A new comparative approach to macroeconomic modeling and policy analysis

In the aftermath of the global financial crisis, the state of macroeconomic modeling and the use of macroeconomic models in policy analysis has come under heavy criticism. Macroeconomists in academia and policy institutions have been blamed for relying too much on a particular class of macroeconomic models. This paper proposes a comparative approach to macroeconomic policy analysis that is open to competing modeling paradigms. Macroeconomic model comparison projects have helped produce some very influential insights such as the Taylor rule. However, they have been infrequent and costly, because they require the input of many teams of researchers and multiple meetings to obtain a limited set of comparative findings. This paper provides a new approach that enables individual researchers to conduct model comparisons easily, frequently, at low cost and on a large scale. Using this approach a model archive is built that includes many well-known empirically estimated models that may be used for quantitative analysis of monetary and fiscal stabilization policies. A computational platform is created that allows straightforward comparisons of models’ implications. Its application is illustrated by comparing different monetary and fiscal policies across selected models. Researchers can easily include new models in the data base and compare the effects of novel extensions to established benchmarks thereby fostering a comparative instead of insular approach to model development

Semiclassical Theory for Parametric Correlation of Energy Levels

Parametric energy-level correlation describes the response of the energy-level statistics to an external parameter such as the magnetic field. Using semiclassical periodic-orbit theory for a chaotic system, we evaluate the parametric energy-level correlation depending on the magnetic field difference. The small-time expansion of the spectral form factor $K(\tau)$ is shown to be in agreement with the prediction of parameter dependent random-matrix theory to all orders in $\tau$.Comment: 25 pages, no figur

Monitoring the antibiotic darobactin modulating the β-barrel assembly factor BamA

The β-barrel assembly machinery (BAM) complex is an essential component of Escherichia coli that inserts and folds outer membrane proteins (OMPs). The natural antibiotic compound darobactin inhibits BamA, the central unit of BAM. Here, we employ dynamic single-molecule force spectroscopy (SMFS) to better understand the structure-function relationship of BamA and its inhibition by darobactin. The five N-terminal polypeptide transport (POTRA) domains show low mechanical, kinetic, and energetic stabilities. In contrast, the structural region linking the POTRA domains to the transmembrane β-barrel exposes the highest mechanical stiffness and lowest kinetic stability within BamA, thus indicating a mechano-functional role. Within the β-barrel, the four N-terminal β-hairpins H1-H4 expose the highest mechanical stabilities and stiffnesses, while the four C-terminal β-hairpins H5-H6 show lower stabilities and higher flexibilities. This asymmetry within the β-barrel suggests that substrates funneling into the lateral gate formed by β-hairpins H1 and H8 can force the flexible C-terminal β-hairpins to change conformations

Periodic-Orbit Theory of Level Correlations

We present a semiclassical explanation of the so-called Bohigas-Giannoni-Schmit conjecture which asserts universality of spectral fluctuations in chaotic dynamics. We work with a generating function whose semiclassical limit is determined by quadruplets of sets of periodic orbits. The asymptotic expansions of both the non-oscillatory and the oscillatory part of the universal spectral correlator are obtained. Borel summation of the series reproduces the exact correlator of random-matrix theory.Comment: 4 pages, 1 figure (+ web-only appendix with 2 pages, 1 figure

Towards annotating the plant epigenome: the Arabidopsis thaliana small RNA locus map.

Based on 98 public and internal small RNA high throughput sequencing libraries, we mapped small RNAs to the genome of the model organism Arabidopsis thaliana and defined loci based on their expression using an empirical Bayesian approach. The resulting loci were subsequently classified based on their genetic and epigenetic context as well as their expression properties. We present the results of this classification, which broadly conforms to previously reported divisions between transcriptional and post-transcriptional gene silencing small RNAs, and to PolIV and PolV dependencies. However, we are able to demonstrate the existence of further subdivisions in the small RNA population of functional significance. Moreover, we present a framework for similar analyses of small RNA populations in all species

Monitoring Backbone Hydrogen-Bond Formation in β-Barrel Membrane Protein Folding

β-barrel membrane proteins are key components of the outer membrane of bacteria, mitochondria and chloroplasts. Their three-dimensional structure is defined by a network of backbone hydrogen bonds between adjacent β-strands. Here, we employ hydrogen-deuterium (H/D) exchange in combination with NMR spectroscopy and mass spectrometry to monitor backbone hydrogen bond formation during folding of the outer membrane protein X (OmpX) from E. coli in detergent micelles. Residue-specific kinetics of interstrand hydrogen-bond formation were found to be uniform in the entire β-barrel and synchronized to formation of the tertiary structure. OmpX folding thus propagates via a long-lived conformational ensemble state in which all backbone amide protons exchange with the solvent and engage in hydrogen bonds only transiently. Stable formation of the entire OmpX hydrogen bond network occurs downhill of the rate-limiting transition state and thus appears cooperative on the overall folding time scale

Universal spectral form factor for chaotic dynamics

We consider the semiclassical limit of the spectral form factor $K(\tau)$ of fully chaotic dynamics. Starting from the Gutzwiller type double sum over classical periodic orbits we set out to recover the universal behavior predicted by random-matrix theory, both for dynamics with and without time reversal invariance. For times smaller than half the Heisenberg time $T_H\propto \hbar^{-f+1}$, we extend the previously known $\tau$-expansion to include the cubic term. Beyond confirming random-matrix behavior of individual spectra, the virtue of that extension is that the ``diagrammatic rules'' come in sight which determine the families of orbit pairs responsible for all orders of the $\tau$-expansion.Comment: 4 pages, 1 figur

First experimental observations on melting and chemical modification of volcanic ash during lightning interaction

Electrification in volcanic ash plumes often leads to syn-eruptive lightning discharges. High temperatures in and around lightning plasma channels have the potential to chemically alter, re-melt, and possibly volatilize ash fragments in the eruption cloud. In this study, we experimentally simulate temperature conditions of volcanic lightning in the laboratory, and systematically investigate the effects of rapid melting on the morphology and chemical composition of ash. Samples of different size and composition are ejected towards an artificially generated electrical arc. Post-experiment ash morphologies include fully melted spheres, partially melted particles, agglomerates, and vesiculated particles. High-speed imaging reveals various processes occurring during the short lightning-ash interactions, such as particle melting and rounding, foaming, and explosive particle fragmentation. Chemical analyses of the flash-melted particles reveal considerable bulk loss of Cl, S, P and Na through thermal vaporization. Element distribution patterns suggest convection as a key process of element transport from the interior of the melt droplet to rim where volatiles are lost. Modeling the degree of sodium loss delivers maximum melt temperatures between 3290 and 3490 K. Our results imply that natural lighting strikes may be an important agent of syn-eruptive morphological and chemical processing of volcanic ash

Software for Dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence, approaches are available to explore the reasoning behind those complex models' predictions. One class of approaches are post-hoc attribution methods, among which Layer-wise Relevance Propagation (LRP) shows high performance. However, the attempt at understanding a DNN's reasoning often stops at the attributions obtained for individual samples in input space, leaving the potential for deeper quantitative analyses untouched. As a manual analysis without the right tools is often unnecessarily labor intensive, we introduce three software packages targeted at scientists to explore model reasoning using attribution approaches and beyond: (1) Zennit - a highly customizable and intuitive attribution framework implementing LRP and related approaches in PyTorch, (2) CoRelAy - a framework to easily and quickly construct quantitative analysis pipelines for dataset-wide analyses of explanations, and (3) ViRelAy - a web-application to interactively explore data, attributions, and analysis results.Comment: 10 pages, 3 figure