Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy

[Abstract] Background The development of large-scale kinetic models is one of the current key issues in computational systems biology and bioinformatics. Here we consider the problem of parameter estimation in nonlinear dynamic models. Global optimization methods can be used to solve this type of problems but the associated computational cost is very large. Moreover, many of these methods need the tuning of a number of adjustable search parameters, requiring a number of initial exploratory runs and therefore further increasing the computation times. Here we present a novel parallel method, self-adaptive cooperative enhanced scatter search (saCeSS), to accelerate the solution of this class of problems. The method is based on the scatter search optimization metaheuristic and incorporates several key new mechanisms: (i) asynchronous cooperation between parallel processes, (ii) coarse and fine-grained parallelism, and (iii) self-tuning strategies. Results The performance and robustness of saCeSS is illustrated by solving a set of challenging parameter estimation problems, including medium and large-scale kinetic models of the bacterium E. coli, bakerés yeast S. cerevisiae, the vinegar fly D. melanogaster, Chinese Hamster Ovary cells, and a generic signal transduction network. The results consistently show that saCeSS is a robust and efficient method, allowing very significant reduction of computation times with respect to several previous state of the art methods (from days to minutes, in several cases) even when only a small number of processors is used. Conclusions The new parallel cooperative method presented here allows the solution of medium and large scale parameter estimation problems in reasonable computation times and with small hardware requirements. Further, the method includes self-tuning mechanisms which facilitate its use by non-experts. We believe that this new method can play a key role in the development of large-scale and even whole-cell dynamic models.Ministerio de Economía y Competitividad; DPI2011-28112-C04-03Ministerio de Economía y Competitividad; DPI2011-28112-C04-04Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RMinisterio de Economía y Competitividad; TIN2013-42148-PMinisterio de Economía y Competitividad; TIN2016-75845-PGalicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2014/041Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; R2016/045Galicia. Consellería de Cultura, Educación e Ordenación Universitaria; GRC2013/05

Advanced analytics through FPGA based query processing and deep reinforcement learning

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics.Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem.Postprint (published version

Advanced analytics through FPGA based query processing and deep reinforcement learning

Today, vast streams of structured and unstructured data have been incorporated in databases, and analytical processes are applied to discover patterns, correlations, trends and other useful relationships that help to take part in a broad range of decision-making processes. The amount of generated data has grown very large over the years, and conventional database processing methods from previous generations have not been sufficient to provide satisfactory results regarding analytics performance and prediction accuracy metrics. Thus, new methods are needed in a wide array of fields from computer architectures, storage systems, network design to statistics and physics. This thesis proposes two methods to address the current challenges and meet the future demands of advanced analytics. First, we present AxleDB, a Field Programmable Gate Array based query processing system which constitutes the frontend of an advanced analytics system. AxleDB melds highly-efficient accelerators with memory, storage and provides a unified programmable environment. AxleDB is capable of offloading complex Structured Query Language queries from host CPU. The experiments have shown that running a set of TPC-H queries, AxleDB can perform full queries between 1.8x and 34.2x faster and 2.8x to 62.1x more energy efficient compared to MonetDB, and PostgreSQL on a single workstation node. Second, we introduce TauRieL, a novel deep reinforcement learning (DRL) based method for combinatorial problems. The design idea behind combining DRL and combinatorial problems is to apply the prediction capabilities of deep reinforcement learning and to use the universality of combinatorial optimization problems to explore general purpose predictive methods. TauRieL utilizes an actor-critic inspired DRL architecture that adopts ordinary feedforward nets. Furthermore, TauRieL performs online training which unifies training and state space exploration. The experiments show that TauRieL can generate solutions two orders of magnitude faster and performs within 3% of accuracy compared to the state-of-the-art DRL on the Traveling Salesman Problem while searching for the shortest tour. Also, we present that TauRieL can be adapted to the Knapsack combinatorial problem. With a very minimal problem specific modification, TauRieL can outperform a Knapsack specific greedy heuristics.Hoy en día, se han incorporado grandes cantidades de datos estructurados y no estructurados en las bases de datos, y se les aplican procesos analíticos para descubrir patrones, correlaciones, tendencias y otras relaciones útiles que se utilizan mayormente para la toma de decisiones. La cantidad de datos generados ha crecido enormemente a lo largo de los años, y los métodos de procesamiento de bases de datos convencionales utilizados en las generaciones anteriores no son suficientes para proporcionar resultados satisfactorios respecto al rendimiento del análisis y respecto de la precisión de las predicciones. Por lo tanto, se necesitan nuevos métodos en una amplia gama de campos, desde arquitecturas de computadoras, sistemas de almacenamiento, diseño de redes hasta estadísticas y física. Esta tesis propone dos métodos para abordar los desafíos actuales y satisfacer las demandas futuras de análisis avanzado. Primero, presentamos AxleDB, un sistema de procesamiento de consultas basado en FPGAs (Field Programmable Gate Array) que constituye la interfaz de un sistema de análisis avanzado. AxleDB combina aceleradores altamente eficientes con memoria, almacenamiento y proporciona un entorno programable unificado. AxleDB es capaz de descargar consultas complejas de lenguaje de consulta estructurado desde la CPU del host. Los experimentos han demostrado que al ejecutar un conjunto de consultas TPC-H, AxleDB puede realizar consultas completas entre 1.8x y 34.2x más rápido y 2.8x a 62.1x más eficiente energéticamente que MonetDB, y PostgreSQL en un solo nodo de una estación de trabajo. En segundo lugar, presentamos TauRieL, un nuevo método basado en Deep Reinforcement Learning (DRL) para problemas combinatorios. La idea central que está detrás de la combinación de DRL y problemas combinatorios, es aplicar las capacidades de predicción del aprendizaje de refuerzo profundo y el uso de la universalidad de los problemas de optimización combinatoria para explorar métodos predictivos de propósito general. TauRieL utiliza una arquitectura DRL inspirada en el actor-crítico que se adapta a redes feedforward. Además, TauRieL realiza el entrenamieton en línea que unifica el entrenamiento y la exploración espacial de los estados. Los experimentos muestran que TauRieL puede generar soluciones dos órdenes de magnitud más rápido y funciona con un 3% de precisión en comparación con el estado del arte en DRL aplicado al problema del viajante mientras busca el recorrido más corto. Además, presentamos que TauRieL puede adaptarse al problema de la Mochila. Con una modificación específica muy mínima del problema, TauRieL puede superar a una heurística codiciosa de Knapsack Problem

