Hours:
16 hours (4 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

Artificial Intelligence (AI) is already happening today and it is pervasive, often invisibly embedded in our day-to-day tools. As AI evolves, so do the many controversies that surround the use of this advanced technology. From military drones to shopping recommendations, AI is powering a wide array of smart products and services across nearly every industry—and with it, creating new ethical dilemmas for which there are no easy answers. As technology continues to develop at an unprecedented rate, those involved with AI often lack the tools and knowledge to expertly navigate ethical challenges.
This course examines today’s most pressing ethical issues related to AI and explores ways to leverage technology to benefit mankind. It provides insights into how to achieve responsible innovation of technology, in order to contribute to the quality of human life, sustainability and fair allocation of risks and benefits.

Course Contents in brief:

1. Explore the foundations of Philosophy of Technology and Responsible Innovation for the benefit of mankind
2. Understand the technological basis of ethics in AI
3. Analyze machine bias and other ethical risks
4. Assess the individual and corporate responsibilities related to AI deployment
5. Examine what it means to be human, and what differentiates humans from machines
6. Explore issues of AI in safety and progress, human rights, economics of happiness and deep ecology
7. Exploit AI and Business Models Innovation in the space industry through the lenses of ethical challenges

Schedule:

1. Day 1: 9.00-13.00
2. Day 2: 9.00-13.00
3. Day 3: 9.00-13.00
4. Day 4: 9.00-13.00

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

The need of storing data in compact form is increasingly important for the ever-growing rate of data produced on a daily basis. To keep up with this data explosion phenomenon, data compression is a mandatory step to deliver good quality of service in concrete applications. In this introductory course you will learn about fundamental data compression algorithms that are all widely adopted in practice by tools that we use every day, like filesystems, computer networks, search engines, databases, and so on. These algorithms have now become indispensable knowledge across many fields in computing, including Information Retrieval, Machine Learning, Natural Language Processing, Applied Physics, and Bioinformatics. To better grasp the beauty behind data compression, we will also learn how to implement some of these algorithms in C++ through several "hands-on" sessions.

Course Contents in brief:

1. Introduction
   1. What is and Why Data Compression?
   2. Motivations
   3. Technological Limitations: Memories and Hierarchies
   4. Applications
   5. Basic Notions: Entropy, Information-Content, Data-Redundancy, Compression-Ratio
2. Integer Codes
   
   1. Basic Notions: Distributions, Kraft-McMillan Inequality
   2. Run-Length Encoding, Gamma, Delta, Golomb, Rice, Zeta, Fibonacci, Variable-Byte
   3. Encoding/Decoding of Prefix-Free Codes
3. Lab Session 1 on Integer Codes
4. Sequence Compressors
   1. Basic Notions: Combinatorial Lower Bound
   2. Binary Packing, Simple, PForDelta, Elias-Fano, Interpolative, Directly-Addressable, Hybrid
   3. Inverted Indexes and Social Networks
5. Lab Session 2 on Sequence Compressors
6. Statistical Compressors
   1. Shannon-Fano, Huffman, Arithmetic Coding, Asymmetric Numeral Systems
7. Dictionary-Based Compressors
   1. LZ77, LZ78, LZW, variants: gzip, LZO, Zstd

Schedule:

11-15 April 2022, 14:00 - 18:00

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

Constitutive modelling in Mechanics is the art of describing the mechanical properties of materials by means of mathematical problems, which are formulated in connection with physical concepts and experimental evidence. The analysis and design of novel products can take advantage from numerical simulations only if constitutive models provide a comprehensive description of materials behavior. For advanced applications, material responses have to be analyzed from a multiscale and multiphysical perspective. This is true for instance when addressing material design for smart behaviors (e.g., hydrogels) or in the analysis of biological tissues and bioprosthetic products. Therefore, constitutive models should correlate macroscale mechanical properties with the behavior and the arrangement of constituents. Moreover, physico-chemical processes taking place at small scales have to be modelled since triggering an effective behavior at larger scales.
This course opens with the general mathematical requirements of constitutive laws for the mechanical behaviour of materials in a finite strain framework. Then, the micromechanical approach for material homogenization is described, introducing the rationale behind multiscale approaches. Furthermore, thermodynamic requirements of multiphysical constitutive laws are outlined and applied in the context of chemomechanical systems. Specific applications on smart hydrogels and biological tissues will be presented, describing and connecting multiphysical descriptions and multiscale effects.

