A logical characterization of well branching event structures

<p>This paper introduces a non-interleaved model for the behaviour of distributed computing systems, and an accompanying temporal logic with an explicit treatment of concurrency (based on a notion of local rather than global states).</p><p>A subclass of event structures (called n-agent event structures) is used as the underlying model -- intended to describe the computational behaviour of n communicating, sequential (and possibly non-deterministic) agents. The logic is centered around indexed modalities to describe the states of knowledge of the individual agents during such a computation.</p><p>An axiom system for the logic is presented, and a full proof of its soundness and completeness (Henkin style proof) is given.</p>

Diagnosing behavioral differences between business process models: An approach based on event structures

We give an event structures based true-concurrency characterization of deep inference proofs. The method is general to all deep inference systems that can be expressed as term rewriting systems. This delivers three consequences in a spectrum from theoretical to practical: the event structure characterization i provides a qualification of proof identity akini¾?to proof nets for multiplicative linear logic and to atomic flows for classical logic; ii provides a concurrency theoretic interpretation for applications in logic programming; iii reduces the length of the proofs, and thereby extends the margin of proof search applications.

Event Structures for Local Traces

The goal of this paper is to evaluate the hedging strategies performance of a range of copula and traditional methods for three spot and futures oil markets: WTI crude oil, propane and heating oil. Our contribution is two-fold. First, we model dependence structure between spot and futures oil markets using copula theory applied to bivariate standardised residuals data obtained from two fitted univariate FIEGARCH models. To take in consideration the presence of extremes, we model residuals by a generalised Pareto distribution (GPD). This procedure permits to simultaneously capturing asymmetric non-linear behaviour, dependence structure, long memory and occurrence of extreme events. Second, we use this method with different Archimedean copulas functions (Joe, Frank, bb1, bb2, bb6, and Gumbel) to investigate hedging performance and the efficiency of copula methods in risk reduction and return improvement. Empirical results show that copulas methods perform better than tradition hedging strategies in terms of return and variance. bb6 copula provide the best performed hedge ratios for both WTI crude oil and propane markets while Frank copula prove effective risk reducers compared with other copulas and traditional methods for heating oil market.

The representation of extreme precipitation at small spatio-temporal scales is of major importance in urban hydrology. The present study compares point and radar observations to reanalyse climate model output data for a period of 14 years where there is full spatial and temporal overlap between datasets. The datasets are compared with respect to seasonality of occurrence, intensity levels and spatial structure of the extreme events. All datasets have similar seasonal distributions and comparable intensity levels. There are, however, clear differences in the spatial correlation structure of the extremes. Seemingly, the radar data is the best representation of a “real” spatial structure for extreme precipitation, even though challenges appear in data when moving far from the physical radar. The spatial correlation in point observations is a valid representation of the spatial structure of extreme precipitation. The convective-permitting climate model seems to represent the spatial structure of extreme precipitation much more realistically, compared to the coarser convective parameterized model. However, there is still room for improvement of the convective-permitting climate model for the shortest rainfall durations and smallest spatial scales in comparison with point and radar data.

Investigated whether recall of events by children under 2 years of age is similar to that of older preschoolers and adults. Experiment 1 used elicited-imitation to test 16- and 20-month-olds' immediate and delayed recall (2-week delay) of familiar and novel events. Ordered recall at immediate and delayed test was superior for familiar events and for novel events with causal relations among the elements; ordered recall of novel events lacking causal relations was significantly lower. Experiment 2 tested children's sensitivity to differences in underlying structure of novel events. Nineteen-, 25-, and 31 -month-olds organized recall around causal relations, in spite of experimental manipulations that interrupted causally connected pairs of elements. The experiments provide clear evidence that, like preschoolers and adults, children as young as 16 months include temporal order information in their representations of both familiar and novel events and that the causal structure of novel events influences their recall. Research on children's memory has amply demonstrated that when children are working in a familiar and meaningful context, they exhibit memory skills far more advanced than they do when working in an unfamiliar context stripped of meaning. This lesson provided the impetus for research on children's memory for everyday events and routines. By interviewing 3- to 8-year-old children about their participation in everyday events, Nelson and her colleagues (e.g., Nelson, 1986; Nelson & Gruendel, 1981,1986) have shown that children as young as 3 years of age recall events in a fashion similar to that of older children and adults. The apparent similarities in the structure underlying the event representations of preschoolers and adults has led to the question of whether the event representations of even younger preverbal and early verbal children are similar as well. It is this question that provides the focus for the present research.

Learning commonsense causal and temporal relation between events is one of the major steps towards deeper language understanding. This is even more crucial for understanding stories and script learning. A prerequisite for learning scripts is a semantic framework which enables capturing rich event structures. In this paper we introduce a novel semantic annotation framework, called Causal and Temporal Relation Scheme (CaTeRS), which is unique in simultaneously capturing a comprehensive set of temporal and causal relations between events. By annotating a total of 1,600 sentences in the context of 320 five-sentence short stories sampled from ROCStories corpus, we demonstrate that these stories are indeed full of causal and temporal relations. Furthermore, we show that the CaTeRS annotation scheme enables high inter-annotator agreement for broad-coverage event entity annotation and moderate agreement on semantic link annotation.

