A hybrid representation based simile component extraction

Simile, a special type of metaphor, can help people to express their ideas more clearly. Simile component extraction is to extract tenors and vehicles from sentences. This task has a realistic significance since it is useful for building cognitive knowledge base. With the development of deep neural networks, researchers begin to apply neural models to component extraction. Simile components should be in cross-domain. According to our observations, words in cross-domain always have different concepts. Thus, concept is important when identifying whether two words are simile components or not. However, existing models do not integrate concept into their models. It is difficult for these models to identify the concept of a word. What’s more, corpus about simile component extraction is limited. There are a number of rare words or unseen words, and the representations of these words are always not proper enough. Exiting models can hardly extract simile components accurately when there are low-frequency words in sentences. To solve these problems, we propose a hybrid representation-based component extraction (HRCE) model. Each word in HRCE is represented in three different levels: word level, concept level and character level. Concept representations (representations in concept level) can help HRCE to identify the words in cross-domain more accurately. Moreover, with the help of character representations (representations in character levels), HRCE can represent the meaning of a word more properly since words are consisted of characters and these characters can partly represent the meaning of words. We conduct experiments to compare the performance between HRCE and existing models. The experiment results show that HRCE significantly outperforms current models

Recurrent Neural Networks for Multivariate Loss Reserving and Risk Capital Analysis

Reserves comprise most of the liabilities of a property and casualty (P&C) company and are actuaries' best estimate for unpaid future claims. Notably, the reserves for different lines of business (LOB) are related, as there may be dependence between events related to claims. There have been parametric and non-parametric methods in the actuarial industry for loss reserving; only a few tools have been developed to use the recurrent neural network (RNN) for multivariate loss reserving and risk capital analyses. This paper aims to study RNN methods to model dependence between loss triangles and develop predictive distribution for reserves using machine learning. Thus, we create an RNN model to capture dependence between LOBs by extending the Deep Triangle (DT) model from Kuo (2019). In the extended Deep Triangle (EDT), we use the incremental paid loss from two LOBs as input and the symmetric squared loss of two LOBs as the loss function. Then, we extend generative adversarial networks (GANs) by transforming the two loss triangles into a tabular format and generating synthetic loss triangles to obtain the predictive distribution for reserves. To illustrate our method, we apply and calibrate these methods on personal and commercial automobile lines from a large US P&C insurance company and compare the results with copula regression models. The results show that the EDT model performs better than the copula regression models in predicting total loss reserve. In addition, with the obtained predictive distribution for reserves, we show that risk capitals calculated from EDT combined with GAN are smaller than that of the copula regression models, which implies a more considerable diversification benefit. Finally, these findings are also confirmed in a simulation study

User Interface Fo Feature-pair Based Design and Analysis of Mechanical Assemblies

School of Mechanical and Aerospace Engineerin

Molecular characterization and ligand binding specificity of the PDZ domain-containing protein GIPC3 from Schistosoma japonicum

BACKGROUND: Schistosomiasis is a serious global health problem that afflicts more than 230 million people in 77 countries. Long-term mass treatments with the only available drug, praziquantel, have caused growing concerns about drug resistance. PSD-95/Dlg/ZO-1 (PDZ) domain-containing proteins are recognized as potential targets for the next generation of drug development. However, the PDZ domain-containing protein family in parasites has largely been unexplored. METHODS: We present the molecular characteristics of a PDZ domain-containing protein, GIPC3, from Schistosoma japonicum (SjGIPC3) according to bioinformatics analysis and experimental approaches. The ligand binding specificity of the PDZ domain of SjGIPC3 was confirmed by screening an arbitrary peptide library in yeast two-hybrid (Y2H) assays. The native ligand candidates were predicted by Tailfit software based on the C-terminal binding specificity, and further validated by Y2H assays. RESULTS: SjGIPC3 is a single PDZ domain-containing protein comprised of 328 amino acid residues. Structural prediction revealed that a conserved PDZ domain was presented in the middle region of the protein. Phylogenetic analysis revealed that SjGIPC3 and other trematode orthologues clustered into a well-defined cluster but were distinguishable from those of other phyla. Transcriptional analysis by quantitative RT-PCR revealed that the SjGIPC3 gene was relatively highly expressed in the stages within the host, especially in male adult worms. By using Y2H assays to screen an arbitrary peptide library, we confirmed the C-terminal binding specificity of the SjGIPC3-PDZ domain, which could be deduced as a consensus sequence, -[SDEC]-[STIL]-[HSNQDE]-[VIL]*. Furthermore, six proteins were predicted to be native ligand candidates of SjGIPC3 based on the C-terminal binding properties and other biological information; four of these were confirmed to be potential ligands using the Y2H system. CONCLUSIONS: In this study, we first characterized a PDZ domain-containing protein GIPC3 in S. japonicum. The SjGIPC3-PDZ domain is able to bind both type I and II ligand C-terminal motifs. The identification of native ligand will help reveal the potential biological function of SjGIPC3. These data will facilitate the identification of novel drug targets against S. japonicum infections

Identification and characterization of microRNAs and endogenous siRNAs in Schistosoma japonicum

