nils-herrmann/clean_scirepeval huggingface dataset:
An IDA algorithm for optimal spare allocation

A new algorithm for the Optimal Spare Allocation problem in reconfigurable arrays is presented. It is based on a previously published branch and bound search method . Enhanced with a strong heuristic function, it yields an effective A∗ search, which performs more efficiently in the iterative deepening implementation (IDA∗). A parallel implementation of the algorithm for a distributed shared memory machine using TreadMarks was used for evaluation. The results demonstrate the efficiency of the scheme and show how computationally intensive problems can be handled by appropriate domain heuristics and cooperative distributed computational power. 1 The Optimal Spare Allocation Problem The Optimal Spare Allocation (OSA) problem (or equivalently Vertex Cover in Bipartite Graphs)  is an NP-HARD problem arising in fault tolerant computing. It deals with the optimal allocation of spare rows and columns over a two-dimensional array of cells where some cells are faulty. The cells can be processors, memory units or VLSI components in practical applications. A number of spare rows and a number of spare columns are given, with two costs associated to spare rows and columns respectively. One spare row (column) can replace any row (column) in the array, repairing all the faulty cells along this row (column). The purpose is to repair all the faulty cells using spares, with the minimum possible overall cost. The sample instance [4] given below, consists of a (10×10) array with 12 faulty cells. There are 3 spare rows with a cost of 8 associated to each one of them and 3 spare columns with a cost of 15 associated to each one of them. An optimal solution with a cost of 39 would include the spare row allocations {4, 2, 8} and the spare column allocation {9}. ∗Currently with the Department of Computer Science, Duke University, Durham, NC 27708;  1 2 X X X 3 4 X X X X X 5 6 7 8 X X X 9 10 X
Near-Optimal Solutions for the Minimum Cost Spare Allocation Problem using Hopfield-T ype Neural Network Optimizers 1

Balancing apparatus for performing in-process balancing of a rotary mass by capacitor discharge to deliver an arc to erode material from the mass. A capacitor bank is operatively coupled through a coupling circuit to electrode structure which delivers the arc to the mass. A trigger pulse is injected into the coupling circuit to initiate the arc discharge. The coupling circuit is so constructed and arranged as not to impair the effectiveness of the trigger pulse in initiating the arc, yet it provides no significant resistance to the high amperage capacitor discharge current which is enabled to flow to the electrodes once the gap between the electrode structure and the rotary mass has been broken down. Several embodiments of the invention are disclosed. One embodiment comprises multiple capacitor banks which are sequentially placed on-line. Associated with each capacitor bank is a "contactless switch". The contactless switch comprises a pair of contacts, preferably graphite blocks, having confronting faces forming a gap between them. A branch circuit shunts this gap and is selectively operable to selectively allow and disallow the gap to be broken down by the same trigger pulse that initiates the arc and in this way selectively allow and disallow the capacitor from discharging. When a capacitor bank is off-line it is recharged.
Spatial Knowledge in Humans, Animals and Robots

Humans, animals and robots are physically existing agents situated in the real world. Their common ability to extract, store and use spatial information is crucial for their successful operation. On the other hand, their idiosyncracies seem to be reflected on their spatial knowledge. The paper attempts a survey around the term cognitive map, coined to describe exactly the body of spatial knowledge held by an agent. The topic is discussed at both a global and an individual level.
Mobile Robot Local Navigation with a Polar Neural Map

The application of (artificial) neural maps on mobile robot navigation is investigated. Neural maps are dynamic representations with computational capabilities, inspired by the cortical maps of the brain. The methodology is presented in detail, based on previous theoretical works, and simulation results demonstrate the applicability of the method on basic global (map-based) navigation problems. However, the real power of the method is revealed in local (sensory-based) navigation for a class of mobile robots equipped with range-finding sensors on their periphery. A complete local navigation scheme for a real Nomad 200 robot has been proposed, implemented and tested in real-world indoor environments. It is based on a topographic neural map and uses ultrasonic sensor (sonar) information. The map is polar and egocentric attached to the center of the robot and represents the robot’s environment in a local (spatial and temporal) sense. Paths are computed incrementally and a separate motion controller determines the control input (velocity level) to drive the robot, respecting its kinematic and dynamic constraints. The system is enhanced with a sensor short-term memory and temporal adaptation methods that compensate for potential uncertainties. The system was tested thoroughly and detailed analytical and experimental results are presented. The potential applications and usability of the system reveal how it can be used further for global navigation, exploration and cognitive mapping.
An Algorithm for Optimal Spare Allocation Parallel Implementation for Distributed Shared Memory Machine using TreadmarksTM

