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An information theory–based framework is developed to assess the predictability and quantify the forecast uncertainty of ENSO complexity, which includes different types of ENSO events with diverse characteristics. With the assistance of a recently developed multiscale stochastic conceptual model that successfully captures both the large-scale dynamics and many crucial statistical properties of the observed ENSO complexity, it is shown that different ENSO events possess distinct predictability limits. Beyond the ensemble mean value, the spread of the ensemble members also contains valuable information about predictability. First, La Niña events are most predictable at long lead times, especially as a subsequent transition after eastern Pacific (EP) El Niño events or during multiyear La Niña phases. Second, EP El Niños tend to be more predictable than the central Pacific (CP) El Niño events up to one year ahead due to a more favorable signal-to-noise ratio, even though their onset remains hard to predict. Third, 4 out of 6 CP El Niño events seem to be predictable up to 24 months ahead, where such strong predictability is often converted to skillful forecast. Fourth, strengthening/weakening the Walker circulation intensity increases/decreases CP predictability at long leads. Fifth, accounting for intraseasonal wind events in the initial condition strongly contributes to EP predictability at lead times of less than one year. Finally, it is shown that a Gaussian approximation of the information gain computation is accurate, making the information theory approach tractable for studying the predictability of more sophisticated models. [ABSTRACT FROM AUTHOR]

Molecular motors, kinesin and myosin, are dimeric consisting of two linked identical monomeric globular proteins. Fueled by the free energy generated by ATP hydrolysis, they walk on polar tracks (microtubule or filamentous actin) processively, which means that only one head detaches and executes a mechanical step while the other stays bound to the track. One motor head must regulate the chemical state of the other, referred to as "gating", a concept that is still not fully understood. Inspired by experiments, showing that only a fraction of the energy from ATP hydrolysis is used to advance the kinesin motors against load, we demonstrate that the rest of the energy is associated with chemical transitions in the two heads. The coordinated chemical transitions involve communication between the two heads - a feature that characterizes gating. We develop a general framework, based on information theory and stochastic thermodynamics, and establish that gating could be quantified in terms of information flow between the motor heads. Applications tokinesin-1 and Myosin V show that information flow, with positive cooperativity, at external resistive loads less than a critical value, Fc. When force exceeds Fc, effective information flow ceases. Interestingly, Fc, which is independent of the input energy generated through ATP hydrolysis, coincides with the force at which the probability of backward steps starts to increase. Our findings suggest that transport efficiency is optimal only at forces less than Fc, which implies that these motors must operate at low loads under in vivo conditions. [ABSTRACT FROM AUTHOR]

Disintegration is a physical phenomenon of atomic nuclei —radioactive isotopes decay— has been modeled with different approaches (deterministic and random), from didactic toy models using as reference the roll (experiment) of standard six-sided dice (Arthur and Ian, 2012), to the generalization of probabilistic methods. Using for example the moment generating function (MGF) method, to obtain the behavior of its probability distribution for n multi-sided dice, —i.e., dice of s ≥ 6 sided (Singh et al., 2011, Sánchez-Sánchez et al., 2022)—. Radioactive decay is essentially statistical (random) in nature, so we cannot predict when any of the atoms will decay. The MGF method and stochastic models were applied to the so-called radioactive dice (toy model), to obtain a theoretical Poisson-like distribution (stochastic model). In this work, we carry out an exhaustive study and a comparison —on the apparent discrepancy — of the Binomial and Poisson statistics associated with the decay of radioactive nuclei. To gain a deeper understanding of this phenomenon —radioactive decay— we use the theory of stochastic processes, i.e., modeling these distributions in a stochastic context using the efficient mathematical tools of this theory. They are discrete random variable processes in continuous time. So, we use an approach of the so-called master equation (Kolmogorov equations). We study them in general as Birth-Death processes —both processes separately, highlighting their disagreements— modeling the Poisson process as a Pure Death process. We solve the master equations of the Poisson process by introducing the so-called sojourn time. Also, we study the relative fluctuations through the Fano factor. We analyze the deeper concept of Entropy of the binomial and Poisson processes by calculating their metrics —In Shannon's information theory context—. We show that they have a corresponding statistical link between both images and the radioactive decay distributions. In this way, we gain a deeper insight into the random nature of nuclear decay with its stochastic distributions. In summary, this paper addresses the extension of deterministic systems that are, in reality, of a random nature within a theoretical framework of so-called stochastic processes and information theory (entropy). We link entropy stochastic (binomial and Poisson) and its intrinsic fluctuations with the physical mechanisms of the collective dynamics of radioactive decay. • Binomial and Poisson distributions are modeled as a stochastic process that evolves over time. • Transition from a toy model —multiple-sided dice— to a stochastic mathematical model for radioisotope decay. • Nuclear Radioactive Decay is studied strictly as a process of random nature. • Entropy —stochastic— metrics for statistical distributions such as Poisson and binomial. [ABSTRACT FROM AUTHOR]

Obtaining high‐quality solutions to the minimum label spanning tree problem is of crucial importance to efficient communication network design, since such solutions can reduce both the construction cost and the complexity of the ultimate architecture. However, the corresponding optimization task was shown to be hard even for complete graphs. As a consequence, no computationally efficient method for solving this problem exactly in a reasonable time is known to exist and one has to rely on approximation techniques such as heuristic and evolutionary algorithms. In this study, we investigate the performance of a different method called the Cross‐Entropy algorithm which relies on rigorous developments in the fields of information theory and stochastic simulation. Our findings indicate that the mathematical soundness of the Cross‐Entropy method makes it very reliable and robust as compared to its counterparts. In particular, the obtained results suggest that the Cross‐Entropy method is not sensitive to different graph models and that the proposed algorithm can obtain optimal or near‐optimal solutions while using a reasonable computational effort. [ABSTRACT FROM AUTHOR]

