Measuring αs(Q2)\alpha_s(Q^2) in τ\tau Decays

The decay rate of the $\tau$ lepton into hadrons of invariant mass smaller than $Q\gg\Lambda_{\rm QCD}$ can be calculated in QCD using the OPE. Using experimental data on the hadronic mass distribution, the running coupling constant $\alpha_s(Q^2)$ is extracted in the range 0.85~\mbox{GeV}, where its value changes by about a factor~2. At $Q=m_\tau$, the result is $\alpha_s(m_\tau^2)=0.33\pm 0.03$, corresponding to $\alpha_s(m_Z^2)=0.119\pm 0.004$. The running of the coupling constant is in excellent agreement with the QCD prediction based on the three-loop $\beta$-function.Comment: 12 pages, 7 figures appended, to appear in the Proceedings of Les Rencontres de Physique de la Vall\'ee d'Aoste (La Thuile, Italy, March 1996), and Second Workshop on Continuous Advances in QCD (Minneapolis, Minnesota, March 1996

Fish otoliths from the Pliocene Heraklion Basin (Crete Island, Eastern Mediterranean)

The Pliocene Eastern Mediterranean fish record is revealed through the study of a 60-m thick stratigraphic sequence near the village Voutes (Heraklion, Crete). Forty-two species belonging to twenty families are identified. Calcareous nannoplankton biostratigraphy places the studied sequence within the biozone MNN16a (latest Zanclean). The stratigraphic distribution of 31 species is modified. Among these, 12 species are reported for the first time in the Eastern Mediterranean Zanclean, while 19 species are first reported outside the Ionian Sea. The Voutes fish fauna presents a diversified benthic and benthopelagic assemblage filling a significant gap in the fossil record

Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing

Hyperparameter Optimization (HPO) of Deep Learning-based models tends to be a compute resource intensive process as it usually requires to train the target model with many different hyperparameter configurations. We show that integrating model performance prediction with early stopping methods holds great potential to speed up the HPO process of deep learning models. Moreover, we propose a novel algorithm called Swift-Hyperband that can use either classical or quantum support vector regression for performance prediction and benefit from distributed High Performance Computing environments. This algorithm is tested not only for the Machine-Learned Particle Flow model used in High Energy Physics, but also for a wider range of target models from domains such as computer vision and natural language processing. Swift-Hyperband is shown to find comparable (or better) hyperparameters as well as using less computational resources in all test cases

Hyperparameter optimization, quantum-assisted model performance prediction, and benchmarking of AI-based High Energy Physics workloads using HPC

Training and Hyperparameter Optimization (HPO) of deep learning-based AI models are often compute resource intensive and calls for the use of large-scale distributed resources as well as scalable and resource efficient hyperparameter search algorithms. This work studies the potential of using model performance prediction to aid the HPO process carried out on High Performance Computing systems. In addition, a quantum annealer is used to train the performance predictor and a method is proposed to overcome some of the problems derived from the current limitations in quantum systems as well as to increase the stability of solutions. This allows for achieving results on a quantum machine comparable to those obtained on a classical machine, showing how quantum computers could be integrated within classical machine learning tuning pipelines. Furthermore, results are presented from the development of a containerized benchmark based on an AI-model for collision event reconstruction that allows us to compare and assess the suitability of different hardware accelerators for training deep neural networks.Comment: 5 pages, 7 figures. Submitted to the proceedings of the ACAT 2022 conference and is to be published in the Journal Of Physics: Conference Serie

Junior Students’ with Hearing Impairment Psychological Correction of Learning Motivation Development

У статті розглянуто основні методологічні принципи, методи, етапи корекційного процесу. Обґрунтовано використання гуманістичного підходу до корекції мотиваційної сфери учіння та підібрано комплекс корекційних завдань для розвитку цієї сфери в молодших школярів із порушеннями слуху. The article presents basic methodological principles, methods, main stages of correctional process. A humanitarian approach to learning motivation development correction has been grounded and a complex of correctional tasks for junior students with hearing impairment has been selected

Scalable neural network models and terascale datasets for particle-flow reconstruction

We study scalable machine learning models for full event reconstruction in high-energy electron-positron collisions based on a highly granular detector simulation. Particle-flow (PF) reconstruction can be formulated as a supervised learning task using tracks and calorimeter clusters or hits. We compare a graph neural network and kernel-based transformer and demonstrate that both avoid quadratic memory allocation and computational cost while achieving realistic PF reconstruction. We show that hyperparameter tuning on a supercomputer significantly improves the physics performance of the models. We also demonstrate that the resulting model is highly portable across hardware processors, supporting Nvidia, AMD, and Intel Habana cards. Finally, we demonstrate that the model can be trained on highly granular inputs consisting of tracks and calorimeter hits, resulting in a competitive physics performance with the baseline. Datasets and software to reproduce the studies are published following the findable, accessible, interoperable, and reusable (FAIR) principles.Comment: 19 pages, 7 figure

