AugDMC: Data Augmentation Guided Deep Multiple Clustering

Clustering aims to group similar objects together while separating dissimilar ones apart. Thereafter, structures hidden in data can be identified to help understand data in an unsupervised manner. Traditional clustering methods such as k-means provide only a single clustering for one data set. Deep clustering methods such as auto-encoder based clustering methods have shown a better performance, but still provide a single clustering. However, a given dataset might have multiple clustering structures and each represents a unique perspective of the data. Therefore, some multiple clustering methods have been developed to discover multiple independent structures hidden in data. Although deep multiple clustering methods provide better performance, how to efficiently capture the alternative perspectives in data is still a problem. In this paper, we propose AugDMC, a novel data Augmentation guided Deep Multiple Clustering method, to tackle the challenge. Specifically, AugDMC leverages data augmentations to automatically extract features related to a certain aspect of the data using a self-supervised prototype-based representation learning, where different aspects of the data can be preserved under different data augmentations. Moreover, a stable optimization strategy is proposed to alleviate the unstable problem from different augmentations. Thereafter, multiple clusterings based on different aspects of the data can be obtained. Experimental results on three real-world datasets compared with state-of-the-art methods validate the effectiveness of the proposed method

Tip-Based Nanofabrication of Arbitrary Shapes of Graphene Nanoribbons for Device Applications

Graphene nanoribbons (GNRs) have promising applications in future nanoelectronics, chemical sensing and electrical interconnects. Although there are quite a few GNR nanofabrication methods reported, a rapid and low-cost fabrication method that is capable of fabricating arbitrary shapes of GNRs with good-quality is still in demand for using GNRs for device applications. In this paper, we present a tip-based nanofabrication method capable of fabricating arbitrary shapes of GNRs. A heated atomic force microscope (AFM) tip deposits polymer nanowires atop a CVD-grown graphene surface. The polymer nanowires serve as an etch mask to define GNRs through one step of oxygen plasma etching similar to photoresist in conventional photolithography. Various shapes of GNRs with either linear or curvilinear features are demonstrated. The width of the GNR is around 270 nm and is determined by the width of depositing polymer nanowire, which we estimate can be scaled down 15 nms. We characterize our TBN-fabricated GNRs using Raman spectroscopy and I-V measurements. The measured sheet resistances of our GNRs fall within the range of 1.65 kΩ - 2.64 kΩ-1 in agreement with previously reported values. Furthermore, we determined the high-field breakdown current density of GNRs to be approximately 2.94x108 A/cm2. This TBN process is seamlessly compatible with existing nanofabrication processes, and is particularly suitable for fabricating GNR based electronic devices including next generation DNA sequencing technologies and beyond silicon field effect transistors

Fast Extraction and Characterization of Fundamental Frequency Events from a Large PMU Dataset using Big Data Analytics

A novel method for fast extraction of fundamental frequency events (FFE) based on measurements of frequency and rate of change of frequency by Phasor Measurement Units (PMU) is introduced. The method is designed to work with exceptionally large historical PMU datasets. Statistical analysis was used to extract the features and train Random Forest and Catboost classifiers. The method is capable of fast extraction of FFE from a historical dataset containing measurements from hundreds of PMUs captured over multiple years. The reported accuracy of the best algorithm for classification expressed as Area Under the receiver operating Characteristic curve reaches 0.98, which was obtained in out-of-sample evaluations on 109 system-wide events over 2 years observed at 43 PMUs. Then Minimum Volume Enclosing Ellipsoid Algorithm was used to further analyze the events. 93.72% events were correctly characterized, where average duration of the event as seen by the PMU was 9.93 sec

The concept of a forward scattering micro-sensors radar network for situational awareness

The concept of a novel forward scattering micro-radar wireless network for ground targets detection and recognition is presented. The system topology and structure are described first, followed by a summary of the system’s capabilities and applications. Signal processing strategies used for target detection, parameter estimation and automatic target recognition are briefly explained and supported with experimental results

HIV-1 genetic diversity a challenge for AIDS vaccine development: A retrospective bibliometric analysis

Background: Despite recent advances in human immunodeficiency virus-1 (HIV-1) prevention, a fast, safe, and effective vaccine will probably be necessary to end the HIV/AIDS pandemic. This study was conducted to evaluate global research trends and map the key bibliometric indices in HIV-1 genetic diversity from 1998 to 2021.Methods: A comprehensive online search was conducted in the Web of Science Core Collection database to retrieve published literature on HIV-1 genetic diversity. Key bibliometric indicators were calculated and evaluated using HistCiteTM, Bibliometrix: An R-tool, and VOSviewer software for windows.Results: A total of 2,060 documents written by 9,201 authors and published in 250 journals were included in the final analysis. Year 2012 was the most productive year with 121 (5.87%) publications. The most prolific author was Shao Yiming (n = 74, 3.59%) from Chinese Center for Disease Control and Prevention. The United States of America was the highly contributing and influential country (n = 681, 33.05%). AIDS Research and Human Retroviruses was the most productive journal (n = 562, 27.2%). Network visualization shows that HIV-1 was the most widely used author keyword.Conclusion: This study provides global research trends and detailed information on HIV-1 genetic diversity. The amount of scientific literature on HIV-1 genetic diversity research has rapidly increased in the last two decades. The maximum number of articles on HIV-1 genetic diversity was published in developed countries; therefore, a scientific research collaboration among researchers and institutes in low-income countries should be promoted and supported