nils-herrmann/clean_scirepeval huggingface dataset:
Downscaling Satellite-Based Soil Moisture in Heterogeneous Regions Using High-Resolution Remote Sensing Products and Information Theory: A Synthetic Study

In this study, a novel methodology based upon the information-theoretic measures of entropy and mutual information was implemented to downscale soil moisture (SM) observations from 10 km to 1 km. It included a transformation function that related auxiliary remotely sensed (RS) products at high resolution to in situ SM observations to obtain first estimates of SM at 1 km and merging this estimate with SM at coarse resolutions through Principle of Relevant Information (PRI). The PRI-based estimates were evaluated using synthetic observations in NC Florida for heterogeneous agricultural land covers (LC), with two growing seasons of sweet corn and one of cotton, annually. The cumulative density function showed an overall error in SM of <; 0.03 cubic meter/cubic meter in the region, with a confidence interval of 95% during the simulation period. The PRI estimates at 1 km were also compared with those from the method based upon Universal Triangle (UT). The spatially averaged root mean square error (RMSE) aggregated over the vegetative LC were 0.01 cubic meter/cubic meter and 0.15 cubic meter/cubic meter using the PRI and UT methods, respectively. The RMSE for downscaled estimates using the UT method increased to 0.28 cubic meter/cubic meter when Laplacian errors are used, while the corresponding RMSE for the PRI remains the same for both Laplacian or Gaussian errors. The Kullback-Liebler divergence (KLD) for estimates using PRI is about 50% lower than those using the method based upon UT indicating that the probability density function (PDF) of the PRI estimate is closer to PDF of the true SM, than the UT method.
Neural network classification of metal surface properties using a dynamic touch sensor

Discusses an application of neural networks to classify signals produced by a dynamic touch sensor, and achieve automated characterization of metal surfaces during machining. Data are first preprocessed and the spectral features are input to a feedforward one-hidden-layer neural network, trained with backpropagation. The classification accuracy was over 90% for most of the surfaces. The authors discuss the experimental setup, the preprocessing, and a critical view of the classification results.<<ETX>>
Parametric least squares approximation using gamma bases

We study the problem of linear approximation of a signal using the parametric gamma bases in L/sup 2/ space. These bases have a time scale parameter, which has the effect of modifying the relative angle between the signal and the projection space, thereby yielding an extra degree of freedom in the approximation. Gamma bases have a simple analog implementation that is a cascade of identical lowpass filters. We derive the normal equation for the optimum value of the time scale parameter and decouple it from that of the basis weights. Using statistical signal processing tools, we further develop a numerical method for estimating the optimum time scale.
Sleep staging automaton based on the theory of evidence

The authors address sleep staging as a medical decision problem. They develop a model for automated sleep staging by combining signal information, human heuristic knowledge in the form of rules, and a mathematical framework. The EEG/EOG/EMG (electroencephalogram/electroculogram/electromyogram) events relevant for sleep staging are detected in real time by an existing front-end system and are summarized per minute. These token data are translated, normalized and constitute the input alphabet to a finite-state machine (automaton). The processed token events are used as partial belief in a set of anthropomimetic rules, which encode human knowledge about the occurrence of a particular sleep stage. The Dempster-Shafer theory of evidence weighs the partial beliefs and attributes the minute sleep stage to the machine state transition that displays the highest final belief. Results are briefly presented.<<ETX>>
A Closed Form Solution for a Nonlinear Wiener Filter

In this paper a nonlinear extension to the Wiener filter is presented. A direct approach has been devised of replacing the autocorrelation function with a novel function called correntropy, derived from ideas on kernel-based learning theory and information theoretic learning. The linear Wiener filter, widely used because of its simplicity and optimality for linear systems and Gaussian distribution, is no longer effective when dealing with nonlinear time series data. The proposed method incorporates higher order moments in the general form of autocorrelation and improves upon the linear filter. Moreover, the computation cost is still lower than some kernel based methods and has a closed form solution to the problem unlike neural network based methods
A new nonparametric measure of conditional independence

In this paper we propose a new measure of conditional independence that is loosely based on measuring the L2 distance between the conditional joint and the product of the conditional marginal density functions. However, we propose to smooth the arguments prior to measuring the distance and use kernel density estimation to derive the estimator. We show that under suitable conditions the proposed smoothing does not affect the conditional independence but using proper smoothing function helps in choosing the bandwidth parameter robustly. We discuss the computational issues and propose an approximation to evaluate the estimator efficiently. We apply the proposed measure in different experiments to show its validity.
Information processing models for automatic sleep scoring

Three different methods of information processing are described (a knowledge-based system, an automaton based on the theory of evidence, and a neural network). Their performance is compared and analyzed. One factor that is discussed is the degradation of the man-machine agreement, which is very high in the input tokens, and only high in the final result. The man-machine agreement for five sleep records, from normal subjects spanning the ages from 13 to 70 years, is shown. The records were chosen because they represent the normal sleep patterns of each age group. The token data and the human scoring are the same for all cases, so that the relative performance of the methods can be compared.<<ETX>>
Minimax Mutual Information Approach for Independent Component Analysis

Minimum output mutual information is regarded as a natural criterion for independent component analysis (ICA) and is used as the performance measure in many ICA algorithms. Two common approaches in information-theoretic ICA algorithms are minimum mutual information and maximum output entropy approaches. In the former approach, we substitute some form of probability density function (pdf) estimate into the mutual information expression, and in the latter we incorporate the source pdf assumption in the algorithm through the use of nonlinearities matched to the corresponding cumulative density functions (cdf). Alternative solutions to ICA use higher-order cumulant-based optimization criteria, which are related to either one of these approaches through truncated series approximations for densities. In this article, we propose a new ICA algorithm motivated by the maximum entropy principle (for estimating signal distributions). The optimality criterion is the minimum output mutual information, where the estimated pdfs are from the exponential family and are approximate solutions to a constrained entropy maximization problem. This approach yields an upper bound for the actual mutual information of the output signalshence, the name minimax mutual information ICA algorithm. In addition, we demonstrate that for a specific selection of the constraint functions in the maximum entropy density estimation procedure, the algorithm relates strongly to ICA methods using higher-order cumulants.
Minimizing Fisher information of the error in supervised adaptive filter training

In this paper, we propose minimizing the Fisher information of the error in supervised training of linear and nonlinear adaptive filters. Fisher information considers the local structure of the error probability distribution and therefore it is a criterion that deserves to be investigated as an alternative to more common statistics such as minimum mean-square-error or minimum-error-entropy. A gradient-based training algorithm, based on a nonparametric estimator of Fisher information is presented and the performances of the three mentioned optimization criteria are compared using Monte Carlo simulations.
A recursive Renyi's entropy estimator

Estimating the entropy of a sample set is required, in solving numerous learning scenarios involving information theoretic optimization criteria. A number of entropy estimators are available in the literature; however, these require a batch of samples to operate on in order to yield an estimate. We derive a recursive formula to estimate Renyi's (1970) quadratic entropy on-line, using each new sample to update the entropy estimate to obtain more accurate results in stationary situations or to track the changing entropy of a signal in nonstationary situations.
A new clustering evaluation function using Renyi's information potential

Clustering is an important unsupervised learning paradigm, but so far the traditional methodologies are mostly based on the minimization of the variance between the data and the cluster means. Here we propose a new evaluation function based on a previously developed information theoretic measure defined from Renyi's (1960) entropy. We show how to apply Renyi's entropy to clustering and analyze the resulting staircase nature of the performance function that can be expected during learning. We suggest simulated annealing as a possible optimization criterion.
Subspace matching thalamic microstimulation to tactile evoked potentials in rat somatosensory cortex

We show experimental results that the evoked local field potentials of the rat somatosensory cortex from natural tactile touch of forepaw digits and matched thalamic microstimulation can be qualitatively and quantitively similar. In ongoing efforts to optimize the microstimulation settings (e.g., location, amplitude, etc.) to match the natural response, we investigate whether subspace projection methods, specifically the eigenface approach proposed in the computer vision community (Turk and Pentland 1991 ), can be used to choose the parameters of microstimulation such that the response matches a single tactile touch realization. Since the evoked potentials from multiple electrodes are high dimensional spatio-temporal data, the subspace projections improve computational efficiency and can reduce the effect of noisy realizations. In computing the PCA projections we use the peristimulus averages instead of the realizations. The dataset is pruned of unreliable stimulation types. A new subspace is computed for the pruned stimulation type, and is used to estimate a sequence of microstimulations to best match the natural responses. This microstimulation sequence is applied in vivo and quantitative analysis shows that per realization matching does statistically better than choosing randomly from the pruned subset.
Direct adaptive control: an echo state network and genetic algorithm approach

