Tootfinder

Improving AEBS Validation Through Objective Intervention Classification Leveraging the Prediction Divergence Principle
Daniel Betschinske, Steven Peters
https://arxiv.org/abs/2507.07872

Improving AEBS Validation Through Objective Intervention Classification Leveraging the Prediction Divergence Principle
The safety validation of automatic emergency braking system (AEBS) requires accurately distinguishing between false positive (FP) and true positive (TP) system activations. While simulations allow straightforward differentiation by comparing scenarios with and without interventions, analyzing activations from open-loop resimulations - such as those from field operational testing (FOT) - is more complex. This complexity arises from scenario parameter uncertainty and the influence of driver inter…

FASE: FPGA-Assisted Syscall Emulation for Rapid End-to-End Processor Performance Validation
Chengzhen Meng, Xiuzhuang Chen, Hongjun Dai
https://arxiv.org/abs/2509.08405 https://…

FASE: FPGA-Assisted Syscall Emulation for Rapid End-to-End Processor Performance Validation
The rapid advancement of AI workloads and domain-specific architectures has led to increasingly diverse processor microarchitectures, whose design exploration requires fast and accurate performance validation. However, traditional workflows defer validation process until RTL design and SoC integration are complete, significantly prolonging development and iteration cycle. In this work, we present FASE framework, FPGA-Assisted Syscall Emulation, the first work for adapt syscall emulation on FP…

An upper bound of the silhouette validation metric for clustering
Hugo Str\"ang, Tai Dinh
https://arxiv.org/abs/2509.08625 https://arxiv.org/pdf/2509.…

An upper bound of the silhouette validation metric for clustering
The silhouette coefficient summarizes, per observation, cohesion versus separation in [-1, 1]; the average silhouette width (ASW) is a common internal measure of clustering quality where higher values indicate more coveted results. However, the dataset-specific maximum of ASW is typically unknown, and the standard upper limit 1 is often unattainable. In this work, we derive for each data point in a given dataset a sharp upper bound on its silhouette width. By aggregating these individual bounds…

ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework
Abrar Faiyaz, Nhat Hoang, Giovanni Schifitto, Md Nasir Uddin
https://arxiv.org/abs/2507.07920

ArteryX: Advancing Brain Artery Feature Extraction with Vessel-Fused Networks and a Robust Validation Framework
Cerebrovascular pathology significantly contributes to cognitive decline and neurological disorders, underscoring the need for advanced tools to assess vascular integrity. Three-dimensional Time-of-Flight Magnetic Resonance Angiography (3D TOF MRA) is widely used to visualize cerebral vasculature, however, clinical evaluations generally focus on major arterial abnormalities, overlooking quantitative metrics critical for understanding subtle vascular changes. Existing methods for extracting stru…

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation
Peizhuo Liu
https://arxiv.org/abs/2508.05677

Adversarial Attacks on Reinforcement Learning-based Medical Questionnaire Systems: Input-level Perturbation Strategies and Medical Constraint Validation
RL-based medical questionnaire systems have shown great potential in medical scenarios. However, their safety and robustness remain unresolved. This study performs a comprehensive evaluation on adversarial attack methods to identify and analyze their potential vulnerabilities. We formulate the diagnosis process as a Markov Decision Process (MDP), where the state is the patient responses and unasked questions, and the action is either to ask a question or to make a diagnosis. We implemented six …

Validation of a CT-brain analysis tool for measuring global cortical atrophy in older patient cohorts
Sukhdeep Bal, Emma Colbourne, Jasmine Gan, Ludovica Griffanti, Taylor Hanayik, Nele Demeyere, Jim Davies, Sarah T Pendlebury, Mark Jenkinson
https://arxiv.org/abs/2509.08012

Validation of a CT-brain analysis tool for measuring global cortical atrophy in older patient cohorts
Quantification of brain atrophy currently requires visual rating scales which are time consuming and automated brain image analysis is warranted. We validated our automated deep learning (DL) tool measuring the Global Cerebral Atrophy (GCA) score against trained human raters, and associations with age and cognitive impairment, in representative older (>65 years) patients. CT-brain scans were obtained from patients in acute medicine (ORCHARD-EPR), acute stroke (OCS studies) and a legacy sample. …

On the Operational Resilience of CBDC: Threats and Prospects of Formal Validation for Offline Payments
Marco Bernardo, Federico Calandra, Andrea Esposito, Francesco Fabris
https://arxiv.org/abs/2508.08064

On the Operational Resilience of CBDC: Threats and Prospects of Formal Validation for Offline Payments
Information and communication technologies are by now employed in most activities, including economics and finance. Despite the extraordinary power of modern computers and the vast amount of memory, some results of theoretical computer science imply the impossibility of certifying software quality in general. With the exception of safety-critical systems, this has primarily concerned the information processed by confined systems, with limited socio-economic consequences. In the emerging era of …

Cluster Catalog Validation with Multiwavelength Data
Ethan K. Steelberg, Tesla E. Jeltema, Jackson H. O'Donnell, Rance Solomon, The LSST Dark Energy Science Collaboration
https://arxiv.org/abs/2509.07268

Cluster Catalog Validation with Multiwavelength Data
The Legacy Survey of Space and Time (LSST) will provide a ground-breaking data set for cosmology, but to achieve the precision needed, the data, data reduction, and algorithms measuring the cosmological data vectors must be thoroughly validated and calibrated. In this note, we focus on clusters of galaxies and present a set of validation tests for optical cluster finding algorithms through comparison to X-ray and Sunyaev-Zel'dovich effect cluster catalogs. As an example, we apply our pipeline t…

From Explainable to Explanatory Artificial Intelligence: Toward a New Paradigm for Human-Centered Explanations through Generative AI
Christian Meske, Justin Brenne, Erdi Uenal, Sabahat Oelcer, Ayseguel Doganguen
https://arxiv.org/abs/2508.06352

From Explainable to Explanatory Artificial Intelligence: Toward a New Paradigm for Human-Centered Explanations through Generative AI
Current explainable AI (XAI) approaches prioritize algorithmic transparency and present explanations in abstract, non-adaptive formats that often fail to support meaningful end-user understanding. This paper introduces "Explanatory AI" as a complementary paradigm that leverages generative AI capabilities to serve as explanatory partners for human understanding rather than providers of algorithmic transparency. While XAI reveals algorithmic decision processes for model validation, Explanatory AI…

An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects
Kaveh Shahedi, Nana Gyambrah, Heng Li, Maxime Lamothe, Foutse Khomh
https://arxiv.org/abs/2508.07084

An Empirical Study on Method-Level Performance Evolution in Open-Source Java Projects
Performance is a critical quality attribute in software development, yet the impact of method-level code changes on performance evolution remains poorly understood. While developers often make intuitive assumptions about which types of modifications are likely to cause performance regressions or improvements, these beliefs lack empirical validation at a fine-grained level. We conducted a large-scale empirical study analyzing performance evolution in 15 mature open-source Java projects hosted on…

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
Zakaria El Kassimi, Fares Fourati, Mohamed-Slim Alouini
https://arxiv.org/abs/2509.09651 https://…

Retrieval-Augmented Generation for Reliable Interpretation of Radio Regulations
We study question answering in the domain of radio regulations, a legally sensitive and high-stakes area. We propose a telecom-specific Retrieval-Augmented Generation (RAG) pipeline and introduce, to our knowledge, the first multiple-choice evaluation set for this domain, constructed from authoritative sources using automated filtering and human validation. To assess retrieval quality, we define a domain-specific retrieval metric, under which our retriever achieves approximately 97% accuracy. B…

Fast End-to-End Simulation and Exploration of Many-RISCV-Core Baseband Transceivers for Software-Defined Radio-Access Networks
Marco Bertuletti, Yichao Zhang, Mahdi Abdollahpour, Samuel Riedel, Alessandro Vanelli-Coralli
https://arxiv.org/abs/2508.06141

Fast End-to-End Simulation and Exploration of Many-RISCV-Core Baseband Transceivers for Software-Defined Radio-Access Networks
The fast-rising demand for wireless bandwidth requires rapid evolution of high-performance baseband processing infrastructure. Programmable many-core processors for software-defined radio (SDR) have emerged as high-performance baseband processing engines, offering the flexibility required to capture evolving wireless standards and technologies. This trend must be supported by a design framework enabling functional validation and end-to-end performance analysis of SDR hardware within realistic r…

