OpenRaiser/Intern-Atlas huggingface dataset:
Robot localization aided by quantum algorithms
Venue: arXiv cs.RO 2025
Year: 2025

Localization is a critical aspect of mobile robotics, enabling robots to navigate their environment efficiently and avoid obstacles. Current probabilistic localization methods, such as the Adaptive-Monte Carlo localization (AMCL) algorithm, are computationally intensive and may struggle with large maps or high-resolution sensor data. This paper explores the application of quantum computing in robotics, focusing on the use of Grover's search algorithm to improve the efficiency of localization in mobile robots. We propose a novel approach to utilize Grover's algorithm in a 2D map, enabling faster and more efficient localization. Despite the limitations of current physical quantum computers, our experimental results demonstrate a significant speedup over classical methods, highlighting the potential of quantum computing to improve robotic localization. This work bridges the gap between quantum computing and robotics, providing a practical solution for robotic localization and paving the way for future research in quantum robotics.
Causes and Effects of Fitness Landscapes in System Test Generation: A Replication Study
Venue: arXiv cs.SE 2025
Year: 2025

Search-Based Software Testing (SBST) has seen several success stories in academia and industry. The effectiveness of a search algorithm at solving a software engineering problem strongly depends on how such algorithm can navigate the fitness landscape of the addressed problem. The fitness landscape depends on the used fitness function. Understanding the properties of a fitness landscape can help to provide insight on how a search algorithm behaves on it. Such insight can provide valuable information to researchers to being able to design novel, more effective search algorithms and fitness functions tailored for a specific problem. Due to its importance, few fitness landscape analyses have been carried out in the scientific literature of SBST. However, those have been focusing on the problem of unit test generation, e.g., with state-of-the-art tools such as EvoSuite. In this paper, we replicate one such existing study. However, in our work we focus on system test generation, with the state-of-the-art tool EvoMaster. Based on an empirical study involving the testing of 23 web services, this enables us to provide valuable insight into this important testing domain of practical industrial relevance.
The role of positional encodings in the ARC benchmark
Venue: arXiv cs.AI 2025
Year: 2025

The Abstraction and Reasoning Corpus challenges AI systems to perform abstract reasoning with minimal training data, a task intuitive for humans but demanding for machine learning models. Using CodeT5+ as a case study, we demonstrate how limitations in positional encoding hinder reasoning and impact performance. This work further examines the role of positional encoding across transformer architectures, highlighting its critical influence on models of varying sizes and configurations. Comparing several strategies, we find that while 2D positional encoding and Rotary Position Embedding offer competitive performance, 2D encoding excels in data-constrained scenarios, emphasizing its effectiveness for ARC tasks
Physics-Informed Neural Network based Damage Identification for Truss Railroad Bridges
Venue: arXiv cs.LG 2025
Year: 2025

Railroad bridges are a crucial component of the U.S. freight rail system, which moves over 40 percent of the nation's freight and plays a critical role in the economy. However, aging bridge infrastructure and increasing train traffic pose significant safety hazards and risk service disruptions. The U.S. rail network includes over 100,000 railroad bridges, averaging one every 1.4 miles of track, with steel bridges comprising over 50% of the network's total bridge length. Early identification and assessment of damage in these bridges remain challenging tasks. This study proposes a physics-informed neural network (PINN) based approach for damage identification in steel truss railroad bridges. The proposed approach employs an unsupervised learning approach, eliminating the need for large datasets typically required by supervised methods. The approach utilizes train wheel load data and bridge response during train crossing events as inputs for damage identification. The PINN model explicitly incorporates the governing differential equations of the linear time-varying (LTV) bridge-train system. Herein, this model employs a recurrent neural network (RNN) based architecture incorporating a custom Runge-Kutta (RK) integrator cell, designed for gradient-based learning. The proposed approach updates the bridge finite element model while also quantifying damage severity and localizing the affected structural members. A case study on the Calumet Bridge in Chicago, Illinois, with simulated damage scenarios, is used to demonstrate the model's effectiveness in identifying damage while maintaining low false-positive rates. Furthermore, the damage identification pipeline is designed to seamlessly integrate prior knowledge from inspections and drone surveys, also enabling context-aware updating and assessment of bridge's condition.
Reward-aware Preference Optimization: A Unified Mathematical Framework for Model Alignment
Venue: arXiv cs.LG 2025
Year: 2025