We propose a hybrid grasshopper optimizer to reduce the size of the feature set in the steganalysis process using information theory and other stochastic optimization techniques. This paper results from the stagnancy of local minima and slow convergence rate by the grasshopper algorithm in optimization problems. Therefore, we enhance the grasshopper optimization (GOA) performance with chaotic maps to make it Chaotic GOA (CGOA). Then, we combine the CGOA with adaptive particle swarm optimization (APSO) to make it Chaotic Particle-Swarm Grasshopper Optimization Algorithm (CPGOA). Next, we use the proposed optimizer with entropy to find the best feature subset of the original Subtractive Pixel Adjacency Model (SPAM) and Spatial Rich Model (SRM) feature set. Finally, the proposed technique is experimented with to detect the spatial domain steganography with different embedding rates on the BOSSbase 1.01 grayscale image database. The results show the improved results from the proposed hybrid optimizer compared to the original GOA and other state-of-the-art feature selection methods in steganalysis. [ABSTRACT FROM AUTHOR]

In 1880-1890, a series of books came out in the Don region, which contained elements of military patriotic propaganda. A noteworthy fact is that their publication was initiated by the staff of Ataman N.I. Svyatopolk-Mirsky. The ataman also took other steps that can be interpreted as an attempt to arrange targeted military propaganda - his office coordinated preparations to establish the Don Museum, it tested and refined measures of "moral influence" on Cossacks to maintain their morale, etc. The paper generally endeavors to explain the reasons behind the development of such military government propaganda among Don Cossacks. To achieve the goal, the author reviews the essential military patriotic texts of the time, written by N.A. Maslakovets (assistant to the ataman), A.N. Pivovarov (army headquarters aide-de-camp), A.A. Karasev, S.F. Nomikosov and Kh.I. Popov (members of the Don Statistical Committee). The most important finding of the research suggests that the Don military propaganda naturally evolved from the bureaucratic infighting of army officials against zemstvo leaders who demanded to make the army service easier for Cossacks. [ABSTRACT FROM AUTHOR]

This paper presents new alternative derivations of the well‐known constrained Cramér–Rao bound for estimating parameters that satisfy a set of deterministic and differentiable equality constraints. Specifically, for unbiased parameter estimation, the equality constraints are treated as pseudo‐measurements corrupted by independent zero‐mean Gaussian noise with infinitely small covariance. In this way, the desired constrained Cramér–Rao bound can be established via utilising the property that the Fisher information matrices for independent measurements are additive. This paper also provides a new simple way for deriving the constrained Cramér–Rao bound when the parameter estimate has a specified bias through exploring that the estimation error lies in the null space of the gradient matrix of the constraints and invoking the Cauchy–Schwarz inequality. [ABSTRACT FROM AUTHOR]

Gradient descent method is an essential algorithm for learning of neural networks. Among diverse variations of gradient descent method that have been developed for accelerating learning speed, the natural gradient learning is based on the theory of information geometry on stochastic neuromanifold, and is known to have ideal convergence properties. Despite its theoretical advantages, the pure natural gradient has some limitations that prevent its practical usage. In order to get the explicit value of the natural gradient, it is required to know true probability distribution of input variables, and to calculate inverse of a matrix with the square size of the number of parameters. Though an adaptive estimation of the natural gradient has been proposed as a solution, it was originally developed for online learning mode, which is computationally inefficient for the learning of large data set. In this paper, we propose a novel adaptive natural gradient estimation for mini-batch learning mode, which is commonly adopted for big data analysis. For two representative stochastic neural network models, we present explicit rules of parameter updates and learning algorithm. Through experiments on three benchmark problems, we confirm that the proposed method has superior convergence properties to the conventional methods. [ABSTRACT FROM AUTHOR]

Null hypothesis significance testing (NHST) remains popular despite decades of concern about misuse and misinterpretation. There are many recent suggestions for mitigating problems arising from NHST, including calls for abandoning NHST in favour of Bayesian or information‐theoretic approaches. We believe that NHST will continue to be widely used, and can be most usefully interpreted as a guide to whether a certain effect can be seen clearly in a particular context (e.g. whether we can clearly see that a correlation or between‐group difference is positive or negative).We believe that much misinterpretation of NHST is due to language: significance testing has little to do with other meanings of the word 'significance'. We therefore suggest that researchers describe the conclusions of null‐hypothesis tests in terms of statistical 'clarity' rather than 'significance'. We illustrate our point by rewriting common misinterpretations of the meaning of statistical tests found in the literature using the language of 'clarity'.The meaning of statistical tests become easier to interpret and explain when viewed through the lens of 'statistical clarity'.Our suggestion is mild, but practical: this simple semantic change could enhance clarity in statistical communication. [ABSTRACT FROM AUTHOR]

In order to reduce the Gaussian noise introduced during image generation, this paper presents an image denoising algorithm based on variational Bayes (V-Bayes) estimation and nonsubsampled contourlet transform (NSCT) bivariate model. First, the proposed algorithm uses the NSCT's advantages of translation-invariant and multidirection-selectivity, exploits the intra-scale and inter-scale correlations of NSCT coefficients. Then, the corresponding nonlinear bivariate threshold function of the model is deduced by using V-Bayes estimation theory. Finally, the noise-reduced coefficients are inverse-transformed by NSCT to obtain denoised image. The simulation results show that the denoised image has obvious improvement in subjective visual effects and performance indicators, and effectively preserves the details and texture information in the original image. [ABSTRACT FROM AUTHOR]

