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1. 发明申请

US20120157517A1 PHARMACEUTICAL SOLUTION OF TAXANES COMPRISING PH REGULATOR AND PREPARATION METHOD THEREOF 有权
标题翻译：包含PH调节剂的TAXANES的药物溶液及其制备方法
公开(公告)号：US20120157517A1
公开(公告)日：2012-06-21
申请号：US13393233
申请日：2010-10-22
申请人： Jianming Chen , Baoan Gao , Li Deng , Qiuxia Yang , Jing Sun , Wei Liu , Peng Gu , Yingying Zhang , Jialiang Zhang
发明人： Jianming Chen , Baoan Gao , Li Deng , Qiuxia Yang , Jing Sun , Wei Liu , Peng Gu , Yingying Zhang , Jialiang Zhang
IPC分类号： A61K31/337 , A61P35/00
CPC分类号： A61K31/337 , A61K9/0019 , A61K47/10 , A61K47/12
摘要： A pharmaceutical solution of taxanes, its preparation method, a composition comprising said solution and its pharmaceutical combination package are disclosed. Said pharmaceutical solution comprises taxanes, a pH regulator and a solvent, wherein the pH regulator is a water-soluble acid.
摘要翻译：紫杉烷的药物溶液，其制备方法，包含所述溶液的组合物及其药物组合包装。所述药物溶液包括紫杉烷，pH调节剂和溶剂，其中pH调节剂是水溶性酸。

2. 发明授权

US09241922B2 Pharmaceutical solution of taxanes comprising pH regulator and preparation method thereof 有权
标题翻译：包含pH调节剂的紫杉烷的药物溶液及其制备方法
公开(公告)号：US09241922B2
公开(公告)日：2016-01-26
申请号：US13393233
申请日：2010-10-22
申请人： Jianming Chen , Baoan Gao , Li Deng , Qiuxia Yang , Jing Sun , Wei Liu , Peng Gu , Yingying Zhang , Jialiang Zhang
发明人： Jianming Chen , Baoan Gao , Li Deng , Qiuxia Yang , Jing Sun , Wei Liu , Peng Gu , Yingying Zhang , Jialiang Zhang
IPC分类号： A61K31/337 , A61K9/00 , A61K47/10 , A61K47/12
CPC分类号： A61K31/337 , A61K9/0019 , A61K47/10 , A61K47/12
摘要： A pharmaceutical solution of taxanes, its preparation method, a composition comprising said solution and its pharmaceutical combination package are disclosed. Said pharmaceutical solution comprises taxanes, a pH regulator and a solvent, wherein the pH regulator is a water-soluble acid.
摘要翻译：紫杉烷的药物溶液，其制备方法，包含所述溶液的组合物及其药物组合包装。所述药物溶液包括紫杉烷，pH调节剂和溶剂，其中pH调节剂是水溶性酸。

3. 发明授权

US09165243B2 Tensor deep stacked neural network 有权
标题翻译：张量深层神经网络
公开(公告)号：US09165243B2
公开(公告)日：2015-10-20
申请号：US13397580
申请日：2012-02-15
申请人： Dong Yu , Li Deng , Brian Hutchinson
发明人： Dong Yu , Li Deng , Brian Hutchinson
IPC分类号： G06N3/04 , G06N3/08
CPC分类号： G06N3/04 , G06N3/08
摘要： A tensor deep stacked neural (T-DSN) network for obtaining predictions for discriminative modeling problems. The T-DSN network and method use bilinear modeling with a tensor representation to map a hidden layer to the predication layer. The T-DSN network is constructed by stacking blocks of a single hidden layer tensor neural network (SHLTNN) on top of each other. The single hidden layer for each block then is separated or divided into a plurality of two or more sections. In some embodiments, the hidden layer is separated into a first hidden layer section and a second hidden layer section. These multiple sections of the hidden layer are combined using a product operator to obtain an implicit hidden layer having a single section. In some embodiments the product operator is a Khatri-Rao product. A prediction is made using the implicit hidden layer and weights, and the output prediction layer is consequently obtained.
摘要翻译：张量深层次神经（T-DSN）网络，用于获得鉴别建模问题的预测。 T-DSN网络和方法使用具有张量表示的双线性建模来将隐藏层映射到预测层。 T-DSN网络由单个隐层张量神经网络（SHLTNN）的堆叠堆叠构成。然后，每个块的单个隐藏层被分离或分成多个两个或更多个部分。在一些实施例中，隐藏层被分成第一隐藏层部分和第二隐藏层部分。使用产品运算符组合隐藏层的这些多个部分以获得具有单个部分的隐式隐藏层。在一些实施例中，产品操作者是Khatri-Rao产品。使用隐式隐层和权重进行预测，从而获得输出预测层。

