公开(公告)号：US20120143792A1

公开(公告)日：2012-06-07

申请号：US12959060

申请日：2010-12-02

申请人： Taifeng Wang ,  Bin Gao ,  Tie-Yan Liu

发明人： Taifeng Wang ,  Bin Gao ,  Tie-Yan Liu

IPC分类号： G06F17/30 ,  G06F15/18

CPC分类号： G06F17/30873 ,  G06F17/30867

摘要： Some implementations provide techniques for selecting web pages for inclusion in an index. For example, some implementations apply regularization to select a subset of the crawled web pages for indexing based on link relationships between the crawled web pages, features extracted from the crawled web pages, and user behavior information determined for at least some of the crawled web pages. Further, in some implementations, the user behavior information may be used to sort a training set of crawled web pages into a plurality of labeled groups. The labeled groups may be represented in a directed graph that indicates relative priorities for being selected for indexing.

摘要翻译： 一些实现提供用于选择包括在索引中的网页的技术。 例如，一些实现应用正则化来基于被爬网的网页之间的链接关系，从被爬网的网页提取的特征以及为至少一些被爬网的网页确定的用户行为信息来选择用于索引的被爬网网页的子集 。 此外，在一些实现中，可以使用用户行为信息来将爬网网页的训练集合分类成多个标记的组。 标记的组可以在有向图中表示，其指示被选择用于索引的相对优先级。

公开(公告)号：US20110295845A1

公开(公告)日：2011-12-01

申请号：US12789278

申请日：2010-05-27

申请人： Bin Gao ,  Taifeng Wang ,  Tie-Yan Liu

发明人： Bin Gao ,  Taifeng Wang ,  Tie-Yan Liu

IPC分类号： G06F17/30

CPC分类号： G06F16/951

摘要： Importance ranking of web pages is performed by defining a graph-based regularization term based on document features, edge features, and a web graph of a plurality of web pages, and deriving a loss term based on human feedback data. The graph-based regularization term and the loss term are combined to obtain a global objective function. The global objective function is optimized to obtain parameters for the document features and edge features and to produce static rank scores for the plurality of web pages. Further, the plurality of web pages is ordered based on the static rank scores.

摘要翻译： 通过基于文档特征，边缘特征和多个网页的网络图定义基于图形的正则化术语，并且基于人类反馈数据导出丢失项来执行网页的重要性排名。 基于图形的正则化项和损失项被组合以获得全局目标函数。 优化全局目标函数以获得文档特征和边缘特征的参数，并且为多个网页产生静态等级分数。 此外，基于静态等级分数来排序多个网页。

公开(公告)号：US07974977B2

公开(公告)日：2011-07-05

申请号：US11743942

申请日：2007-05-03

申请人： Tie-Yan Liu ,  Bin Gao ,  Wei-Ying Ma

发明人： Tie-Yan Liu ,  Bin Gao ,  Wei-Ying Ma

IPC分类号： G06F7/00 ,  G06F17/30

CPC分类号： G06K9/6224 ,  G06F17/3071

摘要： A clustering system generates an original Laplacian matrix representing objects and their relationships. The clustering system initially applies an eigenvalue decomposition solver to the original Laplacian matrix for a number of iterations. The clustering system then identifies the elements of the resultant eigenvector that are stable. The clustering system then aggregates the elements of the original Laplacian matrix corresponding to the identified stable elements and forms a new Laplacian matrix that is a compressed form of the original Laplacian matrix. The clustering system repeats the applying of the eigenvalue decomposition solver and the generating of new compressed Laplacian matrices until the new Laplacian matrix is small enough so that a final solution can be generated in a reasonable amount of time.

