Sensitivity to double beta decay of 130Te to the first 0+ excited state of 130Xe in CUORE

We report a preliminary result on the sensitivity and analysis techniques to search for double beta decay of 130Te to the first 0+ excited state of 130Xe in CUORE. With a TeO2 exposure of 369:9 kg y, we find an expected limit setting sensitivity of T0v 1=2 > 3 1024 y at 90 % C:I:

Structural studies of mesoporous ZrO2_{2}-CeO2_{2} and ZrO2_{2}-CeO2_{2}/SiO2_{2} mixed oxides for catalytical applications

In this work the synthesis of ZrO$_{2}$-CeO$_{2}$ and ZrO$_{2}$-CeO$_{2}$/SiO$_{2}$ were developed, based on the process to form ordered mesoporous materials such as SBA-15 silica. The triblock copolymer Pluronic P-123 was used as template, aiming to obtain crystalline single phase walls and larger specific surface area, for future applications in catalysis. SAXS and XRD results showed a relationship between ordered pores and the material crystallization. 90% of CeO$_{2}$ leaded to single phase homogeneous ceria-zirconia solid solution of cubic fluorite structure (Fm$\bar{3}$m). The SiO$_{2}$ addition improved structural and textural properties as well as the reduction behavior at lower temperatures, investigated by XANES measurements under H$_{2}$ atmosphere

IL-21 comes of age as a regulator of effector T cells in the gut

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A Novel Deep Learning Approach to CSI Feedback Reporting for NR 5G Cellular Systems

In this paper, we study 5G Channel State Information feedback reporting. We show that a Deep Learning approach based on Convolutional Neural Networks can be used to learn efficient encoding and decoding algorithms. We set up a fully compliant link level 5G-New Radio simulator with clustered delay line channel model and we consider a realistic scenario with multiple transmitting/receiving antenna schemes and noisy downlink channel estimation. Results show that our Deep Learning approach achieves results comparable with traditional methods and can also outperform them in some conditions

The CloudSME Simulation Platform and its Applications: A Generic Multi-cloud Platform for Developing and Executing Commercial Cloud-based Simulations

Simulation is used in industry to study a large variety of problems ranging from increasing the productivity of a manufacturing system to optimizing the design of a wind turbine. However, some simulation models can be computationally demanding and some simulation projects require time consuming experimentation. High performance computing infrastructures such as clusters can be used to speed up the execution of large models or multiple experiments but at a cost that is often too much for Small and Medium-sized Enterprises (SMEs). Cloud computing presents an attractive, lower cost alternative. However, developing a cloud-based simulation application can again be costly for an SME due to training and development needs, especially if software vendors need to use resources of different heterogeneous clouds to avoid being locked-in to one particular cloud provider. In an attempt to reduce the cost of development of commercial cloud-based simulations, the CloudSME Simulation Platform (CSSP) has been developed as a generic approach that combines an AppCenter with the workflow of the WS-PGRADE/gUSE science gateway framework and the multi-cloud-based capabilities of the CloudBroker Platform. The paper presents the CSSP and two representative case studies from distinctly different areas that illustrate how commercial multi-cloud-based simulations can be created

Implementation of Deep-Learning-Based CSI Feedback Reporting on 5G NR-Compliant Link-Level Simulator

Advances in machine learning have widened the range of its applications in many fields. In particular, deep learning has attracted much interest for its ability to provide solutions where the derivation of a rigorous mathematical model of the problem is troublesome. Our interest was drawn to the application of deep learning for channel state information feedback reporting, a crucial problem in frequency division duplexing (FDD) 5G networks, where knowledge of the channel characteristics is fundamental to exploiting the full potential of multiple-input multiple-output (MIMO) systems. We designed a framework adopting a 5G New Radio convolutional neural network, called NR-CsiNet, with the aim of compressing the channel matrix experienced by the user at the receiver side and then reconstructing it at the transmitter side. In contrast to similar solutions, our framework is based on a 5G New Radio fully compliant simulator, thus implementing a channel generator based on the latest 3GPP 3-D channel model. Moreover, realistic 5G scenarios are considered by including multi-receiving antenna schemes and noisy downlink channel estimation. Simulations were carried out to analyze and compare the performance with current feedback reporting schemes, showing promising results for this approach from the point of view of the block error rate and throughput of the 5G data channel