The rapid development of large language model (LLM) alignment algorithms has resulted in a complex and fragmented landscape, with limited clarity on the effectiveness of different methods and their inter-connections. This paper introduces Reward-Aware Preference Optimization (RPO), a mathematical framework that unifies popular preference optimization techniques in LLM alignment, including DPO, IPO, SimPO, and REINFORCE (LOO), among others. RPO provides a structured approach to disentangle and systematically study the impact of various design choices, such as the optimization objective, the number of responses per prompt, and the use of implicit versus explicit reward models, on LLM preference optimization. We additionally propose a new experimental setup that enables the clean and direct ablation of such design choices. Through an extensive series of ablation studies within the RPO framework, we gain insights into the critical factors shaping model alignment, offering practical guidance on the most effective strategies for improving LLM alignment.
A Taxonomy of Real-World Defeaters in Safety Assurance Cases
Venue: arXiv cs.SE 2025
Year: 2025

The rise of cyber-physical systems in safety-critical domains calls for robust risk-evaluation frameworks. Assurance cases, often required by regulatory bodies, are a structured approach to demonstrate that a system meets its safety requirements. However, assurance cases are fraught with challenges, such as incomplete evidence and gaps in reasoning, called defeaters, that can call into question the credibility and robustness of assurance cases. Identifying these defeaters increases confidence in the assurance case and can prevent catastrophic failures. The search for defeaters in an assurance case, however, is not structured, and there is a need to standardize defeater analysis. The software engineering community thus could benefit from having a reusable classification of real-world defeaters in software assurance cases. In this paper, we conducted a systematic study of literature from the past 20 years. Using open coding, we derived a taxonomy with seven broad categories, laying the groundwork for standardizing the analysis and management of defeaters in safety-critical systems. We provide our artifacts as open source for the community to use and build upon, thus establishing a common framework for understanding defeaters.
Measuring the Mental Health of Content Reviewers, a Systematic Review
Venue: arXiv cs.CY 2025
Year: 2025

Artificial intelligence and social computing rely on hundreds of thousands of content reviewers to classify high volumes of harmful and forbidden content. Many workers report long-term, potentially irreversible psychological harm. This work is similar to activities that cause psychological harm to other kinds of helping professionals even after small doses of exposure. Yet researchers struggle to measure the mental health of content reviewers well enough to inform diagnoses, evaluate workplace improvements, hold employers accountable, or advance scientific understanding. This systematic review summarizes psychological measures from other professions and relates them to the experiences of content reviewers. After identifying 1,673 potential papers, we reviewed 143 that validate measures in related occupations. We summarize the uses of psychological measurement for content reviewing, differences between clinical and research measures, and 12 measures that are adaptable to content reviewing. We find serious gaps in measurement validity in regions where content review labor is common. Overall, we argue for reliable measures of content reviewer mental health that match the nature of the work and are culturally-relevant.
RADx Data Hub: A Cloud Platform for FAIR, Harmonized COVID-19 Data
Venue: arXiv cs.DB 2025
Year: 2025

