OpenRaiser/Intern-Atlas huggingface dataset:
Cryptanalysis of a Lattice-Based PIR Scheme for Arbitrary Database Sizes
Venue: arXiv cs.CR 2025
Year: 2025

Private Information Retrieval (PIR) schemes enable users to securely retrieve files from a server without disclosing the content of their queries, thereby preserving their privacy. In 2008, Melchor and Gaborit proposed a PIR scheme that achieves a balance between communication overhead and server-side computational cost. However, for particularly small databases, Liu and Bi identified a vulnerability in the scheme using lattice-based methods. Nevertheless, the rapid increase in computational cost associated with the attack limited its practical applicability, leaving the scheme's overall security largely intact. In this paper, we present a novel two-stage attack that extends the work of Liu and Bi to databases of arbitrary sizes. To this end, we employ a binary-search-like preprocessing technique, which enables a significant reduction in the number of lattice problems that need to be considered. Specifically, we demonstrate how to compromise the scheme in a matter of minutes using an ordinary laptop. Our findings are substantiated through both rigorous analytical proofs and comprehensive numerical experiments.
MER-CLIP: AU-Guided Vision-Language Alignment for Micro-Expression Recognition
Venue: arXiv cs.HC 2025
Year: 2025

As a critical psychological stress response, micro-expressions (MEs) are fleeting and subtle facial movements revealing genuine emotions. Automatic ME recognition (MER) holds valuable applications in fields such as criminal investigation and psychological diagnosis. The Facial Action Coding System (FACS) encodes expressions by identifying activations of specific facial action units (AUs), serving as a key reference for ME analysis. However, current MER methods typically limit AU utilization to defining regions of interest (ROIs) or relying on specific prior knowledge, often resulting in limited performance and poor generalization. To address this, we integrate the CLIP model's powerful cross-modal semantic alignment capability into MER and propose a novel approach namely MER-CLIP. Specifically, we convert AU labels into detailed textual descriptions of facial muscle movements, guiding fine-grained spatiotemporal ME learning by aligning visual dynamics and textual AU-based representations. Additionally, we introduce an Emotion Inference Module to capture the nuanced relationships between ME patterns and emotions with higher-level semantic understanding. To mitigate overfitting caused by the scarcity of ME data, we put forward LocalStaticFaceMix, an effective data augmentation strategy blending facial images to enhance facial diversity while preserving critical ME features. Finally, comprehensive experiments on four benchmark ME datasets confirm the superiority of MER-CLIP. Notably, UF1 scores on CAS(ME)3 reach 0.7832, 0.6544, and 0.4997 for 3-, 4-, and 7-class classification tasks, significantly outperforming previous methods.
Fast Differentiable Modal Simulation of Non-linear Strings, Membranes, and Plates
Venue: arXiv cs.SD 2025
Year: 2025

Modal methods for simulating vibrations of strings, membranes, and plates are widely used in acoustics and physically informed audio synthesis. However, traditional implementations, particularly for non-linear models like the von Kármán plate, are computationally demanding and lack differentiability, limiting inverse modelling and real-time applications. We introduce a fast, differentiable, GPU-accelerated modal framework built with the JAX library, providing efficient simulations and enabling gradient-based inverse modelling. Benchmarks show that our approach significantly outperforms CPU and GPU-based implementations, particularly for simulations with many modes. Inverse modelling experiments demonstrate that our approach can recover physical parameters, including tension, stiffness, and geometry, from both synthetic and experimental data. Although fitting physical parameters is more sensitive to initialisation compared to other methods, it provides greater interpretability and more compact parameterisation. The code is released as open source to support future research and applications in differentiable physical modelling and sound synthesis.
Towards Quantum Resilience: Data-Driven Migration Strategy Design
Venue: arXiv cs.CR 2025
Year: 2025

