133 research outputs found
Detecting objects using Rolling Convolution and Recurrent Neural Network
AbstractāAt present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image. The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency. Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations. This is mainly because these approaches use coarse feature. Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance.Ā Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers. Furthermore, we use a recurrent neural networkĀ of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects. Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCALĀ VOC 2007 and 72.3% mAP in PASCAL VOCĀ 2012 dataset. It has been verified through many experiments that this method has reached a more advanced level of detection
Detecting objects using Rolling Convolution and Recurrent Neural Network
AbstractāAt present, most of the existing target detection algorithms use the method of region proposal to search for the target in the image. The most effective regional proposal method usually requires thousands of target prediction areas to achieve high recall rate.This lowers the detection efficiency. Even though recent region proposal network approach have yielded good results by using hundreds of proposals, it still faces the challenge when applied to small objects and precise locations. This is mainly because these approaches use coarse feature. Therefore, we propose a new method for extracting more efficient global features and multi-scale features to provide target detection performance.Ā Given that feature maps under continuous convolution lose the resolution required to detect small objects when obtaining deeper semantic information; hence, we use rolling convolution (RC) to maintain the high resolution of low-level feature maps to explore objects in greater detail, even if there is no structure dedicated to combining the features of multiple convolutional layers. Furthermore, we use a recurrent neural networkĀ of multiple gated recurrent units (GRUs) at the top of the convolutional layer to highlight useful global context locations for assisting in the detection of objects. Through experiments in the benchmark data set, our proposed method achieved 78.2% mAP in PASCALĀ VOC 2007 and 72.3% mAP in PASCAL VOCĀ 2012 dataset. It has been verified through many experiments that this method has reached a more advanced level of detection
Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest
Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Single-frame image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each stepĀ to get the most out of the details and tightly connect the internal logic of each sequential step.This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image
Multi-frame Image Super-resolution Reconstruction Using Multi-grained Cascade Forest
Super-resolution image reconstruction utilizes two algorithms, where one is for single-frame image reconstruction, and the other is for multi-frame image reconstruction. Single-frame image reconstruction generally takes the first degradation and is followed by reconstruction, which essentially creates a problem of insufficient characterization. Multi-frame images provide additional information for image reconstruction relative to single frame images due to the slight differences between sequential frames. However, the existing super-resolution algorithm for multi-frame images do not take advantage of this key factor, either because of loose structure and complexity, or because the individual frames are restored poorly. This paper proposes a new SR reconstruction algorithm for images using Multi-grained Cascade Forest. Multi-frame image reconstruction is processed sequentially. Firstly, the image registration algorithm uses a convolutional neural network to register low-resolution image sequences, and then the images are reconstructed after registration by the Multi-grained Cascade Forest reconstruction algorithm. Finally, the reconstructed images are fused. The optimal algorithm is selected for each stepĀ to get the most out of the details and tightly connect the internal logic of each sequential step.This novel approach proposed in this paper, in which the depth of the cascade forest is procedurally generated for recovered images, rather than being a constant. After training each layer, the recovered image is automatically evaluated, and new layers are constructed for training until an optimal restored image is obtained. Experiments show that this method improves the quality of image reconstruction while preserving the details of the image
Plasmodium vivax populations revisited: mitochondrial genomes of temperate strains in Asia suggest ancient population expansion
<p>Abstract</p> <p>Background</p> <p><it>Plasmodium vivax </it>is the most widely distributed human malaria parasite outside of Africa, and its range extends well into the temperate zones. Previous studies provided evidence for vivax population differentiation, but temperate vivax parasites were not well represented in these analyses. Here we address this deficit by using complete mitochondrial (mt) genome sequences to elucidate the broad genetic diversity and population structure of <it>P. vivax </it>from temperate regions in East and Southeast Asia.</p> <p>Results</p> <p>From the complete mtDNA sequences of 99 clinical samples collected in China, Myanmar and Korea, a total of 30 different haplotypes were identified from 26 polymorphic sites. Significant differentiation between different East and Southeast Asian parasite populations was observed except for the comparison between populations from Korea and southern China. Haplotype patterns and structure diversity analysis showed coexistence of two different groups in East Asia, which were genetically related to the Southeast Asian population and Myanmar population, respectively. The demographic history of <it>P. vivax</it>, examined using neutrality tests and mismatch distribution analyses, revealed population expansion events across the entire <it>P. vivax </it>range and the Myanmar population. Bayesian skyline analysis further supported the occurrence of ancient <it>P. vivax </it>population expansion.</p> <p>Conclusions</p> <p>This study provided further resolution of the population structure and evolution of <it>P. vivax</it>, especially in temperate/warm-temperate endemic areas of Asia. The results revealed divergence of the <it>P. vivax </it>populations in temperate regions of China and Korea from other populations. Multiple analyses confirmed ancient population expansion of this parasite. The extensive genetic diversity of the <it>P. vivax </it>populations is consistent with phenotypic plasticity of the parasites, which has implications for malaria control.</p
Molecular characterization, structural analysis and determination of host range of a novel bacteriophage LSB-1
