# קולוקוויום וסמינרים

כדי להצטרף לרשימת תפוצה של קולוקוויום מדעי המחשב, אנא בקר בדף מנויים של הרשימה.Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.

Academic Calendar at Technion site.

- Bioinformatics Forum
- BizTEC Forum
- ceClub
- CGGC Weekly Seminar
- Coding Theory Seminar
- Colloquia
- Haifux, Haifa Linux Club
- Hardware Security Seminar
- Pixel Club
- Theory Seminar

## קולוקוויום וסמינרים בקרוב

### Pixel Club: Photometric Stereo by Non-convex Minimisation

- דובר:
- גאורג ראדו (אונ' ברנדנברוג)
- תאריך:
- יום ראשון, 26.5.2019, 11:30
- מקום:
- חדר 337, בניין טאוב למדעי המחשב

The aim of photometric stereo is to estimate shape and appearance of a three-dimensional object from multiple input images taken from the same point of view but under different lighting conditions. The most common techniques are conceptually close to the classic photometric stereo problem, meaning that the modelling encompasses a linearisation step and that the shape information is computed in terms of surface normals. Instead of linearising we aim to stick to the original formulation of the photometric stereo problem, and we propose to minimise a much more natural objective function, namely the reprojection error in terms of depth. Minimising the resulting non-trivial variational model for photometric stereo allows to recover the depth of the photographed scene directly. At hand of an experimental evaluation we discuss important properties of the method.

### Coding Theory: Nearly Optimal Robust Positioning Sequences

- דובר:
- וואי הנג'יה (אונ' בן-גוריון)
- תאריך:
- יום ראשון, 26.5.2019, 14:30
- מקום:
- טאוב 601

A robust positioning pattern is a large array that allows a mobile device to locate its position by reading a possibly corrupted small window around it. In this talk, we focus on the 1-dimensional case, i.e., robust positioning sequences (RPS). We present constructions of RPSs along with efficient locating algorithms. In particular, we construct a class of q-ary RPSs which are robust to a constant fraction of errors and have asymptotically optimal rate. We also obtain a class of binary RPSs which are robust to a constant number of errors, and the redundancy of the proposed sequences differs from the lower bound only by a small term.

### Deep Learning: Optimization, Generalization and Architectures

- דובר:
- Amir Globerson - COLLOQUIUM LECTURE - RESCHEDULED
- תאריך:
- יום שלישי, 28.5.2019, 14:30
- מקום:
- חדר 337 טאוב.
- השתייכות:
- Tel-Aviv University
- מארח:
- Yuval Filmus

Artificial neural networks have recently revolutionized the field of machine learning, demonstrating striking empirical success on tasks such as image understanding, speech recognition and natural language processing. However, we still do not have sufficient theoretical understanding of how such models can be successfully learned. Two specific questions in this context are: how can neural nets be learned despite the non-convexity of the learning problem, and how can they generalize well despite often having more parameters than training data. I will describe some of our results in this context, focusing on the particular properties of common optimization algorithms such as stochastic gradient descent. I will also discuss our recent work on using deep learning for complex-output problems, and describe principles for constructing architectures for these. The resulting models show competitive performance on challenging image understanding tasks such as scene graph generation. Short bio:

Amir Globerson received a BSc in computer science and physics in 1997 from the Hebrew University, and a PhD in computational neuroscience from the Hebrew University in 2006. After his PhD, he was a postdoctoral fellow at the University of Toronto and a Rothschild postdoctoral fellow at MIT. His research interests include machine learning, deep learning, graphical models, optimization, machine vision, and natural language processing. He is an associate editor for the Journal of Machine Learning Research, and was the Associate Editor in Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. His work has received several prizes including five paper awards at NeurIPS, ICML and UAI. In 2018 he was the program co-chair for UAI, and general chair for UAI 2019 to be held in Tel Aviv. In 2019 he received the ERC Consolidator Grant.### ceClub: Memory Channels Are Needed for High Performance Data Analytics

- דובר:
- עודד גרין (Invidia ארה"ב)
- תאריך:
- יום רביעי, 29.5.2019, 11:30
- מקום:
- חדר 861, בניין מאייר, הפקולטה להנדסת חשמל

Graph processing is typically considered to be a memory-bound rather than compute-bound problem. One common line of thought is that more available memory bandwidth corresponds to better graph processing performance. However, in this talk, I will show that this is not necessarily the case. I will demonstrate that the key factor in the utilization of the memory system for graph algorithms is not the raw bandwidth, or even latency of memory requests, but instead is the number of memory channels available to handle small data transfers with low locality.

