Deep reinforcement learning (DRL) has shown incredible performance in learning various tasks to the human level. However, unlike human perception, current DRL models connect the entire low-level sensory input to the s...
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The current rapid assessment of post-earthquake damaged houses has been done manually. The determination of the damage level is also very subjective. Moreover, the available time during a rapid assessment and limited ...
The current rapid assessment of post-earthquake damaged houses has been done manually. The determination of the damage level is also very subjective. Moreover, the available time during a rapid assessment and limited number of enumerators lead to ineffective decisions for responses and assistances. There are several application had appeared trying to answer the problems, but still they have many types of data that need to be input and even need a lot of time to process. The system design to be simple, more user-friendly and time efficient to be operated during the assessment. This study is aimed to develop a conceptual design of a system to collect data of houses damaged by earthquake using mobile application which propose to work both online and offline. The system will consists of five components: mobile device, user, data center (web server and database), internet, and data processing center (administrator and manager to verify and analyze the data). The application design to be integrated with Google Maps containing concise data entry to facilitate users (trained users and common users) and to shorten the time of data collection. Final collected data will be presented through the website and perform descriptive and spatial analysis.
The critical infrastructures of the nation such as the power grid and the communication network are highly interdependent. Also, it has been observed that there exists complex interdependent relationships between indi...
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The number of topological defects created in a system driven through a quantum phase transition exhibits a power-law scaling with the driving time. This universal scaling law is the key prediction of the Kibble-Zurek ...
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The number of topological defects created in a system driven through a quantum phase transition exhibits a power-law scaling with the driving time. This universal scaling law is the key prediction of the Kibble-Zurek mechanism (KZM), and testing it using a hardware-based quantum simulator is a coveted goal of quantum information science. Here we provide such a test using quantum annealing. Specifically, we report on extensive experimental tests of topological defect formation via the one-dimensional transverse-field Ising model on two different D-Wave quantum annealing devices. We find that the quantum simulator results can indeed be explained by the KZM for open-system quantum dynamics with phase-flip errors, with certain quantitative deviations from the theory likely caused by factors such as random control errors and transient effects. In addition, we probe physics beyond the KZM by identifying signatures of universality in the distribution and cumulants of the number of kinks and their decay, and again find agreement with the quantum simulator results. This implies that the theoretical predictions of the generalized KZM theory, which assumes isolation from the environment, applies beyond its original scope to an open system. We support this result by extensive numerical computations. To check whether an alternative, classical interpretation of these results is possible, we used the spin-vector Monte Carlo model, a candidate classical description of the D-Wave device. We find that the degree of agreement with the experimental data from the D-Wave annealing devices is better for the KZM, a quantum theory, than for the classical spin-vector Monte Carlo model, thus favoring a quantum description of the device. Our work provides an experimental test of quantum critical dynamics in an open quantum system, and paves the way to new directions in quantum simulation experiments.
Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems. Deep learning algorithms have shown groundb...
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Hopfions are three-dimensional (3D) topological textures characterized by the integer Hopf invariant QH. Here, we present the realization of a zero-field, stable hopfion spin texture in a magnetic system consisting of...
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In this paper we present the system called Bardic, which was developed over three years as the core technology in the Narrative for Sensemaking project, an effort to automatically generate narrative from low-level eve...
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The ability to generate high-order meshes that conform to the boundary of curved geometries is a hurdle in the adoption of high-order computational methods for the numerical solution of partial differential equations....
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The ability to generate high-order meshes that conform to the boundary of curved geometries is a hurdle in the adoption of high-order computational methods for the numerical solution of partial differential equations. In this paper, we propose a method for generating and warping second-order Lagrange triangular and tetrahedral meshes based on a log barrier method. In the case of generation, the approach consists of modifying an initial linear mesh by first, adding nodes at the midpoint of each edge; second, displacing the newly added boundary midpoints to the curved boundary, and third, solving for the final positions of the interior nodes based on the boundary deformation. By allowing all of the boundary nodes to move, the approach can also be used to warp second-order triangular and tetrahedral meshes. We present several numerical examples in both two and three dimensions which demonstrate the capabilities of our method in generating and warping second-order curvilinear meshes.
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