Tootfinder

AI is just a tool, like a pencil. A pencil that can only write and draw what some faceless corporation somewhere allows you to write and draw.
#AI #theMastersTools

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
Mahdi Dhaini, Juraj Vladika, Ege Erdogan, Zineb Attaoui, Gjergji Kasneci
https://arxiv.org/abs/2508.09776

Can LLM-Generated Textual Explanations Enhance Model Classification Performance? An Empirical Study
In the rapidly evolving field of Explainable Natural Language Processing (NLP), textual explanations, i.e., human-like rationales, are pivotal for explaining model predictions and enriching datasets with interpretable labels. Traditional approaches rely on human annotation, which is costly, labor-intensive, and impedes scalability. In this work, we present an automated framework that leverages multiple state-of-the-art large language models (LLMs) to generate high-quality textual explanations. …

Enhance the machine learning algorithm performance in phishing detection with keyword features
Zijiang Yang
https://arxiv.org/abs/2508.09765 https://arxiv.…

Enhance the machine learning algorithm performance in phishing detection with keyword features
Recently, we can observe a significant increase of the phishing attacks in the Internet. In a typical phishing attack, the attacker sets up a malicious website that looks similar to the legitimate website in order to obtain the end-users' information. This may cause the leakage of the sensitive information and the financial loss for the end-users. To avoid such attacks, the early detection of these websites' URLs is vital and necessary. Previous researchers have proposed many machine learning a…

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Marco De Nadai, Andreas Damianou, Mounia Lalmas
https://arxiv.org/abs/2508.09789 htt…

Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Existing video recommender systems rely primarily on user-defined metadata or on low-level visual and acoustic signals extracted by specialised encoders. These low-level features describe what appears on the screen but miss deeper semantics such as intent, humour, and world knowledge that make clips resonate with viewers. For example, is a 30-second clip simply a singer on a rooftop, or an ironic parody filmed amid the fairy chimneys of Cappadocia, Turkey? Such distinctions are critical to pers…

Und hier haben wir es: eine logisch-semantische Injektion: unmarkierte Modifikationen der Funktionsweise von LLMs, die unser Denken nudgen und damit unsere logisch-semantische Souveränität untergraben: https://toot.cafe/@baldur/113934035009657696
Hintergrund:

"It is with great pleasure that we welcome Nicholas E. Johnson as the Director of AI at SBU Libraries. This newly minted position is among the first of its kind in libraries nationwide. Nicholas brings a wealth of experience leading the strategic integration of AI technologies to enhance library services, support academic engagement and promote ethical and responsible use of AI."

🔋 Reusing EV batteries for energy storage can offer greater carbon savings than direct recycling
https://techxplore.com/news/2025-07-reusing-ev-batteries-energy-storage.html

Reusing EV batteries for energy storage can offer greater carbon savings than direct recycling
When electric vehicle (EV) batteries reach the end of their service life, they can be recycled to recover valuable raw materials for the production of new batteries. Alternatively, retired EV batteries can be repurposed for use as stationary energy storage systems, helping to integrate renewable energy into the power grid, manage peak loads, and enhance energy security.

ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
Rajarshi Roy, Devleena Das, Ankesh Banerjee, Arjya Bhattacharjee, Kousik Dasgupta, Subarna Tripathi
https://arxiv.org/abs/2507.08679

ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way
We introduce ByDeWay, a training-free framework designed to enhance the performance of Multimodal Large Language Models (MLLMs). ByDeWay uses a novel prompting strategy called Layered-Depth-Based Prompting (LDP), which improves spatial reasoning and grounding without modifying any model parameters. It segments the scene into closest, mid-range, and farthest layers using monocular depth estimation, then generates region-specific captions with a grounded vision-language model. These structured, d…

Robot and Overhead Crane Collaboration Scheme to Enhance Payload Manipulation
Antonio Rosales, Alaa Abderrahim, Markku Suomalainen, Mikael Haag, Tapio Heikkil\"a
https://arxiv.org/abs/2508.07758

Robot and Overhead Crane Collaboration Scheme to Enhance Payload Manipulation
This paper presents a scheme to enhance payload manipulation using a robot collaborating with an overhead crane. In the current industrial practice, when the crane's payload has to be accurately manipulated and located in a desired position, the task becomes laborious and risky since the operators have to guide the fine motions of the payload by hand. In the proposed collaborative scheme, the crane lifts the payload while the robot's end-effector guides it toward the desired position. The only …

Teaching Code Refactoring Using LLMs
Anshul Khairnar, Aarya Rajoju, Edward F. Gehringer
https://arxiv.org/abs/2508.09332 https://arxiv.org/pdf/2508.09332…

Teaching Code Refactoring Using LLMs
This Innovative Practice full paper explores how Large Language Models (LLMs) can enhance the teaching of code refactoring in software engineering courses through real-time, context-aware feedback. Refactoring improves code quality but is difficult to teach, especially with complex, real-world codebases. Traditional methods like code reviews and static analysis tools offer limited, inconsistent feedback. Our approach integrates LLM-assisted refactoring into a course project using structured pro…

Virtual Reality User Interface Design: Best Practices and Implementation
Esin Mehmedova, Santiago Berrezueta-Guzman, Stefan Wagner
https://arxiv.org/abs/2508.09358 https://

Virtual Reality User Interface Design: Best Practices and Implementation
Designing effective user interfaces (UIs) for virtual reality (VR) is essential to enhance user immersion, usability, comfort, and accessibility in virtual environments. Despite the growing adoption of VR across domains such as education, healthcare, gaming, and rehabilitation, there is a noticeable lack of unified and comprehensive design guidelines for VR UI design. To address this gap, we conducted a systematic literature review to identify existing best practices and propose complete and un…

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights
Klinsmann Agyei, Pouria Sarhadi, Daniel Polani
https://arxiv.org/abs/2507.08196

Deep Reinforcement Learning in Applied Control: Challenges, Analysis, and Insights
Over the past decade, remarkable progress has been made in adopting deep neural networks to enhance the performance of conventional reinforcement learning. A notable milestone was the development of Deep Q-Networks (DQN), which achieved human-level performance across a range of Atari games, demonstrating the potential of deep learning to stabilise and scale reinforcement learning. Subsequently, extensions to continuous control algorithms paved the way for a new paradigm in control, one that has…

Intrinsic defects and 4d/5d transition metal defects in Cr$_2$O$_3$: pathways to enhance the N\'eel temperature
Xuecong Wang, Sai Mu
https://arxiv.org/abs/2506.10883

Intrinsic defects and 4d/5d transition metal defects in Cr$_2$O$_3$: pathways to enhance the Néel temperature
First-principles calculations are employed to explore avenues to increase the Néel temperature ($T_{\mathrm{N}}$) of the magnetoelectric antiferromagnet Cr$_2$O$_3$ through doping. Employing the hybrid functional method, we calculate the formation energy of intrinsic defects and transition metal dopants (Mo, W, Nb, Ta, Zr, and Hf) to assess their likelihood of formation. Intrinsic defect calculations indicate that Cr interstitials and oxygen vacancies dominate under Cr-rich conditions, whereas…

Vibrotactile speech cues are associated with enhanced auditory processing in middle and superior temporal gyri #metamodal

Vibrotactile speech cues are associated with enhanced auditory processing in middle and superior temporal gyri - Scientific Reports
Combined auditory and tactile stimuli have been found to enhance speech-in-noise perception both in individuals with normal hearing and in those with hearing loss. While behavioral benefits of audio-tactile enhancements in speech understanding have been repeatedly demonstrated, the impact of vibrotactile cues on cortical auditory speech processing remains unknown. Using functional near-infrared spectroscopy (fNIRS) with a dense montage setup, we first identified a region-of-interest highly sens…

Nonlinear Symmetry Breaking to Enhance the Sagnac Effect in a Microresonator Gyroscope
Thariq Shanavas, Gregory Krueper, Jiangang Zhu, Wounjhang Park, Juliet T. Gopinath
https://arxiv.org/abs/2508.09132

