OpenRaiser/Intern-Atlas huggingface dataset:
STAR-RIS-Assisted Cell-Free Massive MIMO with Multi-antenna Users and Hardware Impairments Over Correlated Rayleigh Fading Channels
Venue: arXiv cs.IT 2025
Year: 2025

Integrating cell-free massive multiple-input multiple-output (MIMO) with simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can provide ubiquitous connectivity and enhance coverage. This paper explores a STAR-RIS-assisted cell-free massive MIMO system featuring multi-antenna users, multi-antenna access points (APs), and multi-element STAR-RISs, accounting for transceiver hardware impairments. We first establish the system model of STAR-RIS-assisted cell-free massive MIMO systems with multi-antenna users. Subsequently, we analyze two uplink implementations: local processing and centralized decoding (Level 1), and fully centralized processing (Level 2), both implementations incorporating hardware impairments. We study the local and global minimum mean square error (MMSE) combining schemes to maximize the uplink spectral efficiency (SE) for Level 1 and Level 2, respectively. The MMSE-based successive interference cancellation detector is utilized to compute the uplink SE. We introduce the optimal large-scale fading decoding at the central processing unit and derive closed-form SE expressions utilizing maximum ratio combining at APs for Level 1. Our numerical results reveal that hardware impairments negatively affect SE performance, particularly at the user end. However, this degradation can be mitigated by increasing the number of user antennas. Enhancing the number of APs and STAR-RIS elements also improves performance and mitigates performance degradation. Notably, unlike conventional results based on direct links, our findings show that Level 2 consistently outperforms Level 1 with arbitrary combining schemes for the proposed STAR-RIS-assisted system.
Towards Scalable Foundation Model for Multi-modal and Hyperspectral Geospatial Data
Venue: arXiv cs.CV 2025
Year: 2025

Geospatial raster data, such as that collected by satellite-based imaging systems at different times and spectral bands, hold immense potential for enabling a wide range of high-impact applications. This potential stems from the rich information that is spatially and temporally contextualized across multiple channels and sensing modalities. Recent work has adapted existing self-supervised learning approaches for such geospatial data. However, they fall short of scalable model architectures, leading to inflexibility and computational inefficiencies when faced with an increasing number of channels and modalities. To address these limitations, we introduce Low-rank Efficient Spatial-Spectral Vision Transformer with three key innovations: i) the LESS Attention Block that approximates high-dimensional spatial-spectral attention through Kronecker's product of the low-dimensional spatial and spectral attention components; ii) the Continuous Positional-Channel Embedding Layer that preserves both the continuity and physical characteristics of each spatial-spectral patch; and iii) the Perception Field Mask that exploits local spatial dependencies by constraining attention to neighboring patches. To evaluate the proposed innovations, we construct GFM-Bench, which serves as a comprehensive benchmark for such geospatial raster data. We pretrain LESS ViT using a Hyperspectral Masked Autoencoder framework with integrated positional and channel masking strategies. Experimental results demonstrate that our proposed method achieves competitive performance against state-of-the-art multi-modal geospatial foundation models while outperforming them on cross-satellite generalization tasks with higher computational efficiency. The flexibility and extensibility of our framework make it a promising direction for future geospatial data analysis tasks that involve a wide range of modalities and channels.
Enhancing LLM Reasoning with Iterative DPO: A Comprehensive Empirical Investigation
Venue: arXiv cs.CL 2025
Year: 2025

Recent advancements in post-training methodologies for large language models (LLMs) have highlighted reinforcement learning (RL) as a critical component for enhancing reasoning. However, the substantial computational costs associated with RL-based approaches have led to growing interest in alternative paradigms, such as Direct Preference Optimization (DPO). In this study, we investigate the effectiveness of DPO in facilitating self-improvement for LLMs through iterative preference-based learning. We demonstrate that a single round of DPO with coarse filtering significantly enhances mathematical reasoning performance, particularly for strong base model. Furthermore, we design an iterative enhancement framework for both the generator and the reward model (RM), enabling their mutual improvement through online interaction across multiple rounds of DPO. Finally, with simple verifiable rewards, our model DPO-VP achieves RL-level performance with significantly lower computational overhead. These findings highlight DPO as a scalable and cost-effective alternative to RL, offering a practical solution for enhancing LLM reasoning in resource-constrained situations.
Enable Time-Sensitive Applications in Kubernetes with Container Network Interface Plugin Agnostic Metadata Proxy
Venue: arXiv cs.NI 2025
Year: 2025

