Controlling oscillatory behaviour of a two neuron recurrent neural network using inputs

Haschke R, Steil JJ, Ritter H. Controlling oscillatory behaviour of a two neuron recurrent neural network using inputs. In: Dorffner G, Bischof H, Hornik K, eds. Artificial Neural Networks - ICANN 2001. Lecture notes in computer science. Vol 2130. Springer; 2001: 1109-1114.We derive analytical expressions of codim-1-bifurcations for a fully connected, additive two-neuron network with sigmoidal activations, where the two external inputs are regarded as bifurcation parameters. The obtained Neimark-Sacker bifurcation curve encloses a region in input space with stable oscillatory behaviour, in which it is possible to control the oscillation frequency by adjusting the inputs

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

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

Symmetry constrained machine learning

Symmetry, a central concept in understanding the laws of nature, has been used for centuries in physics, mathematics, and chemistry, to help make mathematical models tractable. Yet, despite its power, symmetry has not been used extensively in machine learning, until rather recently. In this article we show a general way to incorporate symmetries into machine learning models. We demonstrate this with a detailed analysis on a rather simple real world machine learning system - a neural network for classifying handwritten digits, lacking bias terms for every neuron. We demonstrate that ignoring symmetries can have dire over-fitting consequences, and that incorporating symmetry into the model reduces over-fitting, while at the same time reducing complexity, ultimately requiring less training data, and taking less time and resources to train

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

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

A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks

info:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FCCI-INF%2F29168%2F2017/PT" Custode, L. L., Tecce, C. L., Bakurov, I., Castelli, M., Cioppa, A. D., & Vanneschi, L. (2020). A Greedy Iterative Layered Framework for Training Feed Forward Neural Networks. In P. A. Castillo, J. L. Jiménez Laredo, & F. Fernández de Vega (Eds.), Applications of Evolutionary Computation - 23rd European Conference, EvoApplications 2020, Held as Part of EvoStar 2020, Proceedings (pp. 513-529). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12104 LNCS). Springer. https://doi.org/10.1007/978-3-030-43722-0_33In recent years neuroevolution has become a dynamic and rapidly growing research field. Interest in this discipline is motivated by the need to create ad-hoc networks, the topology and parameters of which are optimized, according to the particular problem at hand. Although neuroevolution-based techniques can contribute fundamentally to improving the performance of artificial neural networks (ANNs), they present a drawback, related to the massive amount of computational resources needed. This paper proposes a novel population-based framework, aimed at finding the optimal set of synaptic weights for ANNs. The proposed method partitions the weights of a given network and, using an optimization heuristic, trains one layer at each step while “freezing” the remaining weights. In the experimental study, particle swarm optimization (PSO) was used as the underlying optimizer within the framework and its performance was compared against the standard training (i.e., training that considers the whole set of weights) of the network with PSO and the backward propagation of the errors (backpropagation). Results show that the subsequent training of sub-spaces reduces training time, achieves better generalizability, and leads to the exhibition of smaller variance in the architectural aspects of the network.authorsversionpublishe

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

Association between oral sildenafil dosing, predicted exposure, and systemic hypotension in hospitalised infants

Abstract Background The relationship between sildenafil dosing, exposure, and systemic hypotension in infants is incompletely understood. Objectives The aim of this study was to characterise the relationship between predicted sildenafil exposure and hypotension in hospitalised infants. Methods We extracted information on sildenafil dosing and clinical characteristics from electronic health records of 348 neonatal ICUs from 1997 to 2013, and we predicted drug exposure using a population pharmacokinetic model. Results We identified 232 infants receiving sildenafil at a median dose of 3.2 mg/kg/day (2.0, 6.0). The median steady-state area under the concentration–time curve over 24 hours (AUC 24,SS ) and maximum concentration of sildenafil (C max,SS,SIL ) were 712 ng×hour/ml (401, 1561) and 129 ng/ml (69, 293), respectively. Systemic hypotension occurred in 9% of the cohort. In multivariable analysis, neither dosing nor exposure were associated with systemic hypotension: odds ratio=0.96 (95% confidence interval: 0.81, 1.14) for sildenafil dose; 0.87 (0.59, 1.28) for AUC 24,SS ; 1.19 (0.78, 1.82) for C max,SS,SIL . Conclusions We found no association between sildenafil dosing or exposure with systemic hypotension. Continued assessment of sildenafil’s safety profile in infants is warranted

Neural parameters estimation for brain tumor growth modeling

Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a system of partial differential equations, wherein the physiological processes that govern the growth of the tumor are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model is informed from empirical data, e.g., medical images obtained from diagnostic modalities, such as magnetic-resonance imaging. Existing model-observation coupling schemes require a large number of forward integrations of the biophysical model and rely on simplifying assumption on the functional form, linking the output of the model with the image information. In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. The technique allows for explicit evaluation of the posterior distribution of the parameters by sequentially training a mixture-density network, relaxing the constraint on the functional form and reducing the number of samples necessary to propagate through the forward model for the estimation. We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression