Tootfinder

MeloKids: Multisensory VR System to Enhance Speech and Motor Coordination in Children with Hearing Loss
Yichen Yu, Qiaoran Wang
https://arxiv.org/abs/2507.18619 https://

MeloKids: Multisensory VR System to Enhance Speech and Motor Coordination in Children with Hearing Loss
Children with hearing impairments face ongoing challenges in language and motor development. This study explores how multi-sensory feedback technology based on virtual reality (VR), integrating auditory, visual, and tactile stimuli, can enhance rehabilitation outcomes. Using functional near-infrared spectroscopy (fNIRS) technology, we assessed cortical activation patterns in children during pitch-matching tasks across different interaction modes. Our findings aim to provide evidence for designi…

Implementing Zero Trust Architecture to Enhance Security and Resilience in the Pharmaceutical Supply Chain
Saeid Ghasemshirazi, Ghazaleh Shirvani, Marziye Ranjbar Tavakoli, Bahar Ghaedi, Mohammad Amin Langarizadeh
https://arxiv.org/abs/2508.15776

Implementing Zero Trust Architecture to Enhance Security and Resilience in the Pharmaceutical Supply Chain
The pharmaceutical supply chain faces escalating cybersecurity challenges threatening patient safety and operational continuity. This paper examines the transformative potential of zero trust architecture for enhancing security and resilience within this critical ecosystem. We explore the challenges posed by data breaches, counterfeiting, and disruptions and introduce the principles of continuous verification, least-privilege access, and data-centric security inherent in zero trust. Real-world …

A Modular Residual Learning Framework to Enhance Model-Based Approach for Robust Locomotion
Min-Gyu Kim, Dongyun Kang, Hajun Kim, Hae-Won Park
https://arxiv.org/abs/2507.18138 h…

A Modular Residual Learning Framework to Enhance Model-Based Approach for Robust Locomotion
This paper presents a novel approach that combines the advantages of both model-based and learning-based frameworks to achieve robust locomotion. The residual modules are integrated with each corresponding part of the model-based framework, a footstep planner and dynamic model designed using heuristics, to complement performance degradation caused by a model mismatch. By utilizing a modular structure and selecting the appropriate learning-based method for each residual module, our framework dem…

Healthy European peatlands require specific temperature and water level parameters #Europe

Healthy European peatlands require specific temperature and water level parameters
Efforts to enhance peat accumulation in European peatlands, which contain around half of Europe's soil carbon, will require a combination of warm temperatures and a specific depth of the water table, according to a study published in PLOS One by Graeme Swindles of Queen's University Belfast, U.K. and colleagues.

Micro-variations in timing and loudness affect music-evoked mental imagery https://www.nature.com/articles/s41598-025-12604-4 "repetitive quasi-isochronous drumming enhanced mental imagery vividness, with a stronger effect observed when the drumming contained random micro-variat…

Micro-variations in timing and loudness affect music-evoked mental imagery - Scientific Reports
Music can shape the vividness, sentiment, and content of directed mental imagery. Yet, the role of specific musical features in these effects remains elusive. One important aspect of human musical performances is the presence of micro-variations—small deviations in timbre, pitch, and timing, driven by motor and attentional processes. These variations enhance perceived “naturalness” compared to mechanical playing without such variations. Here, we investigated whether random micro-variation…

Synthesizing Artifact Dataset for Pixel-level Detection
Dennis Menn, Feng Liang, Diana Marculescu
https://arxiv.org/abs/2509.19589 https://arxiv.org/pdf/25…

Synthesizing Artifact Dataset for Pixel-level Detection
Artifact detectors have been shown to enhance the performance of image-generative models by serving as reward models during fine-tuning. These detectors enable the generative model to improve overall output fidelity and aesthetics. However, training the artifact detector requires expensive pixel-level human annotations that specify the artifact regions. The lack of annotated data limits the performance of the artifact detector. A naive pseudo-labeling approach-training a weak detector and using…

SMARTAPS: Tool-augmented LLMs for Operations Management
Timothy Tin Long Yu, Mahdi Mostajabdaveh, Jabo Serge Byusa, Rindra Ramamonjison, Giuseppe Carenini, Kun Mao, Zirui Zhou, Yong Zhang
https://arxiv.org/abs/2507.17927

SMARTAPS: Tool-augmented LLMs for Operations Management
Large language models (LLMs) present intriguing opportunities to enhance user interaction with traditional algorithms and tools in real-world applications. An advanced planning system (APS) is a sophisticated software that leverages optimization to help operations planners create, interpret, and modify an operational plan. While highly beneficial, many customers are priced out of using an APS due to the ongoing costs of consultants responsible for customization and maintenance. To address the n…

MuST2-Learn: Multi-view Spatial-Temporal-Type Learning for Heterogeneous Municipal Service Time Estimation
Nadia Asif, Zhiqing Hong, Shaogang Ren, Xiaonan Zhang, Xiaojun Shang, Yukun Yuan
https://arxiv.org/abs/2508.16503

MuST2-Learn: Multi-view Spatial-Temporal-Type Learning for Heterogeneous Municipal Service Time Estimation
Non-emergency municipal services such as city 311 systems have been widely implemented across cities in Canada and the United States to enhance residents' quality of life. These systems enable residents to report issues, e.g., noise complaints, missed garbage collection, and potholes, via phone calls, mobile applications, or webpages. However, residents are often given limited information about when their service requests will be addressed, which can reduce transparency, lower resident satisfac…

Extractive Fact Decomposition for Interpretable Natural Language Inference in one Forward Pass
Nicholas Popovi\v{c}, Michael F\"arber
https://arxiv.org/abs/2509.18901 https…

Assertion Messages with Large Language Models (LLMs) for Code
Ahmed Aljohani, Anamul Haque Mollah, Hyunsook Do
https://arxiv.org/abs/2509.19673 https://arx…

Assertion Messages with Large Language Models (LLMs) for Code
Assertion messages significantly enhance unit tests by clearly explaining the reasons behind test failures, yet they are frequently omitted by developers and automated test-generation tools. Despite recent advancements, Large Language Models (LLMs) have not been systematically evaluated for their ability to generate informative assertion messages. In this paper, we introduce an evaluation of four state-of-the-art Fill-in-the-Middle (FIM) LLMs - Qwen2.5-Coder-32B, Codestral-22B, CodeLlama-13B, a…

Above 99.9% Fidelity Single-Qubit Gates, Two-Qubit Gates, and Readout in a Single Superconducting Quantum Device
Fabian Marxer, Jakub Mro\.zek, Joona Andersson, Leonid Abdurakhimov, Janos Adam, Ville Bergholm, Rohit Beriwal, Chun Fai Chan, Saga Dahl, Soumya Ranjan Das, Frank Deppe, Olexiy Fedorets, Zheming Gao, Alejandro Gomez Frieiro, Daria Gusenkova, Andrew Guthrie, Tuukka Hiltunen, Hao Hsu, Eric Hyypp\"a, Joni Ikonen, Sinan Inel, Shan W. Jolin, Azad Karis, Seung-Goo Kim, Willia…

Above 99.9% Fidelity Single-Qubit Gates, Two-Qubit Gates, and Readout in a Single Superconducting Quantum Device
Achieving high-fidelity single-qubit gates, two-qubit gates, and qubit readout is critical for building scalable, error-corrected quantum computers. However, device parameters that enhance one operation often degrade the others, making simultaneous optimization challenging. Here, we demonstrate that careful tuning of qubit-coupler coupling strengths in a superconducting circuit with two transmon qubits coupled via a tunable coupler enables high-fidelity single- and two-qubit gates, without comp…

C-Koordinator: Interference-aware Management for Large-scale and Co-located Microservice Clusters
Shengye Song, Minxian Xu, Zuowei Zhang, Chengxi Gao, Fansong Zeng, Yu Ding, Kejiang Ye, Chengzhong Xu
https://arxiv.org/abs/2507.18005

C-Koordinator: Interference-aware Management for Large-scale and Co-located Microservice Clusters
Microservices transform traditional monolithic applications into lightweight, loosely coupled application components and have been widely adopted in many enterprises. Cloud platform infrastructure providers enhance the resource utilization efficiency of microservices systems by co-locating different microservices. However, this approach also introduces resource competition and interference among microservices. Designing interference-aware strategies for large-scale, co-located microservice clus…

