Tootfinder

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 …

📁 Three essential tools: read_file for content inspection, list_files for directory navigation & edit_file for code modifications
⚡ #Anthropic #Claude integration with tool definitions using JSON schema validation & structured input parameters

You probably know I get frowny when browser makers lie about support or pretend their late implementation of a feature is somehow new, so I am suffering from a bunch of confirmation bias (and validation) here:
“Safari at WWDC '25: The Ghost of Christmas Past”
https://infrequently.org/2025…

Safari at WWDC '25: The Ghost of Christmas Past
Alex Russell on browsers, standards, and the process of progress.

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
Zuzanna Skorniewska, Bartlomiej W. Papiez
https://arxiv.org/abs/2506.11753

Exploring the Effectiveness of Deep Features from Domain-Specific Foundation Models in Retinal Image Synthesis
The adoption of neural network models in medical imaging has been constrained by strict privacy regulations, limited data availability, high acquisition costs, and demographic biases. Deep generative models offer a promising solution by generating synthetic data that bypasses privacy concerns and addresses fairness by producing samples for under-represented groups. However, unlike natural images, medical imaging requires validation not only for fidelity (e.g., Fréchet Inception Score) but also…

AI5GTest: AI-Driven Specification-Aware Automated Testing and Validation of 5G O-RAN Components
Abiodun Ganiyu, Pranshav Gajjar, Vijay K Shah
https://arxiv.org/abs/2506.10111

AI5GTest: AI-Driven Specification-Aware Automated Testing and Validation of 5G O-RAN Components
The advent of Open Radio Access Networks (O-RAN) has transformed the telecommunications industry by promoting interoperability, vendor diversity, and rapid innovation. However, its disaggregated architecture introduces complex testing challenges, particularly in validating multi-vendor components against O-RAN ALLIANCE and 3GPP specifications. Existing frameworks, such as those provided by Open Testing and Integration Centres (OTICs), rely heavily on manual processes, are fragmented and prone t…

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
Yuan Gao, Mattia Piccinini, Yuchen Zhang, Dingrui Wang, Korbinian Moller, Roberto Brusnicki, Baha Zarrouki, Alessio Gambi, Jan Frederik Totz, Kai Storms, Steven Peters, Andrea Stocco, Bassam Alrifaee, Marco Pavone, Johannes Betz
https://

Foundation Models in Autonomous Driving: A Survey on Scenario Generation and Scenario Analysis
For autonomous vehicles, safe navigation in complex environments depends on handling a broad range of diverse and rare driving scenarios. Simulation- and scenario-based testing have emerged as key approaches to development and validation of autonomous driving systems. Traditional scenario generation relies on rule-based systems, knowledge-driven models, and data-driven synthesis, often producing limited diversity and unrealistic safety-critical cases. With the emergence of foundation models, wh…

Drove through the Blackwall Tunnel to Greenwich and back to Islington yesterday.
South of the river it's all 30MPH speed limits and everything feels too fast and scary compared to the sedate and pedestrian-friendly 20MPH limits in the north.
Since I last went through it, it seems the Blackwall tunnel has added a toll.
The TFL website for setting up to pay the tolls is absolutely awful. Failed in Librewolf, Failed in Firefox. Failed because VPN. Took me four attempts in three different browsers to get it to take payment card details.
Demands *no* special characters in the password?!? And doesn't even tell you what the specific problem with the password is, just "doesn't meet the rules above"
Uses awful validation questions like "mothers maiden name". Bad enough practice to use them at all, but the "memorable date" question restricts to ddmmyy format so you can't even put in a date from outside this century.
For some reason asks for a PIN and and Password both!? Pointless. Then actually refuses the login form if you supply both!?

Setting up the auto-payment doesn't seem to have covered the charges added to the registration plate yesterday, so had to do the payment-details entering yet again to deal with yesterday's charges.
Email validation message contains the link only in the HTML of the email, not the text version, so viewed in Mutt it has no link.
And this all seems to be separate from the Dartford Crossing charge, which had a less crappy but still pretty crappy signup.
Good god council programmers suck at web-dev.
#driving #london #tfl #blackwallTunnel #greenwich

Very, very useful:
https://abyssdomain.expert/@filippo/114496179105976467

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Tahiya Chowdhury, Veronica Romero
https://arxiv.org/abs/2506.11072

Can We Trust Machine Learning? The Reliability of Features from Open-Source Speech Analysis Tools for Speech Modeling
Machine learning-based behavioral models rely on features extracted from audio-visual recordings. The recordings are processed using open-source tools to extract speech features for classification models. These tools often lack validation to ensure reliability in capturing behaviorally relevant information. This gap raises concerns about reproducibility and fairness across diverse populations and contexts. Speech processing tools, when used outside of their design context, can fail to capture b…