Nonlinear Symmetry Breaking to Enhance the Sagnac Effect in a Microresonator Gyroscope
Optical gyroscopes based on the Sagnac effect have been widely used for inertial navigation in aircrafts, submarines, satellites and unmanned robotics. With the rapid progress in the field of ultrahigh-quality whispering gallery mode and ring resonators in recent years, these devices offer the promise of a compact alternative to ring-laser gyroscopes (RLGs) and fiber-optic gyroscopes (FOGs). Yet, successful commercialization of a microresonator gyroscope has been hindered by the scaling of the …

Advances in reconstructing the cosmic-ray energy spectrum with IceTop
Lilly Pyras (for the IceCube Collaboration), Larissa Paul (for the IceCube Collaboration), Katherine Rawlins (for the IceCube Collaboration), Ian Reistroffer (for the IceCube Collaboration), Amar Thakuri (for the IceCube Collaboration)
https://arxiv.org/abs/25…

Advances in reconstructing the cosmic-ray energy spectrum with IceTop
The IceTop array at the surface of the IceCube Neutrino Observatory measures extensive air showers produced by cosmic-ray particles with energies from PeV up to EeV, covering the transition region from galactic to extragalactic sources. This contribution presents significant improvements that will enhance the measurement of the cosmic-ray energy spectrum. (I) To analyze more than a decade of data with increasing snow overburdens on the detector, an improved method to handle the time-dependent a…

Variational boundary based tensor network renormalization group
Feng-Feng Song, Naoki Kawashima
https://arxiv.org/abs/2508.10418 https://arxiv.org/pdf/2508…

Variational boundary based tensor network renormalization group
We propose a real-space renormalization group algorithm for accurately coarse-graining two-dimensional tensor networks. The central innovation of our method lies in utilizing variational boundary tensors as a globally optimized environment for the entire system. Based on this optimized environment, we construct renormalization projectors that significantly enhance accuracy. By leveraging the canonical form of tensors, our algorithm maintains the same computational complexity as the original ten…

Toward High-Precision Astrometry with CSST Using Multi-Gaussian Fitting of PSF
Jialu Nie, Peng Wei, Zihuang Cao, Yibo Yan, Chao Liu, Hao Tian, Xin Zhang, Haijun Tian
https://arxiv.org/abs/2508.10329

Toward High-Precision Astrometry with CSST Using Multi-Gaussian Fitting of PSF
The Chinese Space Station Survey Telescope (CSST) presents significant potential for high-precision astrometry. In this study, we show that the point spread function (PSF) modeled by the discrete PSF with Multi-Gaussian function can effectively enhance the astrometric accuracy. We determine that the PSF profile can be accurately modeled by three Gaussians, which takes advantage of reduced computational complexity in PSF convolution. In sparse star fields, the lowest centering accuracy we obtain…

Boreal Afforestation's Underestimated Cloud Influence on Earth's Energy Imbalance
Enoch Ofosu, Kevin Bradley Dsouza, Daniel Chukwuemeka Amaogu, J\'er\^ome Pigeon, Richard Boudreault, Juan Moreno-Cruz, Pooneh Maghoul, Yuri Leonenko
https://arxiv.org/abs/2508.09295

Boreal Afforestation's Underestimated Cloud Influence on Earth's Energy Imbalance
Earth's Energy Imbalance (EEI) is accelerating, partly due to declining planetary albedo from reduced cloud cover. Boreal afforestation can either mitigate or exacerbate this trend through competing biophysical feedbacks. While snow masking reduces surface albedo (+0.5 to +2.5 $W/m^2$ warming), forests can enhance low-level cloud cover (0.1-0.5%) and increase cloud reflectivity via biogenic volatile organic compounds (BVOCs), producing potential cooling (-1.8 to -6.7 $W/m^2$). This BVOC-aerosol…

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Junyan Ye, Dongzhi Jiang, Zihao Wang, Leqi Zhu, Zhenghao Hu, Zilong Huang, Jun He, Zhiyuan Yan, Jinghua Yu, Hongsheng Li, Conghui He, Weijia Li
https://arxiv.org/abs/2508.09987

Echo-4o: Harnessing the Power of GPT-4o Synthetic Images for Improved Image Generation
Recently, GPT-4o has garnered significant attention for its strong performance in image generation, yet open-source models still lag behind. Several studies have explored distilling image data from GPT-4o to enhance open-source models, achieving notable progress. However, a key question remains: given that real-world image datasets already constitute a natural source of high-quality data, why should we use GPT-4o-generated synthetic data? In this work, we identify two key advantages of syntheti…

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Peng Wang, Xuesi Hu, Jiageng Wu, Yuntao Zou, Qiancheng Zhang, Dagang Li
https://arxiv.org/abs/2507.08339

What Factors Affect LLMs and RLLMs in Financial Question Answering?
Recently, the development of large language models (LLMs) and reasoning large language models (RLLMs) have gained considerable attention from many researchers. RLLMs enhance the reasoning capabilities of LLMs through Long Chain-of-Thought (Long CoT) processes, significantly improving the performance of LLMs in addressing complex problems. However, there are few works that systematically explore what methods can fully unlock the performance of LLMs and RLLMs within the financial domain. To inves…

Quasi-1D Electronic Metadevices with Enhanced Electrical Properties
Abdallah Abushawish, Ziwen Huang, Mohammad Samizadeh Nikoo
https://arxiv.org/abs/2508.09656 https://

Quasi-1D Electronic Metadevices with Enhanced Electrical Properties
Electronic metadevices leveraging sub-wavelength metallic features have shown great potential for high-frequency switching. Theoretical analysis based on a one-dimensional (1D) model indicates that reducing the size of subwavelength features can enhance electrical properties, such as contact resistance and switching cutoff frequency. Here, we report higher-than-expected contact resistance in electronic metadevices when the subwavelength feature size is aggressively downscaled. We attribute this…

Replaced article(s) found for physics.comp-ph. https://arxiv.org/list/physics.comp-ph/new/
[1/1]:
Defects Enhance Stability in 12-fold Symmetric Soft-Matter Quasicrystals

HapticGiant: A Novel Very Large Kinesthetic Haptic Interface with Hierarchical Force Control
Michael Fennel, Markus Walker, Dominik Pikos, Uwe D. Hanebeck
https://arxiv.org/abs/2508.09595

HapticGiant: A Novel Very Large Kinesthetic Haptic Interface with Hierarchical Force Control
Research in virtual reality and haptic technologies has consistently aimed to enhance immersion. While advanced head-mounted displays are now commercially available, kinesthetic haptic interfaces still face challenges such as limited workspaces, insufficient degrees of freedom, and kinematics not matching the human arm. In this paper, we present HapticGiant, a novel large-scale kinesthetic haptic interface designed to match the properties of the human arm as closely as possible and to facilitat…

DEV: A Driver-Environment-Vehicle Closed-Loop Framework for Risk-Aware Adaptive Automation of Driving
Ana\"is Halin, Christel Devue, Marc Van Droogenbroeck
https://arxiv.org/abs/2508.10618

DEV: A Driver-Environment-Vehicle Closed-Loop Framework for Risk-Aware Adaptive Automation of Driving
The increasing integration of automation in vehicles aims to enhance both safety and comfort, but it also introduces new risks, including driver disengagement, reduced situation awareness, and mode confusion. In this work, we propose the DEV framework, a closed-loop framework for risk-aware adaptive driving automation that captures the dynamic interplay between the driver, the environment, and the vehicle. The framework promotes to continuously adjusting the operational level of automation base…

LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations
Junxiao Han, Yarong Wang, Xiaodong Gu, Cuiyun Gao, Yao Wan, Song Han, David Lo, Shuiguang Deng
https://arxiv.org/abs/2508.09791

LibRec: Benchmarking Retrieval-Augmented LLMs for Library Migration Recommendations
In this paper, we propose LibRec, a novel framework that integrates the capabilities of LLMs with retrieval-augmented generation(RAG) techniques to automate the recommendation of alternative libraries. The framework further employs in-context learning to extract migration intents from commit messages to enhance the accuracy of its recommendations. To evaluate the effectiveness of LibRec, we introduce LibEval, a benchmark designed to assess the performance in the library migration recommendation…

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Richa Dasila, Vatsala Upadhyay, Samo Bobek, Abhishek Vaish
https://arxiv.org/abs/2508.10652 …

A Novel Study on Intelligent Methods and Explainable AI for Dynamic Malware Analysis
Deep learning models are one of the security strategies, trained on extensive datasets, and play a critical role in detecting and responding to these threats by recognizing complex patterns in malicious code. However, the opaque nature of these models-often described as "black boxes"-makes their decision-making processes difficult to understand, even for their creators. This research addresses these challenges by integrating Explainable AI (XAI) techniques to enhance the interpretability and tr…

Apple adds a 3x playback option for podcasts in iOS26, an Enhance Dialogue tool to hear speech more clearly, and a puzzle game for Apple News Plus subscribers (Jay Peters/The Verge)
https://www.theverge.com/news/684708/apple-podcasts-speed-news-emoji-game…

Apple turns up the speed on Podcasts and adds a new emoji game to News
Apple will let users listen to podcasts at up to 3x speed in the Podcast app, and Apple News Plus subscribers will be able to play a new Emoji Game.

