11,958 research outputs found
UMSL Bulletin 2023-2024
The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp
Spatial adaptive settlement systems in archaeology. Modelling long-term settlement formation from spatial micro interactions
Despite research history spanning more than a century, settlement patterns still hold a promise to contribute to the theories of large-scale processes in human history. Mostly they have been presented as passive imprints of past human activities and spatial interactions they shape have not been studied as the driving force of historical processes. While archaeological knowledge has been used to construct geographical theories of evolution of settlement there still exist gaps in this knowledge. Currently no theoretical framework has been adopted to explore them as spatial systems emerging from micro-choices of small population units.
The goal of this thesis is to propose a conceptual model of adaptive settlement systems based on complex adaptive systems framework. The model frames settlement system formation processes as an adaptive system containing spatial features, information flows, decision making population units (agents) and forming cross scale feedback loops between location choices of individuals and space modified by their aggregated choices. The goal of the model is to find new ways of interpretation of archaeological locational data as well as closer theoretical integration of micro-level choices and meso-level settlement structures.
The thesis is divided into five chapters, the first chapter is dedicated to conceptualisation of the general model based on existing literature and shows that settlement systems are inherently complex adaptive systems and therefore require tools of complexity science for causal explanations. The following chapters explore both empirical and theoretical simulated settlement patterns based dedicated to studying selected information flows and feedbacks in the context of the whole system.
Second and third chapters explore the case study of the Stone Age settlement in Estonia comparing residential location choice principles of different periods. In chapter 2 the relation between environmental conditions and residential choice is explored statistically. The results confirm that the relation is significant but varies between different archaeological phenomena. In the third chapter hunter-fisher-gatherer and early agrarian Corded Ware settlement systems were compared spatially using inductive models. The results indicated a large difference in their perception of landscape regarding suitability for habitation. It led to conclusions that early agrarian land use significantly extended land use potential and provided a competitive spatial benefit. In addition to spatial differences, model performance was compared and the difference was discussed in the context of proposed adaptive settlement system model. Last two chapters present theoretical agent-based simulation experiments intended to study effects discussed in relation to environmental model performance and environmental determinism in general. In the fourth chapter the central place foragingmodel was embedded in the proposed model and resource depletion, as an environmental modification mechanism, was explored. The study excluded the possibility that mobility itself would lead to modelling effects discussed in the previous chapter.
The purpose of the last chapter is the disentanglement of the complex relations between social versus human-environment interactions. The study exposed non-linear spatial effects expected population density can have on the system and the general robustness of environmental inductive models in archaeology to randomness and social effect. The model indicates that social interactions between individuals lead to formation of a group agency which is determined by the environment even if individual cognitions consider the environment insignificant. It also indicates that spatial configuration of the environment has a certain influence towards population clustering therefore providing a potential pathway to population aggregation. Those empirical and theoretical results showed the new insights provided by the complex adaptive systems framework. Some of the results, including the explanation of empirical results, required the conceptual model to provide a framework of interpretation
Machine learning in solar physics
The application of machine learning in solar physics has the potential to
greatly enhance our understanding of the complex processes that take place in
the atmosphere of the Sun. By using techniques such as deep learning, we are
now in the position to analyze large amounts of data from solar observations
and identify patterns and trends that may not have been apparent using
traditional methods. This can help us improve our understanding of explosive
events like solar flares, which can have a strong effect on the Earth
environment. Predicting hazardous events on Earth becomes crucial for our
technological society. Machine learning can also improve our understanding of
the inner workings of the sun itself by allowing us to go deeper into the data
and to propose more complex models to explain them. Additionally, the use of
machine learning can help to automate the analysis of solar data, reducing the
need for manual labor and increasing the efficiency of research in this field.Comment: 100 pages, 13 figures, 286 references, accepted for publication as a
Living Review in Solar Physics (LRSP
Layer-wise Adaptive Step-Sizes for Stochastic First-Order Methods for Deep Learning
We propose a new per-layer adaptive step-size procedure for stochastic
first-order optimization methods for minimizing empirical loss functions in
deep learning, eliminating the need for the user to tune the learning rate
(LR). The proposed approach exploits the layer-wise stochastic curvature
information contained in the diagonal blocks of the Hessian in deep neural
networks (DNNs) to compute adaptive step-sizes (i.e., LRs) for each layer. The
method has memory requirements that are comparable to those of first-order
methods, while its per-iteration time complexity is only increased by an amount
that is roughly equivalent to an additional gradient computation. Numerical
experiments show that SGD with momentum and AdamW combined with the proposed
per-layer step-sizes are able to choose effective LR schedules and outperform
fine-tuned LR versions of these methods as well as popular first-order and
second-order algorithms for training DNNs on Autoencoder, Convolutional Neural
Network (CNN) and Graph Convolutional Network (GCN) models. Finally, it is
proved that an idealized version of SGD with the layer-wise step sizes
converges linearly when using full-batch gradients
Minimax rates of convergence for nonparametric location-scale models
This paper studies minimax rates of convergence for nonparametric
location-scale models, which include mean, quantile and expectile regression
settings. Under Hellinger differentiability on the error distribution and other
mild conditions, we show that the minimax rate of convergence for estimating
the regression function under the squared loss is determined by the
metric entropy of the nonparametric function class. Different error
distributions, including asymmetric Laplace distribution, asymmetric connected
double truncated gamma distribution, connected normal-Laplace distribution,
Cauchy distribution and asymmetric normal distribution are studied as examples.
