京都大学 / 新制・課程博士 / 博士(理学) / 甲第23673号 / 理博第4763号 / 新制||理||1683(附属図書館) / 京都大学大学院理学研究科数学・数理解析専攻 / (主査)准教授 市野 篤史, 教授 雪江 明彦, 教授 池田 保 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DFAM

Bed bugs (Hemiptera:Cimicidae) are a common, hematophagous ectoparasite of humans and other animals, and are experiencing an international resurgence. Cimicids have been implicated in the transmission many disease agents, including various Bartonella species, however disease transmission has not yet been confirmed. Bartonella spp. are transmitted by a variety of arthropods, including fleas, lice and sand flies, and it is speculated that bed bugs may also serve as a potential vector. In this study, we used an artificial membrane to feed two groups of adult Cimex lectularius rabbit blood, either infected or uninfected with Bartonella henselae. After two weeks, the presence of Bartonella henselae was assessed via PCR, and salivary glands from infected and uninfected bed bugs were dissected and processed for transmission electron microscopy. We were unable to visually identify Bartonella henselae in the images, and therefore unable to confirm the role of bed bugs in B. henselae transmission.

Structural algorithms incorporate the interdependence of outputs into the prediction, the loss, or both. Frank-Wolfe optimizations of pairwise losses and Gaussian conditional random fields for multivariate output regression are two such structural algorithms. Pairwise losses are standard 0-1 classification surrogate losses applied to pairs of features and outputs, resulting in improved ranking performance (area under the ROC curve, average precision, and F-1 score) at the cost of increased learning complexity. In this dissertation, it is proven that the balanced loss 0-1 SVM and the pairwise SVM have the same dual loss and the pairwise dual coefficient domain is a subdomain of the balanced loss 0-1 SVM with bias dual coefficient domain. This provides a theoretical advancement in the understanding of pairwise loss, which we exploit for the development of a novel ranking algorithm that is fast and memory efficient method with state the art ranking metric performance across eight benchmark data sets. Various practical advancements are also made in multivariate output regression. The learning time for Gaussian conditional random fields is greatly reduced and the parameter domain is expanded to enable repulsion between outputs. Last, a novel multivariate regression is presented that keeps the desirable elements of GCRF and infuses them into a local regression model that improves mean squared error and reduces learning complexity. / Computer and Information Science

This study demonstrates the use of an adenovirus vector system to study the effect of a DNA repair gene on untransformed human fibroblasts. The bacteriophage T4 pyrimidine dimer DNA glycosylase (den V) gene has been inserted into the E3 region of human adenovirus type 5. The resulting recombinant virus Ad Den V was determined to be producing correctly initiated RNA from the RSV 3' LTR promoter used in the den V expression cartridge inserted into the virus. The effect of the den V gene product on human fibroblasts 'liras examined by assaying for the percent host cell reactivation (%HCR) of Vag production for UV irradiated Ad Den V in comparison to that for a control virus. It was shown that the %HCR was significantly greater for Ad Den V as compared to the control virus in xeroderma pigmentosum (XP) cells. UV survival of adenovirus in XP cells exhibited a two component nature. Introduction of the den V gene into XP group A cells increased the D0 value of the first component of the viral survival curve to a level similar to that of XPC cells, which showed no change in this component irrespective of the presence of the den V gene. It has been suggested that the den V gene is able to partially complement the deficiency in some XP cells because of its small size, allowing it to gain access to the DNA damage site where as the cellular repair enzyme complex can not. Since XPC cells are proficient in their alteration of DNA secondary structure prior to DNA excision repair, these results are consistant with the hypothesis that the first component of UV viral survival curves reflects the pathway involved in accessing the damaged sites. The manuscript of a paper has been included as an appendix. The work theorizes on the origin of mammalian immune system diversity and bacteriophage lambda, and their possible relationship to prokaryotic DNA repair genes. / Thesis / Master of Science (MS)

Through the specialized lens of one-class classification, anomalies–irregular observations that uncharacteristically diverge from normative data patterns–are comprehensively studied. This dissertation focuses on advancing boundary-based methods in one-class classification, a critical approach to anomaly detection. These methodologies delineate optimal decision boundaries, thereby facilitating a distinct separation between normal and anomalous observations. Encompassing traditional approaches such as One-Class Support Vector Machine and Support Vector Data Description, recent adaptations in deep learning offer a rich ground for innovation in anomaly detection. This dissertation proposes three novel deep learning methods for one-class classification, aiming to enhance the efficacy and accuracy of anomaly detection in an era where data volume and complexity present unprecedented challenges. The first two methods are designed for tabular data from a least squares perspective. Formulating these optimization problems within a least squares framework offers notable advantages. It facilitates the derivation of closed-form solutions for critical gradients that largely influence the optimization procedure. Moreover, this approach circumvents the prevalent issue of degenerate or uninformative solutions, a challenge often associated with these types of deep learning algorithms. The third method is designed for second-order tensors. This proposed method has certain computational advantages and alleviates the need for vectorization, which can lead to structural information loss when spatial or contextual relationships exist in the data structure. The performance of the three proposed methods are demonstrated with simulation studies and real-world datasets. Compared to kernel-based one-class classification methods, the proposed deep learning methods achieve significantly better performance under the settings considered.

