Mechanism Analysis and Dynamics Simulation of Assist Manipulator

In order to reduce labour intensity and improve working efficiency, a kind of assist manipulator was designed which is an auxiliary tool used for the assembly line of the marine diesel engine that can conveniently realize the delivery of parts and field assembly. Motion and force analysis of the mechanism of assist manipulator was examined with the help of MATLAB software on the base of d\u27Alembert principle, the disciplinary of displacement, velocity, acceleration, and force rules in the process of mechanism movement was obtained by mechanical analysis. Based on the kinematical analysis, the parameters of mechanism size were optimized to improve the loading state. The Creo software, ANSYS software, and RecurDyn software were used to model and analyse the rigid-flexible coupling dynamics of the manipulator, and the motion law and stress distribution of the key components was obtained

Private Model Compression via Knowledge Distillation

The soaring demand for intelligent mobile applications calls for deploying powerful deep neural networks (DNNs) on mobile devices. However, the outstanding performance of DNNs notoriously relies on increasingly complex models, which in turn is associated with an increase in computational expense far surpassing mobile devices' capacity. What is worse, app service providers need to collect and utilize a large volume of users' data, which contain sensitive information, to build the sophisticated DNN models. Directly deploying these models on public mobile devices presents prohibitive privacy risk. To benefit from the on-device deep learning without the capacity and privacy concerns, we design a private model compression framework RONA. Following the knowledge distillation paradigm, we jointly use hint learning, distillation learning, and self learning to train a compact and fast neural network. The knowledge distilled from the cumbersome model is adaptively bounded and carefully perturbed to enforce differential privacy. We further propose an elegant query sample selection method to reduce the number of queries and control the privacy loss. A series of empirical evaluations as well as the implementation on an Android mobile device show that RONA can not only compress cumbersome models efficiently but also provide a strong privacy guarantee. For example, on SVHN, when a meaningful $(9.83,10^{-6})$-differential privacy is guaranteed, the compact model trained by RONA can obtain 20$\times$ compression ratio and 19$\times$ speed-up with merely 0.97% accuracy loss.Comment: Conference version accepted by AAAI'1

Families of transposable elements, population structure and the origin of species

<p>Abstract</p> <p>Background</p> <p>Eukaryotic genomes harbor diverse families of repetitive DNA derived from transposable elements (TEs) that are able to replicate and insert into genomic DNA. The biological role of TEs remains unclear, although they have profound mutagenic impact on eukaryotic genomes and the origin of repetitive families often correlates with speciation events. We present a new hypothesis to explain the observed correlations based on classical concepts of population genetics.</p> <p>Presentation of the hypothesis</p> <p>The main thesis presented in this paper is that the TE-derived repetitive families originate primarily by genetic drift in small populations derived mostly by subdivisions of large populations into subpopulations. We outline the potential impact of the emerging repetitive families on genetic diversification of different subpopulations, and discuss implications of such diversification for the origin of new species.</p> <p>Testing the hypothesis</p> <p>Several testable predictions of the hypothesis are examined. First, we focus on the prediction that the number of diverse families of TEs fixed in a representative genome of a particular species positively correlates with the cumulative number of subpopulations (demes) in the historical metapopulation from which the species has emerged. Furthermore, we present evidence indicating that human AluYa5 and AluYb8 families might have originated in separate proto-human subpopulations. We also revisit prior evidence linking the origin of repetitive families to mammalian phylogeny and present additional evidence linking repetitive families to speciation based on mammalian taxonomy. Finally, we discuss evidence that mammalian orders represented by the largest numbers of species may be subject to relatively recent population subdivisions and speciation events.</p> <p>Implications of the hypothesis</p> <p>The hypothesis implies that subdivision of a population into small subpopulations is the major step in the origin of new families of TEs as well as of new species. The origin of new subpopulations is likely to be driven by the availability of new biological niches, consistent with the hypothesis of punctuated equilibria. The hypothesis also has implications for the ongoing debate on the role of genetic drift in genome evolution.</p> <p>Reviewers</p> <p>This article was reviewed by Eugene Koonin, Juergen Brosius and I. King Jordan.</p

