THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

FPGA BASED DESIGN FOR ACCELERATED FAULT-TESTING OF INTEGRATED CIRCUITS

In the past few decades, integrated circuits have become a major part of everyday life. Every circuit that is created needs to be tested for faults so faulty circuits are not sent to end-users. The creation of these tests is time consuming, costly and difficult to perform on larger circuits. This research presents a novel method for fault detection and test pattern reduction in integrated circuitry under test. By leveraging the FPGA's reconfigurability and parallel processing capabilities, a speed up in fault detection can be achieved over previous computer simulation techniques. This work presents the following contributions to the field of Stuck-At-Fault detection: We present a new method for inserting faults into a circuit net list. Given any circuit netlist, our tool can insert multiplexers into a circuit at correct internal nodes to aid in fault emulation on reconfigurable hardware. We present a parallel method of fault emulation. The benefit of the FPGA is not only its ability to implement any circuit, but its ability to process data in parallel. This research utilizes this to create a more efficient emulation method that implements numerous copies of the same circuit in the FPGA. A new method to organize the most efficient faults. Most methods for determinin the minimum number of inputs to cover the most faults require sophisticated softwareprograms that use heuristics. By utilizing hardware, this research is able to process data faster and use a simpler method for an efficient way of minimizing inputs.

Verbal instructions and top-down saccade control.

Few studies have addressed the interaction between instruction content and saccadic eye movement control. To assess the impact of instructions on top-down control, we instructed 20 healthy volunteers to deliberately delay saccade triggering, to make inaccurate saccades or to redirect saccades--i.e. to glimpse towards and then immediately opposite to the target. Regular pro- and antisaccade tasks were used for comparison. Bottom-up visual input remained unchanged and was a gap paradigm for all instructions. In the inaccuracy and delay tasks, both latencies and accuracies were detrimentally impaired by either type of instruction and the variability of latency and accuracy was increased. The intersaccadic interval (ISI) required to correct erroneous antisaccades was shorter than the ISI for instructed direction changes in the redirection task. The word-by-word instruction content interferes with top-down saccade control. Top-down control is a time consuming process, which may override bottom-up processing only during a limited time period. It is questionable whether parallel processing is possible in top-down control, since the long ISI for instructed direction changes suggests sequential planning.

U7 snRNP-specific Lsm11 protein: dual binding contacts with the 100 kDa zinc finger processing factor (ZFP100) and a ZFP100-independent function in histone RNA 3' end processing

The 3' cleavage generating non-polyadenylated animal histone mRNAs depends on the base pairing between U7 snRNA and a conserved histone pre-mRNA downstream element. This interaction is enhanced by a 100 kDa zinc finger protein (ZFP100) that forms a bridge between an RNA hairpin element upstream of the processing site and the U7 small nuclear ribonucleoprotein (snRNP). The N-terminus of Lsm11, a U7-specific Sm-like protein, was shown to be crucial for histone RNA processing and to bind ZFP100. By further analysing these two functions of Lsm11, we find that Lsm11 and ZFP100 can undergo two interactions, i.e. between the Lsm11 N-terminus and the zinc finger repeats of ZFP100, and between the N-terminus of ZFP100 and the Sm domain of Lsm11, respectively. Both interactions are not specific for the two proteins in vitro, but the second interaction is sufficient for a specific recognition of the U7 snRNP by ZFP100 in cell extracts. Furthermore, clustered point mutations in three phylogenetically conserved regions of the Lsm11 N-terminus impair or abolish histone RNA processing. As these mutations have no effect on the two interactions with ZFP100, these protein regions must play other roles in histone RNA processing, e.g. by contacting the pre-mRNA or additional processing factors.

A hybrid method for large-scale short-term scheduling of make-and-pack production processes

Due to the ongoing trend towards increased product variety, fast-moving consumer goods such as food and beverages, pharmaceuticals, and chemicals are typically manufactured through so-called make-and-pack processes. These processes consist of a make stage, a pack stage, and intermediate storage facilities that decouple these two stages. In operations scheduling, complex technological constraints must be considered, e.g., non-identical parallel processing units, sequence-dependent changeovers, batch splitting, no-wait restrictions, material transfer times, minimum storage times, and finite storage capacity. The short-term scheduling problem is to compute a production schedule such that a given demand for products is fulfilled, all technological constraints are met, and the production makespan is minimised. A production schedule typically comprises 500–1500 operations. Due to the problem size and complexity of the technological constraints, the performance of known mixed-integer linear programming (MILP) formulations and heuristic approaches is often insufficient. We present a hybrid method consisting of three phases. First, the set of operations is divided into several subsets. Second, these subsets are iteratively scheduled using a generic and flexible MILP formulation. Third, a novel critical path-based improvement procedure is applied to the resulting schedule. We develop several strategies for the integration of the MILP model into this heuristic framework. Using these strategies, high-quality feasible solutions to large-scale instances can be obtained within reasonable CPU times using standard optimisation software. We have applied the proposed hybrid method to a set of industrial problem instances and found that the method outperforms state-of-the-art methods.

A Parallel Solver for the Time-Periodic Navier–Stokes Equations

We investigate parallel algorithms for the solution of the Navier–Stokes equations in space-time. For periodic solutions, the discretized problem can be written as a large non-linear system of equations. This system of equations is solved by a Newton iteration. The Newton correction is computed using a preconditioned GMRES solver. The parallel performance of the algorithm is illustrated.

