Novel arithmetic implementations using cellular neural network arrays.

The primary goal of this research is to explore the use of arrays of analog self-synchronized cells---the cellular neural network (CNN) paradigm---in the implementation of novel digital arithmetic architectures. In exploring this paradigm we also discover that the implementation of these CNN arrays produces very low system noise; that is, noise generated by the rapid switching of current through power supply die connections---so called di/dt noise. With the migration to sub 100 nanometer process technology, signal integrity is becoming a critical issue when integrating analog and digital components onto the same chip, and so the CNN architectural paradigm offers a potential solution to this problem. A typical example is the replacement of conventional digital circuitry adjacent to sensitive bio-sensors in a SoC Bio-Platform. The focus of this research is therefore to discover novel approaches to building low-noise digital arithmetic circuits using analog cellular neural networks, essentially implementing asynchronous digital logic but with the same circuit components as used in analog circuit design. We address our exploration by first improving upon previous research into CNN binary arithmetic arrays. The second phase of our research introduces a logical extension of the binary arithmetic method to implement binary signed-digit (BSD) arithmetic. To this end, a new class of CNNs that has three stable states is introduced, and is used to implement arithmetic circuits that use binary inputs and outputs but internally uses the BSD number representation. Finally, we develop CNN arrays for a 2-dimensional number representation (the Double-base Number System - DBNS). A novel adder architecture is described in detail, that performs the addition as well as reducing the representation for further processing; the design incorporates an innovative self-programmable array. Extensive simulations have shown that our new architectures can reduce system noise by almost 70dB and crosstalk by more than 23dB over standard digital implementations.Dept. of Electrical and Computer Engineering. Paper copy at Leddy Library: Theses & Major Papers - Basement, West Bldg. / Call Number: Thesis2005 .I27. Source: Dissertation Abstracts International, Volume: 66-11, Section: B, page: 6159. Thesis (Ph.D.)--University of Windsor (Canada), 2005

A non-homogeneous dynamic Bayesian network with sequentially coupled interaction parameters for applications in systems and synthetic biology

An important and challenging problem in systems biology is the inference of gene regulatory networks from short non-stationary time series of transcriptional profiles. A popular approach that has been widely applied to this end is based on dynamic Bayesian networks (DBNs), although traditional homogeneous DBNs fail to model the non-stationarity and time-varying nature of the gene regulatory processes. Various authors have therefore recently proposed combining DBNs with multiple changepoint processes to obtain time varying dynamic Bayesian networks (TV-DBNs). However, TV-DBNs are not without problems. Gene expression time series are typically short, which leaves the model over-flexible, leading to over-fitting or inflated inference uncertainty. In the present paper, we introduce a Bayesian regularization scheme that addresses this difficulty. Our approach is based on the rationale that changes in gene regulatory processes appear gradually during an organism's life cycle or in response to a changing environment, and we have integrated this notion in the prior distribution of the TV-DBN parameters. We have extensively tested our regularized TV-DBN model on synthetic data, in which we have simulated short non-homogeneous time series produced from a system subject to gradual change. We have then applied our method to real-world gene expression time series, measured during the life cycle of Drosophila melanogaster, under artificially generated constant light condition in Arabidopsis thaliana, and from a synthetically designed strain of Saccharomyces cerevisiae exposed to a changing environment

Dynamic Bayesian networks in molecular plant science: inferring gene regulatory networks from multiple gene expression time series

To understand the processes of growth and biomass production in plants, we ultimately need to elucidate the structure of the underlying regulatory networks at the molecular level. The advent of high-throughput postgenomic technologies has spurred substantial interest in reverse engineering these networks from data, and several techniques from machine learning and multivariate statistics have recently been proposed. The present article discusses the problem of inferring gene regulatory networks from gene expression time series, and we focus our exposition on the methodology of Bayesian networks. We describe dynamic Bayesian networks and explain their advantages over other statistical methods. We introduce a novel information sharing scheme, which allows us to infer gene regulatory networks from multiple sources of gene expression data more accurately. We illustrate and test this method on a set of synthetic data, using three different measures to quantify the network reconstruction accuracy. The main application of our method is related to the problem of circadian regulation in plants, where we aim to reconstruct the regulatory networks of nine circadian genes in Arabidopsis thaliana from four gene expression time series obtained under different experimental conditions

p-Adic Statistical Field Theory and Deep Belief Networks

In this work we initiate the study of the correspondence between p-adic statistical field theories (SFTs) and neural networks (NNs). In general quantum field theories over a p-adic spacetime can be formulated in a rigorous way. Nowadays these theories are considered just mathematical toy models for understanding the problems of the true theories. In this work we show these theories are deeply connected with the deep belief networks (DBNs). Hinton et al. constructed DBNs by stacking several restricted Boltzmann machines (RBMs). The purpose of this construction is to obtain a network with a hierarchical structure (a deep learning architecture). An RBM corresponds to a certain spin glass, we argue that a DBN should correspond to an ultrametric spin glass. A model of such a system can be easily constructed by using p-adic numbers. In our approach, a p-adic SFT corresponds to a p-adic continuous DBN, and a discretization of this theory corresponds to a p-adic discrete DBN. We show that these last machines are universal approximators. In the p-adic framework, the correspondence between SFTs and NNs is not fully developed. We point out several open problems.Comment: The introduction was fully revisited. The condition p>2 was remove