Is Sparse Matrix Reordering Effective for Sparse Matrix-Vector Multiplication?
Omid Asudeh, Sina Mahdipour Saravani, Gerald Sabin, Fabrice Rastello, P Sadayappan
https://arxiv.org/abs/2506.10356

Is Sparse Matrix Reordering Effective for Sparse Matrix-Vector Multiplication?
This work evaluates the impact of sparse matrix reordering on the performance of sparse matrix-vector multiplication across different multicore CPU platforms. Reordering can significantly enhance performance by optimizing the non-zero element patterns to reduce total data movement and improve the load-balancing. We examine how these gains vary over different CPUs for different reordering strategies, focusing on both sequential and parallel execution. We address multiple aspects, including appro…

Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment
Farzana Zahid, Anjalika Sewwandi, Lee Brandon, Vimal Kumar, Roopak Sinha
https://arxiv.org/abs/2508.08629 …

Securing Educational LLMs: A Generalised Taxonomy of Attacks on LLMs and DREAD Risk Assessment
Due to perceptions of efficiency and significant productivity gains, various organisations, including in education, are adopting Large Language Models (LLMs) into their workflows. Educator-facing, learner-facing, and institution-facing LLMs, collectively, Educational Large Language Models (eLLMs), complement and enhance the effectiveness of teaching, learning, and academic operations. However, their integration into an educational setting raises significant cybersecurity concerns. A comprehensi…

The DKU System for Multi-Speaker Automatic Speech Recognition in MLC-SLM Challenge
Yuke Lin, Ming Cheng, Ze Li, Ming Li
https://arxiv.org/abs/2507.09499 ht…

The DKU System for Multi-Speaker Automatic Speech Recognition in MLC-SLM Challenge
We present the DKU system for Task 2 of the MLC-SLM Challenge, which aims to perform multi-speaker automatic speech recognition directly from raw audio without Oracle speaker labels or time boundaries. Our approach builds upon a diarization-aware framework integrating speaker embeddings and temporal utterance boundaries into a Qwen2.5-based large language model (LLM). Then, we enhance the system's multilingual performance by fine-tuning language-specific adapters and LoRA modules within the LLM…

"The real threat posed by generative AI is not that it will eliminate work on a mass scale, rendering human labour obsolete. It is that, left unchecked, it will continue to transform work in ways that deepen precarity, intensify surveillance, and widen existing inequalities."
"The current trajectory of generative AI reflects the priorities of firms seeking to lower costs, discipline workers, and consolidate profits — not any drive to enhance human flourishing. If we allo…

Is the AI Bubble About to Burst?
In the years since Automation and the Future of Work first appeared, a new wave of technological enthusiasm has swept across the popular imagination. The catalyst this time has been the rapid advances in generative artificial intelligence, spearheaded by companies like OpenAI, Google DeepMind, and Meta. Once again, a c

Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Masahiro Yasuda, Binh Thien Nguyen, Noboru Harada, Romain Serizel, Mayank Mishra, Marc Delcroix, Shoko Araki, Daiki Takeuchi, Daisuke Niizumi, Yasunori Ohishi, Tomohiro Nakatani, Takao Kawamura, Nobutaka Ono
https://arxiv.org…

Description and Discussion on DCASE 2025 Challenge Task 4: Spatial Semantic Segmentation of Sound Scenes
Spatial Semantic Segmentation of Sound Scenes (S5) aims to enhance technologies for sound event detection and separation from multi-channel input signals that mix multiple sound events with spatial information. This is a fundamental basis of immersive communication. The ultimate goal is to separate sound event signals with 6 Degrees of Freedom (6DoF) information into dry sound object signals and metadata about the object type (sound event class) and representing spatial information, including d…

Optimal Voltage Control Using Online Exponential Barrier Method
Peng Zhang, Baosen Zhang
https://arxiv.org/abs/2506.10247 https://arx…

Optimal Voltage Control Using Online Exponential Barrier Method
This paper address the optimal voltage control problem of distribution systems with high penetration of inverter-based renewable energy resources, under inaccurate model information. We propose the online exponential barrier method that explicitly leverages the online feedback from grids to enhance the robustness to model inaccuracy and incorporates the voltage constraints to maintain the safety requirements. We provide analytical results on the optimal barrier parameter selection and sufficien…

EasyDRAM: An FPGA-based Infrastructure for Fast and Accurate End-to-End Evaluation of Emerging DRAM Techniques
O\u{g}uzhan Canpolat, Ataberk Olgun, David Novo, O\u{g}uz Ergin, Onur Mutlu
https://arxiv.org/abs/2506.10441

EasyDRAM: An FPGA-based Infrastructure for Fast and Accurate End-to-End Evaluation of Emerging DRAM Techniques
DRAM is a critical component of modern computing systems. Recent works propose numerous techniques (that we call DRAM techniques) to enhance DRAM-based computing systems' throughput, reliability, and computing capabilities (e.g., in-DRAM bulk data copy). Evaluating the system-wide benefits of DRAM techniques is challenging as they often require modifications across multiple layers of the computing stack. Prior works propose FPGA-based platforms for rapid end-to-end evaluation of DRAM techniques…

Today seems like a good day to repost this piece I wrote a few weeks back.
How CISOs can defend against Scattered Spider ransomware attacks
https://www.csoonline.com/article/3994369/how-cisos-can-defend-against-scattered-s…

How CISOs can defend against Scattered Spider ransomware attacks
CISOs should fortify help desk and employee defenses, enhance intrusion detection and tracking capabilities, and recognize that paying ransoms is not a viable strategy.

Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs
Behnoush Khavari, Mehran Shakerinava, Jayesh Khullar, Jerry Huang, Fran\c{c}ois Rivest, Siamak Ravanbakhsh, Sarath Chandar
https://arxiv.org/abs/2508.07395

Parity Requires Unified Input Dependence and Negative Eigenvalues in SSMs
Recent work has shown that LRNN models such as S4D, Mamba, and DeltaNet lack state-tracking capability due to either time-invariant transition matrices or restricted eigenvalue ranges. To address this, input-dependent transition matrices, particularly those that are complex or non-triangular, have been proposed to enhance SSM performance on such tasks. While existing theorems demonstrate that both input-independent and non-negative SSMs are incapable of solving simple state-tracking tasks, such…

MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
Tianqi Xiang, Yi Li, Qixiang Zhang, Xiaomeng Li
https://arxiv.org/abs/2508.09967 https://

MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
Recent advances in histopathology vision-language foundation models (VLFMs) have shown promise in addressing data scarcity for whole slide image (WSI) classification via zero-shot adaptation. However, these methods remain outperformed by conventional multiple instance learning (MIL) approaches trained on large datasets, motivating recent efforts to enhance VLFM-based WSI classification through fewshot learning paradigms. While existing few-shot methods improve diagnostic accuracy with limited a…

UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech
Shuhei Kato
https://arxiv.org/abs/2508.09767 https://ar…

UtterTune: LoRA-Based Target-Language Pronunciation Edit and Control in Multilingual Text-to-Speech
We propose UtterTune, a lightweight adaptation method that fine-tunes a multilingual text-to-speech (TTS) system based on a large language model (LLM) architecture, designed to enhance the controllability of pronunciation in a target language while preserving performance in others. While LLM architectures have enabled TTS models to achieve remarkable naturalness, accurately modeling grapheme-to-phoneme (G2P) mapping and prosody remains challenging, especially when the model omits an explicit G2…

