169,487 research outputs found
Isomorphism Checking for Symmetry Reduction
In this paper, we show how isomorphism checking can be used as an effective technique for symmetry reduction. Reduced state spaces are equivalent to the original ones under a strong notion of bisimilarity which preserves the multiplicity of outgoing transitions, and therefore also preserves stochastic temporal logics. We have implemented this in a setting where states are arbitrary graphs. Since no efficiently computable canonical representation is known for arbitrary graphs modulo isomorphism, we define an isomorphism-predicting hash function on the basis of an existing partition refinement algorithm. As an example, we report a factorial state space reduction on a model of an ad-hoc network connectivity protocol
A Temporal Web Ontology Language
The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies on the Semantic Web. In this paper, we present a temporal extension of the very expressive fragment SHIN(D) of the OWL-DL language resulting in the tOWL language. Through a layered approach we introduce 3 extensions: i) Concrete Domains, that allows the representation of restrictions using concrete domain binary predicates, ii) Temporal Representation, that introduces timepoints, relations between timepoints, intervals, and Allenâs 13 interval relations into the language, and iii) TimeSlices/Fluents, that implements a perdurantist view on individuals and allows for the representation of complex temporal aspects, such as process state transitions. We illustrate the expressiveness of the newly introduced language by providing a TBox representation of Leveraged Buy Out (LBO) processes in financial applications and an ABox representation of one specific LBO
Hierarchical recurrent neural encoder for video representation with application to captioning
© 2016 IEEE. Recently, deep learning approach, especially deep Convolutional Neural Networks (ConvNets), have achieved overwhelming accuracy with fast processing speed for image classification. Incorporating temporal structure with deep ConvNets for video representation becomes a fundamental problem for video content analysis. In this paper, we propose a new approach, namely Hierarchical Recurrent Neural Encoder (HRNE), to exploit temporal information of videos. Compared to recent video representation inference approaches, this paper makes the following three contributions. First, our HRNE is able to efficiently exploit video temporal structure in a longer range by reducing the length of input information flow, and compositing multiple consecutive inputs at a higher level. Second, computation operations are significantly lessened while attaining more non-linearity. Third, HRNE is able to uncover temporal tran-sitions between frame chunks with different granularities, i.e. it can model the temporal transitions between frames as well as the transitions between segments. We apply the new method to video captioning where temporal information plays a crucial role. Experiments demonstrate that our method outperforms the state-of-the-art on video captioning benchmarks
Verifying Temporal Regular Properties of Abstractions of Term Rewriting Systems
The tree automaton completion is an algorithm used for proving safety
properties of systems that can be modeled by a term rewriting system. This
representation and verification technique works well for proving properties of
infinite systems like cryptographic protocols or more recently on Java Bytecode
programs. This algorithm computes a tree automaton which represents a (regular)
over approximation of the set of reachable terms by rewriting initial terms.
This approach is limited by the lack of information about rewriting relation
between terms. Actually, terms in relation by rewriting are in the same
equivalence class: there are recognized by the same state in the tree
automaton.
Our objective is to produce an automaton embedding an abstraction of the
rewriting relation sufficient to prove temporal properties of the term
rewriting system.
We propose to extend the algorithm to produce an automaton having more
equivalence classes to distinguish a term or a subterm from its successors
w.r.t. rewriting. While ground transitions are used to recognize equivalence
classes of terms, epsilon-transitions represent the rewriting relation between
terms. From the completed automaton, it is possible to automatically build a
Kripke structure abstracting the rewriting sequence. States of the Kripke
structure are states of the tree automaton and the transition relation is given
by the set of epsilon-transitions. States of the Kripke structure are labelled
by the set of terms recognized using ground transitions. On this Kripke
structure, we define the Regular Linear Temporal Logic (R-LTL) for expressing
properties. Such properties can then be checked using standard model checking
algorithms. The only difference between LTL and R-LTL is that predicates are
replaced by regular sets of acceptable terms
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
Session-based recommendation nowadays plays a vital role in many websites,
which aims to predict users' actions based on anonymous sessions. There have
emerged many studies that model a session as a sequence or a graph via
investigating temporal transitions of items in a session. However, these
methods compress a session into one fixed representation vector without
considering the target items to be predicted. The fixed vector will restrict
the representation ability of the recommender model, considering the diversity
of target items and users' interests. In this paper, we propose a novel target
attentive graph neural network (TAGNN) model for session-based recommendation.
