Tootfinder

Orlovsky ranks the best NFL QBs in seven different traits: Who makes each top-10 list? https://www.espn.com/nfl/story/_/id/45913841/2025-ranking-best-nfl-quarterbacks-top-10-trait-skill-arm-accuracy-rushing

Very excited about this! Code to access GRIN will help lots of Google Books partners, and the example might open other doors, as well as the obvious benefits of access to data!
'Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability' https://arxiv.org/abs/2506…

Institutional Books 1.0: A 242B token dataset from Harvard Library's collections, refined for accuracy and usability
Large language models (LLMs) use data to learn about the world in order to produce meaningful correlations and predictions. As such, the nature, scale, quality, and diversity of the datasets used to train these models, or to support their work at inference time, have a direct impact on their quality. The rapid development and adoption of LLMs of varying quality has brought into focus the scarcity of publicly available, high-quality training data and revealed an urgent need to ground the steward…

Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution
Zailong Tian, Zhuoheng Han, Yanzhe Chen, Haozhe Xu, Xi Yang, richeng xuan, Hongfeng Wang, Lizi Liao
https://arxiv.org/abs/2508.06225

Overconfidence in LLM-as-a-Judge: Diagnosis and Confidence-Driven Solution
Large Language Models (LLMs) are widely used as automated judges, where practical value depends on both accuracy and trustworthy, risk-aware judgments. Existing approaches predominantly focus on accuracy, overlooking the necessity of well-calibrated confidence, which is vital for adaptive and reliable evaluation pipelines. In this work, we advocate a shift from accuracy-centric evaluation to confidence-driven, risk-aware LLM-as-a-Judge systems, emphasizing the necessity of well-calibrated confi…

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Hiroshi Matsuda, Chunpeng Ma, Masayuki Asahara
https://arxiv.org/abs/2506.09983

Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
Recent advances in large language models (LLMs) have enabled impressive performance in various tasks. However, standard prompting often struggles to produce structurally valid and accurate outputs, especially in dependency parsing. We propose a novel step-by-step instruction strategy, where universal part-of-speech tagging precedes the prediction of syntactic heads and dependency labels, and a simplified CoNLL-U like output format, our method achieves state-of-the-art accuracy on Universal Depe…

Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
Zihan Zhang, Leon Wong, Blesson Varghese
https://arxiv.org/abs/2507.07130 https:…

Ampere: Communication-Efficient and High-Accuracy Split Federated Learning
A Federated Learning (FL) system collaboratively trains neural networks across devices and a server but is limited by significant on-device computation costs. Split Federated Learning (SFL) systems mitigate this by offloading a block of layers of the network from the device to a server. However, in doing so, it introduces large communication overheads due to frequent exchanges of intermediate activations and gradients between devices and the server and reduces model accuracy for non-IID data. W…

Impact of Pretraining Word Co-occurrence on Compositional Generalization in Multimodal Models
Helen Qu, Sang Michael Xie
https://arxiv.org/abs/2507.08000 h…

Impact of Pretraining Word Co-occurrence on Compositional Generalization in Multimodal Models
CLIP and large multimodal models (LMMs) have better accuracy on examples involving concepts that are highly represented in the training data. However, the role of concept combinations in the training data on compositional generalization is largely unclear -- for instance, how does accuracy vary when a common object appears in an uncommon pairing with another object? In this paper, we investigate how word co-occurrence statistics in the pretraining dataset (a proxy for co-occurrence of visual co…

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

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

Mitigating Distribution Shift in Graph-Based Android Malware Classification via Function Metadata and LLM Embeddings
Ngoc N. Tran, Anwar Said, Waseem Abbas, Tyler Derr, Xenofon D. Koutsoukos
https://arxiv.org/abs/2508.06734

Mitigating Distribution Shift in Graph-Based Android Malware Classification via Function Metadata and LLM Embeddings
Graph-based malware classifiers can achieve over 94% accuracy on standard Android datasets, yet we find they suffer accuracy drops of up to 45% when evaluated on previously unseen malware variants from the same family - a scenario where strong generalization would typically be expected. This highlights a key limitation in existing approaches: both the model architectures and their structure-only representations often fail to capture deeper semantic patterns. In this work, we propose a robust se…

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Yonatan Kurniawan (Department of Physics and Astronomy, Brigham Young University, Provo, Utah, USA), Mingjian Wen (Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China), Ellad B. Tadmor (Department of Aerospace Engineering and Mechanics, University of Minnesota, Minneapolis, Minnesota, USA), Mark K. Transtru…

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials
Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful tools for large-scale materials modeling. The accuracy of MLIPs is typically validated on a held-out dataset of \emph{ab initio} energies and atomic forces. However, accuracy on these small-scale properties does not guarantee reliability for emergent, system-level behavior -- precisely the regime where atomistic simula…

Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
Joshua Murphy, Conor Rosato, Andrew Millard, Lee Devlin, Paul Horridge, Simon Maskell
https://arxiv.org/abs/2507.07461

Hess-MC2: Sequential Monte Carlo Squared using Hessian Information and Second Order Proposals
When performing Bayesian inference using Sequential Monte Carlo (SMC) methods, two considerations arise: the accuracy of the posterior approximation and computational efficiency. To address computational demands, Sequential Monte Carlo Squared (SMC$^2$) is well-suited for high-performance computing (HPC) environments. The design of the proposal distribution within SMC$^2$ can improve accuracy and exploration of the posterior as poor proposals may lead to high variance in importance weights and …

Event generation at MEPS@NLO accuracy in neutral and charged current DIS at the EIC
Peter Meinzinger, Daniel Reichelt, Federico Silvetti
https://arxiv.org/abs/2506.08994

Event generation at MEPS@NLO accuracy in neutral and charged current DIS at the EIC
We present the first hadron-level predictions at MEPS@NLO precision for both neutral and charged current DIS at the EIC and the first application of the MEPS@NLO technique to charged current DIS in general. We critically examine state-of-the-art inclusive predictions using multi-leg merging techniques, contrasting perturbative and non-perturbative uncertainties. We study typical kinematic DIS observables as well as jet measurements and 1-jettiness with realistic cuts implied by expected and pas…

Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
Md. Yeasin Rahat, Rajan Das Gupta, Nur Raisa Rahman, Sudipto Roy Pritom, Samiur Rahman Shakir, Md Imrul Hasan Showmick, Md. Jakir Hossen
https://arxiv.org/abs/2506.09851

Advancing Exchange Rate Forecasting: Leveraging Machine Learning and AI for Enhanced Accuracy in Global Financial Markets
The prediction of foreign exchange rates, such as the US Dollar (USD) to Bangladeshi Taka (BDT), plays a pivotal role in global financial markets, influencing trade, investments, and economic stability. This study leverages historical USD/BDT exchange rate data from 2018 to 2023, sourced from Yahoo Finance, to develop advanced machine learning models for accurate forecasting. A Long Short-Term Memory (LSTM) neural network is employed, achieving an exceptional accuracy of 99.449%, a Root Mean Sq…

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…

MOSS: Multi-Objective Optimization for Stable Rule Sets
Brian Liu, Rahul Mazumder
https://arxiv.org/abs/2506.08030 https://arxiv.org/…

MOSS: Multi-Objective Optimization for Stable Rule Sets
We present MOSS, a multi-objective optimization framework for constructing stable sets of decision rules. MOSS incorporates three important criteria for interpretability: sparsity, accuracy, and stability, into a single multi-objective optimization framework. Importantly, MOSS allows a practitioner to rapidly evaluate the trade-off between accuracy and stability in sparse rule sets in order to select an appropriate model. We develop a specialized cutting plane algorithm in our framework to rapi…

