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Linguistic Structure Prediction

A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference

A major part of natural language processing now depends on the use of text data to build linguistic analyzers.

A Handbook for Analytical Writing

Keys to Strategic Thinking

This handbook accelerates the development of analytical writing skills for high school students, students in higher education, and working professionals in a broad range of careers. This handbook builds on the idea that writing clarifies thought, and that through analytical writing comes improved insight and understanding for making decisions about innovation necessary for socioeconomic development. This short handbook is a simple, comprehensive guide that shows differences between descriptive writing and analytical writing, and how students and teachers work together during the process of discovery-based learning. This handbook provides nuts and bolts ideas for team projects, organizing writing, the process of writing, constructing tables, presenting figures, documenting reference lists, avoiding the barriers to clear writing, and outlines the importance of ethical issues and bias for writers. Finally, there are ideas for evaluating writing, and examples of classroom exercises for students and teachers. Table of Contents: Preface / Introduction / Descriptive and Analytical Writing / Guidelines for Students and Teachers / Choosing Topics / Writing Teams / Organization / The Writing Process / Construction / Top Ten Writing Tips / Ethics: Bias and Plagiarism / Final Products / Evaluating Analytical Writing / Classroom Exercises for Teachers and Students / Bibliography

Learning to write in writing teams is important for many careers, and requires
skills beyond those of single—authored documents. Analytical writing is often the
effort of several writers that form a writing team, and examples of analytical
writing ...

Developing Embedded Software Using DaVinci & OMAP Technology

This book discusses how to develop embedded products using DaVinci & OMAP Technology from Texas Instruments Incorporated. It presents a single software platform for diverse hardware platforms. DaVinci & OMAP Technology refers to the family of processors, development tools, software products, and support. While DaVinci Technology is driven by the needs of consumer video products such as IP network cameras, networked projectors, digital signage and portable media players, OMAP Technology is driven by the needs of wireless products such as smart phones. Texas Instruments offers a wide variety of processing devices to meet our users' price and performance needs. These vary from single digital signal processing devices to complex, system-on-chip (SoC) devices with multiple processors and peripherals. As a software developer you question: Do I need to become an expert in signal processing and learn the details of these complex devices before I can use them in my application? As a senior executive you wonder: How can I reduce my engineering development cost? How can I move from one processor to another from Texas Instruments without incurring a significant development cost? This book addresses these questions with sample code and gives an insight into the software architecture and associated component software products that make up this software platform. As an example, we show how we develop an IP network camera. Using this software platform, you can choose to focus on the application and quickly create a product without having to learn the details of the underlying hardware or signal processing algorithms. Alternatively, you can choose to differentiate at both the application as well as the signal processing layer by developing and adding your algorithms using the xDAIS for Digital Media, xDM, guidelines for component software. Finally, you may use one code base across different hardware platforms. Table of Contents: Software Platform / More about xDM, VISA, & CE / Building a Product Based on DaVinci Technology / Reducing Development Cost / eXpressDSP Digital Media (xDM) / Sample Application Using xDM / Embedded Peripheral Software Interface (EPSI) / Sample Application Using EPSI / Sample Application Using EPSI and xDM / IP Network Camera on DM355 Using TI Software / Adding your secret sauce to the Signal Processing Layer (SPL) / Further Reading

Using the sample code as a reference you will call and test your new IP along
with the rest of the system.These tasks all belong to the second step of Reuse
and Add. Finally, you conclude with the third step of Test and Release. Figure 4.2
 ...

Arabic Natural Language Processing

This book provides system developers and researchers in natural language processing and computational linguistics with the necessary background information for working with the Arabic language. The goal is to introduce Arabic linguistic phenomena and review the state-of-the-art in Arabic processing. The book discusses Arabic script, phonology, orthography, morphology, syntax and semantics, with a final chapter on machine translation issues. The chapter sizes correspond more or less to what is linguistically distinctive about Arabic, with morphology getting the lion's share, followed by Arabic script. No previous knowledge of Arabic is needed. This book is designed for computer scientists and linguists alike. The focus of the book is on Modern Standard Arabic; however, notes on practical issues related to Arabic dialects and languages written in the Arabic script are presented in different chapters. Table of Contents: What is "Arabic"? / Arabic Script / Arabic Phonology and Orthography / Arabic Morphology / Computational Morphology Tasks / Arabic Syntax / A Note on Arabic Semantics / A Note on Arabic and Machine Translation

This book provides system developers and researchers in natural language processing and computational linguistics with the necessary background information for working with the Arabic language.

