Next Generation Transcriptome Sequencing and Quantitative Real-Time PCR Technologies for Characterisation of the Bemisia tabaci Asia 1 mtCOI Phylogenetic Clade

A programme of functional genomics research is underway at the University of Greenwich,UK,to develop and apply genomics technologies to characterise an economically-important but under-researched Bemisia tabaci（Hemiptera：Aleyrodidae）,the Asia 1 mtCOI phylogenetic group.A comparison of this putative species from India with other important B.tabaci populations and insect species may provide targets for the development of more effective whitefly control strategies.As a first step,next-generation sequencing（NGS）has been used to survey the transcriptome of adult female whitefly,with high quality RNA preparations being used to generate cDNA libraries for NGS using the Roche 454 Titanium DNA sequencing platform.Contig assemblies constructed from the resultant sequences（301 094 reads）using the software program CLC Genomics Workbench generated 3 821 core contigs.Comparison of a selection of these contigs with related sequences from other B.tabaci genetic groups has revealed good alignment for some genes（e.g.,HSP90）but misassemblies in other datasets（e.g.,the vitellogenin gene family）,highlighting the need for manual curation as well as collaborative international efforts to obtain accurate assemblies from the existing next generation sequence datasets.Nevertheless,data emerging from the NGS has facilitated the development of accurate and reliable methods for analysing gene expression based on quantitative real-time RT-PCR,illustrating the power of this approach to enable rapid expression analyses in an organism for which a complete genome sequence is currently lacking.

Dynamic Scene Graph Generation of Point Clouds with Structural Representation Learning

Scene graphs of point clouds help to understand object-level relationships in the 3D space.Most graph generation methods work on 2D structured data,which cannot be used for the 3D unstructured point cloud data.Existing point-cloud-based methods generate the scene graph with an additional graph structure that needs labor-intensive manual annotation.To address these problems,we explore a method to convert the point clouds into structured data and generate graphs without given structures.Specifically,we cluster points with similar augmented features into groups and establish their relationships,resulting in an initial structural representation of the point cloud.Besides,we propose a Dynamic Graph Generation Network(DGGN)to judge the semantic labels of targets of different granularity.It dynamically splits and merges point groups,resulting in a scene graph with high precision.Experiments show that our methods outperform other baseline methods.They output reliable graphs describing the object-level relationships without additional manual labeled data.

Simulation of large-scale numerical substructure in real-time dynamic hybrid testing

A solution scheme is proposed in this paper for an existing RTDHT system to simulate large-scale finite element （FE） numerical substructures. The analysis of the FE numerical substructure is split into response analysis and signal generation tasks, and executed in two different target computers in real-time. One target computer implements the response analysis task, wherein a large time-step is used to solve the FE substructure, and another target computer implements the signal generation task, wherein an interpolation program is used to generate control signals in a small time-step to meet the input demand of the controller. By using this strategy, the scale of the FE numerical substructure simulation may be increased significantly. The proposed scheme is initially verified by two FE numerical substructure models with 98 and 1240 degrees of freedom （DOFs）. Thereafter, RTDHTs of a single frame-foundation structure are implemented where the foundation, considered as the numerical substructure, is simulated by the FE model with 1240 DOFs. Good agreements between the results of the RTDHT and those from the FE analysis in ABAQUS are obtained.