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises
Minghui Du, Ziren Luo, Peng Xu
https://arxiv.org/abs/2506.10599

Enhancing Taiji's Parameter Estimation under Non-Stationarity: a Time-Frequency Domain Framework for Galactic Binaries and Instrumental Noises
The data analysis of space-based gravitational wave detectors like Taiji faces significant challenges from non-stationary noise, which compromises the efficacy of traditional frequency-domain analysis. This work proposes a unified framework based on short-time Fourier transform (STFT) to enhance parameter estimation of Galactic binary and characterization of instrumental noise under non-stationarity. Segmenting data into locally stationary intervals, we derive STFT-based models for signals and …

The IceCube Collaboration -- Contributions to the 39th International Cosmic Ray Conference (ICRC2025)
R. Abbasi, et al.
https://arxiv.org/abs/2507.08666 ht…

The IceCube Collaboration -- Contributions to the 39th International Cosmic Ray Conference (ICRC2025)
The IceCube Observatory at the South Pole has been operating in its full configuration since May 2011 with a duty cycle of about 99%. Its main component consists of a cubic-kilometer array of optical sensors deployed deep in the Glacial ice designed for the detection of high-energy astrophysical neutrinos. A surface array for cosmic ray air shower detection, IceTop, and a denser inner subdetector, DeepCore, significantly enhance the capabilities of the observatory, making it a multipurpose faci…

LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework
Azin Sabzian, Mohammad Jalili Torkamani, Negin Mahmoudi, Kiana Kiashemshaki
https://arxiv.org/abs/2508.08535

LLM-Driven Adaptive 6G-Ready Wireless Body Area Networks: Survey and Framework
Wireless Body Area Networks (WBANs) enable continuous monitoring of physiological signals for applications ranging from chronic disease management to emergency response. Recent advances in 6G communications, post-quantum cryptography, and energy harvesting have the potential to enhance WBAN performance. However, integrating these technologies into a unified, adaptive system remains a challenge. This paper surveys some of the most well-known Wireless Body Area Network (WBAN) architectures, routi…

Spatial and Pulse Efficiency Constraints in Atom Interferometric Gravitational Wave Detectors
Patrik Schach, Enno Giese
https://arxiv.org/abs/2506.09511 ht…

Spatial and Pulse Efficiency Constraints in Atom Interferometric Gravitational Wave Detectors
Currently planned and constructed terrestrial detectors for gravitational waves and dark matter based on differential light-pulse atom interferometry are designed around three primary strategies to enhance their sensitivity: (i) Resonant-mode enhancement using multiple diamonds, (ii) large-momentum-transfer techniques to increase arm separation within the interferometer, and (iii) very-long baseline schemes that increase the distance between the two interferometers. Both resonant-mode enhanceme…

Enhanced randomized Douglas-Rachford method: Improved probabilities and adaptive momentum
Liqi Guo, Ruike Xiang, Deren Han, Jiaxin Xie
https://arxiv.org/abs/2506.10261

Enhanced randomized Douglas-Rachford method: Improved probabilities and adaptive momentum
Randomized iterative methods have gained recent interest in machine learning and signal processing for solving large-scale linear systems. One such example is the randomized Douglas-Rachford (RDR) method, which updates the iterate by reflecting it through two randomly selected hyperplanes and taking a convex combination with the current point. In this work, we enhance RDR by introducing improved sampling strategies and an adaptive heavy-ball momentum scheme. Specifically, we incorporate without…

Token Perturbation Guidance for Diffusion Models
Javad Rajabi, Soroush Mehraban, Seyedmorteza Sadat, Babak Taati
https://arxiv.org/abs/2506.10036 https://

Token Perturbation Guidance for Diffusion Models
Classifier-free guidance (CFG) has become an essential component of modern diffusion models to enhance both generation quality and alignment with input conditions. However, CFG requires specific training procedures and is limited to conditional generation. To address these limitations, we propose Token Perturbation Guidance (TPG), a novel method that applies perturbation matrices directly to intermediate token representations within the diffusion network. TPG employs a norm-preserving shuffling…

Learning Robust Motion Skills via Critical Adversarial Attacks for Humanoid Robots
Yang Zhang, Zhanxiang Cao, Buqing Nie, Haoyang Li, Yue Gao
https://arxiv.org/abs/2507.08303

Learning Robust Motion Skills via Critical Adversarial Attacks for Humanoid Robots
Humanoid robots show significant potential in daily tasks. However, reinforcement learning-based motion policies often suffer from robustness degradation due to the sim-to-real dynamics gap, thereby affecting the agility of real robots. In this work, we propose a novel robust adversarial training paradigm designed to enhance the robustness of humanoid motion policies in real worlds. The paradigm introduces a learnable adversarial attack network that precisely identifies vulnerabilities in motio…

Artificial intelligence-enabled precision medicine for inflammatory skin diseases
Alice Tang, Maria Wei, Anna Haemel, Cindy La, Marina Sirota, Ernest Y. Lee
https://arxiv.org/abs/2505.09527

Artificial intelligence-enabled precision medicine for inflammatory skin diseases
Recent advances in artificial intelligence (AI) and multimodal data collection are revolutionizing dermatology. Generative AI and machine learning approaches offer opportunities to enhance the diagnosis and treatment of inflammatory skin diseases, including atopic dermatitis, psoriasis, hidradenitis suppurativa, and autoimmune connective tissue disease. This review examines the current landscape of AI applications for inflammatory skin diseases and explores how generative AI and machine learnin…

When and How Ultrasound Enhances Nanoparticle Diffusion in Hydrogels: A Stick-and-Release Mechanism
Pablo M. Blanco, Hedda H. R{\o}nneberg, Rita S. Dias
https://arxiv.org/abs/2508.08918

When and How Ultrasound Enhances Nanoparticle Diffusion in Hydrogels: A Stick-and-Release Mechanism
Nanoparticles (NPs) are widely used as drug carriers in cancer therapy due to their ability to accumulate in tumor tissue via the enhanced permeability and retention effect. However, their transport within tumors is often hindered by the dense extracellular matrix, where diffusion dominates. Several studies suggest that ultrasound (US) irradiation can enhance NP diffusion in ECM-mimicking hydrogels, yet the underlying molecular mechanisms remain unclear, and experimental findings are often cont…

It would enhance Alan Dershowitz's reputation if he voluntarily admitted to statutory rape.

#AI impact on work force: killing jobs, enhancing jobs or what?
Interesting read: https://www.ineteconomics.org/perspectives/b…

Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction
Haonan Zhang, Guoyan Lao, Yuyao Zhang, Hongjiang Wei
https://arxiv.org/abs/2506.09100

Low-Rank Augmented Implicit Neural Representation for Unsupervised High-Dimensional Quantitative MRI Reconstruction
Quantitative magnetic resonance imaging (qMRI) provides tissue-specific parameters vital for clinical diagnosis. Although simultaneous multi-parametric qMRI (MP-qMRI) technologies enhance imaging efficiency, robustly reconstructing qMRI from highly undersampled, high-dimensional measurements remains a significant challenge. This difficulty arises primarily because current reconstruction methods that rely solely on a single prior or physics-informed model to solve the highly ill-posed inverse pr…

Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Zhixiang Su, Di Wang, Chunyan Miao
https://arxiv.org/abs/2507.07595

Context Pooling: Query-specific Graph Pooling for Generic Inductive Link Prediction in Knowledge Graphs
Recent investigations on the effectiveness of Graph Neural Network (GNN)-based models for link prediction in Knowledge Graphs (KGs) show that vanilla aggregation does not significantly impact the model performance. In this paper, we introduce a novel method, named Context Pooling, to enhance GNN-based models' efficacy for link predictions in KGs. To our best of knowledge, Context Pooling is the first methodology that applies graph pooling in KGs. Additionally, Context Pooling is first-of-its-ki…

Harnessing Input-Adaptive Inference for Efficient VLN
Dongwoo Kang, Akhil Perincherry, Zachary Coalson, Aiden Gabriel, Stefan Lee, Sanghyun Hong
https://arxiv.org/abs/2508.09262

