OpenRaiser/Intern-Atlas huggingface dataset:
Toward Fair Federated Learning under Demographic Disparities and Data Imbalance
Venue: arXiv cs.CY 2025
Year: 2025

Ensuring fairness is critical when applying artificial intelligence to high-stakes domains such as healthcare, where predictive models trained on imbalanced and demographically skewed data risk exacerbating existing disparities. Federated learning (FL) enables privacy-preserving collaboration across institutions, but remains vulnerable to both algorithmic bias and subgroup imbalance - particularly when multiple sensitive attributes intersect. We propose FedIDA (Fed erated Learning for Imbalance and D isparity A wareness), a framework-agnostic method that combines fairness-aware regularization with group-conditional oversampling. FedIDA supports multiple sensitive attributes and heterogeneous data distributions without altering the convergence behavior of the underlying FL algorithm. We provide theoretical analysis establishing fairness improvement bounds using Lipschitz continuity and concentration inequalities, and show that FedIDA reduces the variance of fairness metrics across test sets. Empirical results on both benchmark and real-world clinical datasets confirm that FedIDA consistently improves fairness while maintaining competitive predictive performance, demonstrating its effectiveness for equitable and privacy-preserving modeling in healthcare. The source code is available on GitHub.
Adaptive Noise Resilient Keyword Spotting Using One-Shot Learning
Venue: arXiv cs.SD 2025
Year: 2025

Keyword spotting (KWS) is a key component of smart devices, enabling efficient and intuitive audio interaction. However, standard KWS systems deployed on embedded devices often suffer performance degradation under real-world operating conditions. Resilient KWS systems address this issue by enabling dynamic adaptation, with applications such as adding or replacing keywords, adjusting to specific users, and improving noise robustness. However, deploying resilient, standalone KWS systems with low latency on resource-constrained devices remains challenging due to limited memory and computational resources. This study proposes a low computational approach for continuous noise adaptation of pretrained neural networks used for KWS classification, requiring only 1-shot learning and one epoch. The proposed method was assessed using two pretrained models and three real-world noise sources at signal-to-noise ratios (SNRs) ranging from 24 to -3 dB. The adapted models consistently outperformed the pretrained models across all scenarios, especially at SNR $\leq$ 18 dB, achieving accuracy improvements of 4.9% to 46.0%. These results highlight the efficacy of the proposed methodology while being lightweight enough for deployment on resource-constrained devices.
Procedural Low-Poly Terrain Generation with Terracing for Computer Games
Venue: arXiv cs.GR 2025
Year: 2025

In computer games, traditional procedural terrain generation relies on a grid of vertices, with each point representing terrain elevation. For each square in the grid, two triangles are created by connecting fixed vertex indices, resulting in a continuous 3D surface. While this method is efficient for modelling smooth terrain, the grid-like structure lacks the distinct, chaotic appearance of low-poly objects and is not suitable to be used for our purposes. The technique presented in this paper aims to solve the following problem: Generate random, low-poly looking terraced terrain with different biomes and add vegetation to create an interesting environment.
eqsat: An Equality Saturation Dialect for Non-destructive Rewriting
Venue: arXiv cs.PL 2025
Year: 2025

With recent algorithmic improvements and easy-to-use libraries, equality saturation is being picked up for hardware design, program synthesis, theorem proving, program optimization, and more. Existing work on using equality saturation for program optimization makes use of external equality saturation libraries such as egg, typically generating a single optimized expression. In the context of a compiler, such an approach uses equality saturation to replace a small number of passes. In this work, we propose an alternative approach that represents equality saturation natively in the compiler's intermediate representation, facilitating the application of constructive compiler passes that maintain the e-graph state throughout the compilation flow. We take LLVM's MLIR framework and propose a new MLIR dialect named eqsat that represents e-graphs in MLIR code. This not only provides opportunities to rethink e-matching and extraction techniques by orchestrating existing MLIR passes, such as common subexpression elimination, but also avoids translation overhead between the chosen e-graph library and MLIR. Our eqsat intermediate representation (IR) allows programmers to apply equality saturation on arbitrary domain-specific IRs using the same flow as other compiler transformations in MLIR.
Personalized Control for Lower Limb Prosthesis Using Kolmogorov-Arnold Networks
Venue: arXiv cs.LG 2025
Year: 2025

