An exactly solvable record model for rainfall

Daily precipitation time series are composed of null entries corresponding to dry days and nonzero entries that describe the rainfall amounts on wet days. Assuming that wet days follow a Bernoulli process with success probability $p$, we show that the presence of dry days induces negative correlations between record-breaking precipitation events. The resulting non-monotonic behavior of the Fano factor of the record counting process is recovered in empirical data. We derive the full probability distribution $P(R,n)$ of the number of records $R_n$ up to time $n$, and show that for large $n$, its large deviation form coincides with that of a Poisson distribution with parameter $\ln(p\,n)$. We also study in detail the joint limit $p \to 0$, $n \to \infty$, which yields a random record model in continuous time $t = pn$.Comment: 11 pages, 2 figures + 13 pages and 2 figures of supplemental materia

A mesocosm experiment investigating the effects of substratum quality and wave exposure on the survival of fish eggs

In a mesocosm experiment, the attachment of bream (Abramis brama) eggs to spawning substrata with and without periphytic biofilm coverage and their subsequent survival with and without low-intensity wave exposure were investigated. Egg attachment was reduced by 73% on spawning substrata with a natural periphytic biofilm, compared to clean substrata. Overall, this initial difference in egg numbers persisted until hatching. The difference in egg numbers was even increased in the wave treatment, while it was reduced in the no-wave control treatment. Exposure to a low-intensity wave regime affected egg development between the two biofilm treatments differently. Waves enhanced egg survival on substrata without a biofilm but reduced the survival of eggs on substrata with biofilm coverage. In the treatment combining biofilm-covered substrata and waves, no attached eggs survived until hatching. In all treatments, more than 75% of the eggs became detached from the spawning substrata during the egg incubation period, an

The role of decarboxylation reactions during the initiation of the methanol-to-olefins process

The mechanism for direct Csingle bondC bond formation during the initiation of the methanol-to-olefins (MTO) process is still under discussion. Carbon dioxide formation is often observed during initiation, but there are only few investigations into the role of decarboxylation. We investigate decarboxylation pathways in the H-SSZ-13 zeolite from methanol to olefins via direct carbon–carbon coupling. Additionally, the rate-determining steps were recomputed in the H-ZSM-5 and H-SAPO-34 zeolite. Gibbs free energy barriers were calculated using periodic density functional theory in combination with CCSD(T) calculations on cluster models. For H-SSZ-13, kinetic batch reactor simulations were performed. We found for H-SSZ-13 that pathways via decarboxylation reactions are equally likely as previously computed pathways including decarbonylation mechanisms (also known as ketene or CO pathway). Lactones formed from ketenes and formaldehyde were identified as the main intermediates. The decarboxylation mechanism has similar barriers in H-SSZ-13, H-ZSM-5, and H-SAPO-34, while the barriers for methylation and decarbonylation reactions are significantly lower in H-ZSM-5 and higher in H-SAPO-34. Decarboxylation reactions of lactones could explain experimentally detected carbon dioxide during the initial phase of the MTO process

Interference of quantum critical excitations and soft diffusive modes in a disordered antiferromagnetic metal

We study the temperature-dependent quantum correction to conductivity due to the interplay of spin density fluctuations and weak disorder for a two-dimensional metal near an antiferromagnetic (AFM) quantum critical point. AFM spin density fluctuations carry large momenta around the ordering vector $\mathbf{Q}$ and, at lowest order of the spin-fermion coupling, only scatter electrons between "hot spots" of the Fermi surface which are connected by $\mathbf{Q}$. Earlier, it was seen that the quantum interference between AFM spin density fluctuations and soft diffusive modes of the disordered metal is suppressed, a consequence of the large-momentum scattering. The suppression of this interference results in a non-singular temperature dependence of the corresponding interaction correction to conductivity. However, at higher order of the spin-fermion coupling, electrons on the entire Fermi surface can be scattered successively by two spin density fluctuations and, in total, suffer a small momentum transfer. This higher-order process can be described by composite modes which carry small momenta. We show that the interference between formally subleading composite modes and diffusive modes generates singular interaction corrections which ultimately dominate over the non-singular first-order correction at low temperatures. We derive an effective low-energy theory from the spin-fermion model which includes the above-mentioned higher-order process implicitly and show that for weak spin-fermion coupling the small-momentum transfer is mediated by a composite propagator. Employing the conventional diagrammatic approach to impurity scattering, we find the correction $\delta \sigma \sim +\ln^2 T$ for temperatures above an exponentially small crossover scale.Comment: 13 pages, 7 figures. Published versio

Learning Multiple Defaults for Machine Learning Algorithms

The performance of modern machine learning methods highly depends on their hyperparameter configurations. One simple way of selecting a configuration is to use default settings, often proposed along with the publication and implementation of a new algorithm. Those default values are usually chosen in an ad-hoc manner to work good enough on a wide variety of datasets. To address this problem, different automatic hyperparameter configuration algorithms have been proposed, which select an optimal configuration per dataset. This principled approach usually improves performance, but adds additional algorithmic complexity and computational costs to the training procedure. As an alternative to this, we propose learning a set of complementary default values from a large database of prior empirical results. Selecting an appropriate configuration on a new dataset then requires only a simple, efficient and embarrassingly parallel search over this set. We demonstrate the effectiveness and efficiency of the approach we propose in comparison to random search and Bayesian Optimization

Quantification of genomic DNA repair capabilities in CHO and identification of genes impacting genomic stability

Genomic instability in CHO cells poses a challenge for biopharmaceutical production because it is associated with decline of productivity, product quality, and culture viability. Chromosome rearrangements are particularly problematic since these can decrease or eliminate transgene expression. These are caused by DNA double-strand breaks (DSBs) that are not adequately repaired by the cell, presumably due to deficiencies in DNA repair genes. In this study we have conducted a genome-wide bioinformatic analysis of single-nucleotide variants (SNVs) in DNA-repair genes in the CHO genome. We implement a reporter system in CHO cells that facilitates the quantification of the cell’s capability to repair DSBs in genomic DNA. This provides a DNA stability assessment that is superior to previous assays since these would merely read out the capability to repair artificial plasmids. By utilizing this genomic DSB repair assay, we can quantify DNA stability in standard CHO cells, various DNA repair-deficient CHO mutants, as well as in primary Chinese hamster cells. Finally, we explore how by targeting defective candidate genes from our bioinformatic analysis, this assay can be used to engineer CHO cell lines with increased genomic stability

The transformation of cuboctahedral to icosahedral nanoparticles: atomic structure and dynamics

The rearrangement of transition metal nanoparticles from cuboctahedral to icosahedral structures is studied for up to 923 atoms. The atomic structure and temperature dependence of the transition are investigated with a well-defined collective variable. This collective variable describes the folding of the square fcc(100) facets into two triangular facets through a linear combination of the diagonals of all fcc(100) facets of all shells of the particle. Activation barriers are determined through harmonic transition state theory and constrained molecular dynamics simulations based on force field potentials. These calculations predict an activation entropy larger than 1 meV K$^{-1}$, leading to strongly temperature dependent activation barriers. Density functional theory calculations were additionally performed both as single point calculations and as full optimizations. Cu, Ag, Au and Ni clusters show low barriers for concerted, symmetric transition up to the 309-atomic clusters. In contrast, for Pd, Pt, Rh and Ir higher barriers are required, already for the 147-atomic clusters. With increasing barriers, an asymmetric but still concerted rearrangement becomes energetically more favorable than the fully symmetric transformation. The material-dependence of the transition can be correlated with the melting point of the bulk metals