Submitted to the faculty of the School of Informatics in partial fulfillment of the requirements for the degree Master of Science in Chemical Informatics (Laboratory Informatics Specialization)Indiana University May 2006 / The Zymark™ System is a lab automation workstation that uses the Caliper Life Sciences (Hopkinton, MA) Zymate XP robot. At Indiana University-Purdue University Indianapolis, a Zymate is used in a course, INFO I510 Data Acquisition and Laboratory Automation, to demonstrate the fundamentals of laboratory robotics. This robot has been re-engineered to function with National Instruments™ graphical software program LabVIEW™. LabVIEW is an excellent tool for robotic control. Based on changing conditions, it is able to dynamically use data from any source to modify the operating parameters of a robot. For dynamically changing information, storage of that information must be readily accessible. For example, there is a need to continuously store and update the calibration data of the robot, populate the setting of each axis and positioning inside the workplace, and also store robot positioning information. This can be achieved by using a database which allows for robotic control data to be easily searched and accessed. To address this need, an interface was developed which would allow full, dynamic communication between any LabVIEW program (called “virtual instruments,” or VIs) and the database. This has been accomplished by developing a set of subVIs that can be dropped into the calling robotic control VIs. With these subVIs, a user has the ability to create table and column information, delete a table, retrieve table information by clicking a particular table name on the user interface, or query using any SQL-specific combination of columns or tables within the database. For robot functionality, subVIs were created to store and retrieve data such as calibration data points and regression calculations. / Chemical Informatics

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1980. / Includes bibliographical references. / by Ifiyenia Kececioglu. / Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Chemical Engineering, 1980.

L'augmentation constante de la demande alimentaire et énergétique dans le monde associée au réchauffement et changements climatiques ont poussé les recherches en agronomie vers le développement d'une agriculture plus durable et l'amélioration de la performance des cultures. Pour répondre à ces demandes, les chercheurs ont concentré leurs efforts sur le développement de méthodes de génotypage à haut débit (l'étude de la séquence génomique des plantes) et ont permis aux biologistes d'identifier les génotypes d'une grande quantité de plantes. De plus, comprendre les relations qui lient les génotypes (ADN) aux phénotypes (caractéristiques visuelles) qui évoluent en fonction des conditions d'irrigation, d'illumination ou de température est devenu un enjeu majeur dans la recherche agricole. Alors que les méthodes de génotypage ont été rapidement améliorées et automatisées au cours de la dernière décennie, les méthodes de phénotypage restent manuelles et parfois destructrices. Ces méthodes consistent à mesurer certains paramètres visuels d'une plante telle que : la hauteur de la tige principale, le nombre de feuilles, les angles d'initiation des feuilles ou la surface foliaire et plus important encore, à suivre ces paramètres tout au long de la croissance des plantes. Par conséquent, le nombre de plantes à cultiver est très important et les mesures prennent beaucoup de temps. Avec l'émergence des nouvelles technologies en vision par ordinateur et en robotique, les chercheurs en agronomie y ont vu un intérêt certain en vue d'automatiser la collecte et les mesures des données visuelles sur les plantes. La thèse porte sur la conception, le développement et la validation de traitements haut débit à exécuter automatiquement sur des images acquises sur des plantes de tournesol, en vue d'amplifier les capacités de phénotypage par les chercheurs en agronomie (et ultérieurement les évaluateurs de variétés et les semenciers). L'objectif est la mise au point d'un protocole d'acquisition d'images (en plante isolée) depuis un robot mobile (ou un système d'acquisition autonome) permettant d'améliorer, de moderniser et d'automatiser les méthodes de phénotypage actuelles afin d'aider les chercheurs en agronomie à collecter une grande quantité de données. Motivés par le souhait d'effectuer un phénotypage à haut débit, nous proposons une approche 3D pour extraire automatiquement les caractéristiques visuelles des plantes de tournesol cultivées en pot. Tout d'abord, un nuage de points 3D d'une plante est acquis avec des techniques classiques de Structure-from-Motion. Une étape de segmentation est ensuite effectuée pour extraire la tige principale et les feuilles. Dans le but de suivre les caractéristiques visuelles pendant la croissance des plantes, en particulier, suivre l'expansion foliaire de chaque feuille, une étape de labellisation basée sur le modèle botanique d'une plante est appliquée pour leur affecter une étiquette unique qui ne changera pas avec le temps. Enfin, les caractéristiques visuelles sont extraites et les résultats obtenus sur les plantes de tournesol démontrent l'efficacité de notre méthode et en font une étape encourageante vers le phénotypage haut débit. / The constant increasing food and energy demand in the world associated to global warming and climate change issues, pushed the researchs in plant breeding to move towards the improvement of crops performance and development of a more sustainable agriculture. To meet these demands, the effort made by the researchers were focused on the development of high-throughput genotyping methods (i.e., the study of genome sequence of plants) and allowed the biologists to indentified the genotypes of a large amount of plants. Moreover, understanding the relationships that link the genotypes (DNA) to the phenotypes (visual characteristics) that evolve according environmental conditions like: light, water, drought, heat, etc. has become a main issue in agricultural research. While the genotyping methods were rapidly improved and automatized during the last decade, the phenotyping methods remain manual, sometimes destructive and non-replicable. The usual phenotyping methods consist to measure certain visual parameters of a plant such as: main stem heigh, number of leaves, leaf initiation angle or leaf area, but more importantly, be able to follow these parameters along the plant growth. Consequently, the number of plants to harvest is very important and the measurements are extremely time-consuming. The emergence and reliability of new technologies in computer vision and robotic have led the researchers to take an interest in them and to seek how they can be used in plant science. The thesis is focused on the design, development and validation of a high-throughput phenotyping method design for sunflower plant with an eye to amplify phenotyping capacities by Agronomists and Geneticists (and later varieties evaluators and seed producers). The aim is to improve, modernize and automatize the current phenotyping methods as a way to help the plant scientists to collect a large amount of data. Motivated by the wish to perform high-throughput plant phenotyping, we propose a 3D approach to automatically extract visual characteristics of sunflower plants grown in pot. First, a 3D point cloud of a plant is acquired with classical Structure-from-Motion techniques. A segmentation step is then proceeded to retrieve the main stem and the leaves. With the intention of following the visual characteristics during the plant growth, especially, the leaf area expansion rate of each leaf, a labelling step relying on the botanical model of a plant is performed to affect them a unique label that will not change over time. Finally, the visual characteristics are extracted and results obtained on sunflower plants demonstrate the efficiency of our method and make it an encouraging step toward high-throughput plant phenotyping.

