Picosecond laser studies of V-T processes in gases and electronic excitation transport in disordered systems

SVL fluorescence spectroscopy was used to study intra- molecular energy transfer from the 0(DEGREES) level of aniline induced by collisions with CO(,2). The populations of eight aniline vibronic growth levels, as a function of CO(,2) pressure, were monitored. Collision gas pressures were adjusted to keep aniline-CO(,2) interactions within the single-collision regime. To first order, collision-induced energy transfer from the 0(DEGREES) level of aniline for CO(,2) as the collision gas follows the same flow pattern as was found in previous studies when Ar, H(,2)O or CH(,3)F were the collision partners(\u271,2);Time-correlated photon counting was used to measure concen- tration dependent fluorescence depolarization for rhodamine 6G in glycerol. Fluorescence decays from these viscous solutions provide data for analyzing the three-dimensional, three-body excitation transport theory developed by Gochanour, Andersen and Fayer for disordered systems(\u273). Solution concentrations of rhodamine 6G range from 1.7 x 10(\u27-4) to 2.4 x 10(\u27-3) M. Differences between optimized theoretical and experimental profiles are shown to be dominated by experimental artifacts arising from excitation trapping by rhodamine 6G aggregates and from self-absorption in solution cells thicker than 10 (mu)m;The two-dimensional, two-body excitation theory developed by Loring and Fayer(\u274) was also examined using time-resolved fluores- cence depolarization techniques. The samples, made up of sub- monolayers of rhodamine 3B adsorbed onto optically flat fused silica yield fluorescence profiles which agree well with profiles developed from the theory for reduced surface coverages up to (TURN)0.4. At higher coverages, excitation trapping by rhodamine 3B aggregates;truncates the depolarization process, yielding apparent reduced coverages which are appreciably lower than the true coverages; (\u271,2,3,4)Please see dissertation for references

CRS-FL: Conditional Random Sampling for Communication-Efficient and Privacy-Preserving Federated Learning

Federated Learning (FL), a privacy-oriented distributed ML paradigm, is being gaining great interest in Internet of Things because of its capability to protect participants data privacy. Studies have been conducted to address challenges existing in standard FL, including communication efficiency and privacy-preserving. But they cannot achieve the goal of making a tradeoff between communication efficiency and model accuracy while guaranteeing privacy. This paper proposes a Conditional Random Sampling (CRS) method and implements it into the standard FL settings (CRS-FL) to tackle the above-mentioned challenges. CRS explores a stochastic coefficient based on Poisson sampling to achieve a higher probability of obtaining zero-gradient unbiasedly, and then decreases the communication overhead effectively without model accuracy degradation. Moreover, we dig out the relaxation Local Differential Privacy (LDP) guarantee conditions of CRS theoretically. Extensive experiment results indicate that (1) in communication efficiency, CRS-FL performs better than the existing methods in metric accuracy per transmission byte without model accuracy reduction in more than 7% sampling ratio (# sampling size / # model size); (2) in privacy-preserving, CRS-FL achieves no accuracy reduction compared with LDP baselines while holding the efficiency, even exceeding them in model accuracy under more sampling ratio conditions

Approximation Algorithms for Resource Allocation

This thesis is devoted to designing new techniques and algorithms for combinatorial optimization problems arising in various applications of resource allocation. Resource allocation refers to a class of problems where scarce resources must be distributed among competing agents maintaining certain optimization criteria. Examples include scheduling jobs on one/multiple machines maintaining system performance; assigning advertisements to bidders, or items to people maximizing profit/social fairness; allocating servers or channels satisfying networking requirements etc. Altogether they comprise a wide variety of combinatorial optimization problems. However, a majority of these problems are NP-hard in nature and therefore, the goal herein is to develop approximation algorithms that approximate the optimal solution as best as possible in polynomial time. The thesis addresses two main directions. First, we develop several new techniques, predominantly, a new linear programming rounding methodology and a constructive aspect of a well-known probabilistic method, the Lov\'{a}sz Local Lemma (LLL). Second, we employ these techniques to applications of resource allocation obtaining substantial improvements over known results. Our research also spurs new direction of study; we introduce new models for achieving energy efficiency in scheduling and a novel framework for assigning advertisements in cellular networks. Both of these lead to a variety of interesting questions. Our linear programming rounding methodology is a significant generalization of two major rounding approaches in the theory of approximation algorithms, namely the dependent rounding and the iterative relaxation procedure. Our constructive version of LLL leads to first algorithmic results for many combinatorial problems. In addition, it settles a major open question of obtaining a constant factor approximation algorithm for the Santa Claus problem. The Santa Claus problem is a $NP$-hard resource allocation problem that received much attention in the last several years. Through out this thesis, we study a number of applications related to scheduling jobs on unrelated parallel machines, such as provisionally shutting down machines to save energy, selectively dropping outliers to improve system performance, handling machines with hard capacity bounds on the number of jobs they can process etc. Hard capacity constraints arise naturally in many other applications and often render a hitherto simple combinatorial optimization problem difficult. In this thesis, we encounter many such instances of hard capacity constraints, namely in budgeted allocation of advertisements for cellular networks, overlay network design, and in classical problems like vertex cover, set cover and k-median

Learning High-Dimensional Markov Forest Distributions: Analysis of Error Rates

The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive thresholding is proposed. It is shown that this algorithm is both structurally consistent and risk consistent and the error probability of structure learning decays faster than any polynomial in the number of samples under fixed model size. For the high-dimensional scenario where the size of the model d and the number of edges k scale with the number of samples n, sufficient conditions on (n,d,k) are given for the algorithm to satisfy structural and risk consistencies. In addition, the extremal structures for learning are identified; we prove that the independent (resp. tree) model is the hardest (resp. easiest) to learn using the proposed algorithm in terms of error rates for structure learning.Comment: Accepted to the Journal of Machine Learning Research (Feb 2011

Pattern-theoretic foundations of automatic target recognition in clutter

Issued as final reportAir Force Office of Scientific Research (U.S.