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• 1. Introduction
• 1.1. The rise of AI in architecture
• 1.2. The scope of AI
• 1.3. Fundamental applicability
• 1.4. Levels of AI for architecture

• 2. Basics of machine learning in architecture
• 2.1. Supervised learning
• 2.2. Unsupervised learning
• 2.3. Semi-supervised learning.
• 2.4. Reinforcement learning
• 2.5. Evaluation metrics

• 3. Literature review
• 3.1. System simulation
• 3.2. GPUs
• 3.3. Memory systems and branch prediction
• 3.4. Networks-on-chip
• 3.5. System-level optimization
• 3.6. Approximate computing

• 4. Case studies
• 4.1. Supervised learning in branch prediction
• 4.2. Reinforcement learning in NoCs
• 4.3. Unsupervised learning in memory systems

• 5. Analysis of current practice
• 5.1. Online machine learning applications
• 5.2. Offline machine learning applications
• 5.3. Integrating domain knowledge

• 6. Future directions of AI for architecture
• 6.1. Investigating models and algorithms
• 6.2. Enhancing implementation strategies
• 6.3. Developing generalized tools
• 6.4. Embracing novel applications
• 7. Conclusions.

Artificial intelligence has already enabled pivotal advances in diverse fields, yet its impact on computer architecture has only just begun. In particular, recent work has explored broader application to the design, optimization, and simulation of computer architecture. Notably, machine-learning-based strategies often surpass prior state-of-the-art analytical, heuristic, and human-expert approaches. This book reviews the application of machine learning in system-wide simulation and run-time optimization, and in many individual components such as caches/memories, branch predictors, networks-on-chip, and GPUs. The book further analyzes current practice to highlight useful design strategies and identify areas for future work, based on optimized implementation strategies, opportune extensions to existing work, and ambitious long term possibilities. Taken together, these strategies and techniques present a promising future for increasingly automated computer architecture designs.

• 1. Introduction
• 1.1. Overview of vibroacoustic modal analysis
• 1.2. Background
• 1.3. References

• 2. Classical modal analysis with random excitations
• 2.1. Introduction
• 2.2. Structural system
• 2.3. Acoustic cavity system
• 2.4. Coupled structural-acoustic system
• 2.5. Summary
• 2.6. References

• 3. Asymptotic modal analysis of structural systems
• 3.1. Introduction
• 3.2. Classical modal analysis
• 3.3. Asymptotic modal analysis
• 3.4. AMA applications
• 3.5. Summary
• 3.6. References

• 4. Asymptotic modal analysis of acoustic cavity systems
• 4.1. Introduction
• 4.2. Classical modal analysis
• 4.3. Asymptotic modal analysis
• 4.4. AMA applications
• 4.5. Summary
• 4.6. References

• 5. Asymptotic modal analysis of coupled systems
• 5.1. Introduction
• 5.2. Classical modal analysis
• 5.3. Discrete classical modal analysis
• 5.4. Asymptotic modal analysis
• 5.5. Applications
• 5.6. Summary
• 5.7. References

• 6. Asymptotic modal analysis of nonlinear systems
• 6.1. Introduction
• 6.2. Classical modal analysis formulation
• 6.3. CMA transient solution method
• 6.4. CMA frequency response method
• 6.5. Dominance-reduced CMA method
• 6.6. Asymptotic modal analysis method
• 6.7. Results and discussion
• 6.8. Summary
• 6.9. References

• 7. Summary
• 7.1. State of the art in predicting the response of systems with high modal density
• 7.2. Two-component system example
• 7.3. Future developments in AMA
• 7.4. References.