Course Contents in brief:

1. Constitutive laws for the mechanical behaviour of materials in a finite strain framework
2. Micromechanical approach for material homogenization
3. Thermodynamic requirements of multiphysical constitutive laws
4. Applications on smart hydrogels and biological tissues

Schedule:

1. Day1 - 4 hours - Constitutive laws for the mechanical behaviour of materials in a finite strain framework
2. Day2 - 4 hours - Micromechanical approach for material homogenization
3. Day3 - 4 hours - Thermodynamic requirements of multiphysical constitutive laws
4. Day4 – 4 hours - Applications on smart hydrogels
5. Day5 – 4 hours - Applications on biological tissues

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa - Ground Floor

To register to the course, click here

Short Abstract:

Advances from the natural language processing community have recently sparked a renaissance in the task of ad-hoc search. Particularly, large contextualized language modeling techniques, such as BERT, have equipped ranking models with a far deeper understanding of language than the capabilities of previous bag-of-words models. Applying these techniques to a new task is tricky, requiring knowledge of deep learning frameworks, and significant scripting and data munging. In this course, we provide background on classical (e.g., Bag of Words), modern (e.g., Learning to Rank). We introduce students to the Transformer architecture also showing how they are used in foundational aspects of modern large language models (e.g., BERT) and contemporary search ranking and re-ranking techniques. Going further, we detail and demonstrate how these can be easily experimentally applied to new search tasks in a new declarative style of conducting experiments exemplified by the PyTerrier search toolkit.

Course Contents in brief:

1. PyTorch
2. Language Models
3. Self-attention
4. Transformers
5. BERT and beyond
6. HuggingFace Transformers
7. PyTerrier
8. Classical IR: bag of words and probabilistic ranking
9. Modern IR: learning to rank
10. Contemporary IR: neural models and techniques

Schedule:

1. Day 1 – 9 – 13. Intro to PyTorch, Language Models, Implementing Word2Vec in PyTorch. Examples in Google Colab.
2. Day 2 – 9 – 13. Self-attention, Transformers, BERT, and Beyond. HuggingFace Transformers. Examples in Google Colab.
3. Day 3 – 9 – 13. Intro to Information Retrieval. Classical models and limitations. PyTerrier. Examples in Google Colab.
4. Day 4 – 9 – 13. Neural Models for IR. Examples in Google Colab.
5. Day 5 – 9 – 13. Exam

Hours:
20 hours (5 credits)

Room:

Aula Riunioni, Dipartimento di Ingegneria dell’Informazione, Largo L. Lazzarino 1, 56122 Pisa, Edificio A, piano 6

To register to the course, click here

Short Abstract:

Machine Vision and Augmented Reality are currently hot topics given the possible applications in many fields. This course gives a theoretical and practical introduction of these topics taking into account optical, geometrical, algebraic, SW, HW, and human factors aspects.

Course Contents in brief:

1. Course introduction and possible applications
2. Camera models and principles of geometric optics
3. Geometric and algebraic pinhole model of common cameras
4. Element of rigid transformations
5. Zhang method to determine the pinhole camera parameters
6. Tracking with single and multiple camera systems
7. 3D surface reconstruction with single and multiple camera systems
8. Basic principles of Virtual Reality and main HW&SW components
9. Augmented Reality definition and main HW&SW components
10. How to obtain the registration in VST systems
11. Challenges in OST systems

Schedule:

Hours:
12 hours (3 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa – Ground Floor

To register to the course, click here

Short Abstract:

We live in a time of automated information warfare, where hordes of bots (automated software programs) rampage in our online social ecosystems. These content-polluting bots are employed by both malicious actors aiming to spread misinformation, as well as by traditional news outlets fighting for readers’ attention.
In this course, we introduce and we experiment with the fundamentals of social media crawling and analysis, we discuss the issues related to information credibility in social media, and we investigate the role of social bots in the spread of high- and low-quality (e.g., fake news) information. We also introduce the task and the challenges of social bot detection. Then, we report on social media platforms capabilities of detecting bots, and on human performance in discriminating between legitimate and bot accounts. Finally, we thoroughly discuss different machine learning and AI approaches to the automatic detection of social bots (e.g., network-, content-, and behavior-based), highlighting those that currently represent the most promising ones. The course also includes hands-on sessions in Python, where participants will write scripts for collecting live data from Twitter and will learn to perform bot detection. The course also covers points such as available datasets, software, and APIs for supporting the study of information credibility and the detection of social bots.

