South Asian students' educational experience and attainment: learning Chinese as a second/additional language in Hong Kong

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THE RHYTHM OF FAIRALL 9. I. OBSERVING THE SPECTRAL VARIABILITY WITH XMM-NEWTON AND NuSTAR

© 2016. The American Astronomical Society. All rights reserved. We present a multi-epoch X-ray spectral analysis of the Seyfert 1 galaxy Fairall 9. Our analysis shows that Fairall 9 displays unique spectral variability in that its ratio residuals to a simple absorbed power law in the 0.5-10 keV band remain constant with time in spite of large variations in flux. This behavior implies an unchanging source geometry and the same emission processes continuously at work at the timescale probed. With the constraints from NuSTAR on the broad-band spectral shape, it is clear that the soft excess in this source is a superposition of two different processes, one being blurred ionized reflection in the innermost parts of the accretion disk, and the other a continuum component such as a spatially distinct Comptonizing region. Alternatively, a more complex primary Comptonization component together with blurred ionized reflection could be responsible

Fast and Accurate Camera Covariance Computation for Large 3D Reconstruction

Estimating uncertainty of camera parameters computed in Structure from Motion (SfM) is an important tool for evaluating the quality of the reconstruction and guiding the reconstruction process. Yet, the quality of the estimated parameters of large reconstructions has been rarely evaluated due to the computational challenges. We present a new algorithm which employs the sparsity of the uncertainty propagation and speeds the computation up about ten times \wrt previous approaches. Our computation is accurate and does not use any approximations. We can compute uncertainties of thousands of cameras in tens of seconds on a standard PC. We also demonstrate that our approach can be effectively used for reconstructions of any size by applying it to smaller sub-reconstructions.Comment: ECCV 201

Electric-field-induced alignment of electrically neutral disk-like particles: modelling and calculation

This work reveals a torque from electric field to electrically neutral flakes that are suspended in a higher electrical conductive matrix. The torque tends to rotate the particles toward an orientation with its long axis parallel to the electric current flow. The alignment enables the anisotropic properties of tiny particles to integrate together and generate desirable macroscale anisotropic properties. The torque was obtained from thermodynamic calculation of electric current free energy at various microstructure configurations. It is significant even when the electrical potential gradient becomes as low as 100 v/m. The changes of electrical, electroplastic and thermal properties during particles alignment were discussed

From Relational Data to Graphs: Inferring Significant Links using Generalized Hypergeometric Ensembles

The inference of network topologies from relational data is an important problem in data analysis. Exemplary applications include the reconstruction of social ties from data on human interactions, the inference of gene co-expression networks from DNA microarray data, or the learning of semantic relationships based on co-occurrences of words in documents. Solving these problems requires techniques to infer significant links in noisy relational data. In this short paper, we propose a new statistical modeling framework to address this challenge. It builds on generalized hypergeometric ensembles, a class of generative stochastic models that give rise to analytically tractable probability spaces of directed, multi-edge graphs. We show how this framework can be used to assess the significance of links in noisy relational data. We illustrate our method in two data sets capturing spatio-temporal proximity relations between actors in a social system. The results show that our analytical framework provides a new approach to infer significant links from relational data, with interesting perspectives for the mining of data on social systems.Comment: 10 pages, 8 figures, accepted at SocInfo201

First direct observation of Dirac fermions in graphite

Originating from relativistic quantum field theory, Dirac fermions have been recently applied to study various peculiar phenomena in condensed matter physics, including the novel quantum Hall effect in graphene, magnetic field driven metal-insulator-like transition in graphite, superfluid in 3He, and the exotic pseudogap phase of high temperature superconductors. Although Dirac fermions are proposed to play a key role in these systems, so far direct experimental evidence of Dirac fermions has been limited. Here we report the first direct observation of massless Dirac fermions with linear dispersion near the Brillouin zone (BZ) corner H in graphite, coexisting with quasiparticles with parabolic dispersion near another BZ corner K. In addition, we report a large electron pocket which we attribute to defect-induced localized states. Thus, graphite presents a novel system where massless Dirac fermions, quasiparticles with finite effective mass, and defect states all contribute to the low energy electronic dynamics.Comment: Nature Physics, in pres

A pan-Asian survey of risk perception, attitudes and practices associated with live animal markets.

Key Messages 1. Causal attributions for highly pathogenic avian influenza (HPAI) of the H5N1 virus among live poultry, consumers, retailers and breeders in Vietnam, Thailand, Guangzhou and Hong Kong were studied. 2. Three main themes embodying lay explanation for the causes of H5N1 HPAI emerged: viruses, husbandry-related factors, and vulnerability factors. 3. A deeper understanding of the perceptions of risks, biases, causal attributions, and both the facilitators and barriers to change is needed for planning effective changes in health related behaviour.published_or_final_versio

Suppression of esophageal tumor growth and chemoresistance by directly targeting the PI3K/AKT pathway

Esophageal cancer is the sixth most common cause of cancer-related deaths worldwide. Novel therapeutic intervention is urgently needed for this deadly disease. The functional role of PI3K/AKT pathway in esophageal cancer is little known. In this study, our results from 49 pairs of human esophageal tumor and normal specimens demonstrated that AKT was constitutively active in the majority (75.5%) of esophageal tumors compared with corresponding normal tissues. Inhibition of the PI3K/AKT pathway with specific inhibitors, wortmannin and LY294002, significantly reduced Bcl-xL expression, induced caspase-3-dependent apoptosis, and repressed cell proliferation and tumor growth in vitro and in vivo without obvious toxic effects. Moreover, significantly higher expression level of p-AKT was observed in fluorouracil (5-FU)-resistant esophageal cancer cells. Inactivation of PI3K/AKT pathway markedly increased the sensitivity and even reversed acquired resistance of esophageal cancer cells to chemotherapeutic drugs in vitro. More importantly, the resistance of tumor xenografts derived from esophageal cancer cells with acquired 5-FU resistance to chemotherapeutic drugs was significantly abrogated by wortmannin treatment in animals. In summary, our data support PI3K/AKT as a valid therapeutic target and strongly suggest that PI3K/AKT inhibitors used in conjunction with conventional chemotherapy may be a potentially useful therapeutic strategy in treating esophageal cancer patients.published_or_final_versio

Excessive Activation of TGFβ by Spinal Instability Causes Vertebral Endplate Sclerosis

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Learning Interpretable Rules for Multi-label Classification

Multi-label classification (MLC) is a supervised learning problem in which, contrary to standard multiclass classification, an instance can be associated with several class labels simultaneously. In this chapter, we advocate a rule-based approach to multi-label classification. Rule learning algorithms are often employed when one is not only interested in accurate predictions, but also requires an interpretable theory that can be understood, analyzed, and qualitatively evaluated by domain experts. Ideally, by revealing patterns and regularities contained in the data, a rule-based theory yields new insights in the application domain. Recently, several authors have started to investigate how rule-based models can be used for modeling multi-label data. Discussing this task in detail, we highlight some of the problems that make rule learning considerably more challenging for MLC than for conventional classification. While mainly focusing on our own previous work, we also provide a short overview of related work in this area.Comment: Preprint version. To appear in: Explainable and Interpretable Models in Computer Vision and Machine Learning. The Springer Series on Challenges in Machine Learning. Springer (2018). See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3077 for further informatio