This book describes the Asymptotic Modal Analysis (AMA) method to predict the high-frequency vibroacoustic response of structural and acoustical systems. The AMA method is based on taking the asymptotic limit of Classical Modal Analysis (CMA) as the number of modes in the structural system or acoustical system becomes large in a certain frequency bandwidth. While CMA requires both the computation of individual modes and a modal summation, AMA evaluates the averaged modal response only at a center frequency of the bandwidth and does not sum the individual contributions from each mode to obtain a final result. It is similar to Statistical Energy Analysis (SEA) in this respect. However, while SEA is limited to obtaining spatial averages or mean values (as it is a statistical method), AMA is derived systematically from CMA and can provide spatial information as well as estimates of the accuracy of the solution for a particular number of modes. A principal goal is to present the state-of-the-art of AMA and suggest where further developments may be possible. A short review of the CMA method as applied to structural and acoustical systems subjected to random excitation is first presented. Then the development of AMA is presented for an individual structural system and an individual acoustic cavity system, as well as a combined structural-acoustic system. The extension of AMA for treating coupled or multi-component systems is then described, followed by its application to nonlinear systems. Finally, the AMA method is summarized and potential further developments are discussed.

• 1. Introduction
• 1.1. From polydisperse nanoparticles to monodisperse nanoparticles.
• 1.2. From monodisperse nanoparticles to atomically precise nanoclusters
• 1.3. About this book
• 1.4. References

• 2. Synthesis of atomically precise metal nanoclusters
• 2.1. Introduction
• 2.2. Synthesis strategy
• 2.3. Two major methods of nanocluster conversion
• 2.4. Conclusion
• 2.5. References

• 3. Characterization of atomically precise metal nanoclusters
• 3.1. Introduction
• 3.2. Structure characterizations
• 3.3. Size and composition characterizations
• 3.4. Optical property characterizations
• 3.5. Characterization of electrochemical properties
• 3.6. Characterization of magnetic properties
• 3.7. Conclusion
• 3.8. References

• 4. Applications of atomically precise metal nanoclusters
• 4.1. Introduction
• 4.2. Catalysis
• 4.3. Sensing
• 4.4. Biology and biomedicine
• 4.5. Energy
• 4.6. Conclusion
• 4.7. References
• 5. Summary and outlook.

Atomically precise metal nanocluster research has emerged as a new frontier. This book serves as an introduction to metal nanoclusters protected by ligands. The authors have summarized the synthesis principles and methods, the characterization methods and new physicochemical properties, and some potential applications. By pursuing atomic precision, such nanocluster materials provide unprecedented opportunities for establishing precise relationships between the atomic-level structures and the properties. The book should be accessible to senior undergraduate and graduate students, researchers in various fields (e.g., chemistry, physics, materials, biomedicine, and engineering), R&D scientists, and science policy makers.

• 1. Introduction
• 1.1. Semantic representation
• 1.2. Vector space models
• 1.3. The evolution path of representations

• 2. Background
• 2.1. Natural language processing fundamentals
• 2.2 deep learning for NLP
• 2.3. Knowledge resources

• 3. Word embeddings
• 3.1. Count-based models
• 3.2. Predictive models
• 3.3. Character embeddings
• 3.4. Knowledge-enhanced word embeddings
• 3.5. Cross-lingual word embeddings
• 3.6. Evaluation

• 4. Graph embeddings
• 4.1. Node embedding
• 4.2. Knowledge-based relation embeddings
• 4.3. Unsupervised relation embeddings
• 4.4. Applications and evaluation

• 5. Sense embeddings
• 5.1. Unsupervised sense embeddings
• 5.2. Knowledge-based sense embeddings
• 5.3. Evaluation and application

• 6. Contextualized embeddings
• 6.1. The need for contextualization.
• 6.2. Background : transformer model
• 6.3. Contextualized word embeddings
• 6.4. Transformer-based models : BERT
• 6.5. Extensions
• 6.6. Feature extraction and finetuning
• 6.7. Analysis and evaluation

• 7. Sentence and document embeddings
• 7.1. Unsupervised sentence embeddings
• 7.2. Supervised sentence embeddings
• 7.3. Document embeddings
• 7.4. Application and evaluation

• 8. Ethics and bias
• 8.1. Bias in word embeddings
• 8.2. Debiasing word embeddings
• 9. Conclusions.