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This paper compares the localization performance of array invariant (AI) and matched field processing (MFP) using a ship of opportunity radiating random noise (200–900 Hz) and a tilted vertical array. AI is a deterministic approach to source-range estimation (i.e., depth-blind), exploiting the dispersion characteristics of broadband signals with minimal/no knowledge of the environment in shallow water. It involves time-domain plane-wave beamforming to separate multiple coherent arrivals (eigenrays) in beam angle and travel time, called "beam-time migration," from which the source range is directly estimated. In contrast, MFP is a model-based approach that requires accurate knowledge of the environment and array geometry (e.g., array tilt) to generate "replicas" for all possible source locations, finding the best match in the two-dimensional ambiguity surface of range and depth. While AI and MFP are both sensitive to array tilt, AI is equipped with self-calibration capability to estimate the array tilt and source range simultaneously. With the array tilt information from AI incorporated, the performance of MFP for range estimation can be comparable to that of AI to such an extent that the environmental knowledge is accurate. [ABSTRACT FROM AUTHOR]

Highly excited states for model Hamiltonians of two strongly coupled modes are calculated by adiabatic and by the self-consistent-field methods. The energy spectra obtained by the two separation methods are similar and deviate from the accurate spectrum in the stochastic energy regime where the system is far from separability. However, the natural expansion analysis of the adiabatic wave functions in the stochastic energy region shows that the adiabatic approach did not totally suppress the stochastic information about the eigenstates, and the adiabatic natural expansion coefficients are in very good agreement with the accurate ones. [ABSTRACT FROM AUTHOR]

Is it reasonable to believe (as many critics do) that informational advertising is okay, but that persuasive advertising is not okay? This article investigates the so-called "information-persuasion" dichotomy and concludes that it is manifestly illogical, a phony dichotomy. A revised version is then proposed: the "high information-content vs. low information-content" dichotomy in advertising. Analysis reveals some substantial problems in operationalizing the new dichotomy. [ABSTRACT FROM AUTHOR]

We present a Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir. The specific aims of this study are to use the volumetric method, “heat in place,” to estimate electrical energy production ability from a geothermal liquid-dominated reservoir, and to build a Python-based stochastic library with useful methods for running such simulations. Although licensed software is available, we selected the open-source programming language Python for this task. The Geothermal Power Potential Evaluation stochastic library (GPPeval) is structured as three essential objects including a geothermal power plant module, a Monte Carlo simulation module, and a tools module. In this study, we use hot spring data from the municipality of Nombre de Jesus, El Salvador, to demonstrate how the GPPeval can be used to assess geothermal power potential. Frequency distribution results from the stochastic simulation shows that this area could initially support a 9.16-MWe power plant for 25 years, with a possible expansion to 17.1 MWe. Further investigations into the geothermal power potential will be conducted to validate the new data. [ABSTRACT FROM AUTHOR]

While people often think they veridically perceive much of the visual surround, recent findings indicate that when asked to detect targets such as gratings embedded in visual noise, observers make more false alarms in the unattended periphery. Do these results from psychophysics studies generalize to more ecologically valid settings? We used a modern game engine to create a simulated driving environment where participants (as drivers) had to make judgments about the colors of pedestrians’ clothing in the periphery. Confirming our hypothesis based on previous psychophysics studies, we found that subjects showed liberal biases for unattended locations when detecting specific colors of pedestrians’ clothing. A second experiment showed that this finding was not simply due to a confirmation bias in decision-making when subjects were uncertain. Together, these results support the idea that in everyday visual experience, there is subjective inflation of experienced detail in the periphery, which may happen at the decisional level. [ABSTRACT FROM AUTHOR]

In this paper, the problem of minimizing the damaging effect on the frequency-hopping spread-spectrum satellite is formulated using the two-player asymmetric zero-sum game framework. The payoff is modeled as the channel capacity of the defender under white additive Gaussian noise. The defender and the attacker are capable of spreading their signals over a prespecified frequency band. Two scenarios are considered: 1) both the attacker and defender know each other's strategies; and 2) the defender knows the attacker's strategy, but not vice versa. In each of these scenarios, we further consider whether the players have knowledge about the environments. We show how to analytically determine optimal strategies for the players in each scenario and provide simulation results to verify our approach. [ABSTRACT FROM AUTHOR]

Rationale: This work focuses on direct matrix‐assisted laser desorption/ionization imaging mass spectrometry (MALDI‐IMS) detection of intraperitoneally (IP)‐injected dipeptide ZP1609 in mouse brain tissue. Direct analysis of drug detection in intact tissue sections provides distribution information that can impact drug development. MALDI‐IMS capabilities of uncovering drug transport across the blood–brain barrier are demonstrated. Methods: Successful peptide detection using MALDI‐IMS was achieved using a MALDI TOF/TOF system. Upon optimization of sample preparation procedures for dipeptide ZP1609, an additional tissue acidification procedure was found to greatly enhance signal detection. The imaging data acquired was able to determine successful transport of ZP1609 across the blood–brain barrier. Data obtained from MALDI‐IMS can help shape our understanding of biological functions, disease progression, and effects of drug delivery. Results: Direct detection of ZP1609 throughout the brain tissue sections was observed from MALDI‐MS images. However, in cases where there was induction of stroke, a peak of lower signal intensity was also detected in the target m/z region. Although distinct differences in signal intensity can be seen between control and experimental groups, fragments and adducts of ZP1609 were investigated using MALDI‐IMS to verify detection of the target analyte. Conclusions: Overall, the data reveals successful penetration of ZP1609 across the blood–brain barrier. The benefits of tissue acidification in the enhancement of detection sensitivity for low‐abundance peptides were demonstrated. MALDI‐IMS has been shown to be a useful technique in the direct detection of drugs within intact brain tissue sections. [ABSTRACT FROM AUTHOR]

