Schroedinger Eigenmaps for Manifold Alignment of Multimodal Hyperspectral Images

Multimodal remote sensing is an upcoming field as it allows for many views of the same region of interest. Domain adaption attempts to fuse these multimodal remotely sensed images by utilizing the concept of transfer learning to understand data from different sources to learn a fused outcome. Semisupervised Manifold Alignment (SSMA) maps multiple Hyperspectral images (HSIs) from high dimensional source spaces to a low dimensional latent space where similar elements reside closely together. SSMA preserves the original geometric structure of respective HSIs whilst pulling similar data points together and pushing dissimilar data points apart. The SSMA algorithm is comprised of a geometric component, a similarity component and dissimilarity component. The geometric component of the SSMA method has roots in the original Laplacian Eigenmaps (LE) dimension reduction algorithm and the projection functions have roots in the original Locality Preserving Projections (LPP) dimensionality reduction framework. The similarity and dissimilarity component is a semisupervised component that allows expert labeled information to improve the image fusion process. Spatial-Spectral Schroedinger Eigenmaps (SSSE) was designed as a semisupervised enhancement to the LE algorithm by augmenting the Laplacian matrix with a user-defined potential function. However, the user-defined enhancement has yet to be explored in the LPP framework. The first part of this thesis proposes to use the Spatial-Spectral potential within the LPP algorithm, creating a new algorithm we call the Schroedinger Eigenmap Projections (SEP). Through experiments on publicly available data with expert-labeled ground truth, we perform experiments to compare the performance of the SEP algorithm with respect to the LPP algorithm. The second part of this thesis proposes incorporating the Spatial Spectral potential from SSSE into the SSMA framework. Using two multi-angled HSI’s, we explore the impact of incorporating this potential into SSMA

Estimating Information in Earth System Data with Machine Learning

El aprendizaje automático ha hecho grandes avances en la ciencia e ingeniería actuales en general y en las ciencias de la Tierra en particular. Sin embargo, los datos de la Tierra plantean problemas particularmente difíciles para el aprendizaje automático debido no sólo al volumen de datos implicado, sino también por la presencia de correlaciones no lineales tanto espaciales como temporales, por una gran diversidad de fuentes de ruido y de incertidumbre, así como por la heterogeneidad de las fuentes de información involucradas. Más datos no implica necesariamente más información. Por lo tanto, extraer conocimiento y contenido informativo mediante el análisis y el modelado de datos resulta crucial, especialmente ahora donde el volumen y la heterogeneidad de los datos aumentan constantemente. Este hecho requiere avances en métodos que puedan cuantificar la información y caracterizar las distribuciones e incertidumbres con precisión. Cuantificar el contenido informativo a los datos y los modelos de nuestro sistema son problemas no resueltos en estadística y el aprendizaje automático. Esta tesis introduce nuevos modelos de aprendizaje automático para extraer conocimiento e información a partir de datos de observación de la Tierra. Proponemos métodos núcleo ('kernel methods'), procesos gaussianos y gaussianización multivariada para tratar la incertidumbre y la cuantificación de la información, y aplicamos estos métodos a una amplia gama de problemas científicos del sistema terrestre. Estos conllevan muchos tipos de problemas de aprendizaje, incluida la clasificación, regresión, estimación de densidad, síntesis, propagación de errores y estimación de medidas teóricas de la información. También demostramos cómo funcionan estos métodos con diferentes fuentes de datos, provenientes de distintos sensores (radar, multiespectrales, hiperespectrales), productos de datos (observaciones, reanálisis y simulaciones de modelos) y cubos de datos (agregados de varias fuentes de datos espacial-temporales ). Las metodologías presentadas nos permiten cuantificar y visualizar cuáles son las características relevantes que gobiernan distintos métodos núcleo, tales como clasificadores, métodos de regresión o incluso las medidas de independencia estadística, como propagar mejor los errores y las distorsiones de los datos de entrada con procesos gaussianos, así como dónde y cuándo se puede encontrar más información en cubos arbitrarios espacio-temporales. Las técnicas presentadas abren una amplia gama de posibles casos de uso y de aplicaciones, con las que prevemos un uso más extenso y robusto de algoritmos estadísticos en las ciencias de la Tierra y el clima.Machine learning has made great strides in today's Science and engineering in general and Earth Sciences in particular. However, Earth data poses particularly challenging problems for machine learning due to not only the volume of data, but also the spatial-temporal nonlinear correlations, noise and uncertainty sources, and heterogeneous sources of information. More data does not necessarily imply more information. Therefore, extracting knowledge and information content using data analysis and modeling is important and is especially prevalent in an era where data volume and heterogeneity is steadily increasing. This calls for advances in methods that can quantify information and characterize distributions accurately. Quantifying information content within our system's data and models are still unresolved problems in statistics and machine learning. This thesis introduces new machine learning models to extract knowledge and information from Earth data. We propose kernel methods, Gaussian processes and multivariate Gaussianization to handle uncertainty and information quantification and we apply these methods to a wide range of Earth system science problems. These involve many types of learning problems including classification, regression, density estimation, synthesis, error propagation and information-theoretic measures estimation. We also demonstrate how these methods perform with different data sources including sensory data (radar, multispectral, hyperspectral, infrared sounders), data products (observations, reanalysis and model simulations) and data cubes (aggregates of various spatial-temporal data sources). The presented methodologies allow us to quantify and visualize what are the salient features driving kernel classifiers, regressors or dependence measures, how to better propagate errors and distortions of input data with Gaussian processes, and where and when more information can be found in arbitrary spatial-temporal data cubes. The presented techniques open a wide range of possible use cases and applications and we anticipate a wider adoption in the Earth sciences

