337,963 research outputs found
A Unified Approach for Representing Structurally-Complex Models in SBML Level 3
The aim of this document is to explore a unified approach to handling several of the proposed extensions to the SBML Level 3 Core specification. The approach is illustrated with reference to Simile, a modelling environment which appears to have most of the capabilities of the various SBML Level 3 package proposals which deal with model structure. Simile (http://www.simulistics.com) is a visual modelling environment for continuous systems modelling which includes the ability to handle complex disaggregation of model structure, by allowing the modeller to specify classes of object and the relationships between them.

The note is organised around the 6 packages listed on the SBML Level 3 Proposals web page (http://sbml.org/Community/Wiki/SBML_Level_3_Proposals) which deal with model structure, namely comp, arrays, spatial, geom, dyn and multi. For each one, I consider how the requirements which motivated the package can be handled using Simile's unified approach. Although Simile has a declarative model-representation language (in both Prolog and XML syntax), I use Simile diagrams and equation syntax throughout, since this is more compact and readable than large chunks of XML.

The conclusion is that Simile can indeed meet most of the requirements of these various packages, using a generic set of constructs - basically, the multiple-instance submodel, the concept of a relationship (association) between submodels, and array variables. This suggests the possibility of having a single SBML Level 3 extension package similar to the Simile data model, rather than a series of separate packages. Such an approach has a number of potential advantages and disadvantages compared with having the current set of discrete packages: these are discussed in this paper
Models of incremental concept formation
Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions
Neurally Implementable Semantic Networks
We propose general principles for semantic networks allowing them to be
implemented as dynamical neural networks. Major features of our scheme include:
(a) the interpretation that each node in a network stands for a bound
integration of the meanings of all nodes and external events the node links
with; (b) the systematic use of nodes that stand for categories or types, with
separate nodes for instances of these types; (c) an implementation of
relationships that does not use intrinsically typed links between nodes.Comment: 32 pages, 12 figure
Architecture Diagrams: A Graphical Language for Architecture Style Specification
Architecture styles characterise families of architectures sharing common
characteristics. We have recently proposed configuration logics for
architecture style specification. In this paper, we study a graphical notation
to enhance readability and easiness of expression. We study simple architecture
diagrams and a more expressive extension, interval architecture diagrams. For
each type of diagrams, we present its semantics, a set of necessary and
sufficient consistency conditions and a method that allows to characterise
compositionally the specified architectures. We provide several examples
illustrating the application of the results. We also present a polynomial-time
algorithm for checking that a given architecture conforms to the architecture
style specified by a diagram.Comment: In Proceedings ICE 2016, arXiv:1608.0313
BL-MNE: Emerging Heterogeneous Social Network Embedding through Broad Learning with Aligned Autoencoder
Network embedding aims at projecting the network data into a low-dimensional
feature space, where the nodes are represented as a unique feature vector and
network structure can be effectively preserved. In recent years, more and more
online application service sites can be represented as massive and complex
networks, which are extremely challenging for traditional machine learning
algorithms to deal with. Effective embedding of the complex network data into
low-dimension feature representation can both save data storage space and
enable traditional machine learning algorithms applicable to handle the network
data. Network embedding performance will degrade greatly if the networks are of
a sparse structure, like the emerging networks with few connections. In this
paper, we propose to learn the embedding representation for a target emerging
network based on the broad learning setting, where the emerging network is
aligned with other external mature networks at the same time. To solve the
problem, a new embedding framework, namely "Deep alIgned autoencoder based
eMbEdding" (DIME), is introduced in this paper. DIME handles the diverse link
and attribute in a unified analytic based on broad learning, and introduces the
multiple aligned attributed heterogeneous social network concept to model the
network structure. A set of meta paths are introduced in the paper, which
define various kinds of connections among users via the heterogeneous link and
attribute information. The closeness among users in the networks are defined as
the meta proximity scores, which will be fed into DIME to learn the embedding
vectors of users in the emerging network. Extensive experiments have been done
on real-world aligned social networks, which have demonstrated the
effectiveness of DIME in learning the emerging network embedding vectors.Comment: 10 pages, 9 figures, 4 tables. Full paper is accepted by ICDM 2017,
In: Proceedings of the 2017 IEEE International Conference on Data Mining
Multi-view constrained clustering with an incomplete mapping between views
Multi-view learning algorithms typically assume a complete bipartite mapping
between the different views in order to exchange information during the
learning process. However, many applications provide only a partial mapping
between the views, creating a challenge for current methods. To address this
problem, we propose a multi-view algorithm based on constrained clustering that
can operate with an incomplete mapping. Given a set of pairwise constraints in
each view, our approach propagates these constraints using a local similarity
measure to those instances that can be mapped to the other views, allowing the
propagated constraints to be transferred across views via the partial mapping.
It uses co-EM to iteratively estimate the propagation within each view based on
the current clustering model, transfer the constraints across views, and then
update the clustering model. By alternating the learning process between views,
this approach produces a unified clustering model that is consistent with all
views. We show that this approach significantly improves clustering performance
over several other methods for transferring constraints and allows multi-view
clustering to be reliably applied when given a limited mapping between the
views. Our evaluation reveals that the propagated constraints have high
precision with respect to the true clusters in the data, explaining their
benefit to clustering performance in both single- and multi-view learning
scenarios
- …