nils-herrmann/clean_scirepeval huggingface dataset:
Using Correntropy as a cost function in linear adaptive filters

Correntropy has been recently defined as a localised similarity measure between two random variables, exploiting higher order moments of the data. This paper presents the use of Correntropy as a cost function for minimizing the error between the desired signal and the output of an adaptive filter, in order to train the filter weights.We have shown that this cost function has the computational simplicity of the popular LMS algorithm, along with the robustness that is obtained by using higher order moments for error minimization. We apply this technique for system identification and noise cancellation configurations. The results demonstrate the advantages of the proposed cost function as compared to LMS algorithm, and the recently proposed Minimum Error Entropy (MEE) cost function.
Learning Recurrent Waveforms Within EEGs

We demonstrate an algorithm to automatically learn the time-limited waveforms associated with phasic events that repeatedly appear throughout an electroencephalogram.  To learn the phasic event waveforms we propose a multiscale modeling process that is based on existing shift-invariant dictionary learning algorithms. For each channel, waveforms at different temporal scales are learned based on the assumption that only a few waveforms occur in any window of the time-series, but the same waveforms reoccur throughout the signal. Once the waveforms are learned, the timing and amplitude of the phasic event occurrences are estimated using matching pursuit. To summarize the waveforms learned across multiple channels and subjects, we analyze their frequency content, their similarity to Gabor-Morlet wavelets, and perform shift-invariant k-means to cluster the waveforms. A prototype waveform from each cluster is then tested for differential spatial patterns between different motor imagery conditions.  On multiple human EEG datasets, the learned waveforms capture key characteristics of signals they were trained to represent, with a consistency in waveform morphology and frequency content across multiple training sections and initializations. On multichannel datasets, the spatial amplitude patterns of the waveforms are also consistent and can be used to distinguish different modalities of motor imagery.  We explored a methodology that can be used for modeling the recurrent waveforms in EEG traces.  The methodology automatically identifies the most frequent phasic event waveforms in EEG, which could then be used as features for automatic evaluation and comparison of EEG during sleep, pathology, or mentally engaging tasks.
Linear-least-squares initialization of multilayer perceptrons through backpropagation of the desired response

Training multilayer neural networks is typically carried out using descent techniques such as the gradient-based backpropagation (BP) of error or the quasi-Newton approaches including the Levenberg-Marquardt algorithm. This is basically due to the fact that there are no analytical methods to find the optimal weights, so iterative local or global optimization techniques are necessary. The success of iterative optimization procedures is strictly dependent on the initial conditions, therefore, in this paper, we devise a principled novel method of backpropagating the desired response through the layers of a multilayer perceptron (MLP), which enables us to accurately initialize these neural networks in the minimum mean-square-error sense, using the analytic linear least squares solution. The generated solution can be used as an initial condition to standard iterative optimization algorithms. However, simulations demonstrate that in most cases, the performance achieved through the proposed initialization scheme leaves little room for further improvement in the mean-square-error (MSE) over the training set. In addition, the performance of the network optimized with the proposed approach also generalizes well to testing data. A rigorous derivation of the initialization algorithm is presented and its high performance is verified with a number of benchmark training problems including chaotic time-series prediction, classification, and nonlinear system identification with MLPs.
Adaptive WRLS-VFF for speech analysis

The paper shows that an adaptive weighted recursive least squares algorithm with a variable forgetting factor (WRLS-VFF) will adjust the size of the data segment to be analyzed according to its time-varying characteristics, as during the transitions between vowels and consonants. The algorithm can accurately estimate the vocal tract formants, anti-formants, and their bandwidths, be used for glottal inverse filtering, perform voiced (V)/unvoiced (U)/silent (S) classification of speech segments, estimate the input excitation (either white noise or periodic pulse trains), and estimate the instant of glottal closure.
A Monte Carlo Sequential Estimation of Point Process Optimum Filtering for Brain Machine Interfaces

The previous decoding algorithms for brain machine interfaces are normally utilized to estimate animal's movement from binned spike rates, which loses spike timing resolution and may exclude rich neural dynamics due to single spikes. Based on recently proposed Monte Carlo sequential estimation algorithm on point process, we present a decoding framework to reconstruct the kinematic states directly from the multi-channel spike trains. Starting with analysis on the differences between the simulation and real BMI data, neural tuning properties are modeled to encode the movement information of the experimental primate as the pre-knowledge for Monte-Carlo sequential estimation for BMI. The preliminary kinematics reconstruction shows better results when compared with Kalman filter.
An Explicit Construction Of A Reproducing Gaussian Kernel Hilbert Space

