Strange to see a lock-free ring buffer without seeing mention of LMAX/Martin Thompson's Java Disruptor (https://github.com/LMAX-Exchange/disruptor)
31 karma · joined July 14, 2015
One outstanding 'todo' was textures!
Then I wanted to make ASCII art automatically from any picture, so I played around and made this: http://ascii.pixelweasel.com/ (again, playing with javascript)
On mobile, I tried recording to /c/PepTalk, but after recording my voice, none of the buttons ('save', 'play' or 'stop') did anything. So I wasn't sure if it had actually uploaded.
Then when I went back to the /c/PepTalk channel to check I had a popup saying something like 'failed to fetch waves' (sorry, I don't recall the exact error message!).
I'll keep an eye on this ongoing though, as I adore the concept.
I work at Anaplan, and the most common way that our biggest customers discover us is when they've been bitten by spreadsheets as they've scaled, and now they have users emailing spreadsheets around and someone with a full time job collating them.
We've modelled the product around the flexibility, but rigor and scale on top of it.
Interesting also that the gold standard of the article is 'in the ring'. I would have thought messy 'real world' combat is gold standard (but obviously less reproducible, from the science perspective). Additionally, multiple opponents.
And the abstract is: "Clinical medical data, especially in the intensive care unit (ICU), consist of multivariate time series of observations. For each patient visit (or episode), sensor data and lab test results are recorded in the patient's Electronic Health Record (EHR). While potentially containing a wealth of insights, the data is difficult to mine effectively, owing to varying length, irregular sampling and missing data. Recurrent Neural Networks (RNNs), particularly those using Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for learning from sequence data. They effectively model varying length sequences and capture long range dependencies. We present the first study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements. Specifically, we consider multilabel classification of diagnoses, training a model to classify 128 diagnoses given 13 frequently but irregularly sampled clinical measurements. First, we establish the effectiveness of a simple LSTM network for modeling clinical data. Then we demonstrate a straightforward and effective training strategy in which we replicate targets at each sequence step. Trained only on raw time series, our models outperform several strong baselines, including a multilayer perceptron trained on hand-engineered features."
I think you REALLY don't want typos on a site about education! :D
There's no mention of tea whatsoever! seethe