Tootfinder

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)

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.

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…

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, …

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…

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…

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 …

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…

@… 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 😬

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…

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…

Thought I might try jdupes as an alternative to old fashioned fdupes. Then went to the project home page, ignoring the SSL error, and found a bunch of edgelord shit there. J is real mad about people who are trying to be decent. I'll find some other tool.

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.…

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…

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 $\…

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…

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 …

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/

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…

COCO: Cognitive Operating System with Continuous Oversight for Multi-Agent Workflow Reliability
Churong Liang, Jinling Gan, Kairan Hong, Qiushi Tian, Zongze Wu, Runnan Li
https://arxiv.org/abs/2508.13815

COCO: Cognitive Operating System with Continuous Oversight for Multi-Agent Workflow Reliability
Large-scale multi-agent workflows exhibit inherent vulnerability to error propagation and quality degradation, where downstream agents compound upstream failures without corrective mechanisms. We introduce COCO (Cognitive Operating System with Continuous Oversight), a theoretically-grounded framework that implements asynchronous self-monitoring and adaptive error correction in multi-agent driven systems. COCO addresses the fundamental trade-off between quality assurance and computational effici…

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

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…

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)

Nearly Optimal Bounds on the Fourier sampling numbers of Besov Spaces
Jonathan W. Siegel
https://arxiv.org/abs/2508.13991 https://arxiv.org/pdf/2508.13991

Nearly Optimal Bounds on the Fourier sampling numbers of Besov Spaces
Let $\mathbb{T}^d$ denote the $d$-dimensional torus. We consider the problem of optimally recovering a target function $f^*:\mathbb{T}^d\rightarrow \mathbb{C}$ from samples of its Fourier coefficients. We make classical smoothness assumptions on $f^*$, specifically that $f^*$ lies in a Besov space $B^s_\infty(L_q)$ with $s > 0$ and $1\leq q\leq \infty$, and measure recovery error in the $L_p$-norm with $1\leq p\leq \infty$. Abstractly, the optimal recovery error is characterized by a `restricte…

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 …

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…

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…

Consistent causal discovery with equal error variances: a least-squares perspective
Anamitra Chaudhuri, Yang Ni, Anirban Bhattacharya
https://arxiv.org/abs/2509.15197 https://…

Consistent causal discovery with equal error variances: a least-squares perspective
We consider the problem of recovering the true causal structure among a set of variables, generated by a linear acyclic structural equation model (SEM) with the error terms being independent and having equal variances. It is well-known that the true underlying directed acyclic graph (DAG) encoding the causal structure is uniquely identifiable under this assumption. In this work, we establish that the sum of minimum expected squared errors for every variable, while predicted by the best linear c…

**NuGet.org Package Deletion – Learnings & Prevention**
https://github.com/NuGet/Home/discussions/14429

Accurate bootstrap bounds from optimal interpolation
Cyuan-Han Chang, Vasiliy Dommes, Petr Kravchuk, David Poland, David Simmons-Duffin
https://arxiv.org/abs/2509.14307 https://…

Accurate bootstrap bounds from optimal interpolation
We develop new methods for approximating conformal blocks as positive functions times polynomials, with applications to the numerical bootstrap. We argue that to obtain accurate bootstrap bounds, conformal block approximations should minimize a certain error norm related to the asymptotics of dispersive functionals. This error norm can be made small using interpolation nodes with an appropriate optimal density. The optimal density turns out to satisfy a kind of force-balance equation for charge…

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…

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…

catherine writes error messages
existence of challenge implies possibility of defeat

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 …

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.

“ upstream connect error or disconnect/reset before headers. reset reason: remote connection failure, transport failure reason: delayed connect error: 111”
Clear the decks

Me and my new #yubikey5 part 2:
Now we get to the nitty-gritty parts. I'm using #mbsync to sync multiple #imap accounts to local

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…

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…

Wasserstein error estimates between telegraph processes and Brownian motion
Gerardo Barrera, Jani Lukkarinen, Mikko S. Pakkanen
https://arxiv.org/abs/2509.11871 https://

Wasserstein error estimates between telegraph processes and Brownian motion
We provide non-asymptotic error bounds in the path Wasserstein distance with quadratic integral cost between suitable functionals of the telegraph process and the corresponding functional of Brownian motion with explicit diffusivity constant. These results cover, in particular, the well-known example of the exponential integral functional of the geometric Brownian motion. The non-asymptotic error bounds tend to zero in the so-called Kac regime. Moreover, the error bounds remain valid when the f…

Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
Oliver A. Dicks, Solveig S. Aamlid, Alannah M. Hallas, Joerg Rottler
https://arxiv.org/abs/2508.13389

Expanding the search space of high entropy oxides and predicting synthesizability using machine learning interatomic potentials
We propose an efficient computational methodology for predicting the synthesizability of high entropy oxides (HEOs) in a large space of possible candidate compounds. HEOs are a growing field with an enormous potential chemical composition space, and yet the discovery of new HEOs is slow and driven by experimental trial-and-error. In this work, we attempt to speed up this process by using a machine learned interatomic potential offering DFT-level accuracy. Our methodology starts by identifying a…

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
Jun Chen, Hong Chen, Yonghua Yu, Yiming Ying
https://arxiv.org/abs/2507.11161

How does Labeling Error Impact Contrastive Learning? A Perspective from Data Dimensionality Reduction
In recent years, contrastive learning has achieved state-of-the-art performance in the territory of self-supervised representation learning. Many previous works have attempted to provide the theoretical understanding underlying the success of contrastive learning. Almost all of them rely on a default assumption, i.e., the label consistency assumption, which may not hold in practice (the probability of failure is called labeling error) due to the strength and randomness of common augmentation st…

Jupyter simulations Using NGS Intensity with PyWFS for REDWOODS
Joel Hurtado, Benjamin Gerard, Cesar Laguna, Dominic Francisco Sanchez
https://arxiv.org/abs/2508.14256 https://

Jupyter simulations Using NGS Intensity with PyWFS for REDWOODS
REDWOODS on ShaneAO at Lick Observatory implements a second-stage, 3-sided reflective pyramid wavefront sensor (PWFS), which, under low-light conditions, offers an improved signal-to-noise ratio for deformable mirror commands to correct dynamic atmospheric aberrations. We modeled the REDWOODS limiting natural guide star magnitude. We also investigated performance tradeoffs between two PWFS modes that are available to REDWOODS, one with higher bandwidth error and another with higher aliasing err…

Day 18
Today I debugged an issue with accessing backend endpoints from a Next.js frontend talking to a NestJS API.
The browser was blocking requests due to a CORS error — the Authorization header was not allowed in the preflight response. Even though frontend domains were correctly set, I forgot to include Authorization in allowedHeaders.
After updating enableCors() to:
`allowedHeaders: 'Authorization, Content-Type, Accept'`
…the issue disappeared, and t…

{purrr} has some lesser known functions that make handling of failing function calls easier: safely, quietly, possibly: #rstats

Wrap a function to capture errors — safely
Creates a modified version of .f that always succeeds. It returns a list with components result and error. If the function succeeds, result contains the returned value and error is NULL. If an error occurred, error is an error object and result is either NULL or otherwise.

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Ronit Virwani, Ruchika Suryawanshi
https://arxiv.org/abs/2508.13371 https://

LOOP: A Plug-and-Play Neuro-Symbolic Framework for Enhancing Planning in Autonomous Systems
Planning is one of the most critical tasks in autonomous systems, where even a small error can lead to major failures or million-dollar losses. Current state-of-the-art neural planning approaches struggle with complex domains, producing plans with missing preconditions, inconsistent goals, and hallucinations. While classical planners provide logical guarantees, they lack the flexibility and natural language understanding capabilities needed for modern autonomous systems. Existing neuro-symbolic…

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,…

Sparse, Geometry- and Material-Aware Bases for Multilevel Elastodynamic Simulation
Ty Trusty, David I. W. Levin, Danny M. Kaufman
https://arxiv.org/abs/2508.13386 https://

Sparse, Geometry- and Material-Aware Bases for Multilevel Elastodynamic Simulation
We present a multi-level elastodynamics timestep solver for accelerating incremental potential contact (IPC) simulations. Our method retains the robustness of gold standard IPC in the face of intricate geometry, complex heterogeneous material distributions and high resolution input data without sacrificing visual fidelity (per-timestep relative displacement error of $\approx1\%$). The success of our method is enabled by a novel, sparse, geometry- and material-aware basis construction method whi…

MIA-EPT: Membership Inference Attack via Error Prediction for Tabular Data
Eyal German, Daniel Samira, Yuval Elovici, Asaf Shabtai
https://arxiv.org/abs/2509.13046 https://

MIA-EPT: Membership Inference Attack via Error Prediction for Tabular Data
Synthetic data generation plays an important role in enabling data sharing, particularly in sensitive domains like healthcare and finance. Recent advances in diffusion models have made it possible to generate realistic, high-quality tabular data, but they may also memorize training records and leak sensitive information. Membership inference attacks (MIAs) exploit this vulnerability by determining whether a record was used in training. While MIAs have been studied in images and text, their use …

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.

