A new approach for diagnosability analysis of Petri nets using Verifier Nets

In this paper, we analyze the diagnosability properties of labeled Petri nets. We consider the standard notion of diagnosability of languages, requiring that every occurrence of an unobservable fault event be eventually detected, as well as the stronger notion of diagnosability in K steps, where the detection must occur within a fixed bound of K event occurrences after the fault. We give necessary and sufficient conditions for these two notions of diagnosability for both bounded and unbounded Petri nets and then present an algorithmic technique for testing the conditions based on linear programming. Our approach is novel and based on the analysis of the reachability/coverability graph of a special Petri net, called Verifier Net, that is built from the Petri net model of the given system. In the case of systems that are diagnosable in K steps, we give a procedure to compute the bound K. To the best of our knowledge, this is the first time that necessary and sufficient conditions for diagnosability and diagnosability in K steps of labeled unbounded Petri nets are presented

A new method for determining small earthquake source parameters using short-period P waves

We developed a new technique of inverting short-period (0.5–2 Hz) P waveforms for determining small earthquake (M <3.5) focal mechanisms and moments, where magnitude ~4 events with known source mechanisms are used to calibrate the "unmodeled" structural effect. The calibration is based on a waveform cluster analysis, where we show that clustered events of different sizes, for example, M ~4 versus M ~2, display similar signals in the short-period (SP, 0.5–2 Hz) frequency band, implying propagational stability. Since both M ~4 and M ~2 events have corner frequencies higher than 2 Hz, they can be treated as point sources, and the "unmodeled" structural effect on the SP P waves can be derived from the magnitude 4 events with known source mechanisms. Similarly, well-determined magnitude 2’s can provide calibration for studying even smaller events at higher frequencies, for example, 2–8 Hz. In particular, we find that the "unmodeled" structural effect on SP P waves is mainly an amplitude discrepancy between data and 1D synthetics. The simple function of "amplitude amplification factor" (AAF) defined as the amplitude ratio between data and synthetics provides useful calibration, in that the AAFs derived from different clustered events appear consistent, hence stable and mechanism independent. We take a grid-search approach to determine source mechanisms by minimizing the misfit error between corrected data and synthetics of SP P waves. The validation tests with calibration events demonstrate the importance and usefulness of the AAF corrections in recovering reliable results. We introduce the method with the 2003 Big Bear sequence. However, it applies equally well to other source regions in southern California, because we have shown that the mechanism independence and stability of the AAFs for source regions of 10 km by 10 km are typical. By definition, the AAFs contain the effects from the station site, the path, and crustal scattering. Although isolating their contributions proves difficult, the mechanism independence and stability of the AAFs suggest that they are mainly controlled by the near-receiver structure. Moreover, the ratios between the AAFs for the vertical and radial components from various events at different locations appear consistent, suggesting that these AAF(v)/AAF(r) ratios might be simple functions of site conditions. In this study, we obtained the focal mechanisms and moments for 92 Big Bear events with M_L down to 2.0. The focal planes correlate well with the seismicity patterns, while containing abundant finer-scale fault complexity. We find a linear relationship between log(M_0) and M_L, that is, log(M_0) = 1.12M_L + 17.29, which explains all the data points spanning three orders of magnitude (2.0 < M_L < 5.5)

Uncertainty in Economic Growth and Inequality

A step to consilience, starting with a deconstruction of the causality of uncertainty that is embedded in the fundamentals of growth and inequality, following a construction of aggregation laws that disclose the invariance principle across heterogeneous individuals, ending with a reconstruction of metric models that yields deeper structural connections via U.S. GDP and income data

The Evolution of Beliefs over Signed Social Networks

We study the evolution of opinions (or beliefs) over a social network modeled as a signed graph. The sign attached to an edge in this graph characterizes whether the corresponding individuals or end nodes are friends (positive links) or enemies (negative links). Pairs of nodes are randomly selected to interact over time, and when two nodes interact, each of them updates its opinion based on the opinion of the other node and the sign of the corresponding link. This model generalizes DeGroot model to account for negative links: when two enemies interact, their opinions go in opposite directions. We provide conditions for convergence and divergence in expectation, in mean-square, and in almost sure sense, and exhibit phase transition phenomena for these notions of convergence depending on the parameters of the opinion update model and on the structure of the underlying graph. We establish a {\it no-survivor} theorem, stating that the difference in opinions of any two nodes diverges whenever opinions in the network diverge as a whole. We also prove a {\it live-or-die} lemma, indicating that almost surely, the opinions either converge to an agreement or diverge. Finally, we extend our analysis to cases where opinions have hard lower and upper limits. In these cases, we study when and how opinions may become asymptotically clustered to the belief boundaries, and highlight the crucial influence of (strong or weak) structural balance of the underlying network on this clustering phenomenon

