Face Recognition with Multi-stage Matching Algorithms

For every face recognition method, the primary goal is to achieve higher recognition accuracy and spend less computational costs. However, as the gallery size increases, especially when one probe image corresponds to only one training image, face recognition becomes more and more challenging. First, a larger gallery size requires more computational costs and memory usage. Meanwhile, that the large gallery sizes degrade the recognition accuracy becomes an even more significant problem to be solved. A coarse parallel algorithm that equally divides training images and probe images into multiple processors is proposed to deal with the large computational costs and huge memory usage of the Non-Graph Matching (NGM) feature-based method. First, each processor finishes its own training workload and stores the extracted feature information, respectively. And then, each processor simultaneously carries out the matching process for their own probe images by communicating their own stored feature information with each other. Finally, one processor collects the recognition result from the other processors. Due to the well-balanced workload, the speedup increases with the number of processors and thus the efficiency is excellently maintained. Moreover, the memory usage on each processor also evidently reduces as the number of processors increases. In sum, the parallel algorithm simultaneously brings less running time and memory usage for one processor. To solve the recognition degradation problem, a set of multi-stage matching algorithms that determine the recognition result step-by-step are proposed. Each step picks a small proportion of the best similar candidates for the next step and removes the others. The behavior of picking and removing repeats until the number of remaining candidates is small enough to produce the final recognition result. Three multi-stage matching algorithms— n-ary elimination, divide and conquer, and two-stage hybrid— are introduced to the matching process of traditional face recognition methods, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Non-graph Matching (NGM). N-ary elimination accomplishes the multi-stage matching from the global perspective by ranking the similarities and picking the best candidates. Divide and conquer implements the multi-stage matching from the local perspective by dividing the candidates into groups and selecting the best one of each group. For two-stage hybrid, it uses a holistic method to choose a small amount of candidates and then utilizes a feature-based method to find out the final recognition result from them. From the experimental results, three conclusions can be drawn. First, with the multi-stage matching algorithms, higher recognition accuracy can be achieved. Second, the larger the gallery size, the greater the improved accuracy brought by the multi-stage matching algorithms. Finally, the multi-stage matching algorithms achieve little extra computational costs

Few-shot Image Classification based on Gradual Machine Learning

Few-shot image classification aims to accurately classify unlabeled images using only a few labeled samples. The state-of-the-art solutions are built by deep learning, which focuses on designing increasingly complex deep backbones. Unfortunately, the task remains very challenging due to the difficulty of transferring the knowledge learned in training classes to new ones. In this paper, we propose a novel approach based on the non-i.i.d paradigm of gradual machine learning (GML). It begins with only a few labeled observations, and then gradually labels target images in the increasing order of hardness by iterative factor inference in a factor graph. Specifically, our proposed solution extracts indicative feature representations by deep backbones, and then constructs both unary and binary factors based on the extracted features to facilitate gradual learning. The unary factors are constructed based on class center distance in an embedding space, while the binary factors are constructed based on k-nearest neighborhood. We have empirically validated the performance of the proposed approach on benchmark datasets by a comparative study. Our extensive experiments demonstrate that the proposed approach can improve the SOTA performance by 1-5% in terms of accuracy. More notably, it is more robust than the existing deep models in that its performance can consistently improve as the size of query set increases while the performance of deep models remains essentially flat or even becomes worse.Comment: 17 pages,6 figures,5 tables, 55 conference

Constructing Physical and Genomic Maps for Puccinia striiformis f. sp. tritici, the Wheat Stripe Rust Pathogen, by Comparing Its EST Sequences to the Genomic Sequence of P. graminis f. sp. tritici, the Wheat Stem Rust Pathogen