The behavior of complex software-intensive systems of systems often only fully emerges during operation, when all systems interact with each other and with their environment. Runtime monitoring approaches are thus used to detect deviations from the expected behavior, which is commonly defined by engineers, e.g., using temporal logic or domain-specific languages. However, the deep domain knowledge required to specify constraints is often not available during the development of systems of systems with multiple teams independently working on heterogeneous components. In this paper, we thus describe our ongoing PhD research to automatically mine constraints for runtime monitoring from recorded events. Our approach mines constraints on event occurrence, timing, data, and combinations of these properties. The approach further presents the mined constraints to users offering multiple ranking strategies and can also be used to support users in system evolution scenarios.

We present an omega-U+00edU+00b0U+0080complete algebra of a class of deterministic event structures which are labelled prime event structures where the labelling function satises a certain distinctness condition. The operators of the algebra are summation sequential composition and join. Each of these gives rise to a monoid in addition a number of distributivity properties hold Summation loosely corresponds to choice and join to parallel composition with however some nonstandard aspects The space of models is a complete partial order in fact a complete lattice, in which all operators are continuous hence minimal fixpoints can be defined inductively. Moreover the submodel relation can be captured within the algebra by summation x \sqsubseteq y iff x + y = y; therefore, the effect of fixpoints can be captured by an infinitary proof rule, yielding a complete proof system for recursively defined deterministic event structures.

This paper introduces projective systems for topological and probabilistic event structures. The projective formalism is used for studying the domain of configurations of a prime event structure and its space of maximal elements. This is done from both a topological and a probabilistic viewpoint. We give probability measure extension theorems in this framework.

Models will play a central role in the representation, storage, manipulation, and communication of knowledge in systems biology. Models capable of fulfilling such a role will likely differ from the all familiar styles deployed with great success in the physical sciences. Molecular systems at the basis of cellular decision processes are concurrent and combinatorial. Their behavior is as much constrained by relationships of causality between molecular interactions as it is by chemical kinetics. Understanding how such systems give rise to coherent behavior and designing effective interventions to fight disease will require a notion of model that is akin to the concept of program in computer science. I will discuss recent progress in implementing a platform and tools for formal analysis that bring us closer to this vision. Protein interactions are represented by means of rules expressed in a formal language that captures a very simple, yet effective and biologically meaningful level of abstraction. Models, then, are collections of rules operating on an initial set of agents, in complete analogy to rules of organic chemical reactions. I will describe tools for analyzing and navigating rule collections as well as exploring their dynamics. We draw on concepts familiar to computer science, especially event structures, and adapt them to biological needs with the goal of formalizing the notion of pathway. The challenges are many, but a road map for the future is discernible. Computer science will play a central role in providing an additional foundational layer, both theoretical and practical, that neither physics nor chemistry can offer on their own in the future definition of the biological sciences.

In this note, we consider the problem of introducing variables in temporal logic programs under the formalism of Temporal Equilibrium Logic, an extension of Answer Set Programming for dealing with linear-time modal operators. To this aim, we provide a definition of a first-order version of Temporal Equilibrium Logic that shares the syntax of first-order Linear-time Temporal Logic but has different semantics, selecting some Linear-time Temporal Logic models we call temporal stable models. Then, we consider a subclass of theories (called splittable temporal logic programs) that are close to usual logic programs but allowing a restricted use of temporal operators. In this setting, we provide a syntactic definition of safe variables that suffices to show the property of domain independence – that is, addition of arbitrary elements in the universe does not vary the set of temporal stable models. Finally, we present a method for computing the derivable facts by constructing a non-temporal logic program with variables that is fed to a standard Answer Set Programming grounder. The information provided by the grounder is then used to generate a subset of ground temporal rules which is equivalent to (and generally smaller than) the full program instantiation.

Recent development of logic programming languages based on linear logic suggests a successful direction to extend logic programming to be more expressive and more efficient. The treatment of formulasas-resources gives us not only powerful expressiveness, but also efficient access to a large set of data. However, in linear logic, whole resources are kept in one context, and there is no straight way to represent complex data structures as resources. For example, in order to represent an ordered list and time-dependent data, we need to put additional indices for each resource formula. This paper describes a logic programming language, called TLLP, based on intuitionistic temporal linear logic. This logic, an extension of linear logic with some features from temporal logics, allows the use of the modal operators '?'(next-time) and '?'(always) in addition to the operators used in intuitionistic linear logic. The intuitive meaning of modal operators is as follows: ? B means that B can be used exactly once at the next moment in time; ? B means that B can be used exactly once any time; !B means that B can be used arbitrarily many times (including 0 times) at any time. We first give a proof theoretic formulation of the logic of the TLLP language. We then present a series of resource management systems designed to implement not only interpreters but also compilers based on an extension of the standard WAM model.

As part of an effort to give a “truly concurrent” semantics to process algebra, we propose a framework of refinements of the failures model for CSP with concurrency, conflict and causality relations on traces. These relations are defined by induction over syntax of CSP processes. We study in detail two new semantics: the possible concurrency (where two traces are said to be concurrent if they may be observations of the same concurrent run) and the possible conflict (two traces are said to be in conflict if they may be observations of two different runs). The guaranteed concurrency is obtained from the possible conflict semantics. Although the expansion law is necessarily weakened to an inequality, we show that most of the CSP laws are preserved, the exception being the idempotency of choice for the possible conflict refinement. Finally, we show that our semantics is well-founded by demonstrating a strong connection with the existing event structures semantics for CSP. The latter results show that, in a certain sense, concurrency distinctions can be made at the level of syntax, without resorting to reasoning about event occurrences.