OpenRaiser/Intern-Atlas huggingface dataset:
Harmonic balance-automatic differentiation method: an out-of-the-box and efficient solver for general nonlinear dynamics simulation
Venue: arXiv cs.CE 2025
Year: 2025

The Harmonic Balance-Alternating Frequency-Time domain (HB-AFT) method is extensively employed for dynamic response analysis of nonlinear systems. However, its application to high-dimensional complex systems is constrained by the manual derivation of Jacobian matrices during Newton-Raphson iterations, which become computationally intractable or error-prone for intricate nonlinearities. The Harmonic Balance-Automatic Differentiation (HB-AD) method is proposed to address this limitation, in which AD is integrated with the harmonic balance framework. This approach eliminates all manual derivations by leveraging AD to compute exact Jacobians numerically, enabling generic and efficient analysis of high-dimensional complex nonlinear systems. The implementation utilizes advanced deep learning frameworks for native parallel computing and CUDA acceleration, and combines AD with arc-length continuation, establishing an out-of-the-box and high efficiency computational architecture. Users need only supply the system's dynamic equations, HB-AD then autonomously trace the complete panorama of periodic responses -- including stable/unstable solution branches. Computational experiments on a rotor system with squeeze-film damper (SFD) demonstrate HB-AD's capability in handling complex nonlinear expressions with automated Jacobian calculations. For a high-dimensional aero-engine rotor-bearing-casing system with complex bearing nonlinearities, HB-AD achieves 17-fold higher efficiency than traditional HB-AFT and 144-fold acceleration over the Newmark method. The HB-AD method is a synergistic merger of computational mechanics and machine learning primitives, delivers an easy to use, general-purpose, high efficiency platform for high-fidelity dynamic characterization of high-dimensional engineering systems.
An Experimental Exploration of In-Memory Computing for Multi-Layer Perceptrons
Venue: arXiv cs.DC 2025
Year: 2025

In modern computer architectures, the performance of many memory-bound workloads (e.g., machine learning, graph processing, databases) is limited by the data movement bottleneck that emerges when transferring large amounts of data between the main memory and the central processing unit (CPU). Processing-in-memory is an emerging computing paradigm that aims to alleviate this data movement bottleneck by performing computation close to or within the memory units, where data resides. One example of a prevalent workload whose performance is bound by the data movement bottleneck is the training and inference process of artificial neural networks. In this work, we analyze the potential of modern general-purpose PiM architectures to accelerate neural networks. To this end, we selected the UPMEM PiM system, the first commercially available real-world general-purpose PiM architecture. We compared the implementation of multilayer perceptrons (MLPs) in PiM with a sequential baseline running on an Intel Xeon CPU. The UPMEM implementation achieves up to $259\times$ better performance for inference of large batch sizes when compared against the CPU that exploits the size of the available PiM memory. Additionally, two smaller MLPs were implemented using UPMEM's working SRAM (WRAM), a scratchpad memory, to evaluate their performance against a low-power Nvidia Jetson graphics processing unit (GPU), providing further insights into the efficiency of UPMEM's PiM for neural network inference. Results show that using WRAM achieves kernel execution times for MLP inference of under $3$ ms, which is within the same order of magnitude as low-power GPUs.
RORPCap: Retrieval-based Objects and Relations Prompt for Image Captioning
Venue: arXiv cs.CV 2025
Year: 2025