Harnessing Input-Adaptive Inference for Efficient VLN
An emerging paradigm in vision-and-language navigation (VLN) is the use of history-aware multi-modal transformer models. Given a language instruction, these models process observation and navigation history to predict the most appropriate action for an agent. While they have significantly improved performance, the scale of these models can be a bottleneck in practical settings with limited computational resources. In this work, we propose a novel input-adaptive navigation method to enhance VLN …

Enhanced superconductivity in ultrathin FeSe films on SrTiO3 via resonant anti-shielding: Superconductivity meets superfluidity
Krzysztof Kempa, Michael J. Naughton, Hanno H. Weitering
https://arxiv.org/abs/2508.09063

Enhanced superconductivity in ultrathin FeSe films on SrTiO3 via resonant anti-shielding: Superconductivity meets superfluidity
A vanishingly small dielectric function reflects a singular polarization response in a medium, leading to collective plasmonic or polaronic excitations that can enhance Cooper pairing in superconductors via a resonant anti-shielding (RAS) effect. Here, we show that RAS can explain the dramatic enhancement of superconductivity-relative to bulk FeSe, observed in single-unit-cell FeSe films on SrTiO$_3$ (STO) and related substrates. Moreover, we present evidence that RAS may play a central role in…

Joint Beamforming Optimization for Pinching-Antenna Systems (PASS)-assisted Symbiotic Radio
Ze Wang, Guoping Zhang, Hongbo Xu
https://arxiv.org/abs/2508.07002 https://

Joint Beamforming Optimization for Pinching-Antenna Systems (PASS)-assisted Symbiotic Radio
This paper investigates a novel downlink symbiotic radio (SR) framework empowered by the pinching antenna system (PASS), aiming to enhance both primary and secondary transmissions through reconfigurable antenna positioning. PASS consists of multiple waveguides equipped with numerous low-cost pinching antennas (PAs), whose positions can be flexibly adjusted to simultaneously manipulate large-scale path loss and signal phases.We formulate a joint transmit and pinching beamforming optimization pro…

Multilingual Multimodal Software Developer for Code Generation
Linzheng Chai, Jian Yang, Shukai Liu, Wei Zhang, Liran Wang, Ke Jin, Tao Sun, Congnan Liu, Chenchen Zhang, Hualei Zhu, Jiaheng Liu, Xianjie Wu, Ge Zhang, Tianyu Liu, Zhoujun Li
https://arxiv.org/abs/2507.08719

Multilingual Multimodal Software Developer for Code Generation
The rapid advancement of Large Language Models (LLMs) has significantly improved code generation, yet most models remain text-only, neglecting crucial visual aids like diagrams and flowcharts used in real-world software development. To bridge this gap, we introduce MM-Coder, a Multilingual Multimodal software developer. MM-Coder integrates visual design inputs-Unified Modeling Language (UML) diagrams and flowcharts (termed Visual Workflow)-with textual instructions to enhance code generation ac…

Alcohol additives to enhance ammonia-methane combustion efficiency and reduce emissions: A reactive force field analysis
Amirali Shateri, Zhiyin Yang, Jianfei Xie, Nasser Sherkat
https://arxiv.org/abs/2507.06314

Alcohol additives to enhance ammonia-methane combustion efficiency and reduce emissions: A reactive force field analysis
Exploring the impact of alcohol additives on combustion and pyrolysis of ammonia/methane is of great importance in the pursuit of sustainable energy technologies. This work employs Reactive Force Field (ReaxFF) molecular dynamics (MD) simulations to investigate the underlying mechanism of how ethanol and methanol additives affect reaction pathways, NOx emissions and bond energy characteristics in ammonia-methane pyrolysis and combustion processes. It shows that adding alcohols altered NOx forma…

Large Language Models for Design Structure Matrix Optimization
Shuo Jiang, Min Xie, Jianxi Luo
https://arxiv.org/abs/2506.09749 https://

Large Language Models for Design Structure Matrix Optimization
In complex engineering systems, the interdependencies among components or development activities are often modeled and analyzed using Design Structure Matrix (DSM). Reorganizing elements within a DSM to minimize feedback loops and enhance modularity or process efficiency constitutes a challenging combinatorial optimization (CO) problem in engineering design and operations. As problem sizes increase and dependency networks become more intricate, traditional optimization methods that solely use m…

Leveraging GNN to Enhance MEF Method in Predicting ENSO
Saghar Ganji, Mohammad Naisipour
https://arxiv.org/abs/2508.07410 https://arxiv.org/pdf/2508.07410

Leveraging GNN to Enhance MEF Method in Predicting ENSO
Reliable long-lead forecasting of the El Nino Southern Oscillation (ENSO) remains a long-standing challenge in climate science. The previously developed Multimodal ENSO Forecast (MEF) model uses 80 ensemble predictions by two independent deep learning modules: a 3D Convolutional Neural Network (3D-CNN) and a time-series module. In their approach, outputs of the two modules are combined using a weighting strategy wherein one is prioritized over the other as a function of global performance. Sepa…

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Junkai Jiang, Yihe Chen, Yibin Yang, Ruochen Li, Shaobing Xu, Jianqiang Wang
https://arxiv.org/abs/2508.09581

ESCoT: An Enhanced Step-based Coordinate Trajectory Planning Method for Multiple Car-like Robots
Multi-vehicle trajectory planning (MVTP) is one of the key challenges in multi-robot systems (MRSs) and has broad applications across various fields. This paper presents ESCoT, an enhanced step-based coordinate trajectory planning method for multiple car-like robots. ESCoT incorporates two key strategies: collaborative planning for local robot groups and replanning for duplicate configurations. These strategies effectively enhance the performance of step-based MVTP methods. Through extensive ex…

Intracortical bipolar stimulation allows selective activation of neuronal populations in the cortex #BCI

Intracortical bipolar stimulation allows selective activation of neuronal populations in the cortex
Background Intracortical electrical stimulation has emerged as a promising approach for sensory restoration, such as a cortical visual prosthesis, yet its effectiveness is limited by current spread and electrode density constraints. Objective To determine whether intracortical bipolar current steering—via modulation of the return electrode position—can enhance neural activation selectivity compared to traditional monopolar stimulation, with the aim of improving spatial precision in sensory…

Influence of photon-magnon coupling to enhance spin-wave excitation
Liubov Ivzhenko, Sergey Polevoy, Sergey Nedukh, Maciej Krawczyk
https://arxiv.org/abs/2506.09808

Influence of photon-magnon coupling to enhance spin-wave excitation
One of the main challenges in magnonics is the efficiency of the conversion of microwave signals into spin waves. This efficiency is low due to the significant mismatch between microwave and spin wave wavelengths in the GHz range $10^{-2}$ m and $10^{-8}$ m, respectively, leading to high energy consumption in magnonic circuits. To address this issue, we propose an approach based on a planar inverse split-ring resonator (ISRR) loaded with a nanometer-thick Py film and exploiting the photon-magno…

Neural Bridge Processes
Jian Xu, Yican Liu, Qibin Zhao, John Paisley, Delu Zeng
https://arxiv.org/abs/2508.07220 https://arxiv.org/pdf/2508.07220

Neural Bridge Processes
Learning stochastic functions from partially observed context-target pairs is a fundamental problem in probabilistic modeling. Traditional models like Gaussian Processes (GPs) face scalability issues with large datasets and assume Gaussianity, limiting their applicability. While Neural Processes (NPs) offer more flexibility, they struggle with capturing complex, multi-modal target distributions. Neural Diffusion Processes (NDPs) enhance expressivity through a learned diffusion process but rely …

Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models
Marc Oriol, Quim Motger, Jordi Marco, Xavier Franch
https://arxiv.org/abs/2507.05981

Multi-Agent Debate Strategies to Enhance Requirements Engineering with Large Language Models
Context: Large Language Model (LLM) agents are becoming widely used for various Requirements Engineering (RE) tasks. Research on improving their accuracy mainly focuses on prompt engineering, model fine-tuning, and retrieval augmented generation. However, these methods often treat models as isolated black boxes - relying on single-pass outputs without iterative refinement or collaboration, limiting robustness and adaptability. Objective: We propose that, just as human debates enhance accuracy a…

