Annual Research Report, 2010-2011

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and government.https://digitalcommons.odu.edu/or_researchreports/1000/thumbnail.jp

Responsible machine learning: supporting privacy preservation and normative alignment with multi-agent simulation

This dissertation aims to advance responsible machine learning through multi-agent simulation (MAS). I introduce and demonstrate an open source, multi-domain discrete event simulation framework and use it to: (1) improve state-of-the-art privacy-preserving federated learning and (2) construct a novel method for normatively-aligned learning from synthetic negative examples. Due to their complexity and capacity, the training of modern machine learning (ML) models can require vast user-collected data sets. The current formulation of federated learning arose in 2016 after repeated exposure of sensitive user information from centralized data stores where mobile and wearable training data was aggregated. Privacy-preserving federated learning (PPFL) soon added stochastic and cryptographic layers to protect against additional vectors of data exposure. Recent state of the art protocols have combined differential privacy (DP) and secure multiparty computation (MPC) to keep client training data set parameters private from an ``honest but curious'' server which is legitimately involved in the learning process, but attempting to infer information it should not have. Investigation of PPFL can be cost prohibitive if each iteration of a proposed experimental protocol is distributed to virtual computational nodes geolocated around the world. It can also be inaccurate when locally simulated without concern for client parallelism, accurate timekeeping, or computation and communication loads. In this work, a recent PPFL protocol is instantiated as a single-threaded MAS to show that its model accuracy, deployed parallel running time, and resistance to inference of client model parameters can be inexpensively evaluated. The protocol is then extended using oblivious distributed differential privacy to a new state of the art secure against attacks of collusion among all except one participant, with an empirical demonstration that the new protocol improves privacy with no loss of accuracy to the final model. State of the art reinforcement learning (RL) is also increasingly complex and hard to interpret, such that a sequence of individually innocuous actions may produce an unexpectedly harmful result. Safe RL seeks to avoid these results through techniques like reward variance reduction, error state prediction, or constrained exploration of the state-action space. Development of the field has been heavily influenced by robotics and finance, and thus it is primarily concerned with physical failures like a helicopter crash or a robot-human workplace collision, or monetary failures like the depletion of an investment account. The related field of Normative RL is concerned with obeying the behavioral expectations of a broad human population, like respecting personal space or not sneaking up behind people. Because normative behavior often implicates safety, for example the assumption that an autonomous navigation robot will not walk through a human to reach its goal more quickly, there is significant overlap between the two areas. There are problem domains not easily addressed by current approaches in safe or normative RL, where the undesired behavior is subtle, violates legal or ethical rather than physical or monetary constraints, and may be composed of individually-normative actions. In this work, I consider an intelligent stock trading agent that maximizes profit but may inadvertently learn ``spoofing'', a form of illegal market manipulation that can be difficult to detect. Using a financial market based on MAS, I safely coerce a variety of spoofing behaviors, learn to distinguish them from other profit-driven strategies, and carefully analyze the empirical results. I then demonstrate how this spoofing recognizer can be used as a normative guide to train an intelligent trading agent that will generate positive returns while avoiding spoofing behaviors, even if their adoption would increase short-term profits. I believe this contribution to normative RL, of deriving an method for normative alignment from synthetic non-normative action sequences, should generalize to many other problem domains.Ph.D

A pure-jump market-making model for high-frequency trading

We propose a new market-making model which incorporates a number of realistic features relevant for high-frequency trading. In particular, we model the dependency structure of prices and order arrivals with novel self- and cross-exciting point processes. Furthermore, instead of assuming the bid and ask prices can be adjusted continuously by the market maker, we formulate the market maker\u27s decisions as an optimal switching problem. Moreover, the risk of overtrading has been taken into consideration by allowing each order to have different size, and the market maker can make use of market orders, which are treated as impulse control, to get rid of excessive inventory. Because of the stochastic intensities of the cross-exciting point processes, the optimality condition cannot be formulated using classical Hamilton-Jacobi-Bellman quasi-variational inequality (HJBQVI), so we extend the framework of constrained forward backward stochastic differential equation (CFBSDE) to solve our optimal control problem

Annual Research Report, 2009-2010

Annual report of collaborative research projects of Old Dominion University faculty and students in partnership with business, industry and governmenthttps://digitalcommons.odu.edu/or_researchreports/1001/thumbnail.jp

Campus Telecommunications Systems: Managing Change

The purpose of this book is to provide a broadbased understanding of the rapidly changing environment of campus telecommunications. The anticipated audience for this material is the non-technical university administrator who may not have direct responsibility for telecommunications, but has a need to understand the general environment in which his telecommunications manager functions and the basic concepts of the technology. Five topic areas were selected that best cover the preponderance of issues. No attempt has been made to associate or closely coordinate materials from one chapter\u27s subject to that of any other. Each chapter generally stands alone. In total, however, the five chapters address the topics and issues that most often generate inquiries from university administrators outside the telecommunications department. Introduction 1 The Changing Telecommunications Environment 2 Telecommunications Technology and the Campus 3 Student Services 4 Financing a New Telecommunications System . 5 Selecting a Consultant Glossary Inde

