Tootfinder

Perspective-Aware AI in Extended Reality
Daniel Platnick, Matti Gruener, Marjan Alirezaie, Kent Larson, Dava J. Newman, Hossein Rahnama
https://arxiv.org/abs/2507.11479

Perspective-Aware AI in Extended Reality
AI-enhanced Extended Reality (XR) aims to deliver adaptive, immersive experiences-yet current systems fall short due to shallow user modeling and limited cognitive context. We introduce Perspective-Aware AI in Extended Reality (PAiR), a foundational framework for integrating Perspective-Aware AI (PAi) with XR to enable interpretable, context-aware experiences grounded in user identity. PAi is built on Chronicles: reasoning-ready identity models learned from multimodal digital footprints that ca…

Cost-aware Stopping for Bayesian Optimization
Qian Xie, Linda Cai, Alexander Terenin, Peter I. Frazier, Ziv Scully
https://arxiv.org/abs/2507.12453 https:/…

Cost-aware Stopping for Bayesian Optimization
In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive black-box functions is an important practical consideration. While several adaptive stopping rules have been proposed, in the cost-aware setting they lack guarantees ensuring they stop before incurring excessive function evaluation costs. We propose a cost-aware stopping rule for Bayesian optimization that adapts to varying evaluation costs and is free…

Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments
Jeongyong Yang, KwangBin Lee, SooJean Han
https://arxiv.org/abs/2507.11920 …

Hybrid Conformal Prediction-based Risk-Aware Model Predictive Planning in Dense, Uncertain Environments
Real-time path planning in dense, uncertain environments remains a challenging problem, as predicting the future motions of numerous dynamic obstacles is computationally burdensome and unrealistic. To address this, we introduce Hybrid Prediction-based Risk-Aware Planning (HyPRAP), a prediction-based risk-aware path-planning framework which uses a hybrid combination of models to predict local obstacle movement. HyPRAP uses a novel Prediction-based Collision Risk Index (P-CRI) to evaluate the ris…

Binarization-Aware Adjuster: Bridging Continuous Optimization and Binary Inference in Edge Detection
Hao Shu
https://arxiv.org/abs/2506.12460 https://

Binarization-Aware Adjuster: Bridging Continuous Optimization and Binary Inference in Edge Detection
Image edge detection (ED) faces a fundamental mismatch between training and inference: models are trained using continuous-valued outputs but evaluated using binary predictions. This misalignment, caused by the non-differentiability of binarization, weakens the link between learning objectives and actual task performance. In this paper, we propose a theoretical method to design a Binarization-Aware Adjuster (BAA), which explicitly incorporates binarization behavior into gradient-based optimizat…

A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
Kwassi Joseph Dzahini, Jeffrey M. Larson, Matt Menickelly, Stefan M. Wild
https://arxiv.org/abs/2507.10992

A Noise-Aware Scalable Subspace Classical Optimizer for the Quantum Approximate Optimization Algorithm
We introduce ANASTAARS, a noise-aware scalable classical optimizer for variational quantum algorithms such as the quantum approximate optimization algorithm (QAOA). ANASTAARS leverages adaptive random subspace strategies to efficiently optimize the ansatz parameters of a QAOA circuit, in an effort to address challenges posed by a potentially large number of QAOA layers. ANASTAARS iteratively constructs random interpolation models within low-dimensional affine subspaces defined via Johnson--Lind…

DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models
Yunnong Chen, Shixian Ding, YingYing Zhang, Wenkai Chen, Jinzhou Du, Lingyun Sun, Liuqing Chen
https://arxiv.org/abs/2506.13663

DesignCoder: Hierarchy-Aware and Self-Correcting UI Code Generation with Large Language Models
Multimodal large language models (MLLMs) have streamlined front-end interface development by automating code generation. However, these models also introduce challenges in ensuring code quality. Existing approaches struggle to maintain both visual consistency and functional completeness in the generated components. Moreover, they lack mechanisms to assess the fidelity and correctness of the rendered pages. To address these issues, we propose DesignCoder, a novel hierarchical-aware and self-corr…

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Yifan Gao, Shaohao Rui, Haoyang Su, Jinyi Xiang, Lianming Wu, Xiaosong Wang
https://arxiv.org/abs/2507.11886

A Composite Alignment-Aware Framework for Myocardial Lesion Segmentation in Multi-sequence CMR Images
Accurate segmentation of myocardial lesions from multi-sequence cardiac magnetic resonance imaging is essential for cardiac disease diagnosis and treatment planning. However, achieving optimal feature correspondence is challenging due to intensity variations across modalities and spatial misalignment caused by inconsistent slice acquisition protocols. We propose CAA-Seg, a composite alignment-aware framework that addresses these challenges through a two-stage approach. First, we introduce a sel…

Asymptotically Optimal Repair of Reed-Solomon Codes with Small Sub-Packetization under Rack-Aware Model
Ke Wang, Zhongyan Liu, Rengang Li, Yaqian Zhao, Yaqiang Zhang
https://arxiv.org/abs/2507.11009

Asymptotically Optimal Repair of Reed-Solomon Codes with Small Sub-Packetization under Rack-Aware Model
This paper presents a comprehensive study on the asymptotically optimal repair of Reed-Solomon (RS) codes with small sub-packetization, specifically tailored for rack-aware distributed storage systems. Through the utilization of multi-base expansion, we introduce a novel approach that leverages monomials to construct linear repair schemes for RS codes. Our repair schemes which adapt to all admissible parameters achieve asymptotically optimal repair bandwidth while significantly reducing the sub…

Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translation
Ziyu Ge, Gabriel Chua, Leanne Tan, Roy Ka-Wei Lee
https://arxiv.org/abs/2507.11966 …

Toxicity-Aware Few-Shot Prompting for Low-Resource Singlish Translation
As online communication increasingly incorporates under-represented languages and colloquial dialects, standard translation systems often fail to preserve local slang, code-mixing, and culturally embedded markers of harmful speech. Translating toxic content between low-resource language pairs poses additional challenges due to scarce parallel data and safety filters that sanitize offensive expressions. In this work, we propose a reproducible, two-stage framework for toxicity-preserving translat…

A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
Mingcong Li
https://arxiv.org/abs/2507.11420 https://arxiv.o…

A Risk-Aware Adaptive Robust MPC with Learned Uncertainty Quantification
Solving chance-constrained optimal control problems for systems subject to non-stationary uncertainties is a significant challenge.Conventional robust model predictive control (MPC) often yields excessive conservatism by relying on static worst-case assumptions, while standard stochastic MPC methods struggle when underlying uncertainty distributions are unknown a priori.This article presents a Risk-Aware Adaptive Robust MPC (RAAR-MPC) framework,a hierarchical architecture that systematically or…

KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis
Zhijie Liu, Qiyi Tang, Sen Nie, Shi Wu, Liang Feng Zhang, Yutian Tang
https://arxiv.org/abs/2506.11612

KEENHash: Hashing Programs into Function-Aware Embeddings for Large-Scale Binary Code Similarity Analysis
Binary code similarity analysis (BCSA) is a crucial research area in many fields such as cybersecurity. Specifically, function-level diffing tools are the most widely used in BCSA: they perform function matching one by one for evaluating the similarity between binary programs. However, such methods need a high time complexity, making them unscalable in large-scale scenarios (e.g., 1/n-to-n search). Towards effective and efficient program-level BCSA, we propose KEENHash, a novel hashing approach…

