林倩：为梦想而奋斗

每天早上7点起床,9点回家,每月8个双休日有一半时间用来工作。林倩给自己设定的底线是每个月能够有2个休息日可以睡到自然醒。每2个月可以和亲人团聚一次,每一年能有一次和丈夫外出旅行的机会。     

峰声：流行那一年

春去春又来．花谢花又开。在制作这一期的时候．已经是公历年的岁来年初,恰好刚过了圣诞,又来了新年。人们在平安夜短信（飞信）、QQ、伊妹儿三枪合并,传递祝福。过了一周的时间,又要重复同样的事情。     

写在前边的话

前段时间,本着普及中国传统文化的目的,我为一位MM讲述了春节的来历。传说在很久很久以前,有一头凶猛的怪兽,每到特定的一天（现在的除夕）才爬上岸,吞食牲畜伤害人命。后来一位乞丐老人用鞭炮吓跑了怪兽。原来怪兽最怕红色、火光和炸响。后来人们都知道了驱赶怪兽的办法。从此每年除夕,家家贴红对联、燃放爆竹。这风俗广泛流传,成了中国民间最隆重的传统节日。     

杨逸鸿：乖女孩的怪娃娃

她看上去像是一个再平常不过的邻家女孩,也让人觉得她是那种从小抱着洋娃娃、坐在童话书堆里长大的女孩。可她偏偏形容自己“很怪,怪到不能再怪”,“我只记得小时候马桶旁边的橱子里放过几本童话,”她说。     

我们叫蚁族，我们在蜗居——“蚁族”不是传说

“蚁族”是当前网络中的顶级红词。它不是指华纳兄弟电影里的蚂蚁王国,也不是指生态玩具中那些被人们用来观察赏玩的昆虫小群落。当然,更不是玄幻小说中的类人种族……     

我那BH的妈妈哟

说到妈妈,我们往往都会立刻联想到轻声细语、温柔体贴……。但其实,妈妈们也有很潮、很幽默．甚至很BH（剽悍）的一面!一起来看看网友们心中的BH妈妈!     

科技依赖症

最近听朋友讲了一个很有意思的小故事,他当时去一个公寓见A君,A君的室友为他开了门,他说明了是来找A君后,希望这位室友能够帮忙敲一下A君的卧室门,通知一下,结果这位室友却说：“我给他发个短信让他开门吧”,然后这位室友就当着他的面给A君发了一条短信。     

他们有话说

一群为了杂志内容四处奔波工作的人们,在工作中、在闲暇时、在面对镜头的时候,都那么欢乐……可遇而不可求 在两场大雪之后。蒋西河同学在周末路过昌平一个不知道叫什么名字的村庄,看见这座独木桥如此的“原生态”,赶紧借着雪景留了一张美照难道这就是所谓的“可遇而不可求”？     

这××的才是生活！

初中时,学校离家近,每次放小假我都步行回家。有一年冬天放假,我独自走在回家的路上,不经意间看到天上飞着一只老鹰。     

时间都去哪儿了

时下最流行的歌曲恐怕非《时间都去哪儿了》莫属。虽是一首老歌,却因马年春晚红遍大江南北。一时间,“时间都去哪儿了”由流行歌演变成了流行语。     

Scalable Bloom Filters

Bloom filters provide space-efficient storage of sets at the cost of a probability of false positives on membership queries. The size of the filter must be defined a priori based on the number of elements to store and the desired false positive probability, being impossible to store extra elements without increasing the false positive probability. This leads typically to a conservative assumption regarding maximum set size, possibly by orders of magnitude, and a consequent space waste. This paper proposes Scalable Bloom Filters, a variant of Bloom filters that can adapt dynamically to the number of elements stored, while assuring a maximum false positive probability.     

