On Arousal and the Internal Regulation of Brain Function: Theory and Evidence across Modalities and Species

The brain is an organ. It is subject to the same physiological regulatory processes that engage the rest of the body’s organs, sculpted over hundreds of millions of years to sustain life so effectively. The central message of this thesis is that the holistic functioning of the brain, rather than operating at some level above or independent from these systemic regulatory processes, is deeply related to them. In short, as our limited attention spans might suggest: brain function is internally regulated. I propose that this internal regulation is a primary function of intrinsic brain activity. Chapter 2 provides a theoretical treatment of this issue, recasting intrinsic activity as an internal regulatory process operating on the brain’s temporal “states” and spatial “networks”. After establishing this framework, Chapters 3 and 4 provide tests of specific predictions. Thus, Chapter 3 confirms, in humans and macaque monkeys, the presence of topographically organized traveling waves occurring in synchrony with ongoing arousal fluctuations, with propagation occurring in parallel within the neocortex, striatum, thalamus, and cerebellum. This process is argued to provide a heretofore lacking physiological account of “resting-state functional connectivity” and related phenomenology. Chapter 4 extends this observation by demonstrating a continuous and tightly coordinated temporal evolution of brain, body, and behavioral states along a latent arousal cycle. Across multiple recording techniques and species, this cyclic trajectory is shown to be coupled to the traveling wave process described in Chapter 3, thus providing a parsimonious and integrative account of intrinsic brain activity and its spatiotemporal dynamics. Taken together, this thesis argues for the existence of an intrinsic regulatory process for global brain function

Transportation Sustainability in the Urban Context: A Comprehensive Review

Although the term “sustainability” did not gain traction until the 1980s, concerns about the consequences of transportation technology started long before. This paper reviews the literature on urban transportation sustainability using three frameworks. First, urban transportation can be unsustainable environmentally, economically, and socially (the three pillars of sustainability). Second, sustainable strategies tend to fall into two paradigms. Sustainable Transport Technology improves current patterns of modes and trips by consuming less resources and generating less waste. Sustainable Travel Behavior and Built Environment takes a more holistic approach that targets more sustainable travel choices, recognizing that changes in the built environment that currently constrains those choices are also essential. Third, the Planner’s Triangle helps explain commonly encountered situations where inherent tradeoffs can impede win-win-win strategies across environmental, economic, and social domains. The paper concludes with future research directions and concluding thoughts about urban transportation and sustainability

A Quantitative Framework for Assessing Vulnerability and Redundancy of Freight Transportation Networks

Freight transportation networks are an important component of everyday life in modern society. Disruption to these networks can make peoples’ daily lives extremely difficult as well as seriously cripple economic productivity. This dissertation develops a quantitative framework for assessing vulnerability and redundancy of freight transportation networks. The framework consists of three major contributions: (1) a two- stage approach for estimating a statewide truck origin-destination (O-D) trip table, (2) a decision support tool for assessing vulnerability of freight transportation networks, and (3) a quantitative approach for measuring redundancy of freight transportation networks.The dissertation first proposes a two-stage approach to estimate a statewide truck O-D trip table. The proposed approach is supported by two sequential stages: the first stage estimates a commodity-based truck O-D trip table using the commodity flows derived from the Freight Analysis Framework (FAF) database, and the second stage uses the path flow estimator (PFE) concept to refine the truck trip table obtained from the first stage using the truck counts from the statewide truck count program. The model allows great flexibility of incorporating data at different spatial levels for estimating the truck O- D trip table. The results from the second stage provide us a better understanding of truck flows on the statewide truck routes and corridors, and allow us to better manage the anticipated impacts caused by network disruptions.A decision support tool is developed to facilitate the decision making system through the application of its database management capabilities, graphical user interface, GIS-based visualization, and transportation network vulnerability analysis. The vulnerability assessment focuses on evaluating the statewide truck-freight bottlenecks/chokepoints. This dissertation proposes two quantitative measures: O-D connectivity (or detour route) in terms of distance and freight flow pattern change in terms of vehicle miles traveled (VMT). The case study adopts a “what-if” analysis approach by generating the disruption scenarios of the structurally deficient bridges in Utah due to earthquakes. In addition, the potential impacts of disruptions to multiple bridges in both rural and urban areas are evaluated and compared to the single bridge failure scenarios.This dissertation also proposes an approach to measure the redundancy of freight transportation networks based on two main dimensions: route diversity and network spare capacity. The route diversity dimension is used to evaluate the existence of multiple efficient routes available for users or the degree of connections between a specific O-D pair. The network spare capacity dimension is used to quantify the network- wide spare capacity with an explicit consideration of congestion effect. These two dimensions can complement each other by providing a two-dimensional characterization of freight transportation network redundancy. Case studies of the Utah statewide transportation network and coal multimodal network are conducted to demonstrate the features of the vulnerability and redundancy measures and the applicability of the quantitative assessment methodology