A Model Aware AIGC Task Offloading Algorithm in IIoT Edge Computing
Xin Wang, Xiao Huan Li, Xun Wang
https://arxiv.org/abs/2507.11560 https://

A Model Aware AIGC Task Offloading Algorithm in IIoT Edge Computing
The integration of the Industrial Internet of Things (IIoT) with Artificial Intelligence-Generated Content (AIGC) offers new opportunities for smart manufacturing, but it also introduces challenges related to computation-intensive tasks and low-latency demands. Traditional generative models based on cloud computing are difficult to meet the real-time requirements of AIGC tasks in IIoT environments, and edge computing can effectively reduce latency through task offloading. However, the dynamic n…

Peer-to-Peer-WLAN auf Anordnung der EU: Apple integriert Wi-Fi Aware
Mit iOS 26 können Apps WLAN-Direktverbindungen aufbauen – auch zu Android und Accessoires. Das ist die Vorstufe für richtige AirDrop-Alternativen.

⚠️ Please BE AWARE that there is a chance that if you transfer your save file from Switch to Switch 2, you may lose it. This person lost 20 years of data that they'd had since their Game Boy Advance. Totally brutal. It's a huge gamble, and to me, might not be worth it if you really value your save data.

'There's nothing I can do. I don't even know if I wanna play anymore' — Pokémon fan left devastated after '20 years worth of data' lost in Switch to Switch 2 transfer
‘My Scarlet save file is gone,’ sobs gamer. They say no error messages popped up during their Friday the 13th transfer attempt.

Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
An-Lun Liu, Yu-Wei Chao, Yi-Ting Chen
https://arxiv.org/abs/2507.11287 https://…

Task-Oriented Human Grasp Synthesis via Context- and Task-Aware Diffusers
In this paper, we study task-oriented human grasp synthesis, a new grasp synthesis task that demands both task and context awareness. At the core of our method is the task-aware contact maps. Unlike traditional contact maps that only reason about the manipulated object and its relation with the hand, our enhanced maps take into account scene and task information. This comprehensive map is critical for hand-object interaction, enabling accurate grasping poses that align with the task. We propose…

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Xiucheng Wang, Qiming Zhang, Nan Cheng, Junting Chen, Zezhong Zhang, Zan Li, Shuguang Cui, Xuemin Shen
https://arxiv.org/abs/2507.12166

RadioDiff-3D: A 3D$\times$3D Radio Map Dataset and Generative Diffusion Based Benchmark for 6G Environment-Aware Communication
Radio maps (RMs) serve as a critical foundation for enabling environment-aware wireless communication, as they provide the spatial distribution of wireless channel characteristics. Despite recent progress in RM construction using data-driven approaches, most existing methods focus solely on pathloss prediction in a fixed 2D plane, neglecting key parameters such as direction of arrival (DoA), time of arrival (ToA), and vertical spatial variations. Such a limitation is primarily due to the relian…

TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati, Haritha Ananthakrishnan, Oktie Hassanzadeh, Horst Samulowitz, Kavitha Srinivas
https://arxiv.org/abs/2507.11505

TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
One of the major challenges in enterprise data analysis is the task of finding joinable tables that are conceptually related and provide meaningful insights. Traditionally, joinable tables have been discovered through a search for similar columns, where two columns are considered similar syntactically if there is a set overlap or they are considered similar semantically if either the column embeddings or value embeddings are closer in the embedding space. However, for enterprise data lakes, col…

Topology-Aware Virtualization over Inter-Core Connected Neural Processing Units
Dahu Feng, Erhu Feng, Dong Du, Pinjie Xu, Yubin Xia, Haibo Chen, Rong Zhao
https://arxiv.org/abs/2506.11446

Topology-Aware Virtualization over Inter-Core Connected Neural Processing Units
With the rapid development of artificial intelligence (AI) applications, an emerging class of AI accelerators, termed Inter-core Connected Neural Processing Units (NPU), has been adopted in both cloud and edge computing environments, like Graphcore IPU, Tenstorrent, etc. Despite their innovative design, these NPUs often demand substantial hardware resources, leading to suboptimal resource utilization due to the imbalance of hardware requirements across various tasks. To address this issue, prio…

Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming
Ali Khandan Boroujeni, Kuranage Roche Rayan Ranasinghe, Giuseppe Thadeu Freitas de Abreu, Stefan K\"opsell, Ghazal Bagheri, Rafael F. Schaefer
https://arxiv.org/abs/2507.11224

Fairness-Aware Secure Integrated Sensing and Communications with Fractional Programming
We propose a novel secure integrated sensing and communications (ISAC) system designed to serve multiple communication users (CUs) and targets. To that end, we formulate an optimization problem that maximizes the secrecy rate under constraints balancing both communication and sensing requirements. To enhance fairness among users, an entropy-regularized fairness metric is introduced within the problem framework. We then propose a solution employing an accelerated quadratic transform (QT) with a …

RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection
Sungkyun Chang, Simon Dixon, Emmanouil Benetos
https://arxiv.org/abs/2507.12175

RUMAA: Repeat-Aware Unified Music Audio Analysis for Score-Performance Alignment, Transcription, and Mistake Detection
This study introduces RUMAA, a transformer-based framework for music performance analysis that unifies score-to-performance alignment, score-informed transcription, and mistake detection in a near end-to-end manner. Unlike prior methods addressing these tasks separately, RUMAA integrates them using pre-trained score and audio encoders and a novel tri-stream decoder capturing task interdependencies through proxy tasks. It aligns human-readable MusicXML scores with repeat symbols to full-length p…

"…so what exactly is going on between publishers and the NIH?" @ ACRLog: https://acrlog.org/2025/07/15/so-what-exactly-is-going-on-between-publishers-and-the-nih/

On QoE-Aware Traffic Management for Real-time, Interactive Video with Time-variant Spatial Complexity
Szilveszter N\'adas, Lars Ernstr\"om, David Lindero, Jonathan Lynam
https://arxiv.org/abs/2507.11798

On QoE-Aware Traffic Management for Real-time, Interactive Video with Time-variant Spatial Complexity
We analyzed spatial complexity, defined as the relationship between the required bitrate and a corresponding picture Quality of Experience (QoE) metric, for realistic, long, real-time, interactive video clips. Apart from variation across different content types, e.g., game genres, we discovered time-variability within a clip from second to second, and explored the ramifications for traffic management. We introduced utility as an elegant way to manage resource sharing preferences. Our analysis o…

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
Fuaing Niu, Zini Chen, Zhiyu Xie, Genan Dai, Bowen Zhang
https://arxiv.org/abs/2506.13343

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduce TwiUSD, the first large-scale, manually annotated UserSD benchmark with explicit followee relationships, containing 16,211 users and 47,757 tweets. TwiUSD enables rigorous evaluation of stance models by integrating tweet content and social links, with superior scale and annotation quality. Building on this resource,…

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Jiahui Geng, Fengyu Cai, Shaobo Cui, Qing Li, Liangwei Chen, Chenyang Lyu, Haonan Li, Derui Zhu, Walter Pretschner, Heinz Koeppl, Fakhri Karray
https://arxiv.org/abs/2506.11066

