Tootfinder

RANA: Robust Active Learning for Noisy Network Alignment
Yixuan Nan, Xixun Lin, Yanmin Shang, Zhuofan Li, Can Zhao, Yanan Cao
https://arxiv.org/abs/2507.22434 https://

RANA: Robust Active Learning for Noisy Network Alignment
Network alignment has attracted widespread attention in various fields. However, most existing works mainly focus on the problem of label sparsity, while overlooking the issue of noise in network alignment, which can substantially undermine model performance. Such noise mainly includes structural noise from noisy edges and labeling noise caused by human-induced and process-driven errors. To address these problems, we propose RANA, a Robust Active learning framework for noisy Network Alignment. …

Tiny Noise-Robust Voice Activity Detector for Voice Assistants
Hamed Jafarzadeh Asl, Mahsa Ghazvini Nejad, Amin Edraki, Masoud Asgharian, Vahid Partovi Nia
https://arxiv.org/abs/2507.22157

Tiny Noise-Robust Voice Activity Detector for Voice Assistants
Voice Activity Detection (VAD) in the presence of background noise remains a challenging problem in speech processing. Accurate VAD is essential in automatic speech recognition, voice-to-text, conversational agents, etc, where noise can severely degrade the performance. A modern application includes the voice assistant, specially mounted on Artificial Intelligence of Things (AIoT) devices such as cell phones, smart glasses, earbuds, etc, where the voice signal includes background noise. Therefo…

Noise-Resilient Quantum Reinforcement Learning
Jing-Ci Yue, Jun-Hong An
https://arxiv.org/abs/2508.20601 https://arxiv.org/pdf/2508.20601

Noise-Resilient Quantum Reinforcement Learning
As a branch of quantum machine learning, quantum reinforcement learning (QRL) aims to solve complex sequential decision-making problems more efficiently and effectively than its classical counterpart by exploiting quantum resources. However, in the noisy intermediate-scale quantum (NISQ) era, its realization is challenged by the ubiquitous noise-induced decoherence. Here, we propose a noise-resilient QRL scheme for a quantum eigensolver. By investigating the non-Markovian decoherence effect on …

Privacy-Preserving Anonymization of System and Network Event Logs Using Salt-Based Hashing and Temporal Noise
Shreyas Bargale, Akshit Vakati Venkata, Jaimandeep Singh, Chester Rebeiro
https://arxiv.org/abs/2507.21904

Privacy-Preserving Anonymization of System and Network Event Logs Using Salt-Based Hashing and Temporal Noise
System and network event logs are essential for security analytics, threat detection, and operational monitoring. However, these logs often contain Personally Identifiable Information (PII), raising significant privacy concerns when shared or analyzed. A key challenge in log anonymization is balancing privacy protection with the retention of sufficient structure for meaningful analysis. Overly aggressive anonymization can destroy contextual integrity, while weak techniques risk re-identificatio…

Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
Qi Gao, Zhihao Chen, Dong Zeng, Junping Zhang, Jianhua Ma, Hongming Shan
https://arxiv.org/abs/2506.22012

Noise-Inspired Diffusion Model for Generalizable Low-Dose CT Reconstruction
The generalization of deep learning-based low-dose computed tomography (CT) reconstruction models to doses unseen in the training data is important and remains challenging. Previous efforts heavily rely on paired data to improve the generalization performance and robustness through collecting either diverse CT data for re-training or a few test data for fine-tuning. Recently, diffusion models have shown promising and generalizable performance in low-dose CT (LDCT) reconstruction, however, they …

Decoherence of Majorana qubits by 1/f noise
Abhijeet Alase, Marcus C. Goffage, Maja C. Cassidy, Susan N. Coppersmith
https://arxiv.org/abs/2506.22394 https…

Decoherence of Majorana qubits by 1/f noise
Qubits based on Majorana Zero Modes (MZMs) in superconductor-semiconductor nanowires have attracted intense interest as a platform for utility-scale quantum computing. These qubits have been predicted to show extremely low error rates due to topological protection of the MZMs, where decoherence processes have been thought to be exponentially suppressed by either the nanowire length or the temperature. However, here we show that 1/f noise, which is ubiquitous in semiconductors, gives rise to a p…

Impact of Phase Noise and Power Amplifier Non-Linearities on Downlink Cell-Free Massive MIMO-OFDM Systems
\"Ozlem Tu\u{g}fe Demir, Emil Bj\"ornson
https://arxiv.org/abs/2507.21635

Impact of Phase Noise and Power Amplifier Non-Linearities on Downlink Cell-Free Massive MIMO-OFDM Systems
Cell-free massive MIMO (multiple-input multiple-output) is a key enabler for the sixth generation (6G) of mobile networks, offering significant spectral and energy efficiency gains through user-centric operation of distributed access points (APs). However, its reliance on low-cost APs introduces inevitable hardware impairments, whose combined impact on wideband downlink systems remains unexplored when analyzed using behavioral models. This paper presents a comprehensive analysis of the downlink…

Distinguishing Quantum Software Bugs from Hardware Noise: A Statistical Approach
Ahmik Virani, Devraj, Anirudh Suresh, Lei Zhang, M V Panduranga Rao
https://arxiv.org/abs/2507.20475

Distinguishing Quantum Software Bugs from Hardware Noise: A Statistical Approach
Quantum computing in the Noisy Intermediate-Scale Quantum (NISQ) era presents significant challenges in differentiating quantum software bugs from hardware noise. Traditional debugging techniques from classical software engineering cannot directly resolve this issue due to the inherently stochastic nature of quantum computation mixed with noises from NISQ computers. To address this gap, we propose a statistical approach leveraging probabilistic metrics to differentiate between quantum software …

Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control
Mahmood Mazare, Hossein Ramezani
https://arxiv.org/abs/2506.20780 https://

Noise-Tolerant Hybrid Approach for Data-Driven Predictive Control
This paper focuses on a key challenge in hybrid data-driven predictive control: the effect of measurement noise on Hankel matrices. While noise is handled in direct and indirect methods, hybrid approaches often overlook its impact during trajectory estimation. We propose a Noise-Tolerant Data-Driven Predictive Control (NTDPC) framework that integrates singular value decomposition to separate system dynamics from noise within reduced-order Hankel matrices. This enables accurate prediction with s…

Consider: we came fairly close to extincting the various species crucial to this cycle *AND STILL COULD.*
The problem today isn’t the ongoing symbolic & subsistence whaling, it is that we have ravaged the ocean ecosystem in ways we don’t understand, such as noise pollution.
We really don’t understand enough about the subsystems of the oceans to be fucking with them in the ways we have been during the past century.

An Image Noise Level Estimation Based on Tensor T-Product
Hanxin Liu, Yisheng Song
https://arxiv.org/abs/2507.20515 https://arxiv.org/pdf/2507.20515…

An Image Noise Level Estimation Based on Tensor T-Product
Currently, the noise level of color images is estimated by many algorithms through separate selection of each page of the third-order tensor using sliding blocks of size ${M_1} \times {M_1}$. The data structure of the tensor is disrupted by this method, leading to errors in the estimation results. In order not to disrupt the data structure of the tensor, we directly select the tensor using a sliding block of size ${M_1} \times {M_1} \times 3$ and then re-arrange it. The newly obtained tensor is…

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability
Syamantak Sarkar, Revoti P. Bora, Bhupender Kaushal, Sudhish N George, Kiran Raja
https://arxiv.org/abs/2508.18154

Assessing the Noise Robustness of Class Activation Maps: A Framework for Reliable Model Interpretability
Class Activation Maps (CAMs) are one of the important methods for visualizing regions used by deep learning models. Yet their robustness to different noise remains underexplored. In this work, we evaluate and report the resilience of various CAM methods for different noise perturbations across multiple architectures and datasets. By analyzing the influence of different noise types on CAM explanations, we assess the susceptibility to noise and the extent to which dataset characteristics may impa…

