What Is An Innovative Educational Leader?

This paper outlined the traits of an innovative educational leader in our changing society. It discussed the difference in a manager and leader, as well as the specific dispositions that differentiate the innovative educational leader from what many consider the average leader. The authors used the acronym “HELPSS” to highlight the leadership skills and traits that many practitioners believe are necessary to qualify a person as an innovative leader

Fabrication techniques for very fast diffractive lenses

Aspheric lenses with arbitrary phase functions can be fabricated on thin light weight substrates via the binary optics fabrication technique. However, it is difficult and costly to fabricate a fast lens (f/number less than 1) for use as the shorter wavelengths. The pitch of the masks and the alignment accuracy must be very fine. For a large lens, the space-bandwidth product of the element can also become impractically large. In this paper, two alternate approaches for the fabrication of fast aspheric diffractive lenses are described. The first approach fabricates the diffractive lens interferometrically, utilizing a spherical wavefront to provide the optical power of the lens and a computer generated hologram to create the aspheric components. The second approach fabricates the aspheric diffractive lens in the form if a higher order kinoform which trades groove profile fidelity for coarser feature size. The design and implementation issues for these two fabrication techniques are discussed

Non-linear Hypothesis Testing of Geometric Object Properties of Shapes Applied to Hippocampi

This paper presents a novel method to test mean differences of geometric object properties (GOPs). The method is designed for data whose representations include both Euclidean and non-Euclidean elements. It is based on advanced statistical analysis methods such as backward means on spheres. We develop a suitable permutation test to find global and simultaneously individual morphological differences between two populations based on the GOPs. To demonstrate the sensitivity of the method, an analysis exploring differences between hippocampi of first-episode schizophrenics and controls is presented. Each hippocampus is represented by a discrete skeletal representation (s-rep). We investigate important model properties using the statistics of populations. These properties are highlighted by the s-rep model that allows accurate capture of the object interior and boundary while, by design, being suitable for statistical analysis of populations of objects. By supporting non-Euclidean GOPs such as direction vectors, the proposed hypothesis test is novel in the study of morphological shape differences. Suitable difference measures are proposed for each GOP. Both global and simultaneous GOP analyses showed statistically significant differences between the first-episode schizophrenics and controls

Composition-Dependent Passivation Efficiency at the CdS/CuIn1-xGaxSe2 Interface

International audienc

Frailty and walking ability as integrated markers of aging and their metabolomic signatures

Frailty and slowed gait become more prevalent with advanced age and predict major health outcomes. These complex phenotypes are influenced by multiple aspects of aging and multimorbidity, and may be manifestations of dysregulation in physiologic systems. Metabolomics, the large-scale study of small molecules that are intermediates or end-products of metabolism, can help us better understand aging-related metabolic changes that contribute to frailty and walking ability by capturing global metabolic profiles occurring most closely to the phenotypes. Here, I aimed to 1) identify metabolites and pathways associated with high versus low walking ability using a nested case-control study of 120 older adults matched on age, gender, race, and fasting time, 2) determine metabolites and pathways associated with frailty to vigor among 287 older black men, and 3) develop and validate a metabolite composite score to determine whether it explains the frailty-associated higher mortality. Regarding aim 1, I found 96 metabolites, mostly lipids/lipid-like molecules, especially triacylglycerols, associated with walking ability. Body composition partly explained associations between select metabolites and walking ability, though many remained independently associated. Triaclyglycerols containing mostly polyunsaturated fatty acids were higher, whereas triaclyglycerols containing mostly saturated or monounsaturated fatty acids were lower among those with high walking ability. Arginine and proline metabolism was a top pathway associated with walking ability. In aims 2 and 3, I found 37 metabolites associated with frailty to vigor and used those metabolites to develop a novel composite score. The metabolite composite score significantly predicted mortality and explained 56% of the higher mortality associated with frailty, where organic acids/derivatives (mostly amino acids) and lipids/lipid-like molecules accounted for almost all of the attenuation. The metabolite composite score also predicted mortality in a validation cohort. Differences in patterns of plasma lipids and amino acids were common classes of metabolites associated with these aging-related phenotypes. Knowledge on differences in these metabolites and metabolic pathways associated with frailty to vigor and walking ability is of public health interest because it provides a better characterization of these complex aging-related phenotypes that can inform points in their pathophysiology to intervene on to promote healthy aging and preserve independence throughout late-life

