train SLR models on non-realistic skeletal representations; 2) they tend to assume complete data availability in both training or inference phases, and capture intricate relationships among different body parts collectively; 3) these methods treat all sign glosses uniformly, failing to account for differences in complexity levels regarding skeletal representations. To enhance the realism of hand skeletal representations, we present a kinematic hand pose rectification method for enforcing constraints. Mitigating the impact of missing data, we propose a feature-isolated mechanism to focus on capturing local spatial-temporal context. This method captures the context concurrently and independently from individual features, thus enhancing the robustness of the SLR model. Additionally, to adapt to varying complexity levels of sign glosses, we develop an input-adaptive inference approach to optimise computational efficiency and accuracy. Experimental results demonstrate the effectiveness of our approach, as evidenced by achieving a new state-of-the-art (SOTA) performance on WLASL100 and LSA64. For WLASL100, we achieve a top-1 accuracy of 86.50\%, marking a relative improvement of 2.39% over the previous SOTA. For LSA64, we achieve a top-1 accuracy of 99.84%.
An Encoding of Interaction Nets in OCaml
Venue: arXiv cs.PL 2025
Year: 2025

Interaction nets constitute a visual programming language grounded in graph transformation. Owing to their distinctive properties, they inherently facilitate parallelism in the rewriting step. This paper showcases a simple and concise approach to encoding interaction nets within the programming language OCaml, emphasising correctness guarantees. To achieve this objective, we encode not only the interaction net primitives, but also Lafont's original type system.
Enhancing Depression Detection via Question-wise Modality Fusion
Venue: arXiv cs.CL 2025
Year: 2025

Depression is a highly prevalent and disabling condition that incurs substantial personal and societal costs. Current depression diagnosis involves determining the depression severity of a person through self-reported questionnaires or interviews conducted by clinicians. This often leads to delayed treatment and involves substantial human resources. Thus, several works try to automate the process using multimodal data. However, they usually overlook the following: i) The variable contribution of each modality for each question in the questionnaire and ii) Using ordinal classification for the task. This results in sub-optimal fusion and training methods. In this work, we propose a novel Question-wise Modality Fusion (QuestMF) framework trained with a novel Imbalanced Ordinal Log-Loss (ImbOLL) function to tackle these issues. The performance of our framework is comparable to the current state-of-the-art models on the E-DAIC dataset and enhances interpretability by predicting scores for each question. This will help clinicians identify an individual's symptoms, allowing them to customise their interventions accordingly. We also make the code for the QuestMF framework publicly available.
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems
Venue: arXiv cs.AR 2025
Year: 2025

Hybrid storage systems (HSS) integrate multiple storage devices with diverse characteristics to deliver high performance and capacity at low cost. The performance of an HSS highly depends on the effectiveness of two key policies: (1) the data-placement policy, which determines the best-fit storage device for incoming data, and (2) the data-migration policy, which dynamically rearranges stored data (i.e., prefetches hot data and evicts cold data) across the devices to sustain high HSS performance. Prior works optimize either data placement or data migration in isolation, which leads to suboptimal HSS performance. Unfortunately, no prior work tries to optimize both policies together. Our goal is to design a holistic data-management technique that optimizes both data-placement and data-migration policies to fully exploit the potential of an HSS, and thus significantly improve system performance. We propose Harmonia, a multi-agent reinforcement learning (RL)-based data-management technique that employs two lightweight autonomous RL agents, a data-placement agent and a data-migration agent, that adapt their policies for the current workload and HSS configuration while coordinating with each other to improve overall HSS performance. We evaluate Harmonia on real HSS configurations with up to four heterogeneous storage devices and seventeen data-intensive workloads. On performance-optimized (cost-optimized) HSS with two storage devices, Harmonia outperforms the best-performing prior approach by 49.5% (31.7%) on average. On an HSS with three (four) devices, Harmonia outperforms the best-performing prior work by 37.0% (42.0%) on average. Harmonia's performance benefits come with low latency (240ns for inference) and storage overheads (206 KiB in DRAM for both RL agents combined). We will open-source Harmonia's implementation to aid future research on HSS.