Course Contents in brief:

1. Social media and information credibility (e.g., fake news, coordinated inauthentic behavior);
2. Social bots and their role in the spread of low-quality information;
3. Early (i.e., supervised) and recent (i.e., unsupervised, group-based) machine learning/AI approaches to the detection of social bots;
4. A look into the future of information credibility and social bot detection;
5. Data collection from Twitter and bot detection experiments in Python.

Schedule:

1. Day1 – 25 January 2022, 9:00 - 12:00
2. Day2 – 26 January 2022, 9:00 - 12:00
3. Day3 – 27 January 2022, 9:00 - 12:00
4. Day4 – 28 January 2022, 9:00 - 12:00

Hours:
12 hours (3 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa – Ground Floor

To register to the course, click here

Short Abstract:

The course aims at introducing the students to the physics and multi-physics based modelling, focusing on applications in the field of information engineering, in particular biomedical ones. The first part of the course will provide basic abilities of using COMSOL Multi-physics software for modelling physical phenomena, in particular transport and reaction of chemical species, heat transfer and solid mechanics. Then, the second part of the course will be devoted to hands-on using the software, identifying exercises close to the activities of the students during their PhD experience.

Course Contents in brief:

• Brief introduction to Physics and Multi-Physics modelling and to the COMSOL platform
• Definition of easy and complex geometries and meshing
• Parametric sweep solutions
• “Transport and reaction of chemical species”, “heat transfer” and “solid mechanics” modules
• Data handling: post-processing and plotting
• Examples and applications

Schedule:

• Day1: December 6th, 15.00-18.00 (3h)
• Day2 – December 7th, 10.00-13.00 (3h)
• Day3 – December 9th, 10.00-13.00 (3h)
• Day 4 – December 10th, 10.00-13.00 (3h)

Hours:
20 hours (5 credits)

Room:

Aula Riunioni del Dipartimento di Ingegneria dell’Informazione, Via G. Caruso 16, Pisa – Ground Floor

To register to the course, click here

Short Abstract:

The objectives of this course are to:

1. Introduce you to typical properties and uses of porous materials in general, and nanoporous silicon in particular
2. Survey why the semiconductor silicon has been such an influential material, shaping modern society
3. Demonstrate how nanostructuring a material can both radically change its properties and provide size-tunability of properties
4. Talk about the challenges of nanotechnology, using nanoporous silicon examples, such as the need for more sustainable (“green”) synthesis and nanomaterial stability and cost
5. Survey a broad range of applications being investigated for this nanomaterial from both a technical and commercial perspective

The course will particularly suit those students who have an interest in multidisciplinary science, nanotechnology and entrepreneurship.

Course Contents in brief:

1. Introduction to porous materials: Classes of porosity. Characterization techniques. Applications of porous metals, ceramics, organics.
2. Introduction to silicon technologies: Electronics to nanoelectronics. MEMS. Solar power.
3. A brief history of porous silicon: Mr & Mrs Uhlir. FIPOS. Visible luminescence. Multifunctionality. Academic and industrial activity.
4. Routes of synthesis: Electrochemical etching. Magnesiothermic reduction. Dealloying. Green synthesis. Other techniques.
5. Properties: Tuning via porosity, composition and quantum mechanics. Mechanical. Thermal. Optical. Luminescent. Magnetic composites.
6. Processing: Substrates, membranes, particles or composites? Techniques for patterning, passivating, pore loading and communition.
7. Electronic applications: Electrical or thermal isolation of circuitry. Uses in micromachining and MEMS.
8. Analytical applications: Gas sensing. Biosensing. Mass spectrometry. SERS Analysis.
9. Optoelectronic applications: Waveguides. LEDs. Photonic crystals.
10. Energy applications: Solar and fuel cell uses. Anodes in Li batteries. Supercapacitors. Thermoelectrics. Explosives. Hydrogen storage.
11. Medical applications: Medical device and pharmaceutical industry. Brachytherapy. Imaging. Drug delivery. Wound repair. Orthopaedic tissue engineering.
12. Other applications. Toothpaste. Nutraceuticals, Cosmetics. Emerging uses.

