LogBase: A Scalable Log-structured Database System in the Cloud

Numerous applications such as financial transactions (e.g., stock trading) are write-heavy in nature. The shift from reads to writes in web applications has also been accelerating in recent years. Write-ahead-logging is a common approach for providing recovery capability while improving performance in most storage systems. However, the separation of log and application data incurs write overheads observed in write-heavy environments and hence adversely affects the write throughput and recovery time in the system. In this paper, we introduce LogBase - a scalable log-structured database system that adopts log-only storage for removing the write bottleneck and supporting fast system recovery. LogBase is designed to be dynamically deployed on commodity clusters to take advantage of elastic scaling property of cloud environments. LogBase provides in-memory multiversion indexes for supporting efficient access to data maintained in the log. LogBase also supports transactions that bundle read and write operations spanning across multiple records. We implemented the proposed system and compared it with HBase and a disk-based log-structured record-oriented system modeled after RAMCloud. The experimental results show that LogBase is able to provide sustained write throughput, efficient data access out of the cache, and effective system recovery.Comment: VLDB201

Non-Markovian finite-temperature two-time correlation functions of system operators: beyond the quantum regression theorem

An extremely useful evolution equation that allows systematically calculating the two-time correlation functions (CF's) of system operators for non-Markovian open (dissipative) quantum systems is derived. The derivation is based on perturbative quantum master equation approach, so non-Markovian open quantum system models that are not exactly solvable can use our derived evolution equation to easily obtain their two-time CF's of system operators, valid to second order in the system-environment interaction. Since the form and nature of the Hamiltonian are not specified in our derived evolution equation, our evolution equation is applicable for bosonic and/or fermionic environments and can be applied to a wide range of system-environment models with any factorized (separable) system-environment initial states (pure or mixed). When applied to a general model of a system coupled to a finite-temperature bosonic environment with a system coupling operator L in the system-environment interaction Hamiltonian, the resultant evolution equation is valid for both L = L^+ and L \neq L^+ cases, in contrast to those evolution equations valid only for L = L^+ case in the literature. The derived equation that generalizes the quantum regression theorem (QRT) to the non-Markovian case will have broad applications in many different branches of physics. We then give conditions on which the QRT holds in the weak system-environment coupling case, and apply the derived evolution equation to a problem of a two-level system (atom) coupled to a finite-temperature bosonic environment (electromagnetic fields) with L \neq L^+.Comment: To appear in the Journal of Chemical Physics (12 pages, 1 figure

Automatic Learning of A Supervised Classifier for Patent Prior Art Retrieval

Prior art retrieval is the process of determining a set of possibly relevant prior arts for a specific patent or patent application. Such process is essential for various patent practices, e.g. patentability search, validity search, and infringement search. To support the automatic retrieval of prior arts, existing studies generally adopt the traditional information retrieval (IR) approach or extend the IR approach by incorporating additional information such as citations, classes of patents. Those approaches only exploit partial information of patents and thus may limit the performance of prior art retrieval. In response, we propose a novel approach which employs comprehensive information of patents and performs a supervised approach for prior art retrieval. Unlike traditional supervised learning approach which requires manual preparation of a set of positive and negative training examples, the proposed supervised technique includes a simple but effective mechanism for automatic generation of training examples. Our empirical evaluation on a large dataset consisted of 52,311 semiconductor-related patents indicates that the proposed supervised technique significantly outperforms the traditional full-text-based IR approach

Acute effects of kinesiology tape tension on soleus muscle h-reflex modulations during lying and standing postures

