ltaic (PV) electricity production from an intermittent Since 1978, compressed air energy storage (CAES) compressed air can then be released on demand to the CAES plant’s turbo-generator set to generate premium value electricity. The first CAES plant was built in broadened in the ittency of wind g with Cavallo,2 nomic feasibility as turbine (GT) oduction.3–8 The ate underground o the wind farms and shape the

Several parametric representations of the acoustic signal were compared as to word recognition performance in a syllable-oriented continuous speech recognition system. The vocabulary in-cluded many phonetically similar monosyllabic words, therefore the emphasis was on ability to retain phonetically significant acoustic information in the face of syntactic and duration variations. For each ~ arameter set (based on a mel-frequency cepstrum, a linear frequency cepstrum, a linear prediction cepstrum, a linear predic-tion spectrum, or a set of reflection coefficients), word templates were generated using an efficient dynamic method, and test data were time registered wi th the templates. A set of ten mel-frequency cepstrum coefficients computed every 6 " 4 ms resulted in the best performance, namely 96.. 5 % and 9500 % recognition with each of two speakers.. The superior performance of the mel-frequency cepstrum coefficients may be attributed to the fact that they better represent the perceptually relevant aspects of the short-term speech spectrum.

After [10, 15, 12, 2, 4] minimum cut/maximum flow algorithms on graphs emerged as an increasingly useful tool for exact or approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper

This book presents a unified treatment of many different kinds of planning algorithms. The subject lies at the crossroads between robotics, control theory, artificial intelligence, algorithms, and computer graphics. The particular subjects covered include motion planning, discrete planning, planning under uncertainty, sensor-based planning, visibility, decision-theoretic planning, game theory, information spaces, reinforcement learning, nonlinear systems, trajectory planning, nonholonomic planning, and kinodynamic planning.

We present a consumption-based model that explains a wide variety of dynamic asset pricing phenomena, including the procyclical variation of stock prices, the long-horizon predictability of excess stock returns, and the countercyclical variation of stock market volatility. The model captures much of the history of stock prices from consumption data. It explains the short- and long-run equity premium puzzles despite a low and constant risk-free rate. The results are essentially the same whether we model stocks as a claim to the consumption stream or as a claim to volatile dividends poorly correlated with consumption. The model is driven by an independently and identically distributed consumption growth process and adds a slow-moving external habit to the standard power utility function. These features generate slow countercyclical variation in risk premia. The model posits a fundamentally novel description of risk premia: Investors fear stocks primarily because they do poorly in recessions unrelated to the risks of long-run average consumption growth.

Composed of tens of thousands of tiny devices with very limited resources ("motes"), sensor networks are subject to novel systems problems and constraints. The large number of motes in a sensor network means that there will often be some failing nodes; networks must be easy to repopu-late. Often there is no feasible method to recharge motes, so energy is a precious resource. Once deployed, a network must be reprogrammable although physically unreachable, and this reprogramming can be a significant energy cost. We present Maté, a tiny communication-centric virtual machine designed for sensor networks. Mat~'s high-level in-terface allows complex programs to be very short (under 100 bytes), reducing the energy cost of transmitting new programs. Code is broken up into small capsules of 24 instructions, which can self-replicate through the network. Packet sending and reception capsules enable the deploy-ment of ad-hoc routing and data aggregation algorithms. Maté's concise, high-level program representation simplifies programming and allows large networks to be frequently re-programmed in an energy-efficient manner; in addition, its safe execution environment suggests a use of virtual ma-chines to provide the user/kernel boundary on motes that have no hardware protection mechanisms.

Suppose we are given a vector f in RN. How many linear measurements do we need to make about f to be able to recover f to within precision ɛ in the Euclidean (ℓ2) metric? Or more exactly, suppose we are interested in a class F of such objects— discrete digital signals, images, etc; how many linear measurements do we need to recover objects from this class to within accuracy ɛ? This paper shows that if the objects of interest are sparse or compressible in the sense that the reordered entries of a signal f ∈ F decay like a power-law (or if the coefficient sequence of f in a fixed basis decays like a power-law), then it is possible to reconstruct f to within very high accuracy from a small number of random measurements. typical result is as follows: we rearrange the entries of f (or its coefficients in a fixed basis) in decreasing order of magnitude |f | (1) ≥ |f | (2) ≥... ≥ |f | (N), and define the weak-ℓp ball as the class F of those elements whose entries obey the power decay law |f | (n) ≤ C · n −1/p. We take measurements 〈f, Xk〉, k = 1,..., K, where the Xk are N-dimensional Gaussian

This dissertation is a defense of the hypothesis that the noun phrase is headed by afunctional element (i.e., \non-lexical " category) D, identi ed with the determiner. In this way, the structure of the noun phrase parallels that of the sentence, which is headed by In (ection), under assumptions now standard within the Government-Binding (GB) framework. The central empirical problem addressed is the question of the proper analysis of the so-called \Poss-ing " gerund in English. This construction possesses simultaneously many properties of sentences, and many properties of noun phrases. The problem of capturing this dual aspect of the Possing construction is heightened by current restrictive views of X-bar theory, which, in particular, rule out the obvious structure for Poss-ing, [NP NP VPing], by virtue of its exocentricity. Consideration of languages in which nouns, even the most basic concrete nouns, show agreement (AGR) with their possessors, points to an analysis

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