Bayesian nonparametric tests via sliced inverse modeling

We study the problem of independence and conditional independence tests between categorical covariates and a continuous response variable, which has an immediate application in genetics. Instead of estimating the conditional distribution of the response given values of covariates, we model the conditional distribution of covariates given the discretized response (aka "slices"). By assigning a prior probability to each possible discretization scheme, we can compute efficiently a Bayes factor (BF)-statistic for the independence (or conditional independence) test using a dynamic programming algorithm. Asymptotic and finite-sample properties such as power and null distribution of the BF statistic are studied, and a stepwise variable selection method based on the BF statistic is further developed. We compare the BF statistic with some existing classical methods and demonstrate its statistical power through extensive simulation studies. We apply the proposed method to a mouse genetics data set aiming to detect quantitative trait loci (QTLs) and obtain promising results.Comment: 32 pages, 7 figure

Observations on shifted cumulative regulation

A response to Dynamic cumulative activity of transcription factors as a mechanism of quantitative gene regulation by F He, J Buer, AP Zeng and R Balling. Genome Biol 2007, 8:R181

Vegetation Changes in Alberta Oil Sands, Canada, Based on Remotely Sensed Data from 1995 to 2020

There are rich oil and gas resources in Alberta oil sand mining area in Canada. Since the 1960s, the Canadian government decided to increase the mining intensity. However, the exploitation will bring many adverse effects. In recent years, more people pay attention to the environmental protection and ecological restoration of mining area, such as issues related with changes of vegetated lands. Thus, the authors used the Landsat-5 TM and Landsat-8 OLI remote sensing images as the basic data sources, and obtained the land cover classification maps from 1995 to 2020 by ENVI. Based on the NDVI, NDMI and RVI, three images in each period are processed and output to explore the long-term impact of exploitation. The results show that from 1995 to 2020, the proportion of vegetation around mining areas decreased sharply, the scale of construction land in the mining area increased, and the vegetated land was changed to land types such as tailings pond, oil sand mine and other land types. In addition, three vegetation indexes decreased from 1995 to 2020. Although the exploitation of oil sand mining area brings great economic benefits, the environmental protection (especially vegetation) in oil sand mining areas should be paid more attention

Verification of arbitrary entangled states with homogeneous local measurements

Quantum state verification (QSV) is the task of using local measurements only to verify that a given quantum device does produce the desired target state. Up to now, certain types of entangled states can be verified efficiently or even optimally by QSV. However, given an arbitrary entangled state, how to design its verification protocol remains an open problem. In this work, we present a systematic strategy to tackle this problem by considering the locality of what we initiate as the choice-independent measurement protocols, whose operators can be directly achieved when they are homogeneous. Taking several typical entangled states as examples, we demonstrate the explicit procedures of the protocol design using standard Pauli projections. Moreover, our framework can be naturally extended to other tasks such as the construction of entanglement witness, and even parameter estimation.Comment: 6+7 pages, 1 figure; Comments are welcome

Accelerated Nonconvex ADMM with Self-Adaptive Penalty for Rank-Constrained Model Identification

The alternating direction method of multipliers (ADMM) has been widely adopted in low-rank approximation and low-order model identification tasks; however, the performance of nonconvex ADMM is highly reliant on the choice of penalty parameter. To accelerate ADMM for solving rankconstrained identification problems, this paper proposes a new self-adaptive strategy for automatic penalty update. Guided by first-order analysis of the increment of the augmented Lagrangian, the self-adaptive penalty updating enables effective and balanced minimization of both primal and dual residuals and thus ensures a stable convergence. Moreover, improved efficiency can be obtained within the Anderson acceleration scheme. Numerical examples show that the proposed strategy significantly accelerates the convergence of nonconvex ADMM while alleviating the critical reliance on tedious tuning of penalty parameters.Comment: 7 pages, 4 figures. Submitted to 62nd IEEE Conference on Decision and Control (CDC 2023