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Large-Scale Parallelization of Human Migration Simulation
No full textForced displacement of people worldwide, for example, due to violent conflicts, is common in the modern world, and today more than 82 million people are forcibly displaced. This puts the problem of migration at the forefront of the most important problems of humanity. The Flee simulation code is an agent-based modeling tool that can forecast population displacements in civil war settings, but performing accurate simulations requires nonnegligible computational capacity. In this article, we present our approach to Flee parallelization for fast execution on multicore platforms, as well as discuss the computational complexity of the algorithm and its implementation. We benchmark parallelized code using supercomputers equipped with AMD EPYC Rome 7742 and Intel Xeon Platinum 8268 processors and investigate its performance across a range of alternative rule sets, different refinements in the spatial representation, and various numbers of agents representing displaced persons. We find that Flee scales excellently to up to 8192 cores for large cases, although very detailed location graphs can impose a large initialization time overhead
Panning for gold, but finding helium: Discovery of the ultra-stripped supernova SN 2019wxt from gravitational-wave follow-up observations
Get PDFWe present the results from multi-wavelength observations of a transient discovered during an intensive follow-up campaign of S191213g, a gravitational wave (GW) event reported by the LIGO-Virgo Collaboration as a possible binary neutron star merger in a low latency search. This search yielded SN 2019wxt, a young transient in a galaxy whose sky position (in the 80% GW contour) and distance (∼150 Mpc) were plausibly compatible with the localisation uncertainty of the GW event. Initially, the transienta\u27s tightly constrained age, its relatively faint peak magnitude (Mi ∼ -16.7 mag), and the r-band decline rate of ∼1 mag per 5 days appeared suggestive of a compact binary merger. However, SN 2019wxt spectroscopically resembled a type Ib supernova, and analysis of the optical-near-infrared evolution rapidly led to the conclusion that while it could not be associated with S191213g, it nevertheless represented an extreme outcome of stellar evolution. By modelling the light curve, we estimated an ejecta mass of only ∼0.1 M\ub7, with 56Ni comprising ∼20% of this. We were broadly able to reproduce its spectral evolution with a composition dominated by helium and oxygen, with trace amounts of calcium. We considered various progenitor channels that could give rise to the observed properties of SN 2019wxt and concluded that an ultra-stripped origin in a binary system is the most likely explanation. Disentangling genuine electromagnetic counterparts to GW events from transients such as SN 2019wxt soon after discovery is challenging: in a bid to characterise this level of contamination, we estimated the rate of events with a volumetric rate density comparable to that of SN 2019wxt and found that around one such event per week can occur within the typical GW localisation area of O4 alerts out to a luminosity distance of 500 Mpc, beyond which it would become fainter than the typical depth of current electromagnetic follow-up campaigns
Permanent magnet motor with field weakening arrangement
No full textThe invention relates to a permanent magnet motor, comprising: a stator configured for generating a time-varying magnetic field; a rotor, arranged inside of the stator, comprising at least one permanent magnet providing a constant magnetic field; the permanent magnet being made of a flexible material; the stator being configured for rotating the rotor by taking along the constant magnetic field of the rotor with the time-varying magnetic field of the stator; the rotor comprising a field weakening mechanical arrangement configured for weakening a flux of the permanent magnet motor; the field weakening mechanical arrangement supporting the permanent magnet and comprising a recess configured for allowing the permanent magnet to be deformed by a centrifugal force provided by the rotation of the rotor
AI-based quantification of whole-body tumour burden on somatostatin receptor PET/CT
Get PDFBackground: Segmenting the whole-body somatostatin receptor-expressing tumour volume (SRETVwb) on positron emission tomography/computed tomography (PET/CT) images is highly time-consuming but has shown value as an independent prognostic factor for survival. An automatic method to measure SRETVwb could improve disease status assessment and provide a tool for prognostication. This study aimed to develop an artificial intelligence (AI)-based method to detect and quantify SRETVwb and total lesion somatostatin receptor expression (TLSREwb) from [68Ga]Ga-DOTA-TOC/TATE PET/CT images. Methods: A UNet3D convolutional neural network (CNN) was used to train an AI model with [68Ga]Ga-DOTA-TOC/TATE PET/CT images, where all tumours were manually segmented with a semi-automatic method. The training set consisted of 148 patients, of which 108 had PET-positive tumours. The test group consisted of 30 patients, of which 25 had PET-positive tumours. Two physicians segmented tumours in the test group for comparison with the AI model. Results: There were good correlations between the segmented SRETVwb and TLSREwb by the AI model and the physicians, with Spearman rank correlation coefficients of r = 0.78 and r = 0.73, respectively, for SRETVwb and r = 0.83 and r = 0.81, respectively, for TLSREwb. The sensitivity on a lesion detection level was 80% and 79%, and the positive predictive value was 83% and 84% when comparing the AI model with the two physicians. Conclusion: It was possible to develop an AI model to segment SRETVwb and TLSREwb with high performance. A fully automated method makes quantification of tumour burden achievable and has the potential to be more widely used when assessing PET/CT images
