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Time-resolved microscopy is a widely used approach for imaging and quantifying charge and energy transport in functional materials. While it is generally recognized that resolving small diffusion lengths is limited by measurement noise, the impacts of noise have not been systematically assessed or quantified. This article reports modeling efforts to elucidate the impact of noise on optical probes of transport. Excited state population distributions, modeled as Gaussians with additive white noise typical of experimental conditions, are subject to decay and diffusive evolution. Using a conventional composite least-squares fitting algorithm, the resulting diffusion constant estimates are compared with the model input parameter. The results show that heteroscedasticity (i.e., time-varying noise levels), insufficient spatial and/or temporal resolution, and small diffusion lengths relative to the magnitude of noise lead to a surprising degree of imprecision under moderate experimental parameters. Moreover, the compounding influence of low initial contrast and small diffusion length leads to systematic overestimation of diffusion coefficients. Each of these issues is quantitatively analyzed herein, and experimental approaches to mitigate them are proposed. General guidelines for experimentalists to rapidly assess measurement precision are provided, as is an open-source tool for customizable evaluation of noise effects on time-resolved microscopy transport measurements. [ABSTRACT FROM AUTHOR]

The precision of stereopsis and vergence are ultimately limited by internal binocular disparity noise. Here we propose an equivalent noise model with both global and local internal disparity noises to provide a unified explanation of both absolute and relative disparity thresholds. To test this model, we developed a psychophysical procedure to measure the equivalent internal disparity noise by adding external disparity noise to random-Gabor-patch stereograms. We used the method of constant stimuli to measure the minimum and maximum disparity thresholds (Dmin and Dmax) for both absolute and relative disparity. Consistent with previous studies, we found that Dmin thresholds are substantially worse for absolute disparity than for relative disparity. We tested three relative disparity mechanisms: (1) the difference between the monocular separations of targets projecting to the two eyes; (2) the direct measurement of relative disparity; and (3) the difference of absolute disparities of targets. Computing the difference of absolute disparities when detecting relative disparity, Mechanism 3 cancels global noise, resulting in a much lower relative Dmin threshold, and provides a reasonable fit to the experimental data. We also found that the presence of as much as 2400 arcsec of external disparity noise does not appear to affect the Dmax threshold. This observation suggests that Dmax is implicated in a mechanism that disregards the disparity variance of individual items, relying instead on the average disparity across all items, supporting the depth model proposed in our previous study (Ding & Levi, 2021), which posits distinct mechanisms governing Dmin and Dmax thresholds.

Protected areas are typically managed as a network of sites exposed to varying anthropogenic conditions. Managing these networks benefits from monitoring of conditions across sites to help prioritize coordinated efforts. Monitoring marine vessel activity and related underwater radiated noise impacts across a network of protected areas, like the U.S. National Marine Sanctuary system, helps managers ensure the quality of habitats used by a wide range of marine species. Here, we use underwater acoustic detections of vessels to quantify different characteristics of vessel noise at 25 locations within eight marine sanctuaries including the Hawaiian Archipelago and the U.S. east and west coasts. Vessel noise metrics, including temporal presence and sound levels, were paired with Automatic Identification System (AIS) vessel tracking data to derive a suite of robust vessel noise indicators for use across the network of marine protected areas. Network-wide comparisons revealed a spectrum of vessel noise conditions that closely matched AIS vessel traffic composition. Shifts in vessel noise were correlated with the decrease in vessel activity early in the COVID-19 pandemic, and vessel speed reduction management initiatives. Improving our understanding of vessel noise conditions in these protected areas can help direct opportunities for reducing vessel noise, such as establishing and maintaining noise-free periods, enhancing port efficiency, engaging with regional and international vessel quieting initiatives, and leveraging co-benefits of management actions for reducing ocean noise.

Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions. [ABSTRACT FROM AUTHOR]

Current–voltage characteristics and low-frequency (LF) noise of industrial-quality AlGaAs/InGaAs/GaAs pseudomorphic high-electron-mobility transistors are evaluated as a function of bias stress and temperature. A small positive shift of threshold voltage Vth and negligible degradation in peak transconductance GM are observed under ON-state bias conditions at elevated temperatures. The Vth measurements suggest activation of an acceptor-like defect or impurity center. The GM measurements demonstrate that newly activated defects are not located close enough to the two-dimensional electron gas to scatter carriers strongly. First-principles calculations and comparisons with previous work suggest that OAs impurity centers, other oxygen-related defects, isolated AsGa antisites, and dopant-based DX centers may contribute significantly to low-frequency (LF) noise in as-processed devices. LF noise is relatively unaffected by voltage stress at elevated temperatures, consistent with the small changes in Vth and peak GM. [ABSTRACT FROM AUTHOR]

