599 research outputs found

    BlackBIRDS: Black-Box Inference foR Differentiable Simulators

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    BlackBIRDS is a Python package consisting of generically applicable, black-box inference methods for differentiable simulation models. It facilitates both (a) the differentiable implementation of simulation models by providing a common object-oriented framework for their implementation in PyTorch (Paszke et al., 2019), and (b) the use of a variety of gradient-assisted inference procedures for these simulation models, allowing researchers to easily exploit the differentiable nature of their simulator in parameter estimation tasks. The package consists of both Bayesian and non-Bayesian inference methods, and relies on well-supported software libraries (e.g., normflows, Stimper et al., 2023) to provide this broad functionality

    Gradient-assisted calibration for financial agent-based models

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    Agent-based modelling (ABMing) is a promising approach to modelling and reasoning about complex systems such as financial markets. However, the application of ABMs in practice is often impeded by the models’ complexity and the ensuing difficulty of performing parameter inference and optimisation tasks. This in turn has motivated efforts directed towards the construction of differentiable ABMs, enabled by recently developed effective auto-differentiation frameworks, as a strategy for addressing these challenges. In this paper, we discuss and present experiments that demonstrate how differentiable programming may be used to implement and calibrate heterogeneous ABMs in finance. We begin by considering in more detail the difficulties inherent in constructing gradients for discrete ABMs. Secondly, we illustrate solutions to these difficulties, by using a discrete agent-based market simulation model as a case study. Finally, we show through numerical experiments how our differentiable implementation of this discrete ABM enables the use of powerful tools from probabilistic machine learning and conditional generative modelling to perform robust parameter inferences and uncertainty quantification, in a simulation-efficient manner

    Supply driven mortgage choice

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    Variable mortgage contracts dominate the UK mortgage market (Miles, 2004). The dominance of the variable rate mortgage contracts has important consequences for the transmission mechanism of monetary policy decisions and systemic risks (Khandani et al., 2012; Fuster and Vickery, 2013). This raises an obvious concern that a mortgage market such as that in the UK, where the major proportion of mortgage debt is either at a variable or fixed for less than two years rate (Badarinza, et al., 2013; CML, 2012), is vulnerable to alterations in the interest rate regime. Theoretically, mortgage choice is determined by demand and supply factors. So far, most of the existing literature has focused on the demand side perspective, and what is limited is consideration of supply side factors in empirical investigation on mortgage choice decisions. This paper uniquely explores whether supply side factors may partially explain observed/ex-post mortgage type decisions. Empirical results detect that lenders’ profit motives and mortgage funding/pricing issues may have assisted in preferences toward variable rate contracts. Securitisation is found to positively impact upon gross mortgage lending volumes while negatively impacting upon the share of variable lending flows. This shows that an increase in securitisation not only improves liquidity in the supply of mortgage funds, but also has the potential to shift mortgage choices toward fixed mortgage debt. The policy implications may involve a number of measures, including reconsideration of the capital requirements for the fixed, as opposed to the variable rate mortgage debt, growing securitisation and optimisation of the mortgage pricing policies

    Heterogeneous N2O5 Uptake During Winter: Aircraft Measurements During the 2015 WINTER Campaign and Critical Evaluation of Current Parameterizations

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    Nocturnal dinitrogen pentoxide (N2O5) heterogeneous chemistry impacts regional air quality and the distribution and lifetime of tropospheric oxidants. Formed from the oxidation of nitrogen oxides, N2O5 is heterogeneously lost to aerosol with a highly variable reaction probability, γ(N2O5), dependent on aerosol composition and ambient conditions. Reaction products include soluble nitrate (HNO3 or NO3−) and nitryl chloride (ClNO2). We report the first‐ever derivations of γ(N2O5) from ambient wintertime aircraft measurements in the critically important nocturnal residual boundary layer. Box modeling of the 2015 Wintertime INvestigation of Transport, Emissions, and Reactivity (WINTER) campaign over the eastern United States derived 2,876 individual γ(N2O5) values with a median value of 0.0143 and range of 2 × 10−5 to 0.1751. WINTER γ(N2O5) values exhibited the strongest correlation with aerosol water content, but weak correlations with other variables, such as aerosol nitrate and organics, suggesting a complex, nonlinear dependence on multiple factors, or an additional dependence on a nonobserved factor. This factor may be related to aerosol phase, morphology (i.e., core shell), or mixing state, none of which are commonly measured during aircraft field studies. Despite general agreement with previous laboratory observations, comparison of WINTER data with 14 literature parameterizations (used to predict γ(N2O5) in chemical transport models) confirms that none of the current methods reproduce the full range of γ(N2O5) values. Nine reproduce the WINTER median within a factor of 2. Presented here is the first field‐based, empirical parameterization of γ(N2O5), fit to WINTER data, based on the functional form of previous parameterizations