High-Dimensional Model Averaging via Cross-Validation
Zhengyan Wan, Fang Fang, Binyan Jiang
https://arxiv.org/abs/2506.08451 https://…

High-Dimensional Model Averaging via Cross-Validation
Model averaging is an important alternative to model selection with attractive prediction accuracy. However, its application to high-dimensional data remains under-explored. We propose a high-dimensional model averaging method via cross-validation under a general framework and systematically establish its theoretical properties. Each candidate model is fitted using a flexible loss function paired with a general regularizer, and the optimal weights are determined by minimizing a cross-validation…

Optimization and validation of charge transport simulation for hybrid pixel detectors incorporating the repulsion effect
X. Xie, A. Bergamaschi, M. Br\"uckner, M. Carulla, R. Dinapoli, S. Ebner, K. Ferjaoui, A. Francesca Mazzoleni, J. Franklin Mulvey, V. Gautam, D. Greiffenberg, S. Hasanaj, J. Heymes, V. Hinger, V. Kedych, T. King, S. Li, C. Lopez-Cuenca, D. Mezza, K. Moustakas, A. Mozzanica, M. M\"uller, K. A. Paton, C. Ruder, B. Schmitt, P. Sieberer, S. Silletta, D. Thattil…

Optimization and validation of charge transport simulation for hybrid pixel detectors incorporating the repulsion effect
For emerging applications of hybrid pixel detectors which require high spatial resolution, e.g., subpixel interpolation in X-ray imaging and deep learning-based electron localization, accurate modeling of charge transport processes in the sensor is highly demanded. To address this, two open-source, time-stepping Monte Carlo simulation methods have been developed, both explicitly incorporating charge repulsion, which are found necessary for accurate simulation when charge sharing becomes importa…

Measurement of radionuclide production probabilities in negative muon nuclear capture and validation of Monte Carlo simulation model
Y. Yamaguchi, M. Niikura, R. Mizuno, M. Tampo, M. Harada, N. Kawamura, I. Umegaki, S. Takeshita, K. Haga
https://arxiv.org/abs/2506.08301

Measurement of radionuclide production probabilities in negative muon nuclear capture and validation of Monte Carlo simulation model
As part of the development of a sample radioactivity calculation program, we have measured radionuclide production probabilities in negative muon nuclear capture to update experimental data and to validate a calculation dataset obtained by a Monte Carlo simulation code. The probabilities have been obtained by an activation experiment on $^{27}$Al, $^\mathrm{nat}$Si, $^{59}$Co, and $^\mathrm{nat}$Ta targets. The obtained probabilities expand the validation scope to the radionuclide production pr…

Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis
Xueying Du, Kai Yu, Chong Wang, Yi Zou, Wentai Deng, Zuoyu Ou, Xin Peng, Lingming Zhang, Yiling Lou
https://arxiv.org/abs/2506.10322

Minimizing False Positives in Static Bug Detection via LLM-Enhanced Path Feasibility Analysis
Static bug analyzers play a crucial role in ensuring software quality. However, existing analyzers for bug detection in large codebases often suffer from high false positive rates. This is primarily due to the limited capabilities of analyzers in path feasibility validation with multiple conditional branches and complex data dependencies. While current LLM-based approaches attempt to address this issue, their effectiveness remains limited due to insufficient constraint cascade analysis and scal…

Validation of the Critical Reflection and Agency in Computing Index: Do Computing Ethics Courses Make a Difference?
Aadarsh Padiyath, Casey Fiesler, Mark Guzdial, Barbara Ericson
https://arxiv.org/abs/2506.06193

Validation of the Critical Reflection and Agency in Computing Index: Do Computing Ethics Courses Make a Difference?
Computing ethics education aims to develop students' critical reflection and agency. We need validated ways to measure whether our efforts succeed. Through two survey administrations (N=474, N=464) with computing students and professionals, we provide evidence for the validity of the Critical Reflection and Agency in Computing Index. Our psychometric analyses demonstrate distinct dimensions of ethical development and show strong reliability and construct validity. Participants who completed com…

UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization
Runze Zhang, Ishfaq Aziz, Derek Houtz, Yuxiang Zhao, Trent W. Ford, Adam C. Watts, Mohamad Alipour
https://arxiv.org/abs/2506.05572

UAV-Based Remote Sensing of Soil Moisture Across Diverse Land Covers: Validation and Bayesian Uncertainty Characterization
High-resolution soil moisture (SM) observations are critical for agricultural monitoring, forestry management, and hazard prediction, yet current satellite passive microwave missions cannot directly provide retrievals at tens-of-meter spatial scales. Unmanned aerial vehicle (UAV) mounted microwave radiometry presents a promising alternative, but most evaluations to date have focused on agricultural settings, with limited exploration across other land covers and few efforts to quantify retrieval…

