Efficient Online Timed Pattern Matching by Automata-Based Skipping

The timed pattern matching problem is an actively studied topic because of its relevance in monitoring of real-time systems. There one is given a log $w$ and a specification $\mathcal{A}$ (given by a timed word and a timed automaton in this paper), and one wishes to return the set of intervals for which the log $w$, when restricted to the interval, satisfies the specification $\mathcal{A}$. In our previous work we presented an efficient timed pattern matching algorithm: it adopts a skipping mechanism inspired by the classic Boyer--Moore (BM) string matching algorithm. In this work we tackle the problem of online timed pattern matching, towards embedded applications where it is vital to process a vast amount of incoming data in a timely manner. Specifically, we start with the Franek-Jennings-Smyth (FJS) string matching algorithm---a recent variant of the BM algorithm---and extend it to timed pattern matching. Our experiments indicate the efficiency of our FJS-type algorithm in online and offline timed pattern matching

Robust Model-Checking of Linear-Time Properties in Timed Automata

International audienceFormal verification of timed systems is well understood, but their \emphimplementation is still challenging. Recent works by Raskin \emphet al. have brought out a model of parameterized timed automata that can be used to prove \emphimplementability of timed systems for safety properties. We define here a more general notion of robust model-checking for linear-time properties, which consists in verifying whether a property still holds even if the transitions are slightly delayed or expedited. We provide PSPACE algorithms for the robust model-checking of Büchi-like and LTL properties. We also verify bounded-response-time properties

Probabilistic Bisimulation: Naturally on Distributions

In contrast to the usual understanding of probabilistic systems as stochastic processes, recently these systems have also been regarded as transformers of probabilities. In this paper, we give a natural definition of strong bisimulation for probabilistic systems corresponding to this view that treats probability distributions as first-class citizens. Our definition applies in the same way to discrete systems as well as to systems with uncountable state and action spaces. Several examples demonstrate that our definition refines the understanding of behavioural equivalences of probabilistic systems. In particular, it solves a long-standing open problem concerning the representation of memoryless continuous time by memory-full continuous time. Finally, we give algorithms for computing this bisimulation not only for finite but also for classes of uncountably infinite systems

Spatial contrast sensitivity in adolescents with autism spectrum disorders

Adolescents with autism spectrum disorders (ASD) and typically developing (TD) controls underwent a rigorous psychophysical assessment that measured contrast sensitivity to seven spatial frequencies (0.5-20 cycles/degree). A contrast sensitivity function (CSF) was then fitted for each participant, from which four measures were obtained: visual acuity, peak spatial frequency, peak contrast sensitivity, and contrast sensitivity at a low spatial frequency. There were no group differences on any of the four CSF measures, indicating no differential spatial frequency processing in ASD. Although it has been suggested that detail-oriented visual perception in individuals with ASD may be a result of differential sensitivities to low versus high spatial frequencies, the current study finds no evidence to support this hypothesis

Symbolic Verification and Strategy Synthesis for Linearly-Priced Probabilistic Timed Automata

Probabilistic timed automata are a formalism for modelling systems whose dynamics includes probabilistic, nondeterministic and timed aspects including real-time systems. A variety of techniques have been proposed for the analysis of this formalism and successfully employed to analyse, for example, wireless communication protocols and computer security systems. Augmenting the model with prices (or, equivalently, costs or rewards) provides a means to verify more complex quantitative properties, such as the expected energy usage of a device or the expected number of messages sent during a protocol’s execution. However, the analysis of these properties on probabilistic timed automata currently relies on a technique based on integer discretisation of real-valued clocks, which can be expensive in some cases. In this paper, we propose symbolic techniques for verification and optimal strategy synthesis for priced probabilistic timed automata which avoid this discretisation. We build upon recent work for the special case of expected time properties, using value iteration over a zone-based abstraction of the model

Configural and featural processing in humans with congenital prosopagnosia.