Random noise suppression plays an important role in microseismic data processing. The microseismic data is often corrupted by strong random noise, which would directly influence identification and location of microseismic events. Shearlet transform is a new multiscale transform, which can effectively process the low magnitude of microseismic data. In shearlet domain, due to different distributions of valid signals and random noise, shearlet coefficients can be shrunk by threshold. Therefore, threshold is vital in suppressing random noise. The conventional threshold denoising algorithms usually use the same threshold to process all coefficients, which causes noise suppression inefficiency or valid signals loss. In order to solve above problems, we propose the adaptive threshold shearlet transform (ATST) for surface microseismic data denoising. In the new algorithm, we calculate the fundamental threshold for each direction subband firstly. In each direction subband, the adjustment factor is obtained according to each subband coefficient and its neighboring coefficients, in order to adaptively regulate the fundamental threshold for different shearlet coefficients. Finally we apply the adaptive threshold to deal with different shearlet coefficients. The experimental denoising results of synthetic records and field data illustrate that the proposed method exhibits better performance in suppressing random noise and preserving valid signal than the conventional shearlet denoising method. [ABSTRACT FROM AUTHOR]

This paper studies the stochastic heat equation driven by time fractional Gaussian noise with Hurst parameter H∈(0,1/2). We establish the Feynman-Kac representation of the solution and use this representation to obtain matching lower and upper bounds for the Lp(Ω) moments of the solution. [ABSTRACT FROM AUTHOR]

Coprime sensor arrays (CSAs) can estimate the directions of arrival of O ( M N ) narrowband plane wave sources using only O ( M + N ) sensors with the CSA product processor. Processing data from a finite aperture array effectively smears the true spatial power spectral density (PSD) with a kernel function determined by both the array geometry and the processing of the signals observed by the array. This paper examines the asymptotic behaviors of the kernel functions resulting from two different processors applied to a CSA sampling geometry in the limit of large aperture. The kernel functions of the product processed CSA and conventionally beamformed coprime sensor arrays (CBF CSA) are compared to the baseline of the kernel of a densely populated uniform line array (ULA) of similar aperture. At the limit of large aperture, the product processed CSA estimate is asymptotically unbiased like the ULA, while the CBF CSA estimate is not. The PSD estimates computed from the CSA processors are compared when spatially correlated Gaussian noise is an input to the array to highlight the bias issues. [ABSTRACT FROM AUTHOR]

Summary: This short communication introduces a quantitative approach for the engineering validation of ground‐motion simulations based on information theory concepts and statistical hypothesis testing. Specifically, we use the Kullback‐Leibler divergence to measure the similarity of the probability distributions of recorded and simulated ground‐motion intensity measures (IMs). We demonstrate the application of the proposed validation approach to ground‐motion simulations computed by using a variety of methods, including Graves and Pitarka hybrid broadband, the deterministic composite source model, and a stochastic white noise finite‐fault model. Ground‐motion IMs, acting as proxies for the (nonlinear) seismic response of more complex engineered systems, are considered herein to validate the considered ground‐motion simulation methods. The list of considered IMs includes both spectral‐shape and duration‐related proxies, shown to be the optimal IMs in several probabilistic seismic demand models of different structural types, within the framework of performance‐based earthquake engineering. The proposed validation exercise (1) can highlight the similarities and differences between simulated and recorded ground motions for a given simulation method and/or (2) allow the ranking of the performance of alternative simulation methods. The similarities between records and simulations should provide confidence in using the simulation method for engineering applications, while the discrepancies should help in improving the tested method for the generation of synthetic records. [ABSTRACT FROM AUTHOR]

Electrochemical noise measured during the stress corrosion cracking (SCC) of alpha brass conducted in Mattsson’s solution of pH 7.2 was investigated under free corrosion potential and at ambient temperature of 25 °C. The Burg’s maximum entropy method was used to analyse the obtained readings. The electrochemical noise was observed as the spontaneous potential fluctuations. All the analysed result parameters of different standard deviations (spectrum, potential, magnitude), the mean potentials, total magnitude and the coefficient of variations (magnitude), appeared to give the clearest indication of the results and they bear close correlations with the already previously determined spectral density curves. Cracking of the specimen was indicated by the highest standard deviation peaks and the mean potential values. Scanning electron micrographs were made from the specimens’ surface examinations. The observed corrosion behaviours as obtained from the results are related to the existing known SCC mechanism. [ABSTRACT FROM AUTHOR]

Background segmentation methods are exposed to the effects of different kinds of noise due to the limitations of image acquisition devices. This type of distortion can worsen the performance of segmentation methods because the input pixel values are altered. In this paper we study how several well-known background segmentation methods perform when the input is corrupted with several levels of uniform and Gaussian noise. Furthermore, few situations are reported where instead of an inconvenience, adding noise to the input may be desirable to attenuate some limitations of a method. In this work, the performance of nine well known methods is studied under both kinds of noise. [ABSTRACT FROM AUTHOR]

