Kernel conditional quantile estimation via reduction revisited

Quantile regression refers to the process of estimating the quantiles of a conditional distribution and has many important applications within econometrics and data mining, among other domains. In this paper, we show how to estimate these conditional quantile functions within a Bayes risk minimization framework using a Gaussian process prior. The resulting non-parametric probabilistic model is easy to implement and allows non-crossing quantile functions to be enforced. Moreover, it can directly be used in combination with tools and extensions of standard Gaussian Processes such as principled hyperparameter estimation, sparsification, and quantile regression with input-dependent noise rates. No existing approach enjoys all of these desirable properties. Experiments on benchmark datasets show that our method is competitive with state-of-the-art approaches. © 2009 IEEE.

Determinantal Clustering Processes - A Nonparametric Bayesian Approach to Kernel Based Semi-Supervised Clustering

Semi-supervised clustering is the task of clustering data points into clusters where only a fraction of the points are labelled. The true number of clusters in the data is often unknown and most models require this parameter as an input. Dirichlet process mixture models are appealing as they can infer the number of clusters from the data. However, these models do not deal with high dimensional data well and can encounter difficulties in inference. We present a novel nonparameteric Bayesian kernel based method to cluster data points without the need to prespecify the number of clusters or to model complicated densities from which data points are assumed to be generated from. The key insight is to use determinants of submatrices of a kernel matrix as a measure of how close together a set of points are. We explore some theoretical properties of the model and derive a natural Gibbs based algorithm with MCMC hyperparameter learning. The model is implemented on a variety of synthetic and real world data sets.

Unified analytic model for current-voltage behavior in amorphous oxide semiconductor TFTs

We present a simple and semi-physical analytical description of the current-voltage characteristics of amorphous oxide semiconductor thin-film transistors in the above-threshold and sub-threshold regions. Both regions are described by single unified expression that employs the same set of model parameter values directly extracted from measured terminal characteristics. The model accurately reproduces measured characteristics of amorphous semiconductor thin film transistors in general, yielding a scatter of < 4%. © 1980-2012 IEEE.

The Random Forest Kernel and other kernels for big data from random partitions

We present Random Partition Kernels, a new class of kernels derived by demonstrating a natural connection between random partitions of objects and kernels between those objects. We show how the construction can be used to create kernels from methods that would not normally be viewed as random partitions, such as Random Forest. To demonstrate the potential of this method, we propose two new kernels, the Random Forest Kernel and the Fast Cluster Kernel, and show that these kernels consistently outperform standard kernels on problems involving real-world datasets. Finally, we show how the form of these kernels lend themselves to a natural approximation that is appropriate for certain big data problems, allowing $O(N)$ inference in methods such as Gaussian Processes, Support Vector Machines and Kernel PCA.

Student-t processes as alternatives to Gaussian processes

We investigate the Student-t process as an alternative to the Gaussian process as a non-parametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the co-variance kernel of a Gaussian process model. We show surprising equivalences between different hierarchical Gaussian process models leading to Student-t processes, and derive a new sampling scheme for the inverse Wishart process, which helps elucidate these equivalences. Overall, we show that a Student-t process can retain the attractive properties of a Gaussian process - a nonparamet-ric representation, analytic marginal and predictive distributions, and easy model selection through covariance kernels - but has enhanced flexibility, and predictive covariances that, unlike a Gaussian process, explicitly depend on the values of training observations. We verify empirically that a Student-t process is especially useful in situations where there are changes in covariance structure, or in applications such as Bayesian optimization, where accurate predictive covariances are critical for good performance. These advantages come at no additional computational cost over Gaussian processes.

Differential expression of vasa RNA and protein during spermatogenesis and oogenesis in the gibel carp (Carassius auratus gibelio), a bisexually and gynogenetically reproducing vertebrate

The RNA helicase Vasa is a germ cell marker in animals, and its homolog in vertebrates to date has been limited to bisexual reproduction. We cloned and characterized CagVasa, a Vasa homolog from the gibel carp, a fish that reproduces bisexually or gynogenetically. CagVasa possesses 14 RGG repeats and eight conserved motifs of Vasa proteins. In bisexually reproducing gibel carp, vasa is maternally supplied and its zygotic expression is restricted to gonads. By in situ hybridization on testicular sections, vasa is low in spermatogonia, high in primary spermatocytes, reduced in secondary spermatocytes, but disappears in spermatids and sperm. In contrast, vasa persists throughout oogenesis, displaying low-high-low levels from oogonia over vitellogenic oocytes to maturing oocytes. A rabbit anti-Vasa antibody (alpha Vasa) was raised against the N-terminal CagVasa for fluorescent immunohistochemistry. On testicular sections, Vasa is the highest in spermatogonia, reduced in spermatocytes, low in spermatids, and absent in sperm. In the ovary, Vasa is the highest in oogonia but persists throughout oogenesis. Subcellular localization of vasa and its protein changes dynamically during oogenesis. The aVasa stains putative primordial germ cells in gibel carp fry. It detects gonadal germ cells also in several other teleosts. Therefore, Cagvasa encodes a Vasa ortholog that is differentially expressed in the testis and ovary. Interestingly, the alpha Vasa in combination with a nuclear dye can differentiate critical stages of spermatogenesis and oogenesis in fish. The cross-reactivity and the ability to stain stage-specific germ cells make this antibody a useful tool to identify fish germ cell development and differentiation. (c) 2005 Wiley-Liss, Inc.

