nils-herrmann/clean_scirepeval huggingface dataset:
Correntropy induced joint power and admission control algorithm for dense small cell network

The authors consider the joint admission and power control problem in a dense small cell network, which contains multiple interference links. The goal is to mainly maximise the number of the admitted links, and at the same time minimise the transmit power. The authors formulate the admission control and power control problem as a joint optimisation problem, which is however non-deterministic polynomial hard (NP-hard). Such NP-hard problem can be relaxed to a p-norm problem (0 < p < 1) by using the correntropy induced metric. The correntropy is a novel non-linear similarity measure, which has been successfully used in the robust and spares signal processing, especially when the data contain large outliers. Thus, in this work the authors propose a new correntropy induced joint power and admission control algorithm. To achieve a faster convergence speed, the authors also propose an adaptive kernel size method, in which the kernel size is determined by the error so that the convergence speed is the fastest during the iterations. Simulation results show that the proposed approach can achieve much better results than the existing works.
On the convergence of SIPEX: a simultaneous principal components extraction algorithm

We have previously proposed SIPEX as a fast-converging and accurate principal components algorithm (Erdogmus, D. et al.,  ICASSP'02, vol.1, p.1069-72, 2002;  EUSIPCO'02, vol.2, p.335-8, 2002). Its superiority in terms of data efficiency and solution accuracy was demonstrated through Monte Carlo simulations. We focus on the convergence properties of the original gradient-based algorithm as well as two modified versions of SIPEX based on approximations to the Hessian matrix of the cost function. We provide practical bounds on the step sizes of these algorithms and compare their convergence properties.
Reinforcement learning via kernel temporal difference

This paper introduces a kernel adaptive filter implemented with stochastic gradient on temporal differences, kernel Temporal Difference (TD)(λ), to estimate the state-action value function in reinforcement learning. The case λ=0 will be studied in this paper. Experimental results show the method's applicability for learning motor state decoding during a center-out reaching task performed by a monkey. The results are compared to the implementation of a time delay neural network (TDNN) trained with backpropagation of the temporal difference error. From the experiments, it is observed that kernel TD(0) allows faster convergence and a better solution than the neural network.
A fixed point update for kernel width adaptation in information theoretic criteria

This paper presents a fixed point update for adaptation of the kernel width parameter in information theoretic criteria. These criteria are typically non-parametric and require a kernel width parameter to be appropriately set. The kernel width sets the smoothing bandwidth for estimation of the probability distribution of the error and, consequently, affects the performance surface. Hence, adaptation of the kernel width allows for the criterion, and its performance surface, to be adjusted to changes in the signal distribution. It is shown that the proposed fixed point update converges faster and is more stable when compared to a gradient update, and has no parameters. Moreover, it can be simplified to achieve the same computational complexity as the stochastic gradient update.
Feature evaluation using quadratic mutual information

Methods of feature evaluation are developed and discussed based on information theoretical learning (ITL). Mutual information was shown in the literature to be more robust and precise to evaluate a feature set. We propose to use quadratic mutual information (QMI) for feature evaluation. The concept of information potential leads to a more clearly physical meaning of the evaluation functions. Moreover, evaluation for feature sets in high-dimensional space could also be implemented efficiently. Experimental results are compared to classifier performances.
Incremental backpropagation learning networks

How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a new incremental learning method for pattern recognition, called the "incremental backpropagation learning network", which employs bounded weight modification and structural adaptation learning rules and applies initial knowledge to constrain the learning process. The viability of this approach is demonstrated for classification problems including the iris and the promoter domains.
Sparse analog associative memory via L1-regularization and thresholding

The CA3 region of the hippocampus acts as an auto-associative memory and is responsible for the consolidation of episodic memory. Two important characteristics of such a network is the sparsity of the stored patterns and the nonsaturating firing rate dynamics. To construct such a network, here we use a maximum a posteriori based cost function, regularized with L1-norm, to change the internal state of the neurons. Then a linear thresholding function is used to obtain the desired output firing rate. We show how such a model leads to a more biologically reasonable dynamic model which can produce a sparse output and recalls with good accuracy when the network is presented with a corrupted input.
An on-line system for intraoperative focus localization from array electrocorticography

A system for the intraoperative localization of the focus of epilepsy from array electrocorticography is presented. The spatio-temporal structure that characterizes the surface potential field during the occurrence of focal interictal events is discussed. The spatio-temporal Laplacian transformation is proposed for selectively emphasizing these occurrences. The implementation of the online localization system in a multiprocessor platform is outlined and detailed results from three surgery cases are shown.<<ETX>>
On Spatio-Temporal Dependency Changes in Epileptic Intracranial EEG: A Statistical Assessment

