2,704 research outputs found
Domain Conditioned Adaptation Network
Tremendous research efforts have been made to thrive deep domain adaptation
(DA) by seeking domain-invariant features. Most existing deep DA models only
focus on aligning feature representations of task-specific layers across
domains while integrating a totally shared convolutional architecture for
source and target. However, we argue that such strongly-shared convolutional
layers might be harmful for domain-specific feature learning when source and
target data distribution differs to a large extent. In this paper, we relax a
shared-convnets assumption made by previous DA methods and propose a Domain
Conditioned Adaptation Network (DCAN), which aims to excite distinct
convolutional channels with a domain conditioned channel attention mechanism.
As a result, the critical low-level domain-dependent knowledge could be
explored appropriately. As far as we know, this is the first work to explore
the domain-wise convolutional channel activation for deep DA networks.
Moreover, to effectively align high-level feature distributions across two
domains, we further deploy domain conditioned feature correction blocks after
task-specific layers, which will explicitly correct the domain discrepancy.
Extensive experiments on three cross-domain benchmarks demonstrate the proposed
approach outperforms existing methods by a large margin, especially on very
tough cross-domain learning tasks.Comment: Accepted by AAAI 202
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning
Automatic decision-making approaches, such as reinforcement learning (RL),
have been applied to (partially) solve the resource allocation problem
adaptively in the cloud computing system. However, a complete cloud resource
allocation framework exhibits high dimensions in state and action spaces, which
prohibit the usefulness of traditional RL techniques. In addition, high power
consumption has become one of the critical concerns in design and control of
cloud computing systems, which degrades system reliability and increases
cooling cost. An effective dynamic power management (DPM) policy should
minimize power consumption while maintaining performance degradation within an
acceptable level. Thus, a joint virtual machine (VM) resource allocation and
power management framework is critical to the overall cloud computing system.
Moreover, novel solution framework is necessary to address the even higher
dimensions in state and action spaces. In this paper, we propose a novel
hierarchical framework for solving the overall resource allocation and power
management problem in cloud computing systems. The proposed hierarchical
framework comprises a global tier for VM resource allocation to the servers and
a local tier for distributed power management of local servers. The emerging
deep reinforcement learning (DRL) technique, which can deal with complicated
control problems with large state space, is adopted to solve the global tier
problem. Furthermore, an autoencoder and a novel weight sharing structure are
adopted to handle the high-dimensional state space and accelerate the
convergence speed. On the other hand, the local tier of distributed server
power managements comprises an LSTM based workload predictor and a model-free
RL based power manager, operating in a distributed manner.Comment: accepted by 37th IEEE International Conference on Distributed
Computing (ICDCS 2017
Non-minimal Lorentz invariance violation in light of muon anomalous magnetic moment and long-baseline neutrino oscillation data
In light of the increasing hints of new physics at the muon and
neutrino oscillation experiments, we consider the recently observed tension in
the long-baseline neutrino oscillation experiments as a potential indication of
Lorentz invariance violation. For this purpose, the latest data from T2K and
NOA is analysed in presence of non-minimal Lorentz invariance violation.
Indeed, we find that isotropic violation in dimensions 4, 5 and 6 can
alleviate the tension in neutrino oscillation data by 0.42.4 CL
significance, with the isotropic coefficient
3.5810GeV yielding the best fit. At the same time, the
anomalous muon result can be reproduced with an additional non-isotropic
violation of -1.710. The analysis highlights the
possibility of simultaneous relaxation of experimental tensions with Lorentz
invariance violation of mixed nature.Comment: 11 pages, 4 figures, 2 tables; accepted for publication in Physical
Review
Poly[μ2-benzene-1,3-dicarboxylÂato-κ2 O:O′-μ2-1,3-di-4-pyridylpropane-κ2 N:N′-zinc(II)]
The title compound, [Zn(C8H4O4)(C13H14N2)]n, was obtained by the hydroÂthermal reaction of Zn(OAc)2·H2O with 1,3-di-4-pyridylpropane (bpp) and isophthalic acid (H2ip). The ZnII ion is coordinated by two bpp and two ip ligands in a distorted tetraÂhedral environment. Each ligand coordinates in a bridging mode to connect ZnII ions into a three-dimensional diamondoid-type structure
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