A Neural Networks Committee for the Contextual Bandit Problem

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.Comment: 21st International Conference on Neural Information Processin

Functional Federated Learning in Erlang (ffl-erl)

The functional programming language Erlang is well-suited for concurrent and distributed applications. Numerical computing, however, is not seen as one of its strengths. The recent introduction of Federated Learning, a concept according to which client devices are leveraged for decentralized machine learning tasks, while a central server updates and distributes a global model, provided the motivation for exploring how well Erlang is suited to that problem. We present ffl-erl, a framework for Federated Learning, written in Erlang, and explore how well it performs in two scenarios: one in which the entire system has been written in Erlang, and another in which Erlang is relegated to coordinating client processes that rely on performing numerical computations in the programming language C. There is a concurrent as well as a distributed implementation of each case. Erlang incurs a performance penalty, but for certain use cases this may not be detrimental, considering the trade-off between conciseness of the language and speed of development (Erlang) versus performance (C). Thus, Erlang may be a viable alternative to C for some practical machine learning tasks.Comment: 16 pages, accepted for publication in the WFLP 2018 conference proceedings; final post-prin

Tracheostomy after Surgery for Congenital Heart Disease: An Analysis of the Society of Thoracic Surgeons Congenital Heart Surgery Database

Background Information concerning tracheostomy after operations for congenital heart disease has come primarily from single-center reports. We aimed to describe the epidemiology and outcomes associated with postoperative tracheostomy in a multi-institutional registry. Methods The Society of Thoracic Surgeons Congenital Heart Database (2000 to 2014) was queried for all index operations with the adverse event “postoperative tracheostomy” or “respiratory failure, requiring tracheostomy.” Patients with preoperative tracheostomy or weighing less than 2.5 kg undergoing isolated closure of patent ductus arteriosus were excluded. Trends in tracheostomy incidence over time from January 2000 to June 2014 were analyzed with a Cochran-Armitage test. The patient characteristics associated with operative mortality were analyzed for January 2010 to June 2014, including deaths occurring up to 6 months after transfer of patients to long-term care facilities. Results From 2000 to 2014, the incidence of tracheostomy after operations for congenital heart disease increased from 0.11% in 2000 to a high of 0.76% in 2012 (p < 0.0001). From 2010 to 2014, 648 patients underwent tracheostomy. The median age at operation was 2.5 months (25th, 75th percentile: 0.4, 7). Prematurity (n = 165, 26%), genetic abnormalities (n = 298, 46%), and preoperative mechanical ventilation (n = 275, 43%) were common. Postoperative adverse events were also common, including cardiac arrest (n = 131, 20%), extracorporeal support (n = 87, 13%), phrenic or laryngeal nerve injury (n = 114, 18%), and neurologic deficit (n = 51, 8%). The operative mortality was 25% (n = 153). Conclusions Tracheostomy as an adverse event of operations for congenital heart disease remains rare but has been increasingly used over the past 15 years. This trend and the considerable mortality risk among patients requiring postoperative tracheostomy support the need for further research in this complex population

Learning Two-input Linear and Nonlinear Analog Functions with a Simple Chemical System

The current biochemical information processing systems behave in a predetermined manner because all features are defined during the design phase. To make such unconventional computing systems reusable and programmable for biomedical applications, adaptation, learning, and self-modification baaed on external stimuli would be highly desirable. However, so far, it haa been too challenging to implement these in real or simulated chemistries. In this paper we extend the chemical perceptron, a model previously proposed by the authors, to function as an analog instead of a binary system. The new analog asymmetric signal perceptron learns through feedback and supports MichaelisMenten kinetics. The results show that our perceptron is able to learn linear and nonlinear (quadratic) functions of two inputs. To the best of our knowledge, it is the first simulated chemical system capable of doing so. The small number of species and reactions allows for a mapping to an actual wet implementation using DNA-strand displacement or deoxyribozymes. Our results are an important step toward actual biochemical systems that can learn and adapt

Representing complex data using localized principal components with application to astronomical data

Often the relation between the variables constituting a multivariate data space might be characterized by one or more of the terms: ``nonlinear'', ``branched'', ``disconnected'', ``bended'', ``curved'', ``heterogeneous'', or, more general, ``complex''. In these cases, simple principal component analysis (PCA) as a tool for dimension reduction can fail badly. Of the many alternative approaches proposed so far, local approximations of PCA are among the most promising. This paper will give a short review of localized versions of PCA, focusing on local principal curves and local partitioning algorithms. Furthermore we discuss projections other than the local principal components. When performing local dimension reduction for regression or classification problems it is important to focus not only on the manifold structure of the covariates, but also on the response variable(s). Local principal components only achieve the former, whereas localized regression approaches concentrate on the latter. Local projection directions derived from the partial least squares (PLS) algorithm offer an interesting trade-off between these two objectives. We apply these methods to several real data sets. In particular, we consider simulated astrophysical data from the future Galactic survey mission Gaia.Comment: 25 pages. In "Principal Manifolds for Data Visualization and Dimension Reduction", A. Gorban, B. Kegl, D. Wunsch, and A. Zinovyev (eds), Lecture Notes in Computational Science and Engineering, Springer, 2007, pp. 180--204, http://www.springer.com/dal/home/generic/search/results?SGWID=1-40109-22-173750210-

