Hard X-ray emission cutoff in anomalous X-ray pulsar 4U 0142+61 detected by INTEGRAL

The anomalous X-ray pulsar 4U 0142+61 was studied by the INTEGRAL observations. The hard X-ray spectrum of 18 -- 500 keV for 4U 0142+61 was derived using near 9 years of INTEGRAL/IBIS data. We obtained the average hard X-ray spectrum of 4U 0142+61 with all available data. The spectrum of 4U 0142+61 can be fitted with a power-law with an exponential high energy cutoff. This average spectrum is well fitted with a power-law of $\Gamma\sim 0.51\pm 0.11$ plus a cutoff energy at $128.6\pm 17.2$ keV. The hard X-ray flux of the source from 20 -- 150 keV showed no significant variations (within 20$\%$) from 2003 -- 2011. The spectral profiles have some variability in nine years: photon index varied from 0.3 -- 1.5, and cutoff energies of 110 -- 250 keV. The detection of the high energy cutoff around 130 keV shows some constraints on the radiation mechanisms of magnetars and possibly probes the differences between magnetar and accretion models for these special class of neutron stars. Future HXMT observations could provide stronger constraints on the hard X-ray spectral properties of this source and other magnetar candidates.Comment: 9 pages, 5 figures, 2 tables, figures are updated, new data are added, conclusion does not change, to be published in RA

BayesNAS: A Bayesian Approach for Neural Architecture Search

One-Shot Neural Architecture Search (NAS) is a promising method to significantly reduce search time without any separate training. It can be treated as a Network Compression problem on the architecture parameters from an over-parameterized network. However, there are two issues associated with most one-shot NAS methods. First, dependencies between a node and its predecessors and successors are often disregarded which result in improper treatment over zero operations. Second, architecture parameters pruning based on their magnitude is questionable. In this paper, we employ the classic Bayesian learning approach to alleviate these two issues by modeling architecture parameters using hierarchical automatic relevance determination (HARD) priors. Unlike other NAS methods, we train the over-parameterized network for only one epoch then update the architecture. Impressively, this enabled us to find the architecture on CIFAR-10 within only 0.2 GPU days using a single GPU. Competitive performance can be also achieved by transferring to ImageNet. As a byproduct, our approach can be applied directly to compress convolutional neural networks by enforcing structural sparsity which achieves extremely sparse networks without accuracy deterioration.Comment: International Conference on Machine Learning 201

Actor-Critic Reinforcement Learning for Control with Stability Guarantee

Reinforcement Learning (RL) and its integration with deep learning have achieved impressive performance in various robotic control tasks, ranging from motion planning and navigation to end-to-end visual manipulation. However, stability is not guaranteed in model-free RL by solely using data. From a control-theoretic perspective, stability is the most important property for any control system, since it is closely related to safety, robustness, and reliability of robotic systems. In this paper, we propose an actor-critic RL framework for control which can guarantee closed-loop stability by employing the classic Lyapunov's method in control theory. First of all, a data-based stability theorem is proposed for stochastic nonlinear systems modeled by Markov decision process. Then we show that the stability condition could be exploited as the critic in the actor-critic RL to learn a controller/policy. At last, the effectiveness of our approach is evaluated on several well-known 3-dimensional robot control tasks and a synthetic biology gene network tracking task in three different popular physics simulation platforms. As an empirical evaluation on the advantage of stability, we show that the learned policies can enable the systems to recover to the equilibrium or way-points when interfered by uncertainties such as system parametric variations and external disturbances to a certain extent.Comment: IEEE RA-L + IROS 202

LERC: Coordinated Cache Management for Data-Parallel Systems

Memory caches are being aggressively used in today's data-parallel frameworks such as Spark, Tez and Storm. By caching input and intermediate data in memory, compute tasks can witness speedup by orders of magnitude. To maximize the chance of in-memory data access, existing cache algorithms, be it recency- or frequency-based, settle on cache hit ratio as the optimization objective. However, unlike the conventional belief, we show in this paper that simply pursuing a higher cache hit ratio of individual data blocks does not necessarily translate into faster task completion in data-parallel environments. A data-parallel task typically depends on multiple input data blocks. Unless all of these blocks are cached in memory, no speedup will result. To capture this all-or-nothing property, we propose a more relevant metric, called effective cache hit ratio. Specifically, a cache hit of a data block is said to be effective if it can speed up a compute task. In order to optimize the effective cache hit ratio, we propose the Least Effective Reference Count (LERC) policy that persists the dependent blocks of a compute task as a whole in memory. We have implemented the LERC policy as a memory manager in Spark and evaluated its performance through Amazon EC2 deployment. Evaluation results demonstrate that LERC helps speed up data-parallel jobs by up to 37% compared with the widely employed least-recently-used (LRU) policy