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Srinivas JONNALAGADDA
92 followers -
Let everyone have happiness and peace!
Let everyone have happiness and peace!

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In Internet conversations, we read sentences like "I did not even install Uber on my new iPhone." Given the context, the person could have said "... on my new phone," without loss of generality.

This insistence on spelling "iPhone" out, on the part of several users of iPhones, comes across as either silliness or childishness, depending on the situation.
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The "OzSky Star Safari" is an exclusive event held on the outskirts of Coonabarabran, New South Wales - The Astronomy Capital of Australia.

Registrations are extremely limited, so be sure to register now to secure your place at OzSky 2017!

http://www.OzSky.org

OzSky's primary aim is to provide astronomers from the Northern Hemisphere with not only a unique Australian cultural experience, but more importantly a unique opportunity to experience the grandeur of the southern night skies through a number of large telescopes, saving the need to transport large telescopes from one side of the world to the other.
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I'm very excited about the work our group (g.co/brain) is doing in various areas of medical imaging. Today, we published a preprint titled _"Detecting Cancer Metastases on Gigapixel Pathology Images" that showed that our ML model achieves a tumor localization score of 0.89 vs. 0.73 achieved by human pathologists with infinite time. The paper authors are Yun Liu, Krishna Gadepalli, Mohammad Norouzi, George E. Dahl, Timo Kohlberger, Aleksey Boyko, Subhashini Venugopalan, Aleksei Timofeev, Philip Q. Nelson, Greg Corrado, Jason D. Hipp, Lily Peng, and Martin C. Stumpe.

Some key statistics from the paper used to evaluate the effectiveness of this work:

Tumor localization score (FROC): ("find all the tumors")
0.89 (our model)
0.73 (human pathologists with infinite time)

Sensitivity at 8 false positives:
0.92 (our model)
0.73 (human pathologists with infinite time)

(It's also worth point out that Yun Liu is a member of the Google Brain Residency program: g.co/brainresidency)

This work follows on our earlier work on detection of diabetic retinopathy in retinal images.

Pathology blog post: https://research.googleblog.com/2017/03/assisting-pathologists-in-detecting.html
Paper preprint: https://drive.google.com/file/d/0BwScg2QTKNZ0VVdwTURXeDluQVk/view

Earlier blog post on diabetic retinopathy work: https://research.googleblog.com/2016/11/deep-learning-for-detection-of-diabetic.html

(Edit: Slight rewording)
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Location: Ayyappa Society 100 feet road.

Interesting caption!
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Interesting!
Setting up my new laptop. Am once again reminded that the crazy UI people continue to think that "DPI" is something meaningful. No it is not. The number of pixels matters. Not the DPI.

The new laptop has the same resolution as my desktop, but apparently because the laptop screen is smaller, gnome seems to decide on its own that I need an automatic scaling factor of 2, which blows up all the stupid things (window decorations, icons etc) to a ridiculous degree.

And obviously none of this is documented anywhere, nor are there any sane settings to edit it. Because "settings confuse people". So instead, you have to do this:

gsettings set org.gnome.desktop.interface scaling-factor 1

because unlike having some UI in the gnome settings center, that confuses nobody.

What the hell is wrong with UI people? What insane person thought that it's a good idea to say "oh, you have a 13.3" QHD+ display, let's make it act completely different from your 28" QHD+ desktop display and waste all that precious screen real estate with stupid window borders and big fonts in your title bars?"

And especially if you have some heuristic tunable that makes random UI decisions for people, give me a simple setting in the control center that says "no, I really know better than you do".

But no.

Writing a G+ rant just to remind myself for next time this insanity happens.
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Came across this auto near RTC X-roads!
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Ask your AI/ML questions away!
I and a bunch of others from the Google Brain team are doing a Reddit AMA on /r/MachineLearning to answer people's questions about AI and Machine Learning. The thread is posted now to allow people to post and vote on questions they want to see answered, and we'll be spending several hours this Thursday, 8/11 answering questions, starting at 10 AM Pacific Daylight Time.

(We have 279 comments posted to the thread, and it's the most up-voted post of all time on /r/MachineLearning, and we haven't even started answering questions. We'll have to see how many questions we can get through on Thursday!).
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Nice blog post by Tevian on teaching math to people who might one day become physicists.
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