Action-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes
Milad Taghipour, Bane Vasic
https://arxiv.org/abs/2507.17893 https://arx…

Action-List Reinforcement Learning Syndrome Decoding for Binary Linear Block Codes
This paper explores the application of reinforcement learning techniques to enhance the performance of decoding of linear block codes based on flipping bits and finding optimal decisions. We describe the methodology for mapping the iterative decoding process into Markov Decision Processes (MDPs) and propose different methods to reduce the number of states in the MDP. A truncated MDP is proposed to reduce the number of states in the MDP by learning a Hamming ball with a specified radius around c…

U-Net Based Healthy 3D Brain Tissue Inpainting
Juexin Zhang, Ying Weng, Ke Chen
https://arxiv.org/abs/2507.18126 https://arxiv.org/pdf/2507.18126

U-Net Based Healthy 3D Brain Tissue Inpainting
This paper introduces a novel approach to synthesize healthy 3D brain tissue from masked input images, specifically focusing on the task of 'ASNR-MICCAI BraTS Local Synthesis of Tissue via Inpainting'. Our proposed method employs a U-Net-based architecture, which is designed to effectively reconstruct the missing or corrupted regions of brain MRI scans. To enhance our model's generalization capabilities and robustness, we implement a comprehensive data augmentation strategy that involves random…

Improving Disease Risk Estimation in Small Areas by Accounting for Spatiotemporal Local Discontinuities
Santaf\'e, G., Adin, A., Ugarte, M. L
https://arxiv.org/abs/2509.19889

Improving Disease Risk Estimation in Small Areas by Accounting for Spatiotemporal Local Discontinuities
This work proposes a two-step method to enhance disease risk estimation in small areas by integrating spatiotemporal cluster detection within a Bayesian hierarchical spatiotemporal model. First, we introduce an efficient scan-statistic-based clustering algorithm that employs a greedy search within the scan window, enabling flexible cluster detection across large spatial domains. We then integrate these detected clusters into a Bayesian spatiotemporal model to estimate relative risks, explicitly…

A Secure Affine Frequency Division Multiplexing for Wireless Communication Systems
Ping Wang, Zulin Wang, Yuanfang Ma, Xiaosi Tian, Yuanhan Ni
https://arxiv.org/abs/2509.18555 h…

A Secure Affine Frequency Division Multiplexing for Wireless Communication Systems
This paper introduces a secure affine frequency division multiplexing (SE-AFDM) for wireless communication systems to enhance communication security. Besides configuring the parameter c1 to obtain communication reliability under doubly selective channels, we also utilize the time-varying parameter c2 to improve the security of the communications system. The derived input-output relation shows that the legitimate receiver can eliminate the nonlinear impact introduced by the time-varying c2 witho…

Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies
Qizhao Chen
https://arxiv.org/abs/2508.16378 https://arxiv.org/pdf/2508.16378

Sentiment-Aware Mean-Variance Portfolio Optimization for Cryptocurrencies
This paper presents a dynamic cryptocurrency portfolio optimization strategy that integrates technical indicators and sentiment analysis to enhance investment decision-making. The proposed method employs the 14-day Relative Strength Index (RSI) and 14-day Simple Moving Average (SMA) to capture market momentum, while sentiment scores are extracted from news articles using the VADER (Valence Aware Dictionary and sEntiment Reasoner) model, with compound scores quantifying overall market tone. The …

Safe Reinforcement Learning-based Automatic Generation Control
Amr S. Mohamed, Emily Nguyen, Deepa Kundur
https://arxiv.org/abs/2507.17868 https://arxiv.or…

Safe Reinforcement Learning-based Automatic Generation Control
Amidst the growing demand for implementing advanced control and decision-making algorithms|to enhance the reliability, resilience, and stability of power systems|arises a crucial concern regarding the safety of employing machine learning techniques. While these methods can be applied to derive more optimal control decisions, they often lack safety assurances. This paper proposes a framework based on control barrier functions to facilitate safe learning and deployment of reinforcement …

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge
Hongfei Xue, Kaixun Huang, Zhikai Zhou, Shen Huang, Shidong Shang
https://arxiv.org/abs/2507.18051

The TEA-ASLP System for Multilingual Conversational Speech Recognition and Speech Diarization in MLC-SLM 2025 Challenge
This paper presents the TEA-ASLP's system submitted to the MLC-SLM 2025 Challenge, addressing multilingual conversational automatic speech recognition (ASR) in Task I and speech diarization ASR in Task II. For Task I, we enhance Ideal-LLM model by integrating known language identification and a multilingual MOE LoRA structure, along with using CTC-predicted tokens as prompts to improve autoregressive generation. The model is trained on approximately 180k hours of multilingual ASR data. In Task …

"Anyone can adapt this model to explore the potential impact of religion, spirituality and health, including devotional practices."
—Benjamin Doolittle '94 M.Div., '97 M.D., Director of the Yale Program for Medicine, Spirituality, and Religion, speaking about a new research model to enhance investigation conducted at the intersection of religion and medicine

Effects of galactic environment on accretion dynamics onto a rotating centrally located black hole and on emergent analogue gravity
Ripon Sk, Sangita Chatterjee, Sankhasubhra Nag
https://arxiv.org/abs/2509.18833

A Divergence-free Preserving Mixed Finite Element Method for Thermally Driven Active Fluid Model
Nan Zheng, Qingguang Guan, Wenlong Pei, Wenju Zhao
https://arxiv.org/abs/2509.19053

Phase Stability and Superconductivity in Hydrogenated and Lithiated Janus GaXS2 (X = Ga, In) Monolayers
Jakkapat Seeyangnok, Udomsilp Pinsook
https://arxiv.org/abs/2509.19922 ht…

Phase Stability and Superconductivity in Hydrogenated and Lithiated Janus GaXS2 (X = Ga, In) Monolayers
Hydrogen and lithium functionalization of two-dimensional (2D) materials offers a promising route to enhance electronic properties and induce superconductivity. Here, we employ first-principles calculations to explore the phase stability and superconducting behavior of hydrogenated and lithiated Janus GaXS2 (X = Ga, In) monolayers. Among Ga2SH, Ga2SLi, GaInSH, and GaInSLi, only the 2H-GaInSLi structure is dynamically, thermally, and mechanically stable, as confirmed by phonon dispersion, ab ini…

Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation
Tony Chahoud, Lorenzo Mario Amorosa, Riccardo Marini, Luca De Nardis
https://arxiv.org/abs/2509.19405

Improving Outdoor Multi-cell Fingerprinting-based Positioning via Mobile Data Augmentation
Accurate outdoor positioning in cellular networks is hindered by sparse, heterogeneous measurement collections and the high cost of exhaustive site surveys. This paper introduces a lightweight, modular mobile data augmentation framework designed to enhance multi-cell fingerprinting-based positioning using operator-collected minimization of drive test (MDT) records. The proposed approach decouples spatial and radio-feature synthesis: kernel density estimation (KDE) models the empirical spatial d…

Molecular gas in a system of two interacting galaxies overlapping on the line-of-sight
Ana\"elle Halle, Barbara Mazzilli Ciraulo, Daniel Maschmann, Anne-Laure Melchior, Fran\c{c}oise Combes
https://arxiv.org/abs/2507.18355

Molecular gas in a system of two interacting galaxies overlapping on the line-of-sight
Galaxy interactions can disturb gas in galactic discs, compress it, excite it, and enhance star formation. An intriguing system likely consisting of two interacting galaxies overlapping on the line-of-sight was previously studied through ionised gas observations from the integral-field spectrograph Mapping Nearby Galaxies at APO (MaNGA). A decomposition into two components using MaNGA spectra, together with a multi-wavelength study, allowed to characterise the system as a minor-merger with inte…

The archetypical "Enhance!" trope is of course from Blade Runner (1982), which is only 7 years later.