SinGRAV: Learning a Generative Radiance Volume from a Single Natural Scene

We present SinGRAV, an attempt to learn a generative radiance volume from multi-view observations of a single natural scene, in stark contrast to existing category-level 3D generative models that learn from images of many object-centric scenes. Inspired by SinGAN, we also learn the internal distribution of the input scene, which necessitates our key designs w.r.t. the scene representation and network architecture. Unlike popular multi-layer perceptrons (MLP)-based architectures, we particularly employ convolutional generators and discriminators, which inherently possess spatial locality bias, to operate over voxelized volumes for learning the internal distribution over a plethora of overlapping regions. On the other hand, localizing the adversarial generators and discriminators over confined areas with limited receptive fields easily leads to highly implausible geometric structures in the spatial. Our remedy is to use spatial inductive bias and joint discrimination on geometric clues in the form of 2D depth maps. This strategy is effective in improving spatial arrangement while incurring negligible additional computational cost. Experimental results demonstrate the ability of SinGRAV in generating plausible and diverse variations from a single scene, the merits of SinGRAV over state-of-the-art generative neural scene models, and the versatility of SinGRAV by its use in a variety of applications. Code and data will be released to facilitate further research.

Comprehensive Relation Modelling for Image Paragraph Generation

Image paragraph generation aims to generate a long description composed of multiple sentences,which is different from traditional image captioning containing only one sentence.Most of previous methods are dedicated to extracting rich features from image regions,and ignore modelling the visual relationships.In this paper,we propose a novel method to generate a paragraph by modelling visual relationships comprehensively.First,we parse an image into a scene graph,where each node represents a specific object and each edge denotes the relationship between two objects.Second,we enrich the object features by implicitly encoding visual relationships through a graph convolutional network(GCN).We further explore high-order relations between different relation features using another graph convolutional network.In addition,we obtain the linguistic features by projecting the predicted object labels and their relationships into a semantic embedding space.With these features,we present an attention-based topic generation network to select relevant features and produce a set of topic vectors,which are then utilized to generate multiple sentences.We evaluate the proposed method on the Stanford image-paragraph dataset which is currently the only available dataset for image paragraph generation,and our method achieves competitive performance in comparison with other state-of-the-art(SOTA)methods.

A formal method to real-time protocol interoperability testing

Interoperability testing is an important technique to ensure the quality of implementations of network communication protocol. In the next generation Internet protocol, real-time applications should be supported effectively. However, time constraints were not considered in the related studies of protocol interoperability testing, so existing interoperability testing methods are difficult to be applied in real-time protocol interoperability testing. In this paper, a formal method to real-time protocol interoperability testing is proposed. Firstly, a formal model CMpTIOA （communicating multi-port timed input output automata） is defined to specify the system under test （SUT） in real-time protocol interoperability testing; based on this model, timed interoperability relation is then defined. In order to check this relation, a test generation method is presented to generate a parameterized test behavior tree from SUT model; a mechanism of executability pre-determination is also integrated in the test generation method to alleviate state space explosion problem to some extent. The proposed theory and method are then applied in interoperability testing of IPv6 neighbor discovery protocol to show the feasibility of this method.

Exploring the hepatitis C virus genome using single molecule realtime sequencing

Single molecular real-time(SMRT)sequencing,also called third-generation sequencing,is a novel sequencing technique capable of generating extremely long contiguous sequence reads.While conventional short-read sequencing cannot evaluate the linkage of nucleotide substitutions distant from one another,SMRT sequencing can directly demonstrate linkage of nucleotide changes over a span of more than 20 kbp,and thus can be applied to directly examine the haplotypes of viruses or bacteria whose genome structures are changing in real time.In addition,an error correction method(circular consensus sequencing)has been established and repeated sequencing of a single-molecule DNA template can result in extremely high accuracy.The advantages of long read sequencing enable accurate determination of the haplotypes of individual viral clones.SMRT sequencing has been applied in various studies of viral genomes including determination of the full-length contiguous genome sequence of hepatitis C virus(HCV),targeted deep sequencing of the HCV NS5A gene,and assessment of heterogeneity among viral populations.Recently,the emergence of multi-drug resistant HCV viruses has become a significant clinical issue and has been also demonstrated using SMRT sequencing.In this review,we introduce the novel third-generation PacBio RSII/Sequel systems,compare them with conventional next-generation sequencers,and summarize previous studies in which SMRT sequencing technology has been applied for HCV genome analysis.We also refer to another long-read sequencing platform,nanopore sequencing technology,and discuss the advantages,limitations and future perspectives in using these thirdgeneration sequencers for HCV genome analysis.