Quantitative Benchmarking of Remote Excitation in Plasmonic Sensing with Enhanced Signal-to-Noise Ratio
Tao He, Haoran Liu, Zihe Jiang, Zhiwei Hu, Banghuan Zhang, Xiaohui Dong, Chaowei Sun, Wei Jiang, Jiawei Sun, Yang Li, Huatian Hu, Wen Chen, Hongxing Xu
https://arxiv.org/abs/2507.22725

Quantitative Benchmarking of Remote Excitation in Plasmonic Sensing with Enhanced Signal-to-Noise Ratio
Remote excitation using guided optical modes -- such as waveguides, fibers, or surface waves -- offers a promising alternative to direct optical excitation for surface-enhanced Raman scattering (SERS), particularly in applications requiring reduced heating, minimal invasiveness, and on-chip integration. However, despite its widespread use, systematic comparisons between remote and direct excitation remain limited. Here, we quantitatively benchmark both schemes by measuring power-dependent SERS …

A Fast Parallel Median Filtering Algorithm Using Hierarchical Tiling
Louis Sugy (NVIDIA)
https://arxiv.org/abs/2507.19926 https://arxiv.org/pdf/2507.19926

A Fast Parallel Median Filtering Algorithm Using Hierarchical Tiling
Median filtering is a non-linear smoothing technique widely used in digital image processing to remove noise while retaining sharp edges. It is particularly well suited to removing outliers (impulse noise) or granular artifacts (speckle noise). However, the high computational cost of median filtering can be prohibitive. Sorting-based algorithms excel with small kernels but scale poorly with increasing kernel diameter, in contrast to constant-time methods characterized by higher constant factors…

SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization
Chien-Chun Wang, En-Lun Yu, Jeih-Weih Hung, Shih-Chieh Huang, Berlin Chen
https://arxiv.org/abs/2508.20885

SincQDR-VAD: A Noise-Robust Voice Activity Detection Framework Leveraging Learnable Filters and Ranking-Aware Optimization
Voice activity detection (VAD) is essential for speech-driven applications, but remains far from perfect in noisy and resource-limited environments. Existing methods often lack robustness to noise, and their frame-wise classification losses are only loosely coupled with the evaluation metric of VAD. To address these challenges, we propose SincQDR-VAD, a compact and robust framework that combines a Sinc-extractor front-end with a novel quadratic disparity ranking loss. The Sinc-extractor uses le…

*Hacker noise*

Nonparametric Inference for Noise Covariance Kernels in Parabolic SPDEs using Space-Time Infill-Asymptotics
Andreas Petersson, Dennis Schroers
https://arxiv.org/abs/2508.20947 h…

Nonparametric Inference for Noise Covariance Kernels in Parabolic SPDEs using Space-Time Infill-Asymptotics
We develop an asymptotic limit theory for nonparametric estimation of the noise covariance kernel in linear parabolic stochastic partial differential equations (SPDEs) with additive colored noise, using space-time infill asymptotics. The method employs discretized infinite-dimensional realized covariations and requires only mild regularity assumptions on the kernel to ensure consistent estimation and asymptotic normality of the estimator. On this basis, we construct omnibus goodness-of-fit test…

On the infinite time horizon approximation for L\'evy-driven McKean-Vlasov SDEs with common noise
Ke Xu, Fen-Fen Yang, Chenggui Yuan
https://arxiv.org/abs/2508.20807 https:/…

On the infinite time horizon approximation for Lévy-driven McKean-Vlasov SDEs with common noise
In this work, we establish the existence and uniqueness of solutions to McKean-Vlasov stochastic differential equations (SDEs) driven by Lévy processes with common noise on an infinite time horizon, by means of a contraction mapping principle in the space of probability measures. In addition, we analyse the propagation of chaos for Lévy-driven McKean-Vlasov SDEs in the presence of common noise.

Interesting
https://www.premierguitar.com/news/new-bzzzzkill-device-solves-stratocaster-60hz-hum-problem

New BzzzzKill Device Solves Stratocaster 60Hz Hum Problem
The newly launched company BzzzzKill has introduced its debut product: a specially designed coil that drastically reduces hum in Stratocaster-style guitars.BzzzzKill solves a problem that has plagued Strat players for more than 70 years: single-coil pickup noise. The new BzzzzKill hum reduction dev...

🇺🇦 #NowPlaying on KEXP's #AfternoonShow
Public Enemy:
🎵 Bring the Noise
#PublicEnemy
https://leksa.bandcamp.com/track/public-enemy-bring-the-noise-leksa-remix
https://open.spotify.com/track/5BiuxSuxOZ9CcQ6SqICsDv

Bookmarked: Talking About Muslims in Middle French: The Potential of Word-to-Vector Models for Studying Semantic Relationships in Medieval Languages – DH Lab #Digital_Humanities

I have given The Train Noise corporeal form and released it onto the internet. I am sorry. https://youtu.be/5PfCp1AVH_o

I have given The Train Noise corporeal form and released it onto the internet. I am sorry. https://youtu.be/5PfCp1AVH_o

kind of uncanny to hear a hi-fi recording of the ecstatic #austin #drone-#noise collective water damage & actually be able to pick out (some of) the different musicians, maybe even more beautiful.

Berlin-based Synthflow, a no-code service letting enterprises build and deploy customized voice AI customer service agents, raised a $20M Series A led by Accel (Rebecca Szkutak/TechCrunch)
https://techcrunch.com/2025/06/24/how-

How Synthflow AI is cutting through the noise in a loud AI voice category | TechCrunch
Synthflow AI was founded in 2023 and has amassed more than 1,000 customers and handled more than 45 million calls.

Identification of Noise-Associated Glitches in KAGRA O3GK with Hveto
T. Akutsu, M. Ando, M. Aoumi, A. Araya, Y. Aso, L. Baiotti, R. Bajpai, K. Cannon, A. H. -Y. Chen, D. Chen, H. Chen, A. Chiba, C. Chou, M. Eisenmann, K. Endo, T. Fujimori, S. Garg, D. Haba, S. Haino, R. Harada, H. Hayakawa, K. Hayama, S. Fujii, Y. Himemoto, N. Hirata, C. Hirose, H. -F. Hsieh, H. -Y. Hsieh, C. Hsiung, S. -H. Hsu, K. Ide, R. Iden, S. Ikeda, H. Imafuku, R. Ishikawa, Y. Itoh, M. Iwaya, H-B. Jin, K. Jung, T…

Identification of Noise-Associated Glitches in KAGRA O3GK with Hveto
Transient noise ("glitches") in gravitational wave detectors can mimic or obscure true signals, significantly reducing detection sensitivity. Identifying and excluding glitch-contaminated data segments is therefore crucial for enhancing the performance of gravitational-wave searches. We perform a noise analysis of the KAGRA data obtained during the O3GK observation. Our analysis is performed with hierarchical veto (Hveto) which identifies noises based on the statistical time correlation between…

A morning thought: claiming that someone is "bothered" by the intensive smell of perfume is an unfair simplification.
It's not that I "don't like" intensive smells. It's an actual physiological reaction. I recall that a long time ago, I accidentally sat behind a classmate who used a lot of perfume. Took me a while to realize why I was getting dizzy wherever I leaned down to note something (at first I thought that leaning was the problem, not entering the cloud of perfume). Using perfumes is a choice, not getting dizzy over smells.
Same goes for noise. It's not that I "don't like" noise. It's that noise kills my nervous system. It's not that I have a choice.
#ActuallyAutistic

Rainbow Noise: Stress-Testing Multimodal Harmful-Meme Detectors on LGBTQ Content
Ran Tong, Songtao Wei, Jiaqi Liu, Lanruo Wang
https://arxiv.org/abs/2507.19551 https://