Scalar quasinormal modes of magnetically charged black holes in a quintessence field
Ali Hasnain
https://arxiv.org/abs/2509.08845 https://arxiv.org/pdf/250…

Scalar quasinormal modes of magnetically charged black holes in a quintessence field
We investigate the quasinormal mode (QNM) spectrum for scalar perturbations of static, magnetically charged black holes in the presence of a quintessence field. The background geometry is obtained from the Einstein-Power-Maxwell action with a Kiselev-type contribution for quintessence. The associated master wave equation is solved using complementary numerical approaches, including high-order WKB expansions, the asymptotic iteration method, and time-domain integration, with cross-validation to …

Replaced article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Cross-validation of meshless Navier-Stokes solvers in porous media flows
Dawid Strzelczyk, Miha Rot, Gregor Kosec, Maciej Matyka

The Relative Instability of Model Comparison with Cross-validation
Alexandre Bayle, Lucas Janson, Lester Mackey
https://arxiv.org/abs/2508.04409 https://ar…

The Relative Instability of Model Comparison with Cross-validation
Existing work has shown that cross-validation (CV) can be used to provide an asymptotic confidence interval for the test error of a stable machine learning algorithm, and existing stability results for many popular algorithms can be applied to derive positive instances where such confidence intervals will be valid. However, in the common setting where CV is used to compare two algorithms, it becomes necessary to consider a notion of relative stability which cannot easily be derived from existin…

Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2
Pjotrs \v{Z}guns, Inga Pudza, Alexei Kuzmin
https://arxiv.org/abs/2509.08498

Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2
Universal machine learning interatomic potentials (uMLIPs) deliver near ab initio accuracy in energy and force calculations at low computational cost, making them invaluable for materials modeling. Although uMLIPs are pre-trained on vast ab initio datasets, rigorous validation remains essential for their ongoing adoption. In this study, we use the CHGNet uMLIP to model thermal disorder in isostructural layered 2Hc-WS2 and 2Hc-MoS2, benchmarking it against ab initio data and extended X-ray absor…

Study of a Compact Device for Water Attenuation Length Measurements
Junyou Chen, Jilei Xu, Yongbo Huang, Sibo Wang, Chuanshi Dong, Haoqi Lu, Changgen Yang, Yongpeng Zhang, Yi Wang
https://arxiv.org/abs/2509.09260

Study of a Compact Device for Water Attenuation Length Measurements
This study presents the development and validation of a compact device for measuring the water attenuation length (WAL), utilizing photomultiplier tubes (PMTs), optical fibers, and LEDs. An 8 m water tank and the device was constructed and validated in the laboratory. The device is capable of measuring WAL values up to 50 m. The stray light was blocked mainly by a custom-designed shutter. Toy Monte Carlo simulations were employed to evaluate the measurement uncertainty, which was found to be wi…

Random Forest Stratified K-Fold Cross Validation on SYN DoS Attack SD-IoV
Muhammad Arif Hakimi Zamrai, Kamaludin Mohd Yusof
https://arxiv.org/abs/2509.07016 https://

Random Forest Stratified K-Fold Cross Validation on SYN DoS Attack SD-IoV
In response to the prevalent concern of TCP SYN flood attacks within the context of Software-Defined Internet of Vehicles (SD-IoV), this study addresses the significant challenge of network security in rapidly evolving vehicular communication systems. This research focuses on optimizing a Random Forest Classifier model to achieve maximum accuracy and minimal detection time, thereby enhancing vehicular network security. The methodology involves preprocessing a dataset containing SYN attack insta…

Unlocking Stopped-Rotor Flight: Development and Validation of SPERO, a Novel UAV Platform
Kristan Hilby, Ian Hunter
https://arxiv.org/abs/2509.07812 https://

Unlocking Stopped-Rotor Flight: Development and Validation of SPERO, a Novel UAV Platform
Stop-rotor aircraft have long been proposed as the ideal vertical takeoff and landing (VTOL) aircraft for missions with equal time spent in both flight regimes, such as agricultural monitoring, search and rescue, and last-mile delivery. Featuring a central lifting surface that rotates in VTOL to generate vertical thrust and locks in forward flight to generate passive lift, the stop-rotor offers the potential for high efficiency across both modes. However, practical implementation has remained i…

TOI-1743 b, TOI-5799 b, TOI-5799 c and TOI-6223 b: TESS discovery and validation of four super-Earth to Neptune-sized planets around M dwarfs
S. Yal\c{c}{\i}nkaya, K. Barkaoui, \"O. Ba\c{s}t\"urk, M. Gillon, F. J. Pozuelos, M. Timmermans, B. V. Rackham, A. J. Burgasser, P. Mistry, A. Pel\'aez-Torres, G. Morello, E. K. Pass, A. Bieryla, D. W. Latham, K. A. Collins, F. Akar, Z. Benkhaldoun, A. Burdanov, J. Brande, D. R. Ciardi, C. A. Clark, E. Ducrot, J. de Wit, B. O. Demor…

TOI-1743 b, TOI-5799 b, TOI-5799 c and TOI-6223 b: TESS discovery and validation of four super-Earth to Neptune-sized planets around M dwarfs
We present the discovery by the TESS mission of one transiting Neptune-sized planet, TOI-6223 b and two transiting super-Earths, TOI-1743 b and TOI-5799 b. We validate these planets using a statistical validation method, multi-color light curves and other ancillary observations. We combined TESS and ground-based photometric data to constrain the physical properties of the planets. TOI-6223-b is slightly larger than Neptune ($R_p=5.12^{+0.24}_{-0.25}$ $R_\oplus$) orbiting an early M dwarf in 3.8…

Day 16
Just published a deep dive into building a secure login page with Next.js, NestJS, JWT, and PostgreSQL.
- Email verification
- Role-based access control
- Subscription enforcement
- Token decoding in frontend
- SQL-level inserts for system roles
Includes full code snippets and explanation of the entire flow.
Perfect if you're working on full-stack apps with JavaScript, TypeScript, and SQL.

Image Recognition with Vision and Language Embeddings of VLMs
Illia Volkov, Nikita Kisel, Klara Janouskova, Jiri Matas
https://arxiv.org/abs/2509.09311 https://

Image Recognition with Vision and Language Embeddings of VLMs
Vision-language models (VLMs) have enabled strong zero-shot classification through image-text alignment. Yet, their purely visual inference capabilities remain under-explored. In this work, we conduct a comprehensive evaluation of both language-guided and vision-only image classification with a diverse set of dual-encoder VLMs, including both well-established and recent models such as SigLIP 2 and RADIOv2.5. The performance is compared in a standard setup on the ImageNet-1k validation set and i…

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Alireza Sameh, Mehrdad Rostami, Mourad Oussalah, Vahid Farrahi
https://arxiv.org/abs/2509.08606

Classification of 24-hour movement behaviors from wrist-worn accelerometer data: from handcrafted features to deep learning techniques
Purpose: We compared the performance of deep learning (DL) and classical machine learning (ML) algorithms for the classification of 24-hour movement behavior into sleep, sedentary, light intensity physical activity (LPA), and moderate-to-vigorous intensity physical activity (MVPA). Methods: Open-access data from 151 adults wearing a wrist-worn accelerometer (Axivity-AX3) was used. Participants were randomly divided into training, validation, and test sets (121, 15, and 15 participants each). Ra…

Does This Look Familiar to You? Knowledge Analysis via Model Internal Representations
Sihyun Park
https://arxiv.org/abs/2509.07311 https://arxiv.org/pdf/25…

Does This Look Familiar to You? Knowledge Analysis via Model Internal Representations
Recent advances in large language models (LLMs) have been driven by pretraining, supervised fine tuning (SFT), and alignment tuning. Among these, SFT plays a crucial role in transforming a model 's general knowledge into structured responses tailored to specific tasks. However, there is no clearly established methodology for effective training data selection. Simply increasing the volume of data does not guarantee performance improvements, while preprocessing, sampling, and validation require s…

A Comprehensive MARTINI Coarse-Grained Framework for Phyllosilicate Clay/Polymer Nanocomposites: From Atomistic Validation to Mesoscale Insights
Ankit Patidar, Gaurav Goel
https://arxiv.org/abs/2507.06159

