Disentangled Variational Auto-Encoder for Semi-supervised Learning

Semi-supervised learning is attracting increasing attention due to the fact that datasets of many domains lack enough labeled data. Variational Auto-Encoder (VAE), in particular, has demonstrated the benefits of semi-supervised learning. The majority of existing semi-supervised VAEs utilize a classifier to exploit label information, where the parameters of the classifier are introduced to the VAE. Given the limited labeled data, learning the parameters for the classifiers may not be an optimal solution for exploiting label information. Therefore, in this paper, we develop a novel approach for semi-supervised VAE without classifier. Specifically, we propose a new model called Semi-supervised Disentangled VAE (SDVAE), which encodes the input data into disentangled representation and non-interpretable representation, then the category information is directly utilized to regularize the disentangled representation via the equality constraint. To further enhance the feature learning ability of the proposed VAE, we incorporate reinforcement learning to relieve the lack of data. The dynamic framework is capable of dealing with both image and text data with its corresponding encoder and decoder networks. Extensive experiments on image and text datasets demonstrate the effectiveness of the proposed framework.Comment: 6 figures, 10 pages, Information Sciences 201

Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis

Target-based sentiment analysis or aspect-based sentiment analysis (ABSA) refers to addressing various sentiment analysis tasks at a fine-grained level, which includes but is not limited to aspect extraction, aspect sentiment classification, and opinion extraction. There exist many solvers of the above individual subtasks or a combination of two subtasks, and they can work together to tell a complete story, i.e. the discussed aspect, the sentiment on it, and the cause of the sentiment. However, no previous ABSA research tried to provide a complete solution in one shot. In this paper, we introduce a new subtask under ABSA, named aspect sentiment triplet extraction (ASTE). Particularly, a solver of this task needs to extract triplets (What, How, Why) from the inputs, which show WHAT the targeted aspects are, HOW their sentiment polarities are and WHY they have such polarities (i.e. opinion reasons). For instance, one triplet from "Waiters are very friendly and the pasta is simply average" could be ('Waiters', positive, 'friendly'). We propose a two-stage framework to address this task. The first stage predicts what, how and why in a unified model, and then the second stage pairs up the predicted what (how) and why from the first stage to output triplets. In the experiments, our framework has set a benchmark performance in this novel triplet extraction task. Meanwhile, it outperforms a few strong baselines adapted from state-of-the-art related methods.Comment: This paper is accepted in AAAI 202

Three more leaves of the Sanskrit – Uighur bilingual Dharmaśarīrasūtra in Brāhmī script

Three leaves written in Brāhmī script and kept in the Dunhuang Research Academy turn out to be parts of a bilingual text of Dharmaśarīrasūtra in Sanskrit and Uighur. After analysing several versions of Dharmaśarīrasūtra, it can be inferred that these three fragments belong to the Northern Brāhmī recensions which were circulated along the Northern Silk Road and are different from the Southern Brāhmī recensions popular along the Southern Route, such as the Khotanese version. This paper attempts to transcribe these fragments and make a thorough research on Dharmaśarīrasūtra, taking five relevant Chinese versions into account

Structural behavior of CFST arch foots under pumping pressure of concrete infills

Prestressed concrete (PC) continuous girder-arch composite bridges, using concrete filled steel tubes (CFST) as the arch ribs to support the PC main girder, have gained their popularity in recently constructed large span bridges. The adoption of CFSTs brings many advantages, such as the concrete infill help to prevent the occurrence of local buckling in steel tubes, while the steel tubes can serve as framework for concrete core and strengthen the encased concrete. In order to achieve the above merits, the arch foots connecting the CFST arch ribs and PC girder are supposed to have sufficient strength and stiffness. However, the extensive use of CFST arch ribs showed that concrete cracking are commonly found at the arch foots. This paper studied the structural behavior of arch foot by employing a comprehensive numerical model. The influence of pumping pressure from concrete infill at construction stage on local stress of the arch foots was presented and discussed. The results indicated that the radial stress produced by pumping pressure at the joint zone squeezed the concrete around the arch foots, which resulted in significant hoop tensile stresses in the surrounding concrete. The outcomes of this study can provide technical support to improve the construction quality and structure stability, and optimize the construction procedures for this type of bridges

Unusual Fermi Surface Sheet-Dependent Band Splitting in Sr2RuO4 Revealed by High Resolution Angle-Resolved Photoemission

High resolution angle-resolved photoemission measurements have been carried out on Sr2RuO4. We observe clearly two sets of Fermi surface sheets near the (\pi,0)-(0,\pi) line which are most likely attributed to the surface and bulk Fermi surface splitting of the \beta band. This is in strong contrast to the nearly null surface and bulk Fermi surface splitting of the \alpha band although both have identical orbital components. Extensive band structure calculations are performed by considering various scenarios, including structural distortion, spin-orbit coupling and surface ferromagnetism. However, none of them can explain such a qualitative difference of the surface and bulk Fermi surface splitting between the \alpha and \beta sheets. This unusual behavior points to an unknown order on the surface of Sr2RuO4 that remains to be uncovered. Its revelation will be important for studying and utilizing novel quantum phenomena associated with the surface of Sr2RuO4 as a result of its being a possible p-wave chiral superconductor and a topological superconductor.Comment: 13 pages, 4 figure

Extraction of Electron Self-Energy and Gap Function in the Superconducting State of Bi_2Sr_2CaCu_2O_8 Superconductor via Laser-Based Angle-Resolved Photoemission

Super-high resolution laser-based angle-resolved photoemission measurements have been performed on a high temperature superconductor Bi_2Sr_2CaCu_2O_8. The band back-bending characteristic of the Bogoliubov-like quasiparticle dispersion is clearly revealed at low temperature in the superconducting state. This makes it possible for the first time to experimentally extract the complex electron self-energy and the complex gap function in the superconducting state. The resultant electron self-energy and gap function exhibit features at ~54 meV and ~40 meV, in addition to the superconducting gap-induced structure at lower binding energy and a broad featureless structure at higher binding energy. These information will provide key insight and constraints on the origin of electron pairing in high temperature superconductors.Comment: 4 pages, 4 figure

MethylPurify: tumor purity deconvolution and differential methylation detection from single tumor DNA methylomes

We propose a statistical algorithm MethylPurify that uses regions with bisulfite reads showing discordant methylation levels to infer tumor purity from tumor samples alone. MethylPurify can identify differentially methylated regions (DMRs) from individual tumor methylome samples, without genomic variation information or prior knowledge from other datasets. In simulations with mixed bisulfite reads from cancer and normal cell lines, MethylPurify correctly inferred tumor purity and identified over 96% of the DMRs. From patient data, MethylPurify gave satisfactory DMR calls from tumor methylome samples alone, and revealed potential missed DMRs by tumor to normal comparison due to tumor heterogeneity. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0419-x) contains supplementary material, which is available to authorized users