A Geometric Morphometric Approach to the Analysis of Lip Shape during Speech: Development of a Clinical Outcome Measure

Objective assessments of lip movement can be beneficial in many disciplines including visual speech recognition, for surgical outcome assessment in patients with cleft lip and for the rehabilitation of patients with facial nerve impairments. The aim of this study was to develop an outcome measure for lip shape during speech using statistical shape analysis techniques. Lip movements during speech were captured from a sample of adult subjects considered as average using a three-dimensional motion capture system. Geometric Morphometrics was employed to extract three-dimensional coordinate data for lip shape during four spoken words decomposed into seven visemes (which included the resting lip shape). Canonical variate analysis was carried out in an attempt to statistically discriminate the seven visemes. The results showed that the second canonical variate discriminated the resting lip shape from articulation of the utterances and accounted for 17.2% of the total variance of the model. The first canonical variate was significant in discriminating between the utterances and accounted for 72.8% of the total variance of the model. The outcome measure was created using the 95% confidence intervals of the canonical variate scores for each subject plotted as ellipses for each viseme. The method and outcome model is proposed as reference to compare lip movement during speech in similar population groups.     

拟似然非线性模型中的置信域：几何法

对拟似然非线性模型在欧氏内积实间建立了修改的Ｂａｔｅｓ＆Ｗａｔｔｓ几何结构，基于此几何结构，导出了参数和子集系数的与统计曲率有关的三种近似置信域，进一步推广和发展了Ｈａｍｉｌｔｏｎ ｅｔ ａｌ．（１９８２）。Ｈａｍｉｌｔｏｎ（１９８６）和Ｗｅｉ（１９９４，１９９８）等人的相应结果。     

A Novel Approach to High Accuracy of Video-Based Microrheology

Video-based particle tracking monitors the microscopic movement of labeled biomolecules and fluorescent probes within a complex cellular environment. Information gained from this technique enables us to extract the dynamic behavior of biomolecules and the local mechanical properties inside the cell from a tracked particle's mean-square displacement (MSD). However, MSD measurements are highly susceptible to static error introduced by noise in the image acquisition process that leads to an incorrect positioning of the particle. Static error can mask the subtle effects from the local microenvironment on the MSD and potentially generate misleading conclusions about the biophysical properties of cells. An approach that greatly increases the accuracy of MSD measurements is presented herein by combining experimental data with Monte Carlo simulations to eliminate the inherent static error. This practical method of static error correction greatly advances particle-tracking techniques.     

Locally Embedding Autoencoders: A Semi-Supervised Manifold Learning Approach of Document Representation

Topic models and neural networks can discover meaningful low-dimensional latent representations of text corpora; as such, they have become a key technology of document representation. However, such models presume all documents are non-discriminatory, resulting in latent representation dependent upon all other documents and an inability to provide discriminative document representation. To address this problem, we propose a semi-supervised manifold-inspired autoencoder to extract meaningful latent representations of documents, taking the local perspective that the latent representation of nearby documents should be correlative. We first determine the discriminative neighbors set with Euclidean distance in observation spaces. Then, the autoencoder is trained by joint minimization of the Bernoulli cross-entropy error between input and output and the sum of the square error between neighbors of input and output. The results of two widely used corpora show that our method yields at least a 15% improvement in document clustering and a nearly 7% improvement in classification tasks compared to comparative methods. The evidence demonstrates that our method can readily capture more discriminative latent representation of new documents. Moreover, some meaningful combinations of words can be efficiently discovered by activating features that promote the comprehensibility of latent representation.     

没有止住的开荒——塔河溯源行

随着大块条田的出现,我们开始进入阿拉尔范围。这里属于农一师,当初是一片荒原．1957年新疆生产建设兵团农一师奉命进驻阿拉尔屯垦戍边。这支部队就是著名的开垦了南泥湾的359旅。     

A General Approach to the Predictability Issue in Survival Analysis with Applications

Very often in survival analysis one has to study martingaleintegrals where the integrand is not predictable and where thecounting process theory of martingales is not directly applicable,as for example in nonparametric and semiparametric applicationswhere the integrand is based on a pilot estimate. We call thisthe predictability issue in survival analysis. The problem hasbeen resolved by approximations of the integrand by predictablefunctions which have been justified by ad hoc procedures. Wepresent a general approach to the solution of this problem.The usefulness of the approach is shown in three applications.In particular, we argue that earlier ad hoc procedures do notwork in higher-dimensional smoothing problems in survival analysis.     