A Comprehensive MARTINI Coarse-Grained Framework for Phyllosilicate Clay/Polymer Nanocomposites: From Atomistic Validation to Mesoscale Insights
Phyllosilicate clays have diverse applications in packaging industries and are found highly suitable for formulation, including thermoplastic starch (TPS), polyethylene (PE), or their combination. We developed CG MARTINI-3 parameters of the pyrophyllite using Lifshitz theory and experimental surface tension data. These initial bead assignments of pyrophyllite and periodic tetramethylammonium-montmorillonite (TMA-MMT) sheet were fine-tuned using optimal reproduction of structural, thermodynamic,…

System Identification via Validation and Adaptation for Model Updating Applied to a Nonlinear Cantilever Beam
Cristian L\'opez, Jackson E. Herzlieb, Keegan J. Moore
https://arxiv.org/abs/2508.00931

System Identification via Validation and Adaptation for Model Updating Applied to a Nonlinear Cantilever Beam
The recently proposed System Identification via Validation and Adaptation (SIVA) method allows system identification, uncertainty quantification, and model validation directly from data. Inspired by generative modeling, SIVA employs a neural network that converts random noise to physically meaningful parameters. The known equation of motion utilizes these parameters to generate fake accelerations, which are compared to real training data using a mean square error loss. For concurrent parameter …

Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
Jia Fu, Xinyu Yang, Hongzhi Zhang, Yahui Liu, Jingyuan Zhang, Qi Wang, Fuzheng Zhang, Guorui Zhou
https://arxiv.org/abs/2508.05710

Klear-CodeTest: Scalable Test Case Generation for Code Reinforcement Learning
Precise, correct feedback is crucial for effectively training large language models (LLMs) in code reinforcement learning. However, synthesizing high-quality test cases remains a profoundly challenging and unsolved problem. In this work, we present Klear-CodeTest, a comprehensive test case synthesis framework featuring rigorous verification to ensure quality and reliability of test cases. Our approach achieves broad coverage of programming problems via a novel Generator-Validation (G-V) framewo…

Dependency-Aware Execution Mechanism in Hyperledger Fabric Architecture
Sanyam Kaul, Manaswini Piduguralla, Gayathri Shreeya Patnala, Sathya Peri
https://arxiv.org/abs/2509.07425

Dependency-Aware Execution Mechanism in Hyperledger Fabric Architecture
Hyperledger Fabric is a leading permissioned blockchain framework for enterprise use, known for its modular design and privacy features. While it strongly supports configurable consensus and access control, Fabric can face challenges in achieving high transaction throughput and low rejection rates under heavy workloads. These performance limitations are often attributed to endorsement, ordering, and validation bottlenecks. Further, optimistic concurrency control and deferred validation in Fabri…

oh, i almost forgot:
- #PHP is great
- #JSON schema validation is great: https://github.com/opis/json-schema

Elliptic interface problem approximated by CutFEM: I. Conservative flux recovery and numerical validation of adaptive mesh refinement
Daniela Capatina, Aimene Gouasmi, Cuiyu He
https://arxiv.org/abs/2507.03492

Elliptic interface problem approximated by CutFEM: I. Conservative flux recovery and numerical validation of adaptive mesh refinement
We study an elliptic interface problem with discontinuous diffusion coefficients on unfitted meshes using the CutFEM method. Our main contribution is the reconstruction of conservative fluxes from the CutFEM solution and their use in a posteriori error estimation. We introduce a hybrid mixed formulation with locally computable Lagrange multipliers and reconstruct the flux in the immersed Raviart-Thomas space. Based on this, we propose a new a posteriori error estimator that includes both volume…

When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation
Zongwei Wang, Min Gao, Junliang Yu, Shazia Sadiq, Hongzhi Yin, Ling Liu
https://arxiv.org/abs/2507.07436

When Graph Contrastive Learning Backfires: Spectral Vulnerability and Defense in Recommendation
Graph Contrastive Learning (GCL) has demonstrated substantial promise in enhancing the robustness and generalization of recommender systems, particularly by enabling models to leverage large-scale unlabeled data for improved representation learning. However, in this paper, we reveal an unexpected vulnerability: the integration of GCL inadvertently increases the susceptibility of a recommender to targeted promotion attacks. Through both theoretical investigation and empirical validation, we iden…

An amazing example of how human testimonies of suffering might fail, and why - as in any other context - validation through fact-checking and triangulation are crucial in order to understand history.
https://forward.com/news/761437/holocaust-survivor-ari-schnei…

His grandfather’s Holocaust stories didn’t add up — so he set out to tell the fact-checked version
In "Stolen Headstones," Ari Schneider tells the fact-checked version of his family's Holocaust survival story.

Implementation and Assessment of an Augmented Training Curriculum for Surgical Robotics
Alberto Rota, Ke Fan, Elena De Momi
https://arxiv.org/abs/2507.07718

Implementation and Assessment of an Augmented Training Curriculum for Surgical Robotics
The integration of high-level assistance algorithms in surgical robotics training curricula may be beneficial in establishing a more comprehensive and robust skillset for aspiring surgeons, improving their clinical performance as a consequence. This work presents the development and validation of a haptic-enhanced Virtual Reality simulator for surgical robotics training, featuring 8 surgical tasks that the trainee can interact with thanks to the embedded physics engine. This virtual simulated e…

Visual Execution and Validation of Finite-State Machines and Pushdown Automata
Marco T. Moraz\'an (Seton Hall University), David Anthony K. Fields (Seton Hall University), Andr\'es M. Garced (Seton Hall University), Tijana Mini\'c (University of Washington)
https://arxiv.org/abs/2508.03641

Visual Execution and Validation of Finite-State Machines and Pushdown Automata
In Formal Languages and Automata Theory courses, students find understanding nondeterministic finite-state and pushdown automata difficult. In many cases, this means that it is challenging for them to comprehend the operational semantics of such machines and, as a consequence, determine why a word is accepted or rejected. This is not entirely surprising, because students are mostly trained to design and implement deterministic programs. Comprehension of pushdown automata is further complicated,…

Prototype-Guided Robust Learning against Backdoor Attacks
Wei Guo, Maura Pintor, Ambra Demontis, Battista Biggio
https://arxiv.org/abs/2509.08748 https://a…

Prototype-Guided Robust Learning against Backdoor Attacks
Backdoor attacks poison the training data to embed a backdoor in the model, causing it to behave normally on legitimate inputs but maliciously when specific trigger signals appear. Training a benign model from a dataset poisoned by backdoor attacks is challenging. Existing works rely on various assumptions and can only defend against backdoor attacks with specific trigger signals, high poisoning ratios, or when the defender possesses a large, untainted, validation dataset. In this paper, we pro…

Operationalising AI Regulatory Sandboxes under the EU AI Act: The Triple Challenge of Capacity, Coordination and Attractiveness to Providers
Deirdre Ahern
https://arxiv.org/abs/2509.05985

Operationalising AI Regulatory Sandboxes under the EU AI Act: The Triple Challenge of Capacity, Coordination and Attractiveness to Providers
The EU AI Act provides a rulebook for all AI systems being put on the market or into service in the European Union. This article investigates the requirement under the AI Act that Member States establish national AI regulatory sandboxes for testing and validation of innovative AI systems under regulatory supervision to assist with fostering innovation and complying with regulatory requirements. Against the backdrop of the EU objective that AI regulatory sandboxes would both foster innovation an…

Dense photometry validation of super-fast-rotating asteroid candidates
Bojan Novakovi\'c, Pedro J. Guti\'errez
https://arxiv.org/abs/2509.06533 https://

Dense photometry validation of super-fast-rotating asteroid candidates
Super-fast rotators (SFRs; P < 2.2 hr) are of great importance in asteroid studies; yet, many reported detections suffer from aliasing caused by an insufficient observation cadence. We present dense CCD photometry for 15 SFRs candidates (14 after excluding 11219 Benbohn, whose published period already exceeds the spin barrier) observed from 2023 Aug 11 to 2024 Aug 11 with the 1.5 m Sierra Nevada and 1.4 m AS Vidojevica telescopes. Our dataset comprises approximately 2,400 calibrated data points…

From Coarse to Fine-Grained Emotion Annotation: An Immediate Recall Paradigm with Validation through Physiological Evidence and Recognition Performance
Hao Tang, Songyun Xie, Xinzhou Xie, Can Liao, Xin Zhang, Bohan Li, Zhongyu Tian, Dalu Zheng
https://arxiv.org/abs/2507.02350

