Coure “LMIs for Optimization and Control”

Davide Di Ruscio PhD School
This year we are happy to host a EECI school in control at L’Aquila. Given the pandemic situation unfortunately the course will be given online.
 
The course is entitled
 
“LMIs for Optimization and Control”
 
and will be given by
 
Professor Didier Henrion
http://homepages.laas.fr/henrion
 
All information regarding the contents of the course and the time table are here:
 
 
The course will be given online with Zoom, you can find the connection details below.
 
Time: Apr 26, 2021 09:30 AM Paris
         Every day, 5 occurrence(s)
         Apr 26, 2021 09:30 AM
         Apr 27, 2021 09:30 AM
         Apr 28, 2021 09:30 AM
         Apr 29, 2021 09:30 AM
         Apr 30, 2021 09:30 AM


Join Zoom Meeting
https://zoom.us/j/95988136379?pwd=NHA0eVlmOEJwamViMENiZGRWdEdwQT09

Meeting ID: 959 8813 6379
Passcode: 611995
One tap mobile
+13017158592,,95988136379#,,,,*611995# US (Washington DC)
+13126266799,,95988136379#,,,,*611995# US (Chicago)

Dial by your location
         +1 301 715 8592 US (Washington DC)
         +1 312 626 6799 US (Chicago)
         +1 346 248 7799 US (Houston)
         +1 669 900 6833 US (San Jose)
         +1 929 205 6099 US (New York)
         +1 253 215 8782 US (Tacoma)
Meeting ID: 959 8813 6379
Passcode: 611995
Find your local number: https://zoom.us/u/abxCvIUNxj
 

Course on “Machine Learning for Smart Cities Automation”

Davide Di Ruscio PhD Courses
Teacher: Prof. Alessandro D’Innocenzo
Duration: 12 hours
Where: Via the MS Team “PhD ICT – Seminars and Courses”
 
The course schedule will be the following:
 
Lecture 1 – 24/03/2021 – 9:00-12:00: Regression Trees (RT) and Random Forests (RF)
 
Lecture 2 – 26/03/2021 – 9:00-12:00: Switching ARX (SARX) models and Model Predictive Control (MPC)
 
Lecture 3 – 09/04/2021 – 9:00-12:00: Joint mathematical framework for RT/RF, SARX and MPC
 
Lecture 4 – 16/04/2021 – 9:00-12:00: Application to Climate control and Structural Health Monitoring in Building Management Systems

Course on Introduction to Quantum Computing

Davide Di Ruscio PhD Courses

Speakers:
Leonardo Guidoni (Univaq)
Hands on tutorial lead by Experts from IBM

The present short course is a joint PhD course between the PhD in Mathematics and Models and the PhD in Informatics. The aim of the short course is to provide to students with background in mathematics and informatics the foundation of quantum computation. The course will consist of theoretical lectures as well as hands-on tutorial lead by the Quantum Computing experts from IBM-Italia.

Arguments: General overview on quantum computation. Introduction to Quantum Mechanics and Qubits. Quantum circuits and algorithms. Single and double Qubit gates with examples. Present and future applications. Perspective of quantum computation and practical implementation of algorithms on the IBM-Q quantum computer and simulator.

 

Lectures (14 hours):
January 22nd 11.00-13.00
January 29th 11.00-13.00
February 5th 11.00-13.00
February 12th 11.00-13.00
February 19th 11.00-13.00 IBM hands-on tutorial
February 26th 11.00-13.00 + 14.30-16.30 IBM hands-on tutorial

Machine Learning over Networks

Davide Di Ruscio External Courses

Machine Learning over Networks
Prof. Carlo Fischione,  https://people.kth.se/~carlofi/

January 19th 10-13, January 20th 10-12

Microsoft Team: PHD ICT – Seminars and Courses

 

Short description:
One of the main characteristics of the Internet of Things (IoT) technological revolution is the generation of huge quantities of data. Such wealth of data and their use in several new IoT technologies is forcefully motivating the development of data analysis methods, namely machine learning. Currently, machine learning needs big datasets and very huge computational and communication resources. However, in IoT, data sets of any size will be distributed among several nodes (people, devices, objects, or machines) that might not be able to perform the computations and to share data.