Parallel and Distributed Data Management. Introduction

The manipulation and handling of an ever increasing volume of data by current data-intensive applications require novel techniques for e?cient data management. Despite recent advances in every aspect of data management (storage, access, querying, analysis, mining), future applications are expected to scale to even higher degrees, not only in terms of volumes of data handled but also in terms of users and resources, often making use of multiple, pre-existing autonomous, distributed or heterogeneous resources.

A parallel implementation of 3D Zernike moment analysis

Zernike polynomials are a well known set of functions that find many applications in image or pattern characterization because they allow to construct shape descriptors that are invariant against translations, rotations or scale changes. The concepts behind them can be extended to higher dimension spaces, making them also fit to describe volumetric data. They have been less used than their properties might suggest due to their high computational cost. We present a parallel implementation of 3D Zernike moments analysis, written in C with CUDA extensions, which makes it practical to employ Zernike descriptors in interactive applications, yielding a performance of several frames per second in voxel datasets about 2003 in size. In our contribution, we describe the challenges of implementing 3D Zernike analysis in a general-purpose GPU. These include how to deal with numerical inaccuracies, due to the high precision demands of the algorithm, or how to deal with the high volume of input data so that it does not become a bottleneck for the system.

Flexible scheduling for non-deterministic, and-parallel execution of logic programs

Abstract is not available.

IDRA (IDeal Resource Allocation): Computing ideal speedups in parallel logic programming

We present a technique to estimate accurate speedups for parallel logic programs with relative independence from characteristics of a given implementation or underlying parallel hardware. The proposed technique is based on gathering accurate data describing one execution at run-time, which is fed to a simulator. Alternative schedulings are then simulated and estimates computed for the corresponding speedups. A tool implementing the aforementioned techniques is presented, and its predictions are compared to the performance of real systems, showing good correlation.

Memory performance of and-parallel prolog on shared-memory architectures

The goal of the RAP-WAM AND-parallel Prolog abstract architecture is to provide inference speeds significantly beyond those of sequential systems, while supporting Prolog semantics and preserving sequential performance and storage efficiency. This paper presents simulation results supporting these claims with special emphasis on memory performance on a two-level sharedmemory multiprocessor organization. Several solutions to the cache coherency problem are analyzed. It is shown that RAP-WAM offers good locality and storage efficiency and that it can effectively take advantage of broadcast caches. It is argued that speeds in excess of 2 ML IPS on real applications exhibiting medium parallelism can be attained with current technology.

B-LOG: A branch and bound methodology for the parallel execution of logic programs

We propose a computational methodology -"B-LOG"-, which offers the potential for an effective implementation of Logic Programming in a parallel computer. We also propose a weighting scheme to guide the search process through the graph and we apply the concepts of parallel "branch and bound" algorithms in order to perform a "best-first" search using an information theoretic bound. The concept of "session" is used to speed up the search process in a succession of similar queries. Within a session, we strongly modify the bounds in a local database, while bounds kept in a global database are weakly modified to provide a better initial condition for other sessions. We also propose an implementation scheme based on a database machine using "semantic paging", and the "B-LOG processor" based on a scoreboard driven controller.

Experimenting with independent and-parallel prolog using standard prolog

This paper presents an approximation to the study of parallel systems using sequential tools. The Independent And-parallelism in Prolog is an example of parallel processing paradigm in the framework of logic programming, and implementations like -Prolog uncover the potential performance of parallel processing. But this potential can also be explored using only sequential systems. Being the spirit of this paper to show how this can be done with a standard system, only standard Prolog will be used in the implementations included. Such implementations include tests for parallelism in And-Prolog, a correctnesschecking meta-interpreter of -Prolog and a simulator of parallel execution for -Prolog.

Experimenting with independent and-parallel prolog using standard prolog

This paper presents an approximation to the study of parallel systems using sequential tools. The Independent And-parallelism in Prolog is an example of parallel processing paradigm in the framework of logic programming, and implementations like -Prolog uncover the potential performance of parallel processing. But this potential can also be explored using only sequential systems. Being the spirit of this paper to show how this can be done with a standard system, only standard Prolog will be used in the implementations included. Such implementations include tests for parallelism in And-Prolog, a correctnesschecking meta-interpreter of -Prolog and a simulator of parallel execution for -Prolog.

Towards a unified sentiment lexicon based on graphics processing units

This paper presents an approach to create what we have called a Unified Sentiment Lexicon (USL). This approach aims at aligning, unifying, and expanding the set of sentiment lexicons which are available on the web in order to increase their robustness of coverage. One problem related to the task of the automatic unification of different scores of sentiment lexicons is that there are multiple lexical entries for which the classification of positive, negative, or neutral {P, Z, N} depends on the unit of measurement used in the annotation methodology of the source sentiment lexicon. Our USL approach computes the unified strength of polarity of each lexical entry based on the Pearson correlation coefficient which measures how correlated lexical entries are with a value between 1 and -1, where 1 indicates that the lexical entries are perfectly correlated, 0 indicates no correlation, and -1 means they are perfectly inversely correlated and so is the UnifiedMetrics procedure for CPU and GPU, respectively. Another problem is the high processing time required for computing all the lexical entries in the unification task. Thus, the USL approach computes a subset of lexical entries in each of the 1344 GPU cores and uses parallel processing in order to unify 155802 lexical entries. The results of the analysis conducted using the USL approach show that the USL has 95.430 lexical entries, out of which there are 35.201 considered to be positive, 22.029 negative, and 38.200 neutral. Finally, the runtime was 10 minutes for 95.430 lexical entries; this allows a reduction of the time computing for the UnifiedMetrics by 3 times.