nils-herrmann/clean_scirepeval huggingface dataset:
An adaptive kernel width update for correntropy

Correntropy, as an adaptive criterion of Information Theoretic Learning (ITL), has been successfully used in signal processing and machine learning. How to appropriately select the kernel width of correntropy is a crucial problem in correntropy applications. Existing kernel width selection methods are not suitable enough for this problem. In this paper, we develop an adaptive method for kernel width selection in correntropy. Based on the Middleton's non-Gaussian models, this method utilizes the kurtosis as a ratio to adjust the standard deviation of the prediction error to obtain the kernel width online. The superior performance of the new method has been demonstrated by simulation examples in the noisy frequency doubling and echo cancelation problems.
Inferring the stability of LIFE through Brain Machine Interfaces

We examine neural signals from Longitudinally implanted Intra-Fascicular Electrodes (LIFE) in a chronic, rabbit model. Translation-invariant wavelet de-noising methods are used to improve S%R. Then traditional template-based spike sorting is applied to discriminate single units. We investigate the effect of discriminating between identified units on Brain Machine Interface (BMI) decoding performance. We infer the stability of LIFE based on decoding performance with and without current BMI methods to counter-act electrode neural signal degradation.
Quantifying Cognitive State From EEG Using Dependence Measures

The exquisite human ability to perceive facial features has been explained by the activity of neurons particularly responsive to faces, found in the fusiform gyrus and the anterior part of the superior temporal sulcus. This study hypothesizes and demonstrates that it is possible to automatically discriminate face processing from processing of a simple control stimulus based on processed EEGs in an online fashion with high temporal resolution using measures of statistical dependence applied on steady-state visual evoked potentials. Correlation, mutual information, and a novel measure of association, referred to as generalized measure of association (GMA), were applied on filtered current source density data. Dependences between channel locations were assessed for two separate conditions elicited by distinct pictures (a face and a Gabor grating) flickering at a rate of 17.5 Hz. Filter settings were chosen to minimize the distortion produced by bandpassing parameters on dependence estimation. Statistical analysis was performed for automated stimulus classification using the Kolmogorov-Smirnov test. Results show active regions in the occipito-parietal part of the brain for both conditions with a greater dependence between occipital and inferotemporal sites for the face stimulus. GMA achieved a higher performance in discriminating the two conditions. Because no additional face-like stimuli were examined, this study established a basic difference between one particular face and one nonface stimulus. Future work may use additional stimuli and experimental manipulations to determine the specificity of the current connectivity results.
A Gaussianity measure for blind source separation insensitive to the sign of kurtosis

Various existing criteria to characterize the statistical independence are applied in blind source separation and independent component analysis. However, almost all of them are based on parametric models. The distribution model mismatch between the output PDF (probability density functions) and the chosen underlying distribution model is a serious problem in blind signal processing. Nonparametric PDF estimates like the Parzen window applied to the popular Kullback-Leibler divergence produce computational difficulties. Hence we propose a new measure, the quadratic Gaussianity measure, which is associated with the Euclidean distance between the marginal probability density function and the Gaussian distribution. We show that it outperforms other Gaussianity measures in signal processing applications, such as standardized kurtosis tests because our novel Gaussianity measure is robust to changes in the distribution form.
Correntropy based matched filtering for classification in sidescan sonar imagery

This paper presents an automated way of classifying mines in sidescan sonar imagery. A nonlinear extension to the matched filter is introduced using a new metric called correntropy. This method features high order moments in the decision statistic showing improvements in classification especially in the presence of noise. Templates have been designed using prior knowledge about the objects in the dataset. During classification, these templates are linearly transformed to accommodate for the shape variability in the observation. The template resulting in the largest correntropy cost function is chosen as the object category. The method is tested on real sonar images producing promising results considering the low number of images required to design the templates.
Hidra: A Hierarchical Instrument for Distributed Real-Time Analysis of Biological Signals

This work presents a locally distributed common bus multiple-processor system intended to support a hierarchical signal processing scheme for biological signal analysis. Each peripheral unit¿the so called preprocessor (PP)¿executes real-time signal processsing algorithms over data multiplexed in an input bus. The result of this analysis is transferred to a host computer that integrates the received information from the PP's, saves it in disk when necessary, performs higher level data analysis, and interfaces to the user. The processing of electroencelographic (EEG) data will be used as a case study, due to the multidimensional aspects involved.
A Markov Chain Framework for the Simple Genetic Algorithm

