Fuels for Future Electric Power

OVER THE NEXT FORTY YEARS, THE U.S. WILL EXPERIENCE PROBLEMS BECAUSE OF DWINDLING SUPPLIES OF FOSSIL FUELS AND AN INCREASING DEPENDENCE ON FOREIGN OIL. SEVERAL ALTERNATIVES ARE AVAILABLE, SUCH AS MORE STRINGENT CONSERVATION MEASURES OR ALTERNATIVE SOURCES OF ENERGY. HOWEVER, NO SINGLE ALTERNATIVE WILL BE SUFFICIENT. A STUDY WAS CONDUCTED TO DETERMINE THE MOST EFFICIENT ALLOCATION POSSIBLE OF RESOURCES. THE ANALYSIS WAS CONDUCTED ON THE BASIS OF ASSUMED HAPPENINGS IN THE FUTURE RATHER THAN BY PROJECTING HISTORIC TRENDS INTO THE FUTURE. FOR EXAMPLE, AS ONE SOURCE OF ENERGY SUCH AS OIL BECOMES MORE SCARCE, THE COST WILL GO UP, INDUCING A CHANGE TO ANOTHER SOURCE. SYNTHETIC FUELS FROM COAL AND HYDROGEN FROM ELECTROLYSIS WILL BECOME MORE PRACTICAL BY THE END OF THE CENTURY. COAL AND OIL WILL BE USED. HEAVILY THIS CENTURY WITH NUCLEAR FUEL BECOMING MORE EFFICIENT EARLY IN THE NEXT CENTURY. CHART

On Lyapunov-Krasovskii Functionals for Switched Nonlinear Systems with Delay

We present a set of results concerning the existence of Lyapunov-Krasovskii functionals for classes of nonlinear switched systems with time-delay. In particular, we first present a result for positive systems that relaxes conditions recently described in \cite{SunWang} for the existence of L-K functionals. We also provide related conditions for positive coupled differential-difference positive systems and for systems of neutral type that are not necessarily positive. Finally, corresponding results for discrete-time systems are described.Comment: 19 Page

Training an adaptive dialogue policy for interactive learning of visually grounded word meanings

We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic parsing/generation framework - Dynamic Syntax and Type Theory with Records (DS-TTR) - with a set of visual classifiers that are learned throughout the interaction and which ground the meaning representations that it produces. We use this system in interaction with a simulated human tutor to study the effects of different dialogue policies and capabilities on the accuracy of learned meanings, learning rates, and efforts/costs to the tutor. We show that the overall performance of the learning agent is affected by (1) who takes initiative in the dialogues; (2) the ability to express/use their confidence level about visual attributes; and (3) the ability to process elliptical and incrementally constructed dialogue turns. Ultimately, we train an adaptive dialogue policy which optimises the trade-off between classifier accuracy and tutoring costs.Comment: 11 pages, SIGDIAL 2016 Conferenc

A natural derivative on [0,n] and a binomial Poincar\'e inequality

We consider probability measures supported on a finite discrete interval $[0,n]$. We introduce a new finitedifference operator $\nabla_n$, defined as a linear combination of left and right finite differences. We show that this operator $\nabla_n$ plays a key role in a new Poincar\'e (spectral gap) inequality with respect to binomial weights, with the orthogonal Krawtchouk polynomials acting as eigenfunctions of the relevant operator. We briefly discuss the relationship of this operator to the problem of optimal transport of probability measures

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc

The Coming Boom in Computer Loads

Computers and other electronic equipment now consume as much electricity as electric steel furnaces, and their growth shows no signs of slowing. Utilities are active participants in the computer revolution. Northeast Utilities, for example, reports that 20% of electricity use in a typical new office building in its service area goes to computers. Given the expected growth in computers and computer loads, this technology deserves greater attention from utility planners and other energy analysts. It is shown that the commercial sector has been the largest contributor to kilowatt-hour (kwh) sales growth and that new uses within the commercial sector have accounted for the biggest portion of this growth. Confirming this conclusion are a 4-year Department of Energy-funded study of the Park Plaza Building office tower and a 1985 study of 181 office buildings by Northwest Utilities. A prospective study suggests that computers could account for as much as 150 billion kwh by the early 1990s

Economic Resilience of German Lignite Regions in Transition

This paper recalls the development of the German lignite regions Rhineland and Lusatia since 1945 to allow for a better understanding of their situation in 2019. We analyze their economic resilience, defined as adaptive capacity, using Holling’s adaptive cycle model. We find that the Rhineland is currently in the conservation phase, while Lusatia experiences a reorganization phase following the economic shock of the German reunification. Key policy recommendations for the upcoming coal phase-out are to foster innovation within the Rhineland’s infrastructures to avoid overconnection, and to expand digital and transportation infrastructure in Lusatia so that the structurally weak region can enter the exploitation phase. Future policymaking should take into consideration the differences between the two regions in order to enable a just and timely transition during which lasting adaptive capacity can be built.BMBF, 01LN1704A, Nachwuchsgruppe Globaler Wandel: CoalExit - Die Ökonomie des Kohleausstiegs - Identifikation von Bausteinen für Rahmenpläne zukünftiger regionaler StrukturwandelDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17

Concavity of entropy under thinning

Building on the recent work of Johnson (2007) and Yu (2008), we prove that entropy is a concave function with respect to the thinning operation T_a. That is, if X and Y are independent random variables on Z_+ with ultra-log-concave probability mass functions, then H(T_a X+T_{1-a} Y)>= a H(X)+(1-a)H(Y), 0 <= a <= 1, where H denotes the discrete entropy. This is a discrete analogue of the inequality (h denotes the differential entropy) h(sqrt(a) X + sqrt{1-a} Y)>= a h(X)+(1-a) h(Y), 0 <= a <= 1, which holds for continuous X and Y with finite variances and is equivalent to Shannon's entropy power inequality. As a consequence we establish a special case of a conjecture of Shepp and Olkin (1981).Comment: To be presented at ISIT0