In this paper, we propose a method to explicitly construct a reproducing kernel Hilbert space (RKHS) associated with a Gaussian kernel by means of polynomial spaces. In contrast to the conventional Mercer's theorem approach that implicitly defines kernels by an eigendecomposition, the functionals in this reproducing kernel Hilbert space are explicitly constructed and are not necessary orthonormal. We also point out an intriguing connection between this reproducing kernel Hilbert space and a generalized Fock space. We give an experimental result on approximation of the constructed kernel to a Gaussian kernel
Epileptic seizure detection from ECoG using recurrence time statistics

A recurrence time statistics T1 is defined and used as a feature extraction method for seizure detection. The preliminary data shows that during seizure T1 generates a peak and this peak clearly distinguishes the seizure state from background activity. When applied to multi-channel ECoG recordings, the spatial-temporal signature of T1 can be clearly observed to discriminate seizures. The T1 feature was used for automated seizure detection on two sets of long term monitoring ECoG data. The detection probability reached 97% with a 0.29 per hour average false alarm rate.
Nonlinear Component Analysis Based on Correntropy

In this paper, we propose a new nonlinear principal component analysis based on a generalized correlation function which we call correntropy. The data is nonlinearly transformed to a feature space, and the principal directions are found by eigen-decomposition of the correntropy matrix, which has the same dimension as the standard covariance matrix for the original input data. The correntropy matrix characterizes the nonlinear correlations between the data. With the correntropy function, one can efficiently compute the principal components in the feature space by projecting the transformed data onto those principal directions. We give the derivation of the new method and present simulation results.
A fast proximal method for convolutional sparse coding

Sparse coding, an unsupervised feature learning technique, is often used as a basic building block to construct deep networks. Convolutional sparse coding is proposed in the literature to overcome the scalability issues of sparse coding techniques to large images. In this paper we propose an efficient algorithm, based on the fast iterative shrinkage thresholding algorithm (FISTA), for learning sparse convolutional features. Through numerical experiments, we show that the proposed convolutional extension of FISTA can not only lead to faster convergence compared to existing methods but can also easily generalize to other cost functions.
Mixture of competitive linear models for phased-array magnetic resonance imaging

Phased-array magnetic resonance imaging is an important contemporary research field in terms of the expected clinical gains in medical imaging technology. Recent research focused on heuristic coil image recombination methods as well as statistical signal processing approaches. In this paper, we investigate the performance of an adaptive signal processing approach, namely mixture of competitively trained models. The proposed method has the ability to train on a set of images and generalize its performance to previously unseen images. Performance evaluations on real data validate the effectiveness of this method.
Autocorrelation features for synthetic aperture sonar image seabed segmentation

High-resolution synthetic aperture sonar (SAS) systems yield richly detailed images of seabed environments. Algorithms that automatically segment and label seabed textures such as coral, sea grass, sand ripple, and mud, require suitable features that discriminate between the texture classes. Here we present a robust, parameterized SAS image texture model based on the autocorrelation function (ACF) of the intensity image. This ACF texture model has been shown to accurately model first- and second-order statistical features of various seabed environments. An unsupervised multi-class k-means segmentation algorithm that uses the features derived from the ACF model is employed to label rock and ripple textures from a set of textured SAS images. The results of the segmentation are compared against the performance of the segmentation approach using biorthogonal wavelets and Haralick features. In the described experiments, the ACF model features are shown to produce better segmentations than the features based on wavelet coefficients and Haralick features for classifiers of low complexity.
Model development, testing and experimentation in a CyberWorkstation for Brain-Machine Interface research

The CyberWorkstation (CW) is an advanced cyber-infrastructure for Brain-Machine Interface (BMI) research. It allows the development, configuration and execution of BMI computational models using high-performance computing resources. The CW's concept is implemented using a software structure in which an “experiment engine” is used to coordinate all software modules needed to capture, communicate and process brain signals and motor-control commands. A generic BMI-model template, which specifies a common interface to the CW's experiment engine, and a common communication protocol enable easy addition, removal or replacement of models without disrupting system operation. This paper reviews the essential components of the CW and shows how templates can facilitate the processes of BMI model development, testing and incorporation into the CW. It also discusses the ongoing work towards making this process infrastructure independent.
Nonlinear dynamic modeling of the voiced excitation for improved speech synthesis

This paper describes the implementation of a waveform-based global dynamic model with the goal of capturing vocal folds variability. The residue extracted from speech by inverse filtering is pre-processed to remove phoneme dependence and is used as the input time series to the dynamic model. After training, the dynamic model is seeded with a point from the trajectory of the time series, and iterated to produce the synthetic excitation waveform. The output of the dynamic model is compared with the input time series. These comparisons confirmed that the dynamic model had captured the variability in the residue. The output of the dynamic models is used to synthesize speech using a pitch-synchronous speech synthesizer, and the output is observed to be close to natural speech.
Towards the modeling of dissociated cortical tissue in the liquid state machine framework

