203 research outputs found
NNQS-Transformer: an Efficient and Scalable Neural Network Quantum States Approach for Ab initio Quantum Chemistry
Neural network quantum state (NNQS) has emerged as a promising candidate for
quantum many-body problems, but its practical applications are often hindered
by the high cost of sampling and local energy calculation. We develop a
high-performance NNQS method for \textit{ab initio} electronic structure
calculations. The major innovations include: (1) A transformer based
architecture as the quantum wave function ansatz; (2) A data-centric
parallelization scheme for the variational Monte Carlo (VMC) algorithm which
preserves data locality and well adapts for different computing architectures;
(3) A parallel batch sampling strategy which reduces the sampling cost and
achieves good load balance; (4) A parallel local energy evaluation scheme which
is both memory and computationally efficient; (5) Study of real chemical
systems demonstrates both the superior accuracy of our method compared to
state-of-the-art and the strong and weak scalability for large molecular
systems with up to spin orbitals.Comment: Accepted by SC'2
Ponder: Point Cloud Pre-training via Neural Rendering
We propose a novel approach to self-supervised learning of point cloud
representations by differentiable neural rendering. Motivated by the fact that
informative point cloud features should be able to encode rich geometry and
appearance cues and render realistic images, we train a point-cloud encoder
within a devised point-based neural renderer by comparing the rendered images
with real images on massive RGB-D data. The learned point-cloud encoder can be
easily integrated into various downstream tasks, including not only high-level
tasks like 3D detection and segmentation, but low-level tasks like 3D
reconstruction and image synthesis. Extensive experiments on various tasks
demonstrate the superiority of our approach compared to existing pre-training
methods.Comment: Project page: https://dihuang.me/ponder
Testing General Relativity with Black Hole X-Ray Data and ABHModels
The past 10 years have seen tremendous progress in our capability of testing
General Relativity in the strong field regime with black hole observations. 10
years ago, the theory of General Relativity was almost completely unexplored in
the strong field regime. Today, we have gravitational wave data of the
coalescence of stellar-mass black holes, radio images of the supermassive black
holes SgrA and M87, and high-quality X-ray data of stellar-mass black
holes in X-ray binaries and supermassive black holes in active galactic nuclei.
In this manuscript, we will review current efforts to test General Relativity
with black hole X-ray data and we will provide a detailed description of the
public codes available on ABHModels.Comment: 31 pages, 5 figures. Talk given at the Frascati Workshop 2023
"Multifrequency Behaviour of High Energy Cosmic Sources - XIV" (Palermo,
Italy, 12-17 June 2023
Horizontal Pyramid Matching for Person Re-identification
Despite the remarkable recent progress, person re-identification (Re-ID)
approaches are still suffering from the failure cases where the discriminative
body parts are missing. To mitigate such cases, we propose a simple yet
effective Horizontal Pyramid Matching (HPM) approach to fully exploit various
partial information of a given person, so that correct person candidates can be
still identified even even some key parts are missing. Within the HPM, we make
the following contributions to produce a more robust feature representation for
the Re-ID task: 1) we learn to classify using partial feature representations
at different horizontal pyramid scales, which successfully enhance the
discriminative capabilities of various person parts; 2) we exploit average and
max pooling strategies to account for person-specific discriminative
information in a global-local manner. To validate the effectiveness of the
proposed HPM, extensive experiments are conducted on three popular benchmarks,
including Market-1501, DukeMTMC-ReID and CUHK03. In particular, we achieve mAP
scores of 83.1%, 74.5% and 59.7% on these benchmarks, which are the new
state-of-the-arts. Our code is available on GithubComment: Accepted by AAAI 201
Testing General Relativity with NuSTAR Data of Galactic Black Holes
Einstein's theory of General Relativity predicts that the spacetime metric
around astrophysical black holes is described by the Kerr solution. In this
work, we employ state-of-the-art in relativistic reflection modeling to analyze
a selected set of NuSTAR spectra of Galactic black holes to obtain the most
robust and precise constraints on the Kerr black hole hypothesis possible
today. Our constraints are much more stringent than those from other
electromagnetic techniques, and with some sources we get stronger constraints
than those currently available from gravitational waves.Comment: 15 pages, 11 figures. v2: refereed versio
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