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

Machine vision-assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes

With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For this purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image feature analysis revealed how variation in one or the other building block of the styryl molecules led to variations in the molecules' visual signals. Across each pair of probes in the library, chemical similarity was significantly associated with spectral and total signal intensity similarity. However, chemical similarity was much less associated with similarity in subcellular probe fluorescence patterns. Quantitative analysis and visual inspection of pairs of images acquired from pairs of styryl isomers confirm that many closely-related probes exhibit different subcellular localization patterns. Therefore, idiosyncratic interactions between styryl molecules and specific cellular components greatly contribute to the subcellular distribution of the styryl probes' fluorescence signal. These results demonstrate how machine vision and cheminformatics can be combined to analyze the targeting properties of bioimaging probes, using large image data sets acquired with automated screening systems. © 2009 International Society for Advancement of CytometryPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63004/1/20713_ftp.pd

Genomic diversity among Basmati rice (Oryza sativa L) mutants obtained through 60Co gamma radiations using AFLP markers

Mutation breeding can be considered successful in obtaining new cultivars and broadening the genetic base of rice crop. In order to obtain new varieties of rice with improved agronomic and grain characteristics, gamma radiation (60Co) has been used to generate novel mutants of the Basmati rice. In this study rice cultivars; Basmati-370 and Basmati-Pak, were exposed to different doses of gamma radiations and stable mutants along with parents were studied for genomic diversity on the basis of molecular marker (AFLP). Morphological data showed that mutants of Basmati-370 performed well for yield and yield components and grain physical parameters whereas, the mutant EL-30-2-1 has extra long rain trait as compared to the parent (Basmati-Pak). The genetic variations determined through AFLP revealed a total of 282 scorable bands, out of which 108 (37.81%) were polymorphic. The number of fragments produced by various primers combinations ranged from 11 - 26 with an average of 17.63fragments per primer combination. Maximum 26 bands were amplified with P-AAG/M-CAG primer combination and minimum one band was amplified with P-ATG/M-CTA primer combination. Two groups of genotypes were detected; group-A had DM-1-30-3-99, DM-1-30-34-99 and EF-1-20-52-04 mutants along with parent Basmati-370, whereas the group-B contained EL-30-2-1 and parent Basmati-Pak. The results of AFLP analysis indicated that the rate of polymorphism was 4.43% (DM-1-30-3-99), 4.25% (DM-1-30-34-99) and 6.38% (EF-1-20-52-04) among the genomes of mutants and parent Basmati-370, respectively, whereas polymorphism rate was 5.32% between genome of EL-30-2-1 and Basmati-Pak. The study further confirmed that the use of gamma radiations is an effective approach for creating new rice germplasm

Development of a Short Form of the Attitudes to Ageing Questionnaire (AAQ)

Objectives: The original 24-item Attitudes to Ageing Questionnaire (AAQ) is well-established as a measure of attitudes to aging, comprising domains of Psychosocial Loss (PL), Physical Change (PC), and Psychological Growth (PG). This paper presents a new 12-item short form Attitudes to Ageing Questionnaire (AAQ-SF). Methods: The original field trial data used to develop the AAQ-24 were used to compare 6-,9- and 12-item versions of AAQ-SF (Sample 1, n = 2,487) and to test the discriminative validity of the selected 12-item AAQ-SF (Sample 2, n = 2,488). Data from a separate study reporting on the AAQ-24 (sample 3, n = 792) verified analyses. Results: The 12-item AAQ-SF reported adequate internal consistency in both Sample 1 (PL α = .72, PC α = .72, and PG α = .62) and Sample 3 (PL α = .68, PC α = .73, and PG α = .61). The AAQ-SF functioned consistently with the profile of the AAQ-24 in that subscales in both formats of this measure discriminate between respondents on key parameters such as depression, subjective health status, and overall quality of life in Sample 2. Sample 3 also demonstrated the AAQ-SF can detect the differences in attitudes toward aging between individuals experiencing anxiety and depression and those without psychological symptoms. Confirmatory Factor Analysis confirmed the structure of the AAQ-SF mirrors that of the original 24-item AAQ. Conclusions: The AAQ-SF is a robust measure of attitudes toward aging, which can reduce respondent burden when used within longer questionnaire batteries or longitudinal research