Spatiotemporal Bloch states of a spin–orbit coupled Bose–Einstein condensate in an optical lattice

We study the spatiotemporal Bloch states of a high-frequency driven two-component Bose–Einstein condensate(BEC)with spin–orbit coupling(SOC) in an optical lattice. By adopting the rotating-wave approximation(RWA) and applying an exact trial-solution to the corresponding quasistationary system, we establish a different method for tuning SOC via external field such that the existence conditions of the exact particular solutions are fitted. Several novel features related to the exact states are demonstrated; for example, SOC leads to spin–motion entanglement for the spatiotemporal Bloch states, SOC increases the population imbalance of the two-component BEC, and SOC can be applied to manipulate the stable atomic flow which is conducive to control quantum transport of the BEC for different application purposes.

Chaos in coupled atom–molecular Bose–Einstein condensates

We investigate the chaotic dynamics of a coupled three-level atom–molecular Bose–Einstein condensate system composed by one molecular mode and two atomic modes.With the increase of atom–molecular coupling strength,we reveal the emergence of chaotic oscillations of the relative population difference between two atomic modes,which can be proven by the broad windows with a huge number of frequencies in spectral density and the chaotic trajectories in phase space diagrams.The different effects of initial states on atomic population oscillations are revealed,where for more particles in the initial state of the molecular model,chaos appears in the larger parameter region of system dynamics.Furthermore,we find that strong intermolecular interaction strength can suppress chaos resulting from strong atom–molecule coupling.This is due to the difficult transformation between atomic and molecular modes,as well as the relatively independent dynamic evolution of atoms and molecules.

EnAli:entity alignment across multiple heterogeneous data sources

Entity alignment is the problem of identifying which entities in a data source refer to the same real-world entity in the others.Identifying entities across heterogeneous data sources is paramount to many research fields,such as data cleaning,data integration,.information retrieval and machine learning.The aligning process is not only overwhelmingly expensive for large data sources since it involves all tuples from two or more data sources,but also need to handle heterogeneous entity attributes.In this paper,we propose an unsupervised approach,called EnAli,to match entities across two or more heterogeneous data sources.EnAli employs a generative probabilistic model to incorporate the heterogeneous entity attributes via employing exponential family,handle missing values,and also utilize the locality sensitive hashing schema to reduce the candidate tuples and speed up the aligning process.EnAli is highly accurate and efficient even without any ground-truth tuples.We illustrate the performance of EnAli on re-identifying entities from the same data source,as well as aligning entities across three real data sources.Our experimental results manifest that our proposed approach outperforms the comparable baseline.

Investigation of the effects of MoO_3 buffer layer on charge carrier injection and extraction by capacitance–voltage measurement

The effects of MoO3thin buffer layer on charge carrier injection and extraction in inverted configuration ITO/ZnO/MEH-PPV(poly(2-methoxy-5-(2-ethylhexyloxy)-1,4-phenylenevinylene))/MoO3(0,5 nm)/Ag hybrid solar cells are investigated by capacitance–voltage measurement under dark and light illumination conditions.The efficiency of charge carrier injection and extraction is enhanced by inserting 5 nm MoO3thin layer,resulting in better device performances.Charge carrier transport of the whole device is improved and the interface energy barrier is reduced by inserting 5 nm MoO3thin buffer layer.The device fill factor is increased from 54.1%to 57.5%after modifying 5 nm MoO3.Simulations and experimental results consistently show that in the forward voltage under dark,the device with the 5 nm MoO3thin layer modification generates larger value of capacitance than the device without MoO3layer.While under illumination,the device with the 5 nm MoO3layer generates smaller value of capacitance than the device without the 5 nm MoO3layer in the bias region of reverse and before the peak position of maximum capacitance(VCmax).The underlying mechanism of the MoO3anode buffer layer on device current density–voltage characteristics is discussed.

DEM:Deep Entity Matching Across Heterogeneous Information Networks

Heterogeneous information networks,which consist of multi-typed vertices representing objects and multi-typed edges representing relations between objects,are ubiquitous in the real world.In this paper,we study the problem of entity matching for heterogeneous information networks based on distributed network embedding and multi-layer perceptron with a highway network,and we propose a new method named DEM short for Deep Entity Matching.In contrast to the traditional entity matching methods,DEM utilizes the multi-layer perceptron with a highway network to explore the hidden relations to improve the performance of matching.Importantly,we incorporate DEM with the network embedding methodology,enabling highly efficient computing in a vectorized manner.DEM's generic modeling of both the network structure and the entity attributes enables it to model various heterogeneous information networks flexibly.To illustrate its functionality,we apply the DEM algorithm to two real-world entity matching applications:user linkage under the social network analysis scenario that predicts the same or matched users in different social platforms and record linkage that predicts the same or matched records in different citation networks.Extensive experiments on real-world datasets demonstrate DEM's effectiveness and rationality.