Application deployment in cloud environment is dominated by Kubernetes-orchestrated microservices. Provides a secure environment, networking, storage, isolation, scheduling, and many other abstractions that can be easily extended to meet our needs. Time-Sensitive Applications (TSAs) have special requirements for compute and network. Deploying TSAs in Kubernetes is challenging because the networking implemented by Container Network Interface (CNI) plugins is not aware of the traffic characteristic required by Time-Sensitive Network. Even if a network interface supports TSN features (e.g.: Scheduled Traffic) and a modified CNI plugin is aware of this interface, the pod network isolation built on top of Linux deletes the metadata required for TSN protocols to work with. We propose TSN metadata proxy, a simple architecture that allows any TSA microservice to use the TSN capabilities of the physical NIC, without any modification. This architecture is tightly integrated with the Kubernetes networking model, works with popular CNI plugins, and supports services such as ClusterIP, NodePort, or LoadBalancer without additional configuration. Unlike former proposals, this architecture does not require either bypassing the Linux kernel network stack, direct access to the physical NIC, escalated privileges for the TSA microservice, or even modification of the TSA.
Low-PAPR OFDM-ISAC Waveform Design Based on Frequency-Domain Phase Differences
Venue: arXiv cs.IT 2025
Year: 2025

Low peak-to-average power ratio (PAPR) orthogonal frequency division multiplexing (OFDM) waveform design is a crucial issue in integrated sensing and communications (ISAC). This paper introduces an OFDM-ISAC waveform design that utilizes the entire spectrum simultaneously for both communication and sensing by leveraging a novel degree of freedom (DoF): the frequency-domain phase difference (PD). Based on this concept, we develop a novel PD-based OFDM-ISAC waveform structure and utilize it to design a PD-based Low-PAPR OFDM-ISAC (PLPOI) waveform. The design is formulated as an optimization problem incorporating four key constraints: the time-frequency relationship equation, frequency-domain unimodular constraints, PD constraints, and time-domain low PAPR requirements. To solve this challenging non-convex problem, we develop an efficient algorithm, ADMM-PLPOI, based on the alternating direction method of multipliers (ADMM) framework. Extensive simulation results demonstrate that the proposed PLPOI waveform achieves significant improvements in both PAPR and bit error rate (BER) performance compared to conventional OFDM-ISAC waveforms.
Objective Measurement of AI Literacy: Development and Validation of the AI Competency Objective Scale (AICOS)
Venue: arXiv cs.HC 2025
Year: 2025

As Artificial Intelligence (AI) becomes more pervasive in various aspects of life, AI literacy is becoming a fundamental competency that enables individuals to move safely and competently in an AI-pervaded world. There is a growing need to measure this competency, e.g., to develop targeted educational interventions. Although several measurement tools already exist, many have limitations regarding subjective data collection methods, target group differentiation, validity, and integration of current developments such as Generative AI Literacy. This study develops and validates the AI Competency Objective Scale (AICOS) for measuring AI literacy objectively. The presented scale addresses weaknesses and offers a robust measurement approach that considers established competency and measurement models, captures central sub-competencies of AI literacy, and integrates the dimension of Generative AI Literacy. The AICOS provides a sound and comprehensive measure of AI literacy, and initial analyses show potential for a modular structure. Furthermore, a first edition of a short version of the AICOS is developed. Due to its methodological foundation, extensive validation, and integration of recent developments, the test represents a valuable resource for scientific research and practice in educational institutions and professional contexts. The AICOS significantly contributes to the development of standardized measurement instruments and enables the targeted assessment and development of AI skills in different target groups.
Action tube generation by person query matching for spatio-temporal action detection
Venue: arXiv cs.CV 2025
Year: 2025

This paper proposes a method for spatio-temporal action detection (STAD) that directly generates action tubes from the original video without relying on post-processing steps such as IoU-based linking and clip splitting. Our approach applies query-based detection (DETR) to each frame and matches DETR queries to link the same person across frames. We introduce the Query Matching Module (QMM), which uses metric learning to bring queries for the same person closer together across frames compared to queries for different people. Action classes are predicted using the sequence of queries obtained from QMM matching, allowing for variable-length inputs from videos longer than a single clip. Experimental results on JHMDB, UCF101-24, and AVA datasets demonstrate that our method performs well for large position changes of people while offering superior computational efficiency and lower resource requirements.
Enhancing Job Salary Prediction with Disentangled Composition Effect Modeling: A Neural Prototyping Approach
Venue: arXiv cs.LG 2025
Year: 2025