The Spare Allocation problem (or equivalently Vertex Cover in bipartite graphs) deals with the optimal allocation of spare rows and columns over a two-dimensional array of cells, some of which are faulty. The purpose is to repair all the faulty cells using spares with the minimum possible overall cost. In order to solve the problem optimally, a branch and bound algorithm is employed. Several heuristics for pruning the search tree and a heuristic that estimates the final cost from partial solutions are used to inform and guide the search procedure, leading to an effective A* search algorithm. However, memory limitations on one hand and the good enough quality of the heuristic function that low bounds the cost, on the other, force an Iterative Deepening implementation of the algorithm (IDA * ). Moving in the parallel implementation, many complications arise. A * is by its nature sequential and attempts to parallelize it, led to an amount of redundant search and/or early memory overflows. IDA * provides for parallelism, but work balancing issues should be addressed carefully. The final parallel IDA * search algorithm is scalable and leads to a significant amount of speedup, as demonstrated by the experimental results.
Neural Networks and Optimization Problems A Case Study: The Minimum Cost Spare Allocation Problem

We are interested in finding near-optimal solutions to hard optimization problems using Hopfield-type neural networks. The methodology is based on a basic property of such networks, that of reducing their ‘energy’ during evolution, leading to a local or global minimum. The methodology is presented and several different network models usually employed as optimizers (Analog Hopfield net with Simulated Annealing, Boltzmann Machine, Cauchy Machine and a hybrid scheme) are applied on the Minimum Cost Spare Allocation Problem (or equivalently Vertex Cover in bipartite graphs). The experimental results (compared with a conventional exact algorithm) demonstrate the advantages and limitations of the approach in terms of solution quality and computation time.
The 0 – 1 Knapsack Problem An Introductory Survey

The 0–1 Knapsack problem has been studied extensively during the past four decades. The reason is that it appears in many real domains with practical importance. Although its NP-completeness, many algorithms have been proposed that exhibit impressive behavior in the average case. This paper introduces the problem and presents a proof that it belongs to the NP-complete class, as well as a list of directly related problems. An overview of the previous research and the most known algorithms for solving it are presented. The paper concludes with a reference to the practical situations where the problem arises.
Planning and Intelligent Systems an Introductory Overview Planning and Intelligent Systems 1

Planning, as an inseparable component of intelligent behavior, became a research interest as early as AI was founded. The possibility of an automated planning system has been studied at different levels of complexity and over a wide area of application domains. Many methodologies have been proposed and several planning systems have been built, nevertheless there is a long way to go before we can speak of practical success. A planning system must meet the real world requirements. It must respond in real time and cope with the unpredictability and uncertainty which are inherent characteristics of the real environments. Planning as a component of Intelligent Systems and the corresponding research are the objectives of this report which is organized in three parts. The first part contains an introduction and the statement of the problem. The second part reviews the proposed techniques and methodologies, as well as the related problems and difficulties. Finally, in the last part four well-known planning systems are described: STRIPS, NOAH, SIPE, FORBIN. 1 This report serves as a partial fulfillment for the requirements of CMPS 619 course during Fall 1996 semester.
Extracting Dynamical Structure Embedded in Neural Activity

Spiking activity from neurophysiological experiments often exhibits dynamics beyond that driven by external stimulation, presumably reflecting the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, particularly during internally-driven cognitive operations. For example, the activity of premotor cortex (PMd) neurons during an instructed delay period separating movement-target specification and a movement-initiation cue is believed to be involved in motor planning. We show that the dynamics underlying this activity can be captured by a low-dimensional non-linear dynamical systems model, with underlying recurrent structure and stochastic point-process output. We present and validate latent variable methods that simultaneously estimate the system parameters and the trial-by-trial dynamical trajectories. These methods are applied to characterize the dynamics in PMd data recorded from a chronically-implanted 96-electrode array while monkeys perform delayed-reach tasks.
Extracting Dynamical Structure Embedded in Motor Preparatory Activity

At present, the best view of the activity of a neural circuit is provided by multiple-electrode extracellular recording technologies. These technologies are able to simultaneously measure spike trains from up to a few hundred cells in one or more brain areas during each trial. However, characterizing the dynamics of the circuit from such data is made difficult, both by the point-like nature of the spikes and by the variability in the responses to identical repeated trials. By their nature, spikes give us only an occasional view of the process from which they are generated.
Bootstrap-based statistical thresholding for MEG source reconstruction images

