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The statistical properties in various medical images demonstrate uncorrelated noise fluctuations. The signal noise fluctuations are generally due to physical imaging processes and have nothing to do with the tissue textures. Adding the noise types (e.g., quantization, electronics, photon) usually degrade medical images. The noise variation is usually assumed to be additive with zero‐mean, constant variance Gaussian distribution. However, close consideration of different medical images indicates the need for better model representation to minimise the noise that can be vital in decision‐making. This research proposed a probabilistic method to represent all real‐type noise in general medical images. The method aims to cover most classical statistical models such as Gaussian, lognormal, Rayleigh, Weibull, and Nakagami without a prior examination to test for fitness. The proposed model was applied to actual clinical images to test the performance of the noise originating from the physical processes. The noise is assumed to be additive white Gaussian type with a zero mean and constant variance. The theoretical literature indicates that a nonlinear function can better represent noise. This research helps to form a relationship between the image intensity and the noise variance that yields the fitting parameters in the introduced nonlinear function. The validity of the proposed method was proved mathematically and tested using the well know Kolmogorov–Smirnov (K‐S) and Akaike Information Criteria (AIC) tests. The method was successfully applied to various clinical images such as magnetic resonance, x‐ray, and panoramic images. The model's performance is compared with the classical models using root mean squared error (RMSE), relative error (RE), and R2 as the evaluation matrices. The presented model has outperformed all classic models. [ABSTRACT FROM AUTHOR]

This study aims to develop a framework to analyze the efficiency of the national ecosystems for supporting high-quality early-stage entrepreneurship to gain insights into the rationales and effectiveness of public policies. A model combining a stochastic DEA model and the metafrontier method is used to explore the entrepreneurial ecosystem efficiency across countries that operate under different technologies. Using data from 30 countries over the period 2013–2018, we highlight the efficiency variation of countries according to their level of economic development. Our results suggest that efficiency-driven countries are the technology leaders in entrepreneurship, and innovation-driven countries are the followers. Then a convergence analysis is applied to determine if similar or divergent paths are observed between countries regarding their entrepreneurial efficiency and technology. The results indicate that there is a lack of overall convergence; instead, countries are converging into several clubs with different characteristics. Finally, by combining the convergence clubs of efficiency and technology, we construct a managerial decision-making matrix to provide benchmarks and improvement suggestions. [ABSTRACT FROM AUTHOR]

This article presents a table containing redacted data from a real study. The table contains three curiosities: statistical significance in the absence of clinical significance, narrow standard deviations, and the absence of a placebo effect. The data in the table had been obtained by an inexperienced rater; how the inexperience compromised the data is explained. Action points for rater experience, rater training, and rating procedures are suggested. [ABSTRACT FROM AUTHOR]

Various studies that have investigated the detection of gamma coincidence events have revealed that design factors and image reconstruction approaches dictate the spatial resolution, coincidence efficiency, and levels of statistical noise of the detection system. In the case of imaging, cascade gamma-ray coincidence (CGC) imagers coupled with collimated detectors offer promising values for both spatial resolution and coincidence efficiency. However, to date, no CGC imager with single or multiple collimated detectors has reported a performance level beyond 6.7 mm spatial resolution (FWHM) and 6.0 × 10-6 coincidence efficiency. Given the recent developments and the current interests in high resolution and localization of an individual decaying nucleus, there is a need for CGC imagers with higher performance in terms of spatial resolution and efficiency. Therefore, deploying a CGC imager coupled with multiple collimated detectors may prove to be of value in nuclear imaging and probably in clinical applications. [ABSTRACT FROM AUTHOR]

Abstract Background The amount of signal decreases when the acquisition duration is shortened. However, it is not clear how this affects the quantitative values. This study aims to clarify the effect of acquisition time shortening in brain tumor PET/CT using 11C-methionine on the quantitative values. Method This study was a retrospective analysis of 30 patients who underwent clinical 11C-methionine PET/CT examination. PET images were acquired in list mode for 10 min. PET images of acquisition duration from 1 to 10 min with 1-min step were reconstructed. We examined the effect on the quantitative values of acquisition duration. We placed a volume of interest to include the entire tumor and regions of interest in the shape of a large crescent in the contralateral hemisphere in 5 contiguous axial slices as normal tissue. Quantitative values examined were maximum, peak, and mean standardized uptake values (SUVmax, SUVpeak, SUVmean), metabolic tumor volume (MTV), and maximum tumor to normal tissue ratio (TNRmax), with each duration compared to that with 10 min. Results SUVmax, MTV, and TNRmax showed the highest values due to the effects of statistical noise when the acquisition time was 1 min. These values were stable when the acquisition duration was > 6 min. SUVpeak and SUVmean showed mostly consistent values regardless of duration. Conclusions SUVmax, MTV, and TNRmax are affected by acquisition time. If the acquisition duration was > 6 min, the fluctuation could be suppressed within 5% in these quantitative values. However, SUVpeak was suggested to be a robust index regardless of the acquisition duration.