Detecting obstructive apnea episodes using dynamic bayesian networks and ECG-based time-series

In this study, we proposed an automatic detector for obstructive apnea episodes using only ECG-based time-series from a single-ECG channel. Several obstructive apnea episodes were provoked for different separated sequences of 15 minutes in anesthetized Sprague-Dawley rats. In this recurrent obstructive sleep apnea (OSA) model, each episode lasted 15s, while the number of total events per sequence was randomly selected. The beat-to-beat interval (RR) and the R-wave amplitude (Ra) time-series were extracted and processed for each sequence, and used to train Dynamic Bayesian Networks with different lags. An optimal trade-off between the lag (mathcal{L) and RMSE values was considered to select the best model to be used when detecting apnea episodes. The selected models were then used to estimate the occurrence probability of apnea episodes, p(At), by using a filtering approach. Finally, the time-series of the estimated probabilities were post-processed using non-overlapped 15-s epochs, to determine whether they are classified as apneic or non-apneic segments. Results showed that those lagged models with orders greater than 5, presented suitable RMSE values and become more sensitive as the order increased. A detection threshold of 0.2 seems to provide the best apnea detection performance overall, with Acc=0.81,Se=0.83 and Sp=0.79, using two ECG parameters and {L}=10. Clinical relevance - Dynamic Bayesian Networks represent a powerful tool to develop personalized models for apnea detection and diagnosis in OSA patients. © 2022 IEEE.Peer ReviewedPostprint (published version

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease.

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.This work was partly supported by the MICINN un der the projects TEC2012-34306 and PSI2015-65848- R, and the Consejer´ıa de Innovaci´on, Ciencia y Em presa (Junta de Andaluc´ıa, Spain) under the Ex cellence Projects P09-TIC-4530, P11-TIC-7103 and the Universidad de M´alaga. Programa de fortalec imiento de las capacidades de I+D+I en las Uni versidades 2014-2015, de la Consejer´ıa de Econom´ıa, Innovaci´on, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER) un der the project FC14-SAF30. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Ini tiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bio engineering, and through generous contributions from the following: AbbVie, Alzheimer’s Associa tion; Alzheimer’s Drug Discovery Foundation; Ara clon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; Eu roImmun; F. Hoffmann-La Roche Ltd and its affili ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.;Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity ; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Re search is providing funds to support ADNI clinical sites in Canada. Private sector contributions are fa cilitated by the Foundation for the National Insti tutes of Health (www.fnih.org). The grantee organi zation is the Northern California Institute for Re search and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

CANCER DETECTION FOR LOW GRADE SQUAMOUS ENTRAEPITHELIAL LESION

The National Cancer Institute estimates in 2012, about 577,190 Americans are expected to die of cancer, more than 1,500 people a day. Cancer is the second most common cause of death in the US, accounting for nearly 1 of every 4 deaths. Cancer diagnosis has a very important role in the early detection and treatment of cancer. Automating the cancer diagnosis process can play a very significant role in reducing the number of falsely identified or unidentified cases. The aim of this thesis is to demonstrate different machine learning approaches for cancer detection. Dr. Tawfik, pathologist from University of Kansas medical Center (KUMC) is an inventor of a novel pathology tissue slicer. The data used in this study comes from this slicer, which successfully allows semi-automated cancer diagnosis and it has the potential to improve patient care. In this study the slides are processed and visual features are computed and the dataset is made from scratch. After features extraction, different machine learning approaches are applied on the dataset which has shown its capability of extracting high-level representations from high-dimensional data. Support Vector Machine and Deep Belief Networks (DBN) are the concentration in this study. In the first section, Support vector machine is applied on the dataset. Next Deep Belief Network which is capable of extracting features in an unsupervised manner is implemented and with back-propagation the network is fine tuned. The results show that DBN can be effective when applied to cytological cancer diagnosis by increasing the accuracy in cancer detection. In the last section a subset of DBN features are selected and then appended with raw features and Support Vector Machine is trained and tested with that. It shows improvement over the first section results. In the end the study infers that Deep Belief Network can be successfully used over other leading classification methods for cancer detection