OpenRaiser/Intern-Atlas huggingface dataset:
HyboWaveNet: Hyperbolic Graph Neural Networks with Multi-Scale Wavelet Transform for Protein-Protein Interaction Prediction
Venue: arXiv cs.LG 2025
Year: 2025

Protein-protein interactions (PPIs) are fundamental for deciphering cellular functions,disease pathways,and drug discovery.Although existing neural networks and machine learning methods have achieved high accuracy in PPI prediction,their black-box nature leads to a lack of causal interpretation of the prediction results and difficulty in capturing hierarchical geometries and multi-scale dynamic interaction patterns among proteins.To address these challenges, we propose HyboWaveNet,a novel deep learning framework that collaborates with hyperbolic graphical neural networks (HGNNs) and multiscale graphical wavelet transform for robust PPI prediction. Mapping protein features to Lorentz space simulates hierarchical topological relationships among biomolecules via a hyperbolic distance metric,enabling node feature representations that better fit biological a priori.HyboWaveNet inherently simulates hierarchical and scale-free biological relationships, while the integration of wavelet transforms enables adaptive extraction of local and global interaction features across different resolutions. Our framework generates node feature representations via a graph neural network under the Lorenz model and generates pairs of positive samples under multiple different views for comparative learning, followed by further feature extraction via multi-scale graph wavelet transforms to predict potential PPIs. Experiments on public datasets show that HyboWaveNet improves over both existing state-of-the-art methods. We also demonstrate through ablation experimental studies that the multi-scale graph wavelet transform module improves the predictive performance and generalization ability of HyboWaveNet. This work links geometric deep learning and signal processing to advance PPI prediction, providing a principled approach for analyzing complex biological systems
Transforming Evidence Synthesis: A Systematic Review of the Evolution of Automated Meta-Analysis in the Age of AI
Venue: arXiv cs.AI 2025
Year: 2025

Exponential growth in scientific literature has heightened the demand for efficient evidence-based synthesis, driving the rise of the field of Automated Meta-analysis (AMA) powered by natural language processing and machine learning. This PRISMA systematic review introduces a structured framework for assessing the current state of AMA, based on screening 978 papers from 2006 to 2024, and analyzing 54 studies across diverse domains. Findings reveal a predominant focus on automating data processing (57%), such as extraction and statistical modeling, while only 17% address advanced synthesis stages. Just one study (2%) explored preliminary full-process automation, highlighting a critical gap that limits AMA's capacity for comprehensive synthesis. Despite recent breakthroughs in large language models (LLMs) and advanced AI, their integration into statistical modeling and higher-order synthesis, such as heterogeneity assessment and bias evaluation, remains underdeveloped. This has constrained AMA's potential for fully autonomous meta-analysis. From our dataset spanning medical (67%) and non-medical (33%) applications, we found that AMA has exhibited distinct implementation patterns and varying degrees of effectiveness in actually improving efficiency, scalability, and reproducibility. While automation has enhanced specific meta-analytic tasks, achieving seamless, end-to-end automation remains an open challenge. As AI systems advance in reasoning and contextual understanding, addressing these gaps is now imperative. Future efforts must focus on bridging automation across all meta-analysis stages, refining interpretability, and ensuring methodological robustness to fully realize AMA's potential for scalable, domain-agnostic synthesis.
Integration Flow Models
Venue: arXiv cs.CV 2025
Year: 2025

Ordinary differential equation (ODE) based generative models have emerged as a powerful approach for producing high-quality samples in many applications. However, the ODE-based methods either suffer the discretization error of numerical solvers of ODE, which restricts the quality of samples when only a few NFEs are used, or struggle with training instability. In this paper, we proposed Integration Flow, which directly learns the integral of ODE-based trajectory paths without solving the ODE functions. Moreover, Integration Flow explicitly incorporates the target state $\mathbf{x}_0$ as the anchor state in guiding the reverse-time dynamics. We have theoretically proven this can contribute to both stability and accuracy. To the best of our knowledge, Integration Flow is the first model with a unified structure to estimate ODE-based generative models and the first to show the exact straightness of 1-Rectified Flow without reflow. Through theoretical analysis and empirical evaluations, we show that Integration Flows achieve improved performance when it is applied to existing ODE-based models, such as diffusion models, Rectified Flows, and PFGM++. Specifically, Integration Flow achieves one-step generation on CIFAR10 with FIDs of 2.86 for the Variance Exploding (VE) diffusion model, 3.36 for rectified flow without reflow, and 2.91 for PFGM++; and on ImageNet with FIDs of 4.09 for VE diffusion model, 4.35 for rectified flow without reflow and 4.15 for PFGM++.
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we present an adherence-aware reinforcement learning (RL) approach aimed at seeking optimal lane-changing recommendations within a semi-autonomous driving environment to enhance a single vehicle's travel efficiency. The problem is framed within a Markov decision process setting and is addressed through an adherence-aware deep Q network, which takes into account the partial compliance of human drivers with the recommended actions. This approach is evaluated within CARLA's driving environment under realistic scenarios.
Performance Smells in ML and Non-ML Python Projects: A Comparative Study
Venue: arXiv cs.SE 2025
Year: 2025

