USING GENERATIVE ADVERSARIAL NETWORK AS A VALUE-AT-RISK ESTIMATOR

Value-at-risk (VaR) estimation is a critical task for modern financial institution. Most methods to estimate VaR rely on classical statistical methods. They produce reliable estimates but there is demand for ever more accurate estimates. Recently there has been major breakthroughs for machine learning models in other fields. This has led to increasing interest in applying machine learning for financial applications. This thesis applies new data-driven machine learning method, generative adversarial network (GAN), for (VaR) estimation. GAN was proposed for fake image generation. Since then it has found applications in multiple domains, such as finance. Estimating the true underlying distribution of financial time series is notoriously difficult task. GAN doesn’t explicitly estimate the underlying distribution but tries to generate new samples from the distribution. This thesis applies a basic GAN model to simulate stock market returns and then estimate the VaR from these. The experiments are conducted on S&P500-index. The GAN model is compared to a simple historical simulation baseline. In the experiments it becomes evident that the GAN model lacks robustness and responds poorly to changes in market. The GAN is unable to fully capture the statistical properties of stock market returns. It can replicate a little of the excess kurtosis present in stock market returns and some of the volatility clustering. The results show that the GAN model has tendency to estimate the VaR between a fairly narrow range. This is in contrast to historical simulation, which can respond to changes in the stock market. Machine learning models, especially neural networks like GANs, present challenges to financial practitioners. Although they provide sometimes more accurate estimates than traditional methods, they lack transparency. GANs have shown promise in the literature but suffer from being unstable to train. It is difficult to guess will a trained GAN work as it is meant to work. Regardless of these shortcomings, it is worthwhile to study GANs and other neural networks in finance. They have performed exceptionally in other fields. Researchers must try to open the black-box nature of the models. Interpretability of the models will allow their use in the financial industry. This thesis shows that more research is needed to provide robust estimates that can be relied on

The FISKMÖ Project : Resources and Tools for Finnish-Swedish Machine Translation and Cross-Linguistic Research

This paper presents FISKMÖ, a project that focuses on the development of resources and tools for cross-linguistic research and machine translation between Finnish and Swedish. The goal of the project is the compilation of a massive parallel corpus out of translated material collected from web sources, public and private organisations and language service providers in Finland with its two official languages. The project also aims at the development of open and freely accessible translation services for those two languages for the general purpose and for domain-specific use. We have released new data sets with over 3 million translation units, a benchmark test set for MT development, pre-trained neural MT models with high coverage and competitive performance and a self-contained MT plugin for a popular CAT tool. The latter enables offline translation without dependencies on external services making it possible to work with highly sensitive data without compromising security concerns.Peer reviewe

Self-Applied Electrode Set Provides a Clinically Feasible Solution Enabling EEG Recording in Home Sleep Apnea Testing

Home sleep apnea testing (HSAT) without electroencephalography (EEG) recording is increasingly used as an alternative to in-laboratory polysomnography for the diagnosis of obstructive sleep apnea (OSA). However, without EEG, electrooculography (EOG), and chin electromyography (EMG) recordings, the OSA severity may be significantly underestimated. Although several ambulatory EEG systems have been recently introduced, no patient-applied systems including EEG, EOG, and chin EMG suitable for home polysomnography are currently in clinical use. We have recently developed and pre-clinically tested a self-applied ambulatory electrode set (AES), consisting of frontal EEG, EOG, and EMG, in subjects with possible sleep bruxism. Now, in this clinical feasibility study, we investigated the signal scorability and usability of the AES as a self-administered sleep assessment approach supplementing the conventional HSAT device. We also investigated how the diagnostic parameters and OSA severity changed when utilizing the AES. Thirty-eight patients (61 % male, 25-78 years) with a clinical suspicion of OSA conducted a single-night, self-administered HSAT with a portable polysomnography device (Nox A1, Nox Medical, Reykjavik, Iceland) supplemented with AES. Only one AES recording failed. The use of AES signals in data analysis significantly affected the median apnea-hypopnea index (AHI), increasing it from 9.4 to 12.7 events/h (p < 0.001) compared to the conventional HSAT. Also, in eight patients, the OSA severity class changed to one class worse. Perceived ease of use was well in line with that previously found among healthy volunteers. These results suggest that the AES provides an easy, clinically feasible solution to record EEG as a part of conventional HSAT.Peer reviewe

Deep Learning Enables Accurate Automatic Sleep Staging Based on Ambulatory Forehead EEG

We have previously developed an ambulatory electrode set (AES) for the measurement of electroencephalography (EEG), electrooculography (EOG), and electromyography (EMG). The AES has been proven to be suitable for manual sleep staging and self-application in in-home polysomnography (PSG). To further facilitate the diagnostics of various sleep disorders, this study aimed to utilize a deep learning-based automated sleep staging approach for EEG signals acquired with the AES. The present neural network architecture comprises a combination of convolutional and recurrent neural networks previously shown to achieve excellent sleep scoring accuracy with a single standard EEG channel (F4-M1). In this study, the model was re-trained and tested with 135 EEG signals recorded with AES. The recordings were conducted for subjects suspected of sleep apnea or sleep bruxism. The performance of the deep learning model was evaluated with 10-fold cross-validation using manual scoring of the AES signals as a reference. The accuracy of the neural network sleep staging was 79.7% (kappa = 0.729) for five sleep stages (W, N1, N2, N3, and R), 84.1% (kappa = 0.773) for four sleep stages (W, light sleep, deep sleep, R), and 89.1% (kappa = 0.801) for three sleep stages (W, NREM, R). The utilized neural network was able to accurately determine sleep stages based on EEG channels measured with the AES. The accuracy is comparable to the inter-scorer agreement of standard EEG scorings between international sleep centers. The automatic AES-based sleep staging could potentially improve the availability of PSG studies by facilitating the arrangement of self-administrated in-home PSGs.Peer reviewe

Increased nocturnal arterial pulsation frequencies of obstructive sleep apnoea patients is associated with an increased number of lapses in a psychomotor vigilance task.

To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked DownloadObjectives: Besides hypoxaemia severity, heart rate variability has been linked to cognitive decline in obstructive sleep apnoea (OSA) patients. Thus, our aim was to examine whether the frequency domain features of a nocturnal photoplethysmogram (PPG) can be linked to poor performance in the psychomotor vigilance task (PVT). Methods: PPG signals from 567 suspected OSA patients, extracted from Type 1 diagnostic polysomnography, and corresponding results of PVT were retrospectively examined. The frequency content of complete PPGs was determined, and analyses were conducted separately for men (n=327) and women (n=240). Patients were grouped into PVT performance quartiles based on the number of lapses (reaction times ≥500 ms) and within-test variation in reaction times. The best-performing (Q1) and worst-performing (Q4) quartiles were compared due the lack of clinical thresholds in PVT. Results: We found that the increase in arterial pulsation frequency (APF) in both men and women was associated with a higher number of lapses. Higher APF was also associated with higher within-test variation in men, but not in women. Median APF (β=0.27, p=0.01), time spent under 90% saturation (β=0.05, p<0.01), female sex (β=1.29, p<0.01), older age (β=0.03, p<0.01) and subjective sleepiness (β=0.07, p<0.01) were significant predictors of belonging to Q4 based on lapses. Only female sex (β=0.75, p<0.01) and depression (β=0.91, p<0.02) were significant predictors of belonging to Q4 based on the within-test variation. Conclusions: In conclusion, increased APF in PPG provides a possible polysomnography indicator for deteriorated vigilance especially in male OSA patients. This finding highlights the connection between cardiorespiratory regulation, vigilance and OSA. However, our results indicate substantial sex-dependent differences that warrant further prospective studies.Research Committee of the Kuopio University Hospital Catchment Area for the State Research Funding Academy of Finland Seinajoki Central Hospital Competitive State Research Financing of Expert Responsibility Area of Tampere University Hospital VTR3242 Business Finland Paulo Foundation Paivikki & Sakari Sohlberg Foundation Research Foundation of the Pulmonary Diseases Finnish Cultural Foundation Alfred Kordelin Foundation Tampere Tuberculosis Foundation Respiratory Foundation of Kuopio Regio

Quality control of displays used for viewing radiological images

Tämän tutkimuksen tarkoituksena oli selvittää radiologisten kuvien katseluun käytettävien näyttöjen laatua Satakunnan sairaanhoitopiirin alueella vuonna 2016. Tutkimukseen pyrittiin valitsemaan kalibroimattomia LCD-värinäyttöjä, joilta tehdään diagnooseja ilman radiologin lausuntoa. Lisäksi referenssinä oli sekä DICOM-kalibroituja harmaasävynäyttöjä että DICOM-kalibroituja värinäyttöjä. Näytöille suoritettiin American Association of Physicistsin (AAPM) suosittelemia mittauksia ja arviointeja. Mittaukset suoritettiin AAPM:n laadunvalvontaraportin suosittelemilla mittausmenetelmillä ja raportin tarjoamilla testikuvilla. Tärkeimmät mitatut laadunvalvontaparametrit olivat näytön luminanssivaste ja ympäristön illuminanssi. Lisäksi näytöille suoritettiin useita muita AAPM:n suosittelemia laadunvalvontatestejä. Mittaukset suoritettiin Unfors Xi -yleismittarilla, johon oli kiinnitetty valosensori luminanssin ja illuminanssin mittausta varten. Tutkimuksen tulokset osoittavat, että Satakunnan sairaanhoitopiirin sairaaloissa ja terveyskeskuksissa käytetyt kuluttajatason kalibroimattomat värinäytöt eivät täytä AAPM:n radiologisille näytöille antamia laadunvalvontasuosituksia. Suurimmat puutteet kalibroimattomien värinäyttöjen suorituskyvyssä ovat näyttöjen luminanssivasteissa, jotka eivät vastaa GSDF-standardin mukaista luminanssivastetta. Lisäksi kuvankatselutilat olivat paljon suosituksia valoisampia ja kuvankatselutilojen sekä näyttöjen puhtaa-napidossa oli puutteita. DICOM-kalibroidut värinäytöt vastaavat luminanssivasteeltaan GSDF-standardia, kuten myös tässä tutkimuksessa referenssinä toimineet DICOM-kalibroidut harmaasävynäytöt. Lisäksi kalibroidut näytöt olivat sijoitettu kalibroimattomia näyttöjä hämärämpiin huoneisiin. Aikaisempien radiologisten näyttöjen suorituskykyä mittaavien tutkimusten ja tämän työn tulosten perusteella annetaan suositus, että kaikki kalibroimattomat värinäytöt DICOM-kalibroidaan vastaanottotarkastuksen yhteydessä. Lisäksi kuvankatselutilojen suunnitteluun tulee kiinnittää enemmän huomiota ja kuvankatselutilat tulisi muuttaa helposti pimennettäviksi esimerkiksi pimennysverhojen ja pöydän lähellä olevien valokatkaisijoiden avulla