Tootfinder

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
Saideep Nannapaneni, Joseph Sakaya, Kyle Caron, Pedro HM Albuquerque, Zaid Tashman
https://arxiv.org/abs/2507.15909

Bayesian implementation of Targeted Maximum Likelihood Estimation for uncertainty quantification in causal effect estimation
Robust decision making involves making decisions in the presence of uncertainty and is often used in critical domains such as healthcare, supply chains, and finance. Causality plays a crucial role in decision-making as it predicts the change in an outcome (usually a key performance indicator) due to a treatment (also called an intervention). To facilitate robust decision making using causality, this paper proposes three Bayesian approaches of the popular Targeted Maximum Likelihood Estimation (…

Self-Supervised Distillation of Legacy Rule-Based Methods for Enhanced EEG-Based Decision-Making
Yipeng Zhang, Yuanyi Ding, Chenda Duan, Atsuro Daida, Hiroki Nariai, Vwani Roychowdhury
https://arxiv.org/abs/2507.14542

Self-Supervised Distillation of Legacy Rule-Based Methods for Enhanced EEG-Based Decision-Making
High-frequency oscillations (HFOs) in intracranial Electroencephalography (iEEG) are critical biomarkers for localizing the epileptogenic zone in epilepsy treatment. However, traditional rule-based detectors for HFOs suffer from unsatisfactory precision, producing false positives that require time-consuming manual review. Supervised machine learning approaches have been used to classify the detection results, yet they typically depend on labeled datasets, which are difficult to acquire due to t…

Mini-Batch Robustness Verification of Deep Neural Networks
Saar Tzour-Shaday, Dana Drachsler Cohen
https://arxiv.org/abs/2508.15454 https://arxiv.org/pdf/2…

Mini-Batch Robustness Verification of Deep Neural Networks
Neural network image classifiers are ubiquitous in many safety-critical applications. However, they are susceptible to adversarial attacks. To understand their robustness to attacks, many local robustness verifiers have been proposed to analyze $ε$-balls of inputs. Yet, existing verifiers introduce a long analysis time or lose too much precision, making them less effective for a large set of inputs. In this work, we propose a new approach to local robustness: group local robustness verificatio…

CMP: A Composable Meta Prompt for SAM-Based Cross-Domain Few-Shot Segmentation
Shuai Chen, Fanman Meng, Chunjin Yang, Haoran Wei, Chenhao Wu, Qingbo Wu, Hongliang Li
https://arxiv.org/abs/2507.16753

CMP: A Composable Meta Prompt for SAM-Based Cross-Domain Few-Shot Segmentation
Cross-Domain Few-Shot Segmentation (CD-FSS) remains challenging due to limited data and domain shifts. Recent foundation models like the Segment Anything Model (SAM) have shown remarkable zero-shot generalization capability in general segmentation tasks, making it a promising solution for few-shot scenarios. However, adapting SAM to CD-FSS faces two critical challenges: reliance on manual prompt and limited cross-domain ability. Therefore, we propose the Composable Meta-Prompt (CMP) framework t…

Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack
Eldor Abdukhamidov, Mohammed Abuhamad, Simon S. Woo, Hyoungshick Kim, Tamer Abuhmed
https://arxiv.org/abs/2507.14248

Breaking the Illusion of Security via Interpretation: Interpretable Vision Transformer Systems under Attack
Vision transformer (ViT) models, when coupled with interpretation models, are regarded as secure and challenging to deceive, making them well-suited for security-critical domains such as medical applications, autonomous vehicles, drones, and robotics. However, successful attacks on these systems can lead to severe consequences. Recent research on threats targeting ViT models primarily focuses on generating the smallest adversarial perturbations that can deceive the models with high confidence, …

PAC Off-Policy Prediction of Contextual Bandits
Yilong Wan, Yuqiang Li, Xianyi Wu
https://arxiv.org/abs/2507.16236 https://arxiv.org/…

PAC Off-Policy Prediction of Contextual Bandits
This paper investigates off-policy evaluation in contextual bandits, aiming to quantify the performance of a target policy using data collected under a different and potentially unknown behavior policy. Recently, methods based on conformal prediction have been developed to construct reliable prediction intervals that guarantee marginal coverage in finite samples, making them particularly suited for safety-critical applications. To further achieve coverage conditional on a given offline data set…

Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks
Asadullah Bin Rahman, Masud Ibn Afjal, Md. Abdulla Al Mamun
https://arxiv.org/abs/2508.15011

Systematic Evaluation of Wavelet-Based Denoising for MRI Brain Images: Optimal Configurations and Performance Benchmarks
Medical imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound are essential for accurate diagnosis and treatment planning in modern healthcare. However, noise contamination during image acquisition and processing frequently degrades image quality, obscuring critical diagnostic details and compromising clinical decision-making. Additionally, enhancement techniques such as histogram equalization may inadvertently amplify existing noise artifacts, …

LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa
Graham Hill, JingYuan Gong, Thulani Babeli, Moseli Mots'oehli, James Gachomo Wanjiku
https://arxiv.org/abs/2508.15110

LLMs and Agentic AI in Insurance Decision-Making: Opportunities and Challenges For Africa
In this work, we highlight the transformative potential of Artificial Intelligence (AI), particularly Large Language Models (LLMs) and agentic AI, in the insurance sector. We consider and emphasize the unique opportunities, challenges, and potential pathways in insurance amid rapid performance improvements, increased open-source access, decreasing deployment costs, and the complexity of LLM or agentic AI frameworks. To bring it closer to home, we identify critical gaps in the African insurance …

Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning
Beinuo Yang, Qishen Zhou, Junyi Li, Xingchen Su, Simon Hu
https://arxiv.org/abs/2508.14410 h…

Automated Optimization Modeling through Expert-Guided Large Language Model Reasoning
Optimization Modeling (OM) is essential for solving complex decision-making problems. However, the process remains time-consuming and error-prone, heavily relying on domain experts. While Large Language Models (LLMs) show promise in addressing these challenges through their natural language understanding and reasoning capabilities, current approaches face three critical limitations: high benchmark labeling error rates reaching up to 42\%, narrow evaluation scope that only considers optimal valu…

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading
Chengwei Lou, Zekai Jin, Wei Tang, Guangfei Geng, Jin Yang, Lu Zhang
https://arxiv.org/abs/2507.14995

LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading
Real-time peer-to-peer (P2P) electricity markets dynamically adapt to fluctuations in renewable energy and variations in demand, maximizing economic benefits through instantaneous price responses while enhancing grid flexibility. However, scaling expert guidance for massive personalized prosumers poses critical challenges, including diverse decision-making demands and lack of customized modeling frameworks. This paper proposed an integrated large language model-multi-agent reinforcement learnin…

The Automatic Calibration Method of the Compton Edge Based on Normalized Cross-correlation and Simulated Annealing Algorithm
Dehua Kong, Yanbiao Zhang, Zixi Lin, Yehao Qiu, Xiulian Chen, Zhonghai Wang
https://arxiv.org/abs/2507.16282

The Automatic Calibration Method of the Compton Edge Based on Normalized Cross-correlation and Simulated Annealing Algorithm
Accurate energy channel calibration in scintillation detectors is essential for reliable radiation detection across nuclear physics, medical imaging, and environmental monitoring. Organic scintillators like BC408 and EJ309 lack full-energy peaks, making their Compton edge a critical calibration alternative where traditional peak methods fail. Existing Compton edge identification techniques - Gaussian fitting for the 50%-70% amplitude point, first derivative minimum detection, and Monte Carlo si…

#GISjob at Uni Twente: https://utwentecareers.nl/en/vacancies/2140/phd-candidate-in-explainable-ai-for-earth-observation/

