440 research outputs found
Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views
In this paper, we present a conditional GAN with two generators and a common
discriminator for multiview learning problems where observations have two
views, but one of them may be missing for some of the training samples. This is
for example the case for multilingual collections where documents are not
available in all languages. Some studies tackled this problem by assuming the
existence of view generation functions to approximately complete the missing
views; for example Machine Translation to translate documents into the missing
languages. These functions generally require an external resource to be set and
their quality has a direct impact on the performance of the learned multiview
classifier over the completed training set. Our proposed approach addresses
this problem by jointly learning the missing views and the multiview classifier
using a tripartite game with two generators and a discriminator. Each of the
generators is associated to one of the views and tries to fool the
discriminator by generating the other missing view conditionally on the
corresponding observed view. The discriminator then tries to identify if for an
observation, one of its views is completed by one of the generators or if both
views are completed along with its class. Our results on a subset of Reuters
RCV1/RCV2 collections show that the discriminator achieves significant
classification performance; and that the generators learn the missing views
with high quality without the need of any consequent external resource.Comment: 15 pages, 3 figur
Hund's rule and metallic ferromagnetism
We study tight-binding models of itinerant electrons in two different bands,
with effective on-site interactions expressing Coulomb repulsion and Hund's
rule. We prove that, for sufficiently large on-site exchange anisotropy, all
ground states show metallic ferromagnetism: They exhibit a macroscopic
magnetization, a macroscopic fraction of the electrons is spatially
delocalized, and there is no energy gap for kinetic excitations.Comment: 17 page
Discovering a junction tree behind a Markov network by a greedy algorithm
In an earlier paper we introduced a special kind of k-width junction tree,
called k-th order t-cherry junction tree in order to approximate a joint
probability distribution. The approximation is the best if the Kullback-Leibler
divergence between the true joint probability distribution and the
approximating one is minimal. Finding the best approximating k-width junction
tree is NP-complete if k>2. In our earlier paper we also proved that the best
approximating k-width junction tree can be embedded into a k-th order t-cherry
junction tree. We introduce a greedy algorithm resulting very good
approximations in reasonable computing time.
In this paper we prove that if the Markov network underlying fullfills some
requirements then our greedy algorithm is able to find the true probability
distribution or its best approximation in the family of the k-th order t-cherry
tree probability distributions. Our algorithm uses just the k-th order marginal
probability distributions as input.
We compare the results of the greedy algorithm proposed in this paper with
the greedy algorithm proposed by Malvestuto in 1991.Comment: The paper was presented at VOCAL 2010 in Veszprem, Hungar
TVL<sub>1</sub> Planarity Regularization for 3D Shape Approximation
The modern emergence of automation in many industries has given impetus to extensive research into mobile robotics. Novel perception technologies now enable cars to drive autonomously, tractors to till a field automatically and underwater robots to construct pipelines. An essential requirement to facilitate both perception and autonomous navigation is the analysis of the 3D environment using sensors like laser scanners or stereo cameras. 3D sensors generate a very large number of 3D data points when sampling object shapes within an environment, but crucially do not provide any intrinsic information about the environment which the robots operate within.
This work focuses on the fundamental task of 3D shape reconstruction and modelling from 3D point clouds. The novelty lies in the representation of surfaces by algebraic functions having limited support, which enables the extraction of smooth consistent implicit shapes from noisy samples with a heterogeneous density. The minimization of total variation of second differential degree makes it possible to enforce planar surfaces which often occur in man-made environments. Applying the new technique means that less accurate, low-cost 3D sensors can be employed without sacrificing the 3D shape reconstruction accuracy
Supervised inference of gene-regulatory networks
<p>Abstract</p> <p>Background</p> <p>Inference of protein interaction networks from various sources of data has become an important topic of both systems and computational biology. Here we present a supervised approach to identification of gene expression regulatory networks.</p> <p>Results</p> <p>The method is based on a kernel approach accompanied with genetic programming. As a data source, the method utilizes gene expression time series for prediction of interactions among regulatory proteins and their target genes. The performance of the method was verified using Saccharomyces cerevisiae cell cycle and DNA/RNA/protein biosynthesis gene expression data. The results were compared with independent data sources. Finally, a prediction of novel interactions within yeast gene expression circuits has been performed.</p> <p>Conclusion</p> <p>Results show that our algorithm gives, in most cases, results identical with the independent experiments, when compared with the YEASTRACT database. In several cases our algorithm gives predictions of novel interactions which have not been reported.</p
Sparse kernel canonical correlation analysis for discovery of nonlinear interactions in high-dimensional data
Network Analysis of Differential Expression for the Identification of Disease-Causing Genes
