ogn

perceptual,是指和抽象概念相对的感官刺激的感知，例如可触摸、嗅到等感知cognitive,是指精神、意识行为方面的认知，例如，通过思考、学习、回忆、使用语言等认知区别还是比较明显的。

不可以，cognitive只有形容词词性。这个词的名词应该是：cognition「n.认识，认知，认识能力」

recognizant

cognitive生词本去背诵英 [u02c8ku0252gnu0259tu026av] 美 [u02c8kɑ:gnu0259tu026av]adj.认知的； 认识的网 络认知的；认识的；认识能力的；认知能力派生词：cognitively1. a child"s cognitive development 儿童的认知开发》2. As children grow older, their cognitive processes become sharper. 孩子们越长越大，他们的认知过程变得更为敏锐。

Testata中文意思说“头”

NormanBognerNormanBogner是一名编剧，代表作品有《SeventhAvenue》、《特权》等。外文名：NormanBogner职业：编剧代表作品：《SeventhAvenue》合作人物：RichardIrving

function pca (path, trainList, subDim) % % PROTOTYPE % function pca (path, trainList, subDim) % % USAGE EXAMPLE(S) % pca ("C:/FERET_Normalised/", trainList500Imgs, 200); % % GENERAL DESCRIPTION % Implements the standard Turk-Pentland Eigenfaces method. As a final % result, this function saves pcaProj matrix to the disk with all images % projected onto the subDim-dimensional subspace found by PCA. % % REFERENCES % M. Turk, A. Pentland, Eigenfaces for Recognition, Journal of Cognitive % Neurosicence, Vol. 3, No. 1, 1991, pp. 71-86 % % M.A. Turk, A.P. Pentland, Face Recognition Using Eigenfaces, Proceedings % of the IEEE Conference on Computer Vision and Pattern Recognition, % 3-6 June 1991, Maui, Hawaii, USA, pp. 586-591 % % % INPUTS: % path - full path to the normalised images from FERET database % trainList - list of images to be used for training. names should be % without extension and .pgm will be added automatically % subDim - Numer of dimensions to be retained (the desired subspace % dimensionality). if this argument is ommited, maximum % non-zero dimensions will be retained, i.e. (number of training images) - 1 % % OUTPUTS: % Function will generate and save to the disk the following outputs: % DATA - matrix where each column is one image reshaped into a vector % - this matrix size is (number of pixels) x (number of images), uint8 % imSpace - same as DATA but only images in the training set % psi - mean face (of training images) % zeroMeanSpace - mean face subtracted from each row in imSpace % pcaEigVals - eigenvalues % w - lower dimensional PCA subspace % pcaProj - all images projected onto a subDim-dimensional space % % NOTES / COMMENTS % * The following files must either be in the same path as this function % or somewhere in Matlab"s path: % 1. listAll.mat - containing the list of all 3816 FERET images % % ** Each dimension of the resulting subspace is normalised to unit length % % *** Developed using Matlab 7 % % % REVISION HISTORY % - % % RELATED FUNCTIONS (SEE ALSO) % createDistMat, feret % % ABOUT % Created: 03 Sep 2005 % Last Update: - % Revision: 1.0 % % AUTHOR: Kresimir Delac % mailto: kdelac@ieee.org % URL: % % WHEN PUBLISHING A PAPER AS A RESULT OF RESEARCH CONDUCTED BY USING THIS CODE % OR ANY PART OF IT, MAKE A REFERENCE TO THE FOLLOWING PAPER: % Delac K., Grgic M., Grgic S., Independent Comparative Study of PCA, ICA, and LDA % on the FERET Data Set, International Journal of Imaging Systems and Technology, % Vol. 15, Issue 5, 2006, pp. 252-260 % % If subDim is not given, n - 1 dimensions are % retained, where n is the number of training images if nargin < 3 subDim = dim - 1; end; disp(" ") load listAll; % Constants numIm = 3816; % Memory allocation for DATA matrix fprintf("Creating DATA matrix ") tmp = imread ( [path char(listAll(1)) ".pgm"] ); [m, n] = size (tmp); % image size - used later also!!! DATA = uint8 (zeros(m*n, numIm)); % Memory allocated clear str tmp; % Creating DATA matrix for i = 1 : numIm im = imread ( [path char(listAll(i)) ".pgm"] ); DATA(:, i) = reshape (im, m*n, 1); end; save DATA DATA; clear im; % Creating training images space fprintf("Creating training images space ") dim = length (trainList); imSpace = zeros (m*n, dim); for i = 1 : dim index = strmatch (trainList(i), listAll); imSpace(:, i) = DATA(:, index); end; save imSpace imSpace; clear DATA; % Calculating mean face from training images fprintf("Zero mean ") psi = mean(double(imSpace"))"; save psi psi; % Zero mean zeroMeanSpace = zeros(size(imSpace)); for i = 1 : dim zeroMeanSpace(:, i) = double(imSpace(:, i)) - psi; end; save zeroMeanSpace zeroMeanSpace; clear imSpace; % PCA fprintf("PCA ") L = zeroMeanSpace" * zeroMeanSpace; % Turk-Pentland trick (part 1) [eigVecs, eigVals] = eig(L); diagonal = diag(eigVals); [diagonal, index] = sort(diagonal); index = flipud(index); pcaEigVals = zeros(size(eigVals)); for i = 1 : size(eigVals, 1) pcaEigVals(i, i) = eigVals(index(i), index(i)); pcaEigVecs(:, i) = eigVecs(:, index(i)); end; pcaEigVals = diag(pcaEigVals); pcaEigVals = pcaEigVals / (dim-1); pcaEigVals = pcaEigVals(1 : subDim); % Retaining only the largest subDim ones pcaEigVecs = zeroMeanSpace * pcaEigVecs; % Turk-Pentland trick (part 2) save pcaEigVals pcaEigVals; % Normalisation to unit length fprintf("Normalising ") for i = 1 : dim pcaEigVecs(:, i) = pcaEigVecs(:, i) / norm(pcaEigVecs(:, i)); end; % Dimensionality reduction. fprintf("Creating lower dimensional subspace ") w = pcaEigVecs(:, 1:subDim); save w w; clear w; % Subtract mean face from all images load DATA; load psi; zeroMeanDATA = zeros(size(DATA)); for i = 1 : size(DATA, 2) zeroMeanDATA(:, i) = double(DATA(:, i)) - psi; end; clear psi; clear DATA; % Project all images onto a new lower dimensional subspace (w) fprintf("Projecting all images onto a new lower dimensional subspace ") load w; pcaProj = w" * zeroMeanDATA; clear w; clear zeroMeanDATA; save pcaProj pcaProj

应该不会，怕出问题卸载重装下吧！！我用98画原理图及PCB快OK了，再用99修正！但GERBER我还是用98转的！ 记得做图时5分钟备份一次！！另存档案；切记！！我是怕了！！！！！！