From Coarse to Fine-Grained Emotion Annotation: An Immediate Recall Paradigm with Validation through Physiological Evidence and Recognition Performance
Traditional video-induced emotion physiological datasets often use whole-trial annotation, assigning a single emotion label to all data collected during an entire trial. This coarse-grained annotation approach misaligns with the dynamic and temporally localized nature of emotional responses as they unfold with video narratives, introducing label noise that limits emotion recognition algorithm evaluation and performance. To solve the label noise problem caused by coarse-grained annotation, we pr…

Simplicial clustering using the $\alpha$--transformation
Michail Tsagris, Nikolaos Kontemeniotis
https://arxiv.org/abs/2509.05945 https://arxiv.org/pdf/250…

Simplicial clustering using the $α$--transformation
We introduce two simplicial clustering approaches for compositional data, that are adaptations of the $K$--means and of the Gaussian mixture models algorithms, by employing the $α$--transformation. By utilizing clustering validation indices we can decide on the number of clusters and choose the value of $α$ for the $K$--means, while for the model-based clustering approach information criteria complete this task. extensive simulation studies compare the performance of these two approaches and …

GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query
Mingchang Ge, Liping Wang, Xuemin Lin, Yuang Zhang, Kunming Wang
https://arxiv.org/abs/2507.05542

GTRSS: Graph-based Top-$k$ Representative Similar Subtrajectory Query
Trajectory mining has attracted significant attention. This paper addresses the Top-k Representative Similar Subtrajectory Query (TRSSQ) problem, which aims to find the k most representative subtrajectories similar to a query. Existing methods rely on costly filtering-validation frameworks, resulting in slow response times. Addressing this, we propose GTRSS, a novel Graph-based Top-k Representative Similar Subtrajectory Query framework. During the offline phase, GTRSS builds a dual-layer graph …

A Closed-Loop Multi-Agent Framework for Aerodynamics-Aware Automotive Styling Design
Xinyu Jin, Shengmao Yan, Qingtao Wang, Shisong Deng, Yanzhen Jiang, Shuangyao Zhao
https://arxiv.org/abs/2508.03370 …

A Closed-Loop Multi-Agent Framework for Aerodynamics-Aware Automotive Styling Design
The core challenge in automotive exterior design is balancing subjective aesthetics with objective aerodynamic performance while dramatically accelerating the development cycle. To address this, we propose a novel, LLM-driven multi-agent framework that automates the end-to-end workflow from ambiguous requirements to 3D concept model performance validation. The workflow is structured in two stages: conceptual generation and performance validation. In the first stage, agents collaborate to interp…

Verti-Arena: A Controllable and Standardized Indoor Testbed for Multi-Terrain Off-Road Autonomy
Haiyue Chen, Aniket Datar, Tong Xu, Francesco Cancelliere, Harsh Rangwala, Madhan Balaji Rao, Daeun Song, David Eichinger, Xuesu Xiao
https://arxiv.org/abs/2508.08226

Verti-Arena: A Controllable and Standardized Indoor Testbed for Multi-Terrain Off-Road Autonomy
Off-road navigation is an important capability for mobile robots deployed in environments that are inaccessible or dangerous to humans, such as disaster response or planetary exploration. Progress is limited due to the lack of a controllable and standardized real-world testbed for systematic data collection and validation. To fill this gap, we introduce Verti-Arena, a reconfigurable indoor facility designed specifically for off-road autonomy. By providing a repeatable benchmark environment, Ver…

Replaced article(s) found for stat.CO. https://arxiv.org/list/stat.CO/new
[1/1]:
- Adaptive sequential Monte Carlo for structured cross validation in Bayesian hierarchical models
Geonhee Han, Andrew Gelman

Weekend Reads
* A new RPKI design https://arxiv.org/abs/2507.01465
* Finding New IP KVMs https://www.runzero.com/blog/oob-p1-ip-kvm/

A new efficient RPKI Design
Resource Public Key Infrastructure (RPKI) is a critical security mechanism for BGP, but the complexity of its architecture is a growing concern as its adoption scales. Current RPKI design heavily reuses legacy PKI components, such as X.509 EE-certificates, ASN.1 encoding, and XML-based repository protocols, all these introduce excessive cryptographic validation, redundant metadata, and inefficiencies in both storage and processing. We show that these design choices, although based on establishe…

Spherically Symmetric Accretion with Self-Gravity: Analytical Formulae and Numerical Validation
Cheng-Liang Jiao, Er-gang Zhao, Liying Zhu, Xiang-dong Shi
https://arxiv.org/abs/2507.02621

Spherically Symmetric Accretion with Self-Gravity: Analytical Formulae and Numerical Validation
Spherically symmetric accretion incorporating self-gravity constitutes a three-point boundary value problem (TPBVP) governed by constraints at the outer boundary, sonic point, and accretor surface. Previous studies have two limitations: either employing an incorrect formula for self-gravity potential in analytical treatments, or introducing additional input parameters in numerical implementations to circumvent solving the full TPBVP. To address these issues, we present a self-consistent TPBVP f…

Hydrodynamic Modeling Improvements for Floating Offshore Wind Turbines with Validation Results
Doyal Sarker, Md Sakif, Tri Ngo, Tuhin Das
https://arxiv.org/abs/2508.01093 https:…

Hydrodynamic Modeling Improvements for Floating Offshore Wind Turbines with Validation Results
This study presents key enhancements in hydrodynamic modeling using the strip-based Morison's equation approach to enable rapid simulations of Floating Offshore Wind Turbines (FOWT). The modeling framework employs the relative form of the Morison equation, incorporating nonlinear irregular wave kinematics, vertical wave stretching, and diffraction corrections based on MacCamy-Fuchs (MCF) theory for large-scale, non-slender structures. Wave kinematics are iteratively applied at dynamically displ…

Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
Gabriella Waters
https://arxiv.org/abs/2507.04555 https…

Testing, Evaluation, Verification and Validation (TEVV) of Digital Twins: A Comprehensive Framework
Digital twins have emerged as a powerful technology for modeling and simulating complex systems across various domains (Fuller et al., 2020; Tao et al., 2019). As virtual representations of physical assets, processes, or systems, digital twins enable real-time monitoring, predictive analysis, and optimization. However, as digital twins become more sophisticated and integral to decision-making processes, ensuring their accuracy, reliability, and ethical implementation is essential. This paper pr…

Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation
Moo Hyun Son, Juyoung Bae, Zelin Qiu, Jiale Peng, Kai Xin Li, Yifan Lin, Hao Chen
https://arxiv.org/abs/2509.07923

Multimodal Contrastive Pretraining of CBCT and IOS for Enhanced Tooth Segmentation
Digital dentistry represents a transformative shift in modern dental practice. The foundational step in this transformation is the accurate digital representation of the patient's dentition, which is obtained from segmented Cone-Beam Computed Tomography (CBCT) and Intraoral Scans (IOS). Despite the growing interest in digital dental technologies, existing segmentation methodologies frequently lack rigorous validation and demonstrate limited performance and clinical applicability. To the best of…

Deep Learning-based Human Gesture Channel Modeling for Integrated Sensing and Communication Scenarios
Zhengyu Zhang, Neeraj Varshney, Jelena Senic, Raied Caromi, Samuel Berweger, Camillo Gentile, Enrico M. Vitucci, Ruisi He, Vittorio Degli-Esposti
https://arxiv.org/abs/2507.06588

Deep Learning-based Human Gesture Channel Modeling for Integrated Sensing and Communication Scenarios
With the development of Integrated Sensing and Communication (ISAC) for Sixth-Generation (6G) wireless systems, contactless human recognition has emerged as one of the key application scenarios. Since human gesture motion induces subtle and random variations in wireless multipath propagation, how to accurately model human gesture channels has become a crucial issue for the design and validation of ISAC systems. To this end, this paper proposes a deep learning-based human gesture channel modelin…

Numerical Validation of the Yarkovsky Effect in Super-Fast Rotating Asteroids
Dusan Marceta, Bojan Novakovic, Marko Gavrilovic
https://arxiv.org/abs/2507.05789

Numerical Validation of the Yarkovsky Effect in Super-Fast Rotating Asteroids
Recent discoveries show that asteroids spinning in less than a few minutes undergo sizeable semi-major-axis drifts, possibly driven by the Yarkovsky effect. Analytical formulas can match these drifts only if very low thermal inertia is assumed, implying a dust-fine regolith or a highly porous interior that is difficult to retain under such extreme centrifugal forces. With analytical theories of the Yarkovsky effect resting on a set of assumptions, their applicability to cases of super-fast rota…

