~~ (The talk will be given in English)

Speaker:  Dr. Merav Parter
                         MIT

Sunday, May 1st, 2016
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering

MST in Log-Star Rounds of Congested Clique

Abstract
We present a randomized algorithm that computes a Minimum Spanning Tree (MST) in O(log^* n) rounds, with high probability, in the Congested Clique model of distributed computing. In this model, the input is a graph on n nodes, initially each node knows only its incident edges, and per round each two nodes can exchange O(log n) bits.
Our key technical novelty is an O(log^* n) Graph Connectivity algorithm, the heart of which is a (recursive) forest growth method, based on a combination of two ideas: a sparsity-sensitive sketching aimed at sparse graphs and a random edge sampling aimed at dense graphs.
Our result improves significantly over the $O(\log \log \log n)$ algorithm of Hegeman et al. [PODC 2015] and the $O(\log \log n)$ algorithm of Lotker et al. [SPAA 2003; SICOMP 2005].
This join work with Mohsen Ghaffari, MIT, CSAIL.

Bio: Merav Parter is a Postdoctoral Fellow at MIT hosted by Prof. Nancy Lynch. She received a Ph.D. degree in Computer Science from the Weizmann Institute of Science under the guidance of Prof. David Peleg.
Her thesis "The Topology of Wireless Communication and Applications" won the first place Feder prize award for best student work in communication technology. Parter is a Rothschild and Fulbright Fellow.
In the past, she was a Google European Fellow in Distributed Computing, 2012. Her research interests focus on two aspects of reliable communication: fault tolerant graph structures and wireless communication. She's particularly intrigued with bridging the gap between Electrical Engineering and Theoretical Computer Science.

You are invited to attend a lecture

By

Moran Assif

M.Sc. student under supervision of Prof. Yossi Rosenwaks

School of Electrical Engineering, Dept. of Physical Electronics

Tel Aviv University

Analysis and Applications of Multiple State

Electrostatically Formed Nanowire Transistors

Electrostatically formed nanowire (EFN)-based transistors have recently been suggested as a single device with multiplexer functionality. In these transistors multiple gates and multiple drains transistors, the conduction path between source to one of the drains is determined by the bias applied to the two junction-side gates. If a specific bias is applied to the side gates, the conduction band electrons between them are confined to a well-defined area forming a narrow channel—the EFN. By applying a nonsymmetric bias on the side gates, the lateral position of the EFN can be controlled.

We present a simulation analysis of the different states of the MSET device, applications, the transient time between them and the power exerted during each transition. The dependence of transition time between states and leakage currents in cutoff state on different geometry parameters is also presented.

A discussion on the usage of the MSET device in digital applications will be presented along with an analog application of an RF switch.

Sunday, April 17, 2016, at 14:00

Room 200, Wolfson Building (deans office)

~~ (The talk will be given in English)

Speaker:   Prof. William T. Freeman
                        Massachusetts Institute of Technology and Google

Monday, May 23rd, 2016
15:00 - 16:00
Room 011, Kitot Bldg., Faculty of Engineering

Learning to see by listening

Abstract
Children may learn about the world by pushing, banging, and manipulating things, watching and listening as materials make their distinctive sounds-- dirt makes a thud; ceramic makes a clink. These sounds reveal physical properties of the objects, as well as the force and motion of the physical interaction. We've explored a toy version of such learning-through-interaction by recording audio and video while we hit many things with a drumstick.
We developed an algorithm the predict sounds from silent videos of the drumstick interactions. The algorithm uses a recurrent neural network to predict sound features from videos and then produces a waveform from these features with an example-based synthesis procedure. We demonstrate that the sounds generated by our model are realistic enough to fool participants in a "real or fake" psychophysical experiment, and that the task of predicting sounds allows our system to learn to visually distinguish different materials.
Joint work with: Andrew Owens, Phillip Isola, Josh McDermott, Antonio Torralba, Edward H. Adelson http://arxiv.org/abs/1512.08512 to appear in CVPR 2016

Bio: 
William T. Freeman is the Thomas and Gerd Perkins Professor of Electrical Engineering and Computer Science at MIT, and a member of the Computer Science and Artificial Intelligence Laboratory (CSAIL) there. He was the Associate Department Head from 2011 - 2014. His current research interests include machine learning applied to computer vision, Bayesian models of visual perception, and computational photography. He received outstanding paper awards at computer vision or machine learning conferences in 1997, 2006, 2009 and 2012, and test-of-time awards for papers from 1990 and 1995. Previous research topics include steerable filters and pyramids, orientation histograms, the generic viewpoint assumption, color constancy, computer vision for computer games, and belief propagation in networks with loops. He is active in the program or organizing committees of computer vision, graphics, and machine learning conferences. He was the program co-chair for ICCV 2005, and for CVPR 2013.