Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Lithium- or potassium-doped carbon nanotubes can absorb approximately 20 or approximately 14 weight percent of hydrogen at moderate (200 degrees to 400 degrees C) or room temperatures, respectively, under ambient pressure. These values are greater than those of metal hydride and cryoadsorption systems. The hydrogen stored in the lithium- or potassium-doped carbon nanotubes can be released at higher temperatures, and the sorption-desorption cycle can be repeated with little decrease in the sorption capacity. The high hydrogen-uptake capacity of these systems may be derived from the special open-edged, layered structure of the carbon nanotubes made from methane, as well as the catalytic effect of alkali metals.

https://science.sciencemag.org/content/sci/285/5424/91.full.pdf

High H2 uptake by alkali-doped carbon nanotubes under ambient pressure and moderate temperatures

Solid Oxide Fuel Cells (SOFCs) are one of the most promising technologies for future efficient conversion of the chemical energy stored in fuels to electrical energy. One of the primary advantages of SOFCs is the potential to operate with a wide variety of fuels. While H2 is the fuel of choice for most fuel cells, operation with fossil-derived and bio-derived hydrocarbon fuels would bypass the costly requirement of a new H2 infrastructure, and accelerate adoption of fuel cell technology. This is feasible as SOFCs transport oxygen anions from the air electrode (cathode) to the fuel electrode (anode). The primary barrier to the realization of fuel flexible SOFCs is the anode material set. Traditional SOFC anodes are based on Ni composites. While these are very efficient for H2 and CO fuels, Ni catalyzes graphite formation from dry hydrocarbons, leading to rapid degradation of cell performance and possible mechanical failure. This motivates the development of new anode materials and composites. The principle requirements of an anode are oxygen anion conductivity, electronic conductivity, and electrocatalytic activity toward the desired reaction. This entry reviews the primary issues in direct hydrocarbon anode development and discusses the new materials and composites that have been developed to meet this challenge.

Direct hydrocarbon solid oxide fuel cells.

Catalyst deactivation is usually inevitable, although the rate at which it occurs varies greatly. This article discusses the causes of deactivation and the influence on reaction rate. Methods for minimising catalyst deactivation, by tailoring catalyst properties and/or process operations, are presented, as well as reactor configurations suitable for the regeneration of deactivated catalysts. Alkane dehydrogenation is used as an example to demonstrate the variety of engineering solutions possible.

Catalyst deactivation: is it predictable?: What to do?

The catalyst in the CCVD of carbon nanotubes—a review

Growth of carbon nanotubes by catalytic decomposition of CH4 or CO on a NiMgO catalyst

A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis

This paper analyses topic segmentation based on the LDA (Latent Dirichlet Allocation) model, and performs the topic segmentation and topic evolution of stem cell research literatures in PubMed from 2001 to 2012 by combining the HMM (Hidden Markov Model) and co-occurrence theory. Stem cell research topics were obtained with LDA and expert judgements made on these topics to test the feasibility of the model classification. Further, the correlation between topics was analysed. HMM was used to predict the trend evolution of topics over various years, and a time series map was used to visualize the evolutional relationships among the stem cell topics.

Topic evolution based on LDA and HMM and its application in stem cell research

Vasculature geometry reconstruction from volumetric medical data is a crucial task in the development of computer guided minimally invasive vascular surgery systems. In this paper, a technique for reconstructing the geometry of vasculatures using bivariate implicit splines is developed. With the proposed technique, an implicit geometry representation of the vascular tree can be accurately constructed based on the voxels extracted directly from the surface of a certain vascular structure in a given volumetric medical dataset. Experimental results show that the geometric representation built using our method can faithfully represent the morphology and topology of vascular structures. In addition, both the qualitative and the quantitative validations have been performed to show that the reconstructed vessel geometry is of high accuracy and smoothness. An virtual angioscopy system has been implemented to indicate one of the strengths of our proposed method.

/pdf/implicit-reconstruction-of-vasculatures-using-bivariate-1id8ep2zh0.pdf

Implicit Reconstruction of Vasculatures Using Bivariate Piecewise Algebraic Splines

The human eye's state of motion and content of interest can express people's cognitive status and emotional status based on their situation. When observing the surrounding things, the human eyes make different eye movements according to the observed objects which reflects human's attention and interest. In this paper, we capture and analyze patterns of human eye-gaze behavior and head motion and classify them into different categories. Besides, we compute and train the eye-object movement attention model and eye-object feature preference model based on different peoples' eye-gaze behaviors by using machine learning algorithms. These models are used to predict humans' object of interest and the interaction intention according to people's real-time situation. Furthermore, the eye-gaze behavior and head motion patterns can be used as a modality of non-verbal information in the computing of human emotional states based on the PAD affective computing model. Our methodology analyzes human emotion and cognition status from the aspect of eye-gaze behavior and head motion, understands the cognitive information that human eyes can express, and effectively improves the efficiency of human-computer interaction in different circumstances.

Qingqi Hong

Papers

Growth of carbon nanotubes by catalytic decomposition of CH4 or CO on a NiMgO catalyst

A novel ECOC algorithm for multiclass microarray data classification based on data complexity analysis

Topic evolution based on LDA and HMM and its application in stem cell research

Implicit Reconstruction of Vasculatures Using Bivariate Piecewise Algebraic Splines

Real-Time Eye-Gaze Based Interaction for Human Intention Prediction and Emotion Analysis