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A comprehensive framework for the theoretical and experimental investigation of thin conducting films for terahertz applications is presented. The electromagnetic properties of conducting polymers spin-coated on low-loss dielectric substrates are characterized by means of terahertz time-domain spectroscopy and interpreted through the Drude-Smith model. The analysis is complemented by an advanced finite-difference time-domain algorithm, which rigorously deals with both the dispersive nature of the involved materials and the extremely subwavelength thickness of the conducting films. Significant agreement is observed among experimental measurements, numerical simulations, and theoretical results. The proposed approach provides a complete toolbox for the engineering of terahertz optoelectronic devices.
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This program aims to evaluate the grain size distribution (equivalent radii) of polycrystals, based on measurements made from 2D sections.
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TwiRole is a hybrid model for role-related user classification on Twitter. It makes use of multiple classifiers and various types of features to predict the roles of users (i.e., female, male, and brand). TwiRole is a graduate research of the Global Event and Trend Archive Research (GETAR) project supported by NSF (IIS-1619028 and 1619371) in Digital Library Research Laboratory (DLRL) at Virginia Tech.
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This is a Matlab code for simulating the performances, in terms of the area under the receiver operating characteristic curve (the AUC) of the sliding window based Gini index detector (SGID), the sliding window based Gershgorin radii and centers ratio (SGRCR), and the Hybrid HSGG test statistics for detecting pulse radar signals for cognitive radio applications, under uniform or nonuniform noise.
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CNN and LSTM have proven their ability in feature extraction and natural language processing respectively. So, we tried to use their ability to process the language of RNAs, i.e. predicting sequence of microRNAs using the sequence of mRNA. Idea is to extract the features from sequence form mRNA and use LSTM network for prediction of miRNA. The model has learned the basic features such as seed match at first 2–8 nucleotides starting at the 5′ end and counting toward the 3′ end. Also, it was able to predict G-U wobble base pair in seed region. While validating on experimentally validated data the model was able to predict on average 72% of miRNAs for specific mRNA and shows highest positive expression fold change of predicted targets on a microarray data generated using anti 25 miRNAs compare to other predicted tools.
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Ensemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. The common point is to maximize accuracy; however, a natural limitation occurs if incremental costs are also assigned to the individual voters. Consequently, we investigate creating ensembles under an additional constraint on the total cost of the members. This task can be formulated as a knapsack problem, where the energy is the ensemble accuracy formed by some aggregation rules. However, the generally applied aggregation rules lead to a nonseparable energy function. This demo is based on the the article 'András Hajdu,György Terdik, Attila Tiba, and Henrietta Tomán, A stochastic approach to handle knapsack problems in the creation of ensembles'. The simulation creates an ensemble from a pool of 30 (100) elements beta distributions under a time constraint condition.
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  • Software/Code
Glycans are fundamental cellular building blocks, involved in many organismal functions. Advances in glycomics are elucidating the roles of glycans, but it remains challenging to properly analyze large glycomics datasets, since the data are sparse (each sample often has only a few measured glycans) and detected glycans are non-independent, sharing many intermediate biosynthetic steps with other glycans. We address these challenges with GlyCompare, a glycomic data analysis approach that leverages shared biosynthetic pathway intermediates to correct for sparsity and non-independence in glycomics. Specifically, quantities of measured glycan are propagated to intermediate glycan substructures, which enable direct comparison of different glycoprofiles. Using this, we studied diverse N-glycan profiles from glycoengineered erythropoietin. We obtained biologically meaningful clustering of mutant cells and identified knockout-specific effects of fucosyltransferase mutants on tetra-antennary structures. We further analyzed human milk oligosaccharide profiles and identified novel impacts that the mother’s secretor-status has on fucosylation and sialylation. Our GlyCompare substructure-oriented approach will enable researchers to take full advantage of the growing power and size of glycomics data. In this capsule, we run through the analysis of the EPO N-glycosylation glycoprofiles performed in our manuscript [Bao, Kellman 2019] Bokan Bao+, Benjamin P. Kellman+, Austin W. T. Chiang, Austin K. York, Mahmoud A. Mohammad, Morey W. Haymond, Lars Bode, and Nathan E. Lewis. 2019. “Correcting for Sparsity and Non-Independence in Glycomic Data through a Systems Biology Framework.” bioRxiv. https://doi.org/10.1101/693507.
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IMToolkit, an open-source index modulation (IM) toolkit, attempts to facilitate reproducible research in the field of wireless communications and IM studies.
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Abstract: Wandering spurs are a little-studied phenomenon seen in MASH and SQ-DDSM modulators. They take the form of frequency-modulated spurs which periodically appear in-band. Since modulators are often employed as divide ratio controllers in fractional-N phase lock loops, these spurs can feed into the output phase noise spectrum. In this paper we explain the mechanism which creates the wandering spurs, and offer a prediction for the behavior of these spurs in the MASH 1-1 modulator. Simulation results are presented which confirm our theoretical predictions.
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Matlab code to generate the ripple-tank simulation featured in the article. From the text of the article: "To understand how much range you can get from a passive radar, it's necessary to have an appreciation of the radar range equation. For passive radar, the appropriate form of the equation is the bistatic radar equation. 'Bistatic' means that the transmitter and receiver are physically separated, which is always the case in passive radar. The opposite of bistatic is monostatic, where transmitter and receiver are collocated—that's the stereotypical active radar. "We'll go about describing the bistatic range equation by appealing to what may be familiar to many readers—the ripple tank from high-school physics class. We're describing the propagation of electromagnetic waves by analogy to how ripples propagate in water. The ripple tank, for those who are not familiar with it, is a wide, shallow tank of water in which we can easily observe the propagation of ripples as we look down on it from above. This is illustrated in Figure 2 in a series of snapshots generated from an animated computer simulation in Matlab."
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  • Software/Code