浅谈布隆过滤器在内容管理系统中的应用

内容管理系统的内容采集主要由爬虫进行搜集,但内容重复与否绝大多数情况下是根据内容所在的页面URI进行判定。作为一个完善的内容管理系统,必须具备对已有内容资源的识别功能。本文通过介绍布隆过滤器,并与传统的判重方式进行对比,同时改进布隆过滤器并应用于内容管理系统的资源判重的功能中,解决了内存占用无限增加,查询时间不断增长,记录内容无法删除等问题,实现了高效快速的资源判重。     

The Dynamic Bloom Filters

A Bloom filter is an effective, space-efficient data structure for concisely representing a set, and supporting approximate membership queries. Traditionally, the Bloom filter and its variants just focus on how to represent a static set and decrease the false positive probability to a sufficiently low level. By investigating mainstream applications based on the Bloom filter, we reveal that dynamic data sets are more common and important than static sets. However, existing variants of the Bloom filter cannot support dynamic data sets well. To address this issue, we propose dynamic Bloom filters to represent dynamic sets, as well as static sets and design necessary item insertion, membership query, item deletion, and filter union algorithms. The dynamic Bloom filter can control the false positive probability at a low level by expanding its capacity as the set cardinality increases. Through comprehensive mathematical analysis, we show that the dynamic Bloom filter uses less expected memory than the Bloom filter when representing dynamic sets with an upper bound on set cardinality, and also that the dynamic Bloom filter is more stable than the Bloom filter due to infrequent reconstruction when addressing dynamic sets without an upper bound on set cardinality. Moreover, the analysis results hold in stand-alone applications, as well as distributed applications.     

On the false-positive rate of Bloom filters

Bloom filters are a randomized data structure for membership queries dating back to 1970. Bloom filters sometimes give erroneous answers to queries, called false positives. Bloom analyzed the probability of such erroneous answers, called the false-positive rate, and Bloom's analysis has appeared in many publications throughout the years. We show that Bloom's analysis is incorrect and give a correct analysis.     

一种基于树形结构的布鲁姆过滤器

本文提出一种基于多层次结构的树形布鲁姆过滤器TBF。多层次结构是近年来布鲁姆过滤器及相关数据结构研究的热点。这一结构使得多层次的存储方式得以实现,减轻了片上存储的负担,而且也加快了片上查找的速度。TBF是针对BloomingTree算法存在的缺陷所改进的一种更高效的算法,它能够在低于CBF的空间需求的条件下实现与CBF相同的功能。实验证明:与BloomingTree算法相比,TBF能够有效地解决BloomingTree算法在逻辑索引时的错误问题,而且比BloomingTree算法时间上更加高效:在层数不变假阳性相同条件下,查询时间平均提高13.4%;在假阳性不变层数相同条件下,插入时间平均提高17.9%,删除时间平均提高12%。     

布鲁姆过滤器查询算法

从理论和应用两方面系统地综述了布鲁姆过滤器查询算法迄今为止的主要研究成果,分析了目前布鲁姆过滤器查询算法的研究现状,最后展望了布鲁姆过滤器查询算法未来可能的研究方向.     

偏态数据流中的Bloom Filters自适应机制研究

针对Count Bloom Filters(CBF)在对偏态分布的网络数据流进行频度检测时,其使用的固定位数的计数器容易溢出的不足,提出了一种自适应性Bloom Filters(Adaptive Bloom Filters ABF),ABF使用可扩展的逻辑计数器替代CBF中大小固定的物理计数器进行计数,逻辑计数器由数目动态变化的若干个物理计数器组成,初始状态逻辑计数器等同于物理计数器,但逻辑计数器在频度数值上溢时会自适应扩展,覆盖其外部的物理计数器,增加数值容量,保证数值的测量准确性.实验表明ABF能够更好地适应检测频度的变化,并且不显著增加误判率,在对数据偏态分布的频度测量场合比其它Count Bloom Filters更具有优势.     

Improving the performance of Invertible Bloom Lookup Tables

Invertible Bloom Lookup Tables (IBLTs) have been recently introduced as an extension of traditional Bloom filters. IBLTs store key-value pairs. Unlike traditional Bloom filters, IBLTs support both a lookup operation (given a key, return a value) and an operation that lists out all the key-value pairs stored. One issue with IBLTs is that there is a probability that a lookup operation will return “not found” for a key. In this paper, a technique to reduce this probability without affecting the storage requirement and only moderately increasing the search time is presented and evaluated. The results show that it can significantly reduce the probability of not returning a value that is actually stored in the IBLT. The overhead of the modified search procedure, compared to the standard IBLT search procedure, is small and has little impact on the average search time.