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Machine learning classifiers are increasingly widely used. This research note explores how a particular widely used classifier, the Random Forest, performs when faced with samples which are imbalanced and noisy data. Both are known to affect accuracy, but if their effects are independent or not has not been explored. Based on an experiment using synthetic data generated for the study we find that the effects of noise and sample balance interact with each other; classification accuracy is worse when faced with both noisy data and sample imbalance. This has implications for the use of RF in market research, but also for how methods to address either sample imbalance or noise are assessed. [ABSTRACT FROM AUTHOR]

Generic open quantum systems are notoriously difficult to simulate unless one looks at specific regimes. In contrast, classical dissipative systems can often be effectively described by stochastic processes, which are generally less computationally expensive. Here, we use the paradigmatic case of a dissipative quantum oscillator to give a pedagogic introduction to the modeling of open quantum systems using quasiclassical methods, i.e., classical stochastic methods that use a "quantum" noise spectrum to capture the influence of the environment on the system. Such quasiclassical methods have the potential to offer insights into the impact of the quantum nature of the environment on the dynamics of the system of interest while still being computationally tractable. [ABSTRACT FROM AUTHOR]

In this paper, we present the design and performance evaluation of a prototype device based on gain-driven polariton, which can function as an amplifier. This device can outperform a single microwave cavity in terms of phase noise and stability. Employed as a standalone amplifier, it demonstrates consistency between input and output phase noise, achieving a gain of up to 35 dB with a typical output of 5 dBm at 4.26 GHz. Despite its limited bandwidth, the device provides an effective solution for small signal amplification, ensuring minimal noise introduction. [ABSTRACT FROM AUTHOR]

A preceding paper [M. Dhar, J. A. Dickinson, and M. A. Berg, J. Chem. Phys. 159, 054110 (2023)] shows how to remove additive noise from an experimental time series, allowing both the equilibrium distribution of the system and its Green's function to be recovered. The approach is based on nonlinear-correlation functions and is fully nonparametric: no initial model of the system or of the noise is needed. However, single-molecule spectroscopy often produces time series with either photon or photon-counting noise. Unlike additive noise, photon noise is signal-size correlated and quantized. Photon counting adds the potential for bias. This paper extends noise-corrected-correlation methods to these cases and tests them on synthetic datasets. Neither signal-size correlation nor quantization is a significant complication. Analysis of the sampling error yields guidelines for the data quality needed to recover the properties of a system with a given complexity. We show that bias in photon-counting data can be corrected, even at the high count rates needed to optimize the time resolution. Using all these results, we discuss the factors that limit the time resolution of single-molecule spectroscopy and the conditions that would be needed to push measurements into the submicrosecond region. [ABSTRACT FROM AUTHOR]

Eddy current shielding by a Faraday cage is an effective way to shield alternating-current magnetic fields in scientific instrumentation. In a strong static magnetic field, however, the eddy current in the conductive shield is subject to the Lorentz force, which causes the shield to vibrate. In addition to mechanical issues (e.g., acoustic noise), such vibration induces motional eddy current in the shield that can dominate the original, electromagnetic eddy current to undermine the conductor's shielding capability. In this work, we investigate a method to control motional eddy current by making cut-out patterns in the conductor that follow the electromagnetic eddy current image. This effectively limits the surface current of the plate to a single mode and prevents the proliferation of uncontrolled motion-induced surface currents that disrupts eddy current shielding. After developing a comprehensive theory of magneto-mechanical interaction in a conductive plate, the proposed method was tested on a flat-geometry testbed experiment inside a 3 T magnetic resonance imaging (MRI) magnet. It was found that the magnetic field generated by the motional eddy current was much more localized in space and frequency for a patterned-copper shield compared to a solid copper. The magnetic field of the patterned shield could be accurately predicted from the impedance measurement in the magnet. Implications of our results for improved shielding of gradient fields in high-field MRI are discussed. [ABSTRACT FROM AUTHOR]

The biochemical activity inside a cell has recently been suggested to act as a source of hydrodynamic fluctuations that can speed up or slow down enzyme catalysis [Tripathi et al., Commun. Phys. 5, 101 (2022).] The idea has been tested against and largely corroborated by simulations of activated barrier crossing in a simple fluid in the presence of thermal and athermal noise. The present paper attempts a wholly analytic solution to the same noise-driven barrier crossing problem but generalizes it to include viscoelastic memory effects of the kind likely to be present in cellular interiors. A calculation of the model's barrier crossing rate, using Kramers' flux-over-population formalism, reveals that in relation to the case where athermal noise is absent, athermal noise always accelerates barrier crossing, though the extent of enhancement depends on the duration τ0 over which the noise acts. More importantly, there exists a critical τ0—determined by the properties of the medium—at which Kramers' theory breaks down and, on approach to which, the rate grows significantly. The possibility of such a giant enhancement is potentially open to experimental validation using optically trapped nanoparticles in viscoelastic media that are acted on by externally imposed colored noise. [ABSTRACT FROM AUTHOR]