Analysis of RF Sheath-Driven Tungsten Erosion at RF Antenna in the WEST Tokamak
A. Kumar, W. Tierens, T. Younkin, C. Johnson, C. Klepper, A. Diaw, J. Lore, A. Grosjean, G. Urbanczyk, J. Hillairet, P. Tamain, L. Colas, C. Guillemaut, D. Current, S. Shiraiwa, N. Bertelli, the WEST Team
https://arxiv.org/abs/2507.06059

Analysis of RF Sheath-Driven Tungsten Erosion at RF Antenna in the WEST Tokamak
This study applies the newly developed STRIPE (Simulated Transport of RF Impurity Production and Emission) framework to interpret tungsten (W) erosion at RF antenna structures in the WEST tokamak. STRIPE integrates SolEdge3x for edge plasma backgrounds, COMSOL for 3D RF sheath potentials, RustBCA for sputtering yields, and GITR for impurity transport and ion energy-angle distributions. In contrast to prior work by Kumar et al. 2025 Nucl. Fusion 65, 076039, which focused on framework validation …

A Thermodynamic Model for Thermomigration in Metals
Daniel J. Long, Edmund Tarleton, Alan C. F. Cocks, Felix Hofmann
https://arxiv.org/abs/2508.05327 https://

A Thermodynamic Model for Thermomigration in Metals
We investigate the mechanisms involved in the thermomigration of interstitial hydrogen in metals. Using statistical thermodynamics, we develop a comprehensive mechanistic model to capture the controlling effects. Crucially, through validation against published experimental data, our results demonstrate that an electron-wind effect plays a significant role, particularly for materials in which the thermomigration direction matches the heat flux. These findings provide new insights into the factor…

Modelling Intertextuality with N-gram Embeddings
Yi Xing
https://arxiv.org/abs/2509.06637 https://arxiv.org/pdf/2509.06637…

Modelling Intertextuality with N-gram Embeddings
Intertextuality is a central tenet in literary studies. It refers to the intricate links between literary texts that are created by various types of references. This paper proposes a new quantitative model of intertextuality to enable scalable analysis and network-based insights: perform pairwise comparisons of the embeddings of n-grams from two texts and average their results as the overall intertextuality. Validation on four texts with known degrees of intertextuality, alongside a scalability…

An amazing example of how human testimonies of suffering might fail, and why - as in any other context - validation through fact-checking and triangulation are crucial in order to understand history.
https://forward.com/news/761437/holocaust-survivor-ari-schnei…

Comparing Cluster-Based Cross-Validation Strategies for Machine Learning Model Evaluation
Afonso Martini Spezia, Mariana Recamonde-Mendoza
https://arxiv.org/abs/2507.22299 https…

Comparing Cluster-Based Cross-Validation Strategies for Machine Learning Model Evaluation
Cross-validation plays a fundamental role in Machine Learning, enabling robust evaluation of model performance and preventing overestimation on training and validation data. However, one of its drawbacks is the potential to create data subsets (folds) that do not adequately represent the diversity of the original dataset, which can lead to biased performance estimates. The objective of this work is to deepen the investigation of cluster-based cross-validation strategies by analyzing the perform…

OpenAlpha: A Community-Led Adversarial Strategy Validation Mechanism for Decentralised Capital Management
Arman Abgaryan, Utkarsh Sharma
https://arxiv.org/abs/2506.21809

OpenAlpha: A Community-Led Adversarial Strategy Validation Mechanism for Decentralised Capital Management
We propose \textit{OpenAlpha}, a community-led strategy validation framework for decentralised capital management on a host blockchain network, which integrates game-theoretic validation, adversarial auditing, and market-based belief aggregation. This work formulates treasury deployment as a capital optimisation problem under verification costs and strategic misreporting, and operationalises it through a decision waterfall that sequences intention declaration, strategy proposal, prediction-mark…

Extending OpenMC Validation to Spent Fuel Canisters: A Criticality Benchmark Against MCNP
Javier Ruiz-Pineda, Jaime Romero-Barrientos, Francisco Molina, Marcelo Zambra, Franco L\'opez-Usquiano
https://arxiv.org/abs/2506.22559

Extending OpenMC Validation to Spent Fuel Canisters: A Criticality Benchmark Against MCNP
OpenMC is an open-source Monte Carlo code with increasing relevance in criticality safety and reactor physics applications. While its validation has covered a broad range of systems, its performance in spent nuclear fuel storage scenarios remains limited in the literature. This work benchmarks OpenMC against MCNP for eleven configurations based on the KBS-3 disposal concept, involving variations in geometry, fuel composition (fresh vs spent), and environmental conditions (e.g., air, argon, floo…

Spatial Patterning and Selection: How the Environment Shapes Molecular Complexity
Alexandre Champagne-Ruel, Christopher P. Kempes, Cole Mathis
https://arxiv.org/abs/2509.04547 h…

Spatial Patterning and Selection: How the Environment Shapes Molecular Complexity
Assembly theory predicts that a distinguishing signature of life is its ability to produce complex molecules in abundance, opening new possibilities for life detection. Experimental validation of this approach has so far relied on abiotic controls like meteoritic material, or simple, well-mixed chemical systems. However, decades of research in self-organization have shown that spatial patterning can foster dynamical self-organization. This raises the possibility that systems with nontrivial spa…

Structural System Identification via Validation and Adaptation
Cristian L\'opez, Keegan J. Moore
https://arxiv.org/abs/2506.20799 https://

Structural System Identification via Validation and Adaptation
Estimating the governing equation parameter values is essential for integrating experimental data with scientific theory to understand, validate, and predict the dynamics of complex systems. In this work, we propose a new method for structural system identification (SI), uncertainty quantification, and validation directly from data. Inspired by generative modeling frameworks, a neural network maps random noise to physically meaningful parameters. These parameters are then used in the known equa…

I have worked for 2 different Global 50 enterprises that did their own in-house development, one of which is largely to blame for the "Agile" model. Neither could stand up a new app in 4 months, much less 4 days. That was not because they coded slowly.
Enterprise grade software has had weeks of iterative development, technical validation, and user acceptance testing. It is built to be maintainable and enhanceable. It is fit for its environment.

Conditional validity and a fast approximation formula of full conformal prediction sets
Nicolai Amann
https://arxiv.org/abs/2508.05272 https://arxiv.org/pd…

Conditional validity and a fast approximation formula of full conformal prediction sets
Prediction sets based on full conformal prediction have seen an increasing interest in statistical learning due to their universal marginal coverage guarantees. However, practitioners have refrained from using it in applications for two reasons: Firstly, it comes at very high computational costs, exceeding even that of cross-validation. Secondly, an applicant is typically not interested in a marginal coverage guarantee which averages over all possible (but not available) training data sets, but…

MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation
Dipo Dunsin, Mohamed Chahine Ghanem, Eduardo Almeida Palmieri
https://arxiv.org/abs/2507.03993

MalVol-25: A Diverse, Labelled and Detailed Volatile Memory Dataset for Malware Detection and Response Testing and Validation
This paper addresses the critical need for high-quality malware datasets that support advanced analysis techniques, particularly machine learning and agentic AI frameworks. Existing datasets often lack diversity, comprehensive labelling, and the complexity necessary for effective machine learning and agent-based AI training. To fill this gap, we developed a systematic approach for generating a dataset that combines automated malware execution in controlled virtual environments with dynamic moni…

Dual-Attention U-Net with Class-Specific Ensembles and Bayesian Hyperparameter Optimization for Precise Wound and Scale Marker Segmentation
Daniel Cie\'slak, Miriam Reca, Olena Onyshchenko, Jacek Rumi\'nski
https://arxiv.org/abs/2507.05314

Dual-Attention U-Net++ with Class-Specific Ensembles and Bayesian Hyperparameter Optimization for Precise Wound and Scale Marker Segmentation
Accurate segmentation of wounds and scale markers in clinical images remainsa significant challenge, crucial for effective wound management and automatedassessment. In this study, we propose a novel dual-attention U-Net++ archi-tecture, integrating channel-wise (SCSE) and spatial attention mechanisms toaddress severe class imbalance and variability in medical images effectively.Initially, extensive benchmarking across diverse architectures and encoders via 5-fold cross-validation identified Eff…

Situated Epistemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge
Matthew Kelly
https://arxiv.org/abs/2508.04995 https://arxiv.org/pd…

