123 research outputs found
Incremental inference on higher-order probabilistic graphical models applied to constraint satisfaction problems
Thesis (PhD)--Stellenbosch University, 2022.ENGLISH ABSTRACT: Probabilistic graphical models (PGMs) are used extensively in the probabilistic
reasoning domain. They are powerful tools for solving systems of complex relationships over a variety of probability distributions, such as medical and fault diagnosis, predictive modelling, object recognition, localisation and mapping, speech recognition, and language processing [5, 6, 7, 8, 9, 10, 11]. Furthermore, constraint
satisfaction problems (CSPs) can be formulated as PGMs and solved with PGM inference techniques. However, the prevalent literature on PGMs shows that suboptimal PGM structures are primarily used in practice and a suboptimal formulation
for constraint satisfaction PGMs.
This dissertation aimed to improve the PGM literature through accessible algorithms and tools for improved PGM structures and inference procedures, specifically focusing on constraint satisfaction. To this end, this dissertation presents three
published contributions to the current literature:
a comparative study to compare cluster graph topologies to the prevalent factor graphs [1],
an application of cluster graphs in land cover classification in the field of cartography [2], and
a comprehensive integration of various aspects required to formulate CSPs as
PGMs and an algorithm to solve this formulation for problems too complex
for traditional PGM tools [3].
First, we present a means of formulating and solving graph colouring problems with probabilistic graphical models. In contrast to the prevailing literature
that mostly uses factor graph configurations, we approach it from a cluster graph perspective, using the general-purpose cluster graph construction algorithm, LTRIP.
Our experiments indicate a significant advantage for preferring cluster graphs over factor graphs, both in terms of accuracy as well as computational efficiency.
Secondly, we use these tools to solve a practical problem: land cover classification. This process is complex due to measuring errors, inefficient algorithms, and
low-quality data. We proposed a PGM approach to boost geospatial classifications
from different sources and consider the effects of spatial distribution and inter-class dependencies (similarly to graph colouring). Our PGM tools were shown to be
robust and were able to produce a diverse, feasible, and spatially-consistent land cover classification even in areas of incomplete and conflicting evidence.
Lastly, in our third publication, we investigated and improved the PGM structures used for constraint satisfaction. It is known that tree-structured PGMs always result in an exact solution [12, p355], but is usually impractical for interesting
problems due to exponential blow-up. We, therefore, developed the āpurge-and mergeā algorithm to incrementally approximate a tree-structured PGM. This algorithm iteratively nudges a malleable graph structure towards a tree structure by selectively merging factors. The merging process is designed to avoid exponential
blow-up through sparse data structures from which redundancy is purged as the algorithm progresses. This algorithm is tested on constraint satisfaction puzzles such
as Sudoku, Fill-a-pix, and Kakuro and manages to outperform other PGM-based
approaches reported in the literature [13, 14, 15]. Overall, the research reported in
this dissertation contributed to developing a more optimised approach for higher order probabilistic graphical models. Further studies should concentrate on applying purge-and-merge on problems closer to probabilistic reasoning than constraint
satisfaction and report its effectiveness in that domain.AFRIKAANSE OPSOMMING: Grafiese waarskynlikheidsmodelle (PGM) word wyd gebruik vir komplekse
waarskynlikheidsprobleme. Dit is kragtige gereedskap om sisteme van komplekse
verhoudings oor ān versameling waarskynlikheidsverspreidings op te los, soos die
mediese en foutdiagnoses, voorspellingsmodelle, objekherkenning, lokalisering en
kartering, spraakherkenning en taalprosessering [5, 6, 7, 8, 9, 10, 11]. Voorts kan
beperkingvoldoeningsprobleme (CSP) as PGMās geformuleer word en met PGM
gevolgtrekkingtegnieke opgelos word. Die heersende literatuur oor PGMās toon
egter dat sub-optimale PGM-strukture hoofsaaklik in die praktyk gebruik word en
ān sub-optimale PGM-formulering vir CSPās.
