Solving linear systems of equations plays a fundamental role in numerous computational problems from different fields of science. The widespread use of numerical methods to solve these systems motivates investigating ...
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作者：
Bin ZhangXin GaoComputer Science Program
Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia. KAUST Computational Bioscience Research Center
King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program
Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia. xin.gao@kaust.edu.sa. KAUST Computational Bioscience Research Center
King Abdullah University of Science and Technology Thuwal Saudi Arabia. xin.gao@kaust.edu.sa.
Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave ...
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Networking superconducting quantum computers is a longstanding challenge in quantum science. The typical approach has been to cascade transducers: converting to optical frequencies at the transmitter and to microwave frequencies at the receiver. However, the small microwave-optical coupling and added noise have proven formidable obstacles. Instead, we propose optical networking via heralding end-to-end entanglement with one detected photon and teleportation. This new protocol can be implemented on standard transduction hardware while providing significant performance improvements over transduction. In contrast to cascaded direct transduction, our scheme absorbs the low optical-microwave coupling efficiency into the heralding step, thus breaking the rate-fidelity trade-off. Moreover, this technique unifies and simplifies entanglement generation between superconducting devices and other physical modalities in quantum networks.
Synthetic data is becoming the way forward to manage legal and regulatory aspects of biomedical research involving personal and clinical data. As no matches are expected between artificial instances and real samples a...
Synthetic data is becoming the way forward to manage legal and regulatory aspects of biomedical research involving personal and clinical data. As no matches are expected between artificial instances and real samples and/or subjects, external researchers performing secondary analyses could benefit significantly by having unlimited access to uncompromised information. In this context, one of the main objectives of the H2020 VITALISE project is to develop a platform for providing synthetic data generated from real data collected in Living Labs to those external researchers. In addition, while some time series specific synthetic data generation models exist, only a few of them consider metadata (e.g., patient demographics) as part of the time series generation process itself. Therefore, the objective of this research is to perform a comparative assessment of two synthetic data generation models that use and process the metadata of subjects differently: The Wasserstein GAN with Gradient Penalty (WGAN-GP) and the DöppelGANger (DGAN). To achieve this goal making sure the analyses were data-independent, we selected two healthcare-related longitudinal datasets: (1) Treadmill Maximal Effort Test (TMET) measurements from the University of Málaga; and (2) a hypotension subset derived from the MIMIC-III v1.4 database. After synthetic data was generated, we assessed three pivotal aspects: resemblance to the original data, utility, and level of privacy. As a main conclusion, the importance of using metadata as context variables and the methodology to take them into account was proved to be significant and valuable, the DGAN model offering better results overall. A more extensive time series specific evaluation is left as the main avenue for future research.
Continuous-depth neural networks, such as the Neural Ordinary Differential Equations (ODEs), have aroused a great deal of interest from the communities of machine learning and data science in recent years, which bridg...
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The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and...
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The Joint Automated Repository for Various Integrated Simulations(JARVIS)is an integrated infrastructure to accelerate materials discovery and design using density functional theory(DFT),classical force-fields(FF),and machine learning(ML)*** is motivated by the Materials Genome Initiative(MGI)principles of developing open-access databases and tools to reduce the cost and development time of materials discovery,optimization,and deployment.
Understanding how interacting particles approach thermal equilibrium is a major challenge of quantum simulators1,2. Unlocking the full potential of such systems toward this goal requires flexible initial state prepara...
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In a nonlocal game, two noncommunicating players cooperate to convince a referee that they possess a strategy that does not violate the rules of the game. Quantum strategies allow players to optimally win some games b...
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In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations an...
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