Gallery — Project / Lab Report

tratamientos de aguas tanto negras como residuales y su importancia

A template that is similar to the HAISA conference style v2018 HAISE recommends using the Microsoft Word (doc) template though. The page limit is 10, including references. I am really thankful Peter Williams (peterw@archsci.arch.su.edu.au) for the harvard bibliography style Version 2.0.5. I have used his jasa_harvard.sty file.

The swappiness parameter was modified in order to see performance improvement when compressing a file.

Plantilla para escribir los planes de monografía en la Especialización en Química Ambiental.

The purpose of this report is to explain how – by leveraging on the capabilities of the amazon web services – it is possible to manage and process a set of data that is too large and complex for traditional data processing techniques and technologies. The report discusses the implementation of a set of services – from the retrieval of external data to its transformation, through the storage on non relational databases and finally the parallel computation on an external cluster – meant for the management of discographic information in order to easily join different data in an agile manner and subsequently perform additional processing based on the joined output.

Accurate prognosis and prediction of a patient's current disease state is critical in an ICU. The use of vast amounts of digital medical information can help in predicting the best course of action for the diagnosis and treatment of patients. The proposed technique investigates the strength of using a combination of latent variable models (latent dirichlet allocation) and structured data to transform the information streams into potentially actionable knowledge. In this project, I use Apache Spark to predict mortality among ICU patients so that it can be used as an acuity surrogate to help physicians identify the patients in need of immediate care.

This project focuses on a modification of a greedy transition based dependency parser. Typically a Part-Of-Speech (POS) tagger models a probability distribution over all the possible tags for each word in the given sentence and chooses one as its best guess. This is then pass on to the parser which uses this information to build a parse tree. The current state of the art for POS tagging is about 97% word accuracy, which seems high but results in a around 56% sentence accuracy. Small errors at the POS tagging phase can lead to large errors down the NLP pipeline and transition based parsers are particularity sensitive to these types of mistakes. A maximum entropy Markov model was trained as a POS multi-tagger passing more than its 1-best guess to the parser which was thought could make a better decision when committing to a parse for the sentence. This has been shown to give improved accuracy in other parsing approaches. We shown there is a correlation between tagging ambiguity and parsers accuracy and in fact the higher the average tags per word the higher the accuracy.

Israel Science Foundation report template

Đây là template để viết báo cáo. Có logo đại học Khoa Học tự nhiên. Template hỗ trợ tiếng việt This is a typical University Report, suitable for course project or whatever. You can replace the University logo. Full support Vietnamese