OpenRaiser/Intern-Atlas huggingface dataset:
Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models
Venue: arXiv cs.SE 2025
Year: 2025

While modern Requirements Engineering (RE) heavily relies on natural language processing and Machine Learning (ML) techniques, their effectiveness is limited by the scarcity of high-quality datasets. This paper introduces Synthline, a Product Line (PL) approach that leverages Large Language Models to systematically generate synthetic RE data for classification-based use cases. Through an empirical evaluation conducted in the context of using ML for the identification of requirements specification defects, we investigated both the diversity of the generated data and its utility for training downstream models. Our analysis reveals that while synthetic datasets exhibit less diversity than real data, they are good enough to serve as viable training resources. Moreover, our evaluation shows that combining synthetic and real data leads to substantial performance improvements. Specifically, hybrid approaches achieve up to 85% improvement in precision and a 2x increase in recall compared to models trained exclusively on real data. These findings demonstrate the potential of PL-based synthetic data generation to address data scarcity in RE. We make both our implementation and generated datasets publicly available to support reproducibility and advancement in the field.
The Tensor-Core Beamformer: A High-Speed Signal-Processing Library for Multidisciplinary Use
Venue: arXiv cs.DC 2025
Year: 2025

Beamforming is a well-known technique to combine signals from multiple sensors. It has a wide range of application domains. This paper introduces the Tensor-Core Beamformer: a generic, optimized beamformer library that harnesses the computational power of GPU tensor cores to accelerate beamforming computations. The library hides the complexity of tensor cores from the user, and supports 16-bit and 1-bit precision. An extensive performance evaluation on NVIDIA and AMD GPUs shows that the library outperforms traditional beamforming on regular GPU cores by a wide margin, at much higher energy efficiency. In the 16-bit mode, it achieves over 600 TeraOps/s on an AMD MI300X GPU, while approaching 1 TeraOp/J. In the 1-bit mode, it breaks the 3 PetaOps/s barrier and achieves over 10 TeraOps/J on an NVIDIA A100 GPU. The beamforming library can be easily integrated into existing pipelines. We demonstrate its use for medical ultrasound and radio-astronomical instruments.
MDPs with a State Sensing Cost
Venue: arXiv cs.LG 2025
Year: 2025

In many practical sequential decision-making problems, tracking the state of the environment incurs a sensing/communication/computation cost. In these settings, the agent's interaction with its environment includes the additional component of deciding when to sense the state, in a manner that balances the value associated with optimal (state-specific) actions and the cost of sensing. We formulate this as an expected discounted cost Markov Decision Process (MDP), wherein the agent incurs an additional cost for sensing its next state, but has the option to take actions while remaining `blind' to the system state. We pose this problem as a classical discounted cost MDP with an expanded (countably infinite) state space. While computing the optimal policy for this MDP is intractable in general, we derive lower bounds on the optimal value function, which allow us to bound the suboptimality gap of any policy. We also propose a computationally efficient algorithm SPI, based on policy improvement, which in practice performs close to the optimal policy. Finally, we benchmark against the state-of-the-art via a numerical case study.
3D Can Be Explored In 2D: Pseudo-Label Generation for LiDAR Point Clouds Using Sensor-Intensity-Based 2D Semantic Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Semantic segmentation of 3D LiDAR point clouds, essential for autonomous driving and infrastructure management, is best achieved by supervised learning, which demands extensive annotated datasets and faces the problem of domain shifts. We introduce a new 3D semantic segmentation pipeline that leverages aligned scenes and state-of-the-art 2D segmentation methods, avoiding the need for direct 3D annotation or reliance on additional modalities such as camera images at inference time. Our approach generates 2D views from LiDAR scans colored by sensor intensity and applies 2D semantic segmentation to these views using a camera-domain pretrained model. The segmented 2D outputs are then back-projected onto the 3D points, with a simple voting-based estimator that merges the labels associated to each 3D point. Our main contribution is a global pipeline for 3D semantic segmentation requiring no prior 3D annotation and not other modality for inference, which can be used for pseudo-label generation. We conduct a thorough ablation study and demonstrate the potential of the generated pseudo-labels for the Unsupervised Domain Adaptation task.
Unraveling the Rainbow: can value-based methods schedule?
Venue: arXiv cs.LG 2025
Year: 2025

