OpenRaiser/Intern-Atlas huggingface dataset:
MSMVD: Exploiting Multi-scale Image Features via Multi-scale BEV Features for Multi-view Pedestrian Detection
Venue: arXiv cs.CV 2025
Year: 2025

Multi-View Pedestrian Detection (MVPD) aims to detect pedestrians in the form of a bird's eye view (BEV) from multi-view images. In MVPD, end-to-end trainable deep learning methods have progressed greatly. However, they often struggle to detect pedestrians with consistently small or large scales in views or with vastly different scales between views. This is because they do not exploit multi-scale image features to generate the BEV feature and detect pedestrians. To overcome this problem, we propose a novel MVPD method, called Multi-Scale Multi-View Detection (MSMVD). MSMVD generates multi-scale BEV features by projecting multi-scale image features extracted from individual views into the BEV space, scale-by-scale. Each of these BEV features inherits the properties of its corresponding scale image features from multiple views. Therefore, these BEV features help the precise detection of pedestrians with consistently small or large scales in views. Then, MSMVD combines information at different scales of multiple views by processing the multi-scale BEV features using a feature pyramid network. This improves the detection of pedestrians with vastly different scales between views. Extensive experiments demonstrate that exploiting multi-scale image features via multi-scale BEV features greatly improves the detection performance, and MSMVD outperforms the previous highest MODA by $4.5$ points on the GMVD dataset.
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers
Venue: arXiv cs.CL 2025
Year: 2025

We introduce MCP-Bench, a benchmark for evaluating large language models (LLMs) on realistic, multi-step tasks that demand tool use, cross-tool coordination, precise parameter control, and planning/reasoning for solving tasks. Built on the Model Context Protocol (MCP), MCP-Bench connects LLMs to 28 representative live MCP servers spanning 250 tools across domains such as finance, traveling, scientific computing, and academic search. Unlike prior API-based benchmarks, each MCP server provides a set of complementary tools designed to work together, enabling the construction of authentic, multi-step tasks with rich input-output coupling. Tasks in MCP-Bench test agents' ability to retrieve relevant tools from fuzzy instructions without explicit tool names, plan multi-hop execution trajectories for complex objectives, ground responses in intermediate tool outputs, and orchestrate cross-domain workflows - capabilities not adequately evaluated by existing benchmarks that rely on explicit tool specifications, shallow few-step workflows, and isolated domain operations. We propose a multi-faceted evaluation framework covering tool-level schema understanding and usage, trajectory-level planning, and task completion. Experiments on 20 advanced LLMs reveal persistent challenges in MCP-Bench. Code and data: https://github.com/Accenture/mcp-bench.
Prediction of mortality and resource utilization in critical care: a deep learning approach using multimodal electronic health records with natural language processing techniques
Venue: arXiv cs.CL 2025
Year: 2025

Background Predicting mortality and resource utilization from electronic health records (EHRs) is challenging yet crucial for optimizing patient outcomes and managing costs in intensive care unit (ICU). Existing approaches predominantly focus on structured EHRs, often ignoring the valuable clinical insights in free-text notes. Additionally, the potential of textual information within structured data is not fully leveraged. This study aimed to introduce and assess a deep learning framework using natural language processing techniques that integrates multimodal EHRs to predict mortality and resource utilization in critical care settings. Methods Utilizing two real-world EHR datasets, we developed and evaluated our model on three clinical tasks with leading existing methods. We also performed an ablation study on three key components in our framework: medical prompts, free-texts, and pre-trained sentence encoder. Furthermore, we assessed the model's robustness against the corruption in structured EHRs. Results Our experiments on two real-world datasets across three clinical tasks showed that our proposed model improved performance metrics by 1.6\%/0.8\% on BACC/AUROC for mortality prediction, 0.5%/2.2% on RMSE/MAE for LOS prediction, 10.9%/11.0% on RMSE/MAE for surgical duration estimation compared to the best existing methods. It consistently demonstrated superior performance compared to other baselines across three tasks at different corruption rates. Conclusions The proposed framework is an effective and accurate deep learning approach for predicting mortality and resource utilization in critical care. The study also highlights the success of using prompt learning with a transformer encoder in analyzing multimodal EHRs. Importantly, the model showed strong resilience to data corruption within structured data, especially at high corruption levels.
Entropy-Bounded Computational Geometry Made Easier and Sensitive to Sortedness
Venue: arXiv cs.CG 2025
Year: 2025

We study entropy-bounded computational geometry, that is, geometric algorithms whose running times depend on a given measure of the input entropy. Specifically, we introduce a measure that we call range-partition entropy, which unifies and subsumes previous definitions of entropy used for sorting problems and structural entropy used in computational geometry. We provide simple algorithms for several problems, including 2D maxima, 2D and 3D convex hulls, and some visibility problems, and we show that they have running times depending on the range-partition entropy.
VisiTrail: A Cognitive Visualization Tool for Time-Series Analysis of Eye Tracking Data from Attention Game
Venue: arXiv cs.HC 2025
Year: 2025

