[Seminar] What we can learn from the system structure in biology and epidemiology

Speaker: Giulia Giordano (Università di Trento) 

Title: What we can learn from the system structure in biology and epidemiology 

Abstract: Biological, ecological and epidemiological systems can be seen as dynamical networks, namely dynamical systems that are naturally endowed with an underlying network structure, because they are composed of several subsystems that interact according to an interconnection topology. Despite their large scale and complexity, natural systems often exhibit an extraordinary robustness that preserves fundamental properties and qualitative behaviours even in the presence of huge parameter variations and environmental fluctuations.
First, we focus on biochemical reaction networks and look for the source of the amazing robustness that often characterises them, by identifying properties and emerging behaviours that exclusively depend on the system structure (the graph topology along with qualitative information), regardless of parameter values. We introduce the BDC-decomposition to capture the system structure and enable the parameter-free assessment of important properties, including the stability of equilibria and the sign of steady-state input-output influences, thus allowing structural model falsification and structural comparison of alternative mechanisms proposed to explain the same phenomenon.Then, inspired by the COVID-19 pandemic and the observation that compartmental models for epidemics can be seen as a special class of chemical reaction networks, we consider epidemiological systems describing the spread of infectious diseases within a population, along with control approaches to curb the contagion: we illustrate strategies to cope with the deep uncertainty affecting parameter values and optimally control the epidemic. 

Bio: Giulia Giordano received the B.Sc. and M.Sc. degrees in electrical engineering and the Ph.D. degree in systems and control theory from the University of Udine, Italy, in 2010, 2012, and 2016, respectively. She visited the Control and Dynamical Systems Group, California Institute of Technology, Pasadena, CA, USA, in 2012, and the Institute of Systems Theory and Automatic Control, University of Stuttgart, Germany, in 2015. She was a Research Fellow with the LCCC Linnaeus Center and the Department of Automatic Control, Lund University, Sweden, from 2016 to 2017, and an Assistant Professor with the Delft Center for Systems and Control, Delft University of Technology, The Netherlands, from 2017 to 2019; she is currently with the Department of Industrial Engineering, University of Trento, Italy. She has been an Associate Editor for the IEEE Control Systems Letters since 2020 and for Automatica since 2022. She was recognised with the Outstanding Reviewer Letter from the IEEE Transactions on Automatic Control in 2016 and from the Annals of Internal Medicine in 2020, and chosen as Outstanding Associate Editor of the IEEE Control Systems Letters for the year 2021. She received the EECI Ph.D. Award 2016 from the European Embedded Control Institute, the NAHS Best Paper Prize 2017, and the SIAM Activity Group on Control and Systems Theory Prize 2021. Her main research interests include the analysis and the control of dynamical networks, with applications especially to biology and epidemiology.  

When: Monday November 7th 2022 from 11 am to 12 am 

Where: Webex at https://univaq.webex.com/univaq-en/j.php?MTID=mc025f51685b16e943c96761b9b7fb79c

[COURSE] Development of recommendation systems in software engineering: challenges and lessons learned

Lecturer: Prof. Davide Di Ruscio

Abstract: Open-source software (OSS) forges contain rich data sources useful for supporting development activities. Several techniques and tools have been promoted to provide open-source developers with innovative features, aiming to obtain improvements in development effort, cost savings, and developer productivity. In the context of the EU H2020 CROSSMINER and TYPHON projects, different recommendation systems have been conceived to assist software programmers in different phases of the development process by providing them with various artifacts, such as third-party libraries, or documentation about how to use the APIs being adopted, or relevant API function calls. To develop such recommendations, various technical choices have been made to overcome issues related to several aspects, including the lack of baselines, limited data availability, decisions about the performance measures, and evaluation approaches. This lecture introduces Recommendation Systems in Software Engineering (RSSE) and describes the challenges that have been encountered in the context of the CROSSMINER and TYPHON projects. Specific attention is devoted to present the intricacies related to the development and evaluation techniques that have been employed to conceive and evaluate the CROSSMINER recommendation systems. The lessons that have been learned while working on the project are also discussed.

