SRI Blog

-By Ophir Gozes, Ma’ayan Frid-Adar, Hayit Greenspan, PhD, Patrick D. Browning, MD, Huangqi Zhang, MD, Wenbin Ji, MD, Adam Bernheim, MD, and Eliot Siegel, MD Paper Link Abstract Rapidly developed AI-based automated CT image analysis tools can achieve high accuracy in detection of Coronavirus positive patients as well as quantification of disease burden. Utilizing the deep-learning image analysis system developed, we achieved classification results for Coronavirus vs Non-coronavirus cases per thoracic CT studies of 0.996 AUC (95%CI: 0.989-1.00) on Chinese control and infected patients. Possible working point: 98.2% sensitivity, 92.2% specificity. For Coronavirus patients the system outputs quantitative opacity measurements and a visualization of the larger opacities in a slice-based “heat map” or a 3D volume display. A suggested “Corona score” measures the progression of patients over time. The coronavirus infection surprised the world with its rapid spread and has had

-By Francisco J. Chiyah Garcia José Lopes Helen Hastie School of Mathematical and Computer Sciences, Heriot-Watt University Edinburgh, United Kingdom Paper Link ABSTRACT Increasingly complex and autonomous robots are being deployed in real-world environments with far-reaching consequences. High stakes scenarios, such as emergency response or offshore energy platform and nuclear inspections, require robot operators to have clear mental models of what the robots can and can’t do. However, operators are often not the original designers of the robots and thus, they do not necessarily have such clear mental models, especially if they are novice users. This lack of mental model clarity can slow adoption and can negatively impact human-machine teaming. We propose that interaction with a conversational assistant, who acts as a mediator, can help the user with understanding the functionality of remote robots and increase transparency through natural language explanations, as well

-By Jerry O. Talton Intel Corporation,  Lingfeng, Yang Stanford University, Ranjitha Kumar Stanford University, Maxine Lim Stanford University, Noah D. Goodman Stanford University, Radom´ır Mech ˇ Adobe Corporation This blog is extension to the earlier blog . Paper Link ABSTRACT Design patterns have proven useful in many creative fields, providing content creators with archetypal, reusable guidelines to leverage in projects. Creating such patterns, however, is a time-consuming, manual process, typically relegated to a few experts in any given domain. In this paper, we describe an algorithmic method for learning design patterns directly from data using techniques from natural language processing and structured concept learning. Given a set of labeled, hierarchical designs as input, we induce a probabilistic formal grammar over these exemplars. Once learned, this grammar encodes a set of generative rules for the class of designs, which can be sampled to synthesize novel art

-By Ranjitha Kumar, Department of Computer Science, Stanford University Paper Link ABSTRACT This thesis describes how data-driven approaches to Web design problems can enable useful interactions for designers. It presents three machine learning applications which enable new interaction mechanisms for Web design: rapid retargeting between page designs, scalable design search, and generative probabilistic model induction to support design interactions cast as probabilistic inference. It also presents a scalable architecture for efficient data-mining on Web designs, which supports these three applications. MACHINE LEARNING FOR WEB DESIGN The Web provides an enormous repository of design knowledge: every page represents a concrete example of human creativity and aesthetics. Given the ready availability of Web data, how can we leverage it to help designers? This thesis describes three machine learning applications which enable new interaction mechanisms for Web design: rapi