The Last Layer

Senior Inspector Gerard de Rochenoir of the elite French National Police is attempting to solve two daring jewelry robberies in the heart of Paris when one of the victims turns up murdered. Gerards investigation takes him to the glamorous Caribbean island of St. Barth where he crosses paths with Sofia Mostov, a striking jeweler with a mysterious past and a possible link to the crimes. While Gerard keeps a suspicious eye on Mostov, he meets Catherine York, an attractive American insurance executive twenty years his junior, who happens to be investigating the same two Paris robberies as well as others that may be related. When Pierre Abou, a Sherlock Holmes obsessed cop, makes a stunning discovery at a farmhouse on the Brittany coast, the mystery begins to unravel and leads Gerard and Catherine around the world and straight to another murder. As this unlikely couple becomes intertwined in the complexities of a passionate relationship, they soon discover that Sofia Mostov is not only mysterious and beautiful, but also very dangerous.

In The Last Layer–the follow-up to Digital Alchemy, her successful book on alternative printmaking techniques–Bonny Lhotka teaches how to make prints that take their inspiration from early printmaking processes. In this book, Lhotka shows readers step-by-step how to create modern-day versions of anthotypes, cyanotypes, tintypes, and daguerreotypes as well as platinum and carbon prints. She also reinvents the photogravure and Polaroid transfer processes and explores and explains groundbreaking techniques for combining digital images with traditional monotype, collograph, and etching press prints. By applying these classic techniques to modern images, readers will be able to recreate the look of historical printmaking techniques and explore the limits of their creative voice. Best of all, the only equipment required is a desktop inkjet printer that uses pigment inks, and a handful of readily available materials and supplies–not the toxic chemicals once required to perform these very same processes. Leveraging her training as a traditional painter and printmaker, Bonny Lhotka brings new innovations and inventions that combine the best of centuries of printmaking technique with modern technology to create unique works of art and photography. After years of experimentation and development, these new processes allow alternative photographers, traditional printer makers, and 21st century digital artists to express their creative voice in ways never before possible.

There are five layers of the ocean, though most of us alive will only ever see one. The deepest layer of the ocean is called by some the midnight zone. The only light comes from bioluminescence, created by animals themselves. In order to see, the creatures there must create their own light. They must move like solitary suns, encased in their own bubbles of freezing water. This layer is the most completely unexplored zone on the planet. Though it is hostile to humans, it also is fascinating beyond belief. If you had a chance to see it, wouldn't you want to go there? The year Mary is 38, the suicide of a stranger in a nearby reservoir compels her to make a change. She decides to strike out for Alaska and take a chance on love and home. She begins to learn how to travel in a small yellow kayak along the coast, contending with gales, high seas, and bears. She explores the different meanings of home: the perspectives of people who were born in this place and others who chose it, the first peoples who have been here for generations, and the ones who eventually leave. When she marries a man from another island, she is convinced that this time love will stick. She soon learns that navigating marriage is just as difficult as learning the ocean. Divided into sections detailing the main kayaking strokes, this memoir shows how each can be a metaphor for the lives we all pass through and the tools we need to stay afloat.

Learn how to process and analysis data using Python Key Features a- The book has theories explained elaborately along with Python code and corresponding output to support the theoretical explanations. The Python codes are provided with step-by-step comments to explain each instruction of the code. a- The book is quite well balanced with programs and illustrative real-case problems. a- The book not only deals with the background mathematics alone or only the programs but also beautifully correlates the background mathematics to the theory and then finally translating it into the programs. a- A rich set of chapter-end exercises are provided, consisting of both short-answer questions and long-answer questions. Description This book introduces the fundamental concepts of Data Science, which has proved to be a major game-changer in business solving problems. Topics covered in the book include fundamentals of Data Science, data preprocessing, data plotting and visualization, statistical data analysis, machine learning for data analysis, time-series analysis, deep learning for Data Science, social media analytics, business analytics, and Big Data analytics. The content of the book describes the fundamentals of each of the Data Science related topics together with illustrative examples as to how various data analysis techniques can be implemented using different tools and libraries of Python programming language. Each chapter contains numerous examples and illustrative output to explain the important basic concepts. An appropriate number of questions is presented at the end of each chapter for self-assessing the conceptual understanding. The references presented at the end of every chapter will help the readers to explore more on a given topic. What will you learn a- Understand what machine learning is and how learning can be incorporated into a program. a- Perform data processing to make it ready for visual plot to understand the pattern in data over time. a- Know how tools can be used to perform analysis on big data using python a- Perform social media analytics, business analytics, and data analytics on any data of a company or organization. Who this book is for The book is for readers with basic programming and mathematical skills. The book is for any engineering graduates that wish to apply data science in their projects or wish to build a career in this direction. The book can be read by anyone who has an interest in data analysis and would like to explore more out of interest or to apply it to certain real-life problems. Table of Contents 1. Fundamentals of Data Science1 2. Data Preprocessing 3. Data Plotting and Visualization 4. Statistical Data Analysis 5. Machine Learning for Data Science 6. Time-Series Analysis 7. Deep Learning for Data Science 8. Social Media Analytics 9. Business Analytics 10. Big Data Analytics About the Authors Dr. Gypsy Nandi is an Assistant Professor (Sr) in the Department of Computer Applications, Assam Don Bosco University, India. Her areas of interest include Data Science, Social Network Mining, and Machine Learning. She has completed her Ph.D. in the field of 'Social Network Analysis and Mining'. Her research scholars are currently working mainly in the field of Data Science. She has several research publications in reputed journals and book series. Dr. Rupam Kumar Sharma is an Assistant Professor in the Department of Computer Applications, Assam Don Bosco University, India. His area of interest includes Machine Learning, Data Analytics, Network, and Cyber Security. He has several research publications in reputed SCI and Scopus journals. He has also delivered lectures and trained hundreds of trainees and students across different institutes in the field of security and android app development.

The seven-volume set of LNCS 11301-11307, constitutes the proceedings of the 25th International Conference on Neural Information Processing, ICONIP 2018, held in Siem Reap, Cambodia, in December 2018. The 401 full papers presented were carefully reviewed and selected from 575 submissions. The papers address the emerging topics of theoretical research, empirical studies, and applications of neural information processing techniques across different domains. The 4th volume, LNCS 11304, is organized in topical sections on feature selection, clustering, classification, and detection.

This handbook gathers together the state of the art on mathematical models and algorithms for imaging and vision. Its emphasis lies on rigorous mathematical methods, which represent the optimal solutions to a class of imaging and vision problems, and on effective algorithms, which are necessary for the methods to be translated to practical use in various applications. Viewing discrete images as data sampled from functional surfaces enables the use of advanced tools from calculus, functions and calculus of variations, and nonlinear optimization, and provides the basis of high-resolution imaging through geometry and variational models. Besides, optimization naturally connects traditional model-driven approaches to the emerging data-driven approaches of machine and deep learning. No other framework can provide comparable accuracy and precision to imaging and vision. Written by leading researchers in imaging and vision, the chapters in this handbook all start with gentle introductions, which make this work accessible to graduate students. For newcomers to the field, the book provides a comprehensive and fast-track introduction to the content, to save time and get on with tackling new and emerging challenges. For researchers, exposure to the state of the art of research works leads to an overall view of the entire field so as to guide new research directions and avoid pitfalls in moving the field forward and looking into the next decades of imaging and information services. This work can greatly benefit graduate students, researchers, and practitioners in imaging and vision; applied mathematicians; medical imagers; engineers; and computer scientists.