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Author (down) Tronci, Enrico
Title Defining Data Structures via Böhm-Out Type Journal Article
Year 1995 Publication J. Funct. Program. Abbreviated Journal
Volume 5 Issue 1 Pages 51-64
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Abstract We show that any recursively enumerable subset of a data structure can be regarded as the solution set to a B??hm-out problem.
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Call Number Sapienza @ mari @ jfp95 Serial 57
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Author (down) Tronci, Enrico
Title Equational Programming in lambda-calculus Type Conference Article
Year 1991 Publication Sixth Annual IEEE Symposium on Logic in Computer Science (LICS) Abbreviated Journal
Volume Issue Pages 191-202
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Publisher IEEE Computer Society Place of Publication Amsterdam, The Netherlands Editor
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Notes Approved yes
Call Number Sapienza @ mari @ lics91 Serial 58
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Author (down) Tronci, Enrico
Title On Computing Optimal Controllers for Finite State Systems Type Conference Article
Year 1997 Publication CDC '97: Proceedings of the 36th IEEE International Conference on Decision and Control Abbreviated Journal
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Publisher IEEE Computer Society Place of Publication Washington, DC, USA Editor
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Notes Approved yes
Call Number Sapienza @ mari @ cdc97 Serial 66
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Author (down) Tronci, Enrico
Title Optimal Finite State Supervisory Control Type Conference Article
Year 1996 Publication CDC '96: Proceedings of the 35th IEEE International Conference on Decision and Control Abbreviated Journal
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Abstract Supervisory Controllers are Discrete Event Dynamic Systems (DEDSs) forming the discrete core of a Hybrid Control System. We address the problem of automatic synthesis of Optimal Finite State Supervisory Controllers (OSCs). We show that Boolean First Order Logic (BFOL) and Binary Decision Diagrams (BDDs) are an effective methodological and practical framework for Optimal Finite State Supervisory Control. Using BFOL programs (i.e. systems of boolean functional equations) and BDDs we give a symbolic (i.e. BDD based) algorithm for automatic synthesis of OSCs. Our OSC synthesis algorithm can handle arbitrary sets of final states as well as plant transition relations containing loops and uncontrollable events (e.g. failures). We report on experimental results on the use of our OSC synthesis algorithm to synthesize a C program implementing a minimum fuel OSC for two autonomous vehicles moving on a 4 x 4 grid.
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Publisher IEEE Computer Society Place of Publication Washington, DC, USA Editor
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Notes Approved yes
Call Number Sapienza @ mari @ cdc96 Serial 67
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Author (down) Tronci, E.; Mancini, T.; Salvo, I.; Mari, F.; Melatti, I.; Massini, A.; Sinisi, S.; Davì, F.; Dierkes, T.; Ehrig, R.; Röblitz, S.; Leeners, B.; Krüger, T.; Egli, M.; Ille, F.
Title Patient-Specific Models from Inter-Patient Biological Models and Clinical Records Type Conference Article
Year 2014 Publication Formal Methods in Computer-Aided Design (FMCAD) Abbreviated Journal
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Call Number Sapienza @ mari @ Serial 120
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Author (down) Tronci, E.; Mancini, T.; Mari, F.; Melatti, I.; Salvo, I.; Prodanovic, M.; Gruber, J. K.; Hayes, B.; Elmegaard, L.
Title Demand-Aware Price Policy Synthesis and Verification Services for Smart Grids Type Conference Article
Year 2014 Publication Proceedings of Smart Grid Communications (SmartGridComm), 2014 IEEE International Conference On Abbreviated Journal
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Call Number Sapienza @ melatti @ Serial 121
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Author (down) Tronci, E.; Mancini, T.; Mari, F.; Melatti, I.; Jacobsen, R. H.; Ebeid, E.; Mikkelsen, S. A.; Prodanovic, M.; Gruber, J. K.; Hayes, B.
Title SmartHG: Energy Demand Aware Open Services for Smart Grid Intelligent Automation Type Conference Article
Year 2014 Publication Proceedings of the Work in Progress Session of SEAA/DSD 2014 Abbreviated Journal
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ISSN ISBN 978-3-902457-40-0 Medium
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Call Number Sapienza @ mari @ Serial 119
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Author (down) Tortora, L.; Meynen, G.; Bijlsma, J.; Tronci, E.; Ferracuti, S.
Title Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective Type Journal Article
Year 2020 Publication Frontiers in Psychology Abbreviated Journal
Volume 11 Issue Pages 220
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Abstract Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as ÃâA.I. neuroprediction,Ãâ and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.
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ISSN 1664-1078 ISBN Medium
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Call Number MCLab @ davi @ Neuroprediction-2020 Serial 180
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Author (down) Toni Mancini; Enrico Tronci; Ivano Salvo; Federico Mari; Annalisa Massini; Igor Melatti
Title Computing Biological Model Parameters by Parallel Statistical Model Checking Type Journal Article
Year 2015 Publication International Work Conference on Bioinformatics and Biomedical Engineering (IWBBIO 2015) Abbreviated Journal
Volume 9044 Issue Pages 542-554
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Call Number MCLab @ davi @ Serial 124
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Author (down) Sinisi, S.; Alimguzhin, V.; Mancini, T.; Tronci, E.; Mari, F.; Leeners, B.
Title Optimal Personalised Treatment Computation through In Silico Clinical Trials on Patient Digital Twins Type Journal Article
Year 2020 Publication Abbreviated Journal Fundamenta Informaticae
Volume 174 Issue Pages 283-310
Keywords Artificial Intelligence; Virtual Physiological Human; In Silico Clinical Trials; Simulation; Personalised Medicine; In Silico Treatment Optimisation
Abstract In Silico Clinical Trials (ISCT), i.e. clinical experimental campaigns carried out by means of computer simulations, hold the promise to decrease time and cost for the safety and efficacy assessment of pharmacological treatments, reduce the need for animal and human testing, and enable precision medicine. In this paper we present methods and an algorithm that, by means of extensive computer simulation-based experimental campaigns (ISCT) guided by intelligent search, optimise a pharmacological treatment for an individual patient (precision medicine ). We show the effectiveness of our approach on a case study involving a real pharmacological treatment, namely the downregulation phase of a complex clinical protocol for assisted reproduction in humans.
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Publisher IOS Press Place of Publication Editor
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ISSN 1875-8681 ISBN Medium
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Notes Approved no
Call Number MCLab @ davi @ Serial 187
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