作者：Richardmore这本书可以说是机器学习的经典学习之作。以前在上机器学习这么课的时候，很多细节还没联系到，结果在读论文中就显得捉襟见肘。本文打算理清楚这本书的脉络，也顺便为学习机器学习的人打下一个学习路线图。1. 排除两块内容现排除第五章的内容神经网络，之所以把神经网络先单列出来，原因一是一个比较独立的研究脉络，二是因为这部分因为深度学习的原因太热了，所以我认为在学习机器学习中把神经网络单列出来学习，在交大的研究生课程安排中，神经网络是机器学习的后续课程。对于第6，7章，也不在下面的学习路线中，因为这部分是关于核技巧方面的，主要是就是高斯过程回归，高斯过程分类以及SVM等内容。2. 一个概率图框架为中心视角排除了上面几章的内容，PRML书中可以用下面的学习路线图覆盖，通过这个图可以理清楚了各个内容的不同角色。<img src="https://pic3.zhimg.com/d82ec8dc52d4bc5d35ada816f156ed6a_b.png" data-rawwidth="1888" data-rawheight="412" class="origin_image zh-lightbox-thumb" width="1888" data-original="https://pic3.zhimg.com/d82ec8dc52d4bc5d35ada816f156ed6a_r.png">说明：（1）一般模型中都会有隐变量因此，，因此对于P（X）的采用MLE学习的另一个技巧,便是第九章 EM算法。条件是在M步时，Q要可以被analytically computed。（2）至于为什么近似，Exact Inference is hard we resort to approximation3. 隐变量技巧下面我们看看另外一个视角：隐变量技巧。隐变量不仅可以使得模型的表达能力丰富起来，而且通常对于隐变量往往富有一定的实际意义。<img src="https://pic1.zhimg.com/bb004397ad1d16a93051d81c8822d2a8_b.png" data-rawwidth="1764" data-rawheight="422" class="origin_image zh-lightbox-thumb" width="1764" data-original="https://pic1.zhimg.com/bb004397ad1d16a93051d81c8822d2a8_r.png">说明：（1）这里所谓的结合模型中，在PRML中最后一章仅仅提到了以加法的方式进行模型集合，也就是mixture of experts，在论文Hinton G E. Training products of experts by minimizing contrastive divergence[J]. Neural computation, 2002, 14(8): 1771-1800. 提出了product of experts 模型，也就是以乘法的方式进行结合，RBM就是一种特殊的product of experts 模型，而高斯混合模型便是加法模型的代表。（2）隐变量的技巧是机器学习中一种重要的技巧，隐变量的加入不仅仅增加了模型的表达能力，而且，隐变量还可以被赋予某种特殊的意义，比如RBM模型中隐变量h被当成显变量v的特征抽象。这当然归根结底是因为隐变量模型确实是现实世界真实存在的情况，unobserved but important variables do exist! 当然隐变量的引入也为模型的推断带来了新的挑战，有很多比较好的隐变量模型往往找不到很高效的方法，而被限制着。4. 例子说明 下面分别从上面两个视角来分析RBM模型，贝叶斯线性回归和序列模型。 4.1 RBM模型 RBM模型是一个无向2层对称的图模型，从隐变量的视角来看，它是一个以乘法方式结合的distributed models。当然隐变量的引入增加了模型的复杂性和表达能力，但是也为学习，推断带来了问题。对于RBM的参数学习，因为是无向图，所以采用MLE最大化P（X），但是由于此时P（X，Z）难以评估，所以<img src="https://pic2.zhimg.com/v2-bda902a2f7f25d45c72a79ba99280c81_b.png" data-rawwidth="834" data-rawheight="94" class="origin_image zh-lightbox-thumb" width="834" data-original="https://pic2.zhimg.com/v2-bda902a2f7f25d45c72a79ba99280c81_r.png">很难计算，没有在RBM的学习中不能像高斯混合模型那样可以采取EM算法。因此只能采取最为标准的做法，求取P（X）的梯度，结果梯度公式如下：<img src="https://pic2.zhimg.com/v2-5fac4d2389b7cfd55118d156225819bd_b.png" data-rawwidth="800" data-rawheight="90" class="origin_image zh-lightbox-thumb" width="800" data-original="https://pic2.zhimg.com/v2-5fac4d2389b7cfd55118d156225819bd_r.png">然而对于计算后面的model部分的积分需要知道模型的概率分布，评估模型的概率分布需要计算一个标准化的分母，难以计算。因此就需要依赖近似，由于p（v｜h），p（h｜v）都是可以分析公式表达，因此采用Gibbs sampler来数值逼近积分。当然后来Hinton G E. Training products of experts by minimizing contrastive divergence[J].发现对于这一部分，Gibbs sampler 不需要多部的迭代，一次迭代就可以了，从而使的训练RBM的时间代价大大降低了，后来（A fast learning algorithm for deep belief nets，2006）提出了贪婪式的训练多层DBN（stacked RBM），每层都是训练RBM，从而使的深度学习焕发新的活力（Reducing the dimensionality of data with neural networks，2006）。4.2 贝叶斯线性回归Bayesian Linear Regression BLR这个模型是最为基础的，这个模型在PRML中，利用直接推断，变分法推断，MCMC采样都是可以做的；因此便于比较不同算法得到的结果。之前，本来打算在这里以LDA主题模型来举例，虽然LDA的EM算法， 变分法，以及Gibbs sampling 都是可以做的，但是模型太复杂，所以果断放弃了，以BLR模型作为例子说明。BLR是一个有向图模型，是一个典型的贝叶斯网络（虽然简单一点）。如果以一个贝叶斯的视角来看，其中的隐变量便是线性参数w，以及各种超参数α,β.....，在贝叶斯的处理视角之下，这些都会赋予一个先验分布。当然，有些模型书中也提到，有不同层次上的贝叶斯网络。有的是仅仅对参数w赋予一个先验分布，而对于其他的参数（hyperparameter）仅仅是作为模型参数，就是假设是一个渡固定的数值，然后再通过learn evidence function，其实说白了就是MLE，来寻找最佳的超参数α,β....。相比于把线性参数w，以及各种超参数α,β.....全部作为放入到贝叶斯网络中，这样的做法显然简化了模型，降低了贝叶斯网络的复杂性。这个技巧也在多处的论文中出现。从隐变量的角度来看，由于BLR模型相对简单，其中并没有随机隐变量，仅仅是一些参数w，以及各种超参数α,β..的环境隐变量。4.3 序列模型：隐马尔可夫链HMM与条件随机CRF隐马尔可夫链HMM这个模型是一个有向图模型，典型的贝叶斯网络，只不过这个网络是一个线性链（linear chains），因此可以进行分析上推断，要知道对于一般网络，并不存在通用的实用的inference算法。因为HMM是一个有向图模型。但是（1）在PRML书中，以及李航《统计学习》中并没有把其当作一个贝叶斯网络来进行处理，对所有的参数比如发射概率，转移矩阵概率都是模型的参数，而不是通过赋予一个先验分布，从而纳入到贝叶斯网络框架之中。因此对于模型而言，关键的便是通过MLE最大化P（X）来学习模型的参数，因为这里的有隐变量，因此在PRML，以及《统计学习》中都是通过EM算法做的。（2）其实，HMM是一个典型的线性链式的贝叶斯网络，因此对于通过对其参数赋予先验分布，进而从贝叶斯的角度，来对模型进行推断是一个非常自然的想法。我在论文Sharon Goldwater， Thomas L Griffiths 论文 A Fully Bayesian Approach to Unsupervised Part-of-Speech Tagging，中作者采用了Bayesian HMM 重新做了POS任务。作者在文中还详细罗列了Bayesian HMM 相比普通的HMM的优点：（a）可以使用先验知识，例如在POS中语言的认知可以加入到先验分布之中，而且（b）贝叶斯的推断，是通过一个后验分布推断参数，相比MLE点估计，会更加准确。对于贝叶斯的推断，作者在文中使用了Gibbs sample抽样实现了数值采样推断模型。最后作者比较了Gibbs sample＋Bayesian HMM和普通的HMM ＋EM，在POS任务效果更加好。另外，对于本论文的作者Thomas L Griffiths，第一次接触这个学者，是在读Gibbs sample in LDA这篇文章，作者推导了LDA的各种的条件分布，然后基于Gibbs sample 进行采样，记得Github上有Java版的实现代码，其推导十分严谨，并且有代码辅助，是学习LDA的一个捷径。在近似推断方面可以看出Thomas L Griffiths是一个坚定的数值采样学派，而LDA的开山之作《Latent Dirichlet Allocation 》的作者David M. Blei，看了作者部分文章以后，发现这个人是在近似推断方面是一个变分法的坚定学派，在《Latent Dirichlet Allocation 》之中，便是通过变分法进行推断了，David M. Blei还写了一个关于变分法的入门讲义pdf，网上可以搜到。所以回看我们概率图视角，做机器学习推断是不可避免的，有的是变分法近似，有的是数值采样近似，也有的是EM算法试一试。至于选择哪一种，就看你的问题哪一个比较简单了。但是好像有的人对这些方面各有偏爱。再说一下条件随机场CRF，相比与HMM，这也是一个序列模型，在很多的NLP任务中，CRF都是state of art 的算法，毕竟人家可以方便的特征工程嘛。但是这种日子被深度学习取代了，在NLP方面，RNN（递归神经网络）要比CRF表现更好，见我之前博文基于RNN做语义理解和词向量。先不说这么远，CRF的模型架构上是一个典型的无向的链式概率图模型，因此，（回看我们概率图的视角），CRF的关键问题便是如何进行学习了P（X），好在求其该模型直接求其梯度并没有太大的困难，具体可以参见李航的《统计学习》。5 结束语这篇文章，从概率图，隐变量两个视角对PRML中各个章节进行了串联，并以RBM，BLR，序列模型（HMM&CRF）具体说明这种串联。

