נפלאות הקריפטוגרפיה: כיצד לסמוך על שקרנים

מפגש העשרה למדעים דיגיטליים להיי-טק - הרצאה מרתקת של פרופ' בני אפלבאום, ראש התוכנית

07 בינואר 2021, 20:30 
 
נפלאות הקריפטוגרפיה: כיצד לסמוך על שקרנים

סטודנטים וסטודנטיות של מדעים דיגטים להיי-טק, צוות התוכנית מזמין אתכם.ן להרצאת העשרה של פרופ' בני אפלבאום.

בואו לטעום ולשמוע על אחד מתחומי המחקר המרתקים והמשפיעים בתעשיית ההיי-טק.

קישור לזום נשלח במייל.

QA Student

  • A student in Computer Science/SW Engineering or other relevant field.
  • At least 2 years until graduation
  • Excellent attention to details and dedication to quality
  • Strong analytical and troubleshooting skills.
  • Fast learner with the ability to work independently.
  • Hi-level English.
  • Experience in QA Methodology, Processes and tools – an advantage.
« חזרה לכל המשרות

Business Analyst student

What you will need to succeed

  • Industrial management Student 3rd year, or second degree (year 1). At least 2 years until graduation.
  • PMO at multi-disciplinary organization
  • MS Project / EPM and Information Systems
  • High skilled at Excel, VBA, Macro experience
  • Strong communication skills

 

Advantages:

  • ERP knowledge
  • SQL server knowledge
  • Familiarity with BI tools
« חזרה לכל המשרות

Application Student

  • B.Sc. Student in the field of Electronics/Mechanics / Bio-Medical/ Physics/ Material engineering
  • At least 2 years until graduation
  • Availability to work at least 25 hours a week (preferably more)
  • Good system understanding and technical affiliation
  • Good interpersonal communication skills
  • Ability to work well under pressure
  • Maintaining a proactive, problem solving attitude
  • Productive working independently and as part of a team  
  • Willingness to travel abroad- advantage
« חזרה לכל המשרות

סמינר מחלקתי - אלקטרוניקה פיסיקלית

Adaptive Multilevel Non-uniform Grid Algorithm for the Accelerated Analysis of Composite Metallic-Dielectric Radomes

10 בדצמבר 2020, 15:00 
 
סמינר

You are invited to attend a lecture on Thursday, December 10, 2020 at 15:00

Join Zoom Meeting

https://us02web.zoom.us/j/86555657602?pwd=N2dyZCtUcGd2djY5V0RiYmRBSEkyUT09

 

Adaptive Multilevel Non-uniform Grid Algorithm for the Accelerated Analysis of Composite Metallic-Dielectric Radomes

 

by:

Yair Hollander

Ph.D. student under the supervision of Prof. Amir Boag

 

A radome (radar dome) is a structure that encloses an antenna to shield it from the environment and provide the needed shape characteristics. Ideally a radome is transparent at the antenna’s operating frequencies, but realistically, it introduces a multitude of electromagnetic (EM) effects. Radomes are usually electrically large and, thus, mostly prohibit the use of full wave analysis methods, such as the Method of Moments (MoM). This work presents a three-dimensional frequency domain algorithm for numerically rigorous analysis of electrically large composite metallic-dielectric radomes in a fast, accurate, and efficient manner. The algorithm employs two coupled electric field integral equations in the mixed potential form – one for the metallic and the other for the dielectric domains. The solution of these equations is effected by an MoM-based iterative solver.

The MoM matrix-vector multiplication is accelerated by the multilevel non-uniform grid (MLNG) approach, which follows a multilevel tree-like scheme that is constructed by an algorithm that adaptively decomposes the geometry.

The geometrically adaptive scheme improves the computational accuracy in the highest tree levels and provides control over the computer memory usage needed for solving the problem, thus enabling larger problems to be solved on a given computer.