Empowering Children to Create AI-Enabled Augmented Reality Experiences
Lei Zhang, Shuyao Zhou, Amna Liaqat, Tinney Mak, Brian Berengard, Emily Qian, Andr\'es Monroy-Hern\'andez
https://arxiv.org/abs/2508.08467

Empowering Children to Create AI-Enabled Augmented Reality Experiences
Despite their potential to enhance children's learning experiences, AI-enabled AR technologies are predominantly used in ways that position children as consumers rather than creators. We introduce Capybara, an AR-based and AI-powered visual programming environment that empowers children to create, customize, and program 3D characters overlaid onto the physical world. Capybara enables children to create virtual characters and accessories using text-to-3D generative AI models, and to animate thes…

Broadband Tunable Deep-UV Emission from AI-Optimized Nonlinear Metasurface Architectures
Omar A. M. Abdelraouf
https://arxiv.org/abs/2506.10442 https://

Broadband Tunable Deep-UV Emission from AI-Optimized Nonlinear Metasurface Architectures
Metasurfaces represent a pivotal advancement in nonlinear optics, leveraging high-Q resonant cavities to enhance harmonic generation. Multi-layer metasurfaces (MLMs) further amplify this potential by intensifying light-matter interactions within individual meta-atoms at the nanoscale. However, maximizing nonlinear efficiency demands extreme field confinement through optimized designs of large geometric and material parameters, which exceed traditional simulation's computational ability. To over…

AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of Visuomotor Policies
Yinpei Dai, Jayjun Lee, Yichi Zhang, Ziqiao Ma, Jed Yang, Amir Zadeh, Chuan Li, Nima Fazeli, Joyce Chai
https://arxiv.org/abs/2508.08113

AimBot: A Simple Auxiliary Visual Cue to Enhance Spatial Awareness of Visuomotor Policies
In this paper, we propose AimBot, a lightweight visual augmentation technique that provides explicit spatial cues to improve visuomotor policy learning in robotic manipulation. AimBot overlays shooting lines and scope reticles onto multi-view RGB images, offering auxiliary visual guidance that encodes the end-effector's state. The overlays are computed from depth images, camera extrinsics, and the current end-effector pose, explicitly conveying spatial relationships between the gripper and obje…

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Morteza Kimiaei, Vyacheslav Kungurtsev, Brian Olimba
https://arxiv.org/abs/2508.06906

Machine Learning Algorithms for Improving Exact Classical Solvers in Mixed Integer Continuous Optimization
Integer and mixed-integer nonlinear programming (INLP, MINLP) are central to logistics, energy, and scheduling, but remain computationally challenging. This survey examines how machine learning and reinforcement learning can enhance exact optimization methods - particularly branch-and-bound (BB), without compromising global optimality. We cover discrete, continuous, and mixed-integer formulations, and highlight applications such as crew scheduling, vehicle routing, and hydropower planning. We i…

Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement
Xiao Yang, Yuxuan Fan, Can Liu, Houcheng Su, Weichen Guo, Jiyao Wang, Dengbo He
https://arxiv.org/abs/2507.07908

Not Only Consistency: Enhance Test-Time Adaptation with Spatio-temporal Inconsistency for Remote Physiological Measurement
Remote photoplethysmography (rPPG) has emerged as a promising non-invasive method for monitoring physiological signals using the camera. Although various domain adaptation and generalization methods were proposed to promote the adaptability of deep-based rPPG models in unseen deployment environments, considerations in aspects like privacy concerns and real-time adaptation restrict their application in real-world deployment. Thus, we aim to propose a novel fully Test-Time Adaptation (TTA) strate…

ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning
Yichen Lu, Wei Dai, Jiaen Liu, Ching Wing Kwok, Zongheng Wu, Xudong Xiao, Ao Sun, Sheng Fu, Jianyuan Zhan, Yian Wang, Takatomo Saito, Sicheng Lai
https://arxiv.org/abs/2507.07306

ViDove: A Translation Agent System with Multimodal Context and Memory-Augmented Reasoning
LLM-based translation agents have achieved highly human-like translation results and are capable of handling longer and more complex contexts with greater efficiency. However, they are typically limited to text-only inputs. In this paper, we introduce ViDove, a translation agent system designed for multimodal input. Inspired by the workflow of human translators, ViDove leverages visual and contextual background information to enhance the translation process. Additionally, we integrate a multimo…

Channel Charting in Smart Radio Environments
Mahdi Maleki, Reza Agahzadeh Ayoubi, Marouan Mizmizi, Umberto Spagnolini
https://arxiv.org/abs/2508.07305 https://

Channel Charting in Smart Radio Environments
This paper introduces the use of static electromagnetic skins (EMSs) to enable robust device localization via channel charting (CC) in realistic urban environments. We develop a rigorous optimization framework that leverages EMS to enhance channel dissimilarity and spatial fingerprinting, formulating EMS phase profile design as a codebook-based problem targeting the upper quantiles of key embedding metric, localization error, trustworthiness, and continuity. Through 3D ray-traced simulations of…

Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Yuqin Dai, Shuo Yang, Guoqing Wang, Yong Deng, Zhanwei Zhang, Jun Yin, Pengyu Zeng, Zhenzhe Ying, Changhua Meng, Can Yi, Yuchen Zhou, Weiqiang Wang, Shuai Lu
https://arxiv.org/abs/2508.07956…

Careful Queries, Credible Results: Teaching RAG Models Advanced Web Search Tools with Reinforcement Learning
Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by integrating up-to-date external knowledge, yet real-world web environments present unique challenges. These limitations manifest as two key challenges: pervasive misinformation in the web environment, which introduces unreliable or misleading content that can degrade retrieval accuracy, and the underutilization of web tools, which, if effectively employed, could enhance query precision and help mitigate this noise, ul…

Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations
Andrea Gaggioli, Sabrina Bartolotta, Andrea Ubaldi, Katusha Gerardini, Eleonora Diletta Sarcinella, Alice Chirico
https://arxiv.org/abs/2506.10249

Extended Creativity: A Conceptual Framework for Understanding Human-AI Creative Relations
Artificial Intelligence holds significant potential to enhance human creativity. However, achieving this vision requires a clearer understanding of how such enhancement can be effectively realized. Adopting the perspective of distributed creativity, we identify three primary modes through which AI can contribute to creative processes: Support, where AI acts as a tool; Synergy, where AI and humans collaborate in complementary ways; and Symbiosis, where human and AI cognition become so integrated…

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
Yu-Chao Hsu, Jiun-Cheng Jiang, Chun-Hua Lin, Wei-Ting Chen, Kuo-Chung Peng, Prayag Tiwari, Samuel Yen-Chi Chen, En-Jui Kuo
https://arxiv.org/abs/2508.06078

Federated Quantum Kernel-Based Long Short-term Memory for Human Activity Recognition
In this work, we introduce the Federated Quantum Kernel-Based Long Short-term Memory (Fed-QK-LSTM) framework, integrating the quantum kernel methods and Long Short-term Memory into federated learning.Within Fed-QK-LSTM framework, we enhance human activity recognition (HAR) in privacy-sensitive environments and leverage quantum computing for distributed learning systems.The DeepConv-QK-LSTM architecture on each client node employs convolutional layers for efficient local pattern capture, this de…

Weighted and unweighted enrichment strategies for solving the Poisson problem with Dirichlet boundary conditions
Francesco Dell'Accio, Luca Desiderio, Allal Guessab, Federico Nudo
https://arxiv.org/abs/2508.07238

Weighted and unweighted enrichment strategies for solving the Poisson problem with Dirichlet boundary conditions
In this paper, we propose weighted and unweighted enrichment strategies to enhance the accuracy of the linear lagrangian finite element for solving the Poisson problem with Dirichlet boundary conditions. We first recall key examples of admissible enrichment functions, specifically designed to overcome the limitations of the linear lagrangian finite element in capturing solution features such as sharp gradients and boundary-layer phenomena. We then introduce two novel three-parameter families of…

SinLlama - A Large Language Model for Sinhala
H. W. K. Aravinda, Rashad Sirajudeen, Samith Karunathilake, Nisansa de Silva, Surangika Ranathunga, Rishemjit Kaur
https://arxiv.org/abs/2508.09115