Eye Tracking (ET) can help to understand visual attention and cognitive processes in interactive environments. In attention tasks, distinguishing between relevant target objects and distractors is crucial for effective performance, yet the underlying gaze patterns that drive successful task completion remain incompletely understood. Traditional gaze analyses lack comprehensive insights into the temporal dynamics of attention allocation and the relationship between gaze behavior and task performance. When applied to complex visual search scenarios, current gaze analysis methods face several limitations, including the isolation of measurements, visual stability, search efficiency, and the decision-making processes involved in these scenarios. This paper proposes an analysis tool that considers time series for eye tracking data from task performance and also gaze measures (fixations, saccades and smooth pursuit); temporal pattern analysis that reveals how attention evolves throughout task performance; object-click sequence tracking that directly links visual attention to user actions; and performance metrics that quantify both accuracy and efficiency. This tool provides comprehensive visualization techniques that make complex patterns of stimuli and gaze connections interpretable.
Enhancing Semantic Document Retrieval- Employing Group Steiner Tree Algorithm with Domain Knowledge Enrichment
Venue: arXiv cs.IR 2025
Year: 2025

Retrieving pertinent documents from various data sources with diverse characteristics poses a significant challenge for Document Retrieval Systems. The complexity of this challenge is further compounded when accounting for the semantic relationship between data and domain knowledge. While existing retrieval systems using semantics (usually represented as Knowledge Graphs created from open-access resources and generic domain knowledge) hold promise in delivering relevant outcomes, their precision may be compromised due to the absence of domain-specific information and reliance on outdated knowledge sources. In this research, the primary focus is on two key contributions- a) the development of a versatile algorithm- 'Semantic-based Concept Retrieval using Group Steiner Tree' that incorporates domain information to enhance semantic-aware knowledge representation and data access, and b) the practical implementation of the proposed algorithm within a document retrieval system using real-world data. To assess the effectiveness of the SemDR system, research work conducts performance evaluations using a benchmark consisting of 170 real-world search queries. Rigorous evaluation and verification by domain experts are conducted to ensure the validity and accuracy of the results. The experimental findings demonstrate substantial advancements when compared to the baseline systems, with precision and accuracy achieving levels of 90% and 82% respectively, signifying promising improvements.
diveXplore at the Video Browser Showdown 2024
Venue: arXiv cs.MM 2025
Year: 2025

According to our experience from VBS2023 and the feedback from the IVR4B special session at CBMI2023, we have largely revised the diveXplore system for VBS2024. It now integrates OpenCLIP trained on the LAION-2B dataset for image/text embeddings that are used for free-text and visual similarity search, a query server that is able to distribute different queries and merge the results, a user interface optimized for fast browsing, as well as an exploration view for large clusters of similar videos (e.g., weddings, paraglider events, snow and ice scenery, etc.).
Precoded Polar Product Decoder Based on Soft-Output SCL Decoding and Maximization of Generalized Mutual Information
Venue: arXiv cs.IT 2025
Year: 2025

We combine two approaches to optimize the iterative decoding of product codes with precoded polar component codes. On one side, we generate bitwise soft messages based on the codebook probability, an approximation of an auxiliary quantity that considers all valid decoding paths of a successive cancellation list (SCL) decoder. On the other side, we scale the soft information during message passing with offline-computed coefficients, which maximize the generalized mutual information (GMI) between the channel input and the outgoing message in each half iteration. Simulation results show significant improvement of the error-correcting performance compared to heuristic scaling and soft information generation based solely on the candidate list of the decoder. Moreover, we present an extrinsic version of the SCL decoder, which we use in a Monte Carlo density evolution analysis to derive decoding thresholds. The computed thresholds accurately predict the performance of the decoder.
Bitcoin as an Interplanetary Monetary Standard with Proof-of-Transit Timestamping
Venue: arXiv cs.CR 2025
Year: 2025

We explore the feasibility of deploying Bitcoin as the shared monetary standard between Earth and Mars, accounting for physical constraints of interplanetary communication. We introduce a novel primitive, Proof-of-Transit Timestamping (PoTT), to provide cryptographic, tamper-evident audit trails for Bitcoin data across high-latency, intermittently-connected links. Leveraging Delay/Disruption-Tolerant Networking (DTN) and optical low-Earth-orbit (LEO) mesh constellations, we propose an architecture for header-first replication, long-horizon Lightning channels with planetary watchtowers, and secure settlement through federated sidechains or blind-merge-mined (BMM) commit chains. We formalize PoTT, analyze its security model, and show how it measurably improves reliability and accountability without altering Bitcoin consensus or its monetary base. Near-term deployments favor strong federations for local settlement; longer-term, blind-merge-mined commit chains (if adopted) provide an alternative. The Earth L1 monetary base remains unchanged, while Mars can operate a pegged commit chain or strong federation with 1:1 pegged assets for local block production. For transparency, if both time-beacon regimes are simultaneously compromised, PoTT-M2 (and PoTT generally) reduces to administrative assertions rather than cryptographic time-anchoring.
Physics Informed Generative Models for Magnetic Field Images
Venue: arXiv cs.CV 2025
Year: 2025

