324 research outputs found
Concept and Construction of Group Signature with self-proof capacity for confirming and denying
With privacy-preserving and traceability properties, group signature is a
cryptosystem with central role in cryptography. And there are lots of
application scenarios. A new extension concept of group signature is presented,
namely group signature with self-proof capacity. For a legitimate group
signature, the real signer can prove that the signature is indeed signed by
him/her. While for the other members of the group, they can prove that the
signature is not signed by him/her. The former can be used for claiming money
reward from the police, while the latter can be used for proving one's innocent
in a criminal investigation
Better Quantum Seal Schemes based on Trapdoor Claw-Free Functions
Seal in classical information is simply impossible. Since classical
information can be easily copied any number of times. Based on quantum
information, esp. quantum unclonable theorem, quantum seal maybe constructed
perfectly. But it is shown that perfect quantum seal is impossible, and the
success probability is bounded. In this paper, we show how to exceed the
optimal bound by using the TCF (Trapdoor Claw Free) functions, which can be
constructed based on LWE assumption. Hence it is post-quantum secure
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
Residual Attention Network for Image Classification
In this work, we propose "Residual Attention Network", a convolutional neural
network using attention mechanism which can incorporate with state-of-art feed
forward network architecture in an end-to-end training fashion. Our Residual
Attention Network is built by stacking Attention Modules which generate
attention-aware features. The attention-aware features from different modules
change adaptively as layers going deeper. Inside each Attention Module,
bottom-up top-down feedforward structure is used to unfold the feedforward and
feedback attention process into a single feedforward process. Importantly, we
propose attention residual learning to train very deep Residual Attention
Networks which can be easily scaled up to hundreds of layers. Extensive
analyses are conducted on CIFAR-10 and CIFAR-100 datasets to verify the
effectiveness of every module mentioned above. Our Residual Attention Network
achieves state-of-the-art object recognition performance on three benchmark
datasets including CIFAR-10 (3.90% error), CIFAR-100 (20.45% error) and
ImageNet (4.8% single model and single crop, top-5 error). Note that, our
method achieves 0.6% top-1 accuracy improvement with 46% trunk depth and 69%
forward FLOPs comparing to ResNet-200. The experiment also demonstrates that
our network is robust against noisy labels.Comment: accepted to CVPR201
Online decentralized tracking for nonlinear time-varying optimal power flow of coupled transmission-distribution grids
The coordinated alternating current optimal power flow (ACOPF) for coupled
transmission-distribution grids has become crucial to handle problems related
to high penetration of renewable energy sources (RESs). However, obtaining all
system details and solving ACOPF centrally is not feasible because of privacy
concerns. Intermittent RESs and uncontrollable loads can swiftly change the
operating condition of the power grid. Existing decentralized optimization
methods can seldom track the optimal solutions of time-varying ACOPFs. Here, we
propose an online decentralized optimization method to track the time-varying
ACOPF of coupled transmission-distribution grids. First, the time-varying ACOPF
problem is converted to a dynamic system based on Karush-Kuhn-Tucker conditions
from the control perspective. Second, a prediction term denoted by the partial
derivative with respect to time is developed to improve the tracking accuracy
of the dynamic system. Third, a decentralized implementation for solving the
dynamic system is designed based on only a few information exchanges with
respect to boundary variables. Moreover, the proposed algorithm can be used to
directly address nonlinear power flow equations without relying on convex
relaxations or linearization techniques. Numerical test results reveal the
effectiveness and fast-tracking performance of the proposed algorithm.Comment: 18 pages with 15 figure
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