Perceptual learning changes the amplitude not the shape of the temporal window of visual processing.

To explore the relationship between category and perceptual learning, we examined both category and perceptual learning in patients with treated Wilson's disease (WD), whose basal ganglia, known to be important in category learning, were damaged by the disease. We measured their learning rate and accuracy in rule-based and information-integration category learning, and magnitudes of perceptual learning in a wide range of external noise conditions, and compared the results with those of normal controls. The WD subjects exhibited deficits in both forms of category learning and in perceptual learning in high external noise. However, their perceptual learning in low external noise was relatively spared. There was no significant correlation between the two forms of category learning, nor between perceptual learning in low external noise and either form of category learning. Perceptual learning in high external noise was, however, significantly correlated with information-integration but not with rule-based category learning. The results suggest that there may be a strong link between information-integration category learning and perceptual learning in high external noise. Damage to brain structures that are important for information-integration category learning may lead to poor perceptual learning in high external noise, yet spare perceptual learning in low external noise. Perceptual learning in high and low external noise conditions may involve separate neural substrates.

What underlies contrast sensitivity improvements in adults with anisometropic amblyopia following perceptual learning in grating contrast detection? In this paper, we adopted the external noise approach (Z.-L. Lu & B. A. Dosher, 1998) to identify the mechanisms underlying perceptual learning in adults with anisometropic amblyopia. By measuring contrast thresholds in a range of external noise conditions at two performance levels (79.3% and 70.7%), we found that a mixture of internal additive noise reduction and external noise exclusion underlay training induced contrast sensitivity improvements in adults with anisometropic amblyopia. In comparison, normal adults exhibited only small amount of external noise exclusion under the same training conditions. The results suggest that neural plasticity may be more robust in amblyopia, lending further support of perceptual learning as a potential treatment for adult amblyopia.

Modern network-on-chip (NoC) hardware is an emerging target for side-channel security attacks. A recent work implemented and characterized timing-based software side-channel attacks that target NoC hardware on a real multicore machine. This article studies the impact of system noise on prior attack setups and shows that high noise is sufficient to defeat the attacker. We propose an information theory-based attack setup that uses repetition codes and differential signaling techniques to de-noise the unwanted noise from the NoC channel to successfully implement a practical covert-communication attack on a real multicore machine. The evaluation demonstrates an attack efficacy of 97%, 88%, and 78% under low, medium, and high external noise, respectively. Our attack characterization reveals that noise-based mitigation schemes are inadequate to prevent practical covert communication, and thus isolation-based mitigation schemes must be considered to ensure strong security. Isolation-based schemes are shown to mitigate timing-based side-channel attacks. However, their impact on the performance of real-world security critical workloads is not well understood in the literature. This article evaluates the performance implications of state-of-the-art spatial and temporal isolation schemes. The performance impact is shown to range from 2–3% for a set of graph and machine learning workloads, thus making isolation-based mitigations practical.

Participants in the Asia Pacific Conference of the Prognostics and Health Management Society 2017 (PHMAP 2017) Data Challenge were given measured vibration signals from motor-driven gearboxes used in pulverizers. Using this information, participants were requested to predict failure dates and the faulty components. The measured signals were affected by significant noise and disturbance, as the pulverizers in the provided data worked under actual operating conditions. This paper thus presents a fault prediction method for a motor-driven gearbox in a pulverizer system that can perform under external noise and disturbance conditions. First, two fault features, an RMS value in the higher frequency zones (HRMS) and an amplitude of a period for high-speed shaft in the quefrency domain (QAHSS), were extracted based on frequency analysis using the higher and lower sampling rate data. The two features were applied to each pulverizer based on results of frequency responses to impact loadings. Then, a regression analysis was used to predict the failure date using the two extracted features. A weighted regression analysis was used to compensate for the imbalance of the features in the given period. In addition, the faulty components in the motor-driven gearboxes were predicted based on the modulated frequency components. The score predicted by the proposed approach was ranked first in the PHMAP 2017 Data Challenge.

The functional form of performance improvements has been extensively studied in speeded cognitive and motor tasks; in such tasks, reductions in response times have been characterized by the ubiquitous power law of learning or by a simpler exponential form. Performance improvements in perceptual capacities are also important in expertise, but their functional form is unknown. This study investigated the functional form of perceptual learning. For individual observers, reductions in thresholds were best described by an exponential function, rather than a power or compound exponential and power (apex) function. Learning was specific to orientation, a result that supports the perceptual locus of the learning, and was decoupled in high and low external noise, a result that reflects separable learning mechanisms in the two conditions. The simple exponential form of learning implies a constant relative rate of learning throughout practice; there was no evidence supporting multilevel hypotheses, such as serial reverse hierarchical and parallel-learning models, that posit multiple processes of learning characterized by different rates.

