LNCS

We solve the offline monitoring problem for timed propositional temporal logic (TPTL), interpreted over dense-time Boolean signals. The variant of TPTL we consider extends linear temporal logic (LTL) with clock variables and reset quantifiers, providing a mechanism to specify real-time constraints. We first describe a general monitoring algorithm based on an exhaustive computation of the set of satisfying clock assignments as a finite union of zones. We then propose a specialized monitoring algorithm for the one-variable case using a partition of the time domain based on the notion of region equivalence, whose complexity is linear in the length of the signal, thereby generalizing a known result regarding the monitoring of metric temporal logic (MTL). The region and zone representations of time constraints are known from timed automata verification and can also be used in the discrete-time case. Our prototype implementation appears to outperform previous discrete-time implementations of TPTL monitoring

Self unbound: ego dissolution in psychedelic experience

Users of psychedelic drugs often report that their sense of being a self or ‘I’ distinct from the rest of the world has diminished or altogether dissolved. Neuroscientific study of such ‘ego dissolution’ experiences offers a window onto the nature of self-awareness. We argue that ego dissolution is best explained by an account that explains self-awareness as resulting from the integrated functioning of hierarchical predictive models which posit the existence of a stable and unchanging entity to which representations are bound. Combining recent work on the ‘integrative self' and the phenomenon of self-binding with predictive processing principles yields an explanation of ego dissolution according to which self-representation is a useful Cartesian fiction: an ultimately false representation of a simple and enduring substance to which attributes are bound which serves to integrate and unify cognitive processing across levels and domains. The self-model is not a mere narrative posit, as some have suggested; it has a more robust and ubiquitous cognitive function than that. But this does not mean, as others have claimed, that the self-model has the right attributes to qualify as a self. It performs some of the right kinds of functions, but it is not the right kind of entity. Ego dissolution experiences reveal that the self-model plays an important binding function in cognitive processing, but the self does not exist

Monitoring-Oriented Programming: A Tool-Supported Methodology for Higher Quality Object-Oriented Software

This paper presents a tool-supported methodological paradigm for object-oriented software development, called monitoring-oriented programming and abbreviated MOP, in which runtime monitoring is a basic software design principle. The general idea underlying MOP is that software developers insert specifications in their code via annotations. Actual monitoring code is automatically synthesized from these annotations before compilation and integrated at appropriate places in the program, according to user-defined configuration attributes. This way, the specification is checked at runtime against the implementation. Moreover, violations and/or validations of specifications can trigger user-defined code at any points in the program, in particular recovery code, outputting or sending messages, or raising exceptions. The MOP paradigm does not promote or enforce any specific formalism to specify requirements: it allows the users to plug-in their favorite or domain-specific specification formalisms via logic plug-in modules. There are two major technical challenges that MOP supporting tools unavoidably face: monitor synthesis and monitor integration. The former is heavily dependent on the specification formalism and comes as part of the corresponding logic plug-in, while the latter is uniform for all specification formalisms and depends only on the target programming language. An experimental prototype tool, called Java-MOP, is also discussed, which currently supports most but not all of the desired MOP features. MOP aims at reducing the gap between formal specification and implementation, by integrating the two and allowing them together to form a system

Road Traffic Congestion Analysis Via Connected Vehicles

La congestion routière est un état particulier de mobilité où les temps de déplacement augmentent et de plus en plus de temps est passé dans le véhicule. En plus d’être une expérience très stressante pour les conducteurs, la congestion a également un impact négatif sur l’environnement et l’économie. Dans ce contexte, des pressions sont exercées sur les autorités afin qu’elles prennent des mesures décisives pour améliorer le flot du trafic sur le réseau routier. En améliorant le flot, la congestion est réduite et la durée totale de déplacement des véhicules est réduite. D’une part, la congestion routière peut être récurrente, faisant référence à la congestion qui se produit régulièrement. La congestion non récurrente (NRC), quant à elle, dans un réseau urbain, est principalement causée par des incidents, des zones de construction, des événements spéciaux ou des conditions météorologiques défavorables. Les opérateurs d’infrastructure surveillent le trafic sur le réseau mais sont contraints à utiliser le moins de ressources possibles. Cette contrainte implique que l’état du trafic ne peut pas être mesuré partout car il n’est pas réaliste de déployer des équipements sophistiqués pour assurer la collecte précise des données de trafic et la détection en temps réel des événements partout sur le réseau routier. Alors certains emplacements où le flot de trafic doit être amélioré ne sont pas surveillés car ces emplacements varient beaucoup. D’un autre côté, de nombreuses études sur la congestion routière ont été consacrées aux autoroutes plutôt qu’aux régions urbaines, qui sont pourtant beaucoup plus susceptibles d’être surveillées par les autorités de la circulation. De plus, les systèmes actuels de collecte de données de trafic n’incluent pas la possibilité d’enregistrer des informations détaillées sur les événements qui surviennent sur la route, tels que les collisions, les conditions météorologiques défavorables, etc. Aussi, les études proposées dans la littérature ne font que détecter la congestion ; mais ce n’est pas suffisant, nous devrions être en mesure de mieux caractériser l’événement qui en est la cause. Les agences doivent comprendre quelle est la cause qui affecte la variabilité de flot sur leurs installations et dans quelle mesure elles peuvent prendre les actions appropriées pour atténuer la congestion.----------ABSTRACT: Road traffic congestion is a particular state of mobility where travel times increase and more and more time is spent in vehicles. Apart from being a quite-stressful experience for drivers, congestion also has a negative impact on the environment and the economy. In this context, there is pressure on the authorities to take decisive actions to improve the network traffic flow. By improving network flow, congestion is reduced and the total travel time of vehicles is decreased. In fact, congestion can be classified as recurrent and non-recurrent (NRC). Recurrent congestion refers to congestion that happens on a regular basis. Non-recurrent congestion in an urban network is mainly caused by incidents, workzones, special events and adverse weather. Infrastructure operators monitor traffic on the network while using the least possible resources. Thus, traffic state cannot be directly measured everywhere on the traffic road network. But the location where traffic flow needs to be improved varies highly and certainly, deploying highly sophisticated equipment to ensure the accurate estimation of traffic flows and timely detection of events everywhere on the road network is not feasible. Also, many studies have been devoted to highways rather than highly congested urban regions which are intricate, complex networks and far more likely to be monitored by the traffic authorities. Moreover, current traffic data collection systems do not incorporate the ability of registring detailed information on the altering events happening on the road, such as vehicle crashes, adverse weather, etc. Operators require external data sources to retireve this information in real time. Current methods only detect congestion but it’s not enough, we should be able to better characterize the event causing it. Agencies need to understand what is the cause affecting variability on their facilities and to what degree so that they can take the appropriate action to mitigate congestion