This paper develops a theoretical framework for the simple genetic algorithm (combinations of the reproduction, mutation, and crossover operators) based on the asymptotic state behavior of a nonstationary Markov chain algorithm model. The methodology borrows heavily from that of simulated annealing. We prove the existence of a unique asymptotic probability distribution (stationary distribution) for the Markov chain when the mutation probability is used with any constant nonzero probability value. We develop a Cramer's Rule representation of the stationary distribution components for all nonzero mutation probability values and then extend the representation to show that the stationary distribution possesses a zero mutation probability limit. Finally, we present a strong ergodicity bound on the mutation probability sequence that ensures that the nonstationary algorithm (which results from varying mutation probability during algorithm execution) achieves the limit distribution asymptotically. Although the focus of this work is on a nonstationary algorithm in which mutation probability is reduced asymptotically to zero via a schedule (in a fashion analogous to simulated annealing), the stationary distribution results (existence, Cramer's Rule representation, and zero mutation probability limit) are directly applicable to conventional, simple genetic algorithm implementations as well.
Unsupervised robust detection of behavioral correlates in ECoG

Electrocorticogram (ECoG) based Brain-Computer Interfaces (BCI) provide finer spatial resolution and improved signal-to-noise ratio than its noninvasive counterpart, Electroencephalogram (EEG). This remarkable feature allows for processing in higher spectral bands in order to elucidate more spatially localized encoding mechanisms. We propose an automatic, fully data-driven method to extract relevant neuromodulation events from single-channel, single-trial traces. In particular, our scheme involves two alternating optimizations that resemble k-means; moreover, correntropy is utilized to provide robust estimation and protection against outliers. In this way, we find distinct behavioral correlates in the low-gamma band (76 – 100 Hz) that encode finger flexion movements in a cued task. The results show that correntropy should be used when working with neuronal oscillations due to the high probability of outliers.
Supervised training of adaptive systems with partially labeled data

Supervised adaptive system training is traditionally performed with available pairs of input-output data and the system weights are fixed following this training procedure. Recently, in the context of machine learning, where the desired outputs are discrete-valued, the idea of exploiting unlabeled samples for improving classification performance has been proposed. We introduce an information theoretic framework based on density divergence minimization to obtain extended training algorithms. Our goal is to provide a theoretical framework upon which we can build efficient algorithms to this end.
Selecting neural subsets for kinematics decoding by information theoretical analysis in motor Brain Machine Interfaces

Previous decoding algorithms for Brain Machine Interfaces (BMIs) reconstruct the kinematics from recorded activities of hundreds of neurons, which are not all related to the movement task. Decoding from all neurons not only brings problem towards model generalization but also a significant computation burden. Knowledge of neural receptive fields helps ascertain the neuron importance associate with the movements. We propose to apply information theoretical analysis based on an instantaneous tuning model to extract the candidate neuron subsets, which also reduces the computation complexity for the decoding process. The cortical distribution of extracted neuron subsets is analyzed and the statistical decoding performances using neuron subset selection are compared to the one by the full neuron ensemble.
Mutual information analysis on non-stationary neuron importance for brain machine interfaces

Decoding with the important neuron subset has been widely used in brain machine interfaces (BMIs), as an effective strategy to reduce computational complexity. Previous works usually assume stationary of neuron importance, which may not be true according to recent research. We propose to conduct a mutual information evaluation to track the time-varying neuron importance over time. We found worth noting changes both in information amount and space distribution in our experiment. When the method is applied with a Kalman filter, the decoding performance achieve is better (with higher correlation coefficient) than when a fixed subset, which shows that time-varying neuron importance should be considered in adaptive algorithms.
A new method of concurrently visualizing states, values, and actions in reinforcement based brain machine interfaces

This paper presents the first attempt to quantify the individual performance of the subject and of the computer agent on a closed loop Reinforcement Learning Brain Machine Interface (RLBMI). The distinctive feature of the RLBMI architecture is the co-adaptation of two systems (a BMI decoder in agent and a BMI user in environment). In this work, an agent implemented using Q-learning via kernel temporal difference (KTD)(λ) decodes the neural states of a monkey and transforms them into action directions of a robotic arm. We analyze how each participant influences the overall performance both in successful and missed trials by visualizing states, corresponding action value Q, and resulting actions in two-dimensional space. With the proposed methodology, we can observe how the decoder effectively learns a good state to action mapping, and how neural states affect the prediction performance.
Analysis of Agent Expertise in Ms. Pac-Man Using Value-of-Information-Based Policies