In this work, we conduct an extensive empirical study of several deep reinforcement learning algorithms on two challenging combinatorial optimization problems: the job-shop and flexible job-shop scheduling problems, both fundamental challenges with multiple industrial applications. Broadly, deep reinforcement learning algorithms fall into two categories: policy-gradient and value-based. While value-based algorithms have achieved notable success in domains such as the Arcade Learning Environment, the combinatorial optimization community has predominantly favored policy-gradient algorithms, often overlooking the potential of value-based alternatives. From our results, value-based algorithms demonstrated a lower variance and a more stable convergence profile compared to policy-gradient ones. Moreover, they achieved superior cross-size and cross-distribution generalization, that is, effectively solving instances that are substantially larger or structurally distinct from those seen during training. Finally, our analysis also suggests that the relative performance of each category of algorithms may be dependent on structural properties of the problem, such as problem flexibility and instance size. Overall, our findings challenge the prevailing assumption that policy-gradient algorithms are inherently superior for combinatorial optimization. We show instead that value-based algorithms can match or even surpass the performance of policy-gradient algorithms, suggesting that they deserve greater attention from the combinatorial optimization community. Our code is openly available at: https://github.com/AJ-Correa/Unraveling-the-Rainbow
Stochastic scheduling with Bernoulli-type jobs through policy stratification
Venue: arXiv cs.DS 2025
Year: 2025

This paper addresses the problem of computing a scheduling policy that minimizes the total expected completion time of a set of $N$ jobs with stochastic processing times on $m$ parallel identical machines. When all processing times follow Bernoulli-type distributions, Gupta et al. (SODA '23) exhibited approximation algorithms with an approximation guarantee $\tilde{\text{O}}(\sqrt{m})$, where $m$ is the number of machines and $\tilde{\text{O}}(\cdot)$ suppresses polylogarithmic factors in $N$, improving upon an earlier ${\text{O}}(m)$ approximation by Eberle et al. (OR Letters '19) for a special case. The present paper shows that, quite unexpectedly, the problem with Bernoulli-type jobs admits a PTAS whenever the number of different job-size parameters is bounded by a constant. The result is based on a series of transformations of an optimal scheduling policy to a "stratified" policy that makes scheduling decisions at specific points in time only, while losing only a negligible factor in expected cost. An optimal stratified policy is computed using dynamic programming. Two technical issues are solved, namely (i) to ensure that, with at most a slight delay, the stratified policy has an information advantage over the optimal policy, allowing it to simulate its decisions, and (ii) to ensure that the delays do not accumulate, thus solving the trade-off between the complexity of the scheduling policy and its expected cost. Our results also imply a quasi-polynomial $\text{O}(\log N)$-approximation for the case with an arbitrary number of job sizes.
A Vision-Language Model for Focal Liver Lesion Classification
Venue: arXiv cs.CV 2025
Year: 2025

Accurate classification of focal liver lesions is crucial for diagnosis and treatment in hepatology. However, traditional supervised deep learning models depend on large-scale annotated datasets, which are often limited in medical imaging. Recently, Vision-Language models (VLMs) such as Contrastive Language-Image Pre-training model (CLIP) has been applied to image classifications. Compared to the conventional convolutional neural network (CNN), which classifiers image based on visual information only, VLM leverages multimodal learning with text and images, allowing it to learn effectively even with a limited amount of labeled data. Inspired by CLIP, we pro-pose a Liver-VLM, a model specifically designed for focal liver lesions (FLLs) classification. First, Liver-VLM incorporates class information into the text encoder without introducing additional inference overhead. Second, by calculating the pairwise cosine similarities between image and text embeddings and optimizing the model with a cross-entropy loss, Liver-VLM ef-fectively aligns image features with class-level text features. Experimental results on MPCT-FLLs dataset demonstrate that the Liver-VLM model out-performs both the standard CLIP and MedCLIP models in terms of accuracy and Area Under the Curve (AUC). Further analysis shows that using a lightweight ResNet18 backbone enhances classification performance, particularly under data-constrained conditions.
Reducing Annotation Burden in Physical Activity Research Using Vision-Language Models
Venue: arXiv cs.CV 2025
Year: 2025

Introduction: Data from wearable devices collected in free-living settings, and labelled with physical activity behaviours compatible with health research, are essential for both validating existing wearable-based measurement approaches and developing novel machine learning approaches. One common way of obtaining these labels relies on laborious annotation of sequences of images captured by cameras worn by participants through the course of a day. Methods: We compare the performance of three vision language models and two discriminative models on two free-living validation studies with 161 and 111 participants, collected in Oxfordshire, United Kingdom and Sichuan, China, respectively, using the Autographer (OMG Life, defunct) wearable camera. Results: We found that the best open-source vision-language model (VLM) and fine-tuned discriminative model (DM) achieved comparable performance when predicting sedentary behaviour from single images on unseen participants in the Oxfordshire study; median F1-scores: VLM = 0.89 (0.84, 0.92), DM = 0.91 (0.86, 0.95). Performance declined for light (VLM = 0.60 (0.56,0.67), DM = 0.70 (0.63, 0.79)), and moderate-to-vigorous intensity physical activity (VLM = 0.66 (0.53, 0.85); DM = 0.72 (0.58, 0.84)). When applied to the external Sichuan study, performance fell across all intensity categories, with median Cohen's kappa-scores falling from 0.54 (0.49, 0.64) to 0.26 (0.15, 0.37) for the VLM, and from 0.67 (0.60, 0.74) to 0.19 (0.10, 0.30) for the DM. Conclusion: Freely available computer vision models could help annotate sedentary behaviour, typically the most prevalent activity of daily living, from wearable camera images within similar populations to seen data, reducing the annotation burden.
Efficient Wi-Fi Sensing for IoT Forensics with Lossy Compression of CSI Data
Venue: arXiv cs.NI 2025
Year: 2025

