Ricerc@Sapienza

A modern lighting system should automatically calibrate itself (light commissioning), assess its own status (which lights are on/off and how dimmed), and allow for the creation or preservation of lighting patterns (adjustability), e.g. after the sunset. Such a system does not exist today, nor (real) data, labels, or metrics are available to compare with and foster progress. In this paper we set the baselines to such a computational system, called LIT, and its applications.

Human activity recognition and forecasting can be used as a primary cue for scene understanding. Acquiring details from the scene has vast applications in different fields such as computer vision, robotics and more recently smart lighting. This work brings together advanced research in computer vision and the most modern technology in lighting. The goal of this work is to eliminate the need for any switches for lighting, which means that each person in the office perceives the entire office as all lit, while lights, which are not visible by the person, are switched off by the system.

The rapid advances of transportation infrastructure have led to a dramatic increase in the demand for smart systems capable of monitoring traffic and street safety. Fundamental to these applications are a community-based evaluation platform and benchmark for object detection and multi-object tracking.

Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only “real world” data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used.

Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progression remain suboptimal. This limits the opportunity to tailor therapy on an individual patient's prognosis, in spite of the choice of several therapeutic options.

Introduction: Blood-based biomarkers of pathophysiological brain amyloid β (Aβ) accumulation, particularly for preclinical target and large-scale interventions, are warranted to effectively enrich Alzheimer's disease clinical trials and management. Methods: We investigated whether plasma concentrations of the Aβ1–40/Aβ1–42 ratio, assessed using the single-molecule array (Simoa) immunoassay, may predict brain Aβ positron emission tomography status in a large-scale longitudinal monocentric cohort (N = 276) of older individuals with subjective memory complaints.

We firstly introduce the new Springer series Human Perspectives in Health Sciences and Technology (HPHST), and then we move on to illustrate the topic this volume deals with, namely whether machines will replace scientists in scientific development. We then explain the decision of having this volume to be the first volume of the HPHST series. Finally, we describe the organization of this book and give a brief presentation of each chapter.

This book provides a critical reflection on automated science and addresses the question whether the computational tools we developed in last decades are changing the way we humans do science. More concretely: Can machines replace scientists in crucial aspects of scientific practice? The contributors to this book re-think and refine some of the main concepts by which science is understood, drawing a fascinating picture of the developments we expect over the next decades of human-machine co-evolution.

Diatoms (Bacillariophyta), one of the most abundant and diverse groups of marine phytoplankton, respond rapidly to the supply of new nutrients, often out-competing other phytoplankton. Herein, we integrated analyses of the evolution, distribution, and expression modulation of two gene families involved in diatom nitrogen uptake (DiAMT1 and DiNRT2), in order to infer the main drivers of divergence in a key functional trait of phytoplankton. Our results suggest that major steps in the evolution of the two gene families reflected key events triggering diatom radiation and diversification.

Multi-agent systems recently gained a lot of attention for solving machine learning and data mining problems. Furthermore, their peculiar divide-and-conquer approach is appealing when large datasets have to be analyzed. In this paper, we propose a multi-agent classification system able to tackle large datasets where each agent independently explores a random small portion of the overall dataset, searching for meaningful clusters in proper subspaces where they are well-formed (i.e., compact and populated).