Development and Application of a Statistically-Based Quality Control for Crowdsourced Air Temperature Data

In urban areas, dense atmospheric observational networks with high-quality data are still a challenge due to high costs for installation and maintenance over time. Citizen weather stations (CWS) could be one answer to that issue. Since more and more owners of CWS share their measurement data publicly, crowdsourcing, i.e., the automated collection of large amounts of data from an undefined crowd of citizens, opens new pathways for atmospheric research. However, the most critical issue is found to be the quality of data from such networks. In this study, a statistically-based quality control (QC) is developed to identify suspicious air temperature (T) measurements from crowdsourced data sets. The newly developed QC exploits the combined knowledge of the dense network of CWS to statistically identify implausible measurements, independent of external reference data. The evaluation of the QC is performed using data from Netatmo CWS in Toulouse, France, and Berlin, Germany, over a 1-year period (July 2016 to June 2017), comparing the quality-controlled data with data from two networks of reference stations. The new QC efficiently identifies erroneous data due to solar exposition and siting issues, which are common error sources of CWS. Estimation of T is improved when averaging data from a group of stations within a restricted area rather than relying on data of individual CWS. However, a positive deviation in CWS data compared to reference data is identified, particularly for daily minimum T. To illustrate the transferability of the newly developed QC and the applicability of CWS data, a mapping of T is performed over the city of Paris, France, where spatial density of CWS is especially high.DFG, 322579844, Hitzewellen in Berlin, Deutschland - StadtklimamodifkationenBMBF, 01LP1602A, Verbundprojekt Stadtklima: Evaluierung von Stadtklimamodellen (Modul B), 3DO Teilprojekt 1: Dreidimensionales Monitoring atmosphärischer Prozesse in Berli

The Power Control Rack: A Modular Solution for Building Power Systems

LDRD proposal presentation to ETA ALD at LBNL. Proposing use of SST to create a modular efficient single point of common coupling for buildings

Managing Wolf Depredation in the United States: Past, Present, and Future

With the successful recolonization and reintroduction of wolves (Canis lupus) in parts of the western United States (Bangs and Fritts, 1996; Bangs et al., 1998) and the natural expansion of wolves in the upper Midwest (Fuller et al., 1992; Thiel, 2001), managing conflicts between wolves and livestock is a growing issue for livestock producers, resource professionals, and the general public (Mech, 1996). Unlike the coyote, (Canis latrans) where a great deal is known regarding the biology and ecology of depredation and methods for managing it (Knowlton et al., 1999), very little is known regarding patterns and processes of wolves preying on livestock and effective ways to mitigate this conflict. Understanding the ramifications of growing wolf populations for livestock production and successfully managing these problems will require knowledge of depredation patterns, wolf ecology, livestock husbandry, and the effectiveness of different tools and techniques to manage wolves. As wolf populations expand into more agricultural areas (Mech et al., 2000) such knowledge will become increasingly important. Here historic records were compared to current data on wolf depredation rates and wolf management techniques relative to the wolf’s status on the endangered species list. The objectives were to synthesize the history of wolf depredation and management, present current data of wolf impacts on livestock, and speculate on the future management of wolves so that producers can consider the ramifications of a growing wolf population and possible mechanisms for decreasing the threat

Managing Wolf Depredation in the United States: Past, Present, and Future

With the successful recolonization and reintroduction of wolves (Canis lupus) in parts of the western United States (Bangs and Fritts, 1996; Bangs et al., 1998) and the natural expansion of wolves in the upper Midwest (Fuller et al., 1992; Thiel, 2001), managing conflicts between wolves and livestock is a growing issue for livestock producers, resource professionals, and the general public (Mech, 1996). Unlike the coyote, (Canis latrans) where a great deal is known regarding the biology and ecology of depredation and methods for managing it (Knowlton et al., 1999), very little is known regarding patterns and processes of wolves preying on livestock and effective ways to mitigate this conflict. Understanding the ramifications of growing wolf populations for livestock production and successfully managing these problems will require knowledge of depredation patterns, wolf ecology, livestock husbandry, and the effectiveness of different tools and techniques to manage wolves. As wolf populations expand into more agricultural areas (Mech et al., 2000) such knowledge will become increasingly important. Here historic records were compared to current data on wolf depredation rates and wolf management techniques relative to the wolf’s status on the endangered species list. The objectives were to synthesize the history of wolf depredation and management, present current data of wolf impacts on livestock, and speculate on the future management of wolves so that producers can consider the ramifications of a growing wolf population and possible mechanisms for decreasing the threat

The effect of heterogeneity on decorrelation mechanisms in spiking neural networks: a neuromorphic-hardware study

