Statistical Tools for Directed and Bipartite Networks

Directed networks and bipartite networks, which exhibit unique asymmetric connectivity structures, are commonly observed in a variety of scientific and engineering fields. Despite their abundance and utility, most network analysis methods only consider symmetric networks. In this thesis, we develop statistical methods and theory for directed and bipartite networks. The first chapter focuses on matched community detection in a bipartite network. The detection of matched communities, i.e. communities that consist of nodes of two types that are closely connected with one another, is a fundamental and challenging problem. Most widely used approaches for matched community detection are either computationally inefficient or prone to non-ideal performance. We propose a new two-stage algorithm that uses fast spectral methods to recover matched communities. We show that, for bipartite networks, it is critical to adjust for the community size in matched community detection, which had not been considered before. We also provide theoretical error bounds for the proposed algorithm on the number of mis-clustered nodes under a variant of the stochastic block model. Numerical studies indicate that the proposed method outperforms existing spectral algorithms, especially when the sizes of the matched communities are proportionally different between the two types. The second chapter of the thesis introduces a new preference-based block model for community detection in a directed network. Unlike existing models, the proposed model allows different sender nodes to have different preferences to communities in the network. We argue that the right singular vectors of a graph Laplacian matrix contain community structures under the model. Further, we propose a spectral clustering algorithm to detect communities and estimate parameters of the model. Theoretical results show insights on how the heterogeneity of preferences and out-degrees contribute to an upper bound of the number of mis-clustered nodes. Numerical studies support the theoretical results and illustrate the outstanding performance of the proposed method. The model can also be naturally extended to bipartite networks. In the third chapter, we propose a dyadic latent space model which accommodates the reciprocity between a pair of nodes in directed networks. Nodes in a pair in directed networks often exhibit strong dependencies with each other, though most widely used approaches usually account for this phenomenon with limited flexibility. We propose a new latent space model for directed networks that incorporates the reciprocity in a flexible way, allowing for important characteristics such as homophily and heterogeneity of the nodes. A fast and scalable algorithm based on projected gradient descent has been developed to fit the model by maximizing the likelihood. Both simulation studies and real-world data examples illustrate that the proposed model is effective in various network analysis tasks including link prediction and community detection.PHDStatisticsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163156/1/yoohs_1.pd

Design of Object-based Information System Prototype

Researchers who use science and technology information were found to ask an information service in which they can excerpt the contents they needed, rather than using the information at article level. In this study, we micronized the contents of scholarly articles into text, image, and table and then constructed a micro-content DB to design a new information system prototype based on this micro-content. After designing the prototype, we performed usability test for this prototype so as to confirm the usefulness of the system prototype. We expect that the outcome of this study will fulfill the segmented and diversified information need of researchers

The Micro-Randomized Trial for Developing Digital Interventions: Data Analysis Methods

Although there is much excitement surrounding the use of mobile and wearable technology for the purposes of delivering interventions as people go through their day-to-day lives, data analysis methods for constructing and optimizing digital interventions lag behind. Here, we elucidate data analysis methods for primary and secondary analyses of micro-randomized trials (MRTs), an experimental design to optimize digital just-in-time adaptive interventions. We provide a definition of causal "excursion" effects suitable for use in digital intervention development. We introduce the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators for digital interventions from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as SAS or R. Throughout we use HeartSteps, an MRT designed to increase physical activity among sedentary individuals, to illustrate potential primary and secondary analyses

Uncovering Biological Factors That Regulate Hepatocellular Carcinoma Growth Using Patient‐Derived Xenograft Assays

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/162740/3/hep31096.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162740/2/hep31096-sup-0001-Suppinfo.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/162740/1/hep31096_am.pd

Der Reaktionsmechanismus zwischen Cu-Zn-Sn-Se Komponenten für die Bildung von Cu2ZnSnSe4 Filmen