Rainbow Noise: Stress-Testing Multimodal Harmful-Meme Detectors on LGBTQ Content
Hateful memes aimed at LGBTQ\,+ communities often evade detection by tweaking either the caption, the image, or both. We build the first robustness benchmark for this setting, pairing four realistic caption attacks with three canonical image corruptions and testing all combinations on the PrideMM dataset. Two state-of-the-art detectors, MemeCLIP and MemeBLIP2, serve as case studies, and we introduce a lightweight \textbf{Text Denoising Adapter (TDA)} to enhance the latter's resilience. Across t…

Precision spectral estimation at sub-Hz frequencies: closed-form posteriors and Bayesian noise projection
Lorenzo Sala, Stefano Vitale
https://arxiv.org/abs/2507.20846 https://

Precision spectral estimation at sub-Hz frequencies: closed-form posteriors and Bayesian noise projection
We present a Bayesian method for estimating spectral quantities in multivariate Gaussian time series. The approach, based on periodograms and Wishart statistics, yields closed-form expressions at any given frequency for the marginal posterior distributions of the individual power spectral densities, the pairwise coherence, and the multiple coherence, as well as for the joint posterior distribution of the full cross-spectral density matrix. In the context of noise projection - where one series i…

TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
Tianrong Chen, Huangjie Zheng, David Berthelot, Jiatao Gu, Josh Susskind, Shuangfei Zhai
https://arxiv.org/abs/2506.21757

TADA: Improved Diffusion Sampling with Training-free Augmented Dynamics
Diffusion models have demonstrated exceptional capabilities in generating high-fidelity images but typically suffer from inefficient sampling. Many solver designs and noise scheduling strategies have been proposed to dramatically improve sampling speeds. In this paper, we introduce a new sampling method that is up to $186\%$ faster than the current state of the art solver for comparative FID on ImageNet512. This new sampling method is training-free and uses an ordinary differential equation (OD…

Sound Source Localization for Human-Robot Interaction in Outdoor Environments
Victor Liu, Timothy Du, Jordy Sehn, Jack Collier, Fran\c{c}ois Grondin
https://arxiv.org/abs/2507.21431

Sound Source Localization for Human-Robot Interaction in Outdoor Environments
This paper presents a sound source localization strategy that relies on a microphone array embedded in an unmanned ground vehicle and an asynchronous close-talking microphone near the operator. A signal coarse alignment strategy is combined with a time-domain acoustic echo cancellation algorithm to estimate a time-frequency ideal ratio mask to isolate the target speech from interferences and environmental noise. This allows selective sound source localization, and provides the robot with the di…

An iterative CMB lensing estimator minimizing instrumental noise bias
Louis Legrand, Blake Sherwin, Anthony Challinor, Julien Carron, Gerrit S. Farren
https://arxiv.org/abs/2506.20667

An iterative CMB lensing estimator minimizing instrumental noise bias
Noise maps from CMB experiments are generally statistically anisotropic, due to scanning strategies, atmospheric conditions, or instrumental effects. Any mis-modeling of this complex noise can bias the reconstruction of the lensing potential and the measurement of the lensing power spectrum from the observed CMB maps. We introduce a new CMB lensing estimator based on the maximum a posteriori (MAP) reconstruction that is minimally sensitive to these instrumental noise biases. By modifying the li…

🤬 Study documents unhealthy noise in Portland, provides research framework for other cities
https://medicalxpress.com/news/2025-07-documents-unhealthy-noise-portland-framework.html

Study documents unhealthy noise in Portland, provides research framework for other cities
A groundbreaking study led by Oregon State University scientists shows that multiple Portland neighborhoods have levels of noise that are likely unhealthy.

Derivative Estimation from Coarse, Irregular, Noisy Samples: An MLE-Spline Approach
Konstantin E. Avrachenkov, Leonid B. Freidovich
https://arxiv.org/abs/2507.22176 https://

Derivative Estimation from Coarse, Irregular, Noisy Samples: An MLE-Spline Approach
We address numerical differentiation under coarse, non-uniform sampling and Gaussian noise. A maximum-likelihood estimator with $L_2$-norm constraint on a higher-order derivative is obtained, yielding spline-based solution. We introduce a non-standard parameterization of quadratic splines and develop recursive online algorithms. Two formulations -- quadratic and zero-order -- offer tradeoff between smoothness and computational speed. Simulations demonstrate superior performance over high-gain o…

Differentially Private Federated Quantum Learning via Quantum Noise
Atit Pokharel, Ratun Rahman, Shaba Shaon, Thomas Morris, Dinh C. Nguyen
https://arxiv.org/abs/2508.20310 http…

Differentially Private Federated Quantum Learning via Quantum Noise
Quantum federated learning (QFL) enables collaborative training of quantum machine learning (QML) models across distributed quantum devices without raw data exchange. However, QFL remains vulnerable to adversarial attacks, where shared QML model updates can be exploited to undermine information privacy. In the context of noisy intermediate-scale quantum (NISQ) devices, a key question arises: How can inherent quantum noise be leveraged to enforce differential privacy (DP) and protect model infor…

Electromagnetic Noise Characterization and Suppression in Low-Field MRI Systems
Teresa Guallart-Naval, Joseba Alonso
https://arxiv.org/abs/2507.20413 https://

Electromagnetic Noise Characterization and Suppression in Low-Field MRI Systems
Purpose: Low-field MRI systems operate at single MHz-range frequencies, where signal losses are primarily dominated by thermal noise from the radio-frequency (RF) receive coils. Achieving operation close to this limit is essential for maximizing imaging performance and signal-to-noise ratio (SNR). However, electromagnetic interference (EMI) from cabling, electronics, and patient loading often degrades system performance. Our goal is to develop and validate a practical protocol that guides users…

Broncos' Patrick Surtain II: Bo Nix 'going to make a lot of noise' in 2025 https://www.nfl.com/news/broncos-patrick-surtain-ii-bo-nix-going-to-make-a-lot-of-noise-in-2025

Collective oscillations in the finite-size Kuramoto model below the critical coupling: shot-noise approach
Sergei Kirillov, Vladimir Klinshov
https://arxiv.org/abs/2506.22160

Collective oscillations in the finite-size Kuramoto model below the critical coupling: shot-noise approach
The Kuramoto model, a paradigmatic framework for studying synchronization, exhibits a transition to collective oscillations only above a critical coupling strength in the thermodynamic limit. However, real-world systems are finite, and their dynamics can deviate significantly from mean-field predictions. Here, we investigate finite-size effects in the Kuramoto model below the critical coupling, where the infinite-size theory predicts complete asynchrony. Using a shot-noise approach, we derive a…

Listen, children:
Your father is dead.
From his old coats
I'll make you little jackets;
I'll make you little trousers
From his old pants.
There'll be in his pockets
Things he used to put there,
Keys and pennies
Covered with tobacco;
Dan shall have the pennies
To save in his bank;
Anne shall have the keys
To make a pretty noise with.
...cont. 1/2

Comprehensive Analysis of Behavioral Hardware Impairments in Cell-Free Massive MIMO-OFDM Uplink: Centralized Operation
\"Ozlem Tu\u{g}fe Demir, Muhammed Selman Somuncu, Ahmet M. Elbir, Emil Bj\"ornson
https://arxiv.org/abs/2507.21626

Comprehensive Analysis of Behavioral Hardware Impairments in Cell-Free Massive MIMO-OFDM Uplink: Centralized Operation
Cell-free massive MIMO is a key 6G technology, offering superior spectral and energy efficiency. However, its dense deployment of low-cost access points (APs) makes hardware impairments unavoidable. While narrowband impairments are well-studied, their impact in wideband systems remains unexplored. This paper provides the first comprehensive analysis of hardware impairments, such as nonlinear distortion in low-noise amplifiers, phase noise, in-phase-quadrature imbalance, and low-resolution analo…

