Tootfinder

Universal Error Correction for Distributed Quantum Computing
Daowen Qiu, Ligang Xiao, Le Luo, Paulo Mateus
https://arxiv.org/abs/2508.15580 https://arxiv.o…

Universal Error Correction for Distributed Quantum Computing
In distributed quantum computing, the final solution of a problem is usually achieved by catenating these partial solutions resulted from different computing nodes, but intolerable errors likely yield in this catenation process. In this paper, we propose a universal error correction scheme to reduce errors and obtain effective solutions. Then, we apply this error correction scheme to designing a distributed phase estimation algorithm that presents a basic tool for studying distributed Shor's al…

Ring said on social media that strange login patterns were caused by a programming error, not hackers.
https://www.snopes.com/news/2025/07/21/ring-hacked-may-28-2025/

What to know about supposed Ring doorbell security breach in May 2025
Ring said on social media that strange login patterns were caused by a programming error, not hackers.

Correlations of error terms for weighted prime counting functions
Shubhrajit Bhattacharya, Greg Martin, Reginald M. Simpson
https://arxiv.org/abs/2507.13504

Correlations of error terms for weighted prime counting functions
Standard prime-number counting functions, such as $ψ(x)$, $θ(x)$, and $π(x)$, have error terms with limiting logarithmic distributions once suitably normalized. The same is true of weighted versions of those sums, like $π_r(x) = \sum_{p\le x} \frac1p$ and $π_\ell(x) = \sum_{p\le x} \log(1-\frac1p)^{-1}$, that were studied by Mertens. These limiting distributions are all identical, but passing to the limit loses information about how these error terms are correlated with one another. In t…

Successfully dodged my first dependency issue update snag. A good experience to have for others to come ->
Error updating cachyOS - General - CachyOS Forum
https://discuss.cachyos.org/t/error-updating-cachyos/15861
(It helped a lot that, thankfully, a par…

Error updating cachyOS
Hello, I get the following error when updating cachyOS: error: no se pudo preparar la operación (no se pudieron satisfacer las dependencias)

An Optimization-Based Framework for Solving Forward-Backward Stochastic Differential Equations: Convergence Analysis and Error Bounds
Yutian Wang, Yuan-Hua Ni, Xun Li
https://arxiv.org/abs/2507.15234

An Optimization-Based Framework for Solving Forward-Backward Stochastic Differential Equations: Convergence Analysis and Error Bounds
In this paper, we develop an optimization-based framework for solving coupled forward-backward stochastic differential equations. We introduce an integral-form objective function and prove its equivalence to the error between consecutive Picard iterates. Our convergence analysis establishes that minimizing this objective generates sequences that converge to the true solution. We provide explicit upper and lower bounds that relate the objective value to the error between trial and exact solution…

A priori error analysis of the proximal Galerkin method
Brendan Keith, Rami Masri, Marius Zeinhofer
https://arxiv.org/abs/2507.13516 https://

A priori error analysis of the proximal Galerkin method
The proximal Galerkin (PG) method is a finite element method for solving variational problems with inequality constraints. It has several advantages, including constraint-preserving approximations and mesh independence. This paper presents the first abstract a priori error analysis of PG methods, providing a general framework to establish convergence and error estimates. As applications of the framework, we demonstrate optimal convergence rates for both the obstacle and Signorini problems using…

Error Correcting Codes for Segmented Burst-Deletion Channels
Yajuan Liu, Tolga M. Duman
https://arxiv.org/abs/2507.14070 https://arxi…

Error Correcting Codes for Segmented Burst-Deletion Channels
We study segmented burst-deletion channels motivated by the observation that synchronization errors commonly occur in a bursty manner in real-world settings. In this channel model, transmitted sequences are implicitly divided into non-overlapping segments, each of which may experience at most one burst of deletions. In this paper, we develop error correction codes for segmented burst-deletion channels over arbitrary alphabets under the assumption that each segment may contain only one burst of …

Today I learned that regular expressions have a size limit and you get a “regular expression too large” error if you go over it.
Still trying to find some definitive documentation on what that size limit is in v8/latest Node.js.
#web #dev

An adaptive procedure for detecting replicated signals with $k$-family-wise error rate control
Ninh Tran
https://arxiv.org/abs/2508.15363 https://arxiv.org…

An adaptive procedure for detecting replicated signals with $k$-family-wise error rate control
Partial conjunction (PC) hypothesis testing is widely used to assess the replicability of scientific findings across multiple comparable studies. In high-throughput meta-analyses, testing a large number of PC hypotheses with k-family-wise error rate (k-FWER) control often suffers from low statistical power due to the multiplicity burden. The state-of-the-art AdaFilter-Bon procedure by Wang et al. (2022, Ann. Stat., 50(4), 1890-1909) alleviates this problem by filtering out hypotheses unlikely t…

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

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

Invariant Generation for Floating-Point Programs via Constraint Solving
Xuran Cai, Liqian Chen, Hongfei Fu
https://arxiv.org/abs/2507.15017 https://…

Invariant Generation for Floating-Point Programs via Constraint Solving
In numeric-intensive computations, it is well known that the execution of floating-point programs is imprecise as floating point arithmetics (e.g., addition, subtraction, multiplication, division, etc.) incurs rounding errors. Albeit the rounding error is small for every single floating-point operation, the aggregation of such error in multiple operations may be dramatic and cause catastrophic program failures. Therefore, to ensure the correctness of floating-point programs, the effect of float…

Improving VQE Parameter Quality on Noisy Quantum Processors with Cost-Effective Readout Error Mitigation
Nacer Eddine Belaloui, Abdellah Tounsi, Abdelmouheymen Rabah Khamadja, Hamza Benkadour, Mohamed Messaoud Louamri, Achour Benslama, Mohamed Taha Rouabah
https://arxiv.org/abs/2508.15072

Improving VQE Parameter Quality on Noisy Quantum Processors with Cost-Effective Readout Error Mitigation
The inherent noise in current Noisy Intermediate-Scale Quantum (NISQ) devices presents a major obstacle to the accurate implementation of quantum algorithms such as the Variational Quantum Eigensolver (VQE) for quantum chemistry applications. This study examines the impact of error mitigation strategies on VQE performance. We show that, for small molecular systems, an older-generation 5-qubit quantum processing unit (IBMQ Belem), when combined with optimized Twirled Readout Error Extinction (T-…

FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models
Yufan Song, Jiatao Zhang, Zeng Gu, Qingmiao Liang, Tuocheng Hu, Wei Song, Shiqiang Zhu
https://arxiv.org/abs/2507.14975

FCRF: Flexible Constructivism Reflection for Long-Horizon Robotic Task Planning with Large Language Models
Autonomous error correction is critical for domestic robots to achieve reliable execution of complex long-horizon tasks. Prior work has explored self-reflection in Large Language Models (LLMs) for task planning error correction; however, existing methods are constrained by inflexible self-reflection mechanisms that limit their effectiveness. Motivated by these limitations and inspired by human cognitive adaptation, we propose the Flexible Constructivism Reflection Framework (FCRF), a novel Ment…

“maybe we should print a better error than 500 - internal server error"
YEAH, MAYBE

@… ah my Windows machine is packed up at the moment due to some flooding in my basement but my hunch is that you need to use Windows directory separators \\ for that path to the eleventy bin — if that isn’t it, it might be a deeper bug outside of my control — I don’t think that error message is 11ty code 😬

A paperwork mistake by a South Florida dealership
led to a U.S. Coast Guard officer being handcuffed at gunpoint
and detained for hours
on suspicion of stealing a pickup he had bought and financed there 10 days earlier,
according to his lawsuit against the store
https://www.

