Latent Self-Exciting Point Process Model for Spatial-Temporal Networks

We propose a latent self-exciting point process model that describes geographically distributed interactions between pairs of entities. In contrast to most existing approaches that assume fully observable interactions, here we consider a scenario where certain interaction events lack information about participants. Instead, this information needs to be inferred from the available observations. We develop an efficient approximate algorithm based on variational expectation-maximization to infer unknown participants in an event given the location and the time of the event. We validate the model on synthetic as well as real-world data, and obtain very promising results on the identity-inference task. We also use our model to predict the timing and participants of future events, and demonstrate that it compares favorably with baseline approaches.Comment: 20 pages, 6 figures (v3); 11 pages, 6 figures (v2); previous version appeared in the 9th Bayesian Modeling Applications Workshop, UAI'1

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin

A silent speech system based on permanent magnet articulography and direct synthesis

In this paper we present a silent speech interface (SSI) system aimed at restoring speech communication for individuals who have lost their voice due to laryngectomy or diseases affecting the vocal folds. In the proposed system, articulatory data captured from the lips and tongue using permanent magnet articulography (PMA) are converted into audible speech using a speaker-dependent transformation learned from simultaneous recordings of PMA and audio signals acquired before laryngectomy. The transformation is represented using a mixture of factor analysers, which is a generative model that allows us to efficiently model non-linear behaviour and perform dimensionality reduction at the same time. The learned transformation is then deployed during normal usage of the SSI to restore the acoustic speech signal associated with the captured PMA data. The proposed system is evaluated using objective quality measures and listening tests on two databases containing PMA and audio recordings for normal speakers. Results show that it is possible to reconstruct speech from articulator movements captured by an unobtrusive technique without an intermediate recognition step. The SSI is capable of producing speech of sufficient intelligibility and naturalness that the speaker is clearly identifiable, but problems remain in scaling up the process to function consistently for phonetically rich vocabularies

Expectation-Maximizing Network Reconstruction and MostApplicable Network Types Based on Binary Time Series Data

Based on the binary time series data of social infection dynamics, we propose a general framework to reconstruct 2-simplex complexes with two-body and three-body interactions by combining the maximum likelihood estimation in statistical inference and introducing the expectation maximization. In order to improve the code running efficiency, the whole algorithm adopts vectorization expression. Through the inference of maximum likelihood estimation, the vectorization expression of the edge existence probability can be obtained, and through the probability matrix, the adjacency matrix of the network can be estimated. We apply a two-step scheme to improve the effectiveness of network reconstruction while reducing the amount of computation significantly. The framework has been tested on different types of complex networks. Among them, four kinds of networks can obtain high reconstruction effectiveness. Besides, we study the influence of noise data or random interference and prove the robustness of the framework, then the effects of two kinds of hyper-parameters on the experimental results are tested. Finally, we analyze which type of network is more suitable for this framework, and propose methods to improve the effectiveness of the experimental results

The inference of gene trees with species trees.

This article reviews the various models that have been used to describe the relationships between gene trees and species trees. Molecular phylogeny has focused mainly on improving models for the reconstruction of gene trees based on sequence alignments. Yet, most phylogeneticists seek to reveal the history of species. Although the histories of genes and species are tightly linked, they are seldom identical, because genes duplicate, are lost or horizontally transferred, and because alleles can coexist in populations for periods that may span several speciation events. Building models describing the relationship between gene and species trees can thus improve the reconstruction of gene trees when a species tree is known, and vice versa. Several approaches have been proposed to solve the problem in one direction or the other, but in general neither gene trees nor species trees are known. Only a few studies have attempted to jointly infer gene trees and species trees. These models account for gene duplication and loss, transfer or incomplete lineage sorting. Some of them consider several types of events together, but none exists currently that considers the full repertoire of processes that generate gene trees along the species tree. Simulations as well as empirical studies on genomic data show that combining gene tree-species tree models with models of sequence evolution improves gene tree reconstruction. In turn, these better gene trees provide a more reliable basis for studying genome evolution or reconstructing ancestral chromosomes and ancestral gene sequences. We predict that gene tree-species tree methods that can deal with genomic data sets will be instrumental to advancing our understanding of genomic evolution