On the basis of problems which is based on external power station power plant operating conditions partitioning historical operational data analysis, it points out the key of data mining results will depend on the adaptability of the data mining algorithms. To solve the above problems, the new k-mean algorithms and equal width method which are suitable for power plant historical operating data divided external conditions are proposed. First of all, two methods are study. One is calculation method combines statistical theory of k-means clustering algorithm for the initial number of cluster centers. And another is the equal width method determination of the number of clusters and the interval size. Secondly, new K-means algorithm is to apply the history running data of the power stations in order to mine the unit load, the external condition of coal quality characteristics. Meaning while, the way to mine the external environmental temperature condition is the proposed method of equal width. Finally, to provide data for the reference site operating personnel, by combining obtain the optimal combination of external operating conditions described unit operation.

In most of the industrial process plants, PI/PID controllers have been widely used because of its simple design, easy tuning, and operational advantages. However, the performance of these controllers degrades for the processes with long dead-time and variation in set-point. Up next, a PPI controller is designed based on the Smith predictor to handle dead-time processes by compensation technique, but it failed to achieve adequate performance in the presence of external noise, large disturbances, and higher-order systems. Furthermore, the model-based controllers structure is complex in nature and requires the exact model of the process with more tunable parameters. Therefore, in this research, a fractional-order predictive PI controller has been proposed for dead-time processes with added filtering abilities. The controller uses the dead-time compensation characteristics of the Smith predictor and the fractional-order controller's robustness nature. For the high peak overshoot, external noise, and disturbance problems, a new set-point and noise filtering technique is proposed, and later it is compared with different conventional methods. In servo and regulatory operations, the proposed controller and filtering techniques produced optimal performance. Multiple real-time industrial process models are simulated with long dead-time to evaluate the proposed technique's flexibility, set-point tracking, disturbance rejection, signal smoothing, and dead-time compensation capabilities.

The improvements due to small incision lenticule extraction (SMILE) in vision, e.g., in spherical equivalent (SE) and visual acuity (VA), has been widely recognized. However, the contrast sensitivity (CS) change after SMILE was not certain. Here, we investigated the effect of SMILE on CS before, 1 day after and 7 days after surgery and then clarified the corresponding mechanism by using a perceptual template model (PTM). In addition, the relationship among SE, VA, and CS was discussed. The quick contrast sensitivity function (qCSF) was applied to measure CS with high precision and accuracy. We found that (1) CS was significantly improved 1 day after SMILE and was also increased 7 days after the surgery, (2) CS improvements were dependent on spatial frequency and external noise, (3) the increase in CS was due to the decreased internal additive noise and an enhanced perceptual template, and (4) Greater SE improvements predicted better VA improvements 1 day after SMILE, and a positive correlation between SE improvements and AULCSF improvements 7 days after SMILE was observed. These findings help us better understand the effect of SMILE and provide effective indicators for future visual research.

As a crucial element in photovoltaic power generation systems, the condition of solar panels significantly impacts the efficiency of power generation. The ability to accurately and promptly detect defects in solar panels is essential for enhancing system performance. This study introduces a novel model for identifying defects in photovoltaic modules, leveraging an enhanced version of EfficientNet-V2. This model aims to address challenges in identifying defects in infrared images of solar panels under conditions of high-noise and low-model efficiency. To address the challenges of high image noise and blur, this article initially presents a methodology that combines the Db4 wavelet transform with a blind deconvolution algorithm for comprehensive preprocessing of the original image. Furthermore, this study optimizes the model's feature representation capabilities by implementing key transformations within the EfficientNet-V2 network framework. Notably, we replaced the traditional SE block with the more efficient channel attention (ECA) mechanism module. Due to its lightweight structure and effective performance, ECA substantially improves the model's capacity to extract complex and abstract image features, while also accelerating the training process's convergence speed and enhancing overall computational efficiency. At the classifier level, this paper innovatively integrates the XGBoost ensemble learning algorithm into the model, substituting the conventional softmax classifier used in traditional convolutional neural network (CNN). With its superior generalization capabilities, robust nonlinear modeling skills, and efficient computational characteristics, XGBoost can more accurately detect minute defects in solar panels based on the deep features produced by EfficientNet-V2, thereby significantly improving the accuracy and robustness of defect detection. Simulation results demonstrate that the proposed model structure outperforms traditional CNN models in terms of accuracy and stability, underscoring the efficacy of the enhanced EfficientNet-V2 model in detecting solar panel defects under high-noise conditions.

