19,472 research outputs found
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 plus a cutoff energy at 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
Superfluidity enhanced by spin-flip tunnelling in the presence of a magnetic field
It is well-known that when the magnetic field is stronger than a critical
value, the spin imbalance can break the Cooper pairs of electrons and hence
hinder the superconductivity in a spin-singlet channel. In a bilayer system of
ultra-cold Fermi gases, however, we demonstrate that the critical value of the
magnetic field at zero temperature can be significantly increased by including
a spin-flip tunnelling, which opens a gap in the spin-triplet channel near the
Fermi surface and hence reduces the influence of the effective magnetic field
on the superfluidity. The phase transition also changes from first order to
second order when the tunnelling exceeds a critical value. Considering a
realistic experiment, this mechanism can be implemented by applying an
intralayer Raman coupling between the spin states with a phase difference
between the two layers.Comment: 10+4 pages, 8 figure
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
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