Embeddings have undoubtedly been one of the most influential research areas in Natural Language Processing (NLP). Encoding information into a low-dimensional vector representation, which is easily integrable in modern machine learning models, has played a central role in the development of NLP. Embedding techniques initially focused on words, but the attention soon started to shift to other forms: from graph structures, such as knowledge bases, to other types of textual content, such as sentences and documents. This book provides a high-level synthesis of the main embedding techniques in NLP, in the broad sense. The book starts by explaining conventional word vector space models and word embeddings (e.g., Word2Vec and GloVe) and then moves to other types of embeddings, such as word sense, sentence and document, and graph embeddings. The book also provides an overview of recent developments in contextualized representations (e.g., ELMo and BERT) and explains their potential in NLP. Throughout the book, the reader can find both essential information for understanding a certain topic from scratch and a broad overview of the most successful techniques developed in the literature.

• Introduction / Maria Polinsky
• Chechen and Ingush / Erwin R. Komen, Zarina Molochieva, Johanna Nichols
• Lak / Victor A. Friedman
• Dargwa / Nina Sumbatova
• Avar / Diana Forker
• Archi / Marina Chumakina
• Abaza and Abkhaz / Brian O'Herin
• Iron Ossetic / David Erschler
• Segmental Phonetics and Phonology in Caucasian Languages / Gašper Beguš
• Word Stress in Languages of the Caucasus / Lena Borise
• North Caucasus: Regions and their Demography / Konstantin Kazenin
• Correlatives in Languages of the Caucasus / Ömer Demirok, Balkız Öztürk
• The Nominal Domain in Languages of the Caucasus / Balkız Öztürk, Ömer Eren
• Agreement in Languages of the Caucasus / Steven Foley
• Ergativity in the Caucasus / Dmitry Ganenkov
• Ellipsis in Languages of the Caucasus / David Erschler
• Binding and indexicality in the Caucasus / Dmitry Ganenkov, Natalia Bogomolova
• Information Structure in Languages of the Caucasus / Diana Forker
• The Northwest Caucasian Languages / Peter Arkadiev, Yury Lander
• Languages and Sociolinguistics of the Caucasus / Nina Dobrushina, Michael Daniel, Yuri Koryakov
• Tone and Intonation in Languages of the Caucasus / Lena Borise
• Nakh-Dagestanian Languages / Dmitry Ganenkov, Timur Maisak
• Kartvelian (South Caucasian) Languages / Yakov G. Testelets
• Indo-European Languages of the Caucasus / Oleg Belyaev
• Megrelian / Alexander Rostovtsev-Popiel.

This handbook introduces and overviews the linguistically diverse languages of southern Russia, Georgia, Azerbaijan, and Armenia. Though the languages of the Caucasus have often been mischaracterised or exoticised, many of them have cross-linguistically rare features found in few or no other languages. The first half of the book is an introduction to the languages, with the linguistic profiles enriched by demographic research about their speakers. It features overviews of the main language families as well as detailed grammatical descriptions of several individual languages. The second half of the book delves more deeply into theoretical analyses of features, such as agreement, ellipsis, and discourse properties, which are found in some languages of the Caucasus.

• 1. Introduction
• 1.1. Objects and attributes
• 1.2. The concept of dominance
• 1.3. Best points
• 1.4. Book roadmap
• 1.5. Summary

• 2. Skyline queries
• 2.1. Main-memory computation
• 2.2. Algorithms for secondary memory
• 2.3. Advanced skyline processing
• 2.4. Skyline cardinality
• 2.5. Summary

• 3. Variations of skyline queries
• 3.1. K-dominant skyline queries
• 3.2. Skycube
• 3.3. Dynamic skyline queries
• 3.4. Spatial skyline queries
• 3.5. Metric space skyline queries
• 3.6. Range-based skyline queries
• 3.7. Other variations
• 3.8. Summary

• 4. Top-k dominating queries
• 4.1. Problem definition
• 4.2. A skyline-based algorithm
• 4.3. Methods based on R-tree variants
• 4.4. Index-free algorithms
• 4.5. Subspace progressive algorithms
• 4.6. Metric-based approaches
• 4.7. Top-k dominating queries in other environments
• 4.8. Summary

• 5. Applications of dominance-based queries.
• 5.1. Multi-criteria decision making
• 5.2. Machine learning
• 5.3. Network analysis
• 5.4. Marketing
• 5.5. Healthcare
• 5.6. Other interesting applications
• 5.7. Database support for dominance queries
• 5.8. Summary.