Feasibility study for a numerical aerodynamic simulation facility. Volume 1

A Numerical Aerodynamic Simulation Facility (NASF) was designed for the simulation of fluid flow around three-dimensional bodies, both in wind tunnel environments and in free space. The application of numerical simulation to this field of endeavor promised to yield economies in aerodynamic and aircraft body designs. A model for a NASF/FMP (Flow Model Processor) ensemble using a possible approach to meeting NASF goals is presented. The computer hardware and software are presented, along with the entire design and performance analysis and evaluation

Future Computer Requirements for Computational Aerodynamics

Recent advances in computational aerodynamics are discussed as well as motivations for and potential benefits of a National Aerodynamic Simulation Facility having the capability to solve fluid dynamic equations at speeds two to three orders of magnitude faster than presently possible with general computers. Two contracted efforts to define processor architectures for such a facility are summarized

Sixth Goddard Conference on Mass Storage Systems and Technologies Held in Cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems

This document contains copies of those technical papers received in time for publication prior to the Sixth Goddard Conference on Mass Storage Systems and Technologies which is being held in cooperation with the Fifteenth IEEE Symposium on Mass Storage Systems at the University of Maryland-University College Inn and Conference Center March 23-26, 1998. As one of an ongoing series, this Conference continues to provide a forum for discussion of issues relevant to the management of large volumes of data. The Conference encourages all interested organizations to discuss long term mass storage requirements and experiences in fielding solutions. Emphasis is on current and future practical solutions addressing issues in data management, storage systems and media, data acquisition, long term retention of data, and data distribution. This year's discussion topics include architecture, tape optimization, new technology, performance, standards, site reports, vendor solutions. Tutorials will be available on shared file systems, file system backups, data mining, and the dynamics of obsolescence

Unconventional computing platforms and nature-inspired methods for solving hard optimisation problems

The search for novel hardware beyond the traditional von Neumann architecture has given rise to a modern area of unconventional computing requiring the efforts of mathematicians, physicists and engineers. Many analogue physical systems, including networks of nonlinear oscillators, lasers, condensates, and superconducting qubits, are proposed and realised to address challenging computational problems from various areas of social and physical sciences and technology. Understanding the underlying physical process by which the system finds the solutions to such problems often leads to new optimisation algorithms. This thesis focuses on studying gain-dissipative systems and nature-inspired algorithms that form a hybrid architecture that may soon rival classical hardware. Chapter 1 lays the necessary foundation and explains various interdisciplinary terms that are used throughout the dissertation. In particular, connections between the optimisation problems and spin Hamiltonians are established, their computational complexity classes are explained, and the most prominent physical platforms for spin Hamiltonian implementation are reviewed. Chapter 2 demonstrates a large variety of behaviours encapsulated in networks of polariton condensates, which are a vivid example of a gain-dissipative system we use throughout the thesis. We explain how the variations of experimentally tunable parameters allow the networks of polariton condensates to represent different oscillator models. We derive analytic expressions for the interactions between two spatially separated polariton condensates and show various synchronisation regimes for periodic chains of condensates. An odd number of condensates at the vertices of a regular polygon leads to a spontaneous formation of a giant multiply-quantised vortex at the centre of a polygon. Numerical simulations of all studied configurations of polariton condensates are performed with a mean-field approach with some theoretically proposed physical phenomena supported by the relevant experiments. Chapter 3 examines the potential of polariton graphs to find the low-energy minima of the spin Hamiltonians. By associating a spin with a condensate phase, the minima of the XY model are achieved for simple configurations of spatially-interacting polariton condensates. We argue that such implementation of gain-dissipative simulators limits their applicability to the classes of easily solvable problems since the parameters of a particular Hamiltonian depend on the node occupancies that are not known a priori. To overcome this difficulty, we propose to adjust pumping intensities and coupling strengths dynamically. We further theoretically suggest how the discrete Ising and $n$-state planar Potts models with or without external fields can be simulated using gain-dissipative platforms. The underlying operational principle originates from a combination of resonant and non-resonant pumping. Spatial anisotropy of pump and dissipation profiles enables an effective control of the sign and intensity of the coupling strength between any two neighbouring sites, which we demonstrate with a two dimensional square lattice of polariton condensates. For an accurate minimisation of discrete and continuous spin Hamiltonians, we propose a fully controllable polaritonic XY-Ising machine based on a network of geometrically isolated polariton condensates. In Chapter 4, we look at classical computing rivals and study nature-inspired methods for optimising spin Hamiltonians. Based on the operational principles of gain-dissipative machines, we develop a novel class of gain-dissipative algorithms for the optimisation of discrete and continuous problems and show its performance in comparison with traditional optimisation techniques. Besides looking at traditional heuristic methods for Ising minimisation, such as the Hopfield-Tank neural networks and parallel tempering, we consider a recent physics-inspired algorithm, namely chaotic amplitude control, and exact commercial solver, Gurobi. For a proper evaluation of physical simulators, we further discuss the importance of detecting easy instances of hard combinatorial optimisation problems. The Ising model for certain interaction matrices, that are commonly used for evaluating the performance of unconventional computing machines and assumed to be exponentially hard, is shown to be solvable in polynomial time including the Mobius ladder graphs and Mattis spin glasses. In Chapter 5 we discuss possible future applications of unconventional computing platforms including emulation of search algorithms such as PageRank, realisation of a proof-of-work protocol for blockchain technology, and reservoir computing