OpenRaiser/Intern-Atlas huggingface dataset:
BARD: Reducing Write Latency of DDR5 Memory by Exploiting Bank-Parallelism
Venue: arXiv cs.AR 2025
Year: 2025

This paper studies the impact of DRAM writes on DDR5-based system. To efficiently perform DRAM writes, modern systems buffer write requests and try to complete multiple write operations whenever the DRAM mode is switched from read to write. When the DRAM system is performing writes, it is not available to service read requests, thus increasing read latency and reducing performance. We observe that, given the presence of on-die ECC in DDR5 devices, the time to perform a write operation varies significantly: from 1x (for writes to banks of different bankgroups) to 6x (for writes to banks within the same bankgroup) to 24x (for conflicting requests to the same bank). If we can orchestrate the write stream to favor write requests that incur lower latency, then we can reduce the stall time from DRAM writes and improve performance. However, for current systems, the write stream is dictated by the cache replacement policy, which makes eviction decisions without being aware of the variable latency of DRAM writes. The key insight of our work is to improve performance by modifying the cache replacement policy to increase bank-parallelism of DRAM writes. Our paper proposes {\em BARD (Bank-Aware Replacement Decisions)}, which modifies the cache replacement policy to favor dirty lines that belong to banks without pending writes. We analyze two variants of BARD: BARD-E (Eviction-based), which changes the eviction policy to evict low-cost dirty lines, and BARD-C (Cleansing-Based), which proactively cleans low-cost dirty lines without modifying the eviction decisions. We develop a hybrid policy (BARD-H), which uses a selective combination of both eviction and writeback. Our evaluations across workloads from SPEC2017, LIGRA, STREAM, and Google server traces show that BARD-H improves performance by 4.3\% on average and up-to 8.5\%. BARD requires only 8 bytes of SRAM per LLC slice.
Benchmarking metaheuristic algorithms for the bi-objective redundancy allocation problem in repairable systems with multiple strategies
Venue: arXiv cs.NE 2025
Year: 2025

This article investigates a bi-objective redundancy allocation problem (RAP) for repairable systems, defined as cost minimization and availability maximization. Binary decisions jointly select the number of components and the standby strategy at the subsystem level. Four redundancy strategies are considered: cold standby, warm standby, hot standby, and a mixed strategy. System availability is evaluated using continuous-time Markov chains. The main novelty is a large, controlled benchmark that compares 65 multi-objective metaheuristics under two initialization settings, with and without Scaled Binomial Initialization (SBI), on six case studies of rising structural and dimensional complexity and four weight limits. Each run uses a fixed budget of 2x10^6 evaluations, and repeated runs support statistical comparisons based on hypervolume and budget-based performance. The Pareto-optimal sets are dominated by hot standby and mixed redundancy, while cold and warm standby are rare in the full populations and almost absent from the Pareto fronts. Hot standby is favored under tight weight limits, whereas mixed redundancy becomes dominant when more spares are allowed. Algorithm results show strong budget effects, so a single overall ranking can be misleading. SBI gives a clear hypervolume gain and can change method rankings; in several cases, the SBI initial population is already close to the best-found reference. NSGAIIARSBX-SBI performs well for medium and large budgets, while NNIA-SBI and CMOPSO-SBI are strongest when the budget is tight. Finally, larger systems require much more search effort to reach high-quality fronts, highlighting the need to plan the evaluation budget in practical RAP studies. The code and the results are available at a Zenodo repository https://doi.org/10.5281/zenodo.17981720.
Theodosian: A Deep Dive into Memory-Hierarchy-Centric FHE Acceleration
Venue: arXiv cs.CR 2025
Year: 2025