A Retrospective Approach to Testing the DNA Barcoding Method

A decade ago, DNA barcoding was proposed as a standardised method for identifying existing species and speeding the discovery of new species. Yet, despite its numerous successes across a range of taxa, its frequent failures have brought into question its accuracy as a short-cut taxonomic method. We use a retrospective approach, applying the method to the classification of New Zealand skinks as it stood in 1977 (primarily based upon morphological characters), and compare it to the current taxonomy reached using both morphological and molecular approaches. For the 1977 dataset, DNA barcoding had moderate-high success in identifying specimens (78-98%), and correctly flagging specimens that have since been confirmed as distinct taxa (77-100%). But most matching methods failed to detect the species complexes that were present in 1977. For the current dataset, there was moderate-high success in identifying specimens (53-99%). For both datasets, the capacity to discover new species was dependent on the methodological approach used. Species delimitation in New Zealand skinks was hindered by the absence of either a local or global barcoding gap, a result of recent speciation events and hybridisation. Whilst DNA barcoding is potentially useful for specimen identification and species discovery in New Zealand skinks, its error rate could hinder the progress of documenting biodiversity in this group. We suggest that integrated taxonomic approaches are more effective at discovering and describing biodiversity.     

A Kramers-Moyal Approach to the Analysis of Third-Order Noise with Applications in Option Valuation

We propose the use of the Kramers-Moyal expansion in the analysis of third-order noise. In particular, we show how the approach can be applied in the theoretical study of option valuation. Despite Pawula’s theorem, which states that a truncated model may exhibit poor statistical properties, we show that for a third-order Kramers-Moyal truncation model of an option’s and its underlier’s price, important properties emerge: (i) the option price can be written in a closed analytical form that involves the Airy function, (ii) the price is a positive function for positive skewness in the distribution, (iii) for negative skewness, the price becomes negative only for price values that are close to zero. Moreover, using third-order noise in option valuation reveals additional properties: (iv) the inconsistencies between two popular option pricing approaches (using a “delta-hedged” portfolio and using an option replicating portfolio) that are otherwise equivalent up to the second moment, (v) the ability to develop a measure R of how accurately an option can be replicated by a mixture of the underlying stocks and cash, (vi) further limitations of second-order models revealed by introducing third-order noise.     

Predetermined Conformations in Bends of Polypeptide Chains: A Geometric Analysis

Biophysics - β-Bends are typical local structures of the polypeptide chain, are widespread in proteins, and play an important structural and functional role. It is possible to expect a priori...     

Dietary Ecology of Murinae (Muridae,Rodentia): A Geometric Morphometric Approach

Murine rodents represent a highly diverse group, which displays great ecological versatility. In the present paper we analyse the relationship between dental morphology, on one hand, using geometric morphometrics based upon the outline of first upper molar and the dietary preference of extant murine genera, on the other. This ecomorphological study of extant murine rodents demonstrates that dietary groups can be distinguished with the use of a quantitative geometric morphometric approach based on first upper molar outline. A discriminant analysis of the geometric morphometric variables of the first upper molars enables us to infer the dietary preferences of extinct murine genera from the Iberian Peninsula. Most of the extinct genera were omnivore; only Stephanomys showed a pattern of dental morphology alike that of the herbivore genera.     

城市生态系统的人机交互式模拟研究

城市生态系统的人机交互式模拟研究吕永龙（中国科学院生态环境研究中心,北京１０００８５）ＡｎＡｐｐｒｏａｃｈｔｏＡＭｅｔｈｏｄｏｆＵｒｂａｎＥｃｏｓｙｓｔｅｍＳｉｍｕｌａｔｉｏｎ．￥ＬｕＹｏｎｇｌｏｎｇ（ＲｅｓｅａｒｃｈＣｅｎｔｅｒｆｏｒＥｃｏ－Ｅｎｖ...     

