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361 条记录 (耗时 0.025 秒)

    Method of synthesizing radioisotopically labeled oligonucleotides by direct solid-phase 5' phosphitylation
    61.
    发明授权
    Method of synthesizing radioisotopically labeled oligonucleotides by direct solid-phase 5' phosphitylation 失效
    通过直接固相5'磷酸化合成放射性同位素标记的寡核苷酸的方法

    公开(公告)号：US5631361A

    公开(公告)日：1997-05-20

    申请号：US447092

    申请日：1995-05-22

    申请人： Weitian Tan ,  Radhakrishnan P. Iyer ,  Zhiwei Jiang ,  Dong Yu ,  Sudhir Agrawal

    发明人： Weitian Tan ,  Radhakrishnan P. Iyer ,  Zhiwei Jiang ,  Dong Yu ,  Sudhir Agrawal

    IPC分类号： C07H21/00 ,  C07H1/02

    CPC分类号： C07H21/00

    摘要： The present invention comprises a novel method of incorporating radiolabels and other type of labels at one or more predetermined sites within an oligonucleotide. In particular, the method comprises contacting a nascent, support-bound oligonucleotide having an unprotected 5' hydroxyl group with a suitable activating agent, followed by contacting the resulting activated nascent oligonucleotide with a labeled, Y-protected mononucleotide having an unprotected 3'-hydroxyl, thereby condensing the labeled mononucleotide and nascent oligonucleotide. Normal automated synthesis can then be continued to yield the oligonucleotide of desired length having the label in the desired location. This method advantageously yields oligonucleotides with high specific activity. The oligonucleotides thereby produced are useful for determining the pharmacokinetics and biodistribution of their non-radiolabeled counterparts, both in vitro and in vivo.

    摘要翻译： 本发明包括在寡核苷酸内的一个或多个预定位点掺入放射性标记和其他类型的标记的新方法。 特别地，该方法包括将具有未保护的5'羟基的新生支持结合的寡核苷酸与合适的活化剂接触，然后将所得活化的新生寡核苷酸与具有未保护的3'-羟基的标记的受Y保护的单核苷酸接触 从而使标记的单核苷酸和新生寡核苷酸缩合。 然后可以继续进行正常的自动合成，得到所需长度的寡核苷酸，其具有所需位置的标记。 该方法有利地产生具有高比活性的寡核苷酸。 由此产生的寡核苷酸可用于在体外和体内测定其非放射性标记的对应物的药代动力学和生物分布。

    Microwave brewing apparatus and method
    63.
    发明授权
    Microwave brewing apparatus and method 失效
    微波酿造设备及方法

    公开(公告)号：US4908222A

    公开(公告)日：1990-03-13

    申请号：US322980

    申请日：1989-03-14

    申请人： Dong Yu

    发明人： Dong Yu

    IPC分类号： A23F5/26 ,  A47J31/54 ,  A47J36/02 ,  B65D81/34

    CPC分类号： A23F5/26 ,  A47J31/547 ,  A47J36/027 ,  B65D2581/3409 ,  Y10S99/14

    摘要： A beverage brewing apparatus and method using microwave energy is disclosed. The apparatus comprises a pressurizable water reservoir and a filter chamber disposed below the reservoir. The reservoir is provided with pressure overflow means which, in operation, prevents water in the reservoir from overflowing into the filter chamber until vapor pressure is built up in the reservoir due to the influx of microwave energy so as to force the heated water to overflow into the filter chamber to mix with an infusible material, e.g. ground coffee to make a beverage.

    摘要翻译： 公开了一种使用微波能量的饮料冲泡设备和方法。 该装置包括可加压的储水器和设置在贮存器下方的过滤室。 储存器设置有压力溢流装置，其在操作中防止储存器中的水溢出到过滤室中，直到由于微波能量的涌入而在储存器中形成蒸气压力，以便迫使加热的水溢出 过滤室与不熔材料混合，例如 研磨咖啡来制作饮料。

    Discriminative pretraining of deep neural networks
    66.
    发明授权
    Discriminative pretraining of deep neural networks 有权
    深层神经网络的辨别性预训练

