One of the fundamental requirements of Grid computing is efficient and effective resource discovery mechanism. Resource discovery involves discovery of appropriate resources required by user applications. In this regard various resource discovery mechanisms have been proposed during the recent years. These mechanisms range from centralized to hierarchical information servers approach. Most of the techniques developed based on these approaches have scalability and fault tolerance limitations. To overcome these limitations Peer to Peer based discovery mechanisms are proposed. / shairbaz@gmail.com

Deciphering the activity of chemical molecules against a pathogenic organism is an essential task in drug discovery process. Virtual screening, in which few plausible molecules are selected from a large set for further processing using computational methods, has become an integral part and complements the expensive and time-consuming in vivo and in vitro experiments. To this end, it is essential to extract certain features from molecules which in the one hand are relevant to the biological activity under consideration, and on the other are suitable for designing fast and robust algorithms. The features/representations are derived either from physicochemical properties or their structures in numerical form and are known as descriptors. In this work we develop two new molecular-fragment descriptors based on the critical analysis of existing descriptors. This development is primarily guided by the notion of coding degeneracy, and the ordering induced by the descriptor on the fragments. One of these descriptors is derived based on the simple graph representation of the molecule, and attempts to encode topological feature or the connectivity pattern in a hierarchical way without discriminating atom or bond types. Second descriptor extends the first one by weighing the atoms (vertices) in consideration with the bonding pattern, valence state and type of the atom. Further, the usefulness of these indices is tested by ranking and classifying molecules in two previously studied large heterogeneous data sets with regard to their anti-tubercular and other bacterial activity. This is achieved by developing a scoring function based on clustering using these new descriptors. Clusters are obtained by ordering the descriptors of training set molecules, and identifying the regions which are (almost) exclusively coming from active/inactive molecules. To test the activity of a new molecule, overlap of its descriptors in those cluster (interpolation) is weighted. Our results are found to be superior compared to previous studies: we obtained better classification performance by using only structural information while previous studies used both structural features and some physicochemical parameters. This makes our model simple, more interpretable and less vulnerable to statistical problems like chance correlation and over fitting. With focus on predictive modeling, we have carried out rigorous statistical validation. New descriptors utilize primarily the topological information in a hierarchical way. This can have significant implications in the design of new bioactive molecules (inverse QSAR, combinatorial library design) which is plagued by combinatorial explosion due to use of large number of descriptors. While the combinatorial generation of molecules with desirable properties is still a problem to be satisfactorily solved, our model has potential to reduce the number of degrees of freedom, thereby reducing the complexity.

Cette thèse s’intéresse à l’exploitation de la sémantique de données pour la recherche exploratoire. La recherche exploratoire se réfère à des tâches de recherche qui sont très ouvertes, avec de multiples facettes, et itératives. Les données sémantiques et les données liées en particulier, offrent de nouvelles possibilités pour répondre à des requêtes de recherche et des besoins d’information complexes. Dans ce contexte, le nuage de données ouvertes liées (LOD) joue un rôle important en permettant des traitements de données avancés et des interactions innovantes. Nous détaillons un état de l’art de la recherche exploratoire sur les données liées. Puis nous proposons un algorithme de recherche exploratoire à base de données liées basé sur une recherche associative. A partir d’un algorithme de propagation d’activation nous proposons une nouvelle formule de diffusion optimisée pour les graphes typés. Nous proposons ensuite des formalisations supplémentaires de plusieurs modes d’interrogation avancée. Nous présentons également une architecture logicielle innovante basée sur deux choix de conception paradigmatiques. D’abord, les résultats doivent être calculés à la demande. Deuxièmement, les données sont consommées à distance à partir de services SPARQL distribués. Cela nous permet d’atteindre un niveau élevé de flexibilité en termes d’interrogation et de sélection des données. L’application Discovery Hub implémente ces résultats et les présente dans une interface optimisée pour l’exploration. Nous évaluons notre approche grâce à plusieurs campagnes avec des utilisateurs et nous ouvrons le débat sur de nouvelles façons d’évaluer les moteurs de recherche exploratoires. / The general topic of the thesis is web search. It focused on how to leverage the data semantics for exploratory search. Exploratory search refers to cognitive consuming search tasks that are open-ended, multi-faceted, and iterative like learning or topic investigation. Semantic data and linked data in particular offer new possibilities to solve complex search queries and information needs including exploratory search ones. In this context the linked open data cloud plays an important role by allowing advanced data processing and innovative interactions model elaboration. First, we detail a state-of-the-art review of linked data based exploratory search approaches and systems. Then we propose a linked data based exploratory search solution which is mainly based on an associative retrieval algorithm. We started from a spreading activation algorithm and proposed new diffusion formula optimized for typed graph. Starting from this formalization we proposed additional formalizations of several advanced querying modes in order to solve complex exploratory search needs. We also propose an innovative software architecture based on two paradigmatic design choices. First the results have to be computed at query-time. Second the data are consumed remotely from distant SPARQL endpoints. This allows us to reach a high level of flexibility in terms of querying and data selection. We specified, designed and evaluated the Discovery Hub web application that retrieves the results and present them in an interface optimized for exploration. We evaluate our approach thanks to several human evaluations and we open the discussion about new ways to evaluate exploratory search engines.