To find an approach to prepare a homogenous kesterite-based Cu2ZnSnSe4 thin film, it is necessary to understand the reaction mechanism between four elements. Thus, various samples are analysed by time-resolved in situ X-ray powder diffraction (XRD) to observe the reaction process of each element. To better understand the formation reactions, samples with different number of metallic layers (one-, two- and three-metal samples) are prepared. The obtained diffractograms are analysed on the basis of the relevant alloy phase diagrams and XRD reference patterns of each phase. When necessary, grazing-incidence XRD measurement and Raman spectroscopy are performed on several samples. Reaction paths of one-metal samples show the formation temperature of a metal with Se without any disturbance by other elements. On the basis of the results, the formation temperature of each binary selenide in the results of two-metal samples will be compared. This comparison may show the influence of other elements on the formation of binary selenides. Results for two-metal samples suggest different formation processes depending on the sequence of stacking layers in the initial precursor, although these samples include only two metallic layers. This means that the stacking order of precursors significantly affects the sequence of alloy formation. From the different reaction paths, the tendency of four elements to react may be determined together with several characteristics of formation reactions of the alloy phases. Based on these analyses, the reaction paths of three-metal samples may be revealed, and several characteristics of formation reactions of CZTSe may be observed. Therefore, the three-metal samples are sub-divided again according to these characteristics for detailed analysis. Formation processes of each component for the CZTSe formation are separately described in section 4.3, along with the cause of the remaining secondary phases in the kesterite film. The reaction characteristics revealed in this study provide information on the optimum stacking order of precursors and lead to a conclusion on one proposed precursor.Das Herstellen von homogenen kesterit-basierten Cu2ZnSnSe4 Dünnschichten erfordert ein tiefgehendes Verständnis der Reaktionsmechanismen zwischen vier Elementen. Zu diesem Zweck werden verschiedene Proben mithilfe der zeitaufgelösten Röntgenpulverdiffraktometrie untersucht, und so die Reaktionspfade der einzelnen Elemente ermittelt. Um die einzelnen Reaktionen voneinander zu trennen werden Proben mit unterschiedlicher Anzahl metallischer Schichten (Ein-, Zwei- und Dreimetallproben) hergestellt. Die gemessenen Diffraktogramme werden basierend auf den entsprechenden Phasendiagrammen der Legierungen und den Referenzdiffraktogrammen der einzelnen Phasen ausgewertet. In einigen Fällen kommen zusätzlich Röntgenpulverbeugung unter streifendem Einfall und Ramanspektroskopie zum Einsatz. Die Reaktionen der Einmetallproben zeigen den Temperaturbereich, in dem ein bestimmtes Metall eine Verbindung mit Se eingeht, ohne den störenden Einfluss anderer Elemente. Basierend auf diesen Ergebnissen lassen sich die Reaktionstemperaturen aller binären Selenide, die in den Zweimetallproben entstehen, miteinander vergleichen. Dieser Vergleich kann den Einfluss anderer Elemente auf die Bildung der binären Selenide aufzeigen. Die Ergebnisse der Zweimetallproben lassen vermuten, dass verschiedene Bildungsmechanismen eine Rolle spielen, abhängig von der Stapelfolge im Präkursor, auch bei nur zwei metallischen Schichten. Folglich hängt die Reihenfolge der Legierungsreaktionen stark von der Stapelfolge ab. Aus der Analyse der verschiedenen Reaktionspfade lassen sich Rückschlüsse auf die Reaktionspräferenzen der einzelnen Elemente und Bildungsmechanismen der Legierungen ziehen. Mithilfe dieser Erkenntnisse können die Reaktionspfade der Dreimetallproben und die Bildungsmechanismen von CZTSe untersucht werden. Entsprechend sind die Dreimetallproben für die genauere Analyse je nach Bildungsmechanismus in Unterkategorien eingeteilt. Die Bildungsmechanismen für jeden Bestandteil der CZTSe Synthese sind in Kapitel 4.3 zusammen mit den Restphasen, die in der Schicht zurückbleiben, einzeln aufgeführt. Durch die in dieser Arbeit herausgearbeiteten Reaktionscharakteristika lässt sich eine optimale Stapelfolge für den vorgeschlagenen Modellpräkursor vorhersagen

Estimating Time-Varying Causal Excursion Effect in Mobile Health with Binary Outcomes

Advances in wearables and digital technology now make it possible to deliver behavioral mobile health interventions to individuals in their everyday life. The micro-randomized trial (MRT) is increasingly used to provide data to inform the construction of these interventions. In an MRT, each individual is repeatedly randomized among multiple intervention options, often hundreds or even thousands of times, over the course of the trial. This work is motivated by multiple MRTs that have been conducted, or are currently in the field, in which the primary outcome is a longitudinal binary outcome. The primary aim of such MRTs is to examine whether a particular time-varying intervention has an effect on the longitudinal binary outcome, often marginally over all but a small subset of the individual's data. We propose the definition of causal excursion effect that can be used in such primary aim analysis for MRTs with binary outcomes. Under rather restrictive assumptions one can, based on existing literature, derive a semiparametric, locally efficient estimator of the causal effect. We, starting from this estimator, develop an estimator that can be used as the basis of a primary aim analysis under more plausible assumptions. Simulation studies are conducted to compare the estimators. We illustrate the developed methods using data from the MRT, BariFit. In BariFit, the goal is to support weight maintenance for individuals who received bariatric surgery

Optical Detection of SnSe2 Formation on CZTSSe Thin-Film Solar Cells

Cu2ZnSn(S1-xSex)4 (CZTSSe) is a promising candidate for the absorber layer of low-cost thin-film solar cells, thanks to the advantage of using earth-abundant, non-toxic elements. However, since the stable phase region of CZTSSe is very narrow, secondary phases are easily formed during the thinfilm deposition or the post-deposition treatments, and some of the secondary phases are detrimental to the solar conversion efficiency. In this work, we investigated the influence of the SnSe2 secondary phase to the performance of a solar cell using laser-beam-induced current (LBIC) measurements and resonance Raman spectroscopy. We found that the SnSe2 secondary phase has a critical impact on the characteristics of the solar cell even if the amount of the secondary phase is so little that it can be detected only with a resonant excitation source. We established that the points with the SnSe2 secondary phase Raman signal had a lower photocurrent. From macro-scale resonance Raman measurements, we show that the existence of the SnSe2 secondary phase directly correlated with the lower efficiency of a cell. Therefore, we conclude that controlling the formation of the SnSe2 secondary phase is a crucial factor to obtain CZTSSe solar cells with high efficiencies. © 2022 American Chemical Society.FALS

The Micro-Randomized Trial for Developing Digital Interventions: Data Analysis Methods

Although there is much excitement surrounding the use of mobile and wearable technology for the purposes of delivering interventions as people go through their day-to-day lives, data analysis methods for constructing and optimizing digital interventions lag behind. Here, we elucidate data analysis methods for primary and secondary analyses of micro-randomized trials (MRTs), an experimental design to optimize digital just-in-time adaptive interventions. We provide a definition of causal "excursion" effects suitable for use in digital intervention development. We introduce the weighted and centered least-squares (WCLS) estimator which provides consistent causal excursion effect estimators for digital interventions from MRT data. We describe how the WCLS estimator along with associated test statistics can be obtained using standard statistical software such as SAS or R. Throughout we use HeartSteps, an MRT designed to increase physical activity among sedentary individuals, to illustrate potential primary and secondary analyses