109,717 research outputs found
Avoiding Unnecessary Information Loss: Correct and Efficient Model Synchronization Based on Triple Graph Grammars
Model synchronization, i.e., the task of restoring consistency between two
interrelated models after a model change, is a challenging task. Triple Graph
Grammars (TGGs) specify model consistency by means of rules that describe how
to create consistent pairs of models. These rules can be used to automatically
derive further rules, which describe how to propagate changes from one model to
the other or how to change one model in such a way that propagation is
guaranteed to be possible. Restricting model synchronization to these derived
rules, however, may lead to unnecessary deletion and recreation of model
elements during change propagation. This is inefficient and may cause
unnecessary information loss, i.e., when deleted elements contain information
that is not represented in the second model, this information cannot be
recovered easily. Short-cut rules have recently been developed to avoid
unnecessary information loss by reusing existing model elements. In this paper,
we show how to automatically derive (short-cut) repair rules from short-cut
rules to propagate changes such that information loss is avoided and model
synchronization is accelerated. The key ingredients of our rule-based model
synchronization process are these repair rules and an incremental pattern
matcher informing about suitable applications of them. We prove the termination
and the correctness of this synchronization process and discuss its
completeness. As a proof of concept, we have implemented this synchronization
process in eMoflon, a state-of-the-art model transformation tool with inherent
support of bidirectionality. Our evaluation shows that repair processes based
on (short-cut) repair rules have considerably decreased information loss and
improved performance compared to former model synchronization processes based
on TGGs.Comment: 33 pages, 20 figures, 3 table
Type Annotation for Adaptive Systems
We introduce type annotations as a flexible typing mechanism for graph
systems and discuss their advantages with respect to classical typing based on
graph morphisms. In this approach the type system is incorporated with the
graph and elements can adapt to changes in context by changing their type
annotations. We discuss some case studies in which this mechanism is relevant.Comment: In Proceedings GaM 2016, arXiv:1612.0105
Towards a Model of Life and Cognition
What should be the ontology of the world such that life and cognition are possible? In this essay, I undertake to outline an alternative ontological foundation which makes biological and cognitive phenomena possible. The foundation is built by defining a model, which is presented in the form of a description of a hypothetical but a logically possible world with a defined ontological base.
Biology rests today on quite a few not so well connected foundations: molecular biology based on the genetic dogma; evolutionary biology based on neo-Darwinian model; ecology based on systems view; developmental biology by morphogenetic models; connectionist models for neurophysiology and cognitive biology; pervasive teleonomic
explanations for the goal-directed behavior across the discipline; etc. Can there be an underlying connecting theme or a model which could make these seemingly disparate domains interconnected? I shall atempt to answer this question.
By following the semantic view of scientific theories, I tend to believe that the models employed by the present physical sciences are not rich enough to capture biological (and some of the non-biological) systems. A richer theory that could capture biological reality could also capture physical and chemical phenomena as limiting cases, but
not vice versa
Evolutionary dynamics of adult stem cells: Comparison of random and immortal strand segregation mechanisms
This paper develops a point-mutation model describing the evolutionary
dynamics of a population of adult stem cells. Such a model may prove useful for
quantitative studies of tissue aging and the emergence of cancer. We consider
two modes of chromosome segregation: (1) Random segregation, where the daughter
chromosomes of a given parent chromosome segregate randomly into the stem cell
and its differentiating sister cell. (2) ``Immortal DNA strand''
co-segregation, for which the stem cell retains the daughter chromosomes with
the oldest parent strands. Immortal strand co-segregation is a mechanism,
originally proposed by Cairns (J. Cairns, {\it Nature} {\bf 255}, 197 (1975)),
by which stem cells preserve the integrity of their genomes. For random
segregation, we develop an ordered strand pair formulation of the dynamics,
analogous to the ordered strand pair formalism developed for quasispecies
dynamics involving semiconservative replication with imperfect lesion repair
(in this context, lesion repair is taken to mean repair of postreplication
base-pair mismatches). Interestingly, a similar formulation is possible with
immortal strand co-segregation, despite the fact that this segregation
mechanism is age-dependent. From our model we are able to mathematically show
that, when lesion repair is imperfect, then immortal strand co-segregation
leads to better preservation of the stem cell lineage than random chromosome
segregation. Furthermore, our model allows us to estimate the optimal lesion
repair efficiency for preserving an adult stem cell population for a given
period of time. For human stem cells, we obtain that mispaired bases still
present after replication and cell division should be left untouched, to avoid
potentially fixing a mutation in both DNA strands.Comment: 9 pages, 3 figure
SPEECH PLANNINGS IN THE STUDENTS’ CONVERSATION (A CASE STUDY OF FOURTH SEMESTER STUDENTS OF ENGLISH DEPARTMENT, DIAN NUSWANTORO UNIVERSITY)
This study is aimed at describing the speech plannings employed by the fourth semester
students of English Department, Dian Nuswantoro University in making conversation with
their friends. The data was collected by recording the students’ conversation. The
conversation lasted for about 30 minutes. The data, then, was transcribed into the written
form. In analyzing the data, the writer used the framework proposed by Faerch and Kasper
(1983:214). The result showed that the speech plannings the students usually attempted in
making conversation are: temporal variables such as pause (filled), drawls ; hesitation
phenomena such as filled pause, repetition, and correction; and other phenomena like slip,
switch, uptake signal, and interjection. From the kinds of speech plannings mentioned above
it can be said that pauses (filled) were the most attempted by the students so they could gain
time for execution. In general, the speech plannings attempted by the students indicate that
the students’ speaking readiness is low. In other words, they often find problems in their
conversation
Certifying and removing disparate impact
What does it mean for an algorithm to be biased? In U.S. law, unintentional
bias is encoded via disparate impact, which occurs when a selection process has
widely different outcomes for different groups, even as it appears to be
neutral. This legal determination hinges on a definition of a protected class
(ethnicity, gender, religious practice) and an explicit description of the
process.
When the process is implemented using computers, determining disparate impact
(and hence bias) is harder. It might not be possible to disclose the process.
In addition, even if the process is open, it might be hard to elucidate in a
legal setting how the algorithm makes its decisions. Instead of requiring
access to the algorithm, we propose making inferences based on the data the
algorithm uses.
We make four contributions to this problem. First, we link the legal notion
of disparate impact to a measure of classification accuracy that while known,
has received relatively little attention. Second, we propose a test for
disparate impact based on analyzing the information leakage of the protected
class from the other data attributes. Third, we describe methods by which data
might be made unbiased. Finally, we present empirical evidence supporting the
effectiveness of our test for disparate impact and our approach for both
masking bias and preserving relevant information in the data. Interestingly,
our approach resembles some actual selection practices that have recently
received legal scrutiny.Comment: Extended version of paper accepted at 2015 ACM SIGKDD Conference on
Knowledge Discovery and Data Minin
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