A Survey of Deep Learning-Based Object Detection

Object detection is one of the most important and challenging branches of computer vision, which has been widely applied in peoples life, such as monitoring security, autonomous driving and so on, with the purpose of locating instances of semantic objects of a certain class. With the rapid development of deep learning networks for detection tasks, the performance of object detectors has been greatly improved. In order to understand the main development status of object detection pipeline, thoroughly and deeply, in this survey, we first analyze the methods of existing typical detection models and describe the benchmark datasets. Afterwards and primarily, we provide a comprehensive overview of a variety of object detection methods in a systematic manner, covering the one-stage and two-stage detectors. Moreover, we list the traditional and new applications. Some representative branches of object detection are analyzed as well. Finally, we discuss the architecture of exploiting these object detection methods to build an effective and efficient system and point out a set of development trends to better follow the state-of-the-art algorithms and further research.Comment: 30 pages,12 figure

Exceptional lability of a genomic complex in rice and its close relatives revealed by interspecific and intraspecific comparison and population analysis

<p>Abstract</p> <p>Background</p> <p>Extensive DNA rearrangement of genic colinearity, as revealed by comparison of orthologous genomic regions, has been shown to be a general concept describing evolutionary dynamics of plant genomes. However, the nature, timing, lineages and adaptation of local genomic rearrangement in closely related species (<it>e.g</it>., within a genus) and haplotype variation of genomic rearrangement within populations have not been well documented.</p> <p>Results</p> <p>We previously identified a hotspot for genic rearrangement and transposon accumulation in the <it>Orp </it>region of Asian rice (<it>Oryza sativa</it>, AA) by comparison with its orthologous region in sorghum. Here, we report the comparative analysis of this region with its orthologous regions in the wild progenitor species (<it>O. nivara</it>, AA) of Asian rice and African rice (<it>O. glaberrima</it>) using the BB genome <it>Oryza </it>species (<it>O. punctata</it>) as an outgroup, and investigation of transposon insertion sites and a segmental inversion event in the AA genomes at the population level. We found that <it>Orp </it>region was primarily and recently expanded in the Asian rice species <it>O. sativa </it>and <it>O. nivara</it>. LTR-retrotransposons shared by the three AA-genomic regions have been fixed in all the 94 varieties that represent different populations of the AA-genome species/subspecies, indicating their adaptive role in genome differentiation. However, LTR-retrotransposons unique to either <it>O. nivara </it>or <it>O. sativa </it>regions exhibited dramatic haplotype variation regarding their presence or absence between or within populations/subpopulations.</p> <p>Conclusions</p> <p>The LTR-retrotransposon insertion hotspot in the <it>Orp </it>region was formed recently, independently and concurrently in different AA-genome species, and that the genic rearrangements detected in different species appear to be differentially triggered by transposable elements. This region is located near the end of the short arm of chromosome 8 and contains a high proportion of LTR-retrotransposons similar to observed in the centromeric region of this same chromosome, and thus may represent a genomic region that has recently switched from euchromatic to heterochromatic states. The haplotype variation of LTR-retrotransposon insertions within this region reveals substantial admixture among various subpopulations as established by molecular markers at the whole genome level, and can be used to develop retrotransposon junction markers for simple and rapid classification of <it>O. sativa </it>germplasm.</p

High‐Efficiency Graphene‐Oxide/Silicon Solar Cells with an Organic‐Passivated Interface

A breakthrough in graphene-oxide/silicon heterojunction solar cells is presented in which edge-oxidized graphene and an in-plane charge transfer dopant (Nafion) are combined to form a high-quality passivating contact scheme. A graphene oxide (GO):Nafion ink is developed and an advanced back-junction GO:Nafion/n-Si solar cell with a high-power conversion efficiency (18.8%) and large area (5.5 cm2) is reported. This scalable solution-based processing technique has the potential to enable low-cost carbon/silicon heterojunction photovoltaic devices

