A Neural Pattern Generator that Exhibits Arousal-Dependant Human Gait Transitions

A neural pattern generator based upon a non-linear cooperative-competitive feedback neural network is presented. It can generate the two standard human gaits: the walk and the run. A scalar arousal or GO signal causes a bifurcation from one gait to the next. Although these two gaits are qualitatively different, they both have the same limb order and may exhibit oscillation frequencies that overlap. The model simulates the walk and the run via qualitatively different waveform shapes. The fraction of cycle that activity is above threshold distinguishes the two gaits, much as the duty cycles of the feet are longer in the walk than in the run.

Quadruped Gait Transitions from a Neural Pattern Generator with Arousal Modulated Interactions

A four-channel neural pattern generator is described in which both the frequency and the relative phase of oscillations are controlled by a scalar arousal or GO signal. The generator is used to simulate quadruped gaits; in particular, rapid transitions are simulated in the order - walk, trot, pace, and gallop - that occurs in the cat. Precise switching control is achieved by using an arousal dependent modulation of the model's inhibitory interactions. This modulation generates a different functional connectivity in a single network at different arousal levels.

Fuzzy ART Choice Functions

Adaptive Resonance Theory (ART) models are real-time neural networks for category learning, pattern recognition, and prediction. Unsupervised fuzzy ART and supervised fuzzy ARTMAP networks synthesize fuzzy logic and ART by exploiting the formal similarity between tile computations of fuzzy subsethood and the dynamics of ART category choice, search, and learning. Fuzzy ART self-organizes stable recognition categories in response to arbitrary sequences of analog or binary input patterns. It generalizes the binary ART 1 model, replacing the set-theoretic intersection (∩) with the fuzzy intersection(∧), or component-wise minimum. A normalization procedure called complement coding leads to a symmetric theory in which the fuzzy intersection and the fuzzy union (∨), or component-wise maximum, play complementary roles. A geometric interpretation of fuzzy ART represents each category as a box that increases in size as weights decrease. This paper analyzes fuzzy ART models that employ various choice functions for category selection. One such function minimizes total weight change during learning. Benchmark simulations compare peformance of fuzzy ARTMAP systems that use different choice functions.

Hippocampal Modulation of Recognition, Conditioning, Timing, and Space: Why So Many Functions?

Advanced Research Projects Agency (ONR N00014-92-J-4015); National Science Foundation (IRI-90-24877); Office of Naval Research (N00014-91-J-1309)

A Neural Pattern Generator that Exhibits Frequency-Dependent In-Phase and Anti-Phase Oscillations

This article describes a. neural pattern generator based on a cooperative-competitive feedback neural network. The two-channel version of the generator supports both in-phase and anti-phase oscillations. A scalar arousal level controls both the oscillation phase and frequency. As arousal increases, oscillation frequency increases and bifurcations from in-phase to anti-phase, or anti-phase to in-phase oscillations can occur. Coupled versions of the model exhibit oscillatory patterns which correspond to the gaits used in locomotion and other oscillatory movements by various animals.