Python is widely adopted across various domains, especially in Machine Learning (ML) and traditional software projects. Despite its versatility, Python is susceptible to performance smells, i.e., suboptimal coding practices that can reduce application efficiency. This study provides a comparative analysis of performance smells between ML and non-ML projects, aiming to assess the occurrence of these inefficiencies while exploring their distribution across stages in the ML pipeline. For that, we conducted an empirical study analyzing 300 Python-based GitHub projects, distributed across ML and non-ML projects, categorizing performance smells based on the RIdiom tool. Our results indicate that ML projects are more susceptible to performance smells likely due to the computational and data-intensive nature of ML workflows. We also observed that performance smells in the ML pipeline predominantly affect the Data Processing stage. However, their presence in the Model Deployment stage indicates that such smells are not limited to the early stages of the pipeline. Our findings offer actionable insights for developers, emphasizing the importance of targeted optimizations for smells prevalent in ML projects. Furthermore, our study underscores the need to tailor performance optimization strategies to the unique characteristics of ML projects, with particular attention to the pipeline stages most affected by performance smells.
Tree embedding based mapping system for low-latency mobile applications in multi-access networks
Venue: arXiv cs.NI 2025
Year: 2025

Low-latency applications like AR/VR and online gaming need fast, stable connections. New technologies such as V2X, LEO satellites, and 6G bring unique challenges in mobility management. Traditional solutions based on centralized or distributed anchors often fall short in supporting rapid mobility due to inefficient routing, low versatility, and insufficient multi-access support. In this paper, we design a new end-to-end system for tracking multi-connected mobile devices at scale and optimizing performance for latency-sensitive, highly dynamic applications. Our system, based on the locator/ID separation principle, extends to multi-access networks without requiring specialized routers or caching. Using a novel tree embedding-based overlay, we enable fast session setup while allowing endpoints to directly handle mobility between them. Evaluation with real network data shows our solution cuts connection latency to 7.42% inflation over the shortest path, compared to LISP's 359\% due to cache misses. It also significantly reduces location update overhead and disruption time during mobility.
Temporal Neural Operator for Modeling Time-Dependent Physical Phenomena
Venue: arXiv cs.LG 2025
Year: 2025

Neural Operators (NOs) are machine learning models designed to solve partial differential equations (PDEs) by learning to map between function spaces. Neural Operators such as the Deep Operator Network (DeepONet) and the Fourier Neural Operator (FNO) have demonstrated excellent generalization properties when mapping between spatial function spaces. However, they struggle in mapping the temporal dynamics of time-dependent PDEs, especially for time steps not explicitly seen during training. This limits their temporal accuracy as they do not leverage these dynamics in the training process. In addition, most NOs tend to be prohibitively costly to train, especially for higher-dimensional PDEs. In this paper, we propose the Temporal Neural Operator (TNO), an efficient neural operator specifically designed for spatio-temporal operator learning for time-dependent PDEs. TNO achieves this by introducing a temporal-branch to the DeepONet framework, leveraging the best architectural design choices from several other NOs, and a combination of training strategies including Markov assumption, teacher forcing, temporal bundling, and the flexibility to condition the output on the current state or past states. Through extensive benchmarking and an ablation study on a diverse set of example problems we demonstrate the TNO long range temporal extrapolation capabilities, robustness to error accumulation, resolution invariance, and flexibility to handle multiple input functions.
Financial Data Analysis with Robust Federated Logistic Regression
Venue: arXiv cs.LG 2025
Year: 2025

