155,959 research outputs found
Detecting quantum speedup in closed and open systems
We construct a general measure for detecting the quantum speedup in both
closed and open systems. The speed measure is based on the changing rate of the
position of quantum states on a manifold with appropriate monotone Riemannian
metrics. Any increase in speed is a clear signature of dynamical speedup. To
clarify the mechanisms for quantum speedup, we first introduce the concept of
longitudinal and transverse types of speedup: the former stems from the time
evolution process itself with fixed initial conditions, while the latter is a
result of adjusting initial conditions. We then apply the proposed measure to
several typical closed and open quantum systems, illustrating that quantum
coherence (or entanglement) and the memory effect of the environment together
can become resources for longitudinally or transversely accelerating dynamical
evolution under specific conditions and assumptions.Comment: 7 pages, 4 figures; Accepted for publication in New Journal of
Physic
Distributionally Robust Counterpart in Markov Decision Processes
This paper studies Markov Decision Processes under parameter uncertainty. We
adapt the distributionally robust optimization framework, and assume that the
uncertain parameters are random variables following an unknown distribution,
and seeks the strategy which maximizes the expected performance under the most
adversarial distribution. In particular, we generalize previous study
\cite{xu2012distributionally} which concentrates on distribution sets with very
special structure to much more generic class of distribution sets, and show
that the optimal strategy can be obtained efficiently under mild technical
condition. This significantly extends the applicability of distributionally
robust MDP to incorporate probabilistic information of uncertainty in a more
flexible way.Comment: Added references. Corrected typos. Modified a mistake in Example 2
(Variance). Provided more details of the simulatio
The magnetospheric activity of bare strange quark stars
In Ruderman & Sutherland (RS75) model, the normal neutron stars as pulsars
bear a severe problem, namely the binding energy problem that both ions (e.g.,
Fe) and electrons on normal neutron star surface can be pulled
out freely by the unipolar generator induced electric field so that sparking on
polar cap can hardly occur. {\bf This problem could be solved within the
Partially Screened Gap (PSG) model in the regime of neutron stars}. However, in
this paper we extensively study this problem in a bare strange quark star (BSS)
model. We find that the huge potential barrier built by the electric field in
the vacuum gap above polar cap could usually prevent electrons from streaming
into the magnetosphere unless the electric potential of a pulsar is
sufficiently lower than that at infinite interstellar medium. Other processes,
such as the diffusion and thermionic emission of electrons have also been
included here. Our conclusions are as follows: both positive and negative
particles on a BSS's surface would be bound strongly enough to form a vacuum
gap above its polar cap as long as the BSS is not charged (or not highly
negative charged), and multi-accelerators could occur in a BSS's magnetosphere.
Our results would be helpful to distinguish normal neutron stars and bare quark
stars through pulsar's magnetospheric activities.Comment: 6 pages, 4 figures, 3 tables; accepted for publication in MNRA
Categorization Axioms for Clustering Results
Cluster analysis has attracted more and more attention in the field of
machine learning and data mining. Numerous clustering algorithms have been
proposed and are being developed due to diverse theories and various
requirements of emerging applications. Therefore, it is very worth establishing
an unified axiomatic framework for data clustering. In the literature, it is an
open problem and has been proved very challenging. In this paper, clustering
results are axiomatized by assuming that an proper clustering result should
satisfy categorization axioms. The proposed axioms not only introduce
classification of clustering results and inequalities of clustering results,
but also are consistent with prototype theory and exemplar theory of
categorization models in cognitive science. Moreover, the proposed axioms lead
to three principles of designing clustering algorithm and cluster validity
index, which follow many popular clustering algorithms and cluster validity
indices.Comment: 16 page
A Note on (3,1)-Choosable Toroidal Graphs
An -coloring is a mapping that assigns a color to each vertex such that at most neighbors of receive
colore . A graph is called -choosable, if admits an
-coloring for every list assignment with for all
. In this note, it is proved that every toroidal graph, which
contains no adjacent triangles and contains no 6-cycles and -cycles for some
, is -choosable.Comment: 7 page
Central-limit approach to risk-aware Markov decision processes
Whereas classical Markov decision processes maximize the expected reward, we
consider minimizing the risk. We propose to evaluate the risk associated to a
given policy over a long-enough time horizon with the help of a central limit
theorem. The proposed approach works whether the transition probabilities are
known or not. We also provide a gradient-based policy improvement algorithm
that converges to a local optimum of the risk objective.Comment: arXiv admin note: text overlap with arXiv:1403.6530 by other author
An adaptive gradient method for computing generalized tensor eigenpairs
High order tensor arises more and more often in signal processing,data
analysis, higher-order statistics, as well as imaging sciences. In this paper,
an adaptive gradient (AG) method is presented for generalized tensor
eigenpairs. Global convergence and linear convergence rate are established
under some suitable conditions. Numerical results are reported to illustrate
the efficiency of the proposed method. Comparing with the GEAP method, an
adaptive shifted power method proposed by Tamara G. Kolda and Jackson R. Mayo
[SIAM J. Matrix Anal. Appl., 35 (2014), pp. 1563-1581], the AG method is much
faster and could reach the largest eigenpair with a higher probability
Incentivizing Users of Data Centers Participate in The Demand Response Programs via Time-Varying Monetary Rewards
Demand response is widely employed by today's data centers to reduce energy
consumption in response to the increasing of electricity cost. To incentivize
users of data centers participate in the demand response programs, i.e.,
breaking the "split incentive" hurdle, some prior researches propose
market-based mechanisms such as dynamic pricing and static monetary rewards.
However, these mechanisms are either intrusive or unfair. In this paper, we use
time-varying rewards to incentivize users, who have flexible deadlines and are
willing to trading performance degradation for monetary rewards, grant
time-shifting of their requests. With a game-theoretic framework, we model the
game between a single data center and its users. Further, we extend our design
via integrating it with two other emerging practical demand response
strategies: server shutdown and local renewable energy generation. With
real-world data traces, we show that a DC with our design can effectively shed
its peak electricity load and overall electricity cost without reducing its
profit, when comparing it with the current practice where no incentive
mechanism is established
Spectral projected gradient methods for generalized tensor eigenvalue complementarity problem
This paper looks at the tensor eigenvalue complementarity problem (TEiCP)
which arises from the stability analysis of finite dimensional mechanical
systems and is closely related to the optimality conditions for polynomial
optimization. We investigate two monotone ascent spectral projected gradient
(SPG) methods for TEiCP. We also present a shifted scaling-and-projection
algorithm (SPA), which is a great improvement of the original SPA method
proposed by Ling, He and Qi [Comput. Optim. Appl., DOI
10.1007/s10589-015-9767-z]. Numerical comparisons with some existed gradient
methods in the literature are reported to illustrate the efficiency of the
proposed methods.Comment: arXiv admin note: text overlap with arXiv:1601.0139
Log-Harnack Inequality for Gruschin Type Semigroups
By constructing a coupling in two steps and using the Girsanov theorem under
a regular conditional probability, the log-Harnack inequality is established
for a large class of Gruschin type semigroups whose generator might be both
degenerate and non-Lipschitzian.Comment: 14 page
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