Situated Epistemic Infrastructures: A Diagnostic Framework for Post-Coherence Knowledge
Large Language Models (LLMs) such as ChatGPT have rendered visible the fragility of contemporary knowledge infrastructures by simulating coherence while bypassing traditional modes of citation, authority, and validation. This paper introduces the Situated Epistemic Infrastructures (SEI) framework as a diagnostic tool for analyzing how knowledge becomes authoritative across hybrid human-machine systems under post-coherence conditions. Rather than relying on stable scholarly domains or bounded co…

Operational Validation of Large-Language-Model Agent Social Simulation: Evidence from Voat v/technology
Aleksandar Toma\v{s}evi\'c, Darja Cvetkovi\'c, Sara Major, Slobodan Maleti\'c, Miroslav An{\dj}elkovi\'c, Ana Vrani\'c, Boris Stupovski, Du\v{s}an Vudragovi\'c, Aleksandar Bogojevi\'c, Marija Mitrovi\'c Dankulov
https://…

Operational Validation of Large-Language-Model Agent Social Simulation: Evidence from Voat v/technology
Large Language Models (LLMs) enable generative social simulations that can capture culturally informed, norm-guided interaction on online social platforms. We build a technology community simulation modeled on Voat, a Reddit-like alt-right news aggregator and discussion platform active from 2014 to 2020. Using the YSocial framework, we seed the simulation with a fixed catalog of technology links sampled from Voat's shared URLs (covering 30+ domains) and calibrate parameters to Voat's v/technolo…

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
Marco Roschkowski
https://arxiv.org/abs/2507.05807 https://

Improving Robustness of Foundation Models in Domain Adaptation with Soup-Adapters
In this paper, we tackle two fundamental problems in few-shot domain adaptation of foundation models. First, hyperparameter tuning is often impractical due to the lack of large validation datasets. Second, model robustness under distribution shifts where test time data deviates slightly from training distributions, remains a concern. We show that by training multiple independent adapters and averaging their outputs, the new model has a higher performance and is more robust to distribution shift…

Automated Validation of LLM-based Evaluators for Software Engineering Artifacts
Ora Nova Fandina, Eitan Farchi, Shmulik Froimovich, Rami Katan, Alice Podolsky, Orna Raz, Avi Ziv
https://arxiv.org/abs/2508.02827

Automated Validation of LLM-based Evaluators for Software Engineering Artifacts
Automation in software engineering increasingly relies on large language models (LLMs) to generate, review, and assess code artifacts. However, establishing LLMs as reliable evaluators remains an open challenge: human evaluations are costly, subjective and non scalable, while existing automated methods fail to discern fine grained variations in artifact quality. We introduce REFINE (Ranking Evaluators for FIne grained Nuanced Evaluation), an automated framework for benchmarking LLM based eval…

Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso
Ryan Burn
https://arxiv.org/abs/2508.14368 https://arxiv.org/pdf/2508.14368

Evaluation and Optimization of Leave-one-out Cross-validation for the Lasso
I develop an algorithm to produce the piecewise quadratic that computes leave-one-out cross-validation for the lasso as a function of its hyperparameter. The algorithm can be used to find exact hyperparameters that optimize leave-one-out cross-validation either globally or locally, and its practicality is demonstrated on real-world data sets.

Experimental Implementation and Validation of Predictor-Based CACC for Vehicular Platoons With Distinct Actuation Delays
Amirhossein Samii, Redmer de Haan, Nikolaos Bekiaris-Liberis
https://arxiv.org/abs/2507.21579

Experimental Implementation and Validation of Predictor-Based CACC for Vehicular Platoons With Distinct Actuation Delays
We provide experimental validation, in a pair of vehicles, of a recently introduced predictor-based cooperative adaptive cruise control (CACC) design, developed for achieving delay compensation in heterogeneous vehicular platoons subject to long actuation delays that may be distinct for each individual vehicle. We provide the explicit formulae of the control design that is implemented, accounting for the effect of zero-order hold and sampled measurements; as well as we obtain vehicle and string…

SDR-Empowered Environment Sensing Design and Experimental Validation Using OTFS-ISAC Signals
Jun Wu, Yuye Shi, Weijie Yuan, Qingqing Cheng, Buyi Li, Xinyuan Wei
https://arxiv.org/abs/2507.01427

SDR-Empowered Environment Sensing Design and Experimental Validation Using OTFS-ISAC Signals
This paper investigates the system design and experimental validation of integrated sensing and communication (ISAC) for environmental sensing, which is expected to be a critical enabler for next-generation wireless networks. We advocate exploiting orthogonal time frequency space (OTFS) modulation for its inherent sparsity and stability in delay-Doppler (DD) domain channels, facilitating a low-overhead environment sensing design. Moreover, a comprehensive environmental sensing framework is deve…

Per-sender neural network classifiers for email authorship validation
Rohit Dube
https://arxiv.org/abs/2509.00005 https://arxiv.org/pdf/2509.00005

Per-sender neural network classifiers for email authorship validation
Business email compromise and lateral spear phishing attacks are among modern organizations' most costly and damaging threats. While inbound phishing defenses have improved significantly, most organizations still trust internal emails by default, leaving themselves vulnerable to attacks from compromised employee accounts. In this work, we define and explore the problem of authorship validation: verifying whether a claimed sender actually authored a given email. Authorship validation is a lightw…

Deconstructing the Crystal Ball: From Ad-Hoc Prediction to Principled Startup Evaluation with the SAISE Framework
Seyed Mohammad Ali Jafari, Ali Mobini Dehkordi, Ehsan Chitsaz, Yadollah Yaghoobzadeh
https://arxiv.org/abs/2508.05491

Deconstructing the Crystal Ball: From Ad-Hoc Prediction to Principled Startup Evaluation with the SAISE Framework
The integration of Artificial Intelligence (AI) into startup evaluation represents a significant technological shift, yet the academic research underpinning this transition remains methodologically fragmented. Existing studies often employ ad-hoc approaches, leading to a body of work with inconsistent definitions of success, atheoretical features, and a lack of rigorous validation. This fragmentation severely limits the comparability, reliability, and practical utility of current predictive mod…

Compressing Chain-of-Thought in LLMs via Step Entropy
Zeju Li, Jianyuan Zhong, Ziyang Zheng, Xiangyu Wen, Zhijian Xu, Yingying Cheng, Fan Zhang, Qiang Xu
https://arxiv.org/abs/2508.03346

Compressing Chain-of-Thought in LLMs via Step Entropy
Large Language Models (LLMs) using Chain-of-Thought (CoT) prompting excel at complex reasoning but generate verbose thought processes with considerable redundancy, leading to increased inference costs and reduced efficiency. We introduce a novel CoT compression framework based on step entropy, a metric that quantifies the informational contribution of individual reasoning steps to identify redundancy. Through theoretical analysis and extensive empirical validation on mathematical reasoning benc…

Mind the Gap: Evaluating Model- and Agentic-Level Vulnerabilities in LLMs with Action Graphs
Ilham Wicaksono, Zekun Wu, Theo King, Adriano Koshiyama, Philip Treleaven
https://arxiv.org/abs/2509.04802

Mind the Gap: Evaluating Model- and Agentic-Level Vulnerabilities in LLMs with Action Graphs
As large language models transition to agentic systems, current safety evaluation frameworks face critical gaps in assessing deployment-specific risks. We introduce AgentSeer, an observability-based evaluation framework that decomposes agentic executions into granular action and component graphs, enabling systematic agentic-situational assessment. Through cross-model validation on GPT-OSS-20B and Gemini-2.0-flash using HarmBench single turn and iterative refinement attacks, we demonstrate funda…

Replaced article(s) found for astro-ph.CO. https://arxiv.org/list/astro-ph.CO/new
[1/1]:
- CRAFTS for HI cosmology: I. data processing pipeline and validation tests
Wenxiu Yang, et al.