Understanding biological information processing systems is key in overcoming the limitations of traditional engineered systems. The advent of the liquid state machine (LSM) as a computational model for cortical processing, as well as the ability to experiment with dissociated cortical tissue (DCT) cultures in a controlled environment provide new opportunities in advancing our understanding of such systems. In this paper we examine the possibilities for modeling the behavior of the DCT cultures in the LSM framework. We show that the LSM framework has the capability to model the spontaneous activity and burstiness of the DCT cultures. Finally, multifractal measures are used to characterize the long range dependencies of the recorded and simulated data. Though the detrended fluctuation analysis (DFA) used to estimate the long range dependencies in the data does not show wholly consistent similarities, the endogenously active neurons that drive the biological and model networks are found to have similar fractal structure.
Integrate and Fire Pulse Train Automaton for QRS detection

Monitoring heart activity from electrocardiograms (ECG) is crucial to avoid unnecessary fatalities; therefore, detection of QRS complex is fundamental to automated ECG monitoring. Continuous, portable 24/7 ECG monitoring requires wireless technology with constraints on power, bandwidth, area, and resolution. In order to provide continuous remote monitoring of patients and fast transmission of data to medical personnel for instantaneous intervention, we propose a methodology that converts analog inputs into pulses for ultralow power implementation. The signal encoding scheme is the time-based integrate and fire (IF) sampler from which a set of signal descriptors in the pulse domain are proposed. Furthermore, a logical decision rule for QRS detection based on morphological checking is derived. The proposed decision logic depends exclusively on relational and logical operators resulting in ultrafast recognition and can be implemented using combinatorial logic hardware to guarantee power consumption orders of magnitude lower than any microprocessor device. The algorithm was evaluated using the MIT-BIH arrhythmia database and results show that our algorithm performance is comparable to the state-of-the art software-based detection.
Cooperative Diversity of Spectrum Sensing in Cognitive Radio Networks

Spectrum sensing is a critical issue in cognitive radio networks. Cooperation among the secondary users is utilized to improve the performance of spectrum sensing. In this paper, we quantify the gain of cooperation in spectrum sensing by introducing the concept of diversity order. With different system performance metrics, we introduce different diversity quantities. We analyze the single-user sensing and the multi-user sensing with soft and hard information fusion strategies using the diversity quantities as our figure of merit. In particular, we discuss the selection of threshold in each sensing scheme with respect to the diversity performance and obtain the quantitative relationship among the diversity performance, threshold, and the number of cooperative users. We also observe and quantify the tradeoff between false alarm and missed detection performance in all spectrum sensing schemes.
Analysis of the time-frequency representation using the Gamma filter

We analyze the performance of the time-frequency representation method that utilizes the Gamma filter. Gamma filter which can be implemented as a cascade of identical first order lowpass filters generates at its taps the Poisson moments of the input signal. These moments carry spectral information about the recent history of the input signal. Due to the inherent time window embedded in the Gamma filter, the moments are local both in time and frequency. Hence, they can be used to construct a time-frequency representation as an alternative to the conventional methods of short term Fourier transform (STFT), cepstrum, etc. The appeal of the proposed method comes from the fact that in the analog domain the moments are readily available as a continuous time electrical signal and can be physically measured, rather than computed offline by a digital computer. Furthermore, for a faithful representation, it is sufficient to observe the moments at the information rate (nonstationarity rate) rather than the usually higher Nyquist rate. The observed moments can be fed into an artificial neural network (ANN) for tasks like prediction, classification and identification. This work studies the performance of the proposed representation scheme as a function of the system parameters, such as; time scale, number of moments and number of bands on the estimation quality.
Innovating Signal Processing for Spike Train Data

It is well known that the conventional algorithms of optimal signal processing developed for random processes can not be easily applied to the analysis and quantification of spike trains. This talk will present our efforts to derive a reproducing kernel Hilbert space (RKHS) for spike train analysis. The advantage of a RKHS is that it has a linear structure and therefore the conventional techniques of principal component analysis, optimal filtering, classification and clustering can be readily applied. We will briefly present the methodology and show some preliminary examples with synthetic and real spike data.
Closed-form cauchy-schwarz PDF divergence for mixture of Gaussians