New approaches in statistical network data analysis

This cumulative dissertation is dedicated to the statistical analysis of network data. The general approach of combining network science with statistical methodology became very popular in recent years. An important reason for this development lies in the ability of statistical network data analysis to provide a means to model and quantify interdependencies of complex systems. A network can be comprehended as a structure consisting of nodes and edges. The nodes represent general entities that are related via the edges. Depending on the research question at hand, it is either of interest to analyze the dependence structure among the nodes or the distribution of the edges given the nodes. This thesis consists of six contributed manuscripts that are concerned with the latter. Based on statistical models, edges in different dynamic and weighted networks are investigated or reconstructed. To put the contributing articles in a general context, the thesis starts with an introductory chapter. In this introduction, central concepts and models from statistical network data analysis are explained. Besides giving an overview of the available methodology, the advantages and drawbacks of the models are given, supplemented with a discussion of potential extensions and modifications. Content-wise it is possible to divide the articles into two projects. One project is focused on the statistical analysis of international arms trade networks. Two articles are devoted to the global exchange of major conventional weapons with a focus on the dynamic structure of the system and the volume traded. A third article explores latent patterns in the international trade system of small arms and ammunition. Additionally, the arms trade data is used in a survey paper that is concerned with dynamic network models. The second project regards the reconstruction of financial networks from their marginals and includes two articles. All contributing articles are attached in the form as published as a preprint. For publications in scientific journals, the respective sources are given. Additionally, the contributions of all authors are included. All computations were done with the statistical software R and the corresponding code is available from Github.Diese kumulative Dissertation beschäftigt sich mit der statistischen Analyse von Netzwerkdaten. Der generelle Ansatz, interdependente Systeme als Netzwerke zu konzeptualisieren um sie anschließend mit statistischer Methodik zu analysieren, hat in den vergangenen Jahren deutlich an Relevanz gewonnen. Insbesondere die Flexibilität der Methodik, zusammen mit der Möglichkeit komplexe Abhängigkeitsstrukturen zu modellieren, hat zu ihrer Popularität beigetragen. Ein Netzwerk ist ein System, das sich aus Knoten und Kanten zusammensetzt. Dabei sind die Knoten generelle Einheiten, die durch die Kanten miteinander in Verbindung gebracht werden. Je nach Forschungsfrage interessieren entweder die Abhängigkeiten zwischen den Knoten oder die Verteilung der Kanten mit gegebenen Knoten. Diese Arbeit greift mit insgesamt sechs Artikeln den zweiten Ansatz auf. Unter Zuhilfenahme von statistischen Modellen werden die Kanten in verschiedenen binären und gewichteten Netzwerken analysiert, beziehungsweise rekonstruiert. Um der Arbeit einen generellen Kontext zu geben, wird den angehängten Artikeln ein Mantelteil vorangestellt. In diesem wird auf zentrale Konzepte und Modelle der statistischen Netzwerkanalyse eingegangen. Dabei werden die Vorteile als auch die Nachteile der Modelle diskutiert und potenzielle Erweiterungen und Modifikationen beschrieben. Die in dieser Dissertation enthaltenen Artikel lassen sich grob in zwei verschiedene Projekte einordnen. In einem Projekt steht die statistische Modellierung des internationalen Waffenhandels im Fokus. Zwei Artikel untersuchen den globalen Austausch von Großwaffen (Major Conventional Weapons), dabei wird sowohl die dynamische Struktur als auch das gehandelte Waffenvolumen analysiert. Ein weiterer Artikel widmet sich den latenten Strukturen im internationalen Kleinwaffenhandel (Small Arms and Ammunition). Weiterhin werden die Waffenhandelsdaten in einem Übersichtsartikel, der sich mit dynamischen Netzwerkmodellen beschäftigt, verwendet. Das zweite Projekt befasst sich, verteilt über zwei Artikel, mit der Rekonstruktion von finanziellen Netzwerken basierend auf den Randsummen von Netzwerkmatrizen. Alle in dieser Dissertation angehängten Artikel befinden sich in der Form, in der sie als Vorabversion veröffentlicht wurden. Bei Veröffentlichungen in Fachjournalen wird die jeweilige Quelle angegeben. Zudem wird vor jedem Artikel der Beitrag des jeweiligen Autors angegeben. Sämtliche Analysen wurden mit der statistischen Software R durchgeführt. Der dazugehörige Code ist über Github verfügbar

AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

New approaches in statistical network data analysis

This cumulative dissertation is dedicated to the statistical analysis of network data. The general approach of combining network science with statistical methodology became very popular in recent years. An important reason for this development lies in the ability of statistical network data analysis to provide a means to model and quantify interdependencies of complex systems. A network can be comprehended as a structure consisting of nodes and edges. The nodes represent general entities that are related via the edges. Depending on the research question at hand, it is either of interest to analyze the dependence structure among the nodes or the distribution of the edges given the nodes. This thesis consists of six contributed manuscripts that are concerned with the latter. Based on statistical models, edges in different dynamic and weighted networks are investigated or reconstructed. To put the contributing articles in a general context, the thesis starts with an introductory chapter. In this introduction, central concepts and models from statistical network data analysis are explained. Besides giving an overview of the available methodology, the advantages and drawbacks of the models are given, supplemented with a discussion of potential extensions and modifications. Content-wise it is possible to divide the articles into two projects. One project is focused on the statistical analysis of international arms trade networks. Two articles are devoted to the global exchange of major conventional weapons with a focus on the dynamic structure of the system and the volume traded. A third article explores latent patterns in the international trade system of small arms and ammunition. Additionally, the arms trade data is used in a survey paper that is concerned with dynamic network models. The second project regards the reconstruction of financial networks from their marginals and includes two articles. All contributing articles are attached in the form as published as a preprint. For publications in scientific journals, the respective sources are given. Additionally, the contributions of all authors are included. All computations were done with the statistical software R and the corresponding code is available from Github.Diese kumulative Dissertation beschäftigt sich mit der statistischen Analyse von Netzwerkdaten. Der generelle Ansatz, interdependente Systeme als Netzwerke zu konzeptualisieren um sie anschließend mit statistischer Methodik zu analysieren, hat in den vergangenen Jahren deutlich an Relevanz gewonnen. Insbesondere die Flexibilität der Methodik, zusammen mit der Möglichkeit komplexe Abhängigkeitsstrukturen zu modellieren, hat zu ihrer Popularität beigetragen. Ein Netzwerk ist ein System, das sich aus Knoten und Kanten zusammensetzt. Dabei sind die Knoten generelle Einheiten, die durch die Kanten miteinander in Verbindung gebracht werden. Je nach Forschungsfrage interessieren entweder die Abhängigkeiten zwischen den Knoten oder die Verteilung der Kanten mit gegebenen Knoten. Diese Arbeit greift mit insgesamt sechs Artikeln den zweiten Ansatz auf. Unter Zuhilfenahme von statistischen Modellen werden die Kanten in verschiedenen binären und gewichteten Netzwerken analysiert, beziehungsweise rekonstruiert. Um der Arbeit einen generellen Kontext zu geben, wird den angehängten Artikeln ein Mantelteil vorangestellt. In diesem wird auf zentrale Konzepte und Modelle der statistischen Netzwerkanalyse eingegangen. Dabei werden die Vorteile als auch die Nachteile der Modelle diskutiert und potenzielle Erweiterungen und Modifikationen beschrieben. Die in dieser Dissertation enthaltenen Artikel lassen sich grob in zwei verschiedene Projekte einordnen. In einem Projekt steht die statistische Modellierung des internationalen Waffenhandels im Fokus. Zwei Artikel untersuchen den globalen Austausch von Großwaffen (Major Conventional Weapons), dabei wird sowohl die dynamische Struktur als auch das gehandelte Waffenvolumen analysiert. Ein weiterer Artikel widmet sich den latenten Strukturen im internationalen Kleinwaffenhandel (Small Arms and Ammunition). Weiterhin werden die Waffenhandelsdaten in einem Übersichtsartikel, der sich mit dynamischen Netzwerkmodellen beschäftigt, verwendet. Das zweite Projekt befasst sich, verteilt über zwei Artikel, mit der Rekonstruktion von finanziellen Netzwerken basierend auf den Randsummen von Netzwerkmatrizen. Alle in dieser Dissertation angehängten Artikel befinden sich in der Form, in der sie als Vorabversion veröffentlicht wurden. Bei Veröffentlichungen in Fachjournalen wird die jeweilige Quelle angegeben. Zudem wird vor jedem Artikel der Beitrag des jeweiligen Autors angegeben. Sämtliche Analysen wurden mit der statistischen Software R durchgeführt. Der dazugehörige Code ist über Github verfügbar