Entanglement and purity of single- and two-photon states

Whereas single- and two-photon wave packets are usually treated as pure states, in practice they will be mixed. We study how entanglement created with mixed photon wave packets is degraded. We find in particular that the entanglement of a delocalized single-photon state of the electro-magnetic field is determined simply by its purity. We also discuss entanglement for two-photon mixed states, as well as the influence of a vacuum component.Comment: 11 pages, 10 figures, 1 debuting autho

Sequence Classification Restricted Boltzmann Machines With Gated Units

For the classification of sequential data, dynamic Bayesian networks and recurrent neural networks (RNNs) are the preferred models. While the former can explicitly model the temporal dependences between the variables, and the latter have the capability of learning representations. The recurrent temporal restricted Boltzmann machine (RTRBM) is a model that combines these two features. However, learning and inference in RTRBMs can be difficult because of the exponential nature of its gradient computations when maximizing log likelihoods. In this article, first, we address this intractability by optimizing a conditional rather than a joint probability distribution when performing sequence classification. This results in the ``sequence classification restricted Boltzmann machine'' (SCRBM). Second, we introduce gated SCRBMs (gSCRBMs), which use an information processing gate, as an integration of SCRBMs with long short-term memory (LSTM) models. In the experiments reported in this article, we evaluate the proposed models on optical character recognition, chunking, and multiresident activity recognition in smart homes. The experimental results show that gSCRBMs achieve the performance comparable to that of the state of the art in all three tasks. gSCRBMs require far fewer parameters in comparison with other recurrent networks with memory gates, in particular, LSTMs and gated recurrent units (GRUs)

Universal Tomonaga-Luttinger liquid phases in one-dimensional strongly attractive SU(N) fermionic cold atoms

A simple set of algebraic equations is derived for the exact low-temperature thermodynamics of one-dimensional multi-component strongly attractive fermionic atoms with enlarged SU(N) spin symmetry and Zeeman splitting. Universal multi-component Tomonaga-Luttinger liquid (TLL) phases are thus determined. For linear Zeeman splitting, the physics of the gapless phase at low temperatures belongs to the universality class of a two-component asymmetric TLL corresponding to spin-neutral N-atom composites and spin-(N-1)/2 single atoms. The equation of states is also obtained to open up the study of multi-component TLL phases in 1D systems of N-component Fermi gases with population imbalance.Comment: 12 pages, 3 figure

Cooling curves for neutron stars with hadronic matter and quark matter

The thermal evolution of isothermal neutron stars is studied with matter both in the hadronic phase as well as in the mixed phase of hadronic matter and strange quark matter. In our models, the dominant early-stage cooling process is neutrino emission via the direct Urca process. As a consequence, the cooling curves fall too fast compared to observations. However, when superfluidity is included, the cooling of the neutron stars is significantly slowed down. Furthermore, we find that the cooling curves are not very sensitive to the precise details of the mixing between the hadronic phase and the quark phase and also of the pairing that leads to superfluidity.Comment: 19 pages, 25 figure

Magnetism and Charge Dynamics in Iron Pnictides

In a wide variety of materials, such as copper oxides, heavy fermions, organic salts, and the recently discovered iron pnictides, superconductivity is found in close proximity to a magnetically ordered state. The character of the proximate magnetic phase is thus believed to be crucial for understanding the differences between the various families of unconventional superconductors and the mechanism of superconductivity. Unlike the AFM order in cuprates, the nature of the magnetism and of the underlying electronic state in the iron pnictide superconductors is not well understood. Neither density functional theory nor models based on atomic physics and superexchange, account for the small size of the magnetic moment. Many low energy probes such as transport, STM and ARPES measured strong anisotropy of the electronic states akin to the nematic order in a liquid crystal, but there is no consensus on its physical origin, and a three dimensional picture of electronic states and its relations to the optical conductivity in the magnetic state is lacking. Using a first principles approach, we obtained the experimentally observed magnetic moment, optical conductivity, and the anisotropy of the electronic states. The theory connects ARPES, which measures one particle electronic states, optical spectroscopy, probing the particle hole excitations of the solid and neutron scattering which measures the magnetic moment. We predict a manifestation of the anisotropy in the optical conductivity, and we show that the magnetic phase arises from the paramagnetic phase by a large gain of the Hund's rule coupling energy and a smaller loss of kinetic energy, indicating that iron pnictides represent a new class of compounds where the nature of magnetism is intermediate between the spin density wave of almost independent particles, and the antiferromagnetic state of local moments.Comment: 4+ pages with additional one-page supplementary materia

DRS: Dynamic Resource Scheduling for Real-Time Analytics over Fast Streams

In a data stream management system (DSMS), users register continuous queries, and receive result updates as data arrive and expire. We focus on applications with real-time constraints, in which the user must receive each result update within a given period after the update occurs. To handle fast data, the DSMS is commonly placed on top of a cloud infrastructure. Because stream properties such as arrival rates can fluctuate unpredictably, cloud resources must be dynamically provisioned and scheduled accordingly to ensure real-time response. It is quite essential, for the existing systems or future developments, to possess the ability of scheduling resources dynamically according to the current workload, in order to avoid wasting resources, or failing in delivering correct results on time. Motivated by this, we propose DRS, a novel dynamic resource scheduler for cloud-based DSMSs. DRS overcomes three fundamental challenges: (a) how to model the relationship between the provisioned resources and query response time (b) where to best place resources; and (c) how to measure system load with minimal overhead. In particular, DRS includes an accurate performance model based on the theory of \emph{Jackson open queueing networks} and is capable of handling \emph{arbitrary} operator topologies, possibly with loops, splits and joins. Extensive experiments with real data confirm that DRS achieves real-time response with close to optimal resource consumption.Comment: This is the our latest version with certain modificatio