A Conditional Variational Framework for Dialog Generation

Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems. However, these latent variables are highly randomized, leading to uncontrollable generated responses. In this paper, we propose a framework allowing conditional response generation based on specific attributes. These attributes can be either manually assigned or automatically detected. Moreover, the dialog states for both speakers are modeled separately in order to reflect personal features. We validate this framework on two different scenarios, where the attribute refers to genericness and sentiment states respectively. The experiment result testified the potential of our model, where meaningful responses can be generated in accordance with the specified attributes.Comment: Accepted by ACL201

Automated Design of Metaheuristics Using Reinforcement Learning within a Novel General Search Framework

Metaheuristic algorithms have been investigated intensively to address highly complex combinatorial optimisation problems. However, most metaheuristic algorithms have been designed manually by researchers of different expertise without a consistent framework to support effective algorithm design. This paper proposes a general search framework to formulate in a unified way a range of different metaheuristics. This framework defines generic algorithmic components, including selection heuristics and evolution operators. The unified general search framework aims to serve as the basis of analysing algorithmic components for automated algorithm design. With the established new general search framework, two reinforcement learning based methods, deep Q-network based and proximal policy optimisation based methods, have been developed to automatically design a new general population-based algorithm. The proposed reinforcement learning based methods are able to intelligently select and combine appropriate algorithmic components during different stages of the optimisation process. The effectiveness and generalization of the proposed reinforcement learning based methods are validated comprehensively across different benchmark instances of the capacitated vehicle routing problem with time windows. This study contributes to making a key step towards automated algorithm design with a general framework supporting fundamental analysis by effective machine learning

Genome Composition and Divergence of the Novel Coronavirus (2019-nCoV) Originating in China.

An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV

Exploring the supersymmetric U(1)B−L×_{B-L} \times U(1)R_{R} model with dark matter, muon g−2g-2 and Z′Z^\prime mass limits

We study the low scale predictions of supersymmetric standard model extended by $U(1)_{B-L}\times U(1)_{R}$ symmetry, obtained from $SO(10)$ breaking via a left-right supersymmetric model, imposing universal boundary conditions. Two singlet Higgs fields are responsible for the radiative $U(1)_{B-L}\times U(1)_{R}$ symmetry breaking, and a singlet fermion $S$ is introduced to generate neutrino masses through inverse seesaw mechanism. The lightest neutralino or sneutrino emerge as dark matter candidates, with different low scale implications. We find that the composition of the neutralino LSP changes considerably depending on the neutralino LSP mass, from roughly half $U(1)_R$ bino, half MSSM bino, to singlet higgsino, or completely dominated by MSSM higgsino. The sneutrino LSP is statistically much less likely, and when it occurs it is a 50-50 mixture of right-handed sneutrino and the scalar $\tilde S$. Most of the solutions consistent with the relic density constraint survive the XENON 1T exclusion curve for both LSP cases. We compare the two scenarios and investigate parameter space points and find consistency with the muon anomalous magnetic moment only at the edge of $2\sigma$ deviation from the measured value. However, we find that the sneutrino LSP solutions could be ruled out completely by strict reinforcement of the recent $Z^\prime$ mass bounds. We finally discuss collider prospects for testing the model

Category-Specific CNN for Visual-aware CTR Prediction at JD.com

As one of the largest B2C e-commerce platforms in China, JD com also powers a leading advertising system, serving millions of advertisers with fingertip connection to hundreds of millions of customers. In our system, as well as most e-commerce scenarios, ads are displayed with images.This makes visual-aware Click Through Rate (CTR) prediction of crucial importance to both business effectiveness and user experience. Existing algorithms usually extract visual features using off-the-shelf Convolutional Neural Networks (CNNs) and late fuse the visual and non-visual features for the finally predicted CTR. Despite being extensively studied, this field still face two key challenges. First, although encouraging progress has been made in offline studies, applying CNNs in real systems remains non-trivial, due to the strict requirements for efficient end-to-end training and low-latency online serving. Second, the off-the-shelf CNNs and late fusion architectures are suboptimal. Specifically, off-the-shelf CNNs were designed for classification thus never take categories as input features. While in e-commerce, categories are precisely labeled and contain abundant visual priors that will help the visual modeling. Unaware of the ad category, these CNNs may extract some unnecessary category-unrelated features, wasting CNN's limited expression ability. To overcome the two challenges, we propose Category-specific CNN (CSCNN) specially for CTR prediction. CSCNN early incorporates the category knowledge with a light-weighted attention-module on each convolutional layer. This enables CSCNN to extract expressive category-specific visual patterns that benefit the CTR prediction. Offline experiments on benchmark and a 10 billion scale real production dataset from JD, together with an Online A/B test show that CSCNN outperforms all compared state-of-the-art algorithms

