1,426 research outputs found
Unmerited Inheritance: An Exposition of the Twelve Divine Elements, Fulfillment, and Typology of Yahweh’s Tithe to Levi
This dissertation seeks to advance the academy’s conversation about the Mosaic tithe ordinance by providing the first published reconciliation of what many scholars consider irreconcilable statutes. It does so by providing the first published exposition of the twelve divine elements of Yahweh’s sacred tithe against the land sabbatical and Jubilee statutes. Unless scholars can agree upon its divine elements, there is little hope for unity and progress towards edifying the saints with the typology of Yahweh’s inheritance tithe to Levi (Num 18:26)
Dielectric and Spin Susceptibilities using Density Functional Theory
The response of a system to some external perturbation is almost ubiquitous in Physics. The application of perturbation theory through an electronic structure method such as Density Functional Theory has had significant contributions over the last few decades. Its implementation, aptly named Density Functional Perturbation Theory has seen use in a number of ab initio calculations on a variety of physical properties of materials which depend on their lattice-dynamical behaviour. Specific heats, thermal expansion, infrared, Raman and optical spectra are to name just a few. Understanding the complex phenomena has significantly corroborated the current understanding of the quantum picture of solids. The Sternheimer scheme falls under the umbrella of methods to compute response functions in Time-Dependent Density Functional Theory. Initially developed to study the electronic polarisability, it is now commonly utilised in the field of lattice dynamics to study phonons and related crystal properties. The Sternheimer equation has also been used to model spin wave excitations by computation of the magnetic susceptibility. The poles of the susceptibility are known to correspond to magnon excitations and these computations have been corroborated by experimental inelastic neutron scattering data. These excitations are of a transverse nature, in that they involve fluctuations of the magnetisation perpendicular to a chosen z axis. The lesser-known longitudinal excitations involve fluctuations of the magnetisation along z, an investigation of collective modes present in transition metals may be carried out from self-consistent computations of the Sternheimer equation. The dielectric response is an important linear response function in solid-state physics. Its computation from first principles provides an invaluable tool in the characterisation of optical properties and can be compared to the experimental method of spectroscopic ellipsometry.The work in this thesis concerns the implementation of the Sternheimer method in computing the dynamical response from either an external plane wave or spin-polarised perturbation. These response functions are the dielectric and spin (magnetisation) susceptibilities respectively.The scheme to compute the frequency-dependent dielectric response is implemented in a plane-wave pseudopotential DFT package. Calculations are performed on the semiconducting systems of Silicon, Gallium Arsenide, Zinc Oxide and perovskite Methylammonium Lead Triiodide. The overall shape of the dielectric spectra is in good agreement with spectroscopic ellipsometry data, however, there is a shift which is attributed to the limitations of DFT.The scheme developed to compute longitudinal spin dynamics is applied to the transitionmetal systems of body-centred cubic Iron and face-centred cubic Nickel. In a similar manner to another first principles approach, a single dominant peak is shown to be present in the magnetisation channel with the charge dynamics being effectively null in comparison. However, the exact position of these peaks is not in agreement with the other approach, a discussion is made regarding difficulties pertaining to self-consistent optimisation
FineMorphs: Affine-diffeomorphic sequences for regression
A multivariate regression model of affine and diffeomorphic transformation
sequences - FineMorphs - is presented. Leveraging concepts from shape analysis,
model states are optimally "reshaped" by diffeomorphisms generated by smooth
vector fields during learning. Affine transformations and vector fields are
optimized within an optimal control setting, and the model can naturally reduce
(or increase) dimensionality and adapt to large datasets via suboptimal vector
fields. An existence proof of solution and necessary conditions for optimality
for the model are derived. Experimental results on real datasets from the UCI
repository are presented, with favorable results in comparison with
state-of-the-art in the literature and densely-connected neural networks in
TensorFlow.Comment: 39 pages, 7 figure
Child–robot interactions using educational robots: an ethical and inclusive perspective
This research was funded by the Spanish Ministerio de Ciencia e Innovación under Grant FECYT FCT-20-15626, Line of action 2. Education and scientific vocations (2nd place out of 120 awarded).Peer ReviewedObjectius de Desenvolupament Sostenible::5 - Igualtat de GènereObjectius de Desenvolupament Sostenible::9 - Indústria, Innovació i InfraestructuraObjectius de Desenvolupament Sostenible::4 - Educació de QualitatObjectius de Desenvolupament Sostenible::10 - Reducció de les DesigualtatsPostprint (published version
StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
Most existing chain-of-thought (CoT) prompting methods suffer from the issues
of generalizability and consistency, as they often rely on instance-specific
solutions that may not be applicable to other cases and lack task-level
consistency in their reasoning steps. To address these limitations, we propose
a comprehensive framework, StrategyLLM, harnessing the capabilities of LLMs to
tackle various tasks. The framework improves generalizability by formulating
general problem-solving strategies and enhances consistency by producing
consistent solutions using these strategies. StrategyLLM employs four LLM-based
agents: strategy generator, executor, optimizer, and evaluator, working
together to generate, evaluate, and select promising strategies for a given
task automatically. The experimental results demonstrate that StrategyLLM
outperforms the competitive baseline CoT-SC that requires human-annotated
solutions on 13 datasets across 4 challenging tasks without human involvement,