Diffusion Models for Safety Validation of Autonomous Driving Systems
Juanran Wang, Marc R. Schlichting, Harrison Delecki, Mykel J. Kochenderfer
https://arxiv.org/abs/2506.08459

Diffusion Models for Safety Validation of Autonomous Driving Systems
Safety validation of autonomous driving systems is extremely challenging due to the high risks and costs of real-world testing as well as the rarity and diversity of potential failures. To address these challenges, we train a denoising diffusion model to generate potential failure cases of an autonomous vehicle given any initial traffic state. Experiments on a four-way intersection problem show that in a variety of scenarios, the diffusion model can generate realistic failure samples while capt…

Inference on panel data models with a generalized factor structure
Juan M. Rodriguez-Poo, Alexandra Soberon, Stefan Sperlich
https://arxiv.org/abs/2506.10690

Inference on panel data models with a generalized factor structure
We consider identification, inference and validation of linear panel data models when both factors and factor loadings are accounted for by a nonparametric function. This general specification encompasses rather popular models such as the two-way fixed effects and the interactive fixed effects ones. By applying a conditional mean independence assumption between unobserved heterogeneity and the covariates, we obtain consistent estimators of the parameters of interest at the optimal rate of conve…

The Atacama Cosmology Telescope: DR6 Power Spectrum Foreground Model and Validation
Benjamin Beringue, Kristen M. Surrao, J. Colin Hill, Zachary Atkins, Nicholas Battaglia, Boris Bolliet, Erminia Calabrese, Steve K. Choi, Susan E. Clark, Adriaan J. Duivenvoorden, Jo Dunkley, Serena Giardiello, Samuel Goldstein, Brandon S. Hensley, Ren\'ee Hlo\v{z}ek, Hidde T. Jense, Darby Kramer, Adrien La Posta, Thibaut Louis, Yogesh Mehta, Kavilan Moodley, Sigurd Naess, Bruce Partridge, Frank J. …

The Atacama Cosmology Telescope: DR6 Power Spectrum Foreground Model and Validation
We discuss the model of astrophysical emission at millimeter wavelengths used to characterize foregrounds in the multi-frequency power spectra of the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6), expanding on Louis et al. (2025). We detail several tests to validate the capability of the DR6 parametric foreground model to describe current observations and complex simulations, and show that cosmological parameter constraints are robust against model extensions and variations. We demonst…

What Causes Errors in Wang-Sheeley-Arge Solar Wind Modeling at L1 ?
Satabdwa Majumdar, Martin Reiss, Karin Muglach, Charles N. Arge
https://arxiv.org/abs/2506.09676

What Causes Errors in Wang-Sheeley-Arge Solar Wind Modeling at L1 ?
Previous ambient solar wind (SW) validation studies have reported on discrepancies between modeled and observed SW conditions at L1. They indicated that a major source of discrepancies stems from how we model the solar corona. Thus, enhancing predictive capabilities demands a thorough examination of coronal modeling. The Wang-Sheeley-Arge (WSA) model has been a workhorse model that provides the near-Sun SW conditions. An important component of it is the Potential Field Source Surface (PFSS) mod…

Simulation of irradiated hybrid planar pixels modules at fluences expected at HL-LHC
Marco Bomben
https://arxiv.org/abs/2506.10823 https://

Simulation of irradiated hybrid planar pixels modules at fluences expected at HL-LHC
Signal loss is the main limitation on tracking/vertexing performance due to radiation damage effect to hybrid pixel detectors when irradiated at fluences expected at High Luminosity LHC (HL-LHC). It is important to have reliable predictions on the charge collection performance after irradiation in order to predict operational voltage values and test tracking algorithms robustness. In this paper the validation of combined TCAD and Monte Carlo simulations of hybrid silicon planar pixels sensors w…

Empirical Validation of the Independent Chip Model
Juho Kim
https://arxiv.org/abs/2506.00180 https://arxiv.org/pdf/2506.00180

Empirical Validation of the Independent Chip Model
The independent chip model (ICM) forms a cornerstone of all modern poker tournament strategy. However, despite its prominence, the ICM's performance in the real world has not been sufficiently scrutinized, especially at a large scale. In this paper, we introduce our new dataset of poker tournaments, consisting of results of over ten thousand events. Then, using this dataset, we perform two experiments as part of a large-scale empirical validation of the ICM. First, we verify that the ICM perfor…

Design and Validation of the Digital Receiver System for the next-generation radio interferometer
Donghao Qu, Jiajun Zhang, Yajun Wu, Zhang Zhao, Yanbin Yang, Zixuan Liu
https://arxiv.org/abs/2506.00464