Wi-Fi sensing is an emerging technology that uses channel state information (CSI) from ambient Wi-Fi signals to monitor human activity without the need for dedicated sensors. Wi-Fi sensing does not only represent a pivotal technology in intelligent Internet of Things (IoT) systems, but it can also provide valuable insights in forensic investigations. However, the high dimensionality of CSI data presents major challenges for storage, transmission, and processing in resource-constrained IoT environments. In this paper, we investigate the impact of lossy compression on the accuracy of Wi-Fi sensing, evaluating both traditional techniques and a deep learning-based approach. Our results reveal that simple, interpretable techniques based on principal component analysis can significantly reduce the CSI data volume while preserving classification performance, making them highly suitable for lightweight IoT forensic scenarios. On the other hand, deep learning models exhibit higher potential in complex applications like activity recognition (achieving compression ratios up to 16000:1 with minimal impact on sensing performance) but require careful tuning and greater computational resources. By considering two different sensing applications, this work demonstrates the feasibility of integrating lossy compression schemes into Wi-Fi sensing pipelines to make intelligent IoT systems more efficient and improve the storage requirements in forensic applications.
Physics-informed neural network estimation of active material properties in time-dependent cardiac biomechanical models
Venue: arXiv cs.LG 2025
Year: 2025

Active stress models in cardiac biomechanics account for the mechanical deformation caused by muscle activity, thus providing a link between the electrophysiological and mechanical properties of the tissue. The accurate assessment of active stress parameters is fundamental for a precise understanding of myocardial function but remains difficult to achieve in a clinical setting, especially when only displacement and strain data from medical imaging modalities are available. This work investigates, through an in-silico study, the application of physics-informed neural networks (PINNs) for inferring active contractility parameters in time-dependent cardiac biomechanical models from these types of imaging data. In particular, by parametrising the sought state and parameter field with two neural networks, respectively, and formulating an energy minimisation problem to search for the optimal network parameters, we are able to reconstruct in various settings active stress fields in the presence of noise and with a high spatial resolution. To this end, we also advance the vanilla PINN learning algorithm with the use of adaptive weighting schemes, ad-hoc regularisation strategies, Fourier features, and suitable network architectures. In addition, we thoroughly analyse the influence of the loss weights in the reconstruction of active stress parameters. Finally, we apply the method to the characterisation of tissue inhomogeneities and detection of fibrotic scars in myocardial tissue. This approach opens a new pathway to significantly improve the diagnosis, treatment planning, and management of heart conditions associated with cardiac fibrosis.
Improving Omics-Based Classification: The Role of Feature Selection and Synthetic Data Generation
Venue: arXiv cs.LG 2025
Year: 2025

Given the increasing complexity of omics datasets, a key challenge is not only improving classification performance but also enhancing the transparency and reliability of model decisions. Effective model performance and feature selection are fundamental for explainability and reliability. In many cases, high dimensional omics datasets suffer from limited number of samples due to clinical constraints, patient conditions, phenotypes rarity and others conditions. Current omics based classification models often suffer from narrow interpretability, making it difficult to discern meaningful insights where trust and reproducibility are critical. This study presents a machine learning based classification framework that integrates feature selection with data augmentation techniques to achieve high standard classification accuracy while ensuring better interpretability. Using the publicly available dataset (E MTAB 8026), we explore a bootstrap analysis in six binary classification scenarios to evaluate the proposed model's behaviour. We show that the proposed pipeline yields cross validated perfomance on small dataset that is conserved when the trained classifier is applied to a larger test set. Our findings emphasize the fundamental balance between accuracy and feature selection, highlighting the positive effect of introducing synthetic data for better generalization, even in scenarios with very limited samples availability.
Concept Factorization via Self-Representation and Adaptive Graph Structure Learning
Venue: arXiv cs.LG 2025
Year: 2025

