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Amir Shachar

Publications and source records attributed to Amir Shachar.

3 recordsLinked to original sources

Introduction to Algogens

This book introduces the concept of Algogens, a promising integration of generative AI with traditional algorithms aimed at improving problem-solving techniques across various fields. It provides an accessible overview of how Algogens combine AI's innovative potential with algorithms' reliability to tackle complex challenges more effectively than either could alone. The text explores the basics of Algogens, their development, applications, and advantages, such as better adaptability and efficiency. Through examples and case studies, readers will learn about Algogens' practical uses today and their potential for future cybersecurity, healthcare, and environmental science innovation. Acknowledging new technologies' challenges and ethical considerations, the book offers a balanced look at the prospects and obstacles facing Algogens. It invites a broad audience, including experts and newcomers, to engage with the topic and consider Algogens' role in advancing our problem-solving capabilities. This work is presented as a starting point for anyone interested in the intersection of AI and algorithms, encouraging further exploration and discussion on this emerging field. It aims to spark curiosity and contribute to the ongoing conversation about how technology can evolve to meet the complex demands of the AI era.

cs.LG

Introduction to Semi-discrete Calculus

The Infinitesimal Calculus explores mainly two measurements: the instantaneous rates of change and the accumulation of quantities. This work shows that scientists, engineers, mathematicians, and teachers increasingly apply another change measurements tool: functions' local trends. While it seems to be a special case of the rate (via the derivative sign), this work proposes a separate and favorable mathematical framework for the trend, called Semi-discrete Calculus.

cs.DM

On a Relation Between the Integral Image Algorithm and Calculus

The Integral Image algorithm is often applied in tasks that require efficient integration over images, such as object detection. In this paper we discuss theoretical aspects of the algorithm's continuous version. We suggest to define the coefficients at the formulation of the algorithm by applying a novel kind of discrete derivative. Based on that operator we build a novel integration method over curves in the plane, and apply it in a theorem that extends the algorithm to general continuous domains.

cs.DM