Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

Human parsing, or human body part semantic segmentation, has been an active research topic due to its wide potential applications. In this paper, we propose a novel GRAph PYramid Mutual Learning (Grapy-ML) method to address the cross-dataset human parsing problem, where the annotations are at different granularities. Starting from the prior knowledge of the human body hierarchical structure, we devise a graph pyramid module (GPM) by stacking three levels of graph structures from coarse granularity to fine granularity subsequently. At each level, GPM utilizes the self-attention mechanism to model the correlations between context nodes. Then, it adopts a top-down mechanism to progressively refine the hierarchical features through all the levels. GPM also enables efficient mutual learning. Specifically, the network weights of the first two levels are shared to exchange the learned coarse-granularity information across different datasets. By making use of the multi-granularity labels, Grapy-ML learns a more discriminative feature representation and achieves state-of-the-art performance, which is demonstrated by extensive experiments on the three popular benchmarks, e.g. CIHP dataset. The source code is publicly available at https://github.com/Charleshhy/Grapy-ML.Comment: Accepted as an oral paper in AAAI2020. 9 pages, 4 figures. https://www.aaai.org/Papers/AAAI/2020GB/AAAI-HeH.2317.pd

Using POMDP as Modeling Framework for Network Fault Management

For highדּpeed networks, it is important that fault management be proactive--i.e., detect, diagnose, and mitigate problems before they result in severe degradation of network performance. Proactive fault manageשּׂent depends on monitoring the network to obtain the data on which to base manager decisions. However, monitoring introduces additional overhead that may itself degrade network performance especially when the network is in a stressed state. Thus, a tradeoff must be made be﫠tween the amount of data collected and transferred on one hand, and the speed and accuracy of fault detection and diagnosis on the other hand. Such a tradeoff can be naturally formulated as a Partially Observable Markov decision process (POMDP).Since exact solution of POMDPs for a realistic number of states is computationally prohibitive, we develop a reinforcementשּׁearningﬢased fast algorithm which learns the decisionגּule in an approximate network simulator and makes it fast deployable to the real network. Simulation results are given to diagnose a switch fault in an ATM network. This approach can be applied to centralized fault management or to construct intelligent agents for distributed fault management

Solving POMDP by On﬐olicy Linear Approximate Learning Algorithm

This paper presents a fast Reinforcement Learning (RL) algorithm to solve Partially Observable Markov Decision Processes (POMDP) prob﫠lem. The proposed algorithm is devised to provide a policyשּׂaking frame﫠work for Network Management Systems (NMS) which is in essence an engineering application without an exact model.The algorithm consists of two phases. Firstly, the model is estimated and policy is learned in a completely observable simulator. Secondly, the estimated model is brought into the partially observed real﬷orld where the learned policy is then fineהּuned.The learning algorithm is based on the onאּolicy linear gradientﬤescent learning algorithm with eligibility traces. This implies that the Qזּalue on belief space is linearly approximated by the Qזּalue at vertex over the belief space where onשּׁine TD method will be applied.The proposed algorithm is tested against the exact solutions to exten﫠sive small/middleדּize benchmark examples from POMDP literature and found near optimal in terms of averageﬤiscountedגּeward and stepהּo﫠goal. The proposed algorithm significantly reduces the convergence time and can easily be adapted to large stateאַumber problems

Role of dimensional crossover on spin-orbit torque efficiency in magnetic insulator thin films

Magnetic insulators (MIs) attract tremendous interest for spintronic applications due to low Gilbert damping and absence of Ohmic loss. Magnetic order of MIs can be manipulated and even switched by spin-orbit torques (SOTs) generated through spin Hall effect and Rashba-Edelstein effect in heavy metal/MI bilayers. SOTs on MIs are more intriguing than magnetic metals since SOTs cannot be transferred to MIs through direct injection of electron spins. Understanding of SOTs on MIs remains elusive, especially how SOTs scale with the film thickness. Here, we observe the critical role of dimensionality on the SOT efficiency by systematically studying the MI layer thickness dependent SOT efficiency in tungsten/thulium iron garnet (W/TmIG) bilayers. We first show that the TmIG thin film evolves from two-dimensional to three-dimensional magnetic phase transitions as the thickness increases, due to the suppression of long-wavelength thermal fluctuation. Then, we report the significant enhancement of the measured SOT efficiency as the thickness increases. We attribute this effect to the increase of the magnetic moment density in concert with the suppression of thermal fluctuations. At last, we demonstrate the current-induced SOT switching in the W/TmIG bilayers with a TmIG thickness up to 15 nm. The switching current density is comparable with those of heavy metal/ferromagnetic metal cases. Our findings shed light on the understanding of SOTs in MIs, which is important for the future development of ultrathin MI-based low-power spintronics