Algebraic service composition for user-centric IoT applications

The Internet of Things (IoT) requires a shift in our way of building applications, as it is aimed at providing many services to society in general. Non-developer people require increasingly complex IoT applications and support for their ever changing run-time requirements. Although service composition allows the combination of functionality into more complex behaviours, current approaches provide support for dealing with one IoT scenario at a time, as they allow the definition of only one workflow. In this paper, we present DX-MAN, an algebraic model for static service composition that allows the definition of composite services that encompass multiple workflows for run-time scenarios. We evaluate our proposal on an example in the domain of smart homes

A composite transcriptional signature differentiates responses towards closely related herbicides in Arabidopsis thaliana and Brassica napus

In this study, genome-wide expression profiling based on Affymetrix ATH1 arrays was used to identify discriminating responses of Arabidopsis thaliana to five herbicides, which contain active ingredients targeting two different branches of amino acid biosynthesis. One herbicide contained glyphosate, which targets 5-enolpyruvylshikimate-3-phosphate synthase (EPSPS), while the other four herbicides contain different acetolactate synthase (ALS) inhibiting compounds. In contrast to the herbicide containing glyphosate, which affected only a few transcripts, many effects of the ALS inhibiting herbicides were revealed based on transcriptional changes related to ribosome biogenesis and translation, secondary metabolism, cell wall modification and growth. The expression pattern of a set of 101 genes provided a specific, composite signature that was distinct from other major stress responses and differentiated among herbicides targeting the same enzyme (ALS) or containing the same chemical class of active ingredient (sulfonylurea). A set of homologous genes could be identified in Brassica napus that exhibited a similar expression pattern and correctly distinguished exposure to the five herbicides. Our results show the ability of a limited number of genes to classify and differentiate responses to closely related herbicides in A. thaliana and B. napus and the transferability of a complex transcriptional signature across species

Event-by-event reconstruction of the shower maximum Xmax with the Surface Detector of the Pierre Auger Observatory using deep learning

The measurement of the mass composition of ultra-high energy cosmic rays constitutes a prime challenge in astroparticle physics. Most detailed information on the composition can be obtained from measurements of the depth of maximum of air showers, Xmax, with the use of fluorescence telescopes, which can be operated only during clear and moonless nights. Using deep neural networks, it is now possible for the first time to perform an event-by-event reconstruction of Xmax with the Surface Detector (SD) of the Pierre Auger Observatory. Therefore, previously recorded data can be analyzed for information on Xmax, and thus, the cosmic-ray composition. Since the SD operates with a duty cycle of almost 100% and its event selection is less strict than for the Fluorescence Detector (FD), the gain in statistics with respect to the FD is almost a factor of 15 for energies above 1019.5 eV. In this contribution, we introduce the neural network particularly designed for the SD of the Pierre Auger Observatory. We evaluate its performance using three different hadronic interaction models, verify its functionality using Auger hybrid measurements, and find that the method can extract mass information on an event level

The depth of the shower maximum of air showers measured with AERA

The Auger Engineering Radio Array (AERA) is currently the largest array of radio antennas for the detection of cosmic rays, spanning an area of 17 km2 with 153 radio antennas, measuring in the energy range from 1017.0 to 1019.0 eV. It detects the radio emission of extensive air showers produced by cosmic rays in the 30 − 80 MHz band. The cosmic-ray mass composition is a crucial piece of information in determining the sources of cosmic rays and their acceleration mechanisms. The depth of the shower maximum, Xmax, a probe for mass composition can be determined with a likelihood analysis that compares the measured radio-emission footprint on the ground to an ensemble of footprints from CORSIKA/CoREAS Monte-Carlo air shower simulations. These simulations are also used to determine the resolution of the method and to validate the reconstruction by identifying and correcting for systematic uncertainties. We will present the method for the reconstruction of the depth of the shower maximum, achieving a resolution of up to 15 g/cm2, show compatibility with the independent fluorescence detector reconstruction measured on an event-by-event basis, and show that the data taken over the past seven years with AERA shows a light cosmic-ray mass composition reconstruction in the energy range from 1017.5 to 1018.8 eV