The wheat stripe rust fungus, Puccinia striiformis f. sp. tritici (Pst), does not have a known alternate host for sexual reproduction, which makes it impossible to study gene linkages through classic genetic and molecular mapping approaches. In this study, we compared 4,219 Pst expression sequence tags (ESTs) to the genomic sequence of P. graminis f. sp. tritici (Pgt), the wheat stem rust fungus, using BLAST searches. The percentages of homologous genes varied greatly among different Pst libraries with 54.51%, 51.21%, and 13.61% for the urediniospore, germinated urediniospore, and haustorial libraries, respectively, with an average of 33.92%. The 1,432 Pst genes with significant homology with Pgt sequences were grouped into physical groups corresponding to 237 Pgt supercontigs. The physical relationship was demonstrated by 12 pairs (57%), out of 21 selected Pst gene pairs, through PCR screening of a Pst BAC library. The results indicate that the Pgt genome sequence is useful in constructing Pst physical maps

The correspondence between shadows and test fields in four-dimensional charged Einstein-Gauss-Bonnet black holes

In this paper, we investigate the photon sphere, shadow radius and quasinormal modes of a four-dimensional charged Einstein-Gauss-Bonnet black hole. The perturbation of a massless scalar field in the background of the black hole is adopted. The quasinormal modes are gotten by the $6th$ order WKB approximation approach and shadow radius, respectively. The degree of coincidence of the quasinormal modes derived by the two approaches increases with the increase of the values of the Gauss-Bonnet coupling constant and multiple number. It shows the correspondence between the shadow and test field in the four-dimensional Einstein-Gauss-Bonnet-Maxwell gravity. The radii of the photon sphere and shadow increase with the decrease of the Gauss-Bonnet coupling constant.Comment: 16 page

Large-scale analysis of antisense transcription in wheat using the Affymetrix GeneChip Wheat Genome Array

<p>Abstract</p> <p>Background</p> <p>Natural antisense transcripts (NATs) are transcripts of the opposite DNA strand to the sense-strand either at the same locus (<it>cis</it>-encoded) or a different locus (<it>trans</it>-encoded). They can affect gene expression at multiple stages including transcription, RNA processing and transport, and translation. NATs give rise to sense-antisense transcript pairs and the number of these identified has escalated greatly with the availability of DNA sequencing resources and public databases. Traditionally, NATs were identified by the alignment of full-length cDNAs or expressed sequence tags to genome sequences, but an alternative method for large-scale detection of sense-antisense transcript pairs involves the use of microarrays. In this study we developed a novel protocol to assay sense- and antisense-strand transcription on the 55 K Affymetrix GeneChip Wheat Genome Array, which is a 3' <it>in vitro </it>transcription (3'IVT) expression array. We selected five different tissue types for assay to enable maximum discovery, and used the 'Chinese Spring' wheat genotype because most of the wheat GeneChip probe sequences were based on its genomic sequence. This study is the first report of using a 3'IVT expression array to discover the expression of natural sense-antisense transcript pairs, and may be considered as proof-of-concept.</p> <p>Results</p> <p>By using alternative target preparation schemes, both the sense- and antisense-strand derived transcripts were labeled and hybridized to the Wheat GeneChip. Quality assurance verified that successful hybridization did occur in the antisense-strand assay. A stringent threshold for positive hybridization was applied, which resulted in the identification of 110 sense-antisense transcript pairs, as well as 80 potentially antisense-specific transcripts. Strand-specific RT-PCR validated the microarray observations, and showed that antisense transcription is likely to be tissue specific. For the annotated sense-antisense transcript pairs, analysis of the gene ontology terms showed a significant over-representation of transcripts involved in energy production. These included several representations of ATP synthase, photosystem proteins and RUBISCO, which indicated that photosynthesis is likely to be regulated by antisense transcripts.</p> <p>Conclusion</p> <p>This study demonstrated the novel use of an adapted labeling protocol and a 3'IVT GeneChip array for large-scale identification of antisense transcription in wheat. The results show that antisense transcription is relatively abundant in wheat, and may affect the expression of valuable agronomic phenotypes. Future work should select potentially interesting transcript pairs for further functional characterization to determine biological activity.</p