Analytic expressions for alpha particle preformation in heavy nuclei

Experimental alpha decay energies and half-lives are investigated systematically to extract alpha particle preformation in heavy nuclei. Formulas for the preformation factors are proposed that can be used to guide microscopic studies on preformation factors and perform accurate calculations of the alpha decay half-lives. There is little evidence for the existence of an island of long stability of superheavy nuclei.

An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

T-Kernel在Blackfin处理器上的移植分析

T-Kernel是日本T-Engine组织推出的开源免费的嵌入式实时操作系统(RTOS),以其强实时小体积内核著称。本文针对T-Kernel在Blackfin处理器(BF533)上的移植过程进行了分析,给出了中断管理,任务切换和系统调用入口的实现方法,并进行了稳定性和实时性测试,保证了移植系统的性能。

开源RTOS T-Kernel在Blackfin处理器上的移植

随着嵌入式计算技术的飞速发展,多种功能强大的微处理器和相应嵌入式操作系统也相继问世。选择合适的嵌入式操作系统和处理器平台进行移植成为嵌入式开发的重要技术环节。本文介绍了开源嵌入式实时操作系统RTOS(Real Time Operating System)T-Kernel和Blackfin(BF)533处理器及其开发环境VisualDSP++4.5Environment,给出了中断管理、任务切换和系统调用入口三个模块的移植方法,并讨论了相应的系统稳定性和实时性测试方法。日本T-Engine组织推出的T-Kernel RTOS拥有高实时性、小体积的内核,并强调对底层处理器全面封装;ADI Blackfin系列微处理器同时具有DSP和MCU的特点,非常适合于移植RTOS。因此T-Kernel在BF533上的移植是一个典型的开发应用实例,其移植分析方法对于其他嵌入式RTOS移植也具有参考价值。

Syntax-Based Analytic Reading of Musical Scores

As part of a larger research project in musical structure, a program has been written which "reads" scores encoded in an input language isomorphic to music notation. The program is believed to be the first of its kind. From a small number of parsing rules the program derives complex configurations, each of which is associated with a set of reference points in a numerical representation of a time-continuum. The logical structure of the program is such that all and only the defined classes of events are represented in the output. Because the basis of the program is syntactic (in the sense that parsing operations are performed on formal structures in the input string), many extensions and refinements can be made without excessive difficulty. The program can be applied to any music which can be represented in the input language. At present, however, it constitutes the first stage in the development of a set of analytic tools for the study of so-called atonal music, the revolutionary and little understood music which has exerted a decisive influence upon contemporary practice of the art. The program and the approach to automatic data-structuring may be of interest to linguists and scholars in other fields concerned with basic studies of complex structures produced by human beings.

Natural Language Input for a Computer Problem Solving System

The STUDENT problem solving system, programmed in LISP, accepts as input a comfortable but restricted subset of English which can express a wide variety of algebra story problems. STUDENT finds the solution to a large class of these problems. STUDENT can utilize a store of global information not specific to any one problem, and may make assumptions about the interpretation of ambiguities in the wording of the problem being solved. If it uses such information or makes any assumptions, STUDENT communicates this fact to the user. The thesis includes a summary of other English language questions-answering systems. All these systems, and STUDENT, are evaluated according to four standard criteria. The linguistic analysis in STUDENT is a first approximation to the analytic portion of a semantic theory of discourse outlined in the thesis. STUDENT finds the set of kernel sentences which are the base of the input discourse, and transforms this sequence of kernel sentences into a set of simultaneous equations which form the semantic base of the STUDENT system. STUDENT then tries to solve this set of equations for the values of requested unknowns. If it is successful it gives the answers in English. If not, STUDENT asks the user for more information, and indicates the nature of the desired information. The STUDENT system is a first step toward natural language communication with computers. Further work on the semantic theory proposed should result in much more sophisticated systems.

A Christian analytic approach of marriage through abduction among the Basotho people of Lesotho: A challenge for pastoral care ministry

University of Pretoria / MA Dissertation / Department of Practical Theology / Advised by Prof M J S Masango

Active Hidden Models for Tracking with Kernel Projections

We introduce Active Hidden Models (AHM) that utilize kernel methods traditionally associated with classification. We use AHMs to track deformable objects in video sequences by leveraging kernel projections. We introduce the "subset projection" method which improves the efficiency of our tracking approach by a factor of ten. We successfully tested our method on facial tracking with extreme head movements (including full 180-degree head rotation), facial expressions, and deformable objects. Given a kernel and a set of training observations, we derive unbiased estimates of the accuracy of the AHM tracker. Kernels are generally used in classification methods to make training data linearly separable. We prove that the optimal (minimum variance) tracking kernels are those that make the training observations linearly dependent.

Analytic-domain lens design with proximate ray tracing.

We have developed an alternative approach to optical design which operates in the analytical domain so that an optical designer works directly with rays as analytical functions of system parameters rather than as discretely sampled polylines. This is made possible by a generalization of the proximate ray tracing technique which obtains the analytical dependence of the rays at the image surface (and ray path lengths at the exit pupil) on each system parameter. The resulting method provides an alternative direction from which to approach system optimization and supplies information which is not typically available to the system designer. In addition, we have further expanded the procedure to allow asymmetric systems and arbitrary order of approximation, and have illustrated the performance of the method through three lens design examples.