Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning

A Review on Automatic Analysis of Human Embryo Microscope Images

Over the last 30 years the process of in vitro fertilisation (IVF) has evolved considerably, yet the efficiency of this treatment remains relatively poor. The principal challenge faced by doctors and embryologists is the identification of the embryo with the greatest potential for producing a child. Current methods of embryo viability assessment provide only a rough guide to potential. In order to improve the odds of a successful pregnancy it is typical to transfer more than one embryo to the uterus. However, this often results in multiple pregnancies (twins, triplets, etc), which are associated with significantly elevated risks of serious complications. If embryo viability could be assessed more accurately, it would be possible to transfer fewer embryos without negatively impacting IVF pregnancy rates. In order to assist with the identification of viable embryos, several scoring systems based on morphological criteria have been developed. However, these mostly rely on a subjective visual analysis. Automated assessment of morphological features offers the possibility of more accurate quantification of key embryo characteristics and elimination of inter- and intra-observer variation. In this paper, we describe the main embryo scoring systems currently in use and review related works on embryo image analysis that could lead to an automatic and precise grading of embryo quality. We summarise achievements, discuss challenges ahead, and point to some possible future directions in this research field

Artificial Intelligence Based Classification for Urban Surface Water Modelling

Estimations and predictions of surface water runoff can provide very useful insights, regarding flood risks in urban areas. To automatically predict the flow behaviour of the rainfall-runoff water, in real-world satellite images, it is important to precisely identify permeable and impermeable areas. This identification indicates and helps to calculate the amount of surface water, by taking into account the amount of water being absorbed in a permeable area and what remains on the impermeable area. In this research, a model of surface water has been established, to predict the behavioural flow of rainfall-runoff water. This study employs a combination of image processing, artificial intelligence and machine learning techniques, for automatic segmentation and classification of permeable and impermeable areas, in satellite images. These techniques investigate the image classification approaches for classifying three land-use categories (roofs, roads, and pervious areas), commonly found in satellite images of the earth’s surface. Three different classification scenarios are investigated, to select the best classification model. The first scenario involves pixel by pixel classification of images, using Classification Tree and Random Forest classification techniques, in 2 different settings of sequential and parallel execution of algorithms. In the second classification scenario, the image is divided into objects, by using Superpixels (SLIC) segmentation method, while three kinds of feature sets are extracted from the segmented objects. The performance of eight different supervised machine learning classifiers is probed, using 5-fold cross-validation, for multiple SLIC values, while detailed performance comparisons lead to conclusions about the classification into different classes, regarding Object-based and Pixel-based classification schemes. Pareto analysis and Knee point selection are used to select SLIC value and the suitable type of classification, among the aforementioned two. Furthermore, a new diversity and weighted sum-based ensemble classification model, called ParetoEnsemble, is proposed, in this classification scenario. The weights are applied to selected component classifiers of an ensemble, creating a strong classifier, where classification is done based on multiple votes from candidate classifiers of the ensemble, as opposed to individual classifiers, where classification is done based on a single vote, from only one classifier. Unbalanced and balanced data-based classification results are also evaluated, to determine the most suitable mode, for satellite image classifications, in this study. Convolutional Neural Networks, based on semantic segmentation, are also employed in the classification phase, as a third scenario, to evaluate the strength of deep learning model SegNet, in the classification of satellite imaging. The best results, from the three classification scenarios, are compared and the best classification method, among the three scenarios, is used in the next phase of water modelling, with the InfoWorks ICM software, to explore the potential of modelling process, regarding a partially automated surface water network. By using the parameter settings, with a specified amount of simulated rain falling, onto the imaged area, the amount of surface water flow is estimated, to get predictions about runoff situations in urban areas, since runoff, in such a situation, can be high enough to pose a dangerous flood risk. The area of Feock, in Cornwall, is used as a simulation area of study, in this research, where some promising results have been derived, regarding classification and modelling of runoff. The correlation coefficient estimation, between classification and runoff accuracy, provides useful insight, regarding the dependence of runoff performance on classification performance. The trained system was tested on some unknown area images as well, demonstrating a reasonable performance, considering the training and classification limitations and conditions. Furthermore, in these unknown area images, reasonable estimations were derived, regarding surface water runoff. An analysis of unbalanced and balanced data-based classification and runoff estimations, for multiple parameter configurations, provides aid to the selection of classification and modelling parameter values, to be used in future unknown data predictions. This research is founded on the incorporation of satellite imaging into water modelling, using selective images for analysis and assessment of results. This system can be further improved, and runoff predictions of high precision can be better achieved, by adding more high-resolution images to the classifiers training. The added variety, to the trained model, can lead to an even better classification of any unknown image, which could eventually provide better modelling and better insights into surface water modelling. Moreover, the modelling phase can be extended, in future research, to deal with real-time parameters, by calibrating the model, after the classification phase, in order to observe the impact of classification on the actual calibration

