118 research outputs found
Online Dynamics Learning for Predictive Control with an Application to Aerial Robots
In this work, we consider the task of improving the accuracy of dynamic
models for model predictive control (MPC) in an online setting. Even though
prediction models can be learned and applied to model-based controllers, these
models are often learned offline. In this offline setting, training data is
first collected and a prediction model is learned through an elaborated
training procedure. After the model is trained to a desired accuracy, it is
then deployed in a model predictive controller. However, since the model is
learned offline, it does not adapt to disturbances or model errors observed
during deployment. To improve the adaptiveness of the model and the controller,
we propose an online dynamics learning framework that continually improves the
accuracy of the dynamic model during deployment. We adopt knowledge-based
neural ordinary differential equations (KNODE) as the dynamic models, and use
techniques inspired by transfer learning to continually improve the model
accuracy. We demonstrate the efficacy of our framework with a quadrotor robot,
and verify the framework in both simulations and physical experiments. Results
show that the proposed approach is able to account for disturbances that are
possibly time-varying, while maintaining good trajectory tracking performance.Comment: 8 pages, 4 figure
Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks
Variational quantum eigensolvers have recently received increased attention,
as they enable the use of quantum computing devices to find solutions to
complex problems, such as the ground energy and ground state of
strongly-correlated quantum many-body systems. In many applications, it is the
optimization of both continuous and discrete parameters that poses a formidable
challenge. Using reinforcement learning (RL), we present a hybrid policy
gradient algorithm capable of simultaneously optimizing continuous and discrete
degrees of freedom in an uncertainty-resilient way. The hybrid policy is
modeled by a deep autoregressive neural network to capture causality. We employ
the algorithm to prepare the ground state of the nonintegrable quantum Ising
model in a unitary process, parametrized by a generalized quantum approximate
optimization ansatz: the RL agent solves the discrete combinatorial problem of
constructing the optimal sequences of unitaries out of a predefined set and, at
the same time, it optimizes the continuous durations for which these unitaries
are applied. We demonstrate the noise-robust features of the agent by
considering three sources of uncertainty: classical and quantum measurement
noise, and errors in the control unitary durations. Our work exhibits the
beneficial synergy between reinforcement learning and quantum control
Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuits
Variational quantum algorithms stand at the forefront of simulations on
near-term and future fault-tolerant quantum devices. While most variational
quantum algorithms involve only continuous optimization variables, the
representational power of the variational ansatz can sometimes be significantly
enhanced by adding certain discrete optimization variables, as is exemplified
by the generalized quantum approximate optimization algorithm (QAOA). However,
the hybrid discrete-continuous optimization problem in the generalized QAOA
poses a challenge to the optimization. We propose a new algorithm called
MCTS-QAOA, which combines a Monte Carlo tree search method with an improved
natural policy gradient solver to optimize the discrete and continuous
variables in the quantum circuit, respectively. We find that MCTS-QAOA has
excellent noise-resilience properties and outperforms prior algorithms in
challenging instances of the generalized QAOA
paper2repo: GitHub Repository Recommendation for Academic Papers
GitHub has become a popular social application platform, where a large number
of users post their open source projects. In particular, an increasing number
of researchers release repositories of source code related to their research
papers in order to attract more people to follow their work. Motivated by this
trend, we describe a novel item-item cross-platform recommender system,
, that recommends relevant repositories on GitHub that
match a given paper in an academic search system such as Microsoft Academic.
The key challenge is to identify the similarity between an input paper and its
related repositories across the two platforms, . Towards that end, paper2repo integrates text encoding and
constrained graph convolutional networks (GCN) to automatically learn and map
the embeddings of papers and repositories into the same space, where proximity
offers the basis for recommendation. To make our method more practical in real
life systems, labels used for model training are computed automatically from
features of user actions on GitHub. In machine learning, such automatic
labeling is often called {\em distant supervision\/}. To the authors'
knowledge, this is the first distant-supervised cross-platform (paper to
repository) matching system. We evaluate the performance of paper2repo on
real-world data sets collected from GitHub and Microsoft Academic. Results
demonstrate that it outperforms other state of the art recommendation methods
Super-resolution microscopy and its applications in neuroscience
Optical microscopy promises researchers to see most tiny substances directly. However, the resolution of conventional microscopy is restricted by the diffraction limit. This makes it a challenge to observe subcellular processes happened in nanoscale. The development of super-resolution microscopy provides a solution to this challenge. Here, we briefly review several commonly used super-resolution techniques, explicating their basic principles and applications in biological science, especially in neuroscience. In addition, characteristics and limitations of each technique are compared to provide a guidance for biologists to choose the most suitable tool
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