133 research outputs found
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
Recommended from our members
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) U(1) model with dark matter, muon and mass limits
We study the low scale predictions of supersymmetric standard model extended
by symmetry, obtained from breaking via a
left-right supersymmetric model, imposing universal boundary conditions. Two
singlet Higgs fields are responsible for the radiative symmetry breaking, and a singlet fermion 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
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 . 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 deviation from the
measured value. However, we find that the sneutrino LSP solutions could be
ruled out completely by strict reinforcement of the recent 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
- …