The aim of this thesis is to conduct a nuanced investigation of connectedness in the global futures market across time and market conditions through a Quantile Vector Autoregression (QVAR) model. Later, a linear regression is utilized to identify determinants of futures market connectedness across market conditions. The sample period consists of daily data from December 2017 to August 2023. Our dataset includes five uncertainties and 19 continuous futures contracts, making it the most comprehensive study of futures market connectedness after the Russian invasion of Ukraine to our knowledge.  The results highlight heterogeneous effects across time and market conditions for all assets, with the futures market connectedness increasing during times of uncertainty. US equity, German Equity, Japanese equity, British equity, gold, silver, USD and EUR are identified as net transmitters of spillovers, whereas the rest of the futures are identified as net receivers. These findings are interesting in the concept of theory as they highlight potential periods of flight-to-safety and safe haven properties for certain futures. When including uncertainties in the QVAR model, financial uncertainty is identified as the only net transmitter, whereas the other uncertainties are net receivers.  Drivers of futures market connectedness depend on market conditions and time, with energy uncertainty being significant for normal markets and the world equity index being significant for bearish markets in both the full sample and a Covid-19 subsample. For the full sample only, financialization is identified as a driver during bullish markets. More variables are significant for the Covid-19 subsample. The commodity index and US dollar index becomes significant in bearish markets and monetary uncertainty in bullish markets.  Our findings are relevant for both investors and policymakers. The results suggest that investors should monitor market conditions when investing in the futures market to suitably optimize, diversify, and hedge their portfolios. For policymakers, monitoring spillover from the futures market is important as it can impact the overall economy by using the industrial sector as a transmission channel. This can aid in early decision-making and minimize the impact of economic downturns. / Das Ziel dieser Arbeit ist, eine nuancierte Untersuchung der Verbundenheit im globalen Terminmarkt über Zeit und Marktbedingungen durch ein Quantile Vector Autoregression (QVAR) Modell durchzuführen. Später benutzen wir eine lineare Regression, um Determinanten der Terminmarktverbundenheit unter verschiedene Marktbedingungen zu identifizieren. Der Zeitraum dieser Untersuchung besteht aus täglichen Daten von Dezember 2017 bis August 2023. Die Daten umfasst fünf Unsicherheitsmaße und 19 kontinuierliche Terminkontrakte, damit ist es nach unserem Wissen die umfassendste Untersuchung über die Verbundenheit des Terminmarkts nach der russischen Invasion die Ukraine.  Die Ergebnisse hervorheben heterogene Effekte über Zeit und Marktbedingungen für alle Variablen, wobei die Verbundenheit des Terminmarkts während unsicherer Perioden verstärkt ist. Der amerikanische Aktienindex, deutsche Aktienindex, japanische Aktienindex, britische Aktienindex, Gold, Silber, US-Dollar und Euro werden als Nettoübermittler von Spillovern identifiziert, während die andere Terminkontrakte als Nettoempfänger identifiziert werden. Die Ergebnisse sind interessant im Kontext der Theorie, da sie sowohl potenzielle Perioden von Flight-to-Safety als auch Safe Haven-Eigenschaften für die Terminkontrakte hinweisen. Bei der Einbeziehung von Unsicherheitsmaßen in das QVAR-Modell wird die finanzielle Unsicherheit als einziger Nettoübermittler identifiziert, während die anderen Unsicherheiten Nettoempfänger sind.  Die Determinanten der Verbundenheit an den Terminmarkt sind von Zeit und Marktbedingungen abhängig, wobei die Energieunsicherheit für normale Marktbedingungen und die Weltaktienindex für bärische Marktbedingungen während sowohl des ganzen Zeitraums als auch des Covid-19 Zeitraums signifikant ist. Finanzialisierung ist nur während des ganzen Zeitraums als Determinant für bullische Marktbedingungen signifikant. Im Covid-19 Zeitraum sind weitere Variablen signifikant. Der Rohstoffindex wird in bärische Marktbedingungen und die US-Dollar-Index wird in bullische Marktbedingungen signifikant.  Die Ergebnisse dieser Untersuchung sind sowohl für Investoren als auch für politische und finanzielle Entscheidungsträger relevant. Die Ergebnisse andeuten, dass Investoren die Marktbedingungen beobachten sollten, wenn sie in den Terminmarkt investieren, um ihre Portfolios zu optimieren, diversifizieren und abzusichern. Für politische und finanzielle Entscheidungsträger ist die Beobachtung von Spillover-Effekten vom Terminmarkt wichtig, da sie auf die Gesamtwirtschaft durch die Industriesektor auswirken können. Darum kann diese kontinuierliche Beobachtung zu früheren makroökonomischen Entscheidungen führen und damit ungünstige wirtschaftliche Auswirkungen minimieren.

