43 research outputs found
Long-time nonlinear dynamical evolution for P-band ultracold atoms in an optical lattice
We report the long-time nonlinear dynamical evolution of ultracold atomic
gases in the P-band of an optical lattice. A Bose-Einstein condensate (BEC) is
fast and efficiently loaded into the Pband at zero quasi-momentum with a
non-adiabatic shortcut method. For the first one and half milliseconds, these
momentum states undergo oscillations due to coherent superposition of different
bands, which are followed by oscillations up to 60ms of a much longer period.
Our analysis shows the dephasing from the nonlinear interaction is very
conducive to the long-period oscillations induced by the variable force due to
the harmonic confinement.Comment: 8 pages, 7 figure
GCN-RL Circuit Designer: Transferable Transistor Sizing with Graph Neural Networks and Reinforcement Learning
Automatic transistor sizing is a challenging problem in circuit design due to
the large design space, complex performance trade-offs, and fast technological
advancements. Although there has been plenty of work on transistor sizing
targeting on one circuit, limited research has been done on transferring the
knowledge from one circuit to another to reduce the re-design overhead. In this
paper, we present GCN-RL Circuit Designer, leveraging reinforcement learning
(RL) to transfer the knowledge between different technology nodes and
topologies. Moreover, inspired by the simple fact that circuit is a graph, we
learn on the circuit topology representation with graph convolutional neural
networks (GCN). The GCN-RL agent extracts features of the topology graph whose
vertices are transistors, edges are wires. Our learning-based optimization
consistently achieves the highest Figures of Merit (FoM) on four different
circuits compared with conventional black-box optimization methods (Bayesian
Optimization, Evolutionary Algorithms), random search, and human expert
designs. Experiments on transfer learning between five technology nodes and two
circuit topologies demonstrate that RL with transfer learning can achieve much
higher FoMs than methods without knowledge transfer. Our transferable
optimization method makes transistor sizing and design porting more effective
and efficient.Comment: Accepted to the 57th Design Automation Conference (DAC 2020); 6
pages, 8 figure
Interactive Generalized Additive Model and Its Applications in Electric Load Forecasting
Electric load forecasting is an indispensable component of electric power
system planning and management. Inaccurate load forecasting may lead to the
threat of outages or a waste of energy. Accurate electric load forecasting is
challenging when there is limited data or even no data, such as load
forecasting in holiday, or under extreme weather conditions. As high-stakes
decision-making usually follows after load forecasting, model interpretability
is crucial for the adoption of forecasting models. In this paper, we propose an
interactive GAM which is not only interpretable but also can incorporate
specific domain knowledge in electric power industry for improved performance.
This boosting-based GAM leverages piecewise linear functions and can be learned
through our efficient algorithm. In both public benchmark and electricity
datasets, our interactive GAM outperforms current state-of-the-art methods and
demonstrates good generalization ability in the cases of extreme weather
events. We launched a user-friendly web-based tool based on interactive GAM and
already incorporated it into our eForecaster product, a unified AI platform for
electricity forecasting