In an analysis of more than 11 million U.S. veterans’ health records, researchers found the risk of 20 different heart and vessel maladies was substantially increased in veterans who had COVID-19 1 year earlier, compared with those who didn’t. The risk rose with severity of initial disease and extended to every outcome the team examined, including heart attacks, arrhythmias, strokes, cardiac arrest, and more. Even people who never went to the hospital had more cardiovascular disease than those who were never infected. The results are “stunning … worse than I expected, for sure,” says Eric Topol, a cardiologist at Scripps Research. “All of these are very serious disorders. … If anybody ever thought that COVID was like the flu this should be one of the most powerful data sets to point out it’s not.” He adds that the new study “may be the most impressive Long Covid paper we have seen to date.” […] “In the post-COVID era, COVID might become the highest risk factor for cardiovascular outcomes,” greater than well-documented risks such as smoking and obesity, says Larisa Tereshchenko.
Bananas. Crappy code in Mazda Connectivity Master Units (CMUs), a component in the Mazda infotainment system in models built between 2014 and 2017, had a massive bug: they would crash and enter a crash/reboot cycle on receiving unexpected input via radio.
The problem, according to Mazda, was that the radio station sent out image files in its HD radio stream that did not have extensions, and it seems that Mazda’s infotainment system of that generation needs an extension (and not a header) to tell what a file is. No extension, no idea, and the system gets corrupted.Just to add insult to injury, there’s no way to field-repair this embedded system — no “factory reset” switch was provided — so the only way to fix it is to install a new CMU at the cost of $1,500, and none are available due to “supply chain issues”. Goes to show you that image decoding libraries remain a fine source of vulnerability surfaces…
Spectacular inbuilt algorithmic discrimination in the UK:
The visa algorithm discriminated on the basis of nationality – by design. Applications made by people holding ‘suspect’ nationalities received a higher risk score. Their applications received intensive scrutiny by Home Office officials, were approached with more scepticism, took longer to determine, and were much more likely to be refused. We argued this was racial discrimination and breached the Equality Act 2010. Entrenched bias and racism in the visa system breaks hearts and tears families apart, like the four siblings from Nigeria unable to travel to the UK for their sister’s wedding, or the countless skilled professionals refused unable to contribute to conferences and events in the UK just because they don’t come from a rich white country – including scores of African academics and artists denied entry for no good reason. The streaming tool was opaque. Aside from admitting the existence of a secret list of suspect nationalities, the Home Office refused to provide meaningful information about the algorithm. It remains unclear what other factors were used to grade applications. The algorithm suffered from a feedback loop — a vicious circle in which biased enforcement and visa statistics reinforce which countries stay on the list of suspect nationalities. In short, applicants from suspect nationalities were more likely to have their visa application rejected. These visa rejections then informed which nationalities appeared on the list of ‘suspect’ nations. This error, combined with the pre-existing bias in Home Office enforcement (in which some nationalities are targeted for enforcement because they are believed to be easier to remove), accelerated bias in the Home Office’s visa process. Such feedback loops are a well-documented problem with automated decision systems.
A neat integration of Scaphandre into an OpenStack cluster by BBC R&D:
While researching tools to monitor VM power usage, we evaluated Scaphandre – an open-source monitoring agent for energy consumption metrics created by Hubblo and Benoit Petit. Scaphandre can measure the CPU power consumption of the whole server and its processes using Intel RAPL alongside CPU utilisation statistics stored in proc/stat. Scaphandre estimates how many CPU watts each process is responsible for by looking at the CPU time spent on it, and the CPU power consumption for the whole server reported by Intel RAPL. Each running VM appears as a process running on the server – therefore, Scaphandre can report the CPU power consumption for each VM. We then used the Carbon Intensity API, created by the UK National Grid ESO, to calculate the carbon dioxide emissions corresponding to each VM’s CPU power consumption. This API provides the number of grams of carbon dioxide (gCO2) emitted to generate a kilowatt-hour (kWh) of electricity consumed at a UK regional level. This figure, referred to as the carbon intensity of electricity generation, varies over time according to the type of generation and electricity demand. Multiplying the carbon intensity figure by the CPU power consumption of a VM at a given point in time results in the carbon dioxide emissions the VM is responsible for.