This paper presents an efficient approach to calculate the difference between two probability density functions (pdfs), each of which is a mixture of Gaussians (MoG). Unlike Kullback-Leibler divergence (DKL), the authors propose that the Cauchy-Schwarz (CS) pdf divergence measure (DCS) can give an analytic, closed-form expression for MoG. This property of the DCS makes fast and efficient calculations possible, which is tremendously desired in real-world applications where the dimensionality of the data/features is very high. We show that DCS follows similar trends to DKL, but can be computed much faster, especially when the dimensionality is high. Moreover, the proposed method is shown to significantly outperform DKL in classifying real-world 2D and 3D objects, and static hand posture recognition based on distances alone.
Multiple model based flight control design

Flight control system for high performance aircraft has been an area of intense research. There is a need for sophisticated control strategies that aim for better performance under severe flying conditions. The coupled and time varying dynamics of these systems make the identification problem complicated. This paper addresses the problem of modeling and controlling non-linear systems by utilizing a self-organizing map (SOM). The proposed method uses multiple models of the non-linear plant for identification, as well as a multi-model controller. The mathematical formulation of the controller algorithm and the switching strategy are discussed. Preliminary results of using the proposed strategy for the identification and control of a high performance aircraft are presented to demonstrate the versatility of the algorithm.
Convergence analysis of the information potential criterion in Adaline training

In our recent studies we have proposed the use of minimum error entropy criterion as an alternative to minimum square error (MSE) in supervised adaptive system training. We have formulated a nonparametric estimator for Renyi's entropy with the help of Parzen windowing. This formulation revealed interesting insights about the process of information theoretical learning. We have applied this new criterion to the training of linear and nonlinear adaptive topologies under the problems of blind source separation, channel equalization, and time-series prediction with superb results. In this paper, we analyze the structure of the entropy criterion performance surface around the optimal solution and we derive the upper bound for the step size in Adaline training with the steepest descent algorithm. We also investigate the effects on adaptation of the kernel size in the Parzen windowing, and order of Renyi's entropy.
Mixture kernel least mean square

Instead of using single kernel, different approaches of using multiple kernels have been proposed recently in kernel learning literature, one of which is multiple kernel learning (MKL). In this paper, we propose an alternative to MKL in order to select the appropriate kernel given a pool of predefined kernels, for a family of online kernel filters called kernel adaptive filters (KAF). The need for an alternative is that, in a sequential learning method where the hypothesis is updated at every incoming sample, MKL would provide a new kernel, and thus a new hypothesis in the new reproducing kernel Hilbert space (RKHS) associated with the kernel. This does not fit well in the KAF framework, as learning a hypothesis in a fixed RKHS is the core of the KAF algorithms. Hence, we introduce an adaptive learning method to address the kernel selection problem for the KAF, based on competitive mixture of models. We propose mixture kernel least mean square (MxKLMS) adaptive filtering algorithm, where the kernel least mean square (KLMS) filters learned with different kernels, act in parallel at each input instance and are competitively combined such that the filter with the best kernel is an expert for each input regime. The competition among these experts is created by using a performance based gating, that chooses the appropriate expert locally. Therefore, the individual filter parameters as well as the weights for combination of these filters are learned simultaneously in an online fashion. The results obtained suggest that the model not only selects the best kernel, but also significantly improves the prediction accuracy.
On speeding up computation in information theoretic learning

With the recent progress in kernel based learning methods, computation with Gram matrices has received immense attention. However, the complexity of computing the entire Gram matrix is quadratic in terms of number of samples. Therefore, a considerable amount of work has been focused on extracting relevant information from the Gram matrix without accessing all the elements. Most of these methods exploits the positive definiteness and rapidly decaying eigenstructure of the Gram matrix. Although information theoretic learning (ITL) is conceptually different from kernel based learning, several ITL estimators can be written in terms of Gram matrices. However, the difference between ITL and kernel based methods is that a few ITL estimators include a special type of matrix which is neither positive definite nor symmetric. In this paper we discuss how the techniques applied in kernel based learning can be applied to reduce computational complexity of the ITL estimators involving both Gram matrices and these other matrices.
Visualization of the Short Term Maximum Lyapunov Exponent Topography in the Epileptic Brain

In this paper, a new kind of brain topography is introduced and applied to data from four patients affected by intractable epilepsy. Experience has shown that the short term maximum Lyapunov exponent (STLmax) is a robust parameter when optimized for the dynamical analysis of the electroencephalography (EEG). The objective of this work is to map the spatial distribution of STLmax over time. STLmax is estimated from segments of each channel of long term continuous scalp EEG recordings and a movie of the STLmax segment estimates is created over the head. Movies allow for a simple visualization of which electrodes are related to the highest or lowest chaoticity for the longest time. We found out that the interictal epileptiform activity is related to the highest STLmax level, whereas the focal area is related to low STLmax levels during either the interictal and preictal stages