Applications on low order interaction models and multiple index models are also
given
Equivariance with Learned Canonicalization Functions
Symmetry-based neural networks often constrain the architecture in order to
achieve invariance or equivariance to a group of transformations. In this
paper, we propose an alternative that avoids this architectural constraint by
learning to produce canonical representations of the data. These
canonicalization functions can readily be plugged into non-equivariant backbone
architectures. We offer explicit ways to implement them for some groups of
interest. We show that this approach enjoys universality while providing
interpretable insights. Our main hypothesis, supported by our empirical
results, is that learning a small neural network to perform canonicalization is
better than using predefined heuristics. Our experiments show that learning the
canonicalization function is competitive with existing techniques for learning
equivariant functions across many tasks, including image classification,
-body dynamics prediction, point cloud classification and part segmentation,
while being faster across the board.Comment: 21 pages, 5 figure
Adaptive Certified Training: Towards Better Accuracy-Robustness Tradeoffs
As deep learning models continue to advance and are increasingly utilized in
real-world systems, the issue of robustness remains a major challenge. Existing
certified training methods produce models that achieve high provable robustness
guarantees at certain perturbation levels. However, the main problem of such
models is a dramatically low standard accuracy, i.e. accuracy on clean
unperturbed data, that makes them impractical. In this work, we consider a more
realistic perspective of maximizing the robustness of a model at certain levels
of (high) standard accuracy. To this end, we propose a novel certified training
method based on a key insight that training with adaptive certified radii helps
to improve both the accuracy and robustness of the model, advancing
state-of-the-art accuracy-robustness tradeoffs. We demonstrate the
effectiveness of the proposed method on MNIST, CIFAR-10, and TinyImageNet
datasets. Particularly, on CIFAR-10 and TinyImageNet, our method yields models
with up to two times higher robustness, measured as an average certified radius
of a test set, at the same levels of standard accuracy compared to baseline
approaches.Comment: Presented at ICML 2023 workshop "New Frontiers in Adversarial Machine
Learning
The instabilities of large learning rate training: a loss landscape view
Modern neural networks are undeniably successful. Numerous works study how
the curvature of loss landscapes can affect the quality of solutions. In this
work we study the loss landscape by considering the Hessian matrix during
network training with large learning rates - an attractive regime that is
(in)famously unstable. We characterise the instabilities of gradient descent,
and we observe the striking phenomena of \textit{landscape flattening} and
\textit{landscape shift}, both of which are intimately connected to the
instabilities of training.Comment: arXiv admin note: text overlap with arXiv:2305.1849
Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives
Deep learning has demonstrated remarkable performance across various tasks in
medical imaging. However, these approaches primarily focus on supervised
learning, assuming that the training and testing data are drawn from the same
distribution. Unfortunately, this assumption may not always hold true in
practice. To address these issues, unsupervised domain adaptation (UDA)
techniques have been developed to transfer knowledge from a labeled domain to a
related but unlabeled domain. In recent years, significant advancements have
been made in UDA, resulting in a wide range of methodologies, including feature
alignment, image translation, self-supervision, and disentangled representation
methods, among others. In this paper, we provide a comprehensive literature
review of recent deep UDA approaches in medical imaging from a technical
perspective. Specifically, we categorize current UDA research in medical
imaging into six groups and further divide them into finer subcategories based
on the different tasks they perform. We also discuss the respective datasets
used in the studies to assess the divergence between the different domains.
Finally, we discuss emerging areas and provide insights and discussions on
future research directions to conclude this survey.Comment: Under Revie
Unconventional Cognitive Intelligent Robotic Control: Quantum Soft Computing Approach in Human Being Emotion Estimation -- QCOptKB Toolkit Application
Strategy of intelligent cognitive control systems based on quantum and soft
computing presented. Quantum self-organization knowledge base synergetic effect
extracted from intelligent fuzzy controllers imperfect knowledge bases
described. That technology improved of robustness of intelligent cognitive
control systems in hazard control situations described with the cognitive
neuro-interface and different types of robot cooperation. Examples demonstrated
the introduction of quantum fuzzy inference gate design as prepared
programmable algorithmic solution for board embedded control systems. The
possibility of neuro-interface application based on cognitive helmet with
quantum fuzzy controller for driving of the vehicle is shown
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