In this paper, we address the graph matching problem.

Monitoring people's breathing could help diagnose, or even treat, various conditions Forget facial recognition – there could be a new way to identify you. Researchers have discovered that we all seem to have a "respiratory fingerprint", a unique way of breathing that could revolutionise how we diagnose and treat various health conditions, from obesity to depression. The breakthrough comes from Timna Soroka at the Weizmann Institute of Science in Israel and her colleagues, who have developed a wearable device that captures the subtle nuances of how we breathe. It addresses many longstanding questions about how respiratory signals relate to health and mental state – all in one body of work," says Torben Noto, who wasn't involved in the research, at Osmo in New York, an AI company aiming to give computers a sense of smell. The idea that breathing patterns contain health information isn't new – work dating back to the 1950s hints at this connection.

Scientists think they could have come across the location of the world's oldest campfire - and it's over a million years old. Flint tools and animal bones had been excavated from a quarry in Israel, thought to have been inhabited by our ancient ancestors, Homo erectus. Researchers investigated the ability of these artefacts to absorb ultraviolet (UV) and infrared (IR) radiation – which is affected by burning. They compared the results to those from similar unburnt materials, and concluded that they had been heated to temperatures between 390 F (200 C) and 1100 F (600 C). The team from the Weizmann Institute of Science in Israel also analysed bits of tusk from of an elephant-like animal that had been found in the same sedimentary layer as the tools.

New technology aims to speed up coronavirus testing by'sniffing' the patient's nasal cavity and produce a diagnosis in just 80 seconds. Scientists at the Weizmann Institute of Science in Israel designed a 3D-printed electric nose that analyzes aromas of chemicals in those infected with COVID-19. Dubbed Pen3, the instrument consists of a long tube fitted with sensors, which fits into the nostril to analyze the naval cavity. Deep learning algorithms sniff around the nose and return real-time detection of COVID-19 infections – and with 94 percent accuracy. New technology aims to speed up coronavirus testing by'sniffing' the patient's nasal cavity and produce a diagnosis in just 80 seconds Project leader Professor Noam Sobel said in a statement: 'The e-nose generates a pattern in every odor – it characterizes the smell of COVID-19.

When developing autonomous systems, engineers and other stakeholders make great effort to prepare the system for all foreseeable events and conditions. However, these systems are still bound to encounter events and conditions that were not considered at design time. For reasons like safety, cost, or ethics, it is often highly desired that these new situations be handled correctly upon first encounter. In this paper we first justify our position that there will always exist unpredicted events and conditions, driven among others by: new inventions in the real world; the diversity of world-wide system deployments and uses; and, the non-negligible probability that multiple seemingly unlikely events, which may be neglected at design time, will not only occur, but occur together. We then argue that despite this unpredictability property, handling these events and conditions is indeed possible. Hence, we offer and exemplify design principles that when applied in advance, can enable systems to deal, in the future, with unpredicted circumstances. We conclude with a discussion of how this work and a broader theoretical study of the unexpected can contribute toward a foundation of engineering principles for developing trustworthy next-generation autonomous systems.

Imagine showing a photo taken through a storefront window to someone who has never opened her eyes before, and asking her to point to what's in the reflection and what's in the store. To her, everything in the photo would just be a big jumble. Computers can perform image separations, but to do it well, they typically require handcrafted rules or many, many explicit demonstrations: here's an image, and here are its component parts. New research finds that a machine-learning algorithm given just one image can discover patterns that allow it to separate the parts you want from the parts you don't. The multi-purpose method might someday benefit any area where computer vision is used, including forensics, wildlife observation, and artistic photo enhancement.

In this book, we explore the task of automatically predicting human decision-making and its use in designing intelligent human-aware automated computer systems of varying natures - from purely conflicting interaction settings (e.g., security and games) to fully cooperative interaction settings (e.g., autonomous driving and personal robotic assistants). We explore the techniques, algorithms, and empirical methodologies for meeting the challenges that arise from the above tasks and illustrate major benefits from the use of these computational solutions in real-world application domains such as security, negotiations, argumentative interactions, voting systems, autonomous driving, and games. The book presents both the traditional and classical methods as well as the most recent and cutting edge advances, providing the reader with a panorama of the challenges and solutions in predicting human decision-making. Top Description Table of Contents Author Information Table of Contents Preface Acknowledgments Introduction Utility Maximization Paradigm Predicting Human Decision-Making From Human Prediction to Intelligent Agents Which Model Should I Use? Concluding Remarks Bibliography Authors' Biographies Index Top Description Table of Contents Author Information About the Author(s)Ariel Rosenfeld, Weizmann Institute of Science Ariel Rosenfeld is a Koshland Postdoctoral Fellow at Weizmann Institute of Science, Israel. He obtained a B.Sc. in Computer Science and Economics, graduating magna cum laude from Tel Aviv University, and a Ph.D. in Computer Science from Bar-Ilan University.