The COVID-19 pandemic highlighted the urgent need for robust systems to enable rapid data collection, integration, and analysis for public health responses. Existing approaches often relied on disparate, non-interoperable systems, creating bottlenecks in comprehensive analyses and timely decision-making. To address these challenges, the U.S. National Institutes of Health (NIH) launched the Rapid Acceleration of Diagnostics (RADx) initiative in 2020, with the RADx Data Hub, a centralized repository for de-identified and curated COVID-19 data, as its cornerstone. The RADx Data Hub hosts diverse study data, including clinical data, testing results, smart sensor outputs, self-reported symptoms, and information on social determinants of health. Built on cloud infrastructure, the RADx Data Hub integrates metadata standards, interoperable formats, and ontology-based tools to adhere to the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data sharing. Initially developed for COVID-19 research, its architecture and processes are adaptable to other scientific disciplines. This paper provides an overview of the data hosted by the RADx Data Hub and describes the platform's capabilities and architecture.
Simultaneous Estimation of Manipulation Skill and Hand Grasp Force from Forearm Ultrasound Images
Venue: arXiv cs.RO 2025
Year: 2025

Accurate estimation of human hand configuration and the forces they exert is critical for effective teleoperation and skill transfer in robotic manipulation. A deeper understanding of human interactions with objects can further enhance teleoperation performance. To address this need, researchers have explored methods to capture and translate human manipulation skills and applied forces to robotic systems. Among these, biosignal-based approaches, particularly those using forearm ultrasound data, have shown significant potential for estimating hand movements and finger forces. In this study, we present a method for simultaneously estimating manipulation skills and applied hand force using forearm ultrasound data. Data collected from seven participants were used to train deep learning models for classifying manipulation skills and estimating grasp force. Our models achieved an average classification accuracy of 94.87 percent plus or minus 10.16 percent for manipulation skills and an average root mean square error (RMSE) of 0.51 plus or minus 0.19 Newtons for force estimation, as evaluated using five-fold cross-validation. These results highlight the effectiveness of forearm ultrasound in advancing human-machine interfacing and robotic teleoperation for complex manipulation tasks. This work enables new and effective possibilities for human-robot skill transfer and tele-manipulation, bridging the gap between human dexterity and robotic control.
SigWavNet: Learning Multiresolution Signal Wavelet Network for Speech Emotion Recognition
Venue: arXiv cs.SD 2025
Year: 2025

In the field of human-computer interaction and psychological assessment, speech emotion recognition (SER) plays an important role in deciphering emotional states from speech signals. Despite advancements, challenges persist due to system complexity, feature distinctiveness issues, and noise interference. This paper introduces a new end-to-end (E2E) deep learning multi-resolution framework for SER, addressing these limitations by extracting meaningful representations directly from raw waveform speech signals. By leveraging the properties of the fast discrete wavelet transform (FDWT), including the cascade algorithm, conjugate quadrature filter, and coefficient denoising, our approach introduces a learnable model for both wavelet bases and denoising through deep learning techniques. The framework incorporates an activation function for learnable asymmetric hard thresholding of wavelet coefficients. Our approach exploits the capabilities of wavelets for effective localization in both time and frequency domains. We then combine one-dimensional dilated convolutional neural networks (1D dilated CNN) with a spatial attention layer and bidirectional gated recurrent units (Bi-GRU) with a temporal attention layer to efficiently capture the nuanced spatial and temporal characteristics of emotional features. By handling variable-length speech without segmentation and eliminating the need for pre or post-processing, the proposed model outperformed state-of-the-art methods on IEMOCAP and EMO-DB datasets. The source code of this paper is shared on the Github repository: https://github.com/alaaNfissi/SigWavNet-Learning-Multiresolution-Signal-Wavelet-Network-for-Speech-Emotion-Recognition.
Physics-Inspired Distributed Radio Map Estimation
Venue: arXiv cs.LG 2025
Year: 2025