The TPLATE complex mediates membrane bending during plant clathrin-mediated endocytosis

Clathrin-mediated endocytosis in plants is an essential process but the underlying mechanisms are poorly understood, not least because of the extreme intracellular turgor pressure acting against the formation of endocytic vesicles. In contrast to other models, plant endocytosis is independent of actin, indicating a mechanistically distinct solution. Here, by using biochemical and advanced microscopy approaches, we show that the plant-specific TPLATE complex acts outside of endocytic vesicles as a mediator of membrane bending. Cells with disrupted TPLATE fail to generate spherical vesicles, and in vitro biophysical assays identified protein domains with membrane bending capability. These results redefine the role of the TPLATE complex as a key component of the evolutionarily distinct mechanism mediating membrane bending against high turgor pressure to drive endocytosis in plant cells. One Sentence Summary While plant CME is actin independent, we identify that the evolutionarily ancient octameric TPLATE complex mediates membrane bending against high turgor pressure in plant clathrin-mediated endocytosis

The effect of multiple adverse childhood experiences on health: a systematic review and meta-analysis

Background A growing body of research identifies the harmful effects that adverse childhood experiences (ACEs; occurring during childhood or adolescence; eg, child maltreatment or exposure to domestic violence) have on health throughout life. Studies have quantified such effects for individual ACEs. However, ACEs frequently co-occur and no synthesis of findings from studies measuring the effect of multiple ACE types has been done. Methods In this systematic review and meta-analysis, we searched five electronic databases for cross-sectional, case-control, or cohort studies published up to May 6, 2016, reporting risks of health outcomes, consisting of substance use, sexual health, mental health, weight and physical exercise, violence, and physical health status and conditions, associated with multiple ACEs. We selected articles that presented risk estimates for individuals with at least four ACEs compared with those with none for outcomes with sufficient data for meta-analysis (at least four populations). Included studies also focused on adults aged at least 18 years with a sample size of at least 100. We excluded studies based on high-risk or clinical populations. We extracted data from published reports. We calculated pooled odds ratios (ORs) using a random-effects model. Findings Of 11 621 references identified by the search, 37 included studies provided risk estimates for 23 outcomes, with a total of 253 719 participants. Individuals with at least four ACEs were at increased risk of all health outcomes compared with individuals with no ACEs. Associations were weak or modest for physical inactivity, overweight or obesity, and diabetes (ORs of less than two); moderate for smoking, heavy alcohol use, poor self-rated health, cancer, heart disease, and respiratory disease (ORs of two to three), strong for sexual risk taking, mental ill health, and problematic alcohol use (ORs of more than three to six), and strongest for problematic drug use and interpersonal and self-directed violence (ORs of more than seven). We identified considerable heterogeneity (I 2 of > 75%) between estimates for almost half of the outcomes. Interpretation To have multiple ACEs is a major risk factor for many health conditions. The outcomes most strongly associated with multiple ACEs represent ACE risks for the next generation (eg, violence, mental illness, and substance use). To sustain improvements in public health requires a shift in focus to include prevention of ACEs, resilience building, and ACE-informed service provision. The Sustainable Development Goals provide a global platform to reduce ACEs and their life-course effect on health. Funding Public Health Wales. © 2017 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY-NC-ND 4.0 licens

