Particle Trapping and Conveying Using an Optical Archimedes’ Screw

:By

Sahar Froim

MSc student under the supervision of Dr. Alon Bahabad

Abstract

Trapping and manipulation of particles using laser beams has become an important tool in diverse fields of research.

In recent years, particular interest has been devoted to the problem of conveying optically trapped particles over extended distances either downstream or upstream of the direction of photon momentum flow. In this work, we proposed and experimentally demonstrated an optical analog of the famous Archimedes’ screw where the rotation of a helical intensity beam is transferred to the axial motion of optically trapped micrometer-scale, airborne, carbon-based particles. With this optical screw, particles were easily conveyed with controlled velocity and direction, upstream or downstream of the optical flow, over a distance of half a centimeter. Our results offer a very simple optical conveyor that could be adapted to a wide range of optical trapping scenarios.

On Thursday, June 14, 2018, 15:00

Room 011, Kitot building

Speaker: Adi Hayat

M.Sc. student under the supervision of Prof. Daniel Cohen-Or

Sunday, June 17^th, 2018 at 15:30

Room 206, Wolfson Mechanical Bldg., Faculty of Engineering

Generative Low-Shot Network Expansion​

Conventional deep learning classifiers are static in the sense that they are trained on a predefined set of classes and learning to classify a novel class typically requires re-training. In our work, we address the problem of Low-Shot network-expansion learning. We introduce a learning framework which enables expanding a pre-trained (base) deep network to classify novel classes when the number of examples for the novel classes is particularly small. We present a simple yet powerful distillation method where the base network is augmented with additional weights to classify the novel classes, while keeping the weights of the base network unchanged. We term this learning hard distillation, since we preserve the response of the network  on the old classes to be equal in both the base and the expanded network. We show that since only a small number of weights needs to be trained, the hard distillation excels for low-shot training scenarios. Furthermore, hard distillation avoids detriment to classification performance on the base classes. Finally, we show that low-shot network expansion can be done with a very small memory footprint by using a compact generative model of the base classes training data with only a negligible degradation relative to learning with the full training set.

Speaker: Itzik Nanikashvili

M.Sc. student under the supervision of Prof. Michael Margaliot

Wednesday, June 13^th 2018 at 15:30

Room 011, Kitot Bldg., Faculty of Engineering

Networks of ribosome flow models for modeling and analyzing intracellular traffic

Abstract

The ribosome flow model with input and output (RFMIO) is a deterministic dynamical system that has been used to study the flow of ribosomes during mRNA translation. The input of the RFMIO controls its initiation rate and the output represents the ribosome exit rate (and thus the protein production rate) at the 3' end of the mRNA molecule. The RFMIO and its variants can be used for studying additional intracellular processes such as transcription, transport, and more.

Here we consider networks of interconnected RFMIOs as a fundamental tool for modeling, analyzing and re-engineering the complex mechanisms of protein production. In these networks, the output of each RFMIO may be divided between several inputs of other RFMIOs. For the specific case of feed-forward network of RFMIOs we prove three important properties. First, the entire network converges to a steady-state that depends on all the translation rates in all the RFMIOs, but not on the initial conditions in the network. Second, there exists a spectral expression for the steady-state, and thus it can be determined without any numerical simulations of the dynamics. Third, the problem of dividing the output of an RFMIO between the inputs of other RFMIOs in a way that maximizes the total steady-state production rate of the network is a convex optimization problem. Hence, this problem is tractable even for very large networks. We describe the implications of these results to several fundamental biological phenomena and biotechnological objectives.