Consistent and Asymptotically Efficient Localization from Range-Difference Measurements

We consider signal source localization from range-difference measurements. First, we give some readily-checked conditions on measurement noises and sensor deployment to guarantee the asymptotic identifiability of the model and show the consistency and asymptotic normality of the maximum likelihood (ML) estimator. Then, we devise an estimator that owns the same asymptotic property as the ML one. Specifically, we prove that the negative log-likelihood function converges to a function, which has a unique minimum and positive definite Hessian at the true source's position. Hence, it is promising to execute local iterations, e.g., the Gauss-Newton (GN) algorithm, following a consistent estimate. The main issue involved is obtaining a preliminary consistent estimate. To this aim, we construct a linear least-squares problem via algebraic operation and constraint relaxation and obtain a closed-form solution. We then focus on deriving and eliminating the bias of the linear least-squares estimator, which yields an asymptotically unbiased (thus consistent) estimate. Noting that the bias is a function of the noise variance, we further devise a consistent noise variance estimator that involves $3$-order polynomial rooting. Based on the preliminary consistent location estimate, a one-step GN iteration suffices to achieve the same asymptotic property as the ML estimator. Simulation results demonstrate the superiority of our proposed algorithm in the large sample case

Robust prior-based single image super resolution under multiple Gaussian degradations

Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance. These priori hypotheses often fail and lead to reconstruction error in real scenario. In this paper, we propose an end-to-end CNN model RPSRMD to handle SR problem in multiple Gaussian degradations by extracting and using as side information a shared image prior that is consistent in different Gaussian degradations. The shared image prior is generated by an AED network RPGen with a rationally designed loss function that contains two parts: consistency loss and validity loss. These losses supervise the training of AED to guarantee that the image priors of one image with different Gaussian blurs to be very similar. Afterwards we carefully designed a SR network, which is termed as PResNet (Prior based Residual Network) in this paper, to efficiently use the image priors and generate high quality and robust SR images when unknown Gaussian blur is presented. When we applied variant Gaussian blurs to the low resolution images, the experiments prove that our proposed RPSRMD, which includes RPGen and PResNet as two core components, is superior to many state-of-the-art SR methods that were designed and trained to handle multi-degradation

Curcumin Enhanced Busulfan-Induced Apoptosis through Downregulating the Expression of Survivin in Leukemia Stem-Like KG1a Cells

Leukemia relapse and nonrecurrence mortality (NRM) due to leukemia stem cells (LSCs) represent major problems following hematopoietic stem cell transplantation (HSCT). To eliminate LSCs, the sensitivity of LSCs to chemotherapeutic agents used in conditioning regimens should be enhanced. Curcumin (CUR) has received considerable attention as a result of its anticancer activity in leukemia and solid tumors. In this study, we investigated the cytotoxic effects and underlying mechanisms in leukemia stem-like KG1a cells exposed to busulfan (BUS) and CUR, either alone or in combination. KG1a cells exhibiting BUS-resistance demonstrated by MTT and annexin V/propidium iodide (PI) assays, compared with HL-60 cells. CUR induced cell growth inhibition and apoptosis in KG1a cells. Apoptosis of KG1a cells was significantly enhanced by treatment with CUR+BUS, compared with either agent alone. CUR synergistically enhanced the cytotoxic effect of BUS. Seven apoptosis-related proteins were modulated in CUR- and CUR+BUS-treated cells analyzed by proteins array analysis. Importantly, the antiapoptosis protein survivin was significantly downregulated, especially in combination group. Suppression of survivin with specific inhibitor YM155 significantly increased the susceptibility of KG1a cells to BUS. These results demonstrated that CUR could increase the sensitivity of leukemia stem-like KG1a cells to BUS by downregulating the expression of survivin

PRED : a parallel network for handling multiple degradations via single model in single image super-resolution

Existing SISR (single image super-resolution) methods mostly assume that a low-resolution (LR) image is bicubicly down-sampled from its high-resolution (HR) counterpart, which inevitably give rise to poor performance when the degradation is out of assumption. To address this issue, we propose a framework PRED (parallel residual and encoder-decoder network) with an innovative training strategy to enhance the robustness to multiple degradations. Consequently, the network can handle spatially variant degradations, which significantly improves the practicability of the proposed method. Extensive experimental results on real LR images show that the proposed method can not only produce favorable results on multiple degradations, but also reconstruct visually plausible HR images