33,452 research outputs found
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
. However, with the recent popularity of graph neural networks (GNNs),
directly encoding graph structure into a model, i.e., , 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
An Effective Way to Determine the Separability of Quantum State
We propose in this work a practical approach, by virtue of correlation
matrices of the generic observables, to study the long lasting tough issue of
quantum separability. Some general separability conditions are set up through
constructing a measurement-induced Bloch space. In essence, these conditions
are established due to the self constraint in the space of quantum states. The
new approach can not only reproduce many of the prevailing entanglement
criteria, but also lead to even stronger results and manifest superiority for
some bound entangled states. Moreover, as a by product, the new criteria are
found directly transformable to the entanglement witness operators.Comment: 25 pages, 2 figure
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 has the most significant difference
in flavor structure at large 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 beams on isoscalar targets can be used to
test different predictions concerning the valence quark flavor structure of the
.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) in the - 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
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