This work was done in collaboration with James, Dr. Jeff Young, Dr. Jun Shirako, and Prof. David Bader.

Using several widely used graph frameworks, including Gunrock (on the GPU) and GAPBS & Ligra (for CPUs), we characterize two very distinct memory hierarchies with respect to key graph analytics kernels. Our results show that the differences in peak bandwidths of several of the Pascal-generation GPU memory subsystems aren't reflected in the performance of various analytics. Furthermore, our experiments on CPU and Xeon Phi systems show that the number of memory channels utilized can be a decisive factor in performance across several different applications. For CPU systems with smaller thread counts, the memory channels can be underutilized while systems with high thread counts can oversaturate the memory subsystem, which leads to limited performance. Lastly, we model the performance of including more channels with narrower access widths than those found in existing memory subsystems, and we analyze the trade-offs in terms of the two most prominent types of memory accesses found in graph algorithms, streaming and random accesses.

Bio:

Dr. Oded Green is a Senior Graph Software Engineer with NVIDIA's AI infrastructure. Oded is also currently an Adjunct Research Scientist at the Georgia Institute of Technology (Georgia Tech) in Computational Sciences and Engineering, where he also received his PhD. Oded received both his MSc in electrical engineering and his BSc in computer engineering from Technion – Israel Institute of Technology

Oded's research primarily focuses on improving the performance and scalability of large-scale data analytics, with an emphasis on static & dynamic graph analytics. In recent years, Oded has also worked on designing and implementing efficient sorting algorithms for a wide range of accelerators, including GPUs. Oded is also very interested in architecture-algorithm codesign.### CGGC Seminar: Accessibility for Line-Cutting in Freeform Surfaces

- דובר:
- בוריס סוסין (מדעי המחשב, טכניון)
- תאריך:
- יום ראשון, 2.6.2019, 13:30
- מקום:
- חדר 337, בניין טאוב למדעי המחשב

Manufacturing techniques such as hot-wire cutting, wire-EDM, wire-saw cutting, and flank CNC machining all belong to a class of processes called line-cutting where the cutting tool moves tangentially along the reference geometry. From a geometric point of view, line-cutting brings a unique set of challenges in guaranteeing that the process is collision-free. In this work, given a set of cut-paths on a freeform geometry as the input, we propose a conservative algorithm for finding collision-free tangential cutting directions. These directions, if they exist, are guaranteed to be globally accessible for fabricating the geometry by line-cutting. We then demonstrate how this information can be used to generate globally collision-free cut-paths. We apply our algorithm to freeform models of varying complexity.

### Computational Integrity: from theory to practice

- דובר:
- מיכאל ריאבצב, הרצאה סמינריונית לדוקטורט
- תאריך:
- יום שני, 3.6.2019, 12:30
- מקום:
- טאוב 601
- מנחה:
- Prof. E. Ben-Sasson and Prof. Y. Ishai

Computation integrity (CI) protocols allow a strong prover to convince a skeptic verifier it has knowledge of an input satisfying a program. Although theoretical CI constructions are well known for almost 30 years, industrial adaption has only recently started. In this seminar we will go over recent progress in industrial adaption, challenges addressed by our work, and the connection to blockchain based systems.

- תאריך:
- מקום:

### CSpecial Talk: Exploring New Frontiers in Container Technology

- דובר:
- ג'יימס בוטומלי (י.ב.מ. מחקר)
- תאריך:
- יום חמישי, 6.6.2019, 11:30
- מקום:
- חדר 337, בניין טאוב למדעי המחשב

Containers (or Operating System based Virtualization) are an old technology; however, the current excitement (and consequent investment) around containers provides interesting avenues for research on updating the way we build and manage container technology. The most active area of research today, thanks to concerns raised by groups supporting other types of virtualization, is in improving the security properties of containers.

The first step in improving security is actually being able to measure it in the first place, so the initial goal of a research programme for container security involves finding that measure. In this talk I'll outline one such measure (attack profiles) developed by IBM research, the useful results that can be derived from it, the problems it has and the avenues that can be explored to refine future measurements of containment.