A Berny green, flieger - type b style watch, with a green dial.
Yes, it looks similar to a certain Seiko with this dial color and the four o'clock crown but, that watch too steals 90 % of it's design from classic fliegers, so I'm ok with it. 😆
Plus it has a couple of things extra I wanted. Titanium case, sapphire crystal, no logo or extraneous text on the face (like classic fliegers) and it features a "mecha-quartz" (quartz accuracy but the second hand is m…

Effects of task difficulty and music expertise in virtual reality: Observations of cognitive load and task accuracy in a rhythm exergame
Kyla Ellahiyoun, Emma Jane Pretty, Renan Guarese, Marcel Takac, Haytham Fayek, Fabio Zambetta
https://arxiv.org/abs/2507.06691

Effects of task difficulty and music expertise in virtual reality: Observations of cognitive load and task accuracy in a rhythm exergame
This study explores the relationship between musical training, cognitive load (CL), and task accuracy within the virtual reality (VR) exergame Beat Saber across increasing levels of difficulty. Participants (N=32) completed a series of post-task questionnaires after playing the game under three task difficulty levels while having their physiological data measured by an Emotibit. Using regression analyses, we found that task difficulty and gaming experience significantly predicted subjective CL,…

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems
Youngjoon Jang, Seongtae Hong, Junyoung Son, Sungjin Park, Chanjun Park, Heuiseok Lim
https://arxiv.org/abs/2507.07847

From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems
Retrieval-Augmented Generation (RAG) has emerged as a crucial framework in natural language processing (NLP), improving factual consistency and reducing hallucinations by integrating external document retrieval with large language models (LLMs). However, the effectiveness of RAG is often hindered by coreferential complexity in retrieved documents, introducing ambiguity that disrupts in-context learning. In this study, we systematically investigate how entity coreference affects both document re…

😶‍🌫️ Generating real-time detailed ground visualisations from sparse aerial point clouds
#3d #xr

Generating real-time detailed ground visualisations from sparse aerial point clouds
Building realistic wide scale outdoor 3D content with sufficient visual quality to observe at walking eye level or from driven vehicles is often carried out by large teams of artists skilled in modelling, texturing, material shading and lighting, which typically leads to both prohibitive costs and reduced accuracy honoring the variety of real world ground truth landscapes. In our proposed method, we define a process to automatically amplify real-world scanned data and render real-time in animat…

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Yilin Zhuang, Karthik Duraisamy
https://arxiv.org/abs/2506.09193 https://…

LaDCast: A Latent Diffusion Model for Medium-Range Ensemble Weather Forecasting
Accurate probabilistic weather forecasting demands both high accuracy and efficient uncertainty quantification, challenges that overburden both ensemble numerical weather prediction (NWP) and recent machine-learning methods. We introduce LaDCast, the first global latent-diffusion framework for medium-range ensemble forecasting, which generates hourly ensemble forecasts entirely in a learned latent space. An autoencoder compresses high-dimensional ERA5 reanalysis fields into a compact representa…

Majorana Signatures in the Tripartite Uncertainty Relations with Quantum Memory
D. Maroulakos (Doctoral School at the University of Rzeszow), C. Jasiukiewicz (Department of Physics and Medical Engineering Rzeszow University of Technology), A. Wal (Institute of Physics Faculty of Exact and Technical Sciences University of Rzeszow), A. Sinner (Institute of Physics University of Opole), I. Weymann (Institute of Spintronics and Quantum Information Faculty of Physics and Astronomy A. Mickie…

Majorana Signatures in the Tripartite Uncertainty Relations with Quantum Memory
Quantumness imposes a fundamental limit on measurement accuracy. The paradigmatic cases are Heisenberg's uncertainty relation in the original formulation, Robertson's formulation, and improved uncertainty relations. However, the more universal measures are given in terms of quantum entropies. Uncertainties of measurements done on one quantum system correlated with another quantum system constitute a more intriguing question. Quantum correlations can influence the lower bound of uncertainties, a…

The problem of course is that our problems are complex.
“Advanced AI suffers ‘complete accuracy collapse’ in face of complex problems, study finds” - https://www.theguardian.com/technology/2025/jun/09/apple-artificial-intelligence-ai-study-c…

g2o vs. Ceres: Optimizing Scan Matching in Cartographer SLAM
Quanjie Qiu, MengCheng Lau
https://arxiv.org/abs/2507.07142 https://arxi…

g2o vs. Ceres: Optimizing Scan Matching in Cartographer SLAM
This article presents a comparative analysis of g2o and Ceres solvers in enhancing scan matching performance within the Cartographer framework. Cartographer, a widely-used library for Simultaneous Localization and Mapping (SLAM), relies on optimization algorithms to refine pose estimates and improve map accuracy. The research aims to evaluate the performance, efficiency, and accuracy of the g2o solver in comparison to the Ceres solver, which is the default in Cartographer. In our experiments co…

AutoAssert 1: A LoRA Fine-Tuned LLM Model for Efficient Automated Assertion Generation
Yi Zhong, Hongchao Liu, Di ZHao
https://arxiv.org/abs/2508.07371 https://

AutoAssert 1: A LoRA Fine-Tuned LLM Model for Efficient Automated Assertion Generation
As the complexity of software systems continues to increase, the demand for automated testing and maintenance tools is growing exponentially. To meet this urgent need, we propose a new assertion generation method based on Hardware Description Language (HDL). This method combines a lightweight, parameter-adjustable large language model (LLM) with the Unsloth platform to automatically generate test cases, thereby significantly reducing training costs without sacrificing accuracy or generalization…

MIND: A Noise-Adaptive Denoising Framework for Medical Images Integrating Multi-Scale Transformer
Tao Tang, Chengxu Yang
https://arxiv.org/abs/2508.07817 https://

MIND: A Noise-Adaptive Denoising Framework for Medical Images Integrating Multi-Scale Transformer
The core role of medical images in disease diagnosis makes their quality directly affect the accuracy of clinical judgment. However, due to factors such as low-dose scanning, equipment limitations and imaging artifacts, medical images are often accompanied by non-uniform noise interference, which seriously affects structure recognition and lesion detection. This paper proposes a medical image adaptive denoising model (MI-ND) that integrates multi-scale convolutional and Transformer architecture…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification.
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated…

VLAI: A RoBERTa-Based Model for Automated Vulnerability Severity Classification
This paper presents VLAI, a transformer-based model that predicts software vulnerability severity levels directly from text descriptions. Built on RoBERTa, VLAI is fine-tuned on over 600,000 real-world vulnerabilities and achieves over 82% accuracy in predicting severity categories, enabling faster and more consistent triage ahead of manual CVSS scoring. The model and dataset are open-source and integrated into the Vulnerability-Lookup service.

Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
Vibhor Agrawal, Fay Wang, Rishi Puri
https://arxiv.org/abs/2508.05647 https://

Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
We present a novel graph neural network (GNN) architecture for retrieval-augmented generation (RAG) that leverages query-aware attention mechanisms and learned scoring heads to improve retrieval accuracy on complex, multi-hop questions. Unlike traditional dense retrieval methods that treat documents as independent entities, our approach constructs per-episode knowledge graphs that capture both sequential and semantic relationships between text chunks. We introduce an Enhanced Graph Attention Ne…

Entropy proxy inversions as tracers of the evolution of physical conditions at the base of the solar convective envelope
G. Buldgen, A. Noels, V. A. Baturin, J. Christensen-Dalsgaard, S. V. Ayukov, A. V. Oreshina, R. Scuflaire
https://arxiv.org/abs/2506.09503

Entropy proxy inversions as tracers of the evolution of physical conditions at the base of the solar convective envelope
The Sun is an important calibrator for the theory of stellar structure and evolution. However, the accuracy of our solar evolution models is tightly linked to the physical ingredients that enter their computations. This include, amongst other, the equation of state, the opacities, the transport of chemicals and the modelling of turbulent convection. Deriving model-independent probes of these ingredients is therefore crucial to further test the quality of these ingredients and potentially reveal…

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models
Chen Feng, Yicheng Lin, Shaojie Zhuo, Chenzheng Su, Ramchalam Kinattinkara Ramakrishnan, Zhaocong Yuan, Xiaopeng Zhang
https://arxiv.org/abs/2507.07877

Edge-ASR: Towards Low-Bit Quantization of Automatic Speech Recognition Models
Recent advances in Automatic Speech Recognition (ASR) have demonstrated remarkable accuracy and robustness in diverse audio applications, such as live transcription and voice command processing. However, deploying these models on resource constrained edge devices (e.g., IoT device, wearables) still presents substantial challenges due to strict limits on memory, compute and power. Quantization, particularly Post-Training Quantization (PTQ), offers an effective way to reduce model size and infere…

Secure Cooperative Gradient Coding: Optimality, Reliability, and Global Privacy
Shudi Weng
https://arxiv.org/abs/2507.07565 https://a…

Secure Cooperative Gradient Coding: Optimality, Reliability, and Global Privacy
This paper studies privacy-sensitive federated learning (FL) with unreliable communication, focusing on secure aggregation and straggler mitigation. While secure aggregation cryptographically reconstructs the global model without exposing client updates, random link failures disrupt its key coordination, degrading model accuracy. Moreover, unreliable communication can lead to objective inconsistency, causing the global model to converge to arbitrary, sub-optimal points far from the intended opt…

A new class of one-step A-stable and L-stable schemes of high-order accuracy for parabolic type equations
Xiaoyi Li, Aijie Cheng, Zhengguang Liu
https://arxiv.org/abs/2507.06985

A new class of one-step A-stable and L-stable schemes of high-order accuracy for parabolic type equations
Recently, a new class of BDF schemes proposed in [F. Huang and J. Shen, SIAM J Numer. Anal., 62.4, 1609--1637] for the parabolic type equations are studied in this paper. The basic idea is based on the Taylor expansions at time $t^{n+β}$ with $β>1$ being a tunable parameter. These new BDF schemes allow larger time steps at higher order r for stiff problems than that which allowed with a usual higher-order scheme. However, multi-step methods like BDF exhibit inherent disadvantages relative to …

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Yingze Li, Xianglong Liu, Dong Wang, Zixuan Wang, Hongzhi Wang, Kaixing Zhang, Yiming Guan
https://arxiv.org/abs/2507.07438

Algorithmic Complexity Attacks on All Learned Cardinality Estimators: A Data-centric Approach
Learned cardinality estimators show promise in query cardinality prediction, yet they universally exhibit fragility to training data drifts, posing risks for real-world deployment. This work is the first to theoretical investigate how minimal data-level drifts can maximally degrade the accuracy of learned estimators. We propose data-centric algorithmic complexity attacks against learned estimators in a black-box setting, proving that finding the optimal attack strategy is NP-Hard. To address th…

Accuracy and stability of the accelerated multi-direct-forcing immersed boundary method
Kosuke Suzuki, Emmanouil Falagkaris, Timm Kr\"uger, Takaji Inamuro
https://arxiv.org/abs/2507.04986

Accuracy and stability of the accelerated multi-direct-forcing immersed boundary method
The multi-direct-forcing immersed boundary method allows for high accuracy of the no-slip condition in moving-particle problems but suffers from numerical instability if simulation parameters are not carefully chosen. This study investigates the numerical accuracy and stability of the accelerated multidirect-forcing immersed boundary method. An analysis of the discretized equations of body motion in moving boundary problems identifies a critical parameter that solely determines the numerical st…

Diffusion Non-Additive Model for Multi-Fidelity Simulations with Tunable Precision
Junoh Heo, Romain Boutelet, Chih-Li Sung
https://arxiv.org/abs/2506.08328

Diffusion Non-Additive Model for Multi-Fidelity Simulations with Tunable Precision
Computer simulations are indispensable for analyzing complex systems, yet high-fidelity models often incur prohibitive computational costs. Multi-fidelity frameworks address this challenge by combining inexpensive low-fidelity simulations with costly high-fidelity simulations to improve both accuracy and efficiency. However, certain scientific problems demand even more accurate results than the highest-fidelity simulations available, particularly when a tuning parameter controlling simulation a…

Global optimization of harmonic oscillator basis in covariant density functional theory
B. Osei, A. V. Afanasjev, A. Dalbah
https://arxiv.org/abs/2508.06698 https://

Global optimization of harmonic oscillator basis in covariant density functional theory
The present investigation focuses on the improvement of the accuracy of the description of binding energies within moderately sized fermionic basis. Using the solutions corresponding to infinite fermionic basis it was shown for the first time that in the case of meson exchange (ME) covariant energy density functionals (CEDFs) the global accuracy of the description of binding energies in the finite $N_F=16-20$ bases can be drastically (by a factor of $\approx 3.5$) improved by a global optimizat…

POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration
Mukul Lokhande, Santosh Kumar Vishvakarma
https://arxiv.org/abs/2506.08785

POLARON: Precision-aware On-device Learning and Adaptive Runtime-cONfigurable AI acceleration
The increasing complexity of AI models requires flexible hardware capable of supporting diverse precision formats, particularly for energy-constrained edge platforms. This work presents PARV-CE, a SIMD-enabled, multi-precision MAC engine that performs efficient multiply-accumulate operations using a unified data-path for 4/8/16-bit fixed-point, floating point, and posit formats. The architecture incorporates a layer adaptive precision strategy to align computational accuracy with workload sensi…

Hybrid Approaches for Black Hole Spin Estimation: From Classical Spectroscopy to Physics-Informed Machine Learning
Stella Menziltsidou
https://arxiv.org/abs/2508.06506 https://

Hybrid Approaches for Black Hole Spin Estimation: From Classical Spectroscopy to Physics-Informed Machine Learning
The measurement of black hole spin is considered one of the key problems in relativistic astrophysics. Existing methods, such as continuum fitting, X-ray reflection spectroscopy, and quasi-periodic oscillation analysis, suffer from limitations in accuracy, interpretability, and scalability. In this work, a hybrid approach is proposed in which theoretical models based on the Teukolsky formalism are integrated with Physics-Informed Neural Networks (PINNs). A PINN model is developed to solve the l…

Detecting malignant dynamics on very few blood sample using signature coefficients
R\'emi Vaucher, St\'ephane Chr\'etien
https://arxiv.org/abs/2506.09097

Detecting malignant dynamics on very few blood sample using signature coefficients
Recent discoveries have suggested that the promising avenue of using circulating tumor DNA (ctDNA) levels in blood samples provides reasonable accuracy for cancer monitoring, with extremely low burden on the patient's side. It is known that the presence of ctDNA can result from various mechanisms leading to DNA release from cells, such as apoptosis, necrosis or active secretion. One key idea in recent cancer monitoring studies is that monitoring the dynamics of ctDNA levels might be sufficient …

DEKC: Data-Enable Control for Tethered Space Robot Deployment in the Presence of Uncertainty via Koopman Operator Theory
Ao Jin, Qinyi Wang, Sijie Wen, Ya Liu, Ganghui Shen, Panfeng Huang, Fan Zhang
https://arxiv.org/abs/2506.08319

DEKC: Data-Enable Control for Tethered Space Robot Deployment in the Presence of Uncertainty via Koopman Operator Theory
This work focuses the deployment of tethered space robot in the presence of unknown uncertainty. A data-enable framework called DEKC which contains offline training part and online execution part is proposed to deploy tethered space robot in the presence of uncertainty. The main idea of this work is modeling the unknown uncertainty as a dynamical system, which enables high accuracy and convergence of capturing uncertainty. The core part of proposed framework is a proxy model of uncertainty, whi…

