Report of the Regional Co-ordination Meeting for the North Sea and Eastern Arctic (RCM NS&EA) 2014

Report of the Regional Co-ordination Meeting for the North Sea and Eastern Arctic (RCM NS&EA) 2014 Swedish University of Agriculture Sciences (SLU Aqua) Lysekil, Sweden 08-12 September 2014The RCM NS&EA met in Lysekil (Sweden) between 8-12 September 2014. The main purpose of the RCM is coordinate the National Programmes (NP) of the Member States (MS) in the North Sea region for 2015

Computational approaches to explainable artificial intelligence: Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.MCIU - Nvidia(UMA18-FEDERJA-084

Report of the Regional Co-ordination Meeting for the North Sea and Eastern Arctic (RCM NS&EA) 2015

The RCM NS&EA met 31st August - 4th September 2015 at den Haag, Netherlands with 27 participants form 11 member states and autonomous regions attending, including representatives of ICES and the Commission. National correspondents from Spain, UK, Denmark, Lithuania, Germany, Sweden and the Netherlands were present. The meeting was co-chaired by Katja Ringdahl (Sweden) and Alastair Pout (Scotland). The RCM N&SEA considered the recommendations from the 11th Liasion meeting and summaries were presented of the work of expert groups and end users for the 2014-15 period to the plenary session of the meeting. The expert groups included WGCATCH, PGDATA, WKISCON2, WKRDB 2014-01, RDB–SC, STECF and the Zagreb meeting on transversal variables. ICES, as a main end user, provided feedback. A summary was presented of the progress in the regional coordination project (fishPi). This project involves over 40 participants from 12 members states from NS&EA, NA and Baltic regions, two external statistical experts, and ICES. The project has a wide scope of regional cooperation issues including sampling designs, data formats, code lists, PETS, stomach sampling, small scale and recreational sampling, and data quality software production. It has a budget of €400,000, and a one year time line and with a planned completion date of April 2016. A project with identical aims is running in paralleled in the Mediterranean and Black Sea regions The majority of the ToRs of the RCM NS&EA were addressed by three subgroups: one concerned with data analysis, one with the landing obligation, and one with issues particularly related to role and work of national correspondents

Computational Approaches to Explainable Artificial Intelligence:Advances in Theory, Applications and Trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications

Sensitivity projections for a dual-phase argon TPC optimized for light dark matter searches through the ionization channel

Dark matter lighter than 10  GeV/c2 encompasses a promising range of candidates. A conceptual design for a new detector, DarkSide-LowMass, is presented, based on the DarkSide-50 detector and progress toward DarkSide-20k, optimized for a low-threshold electron-counting measurement. Sensitivity to light dark matter is explored for various potential energy thresholds and background rates. These studies show that DarkSide-LowMass can achieve sensitivity to light dark matter down to the solar neutrino fog for GeV-scale masses and significant sensitivity down to 10  MeV/c2 considering the Migdal effect or interactions with electrons. Requirements for optimizing the detector’s sensitivity are explored, as are potential sensitivity gains from modeling and mitigating spurious electron backgrounds that may dominate the signal at the lowest energies

Computational approaches to Explainable Artificial Intelligence:Advances in theory, applications and trends

Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.</p

Measurement of isotopic separation of argon with the prototype of the cryogenic distillation plant Aria for dark matter searches

The Aria cryogenic distillation plant, located in Sardinia, Italy, is a key component of the DarkSide-20k experimental program for WIMP dark matter searches at the INFN Laboratori Nazionali del Gran Sasso, Italy. Aria is designed to purify the argon, extracted from underground wells in Colorado, USA, and used as the DarkSide-20k target material, to detector-grade quality. In this paper, we report the first measurement of argon isotopic separation by distillation with the 26&nbsp;m tall Aria prototype. We discuss the measurement of the operating parameters of the column and the observation of the simultaneous separation of the three stable argon isotopes: 36Ar , 38Ar , and 40Ar . We also provide a detailed comparison of the experimental results with commercial process simulation software. This measurement of isotopic separation of argon is a significant achievement for the project, building on the success of the initial demonstration of isotopic separation of nitrogen using the same equipment in 2019

Study of cosmogenic activation above ground for the DarkSide-20k experiment

The activation of materials due to exposure to cosmic rays may become an important background source for experiments investigating rare event phenomena. DarkSide-20k, currently under construction at the Laboratori Nazionali del Gran Sasso, is a direct detection experiment for galactic dark matter particles, using a two-phase liquid-argon Time Projection Chamber (TPC) filled with 49.7 tonnes (active mass) of Underground Argon (UAr) depleted in 39Ar. Despite the outstanding capability of discriminating / background in argon TPCs, this background must be considered because of induced dead time or accidental coincidences mimicking dark-matter signals and it is relevant for low-threshold electron-counting measurements. Here, the cosmogenic activity of relevant long-lived radioisotopes induced in the experiment has been estimated to set requirements and procedures during preparation of the experiment and to check that it is not dominant over primordial radioactivity; particular attention has been paid to the activation of the 120 t of UAr used in DarkSide-20k. Expected exposures above ground and production rates, either measured or calculated, have been considered in detail. From the simulated counting rates in the detector due to cosmogenic isotopes, it is concluded that activation in copper and stainless steel is not problematic. The activity of 39Ar induced during extraction, purification and transport on surface is evaluated to be 2.8% of the activity measured in UAr by DarkSide-50 experiment, which used the same underground source, and thus considered acceptable. Other isotopes in the UAr such as 37Ar and 3H are shown not to be relevant due to short half-life and assumed purification methods

Study on cosmogenic activation above ground for the DarkSide-20k project

The activation of materials due to the exposure to cosmic rays may become an important background source for experiments investigating rare event phenomena. DarkSide-20k is a direct detection experiment for galactic dark matter particles, using a two-phase liquid argon time projection chamber filled with 49.7 tonnes (active mass) of Underground Argon (UAr) depleted in 39Ar. Here, the cosmogenic activity of relevant long-lived radioisotopes induced in the argon and other massive components of the set-up has been estimated; production of 120 t of radiopure UAr is foreseen. The expected exposure above ground and production rates, either measured or calculated, have been considered. From the simulated counting rates in the detector due to cosmogenic isotopes, it is concluded that activation in copper and stainless steel is not problematic. Activation of titanium, considered in early designs but not used in the final design, is discussed. The activity of 39Ar induced during extraction, purification and transport on surface, in baseline conditions, is evaluated to be 2.8% of the activity measured in UAr from the same source, and thus considered acceptable. Other products in the UAr such as 37Ar and 3H are shown to not be relevant due to short half-life and assumed purification methods