18,728 research outputs found
A note on morphisms determined by objects
We prove that a Hom-finite additive category having determined morphisms on
both sides is a dualizing variety. This complements a result by Krause. We
prove that in a Hom-finite abelian category having Serre duality, a morphism is
right determined by some object if and only if it is an epimorphism. We give a
characterization to abelian categories having Serre duality via determined
morphisms
Atomic and photonic entanglement concentration via photonic Faraday rotation
We propose two alternative entanglement concentration protocols (ECPs) using
the Faraday rotation of photonic polarization. Through the single-photon
input-output process in cavity QED, it is shown that the maximally entangled
atomic (photonic) state can be extracted from two partially entangled states.
The distinct feature of our protocols is that we can concentrate both atomic
and photonic entangled states via photonic Faraday rotation, and thus they may
be universal and useful for entanglement concentration in the experiment.
Furthermore, as photonic Faraday rotation works in low- cavities and only
involves virtual excitation of atoms, our ECPs are insensitive to both cavity
decay and atomic spontaneous emission.Comment: 5 pages, 3 figure
Tunable ultra-high-efficiency light absorption of monolayer graphene using critical coupling with guided resonance
We numerically demonstrate a novel monolayer graphene-based perfect
absorption multi-layer photonic structure by the mechanism of critical coupling
with guided resonance, in which the absorption of graphene can significantly
close to 99% at telecommunication wavelengths. The highly efficient absorption
and spectral selectivity can be obtained with designing structural parameters
in the near infrared ranges. Compared to previous works, we achieve the
complete absorption of single-atomic-layer graphene in the perfect absorber for
the first time, which not only opens up new methods of enhancing the
light-graphene interaction, but also makes for practical applications in
high-performance optoelectronic devices, such as modulators and sensors
Bulk viscosity of hot dense Quark matter in PNJL model
Starting from the Kubo formula and the the QCD low energy theorem, we study
the the bulk viscosity of hot dense quark matter in the PNJL model from the
equation of state . We show that the bulk viscosity has a sharp peak near the
chiral phase transition, and the ratio of bulk viscosity over entropy rises
dramatically in the vicinity of the phase transition. These results agrees with
that from lattice and other model calculations. In addition, we show that the
increase of chemical potential raises the bulk viscosity.Comment: 8 Pages, 3 figures in Latex. arXiv admin note: text overlap with
arXiv:1107.5113 by other author
Sharp Dimension Estimates of Holomorphic Functions and Rigidity
Let be a complete noncompact Khler manifold of complex
dimension with nonnegative holomorphic bisectional curvature. Denote by
the space of holomorphic functions of polynomial growth
of degree at most on . In this paper we prove that
for all , with
equality for some positive integer if and only if is holomorphically
isometric to . We also obtain sharp improved dimension estimates
when its volume growth is not maximal or its Ricci curvature is positive
somewhere.Comment: 24 page
The excitation functions of 187Re(n,2n)186m,gRe Reactions
A new value for the emission probability of 137.144keV g-ray of 186gRe decay
are re-recommended to be 9.47+-0.03 (%). From this new g-ray emission
probability, the measured cross sections for 187Re(n,2n)186mRe and
187Re(n,2n)186gRe reactions around 14MeV are evaluated, and the total cross
section for 187Re(n,2n)186m+gRe reaction at 14.8MeV is recommended to be
2213+-116 mb. The UNF code are adopted to calculate the total cross sections
for 187Re(n,2n)186m+gRe reaction below 20 MeV fitting to the recommended value
2213+-116 mb at 14.8MeV using a set of optimum neutron optical potential
parameters which obtained on the relevant experimental data of Re. Then the
isomeric cross section ratios for 187Re(n,2n)186m,gRe reaction are calculated
using the method of Monte Carlo calculations based on the nuclear statistical
theory. Combining these two calculated results, the excitation functions for
187Re(n,2n)186mRe and 187Re(n,2n)186gRe reactions are obtained. The obtained
results are in good agreement with the available experimental data, which
indicates that present method is useful to deduce the isomeric cross sections
for (n,2n) reaction.Comment: 14 pages, 6 figure
Spectrum Cartography via Coupled Block-Term Tensor Decomposition
Spectrum cartography aims at estimating power propagation patterns over a
geographical region across multiple frequency bands (i.e., a radio map)---from
limited samples taken sparsely over the region. Classic cartography methods are
mostly concerned with recovering the aggregate radio frequency (RF) information
while ignoring the constituents of the radio map---but fine-grained
