734 research outputs found
Generalized Permutohedra from Probabilistic Graphical Models
A graphical model encodes conditional independence relations via the Markov
properties. For an undirected graph these conditional independence relations
can be represented by a simple polytope known as the graph associahedron, which
can be constructed as a Minkowski sum of standard simplices. There is an
analogous polytope for conditional independence relations coming from a regular
Gaussian model, and it can be defined using multiinformation or relative
entropy. For directed acyclic graphical models and also for mixed graphical
models containing undirected, directed and bidirected edges, we give a
construction of this polytope, up to equivalence of normal fans, as a Minkowski
sum of matroid polytopes. Finally, we apply this geometric insight to construct
a new ordering-based search algorithm for causal inference via directed acyclic
graphical models.Comment: Appendix B is expanded. Final version to appear in SIAM J. Discrete
Mat
‘‘There’s so much more to it than what I initially thought’’: Stepping into researchers’ shoes with a class activity in a first year psychology survey course
In psychology, it is widely agreed that research methods, although central to the discipline, are particularly challenging to learn and teach, particularly at introductory level. This pilot study explored the potential of embedding a student-conducted research activity in a one-semester undergraduate Introduction to Psychology survey course, with the aims of (a) engaging students with the topic of research methods; (b) developing students’ comprehension and application of research methods concepts; and (c) building students’ ability to link research with theory. The research activity explored shoe ownership, examining gender differences and relationships with age, and linking to theories of gender difference and of consumer identity. The process of carrying out the research and reflecting on it created a contextualized, active learning environment in which students themselves raised many issues that research methods lectures seek to cover. Students also wrote richer assignments than standard first year mid-term essay
OpenEssayist: a supply and demand learning analytics tool for drafting academic essays
This paper focuses on the use of a natural language analytics engine to provide feedback to students when preparing an essay for summative assessment. OpenEssayist is a real-time learning analytics tool, which operates through the combination of a linguistic analysis engine that processes the text in the essay, and a web application that uses the output of the linguistic analysis engine to generate the feedback. We outline the system itself and present analysis of observed patterns of activity as a cohort of students engaged with the system for their module assignments. We report a significant positive correlation between the number of drafts submitted to the system and the grades awarded for the first assignment. We can also report that this cohort of students gained significantly higher overall grades than the students in the previous cohort, who had no access to OpenEssayist. As a system that is content free, OpenEssayist can be used to support students working in any domain that requires the writing of essays
The IBMAP approach for Markov networks structure learning
In this work we consider the problem of learning the structure of Markov
networks from data. We present an approach for tackling this problem called
IBMAP, together with an efficient instantiation of the approach: the IBMAP-HC
algorithm, designed for avoiding important limitations of existing
independence-based algorithms. These algorithms proceed by performing
statistical independence tests on data, trusting completely the outcome of each
test. In practice tests may be incorrect, resulting in potential cascading
errors and the consequent reduction in the quality of the structures learned.
IBMAP contemplates this uncertainty in the outcome of the tests through a
probabilistic maximum-a-posteriori approach. The approach is instantiated in
the IBMAP-HC algorithm, a structure selection strategy that performs a
polynomial heuristic local search in the space of possible structures. We
present an extensive empirical evaluation on synthetic and real data, showing
that our algorithm outperforms significantly the current independence-based
algorithms, in terms of data efficiency and quality of learned structures, with
equivalent computational complexities. We also show the performance of IBMAP-HC
in a real-world application of knowledge discovery: EDAs, which are
evolutionary algorithms that use structure learning on each generation for
modeling the distribution of populations. The experiments show that when
IBMAP-HC is used to learn the structure, EDAs improve the convergence to the
optimum
Current-induced cooling phenomenon in a two-dimensional electron gas under a magnetic field
We investigate the spatial distribution of temperature induced by a dc
current in a two-dimensional electron gas (2DEG) subjected to a perpendicular
magnetic field. We numerically calculate the distributions of the electrostatic
potential phi and the temperature T in a 2DEG enclosed in a square area
surrounded by insulated-adiabatic (top and bottom) and isopotential-isothermal
(left and right) boundaries (with phi_{left} < phi_{right} and T_{left}
=T_{right}), using a pair of nonlinear Poisson equations (for phi and T) that
fully take into account thermoelectric and thermomagnetic phenomena, including
the Hall, Nernst, Ettingshausen, and Righi-Leduc effects. We find that, in the
vicinity of the left-bottom corner, the temperature becomes lower than the
fixed boundary temperature, contrary to the naive expectation that the
temperature is raised by the prevalent Joule heating effect. The cooling is
attributed to the Ettingshausen effect at the bottom adiabatic boundary, which
pumps up the heat away from the bottom boundary. In order to keep the adiabatic
condition, downward temperature gradient, hence the cooled area, is developed
near the boundary, with the resulting thermal diffusion compensating the upward
heat current due to the Ettingshausen effect.Comment: 25 pages, 7 figure
Recommended from our members
HVEM quantitative stereoscopy through the full damage range of an ion- bombarded Fe--Ni--Cr alloy
The swelling of a Ni-ion irradiated Fe--25 percent Ni--15 percent Cr alloy has been investigated employing high voltage (1 MeV) electron microscopy. Helium pre-injected samples were irradiated to a maximum dose of 92 dpa with 3.5 MeV Ni ions at 600, 650, 700 and 750C. By means of quantitative stereoscopy throughout the full range of ion damage, the void morphology, swelling, and dislocation morphology as functions of the distance from the ion-entry surface of the foil were obtained. The dose also varied as a function of depth and, with this technique, swelling as a function of dose was determined from a single sample at each temperature. Swelling, void concentration, and void size varied with irradiation temperature with the maximum swelling of 7.4 percent occurring at 700C. Several distinct dislocation configurations could be distinguished progressing inward from the ion-entry surface. The observations suggest that nucleation and growth of voids are related to dislocation densities and possibly to dislocation structure. The swelling-dose relationships were analyzed using a linear expression for each apparent swelling regime and the results interpreted in terms of the Brailsford and Bullough statistical rate theory. Two distinct steady-state swelling regimes exist at each irradiation temperature and the difference in the rates is attributed to the different kinetic development of the dislocation structure and densities in the two stages. (auth
The identification of informative genes from multiple datasets with increasing complexity
Background
In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.
Results
In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.
Conclusions
We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events
Corrected score methods for estimating Bayesian networks with error-prone nodes
Motivated by inferring cellular signaling networks using noisy flow cytometry
data, we develop procedures to draw inference for Bayesian networks based on
error-prone data. Two methods for inferring causal relationships between nodes
in a network are proposed based on penalized estimation methods that account
for measurement error and encourage sparsity. We discuss consistency of the
proposed network estimators and develop an approach for selecting the tuning
parameter in the penalized estimation methods. Empirical studies are carried
out to compare the proposed methods and a naive method that ignores measurement
error with applications to synthetic data and to single cell flow cytometry
data
Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset
<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.</p> <p>Results</p> <p>We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC) devoted to BN structure learning.</p> <p>Conclusion</p> <p>We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.</p
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