Using the QR Factorization to swiftly update least squares problems

In this paper we study how to update the solution of the linear system Ax = b after the matrix A is changed by addition or deletion of rows or columns. Studying the QR Factorization of the system, more specifically, the factorization created by the Householder reflection algorithm, we find that we can split the algorithm in two parts. The result from the first part is trivial to update and is the only dependency for calculating the second part. We find that not only can this save a considerable amount of time when solving least squares problems but the algorithm is also very easy to implement

Towards Machine Learning on data from Professional Cyclists

Professional sports are developing towards increasingly scientific training methods with increasing amounts of data being collected from laboratory tests, training sessions and competitions. In cycling, it is standard to equip bicycles with small computers recording data from sensors such as power-meters, in addition to heart-rate, speed, altitude etc. Recently, machine learning techniques have provided huge success in a wide variety of areas where large amounts of data (big data) is available. In this paper, we perform a pilot experiment on machine learning to model physical response in elite cyclists. As a first experiment, we show that it is possible to train a LSTM machine learning algorithm to predict the heart-rate response of a cyclist during a training session. This work is a promising first step towards developing more elaborate models based on big data and machine learning to capture performance aspects of athletes.Comment: Accepted for the 12th World Congress on Performance Analysis of Sports, Opatija, Croatia, 201

Data-driven models for predicting microbial water quality in the drinking water source using E. coli monitoring and hydrometeorological data

Rapid changes in microbial water quality in surface waters pose challenges for production of safe drinking water. If not treated to an acceptable level, microbial pathogens present in the drinking water can result in severe consequences for public health. The aim of this paper was to evaluate the suitability of data-driven models of different complexity for predicting the concentrations of E. coli in the river G\uf6ta \ue4lv at the water intake of the drinking water treatment plant in Gothenburg, Sweden. The objectives were to (i) assess how the complexity of the model affects the model performance; and (ii) identify relevant factors and assess their effect as predictors of E. coli levels. To forecast E. coli levels one day ahead, the data on laboratory measurements of E. coli and total coliforms, Colifast measurements of E. coli, water temperature, turbidity, precipitation, and water flow were used. The baseline approaches included Exponential Smoothing and ARIMA (Autoregressive Integrated Moving Average), which are commonly used univariate methods, and a naive baseline that used the previous observed value as its next prediction. Also, models common in the machine learning domain were included: LASSO (Least Absolute Shrinkage and Selection Operator) Regression and Random Forest, and a tool for optimising machine learning pipelines – TPOT (Tree-based Pipeline Optimization Tool). Also, a multivariate autoregressive model VAR (Vector Autoregression) was included. The models that included multiple predictors performed better than univariate models. Random Forest and TPOT resulted in higher performance but showed a tendency of overfitting. Water temperature, microbial concentrations upstream and at the water intake, and precipitation upstream were shown to be important predictors. Data-driven modelling enables water producers to interpret the measurements in the context of what concentrations can be expected based on the recent historic data, and thus identify unexplained deviations warranting further investigation of their origin

Artificial Intelligence, 3D Documentation, and Rock Art - Approaching and Reflecting on the Automation of Identification and Classification of Rock Art Images

Rock art carvings, which are best described as petroglyphs, were produced by removing parts of the rock surface to create a negative relief. This tradition was particularly strong during the Nordic Bronze Age (1700–550 BC) in southern Scandinavia with over 20,000 boats and thousands of humans, animals, wagons, etc. This vivid and highly engaging material provides quantitative data of high potential to understand Bronze Age social structures and ideologies. The ability to provide the technically best possible documentation and to automate identification and classification of images would help to take full advantage of the research potential of petroglyphs in southern Scandinavia and elsewhere. We, therefore, attempted to train a model that locates and classifies image objects using faster region-based convolutional neural network (Faster-RCNN) based on data produced by a novel method to improve visualizing the content of 3D documentations. A newly created layer of 3D rock art documentation provides the best data currently available and has reduced inscribed bias compared to older methods. Several models were trained based on input images annotated with bounding boxes produced with different parameters to find the best solution. The data included 4305 individual images in 408 scans of rock art sites. To enhance the models and enrich the training data, we used data augmentation and transfer learning. The successful models perform exceptionally well on boats and circles, as well as with human figures and wheels. This work was an interdisciplinary undertaking which led to important reflections about archaeology, digital humanities, and artificial intelligence. The reflections and the success represented by the trained models open novel avenues for future research on rock art

JAG VILL LEVA JAG VILL DÖ PÅ JORDEN : En kvalitativ studie av kritiken Gina Dirawi och SVT fått på Expressen, Aftonbladet och Göteborgs-Postens kommentarsfält på Facebook.

