'It's destroying our town': Residents tell of concern after violent incidents near Middlesbrough To play this video you need to enable JavaScript in your browser. 'It's destroying our town': Residents tell of concern after violent incidents near Middlesbrough Close Residents near Middlesbrough have told the BBC of their concern after a spate of violent incidents in their area, including a crash that killed seven people including two police officers and a house fire that killed a woman and a seven-year-old girl . Police have said the two incidents are not connected. They have made a series of arrests in relation to the crash and two arrests in relation to the house fire. On Friday Redcar MP Anna Turley said disorder at a balloon release for one of the young men who died in the crash was unacceptable.

To play this video you need to enable JavaScript in your browser. Security camera footage appears to show several police vehicles chasing a car just minutes before a fatal crash that killed seven people, including two police officers, near Middlesbrough. The clip is thought to have been filmed on Lime Road at 03:36 BST on Saturday morning, around three minutes before a Volkswagen Passat travelling the wrong way on the A66 nearby collided into a marked police vehicle. A police source said the chase had been abandoned before the collision. Updates from your News topics will appear in My News and in a collection on the News homepage .

Multiple people have died in a crash involving a police vehicle and a car following a pursuit, police have said. The collision involved a police vehicle and a Volkswagen Passat on the A66 in South Bank, near Middlesbrough in Teesside, at about 03:39 BST, Cleveland Police said. North East Ambulance Service said 14 ambulance resources attended and a number of patients died at the scene. A police spokesman said: Sadly, the collision has resulted in multiple fatalities. We are working to identify the victims and contact their families.

Neural Information Processing Systems http://nips.cc/

Existing MIPL algorithms often overlook the margins for attention scores andpredicted probabilities, leading tosuboptimal generalization performance.

Theoretical and empirical studies [21,77,9,55,49,5,12]postulate that such flatter regions generalize better than sharper minima, e.g., due to the flat minimizer's robustness against loss function shifts between trainandtestdata,asillustrated inFig.1.

For structured pruning, one promising method is to associate each neuron with a scaling factor, and regularize these scaling factors in training.

Our bound addresses the second question; it suggests that learning algorithms that bias towards models with small variation across the source threat model exhibit smaller drop in robustness to particularunforeseenattacks.