We consider the problem of estimating a low-rank signal matrix from noisy measurements under the assumption that the distribution of the data matrix belongs to an exponential family. In this setting, we derive generalized Stein's unbiased risk estimation (SURE) formulas that hold for any spectral estimators which shrink or threshold the singular values of the data matrix. This leads to new data-driven spectral estimators, whose optimality is discussed using tools from random matrix theory and through numerical experiments. Under the spiked population model and in the asymptotic setting where the dimensions of the data matrix are let going to infinity, some theoretical properties of our approach are compared to recent results on asymptotically optimal shrinking rules for Gaussian noise. It also leads to new procedures for singular values shrinkage in finite-dimensional matrix denoising for Gamma-distributed and Poisson-distributed measurements. [ABSTRACT FROM AUTHOR]

The spectral linewidth from two cross-correlated fiber Bragg gratings (FBGs) are interrogated and characterized using a delayed self-homodyne method for fiber strain sensing. This approach employs a common higher frequency resolution instead of wavelength. A sensitivity and resolution of 166 MHz/με and 50 nε were demonstrated from 4 GHz spectral linewidth characterization on the electric spectrum analyzer. A 10 nε higher resolution can be expected through random noise analyses when the spectral linewidth from two FBG correlations is reduced to 1 GHz. Moreover, the FBG spectrum is broadened during strain and experimentally shows a 0.44 pm/με sensitivity, which is mainly caused by the photo elastic effect from the fiber grating period stretch. [ABSTRACT FROM AUTHOR]

The presence of a light flash near to the body not only increases the ability to detect a weak touch but also increases reports of feeling a weak touch that did not occur. The somatic signal detection task (SSDT) provides a behavioural marker by which to clarify the spatial extent of such visuotactile interactions in peripersonal space. Whilst previous evidence suggests a limit to the spatial extent over which visual input can distort the perception of tactile stimulation during the rubber hand illusion, the spatial boundaries of light-induced tactile sensations are not known. In a repeated measures design, 41 participants completed the SSDT with the light positioned 1 cm (near), 17.5 cm (mid) or 40 cm (far) from the tactile stimulation. In the far condition, the light did not affect hit, or false alarm rates during the SSDT. In the near and mid conditions, the light significantly increased hit rates and led to a more liberal response criterion, that is, participants reported feeling the touch more often regardless of whether or not it actually occurred. Our results demonstrate a spatial boundary over which visual input influences veridical and non-veridical touch perception during the SSDT, and provide further behavioural evidence to show that the boundaries of the receptive fields of visuotactile neurons may be limited to reach space. [ABSTRACT FROM AUTHOR]

The preservation of privacy on information networks has been studied extensively in recent years. Although early work concentrated on preserving sensitive node information and link information to prevent re-identification attacks, recent development has instigated a focus on preserving sensitive edge weight information such as shortest paths. Two types of privacy on edge weights have been proposed. One type of privacy attempts to add random noise edge weights to the graph while still maintaining the same shortest path. The other type of privacy, k-shortest path privacy, minimally perturbs edge weights so that there are at least k shortest paths. However, there might be insufficient paths that can be modified to the same path length. In this work, we present a new concept, called $$ k^{-}$$ -shortest path privacy, to allow anonymizing different numbers of shortest paths for different sources and destination vertex pairs. For a given privacy level k and a pair of source and destination vertices with $$k_i$$ paths between the two vertices, we propose a greedy-based algorithm trying to modify Never-Visited, Partially-Visited, and All-Visited edges, in sequence, from top $$-k$$ shortest paths (or $$k_i$$ available paths) so that all possess the same path length after modification. We use the weighted-proportional-based strategy to modify the edge weights. We also define a new privacy value to quantify the privacy level of $$k^{-}$$ -shortest path privacy. Numerical experiments showing the characteristics of the proposed algorithm are given. Comparison to k-shortest path privacy demonstrates that the proposed approach is more efficient and flexible than the previous model. [ABSTRACT FROM AUTHOR]

Extensive behavioural evidence has shown that older people have declined ability in facial emotion perception. Recent work has begun to examine the neural mechanism that contribute to this, and potential tools to support emotion perception during aging. The aim of this study was to investigate whether high frequency tRNS applied to the inferior frontal cortex would enhance facial expression perception in older adults. Healthy aged adults (60+ years) were randomly assigned to receive active high-frequency or sham tRNS targeted at bilateral inferior frontal cortices. Each group completed tests of facial identity perception, facial happiness perception and facial anger perception. These tasks were completed before and after stimulation. The results showed that, compared to the sham group, the active tRNS group showed greater gains in performance after stimulation in anger perception (relative to performance before stimulation). The same tRNS stimulation did not significantly change performance on the two other face perception tasks assessing facial identity and facial happiness perception. Examination of how inter-individual variability related to changes in anger perception following tRNS indicated that the degree of performance change in anger perception following active tRNS to inferior frontal cortex was predicted by baseline ability and gender of older adult participants. The findings suggest that high frequency tRNS may be a potential tool to aid anger perception in typical aging, but flag that performance variability and gender may interact with stimulation leading to different outcomes. [ABSTRACT FROM AUTHOR]

The application of optical heterodyne detection for transient grating spectroscopy (TGS) using a fixed, binary phase mask often relies on taking the difference between signals captured at multiple heterodyne phases. To date, this has been accomplished by manually controlling the heterodyne phase between measurements with an optical flat. In this letter, an optical configuration is presented which allows for collection of TGS measurements at two heterodyne phases concurrently through the use of two independently phase controlled interrogation paths. This arrangement allows for complete, heterodyne amplified TGS measurements to be made in a manner not constrained by a mechanical actuation time. Measurements are instead constrained only by the desired signal-to-noise ratio. A temporal resolution of between 1 and 10 s, demonstrated here on single crystal metallic samples, will allow TGS experiments to be used as an in-situ, time-resolved monitoring technique for many material processing applications. [ABSTRACT FROM AUTHOR]

