A5057128860- Quantum Computing Algorithms and Architecture
- Meteorological Phenomena and Simulations
- Quantum Information and Cryptography
- Quantum-Dot Cellular Automata
- Machine Learning and Data Classification
- Quantum and electron transport phenomena
- Reading and Literacy Development
- Machine Learning in Materials Science
- Climate variability and models
- Fluid Dynamics and Turbulent Flows
- Advanced Neural Network Applications
- Model Reduction and Neural Networks
- Educational Methods and Media Use
- Parallel Computing and Optimization Techniques
- Literacy, Media, and Education
- Adversarial Robustness in Machine Learning
- Machine Learning in Bioinformatics
- Ferroelectric and Negative Capacitance Devices
- Computational Physics and Python Applications
- Tropical and Extratropical Cyclones Research
- Educational Practices and Challenges
- Graph theory and applications
- Cell Image Analysis Techniques
- Text Readability and Simplification
- Machine Learning and Algorithms
Google (United States)
2017-2024
University of Waterloo
2011-2012
The Ohio State University
2011
University of Minnesota
2011
Significance Accurate simulation of fluids is important for many science and engineering problems but very computationally demanding. In contrast, machine-learning models can approximate physics quickly at the cost accuracy. Here we show that using machine learning inside traditional fluid simulations improve both accuracy speed, even on examples different from training data. Our approach opens door to applying large-scale physical modeling tasks like airplane design climate prediction.
General circulation models (GCMs) are the foundation of weather and climate prediction
The last decade has witnessed substantial interest in protocols for transferring information on networks of quantum mechanical objects. A variety control methods and network topologies have been proposed, the basis that transfer with perfect fidelity --- i.e. deterministic without loss is impossible through unmodulated spin chains more than a few particles. Solving original problem formulated by Bose [Phys. Rev. Lett. 91, 207901 (2003)], we determine exact number qubits (with XY Hamiltonian)...
The explosion in workload complexity and the recent slow-down Moore's law scaling call for new approaches towards efficient computing. Researchers are now beginning to use advances machine learning software optimizations, augmenting or replacing traditional heuristics data structures. However, space of computer hardware architecture is only lightly explored. In this paper, we demonstrate potential deep address von Neumann bottleneck memory performance. We focus on critical problem access...
General circulation models (GCMs) are the foundation of weather and climate prediction. GCMs physics-based simulators which combine a numerical solver for large-scale dynamics with tuned representations small-scale processes such as cloud formation. Recently, machine learning (ML) trained on reanalysis data achieved comparable or better skill than deterministic forecasting. However, these have not demonstrated improved ensemble forecasts, shown sufficient stability long-term simulations....
Abstract The ionosphere is a layer of weakly ionized plasma bathed in Earth’s geomagnetic field extending about 50–1,500 kilometres above Earth 1 . ionospheric total electron content varies response to space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting one the largest sources error for position, navigation and timing services 2 Networks high-quality ground-based GNSS stations provide maps correct these errors, but large spatiotemporal gaps data...
Two vertices $a$ and $b$ in a graph $X$ are cospectral if the vertex-deleted subgraphs $X\setminus a$ b$ have same characteristic polynomial. In this paper we investigate strengthening of relation on vertices, that arises investigations continuous quantum walks. Suppose vectors $e_a$ for $V(X)$ standard basis $\mathbb{R}^{V(X)}$. We say strongly if, each eigenspace $U$ $A(X)$, orthogonal projections $e_b$ either equal or differ only sign. develop basic theory concept provide constructions...
We present a framework for specifying, training, evaluating, and deploying machine learning models. Our focus is on simplifying cutting edge practitioners in order to bring such technologies into production. Recognizing the fast evolution of field deep learning, we make no attempt capture design space all possible model architectures domain- specific language (DSL) or similar configuration language. allow users write code define their models, but provide abstractions that guide develop- ers...
In this paper, we define k-equivalence, a relation on graphs that relies their associated cellular algebras. We show k-Boson quantum walk cannot distinguish pairs of are k- equivalent. The existence k-equivalent has been shown by Ponomarenko et al. [2, 6]. This gives negative answer to question posed Gamble [7].
In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not good tools for understanding how these models arrive their predictions. This has hindered adoption in fields such as the life medical sciences, where researchers require that base decisions on underlying biological phenomena rather than peculiarities of dataset. We propose a set methods critiquing deep learning demonstrate protein family classification, task...
In many application domains, neural networks are highly accurate and have been deployed at large scale. However, users often do not good tools for understanding how these models arrive their predictions. This has hindered adoption in fields such as the life medical sciences, where researchers require that base decisions on underlying biological phenomena rather than peculiarities of dataset introduced. response, we propose a set methods critiquing deep learning demonstrate protein family...