Holocene climate variability of the Western Mediterranean: surface water dynamics inferred from calcareous plankton assemblages

A high-resolution study (centennial scale) has been performed on the calcareous plankton assemblage of the Holocene portion of the Ocean Drilling Program Site 976 (Alboran Sea) with the aim to identify the main changes in the surface water dynamic. The dataset also provided a seasonal foraminiferal sea surface water temperatures (SSTs), estimated using the modern analog technique SIMMAX 28, and it was compared with available geochemical and pollen data at the site. Three main climate shifts were identified as (1) the increase in abundance of Syracosphaera spp. and Turborotalita quinqueloba marks the early Holocene humid phase, during maximum summer insolation and enhanced river runoff. It is concomitant with the expansion of Quercus, supporting high humidity on land. It ends at 8.2 ka, registering a sudden temperature and humidity reduction; (2) the rise in the abundances of Florisphaera profunda and Globorotalia inflata, at ca. 8 ka, indicates the development of the modern geostrophic front, gyre circulation, and of a deep nutricline following the sea-level rise; and (3) the increase of small Gephyrocapsa and Globigerina bulloides at 5.3 ka suggests enhanced nutrient availability in surface waters, related to more persistent wind-induced upwelling conditions. Relatively higher winter SST in the last 3.5 ka favored the increase of Trilobatus sacculifer, likely connected to more stable surface water conditions. Over the main trends, a short-term cyclicity is registered in coccolithophore productivity during the last 8 ka. Short periods of increased productivity are in phase with Atlantic waters inflow, and more arid intervals on land. This cyclicity has been related with periods of positive North Atlantic Oscillation (NAO) circulations. Spectral analysis on coccolithophore productivity confirms the occurrence of millennial-scale cyclicity, suggesting an external (i.e. solar) and an internal (i.e. atmospheric/oceanic) forcing.Geoscience PhD scholarship, Universita degli Studi di BariPotenziamento Strutturale dell'Universita degli Studi di Bari, Laboratorio per lo Sviluppo Integrato delle Scienze e delle Tecnologie dei Materiali Avanzati e per dispositivi innovativi (SISTEMA) [PONa3_00369]Fundacao para a Ciencia e a Tecnologia (FCT)Portuguese Foundation for Science and TechnologyEuropean Commission [SFRH/BPD/111433/2015]info:eu-repo/semantics/submittedVersio

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing nterest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized both local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. In addition, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.Comment: 15 pages, 9 figure

Model Performance Prediction for Hyperparameter Optimization of Deep Learning Models Using High Performance Computing and Quantum Annealing

Hyperparameter Optimization (HPO) of Deep Learning (DL)-based models tends to be a compute resource intensive process as it usually requires to train the target model with many different hyperparameter configurations. We show that integrating model performance prediction with early stopping methods holds great potential to speed up the HPO process of deep learning models. Moreover, we propose a novel algorithm called Swift-Hyperband that can use either classical or quantum Support Vector Regression (SVR) for performance prediction and benefit from distributed High Performance Computing (HPC) environments. This algorithm is tested not only for the Machine-Learned Particle Flow (MLPF), model used in High-Energy Physics (HEP), but also for a wider range of target models from domains such as computer vision and natural language processing. Swift-Hyperband is shown to find comparable (or better) hyperparameters as well as using less computational resources in all test cases

Test of the Running of αs\alpha_s in τ\tau Decays

The $\tau$ decay rate into hadrons of invariant mass smaller than $\sqrt{s_0}\gg\Lambda_{\rm QCD}$ can be calculated in QCD assuming global quark--hadron duality. It is shown that this assumption holds for $s_0>0.7$~GeV$^2$. From measurements of the hadronic mass distribution, the running coupling constant $\alpha_s(s_0)$ is extracted in the range 0.7~GeV$^2<s_0<m_\tau^2$. At $s_0=m_\tau^2$, the result is $\alpha_s(m_\tau^2)=0.329\pm 0.030$. The running of $\alpha_s$ is in good agreement with the QCD prediction.Comment: 9 pages, 3 figures appended; shortened version with new figures, to appear in Physical Review Letters (April 1996