Ten-dimensional neural network emulator for the nonlinear matter power spectrum
Yanhui Yang, Simeon Bird, Ming-Feng Ho, Mahdi Qezlou
https://arxiv.org/abs/2507.07177

Ten-dimensional neural network emulator for the nonlinear matter power spectrum
We present GokuNEmu, a ten-dimensional neural network emulator for the nonlinear matter power spectrum, designed to support next-generation cosmological analyses. Built on the Goku $N$-body simulation suite and the T2N-MusE emulation framework, GokuNEmu predicts the matter power spectrum with $\sim 0.5 \%$ average accuracy for redshifts $0 \leq z \leq 3$ and scales $0.006 \leq k/(h\,\mathrm{Mpc}^{-1}) \leq 10$. The emulator models a 10D parameter space that extends beyond $Λ$CDM to include dyn…

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
Tom Boot, Bart Keijsers
https://arxiv.org/abs/2506.09575 htt…

Diffusion index forecasts under weaker loadings: PCA, ridge regression, and random projections
We study the accuracy of forecasts in the diffusion index forecast model with possibly weak loadings. The default option to construct forecasts is to estimate the factors through principal component analysis (PCA) on the available predictor matrix, and use the estimated factors to forecast the outcome variable. Alternatively, we can directly relate the outcome variable to the predictors through either ridge regression or random projections. We establish that forecasts based on PCA, ridge regres…

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training
Renyuan Liu (Richard), Yuyang Leng (Richard), Kaiyan Liu (Richard), Shaohan Hu (Richard), Chun-Fu (Richard), Chen, Peijun Zhao, Heechul Yun, Shuochao Yao
https://arxiv.org/abs/2507.07149

DAF: An Efficient End-to-End Dynamic Activation Framework for on-Device DNN Training
Recent advancements in on-device training for deep neural networks have underscored the critical need for efficient activation compression to overcome the memory constraints of mobile and edge devices. As activations dominate memory usage during training and are essential for gradient computation, compressing them without compromising accuracy remains a key research challenge. While existing methods for dynamic activation quantization promise theoretical memory savings, their practical deployme…

A tunable Monte Carlo method for mixing correlated-k opacities. PRAS: polynomial reconstruction and sampling
Elspeth K. H. Lee
https://arxiv.org/abs/2508.07072 https://

A tunable Monte Carlo method for mixing correlated-k opacities. PRAS: polynomial reconstruction and sampling
Accurately accounting for mixed-gas opacities is critical for radiative-transfer (RT) calculations in sub-stellar atmospheres. To produce the total k-coefficients of an arbitrary mixture of gases and their associated volume mixing ratios (VMRs), several methods are applied in the literature with various levels of overall accuracy and ease of computation. We propose a simple, tunable random overlap method, polynomial reconstruction and sampling (PRAS). PRAS is a Monte Carlo-based technique, samp…

Bounds for the change of the Weyr characteristic of matrix pencils after 1-rank perturbations
Itziar Baraga\~na, Alicia Roca
https://arxiv.org/abs/2507.07152

Bounds for the change of the Weyr characteristic of matrix pencils after 1-rank perturbations
The complete characterization of the Kronecker structure of a matrix pencil perturbed by another pencil of rank one is known, and it is stated in terms of very involved conditions. This paper is devoted to, loosing accuracy, better understand the meaning of those conditions. The Kronecker structure of a pencil is determined by the sequences of the column and row minimal indices and of the partial multiplicities of the eigenvalues. We introduce the Weyr characteristic of a matrix pencil as the c…

Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime
Alberto Aziani, Michael V. Lo Giudice, Ali Shadman Yazdi
https://arxiv.org/abs/2507.07884

Conspiracy to Commit: Information Pollution, Artificial Intelligence, and Real-World Hate Crime
Is demand for conspiracy theories online linked to real-world hate crimes? By analyzing online search trends for 36 racially and politically-charged conspiracy theories in Michigan (2015-2019), we employ a one-dimensional convolutional neural network (1D-CNN) to predict hate crime occurrences offline. A subset of theories including the Rothschilds family, Q-Anon, and The Great Replacement improves prediction accuracy, with effects emerging two to three weeks after fluctuations in searches. Howe…

Deriving physical parameters of unresolved star clusters. IX. Sky background effects in the aperture photometry
Karolis Daugevi\v{c}ius, Rima Stonkut\.e, Eimantas Kri\v{s}\v{c}i\=unas, Erikas Cic\.enas, Vladas Vansevi\v{c}ius
https://arxiv.org/abs/2506.09622

Deriving physical parameters of unresolved star clusters. IX. Sky background effects in the aperture photometry
The aperture photometry method is a powerful tool that enables us to study large star cluster systems efficiently. However, its accuracy depends on various factors, including the stochasticity of the stellar initial mass function and variations in the sky background. Previously, in the eighth paper of this series, we established the best achievable limits of the aperture photometry method for star cluster studies in the local universe. The aim of this study is to determine how the sky backgroun…

MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation
Dong Xu, Zhangfan Yang, Sisi Yuan, Jenna Xinyi Yao, Jiangqiang Li, Junkai Ji
https://arxiv.org/abs/2507.07201

MODA: A Unified 3D Diffusion Framework for Multi-Task Target-Aware Molecular Generation
Three-dimensional molecular generators based on diffusion models can now reach near-crystallographic accuracy, yet they remain fragmented across tasks. SMILES-only inputs, two-stage pretrain-finetune pipelines, and one-task-one-model practices hinder stereochemical fidelity, task alignment, and zero-shot transfer. We introduce MODA, a diffusion framework that unifies fragment growing, linker design, scaffold hopping, and side-chain decoration with a Bayesian mask scheduler. During training, a c…

How Your Phone Gets the Weather (New York Times)
https://www.nytimes.com/interactive/2025/08/07/weather/weather-app-phone-accuracy.html
http://www.memeorandum.com/250807/p158#a250807p158

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Sizhen Bian, Mengxi Liu, Vitor Fortes Rey, Daniel Geissler, Paul Lukowicz
https://arxiv.org/abs/2507.07949

TinierHAR: Towards Ultra-Lightweight Deep Learning Models for Efficient Human Activity Recognition on Edge Devices
Human Activity Recognition (HAR) on resource-constrained wearable devices demands inference models that harmonize accuracy with computational efficiency. This paper introduces TinierHAR, an ultra-lightweight deep learning architecture that synergizes residual depthwise separable convolutions, gated recurrent units (GRUs), and temporal aggregation to achieve SOTA efficiency without compromising performance. Evaluated across 14 public HAR datasets, TinierHAR reduces Parameters by 2.7x (vs. TinyHA…

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
Yeonju Ro, Zhenyu Zhang, Souvik Kundu, Zhangyang Wang, Aditya Akella
https://arxiv.org/abs/2506.09316

On-the-Fly Adaptive Distillation of Transformer to Dual-State Linear Attention
Large language models (LLMs) excel at capturing global token dependencies via self-attention but face prohibitive compute and memory costs on lengthy inputs. While sub-quadratic methods (e.g., linear attention) can reduce these costs, they often degrade accuracy due to overemphasizing recent tokens. In this work, we first propose \textit{dual-state linear attention} (\textbf{\dsla}), a novel design that maintains two specialized hidden states-one for preserving historical context and one for tr…

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
Sita Sch\"onbauer, Johanna P. Carbone, Andreas Gr\"uneis
https://arxiv.org/abs/2507.06929

