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You searched for subject:(Module weight). Showing records 1 – 3 of 3 total matches.

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1. Hume, Thomas. Practical and theoretical approaches for module analysis of protein-protein interaction networks : Approches pratiques et théoriques pour l'analyse de modules au sein de réseaux d'interaction protéine-protéine.

Degree: Docteur es, Informatique, 2016, Bordeaux

Un des principaux défis de la bioinformatique moderne est de saisir le sens des données biologiques en constante croissance. Il est prépondérant de trouver de bons modèles pour toutes ces données, modèles qui servent à la fois à expliquer les données et à produire des réponses aux questions biologiques sous-jacentes. Une des nombreuses difficultés d’une telle approche est la grande variété dans les types des données manipulées. La biologie computationnelle moderne propose des approches qui combinent ces types de données dans des techniques dites intégratives. Cette thèse contribue au problème de l’identification de module biologique en intégrant les informations de conservation dans les modèles modernes d’identification d’ensemble de protéines. Nous introduisons un modèle pour la détection de modules connexes actifs et conservés, c’est-à-dire des modules connexes dont une majorité d’éléments sont similaires entre deux espèces. Nous présentons une formulation de notre modèle sous forme de programmation linéaire en nombres entiers, et proposons un algorithme branch-and-cut qui résout le modèle à l’optimalité en temps raisonnable. Nous appliquons notre modèle sur des données de différentiation cellulaire, à savoir les cellules Th0 en Th17 pour l’humain et la sourie. Nous analysons également notre modèle du point du vue de la complexité algorithmique, et fournissons des résultats pour le cas général ainsi que des cas spéciaux.

One of the major challenge for modern bioinformatics is making sense of the ever increasing size of biological data. Finding good models for all this data, models that can both explain the data and provide insight into biological questions, is paramount. One of the many difficulties of such path is the variety in the types of data. Modern computational biology approaches combine these many data into integrative approaches, that combine the knowledge inside the data in the hope to extract higher level information. This thesis contribute to the biological module identification problem by integrating conservation information with modern models of modular detection of protein sets. We introduce a model for the detection of conserved active connected modules, that is connected modules that are conversed across two species. These active connected modules are similar in sequence composition between the two species. We present a mixed-integer linear programming formulation of our model, and propose a branch-and-cut algorithm to solve to provable optimality in reasonable run time. We apply our model to cell line differentiation data, namely Th0 into Th17 for both human and mouse. We also analyse the model from a complexity standpoint, and provide general as well as special cases complexity results.

Advisors/Committee Members: Nikolski, Macha (thesis director).

Subjects/Keywords: Bioinformatique; Optimisation combinatoire; Maximum-Weight Connected Subgraph; Recherche de modules biologiques; Bioinformatics; Combinatorial optimization; Maximum-Weight Connected Subgraph; Biological module discovery

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Hume, T. (2016). Practical and theoretical approaches for module analysis of protein-protein interaction networks : Approches pratiques et théoriques pour l'analyse de modules au sein de réseaux d'interaction protéine-protéine. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2016BORD0006

Chicago Manual of Style (16th Edition):

Hume, Thomas. “Practical and theoretical approaches for module analysis of protein-protein interaction networks : Approches pratiques et théoriques pour l'analyse de modules au sein de réseaux d'interaction protéine-protéine.” 2016. Doctoral Dissertation, Bordeaux. Accessed October 20, 2020. http://www.theses.fr/2016BORD0006.

MLA Handbook (7th Edition):

Hume, Thomas. “Practical and theoretical approaches for module analysis of protein-protein interaction networks : Approches pratiques et théoriques pour l'analyse de modules au sein de réseaux d'interaction protéine-protéine.” 2016. Web. 20 Oct 2020.

Vancouver:

Hume T. Practical and theoretical approaches for module analysis of protein-protein interaction networks : Approches pratiques et théoriques pour l'analyse de modules au sein de réseaux d'interaction protéine-protéine. [Internet] [Doctoral dissertation]. Bordeaux; 2016. [cited 2020 Oct 20]. Available from: http://www.theses.fr/2016BORD0006.

Council of Science Editors:

Hume T. Practical and theoretical approaches for module analysis of protein-protein interaction networks : Approches pratiques et théoriques pour l'analyse de modules au sein de réseaux d'interaction protéine-protéine. [Doctoral Dissertation]. Bordeaux; 2016. Available from: http://www.theses.fr/2016BORD0006

2. Zhang, Ruohan. Action selection in modular reinforcement learning.

Degree: MSin Computer Sciences, Computer Sciences, 2014, University of Texas – Austin

Modular reinforcement learning is an approach to resolve the curse of dimensionality problem in traditional reinforcement learning. We design and implement a modular reinforcement learning algorithm, which is based on three major components: Markov decision process decomposition, module training, and global action selection. We define and formalize module class and module instance concepts in decomposition step. Under our framework of decomposition, we train each modules efficiently using SARSA(λ) algorithm. Then we design, implement, test, and compare three action selection algorithms based on different heuristics: Module Combination, Module Selection, and Module Voting. For last two algorithms, we propose a method to calculate module weights efficiently, by using standard deviation of Q-values of each module. We show that Module Combination and Module Voting algorithms produce satisfactory performance in our test domain. Advisors/Committee Members: Ballard, Dana H. (Dana Harry), 1946- (advisor).

