OpenRaiser/Intern-Atlas huggingface dataset:
Tractable Representations for Convergent Approximation of Distributional HJB Equations
Venue: arXiv cs.LG 2025
Year: 2025

In reinforcement learning (RL), the long-term behavior of decision-making policies is evaluated based on their average returns. Distributional RL has emerged, presenting techniques for learning return distributions, which provide additional statistics for evaluating policies, incorporating risk-sensitive considerations. When the passage of time cannot naturally be divided into discrete time increments, researchers have studied the continuous-time RL (CTRL) problem, where agent states and decisions evolve continuously. In this setting, the Hamilton-Jacobi-Bellman (HJB) equation is well established as the characterization of the expected return, and many solution methods exist. However, the study of distributional RL in the continuous-time setting is in its infancy. Recent work has established a distributional HJB (DHJB) equation, providing the first characterization of return distributions in CTRL. These equations and their solutions are intractable to solve and represent exactly, requiring novel approximation techniques. This work takes strides towards this end, establishing conditions on the method of parameterizing return distributions under which the DHJB equation can be approximately solved. Particularly, we show that under a certain topological property of the mapping between statistics learned by a distributional RL algorithm and corresponding distributions, approximation of these statistics leads to close approximations of the solution of the DHJB equation. Concretely, we demonstrate that the quantile representation common in distributional RL satisfies this topological property, certifying an efficient approximation algorithm for continuous-time distributional RL.
Compliance of AI Systems
Venue: arXiv cs.CY 2025
Year: 2025

The increasing integration of artificial intelligence (AI) systems in various fields requires solid concepts to ensure compliance with upcoming legislation. This paper systematically examines the compliance of AI systems with relevant legislation, focusing on the EU's AI Act and the compliance of data sets. The analysis highlighted many challenges associated with edge devices, which are increasingly being used to deploy AI applications closer and closer to the data sources. Such devices often face unique issues due to their decentralized nature and limited computing resources for implementing sophisticated compliance mechanisms. By analyzing AI implementations, the paper identifies challenges and proposes the first best practices for legal compliance when developing, deploying, and running AI. The importance of data set compliance is highlighted as a cornerstone for ensuring the trustworthiness, transparency, and explainability of AI systems, which must be aligned with ethical standards set forth in regulatory frameworks such as the AI Act. The insights gained should contribute to the ongoing discourse on the responsible development and deployment of embedded AI systems.
InDRiVE: Intrinsic Disagreement based Reinforcement for Vehicle Exploration through Curiosity Driven Generalized World Model
Venue: arXiv cs.RO 2025
Year: 2025

Model-based Reinforcement Learning (MBRL) has emerged as a promising paradigm for autonomous driving, where data efficiency and robustness are critical. Yet, existing solutions often rely on carefully crafted, task specific extrinsic rewards, limiting generalization to new tasks or environments. In this paper, we propose InDRiVE (Intrinsic Disagreement based Reinforcement for Vehicle Exploration), a method that leverages purely intrinsic, disagreement based rewards within a Dreamer based MBRL framework. By training an ensemble of world models, the agent actively explores high uncertainty regions of environments without any task specific feedback. This approach yields a task agnostic latent representation, allowing for rapid zero shot or few shot fine tuning on downstream driving tasks such as lane following and collision avoidance. Experimental results in both seen and unseen environments demonstrate that InDRiVE achieves higher success rates and fewer infractions compared to DreamerV2 and DreamerV3 baselines despite using significantly fewer training steps. Our findings highlight the effectiveness of purely intrinsic exploration for learning robust vehicle control behaviors, paving the way for more scalable and adaptable autonomous driving systems.
QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution
Venue: arXiv cs.CV 2025
Year: 2025

One-step diffusion-based image super-resolution (OSDSR) models are showing increasingly superior performance nowadays. However, although their denoising steps are reduced to one and they can be quantized to 8-bit to reduce the costs further, there is still significant potential for OSDSR to quantize to lower bits. To explore more possibilities of quantized OSDSR, we propose an efficient method, Quantization via reverse-module and timestep-retraining for OSDSR, named QArtSR. Firstly, we investigate the influence of timestep value on the performance of quantized models. Then, we propose Timestep Retraining Quantization (TRQ) and Reversed Per-module Quantization (RPQ) strategies to calibrate the quantized model. Meanwhile, we adopt the module and image losses to update all quantized modules. We only update the parameters in quantization finetuning components, excluding the original weights. To ensure that all modules are fully finetuned, we add extended end-to-end training after per-module stage. Our 4-bit and 2-bit quantization experimental results indicate that QArtSR obtains superior effects against the recent leading comparison methods. The performance of 4-bit QArtSR is close to the full-precision one. Our code will be released at https://github.com/libozhu03/QArtSR.
Bridging Classical and Quantum String Matching: A Computational Reformulation of Bit-Parallelism
Venue: arXiv cs.DS 2025
Year: 2025