PhD Candidate in Explainable AI for Earth Observation - Looking for a job that matters?
With the growing integration of Artificial Intelligence (AI) in geospatial decision-making processes, ensuring transparency, accountability, and trust in model outputs has become imperative, especially in critical domains such as disaster risk management,…

Supernova Event Dataset: Interpreting Large Language Model's Personality through Critical Event Analysis
Pranav Agarwal, Ioana Ciuc\u{a}
https://arxiv.org/abs/2506.12189

Supernova Event Dataset: Interpreting Large Language Model's Personality through Critical Event Analysis
Large Language Models (LLMs) are increasingly integrated into everyday applications. As their influence grows, understanding their decision making and underlying personality becomes essential. In this work, we interpret model personality using our proposed Supernova Event Dataset, a novel dataset with diverse articles spanning biographies, historical events, news, and scientific discoveries. We use this dataset to benchmark LLMs on extracting and ranking key events from text, a subjective and c…

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Chan Young Park, Jillian Fisher, Marius Memmel, Dipika Khullar, Andy Yun, Abhishek Gupta, Yejin Choi
https://arxiv.org/abs/2507.08224

Making VLMs More Robot-Friendly: Self-Critical Distillation of Low-Level Procedural Reasoning
Large language models (LLMs) have shown promise in robotic procedural planning, yet their human-centric reasoning often omits the low-level, grounded details needed for robotic execution. Vision-language models (VLMs) offer a path toward more perceptually grounded plans, but current methods either rely on expensive, large-scale models or are constrained to narrow simulation settings. We introduce SelfReVision, a lightweight and scalable self-improvement framework for vision-language procedural …

DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality
Qitong Chu, Yufeng Yue, Danya Yao, Huaxin Pei
https://arxiv.org/abs/2508.11514

DiCriTest: Testing Scenario Generation for Decision-Making Agents Considering Diversity and Criticality
The growing deployment of decision-making agents in dynamic environments increases the demand for safety verification. While critical testing scenario generation has emerged as an appealing verification methodology, effectively balancing diversity and criticality remains a key challenge for existing methods, particularly due to local optima entrapment in high-dimensional scenario spaces. To address this limitation, we propose a dual-space guided testing framework that coordinates scenario param…

Enhancement Report Approval Prediction: A Comparative Study of Large Language Models
Haosheng Zuo, Feifei Niu, Chuanyi Li
https://arxiv.org/abs/2506.15098 …

Enhancement Report Approval Prediction: A Comparative Study of Large Language Models
Enhancement reports (ERs) serve as a critical communication channel between users and developers, capturing valuable suggestions for software improvement. However, manually processing these reports is resource-intensive, leading to delays and potential loss of valuable insights. To address this challenge, enhancement report approval prediction (ERAP) has emerged as a research focus, leveraging machine learning techniques to automate decision-making. While traditional approaches have employed fe…

Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology
Mingzhe Hu, Zach Eidex, Shansong Wang, Mojtaba Safari, Qiang Li, Xiaofeng Yang
https://arxiv.org/abs/2508.13192

Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology
Radiology, radiation oncology, and medical physics require decision-making that integrates medical images, textual reports, and quantitative data under high-stakes conditions. With the introduction of GPT-5, it is critical to assess whether recent advances in large multimodal models translate into measurable gains in these safety-critical domains. We present a targeted zero-shot evaluation of GPT-5 and its smaller variants (GPT-5-mini, GPT-5-nano) against GPT-4o across three representative task…

Balanced quasistatic evolutions of critical points in metric spaces
Stefano Almi, Massimo Fornasier, Jona Klemenc, Alessandro Scagliotti
https://arxiv.org/abs/2506.09812

Balanced quasistatic evolutions of critical points in metric spaces
Quasistatic evolutions of critical points of time-dependent energies exhibit piecewise smooth behavior, making them useful for modeling continuum mechanics phenomena like elastic-plasticity and fracture. Traditionally, such evolutions have been derived as vanishing viscosity and inertia limits, leading to balanced viscosity solutions. However, for nonconvex energies, these constructions have been realized in Euclidean spaces and assume non-degenerate critical points. In this paper, we take a di…

AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling
Qijun Zhang, Yao Lu, Mengming Li, Zhiyao Xie
https://arxiv.org/abs/2508.12294 https://

AutoPower: Automated Few-Shot Architecture-Level Power Modeling by Power Group Decoupling
Power efficiency is a critical design objective in modern CPU design. Architects need a fast yet accurate architecture-level power evaluation tool to perform early-stage power estimation. However, traditional analytical architecture-level power models are inaccurate. The recently proposed machine learning (ML)-based architecture-level power model requires sufficient data from known configurations for training, making it unrealistic. In this work, we propose AutoPower targeting fully automated…

Consistency analysis and nuclear data validation for two series of beryllium reflector critical benchmark experiments
Shengli Chen, Tianxiang Wang
https://arxiv.org/abs/2507.12757

Consistency analysis and nuclear data validation for two series of beryllium reflector critical benchmark experiments
Neutron-induced nuclear reaction data on beryllium playing a crucial role in nuclear application. However, discrepancies have been observed in two closely related series of beryllium-reflector fast-spectrum critical benchmark experiments, HMF-058 and HMF-066, which are widely used in current nuclear data validation. In this work, we address these inconsistencies by improving the secondary angular distributions of the (n,n) and (n,2n) reactions of beryllium, thereby making the theoretical calcul…

VisionLaw: Inferring Interpretable Intrinsic Dynamics from Visual Observations via Bilevel Optimization
Jailing Lin, Shu Jiang, Qingyuan Zeng, Zhenzhong Wang, Min Jiang
https://arxiv.org/abs/2508.13792

VisionLaw: Inferring Interpretable Intrinsic Dynamics from Visual Observations via Bilevel Optimization
The intrinsic dynamics of an object governs its physical behavior in the real world, playing a critical role in enabling physically plausible interactive simulation with 3D assets. Existing methods have attempted to infer the intrinsic dynamics of objects from visual observations, but generally face two major challenges: one line of work relies on manually defined constitutive priors, making it difficult to generalize to complex scenarios; the other models intrinsic dynamics using neural networ…

Ukraine hits Russian electronic warfare facility making Shahed, Iskander components, General Staff says: https://benborges.xyz/2025/07/05/ukraine-hits-russian-electronic-warfare.html

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Sebastian Hirt, Lukas Theiner, Maik Pfefferkorn, Rolf Findeisen
https://arxiv.org/abs/2508.12738

A Hierarchical Surrogate Model for Efficient Multi-Task Parameter Learning in Closed-Loop Contro
Many control problems require repeated tuning and adaptation of controllers across distinct closed-loop tasks, where data efficiency and adaptability are critical. We propose a hierarchical Bayesian optimization (BO) framework that is tailored to efficient controller parameter learning in sequential decision-making and control scenarios for distinct tasks. Instead of treating the closed-loop cost as a black-box, our method exploits structural knowledge of the underlying problem, consisting of a…

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments
Jennah Gosciak (Cornell University), Aparna Balagopalan (Massachusetts Institute of Technology), Derek Ouyang (Stanford University), Allison Koenecke (Cornell University), Marzyeh Ghassemi (Massachusetts Institute of Technology), Daniel E. Ho (Stanford University)

Bias Delayed is Bias Denied? Assessing the Effect of Reporting Delays on Disparity Assessments
Conducting disparity assessments at regular time intervals is critical for surfacing potential biases in decision-making and improving outcomes across demographic groups. Because disparity assessments fundamentally depend on the availability of demographic information, their efficacy is limited by the availability and consistency of available demographic identifiers. While prior work has considered the impact of missing data on fairness, little attention has been paid to the role of delayed dem…

Deciphering the global production network from cross-border firm transactions
Neave O'Clery, Ben Radcliffe-Brown, Thomas Spencer, Daniel Tarling-Hunter
https://arxiv.org/abs/2508.12315

Deciphering the global production network from cross-border firm transactions
Critical for policy-making and business operations, the study of global supply chains has been severely hampered by a lack of detailed data. Here we harness global firm-level transaction data covering 20m global firms, and 1 billion cross-border transactions, to infer key inputs for over 1200 products. Transforming this data to a directed network, we find that products are clustered into three large groups including textiles, chemicals and food, and machinery and metals. European industrial nat…

Science submits CE mark application for PRIMA retinal implant – A critical step towards making it available to patients in Europe #AMD

Science Submits CE Mark Application for PRIMA Retinal Implant – A Critical Step Towards Making It Available To Patients | Science Corporation
Science Corporation is a clinical-stage medical technology company.