Unfortunately, existing machine learning methods are mostly intended for proprietary or high performing networks such as in data centres. They would greatly stress the public communication networks of IoT, such as 5G wireless networks and beyond. In these networks, machine learning methods will encounter new challenges in terms of computation, bandwidth, scalability, privacy, and security.  Machine learning over networks face a lack of understanding of the fundamental methods, and poor performance of their algorithms.

In this PhD course, we highlight the need of establishing a new fundamental theory for machine learning over networks. We give an overview of the state-of-the-art and some of our recently proposed developments. The syllabus will be around the following topics:

  1. Introduction to Machine Learning for the IoT
  2. Background on Machine Learning
  3. Distributed Machine Learning
  4. Wireless for Machine Learning
  5. Co-design of Machine Learning and Wireless

Collective decision making and swarm robotics

Davide Di Ruscio PhD Courses

Speaker: Giulia De Masi (Adjunct Professor at Zayed University) 

Duration:10h

Dates: 9, 11, 14, 17, 21 December 

Time: 9:30-11:30

Where: online on “PHD ICT – Seminars and Courses” Microsoft Team

Description: This course is giving a self-contained introduction to collective decision making with applications to swarm robotics, from theoretical aspects, to simulation, modeling and principles for experimental applications. At the end of this course, the student will be able to recognize in the real life the scenarios where collective decision making is playing a role, will be able to design a swarm of robots for a proper predefined task, model the system and analyze the results. 

Prerequisites: Python, Calculus 

 

1. Introduction to swarm robotics (2h)

  • What is and what is not swarm robotics
  • Biological inspiration
  • From social systems to robotics
  • Applications
  • Pros and cons of swarm robotics

2. Scenarios of swarm robotics (2h)

  • Collective decision mechanisms
  • Aggregation
  • Task allocation
  • Pattern formation
  • Collective motion and flocking

3. Collective decision making (2h)

  • Single vs group decision making
  • Collective decision mechanisms in animals
  • From micro to macro
  • Models for collective decision making processes
  • Design of experiment

4. Simulations and statistical analyses (2h)

  • Netlogo implementation
  • Overview of open source software (example: Argos, Gazebo)
  • Post-simulation statistical analyses for swarm robotics       
  • Practical session

5. Modeling (2h)

  • ODE and chemical reaction networks
  • Nonlinear stochastic systems
  • Practical session

CINECA “HPC and Quantum Computing – third edition”: Talk submission invitation

Davide Di Ruscio External Seminars No Comments

Also this year, CINECA is pleased to announce the third edition of the “HPC and Quantum Computing” workshop.
Due to the pandemic that is afflicting the whole world, this year the event will be an online edition, completely free as always.

The event will take place on Thursday 15 December, from morning (9:00) to evening (18:00).
https://eventi.cineca.it/en/events/quantum-computing-and-high-performance-computing-3rd-edition

The virtual conference hall that will host the event has yet to be precisely decided, but the choice will most likely fall on platforms such as Teams or Zoom.

If you want to share your quantum computing achievements to a large audience of online viewers or you are a company that works in the field of quantum computing and you want to introduce yourself to the Italian public, you are warmly invited to propose a talk. All the slots are avaiable until the end of our time! We will choose talks using arrival order and relevance to the covered topics. All interventions must be in English. Your intervention can take place in two ways: live or recorded.

For those who are not too experienced in online conferences, I will briefly explain how they work. There will be three main figures: the producer (which will most likely be me and/or professor Prati), the speaker (all those who will give a talk) and the audience. The producer manages everything: it is he who, following a set schedule, will hand over the speak permissions to all the speakers in the hall. The public will have no power of speech, but will be able to ask questions to the speakers through the use of a Q&A platform. For his part, the speaker will moderate and answer the questions he deems most appropriate, interacting with the same platform. In this regard, if you opt for a live intervention, I strongly suggest you find an “assistant” to help you in moderating the questions while you are talking. If you decide to record your speech, however, just press “play” when the producer will pass the word and pay attention to the Q&A tab. Don’t go afk (away from keyboard) during your speech, it is very important!

If you want to participate in the event you can send an email to me (d.ottaviani@cineca.it) or to Professor Enrico Prati (enrico.prati@cnr.it) containing the title and abstract of your speech by the end of this month (deadline: 20/11/2020). After receiving your requests, we will create a schedule that will respects every single intervention. You will be notified how much time you will have available to tell what is summarized in your abstract. Keep in mind that no talks will go under 15 minutes, nor above 25.

