In this study, both memcapacitive and memristive characteristics in the composite material based on the rhenium disulfide (ReS
) rich in rhenium (VI) oxide (ReO
) surface overlayer (ReO
@ReS
) and in ...the indium tin oxide (ITO)/ReO
@ReS
/aluminum (Al) device configuration is presented. Comprehensive experimental analysis of the ReO
@ReS
material properties' dependence on the memcapacitor electrical characteristics was carried out by standard as well as frequency-dependent current-voltage, capacitance-voltage, and conductance-voltage studies. Furthermore, determination of the charge carrier conduction model, charge carrier mobility, density of the trap states, density of the available charge carrier, free-carrier concentration, effective density of states in the conduction band, activation energy of the carrier transport, as well as ion hopping was successfully conducted for the ReO
@ReS
based on the experimental data. The ITO/ReO
@ReS
/Al charge carrier conduction was found to rely on the mixed electronic-ionic processes, involving electrochemical metallization and lattice oxygen atoms migration in response to the externally modulated electric field strength. The chemical potential generated by the electronic-ionic ITO/ReO
@ReS
/Al resistive memory cell non-equlibrium processes leads to the occurrence of the nanobattery effect. This finding supports the possibility of a nonvolatile memory cell with a new operation principle based on the potential read function.
Machine Learning Developments in ROOT Bagoly, A; Bevan, A; Carnes, A ...
22nd International Conference on Computing in High Energy and Nuclear Physics, CHEP 2016, San Francisco, CA, USA,
10/2017, Volume:
898, Issue:
7
Journal Article, Conference Proceeding
Peer reviewed
Open access
ROOT is a software framework for large-scale data analysis that provides basic and advanced statistical methods used by high-energy physics experiments. It includes machine learning tools from the ...ROOT-integrated Toolkit for Multivariate Analysis (TMVA). We present several recent developments in TMVA, including a new modular design, new algorithms for pre-processing, cross-validation, hyperparameter-tuning, deep-learning and interfaces to other machine-learning software packages. TMVA is additionally integrated with Jupyter, making it accessible with a browser.
Structural equation modeling was used to explore the direct and indirect association of childhood experiences, attitudes, subjective norms, and intentions on the alcohol consumption of adolescents ...attending faith-based Seventh-day Adventist schools in Australia. Data were collected on 1,266 adolescents and the structural model developed explained 48% of the variance for alcohol consumption. Intentions had the highest degree of association with Alcohol Consumption Status (ACS) (β = 0.52). Attitudes were more strongly associated to ACS (β
total
= 0.36) than subjective norms (β
total
= 0.17). Adverse Childhood Experiences (ACEs) were associated with every variable in the model and had a combined direct and indirect association with ACS of β
total
= 0.14. Multigroup analysis found significant pathway differences in the model for gender and age with regards to the association of intentions, attitudes, ACEs, and Childhood Family Dynamics with alcohol consumption status. The study fills a gap in the alcohol literature by presenting a model describing the complex network of factors that predict alcohol consumption in a low-ACS population. The outcomes of the study highlight the importance of early intervention for children and their families to delay or minimize alcohol consumption in adolescents.
The Cherwell is a 4T CMOS sensor in 180 nm technology developed for the detection of charged particles. Here, the different test structures on the sensor will be described and first results from ...tests on the reference pixel variant are shown. The sensors were shown to have a noise of 12 e- and a signal to noise up to 150 in 55Fe.
The Cherwell is a 4T CMOS sensor in 180nm technology developed for the detection of charged particles. Here, the different test structures on the sensor will be described and first results from tests ...on the reference pixel variant are shown. The sensors were shown to have a noise of 12 e− and a signal to noise up to 150 in 55Fe.
Machine learning is an important applied research area in particle physics, beginning with applications to high-level physics analysis in the 1990s and 2000s, followed by an explosion of applications ...in particle and event identification and reconstruction in the 2010s. In this document we discuss promising future research and development areas in machine learning in particle physics with a roadmap for their implementation, software and hardware resource requirements, collaborative initiatives with the data science community, academia and industry, and training the particle physics community in data science. The main objective of the document is to connect and motivate these areas of research and development with the physics drivers of the High-Luminosity Large Hadron Collider and future neutrino experiments and identify the resource needs for their implementation. Additionally we identify areas where collaboration with external communities will be of great benefit.
PDF