Weekend Reads
* PQC for the RPKI https://labs.ripe.net/author/dirk/pqc-for-the-rpki/
* Big tech in Taiwan https://

PQC for the RPKI
Future capabilities of quantum attackers will present a host of new vulnerabilities for RPKI. A research student from SIDN Labs presents the first work on post-quantum cryptography for the RPKI, establishing the foundation for making this critical Internet infrastructure quantum-safe.

Causality and Decision-making: A Logical Framework for Systems and Security Modelling
Pinaki Chakraborty, Tristan Caulfield, David Pym
https://arxiv.org/abs/2508.01758 https://

Causality and Decision-making: A Logical Framework for Systems and Security Modelling
Causal reasoning is essential for understanding decision-making about the behaviour of complex `ecosystems' of systems that underpin modern society, with security -- including issues around correctness, safety, resilience, etc. -- typically providing critical examples. We present a theory of strategic reasoning about system modelling based on minimal structural assumptions and employing the methods of transition systems, supported by a modal logic of system states in the tradition of van Benthe…

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
Kelen C. Teixeira Vivaldini, Robert P\v{e}ni\v{c}ka, Martin Saska
https://arxiv.org/abs/2508.09304 https://

Decision-Making-Based Path Planning for Autonomous UAVs: A Survey
One of the most critical features for the successful operation of autonomous UAVs is the ability to make decisions based on the information acquired from their surroundings. Each UAV must be able to make decisions during the flight in order to deal with uncertainties in its system and the environment, and to further act upon the information being received. Such decisions influence the future behavior of the UAV, which is expressed as the path plan. Thus, decision-making in path planning is an e…

Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architectures
Amir Hossein Baradaran
https://arxiv.org/abs/2506.15043 https://

Advanced Prediction of Hypersonic Missile Trajectories with CNN-LSTM-GRU Architectures
Advancements in the defense industry are paramount for ensuring the safety and security of nations, providing robust protection against emerging threats. Among these threats, hypersonic missiles pose a significant challenge due to their extreme speeds and maneuverability, making accurate trajectory prediction a critical necessity for effective countermeasures. This paper addresses this challenge by employing a novel hybrid deep learning approach, integrating Convolutional Neural Networks (CNNs)…

AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
Zixian Zhang, Takfarinas Saber
https://arxiv.org/abs/2506.14470

AST-Enhanced or AST-Overloaded? The Surprising Impact of Hybrid Graph Representations on Code Clone Detection
As one of the most detrimental code smells, code clones significantly increase software maintenance costs and heighten vulnerability risks, making their detection a critical challenge in software engineering. Abstract Syntax Trees (ASTs) dominate deep learning-based code clone detection due to their precise syntactic structure representation, but they inherently lack semantic depth. Recent studies address this by enriching AST-based representations with semantic graphs, such as Control Flow Gra…

Log Gaussian Cox Process Background Modeling in High Energy Physics
Yuval Frid, Liron Barak, Pavani Jairam, Michael Kagan, Rachel Jordan Hyneman
https://arxiv.org/abs/2508.11740

Log Gaussian Cox Process Background Modeling in High Energy Physics
Background modeling is one of the most critical components in high energy physics data analyses, and for smooth backgrounds it is often performed by fitting using an analytic functional form. In this paper a novel method based on Log Gaussian Cox Processes (LGCP) is introduced to model smooth backgrounds while making minimal assumptions on the underlying shape. In LGCP, samples are assumed to be drawn from a non-homogeneous Poisson process, with an intensity function drawn from a Gaussian proce…

Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis
Xi Long, Christy Boscardin, Lauren A. Maggio, Joseph A. Costello, Ralph Gonzales, Rasmyah Hammoudeh, Ki Lai, Yoon Soo Park, Brian C. Gin
https://arxiv.org/abs/2508.09458

Hallucination vs interpretation: rethinking accuracy and precision in AI-assisted data extraction for knowledge synthesis
Knowledge syntheses (literature reviews) are essential to health professions education (HPE), consolidating findings to advance theory and practice. However, they are labor-intensive, especially during data extraction. Artificial Intelligence (AI)-assisted extraction promises efficiency but raises concerns about accuracy, making it critical to distinguish AI 'hallucinations' (fabricated content) from legitimate interpretive differences. We developed an extraction platform using large language m…

LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Parisa Fard Moshiri, Xinyu Zhu, Poonam Lohan, Burak Kantarci, Emil Janulewicz
https://arxiv.org/abs/2507.10903

LiLM-RDB-SFC: Lightweight Language Model with Relational Database-Guided DRL for Optimized SFC Provisioning
Effective management of Service Function Chains (SFCs) and optimal Virtual Network Function (VNF) placement are critical challenges in modern Software-Defined Networking (SDN) and Network Function Virtualization (NFV) environments. Although Deep Reinforcement Learning (DRL) is widely adopted for dynamic network decision-making, its inherent dependency on structured data and fixed action rules often limits adaptability and responsiveness, particularly under unpredictable network conditions. This…

Critical demand in a stochastic model of flows in supply networks
Yannick Feld, Marc Barthelemy
https://arxiv.org/abs/2505.24813 https://

Critical demand in a stochastic model of flows in supply networks
Supply networks are essential for modern production, yet their critical properties remain understudied. We present a stochastic model with random production capacities to analyze material flow to a root node, focusing on topology and buffer stocks. The critical demand, where unsatisfied demand diverges, is examined mostly through numerical simulations. Without stocks, minimal production dictates behavior, making topology irrelevant. With stocks, memory effects arise, making topology crucial. In…

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Chandana Cheerla
https://arxiv.org/abs/2507.12425 https://

Advancing Retrieval-Augmented Generation for Structured Enterprise and Internal Data
Organizations increasingly rely on proprietary enterprise data, including HR records, structured reports, and tabular documents, for critical decision-making. While Large Language Models (LLMs) have strong generative capabilities, they are limited by static pretraining, short context windows, and challenges in processing heterogeneous data formats. Conventional Retrieval-Augmented Generation (RAG) frameworks address some of these gaps but often struggle with structured and semi-structured data.…

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Waylon Luo (Kent State University), Jiapeng Zhao (Cisco), Tong Zhan (Meta), Qiang Guan (Kent State University)
https://arxiv.org/abs/2506.10889

Adaptive Job Scheduling in Quantum Clouds Using Reinforcement Learning
Present-day quantum systems face critical bottlenecks, including limited qubit counts, brief coherence intervals, and high susceptibility to errors-all of which obstruct the execution of large and complex circuits. The advancement of quantum algorithms has outpaced the capabilities of existing quantum hardware, making it difficult to scale computations effectively. Additionally, inconsistencies in hardware performance and pervasive quantum noise undermine system stability and computational accu…

The Arctic island of Svalbard is so reliably frigid that humanity bet its future on the place.
Since 2008, the "Svalbard Global Seed Vault" — set deep in frozen soil known as permafrost — has accepted nearly 1.4 million samples of more than 6,000 species of critical crops.
⭐️ But, the island is warming six to seven times faster than the rest of the planet, making even winters freakishly hot -- at least by Arctic standards.
Indeed, in 2017, an access tunnel to the…

Troubling scenes from an Arctic in full-tilt crisis
The heat that hit Svalbard in February was so intense that scientists could dig into the ground with spoons, "like it was soft ice cream."

Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems
Florian Bayer, Maximilian Russo, Christian Rathgeb
https://arxiv.org/abs/2508.11419

Training-free Dimensionality Reduction via Feature Truncation: Enhancing Efficiency in Privacy-preserving Multi-Biometric Systems
Biometric recognition is widely used, making the privacy and security of extracted templates a critical concern. Biometric Template Protection schemes, especially those utilizing Homomorphic Encryption, introduce significant computational challenges due to increased workload. Recent advances in deep neural networks have enabled state-of-the-art feature extraction for face, fingerprint, and iris modalities. The ubiquity and affordability of biometric sensors further facilitate multi-modal fusion…

SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations
Amit Jaspal, Kapil Dalwani, Ajantha Ramineni
https://arxiv.org/abs/2508.07241 https://

SocRipple: A Two-Stage Framework for Cold-Start Video Recommendations
Most industry scale recommender systems face critical cold start challenges new items lack interaction history, making it difficult to distribute them in a personalized manner. Standard collaborative filtering models underperform due to sparse engagement signals, while content only approaches lack user specific relevance. We propose SocRipple, a novel two stage retrieval framework tailored for coldstart item distribution in social graph based platforms. Stage 1 leverages the creators social con…

Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
Yue Kang, Mingshuo Liu, Bongsoo Yi, Jing Lyu, Zhi Zhang, Doudou Zhou, Yao Li
https://arxiv.org/abs/2506.12751

Single Index Bandits: Generalized Linear Contextual Bandits with Unknown Reward Functions
Generalized linear bandits have been extensively studied due to their broad applicability in real-world online decision-making problems. However, these methods typically assume that the expected reward function is known to the users, an assumption that is often unrealistic in practice. Misspecification of this link function can lead to the failure of all existing algorithms. In this work, we address this critical limitation by introducing a new problem of generalized linear bandits with unknown…

Might I suggest that the D-party form a shadow government with shadow agencies to replicate at least the policy making aspects of the gov't that FFOTUS is tearing down.
For example, begin with a medical advisory panel to give us trustworthy policy regarding vaccinations and drugs.
Etc.
It need not be large or expensive, just cherry pick, at least at the start, the most critical places to give the public information that it most needs.

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
Kevin T Webster
https://arxiv.org/abs/2507.11548 https:/…

Fairness Is Not Enough: Auditing Competence and Intersectional Bias in AI-powered Resume Screening
The increasing use of generative AI for resume screening is predicated on the assumption that it offers an unbiased alternative to biased human decision-making. However, this belief fails to address a critical question: are these AI systems fundamentally competent at the evaluative tasks they are meant to perform? This study investigates the question of competence through a two-part audit of eight major AI platforms. Experiment 1 confirmed complex, contextual racial and gender biases, with some…

As if overburdened CISOs don't have enough challenges, they also face a central problem in their organizations: their peers don't really know what they do.
Check out my latest CSO piece on this stumbling block and how CISOs can communicate their missions to colleagues.
Thanks to Andy Ellis, Bethany De Lude, Susan Chiang, and Omar Khawaja for their insights.

The CISO’s challenge: Getting colleagues to understand what you do
CISOs often operate with significant responsibility but limited formal authority, making it critical to articulate their role clearly. Experts offer strategies for CISOs to communicate their mission to colleagues and customers.

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Kasper Lien Oftebro, Anh Nguyen-Duc, Kai-Kristian Kemell
https://arxiv.org/abs/2507.10753

GenAI-Enabled Backlog Grooming in Agile Software Projects: An Empirical Study
Effective backlog management is critical for ensuring that development teams remain aligned with evolving requirements and stakeholder expectations. However, as product backlogs consistently grow in scale and complexity, they tend to become cluttered with redundant, outdated, or poorly defined tasks, complicating prioritization and decision making processes. This study investigates whether a generative-AI (GenAI) assistant can automate backlog grooming in Agile software projects without sacrifi…

DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model
Jingkai Xu, De Cheng, Xiangqian Zhao, Jungang Yang, Zilong Wang, Xinyang Jiang, Xufang Luo, Lili Chen, Xiaoli Ning, Chengxu Li, Xinzhu Zhou, Xuejiao Song, Ang Li, Qingyue Xia, Zhou Zhuang, Hongfei Ouyang, Ke Xue, Yujun Sheng, Rusong Meng, Feng Xu, Xi Yang, Weimin Ma, Yusheng Lee, Dongsheng Li, Xinbo Gao, Jianming Liang, Lili Qiu, Nannan Wang, Xianbo Zuo, Cui Yong

DermINO: Hybrid Pretraining for a Versatile Dermatology Foundation Model
Skin diseases impose a substantial burden on global healthcare systems, driven by their high prevalence (affecting up to 70% of the population), complex diagnostic processes, and a critical shortage of dermatologists in resource-limited areas. While artificial intelligence(AI) tools have demonstrated promise in dermatological image analysis, current models face limitations-they often rely on large, manually labeled datasets and are built for narrow, specific tasks, making them less effective in…

Kevin: Multi-Turn RL for Generating CUDA Kernels
Carlo Baronio, Pietro Marsella, Ben Pan, Simon Guo, Silas Alberti
https://arxiv.org/abs/2507.11948 https:/…

Kevin: Multi-Turn RL for Generating CUDA Kernels
Writing GPU kernels is a challenging task and critical for AI systems' efficiency. It is also highly iterative: domain experts write code and improve performance through execution feedback. Moreover, it presents verifiable rewards like correctness and speedup, making it a natural environment to apply Reinforcement Learning (RL). To explicitly incorporate the iterative nature of this process into training, we develop a flexible multi-turn RL recipe that addresses unique challenges encountered in…

High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic
George Alexakis, Dimitrios Schoinianakis, Giorgos Dimitrakopoulos
https://arxiv.org/abs/2507.12418

High-Performance Pipelined NTT Accelerators with Homogeneous Digit-Serial Modulo Arithmetic
The Number Theoretic Transform (NTT) is a fundamental operation in privacy-preserving technologies, particularly within fully homomorphic encryption (FHE). The efficiency of NTT computation directly impacts the overall performance of FHE, making hardware acceleration a critical technology that will enable realistic FHE applications. Custom accelerators, in FPGAs or ASICs, offer significant performance advantages due to their ability to exploit massive parallelism and specialized optimizations. …

Characterizing gas flows through observations of the disk-CGM interface with the HWO
Sanchayeeta Borthakur, Joseph N. Burchett, Frances Cashman, Andrew J. Fox, Yong Zheng, David M. French, Rongmon Bordoloi, Brad Koplitz
https://arxiv.org/abs/2506.10517

Characterizing gas flows through observations of the disk-CGM interface with the HWO
How gas gets into, through, and out of galaxies is critical to understanding galactic ecosystems. The disk-CGM interface region is uniquely suited for studying processes that drive gas flows. Matter and energy that enter and leave a galaxy pass through this region; however, the precise pathways are yet to be explored. In this paper, we discuss future observations that will facilitate the discovery of the gas flow pathways in galaxies and the telescope parameters necessary for making those obser…

The PacifAIst Benchmark:Would an Artificial Intelligence Choose to Sacrifice Itself for Human Safety?
Manuel Herrador
https://arxiv.org/abs/2508.09762 https://

The PacifAIst Benchmark:Would an Artificial Intelligence Choose to Sacrifice Itself for Human Safety?
As Large Language Models (LLMs) become increasingly autonomous and integrated into critical societal functions, the focus of AI safety must evolve from mitigating harmful content to evaluating underlying behavioral alignment. Current safety benchmarks do not systematically probe a model's decision-making in scenarios where its own instrumental goals - such as self-preservation, resource acquisition, or goal completion - conflict with human safety. This represents a critical gap in our ability t…

