5,482 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
Multidisciplinary perspectives on Artificial Intelligence and the law
This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
UMSL Bulletin 2022-2023
The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp
First order conservation law framework for large strain explicit contact dynamics
This thesis presents a novel vertex-centred finite volume algorithm for explicit large strain solid contact dynamic problems where potential contact loci are known a priori. This methodology exploits the use of a system of first order conservation equations written in terms of the linear momentum and a triplet of geometric deformation measures, consisting of the deformation gradient tensor, its co-factor and its determinant, in combination with their associated Rankine-Hugoniot jump conditions. These jump conditions are used to derive several dynamic contact models ensuring the preservation of hyperbolic characteristic structure across solution discontinuities at the contact interface, which is a significant advantage over standard quasi-static contact models where the influence of inertial effects at the contact interface is completely neglected. By taking advantage of this conservative formalism, both kinematic (velocity) and kinetic (traction) contact-impact conditions are explicitly enforced at the fluxes through the use of the appropriate jump conditions. Specifically, the kinetic contact condition was enforced, in the traditional manner, through the linear momentum equation, while the kinematic contact condition was easily enforced through the geometric conservation equations without requiring a computationally demanding iterative scheme. Additionally, a Total Variation Diminishing shock capturing technique can be suitably incorporated in order to improve dramatically the performance of the algorithm at the vicinity of shocks, importantly no ad-hoc regularisation procedure is required to accurately capture shock phenomena. Moreover, to guarantee stability from the spatial discretisation standpoint, global entropy production is demonstrated through the satisfaction of semi-discrete version of the classical Coleman-Noll procedure expressed in terms of the time rate of the Hamiltonian energy of the system. Finally, a series of numerical examples is presented in order to assess the performance and applicability of the proposed algorithm suitably implemented across MATLAB and a purpose built OpenFOAM solver
Understanding and controlling structural distortions underlying superconductivity in lanthanum cuprates
The suppression of superconductivity in layered lanthanum cuprates near x = 1/8 coincides with a structural phase transition from a low-temperature orthorhombic to a low-temperature tetragonal phase. The low-temperature phases are characterised by a static tilt of the CuO6 octahedra away from the layering axis in distinct directions. It remained an open question whether the orthorhombic-to-tetragonal phase transition would only occur in the context of competing electronic orders in the lanthanum cuprates.
This thesis proposes a novel approach to studying the orthorhombic-to-tetragonal phase transition using the novel La2MgO4 system. La2MgO4 adopts the layered Ruddlesden-Pepper structure of the lanthanum cuprates but lacks the strong electron correlations and octahedral distortions associated with the Jahn-Teller active Cu site. Combining first-principles simula- tions using density-functional theory with experimental data on the novel La2MgO4 system, the context in which these structural phases can occur is detailed, outlining the key param- eters determining the stability of the phase which suppresses bulk superconductivity. The same sequence of structural phase transitions occurs in La2MgO4 as in La1.875Ba0.125CuO4, and the tetragonal phase is stabilised via steric effects beyond a critical octahedral tilt magnitude. Larger Jahn-Teller distortions favour the orthorhombic phase.
The effect of isotropic and anisotropic pressure on La2MgO4 and La2CuO4 is explored. These form the basis for a structural mechanism to understand the experimental trends of the bulk superconducting transition temperature under uniaxial pressure. Finally, the justification for the methodology used throughout this thesis to simulate these systems is provided, highlighting that DFT+U accurately describes their atomic and electronic structure.Open Acces
Variational Bonded Discrete Element Method with Manifold Optimization
This paper proposes a novel approach that combines variational integration
with the bonded discrete element method (BDEM) to achieve faster and more
accurate fracture simulations. The approach leverages the efficiency of
implicit integration and the accuracy of BDEM in modeling fracture phenomena.
We introduce a variational integrator and a manifold optimization approach
utilizing a nullspace operator to speed up the solving of
quaternion-constrained systems. Additionally, the paper presents an element
packing and surface reconstruction method specifically designed for bonded
discrete element methods. Results from the experiments prove that the proposed
method offers 2.8 to 12 times faster state-of-the-art methods
Automated Distinct Bone Segmentation from Computed Tomography Images using Deep Learning
Large-scale CT scans are frequently performed for forensic and diagnostic purposes, to plan and
direct surgical procedures, and to track the development of bone-related diseases. This often
involves radiologists who have to annotate bones manually or in a semi-automatic way, which is
a time consuming task. Their annotation workload can be reduced by automated segmentation
and detection of individual bones. This automation of distinct bone segmentation not only has
the potential to accelerate current workflows but also opens up new possibilities for processing
and presenting medical data for planning, navigation, and education.
In this thesis, we explored the use of deep learning for automating the segmentation of all
individual bones within an upper-body CT scan. To do so, we had to find a network architec-
ture that provides a good trade-off between the problem’s high computational demands and the
results’ accuracy. After finding a baseline method and having enlarged the dataset, we set out
to eliminate the most prevalent types of error. To do so, we introduced an novel method called
binary-prediction-enhanced multi-class (BEM) inference, separating the task into two: Distin-
guishing bone from non-bone is conducted separately from identifying the individual bones.
Both predictions are then merged, which leads to superior results. Another type of error is tack-
led by our developed architecture, the Sneaky-Net, which receives additional inputs with larger
fields of view but at a smaller resolution. We can thus sneak more extensive areas of the input
into the network while keeping the growth of additional pixels in check.
Overall, we present a deep-learning-based method that reliably segments most of the over
one hundred distinct bones present in upper-body CT scans in an end-to-end trained matter
quickly enough to be used in interactive software. Our algorithm has been included in our
groups virtual reality medical image visualisation software SpectoVR with the plan to be used
as one of the puzzle piece in surgical planning and navigation, as well as in the education of
future doctors
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