The advancements in quantum computing are a threat to classical cryptographic systems. The traditional cryptographic methods that utilize factorization-based or discrete-logarithm-based algorithms, such as RSA and ECC, are some of these. This paper thoroughly investigates the vulnerabilities of traditional cryptographic methods against quantum attacks and provides a decision-support framework to help organizations in recommending mitigation plans and determining appropriate transition strategies to post-quantum cryptography. A semi-synthetic dataset, consisting of key features such as key size, network complexity, and sensitivity levels, is crafted, with each configuration labeled according to its recommended mitigation plan. Using decision tree and random forest models, a classifier is trained to recommend appropriate mitigation/transition plans such as continuous monitoring, scheduled transitions, and immediate hybrid implementation. The proposed approach introduces a data-driven and dynamic solution for organizations to assess the scale of the migration, specifying a structured roadmap toward quantum resilience. The results highlight important features that influence strategy decisions and support actionable recommendations for cryptographic modernization based on system context.
Minimal $L^p$-congestion spanning trees on weighted graphs
Venue: arXiv cs.DM 2025
Year: 2025

A generalization of the notion of spanning tree congestion for weighted graphs is introduced. The $L^p$ congestion of a spanning tree is defined as the $L^p$ norm of the edge congestion of that tree. In this context, the classical congestion is the $L^\infty$-congestion. Explicit estimations of the minimal spanning tree $L^p$ congestion for some families of graphs are given. In addition, we introduce a polynomial-time algorithm for approximating the minimal $L^p$-congestion spanning tree in any weighted graph and another two similar algorithms for weighted planar graphs. The performance of these algorithms is tested in several graphs.
CogniSNN: An Exploration to Random Graph Architecture based Spiking Neural Networks with Enhanced Depth-Scalability and Path-Plasticity
Venue: arXiv cs.NE 2025
Year: 2025

Currently, most spiking neural networks (SNNs) still mimic the chain-like hierarchical architecture in traditional artificial neural networks (ANNs). This method significantly differs from random connections between neurons found in biological brains, limiting the ability to model the evolving mechanisms of neural pathways in biological neural systems, particularly in terms of dynamic depth-scalability and adaptive path-plasticity. This paper develops a new modeling paradigm for SNNs with random graph architecture (RGA), termed Cognition-aware SNN (CogniSNN). Furthermore, we model the depth-scalability and path-plasticity in CogniSNN by introducing a modified spiking residual neural node (ResNode) to counteract network degradation in deeper graph pathways, as well as a critical path-based algorithm that enables CogniSNN to perform path reusability on new tasks leveraging the features of the data and the RGA learned in old tasks. Experiments show that the performance of CogniSNN with redesigned ResNode is comparable, even superior, to current state-of-the-art SNNs on neuromorphic datasets. The critical path-based approach effectively achieves path reuse capability while maintaining expected performance in learning new tasks that are similar to or distinct from the old ones. This study showcases the potential of RGA-based SNNs and paves a new path for modeling the fusion of computational neuroscience and deep intelligent agents. The code is available at github.com/Yongsheng124/CogniSNN.
Unilogit: Robust Machine Unlearning for LLMs Using Uniform-Target Self-Distillation
Venue: arXiv cs.CL 2025
Year: 2025

This paper introduces Unilogit, a novel self-distillation method for machine unlearning in Large Language Models. Unilogit addresses the challenge of selectively forgetting specific information while maintaining overall model utility, a critical task in compliance with data privacy regulations like GDPR. Unlike prior methods that rely on static hyperparameters or starting model outputs, Unilogit dynamically adjusts target logits to achieve a uniform probability for the target token, leveraging the current model's outputs for more accurate self-distillation targets. This approach not only eliminates the need for additional hyperparameters but also enhances the model's ability to approximate the golden targets. Extensive experiments on public benchmarks and an in-house e-commerce dataset demonstrate Unilogit's superior performance in balancing forget and retain objectives, outperforming state-of-the-art methods such as NPO and UnDIAL. Our analysis further reveals Unilogit's robustness across various scenarios, highlighting its practical applicability and effectiveness in achieving efficacious machine unlearning.
Learning Music Audio Representations With Limited Data
Venue: arXiv cs.SD 2025
Year: 2025