StrCGAN: A Generative Framework for Stellar Image Restoration
Shantanusinh Parmar
https://arxiv.org/abs/2509.19805 https://arxiv.org/pdf/2509.19805

StrCGAN: A Generative Framework for Stellar Image Restoration
We introduce StrCGAN (Stellar Cyclic GAN), a generative model designed to enhance low-resolution astrophotography images. Our goal is to reconstruct high-fidelity ground truth-like representations of celestial objects, a task that is challenging due to the limited resolution and quality of small-telescope observations such as the MobilTelesco dataset. Traditional models such as CycleGAN provide a foundation for image-to-image translation but are restricted to 2D mappings and often distort the m…

Climate-Adaptive and Cascade-Constrained Machine Learning Prediction for Sea Surface Height under Greenhouse Warming
Tianmu Zheng, Ru Chen, Xin Su, Gang Huang, Bingzheng Yan
https://arxiv.org/abs/2509.18741

MultiSoundGen: Video-to-Audio Generation for Multi-Event Scenarios via SlowFast Contrastive Audio-Visual Pretraining and Direct Preference Optimization
Jianxuan Yang, Xiaoran Yang, Lipan Zhang, Xinyue Guo, Zhao Wang, Gongping Huang
https://arxiv.org/abs/2509.19999

MultiSoundGen: Video-to-Audio Generation for Multi-Event Scenarios via SlowFast Contrastive Audio-Visual Pretraining and Direct Preference Optimization
Current video-to-audio (V2A) methods struggle in complex multi-event scenarios (video scenarios involving multiple sound sources, sound events, or transitions) due to two critical limitations. First, existing methods face challenges in precisely aligning intricate semantic information together with rapid dynamic features. Second, foundational training lacks quantitative preference optimization for semantic-temporal alignment and audio quality. As a result, it fails to enhance integrated generat…

Hey #dotnet folks and #security wonks, join our #livestream today to learn about FAPI 2.0 and how to enhance security at your organization with the latest specification.
Also, drop in and say h…

Microsoft and the NFL extend their partnership to bring real-time game data and analysis to coaches and players using Microsoft Copilot and Azure AI (Ali McCadden/CNBC)
https://www.cnbc.com/2025/08/20/microsoft-nfl-ai-analysis.html

Microsoft and NFL announce multiyear partnership to use AI to enhance game day analysis
Microsoft and the NFL announced a multiyear extension of their partnership to bring real-time analysis to the sidelines using Microsoft Copilot and Azure AI.

Retrieval Enhanced Feedback via In-context Neural Error-book
Jongyeop Hyun, Bumsoo Kim
https://arxiv.org/abs/2508.16313 https://arxiv.org/pdf/2508.16313

Retrieval Enhanced Feedback via In-context Neural Error-book
Recent advancements in Large Language Models (LLMs) have significantly improved reasoning capabilities, with in-context learning (ICL) emerging as a key technique for adaptation without retraining. While previous works have focused on leveraging correct examples, recent research highlights the importance of learning from errors to enhance performance. However, existing methods lack a structured framework for analyzing and mitigating errors, particularly in Multimodal Large Language Models (MLLM…

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Junyu Lu, Songxin Zhang, Zejian Xie, Zhuoyang Song, Jiaxing Zhang
https://arxiv.org/abs/2509.17917 https://…

Orcust: Stepwise-Feedback Reinforcement Learning for GUI Agent
Recent advances in GUI agents have achieved remarkable grounding and action-prediction performance, yet existing models struggle with unreliable reward signals and limited online trajectory generation. In this paper, we introduce Orcust, a framework that integrates Principle-Constrained Reward Modeling (PCRM) and Online VM-Grounded Trajectory Construction (OVTC) to enhance reasoning reliability and data efficiency in interactive GUI tasks. We leverages environment-verifiable and LLM-derived pri…

A Biomimetic Vertebraic Soft Robotic Tail for High-Speed, High-Force Dynamic Maneuvering
Sicong Liu, Jianhui Liu, Fang Chen, Wenjian Yang, Juan Yi, Yu Zheng, Zheng Wang, Wanchao Chi, Chaoyang Song
https://arxiv.org/abs/2509.20219

A Biomimetic Vertebraic Soft Robotic Tail for High-Speed, High-Force Dynamic Maneuvering
Robotic tails can enhance the stability and maneuverability of mobile robots, but current designs face a trade-off between the power of rigid systems and the safety of soft ones. Rigid tails generate large inertial effects but pose risks in unstructured environments, while soft tails lack sufficient speed and force. We present a Biomimetic Vertebraic Soft Robotic (BVSR) tail that resolves this challenge through a compliant pneumatic body reinforced by a passively jointed vertebral column inspir…

SIM-CoT: Supervised Implicit Chain-of-Thought
Xilin Wei, Xiaoran Liu, Yuhang Zang, Xiaoyi Dong, Yuhang Cao, Jiaqi Wang, Xipeng Qiu, Dahua Lin
https://arxiv.org/abs/2509.20317 ht…

SIM-CoT: Supervised Implicit Chain-of-Thought
Implicit Chain-of-Thought (CoT) methods present a promising, token-efficient alternative to explicit CoT reasoning in Large Language Models (LLMs), but a persistent performance gap has limited the application of implicit CoT. We identify a core latent instability issue by scaling the computational budget of implicit CoT approaches: as we increase the number of implicit reasoning tokens to enhance performance, the training process often becomes unstable and collapses. Our analysis reveals that t…

Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
Yinghan Zhou, Juan Wen, Wanli Peng, Zhengxian Wu, Ziwei Zhang, Yiming Xue
https://arxiv.org/abs/2508.15848

Self-Disguise Attack: Induce the LLM to disguise itself for AIGT detection evasion
AI-generated text (AIGT) detection evasion aims to reduce the detection probability of AIGT, helping to identify weaknesses in detectors and enhance their effectiveness and reliability in practical applications. Although existing evasion methods perform well, they suffer from high computational costs and text quality degradation. To address these challenges, we propose Self-Disguise Attack (SDA), a novel approach that enables Large Language Models (LLM) to actively disguise its output, reducing…

Efficient and optimal quantum state discrimination via quantum belief propagation
Christophe Piveteau, Joseph M. Renes
https://arxiv.org/abs/2509.19441 https://

Efficient and optimal quantum state discrimination via quantum belief propagation
We present an efficient quantum algorithm for a structured state discrimination problem we call the subspace decoding task. Building on this, we show that the algorithm enables efficient and optimal decoding of certain families of structured classical linear codes transmitted over binary-input classical-quantum pure-state channels. Such decoders can substantially enhance the performance of quantum algorithms based on Regev's reduction, such as decoded quantum interferometry. In particular, we o…

Factors Impacting Faculty Adoption of Project-Based Learning in Computing Education: a Survey
Ahmad D. Suleiman, Yiming Tang, Daqing Hou
https://arxiv.org/abs/2507.18039 https:/…

Factors Impacting Faculty Adoption of Project-Based Learning in Computing Education: a Survey
This research full paper investigates the factors influencing computing educators' adoption of project-based learning (PjBL) in software engineering and computing curricula. Recognized as a student-centered pedagogical approach, PjBL has the potential to enhance student motivation, engagement, critical thinking, collaboration, and problem-solving skills. Despite these benefits, faculty adoption remains inconsistent due to challenges such as insufficient institutional support, time constraints, …

SCORE: Scaling audio generation using Standardized COmposite REwards
Jaemin Jung, Jaehun Kim, Inkyu Shin, Joon Son Chung
https://arxiv.org/abs/2509.19831 https://

SCORE: Scaling audio generation using Standardized COmposite REwards
The goal of this paper is to enhance Text-to-Audio generation at inference, focusing on generating realistic audio that precisely aligns with text prompts. Despite the rapid advancements, existing models often fail to achieve a reliable balance between perceptual quality and textual alignment. To address this, we adopt Inference-Time Scaling, a training-free method that improves performance by increasing inference computation. We establish its unexplored application to audio generation and prop…

Comparative Analysis of STEM and non-STEM Teachers' Needs for Integrating AI into Educational Environments
Bahare Riahi, Veronica Catete
https://arxiv.org/abs/2509.16276 htt…

Comparative Analysis of STEM and non-STEM Teachers' Needs for Integrating AI into Educational Environments
There is an increasing imperative to integrate programming platforms within AI frameworks to enhance educational tasks for both teachers and students. However, commonly used platforms such as Code.org, Scratch, and Snap fall short of providing the desired AI features and lack adaptability for interdisciplinary applications. This study explores how educational platforms can be improved by incorporating AI and analytics features to create more effective learning environments across various subjec…

