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Terahertz components and devices are typically interfaced with measurement instrumentation and characterized using fixtures equipped with waveguide flanges. Because such fixtures are known to introduce significant uncertainty and error in measurement, it is preferable to characterize such devices in-situ, where the device under test can be measured on-wafer, prior to dicing and separately from the...
Future wide-area measurement and control applications in large electric power systems will require a new decentralized architecture that scales up with the rapidly growing deployment of Phasor Measurement Units (PMUs). The emerging cloud computing paradigm that allows dynamic creation of virtual machines to form virtual data centers would help better support this new architecture through more efficient...
Social learning is analyzed when agents choose the type of information in a set of signals to identify the state of a fundamental variable. It is shown that agents may herd on the type of signal that is chosen and herding is socially inefficient. The public reports of agents may have to be restricted to improve the efficiency of social learning. As an example of application, the mechanism is relevant...
The paper is concerned with learning in large-scale multi-agent games. The empirical centroid fictitious play (ECFP) algorithm is a variant of the well-known fictitious play algorithm that is practical and computationally tractable in large-scale games. ECFP has been shown to be an effective tool in learning consensus equilibria (a subset of the Nash equilibria) in certain games. However, the behavior...
We consider the design of channel-optimized scalar quantizers for secure communications. We derive necessary conditions for an optimal system for which the eavesdropper's minimum achievable distortion is higher then a prescribed threshold. Moreover, we present an iterative optimization scheme for the quantizer design, which follows the spirit of the Lloyd-Max algorithm. Our quantizer design offers...
In this paper, we investigate the resource allocation schemes in a two-user Gaussian multiple access channel (MAC) with conferencing links and a shared energy harvesting (EH) source to maximize the weighted throughput for the two transmitters over a finite horizon of N time slots. In particular, we adopt a block-based EH model, for which the energy arrive at the beginning of each slot and the amount...
In this paper, we study the problem of simultaneous information and power transfer in a wireless multi-user, single hop network. Specifically, we develop an optimal resource allocation algorithm that maximizes the long term average rate of the network while harvesting energy from both the received signal and the interference. We focus on receivers that split the received signal power into two streams;...
This paper studies the decentralized solution of a multi-agent sparse regression problem in the form of a globally coupled objective function with a non-smooth sparsity promoting constraint. In particular, we propose a distributed primal-dual perturbation (PDP) method which combines the average consensus technique and the primaldual perturbed subgradient method. Compared to the conventional primal-dual...
In game theory, a trusted mediator acting on behalf of the players can enable the attainment of correlated equilibria, which may provide better payoffs than those available from the Nash equilibria alone. We explore the approach of replacing the trusted mediator with an unconditionally secure sampling protocol that jointly generates the players' actions. We characterize the joint distributions that...
Differential privacy is a recent framework for computation on sensitive data, which has shown considerable promise in the regime of large datasets. Stochastic gradient methods are a popular approach for learning in the data-rich regime because they are computationally tractable and scalable. In this paper, we derive differentially private versions of stochastic gradient descent, and test them empirically...
In recent years, targeted therapy to treat cancer is gaining popularity. However, how to quantitatively and dynamically analyze the drug effect for molecularly targeted agents is quite different from traditional cytotoxic drugs. A novel preclinical model combining experimental methods and theoretical analysis is proposed to investigate the mechanisms of action and identify pharmacodynamic characteristic...
The advent of the smartphone and its impact on manufacturing are changing the technology landscape. The cost of smartphone components has decreased drastically in recent years due to mass production. Elements that have benefited from this revolution include sensing modules. It is now reasonable to assume that advanced wireless devices are orientation aware. This, coupled with the asymmetrical radiation...
Optimal control policies for Markovian gene regulatory networks assume that external intervention is 100% specific to control genes. In practice, however, this effect may be unpredictable in the sense that intervention may also target alternative genes. Our goal is to find an optimal control policy that performs well in such cases. We model this by an uncertainty class of controlled networks corresponding...
The tumor proliferation pathways for each individual patient encompass variations and a successful treatment regime based on targeted drugs necessitates the estimation of the influences of target inhibition on cell viability. In this article, we consider an inference approach to decipher the significant blocks of protein targets and the effect of their inhibition on tumor proliferation. Our framework...
Inferring Gene Regulatory Networks (GRNs) from high-throughput experimental data is an important problem in Systems Biology. In this paper we present a new algorithm for the task. Our algorithm is based on a sparse Bayesian learning framework and works well with steady state gene expression data. To evaluate its performance, we compare our algorithm with two state of the art algorithms on multiple...
In this paper, we investigated Deep Learning (DL) for characterizing and detecting target images in an image rapid serial visual presentation (RSVP) task based on EEG data. We exploited DL technique with input feature clusters to handle high dimensional features related to time - frequency events. The method was applied to EEG recordings of a RSVP experiment with multiple sessions and subjects. For...
Discovering genotype of structural variations (SV) is a new and challenging topic. To the best of our knowledge, estimation of variant allele frequency (VAF) of an SV from both read depth and read alignment has not been done. In this study, we propose BreakDown, a new statistical model that integrates read depth, discordant and split paired-end read alignment to accurately estimate SVs' genotypes...
The current EEG analysis➔publication workflow mostly documents qualitative descriptions of event-related EEG dynamics. This makes it difficult to look for comparable results in the literature since search options are limited to textual descriptions and/or similar-appearing results depicted in the paper figures. We demonstrate a method for quantitative comparison of source-resolved results (e.g., ERPs,...
We introduce a parallel algorithmic architecture for metagenomic sequence assembly, termed MetaPar, which allows for significant reductions in assembly time and consequently enables the processing of large genomic datasets on computers with low memory usage. The gist of the approach is to iteratively perform read (re)classification based on phylogenetic marker genes and assembler outputs generated...
Many securities markets are organized as double auctions where each incoming limit order—i.e., an order to buy or sell at a specific price—is stored in a data structure called the limit order book [1]. A trade happens whenever a marketable order arrives—i.e., an order to buy or sell at the best currently available price on the opposite side of the order book. This order flow is visible to every market...
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