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Phuc Hao Do, Tran Duc Le
https://arxiv.org/abs/2508.04288 https://

Challenges in Applying Variational Quantum Algorithms to Dynamic Satellite Network Routing
Applying near-term variational quantum algorithms to the problem of dynamic satellite network routing represents a promising direction for quantum computing. In this work, we provide a critical evaluation of two major approaches: static quantum optimizers such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA) for offline route computation, and Quantum Reinforcement Learning (QRL) methods for online decision-making. Using ideal, noise-free sim…

From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Jinghui Zhang, Kaiyang Wan, Longwei Xu, Ao Li, Zongfang Liu, Xiuying Chen
https://arxiv.org/abs/2508.00497

From Individuals to Crowds: Dual-Level Public Response Prediction in Social Media
Public response prediction is critical for understanding how individuals or groups might react to specific events, policies, or social phenomena, making it highly valuable for crisis management, policy-making, and social media analysis. However, existing works face notable limitations. First, they lack micro-level personalization, producing generic responses that ignore individual user preferences. Moreover, they overlook macro-level sentiment distribution and only deal with individual-level se…

Neural-Augmented Kelvinlet: Real-Time Soft Tissue Deformation with Multiple Graspers
Ashkan Shahbazi, Kyvia Pereira, Jon S. Heiselman, Elaheh Akbari, Annie C. Benson, Sepehr Seifi, Xinyuan Liu, Garrison L. Johnston, Erwin Terpstra, Anne Draaisma, Jan-Jaap Severes, Jie Ying Wu, Nabil Simaan, Michael L. Miga, Soheil Kolouri
https://

Neural-Augmented Kelvinlet: Real-Time Soft Tissue Deformation with Multiple Graspers
Fast and accurate simulation of soft tissue deformation is a critical factor for surgical robotics and medical training. In this paper, we introduce a novel physics-informed neural simulator that approximates soft tissue deformations in a realistic and real-time manner. Our framework integrates Kelvinlet-based priors into neural simulators, making it the first approach to leverage Kelvinlets for residual learning and regularization in data-driven soft tissue modeling. By incorporating large-sca…

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Hyeonseo Lee, Juhyun Park, Jihyong Oh, Chanho Eom
https://arxiv.org/abs/2506.12413

Domain Generalization for Person Re-identification: A Survey Towards Domain-Agnostic Person Matching
Person Re-identification (ReID) aims to retrieve images of the same individual captured across non-overlapping camera views, making it a critical component of intelligent surveillance systems. Traditional ReID methods assume that the training and test domains share similar characteristics and primarily focus on learning discriminative features within a given domain. However, they often fail to generalize to unseen domains due to domain shifts caused by variations in viewpoint, background, and l…

First-passage time for PDifMPs: an Exact simulation approach for time-varying thresholds
Sascha Desmettre, Devika Khurana, Amira Meddah
https://arxiv.org/abs/2507.07822

First-passage time for PDifMPs: an Exact simulation approach for time-varying thresholds
Piecewise Diffusion Markov Processes (PDifMPs) are valuable for modelling systems where continuous dynamics are interrupted by sudden shifts and/or changes in drift and diffusion. The first-passage time (FPT) in such models plays a central role in understanding when a process first reaches a critical boundary. In many systems, time-dependent thresholds provide a flexible framework for reflecting evolving conditions, making them essential for realistic modelling. We propose a hybrid exact simula…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Ya-Ting Yang, Quanyan Zhu
https://arxiv.org/abs/2507.03021 htt…

A Multi-Resolution Dynamic Game Framework for Cross-Echelon Decision-Making in Cyber Warfare
Cyber warfare has become a critical dimension of modern conflict, driven by society's increasing dependence on interconnected digital and physical infrastructure. Effective cyber defense often requires decision-making at different echelons, where the tactical layer focuses on detailed actions such as techniques, tactics, and procedures, while the strategic layer addresses long-term objectives and coordinated planning. Modeling these interactions at different echelons remains challenging due to …

Labour government running that line between "there's not enough available resources to support immigrants" and "it's critical for the country to have babies that won't be economically productive for many years".
(An apparent dichotomy I imagine there's a non-racist reason for. And to be fair, despite the headlines, they haven't gone full national-natalist, just suggesting making it easier for people to start families.)

Have more children, says Bridget Phillipson
Education Secretary warns Britons of ‘worrying repercussions’ of falling birth rate

This https://arxiv.org/abs/2409.08665 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Agile Decision-Making and Safety-Critical Motion Planning for Emergency Autonomous Vehicles
Efficiency is critical for autonomous vehicles (AVs), especially for emergency AVs. However, most existing methods focus on regular vehicles, overlooking the distinct strategies required by emergency vehicles to address the challenge of maximizing efficiency while ensuring safety. In this paper, we propose an Integrated Agile Decision-Making with Active and Safety-Critical Motion Planning System (IDEAM). IDEAM focuses on enabling emergency AVs, such as ambulances, to actively attain efficiency …

Towards Data-Driven Model-Free Safety-Critical Control
Zhe Shen, Yitaek Kim, Christoffer Sloth
https://arxiv.org/abs/2506.06931 https://

Towards Data-Driven Model-Free Safety-Critical Control
This paper presents a framework for enabling safe velocity control of general robotic systems using data-driven model-free Control Barrier Functions (CBFs). Model-free CBFs rely on an exponentially stable velocity controller and a design parameter (e.g. alpha in CBFs); this design parameter depends on the exponential decay rate of the controller. However, in practice, the decay rate is often unavailable, making it non-trivial to use model-free CBFs, as it requires manual tuning for alpha. To ad…

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Asim H. Gazi, Bhanu T. Gullapalli, Daiqi Gao, Benjamin M. Marlin, Vivek Shetty, Susan A. Murphy
https://arxiv.org/abs/2507.10798

Uncertainty-Informed Scheduling of Decision Points for Intelligent Mobile Health Interventions
Timely decision making is critical to the effectiveness of mobile health (mHealth) interventions. At predefined timepoints called "decision points," intelligent mHealth systems such as just-in-time adaptive interventions (JITAIs) estimate an individual's biobehavioral context from sensor or survey data and determine whether and how to intervene. For interventions targeting habitual behavior (e.g., oral hygiene), effectiveness often hinges on delivering support shortly before the target behavior…

Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings
Lucas Joos, Gavin J. Mooney, Maximilian T. Fischer, Daniel A. Keim, Falk Schreiber, Helen C. Purchase, Karsten Klein
https://arxiv.org/abs/2506.09212

Show Me Your Best Side: Characteristics of User-Preferred Perspectives for 3D Graph Drawings
The visual analysis of graphs in 3D has become increasingly popular, accelerated by the rise of immersive technology, such as augmented and virtual reality. Unlike 2D drawings, 3D graph layouts are highly viewpoint-dependent, making perspective selection critical for revealing structural and relational patterns. Despite its importance, there is limited empirical evidence guiding what constitutes an effective or preferred viewpoint from the user's perspective. In this paper, we present a systema…

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Jonas Svedas, Hannah Watson, Nathan Laubeuf, Diksha Moolchandani, Abubakr Nada, Arjun Singh, Dwaipayan Biswas, James Myers, Debjyoti Bhattacharjee
https://arxiv.org/abs/2506.09275

A Survey of End-to-End Modeling for Distributed DNN Training: Workloads, Simulators, and TCO
Distributed deep neural networks (DNNs) have become a cornerstone for scaling machine learning to meet the demands of increasingly complex applications. However, the rapid growth in model complexity far outpaces CMOS technology scaling, making sustainable and efficient system design a critical challenge. Addressing this requires coordinated co-design across software, hardware, and technology layers. Due to the prohibitive cost and complexity of deploying full-scale training systems, simulators …

