470 research outputs found
Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment
We study the question of how visual analysis can support the comparison of
spatio-temporal ensemble data of liquid and gas flow in porous media. To this
end, we focus on a case study, in which nine different research groups
concurrently simulated the process of injecting CO2 into the subsurface. We
explore different data aggregation and interactive visualization approaches to
compare and analyze these nine simulations. In terms of data aggregation, one
key component is the choice of similarity metrics that define the relation
between the different simulations. We test different metrics and find that a
fine-tuned machine-learning based metric provides the best visualization
results. Based on that, we propose different visualization methods. For
overviewing the data, we use dimensionality reduction methods that allow us to
plot and compare the different simulations in a scatterplot. To show details
about the spatio-temporal data of each individual simulation, we employ a
space-time cube volume rendering. We use the resulting interactive, multi-view
visual analysis tool to explore the nine simulations and also to compare them
to data from experimental setups. Our main findings include new insights into
ranking of simulation results with respect to experimental data, and the
development of gravity fingers in simulations.Comment: arXiv preprin
Complex model calibration through emulation, a worked example for a stochastic epidemic model
Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions
Visual Analysis of Variability and Features of Climate Simulation Ensembles
This PhD thesis is concerned with the visual analysis of time-dependent scalar field ensembles as occur in climate simulations.
Modern climate projections consist of multiple simulation runs (ensemble members) that vary in parameter settings and/or initial values, which leads to variations in the resulting simulation data.
The goal of ensemble simulations is to sample the space of possible futures under the given climate model and provide quantitative information about uncertainty in the results.
The analysis of such data is challenging because apart from the spatiotemporal data, also variability has to be analyzed and communicated.
This thesis presents novel techniques to analyze climate simulation ensembles visually.
A central question is how the data can be aggregated under minimized information loss.
To address this question, a key technique applied in several places in this work is clustering.
The first part of the thesis addresses the challenge of finding clusters in the ensemble simulation data.
Various distance metrics lend themselves for the comparison of scalar fields which are explored theoretically and practically.
A visual analytics interface allows the user to interactively explore and compare multiple parameter settings for the clustering and investigate the resulting clusters, i.e. prototypical climate phenomena.
A central contribution here is the development of design principles for analyzing variability in decadal climate simulations, which has lead to a visualization system centered around the new Clustering Timeline.
This is a variant of a Sankey diagram that utilizes clustering results to communicate climatic states over time coupled with ensemble member agreement.
It can reveal
several interesting properties of the dataset, such as:
into how many inherently similar groups the ensemble can be divided at any given time,
whether the ensemble diverges in general,
whether there are different phases in the time lapse, maybe periodicity, or outliers.
The Clustering Timeline is also used to compare multiple climate simulation models and assess their performance.
The Hierarchical Clustering Timeline is an advanced version of the above.
It introduces the concept of a cluster hierarchy that may group the whole dataset down to the individual static scalar fields into clusters of various sizes and densities recording the nesting relationship between them.
One more contribution of this work in terms of visualization research is, that ways are investigated how to practically utilize a hierarchical clustering of time-dependent scalar fields to analyze the data.
To this end, a system of different views is proposed which are linked through various interaction possibilities.
The main advantage of the system is that a dataset can now be inspected at an arbitrary level of detail without having to recompute a clustering with different parameters.
Interesting branches of the simulation can be expanded to reveal smaller differences in critical clusters or folded to show only a coarse representation of the less interesting parts of the dataset.
The last building block of the suit of visual analysis methods developed for this thesis aims at a robust, (largely) automatic detection and tracking of certain features in a scalar field ensemble.
Techniques are presented that I found can identify and track super- and sub-levelsets.
And I derive “centers of action” from these sets which mark the location of extremal climate phenomena that govern the weather (e.g. Icelandic Low and Azores High).
The thesis also presents visual and quantitative techniques to evaluate the temporal change of the positions of these centers; such a displacement would be likely to manifest in changes in weather.
In a preliminary analysis with my collaborators, we indeed observed changes in the loci of the centers of action in a simulation with increased greenhouse gas concentration as compared to pre-industrial concentration levels
Representability of algebraic topology for biomolecules in machine learning based scoring and virtual screening
This work introduces a number of algebraic topology approaches, such as
multicomponent persistent homology, multi-level persistent homology and
electrostatic persistence for the representation, characterization, and
description of small molecules and biomolecular complexes. Multicomponent
persistent homology retains critical chemical and biological information during
the topological simplification of biomolecular geometric complexity.