4. 发明申请

US20140323404A1 ANTICOAGULANT POLYPEPTIDE AND APPLICATIONS THEREOF 有权
标题翻译：抗凝剂多肽及其应用
公开(公告)号：US20140323404A1
公开(公告)日：2014-10-30
申请号：US14111470
申请日：2012-04-05
申请人： Lifei Peng , Weiqiong Gan , Zheng Shao , Qingfeng He , Li Deng , Jingjing Hu , Shuli Liao , Jida Peng
发明人： Lifei Peng , Weiqiong Gan , Zheng Shao , Qingfeng He , Li Deng , Jingjing Hu , Shuli Liao , Jida Peng
IPC分类号： C07K14/435
CPC分类号： C07K14/4354 , A61K38/00
摘要： Disclosed is an anticoagulant polypeptide and applications thereof. The anticoagulant polypeptide comprises a polypeptide formed by an amino acid sequence as represented in Seq. ID No. 1; or comprises a derived polypeptide that selectively inhibits coagulation factor XIa and is formed by an amino acid sequence, as represented in Seq. ID No. 1, that has undergone one or multiple amino acid residue substitutions, deletions, or insertions. The anticoagulant polypeptide is a selective inhibitor for coagulation factor XIa, has anticoagulant activity and small side-effect, and can be used in preparing medicines for the prevention and treatment of thrombotic diseases.
摘要翻译：公开了一种抗凝多肽及其应用。抗凝多肽包含由Seq所示的氨基酸序列形成的多肽。身份证号1; 或者包含选择性抑制凝血因子XIa并由Seq。所示的氨基酸序列形成的衍生多肽。 ID No.1，已经经历一个或多个氨基酸残基的取代，缺失或插入。抗凝多肽是凝血因子XIa的选择性抑制剂，具有抗凝活性和副作用小，可用于制备预防和治疗血栓性疾病的药物。

5. 发明授权

US08765955B2 Asymmetric aldol additions using bifunctional cinchona-alkaloid-based catalysts 有权
标题翻译：使用双功能胡萝卜素 - 生物碱类催化剂的不对称醛醇醛添加剂
公开(公告)号：US08765955B2
公开(公告)日：2014-07-01
申请号：US11442667
申请日：2006-05-26
申请人： Li Deng , Hongming Li
发明人： Li Deng , Hongming Li
IPC分类号： C07D453/04
CPC分类号： C07D453/04
摘要： One aspect of the present invention relates to asymmetric catalytic nitroaldol (Henry) reactions with ketones as the electrophilic component. In one embodiment, the present invention relates to asymmetric nitroaldol reactions with α-keto esters catalyzed by a new C6′-OH cinchona alkaloid catalyst. In certain embodiments, this reaction is operationally simple and affords high enantioselectivity as well as good to excellent yield for an exceptionally broad range of α-keto esters.
摘要翻译：本发明的一个方面涉及与酮作为亲电子组分的不对称催化硝基醛（Henry）反应。在一个实施方案中，本发明涉及与新的C6'-OH cinchona生物碱催化剂催化的α-酮酯的不对称硝基醛反应。在某些实施方案中，该反应在操作上是简单的，并且对于特别宽范围的α-酮酯而言具有高的对映选择性以及良好至极好的收率。