CoQuIR: A Comprehensive Benchmark for Code Quality-Aware Information Retrieval
Code retrieval is essential in modern software development, as it boosts code reuse and accelerates debugging. However, current benchmarks primarily emphasize functional relevance while neglecting critical dimensions of software quality. Motivated by this gap, we introduce CoQuIR, the first large-scale, multilingual benchmark specifically designed to evaluate quality-aware code retrieval across four key dimensions: correctness, efficiency, security, and maintainability. CoQuIR provides fine-gra…

Array-Aware Ambisonics and HRTF Encoding for Binaural Reproduction With Wearable Arrays
Yhonatan Gayer, Vladimir Tourbabin, Zamir Ben Hur, David Lou Alon, Boaz Rafaely
https://arxiv.org/abs/2507.11091

Array-Aware Ambisonics and HRTF Encoding for Binaural Reproduction With Wearable Arrays
This work introduces a novel method for binaural reproduction from arbitrary microphone arrays, based on array-aware optimization of Ambisonics encoding through Head-Related Transfer Function (HRTF) pre-processing. The proposed approach integrates array-specific information into the HRTF processing pipeline, leading to improved spatial accuracy in binaural rendering. Objective evaluations demonstrate superior performance under simulated wearable-array and head rotations compared to conventional…

MultiVox: Benchmarking Voice Assistants for Multimodal Interactions
Ramaneswaran Selvakumar, Ashish Seth, Nishit Anand, Utkarsh Tyagi, Sonal Kumar, Sreyan Ghosh, Dinesh Manocha
https://arxiv.org/abs/2507.10859

MultiVox: Benchmarking Voice Assistants for Multimodal Interactions
The rapid progress of Large Language Models (LLMs) has empowered omni models to act as voice assistants capable of understanding spoken dialogues. These models can process multimodal inputs beyond text, such as speech and visual data, enabling more context-aware interactions. However, current benchmarks fall short in comprehensively evaluating how well these models generate context-aware responses, particularly when it comes to implicitly understanding fine-grained speech characteristics, such …

KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding
Luohe Shi, Zuchao Li, Lefei Zhang, Guoming Liu, Baoyuan Qi, Hai Zhao
https://arxiv.org/abs/2507.11273

KV-Latent: Dimensional-level KV Cache Reduction with Frequency-aware Rotary Positional Embedding
Large language models (LLMs) based on Transformer Decoders have become the preferred choice for conversational generative AI. Despite the overall superiority of the Decoder architecture, the gradually increasing Key-Value (KV) cache during inference has emerged as a primary efficiency bottleneck, both in aspects of memory consumption and data transfer bandwidth limitations. To address these challenges, we propose a paradigm called KV-Latent. By down-sampling the Key-Value vector dimensions into…

Raiders' Tyree Wilson fully aware what's at stake ahead of 2025 NFL season https://www.sportingnews.com/us/nfl/las-vegas-raiders/news/raiders-tyree-wilson-fully-aware-whats-sta…

Uncertainty Aware Mapping for Vision-Based Underwater Robots
Abhimanyu Bhowmik, Mohit Singh, Madhushree Sannigrahi, Martin Ludvigsen, Kostas Alexis
https://arxiv.org/abs/2507.10991

Uncertainty Aware Mapping for Vision-Based Underwater Robots
Vision-based underwater robots can be useful in inspecting and exploring confined spaces where traditional sensors and preplanned paths cannot be followed. Sensor noise and situational change can cause significant uncertainty in environmental representation. Thus, this paper explores how to represent mapping inconsistency in vision-based sensing and incorporate depth estimation confidence into the mapping framework. The scene depth and the confidence are estimated using the RAFT-Stereo model an…

NFLPA dispute emerges over lawsuit awareness https://www.espn.com/nfl/story/_/id/45762449/nflpa-members-disagree-knowledge-lloyd-howell-lawsuit

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
Yucheng Li, Surin Ahn, Huiqiang Jiang, Amir H. Abdi, Yuqing Yang, Lili Qiu
https://arxiv.org/abs/2506.12707

SecurityLingua: Efficient Defense of LLM Jailbreak Attacks via Security-Aware Prompt Compression
Large language models (LLMs) have achieved widespread adoption across numerous applications. However, many LLMs are vulnerable to malicious attacks even after safety alignment. These attacks typically bypass LLMs' safety guardrails by wrapping the original malicious instructions inside adversarial jailbreaks prompts. Previous research has proposed methods such as adversarial training and prompt rephrasing to mitigate these safety vulnerabilities, but these methods often reduce the utility of LL…

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
Ashutosh Shankar (Indian Institute Of Technology Kharagpur), Astha Kumari (Indian Institute Of Technology Kharagpur)
https://arxiv.org/abs/2506.12415

QoS-aware Scheduling of Periodic Real-time Task Graphs on Heterogeneous Pre-occupied MECs
In latency-sensitive applications, efficient task scheduling is crucial for maintaining Quality of Service (QoS) while meeting strict timing constraints. This paper addresses the challenge of scheduling periodic tasks structured as directed acyclic graphs (DAGs) within heterogeneous, pre-occupied Mobile Edge Computing (MEC) networks. We propose a modified version of the Heterogeneous Earliest Finish Time (HEFT) algorithm designed to exploit residual processing capacity in preoccupied MEC enviro…

FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
Zhaoyang Wang, Jie Li, Wen Lu, Lihuo He, Maoguo Gong, Xinbo Gao
https://arxiv.org/abs/2506.11545

FCA2: Frame Compression-Aware Autoencoder for Modular and Fast Compressed Video Super-Resolution
State-of-the-art (SOTA) compressed video super-resolution (CVSR) models face persistent challenges, including prolonged inference time, complex training pipelines, and reliance on auxiliary information. As video frame rates continue to increase, the diminishing inter-frame differences further expose the limitations of traditional frame-to-frame information exploitation methods, which are inadequate for addressing current video super-resolution (VSR) demands. To overcome these challenges, we pro…

Headlines like this really make me want to just give up (by which I mean move to one of those tiny offshore islands in the North Atlantic)
https://theconversation.com/ai-tools-collect-and-store-da…

AI tools collect and store data about you from all your devices – here’s how to be aware of what you’re revealing
AI tools that provide information to you also gather lots of information from you and may provide it to third parties.