Large deep-learning models for music, including those focused on learning general-purpose music audio representations, are often assumed to require substantial training data to achieve high performance. If true, this would pose challenges in scenarios where audio data or annotations are scarce, such as for underrepresented music traditions, non-popular genres, and personalized music creation and listening. Understanding how these models behave in limited-data scenarios could be crucial for developing techniques to tackle them. In this work, we investigate the behavior of several music audio representation models under limited-data learning regimes. We consider music models with various architectures, training paradigms, and input durations, and train them on data collections ranging from 5 to 8,000 minutes long. We evaluate the learned representations on various music information retrieval tasks and analyze their robustness to noise. We show that, under certain conditions, representations from limited-data and even random models perform comparably to ones from large-dataset models, though handcrafted features outperform all learned representations in some tasks.
Scheduled Jacobian Chaining
Venue: arXiv cs.DM 2025
Year: 2025

This paper addresses the efficient computation of Jacobian matrices for programs composed of sequential differentiable subprograms. By representing the overall Jacobian as a chain product of the Jacobians of these subprograms, we reduce the problem to optimizing the sequence of matrix multiplications, known as the Jacobian Matrix Chain Product problem. Solutions to this problem yield "optimal bracketings", which induce a precedence-constraint scheduling problem. We investigate the inherent parallelism in the solutions and develop a new dynamic programming algorithm as a heuristic that incorporates the scheduling. To assess its performance, we benchmark it against the global optimum, which is computed via a branch-and-bound algorithm.
Operator Spaces, Linear Logic and the Heisenberg-Schrödinger Duality of Quantum Theory
Venue: arXiv cs.LO 2025
Year: 2025

We show that the category OS of operator spaces, with complete contractions as morphisms, is locally countably presentable and a model of Intuitionistic Linear Logic in the sense of Lafont. We then describe a model of Classical Linear Logic, based on OS, whose duality is compatible with the Heisenberg-Schrödinger duality of quantum theory. We also show that OS provides a good setting for studying pure state and mixed state quantum information, the interaction between the two, and even higher-order quantum maps such as the quantum switch.
Fault Diagnosis of 3D-Printed Scaled Wind Turbine Blades
Venue: arXiv cs.LG 2025
Year: 2025

This study presents an integrated methodology for fault detection in wind turbine blades using 3D-printed scaled models, finite element simulations, experimental modal analysis, and machine learning techniques. A scaled model of the NREL 5MW blade was fabricated using 3D printing, and crack-type damages were introduced at critical locations. Finite Element Analysis was employed to predict the impact of these damages on the natural frequencies, with the results validated through controlled hammer impact tests. Vibration data was processed to extract both time-domain and frequency-domain features, and key discriminative variables were identified using statistical analyses (ANOVA). Machine learning classifiers, including Support Vector Machine and K-Nearest Neighbors, achieved classification accuracies exceeding 94%. The results revealed that vibration modes 3, 4, and 6 are particularly sensitive to structural anomalies for this blade. This integrated approach confirms the feasibility of combining numerical simulations with experimental validations and paves the way for structural health monitoring systems in wind energy applications.
Assessing Tenstorrent's RISC-V MatMul Acceleration Capabilities
Venue: arXiv cs.PF 2025
Year: 2025