SinLlama - A Large Language Model for Sinhala
Low-resource languages such as Sinhala are often overlooked by open-source Large Language Models (LLMs). In this research, we extend an existing multilingual LLM (Llama-3-8B) to better serve Sinhala. We enhance the LLM tokenizer with Sinhala specific vocabulary and perform continual pre-training on a cleaned 10 million Sinhala corpus, resulting in the SinLlama model. This is the very first decoder-based open-source LLM with explicit Sinhala support. When SinLlama was instruction fine-tuned for …

Efficient Modular Multiplier over GF (2^m) for ECPM
Ruby Kumari, Gaurav Purohit, Abhijit Karmakar
https://arxiv.org/abs/2506.09464 https://

Efficient Modular Multiplier over GF (2^m) for ECPM
Elliptic curve cryptography (ECC) has emerged as the dominant public-key protocol, with NIST standardizing parameters for binary field GF(2^m) ECC systems. This work presents a hardware implementation of a Hybrid Multiplication technique for modular multiplication over binary field GF(2m), targeting NIST B-163, 233, 283, and 571 parameters. The design optimizes the combination of conventional multiplication (CM) and Karatsuba multiplication (KM) to enhance elliptic curve point multiplication (E…

This https://arxiv.org/abs/2506.01348 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Distributionally Robust Learning in Survival Analysis
We introduce an innovative approach that incorporates a Distributionally Robust Learning (DRL) approach into Cox regression to enhance the robustness and accuracy of survival predictions. By formulating a DRL framework with a Wasserstein distance-based ambiguity set, we develop a variant Cox model that is less sensitive to assumptions about the underlying data distribution and more resilient to model misspecification and data perturbations. By leveraging Wasserstein duality, we reformulate the …

Dirac edge states of two-dimensional altermagnetic topological crystalline insulators
Raghottam M. Sattigeri, Xujia Gong, Amar Fakhredine, Carmine Autieri, Giuseppe Cuono
https://arxiv.org/abs/2506.10782

Dirac edge states of two-dimensional altermagnetic topological crystalline insulators
Two-dimensional (2D) metallic altermagnets present exciting opportunities for both fundamental research and practical innovations. Their ability to enhance tunneling magnetoresistance in magnetic tunnel junctions, combined with the direct control of spin currents via electric fields, makes them highly promising for spintronic devices. Moreover, the rich electronic structure of altermagnets can host nontrivial topological phases. In particular, topological crystalline insulators are compounds wh…

Electric Vehicle Scheduling and Vehicle-to-Grid Integration in Microgrids
Nathan Cho, Andrea Lodi, Anna Scaglione
https://arxiv.org/abs/2508.06752 https://…

Electric Vehicle Scheduling and Vehicle-to-Grid Integration in Microgrids
The logistical challenges and high costs associated with procuring and transporting fuel to remote military bases underscore the need for sustainable and resilient energy solutions. Integrating renewable energy sources and electric vehicles into military microgrids offers a promising approach to enhance energy security and operational readiness. This paper explores the optimization of travel, charging, and discharging schedules for a fleet of military electric trucks, aiming to minimize relianc…

Robust Design of Beyond-Diagonal Reconfigurable Intelligent Surface Empowered RSMA-SWIPT System Under Channel Estimation Errors
Muhammad Asif, Zain Ali, Asim Ihsan, Ali Ranjha, Zhu Shoujin, Manzoor Ahmed, Xingwang Li, Symeon Chatzinotas
https://arxiv.org/abs/2508.08097

Robust Design of Beyond-Diagonal Reconfigurable Intelligent Surface Empowered RSMA-SWIPT System Under Channel Estimation Errors
This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy-efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, th…

From Noise to Knowledge: Interactive Summaries for Developer Alerts
Burak Yeti\c{s}tiren, Hong Jin Kang, Miryung Kim
https://arxiv.org/abs/2508.07169 https://

From Noise to Knowledge: Interactive Summaries for Developer Alerts
Programmers using bug-finding tools often review their reported warnings one by one. Based on the insight that identifying recurring themes and relationships can enhance the cognitive process of sensemaking, we propose CLARITY, which supports interpreting tool-generated warnings through interactive inquiry. CLARITY derives summary rules for custom grouping of related warnings with active feedback. As users mark warnings as interesting or uninteresting, CLARITY's rule inference algorithm surface…

Electromagnetic signatures from pulsar remnants of binary neutron star mergers: prospects for unique identification using multi-wavelength signatures
Mainak Mukhopadhyay, Shigeo S. Kimura
https://arxiv.org/abs/2506.09157

Electromagnetic signatures from pulsar remnants of binary neutron star mergers: prospects for unique identification using multi-wavelength signatures
Binary neutron star (BNS) mergers can result in the formation of long-lived magnetar remnants which can enhance neutrino and electromagnetic (EM) emissions. In this work, we study the resulting multi-wavelength EM emissions and the prospects of their detectability in the current and upcoming EM telescopes. We model the pulsar-wind neubla system where the long-lived pulsar with dipolar magnetic fields of $10^{13} - 10^{15}$ G (magnetar) spins down and is surrounded by an outward expanding nebula…

"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy
Eunhye Grace Ko, Shaini Nanayakkara, Earl W. Huff Jr
https://arxiv.org/abs/2506.06225

"We need to avail ourselves of GenAI to enhance knowledge distribution": Empowering Older Adults through GenAI Literacy
As generative AI (GenAI) becomes increasingly widespread, it is crucial to equip users, particularly vulnerable populations such as older adults (65 and older), with the knowledge to understand its benefits and potential risks. Older adults often exhibit greater reservations about adopting emerging technologies and require tailored literacy support. Using a mixed methods approach, this study examines strategies for delivering GenAI literacy to older adults through a chatbot named Litti, evaluat…

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Morteza Ziabakhsh, Kiyan Rezaee, Sadegh Eskandari, Seyed Amir Hossein Tabatabaei, Mohammad M. Ghassemi
https://arxiv.org/abs/2508.07486

Extracting Overlapping Microservices from Monolithic Code via Deep Semantic Embeddings and Graph Neural Network-Based Soft Clustering
Modern software systems are increasingly shifting from monolithic architectures to microservices to enhance scalability, maintainability, and deployment flexibility. Existing microservice extraction methods typically rely on hard clustering, assigning each software component to a single microservice. This approach often increases inter-service coupling and reduces intra-service cohesion. We propose Mo2oM (Monolithic to Overlapping Microservices), a framework that formulates microservice extract…

Multi-RIS Deployment Optimization for mmWave ISAC Systems in Real-World Environments
Yueheng Li, Xueyun Long, Mario Pauli, Suheng Tian, Xiang Wan, Benjamin Nuss, Tiejun Cui, Haixia Zhang, Thomas Zwick
https://arxiv.org/abs/2508.07226

Multi-RIS Deployment Optimization for mmWave ISAC Systems in Real-World Environments
Reconfigurable intelligent surface-assisted integrated sensing and communication (RIS-ISAC) presents a promising system architecture to leverage the wide bandwidth available at millimeter-wave (mmWave) frequencies, while mitigating severe signal propagation losses and reducing infrastructure costs. To enhance ISAC functionalities in the future air-ground integrated network applications, RIS deployment must be carefully designed and evaluated, which forms the core motivation of this paper. To en…

On the Ethics of Using LLMs for Offensive Security
Andreas Happe, J\"urgen Cito
https://arxiv.org/abs/2506.08693 https://arxiv.o…

On the Ethics of Using LLMs for Offensive Security
Large Language Models (LLMs) have rapidly evolved over the past few years and are currently evaluated for their efficacy within the domain of offensive cyber-security. While initial forays showcase the potential of LLMs to enhance security research, they also raise critical ethical concerns regarding the dual-use of offensive security tooling. This paper analyzes a set of papers that leverage LLMs for offensive security, focusing on how ethical considerations are expressed and justified in th…

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Wuwei Zhang, Fangcong Yin, Howard Yen, Danqi Chen, Xi Ye
https://arxiv.org/abs/2506.09944