Objective: This paper investigates the potential of learnable activation functions in Kolmogorov-Arnold Networks (KANs) for personalized control in a lower-limb prosthesis. In addition, user-specific vs. pooled training data is evaluated to improve machine learning (ML) and Deep Learning (DL) performance for turn intent prediction. Method: Inertial measurement unit (IMU) data from the shank were collected from five individuals with lower-limb amputation performing turning tasks in a laboratory setting. Ability to classify an upcoming turn was evaluated for Multilayer Perceptron (MLP), Kolmogorov-Arnold Network (KAN), convolutional neural network (CNN), and fractional Kolmogorov-Arnold Networks (FKAN). The comparison of MLP and KAN (for ML models) and FKAN and CNN (for DL models) assessed the effectiveness of learnable activation functions. Models were trained separately on user-specific and pooled data to evaluate the impact of training data on their performance. Results: Learnable activation functions in KAN and FKAN did not yield significant improvement compared to MLP and CNN, respectively. Training on user-specific data yielded superior results compared to pooled data for ML models ($p < 0.05$). In contrast, no significant difference was observed between user-specific and pooled training for DL models. Significance: These findings suggest that learnable activation functions may demonstrate distinct advantages in datasets involving more complex tasks and larger volumes. In addition, pooled training showed comparable performance to user-specific training in DL models, indicating that model training for prosthesis control can utilize data from multiple participants.
DNS Query Forgery: A Client-Side Defense Against Mobile App Traffic Profiling
Venue: arXiv cs.CR 2025
Year: 2025

Mobile applications continuously generate DNS queries that can reveal sensitive user behavioral patterns even when communications are encrypted. This paper presents a privacy enhancement framework based on query forgery to protect users against profiling attempts that leverage these background communications. We first mathematically model user profiles as probability distributions over interest categories derived from mobile application traffic. We then evaluate three query forgery strategies -- uniform sampling, TrackMeNot-based generation, and an optimized approach that minimizes Kullback-Leibler divergence -- to quantify their effectiveness in obfuscating user profiles. Then we create a synthetic dataset comprising 1,000 user traces constructed from real mobile application traffic and we extract the user profiles based on DNS traffic. Our evaluation reveals that a 50\% privacy improvement is achievable with less than 20\% traffic overhead when using our approach, while achieving 100\% privacy protection requires approximately 40-60\% additional traffic. We further propose a modular system architecture for practical implementation of our protection mechanisms on mobile devices. This work offers a client-side privacy solution that operates without third-party trust requirements, empowering individual users to defend against traffic analysis without compromising application functionality.
The Voice Timbre Attribute Detection 2025 Challenge Evaluation Plan
Venue: arXiv cs.SD 2025
Year: 2025

Voice timbre refers to the unique quality or character of a person's voice that distinguishes it from others as perceived by human hearing. The Voice Timbre Attribute Detection (VtaD) 2025 challenge focuses on explaining the voice timbre attribute in a comparative manner. In this challenge, the human impression of voice timbre is verbalized with a set of sensory descriptors, including bright, coarse, soft, magnetic, and so on. The timbre is explained from the comparison between two voices in their intensity within a specific descriptor dimension. The VtaD 2025 challenge starts in May and culminates in a special proposal at the NCMMSC2025 conference in October 2025 in Zhenjiang, China.
Multilingual Machine Translation with Quantum Encoder Decoder Attention-based Convolutional Variational Circuits
Venue: arXiv cs.CL 2025
Year: 2025