歌曲名:Ogni Volta歌手:Vasco Rossi专辑:The Platinum CollectionForza MilanBy EricOgni voltaVasco RossiVado al massimoE ogni volta che viene giornoogni volta che ritornoogni volta che cammino emi sembra di averti vicinoogni volta che mi guardo intornoogni volta che non me ne accorgoogni volta che viene giornoE ogni volta che mi sveglioogni volta che mi sbaglioogni volta che sono sicuro eogni volta che mi sento soloogni volta che mi viene in mentequalche cosa che non c"entra nienteogni voltaE ogni volta che non sono coerentee ogni volta che non è importanteogni volta che qualcuno si preoccupa per meogni volta che non c"èproprio quanto la stavo cercandoogni voltaogni volta quando....E ogni volta che torna serami prende la paurae ogni volta che torna serami prende la pauraE ogni volta che non c"entroogni volta che non sono statoogni volta che non guardo in faccia a nientee ogni volta che dopo piangoogni volta che rimangocon la testa tra le manie rimando tutto a domanihttp://music.baidu.com/song/2637574

歌曲名:Ogni Volta歌手:Paul Anka专辑:Classic Songs, My WayForza MilanBy EricOgni voltaVasco RossiVado al massimoE ogni volta che viene giornoogni volta che ritornoogni volta che cammino emi sembra di averti vicinoogni volta che mi guardo intornoogni volta che non me ne accorgoogni volta che viene giornoE ogni volta che mi sveglioogni volta che mi sbaglioogni volta che sono sicuro eogni volta che mi sento soloogni volta che mi viene in mentequalche cosa che non c"entra nienteogni voltaE ogni volta che non sono coerentee ogni volta che non è importanteogni volta che qualcuno si preoccupa per meogni volta che non c"èproprio quanto la stavo cercandoogni voltaogni volta quando....E ogni volta che torna serami prende la paurae ogni volta che torna serami prende la pauraE ogni volta che non c"entroogni volta che non sono statoogni volta che non guardo in faccia a nientee ogni volta che dopo piangoogni volta che rimangocon la testa tra le manie rimando tutto a domanihttp://music.baidu.com/song/8185336

意大利牛肉面

可能是程序有关的用语：含有未被认可的短语和关键字.

perception 感知cognition 认知

certificates of recognition认可证书例句But Wei said the certificates were "more a recognition of scientists "work and achievement than the approval for commercial production".

脚后跟就会更好高交会馆几个

需要。投稿patternrecognition是需要引用本刊的，不然会被视为侵权。而本刊引用次数所占的比例具体算法为：自引率＝(被本刊引用的次数)/(期刊被引用的总次数)。

相互承认

去查english interpreting 就有了

是的啊

谢谢楼上的指点！这两句话分别来自CSR两个蓝牙芯片的datasheet,如下：CSR8465：Voicerecognitionsupportforansweringacall,enablestruehands-freeuse.CSR8670：Supportforspeechrecognition.根据经验，在这里voice和speech是有本质区别的。我的理解是，voice在这里是音频，speech是语音。不知对否，还请大侠指点！

可以.对的. 不过,有个习惯是如果句子后面已经出现了有关人称的代词来表示谢谢的对象, 前面习惯用thanks.. 即：thanks for your recognition.

【答案】：细胞识别(cell recognition)：胞通过其表面的受体与胞外信号物质分子(配体)选择性地相互作用，进而导致胞内一系列生理生化变化，最终表现为细胞整体的生物学效应的过程。

语法------句子分析： 1 it （形式主语）is（系动词谓语） a good opportunity （表语）to get recognition for my invention （句子的真正主语） 2 i （主语） want （谓语） to take the opportunity（宾语） to get recognition for my invention（後面全部是opportunity的定语）

百度

recognize英 [u02c8reku0259gnau026az] 美 [u02c8ru025bku0259ɡu02ccnau026az]vt.认出； 识别； 承认vi.承认，确认； [法律]具结，立保证书过去式： recognized 过去分词： recognized 现在分词： recognizing 第三人称单数： recognizes派生词：recognizer、recognition

revenue recognition 收入确认；营业收入的确认例句:1.Revenue recognition issue relating to a funeral parlour. 有关葬礼间收入确认的问题。2.Amazon"s revenue recognition policies allow them to record only the commission andshipping fees on sales by third-party merchants as revenue. 亚马逊的收入登记政策只允许把第三方商家的佣金和运费收入计作收入

In recognition是跟介词of一起使用的 所有词典给的关于recognition的例句都是in recognition of .. 但是in recognition to ... 也能在古歌上面找到几个million的search results 当然in recognition of是几百个million的results. 可能in recognition to是个经常错的语法吧

recognize的名词为recognition,意思是认出、认识、识别、承认、认可、赞誉、赏识、奖赏,例如:deserve recognition.值得承认、special recognition.特别认可、formal recognition.正式承认。recognize为动词,其意思是认识、承认、意识到、辨别出、认可、接受、赞成,例如recognize sb by sth.通过某事认出某人。

recognization 识别的意思。

作 认出 承认 确认 时用recognition作 识别 时用recognization 这个词比较正式 一般用在写文章时候用吧

recongnition有主观辨别，辨识辨认的意思congnition主要是认知，认识的意思recongnition同义可以用identification，但是congnition不可以

recognized 形容词，认可的recognizing 形容词，认识的recognition 名词，认出

employee recognition员工识别

revenue recognition 收入确认；营业收入的确认 [例句]Revenue recognition issue relating to a funeral parlour.有关葬礼间收入确认的问题.

iris recognition就是虹膜技术优点：1、所有生物识别都具有的优点，身体本身的功能器官，不会像密码一样有忘记的属性；2、和面部识别一样，非接触性，使用者不需要和设备直接接触就获取了图像，干净卫生，避免了疾病的可能的接触传染；3、和指纹及面部容易修改和磨损不同，虹膜在眼睛内部，基本不可能被复制修改。缺点：1、硬件设备小型化不容易，智能手机已经是非常小的设备；2、相较于其它生物识别硬件，虹膜识别硬件造价较高，大范围推广困难；3、使用便捷性较差，识别准度略低，反应速度较慢表2为指纹识别与虹膜识别的比较

handwriting recognition手写识别

　　作 认出 承认 确认 时用recognition作 识别 时用recognization 这个词比较正式 一般用在写文章时候用吧

RecognitionRecognizable

　　beyond recognition　　英 [biu02c8ju0254nd u02ccreku0259ɡu02c8niu0283u0259n]　　美 [biu02c8ɑnd u02ccru025bku0259ɡu02c8nu026au0283u0259n]　　[词典]完全改了模样，面目全非;　　[网络]认不出来; 面目全非; 使人认不出;　　[例句]The play was butchered beyond recognition.　　这剧本给改得面目全非

recognize的名词形式是recognition,意思是认识,识别；承认,认可。 扩展资料 recognize的"名词形式是recognition,意思是认识,识别；承认,认可；褒奖,酬劳。例如This lack of recognition was at the root of the dispute这种不被承认就是这次纷争的根源。

不省略，gn中的g只有在词首或词尾时可以省略不发音。在词中间，g发不完全爆破音，即形成阻塞，发生不完全爆破，如：土地神，英文叫做gnome。咬、啮，英文是gnaw。标记，英文读sign。羚牛，英文读gnu。咬牙切齿，读gnash。设计，是design。希望我能帮助你解疑释惑。

In recognition是跟介词of一起使用的所有词典给的关于recognition的例句都是in recognition of ..但是in recognition to ... 也能在古歌上面找到几个million的search results当然in recognition of是几百个million的results.可能in recognition to是个经常错的语法吧

acknowledge是动词，常用作“承认”的意思，recognition是名词，有“识别、认可”之意。recognition的动词形式是recognize，和acknowledge是近义词，但recognize常用于表示认出某人，或者意识到某件事。例句：I recognized him as soon as he came in the room.(他一进房间我就认出他了）。They recognized the need to take the problem seriously.（他们意识到需要严肃对待那个问题了）。

1. 认证的意思就是交易处理的记录。2. 在交易的时候，参考交易决定的困难度。3. 认证点是非常重要的，它影响着财政决算。人脑产品，呵呵。希望队楼主有帮助。

您好，除了以下两位网友提供的 recognized 和 recognizing 两个形容词以外，recognize（动词）还有3 个形容词：1）recognized（形容词：公认的）【楼下网友已经提供了】/recognised（英式英语拼法）2）recognizing（形容词：识别）【楼下网友已经提供了】/recognising（英式英语拼法）3）recognizable（形容词： 可认出的；可认识的）/ recognisable（英式英语拼法）4）recognizant（形容词：认识到的，意识到的；承认的）/recognisant（英式英语拼法）5）recognitory（形容词：承认的，认识的【这个现在比较少用了】动词：recognize（美式英语拼法）/recognise（英式英语拼法）

recognition只有在认识的时候是可数，其他都是不可数。认识( recognition的名词复数 )His quick recognitions made him frantically impatient of deliberate judgement. 他敏捷的辩别力使他急躁得毫无耐心作深思熟虑的判断.N-UNCOUNT 认识;识别;认出N-UNCOUNT 承认;接受;理解N-UNCOUNT （政府对他国的）外交认可，正式承认N-UNCOUNT 赞赏;好评;认可