Design and Validation of the Digital Receiver System for the next-generation radio interferometer
This paper presents the design and validation of a digital receiver system developed for the next-generation radio interferometer projects. The receiver supports 8 analog inputs with 12-bit, 4GHz sampling and performs real-time signal processing using FPGA-based channelization. Field experiments were conducted to observe the Sun, a satellite beacon, and Cassiopeia A. Interference fringes were analyzed and modeled. Time delay compensation was implemented in two ways: theoretical calculation and …

This https://arxiv.org/abs/2403.12334 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCG_…

Structural Validation Of Synthetic Power Distribution Networks Using The Multiscale Flat Norm
We study the problem of comparing a pair of geometric networks that may not be similarly defined, i.e., when they do not have one-to-one correspondences between their nodes and edges. Our motivating application is to compare power distribution networks of a region. Due to the lack of openly available power network datasets, researchers synthesize realistic networks resembling their actual counterparts. But the synthetic digital twins may vary significantly from one another and from actual netwo…

This https://arxiv.org/abs/2410.03815 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Single-Shot Learning of Multirotor Controller Gains: A Data-Driven Approach with Experimental Validation
This paper demonstrates the single-shot learning capabilities of retrospective cost optimization based data-driven control applied to learning multirotor controller gains for trajectory tracking. In particular, the proposed control approach is first used within a simple multirotor simulation environment to learn appropriate multirotor controller gains to follow a trajectory. Then, the gains resulting from a single simulation run are used in a more complex multirotor simulation environment based…

For no real reason I’ve been writing a Gradle build for Jing and Trang (#jing

This https://arxiv.org/abs/2402.08151 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Perturbative partial moment matching and gradient-flow adaptive importance sampling transformations for Bayesian leave one out cross-validation
Importance sampling (IS) allows one to approximate leave one out (LOO) cross-validation for a Bayesian model, without refitting, by inverting the Bayesian update equation to subtract a given data point from a model posterior. For each data point, one computes expectations under the corresponding LOO posterior by weighted averaging over the full data posterior. This task sometimes requires weight stabilization in the form of adapting the posterior distribution via transformation. So long as one …

This https://arxiv.org/abs/2506.03780 has been replaced.
link: https://scholar.google.com/scholar?q=a

High-Dimensional Learning in Finance
Recent advances in machine learning have shown promising results for financial prediction using large, over-parameterized models. This paper provides theoretical foundations and empirical validation for understanding when and how these methods achieve predictive success. I examine three key aspects of high-dimensional learning in finance. First, I prove that within-sample standardization in Random Fourier Features implementations fundamentally alters the underlying Gaussian kernel approximation…

TOI-2407 b: a warm Neptune in the desert
C. Jan\'o Mu\~noz, M. J. Hooton, P. P. Pedersen, K. Barkaoui, B. V. Rackham, A. J. Burgasser, F. J. Pozuelos, K. G. Stassun, D. Queloz, A. H. M. J. Triaud, C. Ziegler, J. M. Almenara, M. Timmermans, X. Bonfils, K. A. Collins, B. O. Demory, G. Dransfield, M. Ghachoui, M. Gillon, E. Jehin, A. W. Mann, D. Sebastian, S. Thompson, J. D. Twicken, J. de Wit S. Z\'u\~niga-Fern\'andez

TOI-2407 b: a warm Neptune in the desert
We present the validation of TOI-2407 b, a warm Neptune-sized planet with a radius of 4.26 $\pm$ 0.26 R$_\oplus$, orbiting an early M-type star with a period of 2.7 days and an equilibrium temperature of 705 $\pm$ 12 K. The planet was identified by TESS photometry and validated in this work through multi-wavelength ground-based follow-up observations. We include an observation with the novel CMOS-based infrared instrument SPIRIT at the SPECULOOS Southern Observatory. The high-precision transit …

Integrating computational detection and experimental validation for rapid GFRAL-specific antibody discovery
Maria Francesca Abbate, Pierre Toxe, Nicolas Maestrali, Marie Gagnaire, Emmanuelle Vigne, Melody A. Shahsavarian, Thierry Mora, Aleksandra M. Walczak
https://arxiv.org/abs/2506.01995…

Integrating computational detection and experimental validation for rapid GFRAL-specific antibody discovery
The identification and validation of therapeutic antibodies is critical for developing effective treatments for many diseases. We present a computational approach for identifying antibodies targeting GFRAL-specific receptors, receptors implicated in appetite regulation. Using humanized Trianni mice, we conducted a longitudinal study with repeated blood sampling and splenic analysis. We applied the STAR computational method for antibody discovery on bulk antibody repertoire data sampled at key t…

