6,700 research outputs found
Turnpike Property and Convergence Rate for an Investment Model with General Utility Functions
In this paper we aim to address two questions faced by a long-term investor
with a power-type utility at high levels of wealth: one is whether the turnpike
property still holds for a general utility that is not necessarily
differentiable or strictly concave, the other is whether the error and the
convergence rate of the turnpike property can be estimated. We give positive
answers to both questions. To achieve these results, we first show that there
is a classical solution to the HJB equation and give a representation of the
solution in terms of the dual function of the solution to the dual HJB
equation. We demonstrate the usefulness of that representation with some
nontrivial examples that would be difficult to solve with the trial and error
method. We then combine the dual method and the partial differential equation
method to give a direct proof to the turnpike property and to estimate the
error and the convergence rate of the optimal policy when the utility function
is continuously differentiable and strictly concave. We finally relax the
conditions of the utility function and provide some sufficient conditions that
guarantee the turnpike property and the convergence rate in terms of both
primal and dual utility functions.Comment: 29 page
Smooth Value Functions for a Class of Nonsmooth Utility Maximization Problems
In this paper we prove that there exists a smooth classical solution to the
HJB equation for a large class of constrained problems with utility functions
that are not necessarily differentiable or strictly concave. The value function
is smooth if admissible controls satisfy an integrability condition or if it is
continuous on the closure of its domain. The key idea is to work on the dual
control problem and the dual HJB equation. We construct a smooth, strictly
convex solution to the dual HJB equation and show that its conjugate function
is a smooth, strictly concave solution to the primal HJB equation satisfying
the terminal and boundary conditions.Comment: 18 page
Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction
Most existing event extraction (EE) methods merely extract event arguments
within the sentence scope. However, such sentence-level EE methods struggle to
handle soaring amounts of documents from emerging applications, such as
finance, legislation, health, etc., where event arguments always scatter across
different sentences, and even multiple such event mentions frequently co-exist
in the same document. To address these challenges, we propose a novel
end-to-end model, Doc2EDAG, which can generate an entity-based directed acyclic
graph to fulfill the document-level EE (DEE) effectively. Moreover, we
reformalize a DEE task with the no-trigger-words design to ease the
document-level event labeling. To demonstrate the effectiveness of Doc2EDAG, we
build a large-scale real-world dataset consisting of Chinese financial
announcements with the challenges mentioned above. Extensive experiments with
comprehensive analyses illustrate the superiority of Doc2EDAG over
state-of-the-art methods. Data and codes can be found at
https://github.com/dolphin-zs/Doc2EDAG.Comment: Accepted by EMNLP 201
Realtime Profiling of Fine-Grained Air Quality Index Distribution using UAV Sensing
Given significant air pollution problems, air quality index (AQI) monitoring
has recently received increasing attention. In this paper, we design a mobile
AQI monitoring system boarded on unmanned-aerial-vehicles (UAVs), called ARMS,
to efficiently build fine-grained AQI maps in realtime. Specifically, we first
propose the Gaussian plume model on basis of the neural network (GPM-NN), to
physically characterize the particle dispersion in the air. Based on GPM-NN, we
propose a battery efficient and adaptive monitoring algorithm to monitor AQI at
the selected locations and construct an accurate AQI map with the sensed data.
The proposed adaptive monitoring algorithm is evaluated in two typical
scenarios, a two-dimensional open space like a roadside park, and a
three-dimensional space like a courtyard inside a building. Experimental
results demonstrate that our system can provide higher prediction accuracy of
AQI with GPM-NN than other existing models, while greatly reducing the power
consumption with the adaptive monitoring algorithm
Content adaptive sparse illumination for Fourier ptychography
Fourier Ptychography (FP) is a recently proposed technique for large field of
view and high resolution imaging. Specifically, FP captures a set of low
resolution images under angularly varying illuminations and stitches them
together in Fourier domain. One of FP's main disadvantages is its long
capturing process due to the requisite large number of incident illumination
angles. In this letter, utilizing the sparsity of natural images in Fourier
domain, we propose a highly efficient method termed as AFP, which applies
content adaptive sparse illumination for Fourier ptychography by capturing the
most informative parts of the scene's spatial spectrum. We validate the
effectiveness and efficiency of the reported framework with both simulations
and real experiments. Results show that the proposed AFP could shorten the
acquisition time of conventional FP by around 30%-60%
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