Multimodal Behavioral Patterns Analysis with Eye-Tracking and LLM-Based Reasoning
Dongyang Guo, Yasmeen Abdrabou, Enkeleda Thaqi, Enkelejda Kasneci
https://arxiv.org/abs/2507.18252

Multimodal Behavioral Patterns Analysis with Eye-Tracking and LLM-Based Reasoning
Eye-tracking data reveals valuable insights into users' cognitive states but is difficult to analyze due to its structured, non-linguistic nature. While large language models (LLMs) excel at reasoning over text, they struggle with temporal and numerical data. This paper presents a multimodal human-AI collaborative framework designed to enhance cognitive pattern extraction from eye-tracking signals. The framework includes: (1) a multi-stage pipeline using horizontal and vertical segmentation alo…

Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
Bruno Marques da Silva, Larissa Ferreira Rodrigues Moreira, Fl\'avio de Oliveira Silva, Rodrigo Moreira
https://arxiv.org/abs/2507.17843

Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
The latest generation of games and pervasive communication technologies poses challenges in service management and Service-Level Agreement compliance for mobile users. State-of-the-art edge-gaming techniques enhance throughput, reduce latency, and leverage cloud computing. However, further development of core functions such as the User Plane Function (UPF) is needed for non-intrusive user latency measurement. This paper proposes a closed-loop architecture integrating the Network Data Analytics …

Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation
Yuxiao Yi, Qingyao Zhuang, Zhi-Qin John Xu
https://arxiv.org/abs/2509.19353 https://

Frequency-Aware Ensemble Learning for BraTS 2025 Pediatric Brain Tumor Segmentation
Pediatric brain tumor segmentation presents unique challenges due to the rarity and heterogeneity of these malignancies, yet remains critical for clinical diagnosis and treatment planning. We propose an ensemble approach integrating nnU-Net, Swin UNETR, and HFF-Net for the BraTS-PED 2025 challenge. Our method incorporates three key extensions: adjustable initialization scales for optimal nnU-Net complexity control, transfer learning from BraTS 2021 pre-trained models to enhance Swin UNETR's gen…

Optimal Sizing of Community Photovoltaic and Battery Energy Storage Systems with Second-Life Batteries in Peer-to-Peer Energy Communities
J\'ulia Monar, Fernando Garc\'ia-Mu\~noz, Natalia Jorquera Bravo, Joaqu\'in Aballay Araya, Vicente Castro Burgos
https://arxiv.org/abs/2509.18082

Optimal Sizing of Community Photovoltaic and Battery Energy Storage Systems with Second-Life Batteries in Peer-to-Peer Energy Communities
This article presents a mixed-integer second-order cone programming model to determine the optimal sizing of a community-shared photovoltaic and battery energy storage system (PV-BESS) within a peer-to-peer (P2P) energy trading framework. The model accounts for heterogeneous users who may already own individual PV or PV-BESS systems and aims to enhance the overall energy autonomy of the energy community. A key feature of the model is the explicit comparison between first-life (FL) and second-li…

Agentic AI for Low-Altitude Semantic Wireless Networks: An Energy Efficient Design
Zhouxiang Zhao, Ran Yi, Yihan Cang, Boyang Jin, Zhaohui Yang, Mingzhe Chen, Chongwen Huang, Zhaoyang Zhang
https://arxiv.org/abs/2509.19791

Agentic AI for Low-Altitude Semantic Wireless Networks: An Energy Efficient Design
This letter addresses the energy efficiency issue in unmanned aerial vehicle (UAV)-assisted autonomous systems. We propose a framework for an agentic artificial intelligence (AI)-powered low-altitude semantic wireless network, that intelligently orchestrates a sense-communicate-decide-control workflow. A system-wide energy consumption minimization problem is formulated to enhance mission endurance. This problem holistically optimizes key operational variables, including UAV's location, semantic…

Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
Maksym Hrachov, Hans-Peter Piepho, Niaz Md. Farhat Rahman, Waqas Ahmed Malik
https://arxiv.org/abs/2507.18125

Regression approaches for modelling genotype-environment interaction and making predictions into unseen environments
In plant breeding and variety testing, there is an increasing interest in making use of environmental information to enhance predictions for new environments. Here, we will review linear mixed models that have been proposed for this purpose. The emphasis will be on predictions and on methods to assess the uncertainty of predictions for new environments. Our point of departure is straight-line regression, which may be extended to multiple environmental covariates and genotype-specific responses.…

FROQ: Observing Face Recognition Models for Efficient Quality Assessment
\v{Z}iga Babnik, Deepak Kumar Jain, Peter Peer, Vitomir \v{S}truc
https://arxiv.org/abs/2509.17689 https…

FROQ: Observing Face Recognition Models for Efficient Quality Assessment
Face Recognition (FR) plays a crucial role in many critical (high-stakes) applications, where errors in the recognition process can lead to serious consequences. Face Image Quality Assessment (FIQA) techniques enhance FR systems by providing quality estimates of face samples, enabling the systems to discard samples that are unsuitable for reliable recognition or lead to low-confidence recognition decisions. Most state-of-the-art FIQA techniques rely on extensive supervised training to achieve a…

Meson width predictions and symmetry emergence within the deep neural network
Xin Tong, Wei Feng, Weiwei Xu, Chao-Hsi Chang, Guo-Li Wang, Qiang Li
https://arxiv.org/abs/2509.17093

Meson width predictions and symmetry emergence within the deep neural network
We build a deep neural network model to predict meson widths from quantum numbers and masses based on the Transformer architecture. A Gaussian Monte-Carlo data enhancement method is adopted to enhance the meson data by considering the experimental errors, which significantly increase the data samples and improve the robustness and generalization performance of the model. With the meson widths ranging from $\sim10^{-14}$ to 625 MeV, the relative errors of the predictions behave $0.07\%$, $1.0\%$…

Compositional System Dynamics: The Higher Mathematics Underlying System Dynamics Diagrams & Practice
Xiaoyan Li, Evan Patterson, Patricia L. Mabry, Nathaniel D. Osgood
https://arxiv.org/abs/2509.18475

Compositional System Dynamics: The Higher Mathematics Underlying System Dynamics Diagrams & Practice
This work establishes a robust mathematical foundation for compositional System Dynamics modeling, leveraging category theory to formalize and enhance the representation, analysis, and composition of system models. Here, System Dynamics diagrams, such as stock & flow diagrams, system structure diagrams, and causal loop diagrams, are formulated as categorical constructs, enabling scalable, transparent, and systematic reasoning. By encoding these diagrams as data using attributed C-sets and utili…

Crosslisted article(s) found for cs.CE. https://arxiv.org/list/cs.CE/new
[1/1]:
- Implementing Zero Trust Architecture to Enhance Security and Resilience in the Pharmaceutical Sup...
Ghasemshirazi, Shirvani, Tavakoli, Ghaedi, Langarizadeh

SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs
Jiazhen Hong, Geoff Mackellar, Soheila Ghane
https://arxiv.org/abs/2509.19401 https://

SpellerSSL: Self-Supervised Learning with P300 Aggregation for Speller BCIs
Electroencephalogram (EEG)-based P300 speller brain-computer interfaces (BCIs) face three main challenges: low signal-to-noise ratio (SNR), poor generalization, and time-consuming calibration. We propose SpellerSSL, a framework that combines self-supervised learning (SSL) with P300 aggregation to address these issues. First, we introduce an aggregation strategy to enhance SNR. Second, to achieve generalization in training, we employ a customized 1D U-Net backbone and pretrain the model on both …

QvTAD: Differential Relative Attribute Learning for Voice Timbre Attribute Detection
Zhiyu Wu, Jingyi Fang, Yufei Tang, Yuanzhong Zheng, Yaoxuan Wang, Haojun Fei
https://arxiv.org/abs/2508.15931

QvTAD: Differential Relative Attribute Learning for Voice Timbre Attribute Detection
Voice Timbre Attribute Detection (vTAD) plays a pivotal role in fine-grained timbre modeling for speech generation tasks. However, it remains challenging due to the inherently subjective nature of timbre descriptors and the severe label imbalance in existing datasets. In this work, we present QvTAD, a novel pairwise comparison framework based on differential attention, designed to enhance the modeling of perceptual timbre attributes. To address the label imbalance in the VCTK-RVA dataset, we in…

