Wasserstein Distance Guided Representation Learning for Domain Adaptation

Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WDGRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.Comment: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI 2018

Linking Aboveground Traits to Root Traits and Local Environment: Implications of the Plant Economics Spectrum.

The plant economics spectrum proposes that ecological traits are functionally coordinated and adapt along environmental gradients. However, empirical evidence is mixed about whether aboveground and root traits are consistently linked and which environmental factors drive functional responses. Here we measure the strength of relationships between aboveground and root traits, and examine whether community-weighted mean trait values are adapted along gradients of light and soil fertility, based on the seedling censuses of 57 species in a subtropical forest. We found that aboveground traits were good predictors of root traits; specific leaf area, dry matter, nitrogen and phosphorus content were strongly correlated with root tissue density and specific root length. Traits showed patterns of adaptation along the gradients of soil fertility and light; species with fast resource-acquisitive strategies were more strongly associated with high soil phosphorus, potassium, openness, and with low nitrogen, organic matter conditions. This demonstrates the potential to estimate belowground traits from known aboveground traits in seedling communities, and suggests that soil fertility is one of the main factors driving functional responses. Our results extend our understanding of how ecological strategies shape potential responses of plant communities to environmental change

A Latent Clothing Attribute Approach for Human Pose Estimation

As a fundamental technique that concerns several vision tasks such as image parsing, action recognition and clothing retrieval, human pose estimation (HPE) has been extensively investigated in recent years. To achieve accurate and reliable estimation of the human pose, it is well-recognized that the clothing attributes are useful and should be utilized properly. Most previous approaches, however, require to manually annotate the clothing attributes and are therefore very costly. In this paper, we shall propose and explore a \emph{latent} clothing attribute approach for HPE. Unlike previous approaches, our approach models the clothing attributes as latent variables and thus requires no explicit labeling for the clothing attributes. The inference of the latent variables are accomplished by utilizing the framework of latent structured support vector machines (LSSVM). We employ the strategy of \emph{alternating direction} to train the LSSVM model: In each iteration, one kind of variables (e.g., human pose or clothing attribute) are fixed and the others are optimized. Our extensive experiments on two real-world benchmarks show the state-of-the-art performance of our proposed approach.Comment: accepted to ACCV 2014, preceding work http://arxiv.org/abs/1404.492

Quantum Hall effect on centimeter scale chemical vapor deposited graphene films

We report observations of well developed half integer quantum Hall effect (QHE) on mono layer graphene films of 7 mm \times 7 mm in size. The graphene films are grown by chemical vapor deposition (CVD) on copper, then transferred to SiO_{2} /Si substrates, with typical carrier mobilities \approx 4000 cm^{2} /Vs. The large size graphene with excellent quality and electronic homogeneity demonstrated in this work is promising for graphene-based quantum Hall resistance standards, and can also facilitate a wide range of experiments on quantum Hall physics of graphene and practical applications exploiting the exceptional properties of graphene

NLO QCD + NLO EW corrections to WZZWZZ productions with leptonic decays at the LHC

Precision tests of the Standard Model (SM) require not only accurate experiments, but also precise and reliable theoretical predictions. Triple vector boson production provides a unique opportunity to investigate the quartic gauge couplings and check the validity of the gauge principle in the SM. Since the tree-level predictions alone are inadequate to meet this demand, the next-to-leading order (NLO) calculation becomes compulsory. In this paper, we calculate the NLO QCD + NLO electroweak (EW) corrections to the $W^{\pm}ZZ$ productions with subsequent leptonic decays at the $14~{\rm TeV}$ LHC by adopting an improved narrow width approximation which takes into account the off-shell contributions and spin correlations from the $W^{\pm}$- and $Z$-boson leptonic decays. The NLO QCD+EW corrected integrated cross sections for the $W^{\pm}ZZ$ productions and some kinematic distributions of final products are provided. The results show that both the NLO QCD and NLO EW corrections are significant. In the jet-veto event selection scheme with $p_{T,jet}^{cut} = 50~ {\rm GeV}$, the NLO QCD+EW relative corrections to the integrated cross section are $20.5\%$ and $31.1\%$, while the genuine NLO EW relative corrections are $-5.42\%$ and $-4.58\%$, for the $W^+ZZ$ and $W^-ZZ$ productions, respectively. We also investigate the theoretical dependence of the integrated cross section on the factorization/renormalization scale, and find that the scale uncertainty is underestimated at the LO due to the fact that the strong coupling $\alpha_s$ is not involved in the LO matrix elements.Comment: 19 pages, 8 figure

Synthetic Graphene Grown by Chemical Vapor Deposition on Copper Foils

The discovery of graphene, a single layer of covalently bonded carbon atoms, has attracted intense interests. Initial studies using mechanically exfoliated graphene unveiled its remarkable electronic, mechanical and thermal properties. There has been a growing need and rapid development in large-area deposition of graphene film and its applications. Chemical vapour deposition on copper has emerged as one of the most promising methods in obtaining large-scale graphene films with quality comparable to exfoliated graphene. In this chapter, we review the synthesis and characterizations of graphene grown on copper foil substrates by atmospheric pressure chemical vapour deposition. We also discuss potential applications of such large scale synthetic graphene.Comment: 23 pages, 4 figure