CATVis: Context-Aware Thought Visualization
Tariq Mehmood, Hamza Ahmad, Muhammad Haroon Shakeel, Murtaza Taj
https://arxiv.org/abs/2507.11522 https://

CATVis: Context-Aware Thought Visualization
EEG-based brain-computer interfaces (BCIs) have shown promise in various applications, such as motor imagery and cognitive state monitoring. However, decoding visual representations from EEG signals remains a significant challenge due to their complex and noisy nature. We thus propose a novel 5-stage framework for decoding visual representations from EEG signals: (1) an EEG encoder for concept classification, (2) cross-modal alignment of EEG and text embeddings in CLIP feature space, (3) captio…

A4: Microarchitecture-Aware LLC Management for Datacenter Servers with Emerging I/O Devices
Haneul Park, Jiaqi Lou, Sangjin Lee, Yifan Yuan, Kyoung Soo Park, Yongseok Son, Ipoom Jeong, Nam Sung Kim
https://arxiv.org/abs/2506.11329

A4: Microarchitecture-Aware LLC Management for Datacenter Servers with Emerging I/O Devices
In modern server CPUs, the Last-Level Cache (LLC) serves not only as a victim cache for higher-level private caches but also as a buffer for low-latency DMA transfers between CPU cores and I/O devices through Direct Cache Access (DCA). However, prior work has shown that high-bandwidth network-I/O devices can rapidly flood the LLC with packets, often causing significant contention with co-running workloads. One step further, this work explores hidden microarchitectural properties of the Intel Xe…

FasTUSS: Faster Task-Aware Unified Source Separation
Francesco Paissan, Gordon Wichern, Yoshiki Masuyama, Ryo Aihara, Fran\c{c}ois G. Germain, Kohei Saijo, Jonathan Le Roux
https://arxiv.org/abs/2507.11435

FasTUSS: Faster Task-Aware Unified Source Separation
Time-Frequency (TF) dual-path models are currently among the best performing audio source separation network architectures, achieving state-of-the-art performance in speech enhancement, music source separation, and cinematic audio source separation. While they are characterized by a relatively low parameter count, they still require a considerable number of operations, implying a higher execution time. This problem is exacerbated by the trend towards bigger models trained on large amounts of da…

CRAFT: Latency and Cost-Aware Genetic-Based Framework for Node Placement in Edge-Fog Environments
Soheil Mahdizadeh, Amir Mahdi Rasouli, Mohammad Pourashory, Sadra Galavani, Mohsen Ansari
https://arxiv.org/abs/2507.12445

CRAFT: Latency and Cost-Aware Genetic-Based Framework for Node Placement in Edge-Fog Environments
Reducing latency in the Internet of Things (IoT) is a critical concern. While cloud computing facilitates communication, it falls short of meeting real-time requirements reliably. Edge and fog computing have emerged as viable solutions by positioning computing nodes closer to end users, offering lower latency and increased processing power. An edge-fog framework comprises various components, including edge and fog nodes, whose strategic placement is crucial as it directly impacts latency and sy…

8.4 million people affected by data breach at Indian car share company Zoomcar https://therecord.media/8-million-affected-zoomcar-data-breach

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Kanghyun Ryu, Minjun Sung, Piyush Gupta, Jovin D'sa, Faizan M. Tariq, David Isele, Sangjae Bae
https://arxiv.org/abs/2507.11940

IANN-MPPI: Interaction-Aware Neural Network-Enhanced Model Predictive Path Integral Approach for Autonomous Driving
Motion planning for autonomous vehicles (AVs) in dense traffic is challenging, often leading to overly conservative behavior and unmet planning objectives. This challenge stems from the AVs' limited ability to anticipate and respond to the interactive behavior of surrounding agents. Traditional decoupled prediction and planning pipelines rely on non-interactive predictions that overlook the fact that agents often adapt their behavior in response to the AV's actions. To address this, we propose …

From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs
Anh Ho, Thanh Le-Cong, Bach Le, Christine Rizkallah
https://arxiv.org/abs/2506.13182 …

From Empirical Evaluation to Context-Aware Enhancement: Repairing Regression Errors with LLMs
[...] Since then, various APR approaches, especially those leveraging the power of large language models (LLMs), have been rapidly developed to fix general software bugs. Unfortunately, the effectiveness of these advanced techniques in the context of regression bugs remains largely unexplored. This gap motivates the need for an empirical study evaluating the effectiveness of modern APR techniques in fixing real-world regression bugs. In this work, we conduct an empirical study of APR techniqu…

Joint UAV Placement and Transceiver Design in Multi-User Wireless Relay Networks
Tzu-Hsuan Chou, Nicolo Michelusi, David J. Love, James V. Krogmeier
https://arxiv.org/abs/2507.11912

Joint UAV Placement and Transceiver Design in Multi-User Wireless Relay Networks
In this paper, a novel approach is proposed to improve the minimum signal-to-interference-plus-noise-ratio (SINR) among users in non-orthogonal multi-user wireless relay networks, by optimizing the placement of unmanned aerial vehicle (UAV) relays, relay beamforming, and receive combining. The design is separated into two problems: beamforming-aware UAV placement optimization and transceiver design for minimum SINR maximization. A significant challenge in beamforming-aware UAV placement optimiz…

PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decoding
Jiajun He, Tomoki Toda
https://arxiv.org/abs/2506.11064 …

PMF-CEC: Phoneme-augmented Multimodal Fusion for Context-aware ASR Error Correction with Error-specific Selective Decoding
End-to-end automatic speech recognition (ASR) models often struggle to accurately recognize rare words. Previously, we introduced an ASR postprocessing method called error detection and context-aware error correction (ED-CEC), which leverages contextual information such as named entities and technical terms to improve the accuracy of ASR transcripts. Although ED-CEC achieves a notable success in correcting rare words, its accuracy remains low when dealing with rare words that have similar pronu…

Is anyone aware of case law / settlements (in the USA or elsewhere, just curious) about copyright/trademark infringing tattoos?
People get fan-art tattoos all the time and most of the time the rights owner doesn't care... but under some circumstances, they might.
Obviously they could try to go after the artist, that's a "standard" copyright/trademark infringement case.
But what about the infringing work that is now part of your body?
The three plausi…

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Yanhui Li, Dongxia Wang, Zhu Sun, Haonan Zhang, Huizhong Guo
https://arxiv.org/abs/2506.10347

LightKG: Efficient Knowledge-Aware Recommendations with Simplified GNN Architecture
Recently, Graph Neural Networks (GNNs) have become the dominant approach for Knowledge Graph-aware Recommender Systems (KGRSs) due to their proven effectiveness. Building upon GNN-based KGRSs, Self-Supervised Learning (SSL) has been incorporated to address the sparity issue, leading to longer training time. However, through extensive experiments, we reveal that: (1)compared to other KGRSs, the existing GNN-based KGRSs fail to keep their superior performance under sparse interactions even with S…

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Haoqing Li, Ricardo Borsoi, Tales Imbiriba, Pau Closas
https://arxiv.org/abs/2506.13733

Robust Recursive Fusion of Multiresolution Multispectral Images with Location-Aware Neural Networks
Multiresolution image fusion is a key problem for real-time satellite imaging and plays a central role in detecting and monitoring natural phenomena such as floods. It aims to solve the trade-off between temporal and spatial resolution in remote sensing instruments. Although several algorithms have been proposed for this problem, the presence of outliers such as clouds downgrades their performance. Moreover, strategies that integrate robustness, recursive operation and learned models are missin…

Discontinuity-aware KAN-based physics-informed neural networks
Guoqiang Lei, D. Exposito, Xuerui Mao
https://arxiv.org/abs/2507.08338 https://

Discontinuity-aware KAN-based physics-informed neural networks
Physics-informed neural networks (PINNs) have proved to be a promising method for the rapid solving of partial differential equations (PDEs) in both forward and inverse problems. However, due to the smoothness assumption of functions approximated by general neural networks, PINNs are prone to spectral bias and numerical instability and suffer from reduced accuracy when solving PDEs with sharp spatial transitions or fast temporal evolution. To address this limitation, a discontinuity-aware physi…

Raiders defensive end aware he’s facing pressure: ‘It’s the biggest year’ https://www.reviewjournal.com/sports/raiders/raiders-defensive-end-aware-hes-facing-pressure-its-the-biggest-year-3383984/

The public feed of the Meta AI app is filled with private and sensitive information, suggesting users might not be aware they are sharing their chats publicly (Amanda Silberling/TechCrunch)
https://techcrunch.com/2025/06/12/the-meta-ai-app-is-a-privacy-dis…

The Meta AI app is a privacy disaster | TechCrunch
Meta does not indicate to users what their privacy settings are as they post, or where they are even posting to. So, if you log into Meta AI with Instagram, and your Instagram account is public, then so too are your searches.