Cloud-based multilingual translation services like Google Translate and Microsoft Translator achieve state-of-the-art translation capabilities. These services inherently use large multilingual language models such as GRU, LSTM, BERT, GPT, T5, or similar encoder-decoder architectures with attention mechanisms as the backbone. Also, new age natural language systems, for instance ChatGPT and DeepSeek, have established huge potential in multiple tasks in natural language processing. At the same time, they also possess outstanding multilingual translation capabilities. However, these models use the classical computing realm as a backend. QEDACVC (Quantum Encoder Decoder Attention-based Convolutional Variational Circuits) is an alternate solution that explores the quantum computing realm instead of the classical computing realm to study and demonstrate multilingual machine translation. QEDACVC introduces the quantum encoder-decoder architecture that simulates and runs on quantum computing hardware via quantum convolution, quantum pooling, quantum variational circuit, and quantum attention as software alterations. QEDACVC achieves an Accuracy of 82% when trained on the OPUS dataset for English, French, German, and Hindi corpora for multilingual translations.
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection
Venue: arXiv cs.CV 2025
Year: 2025

Reliable autonomous driving systems require accurate detection of traffic participants. To this end, multi-modal fusion has emerged as an effective strategy. In particular, 4D radar and LiDAR fusion methods based on multi-frame radar point clouds have demonstrated the effectiveness in bridging the point density gap. However, they often neglect radar point clouds' inter-frame misalignment caused by object movement during accumulation and do not fully exploit the object dynamic information from 4D radar. In this paper, we propose MoRAL, a motion-aware multi-frame 4D radar and LiDAR fusion framework for robust 3D object detection. First, a Motion-aware Radar Encoder (MRE) is designed to compensate for inter-frame radar misalignment from moving objects. Later, a Motion Attention Gated Fusion (MAGF) module integrate radar motion features to guide LiDAR features to focus on dynamic foreground objects. Extensive evaluations on the View-of-Delft (VoD) dataset demonstrate that MoRAL outperforms existing methods, achieving the highest mAP of 73.30% in the entire area and 88.68% in the driving corridor. Notably, our method also achieves the best AP of 69.67% for pedestrians in the entire area and 96.25% for cyclists in the driving corridor.
SafePath: Conformal Prediction for Safe LLM-Based Autonomous Navigation
Venue: arXiv cs.LG 2025
Year: 2025

Large Language Models (LLMs) show growing promise in autonomous driving by reasoning over complex traffic scenarios to generate path plans. However, their tendencies toward overconfidence, and hallucinations raise critical safety concerns. We introduce SafePath, a modular framework that augments LLM-based path planning with formal safety guarantees using conformal prediction. SafePath operates in three stages. In the first stage, we use an LLM that generates a set of diverse candidate paths, exploring possible trajectories based on agent behaviors and environmental cues. In the second stage, SafePath filters out high-risk trajectories while guaranteeing that at least one safe option is included with a user-defined probability, through a multiple-choice question-answering formulation that integrates conformal prediction. In the final stage, our approach selects the path with the lowest expected collision risk when uncertainty is low or delegates control to a human when uncertainty is high. We theoretically prove that SafePath guarantees a safe trajectory with a user-defined probability, and we show how its human delegation rate can be tuned to balance autonomy and safety. Extensive experiments on nuScenes and Highway-env show that SafePath reduces planning uncertainty by 77\% and collision rates by up to 70\%, demonstrating effectiveness in making LLM-driven path planning more safer.
MrTrack: Register Mamba for Needle Tracking with Rapid Reciprocating Motion during Ultrasound-Guided Aspiration Biopsy
Venue: arXiv cs.CV 2025
Year: 2025