Ginger DNA transposons in eukaryotes and their evolutionary relationships with long terminal repeat retrotransposons

<p>Abstract</p> <p>Background</p> <p>In eukaryotes, long terminal repeat (LTR) retrotransposons such as <it>Copia, BEL </it>and <it>Gypsy </it>integrate their DNA copies into the host genome using a particular type of DDE transposase called integrase (INT). The <it>Gypsy </it>INT-like transposase is also conserved in the <it>Polinton/Maverick </it>self-synthesizing DNA transposons and in the 'cut and paste' DNA transposons known as <it>TDD-4 </it>and <it>TDD-5</it>. Moreover, it is known that INT is similar to bacterial transposases that belong to the IS<it>3</it>, IS<it>481</it>, IS<it>30 </it>and IS<it>630 </it>families. It has been suggested that LTR retrotransposons evolved from a non-LTR retrotransposon fused with a DNA transposon in early eukaryotes. In this paper we analyze a diverse superfamily of eukaryotic cut and paste DNA transposons coding for INT-like transposase and discuss their evolutionary relationship to LTR retrotransposons.</p> <p>Results</p> <p>A new diverse eukaryotic superfamily of DNA transposons, named <it>Ginger </it>(for '<it>Gypsy </it>INteGrasE Related') DNA transposons is defined and analyzed. Analogously to the IS<it>3 </it>and IS<it>481 </it>bacterial transposons, the <it>Ginger </it>termini resemble those of the <it>Gypsy </it>LTR retrotransposons. Currently, <it>Ginger </it>transposons can be divided into two distinct groups named <it>Ginger1 </it>and <it>Ginger2/Tdd</it>. Elements from the <it>Ginger1 </it>group are characterized by approximately 40 to 270 base pair (bp) terminal inverted repeats (TIRs), and are flanked by CCGG-specific or CCGT-specific target site duplication (TSD) sequences. The <it>Ginger1</it>-encoded transposases contain an approximate 400 amino acid N-terminal portion sharing high amino acid identity to the entire <it>Gypsy</it>-encoded integrases, including the YPYY motif, zinc finger, DDE domain, and, importantly, the GPY/F motif, a hallmark of <it>Gypsy </it>and endogenous retrovirus (ERV) integrases. <it>Ginger1 </it>transposases also contain additional C-terminal domains: ovarian tumor (OTU)-like protease domain or Ulp1 protease domain. In vertebrate genomes, at least two host genes, which were previously thought to be derived from the <it>Gypsy </it>integrases, apparently have evolved from the <it>Ginger1 </it>transposase genes. We also introduce a second <it>Ginger </it>group, designated <it>Ginger2/Tdd</it>, which includes the previously reported DNA transposon <it>TDD-4</it>.</p> <p>Conclusions</p> <p>The <it>Ginger </it>superfamily represents eukaryotic DNA transposons closely related to LTR retrotransposons. <it>Ginger </it>elements provide new insights into the evolution of transposable elements and certain transposable element (TE)-derived genes.</p

Waveform-Domain Adaptive Matched Filtering: A Novel Approach to Suppressing Interrupted-Sampling Repeater Jamming

