Transferable Natural Language Interface to Structured Queries aided by Adversarial Generation

A natural language interface (NLI) to structured query is intriguing due to its wide industrial applications and high economical values. In this work, we tackle the problem of domain adaptation for NLI with limited data on target domain. Two important approaches are considered: (a) effective general-knowledge-learning on source domain semantic parsing, and (b) data augmentation on target domain. We present a Structured Query Inference Network (SQIN) to enhance learning for domain adaptation, by separating schema information from NL and decoding SQL in a more structural-aware manner; we also propose a GAN-based augmentation technique (AugmentGAN) to mitigate the issue of lacking target domain data. We report solid results on GeoQuery, Overnight, and WikiSQL to demonstrate state-of-the-art performances for both in-domain and domain-transfer tasks.Comment: 8 pages, 3 figures; accepted by AAAI Workshop 2019; accepted by International Conference of Semantic Computing (ICSC) 201

"Synchronize" to VR Body: Full Body Illusion in VR Space

Virtual Reality (VR) becomes accessible to mimic a "real-like" world now. People who have a VR experience usually can be impressed by the immersive feeling, they might consider themselves are actually existed in the VR space. Self-consciousness is important for people to identify their own characters in VR space, and illusory ownership can help people to "build" their "bodies". The rubber hand illusion can convince us a fake hand made by rubber is a part of our bodies under certain circumstances. Researches about autoscopic phenomena extend this illusory to the so-called full body illusion. We conducted 3 type of experiments to study the illusory ownership in VR space as it shows in Figure 1, and we learned: Human body must receive the synchronized visual signal and somatosensory stimulus at the same time; The visual signal must be the first person perceptive; the subject and the virtual body needs to be the same height as much as possible. All these illusory ownerships accompanied by the body temperature decreases, where the body is stimulated.Comment: 4 pages, 4 figures, Eurographics 2017,Conference short pape

Globally Tuned Cascade Pose Regression via Back Propagation with Application in 2D Face Pose Estimation and Heart Segmentation in 3D CT Images

Recently, a successful pose estimation algorithm, called Cascade Pose Regression (CPR), was proposed in the literature. Trained over Pose Index Feature, CPR is a regressor ensemble that is similar to Boosting. In this paper we show how CPR can be represented as a Neural Network. Specifically, we adopt a Graph Transformer Network (GTN) representation and accordingly train CPR with Back Propagation (BP) that permits globally tuning. In contrast, previous CPR literature only took a layer wise training without any post fine tuning. We empirically show that global training with BP outperforms layer-wise (pre-)training. Our CPR-GTN adopts a Multi Layer Percetron as the regressor, which utilized sparse connection to learn local image feature representation. We tested the proposed CPR-GTN on 2D face pose estimation problem as in previous CPR literature. Besides, we also investigated the possibility of extending CPR-GTN to 3D pose estimation by doing experiments using 3D Computed Tomography dataset for heart segmentation

O(α3αs)\mathcal O(\alpha^{3}\alpha_s) Study on the yields and polarizations of J/ψ(Υ)J/\psi(\Upsilon) within the framework of non-relativistic QCD via γγ→J/ψ(Υ)+γ+X\gamma\gamma \to J/\psi(\Upsilon)+\gamma+X at CEPC

Within the framework of the non-relativistic QCD (NRQCD), we make a systematical study of the yields and polarizations of $J/\psi$ and $\Upsilon$ via $\gamma \gamma \to J/\psi(\Upsilon)+\gamma+X$ in photon-photon collisions at the Circular Electron Positron Collider (CEPC), up to $\mathcal O(\alpha^{3}\alpha_s)$. We find that this process at CEPC is quite "clean", namely the direct photoproduction absolutely dominate over the single- and double- resolved processes, at least 2 orders of magnitude larger. It is found that the next-to-leading order (NLO) QCD corrections will significantly reduce the results due to that the virtual corrections to $^3S_1^1$ is large and negative. For $J/\psi$, as $p_t$ increases, the color octet (CO) processes will provide increasingly important contributions to the total NLO results. Moreover the inclusion of CO contributions will dramatically change the polarizations of $J/\psi$ from toally transverse to longitudinal, which can be regarded as a distinct signal for the CO mechanism. However, for the case of $\Upsilon$, the effects of the CO processes are negligible, both for yields and polarizations. For $J/\psi$, the dependence of the yields on the value of the renormalization scale $\mu_r$ is moderate, while significant for the polarization. The impact of the variation of $\mu_{\lambda}$ is found to be relatively slight. As for the case of $\Upsilon$, the uncertainties of $\mu_{r}$ and $\mu_{\lambda}$ just bring about negligible effects. The future measurements on this semi-inclusive photoproductions of $J/\psi(\Upsilon)+\gamma+X$, especially on the polarization parameters of $J/\psi$, will be a good laboratory for the study of heavy quarkonium production mechanism and helpful to clarify the problems of the $J/\psi$ polarization puzzle

Kahler-Einstein metrics on Fano manifolds, I: approximation of metrics with cone singularities

This is the first of a series of three papers which provide proofs of results announced recently in arXiv:1210.7494

Kahler-Einstein metrics and stability

We annnounce a proof of the fact that a K-stable Fano manifold admits a Kahler-Einstein metric and give a brief outline of the proof

DFANet: Deep Feature Aggregation for Real-Time Semantic Segmentation

This paper introduces an extremely efficient CNN architecture named DFANet for semantic segmentation under resource constraints. Our proposed network starts from a single lightweight backbone and aggregates discriminative features through sub-network and sub-stage cascade respectively. Based on the multi-scale feature propagation, DFANet substantially reduces the number of parameters, but still obtains sufficient receptive field and enhances the model learning ability, which strikes a balance between the speed and segmentation performance. Experiments on Cityscapes and CamVid datasets demonstrate the superior performance of DFANet with 8$\times$ less FLOPs and 2$\times$ faster than the existing state-of-the-art real-time semantic segmentation methods while providing comparable accuracy. Specifically, it achieves 70.3\% Mean IOU on the Cityscapes test dataset with only 1.7 GFLOPs and a speed of 160 FPS on one NVIDIA Titan X card, and 71.3\% Mean IOU with 3.4 GFLOPs while inferring on a higher resolution image

Soft Factor Subtraction and Transverse Momentum Dependent Parton Distributions on Lattice

We study the transverse momentum dependent (TMD) parton distributions in the newly proposed quasi-parton distribution function framework in Euclidean space. A soft factor subtraction is found to be essential to make the TMDs calculable on lattice. We show that the quasi-TMDs with the associated soft factor subtraction can be applied in hard QCD scattering processes such as Drell-Yan lepton pair production in hadronic collisions. This allows future lattice calculations to provide information on the non-perturbative inputs and energy evolutions for the TMDs. Extension to the generalized parton distributions and quantum phase space Wigner distributions will lead to a complete nucleon tomography on lattice.Comment: 7 pages, 2 figure

Boosting with Structural Sparsity: A Differential Inclusion Approach

Boosting as gradient descent algorithms is one popular method in machine learning. In this paper a novel Boosting-type algorithm is proposed based on restricted gradient descent with structural sparsity control whose underlying dynamics are governed by differential inclusions. In particular, we present an iterative regularization path with structural sparsity where the parameter is sparse under some linear transforms, based on variable splitting and the Linearized Bregman Iteration. Hence it is called \emph{Split LBI}. Despite its simplicity, Split LBI outperforms the popular generalized Lasso in both theory and experiments. A theory of path consistency is presented that equipped with a proper early stopping, Split LBI may achieve model selection consistency under a family of Irrepresentable Conditions which can be weaker than the necessary and sufficient condition for generalized Lasso. Furthermore, some $\ell_2$ error bounds are also given at the minimax optimal rates. The utility and benefit of the algorithm are illustrated by several applications including image denoising, partial order ranking of sport teams, and world university grouping with crowdsourced ranking data

Adaptive Gradient Methods with Dynamic Bound of Learning Rate

Adaptive optimization methods such as AdaGrad, RMSprop and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates. Though prevailing, they are observed to generalize poorly compared with SGD or even fail to converge due to unstable and extreme learning rates. Recent work has put forward some algorithms such as AMSGrad to tackle this issue but they failed to achieve considerable improvement over existing methods. In our paper, we demonstrate that extreme learning rates can lead to poor performance. We provide new variants of Adam and AMSGrad, called AdaBound and AMSBound respectively, which employ dynamic bounds on learning rates to achieve a gradual and smooth transition from adaptive methods to SGD and give a theoretical proof of convergence. We further conduct experiments on various popular tasks and models, which is often insufficient in previous work. Experimental results show that new variants can eliminate the generalization gap between adaptive methods and SGD and maintain higher learning speed early in training at the same time. Moreover, they can bring significant improvement over their prototypes, especially on complex deep networks. The implementation of the algorithm can be found at https://github.com/Luolc/AdaBound .Comment: Accepted to ICLR 2019. arXiv admin note: text overlap with arXiv:1904.09237 by other author