The Genomes of Oryza sativa: A History of Duplications

We report improved whole-genome shotgun sequences for the genomes of indica and japonica rice, both with multimegabase contiguity, or almost 1,000-fold improvement over the drafts of 2002. Tested against a nonredundant collection of 19,079 full-length cDNAs, 97.7% of the genes are aligned, without fragmentation, to the mapped super-scaffolds of one or the other genome. We introduce a gene identification procedure for plants that does not rely on similarity to known genes to remove erroneous predictions resulting from transposable elements. Using the available EST data to adjust for residual errors in the predictions, the estimated gene count is at least 38,000–40,000. Only 2%–3% of the genes are unique to any one subspecies, comparable to the amount of sequence that might still be missing. Despite this lack of variation in gene content, there is enormous variation in the intergenic regions. At least a quarter of the two sequences could not be aligned, and where they could be aligned, single nucleotide polymorphism (SNP) rates varied from as little as 3.0 SNP/kb in the coding regions to 27.6 SNP/kb in the transposable elements. A more inclusive new approach for analyzing duplication history is introduced here. It reveals an ancient whole-genome duplication, a recent segmental duplication on Chromosomes 11 and 12, and massive ongoing individual gene duplications. We find 18 distinct pairs of duplicated segments that cover 65.7% of the genome; 17 of these pairs date back to a common time before the divergence of the grasses. More important, ongoing individual gene duplications provide a never-ending source of raw material for gene genesis and are major contributors to the differences between members of the grass family

Morphological diversity of single neurons in molecularly defined cell types.

Dendritic and axonal morphology reflects the input and output of neurons and is a defining feature of neuronal types1,2, yet our knowledge of its diversity remains limited. Here, to systematically examine complete single-neuron morphologies on a brain-wide scale, we established a pipeline encompassing sparse labelling, whole-brain imaging, reconstruction, registration and analysis. We fully reconstructed 1,741 neurons from cortex, claustrum, thalamus, striatum and other brain regions in mice. We identified 11 major projection neuron types with distinct morphological features and corresponding transcriptomic identities. Extensive projectional diversity was found within each of these major types, on the basis of which some types were clustered into more refined subtypes. This diversity follows a set of generalizable principles that govern long-range axonal projections at different levels, including molecular correspondence, divergent or convergent projection, axon termination pattern, regional specificity, topography, and individual cell variability. Although clear concordance with transcriptomic profiles is evident at the level of major projection type, fine-grained morphological diversity often does not readily correlate with transcriptomic subtypes derived from unsupervised clustering, highlighting the need for single-cell cross-modality studies. Overall, our study demonstrates the crucial need for quantitative description of complete single-cell anatomy in cell-type classification, as single-cell morphological diversity reveals a plethora of ways in which different cell types and their individual members may contribute to the configuration and function of their respective circuits

Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches

Detection of Association Features Based on Gene Eigenvalues and MRI Imaging Using Genetic Weighted Random Forest

In the studies of Alzheimer&rsquo;s disease (AD), jointly analyzing imaging data and genetic data provides an effective method to explore the potential biomarkers of AD. AD can be separated into healthy controls (HC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI) and AD. In the meantime, identifying the important biomarkers of AD progression, and analyzing these biomarkers in AD provide valuable insights into understanding the mechanism of AD. In this paper, we present a novel data fusion method and a genetic weighted random forest method to mine important features. Specifically, we amplify the difference among AD, LMCI, EMCI and HC by introducing eigenvalues calculated from the gene p-value matrix for feature fusion. Furthermore, we construct the genetic weighted random forest using the resulting fused features. Genetic evolution is used to increase the diversity among decision trees and the decision trees generated are weighted by weights. After training, the genetic weighted random forest is analyzed further to detect the significant fused features. The validation experiments highlight the performance and generalization of our proposed model. We analyze the biological significance of the results and identify some significant genes (CSMD1, CDH13, PTPRD, MACROD2 and WWOX). Furthermore, the calcium signaling pathway, arrhythmogenic right ventricular cardiomyopathy and the glutamatergic synapse pathway were identified. The investigational findings demonstrate that our proposed model presents an accurate and efficient approach to identifying significant biomarkers in AD