Template-based AADL automatic code generation

Embedded real-time systems employ a variety of operating system platforms. Consequently, for automatic code generation, considerable redevelopment is needed when the platform changes. This results in major challenges with respect to the automatic code generation process of the architecture analysis and design language (AADL). In this paper, we propose a method of template-based automatic code generation to address this issue. Templates are used as carriers of automatic code generation rules from AADL to the object platform. These templates can be easily modified for different platforms. Automatic code generation for different platforms can be accomplished by formulating the corresponding generation rules and transformation templates. We design a set of code generation templates from AADL to the object platform and develop an automatic code generation tool. Finally, we take a typical data processing unit (DPU) system as a case study to test the tool. It is demonstrated that the autogenerated codes can be compiled and executed successfully on the object platform.

Multiprocessing and Dataflow Processing Architecture of a Pixel Processor and Its VHDL Simulation Strategy

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A Survey of 3D Indoor Scene Synthesis

Indoor scene synthesis has become a popular topic in recent years.Synthesizing functional and plausible indoor scenes is an inherently difficult task since it requires considerable knowledge to both choose reasonable object categories and arrange objects appropriately.In this survey,we propose four criteria which group a wide range of 3D(three-dimensional)indoor scene synthesis techniques according to various aspects(specifically,four groups of categories).It also provides hints,througli comprehensively comparing all the techniques to demonstrate their effectiveness and drawbacks,and discussions of potential remaining problems.

A Comprehensive Pipeline for Complex Text-to-Image Synthesis

Synthesizing a complex scene image with multiple objects and background according to text description is a challenging problem.It needs to solve several difficult tasks across the fields of natural language processing and computer vision.We model it as a combination of semantic entity recognition,object retrieval and recombination,and objects’status optimization.To reach a satisfactory result,we propose a comprehensive pipeline to convert the input text to its visual counterpart.The pipeline includes text processing,foreground objects and background scene retrieval,image synthesis using constrained MCMC,and post-processing.Firstly,we roughly divide the objects parsed from the input text into foreground objects and background scenes.Secondly,we retrieve the required foreground objects from the foreground object dataset segmented from Microsoft COCO dataset,and retrieve an appropriate background scene image from the background image dataset extracted from the Internet.Thirdly,in order to ensure the rationality of foreground objects’positions and sizes in the image synthesis step,we design a cost function and use the Markov Chain Monte Carlo(MCMC)method as the optimizer to solve this constrained layout problem.Finally,to make the image look natural and harmonious,we further use Poisson-based and relighting-based methods to blend foreground objects and background scene image in the post-processing step.The synthesized results and comparison results based on Microsoft COCO dataset prove that our method outperforms some of the state-of-the-art methods based on generative adversarial networks(GANs)in visual quality of generated scene images.

A survey on de novo assembly methods for single-molecular sequencing

Background:The single-molecular sequencing(SMS)is under rapid development and generating increasingly long and accurate sequences.De novo assembly of genomes from SMS sequences is a critical step for many genomic studies.To scale well with the developing trends of SMS,many de novo assemblers for SMS have been released.These assembly workflows can be categorized into two different kinds:the correction-and-assembly strategy and the assembly-and-correction strategy,both of which are gaining more and more attentions.Results:In this article we make a discussion on the characteristics of errors in SMS sequences・We then review the currently widely applied de novo assemblers for SMS sequences.We also describe computational methods relevant to de novo assembly,including the alignment methods and the error correction methods.Benchmarks are provided to analyze their performance on different datasets and to provide use guides on applying the computation methods.Conclusion:We make a detailed review on the latest development of de novo assembly and some relevant algorithms for SMS,including their rationales,solutions and results.Besides,we provide use guides on the algorithms based on their benchmark results.Finally we conclude the review by giving some developing trends of third generation sequencing(TGS).