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Xin An, Ruijie Li, Qiao Ning, Hui Li, Qian Ma, Shikai Guo
https://arxiv.org/abs/2509.20240 https://

A HyperGraphMamba-Based Multichannel Adaptive Model for ncRNA Classification
Non-coding RNAs (ncRNAs) play pivotal roles in gene expression regulation and the pathogenesis of various diseases. Accurate classification of ncRNAs is essential for functional annotation and disease diagnosis. To address existing limitations in feature extraction depth and multimodal fusion, we propose HGMamba-ncRNA, a HyperGraphMamba-based multichannel adaptive model, which integrates sequence, secondary structure, and optionally available expression features of ncRNAs to enhance classificat…

How European, national and local policies enhance each other’s effectiveness. And how cities make EU policy work for business and society.
https://www.linkedin.com/posts/jaapburger_how-european-national-and-local-policies-a…

How European, national, and local policies enhance each other's effectiveness. The share of electric vans and light trucks in new sales in the Netherlands has risen to more than three-quarters this… | Jaap Burger
How European, national, and local policies enhance each other's effectiveness. The share of electric vans and light trucks in new sales in the Netherlands has risen to more than three-quarters this year. A clear result of the introduction of local zero-emission zones for freight in an increasing number of Dutch cities, and updated taxation rules. The graph and table are from an excellent The International Council on Clean Transportation post by Dr. Sandra Wappelhorst and Eamonn Mulholland exp…

Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks
Hailay Kidu Teklehaymanot, Gebrearegawi Gidey, Wolfgang Nejdl
https://arxiv.org/abs/2509.20209

Low-Resource English-Tigrinya MT: Leveraging Multilingual Models, Custom Tokenizers, and Clean Evaluation Benchmarks
Despite advances in Neural Machine Translation (NMT), low-resource languages like Tigrinya remain underserved due to persistent challenges, including limited corpora, inadequate tokenization strategies, and the lack of standardized evaluation benchmarks. This paper investigates transfer learning techniques using multilingual pretrained models to enhance translation quality for morphologically rich, low-resource languages. We propose a refined approach that integrates language-specific tokenizat…

Why we need all the organisms: an exploration of the Monarch knowledge graph to aid mechanism discovery
Katherina Cortes, Daniel Korn, Sarah Gehrke, Kevin Schaper, Corey Cox, Patrick Golden, Aaron Odell, Bryan Laraway, Madan Krishnamurthy, Justin Reese, Harry Caufield, Sierra Moxon, Ellen Elias, Christopher J Mungall, Melissa Haendel
https://

Why we need all the organisms: an exploration of the Monarch knowledge graph to aid mechanism discovery
Research done using model organisms has been fundamental to the biological understanding of human genes, diseases and phenotypes. Model organisms provide tractable systems for experiments to enhance understanding of biological mechanisms conserved across the evolutionary tree. Decades of model organism research has generated vast amounts of data; however, this data is split across many domains, organisms, and biological fields of research. Knowledge graphs (KGs) are a computational way to aggre…

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning
Lauren Deason, Adam Bali, Ciprian Bejean, Diana Bolocan, James Crnkovich, Ioana Croitoru, Krishna Durai, Chase Midler, Calin Miron, David Molnar, Brad Moon, Bruno Ostarcevic, Alberto Peltea, Matt Rosenberg, Catalin Sandu, Arthur Saputkin, Sagar Shah, Daniel Stan, Ernest Szocs, Shengye Wan, Spencer Whitman, Sven Krasser, Joshua Saxe

CyberSOCEval: Benchmarking LLMs Capabilities for Malware Analysis and Threat Intelligence Reasoning
Today's cyber defenders are overwhelmed by a deluge of security alerts, threat intelligence signals, and shifting business context, creating an urgent need for AI systems to enhance operational security work. While Large Language Models (LLMs) have the potential to automate and scale Security Operations Center (SOC) operations, existing evaluations do not fully assess the scenarios most relevant to real-world defenders. This lack of informed evaluation impacts both AI developers and those apply…

Development of a Model Order Reduced Arbitrary Lagrangian Eulerian (MORALE) formulation for structures subjected to dynamic moving loads
Atul Anantheswar, Jannick Kehls, Ines Wollny, Tim Brepols, Stefanie Reese, Michael Kaliske
https://arxiv.org/abs/2509.20069

Development of a Model Order Reduced Arbitrary Lagrangian Eulerian (MORALE) formulation for structures subjected to dynamic moving loads
In recent developments, it has been demonstrated that the Arbitrary Lagrangian Eulerian (ALE) formulation can be utilized to improve computational efficiency, when simulating the response of structures subjected to moving loads. It is also well established in literature, that Model Order Reduction (MOR) techniques significantly enhance calculation speed. This contribution details the combination of both these tools into a novel Model Order Reduced Arbitrary Lagrangian Eulerian (MORALE) formulat…

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation
Manasi Mandal, Eunbi Rha, Abhijatmedhi Chotrattanapituk, Denisse C\'ordova Carrizales, Alexander Lygo, Kevin B. Woller, Mouyang Cheng, Ryotaro Okabe, Guomin Zhu, Kiran Mak, Chu-Liang Fu, Chuhang Liu, Lijun Wu, Yimei Zhu, Susanne Stemmer, Mingda Li
https://ar…

Tuning chiral anomaly signature in a Dirac semimetal via fast-ion implantation
Cd$_3$As$_2$ is a prototypical Dirac semimetal that hosts a chiral anomaly and thereby functions as a platform to test high-energy physics hypotheses and to realize energy efficient applications. Here we use a combination of accelerator-based fast ion implantation and theory-driven planning to enhance the negative longitudinal magnetoresistance (NLMR)--a signature of a chiral anomaly--in Nb-doped Cd$_3$As$_2$ thin films. High-energy ion implantation is commonly used to investigate semiconductor…

We were having a discussion about the origin of the "Enhance!" trope (when someone in a movie or TV show zooms in on a photo or video still and it stays amazingly high resolution), the first instance I can think of is in Columbo season 4, episode 5 ("Playback") from 1975.

A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat
Zezhou Yang, Ting Peng, Cuiyun Gao, Chaozheng Wang, Hailiang Huang, Yuetang Deng
https://arxiv.org/abs/2507.18515

A Deep Dive into Retrieval-Augmented Generation for Code Completion: Experience on WeChat
Code completion, a crucial task in software engineering that enhances developer productivity, has seen substantial improvements with the rapid advancement of large language models (LLMs). In recent years, retrieval-augmented generation (RAG) has emerged as a promising method to enhance the code completion capabilities of LLMs, which leverages relevant context from codebases without requiring model retraining. While existing studies have demonstrated the effectiveness of RAG on public repositori…

Deep chemical tagging - Identifying open clusters and moving groups in chemical space with graph attention networks
Lorenzo Spina, Milan Quandt Rodriguez, Laura Magrini, Leda Berni, Sara Lucatello, Marco Canducci
https://arxiv.org/abs/2509.18268

ExpFace: Exponential Angular Margin Loss for Deep Face Recognition
Jinhui Zheng, Xueyuan Gong
https://arxiv.org/abs/2509.19753 https://arxiv.org/pdf/2509.1…

ExpFace: Exponential Angular Margin Loss for Deep Face Recognition
Face recognition is an open-set problem requiring high discriminative power to ensure that intra-class distances remain smaller than inter-class distances. Margin-based softmax losses, such as SphereFace, CosFace, and ArcFace, have been widely adopted to enhance intra-class compactness and inter-class separability, yet they overlook the impact of noisy samples. By examining the distribution of samples in the angular space, we observe that clean samples predominantly cluster in the center region…

BALANCE: Bitrate-Adaptive Limit-Aware Netcast Content Enhancement Utilizing QUBO and Quantum Annealing
Animesh Rajpurohit, Michael Kelley, Wei Wang, Krishna Murthy Kattiyan Ramamoorthy
https://arxiv.org/abs/2509.19616