<p>Abstract</p> <p>Background</p> <p>Bacteriophages (phages) are widespread in the environment and play a crucial role in the evolution of their bacterial hosts and the emergence of new pathogens.</p> <p>Results</p> <p>LSB-1, a reference coliphage strain, was classified as a member of the Podoviridae family with a cystic form (50 Ā± 5 nm diameter) and short tail (60 Ā± 5 nm long). The double stranded DNA was about 30 kilobase pairs in length. We identified its host range and determined the gp17 sequences and protein structure using shotgun analysis and bioinformatics technology.</p> <p>Conclusions</p> <p>Coliphage LSB-1 possesses a tailspike protein with endosialidase activity which is probably responsible for its specific enteroinvasive <it>E.coli </it>host range within the laboratory.</p
Post-marital residence patterns and the timing of reproduction: evidence from a matrilineal society
Humans exhibit a broad range of post-marital residence patterns and there is growing recognition that post-marital residence predicts women's reproductive success; however, the nature of the relationship is probably dependent on whether co-resident kin are cooperators or competitors. Here, we explore this relationship in a Tibetan population, where couples practice a mixture of post-marital residence patterns, co-residing in the same village with the wife's parents, the husband's parents or endogamously with both sets of parents. Using detailed demographic data from 17 villages we find that women who live with only their own parents have an earlier age at first birth (AFB) and age at last birth (ALB) than women who live with only their parents-in-law. Women who co-reside with both sets of parents have the earliest AFB and ALB. However, those with co-resident older siblings postponed reproduction, suggestive of competition-related delay. Shifts to earlier reproductive timing were also observed in relation to the imposition of family planning policies, in line with Fisherian expectations. Our study provides evidence of the costs and benefits to women's direct fitness of co-residing with different kin, against a backdrop of adaptive responses to cultural constraints on completed fertility
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Familial multinodular goiter syndrome with papillary thyroid carcinomas: mutational analysis of the associated genes in 5 cases from 1 Chinese family
Background: Familial papillary thyroid cancer (fPTC) is recognized as a distinct entity only recently and no fPTC predisposing genes have been identified. Several potential regions and susceptibility loci for sporadic PTC have been reported. We aimed to evaluate the role of the reported susceptibility loci and potential risk genomic region in a Chinese familial multinodular goiter (fMNG) with PTC family. Methods: We sequenced the related risk genomic regions and analyzed the known PTC susceptibility loci in the Chinese family members who consented to join the study. These loci included (1) the point mutations of the BRAF and RET; (2) the possible susceptibility loci to sporadic PTC; and (3) the suggested potential fMNG syndrome with PTC risk region. Results: The members showed no mutations in the common susceptible BRAF and RET genomic region, although contained several different heterozygous alleles in the RET introns. All the members were homozygous for PTC risk alleles of rs966423 (C) at chromosome 2q35, rs2910164 (C) at chromosome 5q24 and rs2439302 (G) at chromosome 8p12; while carried no risk allele of rs4733616 (T) at chromosome 8q24, rs965513 (A) or rs1867277 (A) at chromosome 9q22 which were associated with radiation-related PTC. The frequency of the risk allele of rs944289 (T) but not that of rs116909374 (T) at chromosome 14q13 was increased in the MNG or PTC family members. Conclusions: Our work provided additional evidence to the genetic predisposition to a Chinese familial form of MNG with PTC. The family members carried quite a few risk alleles found in sporadic PTC; particularly, homozygous rs944289 (T) at chromosome 14q13 which was previously shown to be linked to a form of fMNG with PTC. Moreover, the genetic determinants of radiation-related PTC were not presented in this family
Suppression of Black-body Radiation Induced Zeeman Shifts in the Optical Clocks due to the Fine-structure Intramanifold Resonances
The roles of the fine-structure intramanifold resonances to the Zeeman shifts
caused by the blackbody radiation (BBRz shifts) in the optical clock
transitions are analyzed. The clock frequency measurement in the
clock transition of the singly charged aluminium ion (Al) has already been
reached the level at which the BBRz effect can be significant in
determining the uncertainty. In view of this, we probe first the BBRz shift in
this transition rigorously and demonstrate the importance of the contributions
from the intramanifold resonances explicitly. To carry out the analysis, we
determine the dynamic magnetic dipole (M1) polarizabilities of the clock states
over a wide range of angular frequencies by employing two variants of
relativistic many-body methods. This showed the BBRz shift is highly suppressed
due to blue-detuning of the BBR spectrum to the fine-structure
intramanifold resonance in Al and it fails to follow the usually assumed
static M1 polarizability limit in the estimation of the BBRz shift. The
resonance also leads to a reversal behavior of the temperature dependence and a
cancellation in the shift. After learning this behavior, we extended our
analyses to other optical clocks and found that these shifts are of the order
of micro-hertz leading to fractional shifts in the clock transitions at the
level or below
Study on Parameter Optimization for Support Vector Regression in Solving the Inverse ECG Problem
The typical inverse ECG problem is to noninvasively reconstruct the transmembrane potentials (TMPs) from body surface potentials (BSPs). In the study, the inverse ECG problem can be treated as a regression problem with multi-inputs (body surface potentials) and multi-outputs (transmembrane potentials), which can be solved by the support vector regression (SVR) method. In order to obtain an effective SVR model with optimal regression accuracy and generalization performance, the hyperparameters of SVR must be set carefully. Three different optimization methods, that is, genetic algorithm (GA), differential evolution (DE) algorithm, and particle swarm optimization (PSO), are proposed to determine optimal hyperparameters of the SVR model. In this paper, we attempt to investigate which one is the most effective way in reconstructing the cardiac TMPs from BSPs, and a full comparison of their performances is also provided. The experimental results show that these three optimization methods are well performed in finding the proper parameters of SVR and can yield good generalization performance in solving the inverse ECG problem. Moreover, compared with DE and GA, PSO algorithm is more efficient in parameters optimization and performs better in solving the inverse ECG problem, leading to a more accurate reconstruction of the TMPs
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