Fully homomorphic encryption (FHE) enables secure computation on encrypted data, mitigating privacy concerns in cloud and edge environments. However, due to its high compute and memory demands, extensive acceleration research has been pursued across diverse hardware platforms, especially GPUs. In this paper, we perform a microarchitectural analysis of CKKS, a popular FHE scheme, on modern GPUs. We focus on on-chip cache behavior, and show that the dominant kernels remain bound by memory bandwidth despite a high-bandwidth L2 cache, exposing a persistent memory wall. We further discover that the overall CKKS pipeline throughput is constrained by low per-kernel hardware utilization, caused by insufficient intra-kernel parallelism. Motivated by these findings, we introduce Theodosian, a set of complementary, memory-aware optimizations that improve cache efficiency and reduce runtime overheads. Our approach delivers consistent speedups across various CKKS workloads. On an RTX 5090, we reduce the bootstrapping latency for 32,768 complex numbers to 15.2ms with Theodosian, and further to 12.8ms with additional algorithmic optimizations, establishing new state-of-the-art GPU performance to the best of our knowledge.
DACE For Railway Acronym Disambiguation
Venue: arXiv cs.CL 2025
Year: 2025

Acronym Disambiguation (AD) is a fundamental challenge in technical text processing, particularly in specialized sectors where high ambiguity complicates automated analysis. This paper addresses AD within the context of the TextMine'26 competition on French railway documentation. We present DACE (Dynamic Prompting, Retrieval Augmented Generation, Contextual Selection, and Ensemble Aggregation), a framework that enhances Large Language Models through adaptive in-context learning and external domain knowledge injection. By dynamically tailoring prompts to acronym ambiguity and aggregating ensemble predictions, DACE mitigates hallucination and effectively handles low-resource scenarios. Our approach secured the top rank in the competition with an F1 score of 0.9069.
Exploration vs. Fixation: Scaffolding Divergent and Convergent Thinking for Human-AI Co-Creation with Generative Models
Venue: arXiv cs.HC 2025
Year: 2025

Generative AI has begun to democratize creative work, enabling novices to produce complex artifacts such as code, images, and videos. However, in practice, existing interaction paradigms often fail to support divergent exploration: users tend to converge too quickly on early ``good enough'' results and struggle to move beyond them, leading to premature convergence and design fixation that constrains their creative potential. To address this, we propose a structured, process-oriented human-AI co-creation paradigm including divergent and convergent thinking stages, grounded in Wallas's model of creativity. To avoid design fixation, our paradigm scaffolds both high-level exploration of conceptual ideas in the early divergent thinking phase and low-level exploration of variations in the later convergent thinking phrase. We instantiate this paradigm in HAIExplore, an image co-creation system that (i) scaffolds divergent thinking through a dedicated brainstorming stage for exploring high-level ideas in a conceptual space, and (ii) scaffolds convergent refinement through an interface that externalizes users' refinement intentions as interpretable parameters and options, making the refinement process more controllable and easier to explore. We report on a within-subjects study comparing HAIExplore with a widely used linear chat interface (ChatGPT) for creative image generation. Our findings show that explicitly scaffolding the creative process into brainstorming and refinement stages can mitigate design fixation, improve perceived controllability and alignment with users' intentions, and better support the non-linear nature of creative work. We conclude with design implications for future creativity support tools and human-AI co-creation workflows.
Object-Centric Framework for Video Moment Retrieval
Venue: arXiv cs.CV 2025
Year: 2025

Most existing video moment retrieval methods rely on temporal sequences of frame- or clip-level features that primarily encode global visual and semantic information. However, such representations often fail to capture fine-grained object semantics and appearance, which are crucial for localizing moments described by object-oriented queries involving specific entities and their interactions. In particular, temporal dynamics at the object level have been largely overlooked, limiting the effectiveness of existing approaches in scenarios requiring detailed object-level reasoning. To address this limitation, we propose a novel object-centric framework for moment retrieval. Our method first extracts query-relevant objects using a scene graph parser and then generates scene graphs from video frames to represent these objects and their relationships. Based on the scene graphs, we construct object-level feature sequences that encode rich visual and semantic information. These sequences are processed by a relational tracklet transformer, which models spatio-temporal correlations among objects over time. By explicitly capturing object-level state changes, our framework enables more accurate localization of moments aligned with object-oriented queries. We evaluated our method on three benchmarks: Charades-STA, QVHighlights, and TACoS. Experimental results demonstrate that our method outperforms existing state-of-the-art methods across all benchmarks.
Age of Information with Age-Dependent Server Selection
Venue: arXiv cs.IT 2025
Year: 2025

