nils-herrmann/clean_scirepeval huggingface dataset:
Fast algorithm for adaptive blind equalization using order-α Renyi's entropy

In this paper a novel blind equalization algorithm based on stochastic gradient descent minimization of order-α Renyi's entropy and designed for constant modulus signals is introduced. The algorithm applies a new nonparametric estimator for Renyi's entropy, which has been recently proposed and allows to compute any order of entropy. In comparison with conventional adaptive blind techniques, such us CMA, the proposed algorithm shows a remarkable increase in convergence speed with only a moderate increase in computational cost.
Artificial neural networks with gamma kernels for automatic target detection

The quadratic gamma detector (QGD) has been used successfully to discriminate man made objects from background clutter in synthetic aperture (SAR) radar imagery. It implements a linear discriminant function based on quadratic terms of the image intensity of a pixel under observation and of its surroundings. This paper extends the QGD to a multilayer perceptron with a first layer built from 2D gamma kernels. We call this nonlinear extension the NL-QGD. We compare the performance of the QGD and NL-QGD based on receiver operating curve of targets in SAR data. The NL-QGD shows improved performance with respect to the QGD for most of the values of the detection probability. However, there is a deterioration of performance at 100% target detection, and the system performance is highly dependent upon the stopping criterion.
A layered processing model for knowledge-based contextual interpretation of multichannel EEG/EOG signals

A layered processing model designed for a knowledge-based expert system for automated classification of multiple channel sleep EEG/EOG (electroencephalogram/electrooculogram) signals is described. A three-layer model is formulated to process and modify the contextual information present in the signal. Each layer is associated with a different processing time frame. It uses a hierarchical architecture that reflects the human expert's information and data abstraction process for the visual inspection of the signal. The model incorporates a certainty handling scheme which uses symbolic representation of certainty levels. The certainty-level information does not participate directly in the rule-execution mechanism, but is manipulated separately and provides augmented information to use effectively for the contextual interpretation in the layered model.<<ETX>>
Modeling and inverse controller design for an unmanned aerial vehicle based on the self-organizing map

The next generation of aircraft will have dynamics that vary considerably over the operating regime. A single controller will have difficulty to meet the design specifications. In this paper, a self-organizing map (SOM)-based local linear modeling scheme of an unmanned aerial vehicle (UAV) is developed to design a set of inverse controllers. The SOM selects the operating regime depending only on the embedded output space information and avoids normalization of the input data. Each local linear model is associated with a linear controller, which is easy to design. Switching of the controllers is done synchronously with the active local linear model that tracks the different operating conditions. The proposed multiple modeling and control strategy has been successfully tested in a simulator that models the LoFLYTE UAV.
The Correntropy Mace Filter for Image Recognition

The minimum average correlation energy (MACE) filter is a well known correlation filter for pattern recognition. This paper proposes a nonlinear extension to the MACE filter using the recently introduced correntropy function in feature space. Correntropy is a positive definite function that generalizes the concept of correlation by utilizing higher order moment information of signal structure. Since the MACE is a spatial matched filter for an image class, the correntropy MACE can potentially improve its performance. We apply the correntropy MACE filter to face recognition and show that the proposed method indeed outperforms the traditional linear MACE in both generalization and rejection abilities.
Feature selection based on survival Cauchy-Schwartz mutual information

Feature selection techniques play a crucial role in machine learning tasks such as regression and classification. Many filter methods of feature selection are based on the mutual information (e.g. MIFS, MIFS-U, NMIFS, and mRMR methods). In this work, a new mutual information is defined based on the cross survival information potential (CSIP) and Cauchy-Schwartz divergence (CSD), called the survival Cauchy-Schwartz mutual information (SCS-MI). We apply this new mutual information to select an informative subset of features for a SVM classifier. Experimental results illustrate the desirable performance of the new method.
Multidimensional filtering of the electrocorticogram (ECoG) for epileptic focus localization

The rationale of a method for the detection and localization of focal epileptic activity from multichannel ECoG recordings is introduced. The proposed method, the spatiotemporal Laplacian, emphasizes the need to consider both the spatial and the temporal attributes of focal epileptic activity. In its current implementation the method discriminates the events of interest by means of combined measures of spatial and temporal sharpness, yielding a two-dimensional representation that facilitates the localization of the focus. Some preliminary offline results are presented.<<ETX>>
Directed generalized measure of association: A data driven approach towards causal inference