    公开(公告)号：US09235799B2

    公开(公告)日：2016-01-12

    申请号：US13304643

    申请日：2011-11-26

    申请人： Dong Yu ,  Li Deng ,  Frank Torsten Bernd Seide ,  Gang Li

    发明人： Dong Yu ,  Li Deng ,  Frank Torsten Bernd Seide ,  Gang Li

    IPC分类号： G10L15/16 ,  G06N3/08

    CPC分类号： G06N3/08 ,  G06N3/04

    摘要： Discriminative pretraining technique embodiments are presented that pretrain the hidden layers of a Deep Neural Network (DNN). In general, a one-hidden-layer neural network is trained first using labels discriminatively with error back-propagation (BP). Then, after discarding an output layer in the previous one-hidden-layer neural network, another randomly initialized hidden layer is added on top of the previously trained hidden layer along with a new output layer that represents the targets for classification or recognition. The resulting multiple-hidden-layer DNN is then discriminatively trained using the same strategy, and so on until the desired number of hidden layers is reached. This produces a pretrained DNN. The discriminative pretraining technique embodiments have the advantage of bringing the DNN layer weights close to a good local optimum, while still leaving them in a range with a high gradient so that they can be fine-tuned effectively.

    摘要翻译： 提出了预先训练深层神经网络（DNN）的隐藏层的识别性预训练技术实施例。 一般来说，首先使用带有误差反向传播（BP）的标签对标签进行单层隐藏层神经网络的训练。 然后，在丢弃前一个隐藏层神经网络中的输出层之后，将另一个随机初始化的隐藏层与先前训练过的隐藏层一起添加，并将其代表表示用于分类或识别的目标的新输出层。 然后使用相同的策略对所得到的多隐层DNN进行鉴别性训练，等等，直到达到所需数量的隐藏层。 这产生了一个预训练的DNN。 鉴别预培训技术实施例具有使DNN层权重接近良好的局部最优值的优点，同时仍将它们留在具有高梯度的范围内，使得它们可以被有效地微调。

    Full-sequence training of deep structures for speech recognition
    68.
    发明授权
    Full-sequence training of deep structures for speech recognition 有权
    用于语音识别的深层结构的全序训练

    公开(公告)号：US09031844B2

    公开(公告)日：2015-05-12

    申请号：US12886568

    申请日：2010-09-21

    申请人： Dong Yu ,  Li Deng ,  Abdel-rahman Samir Abdel-rahman Mohamed

    发明人： Dong Yu ,  Li Deng ,  Abdel-rahman Samir Abdel-rahman Mohamed

    IPC分类号： G10L15/14 ,  G06K9/62 ,  G06N3/04 ,  G06N3/08

    CPC分类号： G06K9/6296 ,  G06N3/0454 ,  G06N3/084 ,  G10L15/14

    摘要： A method includes an act of causing a processor to access a deep-structured model retained in a computer-readable medium, the deep-structured model includes a plurality of layers with respective weights assigned to the plurality of layers, transition probabilities between states, and language model scores. The method further includes 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： 一种方法包括使处理器访问保存在计算机可读介质中的深层结构模型的行为，所述深层结构模型包括分配给所述多个层的各个权重的多个层，状态之间的转移概率和 语言模型得分。 该方法还包括使用基于序列而不是一组不相关帧的优化准则来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。

    Deep belief network for large vocabulary continuous speech recognition
    69.
    发明授权
    Deep belief network for large vocabulary continuous speech recognition 有权
    深层信念网络为大词汇连续语音识别

    公开(公告)号：US08972253B2

    公开(公告)日：2015-03-03

    申请号：US12882233

    申请日：2010-09-15

    申请人： Li Deng ,  Dong Yu ,  George Edward Dahl

    发明人： Li Deng ,  Dong Yu ,  George Edward Dahl

    IPC分类号： G10L15/00 ,  G10L15/16 ,  G10L15/14

    CPC分类号： G10L15/14 ,  G06N3/0454 ,  G06N3/084

    摘要： A method is disclosed herein that includes an act of causing a processor to receive a sample, wherein the sample is one of spoken utterance, an online handwriting sample, or a moving image sample. The method also comprises the act of causing the processor to decode the sample based at least in part upon an output of a combination of a deep structure and a context-dependent Hidden Markov Model (HMM), wherein the deep structure is configured to output a posterior probability of a context-dependent unit. The deep structure is a Deep Belief Network consisting of many layers of nonlinear units with connecting weights between layers trained by a pretraining step followed by a fine-tuning step.

    摘要翻译： 本文公开了一种包括使处理器接收样本的动作的方法，其中样本是口语发音之一，在线手写样本或运动图像样本之一。 该方法还包括使处理器至少部分地基于深结构和上下文相关隐马尔可夫模型（HMM）的组合的输出来对样本进行解码的动作，其中深结构被配置为输出 上下文相关单位的后验概率。 深层结构是由许多非线性单元组成的深层信念网络，其中层之间的连接权重通过预培训步骤后跟微调步骤训练。