Genetic studies (in particular linkage and association studies) identify chromosomal regions involved in a disease or phenotype of interest, but those regions often contain many candidate genes, only a few of which can be followed-up for biological validation. Recently, computational methods to identify (prioritize) the most promising candidates within a region have been proposed, but they are usually not applicable to cases where little is known about the phenotype (no or few confirmed disease genes, fragmentary understanding of the biological cascades involved). We seek to overcome this limitation by replacing knowledge about the biological process by experimental data on differential gene expression between affected and healthy individuals. Considering the problem from the perspective of a gene/protein network, we assess a candidate gene by considering the level of differential expression in its neighborhood under the assumption that strong candidates will tend to be surrounded by differentially expressed neighbors. We define a notion of soft neighborhood where each gene is given a contributing weight, which decreases with the distance from the candidate gene on the protein network. To account for multiple paths between genes, we define the distance using the Laplacian exponential diffusion kernel. We score candidates by aggregating the differential expression of neighbors weighted as a function of distance. Through a randomization procedure, we rank candidates by p-values. We illustrate our approach on four monogenic diseases and successfully prioritize the known disease causing genes
Sequential updating of a new dynamic pharmacokinetic model for caffeine in premature neonates
International audienceCaffeine treatment is widely used in nursing care to reduce the risk of apnoea in premature neonates. To check the therapeutic efficacy of the treatment against apnoea, caffeine concentration in blood is an important indicator. The present study was aimed at building a pharmacokinetic model as a basis for a medical decision support tool. In the proposed model, time dependence of physiological parameters is introduced to describe rapid growth of neonates. To take into account the large variability in the population, the Pharmacokinetic model is embedded in a population structure. The whole model is inferred within a Bayesian framework. To update caffeine concentration predictions as data of an incoming patient are collected, we propose a fast method that can be used in a medical context. This involves the sequential updating of model parameters (at individual and population levels) via a stochastic particle algorithm. Our model provides better predictions than the ones obtained with models previously published. We show, through an example, that sequential updating improves predictions of caffeine concentration in blood (reduce bias and length of credibility intervals). The update of the pharmacokinetic model using body mass and caffeine concentration data is studied. It shows how informative caffeine concentration data are in contrast to body mass data. This study provides the methodological basis to predict caffeine concentration in blood, after a given treatment if data are collected on the treated neonate
Empathy Manipulation Impacts Music-Induced Emotions: A Psychophysiological Study on Opera
This study investigated the effects of voluntarily empathizing with a musical performer (i.e., cognitive empathy) on music-induced emotions and their underlying physiological activity. N = 56 participants watched video-clips of two operatic compositions performed in concerts, with low or high empathy instructions. Heart rate and heart rate variability, skin conductance level (SCL), and respiration rate (RR) were measured during music listening, and music-induced emotions were quantified using the Geneva Emotional Music Scale immediately after music listening. Listening to the aria with sad content in a high empathy condition facilitated the emotion of nostalgia and decreased SCL, in comparison to the low empathy condition. Listening to the song with happy content in a high empathy condition also facilitated the emotion of power and increased RR, in comparison to the low empathy condition. To our knowledge, this study offers the first experimental evidence that cognitive empathy influences emotion psychophysiology during music listening
Graphical modeling of binary data using the LASSO: a simulation study
Background: Graphical models were identified as a promising new approach to modeling high-dimensional clinical data. They provided a probabilistic tool to display, analyze and visualize the net-like dependence structures by drawing a graph describing the conditional dependencies between the variables. Until now, the main focus of research was on building Gaussian graphical models for continuous multivariate data following a multivariate normal distribution. Satisfactory solutions for binary data were missing. We adapted the method of Meinshausen and Buhlmann to binary data and used the LASSO for logistic regression. Objective of this paper was to examine the performance of the Bolasso to the development of graphical models for high dimensional binary data. We hypothesized that the performance of Bolasso is superior to competing LASSO methods to identify graphical models. Methods: We analyzed the Bolasso to derive graphical models in comparison with other LASSO based method. Model performance was assessed in a simulation study with random data generated via symmetric local logistic regression models and Gibbs sampling. Main outcome variables were the Structural Hamming Distance and the Youden Index. We applied the results of the simulation study to a real-life data with functioning data of patients having head and neck cancer. Results: Bootstrap aggregating as incorporated in the Bolasso algorithm greatly improved the performance in higher sample sizes. The number of bootstraps did have minimal impact on performance. Bolasso performed reasonable well with a cutpoint of 0.90 and a small penalty term. Optimal prediction for Bolasso leads to very conservative models in comparison with AIC, BIC or cross-validated optimal penalty terms. Conclusions: Bootstrap aggregating may improve variable selection if the underlying selection process is not too unstable due to small sample size and if one is mainly interested in reducing the false discovery rate. We propose using the Bolasso for graphical modeling in large sample sizes
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