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Author Leeners, B.; Kruger, T.H.C.; Geraedts, K.; Tronci, E.; Mancini, T.; Ille, F.; Egli, M.; Röblitz, S.; Saleh, L.; Spanaus, K.; Schippert, C.; Zhang, Y.; Hengartner, M.P.
Title Lack of Associations between Female Hormone Levels and Visuospatial Working Memory, Divided Attention and Cognitive Bias across Two Consecutive Menstrual Cycles Type Journal Article
Year 2017 Publication Frontiers in Behavioral Neuroscience Abbreviated Journal
Volume (up) 11 Issue Pages 120
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Abstract Background: Interpretation of observational studies on associations between prefrontal cognitive functioning and hormone levels across the female menstrual cycle is complicated due to small sample sizes and poor replicability. Methods: This observational multisite study comprised data of n=88 menstruating women from Hannover, Germany, and Zurich, Switzerland, assessed during a first cycle and n=68 re-assessed during a second cycle to rule out practice effects and false-positive chance findings. We assessed visuospatial working memory, attention, cognitive bias and hormone levels at four consecutive time-points across both cycles. In addition to inter-individual differences we examined intra-individual change over time (i.e., within-subject effects). Results: Oestrogen, progesterone and testosterone did not relate to inter-individual differences in cognitive functioning. There was a significant negative association between intra-individual change in progesterone and change in working memory from pre-ovulatory to mid-luteal phase during the first cycle, but that association did not replicate in the second cycle. Intra-individual change in testosterone related negatively to change in cognitive bias from menstrual to pre-ovulatory as well as from pre-ovulatory to mid-luteal phase in the first cycle, but these associations did not replicate in the second cycle. Conclusions: There is no consistent association between women's hormone levels, in particular oestrogen and progesterone, and attention, working memory and cognitive bias. That is, anecdotal findings observed during the first cycle did not replicate in the second cycle, suggesting that these are false-positives attributable to random variation and systematic biases such as practice effects. Due to methodological limitations, positive findings in the published literature must be interpreted with reservation.
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ISSN 1662-5153 ISBN Medium
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Notes Approved no
Call Number Sapienza @ mari @ ref10.3389/fnbeh.2017.00120 Serial 167
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Author 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 (up) 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 ‘A.I. neuroprediction,’ 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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Notes Approved no
Call Number MCLab @ davi @ Neuroprediction-2020 Serial 180
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Author Pappagallo, A.; Massini, A.; Tronci, E.
Title Monte Carlo Based Statistical Model Checking of Cyber-Physical Systems: A Review Type Journal Article
Year 2020 Publication Information Abbreviated Journal
Volume (up) 11 Issue 558 Pages
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Notes Approved no
Call Number MCLab @ davi @ Serial 181
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Author Fischer, S.; Ehrig, R.; Schaefer, S.; Tronci, E.; Mancini, T.; Egli, M.; Ille, F.; Krueger, T.H.C.; Leeners, B.; Roeblitz, S.
Title Mathematical Modeling and Simulation Provides Evidence for New Strategies of Ovarian Stimulation Type Journal Article
Year 2021 Publication Frontiers in Endocrinology Abbreviated Journal
Volume (up) 12 Issue Pages 117
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Abstract New approaches to ovarian stimulation protocols, such as luteal start, random start or double stimulation, allow for flexibility in ovarian stimulation at different phases of the menstrual cycle. It has been proposed that the success of these methods is based on the continuous growth of multiple cohorts (“waves”) of follicles throughout the menstrual cycle which leads to the availability of ovarian follicles for ovarian controlled stimulation at several time points. Though several preliminary studies have been published, their scientific evidence has not been considered as being strong enough to integrate these results into routine clinical practice. This work aims at adding further scientific evidence about the efficiency of variable-start protocols and underpinning the theory of follicular waves by using mathematical modeling and numerical simulations. For this purpose, we have modified and coupled two previously published models, one describing the time course of hormones and one describing competitive follicular growth in a normal menstrual cycle. The coupled model is used to test ovarian stimulation protocols in silico. Simulation results show the occurrence of follicles in a wave-like manner during a normal menstrual cycle and qualitatively predict the outcome of ovarian stimulation initiated at different time points of the menstrual cycle.
