Bayesian uncertainty quantification in linear models for diffusion MRI

Diffusion MRI (dMRI) is a valuable tool in the assessment of tissue microstructure. By fitting a model to the dMRI signal it is possible to derive various quantitative features. Several of the most popular dMRI signal models are expansions in an appropriately chosen basis, where the coefficients are determined using some variation of least-squares. However, such approaches lack any notion of uncertainty, which could be valuable in e.g. group analyses. In this work, we use a probabilistic interpretation of linear least-squares methods to recast popular dMRI models as Bayesian ones. This makes it possible to quantify the uncertainty of any derived quantity. In particular, for quantities that are affine functions of the coefficients, the posterior distribution can be expressed in closed-form. We simulated measurements from single- and double-tensor models where the correct values of several quantities are known, to validate that the theoretically derived quantiles agree with those observed empirically. We included results from residual bootstrap for comparison and found good agreement. The validation employed several different models: Diffusion Tensor Imaging (DTI), Mean Apparent Propagator MRI (MAP-MRI) and Constrained Spherical Deconvolution (CSD). We also used in vivo data to visualize maps of quantitative features and corresponding uncertainties, and to show how our approach can be used in a group analysis to downweight subjects with high uncertainty. In summary, we convert successful linear models for dMRI signal estimation to probabilistic models, capable of accurate uncertainty quantification.Comment: Added results from a group analysis and a comparison with residual bootstra

Modellering, simulering och dynamisk kontroll av ett vågkraftverk

The energy in ocean waves is a renewable energy resource not yet fully exploited. Research in converting ocean energy to useful electricity has been ongoing for about 40 years, but no one has so far succeed to do it at sufficiently low cost. CorPower Ocean has developed a method, which in theory can achieve this. It uses a light buoy and a control strategy called Phase Control. The purpose of this thesis is to develop a mathematical model of this method - using LinearWave Theory to derive the hydrodynamic forces - and from the simulated results analyze the energy output of the method. In the process we create a program that will help realizing and improving the method further. The model is implemented and simulated in the simulation program Simulink. On the basis of the simulated results, we can concludes that the CorPower Ocean method is promising. The outcome shows that the energy output increases - up to five times- compared to conventional methods.Vågenergi är en förnyelsebar energikälla som ännu inte utnyttjas fullt ut. Forskning inom konvertering av vågenergi till användbar elektricitet har pågått i cirka 40 år, men ingen har hittills lyckas att göra det tillräckligt kostnadseffektivt. CorPower Ocean har utvecklat en metod, som i teorin kan uppnå detta. De använder en lätt boj och en kontrollstrategi kallad Phase Control. Syftet med detta examensarbete är att utveckla en matematisk modell av metoden -genom att använda Linear Wave Theory för att härleda de hydrodynamiska krafterna -och från de simulerade resultaten analysera energiutbytet. Under arbetets gång skapades också ett simuleringsprogram som hjälpmedel till att realisera och förbättra metoden. Modellen implementeras och simuleras i programmet Simulink. Utifrån de simulerade resultaten kan vi dra slutsatsen att CorPower Oceans metod är lovande. Resultatet visar att energiutbytet ökar -upp till fem gånger - jämfört med konventionella metoder

Pre-training Transformers for Molecular Property Prediction Using Reaction Prediction

Molecular property prediction is essential in chemistry, especially for drug discovery applications. However, available molecular property data is often limited, encouraging the transfer of information from related data. Transfer learning has had a tremendous impact in fields like Computer Vision and Natural Language Processng signaling for its potential in molecular property prediction. We present a pre-training procedure for molecular representation learning using reaction data and use it to pre-train a SMILES Transformer. We fine-tune and evaluate the pretrained model on 12 molecular property prediction tasks from MoleculeNet within physical chemistry, biophysics, and physiology and show a statistically significant positive effect on 5 of the 12 tasks compared to a non-pre-trained baseline model

Remote detection of light tolerance in Basil through frequency and transient analysis of light induced fluorescence

© 2016 Elsevier B.V.Artificial lighting control in industrial scale greenhouses has a large potential for increased crop yields, energy savings and timing in greenhouse production. One key component in controlling greenhouse lighting is continuous and accurate measurement of plant performance. This paper presents a novel concept for remote detection of plant performance based on the dynamics of chlorophyll fluorescence (CF) signals induced by a LED-lamp. The dynamic properties of the CF is studied through fitting a linear dynamic model to CF data. The hypothesis is that changes in photochemistry affects the fluorescence dynamics and can therefore be detected as changes in the model parameters and properties. The dynamics was studied in experiments using a sinusoidal varying light intensity (period 60 s) or step changes (step length 300 s). Experiments were performed in a controlled light environment on Basil plants acclimated to different light intensities. It is concluded that the capacity to use a certain light intensity is reflected by how fast and how complex the dynamics are. In particular, the results show that optimal model order is a potential indicator of light tolerance in plants that could be a valuable feedback signal for lighting control in greenhouses

Analyses of Metabolic Dynamics in Saccharomyces cerevisiae

This thesis conducts a study on the stability of steady states in the glycolysis of in silico models of Saccharomyces cerevisiae. Such un- controlled models could reach unstable steady states that are unlikely to occur in vivo. Little work has previously been done to examine stability of such models. The glycolysis is modeled as a system of nonlinear dierential equa- tions. This is done by using rate equations describing the rate of change in concentration of each metabolite involved in glycolysis. By lineariz- ing this system around dierent equilibria and calculating the eigen- values of the associated jacobian matrices the stability of the steady states can be determined. Additionally perturbation analysis adds fur- ther insight into the stability of the steady state. Given the large range of possible initial conditions which result in dierent steady states, a physiologically feasible one, as well as the environment around it, is chosen to be the subject of this study. A steady state is stable if all the eigenvalues of the Jacobian matrix are negative. The model created by Teusink et al, and expanded upon by Pritchard et, for the glycolysis in S.cerevisiae is used as the primary model of the study. The steady state does not have strictly negative eigenvalues: Two of them are very close to zero, with one positive, within error tolerance of our numerical methods. This means that linear analysis cannot determine whether the steady state is stable. The whole nonlinear system has to be considered. After performing perturbation analysis we conclude that the steady state is most likely stable in the Lyapunov sense

Humor and its perception in 2016 Latvian political Internet memes

Bakalaura darba “Humors un tā uztvere 2016. gadā veidotajos Latvijas politiskajos interneta mēmos” mērķis ir saprast, kā politiskajos interneta mēmos veidots humors un vai ar tiem komunicēts nepieņemams vēstījums tiek uztverts pieņemamāk nekā tāds pats vēstījums, kurš komunicēts bez humora. Teorētiskajā pieejā politisko mēmu pētniecībā izmantots semiotikā definētais komutāciju tests un Tomasa E. Forda (Thomas E. Ford) teorija, ka vēstījumi bez humora ir vairāk aizvainojoši nekā, ja tie pausti humoristiski. Tiek aprakstīta arī remiksēšanas kultūras, mēmu un humora teorijas. Datu ieguvei izmantota kvantitatīvā kontentanalīze, interneta aptauja un eksperiments. Darbā secināts, ka politiskajos mēmos humoru veido neatbilstības un tie politiski nepieņemamu vēstījumu liek uztvert pieņemamāk nekā tādu pašu vēstījumu komunicētu lietišķi, bez humora.The aim of bachelor thesis “Humour and its perception in 2016 Latvian political Internet memes” is to understand how humour is constructed in Latvian political Internet memes and does these memes make unacceptable information perceived to be more acceptable than the same information communicated without humour. The Paper`s theoretical part is based on Thomas E. Ford theory that a humorous message is understood to be less offensive than a non-humorous one and on commutation test which is used in semiotics to analyse a signifying system. Also, theories about remixing culture, memes and humour are discussed. Quantitative content analysis, online survey and experimental method are used for research. Main conclusion states that in political Internet memes humour is constructed as incongruity and unacceptable messages about politicians communicated with political memes are perceived as more acceptable than same messages communicated without humour

Variational Elliptical Processes

We present elliptical processes—a family of non-parametric probabilistic models that subsumes Gaussian processes and Student's t processes. This generalization includes a range of new heavy-tailed behaviors while retaining computational tractability. Elliptical processes are based on a representation of elliptical distributions as a continuous mixture of Gaussian distributions. We parameterize this mixture distribution as a spline normalizing flow, which we train using variational inference. The proposed form of the variational posterior enables a sparse variational elliptical process applicable to large-scale problems. We highlight advantages compared to Gaussian processes through regression and classification experiments. Elliptical processes can supersede Gaussian processes in several settings, including cases where the likelihood is non-Gaussian or when accurate tail modeling is essential

Complexity of Chlorophyll Fluorescence Dynamic Response as an Indicator of Excessive Light Intensity

The controllability of LED lighting systems for greenhouses and plant factories offers a possibility for light induced diagnose of plant status. Here, a novel method for proximal remote detection of plant light tolerance is investigated. The method is based on an identification of a transfer function model for the measured chlorophyll fluorescence response to a small step variation in blue LED light. It is postulated that the least required model order decreases as the plants become light stressed due to saturation effects at excess light conditions. We apply this method to basil and lettuce plants under different background light intensities, and the results are compared to measured effective quantum yield (y(II)), relative electron transport rate through PSII (ETR(II)) and non-photochemical quenching (NPQ), all reflecting the photosynthetic performance. For both species it is indeed found that the required model order decreases with increasing background light intensity at the same time as the measured reference parameters indicates a decreased photosynthetic efficiency. It is suggested that the light intensity should be such that the chlorophyll fluorescence response requires a model order of 3 or higher to avoid ineffective irradiation of the plants