A recursive paradigm for aligning observed behavior of large structured process models

The alignment of observed and modeled behavior is a crucial problem in process mining, since it opens the door for conformance checking and enhancement of process models. The state of the art techniques for the computation of alignments rely on a full exploration of the combination of the model state space and the observed behavior (an event log), which hampers their applicability for large instances. This paper presents a fresh view to the alignment problem: the computation of alignments is casted as the resolution of Integer Linear Programming models, where the user can decide the granularity of the alignment steps. Moreover, a novel recursive strategy is used to split the problem into small pieces, exponentially reducing the complexity of the ILP models to be solved. The contributions of this paper represent a promising alternative to fight the inherent complexity of computing alignments for large instances.Peer ReviewedPostprint (author's final draft

Modality effects in implicit artificial grammar learning: An EEG study

Recently, it has been proposed that sequence learning engages a combination of modality-specific operating networks and modality-independent computational principles. In the present study, we compared the behavioural and EEG outcomes of implicit artificial grammar learning in the visual vs. auditory modality. We controlled for the influence of surface characteristics of sequences (Associative Chunk Strength), thus focusing on the strictly structural aspects of sequence learning, and we adapted the paradigms to compensate for known frailties of the visual modality compared to audition (temporal presentation, fast presentation rate). The behavioural outcomes were similar across modalities. Favouring the idea of modality-specificity, ERPs in response to grammar violations differed in topography and latency (earlier and more anterior component in the visual modality), and ERPs in response to surface features emerged only in the auditory modality. In favour of modality-independence, we observed three common functional properties in the late ERPs of the two grammars: both were free of interactions between structural and surface influences, both were more extended in a grammaticality classification test than in a preference classification test, and both correlated positively and strongly with theta event-related-synchronization during baseline testing. Our findings support the idea of modality-specificity combined with modality-independence, and suggest that memory for visual vs. auditory sequences may largely contribute to cross-modal differences. (C) 2018 Elsevier B.V. All rights reserved.Max Planck Institute for Psycholinguistics; Donders Institute for Brain, Cognition and Behaviour; Fundacao para a Ciencia e Tecnologia [PTDC/PSI-PC0/110734/2009, UID/BIM/04773/2013, CBMR 1334, PEst-OE/EQB/1A0023/2013, UM/PSI/00050/2013

Multiscale Analysis of Spreading in a Large Communication Network

In temporal networks, both the topology of the underlying network and the timings of interaction events can be crucial in determining how some dynamic process mediated by the network unfolds. We have explored the limiting case of the speed of spreading in the SI model, set up such that an event between an infectious and susceptible individual always transmits the infection. The speed of this process sets an upper bound for the speed of any dynamic process that is mediated through the interaction events of the network. With the help of temporal networks derived from large scale time-stamped data on mobile phone calls, we extend earlier results that point out the slowing-down effects of burstiness and temporal inhomogeneities. In such networks, links are not permanently active, but dynamic processes are mediated by recurrent events taking place on the links at specific points in time. We perform a multi-scale analysis and pinpoint the importance of the timings of event sequences on individual links, their correlations with neighboring sequences, and the temporal pathways taken by the network-scale spreading process. This is achieved by studying empirically and analytically different characteristic relay times of links, relevant to the respective scales, and a set of temporal reference models that allow for removing selected time-domain correlations one by one

Geodetic model of the 2016 Central Italy earthquake sequence inferred from InSAR and GPS data

We investigate a large geodetic data set of interferometric synthetic aperture radar (InSAR)and GPS measurements to determine the source parameters for the three main shocks of the 2016Central Italy earthquake sequence on 24 August and 26 and 30 October (Mw6.1, 5.9, and 6.5,respectively). Our preferred model is consistent with the activation of four main coseismic asperitiesbelonging to the SW dipping normal fault system associated with the Mount Gorzano-Mount Vettore-Mount Bove alignment. Additional slip, equivalent to aMw~ 6.1–6.2 earthquake, on a secondary (1) NEdipping antithetic fault and/or (2) on a WNW dipping low-angle fault in the hanging wall of the mainsystem is required to better reproduce the complex deformation pattern associated with the greatestseismic event (theMw6.5 earthquake). The recognition of ancillary faults involved in the sequencesuggests a complex interaction in the activated crustal volume between the main normal faults and thesecondary structures and a partitioning of strain releas