Schedule:

1. December 13th, 2021, 9:00 – 13:00
2. December 14th, 2021, 9:00 – 13:00
3. December 15th, 2021, 9:00 – 13:00
4. December 16th, 2021, 9:00 – 13:00
5. December 17th, 2021, 9:00 – 13:00

Hours:
16 hours (4 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:

The evolution of computer systems is bringing them constantly closer to the physical world by making machines interact with their surrounding reality.  Industrial automation, robotics, aerospace and automotive industries drive increasing demands on both deterministic capabilities and compute performance into the Arm computer systems architecture.

This course will introduce elements of the Arm systems architecture and current and future solutions Arm is adopting, together with its partners, to enable the next generation of high-performance real-time computing.

The audience will be introduced to the Arm real-time compute activities, and how those activities will significantly impact all market segments where both performance and determinism are requirements.

Course Contents in brief:

1. Introduction to Arm and the Arm Architecture
2. System Architecture and Composition
3. Heterogeneous multi-core platforms
4. The shared resources interference problem in high performance real-time systems
   1. Shared caches
   2. Shared interconnect
   3. Shared memory
   4. Other: SMMU, accelerators, interrupt controller
5. Introduction to modelling Heterogeneous platforms on gem5
6. Introduction to Adaptive Traffic Profiles

Schedule:

Day 1 (half day) - 4 May 2021 - 9.00-13.00

1. Introduction to Arm and the Arm Architecture
   1. History of Arm, current business models, markets, organization
   2. The concept of Arm Partnership and Ecosystem
   3. The Arm architecture: what is it, what is it for
2. System Architecture and Composition
   1. Designing an Arm-based system, the role of the system architect
3. Heterogeneous multi-core platforms
   1. Challenges and opportunities of next-generation compute platforms

Day 2 (full day) - 5 May 2021 - 9.00-13.00

1. The shared resources interference problem in high performance real-time systems
   1. Shared caches
      1. Introduction to caches
      2. Points of contention, observability, controllability
2. Shared interconnect
   1. Introduction to interconnects
   2. Points of contention, observability, controllability
3. Shared memory
   1. Introduction to main memory
   2. Points of contention, observability, controllability
4. Other: SMMU, accelerators, interrupt controller
   1. Overview of other shared resources, their characteristics and problematics

Day 3 (half day) - 6 May 2021 - 9.00-13.00

1. Introduction to modelling Heterogeneous platform on gem5 and Adaptive Traffic Profiles
   1. The gem5 simulator
2. Introduction to Adaptive Traffic Profiles
   1. ATP as a way to capture the dynamic behavior of memory devices

Day 4 (half day) - 7 May 2021 - 9.00-13.00

1. TBD

Hours:
18 hours (4 credits)

Room:

From remote by using Microsoft Teams. The link will be sent in due time to all students who registered to the seminar.

To register to the course, click here

Short Abstract:

This course introduces foundational representations, models and processes to extract 3D information from streams of images. It introduces the geometric camera model and the relations in a multiview scenario as well as the techniques to compute 3D structure from image sequences. In a second part 3D information is used in a class of problems involving 3D registration: Self-Localization and Mapping (SLAM), 3D shape recognition and tracking. Finally the course will analyse the recent trend towards data-driven tools based in deep-learning models. For each of these topics the a set of software tools will be introduced and the course will be evaluated.

Course Contents in brief:

1. Linear Algebra review
2. Perspective camera and transformations
3. 3D Reconstruction : BA and bilinear methods
4. 3D Registration : ICP
5. SLAM: Solving the Pose-graph
6. Applications and tools

Schedule:

1. Day1 - 19 April 2021 - 9.30-13.00
2. Day2 - 20 April 2021 - 9.30-13.00
3. Day3 - 21 April 2021 - 9.30-13.00
4. Day4 - 22 April 2021 - 9.30-13.00
5. Day5 - 23 April 2021 - 9.30-13.30