    Who should be prioritized for renal transplantation?: Analysis of key stakeholder preferences using discrete choice experiments

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    Background Policies for allocating deceased donor kidneys have recently shifted from allocation based on Human Leucocyte Antigen (HLA) tissue matching in the UK and USA. Newer allocation algorithms incorporate waiting time as a primary factor, and in the UK, young adults are also favoured. However, there is little contemporary UK research on the views of stakeholders in the transplant process to inform future allocation policy. This research project aimed to address this issue. Methods Discrete Choice Experiment (DCE) questionnaires were used to establish priorities for kidney transplantation among different stakeholder groups in the UK. Questionnaires were targeted at patients, carers, donors / relatives of deceased donors, and healthcare professionals. Attributes considered included: waiting time; donor-recipient HLA match; whether a recipient had dependents; diseases affecting life expectancy; and diseases affecting quality of life. Results Responses were obtained from 908 patients (including 98 ethnic minorities); 41 carers; 48 donors / relatives of deceased donors; and 113 healthcare professionals. The patient group demonstrated statistically different preferences for every attribute (i.e. significantly different from zero) so implying that changes in given attributes affected preferences, except when prioritizing those with no rather than moderate diseases affecting quality of life. The attributes valued highly related to waiting time, tissue match, prioritizing those with dependents, and prioritizing those with moderate rather than severe diseases affecting life expectancy. Some preferences differed between healthcare professionals and patients, and ethnic minority and non-ethnic minority patients. Only non-ethnic minority patients and healthcare professionals clearly prioritized those with better tissue matches. Conclusions Our econometric results are broadly supportive of the 2006 shift in UK transplant policy which emphasized prioritizing the young and long waiters. However, our findings suggest the need for a further review in the light of observed differences in preferences amongst ethnic minorities, and also because those with dependents may be a further priority.</p

    Structural mechanism underpinning cross-reactivity of a CD8(+) T-cell clone that recognizes a peptide derived from human telomerase reverse transcriptase

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    T-cell cross-reactivity is essential for effective immune surveillance but has also been implicated as a pathway to autoimmunity. Previous studies have demonstrated that T-cell receptors (TCRs) that focus on a minimal motif within the peptide are able to facilitate a high level of T-cell cross-reactivity. However, the structural database shows that most TCRs exhibit less focused antigen binding involving contact with more peptide residues. To further explore the structural features that allow the clonally expressed TCR to functionally engage with multiple peptide-major histocompatibility complexes (pMHCs), we examined the ILA1 CD8(+) T-cell clone that responds to a peptide sequence derived from human telomerase reverse transcriptase. The ILA1 TCR contacted its pMHC with a broad peptide binding footprint encompassing spatially distant peptide residues. Despite the lack of focused TCR-peptide binding, the ILA1 T-cell clone was still cross-reactive. Overall, the TCR-peptide contacts apparent in the structure correlated well with the level of degeneracy at different peptide positions. Thus, the ILA1 TCR was less tolerant of changes at peptide residues that were at, or adjacent to, key contact sites. This study provides new insights into the molecular mechanisms that control T-cell cross-reactivity with important implications for pathogen surveillance, autoimmunity, and transplant rejection

    Self-productivity and complementarities in human development : evidence from MARS

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    This paper investigates the role of self-productivity and home resources in capability formation from infancy to adolescence. In addition, we study the complementarities between basic cognitive, motor and noncognitive abilities and social as well as academic achievement. Our data are taken from the Mannheim Study of Children at Risk (MARS), an epidemiological cohort study following the long-term outcome of early risk factors. Results indicate that initial risk conditions cumulate and that differences in basic abilities increase during development. Self-productivity rises in the developmental process and complementarities are evident. Noncognitive abilities promote cognitive abilities and social achievement. There is remarkable stability in the distribution of the economic and socio-emotional home resources during the early life cycle. This is presumably a major reason for the evolution of inequality in human development
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