Deep Metric Learning for Computer Vision: A Brief Overview

Objective functions that optimize deep neural networks play a vital role in creating an enhanced feature representation of the input data. Although cross-entropy-based loss formulations have been extensively used in a variety of supervised deep-learning applications, these methods tend to be less adequate when there is large intra-class variance and low inter-class variance in input data distribution. Deep Metric Learning seeks to develop methods that aim to measure the similarity between data samples by learning a representation function that maps these data samples into a representative embedding space. It leverages carefully designed sampling strategies and loss functions that aid in optimizing the generation of a discriminative embedding space even for distributions having low inter-class and high intra-class variances. In this chapter, we will provide an overview of recent progress in this area and discuss state-of-the-art Deep Metric Learning approaches.Comment: Book Chapter Published In Handbook of Statistics, Special Issue - Deep Learning 48, 5

CoNAN: Conditional Neural Aggregation Network For Unconstrained Face Feature Fusion

Face recognition from image sets acquired under unregulated and uncontrolled settings, such as at large distances, low resolutions, varying viewpoints, illumination, pose, and atmospheric conditions, is challenging. Face feature aggregation, which involves aggregating a set of N feature representations present in a template into a single global representation, plays a pivotal role in such recognition systems. Existing works in traditional face feature aggregation either utilize metadata or high-dimensional intermediate feature representations to estimate feature quality for aggregation. However, generating high-quality metadata or style information is not feasible for extremely low-resolution faces captured in long-range and high altitude settings. To overcome these limitations, we propose a feature distribution conditioning approach called CoNAN for template aggregation. Specifically, our method aims to learn a context vector conditioned over the distribution information of the incoming feature set, which is utilized to weigh the features based on their estimated informativeness. The proposed method produces state-of-the-art results on long-range unconstrained face recognition datasets such as BTS, and DroneSURF, validating the advantages of such an aggregation strategy.Comment: Paper accepted at IJCB 202

Hear The Flow: Optical Flow-Based Self-Supervised Visual Sound Source Localization

Learning to localize the sound source in videos without explicit annotations is a novel area of audio-visual research. Existing work in this area focuses on creating attention maps to capture the correlation between the two modalities to localize the source of the sound. In a video, oftentimes, the objects exhibiting movement are the ones generating the sound. In this work, we capture this characteristic by modeling the optical flow in a video as a prior to better aid in localizing the sound source. We further demonstrate that the addition of flow-based attention substantially improves visual sound source localization. Finally, we benchmark our method on standard sound source localization datasets and achieve state-of-the-art performance on the Soundnet Flickr and VGG Sound Source datasets. Code: https://github.com/denfed/heartheflow.Comment: Accepted to WACV 202

Synthesis and Antimicrobial Activity of Novel 3-[1-(3-nitrophenyl)-ethyl]-1-(indole-1-yl) Substituted Aryl/alkyl-phosphinoyl/thiophosphinoyl/ selenophosphinoyl-1H-indole Derivatives

Syntheses of novel 3-[1-(3-nitrophenyl)-ethyl]-1-(indole-1-yl) substituted aryl/alkyl phosphinoyl/thiophosphinoyl/selenophosphinoyl-1H-indole derivatives were accomplished in two steps. The synthetic route involves the cyclisation of equimolar quantities of 3-[1H-3-indolyl(3-nitrophenyl)methyl]-1H-indole with dichlorophenyl phosphine/ethyldichlorophosphite in the presence of triethylamine in dry acetonitrile at room temperature. These compounds were further converted to the corresponding oxides, sulphides and selenides by reacting them with hydrogen peroxide, sulphur and selenium, respectively. The structures of the novel products were established by elemental analyses, IR, 1H, 13C and 31P NMR and mass spectroscopy. They were screened for antibacterial and antifungal activity against Staphylococcus aureus/Klebsiella pneumoniae and Pellicularia solmanicolor/Macrophomina phaseolina, respectively.Keywords: Bisindolylalkanes, alkyl/aryl phosphorodichloridates, antimicrobial activit

Facile and scalable preparation of bovine serum albumin stabilized cobalt sulfide nanostructures with various morphologies

We present a protein-assisted method for the facile and scalable synthesis of Cobalt sulfide (CoS) nanostructures with various morphologies using bovine serum albumin (BSA) as a stabilizing agent. The CoS samples prepared from 10:1 volume ratio of Cobalt (Co):Sulfur (S) and 1:10 volume ratio of Co:S at 0.01% w/v amount of BSA shows 3D flowers with an average diameter of 510 nm and hollow spheres about 900 nm in average diameter, respectively. The CoS samples prepared from 0.01, 0.1 and 0.5% w/v amounts of BSA at 1:1 volume ratio of Co:S shows nanosheet based porous clusters, nanosheet based partially porous clusters and aggregated spheres, respectively. Fourier transform infrared spectroscopy study confirms that the obtained BSA stabilized CoS nanostructures are stabilized by hydroxyl and amine groups present in the BSA molecules. © 2021 Elsevier B.V.1