String matching is a fundamental problem in computer science, with critical applications in text retrieval, bioinformatics, and data analysis. Among the numerous solutions that have emerged for this problem in recent decades, bit-parallelism has significantly enhanced their practical efficiency, leading to the development of several optimized approaches for both exact and approximate string matching. However, their potential in quantum computing remains largely unexplored. This paper presents a novel pathway that not only translates bit-parallel string matching algorithms into the quantum framework but also enhances their performance to achieve a quadratic speedup through Grover's search. By embedding quantum search within a bit-parallel model, we reduce the time complexity of string matching, establishing a structured pathway for transforming classical algorithms into quantum solutions with provable computational advantages. Beyond exact matching, this technique offers a foundation for tackling a wide range of non-standard string matching problems, opening new avenues for efficient text searching in the quantum era. To demonstrate the simplicity and adaptability of the technique presented in this paper, we apply this translation and adaptation process to two landmark bit-parallel algorithms: Shift-And for exact pattern matching and Shift-Add for approximate string matching with up to k errors.
Identification and explanation of disinformation in wiki data streams
Venue: arXiv cs.IR 2025
Year: 2025

Social media platforms, increasingly used as news sources for varied data analytics, have transformed how information is generated and disseminated. However, the unverified nature of this content raises concerns about trustworthiness and accuracy, potentially negatively impacting readers' critical judgment due to disinformation. This work aims to contribute to the automatic data quality validation field, addressing the rapid growth of online content on wiki pages. Our scalable solution includes stream-based data processing with feature engineering, feature analysis and selection, stream-based classification, and real-time explanation of prediction outcomes. The explainability dashboard is designed for the general public, who may need more specialized knowledge to interpret the model's prediction. Experimental results on two datasets attain approximately 90 % values across all evaluation metrics, demonstrating robust and competitive performance compared to works in the literature. In summary, the system assists editors by reducing their effort and time in detecting disinformation.
Physics-based machine learning framework for predicting NOx emissions from compression ignition engines using on-board diagnostics data
Venue: arXiv cs.LG 2025
Year: 2025

This work presents a physics-based machine learning framework to predict and analyze oxides of nitrogen (NOx) emissions from compression-ignition engine-powered vehicles using on-board diagnostics (OBD) data as input. Accurate NOx prediction from OBD datasets is difficult because NOx formation inside an engine combustion chamber is governed by complex processes occurring on timescales much shorter than the data collection rate. Thus, emissions generally cannot be predicted accurately using simple empirically derived physics models. Black box models like genetic algorithms or neural networks can be more accurate, but have poor interpretability. The transparent model presented in this paper has both high accuracy and can explain potential sources of high emissions. The proposed framework consists of two major steps: a physics-based NOx prediction model combined with a novel Divergent Window Co-occurrence (DWC) Pattern detection algorithm to analyze operating conditions that are not adequately addressed by the physics-based model. The proposed framework is validated for generalizability with a second vehicle OBD dataset, a sensitivity analysis is performed, and model predictions are compared with that from a deep neural network. The results show that NOx emissions predictions using the proposed model has around 55% better root mean square error, and around 60% higher mean absolute error compared to the baseline NOx prediction model from previously published work. The DWC Pattern Detection Algorithm identified low engine power conditions to have high statistical significance, indicating an operating regime where the model can be improved. This work shows that the physics-based machine learning framework is a viable method for predicting NOx emissions from engines that do not incorporate NOx sensing.
Network Anomaly Detection in Distributed Edge Computing Infrastructure
Venue: arXiv cs.DC 2025
Year: 2025

As networks continue to grow in complexity and scale, detecting anomalies has become increasingly challenging, particularly in diverse and geographically dispersed environments. Traditional approaches often struggle with managing the computational burden associated with analyzing large-scale network traffic to identify anomalies. This paper introduces a distributed edge computing framework that integrates federated learning with Apache Spark and Kubernetes to address these challenges. We hypothesize that our approach, which enables collaborative model training across distributed nodes, significantly enhances the detection accuracy of network anomalies across different network types. By leveraging distributed computing and containerization technologies, our framework not only improves scalability and fault tolerance but also achieves superior detection performance compared to state-of-the-art methods. Extensive experiments on the UNSW-NB15 and ROAD datasets validate the effectiveness of our approach, demonstrating statistically significant improvements in detection accuracy and training efficiency over baseline models, as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05).
Inference Scaling Reshapes AI Governance
Venue: arXiv cs.CY 2025
Year: 2025