Development of a High-Resolution Land Cover Dataset to Support Integrated Water Resources Planning and Management in Northern Utah

Integrated planning and management approaches, including bioregional planning and integrated water resources planning, are comprehensive strategies that strive to balance the sustainability of natural resources and the integrity of ecosystem processes with human development and activities. Implementation of integrated plans and programs remains complicated. However, geospatial technologies, such as geographic information systems and remote sensing, can significantly enhance planning and management processes. Through a United States Environmental Protection Agency Region 8 Wetland Program Development Grant, a high-resolution land cover dataset, with a primary emphasis on mapping and quantifying impervious surfaces, was developed for three watershed sub-basins in northern Utah - Lower Bear-Malad, Lower Weber, and Jordan - to support integrated water resources planning and management. This high-resolution land cover dataset can serve as an indicator of cumulative stress from urbanization; it can support the development of ecologically relevant metrics that can be integrated into watershed health and wetland condition assessments; it can provide general assessments of watershed condition; and it can support the identification of sites in need of restoration and protection

Heterogeneities in Nanog Expression Drive Stable Commitment to Pluripotency in the Mouse Blastocyst

SummaryThe pluripotent epiblast (EPI) is the founder tissue of almost all somatic cells. EPI and primitive endoderm (PrE) progenitors arise from the inner cell mass (ICM) of the blastocyst-stage embryo. The EPI lineage is distinctly identified by its expression of pluripotency-associated factors. Many of these factors have been reported to exhibit dynamic fluctuations of expression in embryonic stem cell cultures. Whether these fluctuations correlating with ICM fate choice occur in vivo remains an open question. Using single-cell resolution quantitative imaging of a Nanog transcriptional reporter, we noted an irreversible commitment to EPI/PrE lineages in vivo. A period of apoptosis occurred concomitantly with ICM cell-fate choice, followed by a burst of EPI-specific cell proliferation. Transitions were occasionally observed from PrE-to-EPI, but not vice versa, suggesting that they might be regulated and not stochastic. We propose that the rapid timescale of early mammalian embryonic development prevents fluctuations in cell fate

Statistical interaction modeling of bovine herd behaviors

While there has been interest in modeling the group behavior of herds or flocks, much of this work has focused on simulating their collective spatial motion patterns which have not accounted for individuality in the herd and instead assume a homogenized role for all members or sub-groups of the herd. Animal behavior experts have noted that domestic animals exhibit behaviors that are indicative of social hierarchy: leader/follower type behaviors are present as well as dominance and subordination, aggression and rank order, and specific social affiliations may also exist. Both wild and domestic cattle are social species, and group behaviors are likely to be influenced by the expression of specific social interactions. In this paper, Global Positioning System coordinate fixes gathered from a herd of beef cows tracked in open fields over several days at a time are utilized to learn a model that focuses on the interactions within the herd as well as its overall movement. Using these data in this way explores the validity of existing group behavior models against actual herding behaviors. Domain knowledge, location geography and human observations, are utilized to explain the causes of these deviations from this idealized behavior

