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-$Q$ 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 $M^n$ be a complete noncompact K$\ddot{a}$hler manifold of complex dimension $n$ with nonnegative holomorphic bisectional curvature. Denote by $\mathcal{O}$$_d(M^n)$ the space of holomorphic functions of polynomial growth of degree at most $d$ on $M^n$. In this paper we prove that $$dim_{\mathbb{C}}{\mathcal{O}}_d(M^n)\leq dim_{\mathbb{C}}{\mathcal{O}}_{[d]}(\mathbb{C}^n),$$ for all $d>0$, with equality for some positive integer $d$ if and only if $M^n$ is holomorphically isometric to $\mathbb{C}^n$. 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

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

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

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