就是recognition

recognition和memory的区别:recognition是针对记忆回忆的能力而言的。memory是指记忆和回想起来的事物而言的。 、

recognition[英][u02ccreku0259gu02c8nu026au0283n][美][u02ccru025bku0259ɡu02c8nu026au0283u0259n]n.认识，识别; 承认，认可; 褒奖; 酬劳; 复数：recognitions以上结果来自金山词霸例句:1.Facial recognition programs are used in police and security operations. 面部识别计划将用于警方和安全任务之中。

新一代

CET4考 研TOEFLCET6recognition英 [u02ccreku0259gu02c8nu026au0283n] 美 [u02ccru025bku0259ɡu02c8nu026au0283u0259n]n.认识，识别； 承认，认可； 褒奖； 酬劳复数： recognitions派生词：recognitory 双语例句1. But by the year 2020 business computing will have changed beyond recognition. 但是到了2020年，商业计算会变得面目全非。来自柯林斯例句2. His government did not receive full recognition by Britain until July. 他的政府直到7月份才得到英国的正式承认。来自柯林斯例句3. The situation in Eastern Europe has changed out of all recognition. 东欧的形势经历了巨变。来自柯林斯例句4. South Africa gave diplomatic recognition to Rwanda"s new government on September 15. 南非于9月15日正式承认了卢旺达的新政府。来自柯林斯例句5. This lack of recognition was at the root of the dispute. 这种不被承认就是这次纷争的根源。来自柯林斯例句查看更多例句>>柯林斯高阶英汉双解学习词典英汉双向大词典1. N-UNCOUNT 认识;识别;认出 Recognition is the act of recognizing someone or identifying something when you see it. George said, "Ida, how are you?" She frowned for a moment and then recognition dawned. "George Black. Well, I never."... 乔治说：“艾达，你好吗？”她皱了一会儿眉头，然后才认出他。“乔治·布莱克，噢，我一直都不好。”He searched for a sign of recognition on her face, but there was none. 他试图在她的脸上找出一丝认出他的神情，但是根本没有。2. N-UNCOUNT 承认;接受;理解 Recognition of something is an understanding and acceptance of it. The CBI welcomed the Chancellor"s recognition of the recession and hoped for a reduction in interest rates. 英国工业联合会对财政大臣承认经济出现衰退表示欢迎，并希望能出台降低利率的政策。3. N-UNCOUNT （政府对他国的）外交认可，正式承认 When a government gives diplomatic recognition to another country, they officially accept that its status is valid. South Africa gave diplomatic recognition to Rwanda"s new government on September 15... 南非于9月15日正式承认了卢旺达的新政府。His government did not receive full recognition by Britain until July. 他的政府直到7月份才得到英国的正式承认。4. N-UNCOUNT 赞赏;好评;认可 When a person receives recognition for the things that they have done, people acknowledge the value or skill of their work. At last, her father"s work has received popular recognition... 最后，她父亲的工作得到了大众的认可。He is an outstanding goalscorer who doesn"t get the recognition he deserves. 他是一个出色的射手，但并没有获得应有的认可。5. PHRASE 面目全非;认不出来;无法辨认 If you say that someone or something has changed beyond recognition or out of all recognition, you mean that person or thing has changed so much that you can no longer recognize them. The bodies were mutilated beyond recognition... 尸体都残缺不全，无法辨认了。The facilities have improved beyond all recognition... 这些设备经过大幅度的改良，都让人认不出来了。The situation in Eastern Europe has changed out of all recognition. 东欧的形势经历了巨变。6. PREP-PHRASE 获官方认可;正式承认 If something is done in recognition of someone"s achievements, it is done as a way of showing official appreciation of them. Brazil normalised its diplomatic relations with South Africa in recognition of the steps taken to end apartheid... 巴西恢复了与南非的外交关系，以示对其采取措施结束种族隔离的正式认可。He had just received a doctorate in recognition of his contributions to seismology. 他刚被授予了博士头衔以表彰他对地震学作出的贡献。

第二个 pandora tv. lol champion pick ban 才是正解 发这么多干嘛 qq音乐有的

prognosis主要用于医学上的预测prediction则是一些市场趋势等方面的预测

1、表达意思不同recognition：承认，接受；表彰，赞誉；认出，识别；（政府对他国的）外交认可。recognization：认可；识别。2、用法不同recognition：表达意思“作、认出”时用recognition。He glanced briefly towards her but there was no sign of recognition.他瞥了她一眼，但似乎没认出她来。recognization：用在书面上作识别时用recognization。This method has some value in term with other pattern recognization and decision-makingproblems.这种方法对于研究和解决其它模式识别，决策问题具有较高的借鉴意义。3、侧重点不同recognition：recongnition有主观辨别，辨识辨认的意思。recognization：recognization无具体主、客观区别。

be considered 更像被考虑,be recognised as 更 official,respectable吧

linked recognition 英[liu014bkt u02ccreku0259ɡu02c8niu0283u0259n] 美[lu026au014bkt u02ccru025bku0259ɡu02c8nu026au0283u0259n] .[医]连锁识别

cognitive[英][u02c8ku0252gnu0259tu026av][美][u02c8kɑ:gnu0259tu026av]adj.认知的; 认识的; 例句:1.She noticed that literature on cognitive psychology referred to complex multi-tasking, orswitching between mentally challenging tasks. 她注意到有认知心理学文献提到复杂的多任务处理或在具有脑力挑战的任务间切换

D--once,曾经

A 在机场看到你的那一刻我就认出你了

recognize和make out区别1、recognize : 指所辨认的人或物多是以前所熟悉的。He walked along in the shadows hoping no one would recognizehim.他沿着暗处走,希望没有人会认出他。2、make out : 通常指通过人的感觉器官来辨别事物。Can you make out/identifythe number at the end of the car?你能辨认出车尾车牌的号码吗?

70年代轩尼诗VSOP，0,7L,1800元1支，1L，2800元1支VSOP（VERY SUPERIOR OLD PALE）高级白兰地(18-25年) 　　所有白兰地酒厂, 都用字母来分别品质, 例举如下: E代表ESPECIAL (特别的) 　　F代表FINE (好) 　　V代表VERY (很好) 　　O代表OLD (老的) 　　S代表SUPERIOR (上好的) 　　P代表PALE (淡色而苍老) 　　X代表EXTRA (格外的) 　　干邑的级别>法国政府有着极为严格的规则,酒商是不能随意自称的。总括而言,有下列之类别: 　　3-STAR三星干邑：蕴藏期不少于两年 　　V.S.O.P干邑：蕴藏期不少于四年 　　NAPOLEON干邑：蕴藏期不少于六年 　　X.O.干邑：蕴藏期多在八年以上 　　酒名翻译（洋酒） 　　洋酒系列 BRANDY 　　轩尼诗理查43度 Hennessy Richard 43%(v/v) 　　轩尼诗1873 Hennessy Private Reserve 　　轩尼诗XO 大 Hennessy XO 　　轩尼诗XO 小 Hennessy XO 　　轩尼诗 VSOP Hennessy VSOP 　　轩尼诗 VSOP Hennessy VSOP 　　金王马爹利 Cognac L"or De Martel 　　极品马爹利 Martell Cognac Gobelet Royal 　　长颈FOV FOV 　　路易十三 Martin Louis XШ 　　人头马 XO 大 Remy Martin XO (Big) 　　人头马 XO 小 Remy Martin XO (Small) 　　人头马VSOP 70CL Remy Martin VSOP 70cl 　　人头马VSOP 35CL Remy Martin VSOP 35cl 　　人头马VSOP 20CL Remy Martin VSOP 20cl 　　金花VSOP Grand VSOP 　　土龙酒 Clay-dragon Eel Medicated Wine 　　杯莫停 Hennessy Paradis 　　马爹利XO（大） Martell Cordon (Big) 　　人头马特醇70CL Club De Remy Martin 70cl 　　人头马特醇35CL Club De Remy Martin 35cl 　　人头马特醇4500ML Club De Remy Martin 4500ml 　　蓝带马爹利70CL Martell Cordon Blue 70cl 　　蓝带马爹利35CL Martell Cordon Blue 35cl 　　名仕马爹利 Martell Noblige 　　金牌马爹利20CL Martell V.S.O.P 20cl 　　金牌马爹利70CL Martell V.S.O.P 70cl 　　皇家礼炮 Chivas Royal Salute 　　金皇家礼炮 Gold Chivas Royal Salute 　　金牌威士忌 Johnnie Walker Gold Label Finest Scotch Whisky 　　红牌威士忌 Johnnie Walker Red Label Old Scotch Whisky 　　蓝牌威士忌 Johnnie Walker Blue Label Whisky 　　黑牌威士忌 Johnnie Walker Black Label Whisky 　　芝华士威士忌(12年) Chivas Regal Whisky 　　尊爵威士忌 Johnnie Walker Premier Pare Old Scotch Whisky 　　苏格兰威士忌 Scottish Whiskey 　　皇家史道林 　　GRAN"T IS GRANT IS 　　日本盛清酒 Japanese Seisei

