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Phrase comprehension (PC) aims to locate a specific object in an image according to a given linguistic query. The existing PC methods work in either a fully supervised or proposal-based weakly supervised manner, which rely explicitly or implicitly on expensive region annotations. In order to completely remove the dependence on the supervised region information, this paper proposes to address PC in a proposal-free weakly supervised training paradigm. To this end, we developed a novel cascaded searching reinforcement learning agent (CSRLA). Concretely, we first leveraged a visual language pre-trained model to generate a visual–textual cross-modal attention heatmap. Accordingly, a coarse salient initial region of the referential target was located. Then, we formulated the visual object grounding as a Markov decision process (MDP) in a reinforcement learning framework, where an agent was trained to iteratively search for the target's complete region from the salient local region. Additionally, we developed a novel confidence discrimination reward function (ConDis_R) to constrain the model to search for a complete and exclusive object region. The experimental results on three benchmark datasets of Refcoco, Refcoco+, and Refcocog demonstrated the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

In this paper, a one-way error component regression model with measurement errors is considered. The unknown parameter vector is estimated by using the bias-corrected method, and its corresponding asymptotic properties are also developed. For the hypothesis testing problem of the vector of the coefficient parameter in the model, a parametric bootstrap (PB) method is proposed. Under various sample sizes and parameter configurations, the effectiveness of our proposed PB test method is discussed by using some numerical simulations and a real data analysis. [ABSTRACT FROM AUTHOR]

High-dimensional measurement error data are becoming more prevalent across various fields. Research on measurement error regression models has gained momentum due to the risk of drawing inaccurate conclusions if measurement errors are ignored. When the dimension p is larger than the sample size n, it is challenging to develop statistical inference methods for high-dimensional measurement error regression models due to the existence of bias, nonconvexity of the objective function, high computational cost and many other difficulties. Over the past few years, some works have overcome the aforementioned difficulties and proposed several novel statistical inference methods. This paper mainly reviews the current development on estimation, hypothesis testing and variable screening methods for high-dimensional measurement error regression models and shows the theoretical results of these methods with some directions worthy of exploring in future research. [ABSTRACT FROM AUTHOR]

Regression adjustment is often used to estimate average treatment effect (ATE) in randomized experiments. Recently, some penalty-based regression adjustment methods have been proposed to handle the high-dimensional problem. However, these existing high-dimensional regression adjustment methods may fail to achieve satisfactory performance when the covariates are highly correlated. In this paper, we propose a novel adjustment estimation method for ATE by combining the semi-standard partial covariance (SPAC) and regression adjustment methods. Under some regularity conditions, the asymptotic normality of our proposed SPAC adjustment ATE estimator is shown. Some simulation studies and an analysis of HER2 breast cancer data are carried out to illustrate the advantage of our proposed SPAC adjustment method in addressing the highly correlated problem of the Rubin causal model. [ABSTRACT FROM AUTHOR]

Being the largest the Ca2+ store in mammalian cells, endoplasmic reticulum (ER)-mediated Ca2+ signalling often involves both Ca2+ release via inositol 1, 4, 5-trisphosphate receptors (IP3R) and store operated Ca2+ entries (SOCE) through Ca2+ release activated Ca2+ (CRAC) channels on plasma membrane (PM). IP3Rs are functionally coupled with CRAC channels and other Ca2+ handling proteins. However, it still remains less well defined as to whether IP3Rs could regulate ER-mediated Ca2+ signals independent of their Ca2+ releasing ability. To address this, we generated IP3Rs triple and double knockout human embryonic kidney (HEK) cell lines (IP3Rs-TKO, IP3Rs-DKO), and systemically examined ER Ca2+ dynamics and CRAC channel activity in these cells. The results showed that the rate of ER Ca2+ leakage and refilling, as well as SOCE were all significantly reduced in IP3Rs-TKO cells. And these TKO effects could be rescued by over-expression of IP3R3. Further, results showed that the diminished SOCE was caused by NEDD4L-mediated ubiquitination of Orai1 protein. Together, our findings indicate that IP3R3 is one crucial player in coordinating ER-mediated Ca2+ signalling. [ABSTRACT FROM AUTHOR]