Automated,High Accuracy Classification of Parkinsonian Disorders: A Pattern Recognition Approach

Progressive supranuclear palsy (PSP), multiple system atrophy (MSA) and idiopathic Parkinson’s disease (IPD) can be clinically indistinguishable, especially in the early stages, despite distinct patterns of molecular pathology. Structural neuroimaging holds promise for providing objective biomarkers for discriminating these diseases at the single subject level but all studies to date have reported incomplete separation of disease groups. In this study, we employed multi-class pattern recognition to assess the value of anatomical patterns derived from a widely available structural neuroimaging sequence for automated classification of these disorders. To achieve this, 17 patients with PSP, 14 with IPD and 19 with MSA were scanned using structural MRI along with 19 healthy controls (HCs). An advanced probabilistic pattern recognition approach was employed to evaluate the diagnostic value of several pre-defined anatomical patterns for discriminating the disorders, including: (i) a subcortical motor network; (ii) each of its component regions and (iii) the whole brain. All disease groups could be discriminated simultaneously with high accuracy using the subcortical motor network. The region providing the most accurate predictions overall was the midbrain/brainstem, which discriminated all disease groups from one another and from HCs. The subcortical network also produced more accurate predictions than the whole brain and all of its constituent regions. PSP was accurately predicted from the midbrain/brainstem, cerebellum and all basal ganglia compartments; MSA from the midbrain/brainstem and cerebellum and IPD from the midbrain/brainstem only. This study demonstrates that automated analysis of structural MRI can accurately predict diagnosis in individual patients with Parkinsonian disorders, and identifies distinct patterns of regional atrophy particularly useful for this process.     

Planning for Restoration: A Decision Analysis Approach to Prioritization

Ecological restoration often relies on the use of expert opinion to make management decisions in the face of uncertainty. The quantification of expert opinion can be difficult, especially when more than one expert is consulted and experts are not in agreement. Decision analysis can provide a framework to systematically deconstruct a complex problem and provide greater objectivity to restoration decisions. We utilized decision analysis techniques to identify restoration objectives and to quantify expert opinions to prioritize restoration activities at 112 prairie openings in the Edge of Appalachia Preserve in southern Ohio, U.S.A. We first created an objectives hierarchy to model how decision‐makers decide which prairies to manage. We then determined how to measure each component of the hierarchy and sampled all prairies for percent woody cover, geology, indicator species index (an index of plant species richness), slope, aspect, and distance to nearest prairie. We modeled seven different experts’ preferences for managing prairies with varying values for each of these ecological measures. We then interviewed the same decision‐makers to determine relative weights for each component of the objectives hierarchy using trade‐off analysis. By combining the weights, preference relationships, and sampling data, we were able to rank each prairie and management unit based on its management priority. Experts had similar preferences except for the measure of distance to nearest prairie. We found that decision‐makers gave different weights to each of the different components of the hierarchy. Generally, experts weighted percent woody cover, indicator species index, and geology more highly than slope, aspect, and distance to nearest prairie. Despite these differences, priorities for management, once all factors were weighted and combined, were similar.     

Accuracy Analysis with Special Reference to the Prediction of Grassland Yield

In this paper techniques are presented which allow the evaluation of accuracy of prediction methods, with special reference to the prediction of grassland yield. The more prominent methods of predicting grassland yield are discussed. Two coefficients, which are normalizations of the mean-squared-error, are proposed as measures of accuracy. It is shown that the mean-squared-error partitions into the sum of three meaningful components of error. The distribution of the components is discussed under the assumption that the actual and predicted yields have a bivariate normal distribution. The problem of accuracy analysis is addressed when one is only interested in ranking the yields of grassland areas. The ratio of the mean-squared-error for two competing prediction methods is investigated. Finally, a multiplicative model is studied which allows predicting ability to differ over time or over regions.