## Structured segmented network

### **key word**: action detection in video; computing complexity reduction; structurize proposal 

**Abstract**: using a temporal action grouping scheme (TAG) to generate accurate proposals, using a structured pyramid to model the temporal structure of each action instance to tackle the issue that detected actions are not complete, using two classifiers to determine class and completeness and using a regressor for each category to further modify the temporal bound. In this paper, Yue Zhao et al mainly tackle the problem of high computing complexity by sampling video frame and remove redundant proposals in video detection and the lack of action stage modeling.  

**Model**: 
1. generate proposals: find continuous temporal regions with mostly high actioness. $P = \{ p_i = [s_i,e_i]\}_{i = 1}^N$
2. splitting proposals into 3 stages: start, course, and end: first augment the proposal by 2 times symmetrical to center, and course part is the original proposal, while start and end is the left part and right part of the difference between the transformed proposal and original one.
3. build temporal pyramid representation for each stage: first  L samples are sampled from the augmented proposal, then two-stream feature extractor is used on each one of them and pooling features for each stage 
4. build global representation for each proposal by concatenating stage-level representations
5. a global representation for each proposal is used as input for classifiers 

* input  =  ${S_t}_{t = 1} ^{T}$a sequence of T snippet representing the video. each snippet  = the frames + an optical  flow stack
* network: two linear classifiers; L two-steam feature extractor and several pooling layer  
* output: category and completeness and modification for each proposals.
https://i.imgur.com/thM9oWz.png

**Training**: 
* joint loss for classifiers: $L_{cls} = -log(P(c_i|p_i)* P(b_i,c_i,p_i)) $
* loss for location regression:  $\lambda * 1(c_i>=1, b_i = 1)  L(u_i,\varphi _i;p_i)$

**Summary**:
This paper has three highlights:
1. Parallel: it uses a paralleled network structure where proposals can be processed in paralleled which will shorten the processing time based on GPU
2. temporal structure modeling and regression: give each proposal certain structure so that completeness of proposals can be achieved
3. reduce computing complexity: use two tricks: remove video redundancy by sampling frame; remove proposal redundance

Recent evidence suggests that many malignancies, including breast cancer, are driven by a cellular subcomponent that displays stem cell-like properties. The protein phosphatase and tensin homolog (PTEN) is inactivated in a wide range of human cancers, an alteration that is associated with a poor prognosis. Because PTEN has been reported to play a role in the maintenance of embryonic and tissue-specific stem cells, we investigated the role of the PTEN/Akt pathway in the regulation of normal and malignant mammary stem/progenitor cell populations. We demonstrate that activation of this pathway, via PTEN knockdown, enriches for normal and malignant human mammary stem/progenitor cells in vitro and in vivo. Knockdown of PTEN in normal human mammary epithelial cells enriches for the stem/progenitor cell compartment, generating atypical hyperplastic lesions in humanized NOD/SCID mice. Akt-driven stem/progenitor cell enrichment is mediated by activation of the Wnt/β-catenin pathway through the phosphorylation of GSK3-β. In contrast to chemotherapy, the Akt inhibitor perifosine is able to target the tumorigenic cell population in breast tumor xenografts. These studies demonstrate an important role for the PTEN/PI3-K/Akt/β-catenin pathway in the regulation of normal and malignant stem/progenitor cell populations and suggest that agents that inhibit this pathway are able to effectively target tumorigenic breast cancer cells.

This paper attempts to open up the black box of neural machine translation models and inspect what the representations look like, specifically with respect to morphology. The technique they use is to train word-based and character-based seq2seq-style models on multiple source-target language pairs, of varying morphological complexity, and then ignore the target side to focus on the representations learned about the source language. Once they have an encoder trained to generate these representations, they attempt to use the encoder to create feature representations for external tasks that directly evaluate for morphology and part of speech information. (Contrast this with methods that may, for example, try to inspect activation patterns of individual neurons in a trained model.)

The first experiment shows that representations learned from character-based models are superior for POS tagging in the source language. The gap is bigger for morphologically rich languages like Arabic. The same result holds for morphological tagging. For infrequent words the gap is especially large -- the system can memorize morphological information for frequent words. They also show that the increases in accuracy are due to getting  prevoiusly unseen words correct (both for POS and morph prediction) and that the biggest increase in accuracy is in predicting plural and determined noun categories. Next, they show that in a deeper network, the middle layer (of 3) has the best representations for predicting pos/morph information. The authors suggest the higher layers are more focused on semantics or other higher abstractions.

Overall, this work empirically confirms some conventional wisdom, that character representations are better for unseen words because of their ability to represent morphology.

Gilmer et al. study the existence of adversarial examples on a synthetic toy datasets consisting of two concentric spheres. The dataset is created by randomly sampling examples from two concentric spheres, one with radius $1$ and one with radius $R = 1.3$. While the authors argue that difference difficulties of the dataset can be created by varying $R$ and the dimensionality, they merely experiment with $R = 1.3$ and a dimensionality of $500$. The motivation to study this dataset comes form the idea that adversarial examples can easily be found by leaving the data manifold. Based on this simple dataset, the authors provide several theoretical insights – see the paper for details.

Beneath theoretical insights, Gilmer et al. slso discuss the so-called manifold attack, an attack using projected gradient descent which ensures that the adversarial examples stays on the data-manifold – moreover, it is ensured that the class does not change. Unfortunately (as I can tell), this idea of a manifold attack is not studied further – which is very unfortunate and renders the question while this concept was introduced in the first place.

One of the main take-aways is the suggestion that there is a trade-off between accuracy (i.e. the ability of the network to perform well) and the average distance to an adversarial example. Thus, the existence of adversarial examples might be related to the question why deep neural networks perform very well.

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

They improve an existing error correction system by re-ranking its predictions. The basic approach uses machine translation to perform error correction on learner texts – the incorrect text is essentially translated into correct text. Here, they include a ranking SVM to score and reorder the n-best lists from the translation model.

The reranking features include various internal scores from the translation model, the rank in the original ordering, language model probabilities trained on large corpora, language model scores based on only the n-best list, word-level translation probabilities, and sentence length features. They show improvement on two error correction datasets.

https://i.imgur.com/RxAE11a.png

Example output from the models.