2,171 research outputs found
Quantum Operation Time Reversal
The dynamics of an open quantum system can be described by a quantum
operation, a linear, complete positive map of operators. Here, I exhibit a
compact expression for the time reversal of a quantum operation, which is
closely analogous to the time reversal of a classical Markov transition matrix.
Since open quantum dynamics are stochastic, and not, in general, deterministic,
the time reversal is not, in general, an inversion of the dynamics. Rather, the
system relaxes towards equilibrium in both the forward and reverse time
directions. The probability of a quantum trajectory and the conjugate, time
reversed trajectory are related by the heat exchanged with the environment.Comment: 4 page
Zero-Reachability in Probabilistic Multi-Counter Automata
We study the qualitative and quantitative zero-reachability problem in
probabilistic multi-counter systems. We identify the undecidable variants of
the problems, and then we concentrate on the remaining two cases. In the first
case, when we are interested in the probability of all runs that visit zero in
some counter, we show that the qualitative zero-reachability is decidable in
time which is polynomial in the size of a given pMC and doubly exponential in
the number of counters. Further, we show that the probability of all
zero-reaching runs can be effectively approximated up to an arbitrarily small
given error epsilon > 0 in time which is polynomial in log(epsilon),
exponential in the size of a given pMC, and doubly exponential in the number of
counters. In the second case, we are interested in the probability of all runs
that visit zero in some counter different from the last counter. Here we show
that the qualitative zero-reachability is decidable and SquareRootSum-hard, and
the probability of all zero-reaching runs can be effectively approximated up to
an arbitrarily small given error epsilon > 0 (these result applies to pMC
satisfying a suitable technical condition that can be verified in polynomial
time). The proof techniques invented in the second case allow to construct
counterexamples for some classical results about ergodicity in stochastic Petri
nets.Comment: 20 page
Conditions for compatibility of quantum state assignments
Suppose N parties describe the state of a quantum system by N possibly
different density operators. These N state assignments represent the beliefs of
the parties about the system. We examine conditions for determining whether the
N state assignments are compatible. We distinguish two kinds of procedures for
assessing compatibility, the first based on the compatibility of the prior
beliefs on which the N state assignments are based and the second based on the
compatibility of predictive measurement probabilities they define. The first
procedure leads to a compatibility criterion proposed by Brun, Finkelstein, and
Mermin [BFM, Phys. Rev. A 65, 032315 (2002)]. The second procedure leads to a
hierarchy of measurement-based compatibility criteria which is fundamentally
different from the corresponding classical situation. Quantum mechanically none
of the measurement-based compatibility criteria is equivalent to the BFM
criterion.Comment: REVTEX 4, 19 pages, 1 postscript figur
Probabilistic Timed Automata with Clock-Dependent Probabilities
Probabilistic timed automata are classical timed automata extended with
discrete probability distributions over edges. We introduce clock-dependent
probabilistic timed automata, a variant of probabilistic timed automata in
which transition probabilities can depend linearly on clock values.
Clock-dependent probabilistic timed automata allow the modelling of a
continuous relationship between time passage and the likelihood of system
events. We show that the problem of deciding whether the maximum probability of
reaching a certain location is above a threshold is undecidable for
clock-dependent probabilistic timed automata. On the other hand, we show that
the maximum and minimum probability of reaching a certain location in
clock-dependent probabilistic timed automata can be approximated using a
region-graph-based approach.Comment: Full version of a paper published at RP 201
Maximal-entropy random walk unifies centrality measures
In this paper analogies between different (dis)similarity matrices are
derived. These matrices, which are connected to path enumeration and random
walks, are used in community detection methods or in computation of centrality
measures for complex networks. The focus is on a number of known centrality
measures, which inherit the connections established for similarity matrices.
These measures are based on the principal eigenvector of the adjacency matrix,
path enumeration, as well as on the stationary state, stochastic matrix or mean
first-passage times of a random walk. Particular attention is paid to the
maximal-entropy random walk, which serves as a very distinct alternative to the
ordinary random walk used in network analysis.