Webinar by Silvio Micali (MIT) on “ALGORAND: The Truly Distributed Blockchain”

Davide Di Ruscio External Seminars

Schedule: 20  November 2020, 14:00 GMT, 15:00 Italian time

 
Speaker: Silvio Micali (MIT and Algorand)
 
Title: “ALGORAND: The Truly Distributed Blockchain”

Abstract: In its ideal model, a blockchain consists of a digital ledger of unalterable data, readable by everyone, to which everyone can add new data. If adequately implemented, this model stands to revolutionize the way societies and traditional economies operate. By removing costly intermediaries and introducing new paradigms of trust, the model makes traditional transactions (e.g., payments) more efficient, and totally new ways of transacting (e.g., smart contracts) possible.

Unfortunately, as currently implemented, most blockchains cannot achieve their enormous potential. We shall argue, however, that they can be adequately implemented by means of dramatically different approaches.
 
Bio Sketch: Silvio Micali has received his Laurea in Mathematics from the University of Rome, and his PhD in Computer Science from the University of California at Berkeley. Since 1983 he has been on the MIT faculty, in Electrical Engineering and Computer Science Department, where he is Ford Professor of Engineering. Silvio’s research interests are cryptography, zero knowledge, pseudorandom generation, secure protocols, and mechanism design. Silvio is the recipient of the Turing Award (in computer science), of the Goedel Prize (in theoretical computer science) and the RSA prize (in cryptography). He is a member of the National Academy of Sciences, the National Academy of Engineering, and the American Academy of Arts and Sciences.

Joint CS@GSSI/ICE-TCS@Reykjavik University virtual seminar — speaker: Scott Aaronson

Davide Di Ruscio External Seminars
This CS@GSSI/ICE-TCS@Reykjavik University joint webinar is held in cooperation with Vísindafélag Íslands (the Icelandic Academy of Sciences) and the Icelandic Physical Society.

Schedule: 30 November 2020, 16:00 GMT, 17:00 Italian time
Virtual link: TBA

Speaker: Scott Aaronson (University of Texas at Austin, USA)
WWW: https://www.scottaaronson.com/
Title: “Quantum Computing and Quantum Computational Supremacy”

Abstract: Last fall, a team at Google announced the first-ever demonstration of “quantum computational supremacy”—that is, a clear quantum speedup over a classical computer for some task—using a 53-qubit programmable superconducting chip called Sycamore.  In addition to engineering, Google’s accomplishment built on a decade of research in my field of quantum computing theory.  This talk will discuss the intellectual background to the experiment, the interpretation of the results, potential applications to generating cryptographically certified random bits, and the many challenges that remain.

Bio Sketch: Scott Aaronson is David J. Bruton Centennial Professor of Computer Science at the University of Texas at Austin.  He received his bachelor’s from Cornell University and his PhD from UC Berkeley.  Before coming to UT Austin, he spent nine years as a professor in Electrical Engineering and Computer Science at MIT.  Aaronson’s research in theoretical computer science has focused mainly on the capabilities and limits of quantum computers.  His first book, Quantum Computing Since Democritus, was published in 2013 by Cambridge University Press.  He received the National Science Foundation’s Alan T. Waterman Award, the United States PECASE Award, the Vannevar Bush Fellowship, and the Tomassoni-Chisesi Prize in Physics.

Joint CS@GSSI/ICE-TCS@Reykjavik University virtual seminar — speaker: Martina Maggio

Davide Di Ruscio External Seminars
Schedule: 25 November 2020, 16:30 GMT, 17:30 Italian time
Virtual link: TBA
 
 
Speaker: Martina Maggio, Saarland University, Germany
 
 
Title: Testing Adaptive Software with Probabilistic Guarantees
 
Abstract: Testing software that adapts, like a machine learning algorithm,  is very complicated. In most cases, it is very difficult – if not impossible – to conduct exhaustive testing and analyse each possibile configuration. This is not only because the space of the configurations is very large, but also because the software learns and adapts, and running the same function with the same set of inputs may result in different outcomes.
 
In this context, it is impossible to get a deterministic answer to the software correctness, and there is a need for a paradigm shift to the probabilistic setup. In our research, we explored different alternatives to obtain probabilistic guarantees. The classical tools from statistics are Monte Carlo simulations and the Extreme Value Theory. We show  that these tools have limitations that can be overcame by formulating  the problem of testing a software that adapts itself as a chance-constrained optimization problem. In doing so, we employ the scenario theory, from the field of robust control.