In semiconductor manufacturing, defect detection and localization are critical to ensuring product quality and yield. While X-ray imaging is a reliable non-destructive testing method, it is memory-intensive and time-consuming for large-scale scanning, Magnetic Field Imaging (MFI) offers a more efficient means to localize regions of interest (ROI) for targeted X-ray scanning. However, the limited availability of MFI datasets due to proprietary concerns presents a significant bottleneck for training machine learning (ML) models using MFI. To address this challenge, we consider an ML-driven approach leveraging diffusion models with two physical constraints. We propose Physics Informed Generative Models for Magnetic Field Images (PI-GenMFI) to generate synthetic MFI samples by integrating specific physical information. We generate MFI images for the most common defect types: power shorts. These synthetic images will serve as training data for ML algorithms designed to localize defect areas efficiently. To evaluate generated MFIs, we compare our model to SOTA generative models from both variational autoencoder (VAE) and diffusion methods. We present a domain expert evaluation to assess the generated samples. In addition, we present qualitative and quantitative evaluation using various metrics used for image generation and signal processing, showing promising results to optimize the defect localization process.
A Hybrid Stochastic Gradient Tracking Method for Distributed Online Optimization Over Time-Varying Directed Networks
Venue: arXiv cs.LG 2025
Year: 2025

With the increasing scale and dynamics of data, distributed online optimization has become essential for real-time decision-making in various applications. However, existing algorithms often rely on bounded gradient assumptions and overlook the impact of stochastic gradients, especially in time-varying directed networks. This study proposes a novel Time-Varying Hybrid Stochastic Gradient Tracking algorithm named TV-HSGT, based on hybrid stochastic gradient tracking and variance reduction mechanisms. Specifically, TV-HSGT integrates row-stochastic and column-stochastic communication schemes over time-varying digraphs, eliminating the need for Perron vector estimation or out-degree information. By combining current and recursive stochastic gradients, it effectively reduces gradient variance while accurately tracking global descent directions. Theoretical analysis demonstrates that TV-HSGT can achieve improved bounds on dynamic regret without assuming gradient boundedness. Experimental results on logistic regression tasks confirm the effectiveness of TV-HSGT in dynamic and resource-constrained environments.
diveXplore 6.0: ITEC's Interactive Video Exploration System at VBS 2022
Venue: arXiv cs.MM 2025
Year: 2025

Continuously participating since the sixth Video Browser Showdown (VBS2017), diveXplore is a veteran interactive search system that throughout its lifetime has offered and evaluated numerous features. After undergoing major refactoring for the most recent VBS2021, however, the system since version 5.0 is less feature rich, yet, more modern, leaner and faster than the original system. This proved to be a sensible decision as the new system showed increasing performance in VBS2021 when compared to the most recent former competitions. With version 6.0 we reconsider shot segmentation, map search and introduce new features for improving concept as well as temporal context search.
Leveraging Large Language Models for Generating Research Topic Ontologies: A Multi-Disciplinary Study
Venue: arXiv cs.DL 2025
Year: 2025

Ontologies and taxonomies of research fields are critical for managing and organising scientific knowledge, as they facilitate efficient classification, dissemination and retrieval of information. However, the creation and maintenance of such ontologies are expensive and time-consuming tasks, usually requiring the coordinated effort of multiple domain experts. Consequently, ontologies in this space often exhibit uneven coverage across different disciplines, limited inter-domain connectivity, and infrequent updating cycles. In this study, we investigate the capability of several large language models to identify semantic relationships among research topics within three academic domains: biomedicine, physics, and engineering. The models were evaluated under three distinct conditions: zero-shot prompting, chain-of-thought prompting, and fine-tuning on existing ontologies. Additionally, we assessed the cross-domain transferability of fine-tuned models by measuring their performance when trained in one domain and subsequently applied to a different one. To support this analysis, we introduce PEM-Rel-8K, a novel dataset consisting of over 8,000 relationships extracted from the most widely adopted taxonomies in the three disciplines considered in this study: MeSH, PhySH, and IEEE. Our experiments demonstrate that fine-tuning LLMs on PEM-Rel-8K yields excellent performance across all disciplines.
Multi-Lingual Implicit Discourse Relation Recognition with Multi-Label Hierarchical Learning
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces the first multi-lingual and multi-label classification model for implicit discourse relation recognition (IDRR). Our model, HArch, is evaluated on the recently released DiscoGeM 2.0 corpus and leverages hierarchical dependencies between discourse senses to predict probability distributions across all three sense levels in the PDTB 3.0 framework. We compare several pre-trained encoder backbones and find that RoBERTa-HArch achieves the best performance in English, while XLM-RoBERTa-HArch performs best in the multi-lingual setting. In addition, we compare our fine-tuned models against GPT-4o and Llama-4-Maverick using few-shot prompting across all language configurations. Our results show that our fine-tuned models consistently outperform these LLMs, highlighting the advantages of task-specific fine-tuning over prompting in IDRR. Finally, we report SOTA results on the DiscoGeM 1.0 corpus, further validating the effectiveness of our hierarchical approach.
Adaptive Optimisation of Ride-Pooling Personalised Fares in a Stochastic Framework
Venue: arXiv cs.GT 2025
Year: 2025