 

Content

  • Lecture 1, 21/07/2022, 10:00-13:00:  Development of complex software systems by reusing third-party open-source components. [MS Teams code: 9kkf5ny]
  • Lecture 2, 22/07/2022, 10:00-13:00: The recommendation systems developed in the CROSSMINER and TYPHON projects [MS Teams code: 9kkf5ny]

 

Related literature

  1. P. Robillard, W. Maalej, R. J. Walker, e T. Zimmermann, A c. di, Recommendation Systems in Software Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. doi: 10.1007/978-3-642-45135-5.
  2. Juri Di Rocco, Davide Di Ruscio, Claudio Di Sipio, Phuong T. Nguyen, Riccardo Rubei, “Development of recommendation systems for software engineering: the CROSSMINER experience” Empirical Software Engineering (EMSE), 2021, pre-print https://arxiv.org/abs/2103.06987
  3. Phuong T. Nguyen, Juri Di Rocco, Claudio Di Sipio, Davide Di Ruscio, Massimiliano Di Penta “Recommending API Function Calls and Code Snippets to Support Software Development” IEEE Transactions on Software Engineering (TSE), 2021, ISSN: 1939-3520, DOI: 10.1109/TSE.2021.3059907
  4. Phuong T. Nguyen, Juri Di Rocco, Davide Di Ruscio, Massimiliano Di Penta, “CrossRec: Supporting Software Developers by Recommending Third-party Libraries” Journal of Systems and Software (JSS), 2020, ISSN: 0164-1212, DOI: 10.1016/j.jss.2019.110460
  5. Phuong T. Nguyen, Juri Di Rocco, Riccardo Rubei, Davide Di Ruscio, “An Automated Approach to Assess the Similarity of GitHub Repositories” Software Quality Journal (SQJ), 2020, ISSN: 0963-9314, DOI: 10.1007/s11219-019-09483-0
  6. Andrea Capiluppi, Davide Di Ruscio, Juri Di Rocco, Phuong T. Nguyen, Nemitari Ajienka, “Detecting Java Software Similarities by using Different Clustering Techniques” Information and Software Technology (IST), 2020, ISSN: 0950-5849, DOI: 10.1016/j.infsof.2020.106279
  7. Riccardo Rubei, Claudio Di Sipio, Phuong T. Nguyen, Juri Di Rocco, Davide Di Ruscio, “PostFinder: Mining Stack Overflow posts to support software developers” Information and Software and Technology (IST), 2020, ISSN: 0950-5849, DOI: 10.1016/j.infsof.2020.106367
  8. Phuong T. Nguyen, Juri Di Rocco, Davide Di Ruscio, Lina Ochoa, Thomas Degueule, Massimiliano Di Penta, “FOCUS: A Recommender System for Mining API Function Calls and Usage Patterns” In Proceedings of the 41st International Conference on Software Engineering, ICSE 2019, DOI: 10.1109/ICSE.2019.00109

[COURSE] Nonlinear Optimization for Machine Learning Architectures

Speaker: Dr. Andrea Manno

Dates: July 12, 13, 19 and 20

Time: 16:00-18:00 

MS Teams code: 9kkf5ny 

Outline of the course (8 hours)

(4 hours) Introduction to nonlinear optimization

  • unconstrained optimization: optimality conditions, algorithms, con-
    vergence and regularization
  • hints on constrained optimization

(2 hours) Nonlinear optimization for Neural Networks training

  • a loss function minimization problem and the backpropagation
  • adapting general algorithms and regularization for neural networks
    training
  • advanced strategies

(2 hours) The training of Support Vector Machines as a constrained optimization problem

  • linear SVMs for the linearly separable case
  • a mathematical formulation for non-linearly separable datasets
  • nonlinear SVMs

[SEMINAR] Reliable Real-Time Distributed AI for Mobile Autonomous Systems

Speaker: Prof. Marco Levorato, University of California, Irvine

Schedule: June 1st, 14:30-15:30

Room: Seminar room, Alan Turing (Blocco 0) 
MS Teams code: 9kkf5ny – “PHD ICT – Seminars and Courses” (UPDATED)

Title of talk: Reliable Real-Time Distributed AI for Mobile Autonomous Systems

Abstract:
The autonomous operations of Unmanned Aerial Vehicles (UAV) require the execution of continuous streams of heavy-duty mission-critical computing tasks. These tasks often take the form of complex Deep Neural Network (DNN) models applied to information-rich signals such as image and lidar data. Clearly, such strenuous effort may exceed the capabilities and resources (e.g., computing power and energy) of most UAVs. The research community mostly relied on two distinct approaches to address this issue: model simplification and edge computing. The former may lead to performance degradation, while the performance of the latter suffer from the erratic behavior of wireless channels.