In the era of the knowledge economy, understanding how job skills influence salary is crucial for promoting recruitment with competitive salary systems and aligned salary expectations. Despite efforts on salary prediction based on job positions and talent demographics, there still lacks methods to effectively discern the set-structured skills' intricate composition effect on job salary. While recent advances in neural networks have significantly improved accurate set-based quantitative modeling, their lack of explainability hinders obtaining insights into the skills' composition effects. Indeed, model explanation for set data is challenging due to the combinatorial nature, rich semantics, and unique format. To this end, in this paper, we propose a novel intrinsically explainable set-based neural prototyping approach, namely \textbf{LGDESetNet}, for explainable salary prediction that can reveal disentangled skill sets that impact salary from both local and global perspectives. Specifically, we propose a skill graph-enhanced disentangled discrete subset selection layer to identify multi-faceted influential input subsets with varied semantics. Furthermore, we propose a set-oriented prototype learning method to extract globally influential prototypical sets. The resulting output is transparently derived from the semantic interplay between these input subsets and global prototypes. Extensive experiments on four real-world datasets demonstrate that our method achieves superior performance than state-of-the-art baselines in salary prediction while providing explainable insights into salary-influencing patterns.
Intra-neuronal attention within language models Relationships between activation and semantics
Venue: arXiv cs.AI 2025
Year: 2025

This study investigates the ability of perceptron-type neurons in language models to perform intra-neuronal attention; that is, to identify different homogeneous categorical segments within the synthetic thought category they encode, based on a segmentation of specific activation zones for the tokens to which they are particularly responsive. The objective of this work is therefore to determine to what extent formal neurons can establish a homomorphic relationship between activation-based and categorical segmentations. The results suggest the existence of such a relationship, albeit tenuous, only at the level of tokens with very high activation levels. This intra-neuronal attention subsequently enables categorical restructuring processes at the level of neurons in the following layer, thereby contributing to the progressive formation of high-level categorical abstractions.
Frobenius method for Mahler equations
Venue: arXiv cs.SC 2025
Year: 2025

Using Hahn series, one can attach to any linear Mahler equation a basis of solutions at 0 reminiscent of the solutions of linear differential equations at a regular singularity. We show that such a basis of solutions can be produced by using a variant of Frobenius method.
Linear-Size Neural Network Representation of Piecewise Affine Functions in $\mathbb{R}^2$
Venue: arXiv cs.LG 2025
Year: 2025

It is shown that any continuous piecewise affine (CPA) function $\mathbb{R}^2\to\mathbb{R}$ with $p$ pieces can be represented by a ReLU neural network with two hidden layers and $O(p)$ neurons. Unlike prior work, which focused on convex pieces, this analysis considers CPA functions with connected but potentially non-convex pieces.
Hybrid Work in Agile Software Development: Recurring Meetings
Venue: arXiv cs.SE 2025
Year: 2025

The Covid-19 pandemic established hybrid work as the new norm in software development companies. In large-scale agile, meetings of different types are pivotal for collaboration, and decisions need to be taken on how they are organized and carried out in hybrid work. This study investigates how recurring meetings are organized and carried out in hybrid work in a large-scale agile environment. We performed a single case study by conducting 27 semi-structured interviews with members of 15 agile teams, product owners, managers, and specialists from two units of Ericsson, a multinational telecommunications company with a "2 days per week at the office" policy. A key insight from this study is that different types of meetings in agile software development should be primarily organized onsite or remotely based on the meeting intent, i.e., meetings requiring active discussion or brainstorming, such as retrospectives or technical discussions, benefit from onsite attendance, whereas large information sharing meetings work well remotely. In hybrid work, community meetings can contribute to knowledge sharing within organizations, help strengthen social ties, and prevent siloed collaboration. Additionally, the use of cameras is recommended for small discussion-oriented remote and hybrid meetings.
Disentangling the Power Dynamics in Participatory Data Physicalisation
Venue: arXiv cs.HC 2025
Year: 2025

Participatory data physicalisation (PDP) is recognised for its potential to support data-driven decisions among stakeholders who collaboratively construct physical elements into commonly insightful visualisations. Like all participatory processes, PDP is however influenced by underlying power dynamics that might lead to issues regarding extractive participation, marginalisation, or exclusion, among others. We first identified the decisions behind these power dynamics by developing an ontology that synthesises critical theoretical insights from both visualisation and participatory design research, which were then systematically applied unto a representative corpus of 23 PDP artefacts. By revealing how shared decisions are guided by different agendas, this paper presents three contributions: 1) a cross-disciplinary ontology that facilitates the systematic analysis of existing and novel PDP artefacts and processes; which leads to 2) six PDP agendas that reflect the key power dynamics in current PDP practice, revealing the diversity of orientations towards stakeholder participation in PDP practice; and 3) a set of critical considerations that should guide how power dynamics can be balanced, such as by reflecting on how issues are represented, data is contextualised, participants express their meanings, and how participants can dissent with flexible artefact construction. Consequently, this study advances a feminist research agenda by guiding researchers and practitioners in openly reflecting on and sharing responsibilities in data physicalisation and participatory data visualisation.
TA-GNN: Physics Inspired Time-Agnostic Graph Neural Network for Finger Motion Prediction
Venue: arXiv cs.HC 2025
Year: 2025

