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Foundations of Probabilistic Logic Programming
Probabilistic Logic Programming extends Logic Programming by enabling the representation of uncertain information by means of probability theory. Probabilistic Logic Programming is at the intersection of two wider research fields: the integration of logic and probability and Probabilistic Programming. Logic enables the representation of complex relations among entities while probability theory is useful for model uncertainty over attributes and relations. Combining the two is a very active field of study. Probabilistic Programming extends programming languages with probabilistic primitives that can be used to write complex probabilistic models. Algorithms for the inference and learning tasks are then provided automatically by the system. Probabilistic Logic programming is at the same time a logic language, with its knowledge representation capabilities, and a Turing complete language, with its computation capabilities, thus providing the best of both worlds. Since its birth, the field of Probabilistic Logic Programming has seen a steady increase of activity, with many proposals for languages and algorithms for inference and learning. Foundations of Probabilistic Logic Programming aims at providing an overview of the field with a special emphasis on languages under the Distribution Semantics, one of the most influential approaches. The book presents the main ideas for semantics, inference, and learning and highlights connections between the methods. Many examples of the book include a link to a page of the web application http://cplint.eu where the code can be run online.
eBook
Security Architecture
Security Architecture is a driver for proactive, strategic, and comprehensive protection. Whether you are a seasoned security professional or an emerging architect, this book provides actionable insights to help you navigate the complex landscape of modern cybersecurity.
eBook
Hands-on Programmatic In-house Digital Advertising
Guide to Marketing Automation and Accelerated ROI on Advertising Key Features ? Demonstrates how a DSP works, its bidding strategies, impression tracking, and configurations. ? Exemplifies how AI/ML simplifies bidding strategies. ? Illustrates how SSP, exchange, ad-server, and header-bidding (client and server-side) work in detail. Description This book provides you with an in-depth understanding of programmatic advertising. This knowledge can be applied to the checklist for procuring the appropriate stack, optimizing existing platforms, and/or building the system from the ground up.With comprehensive treatment of programmatic issues, this book establishes a solid foundation with ID systems, data management systems, and data thinking, among other topics. It explores the different data sources, attributes, and the real-time bidding protocol in detail (RTB steam). It makes its way even further into the larger systems of DSP and SSP. This book will help assist you in all aspects of running an ad-tech system.By the end of this book, you will gain a vast amount of knowledge about programmatic systems. You will become an independent expert that will help you to evaluate the advertising techniques for your own business. What you will learn ? Learn about the ID mechanics of cookies and GAID/IDFA. ? Gain an intuitive and in-depth understanding of the data's role in AI/ML. ? Learn about various data-centric strategies around buy and sell of media. ? Learn about DSP, bidder, bidding strategies, RTB, paid impression, and various syncs. ? Learn about SSP, Exchange, Ad-Server, header bidding systems, and AI-led floor price optimization. Who this book is for The book is essential for the architects, senior developers, and ad-tech operations to learn about programmatic in-housing from a design, process, strategic thinking, and operational standpoint. It also attracts business professionals who want to learn the tricks of the trade for increasing revenues and learn the art of asking the right questions. Table of Contents 1. ID Management, Cookies, and Sync Mechanics 2. Data and AI Strategies 3. DMP and CDP 4. Exchanges, Ad-Servers, and Header Bidding 5. Bidders and Meta DSPs 6. Data Privacy by Design 7. Buy and Sell Strategies
eBook
Energy-Efficient Ternary Arithmetic Logic Unit Design in CNTFET Technology
This article presents the low-power ternary arithmetic logic unit (ALU) design in carbon nanotube field-effect transistor (CNFET) technology. CNFET unique characteristic of geometry-dependent threshold voltage is employed in the multi-valued logic design. The ternary logic benefit of reduced circuit overhead is exploited by embedding multiple modules within a block. The existence of symmetric literals among various single shift and dual shift operators in addition and subtraction operations results in the optimized realization of adder/subtractor modules. The proposed design is based on the notion of multiplexing either arithmetic, logical or miscellaneous operations, depending upon the status of input selection trits. The results obtained by the synopsis HSPICE simulator with the Stanford 32 nm CNFET technology illustrate that the proposed processing modules outperform their counterparts in terms of power consumption, energy consumption and device count. The proposed methodology leads to saving in power consumption and energy consumption (PDP) of 62% and 58%, respectively, on the benchmark circuit of the ALU [full adder/subtractor (FAS)]. Furthermore, for the 2-trit multiplier design, the enhanced performance at the architecture and circuit level is achieved through the optimized designs of various adder and multiplier circuits.
Journal Article
Logic and computer design fundamentals
Based on the book Computer Engineering Hardware Design (1988), which presented the same combined treatment of logic design, digital system design and computer design basics. Because of its broad coverage of both logic and computer design, this text can be used to provide an overview of logic and computer hardware for computer science, computer engineering, electrical engineering, or engineering students in general. Annotation copyright by Book News, Inc., Portland, OR.
Book
Causal Inference and Discovery in Python
Demystify causal inference and casual discovery by uncovering causal principles and merging them with powerful machine learning algorithms for observational and experimental data Purchase of the print or Kindle book includes a free PDF eBook
Key Features
Examine Pearlian causal concepts such as structural causal models, interventions, counterfactuals, and moreDiscover modern causal inference techniques for average and heterogenous treatment effect estimationExplore and leverage traditional and modern causal discovery methods
Book Description
Causal methods present unique challenges compared to traditional machine learning and statistics. Learning causality can be challenging, but it offers distinct advantages that elude a purely statistical mindset. Causal Inference and Discovery in Python helps you unlock the potential of causality. You’ll start with basic motivations behind causal thinking and a comprehensive introduction to Pearlian causal concepts, such as structural causal models, interventions, counterfactuals, and more. Each concept is accompanied by a theoretical explanation and a set of practical exercises with Python code. Next, you’ll dive into the world of causal effect estimation, consistently progressing towards modern machine learning methods. Step-by-step, you’ll discover Python causal ecosystem and harness the power of cutting-edge algorithms. You’ll further explore the mechanics of how “causes leave traces” and compare the main families of causal discovery algorithms. The final chapter gives you a broad outlook into the future of causal AI where we examine challenges and opportunities and provide you with a comprehensive list of resources to learn more.
What you will learn
Master the fundamental concepts of causal inferenceDecipher the mysteries of structural causal modelsUnleash the power of the 4-step causal inference process in PythonExplore advanced uplift modeling techniquesUnlock the secrets of modern causal discovery using PythonUse causal inference for social impact and community benefit
Who this book is for
This book is for machine learning engineers, data scientists, and machine learning researchers looking to extend their data science toolkit and explore causal machine learning. It will also help developers familiar with causality who have worked in another technology and want to switch to Python, and data scientists with a history of working with traditional causality who want to learn causal machine learning. It’s also a must-read for tech-savvy entrepreneurs looking to build a competitive edge for their products and go beyond the limitations of traditional machine learning.
eBook