Pathological manifestations of epilepsy are generally associated with a set of clinical events that possess both spatial and temporal patterns. In this paper, based on a similar hypothesis, we study the evolution of epileptic seizures by analyzing temporal changes in the spatial bindings between various cortical structures. We propose to apply the Mantel statistics to quantitatively analyze the temporal changes in spatial-correlation matrices. The Mantel test is applied to 6 complex partial seizures of an epileptic patient. We show that, in 5 of the 6 instances, the spatial structures undergo significant connectivity changes in the 2 hours time-interval prior to the occurrence of a seizure.
Reinforcement Learning in Video Games Using Nearest Neighbor Interpolation and Metric Learning

Reinforcement learning (RL) has had mixed success when applied to games. Large state spaces and the curse of dimensionality have limited the ability for RL techniques to learn to play complex games in a reasonable length of time. We discuss a modification of Q-learning to use nearest neighbor states to exploit previous experience in the early stages of learning. A weighting on the state features is learned using metric learning techniques, such that neighboring states represent similar game situations. Our method is tested on the arcade game Frogger, and it is shown that some of the effects of the curse of dimensionality can be mitigated.
Information Theoretical Estimators of Tuning Depth and Time Delay for Motor Cortex Neurons

Knowledge of neural receptive fields helps decode animal's movement from neural activity in motor brain machine interfaces (BMI). The traditional tuning depth is a coarse metric to evaluate the neuron's tuning properties. The major difficulty of the tuning depth is that it does not allow for a direct comparison among neurons that have very different firing rates. An information theoretic tuning depth based on mutual information is proposed to evaluate how neural spikes encode the kinematic direction. This metric is scale invariant and makes the tuning analysis comparable among not only the cortical areas but also among kinematics vectors. It can help identify candidate neuron subsets to reduce the complexity of analysis and decoding in BMI. The application of the metric on neural encoding modeling also provides a way to estimate time delays in motor cortical neural activity
Visualization Of Epileptic Spikes In State Space

Epileptic spike detection in the electroencephalogram (EEG) is still largely done by humans using the polygraph output. Here we will present a state space display methodology that has the potential to enhance spikes from background activio. It uses techniques from dynamical system analysis to map the EEG into state space and quantify there the trajectories that correspond to EEG spikes. The display technique and some examples will be presented in this paper.
The Cortical Mouse: A Piece of Forgotten History in Noninvasive Brain&#8211;Computer Interfaces

Early research on brain-computer interfaces (BCIs) was fueled by the study of event-related potentials (ERPs) by Farwell and Donchin, who are rightly credited for laying important groundwork for the BCI field. However, many other researchers have made substantial contributions that have escaped the radar screen of the current BCI community. For example, in the late 1980s, I worked with a brilliant multidisciplinary research group in electrical engineering at the University of Florida, Gainesville, headed by Dr. Donald Childers. Childers should be well known to long-time members of the IEEE Engineering in Medicine and Biology Society since he was the editor-in-chief of  1973.
Locating spatial patterns of waveforms during sensory perception in scalp EEG

The spatio-temporal oscillations in EEG waves are indicative of sensory and cognitive processing. We propose a method to find the spatial amplitude patterns of a time-limited waveform across multiple EEG channels. It consists of a single iteration of multichannel matching pursuit where the base waveform is obtained via the Hilbert transform of a time-limited tone. The vector of extracted amplitudes across channels is used for classification, and we analyze the effect of deviation in temporal alignment of the waveform on classification performance. Results for a previously published dataset of 6 subjects show comparable results versus a more complicated criteria-based method.
An information-theoretic perspective to kernel independent components analysis

In this paper, we investigate the intriguing relationship between information-theoretic learning (ITL), based on weighted Parzen window density estimator, and kernel-based learning algorithms. We prove the equivalence between kernel independent component analysis (kernel ICA) and the Cauchy-Schwartz (C-S) independence measure. This link gives a theoretical motivation for the selection of the Mercer kernel, based on density estimation. Demonstrating this equivalence requires introducing a weighted kernel density estimator, a modification of Parzen windowing. We also discuss the role of the weights in the weighted Parzen windowing and kernel ICA.
A greedy algorithm for model selection of tensor decompositions

Various tensor decompositions use different arrangements of factors to explain multi-way data. Components from different decompositions can vary in the number of parameters. Allowing a model to contain components from different decompositions results in a combinatoric number of possible models. Model selection balances approximation error and the number of parameters, but due to the number of possible models, post-hoc model selection is infeasible. Instead, we incrementally build a model. This approach is analogous to sparse coding with a union of dictionaries. The proposed greedy approach can estimate a model consisting of a combination of tensor decompositions.
Pulse-based signal compression for implanted neural recording systems

