796 research outputs found
Layers in the Fabric of Mind: A Critical Review of Cognitive Ontogeny
The essay is critically examines the conceptual problems with the influential modularity model of mind. We shall see that one of the essential characters of modules, namely informational encapsulation, is not only inessential, it ties a knot at a crucial place blocking the solution to the problem of understanding the formation of concepts from percepts (nodes of procedural knowledge). Subsequently I propose that concept formation takes place by modulation of modules leading to cross-representations, which were otherwise prevented by encapsulation. It must be noted that the argument is not against modular architecture, but a variety of an architecture that prevents interaction among modules. This is followed by a brief argument demonstrating that module without modularization, i.e. without developmental history, is impossible. Finally the emerging picture of cognitive development is drawn in the form of the layers in the fabric of mind, with a brief statement of the possible implications
Comparison and classification of flexible distributions for multivariate skew and heavy-tailed data
We present, compare and classify popular families of flexible multivariate distributions. Our classification is based on the type of symmetry (spherical, elliptical, central symmetry or asymmetry) and the tail behaviour (a single tail weight parameter or multiple tail weight parameters). We compare the families both theoretically (relevant properties and distinctive features) and with a Monte Carlo study (comparing the fitting abilities in finite samples)
The Right Tools for the Job: The Case for Spatial Science Tool-Building
This paper was presented as the 8th annual Transactions in GIS plenary
address at the American Association of Geographers annual meeting in
Washington, DC. The spatial sciences have recently seen growing calls for more
accessible software and tools that better embody geographic science and theory.
Urban spatial network science offers one clear opportunity: from multiple
perspectives, tools to model and analyze nonplanar urban spatial networks have
traditionally been inaccessible, atheoretical, or otherwise limiting. This
paper reflects on this state of the field. Then it discusses the motivation,
experience, and outcomes of developing OSMnx, a tool intended to help address
this. Next it reviews this tool's use in the recent multidisciplinary spatial
network science literature to highlight upstream and downstream benefits of
open-source software development. Tool-building is an essential but poorly
incentivized component of academic geography and social science more broadly.
To conduct better science, we need to build better tools. The paper concludes
with paths forward, emphasizing open-source software and reusable computational
data science beyond mere reproducibility and replicability
Dagstuhl News January - December 2007
"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic
Reinforcement learning in large, structured action spaces: A simulation study of decision support for spinal cord injury rehabilitation
Reinforcement learning (RL) has helped improve decision-making in several
applications. However, applying traditional RL is challenging in some
applications, such as rehabilitation of people with a spinal cord injury (SCI).
Among other factors, using RL in this domain is difficult because there are
many possible treatments (i.e., large action space) and few patients (i.e.,
limited training data). Treatments for SCIs have natural groupings, so we
propose two approaches to grouping treatments so that an RL agent can learn
effectively from limited data. One relies on domain knowledge of SCI
rehabilitation and the other learns similarities among treatments using an
embedding technique. We then use Fitted Q Iteration to train an agent that
learns optimal treatments. Through a simulation study designed to reflect the
properties of SCI rehabilitation, we find that both methods can help improve
the treatment decisions of physiotherapists, but the approach based on domain
knowledge offers better performance. Our findings provide a "proof of concept"
that RL can be used to help improve the treatment of those with an SCI and
indicates that continued efforts to gather data and apply RL to this domain are
worthwhile.Comment: 31 pages, 7 figure
Selecting the Number of Clusters with a Stability Trade-off: an Internal Validation Criterion
Model selection is a major challenge in non-parametric clustering. There is
no universally admitted way to evaluate clustering results for the obvious
reason that there is no ground truth against which results could be tested, as
in supervised learning. The difficulty to find a universal evaluation criterion
is a direct consequence of the fundamentally ill-defined objective of
clustering. In this perspective, clustering stability has emerged as a natural
and model-agnostic principle: an algorithm should find stable structures in the
data. If data sets are repeatedly sampled from the same underlying
distribution, an algorithm should find similar partitions. However, it turns
out that stability alone is not a well-suited tool to determine the number of
clusters. For instance, it is unable to detect if the number of clusters is too
small. We propose a new principle for clustering validation: a good clustering
should be stable, and within each cluster, there should exist no stable
partition. This principle leads to a novel internal clustering validity
criterion based on between-cluster and within-cluster stability, overcoming
limitations of previous stability-based methods. We empirically show the
superior ability of additive noise to discover structures, compared with
sampling-based perturbation. We demonstrate the effectiveness of our method for
selecting the number of clusters through a large number of experiments and
compare it with existing evaluation methods.Comment: 43 page
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