Image captioning aims to generate natural language descriptions for input images in an open-form manner. To accurately generate descriptions related to the image, a critical step in image captioning is to identify objects and understand their relations within the image. Modern approaches typically capitalize on object detectors or combine detectors with Graph Convolutional Network (GCN). However, these models suffer from redundant detection information, difficulty in GCN construction, and high training costs. To address these issues, a Retrieval-based Objects and Relations Prompt for Image Captioning (RORPCap) is proposed, inspired by the fact that image-text retrieval can provide rich semantic information for input images. RORPCap employs an Objects and relations Extraction Model to extract object and relation words from the image. These words are then incorporate into predefined prompt templates and encoded as prompt embeddings. Next, a Mamba-based mapping network is designed to quickly map image embeddings extracted by CLIP to visual-text embeddings. Finally, the resulting prompt embeddings and visual-text embeddings are concatenated to form textual-enriched feature embeddings, which are fed into a GPT-2 model for caption generation. Extensive experiments conducted on the widely used MS-COCO dataset show that the RORPCap requires only 2.6 hours under cross-entropy loss training, achieving 120.5% CIDEr score and 22.0% SPICE score on the "Karpathy" test split. RORPCap achieves comparable performance metrics to detector-based and GCN-based models with the shortest training time and demonstrates its potential as an alternative for image captioning.
DCoAR: Deep Concept Injection into Unified Autoregressive Models for Personalized Text-to-Image Generation
Venue: arXiv cs.CV 2025
Year: 2025

The unified autoregressive (AR) model excels at multimodal understanding and generation. However, its full potential in the domain of customized image generation has yet to be fully realized. Existing customization approaches for unified AR models face a fundamental dilemma: adaptation-based methods suffer from overfitting and scalability bottlenecks, while concept-injection paradigms are constrained by a shallow injection strategy that leads to poor visual fidelity and impaired re-contextualization. To address this, we propose DCoAR, a novel deep concept injection framework that maintains a completely frozen pre-trained model. DCoAR deeply integrates new concepts through a Layer-wise Multimodal Context Learning (LMCL) strategy, which is stabilized by a multi-faceted regularization scheme: a Dual Prior Preservation (DPP) loss to mitigate semantic drift and a Context-Aware Self-Regularization (CASR) loss to enhance re-contextualization. The framework also enables training-free subject customization in user-provided styles. Experiments demonstrate that DCoAR significantly outperforms previous injection-based methods and achieves performance competitive with adaptation-based approaches while requiring substantially fewer trainable parameters. Code: https://github.com/KZF-kzf/CoAR
ProteoKnight: Convolution-based phage virion protein classification and uncertainty analysis
Venue: arXiv cs.LG 2025
Year: 2025

\textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages. Computational tools, particularly machine learning, have emerged for annotating phage protein sequences from high-throughput sequencing. However, effective annotation requires specialized sequence encodings. Our paper introduces ProteoKnight, a new image-based encoding method that addresses spatial constraints in existing techniques, yielding competitive performance in PVP classification using pre-trained convolutional neural networks. Additionally, our study evaluates prediction uncertainty in binary PVP classification through Monte Carlo Dropout (MCD). \textbf{Methods:} ProteoKnight adapts the classical DNA-Walk algorithm for protein sequences, incorporating pixel colors and adjusting walk distances to capture intricate protein features. Encoded sequences were classified using multiple pre-trained CNNs. Variance and entropy measures assessed prediction uncertainty across proteins of various classes and lengths. \textbf{Results:} Our experiments achieved 90.8% accuracy in binary classification, comparable to state-of-the-art methods. Multi-class classification accuracy remains suboptimal. Our uncertainty analysis unveils variability in prediction confidence influenced by protein class and sequence length. \textbf{Conclusions:} Our study surpasses frequency chaos game representation (FCGR) by introducing novel image encoding that mitigates spatial information loss limitations. Our classification technique yields accurate and robust PVP predictions while identifying low-confidence predictions.
Enhancing Systematic Interoperability: Convergences and Mismatches between Web 3.0 and the EU Data Act
Venue: arXiv cs.CY 2025
Year: 2025

