Research progress on advanced positron acceleration

Plasma wakefield acceleration (PWFA) is a promising method for reducing the scale and cost of future electron-positron collider experiments by using shorter plasma sections to enhance beam energy. While electron acceleration has already achieved breakthroughs in theory and experimentation, generating high-quality positron beams in plasma presents greater challenges, such as controlling emittance and energy spread, improving energy conversion efficiency, and generating positron sources. In this paper, we have summarized the research progress on advanced positron acceleration schemes, including particle beam-driven wakefield acceleration, laser-driven wakefield acceleration, radiation-based acceleration, hollow plasma channels, among others. The strengths and weaknesses of these approaches are analyzed, and the future outlook is discussed to drive experimental advancements.Comment: 20 pages, 5 figure

Task-Driven Common Representation Learning via Bridge Neural Network

This paper introduces a novel deep learning based method, named bridge neural network (BNN) to dig the potential relationship between two given data sources task by task. The proposed approach employs two convolutional neural networks that project the two data sources into a feature space to learn the desired common representation required by the specific task. The training objective with artificial negative samples is introduced with the ability of mini-batch training and it's asymptotically equivalent to maximizing the total correlation of the two data sources, which is verified by the theoretical analysis. The experiments on the tasks, including pair matching, canonical correlation analysis, transfer learning, and reconstruction demonstrate the state-of-the-art performance of BNN, which may provide new insights into the aspect of common representation learning.Comment: To appear in AAAI-19 proceeding

NANOSTRUCTURED MATERIALS FOR MEMORY APPLICATIONS

Ph.DDOCTOR OF PHILOSOPH

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