Estimation of a probability in inverse binomial sampling under normalized linear-linear and inverse-linear loss

Sequential estimation of the success probability $p$ in inverse binomial sampling is considered in this paper. For any estimator $\hat p$, its quality is measured by the risk associated with normalized loss functions of linear-linear or inverse-linear form. These functions are possibly asymmetric, with arbitrary slope parameters $a$ and $b$ for $\hat pp$ respectively. Interest in these functions is motivated by their significance and potential uses, which are briefly discussed. Estimators are given for which the risk has an asymptotic value as $p$ tends to $0$, and which guarantee that, for any $p$ in $(0,1)$, the risk is lower than its asymptotic value. This allows selecting the required number of successes, $r$, to meet a prescribed quality irrespective of the unknown $p$. In addition, the proposed estimators are shown to be approximately minimax when $a/b$ does not deviate too much from $1$, and asymptotically minimax as $r$ tends to infinity when $a=b$.Comment: 4 figure

Recent results from systematic parameterizations of Ginsparg-Wilson fermions

The Fixed Point Dirac Operator and Chirally Improved Fermions both use large numbers of gauge paths and the full Dirac structure to approximate a solution of the Ginsparg-Wilson equation. After a brief review of the two approaches we present recent results for quenched QCD with pion masses down to 210 MeV. We discuss the limits and advantages of approximate parameterizations and outline future perspectives.Comment: Lattice2002(plenary). References and Fig. 5 updated. Final version submitted to the proceeding

Cross-sectional and longitudinal associations between parents\u27 and preschoolers\u27 physical activity and TV viewing: the HAPPY Study

Background: Parental modelling has been shown to be important for school-aged children’s physical activity (PA) and television (TV) viewing, yet little is known about its impact for younger children. This study examined cross-sectional and three-year longitudinal associations between PA and TV viewing behaviours of parents and their preschool children. Method: In 2008-9 (T1), parents in the HAPPY cohort study (n=450) in Melbourne, Australia self-reported their weekly PA and TV viewing, and proxy-reported their partner’s PA and TV viewing, and their 3-5 year-old preschool child’s TV viewing. Children’s PA was assessed via accelerometers. Repeat data collection occurred in 2011-12 (T2). Results: Mothers’ and fathers’ PA were associated with PA among preschool girls at T1, but not boys. Parents’ TV viewing times were significant correlates of girls’ and boys’ TV viewing at T1. Longitudinally, mothers’ PA at baseline predicted boys’ PA at T2, while sex-specific associations were found for TV viewing, with mothers’ and fathers’ TV viewing at T1 associated with girls’ and boys’ TV viewing respectively at T2. Conclusions: The PA and TV viewing of both parents are significantly associated with these behaviours in preschool children. The influence of the sex-matched parent appears to be important longitudinally for children’s TV viewing&period

Quenched QCD with fixed-point and chirally improved fermion

In this contribution we present results from quenched QCD simulations with the parameterized fixed-point (FP) and the chirally improved (CI) Dirac operator. Both these operators are approximate solutions of the Ginsparg-Wilson equation and have good chiral properties. We focus our discussion on observables sensitive to chirality. In particular we explore pion masses down to 210 MeV in light hadron spectroscopy, quenched chiral logs, the pion decay constant and the pion scattering length. We discuss finite volume effects, scaling properties of the FP and CI operators and performance issues in their numerical implementation.Comment: Lattice2002(chiral), 17 pages, 21 figures, (LaTeX style file espcrc2.sty and AMS style files

A weak characterization of slow variables in stochastic dynamical systems

We present a novel characterization of slow variables for continuous Markov processes that provably preserve the slow timescales. These slow variables are known as reaction coordinates in molecular dynamical applications, where they play a key role in system analysis and coarse graining. The defining characteristics of these slow variables is that they parametrize a so-called transition manifold, a low-dimensional manifold in a certain density function space that emerges with progressive equilibration of the system's fast variables. The existence of said manifold was previously predicted for certain classes of metastable and slow-fast systems. However, in the original work, the existence of the manifold hinges on the pointwise convergence of the system's transition density functions towards it. We show in this work that a convergence in average with respect to the system's stationary measure is sufficient to yield reaction coordinates with the same key qualities. This allows one to accurately predict the timescale preservation in systems where the old theory is not applicable or would give overly pessimistic results. Moreover, the new characterization is still constructive, in that it allows for the algorithmic identification of a good slow variable. The improved characterization, the error prediction and the variable construction are demonstrated by a small metastable system