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Weiyuan Xu, Juntao Luo, Tao Huang, Kaixin Sui, Jie Geng, Qijun Ma, Isami Akasaka, Xiaoxue Shi, Jing Tang, Peng Cai
https://arxiv.org/abs/2506.03691

A Two-Staged LLM-Based Framework for CI/CD Failure Detection and Remediation with Industrial Validation
Continuous Integration and Continuous Deployment (CI/CD) pipelines are pivotal to modern software engineering, yet diagnosing and resolving their failures remains a complex and labor-intensive challenge. In this paper, we present LogSage, the first end-to-end LLM-powered framework that performs root cause analysis and solution generation from failed CI/CD pipeline logs. During the root cause analysis stage, LogSage employs a specialized log preprocessing pipeline tailored for LLMs, which extrac…

Predicting Empirical AI Research Outcomes with Language Models
Jiaxin Wen, Chenglei Si, Yueh-han Chen, He He, Shi Feng
https://arxiv.org/abs/2506.00794 htt…

Predicting Empirical AI Research Outcomes with Language Models
Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute. Predicting an idea's chance of success is thus crucial for accelerating empirical AI research, a skill that even expert researchers can only acquire through substantial experience. We build the first benchmark for this task and compare LMs with human experts. Concretely, given two research ideas (e.g., two jailbreaking methods), we aim to predict which will perform better…

Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
Veronica Centorrino, Francesco Bullo, Giovanni Russo
https://arxiv.org/abs/2506.06134

Similarity Matching Networks: Hebbian Learning and Convergence Over Multiple Time Scales
A recent breakthrough in biologically-plausible normative frameworks for dimensionality reduction is based upon the similarity matching cost function and the low-rank matrix approximation problem. Despite clear biological interpretation, successful application in several domains, and experimental validation, a formal complete convergence analysis remains elusive. Building on this framework, we consider and analyze a continuous-time neural network, the \emph{similarity matching network}, for pri…

ChemGraph: An Agentic Framework for Computational Chemistry Workflows
Thang D. Pham, Aditya Tanikanti, Murat Ke\c{c}eli
https://arxiv.org/abs/2506.06363 ht…

ChemGraph: An Agentic Framework for Computational Chemistry Workflows
Atomistic simulations are essential tools in chemistry and materials science, accelerating the discovery of novel catalysts, energy storage materials, and pharmaceuticals. However, running these simulations remains challenging due to the wide range of computational methods, diverse software ecosystems, and the need for expert knowledge and manual effort for the setup, execution, and validation stages. In this work, we present ChemGraph, an agentic framework powered by artificial intelligence an…

This https://arxiv.org/abs/2501.07973 has been replaced.
initial toot: https://mastoxiv.page/@arX…

An Open Source Validation System for Continuous Arterial Blood Pressure Measuring Sensors
Measuring the blood pressure waveform is becoming a more frequently studied area. The development of sensor technologies opens many new ways to be able to measure high-quality signals. The development of such an aim-specific sensor can be time-consuming, expensive, and difficult to test or validate with known and consistent waveforms. In this paper, we present an open source blood pressure waveform simulator with an open source Python validation package to reduce development costs for early-sta…

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
Christian Reichenb\"acher, Philipp Rank, Jochen Hipp, Oliver Bringmann
https://arxiv.org/abs/2506.10098

Estimating the Joint Probability of Scenario Parameters with Gaussian Mixture Copula Models
This paper presents the first application of Gaussian Mixture Copula Models to the statistical modeling of driving scenarios for the safety validation of automated driving systems. Knowledge of the joint probability distribution of scenario parameters is essential for scenario-based safety assessment, where risk quantification depends on the likelihood of concrete parameter combinations. Gaussian Mixture Copula Models bring together the multimodal expressivity of Gaussian Mixture Models and the…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
A. G. R. Sandeepa, Sanka Mohottala
https://arxiv.org/abs/2506.08047 https:…

Evaluation of Machine Learning Models in Student Academic Performance Prediction
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train…

ROSGuard: A Bandwidth Regulation Mechanism for ROS2-based Applications
Jon Altonaga Puente, Enrico Mezzetti, Irune Agirre Troncoso, Jaume Abella Ferrer, Francisco J. Cazorla Almeida
https://arxiv.org/abs/2506.04640

ROSGuard: A Bandwidth Regulation Mechanism for ROS2-based Applications
Multicore timing interference, arising when multiple requests contend for the same shared hardware resources, is a primary concern for timing verification and validation of time-critical applications. Bandwidth control and regulation approaches have been proposed in the literature as an effective method to monitor and limit the impact of timing interference at run time. These approaches seek for fine-grained control of the bandwidth consumption (at the microsecond level) to meet stringent timin…

Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention
Gordon Y. Liao, Ziming Zeng, Mira Belenkiy, Jacob Hirshman
https://arxiv.org/abs/2505.24284

Transaction Proximity: A Graph-Based Approach to Blockchain Fraud Prevention
This paper introduces a fraud-deterrent access validation system for public blockchains, leveraging two complementary concepts: "Transaction Proximity", which measures the distance between wallets in the transaction graph, and "Easily Attainable Identities (EAIs)", wallets with direct transaction connections to centralized exchanges. Recognizing the limitations of traditional approaches like blocklisting (reactive, slow) and strict allow listing (privacy-invasive, adoption barriers), we propose…

Replaced article(s) found for physics.ins-det. https://arxiv.org/list/physics.ins-det/new/
[1/1]:
Thermal Management Design and Key Technology Validation for PandaX Underground Experiment

Join Dennis Berger, Marco Petris, and Volker Carlguth to explore 'Intent-Based Clustering.' An approach to overcome some limitations of modern hybrid search systems. Discover how upfront LLM-supported in-depth query understanding can be applied in various steps, including retrieval, clustering, validation, and presentation. Learn about the process of moving from prototype to production for large-scale, high-volume e-commerce searches.

What you see is what you mean; intent based ecommerce search
In today's e-commerce landscape, hybrid search systems represent the market standard, yet they often struggle with highly inspirational queries, lack of precision in semantic recall, and presentation of diversity. Ambiguity in search terms can lead to disorganized results or overshadowed relevant products. This session will demonstrate how an upfront LLM-supported in depth query understanding can increase recall meaningfully without sacrificing precision. We show how LLM-driven query understand…

A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
Esteban Guti\'errez, Frederic Font, Xavier Serra, Lonce Wyse
https://arxiv.org/abs/2506.04073

A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis
In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds characterized by stochastic structure and perceptual stationarity. Drawing inspiration from the statistical and perceptual framework of McDermott and Simoncelli, TexStat identifies similarities between signals belonging to the same texture category without relying on temporal structure. We also propose using TexStat as a validation metric alongside Frechet Audio Dista…

The Coupling Effect of Sensing Targets on the Environment for 3GPP ISAC Channels: Observation, Modeling, and Validation
Yameng Liu, Jianhua Zhang, Yuxiang Zhang, Hongbo Xing, Yifeng Xiong, Zhiqiang Yuan, Guangyi Liu
https://arxiv.org/abs/2506.00480

The Coupling Effect of Sensing Targets on the Environment for 3GPP ISAC Channels: Observation, Modeling, and Validation
Integrated Sensing And Communication (ISAC) has been identified as a key 6G application by ITU and 3GPP, with standardization efforts already underway. Sensing tasks, such as target localization, demand more precise characterization of the sensing target (ST) in ISAC channel modeling. The ST couples complexly with environmental scatterers, potentially blocking some multipaths and generating new ones, resulting in power variations compared to the original channel. To accurately model this effect…

This https://arxiv.org/abs/2502.17370 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

A Refined Analysis of UCBVI
In this work, we provide a refined analysis of the UCBVI algorithm (Azar et al., 2017), improving both the bonus terms and the regret analysis. Additionally, we compare our version of UCBVI with both its original version and the state-of-the-art MVP algorithm. Our empirical validation demonstrates that improving the multiplicative constants in the bounds has significant positive effects on the empirical performance of the algorithms.

ChatPD: An LLM-driven Paper-Dataset Networking System
Anjie Xu, Ruiqing Ding, Leye Wang
https://arxiv.org/abs/2505.22349 https://arxi…

ChatPD: An LLM-driven Paper-Dataset Networking System
Scientific research heavily depends on suitable datasets for method validation, but existing academic platforms with dataset management like PapersWithCode suffer from inefficiencies in their manual workflow. To overcome this bottleneck, we present a system, called ChatPD, that utilizes Large Language Models (LLMs) to automate dataset information extraction from academic papers and construct a structured paper-dataset network. Our system consists of three key modules: \textit{paper collection},…

Ensemble Modeling of the Solar Wind Flow with Boundary Conditions Governed by Synchronic Photospheric Magnetograms. I. Multi-point Validation in the Inner Heliosphere
Dinesha V. Hegde (Department of Space Science, The University of Alabama in Huntsville, Huntsville, USA, Center for Space Plasma and Aeronomic Research, The University of Alabama in Huntsville, Huntsville, USA), Tae K. Kim (Center for Space Plasma and Aeronomic Research, The University of Alabama in Huntsville, Huntsville…