In this paper, we consider a single-source multi-server generate-at-will discrete-time non-preemptive status update system where update packets are transmitted using {\em only one} of the available servers, according to a server selection policy. In particular, when a transmission is complete, the update system makes a threshold-based decision on whether to wait or transmit, and if latter, which server to use for transmissions, on the basis of the instantaneous value of the age of information (AoI) process. In our setting, servers have general heterogeneous discrete phase-type (DPH) distributed service times, and also heterogeneous transmission costs. The goal is to find an age-dependent multi-threshold policy that minimizes the AoI cost with a constraint on transmission costs, the former cost defined in terms of the time average of an arbitrary function of AoI. For this purpose, we propose a novel tool called \emph{multi-regime absorbing Markov chain} (MR-AMC) in discrete time. Using the MR-AMC framework, we exactly obtain the distribution of AoI, and subsequently the costs associated with AoI and transmissions. With the exact analysis in hand, optimum thresholds can be obtained in the case of a few servers, by exhaustive search. We validate the proposed analytical model, and also demonstrate the benefits of age-dependent server selection, with numerical examples.
Self-organizing maps for water quality assessment in reservoirs and lakes: A systematic literature review
Venue: arXiv cs.LG 2025
Year: 2025

Sustainable water quality underpins ecological balance and water security. Assessing and managing lakes and reservoirs is difficult due to data sparsity, heterogeneity, and nonlinear relationships among parameters. This review examines how Self-Organizing Map (SOM), an unsupervised AI technique, is applied to water quality assessment. It synthesizes research on parameter selection, spatial and temporal sampling strategies, and clustering approaches. Emphasis is placed on how SOM handles multidimensional data and uncovers hidden patterns to support effective water management. The growing availability of environmental data from in-situ sensors, remote sensing imagery, IoT technologies, and historical records has significantly expanded analytical opportunities in environmental monitoring. SOM has proven effective in analysing complex datasets, particularly when labelled data are limited or unavailable. It enables high-dimensional data visualization, facilitates the detection of hidden ecological patterns, and identifies critical correlations among diverse water quality indicators. This review highlights SOMs versatility in ecological assessments, trophic state classification, algal bloom monitoring, and catchment area impact evaluations. The findings offer comprehensive insights into existing methodologies, supporting future research and practical applications aimed at improving the monitoring and sustainable management of lake and reservoir ecosystems.
The Geometry of Abstraction: Continual Learning via Recursive Quotienting
Venue: arXiv cs.LG 2025
Year: 2025

Continual learning systems operating in fixed-dimensional spaces face a fundamental geometric barrier: the flat manifold problem. When experience is represented as a linear trajectory in Euclidean space, the geodesic distance between temporal events grows linearly with time, forcing the required covering number to diverge. In fixed-dimensional hardware, this volume expansion inevitably forces trajectory overlap, manifesting as catastrophic interference. In this work, we propose a geometric resolution to this paradox based on Recursive Metric Contraction. We formalize abstraction not as symbolic grouping, but as a topological deformation: a quotient map that collapses the metric tensor within validated temporal neighborhoods, effectively driving the diameter of local sub-manifolds to zero. We substantiate our framework with four rigorous results. First, the Bounded Capacity Theorem establishes that recursive quotient maps allow the embedding of arbitrarily long trajectories into bounded representational volumes, trading linear metric growth for logarithmic topological depth. Second, the Topological Collapse Separability Theorem, derived via Urysohn's Lemma, proves that recursive quotienting renders non-linearly separable temporal sequences linearly separable in the limit, bypassing the need for infinite-dimensional kernel projections. Third, the Parity-Partitioned Stability Theorem solves the catastrophic forgetting problem by proving that if the state space is partitioned into orthogonal flow and scaffold manifolds, the metric deformations of active learning do not disturb the stability of stored memories. Our analysis reveals that tokens in neural architectures are physically realizable as singularities or wormholes, regions of extreme positive curvature that bridge distant points in the temporal manifold.
QLink: Quantum-Safe Bridge Architecture for Blockchain Interoperability
Venue: arXiv cs.CR 2025
Year: 2025

