Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices, Second Edition takes an interdisciplinary approach to applying principles for efficient implementation of network devices, offering solutions to the problem of network implementation bottlenecks. In designing a network device, there are dozens of decisions that affect the speed with which it will perform - sometimes for better, but sometimes for worse. The book provides a complete and coherent methodology for maximizing speed while meeting network design goals. The book is uniquely focused on the seamless integration of data structures, algorithms, operating systems and hardware/software co-designs for high-performance routers/switches and network end systems. Thoroughly updated based on courses taught by the authors over the past decade, the book lays out the bottlenecks most often encountered at four disparate levels of implementation: protocol, OS, hardware and architecture. It then develops fifteen principles key to breaking these bottlenecks, systematically applying them to bottlenecks found in end-nodes, interconnect devices and specialty functions located along the network. Later sections discuss the inherent challenges of modern cloud computing and data center networking.

Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices (The Morgan Kaufmann Series in Networking)

Computers & Technology

Varghese, George

Xu, Jun

Networking & Cloud Computing

Morgan Kaufmann

Modern Applied Regressions creates an intricate and colorful mural with mosaics of categorical and limited response variable (CLRV) models using both Bayesian and Frequentist approaches. Written for graduate students, junior researchers, and quantitative analysts in behavioral, health, and social sciences, this text provides details for doing Bayesian and frequentist data analysis of CLRV models. Each chapter can be read and studied separately with R coding snippets and template interpretation for easy replication. Along with the doing part, the text provides basic and accessible statistical theories behind these models and uses a narrative style to recount their origins and evolution. 
This book first scaffolds both Bayesian and frequentist paradigms for regression analysis, and then moves onto different types of categorical and limited response variable models, including binary, ordered, multinomial, count, and survival regression. Each of the middle four chapters discusses a major type of CLRV regression that subsumes an array of important variants and extensions. The discussion of all major types usually begins with the history and evolution of the prototypical model, followed by the formulation of basic statistical properties and an elaboration on the doing part of the model and its extension. The doing part typically includes R codes, results, and their interpretation. The last chapter discusses advanced modeling and predictive techniques―multilevel modeling, causal inference and propensity score analysis, and machine learning―that are largely built with the toolkits designed for the CLRV models previously covered. 
The online resources for this book, including R and Stan codes and supplementary 
notes, can be accessed at https://sites.google.com/site/socjunxu/home/statistics/modernapplied- 
regressions.

Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Politics & Social Sciences

Sociology

Chapman and Hall/CRC

Estimate and Interpret Results from Ordered Regression Models\nOrdered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption.\nThe authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R.\nThis book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable.\nWeb ResourceMore detailed examples are available on a supplementary website. The site also contains JAGS, R, and Stata codes to estimate the models along with syntax to reproduce the results.

Ordered Regression Models (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Science & Math

Fullerton, Andrew S.

Mathematics

Routledge

Channel Coding in 5G New Radio

This book provides a comprehensive coverage of major channel codes adopted since the 3rd generation of mobile communication. Modulation schemes suitable for 5G mobile communications are also described based on key New Radio application scenarios and performance requirements. It covers low density parity check (LDPC) codes, Polar codes, tail-biting convolutional codes (TBCC) and Turbo codes. Outer codes and a few advanced coding and modulations are also discussed. In addition, it includes detailed illustration of each channel coding scheme such as the basic code structure, decoding algorithms, performance evaluation and complexity analysis. The book offers insights on why and how channel codes are designed and developed in standardization organizations, which significantly facilitates the reading and understanding of the of 5G channel coding technologies. Channel Coding in 5G New Radio will be an essential read for researchers and students of digital communications, wireless communications engineers, and those who are interested in mobile communications in general.

Yuan, Yifei

Computer Science

CRC-Press

This book provides a comprehensive coverage of major channel codes adopted since the 3rd generation of mobile communication. Modulation schemes suitable for 5G mobile communications are also described based on key New Radio application scenarios and performance requirements.It covers low density parity check (LDPC) codes, Polar codes, tail-biting convolutional codes (TBCC) and Turbo codes. Outer codes and a few advanced coding and modulations are also discussed. In addition, it includes detailed illustration of each channel coding scheme such as the basic code structure, decoding algorithms, performance evaluation and complexity analysis. The book offers insights on why and how channel codes are designed and developed in standardization organizations, which significantly facilitates the reading and understanding of the of 5G channel coding technologies. Channel Coding in 5G New Radio will be an essential read for researchers and students of digital communications, wireless communications engineers, and those who are interested in mobile communications in general.