This bachelor thesis analyzes the type of criticism the TV-hostess Gina Dirawi and Sveriges Television has received on Facebook concerning the intermission of the TV-broadcast of the swedish television show Melodifestivalen where the Swedish national anthems lyrics was changed from “I want to live and die in the north” to “I want to live and die on the earth” and a couple of folksongs where translated from Swedish to Hebrew and Arabic.The thesis was executed with a two-part analysis consisting of a quantitative content analysis followed by a qualitative text analysis on Aftonbladet, Expressen and Göteborgs-Postens Facebook pages to find different themes in the criticism of the host Gina Dirawi and the Swedish television network SVT, this thesis focused on both the ideology and intersectionality perspectives. In addition to these perspectives, terories about discourse, identity, gender and ethnicity have been used to analyse the selected material. The study has showed that the criticism is based on a fear of the Swedish national identity disappearing and changing. Also that there are some conspiratorial theories surrounding the Swedish public service television network Sveriges Television.There is also a small collection of comments that focuses on critizing the media coverage of the performance and other commenters. The positive comments are underrepresented, and often focuses on the quality of the performance from Gina Dirawi or criticize the more negative commenters who should focus on more important events around the world.Validerat; 20160614 (global_studentproject_submitter

Insamling och visualisering av kundspecifik data för webben

As the Internet grows larger and the use of it expands, more people are developing for the web. This rapid development is making it more clear how important it is to think of the intended user. Twingly works with blog tools towards several different customers. Twingly wants to offer their customers the use of a dashboard with visualizations of customer-specific data. It would also be used to attract new customers

Insamling och visualisering av kundspecifik data för webben

As the Internet grows larger and the use of it expands, more people are developing for the web. This rapid development is making it more clear how important it is to think of the intended user. Twingly works with blog tools towards several different customers. Twingly wants to offer their customers the use of a dashboard with visualizations of customer-specific data. It would also be used to attract new customers

Automation of a Data Analysis Pipeline for High-content Screening Data

High-content screening is a part of the drug discovery pipeline dealing with the identification of substances that affect cells in a desired manner. Biological assays with a large set of compounds are developed and screened and the output is generated with a multidimensional structure. Data analysis is performed manually by an expert with a set of tools and this is considered to be too time consuming and unmanageable when the amount of data grows large. This thesis therefore investigates and proposes a way of automating the data analysis phase through a set of machine learning algorithms. The resulting implementation is a cloud based application that can support the user with the selection of which features that are relevant for further analysis. It also provides techniques for automated processing of the dataset and training of classification models which can be utilised for predicting sample labels. An investigation of the workflow for analysing data was conducted before this thesis. It resulted in a pipeline that maps the different tools and software to what goal they fulfil and which purpose they have for the user. This pipeline was then compared with a similar pipeline but with the implemented application included. This comparison demonstrates clear advantages in contrast to previous methodologies in that the application will provide support to work in a more automated way of performing data analysis

JAG VILL LEVA JAG VILL DÖ PÅ JORDEN : En kvalitativ studie av kritiken Gina Dirawi och SVT fått på Expressen, Aftonbladet och Göteborgs-Postens kommentarsfält på Facebook.

This bachelor thesis analyzes the type of criticism the TV-hostess Gina Dirawi and Sveriges Television has received on Facebook concerning the intermission of the TV-broadcast of the swedish television show Melodifestivalen where the Swedish national anthems lyrics was changed from “I want to live and die in the north” to “I want to live and die on the earth” and a couple of folksongs where translated from Swedish to Hebrew and Arabic.The thesis was executed with a two-part analysis consisting of a quantitative content analysis followed by a qualitative text analysis on Aftonbladet, Expressen and Göteborgs-Postens Facebook pages to find different themes in the criticism of the host Gina Dirawi and the Swedish television network SVT, this thesis focused on both the ideology and intersectionality perspectives. In addition to these perspectives, terories about discourse, identity, gender and ethnicity have been used to analyse the selected material. The study has showed that the criticism is based on a fear of the Swedish national identity disappearing and changing. Also that there are some conspiratorial theories surrounding the Swedish public service television network Sveriges Television.There is also a small collection of comments that focuses on critizing the media coverage of the performance and other commenters. The positive comments are underrepresented, and often focuses on the quality of the performance from Gina Dirawi or criticize the more negative commenters who should focus on more important events around the world.Validerat; 20160614 (global_studentproject_submitter