Ultrasound-guided fine needle aspiration (FNA) biopsy is a common minimally invasive diagnostic procedure. However, an aspiration needle tracker addressing rapid reciprocating motion is still missing. MrTrack, an aspiration needle tracker with a mamba-based register mechanism, is proposed. MrTrack leverages a Mamba-based register extractor to sequentially distill global context from each historical search map, storing these temporal cues in a register bank. The Mamba-based register retriever then retrieves temporal prompts from the register bank to provide external cues when current vision features are temporarily unusable due to rapid reciprocating motion and imaging degradation. A self-supervised register diversify loss is proposed to encourage feature diversity and dimension independence within the learned register, mitigating feature collapse. Comprehensive experiments conducted on both robotic and manual aspiration biopsy datasets demonstrate that MrTrack not only outperforms state-of-the-art trackers in accuracy and robustness but also achieves superior inference efficiency. Project page: https://github.com/PieceZhang/MrTrack
SEGA-DCIM: Design Space Exploration-Guided Automatic Digital CIM Compiler with Multiple Precision Support
Venue: arXiv cs.AR 2025
Year: 2025

Digital computing-in-memory (DCIM) has been a popular solution for addressing the memory wall problem in recent years. However, the DCIM design still heavily relies on manual efforts, and the optimization of DCIM is often based on human experience. These disadvantages limit the time to market while increasing the design difficulty of DCIMs. This work proposes a design space exploration-guided automatic DCIM compiler (SEGA-DCIM) with multiple precision support, including integer and floating-point data precision operations. SEGA-DCIM can automatically generate netlists and layouts of DCIM designs by leveraging a template-based method. With a multi-objective genetic algorithm (MOGA)-based design space explorer, SEGA-DCIM can easily select appropriate DCIM designs for a specific application considering the trade-offs among area, power, and delay. As demonstrated by the experimental results, SEGA-DCIM offers solutions with wide design space, including integer and floating-point precision designs, while maintaining competitive performance compared to state-of-the-art (SOTA) DCIMs.
Function-Correcting $b$-symbol Codes for Locally $(λ, ρ,b)$-Functions
Venue: arXiv cs.IT 2025
Year: 2025

The family of functions plays a central role in the design and effectiveness of function-correcting codes. By focusing on a well-defined family of functions, function-correcting codes can be constructed with minimal length while still ensuring full error detection and correction within that family. In this work, we explore the concept of locally $(λ,ρ)$-functions for $b$-symbol read channels and investigate the optimal redundancy of the corresponding function-correcting $b$-symbol codes (FCBSC) by introducing the notions of locally $(λ,ρ,b)$-functions. First, we discuss the values of $λ$ and $ρ$ for which a function can be considered as a locally $(λ,ρ)$-function in $b$-symbol metric. The findings improve some known results in the Hamming metric and present several new results in the $b$-symbol metric. Then we investigate the optimal redundancy of $(f,t)$-FCBSCs for locally $(λ,ρ,b)$-functions. We establish a recurrence relation between the optimal redundancy of $(f,t)$-function-correcting codes for the $(b+1)$-symbol read and $b$-symbol read channels. We present an upper bound on the optimal redundancy of $(f,t)$-function-correcting $b$-symbol codes for general locally ($λ,ρ$, $b$)-functions by associating it to the minimum achievable length of $b$-symbol error-correcting codes and traditional Hamming-metric codes, given a fixed number of codewords and a specified minimum distance. We derive some explicit upper bounds on the redundancy of $(f,t)$-function-correcting $b$-symbol codes for locally $(λ,2t,b)$-functions. Moreover, for the case where $b=1$, we show that a locally ($3,2t,1$)-function achieves the optimal redundancy of $3t$. Additionally, we explicitly investigate the locality and optimal redundancy of FCBSCs for the $b$-symbol weight function and weight distribution function for $b\geq1$.
Deploying Foundation Model-Enabled Air and Ground Robots in the Field: Challenges and Opportunities
Venue: arXiv cs.RO 2025
Year: 2025

