Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters

Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space. Typically the problem is solved by reducing the dimensionality using feature engineering and histograms, whereby the latter technique allows to build the likelihood using Poisson statistics. However, in the presence of systematic uncertainties represented by nuisance parameters in the likelihood, the optimal dimensionality reduction with a minimal loss of information about the parameters of interest is not known. This work presents a novel strategy to construct the dimensionality reduction with neural networks for feature engineering and a differential formulation of histograms so that the full workflow can be optimized with the result of the statistical inference, e.g., the variance of a parameter of interest, as objective. We discuss how this approach results in an estimate of the parameters of interest that is close to optimal and the applicability of the technique is demonstrated with a simple example based on pseudo-experiments and a more complex example from high-energy particle physics

Reducing the dependence of the neural network function to systematic uncertainties in the input space

Applications of neural networks to data analyses in natural sciences are complicated by the fact that many inputs are subject to systematic uncertainties. To control the dependence of the neural network function to variations of the input space within these systematic uncertainties, several methods have been proposed. In this work, we propose a new approach of training the neural network by introducing penalties on the variation of the neural network output directly in the loss function. This is achieved at the cost of only a small number of additional hyperparameters. It can also be pursued by treating all systematic variations in the form of statistical weights. The proposed method is demonstrated with a simple example, based on pseudo-experiments, and by a more complex example from high-energy particle physics

Identifying the relevant dependencies of the neural network response on characteristics of the input space

The relation between the input and output spaces of neural networks (NNs) is investigated to identify those characteristics of the input space that have a large influence on the output for a given task. For this purpose, the NN function is decomposed into a Taylor expansion in each element of the input space. The Taylor coefficients contain information about the sensitivity of the NN response to the inputs. A metric is introduced that allows for the identification of the characteristics that mostly determine the performance of the NN in solving a given task. Finally, the capability of this metric to analyze the performance of the NN is evaluated based on a task common to data analyses in high-energy particle physics experiments

Ecoaldea cafetera : Reasentamiento colectivo en el municipio de la Florida, dpto. de Nariño, debido al riesgo generado por el Volcán Galeras.

Reasentamiento de la población ubicada en la zona de amenaza volcánica alta mediante la planeación y diseño de una pequeña aldea, dando solución al aspecto habitacional , acompañado por una serie de servicios complementarios, una zona productiva dedicada a la actividad cafetera y un parque ecológico, enmarcando todo el proyecto dentro del ámbito ecológico y sostenibleResettlement of people located in a dangerous volcanic area by planning and designing a small village providing a housing solution, it is accompanied by a few complementary services, they include creating a productive area focused on coffee industry and running an eco-parkArquitecto (a)Pregrad

Probing the local activity of CO2 reduction on gold gas diffusion electrodes: effect of the catalyst loading and CO2 pressure

Large scale CO2 electrolysis can be achieved using gas diffusion electrodes (GDEs), and is an essential step towards broader implementation of carbon capture and utilization strategies. Different variables are known to affect the performance of GDEs. Especially regarding the catalyst loading, there are diverging trends reported in terms of activity and selectivity, e.g. for CO2 reduction to CO. We have used shear-force based Au nanoelectrode positioning and scanning electrochemical microscopy (SECM) in the surface-generation tip collection mode to evaluate the activity of Au GDEs for CO2 reduction as a function of catalyst loading and CO2 back-pressure. Using a Au nanoelectrode, we have locally measured the amount of CO produced along a catalyst loading gradient under operando conditions. We observed that an optimum local loading of catalyst is necessary to achieve high activities. However, this optimum is directly dependent on the CO2 back-pressure. Our work does not only present a tool to evaluate the activity of GDEs locally, it also allows drawing a more precise picture regarding the effect of catalyst loading and CO2 back-pressure on their performance.Horizon 2020(H2020)722614-ELCORELCatalysis and Surface Chemistr

In-depth analysis of T cell immunity and antibody responses in heterologous prime-boost-boost vaccine regimens against SARS-CoV-2 and Omicron variant.

