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while(alive){learn;} home about download link sent publications stork文献鸟显示期刊的中科院分区 september 12th, 2018 no comments 每天的新文献太多了，根本没时间读。怎么办？这时候就需要快速地甄别出重要文献。为了帮助大家做到这一点，文献鸟做了两件事情： 高亮标记了高影响因子的文献，并且文献按影响因子排列 显示中科院期刊分区信息，并用不同的颜色标识不同分区 对于 pro用户 ，文献鸟还允许设置过滤分区。比如如果设置最大分区是2，则只有分区为1和2的期刊文献才会被推送，其它的会被自动过滤。 中科院期刊分区的显示 pro用户可以设置分区过滤 如果需要购买pro，可以单击 这个链接 。 stork官网地址： https://www.storkapp.me/ author: categories: stork , web , writing tags: bold5000, a public fmri dataset of 5000 images september 11th, 2018 no comments official website and download full text paper link good news for brain imaging researchers. there is a new dataset available for you to play with. bold5000 is a large-scale, slow event-related fmri dataset collected on 4 subjects, each observing 5,254 images over 15 scanning sessions. the images are selected from three computer vision datasets. 1,000 images from scene images (with scene categories based on sun categories) 2,000 images from the coco dataset 1,916 images from the imagenet dataset bold5000 image data author: categories: brain , web tags: stork api, a single line becomes a list of new publications september 8th, 2018 no comments i want to show a list of my own publications on my webpage, is there an easy way to do so? yes, stork api, a single line of code, allows you to show a list of publications given a keyword. you only need to put the code to your webpage once, and then even if there are new publications, the list will update itself. let’s look at this list: the above list was generated by the following single line of code: <iframe style="border: 0;" src="https://www.storkapp.me/api/new.php?apikey=storkdemo&amp;format=html&amp;num=20&amp;k=cui+xu+(stanford+psychiatry+or+houston)" width="100%" height="600" frameborder="0"></iframe> what about a list of publications in fnirs field? it’s easy too. as you can see, all you need to change the the “k” parameter (which stands for keyword). <iframe style="border: 0;" src="https://www.storkapp.me/api/new.php?apikey=storkdemo&amp;format=html&amp;num=4&amp;k=(nirs or fnirs) brain" width="100%" height="600" frameborder="0"></iframe> the line of code above becomes author: categories: programming , web , writing tags: google dataset search, a great tool for fnirs and fmri? september 6th, 2018 no comments google just launched a new search engine: google dataset search . with this app, scientists can search public datasets published in scientific journals (and possibly other sources). according to google, “dataset search enables users to find datasets stored across thousands of repositories on the web, making these datasets universally accessible and useful.” i searched ‘fnirs’ and it returned 30+ results. see figure below. i clicked the first one, fnirs/eeg/eog classification, and it shows some meta information (e.g. the source and authors). then i clicked the ‘zenodo.org’ website and did see the download link of the mat file. google dataset search i also tried to search ‘fmri’. the number of datasets for fmri is much larger than that for fnirs. currently the number of datasets indexed by google is still limited, but i expect it will grow rapidly and become a very useful tool for scientists and anybody who want to play with data. link: google dataset search author: categories: nirs , web tags: fnirs 2018 august 29th, 2018 no comments fnirs 2010 conference will be held during october 5-8, 2018 in tokyo, japan. you may find more information at http://fnirs2018.org/ . the early registration deadline is 2018-09-05. author: categories: nirs tags: temporal resolution of cw fnirs devices august 10th, 2018 1 comment this is a guest post by ning liu from stanford university. temporal resolution provides information on the distance of time between the acquisitions of two images (data) of the same area. it is the reciprocal of sampling rate (or acquisition rate) of an fnirs device. for some devices, sampling rate is a fixed number; for some other ones, sampling rate may depend on number of sources or detectors to use. why is that? it is because they have different instrumental design. for those with unfixed sampling rate, multiple sources time-share an optical detector by means of a multiplexing circuit that turns the sources on and off in sequence, so that only one source within the detector range is on at any given time. the nirx system, for instance, are using this type of design. for those with fixed sampling rate, they usually use low frequency modulated light source to provide the excitation light, thus one detector can ‘see’ only one source. for instance, hitachi etg4000 system has sampling rate of 10hz (from http://www.hitachi.com/businesses/healthcare/products-support/opt/etg4000/contents2.html), thus its temporal resolution is 100ms. some other device, such as nirscout, has sampling rate from 2.5 – 62.5 hz (from https://nirx.net/nirscout/), thus its temporal resolution is 16 - 400ms. why the sampling rate is changing from 2.5 – 62.5 hz? that’s because users can choose different number of sources and detectors in their configuration. the more number of sources and detectors to use, the smaller the sampling rate. the following table is from a review article (scholkmann, et al., 2014) on neuroimaging volume 85 (2104), a special issue of functional near-infrared spectroscopy. it summarizes the specifications of some popular commercially available fnirs devices, mainly focused on continuous wave devices. time resolution of nirs devices (click to enlarge, f. scholkmann et al. / neuroimage 85 (2014) 6–27) 本文作者为斯坦福大学 刘宁 。她提供nirs培训服务。 author: categories: nirs tags: deep learning training speed with 1080 ti and m1200 june 19th, 2018 no comments i compared the speed of nvidia’s 1080 ti on a desktop (intel i5-3470 cpu, 3.2g hz, 32g memory) and nvidia quadro m1200 w/4gb gddr5, 640 cuda cores on a laptop (cpu: intel core i7-7920hq (quad core 3.10ghz, 4.10ghz turbo, 8mb 45w, memory: 64g). the code i used is keras’ own example (mnist_cnn.py) to classiy mnist dataset: mnist dataset '''trains a simple convnet on the mnist dataset. gets to 99.25% test accuracy after 12 epochs (there is still a lot of margin for parameter tuning). 16 seconds per epoch on a grid k520 gpu. ''' from __future__ import print_function import keras from keras.datasets import mnist from keras.models import sequential from keras.layers import dense, dropout, flatten from keras.layers import conv2d, maxpooling2d from keras import backend as k batch_size = 128 num_classes = 10 epochs = 12 # input image dimensions img_rows, img_cols = 28, 28 # the data, shuffled and split between train and test sets (x_train, y_train), (x_test, y_test) = mnist.load_data() if k.image_data_format() == 'channels_first': x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_test = x_test.reshape(x_test.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) x_train = x_train.astype('float32') x_test = x_test.astype('float32') x_train /= 255 x_test /= 255 print('x_train shape:', x_train.shape) print(x_train.shape[0], 'train samples') print(x_test.shape[0], 'test samples') # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) model = sequential() model.add(conv2d(32, kernel_size=(3, 3), activation='relu', input_shape=input_shape)) model.add(conv2d(64, (3, 3), activation='relu')) model.add(maxpooling2d(pool_size=(2, 2))) model.add(dropout(0.25)) model.add(flatten()) model.add(dense(128, activation='relu')) model.add(dropout(0.5)) #model.add(dropout(1)) model.add(dense(num_classes, activation='softmax')) model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.adadelta(), metrics=['accurac

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