Estimate and Interpret Results from Ordered Regression Models Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives presents regression models for ordinal outcomes, which are variables that have ordered categories but unknown spacing between the categories. The book provides comprehensive coverage of the three major classes of ordered regression models (cumulative, stage, and adjacent) as well as variations based on the application of the parallel regression assumption. The authors first introduce the three "parallel" ordered regression models before covering unconstrained partial, constrained partial, and nonparallel models. They then review existing tests for the parallel regression assumption, propose new variations of several tests, and discuss important practical concerns related to tests of the parallel regression assumption. The book also describes extensions of ordered regression models, including heterogeneous choice models, multilevel ordered models, and the Bayesian approach to ordered regression models. Some chapters include brief examples using Stata and R. This book offers a conceptual framework for understanding ordered regression models based on the probability of interest and the application of the parallel regression assumption. It demonstrates the usefulness of numerous modeling alternatives, showing you how to select the most appropriate model given the type of ordinal outcome and restrictiveness of the parallel assumption for each variable. Web Resource More detailed examples are available on a supplementary website. The site also contains JAGS, R, and Stata codes to estimate the models along with syntax to reproduce the results.

Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

<div>Understand the fundamental factors of data storage system performance and master an essential analytical skill using block trace via applications such as MATLAB and Python tools. You will increase your productivity and learn the best techniques for doing specific tasks (such as analyzing the IO pattern in a quantitative way, identifying the storage system bottleneck, and designing the cache policy). </div> <div> In the new era of IoT, big data, and cloud systems, better performance and higher density of storage systems has become crucial. To increase data storage density, new techniques have evolved and hybrid and parallel access techniques--together with specially designed IO scheduling and data migration algorithms--are being deployed to develop high-performance data storage solutions. Among the various storage system performance analysis techniques, IO event trace analysis (block-level trace analysis particularly) is one of the most common approaches for system optimization and design. However, the task of completing a systematic survey is challenging and very few works on this topic exist. Block Trace Analysis and Storage System Optimization brings together theoretical analysis (such as IO qualitative properties and quantitative metrics) and practical tools (such as trace parsing, analysis, and results reporting perspectives). The book provides content on block-level trace analysis techniques, and includes case studies to illustrate how these techniques and tools can be applied in real applications (such as SSHD, RAID, Hadoop, and Ceph systems). What You'll Learn <ul> <li>Understand the fundamental factors of data storage system performance </li> <li>Master an essential analytical skill using block trace via various applications </li> <li>Distinguish how the IO pattern differs in the block level from the file level </li> <li>Know how the sequential HDFS request becomes "fragmented" in final storage devices </li> <li>Perform trace analysis tasks with a tool based on the MATLAB and Python platforms</li> </ul> Who This Book Is For IT professionals interested in storage system performance optimization: network administrators, data storage managers, data storage engineers, storage network engineers, systems engineers </div>

Block Trace Analysis and Storage System Optimization: A Practical Approach with MATLAB/Python Tools

Apress

Semantic Matching in Search is a systematic and detailed introduction to newly developed machine learning technologies for query document matching (semantic matching) in search, particularly in web search. It focuses on the fundamental problems, as well as the state-of-the-art solutions of query document matching on form aspect, phrase aspect, word sense aspect, topic aspect, and structure aspect. Matching between query and document is not limited to search, and similar problems can be found in question answering, online advertising, cross-language information retrieval, machine translation, recommender systems, link prediction, image annotation, drug design, and other applications where one is faced with the general task of matching between objects from two different spaces. The technologies introduced in this monograph can be generalized into more general machine learning techniques, which are referred to as learning to match in this survey. It is hoped that the ideas and solutions explained in Semantic Matching in Search may motivate industrial practitioners to turn the research results into products. The methods introduced and the discussions around them should also stimulate academic researchers to find new research directions and approaches.

