![]() “I was inspired to start Ransomwhere by Katie Nickels’s tweet that no one really knows the full impact of cybercrime, and especially ransomware,” Cable told TechCrunch. Cybersecurity and Infrastructure Agency (CISA), is looking to solve that problem with the launch of a crowdsourced ransom payments tracking website, Ransomwhere. Jack Cable, a security architect at Krebs Stamos Group who previously worked for the U.S. However, while ransomware attacks continue to make headlines, it’s nearly impossible to understand their full impact, nor is it known whether taking certain decisions - such as paying the cybercriminals’ ransom demands - make a difference. In the last few months alone we’ve witnessed the attack on Colonial Pipeline that forced the company to shut down its systems - and the gasoline supply - to much of the eastern seaboard, the hack on meat supplier JBS that abruptly halted its slaughterhouse operations around the world, and just this month a supply chain attack on IT vendor Kaseya that saw hundreds of downstream victims locked out of their systems. These file-encrypting attacks have continued largely unabated this year, too. I am not sure I understand it, there is no way for OS to release the thread if it does a blocking call such as read, for these purposes kernel has non blocking syscalls such as epoll which doesn't block the thread and immediately returns a list of file descriptors that have some data available.Ransomware attacks, fueled by COVID-19 pandemic turbulence, have become a major money earner for cybercriminals, with the number of attacks rising in 2020. If the thread needs to block in say a Socket read then this releases the underlying kernel thread to do other work Or Did I just misunderstood the use case for Loom ? Thanks in advance Addon ![]() How is it different from regular threads, at least for OS threads I can scale it to thousand to increase the throughput. So as far as I understand, if I have amount of OS threads equals to amount of CPU cores and unbounded amount of virtual threads, all OS threads will still wait for IO and Executor service won't be able to assign new work for Virtual threads because there are no available threads to execute it. Instead, virtual threads automatically give up (or yield) their carrier thread when a blocking call (such as I/O) is made. According to Ben Evans in the article Going inside Java’s Project Loom and virtual threads: ![]() Now let's say I switched a thread pool to use virtual threads instead. So if I have 200 hundred users reaching this endpoint, I need to create 200 threads each waiting for IO. So let's say I have a backend application that has single endpoint, the business logic behind this endpoint is to read some data using JDBC which internally uses InputStream which again will use blocking system call( read() in terms of Linux). So I understand the motivation, for standard servlet based backend, there is always a thread pool that executes a business logic, once thread is blocked because of IO it can't do anything but wait. I was investigating how Project Loom works and what kind of benefits it can bring to my company. Instead you want the rolling sum over the past year and divide that by the number of days in the year, per my code above. I've had a look at your code and I don't think that the use of the pct_change function is correct - that will calculate the change on the rolling differential, so a movement from eg 0.10% to 0.11% would actually equate to a 10% change. Please also note the article cited states that "It was reported that it had correctly predicted a significant stock market decline only 25% of the time." so I'm not sure if we can read too much into this. So from a look at our graph, my interpretation is that the stock market is currently closing at a lot of 52 week highs, but is not showing many 52 week lows. It would be abnormal if both were occurring at the same time." ,įrom the Hindenburg Omen Definition, "The Hindenburg Omen looks for a statistical deviation from the premise that under normal conditions, some stocks are either making new 52-week highs or new 52-week lows.
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