Neural Mechanisms and Psychology of Psychedelic Ego Dissolution

Neuroimaging studies of psychedelics have advanced our understanding of hierarchical brain organization and the mechanisms underlying their subjective and therapeutic effects. The primary mechanism of action of classic psychedelics is binding to serotonergic 5-HT2A receptors. Agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy that can have a profound effect on hierarchical message-passing in the brain. Here, we review the cognitive and neuroimaging evidence for the effects of psychedelics: in particular, their influence on selfhood and subject-object boundaries—known as ego dissolution—surmised to underwrite their subjective and therapeutic effects. Agonism of 5-HT2A recep-tors, located at the apex of the cortical hierarchy, may have a particularly powerful effect on sentience and consciousness. These effects can endure well after the pharmacological half-life, suggesting that psychedelics may have effects on neural plasticity that may play a role in their therapeutic efficacy. Psychologi-cally, this may be accompanied by a disarming of ego resistance that increases the repertoire of perceptual hypotheses and affords alternate pathways for thought and behavior, including those that undergird selfhood. We consider the interaction between serotonergic neuromodulation and sentience through the lens of hierarchical predictive coding, which speaks to the value of psychedelics in understanding how we make sense of the world and specific predictions about effective connectivity in cortical hierarchies that can be tested using functional neuroimaging. Significance Statement——Classic psychedelics bind to serotonergic 5-HT2A receptors. Their agonist activity at these receptors leads to neuromodulatory changes in synaptic efficacy, resulting in a profound effect on information processing in the brain. Here, we synthesize an abundance of brain imaging research with pharmacological and psychological interpretations informed by the framework of predictive coding. Moreover, predictive coding is suggested to offer more sophisticated interpretations of neuroimaging find-ings by bridging the role between the 5-HT2A receptors and large-scale brain networks

One Model to Rule them all: Multitask and Multilingual Modelling for Lexical Analysis

When learning a new skill, you take advantage of your preexisting skills and knowledge. For instance, if you are a skilled violinist, you will likely have an easier time learning to play cello. Similarly, when learning a new language you take advantage of the languages you already speak. For instance, if your native language is Norwegian and you decide to learn Dutch, the lexical overlap between these two languages will likely benefit your rate of language acquisition. This thesis deals with the intersection of learning multiple tasks and learning multiple languages in the context of Natural Language Processing (NLP), which can be defined as the study of computational processing of human language. Although these two types of learning may seem different on the surface, we will see that they share many similarities. The traditional approach in NLP is to consider a single task for a single language at a time. However, recent advances allow for broadening this approach, by considering data for multiple tasks and languages simultaneously. This is an important approach to explore further as the key to improving the reliability of NLP, especially for low-resource languages, is to take advantage of all relevant data whenever possible. In doing so, the hope is that in the long term, low-resource languages can benefit from the advances made in NLP which are currently to a large extent reserved for high-resource languages. This, in turn, may then have positive consequences for, e.g., language preservation, as speakers of minority languages will have a lower degree of pressure to using high-resource languages. In the short term, answering the specific research questions posed should be of use to NLP researchers working towards the same goal.Comment: PhD thesis, University of Groninge

Compendium for Early Career Researchers in Mathematics Education

The purpose of this Open Access compendium, written by experienced researchers in mathematics education, is to serve as a resource for early career researchers in furthering their knowledge of the state of the field and disseminating their research through publishing. To accomplish this, the book is split into four sections: Empirical Methods, Important Mathematics Education Themes, Academic Writing and Academic Publishing, and a section Looking Ahead. The chapters are based on workshops that were presented in the Early Career Researcher Day at the 13th International Congress on Mathematical Education (ICME-13). The combination of presentations on methodological approaches and theoretical perspectives shaping the field in mathematics education research, as well as the strong emphasis on academic writing and publishing, offered strong insight into the theoretical and empirical bases of research in mathematics education for early career researchers in this field. Based on these presentations, the book provides a state-of-the-art overview of important theories from mathematics education and the broad variety of empirical approaches currently widely used in mathematics education research. This compendium supports early career researchers in selecting adequate theoretical approaches and adopting the most appropriate methodological approaches for their own research. Furthermore, it helps early career researchers in mathematics education to avoid common pitfalls and problems while writing up their research and it provides them with an overview of the most important journals for research in mathematics education, helping them to select the right venue for publishing and disseminating their work