Yes / In dealing with big data, we need effective algorithms; effectiveness that depends, among others, on the ability to remove outliers from the data set, especially when dealing with classification problems. To this aim, support vector finder algorithms have been created to save just the most important data in the data pool. Nevertheless, existing classification algorithms, such as Fuzzy C-Means (FCM), suffer from the drawback of setting the initial cluster centers imprecisely. In this paper, we avoid existing shortcomings and aim to find and remove unnecessary data in order to speed up the final classification task without losing vital samples and without harming final accuracy; in this sense, we present a unique approach for finding support vectors, named evolutionary Pentagon Support Vector (PSV) finder method. The originality of the current research lies in using geometrical computations and evolutionary algorithms to make a more effective system, which has the advantage of higher accuracy on some data sets. The proposed method is subsequently tested with seven benchmark data sets and the results are compared to those obtained from performing classification on the original data (classification before and after PSV) under the same conditions. The testing returned promising results.

The bin packing problem has been the corner stone of approximation algorithms and has been extensively studied starting from the early seventies. In the classical bin packing problem, we are given a list of real numbers in the range (0, 1], the goal is to place them in a minimum number of bins so that no bin holds numbers summing to more than 1. In this thesis we study approximation algorithms for three generalizations of bin packing: geometric bin packing, vector bin packing and weighted bipartite edge coloring. In two-dimensional (2-D) geometric bin packing, we are given a collection of rectangular items to be packed into a minimum number of unit size square bins. Geometric packing has vast applications in cutting stock, vehicle loading, pallet packing, memory allocation and several other logistics and robotics related problems. We consider the widely studied orthogonal packing case, where the items must be placed in the bin such that their sides are parallel to the sides of the bin. Here two variants are usually studied, (i) where the items cannot be rotated, and (ii) they can be rotated by 90 degrees. We give a polynomial time algorithm with an asymptotic approximation ratio of $\ln(1.5) + 1 \approx 1.405$ for the versions with and without rotations. We have also shown the limitations of rounding based algorithms, ubiquitous in bin packing algorithms. We have shown that any algorithm that rounds at least one side of each large item to some number in a constant size collection values chosen independent of the problem instance, cannot achieve an asymptotic approximation ratio better than 3/2. In d-dimensional vector bin packing (VBP), each item is a d-dimensional vector that needs to be packed into unit vector bins. The problem is of great significance in resource constrained scheduling and also appears in recent virtual machine placement in cloud computing. Even in two dimensions, it has novel applications in layout design, logistics, loading and scheduling problems. We obtain a polynomial time algorithm with an asymptotic approximation ratio of $\ln(1.5) + 1 \approx 1.405$ for 2-D VBP. We also obtain a polynomial time algorithm with almost tight (absolute) approximation ratio of $1+\ln(1.5)$ for 2-D VBP. For $d$ dimensions, we give a polynomial time algorithm with an asymptotic approximation ratio of $\ln(d/2) + 1.5 \approx \ln d+0.81$. We also consider vector bin packing under resource augmentation. We give a polynomial time algorithm that packs vectors into $(1+\epsilon)Opt$ bins when we allow augmentation in (d - 1) dimensions and $Opt$ is the minimum number of bins needed to pack the vectors into (1,1) bins. In weighted bipartite edge coloring problem, we are given an edge-weighted bipartite graph $G=(V,E)$ with weights $w: E \rightarrow [0,1]$. The task is to find a proper weighted coloring of the edges with as few colors as possible. An edge coloring of the weighted graph is called a proper weighted coloring if the sum of the weights of the edges incident to a vertex of any color is at most one. This problem is motivated by rearrangeability of 3-stage Clos networks which is very useful in various applications in interconnected networks and routing. We show a polynomial time approximation algorithm that returns a proper weighted coloring with at most $\lceil 2.2223m \rceil$ colors where $m$ is the minimum number of unit sized bins needed to pack the weight of all edges incident at any vertex. We also show that if all edge weights are $>1/4$ then $\lceil 2.2m \rceil$ colors are sufficient.