This paper presents a direct adaptive approach to design controllers for nonlinear dynamical systems, where system identification of the unknown dynamical system is not required. The solution is powered by both echo state network (ESN) and genetic algorithm (GA). ESN enables a simple modeling of the controller, with which only a linear readout needs to be trained. GA is used to optimize ESN's linear readout directly so that system identification is not required. Simulation results reveal that the algorithm is capable of achieving very good control performance with computational efficiency.
Similarity classification for bio-signals using fuzzy table look-up by distance and eigenvector

In this paper, two kinds of features, the distance and the angle difference of eigenvectors between two signals, were selected to input a fuzzy similarity inference system. Twelve visual evoked potential (VEP) signals were experimented on to test the fuzzy classification of similarity, and they were compared to each other in similarity. The similarity was taken by the regular section, so the the partial similarity analysis was available, and the total degree of similarity was calculated by averaging to similarity of each section.
On the relationship between the Karhunen-Loeve transform and the prolate spheroidal wave functions

We find a close relationship between the discrete Karhunen-Loeve transform (KLT) and the discrete prolate spheroidal wave functions (DPSWF). We show that the DPSWF form a natural basis for an expansion of the eigenfunctions of the KLT in the frequency domain, and then determine more general conditions that any set of functions must obey to be a valid basis. We also present approximate solutions for small, medium, and large filter orders. The medium order solution suggests that the principal eigenfunction is, to a high degree of approximation, the principal DPSWF modulated so that its center frequency coincides with the peak of maximum energy in the signal spectrum. We then use this result to propose a new basis.
Real-time PCA (principal component analysis) implementation on DSP

PCA (principal component analysis) is a wellknown statistical technique used in many signal processing applications. An on-line temporal PCA learning algorithm is implemented on a floating-point DSP for real-time applications. This algorithm is coded in assembly language to optimize. The experimental results showed that the implemented on-line temporal PCA algorithm not only can accurately estimate the principal components from the input but also can track the principal components from the time varying input. And this algorithm can be applied in space easily by using spacial signals as its inputs instead of using the past inputs as in temporal PCA.
An Association Framework to Analyze Dependence Structure in Time Series

The purpose of this paper is two-fold: first, to propose a modification to the generalized measure of association (GMA) framework that reduces the effect of temporal structure in time series; second, to assess the reliability of using association methods to capture dependence between pairs of EEG channels using their time series or envelopes. To achieve the first goal, the GMA algorithm was updated so as to minimize the effect of the correlation inherent in the time structure. The reliability of the modified scheme was then assessed on both synthetic and real data. Synthetic data was generated from a Clayton copula, for which null hypotheses of uncorrelatedness were constructed for the signal. The signal was processed such that the envelope emulated important characteristics of experimental EEG data. Results show that the modified GMA procedure can capture pairwise dependence between generated signals as well as their envelopes with good statistical power. Furthermore, applying GMA and Kendall's tau to quantify dependence using the extracted envelopes of processed EEG data concords with previous findings using the signal itself.
Generalized information potential criterion for adaptive system training

We have previously proposed the quadratic Renyi's error entropy as an alternative cost function for supervised adaptive system training. An entropy criterion instructs the minimization of the average information content of the error signal rather than merely trying to minimize its energy. In this paper, we propose a generalization of the error entropy criterion that enables the use of any order of Renyi's entropy and any suitable kernel function in density estimation. It is shown that the proposed entropy estimator preserves the global minimum of actual entropy. The equivalence between global optimization by convolution smoothing and the convolution by the kernel in Parzen windowing is also discussed. Simulation results are presented for time-series prediction and classification where experimental demonstration of all the theoretical concepts is presented.
A methodology for information theoretic feature extraction

We discuss an unsupervised feature extraction method which is driven by an information theoretic based criterion: mutual information. While information theoretic signal processing has been examined by many authors the method presented here is more closely related to the approaches of Linsker (1988, 1990), Bell and Sejnowski (1995), and Viola et al. (1996). The method we discuss differs from previous work in several aspects. It is extensible to a feed-forward multilayer perceptron with an arbitrary number of layers. No assumptions are made about the underlying PDF of the input space. It exploits a property of entropy coupled with a saturating nonlinearity resulting in a method for entropy manipulation with computational complexity proportional to the number of data samples squared This represents a significant computational savings over previous methods. As mutual information is a function of two entropy terms, the method for entropy manipulation can be directly applied to the mutual information as well.
Conditional Association