With the emergence of novel Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Variants of Concern (VOCs), vaccination studies that elucidate the efficiency and effectiveness of a vaccination campaign are critical to assess the durability and the protective immunity provided by vaccines. SARS-CoV-2 vaccines have been found to induce robust humoral and cell-mediated immunity in individuals vaccinated with homologous vaccination regimens. Recent studies also suggest improved immune response against SARS-CoV-2 when heterologous vaccination strategies are employed. Yet, few data exist on the extent to which heterologous prime-boost-boost vaccinations with two different vaccine platforms have an impact on the T cell-mediated immune responses with a special emphasis on the currently dominantly circulating Omicron strain. In this study, we collected serum and peripheral blood mononuclear cells (PBMCs) from 57 study participants of median 35-year old's working in the health care field, who have received different vaccination regimens. Neutralization assays revealed robust but decreased neutralization of Omicron VOC, including BA.1 and BA.4/5, compared to WT SARS-CoV-2 in all vaccine groups and increased WT SARS-CoV-2 binding and neutralizing antibodies titers in homologous mRNA prime-boost-boost study participants. By investigating cytokine production, we found that homologous and heterologous prime-boost-boost-vaccination induces a robust cytokine response of CD4+ and CD8+ T cells. Collectively, our results indicate robust humoral and T cell mediated immunity against Omicron in homologous and heterologous prime-boost-boost vaccinated study participants, which might serve as a guide for policy decisions

NCX1 represents an ionic Na+ sensing mechanism in macrophages

Inflammation and infection can trigger local tissue Na(+)accumulation. This Na+-rich environment boosts proinflammatory activation of monocyte/macrophage-like cells (M phi s) and their antimicrobial activity. Enhanced Na+-driven M phi function requires the osmoprotective transcription factor nuclear factor of activated T cells 5 (NFAT5), which augments nitric oxide (NO) production and contributes to increased autophagy. However, the mechanism of Na(+)sensing in M phi s remained unclear. High extracellular Na(+)levels (high salt [HS]) trigger a substantial Na(+)influx and Ca(2+)loss. Here, we show that the Na+/Ca(2+)exchanger 1 (NCX1, also known as solute carrier family 8 member A1 [SLC8A1]) plays a critical role in HS-triggered Na(+)influx, concomitant Ca(2+)efflux, and subsequent augmented NFAT5 accumulation. Moreover, interfering with NCX1 activity impairs HS-boosted inflammatory signaling, infection-triggered autolysosome formation, and subsequent antibacterial activity. Taken together, this demonstrates that NCX1 is able to sense Na(+)and is required for amplifying inflammatory and antimicrobial M phi responses upon HS exposure. Manipulating NCX1 offers a new strategy to regulate M phi function

Microbial and Chemical Characterization of Underwater Fresh Water Springs in the Dead Sea

Due to its extreme salinity and high Mg concentration the Dead Sea is characterized by a very low density of cells most of which are Archaea. We discovered several underwater fresh to brackish water springs in the Dead Sea harboring dense microbial communities. We provide the first characterization of these communities, discuss their possible origin, hydrochemical environment, energetic resources and the putative biogeochemical pathways they are mediating. Pyrosequencing of the 16S rRNA gene and community fingerprinting methods showed that the spring community originates from the Dead Sea sediments and not from the aquifer. Furthermore, it suggested that there is a dense Archaeal community in the shoreline pore water of the lake. Sequences of bacterial sulfate reducers, nitrifiers iron oxidizers and iron reducers were identified as well. Analysis of white and green biofilms suggested that sulfide oxidation through chemolitotrophy and phototrophy is highly significant. Hyperspectral analysis showed a tight association between abundant green sulfur bacteria and cyanobacteria in the green biofilms. Together, our findings show that the Dead Sea floor harbors diverse microbial communities, part of which is not known from other hypersaline environments. Analysis of the water’s chemistry shows evidence of microbial activity along the path and suggests that the springs supply nitrogen, phosphorus and organic matter to the microbial communities in the Dead Sea. The underwater springs are a newly recognized water source for the Dead Sea. Their input of microorganisms and nutrients needs to be considered in the assessment of possible impact of dilution events of the lake surface waters, such as those that will occur in the future due to the intended establishment of the Red Sea−Dead Sea water conduit

A Roadmap for HEP Software and Computing R&D for the 2020s

Particle physics has an ambitious and broad experimental programme for the coming decades. This programme requires large investments in detector hardware, either to build new facilities and experiments, or to upgrade existing ones. Similarly, it requires commensurate investment in the R&D of software to acquire, manage, process, and analyse the shear amounts of data to be recorded. In planning for the HL-LHC in particular, it is critical that all of the collaborating stakeholders agree on the software goals and priorities, and that the efforts complement each other. In this spirit, this white paper describes the R&D activities required to prepare for this software upgrade.Peer reviewe

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio