Multi-Context Attention for Human Pose Estimation

In this paper, we propose to incorporate convolutional neural networks with a multi-context attention mechanism into an end-to-end framework for human pose estimation. We adopt stacked hourglass networks to generate attention maps from features at multiple resolutions with various semantics. The Conditional Random Field (CRF) is utilized to model the correlations among neighboring regions in the attention map. We further combine the holistic attention model, which focuses on the global consistency of the full human body, and the body part attention model, which focuses on the detailed description for different body parts. Hence our model has the ability to focus on different granularity from local salient regions to global semantic-consistent spaces. Additionally, we design novel Hourglass Residual Units (HRUs) to increase the receptive field of the network. These units are extensions of residual units with a side branch incorporating filters with larger receptive fields, hence features with various scales are learned and combined within the HRUs. The effectiveness of the proposed multi-context attention mechanism and the hourglass residual units is evaluated on two widely used human pose estimation benchmarks. Our approach outperforms all existing methods on both benchmarks over all the body parts.Comment: The first two authors contribute equally to this wor

Rethinking Graph Regularization for Graph Neural Networks

The graph Laplacian regularization term is usually used in semi-supervised representation learning to provide graph structure information for a model $f(X)$. However, with the recent popularity of graph neural networks (GNNs), directly encoding graph structure $A$ into a model, i.e., $f(A, X)$, has become the more common approach. While we show that graph Laplacian regularization brings little-to-no benefit to existing GNNs, and propose a simple but non-trivial variant of graph Laplacian regularization, called Propagation-regularization (P-reg), to boost the performance of existing GNN models. We provide formal analyses to show that P-reg not only infuses extra information (that is not captured by the traditional graph Laplacian regularization) into GNNs, but also has the capacity equivalent to an infinite-depth graph convolutional network. We demonstrate that P-reg can effectively boost the performance of existing GNN models on both node-level and graph-level tasks across many different datasets.Comment: AAAI202

Flavor and Spin Structure of Octet Baryons at Large x

The quark flavor and spin distributions in octet baryons are calculated both in the SU(6) quark spectator diquark model and in a perturbative QCD (pQCD) based model. It is shown that the $\Lambda$ has the most significant difference in flavor structure at large $x$ between the two models, though the flavor and spin structure of other baryons can also provide tests of different models. The Drell-Yan process for $\Sigma^{\pm}$ beams on isoscalar targets can be used to test different predictions concerning the valence quark flavor structure of the $\Sigma^{\pm}$.Comment: 24 pages, 11 figures, version published in Nucl.Phys.B 574 (2000) 33

Scaling of disorder operator at deconfined quantum criticality

We study scaling behavior of the disorder parameter, defined as the expectation value of a symmetry transformation applied to a finite region, at the deconfined quantum critical point in (2+1)$d$ in the $J$-$Q_3$ model via large-scale quantum Monte Carlo simulations. We show that the disorder parameter for U(1) spin rotation symmetry exhibits perimeter scaling with a logarithmic correction associated with sharp corners of the region, as generally expected for a conformally-invariant critical point. However, for large rotation angle the universal coefficient of the logarithmic corner correction becomes negative, which is not allowed in any unitary conformal field theory. We also extract the current central charge from the small rotation angle scaling, whose value is much smaller than that of the free theory.Comment: 8 pages, 6 figures; v2 improved measurement on disorder operato

A Unified Contrastive Transfer Framework with Propagation Structure for Boosting Low-Resource Rumor Detection

The truth is significantly hampered by massive rumors that spread along with breaking news or popular topics. Since there is sufficient corpus gathered from the same domain for model training, existing rumor detection algorithms show promising performance on yesterday's news. However, due to a lack of training data and prior expert knowledge, they are poor at spotting rumors concerning unforeseen events, especially those propagated in different languages (i.e., low-resource regimes). In this paper, we propose a unified contrastive transfer framework to detect rumors by adapting the features learned from well-resourced rumor data to that of the low-resourced. More specifically, we first represent rumor circulated on social media as an undirected topology, and then train a Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our model explicitly breaks the barriers of the domain and/or language issues, via language alignment and a novel domain-adaptive contrastive learning mechanism. To enhance the representation learning from a small set of target events, we reveal that rumor-indicative signal is closely correlated with the uniformity of the distribution of these events. We design a target-wise contrastive training mechanism with three data augmentation strategies, capable of unifying the representations by distinguishing target events. Extensive experiments conducted on four low-resource datasets collected from real-world microblog platforms demonstrate that our framework achieves much better performance than state-of-the-art methods and exhibits a superior capacity for detecting rumors at early stages.Comment: A significant extension of the first contrastive approach for low-resource rumor detection (arXiv:2204.08143

Neural-Learning-Based Telerobot Control with Guaranteed Performance

© 2013 IEEE. In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot's end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods

Dynamic response of underground box-type structure to explosion seismic waves

The dynamic response of lined tunnels with a uniform box-type cross-section buried into elastic half-space to explosion seismic waves is studied by employing the matrix force method and treating the structure as a connecting rod system interacting with foundation. The main equations for dynamic analyzing of the hyperstatic structure are deduced and solving method is proposed. A case study is implemented to investigate the influence of span-height ratio of the structure and foundation-structure wave impedance ratio. The results are presented in nondimensional form to obtain a clear physical understanding of the dynamic response of structure. It is shown that the dynamic response of box-type structure can be significantly influenced by the span-height ratio as well as the foundation conditions. Since nondimensional parameters are adopted, the results are independent of dimension and can extend to structures with different size and working conditions. This study provides an analysis method and new insights into the dynamic response of underground box-type structures

Excess caffeine exposure impairs eye development during chick embryogenesis

Caffeine has been an integral component of our diet and medicines for centuries. It is now known that over consumption of caffeine has detrimental effects on our health, and also disrupts normal foetal development in pregnant mothers. In this study, we investigated the potential teratogenic effect of caffeine over-exposure on eye development in the early chick embryo. Firstly, we demonstrated that caffeine exposure caused chick embryos to develop asymmetrical microphthalmia and induced the orbital bone to develop abnormally. Secondly, caffeine exposure perturbed Pax6 expression in the retina of the developing eye. In addition, it perturbed the migration of HNK-1(+) cranial neural crest cells. Pax6 is an important gene that regulates eye development, so altering the expression of this gene might be the cause for the abnormal eye development. Thirdly, we found that reactive oxygen species (ROS) production was significantly increased in eye tissues following caffeine treatment, and that the addition of anti-oxidant vitamin C could rescue the eyes from developing abnormally in the presence of caffeine. This suggests that excess ROS induced by caffeine is one of the mechanisms involved in the teratogenic alterations observed in the eye during embryogenesis. In sum, our experiments in the chick embryo demonstrated that caffeine is a potential teratogen. It causes asymmetrical microphthalmia to develop by increasing ROS production and perturbs Pax6 expression