219 research outputs found
Rational singularities and -birational morphism
In this paper, we generalize the notion of rational singularities for any
reflexive sheaf of rank and prove generalizations of standard facts about
rational singularities. Moreover, we introduce the notion of as a
dual notion of well-known Serre's notion of and prove a theorem
about -birational morphism.Comment: Comments Welcome
Self-Supervised Video Representation Learning with Space-Time Cubic Puzzles
Self-supervised tasks such as colorization, inpainting and zigsaw puzzle have
been utilized for visual representation learning for still images, when the
number of labeled images is limited or absent at all. Recently, this worthwhile
stream of study extends to video domain where the cost of human labeling is
even more expensive. However, the most of existing methods are still based on
2D CNN architectures that can not directly capture spatio-temporal information
for video applications. In this paper, we introduce a new self-supervised task
called as \textit{Space-Time Cubic Puzzles} to train 3D CNNs using large scale
video dataset. This task requires a network to arrange permuted 3D
spatio-temporal crops. By completing \textit{Space-Time Cubic Puzzles}, the
network learns both spatial appearance and temporal relation of video frames,
which is our final goal. In experiments, we demonstrate that our learned 3D
representation is well transferred to action recognition tasks, and outperforms
state-of-the-art 2D CNN-based competitors on UCF101 and HMDB51 datasets.Comment: Accepted to AAAI 201
Modeling the effects of aluminum and ammonium perchlorate addition on the detonation of the high explosives C_4H_8O_8N_8 (HMX) and C_3H_6O_6N_6(RDX)
Metalized high explosives effectively tailor the explosion impulse at lowered detonation pressures of common high performance explosives such as C_3H_6O_6N_6 (RDX) and C_4H_8O_8N_8 (HMX). The presence of aluminum (Al) with and without ammonium perchlorate (AP) allows the subsequent burning for longer and sustained reactions of enhanced blast explosives. The modeling of reaction rate laws for three explosives with varied amounts of Al, AP, RDX, and HMX is reported. The model validation included the rate stick test for understanding the explosive reaction of the three samples and the large-scale gap test for determining their ignition sensitivity. The experimental results confirmed the accuracy of the model in simulating the shock sensitivity and the size effects before detonation failure. The effect of enhanced blast of these explosives in the presence of Al and AP is also reported
Adjoint asymptotic multiplier ideal sheaves
In this paper, we initiate the study of a triple which
consists of a pair and a polarizing pseudoeffective divisor .
The adjoint asymptotic multiplier ideal sheaf associated to the triple gives a simultaneous generalization of the
multiplier ideal sheaf and asymptotic multiplier ideal sheaf
. We describe the closed set defined by the ideal
sheaf in terms of the minimal model
program. We also characterize the case where . Lastly, we also prove a Nadel type vanishing theorem of
cohomology using
Torque-based Deep Reinforcement Learning for Task-and-Robot Agnostic Learning on Bipedal Robots Using Sim-to-Real Transfer
In this paper, we review the question of which action space is best suited
for controlling a real biped robot in combination with Sim2Real training.
Position control has been popular as it has been shown to be more sample
efficient and intuitive to combine with other planning algorithms. However, for
position control gain tuning is required to achieve the best possible policy
performance. We show that instead, using a torque-based action space enables
task-and-robot agnostic learning with less parameter tuning and mitigates the
sim-to-reality gap by taking advantage of torque control's inherent compliance.
Also, we accelerate the torque-based-policy training process by pre-training
the policy to remain upright by compensating for gravity. The paper showcases
the first successful sim-to-real transfer of a torque-based deep reinforcement
learning policy on a real human-sized biped robot. The video is available at
https://youtu.be/CR6pTS39VRE
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