Contrary to popular belief, a "container" doesn't describe one fixed thing, but instead is a collective noun for a group of isolation and resource control primitives (in Linux terminology called namespaces and cgroups) the composition of which can be independently varied. In the second half of this talk, we'll explore how containment can be improved by replacing some of the isolation primitives with either local system call emulation sandboxes, a promising technique used by both the Google gVisor and the IBM Nabla secure container systems, or system call strengthening via address space separation within the kernel. We'll also explore the question of whether sandboxes are the end point of container security research or merely point the way to the next Frontier for container abstraction.

Biio:

James Bottomley is a Distinguished Engineer at IBM Research where he works on Cloud and Container technology. He is also Linux Kernel maintainer of the SCSI subsystem. He has been a Director on the Board of the Linux Foundation and Chair of its Technical Advisory Board. He went to university at Cambridge for both his undergraduate and doctoral degrees after which he joined AT&T Bell labs to work on Distributed Lock Manager technology for clustering. In 2000 he helped found SteelEye Technology, a High availability company for Linux and Windows, becoming Vice President and CTO. He joined Novell in 2008 as a Distinguished Engineer at Novell's SUSE Labs, Parallels (later Odin) in 2011 as CTO of Server Virtualization and IBM Research in 2016.### CGGC Seminar: Volumetric Untrimming: Precise Decomposition of Trimmed Trivariates into Tensor Products

- דובר:
- פאדי מצארווי (מדעי המחשב, טכניון)
- תאריך:
- יום ראשון, 9.6.2019, 13:30
- מקום:
- חדר 337, בניין טאוב למדעי המחשב

3D objects, modeled using Computer Aided Geometric Design (CAGD) tools, are traditionally represented using a boundary representation (B-rep), and typically use spline functions to parameterize these boundary surfaces. However, recent development in physical analysis, in isogeometric analysis (IGA) in specific, necessitates a volumetric parametrization of the interior of the object. IGA is performed directly by integrating over the spline spaces of the volumetric spline representation of the object. Typically, tensor-product B-spline trivariates are used to parameterize the volumetric domain.

A general 3D object, that can be modeled in contemporary B-rep CAD tools, is typically represented using trimmed B-spline surfaces. In order to capture the generality of the contemporary B-rep modeling space, while supporting IGA needs, Massarwi and Elber (2016) proposed the use of trimmed trivariates volumetric elements. However, the use of trimmed geometry makes the integration process more difficult since integration over trimmed B-spline basis functions is a highly challenging task Xu et al. (2017). In this work, we propose an algorithm that precisely decomposes a trimmed B-spline trivariate into a set of (singular only on the boundary) tensor-product B-spline trivariates, that can be utilized to simplify the integration process, in IGA. The trimmed B-spline trivariate is first subdivided into a set of trimmed Béziertrivariates, at all its internal knots. Then, each trimmed Bézier trivariate, is decomposed into a set of mutually exclusive tensor-product B-spline trivariates, that precisely cover the entire trimmed domain. This process, denoted untrimming, can be performed in either the Euclidean space or the parametric space of the trivariate. We present examples of the algorithm on complex trimmed trivariates’ based geometry, and we demonstrate the effectiveness of the method by applying IGA over the (untrimmed) results.### SYNTECH: Synthesis Technologies for Reactive Systems Software Engineers

- דובר:
- Shahar Maoz - COLLOQUIUM LECTURE
- תאריך:
- יום שלישי, 11.6.2019, 14:30
- מקום:
- חדר 337 טאוב.
- השתייכות:
- School of Computer Science, Tel-Aviv University
- מארח:
- Yuval Filmus

T B A Shahar Maoz research interests are in Software Engineering, specifically software and systems modeling, modeling languages, and formal methods. He has recently worked on synthesis of structure and behavior, differencing, and log analysis. He joined the faculty of the School of Computer Science, Tel Aviv University, in summer 2012, as a (tenure-track) Senior Lecturer (assistant professor). Since January 2018 he is an Associate Professor with Tenure. He received his B.Sc and M.Sc. in Computer Science from Tel Aviv University and his PhD in Computer Science from the Weizmann Institute (2009). Before joining TAU as a faculty member he was post-doc research fellow at RWTH Aachen University, Germany (2010-2012). In 2015-2016 he was on sabbatical as visiting scientist at MIT CSAIL.