Background: The amount of signal decreases when the acquisition duration is shortened. However, it is not clear how this affects the quantitative values. This study aims to clarify the effect of acquisition time shortening in brain tumor PET/CT using 11C-methionine on the quantitative values. Method: This study was a retrospective analysis of 30 patients who underwent clinical 11C-methionine PET/CT examination. PET images were acquired in list mode for 10 min. PET images of acquisition duration from 1 to 10 min with 1-min step were reconstructed. We examined the effect on the quantitative values of acquisition duration. We placed a volume of interest to include the entire tumor and regions of interest in the shape of a large crescent in the contralateral hemisphere in 5 contiguous axial slices as normal tissue. Quantitative values examined were maximum, peak, and mean standardized uptake values (SUVmax, SUVpeak, SUVmean), metabolic tumor volume (MTV), and maximum tumor to normal tissue ratio (TNRmax), with each duration compared to that with 10 min. Results: SUVmax, MTV, and TNRmax showed the highest values due to the effects of statistical noise when the acquisition time was 1 min. These values were stable when the acquisition duration was > 6 min. SUVpeak and SUVmean showed mostly consistent values regardless of duration. Conclusions: SUVmax, MTV, and TNRmax are affected by acquisition time. If the acquisition duration was > 6 min, the fluctuation could be suppressed within 5% in these quantitative values. However, SUVpeak was suggested to be a robust index regardless of the acquisition duration. [ABSTRACT FROM AUTHOR]

Organizational signals about corporate social responsibility may have a harmful impact on equity markets for two main reasons. First, corporate social responsibility is not systematically correlated with companies' economic fundamentals. Second, opportunistic managers are incentivized to distort information provided to market participants about their firms' corporate social responsibility. Either causal force, by itself, makes it difficult for market participants to interpret information about corporate social responsibility accurately. This greater noise in financial markets typically invites more noise trading, which in turn leads to excess market volatility (among all publicly traded firms) and, in a particular context of social-institutional processes and structures, to excess market valuations of firms that are widely perceived as socially responsible. [ABSTRACT FROM AUTHOR]

Noise in data is not uncommon in real-world cases, although it is commonly omitted from performance measurement studies. In this paper, we develop a stochastic DEA-based methodology to measure performance when the endogenous (e.g. efficiency) and exogenous variables (e.g. perspectives of patients' satisfaction), which are incorporated in the assessment, are inversely related. This methodology identifies benchmark units that are not only efficient but are also assigned scores for their exogenous variables, which are at least equal to user-defined critical values. We apply the performance measurement methodology to the 14 largest Cypriot health centers. The advantages of our methodology are pointed out through comparative analysis with alternative stochastic and non-stochastic DEA approaches. [ABSTRACT FROM AUTHOR]

In regression analysis, the adjusted R2 value describes the proportion of the variance in the dependent variable that is explained by the independent variables in the equation. What remains is unexplained or residual variance. Residual variance has two components: the contribution of unmeasured variables (residual confounding) and measurement error (noise). These concepts are explained using examples. [ABSTRACT FROM AUTHOR]

A method of determining unknown phase-shifts between elementary images in two-dimensional Structured Illumination Microscopy (2D-SIM) is presented. The proposed method is based on the comparison of the peak intensity of spectral components. These components correspond to the inherent structured illumination spectral content and the residual component that appears from wrongly estimated phase-shifts. The estimation of the phase-shifts is carried out by finding the absolute maximum of a function defined as the normalized peak intensity difference in the Fourier domain. This task is performed by an optimization method providing a fast estimation of the phase-shift. The algorithm stability and robustness are tested for various levels of noise and contrasts of the structured illumination pattern. Furthermore, the proposed approach reduces the number of computations compared to other existing techniques. The method is supported by the theoretical calculations and validated by means of simulated and experimental results. [ABSTRACT FROM AUTHOR]

Variability, stochastic or otherwise, is a central feature of neural activity. Yet the means by which estimates of variation and uncertainty are derived from noisy observations of neural activity is often heuristic, with more weight given to numerical convenience than statistical rigour. For two-photon imaging data, composed of fundamentally probabilistic streams of photon detections, the problem is particularly acute. Here, we present a statistical pipeline for the inference and analysis of neural activity using Gaussian Process regression, applied to two-photon recordings of light-driven activity in ex vivo mouse retina. We demonstrate the flexibility and extensibility of these models, considering cases with non-stationary statistics, driven by complex parametric stimuli, in signal discrimination, hierarchical clustering and other inference tasks. Sparse approximation methods allow these models to be fitted rapidly, permitting them to actively guide the design of light stimulation in the midst of ongoing two-photon experiments. [ABSTRACT FROM AUTHOR]

The Bitcoin market becomes the focus of the economic market since its birth, and it has attracted wide attention from both academia and industry. Due to the absence of regulations in the Bitcoin market, it may be easier to bring some kinds of illegal behaviors. Thus, it raises an interesting question: Is there abnormity or illegal behavior in Bitcoin platforms? To answer this question, we investigate the abnormity in five leading Bitcoin platforms. By analyzing the financial index, i.e. the normalized logarithmic price return, we find that the properties of price return in bitFlyer are completely different from others. To find the possible reasons, we find that the abnormal ask price and bid price appear simultaneously in bitFlyer, which may be potentially linked to either price manipulation or money laundering. It verifies our conjecture that there may be abnormity or price manipulation in Bitcoin platforms. Furthermore, our findings in price return could also provide an innovative and effective method to detect the abnormity in Bitcoin platforms. [ABSTRACT FROM AUTHOR]