parfum是香精 eaude parfum是香水 eaude de toilette淡香水 cologne古龙水

认知弹性理论（Cognitive Flexibility Theory） 认知弹性理论就是一种针对结构不良知识领域， 以获得高级知识为目的的教学思想和方法。 基本原理之一是： 只有在显示多元事实时才能以最佳方式对结构不良领域的现象进行思 考。因此， 认知弹性理论的焦点是试图揭示复杂与结构不良领域中的学习本质。 该理论的中心问题是多元认知表征， 即要求从多于一个观点的角度检查某一概念， 这既能增强对该概念自身的理解， 同时也能增强将这一理解迁移至其它领域的能力。同样， 从同一观点检查不同概念也能导致一种新的见识。 在结构不良领域中这更是一个真实的事实。

In cognitive linguistics, conceptual metaphor, or cognitive metaphor, refers to the understanding of one idea, or conceptual domain, in terms of another, for example, understanding quantity in terms of directionality (e.g. "prices are rising"). A conceptual domain can be any coherent organization of human experience. The regularity with which different languages employ the same metaphors, which often appear to be perceptually based, has led to the hypothesis that the mapping between conceptual domains corresponds to neural mappings in the brain [1]This idea, and a detailed examination of the underlying processes, was first extensively explored by George Lakoff and Mark Johnson in their work Metaphors We Live By. Other cognitive scientists study subjects similar to conceptual metaphor under the labels "analogy" and "conceptual blending".Conceptual metaphors are seen in language in our everyday lives. Conceptual metaphors shape not just our communication, but also shape the way we think and act. In George Lakoff and Mark Johnson"s work, Metaphors We Live By (1980), we see how everyday language is filled with metaphors we may not always notice. An example of one of the commonly used conceptual metaphors is argument as war.[2] This metaphor shapes our language in the way we view argument as war or as a battle to be won. It is not uncommon to hear someone say "He won that argument" or "I attacked every weak point in his argument". The very way argument is thought of is shaped by this metaphor of arguments being war and battles that must be won. Argument can be seen in many other ways other than a battle, but we use this concept to shape the way we think of argument and the way we go about arguing.Conceptual metaphors are used very often to understand theories and models. A conceptual metaphor uses one idea and links it to another to better understand something. For example, the conceptual metaphor of viewing communication as a conduit is one large theory expl

Very Deep Convolutional Networks for Large-Scale Image Recognition翻译 下 code Very Deep Convolutional Networks for Large-Scale Image Recognition 用于大规模图像识别的非常深的卷积网络 论文： http://arxiv.org/pdf/1409.1556v6.pdf ABSTRACT 摘要 ) convolution ufb01lters, which shows that a signiufb01cant improvement on the prior-art conufb01gurations can be achieved by pushing the depth to 16–19 weight layers. These ufb01ndings were the basis of our ImageNet Challenge 2014 submission, where our team secured the ufb01rst and the second places in the localisation and classiufb01cation tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. ）卷积滤波器的体系结构对深度网络进行深入评估，这表明通过将深度推到16-19个重量层可以实现对现有技术配置的显着改进。这些发现是我们ImageNet Challenge 2014提交的基础，我们的团队分别获得了本地化和分类轨道的第一和第二名。我们还表明，我们的表示很好地适用于其他数据集，他们在那里获得最新的结果。我们已经公开发布了两款性能最佳的ConvNet模型，以便于进一步研究在计算机视觉中使用深度视觉表示。 1 INTRODUCTION 1引言 Convolutional networks (ConvNets) have recently enjoyed a great success in large-scale image and video recognition (Krizhevsky et al., 2012; Zeiler & Fergus, 2013; Sermanet et al., 2014; Simonyan & Zisserman, 2014) which has become possible due to the large public image repositories, such as ImageNet (Deng et al., 2009), and high-performance computing systems, such as GPUs or large-scale distributed clusters (Dean et al., 2012). In particular, an important role in the advance of deep visual recognition architectures has been played by the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) (Russakovsky et al., 2014), which has served as a testbed for a few generations of large-scale image classiufb01cation systems, from high-dimensional shallow feature encodings (Perronnin et al., 2010) (the winner of ILSVRC-2011) to deep ConvNets (Krizhevsky et al., 2012) (the winner of ILSVRC-2012). 卷积网络（ConvNets）最近在大规模图像和视频识别（Krizhevsky等，2012; Zeiler＆Fergus，2013; Sermanet等，2014; Simonyan＆Zisserman，2014）方面取得了巨大的成功，这已经成为可能由于大型公共图像库（如ImageNet（Deng等，2009））和高性能计算系统（如GPU或大规模分布式群集）（Dean等，2012）。特别是，ImageNet大规模视觉识别挑战（ILSVRC）（Russakovsky et al。，2014）对深度视觉识别架构的发展起到了重要作用，它已经成为几代大型（Perronnin et al。，2010）（ILSVRC-2011的获胜者）到深层ConvNets（Krizhevsky等，2012）（ILSVRC-2012的获胜者）的高分辨率图像分类系统。 ) convolution ufb01lters in all layers. ）卷积滤波器，这是可行的。 As a result, we come up with signiufb01cantly more accurate ConvNet architectures, which not only achieve the state-of-the-art accuracy on ILSVRC classiufb01cation and localisation tasks, but are also applicable to other image recognition datasets, where they achieve excellent performance even when used as a part of a relatively simple pipelines (e.g. deep features classiufb01ed by a linear SVM without ufb01ne-tuning). We have released our two best-performing models1 to facilitate further research. 因此，我们提出了更加精确的ConvNet架构，它不仅实现了ILSVRC分类和本地化任务的最新准确度，而且还适用于其他图像识别数据集，甚至可以实现卓越的性能当用作相对简单的管道的一部分时（例如，不需要微调的线性SVM对深度特征进行分类）。我们发布了两款性能最好的模型1，以便于进一步研究。 The rest of the paper is organised as follows. In Sect. 2, we describe our ConvNet conufb01gurations. The details of the image classiufb01cation training and evaluation are then presented in Sect. 3, and the u2217current afufb01liation: Google DeepMind +current afufb01liation: University of Oxford and Google DeepMind 1 http://www.robots.ox.ac.uk/ u02dcvgg/research/very_deep/ conufb01gurations are compared on the ILSVRC classiufb01cation task in Sect. 4. Sect. 5 concludes the paper. For completeness, we also describe and assess our ILSVRC-2014 object localisation system in Appendix A, and discuss the generalisation of very deep features to other datasets in Appendix B. Finally, Appendix C contains the list of major paper revisions. 本文的其余部分安排如下。在Sect。 2，我们描述了我们的ConvNet配置。图像分类培训和评估的细节将在第二部分中介绍。 3和*当前补充：Google DeepMind +当前补充：牛津大学和Google DeepMind 1http： //www.robots.ox.ac.uk/~vgg/research/very_deep/ 配置在ILSVRC分类任务中进行比较教派。 4. Sect。 5结束了论文。为了完整起见，我们还在附录A中描述和评估了ILSVRC-2014对象定位系统，并讨论了附录B中对其他数据集的深入特征的概括。最后，附录C包含主要论文修订版的列表。 2 CONVNET CONFIGURATIONS 2 CONVNET配置 To measure the improvement brought by the increased ConvNet depth in a fair setting, all our ConvNet layer conufb01gurations are designed using the same principles, inspired by Ciresan et al. (2011); Krizhevsky et al. (2012). In this section, we ufb01rst describe a generic layout of our ConvNet conufb01gurations (Sect. 2.1) and then detail the speciufb01c conufb01gurations used in the evaluation (Sect. 2.2). Our design choices are then discussed and compared to the prior art in Sect. 2.3. 为了衡量公平环境下ConvNet深度增加所带来的改进，我们所有的ConvNet层配置都采用了Ciresan等人的相同原则设计。 （2011）; Krizhevsky等人。 （2012年）。在本节中，我们首先描述ConvNet配置的一般布局（第2.1节），然后详细介绍评估中使用的特定配置（第2.2节）。然后讨论我们的设计选择，并与Sect中的现有技术进行比较。 2.3。 2.1 ARCHITECTURE 2.1体系结构 A stack of convolutional layers (which has a different depth in different architectures) is followed by three Fully-Connected (FC) layers: the ufb01rst two have 4096 channels each, the third performs 1000way ILSVRC classiufb01cation and thus contains 1000 channels (one for each class). The ufb01nal layer is the soft-max layer. The conufb01guration of the fully connected layers is the same in all networks. 一堆卷积层（在不同的体系结构中具有不同的深度）之后是三个全连接（FC）层：前两个层各有4096个通道，第三层执行1000way ILSVRC分类，因此包含1000个通道（每个类）。最后一层是软 - 最大层。全连接层的配置在所有网络中都是相同的。 All hidden layers are equipped with the rectiufb01cation (ReLU (Krizhevsky et al., 2012)) non-linearity. We note that none of our networks (except for one) contain Local Response Normalisation (LRN) normalisation (Krizhevsky et al., 2012): as will be shown in Sect. 4, such normalisation does not improve the performance on the ILSVRC dataset, but leads to increased memory consumption and computation time. Where applicable, the parameters for the LRN layer are those of (Krizhevsky et al., 2012). 所有隐藏层都配备了整合（ReLU（Krizhevsky et al。，2012））非线性。我们注意到我们的网络（除了一个网络）都没有包含本地响应规范化（LRN）规范化（Krizhevsky et al。，2012）。如图4所示，这种归一化不会提高ILSVRC数据集的性能，但会导致内存消耗和计算时间增加。在适用的情况下，LRN层的参数是（Krizhevsky et al。，2012）的参数。 2.2 CONFIGURATIONS 2.2配置 The ConvNet conufb01gurations, evaluated in this paper, are outlined in Table 1, one per column. In the following we will refer to the nets by their names (A–E). All conufb01gurations follow the generic design presented in Sect. 2.1, and differ only in the depth: from 11 weight layers in the network A (8 conv. and 3 FC layers) to 19 weight layers in the network E (16 conv. and 3 FC layers). The width of conv. layers (the number of channels) is rather small, starting from 64 in the ufb01rst layer and then increasing by a factor of 2 after each max-pooling layer, until it reaches 512. 本文中评估的ConvNet配置在表1中列出，每列一列。下面我们将以他们的名字（A-E）来提及网。所有的配置都遵循Sect中的通用设计。 2.1，并且仅在深度上有所不同：从网络A中的11个权重层（8个转发层和3个FC层）到网络E中的19个权重层（16个转发层和3个FC层）。conv的宽度。层数（通道数量）相当小，从第一层64层开始，然后在每个最大池层后增加2倍，直到达到512。 In Table 2 we report the number of parameters for each conufb01guration. In spite of a large depth, the number of weights in our nets is not greater than the number of weights in a more shallow net with larger conv. layer widths and receptive ufb01elds (144M weights in (Sermanet et al., 2014)). 在表2中，我们报告了每个配置的参数数量。尽管深度很大，但我们的网中的重量数量不会超过更大的转化次数的更浅网中的重量数量。图层宽度和接受域（Sermanet et al。，2014）中的144M权重）。 2.3 DISCUSSION 2.3讨论 3 CLASSIFICATION FRAMEWORK 3分类框架 In the previous section we presented the details of our network conufb01gurations. In this section, we describe the details of classiufb01cation ConvNet training and evaluation. 在上一节中，我们介绍了我们网络配置的细节。在本节中，我们将描述分类ConvNet培训和评估的细节。 3.1 TRAINING 3.1培训

打印机必须是在所有颜色的墨水都有的时候才能正常工作，所以一个颜色没了，就得换，但是只换没了的那个就好了

歌曲名:Sogno歌手:Andrea Bocelli专辑:The Best Of Andrea Bocelli - "Vivere"《Sogno(Dream)》Sung By "Andrea Bocelli"Va ti aspettero"(Go then, I will wait for you)II fiore nel giardino segna il tempo(The flowers in the garden will mark your absence)Qui disegnero" il giomo poi del tuo ritorno(And rejoice the day of your return)Sei cosi sicura del mio amore(Of my love you are so sure)Da portarlo via con te(So sure you can take it with you)Chiuso nelle mani che ti porti al viso(Cupped in the hands that you raise to your face)Ripensando ancora a me(As you still think of me)E se ti servira" lo mostri al mondo(And if you need to)Che non sa che vita c"e`(you can show it to the world)Nel cuore che distratto sembra assente(A world that couldn"t)Non sa che vita c"e`(begin to understand what lives)In quello che soltanto il cuore sente(In an uncaring absent heart)Non sa(That couldn"t begin to understand what a heart can truly feel)Qui ti aspettero"(This is where I will wait for you)E rubero" i baci al tempo(Stealing imaginary kisses as time goes by)Tempo che non basta a cancellare(Time, time cannot erase the memories and the desire)Coi ricordi il desiderio che(That you cup in the hands)Resta chiuso nelle mani che ti porti al viso(you raise to your face)Ripensando a me(As you still think of me)E ti accompagnera" passando le citta" da me(Throughout your journey it will lead you back to me)Da me che sono ancora qui(For I"ll still be waiting here, dreaming)E sogno cose che non so di te(Dreaming of your unknown whereabouts)Dove sara" che strada fara" il tuo ritorno(Picturing the scene you"ll return to, and how you"ll return)Sogno(I dream)Qui ti aspettero"(This is where I will wait for you)E rubero" i baci al tempo(Stealing imaginary kisses as time goes by)Sogno(Dream)Un rumore il vento che mi sveglia(A noise, the wind awakes me)E sie gia" qua(And you"re already here)http://music.baidu.com/song/7379116

歌曲名:Sogno梦歌手:Andrea Bocelli专辑:The Best Of Andrea Bocelli-Vivere《Sogno(Dream)》Sung By "Andrea Bocelli"Va ti aspettero"(Go then, I will wait for you)II fiore nel giardino segna il tempo(The flowers in the garden will mark your absence)Qui disegnero" il giomo poi del tuo ritorno(And rejoice the day of your return)Sei cosi sicura del mio amore(Of my love you are so sure)Da portarlo via con te(So sure you can take it with you)Chiuso nelle mani che ti porti al viso(Cupped in the hands that you raise to your face)Ripensando ancora a me(As you still think of me)E se ti servira" lo mostri al mondo(And if you need to)Che non sa che vita c"e`(you can show it to the world)Nel cuore che distratto sembra assente(A world that couldn"t)Non sa che vita c"e`(begin to understand what lives)In quello che soltanto il cuore sente(In an uncaring absent heart)Non sa(That couldn"t begin to understand what a heart can truly feel)Qui ti aspettero"(This is where I will wait for you)E rubero" i baci al tempo(Stealing imaginary kisses as time goes by)Tempo che non basta a cancellare(Time, time cannot erase the memories and the desire)Coi ricordi il desiderio che(That you cup in the hands)Resta chiuso nelle mani che ti porti al viso(you raise to your face)Ripensando a me(As you still think of me)E ti accompagnera" passando le citta" da me(Throughout your journey it will lead you back to me)Da me che sono ancora qui(For I"ll still be waiting here, dreaming)E sogno cose che non so di te(Dreaming of your unknown whereabouts)Dove sara" che strada fara" il tuo ritorno(Picturing the scene you"ll return to, and how you"ll return)Sogno(I dream)Qui ti aspettero"(This is where I will wait for you)E rubero" i baci al tempo(Stealing imaginary kisses as time goes by)Sogno(Dream)Un rumore il vento che mi sveglia(A noise, the wind awakes me)E sie gia" qua(And you"re already here)http://music.baidu.com/song/384439

Teacher zhang you said yesterday that I didn"t understand.In conclusion, many recognize the pleasure they may even up to an affiliate plays a pivotal role in the one more molding or undoing of a-one "s behavioral which.1. Because not fully understand the whole sentence legal structure, translation is very clumsy, fall like a foreigner speak Chinese. Should be translated into individual behavior tendency: in shaping and recovery process, many scientists were not able to realize personal growth environment plays a crucial role.2. Couriered direct not is not normal word order, but you have no clear sentence structure. Its structure should be as follows:The main clause -- the one plays a pivotal to wish the role in molding or undoing of a-one "s behavioral whichAn attributive clause -- may even up in, including relationship pronouns which/pleasure because the object which is prepositions in