Estimating conditional dependence between two random variables given the knowledge of a third random variable is essential in neuroscientific applications to understand the causal architecture of a distributed network. However, existing methods of assessing conditional dependence, such as the conditional mutual information, are computationally expensive, involve free parameters, and are difficult to understand in the context of realizations. In this letter, we discuss a novel approach to this problem and develop a computationally simple and parameter-free estimator. The difference between the proposed approach and the existing ones is that the former expresses conditional dependence in terms of a finite set of realizations, whereas the latter use random variables, which are not available in practice. We call this approach conditional association, since it is based on a generalization of the concept of association to arbitrary metric spaces. We also discuss a novel and computationally efficient approach of generating surrogate data for evaluating the significance of the acquired association value.
Neuronal tuning in a brain-machine interface during Reinforcement Learning

In this research, we have used neural tuning to quantify the neural representation of prosthetic arm's actions in a new framework of BMI, which is based on Reinforcement Learning (RLBMI). We observed that through closed-loop brain control, the neural representation has changed to encode robot actions that maximize rewards. This is an interesting result because in our paradigm robot actions are directly controlled by a Computer Agent (CA) with reward states compatible with the user's rewards. Through co-adaptation, neural modulation is used to establish the value of robot actions to achieve reward.
Simultaneus prediction of four kinematic variables for a brain-machine interface using a single recurrent neural network

Implementation of brain-machine interface neural-to-motor mapping algorithms in low-power, portable digital signal processors (DSPs) requires efficient use of model resources especially when predicting signals that show interdependencies. We show here that a single recurrent neural network can simultaneously predict hand position and velocity from the same ensemble of cells using a minimalist topology. Analysis of the trained topology showed that the model learns to concurrently represent multiple kinematic parameters in a single state variable. We further assess the expressive power of the state variables for both large and small topologies.
Generalized Correntropy for Robust Adaptive Filtering

As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel, which is, of course, not always the best choice. In this paper, we propose a generalized correntropy that adopts the generalized Gaussian density (GGD) function as the kernel, and present some important properties. We further propose the generalized maximum correntropy criterion (GMCC) and apply it to adaptive filtering. An adaptive algorithm, called the GMCC algorithm, is derived, and the stability problem and steady-state performance are studied. We show that the proposed algorithm is very stable and can achieve zero probability of divergence (POD). Simulation results confirm the theoretical expectations and demonstrate the desirable performance of the new algorithm.
Robust Blind Beamforming Algorithm Using Joint Multiple Matrix Diagonalization

The objective of the blind beamforming is to restore the unknown source signals simply based on the observations, without a priori knowledge of the source signals and the mixing matrix. In this paper, we propose a new joint multiple matrix diagonalization (JMMD) algorithm for the robust blind beamforming. This new JMMD algorithm is based on the iterative eigen decomposition of the fourth-order cumulant matrices. Therefore, it can avoid the problems of the stability and the misadjustment, which arise from the conventional steepest-descent approaches for the constant-modulus or cumulant optimization. Our Monte Carlo simulations show that our proposed algorithm significantly outperforms the ubiquitous joint approximate diagonalization of eigen-matrices algorithm, relying on the Givens rotations for the phase-shift keying source signals in terms of signal-to-interference-and-noise ratio for a wide variety of signal-to-noise ratios
Generalized feedforward structures: a new class of adaptive filters

The authors introduce a new class of filters, the generalized feedforward structures, that combine attractive properties of the moving average (MA) filters for adaptation (i.e. fast algorithms, trivial stability) with some of the power of autoregressive moving average (ARMA) filters (i.e. decoupling of the length of the impulse response with filter order). Preliminary results show that this class of filters is much more efficient than conventional MA filters (i.e. for a given minimum mean square error (MSE) the filter order is much smaller). The authors have extended the Wiener-Hopf solution for this class of filters and have developed some design tools. The generalized feedforward structures accept Widrow's adaptive linear combiner as a special case. An identification example is presented.<<ETX>>
Recursive principal components analysis using eigenvector matrix perturbation

Principal components analysis is an important and well-studied topic in statistics and signal processing. Most algorithms could be grouped into one of the following three approaches: adaptation based on Hebbian updates and deflation, optimization of a second order statistical criterion, and fixed point update rules with deflation. In this paper, we propose a completely different approach that updates the eigenvector and eigenvalue matrices with every new data sample, such that the estimates approximately track their true values. The performance is compared with traditional methods like Sanger and APEX algorithm, as well as with a similar matrix perturbation based method. The results show the efficiency of the algorithm in terms of convergence speed and accuracy