Many biological problems involve the response to multiple perturbations. Examples include response to combinations of many drugs, and the effects of combinations of many mutations. Such problems have an exponentially large space of combinations, which makes it infeasible to cover the entire space experimentally. To overcome this problem, several formulae that predict the effect of drug combinations or fitness landscape values have been proposed. These formulae use the effects of single perturbations and pairs of perturbations to predict triplets and higher order combinations. Interestingly, different formulae perform best on different datasets. Here we use Pareto optimality theory to quantitatively explain why no formula is optimal for all datasets, due to an inherent bias-variance (noise-precision) tradeoff. We calculate the Pareto front of log-linear formulae and find that the optimal formula depends on properties of the dataset: the typical interaction strength and the experimental noise. This study provides an approach to choose a suitable prediction formula for a given dataset, in order to best overcome the combinatorial explosion problem. [ABSTRACT FROM AUTHOR]

Behavioral and neuroimaging evidence shows that human decisions are sensitive to the statistical regularities (mean, variance, skewness, etc.) of reward distributions. However, it is unclear what representations human observers form to approximate reward distributions, or probability distributions in general. When the possible values of a probability distribution are numerous, it is cognitively costly and perhaps unrealistic to maintain in mind the probability of each possible value. Here we propose a Clusters of Samples (CoS) representation model: The samples of the to-be-represented distribution are classified into a small number of clusters and only the centroids and relative weights of the clusters are retained for future use. We tested the behavioral relevance of CoS in four experiments. On each trial, human subjects reported the mean and mode of a sequentially presented multimodal distribution of spatial positions or orientations. By varying the global and local features of the distributions, we observed systematic errors in the reported mean and mode. We found that our CoS representation of probability distributions outperformed alternative models in accounting for subjects’ response patterns. The ostensible influence of positive/negative skewness on the over/under estimation of the reported mean, analogous to the “skewness preference” phenomenon in decisions, could be well explained by models based on CoS. [ABSTRACT FROM AUTHOR]

Two novel image denoising algorithms are proposed which employ goodness of fit (GoF) test at multiple image scales. Proposed methods operate by employing the GoF tests locally on the wavelet coefficients of a noisy image obtained via discrete wavelet transform (DWT) and the dual tree complex wavelet transform (DT-CWT) respectively. We next formulate image denoising as a binary hypothesis testing problem with the null hypothesis indicating the presence of noise and the alternate hypothesis representing the presence of desired signal only. The decision that a given wavelet coefficient corresponds to the null hypothesis or the alternate hypothesis involves the GoF testing based on empirical distribution function (EDF), applied locally on the noisy wavelet coefficients. The performance of the proposed methods is validated by comparing them against the state of the art image denoising methods. [ABSTRACT FROM AUTHOR]

An extreme learning machine (ELM) is a novel training method for single-hidden layer feedforward neural networks (SLFNs) in which the hidden nodes are randomly assigned and fixed without iterative tuning. ELMs have earned widespread global interest due to their fast learning speed, satisfactory generalization ability and ease of implementation. In this paper, we extend this theory to hypercomplex space and attempt to simultaneously consider multisource information using a hypercomplex representation. To illustrate the performance of the proposed hypercomplex extreme learning machine (HELM), we have applied this scheme to the task of multispectral palmprint recognition. Images from different spectral bands are utilized to construct the hypercomplex space. Extensive experiments conducted on the PolyU and CASIA multispectral databases demonstrate that the HELM scheme can achieve competitive results. The source code together with datasets involved in this paper can be available for free download at . [ABSTRACT FROM AUTHOR]

Computational neuroimaging methods aim to predict brain responses (measured e.g. with functional magnetic resonance imaging [fMRI]) on the basis of stimulus features obtained through computational models. The accuracy of such prediction is used as an indicator of how well the model describes the computations underlying the brain function that is being considered. However, the prediction accuracy is bounded by the proportion of the variance of the brain response which is related to the measurement noise and not to the stimuli (or cognitive functions). This bound to the performance of a computational model has been referred to as the noise ceiling. In previous fMRI applications two methods have been proposed to estimate the noise ceiling based on either a split-half procedure or Monte Carlo simulations. These methods make different assumptions over the nature of the effects underlying the data, and, importantly, their relation has not been clarified yet. Here, we derive an analytical form for the noise ceiling that does not require computationally expensive simulations or a splitting procedure that reduce the amount of data. The validity of this analytical definition is proved in simulations, we show that the analytical solution results in the same estimate of the noise ceiling as the Monte Carlo method. Considering different simulated noise structure, we evaluate different estimators of the variance of the responses and their impact on the estimation of the noise ceiling. We furthermore evaluate the effect the interplay between regularization (often used to estimate model fits to the data when the number of computational features in the model is large) and model complexity on the performance with respect to the noise ceiling. Our results indicate that when considering the variance of the responses across runs, the computing the noise ceiling analytically results in similar estimates as the split half estimator and approaches the true noise ceiling under a variety of simulated noise scenarios. Finally, the methods are tested on real fMRI data acquired at 7 Tesla. [ABSTRACT FROM AUTHOR]

Recently, U.S. live cattle futures prices have experienced high levels of intraday price variance, which have raised concerns about the possible impact of microstructure noise from high frequency trading on market instability. This article identifies both the magnitude and the duration of the bias caused by market microstructure noise in measuring efficient price variance in the live cattle futures market from 2011 to 2016, with emphasis on price variance behavior in recent years. Market microstructure noise increases observed price variance, but its effects are not large and do not last more than three to four minutes in response to changing information. Intraday price variance has increased in recent years, but the findings provide little evidence that high frequency traders were responsible for economically meaningful market noise. Informatively, steps taken by the CME and cattle producers to mitigate noise have not been fruitful to date, and signal that the magnitude of noise will likely vary with the magnitude of changes in demand and cyclical supply. [ABSTRACT FROM AUTHOR]

