Hasil untuk "q-bio.OT"

E. Lepeschkin, B. Surawicz

Anissa Ayala Cavalcante

R. Gibson, W. Boden, P. Théroux et al.

R. Shephard

R. Tonn

D. Rorabacher

I. Bezprozvanny, R. Scheller, R. Tsien

A. Jouvenceau, A. Jouvenceau, LH Eunson et al.

M. Bożejko, Burkhard Kummerer, R. Speicher

Abstract: We examine, for −1<q<1, q-Gaussian processes, i.e. families of operators (non-commutative random variables) – where the at fulfill the q-commutation relations for some covariance function – equipped with the vacuum expectation state. We show that there is a q-analogue of the Gaussian functor of second quantization behind these processes and that this structure can be used to translate questions on q-Gaussian processes into corresponding (and much simpler) questions in the underlying Hilbert space. In particular, we use this idea to show that a large class of q-Gaussian processes possesses a non-commutative kind of Markov property, which ensures that there exist classical versions of these non-commutative processes. This answers an old question of Frisch and Bourret [FB].

W. V. D. Hoek, F. Dijkstra, B. Schimmer et al.

Since the steady rise in human cases which started in 2007, Q fever has become a major public health problem in the Netherlands with 2,357 human cases notified in the year 2009. Ongoing research confirms that abortion waves on dairy goat farms are the primary source of infection for humans, primarily affecting people living close (under 5 km) to such a dairy goat farm. To reverse the trend of the last three years, drastic measures have been implemented, including the large-scale culling of pregnant goats on infected farms.

J. Duan

R. Ferreira

Y. Goldberg, M. Kosorok

We develop methodology for a multistage-decision problem with flexible number of stages in which the rewards are survival times that are subject to censoring. We present a novel Q-learning algorithm that is adjusted for censored data and allows a flexible number of stages. We provide finite sample bounds on the generalization error of the policy learned by the algorithm, and show that when the optimal Q-function belongs to the approximation space, the expected survival time for policies obtained by the algorithm converges to that of the optimal policy. We simulate a multistage clinical trial with flexible number of stages and apply the proposed censored-Q-learning algorithm to find individualized treatment regimens. The methodology presented in this paper has implications in the design of personalized medicine trials in cancer and in other life-threatening diseases.

C. Hennessy, Amnon Levy, Toni M. Whited

R. Ferreira

In this paper, we investigate the existence of nontrivial solutions to the nonlinear q-fractional boundary value problem

A. Mehnert, P. Herschbach, Petra Berg et al.

L. Anttila, M. Valkama, M. Renfors

Seth Cable

H. Busemann, H. Baur, R. Wieler

G. Karagiannidis, Athanasios S. Lioumpas