Towards General and Efficient Online Tuning for Spark

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance. Recent studies try to employ auto-tuning techniques to solve this problem but suffer from three issues: limited functionality, high overhead, and inefficient search. In this paper, we present a general and efficient Spark tuning framework that can deal with the three issues simultaneously. First, we introduce a generalized tuning formulation, which can support multiple tuning goals and constraints conveniently, and a Bayesian optimization (BO) based solution to solve this generalized optimization problem. Second, to avoid high overhead from additional offline evaluations in existing methods, we propose to tune parameters along with the actual periodic executions of each job (i.e., online evaluations). To ensure safety during online job executions, we design a safe configuration acquisition method that models the safe region. Finally, three innovative techniques are leveraged to further accelerate the search process: adaptive sub-space generation, approximate gradient descent, and meta-learning method. We have implemented this framework as an independent cloud service, and applied it to the data platform in Tencent. The empirical results on both public benchmarks and large-scale production tasks demonstrate its superiority in terms of practicality, generality, and efficiency. Notably, this service saves an average of 57.00% memory cost and 34.93% CPU cost on 25K in-production tasks within 20 iterations, respectively

Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images

Purpose/Objectives(s)Salivary gland tumors are a rare, histologically heterogeneous group of tumors. The distinction between malignant and benign tumors of the parotid gland is clinically important. This study aims to develop and evaluate a deep-learning network for diagnosing parotid gland tumors via the deep learning of MR images.Materials/MethodsTwo hundred thirty-three patients with parotid gland tumors were enrolled in this study. Histology results were available for all tumors. All patients underwent MRI scans, including T1-weighted, CE-T1-weighted and T2-weighted imaging series. The parotid glands and tumors were segmented on all three MR image series by a radiologist with 10 years of clinical experience. A total of 3791 parotid gland region images were cropped from the MR images. A label (pleomorphic adenoma and Warthin tumor, malignant tumor or free of tumor), which was based on histology results, was assigned to each image. To train the deep-learning model, these data were randomly divided into a training dataset (90%, comprising 3035 MR images from 212 patients: 714 pleomorphic adenoma images, 558 Warthin tumor images, 861 malignant tumor images, and 902 images free of tumor) and a validation dataset (10%, comprising 275 images from 21 patients: 57 pleomorphic adenoma images, 36 Warthin tumor images, 93 malignant tumor images, and 89 images free of tumor). A modified ResNet model was developed to classify these images. The input images were resized to 224x224 pixels, including four channels (T1-weighted tumor images only, T2-weighted tumor images only, CE-T1-weighted tumor images only and parotid gland images). Random image flipping and contrast adjustment were used for data enhancement. The model was trained for 1200 epochs with a learning rate of 1e-6, and the Adam optimizer was implemented. It took approximately 2 hours to complete the whole training procedure. The whole program was developed with PyTorch (version 1.2).ResultsThe model accuracy with the training dataset was 92.94% (95% CI [0.91, 0.93]). The micro-AUC was 0.98. The experimental results showed that the accuracy of the final algorithm in the diagnosis and staging of parotid cancer was 82.18% (95% CI [0.77, 0.86]). The micro-AUC was 0.93.ConclusionThe proposed model may be used to assist clinicians in the diagnosis of parotid tumors. However, future larger-scale multicenter studies are required for full validation

25(OH)D-but not 1,25(OH)2D–Is an independent risk factor predicting graft loss in stable kidney transplant recipients

BackgroundVitamin D deficiency (VDD) or vitamin D insufficiency is common in kidney transplant recipients (KTRs). The impact of VDD on clinical outcomes in KTRs remain poorly defined and the most suitable marker for assessing vitamin D nutritional status in KTRs is unknown so far.MethodsWe conducted a prospective study including 600 stable KTRs (367 men, 233 women) and a meta-analysis to pool existing evidence to determine whether 25(OH)D or 1,25(OH)2D predicted graft failure and all-cause mortality in stable KTRs.ResultsCompared with a higher 25(OH)D concentration, a low concentration of 25(OH)D was a risk factor for graft failure (HR 0.946, 95% CI 0.912−0.981, p = 0.003), whereas 1,25 (OH)2D was not associated with the study end-point graft loss (HR 0.993, 95% CI 0.977−1.009, p = 0.402). No association was found between either 25(OH)D or 1,25 (OH)2D and all-cause mortality. We furthermore conducted a meta-analysis including 8 studies regarding the association between 25(OH)D or 1,25(OH)2D and graft failure or mortality, including our study. The meta-analysis results were consistent with our study in finding that lower 25(OH)D levels were significantly associated with the risk of graft failure (OR = 1.04, 95% CI: 1.01−1.07), but not associated with mortality (OR = 1.00, 95% CI: 0.98−1.03). Lower 1,25(OH)2D levels were not associated with the risk of graft failure (OR = 1.01, 95% CI: 0.99−1.02) and mortality (OR = 1.01, 95% CI: 0.99−1.02).ConclusionBaseline 25(OH)D concentrations but not 1,25(OH)2D concentrations were independently and inversely associated with graft loss in adult KTRs

Contrasting Soil Bacterial Community, Diversity, and Function in Two Forests in China

