Aquaporins are transmembrane proteins that facilitate the flow of water through cellular membranes. An unusual characteristic of yeast aquaporins is that they frequently contain an extended N terminus of unknown function. Here we present the X-ray structure of the yeast aquaporin Aqy1 from Pichia pastoris at 1.15 resolution. Our crystal structure reveals that the water channel is closed by the N terminus, which arranges as a tightly wound helical bundle, with Tyr31 forming H-bond interactions to a water molecule within the pore and thereby occluding the channel entrance. Nevertheless, functional assays show that Aqy1 has appreciable water transport activity that aids survival during rapid freezing of P. pastoris. These findings establish that Aqy1 is a gated water channel. Mutational studies in combination with molecular dynamics simulations imply that gating may be regulated by a combination of phosphorylation and mechanosensitivity.

All living organisms must regulate precisely the flow of water into and out of cells in order to maintain cell shape and integrity. Proteins of one family, the aquaporins, are found in virtually every living organism and play a major role in maintaining water homeostasis by acting as regulated water channels. Here we describe the first crystal structure of a yeast aquaporin, Aqy1, at 1.15 resolution, which represents the highest resolution structural data obtained to date for a membrane protein. Using this structural information, we address an outstanding biological question surrounding yeast aquaporins: what is the functional role of the amino-terminal extension that is characteristic of yeast aquaporins? Our structural data show that the amino terminus of Aqy1 fulfills a novel gate-like function by folding to form a cytoplasmic helical bundle with a tyrosine residue entering the water channel and occluding the cytoplasmic entrance. Molecular dynamics simulations and functional studies in combination with site-directed mutagenesis suggest that water flow is regulated through a combination of mechanosensitive gating and post-translational modifications such as phosphorylation. Our study therefore provides insight into a unique mechanism for the regulation of water flux in yeast.

Model Interpretability aims at explaining the inner workings of a model promoting transparency of any decisions made by the model, however for the sake of human acceptance or understanding, these explanations seem to be more geared toward human trust than remaining faithful to the model.

**Idea**
There is a distinct difference and tradeoff between persuasive and descriptive Interpretations of a model, one promotes human trust while the other stays truthful to the model. Promoting the former can lead to a loss in transparency of the model.

**Questions to be answered:**
- How do we balance between a persuasive strategy and a descriptive strategy?
- How do we combat human cognitive bias?

**Solutions:**
- *Separating the descriptive and persuasive steps: *
    - We first generate a descriptive explanation, without trying to simplify it
    - In our final steps we add persuasiveness to this explanation to make it more understandable
- *Explicit inclusion of cognitive features:*
    - We would include attributes that affect our functional measures of interpretability to our objective function.
    - This approach has some drawbacks however:
        - we would need to map the knowledge of the user which is an expensive process.
        - Any features that we fail to add to the objective function would add to the human cognitive bias
        - Increased complexity in optimizing of a multi-objective loss function.



**Important terms:**
- *Explanation Strategy*: An explanation strategy is defined as an explanation vehicle coupled with the objective function, constraints, and hyper parameters required to generate a model explanation
- *Explanation model*: An explanation model is defined as the implementation of an explanation strategy, which is fit to a model that is to be interpreted.
- *Human Cognitive Bias*: if an explanation model is highly persuasive or tuned toward human trust as opposed to staying true to the model, the overall evaluation of this explanation would be highly biased compared to a descriptive model. This bias can lead from commonalities between human users across a domain, expertise of the application, or the expectation of a model explanation. Such bias is known as implicit human cognitive bias. 
- *Persuasive Explanation Strategy*: A persuasive explanation strategy aim at convincing a user/ humanizing a model so that the user feels more comfortable with the decisions generated by the model. Fidelity or truthfulness to the model in such a strategy can be very low, which can lead to ethical dilemmas as to where to draw the line between being persuasive and being descriptive. Persuasive strategies do promote human understanding and cognition, which are important aspects of interpretability, however they fail to address the certain other aspects such as fidelity to the model.
- *Descriptive Explanation Strategy*: A descriptive explanation strategy stays true to the underlying model, and generates explanations with maximum fidelity to the model. Ideally such a strategy would describe exactly what the inner working of the underlying model is, which is the main purpose of model interpretation in terms of better understanding the actual workings of the model. 

Fernanda Viegas, Marin Wattenberg, and Kushal Dave describe a visualization system they have built called history flow that they use to visualize changes made to Wikipedia articles. The authors suggest that their papers makes three distinct contributions:

* History flow itself which is able to reveal editing patterns in Wikipedia and provide context for editors.
* Several examples of collaboration patterns that become visible using the visualization tool and contribute to the literature on Wikipedia.
* Implications of these patterns for design and governance of online social spaces.

The paper is largely an examination of Wikipedia and the early parts of the paper give background into the sites. It uses shortcomings in the design of the Wikipedia to motivate the history flow visualization which essentially depicts articles, over time, with colors representing authors who contributed text in question. Examples can be seen online at the IBM History Flow website. The interface is particularly good at representing major deletions and insertions.

The authors use a lightweight statistically analysis to reveal patterns of editing on Wikipedia (which at the time, were not widely studied). In particular, they show vandalism including mass-deletion, the creation of phony redirects, and addition of idiosyncratic copy and show that it rarely stays on the site for more than few minutes before being removed.

They also show a zig-zag patten that represents negotiation of content, often in the form of edit wars. They also attempt to provide some basic data on the stability of Wikipedia and the growth of articles on average. They suggest something that is now taken for granted by researchers of wikis: that studying Wikipedia may have important implications for other types of work.

#### Theoretical and practical relevance:

The paper is important more for its path-breaking work on Wikipedia -- now with its track at CHI, than for the history flow visualization which has not, for the most part, been widely deployed outside Wikipedia but which seems to hold promise in a variety of other contexts. The paper has been cited more than 400 times, mostly in the academic literature on Wikipedia.

This paper is a finalist for the Wikimedia France Research Award.

#### Problem addressed: 
Estimation of the position of facial keypoints

#### Summary: 
The authors propose an effective convolutional network cascade for facial point detection. Deep convolutional networks at the first level provide highly robust initial estimations, while shallower convolutional networks at the following two levels finely tune the initial prediction to achieve high accuracy. The method therefore can avoid local minimum caused by ambiguity and data corruption. Networks at the first level are deep convolutional networks with four convolutional stages, absolute value rectification, and locally shared weights. Networks at the second and third levels share a common shallower structure. Since they are designed to extract local features, deep structures and locally sharing weights are unnecessary.

#### Novelty:
Three-level carefully designed convolution networks

#### Drawbacks:
High computational cost because of deep model

#### Datasets:
BioID, LFPW

#### Resources:
http://www.ee.cuhk.edu.hk/~xgwang/papers/sunWTcvpr13.pdf

#### Presenter:
Neeti Narayan

Demontis et al. study transferability of adversarial examples and data poisening attacks in the light of the targeted models gradients. In particular, they experimentally validate the following hypotheses: First, susceptibility to these attacks depends on the size of the model’s gradients; the higher the gradient, the smaller is the perturbation needed to increase the loss. Second, the size of the gradient depends on regularization. And third, the cosine between the target model’s gradients and the surrogate model’s gradients (trained to compute transferable attacks) influences vulnerability. These insights hold for both evasion and poisening attacks and are motivated by a simple linear Taylor expansion of the target model’s loss.

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