組合せ最適化問題のための測定フィードバック型コヒーレント・イジングマシンの実現と評価

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 合原 一幸, 東京大学教授 岩田 覚, 東京大学准教授 平田 祥人, 東京大学准教授 大西 立顕, 東京大学准教授 鈴木 大慈University of Tokyo(東京大学

The Road to General Intelligence

Humans have always dreamed of automating laborious physical and intellectual tasks, but the latter has proved more elusive than naively suspected. Seven decades of systematic study of Artificial Intelligence have witnessed cycles of hubris and despair. The successful realization of General Intelligence (evidenced by the kind of cross-domain flexibility enjoyed by humans) will spawn an industry worth billions and transform the range of viable automation tasks.The recent notable successes of Machine Learning has lead to conjecture that it might be the appropriate technology for delivering General Intelligence. In this book, we argue that the framework of machine learning is fundamentally at odds with any reasonable notion of intelligence and that essential insights from previous decades of AI research are being forgotten. We claim that a fundamental change in perspective is required, mirroring that which took place in the philosophy of science in the mid 20th century. We propose a framework for General Intelligence, together with a reference architecture that emphasizes the need for anytime bounded rationality and a situated denotational semantics. We given necessary emphasis to compositional reasoning, with the required compositionality being provided via principled symbolic-numeric inference mechanisms based on universal constructions from category theory. • Details the pragmatic requirements for real-world General Intelligence. • Describes how machine learning fails to meet these requirements. • Provides a philosophical basis for the proposed approach. • Provides mathematical detail for a reference architecture. • Describes a research program intended to address issues of concern in contemporary AI. The book includes an extensive bibliography, with ~400 entries covering the history of AI and many related areas of computer science and mathematics.The target audience is the entire gamut of Artificial Intelligence/Machine Learning researchers and industrial practitioners. There are a mixture of descriptive and rigorous sections, according to the nature of the topic. Undergraduate mathematics is in general sufficient. Familiarity with category theory is advantageous for a complete understanding of the more advanced sections, but these may be skipped by the reader who desires an overall picture of the essential concepts This is an open access book

Evolutionary Algorithms in Engineering Design Optimization

Evolutionary algorithms (EAs) are population-based global optimizers, which, due to their characteristics, have allowed us to solve, in a straightforward way, many real world optimization problems in the last three decades, particularly in engineering fields. Their main advantages are the following: they do not require any requisite to the objective/fitness evaluation function (continuity, derivability, convexity, etc.); they are not limited by the appearance of discrete and/or mixed variables or by the requirement of uncertainty quantification in the search. Moreover, they can deal with more than one objective function simultaneously through the use of evolutionary multi-objective optimization algorithms. This set of advantages, and the continuously increased computing capability of modern computers, has enhanced their application in research and industry. From the application point of view, in this Special Issue, all engineering fields are welcomed, such as aerospace and aeronautical, biomedical, civil, chemical and materials science, electronic and telecommunications, energy and electrical, manufacturing, logistics and transportation, mechanical, naval architecture, reliability, robotics, structural, etc. Within the EA field, the integration of innovative and improvement aspects in the algorithms for solving real world engineering design problems, in the abovementioned application fields, are welcomed and encouraged, such as the following: parallel EAs, surrogate modelling, hybridization with other optimization techniques, multi-objective and many-objective optimization, etc

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Applied Mathematics and Computational Physics

As faster and more efficient numerical algorithms become available, the understanding of the physics and the mathematical foundation behind these new methods will play an increasingly important role. This Special Issue provides a platform for researchers from both academia and industry to present their novel computational methods that have engineering and physics applications

Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences

Mathematical fuzzy logic (MFL) specifically targets many-valued logic and has significantly contributed to the logical foundations of fuzzy set theory (FST). It explores the computational and philosophical rationale behind the uncertainty due to imprecision in the backdrop of traditional mathematical logic. Since uncertainty is present in almost every real-world application, it is essential to develop novel approaches and tools for efficient processing. This book is the collection of the publications in the Special Issue “Mathematical Fuzzy Logic in the Emerging Fields of Engineering, Finance, and Computer Sciences”, which aims to cover theoretical and practical aspects of MFL and FST. Specifically, this book addresses several problems, such as:- Industrial optimization problems- Multi-criteria decision-making- Financial forecasting problems- Image processing- Educational data mining- Explainable artificial intelligence, etc