摘要翻译： 聚类系统生成表示对象及其关系的原始拉普拉斯矩阵。 聚类系统首先将特征值分解求解器应用于原始拉普拉斯矩阵进行多次迭代。 然后，聚类系统识别所得到的特征向量的元素是稳定的。 然后，聚类系统聚合对应于所识别的稳定元素的原始拉普拉斯矩阵的元素，并形成作为原始拉普拉斯矩阵的压缩形式的新的拉普拉斯矩阵。 聚类系统重复应用特征值分解求解器和生成新的压缩拉普拉斯矩阵，直到新的拉普拉斯矩阵足够小，以便在合理的时间内生成最终解。

公开(公告)号：US07461073B2

公开(公告)日：2008-12-02

申请号：US11354208

申请日：2006-02-14

申请人： Bin Gao ,  Tie-Yan Liu ,  Wei-Ying Ma

发明人： Bin Gao ,  Tie-Yan Liu ,  Wei-Ying Ma

IPC分类号： G06F17/00 ,  G06F17/30

CPC分类号： G06F17/30598 ,  G06K9/6224 ,  Y10S707/99942 ,  Y10S707/99943 ,  Y10S707/99945

摘要： A method and system for high-order co-clustering of objects of heterogeneous types using multiple bipartite graphs is provided. A clustering system represents relationships between objects of a first type and objects of a third type as a first bipartite graph and relationships between objects of a second type and objects of the third type as a second bipartite graph. The clustering system defines an objective function that specifies an objective of the clustering process that combines an objective for the first bipartite graph and an objective for the second bipartite graph. The clustering system solves the objective function and then applies a clustering algorithm such as the K-means algorithm to the solution to identify the clusters of heterogeneous objects.

摘要翻译： 提供了使用多个二分图的异构类型的对象的高阶共聚的方法和系统。 聚类系统表示第一类型的对象与第三类型的对象之间的关系，作为第二个二分图，第二类的对象与第三类的对象之间的关系作为第二个二分图。 聚类系统定义了一个目标函数，该目标函数指定了组合第一个二分图的目标和第二个二分图的目标的聚类过程的目标。 聚类系统解决了目标函数，然后将K-means算法的聚类算法应用于解决方案，以识别异构对象的簇。

公开(公告)号：US20070192350A1

公开(公告)日：2007-08-16

申请号：US11354208

申请日：2006-02-14

申请人： Bin Gao ,  Tie-Yan Liu ,  Wei-Ying Ma

发明人： Bin Gao ,  Tie-Yan Liu ,  Wei-Ying Ma

IPC分类号： G06F7/00

CPC分类号： G06F17/30598 ,  G06K9/6224 ,  Y10S707/99942 ,  Y10S707/99943 ,  Y10S707/99945

摘要： A method and system for high-order co-clustering of objects of heterogeneous types using multiple bipartite graphs is provided. A clustering system represents relationships between objects of a first type and objects of a third type as a first bipartite graph and relationships between objects of a second type and objects of the third type as a second bipartite graph. The clustering system defines an objective function that specifies an objective of the clustering process that combines an objective for the first bipartite graph and an objective for the second bipartite graph. The clustering system solves the objective function and then applies a clustering algorithm such as the K-means algorithm to the solution to identify the clusters of heterogeneous objects.

摘要翻译： 提供了使用多个二分图的异构类型的对象的高阶共聚的方法和系统。 聚类系统表示第一类型的对象与第三类型的对象之间的关系，作为第二个二分图，第二类的对象与第三类的对象之间的关系作为第二个二分图。 聚类系统定义了一个目标函数，该目标函数指定了组合第一个二分图的目标和第二个二分图的目标的聚类过程的目标。 聚类系统解决了目标函数，然后将K-means算法的聚类算法应用于解决方案，以识别异构对象的簇。

公开(公告)号：US07743058B2

公开(公告)日：2010-06-22

申请号：US11621848

申请日：2007-01-10

申请人： Tie-Yan Liu ,  Bin Gao ,  Wei-Ying Ma

发明人： Tie-Yan Liu ,  Bin Gao ,  Wei-Ying Ma

IPC分类号： G06F7/00 ,  G06F17/30 ,  G06F17/00 ,  G06N5/00

CPC分类号： G06F17/30705 ,  G06K9/6226

摘要： A method and system for high-order co-clustering of objects of heterogeneous types is provided. A clustering system co-clusters objects of heterogeneous types based on joint distributions for objects of non-central types and objects of a central type. The clustering system uses an iterative approach to co-clustering the objects of the various types. The clustering system divides the co-clustering into a sub-problem, for each non-central type (e.g., first type and second type), of co-clustering objects of that non-central type and objects of the central type based on the joint distribution for that non-central type. After the co-clustering is completed, the clustering system clusters objects of the central type based on the clusters of the objects of the non-central types identified during co-clustering. The clustering system repeats the iterations until the clusters of objects of the central type converge on a solution.