Hardware design of cryptographic accelerators

With the rapid growth of the Internet and digital communications, the volume of sensitive electronic transactions being transferred and stored over and on insecure media has increased dramatically in recent years. The growing demand for cryptographic systems to secure this data, across a multitude of platforms, ranging from large servers to small mobile devices and smart cards, has necessitated research into low cost, flexible and secure solutions. As constraints on architectures such as area, speed and power become key factors in choosing a cryptosystem, methods for speeding up the development and evaluation process are necessary. This thesis investigates flexible hardware architectures for the main components of a cryptographic system. Dedicated hardware accelerators can provide significant performance improvements when compared to implementations on general purpose processors. Each of the designs proposed are analysed in terms of speed, area, power, energy and efficiency. Field Programmable Gate Arrays (FPGAs) are chosen as the development platform due to their fast development time and reconfigurable nature. Firstly, a reconfigurable architecture for performing elliptic curve point scalar multiplication on an FPGA is presented. Elliptic curve cryptography is one such method to secure data, offering similar security levels to traditional systems, such as RSA, but with smaller key sizes, translating into lower memory and bandwidth requirements. The architecture is implemented using different underlying algorithms and coordinates for dedicated Double-and-Add algorithms, twisted Edwards algorithms and SPA secure algorithms, and its power consumption and energy on an FPGA measured. Hardware implementation results for these new algorithms are compared against their software counterparts and the best choices for minimum area-time and area-energy circuits are then identified and examined for larger key and field sizes. Secondly, implementation methods for another component of a cryptographic system, namely hash functions, developed in the recently concluded SHA-3 hash competition are presented. Various designs from the three rounds of the NIST run competition are implemented on FPGA along with an interface to allow fair comparison of the different hash functions when operating in a standardised and constrained environment. Different methods of implementation for the designs and their subsequent performance is examined in terms of throughput, area and energy costs using various constraint metrics. Comparing many different implementation methods and algorithms is nontrivial. Another aim of this thesis is the development of generic interfaces used both to reduce implementation and test time and also to enable fair baseline comparisons of different algorithms when operating in a standardised and constrained environment. Finally, a hardware-software co-design cryptographic architecture is presented. This architecture is capable of supporting multiple types of cryptographic algorithms and is described through an application for performing public key cryptography, namely the Elliptic Curve Digital Signature Algorithm (ECDSA). This architecture makes use of the elliptic curve architecture and the hash functions described previously. These components, along with a random number generator, provide hardware acceleration for a Microblaze based cryptographic system. The trade-off in terms of performance for flexibility is discussed using dedicated software, and hardware-software co-design implementations of the elliptic curve point scalar multiplication block. Results are then presented in terms of the overall cryptographic system.

Financial modelling with 2-EPT probability density functions

The class of all Exponential-Polynomial-Trigonometric (EPT) functions is classical and equal to the Euler-d’Alembert class of solutions of linear differential equations with constant coefficients. The class of non-negative EPT functions defined on [0;1) was discussed in Hanzon and Holland (2010) of which EPT probability density functions are an important subclass. EPT functions can be represented as ceAxb, where A is a square matrix, b a column vector and c a row vector where the triple (A; b; c) is the minimal realization of the EPT function. The minimal triple is only unique up to a basis transformation. Here the class of 2-EPT probability density functions on R is defined and shown to be closed under a variety of operations. The class is also generalised to include mixtures with the pointmass at zero. This class coincides with the class of probability density functions with rational characteristic functions. It is illustrated that the Variance Gamma density is a 2-EPT density under a parameter restriction. A discrete 2-EPT process is a process which has stochastically independent 2-EPT random variables as increments. It is shown that the distribution of the minimum and maximum of such a process is an EPT density mixed with a pointmass at zero. The Laplace Transform of these distributions correspond to the discrete time Wiener-Hopf factors of the discrete time 2-EPT process. A distribution of daily log-returns, observed over the period 1931-2011 from a prominent US index, is approximated with a 2-EPT density function. Without the non-negativity condition, it is illustrated how this problem is transformed into a discrete time rational approximation problem. The rational approximation software RARL2 is used to carry out this approximation. The non-negativity constraint is then imposed via a convex optimisation procedure after the unconstrained approximation. Sufficient and necessary conditions are derived to characterise infinitely divisible EPT and 2-EPT functions. Infinitely divisible 2-EPT density functions generate 2-EPT Lévy processes. An assets log returns can be modelled as a 2-EPT Lévy process. Closed form pricing formulae are then derived for European Options with specific times to maturity. Formulae for discretely monitored Lookback Options and 2-Period Bermudan Options are also provided. Certain Greeks, including Delta and Gamma, of these options are also computed analytically. MATLAB scripts are provided for calculations involving 2-EPT functions. Numerical option pricing examples illustrate the effectiveness of the 2-EPT approach to financial modelling.

