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The Overproduction of truth: Passion, competition, and integrity in modern science

The way science is done has changed radically in recent years. Scientific research and institutions, which have long been characterized by passion, dedication and reliability, have increasingly less capacity for more ethical pursuits, and are pressed by hard market laws. From the vocation of a few, science has become the profession of many — possibly too many. These trends come with consequences and risks, such as the rise in fraud, plagiarism, and in particular the sheer volume of scientific publications, often of little relevance. We will critically review and assess the present-day policies and behaviors in scientific production and publication. We will touch on the tumultuous growth of scientific journals, in parallel with the growth of self-declared scientists over the world. We will investigate the loopholes and hoaxes of pretend journals and nonexistent congresses, so common today in the scientific arena, and discuss problems connected to the incorrect use of bibliometric indices, which have resulted in large part from the above distortions of scientific life. The solution? A slow approach with more emphasis on quality rather than quantity that will help us to rediscover the essential role of the responsible scientist.

Reference: G. Pacchioni, The Overproduction of truth. Passion, competition, and integrity in modern science, Oxford University Press, Oxford 2018.

Gianfranco Pacchioni received his PhD at the Freie Universität Berlin in 1984. He worked at the IBM Almaden Research Center, and at the Technical University of Munich. He is Full Professor at the University of Milano Bicocca where he has been Vice Rector for Research (2013-2019) and Director of the Department of Materials Science (2003-2009). He has published more than 500 papers (h index 85; WoS) and given nearly 500 invited talks on the electronic structure of oxide surfaces and interfaces, 2D oxides, defects, supported metal clusters, and catalysis. He received several awards and is Fellow of the Accademia Nazionale dei Lincei (2014), the Academia Europaea (2012), and the European Academy of Sciences (2009). He has authored some popular science books, including “The overproduction of truth”, Oxford University Press, 2018, where he presents a personal view of the problems of contemporary science.

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Collision Course: Particle Physics Meets Machine Learning

Modern machine learning has had an outsized impact on many scientific fields, and particle physics is no exception. What is special about particle physics, though, is the vast amount of theoretical and experimental knowledge that we already have about many problems in the field. In this colloquium, I present two cases studies involving quantum chromodynamics (QCD) at the Large Hadron Collider (LHC), highlighting the fascinating interplay between theoretical principles and machine learning strategies. First, by cataloging the space of all possible QCD measurements, we (re)discovered technology relevant for self-driving cars. Second, by quantifying the similarity between two LHC collisions, we unlocked a class of nonparametric machine learning techniques based on optimal transport. In addition to providing new quantitative insights into QCD, these techniques enable new ways to visualize data from the LHC.

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