摘要翻译： 提供了一种用于异构类型对象的高阶共聚集的方法和系统。 基于非中心类型对象和中心类型对象的联合分布，聚类系统将异构类型的对象共同聚集。 聚类系统使用迭代方法来共同分类各种类型的对象。 对于每个非中心类型（例如，第一类型和第二类型），聚类系统将共聚类分为非中心类型的共聚类对象和中心类型的对象的子问题，基于 联合分配为非中央型。 在共同聚集完成之后，聚类系统基于在共聚集期间识别的非中心类型的对象的聚类来聚类中心类型的对象。 聚类系统重复迭代，直到中心类型的对象集合在解上。

公开(公告)号：US20080168061A1

公开(公告)日：2008-07-10

申请号：US11621848

申请日：2007-01-10

申请人： Tie-Yan Liu ,  Bin Gao ,  Wei-Ying Ma

发明人： Tie-Yan Liu ,  Bin Gao ,  Wei-Ying Ma

IPC分类号： G06F17/30

CPC分类号： G06F17/30705 ,  G06K9/6226

摘要： A method and system for high-order co-clustering of objects of heterogeneous types is provided. A clustering system co-clusters objects of heterogeneous types based on joint distributions for objects of non-central types and objects of a central type. The clustering system uses an iterative approach to co-clustering the objects of the various types. The clustering system divides the co-clustering into a sub-problem, for each non-central type (e.g., first type and second type), of co-clustering objects of that non-central type and objects of the central type based on the joint distribution for that non-central type. After the co-clustering is completed, the clustering system clusters objects of the central type based on the clusters of the objects of the non-central types identified during co-clustering. The clustering system repeats the iterations until the clusters of objects of the central type converge on a solution.

摘要翻译： 提供了一种用于异构类型对象的高阶共聚集的方法和系统。 基于非中心类型对象和中心类型对象的联合分布，聚类系统将异构类型的对象共同聚集。 聚类系统使用迭代方法来共同分类各种类型的对象。 对于每个非中心类型（例如，第一类型和第二类型），聚类系统将共聚类分为非中心类型的共聚类对象和中心类型的对象的子问题，基于 联合分配为非中央型。 在共同聚集完成之后，聚类系统基于在共聚集期间识别的非中心类型的对象的聚类来聚类中心类型的对象。 聚类系统重复迭代，直到中心类型的对象集合在解上。

公开(公告)号：US09080152B2

公开(公告)日：2015-07-14

申请号：US13254039

申请日：2009-04-30

申请人： Bin Gao ,  Jie Ding

发明人： Bin Gao ,  Jie Ding

IPC分类号： A61K39/12 ,  A61K39/02 ,  A61K45/06 ,  C12N5/0783 ,  A61K35/12

CPC分类号： C12N5/0636 ,  A61K2035/124 ,  C12N2501/515 ,  C12N2510/00

摘要： Methods for expansion of antigen-specific T cells are provided. Said methods include following steps: generating antigen-specific T cells by stimulation of T cells with antigen A; introducing genes encoding immune recognition molecule specific to major histocompatibility complex (MHC) molecule bound with a peptide derived from antigen B into the antigen A specific T cell to produce bi-specific T cells recognizing both target cells expressing antigen A peptide associated MHC and target cells expressing antigen B peptide associated MHC; stimulating the bi-specific T cells by antigen A for expansion of the bi-specific T cells in vitro or in vivo. Methods of the present invention can be applied to expand various of T cells with specific to cancer cells with tumor antigen peptide loaded MHC molecules for adoptive therapy against unmet medical need such as tumors etc.