Three-dimensional finite element model generation based on CT image for concrete crack

A generation method of three-dimensional finite element model based on CT image for concrete crack is proposed. Aiming at concrete crack, firstly, the paper adopts watershed algorithm to achieve the segmentation of CT image for concrete crack and uses Canny operator to extract the crack edges. Secondly, by using Marching Cubes algorithm the three-dimensional surface model for concrete crack is constructed. Finally, on the basis of three-dimensional surface model, the paper employs constrained Delaunay triangulation (CDT) algorithm to generate three-dimensional finite element model for concrete crack. In the paper, the finite element method (FEM) software ABAQUS is used to achieve the analysis of loading for crack regions. The results show that this proposed method can achieve three-dimensional finite element model generation of concrete structures and mechanical analysis for the cracks which significantly improve the accuracy and efficiency of numerical simulation for the concrete structures

Engineering interface-type resistive switching in BiFeO3 thin film switches by Ti implantation of bottom electrodes

BiFeO3 based MIM structures with Ti-implanted Pt bottom electrodes and Au top electrodes have been fabricated on Sapphire substrates. The resulting metal-insulator-metal (MIM) structures show bipolar resistive switching without an electroforming process. It is evidenced that during the BiFeO3 thin film growth Ti diffuses into the BiFeO3 layer. The diffused Ti effectively traps and releases oxygen vacancies and consequently stabilizes the resistive switching in BiFeO3 MIM structures. Therefore, using Ti implantation of the bottom electrode, the retention performance can be greatly improved with increasing Ti fluence. For the used raster-scanned Ti implantation the lateral Ti distribution is not homogeneous enough and endurance slightly degrades with Ti fluence. The local resistive switching investigated by current sensing atomic force microscopy suggests the capability of down-scaling the resistive switching cell to one BiFeO3 grain size by local Ti implantation of the bottom electrode

Modulation of Gut Microbiota by Low Methoxyl Pectin Attenuates Type 1 Diabetes in Non-obese Diabetic Mice

Intestinal homeostasis underpins the development of type 1 diabetes (T1D), and dietary manipulations to enhance intestinal homeostasis have been proposed to prevent T1D. The current study aimed to investigate the efficacy of supplementing a novel specific low-methoxyl pectin (LMP) dietary fiber in preventing T1D development. Female NOD mice were weaned onto control or 5% (wt/wt) LMP supplemented diets for up to 40 weeks of age, overt diabetes incidence and blood glucose were monitored. Then broad-spectrum antibiotics (ABX) treatment per os for 7 days followed by gut microbiota transfer was performed to demonstrate gut microbiota-dependent effects. Next-generation sequencing was used for analyzing the composition of microbiota in caecum. Concentration of short chain fatty acids were determined by GC-MS. The barrier reinforcing tight junction proteins zonula occludens-2 (ZO-2), claudin-1 and NOD like receptor protein 3 (NLRP3) inflammasome activation were determined by Western blot. The proportion of CD25(+)Foxp3(+)CD4(+) regulatory T cell (Foxp3(+) Treg) in the pancreas, pancreatic and mesenteric lymph nodes was analyzed by flow cytometry. We found that LMP supplementation ameliorated T1D development in non-obese diabetic (NOD) mice, as evidenced by decreasing diabetes incidence and fasting glucose levels in LMP fed NOD mice. Further microbiota analysis revealed that LMP supplementation prevented T1D-associated caecal dysbiosis and selectively enriched caecal bacterial species to produce more SCFAs. The LMP-mediated microbial balance further enhanced caecal barrier function and shaped gut-pancreatic immune environment, as characterized by higher expression of tight junction proteins claudin-1, ZO-2 in caecum, increased Foxp3(+) Treg population and decreased NLRP3 inflammasome activation in both caecum and pancreas. The microbiota-dependent beneficial effect of LMP on T1D was further proven by the fact that aberration of caecal microbiota by ABX treatment worsened T1D autoimmunity and could be restored with transfer of feces of LMP-fed NOD mice. These data demonstrate that this novel LMP limits T1D development by inducing caecal homeostasis to shape pancreatic immune environment. This finding opens a realistic option for gut microbiota manipulation and prevention of T1D in humans