想问什么，把问题发出来

这两行是你写的吗？如果不是你写的，也不知道有什么用那就删除掉，出现问题用其他方法解决，不一定要使用#pragma。

cognitive presence网络认知存在；认知呈现；认知临场感网络释义1. 认知存在...在社区内学习需要通过三个核心部分的相互作用而发生：认知存在（cognitive presence），社会存在(social presence)和教学存 …xkwz.sgedu.gov.cn|基于7个网页2. 认知呈现4.认知呈现(cognitive presence):虚拟实境中的学习者,不一定要扮演人类,亦可采用物件的方式呈现,对於认知上也会有不同的变化.qqgj1.blog.163.com|基于2个网页3. 认知临场感探究社区模型的三要素 为： 认知临场感 （Cognitive Presence）， 社会临场感 （Social Presence）和教学临场感（Teaching Pr…www.docin.com|基于2个网页4. 认知性...型为预测模型的特性，来分辨学生的讨论活动流程是属於认知性(cognitive presence)的活动流程，亦或是社交性(social presence) …

答案C该题考查过去分词在句中用作原因状语。根据句意及句子结构可知，句子的主语Dr．Lillian Glass实际上就是选项动词的主语，相当于As he is known as…引导的原因状语从句，为被动结构，故选过去分词known。

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　　Realize与recognize的用法辨析 两个词都有“认识、识别”的意思,但在具体用法上又有所差别.1．Realize作为及物动词,可以表达“认识到；了解”的意思.如：He didn"t realize his mistake until his mother told him.直到妈妈告诉他,他才认识到自己的错误.I didn"t realize how late it was.我没有意识到天已经那么晚了.She realizes now how hard you worked.现在她了解你工作得多辛苦.When he realized what had happened,he was sorry.当他明白发生了什么事时,他感到很难过.2．realize还可以表示“实现；完成”的意思.如：The girl finally realized her dream of becoming an actress.那个女孩当演员的梦想终于实现了.3．recognize也可以用作及物动词,表示“认出；辩认；认识”的意思.如：I recognized his voice.我认出了他的声音.I recognized her as my friend"s daughter.我认出她是我朋友的女儿.4．recognize还可以表示“清楚知道；认定”的意思.如：I recognized him to be cleverer than I am.我认识到他比我聪明.I recognize that she works harder than I do.我认识到她比我用功.5．recognize还可以表示“承认”的意思.如：I recognize that I have been wrong.我承认我错了.They refused to recognize this government.他们拒不承认这个政府 .诺贝尔团队合作 E=mc?-TZX

revenue recognition收入确认；营业收入的确认;

tell 辨别；分辨recognize 认出；认识distinguish 区别;识别;辨别classify 将...分类;将...分等级

A 试题分析： 句意：—我昨天早上打电话给你。一个女孩接的，但我没听出是谁的声音。—哦，一定是我的小妹妹。她那会正在我的房间。A. 一定是，肯定推测；B. 本应该；C. 可能是；D. 也许是。结合语境，选A。考点: 考查情态动词表推测的用法。