A test statistic, $h^*$, for outlier analysis
Johan F. Hoorn, Johnny K. W. Ho
https://arxiv.org/abs/2508.06792 https://arxiv.org/pdf/2508.06792

A test statistic, $h^*$, for outlier analysis
Outlier analysis is a critical tool across diverse domains, from clinical decision-making to cybersecurity and talent identification. Traditional statistical outlier detection methods, such as Grubb's test and Dixon's Q, are predicated on the assumption of normality and often fail to reckon the meaningfulness of exceptional values within non-normal datasets. In this paper, we introduce the h* statistic, a novel parametric, frequentist approach for evaluating global outliers without the normalit…

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Naomi Fridman, Bubby Solway, Tomer Fridman, Itamar Barnea, Anat Goldshtein
https://arxiv.org/abs/2506.12190

BreastDCEDL: Curating a Comprehensive DCE-MRI Dataset and developing a Transformer Implementation for Breast Cancer Treatment Response Prediction
Breast cancer remains a leading cause of cancer-related mortality worldwide, making early detection and accurate treatment response monitoring critical priorities. We present BreastDCEDL, a curated, deep learning-ready dataset comprising pre-treatment 3D Dynamic Contrast-Enhanced MRI (DCE-MRI) scans from 2,070 breast cancer patients drawn from the I-SPY1, I-SPY2, and Duke cohorts, all sourced from The Cancer Imaging Archive. The raw DICOM imaging data were rigorously converted into standardized…

Camel: Energy-Aware LLM Inference on Resource-Constrained Devices
Hao Xu, Long Peng, Shezheng Song, Xiaodong Liu, Ma Jun, Shasha Li, Jie Yu, Xiaoguang Mao
https://arxiv.org/abs/2508.09173

Camel: Energy-Aware LLM Inference on Resource-Constrained Devices
Most Large Language Models (LLMs) are currently deployed in the cloud, with users relying on internet connectivity for access. However, this paradigm faces challenges such as network latency, privacy concerns, and bandwidth limits. Thus, deploying LLMs on edge devices has become an important research focus. In edge inference, request latency is critical as high latency can impair real-time tasks. At the same time, edge devices usually have limited battery capacity, making energy consumption ano…

Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach
Lucas Erlacher, Samuel Ruip\'erez-Campillo, Holger Michel, Sven Wellmann, Thomas M. Sutter, Ece Ozkan, Julia E. Vogt
https://arxiv.org/abs/2507.11561

Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach
Pulmonary hypertension (PH) in newborns is a critical condition characterized by elevated pressure in the pulmonary arteries, leading to right ventricular strain and heart failure. While right heart catheterization (RHC) is the diagnostic gold standard, echocardiography is preferred due to its non-invasive nature, safety, and accessibility. However, its accuracy highly depends on the operator, making PH assessment subjective. While automated detection methods have been explored, most models foc…

This https://arxiv.org/abs/2411.07161 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csMA_…

RoundTable: Investigating Group Decision-Making Mechanism in Multi-Agent Collaboration
Effective group decision-making is critical in Multi-Agent Systems (MAS). Yet, how different mechanisms for reaching consensus impact collaboration quality and efficiency remains understudied. We conduct a systematic study on group decision-making mechanisms in a decentralized setting. Through controlled experiments, we analyze how different voting rules affect decision quality and efficiency in a multi-round collaboration. Results reveal that majority voting often cause inefficient collaborati…

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Md Sazedur Rahman, Mohamed Elmahallawy, Sanjay Madria, Samuel Frimpong
https://arxiv.org/abs/2508.10212

Detecting Untargeted Attacks and Mitigating Unreliable Updates in Federated Learning for Underground Mining Operations
Underground mining operations rely on distributed sensor networks to collect critical data daily, including mine temperature, toxic gas concentrations, and miner movements for hazard detection and operational decision-making. However, transmitting raw sensor data to a central server for training deep learning models introduces significant privacy risks, potentially exposing sensitive mine-specific information. Federated Learning (FL) offers a transformative solution by enabling collaborative mo…

Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems
Maria J. P. Peixoto, Akriti Pandey, Ahsan Zaman, Peter R. Lewis
https://arxiv.org/abs/2508.10806 …

Who Benefits from AI Explanations? Towards Accessible and Interpretable Systems
As AI systems are increasingly deployed to support decision-making in critical domains, explainability has become a means to enhance the understandability of these outputs and enable users to make more informed and conscious choices. However, despite growing interest in the usability of eXplainable AI (XAI), the accessibility of these methods, particularly for users with vision impairments, remains underexplored. This paper investigates accessibility gaps in XAI through a two-pronged approach. …

Explainable Information Retrieval in the Audit Domain
Alexander Frummet, Emanuel Slany, Jonas Amling, Moritz Lang, Stephan Scheele
https://arxiv.org/abs/2507.03479

Explainable Information Retrieval in the Audit Domain
Conversational agents such as Microsoft Copilot and Google Gemini assist users with complex search tasks but often generate misleading or fabricated references. This undermines trust, particularly in high-stakes domains such as medicine and finance. Explainable information retrieval (XIR) aims to address this by making search results more transparent and interpretable. While most XIR research is domain-agnostic, this paper focuses on auditing -- a critical yet underexplored area. We argue that …

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…

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models
Xingyu Xiao, Hongxu Zhu, Jingang Liang, Jiejuan Tong, Haitao Wang
https://arxiv.org/abs/2507.01017

A Comprehensive Review of Human Error in Risk-Informed Decision Making: Integrating Human Reliability Assessment, Artificial Intelligence, and Human Performance Models
Human error remains a dominant risk driver in safety-critical sectors such as nuclear power, aviation, and healthcare, where seemingly minor mistakes can cascade into catastrophic outcomes. Although decades of research have produced a rich repertoire of mitigation techniques, persistent limitations: scarce high-quality data, algorithmic opacity, and residual reliance on expert judgment, continue to constrain progress. This review synthesizes recent advances at the intersection of risk-informed …

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Nan Mu, Ruiqi Song, Xiaoning Li, Zhihui Xu, Jingfeng Jiang, Chen Zhao
https://arxiv.org/abs/2506.11454

FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography
Background: Coronary artery disease (CAD) remains one of the leading causes of mortality worldwide. Precise segmentation of coronary arteries from invasive coronary angiography (ICA) is critical for effective clinical decision-making. Objective: This study aims to propose a novel deep learning model based on frequency-domain analysis to enhance the accuracy of coronary artery segmentation and stenosis detection in ICA, thereby offering robust support for the stenosis detection and treatment of …

PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Yushi Feng, Junye Du, Yingying Hong, Qifan Wang, Lequan Yu
https://arxiv.org/abs/2508.10501

PASS: Probabilistic Agentic Supernet Sampling for Interpretable and Adaptive Chest X-Ray Reasoning
Existing tool-augmented agentic systems are limited in the real world by (i) black-box reasoning steps that undermine trust of decision-making and pose safety risks, (ii) poor multimodal integration, which is inherently critical for healthcare tasks, and (iii) rigid and computationally inefficient agentic pipelines. We introduce PASS (Probabilistic Agentic Supernet Sampling), the first multimodal framework to address these challenges in the context of Chest X-Ray (CXR) reasoning. PASS adaptivel…

FairLangProc: A Python package for fairness in NLP
Arturo P\'erez-Peralta, Sandra Ben\'itez-Pe\~na, Rosa E. Lillo
https://arxiv.org/abs/2508.03677 https://

FairLangProc: A Python package for fairness in NLP
The rise in usage of Large Language Models to near ubiquitousness in recent years has risen societal concern about their applications in decision-making contexts, such as organizational justice or healthcare. This, in turn, poses questions about the fairness of these models in critical settings, which leads to the developement of different procedures to address bias in Natural Language Processing. Although many datasets, metrics and algorithms have been proposed to measure and mitigate harmful …

