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

Brexit was a wake up call for Africans in the Diaspora

As the African Union moves towards economic integration, Tobi Jaiyesimi discusses what lessons the continent can learn from Brexit

CendR-peptiidiga suunatud terapeutiliste hõbeda nanoosakeste mõju eesnäärmekartsinoomi rakkudele

Käesoleva uurimistöö eesmärgiks oli uurida vähiravimi monometüülauristatiin E (MMAE) ja CendR-peptiidiga RPARPAR (RPAR) funktsionaliseeritud hõbeda nanoosakeste (AgNP) mõju NRP-1 üleekspresseerivale PPC-1 eesnäärmekartsinoomi rakuliinile in vitro. Uurimistöös näidati, et AgNP-MMAE-RPAR seondub spetsiifiliselt NRP-1 retseptorile ja et MMAE lisamine AgNP-RPAR osakestele ei mõjutanud nende seondumisvõimekust. Ag-MMAE-RPAR omas tugevat tsütotoksilist mõju NRP-1 ekspresseerivatele rakkudele. AgNP-MMAE-RPAR-i lisamisel PPC-1 ja M21 rakkude segakultuurile suri 96% sihtmärkrakkudest. Käesolevas uurimistöös väljaarendatud transportsüsteem on mudeliks järgnevatele projektidele. Märksõnad: suunatud kasvajaravi, eesnäärmekartsinoom, hõbeda nanoosakesed, CendR-peptiid, monometüülauristatiin E. CERCS kood: B726 kliiniline bioloogia

Delta Networks for Optimized Recurrent Network Computation

Many neural networks exhibit stability in their activation patterns over time in response to inputs from sensors operating under real-world conditions. By capitalizing on this property of natural signals, we propose a Recurrent Neural Network (RNN) architecture called a delta network in which each neuron transmits its value only when the change in its activation exceeds a threshold. The execution of RNNs as delta networks is attractive because their states must be stored and fetched at every timestep, unlike in convolutional neural networks (CNNs). We show that a naive run-time delta network implementation offers modest improvements on the number of memory accesses and computes, but optimized training techniques confer higher accuracy at higher speedup. With these optimizations, we demonstrate a 9X reduction in cost with negligible loss of accuracy for the TIDIGITS audio digit recognition benchmark. Similarly, on the large Wall Street Journal speech recognition benchmark even existing networks can be greatly accelerated as delta networks, and a 5.7x improvement with negligible loss of accuracy can be obtained through training. Finally, on an end-to-end CNN trained for steering angle prediction in a driving dataset, the RNN cost can be reduced by a substantial 100X

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