Measurements, Models, Systems and Design

531 s.

Learning-Based Modeling of Weather and Climate Events Related To El Niño Phenomenon via Differentiable Programming and Empirical Decompositions

This dissertation is the accumulation of the application of adaptive, empirical learning-based methods in the study and characterization of the El Niño Southern Oscillation. In specific, it focuses on ENSO’s effects on rainfall and drought conditions in two major regions shown to be linked through the strength of the dependence of their climate on ENSO: 1) the southern Pacific Coast of the United States and 2) the Nile River Basin. In these cases, drought and rainfall are tied to deep economic and social factors within the region. The principal aim of this dissertation is to establish, with scientific rigor, an epistemological and foundational justification of adaptive learning models and their utility in the both the modeling and understanding of a wide-reaching climate phenomenon such as ENSO. This dissertation explores a scientific justification for their proven accuracy in prediction and utility as an aide in deriving a deeper understanding of climate phenomenon. In the application of drought forecasting for Southern California, adaptive learning methods were able to forecast the drought severity of the 2015-2016 winter with greater accuracy than established models. Expanding this analysis yields novel ways to analyze and understand the underlying processes driving California drought. The pursuit of adaptive learning as a guiding tool would also lead to the discovery of a significant extractable components of ENSO strength variation, which are used with in the analysis of Nile River Basin precipitation and flow of the Nile River, and in the prediction of Nile River yield to p=0.038. In this dissertation, the duality of modeling and understanding is explored, as well as a discussion on why adaptive learning methods are uniquely suited to the study of climate phenomenon like ENSO in the way that traditional methods lack. The main methods explored are 1) differentiable Programming, as a means of construction of novel self-learning models through which the meaningfulness of parameters arises from emergent phenomenon and 2) empirical decompositions, which are driven by an adaptive rather than rigid component extraction principle, are explored further as both a predictive tool and as a tool for gaining insight and the construction of models

Exploring Liquid Computing in a Hardware Adaptation : Construction and Operation of a Neural Network Experiment

Future increases in computing power strongly rely on miniaturization, large scale integration, and parallelization. Yet, approaching the nanometer realm poses new challenges in terms of device reliability, power dissipation, and connectivity - issues that have been of lesser concern in today's prevailing microprocessor implementations. It is therefore necessary to pursue the research on alternative computing architectures and strategies that can make use of large numbers of unreliable devices and only have a moderate power consumption. This thesis describes the construction of an experiment dedicated to exploring silicon adaptations of artificial neural network paradigms for their general applicability, power efficiency, and fault-tolerance. The presented setup comprises peripheral electronics, programmable logic, and software to accommodate a mixed-signal CMOS microchip implementing a flexible perceptron with 256 McCulloch-Pitts neurons. This neural network experiment is used to explore a recent strategy that allows to access the power of recurrent network topologies. While it has been conjectured that this liquid computing is suited for hardware implementations, this first time adaptation to a CMOS neural network affirms this claim. Not only feasibility but also tolerance to substrate variations and robustness to faults during operation are demonstrated

Cellular automata and dynamical systems

In this thesis we investigate the theoretical nature of the mathematical structures termed cellular automata. Chapter 1: Reviews the origin and history of cellular automata in order to place the current work into context. Chapter 2: Develops a cellular automata framework which contains the main aspects of cellular automata structure which have appeared in the literature. We present a scheme for specifying the cellular automata rules for this general model and present six examples of cellular automata within the model. Chapter 3: Here we develop a statistical mechanical model of cellular automata behaviour. We consider the relationship between variations within the model and their relationship to dynamical systems. We obtain results on the variance of the state changes, scaling of the cellular automata lattice, the equivalence of noise, spatial mixing of the lattice states and entropy, synchronous and asynchronous cellular automata and the equivalence of the rule probability and the time step of a discrete approximation to a dynamical system. Chapter 4: This contains an empirical comparison of cellular automata within our general framework and the statistical mechanical model. We obtain results on the transition from limit cycle to limit point behaviour as the rule probabilities are decreased. We also discuss failures of the statistical mechanical model due to failure of the assumptions behind it. Chapter 5: Here a practical application of the preceding work to population genetics is presented. We study this in the context of some established population models and show it may be most useful in the field of epidemiology. Further generalisations of the statistical mechanical and cellular automata models allow the modelling of more complex population models and mobile populations of organisms. Chapter 6: Reviews the results obtained in the context of the open questions introduced in Chapter 1. We also consider further questions this work raises and make some general comments on how these may apply to related fields

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Research-study of a self-organizing computer

It is shown that a self organizing system has two main components: an organizable physical part, and a programing part. This report presents the organizable part in the form of a programable hardware and its programing language

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

DNA Chemical Reaction Network Design Synthesis and Compilation

The advantages of biomolecular computing include 1) the ability to interface with, monitor, and intelligently protect and maintain the functionality of living systems, 2) the ability to create computational devices with minimal energy needs and hazardous waste production during manufacture and lifecycle, 3) the ability to store large amounts of information for extremely long time periods, and 4) the ability to create computation analogous to human brain function. To realize these advantages over electronics, biomolecular computing is at a watershed moment in its evolution. Computing with entire molecules presents different challenges and requirements than computing just with electric charge. These challenges have led to ad-hoc design and programming methods with high development costs and limited device performance. At the present time, device building entails complete low-level detail immersion. We address these shortcomings by creation of a systems engineering process for building and programming DNA-based computing devices. Contributions of this thesis include numeric abstractions for nucleic acid sequence and secondary structure, and a set of algorithms which employ these abstractions. The abstractions and algorithms have been implemented into three artifacts: DNADL, a design description language; Pyxis, a molecular compiler and design toolset; and KCA, a simulation of DNA kinetics using a cellular automaton discretization. Our methods are applicable to other DNA nanotechnology constructions and may serve in the development of a full DNA computing model