$(H,H^2)$-smoothing effect of Navier-Stokes equations with additive white noise on two-dimensional torus
Hongyong Cui, Hui Liu, Jie Xin
https://arxiv.org/abs/2508.18745 https://…

$(H,H^2)$-smoothing effect of Navier-Stokes equations with additive white noise on two-dimensional torus
This paper is devoted to the regularity of Navier-Stokes (NS) equations with additive white noise on two-dimensional torus $\mathbb T^2$. Under the conditions that the external force $f(x)$ belongs to the phase space $ H$ and the noise intensity function $h(x)$ satisfies $\|\nabla h\|_{L^\infty} \leq \sqrt πνλ_1$, where $ ν$ is the kinematic viscosity of the fluid and $λ_1$ is the first eigenvalue of the Stokes operator, it was proved that the random NS equations possess a temper…

Morph: ChirpTransformer-based Encoder-decoder Co-design for Reliable LoRa Communication
Yidong Ren, Maolin Gan, Chenning Li, Shakhrul Iman Siam, Mi Zhang, Shigang Chen, Zhichao Cao
https://arxiv.org/abs/2507.22851

Morph: ChirpTransformer-based Encoder-decoder Co-design for Reliable LoRa Communication
In this paper, we propose Morph, a LoRa encoder-decoder co-design to enhance communication reliability while improving its computation efficiency in extremely-low signal-to-noise ratio (SNR) situations. The standard LoRa encoder controls 6 Spreading Factors (SFs) to tradeoff SNR tolerance with data rate. SF-12 is the maximum SF providing the lowest SNR tolerance on commercial off-the-shelf (COTS) LoRa nodes. In Morph, we develop an SF-configuration based encoder to mimic the larger SFs beyond S…

Replaced article(s) found for q-bio.QM. https://arxiv.org/list/q-bio.QM/new
[1/1]:
- Inferring biological processes with intrinsic noise from cross-sectional data
Suryanarayana Maddu, Victor Chard\`es, Michael. J. Shelley

Interpolation-Based Gradient-Error Bounds for Use in Derivative-Free Optimization of Noisy Functions
Alejandro G. Marchetti, Dominique Bonvin
https://arxiv.org/abs/2507.19661 ht…

Interpolation-Based Gradient-Error Bounds for Use in Derivative-Free Optimization of Noisy Functions
In this paper, we analyze the accuracy of gradient estimates obtained by linear interpolation when the underlying function is subject to bounded measurement noise. The total gradient error is decomposed into a deterministic component arising from the interpolation (finite-difference) approximation, and a stochastic component due to noise. Various upper bounds for both error components are derived and compared through several illustrative examples. Our comparative study reveals that strict deter…

A Statistical Approach to Quantifying Uncertainty in Meteoroid Physical Properties
Maximilian Vovk, Denis Vida, Peter G. Brown
https://arxiv.org/abs/2506.21701

A Statistical Approach to Quantifying Uncertainty in Meteoroid Physical Properties
Meteoroid bulk density is a critical value required for assessing impact risks to spacecraft, informing shielding and mission design. Direct bulk density measurements for sub-millimeter to millimeter-sized meteoroids are difficult, often relying on forward modeling without robust uncertainty estimates. Methods based solely on select observables can overlook noise-induced biases and non-linear relations between physical parameters. This study aims to automate the inversion of meteoroid physical …

Truncated Gaussian Noise Estimation in State-Space Models
Rodrigo A. Gonz\'alez, Angel L. Cede\~no, Koen Tiels, Tom Oomen
https://arxiv.org/abs/2507.19244 https://

Truncated Gaussian Noise Estimation in State-Space Models
Within Bayesian state estimation, considerable effort has been devoted to incorporating constraints into state estimation for process optimization, state monitoring, fault detection and control. Nonetheless, in the domain of state-space system identification, the prevalent practice entails constructing models under Gaussian noise assumptions, which can lead to inaccuracies when the noise follows bounded distributions. With the aim of generalizing the Gaussian noise assumption to potentially tru…

Provable Mixed-Noise Learning with Flow-Matching
Paul Hagemann, Robert Gruhlke, Bernhard Stankewitz, Claudia Schillings, Gabriele Steidl
https://arxiv.org/abs/2508.18122 https:/…

Provable Mixed-Noise Learning with Flow-Matching
We study Bayesian inverse problems with mixed noise, modeled as a combination of additive and multiplicative Gaussian components. While traditional inference methods often assume fixed or known noise characteristics, real-world applications, particularly in physics and chemistry, frequently involve noise with unknown and heterogeneous structure. Motivated by recent advances in flow-based generative modeling, we propose a novel inference framework based on conditional flow matching embedded with…

When Networks Mislead: How Partisan Communication Undermines Democratic Decision-Making
Hsuan-Wei Lee, Po-Kang Hsiao
https://arxiv.org/abs/2506.21820 https…

When Networks Mislead: How Partisan Communication Undermines Democratic Decision-Making
Democratic societies increasingly rely on communication networks to aggregate citizen preferences and information, yet these same networks can systematically mislead voters under certain conditions. We introduce an agent-based model that captures two rival forces in partisan networks: honest noise filtering that lifts accuracy and strategic bluffing that embeds bias. Extensive simulations show that communication architecture shapes voting accuracy more than any individual-level trait. When cand…

Function space induced by no arbitrage
Kihun Nam, Yunxi Xu
https://arxiv.org/abs/2506.22213 https://arxiv.org/pdf/2506.22213

Function space induced by no arbitrage
In this article, we show necessary and sufficient conditions for a function to transform a continuous Markov semimartingale to a semimartingale. As a result, the no-arbitrage principle guarantees the differentiability of asset prices with respect to the underlying noise, if the asset prices are continuous and the underlying noise is a continuous Markov semimartingale.

Existence of Strong Randomized Equilibria in Mean-Field Games of Optimal Stopping with Common Noise
Giorgio Ferrari, Anna Pajola
https://arxiv.org/abs/2507.19123 https://…

Existence of Strong Randomized Equilibria in Mean-Field Games of Optimal Stopping with Common Noise
We study a mean-field game of optimal stopping and investigate the existence of strong solutions via a connection with the Bank-El Karoui's representation problem. Under certain continuity assumptions, where the common noise is generated by a countable partition, we show that a strong randomized mean-field equilibrium exists, in which the mean-field interaction term is adapted to the common noise and the stopping time is randomized. Furthermore, under suitable monotonicity assumptions and for a…

So I heard fedi like trains

Slicing Through The Noise
Great Australian Pods Podcast Directory: #GreatAusPods

Putting Skill as Nearly Indistinguishable from Noise: An Empirical Bayes Analysis of PGA Tour Performance
Ryan S. Brill, Abraham J. Wyner
https://arxiv.org/abs/2506.21822

Putting Skill as Nearly Indistinguishable from Noise: An Empirical Bayes Analysis of PGA Tour Performance
We revisit a foundational question in golf analytics: how important are the core components of performance--driving, approach play, and putting--in explaining success on the PGA Tour? Building on Mark Broadie's strokes gained analyses, we use an empirical Bayes approach to estimate latent golfer skill and assess statistical significance using a multiple testing procedure that controls the false discovery rate. While tee-to-green skill shows clear and substantial differences across players, putt…

First use of large area SiPM matrices coupled with NaI(Tl) scintillating crystal for low energy dark matter search
Edoardo Martinenghi, Valerio Toso, Fabrizio Bruno Armani, Andrea Castoldi, Giuseppe di Carlo, Luca Frontini, Niccol\`o Gallice, Chiara Guazzoni, Valentino Liberali, Alberto Stabile, Valeria Trabattoni, Andrea Zani, Davide D'Angelo
https://

First use of large area SiPM matrices coupled with NaI(Tl) scintillating crystal for low energy dark matter search
The long-standing claim of dark matter detection by the DAMA experiment remains a crucial open question in astroparticle physics. A key step towards its independent verification is the development of NaI(Tl)-based detectors with improved sensitivity at low energies. The majority of NaI(Tl)-based experiments rely on conventional photomultiplier tubes (PMTs) as single photon detectors, which present technological limitations in terms of light collection, intrinsic radioactivity and a high noise c…

