Colloquia and Seminars

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Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.

Academic Calendar at Technion site.

Upcoming Colloquia & Seminars

  • CGGC Seminar: IGATOOLS: a general purpose C++14 library for Isogeometric Analysis

    Pablo Antolin (Ecole Polytechnique F´ed´erale de Lausanne - EPFL, Switzerland)
    Friday, 25.8.2017, 14:30
    Room 337 Taub Bld.

    We present the design and the implementation of IGATOOLS [1], a C++14 general purpose library for solving PDEs using the isogeometric analysis framework [2]. The most remarkable aspect of isogeometric methods is the use of the same set of spline functions for representing the geometric domain and for describing the solution of PDEs.

    In the IGATOOLS design, the mathematical concepts of the isogeometric method and their relationships are directly mapped into classes and their interactions. This encapsulation gives flexibility to use the library in a wide range of scientific areas and applications. We provide a precise framework for a lot of loose, available information regarding the implementation of the isogeometric method, and also discuss the similarities and differences between this and the finite element method.

    The library uses advanced object oriented and generic programming techniques to ensure reusability, reliability, and maintainability of the source code. Among other capabilities, the library supports the development of dimension independent code (including manifolds and tensor-valued spaces), implements mutlithreaded methods and takes full advantage of the underlying tensor-product structure of the problem at hand (if any). The library also provides a plugin for interfacing with ParaView [3] in order to help the user to visualize the results. A bunch of code examples to illustrate the flexibility and power of the library are presented.

    Finally, new upcoming features are introduced: hierarchical B-Splines, which allows to perform local refinement; and computations in 2D and 3D trimmed domains (by using Irit [4]), in collaboration with Gershon Elber (Technion).

  • On Visibility and Point Clouds

    Nati Kligler, M.Sc. Thesis Seminar
    Tuesday, 29.8.2017, 11:30
    Taub 601
    Prof. A. Tal

    We introduce the concept of visibility detection within a point set to new domains. Specifically, we show that a simple representation of an image as a 3D point cloud lets us use visibility detection in classical image processing tasks, improving state-of-the-art results. Given an image, each pixel is represented as a feature point, a viewpoint is set, and points that are visible to the viewpoint are detected. What does it mean for a point to be visible? Although this question has been widely studied within computer graphics, it has never been regarded when the point set consists of feature vectors (rather than a real scene). We show that the answer to this question reveals unique information about the image, enabling us to modify state-of-the-art algorithms and improve their own results. As proof of concept, we demonstrate this idea within three applications: text image binarization, document unshadowing and stippling-style illustration.

  • Pixel Club: A Quest for a Universal Model for Signals: >From Sparsity to ConvNets

    Yaniv Romano (EE, Technion)
    Wednesday, 30.8.2017, 11:30
    Room 337 Taub Bld.

    Modeling data has led to a revolution in the fields of signal and image processing, and machine learning. Consider the simplest restoration problem - removal of noise from an image. The recent advent of highly effective models for images (e.g. the sparse-land model) has led researchers to believe that existing denoisers are touching the ceiling in terms of restoration performance. Leveraging this impressive achievement, we propose a framework that is able to translate complicated tasks in image processing to a chain of simple denoising steps, leading to cutting edge performance.

    We then proceed and concentrate on the Sparse-Land model, study its limitations and suggest different ways to overcome them. This model assumes that a signal can be represented as a linear combination of a few columns, called atoms, taken from a matrix, termed a dictionary. The learning problem, aiming to adapt the dictionary to a collection of samples, becomes computationally infeasible when dealing with high-dimensional signals. Traditionally, this problem was circumvented by learning a local model on small overlapping patches extracted from the image, and processing (e.g. denoising) these independently. However efficient, this approach is suboptimal since patches are globally connected to each other. To this end, we propose various approaches to tackle this limitation by harnessing ideas from game theory, boosting and graph theory. The suggested algorithms provide a systematic and generic way to improve the performance of existing methods, resulting in state-of-the-art performance.

    A different approach to treat high dimensional signals is the convolutional sparse coding (CSC). This global model assumes that a signal can be represented as a superposition of a few local atoms (small filters) shifted to different positions. A recent work suggested a novel theoretical analysis of this global model, which is based on the observation that while being global, the CSC can be characterized and analyzed locally. Armed with this observation, we extend the classic theory of sparse representations to a multi-layered, or hierarchical, convolutional sparse compositions. The proposed ML-CSC model is shown to be tightly connected to deep-learning - a sub-field of machine learning, offering a highly effective tool for supervised classification and regression. In particular, we reveal that the core algorithm of Convolutional Neural Networks (CNN), called the forward-pass, is a pursuit algorithm aiming to decompose signals that belong to the ML-CSC into their building atoms. With this view, we are able to analyze theoretically this architecture and provide success guarantees and stable estimation of the underlying representations throughout the layers. Furthermore, identifying the weaknesses in the above scheme, we propose theoretically superior alternative to the forward-pass algorithm.

  • The 6th Summer School on Cyber and Computer Security

    The 6th Summer School on Cyber and Computer Security

    Sunday, 10.9.2017, 09:00

    The Hiroshi Fujiwara Cyber Security Research Center will hold the 6th Summer School on Cyber and Computer Security: "Decentralized Cryptographic Currencies and Blockchains".

    The event will take place on Sunday-Thursday, September 10th-14th, 2017 at the Technion, Haifa.

    Tentative topics:
    Bitcoin, Ethereum and ZCash — overview
    Blockchain Scalability and Stability
    Game theory, economic incentives
    Zero knowledge proofs — applications to crypto-currencies
    Legal and regulatroy aspects of crypto-currencies

    Prof. Eli Ben-Sasson – Technion
    Prof. Eli Biham – Technion

    Prof. Eli Ben-Sasson – Technion
    Prof. Joseph Bonneau – New York University
    Vitalik Buterin – Ethereum, Chief Scientist; Foundation President
    Prof. Ittay Eyal – Technion
    Kathryn Haun, J.D. – Stanford University and Coinbase
    Dr. Neha Narula – MIT
    Prof. Rafael Pass – Cornell University
    Peter Van Valkenburgh, J.D. – Coin Center, Director of Research
    Zooko Wilcox – ZCash, Founder and CEO
    Prof. Aviv Zohar – Hebrew University

    Attendance is free, but registration is required.

    More details and information about The Hiroshi Fujiwara Cyber Security Research Center.