Tel Aviv-based Qedma, which is developing an error mitigation software called QESEM for quantum computers, raised a $26M Series A led by Glilot Capital Partners (Anna Heim/TechCrunch)
https://techcrunch.com/2025/07/03/isra

Israeli quantum startup Qedma just raised $26 million, with IBM joining in | TechCrunch
Qedma specializes in error mitigation software. Its main piece of software, QESEM, standing for Quantum Error Suppression and Error Mitigation, analyzes noise patterns to suppress some classes of errors while the algorithm is running and mitigate others in post-processing.

IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion
Wenhao Hu, Zesheng Li, Haonan Zhou, Liu Liu, Xuexiang Wen, Zhizhong Su, Xi Li, Gaoang Wang
https://arxiv.org/abs/2508.13153

IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion
Reconstructing complete and interactive 3D scenes remains a fundamental challenge in computer vision and robotics, particularly due to persistent object occlusions and limited sensor coverage. Multiview observations from a single scene scan often fail to capture the full structural details. Existing approaches typically rely on multi stage pipelines, such as segmentation, background completion, and inpainting or require per-object dense scanning, both of which are error-prone, and not easily sc…

Replaced article(s) found for math.CA. https://arxiv.org/list/math.CA/new
[1/1]:
- Convergence order of the quantization error for self-affine measures on Lalley-Gatzouras carpets
Sanguo Zhu

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…

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…

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…

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 …

EvoVerilog: Large Langugage Model Assisted Evolution of Verilog Code
Ping Guo, Yiting Wang, Wanghao Ye, Yexiao He, Ziyao Wang, Xiaopeng Dai, Ang Li, Qingfu Zhang
https://arxiv.org/abs/2508.13156

EvoVerilog: Large Langugage Model Assisted Evolution of Verilog Code
Large Language Models (LLMs) have demonstrated great potential in automating the generation of Verilog hardware description language code for hardware design. This automation is critical to reducing human effort in the complex and error-prone process of hardware design. However, existing approaches predominantly rely on human intervention and fine-tuning using curated datasets, limiting their scalability in automated design workflows. Although recent iterative search techniques have emerged…

Boosting Scientific Error-Bounded Lossy Compression through Optimized Synergistic Lossy-Lossless Orchestration
Shixun Wu, Jinwen Pan, Jinyang Liu, Jiannan Tian, Ziwei Qiu, Jiajun Huang, Kai Zhao, Xin Liang, Sheng Di, Zizhong Chen, Franck Cappello
https://arxiv.org/abs/2507.11165

Boosting Scientific Error-Bounded Lossy Compression through Optimized Synergistic Lossy-Lossless Orchestration
As high-performance computing architectures evolve, more scientific computing workflows are being deployed on advanced computing platforms such as GPUs. These workflows can produce raw data at extremely high throughputs, requiring urgent high-ratio and low-latency error-bounded data compression solutions. In this paper, we propose cuSZ-Hi, an optimized high-ratio GPU-based scientific error-bounded lossy compressor with a flexible, domain-irrelevant, and fully open-source framework design. Our n…

Singularity-free prescribed performance guaranteed control for perturbed system
Yiwei Liu
https://arxiv.org/abs/2508.13726 https://arxiv.org/pdf/2508.13726…

Singularity-free prescribed performance guaranteed control for perturbed system
This paper addresses the prescribed performance control (PPC) challenge for high-order nonlinear systems affected by mismatched disturbances. The research aims to prevent singularity issues arising from error boundary violations during abrupt changes in reference trajectories. We introduce a novel transformation function with infinite-order differentiability at connection points, advancing beyond mere continuous differentiability. Utilizing this transformation function, we develop a comprehensi…