Indiana University-Purdue University Indianapolis (IUPUI) / Over the last two decades a new theory has been developed and intensively investigated in quantum physics. The theory stipulates that a non-Hermitian Hamiltonian can also represents a physical system as long as its energy spectra can be purely real in certain regime depending on the parameters of the Hamiltonian. It was demonstrated that the reality of the eigenenergy was conditioned by a certain kind of symmetry embedded in the actual non-Hermitian system. Indeed, such systems have a combined reflection (parity) symmetry (P) and time-reversal symmetry (T), PT-symmetry. The theory opens the door to new features particularly in open systems in which there could be gain and/or loss of particle or energy from and/or to the environment. A key property of the theory is the PT-symmetry breaking transition which occurs at the exceptional point (EP). The exceptional points are special degeneracies characterized by a coalescence of not only the eigenvalues but also of the corresponding eigenvectors of the system; and the coalescence happens when the gain-loss strength, a measure of the openness of the system, exceeds the intrinsic energy-scale of the system. In recent years, quantum walks with PT-symmetric non-unitary time evolution have been realized in systems with balanced gain and loss. These systems fall in two categories namely continuous time quantum walks (CTQW) that are characterized by a unitary or non-unitary time evolution Hamiltonian, and discrete-time quantum walks (DTQW) whose dynamic is described by a unitary or non-unitary time evolution operator consisting of a product of shift, coin, and gain-loss operations. In this thesis, we investigate the PT-symmetric phase of CTQW and DTQW in a variety of non-Hermitian lattice systems with both position-dependent and position independent, parity-symmetric tunneling functions in the presence of PT-symmetric impurities located at arbitrary parity-symmetric site on the lattice. Moreover, we explore the topological phase diagram and its novel features in non-Hermitian, homogeneous and non-homogeneous, PT-symmetric DTQW with closed and open boundary conditions. We conduct our study using analytical and numerical approaches that are directly and easily implementable in physical experiments. Among others, we found that, despite their non-unitary evolution, open systems governed by parity-time symmetric Hamiltonian support conserved quantities and that the PT-symmetry breaking threshold depends on the physical structure of the Hamiltonian and its underlying symmetries.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第21684号 / 医博第4490号 / 新制||医||1036(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 松田 道行, 教授 松田 秀一, 教授 椛島 健治 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