The various importance measures, defined both with the use of ordinary random
walk and the maximal-entropy random walk, are compared numerically on a set of
benchmark graphs. It is shown that groups of centrality measures defined with
the two random walks cluster into two separate families. In particular, the
group of centralities for the maximal-entropy random walk, connected to the
eigenvector centrality and path enumeration, is strongly distinct from all the
other measures and produces largely equivalent results.Comment: 7 pages, 2 figure
The thermodynamics of urban population flows
Orderliness, reflected via mathematical laws, is encountered in different
frameworks involving social groups. Here we show that a thermodynamics can be
constructed that macroscopically describes urban population flows. Microscopic
dynamic equations and simulations with random walkers underlie the macroscopic
approach. Our results might be regarded, via suitable analogies, as a step
towards building an explicit social thermodynamics
Tableaux for Policy Synthesis for MDPs with PCTL* Constraints
Markov decision processes (MDPs) are the standard formalism for modelling
sequential decision making in stochastic environments. Policy synthesis
addresses the problem of how to control or limit the decisions an agent makes
so that a given specification is met. In this paper we consider PCTL*, the
probabilistic counterpart of CTL*, as the specification language. Because in
general the policy synthesis problem for PCTL* is undecidable, we restrict to
policies whose execution history memory is finitely bounded a priori.
Surprisingly, no algorithm for policy synthesis for this natural and
expressive framework has been developed so far. We close this gap and describe
a tableau-based algorithm that, given an MDP and a PCTL* specification, derives
in a non-deterministic way a system of (possibly nonlinear) equalities and
inequalities. The solutions of this system, if any, describe the desired
(stochastic) policies.
Our main result in this paper is the correctness of our method, i.e.,
soundness, completeness and termination.Comment: This is a long version of a conference paper published at TABLEAUX
2017. It contains proofs of the main results and fixes a bug. See the
footnote on page 1 for detail
The Effects on Toxicity of Circadian Patterning of Continuous Hepatic Artery Infusion
Long term continuous hepatic artery infusion of FUDR was carried out in 34 rats. In the animals who
received a constant infusion schedule of 15mg/kg/day all died of toxicity with a mean survival of 9.3 days.
If the pattern of the continuous infusion was changed so that over 60% of the infusion was given during
the hours of 3pm to 9pm then all of the animals survived the 14 day infusion. If the maximum dose of
infusion was changed so that 60% of the infusion was given at night from 3am to 9am the infusion
became more toxic and all the animals died in a mean of 5.5 days. Pathologic sectioning of all the livers
reflected the above outcomes with the greatest amount of hepatic necrosis in the animals on the night
cycles. This study underscores the recent advances in chronobiology demonstrating that for continuous
hepatic arterial infusions the timing of delivery is crucial in determining the toxicity
Long-Range Navigation on Complex Networks using L\'evy Random Walks
We introduce a strategy of navigation in undirected networks, including
regular, random, and complex networks, that is inspired by L\'evy random walks,
generalizing previous navigation rules. We obtained exact expressions for the
stationary probability distribution, the occupation probability, the mean first
passage time, and the average time to reach a node on the network. We found
that the long-range navigation using the L\'evy random walk strategy, compared
with the normal random walk strategy, is more efficient at reducing the time to
cover the network. The dynamical effect of using the L\'evy walk strategy is to
transform a large-world network into a small world. Our exact results provide a
general framework that connects two important fields: L\'evy navigation
strategies and dynamics on complex networks.Comment: 6 pages, 3 figure
Probabilistic Guarantees for Safe Deep Reinforcement Learning
Deep reinforcement learning has been successfully applied to many control
tasks, but the application of such agents in safety-critical scenarios has been
limited due to safety concerns. Rigorous testing of these controllers is
challenging, particularly when they operate in probabilistic environments due
to, for example, hardware faults or noisy sensors. We propose MOSAIC, an
algorithm for measuring the safety of deep reinforcement learning agents in
stochastic settings. Our approach is based on the iterative construction of a
formal abstraction of a controller's execution in an environment, and leverages
probabilistic model checking of Markov decision processes to produce
probabilistic guarantees on safe behaviour over a finite time horizon. It
produces bounds on the probability of safe operation of the controller for
different initial configurations and identifies regions where correct behaviour
can be guaranteed. We implement and evaluate our approach on agents trained for
several benchmark control problems
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