Prosopagnosia describes the failure to recognize faces, a deficiency that can be devastating in social interactions. Cases of acquired prosopagnosia have often been described over the last century. In recent years, more and more cases of congenital prosopagnosia (CP) have been reported. In the present study we tried to determine possible cognitive characteristics of this impairment. We used scrambled and blurred images of faces, houses, and sugar bowls to separate featural processing strategies from configural processing strategies. This served to investigate whether congenital prosopagnosia results from process-specific deficiencies, or whether it is a face-specific impairment. Using a delayed matching paradigm, 6 individuals with CP and 6 matched healthy controls indicated whether an intact test stimulus was the same identity as a previously presented scrambled or blurred cue stimulus. Analyses of d´ values indicated that congenital prosopagnosia is a face-specific deficit, but that this shortcoming is particularly pronounced for processing configural facial information

Early Left-Hemispheric Dysfunction of Face Processing in Congenital Prosopagnosia: An MEG Study

Electrophysiological research has demonstrated the relevance to face processing of a negative deflection peaking around 170 ms, labelled accordingly as N170 in the electroencephalogram (EEG) and M170 in magnetoencephalography (MEG). The M170 was shown to be sensitive to the inversion of faces and to familiarity-two factors that are assumed to be crucial for congenital prosopagnosia. In order to locate the cognitive dysfunction and its neural correlates, we investigated the time course of neural activity in response to these manipulations.Seven individuals with congenital prosopagnosia and seven matched controls participated in the experiment. To explore brain activity with high accuracy in time, we recorded evoked magnetic fields (275 channel whole head MEG) while participants were looking at faces differing in familiarity (famous vs. unknown) and orientation (upright vs. inverted). The underlying neural sources were estimated by means of the least square minimum-norm-estimation (L2-MNE) approach.The behavioural data corroborate earlier findings on impaired configural processing in congenital prosopagnosia. For the M170, the overall results replicated earlier findings, with larger occipito-temporal brain responses to inverted than upright faces, and more right- than left-hemispheric activity. Compared to controls, participants with congenital prosopagnosia displayed a general decrease in brain activity, primarily over left occipitotemporal areas. This attenuation did not interact with familiarity or orientation.The study substantiates the finding of an early involvement of the left hemisphere in symptoms of prosopagnosia. This might be related to an efficient and overused featural processing strategy which serves as a compensation of impaired configural processing

Deficits in Long-Term Recognition Memory Reveal Dissociated Subtypes in Congenital Prosopagnosia

The study investigates long-term recognition memory in congenital prosopagnosia (CP), a lifelong impairment in face identification that is present from birth. Previous investigations of processing deficits in CP have mostly relied on short-term recognition tests to estimate the scope and severity of individual deficits. We firstly report on a controlled test of long-term (one year) recognition memory for faces and objects conducted with a large group of participants with CP. Long-term recognition memory is significantly impaired in eight CP participants (CPs). In all but one case, this deficit was selective to faces and didn't extend to intra-class recognition of object stimuli. In a test of famous face recognition, long-term recognition deficits were less pronounced, even after accounting for differences in media consumption between controls and CPs. Secondly, we combined test results on long-term and short-term recognition of faces and objects, and found a large heterogeneity in severity and scope of individual deficits. Analysis of the observed heterogeneity revealed a dissociation of CP into subtypes with a homogeneous phenotypical profile. Thirdly, we found that among CPs self-assessment of real-life difficulties, based on a standardized questionnaire, and experimentally assessed face recognition deficits are strongly correlated. Our results demonstrate that controlled tests of long-term recognition memory are needed to fully assess face recognition deficits in CP. Based on controlled and comprehensive experimental testing, CP can be dissociated into subtypes with a homogeneous phenotypical profile. The CP subtypes identified align with those found in prosopagnosia caused by cortical lesions; they can be interpreted with respect to a hierarchical neural system for face perception