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
Ray Wai Man Kong
https://arxiv.org/abs/2508.06518 https://arxiv.org/pd…

Automated Seam Folding and Sewing Machine on Pleated Pants for Apparel Manufacturing
The applied research is the design and development of an automated folding and sewing machine for pleated pants. It represents a significant advancement in addressing the challenges associated with manual sewing processes. Traditional methods for creating pleats are labour-intensive, prone to inconsistencies, and require high levels of skill, making automation a critical need in the apparel industry. This research explores the technical feasibility and operational benefits of integrating advanc…

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema, Hamid Rezatofighi, Adel N. Toosi
https://arxiv.org/abs/2507.06011

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
Edge computing enables data processing closer to the source, significantly reducing latency an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strateg…

Rapid Modeling Architecture for Lightweight Simulator to Accelerate and Improve Decision Making for Industrial Systems
Takumi Kato, Zhi Li Hu
https://arxiv.org/abs/2507.17990 ht…

Rapid Modeling Architecture for Lightweight Simulator to Accelerate and Improve Decision Making for Industrial Systems
Designing industrial systems, such as building, improving, and automating distribution centers and manufacturing plants, involves critical decision-making with limited information in the early phases. The lack of information leads to less accurate designs of the systems, which are often difficult to resolve later. It is effective to use simulators to model the designed system and find out the issues early. However, the modeling time required by conventional simulators is too long to allow for r…

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Zhonghao Yan, Muxi Diao, Yuxuan Yang, Jiayuan Xu, Kaizhou Zhang, Ruoyan Jing, Lele Yang, Yanxi Liu, Kongming Liang, Zhanyu Ma
https://arxiv.org/abs/2508.08177

MedReasoner: Reinforcement Learning Drives Reasoning Grounding from Clinical Thought to Pixel-Level Precision
Accurately grounding regions of interest (ROIs) is critical for diagnosis and treatment planning in medical imaging. While multimodal large language models (MLLMs) combine visual perception with natural language, current medical-grounding pipelines still rely on supervised fine-tuning with explicit spatial hints, making them ill-equipped to handle the implicit queries common in clinical practice. This work makes three core contributions. We first define Unified Medical Reasoning Grounding (UMRG…

This https://arxiv.org/abs/2501.00296 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

From Pixels to Predicates: Learning Symbolic World Models via Pretrained Vision-Language Models
Our aim is to learn to solve long-horizon decision-making problems in complex robotics domains given low-level skills and a handful of short-horizon demonstrations containing sequences of images. To this end, we focus on learning abstract symbolic world models that facilitate zero-shot generalization to novel goals via planning. A critical component of such models is the set of symbolic predicates that define properties of and relationships between objects. In this work, we leverage pretrained …

This https://arxiv.org/abs/2505.24492 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csLG_…

Object Centric Concept Bottlenecks
Developing high-performing, yet interpretable models remains a critical challenge in modern AI. Concept-based models (CBMs) attempt to address this by extracting human-understandable concepts from a global encoding (e.g., image encoding) and then applying a linear classifier on the resulting concept activations, enabling transparent decision-making. However, their reliance on holistic image encodings limits their expressiveness in object-centric real-world settings and thus hinders their abilit…

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
Kaiyuan Zhang, Siyuan Cheng, Hanxi Guo, Yuetian Chen, Zian Su, Shengwei An, Yuntao Du, Charles Fleming, Ashish Kundu, Xiangyu Zhang, Ninghui Li
https://arxiv.org/abs/2506.10424

SOFT: Selective Data Obfuscation for Protecting LLM Fine-tuning against Membership Inference Attacks
Large language models (LLMs) have achieved remarkable success and are widely adopted for diverse applications. However, fine-tuning these models often involves private or sensitive information, raising critical privacy concerns. In this work, we conduct the first comprehensive study evaluating the vulnerability of fine-tuned LLMs to membership inference attacks (MIAs). Our empirical analysis demonstrates that MIAs exploit the loss reduction during fine-tuning, making them highly effective in re…

Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values
John P. Dickerson, Hadi Hosseini, Samarth Khanna, Leona Pierce
https://arxiv.org/abs/2506.00079

Who Gets the Kidney? Human-AI Alignment, Indecision, and Moral Values
The rapid integration of Large Language Models (LLMs) in high-stakes decision-making -- such as allocating scarce resources like donor organs -- raises critical questions about their alignment with human moral values. We systematically evaluate the behavior of several prominent LLMs against human preferences in kidney allocation scenarios and show that LLMs: i) exhibit stark deviations from human values in prioritizing various attributes, and ii) in contrast to humans, LLMs rarely express indec…

Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
Zhiyi Hu, Siyuan Shen, Tommaso Bonato, Sylvain Jeaugey, Cedell Alexander, Eric Spada, Jeff Hammond, Torsten Hoefler
https://arxiv.org/abs/2507.04786

Demystifying NCCL: An In-depth Analysis of GPU Communication Protocols and Algorithms
The NVIDIA Collective Communication Library (NCCL) is a critical software layer enabling high-performance collectives on large-scale GPU clusters. Despite being open source with a documented API, its internal design remains largely opaque. The orchestration of communication channels, selection of protocols, and handling of memory movement across devices and nodes are not well understood, making it difficult to analyze performance or identify bottlenecks. This paper presents a comprehensive anal…

Supply Chain Optimization via Generative Simulation and Iterative Decision Policies
Haoyue Bai, Haoyu Wang, Nanxu Gong, Xinyuan Wang, Wangyang Ying, Haifeng Chen, Yanjie Fu
https://arxiv.org/abs/2507.07355

Supply Chain Optimization via Generative Simulation and Iterative Decision Policies
High responsiveness and economic efficiency are critical objectives in supply chain transportation, both of which are influenced by strategic decisions on shipping mode. An integrated framework combining an efficient simulator with an intelligent decision-making algorithm can provide an observable, low-risk environment for transportation strategy design. An ideal simulation-decision framework must (1) generalize effectively across various settings, (2) reflect fine-grained transportation dynami…

An Appraisal-Based Approach to Human-Centred Explanations
Rukshani Somarathna, Madhawa Perera, Tom Gedeon, Matt Adcock
https://arxiv.org/abs/2508.01388 https://

An Appraisal-Based Approach to Human-Centred Explanations
Explainability remains a critical challenge in artificial intelligence (AI) systems, particularly in high stakes domains such as healthcare, finance, and decision support, where users must understand and trust automated reasoning. Traditional explainability methods such as feature importance and post-hoc justifications often fail to capture the cognitive processes that underlie human decision making, leading to either too technical or insufficiently meaningful explanations. We propose a novel a…

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Hyeongchan Ham, Heejin Ahn
https://arxiv.org/abs/2507.05710

DRO-EDL-MPC: Evidential Deep Learning-Based Distributionally Robust Model Predictive Control for Safe Autonomous Driving
Safety is a critical concern in motion planning for autonomous vehicles. Modern autonomous vehicles rely on neural network-based perception, but making control decisions based on these inference results poses significant safety risks due to inherent uncertainties. To address this challenge, we present a distributionally robust optimization (DRO) framework that accounts for both aleatoric and epistemic perception uncertainties using evidential deep learning (EDL). Our approach introduces a novel…

ControlMed: Adding Reasoning Control to Medical Language Model
Sung-Min Lee, Siyoon Lee, Juyeon Kim, Kyungmin Roh
https://arxiv.org/abs/2507.22545 https://…