This work proposes a bootstrap-based statistical method for extracting target source activities from MEG/EEG source reconstruction results. The method requires measurements in a control condition, which contains only nontarget source activities. The method derives, at each pixel location, an empirical probability distribution of the nontarget source activity using bootstrapped reconstruction obtained from the control period. The statistical threshold that can extract the target source activities is derived based on the empirical distributions obtained from all pixel locations. Here, the multiple comparison problem is taken into account by using two step procedure: studentizing these empirical distributions and deriving an empirical distribution of the maximum pseudo T value at each pixel location. The results of numerical experiments are presented to demonstrate the method's effectiveness.
An extensible infrastructure for fully automated spike sorting during online experiments

When recording extracellular neural activity, it is often necessary to distinguish action potentials arising from distinct cells near the electrode tip, a process commonly referred to as "spike sorting." In a number of experiments, notably those that involve direct neuroprosthetic control of an effector, this cell-by-cell classification of the incoming signal must be achieved in real time. Several commercial offerings are available for this task, but all of these require some manual supervision per electrode, making each scheme cumbersome with large electrode counts. We present a new infrastructure that leverages existing unsupervised algorithms to sort and subsequently implement the resulting signal classification rules for each electrode using a commercially available Cerebus neural signal processor. We demonstrate an implementation of this infrastructure to classify signals from a cortical electrode array, using a probabilistic clustering algorithm (described elsewhere). The data were collected from a rhesus monkey performing a delayed center-out reach task. We used both sorted and unsorted (thresholded) action potentials from an array implanted in pre-motor cortex to "predict" the reach target, a common decoding operation in neuroprosthetic research. The use of sorted spikes led to an improvement in decoding accuracy of between 3.6 and 6.4%.
Local field potential activity varies with reach distance, direction, and speed in monkey pre-motor cortex.

Local field potentials (LFPs) in motor and pre−motor cortex show 'plan' activity: increased power in the 20−45 Hz frequency band after an instructive cue, but prior to a 'go' cue. Previous research has suggested that LFP plan activity is independent of the specifics of the upcoming movement and does not vary with movement parameters such as distance or direction. In contrast, research in upstream brain areas such as LIP reveals reliable direction tuning of LFP activity. To investigate LFP tuning, we recorded from 16 sites in pre−motor cortex (putatively caudal PMd) and M1 of a rhesus macaque. Recordings were made using 1−5 megaohm tungsten micro−electrodes, simultaneously with the single−unit recordings reported by Churchland and Shenoy in this volume. LFP activity was recorded for reaches to 5 distances in two directions, executed at two speeds. The 20−40 Hz frequency band was analyzed using the Thompson multitaper spectral analysis technique for plan activity. Upon performing an ANOVA (P < 0.05), the LFP plan activity of 10 sites showed a main effect of speed, 3 sites showed a main effect of direction, and 3 sites showed a main effect of distance. (A non−parametric Friedman test of the data resulted in a nearly identical set of sites showing significant effects for speed.) The ANOVA also revealed that a small number of sites exhibit an interaction effect between either two or all of the factors. Interestingly, there was no consistent preference across sites for one speed over the other. The size of the effect at each particular site was typically small and reached statistical significance due to the large number of trials collected (e.g., ~200 reaches per speed, if one collapses across direction and distance).
A Biologically Plausible Algorithm for Reinforcement-shaped Representational Learning

Significant plasticity in sensory cortical representations can be driven in mature animals either by behavioural tasks that pair sensory stimuli with reinforcement, or by electrophysiological experiments that pair sensory input with direct stimulation of neuromodulatory nuclei, but usually not by sensory stimuli presented alone. Biologically motivated theories of representational learning, however, have tended to focus on unsupervised mechanisms, which may play a significant role on evolutionary or developmental timescales, but which neglect this essential role of reinforcement in adult plasticity. By contrast, theoretical reinforcement learning has generally dealt with the acquisition of optimal policies for action in an uncertain world, rather than with the concurrent shaping of sensory representations. This paper develops a framework for representational learning which builds on the relative success of unsupervised generative-modelling accounts of cortical encodings to incorporate the effects of reinforcement in a biologically plausible way.
Reconstructing MEG Sources with Unknown Correlations

Existing source location and recovery algorithms used in magnetoen-cephalographic imaging generally assume that the source activity at different brain locations is independent or that the correlation structure is known. However, electrophysiological recordings of local field potentials show strong correlations in aggregate activity over significant distances. Indeed, it seems very likely that stimulus-evoked activity would follow strongly correlated time-courses in different brain areas. Here, we present, and validate through simulations, a new approach to source reconstruction in which the correlation between sources is modelled and estimated explicitly by variational Bayesian methods, facilitating accurate recovery of source locations and the time-courses of their activation.
Robust neural decoding of reaching movements for prosthetic systems