We proposed a method for segmentation of brain tissues—gray matter, white matter, and cerebrospinal fluid—using multi-contrast images, including a T1 map and a uniform T1-weighted image, from a magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence at 7 Tesla. The proposed method was evaluated with respect to the processing time and the similarity of the segmented masks of brain tissues with those obtained using FSL, FreeSurfer, and SPM12. The processing time of the proposed method (28 ± 0 s) was significantly shorter than those of FSL and SPM12 (444 ± 4 s and 159 ± 2 s for FSL and SPM12, respectively). In the similarity assessment, the tissue mask of the brain obtained by the proposed method showed higher consistency with those obtained using FSL than with those obtained using SPM12. The proposed method misclassified the subcortical structures and large vessels since it is based on the intensities of multi-contrast images obtained using MP2RAGE, which uses a similar segmentation approach as FSL but is not based on a template image or a parcellated brain atlas, which are used for FreeSurfer and SPM12, respectively. However, the proposed method showed good segmentation in the cerebellum and white matter in the medial part of the brain in comparison with the other methods. Thus, because the proposed method using different contrast images of MP2RAGE sequence showed the shortest processing time and similar segmentation ability as the other methods, it may be useful for both neuroimaging research and clinical diagnosis. [ABSTRACT FROM AUTHOR]

A model has two main aims: predicting the behavior of a physical system and understanding its nature, that is how it works, at some desired level of abstraction. A promising recent approach to model building consists in deriving a Langevin-type stochastic equation from a time series of empirical data. Even if the protocol is based upon the introduction of drift and diffusion terms in stochastic differential equations, its implementation involves subtle conceptual problems and, most importantly, requires some prior theoretical knowledge about the system. Here we apply this approach to the data obtained in a rotational granular diffusion experiment, showing the power of this method and the theoretical issues behind its limits. A crucial point emerged in the dense liquid regime, where the data reveal a complex multiscale scenario with at least one fast and one slow variable. Identifying the latter is a major problem within the Langevin derivation procedure and led us to introduce innovative ideas for its solution. [ABSTRACT FROM AUTHOR]

Intravoxel incoherent motion (IVIM) imaging is a magnetic resonance imaging (MRI) technique widely used in clinical applications for various organs. However, IVIM imaging at low b-values is a persistent problem. This paper aims to investigate in a systematic and detailed manner how the number of low b-values influences the estimation of IVIM parameters. To this end, diffusion-weighted (DW) data with different low b-values were simulated to get insight into the distributions of subsequent IVIM parameters. Then, in vivo DW data with different numbers of low b-values and different number of excitations (NEX) were acquired. Finally, least-squares (LSQ) and Bayesian shrinkage prior (BSP) fitting methods were implemented to estimate IVIM parameters. The influence of the number of low b-values on IVIM parameters was analyzed in terms of relative error (RE) and structural similarity (SSIM). The results showed that the influence of the number of low b-values on IVIM parameters is variable. LSQ is more dependent on the number of low b-values than BSP, but the latter is more sensitive to noise. [ABSTRACT FROM AUTHOR]

Functional methods identify joint centres as the centre of rotation (CoR) of two adjacent movements during an ad-hoc movement. The methods have been used for functionally determining hip joint centre in gait analysis and have revealed advantages compared to predictive regression techniques. However, the current implementation of functional methods hinders its application in clinical use when subjects have difficulties performing multi-plane movements over the required range. In this study, we systematically investigated whether functional methods can be used to localise the CoR during a quasi-planar movement. The effects of the following factors were analysed: the algorithms, the range and speed of the movement, marker cluster location, marker cluster size and distance to the joint centre. A mechanical linkage was used in our study to isolate the factors of interest and give insight to variation in implementation of functional methods. Our results showed the algorithms and cluster locations significantly affected the estimate results. For all algorithms, a significantly positive relationship between CoR errors and the distance of proximal cluster coordinate location to the joint centre along the medial-lateral direction was observed while the distal marker clusters were best located as close as possible to the joint centre. By optimising the analytical and experimental factors, the transformation algorithms achieved a root mean square error (RMSE) of 5.3 mm while the sphere fitting methods yielded the best estimation with an RMSE of 2.6 mm. The transformation algorithms performed better in presence of random noise and simulated soft tissue artefacts. [ABSTRACT FROM AUTHOR]

