A Pyrrole-Based Triazolium-Phane with Nh and Cationic Ch Donor Groups as a Receptor for Tetrahedral Oxyanions that Functions in Polar Media

The pyrrole-based triazolium-phane 1(4+)center dot 4BF(4)(-) has been prepared via the tetraalkylation of a macrocycle originally prepared via click chemistry. It displays a high selectivity for tetrahedral oxyanions relative to various test monoanions and trigonal planar anions in mixed polar organic-aqueous media. This selectivity is solvent dependent and is less pronounced in acetonitrile. Theoretical calculations were carried out in with the chloride anion in an effort to understand the influence of solvent on the intrinsic hydrogen bonding ability of the donor groups (pyrrole N-H, benzene C-H and triazolium C-H). The host-guest interactions between receptor 1(4+)center dot 4BF(4)(-) and representative tetrahedral oxyanions were further analysed by H-1 NMR spectroscopy, and the findings proved consistent with the differences in the intrinsic strength of the various H-bond donor groups inferred from the electronic structure calculations carried out in methanol, namely that (CH)(+)-anion interactions are less important in an energetic sense than neutral CH-anion interactions in polar media. Single crystal X-ray diffraction analyses of the mixed salts 1(4+)center dot HP2O73-center dot BF4- and 31(4+)center dot 4H(2)PO(4)(-)center dot 8BF(4)(-) confirmed that receptor 1(4+) can bind the pyrophosphate and phosphate anions in the solid state.Cai, Jiajia, Benjamin P. Hay, Neil J. Young, Xiaoping Yang, and Jonathan L. Sessler. "A pyrrole-based triazolium-phane with NH and cationic CH donor groups as a receptor for tetrahedral oxyanions that functions in polar media." Chemical Science 4, no. 4 (Jan., 2013): 1560-1567.Chemistr

Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds

[EN] To meet the dynamic workload requirements in widespread task-batch based workflow applications, it is important to design algorithms for DAG-based platforms (such as Dryad, Spark and Pegasus) to rent virtual machines from public clouds dynamically. In terms of depths and functionalities, tasks of different task-batches are merged into task-units. A unit-aware deadline division method is investigated for properly dividing workflow deadlines to task deadlines so as to minimize the utilization of rented intervals. A rule-based task scheduling method is presented for allocating tasks to time slots of rented Virtual Machines (VMs) with a task right shifting operation and a weighted priority composite rule. A Unit-aware Rule-based Heuristic (URH) is proposed for elastically provisioning VMs to task-batch based workflows to minimize the rental cost in DAG-based cloud platforms. Effectiveness of the proposed URH methods is verified by comparing them against two adapted existing algorithms for similar problems on some realistic workflows.The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No.61602243 and 61572127), the Natural Science Foundation of Jiangsu Province (Grant No.BK20160846), the Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Grant No. 30916014107). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (DPI2015-65895-R) financed by FEDER funds.Cai, Z.; Li, X.; Ruiz García, R. (2019). Resource Provisioning for Task-Batch Based Workflows with Deadlines in Public Clouds. IEEE Transactions on Cloud Computing. 7(3):814-826. https://doi.org/10.1109/TCC.2017.2663426S8148267

A Voice Disease Detection Method Based on MFCCs and Shallow CNN

The incidence rate of voice diseases is increasing year by year. The use of software for remote diagnosis is a technical development trend and has important practical value. Among voice diseases, common diseases that cause hoarseness include spasmodic dysphonia, vocal cord paralysis, vocal nodule, and vocal cord polyp. This paper presents a voice disease detection method that can be applied in a wide range of clinical. We cooperated with Xiangya Hospital of Central South University to collect voice samples from sixty-one different patients. The Mel Frequency Cepstrum Coefficient (MFCC) parameters are extracted as input features to describe the voice in the form of data. An innovative model combining MFCC parameters and single convolution layer CNN is proposed for fast calculation and classification. The highest accuracy we achieved was 92%, it is fully ahead of the original research results and internationally advanced. And we use Advanced Voice Function Assessment Databases (AVFAD) to evaluate the generalization ability of the method we proposed, which achieved an accuracy rate of 98%. Experiments on clinical and standard datasets show that for the pathological detection of voice diseases, our method has greatly improved in accuracy and computational efficiency

A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds

[EN] Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms. (C) 2017 Elsevier B.V. All rights reserved.The authors would like to thank the reviewers for their constructive and useful comments. This work is supported by the National Natural Science Foundation of China (Grant No. 61602243 and 61572127), the Natural Science Foundation ofJiangsu Province (Grant No. BK20160846), Jiangsu Key Laboratory of Image and Video Understanding for Social Safety (Nanjing University of Science and Technology, Grant No. 30916014107), the Fundamental Research Funds for the Central University (Grant No. 30916015104). Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD" (No. DP12015-65895-R) co-financed by FEDER funds.Cai, Z.; Li, X.; Ruiz García, R.; Li, Q. (2017). A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds. Future Generation Computer Systems. 71:57-72. https://doi.org/10.1016/j.future.2017.01.020S57727

Nonparametric Quantile Estimations for Dynamic Smooth Coefficient Models

We suggest quantile regression methods for a class of smooth coefficient time series models. We use both local polynomial and local constant fitting schemes to estimate the smooth coefficients in a quantile framework. We establish the asymptotic properties of both the local polynomial and local constant estimators for α-mixing time series. We also suggest a bandwidth selector based on the nonparametric version of the Akaike information criterion, along with a consistent estimate of the asymptotic covariance matrix.We evaluate the asymptotic behaviors of the estimators at boundaries and compare the local polynomial quantile estimator and the local constant estimator. A simulation study is carried out to illustrate the performance of estimates. An empirical application of the model to real data further demonstrates the potential of the proposed modeling procedures.This paper was published in Journal of the American Statistical Association, December 2008, Vol. 103, No. 484