Human performance in visual detection, discrimination, identification, and search tasks typically improves with practice. Psychophysical studies suggest that perceptual learning is mediated by an enhancement in the coding of the signal, and physiological studies suggest that it might be related to the plasticity in the weighting or selection of sensory units coding task relevant information (learning through attention optimization). We propose an experimental paradigm (optimal perceptual learning paradigm) to systematically study the dynamics of perceptual learning in humans by allowing comparisons to that of an optimal Bayesian algorithm and a number of suboptimal learning models. We measured improvement in human localization (eight-alternative forced-choice with feedback) performance of a target randomly sampled from four elongated Gaussian targets with different orientations and polarities and kept as a target for a block of four trials. The results suggest that the human perceptual learning can occur within a lapse of four trials (<1 min) but that human learning is slower and incomplete with respect to the optimal algorithm (23.3% reduction in human efficiency from the 1st-to-4th learning trials). The greatest improvement in human performance, occurring from the 1st-to-2nd learning trial, was also present in the optimal observer, and, thus reflects a property inherent to the visual task and not a property particular to the human perceptual learning mechanism. One notable source of human inefficiency is that, unlike the ideal observer, human learning relies more heavily on previous decisions than on the provided feedback, resulting in no human learning on trials following a previous incorrect localization decision. Finally, the proposed theory and paradigm provide a flexible framework for future studies to evaluate the optimality of human learning of other visual cues and/or sensory modalities.

The application of the fractional Fourier transform (FrFT) for the formation and analysis of acoustic holograms created by a moving linear frequency-modulated (LFM) source in a shallow-water waveguide is considered. A holographic method is proposed for constructing an interferogram of the LFM signal of a source and converting it into a Fourier hologram based on FrFT in conditions of high noise. The holographic method based on FrFT ensures the formation of localized focal areas arising from mode interference, even with high-intensity noise interference. The effectiveness of the holographic method based on FrFT is analyzed using numerical experiments in the frequency range of 100-150 Hz. The results demonstrate the high noise immunity of the method for locating the source in realistic shallow water conditions with high noise.

Current baseline correction methods struggle to accurately identify baseline drift types when applied to multi-peak Fiber Bragg Grating (FBG) spectra due to their high noise levels and multi-peak characteristics. This paper proposes a Partial Least Squares (PLS) based spectral baseline correction algorithm specifically for distorted FBG spectra in high-noise environments. This algorithm employs Savitzky-Golay filtering and first-order differentiation to delineate spectral peak regions, optimizes the iterative conditions of the PLS algorithm, and introduces an evaluation index to assess the degree of thenormalized L 1 norm in the fitting results, thereby optimizing the tendency of the AirPLS algorithm to produce suboptimal fitting results under high noise conditions. Experimental results demonstrate that the proposed NtwPLS method can effectively handle multi-peak FBG spectra with arcuate and linear drifts and maintain a high degree of central coincidence in the fitted baseline even as the Signal-to-Noise Ratio gradually decreases. This reflects its robust noise performance and accurate spectral baseline fitting accuracy.

Pharmacokinetics and adhesion of the Agile transdermal contraceptive patch (AG200-15) during daily exposure to external conditions of heat, humidity and exercise

Gravitational waves (GWs) provide a powerful tool to probe energetic astrophysical events and test fundamental physics, with space-based detectors enabling access to low-frequency signals from massive black hole mergers, extreme mass-ratio inspirals, and other sources. However, the sensitivity of space-based interferometers can be severely compromised by tilt-to-length (TTL) coupling noise, which contaminates phase measurements by converting angular jitter into apparent path length fluctuations. This problem is exacerbated when differential wave front sensing (DWS) systems suffer from high readout noise due to optical misalignments and wave front aberrations. In this work, we introduce a novel framework that integrates convolutional neural networks (CNNs) with time delay interferometry (TDI) to improve the robustness of TTL coupling coefficient estimation under high-noise conditions. The CNN is trained to identify and exclude DWS angular measurements corrupted by noise, while the TDI module adaptively selects optimal combinations that minimize TTL-induced contamination. In addition, an analytical error-based monitoring algorithm is incorporated to track the stability of TTL coefficients. When coefficients remain within their nominal range, the predicted uncertainty band covers the true values, while abrupt excursions beyond the monitoring threshold can be reliably detected. Simulations demonstrate that our method maintains TTL coefficient errors below 0.1 mm rad −1 for the coefficients associated with the selected TDI combination even under degraded DWS readout conditions, while simultaneously providing monitoring bands that capture excursions up to tens of millimeters per radian in agreement with analytical predictions. Our approach ensures that space-based detectors remain effective in resolving weak astrophysical signals despite actual measurement imperfections.

Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.