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Tse, David

Works: 16 works in 48 publications in 1 language and 1,084 library holdings
Genres: Fiction  Political fiction  Dystopias  History  Science fiction  Case studies  Academic theses  Popular works 
Roles: Author, Thesis advisor, Contributor
Classifications: PL2840.G84, 895.1352
Publication Timeline
Publications about David Tse
Publications by David Tse
Most widely held works by David Tse
Fundamentals of wireless communication by David Tse( Book )
25 editions published between 2005 and 2013 in English and held by 427 libraries worldwide
The past decade has seen many advances in physical layer wireless communication theory and their implementation in wireless systems. This text presents a unified view of the fundamentals of wireless communication for graduate students and practising engineers
The fat years : the book no one in China dares publish by Guanzhong Chen( Sound Recording )
3 editions published in 2011 in English and held by 24 libraries worldwide
Beijing, sometime in the near future: a month has gone missing from official records. No one has any memory of it, and no one can care less. Except for a small circle of friends, who will stop at nothing to get to the bottom of the sinister cheerfulness and amnesia that has possessed the Chinese nation. When they kidnap a high-ranking official and force him to reveal all, what they learn - not only about their leaders, but also about their own people - stuns them to the core. It is a message that will rock the world
Operating regimes of large wireless networks by Ayfer Özgür( Book )
6 editions published between 2011 and 2014 in English and held by 12 libraries worldwide
Multi-hop is the current communication architecture of wireless mesh and ad hoc networks. Information is relayed from each source to its destination in successive transmissions between intermediate nodes. A major problem regarding this architecture is its poor performance at large system size: as the number of users in a wireless network increases, the communication rate for each user rapidly decreases. Can we design new communication architectures that significantly increase the capacity of large wireless networks? In this monograph, we present a scaling law characterization of the information-theoretic capacity of wireless networks, which sheds some light on this question. We show that the answer depends on the parameter range in which a particular network lies, namely the operating regime of the network. There are operating regimes where the information-theoretic capacity of the network is drastically higher than the capacity of conventional multi-hop. New architectures can provide substantial capacity gains here. We determine what these regimes are and investigate the new architectures that are able to approach the information-theoretic capacity of the network. In some regimes, there is no way to outperform multi-hop. In other words, the conventional multi-hop architecture indeed achieves the information-theoretic capacity of the network. We discuss the fundamental factors limiting the capacity of the network in these regimes and provide an understanding of why conventional multi-hop indeed turns out to be the right architecture
The fat years : a novel by Guanzhong Chen( Book )
1 edition published in 2011 in English and held by 2 libraries worldwide
In near-future Beijing, a month is missing from all official records, mass amnesia has wiped it from collective memory, and people are possessed with an unnatural cheerfulness. A small group of friends will stop at nothing to get to the bottom of the mystery
Blepharoptosis 1 : evaluation and choice of procedure ( visu )
1 edition published in 1984 in English and held by 2 libraries worldwide
The pathogenisis, pre-operative clinical evaluation, and selection of surgical techniques for blepharoptosis are presented
Multiuser diversity in wireless networks : from information theory to architecture ( visu )
1 edition published in 2000 in English and held by 1 library worldwide
This talk presents channel fading as a form of randomization that can be taken advantage of in the design of wireless networks, contradicting the traditional view of it as a form of unreliability that has to be compensated for
Marketing in china and east asia by David Tse( Book )
1 edition published in 2016 in English and held by 1 library worldwide
Efficient learning in sequential optimization by Daniel Russo( file )
1 edition published in 2015 in English and held by 1 library worldwide
We consider a broad class of online optimization problems in which a decision-maker must balance between exploration and exploitation while learning from partial feedback. In these problems, the decision-maker repeatedly chooses among a set of possible actions, observes an outcome, and receives a reward representing the utility derived from this outcome. She is uncertain about the underlying system and is therefore initially unsure of which action is best. However, as outcomes are observed, she is able to learn over time to make increasingly effective decisions. Her objective is to choose actions sequentially so as to maximize the expected cumulative reward. We focus on three algorithmic approaches that accommodate flexible statistical modeling, and are capable of experimenting efficiently in broad classes of problems. The first part of the thesis focuses on a design principle known as optimism in the face of uncertainty, which underlies many of the most effective exploration algorithms. We provide a regret bound for an optimistic algorithm that applies broadly and can be specialized to many specific model classes. Our bound depends on a new notion of dimension that measures the degree of dependence among actions. We compare our notion to the Vapnik-Chervonenkis dimension, and explain why that and other measures of dimension used in the supervised literature do not suffice when it comes to analyzing optimistic algorithms. We then turn our attention to Thompson sampling, an elegant algorithm for learning in online optimization problems with