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Juan Diego Toscano, Daniel T. Chen, Vivek Oommen, George Em Karniadakis
https://arxiv.org/abs/2509.14198

A Variational Framework for Residual-Based Adaptivity in Neural PDE Solvers and Operator Learning
Residual-based adaptive strategies are widely used in scientific machine learning but remain largely heuristic. We introduce a unifying variational framework that formalizes these methods by integrating convex transformations of the residual. Different transformations correspond to distinct objective functionals: exponential weights target the minimization of uniform error, while linear weights recover the minimization of quadratic error. Within this perspective, adaptive weighting is equivalen…

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 …

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…

Datarus-R1: An Adaptive Multi-Step Reasoning LLM for Automated Data Analysis
Ayoub Ben Chaliah, Hela Dellagi
https://arxiv.org/abs/2508.13382 https://arxiv…

Datarus-R1: An Adaptive Multi-Step Reasoning LLM for Automated Data Analysis
We present Datarus-R1-14B, a 14 B-parameter open-weights language model fine-tuned from Qwen 2.5-14B-Instruct to act as a virtual data analyst and graduate-level problem solver. Datarus is trained not on isolated question-answer pairs but on full analytical trajectories including reasoning steps, code execution, error traces, self-corrections, and final conclusions, all captured in a ReAct-style notebook format spanning finance, medicine, numerical analysis, and other quantitative domains. Our …

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

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Xinyuan Liu, Jiahui Chen, Bocheng Hu, Yu Sun, Xinyang Chen, Shaoxu Song
https://arxiv.org/abs/2507.10934

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Data quality remains an important challenge in data-driven systems, as errors in tabular data can severely compromise downstream analytics and machine learning performance. Although numerous error detection algorithms have been proposed, the lack of diverse, real-world error datasets limits comprehensive evaluation. Manual error annotation is both time-consuming and inconsistent, motivating the exploration of synthetic error generation as an alternative. In this work, we introduce TableEG, a fr…

Convergence Rates for Realizations of Gaussian Random Variables
Daniel Winkle, Ingo Steinwart, Bernard Haasdonk
https://arxiv.org/abs/2508.13940 https://ar…

Convergence Rates for Realizations of Gaussian Random Variables
This paper investigates the approximation of Gaussian random variables in Banach spaces, focusing on the high-probability bounds for the approximation of Gaussian random variables using finitely many observations. We derive non-asymptotic error bounds for the approximation of a Gaussian process $ X $ by its conditional expectation, given finitely many linear functionals. Specifically, we quantify the difference between the covariance of $ X $ and its finite-dimensional approximation, establishi…

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…

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…

Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN
Dewi Endah Kharismawati, Toni Kazic
https://arxiv.org/abs/2509.15181

Maize Seedling Detection Dataset (MSDD): A Curated High-Resolution RGB Dataset for Seedling Maize Detection and Benchmarking with YOLOv9, YOLO11, YOLOv12 and Faster-RCNN
Accurate maize seedling detection is crucial for precision agriculture, yet curated datasets remain scarce. We introduce MSDD, a high-quality aerial image dataset for maize seedling stand counting, with applications in early-season crop monitoring, yield prediction, and in-field management. Stand counting determines how many plants germinated, guiding timely decisions such as replanting or adjusting inputs. Traditional methods are labor-intensive and error-prone, while computer vision enables e…

CARES: Collaborative Agentic Reasoning for Error Detection in Surgery
Chang Han Low, Zhu Zhuo, Ziyue Wang, Jialang Xu, Haofeng Liu, Nazir Sirajudeen, Matthew Boal, Philip J. Edwards, Danail Stoyanov, Nader Francis, Jiehui Zhong, Di Gu, Evangelos B. Mazomenos, Yueming Jin
https://arxiv.org/abs/2508.08764

CARES: Collaborative Agentic Reasoning for Error Detection in Surgery
Robotic-assisted surgery (RAS) introduces complex challenges that current surgical error detection methods struggle to address effectively due to limited training data and methodological constraints. Therefore, we construct MERP (Multi-class Error in Robotic Prostatectomy), a comprehensive dataset for error detection in robotic prostatectomy with frame-level annotations featuring six clinically aligned error categories. In addition, we propose CARES (Collaborative Agentic Reasoning for Error De…

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…

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…

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.

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.