### Preventing Collusion in Cloud Computing Auctions

- דובר:
- שונית אגמון, הרצאה סמינריונית למגיסטר
- תאריך:
- יום חמישי, 13.6.2019, 14:30
- מקום:
- טאוב 601
- מנחה:
- Prof. A. Schuster

In recent years, cloud providers have been moving towards offering their clients separate cloud resources for short periods of time instead of offering bundles of resources for longer periods. In parallel, the providers are moving towards using economic mechanisms, such as auctions, to allocate these resources. Vickrey-Clarke-Groves (VCG) auctions are likely to be used for that purpose. These auctions are incentive compatible: by allocating the resource to the highest bidders first, they maximize social welfare---the participants' aggregate valuation of the resources. However, VCG auctions are prone to various types of collusion, where users try to increase their profits at the expense of auction efficiency. We propose a coalition formation mechanism for cloud users that helps providers prevent user collusion. Our mechanism allows the auction participants to collaborate profitably while also maintaining the auction's resource allocation efficiency. By increasing users' profits, we reduce their incentive to collude in ways that harm the social welfare and allocation efficiency. We also propose a negotiation protocol for guests who wish to use the mechanism. Our experiments show that when using our mechanism, participants' mean profit increases up to the maximal possible collusion profit, without harming the provider's allocation efficiency. We also examine types of collusion mechanisms that involve changing the guest bids, and prove that when interactions are limited to two guests, all bid-altering interactions harm the social welfare.

### Multiscale Models for Image Classification and Physics with Deep Networks

- דובר:
- Prof. Stephane Mallat - SPECIAL GUEST LECTURE
- תאריך:
- יום שלישי, 18.6.2019, 14:30
- מקום:
- חדר 337 טאוב.
- השתייכות:
- College de France
- מארח:
- Prof. Alfred Bruckstein

Approximating high-dimensional functionals with low-dimensional models is a central issue of machine learning, image processing, physics and mathematics. Deep convolutional networks are able to approximate such functionals over a wide range of applications. This talk shows that these computational architectures take advantage of scale separation, symmetries and sparse representations. We introduce simplified architectures which can be anlalyzed mathematically. Scale separations is performed with wavelets and scale interactions are captured through phase coherence. We show applications to image classificaiton and generation as well as regression of quantum molecular energies and modelization of turbulence flows. Short Bio.: ========== Stéphane Mallat is a French applied mathematician, Professor at College de France and Ecole Normale Superieure. He has made some fundamental contributions to the development of wavelet theory in the late 1980s and early 1990s. He has also done work in applied mathematics, signal processing, music synthesis and image segmentation. With Yves Meyer, he developed the Multiresolution Analysis (MRA) construction for compactly supported wavelets, which made the implementation of wavelets practical for engineering applications by demonstrating the equivalence of wavelet bases and conjugate mirror filters used in discrete, multirate filter banks in signal processing. He also developed (with Sifen Zhong) the Wavelet transform modulus maxima method for image characterization, a method that uses the local maxima of the wavelet coefficients at various scales to reconstruct images. He introduced the scattering transform that constructs invariance for object recognition purposes. Mallat is the author of A Wavelet Tour of Signal Processing (ISBN 012466606X), a text widely used in applied mathematics and engineering courses. He has held teaching positions at New York University, Massachusetts Institute of Technology, École polytechnique and at the Ecole normale supérieure. He is currently Professor of Data Science at College de France. ========================== Refreshments will be served from 14:15 Lecture starts at 14:30

### יום מחקר 2018 בפקולטה למדעי המחשב

- תאריך:
- יום שני, 24.6.2019, 15:00
- מקום:
- כניסת הקומה - בניין טאוב למדעי המחשב

יום המחקר התשיעי לתארים מתקדמים בפקולטה למדעי המחשב יתקיים ביום ראשון, 24 ביוני, 2019, בין השעות 15:00-17:00, בלובי של בניין טאוב למדעי המחשב.

יום מחקר הוא הזדמנות עבור משתלמי הפקולטה להציג את מחקריהם באמצעות פוסטרים ומצגות בפני אנשי סגל ומנהלים בכירים בטכניון ותלמידים לכל התארים בפקולטה, כמו גם בפני נציגים רמי-דרג מחברות מובילות בתעשייה העילית בארץ ובעולם.

המחקרים המשתתפים ביום המחקר יהיו בנושאים שונים:

Cryptology and Cyber, Data Centers and Clouds, Graphics, Intelligent Systems and Scientific Computation, Machine Learning and Information Retrieval, Systems and Applications, Testing and Verification, Theory of Computer Science.

ההשתתפות באירוע אינה כרוכה בתשלום אך דורשת הרשמה מוקדמת.

פרטים נוספים והרשמה.

**סטודנטים המעוניינים להציג את מחקרם מתבקשים להירשם כאן.**