High-level brain function such as memory, classification or reasoning can be realized by means of recurrent networks of simplified model neurons. Analog neuromorphic hardware constitutes a fast and energy efficient substrate for the implementation of such neural computing architectures in technical applications and neuroscientific research. The functional performance of neural networks is often critically dependent on the level of correlations in the neural activity. In finite networks, correlations are typically inevitable due to shared presynaptic input. Recent theoretical studies have shown that inhibitory feedback, abundant in biological neural networks, can actively suppress these shared-input correlations and thereby enable neurons to fire nearly independently. For networks of spiking neurons, the decorrelating effect of inhibitory feedback has so far been explicitly demonstrated only for homogeneous networks of neurons with linear sub-threshold dynamics. Theory, however, suggests that the effect is a general phenomenon, present in any system with sufficient inhibitory feedback, irrespective of the details of the network structure or the neuronal and synaptic properties. Here, we investigate the effect of network heterogeneity on correlations in sparse, random networks of inhibitory neurons with non-linear, conductance-based synapses. Emulations of these networks on the analog neuromorphic hardware system Spikey allow us to test the efficiency of decorrelation by inhibitory feedback in the presence of hardware-specific heterogeneities. The configurability of the hardware substrate enables us to modulate the extent of heterogeneity in a systematic manner. We selectively study the effects of shared input and recurrent connections on correlations in membrane potentials and spike trains. Our results confirm ...Comment: 20 pages, 10 figures, supplement

Deterministic networks for probabilistic computing

Neural-network models of high-level brain functions such as memory recall and reasoning often rely on the presence of stochasticity. The majority of these models assumes that each neuron in the functional network is equipped with its own private source of randomness, often in the form of uncorrelated external noise. However, both in vivo and in silico, the number of noise sources is limited due to space and bandwidth constraints. Hence, neurons in large networks usually need to share noise sources. Here, we show that the resulting shared-noise correlations can significantly impair the performance of stochastic network models. We demonstrate that this problem can be overcome by using deterministic recurrent neural networks as sources of uncorrelated noise, exploiting the decorrelating effect of inhibitory feedback. Consequently, even a single recurrent network of a few hundred neurons can serve as a natural noise source for large ensembles of functional networks, each comprising thousands of units. We successfully apply the proposed framework to a diverse set of binary-unit networks with different dimensionalities and entropies, as well as to a network reproducing handwritten digits with distinct predefined frequencies. Finally, we show that the same design transfers to functional networks of spiking neurons.Comment: 22 pages, 11 figure

3D Neuro-electronic interface devices for neuromuscular control: Design studies and realisation steps

In order to design the shape and dimensions of new 3D multi-microelectrode information transducers properly, i. e. adapted to the scale of information delivery to and from peripheral nerve fibres, a number of studies were, and still are, being performed on modelling and simulation of electrical volume conduction inside and outside nerves, on animal experiments on stimulation and recording with single wires and linear arrays, and on new technologies for 3D micro-fabrication. This paper presents a selection of the results of these `Neurotechnology¿ studies at the University of Twente. The experimental and simulation results apply primarily to the peripheral motor nerves of the rat, but are also of interest for neural interfacing with myelinated nerves in man, as fascicles in man are about the same size as in the rat

Selectivity of recording of neural signals with micromachined intraneural micro electrodes

The number uf afferent fibers in a peripheral nerve fascicle and the electrical volume conduction in neuraI tissue determine the interelectrode spacing, needed for selective recording with mufticontact devices. These factors taking into account, and also assuming uniform distribution of fibers throughout the fascicle cross section, it is calculated that for selective sensing in peroneal nerve fascicles, the electrode spacing must be about 80 pm. This is for the nelve, embedded in its natural saline environment. The selectivity, thus reached, however reduces dmstically to zero when an insulating cuff is positioued around the nerve at the position of the intraneural multielectrode device

Benchmarking of Optimization Algorithms

In this paper, we present an empirical approach for objective and quantitative benchmarking of optimization algorithms with respect to characteristics induced by the forward calculation. Due to the professional background of the authors, this benchmarking strategy is illustrated on a selection of search methods in regard to expected characteristics of geotechnical parameter back calculation problems. Starting from brief introduction into the approach employed, a strategy for optimization algorithm benchmarking is introduced. The benchmarking utilizes statistical tests carried out on well-known test functions superposed with perturbations, both chosen to mimic objective function topologies found for geotechnical objective function topologies. Here, the moved axis parallel hyper-ellipsoid test function and the generalized Ackley test function in conjunction with an adjustable quantity of objective function topology roughness and fraction of failing forward calculations is analyzed. In total, results for 5 optimization algorithms are presented, compared and discussed