Investigating the performance of transport infrastructure using real-time data and a scalable multi-modal agent based model

The idea that including more information in more dynamic and iterative ways is central to the promise of the big data paradigm. The hope is that via new data sources, such as remote sensors and mobile phones, the reliance on heavily simplified generalised functions for model inputs will be erased. This trade between idealised and actual empirical data will be matched with dynamic models which consider complexity at a fundamental level, inherently mirroring the systems they are attempting to replicate. Cloud computing brings the possibility of doing all of this, in less time than the simplified macro models of the past, thus enabling better answers and at the time of critical decision making junctures. This research was task driven - the question of high speed rail versus aviation led to an investigation into the simplifications and assumptions that back up many of the commonly held beliefs on the sustainability of different modes of transport. The literature ultimately highlighted the need for context specific information; actual load factors, actual journey times considering traffic/engineering works and so on. Thus, rather than being explicitly an exercise in answering a specific question, a specific question was used to drive the development of a tool which may hold promise for answering a range of transportation related questions. The original contributions of this work are, firstly the use of real-time data sources to quantify temporally and spatially dynamic network performance metrics (eg. journey times on different transport models) and secondly to organise these data sources in a framework which can handle the volume and type of the data and organise the data in a way so that it is useful for the dynamic agent based modelling of future scenarios.EPSRC I Case Studentship with Ove Arup & Partner

Problems in Control, Estimation, and Learning in Complex Robotic Systems

In this dissertation, we consider a range of different problems in systems, control, and learning theory and practice. In Part I, we look at problems in control of complex networks. In Chapter 1, we consider the performance analysis of a class of linear noisy dynamical systems. In Chapter 2, we look at the optimal design problems for these networks. In Chapter 3, we consider dynamical networks where interactions between the networks occur randomly in time. And in the last chapter of this part, in Chapter 4, we look at dynamical networks wherein coupling between the subsystems (or agents) changes nonlinearly based on the difference between the state of the subsystems. In Part II, we consider estimation problems wherein we deal with a large body of variables (i.e., at large scale). This part starts with Chapter 5, in which we consider the problem of sampling from a dynamical network in space and time for initial state recovery. In Chapter 6, we consider a similar problem with the difference that the observations instead of point samples become continuous observations that happen in Lebesgue measurable observations. In Chapter 7, we consider an estimation problem in which the location of a robot during the navigation is estimated using the information of a large number of surrounding features and we would like to select the most informative features using an efficient algorithm. In Part III, we look at active perception problems, which are approached using reinforcement learning techniques. This part starts with Chapter 8, in which we tackle the problem of multi-agent reinforcement learning where the agents communicate and classify as a team. In Chapter 9, we consider a single agent version of the same problem, wherein a layered architecture replaces the architectures of the previous chapter. Then, we use reinforcement learning to design the meta-layer (to select goals), action-layer (to select local actions), and perception-layer (to conduct classification)

Seven Defining Features of Terahertz (THz) Wireless Systems: A Fellowship of Communication and Sensing

Wireless communication at the terahertz (THz) frequency bands (0.1-10THz) is viewed as one of the cornerstones of tomorrow's 6G wireless systems. Owing to the large amount of available bandwidth, THz frequencies can potentially provide wireless capacity performance gains and enable high-resolution sensing. However, operating a wireless system at the THz-band is limited by a highly uncertain channel. Effectively, these channel limitations lead to unreliable intermittent links as a result of a short communication range, and a high susceptibility to blockage and molecular absorption. Consequently, such impediments could disrupt the THz band's promise of high-rate communications and high-resolution sensing capabilities. In this context, this paper panoramically examines the steps needed to efficiently deploy and operate next-generation THz wireless systems that will synergistically support a fellowship of communication and sensing services. For this purpose, we first set the stage by describing the fundamentals of the THz frequency band. Based on these fundamentals, we characterize seven unique defining features of THz wireless systems: 1) Quasi-opticality of the band, 2) THz-tailored wireless architectures, 3) Synergy with lower frequency bands, 4) Joint sensing and communication systems, 5) PHY-layer procedures, 6) Spectrum access techniques, and 7) Real-time network optimization. These seven defining features allow us to shed light on how to re-engineer wireless systems as we know them today so as to make them ready to support THz bands. Furthermore, these features highlight how THz systems turn every communication challenge into a sensing opportunity. Ultimately, the goal of this article is to chart a forward-looking roadmap that exposes the necessary solutions and milestones for enabling THz frequencies to realize their potential as a game changer for next-generation wireless systems.Comment: 26 pages, 6 figure