OK, so FFOTUS is making noises that he knew about Israel's plans to attack Iran, or that FFOTUS actually approved and supported that attack.
Whether that is true of not, FFOTUS has planted a very dangerous seed.
Now, there is plausible reason for Iran and Islamic radicals to conclude that there is an actual, but undeclared, war by the US on Iran.
Iran is not a stupid country, it knows that it can not directly attack the US, especially with anything nuclear.
But Ir…

I have recently become aware that I tend to change my profile picture based on my mood.
As someone who is #ActuallyAutistic, I have only just discovered this particular habit of mine.
The more you know!
#Autistic

Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
Yanbo Wang, Zipeng Fang, Lei Zhao, Weidong Chen
https://arxiv.org/abs/2507.11001

Learning to Tune Like an Expert: Interpretable and Scene-Aware Navigation via MLLM Reasoning and CVAE-Based Adaptation
Service robots are increasingly deployed in diverse and dynamic environments, where both physical layouts and social contexts change over time and across locations. In these unstructured settings, conventional navigation systems that rely on fixed parameters often fail to generalize across scenarios, resulting in degraded performance and reduced social acceptance. Although recent approaches have leveraged reinforcement learning to enhance traditional planners, these methods often fail in real-w…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[4/4]:
Improving Large Language Models with Concept-Aware Fine-Tuning
https://

@…
I don't know if you're already aware of this but they have full episodes of all 12 seasons of Welcome to Anthony Bourdain: Parts Unknown on YouTube.
https://www.

Anthony Bourdain Parts Unknown
Welcome to Anthony Bourdain: Parts Unknown YouTube Channel. Watch weekly videos from Chef Anthony Bourdain's travels of the world to discover little-known parts of the world and celebrate diverse cultures by exploring food and culinary rituals. Subscribe to discover all of Anthony Bourdain's adventures around the world: https://www.youtube.com/channel/UCffagYQTThJx4N55UKdr5ew?Sub_confirmation=1

❝I have noticed that we people privileged by supremacy have a tendency to take this same stance toward newly aware people, a stance which is not ours to assume. We seem to feel that it is our business to meet people who are in the same place that we were just a few short years or decades ago, and meet their shock and surprise and anger and dismay with a skepticism and an impatience we haven't earned.
We say things like "are you surprised?"
We say things like "why does this shock you?"
We say "oh so you're only angry now?"
We say things like "where have you been?"
…
Instead of asking “are you surprised?” say “I was surprised once, too; here's what I know.” Instead of “what took you so long?” say “I just got here recently; here's what I've learned.”❞ https://mastodon.social/@JuliusGoat/114847074342312772

Delay-optimal Congestion-aware Routing and Computation Offloading in Arbitrary Network
Jinkun Zhang, Yuezhou Liu, Edmund Yeh
https://arxiv.org/abs/2506.13626

Delay-optimal Congestion-aware Routing and Computation Offloading in Arbitrary Network
Emerging edge computing paradigms enable heterogeneous devices to collaborate on complex computation applications. However, for arbitrary heterogeneous edge networks, delay-optimal forwarding and computation offloading remains an open problem. In this paper, we jointly optimize data/result routing and computation placement in arbitrary networks with heterogeneous node capabilities, and congestion-dependent nonlinear transmission and processing delay. Despite the non-convexity of the formulated …

Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs
Ziwen Wang, Yue Zhang, Zhiqiang Zhang, Sheng Quan Xie, Alexander Lanzon, William P. Heath, Zhenhong Li
https://arxiv.org/abs/2506.10933

Instance-Based Transfer Learning with Similarity-Aware Subject Selection for Cross-Subject SSVEP-Based BCIs
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) can achieve high recognition accuracy with sufficient training data. Transfer learning presents a promising solution to alleviate data requirements for the target subject by leveraging data from source subjects; however, effectively addressing individual variability among both target and source subjects remains a challenge. This paper proposes a novel transfer learning framework, termed instance-based task-relat…

Ask TUI to Stop Supporting Orca Abuse #AnimalRights

Please ask TUI UK to stop supporting orca abuse
TUI UK continues to sell tickets to SeaWorld, despite being fully aware of the company's cruel treatment of animals for profit.

FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Gwanhyeong Koo, Sunjae Yoon, Younghwan Lee, Ji Woo Hong, Chang D. Yoo
https://arxiv.org/abs/2507.08285

FlowDrag: 3D-aware Drag-based Image Editing with Mesh-guided Deformation Vector Flow Fields
Drag-based editing allows precise object manipulation through point-based control, offering user convenience. However, current methods often suffer from a geometric inconsistency problem by focusing exclusively on matching user-defined points, neglecting the broader geometry and leading to artifacts or unstable edits. We propose FlowDrag, which leverages geometric information for more accurate and coherent transformations. Our approach constructs a 3D mesh from the image, using an energy functi…

Remote Safety Monitoring: Significance-Aware Status Updating for Situational Awareness
Tasmeen Zaman Ornee, Md Kamran Chowdhury Shisher, Clement Kam, Yin Sun
https://arxiv.org/abs/2507.09833

Remote Safety Monitoring: Significance-Aware Status Updating for Situational Awareness
In this study, we consider a problem of remote safety monitoring, where a monitor pulls status updates from multiple sensors monitoring several safety-critical situations. Based on the received updates, multiple estimators determine the current safety-critical situations. Due to transmission errors and limited channel resources, the received status updates may not be fresh, resulting in the possibility of misunderstanding the current safety situation. In particular, if a dangerous situation is …

Robot Context Protocol (RCP): A Runtime-Agnostic Interface for Agent-Aware Robot Control
Lambert Lee, Joshua Lau
https://arxiv.org/abs/2506.11650 https://

Robot Context Protocol (RCP): A Runtime-Agnostic Interface for Agent-Aware Robot Control
The Robot Context Protocol (RCP) is a lightweight, middleware-agnostic communication protocol designed to simplify the complexity of robotic systems and enable seamless interaction between robots, users, and autonomous agents. RCP provides a unified and semantically meaningful interface that decouples client-facing operations from backend implementations, supporting a wide range of deployment environments including physical robots, cloud-based orchestrators, and simulated platforms. Built on HT…

🤖 Automate database management tasks - let AI handle queries, table creation & indexing
📊 Generate context-aware application code & tests with real-time database schema understanding
🔄 Dynamic reloading support - update tools without redeploying your application

Surfing the SWAVES: Lifecycle-aware Service Placement in MEC
Federico Giarr\`e, Holger Karl
https://arxiv.org/abs/2506.12265 https://…

