To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that ...
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ISBN:
(数字)9783031019609
ISBN:
(纸本)9783031019593
To date, processing of high-throughput Mass Spectrometry (MS) data is accomplished using serial algorithms. Developing new methods to process MS data is an active area of research but there is no single strategy that focuses on scalability of MS based methods. Mass spectrometry is a diverse and versatile technology for high-throughput functional characterization of proteins, small molecules and metabolites in complex biological mixtures. In the recent years the technology has rapidly evolved and is now capable of generating increasingly large (multiple tera-bytes per experiment) and complex (multiple species/microbiome/high-dimensional) data sets. This rapid advance in MS instrumentation must be matched by equally fast and rapid evolution of scalable methods developed for analysis of these complex data sets. Ideally, the new methods should leverage the rich heterogeneous computational resources available in a ubiquitous fashion in the form of multicore, manycore, CPU-GPU, CPU-FPGA, and IntelPhi architectures. The absence of these high-performance computing algorithms.now hinders scientific advancements for mass spectrometry research. In this book we illustrate the need for high-performance computing algorithms.for MS based proteomics, and proteogenomics and showcase our progress in developing these high-performance algorithms.
This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimiz...
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ISBN:
(数字)9783030637736
ISBN:
(纸本)9783030637729
This book presents an overview of archiving strategies developed over the last years by the authors that deal with suitable approximations of the sets of optimal and nearly optimal solutions of multi-objective optimization problems by means of stochastic search algorithms. All presented archivers are analyzed with respect to the approximation qualities of the limit archives that they generate and the upper bounds of the archive sizes. The convergence analysis will be done using a very broad work that involves all existing stochastic search algorithms.and that will only use minimal assumptions on the process to generate new candidate solutions. All of the presented archivers can effortlessly be coupled with any set-based multi-objective search algorithm such as multi-objective evolutionary algorithms. and the resulting hybrid method takes over the convergence properties of the chosen archiver. This book hence targets at all algorithm designers and practitioners in the field of multi-objective optimization.
Machine Learning algorithms.is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine...
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ISBN:
(数字)9781119769262
ISBN:
(纸本)9781119768852
Machine Learning algorithms.is for current and ambitious machine learning specialists looking to implement solutions to real-world machine learning problems. It talks entirely about the various applications of machine and deep learning techniques, with each chapter dealing with a novel approach of machine learning architecture for a specific application, and then compares the results with previous algorithms. The book discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, sentiment analysis, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All learning algorithms.are explained so that the user can easily move from the equations in the book to a computer program.
As the structure of contemporary communication networks grows more complex, practical networked distributed systems become prone to component failures.Fault-tolerant consensus in message-passing systems allows partici...
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ISBN:
(数字)9781681735672
ISBN:
(纸本)9781681735665
As the structure of contemporary communication networks grows more complex, practical networked distributed systems become prone to component failures.
Fault-tolerant consensus in message-passing systems allows participants in the system to agree on a common value despite the malfunction or misbehavior of some components. It is a task of fundamental importance for distributed computing, due to its numerous applications.
We summarize studies on the topological conditions that determine the feasibility of consensus, mainly focusing on directed networks and the case of restricted topology knowledge at each participant. Recently, significant efforts have been devoted to fully characterize the underlying communication networks in which variations of fault-tolerant consensus can be achieved. Although the deduction of analogous topological conditions for undirected networks of known topology had shortly followed the introduction of the problem, their extension to the directed network case has been proven a highly non-trivial task. Moreover, global knowledge restrictions, inherent in modern large-scale networks, require more elaborate arguments concerning the locality of distributed computations. In this work, we present the techniques and ideas used to resolve these issues.
Recent studies indicate a number of parameters that affect the topological conditions under which consensus can be achieved, namely, the fault model, the degree of system synchrony (synchronous vs. asynchronous), the type of agreement (exact vs. approximate), the level of topology knowledge, and the algorithm class used (general vs. iterative). We outline the feasibility and impossibility results for various combinations of the above parameters, extensively illustrating the relation between network topology and consensus.
In the last two decades a link has been established that, in some cases, proof that a solution exists has enabled an algorithm to find that solution itself. This has had most effect on semialgebraic proof systems and ...
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ISBN:
(数字)9781680836370
ISBN:
(纸本)9781680836363
In the last two decades a link has been established that, in some cases, proof that a solution exists has enabled an algorithm to find that solution itself. This has had most effect on semialgebraic proof systems and linear and semidefinite programming. This monograph details the interplay between proof systems and efficient algorithm design and surveys the state-of-the-art for two of the most important semi-algebraic proof systems: Sherali-Adams and Sum-of-Squares. It provides the readers with a rigorous treatment of these systems both as proof systems, and as a general family of optimization algorithms. The emphasis is on illustrating the main ideas by presenting a small fraction of representative results with detailed intuition and commentary. The monograph is self-contained and includes a review of the necessary mathematical background including basic theory of linear and semidefinite programming. Semialgebraic Proofs and Efficient Algorithm Design provides the advanced reader with a deep insight into the exciting line of research. It will inspire readers in deploying the techniques in their own further research.
Explore and master the most important algorithms.for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms.and understand how they work in depth. One-stop solutio...
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ISBN:
(数字)9781788625906
ISBN:
(纸本)9781788621113
Explore and master the most important algorithms.for solving complex machine learning problems. Key Features Discover high-performing machine learning algorithms.and understand how they work in depth. One-stop solution to mastering supervised, unsupervised, and semi-supervised machine learning algorithms.and their implementation. Master concepts related to algorithm tuning, parameter optimization, and more Book Description Machine learning is a subset of AI that aims to make modern-day computer systems smarter and more intelligent. The real power of machine learning resides in its algorithms. which make even the most difficult things capable of being handled by machines. However, with the advancement in the technology and requirements of data, machines will have to be smarter than they are today to meet the overwhelming data needs; mastering these algorithms.and using them optimally is the need of the hour. Mastering Machine Learning algorithms.is your complete guide to quickly getting to grips with popular machine learning algorithms. You will be introduced to the most widely used algorithms.in supervised, unsupervised, and semi-supervised machine learning, and will learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this book will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries such as scikit-learn. You will also learn how to use Keras and TensorFlow to train effective neural networks. If you are looking for a single resource to study, implement, and solve end-to-end machine learning problems and use-cases, this is the book you need. What you will learn Explore how a ML model can be trained, optimized, and evaluated Understand how to create and learn static and dynamic probabilistic models Successfully cluster high-dimensional data and evaluate model accuracy Discover how artificial neural networks work and how
This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machin...
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ISBN:
(数字)9781316882177
ISBN:
(纸本)9781107184589
This book bridges theoretical computer science and machine learning by exploring what the two sides can teach each other. It emphasizes the need for flexible, tractable models that better capture not what makes machine learning hard, but what makes it easy. Theoretical computer scientists will be introduced to important models in machine learning and to the main questions within the field. Machine learning researchers will be introduced to cutting-edge research in an accessible format, and gain familiarity with a modern, algorithmic toolkit, including the method of moments, tensor decompositions and convex programming relaxations. The treatment beyond worst-case analysis is to build a rigorous understanding about the approaches used in practice and to facilitate the discovery of exciting, new ways to solve important long-standing problems.
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