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1 The Limit of the Boltzmann Equation to the Euler Equations for Riemann Problems

by Feimin Huang, Yi Wang, Yong Wang, Tong Yang
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Source–Channel Coding and Network Communication

by Paolo Minero, Sung Hoon Lim, Young-han Kim , 2014
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Invited Lectures

by Sun Young Jang, Young Rock Kim, Dae-woong Lee, Ikkwon Yie
"... Mathematical models and numerical methods for Bose-Einstein condensation ..."
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Mathematical models and numerical methods for Bose-Einstein condensation

RICE UNIVERSITY Regime Change: Sampling Rate vs. Bit-Depth in Compressive Sensing

by Jason Noah Laska , 2011
"... The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of t ..."
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of the signal complexity. In practice, this enables lower sampling rates that can be more easily achieved by current hardware designs. The primary bottleneck that limits ADC sam-pling rates is quantization, i.e., higher bit-depths impose lower sampling rates. Thus, the decreased sampling rates of CS ADCs

Minimum Variance Estimation of a Sparse Vector Within the Linear Gaussian Model: An

by Zvika Ben-haim, Yonina C. Eldar
"... Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterizat ..."
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Abstract — We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian noise. Our analysis is based on the theory of reproducing kernel Hilbert spaces (RKHS). After a characterization of the RKHS associated with the SLGM, we derive a lower bound on the minimum variance achievable by estimators with a prescribed bias function, including the important special case of unbiased estimation. This bound is obtained via an orthogonal projection of the prescribed mean function onto a subspace of the RKHS associated with the SLGM. It provides an approximation to the minimum achievable variance (Barankin bound) that is tighter than any known bound. Our bound holds for an arbitrary system matrix, including the overdetermined and underdetermined cases. We specialize

Universität Oldenburg, D-26111 OldenburgClimate Policy with Technology Transfers and Permit Trading

by Carsten Helm, Stefan Pichler, Carsten Helm, Stefan Pichler , 2011
"... In this paper, we analyze technology transfers (TT) and tradable emission rights, which are core issues of the ongoing climate negotiations. Subsidizing TT leads to the adoption of better abatement technologies in developing countries, thereby reducing the international permit price. This is benefic ..."
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In this paper, we analyze technology transfers (TT) and tradable emission rights, which are core issues of the ongoing climate negotiations. Subsidizing TT leads to the adoption of better abatement technologies in developing countries, thereby reducing the international permit price

1 Minimum Variance Estimation of a Sparse Vector within the Linear Gaussian Model:

by Alexander Junga (corresponding, Zvika Ben-haimc, Yonina C. Eldard
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1 An Algebraic Approach to Physical-Layer Network Coding

by Chen Feng, Danilo Silva, Frank R. Kschischang
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Proceedings of the 37th Hawaii International Conference on System Sciences- 2004 QoS Evaluation of JMS: An Empirical Approach

by Shiping Chen, Paul Greenfield
"... JMS is an API specification that defines a standard way for Java applications to access messaging services. All JMS products promise good performance and to properly support the QoS attributes specified in the standard, making it hard to choose between them. Customers who want to determine which JMS ..."
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JMS is an API specification that defines a standard way for Java applications to access messaging services. All JMS products promise good performance and to properly support the QoS attributes specified in the standard, making it hard to choose between them. Customers who want to determine which JMS product best meets their requirements need a simple, effective and fair methodology for evaluating and comparing competing implementations. This paper presents an empirical methodology for evaluating the QoS implementation of a JMS product. We present a number of test scenarios and define metrics for measuring performance and message persistence. We then illustrate this methodology by using it to evaluate two leading JMS products. Our evaluation results show differences between these products in terms of their overall performance and the impact of various QoS attributes. The case study demonstrates that our empirical methodology is an effective and practical way to test the performance of JMS and other messaging systems.
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