In this talk, I will present two key components of reliable computing in the edge computing for Mobile Autonomous Systems (MAS) and especially UAVs. Our ultimate objective is to achieve task-level latency and accuracy guarantees. First, I will introduce the notion of data-driven redundant computing. The core idea is to replicate tasks across the system to reduce uncertainty in their total execution time. A Deep Reinforcement Learning (DRL) Agent dynamically controls in real-time how many and which edge servers are selected for computing. Our approach is experimental, and we demonstrate how the DRL agent necessitates features from multiple system blocks (telemetry, network and application) to make effective decisions in real-world deployments. Finally, I will summarize our work in the area of split computing, where we modify DNN models for vision to make them splittable across MAS and edge servers and reduce end-to-end latency in unreliable wireless environments. We pioneered this area by introducing the notion of artificial bottleneck to obtain in-model compression, and by developing innovative training strategies that achieve the best rate distortion curve available to date.

Bio:
Marco Levorato is an Associate Professor in the Computer Science department at UC Irvine. He completed the PhD in Electrical Engineering at the University of Padova, Italy, in 2009. Between 2010 and 2012, he was a postdoctoral researcher with a joint affiliation at Stanford and the University of Southern California. His research interests are focused on distributed computing over unreliable wireless systems, especially for autonomous vehicles and healthcare systems. His work received the best paper award at IEEE GLOBECOM (2012). In 2016 and 2019, he received the UC Hellman Foundation Award and the Dean mid-career research award, respectively. His research is funded by the National Science Foundation, the Department of Defense, Intel and Cisco.

Introduction to Quantum Computing

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

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.

Topics: 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.

Duration: 14 hours (8 hours of lectures + 6 hours of computer lab hands-on tutorial)

When:
January 20th 11-13
January 26th 11-13
February 3rd 11-13
February 9th 11-13
Februart 18th 11-13
February 23rd 11-13 + 15-17

Classroom: Aula 0.6 (Coppito 1)
Microsoft Teams Code: aw13okq

 

5G International PhD School 2021 – Hybrid event

5G International PhD School 2021 is the fourth edition of the international doctorate school that CNIT has conceived as a prestigious event, with a high scientific connotation, aggregated at the 5G Italia conference.

5G International PhD School aspires to be an event in which all of us, researchers potentially interested in 5G technology, can jointly structure our training: to be able to grasp the innumerable research opportunities that the 5G, and its desired applications, can offer us.

Details are available at PhD School – 5G Italy 2021

Complexity and the City: transitioning towards the smart cities of the future

Nell’ambito della manifestazione Futuro Remoto (www.futuroremoto2021.it) organizzata ogni anno da Città della Scienza (www.cittadellascienza.it), il *23 novembre 2021 alle ore 17* si terrà un evento specialmente rivolto a tutti i dottorandi di ricerca interessati ai Sistemi Complessi e alle loro applicazioni di cui vi allego la locandina.

L’evento telematico dal titolo ‘Complexity and the City: transitioning towards the smart cities of the future”, patrocinato dal Consolato Generale USA a Napoli, vedrà la partecipazione di Louis Bettencourt  Inaugural Director of the Mansueto Institute for Urban Innovation at the University of Chicago e external professor del Santa Fe Insititute che parlerà delle città come sistemi complessi.

La presentazione sarà seguita da una sessione interattiva a cui potranno partecipare tutti i dottorandi che si iscriveranno all’evento come nelle istruzioni contenute nella locandina in allegato.

La partecipazione sarà limitata solo agli iscritti all’evento. Per maggiori
informazioni e per iscriversi:

https://www.futuroremoto.eu/evento/complexity-and-the-city-transitioning-towards-the-smart-cities-of-the-future/