The increasing demand for generative AI as Large Language Models (LLMs) services has driven the need for specialized hardware architectures that optimize computational efficiency and energy consumption. This paper evaluates the performance of the Tenstorrent Grayskull e75 RISC-V accelerator for basic linear algebra kernels at reduced numerical precision, a fundamental operation in LLM computations. We present a detailed characterization of Grayskull's execution model, gridsize, matrix dimensions, data formats, and numerical precision impact computational efficiency. Furthermore, we compare Grayskull's performance against state-of-the-art architectures with tensor acceleration, including Intel Sapphire Rapids processors and two NVIDIA GPUs (V100 and A100). Whilst NVIDIA GPUs dominate raw performance, Grayskull demonstrates a competitive trade-off between power consumption and computational throughput, reaching a peak of 1.55 TFLOPs/Watt with BF16.
LLMs Outperform Experts on Challenging Biology Benchmarks
Venue: arXiv cs.LG 2025
Year: 2025

This study systematically evaluates 27 frontier Large Language Models on eight biology benchmarks spanning molecular biology, genetics, cloning, virology, and biosecurity. Models from major AI developers released between November 2022 and April 2025 were assessed through ten independent runs per benchmark. The findings reveal dramatic improvements in biological capabilities. Top model performance increased more than 4-fold on the challenging text-only subset of the Virology Capabilities Test over the study period, with OpenAI's o3 now performing twice as well as expert virologists. Several models now match or exceed expert-level performance on other challenging benchmarks, including the biology subsets of GPQA and WMDP and LAB-Bench CloningScenarios. Contrary to expectations, chain-of-thought did not substantially improve performance over zero-shot evaluation, while extended reasoning features in o3-mini and Claude 3.7 Sonnet typically improved performance as predicted by inference scaling. Benchmarks such as PubMedQA and the MMLU and WMDP biology subsets exhibited performance plateaus well below 100%, suggesting benchmark saturation and errors in the underlying benchmark data. The analysis highlights the need for more sophisticated evaluation methodologies as AI systems continue to advance.
Multimodal Sentiment Analysis on CMU-MOSEI Dataset using Transformer-based Models
Venue: arXiv cs.CL 2025
Year: 2025

This project performs multimodal sentiment analysis using the CMU-MOSEI dataset, using transformer-based models with early fusion to integrate text, audio, and visual modalities. We employ BERT-based encoders for each modality, extracting embeddings that are concatenated before classification. The model achieves strong performance, with 97.87% 7-class accuracy and a 0.9682 F1-score on the test set, demonstrating the effectiveness of early fusion in capturing cross-modal interactions. The training utilized Adam optimization (lr=1e-4), dropout (0.3), and early stopping to ensure generalization and robustness. Results highlight the superiority of transformer architectures in modeling multimodal sentiment, with a low MAE (0.1060) indicating precise sentiment intensity prediction. Future work may compare fusion strategies or enhance interpretability. This approach utilizes multimodal learning by effectively combining linguistic, acoustic, and visual cues for sentiment analysis.
Efficient Sensorimotor Learning for Open-world Robot Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

This dissertation considers Open-world Robot Manipulation, a manipulation problem where a robot must generalize or quickly adapt to new objects, scenes, or tasks for which it has not been pre-programmed or pre-trained. This dissertation tackles the problem using a methodology of efficient sensorimotor learning. The key to enabling efficient sensorimotor learning lies in leveraging regular patterns that exist in limited amounts of demonstration data. These patterns, referred to as ``regularity,'' enable the data-efficient learning of generalizable manipulation skills. This dissertation offers a new perspective on formulating manipulation problems through the lens of regularity. Building upon this notion, we introduce three major contributions. First, we introduce methods that endow robots with object-centric priors, allowing them to learn generalizable, closed-loop sensorimotor policies from a small number of teleoperation demonstrations. Second, we introduce methods that constitute robots' spatial understanding, unlocking their ability to imitate manipulation skills from in-the-wild video observations. Last but not least, we introduce methods that enable robots to identify reusable skills from their past experiences, resulting in systems that can continually imitate multiple tasks in a sequential manner. Altogether, the contributions of this dissertation help lay the groundwork for building general-purpose personal robots that can quickly adapt to new situations or tasks with low-cost data collection and interact easily with humans. By enabling robots to learn and generalize from limited data, this dissertation takes a step toward realizing the vision of intelligent robotic assistants that can be seamlessly integrated into everyday scenarios.
Neuro-Symbolic Concepts
Venue: arXiv cs.AI 2025
Year: 2025

