Bounded parallel-batching scheduling with two competing agents

We consider a scheduling problem in which two agents, each with a set of non-preemptive jobs, compete to perform their jobs on a common bounded parallel-batching machine. Each of the agents wants to minimize an objective function that depends on the completion times of its own jobs. The goal is to schedule the jobs such that the overall schedule performs well with respect to the objective functions of both agents. We focus on minimizing the makespan or the total completion time of one agent, subject to an upper bound on the makespan of the other agent. We distinguish two categories of batch processing according to the compatibility of the agents. In the case where the agents are incompatible, their jobs cannot be processed in the same batch, whereas all the jobs can be processed in the same batch when the agents are compatible. We show that the makespan problem can be solved in polynomial time for the incompatible case and is NP-hard in the ordinary sense for the compatible case. Furthermore, we show that the latter admits a fully polynomial-time approximation scheme. We prove that the total completion time problem is NP-hard and is polynomially solvable for the incompatible case with a fixed number of job types.     

Maximal Pairs of Computably Enumerable Sets in the Computably Lipschitz Degrees

A set A is computably Lipschitz or cl-reducible, for short, to a set B if A is Turing reducible to B by an oracle Turing machine with use function ? such that ? is bounded by the identity function up to an additive constant, i.e., ?(n)??n+O(1). In this paper we study maximal pairs of computably enumerable (c.e.) cl-degrees or maximal pairs, for short, i.e., pairs of c.e. cl-degrees such that there is no c.e. cl-degree that is above both cl-degrees in this pair. Our main results are as follows. (1) A c.e. Turing degree contains a c.e. cl-degree that is half of a maximal pair if and only if this Turing degree contains a maximal pair if and only if this Turing degree is array noncomputable. (2) The cl-degrees of all weak truth-table complete sets are halves of maximal pairs while there is a Turing complete set A such that the cl-degree of A is not half of any maximal pair. In fact, any high c.e. Turing degree contains a c.e. cl-degree that is not half of a maximal pair. (3) Above any c.e. cl-degree there is a maximal pair. (4) There is a maximal pair which at the same time is a minimal pair. (5) There is a pair of c.e. cl-degrees that is not maximal and does not possess a least upper bound. Moreover, we make some observations on the structure of the c.e. cl-degrees in general. For instance, we give a very simple proof of the fact that there are no maximal c.e. cl-degrees.     

Analyzing sentiments in Web 2.0 social media data in Chinese: experiments on business and marketing related Chinese Web forums

Web 2.0 has brought a huge amount of user-generated, social media data that contains rich information about people’s opinions and ideas towards various products, services, and ongoing social and political events. Nowadays, many companies start to look into and try to leverage this new type of data to understand their customers in order to make better business strategies and services. As a nation with rapid economic growth in recently years, China has become visible and started to play an important role in the global business and economy. Also, with the large number of Chinese Internet users, a considerable amount of options about Chinese business and market have been expressed in social media sites. Thus, it will be of interest to explore and understand those user-generated contents in Chinese. In this study, we develop an integrated framework to analyze user sentiments from Chinese social media sites by leveraging sentiment analysis techniques. Based on the framework, we conduct experiments on two popular Chinese Web forums, both related to business and marketing. By utilizing Elastic Net together with a rich body of feature representations, we achieve the highest F-measures of 84.4 and 86.7 % for the two data sets, respectively. We also demonstrate the interpretability of Elastic Net by discussing the top-ranked features with positive or negative sentiments.     

北方缺水地区水库汛限水位调整下的极限风险研究

为了挖掘水库调蓄能力,缓解北方地区出现的水资源危机,探讨水库提高汛限水位的可能性.以海河流域岳城水库为例,采用随机模拟法计算汛限水位调整下水库的极限风险.分别建立了线性白回归模型和典型解集模型模拟入库洪水过程,通过统计特性分析检验其模拟效果.研究表明,岳城水库的汛限水位至少可以从目前的132 m提高到135 m,此时超校核洪水位的极限风险远小于原设计标准,从而可以大大增加水库蓄水,提高水库的经济和社会效益.     

