670 research outputs found
Stochastic Collapsed Variational Inference for Sequential Data
Stochastic variational inference for collapsed models has recently been
successfully applied to large scale topic modelling. In this paper, we propose
a stochastic collapsed variational inference algorithm in the sequential data
setting. Our algorithm is applicable to both finite hidden Markov models and
hierarchical Dirichlet process hidden Markov models, and to any datasets
generated by emission distributions in the exponential family. Our experiment
results on two discrete datasets show that our inference is both more efficient
and more accurate than its uncollapsed version, stochastic variational
inference.Comment: NIPS Workshop on Advances in Approximate Bayesian Inference, 201
Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment
Understanding mobile traffic patterns of large scale cellular towers in urban
environment is extremely valuable for Internet service providers, mobile users,
and government managers of modern metropolis. This paper aims at extracting and
modeling the traffic patterns of large scale towers deployed in a metropolitan
city. To achieve this goal, we need to address several challenges, including
lack of appropriate tools for processing large scale traffic measurement data,
unknown traffic patterns, as well as handling complicated factors of urban
ecology and human behaviors that affect traffic patterns. Our core contribution
is a powerful model which combines three dimensional information (time,
locations of towers, and traffic frequency spectrum) to extract and model the
traffic patterns of thousands of cellular towers. Our empirical analysis
reveals the following important observations. First, only five basic
time-domain traffic patterns exist among the 9,600 cellular towers. Second,
each of the extracted traffic pattern maps to one type of geographical
locations related to urban ecology, including residential area, business
district, transport, entertainment, and comprehensive area. Third, our
frequency-domain traffic spectrum analysis suggests that the traffic of any
tower among the 9,600 can be constructed using a linear combination of four
primary components corresponding to human activity behaviors. We believe that
the proposed traffic patterns extraction and modeling methodology, combined
with the empirical analysis on the mobile traffic, pave the way toward a deep
understanding of the traffic patterns of large scale cellular towers in modern
metropolis.Comment: To appear at IMC 201
Learning For Predictive Control:A Dual Gaussian Process Approach
An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations
Learning For Predictive Control: A Dual Gaussian Process Approach
An important issue in model-based control design is that an accurate dynamic
model of the system is generally nonlinear, complex, and costly to obtain. This
limits achievable control performance in practice. Gaussian process (GP) based
estimation of system models is an effective tool to learn unknown dynamics
directly from input/output data. However, conventional GP-based control methods
often ignore the computational cost associated with accumulating data during
the operation of the system and how to handle forgetting in continuous
adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based
model predictive control (MPC) strategy that enables efficient use of online
learning based predictive control without the danger of catastrophic
forgetting. The bio-inspired DGP structure is a combination of a long-term GP
and a short-term GP, where the long-term GP is used to keep the learned
knowledge in memory and the short-term GP is employed to rapidly compensate
unknown dynamics during online operation. Furthermore, a novel recursive online
update strategy for the short-term GP is proposed to successively improve the
learnt model during online operation. Effectiveness of the proposed strategy is
demonstrated via numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2112.1166
Penalty-based Methods for Simple Bilevel Optimization under H\"{o}lderian Error Bounds
This paper investigates simple bilevel optimization problems where the
upper-level objective minimizes a composite convex function over the optimal
solutions of a composite convex lower-level problem. Existing methods for such
problems either only guarantee asymptotic convergence, have slow sublinear
rates, or require strong assumptions. To address these challenges, we develop a
novel penalty-based approach that employs the accelerated proximal gradient
(APG) method. Under an -H\"{o}lderian error bound condition on the
lower-level objective, our algorithm attains an
-optimal solution for any
within
iterations, where , and denote the Lipschitz constants
of the upper-level objective, the gradients of the smooth parts of the upper-
and lower-level objectives, respectively. If the smooth part of the upper-level
objective is strongly convex, the result improves further. We also establish
the complexity results when both upper- and lower-level objectives are general
convex nonsmooth functions. Numerical experiments demonstrate the effectiveness
of our algorithms
Near-infrared quantum cutting in Ho3+, Yb3+-codoped BaGdF5 nanoparticles via first- and second-order energy transfers
Infrared quantum cutting involving Yb(3+) 950–1,000 nm ((2) F(5/2) → (2) F(7/2)) and Ho(3+) 1,007 nm ((5)S(2),(5)F(4) → (5)I(6)) as well as 1,180 nm ((5)I(6) → (5)I(8)) emissions is achieved in BaGdF(5): Ho(3+), Yb(3+) nanoparticles which are synthesized by a facile hydrothermal route. The mechanisms through first- and second-order energy transfers were analyzed by the dependence of Yb(3+) doping concentration on the visible and infrared emissions, decay lifetime curves of the (5) F(5) → (5)I(8), (5)S(2)/(5)F(4) → (5)I(8), and (5) F(3) → (5)I(8) of Ho(3+), in which a back energy transfer from Yb(3+) to Ho(3+) is first proposed to interpret the spectral characteristics. A modified calculation equation for quantum efficiency of Yb(3+)-Ho(3+) couple by exciting at 450 nm was presented according to the quantum cutting mechanism. Overall, the excellent luminescence properties of BaGdF(5): Ho(3+), Yb(3+) near-infrared quantum cutting nanoparticles could explore an interesting approach to maximize the performance of solar cells
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