Alexis de Tocqueville; chronicler of the American democratic experiment

[Abstract]: The purpose of this work is to develop an interactive tool which helps botanists to extract the vein system with its hierarchical properties with as little user interaction as possible. In this paper, we present a new venation extraction method using independent component analysis (ICA). The popular and efficient FastICA algorithm is applied to patches of leaf images to learn a set of linear basis functions or features for the images and then the basis functions are used as the pattern map for vein extraction. In our experiments, the training sets are randomly generated from different leaf images. Experimental results demonstrate that ICA is a promising technique for extracting leaf veins and edges of objects. ICA, therefore, can play an important role in automatically identifying living plants

A Bayesian adaptive marker‐stratified design for molecularly targeted agents with customized hierarchical modeling

It is well known that the treatment effect of a molecularly targeted agent (MTA) may vary dramatically, depending on each patient's biomarker profile. Therefore, for a clinical trial evaluating MTA, it is more reasonable to evaluate its treatment effect within different marker subgroups rather than evaluating the average treatment effect for the overall population. The marker‐stratified design (MSD) provides a useful tool to evaluate the subgroup treatment effects of MTAs. Under the Bayesian framework, the beta‐binomial model is conventionally used under the MSD to estimate the response rate and test the hypothesis. However, this conventional model ignores the fact that the biomarker used in the MSD is, in general, predictive only for the MTA. The response rates for the standard treatment can be approximately consistent across different subgroups stratified by the biomarker. In this paper, we proposed a Bayesian hierarchical model incorporating this biomarker information into consideration. The proposed model uses a hierarchical prior to borrow strength across different subgroups of patients receiving the standard treatment and, therefore, improve the efficiency of the design. Prior informativeness is determined by solving a “customized” equation reflecting the physician's professional opinion. We developed a Bayesian adaptive design based on the proposed hierarchical model to guide the treatment allocation and test the subgroup treatment effect as well as the predictive marker effect. Simulation studies and a real trial application demonstrate that the proposed design yields desirable operating characteristics and outperforms the existing designs

Unveiling a multiscale view of massive star and cluster formation

Massive stars regulate the physical and chemical evolution of galaxies. Most stars\ua0within these galaxies, including massive ones, appear to be born in star clusters and\ua0associations. However, many questions remain unanswered about how massive stars\ua0and clusters form from the diffuse gas of interstellar space. For example, it is not yet\ua0known whether magnetic fields, turbulence or feedback are the most important actors\ua0in regulating gravitational collapse to give birth to these systems. It is also unclear to\ua0what extent potential protostellar crowding within a protocluster may affect massive\ua0star formation. Overdense clumps within giant molecular clouds (GMCs), often\ua0appearing in their earliest phases as infrared dark clouds (IRDCs), are the nurseries\ua0of massive stars and clusters. Properties of turbulence and magnetic fields in IRDCs\ua0are thus important to measure to give inputs for theoretical models of the formation\ua0processes. On the smaller scales of individual massive star formation, various theories,\ua0including core accretion, competitive accretion and protostellar collisions, may be\ua0viable depending on environmental conditions. Hence, studying how massive stars\ua0are forming in environments with relatively extreme conditions, e.g., in terms of\ua0crowding or isolation, may yield the most stringent constraints on these models.This licentiate thesis first presents a study of a massive protostar (G28.2-0.05)\ua0that appears to be forming in relative isolation. Observational data, especially from\ua0the Atacama Large Millimeter/Submillimeter Array (ALMA), are used to investigate\ua0the nature of the system, including its dense and ionized gas structures, small-scale\ua0kinematics and dynamics and large-scale outflows. Mid to Far Infrared observations\ua0and archival data are used to measure the spectral energy distribution (SED) to\ua0further constrain protostellar properties. We conclude the system is a massive\ua0(∼ 24 M ) protostar that has an accretion powered wide angle bipolar molecular\ua0outflow and is also in the first stages of producing significant ionizing feedback. An\ua0examination of the mm dust continuum emission in the surroundings finds a near\ua0complete dearth of other sources, which is evidence for the system’s isolation and a\ua0strong constraint on competitive accretion models. Overall, core accretion models\ua0appear to give a good description of the protostar.We also present first results of an observational program that attempts to use\ua0molecular gas kinematics and dust polarized continuum emission to measure properties\ua0of turbulence and magnetic fields across a range of scales from GMCs to IRDCs\ua0to individual massive protostars. We discuss the methods to be used and present\ua0the first data collected for the project. The overall work of this thesis leads in a\ua0direction of characterization of both large-scale molecular cloud environments and\ua0individual massive protostars forming within them to reveal a holistic, multi-scale\ua0view of the birth of massive stars and cluster