AskMyBuddy

I currently dual booting between Ubuntu and Windows 10. However, whenever I try to boot from Windows 10 in grub it simply restarts and brings me right back to grub again. I used boot-repair to create a pastebin: http://paste.ubuntu.com/p/pzJqP55FVV/ (It's too long to paste onto here) But I have no idea what anything in the pastebin really means or how to fix grub. I have tried updating grub but the same problem occurs.

I have send this transaction to Exodus wallet but didn't received. now it has more than 100 confirmed. what does mean when it has a lot of confirmation and still stuck there ? what I can do to return it back. is there any thing can I do ?

We are a group of data scientists, trying to build a social media search engine that basically focuses on trends and follows the method of store->index->rank method of storing social data from Twitter, Instagram, Vine, Tumblr (for now; we plan to add more in the future). We have researched Terms of Use of major social networks, and we haven't found anything substantial that it is unlawful to aggregate social media data. So, does someone know or experienced such a use case before that we should be wary of?

I have a dictionary say, stringToListDict = {'foo' : [], 'bar' : []} Now lets say we do +foofoo stringToListDict = {'foo' : ['foofoo'], 'bar' : []} +barbar stringToListDict = {'foo' : ['foofoo'], 'bar' : ['barbar']} +foobarbar stringToListDict = {'foo' : ['foofoo', 'foobarbar'], 'bar' : ['barbar']} +notMatchingAnyKey Simply discard this new string. As you can see the added string goes by matching keys as prefix. I can do this by traversing through each key one by one of the dictionary till I get a matching prefix. But is there any other elegant or efficient approach? You don't have to worry of edge scenarios like what would happen if : stringToListDict = {'foo' : ['foofoo'], 'foobar' : [], 'bar' : ['barbar']} then +foobarbar FYI, this is not an assignment.

I have a dictionary say, stringToListDict = {'foo' : [], 'bar' : []} Now lets say we do +foofoo stringToListDict = {'foo' : ['foofoo'], 'bar' : []} +barbar stringToListDict = {'foo' : ['foofoo'], 'bar' : ['barbar']} +foobarbar stringToListDict = {'foo' : ['foofoo', 'foobarbar'], 'bar' : ['barbar']} +notMatchingAnyKey Simply discard this new string. As you can see the added string goes by matching keys as prefix. I can do this by traversing through each key one by one of the dictionary till I get a matching prefix. But is there any other elegant or efficient approach? You don't have to worry of edge scenarios like what would happen if : stringToListDict = {'foo' : ['foofoo'], 'foobar' : [], 'bar' : ['barbar']} then +foobarbar FYI, this is not an assignment.

I've recently written a Matlab script to develop/simulate PWM output for RGB lights based on sampled audio input. I made this script with plans to actually construct a device that can run the algorithm and thus control RGB lights. A brief explanation of the script (I'll post it below as well) is as follows. Filter the incoming signal into 3 bands of low, mid, and high range frequencies. Creates frames of 0.05 seconds in length (I think) to hold RGB values for the duration based upon the 3 filtered signals. Low = Blue, Mid = Green, High = Red. Use a peak ("bump") detector of sorts on the original sampled signal to look for volume increases in the song. The areas of high volume are recorded. Add Bumps to frames. To be honest, I'm not sure I like the way I implemented this and may change it in the future although I'm still trying to figure out a better way. I intend the "bumps" to change the color FROM the background color (created from the 3 filt...

I want to add word dropout to my network so that I can have sufficient training examples for training the embedding of the "unk" token. As far as I'm aware, this is standard practice. Let's assume the index of the unk token is 0, and the index for padding is 1 (we can switch them if that's more convenient). This is a simple CNN network which implements word dropout the way I would have expected it to work: class Classifier(nn.Module):     def __init__(self, params):         super(Classifier, self).__init__()         self.params = params         self.word_dropout = nn.Dropout(params["word_dropout"])         self.pad = torch.nn.ConstantPad1d(max(params["window_sizes"])-1, 1)         self.embedding = nn.Embedding(params["vocab_size"], params["word_dim"], padding_idx=1)         self.convs = nn.ModuleList([nn.Conv1d(1, params["feature_num"], params["word...