Dealership paperwork error leads to customer handcuffed at gunpoint, accused of stealing pickup he just bought
A Doral Volkswagen customer is suing the dealership after the store made a clerical error leading to the man's detainment by police.

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
John Alderete, Macarious Kin Fung Hui, Aanchan Mohan
https://arxiv.org/abs/2508.13060

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
The Simon Fraser University Speech Error Database (SFUSED) is a public data collection developed for linguistic and psycholinguistic research. Here we demonstrate how its design and annotations can be used to test and evaluate speech recognition models. The database comprises systematically annotated speech errors from spontaneous English speech, with each error tagged for intended and actual error productions. The annotation schema incorporates multiple classificatory dimensions that are of so…

If you are using "docker:latest" in your CI pipeline, you may find your builds broken with a weird "failed to find target default" error.
In that case, switching to "docker:28.3.1" will fit this.
Issue on GitHub: https://github.com/docker/buildx/issues/3328…

As many of you may have already noticed when you try to update your Arch-based distros, you will receive an error mentioning that linux-firmware-nvidia "exists in the filesystem".
To fix this you should run `sudo pacman -Rdd linux-firmware` and then `sudo pacman -Syu linux-firmware`.
h…

GOP wants to cut waste. Critics say SNAP exemption could do opposite. (Mariana Alfaro/Washington Post)
https://www.washingtonpost.com/politics/2025/07/20/snap-tax-law-error-rate/
http://www.memeorandum.com/250720/p7#a250720p7

$\textit{A Priori}$ Error Analysis for the $p$-Stokes Equations with Slip Boundary Conditions: A Discrete Leray Projection Framework
Alex Kaltenbach, J\"orn Wichmann
https://arxiv.org/abs/2507.15016

$\textit{A Priori}$ Error Analysis for the $p$-Stokes Equations with Slip Boundary Conditions: A Discrete Leray Projection Framework
We present an $\textit{a priori}$ error analysis for the kinematic pressure in a fully-discrete finite-differences/-elements discretization of the unsteady $p$-Stokes equations, modelling non-Newtonian fluids. This system is subject to both impermeability and perfect Navier slip boundary conditions, which are incorporated either weakly via Lagrange multipliers or strongly in the discrete velocity space. A central aspect of the $\textit{a priori}$ error analysis is the discrete Leray projection,…

Tighter Lower Bounds for Single Source Personalized PageRank
Xinpeng Jiang, Haoyu Liu, Siqiang Luo, Xiaokui Xiao
https://arxiv.org/abs/2507.14462 https://

Tighter Lower Bounds for Single Source Personalized PageRank
We study lower bounds for approximating the Single Source Personalized PageRank (SSPPR) query, which measures the probability distribution of an $α$-decay random walk starting from a source node $s$. Existing lower bounds remain loose-$Ω\left(\min(m, 1/δ)\right)$ for relative error (SSPPR-R) and $Ω\left(\min(n, 1/ε)\right)$ for additive error (SSPPR-A). To close this gap, we establish tighter bounds for both settings. For SSPPR-R, we show a lower bound of $Ω\left(\min\left(m, \frac{\log(1…

Towards Accurate Phonetic Error Detection Through Phoneme Similarity Modeling
Xuanru Zhou, Jiachen Lian, Cheol Jun Cho, Tejas Prabhune, Shuhe Li, William Li, Rodrigo Ortiz, Zoe Ezzes, Jet Vonk, Brittany Morin, Rian Bogley, Lisa Wauters, Zachary Miller, Maria Gorno-Tempini, Gopala Anumanchipalli
https://arxiv.org/abs/2507.14346…

Towards Accurate Phonetic Error Detection Through Phoneme Similarity Modeling
Phonetic error detection, a core subtask of automatic pronunciation assessment, identifies pronunciation deviations at the phoneme level. Speech variability from accents and dysfluencies challenges accurate phoneme recognition, with current models failing to capture these discrepancies effectively. We propose a verbatim phoneme recognition framework using multi-task training with novel phoneme similarity modeling that transcribes what speakers actually say rather than what they're supposed to s…

On the connection between zero-free regions and the error term in the Prime Number Theorem
Frederik Broucke
https://arxiv.org/abs/2507.13780 https://

On the connection between zero-free regions and the error term in the Prime Number Theorem
We provide for a wide class of zero-free regions an upper bound for the error term in the Prime Number Theorem, refining works of Pintz (1980), Johnston (2024), and Révész (2024). Our method does not only apply to the Riemann zeta function, but to general Beurling zeta functions. Next we construct Beurling zeta functions having infinitely many zeros on a prescribed contour, and none to the right, for a wide class of such contours. We also deduce an oscillation result for the corresponding err…

Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models
Facundo Arga\~naraz, Juan Carlos Escanciano
https://arxiv.org/abs/2507.13788

Debiased Machine Learning for Unobserved Heterogeneity: High-Dimensional Panels and Measurement Error Models
Developing robust inference for models with nonparametric Unobserved Heterogeneity (UH) is both important and challenging. We propose novel Debiased Machine Learning (DML) procedures for valid inference on functionals of UH, allowing for partial identification of multivariate target and high-dimensional nuisance parameters. Our main contribution is a full characterization of all relevant Neyman-orthogonal moments in models with nonparametric UH, where relevance means informativeness about the p…

Interval Estimation for Bounded Jacobian Nonlinear Systems by Zonotope Analysis
Chi Xu, Zhenhua Wang, Nacim Meslem, Tarek Raissi, Yacine Chitour
https://arxiv.org/abs/2509.16000

Interval Estimation for Bounded Jacobian Nonlinear Systems by Zonotope Analysis
This paper introduces an interval state estimation method for discrete-time bounded Jacobian nonlinear systems allying Luenberger-like observer with zonotope set computation. First, a robust observer is designed to obtain bounded-error and point-estimation with a peak-to-peak performance. This allows one to cope with the unknown but bounded process disturbance and measurement noise. Then, based on the stable dynamics of the observation error, tight interval estimation is obtained by applying zo…

Some backend webserver got updated, and it broke the nginx reverse proxy giving an SNI error. "proxy_ssl_server_name on;" was the fix.

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[4/4]:
- Poisson Midpoint Method for Log Concave Sampling: Beyond the Strong Error Lower Bounds
Rishikesh Srinivasan, Dheeraj Nagaraj

Error exponents for tripartite-to-bipartite entanglement transformations
P\'eter Vrana
https://arxiv.org/abs/2507.13778 https://a…

Error exponents for tripartite-to-bipartite entanglement transformations
We consider distillation of ebits between a specified pair of subsystems from pure tripartite states by local operations and classical communication. It is known that, allowing an asymptotically vanishing error, the maximal rate is the minimum of the von Neumann entropies of the two corresponding marginals, and under asymptotic stochastic local operations and classical communication the maximal rate is given by a minimization over a one-parameter family of entanglement measures. In this paper, …

is everyone doing #FediCircles now? fair enough, I suppose.
https://data.natty.sh/fedi-circles/
(for the record, this isn't super accurate IMO :P)

is everyone doing #FediCircles now? fair enough, I suppose.
https://data.natty.sh/fedi-circles/
(for the record, this isn't super accurate IMO :P)

Harnessing the Full Potential of RRAMs through Scalable and Distributed In-Memory Computing with Integrated Error Correction
Huynh Q. N. Vo, Md Tawsif Rahman Chowdhury, Paritosh Ramanan, Murat Yildirim, Gozde Tutuncuoglu
https://arxiv.org/abs/2508.13298