Bacteria are the highest abundant microorganisms in the soil. To investigate bacteria community structures, diversity, and functions, contrasting them in four different seasons all the year round with/within two different forest type soils of China. We analyzed soil bacterial community based on 16S rRNA gene sequencing via Illumina HiSeq platform at a temperate deciduous broad-leaved forest (Baotianman, BTM) and a tropical rainforest (Jianfengling, JFL). We obtained 51,137 operational taxonomic units (OTUs) and classified them into 44 phyla and 556 known genera, 18.2% of which had a relative abundance >1%. The composition in each phylum was similar between the two forest sites. Proteobacteria and Acidobacteria were the most abundant phyla in the soil samples between the two forest sites. The Shannon index did not significantly differ among the four seasons at BTM or JFL and was higher at BTM than JFL in each season. The bacteria community at both BTM and JFL showed two significant (P < 0.05) predicted functions related to carbon cycle (anoxygenic photoautotrophy sulfur oxidizing and anoxygenic photoautotrophy) and three significant (P < 0.05) predicted functions related to nitrogen cycle (nitrous denitrificaton, nitrite denitrification, and nitrous oxide denitrification). We provide the basis on how changes in bacterial community composition and diversity leading to differences in carbon and nitrogen cycles at the two forests

Inorganic molecular sieves: Preparation, modification and industrial application in catalytic processes

[EN] The increasing environmental concern and promotion of “green processes” are forcing the substitution of traditional acid and base homogeneous catalysts by solid ones. Among these heterogeneous catalysts, zeolites and zeotypes can be considered as real “green” catalysts, due to their benign nature from an environmental point of view. The importance of these inorganic molecular sieves within the field of heterogeneous catalysis relies not only on their microporous structure and the related shape selectivity, but also on the flexibility of their chemical composition. Modification of the zeolite framework composition results in materials with acidic, basic or redox properties, whereas multifunctional catalysts can be obtained by introducing metals by ion exchange or impregnation procedures, that can catalyze hydrogenation–dehydrogenation reactions, and the number of commercial applications of zeolite based catalysts is continuously expanding. In this review we discuss determinant issues for the development of zeolite based catalysts, going from zeolite catalyst preparation up to their industrial application. Concerning the synthesis of microporous materials we present some of the new trends moving into larger pore structures or into organic free synthesis media procedures, thanks to the incorporation of novel organic templates or alternative framework elements, and to the use of high-throughput synthesis methods. Post-synthesis zeolite modification and final catalyst conformation for industrial use are briefly discussed. In a last section we give a thorough overview on the application of zeolites in industrial processes. Some of them are well established mature technologies, such as fluid catalytic cracking, hydrocracking or aromatics alkylation. Although the number of zeolite structures commercially used as heterogeneous catalysts in these fields is limited, the development of new catalysts is a continuous challenge due to the need for processing heavier feeds or for increasing the quality of the products. The application of zeolite based catalysts in the production of chemicals and fine chemicals is an emerging field, and will greatly depend on the discovery of new or known structures by alternative, lower cost, synthesis routes, and the fine tuning of their textural properties. Finally, biomass conversion and selective catalytic reduction for conversion of NOx are two active research fields, highlighting the interest in these potential industrial applications.The authors acknowledge financial support from Ministerio de Ciencia e Innovacion (project Consolider-Ingenio 2010 MULTICAT).Martínez Sánchez, MC.; Corma Canós, A. (2011). Inorganic molecular sieves: Preparation, modification and industrial application in catalytic processes. Coordination Chemistry Reviews. 255(13-14):1558-1580. doi:10.1016/j.ccr.2011.03.014S1558158025513-1

Association between Vegetable Consumption and Blood Pressure, Stratified by BMI, among Chinese Adolescents Aged 13–17 Years: A National Cross-Sectional Study

The association between vegetable intake and blood pressure (BP) in adolescents is still inconsistent, and the description of the recommended daily vegetable consumption is abstract and nonfigurative. Here we aimed to investigate the association between vegetable consumption and BP and further look for a simple way to describe a satisfactory level of daily vegetable consumption for adolescents. We recruited 18,757 adolescents, aged 13–17 years, from seven provinces in China in 2013. A standard physical examination, including height, weight and BP was conducted. Information regarding vegetable intake was collected by questionnaire, and one serving of vegetables was defined as the size of an adult’s fist. Multivariable linear and logistic regression models were used for analysis after adjusting for covariates. Approximately 12.2%, 38.0%, 28.7%, and 21.1% of the adolescents reported daily vegetable consumption of <1, 1~2, 2~3, and ≥3 servings, respectively. Adolescents whose daily vegetable consumption was ≥3 servings showed a lower risk of high blood pressure (HBP) (OR = 0.74, 95%CI: 0.58~0.94, p = 0.013) compared to those with daily vegetable consumptions of < 1 serving. When stratified by body mass index (BMI), in overweight adolescents, participants with 2~3 or ≥3 servings/day had an OR of 0.66 (95%CI: 0.45~0.97) or 0.63 (95%CI: 0.42~0.95) compared with the reference group. Daily vegetable intake of at least three servings (three adult’s fists) is associated with a lower HBP risk in adolescents, which leads to a simple message: “consuming at least three fists of vegetables every day will improve your blood pressure profile”

Improved Coprime Linear Array Configuration for Moving Platform in DOA Estimation

International audienceMoving platform based coprime linear arrays(CLAs) have been studied in recent years. It is shown that byshifting a CLA a half wavelength, the resulting difference coarraycan be regarded as the union of the difference coarray of theoriginal CLA and its two shifted versions with one lag to theleft and one lag to the right, respectively, filling the majority orall holes in the difference coarray. However, only the lags whichare neighbors of holes are actually useful, while the shifts of theother lags generate lags which already exist, contributing thennegligibly to the increase of the degrees of freedom (DOFs). Inthis letter, an improved CLA configuration for moving platform isproposed. By judiciously designing the sensor positions, all lagscan be utilized to fill holes, consequently a difference coarraywith more consecutive lags can be obtained. Compared withthe original configuration, the proposed configuration can detectmore sources with the same number of sensors and same lengthof array motion

An Ambiguity-Solving Method for DOA Estimation with Unfolded Co-prime Arrays

International audienc