Analysing energy detector diversity receivers for spectrum sensing

The analysis of energy detector systems is a well studied topic in the literature: numerous models have been derived describing the behaviour of single and multiple antenna architectures operating in a variety of radio environments. However, in many cases of interest, these models are not in a closed form and so their evaluation requires the use of numerical methods. In general, these are computationally expensive, which can cause difficulties in certain scenarios, such as in the optimisation of device parameters on low cost hardware. The problem becomes acute in situations where the signal to noise ratio is small and reliable detection is to be ensured or where the number of samples of the received signal is large. Furthermore, due to the analytic complexity of the models, further insight into the behaviour of various system parameters of interest is not readily apparent. In this thesis, an approximation based approach is taken towards the analysis of such systems. By focusing on the situations where exact analyses become complicated, and making a small number of astute simplifications to the underlying mathematical models, it is possible to derive novel, accurate and compact descriptions of system behaviour. Approximations are derived for the analysis of energy detectors with single and multiple antennae operating on additive white Gaussian noise (AWGN) and independent and identically distributed Rayleigh, Nakagami-m and Rice channels; in the multiple antenna case, approximations are derived for systems with maximal ratio combiner (MRC), equal gain combiner (EGC) and square law combiner (SLC) diversity. In each case, error bounds are derived describing the maximum error resulting from the use of the approximations. In addition, it is demonstrated that the derived approximations require fewer computations of simple functions than any of the exact models available in the literature. Consequently, the regions of applicability of the approximations directly complement the regions of applicability of the available exact models. Further novel approximations for other system parameters of interest, such as sample complexity, minimum detectable signal to noise ratio and diversity gain, are also derived. In the course of the analysis, a novel theorem describing the convergence of the chi square, noncentral chi square and gamma distributions towards the normal distribution is derived. The theorem describes a tight upper bound on the error resulting from the application of the central limit theorem to random variables of the aforementioned distributions and gives a much better description of the resulting error than existing Berry-Esseen type bounds. A second novel theorem, providing an upper bound on the maximum error resulting from the use of the central limit theorem to approximate the noncentral chi square distribution where the noncentrality parameter is a multiple of the number of degrees of freedom, is also derived.

Cryptographic coprocessors for embedded systems

In the field of embedded systems design, coprocessors play an important role as a component to increase performance. Many embedded systems are built around a small General Purpose Processor (GPP). If the GPP cannot meet the performance requirements for a certain operation, a coprocessor can be included in the design. The GPP can then offload the computationally intensive operation to the coprocessor; thus increasing the performance of the overall system. A common application of coprocessors is the acceleration of cryptographic algorithms. The work presented in this thesis discusses coprocessor architectures for various cryptographic algorithms that are found in many cryptographic protocols. Their performance is then analysed on a Field Programmable Gate Array (FPGA) platform. Firstly, the acceleration of Elliptic Curve Cryptography (ECC) algorithms is investigated through the use of instruction set extension of a GPP. The performance of these algorithms in a full hardware implementation is then investigated, and an architecture for the acceleration the ECC based digital signature algorithm is developed. Hash functions are also an important component of a cryptographic system. The FPGA implementation of recent hash function designs from the SHA-3 competition are discussed and a fair comparison methodology for hash functions presented. Many cryptographic protocols involve the generation of random data, for keys or nonces. This requires a True Random Number Generator (TRNG) to be present in the system. Various TRNG designs are discussed and a secure implementation, including post-processing and failure detection, is introduced. Finally, a coprocessor for the acceleration of operations at the protocol level will be discussed, where, a novel aspect of the design is the secure method in which private-key data is handled

A novel syndrome of severe neutrophil dysfunction: unresponsiveness confined to chemotaxin-induced functions.

We have identified a patient with a number of neutrophil dysfunctions. The patient was a female baby who lived for 8 months. During her life, she developed severe bacterial infections and showed omphalitis, impaired wound healing, and a pronounced leukocytosis. She was not a patient with leukocyte adhesion deficiency, because all leukocyte CD18 complex proteins were expressed at normal levels. Yet, neutrophil polarization and chemotaxis to platelet-activating factor, leukotriene B4, or formyl-methionyl-leucyl-phenylalanine (FMLP) were completely absent. We found a strong defect in actin polymerization in response to chemotactic stimuli, but only a retarded or even normal reaction with other stimuli. This indicates that the cellular dysfunctions were not due to an intrinsic defect in actin metabolism. Instead, the regulation of actin polymerization with chemotactic stimuli seemed to be defective. We concentrated on FMLP-induced responses in the patient's neutrophils. Functions dependent on activation of complement receptor type 3, such as aggregation or adherence to endothelial cells, were normally induced. Binding to serum-coated coverslips was normal in cell number; however, spreading was not observed. Exocytosis from the specific granules was readily induced. In contrast, FMLP failed to induce a respiratory burst activity or degranulation of the azurophil granules. FMLP induced a normal increase in free intracellular Ca2+, but a decreased formation of diglycerides (especially the 1-O-alkyl,2-acyl compounds). Thus, we have described a patient whose neutrophils show a severe defect in functional activation via chemotaxin receptors, resulting in a selective absence of NADPH oxidase activity, exocytosis from the azurophil granules, and actin polymerization. Our findings show that actin polymerization for neutrophil spreading and locomotion is regulated differently from that for phagocytosis. Also, the release of azurophil and specific granule contents is clearly shown to be regulated in a different way.