Subjects/Keywords: Modular reinforcement learning; Action selection; Module weight

…before failed (bottom). 16 Chapter 4 Action Selection Algorithms 1 Module Weight… …Hence, to determine a winning module, our next goal is to assign weight, or priority, to each… …module instance, and we simply select the module with highest weight. As we mentioned in… …provided by module instances. 2 Flatness of Q-Values as Module Weight An intuitive way to… …determine the weight of module instances is the magnitude of its optimal action’s Q-value in its… 

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Zhang, R. (2014). Action selection in modular reinforcement learning. (Masters Thesis). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/25916

Chicago Manual of Style (16th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Masters Thesis, University of Texas – Austin. Accessed October 20, 2020. http://hdl.handle.net/2152/25916.

MLA Handbook (7th Edition):

Zhang, Ruohan. “Action selection in modular reinforcement learning.” 2014. Web. 20 Oct 2020.

Vancouver:

Zhang R. Action selection in modular reinforcement learning. [Internet] [Masters thesis]. University of Texas – Austin; 2014. [cited 2020 Oct 20]. Available from: http://hdl.handle.net/2152/25916.

Council of Science Editors:

Zhang R. Action selection in modular reinforcement learning. [Masters Thesis]. University of Texas – Austin; 2014. Available from: http://hdl.handle.net/2152/25916


University of Southern California

3. Lue, Jaw-Chyng Lormen. Neuron unit arrays and nature/nurture adaptation for photonic multichip modules.

Degree: PhD, Electrical Engineering, 2007, University of Southern California

To implement a previously proposed 3-D hybrid electronic/photonic multichip module (PMCM) (mimicking a primate retina structure) capable of low-latency, high-throughput, parallel-processing computations, several critical hardware components are designed, fabricated, and tested. All components are made of MOSIS 1.5 mm n-well BiCMOS (bipolar complimentary metal oxide silicon) fabrication process.; A 12-by-12 dual-input, dual-output silicon neuron unit array chip has been fabricated, and characterized. A desired sigmoid-shape optical output from a vertical surface emitting laser (VCSEL) driven by this chip (with a linear-optical-input) was obtained. A logarithmic amplifier circuitry has been fabricated, and characterized. The dynamic range of its sensed brightness is multiple decades wide. This bipolar-based circuit's high sensitivity at low input signal range can improve the overall optical responsivity of the PMCM if it is integrated. A floating gate design is verified to be a good candidate for the long-term analog weight storage. The floating gate controlled channel resistance can represent the lateral weighted interconnection in the PMCM. A preliminary active pixel sensor design is also characterized, and evaluated for weight storage. Physical constraints, trade-offs, and relationships among the components for optimizing the performance of the PMCM are discussed.; Software-wise, an artificial neural learning algorithm (Nature/Nurture algorithm) is developed for modeling the PMCM. This algorithm describes the weight updating rules for both the vertical fixed (nature-like) and the lateral adaptive (nurture-like) weighted interconnections in the PMCM. The learning algorithm for the lateral weight adaptations is new, and derived based on the multi-layer error back-propagation (BP) supervised learning algorithm using gradient descent method. Results from a simple optical character recognition (OCR) simulation show: (1) A PMCM with only one hidden neuron layer is sufficient to perform the OCR. (2) The Nature/Nurture trained neural network can recognize well the new modified patterns (generated from the original patterns) after the lateral weight adaptations. (3) A neural network similar to the pathways of the PMCM with local connectivity (only 9 vertical and 8 lateral interconnections from each neuron) can also perform pattern recognition with acceptable recognition rate. Advisors/Committee Members: Tanguay, Armand R., Jr. (Committee Chair), Jenkins, B. Keith (Committee Member), O'Brien, John D. (Committee Member), Beiderman, Irving (Committee Member).

Subjects/Keywords: back-propagation based Nature/Nurture algorithm; photonic multichip module; lateral weight adaptation; ASIC VLSI neuron unit; pattern recognition; artificial intelligence; interpenetrating neural

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Lue, J. L. (2007). Neuron unit arrays and nature/nurture adaptation for photonic multichip modules. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/489412/rec/4384

Chicago Manual of Style (16th Edition):

Lue, Jaw-Chyng Lormen. “Neuron unit arrays and nature/nurture adaptation for photonic multichip modules.” 2007. Doctoral Dissertation, University of Southern California. Accessed October 20, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/489412/rec/4384.

MLA Handbook (7th Edition):

Lue, Jaw-Chyng Lormen. “Neuron unit arrays and nature/nurture adaptation for photonic multichip modules.” 2007. Web. 20 Oct 2020.

Vancouver:

Lue JL. Neuron unit arrays and nature/nurture adaptation for photonic multichip modules. [Internet] [Doctoral dissertation]. University of Southern California; 2007. [cited 2020 Oct 20]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/489412/rec/4384.

Council of Science Editors:

Lue JL. Neuron unit arrays and nature/nurture adaptation for photonic multichip modules. [Doctoral Dissertation]. University of Southern California; 2007. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll127/id/489412/rec/4384

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