partial feedback. We derive a close theoretical connection between Thompson sampling and optimistic algorithms. Due to the connection we derive, existing analysis available for specific optimistic algorithms immediately translates to expected regret bounds for Thompson sampling. The second part of the thesis pushes beyond the optimistic principle, and offers a fresh, information-theoretic, perspective on the exploration/exploitation tradeoff. We first revisit Thompson sampling from this perspective and provide novel regret bounds that scale with the entropy of the optimal action distribution. Then, we propose a new algorithm--information-directed sampling (IDS)--and study its performance. IDS quantifies the amount learned by selecting an action through an information theoretic measure, and selects actions by optimizing an objective that explicitly balances attaining a high immediate reward and selecting informative actions. We provide a general regret bound for IDS, demonstrate strong performance in simulation, and show through simple analytic examples that it can dramatically outperform Thompson sampling due to the way it quantifies information
Inference and estimation in high-dimensional data analysis by Adel Javanmard( file )
1 edition published in 2014 in English and held by 1 library worldwide
Modern technologies generate vast amounts of fine-grained data at an unprecedented speed. Nowadays, high-dimensional data, where the number of variables is much larger than the sample size, occur in many applications, such as healthcare, social networks, and recommendation systems, among others. The ubiquitous interest in these applications has spurred remarkable progress in the area of high-dimensional data analysis in terms of point estimation and computation. However, one of the fundamental inference task, namely quantifying uncertainty or assessing statistical significance, is still in its infancy for such models. In the first part of this dissertation, we present efficient procedures and corresponding theory for constructing classical uncertainty measures like confidence intervals and p-values for single regression coefficients in high-dimensional settings. In the second part, we study the compressed sensing reconstruction problem, a well-known example of estimation in high-dimensional settings. We propose a new approach to this problem that is drastically different from the classical wisdom in this area. Our construction of the sensing matrix is inspired by the idea of spatial coupling in coding theory and similar ideas in statistical physics. For reconstruction, we use an approximate message passing algorithm. This is an iterative algorithm that takes advantage of the statistical properties of the problem to improve convergence rate. Finally, we prove that our method can effectively solve the reconstruction problem at (information-theoretically) optimal undersampling rate and show its robustness to measurement noise
Blepharoptosis 2 : surgical technique ( visu )
1 edition published in 1984 in English and held by 1 library worldwide
Varies surgical techniques such as Fasonella-Servat and external levator resection are demonstrated for blepharoptosis correction
1 edition published in 1991 in English and held by 1 library worldwide
Experimental investigation of the flow field during combustion in narrow circular ducts by David Tse( file )
1 edition published in 2012 in English and held by 1 library worldwide
The assessment of stack gas scrubbing and alternative technologies by Julian Szekely( Book )
1 edition published in 1978 in English and held by 1 library worldwide
Hair loss treatment almanac 1998 by David Tse( Book )
1 edition published in 1998 in English and held by 0 libraries worldwide
Rediscovering market niches in a traditional industry by Mary Ho( Book )
2 editions published in 2003 in English and held by 0 libraries worldwide
An Approximation Approach to Network Information Theory by A. Salman Avestimehr( file )
1 edition published in 2015 in English and held by 0 libraries worldwide
This monograph illustrates a novel approach, which is based on changing the focus to seek approximate solutions accompanied by universal guarantees on the gap to optimality, in order to enable progress on several key open problems in network information theory. We seek universal guarantees that are independent of problem parameters, but perhaps dependent on the problem structure. At the heart of this approach is the development of simple, deterministic models that capture the main features of information sources and communication channels, and are utilized to approximate more complex models. The program advocated in this monograph is to use first seek solutions for the simplified deterministic model and use the insights and the solution of the simplified model to connect it to the original problem. The goal of this deterministic-approximation approach is to obtain universal approximate characterizations of the original channel capacity region and source coding rate regions. The translation of the insights from the deterministic framework to the original problem might need non-trivial steps either in the coding scheme or in the outer bounds. The applications of this deterministic approximation approach are demonstrated in four central problems, namely unicast/multicast relay networks, interference channels, multiple descriptions source coding, and joint source-channel coding over networks. For each of these problems, it is illustrated how the proposed approach can be utilized to approximate the solution and draw engineering insights. Throughout the monograph, many extensions and future directions are addressed, and several open problems are presented in each chapter. The monograph is concluded by illustrating other deterministic models that can be utilized to obtain tighter approximation results, and discussing some recent developments on utilization of deterministic models in multi-flow multi-hop wireless networks
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English (48)
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