The shift from scaling up the pre-training compute of AI systems to scaling up their inference compute may have profound effects on AI governance. The nature of these effects depends crucially on whether this new inference compute will primarily be used during external deployment or as part of a more complex training programme within the lab. Rapid scaling of inference-at-deployment would: lower the importance of open-weight models (and of securing the weights of closed models), reduce the impact of the first human-level models, change the business model for frontier AI, reduce the need for power-intense data centres, and derail the current paradigm of AI governance via training compute thresholds. Rapid scaling of inference-during-training would have more ambiguous effects that range from a revitalisation of pre-training scaling to a form of recursive self-improvement via iterated distillation and amplification.
The Impact of Building-Induced Visibility Restrictions on Intersection Accidents
Venue: arXiv cs.CY 2025
Year: 2025

Traffic accidents, especially at intersections, are a major road safety concern. Previous research has extensively studied intersection-related accidents, but the effect of building-induced visibility restrictions at intersections on accident rates has been under-explored, particularly in urban contexts. Using OpenStreetMap data, the UK's geographic and accident datasets, and the UK Traffic Count Dataset, we formulated a novel approach to estimate accident risk at intersections. This method factors in the area visible to drivers, accounting for views blocked by buildings - a distinctive aspect in traffic accident analysis. Our findings reveal a notable correlation between the road visible percentage and accident frequency. In the model, the coefficient for "road visible percentage" is 1.7450, implying a strong positive relationship. Incorporating this visibility factor enhances the model's explanatory power, with increased R-square values and reduced AIC and BIC, indicating a better data fit. This study underscores the essential role of architectural layouts in road safety and suggests that urban planning strategies should consider building-induced visibility restrictions. Such consideration could be an effective approach to mitigate accident rates at intersections. This research opens up new avenues for innovative, data-driven urban planning and traffic management strategies, highlighting the importance of visibility enhancements for safer roads.
The Role of AI, Blockchain, Cloud, and Data (ABCD) in Enhancing Learning Assessments of College Students
Venue: arXiv cs.CY 2025
Year: 2025

This study investigates how ABCD technologies can improve learning assessments in higher education. The objective is to research how students perceive things, plan their behavior, and how ABCD technologies affect individual learning, academic integrity, co-learning, and trust in the assessment. Through a quantitative research design, survey responses were gathered from university students, and statistical tests, such as correlation and regression, were used to establish relationships between Perceived Usefulness (PU), Perceived Ease of Use (PEU), and Behavioral Intention (BI) towards ABCD adoption. The results showed that there was no significant relationship between PU, PEU, and BI, which suggests that students' attitudes, institutional policies, faculty support, and infrastructure matter more in adoption than institutional policies, faculty support, and infrastructure. While students recognize ABCD's efficiency and security benefits, fairness, ease of use, and engagement issues limit their adoption of these technologies. The research adds to Technology Acceptance Model (TAM) and Constructivist Learning Theory (CLT) by emphasizing external drivers of technology adoption. The limitations are based on self-reported data and one institutional sample. It is suggested that universities invest in faculty development, infrastructure, and policy-making to facilitate effective and ethical use of ABCD technologies in higher education.
Modeling Behavior Change for Multi-model At-Risk Students Early Prediction (extended version)
Venue: arXiv cs.CY 2025
Year: 2025

In the educational domain, identifying students at risk of dropping out is essential for allowing educators to intervene effectively, improving both academic outcomes and overall student well-being. Data in educational settings often originate from diverse sources, such as assignments, grades, and attendance records. However, most existing research relies on online learning data and just extracting the quantitative features. While quantification eases processing, it also leads to a significant loss of original information. Moreover, current models primarily identify students with consistently poor performance through simple and discrete behavioural patterns, failing to capture the complex continuity and non-linear changes in student behaviour. We have developed an innovative prediction model, Multimodal- ChangePoint Detection (MCPD), utilizing the textual teacher remark data and numerical grade data from middle schools. Our model achieves a highly integrated and intelligent analysis by using independent encoders to process two data types, fusing the encoded feature. The model further refines its analysis by leveraging a changepoint detection module to pinpoint crucial behavioral changes, which are integrated as dynamic weights through a simple attention mechanism. Experimental validations indicate that our model achieves an accuracy range of 70- 75%, with an average outperforming baseline algorithms by approximately 5-10%. Additionally, our algorithm demonstrates a certain degree of transferability, maintaining high accuracy when adjusted and retrained with different definitions of at-risk, proving its broad applicability.
ChatWise: A Strategy-Guided Chatbot for Enhancing Cognitive Support in Older Adults
Venue: arXiv cs.CY 2025
Year: 2025