Today's implanted neural systems are bound by tight constraints on power and communication bandwidth. Most conventional ADC-based approaches fall into two categories. Either they transmit all of the information at the Nyquist rate but are ultimately limited to only a handful of channels due to communication bandwidth constraints. Or they perform spike detection on the front-end which allows a scale up to 100 or more channels but prevents the use of spike sorting on the backend. Spike sorting is an important step that provides a labeling to multiple neurons on each channel and further improves the accuracy of spike detection. In this paper we describe the pulse-based approach used in the FWIRE (Florida wireless implantable recording electrodes) project. A hardware spiking neuron on each channel is configured either to transmit pulses for full reconstruction on the back-end, or to transmit dramatically fewer pulses but still allow for spike sorting on the back-end. Spike sorting results show that the pulse-based spike sorting accuracy is competitive with conventional methods used in daily practice.
A knowledge-based approach to supervised incremental learning

How to learn new knowledge without forgetting old knowledge is a key issue in designing an incremental-learning neural network. In this paper, we present a rule-based connectionist approach in which old knowledge is preserved by bounding weight modifications. In addition, some heuristics are developed for avoiding overtraining of the network and adding new hidden units. The feasibility of this approach is demonstrated for classification problems including the iris and the promoter domains.<<ETX>>
Robust Blind Simo Channel Estimation Using Adatron

In this paper we apply the structural risk minimization (SRM) principle to derive a blind single-input multiple-output (SIMO) channel estimation algorithm, which is robust to channel order overestimation. Specifically, the blind estimation is formulated as a support vector regression (SVR) problem in which the channel coefficients are the Lagrange multipliers of the dual problem. In this paper, we show that the SRM principle pushes to zero the small leading and trailing terms of the channel impulse response even when its order is highly overestimated. The main drawback of this approach is the high computational cost of the resulting quadratic programming (QP) problem. To alleviate this, in this paper we propose to use a simple and fast algorithm called the Adatron to solve the QP problem. Simulation results are provided to demonstrate the performance of our channel estimator
Information cut and information forces for clustering

We define an information-theoretic divergence measure between probability density functions (pdfs) that has a deep connection to the cut in graph-theory. This connection is revealed when the pdfs are estimated by the Parzen method with a Gaussian kernel. We refer to our divergence measure as the information cut. The information cut provides us with a theoretically sound criterion for cluster evaluation. In this paper we show that it can be used to merge clusters. The initial clusters are obtained based on the related concept of information forces. We create directed trees by selecting the predecessor of a node (pattern) according to the direction of the information force acting on the pattern. Each directed tree corresponds to a cluster, hence enabling us to obtain an initial partitioning of the data set. Subsequently, we utilize the information cut as a cluster evaluation function to merge clusters until the predefined number of clusters is reached. We demonstrate the performance of our novel information-theoretic clustering method when applied to both artificially created data and real data, with encouraging results.
Information theoretic spectral clustering

We discuss a new information-theoretic framework for spectral clustering that is founded on the recently introduced information cut. A novel spectral clustering algorithm is proposed, where the clustering solution is given as a linearly weighted combination of certain top eigenvectors of the data affinity matrix. The information cut provides us with a theoretically well-defined graph-spectral cost function, and also establishes a close link between spectral clustering, and non-parametric density estimation. As a result, a natural criterion for creating the data affinity matrix is provided. We present preliminary clustering results to illustrate some of the properties of our algorithm, and we also make comparative remarks.
Extraction of Signals With Specific Temporal Structure Using Kernel Methods

This work derives and evaluates a method for Blind Source Extraction (BSE) in a reproducing kernel Hilbert space (RKHS) framework. The a priori information about the autocorrelation function of the signal under study is translated in a linear transformation of the Gram matrix of the transformed data in Hilbert space. Our method proved to be more robust than methods presented in the literature of BSE with respect to ambiguities in the available a priori information of the signal to be extracted. The approach here introduced can also be seen as a generalization of Kernel principal component analysis (KPCA) to analyze autocorrelation matrices at specific time lags.
Group feature selection in image classification with multiple kernel learning

Classification of large amount of images calls for diverse types of features, but employing all possible feature types will create unnecessary computation burden, and may result in reduced classification accuracy. Selecting feature vectors individually is not a feasible solution in this scenario due to the high amount of feature vectors needed for reasonable performance. Instead, this paper proposes a measure that effectively evaluates the relative significance of a feature group, employing the minimum redundancy maximum relevance (mRMR) feature selection. Multiple kernel learning (MKL) is used for combining different feature types in classification, which implicitly also serves an alternative way for weighing the feature groups' importance. Results show the proposed group feature selection better reflects a feature type's importance, and improve upon MKL performance. This study also finds that the convolutional neural network (CNN) features have the best discriminative power among all features, but it is still possible to improve classification accuracy with other well-designed features.
Dynamic factor graphs: A novel framework for multiple features data fusion

