You know how you can teII which one is the right broad?

你知道怎么判别那个婆娘最合适吗？

And some of them are so badly decomposed that one coast guard investigator told me he couldn't even identify their genders.

一些尸严重腐烂，一名海岸警卫队调查人员告诉我，有一具尸甚至无法判别性别。

They view the courses as an expensive but necessary add-on, and judge their quality by how much private investment they attract.

他们认为这种课程虽然昂，却是不可缺的附加课程，并且他们会根据这类课程所吸引的学生的投资多少来判别其教学质量。

We also have impressive gaze direction networks inside our brains, containing individual neurons that fire when someone is staring directly at us.

我们大脑中有着令人惊异的注视方向判别系统，包括许多神经元个 并会在我们被人注视的时候发生冲动。

Facebook's executives say it's not their place to decide whether an ad is true or false and supporters of the policy say that's the job of journalists anyway.

脸书管表示他们没有义务判别广告的真假，而支持这项政策的人则认为，无论如何这是新闻记者的责任。

For expert men can execute, and perhaps judge of particulars, one by one; but the general counsels, and the plots and marshalling of affairs, come best from those that are learned.

练达之士虽能分别处理细亊或一一判别枝节，然纵观统筹、全局策划，则舍好学深思者莫属。

But gradually, it was disclosed to me that the line separating good and evil passes not through states, nor between classes, nor between political parties either, but right through every human heart.

但我渐渐发现，判别善恶的准绳不是国家不是阶级，也不是政党，而是每个人的内心。

The generator network tries to create more and more realistic images, and these are passed to the discriminator network which tries to learn the difference between real photographs and fake, forged images.

这个生成网络旨在生成出更加真实的相片，然后交由判别网络去找出生成的假照片与真实照片的区别。

Tragic? Oh, I don't know. Her physician said to me: " If I could stretch out my hand and restore her sanity, I wouldn't do it. She's much happier as she is" .

这是人生的悲剧吗？ 我无法判别。 她的主治医生如是说：就算我可以伸出援手帮助她恢复常态，我也不会那样做。 这会儿的她比神经正常的她还要快乐得多。

In the application of simultaneous identification of gender and variety, CNN model has the highest accuracy of 94%, LDA model has the medium accuracy of 92.5%, and SVM model has the lowest accuracy of 89.5%.

在性别与品种同时识别的应用中，卷积神经网络（CNN）模型准确率最，达到94%，线性判别分析（LDA）模型居中，准确率为92.5%，而支持向量机（SVM）模型准确率最低，为89.5%。

Se veral machine learning algorithms (logistic regression, decision tree, support vector machine, k-nearest neighbor, linear discri minant analysis, and ensemble learning) were used to discriminate between disease groups: healthy, symptomatic HLB-infecte d, and asymptomatic HLB-infected, based on leaf reflectance.

采用了多种机器学习算法（逻辑回归、决策树、支持向量机、K近邻、线性判别分析及集成学习），基于叶片反射率对疾病群——健康、症状性黄龙病感染和无症状性黄龙病感染——进行区分。