This thesis deals about a digitizer design. The digitizer is part of air-band monitoring receiver. Requirement is digitalization of two audio signals. The device communicates with PC through USB interface and it provides reading and writing to SD card. The STM32F4DISCOVERY development board with processor STM32F407VGT6 was used for design and function testing. This development kit is supplemented with other peripherals on an extern board (input audio circuits and SD slots). The thesis describes briefly theory for each issue which this project deals with. One part is engaged to the hardware design. Then there is a description of the PC software for the device controlling. The main part of the thesis is about the development of the firmware for MCU, which manages AD conversion, formatting of the voice signal, USB communication (HID and Mass Storage class), recording and reading from the SD card and additional peripherals.

No description available.

Glaspärlor är en viktig del av vikingatidens dräkt. Syftet med denna studie är att få en inblick i hur befolkningen i Fröjel såg på och använde pärlor. Aktörskap som teori används i studien. Undersökningsmaterialet är 100 glaspärlor som kan dateras mellan 900–1000 e.v.t och har hittats i Fröjel, genom Fröjel Discovery Programme. Pärltyperna sorterades med Callmers typologi och har tillsammans med pärlornas fyndplats analyserats och diskuterats. Undersökningsmaterialet har diskuterats utifrån tidigare forskning kring vikingatiden, handelssystem och glaspärlor, som ett material och föremål. Callmers typologi bidrog till att urskilja pärlornas ursprung vilka stämmer med Fröjels handelsvägar. Studien visar, med hjälp av Callmers typologi och rumslig spridning, att pärlorna är av värde och ett resultat av social struktur, handel och val gjorda av både individen och grupper vilket ökar relevansen av aktörskap vad gäller individers val och kläddetaljer. / Glass beads have long been a part of the viking age costume. The purpose of this study is to get an insight to how the population of Fröjel viewed and used the beads. Agency as a theory was used in the study. The research material is 100 glass beads that can be dated between 900-1000 CE, have been found in Fröjel, through Fröjel Discovery Programme. The bead types have been sorted with Callmers typology and along with the location of the finds have been analyzed and discussed. The research material has been discussed based on previous research surrounding the viking age, trade systems and glass beads as a material and item. The origins of the beads which could be distinguished through Callmers typology matches with Fröjels trade networks. The study shows, with the help of Callmer’s typology and the spatial spread of the beads, that the beads are an accessory of value and a result of social structure, such as trade, and choices made by both the individual and groups which heightens the relevance of agency regarding individuals and clothing details.