Harnessing the Full Potential of RRAMs through Scalable and Distributed In-Memory Computing with Integrated Error Correction
Exponential growth in global computing demand is exacerbated due to the higher-energy requirements of conventional architectures, primarily due to energy-intensive data movement. In-memory computing with Resistive Random Access Memory (RRAM) addresses this by co-integrating memory and processing, but faces significant hurdles related to device-level non-idealities and poor scalability for large computing tasks. Here, we introduce \textbf{MELISO+} (In-\textbf{Me}mory \textbf{Li}near \textbf{So}l…

Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI
Hannah Selder, Florian Fischer, Per Ola Kristensson, Arthur Fleig
https://arxiv.org/abs/2508.15727

Demystifying Reward Design in Reinforcement Learning for Upper Extremity Interaction: Practical Guidelines for Biomechanical Simulations in HCI
Designing effective reward functions is critical for reinforcement learning-based biomechanical simulations, yet HCI researchers and practitioners often waste (computation) time with unintuitive trial-and-error tuning. This paper demystifies reward function design by systematically analyzing the impact of effort minimization, task completion bonuses, and target proximity incentives on typical HCI tasks such as pointing, tracking, and choice reaction. We show that proximity incentives are essent…

Hot Topics and Common Challenges: an Empirical Study of React Discussions on Stack Overflow
Yusuf Sulistyo Nugroho, Ganno Tribuana Kurniaji, Syful Islam, Mohammed Humayun Kabir, Vanesya Aura Ardity, Md. Kamal Uddin
https://arxiv.org/abs/2507.15624

Hot Topics and Common Challenges: an Empirical Study of React Discussions on Stack Overflow
React is a JavaScript library used to build user interfaces for single-page applications. Although recent studies have shown the popularity and advantages of React in web development, the specific challenges users face remain unknown. Thus, this study aims to analyse the React-related questions shared on Stack Overflow. The study utilizes an exploratory data analysis to investigate the most frequently discussed keywords, error classification, and user reputation-based errors, which is the novel…

Soft Error Probability Estimation of Nano-scale Combinational Circuits
Ali Jockar, Mohsen Raji
https://arxiv.org/abs/2508.12345 https://arxiv.org/pdf/2508.…

Soft Error Probability Estimation of Nano-scale Combinational Circuits
As technology scales, nano-scale digital circuits face heightened susceptibility to single event upsets (SEUs) and transients (SETs) due to shrinking feature sizes and reduced operating voltages. While logical, electrical, and timing masking effects influence soft error probability (SEP), the combined impact of process variation (PV) and aging-induced degradation further complicates SEP estimation. Existing approaches often address PV or aging in isolation, or rely on computationally intensive …

Reconstruction Codes for Deletions and Insertions: Connection, Distinction, and Construction
Yubo Sun, Gennian Ge
https://arxiv.org/abs/2508.14386 https://…

Reconstruction Codes for Deletions and Insertions: Connection, Distinction, and Construction
Let $\mathcal{B}(\cdot)$ be an error ball function. A set of $q$-ary sequences of length $n$ is referred to as an \emph{$(n,q,N;\mathcal{B})$-reconstruction code} if each sequence $\boldsymbol{x}$ within this set can be uniquely reconstructed from any $N$ distinct elements within its error ball $\mathcal{B}(\boldsymbol{x})$. The main objective in this area is to determine or establish bounds for the minimum redundancy of $(n,q,N;\mathcal{B})$-reconstruction codes, denoted by $ρ(n,q,N;\mathcal{…

OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Models
Ningyong Wu, Jinzhi Wang, Wenhong Zhao, Chenzhan Yu, Zhigang Xiu, Duwei Dai
https://arxiv.org/abs/2507.13993

OrthoInsight: Rib Fracture Diagnosis and Report Generation Based on Multi-Modal Large Models
The growing volume of medical imaging data has increased the need for automated diagnostic tools, especially for musculoskeletal injuries like rib fractures, commonly detected via CT scans. Manual interpretation is time-consuming and error-prone. We propose OrthoInsight, a multi-modal deep learning framework for rib fracture diagnosis and report generation. It integrates a YOLOv9 model for fracture detection, a medical knowledge graph for retrieving clinical context, and a fine-tuned LLaVA lang…

Mom and child detained over visa error released from US facility - Salon.com
https://www.salon.com/2025/08/16/mom-and-child-detained-over-visa-error-released-from-us-facility/

Error Estimation for Adaptive Mesh Refinement in Droplet Simulations
Darsh Nathawani, Matthew Knepley
https://arxiv.org/abs/2508.15081 https://arxiv.org/pd…

Error Estimation for Adaptive Mesh Refinement in Droplet Simulations
We present a one-dimensional shear force driven droplet formation model with a flux-based error estimation. The presented model is derived using asymptotic expansion and a front-tracking method to simulate the droplet interface. The model is then discretized using the Galerkin finite element method in the mixed form. However, the jumps in the solution gradients are discontinuous and can grow faster due to the highly convective pinch-off process. This leads to an erroneous droplet interface and …

OddEEC: A New Sketch Technique for Error Estimating Coding
Huayi Wang, Jingfan Meng, Jun Xu
https://arxiv.org/abs/2508.11842 https://arxiv.org/pdf/2508.118…

OddEEC: A New Sketch Technique for Error Estimating Coding
Error estimating coding (EEC) is a standard technique for estimating the number of bit errors during packet transmission over wireless networks. In this paper, we propose OddEEC, a novel EEC scheme. OddEEC is a nontrivial adaptation of a data sketching technique named Odd Sketch to EEC, addressing new challenges therein by its bit sampling technique and maximum likelihood estimator. Our experiments show that OddEEC overall achieves comparable estimation accuracy as competing schemes such as gEE…

AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Zhongjun Ding, Yin Lin, Tianjing Zeng
https://arxiv.org/abs/2508.15276 https://arxi…

AmbiSQL: Interactive Ambiguity Detection and Resolution for Text-to-SQL
Text-to-SQL systems translate natural language questions into SQL queries, providing substantial value for non-expert users. While large language models (LLMs) show promising results for this task, they remain error-prone. Query ambiguity has been recognized as a major obstacle for LLM-based Text-to-SQL systems, leading to misinterpretation of user intent and inaccurate SQL generation. We demonstrate AmbiSQL, an interactive system that automatically detects query ambiguities and guides users th…

KoMbine: Propagating Statistical and Systematic Errors to Kaplan--Meier Curves
Jeffrey Roskes
https://arxiv.org/abs/2509.15371 https://arxiv.org/pdf/2509.1…

KoMbine: Propagating Statistical and Systematic Errors to Kaplan--Meier Curves
Kaplan--Meier curves are widely used in medical research to evaluate the performance of biomarkers and predict patient outcomes. These curves are often shown without error bands, and even when error bands are provided, they typically only account for the statistical uncertainty resulting from the finite number of patients in the study. In reality, other sources of uncertainty affect the measurements as well. As datasets grow, the statistical uncertainty on the number of patients no longer domin…

Evaluating Identity Leakage in Speaker De-Identification Systems
Seungmin Seo, Oleg Aulov, Afzal Godil, Kevin Mangold
https://arxiv.org/abs/2508.14012 https://

Evaluating Identity Leakage in Speaker De-Identification Systems
Speaker de-identification aims to conceal a speaker's identity while preserving intelligibility of the underlying speech. We introduce a benchmark that quantifies residual identity leakage with three complementary error rates: equal error rate, cumulative match characteristic hit rate, and embedding-space similarity measured via canonical correlation analysis and Procrustes analysis. Evaluation results reveal that all state-of-the-art speaker de-identification systems leak identity information.…

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime
Hongzhi Zhu, Wei Xu, Xiaohu You
https://arxiv.org/abs/2507.14951 https:/…