This work presents the excess noise and thermoelectric (Seebeck) measurements on polycrystalline vanadium dioxide (VO2) thin films. Noise spectral power density (SPD) of current fluctuations in the semiconducting (SC) phase had a typical flicker noise (f−γ) characteristic with an average slope parameter γ of 1.13. Normalized SPD (Sn) values obtained in the SC-phase indicate that the noise originates in the bulk of the film. On the contrary, in the metallic (M)-phase, γ values were greater than unity, and the observed Sn values indicated that the origin of the noise is most likely from the contacts or surface rather than the bulk. A general decrease was observed in Sn by a factor of 4–5 from the SC- to M-phase. Moreover, Sn in the SC-phase showed no temperature dependence. An interpretation based on the number of charge carrier fluctuations in Hooge's model led to an unrealistically high Hooge parameter and had to be ruled out. We propose that the fluctuations are related to the mobility fluctuations of carriers arising primarily from grain-boundary scattering which explains the observed characteristics well. The Seebeck coefficients (S) obtained under both heating and cooling schedules showed the n-type nature of magnetron-sputtered VO2 films in the SC-phase. Differently, in the M-phase, the S value was positive. The S values obtained from the cooling schedule signified the low percolation threshold of the metal-to-insulator transition already demonstrated for VO2 thin films grown on r-cut sapphire using the Efros–Shklovskii percolation model. [ABSTRACT FROM AUTHOR]

Robust control of a quantum system is essential to utilize the current noisy quantum hardware to its full potential, such as quantum algorithms. To achieve such a goal, a systematic search for an optimal control for any given experiment is essential. The design of optimal control pulses requires accurate numerical models and, therefore, accurate characterization of the system parameters. We present an online Bayesian approach for quantum characterization of qutrit systems, which automatically and systematically identifies optimal experiments that provide maximum information on the system parameters, thereby greatly reducing the number of experiments that need to be performed on the quantum testbed. Unlike most characterization protocols that provide point-estimates of the parameters, the proposed approach is able to estimate their probability distribution. The applicability of the Bayesian experimental design technique was demonstrated on test problems, where each experiment was defined by a parameterized control pulse. In addition to this, we also present an approach for iterative pulse extension, which is robust under uncertainties in transition frequencies and coherence times, and shot noise, despite being initialized with wide uninformative priors. Furthermore, we provide a mathematical proof of the theoretical identifiability of the model parameters and present conditions on the quantum state under which the parameters are identifiable. The proof and conditions for identifiability are presented for both closed and open quantum systems using the Schrödinger equation and the Lindblad master equation, respectively. [ABSTRACT FROM AUTHOR]

Quantum computers hold immense potential in the field of chemistry, ushering new frontiers to solve complex many-body problems that are beyond the reach of classical computers. However, noise in the current quantum hardware limits their applicability to large chemical systems. This work encompasses the development of a projective formalism that aims to compute ground-state energies of molecular systems accurately using noisy intermediate scale quantum (NISQ) hardware in a resource-efficient manner. Our approach is reliant upon the formulation of a bipartitely decoupled parameterized ansatz within the disentangled unitary coupled cluster framework based on the principles of nonlinear dynamics and synergetics. Such decoupling emulates total parameter optimization in a lower dimensional manifold, while a mutual synergistic relationship among the parameters is exploited to ensure characteristic accuracy via a non-iterative energy correction. Without any pre-circuit measurements, our method leads to a highly compact fixed-depth ansatz with shallower circuits and fewer expectation value evaluations. Through analytical and numerical demonstrations, we establish the method's superior performance under noise while concurrently ensuring requisite accuracy in future fault-tolerant systems. This approach enables rapid exploration of emerging chemical spaces by the efficient utilization of near-term quantum hardware resources. [ABSTRACT FROM AUTHOR]