Machine-Learned Force Fields for Lattice Dynamics at Coupled-Cluster Level Accuracy
We investigate Machine-Learned Force Fields (MLFFs) trained on approximate Density Functional Theory (DFT) and Coupled Cluster (CC) level potential energy surfaces for the carbon diamond and lithium hydride solids. We assess the accuracy and precision of the MLFFs by calculating phonon dispersions and vibrational densities of states (VDOS) that are compared to experiment and reference ab initio results. To overcome limitations from long-range effects and the lack of atomic forces in the CC trai…

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
Mridula Vijendran, Shuang Chen, Jingjing Deng, Hubert P. H. Shum
https://arxiv.org/abs/2507.07134

BOOST: Out-of-Distribution-Informed Adaptive Sampling for Bias Mitigation in Stylistic Convolutional Neural Networks
The pervasive issue of bias in AI presents a significant challenge to painting classification, and is getting more serious as these systems become increasingly integrated into tasks like art curation and restoration. Biases, often arising from imbalanced datasets where certain artistic styles dominate, compromise the fairness and accuracy of model predictions, i.e., classifiers are less accurate on rarely seen paintings. While prior research has made strides in improving classification performa…

Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning
Manuel Gallego, Sebasti\'an Roca-Jerat, David Zueco, Jes\'us Carrete
https://arxiv.org/abs/2508.06441

Accelerating Quantum Monte Carlo Calculations with Set-Equivariant Architectures and Transfer Learning
Machine-learning (ML) ansätze have greatly expanded the accuracy and reach of variational quantum Monte Carlo (QMC) calculations, in particular when exploring the manifold quantum phenomena exhibited by spin systems. However, the scalability of QMC is still compromised by several other bottlenecks, and specifically those related to the actual evaluation of observables based on random deviates that lies at the core of the approach. Here we show how the set-transformer architecture can be used t…

Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models
Jikesh Thapa, Gurrehmat Chahal, Serban Voinea Gabreanu, Yazan Otoum
https://arxiv.org/abs/2507.07406

Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models
Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning (ML), Deep Learning (DL), and quantized small-parameter Large Language Models (LLMs) for phishing detection. Through experiments on a curated dataset, we show that while LLMs currently underperform compared to ML and DL methods in terms of raw ac…

This https://arxiv.org/abs/2502.07404 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Human-in-the-Loop Annotation for Image-Based Engagement Estimation: Assessing the Impact of Model Reliability on Annotation Accuracy
Human-in-the-loop (HITL) frameworks are increasingly recognized for their potential to improve annotation accuracy in emotion estimation systems by combining machine predictions with human expertise. This study focuses on integrating a high-performing image-based emotion model into a HITL annotation framework to evaluate the collaborative potential of human-machine interaction and identify the psychological and practical factors critical to successful collaboration. Specifically, we investigate…

A Generalized $\ell_1$-Merit Function SQP Method Using Function Approximations with Tunable Accuracy
Dane S. Grundvig, Matthias Heinkenschloss
https://arxiv.org/abs/2507.06199

A Generalized $\ell_1$-Merit Function SQP Method Using Function Approximations with Tunable Accuracy
This paper develops a generalization of the line-search sequential quadratic programming (SQP) algorithm with $\ell_1$-merit function that uses objective and constraint function approximations with tunable accuracy to solve smooth equality-constrained optimization problems. The evaluation of objective and constraint functions and their gradients is potentially computationally expensive, but it is assumed that one can construct effective, computationally inexpensive models of these functions. Th…

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Xiangchen Li, Dimitrios Spatharakis, Saeid Ghafouri, Jiakun Fan, Dimitrios Nikolopoulos
https://arxiv.org/abs/2506.09397

SLED: A Speculative LLM Decoding Framework for Efficient Edge Serving
Regardless the advancements in device capabilities, efficient inferencing advanced large language models (LLMs) at the edge remains challenging due to limited device memory and power constraints. Existing strategies, such as aggressive quantization, pruning, or remote inference, trade accuracy for efficiency or lead to substantial cost burdens. This position paper introduces a new approach that leverages speculative decoding, previously viewed primarily as a decoding acceleration technique for …

Where Journalism Silenced Voices: Exploring Discrimination in the Representation of Indigenous Communities in Bangladesh
Abhijit Paul, Adity Khisa, Zarif Masud, Sharif Md. Abdullah, Ahmedul Kabir, Shebuti Rayana
https://arxiv.org/abs/2506.09771

Where Journalism Silenced Voices: Exploring Discrimination in the Representation of Indigenous Communities in Bangladesh
In this paper, we examine the intersections of indigeneity and media representation in shaping perceptions of indigenous communities in Bangladesh. Using a mixed-methods approach, we combine quantitative analysis of media data with qualitative insights from focus group discussions (FGD). First, we identify a total of 4,893 indigenous-related articles from our initial dataset of 2.2 million newspaper articles, using a combination of keyword-based filtering and LLM, achieving 77% accuracy and an …

Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
Daniel H. Pak, Shubh Thaker, Kyle Baylous, Xiaoran Zhang, Danny Bluestein, James S. Duncan
https://arxiv.org/abs/2506.08280

Snap-and-tune: combining deep learning and test-time optimization for high-fidelity cardiovascular volumetric meshing
High-quality volumetric meshing from medical images is a key bottleneck for physics-based simulations in personalized medicine. For volumetric meshing of complex medical structures, recent studies have often utilized deep learning (DL)-based template deformation approaches to enable fast test-time generation with high spatial accuracy. However, these approaches still exhibit limitations, such as limited flexibility at high-curvature areas and unrealistic inter-part distances. In this study, we …

Exploring the Eclipsing Binary System IY Aur: First Photometric Insights
N. Alan, M. Alpsoy, Y. Kilic
https://arxiv.org/abs/2506.09158 https://

Exploring the Eclipsing Binary System IY Aur: First Photometric Insights
Eclipsing binary systems play a vital role in astrophysics, as they provide a direct means of measuring fundamental stellar parameters. By combining high-precision space-based observations with ground-based multicolor photometric data, these parameters can be determined with greater accuracy. In this study, we present the first photometric analysis of the IY Aur eclipsing binary system, using a combination of the Transiting Exoplanet Survey Satellite (TESS) light curve and new UBVRI CCD observa…

Large Language Models Facilitate Vision Reflection in Image Classification
Guoyuan An, JaeYoon Kim, SungEui Yoon
https://arxiv.org/abs/2508.06525 https://a…

Large Language Models Facilitate Vision Reflection in Image Classification
This paper presents several novel findings on the explainability of vision reflection in large multimodal models (LMMs). First, we show that prompting an LMM to verify the prediction of a specialized vision model can improve recognition accuracy, even on benchmarks like ImageNet, despite prior evidence that LMMs typically underperform dedicated vision encoders. Second, we analyze the internal behavior of vision reflection and find that the vision-language connector maps visual features into exp…

Robust Power System State Estimation using Physics-Informed Neural Networks
Solon Falas, Markos Asprou, Charalambos Konstantinou, Maria K. Michael
https://arxiv.org/abs/2507.05874

Robust Power System State Estimation using Physics-Informed Neural Networks
Modern power systems face significant challenges in state estimation and real-time monitoring, particularly regarding response speed and accuracy under faulty conditions or cyber-attacks. This paper proposes a hybrid approach using physics-informed neural networks (PINNs) to enhance the accuracy and robustness, of power system state estimation. By embedding physical laws into the neural network architecture, PINNs improve estimation accuracy for transmission grid applications under both normal …

Estimating prevalence with precision and accuracy
Aime Bienfait Igiraneza, Christophe Fraser, Robert Hinch
https://arxiv.org/abs/2507.06061 https://…