BALANCE: Bitrate-Adaptive Limit-Aware Netcast Content Enhancement Utilizing QUBO and Quantum Annealing
In an era of increasing data cap constraints, optimizing video streaming quality while adhering to user-defined data caps remains a significant challenge. This paper introduces Bitrate-Adaptive Limit-Aware Netcast Content Enhancement (BALANCE), a novel Quantum framework aimed at addressing this issue. BALANCE intelligently pre-selects video segments based on visual complexity and anticipated data consumption, utilizing the Video Multimethod Assessment Fusion (VMAF) metric to enhance Quality of …

Deep Learning-based Position-domain Channel Extrapolation for Cell-Free Massive MIMO
Jiajia Guo, Chao-Kai Wen, Xiao Li, Shi Jin
https://arxiv.org/abs/2507.17950 https://

Deep Learning-based Position-domain Channel Extrapolation for Cell-Free Massive MIMO
To reduce channel acquisition overhead, spatial, time, and frequency-domain channel extrapolation techniques have been widely studied. In this paper, we propose a novel deep learning-based Position-domain Channel Extrapolation framework (named PCEnet) for cell-free massive multiple-input multiple-output (MIMO) systems. The user's position, which contains significant channel characteristic information, can greatly enhance the efficiency of channel acquisition. In cell-free massive MIMO, while th…

Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees
Meshi Bashari, Yonghoon Lee, Roy Maor Lotan, Edgar Dobriban, Yaniv Romano
https://arxiv.org/abs/2509.20345

Statistical Inference Leveraging Synthetic Data with Distribution-Free Guarantees
The rapid proliferation of high-quality synthetic data -- generated by advanced AI models or collected as auxiliary data from related tasks -- presents both opportunities and challenges for statistical inference. This paper introduces a GEneral Synthetic-Powered Inference (GESPI) framework that wraps around any statistical inference procedure to safely enhance sample efficiency by combining synthetic and real data. Our framework leverages high-quality synthetic data to boost statistical power, …

Reciprocal Beyond-Diagonal Reconfigurable Intelligent Surface (BD-RIS): Scattering Matrix Design via Manifold Optimization
Marko Fidanovski, Iv\'an Alexander Morales Sandoval, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu, Emil Bj\"ornson
https://arxiv.org/abs/2509.20246

Reciprocal Beyond-Diagonal Reconfigurable Intelligent Surface (BD-RIS): Scattering Matrix Design via Manifold Optimization
Beyond-diagonal reconfigurable intelligent surfaces (BD-RISs) are emerging as a transformative technology in wireless communications, enabling enhanced performance and quality of service (QoS) of wireless systems in harsh urban environments due to their relatively low cost and advanced signal processing capabilities. Generally, BD-RIS systems are employed to improve robustness, increase achievable rates, and enhance energy efficiency of wireless systems in both direct and indirect ways. The dir…

Group Relative Policy Optimization for Text-to-Speech with Large Language Models
Chang Liu, Ya-Jun Hu, Ying-Ying Gao, Shi-Lei Zhang, Zhen-Hua Ling
https://arxiv.org/abs/2509.18798

Group Relative Policy Optimization for Text-to-Speech with Large Language Models
This paper proposes a GRPO-based approach to enhance the performance of large language model (LLM)-based text-to-speech (TTS) models by deriving rewards from an off-the-shelf automatic speech recognition (ASR) model. Compared to previous reinforcement learning methods for LLM-based TTS, our method requires no dedicated model for reward computation or training. Moreover, we design a composite reward function that combines character error rate (CER) with negative log-likelihood (NLL) obtained fro…

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation
Bo Xiong, Linghao Zhang, Chong Wang, Peng Liang
https://arxiv.org/abs/2509.18337 https://

CoRaCMG: Contextual Retrieval-Augmented Framework for Commit Message Generation
Commit messages play a key role in documenting the intent behind code changes. However, they are often low-quality, vague, or incomplete, limiting their usefulness. Commit Message Generation (CMG) aims to automatically generate descriptive commit messages from code diffs to reduce developers' effort and improve message quality. Although recent advances in LLMs have shown promise in automating CMG, their performance remains limited. This paper aims to enhance CMG performance by retrieving simila…

Tenure Under Pressure: Simulating the Disruptive Effects of AI on Academic Publishing
Shan Jiang
https://arxiv.org/abs/2509.16925 https://arxiv.org/pdf/250…

Tenure Under Pressure: Simulating the Disruptive Effects of AI on Academic Publishing
Generative artificial intelligence (AI) has begun to reshape academic publishing by enabling the rapid production of submission-ready manuscripts. While such tools promise to enhance productivity, they also raise concerns about overwhelming journal systems that have fixed acceptance capacities. This paper uses simulation modeling to investigate how AI-driven surges in submissions may affect desk rejection rates, review cycles, and faculty publication portfolios, with a focus on business school …

PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction
Manahil Raza, Ayesha Azam, Talha Qaiser, Nasir Rajpoot
https://arxiv.org/abs/2509.20022

PS3: A Multimodal Transformer Integrating Pathology Reports with Histology Images and Biological Pathways for Cancer Survival Prediction
Current multimodal fusion approaches in computational oncology primarily focus on integrating multi-gigapixel histology whole slide images (WSIs) with genomic or transcriptomic data, demonstrating improved survival prediction. We hypothesize that incorporating pathology reports can further enhance prognostic performance. Pathology reports, as essential components of clinical workflows, offer readily available complementary information by summarizing histopathological findings and integrating ex…

Does spatialized audio enhance the creation of mental representations? Spoiler: No (for their SnapStick-based setup) https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1660373/full "seven blind individuals and se…

Frontiers | Does spatialized audio enhance the creation of mental representations?
Navigating unfamiliar environments without vision is a considerable challenge for blind individuals, as it requires constructing accurate cognitive maps. Bin...

Table Detection with Active Learning
Somraj Gautam, Nachiketa Purohit, Gaurav Harit
https://arxiv.org/abs/2509.20003 https://arxiv.org/pdf/2509.20003

Table Detection with Active Learning
Efficient data annotation remains a critical challenge in machine learning, particularly for object detection tasks requiring extensive labeled data. Active learning (AL) has emerged as a promising solution to minimize annotation costs by selecting the most informative samples. While traditional AL approaches primarily rely on uncertainty-based selection, recent advances suggest that incorporating diversity-based strategies can enhance sampling efficiency in object detection tasks. Our approach…

Extracting Higgs Self-Coupling Constraints through Triple Higgs Boson Production at Future Hadron Colliders
Benjamin Fuks, Andreas Papaefstathiou, Gilberto Tetlalmatzi-Xolocotzi
https://arxiv.org/abs/2509.16364

Extracting Higgs Self-Coupling Constraints through Triple Higgs Boson Production at Future Hadron Colliders
We present a systematic study of triple Higgs boson production at future high-energy hadron colliders, using the six-$b$-jet final state as a probe of the Higgs self-interactions. We conduct, under realistic detector smearing assumptions, both a traditional cut-based analysis, and a multivariate one using gradient boosting. The multivariate strategy is found to enhance sensitivity to beyond the Standard Model effects on the Higgs boson's self-couplings, while preserving large signal event yield…

Enhancing CTAO Monitoring and Alarm Subsystems in Distributed Environments Using ServiMon
Kevin Munari, Alessandro Costa, Federico Incardona, Emilio Mastriani, Sebastiano Spinello, Stefano Germani, Pietro Bruno
https://arxiv.org/abs/2509.16366

Enhancing CTAO Monitoring and Alarm Subsystems in Distributed Environments Using ServiMon
ServiMon is a scalable data collection and auditing pipeline designed for service-oriented, cost-efficient quality control in distributed environments, including the CTAO monitoring, logging, and alarm subsystems. Developed within a Docker-based architecture, it leverages cloud-native technologies and distributed computing principles to enhance system observability and reliability. At its core, ServiMon integrates key technologies such as Prometheus, Grafana, Kafka, and Cassandra. Prometheus se…

Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Javier Bisbal, Patrick Winter, Sebastian Jofre, Aaron Ponce, Sameer A. Ansari, Ramez Abdalla, Michael Markl, Oliver Welin Odeback, Sergio Uribe, Cristian Tejos, Julio Sotelo, Susanne Schnell, David Marlevi
https://arxiv.org/abs/2509.17726…