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Gas gun shock experiments on cyclotrimethylenetrinitramine (RDX)-based explosive compositions that employ embedded gauge particle velocity tracers have noted a significant amount of electrical noise when compared to other explosive formulations. This paper reexamines previously published embedded gauge data on Cyclotols (60–80 wt. % RDX) to quantify the electromagnetic behavior of these materials. The primary observation is a fourfold increase in the electrical noise when Cyclotols are shocked above 4.22 ± 0.08 GPa. Electromagnetic gauge noise is also observed within particle velocity traces in reactive growth and off-Hugoniot shocks, although at higher pressures than the direct shock case, suggesting a temperature- or kinetically dependent transition. In all cases, the electrical noise disappears upon detonation. By comparing with the static high-pressure phase diagram of RDX, we interpret this change in electromagnetic behavior to be a change in the RDX crystal structure to a piezoelectric phase, although it is uncertain whether the γ or ε phase is responsible for the observed behavior. [ABSTRACT FROM AUTHOR]

Cosmic microwave background (CMB) experiments have deployed focal planes with O(104) transition edge sensor (TES) bolometers cooled to sub-Kelvin temperatures by multiplexing the readout of many TES channels onto a single pair of wires. Digital Frequency-domain Multiplexing (DfMux) is a multiplexing technique used in many CMB polarization experiments, such as the Simons Array, SPT-3 G, and EBEX. The DfMux system studied here uses LC filters with resonant frequencies ranging from 1.5 to 4.5 MHz connected to an array of TESs. Each detector has an amplitude-modulated carrier tone at the resonant frequency of its accompanying LC resonator. The signal is recovered via quadrature demodulation where the in-phase (I) component of the demodulated current is in phase with the complex admittance of the circuit and the quadrature (Q) component is orthogonal to I. Observed excess current noise in the Q component is consistent with fluctuations in the resonant frequency. This noise has been shown to be non-orthogonal to the phase of the detector's responsivity. We present a detailed analysis of the phase of responsivity of the TES and noise sources in our DfMux readout system. Further, we investigate how modifications to the TES operating resistance and bias frequency can affect the phase of noise relative to the phase of the detector responsivity, using data from Simons Array to evaluate our predictions. We find that both the phase of responsivity and phase of noise are functions of the two tuning parameters, which can be purposefully selected to maximize signal-to-noise (SNR) ratio.

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]

A numerical model describing a magnonic active ring oscillator (MARO) based on a microscopic spin-wave delay line is proposed. The model considers excitation, propagation, and reception of the magnetostatic surface waves in a yttrium iron garnet (YIG) magnetic film with a thickness in the nanometer range. The waves are excited and received with a microscopic coplanar antenna. We employed the model to analyze the influence of the YIG-film thickness and the distance between the antennas on the MARO performance characteristics. We showed that an increase in the delay time inserted by the delay line reduces the phase noise of the MARO and increases the auto-oscillation threshold. In addition, we found a relation between the auto-oscillation threshold, the thickness of the YIG film, and the distance between the antennas. The relation helps design miniature MAROs and suggests a way to reduce the phase noise of the device. The model predicts a phase noise level of −115 dBc/Hz at a 10 kHz offset from an oscillation frequency in the vicinity of 5 GHz for the MARO based on a 100 nm-thick YIG film and 56 μm of distance between the coplanar nano-antennas of the YIG-film based delay line. We believe that this is a clear way forward to microminiaturize the time-delay feedback microwave auto-oscillators. A further reduction in the phase noise down to −125 dBc/Hz at a 10 kHz offset is found in a model of cascaded connection of several microscopic spin-wave delay lines. [ABSTRACT FROM AUTHOR]

Colloidal probes are often used in force microscopy when the geometry of the tip–sample interaction should be well controlled. Their calibration requires an understanding of their mechanical response, which is very sensitive to the details of the force sensor consisting of a cantilever and the attached colloid. We present some analytical models to describe the dynamics of the cantilever and its load positioned anywhere along its length. The thermal noise calibration of such probes is then studied from a practical point of view, leading to correction coefficients that can be applied in standard force microscope calibration routines. Experimental measurements of resonance frequencies and thermal noise profiles of raw and loaded cantilevers demonstrate the validity of the approach. [ABSTRACT FROM AUTHOR]