Interoperability is increasingly recognised as a foundational principle for fostering innovation, competition, and user autonomy in the evolving digital ecosystem. Existing research on interoperability predominantly focuses either on technological interoperability itself or on the legal regulations concerning interoperability, with insufficient exploration of their interdisciplinary intersection. This paper compares the technological interoperability in Web 3.0 with the theoretical framework of legal interoperability established by the EU Data Act, analysing the areas of convergence and mismatch. The goal is to align technical interoperability with legal concepts of interoperability, thereby enhancing the practical implementation of systematic interoperability in the next generation of the Web. This study finds that, firstly, Web 3.0's concept of interoperability spans data, systems, and applications, while the Data Act focuses solely on data. This narrow scope risks creating a fragmented ecosystem, where data exchange is possible, but full integration of systems and applications is hindered, leading to inefficiencies, and obstructing seamless data flow across platforms. Secondly, while Web 3.0 technically seeks to achieve interoperability through the integration of entire systems and decentralised applications, the compliance with Data Act might negatively limit such system and application interoperability through its data interoperability provisions. This paper suggests interdisciplinary recommendations to enhance the implementation and enforcement of interoperability. On one hand, the Data Act should broaden its concept of interoperability to encompass both the systems and applications layers. On the other hand, it is advisable to introduce provisions for standardised protocols through soft law mechanisms to address legal shortcomings and keep pace with technological advancements.
Tight Bounds for Schrödinger Potential Estimation in Unpaired Data Translation
Venue: arXiv cs.LG 2025
Year: 2025

Modern methods of generative modelling and unpaired data translation based on Schrödinger bridges and stochastic optimal control theory aim to transform an initial density to a target one in an optimal way. In the present paper, we assume that we only have access to i.i.d. samples from initial and final distributions. This makes our setup suitable for both generative modelling and unpaired data translation. Relying on the stochastic optimal control approach, we choose an Ornstein-Uhlenbeck process as the reference one and estimate the corresponding Schrödinger potential. Introducing a risk function as the Kullback-Leibler divergence between couplings, we derive tight bounds on generalization ability of an empirical risk minimizer in a class of Schrödinger potentials including Gaussian mixtures. Thanks to the mixing properties of the Ornstein-Uhlenbeck process, we almost achieve fast rates of convergence up to some logarithmic factors in favourable scenarios. We also illustrate performance of the suggested approach with numerical experiments.
Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we consider the problem of recovering time-varying reward functions from either optimal policies or demonstrations coming from a max entropy reinforcement learning problem. This problem is highly ill-posed without additional assumptions on the underlying rewards. However, in many applications, the rewards are indeed parsimonious, and some prior information is available. We consider two such priors on the rewards: 1) rewards are mostly constant and they change infrequently, 2) rewards can be represented by a linear combination of a small number of feature functions. We first show that the reward identification problem with the former prior can be recast as a sparsification problem subject to linear constraints. Moreover, we give a polynomial-time algorithm that solves this sparsification problem exactly. Then, we show that identifying rewards representable with the minimum number of features can be recast as a rank minimization problem subject to linear constraints, for which convex relaxations of rank can be invoked. In both cases, these observations lead to efficient optimization-based reward identification algorithms. Several examples are given to demonstrate the accuracy of the recovered rewards as well as their generalizability.
AgriVLN: Vision-and-Language Navigation for Agricultural Robots
Venue: arXiv cs.RO 2025
Year: 2025

Agricultural robots have emerged as powerful members in agricultural tasks, nevertheless, still heavily rely on manual operation or untransportable railway for movement, resulting in limited mobility and poor adaptability. Vision-and-Language Navigation (VLN) enables robots to navigate to the target destinations following natural language instructions, demonstrating strong performance on several domains. However, none of the existing benchmarks or methods is specifically designed for agricultural scenes. To bridge this gap, we propose Agriculture to Agriculture (A2A) benchmark, containing 1,560 episodes across six diverse agricultural scenes, in which all realistic RGB videos are captured by front-facing camera on a quadruped robot at a height of 0.38 meters, aligning with the practical deployment conditions. Meanwhile, we propose Vision-and-Language Navigation for Agricultural Robots (AgriVLN) baseline based on Vision-Language Model (VLM) prompted with carefully crafted templates, which can understand both given instructions and agricultural environments to generate appropriate low-level actions for robot control. When evaluated on A2A, AgriVLN performs well on short instructions but struggles with long instructions, because it often fails to track which part of the instruction is currently being executed. To address this, we further propose Subtask List (STL) instruction decomposition module and integrate it into AgriVLN, improving Success Rate (SR) from 0.33 to 0.47. We additionally compare AgriVLN with several existing VLN methods, demonstrating the state-of-the-art performance in the agricultural domain.
RNA-KG v2.0: An RNA-centered Knowledge Graph with Properties
Venue: arXiv cs.DB 2025
Year: 2025