Mitochondrial Phylogenomics of Fagales Provides Insights Into Plant Mitogenome Mosaic Evolution

Fagales are an order of woody plants and comprise more than 1,100 species, most of which produce economically important timbers, nuts, and fruits. Their nuclear and plastid genomes are well-sequenced and provided valuable resources to study their phylogeny, breeding, resistance, etc. However, little is known about the mitochondrial genomes (mitogenomes), which hinder a full understanding of their genome evolution. In this study, we assembled complete mitogenomes of 23 species, covering five of the seven families of Fagales. These mitogenomes had similar gene sets but varied 2.4 times in size. The mitochondrial genes were highly conserved, and their capacity in phylogeny was challenging. The mitogenomic structure was extremely dynamic, and synteny among species was poor. Further analyses of the Fagales mitogenomes revealed extremely mosaic characteristics, with horizontal transfer (HGT)-like sequences from almost all seed plant taxa and even mitoviruses. The largest mitogenome, Carpinus cordata, did not have large amounts of specific sequences but instead contained a high proportion of sequences homologous to other Fagales. Independent and unequal transfers of third-party DNA, including nuclear genome and other resources, may partially account for the HGT-like fragments and unbalanced size expansions observed in Fagales mitogenomes. Supporting this, a mitochondrial plasmid-like of nuclear origin was found in Carpinus. Overall, we deciphered the last genetic materials of Fagales, and our large-scale analyses provide new insights into plant mitogenome evolution and size variation.</p

Radar Emitter Identification with Multi-View Adaptive Fusion Network (MAFN)

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts

Saline soil enzyme activities of four plant communities in Sangong River basin of Xinjiang, China

Soil enzyme activity plays an important role in the conversion of soil organic carbon into inorganic carbon, which is significant for the global carbon cycle. In this study, we investigated the soil enzyme activities of two ligninolytic enzymes (peroxidase and polyphenol oxidase) and five non-ligninolytic enzymes (a-1,4-glucosidase (AG); beta-1,4-glucosidase (BG); N-acetyl-beta-glucosaminidase (NAG); beta-D-cellobiosidase (CBH); and beta-xylosidase (BXYL)) in four plant communities of the Sangong River basin in Fukang, North Xinjiang, China. The four typical plant communities were dominated by Haloxylon ammodendron, Reaumuria soongonica, Salsola passerina, and Tamarix rarmosissima, respectively, with saline soils of varied alkalinity. The results showed that the soil peroxidase activity decreased seasonally. The activities of the five non-ligninolytic enzymes decreased with increasing soil depths, while those of the two ligninolytic enzymes did not show such a trend. In the four plant communities, BG had the highest activity among the five non-ligninolytic enzymes, and the activities of the two ligninolytic enzymes were higher than those of the four non-ligninolytic ones (AG, NAG, CBH, and BXYL). The community of H. ammodendron displayed the highest activity with respect to the two ligninolytic enzymes in most cases, but no significant differences were found among the four plant communities. The geometric mean of soil enzyme activities of the four plant communities was validated through an independently performed principal component analysis (PCA), which indicated that different plant communities had different soil enzyme activities. The correlation analysis showed that soil polyphenol oxidase activity was significantly positively correlated with the activities of the five non-ligninolytic enzymes. The soil pH value was positively correlated with the activities of all soil enzymes except peroxidase. Soil microbial carbon content also showed a significant positive correlation (P< 0.01) with the activities of all soil enzymes except polyphenol oxidase. The results suggested that the H. ammodendron community has the highest ability to utilize soil organic carbon, and glucoside could be the most extensively utilized non-ligninolytic carbon source in the saline soil of arid areas in Xinjiang