Ensemble Modeling of the Solar Wind Flow with Boundary Conditions Governed by Synchronic Photospheric Magnetograms. I. Multi-point Validation in the Inner Heliosphere
The solar wind (SW) is a vital component of space weather, providing a background for solar transients such as coronal mass ejections, stream interaction regions, and energetic particles propagating toward Earth. Accurate prediction of space weather events requires a precise description and thorough understanding of physical processes occurring in the ambient SW plasma. Ensemble simulations of the three-dimensional SW flow are performed using an empirically-driven magnetohydrodynamic heliospher…

fftvis: A Non-Uniform Fast Fourier Transform Based Interferometric Visibility Simulator
Tyler A. Cox, Steven G. Murray, Aaron R. Parsons, Joshua S. Dillon, Kartik Mandar, Zachary E. Martinot, Robert Pascua, Piyanat Kittiwisit, James E. Aguirre
#toXiv_bot_toot

This https://arxiv.org/abs/2505.19713 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGR_…

CAD-Coder: Text-to-CAD Generation with Chain-of-Thought and Geometric Reward
In this work, we introduce CAD-Coder, a novel framework that reformulates text-to-CAD as the generation of CadQuery scripts - a Python-based, parametric CAD language. This representation enables direct geometric validation, a richer modeling vocabulary, and seamless integration with existing LLMs. To further enhance code validity and geometric fidelity, we propose a two-stage learning pipeline: (1) supervised fine-tuning on paired text-CadQuery data, and (2) reinforcement learning with Group Re…

Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review
Lucas Mauser, Eva Zimmermann, Pavel Nedv\v{e}dick\'y, Tobias Eisenreich, Moritz W\"aschle, Stefan Wagner
https://arxiv.org/abs/2506.05822

Towards Mixed-Criticality Software Architectures for Centralized HPC Platforms in Software-Defined Vehicles: A Systematic Literature Review
Centralized electrical/electronic architectures and High-Performance Computers (HPCs) are redefining automotive software development, challenging traditional microcontroller-based approaches. Ensuring real-time, safety, and scalability in software-defined vehicles necessitates reevaluating how mixed-criticality software is integrated into centralized architectures. While existing research on automotive SoftWare Architectures (SWAs) is relevant to the industry, it often lacks validation through …

This https://arxiv.org/abs/2411.03328 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Foundation Models for Rapid Autonomy Validation
We are motivated by the problem of autonomous vehicle performance validation. A key challenge is that an autonomous vehicle requires testing in every kind of driving scenario it could encounter, including rare events, to provide a strong case for safety and show there is no edge-case pathological behavior. Autonomous vehicle companies rely on potentially millions of miles driven in realistic simulation to expose the driving stack to enough miles to estimate rates and severity of collisions. To …

Classifying and Clustering Trading Agents
Mateusz Wilinski, Anubha Goel, Alexandros Iosifidis, Juho Kanniainen
https://arxiv.org/abs/2505.21662 https://

Classifying and Clustering Trading Agents
The rapid development of sophisticated machine learning methods, together with the increased availability of financial data, has the potential to transform financial research, but also poses a challenge in terms of validation and interpretation. A good case study is the task of classifying financial investors based on their behavioral patterns. Not only do we have access to both classification and clustering tools for high-dimensional data, but also data identifying individual investors is fina…

CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs
Mohamed R. Elshamy, Mehdi Elahi, Ahmad Patooghy, Abdel-Hameed A. Badawy
https://arxiv.org/abs/2505.22469

CPINN-ABPI: Physics-Informed Neural Networks for Accurate Power Estimation in MPSoCs
Efficient thermal and power management in modern multiprocessor systems-on-chip (MPSoCs) demands accurate power consumption estimation. One of the state-of-the-art approaches, Alternative Blind Power Identification (ABPI), theoretically eliminates the dependence on steady-state temperatures, addressing a major shortcoming of previous approaches. However, ABPI performance has remained unverified in actual hardware implementations. In this study, we conduct the first empirical validation of ABPI …

This https://arxiv.org/abs/2212.05260 has been replaced.
link: https://scholar.google.com/scholar?q=a

Examining marginal properness in the external validation of survival models with squared and logarithmic losses
Scoring rules promote rational and honest decision-making, which is important for model evaluation and becoming increasingly important for automated procedures such as `AutoML'. In this paper we survey common squared and logarithmic scoring rules for survival analysis, with a focus on their theoretical and empirical properness. We introduce a marginal definition of properness and show that both the Integrated Survival Brier Score (ISBS) and the Right-Censored Log-Likelihood (RCLL) are theoretic…

PyGemini: Unified Software Development towards Maritime Autonomy Systems
Kjetil Vasstein, Christian Le, Simon Lerv{\aa}g Breivik, Trygve Maukon Myhr, Annette Stahl, Edmund F{\o}rland Brekke
https://arxiv.org/abs/2506.06262