Conventional reinforcement-learning methods for Markov decision processes rely on weakly guided, stochastic searches to drive the learning process. It can therefore be difficult to predict what agent behaviors might emerge. In this paper, we consider an information-theoretic cost function for performing constrained stochastic searches that promote the formation of risk-averse to risk-favoring behaviors. This cost function is the value of information, which provides the optimal tradeoff between the expected return of a policy and the policy's complexity; policy complexity is measured by number of bits and controlled by a single hyperparameter on the cost function. As the policy complexity is reduced, the agents will increasingly eschew risky actions. This reduces the potential for high accrued rewards. As the policy complexity increases, the agents will take actions, regardless of the risk, that can raise the long-term rewards. The obtainable reward depends on a single, tunable hyperparameter that regulates the degree of policy complexity. We evaluate the performance of value-of-information-based policies on a stochastic version of Ms. Pac-Man. A major component of this paper is the demonstration that ranges of policy complexity values yield different game-play styles and explaining why this occurs. We also show that our reinforcement-learning search mechanism is more efficient than the others we utilize. This result implies that the value-of-information theory is appropriate for framing the exploitation–exploration tradeoff in reinforcement learning.
Correntropy Based Matched Filtering

In this paper a non-linear extension to the matched filter is proposed and applied to signal detection. The decision statistic used in this novel method is derived from ideas on kernel-based learning theory and in fact, is a generalization of the correlation statistic used in the matched filter. The optimality of the matched filter is merely based on second order statistics and hence leaves room for improvement, especially when the assumption of Gaussianity is no longer valid. The proposed method incorporates higher order moments in the decision statistic and shows different behavior than the matched filter and improvement in the detection rate for non-Gaussian noise. Moreover, unlike kernel based approaches, this method is still computationally tractable and can easily be implemented in real-time
Fusing attention with visual question answering

Visual Question Answering is a complex problem that fuses natural language and image processing to answer a question based on information from the image. The basic architecture for accomplishing this is using a CNN to extract features from the image and an RNN for the language processing, then combine the two in an MLP to produce an answer. These architectures perform well at identifying content, but fail at higher level reasoning such as spatial awareness and combining objects. To help remedy this, we propose using attention to divide the image into separate objects, then using the extracted features along with the location and size information to learn the MLP.
Independently Coupled HMM Switching Classifier for a Bimodel Brain-Machine Interface

Our initial attempt to develop a switching classifier used vector quantization to compress the multi-dimensional neural data recorded from multiple cortical areas of an owl monkey, into a discrete symbol for use in a single hidden Markov model (HMM) or HMM chain. After classification, different neural data is delegated to local linear predictors when the monkey's arm is moving and when it is at rest. This multiple-model approach helped to validate the hypothesis that by switching the neuronal firing data, the performance of the final linear prediction is improved. In this paper, we take the idea of using multiple models a step further and apply the concept to our actual switching classifier. This new structure uses an ensemble of single neural-channel HMM chains to form an independently coupled hidden Markov model (ICHMM). Consequently, this classifier takes advantage of the neural firing properties and allows for the removal of vector quantization while jointly improving the classification performance and the subsequent linear prediction of the trajectory.
Design and implementation of biologically realistic signal to symbol translators

Reviews the problem of translating signals into symbols preserving maximally the information contained in the signal time structure. In this context, we motivate the use of nonconvergent dynamics for the signal to symbol translator. We then describe a biologically realistic model of the olfactory system proposed by Walter Freeman (1975) that has locally stable dynamics but is globally chaotic. We present results of simulations and measurements obtained from a fabricated analog VLSI chip.
Evaluation of body movement during sleep by thermopile using wavelet and neuro-fuzzy

Body movement is one of the important factors in sleep analysis. Instant body movement signals were evaluated for the upper, lower, and entire part of the body from a combination of the eighth scale detail signals using the Haar wavelet. These signals show the points of time when the upper-body or lower-body movement occurred and the level of body movement. Additionally, partial body movement was decomposed to three parts of body movement comprising head-only, whole body, and leg-only movement using the ANFIS. After training data for 30 epochs, the three parts of the body movement data were acquired with minimized RMSE, 23.4, 12.1, and 20.8 respectively. Finally, three subject's data were evaluated for 60 minutes, and the detection rates of instant and partial body movement, on average, were 96.3% and 89.2%, respectively.
Multi-rate sampling digital audio equalizer

The advantages of implementing a digital audio equalizer using multirate sampling techniques vs. fixed sampling are presented. A theoretical discussion of multirate sampling equalization design issues such as processor computational bandwidth, number of equalizer bands, frequency resolution in a particular band, and filter type and order are reviewed and weighted accordingly. In addition to those multirate sampling equalizer design concepts, an actual implementation is described. A seven slider high-resolution digital audio equalizer has been developed using the Motorola DSP56001 on the NeXT Machine.<<ETX>>
Towards a silicon implementation of the olfactory system