Transactional Memory

The advent of multicore processors has renewed interest in the idea of incorporating transactions into the programming model used to write parallel programs. This approach, known as transactional memory, offers an alternative, and hopefully better, way to coordinate concurrent threads. The ACI (atomicity, consistency, isolation) properties of transactions provide a foundation to ensure that con-current reads and writes of shared data do not produce inconsistent or incorrect results. At a higher level, a computation wrapped in a transaction executes atomically---either it completes successfully and commits its result in its entirety or it aborts. In addition, isolation ensures the transaction produces the same result as if no other transactions were executing concurrently. Although transactions are not a parallel programming panacea, they shift much of the burden of synchronizing and co-ordinating parallel computations from a programmer to a compiler, to a language runtime system, or to hardware. The challenge for the system implementers is to build an efficient transactional memory infrastructure. This book presents an overview of the state of the art in the design and implementation of transactional memory systems, as of early spring 2010.

This book presents an overview of the state of the art in the design and implementation of transactional memory systems, as of early spring 2010.

Active Learning

The key idea behind active learning is that a machine learning algorithm can perform better with less training if it is allowed to choose the data from which it learns. An active learner may pose "queries," usually in the form of unlabeled data instances to be labeled by an "oracle" (e.g., a human annotator) that already understands the nature of the problem. This sort of approach is well-motivated in many modern machine learning and data mining applications, where unlabeled data may be abundant or easy to come by, but training labels are difficult, time-consuming, or expensive to obtain. This book is a general introduction to active learning. It outlines several scenarios in which queries might be formulated, and details many query selection algorithms which have been organized into four broad categories, or "query selection frameworks." We also touch on some of the theoretical foundations of active learning, and conclude with an overview of the strengths and weaknesses of these approaches in practice, including a summary of ongoing work to address these open challenges and opportunities. Table of Contents: Automating Inquiry / Uncertainty Sampling / Searching Through the Hypothesis Space / Minimizing Expected Error and Variance / Exploiting Structure in Data / Theory / Practical Considerations

Recall that in machine learning parlance, a “hypothesis” is a particular
computational model which attempts to generalize or explain the training data,
and make predictions on new data instances. Let H denote the hypothesis space:
the set ofall hypotheses under consideration. For example,H could be comprised
of all possible weights assigned to parameters in a perceptron or a given neural
network structure, or all possible decision trees that use the input and output
variables as ...

Multitasking in the Digital Age

In our digital age we can communicate, access, create, and share an abundance of information effortlessly, rapidly, and nearly ubiquitously. The consequence of having so many choices is that they compete for our attention: we continually switch our attention between different types of information while doing different types of tasks--in other words, we multitask. The activity of information workers in particular is characterized by the continual switching of attention throughout the day. In this book, empirical work is presented, based on ethnographic and sensor data collection, which reveals how multitasking affects information workers' activities, mood, and stress in real work environments. Multitasking is discussed from various perspectives: activity switching, interruptions as triggers for activity switching, email as a major source of interruptions, and the converse of distractions: focused attention. All of these factors are components of information work. This book begins by defining multitasking and describing different research approaches used in studying multitasking. It then describes how multiple factors occur to encourage multitasking in the digitally-enabled workplace: the abundance and ease of accessing information, the number of different working spheres, the workplace environment, attentional state, habit, and social norms. Empirical work is presented describing the nature of multitasking, the relationship of different types of interruptions and email with overload and stress, and patterns of attention focus. The final chapter ties these factors together and discusses challenges that information workers in our digital age face.

Digital detox resorts have sprouted up in locations from the Samburu reserve in
Kenya to the Trans-Himalaya to the Gobi desert in Mongolia. It is ironic that there
is now a business market for digital-free vacations—paying to relinquish access ...