Certain nonlinear systems can switch between dynamical attractors occupying different regions of phase space, under variation of parameters or initial states. In this work we exploit this feature to obtain reliable logic operations. With logic output 0/1 mapped to dynamical attractors bounded in distinct regions of phase space, and logic inputs encoded by a very small bias parameter, we explicitly demonstrate that the system hops consistently in response to an external input stream, operating effectively as a reliable logic gate. This system offers the advantage that very low-amplitude inputs yield highly amplified outputs. Additionally, different dynamical variables in the system yield complementary logic operations in parallel. Further, we show that in certain parameter regions noise aids the reliability of logic operations, and is actually necessary for obtaining consistent outputs. This leads us to a generalization of the concept of Logical Stochastic Resonance to attractors more complex than fixed point states, such as periodic or chaotic attractors. Lastly, the results are verified in electronic circuit experiments, demonstrating the robustness of the phenomena. So we have combined the research directions of Chaos Computing and Logical Stochastic Resonance here, and this approach has potential to be realized in wide-ranging systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the stochastic transitions in a bistable Schlögl chemical reaction subjected to a nonextensive statistical noise (NESN) and a Gaussian white noise. The NESN is a correlated noise, and able to describe heavy-tailed noise distributions with certain deviation degree relative to Gaussian colored noise. With a unified-colored noise approximation method, the theoretical results of the steady state probability density function (PDF) and mean first passage time (MFPT) are obtained to demonstrate the influences of NESN. The validity of theoretical results has been confirmed by numerical simulations of the PDFs. Besides, stochastic P-bifurcation is discovered based on the changing of PDFs. The results show that the NESN will keep the conversion rate of substrate and the generation rate of products in the low state. Finally, from the MFPT, we find that the intensity and deviation degree of NESN, and Gaussian noise intensity can decrease the switching time between two states. [ABSTRACT FROM AUTHOR]

Analytical CT reconstruction is popular in practice because of its computational efficiency, but it suffers from low reconstruction quality when an insufficient number of projections are used. To address this issue, this paper presents a new analytical method of backprojection Wiener deconvolution (BPWD). BPWD executes backprojection first, and then applies a Wiener deconvolution to the whole backprojected image. The Wiener filter is derived from a ramp filter, enabling the proposed approach to perform reconstruction and denoising simultaneously. The use of a filter after backprojection does not differentiate between real sampled projections and interpolated ones, introducing reconstruction errors. Therefore a weighted ramp filter was applied to increase the contribution of real sampled projections in the reconstruction, thus improving reconstruction quality. Experiments on synthetic data and real phase-contrast x-ray images showed that the proposed approach yields better reconstruction quality compared to the classical filtered backprojection (FBP) method, with comparable reconstruction speed. [ABSTRACT FROM AUTHOR]

We proposed a new efficient image denoising scheme, which mainly leads to four important contributions whose approaches are different from existing ones. The first is to show the equivalence between the group-based sparse representation and the Schatten-p norm minimization problem, so that the sparsity of the coefficients for each group can be measured by estimating the underlying singular values. The second is that we construct the proximal operator for sparse optimization in ℓp space with p ∈ (0, 1] by using fixed-point iteration and obtained a new solution of Schatten-p norm minimization problem, which is more rigorous and accurate than current available results. The third is that we analyze the suitable setting of power p for each noise level σ = 20, 30, 50, 60, 75, 100, respectively. We find that the optimal value of p is inversely proportional to the noise level except for high level of noise, where the best values of p are 1 and 0.95, when the noise levels are respectively 75 and 100. Last we measure the structural similarity between two image patches and extends previous deterministic annealing-based solution to sparsity optimization problem through incorporating the idea of dictionary learning. Experimental results demonstrate that for every given noise level, the proposed Spatially Adaptive Fixed Point Iteration (SAFPI) algorithm attains the best denoising performance on the value of Peak Signal-to-Noise Ratio (PSNR) and structure similarity (SSIM), being able to retain the image structure information, which outperforms many state-of-the-art denoising methods such as Block-matching and 3D filtering (BM3D), Weighted Nuclear Norm Minimization (WNNM) and Weighted Schatten p-Norm Minimization (WSNM). [ABSTRACT FROM AUTHOR]

The method of flicker-noise spectroscopy is used for remote identification of geoeffective disturbances of the solar wind. The method is based on the study of short-term variations in the cosmic-ray flux that arise when it crosses the perturbed regions in the inner heliosphere. The magnitude of the effect is usually small and it is hidden in statistical noise. The analysis uses data from a continuous flux of secondary atmospheric muons. Bumps of the "nonstationarity" parameter of time series of muons indicate the emergence of advanced signals (predictors) 1-3 days before the disturbances enter the Earth's magnetosphere. Geoeffective events with a low threshold (Ap > 30 nT) for various types of solar wind are analyzed. The results are obtained using a ground-based muon hodoscope URAGAN (Moscow). The technique can be used for continuous monitoring of space weather. [ABSTRACT FROM AUTHOR]

A central goal of sensory neuroscience is to construct models that can explain neural responses to natural stimuli. As a consequence, sensory models are often tested by comparing neural responses to natural stimuli with model responses to those stimuli. One challenge is that distinct model features are often correlated across natural stimuli, and thus model features can predict neural responses even if they do not in fact drive them. Here, we propose a simple alternative for testing a sensory model: we synthesize a stimulus that yields the same model response as each of a set of natural stimuli, and test whether the natural and “model-matched” stimuli elicit the same neural responses. We used this approach to test whether a common model of auditory cortex—in which spectrogram-like peripheral input is processed by linear spectrotemporal filters—can explain fMRI responses in humans to natural sounds. Prior studies have that shown that this model has good predictive power throughout auditory cortex, but this finding could reflect feature correlations in natural stimuli. We observed that fMRI responses to natural and model-matched stimuli were nearly equivalent in primary auditory cortex (PAC) but that nonprimary regions, including those selective for music or speech, showed highly divergent responses to the two sound sets. This dissociation between primary and nonprimary regions was less clear from model predictions due to the influence of feature correlations across natural stimuli. Our results provide a signature of hierarchical organization in human auditory cortex, and suggest that nonprimary regions compute higher-order stimulus properties that are not well captured by traditional models. Our methodology enables stronger tests of sensory models and could be broadly applied in other domains. [ABSTRACT FROM AUTHOR]