This study presents findings indicating that the ferroelectric tunnel junction (FTJ) or resistive random-access memory (RRAM) in one cell can be intentionally selected depending on the application. The HfAlO film annealed at 700 °C shows stable FTJ characteristics and can be converted into RRAM by forming a conductive filament inside the same cell, that is, the process of intentionally forming a conductive filament is the result of defect generation and redistribution, and applying compliance current prior to a hard breakdown event of the dielectric film enables subsequent RRAM operation. The converted RRAM demonstrated good memory performance. Through current–voltage fitting, it was confirmed that the two resistance states of the FTJ and RRAM had different transport mechanisms. In the RRAM, the 1/f noise power of the high-resistance state (HRS) was about ten times higher than that of the low-resistance state (LRS). This is because the noise components increase due to the additional current paths in the HRS. The 1/f noise power according to resistance states in the FTJ was exactly the opposite result from the case of the RRAM. This is because the noise component due to the Poole–Frenkel emission is added to the noise component due to the tunneling current in the LRS. In addition, we confirmed the potentiation and depression characteristics of the two devices and further evaluated the accuracy of pattern recognition through a simulation by considering a dataset from the Modified National Institute of Standards and Technology. [ABSTRACT FROM AUTHOR]

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]

Simulations of condensed matter systems often focus on the dynamics of a few distinguished components but require integrating the full system. A prime example is a molecular dynamics simulation of a (macro)molecule in a solution, where the molecule(s) and the solvent dynamics need to be integrated, rendering the simulations computationally costly and often unfeasible for physically/biologically relevant time scales. Standard coarse graining approaches can reproduce equilibrium distributions and structural features but do not properly include the dynamics. In this work, we develop a general data-driven coarse-graining methodology inspired by the Mori–Zwanzig formalism, which shows that macroscopic systems with a large number of degrees of freedom can be described by a few relevant variables and additional noise and memory terms. Our coarse-graining method consists of numerical integrators for the distinguished components, where the noise and interaction terms with other system components are substituted by a random variable sampled from a data-driven model. The model is parameterized using data from multiple short-time full-system simulations, and then, it is used to run long-time simulations. Applying our methodology to three systems—a distinguished particle under a harmonic and a bistable potential and a dimer with two metastable configurations—the resulting coarse-grained models are capable of reproducing not only the equilibrium distributions but also the dynamic behavior due to temporal correlations and memory effects. Remarkably, our method even reproduces the transition dynamics between metastable states, which is challenging to capture correctly. Our approach is not constrained to specific dynamics and can be extended to systems beyond Langevin dynamics, and, in principle, even to non-equilibrium dynamics. [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]

Implantable medical devices, such as pacemakers, have significantly improved the quality of life for patients with cardiac conditions, allowing them to maintain active lifestyles. Nonetheless, these devices can present unique challenges when interacting with the wearers physical activities, potentially leading to unforeseen complications. Here, we present a case of an 81-year-old male golfer, with a history of atrial fibrillation, congestive heart failure, and sick sinus syndrome, who experienced atrial lead noise from his pacemaker, exclusively triggered by his golf swing. This incident, which led to multiple interventions including lead extraction, reimplantation, and eventually a switch to a unipolar lead configuration, represents the first documented case of its kind. It underscores the intricate relationship between the biomechanical forces of certain sports and the functionality of implanted cardiac devices. Through detailed electrophysiology testing, this case demonstrates how specific movements inherent to the patients golf swing could induce micro-damage to the pacemaker leads, causing noise and malfunction. The findings from this case emphasize the need for healthcare providers to perform sport-specific biomechanical evaluations and create tailored rehabilitation strategies that consider the unique physical demands placed on patients with implanted devices. This approach is important not only for diagnosing and managing similar cases but also for advancing our understanding of how to best support the active lifestyles of patients with implanted cardiac devices, ensuring their safety and longevity.

Previous work assessing the effect of additive noise on the postural control system has found a positive effect of additive white noise on postural dynamics. This study covers two separate experiments that were run sequentially to better understand how the structure of the additive noise signal affects postural dynamics, while also furthering our knowledge of how the intensity of auditory stimulation of noise may elicit this phenomenon. Across the two experiments, we introduced three auditory noise stimulations of varying structure (white, pink, and brown noise). Experiment 1 presented the stimuli at 35 dB while Experiment 2 was presented at 75 dB. Our findings demonstrate a decrease in variability of the postural control system regardless of the structure of the noise signal presented, but only for high intensity auditory stimulation.

To understand the extent of anthropogenic noise in the ocean, it is essential to compare the differences between modern noise environments and their pre-industrial equivalents. The Santa Barbara Channel, off the coast of Southern California, is a corridor for the transportation of goods to and from the busiest shipping ports in the Western hemisphere. Commercial ships introduce high levels of underwater noise into the marine environment. To quantify the extent of noise in the region, we modeled pre-industrial ocean noise levels, driven by wind, and modern ocean noise levels, resulting from the presence of both ships and wind. By comparing pre-industrial and modern underwater noise levels, the low-frequency (50 Hz) acoustic environment was found to be degraded by more than 15 dB. These results can be used to identify regions for noise reduction efforts, as well as to model scenarios to identify those with the greatest potential to support marine conservation efforts.