Secure interoperability across heterogeneous blockchains remains one of the most pressing challenges in Web3 with existing bridge protocols vulnerable to both classical exploits and emerging quantum threats. This paper introduces QLink a quantum-safe Layer 3 interoperability protocol that integrates postquantum cryptography (PQC) quantum key distribution (QKD) and hardware security modules (HSMs) into a unified validator architecture. To our knowledge, QLink is the first interoperability framework to combine these mechanisms to secure validator communication proof aggregation and key management. Validators exchange encryption keys through QKD channels, achieving information-theoretic security against interception, while cross-chain proofs are generated and aggregated with NIST-standardized PQC algorithms. Private keys remain sealed inside HSM enclaves mitigating the risk of theft or leakage. Deployed as a dedicated Layer 3 protocol QLink operates independently of Layer 1 and Layer 2 chains providing a scalable decentralized foundation for secure cross-chain messaging and asset transfer. Experimental evaluation using network simulations demonstrates that validator communication overhead remains sub-second while security guarantees extend beyond current bridge architectures to resist both classical and quantum adversaries. By addressing today vulnerabilities and anticipating future quantum threats QLink establishes a practical and future-proof pathway for blockchain interoperability.
WoundNet-Ensemble: A Novel IoMT System Integrating Self-Supervised Deep Learning and Multi-Model Fusion for Automated, High-Accuracy Wound Classification and Healing Progression Monitoring
Venue: arXiv cs.CV 2025
Year: 2025

Chronic wounds, including diabetic foot ulcers which affect up to one-third of people with diabetes, impose a substantial clinical and economic burden, with U.S. healthcare costs exceeding 25 billion dollars annually. Current wound assessment remains predominantly subjective, leading to inconsistent classification and delayed interventions. We present WoundNet-Ensemble, an Internet of Medical Things system leveraging a novel ensemble of three complementary deep learning architectures: ResNet-50, the self-supervised Vision Transformer DINOv2, and Swin Transformer, for automated classification of six clinically distinct wound types. Our system achieves 99.90 percent ensemble accuracy on a comprehensive dataset of 5,175 wound images spanning diabetic foot ulcers, pressure ulcers, venous ulcers, thermal burns, pilonidal sinus wounds, and fungating malignant tumors. The weighted fusion strategy demonstrates a 3.7 percent improvement over previous state-of-the-art methods. Furthermore, we implement a longitudinal wound healing tracker that computes healing rates, severity scores, and generates clinical alerts. This work demonstrates a robust, accurate, and clinically deployable tool for modernizing wound care through artificial intelligence, addressing critical needs in telemedicine and remote patient monitoring. The implementation and trained models will be made publicly available to support reproducibility.
Commercial Vehicle Braking Optimization: A Robust SIFT-Trajectory Approach
Venue: arXiv cs.CV 2025
Year: 2025

A vision-based trajectory analysis solution is proposed to address the "zero-speed braking" issue caused by inaccurate Controller Area Network (CAN) signals in commercial vehicle Automatic Emergency Braking (AEB) systems during low-speed operation. The algorithm utilizes the NVIDIA Jetson AGX Xavier platform to process sequential video frames from a blind spot camera, employing self-adaptive Contrast Limited Adaptive Histogram Equalization (CLAHE)-enhanced Scale-Invariant Feature Transform (SIFT) feature extraction and K-Nearest Neighbors (KNN)-Random Sample Consensus (RANSAC) matching. This allows for precise classification of the vehicle's motion state (static, vibration, moving). Key innovations include 1) multiframe trajectory displacement statistics (5-frame sliding window), 2) a dual-threshold state decision matrix, and 3) OBD-II driven dynamic Region of Interest (ROI) configuration. The system effectively suppresses environmental interference and false detection of dynamic objects, directly addressing the challenge of low-speed false activation in commercial vehicle safety systems. Evaluation in a real-world dataset (32,454 video segments from 1,852 vehicles) demonstrates an F1-score of 99.96% for static detection, 97.78% for moving state recognition, and a processing delay of 14.2 milliseconds (resolution 704x576). The deployment on-site shows an 89% reduction in false braking events, a 100% success rate in emergency braking, and a fault rate below 5%.
Text2Graph VPR: A Text-to-Graph Expert System for Explainable Place Recognition in Changing Environments
Venue: arXiv cs.CV 2025
Year: 2025

