The Visual Social Distancing Problem

One of the main and most effective measures to contain the recent viral outbreak is the maintenance of the so-called Social Distancing (SD). To comply with this constraint, workplaces, public institutions, transports and schools will likely adopt restrictions over the minimum inter-personal distance between people. Given this actual scenario, it is crucial to massively measure the compliance to such physical constraint in our life, in order to figure out the reasons of the possible breaks of such distance limitations, and understand if this implies a possible threat given the scene context. All of this, complying with privacy policies and making the measurement acceptable. To this end, we introduce the Visual Social Distancing (VSD) problem, defined as the automatic estimation of the inter-personal distance from an image, and the characterization of the related people aggregations. VSD is pivotal for a non-invasive analysis to whether people comply with the SD restriction, and to provide statistics about the level of safety of specific areas whenever this constraint is violated. We then discuss how VSD relates with previous literature in Social Signal Processing and indicate which existing Computer Vision methods can be used to manage such problem. We conclude with future challenges related to the effectiveness of VSD systems, ethical implications and future application scenarios.Comment: 9 pages, 5 figures. All the authors equally contributed to this manuscript and they are listed by alphabetical order. Under submissio

A deep Natural Language Inference predictor without language-specific training data

In this paper we present a technique of NLP to tackle the problem of inference relation (NLI) between pairs of sentences in a target language of choice without a language-specific training dataset. We exploit a generic translation dataset, manually translated, along with two instances of the same pre-trained model - the first to generate sentence embeddings for the source language, and the second fine-tuned over the target language to mimic the first. This technique is known as Knowledge Distillation. The model has been evaluated over machine translated Stanford NLI test dataset, machine translated Multi-Genre NLI test dataset, and manually translated RTE3-ITA test dataset. We also test the proposed architecture over different tasks to empirically demonstrate the generality of the NLI task. The model has been evaluated over the native Italian ABSITA dataset, on the tasks of Sentiment Analysis, Aspect-Based Sentiment Analysis, and Topic Recognition. We emphasise the generality and exploitability of the Knowledge Distillation technique that outperforms other methodologies based on machine translation, even though the former was not directly trained on the data it was tested over.Comment: Conference: ICIAP202

A Smart Grid for the city of Rome: A Cost Benefit Analysis

In this work, the JRC applies its Smart Grid CBA methodology to a full-scale project rather than only to a small-size demonstrative one. To this end, the JRC and ACEA - one of Italy’s biggest Distribution System Operators (DSOs), in charge of managing the distribution system of Rome - teamed up to study the merits of deploying Smart Grid technologies (preliminarily tested in a pilot project) in a big city like the Italian capital, hosting several million electricity users. The ACEA Smart Grid Pilot Project (named "Malagrotta" after the area where pilot solutions were first realised) is the starting point for this study, as it displays many of the characteristics of emerging Smart Grids projects and interconnects several diversified generation facilities (like biogas, waste-to-electricity and PV plants) and consumption centres. This study illustrates the outcome of the application of the JRC Cost Benefit Analysis (CBA) to a) the ACEA Smart Grids pilot project; and b) the planned deployment of Smart Grid technologies (tested in the ACEA Smart Grids pilot project) to the whole of the city of Rome. The CBA is conducted from both the private investor’s and the societal perspective, in order to assess whether scaling up the Smart Grid pilot project benefits the distribution operator and the citizens. Finally, this report shows how the JRC's CBA methodology can be effectively used to assess the financial and economic viability of real Smart Grids projects and help the investment decisions of DSOs.JRC.F.3-Energy Security, Systems and Marke

POMP: Pomcp-based Online Motion Planning for active visual search in indoor environments

In this paper we focus on the problem of learning an optimal policy for Active Visual Search (AVS) of objects in known indoor environments with an online setup. Our POMP method uses as input the current pose of an agent (e.g. a robot) and a RGB-D frame. The task is to plan the next move that brings the agent closer to the target object. We model this problem as a Partially Observable Markov Decision Process solved by a Monte-Carlo planning approach. This allows us to make decisions on the next moves by iterating over the known scenario at hand, exploring the environment and searching for the object at the same time. Differently from the current state of the art in Reinforcement Learning, POMP does not require extensive and expensive (in time and computation) labelled data so being very agile in solving AVS in small and medium real scenarios. We only require the information of the floormap of the environment, an information usually available or that can be easily extracted from an a priori single exploration run. We validate our method on the publicly available AVD benchmark, achieving an average success rate of 0.76 with an average path length of 17.1, performing close to the state of the art but without any training needed. Additionally, we show experimentally the robustness of our method when the quality of the object detection goes from ideal to faulty