In this study, we focus on the analysis of financial data in a federated setting, wherein data is distributed across multiple clients or locations, and the raw data never leaves the local devices. Our primary focus is not only on the development of efficient learning frameworks (for protecting user data privacy) in the field of federated learning but also on the importance of designing models that are easier to interpret. In addition, we care about the robustness of the framework to outliers. To achieve these goals, we propose a robust federated logistic regression-based framework that strives to strike a balance between these goals. To verify the feasibility of our proposed framework, we carefully evaluate its performance not only on independently identically distributed (IID) data but also on non-IID data, especially in scenarios involving outliers. Extensive numerical results collected from multiple public datasets demonstrate that our proposed method can achieve comparable performance to those of classical centralized algorithms, such as Logistical Regression, Decision Tree, and K-Nearest Neighbors, in both binary and multi-class classification tasks.
Smart Water Security with AI and Blockchain-Enhanced Digital Twins
Venue: arXiv cs.CR 2025
Year: 2025

Water distribution systems in rural areas face serious challenges such as a lack of real-time monitoring, vulnerability to cyberattacks, and unreliable data handling. This paper presents an integrated framework that combines LoRaWAN-based data acquisition, a machine learning-driven Intrusion Detection System (IDS), and a blockchain-enabled Digital Twin (BC-DT) platform for secure and transparent water management. The IDS filters anomalous or spoofed data using a Long Short-Term Memory (LSTM) Autoencoder and Isolation Forest before validated data is logged via smart contracts on a private Ethereum blockchain using Proof of Authority (PoA) consensus. The verified data feeds into a real-time DT model supporting leak detection, consumption forecasting, and predictive maintenance. Experimental results demonstrate that the system achieves over 80 transactions per second (TPS) with under 2 seconds of latency while remaining cost-effective and scalable for up to 1,000 smart meters. This work demonstrates a practical and secure architecture for decentralized water infrastructure in under-connected rural environments.
FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting
Venue: arXiv cs.LG 2025
Year: 2025

Privacy-preserving distributed model training is crucial for modern machine learning applications, yet existing Federated Learning approaches struggle with heterogeneous data distributions and varying computational capabilities. Traditional solutions either treat all participants uniformly or require costly dynamic clustering during training, leading to reduced efficiency and delayed model specialization. We present FedCCL (Federated Clustered Continual Learning), a framework specifically designed for environments with static organizational characteristics but dynamic client availability. By combining static pre-training clustering with an adapted asynchronous FedAvg algorithm, FedCCL enables new clients to immediately profit from specialized models without prior exposure to their data distribution, while maintaining reduced coordination overhead and resilience to client disconnections. Our approach implements an asynchronous Federated Learning protocol with a three-tier model topology - global, cluster-specific, and local models - that efficiently manages knowledge sharing across heterogeneous participants. Evaluation using photovoltaic installations across central Europe demonstrates that FedCCL's location-based clustering achieves an energy prediction error of 3.93% (+-0.21%), while maintaining data privacy and showing that the framework maintains stability for population-independent deployments, with 0.14 percentage point degradation in performance for new installations. The results demonstrate that FedCCL offers an effective framework for privacy-preserving distributed learning, maintaining high accuracy and adaptability even with dynamic participant populations.
A Cryptographic Perspective on Mitigation vs. Detection in Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

In this paper, we initiate a cryptographically inspired theoretical study of detection versus mitigation of adversarial inputs produced by attackers on Machine Learning algorithms during inference time. We formally define defense by detection (DbD) and defense by mitigation (DbM). Our definitions come in the form of a 3-round protocol between two resource-bounded parties: a trainer/defender and an attacker. The attacker aims to produce inference-time inputs that fool the training algorithm. We define correctness, completeness, and soundness properties to capture successful defense at inference time while not degrading (too much) the performance of the algorithm on inputs from the training distribution. We first show that achieving DbD and achieving DbM are equivalent for ML classification tasks. Surprisingly, this is not the case for ML generative learning tasks, where there are many possible correct outputs for each input. We show a separation between DbD and DbM by exhibiting two generative learning tasks for which it is possible to defend by mitigation but it is provably impossible to defend by detection. The mitigation phase uses significantly less computational resources than the initial training algorithm. In the first learning task we consider sample complexity as the resource and in the second the time complexity. The first result holds under the assumption that the Identity-Based Fully Homomorphic Encryption (IB-FHE), publicly-verifiable zero-knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARK), and Strongly Unforgeable Signatures exist. The second result assumes the existence of Non-Parallelizing Languages with Average-Case Hardness (NPL) and Incrementally-Verifiable Computation (IVC) and IB-FHE.
System Identification of Thrust and Torque Characteristics for a Bipedal Robot with Integrated Propulsion
Venue: arXiv cs.RO 2025
Year: 2025