Continuous prediction of finger joint movement using historical joint positions/rotations is vital in a multitude of applications, especially related to virtual reality, computer graphics, robotics, and rehabilitation. However, finger motions are highly articulated with multiple degrees of freedom, making them significantly harder to model and predict. To address this challenge, we propose a physics-inspired time-agnostic graph neural network (TA-GNN) to accurately predict human finger motions. The proposed encoder comprises a kinematic feature extractor to generate filtered velocity and acceleration and a physics-based encoder that follows linear kinematics. The model is designed to be prediction-time-agnostic so that it can seamlessly provide continuous predictions. The graph-based decoder for learning the topological motion between finger joints is designed to address the higher degree articulation of fingers. We show the superiority of our model performance in virtual reality context. This novel approach enhances finger tracking without additional sensors, enabling predictive interactions such as haptic re-targeting and improving predictive rendering quality.
Study Protocol: Shared Achievements: Exploring the Design of Gameful Collaborative Elements and Fostering Social Relatedness through Team Effort Contributions in a Social Physical Activity App
Venue: arXiv cs.HC 2025
Year: 2025

This study protocol outlines the design and methodology of a research study investigating collaborative game elements to promote physical activity within digital health interventions. The study aims to examine how social relatedness influences motivation and adherence to step-count goals. Participants will use Shared Achievements, a minimalistic multiplayer step counter game, over two weeks, one week contributing absolute step counts and one week sharing step counts as a relative percentage of a team goal. Data will be collected through usage metrics and participant feedback to evaluate engagement, motivation, and perceived challenges. Findings will inform the design of digital health tools that balance competition and collaboration, optimising social and behavioural support mechanisms.
All You Need to Know About Training Image Retrieval Models
Venue: arXiv cs.CV 2025
Year: 2025

Image retrieval is the task of finding images in a database that are most similar to a given query image. The performance of an image retrieval pipeline depends on many training-time factors, including the embedding model architecture, loss function, data sampler, mining function, learning rate(s), and batch size. In this work, we run tens of thousands of training runs to understand the effect each of these factors has on retrieval accuracy. We also discover best practices that hold across multiple datasets. The code is available at https://github.com/gmberton/image-retrieval
Multi-Touch and Bending Perception Using Electrical Impedance Tomography for Robotics
Venue: arXiv cs.RO 2025
Year: 2025

Electrical Impedance Tomography (EIT) offers a promising solution for distributed tactile sensing with minimal wiring and full-surface coverage in robotic applications. However, EIT-based tactile sensors face significant challenges during surface bending. Deformation alters the baseline impedance distribution and couples with touch-induced conductivity variations, complicating signal interpretation. To address this challenge, we present a novel sensing framework that integrates a deep neural network for interaction state classification with a dynamic adaptive reference strategy to decouple touch and deformation signals, while a data-driven regression model translates EIT voltage changes into continuous bending angles. The framework is validated using a magnetic hydrogel composite sensor that conforms to bendable surfaces. Experimental evaluations demonstrate that the proposed framework achieves precise and robust bending angle estimation, high accuracy in distinguishing touch, bending, and idle states, and significantly improves touch localization quality under bending deformation compared to conventional fixed-reference methods. Real-time experiments confirm the system's capability to reliably detect multi-touch interactions and track bending angles across varying deformation conditions. This work paves the way for flexible EIT-based robotic skins capable of rich multimodal sensing in robotics and human-robot interaction.
Historic Scripts to Modern Vision: A Novel Dataset and A VLM Framework for Transliteration of Modi Script to Devanagari
Venue: arXiv cs.CV 2025
Year: 2025

In medieval India, the Marathi language was written using the Modi script. The texts written in Modi script include extensive knowledge about medieval sciences, medicines, land records and authentic evidence about Indian history. Around 40 million documents are in poor condition and have not yet been transliterated. Furthermore, only a few experts in this domain can transliterate this script into English or Devanagari. Most of the past research predominantly focuses on individual character recognition. A system that can transliterate Modi script documents to Devanagari script is needed. We propose the MoDeTrans dataset, comprising 2,043 images of Modi script documents accompanied by their corresponding textual transliterations in Devanagari. We further introduce MoScNet (\textbf{Mo}di \textbf{Sc}ript \textbf{Net}work), a novel Vision-Language Model (VLM) framework for transliterating Modi script images into Devanagari text. MoScNet leverages Knowledge Distillation, where a student model learns from a teacher model to enhance transliteration performance. The final student model of MoScNet has better performance than the teacher model while having 163$\times$ lower parameters. Our work is the first to perform direct transliteration from the handwritten Modi script to the Devanagari script. MoScNet also shows competitive results on the optical character recognition (OCR) task.