<p>Abstract</p> <p>Background</p> <p>Small endogenous non-coding RNAs (sncRNAs) such as small interfering RNA (siRNA), microRNA and other small RNA transcripts are derived from distinct loci in the genome and play critical roles in RNA-mediated gene silencing mechanisms in plants and metazoa. They are approximately 22 nucleotides long; regulate mRNA stability through perfect or imperfect match to the targets. The biological activities of sncRNAs have been related to many biological events, from resistance to microbe infections to cellular differentiation. The development of the zoonotic parasite <it>Schistosoma japonicum </it>parasite includes multiple steps of morphological alterations and biological differentiations, which provide a unique model for studies on the functions of small RNAs. Characterization of the genome-wide transcription of the sncRNAs will be a major step in understanding of the parasite biology. The objective of this study is to investigate the transcriptional profile and potential function of the small non-coding RNAs in the development of <it>S. japanicum</it>.</p> <p>Results</p> <p>The endogenous siRNAs were found mainly derived from transposable elements (TE) or transposons and the natural antisense transcripts (NAT). In contrast to other organisms, the TE-derived siRNAs in <it>S. japonicum </it>were more predominant than other sncRNAs including microRNAs (miRNAs). Further, there were distinct length and 3'end variations in the sncRNAs, which were associated with the developmental differentiation of the parasite. Among the identified miRNA transcripts, there were 38 unique to <it>S. japonicum </it>and 16 that belonged to 13 miRNA families are common to other metazoan lineages. These miRNAs were either ubiquitously expressed, or they exhibited specific expression patterns related to the developmental stages or sex. Genes that encoded miRNAs are mainly located in clusters within the genome of <it>S. japonicum</it>. However, genes within one cluster could be differentially transcribed, which suggested that individual genes might be regulated by distinct mechanisms during parasite development.</p> <p>Conclusions</p> <p>Many miRNA and endogenous siRNA transcripts were identified in <it>S. japonicum </it>and the amount of siRNA was at least 4.4 and 1.6 times more than that of miRNA in both schistosomulum and adult worm stages respectively. SiRNAs are mainly derived from transposable elements (or transposons); while natural antisense transcripts (NAT)-derived siRNAs were much less. A majority of miRNA transcripts identified in the parasite were species-specific and the expression of certain miRNAs was found developmentally regulated. Both miRNA and siRNAs are potentially important regulators in the development of schistosomal parasites.</p

LoS Sensing-based Channel Estimation in UAV-Assisted OFDM Systems

In unmanned aerial vehicle (UAV)-assisted orthogonal frequency division multiplexing (OFDM) systems, the potential advantage of the line-of-sight (LoS) path, characterized by its high probability of existence, has not been fully harnessed, thereby impeding the improvement of channel estimation (CE) accuracy. Inspired by the ideas of integrated sensing and communication (ISAC), this letter develops a LoS sensing method aimed at detecting the presence of LoS path. Leveraging the prior information obtained from LoS path detection, the detection thresholds for resolvable paths are proposed for LoS and Non-LoS (NLoS) scenarios, respectively. By employing these specifically designed detection thresholds, denoising processing is applied to classical least square (LS) CE, thereby improving the CE accuracy. Simulation results validate the effectiveness of the proposed method in enhancing CE accuracy and demonstrate its robustness against parameter variations

Learning Disentangled Semantic Representation for Domain Adaptation

Domain adaptation is an important but challenging task. Most of the existing domain adaptation methods struggle to extract the domain-invariant representation on the feature space with entangling domain information and semantic information. Different from previous efforts on the entangled feature space, we aim to extract the domain invariant semantic information in the latent disentangled semantic representation (DSR) of the data. In DSR, we assume the data generation process is controlled by two independent sets of variables, i.e., the semantic latent variables and the domain latent variables. Under the above assumption, we employ a variational auto-encoder to reconstruct the semantic latent variables and domain latent variables behind the data. We further devise a dual adversarial network to disentangle these two sets of reconstructed latent variables. The disentangled semantic latent variables are finally adapted across the domains. Experimental studies testify that our model yields state-of-the-art performance on several domain adaptation benchmark datasets

Ramp facies in an intracratonic basin: A case study from the Upper Devonian and Lower Carboniferous in central Hunan, southern China

AbstractDetailed studies on Late Devonian to Early Carboniferous carbonate rocks in central Hunan, southern China have led to the recognition of 25 lithofacies which can be grouped into: (1) inner ramp peritidal platform, (2) inner ramp organic bank and mound, (3) mid ramp, (4) outer ramp, and (5) shelf basin facies associations. The peritidal platform facies association dominates the Zimenqiao Formation (Namurian A or late Datangian) and is characterized by gypsum and dolostone-containing sequences, indicating a peritidal platform environment. The other four facies associations dominate the Menggongao Formation (late Famennian), Liujiatang Formation (Tournaisian or Yangruanian), Shidengzi Formations (early Visean or early Datangian). Five upward-shallowing cycles were distinguished in these three Formations. The predominant facies associations developed in each Formation demonstrate an overall transgression–regression cycle in the Late Devonian to Early Carboniferous in central Hunan. The overall transgressive sequence was preserved in the Shaodong, Menggongao, and Liujiatang Formations, and the overall regressive sequence was preserved in the Liujiatang, Shidengzi, Ceshui and Zimenqiao Formations

POD-DEIM Based Model Order Reduction for the Spherical Shallow Water Equations with Turkel-Zwas Finite Difference Discretization

We consider the shallow water equations (SWE) in spherical coordinates solved by Turkel-Zwas (T-Z) explicit large time-step scheme. To reduce the dimension of the SWE model, we use a well-known model order reduction method, a proper orthogonal decomposition (POD). As the computational complexity still depends on the number of variables of the full spherical SWE model, we use discrete empirical interpolation method (DEIM) proposed by Sorensen to reduce the computational complexity of the reduced-order model. DEIM is very helpful in evaluating quadratically nonlinear terms in the reduced-order model. The numerical results show that POD-DEIM is computationally very efficient for implementing model order reduction for spherical SWE