emitter-level RF information is of great interest. In addition, many existing
cartography methods work explicitly or implicitly assume random spatial
sampling schemes that may be difficult to implement, due to
legal/privacy/security issues. The theoretical aspects (e.g., identifiability
of the radio map) of many existing methods are also unclear. In this work, we
propose a joint radio map recovery and disaggregation method that is based on
coupled block-term tensor decomposition. Our method guarantees identifiability
of the individual radio map of \textit{each emitter} (thereby that of the
aggregate radio map as well), under realistic conditions. The identifiability
result holds under a large variety of geographical sampling patterns, including
a number of pragmatic systematic sampling strategies. We also propose effective
optimization algorithms to carry out the formulated radio map disaggregation
problems. Extensive simulations are employed to showcase the effectiveness of
the proposed approach.Comment: Accepted version; IEEE Transactions on Signal Processing
(27-Apr-2020
Learning Temporal Point Processes via Reinforcement Learning
Social goods, such as healthcare, smart city, and information networks, often
produce ordered event data in continuous time. The generative processes of
these event data can be very complex, requiring flexible models to capture
their dynamics. Temporal point processes offer an elegant framework for
modeling event data without discretizing the time. However, the existing
maximum-likelihood-estimation (MLE) learning paradigm requires hand-crafting
the intensity function beforehand and cannot directly monitor the
goodness-of-fit of the estimated model in the process of training. To alleviate
the risk of model-misspecification in MLE, we propose to generate samples from
the generative model and monitor the quality of the samples in the process of
training until the samples and the real data are indistinguishable. We take
inspiration from reinforcement learning (RL) and treat the generation of each
event as the action taken by a stochastic policy. We parameterize the policy as
a flexible recurrent neural network and gradually improve the policy to mimic
the observed event distribution. Since the reward function is unknown in this
setting, we uncover an analytic and nonparametric form of the reward function
using an inverse reinforcement learning formulation. This new RL framework
allows us to derive an efficient policy gradient algorithm for learning
flexible point process models, and we show that it performs well in both
synthetic and real data
Wasserstein Learning of Deep Generative Point Process Models
Point processes are becoming very popular in modeling asynchronous sequential
data due to their sound mathematical foundation and strength in modeling a
variety of real-world phenomena. Currently, they are often characterized via
intensity function which limits model's expressiveness due to unrealistic
assumptions on its parametric form used in practice. Furthermore, they are
learned via maximum likelihood approach which is prone to failure in
multi-modal distributions of sequences. In this paper, we propose an
intensity-free approach for point processes modeling that transforms nuisance
processes to a target one. Furthermore, we train the model using a
likelihood-free leveraging Wasserstein distance between point processes.
Experiments on various synthetic and real-world data substantiate the
superiority of the proposed point process model over conventional ones
Joint Modeling of Event Sequence and Time Series with Attentional Twin Recurrent Neural Networks
A variety of real-world processes (over networks) produce sequences of data
whose complex temporal dynamics need to be studied. More especially, the event
timestamps can carry important information about the underlying network
dynamics, which otherwise are not available from the time-series evenly sampled
from continuous signals. Moreover, in most complex processes, event sequences
and evenly-sampled times series data can interact with each other, which
renders joint modeling of those two sources of data necessary. To tackle the
above problems, in this paper, we utilize the rich framework of (temporal)
point processes to model event data and timely update its intensity function by
the synergic twin Recurrent Neural Networks (RNNs). In the proposed
architecture, the intensity function is synergistically modulated by one RNN
with asynchronous events as input and another RNN with time series as input.
Furthermore, to enhance the interpretability of the model, the attention
mechanism for the neural point process is introduced. The whole model with
event type and timestamp prediction output layers can be trained end-to-end and
allows a black-box treatment for modeling the intensity. We substantiate the
superiority of our model in synthetic data and three real-world benchmark
datasets.Comment: 14 page
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