This sounds more like a medieval court than a modern democracy. Also this incredible gem:
Mr. Bannon remains the president’s dominant adviser, despite Mr. Trump’s anger that he was not fully briefed on details of the executive order he signed giving his chief strategist a seat on the National Security Council, a greater source of frustration to the president than the fallout from the travel ban.
Beringei is different from other in-memory systems, such as memcache, because it has been optimized for storing time series data used specifically for health and performance monitoring. We designed Beringei to have a very high write rate and a low read latency, while being as efficient as possible in using RAM to store the time series data. In the end, we created a system that can store all the performance and monitoring data generated at Facebook for the most recent 24 hours, allowing for extremely fast exploration and debugging of systems and services as we encounter issues in production. Data compression was necessary to help reduce storage overhead. We considered several existing compression schemes and rejected the techniques that applied only to integer data, used approximation techniques, or needed to operate on the entire dataset. Beringei uses a lossless streaming compression algorithm to compress points within a time series with no additional compression used across time series. Each data point is a pair of 64-bit values representing the timestamp and value of the counter at that time. Timestamps and values are compressed separately using information about previous values. Timestamp compression uses a delta-of-delta encoding, so regular time series use very little memory to store timestamps. From analyzing the data stored in our performance monitoring system, we discovered that the value in most time series does not change significantly when compared to its neighboring data points. Further, many data sources only store integers (despite the system supporting floating point values). Knowing this, we were able to tune previous academic work to be easier to compute by comparing the current value with the previous value using XOR, and storing the changed bits. Ultimately, this algorithm resulted in compressing the entire data set by at least 90 percent.
According to Willy Allison, a Las Vegas–based casino security consultant who has been tracking the Russian scam for years, the operatives use their phones to record about two dozen spins on a game they aim to cheat. They upload that footage to a technical staff in St. Petersburg, who analyze the video and calculate the machine’s pattern based on what they know about the model’s pseudorandom number generator. Finally, the St. Petersburg team transmits a list of timing markers to a custom app on the operative’s phone; those markers cause the handset to vibrate roughly 0.25 seconds before the operative should press the spin button. “The normal reaction time for a human is about a quarter of a second, which is why they do that,” says Allison, who is also the founder of the annual World Game Protection Conference. The timed spins are not always successful, but they result in far more payouts than a machine normally awards: Individual scammers typically win more than $10,000 per day. (Allison notes that those operatives try to keep their winnings on each machine to less than $1,000, to avoid arousing suspicion.) A four-person team working multiple casinos can earn upwards of $250,000 in a single week.
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