Generation of four-photon polarization entangled state based on EPR entanglers

We show how to prepare four-photon polarization entangled states based on some Einstein-Podolsky-Rosen (EPR) entanglers. An EPR entangler consists of two single photons, linear optics elements, quantum non-demolition measurement using a weak cross-Kerr nonlinearity, and classical feed forward. This entangler which acts as the most primary part in the construction of our scheme allows us to make two separable polarization qubits entangled near deterministically. Therefore, the efficiency of the present device completely depends on that of EPR entanglers, and it has a high success probability.Comment: 5 pages, 3 figure

Multiple teleportation via the partially entangled states

We investigate the multiple teleportation with some nonmaximally entangled channels. The efficiencies of two multiple teleportation protocols, the separate multiple teleportation protocol (SMTP) and the global multiple teleportation protocol (GMTP), are calculated. We show that GMTP is more efficient than SMTP.Comment: 4 pages, 2 figure

Cross-position Activity Recognition with Stratified Transfer Learning

Human activity recognition aims to recognize the activities of daily living by utilizing the sensors on different body parts. However, when the labeled data from a certain body position (i.e. target domain) is missing, how to leverage the data from other positions (i.e. source domain) to help learn the activity labels of this position? When there are several source domains available, it is often difficult to select the most similar source domain to the target domain. With the selected source domain, we need to perform accurate knowledge transfer between domains. Existing methods only learn the global distance between domains while ignoring the local property. In this paper, we propose a \textit{Stratified Transfer Learning} (STL) framework to perform both source domain selection and knowledge transfer. STL is based on our proposed \textit{Stratified} distance to capture the local property of domains. STL consists of two components: Stratified Domain Selection (STL-SDS) can select the most similar source domain to the target domain; Stratified Activity Transfer (STL-SAT) is able to perform accurate knowledge transfer. Extensive experiments on three public activity recognition datasets demonstrate the superiority of STL. Furthermore, we extensively investigate the performance of transfer learning across different degrees of similarities and activity levels between domains. We also discuss the potential applications of STL in other fields of pervasive computing for future research.Comment: Submit to Pervasive and Mobile Computing as an extension to PerCom 18 paper; First revision. arXiv admin note: substantial text overlap with arXiv:1801.0082

Embedding Representation of Academic Heterogeneous Information Networks Based on Federated Learning

Academic networks in the real world can usually be portrayed as heterogeneous information networks (HINs) with multi-type, universally connected nodes and multi-relationships. Some existing studies for the representation learning of homogeneous information networks cannot be applicable to heterogeneous information networks because of the lack of ability to issue heterogeneity. At the same time, data has become a factor of production, playing an increasingly important role. Due to the closeness and blocking of businesses among different enterprises, there is a serious phenomenon of data islands. To solve the above challenges, aiming at the data information of scientific research teams closely related to science and technology, we proposed an academic heterogeneous information network embedding representation learning method based on federated learning (FedAHE), which utilizes node attention and meta path attention mechanism to learn low-dimensional, dense and real-valued vector representations while preserving the rich topological information and meta-path-based semantic information of nodes in network. Moreover, we combined federated learning with the representation learning of HINs composed of scientific research teams and put forward a federal training mechanism based on dynamic weighted aggregation of parameters (FedDWA) to optimize the node embeddings of HINs. Through sufficient experiments, the efficiency, accuracy and feasibility of our proposed framework are demonstrated

Synthesis and electrochemical properties of Sn-SnO2/C nanocomposite

A Sn-Sn02/C nanocomposite was synthesized using the electrospinning method. Thermal analysis was used to determine the content range of Sn and Sn02 in the composite. The composite was characterized by X-ray diffraction, and the particle size and shape in the Sn-SnOiC composite were determined by scanning and transmission electron microscopy. The results show that the Sn-Sn02/C composite takes on a nanofiber morphology, with the diameters of the nanofibers distributed from 50 to 200 nm. The electrOChemical properties of the Sn-SnOiC composite were also investigated. The Sn-SnOiC composite as an electrode material has both higher reversible capacity (887 mAh· g-I). and good cycling performance in lithium-anode ceUs working at room temperature in a 3.0 V to O.Ot V potential window. The Sn-Sn02/C composite could relain a discharge capacity of 546 mAWg aller 30 cycles. The outstanding electrochemical properties of the Sn-SnOiC composite oblained by this method make it possible for Ihis composite to be used as a promising anode material