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A remarkable part of children’ s development and education happens in educational institutions. Acoustic environments in these institutions are usually highly complex and noisy, hence it is demanding to identify relevant target speakers and to ignore irrelevant sounds. Previous research has analyzed auditory selective attention in adults, both in dichotic and binaural listening environments. Until now, there is little knowledge of auditory selective attention in children. In the present work, the original paradigm was adapted by using a task suited for children which included child-oriented elements. Further, the subject’s anthropometric sizes were considered for an aurally-accurate reproduction of the acoustic scene. Twenty-four adults and twenty-four children participated in an experiment on auditory selective attention. Noise and noise-free conditions and various target-distractor distributions in the room were analyzed among others. The result of this experiment revealed significant differences between adults and children, especially in the way auditory attention was influenced by noise.
We investigate the role of the Pauli Exclusion Principle (PEP) for light nuclei, at the examples of 12C and 16O. We show that ignoring the PEP does lead not only to a too dense spectrum at low energy but also to a wrong grouping into bands. Using a geometrical mapping, a triangular structure for 12C and a tetrahedral structure in 16O in the ground state is obtained by using the indistinguishably of the α-particles.
Based on the positive results of the 0.63 m unmodulated 325 MHz Ladder-RFQ prototype from 2013 to 2016 [1, 2], a modulated 3.3m Ladder-RFQ (s. Fig. 1) has been designed and built for the acceleration of up to 100 mA protons from 95 keV to 3.0 MeV at the FAIR p-Linac [3, 4]. In this paper, we will show the results of manufacturing as well as low level RF measurements of the Ladder-RFQ including flatness and frequency tuning.
LICE is one of the four major LHC experiments at CERN. When the accelerator enters the Run 3 data-taking period, starting in 2021, ALICE expects almost 100 times more Pb-Pb central collisions than now, resulting in a large increase of data throughput. In order to cope with this new challenge, the collaboration had to extensively rethink the whole data processing chain, with a tighter integration between Online and Offline computing worlds. Such a system, code-named ALICE O2, is being developed in collaboration with the FAIR experiments at GSI. It is based on the ALFA framework which provides a generalized implementation of the ALICE High Level Trigger approach, designed around distributed software entities coordinating and communicating via message passing.
We will highlight our efforts to integrate ALFA within the ALICE O2 environment. We analyze the challenges arising from the different running environments for production and development, and conclude on requirements for a flexible and modular software framework. In particular we will present the ALICE O2 Data Processing Layer which deals with ALICE specific requirements in terms of Data Model. The main goal is to reduce the complexity of development of algorithms and managing a distributed system, and by that leading to a significant simplification for the large majority of the ALICE users.
The recognition of pharmacological substances, compounds and proteins is an essential preliminary work for the recognition of relations between chemicals and other biomedically relevant units. In this paper, we describe an approach to Task 1 of the PharmaCoNER Challenge, which involves the recognition of mentions of chemicals and drugs in Spanish medical texts. We train a state-of-the-art BiLSTM-CRF sequence tagger with stacked Pooled Contextualized Embeddings, word and sub-word embeddings using the open-source framework FLAIR. We present a new corpus composed of articles and papers from Spanish health science journals, termed the Spanish Health Corpus, and use it to train domain-specific embeddings which we incorporate in our model training. We achieve a result of 89.76% F1-score using pre-trained embeddings and are able to improve these results to 90.52% F1-score using specialized embeddings.
The Specialized Information Service Biodiversity Research (BIOfid) has been launched to mobilize valuable biological data from printed literature hidden in German libraries for over the past 250 years. In this project, we annotate German texts converted by OCR from historical scientific literature on the biodiversity of plants, birds, moths and butterflies. Our work enables the automatic extraction of biological information previously buried in the mass of papers and volumes. For this purpose, we generated training data for the tasks of Named Entity Recognition (NER) and Taxa Recognition (TR) in biological documents. We use this data to train a number of leading machine learning tools and create a gold standard for TR in biodiversity literature. More specifically, we perform a practical analysis of our newly generated BIOfid dataset through various downstream-task evaluations and establish a new state of the art for TR with 80.23% F-score. In this sense, our paper lays the foundations for future work in the field of information extraction in biology texts.
GSI High Energy Beam Transfer lines (HEST) link the SIS18 synchrotron with two storage rings (Experimental Storage Ring and Cryring) and six experimental caves. The recent upgrades to HEST beam instrumentation enables precise measurements of beam properties along the lines and allow for faster and more precise beams setup on targets. Preliminary results of some of the measurements performed during runs in 2018 and 2019 are presented here. The focus is on response matrix measurements and quadrupole scans performed on HADES beam line. The errors and future improvements are discussed.
The present study focuses on the beam line optimization from the heavy-ion synchrotron SIS18 to the HADES experiment. BOBYQA (Bound Optimization BY Quadratic Approximation) solves bound constrained optimization problems without using derivatives of the objective function. The Bayesian optimization is another strategy for global optimization of costly, noisy functions without using derivatives. A python programming interface to MADX allow the use of the python implementation of BOBYQA and Bayesian method. This gave the possibility to use tracking simulation with MADX to determine the loss budget for each lattice setting during the optimization and compare both optimization methods.
Since the last 20 years, modern heuristic algorithms and machine learning have been increasingly used for several purposes in accelerator technology and physics. Since computing power has become less and less of a limiting factor, these tools have become part of the physicist community's standard toolkit [1][2] [3] [4] [5]. This paper describes the construction of an algorithm that can be used to generate an optimised lattice design for transfer lines under the consideration of restrictions that usually limit design options in reality. The developed algorithm has been applied to the existing SIS18 to HADES transfer line in GSI.
In November 2016, magnetotelluric (MT) data were collected at the Ceboruco Volcano in cooperation with the Centro de Sismología y Volcanología de Occidente (SisVoc, Universidad de Guadalajara, Mexico). The Ceboruco is a 2280 m high stratovolcano, located in Nayarit State, Mexico. It is placed in the central part of the Tepic-Zacoalco Rift (TZR), which constitutes the north-western end of the Trans-Mexican Volcanic Belt. Together with Chapala and Colima (in the Jalisco Block), they form the triple rift system developed as a consequence of the ongoing subduction of the Rivera and Cocos oceanic plates beneath the North American continental crust. Although its last eruption occurred in 1870, it is the most active volcano in the area, showing volcanic-earthquake activity together with ongoing vapor emissions. The survey was part of a geothermal project (CeMIEGeo-P24) and focused on the determination of electrical conductivity properties to characterize the deep structure and the geothermal potential of the Volcano. Frequency dependent magnetotelluric response functions were calculated from 25 broadband MT stations, which covered an area of 10 x 10 km2 including its crater, calderas and foreland. The results were interpreted using anisotropic 3-D forward modelling and isotropic 3-D inversion approaches, considering strong topographical effects. The final resistivity model implies a highly conductive layer, reaching from near-surface to approximately 2 km depth, which might be related to a hydrothermal system. Here, mineralized fluids and clay minerals can cause high conductivities around 1 S/m. For longer periods, the principal axes of the MT response tensors (phase tensor, apparent resistivity tensor) are in good agreement with the strike direction of the underlying rift system. However, they are not rendered by the isotropic inversion. Thus the data suggest an anisotropic electrical conductivity at greater depth with its principal axis determined by the response tensors.