We investigate the statistical efficiency of maximum likelihood estimates (MLE) with nonlinear observation models and additive Gaussian noises. Although we and many others have found empirically that in many cases the sample variances of the parameter estimates via Monte Carlo runs match statistically their corresponding Cramér–Rao lower bounds, there has been, up to now, no formal proof to support this. In this letter, we show that the MLE obtained using the iterated least squares method is unbiased and satisfies the generalized collinearity condition between the estimation error and the gradient of the log-likelihood function w.r.t. the parameter vector that is sufficient for the MLE to be efficient. In other words, the MLE is an efficient estimator regardless of the sample size, although, admittedly, this may not be observed for very ill-conditioned problems. A numerical example is provided to demonstrate the statistical efficiency for such problems. [ABSTRACT FROM PUBLISHER]

We present a scalable computing framework for the solution stochastic multiobjective optimization problems. The proposed framework uses a nested conditional value-at-risk (nCVaR) metric to find compromise solutions among conflicting random objectives. We prove that the associated nCVaR minimization problem can be cast as a standard stochastic programming problem with expected value (linking) constraints. We also show that these problems can be implemented in a modular and compact manner using PLASMO (a Julia -based structured modeling framework) and can be solved efficiently using PIPS-NLP (a parallel nonlinear solver). We apply the framework to a CHP design study in which we seek to find compromise solutions that trade-off cost, water, and emissions in the face of uncertainty in electricity and water demands. [ABSTRACT FROM AUTHOR]

This paper presents a novel detection method for weak sinusoidal signal embedded in strong noise. Two synchronization systems are constructed to implement this method, in which one is used to estimate the frequency and phase angle, the other is used to estimate the amplitude. The detection process is achieved in three steps. First, two synchronization systems are established based on chaotic synchronization theorem and adaptive algorithm. The test signal is injected into their driving ends. Second, in the first synchronization system, the frequency of the sinusoidal signal is obtained by the MUSIC algorithm. Then, the reference sinusoidal signal with this frequency can be generated and correlated with the test signal. The phase difference between them is obtained. Finally, in the second synchronization system, the amplitude of the reference signal is close to the actual amplitude of the sinusoidal signal in the process of synchronization. This detection system is achieved by the Chua's circuit, and its detection characteristics are verified by simulation and experimentation. The results show that it is feasible to obtain the parameters of the sinusoidal signal. It retains excellent detection performance, simplifies parameters setting, avoids the solution of the running state, and is easy to achieve. [ABSTRACT FROM PUBLISHER]

We have developed a new algorithm for the interpretation of magnetic data measured along a profile by a dipping dike like body. The algorithm is based on the second horizontal gradient (SHG) anomalies calculated from the observed magnetic data using filters of successive window lengths (s). The main merit of this new method is its capability to estimate (successively rather than simultaneously) all the model parameters of the buried anomalous body: depth to top (z), half-width (d), index parameter (α) and amplitude coefficient (K). This technique successively estimates the parameters z, α and K of the anomalous structure at an assumed half-width for various window lengths by solving a nonlinear equation for z and by direct substitution in two consequent formulas in order to infer α and K. The best fitting solution is that for which the root mean square error (RMS) between the observed and predicted data is minimal. The method has been applied to synthetic examples with and without random noise. Following this, it has been applied to three field examples from Canada, China and the United Kingdom which assesses the accuracy of the method. The results were compared against drilling information and published approaches, which highlight the usefulness of the developed approach. [ABSTRACT FROM AUTHOR]

The lack of signal to noise ratio increases the final errors of seismic interpretation. In the present study, we apply a new non-local transform domain method called “3 Dimensional Block Matching (3 DBM )” for seismic random noise attenuation. Basically, 3 DBM uses the similarities through the data for retrieving the amplitude of signal in a specific point in the f–x domain, and because of this, it is able to preserve discontinuities in the data such as fractures and faults. 3 DBM considers each seismic profile as an image and thus it can be applied to both pre-stack and post-stack seismic data. It uses the block matching clustering method to gather similar blocks contained in 2 D data into 3 D groups in order to enhance the level of correlation in each 3 D array. By applying a 2 D transform and 1 D transform (instead of a 3 D transform) on each array, we can effectively attenuate the noise by shrinkage of the transform coefficients. The subsequent inverse 2 D transform and inverse 1 D transform yield estimates of all matched blocks. Finally, the random noise attenuated data is computed using the weighted average of all block estimates. We applied 3 DBM on both synthetic and real pre-stack and post-stack seismic data and compared it with a Curvelet transform based denoising method which is one of the most powerful methods in this area. The results show that 3 DBM method eventuates in higher signal to noise ratio, lower execution time and higher visual quality. [ABSTRACT FROM AUTHOR]

Whether to forgive is a key decision supporting cooperation. Like many other evolutionarily recurrent decisions, it is made under uncertainty and requires the trade-off of costs and benefits. This decision can be conceptualized as a signal detection or error management task: Forgiving is adaptive if a relationship with the “harmdoer” will be fitness enhancing and not adaptive if the relationship will be fitness reducing, and the decision should be biased toward lowering the likelihood of the more costly error, which depending on the context may be either erroneously not forgiving or forgiving. Building on such conceptualization, we developed two cognitive models and examined how well they described participants' forgiveness decisions in hypothetical scenarios and predicted their decisions in recalled real-life incidents. We found that the models performed similarly and generally well—around 80% in describing and 70% in prediction. Moreover, this modeling approach allowed us to estimate the decision bias of each participant; we found that the biases were generally consistent with the prescriptions of signal detection theory and were directed at reducing the more costly error. In addition to testing mechanistic models of the forgiveness decision, our study also contributes to forgiveness research by applying a novel experimental method that studied both hypothetical and real-life decisions in tandem. These models and experimental methods could be used to study other evolutionarily recurrent problems, advancing understanding of how they are solved in the mind. [ABSTRACT FROM AUTHOR]