Cognitive health in older adults presents a growing challenge. Although conversational interventions show feasibility in improving cognitive wellness, human caregiver resources remain overloaded. AI-based chatbots have shown promise, yet existing work is often limited to implicit strategies or heavily depends on training and label resources. In response, we propose a strategy-guided AI chatbot named ChatWise that follows a dual-level conversation reasoning framework. It integrates macro-level strategy planning and micro-level utterance generation to enable engaging, multi-turn dialogue tailored to older adults. Empirical results show that ChatWise closely aligns with professional human caregiver behaviors in offline evaluation using real clinic data, and achieves positive user cognitive and emotional responses in interactive simulations with digital twins, which significantly outperforms AI baselines that follow implicit conversation generation.
Design of a Microprocessors and Microcontrollers Laboratory Course Addressing Complex Engineering Problems and Activities
Venue: arXiv cs.CY 2025
Year: 2025

This paper proposes a novel curriculum for the microprocessors and microcontrollers laboratory course. The proposed curriculum blends structured laboratory experiments with an open-ended project phase, addressing complex engineering problems and activities. Microprocessors and microcontrollers are ubiquitous in modern technology, driving applications across diverse fields. To prepare future engineers for Industry 4.0, effective educational approaches are crucial. The proposed lab enables students to perform hands-on experiments using advanced microprocessors and microcontrollers while leveraging their acquired knowledge by working in teams to tackle self-defined complex engineering problems that utilize these devices and sensors, often used in the industry. Furthermore, this curriculum fosters multidisciplinary learning and equips students with problem-solving skills that can be applied in real-world scenarios. With recent technological advancements, traditional microprocessors and microcontrollers curricula often fail to capture the complexity of real-world applications. This curriculum addresses this critical gap by incorporating insights from experts in both industry and academia. It trains students with the necessary skills and knowledge to thrive in this rapidly evolving technological landscape, preparing them for success upon graduation. The curriculum integrates project-based learning, where students define complex engineering problems for themselves. This approach actively engages students, fostering a deeper understanding and enhancing their learning capabilities. Statistical analysis shows that the proposed curriculum significantly improves student learning outcomes, particularly in their ability to formulate and solve complex engineering problems, as well as engage in complex engineering activities.
Diminishing Waters: The Great Salt Lake's Desiccation and Its Mental Health Consequences
Venue: arXiv cs.CY 2025
Year: 2025

This study examines how the desiccation of Utah Great Salt Lake GSL, exacerbated by anthropogenic changes, poses significant health risks, particularly communities mental health. Reduced water inflow has exposed the lakebed, increasing airborne particulate matter PM2.5 and dust storms, which impact air quality. By integrating diverse datasets spanning from 1980 to present including insitu measurements, satellite imagery, and reanalysis products this study synthesizes hydrological, atmospheric, and epidemiological variables to comprehensively track the extent of the GSL surface water, local air quality fluctuations, and their effects on community mental health. The findings indicate a clear relationship between higher pollution days and more severe depressive symptoms. Specifically, individuals exposed to 22 days with PM2.5 levels above the World Health Organizations 24 hour guideline of 15 ug per m3 were more likely to experience severe depressive symptoms. Our results also suggest that people experiencing more severe depression not only face a higher number of high pollution days but also encounter such days more frequently. The study highlights the interconnectedness of poor air quality, environmental degradation and mental health emphasizing the need for more sustainable economic growth in the region.
SEAFL: Enhancing Efficiency in Semi-Asynchronous Federated Learning through Adaptive Aggregation and Selective Training
Venue: arXiv cs.DC 2025
Year: 2025

Federated Learning (FL) is a promising distributed machine learning framework that allows collaborative learning of a global model across decentralized devices without uploading their local data. However, in real-world FL scenarios, the conventional synchronous FL mechanism suffers from inefficient training caused by slow-speed devices, commonly known as stragglers, especially in heterogeneous communication environments. Though asynchronous FL effectively tackles the efficiency challenge, it induces substantial system overheads and model degradation. Striking for a balance, semi-asynchronous FL has gained increasing attention, while still suffering from the open challenge of stale models, where newly arrived updates are calculated based on outdated weights that easily hurt the convergence of the global model. In this paper, we present {\em SEAFL}, a novel FL framework designed to mitigate both the straggler and the stale model challenges in semi-asynchronous FL. {\em SEAFL} dynamically assigns weights to uploaded models during aggregation based on their staleness and importance to the current global model. We theoretically analyze the convergence rate of {\em SEAFL} and further enhance the training efficiency with an extended variant that allows partial training on slower devices, enabling them to contribute to global aggregation while reducing excessive waiting times. We evaluate the effectiveness of {\em SEAFL} through extensive experiments on three benchmark datasets. The experimental results demonstrate that {\em SEAFL} outperforms its closest counterpart by up to $\sim$22\% in terms of the wall-clock training time required to achieve target accuracy.