Latent-attention Based Transformer for Near ML Polar Decoding in Short-code Regime
Transformer architectures have emerged as promising deep learning (DL) tools for modeling complex sequence-to-sequence interactions in channel decoding. However, current transformer-based decoders for error correction codes (ECCs) demonstrate inferior performance and generalization capabilities compared to conventional algebraic decoders, especially in short-code regimes. In this work, we propose a novel latent-attention based transformer (LAT) decoder for polar codes that addresses the limitat…

Fractional-order controller tuning via minimization of integral of time-weighted absolute error without multiple closed-loop tests
Ansei Yonezawa, Heisei Yonezawa, Shuichi Yahagi, Itsuro Kajiwara, Shinya Kijimoto
https://arxiv.org/abs/2507.12987

Fractional-order controller tuning via minimization of integral of time-weighted absolute error without multiple closed-loop tests
This study presents a non-iterative tuning technique for a linear fractional-order (FO) controller, based on the integral of the time-weighted absolute error (ITAE) criterion. Minimizing the ITAE is a traditional approach for tuning FO controllers. This technique reduces the over/undershoot and suppresses the steady-state error. In contrast to conventional approaches of ITAE-based controller tuning, the proposed approach does not require multiple closed-loop experiments or model-based simulatio…

Local limit theorems on relatively hyperbolic groups with respect to virtually nilpotent subgroups
Matthieu Dussaule
https://arxiv.org/abs/2507.15408 https…

Local limit theorems on relatively hyperbolic groups with respect to virtually nilpotent subgroups
Given a probability measure on a finitely generated group, the local limit problem consists in finding asymptotics of $p_n(e,e)$, the probability that the random walk at time $n$ is at the origin. We give the classification of all possible local limit theorems, up to bounded error, for finitely supported, symmetric, admissible probability measures on a relatively hyperbolic group with respect to virtually nilpotent subgroups.

Sublinear-Time Approximation for Graph Frequency Vectors in Hyperfinite Graphs
Gregory Moroie
https://arxiv.org/abs/2508.14324 https://arxiv.org/pdf/2508.1…

Sublinear-Time Approximation for Graph Frequency Vectors in Hyperfinite Graphs
In this work, we address the problem of approximating the $k$-disc distribution (``frequency vector") of a bounded-degree graph in sublinear-time under the assumption of hyperfiniteness. We revisit the partition-oracle framework of Hassidim, Kelner, Nguyen, and Onak \cite{hassidim2009local}, and provide a concise, self-contained analysis that explicitly separates the two sources of error: (i) the cut error, controlled by hyperfiniteness parameter $ϕ$, which incurs at most $\varepsilon/2$ in $\…

Crosslisted article(s) found for physics.flu-dyn. https://arxiv.org/list/physics.flu-dyn/new
[1/1]:
- Error Estimation for Adaptive Mesh Refinement in Droplet Simulations
Darsh Nathawani, Matthew Knepley

Weak Error Estimates of Ergodic Approximations for Monotone Jump-diffusion SODEs
Zhihui Liu, Xiaoming Wu
https://arxiv.org/abs/2509.15698 https://arxiv.org…

Weak Error Estimates of Ergodic Approximations for Monotone Jump-diffusion SODEs
We first derive the exponential ergodicity of the stochastic theta method (STM) with $θ\in (1/2,1]$ for monotone jump-diffusion stochastic ordinary differential equations (SODEs) under a dissipative condition. Then we establish the weak error estimates of the backward Euler method (BEM), corresponding to the STM with $θ=1$. In particular, the time-independent estimate for the BEM in the jump-free case yields a one-order convergence rate between the exact and numerical invariant measures, answ…

Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Frederic Kirstein, Sonu Kumar, Terry Ruas, Bela Gipp
https://arxiv.org/abs/2509.15901

Re-FRAME the Meeting Summarization SCOPE: Fact-Based Summarization and Personalization via Questions
Meeting summarization with large language models (LLMs) remains error-prone, often producing outputs with hallucinations, omissions, and irrelevancies. We present FRAME, a modular pipeline that reframes summarization as a semantic enrichment task. FRAME extracts and scores salient facts, organizes them thematically, and uses these to enrich an outline into an abstractive summary. To personalize summaries, we introduce SCOPE, a reason-out-loud protocol that has the model build a reasoning trace …

Distribution of integer points on determinant surfaces and a $\text{mod-}p$ analogue
Satadal Ganguly, Rachita Guria
https://arxiv.org/abs/2508.14793 https://

Distribution of integer points on determinant surfaces and a $\text{mod-}p$ analogue
We establish an asymptotic formula for counting integer solutions with smooth weights to an equation of the form $xy-zw=r$, where $r$ is a non-zero integer, with an explicit main term and a strong bound on the error term in terms of the size of the variables $x, y, z, w$ as well as of $r$. We also establish an asymptotic formula for counting integer solutions with smooth weights to the congruence $xy-zw \equiv 1 (\text{mod }p)$, where $p$ is a large prime, with a strong bound on the error term.

Composable logical gate error in approximate quantum error correction: reexamining gate implementations in Gottesman-Kitaev-Preskill codes
Lukas Brenner, Beatriz Dias, Robert Koenig
https://arxiv.org/abs/2509.14658

Composable logical gate error in approximate quantum error correction: reexamining gate implementations in Gottesman-Kitaev-Preskill codes
Quantifying the accuracy of logical gates is paramount in approximate error correction, where perfect implementations are often unachievable with the available set of physical operations. To this end, we introduce a single scalar quantity we call the (composable) logical gate error. It captures both the deviation of the logical action from the desired target gate as well as leakage out of the code space. It is subadditive under successive application of gates, providing a simple means for analy…

Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
Marko Tuononen, Heikki Penttinen, Ville Hautam\"aki
https://arxiv.org/abs/2509.15950 https://

Targeted Fine-Tuning of DNN-Based Receivers via Influence Functions
We present the first use of influence functions for deep learning-based wireless receivers. Applied to DeepRx, a fully convolutional receiver, influence analysis reveals which training samples drive bit predictions, enabling targeted fine-tuning of poorly performing cases. We show that loss-relative influence with capacity-like binary cross-entropy loss and first-order updates on beneficial samples most consistently improves bit error rate toward genie-aided performance, outperforming random fi…

Hear Your Code Fail, Voice-Assisted Debugging for Python
Sayed Mahbub Hasan Amiri, Md. Mainul Islam, Mohammad Shakhawat Hossen, Sayed Majhab Hasan Amiri, Mohammad Shawkat Ali Mamun, Sk. Humaun Kabir, Naznin Akter
https://arxiv.org/abs/2507.15007

Hear Your Code Fail, Voice-Assisted Debugging for Python
This research introduces an innovative voice-assisted debugging plugin for Python that transforms silent runtime errors into actionable audible diagnostics. By implementing a global exception hook architecture with pyttsx3 text-to-speech conversion and Tkinter-based GUI visualization, the solution delivers multimodal error feedback through parallel auditory and visual channels. Empirical evaluation demonstrates 37% reduced cognitive load (p<0.01, n=50) compared to traditional stack-trace debugg…

Consistent Pose Estimation of Unmanned Ground Vehicles through Terrain-Aided Multi-Sensor Fusion on Geometric Manifolds
Alexander Raab, Stephan Weiss, Alessandro Fornasier, Christian Brommer, Abdalrahman Ibrahim
https://arxiv.org/abs/2508.14661

Consistent Pose Estimation of Unmanned Ground Vehicles through Terrain-Aided Multi-Sensor Fusion on Geometric Manifolds
Aiming to enhance the consistency and thus long-term accuracy of Extended Kalman Filters for terrestrial vehicle localization, this paper introduces the Manifold Error State Extended Kalman Filter (M-ESEKF). By representing the robot's pose in a space with reduced dimensionality, the approach ensures feasible estimates on generic smooth surfaces, without introducing artificial constraints or simplifications that may degrade a filter's performance. The accompanying measurement models are compati…