Population Pharmacokinetics of Olanzapine in Children

Aims The aim of this study was to evaluate the population pharmacokinetics (PopPK) of olanzapine in children and devise a model-informed paediatric dosing scheme. Methods The PopPK of olanzapine was characterized using opportunistically collected plasma samples from children receiving olanzapine per standard of care for any indication. A nonlinear mixed effect modelling approach was employed for model development using the software NONMEM (v7.4). Simulations from the developed PopPK model were used to devise a paediatric dosing scheme that targeted comparable plasma exposures to adolescents and adults. Results Forty-five participants contributed 83 plasma samples towards the analysis. The median (range) postnatal age and body weight of participants were 3.8 years (0.2–19.2) and 14.1 kg (4.2–111.7), respectively. The analysis was restricted to pharmacokinetic (PK) samples collected following enteral administration (oral and feeding tube). A one-compartment model with linear elimination provided an appropriate fit to the data. The final model included the covariates body weight and postmenstrual age (PMA) on apparent olanzapine clearance (CL/F). Typical CL/F and apparent volume of distribution (scaled to 70 kg) were 16.8 L/h (21% RSE) and 663 L (13% RSE), respectively. Developed dosing schemes used weight-normalized doses for children ≤6 months postnatal age or \u3c15 kg and fixed doses for children ≥15 kg. Conclusion We developed a paediatric PopPK model for enterally-administered olanzapine. To our knowledge, this analysis is the first study to characterize the PK of olanzapine in participants ranging from infants to adolescents. Body weight and PMA were identified as influential covariates for characterizing developmental changes in olanzapine apparent clearance

Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

Building nonparametric nn-body force fields using Gaussian process regression

Constructing a classical potential suited to simulate a given atomic system is a remarkably difficult task. This chapter presents a framework under which this problem can be tackled, based on the Bayesian construction of nonparametric force fields of a given order using Gaussian process (GP) priors. The formalism of GP regression is first reviewed, particularly in relation to its application in learning local atomic energies and forces. For accurate regression it is fundamental to incorporate prior knowledge into the GP kernel function. To this end, this chapter details how properties of smoothness, invariance and interaction order of a force field can be encoded into corresponding kernel properties. A range of kernels is then proposed, possessing all the required properties and an adjustable parameter $n$ governing the interaction order modelled. The order $n$ best suited to describe a given system can be found automatically within the Bayesian framework by maximisation of the marginal likelihood. The procedure is first tested on a toy model of known interaction and later applied to two real materials described at the DFT level of accuracy. The models automatically selected for the two materials were found to be in agreement with physical intuition. More in general, it was found that lower order (simpler) models should be chosen when the data are not sufficient to resolve more complex interactions. Low $n$ GPs can be further sped up by orders of magnitude by constructing the corresponding tabulated force field, here named "MFF".Comment: 31 pages, 11 figures, book chapte

Solithromycin Pharmacokinetics in Plasma and Dried Blood Spots and Safety in Adolescents

ABSTRACT We assessed the pharmacokinetics and safety of solithromycin, a fluoroketolide antibiotic, in a phase 1, open-label, multicenter study of 13 adolescents with suspected or confirmed bacterial infections. On days 3 to 5, the mean (standard deviation) maximum plasma concentration and area under the concentration versus time curve from 0 to 24 h were 0.74 μg/ml (0.61 μg/ml) and 9.28 μg · h/ml (6.30 μg · h/ml), respectively. The exposure and safety in this small cohort of adolescents were comparable to those for adults. (This study has been registered at ClinicalTrials.gov under registration no. NCT01966055.

Training Very Deep Networks via Residual Learning with Stochastic Input Shortcut Connections

Many works have posited the benefit of depth in deep networks. However, one of the problems encountered in the training of very deep networks is feature reuse; that is, features are ’diluted’ as they are forward propagated through the model. Hence, later network layers receive less informative signals about the input data, consequently making training less effective. In this work, we address the problem of feature reuse by taking inspiration from an earlier work which employed residual learning for alleviating the problem of feature reuse. We propose a modification of residual learning for training very deep networks to realize improved generalization performance; for this, we allow stochastic shortcut connections of identity mappings from the input to hidden layers.We perform extensive experiments using the USPS and MNIST datasets. On the USPS dataset, we achieve an error rate of 2.69% without employing any form of data augmentation (or manipulation). On the MNIST dataset, we reach a comparable state-of-the-art error rate of 0.52%. Particularly, these results are achieved without employing any explicit regularization technique