Die doel met die verhandeling is om die PGM-literatuur deur toeganklike algoritmes en gereedskap vir verbeterde PGM-strukture en gevolgtrekking-prosedures
te verbeter deur op CSP toepassings te fokus. Na aanleiding hiervan voeg die verhandeling drie gepubliseerde bydraes by die huidige literatuur:
ān vergelykende studie om bundelgrafieke tot die heersende faktorgrafieke te
vergelyk [1],
ān praktiese toepassing vir die gebruik van bundelgrafieke in āland-coverā-
klassifikasie in die kartografieveld [2] en
ān omvattende integrasie van verskeie aspekte om CSPās as PGMās te formuleer en ān algoritme vir die formulering van probleme te kompleks vir tradisionele PGM-gereedskap [3]
Eerstens bied ons ān wyse van formulering en die oplos van grafiekkleurprobleme met PGMās. In teenstelling met die huidige literatuur wat meestal faktorgrafieke gebruik, benader ons dit van ān bundelgrafiek-perspektief deur die gebruik
van die automatiese bundelgrafiekkonstruksie-algoritme, LTRIP. Ons eksperimente
toon ān beduidende voorkeur vir bundelgrafieke teenoor faktorgrafieke, wat akku raatheid asook berekende doeltreffendheid betref.
Tweedens gebruik ons die gereedskap om ān praktiese probleem op te los: ālandcoverā-klassifikasie. Die proses is kompleks weens metingsfoute, ondoeltreffende
algoritmes en lae-gehalte data. Ons stel ān PGM-benadering voor om die georuimtelike klassifikasies van verskillende bronne te versterk, asook die uitwerking van ruimtelike verspreiding en interklas-afhanklikhede (soortgelyk aan grafiekkleurprobleme). Ons PGM-gereedskap is robuus en kon ān diverse, uitvoerbare en
ruimtelik-konsekwente āland-coverā-klassifikasie selfs in gebiede van onvoltooide
en konflikterende inligting bewys.
Ten slotte het ons in ons derde publikasie die PGM-strukture vir CSPās ondersoek en verbeter. Dit is bekend dat boomstrukture altyd tot ān eksakte oplossing
lei [12, p355], maar is weens eksponensiƫle uitbreiding gewoonlik onprakties vir interessante probleme. Ons het gevolglik die algoritme, purge-and-merge, ontwikkel
om inkrementeel ān boomstruktuur na te doen.
Die algoritme hervorm ān bundelgrafiek stapsgewys in ān boomstruktuur deur
faktore selektief te āmergeā. Die saamsmeltproses is ontwerp om eksponensiĆ«le
uitbreiding te vermy deur van yl datastrukture gebruik te maak waarvan die waarskeinlikheidsruimte ge-āpurgeā word namate die algoritme vorder. Die algoritme
is getoets op CSP-speletjies soos Sudoku, Fill-a-pix en Kakuro en oortref ander
PGM-gegronde benaderings waaroor in die literatuur verslag gedoen word [13,
14, 15]. In die geheel gesien, het die navorsing bygedra tot die ontwikkeling van
ān meer geoptimaliseerde benadering vir hoĆ«r-orde PGMās. Verdere studies behoort te fokus op die toepassing van purge-and-merge op probleme nader aan
waarskynlikheidsredenasie-probleme as aan CSPās en moet sy effektiwiteit in daar die domein rapporteer.Doctora
A fast and scalable algorithm for the Monte Carlo simulation of elastic scattering in perturbed media
Monte Carlo (MC) simulations are frequently used to describe random processes. An important application is multiple Mie scattering in perturbed media. To significantly reduce execution time and increase the statistical accuracy of the MC simulations we have implemented a concurrent algorithm running on graphics processing units. We comment on execution time and scalability
3D Mie-Streuung in Simulation und Streulichtexperiment
Die Lichtausbreitung durch streuende Medien ist fĆ¼r zahlreiche Anwendungsgebiete von groĆem Interesse. Da die rigorose Berechnung sehr aufwendig ist, wird eine massiv parallelisierte Methode zur dreidimensionalen Simulation basierend auf der Mie-Theorie vorgestellt. Die Ergebnisse werden mit einem geeigneten Streulichtexperiment verglichen
Hocheffiziente Simulation von Mehrfachstreuprozessen auf Basis der Mie-Theorie fĆ¼r die optische Sensorik
Wir stellen eine Methode vor, wie die Lichtausbreitung in Medien effizient berechnet werden kann. Hierzu setzen wir programmierbare Grafikprozessoren ein. Die Simulation wird anhand experimenteller Messergebnisse an unterschiedlich streuenden Diffusoren validiert
Lineage tracing and clonal analysis in developing cerebral cortex using mosaic analysis with double markers (MADM)
Beginning from a limited pool of progenitors, the mammalian cerebral cortex forms highly organized functional neural circuits. However, the underlying cellular and molecular mechanisms regulating lineage transitions of neural stem cells (NSCs) and eventual production of neurons and glia in the developing neuroepithelium remains unclear. Methods to trace NSC division patterns and map the lineage of clonally related cells have advanced dramatically. However, many contemporary lineage tracing techniques suffer from the lack of cellular resolution of progeny cell fate, which is essential for deciphering progenitor cell division patterns. Presented is a protocol using mosaic analysis with double markers (MADM) to perform in vivo clonal analysis. MADM concomitantly manipulates individual progenitor cells and visualizes precise division patterns and lineage progression at unprecedented single cell resolution. MADM-based interchromosomal recombination events during the G2-X phase of mitosis, together with temporally inducible CreERT2, provide exact information on the birth dates of clones and their division patterns. Thus, MADM lineage tracing provides unprecedented qualitative and quantitative optical readouts of the proliferation mode of stem cell progenitors at the single cell level. MADM also allows for examination of the mechanisms and functional requirements of candidate genes in NSC lineage progression. This method is unique in that comparative analysis of control and mutant subclones can be performed in the same tissue environment in vivo. Here, the protocol is described in detail, and experimental paradigms to employ MADM for clonal analysis and lineage tracing in the developing cerebral cortex are demonstrated. Importantly, this protocol can be adapted to perform MADM clonal analysis in any murine stem cell niche, as long as the CreERT2 driver is present
Cell-type specificity of genomic imprinting in cerebral cortex
In mammalian genomes, a subset of genes is regulated by genomic imprinting, resulting in silencing of one parental allele. Imprinting is essential for cerebral cortex development, but prevalence and functional impact in individual cells is unclear. Here, we determined allelic expression in cortical cell types and established a quantitative platform to interrogate imprinting in single cells. We created cells with uniparental chromosome disomy (UPD) containing two copies of either the maternal or the paternal chromosome; hence, imprinted genes will be 2-fold overexpressed or not expressed. By genetic labeling of UPD, we determined cellular phenotypes and transcriptional responses to deregulated imprinted gene expression at unprecedented single-cell resolution. We discovered an unexpected degree of cell-type specificity and a novel function of imprinting in the regulation of cortical astrocyte survival. More generally, our results suggest functional relevance of imprinted gene expression in glial astrocyte lineage and thus for generating cortical cell-type diversity
A mathematical insight into cell labelling experiments for clonal analysis
Studying the progression of the proliferative and differentiative patterns of neural stem cells at the individual cell-level is crucial to the understanding of cortex development and how the disruption of such patterns can lead to malformations and neurodevelopmental diseases. However, our understanding of the precise lineage progression program at single cell resolution is still incomplete due to the technical variations in lineage tracing approaches. One of the key challenges involves developing a robust theoretical framework in which we can integrate experimental observations and introduce correction factors to obtain a reliable and representative description of the temporal modulation of proliferation and differentiation. In order to obtain more conclusive insights we carry out virtual clonal analysis using mathematical modelling and compare our results against experimental data. Using a dataset obtained with Mosaic Analysis with Double Markers, we illustrate how the theoretical description can be exploited to interpret and reconcile the disparity between virtual and experimental results
SolNet:PhD-scholarships and courses on solar heating
AbstractSolNet, founded in 2006, is the first coordinated International PhD education program on Solar Thermal Engineering. The SolNet network is coordinated by the Institute of Thermal Engineering at Kassel University, Germany. The network offers PhD courses on solar heating and cooling, conference-accompanying Master courses, placements of internships, and PhD scholarship projects. A new scholarship project, āSHINEā, will be launched in autumn 2013 in the frame work of the Marie Curie program of the European Union (Initial Training Network, ITN). 13 PhD-scholarships on solar district heating, solar heat for industrial processes, as well as sorption stores and materials will be offered, starting in December 2013. Additionally, the project comprises a training program with five PhD courses and several workshops on solar thermal engineering that will be open also for other PhD students working in the field. The research projects will be hosted by six different universities and five companies from all over Europe
A genome-wide library of MADM mice for single-cell genetic mosaic analysis
Mosaic analysis with double markers (MADM) offers one approach to visualize and concomitantly manipulate genetically defined cells in mice with single-cell resolution. MADM applications include the analysis of lineage, single-cell morphology and physiology, genomic imprinting phenotypes, and dissection of cell-autonomous gene functions in vivo in health and disease. Yet, MADM can only be applied to 96% of the entire mouse genome can now be subjected to single-cell genetic mosaic analysis. Beyond a proof of principle, we apply our MADM library to systematically trace sister chromatid segregation in distinct mitotic cell lineages. We find striking chromosome-specific biases in segregation patterns, reflecting a putative mechanism for the asymmetric segregation of genetic determinants in somatic stem cell division
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