Correcting for cryptic relatedness by a regression-based genomic control method

<p>Abstract</p> <p>Background</p> <p>Genomic control (GC) method is a useful tool to correct for the cryptic relatedness in population-based association studies. It was originally proposed for correcting for the variance inflation of Cochran-Armitage's additive trend test by using information from unlinked null markers, and was later generalized to be applicable to other tests with the additional requirement that the null markers are matched with the candidate marker in allele frequencies. However, matching allele frequencies limits the number of available null markers and thus limits the applicability of the GC method. On the other hand, errors in genotype/allele frequencies may cause further bias and variance inflation and thereby aggravate the effect of GC correction.</p> <p>Results</p> <p>In this paper, we propose a regression-based GC method using null markers that are not necessarily matched in allele frequencies with the candidate marker. Variation of allele frequencies of the null markers is adjusted by a regression method.</p> <p>Conclusion</p> <p>The proposed method can be readily applied to the Cochran-Armitage's trend tests other than the additive trend test, the Pearson's chi-square test and other robust efficiency tests. Simulation results show that the proposed method is effective in controlling type I error in the presence of population substructure.</p

Bosonization of quantum sine-Gordon field with a boundary

Boundary operators and boundary ground states in sine-Gordon model with a fixed boundary condition are studied using bosonization and q-deformed oscillators.We also obtain the form-factors of this model.Comment: Latex 25page

Threshold Effects in the Decay of Heavy b' and t' Quarks

A sequential fourth generation is still viable, but the t' and b' quarks are constrained to be not too far apart in mass. The t'{\to}bW and b'{\to}tW decay channels are still being pursued at the Tevatron, which would soon be surpassed by the LHC. We use a convolution method with up to five-body final state to study t' and b' decays. We show how the two decay branches for m_{b'} below the tW threshold, b'{\to}tW^* and t^*W, merge with b'{\to}tW above the threshold. We then consider the heavy-to-heavy transitions b'{\to}t^{\prime(*)}W^{(*)} (or t'{\to}b^{\prime(*)}W^{(*)}), as they are not suppressed by quark mixing. We find that, because of the threshold sensitivity of the branching fraction of t'{\to}b'W^* (or b'{\to}t'W^*), it is possible to measure the strength of the CKM mixing element V_{t'b} (or V_{tb'}), especially when it is rather small. We urge the experiments to pursue and separate the t'{\to}b'W^* (or b'{\to}t'W^*) decay in their search program

A hybrid quantum-classical classifier based on branching multi-scale entanglement renormalization ansatz

Label propagation is an essential semi-supervised learning method based on graphs, which has a broad spectrum of applications in pattern recognition and data mining. This paper proposes a quantum semi-supervised classifier based on label propagation. Considering the difficulty of graph construction, we develop a variational quantum label propagation (VQLP) method. In this method, a locally parameterized quantum circuit is created to reduce the parameters required in the optimization. Furthermore, we design a quantum semi-supervised binary classifier based on hybrid Bell and $Z$ bases measurement, which has shallower circuit depth and is more suitable for implementation on near-term quantum devices. We demonstrate the performance of the quantum semi-supervised classifier on the Iris data set, and the simulation results show that the quantum semi-supervised classifier has higher classification accuracy than the swap test classifier. This work opens a new path to quantum machine learning based on graphs

Storage Fit Learning with Feature Evolvable Streams

Feature evolvable learning has been widely studied in recent years where old features will vanish and new features will emerge when learning with streams. Conventional methods usually assume that a label will be revealed after prediction at each time step. However, in practice, this assumption may not hold whereas no label will be given at most time steps. A good solution is to leverage the technique of manifold regularization to utilize the previous similar data to assist the refinement of the online model. Nevertheless, this approach needs to store all previous data which is impossible in learning with streams that arrive sequentially in large volume. Thus we need a buffer to store part of them. Considering that different devices may have different storage budgets, the learning approaches should be flexible subject to the storage budget limit. In this paper, we propose a new setting: Storage-Fit Feature-Evolvable streaming Learning (SF$^2$EL) which incorporates the issue of rarely-provided labels into feature evolution. Our framework is able to fit its behavior to different storage budgets when learning with feature evolvable streams with unlabeled data. Besides, both theoretical and empirical results validate that our approach can preserve the merit of the original feature evolvable learning i.e., can always track the best baseline and thus perform well at any time step

Turbidimeter based on a refractometer using a charge-coupled device

International audienceSalinity and turbidity are two important seawater properties in oceanography. We have studied the use of a high resolution refractometer to measure the salinity of seawater. The requirement of a multifunctional sensor makes the turbidity measurement based on our refractometer valuable. We measure turbidity according to the attenuation of the laser beam caused by the scattering. With the configuration of our refractometer, several issues impact the laserbeamattenuation measurement, while the measurement of salinity is impacted by the scattering as well. All these issues make light distribution nonsensitive sensors such as position sensitive devices unsuitable for building the refracto-turbidimeters. To overcome these issues, a charge-coupled device combined with a new location algorithm is used to measure both the refractive index and the attenuation. Several simulations and experiments are carried out to evaluate this new method. According to the results, the way to improve the resolution is discussed as well. The validation of our method is proved by comparison to the nephelometer specified by the nephelometric turbidity unit standar

Colloidal quantum dots and metal halide perovskite hybridization for solar cells stability and performance enhancement

Metal halide perovskites and colloidal quantum dots (QDs) are two emerging class of photoactive materials that has been attracted considerable attention for next-generation high-performance solution-processed solar cells. In particular, the hybridization of these two materials has been recently demonstrated remarkable performance enhancement due to the complementary nature of the two constituents. In this review, we will highlight the recent progress of QDs and perovskite hybridization in solar cell applications. More specifically, the unique properties of monophase perovskite QDs will be summarised which are demonstrated by homogeneously hybridizing perovskite QDs into perovskite lattice. We also discuss the recent progress in heterogeneously hybridizing discrete colloidal QDs into perovskite layers which exhibit significant perovskite film stability enhancement as well as corresponding solar cell performance improvement. PbS QDs, other chalcogenides QDs, as well as emerging two-dimensional QDs, are further accounted through multiple methods, such as bilayer architectures, core-shell structures or blending multiple QDs into perovskite layers. In the end, an outlook perspective of this field has been proposed to point out several challenges and possible solutions