To gain panoramic awareness of spectrum coverage in complex wireless environments, data-driven learning approaches have recently been introduced for radio map estimation (RME). While existing deep learning based methods conduct RME given spectrum measurements gathered from dispersed sensors in the region of interest, they rely on centralized data at a fusion center, which however raises critical concerns on data privacy leakages and high communication overloads. Federated learning (FL) enhance data security and communication efficiency in RME by allowing multiple clients to collaborate in model training without directly sharing local data. However, the performance of the FL-based RME can be hindered by the problem of task heterogeneity across clients due to their unavailable or inaccurate landscaping information. To fill this gap, in this paper, we propose a physics-inspired distributed RME solution in the absence of landscaping information. The main idea is to develop a novel distributed RME framework empowered by leveraging the domain knowledge of radio propagation models, and by designing a new distributed learning approach that splits the entire RME model into two modules. A global autoencoder module is shared among clients to capture the common pathloss influence on radio propagation pattern, while a client-specific autoencoder module focuses on learning the individual features produced by local shadowing effects from the unique building distributions in local environment. Simulation results show that our proposed method outperforms the benchmarks in achieving higher performance.
A Novel Approach to Translate Structural Aggregation Queries to MapReduce Code
Venue: arXiv cs.DB 2025
Year: 2025

Data management applications are growing and require more attention, especially in the "big data" era. Thus, supporting such applications with novel and efficient algorithms that achieve higher performance is critical. Array database management systems are one way to support these applications by dealing with data represented in n-dimensional data structures. For instance, software like SciDB and RasDaMan can be powerful tools to achieve the required performance on large-scale problems with multidimensional data. Like their relational counterparts, these management systems support specific array query languages as the user interface. As a popular programming model, MapReduce allows large-scale data analysis, facilitates query processing, and is used as a DB engine. Nevertheless, one major obstacle is the low productivity of developing MapReduce applications. Unlike high-level declarative languages such as SQL, MapReduce jobs are written in a low-level descriptive language, often requiring massive programming efforts and complicated debugging processes. This work presents a system that supports translating array queries expressed in the Array Query Language (AQL) in SciDB into MapReduce jobs. We focus on translating some unique structural aggregations, including circular, grid, hierarchical, and sliding aggregations. Unlike traditional aggregations in relational DBs, these structural aggregations are designed explicitly for array manipulation. Thus, our work can be considered an array-view counterpart of existing SQL to MapReduce translators like HiveQL and YSmart. Our translator supports structural aggregations over arrays to meet various array manipulations. The translator can also help user-defined aggregation functions with minimal user effort. We show that our translator can generate optimized MapReduce code, which performs better than the short handwritten code by up to 10.84x.
Sampling in High-Dimensions using Stochastic Interpolants and Forward-Backward Stochastic Differential Equations
Venue: arXiv cs.LG 2025
Year: 2025

We present a class of diffusion-based algorithms to draw samples from high-dimensional probability distributions given their unnormalized densities. Ideally, our methods can transport samples from a Gaussian distribution to a specified target distribution in finite time. Our approach relies on the stochastic interpolants framework to define a time-indexed collection of probability densities that bridge a Gaussian distribution to the target distribution. Subsequently, we derive a diffusion process that obeys the aforementioned probability density at each time instant. Obtaining such a diffusion process involves solving certain Hamilton-Jacobi-Bellman PDEs. We solve these PDEs using the theory of forward-backward stochastic differential equations (FBSDE) together with machine learning-based methods. Through numerical experiments, we demonstrate that our algorithm can effectively draw samples from distributions that conventional methods struggle to handle.
When End-to-End is Overkill: Rethinking Cascaded Speech-to-Text Translation
Venue: arXiv cs.CL 2025
Year: 2025

Though end-to-end speech-to-text translation has been a great success, we argue that the cascaded speech-to-text translation model still has its place, which is usually criticized for the error propagation between automatic speech recognition (ASR) and machine translation (MT) models. In this paper, we explore the benefits of incorporating multiple candidates from ASR and self-supervised speech features into MT. Our analysis reveals that the primary cause of cascading errors stems from the increased divergence between similar samples in the speech domain when mapped to the text domain. By including multiple candidates and self-supervised speech features, our approach allows the machine translation model to choose the right words and ensure precise translation using various speech samples. This strategy minimizes error spread and takes advantage of large ASR and MT datasets, along with pre-trained ASR/MT models, while addressing associated issues.
Towards a Supporting Framework for Neuro-Developmental Disorder: Considering Artificial Intelligence, Serious Games and Eye Tracking
Venue: arXiv cs.HC 2025
Year: 2025

