nils-herrmann/clean_scirepeval huggingface dataset:
EEG-based communication via dynamic neural network models

The overall aim of this research is to develop an EEG-based computer interface. We report on an offline analysis of EEG data recorded from 7 subjects performing two different pairs of cognitive tasks; motor imagery versus a baseline task and motor imagery versus a maths task. For the imagery versus baseline pairing, discrimination was good in three subjects, marginal in two and not possible in the other two. For the imagery versus maths pairing, discrimination was very good in two subjects, good in 4 and marginal in one. The data was analysed using lagged-AR feature vectors and a Bayesian logistic regression classifier with temporal smoothing. Enhanced spectra are shown highlighting differential spectral activity for each task pairing. The results suggest that combinations of different task pairings and dynamic neural network models have the potential to drastically reduce the time it takes for a new user to learn to use an EEG-based computer interface.
Dynamic Models for Nonstationary Signal Segmentation

This paper investigates Hidden Markov Models (HMMs) in which the observations are generated from an autoregressive (AR) model. The overall model performs nonstationary spectral analysis and automatically segments a time series into discrete dynamic regimes. Because learning in HMMs is sensitive to initial conditions, we initialize the HMM model with parameters derived from a cluster analysis of Kalman filter coefficients. An important aspect of the Kalman filter implementation is that the state noise is estimated on-line. This allows for an initial estimation of AR parameters for each of the different dynamic regimes. These estimates are then fine-tuned with the HMM model. The method is demonstrated on a number of synthetic problems and on electroencephalogram data.
Covariance-based weighting for optimal combination of model predictions

This paper introduces a method for calculating the covariance between different neural network solutions. It is based on a generalisation of the delta method for calculating the network Hessian and generates what we call the `cross-covariance' matrix (its inverse is the `cross-Hessian'). Using this matrix we are able to estimate the covariance between network predictions at each point in input space, using training data alone. Whilst this is a significant result in itself we have also applied the method to the problem of finding optimal linear combinations of models. This results in a `covariance-based' weighted committee, where the weights are input-dependent. If the individual networks are unbiased then the covariance-based weighted committee is optimal in the sense of minimum expected prediction error.
Dynamic logistic regression

We propose an online learning algorithm for training a logistic regression model on nonstationary classification problems. The nonstationarity is captured by modelling the weights in a logistic regression classifier as evolving according to a first order Markov process. The weights are updated using the extended Kalman filter formalism and nonstationarities are tracked by inferring a time-varying state noise variance parameter. We describe an algorithm for doing this based on maximising the evidence of updated predictions. The algorithm is illustrated on a number of synthetic problems.
Temporal and Spatial Complexity Measures for Eeg-based Brain- Computer Interfacing

There has been much interest recently in the concept of using information from the motor cortex region of the brain, recorded using non-invasive scalp electrodes, to construct a crude interface with a computer. It is known that movements of the limbs, for example, are accompanied by desynchro-nisations and synchronisations within the scalp-recorded electroencephalogram (EEG). These Event-Related Desynchronisations and Synchronisations (ERD & ERS), however, appear to be present when volition to move a limb occurs, even when actual movement of the limb does not in fact take place. The determination and classiication of the ERD/S ooers many exciting possibilities for the control of peripheral devices via computer analysis. To date most eeort has concentrated on the analysis of the changes in absolute frequency content of signals recorded from the motor cortex. We present results in this paper which tackle the issues of both the interpretation of changes in signals with time and across channels with simple methods which monitor the temporal and spatiaìcomplexity' of the data. Results are shown on synthetic and real data sets.
The use of advanced information processing methods in EEG analysis

In the field of medical signal and image processing the usual uncertainties in our knowledge are often compounded by a poor understanding of the physical mechanisms by which the data is generated and a subjective evaluation of the data by a human observer. In the case of the EEG, the effects of scalp, fluid and bone on the tiny electrical currents generated in the cortex may be modelled only poorly and the large size of scalp electrodes and the effects of muscle and instrument noise all contribute to the difficulty of EEG analysis. The belief that the EEG contains some objective information regarding brain state is tantalising. We do not discuss in detail the problems of noise and artifact removal, nor the problems of modelling the passage of the EEG from cortex to scalp, but instead concentrate on the EEG as is and suggest that, even if such problems are not solved, the EEG contains significant objective information regarding cortical functioning. We focus upon two main areas of information processing, namely information extraction from an EEG record (feature extraction and representation) and pattern recognition and inference.
Empirical Evaluation of Bayesian Sampling for Neural Classifiers