PyGemini: Unified Software Development towards Maritime Autonomy Systems
Ensuring the safety and certifiability of autonomous surface vessels (ASVs) requires robust decision-making systems, supported by extensive simulation, testing, and validation across a broad range of scenarios. However, the current landscape of maritime autonomy development is fragmented -- relying on disparate tools for communication, simulation, monitoring, and system integration -- which hampers interdisciplinary collaboration and inhibits the creation of compelling assurance cases, demanded…

This https://arxiv.org/abs/2504.02856 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

The epistemic dimension of algorithmic fairness: assessing its impact in innovation diffusion and fair policy making
Algorithmic fairness is an expanding field that addresses a range of discrimination issues associated with algorithmic processes. However, most works in the literature focus on analyzing it only from an ethical perspective, focusing on moral principles and values that should be considered in the design and evaluation of algorithms, while disregarding the epistemic dimension related to knowledge transmission and validation. However, this aspect of algorithmic fairness should also be included in …

This https://arxiv.org/abs/2502.05258 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Searching for Inflationary Physics with the CMB Trispectrum: 2. Code & Validation
To unlock the vast potential of the CMB trispectrum, we require both robust estimators and efficient computational tools. In this work, we introduce the public code PolySpec: a suite of quartic estimators designed to measure the amplitudes of a wide variety of inflationary templates, including local non-Gaussianity, effective field theory models, direction-dependent trispectra, spinning massive particle exchange, and weak gravitational lensing. PolySpec includes a python/cython implementation o…

This https://arxiv.org/abs/2503.20197 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csSE_…

Enhancing the Robustness of LLM-Generated Code: Empirical Study and Framework
Ensuring the robustness of code generated by large language models (LLMs) is crucial for real-world reliability. However, existing evaluations predominantly focus on correctness, often neglecting key robustness concerns such as missing input validation and insufficient error handling. In this paper, we present the first empirical study on the robustness of LLM-generated code. We introduce novel robustness metrics and analyze four state-of-the-art code LLMs, revealing that, on average, 43.1% of …

Internal replication as a tool for evaluating reproducibility in preclinical experiments
Stanley E. Lazic
https://arxiv.org/abs/2506.03468 https://

Internal replication as a tool for evaluating reproducibility in preclinical experiments
Reproducibility is central to the credibility of scientific findings, yet complete replication studies are costly and infrequent. However, many biological experiments contain internal replication, which is defined as repetition across batches, runs, days, litters, or sites that can be used to estimate reproducibility without requiring additional experiments. This internal replication is analogous to internal validation in prediction or machine learning models, but is often treated as a nuisance…

Window observables for benchmarking parton distribution functions
Joe Karpie, Christopher J. Monahan, Kostas Orginos, Savvas Zafeiropoulos
https://arxiv.org/abs/2505.22795

Window observables for benchmarking parton distribution functions
Global analysis of collider and fixed-target experimental data and calculations from lattice quantum chromodynamics (QCD) are used to gain complementary information on the structure of hadrons. We propose novel ``window observables'' that allow for higher precision cross-validation between the different approaches, a critical step for studies that wish to combine the datasets. Global analyses are limited by the kinematic regions accessible to experiment, particularly in a range of Bjorken-$x$, …

This https://arxiv.org/abs/2505.20552 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_ees…

Effect of laboratory conditions on the perception of virtual stages for music
This manuscript presents initial findings critical for supporting augmented acoustics experiments in custom-made hearing booths, addressing a key challenge in ensuring perceptual validity and experimental rigor in these highly sensitive setups. This validation ensures our proposed methodology is sound, guarantees the reliability of future results, and lays the foundational groundwork for subsequent perceptual studies and the development of robust guidelines for laboratory design in virtual acou…

Generative Knowledge Production Pipeline Driven by Academic Influencers
Katalin Feher, Marton Demeter
https://arxiv.org/abs/2505.24681 https://

Generative Knowledge Production Pipeline Driven by Academic Influencers
Generative AI transforms knowledge production, validation, and dissemination, raising academic integrity and credibility concerns. This study examines 53 academic influencer videos that reached 5.3 million viewers to identify an emerging, structured, implementation-ready pipeline balancing originality, ethical compliance, and human-AI collaboration despite the disruptive impacts. Findings highlight generative AI's potential to automate publication workflows and democratize participation in know…

This https://arxiv.org/abs/2505.06787 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csRO_…

Digital-physical testbed for ship autonomy studies in the Marine Cybernetics Laboratory basin
The algorithms developed for Maritime Autonomous Surface Ships (MASS) are often challenging to test on actual vessels due to high operational costs and safety considerations. Simulations offer a cost-effective alternative and eliminate risks, but they may not accurately represent real-world dynamics for the given tasks. Utilizing small-scale model ships and robotic vessels in conjunction with a laboratory basin provides an accessible testing environment for the early stages of validation proces…