A Clockwork Quantum: Symmetry, Noise, and the Emergence of Quantum Order
Eric R. Bittner, Bhavay Tyagi
https://arxiv.org/abs/2507.19348 https://arxiv.org/p…

A Clockwork Quantum: Symmetry, Noise, and the Emergence of Quantum Order
We present a concise review and perspective on noise-induced synchronization and coherence protection in open quantum systems, with emphasis on recent work involving coupled spins, oscillators, and anyons. When local environments exhibit internal correlations, the structure of the noise determines which collective modes become decoherence-protected. This leads to steady-state entanglement, phase locking, and exceptional points (EPs) in the Liouvillian spectrum, signaling a collapse of the mode …

Binaural Localization Model for Speech in Noise
Vikas Tokala, Eric Grinstein, Rory Brooks, Mike Brookes, Simon Doclo, Jesper Jensen, Patrick A. Naylor
https://arxiv.org/abs/2507.20027

Binaural Localization Model for Speech in Noise
Binaural acoustic source localization is important to human listeners for spatial awareness, communication and safety. In this paper, an end-to-end binaural localization model for speech in noise is presented. A lightweight convolutional recurrent network that localizes sound in the frontal azimuthal plane for noisy reverberant binaural signals is introduced. The model incorporates additive internal ear noise to represent the frequency-dependent hearing threshold of a typical listener. The loca…

A Tactical Behaviour Recognition Framework Based on Causal Multimodal Reasoning: A Study on Covert Audio-Video Analysis Combining GAN Structure Enhancement and Phonetic Accent Modelling
Wei Meng
https://arxiv.org/abs/2507.21100

A Tactical Behaviour Recognition Framework Based on Causal Multimodal Reasoning: A Study on Covert Audio-Video Analysis Combining GAN Structure Enhancement and Phonetic Accent Modelling
This paper introduces TACTIC-GRAPHS, a system that combines spectral graph theory and multimodal graph neural reasoning for semantic understanding and threat detection in tactical video under high noise and weak structure. The framework incorporates spectral embedding, temporal causal edge modeling, and discriminative path inference across heterogeneous modalities. A semantic-aware keyframe extraction method fuses visual, acoustic, and action cues to construct temporal graphs. Using graph atten…

Replaced article(s) found for eess.IV. https://arxiv.org/list/eess.IV/new
[1/1]:
- Beyond Image Prior: Embedding Noise Prior into Conditional Denoising Transformer
Yuanfei Huang, Hua Huang

Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Keyi Li, Yuval Kluger, Boris Landa
https://arxiv.org/abs/2507.18520 https://arxiv…

Euclidean Distance Deflation Under High-Dimensional Heteroskedastic Noise
Pairwise Euclidean distance calculation is a fundamental step in many machine learning and data analysis algorithms. In real-world applications, however, these distances are frequently distorted by heteroskedastic noise$\unicode{x2014}$a prevalent form of inhomogeneous corruption characterized by variable noise magnitudes across data observations. Such noise inflates the computed distances in a nontrivial way, leading to misrepresentations of the underlying data geometry. In this work, we addre…

Glitches far from transient gravitational-wave events do not bias inference
Sophie Hourihane, Katerina Chatziioannou
https://arxiv.org/abs/2506.21869 https…

Glitches far from transient gravitational-wave events do not bias inference
Non-Gaussian noise in gravitational-wave detectors, known as "glitches," can bias the inferred parameters of transient signals when they occur nearby in time and frequency. These biases are addressed with a variety of methods that remove or otherwise mitigate the impact of the glitch. Given the computational cost and human effort required for glitch mitigation, we study the conditions under which it is strictly necessary. We consider simulated glitches and gravitational-wave signals in various …

Google unveils $130 Pixel Buds 2A with a Tensor A1 chip, Gemini features, seven hours of battery life with ANC, and a replaceable battery, available October 9 (Victoria Song/The Verge)
https://www.theverge.com/hands-on/760852/g

Google’s Pixel Buds 2A add Gemini, noise cancellation, and a replaceable battery
The $129.99 Pixel Buds 2a are a bit more expensive, but add active noise cancellation, a Tensor A1 chip, Gemini access, and replaceable batteries.

Bootstrap Policy Iteration for Stochastic LQ Tracking with Multiplicative Noise
Jiayu Chen, Zhenhui Xu, Xinghu Wang
https://arxiv.org/abs/2508.20394 https://

Bootstrap Policy Iteration for Stochastic LQ Tracking with Multiplicative Noise
This paper studies the optimal tracking control problem for continuous-time stochastic linear systems with multiplicative noise. The solution framework involves solving a stochastic algebraic Riccati equation for the feedback gain and a Sylvester equation for the feedforward gain. To enable model-free optimal tracking, we first develop a two-phase bootstrap policy iteration (B-PI) algorithm, which bootstraps a stabilizing control gain from the trivially initialized zero-value start and proceeds…

Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation
Eman Ali, Chetan Arora, Muhammad Haris Khan
https://arxiv.org/abs/2507.22075 htt…

Prototype-Guided Pseudo-Labeling with Neighborhood-Aware Consistency for Unsupervised Adaptation
In unsupervised adaptation for vision-language models such as CLIP, pseudo-labels derived from zero-shot predictions often exhibit significant noise, particularly under domain shifts or in visually complex scenarios. Conventional pseudo-label filtering approaches, which rely on fixed confidence thresholds, tend to be unreliable in fully unsupervised settings. In this work, we propose a novel adaptive pseudo-labeling framework that enhances CLIP's adaptation performance by integrating prototype …

Noise-Limited Sensitivity in Cavity Optomechanical Molecular Sensing Enabled by Quantum Zero-Point Displacement Coupling and Strong Photon-Phonon Interaction for Chiral Detection
Giuseppina Simone
https://arxiv.org/abs/2507.19982

Noise-Limited Sensitivity in Cavity Optomechanical Molecular Sensing Enabled by Quantum Zero-Point Displacement Coupling and Strong Photon-Phonon Interaction for Chiral Detection
This work presents a quantum-limited optomechanical sensing platform for real-time detection and discrimination of chiral molecules, based on a multilayer hybrid plasmonic-mechanical resonator. Leveraging quantum zero-point motion and engineered photon-phonon interactions, the system achieves ultrahigh displacement sensitivity that approaches the fundamental quantum limit. The multilayer architecture, composed of alternating dielectric and metallic films, supports mechanical resonances with qua…

Signal Detection under Composite Hypotheses with Identical Distributions for Signals and for Noises
Yiming Xing, Anamitra Chaudhuri, Yifan Chen
https://arxiv.org/abs/2507.21692 …

Signal Detection under Composite Hypotheses with Identical Distributions for Signals and for Noises
In this paper, we consider the problem of detecting signals in multiple, sequentially observed data streams. For each stream, the exact distribution is unknown, but characterized by a parameter that takes values in either of two disjoint composite spaces depending on whether it is a signal or noise. Furthermore, we consider a practical yet underexplored setting where all signals share the same parameter, as do all noises. Compared to the unconstrained case where the parameters in all streams ar…

Relationship between objective and subjective perceptual measures of speech in individuals with head and neck cancer
Bence Mark Halpern, Thomas Tienkamp, Teja Rebernik, Rob J. J. H. van Son, Martijn Wieling, Defne Abur, Tomoki Toda
https://arxiv.org/abs/2507.21426

Relationship between objective and subjective perceptual measures of speech in individuals with head and neck cancer
Meaningful speech assessment is vital in clinical phonetics and therapy monitoring. This study examined the link between perceptual speech assessments and objective acoustic measures in a large head and neck cancer (HNC) dataset. Trained listeners provided ratings of intelligibility, articulation, voice quality, phonation, speech rate, nasality, and background noise on speech. Strong correlations were found between subjective intelligibility, articulation, and voice quality, likely due to a sha…