Semantic Matching in Search (Foundations and Trends(r) in Information Retrieval)

李航

Now Publishers

Matching, which is to measure the relevance of a document to a query or interest of a user to an item, is a key problem in both search and recommendation. Machine learning has been exploited to address the problem and efforts have been made to develop deep learning techniques for matching tasks in search and recommendation. With the availability of a large amount of data, powerful computational resources, and advanced deep learning techniques, deep learning for matching now becomes the state-of-the-art technology for search and recommendation.The key to the success of the deep learning approach is its strong ability in learning of representations and generalization of matching patterns from data. This survey gives a systematic and comprehensive introduction to the deep matching models for search and recommendation. First, it gives a unified view of matching in search and recommendation and the solutions from the two fields can be compared in one framework. Then, the survey categorizes the current deep learning solutions into two types: methods of representation learning and methods of matching function learning. The fundamental problems as well as the state-of-the-art solutions of query-document matching in search and user-item matching in recommendation are described.Deep Learning for Matching in Search and Recommendation aims to help researchers from both search and recommendation communities to get an in-depth understanding and insight into the spaces, stimulate more ideas and discussions, and promote developments of new technologies. As matching is not limited to search and recommendation, the technologies introduced here can be generalized into a more general task of matching between objects from two spaces.

Deep Learning for Matching in Search and Recommendation (Foundations and Trends(r) in Information Retrieval)

He, Xiangnan

Databases & Big Data

一场飘零于纳粹德国与日本帝国的壮烈史诗
中华民国外交官周嘉文在德国建功立业,不久便与桑德拉坠入爱河,娶为续弦。第二次世界大战炮火响起,周嘉文被美军轰炸身亡,临死前将两个女儿交付给妻子,殊不知大女儿周安佳已向桑德拉的儿子——纳粹士兵卢卡斯献出初夜,而小女儿周汉娜也将被送回中国,与姐姐骨肉分离。周汉娜经历了中日战争的烽火连天和国共战争的枪林弹雨,又在国民政府和新政权的更迭中流离失所。卢卡斯带着周安佳的爱情奔赴战场,美军占领德国以后,开始追查周安佳支持纳粹的事情,周安佳尝尽世态炎凉,最终飘零到了不同国家。她虽长着一张中国人的脸,却在华人社区到处碰壁。向来认为是德国人的她,却不被欧洲情人的家人所接受。
桑德拉为何要将从小相依为命的一对女儿拆散?周安佳是与卢卡斯终成眷属?还是依旧在不同情人中颠沛流离?周汉娜在中国的命运又变得如何?40年分离的姐妹是否终将团圆?
本书以周安佳和周汉娜的命运作为主线,用真实细腻的笔触,表现上世纪的中、德、美、奥、日和前苏联等国的军人百姓在面对亲情爱情时心理状态,以及战火下难以掌控的个体命运。一对姐妹花,数场战争,说不尽的悲欢离合,皆在大事件背景下的小人物身上一一展现,……
最终,古今多少事,都付笑谈中。

Gone With War (Chinese Edition)

Literature & Fiction

Dixie W Publishing Corporation

See the best price to sell, buy, or rent books by Xu, Jun. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Xu, Jun

See the best price to sell, buy, or rent books by Xu, Jun. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Network Algorithmics: An Interdisciplinary Approach to Designing Fast Networked Devices (The Morgan Kaufmann Series in Networking)

Modern Applied Regressions: Bayesian and Frequentist Analysis of Categorical and Limited Response Variables with R and Stan (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Ordered Regression Models (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Channel Coding in 5G New Radio

Channel Coding in 5G New Radio

Ordered Regression Models: Parallel, Partial, and Non-Parallel Alternatives (Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences)

Block Trace Analysis and Storage System Optimization: A Practical Approach with MATLAB/Python Tools

Semantic Matching in Search (Foundations and Trends(r) in Information Retrieval)

Deep Learning for Matching in Search and Recommendation (Foundations and Trends(r) in Information Retrieval)

Gone With War (Chinese Edition)