Multi-level persistent homology enables a tailored topological description of
inter- and/or intra-molecular interactions of interest. Electrostatic
persistence incorporates partial charge information into topological
invariants. These topological methods are paired with Wasserstein distance to
characterize similarities between molecules and are further integrated with a
variety of machine learning algorithms, including k-nearest neighbors, ensemble
of trees, and deep convolutional neural networks, to manifest their descriptive
and predictive powers for chemical and biological problems. Extensive numerical
experiments involving more than 4,000 protein-ligand complexes from the PDBBind
database and near 100,000 ligands and decoys in the DUD database are performed
to test respectively the scoring power and the virtual screening power of the
proposed topological approaches. It is demonstrated that the present approaches
outperform the modern machine learning based methods in protein-ligand binding
affinity predictions and ligand-decoy discrimination
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NPAS4 recruits CCK basket cell synapses and enhances cannabinoid-sensitive inhibition in the mouse hippocampus.
Experience-dependent expression of immediate-early gene transcription factors (IEG-TFs) can transiently change the transcriptome of active neurons and initiate persistent changes in cellular function. However, the impact of IEG-TFs on circuit connectivity and function is poorly understood. We investigate the specificity with which the IEG-TF NPAS4 governs experience-dependent changes in inhibitory synaptic input onto CA1 pyramidal neurons (PNs). We show that novel sensory experience selectively enhances somatic inhibition mediated by cholecystokinin-expressing basket cells (CCKBCs) in an NPAS4-dependent manner. NPAS4 specifically increases the number of synapses made onto PNs by individual CCKBCs without altering synaptic properties. Additionally, we find that sensory experience-driven NPAS4 expression enhances depolarization-induced suppression of inhibition (DSI), a short-term form of cannabinoid-mediated plasticity expressed at CCKBC synapses. Our results indicate that CCKBC inputs are a major target of the NPAS4-dependent transcriptional program in PNs and that NPAS4 is an important regulator of plasticity mediated by endogenous cannabinoids
Multi-task deep learning for large-scale building detail extraction from high-resolution satellite imagery
Understanding urban dynamics and promoting sustainable development requires
comprehensive insights about buildings. While geospatial artificial
intelligence has advanced the extraction of such details from Earth
observational data, existing methods often suffer from computational
inefficiencies and inconsistencies when compiling unified building-related
datasets for practical applications. To bridge this gap, we introduce the
Multi-task Building Refiner (MT-BR), an adaptable neural network tailored for
simultaneous extraction of spatial and attributional building details from
high-resolution satellite imagery, exemplified by building rooftops, urban
functional types, and roof architectural types. Notably, MT-BR can be
fine-tuned to incorporate additional building details, extending its
applicability. For large-scale applications, we devise a novel spatial sampling
scheme that strategically selects limited but representative image samples.
This process optimizes both the spatial distribution of samples and the urban
environmental characteristics they contain, thus enhancing extraction
effectiveness while curtailing data preparation expenditures. We further
enhance MT-BR's predictive performance and generalization capabilities through
the integration of advanced augmentation techniques. Our quantitative results
highlight the efficacy of the proposed methods. Specifically, networks trained
with datasets curated via our sampling method demonstrate improved predictive
accuracy relative to those using alternative sampling approaches, with no
alterations to network architecture. Moreover, MT-BR consistently outperforms
other state-of-the-art methods in extracting building details across various
metrics. The real-world practicality is also demonstrated in an application
across Shanghai, generating a unified dataset that encompasses both the spatial
and attributional details of buildings
A Sliced Inverse Regression (SIR) Decoding the Forelimb Movement from Neuronal Spikes in the Rat Motor Cortex
Several neural decoding algorithms have successfully converted brain signals into commands to control a computer cursor and prosthetic devices. A majority of decoding methods, such as population vector algorithms (PVA), optimal linear estimators (OLE), and neural networks (NN), are effective in predicting movement kinematics, including movement direction, speed and trajectory but usually require a large number of neurons to achieve desirable performance. This study proposed a novel decoding algorithm even with signals obtained from a smaller numbers of neurons. We adopted sliced inverse regression (SIR) to predict forelimb movement from single-unit activities recorded in the rat primary motor (M1) cortex in a water-reward lever-pressing task. SIR performed weighted principal component analysis (PCA) to achieve effective dimension reduction for nonlinear regression. To demonstrate the decoding performance, SIR was compared to PVA, OLE, and NN. Furthermore, PCA and sequential feature selection (SFS) which are popular feature selection techniques were implemented for comparison of feature selection effectiveness. Among SIR, PVA, OLE, PCA, SFS, and NN decoding methods, the trajectories predicted by SIR (with a root mean square error, RMSE, of 8.47 ± 1.32 mm) was closer to the actual trajectories compared with those predicted by PVA (30.41 ± 11.73 mm), OLE (20.17 ± 6.43 mm), PCA (19.13 ± 0.75 mm), SFS (22.75 ± 2.01 mm), and NN (16.75 ± 2.02 mm). The superiority of SIR was most obvious when the sample size of neurons was small. We concluded that SIR sorted the input data to obtain the effective transform matrices for movement prediction, making it a robust decoding method for conditions with sparse neuronal information
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