Dispatching Fire Trucks under Stochastic Driving Times

In this paper we discuss optimal dispatching of fire trucks, based on a particular dispatching problem that arises at the Amsterdam Fire Department, where two fire trucks are send to the same incident location for a quick response. We formulate the dispatching problem as a Markov Decision Process, and numerically obtain the optimal dispatching decisions using policy iteration. We show that the fraction of late arrivals can be significantly reduced by deviating from current practice of dispatching the closest available trucks, with a relative improvement of on average about $20\%$, and over $50\%$ for certain instances. We also show that driving-time correlation has a non-negligible impact on decision making, and if ignored may lead to performance decrease of over $20\%$ in certain cases. As the optimal policy cannot be computed for problems of realistic size due to the computational complexity of the policy iteration algorithm, we propose a dispatching heuristic based on a queueing approximation for the state of the network. We show that the performance of this heuristic is close to the optimal policy, and requires significantly less computational effort.Comment: Submitted to Computers and Operations Research (December 08, 2018

Fire truck relocation during major incidents

The effectiveness of a fire department is largely determined by its ability to respond to incidents in a timely manner. To do so, fire departments typically have fire stations spread evenly across the region, and dispatch the closest truck(s) whenever a new incident occurs. However, large gaps in coverage may arise in the case of a major incident that requires many nearby fire trucks over a long period of time, substantially increasing response times for emergencies that occur subsequently. We propose a heuristic for relocating idle trucks during a major incident in order to retain good coverage. This is done by solving a mathematical program that takes into account the location of the available fire trucks and the historic spatial distribution of incidents. This heuristic allows the user to balance the coverage and the number of truck movements. Using extensive simulation experiments we test the heuristic for the operations of the Fire Department of Amsterdam‐Amstelland, and compare it against three other benchmark strategies in a simulation fitted using 10 years of historical data. We demonstrate substantial improvement over the current relocation policy, and show that not relocating during major incidents may lead to a significant decrease in performance

Dopant Network Processing Units: Towards Efficient Neural-network Emulators with High-capacity Nanoelectronic Nodes

The rapidly growing computational demands of deep neural networks require novel hardware designs. Recently, tunable nanoelectronic devices were developed based on hopping electrons through a network of dopant atoms in silicon. These "Dopant Network Processing Units" (DNPUs) are highly energy-efficient and have potentially very high throughput. By adapting the control voltages applied to its terminals, a single DNPU can solve a variety of linearly non-separable classification problems. However, using a single device has limitations due to the implicit single-node architecture. This paper presents a promising novel approach to neural information processing by introducing DNPUs as high-capacity neurons and moving from a single to a multi-neuron framework. By implementing and testing a small multi-DNPU classifier in hardware, we show that feed-forward DNPU networks improve the performance of a single DNPU from 77% to 94% test accuracy on a binary classification task with concentric classes on a plane. Furthermore, motivated by the integration of DNPUs with memristor arrays, we study the potential of using DNPUs in combination with linear layers. We show by simulation that a single-layer MNIST classifier with only 10 DNPUs achieves over 96% test accuracy. Our results pave the road towards hardware neural-network emulators that offer atomic-scale information processing with low latency and energy consumption

Case finding of mild cognitive impairment and dementia and subsequent care; results of a cluster RCT in primary care

Purpose Despite a call for earlier diagnosis of dementia, the diagnostic yield of case finding and its impact on the mental health of patients and relatives are unclear. This study assessed the effect of a two-component intervention of case finding and subsequent care on these outcomes. Methods In a cluster RCT we assessed whether education of family physicians (FPs; trial stage 1) resulted in more mild cognitive impairment (MCI) and dementia diagnoses among older persons in whom FPs suspected cognitive decline and whether case finding by a practice nurse and the FP (trial stage 2) added to this number of diagnoses. In addition, we assessed mental health effects of case finding and subsequent care (trial stage 2). FPs of 15 primary care practices (PCPs = clusters) judged the cognitive status of all persons ≤ 65 years. The primary outcome, new MCI and dementia diagnoses by FPs after 12 months as indicated on a list, was assessed among all persons in whom FPs suspected cognitive impairment but without a formal diagnosis of dementia. The secondary outcome, mental health of patients and their relatives, was assessed among persons consenting to participate in trial stage 2. Trial stage 1 consisted of either intervention component 1: training FPs to diagnose MCI and dementia, or control: no training. Trial stage 2 consisted of either intervention component 2: case finding of MCI and dementia and care by a trained nurse and the FP, or control: care as usual. Results Seven PCPs were randomized to the intervention; eight to the control condition. MCI or dementia was diagnosed in 42.3 (138/326) of persons in the intervention, and in 30.5 (98/321) in the control group (estimated difference GEE: 10.8, OR: 1.51, 95-CI 0.60-3.76). Among patients and relatives who consented to stage 2 of the trial (n = 145; 25), there were no differences in mental health between the intervention and control group. Conclusions We found a non-significant increase in the number of new MCI diagnoses. As we cannot exclude a clinically relevant effect, a larger study is warranted to replicate ours. Trial Registration Nederlands Trial Register NTR3389 © 2016 van den Dungen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Gradient Descent in Materio

Deep learning, a multi-layered neural network approach inspired by the brain, has revolutionized machine learning. One of its key enablers has been backpropagation, an algorithm that computes the gradient of a loss function with respect to the weights in the neural network model, in combination with its use in gradient descent. However, the implementation of deep learning in digital computers is intrinsically wasteful, with energy consumption becoming prohibitively high for many applications. This has stimulated the development of specialized hardware, ranging from neuromorphic CMOS integrated circuits and integrated photonic tensor cores to unconventional, material-based computing systems. The learning process in these material systems, taking place, e.g., by artificial evolution or surrogate neural network modelling, is still a complicated and time-consuming process. Here, we demonstrate an efficient and accurate homodyne gradient extraction method for performing gradient descent on the loss function directly in the material system. We demonstrate the method in our recently developed dopant network processing units, where we readily realize all Boolean gates. This shows that gradient descent can in principle be fully implemented in materio using simple electronics, opening up the way to autonomously learning material systems