[Forum SIS] 8th Seminar D2 Seminar Series FDS - 26th November 2-3.30 pm

[datascience a unifi.it]

Dear all,

We are happy to present the eighth Seminar of the "D2 Seminar Series"
launched by the FDS. The Seminar will be held online FRIDAY 26TH OF
NOVEMBER 2021, from 2-3.30 PM.
The seminar will be held by Giorgio Ricchiuti from the Department of
Economics and Management and Marco Bertini from the Department of
Information Engineering of the University of Florence. 

Register in advance for this webinar:
https://us02web.zoom.us/webinar/register/WN__m4iKOO3R6WBL4uVvT-v4A

After registering, you will receive a confirmation email containing
information about joining the webinar.

We hope to see you there! You are invited to invite also your students,
PhDs and colleagues who may be interested in the Seminar (you find a
Flyer with all the info attached).

Kind Regards,
Florence Center for Data Science 

---------------- 
SPEAKER: Giorgio Ricchiuti - Department of Economics and Management,
University of Florence 
TITLE: State Space Model to Detect Cycles in Heterogeneous Agents Models
(joint work with Filippo Gusella) 

ABSTRACT: We propose an empirical test to depict possible endogenous
cycles within Heterogeneous Agent Models (HAMs). We consider a 2-type
HAM into a standard small-scale dynamic asset pricing framework. On the
one hand, fundamentalists base their expectations on the deviation of
fundamental value from market price expecting a convergence between
them. On the other hand, chartists, subject to self-fulling moods,
consider the level of past prices and relate it to the fundamental value
acting as contrarians. These pricing strategies, by their nature, cannot
be directly observed but can cause the response of the observed data.
For this reason, we consider the agents' beliefs as unobserved state
components from which, through a state space model formulation, the
heterogeneity of
fundamentalist-chartist trader cycles can be mathematically derived and
empirically tested. The model is estimated using the S&P500 index, for
the period 1990-2020 at different time scales, specifically, daily,
monthly, and quarterly.

SPEAKER: Marco Bertini - Department of Information Engineering,
University of Florence 
TITLE: High quality video experience using deep neural networks 

ABSTRACT: Lossy image and video compression algorithms are the enabling
technology for a large variety of multimedia applications, reducing the
bandwidth required for image transmission and video streaming. However,
lossy image and video compression codecs decrease the perceived visual
quality, eliminate higher frequency details and in certain cases add
noise or small image structures. There are two main drawbacks of this
phenomenon. First, images and videos appear much less pleasant to the
human eye, reducing the quality of experience. Second, computer vision
algorithms such as object detectors may be hindered and their
performance reduced. Removing such artefacts means recovering the
original image from a perturbed version of it. This means that one
ideally should invert the compression process through a complicated
non-linear image transformation. In this talk, I'll present our most
recent works based on the GAN framework that allows us to produce images
with photorealistic details from highly compressed inputs.
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