Our main interest is the development of a computational efficient VLSI implementation of a highly complex system proposed by Freeman and Yao (1990) to explain chaotic dynamics in the olfactory cortex using discrete time techniques. One of the issues involved with this implementation approach is the offset. This paper presents a simulation of a novel circuit for offset cancellation in switched current circuits. According to the simulation results, this new technique is able to cancel the offset and has a low sensitivity to changes in feedback parameters associated with the delay (z/sup -1/) operation.
Adaptive background estimation using an information theoretic cost for hidden state estimation

Hidden state estimation in linear systems is a popular and broad research topic which became a mainstream research area after Rudolf Kalman's seminal paper. The Kalman Filter (KF) gives the optimal solution to the estimation problem in a setting where all the processes are Gaussian random processes. However because of the suboptimal behavior of the KF in non-Gaussian settings, there is a need for a new filter that can extract higher order information from the signals. In this paper we propose using an information theoretic cost function utilizing the similarity measure Correntropy as a performance index. This results in a different perspective on hidden state estimation. We present the superior performance of the new filter on both synthetic data and on adaptive background estimation problem and discuss future research directions.
A neighborhood map of competing one step predictors for piecewise segmentation and identification of time series

A new off-line technique for the competitive identification of piecewise stationary time series is described. A neighborhood map of one step predictors competes for the data during training. The winner is granted the largest parameter update, while other predictors are allowed smaller updates, decreasing with distance from the winner on the neighborhood map. In addition to performing piecewise segmentation and identification, the technique maps similar segments of the time series as neighbors on the neighborhood map.
The Kernel Adaptive Autoregressive-Moving-Average Algorithm

In this paper, we present a novel kernel adaptive recurrent filtering algorithm based on the autoregressive-moving-average (ARMA) model, which is trained with recurrent stochastic gradient descent in the reproducing kernel Hilbert spaces. This kernelized recurrent system, the kernel adaptive ARMA (KAARMA) algorithm, brings together the theories of adaptive signal processing and recurrent neural networks (RNNs), extending the current theory of kernel adaptive filtering (KAF) using the representer theorem to include feedback. Compared with classical feedforward KAF methods, the KAARMA algorithm provides general nonlinear solutions for complex dynamical systems in a state-space representation, with a deferred teacher signal, by propagating forward the hidden states. We demonstrate its capabilities to provide exact solutions with compact structures by solving a set of benchmark nondeterministic polynomial-complete problems involving grammatical inference. Simulation results show that the KAARMA algorithm outperforms equivalent input-space recurrent architectures using first- and second-order RNNs, demonstrating its potential as an effective learning solution for the identification and synthesis of deterministic finite automata.
Underdetermined blind source separation in a time-varying environment

The problem of estimating n source signals from m measurements that are an unknown mixture of the sources is known as blind source separation. In the underdetermined —less measurements than sources— linear case, the solution process can be conveniently divided in three stages: represent the signals in a sparse domain, find the mixing matrix, and estimate the sources. In this paper we adhere to that approach and parametrize the performance of these stages as a function of the sparsity of the signals. To find the mixing matrix and track its variations in the dynamic case a nonparametric maximum-likelihood approach based on Parzen windowing is presented. To invert the underdetermined linear problem we present an estimator that chooses the “best” demixing matrix in a sample by sample basis by using some previous knowledge of the statistics of the sources. The results are validated by Montecarlo simulations.
Kernel recurrent system trained by real-time recurrent learning algorithm

This paper presents a kernelized version of recurrent systems (KRS) and develops a kernel real-time recurrent learning (KRTRL) algorithm to train KRS. To avoid instabilities during training, the teacher forcing technique is adopted to modify the KRTRL learning. The proposed algorithms compared with the KLMS in Lorenz time series prediction. The prediction performances of the proposed algorithm outperform the KLMS significantly.
A unifying criterion for blind source separation and decorrelation: simultaneous diagonalization of correlation matrices

Blind source separation and blind output decorrelation are two well-known problems in signal processing. For instantaneous mixtures, blind source separation is equivalent to a generalized eigen-decomposition, while blind output decorrelation can be considered as an iterative method of output orthogonalization. We propose a steepest descent procedure on a new cost function based on the Frobenius norm which measures the diagonalization of correlation matrices to perform blind source separation as well as blind decorrelation. The method is applicable to both stationary and nonstationary signals and instantaneous as well as convolutive mixture models. Simulation results by Monte Carlo trials are provided to show the consistent performance of the proposed algorithm.