In proteomics, a popular method to identify peptides is mass spectrometry. An experimental tandem mass spectrum consists of mass peaks that stem from fragmenting a peptide to fragment ions. 
To identify the peptide, that produced the experimental spectrum, all peptides that could produce the seen fragment ions must be analysed. Unfortunately, the space of possible peptides to search against is large.  The paper simplifies this problem by searching against a peptide database.

The approach describes a 4-step method:

- Data reduction: only the 200 most abundant peaks of the experimental spectrum are kept
- Search: peptides with a mass similar to the experimental spectrum's precursor are selected
- Scoring: the score for each selected peptide is based on the number of predicted fragment ions seen in the experimental spectrum.
- Cross-correlation: For the top 500 scoring peptides, theoretical spectra is constructed. These spectra are are evaluated by cross-correlation with the experimental spectrum. The top scoring peptide is considered the identified peptide.

Since a database of known peptides is needed for this approach, it can not be used for de-novo peptide identification, meaning identifying peptides for species where no sequenced genome or proteome is available.

Comparison of three recurrent architectures for language modelling: LSTMs, Recurrent Highway Networks and the NAS architecture. Each model goes through a substantial hyperparameter search, under the constraint that the total number of parameters is kept constant. They conclude that basic LSTMs still outperform other architectures and achieve state-of-the-art perplexities on two datasets.

[https://www.cs.uic.edu/~jakob/papers/biagioni-trr12.pdf](https://www.cs.uic.edu/~jakob/papers/biagioni-trr12.pdf) is a super nice survey where I almost feel bad to summarize it. Almost.

It is about road map inference in the presence of traces of geo coordinates. So you have many vehicles which log their positions while driving. From this data, you want to infer the latest map.

Problems:
* The map changes: What was a valid map a week ago, might not be anymore (due to construction work, breaking roads, new roads)
* Sensor errors: GPS is not very accurate

## Contributions

* overview over the literature up to 2012 on map generation
* method for the automatic evaluation of generated maps
* an evaluation of three reference algorithms including code
* a [118-h trace data set](https://www.cs.uic.edu/bin/view/Bits/Software) and ground truth map

## How Map Inference Works

* **Preprocess Geo-Traces**: check for unreasonable speed, too extreme acceleration, too abrupt changes
* Inference:
    * k-Means: Reduce candidates to centroid. Works on 3D - 2 coordinates and direction
    * trace merging: Merge edges directly, without reduction
    * kernel density estimation

## Quantitative Evaluation

1. Start at one location in both, the ground truth map and the generated map
2. Follow streets from both and sample points in a fixed range (e.g. each 50cm). Only go in directions that go away from the start
3. Try to match both, ground truth and generated map points. Only match if points are close enough (e.g. a distance of 10cm if this is an acceptable uncertainty). Then use a classification score (e.g. F1)

The model learns to translate using a seq2seq model, an autoencoder objective, and an adversarial objective for language identification.
The system is trained to correct noisy versions of its own output and iteratively improves performance.
Does not require parallel corpora, but relies on a separate method for inducing a parallel dictionary that bootstraps the translation.

https://i.imgur.com/6uXNAgo.png

Hermann et al (2015) created a dataset for testing reading comprehension by extracting summarised bullet points from CNN and Daily Mail. All the entities in the text are anonymised and the task is to place correct entities into empty slots based on the news article.

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

This paper has hand-reviewed 100 samples from the dataset and concludes that around 25% of the questions are difficult or impossible to answer even for a human, mostly due to the anonymisation process. They present a simple classifier that achieves unexpectedly good results, and a neural network based on attention that beats all previous results by quite a margin.