Accelerated Stochastic ADMM with Variance Reduction

Alternating Direction Method of Multipliers (ADMM) is a popular method in solving Machine Learning problems. Stochastic ADMM was firstly proposed in order to reduce the per iteration computational complexity, which is more suitable for big data problems. Recently, variance reduction techniques have been integrated with stochastic ADMM in order to get a fast convergence rate, such as SAG-ADMM and SVRG-ADMM,but the convergence is still suboptimal w.r.t the smoothness constant. In this paper, we propose a new accelerated stochastic ADMM algorithm with variance reduction, which enjoys a faster convergence than all the other stochastic ADMM algorithms. We theoretically analyze its convergence rate and show its dependence on the smoothness constant is optimal. We also empirically validate its effectiveness and show its priority over other stochastic ADMM algorithms

Progress on the Study of PD-L1 Detection Methods in Non-small Cell Lung Cancer

PD-1/PD-L1 inhibitors play an important role in the first-line and second-line treatment of non-small cell lung cancer (NSCLC), indicating a new treatment strategy of NSCLC. Completed clinical trials have shown that effective detection of PD-L1 expression is the key to the use of immunosuppressive agents. However, the gold standard for PD-L1 detection has still lacked. In recent years, immunohistochemistry (IHC) and enzyme-linked immunosorbent assay (ELISA) have been continuously innovated, which accounts for good prospect in PD-L1 detection. The research progress of PD-L1 detection methods in NSCLC is summarized in this review

Prognostic Analysis of EGFR-TKIs Combined with Gamma Knife in EGFR-mutant Lung Adenocarcinoma with Brain Metastasis

Background and objective Advanced epidermal growth factor receptor (EGFR)-mutant lung adenocarcinoma had a high overall incidence of brain metastasis during the full course, and local brain radiotherapy combined with systemic targeted therapy may be a better strategy. This study aimed to identify the prognostic factors of EGFR-mutant brain-metastatic lung adenocarcinoma patients who received EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in combination with gamma knife radiosurgery. Methods Retrospective analysis of EGFR-mutant lung adenocarcinoma patients with brain metastases which developed at initial diagnosis or during EGFR-TKIs treatment period were performed. Intracranial progression free survival (PFS) was statistically analyzed between different subgroups to find out the prognostic factors including gender, age, smoking history, extracranial metastasis, EGFR mutation type, size and number of intracranial lesions, carcino-embryonic antigen (CEA) level, lung-molGPA score and so on. Results A total of 74 EGFR-mutant brain-metastatic lung adenocarcinoma patients were enrolled in this study, with median intracranial PFS of 14.7 months. One-year intracranial-progression-free rate was 58.5%, and two-year rate was 22.2%. Univariate survival analysis showed that patients with lower CEA level at initial diagnosis (3)(15 months vs 12.6 months, P=0.041) were prone to have a superior intracranial PFS. Multivariate analysis showed that CEA≥10 ng/mL and intracranial lesion≥2 cm were the independent risk factors of intracranial PFS. Conclusion EGFR-TKIs in combination with gamma knife radiosurgery was an efficient treatment option to control the cranial tumor lesion. CEA≥10 μg/L at initial diagnosis and intracranial lesion≥2 cm were the risk factors of EGFR-mutant brain-metastatic lung adenocarcinoma patients receiving EGFR-TKIs in combination with gamma knife radiosurgery

Low-Cost and Efficient Indoor 3D Reconstruction Through Annotated Hierarchical Structure-from-Motion

With the widespread application of location-based services, the appropriate representation of indoor spaces and efficient indoor 3D reconstruction have become essential tasks. Due to the complexity and closeness of indoor spaces, it is difficult to develop a versatile solution for large-scale indoor 3D scene reconstruction. In this paper, an annotated hierarchical Structure-from-Motion (SfM) method is proposed for low-cost and efficient indoor 3D reconstruction using unordered images collected with widely available smartphone or consumer-level cameras. Although the reconstruction of indoor models is often compromised by the indoor complexity, we make use of the availability of complex semantic objects to classify the scenes and construct a hierarchical scene tree to recover the indoor space. Starting with the semantic annotation of the images, images that share the same object were detected and classified utilizing visual words and the support vector machine (SVM) algorithm. The SfM method was then applied to hierarchically recover the atomic 3D point cloud model of each object, with the semantic information from the images attached. Finally, an improved random sample consensus (RANSAC) generalized Procrustes analysis (RGPA) method was employed to register and optimize the partial models into a complete indoor scene. The proposed approach incorporates image classification in the hierarchical SfM based indoor reconstruction task, which explores the semantic propagation from images to points. It also reduces the computational complexity of the traditional SfM by avoiding exhausting pair-wise image matching. The applicability and accuracy of the proposed method was verified on two different image datasets collected with smartphone and consumer cameras. The results demonstrate that the proposed method is able to efficiently and robustly produce semantically and geometrically correct indoor 3D point models

Effect of Barley Antifreeze Protein on Dough and Bread during Freezing and Freeze-Thaw Cycles

In order to verify the cryoprotective effect of an antifreeze protein (BaAFP-1) obtained from barley on bread dough, the effect of BaAFP-1 on the rheological properties, microstructure, fermentation, and baking performance including the proofing time and the specific volume of bread dough and bread crumb properties during freezing treatment and freeze-thaw cycles were analysed. BaAFP-1 reduced the rate of decrease in storage modulus and loss modulus values during freezing treatment and freeze-thaw cycles. It influenced the formation and the shape of ice formed during freezing and inhibited ice recrystallization during freeze-thaw. BaAFP-1 maintained gas production ability and gas retention properties, protected gluten network and the yeast cells from deterioration caused by ice formation and ice crystals recrystallisation in dough samples during freezing treatment and freeze-thaw treatment. It slow down the increase rate of hardness of bread crumb. The average area of pores in bread crumbs decreased significantly (p < 0.05) as the total number of pores increased (p < 0.05), and the addition of BaAFP-1 inhibited this deterioration. These results confirmed the cryoprotective activity of BaAFP-1 in bread dough during freezing treatment and freeze-thaw cycles