Microbial natural products have been a source for new drugs for many decades and are unrivaled in their capacity to generate not only future therapeutic agents, but also providing key agents for agricultural and industrial use. LC-MS/MS based metabolomic tools and technologies have been developed that can rapidly dereplicate nonribosomal peptides and statistically identify related congeners in an automated nontargeted process from complex natural product extracts with nanogram sensitivity. This data-base search approach is designed to handle linear, cyclic and cyclic-branched nonribosomal peptides from proteinogenic and nonproteinogenic amino acids without genomic data or traditional bioactivity directed fractionation. Chemometric work-flows combined with a comprehensive metabolomic guided discovery strategy were used to profile the chemical space of a diverse collection of understudied fungal endophytes from fruiting plants. This approach allowed for the prioritization of unique isolates and for the focused discovery, isolation and characterization of distinct outlier metabolites by LC-SPE, 1D and 2D NMR, HRMS and single crystal X-ray analysis. These metabolomic tools and strategies have led to the discovery and characterization of 35 new and over 40 known natural products, many of which are biologically active. This thesis with enabling metabolomic tools and novel discoveries has demonstrated the utility of these analytical methodologies as an effective strategy for the untargeted discovery of new natural products from bacteria and endophytic fungi. / Thesis / Doctor of Philosophy (PhD)

<p dir="ltr">Learning, planning, and representing knowledge at multiple levels of temporal abstractions provide an agent with the ability to predict consequences of different courses of actions, which is essential for improving the performance of sequential decision making. However, discovering effective temporal abstractions, which the agent can use as skills, and adopting the constructed temporal abstractions for efficient policy learning can be challenging. Despite significant advancements in single-agent settings, temporal abstractions in multi-agent systems remains underexplored. This thesis addresses this research gap by introducing novel algorithms for discovering and employing temporal abstractions in both cooperative and competitive multi-agent environments. We first develop an unsupervised spectral-analysis-based discovery algorithm, aiming at finding temporal abstractions that can enhance the joint exploration of agents in complex, unknown environments for goal-achieving tasks. Subsequently, we propose a variational method that is applicable for a broader range of collaborative multi-agent tasks. This method unifies dynamic grouping and automatic multi-agent temporal abstraction discovery, and can be seamlessly integrated into the commonly-used multi-agent reinforcement learning algorithms. Further, for competitive multi-agent zero-sum games, we develop an algorithm based on Counterfactual Regret Minimization, which enables agents to form and utilize strategic abstractions akin to routine moves in chess during strategy learning, supported by solid theoretical and empirical analyses. Collectively, these contributions not only advance the understanding of multi-agent temporal abstractions but also present practical algorithms for intricate multi-agent challenges, including control, planning, and decision-making in complex scenarios.</p>

The media landscape is changing and Internet-based streaming services for children are becoming increasingly popular. The concept of online streaming extends freedom and control over content selection but discovering new content is often experienced as troublesome. The objective of this thesis is to identify parameters to make it easier for users of streaming services for children to discover and watch unfamiliar programs. Guidelines are formed based on literature studies, interviews, observations, a survey, a benchmark, data analysis with Adobe Analytics, a workshop and finally user tests with prototypes. These guidelines conclude that content should be categorized, dynamicized, highlighted and socially engaging. Categorization based on age is primarily important but interests and genres can also be used to make children more interested in a title. Content should be dynamic, customizable and personal to each user to give more accurate recommendations based on age and interests. Making one alternative more visually prominent will make this alternative more interesting especially for younger children and social features with information about other users opinions can motivate a decision to discover new content among older children.

Master of Science / Department of Agricultural Economics / Ted C. Schroeder / Very rapid growth in the ethanol industry has led to a void of information regarding price dynamics and utilization of distiller's grains. Understanding market conditions is essential for livestock producers to make informed decisions in the procurement of feedstuffs, especially as grain price levels have recently increased substantially. In this study, distiller's grain price discovery dynamics are evaluated to develop an understanding of spatial price relationships. The knowledge of price relationships reveals that users of distiller's grain should shop around when procuring the feedstuff. Additionally, because animal performance may be altered with increased inclusion of distiller's grains, regional competitive advantages could shift fed cattle production to geographic regions characterized by high of ethanol production plants. Therefore, the cost of current grain processing methods are evaluated to enhance the awareness of regional competitiveness and long term sustainability. The combination of these two objectives allows producers to better realize the implications of the ethanol industry on their ability to maintain their operations into the near future.