In this paper we propose a new statistical concept called directed generalized measure of association (dGMA) to quantify the amount of association transferred between subsystems in a system evolving in time. This paper is an improvement of the previously established method called generalized measure of association (GMA) by taking the conditional dependence between time-delay representation of subsystems into account. Directed-GMA is a rank-based pair-wise measure which can deal with dynamic data sets. This is done by calculating the rank permutation in a conditional scheme under the framework of conditional causality. In this paper we present a bivariate case and assume that the cause-effect relationship is one directional, e.g. there is no feedback. The preliminary results on the synthetic data sets reveal that the proposed method is able to extract nonlinear causal relationship, which cannot be extracted by traditional approaches. To further assess the performance we compared the results with another rank-based method called symbolic transfer entropy (STE). Our approach can be a promising tool to infer causal relationship in complex systems, e. g. human brain, to reveal their underlying effective connectivity.
Latent state visualization of neural firing rates

Visualizing the collective modulation of multiple neurons during a known behavioral task is useful for exploratory analysis, but handling the large dimensionality of neural recordings is challenging. We further investigate using static dimensionality reduction techniques on neural firing rate data during an arm movement task. This lower-dimensional representation of the data is able to capture the neural states corresponding to different portions of the behavior task. A simulation using a dynamical model lends credence to the ability of the technique to generate a representation that preserves underlying dynamics of the model. This technique is a straightforward way to extract a useful visualization for neural recordings during brain-machine interface tasks. Meaningful visualization confirms underlying structure in data, which can be captured with parametric modeling.
Data-driven tree-structured Bayesian network for image segmentation

This paper presents Data-Driven Tree-structured Bayesian network (DDT), a novel probabilistic graphical model for hierarchical unsupervised image segmentation. The DDT captures long and short-ranged correlations between neighboring regions in each image using a tree-structured prior. Unlike other previous work, DDT first segments an input image into superpixels and learn a tree-structured prior based on the topology of superpixels in different scales. Such a tree structure is referred to as data-driven tree structure. Each superpixel is represented by a variable node taking a discrete value of class/label of the segmentation. The probabilistic relationships among the nodes are represented by edges in the network. The unsupervised image segmentation, hence, can be viewed as an inference problem of the nodes in the tree structure of DDT, which can be carried out efficiently. We evaluate quantitatively our results with respect to the ground-truth segmentation, demonstrating that our proposed framework performs competitively with the state of the art in unsupervised image segmentation and contour detection.
Gravity Transform for Input Conditioning in Brain Machine Interfaces

Gravity transform measures cooperative neural activity being utilized for the analysis of neural assemblies. In this paper we verify the applicability of the gravity transform to specify components of neural assemblies, which could be combined, leading ultimately to a reduction of the input dimensionality in brain-machine interface models. Our analysis was performed on data collected from rats performing a lever pressing task. We compare the results from the gravity transform analysis with the assignment obtained through a sensitivity analysis applied on a linear optimal filter
An information theoretic approach to adaptive system training using unlabeled data

Traditionally, supervised learning is performed with pairwise input-output labelled data. After the training procedure, the adaptive system weights are fixed and the system is tested with unlabelled data. Recently, exploiting the unlabeled data to improve classification performance has been proposed in the machine learning community. In this paper, we present an information theoretic approach based on density divergence minimization to obtain an extended training algorithm using unlabeled data during testing. The simulations for classification problems suggest that our method can improve the performance of adaptive system in the application phase.
A Novel Switching Scheme Between Adaptive Information Algorithms

Switching approaches can improve the performance of adaptive schemes, however a data driven criterion to accomplish the task is unclear. In this paper, we propose a new optimization criterion for switching which is estimated directly from data. We apply the method to the recently introduced MEE and MEE-SAS algorithms. Using this novel switching scheme, we develop a single algorithm which effectively combines the strengths of MEE and MEE-SAS without sacrificing the simplicity and stability properties of MEE. We explain these results analytically, and through simulations.
Effects of Acute Hippocampal Stimulation on EEG Dynamics

Progressive preictal dynamical convergence and postictal divergence of dynamical EEG descriptors among brain regions has been reported in human temporal lobe epilepsy (TLE) and in a rodent model of TLE. There are also reports of anticonvulsant effects of high frequency stimulation of the hippocampus in humans. We postulate that this anticonvulsant effect is due to dynamical resetting by the electrical stimulation. The following study investigated the effects of acute hippocampal electrical stimulation on dynamical transitions in the brain of a spontaneously seizing animal model of TLE to test the hypothesis of divergence in dynamical values by electrical stimulation of the hippocampus
A New Information Theoretic Measure of PDF Symmetry

In this paper, a new quantity called symmetric information potential (SIP) is proposed to measure the reflection symmetry and to estimate the location parameter of probability density functions. SIP is defined as an inner product in the probability density function space and has a close relation to information theoretic learning. A simple nonparametric estimator directly from the data exists. Experiments demonstrate that this concept can be very useful dealing with impulsive data distributions, in particular, α-stable distributions.
Adaptive epileptic seizure prediction system