Estimating prevalence with precision and accuracy
Unlike classification, whose goal is to estimate the class of each data point in a dataset, prevalence estimation or quantification is a task that aims to estimate the distribution of classes in a dataset. The two main tasks in prevalence estimation are to adjust for bias, due to the prevalence in the training dataset, and to quantify the uncertainty in the estimate. The standard methods used to quantify uncertainty in prevalence estimates are bootstrapping and Bayesian quantification methods. …

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

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

Drell--Yan lepton pair production at low invariant masses: transverse-momentum resummation and non-perturbative effects in QCD
Stefano Camarda, Giancarlo Ferrera, Lorenzo Rossi
https://arxiv.org/abs/2508.06201

Drell--Yan lepton pair production at low invariant masses: transverse-momentum resummation and non-perturbative effects in QCD
We consider the transverse-momentum ($q_T$) distribution of Drell-Yan lepton pairs produced with invariant masses ($M$) from low values up to the $Z$-boson peak ($4\leq M \leq 116$~GeV). We present perturbative predictions obtained by consistently combining the resummation of logarithmically enhanced QCD corrections at small $q_T$ ($q_T \ll M$) up to next-to-next-to-next-to-next-to-leading logarithmic accuracy with the available fixed-order calculations at next-to-next-to-leading order (i.e. $\…

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Sahil Bansal, Sai Shruthi Sistla, Aarti Arikatala, Sebastian Schreiber
https://arxiv.org/abs/2508.05888

Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Effective tool retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user queries. Despite its central role in planning, this aspect remains underexplored in the literature. Traditional approaches rely primarily on similarities between user queries and tool descriptions, which significantly limits retrieval accuracy, specifically when handling multi-step user requests. To address these limitations, we propose a …

Enhancing air-shower observations: Results from an IceCube Surface Array Prototype Station
Megha Venugopal (for the IceCube Collaboration)
https://arxiv.org/abs/2506.09806

Enhancing air-shower observations: Results from an IceCube Surface Array Prototype Station
The Surface Array Enhancement of the IceCube Neutrino Observatory is set to equip the existing surface cosmic-ray array of ice-Cherenkov detectors, IceTop, with radio antennas and scintillation detectors. This can lower the energy threshold of detection and increase the measurement accuracy of IceTop. The antenna readout uses a multiplicity trigger from the scintillation detectors. A fully functioning prototype station of the enhancement was deployed at the South Pole in 2020 and upgraded in 20…

CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
Huong Van Le, Weibin Ren, Junhong Kim, Yukyung Yun, Young Bin Park, Young Jun Kim, Bok Kyung Han, Inho Choi, Jong IL Park, Hwi-Yeol Yun, Jae-Mun Choi
https://arxiv.org/abs/2506.08059

CaliciBoost: Performance-Driven Evaluation of Molecular Representations for Caco-2 Permeability Prediction
Caco-2 permeability serves as a critical in vitro indicator for predicting the oral absorption of drug candidates during early-stage drug discovery. To enhance the accuracy and efficiency of computational predictions, we systematically investigated the impact of eight molecular feature representation types including 2D/3D descriptors, structural fingerprints, and deep learning-based embeddings combined with automated machine learning techniques to predict Caco-2 permeability. Using two datasets…

EcBot: Data-Driven Energy Consumption Open-Source MATLAB Library for Manipulators
Juan Heredia, Christian Schlette, Mikkel Baun Kj{\ae}rgaard
https://arxiv.org/abs/2508.06276 ht…

EcBot: Data-Driven Energy Consumption Open-Source MATLAB Library for Manipulators
Existing literature proposes models for estimating the electrical power of manipulators, yet two primary limitations prevail. First, most models are predominantly tested using traditional industrial robots. Second, these models often lack accuracy. To address these issues, we introduce an open source Matlab-based library designed to automatically generate \ac{ec} models for manipulators. The necessary inputs for the library are Denavit-Hartenberg parameters, link masses, and centers of mass. Ad…

Effects of One-particle Reduced Density Matrix Optimization in Variational Quantum Eigensolvers
Amanda Marques de Lima, Erico Souza Teixeira, Eivson Darlivam Rodrigues de Aguiar Silva, Ricardo Luiz Longo
https://arxiv.org/abs/2507.07667

Effects of One-particle Reduced Density Matrix Optimization in Variational Quantum Eigensolvers
The variational quantum eigensolver (VQE) is a promising method for simulating molecular systems on near-term quantum computers. This approach employs energy estimation; however, other relevant molecular properties can be extracted from the one-particle reduced density matrix (1-RDM) generated by VQE. The accuracy of these properties strongly depends on the reliability and convergence of the 1-RDM, which is not guaranteed by energy-only optimization. Thus, we investigate the effect of optimizin…

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

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

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

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

A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery
Ch Muhammad Awais, Marco Reggiannini, Davide Moroni, Oktay Karakus
https://arxiv.org/abs/2508.06407

A Classification-Aware Super-Resolution Framework for Ship Targets in SAR Imagery
High-resolution imagery plays a critical role in improving the performance of visual recognition tasks such as classification, detection, and segmentation. In many domains, including remote sensing and surveillance, low-resolution images can limit the accuracy of automated analysis. To address this, super-resolution (SR) techniques have been widely adopted to attempt to reconstruct high-resolution images from low-resolution inputs. Related traditional approaches focus solely on enhancing image …

ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs
Dhruv Parikh, Viktor Prasanna
https://arxiv.org/abs/2506.09282

ScalableHD: Scalable and High-Throughput Hyperdimensional Computing Inference on Multi-Core CPUs
Hyperdimensional Computing (HDC) is a brain-inspired computing paradigm that represents and manipulates information using high-dimensional vectors, called hypervectors (HV). Traditional HDC methods, while robust to noise and inherently parallel, rely on single-pass, non-parametric training and often suffer from low accuracy. To address this, recent approaches adopt iterative training of base and class HVs, typically accelerated on GPUs. Inference, however, remains lightweight and well-suited fo…

FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction
Sichen Zhao, Wei Shao, Jeffrey Chan, Ziqi Xu, Flora Salim
https://arxiv.org/abs/2508.07518

FairDRL-ST: Disentangled Representation Learning for Fair Spatio-Temporal Mobility Prediction
As deep spatio-temporal neural networks are increasingly utilised in urban computing contexts, the deployment of such methods can have a direct impact on users of critical urban infrastructure, such as public transport, emergency services, and traffic management systems. While many spatio-temporal methods focus on improving accuracy, fairness has recently gained attention due to growing evidence that biased predictions in spatio-temporal applications can disproportionately disadvantage certain …

DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
Donglian Li, Hui Guo, Minglang Chen, Huizhen Chen, Jialing Chen, Bocheng Liang, Pengchen Liang, Ying Tan
https://arxiv.org/abs/2506.08534

DCD: A Semantic Segmentation Model for Fetal Ultrasound Four-Chamber View
Accurate segmentation of anatomical structures in the apical four-chamber (A4C) view of fetal echocardiography is essential for early diagnosis and prenatal evaluation of congenital heart disease (CHD). However, precise segmentation remains challenging due to ultrasound artifacts, speckle noise, anatomical variability, and boundary ambiguity across different gestational stages. To reduce the workload of sonographers and enhance segmentation accuracy, we propose DCD, an advanced deep learning-ba…

The VMC Survey -- LIV. Anomalous Cepheids in the Magellanic Clouds Period-Luminosity relations in the near-infrared bands
Teresa Sicignano, Vincenzo Ripepi, Marina Rejkuba, Martino Romaniello, Marcella Marconi, Roberto Molinaro, Anupam Bhardwaj, Giulia De Somma, Maria-Rosa Cioni, Felice Cusano, Gisella Clementini, Richard de Grijs, Valentin Ivanov, Jesper Storm, Martin Groenewegen

