32,063 research outputs found
Modeling The Time Variability of Accreting Compact Sources
We present model light curves for accreting Black Hole Candidates (BHC) based
on a recently proposed model for their spectro-temporal properties. According
to this model, the observed light curves and aperiodic variability of BHC are
due to a series of soft photon injections at random (Poisson) intervals near
the compact object and their reprocessing into hard radiation in an extended
but non-uniform hot plasma corona surrounding the compact object. We argue that
the majority of the timing characteristics of these light curves are due to the
stochastic nature of the Comptonization process in the extended corona, whose
properties, most notably its radial density dependence, are imprinted in them.
We compute the corresponding Power Spectral Densities (PSD), autocorrelation
functions, time skewness of the light curves and time lags between the light
curves of the sources at different photon energies and compare our results to
observation. Our model light curves compare well with observations, providing
good fits to their overall morphology, as manifest by the autocorrelation and
skewness functions. The lags and PSDs of the model light curves are also in
good agreement with those observed (the model can even accommodate the presence
of QPOs). Finally, while most of the variability power resides at time scales
\gsim a few seconds, at the same time, the model allows also for shots of a
few msec in duration, in accordance with observation. We suggest that
refinements of this type of model along with spectral and phase lag information
can be used to probe the structure of this class of high energy sources.Comment: 23 pages Latex, 15 encapsulated postscript figures, to appear in the
Astrophysical Journa
Sudden jumps and plateaus in the quench dynamics of a Bloch state
We take a one-dimensional tight binding chain with periodic boundary
condition and put a particle in an arbitrary Bloch state, then quench it by
suddenly changing the potential of an arbitrary site. In the ensuing time
evolution, the probability density of the wave function at an arbitrary site
\emph{jumps indefinitely between plateaus}. This phenomenon adds to a former
one in which the survival probability of the particle in the initial Bloch
state shows \emph{cusps} periodically, which was found in the same scenario
[Zhang J. M. and Yang H.-T., EPL, \textbf{114} (2016) 60001]. The plateaus
support the scattering wave picture of the quench dynamics of the Bloch state.
Underlying the cusps and jumps is the exactly solvable, nonanalytic dynamics of
a Luttinger-like model, based on which, the locations of the jumps and the
heights of the plateaus are accurately predicted.Comment: final versio
3D UAV Trajectory and Communication Design for Simultaneous Uplink and Downlink Transmission
In this paper, we investigate the unmanned aerial vehicle (UAV)-Aided simultaneous uplink and downlink transmission networks, where one UAV acting as a disseminator is connected to multiple access points (AP), and the other UAV acting as a base station (BS) collects data from numerous sensor nodes (SNs). The goal of this paper is to maximize the system throughput by jointly optimizing the 3D UAV trajectory, communication scheduling, and UAV-AP/SN transmit power. We first consider a special case where the UAV-BS and UAV-AP trajectories are pre-determined. Although the resulting problem is an integer and non-convex optimization problem, a globally optimal solution is obtained by applying the polyblock outer approximation (POA) method based on the problem's hidden monotonic structure. Subsequently, for the general case considering the 3D UAV trajectory optimization, an efficient iterative algorithm is proposed to alternately optimize the divided sub-problems based on the successive convex approximation (SCA) technique. Numerical results demonstrate that the proposed design is able to achieve significant system throughput gain over the benchmarks. In addition, the SCA-based method can achieve nearly the same performance as the POA-based method with much lower computational complexity
Probing the Structure of Accreting Compact Sources Through X-Ray Time Lags and Spectra
We exhibit, by compiling all data sets we can acquire, that the Fourier
frequency dependent hard X-ray lags, first observed in the analysis of
aperiodic variability of the light curves of the black hole candidate Cygnus
X-1, appear to be a property shared by several other accreting black hole
candidate sources and also by the different spectral states of this source. We
then present both analytic and numerical models of these time lags resulting by
the process of Comptonization in a variety of hot electron configurations. We
argue that under the assumption that the observed spectra are due to
Comptonization, the dependence of the lags on the Fourier period provides a
means for mapping the spatial density profile of the hot electron plasma, while
the period at which the lags eventually level--off provides an estimate of the
size of the scattering cloud. We further examine the influence of the location
and spatial extent of the soft photon source on the form of the resulting lags
for a variety of configurations; we conclude that the study of the X-ray hard
lags can provide clues about these parameters of the Comptonization process
too. Fits of the existing data with our models indicate that the size of the
Comptonizing clouds are quite large in extent ( 1 light second) with
inferred radial density profiles which are in many instances inconsistent with
those of the standard dynamical models, while the extent of the source of soft
photons appears to be much smaller than those of the hot electrons by roughly
two orders of magnitude and its location consistent with the center of the hot
electron corona.Comment: 20 pages Latex, 11 postscript figures, to appear in the Astrophysical
Journal, Vol 512, Feb 20 issu
The Complete Two-Loop Integrated Jet Thrust Distribution In Soft-Collinear Effective Theory
In this work, we complete the calculation of the soft part of the two-loop
integrated jet thrust distribution in e+e- annihilation. This jet mass
observable is based on the thrust cone jet algorithm, which involves a veto
scale for out-of-jet radiation. The previously uncomputed part of our result
depends in a complicated way on the jet cone size, r, and at intermediate
stages of the calculation we actually encounter a new class of multiple
polylogarithms. We employ an extension of the coproduct calculus to
systematically exploit functional relations and represent our results
concisely. In contrast to the individual contributions, the sum of all global
terms can be expressed in terms of classical polylogarithms. Our explicit
two-loop calculation enables us to clarify the small r picture discussed in
earlier work. In particular, we show that the resummation of the logarithms of
r that appear in the previously uncomputed part of the two-loop integrated jet
thrust distribution is inextricably linked to the resummation of the non-global
logarithms. Furthermore, we find that the logarithms of r which cannot be
absorbed into the non-global logarithms in the way advocated in earlier work
have coefficients fixed by the two-loop cusp anomalous dimension. We also show
that, given appropriate L-loop contributions to the integrated hemisphere soft
function, one can straightforwardly predict a number of potentially large
logarithmic contributions at L loops not controlled by the factorization
theorem for jet thrust.Comment: 52 pages, 5 figures; in v2: incorporated referee suggestions in text,
including additional figures and footnotes for the purpose of clarification.
v2 is the version published in PR
Ranked List Loss for Deep Metric Learning
The objective of deep metric learning (DML) is to learn embeddings that can
capture semantic similarity and dissimilarity information among data points.
Existing pairwise or tripletwise loss functions used in DML are known to suffer
from slow convergence due to a large proportion of trivial pairs or triplets as
the model improves. To improve this, ranking-motivated structured losses are
proposed recently to incorporate multiple examples and exploit the structured
information among them. They converge faster and achieve state-of-the-art
performance. In this work, we unveil two limitations of existing
ranking-motivated structured losses and propose a novel ranked list loss to
solve both of them. First, given a query, only a fraction of data points is
incorporated to build the similarity structure. Consequently, some useful
examples are ignored and the structure is less informative. To address this, we
propose to build a set-based similarity structure by exploiting all instances
in the gallery. The learning setting can be interpreted as few-shot retrieval:
given a mini-batch, every example is iteratively used as a query, and the rest
ones compose the gallery to search, i.e., the support set in few-shot setting.
The rest examples are split into a positive set and a negative set. For every
mini-batch, the learning objective of ranked list loss is to make the query
closer to the positive set than to the negative set by a margin. Second,
previous methods aim to pull positive pairs as close as possible in the
embedding space. As a result, the intraclass data distribution tends to be
extremely compressed. In contrast, we propose to learn a hypersphere for each
class in order to preserve useful similarity structure inside it, which
functions as regularisation. Extensive experiments demonstrate the superiority
of our proposal by comparing with the state-of-the-art methods.Comment: Accepted to T-PAMI. Therefore, to read the offical version, please go
to IEEE Xplore. Fine-grained image retrieval task. Our source code is
available online: https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DM
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