Extending Data to Improve Stability and Error Estimates Using Asymmetric Kansa-like Methods to Solve PDEs
Thomas Hangelbroek, Francis J. Narcowich, Joseph D. Ward
https://arxiv.org/abs/2507.15137

Extending Data to Improve Stability and Error Estimates Using Asymmetric Kansa-like Methods to Solve PDEs
In this paper, a theoretical framework is presented for the use of a Kansa-like method to numerically solve elliptic partial differential equations on spheres and other manifolds. The theory addresses both the stability of the method and provides error estimates for two different approximation methods. A Kansa-like matrix is obtained by replacing the test point set $X$, used in the traditional Kansa method, by a larger set $Y$, which is a norming set for the underlying trial space…

FlowForge: Guiding the Creation of Multi-agent Workflows with Design Space Visualization as a Thinking Scaffold
Pan Hao, Dongyeop Kang, Nicholas Hinds, Qianwen Wang
https://arxiv.org/abs/2507.15559

FlowForge: Guiding the Creation of Multi-agent Workflows with Design Space Visualization as a Thinking Scaffold
Multi-agent workflows have become an effective strategy for tackling complicated tasks by decomposing them into multiple sub-tasks and assigning them to specialized agents. However, designing optimal workflows remains challenging due to the vast and intricate design space. Current practices rely heavily on the intuition and expertise of practitioners, often resulting in design fixation or an unstructured, time-consuming exploration of trial-and-error. To address these challenges, this work intr…

Trace-Based Reconstruction of Quantum Circuit Dataflow in Surface Codes
Theodoros Trochatos, Christopher Kang, Andrew Wang, Frederic T. Chong, Jakub Szefer
https://arxiv.org/abs/2508.14533

Trace-Based Reconstruction of Quantum Circuit Dataflow in Surface Codes
Practical applications of quantum computing depend on fault-tolerant devices that employ error correction. A promising quantum error-correcting code for large-scale quantum computing is the surface code. For this code, Fault-Tolerant Quantum Computing (FTQC) can be performed via lattice surgery, i.e. merging and splitting of encoded qubit patches on a 2D grid. Lattice surgery operations result in space-time patterns of activity that are defined in this work as access traces. This work demonstra…

Analysis and experimental validation of decentralized controllers for delayed spacing policies in vehicle-platooning
Paul Wijnbergen, Redmer de Haan, Erjen Lefeber
https://arxiv.org/abs/2508.14587

Analysis and experimental validation of decentralized controllers for delayed spacing policies in vehicle-platooning
In this paper, a novel approach to spacing policies for vehicle platoons and a framework for control design is presented. Whereas traditional approaches aim to mitigate the effect of actuation and communication delays on the spacing error, this paper presents spacing policies that account for delays. A framework for decentralized control design is presented and necessary and sufficient conditions for tracking of delayed-based spacing policies are stated. It is shown that due to the control desi…

Hallucinations in medical devices
Jason Granstedt, Prabhat Kc, Rucha Deshpande, Victor Garcia, Aldo Badano
https://arxiv.org/abs/2508.14118 https://arxiv.o…

Hallucinations in medical devices
Computer methods in medical devices are frequently imperfect and are known to produce errors in clinical or diagnostic tasks. However, when deep learning and data-based approaches yield output that exhibit errors, the devices are frequently said to hallucinate. Drawing from theoretical developments and empirical studies in multiple medical device areas, we introduce a practical and universal definition that denotes hallucinations as a type of error that is plausible and can be either impactful …

Elastic Buffer Design for Real-Time All-Digital Clock Recovery Enabling Free-Running Receiver Clock with Negative and Positive Clock Frequency Offsets
Patrick Matalla, Joel Dittmer, Md Salek Mahmud, Christian Koos, Sebastian Randel
https://arxiv.org/abs/2507.13748

Elastic Buffer Design for Real-Time All-Digital Clock Recovery Enabling Free-Running Receiver Clock with Negative and Positive Clock Frequency Offsets
We present an elastic buffer design that enables all-digital clock recovery implementation with free-running receiver clock featuring negative and positive clock frequency offsets. Error-free real-time data transmission is demonstrated from -400 ppm to +400 ppm.

RulER: Automated Rule-Based Semantic Error Localization and Repair for Code Translation
Shuo Jin, Songqiang Chen, Xiaoyuan Xie, Shing-Chi Cheung
https://arxiv.org/abs/2509.14829

RulER: Automated Rule-Based Semantic Error Localization and Repair for Code Translation
Automated code translation aims to convert programs between different programming languages while maintaining their functionality. Due to the imperfections of code translation models, the generated translations may contain errors that compromise their reliability. Existing automated debugging methods for code translation rely on code alignments and repair patch templates to locate and fix erroneous translations. However, existing methods lack reliable references to construct code alignments and…

On the convergence analysis of MsFEM with oversampling: Interpolation error
Guanglian Li
https://arxiv.org/abs/2507.14548 https://arx…

On the convergence analysis of MsFEM with oversampling: Interpolation error
In this paper, we investigate the approximation properties of two types of multiscale finite element methods with oversampling as proposed in [Hou \& Wu, {\textit{J. Comput. Phys.}}, 1997] and [Efendiev, Hou \& Wu, \textit{SIAM J. Numer. Anal.}, 2000] without scale separation. We develop a general interpolation error analysis for elliptic problems with highly oscillatory rough coefficients, under the assumption of the existence of a macroscopic problem with suitable $L^2$-accuracy. The distinct…

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
Jing Chang, Chang Liu, Jinbin Huang, Rui Mao, Jianbin Qin
https://arxiv.org/abs/2507.13712

LLaPipe: LLM-Guided Reinforcement Learning for Automated Data Preparation Pipeline Construction
Automated data preparation is crucial for democratizing machine learning, yet existing reinforcement learning (RL) based approaches suffer from inefficient exploration in the vast space of possible preprocessing pipelines. We present LLaPipe, a novel framework that addresses this exploration bottleneck by integrating Large Language Models (LLMs) as intelligent policy advisors. Unlike traditional methods that rely solely on statistical features and blind trial-and-error, LLaPipe leverages the se…

PyTOD: Programmable Task-Oriented Dialogue with Execution Feedback
Alexandru Coca, Bo-Hsiang Tseng, Pete Boothroyd, Jianpeng Cheng, Mark Gaynor, Zhenxing Zhang, Joe Stacey, Tristan Guigue, H\'ector Martinez Alonso, Diarmuid \'O S\'eaghdha, Anders Johannsen
https://arxiv.org/abs/2508.15456…

PyTOD: Programmable Task-Oriented Dialogue with Execution Feedback
Programmable task-oriented dialogue (TOD) agents enable language models to follow structured dialogue policies, but their effectiveness hinges on accurate state tracking. We present PyTOD, an agent that generates executable code to track dialogue state and uses policy and execution feedback for efficient error correction. To this end, PyTOD employs a simple constrained decoding approach, using a language model instead of grammar rules to follow API schemata. This leads to state-of-the-art state…

MD-OFDM: An Energy-Efficient and Low-PAPR MIMO-OFDM Variant for Resource-Constrained Applications
Rahul Gulia
https://arxiv.org/abs/2507.13623 https://

