&inCSL Seminars

In the last 10 years, the availability of time-resolved data in many fields has led to the extension of the field of networks to the study of temporal networks. In a static network, nodes represent elements of the system and links between nodes encode the fact that an interaction exists between the corresponding elements. Links are then fixed and no information on the timing of these interactions is available. In a temporal network instead, links are replaced by temporal series of interactions, each with its starting time and duration. Taking into account temporality has important consequences in terms of analysis and modelling. Finding relevant structures in temporal networks is in particular a challenging task, and I will present two methods recently developed: the extraction of a backbone of significant ties on the one hand, and the temporal core decomposition, which allows us to identify dense structures, together with their temporal span.

In this talk, I will describe some of our projects on HCI using advanced computer vision and dynamic projection mapping, including an interactive display on the water, image stabilization of camera embedded ball, and interactive spherical displays with 360 omnidirectional cameras. Then I will talk our recent projects on skill transfer in sports and music.

The Ising model is a graphical model whose parameters can be tuned in order to describe stationary distributions of binary variables. In many practical problems in different domains - e.g. physics, biology, neuroscience, finance, sociology - the topology of the graph and the values of the couplings are unknown and they need to be reconstructed from the data. The inverse Ising problem aims to find the parameters of the model that best fit the data. We propose a new algorithm to learn the network of the interactions of a pairwise Ising models, based on the pseudo-likelihood method. Our present implementation is particularly suitable to address the case of sparse underlying topologies and it is based on a careful search of the most important parameters in their high dimensional space.

The DeepBach model provides a novel way to compose Bach chorales in an interactive manner. In this seminar, we discuss how to extend this model so that it handles other music genres such as traditionnal folk tunes or jazz songs.

Theoretical models of critical mass have shown how minority groups can initiate social change dynamics in the emergence of new social conventions. Here, we study an artificial system of social conventions in which human subjects interact to establish a new coordination equilibrium. The findings provide direct empirical demonstration of the existence of a tipping point in the dynamics of changing social conventions. When minority groups reached the critical mass—that is, the critical group size for initiating social change—they were consistently able to overturn the established behavior. The size of the required critical mass is expected to vary based on theoretically identifiable features of a social setting. Our results show that the theoretically predicted dynamics of critical mass do in fact emerge as expected within an empirical system of social coordination.

Is it possible to capture the socio-economic footprint of the human behavior in our cities or neighborhoods? Nowadays, all human activities, ranging from the people we call, the places we visit, the things we eat and the products we buy, generates data. This can be analyzed over long periods to paint a comprehensive portrait of human behavior within the city boundaries. These geolocated digital traces, when combined with other information streams from national census, or google api, can be used to extract information about the potential needs and the routines in the collective behavior of different groups of citizens. We will analyze this data to understand the extent to which the urban activities of different population groups or communities are driven by both socio-economic differences and cities’ structure. This new quantitative approach will provide new insights for more inclusive policies to help future urban development.

In large musical catalogs such as in streaming companies, manual curation comes at high cost and the amount of data is considerable with tens of thousands of records delivered every week. Automatic systems trained directly from audio data help streaming companies describing audio recordings in their catalogs as well as creating relations between them. We will take a look at what is done by Deezer R&D's team in this domain using machine learning techniques, especially representation learning ones.

Music mixing is the process of combining multitrack recordings into a final product. Sony CSL is involved in music mixing through the AutoMix and DAWGen projects. Beyond making the music merely audible, what is the purpose of mixing? Citing many examples within five categories, we explore a variety of aspects music mixing can address.

LEGO bricks are among the most popular toys for children (and adults) and they also are well known tools capable of fostering individual creativity and problem solving skills. In relatively recent times, some scientific works exploited LEGO bricks for a wide variety of different purposes, from the measurement of cognitive effects on problem solving in social sciences to the representation of molecular structures, while in some cases they became part of the experimental apparatus. In this presentation I will talk about my past work with LEGO Bricks, starting from the first experiments on collective creativity during free building events that took place in Rome. From these experiments, we started to develop a new interactive experience in which the “free building” task is replaced by the task of finding sustainable solutions for problems related to urban environments. This new experiment requires a realistic modeling framework of the dynamics of the cities. I will conclude presenting the current issues and research questions related to it.

Nowadays dealing with R&I implies not only looking after the scientific value of our work, but also having clear in mind the effects of what we are doing for society in a wide perspective: from policymakers to enterprises, up to citizens. Research and innovation therefore should be able to come out from labs and create bridges with their reference context. To this regard, the challenge is to be able of creating economic, social, cultural and environmental impact looking at them as a measurement of "change".Through the seminar we will share and talk about some tools, such as logical framework matrix, and some concepts, like Responsible Research and Innovation, to comply with this issue.

About fifty years ago, linguistics played a central role in cognitive science and its insights were highly influential for developments in models and applications of natural language processing. Today, language is still seen as a major issue, but all recent breakthroughs in language studies - particularly in the fields of computational linguistics and artificial intelligence - have been achieved without influence from developments in linguistics. That is unfortunate, because the most powerful language technologies today are still incapable of understanding natural language, and they would greatly benefit from more linguistic sophistication. In this presentation, I will present how “constructional approaches” to language can put linguistics back on the map of cognitive science and how it can help linguistics to make claim to the position of the science of natural language processing. More specifically, I will present our work on Fluid Construction Grammar, the world’s most advanced computational platform for constructional language processing, which intends to achieve both deep semantic parsing and adequate production using the same linguistic inventories.

Creativity and innovation are key elements in many different areas and disciplines since they represent the primary motor to explore new solutions in ever-changing and unpredictable environments. New biological traits and functions, new technological artefacts, new social, linguistic and cultural structures, new meanings, are very often triggered by the mutated external conditions. Unfortunately the detailed mechanisms through which humans, societies and nature express their creativity and innovate are largely unknown. The common intuition that one new thing often leads to another is captured, mathematically, by the notion of adjacent possible, introduced by Stuart Kauffman. Originally introduced in the framework of biology, the adjacent possible metaphor already expanded its scope to include all those things (ideas, linguistic structures, concepts, molecules, genomes, technological artefacts, etc.) that are one step away from what actually exists, and hence can arise from incremental modifications and recombination of existing material. In this talk I'll present a mathematical framework, describing the expansion of the adjacent possible, whose predictions are borne out in several data sets drawn from social and technological systems. Finally I'll discuss how games could represent a extraordinary framework to experimentally investigate basic mechanisms at play whenever we learn, create and innovate. I'll present a few examples recently developed in the framework of the KREYON project (www.kreyon.net).