Moderate noise exposure induces cochlear synaptopathy, the loss of afferent ribbon synapses between cochlear hair cells and spiral ganglion neurons, which is associated with functional hearing decline. Prior studies have demonstrated noise-induced changes in the distribution and number of synaptic components, but the dynamic changes that occur after noise exposure have not been directly visualized. Here, we describe a live imaging model using RIBEYE-tagRFP to enable direct observation of pre-synaptic ribbons in mature hearing mouse cochleae after synaptopathic noise exposure. Ribbon number does not change, but noise induces an increase in ribbon volume as well as movement suggesting unanchoring from synaptic tethers. A subgroup of basal ribbons displays concerted motion towards the cochlear nucleus with subsequent migration back to the cell membrane after noise cessation. Understanding the immediate dynamics of synaptic damage after noise exposure may facilitate identification of specific target pathways to treat cochlear synaptopathy.

Opinion dynamics are affected by cognitive biases and noise. While mathematical models have focused extensively on biases, we still know surprisingly little about how noise shapes opinion patterns. Here, we use an agent-based opinion dynamics model to investigate the interplay between confirmation bias-represented as bounded confidence-and different types of noise. After analysing where noise can enter social interaction, we propose a type of noise that has not been discussed so far, ambiguity noise. While previously considered types of noise acted on agents either before, after or independent of social interaction, ambiguity noise acts on communicated messages, assuming that socially transmitted opinions are inherently noisy. We find that noise can induce agreement when confirmation bias is moderate, but different types of noise require quite different conditions for this effect to occur. An application of our model to the climate change debate shows that at just the right mix of confirmation bias and ambiguity noise, opinions tend to converge to high levels of climate change concern. This result is not observed with the other types. Our findings highlight the importance of considering and distinguishing between the various types of noise and the unique role of ambiguity in opinion formation.

Coherent Ising Machine (CIM) is a network of optical parametric oscillators that solve combinatorial optimization problems by finding the ground state of an Ising Hamiltonian. In CIMs, a problem arises when attempting to realize the Zeeman term because of the mismatch in size between interaction and Zeeman terms due to the variable amplitude of the optical parametric oscillator pulses corresponding to spins. There have been three approaches proposed so far to address this problem for CIM, including the absolute mean amplitude method, the auxiliary spin method, and the chaotic amplitude control (CAC) method. This paper focuses on the efficient implementation of Zeeman terms within the mean-field CIM model, which is a physics-inspired heuristic solver without quantum noise. With the mean-field model, computation is easier than with more physically accurate models, which makes it suitable for implementation in field programmable gate arrays and large-scale simulations. First, we examined the performance of the mean-field CIM model for realizing the Zeeman term with the CAC method, as well as their performance when compared to a more physically accurate model. Next, we compared the CAC method to other Zeeman term realization techniques on the mean-field model and a more physically accurate model. In both models, the CAC method outperformed the other methods while retaining similar performance. [ABSTRACT FROM AUTHOR]

Suppression of 1/f noise in tunnel magnetoresistance (TMR) sensors is a central issue in the realization of magnetic field sensors with ultrafine magnetic field detectivity. Although AC modulation with an external magnetic field has been proposed as a method to shift the operating frequency of a sensor to a high frequency and substantially suppress 1/f noise, its effects on the two types of 1/f noise, that is, magnetic and electrical 1/f noise, are not well understood. In this study, we investigated the noise characteristics and signal detection performance of TMR sensors with an even-function resistance-magnetic field curve operated by the AC modulation method. For one TMR device in which the magnetic 1/f noise was dominant, AC modulation degraded the magnetic field detectivity owing to the additional noise induced by the AC modulation field. However, in another TMR device, in which the electrical 1/f noise was artificially enhanced by introducing lattice defects in the MgO tunnel barrier, AC modulation effectively suppressed the 1/f noise and improved the magnetic field detectivity by one order. This demonstrates that the AC modulation method using an external magnetic field is effective for magnetic field sensors in which electrical 1/f noise is dominant. [ABSTRACT FROM AUTHOR]