This book is a gentle introduction to dominance-based query processing techniques and their applications. The book aims to present fundamental as well as some advanced issues in the area in a precise, but easy-to-follow, manner. Dominance is an intuitive concept that can be used in many different ways in diverse application domains. The concept of dominance is based on the values of the attributes of each object. An object p dominates another object q if p is better than q. This goodness criterion may differ from one user to another. However, all decisions boil down to the minimization or maximization of attribute values. In this book, we will explore algorithms and applications related to dominance-based query processing. The concept of dominance has a long history in finance and multi-criteria optimization. However, the introduction of the concept to the database community in 2001 inspired many researchers to contribute to the area. Therefore, many algorithmic techniques have been proposed for the efficient processing of dominance-based queries, such as skyline queries, k-dominant queries, and top-k dominating queries, just to name a few.

• Question 1. Citizenship : who are "we the people"?
• George Washington to the Hebrew congregation of Newport, Rhode Island / George Washington
• Fredrick Douglass: from What to the Slave is 4th of July? / Fredrick Douglass
• Elizabeth Cady Stanton: Solitude of self / Elizabeth Cady Stanton -- Henry Cabot Lodge: Speech in the Senate on immigration / Henry Cabot Lodge
• Question 2. Equality : how can it be achieved?
• Horace Mann: from Twelfth Annual Report to the Massachusetts Board of Education / Horace Mann -- Abraham Lincoln: Speech to the 166th Ohio Regiment / Abraham Lincoln
• Jane Addams: from The subtle problems of charity / Jane Addams
• W.E.B. Du Bois: from Black Reconstruction / W.E.B. Du Bois
• Question 3. A more perfect union : what is the government for?
• James Madison: The Federalist No. 51 / James Madison
• John Marshall: from Opinion for the Court in McCulloch v. Maryland / John Marshall
• Alexis de Tocqueville: from Democracy in America / Alexis de Tocqueville
• Franklin D. Roosevelt: Address to the Commonwealth Club of California / Franklin D. Roosevelt
• Paul Nitze et al.: from NSC-68: United States Objectives and Programs for National Security / Paul Nitze
• Question 4. The power of money : how to control it?
• Andrew Jackson: from Veto to the Bank Charter / Andrew Jackson
• Carl Schurz: from Address on Civil Service reform / Carl Schurz
• Theodore Roosevelt: The new nationalism / Theodore Roosevelt
• John Paul Stevens: From Dissent in Citizens United v. Federal Election Commission / John Paul Stevens
• Question 5. Protest : can we disobey the law?
• Henry David Thoreau: from Civil disobedience / Henry David Thoreau
• Martin Luther King, Jr.: Letter from Birmingham Jail / Martin Luther King, Jr.
• Hannah Arendt: from Civil Disobedience / Hannah Arendt.

Architecture and ekphrasis' examines how eighteenth-century prints and drawings of antique architecture operated as representations of thought. Using original archival material, it considers the idea of the past in the period, specifically how it was discovered and described, and investigates how space and time inform visual ekphrasis or descriptions of architecture. The idea of embodiment is used to explore the various methods of describing architecture - including graphic techniques, measurement and perspective - all of which demonstrate choices about different modes of ekphrasis. This well-illustrated, accessibly written study will be of interest to academics and students working in a broad range of subject areas. It will also be an essential teaching tool for increasingly popular cross-disciplinary courses.