OpenRaiser/Intern-Atlas huggingface dataset:
Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and Convolutional Neural Network
Venue: arXiv cs.CV 2025
Year: 2025

Purpose: The scarcity of high-quality curated labeled medical training data remains one of the major limitations in applying artificial intelligence (AI) systems to breast cancer diagnosis. Deep models for mammogram analysis and mass (or micro-calcification) detection require training with a large volume of labeled images, which are often expensive and time-consuming to collect. To reduce this challenge, we proposed a novel method that leverages self-supervised learning (SSL) and a deep hybrid model, named \textbf{HybMNet}, which combines local self-attention and fine-grained feature extraction to enhance breast cancer detection on screening mammograms. Approach: Our method employs a two-stage learning process: (1) SSL Pretraining: We utilize EsViT, a SSL technique, to pretrain a Swin Transformer (Swin-T) using a limited set of mammograms. The pretrained Swin-T then serves as the backbone for the downstream task. (2) Downstream Training: The proposed HybMNet combines the Swin-T backbone with a CNN-based network and a novel fusion strategy. The Swin-T employs local self-attention to identify informative patch regions from the high-resolution mammogram, while the CNN-based network extracts fine-grained local features from the selected patches. A fusion module then integrates global and local information from both networks to generate robust predictions. The HybMNet is trained end-to-end, with the loss function combining the outputs of the Swin-T and CNN modules to optimize feature extraction and classification performance. Results: The proposed method was evaluated for its ability to detect breast cancer by distinguishing between benign (normal) and malignant mammograms. Leveraging SSL pretraining and the HybMNet model, it achieved AUC of 0.864 (95% CI: 0.852, 0.875) on the CMMD dataset and 0.889 (95% CI: 0.875, 0.903) on the INbreast dataset, highlighting its effectiveness.
Lossy Source Coding with Focal Loss
Venue: arXiv cs.IT 2025
Year: 2025

Focal loss has recently gained significant popularity, particularly in tasks like object detection where it helps to address class imbalance by focusing more on hard-to-classify examples. This work proposes the focal loss as a distortion measure for lossy source coding. The paper provides single-shot converse and achievability bounds. These bounds are then used to characterize the distortion-rate trade-off in the infinite blocklength, which is shown to be the same as that for the log loss case. In the non-asymptotic case, the difference between focal loss and log loss is illustrated through a series of simulations.
Skew generalized quasi-cyclic codes over non-chain ring $F_q+vF_q$
Venue: arXiv cs.IT 2025
Year: 2025

For a prime $p$, let $F_q$ be the finite field of order $q= p^d$. This paper presents the study on skew generalized quasi-cyclic (SGQC) codes of length $n$ over the non-chain ring $F_q+vF_q$ where $v^2=v$ and $θ_t$ is the Galois automorphism. Here, first, we prove the dual of an SGQC code of length $n$ is also an SGQC code of the same length and derive a necessary and sufficient condition for the existence of a self-dual SGQC code. Then, we discuss the $1$-generator polynomial and the $ρ$-generator polynomial for skew generalized quasi-cyclic codes. Further, we determine the dimension and BCH type bound for the 1-generator skew generalized quasi-cyclic codes. As a by-product, with the help of MAGMA software, we provide a few examples of SGQC codes and obtain some $2$-generator SGQC codes of index $2$.
From Concept to Practice: an Automated LLM-aided UVM Machine for RTL Verification
Venue: arXiv cs.AR 2025
Year: 2025