6. 发明申请

US20120303565A1 LEARNING PROCESSES FOR SINGLE HIDDEN LAYER NEURAL NETWORKS WITH LINEAR OUTPUT UNITS 有权
标题翻译：具有线性输出单元的单一隐藏层神经网络的学习过程
公开(公告)号：US20120303565A1
公开(公告)日：2012-11-29
申请号：US13113100
申请日：2011-05-23
申请人： Li Deng , Dong Yu
发明人： Li Deng , Dong Yu
IPC分类号： G06N3/08
CPC分类号： G06N3/08 , G05B13/027 , G06N99/005 , G06T2207/20084
摘要： Learning processes for a single hidden layer neural network, including linear input units, nonlinear hidden units, and linear output units, calculate the lower-layer network parameter gradients by taking into consideration a solution for the upper-layer network parameters. The upper-layer network parameters are calculated by a closed form formula given the lower-layer network parameters. An accelerated gradient algorithm can be used to update the lower-layer network parameters. A weighted gradient also can be used. With the combination of these techniques, accelerated training with faster convergence, to a point with a lower error rate, can be obtained.
摘要翻译：通过考虑上层网络参数的解决方案，单层隐层神经网络的学习过程包括线性输入单元，非线性隐藏单元和线性输出单元，计算下层网络参数梯度。给定较低层网络参数，上层网络参数由闭合形式公式计算。可以使用加速梯度算法来更新下层网络参数。也可以使用加权梯度。通过这些技术的组合，可以获得具有较快收敛速度的加速训练，降低错误率的点。

7. 发明申请

US20120254086A1 DEEP CONVEX NETWORK WITH JOINT USE OF NONLINEAR RANDOM PROJECTION, RESTRICTED BOLTZMANN MACHINE AND BATCH-BASED PARALLELIZABLE OPTIMIZATION 有权
标题翻译：连续使用非线性随机投影，限制性BOLTZMANN机器和基于批量的平行优化的深层网络
公开(公告)号：US20120254086A1
公开(公告)日：2012-10-04
申请号：US13077978
申请日：2011-03-31
申请人： Li Deng , Dong Yu , Alejandro Acero
发明人： Li Deng , Dong Yu , Alejandro Acero
IPC分类号： G06N3/08
CPC分类号： G06N3/08 , G06N3/02 , G06N3/04 , G06N3/0454
摘要： A method is disclosed herein that includes an act of causing a processor to access a deep-structured, layered or hierarchical model, called deep convex network, retained in a computer-readable medium, wherein the deep-structured model comprises a plurality of layers with weights assigned thereto. This layered model can produce the output serving as the scores to combine with transition probabilities between states in a hidden Markov model and language model scores to form a full speech recognizer. The method makes joint use of nonlinear random projections and RBM weights, and it stacks a lower module's output with the raw data to establish its immediately higher module. Batch-based, convex optimization is performed to learn a portion of the deep convex network's weights, rendering it appropriate for parallel computation to accomplish the training. The method can further include the act of jointly substantially optimizing the weights, the transition probabilities, and the language model scores of the deep-structured model using the optimization criterion based on a sequence rather than a set of unrelated frames.
摘要翻译：本文公开了一种方法，其包括使处理器访问被保留在计算机可读介质中的称为深凸网络的深层结构的分层或层次模型的动作，其中深层结构模型包括多个具有分配给它的权重。该分层模型可以产生作为分数的输出，以与隐藏的马尔可夫模型和语言模型分数中的状态之间的转移概率相结合，以形成完整的语音识别器。该方法联合使用非线性随机投影和RBM权重，并将较低模块的输出与原始数据叠加以建立其立即更高的模块。执行基于批次的凸优化来学习深凸网络权重的一部分，使其适合于并行计算以完成训练。该方法还可以包括使用基于序列而不是一组不相关帧的优化准则共同基本优化深层结构模型的权重，转移概率和语言模型分数的动作。

8. 发明授权

US08219406B2 Speech-centric multimodal user interface design in mobile technology 有权
标题翻译：以移动技术为中心的多模态用户界面设计
公开(公告)号：US08219406B2
公开(公告)日：2012-07-10
申请号：US11686722
申请日：2007-03-15
申请人： Dong Yu , Li Deng
发明人： Dong Yu , Li Deng
IPC分类号： G10L21/00
CPC分类号： G06F3/038 , G06F2203/0381 , G10L15/24
摘要： A multi-modal human computer interface (HCI) receives a plurality of available information inputs concurrently, or serially, and employs a subset of the inputs to determine or infer user intent with respect to a communication or information goal. Received inputs are respectively parsed, and the parsed inputs are analyzed and optionally synthesized with respect to one or more of each other. In the event sufficient information is not available to determine user intent or goal, feedback can be provided to the user in order to facilitate clarifying, confirming, or augmenting the information inputs.
摘要翻译：多模式人机界面（HCI）同时或串行地接收多个可用信息输入，并且使用输入的子集来确定或推断关于通信或信息目标的用户意图。分别对接收到的输入进行解析，并且解析输入相对于彼此中的一个或多个进行分析并任选地合成。如果没有足够的信息来确定用户意图或目标，则可以向用户提供反馈，以便于澄清，确认或增加信息输入。