Functional Data Analysis of Amplitude and Phase Variation

The abundance of functional observations in scientific endeavors has led to a significant development in tools for functional data analysis (FDA). This kind of data comes with several challenges: infinite-dimensionality of function spaces, observation noise, and so on. However, there is another interesting phenomena that creates problems in FDA. The functional data often comes with lateral displacements/deformations in curves, a phenomenon which is different from the height or amplitude variability and is termed phase variation. The presence of phase variability artificially often inflates data variance, blurs underlying data structures, and distorts principal components. While the separation and/or removal of phase from amplitude data is desirable, this is a difficult problem. In particular, a commonly used alignment procedure, based on minimizing the $\mathbb{L}^2$ norm between functions, does not provide satisfactory results. In this paper we motivate the importance of dealing with the phase variability and summarize several current ideas for separating phase and amplitude components. These approaches differ in the following: (1) the definition and mathematical representation of phase variability, (2) the objective functions that are used in functional data alignment, and (3) the algorithmic tools for solving estimation/optimization problems. We use simple examples to illustrate various approaches and to provide useful contrast between them.Comment: Published at http://dx.doi.org/10.1214/15-STS524 in the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

A Fast and Compact Quantum Random Number Generator

We present the realization of a physical quantum random number generator based on the process of splitting a beam of photons on a beam splitter, a quantum mechanical source of true randomness. By utilizing either a beam splitter or a polarizing beam splitter, single photon detectors and high speed electronics the presented devices are capable of generating a binary random signal with an autocorrelation time of 11.8 ns and a continuous stream of random numbers at a rate of 1 Mbit/s. The randomness of the generated signals and numbers is shown by running a series of tests upon data samples. The devices described in this paper are built into compact housings and are simple to operate.Comment: 23 pages, 6 Figs. To appear in Rev. Sci. Inst

Non-Euclidean classification of medically imaged objects via s-reps

AbstractClassifying medically imaged objects, e.g., into diseased and normal classes, has been one of the important goals in medical imaging. We propose a novel classification scheme that uses a skeletal representation to provide rich non-Euclidean geometric object properties. Our statistical method combines distance weighted discrimination (DWD) with a carefully chosen Euclideanization which takes full advantage of the geometry of the manifold on which these non-Euclidean geometric object properties (GOPs) live. Our method is evaluated via the task of classifying 3D hippocampi between schizophrenics and healthy controls. We address three central questions. 1) Does adding shape features increase discriminative power over the more standard classification based only on global volume? 2) If so, does our skeletal representation provide greater discriminative power than a conventional boundary point distribution model (PDM)? 3) Especially, is Euclideanization of non-Euclidean shape properties important in achieving high discriminative power? Measuring the capability of a method in terms of area under the receiver operator characteristic (ROC) curve, we show that our proposed method achieves strongly better classification than both the classification method based on global volume alone and the s-rep-based classification method without proper Euclideanization of non-Euclidean GOPs. We show classification using Euclideanized s-reps is also superior to classification using PDMs, whether the PDMs are first Euclideanized or not. We also show improved performance with Euclideanized boundary PDMs over non-linear boundary PDMs. This demonstrates the benefit that proper Euclideanization of non-Euclidean GOPs brings not only to s-rep-based classification but also to PDM-based classification

Joint and individual analysis of breast cancer histologic images and genomic covariates

A key challenge in modern data analysis is understanding connections between complex and differing modalities of data. For example, two of the main approaches to the study of breast cancer are histopathology (analyzing visual characteristics of tumors) and genetics. While histopathology is the gold standard for diagnostics and there have been many recent breakthroughs in genetics, there is little overlap between these two fields. We aim to bridge this gap by developing methods based on Angle-based Joint and Individual Variation Explained (AJIVE) to directly explore similarities and differences between these two modalities. Our approach exploits Convolutional Neural Networks (CNNs) as a powerful, automatic method for image feature extraction to address some of the challenges presented by statistical analysis of histopathology image data. CNNs raise issues of interpretability that we address by developing novel methods to explore visual modes of variation captured by statistical algorithms (e.g. PCA or AJIVE) applied to CNN features. Our results provide many interpretable connections and contrasts between histopathology and genetics