Multiplexing, the simultaneous sequencing of multiple barcoded DNA samples on a single flow cell, has made Oxford Nanopore sequencing cost-effective for small genomes. However, it depends on the ability to sort the resulting sequencing reads by barcode, and current demultiplexing tools fail to classify many reads. Here we present Deepbinner, a tool for Oxford Nanopore demultiplexing that uses a deep neural network to classify reads based on the raw electrical read signal. This ‘signal-space’ approach allows for greater accuracy than existing ‘base-space’ tools (Albacore and Porechop) for which signals must first be converted to DNA base calls, itself a complex problem that can introduce noise into the barcode sequence. To assess Deepbinner and existing tools, we performed multiplex sequencing on 12 amplicons chosen for their distinguishability. This allowed us to establish a ground truth classification for each read based on internal sequence alone. Deepbinner had the lowest rate of unclassified reads (7.8%) and the highest demultiplexing precision (98.5% of classified reads were correctly assigned). It can be used alone (to maximise the number of classified reads) or in conjunction with other demultiplexers (to maximise precision and minimise false positive classifications). We also found cross-sample chimeric reads (0.3%) and evidence of barcode switching (0.3%) in our dataset, which likely arise during library preparation and may be detrimental for quantitative studies that use multiplexing. Deepbinner is open source (GPLv3) and available at . [ABSTRACT FROM AUTHOR]

RNA SHAPE experiments have become important and successful sources of information for RNA structure prediction. In such experiments, chemical reagents are used to probe RNA backbone flexibility at the nucleotide level, which in turn provides information on base pairing and therefore secondary structure. Little is known, however, about the statistics of such SHAPE data. In this work, we explore different representations of noise in SHAPE data and propose a statistically sound framework for extracting reliable reactivity information from multiple SHAPE replicates. Our analyses of RNA SHAPE experiments underscore that a normal noise model is not adequate to represent their data. We propose instead a log-normal representation of noise and discuss its relevance. Under this assumption, we observe that processing simulated SHAPE data by directly averaging different replicates leads to bias. Such bias can be reduced by analyzing the data following a log transformation, either by log-averaging or Kalman filtering. Application of Kalman filtering has the additional advantage that a prior on the nucleotide reactivities can be introduced. We show that the performance of Kalman filtering is then directly dependent on the quality of that prior. We conclude the paper with guidelines on signal processing of RNA SHAPE data. [ABSTRACT FROM AUTHOR]

The presence of motion artefacts in ECG signals can cause misleading interpretation of cardiovascular status. Recently, reducing the motion artefact from ECG signal has gained the interest of many researchers. Due to the overlapping nature of the motion artefact with the ECG signal, it is difficult to reduce motion artefact without distorting the original ECG signal. However, the application of an adaptive noise canceler has shown that it is effective in reducing motion artefacts if the appropriate noise reference that is correlated with the noise in the ECG signal is available. Unfortunately, the noise reference is not always correlated with motion artefact. Consequently, filtering with such a noise reference may lead to contaminating the ECG signal. In this paper, a two-stage filtering motion artefact reduction algorithm is proposed. In the algorithm, two methods are proposed, each of which works in one stage. The weighted adaptive noise filtering method (WAF) is proposed for the first stage. The acceleration derivative is used as motion artefact reference and the Pearson correlation coefficient between acceleration and ECG signal is used as a weighting factor. In the second stage, a recursive Hampel filter-based estimation method (RHFBE) is proposed for estimating the ECG signal segments, based on the spatial correlation of the ECG segment component that is obtained from successive ECG signals. Real-World dataset is used to evaluate the effectiveness of the proposed methods compared to the conventional adaptive filter. The results show a promising enhancement in terms of reducing motion artefacts from the ECG signals recorded by a cost-effective single lead ECG sensor during several activities of different subjects. [ABSTRACT FROM AUTHOR]

Humans can meaningfully report their confidence in a perceptual or cognitive decision. It is widely believed that these reports reflect the Bayesian probability that the decision is correct, but this hypothesis has not been rigorously tested against non-Bayesian alternatives. We use two perceptual categorization tasks in which Bayesian confidence reporting requires subjects to take sensory uncertainty into account in a specific way. We find that subjects do take sensory uncertainty into account when reporting confidence, suggesting that brain areas involved in reporting confidence can access low-level representations of sensory uncertainty, a prerequisite of Bayesian inference. However, behavior is not fully consistent with the Bayesian hypothesis and is better described by simple heuristic models that use uncertainty in a non-Bayesian way. Both conclusions are robust to changes in the uncertainty manipulation, task, response modality, model comparison metric, and additional flexibility in the Bayesian model. Our results suggest that adhering to a rational account of confidence behavior may require incorporating implementational constraints. [ABSTRACT FROM AUTHOR]