A new neural prosthetic decoder architecture is presented which uses a hidden Markov model of typical arm movements to assist the reconstruction of intended trajectories from an ensemble of neural signals. The use of such a model results in a decoder which is robust to fewer or smaller neural signals. With limited information, the average error of the reconstructed trajectories produced by the robust decoder is half of that produced by the standard linear filter approach.
Doubly Distributional Population Codes: Simultaneous Representation of Uncertainty and Multiplicity

Perceptual inference fundamentally involves uncertainty, arising from noise in sensation and the ill-posed nature of many perceptual problems. Accurate perception requires that this uncertainty be correctly represented, manipulated, and learned about. The choicessubjects makein various psychophysical experiments suggest that they do indeed take such uncertainty into account when making perceptual inferences, posing the question as to how uncertainty is represented in the activities of neuronal populations. Most theoretical investigations of population coding have ignored this issue altogether; the few existing proposals that address it do so in such a way that it is fatally conflated with another facet of perceptual problems that also needs correct handling: multiplicity (that is, the simultaneous presence of multiple distinct stimuli). We present and validate a more powerful proposal for the way that population activity may encode uncertainty, both distinctly from and simultaneously with multiplicity.
Spectrotemporal structure of receptive fields in areas AI and AAF of mouse auditory cortex.

The mouse is a promising model system for auditory cortex research because of the powerful genetic tools available for manipulating its neural circuitry. Previous studies have identified two tonotopic auditory areas in the mouse-primary auditory cortex (AI) and anterior auditory field (AAF)- but auditory receptive fields in these areas have not yet been described. To establish a foundation for investigating auditory cortical circuitry and plasticity in the mouse, we characterized receptive-field structure in AI and AAF of anesthetized mice using spectrally complex and temporally dynamic stimuli as well as simple tonal stimuli. Spectrotemporal receptive fields (STRFs) were derived from extracellularly recorded responses to complex stimuli, and frequency-intensity tuning curves were constructed from responses to simple tonal stimuli. Both analyses revealed temporal differences between AI and AAF responses: peak latencies and receptive-field durations for STRFs and first-spike latencies for responses to tone bursts were significantly longer in AI than in AAF. Spectral properties of AI and AAF receptive fields were more similar, although STRF bandwidths were slightly broader in AI than in AAF. Finally, in both AI and AAF, a substantial minority of STRFs were spectrotemporally inseparable. The spectrotemporal interaction typically appeared in the form of clearly disjoint excitatory and inhibitory subfields or an obvious spectrotemporal slant in the STRF. These data provide the first detailed description of auditory receptive fields in the mouse and suggest that although neurons in areas AI and AAF share many response characteristics, area AAF may be specialized for faster temporal processing.
Adaptation and Unsupervised Learning

Adaptation is a ubiquitous neural and psychological phenomenon, with a wealth of instantiations and implications. Although a basic form of plasticity, it has, bar some notable exceptions, attracted computational theory of only one main variety. In this paper, we study adaptation from the perspective of factor analysis, a paradigmatic technique of unsupervised learning. We use factor analysis to re-interpret a standard view of adaptation, and apply our new model to some recent data on adaptation in the domain of face discrimination.
How Linear are Auditory Cortical Responses?

By comparison to some other sensory cortices, the functional properties of cells in the primary auditory cortex are not yet well understood. Recent attempts to obtain a generalized description of auditory cortical responses have often relied upon characterization of the spectrotemporal receptive field (STRF), which amounts to a model of the stimulus-response function (SRF) that is linear in the spectrogram of the stimulus. How well can such a model account for neural responses at the very first stages of auditory cortical processing? To answer this question, we develop a novel methodology for evaluating the fraction of stimulus-related response power in a population that can be captured by a given type of SRF model. We use this technique to show that, in the thalamo-recipient layers of primary auditory cortex, STRF models account for no more than 40% of the stimulus-related power in neural responses.
Evidence Optimization Techniques for Estimating Stimulus-Response Functions

An essential step in understanding the function of sensory nervous systems is to characterize as accurately as possible the stimulus-response function (SRF) of the neurons that relay and process sensory information. One increasingly common experimental approach is to present a rapidly varying complex stimulus to the animal while recording the responses of one or more neurons, and then to directly estimate a functional transformation of the input that accounts for the neuronal firing. The estimation techniques usually employed, such as Wiener filtering or other correlation-based estimation of the Wiener or Volterra kernels, are equivalent to maximum likelihood estimation in a Gaussian-output-noise regression model. We explore the use of Bayesian evidence-optimization techniques to condition these estimates. We show that by learning hyper-parameters that control the smoothness and sparsity of the transfer function it is possible to improve dramatically the quality of SRF estimates, as measured by their success in predicting responses to novel input.