Sarkar et al. propose two “learned” adversarial example attacks, UPSET and ANGRI. The former, UPSET, learns to predict universal, targeted adversarial examples. The latter, ANGRI, learns to predict (non-universal) targeted adversarial attacks. For UPSET, a network takes the target label as input and learns to predict a perturbation, which added to the original image results in mis-classification; for ANGRI, a network takes both the target label and the original image as input to predict a perturbation. These networks are then trained using a mis-classification loss while also minimizing the norm of the perturbation. To this end, the target classifier needs to be differentiable – i.e., UPSET and ANGRI require white-box access.

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

This paper presents a combination of the inception architecture
with residual networks. This is done by adding a shortcut connection
to each inception module. This can alternatively be seen as a resnet where
the 2 conv layers are replaced by a (slightly modified) inception module.
The paper (claims to) provide results against the hypothesis that adding residual
connections improves training, rather increasing the model size is what makes the difference.

Problem
---------
Video prediction with human objects


Contribution
--------------
Instead of the common approach of predicting directly in pixel-space, use explicit knowledge of human motion space to predict the future of the video.

Approach
--------------
1. VAE to model the possible future movements of humans in the pose space
2. Conditional GAN - use pose information for to predict video in pixel space.



https://image.ibb.co/b1omVF/The_pose_knows.png

Keypoint detection is an important step in various tasks such as SLAM, panorama stitching, camera calibration, and more. Efficient keypoint detectors, FAST (Features from Accelerated and Segments Test) for example, would detect keypoints where a relatively high brightness change is observed in relation to surrounding pixels. Most probably, the keypoints would be located on edges, as shown below:
https://i.imgur.com/ylC4BM3.jpg
Let's consider another image shown below. Here, while the detector is capable of detecting many keypoints, they are mostly located on trees (see subfigure (a) below). This causes redundancy and this paper focuses on solving it by selecting locally strong keypoints that are well distributed all over the image (subfigure (c)).

https://i.imgur.com/1MqZhmT.png


The algorithm requires input keypoints to be sorted in decreasing order of strength. The keypoints are then processed in that order and points that fall within the suppression range of a current keypoint are removed from the consideration. The process is repeated for the next unsuppressed keypoint in the sorted order. The process continues until no points remain. If the number of filtered points is not close enough to what we require, the suppression range is modified and the process is repeated. The suppression range is modified using binary search.

https://i.imgur.com/qryscZP.png

Binary search requires lower and upper bounds to operate. Naive initialization would be setting lower bound to 1 pixel, while upper bound - image width or height. On the other hand, this paper proposes better initialization of the suppression range that is dependent on image height $H_I$, width $W_I$, number of input keypoints $n$ as well as the number of output keypoints $m$.
* Upper bound: $\frac{H_I + W_I + 2m - \sqrt{\Delta}}{2m - 1}$ (see paper for more details)
* Lower bound: $\frac{1}{2}\sqrt{\frac{n}{m}}$

This initializing helps to decrease the number of iterations to convergence by a factor of three:

https://i.imgur.com/7NCpgbi.png

Homogenous location of keypoints is beneficial for SLAM algorithm and reduce translational and rotational errors compared to naive filtering when evaluated on KITTI:

https://i.imgur.com/4wq0kLK.png

Overall, this paper proposes a fast, efficient, and effective method to post-process noisy and redundant keypoint detections with a clear benefit to SLAM. The codes are publically available in multiple languages: C++, Python, Java, and Matlab. See https://github.com/BAILOOL/ANMS-Codes

There is no correlation between body size and cancer incidence across animal species. From a logical point of view such a correlation would be expected. If one assumes that each proliferating cell in an individual multicellular organism has an equal probability of acquiring a cancerous mutation, then organisms with significantly more cells should have a higher probability of developing a cancerous tumour. This is known as Peto's paradox. 

This paper puts Peto's paradox in the context of an evolutionary strategy to allow large multicellular organisms to live beyond reproductive age. Evolutionary theory describes the genetic instabilities (and variations) leading to the development of tumour suppression mechanisms. The evolutionary rules are clearly stated in box 1 and are reasonable considering the known heterogeneity of tumours.

I particularly like this paper because it puts some captivating numbers on the effect of tumour suppression in some animals. It takes one extreme of animal size, and a well known character in terms of tumour suppression, the blue whale, and makes back of the envelope calculations to what its cancer incidence should theoretically be. Given that the blue whale is 1000 times the size of a human the authors predict that all blue whales should have colorectal cancer by the age of 80. For information, blue whales can live to more than 100 years.

Within a species size is related to cancer risk (Hooray I knew there would be some advantage to being only 165cm) whereby a 3-4 mm increase above the average leg length results in an 80% higher risk of non smoking related cancers. All this suggests that large organisms (that also happen to live longer) have acquired mechanism to suppress cancer.

The authors describe such cancer suppression mechanisms including lower somatic mutation rates, different tissue architecture, redundancy in tumour suppressor genes and a somehow lower selective advantage of mutant cells and increased sensitivity to contact exhibition to name but a few.

A particularly tantalising theory suggested by Nagy et al is that of "hypertumours" whereby a parasitic growth from the cancerous tumour results in a lowering of the overall fitness tumour fitness. This hypothesis has yet to be tested.

My understanding is that comparing cancer suppression mechanisms across the species will lead to a better understanding of the evolutionary process involved in cancer progression and perhaps will reveal knowledge to help better develop strategies for cancer therapies in humans.