In other words, the more you pay attention to political media, the less likely you are to understand the true beliefs of your political opponents. One of the panelists made an earnest and sincere case that it can be extremely difficult to work with religious groups in part because their beliefs can be so unscientific and strange — and as evidence he highlighted a person I’d never heard of who has exactly zero political influence over the Evangelical political movement. Ignorance and misinformation are building enmity, and when America’s most political citizens are the source of the problem, the future of American polarization looks bleak to Consider

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An individual’s cardiac signature is unique, though, and unlike faces or gait, it remains constant and cannot be altered or disguised. Long-range detection

A new device, developed for the Pentagon after US Special Forces requested it, can identify people without seeing their face: instead it detects their unique cardiac signature with an infrared laser. Better than face recognition

Remaly’s team then developed algorithms capable of extracting a cardiac signature from the laser signals.

Britain on Thursday became the world’s first major economy to adopt the tough new target of lowering fossil fuel emissions to a level of net zero by 2050. “Today we’re leading the world yet again in becoming the first major economy to pass new laws to reduce emissions to net zero by 2050 while remaining committed to growing the economy.” “The incredible progress that Britain has made in the past 10 years means we can now say 2019 will be the year zero-carbon power beats fossil fuel-fired generation for the first time,” Pettigrew told reporters.

Secure ShellFish is a SSH and SFTP file manager that works well with other apps. Setup new servers inside Secure ShellFish itself and access files on these servers in the Files app and other apps supporting the document browser or document picker. The app works with Synology and other NAS solutions for fast file access at home or the office even when Internet is lacking.

If you’ve read anything about machine learning in the last ten years, you’ve probably encountered these villains:

These are three samples from the MNIST database, a collection of 70,000 handwritten digits as 28×28 greyscale images. Code and data files used by this step:

And on the command line:

It’s worth mentioning that also attempts to adjust image brightness and contrast, which varies in the original images owing to banding from camera shutter/5100 display synchronisation issues. In machine learning research, fully 60,000 of the 70,000 MNIST digits are commonly reserved as training data—over 85% of the entire dataset.

I was looking through my iPhone device analytics as I plan my iOS 13 updates and noticed something that really jumped out at me. When I look at the iPhone distribution for Pedometer++ the four most popular devices are all that size. These iPhones currently account for 17% of my userbase.

In theory this would have made each query quicker because the desired SNP data would only reside in the ranges of a few of the Parquet chunks within a given region. I ran a query on our already loaded data to get the list of the unique SNPs, their positions, and their chromosomes. # Join the raw data with the snp bins data_w_bin % by =’snp_name’) %>% group_by(chr_bin) %>% arrange(Position) %>% Spark_write_Parquet( path = DUMP_LOC, mode = ‘overwrite’, partition_by = c(‘chr_bin’) ) Notice the use of , this lets Spark know it should send this dataframe to all nodes.

Every six hours, at his home in the high desert outside Kingman, Arizona, midway between Phoenix and Las Vegas, Brian Goss downloads the latest blocks from the bitcoin blockchain via satellite. The company, which mainly deals with text messages, started supporting bitcoin transactions last year, and it recently partnered with Blockstream to let people use GoTenna’s device to rebroadcast the satellite data they receive. Goss tells me about mining bitcoin nine years ago, when the cryptocurrency world was so small that his home desktop at times made up 1 percent or more of the computing power on the network.

Kristoffferson struggled with memory problems in recent years and was told he had Alzheimer’s disease, but it appears he was misdiagnosed and all along has actually been suffering from the tick-borne illness Lyme disease. Kristofferson and his wife Lisa told Rolling Stone that for years, doctors had been telling him that his increasingly debilitating memory loss was due to either Alzheimer’s or to dementia brought on by blows to the head from boxing and playing football and rugby in his teens and early twenties. Earlier this year, though, a doctor decided to test Kristofferson for Lyme disease, which can cause neurological problems, including memory issues and what some describe as “brain fog,” as well as a broad range of other symptoms.

In theory this would have made each query quicker because the desired SNP data would only reside in the ranges of a few of the Parquet chunks within a given region. I ran a query on our already loaded data to get the list of the unique SNPs, their positions, and their chromosomes. # Join the raw data with the snp bins data_w_bin % by =’snp_name’) %>% group_by(chr_bin) %>% arrange(Position) %>% Spark_write_Parquet( path = DUMP_LOC, mode = ‘overwrite’, partition_by = c(‘chr_bin’) ) Notice the use of , this lets Spark know it should send this dataframe to all nodes.