1,434 research outputs found
Optimal Investment with Stopping in Finite Horizon
In this paper, we investigate dynamic optimization problems featuring both
stochastic control and optimal stopping in a finite time horizon. The paper
aims to develop new methodologies, which are significantly different from those
of mixed dynamic optimal control and stopping problems in the existing
literature, to study a manager's decision. We formulate our model to a free
boundary problem of a fully nonlinear equation. Furthermore, by means of a dual
transformation for the above problem, we convert the above problem to a new
free boundary problem of a linear equation. Finally, we apply the theoretical
results to challenging, yet practically relevant and important, risk-sensitive
problems in wealth management to obtain the properties of the optimal strategy
and the right time to achieve a certain level over a finite time investment
horizon
On Efficiently Detecting Overlapping Communities over Distributed Dynamic Graphs
Modern networks are of huge sizes as well as high dynamics, which challenges
the efficiency of community detection algorithms. In this paper, we study the
problem of overlapping community detection on distributed and dynamic graphs.
Given a distributed, undirected and unweighted graph, the goal is to detect
overlapping communities incrementally as the graph is dynamically changing. We
propose an efficient algorithm, called \textit{randomized Speaker-Listener
Label Propagation Algorithm} (rSLPA), based on the \textit{Speaker-Listener
Label Propagation Algorithm} (SLPA) by relaxing the probability distribution of
label propagation. Besides detecting high-quality communities, rSLPA can
incrementally update the detected communities after a batch of edge insertion
and deletion operations. To the best of our knowledge, rSLPA is the first
algorithm that can incrementally capture the same communities as those obtained
by applying the detection algorithm from the scratch on the updated graph.
Extensive experiments are conducted on both synthetic and real-world datasets,
and the results show that our algorithm can achieve high accuracy and
efficiency at the same time.Comment: A short version of this paper will be published as ICDE'2018 poste
Constraining Astrophysical Neutrino Flavor Composition from Leptonic Unitarity
The recent IceCube observation of ultra-high-energy astrophysical neutrinos
has begun the era of neutrino astronomy. In this work, using the unitarity of
leptonic mixing matrix, we derive nontrivial unitarity constraints on the
flavor composition of astrophysical neutrinos detected by IceCube. Applying
leptonic unitarity triangles, we deduce these unitarity bounds from geometrical
conditions, such as triangular inequalities. These new bounds generally hold
for three flavor neutrinos, and are independent of any experimental input or
the pattern of leptonic mixing. We apply our unitarity bounds to derive general
constraints on the flavor compositions for three types of astrophysical
neutrino sources (and their general mixture), and compare them with the IceCube
measurements. Furthermore, we prove that for any sources without
neutrinos, a detected flux ratio will require the initial
flavor composition with more neutrinos than neutrinos.Comment: JCAP Final Version. 24pp. Only minor refinements, references adde
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