Surfing the SWAVES: Lifecycle-aware Service Placement in MEC
In Multi-access Edge Computing (MEC) networks, users covered by a mobile network can exploit edge clouds (ECs), computational resources located at the network's edge, to execute virtual network functions (VNFs). ECs are particularly useful when deploying VNFs with strict delay and availability requirements. As users roam in the network and get handed over between cells, deployed VNFs must follow users to retain the benefits of edge computing. Yet, having VNFs ready at the closest EC can be chal…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/5]:
- METIS: Fast Quality-Aware RAG Systems with Configuration Adaptation
Ray, Pan, Gu, Du, Feng, Ananthanarayanan, Netravali, Jiang

Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Philipp Wiesner, Odej Kao
https://arxiv.org/abs/2507.11377

Moving Beyond Marginal Carbon Intensity: A Poor Metric for Both Carbon Accounting and Grid Flexibility
Marginal Carbon Intensity (MCI) has been promoted as an effective metric for carbon-aware computing. Although it is already considered as impractical for carbon accounting purposes, many still view it as valuable when optimizing for grid flexibility by incentivizing electricity usage during curtailment periods. In this statement paper, we argue that MCI is neither reliable nor actionable for either purpose. We outline its fundamental limitations, including non-observability, reliance on opaque …

Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks
Kamran Shafafi, Alaa Awad Abdellatif, Manuel Ricardo, Rui Campos
https://arxiv.org/abs/2506.13287

Joint Optimization of Multi-UAV Deployment and 3D Positioning in Traffic-Aware Aerial Networks
Unmanned Aerial Vehicles (UAVs) have emerged as a key enabler for next-generation wireless networks due to their on-demand deployment, high mobility, and ability to provide Line-of-Sight (LoS) connectivity. These features make UAVs particularly well-suited for dynamic and mission-critical applications such as intelligent transportation systems and emergency communications. However, effectively positioning multiple UAVs in real-time to meet non-uniform, time-varying traffic demands remains a sig…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[3/5]:
Evidential Spectrum-Aware Contrastive Learning for OOD Detection in Dynamic Graphs

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
Shibo Sun, Xue Li, Donglin Di, Mingjie Wei, Lanshun Nie, Wei-Nan Zhang, Dechen Zhan, Yang Song, Lei Fan
https://arxiv.org/abs/2507.08496

LLaPa: A Vision-Language Model Framework for Counterfactual-Aware Procedural Planning
While large language models (LLMs) have advanced procedural planning for embodied AI systems through strong reasoning abilities, the integration of multimodal inputs and counterfactual reasoning remains underexplored. To tackle these challenges, we introduce LLaPa, a vision-language model framework designed for multimodal procedural planning. LLaPa generates executable action sequences from textual task descriptions and visual environmental images using vision-language models (VLMs). Furthermor…

From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment
Kyubyung Chae, Hyunbin Jin, Taesup Kim
https://arxiv.org/abs/2506.10020

From Threat to Tool: Leveraging Refusal-Aware Injection Attacks for Safety Alignment
Safely aligning large language models (LLMs) often demands extensive human-labeled preference data, a process that's both costly and time-consuming. While synthetic data offers a promising alternative, current methods frequently rely on complex iterative prompting or auxiliary models. To address this, we introduce Refusal-Aware Adaptive Injection (RAAI), a straightforward, training-free, and model-agnostic framework that repurposes LLM attack techniques. RAAI works by detecting internal refusal…

Latent Space Dynamics Identification for Interface Tracking with Application to Shock-Induced Pore Collapse
Seung Whan Chung, Christopher Miller, Youngsoo Choi, Paul Tranquilli, H. Keo Springer, Kyle Sullivan
https://arxiv.org/abs/2507.10647

Latent Space Dynamics Identification for Interface Tracking with Application to Shock-Induced Pore Collapse
Capturing sharp, evolving interfaces remains a central challenge in reduced-order modeling, especially when data is limited and the system exhibits localized nonlinearities or discontinuities. We propose LaSDI-IT (Latent Space Dynamics Identification for Interface Tracking), a data-driven framework that combines low-dimensional latent dynamics learning with explicit interface-aware encoding to enable accurate and efficient modeling of physical systems involving moving material boundaries. At th…

FieldFormer: Self-supervised Reconstruction of Physical Fields via Tensor Attention Prior
Panqi Chen, Siyuan Li, Lei Cheng, Xiao Fu, Yik-Chung Wu, Sergios Theodoridis
https://arxiv.org/abs/2506.11629

FieldFormer: Self-supervised Reconstruction of Physical Fields via Tensor Attention Prior
Reconstructing physical field tensors from \textit{in situ} observations, such as radio maps and ocean sound speed fields, is crucial for enabling environment-aware decision making in various applications, e.g., wireless communications and underwater acoustics. Field data reconstruction is often challenging, due to the limited and noisy nature of the observations, necessitating the incorporation of prior information to aid the reconstruction process. Deep neural network-based data-driven struct…

What is the least politically / ethically awful US cell carrier right now? Is there even a difference?
I feel like I hear AT&T and Verizon mentioned most often as morally objectionable, followed by T-Mobile. Less Sprint, but…is that just because they’re smaller…?
I’m aware of the existence of Credo Mobile, but I’m a little skeptical of how effective it is getting service from them since much of the money goes to Verizon in the end.

Past, Present and Future: Exploring Adaptive AI in Software Development Bots
Omar Elsisi, Glaucia Melo
https://arxiv.org/abs/2507.10822 https://

Past, Present and Future: Exploring Adaptive AI in Software Development Bots
Conversational agents, such as chatbots and virtual assistants, have become essential in software development, boosting productivity, collaboration, and automating various tasks. This paper examines the role of adaptive AI-powered conversational agents in software development, highlighting their ability to offer dynamic, context-aware assistance to developers. Unlike traditional rule-based systems, adaptive AI agents use machine learning and natural language processing to learn from interaction…

Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint
Dominik Schweisgut, Anne Benoit, Yves Robert, Henning Meyerhenke
https://arxiv.org/abs/2507.08725

Carbon-Aware Workflow Scheduling with Fixed Mapping and Deadline Constraint
Large data and computing centers consume a significant share of the world's energy consumption. A prominent subset of the workloads in such centers are workflows with interdependent tasks, usually represented as directed acyclic graphs (DAGs). To reduce the carbon emissions resulting from executing such workflows in centers with a mixed (renewable and non-renewable) energy supply, it is advisable to move task executions to time intervals with sufficient green energy when possible. To this end, …

Vikings' Justin Jefferson aware his presence 'makes a difference' at OTAs: 'I have some type of influence' https://www.nfl.com/news/vikings-justin-jefferson-aware-his-presence-makes-a-diffe…

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/5]:
Critical Influence of Overparameterization on Sharpness-aware Minimization

MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing
Debashis Gupta, Aditi Golder, Rongkhun Zhu, Kangning Cui, Wei Tang, Fan Yang, Ovidiu Csillik, Sarra Alaqahtani, V. Paul Pauca
https://arxiv.org/abs/2507.08683