摘要翻译： 提供了用于扩增抗原特异性T细胞的方法。 所述方法包括以下步骤：通过用抗原A刺激T细胞产生抗原特异性T细胞; 引入编码针对与源自抗原B的肽结合的主要组织相容性复合物（MHC）分子特异性的免疫识别分子的基因进入抗原A特异性T细胞，以产生识别表达抗原A肽相关MHC和靶细胞的两种靶细胞的双特异性T细胞 表达抗原B肽相关MHC; 通过抗原A刺激双特异性T细胞以在体外或体内扩增双特异性T细胞。 本发明的方法可应用于扩增具有癌细胞特异性的各种具有肿瘤抗原肽的MHC分子的T细胞用于针对未满足的医疗需要如肿瘤等的过继疗法。

公开(公告)号：US08929123B2

公开(公告)日：2015-01-06

申请号：US13641832

申请日：2011-11-18

申请人： Jinfeng Kang ,  Feifei Zhang ,  Bin Gao ,  Bing Chen ,  Lifeng Liu ,  Xiaoyan Liu

发明人： Jinfeng Kang ,  Feifei Zhang ,  Bin Gao ,  Bing Chen ,  Lifeng Liu ,  Xiaoyan Liu

IPC分类号： G11C11/00 ,  G11C13/00 ,  G11C11/56

CPC分类号： G11C13/0007 ,  G11C11/5685 ,  G11C13/0069 ,  G11C2013/0073 ,  G11C2013/0083

摘要： The present disclosure provides a resistive-switching device capable of implementing multiary addition operation and a method for implementing multiary addition operation using the resistive-switching device. The resistive-switching device has a plurality of resistance values each corresponding to a respective data value stored by the resistive-switching device and ranging from a high resistance value to a low resistance value. The data value stored by the resistive-switching device is increased by ‘1’ successively with a series of set pulses having a same pulse width and a same voltage amplitude being applied thereto. The data value stored by the resistive-switching device is set to ‘0’ with a reset pulse being applied thereto, and meanwhile a data value stored by a higher-bit resistive-switching device is increased by ‘1’ with a set pulse being applied thereto. In this way, multiary addition operation is implemented.

摘要翻译： 本公开提供了一种能够实现多加法运算的电阻式开关装置和使用电阻式开关装置实现多重加法运算的方法。 电阻开关器件具有多个电阻值，每个电阻值对应于由电阻开关器件存储的各个数据值，并且从高电阻值到低电阻值。 由电阻式开关器件存储的数据值以相同的脉冲宽度和相同的电压幅度施加一系列设定脉冲连续增加“1”。 由施加复位脉冲的电阻开关器件存储的数据值被设定为“0”，同时由高位电阻开关器件存储的数据值增加“1”，设定脉冲为 应用于此。 以这种方式，实现多重添加操作。

公开(公告)号：US08924321B2

公开(公告)日：2014-12-30

申请号：US13502462

申请日：2011-11-03

申请人： Jinfeng Kang ,  Bin Gao ,  Feifei Zhang ,  Bing Chen ,  Lifeng Liu ,  Xiaoyan Liu

发明人： Jinfeng Kang ,  Bin Gao ,  Feifei Zhang ,  Bing Chen ,  Lifeng Liu ,  Xiaoyan Liu

IPC分类号： G06F15/18 ,  G06N3/00 ,  G06N3/063

CPC分类号： G06N3/063

摘要： A neuron device includes a bottom electrode, a top electrode, and a layer of metal oxide variable resistance material sandwiched between the bottom electrode and the top electrode, in which the neuron device is switched to a normal state upon application of reset pulse, and is switched to an excitation state upon application of stimulus pulses. The neuron device has a comprehensive response to different amplitude, different width of a stimulus voltage pulse and different number of a sequence of stimulus pulses, and provides functionalities of a weighting section and a computing section. The neuron device has a simple structure, excellent scalability, quick speed, low operation voltage, and is compatible with the conventional silicon-based CMOS fabrication process, and thus suitable for mass production. The neuron device is capable of performing many biological functions and complex logic operations.

摘要翻译： 神经元装置包括底部电极，顶部电极和夹在底部电极和顶部电极之间的金属氧化物可变电阻材料层，其中神经元装置在施加复位脉冲时切换到正常状态，并且是 在施加刺激脉冲时切换到激发状态。 神经元装置对刺激电压脉冲的不同幅度，不同宽度和不同数量的刺激脉冲序列具有综合响应，并提供加权部分和计算部分的功能。 神经元器件结构简单，可扩展性好，速度快，工作电压低，与传统的硅基CMOS制造工艺兼容，适合批量生产。 神经元器件能够执行许多生物学功能和复杂的逻辑运算。