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"Hodgson, Matthew"
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Machine learning the derivative discontinuity of density-functional theory
Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number or the infamous derivative discontinuity at integer particle numbers. Here we propose a solution to these problems by training a neural network as the universal functional of density-functional theory that (a) depends explicitly on the number of particles with a piece-wise linearity between the integer numbers and (b) reproduces the derivative discontinuity of the exchange-correlation energy. This is achieved by using an ensemble formalism, a training set containing fractional densities, and an explicitly discontinuous formulation.
Journal Article
Systematic review of neurotrophic tropomyosin-related kinase inhibition as a tumor-agnostic management strategy
To conduct a systematic review and meta-analysis feasibility of clinical, quality of life and economic evidence for neurotrophic tropomyosin-related receptor tyrosine kinases (
) inhibitors in patients with
gene fusion-positive tumors.
Databases were searched for studies on
inhibitors in adult and pediatric patients.
27 publications reported clinical data for seven interventions. Efficacy/safety data were available for two interventions only. Four trials each reported data for larotrectinib and entrectinib with pooled analyses reporting objective response rates of 75% (95% CI: 61–85) and 57.4% (43.2–70.8), respectively. No publications reported economic or quality of life evidence.
Preliminary data demonstrate that
inhibitors are well tolerated and show impressive clinical benefit; corroboration of existing studies and real-world data are required.
Journal Article
Scientists as Regulators of Default Inference
As the bridge between science and regulation, risk assessment is an important area of study where generalisations underlying scientific evidence are employed to fulfil legal criteria found in protective statutes. Scientific generalisations in risk assessment often assume the form of presumptive default inferences that function as rules of evidentiary evaluation in the presence of uncertainty. There are no definitive criteria to determine when a body of scientific evidence effectively rebuts a protective standard or assumption of harm established by an administrative agency. In 2011, the Government of Canada established the first Board of Review under the Canadian Environmental Protection Act 1999 in response to an industry led rebuttal concerning the protective regulation of the chemical Siloxane D5. A default logic framework is used to model and visually depict the Board’s reasoning at a critical rule–evidence junction to examine how scientists can become regulators through the imposition of default inference.
Journal Article
Patient selection for anti-PD-1/PD-L1 therapy in advanced non-small-cell lung cancer: implications for clinical practice
Immune checkpoint inhibitors (ICIs) targeting PD-1 or PD-L1 represent a standard treatment option for patients with advanced non-small-cell lung cancer. However, a substantial proportion of patients will not benefit from these treatments, and robust biomarkers are required to help clinicians select patients who are most likely to benefit. Here, we discuss the available evidence on the utility of clinical characteristics in the selection of patients with advanced non-small-cell lung cancer as potential candidates for single-agent anti-PD-1/PD-L1 therapy, and provide practical guidance to clinicians on identifying those patients who are most likely to benefit. Recommendations on the use of immune checkpoint inhibitor in clinically challenging populations are also provided.
Journal Article
Improving the exchange and correlation potential in density functional approximations through constraints
We review and expand on our work to impose constraints on the effective Kohn Sham (KS) potential of local and semi-local density functional approximations. In this work, we relax a previously imposed positivity constraint, which increased the computational cost and we find that it is safe to do so, except in systems with very few electrons. The constrained minimisation leads invariably to the solution of an optimised effective potential (OEP) equation in order to determine the KS potential. We review briefly our previous work on this problem and demonstrate with numerous examples that despite well-known mathematical issues of the OEP with finite basis sets, our OEP equations are well behaved. We demonstrate that constraining the screening charge of the Hartree, exchange and correlation potential not only corrects its asymptotic behaviour but also allows the exchange and correlation potential to exhibit nonzero derivative discontinuity, a feature of the exact KS potential that is necessary for the accurate prediction of band-gaps in solids but very hard to capture with semi-local approximations.
Paper
Machine learning the derivative discontinuity of density-functional theory
Machine learning is a powerful tool to design accurate, highly non-local, exchange-correlation functionals for density functional theory. So far, most of those machine learned functionals are trained for systems with an integer number of particles. As such, they are unable to reproduce some crucial and fundamental aspects, such as the explicit dependency of the functionals on the particle number or the infamous derivative discontinuity at integer particle numbers. Here we propose a solution to these problems by training a neural network as the universal functional of density-functional theory that (i) depends explicitly on the number of particles with a piece-wise linearity between the integer numbers and (ii) reproduces the derivative discontinuity of the exchange-correlation energy. This is achieved by using an ensemble formalism, a training set containing fractional densities, and an explicitly discontinuous formulation.
Paper
Pay diversity across work teams: doubly de-motivating influences?
Purpose - The purpose of this paper is to examine the impact of pay diversity between groups, for example, across competing workplace teams.Design methodology approach - In Study I, 60 future managers from Newcastle, Australia, were paid either $1 or $2 to work on an identical intrinsically motivating task, either on an individual basis or as members of pay-diverse groups. In Study II, with 84 future managers in Darwin, Australia, the $1 $2 group pay dichotomy was made more realistic, by positioning the pay either at the bottom ($1) or top ($2) rungs of a pay ladder, or embedding it within a wider pay scale ($1 at a first, and $2 at the second tertile).Findings - In Study I, between individually paid workers, both below- and above-average payment were linked to low intrinsic motivation, whereas between groups, those in the higher pay bracket remained more motivated compared to their lower-paid group counterparts. In Study II, when pay was polarised, intrinsic motivation was higher in the higher-paid compared to lower-paid groups; but when pay was embedded, this comparative advantage dissipated.Originality value - Taken together, Studies I and II suggest that pay diversity across groups will de-motivate both lower- and higher-paid groups, except perhaps when a group tops the pay ladder.
Journal Article
Spin Relaxation in GaAs: Importance of Electron-Electron Interactions
We study spin relaxation in n-type bulk GaAs, due to the Dyakonov–Perel mechanism, using ensemble Monte Carlo methods. Our results confirm that spin relaxation time increases with the electronic density in the regime of moderate electronic concentrations and high temperature. We show that the electron-electron scattering in the non-degenerate regime significantly slows down spin relaxation. This result supports predictions by Glazov and Ivchenko. Most importantly, our findings highlight the importance of many-body interactions for spin dynamics: we show that only by properly taking into account electron-electron interactions within the simulations, results for the spin relaxation time—with respect to both electron density and temperature—will reach good quantitative agreement with corresponding experimental data. Our calculations contain no fitting parameters.
Journal Article
A spatial model with ordinal responses for grazing impact data
We propose a model for use with ordinal spatial data arising from field assessments
of the grazing and trampling impact by animals on vegetation, and study the
predictive performance of the model on partial surveys. We employ a mixed effects
model, including a term for spatial correlation, which assumes a continuous
underlying scale of grazing impact, and where the classification into discrete
categories is made via cut-points. We analyse two classes of data: full census data
and sample data drawn from the full census. In the latter case, we show that the
estimation of nonsampled data improves as the spatial information included within
the model increases.
Journal Article