2,257 research outputs found
Very Old Isolated Compact Objects as Dark Matter Probes
Very old isolated neutron stars and white dwarfs have been suggested to be
probes of dark matter. To play such a role, two requests should be fulfilled,
i.e., the annihilation luminosity of the captured dark matter particles is
above the thermal emission of the cooling compact objects (request-I) and also
dominate over the energy output due to the accretion of normal matter onto the
compact objects (request-II). Request-I calls for very dense dark matter medium
and the critical density sensitively depends on the residual surface
temperature of the very old compact objects. The accretion of
interstellar/intracluster medium onto the compact objects is governed by the
physical properties of the medium and by the magnetization and rotation of the
stars and may outshine the signal of dark matter annihilation. Only in a few
specific scenarios both requests are satisfied and the compact objects are dark
matter burners. The observational challenges are discussed and a possible way
to identify the dark matter burners is outlined.Comment: 9 pages including 1 Figure, to appear in Phys. Rev.
On the Optimal Batch Size for Byzantine-Robust Distributed Learning
Byzantine-robust distributed learning (BRDL), in which computing devices are
likely to behave abnormally due to accidental failures or malicious attacks,
has recently become a hot research topic. However, even in the independent and
identically distributed (i.i.d.) case, existing BRDL methods will suffer from a
significant drop on model accuracy due to the large variance of stochastic
gradients. Increasing batch sizes is a simple yet effective way to reduce the
variance. However, when the total number of gradient computation is fixed, a
too-large batch size will lead to a too-small iteration number (update number),
which may also degrade the model accuracy. In view of this challenge, we mainly
study the optimal batch size when the total number of gradient computation is
fixed in this work. In particular, we theoretically and empirically show that
when the total number of gradient computation is fixed, the optimal batch size
in BRDL increases with the fraction of Byzantine workers. Therefore, compared
to the case without attacks, the batch size should be set larger when under
Byzantine attacks. However, for existing BRDL methods, large batch sizes will
lead to a drop on model accuracy, even if there is no Byzantine attack. To deal
with this problem, we propose a novel BRDL method, called Byzantine-robust
stochastic gradient descent with normalized momentum (ByzSGDnm), which can
alleviate the drop on model accuracy in large-batch cases. Moreover, we
theoretically prove the convergence of ByzSGDnm for general non-convex cases
under Byzantine attacks. Empirical results show that ByzSGDnm has a comparable
performance to existing BRDL methods under bit-flipping failure, but can
outperform existing BRDL methods under deliberately crafted attacks
Extracting the Quantum Geometric Tensor of an Optical Raman Lattice by Bloch State Tomography
In Hilbert space, the geometry of the quantum state is identified by the
quantum geometric tensor (QGT), whose imaginary part is the Berry curvature and
real part is the quantum metric tensor. Here, we propose and experimentally
implement a complete Bloch state tomography to directly measure eigenfunction
of an optical Raman lattice for ultracold atoms. Through the measured
eigenfunction, the distribution of the complete QGT in the Brillouin zone is
reconstructed, with which the topological invariants are extracted by the Berry
curvature and the distances of quantum states in momentum space are measured by
the quantum metric tensor. Further, we experimentally test a predicted
inequality between the Berry curvature and quantum metric tensor, which reveals
a deep connection between topology and geometry
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