Copyright: © 2020 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Kinesiology tape (KT) has been widely used in the areas of sports and rehabilitation. However, there is no gold standard for the tape tension used during a KT application. The purpose of this study was to examine the effects of KT application with different tension intensities on soleus muscle Hoffmann-reflex (H-reflex) modulation during lying and standing postures. Fifteen healthy university students were tested with 3 tape tension intensities during separate visits with a randomized sequence: tape-on no tension (0KT), moderate (about 50% of the maximal tape tension: (ModKT), and maximal tape tension (MaxKT). During each experimental visit, the H-reflex measurements on the soleus muscle were taken before, during, and after the KT application for both lying and standing postures. The H-wave and M-wave recruitment curves were generated using surface electromyography (EMG). There was a main effect for posture (p = 0.001) for the maximal peak-to-peak amplitude of the H-wave and M-wave (Hmax/Mmax) ratio, showing the depressed Hmax/Mmax ratio during standing, when compared to the lying posture. Even though the tension factor had a large effect (ηp2 = 0.165), different tape tensions showed no significant differential effects for the Hmax/Mmax ratio. The spinal motoneuron excitability was not altered, even during the maximal tension KT application on the soleus muscle. Thus, the tension used during a KT application should not be a concern in terms of modulating the sensorimotor activity ascribed to elastic taping during lying and standing postures

Calculation of Weighted Geometric Dilution of Precision

To achieve high accuracy in wireless positioning systems, both accurate measurements and good geometric relationship between the mobile device and the measurement units are required. Geometric dilution of precision (GDOP) is widely used as a criterion for selecting measurement units, since it represents the geometric effect on the relationship between measurement error and positioning determination error. In the calculation of GDOP value, the maximum volume method does not necessarily guarantee the selection of the optimal four measurement units with minimum GDOP. The conventional matrix inversion method for GDOP calculation demands a large amount of operation and causes high power consumption. To select the subset of the most appropriate location measurement units which give the minimum positioning error, we need to consider not only the GDOP effect but also the error statistics property. In this paper, we employ the weighted GDOP (WGDOP), instead of GDOP, to select measurement units so as to improve the accuracy of location. The handheld global positioning system (GPS) devices and mobile phones with GPS chips can merely provide limited calculation ability and power capacity. Therefore, it is very imperative to obtain WGDOP accurately and efficiently. This paper proposed two formations of WGDOP with less computation when four measurements are available for location purposes. The proposed formulae can reduce the computational complexity required for computing the matrix inversion. The simpler WGDOP formulae for both the 2D and the 3D location estimation, without inverting a matrix, can be applied not only to GPS but also to wireless sensor networks (WSN) and cellular communication systems. Furthermore, the proposed formulae are able to provide precise solution of WGDOP calculation without incurring any approximation error

Branch and Price Solution Approach for Order Acceptance and Capacity Planning in Make-to-Order Operations

The increasing emphasis on mass customization, shortened product lifecycles, synchronized supply chains, when coupled with advances in information system, is driving most firms towards make-to-order (MTO) operations. Increasing global competition, lower profit margins, and higher customer expectations force the MTO firms to plan its capacity by managing the effective demand. The goal of this research was to maximize the operational profits of a make-to-order operation by selectively accepting incoming customer orders and simultaneously allocating capacity for them at the sales stage. For integrating the two decisions, a Mixed-Integer Linear Program (MILP) was formulated which can aid an operations manager in an MTO environment to select a set of potential customer orders such that all the selected orders are fulfilled by their deadline. The proposed model combines order acceptance/rejection decision with detailed scheduling. Experiments with the formulation indicate that for larger problem sizes, the computational time required to determine an optimal solution is prohibitive. This formulation inherits a block diagonal structure, and can be decomposed into one or more sub-problems (i.e. one sub-problem for each customer order) and a master problem by applying Dantzig-Wolfe’s decomposition principles. To efficiently solve the original MILP, an exact Branch-and-Price algorithm was successfully developed. Various approximation algorithms were developed to further improve the runtime. Experiments conducted unequivocally show the efficiency of these algorithms compared to a commercial optimization solver. The existing literature addresses the static order acceptance problem for a single machine environment having regular capacity with an objective to maximize profits and a penalty for tardiness. This dissertation has solved the order acceptance and capacity planning problem for a job shop environment with multiple resources. Both regular and overtime resources is considered. The Branch-and-Price algorithms developed in this dissertation are faster and can be incorporated in a decision support system which can be used on a daily basis to help make intelligent decisions in a MTO operation