In TAGNN, target-aware attention adaptively activates different user interests
with respect to varied target items. The learned interest representation vector
varies with different target items, greatly improving the expressiveness of the
model. Moreover, TAGNN harnesses the power of graph neural networks to capture
rich item transitions in sessions. Comprehensive experiments conducted on
real-world datasets demonstrate its superiority over state-of-the-art methods.Comment: 5 pages, accepted to SIGIR 2020, authors' versio
Integrating Planning and Scheduling : A Constraint-based Approach
Automated decision making is one of the important problems of
Artificial Intelligence (AI).
Planning and scheduling are two sub-fields of AI that research
automated decision making. The
main focus of planning is on general representations of actions,
causal reasoning among actions
and domain-independent solving strategies. Scheduling generally
optimizes problems with
complex temporal and resource constraints that have simpler
causal relations between actions.
However, there are problems that have both planning
characteristics (causal constraints) and
scheduling characteristics (temporal and resource constraints),
and have strong interactions
between these constraints. An integrated approach is needed to
solve this class of problems
efficiently.
The main contribution of this thesis is an integrated
constraint-based planning and scheduling
approach that can model and solve problems that have both
planning and scheduling characteristics.
In our representation problems are described using a multi-valued
state variable
planning language with explicit representation of different types
of resources, and a new action
model where each action is represented by a set of transitions.
This action-transition model
makes the representation of actions with delayed effects, effects
with different durations, and
the representation of complex temporal and resource constraints
like time-windows, deadline
goals, sequence-dependent setup times, etc simpler.
Constraint-based techniques have been successfully applied to
solve scheduling problems.
Therefore, to solve a combined planning/scheduling problem we
compile it into a CSP. This
compilation is bounded by the number of action occurrences. The
constraint model is based
on the notion of âsupportâ for each type of transition. The
constraint model can be viewed
as a system of CSPs, one for each state variable and resource,
that are synchronized by a
simple temporal network for action start times. Central to our
constraint model is the explicit
representation and maintenance of the precedence constraints
between transitions on the same
state variable or resource.
We propose a branching scheme for solving the CSP based on
establishing supports for
transitions, which imply precedence constraints. Furthermore, we
propose new propagation
and inference techniques that infer precedence relations from
temporal and mutex constraints,
and infer tighter temporal bounds from the precedence
constraints. The distinguishing feature
of these inference and propagation techniques is that they not
only consider the transitions and
actions that are included in the plan but can also consider
actions and transitions that are not
yet included in or excluded from the plan.
We conclude the thesis with a modeling case study of a complex
satellite problem domain
to demonstrate the effectiveness of our representation. This
problem domain has action choices
that are tightly coupled with temporal and resource constraints.
We show that most of the
complexities of this problem can be expressed in our
representation in a simple and intuitive
way
Charting the Realms of Mesoscale Cloud Organisation using Unsupervised Learning
Quantifying the driving mechanisms and effect on Earth's energy budget, of
mesoscale shallow cloud organisation, remains difficult. Partly because
quantifying the atmosphere's organisational state through objective means
remains challenging. We present the first map of the full continuum of
convective organisation states by extracting the manifold within an
unsupervised neural networks's internal representation. On the manifold
distinct organisational regimes, defined in prior work, sit as waymarkers in
this continuum. Composition of reanalysis and observations onto the manifold,
shows wind-speed and water vapour concentration as key environmental
characteristics varying with organisation. We show, for the first time, that
mesoscale shallow cloud organisation produces variations in albedo
in addition to variations from cloud-fraction changes alone. We further
demonstrate how the manifold's continuum representation captures the temporal
evolution of organisation. By enabling study of states and transitions in
organisation (in simulations and observations) the presented technique paves
the way for better representation of shallow clouds in simulations of Earth's
future climate
Deep Latent State Space Models for Time-Series Generation
Methods based on ordinary differential equations (ODEs) are widely used to
build generative models of time-series. In addition to high computational
overhead due to explicitly computing hidden states recurrence, existing
ODE-based models fall short in learning sequence data with sharp transitions -
common in many real-world systems - due to numerical challenges during
optimization. In this work, we propose LS4, a generative model for sequences
with latent variables evolving according to a state space ODE to increase
modeling capacity. Inspired by recent deep state space models (S4), we achieve
speedups by leveraging a convolutional representation of LS4 which bypasses the
explicit evaluation of hidden states. We show that LS4 significantly
outperforms previous continuous-time generative models in terms of marginal
distribution, classification, and prediction scores on real-world datasets in
the Monash Forecasting Repository, and is capable of modeling highly stochastic
data with sharp temporal transitions. LS4 sets state-of-the-art for
continuous-time latent generative models, with significant improvement of mean
squared error and tighter variational lower bounds on irregularly-sampled
datasets, while also being x100 faster than other baselines on long sequences
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