Concept Factorization (CF) models have attracted widespread attention due to their excellent performance in data clustering. In recent years, many variant models based on CF have achieved great success in clustering by taking into account the internal geometric manifold structure of the dataset and using graph regularization techniques. However, their clustering performance depends greatly on the construction of the initial graph structure. In order to enable adaptive learning of the graph structure of the data, we propose a Concept Factorization Based on Self-Representation and Adaptive Graph Structure Learning (CFSRAG) Model. CFSRAG learns the affinity relationship between data through a self-representation method, and uses the learned affinity matrix to implement dynamic graph regularization constraints, thereby ensuring dynamic learning of the internal geometric structure of the data. Finally, we give the CFSRAG update rule and convergence analysis, and conduct comparative experiments on four real datasets. The results show that our model outperforms other state-of-the-art models.
EOPose : Exemplar-based object reposing using Generalized Pose Correspondences
Venue: arXiv cs.CV 2025
Year: 2025

Reposing objects in images has a myriad of applications, especially for e-commerce where several variants of product images need to be produced quickly. In this work, we leverage the recent advances in unsupervised keypoint correspondence detection between different object images of the same class to propose an end-to-end framework for generic object reposing. Our method, EOPose, takes a target pose-guidance image as input and uses its keypoint correspondence with the source object image to warp and re-render the latter into the target pose using a novel three-step approach. Unlike generative approaches, our method also preserves the fine-grained details of the object such as its exact colors, textures, and brand marks. We also prepare a new dataset of paired objects based on the Objaverse dataset to train and test our network. EOPose produces high-quality reposing output as evidenced by different image quality metrics (PSNR, SSIM and FID). Besides a description of the method and the dataset, the paper also includes detailed ablation and user studies to indicate the efficacy of the proposed method
Lightweight Clinical Decision Support System using QLoRA-Fine-Tuned LLMs and Retrieval-Augmented Generation
Venue: arXiv cs.CL 2025
Year: 2025

This research paper investigates the application of Large Language Models (LLMs) in healthcare, specifically focusing on enhancing medical decision support through Retrieval-Augmented Generation (RAG) integrated with hospital-specific data and fine-tuning using Quantized Low-Rank Adaptation (QLoRA). The system utilizes Llama 3.2-3B-Instruct as its foundation model. By embedding and retrieving context-relevant healthcare information, the system significantly improves response accuracy. QLoRA facilitates notable parameter efficiency and memory optimization, preserving the integrity of medical information through specialized quantization techniques. Our research also shows that our model performs relatively well on various medical benchmarks, indicating that it can be used to make basic medical suggestions. This paper details the system's technical components, including its architecture, quantization methods, and key healthcare applications such as enhanced disease prediction from patient symptoms and medical history, treatment suggestions, and efficient summarization of complex medical reports. We touch on the ethical considerations-patient privacy, data security, and the need for rigorous clinical validation-as well as the practical challenges of integrating such systems into real-world healthcare workflows. Furthermore, the lightweight quantized weights ensure scalability and ease of deployment even in low-resource hospital environments. Finally, the paper concludes with an analysis of the broader impact of LLMs on healthcare and outlines future directions for LLMs in medical settings.
MedArabiQ: Benchmarking Large Language Models on Arabic Medical Tasks
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have demonstrated significant promise for various applications in healthcare. However, their efficacy in the Arabic medical domain remains unexplored due to the lack of high-quality domain-specific datasets and benchmarks. This study introduces MedArabiQ, a novel benchmark dataset consisting of seven Arabic medical tasks, covering multiple specialties and including multiple choice questions, fill-in-the-blank, and patient-doctor question answering. We first constructed the dataset using past medical exams and publicly available datasets. We then introduced different modifications to evaluate various LLM capabilities, including bias mitigation. We conducted an extensive evaluation with five state-of-the-art open-source and proprietary LLMs, including GPT-4o, Claude 3.5-Sonnet, and Gemini 1.5. Our findings highlight the need for the creation of new high-quality benchmarks that span different languages to ensure fair deployment and scalability of LLMs in healthcare. By establishing this benchmark and releasing the dataset, we provide a foundation for future research aimed at evaluating and enhancing the multilingual capabilities of LLMs for the equitable use of generative AI in healthcare.
Procedural Memory Is Not All You Need: Bridging Cognitive Gaps in LLM-Based Agents
Venue: arXiv cs.AI 2025
Year: 2025

Large Language Models (LLMs) represent a landmark achievement in Artificial Intelligence (AI), demonstrating unprecedented proficiency in procedural tasks such as text generation, code completion, and conversational coherence. These capabilities stem from their architecture, which mirrors human procedural memory -- the brain's ability to automate repetitive, pattern-driven tasks through practice. However, as LLMs are increasingly deployed in real-world applications, it becomes impossible to ignore their limitations operating in complex, unpredictable environments. This paper argues that LLMs, while transformative, are fundamentally constrained by their reliance on procedural memory. To create agents capable of navigating ``wicked'' learning environments -- where rules shift, feedback is ambiguous, and novelty is the norm -- we must augment LLMs with semantic memory and associative learning systems. By adopting a modular architecture that decouples these cognitive functions, we can bridge the gap between narrow procedural expertise and the adaptive intelligence required for real-world problem-solving.