A computational method for type I error rate control in power-maximizing response-adaptive randomization
Stef Baas, Lukas Pin, Sof\'ia S. Villar, William F. Rosenberger
https://arxiv.org/abs/2509.12448

A computational method for type I error rate control in power-maximizing response-adaptive randomization
Maximizing statistical power in experimental design often involves imbalanced treatment allocation, but several challenges hinder its practical adoption: (1) the misconception that equal allocation always maximizes power, (2) when only targeting maximum power, more than half the participants may be expected to obtain inferior treatment, and (3) response-adaptive randomization (RAR) targeting maximum statistical power may inflate type I error rates substantially. Recent work identified issue (3)…

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 …

A Symmetry-Integrated Approach to Surface Code Decoding
Hoshitaro Ohnishi, Hideo Mukai
https://arxiv.org/abs/2509.10164 https://arxiv.org/pdf/2509.10164

A Symmetry-Integrated Approach to Surface Code Decoding
Quantum error correction, which utilizes logical qubits that are encoded as redundant multiple physical qubits to find and correct errors in physical qubits, is indispensable for practical quantum computing. Surface code is considered to be a promising encoding method with a high error threshold that is defined by stabilizer generators. However, previous methods have suffered from the problem that the decoder acquires solely the error probability distribution because of the non-uniqueness of co…

Replaced article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[1/5]:
- Concurrent Linguistic Error Detection (CLED): a New Methodology for Error Detection in Large Lang...
Jinhua Zhu, Javier Conde, Zhen Gao, Pedro Reviriego, Shanshan Liu, Fabrizio Lombardi

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…

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…

Uniquely-Decodable Coding for Zero-Error Network Function Computation
Xuan Guang, Jihang Yang, Ruze Zhang
https://arxiv.org/abs/2509.10775 https://arxiv.or…

Uniquely-Decodable Coding for Zero-Error Network Function Computation
We consider uniquely-decodable coding for zero-error network function computation, where in a directed acyclic graph, the single sink node is required to compute with zero error a target function multiple times, whose arguments are the information sources generated at a set of source nodes. We are interested in the computing capacity from the information theoretic point of view, which is defined as the infimum of the maximum expected number of bits transmitted on all the edges for computing the…

Error analysis of a fully discrete structure-preserving finite element scheme for a diffuse-interface model of tumour growth
Agus L. Soenjaya, Ping Lin, Thanh Tran
https://arxiv.org/abs/2509.14486

Error analysis of a fully discrete structure-preserving finite element scheme for a diffuse-interface model of tumour growth
We develop a fully discrete structure-preserving finite element method for a diffuse-interface model of tumour growth. The system couples a Cahn--Hilliard type equation with a nonlinear reaction-diffusion equation for nutrient concentration and admits a dissipative energy law at the continuous level. For the discretisation, we employ a scalar auxiliary variable (SAV) formulation together with a mixed finite element method for the Cahn--Hilliard part and standard conforming finite elements for t…

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…

Unconditional and optimal error analysis of two linearized finite difference schemes for the logarithmic Schr\"odinger equation
Tingchun Wang, Jingye Yan
https://arxiv.org/abs/2509.14736

Unconditional and optimal error analysis of two linearized finite difference schemes for the logarithmic Schrödinger equation
In this paper, we propose two linearized finite difference schemes for solving the logarithmic Schrödinger equation (LogSE) without the need for regularization of the logarithmic term. These two schemes employ the first-order and the second-order backward difference formula, respectively, for temporal discretization of the LogSE, while using the second-order central finite difference method for spatial discretization. We overcome the singularity posed by the logarithmic nonlinearity $f(u)=u\ln…

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…

$\boldsymbol{H}(\textbf{curl})$-reconstruction of piecewise polynomial fields with application to $hp$-a posteriori nonconforming error analysis for Maxwell's equations
Zhaonan Dong, Alexandre Ern
https://arxiv.org/abs/2508.12904

$\boldsymbol{H}(\textbf{curl})$-reconstruction of piecewise polynomial fields with application to $hp$-a posteriori nonconforming error analysis for Maxwell's equations
We devise and analyse a novel $\boldsymbol{H}(\textbf{curl})$-reconstruction operator for piecewise polynomial fields on shape-regular simplicial meshes. The (non-polynomial) reconstruction is devised over the mesh vertex patches using the partition of unity induced by hat basis functions in combination with local Helmholtz decompositions. Our main focus is on homogeneous tangential boundary conditions. We prove that the difference between the reconstructed $\boldsymbol{H}_0(\textbf{curl})$-fie…