Adopting a Bayesian approach and sampling the network parameters from their posterior distribution is a rather novel and promising method for improving the generalisation performance of neural network predictors. The present empirical study applies this scheme to a set of different synthetic and real-world classification problems. The paper focuses on the dependence of the prediction results on the prior distribution of the network parameters and hyperparameters, and provides a critical evaluation of the automatic relevance determination (ARD) scheme for detecting irrelevant inputs.
Bayesian Approaches to Gaussian Mixture Modeling

A Bayesian-based methodology is presented which automatically penalizes overcomplex models being fitted to unknown data. We show that, with a Gaussian mixture model, the approach is able to select an "optimal" number of components in the model and so partition data sets. The performance of the Bayesian method is compared to other methods of optimal model selection and found to give good results. The methods are tested on synthetic and real data sets.
Regular ArticleNeural Network Modeling of the Level of Observation Decision in an Acute Psychiatric Ward

Patients in an acute psychiatric ward need to be observed with varying levels of closeness. We report a series of experiments in which neural networks were trained to model this “level of observation” decision. One hundred eighty-seven such clinical decisions were used to train and test the networks which were evaluated by a multitrialv-fold cross-validation procedure. One neural network modeling approach was to break down the decision process into four subproblems, each of which was solved by a perceptron unit. This resulted in a hierarchical perceptron network having a structure that was equivalent to a sparsely connected two-layer perceptron. Neural network approaches were compared with nearest neighbor, linear regression, and naive Bayes classifiers. The hierarchical and sparse neural networks were the most accurate classifiers. This shows that the decision process is nonlinear, that neural nets can be more accurate than other statistical approaches, and that hierarchical decomposition is a useful methodology for neural network design.
Neural network modeling of the level of observation decision in an acute psychiatric ward.

Patients in an acute psychiatric ward need to be observed with varying levels of closeness. We report a series of experiments in which neural networks were trained to model this "level of observation" decision. One hundred eighty-seven such clinical decisions were used to train and test the networks which were evaluated by a multitrial v-fold cross-validation procedure. One neural network modeling approach was to break down the decision process into four subproblems, each of which was solved by a perceptron unit. This resulted in a hierarchical perceptron network having a structure that was equivalent to a sparsely connected two-layer perceptron. Neural network approaches were compared with nearest neighbor, linear regression, and naive Bayes classifiers. The hierarchical and sparse neural networks were the most accurate classifiers. This shows that the decision process is nonlinear, that neural nets can be more accurate than other statistical approaches, and that hierarchical decomposition is a useful methodology for neural network design.
Human Brain Function

Principles and methods: Linking brain and behaviour, C. Frith Analyzing brain images - principles and overview, K. Friston Registering brain images to anatomy,  Friston Characterizing brain images with the general linear model, A. Holmes et al Making statistical inferences, J. Poline et al Characterizing distributed functional systems, K. Friston Characterizing functional integration, C. Buechel, K. Friston A taxonomy of study design, K. Friston et al.  Dynamism of a PET image - studies of visual function, S. Zeki Mapping somatosensory systems, E. Paulesu, R. Frackowiak Functional organization of the motor system, R. Passingham The cerebral basis of functional recovery, R. Frackowiak Functional anatomy of reading, C. Price Higher cognitive processes, C. Frith, R. Dolan Human memory systems, R. Dolan et al Measuring neuromodulation with functional imaging, R. Dolan et al Brain maps - linking the present with the future, J. Mazziotta et al Functional imaging with magnetic resonance, R. Turner et al.
Neural networks: friends or foes?

There has been enormous interest over the past decade in the use of artificial neural networks (ANNs) for data processing applications. By and large, this interest has been well‐founded. ANNs, however, offer no panacea to the data analyst. They are as prone to misuse as any other method and the details of their functioning are often clouded in mystique. This has made a firm understanding of their functioning difficult. Presents a brief introduction to the most widely applied class of ANN, the feed‐forward network. Gives an overview of its functioning for both classification and regression problems.
Novelty, confidence and errors in connectionist systems

Artificial neural networks (ANN) have become very popular for data analysis over the past decade. In particular, feedforward neural networks have, it may be argued, become so popular because they can, as classifiers, estimate a posteriori probabilities directly by forming a mapping function from the data space to a probability space and as regressors may correctly estimate the conditional average of target data on a given input. If, however, we are to exploit the undoubted utility of ANN in safety-critical environments then classification or regression performance in itself is not enough. One of the key requirements of any statistical analysis system should be to assess its own confidence in a decision in the case of classification, and estimate probable error bars in the case of regression. Part of the problem for any Bayesian classifier is the fact that the posteriors, by definition, sum to unity. This means that a classification is made into one of a closed set of classes. If rogue data appears then, even if it fails to conform to the statistics of genuine data, it will be classified with apparent confidence into one of the output classes. We must, therefore, monitor the confidence in any classification decision. This paper looks at some of the issues in estimating errors and confidence limits in feed-forward networks, and results are presented on a simple regression problem and an example from medical diagnostics.
Neural Networks in Clinical Medicine

