Resisting the Tradition of Sexism

I stood there, or rather wobbled, standing on a plastic chair, peering over the division of a wall. Three feet away, I saw a boy reading from the Torah. There were men embracing, women cheering from across the wall, and rounds of applause on both sides. I stepped down from the chair and glanced at the sky. In my view, a large barrier towered over me, piercing the blue-- the Western Wall. My haze was then suddenly interrupted by a woman frantically mounting the chair I had previously been standing on. She was desperate to get a view of the boy on his Bar Mitzvah day. Frazzled, I redirected my focus to a new woman with dark hair. She looked as if she was trying to jump across the barrier that separated the men from the women. It was her son’s Bar Mitzvah day, and all she could do was watch from afar. All I could do was watch from afar because I am a woman

Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation

Rapid and low power computation of optical flow (OF) is potentially useful in robotics. The dynamic vision sensor (DVS) event camera produces quick and sparse output, and has high dynamic range, but conventional OF algorithms are frame-based and cannot be directly used with event-based cameras. Previous DVS OF methods do not work well with dense textured input and are designed for implementation in logic circuits. This paper proposes a new block-matching based DVS OF algorithm which is inspired by motion estimation methods used for MPEG video compression. The algorithm was implemented both in software and on FPGA. For each event, it computes the motion direction as one of 9 directions. The speed of the motion is set by the sample interval. Results show that the Average Angular Error can be improved by 30\% compared with previous methods. The OF can be calculated on FPGA with 50\,MHz clock in 0.2\,us per event (11 clock cycles), 20 times faster than a Java software implementation running on a desktop PC. Sample data is shown that the method works on scenes dominated by edges, sparse features, and dense texture.Comment: Published in ISCAS 201

Release Planning for Successful Reentry: A Guide for Corrections, Service Providers, and Community Groups

Outlines the concept of release planning, identifies the fundamental needs released prisoners face in reentering society, and recommends ways for corrections agencies and community organizations to help meet those needs through improved release planning

DDD17: End-To-End DAVIS Driving Dataset

Event cameras, such as dynamic vision sensors (DVS), and dynamic and active-pixel vision sensors (DAVIS) can supplement other autonomous driving sensors by providing a concurrent stream of standard active pixel sensor (APS) images and DVS temporal contrast events. The APS stream is a sequence of standard grayscale global-shutter image sensor frames. The DVS events represent brightness changes occurring at a particular moment, with a jitter of about a millisecond under most lighting conditions. They have a dynamic range of >120 dB and effective frame rates >1 kHz at data rates comparable to 30 fps (frames/second) image sensors. To overcome some of the limitations of current image acquisition technology, we investigate in this work the use of the combined DVS and APS streams in end-to-end driving applications. The dataset DDD17 accompanying this paper is the first open dataset of annotated DAVIS driving recordings. DDD17 has over 12 h of a 346x260 pixel DAVIS sensor recording highway and city driving in daytime, evening, night, dry and wet weather conditions, along with vehicle speed, GPS position, driver steering, throttle, and brake captured from the car's on-board diagnostics interface. As an example application, we performed a preliminary end-to-end learning study of using a convolutional neural network that is trained to predict the instantaneous steering angle from DVS and APS visual data.Comment: Presented at the ICML 2017 Workshop on Machine Learning for Autonomous Vehicle

Teaching and Learning in Interdisciplinary Higher Education: A Systematic Review

Interdisciplinary higher education aims to develop boundary-crossing skills, such as interdisciplinary thinking. In the present review study, interdisciplinary thinking was defined as the capacity to integrate knowledge of two or more disciplines to produce a cognitive advancement in ways that would have been impossible or unlikely through single disciplinary means. It was considered as a complex cognitive skill that constituted of a number of subskills. The review was accomplished by means of a systematic search within four scientific literature databases followed by a critical analysis. The review showed that, to date, scientific research into teaching and learning in interdisciplinary higher education has remained limited and explorative. The research advanced the understanding of the necessary subskills of interdisciplinary thinking and typical conditions for enabling the development of interdisciplinary thinking. This understanding provides a platform from which the theory and practice of interdisciplinary higher education can move forwar