MD-OFDM: An Energy-Efficient and Low-PAPR MIMO-OFDM Variant for Resource-Constrained Applications
Orthogonal Frequency Division Multiplexing (OFDM) combined with Multiple-Input Multiple-Output (MIMO) techniques forms the backbone of modern wireless communication systems. While offering high spectral efficiency and robustness, conventional MIMO-OFDM, especially with complex equalizers like Minimum Mean Square Error (MMSE), suffers from high Peak-to-Average Power Ratio (PAPR) and significant power consumption due to multiple active Radio Frequency (RF) chains. This paper proposes and mathemat…

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio questuin answering
Jinghua Zhao, Hang Su, Lichun Fan, Zhenbo Luo, Jian Luan, Hui Wang, Haoqin Sun, Yong Qin
https://arxiv.org/abs/2509.12275

Omni-CLST: Error-aware Curriculum Learning with guided Selective chain-of-Thought for audio questuin answering
We propose Omni-CLST, an error-aware Curriculum Learning framework with guided Selective Chain-of-Thought for audio question answering. The framework efficiently leverages existing high-quality dataset through two key strategies: an error-aware curriculum that organizes samples by difficulty, and a guided thought dropout mechanism that focuses reasoning on challenging cases. Integrated with GRPO training, these strategies enable the model to learn more effectively from informative samples. Expe…

Asymptotically Optimal Codes Correcting One Substring Edit
Yuting Li, Yuanyuan Tang, Hao Lou, Ryan Gabrys, Farzad Farnoud
https://arxiv.org/abs/2507.13808 …

Asymptotically Optimal Codes Correcting One Substring Edit
The substring edit error is the operation of replacing a substring $u$ of $x$ with another string $v$, where the lengths of $u$ and $v$ are bounded by a given constant $k$. It encompasses localized insertions, deletions, and substitutions within a window. Codes correcting one substring edit have redundancy at least $\log n+k$. In this paper, we construct codes correcting one substring edit with redundancy $\log n+O(\log \log n)$, which is asymptotically optimal.

Session-Level Spoken Language Assessment with a Multimodal Foundation Model via Multi-Target Learning
Hong-Yun Lin, Jhen-Ke Lin, Chung-Chun Wang, Hao-Chien Lu, Berlin Chen
https://arxiv.org/abs/2509.16025

Session-Level Spoken Language Assessment with a Multimodal Foundation Model via Multi-Target Learning
Spoken Language Assessment (SLA) estimates a learner's oral proficiency from spontaneous speech. The growing population of L2 English speakers has intensified the demand for reliable SLA, a critical component of Computer Assisted Language Learning (CALL). Existing efforts often rely on cascaded pipelines, which are prone to error propagation, or end-to-end models that often operate on a short audio window, which might miss discourse-level evidence. This paper introduces a novel multimodal found…

ACS: An interactive framework for conformal selection
Yu Gui, Ying Jin, Yash Nair, Zhimei Ren
https://arxiv.org/abs/2507.15825 https://

ACS: An interactive framework for conformal selection
This paper presents adaptive conformal selection (ACS), an interactive framework for model-free selection with guaranteed error control. Building on conformal selection (Jin and Candès, 2023b), ACS generalizes the approach to support human-in-the-loop adaptive data analysis. Under the ACS framework, we can partially reuse the data to boost the selection power, make decisions on the fly while exploring the data, and incorporate new information or preferences as they arise. The key to ACS is a c…

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
Jihun Kim, Javad Lavaei
https://arxiv.org/abs/2509.15794 https://

Bridging Batch and Streaming Estimations to System Identification under Adversarial Attacks
System identification in modern engineering systems faces emerging challenges from unanticipated adversarial attacks beyond existing detection mechanisms. In this work, we obtain a provably accurate estimate of the Markov parameter matrix of order $k$ to identify partially observed linear systems, in which the probability of having an attack at each time is $O(1/k)$. We show that given the batch data accumulated up to time $T^*$, the $\ell_2$-norm estimator achieves an error decaying exponentia…

Adversarially robust quantum state learning and testing
Maryam Aliakbarpour, Vladimir Braverman, Nai-Hui Chia, Yuhan Liu
https://arxiv.org/abs/2508.13959 https://

Adversarially robust quantum state learning and testing
Quantum state learning is a fundamental problem in physics and computer science. As near-term quantum devices are error-prone, it is important to design error-resistant algorithms. Apart from device errors, other unexpected factors could also affect the algorithm, such as careless human read-out error, or even a malicious hacker deliberately altering the measurement results. Thus, we want our algorithm to work even in the worst case when things go against our favor. We consider the practical …

Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems
Lucian Cristian Iacob, Roland T\'oth, Maarten Schoukens
https://arxiv.org/abs/2507.15093

Exact Finite Koopman Embedding of Block-Oriented Polynomial Systems
The challenge of finding exact and finite-dimensional Koopman embeddings of nonlinear systems has been largely circumvented by employing data-driven techniques to learn models of different complexities (e.g., linear, bilinear, input affine). Although these models may provide good accuracy, selecting the model structure and dimension is still ad-hoc and it is difficult to quantify the error that is introduced. In contrast to the general trend of data-driven learning, in this paper, we develop a …

Refinements for primes in short arithmetic progressions
Michael Harm
https://arxiv.org/abs/2507.15334 https://arxiv.org/pdf/2507.1533…

Refinements for primes in short arithmetic progressions
Given a zero-free region and an averaged zero-density estimate for all Dirichlet $L$-functions modulo $q$, we refine the error terms of the prime number theorem in all and almost all short arithmetic progressions. For example, if we assume the Generalized Density Hypothesis, then for any arithmetic progression modulo $q\leq \log^{\ell} x$ with $\ell>0$ and any $α>\frac{2}{3}$, the prime number theorem holds in all intervals $(x,x+\sqrt{x}\exp(\log^αx)]$ and almost all intervals $(x,x+\exp(\lo…

Interplay Between Belief Propagation and Transformer: Differential-Attention Message Passing Transformer
Chin Wa Lau, Xiang Shi, Ziyan Zheng, Haiwen Cao, Nian Guo
https://arxiv.org/abs/2509.15637

Interplay Between Belief Propagation and Transformer: Differential-Attention Message Passing Transformer
Transformer-based neural decoders have emerged as a promising approach to error correction coding, combining data-driven adaptability with efficient modeling of long-range dependencies. This paper presents a novel decoder architecture that integrates classical belief propagation principles with transformer designs. We introduce a differentiable syndrome loss function leveraging global codebook structure and a differential-attention mechanism optimizing bit and syndrome embedding interactions. E…

RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
Yingshu Li, Yunyi Liu, Lingqiao Liu, Lei Wang, Luping Zhou
https://arxiv.org/abs/2508.15464 https://

RadReason: Radiology Report Evaluation Metric with Reasons and Sub-Scores
Evaluating automatically generated radiology reports remains a fundamental challenge due to the lack of clinically grounded, interpretable, and fine-grained metrics. Existing methods either produce coarse overall scores or rely on opaque black-box models, limiting their usefulness in real-world clinical workflows. We introduce RadReason, a novel evaluation framework for radiology reports that not only outputs fine-grained sub-scores across six clinically defined error types, but also produces h…

Reliable high-accuracy error mitigation for utility-scale quantum circuits
Dorit Aharonov, Ori Alberton, Itai Arad, Yosi Atia, Eyal Bairey, Matan Ben Dov, Asaf Berkovitch, Zvika Brakerski, Itsik Cohen, Eran Fuchs, Omri Golan, Or Golan, Barak D. Gur, Ilya Gurwich, Avieli Haber, Rotem Haber, Dorri Halbertal, Yaron Itkin, Barak A. Katzir, Oded Kenneth, Shlomi Kotler, Roei Levi, Eyal Leviatan, Yotam Y. Lifshitz, Adi Ludmer, Shlomi Matityahu, Ron Aharon Melcer, Adiel Meyer, Omrie Ovdat, Avi…

