Lorentz Boost Networks: Autonomous Physics-Inspired Feature Engineering

We present a two-stage neural network architecture that enables a fully autonomous and comprehensive characterization of collision events by exclusively exploiting the four momenta of final-state particles. We refer to the first stage of the architecture as Lorentz Boost Network (LBN). The LBN allows the creation of particle combinations representing rest frames. The LBN also enables the formation of further composite particles, which are then transformed into said rest frames by Lorentz transformation. The properties of the composite, transformed particles are compiled in the form of characteristic variables that serve as input for a subsequent network. This second network has to be configured for a specific analysis task such as the separation of signal and background events. Using the example of the classification of ttH and ttbb events, we compare the separation power of the LBN approach with that of domain-unspecific deep neural networks (DNN). We observe leading performance with the LBN, even though we provide the DNNs with extensive additional input information beyond the particle four momenta. Furthermore, we demonstrate that the LBN forms physically meaningful particle combinations and autonomously generates suitable characteristic variables

Keep up with the winners: Experimental evidence on risk taking, asset integration, and peer effects

The paper reports the result of an experimental game on asset integration and risk taking. We find some evidence that winnings in earlier rounds affect risk taking in subsequent rounds, but no evidence that real life wealth outside the experiment affects risk taking. Controlling for past winnings, participants receiving a low endowment in a round engage in more risk taking. We test a ‘keeping-up-with-the-Joneses’ hypothesis and find that subjects seek to keep up with winners, though not necessarily with average earnings. Overall, the evidence suggests that risk taking tracks a reference point affected by social comparisons

A field study of data analysis exercises in a bachelor physics course using the internet platform VISPA

Bachelor physics lectures on particle physics and astrophysics were complemented by exercises related to data analysis and data interpretation at the RWTH Aachen University recently. The students performed these exercises using the internet platform VISPA, which provides a development environment for physics data analyses. We describe the platform and its application within the physics course, and present the results of a student survey. The students acceptance of the learning project was positive. The level of acceptance was related to their individual preference for learning with a computer. Furthermore, students with good programming skills favor working individually, while students who attribute themselves having low programming abilities favor working in teams. The students appreciated approaching actual research through the data analysis tasks.Comment: 21 pages, 8 figures, 1 table, for the internet platform VISPA see http://vispa.physik.rwth-aachen.d

Data Science and Machine Learning in Education

The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover, exploiting symmetries inherent in physics data have inspired physics-informed ML as a vibrant sub-field of computer science research. HEP researchers benefit greatly from materials widely available materials for use in education, training and workforce development. They are also contributing to these materials and providing software to DS/ML-related fields. Increasingly, physics departments are offering courses at the intersection of DS, ML and physics, often using curricula developed by HEP researchers and involving open software and data used in HEP. In this white paper, we explore synergies between HEP research and DS/ML education, discuss opportunities and challenges at this intersection, and propose community activities that will be mutually beneficial.Comment: Contribution to Snowmass 202

Fachinformation und EDV-Arbeitstechniken für Historiker: Einführung und Arbeitsbuch

'Der Sammelband versucht, die Erfahrungen einzufangen, die von den Herausgebern in den vergangenen Jahren an der Humboldt-Universität zu Berlin in der studentischen EDV-Ausbildung und beim Aufbau vernetzter Informationsdienste für Historiker gemacht wurden. Es handelt sich weder um einen der üblichen Sammelbände noch um eine einfache Softwareeinführung, sondern um ein praktisches Arbeitsbuch für Studierende und Nachwuchswissenschaftler zu Fragen des EDV-Einsatzes in den Geschichtswissenschaften. Dabei stehen das 'Internet' bzw. der Einsatz der sogenannten 'Neuen Medien' im Mittelpunkt. Im ersten Kapitel wird zunächst das Verhältnis der EDV zu den Geisteswissenschaften geklärt. Dies schließt eine Einführung in die Geschichte der EDV und die Betrachtung der EDV-Einsatzfelder in den Geschichtswissenschaften zu den Themen Bibliotheken im Internet, Wissenschaftlicher Alltag und Lehre sowie spezielle historische Fachinformationsangebote ein. Im zweiten Kapitel wird näher auf die Grundlagen der Datenverarbeitung eingegangen. Dabei liegt neben dem PC- und Netzbasiswissen das Hauptaugenmerk auf dem Internet und seinen Diensten. Darauf aufbauend werden im dritten Kapitel ausgewählte Applikationen aus den Bereichen Textverarbeitung, Beschreibungs- und Skriptsprachen, Tabellenkalkulation, Datenanalyse & Datenbanksysteme sowie Grafik, Präsentation und Multimedia an historischen Beispielen in ihrem Funktionsumfang vorgestellt. Im abschließenden vierten Kapitel wird der EDV-Einsatz in den Geschichtswissenschaften an ausgewählten Projekten in Forschung und Lehre exemplarisch beschrieben.' (Autorenreferat

A multicenter, randomized controlled trial of immediate total-body CT scanning in trauma patients (REACT-2)

Contains fulltext : 110874.pdf (publisher's version ) (Open Access)BACKGROUND: Computed tomography (CT) scanning has become essential in the early diagnostic phase of trauma care because of its high diagnostic accuracy. The introduction of multi-slice CT scanners and infrastructural improvements made total-body CT scanning technically feasible and its usage is currently becoming common practice in several trauma centers. However, literature provides limited evidence whether immediate total-body CT leads to better clinical outcome then conventional radiographic imaging supplemented with selective CT scanning in trauma patients. The aim of the REACT-2 trial is to determine the value of immediate total-body CT scanning in trauma patients. METHODS/DESIGN: The REACT-2 trial is an international, multicenter randomized clinical trial. All participating trauma centers have a multi-slice CT scanner located in the trauma room or at the Emergency Department (ED). All adult, non-pregnant, severely injured trauma patients according to predefined criteria will be included. Patients in whom direct scanning will hamper necessary cardiopulmonary resuscitation or who require an immediate operation because of imminent death (both as judged by the trauma team leader) are excluded. Randomization will be computer assisted. The intervention group will receive a contrast-enhanced total-body CT scan (head to pelvis) during the primary survey. The control group will be evaluated according to local conventional trauma imaging protocols (based on ATLS guidelines) supplemented with selective CT scanning. Primary outcome will be in-hospital mortality. Secondary outcomes are differences in mortality and morbidity during the first year post trauma, several trauma work-up time intervals, radiation exposure, general health and quality of life at 6 and 12 months post trauma and cost-effectiveness. DISCUSSION: The REACT-2 trial is a multicenter randomized clinical trial that will provide evidence on the value of immediate total-body CT scanning during the primary survey of severely injured trauma patients. If immediate total-body CT scanning is found to be the best imaging strategy in severely injured trauma patients it could replace conventional imaging supplemented with CT in this specific group. TRIAL REGISTRATION: ClinicalTrials.gov: (NCT01523626)