A NOTE ON COMONOTONICITY AND POSITIVITY OF THE CONTROL COMPONENTS OF DECOUPLED QUADRATIC FBSDE

In this small note we are concerned with the solution of Forward-Backward Stochastic Differential Equations (FBSDE) with drivers that grow quadratically in the control component (quadratic growth FBSDE or qgFBSDE). The main theorem is a comparison result that allows comparing componentwise the signs of the control processes of two different qgFBSDE. As a byproduct one obtains conditions that allow establishing the positivity of the control process.Comment: accepted for publicatio

Droplet mixer based on siphon-induced flow discretization and phase shifting

We present a novel mixing principle for centrifugal microfluidic platforms. Siphon structures are designed to disrupt continuous flows in a controlled manner into a sequence of discrete droplets, displaying individual volumes as low as 60 nL. When discrete volumes of different liquids are alternately issued into a common reservoir, a striation pattern of alternating liquid layers is obtained. In this manner diffusion distances are drastically decreased and a fast and homogeneous mixing is achieved. Efficient mixing is demonstrated for a range of liquid combinations of varying fluid properties such as aqueous inks or saline solutions and human plasma. Volumes of 5 muL have been mixed in less than 20 s to a high mixing quality. One-step dilutions of plasma in a standard phosphate buffer solution up to 1:5 are also demonstrated

Optimal state reductions of automata with partially specified behaviors

Nondeterministic finite automata with don't care states, namely states which neither accept nor reject, are considered. A characterization of deterministic automata compatible with such a device is obtained. Furthermore, an optimal state bound for the smallest compatible deterministic automata is provided. It is proved that the problem of minimizing deterministic don't care automata is NP-complete and PSPACE-hard in the nondeterministic case. The restriction to the unary case is also considered

Non-parametric contextual stochastic search

Stochastic search algorithms are black-box optimizer of an objective function. They have recently gained a lot of attention in operations research, machine learning and policy search of robot motor skills due to their ease of use and their generality. Yet, many stochastic search algorithms require relearning if the task or objective function changes slightly to adapt the solution to the new situation or the new context. In this paper, we consider the contextual stochastic search setup. Here, we want to find multiple good parameter vectors for multiple related tasks, where each task is described by a continuous context vector. Hence, the objective function might change slightly for each parameter vector evaluation of a task or context. Contextual algorithms have been investigated in the field of policy search, however, the search distribution typically uses a parametric model that is linear in the some hand-defined context features. Finding good context features is a challenging task, and hence, non-parametric methods are often preferred over their parametric counter-parts. In this paper, we propose a non-parametric contextual stochastic search algorithm that can learn a non-parametric search distribution for multiple tasks simultaneously. In difference to existing methods, our method can also learn a context dependent covariance matrix that guides the exploration of the search process. We illustrate its performance on several non-linear contextual tasks

Regularized covariance estimation for weighted maximum likelihood policy search methods

Many episode-based (or direct) policy search algorithms, maintain a multivariate Gaussian distribution as search distribution over the parameter space of some objective function. One class of algorithms, such as episodic REPS, PoWER or PI2 uses, a weighted maximum likelihood estimate (WMLE) to update the mean and covariance matrix of this distribution in each iteration. However, due to high dimensionality of covariance matrices and limited number of samples, the WMLE is an unreliable estimator. The use of WMLE leads to over-fitted covariance estimates, and, hence the variance/entropy of the search distribution decreases too quickly, which may cause premature convergence. In order to alleviate this problem, the estimated covariance matrix can be regularized in different ways, for example by using a convex combination of the diagonal covariance estimate and the sample covariance estimate. In this paper, we propose a new covariance matrix regularization technique for policy search methods that uses the convex combination of the sample covariance matrix and the old covariance matrix used in last iteration. The combination weighting is determined by specifying the desired entropy of the new search distribution. With this mechanism, the entropy of the search distribution can be gradually decreased without damage from the maximum likelihood estimate

Contextual CMA-ES

Many stochastic search algorithms are designed to optimize a fixed objective function to learn a task, i.e., if the objective function changes slightly, for example, due to a change in the situation or context of the task, relearning is required to adapt to the new context. For instance, if we want to learn a kicking movement for a soccer robot, we have to relearn the movement for different ball locations. Such relearning is undesired as it is highly inefficient and many applications require a fast adaptation to a new context/situation. Therefore, we investigate contextual stochastic search algorithms that can learn multiple, similar tasks simultaneously. Current contextual stochastic search methods are based on policy search algorithms and suffer from premature convergence and the need for parameter tuning. In this paper, we extend the well known CMA-ES algorithm to the contextual setting and illustrate its performance on several contextual tasks. Our new algorithm, called contextual CMAES, leverages from contextual learning while it preserves all the features of standard CMA-ES such as stability, avoidance of premature convergence, step size control and a minimal amount of parameter tuning

Electronic polarization in pentacene crystals and thin films

Electronic polarization is evaluated in pentacene crystals and in thin films on a metallic substrate using a self-consistent method for computing charge redistribution in non-overlapping molecules. The optical dielectric constant and its principal axes are reported for a neutral crystal. The polarization energies P+ and P- of a cation and anion at infinite separation are found for both molecules in the crystal's unit cell in the bulk, at the surface, and at the organic-metal interface of a film of N molecular layers. We find that a single pentacene layer with herring-bone packing provides a screening environment approaching the bulk. The polarization contribution to the transport gap P=(P+)+(P-), which is 2.01 eV in the bulk, decreases and increases by only ~ 10% at surfaces and interfaces, respectively. We also compute the polarization energy of charge-transfer (CT) states with fixed separation between anion and cation, and compare to electroabsorption data and to submolecular calculations. Electronic polarization of ~ 1 eV per charge has a major role for transport in organic molecular systems with limited overlap.Comment: 10 revtex pages, 6 PS figures embedde

Dynamic studies of biomimetic coated polycaprolactone nanofiber meshes as bone extracellular matrix analogues

This work aimed at studying the effects of dynamic culture conditions and biomimetic coating on bone cells grown on nanofiber meshes. In our previous work, biomimetic calcium phosphate coated polycaprolactone nanofibre meshes (BCP-NM) proved to be more efficient for supporting cell attachment and proliferation under static conditions, when compared to polycaprolactone nanofibre meshe (PCL-NM). However, no studies on the influence of bioreactors on the behaviour of cells cultivated on these materials were developed so far. [...]info:eu-repo/semantics/publishedVersio