盖下坝水电站输水系统布置

盖下坝水电站是位于重庆市云阳县的一座引水式水电站,装机容量132 MW,安装3台单机容量44MW机组.文章对该水电站输水系统的工程地质条件、输水系统布置以及进水口、引水隧洞、调压井及压力管道的设计作了介绍.     

动态规划模型在灌溉泵站群布局优化中的应用

针对具有耦合约束的多维动态规划模型提出了一种改进的递推方法,即改变相应各阶段对应状态变量的离散范围,以达到降维的目的。以灌溉泵站群总能耗最小为目标函数,以农田灌溉和河道引水能力为约束条件,对动态规划模型进行了求解,确定开机灌溉的泵站组合,并据此对灌溉泵站群及配套渠系的布局进行了调整。结果显示,采用此方法布局的泵站群,既能满足农田灌溉供水及河道最小生态流量的要求,又能使整个泵站群的耗能率最小。     

连续流动分析仪测定水中挥发酚不确定度的评定

文章利用SKALAR SAN++连续分析仪测定水中挥发酚浓度的不确定度,分别对工作曲线、测量的重复性、标准物质及其稀释过程等引起的不确定度进行评定;最终测得挥发酚测量结果为(0.056±0.002)mg/L;k=2.     

基于小波分析的丹江口水库水文年度入库径流变化特征

根据丹江口水库运行调度的实践,采用Morlet小波函数,对丹江口水库水文年度、汛期和非汛期的径流时间序列进行小波分析,揭示了丹江口水库入库径流多时间尺度的复杂结构,并分析了不同尺度下径流序列的周期变化和突变点。研究结果表明：水文年度、汛期、非汛期入库径流序列都存在9,20,40,50a的主要周期,并且水文年度入库径流与汛期径流具有相似的周期演变特征。研究成果可供丹江口水库运行调度参考。     

泛系再现法:科技教育军事经济变革——缘学研产商医美乐八达结合·生命扩变

泛系再现法是复述重证再发现创造和发明的因缘与过程的方法,是在新的平台上对创造过程的泛系模拟QSS*和泛系仿生的扩变life* *,它显生数专多能贵精奇的博才教育理念,也是许多大师泛系生命活力扩变的理法的集成整合.结合作者参与的教学和实践(计算机、互联网、高新技术、军事后勤、现代C10KISR*后勤网络战//科索沃战争等等),在泛系再现法框架下,以美国STEM规划为原型,论述当代科技、教育、军事、经济等等的变革趋势.同时网联千题万技的原创性开拓机理,善憾巧次现实显生,泛系模拟、诠释或解读许多学者大师的智慧.有关的理法包括;泛系运筹原理、泛系控制论、泛系生命说、网络战和缘学研产商医美乐八达结合等等理念.     

Pedestrian Detection and Tracking Using Deformable Part Models and Kalman Filtering

Abstract： Pedestrian detection techniques are important and challenging especially for complex real world scenes. They can be used for ensuring pedestrian safety, ADASs （advance driver assistance systems） and safety surveillance systems. In this paper, we propose a novel approach for multi-person tracking-by-detection using deformable part models in Kalman filtering framework. The Kalman filter is used to keep track of each person and a unique label is assigned to each tracked individual. Based on this approach, people can enter and leave the scene randomly. We test and demonstrate our results on Caltech Pedestrian benchmark, which is two orders of magnitude larger than any other existing datasets and consists of pedestrians varying widely in appearance, pose and scale. Complex situations such as people occluded by each other are handled gracefully and individual persons can be tracked correctly after a group of people split. Experiments confirm the real-time performance and robustness of our system, working in complex scenes. Our tracking model gives a tracking accuracy of 72.8% and a tracking precision of 82.3%. We can further reduce false positives by 2.8%, using Kalman filtering.