377 research outputs found
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
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