ControlMed: Adding Reasoning Control to Medical Language Model
Reasoning Large Language Models (LLMs) with enhanced accuracy and explainability are increasingly being adopted in the medical domain, as the life-critical nature of clinical decision-making demands reliable support. Despite these advancements, existing reasoning LLMs often generate unnecessarily lengthy reasoning processes, leading to significant computational overhead and response latency. These limitations hinder their practical deployment in real-world clinical environments. To address thes…

Leveraging large language models for SQL behavior-based database intrusion detection
Meital Shlezinger, Shay Akirav, Lei Zhou, Liang Guo, Avi Kessel, Guoliang Li
https://arxiv.org/abs/2508.05690

Leveraging large language models for SQL behavior-based database intrusion detection
Database systems are extensively used to store critical data across various domains. However, the frequency of abnormal database access behaviors, such as database intrusion by internal and external attacks, continues to rise. Internal masqueraders often have greater organizational knowledge, making it easier to mimic employee behavior effectively. In contrast, external masqueraders may behave differently due to their lack of familiarity with the organization. Current approaches lack the granul…

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Tingzhi Li, Xuefeng Liu
https://arxiv.org/abs/2506.08602 https://

WGLE:Backdoor-free and Multi-bit Black-box Watermarking for Graph Neural Networks
Graph Neural Networks (GNNs) are increasingly deployed in graph-related applications, making ownership verification critical to protect their intellectual property against model theft. Fingerprinting and black-box watermarking are two main methods. However, the former relies on determining model similarity, which is computationally expensive and prone to ownership collisions after model post-processing such as model pruning or fine-tuning. The latter embeds backdoors, exposing watermarked model…

This https://arxiv.org/abs/2505.22906 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

HiLDe: Intentional Code Generation via Human-in-the-Loop Decoding
While AI programming tools hold the promise of increasing programmers' capabilities and productivity to a remarkable degree, they often exclude users from essential decision-making processes, causing many to effectively "turn off their brains" and over-rely on solutions provided by these systems. These behaviors can have severe consequences in critical domains, like software security. We propose Human-in-the-loop Decoding, a novel interaction technique that allows users to observe and directly …

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation
Fatimaelzahraa Ahmed, Muraam Abdel-Ghani, Muhammad Arsalan, Mahmoud Ali, Abdulaziz Al-Ali, Shidin Balakrishnan
https://arxiv.org/abs/2507.04304

Surg-SegFormer: A Dual Transformer-Based Model for Holistic Surgical Scene Segmentation
Holistic surgical scene segmentation in robot-assisted surgery (RAS) enables surgical residents to identify various anatomical tissues, articulated tools, and critical structures, such as veins and vessels. Given the firm intraoperative time constraints, it is challenging for surgeons to provide detailed real-time explanations of the operative field for trainees. This challenge is compounded by the scarcity of expert surgeons relative to trainees, making the unambiguous delineation of go- and n…

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Weiyu Luo, Chenfeng Xiong
https://arxiv.org/abs/2508.02734 …

Recovering Individual-Level Activity Sequences from Location-Based Service Data Using a Novel Transformer-Based Model
Location-Based Service (LBS) data provides critical insights into human mobility, yet its sparsity often yields incomplete trip and activity sequences, making accurate inferences about trips and activities difficult. We raise a research problem: Can we use activity sequences derived from high-quality LBS data to recover incomplete activity sequences at the individual level? This study proposes a new solution, the Variable Selection Network-fused Insertion Transformer (VSNIT), integrating the In…

This https://arxiv.org/abs/2505.23667 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csAI_…

Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models
Tables are a fundamental structure for organizing and analyzing data, making effective table understanding a critical capability for intelligent systems. While large language models (LMs) demonstrate strong general reasoning abilities, they continue to struggle with accurate numerical or symbolic reasoning over tabular data, especially in complex scenarios. Spreadsheet formulas provide a powerful and expressive medium for representing executable symbolic operations, encoding rich reasoning patt…

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
Raju Ningappa Mulawade, Christoph Garth, Alexander Wiebel
https://arxiv.org/abs/2507.22020 https://…

XAI for Point Cloud Data using Perturbations based on Meaningful Segmentation
We propose a novel segmentation-based explainable artificial intelligence (XAI) method for neural networks working on point cloud classification. As one building block of this method, we propose a novel point-shifting mechanism to introduce perturbations in point cloud data. Recently, AI has seen an exponential growth. Hence, it is important to understand the decision-making process of AI algorithms when they are applied in critical areas. Our work focuses on explaining AI algorithms that class…

Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving V2X Communication
Abdullah Al Mamun, Kyle Yates, Antsa Rakotondrafara, Mashrur Chowdhury, Ryann Cartor, Shuhong Gao
https://arxiv.org/abs/2508.02461

Experimental Evaluation of Post-Quantum Homomorphic Encryption for Privacy-Preserving V2X Communication
Intelligent Transportation Systems (ITS) fundamentally rely on vehicle-generated data for applications such as congestion monitoring and route optimization, making the preservation of user privacy a critical challenge. Homomorphic Encryption (HE) offers a promising solution by enabling computation on encrypted data without revealing underlying content. This study presents the first real-world experimental evaluation of three post-quantum secure HE schemes, i.e., Brakerski-Fan-Vercauteren (BFV),…

BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies
Kourosh Shahnazari, Seyed Moein Ayyoubzadeh, Mohammadali Keshtparvar
https://arxiv.org/abs/2506.00328

BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies
The quest for interpretable reinforcement learning is a grand challenge for the deployment of autonomous decision-making systems in safety-critical applications. Modern deep reinforcement learning approaches, while powerful, tend to produce opaque policies that compromise verification, reduce transparency, and impede human oversight. To address this, we introduce BASIL (Best-Action Symbolic Interpretable Learning), a systematic approach for generating symbolic, rule-based policies via online ev…

Large language models can learn and generalize steganographic chain-of-thought under process supervision
Joey Skaf, Luis Ibanez-Lissen, Robert McCarthy, Connor Watts, Vasil Georgiv, Hannes Whittingham, Lorena Gonzalez-Manzano, David Lindner, Cameron Tice, Edward James Young, Puria Radmard
https://arxiv.org/abs/2506.01926

Large language models can learn and generalize steganographic chain-of-thought under process supervision
Chain-of-thought (CoT) reasoning not only enhances large language model performance but also provides critical insights into decision-making processes, marking it as a useful tool for monitoring model intent and planning. By proactively preventing models from acting on CoT indicating misaligned or harmful intent, CoT monitoring can be used to reduce risks associated with deploying models. However, developers may be incentivized to train away the appearance of harmful intent from CoT traces, by …

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment
Hao Li, Lijun Li, Zhenghao Lu, Xianyi Wei, Rui Li, Jing Shao, Lei Sha
https://arxiv.org/abs/2507.18631

Layer-Aware Representation Filtering: Purifying Finetuning Data to Preserve LLM Safety Alignment
With rapid advancement and increasing accessibility of LLMs, fine-tuning aligned models has become a critical step for adapting them to real-world applications, which makes the safety of this fine-tuning process more important than ever. However, recent studies have highlighted a critical challenge: even when fine-tuning with seemingly benign downstream datasets, the safety of aligned LLMs can be compromised, making them more susceptible to malicious instructions. In this paper, we show that fi…

World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
Changyuan Zhao, Ruichen Zhang, Jiacheng Wang, Gaosheng Zhao, Dusit Niyato, Geng Sun, Shiwen Mao, Dong In Kim
https://arxiv.org/abs/2506.00417

World Models for Cognitive Agents: Transforming Edge Intelligence in Future Networks
World models are emerging as a transformative paradigm in artificial intelligence, enabling agents to construct internal representations of their environments for predictive reasoning, planning, and decision-making. By learning latent dynamics, world models provide a sample-efficient framework that is especially valuable in data-constrained or safety-critical scenarios. In this paper, we present a comprehensive overview of world models, highlighting their architecture, training paradigms, and a…