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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
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
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
A novel ultra-low power 7T full adder design using mixed logic
The key point is to design and implementation of the full adder which provides high-speed, Power efficiency and leas area with good voltage swing”. Where the term ‘Novel’ indicates that If something is so new, genuine and original that it had never been seen, used or even thought of before, call it is considered as ‘novel’ and ‘Ultra low power’ indicates that with the minimal amount of system power is enough for performing the respective operation of its own. In this article, a new High Performance and low power full adder utilizing a distinctive design \"Mixed Logic Design\" is recommended in implementation. The mixed - logic design combines Modified Gate diffusion input (MGDI) Transmission Gate Logic (TGL), Static CMOS logic, Pass transistor logic(PTL )and various logics which requires the recommended circuit. Full adder is a digital circuit which performs the sum of bits. In many PC’s and various kinds of microprocessors, adders are utilized in the ALU. The traditional Complementary Metal Oxide Semiconductor (CMOS) Full adder consisting of 28-Transistors and is built on a traditional Complementary Metal Oxide Semiconductor structure. GDI technique is low power and high-speed design technique where it takes 10 T. Gate Diffusion Input is one of the circuit design logics which occupies less area, simulates with high speed and power-efficient technique. It entails less count of transistors as correlated to traditional Complementary Metal Oxide Semiconductor technology. But the disadvantage with Gate Diffusion Input technique is that it provides an output having poor logic swing after simulation. The Modified-Gate diffusion input (MGDI ) technique rectifies this issue by implementing FA with 8T. But we are implementing another alternative “Mixed logic design” (combining the GDI, CMOS, TGL, etc logics ) and designing the Circuit with least count of transistors and compare with other unique logics which helps them to simulate the circuit in a power-efficient way and time delay.
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
Digital Design and Computer Architecture
This book is designed for courses that combine digital logic design with computer organization/architecture or that teach these subjects as a two-course sequence. The book begins with a modern approach by rigorously covering the fundamentals of digital logic design and then introduces Hardware Description Languages (HDLs). Featuring examples of the two most widely-used HDLs, VHDL and Verilog, the first half of the text prepares the reader for the design of a MIPS processor which is discussed in the second half of the book. By the end of the book, readers will be able to build their own microprocessor and will have a top-to-bottom understanding of how it works, even if they have no formal background in design or architecture beyond an introductory class. The authors combine an engaging and humorous writing style with an updated and hands-on approach to digital design.
eBook