This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent utilizes a vocabulary of neuro-symbolic concepts. These concepts, such as object, relation, and action concepts, are grounded on sensory inputs and actuation outputs. They are also compositional, allowing for the creation of novel concepts through their structural combination. To facilitate learning and reasoning, the concepts are typed and represented using a combination of symbolic programs and neural network representations. Leveraging such neuro-symbolic concepts, the agent can efficiently learn and recombine them to solve various tasks across different domains, ranging from 2D images, videos, 3D scenes, and robotic manipulation tasks. This concept-centric framework offers several advantages, including data efficiency, compositional generalization, continual learning, and zero-shot transfer.
Decoding Algorithms for Two-dimensional Constacyclic Codes over $\mathbb{F}_q$
Venue: arXiv cs.IT 2025
Year: 2025

We derive the spectral domain properties of two-dimensional (2-D) $(λ_1, λ_2)$-constacyclic codes over $\mathbb{F}_q$ using the 2-D finite field Fourier transform (FFFT). Based on the spectral nulls of 2-D $(λ_1, λ_2)$-constacyclic codes, we characterize the structure of 2-D constacyclic coded arrays. The proposed 2-D construction has flexible code rates and works for any code areas, be it odd or even area. We present an algorithm to detect the location of 2-D errors. Further, we also propose decoding algorithms for extracting the error values using both time and frequency domain properties by exploiting the sparsity that arises due to duality in the time and frequency domains. Through several illustrative examples, we demonstrate the working of the proposed decoding algorithms.
Neural Network Operator-Based Fractal Approximation: Smoothness Preservation and Convergence Analysis
Venue: arXiv cs.LG 2025
Year: 2025

This paper presents a new approach of constructing $α$-fractal interpolation functions (FIFs) using neural network operators, integrating concepts from approximation theory. Initially, we construct $α$-fractals utilizing neural network-based operators, providing an approach to generating fractal functions with interpolation properties. Based on the same foundation, we have developed fractal interpolation functions that utilize only the values of the original function at the nodes or partition points, unlike traditional methods that rely on the entire original function. Further, we have constructed \(α\)-fractals that preserve the smoothness of functions under certain constraints by employing a four-layered neural network operator, ensuring that if \(f \in C^{r}[a,b]\), then the corresponding fractal \(f^α \in C^{r}[a,b]\). Furthermore, we analyze the convergence of these $α$-fractals to the original function under suitable conditions. The work uses key approximation theory tools, such as the modulus of continuity and interpolation operators, to develop convergence results and uniform approximation error bounds.
United States Road Accident Prediction using Random Forest Predictor
Venue: arXiv cs.CY 2025
Year: 2025

Road accidents significantly threaten public safety and require in-depth analysis for effective prevention and mitigation strategies. This paper focuses on predicting accidents through the examination of a comprehensive traffic dataset covering 49 states in the United States. The dataset integrates information from diverse sources, including transportation departments, law enforcement, and traffic sensors. This paper specifically emphasizes predicting the number of accidents, utilizing advanced machine learning models such as regression analysis and time series analysis. The inclusion of various factors, ranging from environmental conditions to human behavior and infrastructure, ensures a holistic understanding of the dynamics influencing road safety. Temporal and spatial analysis further allows for the identification of trends, seasonal variations, and high-risk areas. The implications of this research extend to proactive decision-making for policymakers and transportation authorities. By providing accurate predictions and quantifiable insights into expected accident rates under different conditions, the paper aims to empower authorities to allocate resources efficiently and implement targeted interventions. The goal is to contribute to the development of informed policies and interventions that enhance road safety, creating a safer environment for all road users. Keywords: Machine Learning, Random Forest, Accident Prediction, AutoML, LSTM.