Computer-Generated Ovaries to Assist Follicle Counting Experiments

Precise estimation of the number of follicles in ovaries is of key importance in the field of reproductive biology, both from a developmental point of view, where follicle numbers are determined at specific time points, as well as from a therapeutic perspective, determining the adverse effects of environmental toxins and cancer chemotherapeutics on the reproductive system. The two main factors affecting follicle number estimates are the sampling method and the variation in follicle numbers within animals of the same strain, due to biological variability. This study aims at assessing the effect of these two factors, when estimating ovarian follicle numbers of neonatal mice. We developed computer algorithms, which generate models of neonatal mouse ovaries (simulated ovaries), with characteristics derived from experimental measurements already available in the published literature. The simulated ovaries are used to reproduce in-silico counting experiments based on unbiased stereological techniques; the proposed approach provides the necessary number of ovaries and sampling frequency to be used in the experiments given a specific biological variability and a desirable degree of accuracy. The simulated ovary is a novel, versatile tool which can be used in the planning phase of experiments to estimate the expected number of animals and workload, ensuring appropriate statistical power of the resulting measurements. Moreover, the idea of the simulated ovary can be applied to other organs made up of large numbers of individual functional units

Many-Body Quantum Spin Dynamics with Monte Carlo Trajectories on a Discrete Phase Space

Interacting spin systems are of fundamental relevance in different areas of physics, as well as in quantum information science, and biology. These spin models represent the simplest, yet not fully understood, manifestation of quantum many-body systems. An important outstanding problem is the efficient numerical computation of dynamics in large spin systems. Here we propose a new semiclassical method to study many-body spin dynamics in generic spin lattice models. The method is based on a discrete Monte Carlo sampling in phase-space in the framework of the so-called truncated Wigner approximation. Comparisons with analytical and numerically exact calculations demonstrate the power of the technique. They show that it correctly reproduces the dynamics of one- and two-point correlations and spin squeezing at short times, thus capturing entanglement. Our results open the possibility to study the quantum dynamics accessible to recent experiments in regimes where other numerical methods are inapplicable.Comment: 8 pages, 6 figure

PV Cell Characteristic Extraction to Verify Power Transfer Efficiency in Indoor Harvesting System

A method is proposed to verify the efficiency of low-power harvesting systems based on Photovoltaic (PV) cells for indoor applications and a Fractional Open-Circuit Voltage (FOCV) technique to track the Maximum Power Point (MPP). It relies on an algorithm to reconstruct the PV cell Power versus Voltage (P-V) characteristic measuring the open circuit voltage and the voltage/current operating point but not the short-circuit current as required by state-of-the-art algorithms. This way the characteristic is reconstructed starting from the two values corresponding to standard operation modes of dc-dc converters implementing the FOCV Maximum Power Point Tracking (MPPT) technique. The method is applied to a prototype system: an external board is connected between the transducer and the dc-dc converter to measure the open circuit voltage and the voltage/current operating values. Experimental comparisons between the reconstructed and the measured P-V characteristics validate the reconstruction algorithm. Experimental results show the method is able to clearly identify the error between the transducer operating point and the one corresponding to the maximum power transfer, whilst also suggesting corrective action on the programmable factor of the FOCV technique. The proposed technique therefore provides a possible way of estimating MPPT efficiency without sampling the full P-V characteristic

To the practical design of the optical lever intracavity topology of gravitational-wave detectors

The QND intracavity topologies of gravitational-wave detectors proposed several years ago allow, in principle, to obtain sensitivity significantly better than the Standard Quantum Limit using relatively small anount of optical pumping power. In this article we consider an improved more ``practical'' version of the optical lever intracavity scheme. It differs from the original version by the symmetry which allows to suppress influence of the input light amplitude fluctuation. In addition, it provides the means to inject optical pumping inside the scheme without increase of optical losses. We consider also sensitivity limitations imposed by the local meter which is the key element of the intracavity topologies. Two variants of the local meter are analyzed, which are based on the spectral variation measurement and on the Discrete Sampling Variation Measurement, correspondingly. The former one, while can not be considered as a candidate for a practical implementation, allows, in principle, to obtain the best sensitivity and thus can be considered as an ideal ``asymptotic case'' for all other schemes. The DSVM-based local meter can be considered as a realistic scheme but its sensitivity, unfortunately, is by far not so good just due to a couple of peculiar numeric factors specific for this scheme. From our point of view search of new methods of mechanical QND measurements probably based on improved DSVM scheme or which combine the local meter with the pondermotive squeezing technique, is necessary.Comment: 27 pages, 6 figure

Nonparametric inference for second order stochastic dominance

This paper deals with nonparametric inference for second order stochastic dominance of two random variables. If their distribution functions are unknown they have to be inferred from observed realizations. Thus, any results on stochastic dominance are in uenced by sampling errors. We establish two methods to take the sampling error into account. The first one is based on the asymptotic normality of point estimators, while the second one, relying on resampling techniques, can also cope with small sample sizes. Both methods are used to develop statistical tests for second order stochastic dominance. We argue, however, that tests based on resampling techniques are more useful in practical applications. Their power in small samples is estimated by Monte Carlo simulations for a couple of alternative distributions. We further show that these tests can also be used for testing for first order stochastic dominance, often having a higher power than tests specifically designed for first order stochastic dominance such as the Kolmogorov-Smirnov test or the Wilcoxon-Mann-Whitney test. The results of this paper are relevant in various fields such as finance, life testing and decision under risk. --second order stochastic dominance,nonparametric inference,permutation tests,Monte Carlo methods

Video classification based on spatial gradient and optical flow descriptors

Feature point detection and local feature extraction are the two critical steps in trajectory-based methods for video classification. This paper proposes to detect trajectories by tracking the spatiotemporal feature points in salient regions instead of the entire frame. This strategy significantly reduces noisy feature points in the background region, and leads to lower computational cost and higher discriminative power of the feature set. Two new spatiotemporal descriptors, namely the STOH and RISTOH are proposed to describe the spatiotemporal characteristics of the moving object. The proposed method for feature point detection and local feature extraction is applied for human action recognition. It is evaluated on three video datasets: KTH, YouTube, and Hollywood2. The results show that the proposed method achieves a higher classification rate, even when it uses only half the number of feature points compared to the dense sampling approach. Moreover, features extracted from the curvature of the motion surface are more discriminative than features extracted from the spatial gradient