The Dynamic Tree  (DT) Bayesian Network is a powerful analytical tool for image segmentation and object segmentation tasks. Its hierarchical nature makes it possible to analyze and incorporate information from different scales, which is desirable in many applications. Having a flexible structure enables model selection, concurrent with parameter inference. In this paper, we propose a novel framework, dynamic factor graphs (DFG), where data segmentation and fusion tasks are combined in the same framework. Factor graphs (FGs) enable us to have a broader range of modeling applications than Bayesian networks (BNs) since FGs include both directed acyclic and undirected graphs in the same setting. The example in this paper will focus on segmentation and fusion of 2D image features with a linear Gaussian model assumption.
A loss function for classification based on a robust similarity metric

We present a margin-based loss function for classification, inspired by the recently proposed similarity measure called correntropy. We show that correntropy induces a nonconvex loss function that is a closer approximation to the misclassification loss (ideal 0–1 loss). We show that the discriminant function obtained by optimizing the proposed loss function using a neural network is insensitive to outliers and has better generalization performance as compared to using the squared loss function which is common in neural network classifiers. The proposed method of training classifiers is a practical way of obtaining better results on real world classification problems, that uses a simple gradient based online training procedure for minimizing the empirical risk.
The Convergence of Machine and Biological Intelligence

To explore the exciting new domain of brain informatics, we invited several well-known experts to discuss the state of the art, the challenges, the opportunities, and the trends. In "Creating Human-Level AI by Educating a Child Machine," Raj Reddy proposes an architecture for a "child machine" that can learn and is teachable. In "Cyborg Intelligence," Zhaohui Wu, Gang Pan, and Nenggan Zheng describe a biological-machine system consisting of both an organic and a computing part. In "Formal Minds and Biological Brains II: From the Mirage of Intelligence to a Science and Engineering of Consciousness," Paul F.M.J. Verschure discusses human-like cognitive architectures and describes the Distributed Adaptive Control (DAC) architecture for perception, cognition, and action. In "The Challenges of Closed-Loop Invasive Brain-Machine Interfaces," Qiaosheng Zhang and Xiaoxiang Zheng discuss the challenges and trends in closed-loop brain-machine interfaces. In "Neural Signal Processing in Brain-Machine Interfaces," Jose C. Principe takes a critical look at the challenges and opportunities of performing computation with pulses, as neurons do. In "Neuroprosthesis Control via a Noninvasive Hybrid Brain-Computer Interface," Alex Kreilinger, Martin Rohm, Vera Kaiser, Robert Leeb, Rüdiger Rupp, and Gernot R. Müller-Putz describe an example of the convergence of biological intelligence and machine intelligence in a hand-elbow neuroprosthesis control unit.
A closed form recursive solution for Maximum Correntropy training

This paper presents a closed form recursive solution for training adaptive filters using the Maximum Correntropy Criterion (MCC). Correntropy has been recently proposed as a robust similarity measure between two random variables or signals, when the pdfs involved are heavy tailed and non-Gaussian. Maximizing the cross-correntropy between the output of an adaptive filter and the desired response leads to the Maximum Correntropy Criterion for adaptive systems training. We show that a closed form, recursive solution of the filter weights using this criterion yields a simple weighted least squares like formulation. Our simulations show that training the filter weights using this recursive solution is much faster than gradient based training, and more accurate than the RLS algorithm in cases where the error pdf is non-Gaussian and heavy tailed.
Comparison of TDNN training algorithms in brain machine interfaces

Linear or non-linear models are used in brain machine interfaces (BIMIs) to map the neural activity to the associated behavior, typically the primate's hand position. Linear models assume a linear relationship between neural activity and hand position that may not be the case. A solution would be time-delay neural network (TDNN) that provides effectively a nonlinear combination of linear models. However, this model results in a drastic increase of free parameters and slow convergence when trained by an error backpropagation learning rule. We propose to train the TDNN by scaled conjugate gradient, which avoids time-consuming linear search, coupled with weight decay to reduce the free parameters number and produce generally faster convergence.
Reconstructed dynamics and chaotic signal modeling

A nonlinear AR model is derived from the reconstructed dynamics of a signal. The underlying system is assumed to be nonlinear, autonomous, and deterministic. In this formulation, the output error scheme is shown to be more suitable than the equation error scheme in training a network as a nonlinear AR model of the signal. A method to incorporate the information of the dynamical invariants in signal modeling is proposed.<<ETX>>