Verification presents a major bottleneck in Integrated Circuit (IC) development, consuming nearly 70% of the total development effort. While the Universal Verification Methodology (UVM) is widely used in industry to improve verification efficiency through structured and reusable testbenches, constructing these testbenches and generating sufficient stimuli remain challenging. These challenges arise from the considerable manual coding effort required, repetitive manual execution of multiple EDA tools, and the need for in-depth domain expertise to navigate complex designs.Here, we present UVM^2, an automated verification framework that leverages Large Language Models (LLMs) to generate UVM testbenches and iteratively refine them using coverage feedback, significantly reducing manual effort while maintaining rigorous verification standards.To evaluate UVM^2, we introduce a benchmark suite comprising Register Transfer Level (RTL) designs of up to 1.6K lines of code.The results show that UVM^2 reduces testbench setup time by up to UVM^2 compared to experienced engineers, and achieve average code and function coverage of 87.44% and 89.58%, outperforming state-of-the-art solutions by 20.96% and 23.51%, respectively.
3D MPSoC with On-Chip Cache Support -- Design and Exploitation
Venue: arXiv cs.AR 2025
Year: 2025

The increasing density of transistors in Integrated Circuits (ICs) has enabled the development of highly integrated Systems-on-Chip (SoCs) and, more recently, Multiprocessor Systems-on-Chip (MPSoCs). To address scalability challenges in communication and memory performance, three-dimensional (3D) Network-on-Chip (NoC) architectures have emerged, offering improvements in communication latency and throughput. However, memory system efficiency remains a critical bottleneck in NoC-based designs. This work proposes the design and experimental exploration of 3D MPSoCs with on-chip cache support by employing distinct communication infrastructures for inter-processor and memory interactions. Specifically, packet-based NoCs are adopted for inter-processor communication, while a crossbar-based infrastructure supports a cache coherence hierarchy for memory access. A two-layer system architecture is introduced, combining a Uniform Memory Access (UMA) model within clusters and a No Remote Memory Access (NORMA) model between clusters, aiming to balance scalability and coherence requirements. Emerging memory technologies such as PCRAM and MRAM are explored to optimize performance, energy consumption, and area usage. Experimental evaluations are conducted using the Gem5 simulator, targeting a model based on the ARM Versatile Express platform. The outcomes of this study aim to enhance MPSoC scalability while meeting the stringent demands of memory-centric applications.
Monitoring digestate application on agricultural crops using Sentinel-2 Satellite imagery
Venue: arXiv cs.CV 2025
Year: 2025

The widespread use of Exogenous Organic Matter in agriculture necessitates monitoring to assess its effects on soil and crop health. This study evaluates optical Sentinel-2 satellite imagery for detecting digestate application, a practice that enhances soil fertility but poses environmental risks like microplastic contamination and nitrogen losses. In the first instance, Sentinel-2 satellite image time series (SITS) analysis of specific indices (EOMI, NDVI, EVI) was used to characterize EOM's spectral behavior after application on the soils of four different crop types in Thessaly, Greece. Furthermore, Machine Learning (ML) models (namely Random Forest, k-NN, Gradient Boosting and a Feed-Forward Neural Network), were used to investigate digestate presence detection, achieving F1-scores up to 0.85. The findings highlight the potential of combining remote sensing and ML for scalable and cost-effective monitoring of EOM applications, supporting precision agriculture and sustainability.
Simplified and Secure MCP Gateways for Enterprise AI Integration
Venue: arXiv cs.CR 2025
Year: 2025

The increased adoption of the Model Context Protocol (MCP) for AI Agents necessitates robust security for Enterprise integrations. This paper introduces the MCP Gateway to simplify self-hosted MCP server integration. The proposed architecture integrates security principles, authentication, intrusion detection, and secure tunneling, enabling secure self-hosting without exposing infrastructure. Key contributions include a reference architecture, threat model mapping, simplified integration strategies, and open-source implementation recommendations. This work focuses on the unique challenges of enterprise-centric, self-hosted AI integrations, unlike existing public MCP server solutions.
Deciding summability via residues in theory and in practice
Venue: arXiv cs.SC 2025
Year: 2025

In difference algebra, summability arises as a basic problem upon which rests the effective solution of other more elaborate problems, such as creative telescoping problems and the computation of Galois groups of difference equations. In 2012 Chen and Singer introduced discrete residues as a theoretical obstruction to summability for rational functions with respect to the shift and $q$-dilation difference operators. Since then analogous notions of discrete residues have been defined in other difference settings relevant for applications, such as for Mahler and elliptic shift difference operators. Very recently there have been some advances in making these theoretical obstructions computable in practice.
Applying LLM-Powered Virtual Humans to Child Interviews in Child-Centered Design
Venue: arXiv cs.HC 2025
Year: 2025

