30,073 research outputs found
Maximizing the Probability of Arriving on Time: A Practical Q-Learning Method
The stochastic shortest path problem is of crucial importance for the development of sustainable transportation systems. Existing methods based on the probability tail model seek for the path that maximizes the probability of arriving at the destination before a deadline. However, they suffer from low accuracy and/or high computational cost. We design a novel Q-learning method where the converged Q-values have the practical meaning as the actual probabilities of arriving on time so as to improve accuracy. By further adopting dynamic neural networks to learn the value function, our method can scale well to large road networks with arbitrary deadlines. Experimental results on real road networks demonstrate the significant advantages of our method over other counterparts
Information Rates of ASK-Based Molecular Communication in Fluid Media
This paper studies the capacity of molecular communications in fluid media,
where the information is encoded in the number of transmitted molecules in a
time-slot (amplitude shift keying). The propagation of molecules is governed by
random Brownian motion and the communication is in general subject to
inter-symbol interference (ISI). We first consider the case where ISI is
negligible and analyze the capacity and the capacity per unit cost of the
resulting discrete memoryless molecular channel and the effect of possible
practical constraints, such as limitations on peak and/or average number of
transmitted molecules per transmission. In the case with a constrained peak
molecular emission, we show that as the time-slot duration increases, the input
distribution achieving the capacity per channel use transitions from binary
inputs to a discrete uniform distribution. In this paper, we also analyze the
impact of ISI. Crucially, we account for the correlation that ISI induces
between channel output symbols. We derive an upper bound and two lower bounds
on the capacity in this setting. Using the input distribution obtained by an
extended Blahut-Arimoto algorithm, we maximize the lower bounds. Our results
show that, over a wide range of parameter values, the bounds are close.Comment: 31 pages, 8 figures, Accepted for publication on IEEE Transactions on
Molecular, Biological, and Multi-Scale Communication
Adaptive and Resilient Revenue Maximizing Dynamic Resource Allocation and Pricing for Cloud-Enabled IoT Systems
Cloud computing is becoming an essential component of modern computer and
communication systems. The available resources at the cloud such as computing
nodes, storage, databases, etc. are often packaged in the form of virtual
machines (VMs) to be used by remotely located client applications for
computational tasks. However, the cloud has a limited number of VMs available,
which have to be efficiently utilized to generate higher productivity and
subsequently generate maximum revenue. Client applications generate requests
with computational tasks at random times with random complexity to be processed
by the cloud. The cloud service provider (CSP) has to decide whether to
allocate a VM to a task at hand or to wait for a higher complexity task in the
future. We propose a threshold-based mechanism to optimally decide the
allocation and pricing of VMs to sequentially arriving requests in order to
maximize the revenue of the CSP over a finite time horizon. Moreover, we
develop an adaptive and resilient framework based that can counter the effect
of realtime changes in the number of available VMs at the cloud server, the
frequency and nature of arriving tasks on the revenue of the CSP.Comment: American Control Conference (ACC 2018
The Submodular Secretary Problem Goes Linear
During the last decade, the matroid secretary problem (MSP) became one of the
most prominent classes of online selection problems. Partially linked to its
numerous applications in mechanism design, substantial interest arose also in
the study of nonlinear versions of MSP, with a focus on the submodular matroid
secretary problem (SMSP). So far, O(1)-competitive algorithms have been
obtained for SMSP over some basic matroid classes. This created some hope that,
analogously to the matroid secretary conjecture, one may even obtain
O(1)-competitive algorithms for SMSP over any matroid. However, up to now, most
questions related to SMSP remained open, including whether SMSP may be
substantially more difficult than MSP; and more generally, to what extend MSP
and SMSP are related.
Our goal is to address these points by presenting general black-box
reductions from SMSP to MSP. In particular, we show that any O(1)-competitive
algorithm for MSP, even restricted to a particular matroid class, can be
transformed in a black-box way to an O(1)-competitive algorithm for SMSP over
the same matroid class. This implies that the matroid secretary conjecture is
equivalent to the same conjecture for SMSP. Hence, in this sense SMSP is not
harder than MSP. Also, to find O(1)-competitive algorithms for SMSP over a
particular matroid class, it suffices to consider MSP over the same matroid
class. Using our reductions we obtain many first and improved O(1)-competitive
algorithms for SMSP over various matroid classes by leveraging known algorithms
for MSP. Moreover, our reductions imply an O(loglog(rank))-competitive
algorithm for SMSP, thus, matching the currently best asymptotic algorithm for
MSP, and substantially improving on the previously best
O(log(rank))-competitive algorithm for SMSP
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