Evaluating Communication Campaigns

Summarizes presentations from a September 2007 conference on evaluating communication campaigns. Discusses the mechanism of effecting change through communication; the principles of advocacy evaluation; the design, methods, and tools; and lessons learned

Matching With Doses in an Observational Study of a Media Campaign Against Drug Abuse

Multivariate matching with doses of treatment differs from the treatment-control matching in three ways. First, pairs must not only balance covariates, but also must differ markedly in dose. Second, any two subjects may be paired, so that the matching is nonbipartite, and different algorithms are required. Finally, a propensity score with doses must be used in place of the conventional propensity score. We illustrate multivariate matching with doses using pilot data from a media campaign against drug abuse. The media campaign is intended to change attitudes and intentions related to illegal drugs, and the evaluation compares stated intentions among ostensibly comparable teens who reported markedly different exposures to the media campaign

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

Infering Air Quality from Traffic Data using Transferable Neural Network Models

This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Probability of local bifurcation type from a fixed point: A random matrix perspective

Results regarding probable bifurcations from fixed points are presented in the context of general dynamical systems (real, random matrices), time-delay dynamical systems (companion matrices), and a set of mappings known for their properties as universal approximators (neural networks). The eigenvalue spectra is considered both numerically and analytically using previous work of Edelman et. al. Based upon the numerical evidence, various conjectures are presented. The conclusion is that in many circumstances, most bifurcations from fixed points of large dynamical systems will be due to complex eigenvalues. Nevertheless, surprising situations are presented for which the aforementioned conclusion is not general, e.g. real random matrices with Gaussian elements with a large positive mean and finite variance.Comment: 21 pages, 19 figure

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

Consumer behaviour in the waiting area

Objective of the study: To determine consumer behaviour in the pharmacy waiting area. Method: The applied methods for data-collection were direct observations. Three Dutch community pharmacies were selected for the study. The topics in the observation list were based on available services at each waiting area (brochures, books, illuminated new trailer, children’s play area, etc.). Per patient each activity was registered, and at each pharmacy the behaviour was studied for 2 weeks. Results: Most patients only waited during the waiting time at the studied pharmacies. Few consumers obtained written information during their wait. Conclusion: The waiting area may have latent possibilities to expand the information function of the pharmacy and combine this with other activities that distract the consumer from the wait. Transdisciplinary research, combining knowledge from pharmacy practice research with consumer research, has been a useful approach to add information on queueing behaviour of consumers

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

Pharmacokinetics and Safety of Micafungin in Infants Supported With Extracorporeal Membrane Oxygenation

Candida is a leading cause of infection in infants on extracorporeal membrane oxygenation (ECMO). Optimal micafungin dosing is unknown in this population because ECMO can alter drug pharmacokinetics (PK)

A Parallel Incremental Learning Algorithm for Neural Networks with Fault Tolerance

URL : http://vecpar.fe.up.pt/2008/papers/46.pdfInternational audienceThis paper presents a parallel and fault tolerant version of an incremental learning algorithm for feed-forward neural networks used as function approximators. It has been shown in previous works that our incremental algorithm builds networks of reduced size while providing high quality approximations for real data sets. However, for very large sets, the use of our learning process on a single machine may be quite long and even sometimes impossible, due to memory limitations. The parallel algorithm presented in this paper is usable in any parallel system, and in particular, with large dynamical systems such as clusters and grids in which faults may occur. Finally, the quality and performances (without and with faults) of that algorithm are experimentally evaluated