In child-centered design, directly engaging children is crucial for deeply understanding their experiences. However, current research often prioritizes adult perspectives, as interviewing children involves unique challenges such as environmental sensitivities and the need for trust-building. AI-powered virtual humans (VHs) offer a promising approach to facilitate engaging and multimodal interactions with children. This study establishes key design guidelines for LLM-powered virtual humans tailored to child interviews, standardizing multimodal elements including color schemes, voice characteristics, facial features, expressions, head movements, and gestures. Using ChatGPT-based prompt engineering, we developed three distinct Human-AI workflows (LLM-Auto, LLM-Interview, and LLM-Analyze) and conducted a user study involving 15 children aged 6 to 12. The results indicated that the LLM-Analyze workflow outperformed the others by eliciting longer responses, achieving higher user experience ratings, and promoting more effective child engagement.
Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models
Venue: arXiv cs.LG 2025
Year: 2025

Large language models (LLMs) have substantially advanced machine learning research, including natural language processing, computer vision, data mining, etc., yet they still exhibit critical limitations in explainability, reliability, adaptability, and extensibility. In this paper, we overview a promising learning paradigm, i.e., Modular Machine Learning (MML), as an essential approach toward new-generation LLMs capable of addressing these issues. We begin by systematically and comprehensively surveying the existing literature on modular machine learning, with a particular focus on modular data representation and modular models. Then, we propose a unified MML framework for LLMs, which decomposes the complex structure of LLMs into three interdependent components: modular representation, modular model, and modular reasoning. Specifically, the MML paradigm discussed in this article is able to: i) clarify the internal working mechanism of LLMs through the disentanglement of semantic components; ii) allow for flexible and task-adaptive model design; iii) enable an interpretable and logic-driven decision-making process. We further elaborate a feasible implementation of MML-based LLMs via leveraging advanced techniques such as disentangled representation learning, neural architecture search and neuro-symbolic learning. Last but not least, we critically identify the remaining key challenges, such as the integration of continuous neural and discrete symbolic processes, joint optimization, and computational scalability, present promising future research directions that deserve further exploration. Ultimately, we believe the integration of the MML with LLMs has the potential to bridge the gap between statistical (deep) learning and formal (logical) reasoning, thereby paving the way for robust, adaptable, and trustworthy AI systems across a wide range of real-world applications.
Cam-2-Cam: Exploring the Design Space of Dual-Camera Interactions for Smartphone-based Augmented Reality
Venue: arXiv cs.HC 2025
Year: 2025

Off-the-shelf smartphone-based AR systems typically use a single front-facing or rear-facing camera, which restricts user interactions to a narrow field of view and small screen size, thus reducing their practicality. We present Cam-2-Cam, an interaction concept implemented in three smartphone-based AR applications with interactions that span both cameras. Results from our qualitative analysis conducted on 30 participants presented two major design lessons that explore the interaction space of smartphone AR while maintaining critical AR interface attributes like embodiment and immersion: (1) Balancing Contextual Relevance and Feedback Quality serves to outline a delicate balance between implementing familiar interactions people do in the real world and the quality of multimodal AR responses and (2) Preventing Disorientation using Simultaneous Capture and Alternating Cameras which details how to prevent disorientation during AR interactions using the two distinct camera techniques we implemented in the paper. Additionally, we consider observed user assumptions or natural tendencies to inform future implementations of dual-camera setups for smartphone-based AR. We envision our design lessons as an initial pioneering step toward expanding the interaction space of smartphone-based AR, potentially driving broader adoption and overcoming limitations of single-camera AR.
Can Geometry Save Central Views for Sports Field Registration?
Venue: arXiv cs.CV 2025
Year: 2025