Signal detection in the presence of high noise is a challenge in natural sciences. From understanding signals emanating out of deep space probes to signals in protein interactions for systems biology, domain specific innovations are needed. The present work is in the domain of transfer alignment (TA), which deals with estimation of the misalignment of deliverable daughter munitions with respect to that of the delivering mother platform. In this domain, the design of noise filtering scheme has to consider a time varying and nonlinear system dynamics at play. The accuracy of conventional particle filter formulation suffers due to deviations from modeled system dynamics. An evolutionary particle filter can overcome this problem by evolving multiple system models through few support points per particle. However, this variant has even higher time complexity for real-time execution. As a result, measurement update gets deferred and the estimation accuracy is compromised. By running these filter algorithms on multiple processors, the execution time can be reduced, to allow frequent measurement updates. Such scheme ensures better system identification so that performance improves in case of simultaneous ejection of multiple daughters and also results in better convergence of TA algorithms for single daughter. [ABSTRACT FROM AUTHOR]

We consider an L -user multiple access channel where transmitter m has access to the linear equation \textum =\bigoplus l=1^{L} fml{\text{w}}l of independent messages {\text{w}}l \in \mathbb {F}p^{kl} with fml\in ~\mathbb {F}p , and the destination wishes to recover all $L$ messages. This problem may be motivated as the last hop in a network where relay nodes employ the compute-and-forward strategy and decode linear equations of messages; we seek to do the reverse and extract messages from sums over a multiple access channel. In particular, we exploit the particular form of dependencies between the equations at the different relays to improve the reliable communication rates beyond those achievable by simply forwarding all equations to the destination independently. The presented achievable rate region for the discrete memoryless channel model is shown to be capacity for the additive white Gaussian noise channel. [ABSTRACT FROM PUBLISHER]

In the present paper, the optimal detector design is investigated for both detecting and locating light atoms from high resolution scanning transmission electron microscopy (HR STEM) images. The principles of detection theory are used to quantify the probability of error for the detection of light atoms from HR STEM images. To determine the optimal experiment design for locating light atoms, use is made of the so-called Cramér–Rao Lower Bound (CRLB). It is investigated if a single optimal design can be found for both the detection and location problem of light atoms. Furthermore, the incoming electron dose is optimised for both research goals and it is shown that picometre range precision is feasible for the estimation of the atom positions when using an appropriate incoming electron dose under the optimal detector settings to detect light atoms. [ABSTRACT FROM AUTHOR]

Adaptive methods for signal detection and direction finding are considered in the framework of the maximum likelihood method. The possibility of application of adaptive methods for solution of the problem of coherent passive radar is shown. [ABSTRACT FROM AUTHOR]

Vector quantization (VQ) is a general data compression technique that has a scalable implementation complexity and potentially a high compression ratio. In this paper, a novel implementation of VQ using stochastic circuits is proposed and its performance is evaluated against conventional binary designs. The stochastic and binary designs are compared for the same compression quality, and the circuits are synthesized for an industrial 28-nm cell library. The effects of varying the sequence length of the stochastic representation are studied with respect to throughput per area (TPA) and energy per operation (EPO). The stochastic implementations are shown to have higher EPOs than the conventional binary implementations due to longer latencies. When a shorter encoding sequence with 512 bits is used to obtain a lower quality compression measured by the L^1 -norm, squared L^2 -norm, and third-law errors, the TPA ranges from 1.16 to 2.56 times than that of the binary implementation with the same compression quality. Thus, although the stochastic implementation underperforms for a high compression quality, it outperforms the conventional binary design in terms of TPA for a reduced compression quality. By exploiting the progressive precision feature of a stochastic circuit, a readily scalable processing quality can be attained by halting the computation after different numbers of clock cycles. [ABSTRACT FROM PUBLISHER]

As a typical machine-learning based detection technique, deformable part models (DPM) achieve great success in detecting complex object categories. The heavy computational burden of DPM, however, severely restricts their utilization in many real world applications. In this work, we accelerate DPM via parallelization and hypothesis pruning. Firstly, we implement the original DPM approach on a GPU platform and parallelize it, making it 136 times faster than DPM release 5 without loss of detection accuracy. Furthermore, we use a mixture root template as a prefilter for hypothesis pruning, and achieve more than 200 times speedup over DPM release 5, apparently the fastest implementation of DPM yet. The performance of our method has been validated on the Pascal VOC 2007 and INRIA pedestrian datasets, and compared to other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

This letter presents a framework for replay detection in sports videos to generate highlights. For replay detection, the proposed work exploits the following facts: 1) broadcasters introduce gradual transition (GT) effect both at the start and at the end of a replay segment (RS), and 2) the absence of score captions (SCs) in an RS. The dual-threshold-based method is used to detect GT frames from the input video. A pair of successive GT frames is used to extract the candidate RSs. All frames in the selected segment are processed to detect SC. To this end, temporal running average is used to filter out temporal variations. First- and second-order statistics are used to binarize the running average image, which is fed to optical character recognition stage for character recognition. The absence/presence of SC is used for replay/live frame labeling. The SC detection stage complements the GT detection process, therefore, a combination of both is expected to result in superior computational complexity and detection accuracy. The performance of the proposed system is evaluated on 22 videos of four different sports (e.g., Cricket, tennis, baseball, and basketball). Experimental results indicate that the proposed method can achieve average detection accuracy ≥ 94.7%. [ABSTRACT FROM PUBLISHER]

