A Boundary-Oriented Chinese Segmentation Method Using NGram Mutual Information

This paper describes our participation in the Chinese word segmentation task of CIPS-SIGHAN 2010. We implemented an n-gram mutual information (NGMI) based segmentation algorithm with the mixed-up features from unsupervised, supervised and dictionarybased segmentation methods. This algorithm is also combined with a simple strategy for out-of-vocabulary (OOV) word recognition. The evaluation for both open and closed training shows encouraging results of our system. The results for OOV word recognition in closed training evaluation were however found unsatisfactory.

Two-way recommendation methods for social networks

In this work, we present the challenges associated with the two-way recommendation methods in social networks and the solutions. We discuss them from the perspective of community-type social networks such as online dating networks.

Detecting rare events using Kullback-Leibler divergence

One main challenge in developing a system for visual surveillance event detection is the annotation of target events in the training data. By making use of the assumption that events with security interest are often rare compared to regular behaviours, this paper presents a novel approach by using Kullback-Leibler (KL) divergence for rare event detection in a weakly supervised learning setting, where only clip-level annotation is available. It will be shown that this approach outperforms state-of-the-art methods on a popular real-world dataset, while preserving real time performance.

A high performance liquid chromatography and electrospray ionization mass spectrometry method for the analysis of the natural medicine, Forsythia suspensa

Samples of Forsythia suspensa from raw (Laoqiao) and ripe (Qingqiao) fruit were analyzed with the use of HPLC-DAD and the EIS-MS techniques. Seventeen peaks were detected, and of these, twelve were identified. Most were related to the glucopyranoside molecular fragment. Samples collected from three geographical areas (Shanxi, Henan and Shandong Provinces), were discriminated with the use of hierarchical clustering analysis (HCA), discriminant analysis (DA), and principal component analysis (PCA) models, but only PCA was able to provide further information about the relationships between objects and loadings; eight peaks were related to the provinces of sample origin. The supervised classification models-K-nearest neighbor (KNN), least squares support vector machines (LS-SVM), and counter propagation artificial neural network (CP-ANN) methods, indicated successful classification but KNN produced 100% classification rate. Thus, the fruit were discriminated on the basis of their places of origin.

Ecological and Behavioral Methods for the Study of Bats [2nd. Ed.]

"First published in 1988, Ecological and Behavioral Methods for the Study of Bats is widely acknowledged as the primary reference for both amateur and professional bat researchers. Bats are the second most diverse group of mammals on the earth. They live on every continent except Antarctica, ranging from deserts to tropical forests to mountains, and their activities have a profound effect on the ecosystems in which they live. Despite their ubiquity and importance, bats are challenging to study. This volume provides researchers, conservationists, and consultants with the ecological background and specific information essential for studying bats in the wild and in captivity. Chapters detail many of the newest and most commonly used field and laboratory techniques needed to advance the study of bats, describe how these methods are applied to the study of the ecology and behavior of bats, and offer advice on how to interpret the results of research. The book includes forty-three chapters, fourteen of which are new to the second edition, with information on molecular ecology and evolution, bioacoustics, chemical communication, flight dynamics, population models, and methods for assessing postnatal growth and development. Fully illustrated and featuring contributions from the world’s leading experts in bat biology, this reference contains everything bat researchers and natural resource managers need to know for the study and conservation of this wide-ranging, ecologically vital, and diverse taxon."--Publisher website

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Materials, methods and systems for purification and/or seperation

Materials, methods and systems are provided for the purifn., filtration and​/or sepn. of certain mols. such as certain size biomols. Certain embodiments relate to supports contg. at least one polymethacrylate polymer engineered to have certain pore diams. and other properties, and which can be functionally adapted to for certain purifications, filtrations and​/or sepns. Biomols. are selected from a group consisting of: polynucleotide mols., oligonucleotide mols. including antisense oligonucleotide mols. such as antisense RNA and other oligonucleotide mols. that are inhibitory of gene function such as small interfering RNA (siRNA)​, polypeptides including proteinaceous infective agents such as prions, for example, the infectious agent for CJD, and infectious agents such as viruses and phage.

Using personal construct theory and narrative methods to facilitate reflexive constructions of teaching physical education

This paper reports on two lengthy studies in Physical education teacher education (PETE) conducted independently but which are epistemologically and methodologically linked. The paper describes how personal construct theory (PCT) and its associated methods provided a means for PETE students to reflexively construct their ideas about teaching physical education over an extended period. Data are drawn from each study in the form of a story of a single participant to indicate how this came about. Furthermore we suggest that PCT might be both a useful research strategy and an effective approach to facilitate professional development in a teacher education setting.

Finite volume methods for simulating anomalous transport

In this thesis a new approach for solving a certain class of anomalous diffusion equations was developed. The theory and algorithms arising from this work will pave the way for more efficient and more accurate solutions of these equations, with applications to science, health and industry. The method of finite volumes was applied to discretise the spatial derivatives, and this was shown to outperform existing methods in several key respects. The stability and convergence of the new method were rigorously established.