Posterior Contraction Rates of the Phylogenetic Indian Buffet Processes

By expressing prior distributions as general stochastic processes, nonparametric Bayesian methods provide a flexible way to incorporate prior knowledge and constrain the latent structure in statistical inference. The Indian buffet process (IBP) is such an example that can be used to define a prior distribution on infinite binary features, where the exchangeability among subjects is assumed. The phylogenetic Indian buffet process (pIBP), a derivative of IBP, enables the modeling of non-exchangeability among subjects through a stochastic process on a rooted tree, which is similar to that used in phylogenetics, to describe relationships among the subjects. In this paper, we study the theoretical properties of IBP and pIBP under a binary factor model. We establish the posterior contraction rates for both IBP and pIBP and substantiate the theoretical results through simulation studies. This is the first work addressing the frequentist property of the posterior behaviors of IBP and pIBP. We also demonstrated its practical usefulness by applying pIBP prior to a real data example arising in the field of cancer genomics where the exchangeability among subjects is violated

RAN4IQA: Restorative Adversarial Nets for No-Reference Image Quality Assessment

Inspired by the free-energy brain theory, which implies that human visual system (HVS) tends to reduce uncertainty and restore perceptual details upon seeing a distorted image, we propose restorative adversarial net (RAN), a GAN-based model for no-reference image quality assessment (NR-IQA). RAN, which mimics the process of HVS, consists of three components: a restorator, a discriminator and an evaluator. The restorator restores and reconstructs input distorted image patches, while the discriminator distinguishes the reconstructed patches from the pristine distortion-free patches. After restoration, we observe that the perceptual distance between the restored and the distorted patches is monotonic with respect to the distortion level. We further define Gain of Restoration (GoR) based on this phenomenon. The evaluator predicts perceptual score by extracting feature representations from the distorted and restored patches to measure GoR. Eventually, the quality score of an input image is estimated by weighted sum of the patch scores. Experimental results on Waterloo Exploration, LIVE and TID2013 show the effectiveness and generalization ability of RAN compared to the state-of-the-art NR-IQA models.Comment: AAAI'1

MULTIFUNCTIONAL NANOPHOSPHORS FOR TISSUE IMAGING AND DRUG DELIVERY

X-rays have been used for non-invasive high-resolution imaging of thick biological specimens since their discovery in 1895. They are widely used for structural imaging of bone, metal implants, and cavities in soft tissue. Recently, a number of new contrast methodologies have emerged which are expanding X-ray\u27s biomedical applications to functional as well as structural imaging. However, traditional X-ray imaging provides high spatial resolution imaging through tissue but do not measure chemical concentrations. In this dissertation, we describe an X-ray excited optical luminescence (XEOL) technique which uses a scanning X-ray beam to irradiate Gd2O2S phosphors and detect the resulting visible luminescence through the tissue. The amount of light collected is modulated by optical absorption in close proximity to the luminescence source. The ability to specifically target biological processes in vivo makes nanophosphors promising molecular imaging agents for XEOL. We also describe versatile techniques to design and fabricate multifunctional X-ray nanophosphors. The addition of pH-triggred drug release on our X-ray nanophosphors make it possible to monitor pH-triggered drug release rate in real time. The iron oxide encapsulated X-ray nanosctintillators offer promising multimodal MRI/fluorescence/X-ray luminescence contrast agents

Low pressure chemical vapor deposition of tungsten as an absorber for x-ray masks

Tungsten film is one of promising materials for X-ray absorber in X-ray Lithography technology because of its high X-ray absorption and refractory properties. This study focus on CVD method to make tungsten film for X-ray absorber. In this work, a cold wall, single wafer, Spectrum 211 CVD reactor was used for the deposition of tungsten from H, and WF6. The growth kinetics were determined as a function of temperature, pressure and flow ratio. The deposition rate of as deposited tungsten films was found to follow an Arrehnius behavior in the range of 300-500°C with an activation energy of 55.7 kJ/mol. The growth rate was seen to increase linearly with total pressure and H, partial pressure. In the H2/WF6 ratio studies conducted at 500°C and 500mTorr, growth rate increase with flow ratio when lower than 10 followed by saturation above this ratio. The stress of as deposited film strongly dependent on deposition temperature and has weak relationship with pressure and flow ratio. The `buried layer model\u27 can explain the stress of as deposited film very well. The resistivity of the film is no relationship with pressure, flow ratio and dependent on temperature. The deposited films have preferred orientation of the (200) plane

Short-Term Forecasting of Passenger Demand under On-Demand Ride Services: A Spatio-Temporal Deep Learning Approach

Short-term passenger demand forecasting is of great importance to the on-demand ride service platform, which can incentivize vacant cars moving from over-supply regions to over-demand regions. The spatial dependences, temporal dependences, and exogenous dependences need to be considered simultaneously, however, which makes short-term passenger demand forecasting challenging. We propose a novel deep learning (DL) approach, named the fusion convolutional long short-term memory network (FCL-Net), to address these three dependences within one end-to-end learning architecture. The model is stacked and fused by multiple convolutional long short-term memory (LSTM) layers, standard LSTM layers, and convolutional layers. The fusion of convolutional techniques and the LSTM network enables the proposed DL approach to better capture the spatio-temporal characteristics and correlations of explanatory variables. A tailored spatially aggregated random forest is employed to rank the importance of the explanatory variables. The ranking is then used for feature selection. The proposed DL approach is applied to the short-term forecasting of passenger demand under an on-demand ride service platform in Hangzhou, China. Experimental results, validated on real-world data provided by DiDi Chuxing, show that the FCL-Net achieves better predictive performance than traditional approaches including both classical time-series prediction models and neural network based algorithms (e.g., artificial neural network and LSTM). This paper is one of the first DL studies to forecast the short-term passenger demand of an on-demand ride service platform by examining the spatio-temporal correlations.Comment: 39 pages, 10 figure