Low-Noise Phase-Sensitive Optical Parametric Amplifier with Local Pump Generation using Digital Frequency and Phase Control
No full textWe demonstrate a novel, lossless approach, eliminating the need to copropagate pumps in phase-sensitive parametric amplifier-based links, by control loops creating a locked pump within the amplifier. Gain, noise and BER measurements validate the performance
Substituting Animals with Biohybrid Robots: Speculative Interactions with Animal-Robot Hybrids
Get PDFWhat if animals were substituted with biohybrid robots? The replacement of pets with bioinspired robots has long existed within technological imaginaries and HRI research. Addressing developments of bioengineering and biohybrid robots, we depart from such replacement to study futures inhabited by animal-robot hybrids. In this paper, we introduce a speculative concept of assembling and eating biohybrid robots. With this provocation as a starting point, we intend to initiate cross-disciplinary and cross-cultural discussions around human-food interaction practices and related topics
Deep neural networks for electromagnetics applied to optimization and inverse problems
Get PDFThis thesis explores the possibilities to complement full-wave electromagnetic solvers with fully-connected neural networks. We emphasize problems that must be solved a very large number of times in a limited parameter domain.We present and evaluate the normalization method ForwardNorm. ForwardNorm normalizes the outputs of the hidden layers of a neural network and enables training of very deep fully-connected neural networks. To minimize the number of samples needed train the very deep neural networks, we formulate a loss function that includes the misfit in (i) the output of the neural network and (ii) the derivatives of the output of the neural network with respect to its inputs. For certain combinations of input and output, we use continuum sensitivity analysis to compute these derivatives at a low computation cost. We also develop an auto-calibration method that simultaneously determines (i) a set of unknown amplification factors and (ii) the mean permittivity of an unknown medium under test. The method assumes that we have access to a set of measurements that are made a-priori for the purpose of characterization. The method is intended for on-line applications.We test the methods on four different test-problems. For the first three test-problems, we consider a type of microwave measurement-device intended for an inhomogeneous dielectric medium transported through a metal pipe. In the first test-problem, we train a neural network to determine the point-wise mean and variance of the permittivity of the inhomogeneous dielectric. The trained neural network is very computationally cheap to evaluate, which makes the method appealing for real-time applications. For the second test-problem, we apply the auto-calibration method to simultaneously determine (i) the mean permittivity in the pipe and (ii) a set of unknown amplification factors. For the third test-problem, we use a deep neural network to model the microwave measurement-device with a stochastic dielectric medium and estimate high-dimensional histograms. For the fourth test-problem, we train a deep neural network to model the frequency response of an H-plane waveguide filter as a function of its geometrical parameters. We then use the neural network to optimize the geometry of the filter to achieve pass-band characteristics under geometrical uncertainty
A Service-Aware Autoscaling Strategy for Container Orchestration Platforms with Soft Resource Isolation
No full textContainer orchestration platforms like Kubernetes (K8s) allow easy deployment and management of cloud native services. When deploying their services, service providers need to specify a proper amount of resources to K8s, so that the desired Quality of Service (QoS) to their users can be maintained. To cope with the varying traffic demand coming from users, they can rely on the K8s Horizontal Pod Autoscaling (HPA) mechanism. To ensure that enough resources are available when needed, the standard HPA mechanism relies on resource overprovisioning. In this way, the required QoS is achieved most of (or all) the time but at the expense of additional resources that are allocated (and charged for), while they may stay idle for significant periods of time. A way to reduce overprovisioning is provided by the soft resource isolation of K8s, which allows services to compensate for a temporary lack of resources with shared resources, i.e., idle resources of the machines where services are running. However, during traffic spikes, these idle resources may not be enough to serve the whole demand, degrading the QoS. The HPA, which is not aware of how much demand could not be served, is not always able to correctly estimate the required additional resources, further degrading the QoS. To overcome this, service providers need to leverage overprovisioning, limiting the use of shared resources. In this paper, we propose a novel mechanism for autoscaling resources in K8s that relies on service-related data to avoid the additional degradation introduced by the HPA. The proposed strategy also offers a way to tune overprovisioning and shared resources. Simulation results show that our approach can reduce idle resources by up to 60% compared with the HPA mechanism