Speckle reduction remains a critical issue for ultrasound image processing and analysis. The nonlocal means (NLM) filter has recently attached much attention due to its competitive despeckling performance. However, the existing NLM methods usually determine the similarity between two patches by directly utilizing the gray-level information of the noisy image, which renders it difficult to represent the structural similarity of ultrasound images effectively. To address this problem, the NLM method based on the simple deep learning baseline named PCANet is proposed by introducing the intrinsic features of image patches extracted by this network rather than the pixel intensities into the pixel similarity computation. In this approach, the improved two-stage PCANet is proposed by using Parametric Rectified Linear Unit (PReLU) activation function instead of the binary hashing and block histograms in the original PCANet. This model is firstly trained on the ultrasound database to learn the convolution kernels. Then, the trained PCANet is utilized to extract the intrinsic features from the image patches in the pre-denoised version of the noisy image to be despeckled. These obtained features are concatenated together to determine the structural similarity between image patches in the NLM method, based on which the weighted mean of all pixels in a search window is computed to produce the final despeckled image. Extensive experiments have been conducted on a variety of images to demonstrate the superiority of the proposed method over several well-known despeckling algorithm and the PCANet based NLM method using ReLU function and sigmoid function. Visual inspection indicates that the proposed method outperforms the compared methods in reducing speckle noise and preserving image details. The quantitative comparisons show that among all the evaluated methods, our method produces the best structural similarity index metrics (SSIM) values for the synthetic image, as well as the highest equivalent number of looks (ENL) value for the simulated image and the clinical ultrasound images. [ABSTRACT FROM AUTHOR]

Highlights • A framework to evaluate noise level in coherent STEM imaging modes is proposed. • The framework enables one to directly compare signal-to-noise ratios of STEM imaging techniques. • A new noise suppression technique for annular bright field imaging is developed. Abstract Statistical noise, or shot noise, dominates experimental image quality in scanning transmission electron microscopy because efficiencies of recent detectors are close to ideal. We establish a general framework for the statistical noise taking into account two random processes in the electron incidence and the electron scattering. Using this framework, in terms of signal-to-noise ratio, we evaluate several STEM coherent imaging techniques: annular bright field, enhanced annular bright field, differential phase contrast, and ptychography and show that ptychography is the most efficient imaging for weak phase objects. Moreover, we find that normalizing annular-bright-field image by total electron count in the bright field significantly suppress the noise. [ABSTRACT FROM AUTHOR]

In this paper, the statistical properties of near-ultrasound acoustic noise in typical indoor environments are investigated. The results indicate that near-ultrasound noise exhibits impulsive behavior and that its first-order probability density functions can be accurately modeled by using a Gaussian mixture or symmetric alpha-stable (SαS) distributions. Furthermore, a practical approach for estimating the parameters of the SαS noise in near-ultrasound aerial acoustic communication receivers is proposed. The problem of modeling memory effects in near-ultrasound acoustic noise is also considered and a simple noise model with memory is proposed based on multiple linear stable processes. The proposed statistical noise models can be used to analyze, simulate, and optimize the performance of various near-ultrasound aerial acoustic communication systems. It is believed that the analysis may contribute to the development of more efficient near-ultrasound aerial communication systems for a range of practical applications. [ABSTRACT FROM AUTHOR]

Mutational correlation patterns found in population-level sequence data for the Human Immunodeficiency Virus (HIV) and the Hepatitis C Virus (HCV) have been demonstrated to be informative of viral fitness. Such patterns can be seen as footprints of the intrinsic functional constraints placed on viral evolution under diverse selective pressures. Here, considering multiple HIV and HCV proteins, we demonstrate that these mutational correlations encode a modular co-evolutionary structure that is tightly linked to the structural and functional properties of the respective proteins. Specifically, by introducing a robust statistical method based on sparse principal component analysis, we identify near-disjoint sets of collectively-correlated residues (sectors) having mostly a one-to-one association to largely distinct structural or functional domains. This suggests that the distinct phenotypic properties of HIV/HCV proteins often give rise to quasi-independent modes of evolution, with each mode involving a sparse and localized network of mutational interactions. Moreover, individual inferred sectors of HIV are shown to carry immunological significance, providing insight for guiding targeted vaccine strategies. [ABSTRACT FROM AUTHOR]

Batch effects are technical sources of variation introduced by the necessity of conducting gene expression analyses on different dates due to the large number of biological samples in population-based studies. The aim of this study is to evaluate the performances of linear mixed models (LMM) and Combat in batch effect removal. We also assessed the utility of adding quality control samples in the study design as technical replicates. In order to do so, we simulated gene expression data by adding “treatment” and batch effects to a real gene expression dataset. The performances of LMM and Combat, with and without quality control samples, are assessed in terms of sensitivity and specificity while correcting for the batch effect using a wide range of effect sizes, statistical noise, sample sizes and level of balanced/unbalanced designs. The simulations showed small differences among LMM and Combat. LMM identifies stronger relationships between big effect sizes and gene expression than Combat, while Combat identifies in general more true and false positives than LMM. However, these small differences can still be relevant depending on the research goal. When any of these methods are applied, quality control samples did not reduce the batch effect, showing no added value for including them in the study design. [ABSTRACT FROM AUTHOR]

This paper considers the DCSK system under jamming environment. We consider both fast and slow switching pulse jamming (PJ). We derive approximations of the system bit error rates (BERs) in analytic expressions containing only well-known special functions, from which we show that for both jamming cases increasing the spreading factor can enhance the BER. In addition, we reveal that under the fast switching PJ, the BER does not depend on the jamming duty cycle ρ when ρ ≤ 0.5, however when ρ > 0.5, increasing its value degrades the BER. Moreover, we find out that under the slow switching PJ, increasing ρ may either enhance or degrade the BER. [ABSTRACT FROM AUTHOR]