This study analyzes the novels of Kurt Vonnegut, Jr. from the aspect of point of view. Point of view refers to the reflective mind through which a reader perceives the story. Traditionally, the narrator delivers his narrative in either first or third person point of view, but Vonnegut frequently mixes points of view. Mixed point of view presents a particular challenge to the oral interpreter and the adapter/director of readers theatre scripts. The narrator and the narrative structure are discussed, as well as numerous innovative narrative techniques. Suggestions are made for script adaptations and production direction featuring the narrative structure and point of view.

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Most widely accepted measures of performance for learning algorithms, such as accuracy and area under the ROC curve, provide information about behavior at the data set level. They say nothing about which instances are misclassified, whether two learning algorithms with the same classification accuracy on a data set misclassify the same instances, or whether there are instances misclassified by all learning algorithms. These questions about behavior at the instance level motivate our empirical analysis of instance hardness, a measure of expected classification accuracy for an instance. We analyze the classification of 57 data sets using 9 learning algorithms. Of the over 175000 instances investigated, 5% are misclassified by all 9 of the considered learning algorithms, and 15% are misclassified by at least half. We find that the major cause of misclassification for hard instances is class overlap, manifested as outliers and border points which can be exacerbated by class skew. We analyze these causes and show to what extent each leads to misclassifications, both in isolation and jointly. 19.8% of all misclassified instances are outliers; 71.3% are border points; 21% belong to a minority class. We also find that 91.6% of all outliers and 38.3% of all border points are misclassified whereas only 3.5% of instances without class overlap are misclassified. We propose a set of heuristics to predict when an instance will be hard to correctly classify. Additionally, we analyze how different learning algorithms perform on tasks with varying degrees of outliers, border points and class skew.

Ungdomsbrottslighet är ett aktuellt problem som finns i Sverige och framställs i media att den ökar, vilket inte stämmer då det finns rapporter och statistik på att det har skett en minskning. Statistik är en viktig del i brottspreventivt syfte eftersom den hjälper forskare få en bild av hur omfattande ungdomsbrottsligheten är, dess utveckling, omfattning, dess minskande och om det förekommer skillnader mellan de olika regionerna i Sverige. ”Detta var min vändpunkt” är en studie vars syfte är att granska varför ungdomar i kriminalitet slutar att begå brott och även undersöka vilka deras vändpunkt/er var utifrån teoretikerna Robert Sampson och John Laubs teori om vändpunkter. Frågeställningarna som uppsatsen behandlat är: vad fick vissa ungdomar att sluta begå brott, finns det vändpunkter för ungdomsbrottslingar samt om någon av dessa vändpunkter var relationer, miljöombyte eller sysselsättning.För att undersöka och besvara dessa frågor har en kvalitativ ansats använts. Tre stycken semistrukturerade intervjuer genomfördes med medlemmar från Unga Kris. Resultatet analyserades och diskuterades utifrån teorin om turning points samt tidigare forskning. Frågeställningarna besvarades och det informanterna uppgav har varit mest betydelsefullt för att avsluta en brottslig karriär är självinsikt, sysselsättning och relationer till nära och kära. Vändpunkter som har varit bidragande faktorer har varit sysselsättning, inre motivation, nära till förlust av närstående vilket ledde till en vilja att förändra sin levnadssituation. / Juvenile delinquency is a current problem that exists in the Swedish society which is portrayed as increasing, which is not true since there are reports and statistics that there is a decreasing level of juvenile delinquency .Statistics is an important part of crime prevention because it helps scientists understand the extent of juvenile delinquency, its development, its range, its increase and decrease and whether there are differences between the different regions in Sweden. But who are the young people that represent the statistic and why did they stop commit crime?This was my turning point is a study which aims to examine why young people involved in crime stop committing crimes and also examine their turning point/ s which is based on the theorists Robert Sampson and John Laubs theory of turning points .To examine and answer these questions, a qualitative approach was used. Three semi-structured interviews were conducted with members of the Young Kris. The results were analyzed and discussed based on the theory of turning points and previous research. The informants stated that the meaningful factors that lead to the end of their criminal career were self-perception, occupation and relationships with loved ones. Turning points that have been contributing factors was inner motivation, imprisonment, and loss of relatives, which led to a desire to change their life situation.