Automated Labeling of Intracranial Arteries with Uncertainty Quantification Using Deep Learning
Accurate anatomical labeling of intracranial arteries is essential for cerebrovascular diagnosis and hemodynamic analysis but remains time-consuming and subject to interoperator variability. We present a deep learning-based framework for automated artery labeling from 3D Time-of-Flight Magnetic Resonance Angiography (3D ToF-MRA) segmentations (n=35), incorporating uncertainty quantification to enhance interpretability and reliability. We evaluated three convolutional neural network architecture…

RaFD: Flow-Guided Radar Detection for Robust Autonomous Driving
Shuocheng Yang, Zikun Xu, Jiahao Wang, Shahid Nawaz, Jianqiang Wang, Shaobing Xu
https://arxiv.org/abs/2509.16261

RaFD: Flow-Guided Radar Detection for Robust Autonomous Driving
Radar has shown strong potential for robust perception in autonomous driving; however, raw radar images are frequently degraded by noise and "ghost" artifacts, making object detection based solely on semantic features highly challenging. To address this limitation, we introduce RaFD, a radar-based object detection framework that estimates inter-frame bird's-eye-view (BEV) flow and leverages the resulting geometric cues to enhance detection accuracy. Specifically, we design a supervised flow est…

Improving Test-Time Performance of RVQ-based Neural Codecs
Hyeongju Kim, Junhyeok Lee, Jacob Morton, Juheon Lee, Jinhyeok Yang
https://arxiv.org/abs/2509.19186 https://

STAR: Speech-to-Audio Generation via Representation Learning
Zeyu Xie, Xuenan Xu, Yixuan Li, Mengyue Wu, Yuexian Zou
https://arxiv.org/abs/2509.17164 https://

STAR: Speech-to-Audio Generation via Representation Learning
This work presents STAR, the first end-to-end speech-to-audio generation framework, designed to enhance efficiency and address error propagation inherent in cascaded systems. Unlike prior approaches relying on text or vision, STAR leverages speech as it constitutes a natural modality for interaction. As an initial step to validate the feasibility of the system, we demonstrate through representation learning experiments that spoken sound event semantics can be effectively extracted from raw spee…

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Jiaqi Ma, Guo-Sen Xie, Fang Zhao, Zechao Li
https://arxiv.org/abs/2508.16159 https://

Through the Looking Glass: A Dual Perspective on Weakly-Supervised Few-Shot Segmentation
Meta-learning aims to uniformly sample homogeneous support-query pairs, characterized by the same categories and similar attributes, and extract useful inductive biases through identical network architectures. However, this identical network design results in over-semantic homogenization. To address this, we propose a novel homologous but heterogeneous network. By treating support-query pairs as dual perspectives, we introduce heterogeneous visual aggregation (HA) modules to enhance complementa…

A LiDAR-Driven Fallback Longitudinal Controller for Safer Following in Sudden Braking Scenarios
Mohamed Sabry, Enrico Del Re, Walter Morales-Alvarez, Cristina Olaverri-Monreal
https://arxiv.org/abs/2509.16642

A LiDAR-Driven Fallback Longitudinal Controller for Safer Following in Sudden Braking Scenarios
Adaptive Cruise Control has seen significant advancements, with Collaborative Adaptive Cruise Control leveraging Vehicle-to-Vehicle communication to enhance coordination and stability. However, the reliance on stable communication channels limits its reliability. Research on reducing information dependencies in Adaptive Cruise Control systems has remained limited, despite its critical role in mitigating collision risks during sudden braking scenarios. This study proposes a novel fallback longit…

Triplet Loss Based Quantum Encoding for Class Separability
Marco Mordacci, Mahul Pandey, Paolo Santini, Michele Amoretti
https://arxiv.org/abs/2509.15705 https://

Triplet Loss Based Quantum Encoding for Class Separability
An efficient and data-driven encoding scheme is proposed to enhance the performance of variational quantum classifiers. This encoding is specially designed for complex datasets like images and seeks to help the classification task by producing input states that form well-separated clusters in the Hilbert space according to their classification labels. The encoding circuit is trained using a triplet loss function inspired by classical facial recognition algorithms, and class separability is meas…

Enhancing Noise Robustness for Neural Speech Codecs through Resource-Efficient Progressive Quantization Perturbation Simulation
Rui-Chen Zheng, Yang Ai, Hui-Peng Du, Zhen-Hua Ling
https://arxiv.org/abs/2509.19025

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Zhiyu Mou, Yiqin Lv, Miao Xu, Cheems Wang, Yixiu Mao, Qichen Ye, Chao Li, Rongquan Bai, Chuan Yu, Jian Xu, Bo Zheng
https://arxiv.org/abs/2509.15927

Enhancing Generative Auto-bidding with Offline Reward Evaluation and Policy Search
Auto-bidding is an essential tool for advertisers to enhance their advertising performance. Recent progress has shown that AI-Generated Bidding (AIGB), which formulates the auto-bidding as a trajectory generation task and trains a conditional diffusion-based planner on offline data, achieves superior and stable performance compared to typical offline reinforcement learning (RL)-based auto-bidding methods. However, existing AIGB methods still encounter a performance bottleneck due to their negle…

Hybrid FIM and STAR-BD-RIS-Aided Wireless Communications with Short Packet Length: A Meta-TD3 Approach
Ayla Eftekhari, Maryam Cheraghy, Armin Farhadi, Mohammad Robat Mili, Qingqing Wu
https://arxiv.org/abs/2509.16417

Hybrid FIM and STAR-BD-RIS-Aided Wireless Communications with Short Packet Length: A Meta-TD3 Approach
Reconfigurable intelligent surfaces (RIS) and flexible intelligent metasurfaces (FIM) have been widely adopted in multi-user wireless communication systems to enhance channel quality through simultaneous transmission and reflection of signals and three-dimensional reconfiguration of antennas. In this paper, we propose a novel system architecture that integrates the benefits of both technologies by deploying an FIM antenna at the base station (BS) and a simultaneously transmitting and reflecting…

DIO: Refining Mutual Information and Causal Chain to Enhance Machine Abstract Reasoning Ability
Ruizhuo Song, Beiming Yuan
https://arxiv.org/abs/2508.15387 https://

DIO: Refining Mutual Information and Causal Chain to Enhance Machine Abstract Reasoning Ability
Despite the outstanding performance of current deep learning models across various domains, their fundamental bottleneck in abstract reasoning remains unresolved. To address this challenge, the academic community has introduced Raven's Progressive Matrices (RPM) problems as an authoritative benchmark for evaluating the abstract reasoning capabilities of deep learning algorithms, with a focus on core intelligence dimensions such as abstract reasoning, pattern recognition, and complex problem-sol…

Digging Into the Internal: Causality-Based Analysis of LLM Function Calling
Zhenlan Ji, Daoyuan Wu, Wenxuan Wang, Pingchuan Ma, Shuai Wang, Lei Ma
https://arxiv.org/abs/2509.16268

Digging Into the Internal: Causality-Based Analysis of LLM Function Calling
Function calling (FC) has emerged as a powerful technique for facilitating large language models (LLMs) to interact with external systems and perform structured tasks. However, the mechanisms through which it influences model behavior remain largely under-explored. Besides, we discover that in addition to the regular usage of FC, this technique can substantially enhance the compliance of LLMs with user instructions. These observations motivate us to leverage causality, a canonical analysis meth…

EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models
Xinyi Ling, Hanwen Du, Zhihui Zhu, Xia Ning
https://arxiv.org/abs/2508.15721 https://

EcomMMMU: Strategic Utilization of Visuals for Robust Multimodal E-Commerce Models
E-commerce platforms are rich in multimodal data, featuring a variety of images that depict product details. However, this raises an important question: do these images always enhance product understanding, or can they sometimes introduce redundancy or degrade performance? Existing datasets are limited in both scale and design, making it difficult to systematically examine this question. To this end, we introduce EcomMMMU, an e-commerce multimodal multitask understanding dataset with 406,190 sa…

On Fast Attitude Filtering Based on Matrix Fisher Distribution with Stability Guarantee
Shijie Wang, Haichao Gui, Rui Zhong
https://arxiv.org/abs/2509.17827 https://