Data assimilation is an important discipline in geosciences that aims to combine the information contents from both prior geophysical models and observational data (observations) to obtain improved model estimates. Ensemble-based methods are among the state-of-the-art assimilation algorithms in the data assimilation community. When applying ensemble-based methods to assimilate big geophysical data, substantial computational resources are needed in order to compute and/or store certain quantities (e.g., the Kalman-gain-type matrix), given both big model and data sizes. In addition, uncertainty quantification of observational data, e.g., in terms of estimating the observation error covariance matrix, also becomes computationally challenging, if not infeasible. To tackle the aforementioned challenges in the presence of big data, in a previous study, the authors proposed a wavelet-based sparse representation procedure for 2D seismic data assimilation problems (also known as history matching problems in petroleum engineering). In the current study, we extend the sparse representation procedure to 3D problems, as this is an important step towards real field case studies. To demonstrate the efficiency of the extended sparse representation procedure, we apply an ensemble-based seismic history matching framework with the extended sparse representation procedure to a 3D benchmark case, the Brugge field. In this benchmark case study, the total number of seismic data is in the order of . We show that the wavelet-based sparse representation procedure is extremely efficient in reducing the size of seismic data, while preserving the salient features of seismic data. Moreover, even with a substantial data-size reduction through sparse representation, the ensemble-based seismic history matching framework can still achieve good estimation accuracy. [ABSTRACT FROM AUTHOR]

Lack of unallocated spectrum and increasing demand for bandwidth in wireless networks is forcing new devices and technologies to share frequency bands. Spectrum sensing is a key enabler for frequency sharing and there is a large body of existing work on signal detection methods. However a unified methodology that would be suitable for objective comparison of detection methods based on experimental evaluations is missing. In this paper we propose such a methodology comprised of seven steps that can be applied to evaluate methods in simulation or practical experiments. Using the proposed methodology, we perform the most comprehensive experimental evaluation of signal detection methods to date: we compare energy detection, covariance-based and eigenvalue-based detection and cyclostationary detection. We measure minimal detectable signal power, sensitivity to noise power changes and computational complexity using an experimental setup that covers typical capabilities from low-cost embedded to high-end software defined radio devices. Presented results validate our premise that a unified methodology is valuable in obtaining reliable and reproducible comparisons of signal detection methods. [ABSTRACT FROM AUTHOR]

How to run most effectively to catch a projectile, such as a baseball, that is flying in the air for a long period of time? The question about the best solution to the ball catching problem has been subject to intense scientific debate for almost 50 years. It turns out that this scientific debate is not focused on the ball catching problem alone, but revolves around the research question what constitutes the ingredients of intelligent decision making. Over time, two opposing views have emerged: the generalist view regarding intelligence as the ability to solve any task without knowing goal and environment in advance, based on optimal decision making using predictive models; and the specialist view which argues that intelligent decision making does not have to be based on predictive models and not even optimal, advocating simple and efficient rules of thumb (heuristics) as superior to enable accurate decisions. We study two types of approaches to the ball catching problem, one for each view, and investigate their properties using both a theoretical analysis and a broad set of simulation experiments. Our study shows that neither of the two types of approaches can be regarded as superior in solving all relevant variants of the ball catching problem: each approach is optimal under a different realistic environmental condition. Therefore, predictive models neither guarantee nor prevent success a priori, and we further show that the key difference between the generalist and the specialist approach to ball catching is the type of input representation used to control the agent. From this finding, we conclude that the right solution to a decision making or control problem is orthogonal to the generalist and specialist approach, and thus requires a reconciliation of the two views in favor of a representation-centric view. [ABSTRACT FROM AUTHOR]

This paper presents a robust 3D point cloud registration algorithm based on bidirectional Maximum Correntropy Criterion (MCC). Comparing with traditional registration algorithm based on the mean square error (MSE), using the MCC is superior in dealing with complex registration problem with non-Gaussian noise and large outliers. Since the MCC is considered as a probability measure which weights the corresponding points for registration, the noisy points are penalized. Moreover, we propose to use bidirectional measures which can maximum the overlapping parts and avoid the registration result being trapped into a local minimum. Both of these strategies can better apply the information theory method to the point cloud registration problem, making the algorithm more robust. In the process of implementation, we integrate the fixed-point optimization technique based on the iterative closest point algorithm, resulting in the correspondence and transformation parameters that are solved iteratively. The comparison experiments under noisy conditions with related algorithms have demonstrated good performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Ellipse fitting is a highly researched and mature topic. Surprisingly, however, no existing method has thus far considered the data point eccentricity in its ellipse fitting procedure. Here, we introduce the concept of eccentricity of a data point, in analogy with the idea of ellipse eccentricity. We then show empirically that, irrespective of ellipse fitting method used, the root mean square error (RMSE) of a fit increases with the eccentricity of the data point set. The main contribution of the paper is based on the hypothesis that if the data point set were pre-processed to strategically add additional data points in regions of high eccentricity, then the quality of a fit could be improved. Conditional validity of this hypothesis is demonstrated mathematically using a model scenario. Based on this confirmation we propose an algorithm that pre-processes the data so that data points with high eccentricity are replicated. The improvement of ellipse fitting is then demonstrated empirically in real-world application of 3D reconstruction of a plant root system for phenotypic analysis. The degree of improvement for different underlying ellipse fitting methods as a function of data noise level is also analysed. We show that almost every method tested, irrespective of whether it minimizes algebraic error or geometric error, shows improvement in the fit following data augmentation using the proposed pre-processing algorithm. [ABSTRACT FROM AUTHOR]