Examining the role of protein structural dynamics in drug resistance in Mycobacterium tuberculosis

Antimicrobial resistance represents a growing global health problem. The emergence of novel resistance mechanisms necessitates the development of alternative approaches to investigate the molecular fundamentals of resistance, leading ultimately to new strategies for counteracting them. To gain deeper insight into antibiotic-target interactions, the binding of the frontline anti-tuberculosis drug isoniazid (INH) to a target enzyme, InhA, from Mycobacterium tuberculosis was studied using ultrafast two-dimensional infrared (2D-IR) spectroscopy and molecular simulations. Comparing wild-type InhA with a series of single point mutations, it was found that binding of the INH-NAD inhibitor to susceptible forms of the enzyme caused increased vibrational coupling between residues located in the Rossmann fold co-factor binding site of InhA, reducing dynamic fluctuations. The effect correlated with biochemical assay data, being markedly reduced in the INH-resistant S94A mutant and absent in the biochemically-inactive P193A control. Molecular dynamics simulations and calculations of inter-residue couplings indicate that the changes in coupling and dynamics are not localised to the co-factor binding site, but permeate much of the protein. We thus propose that the resistant S94A mutation circumvents subtle changes in global structural dynamics caused by INH upon binding to the wild-type enzyme that may impact upon the formation of important protein-protein complexes in the fatty acid synthase pathway of M. tuberculosis

Mechanism of activation at the selectivity filter of the KcsA K(+) channel

Potassium channels are opened by ligands and/or membrane potential. In voltage-gated K(+) channels and the prokaryotic KcsA channel, conduction is believed to result from opening of an intracellular constriction that prevents ion entry into the pore. On the other hand, numerous ligand-gated K(+) channels lack such gate, suggesting that they may be activated by a change within the selectivity filter, a narrow region at the extracellular side of the pore. Using molecular dynamics simulations and electrophysiology measurements, we show that ligand-induced conformational changes in the KcsA channel removes steric restraints at the selectivity filter, thus resulting in structural fluctuations, reduced K(+) affinity, and increased ion permeation. Such activation of the selectivity filter may be a universal gating mechanism within K(+) channels. The occlusion of the pore at the level of the intracellular gate appears to be secondary

Harmonized gap-filled datasets from 20 urban flux tower sites

A total of 20 urban neighbourhood-scale eddy covariance flux tower datasets are made openly available after being harmonized to create a 50 site-year collection with broad diversity in climate and urban surface characteristics. Variables needed as inputs for land surface models (incoming radiation, temperature, humidity, air pressure, wind and precipitation) are quality controlled, gap-filled and prepended with 10 years of reanalysis-derived local data, enabling an extended spin up to equilibrate models with local climate conditions. For both gap filling and spin up, ERA5 reanalysis meteorological data are bias corrected using tower-based observations, accounting for diurnal, seasonal and local urban effects not modelled in ERA5. The bias correction methods developed perform well compared to methods used in other datasets (e.g. WFDE5 or FLUXNET2015). Other variables (turbulent and upwelling radiation fluxes) are harmonized and quality controlled without gap filling Site description metadata include local land cover fractions (buildings, roads, trees, grass etc.), building height and morphology, aerodynamic roughness estimates, population density and satellite imagery. This open collection can help extend our understanding of urban environmental processes through observational synthesis studies or in the evaluation of land surface environmental models in a wide range of urban settings. These data can be accessed from https://doi.org/10.5281/zenodo.7104984 (Lipson et al., 2022).Peer reviewe

Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented