770 research outputs found
Analytical controllability of deterministic scale-free networks and Cayley trees
According to the exact controllability theory, the controllability is
investigated analytically for two typical types of self-similar bipartite
networks, i.e., the classic deterministic scale-free networks and Cayley trees.
Due to their self-similarity, the analytical results of the exact
controllability are obtained, and the minimum sets of driver nodes (drivers)
are also identified by elementary transformations on adjacency matrices. For
these two types of undirected networks, no matter their links are unweighted or
(nonzero) weighted, the controllability of networks and the configuration of
drivers remain the same, showing a robustness to the link weights. These
results have implications for the control of real networked systems with
self-similarity.Comment: 7 pages, 4 figures, 1 table; revised manuscript; added discussion
about the general case of DSFN; added 3 reference
Global and partitioned reconstructions of undirected complex networks
It is a significant challenge to predict the network topology from a small
amount of dynamical observations. Different from the usual framework of the
node-based reconstruction, two optimization approaches (i.e., the global and
partitioned reconstructions) are proposed to infer the structure of undirected
networks from dynamics. These approaches are applied to evolutionary games
occurring on both homogeneous and heterogeneous networks via compressed
sensing, which can more efficiently achieve higher reconstruction accuracy with
relatively small amounts of data. Our approaches provide different perspectives
on effectively reconstructing complex networks.Comment: 6 pages, 2 figures, 1 table; revised version; added numerical results
of the PR in Table 1 and expanded Section 4; added 7 reference
BakedAvatar: Baking Neural Fields for Real-Time Head Avatar Synthesis
Synthesizing photorealistic 4D human head avatars from videos is essential
for VR/AR, telepresence, and video game applications. Although existing Neural
Radiance Fields (NeRF)-based methods achieve high-fidelity results, the
computational expense limits their use in real-time applications. To overcome
this limitation, we introduce BakedAvatar, a novel representation for real-time
neural head avatar synthesis, deployable in a standard polygon rasterization
pipeline. Our approach extracts deformable multi-layer meshes from learned
isosurfaces of the head and computes expression-, pose-, and view-dependent
appearances that can be baked into static textures for efficient rasterization.
We thus propose a three-stage pipeline for neural head avatar synthesis, which
includes learning continuous deformation, manifold, and radiance fields,
extracting layered meshes and textures, and fine-tuning texture details with
differential rasterization. Experimental results demonstrate that our
representation generates synthesis results of comparable quality to other
state-of-the-art methods while significantly reducing the inference time
required. We further showcase various head avatar synthesis results from
monocular videos, including view synthesis, face reenactment, expression
editing, and pose editing, all at interactive frame rates.Comment: ACM Transactions on Graphics (SIGGRAPH Asia 2023). Project Page:
https://buaavrcg.github.io/BakedAvata
A possible explanation of the phase diagram of cuprate superconductors
A d-p pairing curve that is consistent with the pseudogap curve observed in
experiments is found on a d-p model on phonon mechanism. On the discovery we
suggest that there are two pseudogaps associated with the nearly localized d-p
pairs and nearly free p-p pairs. The p-p pairs look like bosons and are
responsible for superconductivities.Comment: 6 pages, 3 figure
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