Deep and comparative analysis of the mycelium and appressorium transcriptomes of Magnaporthe grisea using MPSS, RL-SAGE, and oligoarray methods

BACKGROUND: Rice blast, caused by the fungal pathogen Magnaporthe grisea, is a devastating disease causing tremendous yield loss in rice production. The public availability of the complete genome sequence of M. grisea provides ample opportunities to understand the molecular mechanism of its pathogenesis on rice plants at the transcriptome level. To identify all the expressed genes encoded in the fungal genome, we have analyzed the mycelium and appressorium transcriptomes using massively parallel signature sequencing (MPSS), robust-long serial analysis of gene expression (RL-SAGE) and oligoarray methods. RESULTS: The MPSS analyses identified 12,531 and 12,927 distinct significant tags from mycelia and appressoria, respectively, while the RL-SAGE analysis identified 16,580 distinct significant tags from the mycelial library. When matching these 12,531 mycelial and 12,927 appressorial significant tags to the annotated CDS, 500 bp upstream and 500 bp downstream of CDS, 6,735 unique genes in mycelia and 7,686 unique genes in appressoria were identified. A total of 7,135 mycelium-specific and 7,531 appressorium-specific significant MPSS tags were identified, which correspond to 2,088 and 1,784 annotated genes, respectively, when matching to the same set of reference sequences. Nearly 85% of the significant MPSS tags from mycelia and appressoria and 65% of the significant tags from the RL-SAGE mycelium library matched to the M. grisea genome. MPSS and RL-SAGE methods supported the expression of more than 9,000 genes, representing over 80% of the predicted genes in M. grisea. About 40% of the MPSS tags and 55% of the RL-SAGE tags represent novel transcripts since they had no matches in the existing M. grisea EST collections. Over 19% of the annotated genes were found to produce both sense and antisense tags in the protein-coding region. The oligoarray analysis identified the expression of 3,793 mycelium-specific and 4,652 appressorium-specific genes. A total of 2,430 mycelial genes and 1,886 appressorial genes were identified by both MPSS and oligoarray. CONCLUSION: The comprehensive and deep transcriptome analysis by MPSS and RL-SAGE methods identified many novel sense and antisense transcripts in the M. grisea genome at two important growth stages. The differentially expressed transcripts that were identified, especially those specifically expressed in appressoria, represent a genomic resource useful for gaining a better understanding of the molecular basis of M. grisea pathogenicity. Further analysis of the novel antisense transcripts will provide new insights into the regulation and function of these genes in fungal growth, development and pathogenesis in the host plants

On the Purity of Atmospheric Glow-Discharge Plasma

Purity of the glow-discharge plasma at atmospheric pressure for surface modification applications is always debatable, since it works at ambient atmosphere. We have demonstrated on the use of optical emission spectroscopy to test the purity of this kind of plasma. The effect of gas flow pattern, nature of gas, and its flow rate on the plasma chemistry was studied. The importance of proper system design in maintaining a uniform flow of heavy and inert gases as carrier gas in atmospheric glow-discharge plasma was confirmed. The surface of a plasma-treated PET sample was analyzed using X-ray photoelectron spectroscopy to verify the studies on plasma purity done using emission spectrum

On the quality of hydrogenated amorphous silicon deposited by sputtering

Amorphous hydrogenated silicon (a-Si:H) is well-known material in the global semiconductor industry. The quality of the a-Si:H films is generally decided by silicon and hydrogen bonding configuration (Si-H-x, x=1,2) and hydrogen concentration (C-H). These quality aspects are correlated with the plasma parameters like ion density (N-i) and electron temperature (T-e) of DC, Pulsed DC (PDC) and RF plasmas during the sputter-deposition of a-Si:H thin films. It was found that the N-i and T-e play a major role in deciding Si-H-x bonding configuration and the C-H value in a-Si:H films. We observed a trend in the variation of Si-H and Si-H-2 bonding configurations, and C-H in the films deposited by DC, Pulsed DC and RF reactive sputtering techniques. Ion density and electron energy are higher in RF plasma followed by PDC and DC plasma. Electrons with two different energies were observed in all the plasmas. At a particular hydrogen partial pressure, RF deposited films have higher C-H followed by PDC and then DC deposited films. The maximum energy that can be acquired by the ions was found to be higher in RF plasma. Floating potential (V-f) is more negative in DC plasma, whereas, plasma potential (V-p) is found to be more positive in RF plasma. (C) 2014 Elsevier Ltd. All rights reserved

Wetting characteristics of vertically aligned graphene nanosheets

Vertically aligned graphene nanosheets (VAGNs) are a class of graphitic carbon in which few layers of graphene nanosheets are aligned perpendicular to the plane of the substrate. The change in water contact angle (from 103 degrees to 135 degrees) with VAGNs, as a function of change in the surface geometry, is analysed. Theoretical calculations and comparison with the experimental data shows that the apparent contact angle values of VAGNs are closer to that of the fully non-wetting mode or ideal Cassie mode of wetting. The ideal Cassie mode of wetting also explains the variation of the water contact angle of VAGNs with the surface morphology of the material and predicts how surface parameters can be modified to get the required wettability for a certain application of this material