2017/01/31 发于微信订阅号 两个问题 1. 什么是元认知（Metacognition)？ 首先"元"是个特别好的词。“元”用来指代那些本源的东西。《易经》：“《彖》曰：大哉乾元，万物资始，乃统天。云行雨施，品物流形。大明终始，六位时成，时乘六龙以御天。乾道变化，各正性命。保合大合，乃利贞。首出庶物，万国咸宁。” 认知本是心理学词汇，指的是获取知识的心理活动过程。突然之间，认知成了个个耳熟能详的词汇。紧接着"元认知”也成了大众词汇。 元认知就是对认知的认知（Thinking about thinkingU0010000f），审视自身思想的能力。意即自我意识和自我调节。 2. 元认知有什么用？ 我们在生活中被很多问题困扰，而且经常发现作为当事人，我们身陷其中，无力自拔。"不识庐山真面目，只缘身在此山中”。但从第三者的角度来看，其实并不难。你需要的是做出选择，你必须取舍，不可能两者或者多者兼得。或者当你无可避免地要面对某种困境时，你需要接受现实，面对它，无论抗拒还是绝望，你都无法改变事实。但改变观念或者改变视角，你有可能看到其他的能改善你的困境的可能性。 元认知就是帮助你改变的力量。认识自己，有意识地自我控制，改变人生。当你的思维改变时，你的人格其实也发生了变化。需要记住的是：大脑并不是一成不变的。人格也不是。当你的自我改变时，一切变得皆有可能。 书名：元认知-改变大脑的顽固思维 Brain changer-How harnessing your brain"s power to adapt can change your life 元认知：冷静的观察者 (The impressive watcher in the tower) “元认知”-即我们审视自身思想的能力。元认知是“对思维的思考”(Thinking about thinkingU0010000f”)。元认知是调整思维、改进思维结果的最有力的内部手段。 反馈回路(Feedback loop): 反馈回路是适应性大脑发动机，它包括四个主要因素 ——事实（Evidence), 联系(Relevance)，结果(Consequence)和行动(Action)。反馈回路为大脑提供驱动力。 元认知回路 元认知回路是无意识的信息（在“系统”中,The system）进入到意识空间（Conscious mind space, 心理剧场-The mental theater），并且转化成能重新转回系统的信息的过程。 元认知回路包括： 系统 (The system): 无意识加（Unconscious）工通过“模块”（Module)结构自动进行。无意识的处理速度为11，000，000 次/秒。 有意识（Conscious)的加工能力聚焦于“系统”的特殊状况，通过元认知加工来有意识地分离（Conscious detachment, 通过冥想或者其他放松技术），这个过程发生于大脑前额叶皮层上（The prefrontal cortex）。 元认知分为： 高级元认知（Higher-order)：又叫意识元表征（Conscious metarepresentation），加工速度40次/秒 低级元认知(Lower-order)：又叫元认知察觉（Epistemic thoughts）。非语言认知感受, 无意识认知会渗入，开始处理无意识认知，但不会投射到心理剧场。一直到输送到高级元认知。 元认知觉察（Metacognitive awareness）包含四个主要因素： 元认知控制（M control）：在意识空间中，我们施加在思想和感情上的有意识控制。 元认知知识（M knowledge）：进入意识空间的知识的数量和质量。 元认知监控（M monitoring）：在意识空间中，我们对知识进行评估的频率和效率。 元认知体验（M experience）：我们能从意识空间的知识中获得什么，以及这种体验怎样使我们在整个过程中获益。 心理化（Mentalization)：认知的游戏 长久以来，认知科学家认为，只有人类才能够完成基本的自我意识任务。事实证明，黑猩猩和其他类人猿，海豚、大象、猕猴和欧洲猿、喜鹊等动物都具有自我意识，他们认识镜中的自己。 但只有人类能够从自我知觉中脱离出来，审视一种脱离了自我的情境。（What humans can do that these other species cannot is detach from their self-perspective and examine a situation that includes oneself om a position outside oneself.） 例：当交通拥挤的时候，旁边的车却突然插到你前面，你怒不可遏地按响喇叭，然后摇下车窗玻璃，呵斥插队的人。你能够对做与不做的后果进行一下分析。在这个心理空间中，你“看到”了下一刻将会发生什么，认为贸然行动结果并不理想，所以你放弃了这样做。你有效地使用了元认知觉察来改变当前局面。 心理理论（TOM)指的是人类能够想象隐藏在他人之前行为背后的动机和感受，并对他们当前或未来的行为做出预测。（Teory of Mind (TOM) refers to the uniquely human ability to imagine the motives and feelings behind other people"s past behavior and to predict how their behavior will unfold under their present and future circumstances.） 丹尼尔·西格尔的工作开创了一个新的领域—“人际神经生物学”。这个学科强调：当我们说起“心智”时，我们实际上是在谈论我们的大脑、自己的心智和他人的心智之间的关系。总结一下就是，心智是内在的、相关的。西格尔强调：“心智是身体的具体表现，而非局限于大脑。“（Daniel Siegel"s work, which touched off a new field,“interpersonal neurobiology,” suggests that when we speak of“mind,” we are speaking of interrelationships between our brain, our mind, and others" minds.) “The mind [is] an emergent property of the body and relationships[and] is created within internal neurophysiological processes and relational experiences. 意向性(Intentionality)： 意向性是人区别于其他动物的重要概念(人类才具有三阶以上意向性，可达六阶）: 一阶意向性（First-order I)：主体能够反思(self-reflect)自我的欲望、需要，他们能够进入自己的头脑中。 二阶意向性(Second-order I): 主体能够形成关于他人心智状态的理念(belief)。 三阶意向性(Third-order I): 个体能够推断(reason)一个人如何思考另一个人的想法。 四阶意向性(Fourth-order I): 个体能够推断(reason)一个人怎样揣测另一个人如何思考第三方的想法。 心声(Inner voice) 心声(Inner voice)人类心智区别于其他动物的另一个与众不同的特征就是“心声”。心声是将行为中的元认知觉察标注出来的通常做法。(The inner voice is really just a popular way of labeling metacognitive awareness in action.) 如果心声得到良好训练，那么它将扮演一个冷静的旁观者的角色。反之，人们将沦为情绪的奴隶。 自律性人格（The autonoetic personality） 通过对元认知的有效利用（心理化增强了其效果），我们具有了越来越自律的人格。自律是指可达到的自我意识的最高水平。 自律型人格的人了解元认知回路并且能够使其发挥最大功效。他们同样知道心智化是怎样起作用的，并且认识到他们的心智会有目的地与他人相互作用。尽管他们知道自己无法控制无意识，但是通过元认知，他们可以影响庞大的系统中的加工模块，进而改变整个生活。 实用主义的适应：改变思维，改变生活 （Pragmatic adaptation) 实用的适应性（Pragmatic adaptation）： 实用主义的进化指的是我们如何修正自身的想法和行为来适应这个由我们大脑创造的世界。(Pragmatic adaptation refers to how we must adapt our thoughtsand behavior to negotiate our way through the world our brains created.) 实用主义适应中最重要的变量就是反馈。(My contention is that the most important variable in the exercise of pragmatic adaptation is feedback.) 所谓大脑可塑性，往往指的是神经化学水平上的变化，尤其是指突触（神经元之间的连接点，它允许类似多巴胺、5-羟色胺、谷氨酸之类的神经递质在神经元间的传递）在形态和大小上的变化。（Not all of the brain"s synapses are “plastic,” but we now know that multiple brain regions are homes to neurons with synapses capable of adjusting for lesser or greater reception and projection of specic neurotransmitters. is is signi cant because it opens up the possibility of training the brain to do things previously thought impossible. ） 人格变化和幸福感(Personality change and well-being) 人格理论学者高尔顿·奥尔波特（Gordon Allport）把这些人格形容词（4000多个）分为三大类： 1.核心特质(Cardinal traits): 会主宰一个人的人格(mindset and outlook)。 2.主要特质(Central traits): 会影响一个人的行为(behavior)。 3.次要特质(Secondary traits):只有在特定的情境下才会表现出来。 保罗·科斯塔（Paul Costa）和罗伯特·麦克雷（Robert McCrae）将人格词汇被简化为五种评价类型： 开放性(Openess) 外向性(Extraversion) 宜人性（Agreeableness) 神经性（Neuroticism) 严谨性（conscientiousness) 人格变化 实用主义的原则聚焦于大脑调节自身使之适应环境的能力上。我们首先判断自身在任何一个特定的人格类型上的局限，然后，本着实用的目的，去打破种种局限。（We must rst identify our limitations in any given personality category, and then pragmatically adapt to address these limitations.） 适应道路上的重点： 应变稳态和自稳态Major Points on the Adaptation Raceway: Allostasis and Homeostasis 人的大脑有两种状态用于自适应世界： 自稳态(Homeostasis)是指一个系统试图去保持稳定、平衡的状态，而不是从一个极端到另一个极端。(Homeostasis refers to the tendency of a system to maintain a stable, balanced condition instead of bounding from one extreme to another.) 应变稳态(Allostasis)是一个需要适应千变万化的内部和外部环境的系统所必备的，目的就是要接近自稳态。 (Allostasis is the necessity of a system to adapt to ever-changing internal and external environments in order to get closer to the holy grail of homeostasis.) 我们的大脑同时包含了这两种动态。为了应对外部和内部的影响，大脑会在这些状态之间来回变换。如果我们长时间停滞于一种状态，那么对我们的身心都会产生不利的后果。 Our brains embody both of these dynamics. For example, our brains seek homeostatic balance between our sympathetic and parasympathetic nervous systems—between the soly focused resting state and the stress-alert, fight-or-fight state—but we never remain only in one or the other. Instead, our brains toggle between these states in response to internal and external infuences. 应变稳态的最好例子，就是人们如何处理所谓的“思维错误”(Thinking distortion)。 "思维错误"能够使反馈回路发生偏移，阻碍我们的适应性能力(Tinking error can distort feedback loops and hamper our ability to adapt.)。 如果思维错误长期存在，会在大脑中形成固定的神经模式。思维错误是自动化意识，元认知有助于增强适应力。 “你不是你的想法”这条原则至关重要。（You are not your thoughts.） 人的常见思维错误： “非黑即白”(All or nothing)的绝对思维， 以偏概全(Overgeneralination) 主观臆测(Mind reading) 否定正面或者负面信息 (Disqualifying the positive/Negative) 认知行为疗法（Cognitive behavioral therapy，CBT）是一种试图通过改变思维来改变情绪反应的治疗技术。它认为，由于我们的认知中存着一系列的“思维错误”，使我们不合理地解释了事件，从而导致心理问题，这一切与事件本身没有关系。 "自我对称”(EgoU0010000e symmetric personality)这个术语是指，在关于自我概念的两极——“自我协调”和“自我失调”之间寻求一种理智的平衡，当你以一种自我协调的方式工作时，你认为出现在你意识中的一连串的想法都真实地代表着你是谁。(“Ego-symmetric” strikes a judicious balanceego-syntonicandego-dystonic. When you function in an ego-syntonic manner, you believe that the ongoing stream of thoughts surfacing in your consciousness truly represent who you are.) 具有自我对称人格的人，能够从消极、错误的信息中脱离出来。它意味着对影响人们适应力的消极方面的有效控制。(The ego-symmetric personality is able to detach from negative and erroneous information that, if indulged, would undermine the the self"s ability to achieve its goals. It"s about being in better control of how negativity affects our ability to adapt and thrive. ) 寻迹叙述性线索：剧本化和突显的力量 （Tracing the narrative thread-the power of scripting and salience） 现在人们确定人格改变对于幸福感来说至关重要，远远超过了一般的外部因素，比如婚姻状况、就业和居住地。 元认知绝不仅仅是一个理论概念，它更是一个神经实体（大脑的一个物理维度）。我们的元认知能力之所以不同于其他物种的自我意识能力，是因为我们可以，从当前的情境中心智分离，审视自身的思维。 “叙述性线索”描述的是我们以一种很相似的方式将多个“自我”整合为一体。研究表明，“我”或者自我身份，实际上并不是一个独立作用的整体，而是一个由相互作用的自我身份结合而成的混合物。其中那个统一的“我”是大脑为我们自己量身定做的身份。(“Narrative thread” describes how we hold our “selves” together in a more or less unified way as we proceed through our lives. “I,” or self-identity is not onecoherent entity, but a composite of interplaying self-identities. The unified “I” within is a useful illusion that our brains foster on our behalf.) 当我们处理当前情境或挑战时，我们需要一个可为我们提供庇护的内部核心机制。我们体验自身的时候都是作为一个“我”，而不是“我们”，这充分说明叙述性线索的必要性。因为感受“我”是出于本能，以至于我们很少觉察到这一事实。 在生活中，既有内部叙述性脚本，也有外部叙述性脚本。叙述从来不是静态的，我们的大脑从来不是一成不变的。人格也从来不是生而定型的。尽管并非时时都能体验到变化，但是我们永远都处在变化之中。 精神世界：循环相连 The mindscape-looping it all together 一段关于元认知的对话： 元认知是影响反馈回路最有效的内部手段。但大脑的大部分活动是在无意识空间进行的。 当利用元认知来控制大脑的适应力时，人们将会体验到三种很重要的影响，更加自律、更加自我对称、更加有效的自我叙述（More autonoetic, more ego-symmetric, and more eective conscious self-narrators.）。 自律，指的是人们自我意识将达到一个更高的水平（By autonoetic, I mean that they"ll attain a higher degree of self-awareness.）。 自我对称，指的是人们能够从阻碍他们达成目标的想法或感受中分离出来。 （They"ll attain a greater ability to detach from thoughts and feelings that conict with the achievement of their goals—the outcomes they want in life.) 有意识的自我叙述，指的是人们将会变成“脚本”的评论者、编著者，在他们自身的叙述中施加更多的有意控制。（They"ll become the reviewers, editors, and writers of those “scripts” we mentioned.） 想法箱：30种改善思维的工具（The thought box) 四大类工具： 个人(Personal)——处理我们的内部世界、个体心理空间问题的工具。 外部(External)——处理内心世界与外部世界的冲突的工具。 联系(Relational)——处理人际关系，我们的思想如何影响他人，又如何被他人影响。 生物化学(Biochemical)——这类工具催化引起思维和行为改变的生物化学变化。 心智资料 （推荐的图书，小说和回忆录， 电影） 众多 （略）