With the advances in smart sensing and data mining technologies of Industry 4.0, condition monitoring of key equipment in manufacturing has brought transformations in production and maintenance management. However, in practical applications, noise from both the working environment and the sensing devices is inevitable, which causes the low performance of data-driven fault diagnosis. To address this challenge, the paper develops a robust two-stage joint denoising method by integrating ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA), with fuzzy entropy discriminant as a threshold. The developed method can filter noisy components from decomposed modal components and reconstruct a new signal with denoised independent components. Moreover, an improved convolutional neural network (CNN) model based on the VGG structure has been constructed as a classifier to achieve end-to-end fault diagnosis. The experimental results demonstrate the high accuracy and superior anti-interference capability of the proposed method for rolling bearing fault diagnosis under various noise levels. Compared with state-of-the-art denoising methods and fault diagnosis methods, the proposed method achieves higher accuracy and robustness under variable noise interference. The proposed method can be applied to broader fault diagnosis tasks of production equipment in complex practical environments. [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]

We present a novel diagnostic system, based on the principle of capacitive sensing, capable of detecting the conduction zone of detonating explosives. The method relies on measuring the change in the capacitive coupling between a printed circuit and its surroundings, induced by an increase in local conductivity. A high frequency wave generator provides the driving signal, which is modified by the change in coupling. This method boasts greater sensitivity than existing conductivity-based techniques, with comparable spatial and temporal resolution. The subject material was pentaerythritol tetranitrate (PETN) powder, pressed into low density (50%–85% theoretical maximum density) cylindrical columns. Previous attempts to measure the reaction zone of low density PETN have faced difficulties because of its short length and low conductivity compared to other explosives. The PETN was initiated using a laser flyer detonator in order to reach detonation promptly and with a minimum of electrical noise. Application of the capacitive sensor diagnostic and subsequent analysis yielded a density dependent steady-state reaction zone length of between 45 and 78 μ m. [ABSTRACT FROM AUTHOR]

Barkhausen noise is a type of magnetic noise that occurs due to the interaction of domain walls with defects. In magnetic sensor applications, this can be a detrimental phenomenon since it disturbs the signal. We study this noise using coupled micro-magneto-mechanical finite element simulations. To this end, we consider in the first step a thermodynamically consistent material model within the generalized standard material approach. In our material model, we include exchange, anisotropy, demagnetizing, Zeeman, and elastic energy. The coupling between mechanics and micro-magnetics is implemented via a magnetostrictive strain contribution. In the following step, we extend the material model to represent the full Landau–Lifschitz–Gilbert magnetization dynamics. For the model extension, we give a detailed exposition of the finite element implementation. In particular, we use a new modified leapfrog/Crank–Nicolson time integration scheme, which preserves the magnetization magnitude exactly. Furthermore, we showcase in detail the scheme for applying our material model to noise computation (based on ensemble averaging). Finally, we investigate various numerical examples based on the magnetostrictive material FeCoSiB to illustrate the different features of our approach. [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.

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.

PURPOSE: To evaluate and compare the effectiveness of nearest neighbor (NN)- and variational autoencoder (VAE)-smoothing algorithms to reduce variability and enhance the performance of glaucoma visual field (VF) progression models. DESIGN: Longitudinal cohort study. SUBJECTS: 7150 eyes (4232 patients), with ≥ 5 years of follow-up and ≥ 6 visits. METHODS: Vsual field thresholds were smoothed with the NN and VAE algorithms. The mean total deviation (mTD) and VF index rates, pointwise linear regression (PLR), permutation of PLR (PoPLR), and the glaucoma rate index were applied to the unsmoothed and smoothed data. MAIN OUTCOME MEASURES: The proportion of progressing eyes and the conversion to progression were compared between the smoothed and unsmoothed data. A simulation series of noiseless VFs with various patterns of glaucoma damage was used to evaluate the specificity of the smoothing models. RESULTS: The mean values of age and follow-up time were 62.8 (standard deviation: 12.6) years and 10.4 (standard deviation: 4.7) years, respectively. The proportion of progression was significantly higher for the NN and VAE smoothed data compared with the unsmoothed data. VF progression occurred significantly earlier with both smoothed data compared with unsmoothed data based on mTD rates, PLR, and PoPLR methods. The ability to detect the progressing eyes was similar for the unsmoothed and smoothed data in the simulation data. CONCLUSIONS: Smoothing VF data with NN and VAE algorithms improves the signal-to-noise ratio for detection of change, results in earlier detection of VF progression, and could help monitor glaucoma progression more effectively in the clinical setting. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.