MoSAiC: Multi-Modal Multi-Label Supervision-Aware Contrastive Learning for Remote Sensing
Contrastive learning (CL) has emerged as a powerful paradigm for learning transferable representations without the reliance on large labeled datasets. Its ability to capture intrinsic similarities and differences among data samples has led to state-of-the-art results in computer vision tasks. These strengths make CL particularly well-suited for Earth System Observation (ESO), where diverse satellite modalities such as optical and SAR imagery offer naturally aligned views of the same geospatial …

PRISM: Distributed Inference for Foundation Models at Edge
Muhammad Azlan Qazi, Alexandros Iosifidis, Qi Zhang
https://arxiv.org/abs/2507.12145 https://

PRISM: Distributed Inference for Foundation Models at Edge
Foundation models (FMs) have achieved remarkable success across a wide range of applications, from image classification to natural langurage processing, but pose significant challenges for deployment at edge. This has sparked growing interest in developing practical and efficient strategies for bringing foundation models to edge environments. In this work, we propose PRISM, a communication-efficient and compute-aware strategy for distributed Transformer inference on edge devices. Our method lev…

Replaced article(s) found for cs.RO. https://arxiv.org/list/cs.RO/new
[2/2]:
Uncertainty-Aware Trajectory Prediction via Rule-Regularized Heteroscedastic Deep Classification

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling
Guilin Zhang, Wulan Guo, Ziqi Tan, Qiang Guan, Hailong Jiang
https://arxiv.org/abs/2507.07932

KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling
Autoscaling GPU inference workloads in Kubernetes remains challenging due to the reactive and threshold-based nature of default mechanisms such as the Horizontal Pod Autoscaler (HPA), which struggle under dynamic and bursty traffic patterns and lack integration with GPU-level metrics. We present KIS-S, a unified framework that combines KISim, a GPU-aware Kubernetes Inference Simulator, with KIScaler, a Proximal Policy Optimization (PPO)-based autoscaler. KIScaler learns latency-aware and resour…

Text-Aware Image Restoration with Diffusion Models
Jaewon Min, Jin Hyeon Kim, Paul Hyunbin Cho, Jaeeun Lee, Jihye Park, Minkyu Park, Sangpil Kim, Hyunhee Park, Seungryong Kim
https://arxiv.org/abs/2506.09993

Text-Aware Image Restoration with Diffusion Models
Image restoration aims to recover degraded images. However, existing diffusion-based restoration methods, despite great success in natural image restoration, often struggle to faithfully reconstruct textual regions in degraded images. Those methods frequently generate plausible but incorrect text-like patterns, a phenomenon we refer to as text-image hallucination. In this paper, we introduce Text-Aware Image Restoration (TAIR), a novel restoration task that requires the simultaneous recovery of…

PDE-aware Optimizer for Physics-informed Neural Networks
Hardik Shukla, Manurag Khullar, Vismay Churiwala
https://arxiv.org/abs/2507.08118 https://arxiv.org/pdf/2507.08118 https://arxiv.org/html/2507.08118
arXiv:2507.08118v1 Announce Type: new
Abstract: Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs) by embedding physical constraints into the loss function. However, standard optimizers such as Adam often struggle to balance competing loss terms, particularly in stiff or ill-conditioned systems. In this work, we propose a PDE-aware optimizer that adapts parameter updates based on the variance of per-sample PDE residual gradients. This method addresses gradient misalignment without incurring the heavy computational costs of second-order optimizers such as SOAP. We benchmark the PDE-aware optimizer against Adam and SOAP on 1D Burgers', Allen-Cahn and Korteweg-de Vries(KdV) equations. Across both PDEs, the PDE-aware optimizer achieves smoother convergence and lower absolute errors, particularly in regions with sharp gradients. Our results demonstrate the effectiveness of PDE residual-aware adaptivity in enhancing stability in PINNs training. While promising, further scaling on larger architectures and hardware accelerators remains an important direction for future research.
toXiv_bot_toot

Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing
Rongfei Li, Francis Assadian
https://arxiv.org/abs/2506.10251

Energy Aware Camera Location Search Algorithm for Increasing Precision of Observation in Automated Manufacturing
Visual servoing technology has been well developed and applied in many automated manufacturing tasks, especially in tools' pose alignment. To access a full global view of tools, most applications adopt eye-to-hand configuration or eye-to-hand/eye-in-hand cooperation configuration in an automated manufacturing environment. Most research papers mainly put efforts into developing control and observation architectures in various scenarios, but few of them have discussed the importance of the camera…

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment
Guoxin Zang, Xue Li, Donglin Di, Lanshun Nie, Dechen Zhan, Yang Song, Lei Fan
https://arxiv.org/abs/2507.07939

SAGE: A Visual Language Model for Anomaly Detection via Fact Enhancement and Entropy-aware Alignment
While Vision-Language Models (VLMs) have shown promising progress in general multimodal tasks, they often struggle in industrial anomaly detection and reasoning, particularly in delivering interpretable explanations and generalizing to unseen categories. This limitation stems from the inherently domain-specific nature of anomaly detection, which hinders the applicability of existing VLMs in industrial scenarios that require precise, structured, and context-aware analysis. To address these chall…

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding
Benjamin Stoler, Juliet Yang, Jonathan Francis, Jean Oh
https://arxiv.org/abs/2507.10749

RCG: Safety-Critical Scenario Generation for Robust Autonomous Driving via Real-World Crash Grounding
Safety-critical scenarios are essential for training and evaluating autonomous driving (AD) systems, yet remain extremely rare in real-world driving datasets. To address this, we propose Real-world Crash Grounding (RCG), a scenario generation framework that integrates crash-informed semantics into adversarial perturbation pipelines. We construct a safety-aware behavior representation through contrastive pre-training on large-scale driving logs, followed by fine-tuning on a small, crash-rich dat…

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Benno Krojer, Mojtaba Komeili, Candace Ross, Quentin Garrido, Koustuv Sinha, Nicolas Ballas, Mahmoud Assran
https://arxiv.org/abs/2506.09987

A Shortcut-aware Video-QA Benchmark for Physical Understanding via Minimal Video Pairs
Existing benchmarks for assessing the spatio-temporal understanding and reasoning abilities of video language models are susceptible to score inflation due to the presence of shortcut solutions based on superficial visual or textual cues. This paper mitigates the challenges in accurately assessing model performance by introducing the Minimal Video Pairs (MVP) benchmark, a simple shortcut-aware video QA benchmark for assessing the physical understanding of video language models. The benchmark is…

Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices
Adel Chehade, Edoardo Ragusa, Paolo Gastaldo, Rodolfo Zunino
https://arxiv.org/abs/2506.11319

Efficient Traffic Classification using HW-NAS: Advanced Analysis and Optimization for Cybersecurity on Resource-Constrained Devices
This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained Internet of Things (IoT) and edge devices. Thanks to HW-NAS, a 1D convolutional neural network (CNN) is tailored on the ISCX VPN-nonVPN dataset to meet strict memory and computational limits while achieving robust performance. The optimized model attains an accuracy…

LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
Ajay Shankar, Keisuke Okumura, Amanda Prorok
https://arxiv.org/abs/2507.11464 https:/…

LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework invokes two processes asynchronously at high frequency: (i) a centralized, discrete, and full-horizon planner for computing collision- and deadlock-free paths rapidly, leveraging recent advances in multi-agent pathfinding (MAPF), and (ii) dynamics-aware, robot-wise optimal trajectory controllers that ensure all robots in…