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ISSN 1664-2392 ISBN Medium
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Notes Approved no
Call Number MCLab @ davi @ ref10.3389/fendo.2021.613048 Serial 189
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Author Bucciarelli, Antonio; Piperno, Adolfo; Salvo, Ivano
Title Intersection types and λ-definability Type Journal Article
Year 2003 Publication Mathematical Structures in Computer Science Abbreviated Journal
Volume (up) 13 Issue 1 Pages 15-53
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Abstract This paper presents a novel method for comparing computational properties of λ-terms that are typeable with intersection types, with respect to terms that are typeable with Curry types. We introduce a translation from intersection typing derivations to Curry typeable terms that is preserved by β-reduction: this allows the simulation of a computation starting from a term typeable in the intersection discipline by means of a computation starting from a simply typeable term. Our approach proves strong normalisation for the intersection system naturally by means of purely syntactical techniques. The paper extends the results presented in Bucciarelli et al. (1999) to the whole intersection type system of Barendregt, Coppo and Dezani, thus providing a complete proof of the conjecture, proposed in Leivant (1990), that all functions uniformly definable using intersection types are already definable using Curry types.
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Publisher Cambridge University Press Place of Publication New York, NY, USA Editor
Language Summary Language Original Title
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ISSN 0960-1295 ISBN Medium
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Notes Approved yes
Call Number Sapienza @ mari @ Bucciarelli-Piperno-Salvo:MSCS-03 Serial 69
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Author Bobbio, Andrea; Ciancamerla, Ester; Minichino, Michele; Tronci, Enrico
Title Functional analysis of a telecontrol system and stochastic measures of its GSM/GPRS connections Type Journal Article
Year 2005 Publication Archives of Transport – International Journal of Transport Problems Abbreviated Journal
Volume (up) 17 Issue 3-4 Pages
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Notes Approved yes
Call Number Sapienza @ mari @ jtp05 Serial 31
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Author Della Penna, Giuseppe; Intrigila, Benedetto; Magazzeni, Daniele; Melatti, Igor; Tronci, Enrico
Title CGMurphi: Automatic synthesis of numerical controllers for nonlinear hybrid systems Type Journal Article
Year 2013 Publication European Journal of Control Abbreviated Journal European Journal of Control
Volume (up) 19 Issue 1 Pages 14-36
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Publisher Elsevier North-Holland, Inc. Place of Publication Editor
Language Summary Language Original Title
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Series Volume Series Issue Edition
ISSN 0947-3580 ISBN Medium
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Notes Approved no
Call Number Sapienza @ melatti @ Serial 114
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Author Mari, Federico; Melatti, Igor; Salvo, Ivano; Tronci, Enrico
Title Model Based Synthesis of Control Software from System Level Formal Specifications Type Journal Article
Year 2014 Publication ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY Abbreviated Journal ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
Volume (up) 23 Issue 1 Pages Article 6
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Publisher ACM Place of Publication Editor
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ISSN 1049-331X ISBN Medium
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Notes Approved no
Call Number Sapienza @ melatti @ Serial 110
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Author Cesta, Amedeo; Finzi, Alberto; Fratini, Simone; Orlandini, Andrea; Tronci, Enrico
Title Validation and verification issues in a timeline-based planning system Type Journal Article
Year 2010 Publication The Knowledge Engineering Review Abbreviated Journal
Volume (up) 25 Issue 03 Pages 299-318
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Abstract One of the key points to take into account to foster effective introduction of AI planning and scheduling systems in real world is to develop end user trust in the related technologies. Automated planning and scheduling systems often brings solutions to the users which are neither “obvious” nor immediately acceptable for them. This is due to the ability of these tools to take into account quite an amount of temporal and causal constraints and to employ resolution processes often designed to optimize the solution with respect to non trivial evaluation functions. To increase technology trust, the study of tools for verifying and validating plans and schedules produced by AI systems might be instrumental. In general, validation and verification techniques represent a needed complementary technology in developing domain independent architectures for automated problem solving. This paper presents a preliminary report of the issues concerned with the use of two software tools for formal verification of finite state systems to the validation of the solutions produced by MrSPOCK, a recent effort for building a timeline based planning tool in an ESA project.
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Publisher Cambridge University Press Place of Publication Editor
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Notes Approved yes
Call Number Sapienza @ mari @ Cffot10 Serial 18
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Author Mancini, T.; Massini, A.; Tronci, E.
Title Parallelization of Cycle-Based Logic Simulation Type Journal Article
Year 2017 Publication Parallel Processing Letters Abbreviated Journal
Volume (up) 27 Issue 02 Pages
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Notes Approved no
Call Number MCLab @ davi @ Serial 166
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