Neural networks are parallel, distributed, adaptive information-processing systems that develop their functionality in response to exposure to information. This paper is a tutorial for researchers intending to use neural nets for medical decision-making applications. It includes detailed discussion of the issues particularly relevant to medical data as well as wider issues relevant to any neural net application. The article is restricted to back-propagation learning in multilayer perceptrons, as this is the neural net model most widely used in medical applications.  neural networks; medical decision making; pattern recognition; nonlinearity; error back-propagation; multi layer perceptron.
Novelty, confidence & errors in connectionist systems

Connectionist systems, or neural networks, have made a signiicant impact in the world of statistical pattern classiication, detection and prediction over the last few years. It is clear that the basic mechanisms involved with the method are sound. It is desirable, however, in all data analysis applications, to have estimates of the errors associated with the network's outputs. Whilst this is an established requirement for prediction and regression problems its eeects are subtle in the classiication case and are often ignored. Errors or uncertainty may be attributed to several causes within the network formalism; target noise and parameter uncertainty as well as, in certain cases, the variance of a committee of networks. We present in this paper a uniied, but simple, framework in which these errors may be evaluated. This includes assessment, even in the case of classiication, of the reduction in certainty in decisions made on`novel' inputs. This is of particular importance as if unseen data falls far outside the statistics of the training set then all input-output mappings are no longer valid and meaningless outputs are obtained.
A Generalized Kalman Filter for Fixed Point Approximation and Efficient Temporal-Difference Learning

The traditional Kalman filter can be viewed as a recursive stochastic algorithm that approximates an unknown function via a linear combination of prespecified basis functions given a sequence of noisy samples. In this paper, we generalize the algorithm to one that approximates the fixed point of an operator that is known to be a Euclidean norm contraction. Instead of noisy samples of the desired fixed point, the algorithm updates parameters based on noisy samples of functions generated by application of the operator, in the spirit of Robbins–Monro stochastic approximation. The algorithm is motivated by temporal-difference learning, and our developments lead to a possibly more efficient variant of temporal-difference learning. We establish convergence of the algorithm and explore efficiency gains through computational experiments involving optimal stopping and queueing problems.
Consensus Propagation

We propose consensus propagation, an asynchronous distributed protocol for averaging numbers across a network. We establish convergence, characterize the convergence rate for regular graphs, and demonstrate that the protocol exhibits better scaling properties than pairwise averaging, an alternative that has received much recent attention. Consensus propagation can be viewed as a special case of belief propagation, and our results contribute to the belief propagation literature. In particular, beyond singly-connected graphs, there are very few classes of relevant problems for which belief propagation is known to converge
Oblivious Equilibrium: A Mean Field Approximation for Large-Scale Dynamic Games

We propose a mean-field approximation that dramatically reduces the computational complexity of solving stochastic dynamic games. We provide conditions that guarantee our method approximates an equilibrium as the number of agents grow. We then derive a performance bound to assess how well the approximation performs for any given number of agents. We apply our method to an important class of problems in applied microeconomics. We show with numerical experiments that we are able to greatly expand the set of economic problems that can be analyzed computationally.
Detecting colluders in pagerank: finding slow mixing states in a markov chain

The PageRank algorithm evaluates webpage reputations based on the hyperlinks that connect them. Webpages that collude to boost their reputations significantly distort the resulting rankings. We introduce a measure for assessing the degree to which a set of webpages boosts its reputation. There is no known efficient algorithm that is guaranteed to detect significantly boosted sets when they exist. However, we provide metrics that, under reasonable conditions, are guaranteed to detect a member of a significantly boosted set, if one exists, and address various implementation issues that arise in incorporating these metrics into PageRank.
Markov Perfect Industry Dynamics with Many Firms