Visual Place Recognition (VPR) in long-term deployment requires reasoning beyond pixel similarity: systems must make transparent, interpretable decisions that remain robust under lighting, weather and seasonal change. We present Text2Graph VPR, an explainable semantic localization system that converts image sequences into textual scene descriptions, parses those descriptions into structured scene graphs, and reasons over the resulting graphs to identify places. Scene graphs capture objects, attributes and pairwise relations; we aggregate per-frame graphs into a compact place representation and perform retrieval with a dual-similarity mechanism that fuses learned Graph Attention Network (GAT) embeddings and a Shortest-Path (SP) kernel for structural matching. This hybrid design enables both learned semantic matching and topology-aware comparison, and -- critically -- produces human-readable intermediate representations that support diagnostic analysis and improve transparency in the decision process. We validate the system on Oxford RobotCar and MSLS (Amman/San Francisco) benchmarks and demonstrate robust retrieval under severe appearance shifts, along with zero-shot operation using human textual queries. The results illustrate that semantic, graph-based reasoning is a viable and interpretable alternative for place recognition, particularly suited to safety-sensitive and resource-constrained settings.
Obnoxious Facility Location Problems: Strategyproof Mechanisms Optimizing $L_p$-Aggregated Utilities and Costs
Venue: arXiv cs.GT 2025
Year: 2025

We study the problem of locating a single obnoxious facility on the normalized line segment $[0,1]$ with strategic agents from a mechanism design perspective. Each agent has a preference for the undesirable location of the facility and would prefer the facility to be far away from their location. We consider the utility of the agent, defined as the distance between the agent's location and the facility location, and the cost of each agent, equal to one minus the utility. Given this standard setting of obnoxious facility location problems, our goal is to design (group) strategyproof mechanisms to elicit agent locations truthfully and determine facility location approximately optimizing the $L_p$-aggregated utility and cost objectives, which generalizes the $L_p$-norm ($p\ge 1$) of the agents' utilities and agents' costs to any $p \in [-\infty, \infty]$, respectively. We establish upper and lower bounds on the approximation ratios of deterministic and randomized (group) strategyproof mechanisms for maximizing the $L_p$-aggregated utilities or minimizing the $L_p$-aggregated costs across the range of \(p\)-values. While there are gaps between upper and lower bounds for randomized mechanisms, our bounds for deterministic mechanisms are tight.
A Multi-agent Text2SQL Framework using Small Language Models and Execution Feedback
Venue: arXiv cs.DB 2025
Year: 2025

Text2SQL, the task of generating SQL queries from natural language text, is a critical challenge in data engineering. Recently, Large Language Models (LLMs) have demonstrated superior performance for this task due to their advanced comprehension and generation capabilities. However, privacy and cost considerations prevent companies from using Text2SQL solutions based on external LLMs offered as a service. Rather, small LLMs (SLMs) that are openly available and can hosted in-house are adopted. These SLMs, in turn, lack the generalization capabilities of larger LLMs, which impairs their effectiveness for complex tasks such as Text2SQL. To address these limitations, we propose MATS, a novel Text2SQL framework designed specifically for SLMs. MATS uses a multi-agent mechanism that assigns specialized roles to auxiliary agents, reducing individual workloads and fostering interaction. A training scheme based on reinforcement learning aligns these agents using feedback obtained during execution, thereby maintaining competitive performance despite a limited LLM size. Evaluation results using on benchmark datasets show that MATS, deployed on a single- GPU server, yields accuracy that are on-par with large-scale LLMs when using significantly fewer parameters. Our source code and data are available at https://github.com/thanhdath/mats-sql.