including math reasoning (39.2% 43.3%), commonsense reasoning
(70.3% 72.5%), algorithmic reasoning (51.7% 62.0%),
and symbolic reasoning (30.0% 79.2%)
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of
discoveries in natural sciences. Today, AI has started to advance natural
sciences by improving, accelerating, and enabling our understanding of natural
phenomena at a wide range of spatial and temporal scales, giving rise to a new
area of research known as AI for science (AI4Science). Being an emerging
research paradigm, AI4Science is unique in that it is an enormous and highly
interdisciplinary area. Thus, a unified and technical treatment of this field
is needed yet challenging. This work aims to provide a technically thorough
account of a subarea of AI4Science; namely, AI for quantum, atomistic, and
continuum systems. These areas aim at understanding the physical world from the
subatomic (wavefunctions and electron density), atomic (molecules, proteins,
materials, and interactions), to macro (fluids, climate, and subsurface) scales
and form an important subarea of AI4Science. A unique advantage of focusing on
these areas is that they largely share a common set of challenges, thereby
allowing a unified and foundational treatment. A key common challenge is how to
capture physics first principles, especially symmetries, in natural systems by
deep learning methods. We provide an in-depth yet intuitive account of
techniques to achieve equivariance to symmetry transformations. We also discuss
other common technical challenges, including explainability,
out-of-distribution generalization, knowledge transfer with foundation and
large language models, and uncertainty quantification. To facilitate learning
and education, we provide categorized lists of resources that we found to be
useful. We strive to be thorough and unified and hope this initial effort may
trigger more community interests and efforts to further advance AI4Science
LIPIcs, Volume 274, ESA 2023, Complete Volume
LIPIcs, Volume 274, ESA 2023, Complete Volum
Antibody responses after influenza and SARS-CoV-2 vaccination and infection: Lessons across the ages
Influensa A og SARS-CoV-2 er RNA-baserte luftvegsvirus som forårsakar pandemiar og muterer raskt for å oppretthalde ein kontinuerleg sirkulasjon. Eit komplekst samspel mellom immunresponsar hjå verten og viruset er forma gjennom etablerte minne frå den første infeksjonen, tidlegare vaksinasjonar og alder. Dette doktorgradsarbeidet karakteriserer antistoff-responsar mot influensa A/H3N2 og SARS-CoV-2 i ulike aldersgrupper etter infeksjon og vaksinasjon.
Den første influensainfeksjonen, i tillegg til summen av eksponeringar gjennom livet, påverkar framtidige immunresponsar. Vi fann kryssreaktive antistoff-responsar hjå vaksne og barn mot A/H3N2-virus tilbake til fødselsåret deira. Sjølv om antistoff mot dei nyaste virusa dominerte landskapet, kunne antistoff også kryssreagere mot framtidige, epidemiske virus.
Ein laboratoriebasert og hurtig hemagglutinasjonstest (HAT) kan forenkle studiar om immunitet mot SARS-CoV-2 og brukast til å måle surrogat-nøytraliserande antistoff. mRNA-vaksiner var mindre immunogene hjå eldre, basert på lågare kryss-reaktivitet mot nye virusvariantar, med mindre dei hadde vore infiserte tidlegare. Vi fann at eldre trengte to vaksinedosar for å produsere tilsvarande HAT-antistoff mengder samanlikna med yngre vaksne med éin vaksinasjon eller tidlegare infiserte personar.
Det er få studiar på vedvarande SARS-CoV-2-symptom, også kjent som post COVID-19-tilstand, blant barn og ungdom, spesielt etter delta- og omikron-infeksjon. Faktorar assosiert med vedvarande symptom i aldersgruppa 10-20 åringar var akutte symptom, høgare alder, høgare antistoff-titer mot piggeproteinet og kvinneleg kjønn. I same kohort fann vi høgare antistoff og færre omikron BA.1/2-reinfeksjonar hjå COVID-19-vaksinerte samanlikna med uvaksinerte. Vaksineeffekten var derimot kortvarig, 22 dagar, trass i hybridimmunitet.
Desse funna understrekar nytten av raske og enkle analysar for å evaluere infeksjon- og vaksinasjonsresponsar. Det er behov for forbetra vaksineeffekt for å redusere byrden av COVID-19 og vedvarande symptom hjå unge menneske. Samla sett kan kryssreaktive antistoff vere gunstige i møte med nye luftvegsvirus.Influenza A and SARS-CoV-2 are respiratory RNA viruses which cause pandemics, and rapidly mutate ensuring their continuous circulation. There is a complex interplay between the host immune responses and the virus, influenced by prior memory from the initial infection, vaccination, and age. This thesis characterises antibody responses to influenza A/H3N2 and SARS-CoV-2 in different age groups after infection and vaccination.
The priming influenza infection, as well as the summary of life-time exposures, is known to affect subsequent immune responses. We found cross-reactive antibody responses in adults and children against A/H3N2 viruses back to their year of birth. Although antibodies to the most recent viruses dominated the antibody landscapes, antibodies also cross-reacted against future epidemic viruses.
Studies of SARS-CoV-2 immunity can be simplified by the use of a rapid, laboratory-based hemagglutination test (HAT) to measure surrogate neutralising antibodies. mRNA vaccines were less immunogenic in the elderly with lower cross-reactivity to new variants, unless they had been previously infected. We found that the elderly required two vaccine doses, to produce HAT antibodies comparable to after one vaccination in younger adults or previously infected subjects.
Long-term SARS-CoV-2 symptoms, known as post COVID-19 condition, are understudied in children and adolescents, especially after infection with the delta and omicron variants. In 10-20 year olds, we identified acute symptoms, older age, higher spike-specific antibody titres and female sex as factors associated with persisting symptoms. In the same cohort, we found higher antibodies and fewer omicron BA.1/2 reinfections in COVID-19 vaccinees than unvaccinated. However, vaccine effectiveness had a short duration of 22 days, despite hybrid immunity.
Our findings highlight the utility of rapid and simple assays for evaluation of infection and vaccination responses. There is a need for improved vaccine effectiveness to reduce the burden of COVID-19 and long-term symptoms in young people. Overall, cross-reactive antibodies can be favourable in the face of emerging respiratory viruses.Doktorgradsavhandlin
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