On Fast Attitude Filtering Based on Matrix Fisher Distribution with Stability Guarantee
This paper addresses two interrelated problems of the nonlinear filtering mechanism and fast attitude filtering with the matrix Fisher distribution (MFD) on the special orthogonal group. By analyzing the distribution evolution along Bayes' rule, we reveal two essential properties that enhance the performance of Bayesian attitude filters with MFDs, particularly in challenging conditions, from a theoretical viewpoint. Benefiting from the new understanding of the filtering mechanism associated w…

Core-elements Subsampling for Alternating Least Squares
Dunyao Xue, Mengyu Li, Cheng Meng, Jingyi Zhang
https://arxiv.org/abs/2509.18024 https://arxiv.org/…

Core-elements Subsampling for Alternating Least Squares
In this paper, we propose a novel element-wise subset selection method for the alternating least squares (ALS) algorithm, focusing on low-rank matrix factorization involving matrices with missing values, as commonly encountered in recommender systems. While ALS is widely used for providing personalized recommendations based on user-item interaction data, its high computational cost, stemming from repeated regression operations, poses significant challenges for large-scale datasets. To enhance t…

Survey of Vision-Language-Action Models for Embodied Manipulation
Haoran Li, Yuhui Chen, Wenbo Cui, Weiheng Liu, Kai Liu, Mingcai Zhou, Zhengtao Zhang, Dongbin Zhao
https://arxiv.org/abs/2508.15201

Survey of Vision-Language-Action Models for Embodied Manipulation
Embodied intelligence systems, which enhance agent capabilities through continuous environment interactions, have garnered significant attention from both academia and industry. Vision-Language-Action models, inspired by advancements in large foundation models, serve as universal robotic control frameworks that substantially improve agent-environment interaction capabilities in embodied intelligence systems. This expansion has broadened application scenarios for embodied AI robots. This survey …

MaskVCT: Masked Voice Codec Transformer for Zero-Shot Voice Conversion With Increased Controllability via Multiple Guidances
Junhyeok Lee, Helin Wang, Yaohan Guan, Thomas Thebaud, Laureano Moro-Velazquez, Jes\'us Villalba, Najim Dehak
https://arxiv.org/abs/2509.17143

MaskVCT: Masked Voice Codec Transformer for Zero-Shot Voice Conversion With Increased Controllability via Multiple Guidances
We introduce MaskVCT, a zero-shot voice conversion (VC) model that offers multi-factor controllability through multiple classifier-free guidances (CFGs). While previous VC models rely on a fixed conditioning scheme, MaskVCT integrates diverse conditions in a single model. To further enhance robustness and control, the model can leverage continuous or quantized linguistic features to enhance intellgibility and speaker similarity, and can use or omit pitch contour to control prosody. These choice…

DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
Rui Yang, Michael Fu, Chakkrit Tantithamthavorn, Chetan Arora, Gunel Gulmammadova, Joey Chua
https://arxiv.org/abs/2509.16870

DecipherGuard: Understanding and Deciphering Jailbreak Prompts for a Safer Deployment of Intelligent Software Systems
Intelligent software systems powered by Large Language Models (LLMs) are increasingly deployed in critical sectors, raising concerns about their safety during runtime. Through an industry-academic collaboration when deploying an LLM-powered virtual customer assistant, a critical software engineering challenge emerged: how to enhance a safer deployment of LLM-powered software systems at runtime? While LlamaGuard, the current state-of-the-art runtime guardrail, offers protection against unsafe in…

Tensorized Multi-Task Learning for Personalized Modeling of Heterogeneous Individuals with High-Dimensional Data
Elif Konyar, Mostafa Reisi Gahrooei, Kamran Paynabar
https://arxiv.org/abs/2508.15676

Tensorized Multi-Task Learning for Personalized Modeling of Heterogeneous Individuals with High-Dimensional Data
Effective modeling of heterogeneous subpopulations presents a significant challenge due to variations in individual characteristics and behaviors. This paper proposes a novel approach to address this issue through multi-task learning (MTL) and low-rank tensor decomposition techniques. Our MTL approach aims to enhance personalized modeling by leveraging shared structures among similar tasks while accounting for distinct subpopulation-specific variations. We introduce a framework where low-rank d…

Fusing Spectral Correlation Density Imaging with Deep Learning for Intelligent Fault Diagnosis in Rotating Machinery
Dilshara Herath, Chinthaka Abeyrathne, Chamindu Adithya, Chathura Seneviratne
https://arxiv.org/abs/2509.16580

Fusing Spectral Correlation Density Imaging with Deep Learning for Intelligent Fault Diagnosis in Rotating Machinery
Bearing fault diagnosis in rotating machinery is critical for ensuring operational reliability, therefore early fault detection is essential to avoid catastrophic failures and expensive emergency repairs. Traditional methods like Fast Fourier Transform (FFT) often fail to capture the complex, non-stationary nature of vibration signals. This study leverages the cyclostationary properties of vibration data through Spectral Correlation Density (SCD) images to enhance fault detection and apply deep…

Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
Ziyou Li, Agnia Sergeyuk, Maliheh Izadi
https://arxiv.org/abs/2509.17096 https://

Prompt-with-Me: in-IDE Structured Prompt Management for LLM-Driven Software Engineering
Large Language Models are transforming software engineering, yet prompt management in practice remains ad hoc, hindering reliability, reuse, and integration into industrial workflows. We present Prompt-with-Me, a practical solution for structured prompt management embedded directly in the development environment. The system automatically classifies prompts using a four-dimensional taxonomy encompassing intent, author role, software development lifecycle stage, and prompt type. To enhance prompt…

Fairness for the People, by the People: Minority Collective Action
Omri Ben-Dov, Samira Samadi, Amartya Sanyal, Alexandru \c{T}ifrea
https://arxiv.org/abs/2508.15374 https://

Fairness for the People, by the People: Minority Collective Action
Machine learning models often preserve biases present in training data, leading to unfair treatment of certain minority groups. Despite an array of existing firm-side bias mitigation techniques, they typically incur utility costs and require organizational buy-in. Recognizing that many models rely on user-contributed data, end-users can induce fairness through the framework of Algorithmic Collective Action, where a coordinated minority group strategically relabels its own data to enhance fairne…

See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
Pengteng Li, Pinhao Song, Wuyang Li, Weiyu Guo, Huizai Yao, Yijie Xu, Dugang Liu, Hui Xiong
https://arxiv.org/abs/2509.16087

See&Trek: Training-Free Spatial Prompting for Multimodal Large Language Model
We introduce SEE&TREK, the first training-free prompting framework tailored to enhance the spatial understanding of Multimodal Large Language Models (MLLMS) under vision-only constraints. While prior efforts have incorporated modalities like depth or point clouds to improve spatial reasoning, purely visualspatial understanding remains underexplored. SEE&TREK addresses this gap by focusing on two core principles: increasing visual diversity and motion reconstruction. For visual diversity, we con…

LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions
Yongju Jia, Jiarui Ma, Xiangxian Li, Baiqiao Zhang, Xianhui Cao, Juan Liu, Yulong Bian
https://arxiv.org/abs/2508.15688

LLM-empowered Dynamic Prompt Routing for Vision-Language Models Tuning under Long-Tailed Distributions
Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive capability in visual tasks, but their fine-tuning often suffers from bias in class-imbalanced scene. Recent works have introduced large language models (LLMs) to enhance VLM fine-tuning with supplementing semantic information. However, they often overlook inherent class imbalance in VLMs' pre-training, which may lead to bias accumulation in downstream tasks. To address this problem, this paper proposes a Multi…

Waver: Wave Your Way to Lifelike Video Generation
Yifu Zhang, Hao Yang, Yuqi Zhang, Yifei Hu, Fengda Zhu, Chuang Lin, Xiaofeng Mei, Yi Jiang, Zehuan Yuan, Bingyue Peng
https://arxiv.org/abs/2508.15761 …

Waver: Wave Your Way to Lifelike Video Generation
We present Waver, a high-performance foundation model for unified image and video generation. Waver can directly generate videos with durations ranging from 5 to 10 seconds at a native resolution of 720p, which are subsequently upscaled to 1080p. The model simultaneously supports text-to-video (T2V), image-to-video (I2V), and text-to-image (T2I) generation within a single, integrated framework. We introduce a Hybrid Stream DiT architecture to enhance modality alignment and accelerate training c…