Ride-pooling systems, to succeed, must provide an attractive service, namely compensate perceived costs with an appealing price. However, because of a strong heterogeneity in a value-of-time, each traveller has his own acceptable price, unknown to the operator. Here, we show that individual acceptance levels can be learned by the operator (over $90\%$ accuracy for pooled travellers in $10$ days) to optimise personalised fares. We propose an adaptive pricing policy, where every day the operator constructs an offer that progressively meets travellers' expectations and attracts a growing demand. Our results suggest that operators, by learning behavioural traits of individual travellers, may improve performance not only for travellers (increased utility) but also for themselves (increased profit). Moreover, such knowledge allows the operator to remove inefficient pooled rides and focus on attractive and profitable combinations.
Unleashing Uncertainty: Efficient Machine Unlearning for Generative AI
Venue: arXiv cs.LG 2025
Year: 2025

We introduce SAFEMax, a novel method for Machine Unlearning in diffusion models. Grounded in information-theoretic principles, SAFEMax maximizes the entropy in generated images, causing the model to generate Gaussian noise when conditioned on impermissible classes by ultimately halting its denoising process. Also, our method controls the balance between forgetting and retention by selectively focusing on the early diffusion steps, where class-specific information is prominent. Our results demonstrate the effectiveness of SAFEMax and highlight its substantial efficiency gains over state-of-the-art methods.
Addressing Personalized Bias for Unbiased Learning to Rank
Venue: arXiv cs.IR 2025
Year: 2025

Unbiased learning to rank (ULTR), which aims to learn unbiased ranking models from biased user behavior logs, plays an important role in Web search. Previous research on ULTR has studied a variety of biases in users' clicks, such as position bias, presentation bias, and outlier bias. However, existing work often assumes that the behavior logs are collected from an ``average'' user, neglecting the differences between different users in their search and browsing behaviors. In this paper, we introduce personalized factors into the ULTR framework, which we term the user-aware ULTR problem. Through a formal causal analysis of this problem, we demonstrate that existing user-oblivious methods are biased when different users have different preferences over queries and personalized propensities of examining documents. To address such a personalized bias, we propose a novel user-aware inverse-propensity-score estimator for learning-to-rank objectives. Specifically, our approach models the distribution of user browsing behaviors for each query and aggregates user-weighted examination probabilities to determine propensities. We theoretically prove that the user-aware estimator is unbiased under some mild assumptions and shows lower variance compared to the straightforward way of calculating a user-dependent propensity for each impression. Finally, we empirically verify the effectiveness of our user-aware estimator by conducting extensive experiments on two semi-synthetic datasets and a real-world dataset.
The Epistemic Support-Point Filter (ESPF): A Bounded Possibilistic Framework for Ordinal State Estimation
Venue: arXiv cs.IT 2025
Year: 2025

Traditional state estimation methods rely on probabilistic assumptions that often collapse epistemic uncertainty into scalar beliefs, risking overconfidence in sparse or adversarial sensing environments. We introduce the Epistemic Support-Point Filter (ESPF), a novel non-Bayesian filtering framework fully grounded in possibility theory and epistemic humility. ESPF redefines the evolution of belief over state space using compatibility-weighted support updates, surprisalaware pruning, and adaptive dispersion via sparse grid quadrature. Unlike conventional filters, ESPF does not seek a posterior distribution, but rather maintains a structured region of plausibility or non-rejection, updated using ordinal logic rather than integration. For multi-model inference, we employ the Choquet integral to fuse competing hypotheses based on a dynamic epistemic capacity function, generalizing classical winner-take-all strategies. The result is an inference engine capable of dynamically contracting or expanding belief support in direct response to information structure, without requiring prior statistical calibration. This work presents a foundational shift in how inference, evidence, and ignorance are reconciled, supporting robust estimation where priors are unavailable, misleading, or epistemically unjustified.