The integration of foundation models (FMs) into robotics has enabled robots to understand natural language and reason about the semantics in their environments. However, existing FM-enabled robots primary operate in closed-world settings, where the robot is given a full prior map or has a full view of its workspace. This paper addresses the deployment of FM-enabled robots in the field, where missions often require a robot to operate in large-scale and unstructured environments. To effectively accomplish these missions, robots must actively explore their environments, navigate obstacle-cluttered terrain, handle unexpected sensor inputs, and operate with compute constraints. We discuss recent deployments of SPINE, our LLM-enabled autonomy framework, in field robotic settings. To the best of our knowledge, we present the first demonstration of large-scale LLM-enabled robot planning in unstructured environments with several kilometers of missions. SPINE is agnostic to a particular LLM, which allows us to distill small language models capable of running onboard size, weight and power (SWaP) limited platforms. Via preliminary model distillation work, we then present the first language-driven UAV planner using on-device language models. We conclude our paper by proposing several promising directions for future research.
Card Sorting Simulator: Augmenting Design of Logical Information Architectures with Large Language Models
Venue: arXiv cs.HC 2025
Year: 2025

Card sorting is a common ideation technique that elicits information on users' mental organization of content and functionality by having them sort items into categories. For more robust card sorting research, digital card sorting tools could benefit from providing quick automated feedback. Our objective of this research is to advance toward an instrument that applies artificial intelligence (AI) to augment card sorting. For this purpose, we develop the Card Sorting Simulator, a prototype tool that leverages Large Language Models (LLMs) to generate informative categorizations of cards. To illuminate how aligned the simulation is with card sorting by actual participants, and to inform the instrument's design decisions, we conducted a generalizability-focused comparative study. We obtained 28 pre-existing card sorting studies from real practitioners, comprising 1,399 participants, along with diverse contents and origins. With this dataset, we conducted a comprehensive and nuanced analysis of the agreement between actual card sorting results (clusterings of cards) and synthetic clusterings across a multitude of LLMs and prompt designs. Mutual information scores indicate a good degree of agreement to real result clustering, although similarity matrices also demonstrate inconsistencies from mental models, which can be attributed to their top-down nature. Furthermore, the number of cards or complexity of their labels impact the accuracy of its simulation. These findings bolster the case for AI augmentation in card sorting research as a source of meaningful preliminary feedback and highlight the need for further study for the development and validation of intelligent user research tools.
Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing
Venue: arXiv cs.CV 2025
Year: 2025

Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios such as payment processing and surveillance. Current multimodal FAS methods often struggle with effective generalization, mainly due to modality-specific biases and domain shifts. To address these challenges, we introduce the \textbf{M}ulti\textbf{m}odal \textbf{D}enoising and \textbf{A}lignment (\textbf{MMDA}) framework. By leveraging the zero-shot generalization capability of CLIP, the MMDA framework effectively suppresses noise in multimodal data through denoising and alignment mechanisms, thereby significantly enhancing the generalization performance of cross-modal alignment. The \textbf{M}odality-\textbf{D}omain Joint \textbf{D}ifferential \textbf{A}ttention (\textbf{MD2A}) module in MMDA concurrently mitigates the impacts of domain and modality noise by refining the attention mechanism based on extracted common noise features. Furthermore, the \textbf{R}epresentation \textbf{S}pace \textbf{S}oft (\textbf{RS2}) Alignment strategy utilizes the pre-trained CLIP model to align multi-domain multimodal data into a generalized representation space in a flexible manner, preserving intricate representations and enhancing the model's adaptability to various unseen conditions. We also design a \textbf{U}-shaped \textbf{D}ual \textbf{S}pace \textbf{A}daptation (\textbf{U-DSA}) module to enhance the adaptability of representations while maintaining generalization performance. These improvements not only enhance the framework's generalization capabilities but also boost its ability to represent complex representations. Our experimental results on four benchmark datasets under different evaluation protocols demonstrate that the MMDA framework outperforms existing state-of-the-art methods in terms of cross-domain generalization and multimodal detection accuracy. The code will be released soon.