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…

Long-time relative error analysis for linear ODEs with perturbed initial value
Stefano Maset
https://arxiv.org/abs/2507.08752 https://

Long-time relative error analysis for linear ODEs with perturbed initial value
We investigate the propagation of initial value perturbations along the solution of a linear ODE \( y'(t) = Ay(t) \). This propagation is analized using the relative error rather than the absolute error. Our focus is on the long-term behavior of this relative error, which differs significantly from that of the absolute error. Understanding this long-term behavior provides insights into the growth of the relative error over all times, not just at large times. Therefore, it represents a crucial a…

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…

Quasi-optimality of the Crouzeix-Raviart FEM for p-Laplace-type problems
Johannes Storn
https://arxiv.org/abs/2507.12742 https://arxi…

Quasi-optimality of the Crouzeix-Raviart FEM for p-Laplace-type problems
We verify quasi-optimality of the Crouzeix-Raviart FEM for nonlinear problems of $p$-Laplace type. More precisely, we show that the error of the Crouzeix-Raviart FEM with respect to a quasi-norm is bounded from above by a uniformly bounded constant times the best-approximation error plus a data oscillation term. As a byproduct, we verify a novel more localized a priori error estimate for the conforming lowest-order Lagrange FEM.

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), …

On approximate quantum error correction for symmetric noise
Gereon Ko{\ss}mann, Julius A. Zeiss, Omar Fawzi, Mario Berta
https://arxiv.org/abs/2507.12326 h…

On approximate quantum error correction for symmetric noise
We revisit the extendability-based semi-definite programming hierarchy introduced by Berta et al. [Mathematical Programming, 1 - 49 (2021)], which provides converging outer bounds on the optimal fidelity of approximate quantum error correction (AQEC). As our first contribution, we introduce a measurement-based rounding scheme that extracts inner sequences of certifiably good encoder-decoder pairs from this outer hierarchy. To address the computational complexity of evaluating fixed levels of th…

Quantum Error Mitigation by Global Randomized Error Cancellation for Adiabatic Evolution in the Schwinger Model
Oleg Kaikov, Theo Saporiti, Vasily Sazonov, Mohamed Tamaazousti
https://arxiv.org/abs/2507.06601

Quantum Error Mitigation by Global Randomized Error Cancellation for Adiabatic Evolution in the Schwinger Model
We extend the global randomized error cancellation (GREC) method for quantum error mitigation (QEM) in an application to adiabatic evolution of states on a noisy quantum device. We apply the adiabatic GREC method to the evolution of eigenstates in the lattice Schwinger model on a simulated quantum device with custom noise. Our results suggest that the corresponding QEM learned in one parameter regime of the model successfully transfers to a different parameter regime. In particular, our finding…

State-adaptive quantum error correction and fault-tolerant quantum computing
D. -S. Wang
https://arxiv.org/abs/2508.06011 https://arxiv.org/pdf/2508.06011

State-adaptive quantum error correction and fault-tolerant quantum computing
We present a theoretical framework for state-adaptive quantum error correction (SAQEC) that bridges the gap between quantum computing and error correction paradigms. By incorporating knowledge of quantum states into the error correction process, we establish a new capacity regime governed by quantum mutual information rather than coherent information. This approach reveals a fundamental connection to entanglement-assisted protocols. We demonstrate practical applications in fault-tolerant quantu…

The superconducting grid-states qubit
Long B. Nguyen, Hyunseong Kim, Dat T. Le, Thomas Ersevim, Sai P. Chitta, Trevor Chistolini, Christian J\"unger, W. Clarke Smith, T. M. Stace, Jens Koch, David I. Santiago, Irfan Siddiqi
https://arxiv.org/abs/2509.14656

The superconducting grid-states qubit
Decoherence errors arising from noisy environments remain a central obstacle to progress in quantum computation and information processing. Quantum error correction (QEC) based on the Gottesman-Kitaev-Preskill (GKP) protocol offers a powerful strategy to overcome this challenge, with successful demonstrations in trapped ions, superconducting circuits, and photonics. Beyond active QEC, a compelling alternative is to engineer Hamiltonians that intrinsically enforce stabilizers, offering passive p…