Reliable high-accuracy error mitigation for utility-scale quantum circuits
Error mitigation is essential for unlocking the full potential of quantum algorithms and accelerating the timeline toward quantum advantage. As quantum hardware progresses to push the boundaries of classical simulation, efficient and robust error mitigation methods are becoming increasingly important for producing accurate and reliable outputs. We introduce QESEM, a reliable, high-accuracy, characterization-based software implementing efficient, unbiased quasi-probabilistic error mitigation. We…

Bridge Sampling Diagnostics
Giorgio Micaletto, Aki Vehtari
https://arxiv.org/abs/2508.14487 https://arxiv.org/pdf/2508.14487…

Bridge Sampling Diagnostics
In Bayesian statistics, the marginal likelihood is used for model selection and averaging, yet it is often challenging to compute accurately for complex models. Approaches such as bridge sampling, while effective, may suffer from issues of high variability of the estimates. We present how to estimate Monte Carlo standard error (MCSE) for bridge sampling, and how to diagnose the reliability of MCSE estimates using Pareto-$\hat{k}$ and block reshuffling diagnostics without the need to repeatedly …

Distribution of prime geodesic traces
Anton Deitmar
https://arxiv.org/abs/2507.13679 https://arxiv.org/pdf/2507.13679

Distribution of prime geodesic traces
This note complements a recent paper of Chatzakos, Harcos and Kaneko \cite{CHK}. We use a Dirichlet style Prime Geodesic Theorem to improve the error term estimate in loc. cit. at the cost of lowering the resolution. The proof relies on the Selberg trace formula.

A DPI-PAC-Bayesian Framework for Generalization Bounds
Muhan Guan, Farhad Farokhi, Jingge Zhu
https://arxiv.org/abs/2507.14795 https://

A DPI-PAC-Bayesian Framework for Generalization Bounds
We develop a unified Data Processing Inequality PAC-Bayesian framework -- abbreviated DPI-PAC-Bayesian -- for deriving generalization error bounds in the supervised learning setting. By embedding the Data Processing Inequality (DPI) into the change-of-measure technique, we obtain explicit bounds on the binary Kullback-Leibler generalization gap for both Rényi divergence and any $f$-divergence measured between a data-independent prior distribution and an algorithm-dependent posterior distributi…

CL$^2$GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction
Shang Qin, Jingheng Ye, Yinghui Li, Hai-Tao Zheng, Qi Li, Jinxiao Shan, Zhixing Li, Hong-Gee Kim
https://arxiv.org/abs/2509.13672

CL$^2$GEC: A Multi-Discipline Benchmark for Continual Learning in Chinese Literature Grammatical Error Correction
The growing demand for automated writing assistance in diverse academic domains highlights the need for robust Chinese Grammatical Error Correction (CGEC) systems that can adapt across disciplines. However, existing CGEC research largely lacks dedicated benchmarks for multi-disciplinary academic writing, overlooking continual learning (CL) as a promising solution to handle domain-specific linguistic variation and prevent catastrophic forgetting. To fill this crucial gap, we introduce CL$^2$GEC,…

Estimating systematic errors in Bayesian inversion using transport maps
Maren Casfor, Philipp Trunschke, Sebastian Heidenreich, Nando Hegemann
https://arxiv.org/abs/2509.16116 h…

Estimating systematic errors in Bayesian inversion using transport maps
In indirect measurements, the measurand is determined by solving an inverse problem which requires a model of the measurement process. Such models are often approximations and introduce systematic errors leading to a bias of the posterior distribution in Bayesian inversion. We propose a unified framework that combines transport maps from a reference distribution to the posterior distribution with the model error approach. This leads to an adaptive algorithm that jointly estimates the posterior …

Error exponents of quantum state discrimination with composite correlated hypotheses
Kun Fang
https://arxiv.org/abs/2508.12901 https://arxiv.org/pdf/2508.1…

Error exponents of quantum state discrimination with composite correlated hypotheses
We study the error exponents in quantum hypothesis testing between two sets of quantum states, extending the analysis beyond the independent and identically distributed case to encompass composite and correlated hypotheses. We introduce and compare two natural extensions of the quantum Hoeffding divergence and anti-divergence to sets of quantum states, establishing their equivalence or quantitative relationships. Our main results generalize the quantum Hoeffding bound to stable sequences of con…

Decay of Chebyshev coefficients and error estimates of associated quadrature on shrinking intervals
Krishna Yamanappa Poojara, Sabhrant Sachan, Ambuj Pandey
https://arxiv.org/abs/2509.14602

Decay of Chebyshev coefficients and error estimates of associated quadrature on shrinking intervals
We analyze decay of Chebyshev coefficients and local Chebyshev approximations for functions of finite regularity on finite intervals, focusing on the framework where the interval length tends to zero while the number of approximation nodes remains fixed. For all four families of Chebyshev polynomials, we derive error estimates that quantify the dependence on the interval length for (i) the decay of Chebyshev coefficients, (ii) the approximation error between continuous and discrete Chebyshev co…

Scalable dissipative quantum error correction for discrete-variable codes
Ivan Rojkov, Elias Zapusek, Florentin Reiter
https://arxiv.org/abs/2507.12534 htt…

Scalable dissipative quantum error correction for discrete-variable codes
Dissipative quantum error correction (QEC) autonomously protects quantum information using engineered dissipation and offers a promising alternative to error correction via measurement and feedback. However, scalability remains a challenge, as correcting high-weight errors typically requires increasing dissipation rates and exponentially many correction operators. Here, we present a scalable dissipative QEC protocol for discrete-variable codes, correcting multi-qubit errors via a trickle-down m…

Lowering Error Floors for Hard Decision Decoding of OFEC Code
Jasper Lagendijk, Yunus Can G\"ultekin, Alexios Balatsoukas-Stimming, Gabriele Liga, Alex Alvarado
https://arxiv.org/abs/2507.12155

Lowering Error Floors for Hard Decision Decoding of OFEC Code
Stall patterns are known to cause an error floor in hard decision decoding of the OFEC code. We propose a novel stall pattern removal algorithm that lowers the error floor of state-of-the-art algorithms by an order of magnitude

Clustering-based aggregate value regression
Kei Hirose, Hidetoshi Matsui, Hiroki Masuda
https://arxiv.org/abs/2508.15567 https://arxiv.org/pdf/2508.15567…

Clustering-based aggregate value regression
In various practical situations, forecasting of aggregate values rather than individual ones is often our main focus. For instance, electricity companies are interested in forecasting the total electricity demand in a specific region to ensure reliable grid operation and resource allocation. However, to our knowledge, statistical learning specifically for forecasting aggregate values has not yet been well-established. In particular, the relationship between forecast error and the number of clus…

Generalized Number-Phase Lattice Encoding of a Bosonic Mode for Quantum Error Correction
Dong-Long Hu, Weizhou Cai, Chang-Ling Zou, Ze-Liang Xiang
https://arxiv.org/abs/2508.12354

Generalized Number-Phase Lattice Encoding of a Bosonic Mode for Quantum Error Correction
Bosonic systems offer unique advantages for quantum error correction, as a single bosonic mode provides a large Hilbert space to redundantly encode quantum information. However, previous studies have been limited to exploiting symmetries in the quadrature phase space. Here we introduce a unified framework for encoding a qubit utilizing the symmetries in the phase space of number and phase variables of a bosonic mode. The logical codewords form lattice structures in the number-phase space, resul…

Controlling IER and EER in replicated regular two-level factorial experiments
Pengfei Li, Oludotun J. Akinlawon, Shengli Zhao
https://arxiv.org/abs/2507.13621