RNA-KG is a recently developed knowledge graph that integrates the interactions involving coding and non-coding RNA molecules extracted from public data sources. It can be used to support the classification of new molecules, identify new interactions through the use of link prediction methods, and reveal hidden patterns among the represented entities. In this paper, we propose RNA-KG v2.0, a new release of RNA-KG that integrates around 100M manually curated interactions sourced from 91 linked open data repositories and ontologies. Relationships are characterized by standardized properties that capture the specific context (e.g., cell line, tissue, pathological state) in which they have been identified. In addition, the nodes are enriched with detailed attributes, such as descriptions, synonyms, and molecular sequences sourced from platforms such as OBO ontologies, NCBI repositories, RNAcentral, and Ensembl. The enhanced repository enables the expression of advanced queries that take into account the context in which the experiments were conducted. It also supports downstream applications in RNA research, including "context-aware" link prediction techniques that combine both topological and semantic information.
Leveraging Learning Bias for Noisy Anomaly Detection
Venue: arXiv cs.CV 2025
Year: 2025

This paper addresses the challenge of fully unsupervised image anomaly detection (FUIAD), where training data may contain unlabeled anomalies. Conventional methods assume anomaly-free training data, but real-world contamination leads models to absorb anomalies as normal, degrading detection performance. To mitigate this, we propose a two-stage framework that systematically exploits inherent learning bias in models. The learning bias stems from: (1) the statistical dominance of normal samples, driving models to prioritize learning stable normal patterns over sparse anomalies, and (2) feature-space divergence, where normal data exhibit high intra-class consistency while anomalies display high diversity, leading to unstable model responses. Leveraging the learning bias, stage 1 partitions the training set into subsets, trains sub-models, and aggregates cross-model anomaly scores to filter a purified dataset. Stage 2 trains the final detector on this dataset. Experiments on the Real-IAD benchmark demonstrate superior anomaly detection and localization performance under different noise conditions. Ablation studies further validate the framework's contamination resilience, emphasizing the critical role of learning bias exploitation. The model-agnostic design ensures compatibility with diverse unsupervised backbones, offering a practical solution for real-world scenarios with imperfect training data. Code is available at https://github.com/hustzhangyuxin/LLBNAD.
Structured Superposition of Autoencoders for UEP Codes at Intermediate Blocklengths
Venue: arXiv cs.IT 2025
Year: 2025