POMP++: Pomcp-based Active Visual Search in unknown indoor environments

In this paper we focus on the problem of learning online an optimal policy for Active Visual Search (AVS) of objects in unknown indoor environments. We propose POMP++, a planning strategy that introduces a novel formulation on top of the classic Partially Observable Monte Carlo Planning (POMCP) framework, to allow training-free online policy learning in unknown environments. We present a new belief reinvigoration strategy which allows to use POMCP with a dynamically growing state space to address the online generation of the floor map. We evaluate our method on two public benchmark datasets, AVD that is acquired by real robotic platforms and Habitat ObjectNav that is rendered from real 3D scene scans, achieving the best success rate with an improvement of >10% over the state-of-the-art methods

The Northern Cross fast radio burst project–I: overview and pilot observations at 408 MHz

Fast radio bursts (FRBs) remain one of the most enigmatic astrophysical sources. Observations have significantly progressed over the last few years, due to the capabilities of new radio telescopes and the refurbishment of existing ones. Here, we describe the upgrade of the Northern Cross radio telescope, operating in the 400–416 MHz frequency band, with the ultimate goal of turning the array into a dedicated instrument to survey the sky for FRBs

Extracellular vesicle-mediated transfer of CLIC1 protein is a novel mechanism for the regulation of glioblastoma growth

Little progresses have been made in the treatment of glioblastoma (GBM), the most aggressive and lethal among brain tumors. Recently we have demonstrated that Chloride Intracellular Channel-1 (CLIC1) is overexpressed in GBM compared to normal tissues, with highest expression in patients with poor prognosis. Moreover, CLIC1-silencing in cancer stem cells (CSCs) isolated from human GBM patients negatively influences proliferative capacity and self-renewal properties in vitro and impairs the in vivo tumorigenic potential. Here we show that CLIC1 exists also as a circulating protein, secreted via extracellular vesicles (EVs) released by either cell lines or GBM-derived CSCs. Extracellular vesicles (EVs), comprising exosomes and microvesicles based on their composition and biophysical properties, have been shown to sustain tumor growth in a variety of model systems, including GBM. Interestingly, treatment of GBM cells with CLIC1-containing EVs stimulates cell growth both in vitro and in vivo in a CLIC1-dose dependent manner. EVs derived from CLIC1-overexpressing GBM cells are strong inducers of proliferation in vitro and tumor engraftment in vivo. These stimulations are significantly attenuated by treatment of GBM cells with EVs derived from CLIC1-silenced cells. However, CLIC1 modulation appears to have no direct role in EV structure, biogenesis and secretion. These findings reveal that, apart from the function of CLIC1 cellular reservoir, CLIC1 contained in EVs is a novel regulator of GBM growth

Technical and Functional Validation of a Teleoperated Multirobots Platform for Minimally Invasive Surgery

Nowadays Robotic assisted Minimally Invasive Surgeries (R-MIS) are the elective procedures for treating highly accurate and scarcely invasive pathologies, thanks to their abil- ity to empower surgeons\u2019 dexterity and skills. The research on new Multi-Robots Surgery (MRS) platform is cardinal to the development of a new SARAS surgical robotic platform, which aims at carrying out autonomously the assistants tasks during R- MIS procedures. In this work, we will present the SARAS MRS platform validation protocol, framed in order to assess: (i) its technical performances in purely dexterity exercises, and (ii) its functional performances. The results obtained show a prototype able to put the users in the condition of accomplishing the tasks requested (both dexterity- and surgical-related), even with rea- sonably lower performances respect to the industrial standard. The main aspects on which further improvements are needed result to be the stability of the end effectors, the depth per- ception and the vision systems, to be enriched with dedicated virtual fixtures. The SARAS\u2019 aim is to reduce the main surgeon\u2019s workload through the automation of assistive tasks which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time