1,933 research outputs found
Superradiantly stable non-extremal Reissner-Nordstrom black holes
The superradiant stability is investigated for non-extremal
Reissner-Nordstrom black hole. We use an algebraic method to demonstrate that
all non-extremal Reissner-Nordstrom black holes are superradiantly stable
against a charged massive scalar perturbation. This improves the results
obtained before for non-extremal Reissner-Nordstrom black holes
Video Frame Interpolation via Adaptive Separable Convolution
Standard video frame interpolation methods first estimate optical flow
between input frames and then synthesize an intermediate frame guided by
motion. Recent approaches merge these two steps into a single convolution
process by convolving input frames with spatially adaptive kernels that account
for motion and re-sampling simultaneously. These methods require large kernels
to handle large motion, which limits the number of pixels whose kernels can be
estimated at once due to the large memory demand. To address this problem, this
paper formulates frame interpolation as local separable convolution over input
frames using pairs of 1D kernels. Compared to regular 2D kernels, the 1D
kernels require significantly fewer parameters to be estimated. Our method
develops a deep fully convolutional neural network that takes two input frames
and estimates pairs of 1D kernels for all pixels simultaneously. Since our
method is able to estimate kernels and synthesizes the whole video frame at
once, it allows for the incorporation of perceptual loss to train the neural
network to produce visually pleasing frames. This deep neural network is
trained end-to-end using widely available video data without any human
annotation. Both qualitative and quantitative experiments show that our method
provides a practical solution to high-quality video frame interpolation.Comment: ICCV 2017, http://graphics.cs.pdx.edu/project/sepconv
Synthetic Engineering of Graphene Nanoribbons with Excellent Liquid-Phase Processability
Motion-Adjustable Neural Implicit Video Representation
Implicit neural representation (INR) has been successful in representing static images. Contemporary image-based INR, with the use of Fourier-based positional encoding, can be viewed as a mapping from sinusoidal patterns with different frequencies to image content. Inspired by that view, we hypothesize that it is possible to generate temporally varying content with a single image-based INR model by displacing its input sinusoidal patterns over time. By exploiting the relation between the phase information in sinusoidal functions and their displacements, we incorporate into the conventional image-based INR model a phase-varying positional encoding module, and couple it with a phase-shift generation module that determines the phase-shift values at each frame. The model is trained end-to-end on a video to jointly determine the phase-shift values at each time with the mapping from the phase-shifted sinusoidal functions to the corresponding frame, enabling an implicit video representation. Experiments on a wide range of videos suggest that such a model is capable of learning to interpret phase-varying positional embeddings into the corresponding time-varying content. More importantly, we found that the learned phase-shift vectors tend to capture meaningful temporal and motion information from the video. In particular, manipulating the phase-shift vectors induces meaningful changes in the temporal dynamics of the resulting video, enabling non-trivial temporal and motion editing effects such as temporal interpolation, motion magnification, motion smoothing, and video loop detection
Scalable and Adaptively Secure Any-Trust Distributed Key Generation and All-hands Checkpointing
The classical distributed key generation protocols (DKG) are resurging due to
their widespread applications in blockchain. While efforts have been made to
improve DKG communication, practical large scale deployments are still yet to
come, due to various challenges including broadcast channel scalability and
worst-case complaint phase. In this paper, we propose a practical DKG for
DL-based cryptosystems, with only (quasi-)linear computation/communication cost
per participant, with the help of a public ledger, and beacon; Notably, our DKG
only incurs constant-size blockchain storage cost for broadcast, even in the
face of worst-case complaints. Moreover, our protocol satisfies adaptive
security. The key to our improvements lies in delegating the most costly
operations to an Any-Trust group. This group is randomly sampled and consists
of a small number of individuals. The population only trusts that at least one
member in the group is honest, without knowing which one. Additionally, we
introduce an extended broadcast channel based on a blockchain and data
dispersal network (such as IPFS), enabling reliable broadcasting of
arbitrary-size messages at the cost of constant-size blockchain storage, which
may be of independent interest.
Our DKG leads to a fully practical instantiation of Filecoin's checkpointing
mechanism, in which all validators of a Proof-of-Stake (PoS) blockcahin
periodically run DKG and threshold signing to create checkpoints on Bitcoin,
thereby enhancing the security of the PoS chain. In comparison with another
checkpointing approach of Babylon (Oakland, 2023), ours enjoys a significally
smaller monetary cost of Bitcoin transaction fees. For a PoS chain with
validators, our cost is merely 0.6\% of that incurred by Babylon's
approach.Comment: 21 pages, 3 figure
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