The inadequate adaptability to flexible interference scenarios remains an unresolved challenge in the majority of techniques utilized for mitigating interrupted-sampling repeater jamming (ISRJ). Matched filtering system based methods is desirable to incorporate anti-ISRJ measures based on prior ISRJ modeling, either preceding or succeeding the matched filtering. Due to the partial matching nature of ISRJ, its characteristics are revealed during the process of matched filtering. Therefore, this paper introduces an extended domain called the waveform domain within the matched filtering process. On this domain, a novel matched filtering model, known as the waveform-domain adaptive matched filtering (WD-AMF), is established to tackle the problem of ISRJ suppression without relying on a pre-existing ISRJ model. The output of the WD-AMF encompasses an adaptive filtering term and a compensation term. The adaptive filtering term encompasses the adaptive integration outcomes in the waveform domain, which are determined by an adaptive weighted function. This function, akin to a collection of bandpass filters, decomposes the integrated function into multiple components, some of which contain interference while others do not. The compensation term adheres to an integrated guideline for discerning the presence of signal components or noise within the integrated function. The integration results are then concatenated to reconstruct a compensated matched filter signal output. Simulations are conducted to showcase the exceptional capability of the proposed method in suppressing ISRJ in diverse interference scenarios, even in the absence of a pre-existing ISRJ model

The mouse and ferret models for studying the novel avian-origin human influenza A (H7N9) virus.

BackgroundThe current study was conducted to establish animal models (including mouse and ferret) for the novel avian-origin H7N9 influenza virus.FindingsA/Anhui/1/2013 (H7N9) virus was administered by intranasal instillation to groups of mice and ferrets, and animals developed typical clinical signs including body weight loss (mice and ferrets), ruffled fur (mice), sneezing (ferrets), and death (mice). Peak virus shedding from respiratory tract was observed on 2 days post inoculation (d.p.i.) for mice and 3-5 d.p.i. for ferrets. Virus could also be detected in brain, liver, spleen, kidney, and intestine from inoculated mice, and in heart, liver, and olfactory bulb from inoculated ferrets. The inoculation of H7N9 could elicit seroconversion titers up to 1280 in ferrets and 160 in mice. Leukopenia, significantly reduced lymphocytes but increased neutrophils were also observed in mouse and ferret models.ConclusionsThe mouse and ferret model enables detailed studies of the pathogenesis of this illness and lay the foundation for drug or vaccine evaluation

Decreased default mode network functional connectivity with visual processing regions as potential biomarkers for delayed neurocognitive recovery: A resting-state fMRI study and machine-learning analysis

ObjectivesThe abnormal functional connectivity (FC) pattern of default mode network (DMN) may be key markers for early identification of various cognitive disorders. However, the whole-brain FC changes of DMN in delayed neurocognitive recovery (DNR) are still unclear. Our study was aimed at exploring the whole-brain FC patterns of all regions in DMN and the potential features as biomarkers for the prediction of DNR using machine-learning algorithms.MethodsResting-state functional magnetic resonance imaging (fMRI) was conducted before surgery on 74 patients undergoing non-cardiac surgery. Seed-based whole-brain FC with 18 core regions located in the DMN was performed, and FC features that were statistically different between the DNR and non-DNR patients after false discovery correction were extracted. Afterward, based on the extracted FC features, machine-learning algorithms such as support vector machine, logistic regression, decision tree, and random forest were established to recognize DNR. The machine learning experiment procedure mainly included three following steps: feature standardization, parameter adjustment, and performance comparison. Finally, independent testing was conducted to validate the established prediction model. The algorithm performance was evaluated by a permutation test.ResultsWe found significantly decreased DMN connectivity with the brain regions involved in visual processing in DNR patients than in non-DNR patients. The best result was obtained from the random forest algorithm based on the 20 decision trees (estimators). The random forest model achieved the accuracy, sensitivity, and specificity of 84.0, 63.1, and 89.5%, respectively. The area under the receiver operating characteristic curve of the classifier reached 86.4%. The feature that contributed the most to the random forest model was the FC between the left retrosplenial cortex/posterior cingulate cortex and left precuneus.ConclusionThe decreased FC of DMN with regions involved in visual processing might be effective markers for the prediction of DNR and could provide new insights into the neural mechanisms of DNR.Clinical Trial Registration: Chinese Clinical Trial Registry, ChiCTR-DCD-15006096