Summary: The paper proposed to improve the interaction modeling between pedestrians by using a graph attention network [22] for the trajectory prediction task and learn multimodal trajectory distributions by using Bicycle-GAN [23]. The experimental results showed the effectiveness of the proposed approach by achieving state-of-the-art performance on the public benchmarks. Also, they showed that the performance of the proposed approach is more robust to varying K than that of the baselines, indicating that the proposed approach was successful in addressing the high variance issue in the existing approaches to a certain extent. Strengths: -- The paper is clearly written so it was easy to follow. The reasoning behind the choice of [22] and [23] for the trajectory prediction task is also clearly presented in the paper.

Summary: This paper draws inspiration from work on psychophysics on classification images. Large-scale human experiments were run, where people were asked to classify images generated from random noise (randomly generated by inverting HOG or CNN feature spaces to more closely approximate the distribution of natural images). The results were used to 1) visualize human perception of different classes, 2) see how well classifiers trained on datasets of random noise would work on real images, and 3) use the results as an additional source of information to regularize classifiers trained on a small number of images. Quality: This is a very unusual paper. It is overall a high quality and well written paper where interesting and novel experiments were carried out; however it is unclear if the results or methods of the paper are of practical value.

The transferabilities of taskonomy have a practical value (they're constructed and are shown to reduce the need for supervision through transfer learning), but Taskonomy's method is computationally expensive. So, the gold standard is duplication of taskonomy's affinity matrix, but with less complexity. Therefore I see the comparison between the transferability matrix by attribution maps and taskonomy's (fig 4) valid and what the main point is. But I don't understand why/how SVCCA vs attribution map's similarity matrix comparisons (figure 3) are useful. What exactly is the value of SVCCA based similarity matrix? Why isn't figure 3 comparing between attribution map's matrix and Taskonomy's affinity matrix (after being made symmetric)?

China is deploying machinegun-carrying robots to its western desert regions amid a standoff with India because troops are struggling with the high-altitude conditions, it has been claimed. Dozens of unmanned vehicles capable of carrying both weapons and supplies are being sent to Tibet, Indian media reports, with the majority deployed in border regions where Chinese troops are locked into a standoff with Indian soldiers. Vehicles include the Sharp Claw, which is mounted with a light machinegun and can be operated wirelessly, and the Mule-200, which is designed as an unmanned supply vehicle but can also be fitted with weapons. Beijing has sent 88 Sharp Claws to Tibet, which borders India high in the Himalayas, of which 38 are deployed to the border region, Times News Now has claimed. Some 120 Mule-200s have also been sent to Tibet, News Now reports, with a majority of them deployed to the border area.

For the past decade, cloud has been all the rage in federal IT circles, as agencies look for ways to decrease the burden of legacy IT spending. Today, the IT modernization push continues, and agencies can now see the light at the end of the tunnel. So far in 2017, there have been positive signs from Congress and the White House that IT modernization will remain a critical part of our government's priorities moving forward. Earlier this year, President Donald Trump signed two executive orders -- one on IT modernization and one on cybersecurity -- that signaled his administration would be taking the push seriously. Similarly, the House of Representatives approved the Modernizing Government Technology Act, commonly referred to as the MGT Act, to set aside funding for federal agencies to upgrade to new platforms, such as cloud, to drastically overall its IT infrastructure.