Speaker: Ofir Lindenbaum

Ph.D. student under the supervision of Prof. Arie Yeredor and Prof. Amir Averbuch

Wednesday, June 28^h, 2017 at 15:00
Room 011, Kitot Bldg., Faculty of Engineering

Learning From Multi-View High Dimensional Data

Abstract

High dimensional “big data” is often encountered in various fields, such as audio analysis, video analytics and data mining.  One of the associated challenges is how to extract meaningful information from such data, which is generally difficult to analyze as is. Dimensionality reduction methodologies reduce the size (dimension) of objects in the dataset while preserving the coherence of the original data, such that clustering, classification, manifold learning and many other data analysis tasks can be applied in the reduced space.

We consider learning a reduced dimensionality representation from datasets obtained under multiple views. Such multiple views of datasets can be obtained, for example, when the same underlying process is observed using several different modalities, or measured with different instrumentation. Our goal is to effectively exploit the availability of such multiple views for various purposes, such as non-linear embedding, manifold learning, spectral clustering, anomaly detection and non-linear system identification.

Our proposed method exploits the intrinsic relation within each view, as well as the mutual relations between views. We do this by defining a cross-view model, in which an implied Random Walk process between objects is restrained to hop between the various views.  In this talk I will describe two frameworks for multi-view dimensionality reduction. Applications for manifold learning, clustering, classification and detection of seismic events will be presented.

School of Mechanical Engineering Seminar
Wednesday, Oct 25, 2017 at 14:00
Wolfson Building of Mechanical Engineering, Room 130

 Evaluating the cleavage energies of Lithium Niobate (LiNbO₃) crystal

Yotam Hirsh

MSc. Student of Prof. Dov Sherman

The cleavage energy of brittle single crystal is scarce due to the difficulties to perform controlled and accurate fracture experiments in these materials. The first estimation would be the lower bound of the cleavage energy, twice the free and relaxed surface energy, or 2g_s, known as Griffith barrier. The most common crystals structures are cubic and hexagonal structures, most of the efforts fracture wise, have been directed toward cubic single crystal silicon. This research explores the second most common structure, the hexagonal crystal structure, where Lithium Niobate (LiNbO₃) is the targeted material. The structure of LiNbO₃ crystal is challenging due to his special thermal expansion behavior and anisotropic mechanical properties in the orthogonal space. This crystal has been widely studied for its physical and optical properties. However, very limited information exists for its fracture properties, cleavage planes, the free surface energy, etc. Our results would be the first time cleavge energies would be given for this hexagonal LiNbO₃ crystal.

We will present our high resolution fracture experiments, performed using our Coefficient of Thermal Expansion Mismatch (CTEM) method, employing heating as the driving force for fracture. Finite element analysis (FEA) was used as a calculation tool to evaluate the cleavage energy of the material in difference cleavage systems. Preliminary results will be presented and discussed.