Master of Agribusiness / Department of Agricultural Economics / Allen M. Featherstone / During the past 10 years, ethanol production in the United States has grown exponentially. From 2000 to 2009 U.S. ethanol production increased from 1.6 billion gallons annually to 10.8 billion gallons annually. In 2010, U.S ethanol production increased by 23 percent from 2009 to 13.23 billion gallons. The increase in ethanol production was due to lawmakers reacting to skyrocketing oil prices by implementing a Renewable Fuels Standard (RFS) in 2005 and expanding the RFS in 2007. The RFS requires the use of specified amounts of biofuels, such as ethanol, through the year 2022. The creation of the RFS represented a step beyond lawmakers’ usual policy of using the tax code to promote ethanol production. There is a long history of encouraging ethanol production by using the tax code, but the implementation of a biofuels mandate is new and therefore there is not a great deal of research on the effects of such a policy. This study analyzes U.S. oil, unleaded gasoline, corn and ethanol prices dating back to 1985 to determine the impact that the RFS has had on corn prices. The key question answered is whether the creation and expansion of the RFS has brought the instability of the oil market into the corn market. The prices that an ethanol plant in western Kansas paid for the grain it used to produce ethanol and the price that the plant received for the ethanol that it produced are also analyzed. The plant began operation in January 2004, so it is possible to analyze the grain and ethanol prices both before and after the implementation and expansion of the RFS. To study the impact of the RFS creation and expansion, the prices were analyzed to see if there was an increase in the correlation after the creation and expansion of the RFS. Regression analysis of the national corn prices and the prices that Western Plains Energy paid for the grain that it used to produce ethanol; and regression analysis of the national price of ethanol and the price that Western Plains Energy sold its ethanol for were also used to study the impact of the RFS. Finally, the vector autoregression (VAR) model is used to analyze the dynamic relationships between the variables in the system: corn price, oil price, ethanol price and unleaded gasoline price. The analysis of the correlation reveals that both at the national and plant level grain and oil prices track much more closely together after the creation and then expansion of the RFS. The VAR reveals that there is some relationship between corn and oil prices contemporaneously. The correlation matrix of residuals reveals that there is not a strong correlation between national corn and oil prices. The results suggest the need for greater research in this area. The creation and expansion of the RFS represented a step into uncharted territory and the consequences are still not known.

Doctor of Philosophy / Department of Industrial & Manufacturing Systems Engineering / Chih-Hang Wu / Recently, interdisciplinary (management, engineering, science, and economics) collaboration research has been growing to achieve the synergy and to reinforce the weakness of each discipline. Along this trend, this research combines three topics: mathematical programming, data mining, and supply chain management. A new pegging algorithm is developed for solving the continuous nonlinear knapsack problem. An efficient solving approach is proposed for solving the ν-support vector machine for classification problem in the field of data mining. The new pegging algorithm is used to solve the subproblem of the support vector machine problem. For the supply chain management, this research proposes an efficient integrated solving approach for the supplier selection problem. The support vector machine is applied to solve the problem of selecting potential supplies in the procedure of the integrated solving approach. In the first part of this research, a new pegging algorithm solves the continuous nonlinear knapsack problem with box constraints. The problem is to minimize a convex and differentiable nonlinear function with one equality constraint and box constraints. Pegging algorithm needs to calculate primal variables to check bounds on variables at each iteration, which frequently is a time-consuming task. The newly proposed dual bound algorithm checks the bounds of Lagrange multipliers without calculating primal variables explicitly at each iteration. In addition, the calculation of the dual solution at each iteration can be reduced by a proposed new method for updating the solution. In the second part, this research proposes several streamlined solution procedures of ν-support vector machine for the classification. The main solving procedure is the matrix splitting method. The proposed method in this research is a specified matrix splitting method combined with the gradient projection method, line search technique, and the incomplete Cholesky decomposition method. The method proposed can use a variety of methods for line search and parameter updating. Moreover, large scale problems are solved with the incomplete Cholesky decomposition and some efficient implementation techniques. To apply the research findings in real-world problems, this research developed an efficient integrated approach for supplier selection problems using the support vector machine and the mixed integer programming. Supplier selection is an essential step in the procurement processes. For companies considering maximizing their profits and reducing costs, supplier selection requires seeking satisfactory suppliers and allocating proper orders to the selected suppliers. In the early stage of supplier selection, a company can use the support vector machine classification to choose potential qualified suppliers using specific criteria. However, the company may not need to purchase from all qualified suppliers. Once the company determines the amount of raw materials and components to purchase, the company then selects final suppliers from which to order optimal order quantities at the final stage of the process. Mixed integer programming model is then used to determine final suppliers and allocates optimal orders at this stage.