#BombayHighCourt begins hearing the plea filed by Kangana Ranaut and her sister Rangoli Chandel challenging the summons and FIR accusing them of sedition.

@KanganaTeam
@MumbaiPolice

(THREAD) The Whitaker scandal is deepening, as it becomes clear that Sessions' Russia recusal was a coordinated sham and his firing an act of obstruction—and that his replacement is a White House plant inside the Mueller probe. I hope you'll retweet and read on for more details.

1/ From 2014-17 Whitaker worked for World Patent Marketing—which during his tenure defrauded consumers out of $26 million and was successfully prosecuted by the feds. His involvement in the scam confirmed he had loose morals and that the feds would never want to employ him again.

2/ Despite the seeming impossibility of a man with Whitaker's background getting a job at Justice ever again—he'd been found to have used his former title as a US attorney to fraudulently threaten consumers with valid complaints with criminal penalties—Whitaker found an opening.

3/ Within 60 days of parachuting out of World Patent Marketing as it was being fined $26 million by the feds, Whitaker was working for CNN and telling a fellow attorney panelist that his purpose in working for CNN was to get noticed by one man—Donald Trump—and thereby get a job.

4/ Whitaker spent his time at CNN making ludicrous statements about the Mueller probe: there was no obstruction or collusion, he said; Mueller had no authority to look into any aspect of Trump's finances or to subpoena him, he said. All the while, he hoped Trump was watching him.

Important paper from Google on large batch optimization. They do impressively careful experiments measuring # iterations needed to achieve target validation error at various batch sizes. The main "surprise" is the lack of surprises. [thread]

https://t.co/7QIx5CFdfJ

The paper is a good example of lots of elements of good experimental design. They validate their metric by showing lots of variants give consistent results. They tune hyperparamters separately for each condition, check that optimum isn't at the endpoints, and measure sensitivity.

They have separate experiments where the hold fixed # iterations and # epochs, which (as they explain) measure very different things. They avoid confounds, such as batch norm's artificial dependence between batch size and regularization strength.

When the experiments are done carefully enough, the results are remarkably consistent between different datasets and architectures. Qualitatively, MNIST behaves just like ImageNet.

Importantly, they don't find any evidence for a "sharp/flat optima" effect whereby better optimization leads to worse final results. They have a good discussion of experimental artifacts/confounds in past papers where such effects were reported.

1 of 16: WE ARE ALL JEWS HERE: THE STORY OF RODDIE EDMONDS

One of the most moving and relevant stories of the Battle of the Bulge, or any American Soldier in any war, is that of Master Sergeant Roddie Edmonds, a Knoxville, Tennessee native, who served with the 106th Infantry.

2 of 16:

Roddie was captured early on in the Battle of the Bulge, on December 19th, when Panzer forces plowed through his unit.

He, along with almost his entire regiment, was forced to surrender.

3 of 16:

The men were transported to the Stalag IX-A POW camp in Ziegenhain, Germany.

Roddie was the senior enlisted American Soldier at the site. As such, he was the conduit between all American Soldiers and their German captors.

4 of 16:

In late January, the camp’s commandant, Major Siegmann [pictured here], ordered Roddie, a Christian, to identify all Jewish Soldiers and order them to stand in formation by themselves the next day.

[Jewish Soldiers were a minority within American units]

5 of 16:

Throughout WWII, captured Jewish Soldiers were often tortured and then killed by their German captors.

In fact, Jewish Soldiers had been told to bury their dog tags before capture. The dog tag identified Jews with the letter “H” for Hebrew.