We propose an approximation method for analyzing Ericson and Pakes (1995)-style dynamic models of imperfect competition. We develop a simple algorithm for computing an ``oblivious equilibrium,'' in which each firm is assumed to make decisions based only on its own state and knowledge of the long run average industry state, but where firms ignore current information about competitors' states. We prove that, as the market becomes large, if the equilibrium distribution of firm states obeys a certain ``light-tail'' condition, then oblivious equilibria closely approximate Markov perfect equilibria. We develop bounds that can be computed to assess the accuracy of the approximation for any given applied problem. Through computational experiments, we find that the method often generates useful approximations for industries with hundreds of firms and in some cases even tens of firms
Transaction-Cost-Conscious Pairs Trading via Approximate Dynamic Programming Undergraduate

In this paper, we develop an algorithm that optimizes logarithmic utility in pairs trading. We assume price processes for two assets, with transaction cost linear with respect to the rate of change in portfolio weights. We then solve the optimization problem via a linear programming approach to approximate dynamic programming. Our simulation results show that when asset price volatility and transaction cost are sufficiently high, our ADP strategy offers significant benefits over the chosen baseline strategy. Our baseline strategy is an optimized version of a pairs trading heuristic studied in the literature .
Feature-Based Methods for Large Scale Dynamic Programming

We develop a methodological framework and present a few different ways in which dynamic programming and compact representations can be combined to solve large scale stochastic control problems. In particular, we develop algorithms that employ two types of feature-based compact representations; that is, representations that involve feature extraction and a relatively simple approximation architecture. We prove the convergence of these algorithms and provide bounds on the approximation error. As an example, one of these algorithms is used to generate a strategy for the game of Tetris. Furthermore, we provide a counter-example illustrating the difficulties of integrating compact representations with dynamic programming, which exemplifies the shortcomings of certain simple approaches.
TD(0) Leads to Better Policies than Approximate Value Iteration

We consider approximate value iteration with a parameterized approximator in which the state space is partitioned and the optimal cost-to-go function over each partition is approximated by a constant. We establish performance loss bounds for policies derived from approximations associated with fixed points. These bounds identify benefits to having projection weights equal to the invariant distribution of the resulting policy. Such projection weighting leads to the same fixed points as TD(0). Our analysis also leads to the first performance loss bound for approximate value iteration with an average cost objective.
A universal scheme for learning

We consider the problem of optimal control of a Kth order Markov process so as to minimize long-term average cost, a framework with many applications in communications and beyond. Specifically, we wish to do so without knowledge of either the transition kernel or even the order K. We develop and analyze two algorithms, based on the Lempel-Ziv scheme for data compression, that maintain probability estimates along variable length contexts. We establish that eventually, with probability 1, the optimal action is taken at each context. Further, in the case of the second algorithm, we establish almost sure asymptotic optimality
On Average Versus Discounted Reward Temporal-Difference Learning

We provide an analytical comparison between discounted and average reward temporal-difference (TD) learning with linearly parameterized approximations. We first consider the asymptotic behavior of the two algorithms. We show that as the discount factor approaches 1, the value function produced by discounted TD approaches the differential value function generated by average reward TD. We further argue that if the constant function—which is typically used as one of the basis functions in discounted TD—is appropriately scaled, the transient behaviors of the two algorithms are also similar. Our analysis suggests that the computational advantages of average reward TD that have been observed in some prior empirical work may have been caused by inappropriate basis function scaling rather than fundamental differences in problem formulations or algorithms.
Optimization for value function approximation

Many problems in control theory are theoretically addressable by dynamic programming. The theory of dynamic programming revolves around the notion of a value function—a function that evaluates the performance of the best possible controller for a given system. Dynamic programming gives methods for computing the value function and for finding an optimal controller once the value function is known. However, these methods break down for many real-world problems; due to their size and complexity, the computation of a value function becomes intractably difficult. As a result, there is growing interest in efficient methods for finding an approximation to the value function. Commonly the value function is approximated by a weighted summation of basis functions. The basis functions are chosen by the practitioner who relies on his intuition to generate a set of functions whose span is likely to contain a good approximation to the value function. The weights are then chosen using computational algorithms that incorporate elements of dynamic programming and statistical regression. 
In this thesis, two new algorithms are proposed for value function approximation. These new algorithms borrow from optimization theory to extend the state-of-the-art in value function approximation. The first algorithm uses elements of the Kalman filter to speed up the generation of the weights. The second algorithm uses elements of the simplex method to automate the selection of basis functions. The contributions of the thesis include a theoretical analysis (a convergence proof and error bounds) of the first algorithm, and computation experiments for both algorithms. The experiments consist of problems in queueing networks, including networks that can be used to model problems such as management of a call center for a business. In the experimental results, the first algorithm successfully accelerates the computation of weights, while the second algorithm successfully chooses basis functions to approximate the value function.