Distributed Learning with Infinitely Many Hypotheses

We consider a distributed learning setup where a network of agents sequentially access realizations of a set of random variables with unknown distributions. The network objective is to find a parametrized distribution that best describes their joint observations in the sense of the Kullback-Leibler divergence. Apart from recent efforts in the literature, we analyze the case of countably many hypotheses and the case of a continuum of hypotheses. We provide non-asymptotic bounds for the concentration rate of the agents' beliefs around the correct hypothesis in terms of the number of agents, the network parameters, and the learning abilities of the agents. Additionally, we provide a novel motivation for a general set of distributed Non-Bayesian update rules as instances of the distributed stochastic mirror descent algorithm.Comment: Submitted to CDC201

Distributed Knowledge Discovery in Large Scale Peer-to-Peer Networks

Explosive growth in the availability of various kinds of data in distributed locations has resulted in unprecedented opportunity to develop distributed knowledge discovery (DKD) techniques. DKD embraces the growing trend of merging computation with communication by performing distributed data analysis and modeling with minimal communication of data. Most of the current state-of-the-art DKD systems suffer from the lack of scalability, robustness and adaptability due to their dependence on a centralized model for building the knowledge discovery model. Peer-to-Peer networks offer a better scalable and fault-tolerant computing platform for building distributed knowledge discovery models than client-server based platforms. Algorithms and communication protocols have been developed for file search and discovery services in peer-to-peer networks. The file search algorithms are concerned with identification of a peer and discovery of a file on that specified peer, so most of the current peer-to-peer networks for file search act as directory services. The problem of distributed knowledge discovery is different from file search services, however new issues and challenges have to be addressed. The algorithms and communication protocols for knowledge discovery deal with implementing algorithms by which every peer in the network discovers the correct knowledge discovery model, as if it were given the combined database. Therefore, algorithms and communication protocols for DKD mainly deal with distributed computing. The distributed computations are entirely asynchronous, impose very little communication overhead, transparently tolerate network topology changes and peer failures and quickly adjust to changes in the data as they occur. Another important aspect of the distributed computations in a peer-to-peer network is that most of the communication between peer nodes is local i.e. the knowledge discovery model is learned at each peer using information gathered from a very small neighborhood, whose size is independent of the size of the peer-to-peer network. The peer-to-peer constraints on data and/or computing are the hard ones, so the challenge is to show that it is still possible to extract useful information from the distributed data effectively and dependably. The implementation of a distributed algorithm in an asynchronous and decentralized environment is the hardest challenge. DKD in a peer-to-peer network raises issues related to impracticality of global communications and global synchronization, on-the-fly data updates, lack of control, accuracy of computation, the need to share resources with other applications, and frequent failure and recovery of resources. We propose a methodology based on novel distributed algorithms and communication protocols to perform DKD in a peer-to-peer network. We investigate the performance of our algorithms and communication protocols by means of analysis and simulations

Real-time sensor data development for smart truck drivetrains

Heavy articulated transport vehicles have a poor reputation associated with dramatic road accidents with frequent fatalities for those in automobiles. The result of this work is a formal data flow structure to enhance real-time decision-making in complex mechanical systems to increase performance capability and responsiveness to human commands. This structure recognizes the multiple layers of highly non-linear mechanical components (actuators, wheel tire & ground surfaces, controllers, power supplies, human/machine interfaces, etc.) that must operate in unison (i.e., reduce conflicts) in real-time (in milli-seconds) to enhance operator (driver) control to maximize human choice. This work contains a discussion on dependable sensor data is vital in complex systems that rely on a suite of sensors for both control as well as condition monitoring purposes as well as discussion on real-time energy distribution analysis in high momentum mechanical systems. The focus will be on tractor trucks of class 7 & 8 that are outfitted with an array of low-cost redundant sensors leveraging advances in intelligent robotic systems. This work details many topics including: Most relevant sensor types and their technologies, Designing, implementing, and maintaining a multi-sensor system using feasible industry standards, Sensor signal integrity and data flow processing for decision making, Asynchronous data flow methods for operating decision making schemes in real-time, Multiple applications to enhance tractor trucks systems with multi-sensor systems for real-time decision making.Mechanical Engineerin

Real-Time Prediction and Decision Making in Connected and Automated Vehicles Under Cyber-Security and Safety Uncertainties

Our current transportation system is on the brink of transforming into a highly connected,automated, and intelligent system as a result of the rapid emergence of connected andautomated vehicles (CAVs). CAVs, with various levels of automation, are expected toincrease overall road safety, reduce travel time, improve comfort, improve fuel efficiency, anddecrease fatal accidents in the near future. CAVs use a combination of cameras, ultrasonicsensors, and radar to build a digital map of their surroundings and operate the vehicleaccordingly. As a result, there are numerous sources of information that can be manipulated,with malicious or non-malicious intent, which may result in dangerous situations. Althoughthe ever-increasing use of CAV technologies in vehicles are expected to have numerousadvantages, they can give rise to new challenges in terms of safety, security, and privacy.As evident by recent crash records and experiments successfully conducting cyber attacks onvehicles, the currently available autonomous systems lack the ability to fully handle novel,complex situations. Hence, the potential drawbacks of CAVs are not negligible and shouldnot be ignored. In this study, we investigate the real-time prediction and decision makingin CAVs under cyber-security and safety uncertainties

Systemic risk: A survey

This paper develops a broad concept of systemic risk, the basic economic concept for the understanding of financial crises. It is claimed that any such concept must integrate systemic events in banking and financial markets as well as in the related payment and settlement systems. At the heart of systemic risk are contagion effects, various forms of external effects. The concept also includes simultaneous financial instabilities following aggregate shocks. The quantitative literature on systemic risk, which was evolving swiftly in the last couple of years, is surveyed in the light of this concept. Various rigorous models of bank and payment system contagion have now been developed, although a general theoretical paradigm is still missing. Direct econometric tests of bank contagion effects seem to be mainly limited to the United States. Empirical studies of systemic risk in foreign exchange and security settlement systems appear to be non-existent. Moreover, the literature surveyed reflects the general difficulty to develop empirical tests that can make a clear distinction between contagion in the proper sense and joint crises caused by common shocks, rational revisions of depositor or investor expectations when information is asymmetric ('information-based' contagion) and 'pure' contagion as well as between 'efficient' and 'inefficient' systemic events. JEL Classification: G21, G29, G12, E49banking crises, Contagion, currency crises, financial markets, financial stability, payment and settlement systems, systemic risk