A Neural Network Architecture to Learn Explicit MPC Controllers from Data. (arXiv:1911.10789v1 [eess.SY])

We present a methodology to learn explicit Model Predictive Control (eMPC) laws from sample data points with tunable complexity. The learning process is cast in a special Neural Network setting where the coefficients of two linear layers and a parametric quadratic program (pQP) implicit layer are optimized to fit the training data. Thanks to this…

Collectivised Pension Investment with Exponential Kihlstrom–Mirman Preferences. (arXiv:1911.02296v2 [q-fin.PM] UPDATED)

In a collectivised pension fund, investors agree that any money remaining in the fund when they die can be shared among the survivors. We give a numerical algorithm to compute the optimal investment-consumption strategy for an infinite collective of identical investors with exponential Kihlstrom–Mirman preferences, investing in the Black–Scholes market in continuous time but consuming…

Unsupervised Attention Mechanism across Neural Network Layers. (arXiv:1902.10658v8 [cs.LG] UPDATED)

Inspired by the adaptation phenomenon of neuronal firing, we propose an unsupervised attention mechanism (UAM) which computes the statistical regularity in the implicit space of neural networks under the Minimum Description Length (MDL) principle. Treating the neural network optimization process as a partially observable model selection problem, UAM constrained the implicit space by a normalization…

On the convergence of the maximum likelihood estimator for the transition rate under a 2-state symmetric model. (arXiv:1903.03919v3 [q-bio.PE] UPDATED)

Maximum likelihood estimators are used extensively to estimate unknown parameters of stochastic trait evolution models on phylogenetic trees. Although the MLE has been proven to converge to the true value in the independent-sample case, we cannot appeal to this result because trait values of different species are correlated due to shared evolutionary history. In this…

Confinement enhances the diversity of microbial flow fields. (arXiv:1904.08319v2 [physics.bio-ph] UPDATED)

Despite their importance in many biological, ecological and physical processes, microorganismal fluid flows under tight confinement have not been investigated experimentally. Strong screening of Stokelets in this geometry suggests that the flow fields of different microorganisms should be universally dominated by the 2D source dipole from the swimmer’s finite-size body. Confinement therefore is poised to…

A partial order and cluster-similarity metric on rooted phylogenetic trees. (arXiv:1906.02411v2 [q-bio.PE] UPDATED)

Metrics on rooted phylogenetic trees are integral to a number of areas of phylogenetic analysis. Cluster-similarity metrics have recently been introduced in order to limit skew in the distribution of distances, and to ensure that trees in the neighbourhood of each other have similar hierarchies. In the present paper we introduce a new cluster-similarity metric…

Information capacity of a network of spiking neurons. (arXiv:1906.05584v2 [q-bio.NC] UPDATED)

We study a model of spiking neurons, with recurrent connections that result from learning a set of spatio-temporal patterns with a spike-timing dependent plasticity rule and a global inhibition. We investigate the ability of the network to store and selectively replay multiple patterns of spikes, with a combination of spatial population and phase-of-spike code. Each…

How tree-based is my network? Proximity measures for unrooted phylogenetic networks. (arXiv:1906.06163v3 [q-bio.PE] UPDATED)

Tree-based networks are a class of phylogenetic networks that attempt to formally capture what is meant by “tree-like” evolution. A given non-tree-based phylogenetic network, however, might appear to be very close to being tree-based, or very far. In this paper, we formalise the notion of proximity to tree-based for unrooted phylogenetic networks, with a range…

EvAn: Neuromorphic Event-based Anomaly Detection. (arXiv:1911.09722v1 [stat.ML])

Event-based cameras are bio-inspired novel sensors that asynchronously record changes in illumination in the form of events, thus resulting in significant advantages over conventional cameras in terms of low power utilization, high dynamic range, and no motion blur. Moreover, such cameras, by design, encode only the relative motion between the scene and the sensor (and…

Phase mapping for cardiac unipolar electrograms with neural network instead of phase transformation. (arXiv:1911.09731v1 [eess.SP])

Digital signal processing can be performed in two major ways. The first is a pipeline of signal transformations, and the second is machine learning approaches that require tagged data for training. This paper studies the third way. We generate a training dataset for a neural network in a series of numerical experiments and uses the…