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    Home Unlabelled Will California's CPRA become the standard for national consumer privacy legislation? Computer Science

    Will California's CPRA become the standard for national consumer privacy legislation? Computer Science

    July 30, 2020

    Will California's CPRA become the standard for national consumer privacy legislation? Computer Science

    Will California's CPRA become the standard for national consumer privacy legislation?

    Posted: 29 Jul 2020 10:58 AM PDT

    Kernel.img Help

    Posted: 29 Jul 2020 06:57 AM PDT

    Hey,

    I am currently having some issues with a project I am currently work on for my undergraduate research(summary of project: emulate a ECU on a RPI and gather HPC(hardware performance counters) data using perf(Linux based analysis tool)).

    So i started off with this GitHub repo:( https://github.com/MichaelBuhler/ecu ) although when I make make this project it produces a new kernel.img which i went on and replace with the kernel.img that was on the RPI, which all is fine although my problem is that the repo is for a bare-metal RPI so it doesn't have OS like Linux which i need for my project.

    So my main question is that is there anyway I could change the makefile so it doesn't create a complete new bare-metal kenrel.img rather a Linux based kernel, or is there any other way to get around this issue.

    Thank you so much, any help or advice would be greatly appreciated

    submitted by /u/ConstantlyClapped22
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    Resources or approaches for making NP-complete reductions

    Posted: 29 Jul 2020 02:19 PM PDT

    I really enjoy this concept and am doing self-learning on it but I have yet to find resources or approaches for making NP-complete reduction proofs or how to think about them? Does anyone have any thoughts on how they approach it or any resources that do a really good job at making these proofs and reductions "click"?

    submitted by /u/hippi345
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    Splitting a dataset

    Posted: 29 Jul 2020 07:37 PM PDT

    I'm working on the applications of A.I. to thermodynamics, and I had to solve for how to split a dataset into two parts, using objective criteria. Specifically, I'm interested in what's moving, and what's not, but the algorithm I came up with is general, and can be used to split any dataset in two, using objective criteria.

    It is only a slight tweak on my original clustering algorithm, that requires an additional outer loop that iterates through levels of granularity, because you end up with a coin toss distribution, which produces very slight changes in entropy.

    It is of course extremely efficient, and the code attached splits a dataset of 50 million data points in about seconds.

    Code here:

    https://derivativedribble.wordpress.com/2020/07/29/splitting-a-dataset/

    submitted by /u/Feynmanfan85
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    What Are the Top Uses of Python For Finance? - Statanalytica

    Posted: 30 Jul 2020 01:26 AM PDT

    Top 10 uses of Python for The Real World

    Posted: 29 Jul 2020 11:28 PM PDT

    [N] MLPerf Training v0.7 Results Released: Google & NVIDIA Lead the Race

    Posted: 29 Jul 2020 02:27 PM PDT

    The industry-standard MLPerf benchmark today released the results of the third round of its ongoing ML Training Systems competition. The competition measures the time it takes to train one of eight ML models to a qualified target on the following tasks: image classification, recommendation, translation, and playing Go. Forerunners Google and NVIDIA set new AI performance records in this third round (v0.7).

    Here is a quick read: MLPerf Training v0.7 Results Released: Google & NVIDIA Lead the Race

    submitted by /u/Yuqing7
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    [R] Google Proposes Lasso Algorithm Variant for Learning Convolutions: ‘Bridging the Gap Between Fully-Connected and Convolutional Nets’

    Posted: 29 Jul 2020 12:51 PM PDT

    While researchers have focused on learning convolution-like structures from scratch to move forward in this regard, they face a dilemma due to a limited understanding of the inductive bias that gives rise to convolutions. How to reduce inductive bias while making sure this won't hurt model efficiency? Is it possible to keep only the core bias to deliver high performance? Google Senior Research Scientist Behnam Neyshabur recently offered his insights on the topic in the paper Towards Learning Convolutions from Scratch.

    Here is a quick read: Google Proposes Lasso Algorithm Variant for Learning Convolutions: 'Bridging the Gap Between Fully-Connected and Convolutional Nets'

    submitted by /u/Yuqing7
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    [QUESTION] A project to work on with 0 CS knowledge?

    Posted: 29 Jul 2020 09:05 PM PDT

    Alright dear computer scientists here is the thing, I am attending my dream college next month to study CS (yay) and since I got in touch with many prospective students of the same major, we decided why not trying to do a little project that is related to CS to impress our teachers right off the start lol. Note that most of us have no previous experience in CS but we would love to learn something while working on the project itself. Can you guys recommend us something to do? (Better if it can be divided and done as a group project)

    submitted by /u/hellytcha
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