Missing data are an unavoidable component of modern statistical genetics. Different array or sequencing technologies cover different single nucleotide polymorphisms (SNPs), leading to a complicated mosaic pattern of missingness where both individual genotypes and entire SNPs are sporadically absent. Such missing data patterns cannot be ignored without introducing bias, yet cannot be inferred exclusively from nonmissing data. In genome-wide association studies, the accepted solution to missingness is to impute missing data using external reference haplotypes. The resulting probabilistic genotypes may be analyzed in the place of genotype calls. A general-purpose paradigm, called Multiple Imputation (MI), is known to model uncertainty in many contexts, yet it is not widely used in association studies. Here, we undertake a systematic evaluation of existing imputed data analysis methods and MI. We characterize biases related to uncertainty in association studies, and find that bias is introduced both at the imputation level, when imputation algorithms generate inconsistent genotype probabilities, and at the association level, when analysis methods inadequately model genotype uncertainty. We find that MI performs at least as well as existing methods or in some cases much better, and provides a straightforward paradigm for adapting existing genotype association methods to uncertain data. [ABSTRACT FROM AUTHOR]

This paper presents a complete study based on signal detection theory (SDT) for deciding the motivation factors that motivate academic researchers to participate in the technology transfer process (university-industry relationship). Moreover, this study determines the researchers' perception about the motivations strategies designed in universities. The paper focuses on positive motivation factors such as academic prestige, competition, generation of resources, the solution of complex problems, professional challenge, personal gains, personal gratification and the solution of society problems. The negative motivation factors studied in the paper are as follows: innovation environment, time required, and lack of incentive and fear of contravening university policies. The importance of SDT lies in the fact that it is a theory that can deal with observer perception and the ways in which choices are made. This paper proposes fuzzy sets type 2 in SDT to expand its potential and understand the decision of the researchers during the technology transfer process under conditions of uncertainty. Although fuzzy type 1 detection theory (FDT) allows signals to overlap (non-binary description), a complete representation of uncertainty is not incorporated. Thus, fuzzy type 2 signal detection theory (FDT2) is proposed to model the uncertainties and noise condition under technology transfer process. High standards of motivation can maintain and attract competent researchers at universities; thus, this paper deals in a deep fashion with all the main aspects about those motivation factors using FDT2. [ABSTRACT FROM AUTHOR]

This paper introduces a robust voiced/non-voiced (VnV) speech classification method using bivariate empirical mode decomposition (bEMD). Fractional Gaussian noise (fGn) is employed as the reference signal to derive a data adaptive threshold for VnV discrimination. The analyzing speech signal and fGn are combined to generate a complex signal which is decomposed into a finite number of complex-valued intrinsic mode functions (IMFs) by using bEMD. The real and imaginary parts of the IMFs represent the IMFs of observed speech and fGn, respectively. The log-energies of both types of IMFs are calculated. There exist similarities between the IMF log-energy representation of fGn and unvoiced speech signals. Hence, the upper confidence limit from IMF log-energies of fGn is used as data adaptive threshold for VnV classification. If the subband log-energy of speech segment exceeds the threshold, the segment is classified as voiced and unvoiced otherwise. The experimental results show that the proposed algorithm performs better than the recently reported methods without requiring any training data for a wide range of SNRs. [ABSTRACT FROM AUTHOR]

Background: Study of emotional responses of antisocial individuals has produced inconsistent findings. Some studies report emotional deficits, while others find no differences between people with and without antisocial behaviours.Aims: Our aim was to apply signal detection theory methods to compare the sensitivity of antisocial and control participants to emotional stimuli. We hypothesised that offenders would show lower ability to discriminate changes in the level of arousal and valence of emotional stimuli relative to the controls.Methods: Signal detection theory was applied to study the sensitivity of recidivist offenders in prison to emotional arousal and valence induced by pictures. This approach, novel in this context, provides a departure from the usual reliance on self-report.Results: Offenders reported higher arousal than controls but showed lower sensitivity to changes between different levels of arousal (whereas no differences were found for valence). Also, offenders showed increased response bias for changes in the levels of arousal, as well as in the higher levels of valence.Conclusions: Our findings show that direct observations of emotional arousal, but not valence, discriminate between recidivist offenders with antisocial personality disorder and non-offending controls. Use of such approaches is likely to provide more valid data than self-reports and may prove particularly useful in studies of intervention for recidivists or in assessment of their readiness for release. [ABSTRACT FROM AUTHOR]

This paper presents an approximate model for the simulation of pulsar aided navigation systems. High fidelity simulations of these systems are computationally intensive and impractical for simulating periods of a day or more. Simulation of yearlong missions is done by abstracting navigation errors as periodic Gaussian noise injections. This paper presents an intermediary approximate model to simulate position errors for periods of several weeks, useful for building more accurate Gaussian error models. This is done by abstracting photon detection and binning, replacing it with a simple deterministic process. The approximate model enables faster computation of error injection models, allowing the error model to be inexpensively updated throughout a simulation. Testing of the approximate model revealed an optimistic performance prediction for non-millisecond pulsars with more accurate predictions for pulsars in the millisecond spectrum. This performance gap was attributed to noise which is not present in the approximate model but can be predicted and added to improve accuracy. [ABSTRACT FROM AUTHOR]