Bipedal robots represent a remarkable and sophisticated class of robotics, designed to emulate human form and movement. Their development marks a significant milestone in the field. However, even the most advanced bipedal robots face challenges related to terrain variation, obstacle negotiation, payload management, weight distribution, and recovering from stumbles. These challenges can be mitigated by incorporating thrusters, which enhance stability on uneven terrain, facilitate obstacle avoidance, and improve recovery after stumbling. Harpy is a bipedal robot equipped with six joints and two thrusters, serving as a hardware platform for implementing and testing advanced control algorithms. This thesis focuses on characterizing Harpy's hardware to improve the system's overall robustness, controllability, and predictability. It also examines simulation results for predicting thrust in propeller-based mechanisms, the integration of thrusters into the Harpy platform and associated testing, as well as an exploration of motor torque characterization methods and their application to hardware in relation to closed-loop force-based impedance control.
Fine-Grained Classification: Connecting Metadata via Cross-Contrastive Pre-Training
Venue: arXiv cs.CV 2025
Year: 2025

Fine-grained visual classification aims to recognize objects belonging to many subordinate categories of a supercategory, where appearance alone often fails to distinguish highly similar classes. We propose a unified framework that integrates image, text, and metadata via cross-contrastive pre-training. We first align the three modality encoders in a shared embedding space and then fine-tune the image and metadata encoders for classification. On NABirds, our approach improves over the baseline by 7.83% and achieves 84.44% top-1 accuracy, outperforming strong multimodal methods.
AI in Software Engineering: Perceived Roles and Their Impact on Adoption
Venue: arXiv cs.SE 2025
Year: 2025

This paper investigates how developers conceptualize AI-powered Development Tools and how these role attributions influence technology acceptance. Through qualitative analysis of 38 interviews and a quantitative survey with 102 participants, we identify two primary Mental Models: AI as an inanimate tool and AI as a human-like teammate. Factor analysis further groups AI roles into Support Roles (e.g., assistant, reference guide) and Expert Roles (e.g., advisor, problem solver). We find that assigning multiple roles to AI correlates positively with Perceived Usefulness and Perceived Ease of Use, indicating that diverse conceptualizations enhance AI adoption. These insights suggest that AI4SE tools should accommodate varying user expectations through adaptive design strategies that align with different Mental Models.
Method Names in Jupyter Notebooks: An Exploratory Study
Venue: arXiv cs.SE 2025
Year: 2025

Method names play an important role in communicating the purpose and behavior of their functionality. Research has shown that high-quality names significantly improve code comprehension and the overall maintainability of software. However, these studies primarily focus on naming practices in traditional software development. There is limited research on naming patterns in Jupyter Notebooks, a popular environment for scientific computing and data analysis. In this exploratory study, we analyze the naming practices found in 691 methods across 384 Jupyter Notebooks, focusing on three key aspects: naming style conventions, grammatical composition, and the use of abbreviations and acronyms. Our findings reveal distinct characteristics of notebook method names, including a preference for conciseness and deviations from traditional naming patterns. We identified 68 unique grammatical patterns, with only 55.57% of methods beginning with a verb. Further analysis revealed that half of the methods with return statements do not start with a verb. We also found that 30.39% of method names contain abbreviations or acronyms, representing mathematical or statistical terms and image processing concepts, among others. We envision our findings contributing to developing specialized tools and techniques for evaluating and recommending high-quality names in scientific code and creating educational resources tailored to the notebook development community.
SoK: Enhancing Privacy-Preserving Software Development from a Developers' Perspective
Venue: arXiv cs.SE 2025
Year: 2025

In software development, privacy preservation has become essential with the rise of privacy concerns and regulations such as GDPR and CCPA. While several tools, guidelines, methods, methodologies, and frameworks have been proposed to support developers embedding privacy into software applications, most of them are proofs-of-concept without empirical evaluations, making their practical applicability uncertain. These solutions should be evaluated for different types of scenarios (e.g., industry settings such as rapid software development environments, teams with different privacy knowledge, etc.) to determine what their limitations are in various industry settings and what changes are required to refine current solutions before putting them into industry and developing new developer-supporting approaches. For that, a thorough review of empirically evaluated current solutions will be very effective. However, the existing secondary studies that examine the available developer support provide broad overviews but do not specifically analyze empirically evaluated solutions and their limitations. Therefore, this Systematic Literature Review (SLR) aims to identify and analyze empirically validated solutions that are designed to help developers in privacy-preserving software development. The findings will provide valuable insights for researchers to improve current privacy-preserving solutions and for practitioners looking for effective and validated solutions to embed privacy into software development.