The VMC Survey -- LIV. Anomalous Cepheids in the Magellanic Clouds Period-Luminosity relations in the near-infrared bands
Anomalous Cepheids (ACs) are less studied metal-poor pulsating stars ([Fe/H]<-1.5) compared to Classical Cepheids (CCs) and RR Lyrae stars. They follow distinct Period-Luminosity (PL) and Period-Wesenheit (PW) relations and pulsate in either the fundamental (F) or first overtone (1O) mode. Our goal is to assess the precision and accuracy of AC-based distances and evaluate their potential for establishing an independent distance scale. We derive new PL and PW relations for F-mode, 1O-mode, and, …

R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
Maurice Brunner, Edoardo Ghignone, Nicolas Baumann, Michele Magno
https://arxiv.org/abs/2506.09629

R-CARLA: High-Fidelity Sensor Simulations with Interchangeable Dynamics for Autonomous Racing
Autonomous racing has emerged as a crucial testbed for autonomous driving algorithms, necessitating a simulation environment for both vehicle dynamics and sensor behavior. Striking the right balance between vehicle dynamics and sensor accuracy is crucial for pushing vehicles to their performance limits. However, autonomous racing developers often face a trade-off between accurate vehicle dynamics and high-fidelity sensor simulations. This paper introduces R-CARLA, an enhancement of the CARLA si…

Classical simulation of noisy quantum circuits via locally entanglement-optimal unravelings
Simon Cichy, Paul K. Faehrmann, Lennart Bittel, Jens Eisert, Hakop Pashayan
https://arxiv.org/abs/2508.05745 …

Classical simulation of noisy quantum circuits via locally entanglement-optimal unravelings
Classical simulations of noisy quantum circuits is instrumental to our understanding of the behavior of real world quantum systems and the identification of regimes where one expects quantum advantage. In this work, we present a highly parallelizable tensor-network-based classical algorithm -- equipped with rigorous accuracy guarantees -- for simulating $n$-qubit quantum circuits with arbitrary single-qubit noise. Our algorithm represents the state of a noisy quantum system by a particular ense…

Non-uniform time-stepping in k-space pseudospectral time domain models of acoustic propagation
Matthew J. King, B. E. Treeby, B. T. Cox
https://arxiv.org/abs/2507.07635

Non-uniform time-stepping in k-space pseudospectral time domain models of acoustic propagation
Non-uniform time stepping in acoustic propagation models can be used to preserve accuracy or reduce computational cost for an acoustic simulation with a wave front propagating through a domain with both heterogeneous and homogenous regions, such as for a simulation of breast ultrasound tomography. The k-space correction already exist within the literature to remove numerical dispersion caused by the time stepping procedure in pseudo-spectral time domain models, but requires a uniform time step.…

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials
Kazutaka Nishiguchi, Ryota Yamamoto, Meguru Yamazaki, Naoki Matsumura, Yuta Yoshimoto, Seiichiro L. Ten-no, Yasufumi Sakai
https://arxiv.org/abs/2506.09372

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials
A precise analysis of point defects in solids requires accurate molecular dynamics (MD) simulations of large-scale systems. However, ab initio MD simulations based on density functional theory (DFT) incur high computational cost, while classical MD simulations lack accuracy. We perform MD simulations using a neural network potential (NNP) model (NNP-MD) to predict the physical quantities of both pristine SrTiO$_3$ and SrTiO$_3$ in the presence of oxygen vacancies (V$_{\text{O}}$). To verify the…

Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU
Petar Jaku\v{s}, Hrvoje D\v{z}apo
https://arxiv.org/abs/2506.08911 https:/…

Implementing Keyword Spotting on the MCUX947 Microcontroller with Integrated NPU
This paper presents a keyword spotting (KWS) system implemented on the NXP MCXN947 microcontroller with an integrated Neural Processing Unit (NPU), enabling real-time voice interaction on resource-constrained devices. The system combines MFCC feature extraction with a CNN classifier, optimized using Quantization Aware Training to reduce model size with minimal accuracy drop. Experimental results demonstrate a 59x speedup in inference time when leveraging the NPU compared to CPU-only execution, …

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Hariharan Ramesh, Jyotikrishna Dass
https://arxiv.org/abs/2506.09199

FLoRIST: Singular Value Thresholding for Efficient and Accurate Federated Fine-Tuning of Large Language Models
Integrating Low-Rank Adaptation (LoRA) into federated learning offers a promising solution for parameter-efficient fine-tuning of Large Language Models (LLMs) without sharing local data. However, several methods designed for federated LoRA present significant challenges in balancing communication efficiency, model accuracy, and computational cost, particularly among heterogeneous clients. These methods either rely on simplistic averaging of local adapters, which introduces aggregation noise, re…

Distributed Training under Packet Loss
Erez Weintraub, Ron Banner, Ariel Orda
https://arxiv.org/abs/2507.07114 https://arxiv.org/pdf/…

Distributed Training under Packet Loss
State-of-the-art language and vision models are routinely trained across thousands of GPUs, often spanning multiple data-centers, yet today's distributed frameworks still assume reliable connections (e.g., InfiniBand or RoCE). The resulting acknowledgment traffic and retransmissions inflate tail latencies and limit scalability. Leveraging unreliable connections will reduce latency but may sacrifice model accuracy and convergence once packets are dropped. A principled, end-to-end solution that p…

Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification
Mobarak Abumohsen, Enrique Costa-Montenegro, Silvia Garc\'ia-M\'endez, Amani Yousef Owda, Majdi Owda
https://arxiv.org/abs/2508.06287

Advanced Deep Learning Techniques for Accurate Lung Cancer Detection and Classification
Lung cancer (LC) ranks among the most frequently diagnosed cancers and is one of the most common causes of death for men and women worldwide. Computed Tomography (CT) images are the most preferred diagnosis method because of their low cost and their faster processing times. Many researchers have proposed various ways of identifying lung cancer using CT images. However, such techniques suffer from significant false positives, leading to low accuracy. The fundamental reason results from employing…

Attention-based Learning for 3D Informative Path Planning
Rui Zhao, Xingjian Zhang, Yuhong Cao, Yizhuo Wang, Guillaume Sartoretti
https://arxiv.org/abs/2506.08434

Attention-based Learning for 3D Informative Path Planning
In this work, we propose an attention-based deep reinforcement learning approach to address the adaptive informative path planning (IPP) problem in 3D space, where an aerial robot equipped with a downward-facing sensor must dynamically adjust its 3D position to balance sensing footprint and accuracy, and finally obtain a high-quality belief of an underlying field of interest over a given domain (e.g., presence of specific plants, hazardous gas, geological structures, etc.). In adaptive IPP task…

Quantum Annealing for the Set Splitting Problem
Sean Borneman
https://arxiv.org/abs/2508.06410 https://arxiv.org/pdf/2508.06410

Quantum Annealing for the Set Splitting Problem
I present a novel use of quantum annealing to solve the Set Splitting Problem using (QUBO) problem formulation. The contribution of the work is in formulating penalty functions that ensure the ground state of the QUBO Hamiltonian corresponds to valid solutions that split the input subsets. This approach scales linearly in terms of the number of logical qubits relative to problem size. Empirical tests of the proposed solution show convergence to globally optimal solutions, with high accuracy rat…

AI for the Routine, Humans for the Complex: Accuracy-Driven Data Labelling with Mixed Integer Linear Programming
Mohammad Hossein Amini, Mehrdad Sabetzadeh, Shiva Nejati
https://arxiv.org/abs/2507.04990

