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 $g-2$ 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 NO$\nu$A is analysed in presence of non-minimal Lorentz invariance violation. Indeed, we find that isotropic violation in dimensions $D =$ 4, 5 and 6 can alleviate the tension in neutrino oscillation data by 0.4$-$2.4$\sigma$ CL significance, with the isotropic coefficient $\gamma^{(5)}_{\tau \tau} =$ 3.58$\times$10$^{-32}$GeV$^{-1}$ yielding the best fit. At the same time, the anomalous muon $g-2$ result can be reproduced with an additional non-isotropic violation of $d^{zt} =$ -1.7$\times$10$^{-25}$. 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-dicarboxyl­ato-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 hydro­thermal 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 tetra­hedral environment. Each ligand coordinates in a bridging mode to connect ZnII ions into a three-dimensional diamondoid-type structure