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    • 3. 发明专利
    • Distributed hosting of web content using partial replication
    • 使用部分复制分发网络内容
    • JP2005327291A
    • 2005-11-24
    • JP2005139956
    • 2005-05-12
    • Microsoft Corpマイクロソフト コーポレーション
    • ZHANG CHALI XIN
    • G06F13/00G06F15/00H04L29/06H04L29/08
    • H04L67/104H04L67/1076H04L67/1095H04L69/329
    • PROBLEM TO BE SOLVED: To host a content of a web site on multiple computing devices. SOLUTION: A relative importance for each file associated with the web site is calculated. This relative importance is used to calculate a plurality of subsets of the content which are distributed to a plurality of devices within a computer cluster, such as a server array, peer-to-peer network, and the like. The subsets may include coded messages created using an erasure coding scheme on packets containing portions of one or more files. Upon retrieving a file, a fixed number of distinct coded messages are retrieved from the devices based on the scheme. The file is re-created with these distinct messages. Because multiple devices hold the content, the web site may be retrieved significantly faster and the reliability is increased without consuming a large amount of storage space or bandwidth of any one computing device. COPYRIGHT: (C)2006,JPO&NCIPI
    • 要解决的问题:在多个计算设备上托管网站的内容。

      解决方案:计算与网站相关联的每个文件的相对重要性。 该相对重要性用于计算分配给诸如服务器阵列,对等网络等的计算机集群内的多个设备的内容的多个子集。 子集可以包括使用擦除编码方案在包含一个或多个文件的部分的分组上创建的编码消息。 在检索文件时,基于该方案从设备检索固定数量的不同编码消息。 使用这些不同的消息重新创建文件。 由于多个设备保存内容,因此可以显着地更快地检索网站,并且可以增加可靠性,而不消耗任何一个计算设备的大量存储空间或带宽。 版权所有(C)2006,JPO&NCIPI

    • 6. 发明专利
    • DE602005001815D1
    • 2007-09-13
    • DE602005001815
    • 2005-06-30
    • MICROSOFT CORP
    • ZHANG CHALI JINCHOU PHILIP A
    • H04L29/08H04L12/18
    • A content distribution method for distributing content over a peer-to-peer network such that the full potential throughput of the network is achieved. The content distribution method divides the content to be distributed into many small blocks. Each of the content blocks then is assigned to a node, which can be a content-requesting node, a non-content-requesting node or a source node. Content is assigned based on a capacity of the node, where nodes having a larger capacity are assigned a greater number of content blocks and nodes having a smaller capacity are assigned a fewer content blocks. The capacity generally is defined as the upload bandwidth of the node. Redistribution queues are employed to control the throughput of the distribution. This bandwidth control strategy ensures that upload bandwidths of the peer and source nodes are fully utilized even with network anomalies such as packet losses and delivery jitters.
    • 7. 发明专利
    • DE602005001815T2
    • 2007-12-06
    • DE602005001815
    • 2005-06-30
    • MICROSOFT CORP
    • ZHANG CHALI JINCHOU PHILIP A
    • H04L29/08H04L12/18
    • A content distribution method for distributing content over a peer-to-peer network such that the full potential throughput of the network is achieved. The content distribution method divides the content to be distributed into many small blocks. Each of the content blocks then is assigned to a node, which can be a content-requesting node, a non-content-requesting node or a source node. Content is assigned based on a capacity of the node, where nodes having a larger capacity are assigned a greater number of content blocks and nodes having a smaller capacity are assigned a fewer content blocks. The capacity generally is defined as the upload bandwidth of the node. Redistribution queues are employed to control the throughput of the distribution. This bandwidth control strategy ensures that upload bandwidths of the peer and source nodes are fully utilized even with network anomalies such as packet losses and delivery jitters.
    • 8. 发明专利
    • AT369007T
    • 2007-08-15
    • AT05105865
    • 2005-06-30
    • MICROSOFT CORP
    • ZHANG CHALI JINCHOU PHILIP A
    • H04L29/08H04L12/18
    • A content distribution method for distributing content over a peer-to-peer network such that the full potential throughput of the network is achieved. The content distribution method divides the content to be distributed into many small blocks. Each of the content blocks then is assigned to a node, which can be a content-requesting node, a non-content-requesting node or a source node. Content is assigned based on a capacity of the node, where nodes having a larger capacity are assigned a greater number of content blocks and nodes having a smaller capacity are assigned a fewer content blocks. The capacity generally is defined as the upload bandwidth of the node. Redistribution queues are employed to control the throughput of the distribution. This bandwidth control strategy ensures that upload bandwidths of the peer and source nodes are fully utilized even with network anomalies such as packet losses and delivery jitters.
    • 10. 发明申请
    • MULTIPLE-INSTANCE PRUNING FOR LEARNING EFFICIENT CASCADE DETECTORS
    • 用于学习有效的CASCADE检测器的多功能校正
    • WO2009012056A3
    • 2009-03-26
    • PCT/US2008068925
    • 2008-07-01
    • MICROSOFT CORP
    • ZHANG CHAVIOLA PAUL
    • G06F15/18
    • G06K9/6256G06K9/00288G06K9/6282
    • A "Classifier Trainer" trains a combination classifier for detecting specific objects in signals (e.g., faces in images, words in speech, patterns in signals, etc.). In one embodiment "multiple instance pruning" (MIP) is introduced for training weak classifiers or "features" of the combination classifier. Specifically, a trained combination classifier and associated final threshold for setting false positive/negative operating points are combined with learned intermediate rejection thresholds to construct the combination classifier. Rejection thresholds are learned using a pruning process which ensures that objects detected by the original combination classifier are also detected by the combination classifier, thereby guaranteeing the same detection rate on the training set after pruning. The only parameter required throughout training is a target detection rate for the final cascade system. In additional embodiments, combination classifiers are trained using various combinations of weight trimming, bootstrapping, and a weak classifier termed a "fat stump" classifier.
    • “分类器训练器”训练用于检测信号中的特定对象的组合分类器(例如,图像中的面部,语音中的词,信号中的模式等)。 在一个实施例中,引入了用于训练组合分类器的弱分类器或“特征”的“多实例修剪”(MIP)。 具体来说,将训练有素的组合分类器和用于设置假正/负操作点的相关联的最终阈值与学习的中间拒绝阈值组合以构建组合分类器。 使用修剪过程学习拒绝阈值,确保由组合分类器检测到原始组合分类器检测到的对象,从而保证修剪后训练集上的相同检测率。 训练所需的唯一参数是最终级联系统的目标检测率。 在另外的实施例中,组合分类器使用称为“胖树桩”分类器的重量修剪,自举和弱分类器的各种组合进行训练。