This paper focuses on developing a framework for uncovering insights about NDD children's performance (e.g., raw gaze cluster analysis, duration analysis \& area of interest for sustained attention, stimuli expectancy, loss of focus/motivation, inhibitory control) and informing their teachers. The hypothesis behind this work is that self-adaptation of games can contribute to improving students' well-being and performance by suggesting personalized activities (e.g., highlighting stimuli to increase attention or choosing a difficulty level that matches students' abilities). The aim is to examine how AI can be used to help solve this problem. The results would not only contribute to a better understanding of the problems of NDD children and their teachers but also help psychologists to validate the results against their clinical knowledge, improve communication with patients and identify areas for further investigation, e.g., by explaining the decision made and preserving the children's private data in the learning process.
Efficient Adaptive Label Refinement for Label Noise Learning
Venue: arXiv cs.CV 2025
Year: 2025

Deep neural networks are highly susceptible to overfitting noisy labels, which leads to degraded performance. Existing methods address this issue by employing manually defined criteria, aiming to achieve optimal partitioning in each iteration to avoid fitting noisy labels while thoroughly learning clean samples. However, this often results in overly complex and difficult-to-train models. To address this issue, we decouple the tasks of avoiding fitting incorrect labels and thoroughly learning clean samples and propose a simple yet highly applicable method called Adaptive Label Refinement (ALR). First, inspired by label refurbishment techniques, we update the original hard labels to soft labels using the model's predictions to reduce the risk of fitting incorrect labels. Then, by introducing the entropy loss, we gradually `harden' the high-confidence soft labels, guiding the model to better learn from clean samples. This approach is simple and efficient, requiring no prior knowledge of noise or auxiliary datasets, making it more accessible compared to existing methods. We validate ALR's effectiveness through experiments on benchmark datasets with artificial label noise (CIFAR-10/100) and real-world datasets with inherent noise (ANIMAL-10N, Clothing1M, WebVision). The results show that ALR outperforms state-of-the-art methods.
Dexterous Cable Manipulation: Taxonomy, Multi-Fingered Hand Design, and Long-Horizon Manipulation
Venue: arXiv cs.RO 2025
Year: 2025

Existing research that addressed cable manipulation relied on two-fingered grippers, which make it difficult to perform similar cable manipulation tasks that humans perform. However, unlike dexterous manipulation of rigid objects, the development of dexterous cable manipulation skills in robotics remains underexplored due to the unique challenges posed by a cable's deformability and inherent uncertainty. In addition, using a dexterous hand introduces specific difficulties in tasks, such as cable grasping, pulling, and in-hand bending, for which no dedicated task definitions, benchmarks, or evaluation metrics exist. Furthermore, we observed that most existing dexterous hands are designed with structures identical to humans', typically featuring only one thumb, which often limits their effectiveness during dexterous cable manipulation. Lastly, existing non-task-specific methods did not have enough generalization ability to solve these cable manipulation tasks or are unsuitable due to the designed hardware. We have three contributions in real-world dexterous cable manipulation in the following steps: (1) We first defined and organized a set of dexterous cable manipulation tasks into a comprehensive taxonomy, covering most short-horizon action primitives and long-horizon tasks for one-handed cable manipulation. This taxonomy revealed that coordination between the thumb and the index finger is critical for cable manipulation, which decomposes long-horizon tasks into simpler primitives. (2) We designed a novel five-fingered hand with 25 degrees of freedom (DoF), featuring two symmetric thumb-index configurations and a rotatable joint on each fingertip, which enables dexterous cable manipulation. (3) We developed a demonstration collection pipeline for this non-anthropomorphic hand, which is difficult to operate by previous motion capture methods.