$(H,H^3)$-smoothing effect and convergence of solutions of stochastic two-dimensional anisotropic Navier-Stokes equations driven by colored noise
Hui Liu, Dong Su, Chengfeng Sun, Jie Xin
https://arxiv.org/abs/2508.16938

$(H,H^3)$-smoothing effect and convergence of solutions of stochastic two-dimensional anisotropic Navier-Stokes equations driven by colored noise
This paper is devoted to the higher regularity and convergence of solutions of anisotropic Navier-Stokes (NS) equations with additive colored noise and white noise on two-dimensional torus $\mathbb T^2$. Under the conditions that the external force $f(\textbf{x})$ belongs to the phase space $ H$ and the noise intensity function $h(\textbf{x})$ satisfies $\|\nabla h\|_{L^\infty} \leq \sqrt{πδ} \frac{νλ_1}{2}$, it was proved that the random anisotropic NS equations possess a tempere…

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation
Sanaa Khobizy
https://arxiv.org/abs/2506.22292 ht…

Scalable inference of large-scale random kronecker graphs via tensor decomposition and Einstein summation
In this paper, we extend the analysis of random Kronecker graphs to multi-dimensional networks represented as tensors, enabling a more detailed and nuanced understanding of complex network structures. We decompose the adjacency tensor of such networks into two components: a low-rank signal tensor that captures the essential network structure and a zero-mean noise tensor that accounts for random variations. Building on recent advancements in tensor decomposition and random tensor theory, we intr…

The Atacama Cosmology Telescope: DR6 Sunyaev-Zel'dovich Selected Galaxy Clusters Catalog
HSC Collaboration, M. Aguena, S. Aiola, S. Allam, F. Andrade-Oliveira, D. Bacon, N. Bahcall, N. Battaglia, E. S. Battistelli, S. Bocquet, B. Bolliet, J. R. Bond, D. Brooks, E. Calabrese, J. Carretero, S. K. Choi, L. N. da Costa, M. Costanzi, W. Coulton, T. M. Davis, S. Desai, M. J. Devlin, S. Dicker, P. Doel, A. J. Duivenvoorden, J. Dunkley, S. Ferraro, B. Flaugher, J. Frieman, P. A. Gallardo, …

The Atacama Cosmology Telescope: DR6 Sunyaev-Zel'dovich Selected Galaxy Clusters Catalog
We present the results of a search for galaxy clusters in the Atacama Cosmology Telescope (ACT) Data Release 6 (DR6) microwave sky maps covering 16293 square degrees in three frequency bands, using data obtained over the lifetime of the project (2008-2022). We report redshifts and mass estimates for 9977 clusters detected via their Sunyaev-Zel'dovich (SZ) effect with signal-to-noise greater than 4 at a 2.4 arcminute filter scale. The catalog includes 1166 clusters at redshifts greater than 1, a…

Flexible Spectral Separation of Multiple Isotropic and Anisotropic Stochastic Gravitational Wave Backgrounds in LISA
Alexander W. Criswell, Sharan Banagiri, Jessica Lawrence, Levi Schult, Steven Rieck, Stephen R. Taylor, Vuk Mandic
https://arxiv.org/abs/2508.20308

Flexible Spectral Separation of Multiple Isotropic and Anisotropic Stochastic Gravitational Wave Backgrounds in LISA
The Laser Interferometer Space Antenna (LISA) will observe mHz gravitational waves from a wide variety of astrophysical sources. Of these, some will be characterizable as individual deterministic signals; the remainder will overlap to create astrophysical confusion noise. These sources of confusion noise are known as stochastic gravitational wave backgrounds (SGWBs). LISA data is expected to include several such astrophysical SGWBs, including the notable Galactic binary foreground, SGWBs from w…

Tuning adaptive gamma correction (TAGC) for enhancing images in low ligh
Ghufran Abualhail Alhamzawi, Ali Saeed Alfoudi, Ali Hakem Alsaeedi, Suha Mohammed Hadi, Amjed Abbas Ahmed, Md. Riad Hassan, Nurhizam Safie Mohd Satar, Waeel Yahya Yasseen
https://arxiv.org/abs/2507.19574

Tuning adaptive gamma correction (TAGC) for enhancing images in low ligh
Enhancing images in low-light conditions is an important challenge in computer vision. Insufficient illumination negatively affects the quality of images, resulting in low contrast, intensive noise, and blurred details. This paper presents a model for enhancing low-light images called tuning adaptive gamma correction (TAGC). The model is based on analyzing the color luminance of the low-light image and calculating the average color to determine the adaptive gamma coefficient. The gamma value is…

Extreme value theory for singular subspace estimation in the matrix denoising model
Junhyung Chang, Joshua Cape
https://arxiv.org/abs/2507.19978 https://ar…

Extreme value theory for singular subspace estimation in the matrix denoising model
This paper studies fine-grained singular subspace estimation in the matrix denoising model where a deterministic low-rank signal matrix is additively perturbed by a stochastic matrix of Gaussian noise. We establish that the maximum Euclidean row norm (i.e., the two-to-infinity norm) of the aligned difference between the leading sample and population singular vectors approaches the Gumbel distribution in the large-matrix limit, under suitable signal-to-noise conditions and after appropriate cent…

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
Yueh-Po Peng, Vincent K. M. Cheung, Li Su
https://arxiv.org/abs/2507.22378

Whole-brain Transferable Representations from Large-Scale fMRI Data Improve Task-Evoked Brain Activity Decoding
A fundamental challenge in neuroscience is to decode mental states from brain activity. While functional magnetic resonance imaging (fMRI) offers a non-invasive approach to capture brain-wide neural dynamics with high spatial precision, decoding from fMRI data -- particularly from task-evoked activity -- remains challenging due to its high dimensionality, low signal-to-noise ratio, and limited within-subject data. Here, we leverage recent advances in computer vision and propose STDA-SwiFT, a tr…

Horseshoe Forests for High-Dimensional Causal Survival Analysis
Tijn Jacobs, Wessel N. van Wieringen, St\'ephanie L. van der Pas
https://arxiv.org/abs/2507.22004 https://

Horseshoe Forests for High-Dimensional Causal Survival Analysis
We develop a Bayesian tree ensemble model to estimate heterogeneous treatment effects in censored survival data with high-dimensional covariates. Instead of imposing sparsity through the tree structure, we place a horseshoe prior directly on the step heights to achieve adaptive global-local shrinkage. This strategy allows flexible regularisation and reduces noise. We develop a reversible jump Gibbs sampler to accommodate the non-conjugate horseshoe prior within the tree ensemble framework. We s…

Shot-Noise-Limited Laser Frequency Stabilization Using a High-Resolution Wavelength Meter
Mengde Gan, Haoyi Zhang, Xiaodong Tan, Jiaming Li, Le Luo
https://arxiv.org/abs/2506.20894

Shot-Noise-Limited Laser Frequency Stabilization Using a High-Resolution Wavelength Meter
We present a compact laser frequency stabilization method by locking a 556 nm laser to a high-precision wavelength meter. Unlike traditional schemes that rely on optical cavities or atomic references, we stabilize the laser frequency via a closed-loop feedback system referenced to a wavelength meter. This configuration effectively suppresses long-term frequency drifts, achieving frequency stability to the wavelength meter's shot-noise-limited resolution. The system enables sub-hundred-kilohertz…

Modality-Specific Speech Enhancement and Noise-Adaptive Fusion for Acoustic and Body-Conduction Microphone Framework
Yunsik Kim, Yoonyoung Chung
https://arxiv.org/abs/2508.17336