Single-frame sports field registration often serves as the foundation for extracting 3D information from broadcast videos, enabling applications related to sports analytics, refereeing, or fan engagement. As sports fields have rigorous specifications in terms of shape and dimensions of their line, circle and point components, sports field markings are commonly used as calibration targets for this task. However, because of the sparse and uneven distribution of field markings, close-up camera views around central areas of the field often depict only line and circle markings. On these views, sports field registration is challenging for the vast majority of existing methods, as they focus on leveraging line field markings and their intersections. It is indeed a challenge to include circle correspondences in a set of linear equations. In this work, we propose a novel method to derive a set of points and lines from circle correspondences, enabling the exploitation of circle correspondences for both sports field registration and image annotation. In our experiments, we illustrate the benefits of our bottom-up geometric method against top-performing detectors and show that our method successfully complements them, enabling sports field registration in difficult scenarios.
Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis
Venue: arXiv cs.SE 2025
Year: 2025

Machine Learning (ML) models offer significant potential for advancing cell counting applications in neuroscience, medical research, pharmaceutical development, and environmental monitoring. However, implementing these models effectively requires robust operational frameworks. This paper introduces Cell Counting Machine Learning Operations (CC-MLOps), a comprehensive framework that streamlines the integration of ML in cell counting workflows. CC-MLOps encompasses data access and preprocessing, model training, monitoring, explainability features, and sustainability considerations. Through a practical use case, we demonstrate how MLOps principles can enhance model reliability, reduce human error, and enable scalable Cell Counting solutions. This work provides actionable guidance for researchers and laboratory professionals seeking to implement machine learning (ML)- powered cell counting systems.
AI Supply Chains: An Emerging Ecosystem of AI Actors, Products, and Services
Venue: arXiv cs.CY 2025
Year: 2025

The widespread adoption of AI in recent years has led to the emergence of AI supply chains: complex networks of AI actors contributing models, datasets, and more to the development of AI products and services. AI supply chains have many implications yet are poorly understood. In this work, we take a first step toward a formal study of AI supply chains and their implications, providing two illustrative case studies indicating that both AI development and regulation are complicated in the presence of supply chains. We begin by presenting a brief historical perspective on AI supply chains, discussing how their rise reflects a longstanding shift towards specialization and outsourcing that signals the healthy growth of the AI industry. We then model AI supply chains as directed graphs and demonstrate the power of this abstraction by connecting examples of AI issues to graph properties. Finally, we examine two case studies in detail, providing theoretical and empirical results in both. In the first, we show that information passing (specifically, of explanations) along the AI supply chains is imperfect, which can result in misunderstandings that have real-world implications. In the second, we show that upstream design choices (e.g., by base model providers) have downstream consequences (e.g., on AI products fine-tuned on the base model). Together, our findings motivate further study of AI supply chains and their increasingly salient social, economic, regulatory, and technical implications.
Can You Mimic Me? Exploring the Use of Android Record & Replay Tools in Debugging
Venue: arXiv cs.SE 2025
Year: 2025

Android User Interface (UI) testing is a critical research area due to the ubiquity of apps and the challenges faced by developers. Record and replay (R&R) tools facilitate manual and automated UI testing by recording UI actions to execute test scenarios and replay bugs. These tools typically support (i) regression testing, (ii) non-crashing functional bug reproduction, and (iii) crashing bug reproduction. However, prior work only examines these tools in fragmented settings, lacking a comprehensive evaluation across common use cases. We address this gap by conducting an empirical study on using R&R tools to record and replay non-crashing failures, crashing bugs, and feature-based user scenarios, and explore combining R&R with automated input generation (AIG) tools to replay crashing bugs. Our study involves one industrial and three academic R&R tools, 34 scenarios from 17 apps, 90 non-crashing failures from 42 apps, and 31 crashing bugs from 17 apps. Results show that 17% of scenarios, 38% of non-crashing bugs, and 44% of crashing bugs cannot be reliably recorded and replayed, mainly due to action interval resolution, API incompatibility, and Android tooling limitations. Our findings highlight key future research directions to enhance the practical application of R&R tools.
Physics-Informed Diffusion Models for SAR Ship Wake Generation from Text Prompts
Venue: arXiv cs.CV 2025
Year: 2025