Distinct functions of POT1 at telomeres.

The mammalian protein POT1 binds to telomeric single-stranded DNA (ssDNA), protecting chromosome ends from being detected as sites of DNA damage. POT1 is composed of an N-terminal ssDNA-binding domain and a C-terminal protein interaction domain. With regard to the latter, POT1 heterodimerizes with the protein TPP1 to foster binding to telomeric ssDNA in vitro and binds the telomeric double-stranded-DNA-binding protein TRF2. We sought to determine which of these functions-ssDNA, TPP1, or TRF2 binding-was required to protect chromosome ends from being detected as DNA damage. Using separation-of-function POT1 mutants deficient in one of these three activities, we found that binding to TRF2 is dispensable for protecting telomeres but fosters robust loading of POT1 onto telomeric chromatin. Furthermore, we found that the telomeric ssDNA-binding activity and binding to TPP1 are required in cis for POT1 to protect telomeres. Mechanistically, binding of POT1 to telomeric ssDNA and association with TPP1 inhibit the localization of RPA, which can function as a DNA damage sensor, to telomeres.

B-lymphocyte effector functions in health and disease.

B-lymphocytes have traditionally been thought to contribute to immunity and autoimmune disease through terminal differentiation into plasma cells that secrete antibody. However, studies in mice and recent clinical studies have demonstrated that genetically altered B-cell function and B-cell-targeted therapies can significantly affect autoimmune diseases that were predominantly thought to be T-cell-mediated. B-cell depletion in mouse models of disease has also led to the identification of alternative B-cell effector functions that regulate normal immune responses and autoimmune disease. This review highlights multiple B-cell effector mechanisms, including the promotion of cellular immunity, the negative regulation of immune responses, and the production of pathogenic antibodies.

Desensitization, internalization, and signaling functions of beta-arrestins demonstrated by RNA interference.

Beta-arrestins bind to activated G protein-coupled receptor kinase-phosphorylated receptors, which leads to their desensitization with respect to G proteins, internalization via clathrin-coated pits, and signaling via a growing list of "scaffolded" pathways. To facilitate the discovery of novel adaptor and signaling roles of beta-arrestins, we have developed and validated a generally applicable interfering RNA approach for selectively suppressing beta-arrestins 1 or 2 expression by up to 95%. Beta-arrestin depletion in HEK293 cells leads to enhanced cAMP generation in response to beta(2)-adrenergic receptor stimulation, markedly reduced beta(2)-adrenergic receptor and angiotensin II receptor internalization and impaired activation of the MAP kinases ERK 1 and 2 by angiotensin II. This approach should allow discovery of novel signaling and regulatory roles for the beta-arrestins in many seven-membrane-spanning receptor systems.

Bayesian Gaussian Copula Factor Models for Mixed Data.

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables or through generalized latent trait models acommodating measurements in the exponential family. However, when generalizing to non-Gaussian measured variables the latent variables typically influence both the dependence structure and the form of the marginal distributions, complicating interpretation and introducing artifacts. To address this problem we propose a novel class of Bayesian Gaussian copula factor models which decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial computational gains. We provide new theoretical and empirical justifications for using this likelihood in Bayesian inference. We propose new default priors for the factor loadings and develop efficient parameter-expanded Gibbs sampling for posterior computation. The methods are evaluated through simulations and applied to a dataset in political science. The models in this paper are implemented in the R package bfa.

Task-driven adaptive statistical compressive sensing of gaussian mixture models

A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity. © 1991-2012 IEEE.