The NANOGrav 15-year Data Set: Evidence for a Gravitational-Wave Background

We report multiple lines of evidence for a stochastic signal that is correlated among 67 pulsars from the 15-year pulsar-timing data set collected by the North American Nanohertz Observatory for Gravitational Waves. The correlations follow the Hellings-Downs pattern expected for a stochastic gravitational-wave background. The presence of such a gravitational-wave background with a power-law-spectrum is favored over a model with only independent pulsar noises with a Bayes factor in excess of $10^{14}$, and this same model is favored over an uncorrelated common power-law-spectrum model with Bayes factors of 200-1000, depending on spectral modeling choices. We have built a statistical background distribution for these latter Bayes factors using a method that removes inter-pulsar correlations from our data set, finding $p = 10^{-3}$ (approx. $3\sigma$) for the observed Bayes factors in the null no-correlation scenario. A frequentist test statistic built directly as a weighted sum of inter-pulsar correlations yields $p = 5 \times 10^{-5} - 1.9 \times 10^{-4}$ (approx. $3.5 - 4\sigma$). Assuming a fiducial $f^{-2/3}$ characteristic-strain spectrum, as appropriate for an ensemble of binary supermassive black-hole inspirals, the strain amplitude is $2.4^{+0.7}_{-0.6} \times 10^{-15}$ (median + 90% credible interval) at a reference frequency of 1/(1 yr). The inferred gravitational-wave background amplitude and spectrum are consistent with astrophysical expectations for a signal from a population of supermassive black-hole binaries, although more exotic cosmological and astrophysical sources cannot be excluded. The observation of Hellings-Downs correlations points to the gravitational-wave origin of this signal.Comment: 30 pages, 18 figures. Published in Astrophysical Journal Letters as part of Focus on NANOGrav's 15-year Data Set and the Gravitational Wave Background. For questions or comments, please email [email protected]

Quality indicators for patients with traumatic brain injury in European intensive care units

Background: The aim of this study is to validate a previously published consensus-based quality indicator set for the management of patients with traumatic brain injury (TBI) at intensive care units (ICUs) in Europe and to study its potential for quality measur

Changing care pathways and between-center practice variations in intensive care for traumatic brain injury across Europe

Purpose: To describe ICU stay, selected management aspects, and outcome of Intensive Care Unit (ICU) patients with traumatic brain injury (TBI) in Europe, and to quantify variation across centers. Methods: This is a prospective observational multicenter study conducted across 18 countries in Europe and Israel. Admission characteristics, clinical data, and outcome were described at patient- and center levels. Between-center variation in the total ICU population was quantified with the median odds ratio (MOR), with correction for case-mix and random variation between centers. Results: A total of 2138 patients were admitted to the ICU, with median age of 49 years; 36% of which were mild TBI (Glasgow Coma Scale; GCS 13–15). Within, 72 h 636 (30%) were discharged and 128 (6%) died. Early deaths and long-stay patients (> 72 h) had more severe injuries based on the GCS and neuroimaging characteristics, compared with short-stay patients. Long-stay patients received more monitoring and were treated at higher intensity, and experienced worse 6-month outcome compared to short-stay patients. Between-center variations were prominent in the proportion of short-stay patients (MOR = 2.3, p < 0.001), use of intracranial pressure (ICP) monitoring (MOR = 2.5, p < 0.001) and aggressive treatme

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio

Penilaian Kinerja Keuangan Koperasi di Kabupaten Pelalawan

This paper describe development and financial performance of cooperative in District Pelalawan among 2007 - 2008. Studies on primary and secondary cooperative in 12 sub-districts. Method in this stady use performance measuring of productivity, efficiency, growth, liquidity, and solvability of cooperative. Productivity of cooperative in Pelalawan was highly but efficiency still low. Profit and income were highly, even liquidity of cooperative very high, and solvability was good