AI for the Routine, Humans for the Complex: Accuracy-Driven Data Labelling with Mixed Integer Linear Programming
The scarcity of accurately labelled data remains a major challenge in deep learning (DL). Many DL approaches rely on semi-supervised methods, which focus on constructing large datasets that require only a minimal amount of human-labelled data. Since DL training algorithms can tolerate moderate label noise, it has generally been acceptable for the accuracy of labels in large training datasets to fall well short of a perfect 100%. However, when it comes to testing DL models, achieving high label …

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
Tuo Zhang, Ning Li, Xin Yuan, Wenchao Xu, Quan Chen, Song Guo, Haijun Zhang
https://arxiv.org/abs/2508.07329

Efficient Edge LLMs Deployment via HessianAware Quantization and CPU GPU Collaborative
With the breakthrough progress of large language models (LLMs) in natural language processing and multimodal tasks, efficiently deploying them on resource-constrained edge devices has become a critical challenge. The Mixture of Experts (MoE) architecture enhances model capacity through sparse activation, but faces two major difficulties in practical deployment: (1) The presence of numerous outliers in activation distributions leads to severe degradation in quantization accuracy for both activat…

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
Gabriele Codega, Anna Ivagnes, Nicola Demo, Gianluigi Rozza
https://arxiv.org/abs/2506.09830

Machine Learning-based quadratic closures for non-intrusive Reduced Order Models
In the present work, we introduce a data-driven approach to enhance the accuracy of non-intrusive Reduced Order Models (ROMs). In particular, we focus on ROMs built using Proper Orthogonal Decomposition (POD) in an under-resolved and marginally-resolved regime, i.e. when the number of modes employed is not enough to capture the system dynamics. We propose a method to re-introduce the contribution of neglected modes through a quadratic correction term, given by the action of a quadratic operator…

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

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

A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation
Isha Puri, David Cox
https://arxiv.org/abs/2506.08183

A System for Accurate Tracking and Video Recordings of Rodent Eye Movements using Convolutional Neural Networks for Biomedical Image Segmentation
Research in neuroscience and vision science relies heavily on careful measurements of animal subject's gaze direction. Rodents are the most widely studied animal subjects for such research because of their economic advantage and hardiness. Recently, video based eye trackers that use image processing techniques have become a popular option for gaze tracking because they are easy to use and are completely noninvasive. Although significant progress has been made in improving the accuracy and robus…

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
Jiaqi Tang, Yu Xia, Yi-Feng Wu, Yuwei Hu, Yuhui Chen, Qing-Guo Chen, Xiaogang Xu, Xiangyu Wu, Hao Lu, Yanqing Ma, Shiyin Lu, Qifeng Chen
https://arxiv.org/abs/2506.09373

LPO: Towards Accurate GUI Agent Interaction via Location Preference Optimization
The advent of autonomous agents is transforming interactions with Graphical User Interfaces (GUIs) by employing natural language as a powerful intermediary. Despite the predominance of Supervised Fine-Tuning (SFT) methods in current GUI agents for achieving spatial localization, these methods face substantial challenges due to their limited capacity to accurately perceive positional data. Existing strategies, such as reinforcement learning, often fail to assess positional accuracy effectively, …

Spectral connvergece of random feature method in one dimension
Pingbing Ming, Hao Yu
https://arxiv.org/abs/2507.07371 https://arxiv.o…

Spectral connvergece of random feature method in one dimension
Among the various machine learning methods solving partial differential equations, the Random Feature Method (RFM) stands out due to its accuracy and efficiency. In this paper, we demonstrate that the approximation error of RFM exhibits spectral convergence when it is applied to the second-order elliptic equations in one dimension, provided that the solution belongs to Gevrey classes or Sobolev spaces. We highlight the significant impact of incorporating the Partition of Unity Method (PUM) to e…

Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Zhanhong Jiang, Dylan Shah, Hsin-Jung Yang, Soumik Sarkar
https://arxiv.org/abs/2507.07370

Data-driven Kinematic Modeling in Soft Robots: System Identification and Uncertainty Quantification
Precise kinematic modeling is critical in calibration and controller design for soft robots, yet remains a challenging issue due to their highly nonlinear and complex behaviors. To tackle the issue, numerous data-driven machine learning approaches have been proposed for modeling nonlinear dynamics. However, these models suffer from prediction uncertainty that can negatively affect modeling accuracy, and uncertainty quantification for kinematic modeling in soft robots is underexplored. In this w…

L2Calib: $SE(3)$-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience
Baorun Li, Chengrui Zhu, Siyi Du, Bingran Chen, Jie Ren, Wenfei Wang, Yong Liu, Jiajun Lv
https://arxiv.org/abs/2508.06330

L2Calib: $SE(3)$-Manifold Reinforcement Learning for Robust Extrinsic Calibration with Degenerate Motion Resilience
Extrinsic calibration is essential for multi-sensor fusion, existing methods rely on structured targets or fully-excited data, limiting real-world applicability. Online calibration further suffers from weak excitation, leading to unreliable estimates. To address these limitations, we propose a reinforcement learning (RL)-based extrinsic calibration framework that formulates extrinsic calibration as a decision-making problem, directly optimizes $SE(3)$ extrinsics to enhance odometry accuracy. Ou…

Multinode Shepard collocation method for pricing of financial derivatives
Francesco Dell'Accio, Filomena Di Tommaso, Elisa Francomano, Clara Lorenzi
https://arxiv.org/abs/2508.08023

Multinode Shepard collocation method for pricing of financial derivatives
This paper explores the use of the multinode Shepard method for the numerical solution of the two-dimensional Black-Scholes equation. The proposed approach integrates a spatial approximation via the multinode Shepard operator with a temporal discretization based on the Backward Difference Formula. Numerical experiments are presented to demonstrate the accuracy and effectiveness of the method.

2N-storage Runge-Kutta methods: c-reflection symmetry and factorization of the Butcher tableau
Alexei Bazavov
https://arxiv.org/abs/2506.08444 https://

2N-storage Runge-Kutta methods: c-reflection symmetry and factorization of the Butcher tableau
Low-storage Runge-Kutta schemes of Williamson's type, so-called 2N-storage schemes, are further examined as a follow-up to the recent work. It is found that the augmented Butcher tableau factorizes into a product of matrices with special properties. Those properties reveal that the 2N-storage methods of the order of global accuracy less than five possess a symmetry, called c-reflection symmetry, i.e. most methods exist in pairs. A transformation that relates the Butcher tableaux of the pairs is…

Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
Guglielmo Padula, Gianluigi Rozza
https://arxiv.org/abs/2506.09721 https…

Generative Models for Parameter Space Reduction applied to Reduced Order Modelling
Solving and optimising Partial Differential Equations (PDEs) in geometrically parameterised domains often requires iterative methods, leading to high computational and time complexities. One potential solution is to learn a direct mapping from the parameters to the PDE solution. Two prominent methods for this are Data-driven Non-Intrusive Reduced Order Models (DROMs) and Parametrised Physics Informed Neural Networks (PPINNs). However, their accuracy tends to degrade as the number of geometric p…

FNPF-SEM: A parallel spectral element model in Firedrake for fully nonlinear water wave simulations
Jens Visbech, Anders Melander, Allan Peter Engsig-Karup
https://arxiv.org/abs/2506.09435

FNPF-SEM: A parallel spectral element model in Firedrake for fully nonlinear water wave simulations
We present a new parallel spectral element solver, FNPF-SEM, for simulating linear and fully nonlinear potential flow-based water waves and their interaction with offshore structures. The tool is designed as a general-purpose wave model for offshore engineering applications. Built within the open-source framework Firedrake, the new FNPF-SEM model is designed as a computational tool capable of capturing both linear and nonlinear wave phenomena with high accuracy and efficiency, with support for …