Current epileptic seizure "prediction" algorithms are generally based on the knowledge of seizure occurring time and analyze the electroencephalogram (EEG) recordings retrospectively. It is then obvious that, although these analyses provide evidence of brain activity changes prior to epileptic seizures, they cannot be applied to develop implantable devices for diagnostic and therapeutic purposes. In this paper, we describe an adaptive procedure to prospectively analyze continuous, long-term EEG recordings when only the occurring time of the first seizure is known. The algorithm is based on the convergence and divergence of short-term maximum Lyapunov exponents (STLmax) among critical electrode sites selected adaptively. A warning of an impending seizure is then issued. Global optimization techniques are applied for selecting the critical groups of electrode sites. The adaptive seizure prediction algorithm (ASPA) was tested in continuous 0.76 to 5.84 days intracranial EEG recordings from a group of five patients with refractory temporal lobe epilepsy. A fixed parameter setting applied to all cases predicted 82% of seizures with a false prediction rate of 0.16/h. Seizure warnings occurred an average of 71.7 min before ictal onset. Similar results were produced by dividing the available EEG recordings into half training and testing portions. Optimizing the parameters for individual patients improved sensitivity (84% overall) and reduced false prediction rate (0.12/h overall). These results indicate that ASPA can be applied to implantable devices for diagnostic and therapeutic purposes.
An adaptive duty-cycle methodology for PV power maximization using a single variable

This paper presents a new methodology to maximize the power output of Photovoltaic panels (PV), based on an adaptive duty-cycle methodology. The approach embeds the DC/DC converter characteristic in the cost function, allowing an optimization based on a single measured variable. Two cost functions, and respective learning rules, are derived. The first, more complex and comprehensive, traces the ground for the second which is less computational intensive and solves stability issues and implementation difficulties. It is also demonstrated that the system is asymptotically stable around the optimum duty-cycle, in the Lyapunov sense. Both methods are compared through simulations and deviations from the optimal solution are assessed.
A New Neural Network Model For Temporal Processing

We present a novel neural network model for real-time processing of space-time patterns. The model is characterized by high order temporal dynamics, a feature that adds computational flexibility in the time domain compared to common neural models. Another important property is that although our model uses temporal synaptic weights (w = w(t)), the system equations can be written as a regular additive (Grossberg) model. This is advantageous, because a substantial body of learning and stability theory frr the additive model is directly applicable to our model. Applications include real-time classification of speech or EEG data and nonlinear prediction.
Peri-event cross-correlation over time for analysis of interactions in neuronal firing

Several methods have been described in the literature to verify the presence of couplings between neurons in the brain. In this paper we introduce the peri-event cross-correlation over time (PECCOT) to describe the interaction among the two neurons as a function of the event onset. Instead of averaging over time, the PECCOT averages the cross-correlation over instances of the event. As a consequence, the PECCOT is able to characterize with high temporal resolution the interactions over time among neurons. To illustrate the method, the PECCOT is applied to a simulated dataset and for analysis of synchrony in recordings of a rat performing a go/no go behavioral lever press task. We verify the presence of synchrony before the lever press time and its suppression afterwards.
Period detection in light curves from astronomical objects using correntropy

In this paper we propose a new method for determining the period in astronomical time series using correntropy, an information theoretical concept recently developed in the computational intelligence field. The time series correspond to the stellar brightness over time, so-called light curves, and are characterized as being noisy and unevenly sampled. The advantages of using correntropy instead of correlation are to escape from the constraints of linearity and Gaussianity and are clearly demonstrated. The performance of the proposed method is compared with other algorithms published in the literature on a set of light curves drawn from the MACHO survey. The results show that the correntropy-based method obtains the correct periods more frequently than the Lomb-Scargle periodogram and the Period04 program.
Special Issue on Deep Reinforcement Learning and Adaptive Dynamic Programming

The sixteen papers in this special section focus on deep reinforcement learning and adaptive dynamic programming (deep RL/ADP). Deep RL is able to output control signal directly based on input images, which incorporates both the advantages of the perception of deep learning (DL) and the decision making of RL or adaptive dynamic programming (ADP). This mechanism makes the artificial intelligence much closer to human thinking modes. Deep RL/ADP has achieved remarkable success in terms of theory and applications since it was proposed. Successful applications cover video games, Go, robotics, smart driving, healthcare, and so on. However, it is still an open problem to perform the theoretical analysis on deep RL/ADP, e.g., the convergence, stability, and optimality analyses. The learning efficiency needs to be improved by proposing new algorithms or combined with other methods. More practical demonstrations are encouraged to be presented. Therefore, the aim of this special issue is to call for the most advanced research and state-of-the-art works in the field of deep RL/ADP.
An asymmetric stagewise least square loss function for imbalanced classification

