the Effect of Noise
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Characterizing the Effect of Noise in Language Generation in the Limit
Announce Type: replace Abstract: Kleinberg and Mullainathan recently proposed a formal framework for studying the phenomenon of language generation, called language generation in the limit. In this model, an adversary gives an enumeration of example strings from an unknown target language, and the algorithm is tasked with correctly generating unseen strings from the target language within finite time. Refined notions of non-uniform and uniform generation were later introduced by Li, Raman,...
Developmental genetic response of the zooplanktonic tunicate Oikopleura dioica to marine noise pollution.
Background: Anthropogenic noise is an emerging threat to marine ecosystems, yet its effects on marine invertebrates, particularly zooplanktonic species, remain poorly understood. Despite increasing evidence of behavioral and physiological impacts in invertebrates, the effects of noise on embryonic development and the molecular mechanisms underlying acoustic responses remain largely unexplored. Here, to address this gap, we investigated the impact of high-intensity underwater noise exposure...
Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning
Announce Type: new Abstract: Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning frameworks trained on simulated data, discrepancies between the noise characteristics of simulated and acquired signals introduce a form of covariate shift, whereby the input signal distribution differs between training and inference. We investigated the impact of this mismatch on...
Realistic noise synthesis reduces bias and improves tissue microstructure estimation with supervised machine learning
Announce Type: cross Abstract: Diffusion MRI enables non-invasive probing of tissue microstructure, but accurate parameter estimation is challenged by noise-related effects. In supervised machine learning frameworks trained on simulated data, discrepancies between the noise characteristics of simulated and acquired signals introduce a form of covariate shift, whereby the input signal distribution differs between training and inference. We investigated the impact of this mismatch on...
A Comparison of Generative and Discriminative Methods for Speech Enhancement: Robustness, Complexity, and Hallucination
arXiv:2606.02913v1 Announce Type: cross Abstract: In this study, we conduct a comprehensive comparative analysis of generative and discriminative deep learning-based speech enhancement methods, specifically in noise reduction tasks. Our investigation focuses on evaluating their effectiveness under high and low signal-to-noise ratio conditions, considering both matched and mismatched training scenarios. We further investigate the impact of training data volume, model convergence speed, and...
Sequential Subspace Noise Injection Prevents Accuracy Collapse in Certified Unlearning
Announce Type: replace Abstract: Certified unlearning based on differential privacy offers strong guarantees but remains largely impractical: the noisy fine-tuning approaches proposed so far achieve these guarantees but severely reduce model accuracy. We propose sequential noise scheduling, which distributes the noise budget across orthogonal subspaces of the parameter space, rather than injecting it all at once. This simple modification mitigates the destructive effect of noise while...
COD10K-C: Benchmarking Robustness of Camouflaged Object Detection Under Natural Image Corruptions
arXiv:2606.02603v1 Announce Type: new Abstract: Camouflaged object detection has improved substantially, but most standard benchmarks evaluate models only on clean images. This is not realistic because real cameras often capture blur, sensor noise, weather effects, and compression artifacts. We present COD10K-C, a corruption robustness benchmark based on COD10K.
Power System Robust State Estimation As a Layer: An Optimization-embedded End-to-end Learning Approach
Announce Type: replace Abstract: Serving as an essential prerequisite for modern power system operation, robust state estimation (RSE) could effectively resist noises and outliers in measurements. The emerging neural network (NN) based end-to-end (E2E) learning framework enables real-time application of RSE but potentially yields solutions that are statistically accurate yet physically inconsistent. To bridge this gap, this work proposes a novel E2E learning based RSE framework, where the...
Training-Free Intelligibility-Guided Observation Addition for Noisy ASR
Announce Type: replace-cross Abstract: Automatic speech recognition (ASR) degrades severely in noisy environments. Although speech enhancement (SE) front-ends effectively suppress background noise, they often introduce artifacts that harm recognition. Observation addition (OA) addressed this issue by fusing noisy and SE enhanced speech, improving recognition without modifying the parameters of the SE or ASR models.
Effective Biological Representation Learning by Masking Gene Expression
Announce Type: new Abstract: RNA sequencing produces rich and diverse datasets of gene expression, offering compelling insights into cellular state and function that have many applications in drug discovery. Modeling such data is challenging due to inherent technical noise and experimental batch effects, as evidenced by many existing transcriptomic foundation models (FMs) underperforming relative to linear baselines. Such results raise the question of whether deep representation learning...