Unequal error protection (UEP) coding that enables differentiated reliability levels within a transmitted message is essential for modern communication systems. Autoencoder (AE)-based code designs have shown promise in the context of learned equal error protection (EEP) coding schemes. However, their application to UEP remains largely unexplored, particularly at intermediate blocklengths, due to the increasing complexity of AE-based models. Inspired by the proven effectiveness of superposition coding and successive interference cancellation (SIC) decoding in conventional UEP schemes, we propose a structured AE-based architecture that extends AE-based UEP codes to substantially larger blocklengths while maintaining efficient training. By structuring encoding and decoding into smaller AE subblocks, our method provides a flexible framework for fine-tuning UEP reliability levels while adapting to diverse system parameters. Numerical results show that the proposed approach improves over established achievability bounds of randomized superposition coding-based UEP schemes with SIC decoding, making the proposed structured AE-based UEP codes a scalable and efficient solution for next-generation networks.
N-BEATS-MOE: N-BEATS with a Mixture-of-Experts Layer for Heterogeneous Time Series Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Deep learning approaches are increasingly relevant for time series forecasting tasks. Methods such as N-BEATS, which is built on stacks of multilayer perceptrons (MLPs) blocks, have achieved state-of-the-art results on benchmark datasets and competitions. N-BEATS is also more interpretable relative to other deep learning approaches, as it decomposes forecasts into different time series components, such as trend and seasonality. In this work, we present N-BEATS-MOE, an extension of N-BEATS based on a Mixture-of-Experts (MoE) layer. N-BEATS-MOE employs a dynamic block weighting strategy based on a gating network which allows the model to better adapt to the characteristics of each time series. We also hypothesize that the gating mechanism provides additional interpretability by identifying which expert is most relevant for each series. We evaluate our method across 12 benchmark datasets against several approaches, achieving consistent improvements on several datasets, especially those composed of heterogeneous time series.
A Learning-Based Framework for Collision-Free Motion Planning
Venue: arXiv cs.RO 2025
Year: 2025

This paper presents a learning-based extension to a Circular Field (CF)-based motion planner for efficient, collision-free trajectory generation in cluttered environments. The proposed approach overcomes the limitations of hand-tuned force field parameters by employing a deep neural network trained to infer optimal planner gains from a single depth image of the scene. The pipeline incorporates a CUDA-accelerated perception module, a predictive agent-based planning strategy, and a dataset generated through Bayesian optimization in simulation. The resulting framework enables real-time planning without manual parameter tuning and is validated both in simulation and on a Franka Emika Panda robot. Experimental results demonstrate successful task completion and improved generalization compared to classical planners.
Commentary Generation for Soccer Highlights
Venue: arXiv cs.CV 2025
Year: 2025

Automated soccer commentary generation has evolved from template-based systems to advanced neural architectures, aiming to produce real-time descriptions of sports events. While frameworks like SoccerNet-Caption laid foundational work, their inability to achieve fine-grained alignment between video content and commentary remains a significant challenge. Recent efforts such as MatchTime, with its MatchVoice model, address this issue through coarse and fine-grained alignment techniques, achieving improved temporal synchronization. In this paper, we extend MatchVoice to commentary generation for soccer highlights using the GOAL dataset, which emphasizes short clips over entire games. We conduct extensive experiments to reproduce the original MatchTime results and evaluate our setup, highlighting the impact of different training configurations and hardware limitations. Furthermore, we explore the effect of varying window sizes on zero-shot performance. While MatchVoice exhibits promising generalization capabilities, our findings suggest the need for integrating techniques from broader video-language domains to further enhance performance. Our code is available at https://github.com/chidaksh/SoccerCommentary.
Multimodal Remote Inference
Venue: arXiv cs.LG 2025
Year: 2025

We consider a remote inference system with multiple modalities, where a multimodal machine learning (ML) model performs real-time inference using features collected from remote sensors. When sensor observations evolve dynamically over time, fresh features are critical for inference tasks. However, timely delivery of features from all modalities is often infeasible because of limited network resources. Towards this end, in this paper, we study a two-modality scheduling problem that seeks to minimize the ML model's inference error, expressed as a penalty function of the Age of Information (AoI) vector of the two modalities. We develop an index-based threshold policy and prove its optimality. Specifically, the scheduler switches to the other modality once the current modality's index function exceeds a predetermined threshold. We show that both modalities share the same threshold and that the index functions and the threshold can be computed efficiently. Our optimality results hold for general AoI functions (which could be non-monotonic and non-separable) and heterogeneous transmission times across modalities. To demonstrate the importance of considering a task-oriented AoI function, we conduct numerical experiments based on robot state prediction and compare our policy with round-robin and uniform random policies (both are oblivious to the AoI and the inference error).n The results show that our policy reduces inference error by up to 55% compared with these baselines.