Controlling IER and EER in replicated regular two-level factorial experiments
Replicated regular two-level factorial experiments are very useful for industry. The goal of these experiments is to identify active effects that affect the mean and variance of the response. Hypothesis testing procedures are widely used for this purpose. However, the existing methods give results that are either too anticonservative or conservative in controlling the individual and experimentwise error rates (IER and EER). In this paper, we propose {a Monte Carlo method} and an exact-variance …

Rare Event Simulation of Quantum Error-Correcting Circuits
Carolyn Mayer, Anand Ganti, Uzoma Onunkwo, Tzvetan Metodi, Benjamin Anker, Jacek Skryzalin
https://arxiv.org/abs/2509.13678

Rare Event Simulation of Quantum Error-Correcting Circuits
We describe a practical approach for accessing the logical failure rates of quantum error-correcting (QEC) circuits under low physical (component) failure rate regimes. Standard Monte Carlo is often the de facto approach for studying the failure rates of quantum circuits. However, in the study of fault-tolerant error-correcting circuits, the ability to extend this approach to low physical failure rates is limited. In particular, the use of Monte Carlo to access circuits that are relatively larg…

The extended horizontal linear complementarity problem: iterative methods and error analysis
Shi-Liang Wu, Cui-Xia Li
https://arxiv.org/abs/2509.14491 https://

The extended horizontal linear complementarity problem: iterative methods and error analysis
To the best of our knowledge, since the extended horizontal linear complementarity problem (EHLCP) was first introduced and studied by Kaneko in 1977, no iterative methods or error analysis have been developed for it due to the interdependence of its multiple unknowns in a 'chain-like' structure. This paper aims to address these gaps by: (1) proposing an equivalent fixed-point formulation of the EHLCP by using a variable transformation technique with the max-min function; (2) developing eff…

Reinforcement learning entangling operations on spin qubits
Mohammad Abedi, Markus Schmitt
https://arxiv.org/abs/2508.14761 https://arxiv.org/pdf/2508.1476…

Reinforcement learning entangling operations on spin qubits
High-fidelity control of one- and two-qubit gates past the error correction threshold is an essential ingredient for scalable quantum computing. We present a reinforcement learning (RL) approach to find entangling protocols for semiconductor-based singlet-triplet qubits in a double quantum dot. Despite the presence of realistically modelled experimental constraints, such as various noise contributions and finite rise-time effects, we demonstrate that an RL agent can yield performative protocols…

On the efficiency of a posteriori error estimators for parabolic partial differential equations in the energy norm
Iain Smears
https://arxiv.org/abs/2507.13188

On the efficiency of a posteriori error estimators for parabolic partial differential equations in the energy norm
For the model problem of the heat equation discretized by an implicit Euler method in time and a conforming finite element method in space, we prove the efficiency of a posteriori error estimators with respect to the energy norm of the error, when considering the numerical solution as the average between the usual continuous piecewise affine-in-time and piecewise constant-in-time reconstructions. This illustrates how the efficiency of the estimators is not only possibly dependent on the choice …

Colour Codes Reach Surface Code Performance using Vibe Decoding
Stergios Koutsioumpas, Tamas Noszko, Hasan Sayginel, Mark Webster, Joschka Roffe
https://arxiv.org/abs/2508.15743

Colour Codes Reach Surface Code Performance using Vibe Decoding
Two-dimensional quantum colour codes hold significant promise for quantum error correction, offering advantages such as planar connectivity and low overhead logical gates. Despite their theoretical appeal, the practical deployment of these codes faces challenges due to complex decoding requirements compared to surface codes. This paper introduces vibe decoding which, for the first time, brings colour code performance on par with the surface code under practical decoding. Our approach leverages …

Sharp Error-Rate Transitions in Quantum QC-LDPC Codes under Joint BP Decoding
Daiki Komoto, Kenta Kasai
https://arxiv.org/abs/2507.11534 https://

Sharp Error-Rate Transitions in Quantum QC-LDPC Codes under Joint BP Decoding
In this study, we report that quantum quasi-cyclic low-density parity-check codes decoded via joint belief propagation (BP) exhibit steep error-rate curves, despite the presence of error floors. To the best of our knowledge, this is the first observation of such threshold-like behavior for quantum codes with non-vanishing coding rate, excluding those decoded with non-binary BP decoders. Moreover, we find that dominant error events contributing to the error floor typically involve only a small n…

Exponential decay of the discrete energy for the wave-wave coupled system
Toni Sayah, Toufic El Arwadi
https://arxiv.org/abs/2508.15514 https://arxiv.org/p…

Exponential decay of the discrete energy for the wave-wave coupled system
In this article, a numerical analysis of the asymptotic behavior of the discrete energy associated to a dissipative coupled wave system is conducted. The numerical approximation of the system is constructed using the P1 finite element method for spatial discretization, combined with the implicit Euler scheme for time integration. An a priori error analysis is established, showing that, under extra regularity assumptions on the continuous solution, the numerical scheme exhibits linear convergenc…

The Proportional Fair Scheduler in Wavelength-Multiplexed Quantum Networks
Sanidhay Bhambay, Siddarth Koduru Joshi, Thirupathaiah Vasantam, Neil Walton
https://arxiv.org/abs/2507.13999

The Proportional Fair Scheduler in Wavelength-Multiplexed Quantum Networks
We address the problem of optimal pumping strategies in quantum networks. These networks enable secure communication by distributing entangled photon pairs to user (or node) pairs. Quantum Key Distribution (QKD) protocols, like BBM92, generate secret keys from entangled photons. While secure communication and error correction are essential for any quantum communication channel, resource contention, optimization, and fairness issues are critical for networks. In this article, we analyze the perf…

Surface Stokes Without Inf-Sup Condition
Ricardo H. Nochetto, Mansur Shakipov
https://arxiv.org/abs/2508.13342 https://arxiv.org/pdf/2508.13342

Surface Stokes Without Inf-Sup Condition
For a $d$-dimensional hypersurface of class $C^3$ without boundary, we reformulate the surface Stokes equations as a nonsymmetric indefinite elliptic problem governed by two Laplacians. We then use this elliptic reformulation as a basis for a numerical method based on lifted parametric FEM. Assuming no geometric error for simplicity, we prove its well-posedness, quasi-best approximation in a robust mesh-dependent $H^1$-norm for any polynomial degree, as well as an optimal $L^2$ error estimate f…

Design Automation in Quantum Error Correction
Archisman Ghosh, Avimita Chatterjee, Swaroop Ghosh
https://arxiv.org/abs/2507.12253 https://

Design Automation in Quantum Error Correction
Quantum error correction (QEC) underpins practical fault-tolerant quantum computing (FTQC) by addressing the fragility of quantum states and mitigating decoherence-induced errors. As quantum devices scale, integrating robust QEC protocols is imperative to suppress logical error rates below threshold and ensure reliable operation, though current frameworks suffer from substantial qubit overheads and hardware inefficiencies. Design automation in the QEC flow is thus critical, enabling automated s…

Enhancing Decoding Performance using Efficient Error Learning
Pavithran Iyer, Aditya Jain, Stephen D. Bartlett, Joseph Emerson
https://arxiv.org/abs/2507.08536

Enhancing Decoding Performance using Efficient Error Learning
Lowering the resource overhead needed to achieve fault-tolerant quantum computation is crucial to building scalable quantum computers. We show that adapting conventional maximum likelihood (ML) decoders to a small subset of efficiently learnable physical error characteristics can significantly improve the logical performance of a quantum error-correcting code. Specifically, we leverage error information obtained from efficient characterization methods based on Cycle Error Reconstruction (CER), …