SCOUT: An in-vivo Methane Sensing System for Real-time Monitoring of Enteric Emissions in Cattle with ex-vivo Validation
Yuelin Deng, Hinayah Rojas de Oliveira, Richard M. Voyles, Upinder Kaur
https://arxiv.org/abs/2508.04056

SCOUT: An in-vivo Methane Sensing System for Real-time Monitoring of Enteric Emissions in Cattle with ex-vivo Validation
Accurate measurement of enteric methane emissions remains a critical bottleneck for advancing livestock sustainability through genetic selection and precision management. Existing ambient sampling approaches suffer from low data retention rates, environmental interference, and limited temporal resolution. We developed SCOUT (Smart Cannula-mounted Optical Unit for Trace-methane), the first robust in-vivo sensing system enabling continuous, high-resolution monitoring of ruminal methane concentrat…

Reassessment of Kepler's habitable zone Earth-like exoplanets with data-driven null-signal templates
Jakob Robnik, Uro\v{s} Seljak
https://arxiv.org/abs/2509.07409 https://

Reassessment of Kepler's habitable zone Earth-like exoplanets with data-driven null-signal templates
One of the primary mission goals of the Kepler space telescope is to detect Earth-like terrestrial planets in the habitable zone around Sun-like stars. Unfortunately, such planets are at the detection limit. Estimating their statistical significance via false alarm probability (FAP) is crucial for their validation, and has a large impact on the estimate of their occurrence rate, which is of central importance for future spectroscopic missions searching for life signatures. Current methods estim…

A one-dimensional numerical model for system performance prediction of loop heat pipes and its validation
Xu Huang, Geert Buckinx, Maria Rosaria Vetrano, Martine Baelmans
https://arxiv.org/abs/2509.02396

A one-dimensional numerical model for system performance prediction of loop heat pipes and its validation
Developing high power density electronics requires effective and highly reliable cooling techniques with low thermal resistance and high heat removal capacity. Loop heat pipes (LHPs) are one kind of two-phase heat transfer device which can meet all these requirements. A physics-based one-dimensional numerical model has been elaborated to further develop this promising electronics cooling solution. In addition, an experimental setup using water as coolant, and which includes an LHP built with tr…

CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
Jifeng Wang, Jiazhe Ju, Ying Wang
https://arxiv.org/abs/2507.04423

CEMP: a platform unifying high-throughput online calculation, databases and predictive models for clean energy materials
The development of materials science is undergoing a shift from empirical approaches to data-driven and algorithm-oriented research paradigm. The state-of-the-art platforms are confined to inorganic crystals, with limited chemical space, sparse experimental data and a lack of integrated online computation for rapid validation. Here, we introduce the Clean Energy Materials Platform (CEMP), an open-access platform that integrates high-throughput computing workflows, multi-scale machine learning (…

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation
Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich
https://arxiv.org/abs/2509.04222

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation
Dimensionality Reduction (DR) is widely used for visualizing high-dimensional data, often with the goal of revealing expected cluster structure. However, such a structure may not always appear in the projections. Existing DR quality metrics assess projection reliability (to some extent) or cluster structure quality, but do not explain why expected structures are missing. Visual Analytics solutions can help, but are often time-consuming due to the large hyperparameter space. This paper addresses…

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
MD Shaikh Rahman, Feiroz Humayara, Syed Maudud E Rabbi, Muhammad Mahbubur Rashid
https://arxiv.org/abs/2508.04790

Advanced Multi-Architecture Deep Learning Framework for BIRADS-Based Mammographic Image Retrieval: Comprehensive Performance Analysis with Super-Ensemble Optimization
Content-based mammographic image retrieval systems require exact BIRADS categorical matching across five distinct classes, presenting significantly greater complexity than binary classification tasks commonly addressed in literature. Current medical image retrieval studies suffer from methodological limitations including inadequate sample sizes, improper data splitting, and insufficient statistical validation that hinder clinical translation. We developed a comprehensive evaluation framework sy…

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Yuanfang Ren, Esra Adiyeke, Ziyuan Guan, Zhenhong Hu, Mackenzie J Meni, Benjamin Shickel, Parisa Rashidi, Tezcan Ozrazgat-Baslanti, Azra Bihorac
https://arxiv.org/abs…

Validation of the MySurgeryRisk Algorithm for Predicting Complications and Death after Major Surgery: A Retrospective Multicenter Study Using OneFlorida Data Trust
Despite advances in surgical techniques and care, postoperative complications are prevalent and effects up to 15% of the patients who underwent a major surgery. The objective of this study is to develop and validate models for predicting postoperative complications and death after major surgery on a large and multicenter dataset, following the previously validated MySurgeryRisk algorithm. This retrospective, longitudinal and multicenter cohort analysis included 508,097 encounters from 366,875 a…

Site-Specific Location Calibration and Validation of Ray-Tracing Simulator NYURay at Upper Mid-Band Frequencies
Mingjun Ying, Dipankar Shakya, Peijie Ma, Guanyue Qian, Theodore S. Rappaport
https://arxiv.org/abs/2507.22027

Site-Specific Location Calibration and Validation of Ray-Tracing Simulator NYURay at Upper Mid-Band Frequencies
Ray-tracing (RT) simulators are essential for wireless digital twins, enabling accurate site-specific radio channel prediction for next-generation wireless systems. Yet, RT simulation accuracy is often limited by insufficient measurement data and a lack of systematic validation. This paper presents site-specific location calibration and validation of NYURay, NYU's in-house ray tracer, at upper mid-band frequencies (6.75 GHz and 16.95 GHz). We propose a location calibration algorithm that correc…

Validation of TESS Planet Candidates with Multi-Color Transit Photometry and TRICERATOPS
Jonathan Gomez Barrientos, Michael Greklek-McKeon, Heather A. Knutson, Steven Giacalone, W. Garrett Levine, Morgan Saidel, Shreyas Vissapragada, David R. Ciardi, Karen A. Collins, David W. Latham, Cristilyn N. Watkins, Polina A. Budnikova, Dmitry V. Cheryasov, Akihiko Fukui, Allyson Bieryla, Avi Shporer, Benjamin M. Tofflemire, Catherine A. Clark, Chris Stockdale, Colin Littlefield, Emily Gilbert,…

Validation of TESS Planet Candidates with Multi-Color Transit Photometry and TRICERATOPS+
We present an upgraded version of TRICERATOPS, a software package designed to calculate false positive probabilities for planet candidates identified by the Transiting Exoplanet Survey Satellite (TESS). This enhanced framework now incorporates ground-based light curves in separate bandpasses, which are routinely obtained as part of the candidate vetting process. We apply this upgraded framework to explore the planetary nature of 14 TESS planet candidates, combining primarily J band light curves…

Case Studies of Generative Machine Learning Models for Dynamical Systems
Nachiket U. Bapat, Randy C. Paffenroth, Raghvendra V. Cowlagi
https://arxiv.org/abs/2508.04459 https://

Case Studies of Generative Machine Learning Models for Dynamical Systems
Systems like aircraft and spacecraft are expensive to operate in the real world. The design, validation, and testing for such systems therefore relies on a combination of mathematical modeling, abundant numerical simulations, and a relatively small set of real-world experiments. Due to modeling errors, simplifications, and uncertainties, the data synthesized by simulation models often does not match data from the system's real-world operation. We consider the broad research question of whether …

The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs
Pengrui Han, Rafal Kocielnik, Peiyang Song, Ramit Debnath, Dean Mobbs, Anima Anandkumar, R. Michael Alvarez
https://arxiv.org/abs/2509.03730

The Personality Illusion: Revealing Dissociation Between Self-Reports & Behavior in LLMs
Personality traits have long been studied as predictors of human behavior.Recent advances in Large Language Models (LLMs) suggest similar patterns may emerge in artificial systems, with advanced LLMs displaying consistent behavioral tendencies resembling human traits like agreeableness and self-regulation. Understanding these patterns is crucial, yet prior work primarily relied on simplified self-reports and heuristic prompting, with little behavioral validation. In this study, we systematicall…

Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation
Simon K\"ummel, Johannes Roth
https://arxiv.org/abs/2506.23699

Development and validation of an electron temperature-dependent interaction potential for silicon and copper for the use in atomistic simulations of laser ablation
Laser pulses with a duration of the order of femtoseconds lead to a strong excitation, heating and potentially to ablation of the irradiated material. During the time of strong excitation, the interaction of the atoms and thus the material dynamics can be strongly altered. To take this effect into account, an ip was developed for copper that takes the excitation of the electrons into account up to an electron temperature of 1.2 eV. Furthermore, several ways to identify non-thermal effects in De…

Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs
Valentina Wu, Alexandra Mendes, Alexandre Abreu
https://arxiv.org/abs/2507.03659

Specification-Guided Repair of Arithmetic Errors in Dafny Programs using LLMs
Formal verification offers strong assurances of software correctness. However, debugging and repairing the underlying faults can be complex and time-consuming when verification fails. Automated Program Repair (APR) aims to ease this by automatically identifying and fixing faults. Traditional APR techniques often depend on test suites for validation, but these may fail to capture all scenarios. In contrast, formal specifications provide stronger correctness criteria for effective repairs. We p…

