397 research outputs found
Machine Learning Techniques for Stellar Light Curve Classification
We apply machine learning techniques in an attempt to predict and classify
stellar properties from noisy and sparse time series data. We preprocessed over
94 GB of Kepler light curves from MAST to classify according to ten distinct
physical properties using both representation learning and feature engineering
approaches. Studies using machine learning in the field have been primarily
done on simulated data, making our study one of the first to use real light
curve data for machine learning approaches. We tuned our data using previous
work with simulated data as a template and achieved mixed results between the
two approaches. Representation learning using a Long Short-Term Memory (LSTM)
Recurrent Neural Network (RNN) produced no successful predictions, but our work
with feature engineering was successful for both classification and regression.
In particular, we were able to achieve values for stellar density, stellar
radius, and effective temperature with low error (~ 2 - 4%) and good accuracy
(~ 75%) for classifying the number of transits for a given star. The results
show promise for improvement for both approaches upon using larger datasets
with a larger minority class. This work has the potential to provide a
foundation for future tools and techniques to aid in the analysis of
astrophysical data.Comment: Accepted to The Astronomical Journa
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks
In this work, we consider the distributed optimization of non-smooth convex
functions using a network of computing units. We investigate this problem under
two regularity assumptions: (1) the Lipschitz continuity of the global
objective function, and (2) the Lipschitz continuity of local individual
functions. Under the local regularity assumption, we provide the first optimal
first-order decentralized algorithm called multi-step primal-dual (MSPD) and
its corresponding optimal convergence rate. A notable aspect of this result is
that, for non-smooth functions, while the dominant term of the error is in
, the structure of the communication network only impacts a
second-order term in , where is time. In other words, the error due
to limits in communication resources decreases at a fast rate even in the case
of non-strongly-convex objective functions. Under the global regularity
assumption, we provide a simple yet efficient algorithm called distributed
randomized smoothing (DRS) based on a local smoothing of the objective
function, and show that DRS is within a multiplicative factor of the
optimal convergence rate, where is the underlying dimension.Comment: 17 page
High Photovoltaic Quantum Efficiency in Ultrathin van der Waals Heterostructures
We report experimental measurements for ultrathin (< 15 nm) van der Waals
heterostructures exhibiting external quantum efficiencies exceeding 50%, and
show that these structures can achieve experimental absorbance > 90%. By
coupling electromagnetic simulations and experimental measurements, we show
that pn WSe2/MoS2 heterojunctions with vertical carrier collection can have
internal photocarrier collection efficiencies exceeding 70%.Comment: ACS Nano, 2017. Manuscript (25 pages, 7 figures) plus supporting
information (7 pages, 4 figures
ReSQueing Parallel and Private Stochastic Convex Optimization
We introduce a new tool for stochastic convex optimization (SCO): a
Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function
convolved with a (Gaussian) probability density. Combining ReSQue with recent
advances in ball oracle acceleration [CJJJLST20, ACJJS21], we develop
algorithms achieving state-of-the-art complexities for SCO in parallel and
private settings. For a SCO objective constrained to the unit ball in
, we obtain the following results (up to polylogarithmic
factors). We give a parallel algorithm obtaining optimization error
with gradient
oracle query depth and gradient queries in total, assuming access to a
bounded-variance stochastic gradient estimator. For , our algorithm matches the state-of-the-art oracle depth of
[BJLLS19] while maintaining the optimal total work of stochastic gradient
descent. Given samples of Lipschitz loss functions, prior works [BFTT19,
BFGT20, AFKT21, KLL21] established that if , -differential
privacy is attained at no asymptotic cost to the SCO utility. However, these
prior works all required a superlinear number of gradient queries. We close
this gap for sufficiently large , by
using ReSQue to design an algorithm with near-linear gradient query complexity
in this regime
Optimal algorithms for smooth and strongly convex distributed optimization in networks
In this paper, we determine the optimal convergence rates for strongly convex and smooth distributed optimization in two settings: centralized and decentralized communications over a network. For centralized (i.e. master/slave) algorithms, we show that distributing Nesterov's accelerated gradient descent is optimal and achieves a precision in time , where is the condition number of the (global) function to optimize, is the diameter of the network, and (resp. ) is the time needed to communicate values between two neighbors (resp. perform local computations). For decentralized algorithms based on gossip, we provide the first optimal algorithm, called the multi-step dual accelerated (MSDA) method, that achieves a precision in time , where is the condition number of the local functions and is the (normalized) eigengap of the gossip matrix used for communication between nodes. We then verify the efficiency of MSDA against state-of-the-art methods for two problems: least-squares regression and classification by logistic regression
- âŠ