Historical policies have been shown to underpin environmental quality. In the 1930s, the federal Home Owners' Loan Corporation (HOLC) developed the most comprehensive archive of neighborhoods that would have been redlined by local lenders and the Federal Housing Administration, often applying racist criteria. Our study explored how redlining is associated with environmental quality across eight California cities. We integrated HOLC's graded maps [grades A (i.e., "best" and "greenlined"), B, C, and D (i.e., "hazardous" and "redlined")] with 10 environmental hazards using data from 2018 to 2021 to quantify the spatial overlap among redlined neighborhoods and environmental hazards. We found that formerly redlined neighborhoods have poorer environmental quality relative to those of other HOLC grades via higher pollution, more noise, less vegetation, and elevated temperatures. Additionally, we found that intraurban disparities were consistently worse for formerly redlined neighborhoods across environmental hazards, with redlined neighborhoods having higher pollution burdens (77% of redlined neighborhoods vs 18% of greenlined neighborhoods), more noise (72% vs 18%), less vegetation (86% vs 12%), and elevated temperature (72% vs 20%), than their respective city's average. Our findings highlight that redlining, a policy abolished in 1968, remains an environmental justice concern by shaping the environmental quality of Californian urban neighborhoods.

Traditional systems for indoor pressure sensing and human activity recognition (HAR) rely on costly, high-resolution mats and computationally intensive neural network-based (NN-based) models that are prone to noise. In contrast, we design a cost-effective and noise-resilient pressure mat system for HAR, leveraging Velostat for intelligent pressure sensing and a novel hyperdimensional computing (HDC) classifier that is lightweight and highly noise resilient. To measure the performance of our system, we collected two datasets, capturing the static and continuous nature of human movements. Our HDC-based classification algorithm shows an accuracy of 93.19%, improving the accuracy by 9.47% over state-of-the-art CNNs, along with an 85% reduction in energy consumption. We propose a new HDC noise-resilient algorithm and analyze the performance of our proposed method in the presence of three different kinds of noise, including memory and communication, input, and sensor noise. Our system is more resilient across all three noise types. Specifically, in the presence of Gaussian noise, we achieve an accuracy of 92.15% (97.51% for static data), representing a 13.19% (8.77%) improvement compared to state-of-the-art CNNs.

Elevated "neural noise" has been advanced as an explanation of autism and autistic sensory experiences. However, functional neuroimaging measures of neural noise may be vulnerable to contamination by recording noise. This study explored variability of electrophysiological responses to tones of different intensities in 127 autistic and 79 typically-developing children aged 2-5 years old. A rigorous data processing pipeline, including advanced visualizations of different signal sources that were maximally independent across different time lags, was used to identify and eliminate putative recording noise. Inter-trial variability was measured using median absolute deviations (MADs) of EEG amplitudes across trials and inter-trial phase coherence (ITPC). ITPC was elevated in autism in the 50 and 60 dB intensity conditions, suggesting diminished (rather than elevated) neural noise in autism, although reduced ITPC to soft 50 dB sounds was associated with increased loudness discomfort. Autistic and non-autistic participants did not differ in MADs, and indeed, the vast majority of the statistical tests examined in this study yielded no significant effects. These results appear inconsistent with the neural noise account.

The effect of active sonars on marine mammal behaviour is a topic of considerable interest and scientific investigation. Some whales, including the largest species (blue whales, Balaenoptera musculus), can be impacted by mid-frequency (1-10 kHz) military sonars. Here we apply complementary experimental methods to provide the first experimentally controlled measurements of behavioural responses to military sonar and similar stimuli for a related endangered species, fin whales (Balaenoptera physalus). Analytical methods include: (i) principal component analysis paired with generalized additive mixed models; (ii) hidden Markov models; and (iii) structured expert elicitation using response severity metrics. These approaches provide complementary perspectives on the nature of potential changes within and across individuals. Behavioural changes were detected in five of 15 whales during controlled exposure experiments using mid-frequency active sonar or pseudorandom noise of similar frequency, duration and source and received level. No changes were detected during six control (no noise) sequences. Overall responses were more limited in occurrence, severity and duration than in blue whales and were less dependent upon contextual aspects of exposure and more contingent upon exposure received level. Quantifying the factors influencing marine mammal responses to sonar is critical in assessing and mitigating future impacts.