Modality-Specific Speech Enhancement and Noise-Adaptive Fusion for Acoustic and Body-Conduction Microphone Framework
Body\-conduction microphone signals (BMS) bypass airborne sound, providing strong noise resistance. However, a complementary modality is required to compensate for the inherent loss of high\-frequency information. In this study, we propose a novel multi\-modal framework that combines BMS and acoustic microphone signals (AMS) to achieve both noise suppression and high\-frequency reconstruction. Unlike conventional multi\-modal approaches that simply merge features, our method employs two special…

Beyond 200 Gb/s/lane: An Analytical Approach to Optimal Detection in Shaped IM-DD Optical Links with Relative Intensity Noise
Felipe Villenas, Kaiquan Wu, Yunus Can G\"ultekin, Jamal Riani, Alex Alvarado
https://arxiv.org/abs/2506.19684

Beyond 200 Gb/s/lane: An Analytical Approach to Optimal Detection in Shaped IM-DD Optical Links with Relative Intensity Noise
Next-generation intensity-modulation (IM) and direct-detection (DD) systems used in data centers are expected to operate at 400 Gb/s/lane and beyond. Such rates can be achieved by increasing the system bandwidth or the modulation format, which in turn requires maintaining or increasing the signal-to-noise ratio (SNR). Such SNR requirements can be achieved by increasing the transmitted optical power. This increase in optical power causes the emergence of relative intensity noise (RIN), a signal-…

Robust Tripartite Entanglement Generation via Correlated Noise in Spin Qubits
Sander Driessen, Ji Zou, Even Thingstad, Jelena Klinovaja, Daniel Loss
https://arxiv.org/abs/2506.20466

Robust Tripartite Entanglement Generation via Correlated Noise in Spin Qubits
We investigate the generation of genuine tripartite entanglement in a triangular spin-qubit system due to spatially correlated noise. In particular, we demonstrate how the formation of a highly entangled dark state -- a W state -- enables robust, long-lived tripartite entanglement. Surprisingly, we find that environmentally induced coherent coupling does not play a crucial role in sustaining this entanglement. This contrasts sharply with the two-qubit case, where the induced coupling significan…

Replaced article(s) found for astro-ph.IM. https://arxiv.org/list/astro-ph.IM/new
[1/1]:
- Deep source separation of overlapping gravitational-wave signals and non-stationary noise artifacts
Niklas Houba

Replaced article(s) found for math.PR. https://arxiv.org/list/math.PR/new
[1/1]:
- Stochastic Partial Differential Equations, Space-time White Noise and Random Fields
Robert C. Dalang, Marta Sanz-Sol\'e

Ensemble Control of Stochastic Oscillators via Periodic and Feedback Control
Kaito Ito, Haruhiro Kume, Hideaki Ishii
https://arxiv.org/abs/2507.21441 https://

Ensemble Control of Stochastic Oscillators via Periodic and Feedback Control
We address the problem of steering the phase distribution of oscillators all receiving the same control input to a given target distribution. In a large population limit, the distribution of oscillators can be described by a probability density. Then, our problem can be seen as that of ensemble control with a constraint on the steady-state density. In particular, we consider the case where oscillators are subject to stochastic noise, for which the theoretical understanding is still lacking. Fir…

All-polarisation beamsplitters for interferometer applications
S. L. Kranzhoff, Z. Van Ranst, J. De Bolle, S. Coessens, S. L. Danilishin, C. Detavernier, P. F. Smet, A. P. Spencer, J. Steinlechner, S. Steinlechner, M. Vardaro, S. Hild
https://arxiv.org/abs/2507.22866

All-polarisation beamsplitters for interferometer applications
Optical beamsplitters with similar properties for orthogonal, linear polarisation modes are required for realising polarisation-based speedmeter schemes to reduce back-action noise in gravitational-wave interferometers. In this paper, we investigate two beamsplitter coatings obtained from Laseroptik GmbH and Optoman on a best-effort basis that aim for a 50/50 power splitting ratio and equal overall phase shift for two orthogonal, linear polarisation modes interacting with the optic. We show tha…

TF-TransUNet1D: Time-Frequency Guided Transformer U-Net for Robust ECG Denoising in Digital Twin
Shijie Wang, Lei Li
https://arxiv.org/abs/2508.20398 https://

TF-TransUNet1D: Time-Frequency Guided Transformer U-Net for Robust ECG Denoising in Digital Twin
Electrocardiogram (ECG) signals serve as a foundational data source for cardiac digital twins, yet their diagnostic utility is frequently compromised by noise and artifacts. To address this issue, we propose TF-TransUNet1D, a novel one-dimensional deep neural network that integrates a U-Net-based encoder-decoder architecture with a Transformer encoder, guided by a hybrid time-frequency domain loss. The model is designed to simultaneously capture local morphological features and long-range tempo…

Growth rates for the H\"older coefficients of the linear stochastic fractional heat equation with rough dependence in space
Chang Liu, Bin Qian, Ran Wang
https://arxiv.org/abs/2507.22379

Growth rates for the Hölder coefficients of the linear stochastic fractional heat equation with rough dependence in space
We study the linear stochastic fractional heat equation $$ \frac{\partial}{\partial t}u(t,x)=-(-Δ)^{\fracα{2}}u(t,x)+\dot{W}(t,x), \quad t>0, \quad x\in\mathbb{R}, $$ where $-(-Δ)^{\fracα{2}}$ denotes the fractional Laplacian with power $α\in (1,2)$, and the driving noise $\dot{W}$ is a centered Gaussian field that is white in time and has the covariance of a fractional Brownian motion with Hurst parameter $H\in\left(\frac{2-α}{2},\frac{1}{2}\right)$. We establish exact asymptotics for …

1-Bit Unlimited Sampling Beyond Fourier Domain: Low-Resolution Sampling of Quantization Noise
Vaclav Pavlicek, Ayush Bhandari
https://arxiv.org/abs/2508.19408 https://

1-Bit Unlimited Sampling Beyond Fourier Domain: Low-Resolution Sampling of Quantization Noise
Analog-to-digital converters (ADCs) play a critical role in digital signal acquisition across various applications, but their performance is inherently constrained by sampling rates and bit budgets. This bit budget imposes a trade-off between dynamic range (DR) and digital resolution, with ADC energy consumption scaling linearly with sampling rate and exponentially with bit depth. To bypass this, numerous approaches, including oversampling with low-resolution ADCs, have been explored. A promine…

Towards Bitrate-Efficient and Noise-Robust Speech Coding with Variable Bitrate RVQ
Yunkee Chae, Kyogu Lee
https://arxiv.org/abs/2506.16538 https://

Towards Bitrate-Efficient and Noise-Robust Speech Coding with Variable Bitrate RVQ
Residual Vector Quantization (RVQ) has become a dominant approach in neural speech and audio coding, providing high-fidelity compression. However, speech coding presents additional challenges due to real-world noise, which degrades compression efficiency. Standard codecs allocate bits uniformly, wasting bitrate on noise components that do not contribute to intelligibility. This paper introduces a Variable Bitrate RVQ (VRVQ) framework for noise-robust speech coding, dynamically adjusting bitrate…

Consequences of Non-Gaussian Instrumental Noise in Perturbed Nulling Interferometers
Felix A. Dannert, Philipp A. Huber, Thomas Birbacher, Romain Laugier, Markus J. Bonse, Emily O. Garvin, Adrian M. Glauser, Veronika Oehl, Sascha P. Quanz
https://arxiv.org/abs/2506.20653

Consequences of Non-Gaussian Instrumental Noise in Perturbed Nulling Interferometers
With the astrophysics community working towards the first observations and characterizations of Earth-like exoplanets, interest in space-based nulling interferometry has been renewed. This technique promises unique scientific and technical advantages by enabling direct mid-infrared observations. However, concept studies of nulling interferometers often overlook the impact of systematic noise caused by instrument perturbations. Earlier research introduced analytical and numerical models to addre…

