117 research outputs found
An efficient stochastic particle method for high-dimensional nonlinear PDEs
Numerical resolution of high-dimensional nonlinear PDEs remains a huge
challenge due to the curse of dimensionality. Starting from the weak
formulation of the Lawson-Euler scheme, this paper proposes a stochastic
particle method (SPM) by tracking the deterministic motion, random jump,
resampling and reweighting of particles. Real-valued weighted particles are
adopted by SPM to approximate the high-dimensional solution, which
automatically adjusts the point distribution to intimate the relevant feature
of the solution. A piecewise constant reconstruction with virtual uniform grid
is employed to evaluate the nonlinear terms, which fully exploits the intrinsic
adaptive characteristic of SPM. Combining both can SPM achieve the goal of
adaptive sampling in time. Numerical experiments on the 6-D Allen-Cahn equation
and the 7-D Hamiltonian-Jacobi-Bellman equation demonstrate the potential of
SPM in solving high-dimensional nonlinear PDEs efficiently while maintaining an
acceptable accuracy
BASAR:Black-box Attack on Skeletal Action Recognition
Skeletal motion plays a vital role in human activity recognition as either an
independent data source or a complement. The robustness of skeleton-based
activity recognizers has been questioned recently, which shows that they are
vulnerable to adversarial attacks when the full-knowledge of the recognizer is
accessible to the attacker. However, this white-box requirement is overly
restrictive in most scenarios and the attack is not truly threatening. In this
paper, we show that such threats do exist under black-box settings too. To this
end, we propose the first black-box adversarial attack method BASAR. Through
BASAR, we show that adversarial attack is not only truly a threat but also can
be extremely deceitful, because on-manifold adversarial samples are rather
common in skeletal motions, in contrast to the common belief that adversarial
samples only exist off-manifold. Through exhaustive evaluation and comparison,
we show that BASAR can deliver successful attacks across models, data, and
attack modes. Through harsh perceptual studies, we show that it achieves
effective yet imperceptible attacks. By analyzing the attack on different
activity recognizers, BASAR helps identify the potential causes of their
vulnerability and provides insights on what classifiers are likely to be more
robust against attack. Code is available at
https://github.com/realcrane/BASAR-Black-box-Attack-on-Skeletal-Action-Recognition.Comment: Accepted in CVPR 202
GFlowCausal: Generative Flow Networks for Causal Discovery
Causal discovery aims to uncover causal structure among a set of variables.
Score-based approaches mainly focus on searching for the best Directed Acyclic
Graph (DAG) based on a predefined score function. However, most of them are not
applicable on a large scale due to the limited searchability. Inspired by the
active learning in generative flow networks, we propose a novel approach to
learning a DAG from observational data called GFlowCausal. It converts the
graph search problem to a generation problem, in which direct edges are added
gradually. GFlowCausal aims to learn the best policy to generate high-reward
DAGs by sequential actions with probabilities proportional to predefined
rewards. We propose a plug-and-play module based on transitive closure to
ensure efficient sampling. Theoretical analysis shows that this module could
guarantee acyclicity properties effectively and the consistency between final
states and fully-connected graphs. We conduct extensive experiments on both
synthetic and real datasets, and results show the proposed approach to be
superior and also performs well in a large-scale setting
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