'Breaking News that nobody is interested in' ...

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My mum bought me a breathalyser for Christmas, because when I drink in the night after the match, etc I usually have to drive somewhere in the morning.

I always try to give it 10 hours and drink loads of water, my last drink was at half 12 and just failed it then.

Just goes to show how easy it is to be done for drink driving.
 
I'll make something up about a post-match brass later.

You can call me ZatMk2 then.

Had a meeting yesterday...casual over drinks, went on for 2.5 hours and i think i had about 10 jack cokes...then ended up down in one of the bar areas here...got chatting to 3 girls and bought them drinks, turns out that they were on the game. Ended up in a hotel room with them and scarpered at 3am...must have had 15 jack cokes and a lot of sambuca.

Did about £400 the whole night which i suppose is good value all in...

Was planning to watch the game at 4am but was too wasted, watched online earlier today.

You can call me Zat.
 
Had a meeting yesterday...casual over drinks, went on for 2.5 hours and i think i had about 10 jack cokes...then ended up down in one of the bar areas here...got chatting to 3 girls and bought them drinks, turns out that they were on the game. Ended up in a hotel room with them and scarpered at 3am...must have had 15 jack cokes and a lot of sambuca.

Did about £400 the whole night which i suppose is good value all in...

Was planning to watch the game at 4am but was too wasted, watched online earlier today.

You can call me Zat.

You're just a natural at this

*doffs cap
 
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141357

Abstract
Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.
 
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0141357

Abstract
Pathologists and radiologists spend years acquiring and refining their medically essential visual skills, so it is of considerable interest to understand how this process actually unfolds and what image features and properties are critical for accurate diagnostic performance. Key insights into human behavioral tasks can often be obtained by using appropriate animal models. We report here that pigeons (Columba livia)—which share many visual system properties with humans—can serve as promising surrogate observers of medical images, a capability not previously documented. The birds proved to have a remarkable ability to distinguish benign from malignant human breast histopathology after training with differential food reinforcement; even more importantly, the pigeons were able to generalize what they had learned when confronted with novel image sets. The birds’ histological accuracy, like that of humans, was modestly affected by the presence or absence of color as well as by degrees of image compression, but these impacts could be ameliorated with further training. Turning to radiology, the birds proved to be similarly capable of detecting cancer-relevant microcalcifications on mammogram images. However, when given a different (and for humans quite difficult) task—namely, classification of suspicious mammographic densities (masses)—the pigeons proved to be capable only of image memorization and were unable to successfully generalize when shown novel examples. The birds’ successes and difficulties suggest that pigeons are well-suited to help us better understand human medical image perception, and may also prove useful in performance assessment and development of medical imaging hardware, image processing, and image analysis tools.

Beats Neural Networks(!)
 
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