The Adaptive Sampling Revisited

The problem of estimating the number $n$ of distinct keys of a large collection of $N$ data is well known in computer science. A classical algorithm is the adaptive sampling (AS). $n$ can be estimated by $R.2^D$, where $R$ is the final bucket (cache) size and $D$ is the final depth at the end of the process. Several new interesting questions can be asked about AS (some of them were suggested by P.Flajolet and popularized by J.Lumbroso). The distribution of $W=\log (R2^D/n)$ is known, we rederive this distribution in a simpler way. We provide new results on the moments of $D$ and $W$. We also analyze the final cache size $R$ distribution. We consider colored keys: assume that among the $n$ distinct keys, $n_C$ do have color $C$. We show how to estimate $p=\frac{n_C}{n}$. We also study colored keys with some multiplicity given by some distribution function. We want to estimate mean an variance of this distribution. Finally, we consider the case where neither colors nor multiplicities are known. There we want to estimate the related parameters. An appendix is devoted to the case where the hashing function provides bits with probability different from $1/2$

The effects of research and development expenditure on long-term stock returns: an analysis of the BRICS nations

Research and development (R&D) facilitate and drive innovation, which plays a critical role in increasing competitiveness for firms and contributing to economic growth. This study examines a sample of 970 firms from Brazil, Russia, India, China and South Africa (BRICS) between 2007-2020 who increased their R&D expenditure or had an unexpected increase in R&D expenditure from one year to the next. The Fama and French (1993) three factor and Carhart (1997) four factor models are used to assess whether these firms earned abnormal returns in the long run. The study finds that value weighted portfolios of firms that increased their R&D expenditure or experienced unexpected R&D expenditure increases exhibited long term positive abnormal returns. This suggests that investors fail to respond immediately to the good news about R&D, consistent with the phenomenon of investor underreaction, and therefore presents an opportunity for market participants to earn abnormal returns by investing in BRICS companies engaged in R&D

Master of Science

thesisMany classic and contemporary fracture models are based on some variant of strain-to-failure with linear accumulation of damage. These models are categorized as strain-to-failure models, even if the damage weighting function is stress-based. Recent experimental investigations suggest that strain-to-failure fracture models are a natural choice when modeling metals. Notably, the third stress invariant (J3) dependence of strain-to-failure has been shown to be nonnegligible. In response to the metal-fracture literature proposing a multitude of new strain-to-failure fracture models with little demonstration of predictiveness in large-scale general-loading simulations, this research implements a strain-to-failure framework into a generalized plasticity model, Kayenta, tested in conjunction with three representative fracture models: constant equivalent-strain-to-failure, Johnson-Cook strain-to-failure theory, and Xue-Wierzbicki strain-to-failure theory. These models constitute a sampling of J2, J3, strain-rate, and temperature dependence that greatly extend the softening options available in Kayenta. As Kayenta is portable and already available in multiple host codes, this research allows analysts to rapidly gauge which failure theory is best suited to their applications, thus potentially allowing one of these theories to emerge as more broadly valid in general loading problems. This fracture framework is designed to operate in the realm of time-to-failure so as to function seamlessly with the current softening implementation in Kayenta and lay the foundation for mixed-response fracture behavior to transition between ductile to brittle fracture models dynamically as the stress state evolves

Discrete Event Simulation for Decision Modeling in Health Care: Lessons from Abdominal Aortic Aneurysm Screening.

Markov models are often used to evaluate the cost-effectiveness of new healthcare interventions but they are sometimes not flexible enough to allow accurate modeling or investigation of alternative scenarios and policies. A Markov model previously demonstrated that a one-off invitation to screening for abdominal aortic aneurysm (AAA) for men aged 65 y in the UK and subsequent follow-up of identified AAAs was likely to be highly cost-effective at thresholds commonly adopted in the UK (£20,000 to £30,000 per quality adjusted life-year). However, new evidence has emerged and the decision problem has evolved to include exploration of the circumstances under which AAA screening may be cost-effective, which the Markov model is not easily able to address. A new model to handle this more complex decision problem was needed, and the case of AAA screening thus provides an illustration of the relative merits of Markov models and discrete event simulation (DES) models. An individual-level DES model was built using the R programming language to reflect possible events and pathways of individuals invited to screening v. those not invited. The model was validated against key events and cost-effectiveness, as observed in a large, randomized trial. Different screening protocol scenarios were investigated to demonstrate the flexibility of the DES. The case of AAA screening highlights the benefits of DES, particularly in the context of screening studies

Discovery of isomers in dysprosium, holmium, and erbium isotopes with N = 94 to 97

High-spin states in the 68164Er96 region were studied using 9Be + 160Gd reactions. Pulsed beam conditions were exploited for enhanced sensitivity to delayed γ-ray transitions. New isomers were identified in 161Dy, 163Ho, 162Er, and 165Er. The 162Er isom

Evaluating Bayesian stable isotope mixing models of wild animal diet and the effects of trophic discrimination factors and informative priors

Funding information University of Exeter; CONICYT, Grant/ Award Number: 3190800; ERC, Grant/ Award Number: 310820Peer reviewedPublisher PD