73,118 research outputs found
Representing Dependencies in Event Structures
Event structures where the causality may explicitly change during a computation have recently gained the stage. In this kind of event structures the changes in the set of the causes of an event are triggered by modifiers that may add or remove dependencies, thus making the happening of an event contextual. Still the focus is always on the dependencies of the event. In this paper we promote the idea that the context determined by the modifiers plays a major role, and the context itself determines not only the causes but also what causality should be. Modifiers are then used to understand when an event (or a set of events) can be added to a configuration, together with a set of events modeling dependencies, which will play a less important role. We show that most of the notions of Event Structure presented in literature can be translated into this new kind of event structure, preserving the main notion, namely the one of configuration
Representing Dependencies in Event Structures
Event structures where the causality may explicitly change during a
computation have recently gained the stage. In this kind of event structures
the changes in the set of the causes of an event are triggered by modifiers
that may add or remove dependencies, thus making the happening of an event
contextual. Still the focus is always on the dependencies of the event. In this
paper we promote the idea that the context determined by the modifiers plays a
major role, and the context itself determines not only the causes but also what
causality should be. Modifiers are then used to understand when an event (or a
set of events) can be added to a configuration, together with a set of events
modeling dependencies, which will play a less important role. We show that most
of the notions of Event Structure presented in literature can be translated
into this new kind of event structure, preserving the main notion, namely the
one of configuration
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
A coalgebraic semantics for causality in Petri nets
In this paper we revisit some pioneering efforts to equip Petri nets with
compact operational models for expressing causality. The models we propose have
a bisimilarity relation and a minimal representative for each equivalence
class, and they can be fully explained as coalgebras on a presheaf category on
an index category of partial orders. First, we provide a set-theoretic model in
the form of a a causal case graph, that is a labeled transition system where
states and transitions represent markings and firings of the net, respectively,
and are equipped with causal information. Most importantly, each state has a
poset representing causal dependencies among past events. Our first result
shows the correspondence with behavior structure semantics as proposed by
Trakhtenbrot and Rabinovich. Causal case graphs may be infinitely-branching and
have infinitely many states, but we show how they can be refined to get an
equivalent finitely-branching model. In it, states are equipped with
symmetries, which are essential for the existence of a minimal, often
finite-state, model. The next step is constructing a coalgebraic model. We
exploit the fact that events can be represented as names, and event generation
as name generation. Thus we can apply the Fiore-Turi framework: we model causal
relations as a suitable category of posets with action labels, and generation
of new events with causal dependencies as an endofunctor on this category. Then
we define a well-behaved category of coalgebras. Our coalgebraic model is still
infinite-state, but we exploit the equivalence between coalgebras over a class
of presheaves and History Dependent automata to derive a compact
representation, which is equivalent to our set-theoretical compact model.
Remarkably, state reduction is automatically performed along the equivalence.Comment: Accepted by Journal of Logical and Algebraic Methods in Programmin
Flux Analysis in Process Models via Causality
We present an approach for flux analysis in process algebra models of
biological systems. We perceive flux as the flow of resources in stochastic
simulations. We resort to an established correspondence between event
structures, a broadly recognised model of concurrency, and state transitions of
process models, seen as Petri nets. We show that we can this way extract the
causal resource dependencies in simulations between individual state
transitions as partial orders of events. We propose transformations on the
partial orders that provide means for further analysis, and introduce a
software tool, which implements these ideas. By means of an example of a
published model of the Rho GTP-binding proteins, we argue that this approach
can provide the substitute for flux analysis techniques on ordinary
differential equation models within the stochastic setting of process algebras
Pattern Reification as the Basis for Description-Driven Systems
One of the main factors driving object-oriented software development for
information systems is the requirement for systems to be tolerant to change. To
address this issue in designing systems, this paper proposes a pattern-based,
object-oriented, description-driven system (DDS) architecture as an extension
to the standard UML four-layer meta-model. A DDS architecture is proposed in
which aspects of both static and dynamic systems behavior can be captured via
descriptive models and meta-models. The proposed architecture embodies four
main elements - firstly, the adoption of a multi-layered meta-modeling
architecture and reflective meta-level architecture, secondly the
identification of four data modeling relationships that can be made explicit
such that they can be modified dynamically, thirdly the identification of five
design patterns which have emerged from practice and have proved essential in
providing reusable building blocks for data management, and fourthly the
encoding of the structural properties of the five design patterns by means of
one fundamental pattern, the Graph pattern. A practical example of this
philosophy, the CRISTAL project, is used to demonstrate the use of
description-driven data objects to handle system evolution.Comment: 20 pages, 10 figure
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