Nonstabilizerness and Error Resilience in Noisy Quantum Circuits
Fabian Ballar Trigueros, Jos\'e Antonio Mar\'in Guzm\'an
https://arxiv.org/abs/2506.18976

Nonstabilizerness and Error Resilience in Noisy Quantum Circuits
We investigate how noise impacts nonstabilizerness - a key resource for quantum advantage - in many-body qubit systems. While noise typically degrades quantum resources, we show that amplitude damping, a nonunital channel, can generate or enhance magic, whereas depolarizing noise provably cannot. In an encoding-decoding protocol, we find that, unlike in the coherent case, a sharp decoding fidelity transition does not match a transition in nonstabilizerness. Our results point toward the possibil…

Backpropagation in unstable diffusions
Angxiu Ni
https://arxiv.org/abs/2507.21497 https://arxiv.org/pdf/2507.21497

Backpropagation in unstable diffusions
We derive the adjoint path-kernel method for the parameter-gradient of SDEs, where the observable is averaged at a particular time or over the stationary measure. Its cost is almost independent of the number of parameters; it extends the conventional backpropagation method to cases with gradient explosion. It works for non-hyperbolic systems with multiplicative noise controlled by parameters. We derive a Monte-Carlo-type algorithm and demonstrate it on the 40-dimensional Lorenz 96 system.

Unleashing Uncertainty: Efficient Machine Unlearning for Generative AI
Christoforos N. Spartalis, Theodoros Semertzidis, Petros Daras, Efstratios Gavves
https://arxiv.org/abs/2508.20773

Unleashing Uncertainty: Efficient Machine Unlearning for Generative AI
We introduce SAFEMax, a novel method for Machine Unlearning in diffusion models. Grounded in information-theoretic principles, SAFEMax maximizes the entropy in generated images, causing the model to generate Gaussian noise when conditioned on impermissible classes by ultimately halting its denoising process. Also, our method controls the balance between forgetting and retention by selectively focusing on the early diffusion steps, where class-specific information is prominent. Our results demon…

A stochastic noise model based excess noise factor expressions for staircase avalanche photodiodes
Ankitha E Bangera
https://arxiv.org/abs/2506.14722 https…

A stochastic noise model based excess noise factor expressions for staircase avalanche photodiodes
Multistep staircase avalanche photodiodes (APDs) are the solid-state analogue of photomultiplier tubes, owing to their deterministic amplification with twofold stepwise gain via impact ionization. Yet, the stepwise impact ionization irregularities worsen with increasing step counts, which are a major source of internal noise in these APDs. Some noise models for staircase APDs have been previously reported, where the excess noise factor expressions are based on Friis' noise factor formula for ca…

ClearMask: Noise-Free and Naturalness-Preserving Protection Against Voice Deepfake Attacks
Yuanda Wang, Bocheng Chen, Hanqing Guo, Guangjing Wang, Weikang Ding, Qiben Yan
https://arxiv.org/abs/2508.17660

ClearMask: Noise-Free and Naturalness-Preserving Protection Against Voice Deepfake Attacks
Voice deepfake attacks, which artificially impersonate human speech for malicious purposes, have emerged as a severe threat. Existing defenses typically inject noise into human speech to compromise voice encoders in speech synthesis models. However, these methods degrade audio quality and require prior knowledge of the attack approaches, limiting their effectiveness in diverse scenarios. Moreover, real-time audios, such as speech in virtual meetings and voice messages, are still exposed to voic…

Noise-tolerant tomography of multimode linear optical interferometers with single photons
Yu. A. Biriukov, R. D. Morozov, I. V. Dyakonov, M. V. Rakhlin, A. I. Galimov, G. V. Klimko, S. V. Sorokin, I. V. Sedova, M. M. Kulagina, Yu. M. Zadiranov, A. A. Toropov, A. A. Korneev, S. P. Kulik, S. S. Straupe
https://arxiv.org/abs/2506.2…

Noise-tolerant tomography of multimode linear optical interferometers with single photons
Linear optical networks are fundamental to the advancement of quantum technologies, including quantum computing, communication, and sensing. The accurate characterization of these networks, described by unitary matrices, is crucial to their effective utilization and scalability. In this work, we present the method for reconstructing the transfer matrix of a linear optical interferometer based on the analysis of cross-correlation functions of photon counts between pairs of output modes. Our appr…

Sharp moment and upper tail asymptotics for the critical $2d$ Stochastic Heat Flow
Shirshendu Ganguly, Kyeongsik Nam
https://arxiv.org/abs/2507.22029 https://

Sharp moment and upper tail asymptotics for the critical $2d$ Stochastic Heat Flow
While $1+1$ dimensional growth models in the Kardar-Parisi-Zhang universality class have witnessed an explosion of activity, higher dimensional models remain much less explored. The special case of $2+1$ dimensions is particularly interesting as it is, in physics parlance, neither ultraviolet nor infrared super-renormalizable. Canonical examples include the stochastic heat equation (SHE) with multiplicative noise and directed polymers. The models exhibit a weak to strong disorder transition as …

Crosslisted article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[1/3]:
- Multi-Objective Optimization of ReRAM Crossbars for Robust DNN Inferencing under Stochastic Noise
Yang, Belakaria, Joardar, Yang, Doppa, Pande, Chakrabarty, Li

Dynamics of colored-noise-driven stochastic Schr\"odinger equations
Pietro De Checchi, Federico Gallina, Barbara Fresch, Giulio G. Giusteri
https://arxiv.org/abs/2507.17864

Dynamics of colored-noise-driven stochastic Schrödinger equations
In this work, we study the effect of colored stochastic noise as a source of fluctuations in the dynamics of a two-level system, e.g. the states of a qubit system or two local sites in a transfer problem. We derive the stochastic Schrödinger equations (SSE) and related quantum master equations (QME) for the average density matrix for different stochastic potentials. We compare the case of memory and memoryless processes, which reflect different short-time behaviors and, in some cases, can lead…

Random measurements are almost maximally incompatible
Andreas Bluhm, C\'ecilia Lancien, Ion Nechita
https://arxiv.org/abs/2507.20600 https://arxiv.org/…

Random measurements are almost maximally incompatible
In this work, we investigate the incompatibility of random quantum measurements. Most previous work has focused on characterizing the maximal amount of white noise that any fixed number of incompatible measurements with a fixed number of outcomes in a fixed dimension can tolerate before becoming compatible. This can be used to quantify the maximal amount of incompatibility available in such systems. The present article investigates the incompatibility of several classes of random measurements, …

Quantitative analysis of the effectiveness of mid-anneal measurement in quantum annealing
Keita Takahashi, Shu Tanaka
https://arxiv.org/abs/2507.20318 https://

Quantitative analysis of the effectiveness of mid-anneal measurement in quantum annealing
Quantum annealing is a promising metaheuristic for solving constrained combinatorial optimization problems. However, parameter tuning difficulties and hardware noise often prevent optimal solutions from being properly encoded as the ground states of the problem Hamiltonian. This study investigates mid-anneal measurement as a mitigation approach for such situations, analyzing its effectiveness and underlying physical mechanisms. We introduce a quantitative metric to evaluate the effectiveness of…

Machine-Learning-Assisted Pulse Design for State Preparation in a Noisy Environment
Zhao-Wei Wang, Hong-Yang Ma, Yun-An Yan, Lian-Ao Wu, Zhao-Ming Wang
https://arxiv.org/abs/2508.20377

Machine-Learning-Assisted Pulse Design for State Preparation in a Noisy Environment
High-precision quantum control is essential for quantum computing and quantum information processing. However, its practical implementation is challenged by environmental noise, which affects the stability and accuracy of quantum systems. In this paper, using machine learning techniques we propose a quantum control approach that incorporates environmental factors into the design of control schemes, improving the control fidelity in noisy environments. Specifically, we investigate arbitrary quan…