9. 发明授权

US08185389B2 Noise suppressor for robust speech recognition 有权
标题翻译：噪声抑制器用于强大的语音识别
公开(公告)号：US08185389B2
公开(公告)日：2012-05-22
申请号：US12335558
申请日：2008-12-16
申请人： Dong Yu , Li Deng , Yifan Gong , Jian Wu , Alejandro Acero
发明人： Dong Yu , Li Deng , Yifan Gong , Jian Wu , Alejandro Acero
IPC分类号： G10L15/20
CPC分类号： G10L21/0208 , G10L15/20
摘要： Described is noise reduction technology generally for speech input in which a noise-suppression related gain value for the frame is determined based upon a noise level associated with that frame in addition to the signal to noise ratios (SNRs). In one implementation, a noise reduction mechanism is based upon minimum mean square error, Mel-frequency cepstra noise reduction technology. A high gain value (e.g., one) is set to accomplish little or no noise suppression when the noise level is below a threshold low level, and a low gain value set or computed to accomplish large noise suppression above a threshold high noise level. A noise-power dependent function, e.g., a log-linear interpolation, is used to compute the gain between the thresholds. Smoothing may be performed by modifying the gain value based upon a prior frame's gain value. Also described is learning parameters used in noise reduction via a step-adaptive discriminative learning algorithm.
摘要翻译：描述了通常用于语音输入的噪声降低技术，其中除了信噪比（SNR）之外，基于与该帧相关联的噪声电平来确定用于帧的噪声抑制相关增益值。在一个实现中，降噪机制基于最小均方误差，Mel-frequency cepstra降噪技术。设置高增益值（例如一个），以在噪声电平低于阈值低电平时实现很少或没有噪声抑制，以及设置或计算的低增益值，以实现高于阈值高噪声电平的大噪声抑制。使用噪声功率相关函数，例如对数线性插值来计算阈值之间的增益。可以通过基于先前帧的增益值修改增益值来执行平滑化。还描述了通过步进自适应识别学习算法在降噪中使用的学习参数。

10. 发明申请

US20110191274A1 Deep-Structured Conditional Random Fields for Sequential Labeling and Classification 有权
标题翻译：用于顺序标记和分类的深层结构条件随机场
公开(公告)号：US20110191274A1
公开(公告)日：2011-08-04
申请号：US12696051
申请日：2010-01-29
申请人： Dong Yu , Li Deng , Shizhen Wang
发明人： Dong Yu , Li Deng , Shizhen Wang
IPC分类号： G06F15/18 , G06N5/02
CPC分类号： G06F15/18 , G06N5/02
摘要： Described is a technology by which a deep-structured (multiple layered) conditional random field model is trained and used for classification of sequential data. Sequential data is processed at each layer, from the lowest layer to a final (highest) layer, to output data in the form of conditional probabilities of classes given the sequential input data. Each higher layer inputs the conditional probability data and the sequential data jointly to output further probability data, and so forth, until the final layer which outputs the classification data. Also described is layer-by-layer training, supervised or unsupervised. Unsupervised training may process raw features to minimize average frame-level conditional entropy while maximizing state occupation entropy, or to minimize reconstruction error. Also described is a technique for back-propagation of error information of the final layer to iteratively fine tune the parameters of the lower layers, and joint training, including joint training via subgroups of layers.
摘要翻译：描述了一种深层结构（多层）条件随机场模型被训练并用于顺序数据分类的技术。在从最低层到最终（最高）层的每个层处理顺序数据，以给定顺序输入数据的类的条件概率的形式输出数据。每个较高层输入条件概率数据和顺序数据，共同输出进一步的概率数据，等等，直到输出分类数据的最后一层。还描述了逐层培训，监督或无监督。无监督训练可以处理原始特征以最小化平均帧级条件熵，同时最大化状态占用熵，或最小化重建误差。还描述了用于反向传播最终层的误差信息的技术，以迭代地微调下层的参数，以及联合训练，包括通过子层的联合训练。

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