This algorithm is shown to be accurate and realizable on real world applications showing a computational complexity, in terms of computer memory usage and CPU times, of , being the number of unknowns. The antenna-radome interactions are fully taken into account in one of the two excitation methods included in our software implementation. Further acceleration is achieved by using a preconditioner and a pre-iterative stage that generates an accurate initial guess for the unknowns’ vector

סמינר בהנדסת חשמל: EE Seminar: Recovering tree models via spectral graph theory

14 בדצמבר 2020, 15:00 
ZOOM  
סמינר בהנדסת חשמל: EE Seminar: Recovering tree models via spectral graph theory

Zoom link: https://us02web.zoom.us/j/87537900653?pwd=RXcrb2N3MnMvek4vZ2VjRis4ei81dz09 

Meeting ID: 875 3790 0653
Passcode: TAUEESYS  

Speaker: Dr.  Ariel Jaffe

Applied Mathematics, Yale University

Monday, December 14th, 2020, at 15:00

Recovering tree models via spectral graph theory

Abstract

Modeling high dimensional data by latent tree graphical models is a common approach in multiple machine learning applications. In these models, the key task is to infer the structure of the tree, given only observations on its leaves. A canonical example of this setting is the tree of life, where the evolutionary history of a set of organisms is inferred by their nucleotide or protein sequences.
In this talk, we will show that the tree structure is strongly related to the spectral properties of a fully connected graph, defined over the terminal nodes of the tree.  This relation forms the theoretical basis of two new methods to recover latent tree models: (i) spectral neighbor joining, where subsets of nodes are iteratively merged to form the full tree, and (ii) spectral top down recovery, where the terminal nodes are iteratively partitioned into smaller subsets. Comparing our approach to several competing methods, we show that in many settings, spectral methods have stronger theoretical guarantees and work better in practice.
Short Bio

Ariel Jaffe is a Gibbs assistant professor in the program of applied mathematics, Yale University.  
Previously, he completed his Ph.D. in the Weizmann Institute of Science under the guidance of Prof. Boaz Nadler. His research interests include statistical machine learning and high dimensional data analysis, with a focus on applications in the fields of computational biology and signal processing.‏

 

 

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז.  בצ'אט

 

EE Seminar: Distributed Interactive Proofs for Decision Tasks הנדסת חשמל

08 בדצמבר 2020, 15:00 
ZOOM  

Zoom link: https://us02web.zoom.us/j/81297775131?pwd=b0dNZGpNaEJDbW5SQ1NyWjVSaWFiZz09
Meeting ID: 812 9777 5131

Passcode: TAUEESYS  

Speaker: Dr.  Ami Paz

CS, University Of Vienna

Tuesday, December 8th, 2020, at 15:00

Distributed Interactive Proofs for Decision Tasks

Abstract

One of the key concepts in complexity theory is decision tasks, where the goal is to decide if some predicate holds on the input. In *distributed* decision tasks, we are given a network of processing units, and wish to have the network decide a predicate on its own structure. Some predicates of interest are being bipartite, having diameter at most k or at least k, or containing a triangle.

In this talk, we will discuss labeling mechanisms that allow a network to decide such predicates fast, and with low communication. On the theoretical side, these can be seen as distributed nondeterministic decision mechanisms, and on the more practical side, as mechanisms for self stabilization. We will survey some techniques for exact and approximate decision, and discuss a recent concept of distributed interactive proofs.

Based on joint works with Keren Censor-Hillel, Pierluigi Crescenzi, Laurent Feuilloley, Pierre Fraigniaud, François Le Gall, Harumichi Nishimura and Mor Perry.

Short Bio

Ami Paz is a researcher in distributed computing. He is currently a post-doc at the University of Vienna, and before that he was a post-doc at IRIF, Paris. He obtained his PhD from the Technion in 2017, titled Distributed Distance Computation and Related Topics.

 

השתתפות בסמינר תיתן קרדיט שמיעה = עפ"י רישום שם מלא + מספר ת.ז.  בצ'אט

 

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