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Eun Som Jeon, Sinjini Mitra, Jisoo Lee, Omik M. Save, Ankita Shukla, Hyunglae Lee, Pavan Turaga
https://arxiv.org/abs/2506.10265

Ground Reaction Force Estimation via Time-aware Knowledge Distillation
Human gait analysis with wearable sensors has been widely used in various applications, such as daily life healthcare, rehabilitation, physical therapy, and clinical diagnostics and monitoring. In particular, ground reaction force (GRF) provides critical information about how the body interacts with the ground during locomotion. Although instrumented treadmills have been widely used as the gold standard for measuring GRF during walking, their lack of portability and high cost make them impracti…

Next-Gen Museum Guides: Autonomous Navigation and Visitor Interaction with an Agentic Robot
Luca Garello, Francesca Cocchella, Alessandra Sciutti, Manuel Catalano, Francesco Rea
https://arxiv.org/abs/2507.12273

Next-Gen Museum Guides: Autonomous Navigation and Visitor Interaction with an Agentic Robot
Autonomous robots are increasingly being tested into public spaces to enhance user experiences, particularly in cultural and educational settings. This paper presents the design, implementation, and evaluation of the autonomous museum guide robot Alter-Ego equipped with advanced navigation and interactive capabilities. The robot leverages state-of-the-art Large Language Models (LLMs) to provide real-time, context aware question-and-answer (Q&A) interactions, allowing visitors to engage in conve…

Latency Optimization for Wireless Federated Learning in Multihop Networks
Shaba Shaon, Van-Dinh Nguyen, Dinh C. Nguyen
https://arxiv.org/abs/2506.12081 htt…

Latency Optimization for Wireless Federated Learning in Multihop Networks
In this paper, we study a novel latency minimization problem in wireless federated learning (FL) across multi-hop networks. The system comprises multiple routes, each integrating leaf and relay nodes for FL model training. We explore a personalized learning and adaptive aggregation-aware FL (PAFL) framework that effectively addresses data heterogeneity across participating nodes by harmonizing individual and collective learning objectives. We formulate an optimization problem aimed at minimizin…

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training
Shurui Gui, Shuiwang Ji
https://arxiv.org/abs/2506.09433 https://

Mitigating Spurious Correlations in LLMs via Causality-Aware Post-Training
While large language models (LLMs) have demonstrated remarkable capabilities in language modeling, recent studies reveal that they often fail on out-of-distribution (OOD) samples due to spurious correlations acquired during pre-training. Here, we aim to mitigate such spurious correlations through causality-aware post-training (CAPT). By decomposing a biased prediction into two unbiased steps, known as \textit{event estimation} and \textit{event intervention}, we reduce LLMs' pre-training biases…

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Han Zhang, Akram Bin Sediq, Ali Afana, Melike Erol-Kantarci
https://arxiv.org/abs/2506.12074

Mobile Traffic Prediction using LLMs with Efficient In-context Demonstration Selection
Mobile traffic prediction is an important enabler for optimizing resource allocation and improving energy efficiency in mobile wireless networks. Building on the advanced contextual understanding and generative capabilities of large language models (LLMs), this work introduces a context-aware wireless traffic prediction framework powered by LLMs. To further enhance prediction accuracy, we leverage in-context learning (ICL) and develop a novel two-step demonstration selection strategy, optimizin…

Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor
Vatsal Agarwal, Matthew Gwilliam, Gefen Kohavi, Eshan Verma, Daniel Ulbricht, Abhinav Shrivastava
https://arxiv.org/abs/2507.07106

Towards Multimodal Understanding via Stable Diffusion as a Task-Aware Feature Extractor
Recent advances in multimodal large language models (MLLMs) have enabled image-based question-answering capabilities. However, a key limitation is the use of CLIP as the visual encoder; while it can capture coarse global information, it often can miss fine-grained details that are relevant to the input query. To address these shortcomings, this work studies whether pre-trained text-to-image diffusion models can serve as instruction-aware visual encoders. Through an analysis of their internal re…

This https://arxiv.org/abs/2505.22772 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Calibrated Value-Aware Model Learning with Stochastic Environment Models
The idea of value-aware model learning, that models should produce accurate value estimates, has gained prominence in model-based reinforcement learning. The MuZero loss, which penalizes a model's value function prediction compared to the ground-truth value function, has been utilized in several prominent empirical works in the literature. However, theoretical investigation into its strengths and weaknesses is limited. In this paper, we analyze the family of value-aware model learning losses, w…

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Mateusz Wasala, Mateusz Smolarczyk, Michal Danilowicz, Tomasz Kryjak
https://arxiv.org/abs/2507.07903

Hardware-Aware Feature Extraction Quantisation for Real-Time Visual Odometry on FPGA Platforms
Accurate position estimation is essential for modern navigation systems deployed in autonomous platforms, including ground vehicles, marine vessels, and aerial drones. In this context, Visual Simultaneous Localisation and Mapping (VSLAM) - which includes Visual Odometry - relies heavily on the reliable extraction of salient feature points from the visual input data. In this work, we propose an embedded implementation of an unsupervised architecture capable of detecting and describing feature po…

SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Juyeop Han, Lukas Lao Beyer, Guilherme V. Cavalheiro, Sertac Karaman
https://arxiv.org/abs/2507.07467

SCREP: Scene Coordinate Regression and Evidential Learning-based Perception-Aware Trajectory Generation
Autonomous flight in GPS denied indoor spaces requires trajectories that keep visual localization error tightly bounded across varied missions. Whereas visual inertial odometry (VIO) accumulates drift over time, scene coordinate regression (SCR) yields drift-free, high accuracy absolute pose estimation. We present a perception-aware framework that couples an evidential learning-based SCR pose estimator with a receding horizon trajectory optimizer. The optimizer steers the onboard camera toward …

Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks
Huan Lin, Chenguang Zhu, Lianghui Ding, Feng Yang
https://arxiv.org/abs/2506.09703

Multi-Level Damage-Aware Graph Learning for Resilient UAV Swarm Networks
Unmanned aerial vehicle (UAV) swarm networks leverage resilient algorithms to address communication network split issues and restore connectivity. However, existing graph learning-based resilient algorithms face over-aggregation and non-convergence problems caused by uneven and sparse topology under massive damage scenarios. To alleviate these problems, we propose a novel Multi-Level Damage-Aware Graph Learning (ML-DAGL) algorithm, which generates recovery trajectories by mining information fro…

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
Haochen Liu, Tianyu Li, Haohan Yang, Li Chen, Caojun Wang, Ke Guo, Haochen Tian, Hongchen Li, Hongyang Li, Chen Lv
https://arxiv.org/abs/2506.09800

Reinforced Refinement with Self-Aware Expansion for End-to-End Autonomous Driving
End-to-end autonomous driving has emerged as a promising paradigm for directly mapping sensor inputs to planning maneuvers using learning-based modular integrations. However, existing imitation learning (IL)-based models suffer from generalization to hard cases, and a lack of corrective feedback loop under post-deployment. While reinforcement learning (RL) offers a potential solution to tackle hard cases with optimality, it is often hindered by overfitting to specific driving cases, resulting i…