Detecting ship presence via wake signatures in SAR imagery is attracting considerable research interest, but limited annotated data availability poses significant challenges for supervised learning. Physics-based simulations are commonly used to address this data scarcity, although they are slow and constrain end-to-end learning. In this work, we explore a new direction for more efficient and end-to-end SAR ship wake simulation using a diffusion model trained on data generated by a physics-based simulator. The training dataset is built by pairing images produced by the simulator with text prompts derived from simulation parameters. Experimental result show that the model generates realistic Kelvin wake patterns and achieves significantly faster inference than the physics-based simulator. These results highlight the potential of diffusion models for fast and controllable wake image generation, opening new possibilities for end-to-end downstream tasks in maritime SAR analysis.
NMPC-based Unified Posture Manipulation and Thrust Vectoring for Agile and Fault-Tolerant Flight of a Morphing Aerial Robot
Venue: arXiv cs.RO 2025
Year: 2025

This thesis presents a unified control framework for agile and fault-tolerant flight of the Multi-Modal Mobility Morphobot (M4) in aerial mode. The M4 robot is capable of transitioning between ground and aerial locomotion. The articulated legs enable more dynamic maneuvers than a standard quadrotor platform. A nonlinear model predictive control (NMPC) approach is developed to simultaneously plan posture manipulation and thrust vectoring actions, allowing the robot to execute sharp turns and dynamic flight trajectories. The framework integrates an agile and fault-tolerant control logic that enables precise tracking under aggressive maneuvers while compensating for actuator failures, ensuring continued operation without significant performance degradation. Simulation results validate the effectiveness of the proposed method, demonstrating accurate trajectory tracking and robust recovery from faults, contributing to resilient autonomous flight in complex environments.
Clustering-Based Evolutionary Federated Multiobjective Optimization and Learning
Venue: arXiv cs.NE 2025
Year: 2025

Federated learning enables decentralized model training while preserving data privacy, yet it faces challenges in balancing communication efficiency, model performance, and privacy protection. To address these trade-offs, we formulate FL as a federated multiobjective optimization problem and propose FedMOEAC, a clustering-based evolutionary algorithm that efficiently navigates the Pareto-optimal solution space. Our approach integrates quantization, weight sparsification, and differential privacy to reduce communication overhead while ensuring model robustness and privacy. The clustering mechanism en-hances population diversity, preventing premature convergence and improving optimization efficiency. Experimental results on MNIST and CIFAR-10 demonstrate that FedMOEAC achieves 98.2% accuracy, reduces communication overhead by 45%, and maintains a privacy budget below 1.0, outperforming NSGA-II in convergence speed by 33%. This work provides a scalable and efficient FL framework, ensuring an optimal balance between accuracy, communication efficiency, and privacy in resource-constrained environments.
Local Prompt Optimization
Venue: arXiv cs.CL 2025
Year: 2025

In recent years, the use of prompts to guide the output of Large Language Models have increased dramatically. However, even the best of experts struggle to choose the correct words to stitch up a prompt for the desired task. To solve this, LLM driven prompt optimization emerged as an important problem. Existing prompt optimization methods optimize a prompt globally, where in all the prompt tokens have to be optimized over a large vocabulary while solving a complex task. The large optimization space (tokens) leads to insufficient guidance for a better prompt. In this work, we introduce Local Prompt Optimization (LPO) that integrates with any general automatic prompt engineering method. We identify the optimization tokens in a prompt and nudge the LLM to focus only on those tokens in its optimization step. We observe remarkable performance improvements on Math Reasoning (GSM8k and MultiArith) and BIG-bench Hard benchmarks across various automatic prompt engineering methods. Further, we show that LPO converges to the optimal prompt faster than global methods.