Query-Focused Retrieval Heads Improve Long-Context Reasoning and Re-ranking
Recent work has identified retrieval heads (Wu et al., 2025b), a subset of attention heads responsible for retrieving salient information in long-context language models (LMs), as measured by their copy-paste behavior in Needle-in-a-Haystack tasks. In this paper, we introduce QRHEAD (Query-Focused Retrieval Head), an improved set of attention heads that enhance retrieval from long context. We identify QRHEAD by aggregating attention scores with respect to the input query, using a handful of exa…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
Bujar Raufi
https://arxiv.org/abs/2508.07283 https://arxiv.o…

Fine-Tuning Large Language Models Using EEG Microstate Features for Mental Workload Assessment
This study explores the intersection of electroencephalography (EEG) microstates and Large Language Models (LLMs) to enhance the assessment of cognitive load states. By utilizing EEG microstate features, the research aims to fine-tune LLMs for improved predictions of distinct cognitive states, specifically 'Rest' and 'Load'. The experimental design is delineated in four comprehensive stages: dataset collection and preprocessing, microstate segmentation and EEG backfitting, feature extraction pa…

Design of an innovative robotic surgical instrument for circular stapling
Paul Tucan (CESTER), Nadim Al Hajjar (UMF), Calin Vaida (CESTER), Alexandru Pusca (CESTER), Tiberiu Antal (CESTER), Corina Radu (UMF), Daniel Jucan (CESTER), Adrian Pisla (CESTER), Damien Chablat (LS2N, LS2N - \'equipe RoMas), Doina Pisla (CESTER)
https://

Design of an innovative robotic surgical instrument for circular stapling
Esophageal cancer remains a highly aggressive malignancy with low survival rates, requiring advanced surgical interventions like esophagectomy. Traditional manual techniques, including circular staplers, face challenges such as limited precision, prolonged recovery times, and complications like leaks and tissue misalignment. This paper presents a novel robotic circular stapler designed to enhance the dexterity in confined spaces, improve tissue alignment, and reduce post-operative risks. Integr…

What Builds Effective In-Context Examples for Code Generation?
Dongze Li, Songqiang Chen, Jialun Cao, Shing-Chi Cheung
https://arxiv.org/abs/2508.06414 https://

What Builds Effective In-Context Examples for Code Generation?
In-Context Learning (ICL) has emerged as a promising solution to enhance the code generation capabilities of Large Language Models (LLMs), which incorporates code examples inside the prompt to let LLMs learn from demonstrations. However, despite the substantial effectiveness of the code example-based ICL approach, the specific features (e.g., identifier naming styles, code formatting, solution insight) within the ICL-provided code examples that significantly contribute to the ICL's effectivenes…

"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Kellie Yu Hui Sim, Roy Ka-Wei Lee, Kenny Tsu Wei Choo
https://arxiv.org/abs/2506.09354

"Is This Really a Human Peer Supporter?": Misalignments Between Peer Supporters and Experts in LLM-Supported Interactions
Mental health is a growing global concern, prompting interest in AI-driven solutions to expand access to psychosocial support. Peer support, grounded in lived experience, offers a valuable complement to professional care. However, variability in training, effectiveness, and definitions raises concerns about quality, consistency, and safety. Large Language Models (LLMs) present new opportunities to enhance peer support interactions, particularly in real-time, text-based interactions. We present …

This https://arxiv.org/abs/2506.03978 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCL_…

Structured Pruning for Diverse Best-of-N Reasoning Optimization
Model pruning in transformer-based language models, traditionally viewed as a means of achieving computational savings, can enhance the model's reasoning capabilities. In this work, we uncover a surprising phenomenon: the selective pruning of certain attention heads leads to improvements in reasoning performance, particularly on challenging tasks. Motivated by this observation, we propose SPRINT, a novel contrastive learning framework that dynamically selects the optimal head and layer to prune…

Integrating Vision Foundation Models with Reinforcement Learning for Enhanced Object Interaction
Ahmad Farooq, Kamran Iqbal
https://arxiv.org/abs/2508.05838 https://

Integrating Vision Foundation Models with Reinforcement Learning for Enhanced Object Interaction
This paper presents a novel approach that integrates vision foundation models with reinforcement learning to enhance object interaction capabilities in simulated environments. By combining the Segment Anything Model (SAM) and YOLOv5 with a Proximal Policy Optimization (PPO) agent operating in the AI2-THOR simulation environment, we enable the agent to perceive and interact with objects more effectively. Our comprehensive experiments, conducted across four diverse indoor kitchen settings, demons…

This https://arxiv.org/abs/2409.05101 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

On the Need to Monitor Continuous Integration Practices -- An Empirical Study
Continuous Integration (CI) encompasses a set of widely adopted practices that enhance software development. However, there are indications that developers may not adequately monitor CI practices. Hence, this paper explores developers' perceptions regarding the monitoring CI practices. To achieve this, we first perform a Document Analysis to assess developers' expressed need for practice monitoring in pull requests comments generated by developers during the development process. After that, we …

Cyberbullying Detection via Aggression-Enhanced Prompting
Aisha Saeid, Anu Sabu, Girish A. Koushik, Ferrante Neri, Diptesh Kanojia
https://arxiv.org/abs/2508.06360 https://

Cyberbullying Detection via Aggression-Enhanced Prompting
Detecting cyberbullying on social media remains a critical challenge due to its subtle and varied expressions. This study investigates whether integrating aggression detection as an auxiliary task within a unified training framework can enhance the generalisation and performance of large language models (LLMs) in cyberbullying detection. Experiments are conducted on five aggression datasets and one cyberbullying dataset using instruction-tuned LLMs. We evaluated multiple strategies: zero-shot, …

Effects of Wrist-Worn Haptic Feedback on Force Accuracy and Task Speed during a Teleoperated Robotic Surgery Task
Brian B. Vuong, Josie Davidson, Sangheui Cheon, Kyujin Cho, Allison M. Okamura
https://arxiv.org/abs/2507.07327

Effects of Wrist-Worn Haptic Feedback on Force Accuracy and Task Speed during a Teleoperated Robotic Surgery Task
Previous work has shown that the addition of haptic feedback to the hands can improve awareness of tool-tissue interactions and enhance performance of teleoperated tasks in robot-assisted minimally invasive surgery. However, hand-based haptic feedback occludes direct interaction with the manipulanda of surgeon console in teleoperated surgical robots. We propose relocating haptic feedback to the wrist using a wearable haptic device so that haptic feedback mechanisms do not need to be integrated …

ReFuzzer: Feedback-Driven Approach to Enhance Validity of LLM-Generated Test Programs
Iti Shree, Karine Even-Mendoz, Tomasz Radzik
https://arxiv.org/abs/2508.03603 https://

ReFuzzer: Feedback-Driven Approach to Enhance Validity of LLM-Generated Test Programs
Existing LLM-based compiler fuzzers often produce syntactically or semantically invalid test programs, limiting their effectiveness in exercising compiler optimizations and backend components. We introduce ReFuzzer, a framework for refining LLM-generated test programs by systematically detecting and correcting compilation and runtime violations (e.g. division by zero or array out-of-bounds accesses). ReFuzzer employs a feedback loop with a local LLM to validate and filter erroneous programs bef…

RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
Sizhe Cheng, Jiaping Li, Huanchen Wang, Yuxin Ma
https://arxiv.org/abs/2508.06056

RAGTrace: Understanding and Refining Retrieval-Generation Dynamics in Retrieval-Augmented Generation
Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to enhance large language models (LLMs) by integrating external knowledge retrieval with generative capabilities. While significant advancements have been made in improving retrieval accuracy and response quality, a critical challenge remains that the internal knowledge integration and retrieval-generation interactions in RAG workflows are largely opaque. This paper introduces RAGTrace, an interactive evaluation s…

Dynamical Trajectory Planning of Disturbance Consciousness for Air-Land Bimodal Unmanned Aerial Vehicles
Shaoting Liu, Zhou Liu
https://arxiv.org/abs/2508.05972 https://

Dynamical Trajectory Planning of Disturbance Consciousness for Air-Land Bimodal Unmanned Aerial Vehicles
Air-land bimodal vehicles provide a promising solution for navigating complex environments by combining the flexibility of aerial locomotion with the energy efficiency of ground mobility. To enhance the robustness of trajectory planning under environmental disturbances, this paper presents a disturbance-aware planning framework that incorporates real-time disturbance estimation into both path searching and trajectory optimization. A key component of the framework is a disturbance-adaptive safet…