Individual difference exploration of cognitive domains is predicated on being able to ascertain how well performance on tasks covary. Yet, establishing correlations among common inhibition tasks such as Stroop or flanker tasks has proven quite difficult. It remains unclear whether this difficulty occurs because there truly is a lack of correlation or whether analytic techniques to localize correlations perform poorly real-world contexts because of excessive measurement error from trial noise. In this paper, we explore how well correlations may localized in large data sets with many people, tasks, and replicate trials. Using hierarchical models to separate trial noise from true individual variability, we show that trial noise in 24 extant tasks is about 8 times greater than individual variability. This degree of trial noise results in massive attenuation in correlations and instability in Spearman corrections. We then develop hierarchical models that account for variation across trials, variation across individuals, and covariation across individuals and tasks. These hierarchical models also perform poorly in localizing correlations. The advantage of these models is not in estimation efficiency, but in providing a sense of uncertainty so that researchers are less likely to misinterpret variability in their data. We discuss possible improvements to study designs to help localize correlations.

The recently developed Projective Quantum Eigensolver (PQE) offers an elegant procedure to evaluate the ground state energies of molecular systems in quantum computers. However, the noise in available quantum hardware can result in significant errors in computed outcomes, limiting the realization of quantum advantage. Although PQE comes equipped with some degree of inherent noise resilience, any practical implementation with apposite accuracy would require additional routines to eliminate or mitigate the errors further. In this work, we propose a way to enhance the efficiency of PQE by developing an optimal framework for introducing Zero Noise Extrapolation (ZNE) in the nonlinear iterative procedure that outlines the PQE, leading to the formulation of ZNE-PQE. Moreover, we perform a detailed analysis of how various components involved in it affect the accuracy and efficiency of the reciprocated energy convergence trajectory. Additionally, we investigate the underlying mechanism that leads to the improvements observed in ZNE-PQE over conventional PQE by performing a comparative analysis of their residue norm landscape. This approach is expected to facilitate practical applications of quantum computing in fields related to molecular sciences, where it is essential to determine molecular energies accurately. [ABSTRACT FROM AUTHOR]

We consider the allocation of m balls (jobs) into n bins (servers). In the standard Two-Choice process, at each step t = 1, 2, . . ., m we first sample two randomly chosen bins, compare their two loads and then place a ball in the least loaded bin. It is well-known that for any m x n, this results in a gap (difference between the maximum and average load) of log2 logn + T(1) (with high probability). In this work, we consider Two-Choice in different settings with noisy load comparisons. One key setting involves an adaptive adversary whose power is limited by some threshold N. In each step, such adversary can determine the result of any load comparison between two bins whose loads differ by at most, while if the load difference is greater than, the comparison is correct. For this adversarial setting, we first prove that for anym x n the gap is O(1 + logn) with high probability. Then through a refined analysiswe prove that if x logn, then for anym x n the gap is O(1 log 1 ·log logn). For constant values of 1, this generalizes the heavily loaded analysis of [19, 61] for the Two-Choice process, and establishes that asymptotically the same gap bound holds even if load comparisons among "similarly loaded" bins are wrong. Finally, we complement these upper bounds with tight lower bounds, which establish an interesting phase transition on how the parameter 1 impacts the gap. The analysis also applies to settings with outdated and delayed information. For example, for the setting of [18] where balls are allocated in consecutive batches of size b = n, we present an improved and tight gap bound of T(log n log log n). This bound also extends for a range of values of b and applies to a relaxed setting where the reported load of a bin can be any load value from the last b steps. [ABSTRACT FROM AUTHOR]

Single-molecule and related experiments yield time series of an observable as it fluctuates due to thermal motion. In such data, it can be difficult to distinguish fluctuating signal from fluctuating noise. We present a method of separating signal from noise using nonlinear-correlation functions. The method is fully nonparametric: No a priori model for the system is required, no knowledge of whether the system is continuous or discrete is needed, the number of states is not fixed, and the system can be Markovian or not. The noise-corrected, nonlinear-correlation functions can be converted to the system's Green's function; the noise-corrected moments yield the system's equilibrium-probability distribution. As a demonstration, we analyze synthetic data from a three-state system. The correlation method is compared to another fully nonparametric approach—time binning to remove noise, and histogramming to obtain the distribution. The correlation method has substantially better resolution in time and in state space. We develop formulas for the limits on data quality needed for signal recovery from time series and test them on datasets of varying size and signal-to-noise ratio. The formulas show that the signal-to-noise ratio needs to be on the order of or greater than one-half before convergence scales at a practical rate. With experimental benchmark data, the positions and populations of the states and their exchange rates are recovered with an accuracy similar to parametric methods. The methods demonstrated here are essential components in building a complete analysis of time series using only high-order correlation functions. [ABSTRACT FROM AUTHOR]