The 2021 Open-Data release by the Pierre Auger Collaboration

The Pierre Auger Observatory is used to study the extensive air-showers produced by cosmic rays above 1017 eV. The Observatory is operated by a Collaboration of about 400 scientists, engineers, technicians and students from more than 90 institutions in 18 countries. The Collaboration is committed to the public release of their data for the purpose of re-use by a wide community including professional scientists, in educational and outreach initiatives, and by citizen scientists. The Open Access Data for 2021 comprises 10% of the samples used for results reported at the Madison ICRC 2019, amounting to over 20000 showers measured with the surface-detector array and over 3000 showers recorded simultaneously by the surface and fluorescence detectors. Data are available in pseudo-raw (JSON) format with summary CSV file containing the reconstructed parameters. A dedicated website is used to host the datasets that are available for download. Their detailed description, along with auxiliary information needed for data analysis, is given. An online event display is also available. Simplified codes derived from those used for published analyses are provided by means of Python notebooks prepared to guide the reader to an understanding of the physics results. Here we describe the Open Access data, discuss the notebooks available and show material accessible to the user at https://opendata.auger.org/

A Search for Photons with Energies above 2 × 1017eV Using Hybrid Data from the Low-Energy Extensions of the Pierre Auger Observatory

Ultra-high-energy photons with energies exceeding 1017 eV offer a wealth of connections to different aspects of cosmic-ray astrophysics as well as to gamma-ray and neutrino astronomy. The recent observations of photons with energies in the 1015 eV range further motivate searches for even higher-energy photons. In this paper, we present a search for photons with energies exceeding 2 × 1017 eV using about 5.5 yr of hybrid data from the low-energy extensions of the Pierre Auger Observatory. The upper limits on the integral photon flux derived here are the most stringent ones to date in the energy region between 1017 and 1018 eV

Deep-learning based reconstruction of the shower maximum Xmax using the water-Cherenkov detectors of the Pierre Auger Observatory

The atmospheric depth of the air shower maximum Xmax is an observable commonly used for the determination of the nuclear mass composition of ultra-high energy cosmic rays. Direct measurements of Xmax are performed using observations of the longitudinal shower development with fluorescence telescopes. At the same time, several methods have been proposed for an indirect estimation of Xmax from the characteristics of the shower particles registered with surface detector arrays. In this paper, we present a deep neural network (DNN) for the estimation of Xmax. The reconstruction relies on the signals induced by shower particles in the ground based water-Cherenkov detectors of the Pierre Auger Observatory. The network architecture features recurrent long short-term memory layers to process the temporal structure of signals and hexagonal convolutions to exploit the symmetry of the surface detector array. We evaluate the performance of the network using air showers simulated with three different hadronic interaction models. Thereafter, we account for long-term detector effects and calibrate the reconstructed Xmax using fluorescence measurements. Finally, we show that the event-by-event resolution in the reconstruction of the shower maximum improves with increasing shower energy and reaches less than 25 g/cm2 at energies above 2×1019 eV

A Search for Photons with Energies Above 2 × 1017^{17} eV Using Hybrid Data from the Low-Energy Extensions of the Pierre Auger Observatory

Ultra-high-energy photons with energies exceeding 10$^{17}$ eV offer a wealth of connections to different aspects of cosmic-ray astrophysics as well as to gamma-ray and neutrino astronomy. The recent observations of photons with energies in the 10$^{15}$ eV range further motivate searches for even higher-energy photons. In this paper, we present a search for photons with energies exceeding 2 × 10$^{17}$ eV using about 5.5 yr of hybrid data from the low-energy extensions of the Pierre Auger Observatory. The upper limits on the integral photon flux derived here are the most stringent ones to date in the energy region between 10$^{17}$ and 10$^{18}$ eV