Dwarf galaxies within the Northern condensation of the Abell 569 galaxy cluster

Abstract. Dwarf galaxies are the most abundant class of galaxies in the universe with an absolute magnitudes MB > -18. They have a large dispersion in the properties, but typically have stellar masses M* < 10^9M⊙. They are especially interesting in the cluster envinronments as they are easily disturbed by the envinronmental mechanisms due to the low masses. Studying the dwarf galaxies in the galaxy clusters give information of the physical processes present, but also on the galaxy evolution in a rich envinronments. This Master’s thesis studies the dwarf galaxy population in the Abell 569 Northern condensation. I aim to present the Abell 569 Northern condensation dwarf galaxy catalogue and the structural parameter behaviours of the galaxies in different parts of the condensation. This study uses the g, r and z-band data from the DESI legacy surveys. Dwarf galaxies were detected from 3.14 deg area of the condensation using Max-Tree Objects (MTO) detection algorithm. Galaxies were modeled with Sérsic models using 2D-fitting algorithm GALFIT, and galaxy g – r colors were measured through elliptical aperture. Cluster galaxies were separated from the background galaxies using radius-, sérsic index-, color- and surface brightness-magnitude relations. Visual classification was conducted to remove artifacts from the cluster galaxies. Remaining galaxies were used to create the final catalogue present in the thesis. MTO detection completeness was evaluated by embedding simulated galaxies into brick images, and resulting completeness limit was used to study the dwarf galaxy luminosity function of the condensation. Catalogue galaxies were distinguished from each other into early- and late-type populations to study population distribution and structural behaviour in the different parts of the condensation. Catalogue includes 499 dwarf galaxies with different morphologies. With the color limit of 0.45, condensation has a populations of 464 early-type and 35 late-type dwarf galaxies. Completness limit of 95% was reached at Mg = -14.5 mag, and using the whole condensation dwarf galaxy population and forementioned completeness limit I obtained faint-end slope of a luminosity function α = -1.11 ± 0.04. Mass-corrected structural parameters show that dwarf galaxies in the inner parts of the condensation are more centrally concentrated, extended, redder and less bright than galaxies in the outer parts.Kääpiögalaksit Abell 569 galaksijoukon pohjoisessa tihentymässä. Tiivistelmä. Kääpiögalaksit ovat maailmankaikkeuden yleisin galaksiluokka, ja ne omaavat absoluuttisen magnitudin MB > -18. Niiden ominaisuudet vaihtelevat suuresti, mutta tyypillisesti kääpiögalaksien massan yläraja on M* < 10^9M⊙. Kääpiögalaksit ovat erityisen kiinnostavia galaksijoukoissa, sillä niiden pieni massa tekee niistä alttiita muutoksille, ja ne toimivatkin hyvinä tutkimuskohteina galaksien kehityksen sekä fysikaalisten prosessien tutkimisessa. Tämä opinnäytetyö käyttää g, r ja z-kaistan dataa, joka on kerätty DESI legacy surveyn avoimesta tietokannasta. Kääpiögalaksit on havaittu 3.14 neliöasteen alueelta käyttäen Max-Tree Objects (MTO) algoritmia. Löydetyt galaksit mallinnettiin GALFIT algoritmilla, joka sovitti kohteisiin Sérsic profiilin. Lisäksi, kääpiögalaksien värit laskettiin elliptisen aukon läpi. Joukon ja taustan galaksit erotettiin toisistaan käyttämällä säde-, sérsic indeksin, värin sekä pintakirkkas-magnitudi relaatioiden avulla. Lopuksi kääpiögalakseista poistettiin viimeiset artefaktit ennen niiden luettelointia. MTO:n löytyvyyttä testattiin lisäämällä alkuperäisiin kuviin simuloituja kääpiögalakseja, joita MTO pyrki havaitsemaan. Saatua löytyvyyden raja-arvoa hyödynnettiin tihentymän luminositeetti funktion mallintamisessa. Galaksi tyyppien jakauman sekä parametrien tarkastelussa kääpiögalaksit jaettiin varhais- sekä myöhäistyypin galakseihin. Lopullinen katalogi sisältää 499 kääpiögalaksia, joista 464 on varhaistyypin ja 35 on myöhäistyypin galakseja. Löytyvyyden raja-arvo 95% saavutettiin absoluuttisella magnitudilla Mg = -14.5 mag, sekä käyttämällä koko joukon kääpiögalakseja luminositeetti funktion heikonpään jyrkkyydeksi saatiin α = -1.11 ± 0.04. Massakorjatut parametrit näyttävät, että tihentymän sisäosissa kääpiögalaksit ovat suurempia, punaisempia, hämärämpiä sekä galaksien keskiosiin kasaantuneempia kuin tihentymän ulko-osissa olevat

Automating assessment of human embryo images and time-lapse sequences for IVF treatment

As the number of couples using In Vitro Fertilization (IVF) treatment to give birth increases, so too does the need for robust tools to assist embryologists in selecting the highest quality embryos for implantation. Quality scores assigned to embryonic structures are critical markers for predicting implantation potential of human blastocyst-stage embryos. Timing at which embryos reach certain cell and development stages in vitro also provides valuable information about their development progress and potential to become a positive pregnancy. The current workflow of grading blastocysts by visual assessment is susceptible to subjectivity between embryologists. Visually verifying when embryo cell stage increases is tedious and confirming onset of later development stages is also prone to subjective assessment. This thesis proposes methods to automate embryo image and time-lapse sequence assessment to provide objective evaluation of blastocyst structure quality, cell counting, and timing of development stages