In this paper, we present an asymmetric stagewise least square (ASLS) loss function for imbalanced classification. While keeping all the advantages of the stagewise least square (SLS) loss function, such as, better robustness, computational efficiency and sparseness, the ASLS loss extends the SLS loss by adding another two parameters, namely, ramp coefficient and margin coefficient. Therefore, asymmetric ramps and margins can be formed which makes the ASLS loss be more flexible and appropriate for processing class imbalance problems. A reduced kernel classifier of the ASLS loss is also developed which only uses a small part of the dataset to generate an efficient nonlinear classifier. Experimental results confirm the effectiveness of the ASLS loss in imbalanced classification.
Universal Approximation with Convex Optimization: Gimmick or Reality? [Discussion Forum]

This paper surveys in a tutorial fashion the recent history of universal learning machines starting with the multilayer perceptron. The big push in recent years has been on the design of universal learning machines using optimization methods linear in the parameters, such as the Echo State Network, the Extreme Learning Machine and the Kernel Adaptive filter. We call this class of learning machines convex universal learning machines or CULMs. The purpose of the paper is to compare the methods behind these CULMs, highlighting their features using concepts of vector spaces (i.e. basis functions and projections), which are easy to understand by the computational intelligence community. We illustrate how two of the CULMs behave in a simple example, and we conclude that indeed it is practical to create universal mappers with convex adaptation, which is an improvement over backpropagation.
Dynamic subgrouping in RTRL provides a faster O(N/sup 2/) algorithm

Static grouping of processing elements (PEs) has been proposed to reduce the computational complexity of real time recurrent learning (RTRL) from O(n/sup 4/) to O(n/sup 2/), but performance suffers. This paper proposes a dynamic subgrouping of PEs estimated from a local approximation of the /spl pi/ matrix based on temporal Hebbian of sensitivities during training. The method is O(n/sup 2/) and leads to better performance.
Innovating adaptive and neural systems instruction with interactive electronic books

This paper describes an integrated strategy to innovate teaching in the undergraduate classroom by appropriately utilizing information technologies. The innovation is an interactive learning environment built around an interactive electronic book (i-book). The i-book is a tight integration of a hypertext document with a simulator. The hypertext specifies the presentation order of the theoretical topics, and the simulator is an essential piece of learning. The lectures exploit the i-book, transforming the classroom into an interactive teaching laboratory. An electronic white board is used in every lecture to teach from the text and run the simulations. Our goal is to teach adaptive systems to undergraduate electrical engineering students. Undergraduates do not have the mathematical background to comprehend the equation-based approach utilized in graduate-level adaptive systems courses. We have used the i-book for undergraduate instruction with very positive results. We are now ready to extend the format for distance learning. This new teaching methodology transcends adaptive systems and can be applied to other engineering and science courses which have access to simulators.
Joint optimization of algorithmic suites for EEG analysis

Electroencephalogram (EEG) data analysis algorithms consist of multiple processing steps each with a number of free parameters. A joint optimization methodology can be used as a wrapper to fine-tune these parameters for the patient or application. This approach is inspired by deep learning neural network models, but differs because the processing layers for EEG are heterogeneous with different approaches used for processing space and time. Nonetheless, we treat the processing stages as a neural network and apply backpropagation to jointly optimize the parameters. This approach outperforms previous results on the BCI Competition II - dataset IV; additionally, it outperforms the common spatial patterns (CSP) algorithm on the BCI Competition III dataset IV. In addition, the optimized parameters in the architecture are still interpretable.
Stochastic kernel temporal difference for reinforcement learning

This paper introduces a kernel adaptive filter using the stochastic gradient on temporal differences, kernel TD(λ), to estimate the state-action value function Q in reinforcement learning. Kernel methods are powerful for solving nonlinear problems, but the growing computational complexity and memory size limit their applicability on practical scenarios. To overcome this, the quantization approach introduced in  is applied. To help understand the behavior and illustrate the role of the parameters, we apply the algorithm on a 2-dimentional spatial navigation task. Eligibility traces are commonly applied in TD learning to improve data efficiency, so the relations of eligibility trace λ and step size and filter size are observed. Moreover, kernel TD (0) is applied to neural decoding of an 8 target center-out reaching task performed by a monkey. Results show the method can effectively learn the brain-state action mapping for this task.