La La LiDAR: Large-Scale Layout Generation from LiDAR Data
Youquan Liu, Lingdong Kong, Weidong Yang, Xin Li, Ao Liang, Runnan Chen, Ben Fei, Tongliang Liu
https://arxiv.org/abs/2508.03691

La La LiDAR: Large-Scale Layout Generation from LiDAR Data
Controllable generation of realistic LiDAR scenes is crucial for applications such as autonomous driving and robotics. While recent diffusion-based models achieve high-fidelity LiDAR generation, they lack explicit control over foreground objects and spatial relationships, limiting their usefulness for scenario simulation and safety validation. To address these limitations, we propose Large-scale Layout-guided LiDAR generation model ("La La LiDAR"), a novel layout-guided generative framework tha…

Digital and Robotic Twinning for Validation of Proximity Operations and Formation Flying
Aviad Golan, Gregory Zin, Zahra Ahmed, Emily Bates, Toby Bell, Pol Francesch Huc, Samuel Y. W. Low, Juergen Bosse, Simone D'Amico
https://arxiv.org/abs/2507.20034

Digital and Robotic Twinning for Validation of Proximity Operations and Formation Flying
In spacecraft Rendezvous, Proximity Operations (RPO), and Formation Flying (FF), the Guidance Navigation and Control (GNC) system is safety-critical and must meet strict performance requirements. However, validating such systems is challenging due to the complexity of the space environment, necessitating a verification and validation (V&V) process that bridges simulation and real-world behavior. The key contribution of this paper is a unified, end-to-end digital and robotic twinning framework t…

MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation
Kareem Elozeiri, Mervat Abassy, Preslav Nakov, Yuxia Wang
https://arxiv.org/abs/2508.13130 https://

MuDRiC: Multi-Dialect Reasoning for Arabic Commonsense Validation
Commonsense validation evaluates whether a sentence aligns with everyday human understanding, a critical capability for developing robust natural language understanding systems. While substantial progress has been made in English, the task remains underexplored in Arabic, particularly given its rich linguistic diversity. Existing Arabic resources have primarily focused on Modern Standard Arabic (MSA), leaving regional dialects underrepresented despite their prevalence in spoken contexts. To bri…

Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems
Muhammad Azmi Umer, Chuadhry Mujeeb Ahmed, Aditya Mathur, Muhammad Taha Jilani
https://arxiv.org/abs/2508.04561

Attack Pattern Mining to Discover Hidden Threats to Industrial Control Systems
This work focuses on validation of attack pattern mining in the context of Industrial Control System (ICS) security. A comprehensive security assessment of an ICS requires generating a large and variety of attack patterns. For this purpose we have proposed a data driven technique to generate attack patterns for an ICS. The proposed technique has been used to generate over 100,000 attack patterns from data gathered from an operational water treatment plant. In this work we present a detailed cas…

Design and Experimental Validation of UAV Swarm-Based Phased Arrays with MagSafe- and LEGO-Inspired RF Connectors
Bidya Debnath, Mst Mostary Begum, Prashant Neupant, Brooke E. Molen, Junming Diao
https://arxiv.org/abs/2507.22295

Design and Experimental Validation of UAV Swarm-Based Phased Arrays with MagSafe- and LEGO-Inspired RF Connectors
This paper presents a novel UAV swarm-based phased array antenna system that leverages MagSafe- and LEGO-inspired radio frequency (RF) connectors to address key challenges in distributed phased arrays, including inter-element oscillator synchronization, localization, phase coherence, and positional accuracy. The proposed non-threaded, hands-free connectors enable precise inter-element spacing and establish a continuous, low-loss RF signal propagation path during mid-flight docking. A multi-stag…

Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems
Halit Eris, Stefan Wagner
https://arxiv.org/abs/2506.16876

Revolutionizing Validation and Verification: Explainable Testing Methodologies for Intelligent Automotive Decision-Making Systems
Autonomous Driving Systems (ADS) use complex decision-making (DM) models with multimodal sensory inputs, making rigorous validation and verification (V&V) essential for safety and reliability. These models pose challenges in diagnosing failures, tracing anomalies, and maintaining transparency, with current manual testing methods being inefficient and labor-intensive. This vision paper presents a methodology that integrates explainability, transparency, and interpretability into V&V processes. W…

Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems
Erwin de Gelder, Maren Buermann, Olaf Op den Camp
https://arxiv.org/abs/2507.22429

Comparing Normalizing Flows with Kernel Density Estimation in Estimating Risk of Automated Driving Systems
The development of safety validation methods is essential for the safe deployment and operation of Automated Driving Systems (ADSs). One of the goals of safety validation is to prospectively evaluate the risk of an ADS dealing with real-world traffic. Scenario-based assessment is a widely-used approach, where test cases are derived from real-world driving data. To allow for a quantitative analysis of the system performance, the exposure of the scenarios must be accurately estimated. The exposur…

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Arash Jamshidi, Lauri Sepp\"al\"ainen, Katsiaryna Haitsiukevich, Hoang Phuc Hau Luu, Anton Bj\"orklund, Kai Puolam\"aki
https://arxiv.org/abs/2508.19028

GRADSTOP: Early Stopping of Gradient Descent via Posterior Sampling
Machine learning models are often learned by minimising a loss function on the training data using a gradient descent algorithm. These models often suffer from overfitting, leading to a decline in predictive performance on unseen data. A standard solution is early stopping using a hold-out validation set, which halts the minimisation when the validation loss stops decreasing. However, this hold-out set reduces the data available for training. This paper presents {\sc gradstop}, a novel stochast…

GUI-ReRank: Enhancing GUI Retrieval with Multi-Modal LLM-based Reranking
Kristian Kolthoff, Felix Kretzer, Christian Bartelt, Alexander Maedche, Simone Paolo Ponzetto
https://arxiv.org/abs/2508.03298

GUI-ReRank: Enhancing GUI Retrieval with Multi-Modal LLM-based Reranking
GUI prototyping is a fundamental component in the development of modern interactive systems, which are now ubiquitous across diverse application domains. GUI prototypes play a critical role in requirements elicitation by enabling stakeholders to visualize, assess, and refine system concepts collaboratively. Moreover, prototypes serve as effective tools for early testing, iterative evaluation, and validation of design ideas with both end users and development teams. Despite these advantages, the…

A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model
Yi Wang, Peiyao Zhang, Mojtaba Esfandiari, Peter Gehlbach, Iulian I. Iordachita
https://arxiv.org/abs/2507.21965

A Deep Learning-Driven Autonomous System for Retinal Vein Cannulation: Validation Using a Chicken Embryo Model
Retinal vein cannulation (RVC) is a minimally invasive microsurgical procedure for treating retinal vein occlusion (RVO), a leading cause of vision impairment. However, the small size and fragility of retinal veins, coupled with the need for high-precision, tremor-free needle manipulation, create significant technical challenges. These limitations highlight the need for robotic assistance to improve accuracy and stability. This study presents an automated robotic system with a top-down microsco…

Challenges of Virtual Validation and Verification for Automotive Functions
Beatriz Cabrero-Daniel, Mazen Mohamad
https://arxiv.org/abs/2508.14747 https://a…

Challenges of Virtual Validation and Verification for Automotive Functions
Verification and validation of vehicles is a complex yet critical process, particularly for ensuring safety and coverage through simulations. However, achieving realistic and useful simulations comes with significant challenges. To explore these challenges, we conducted a workshop with experts in the field, allowing them to brainstorm key obstacles. Following this, we distributed a survey to consolidate findings and gain further insights into potential solutions. The experts identified 17 key c…

Towards more efficient quantitative safety validation of residual risk for assisted and automated driving
Daniel Betschinske, Malte Schrimpf, Steven Peters, Kamil Klonecki, Jan Peter Karch, Moritz Lippert
https://arxiv.org/abs/2506.10363

Towards more efficient quantitative safety validation of residual risk for assisted and automated driving
The safety validation of Advanced Driver Assistance Systems (ADAS) and Automated Driving Systems (ADS) increasingly demands efficient and reliable methods to quantify residual risk while adhering to international standards such as ISO 21448. Traditionally, Field Operational Testing (FOT) has been pivotal for macroscopic safety validation of automotive driving functions up to SAE automation level 2. However, state-of-the-art derivations for empirical safety demonstrations using FOT often result …