ML-Based Analysis of Particle Distributions in High-Intensity Laser Experiments: Role of Binning Strategy

When entering the phase of big data processing and statistical inferences in experimental physics, the efficient use of machine learning methods may require optimal data preprocessing methods and, in particular, optimal balance between details and noise. In experimental studies of strong-field quantum electrodynamics with intense lasers, this balance concerns data binning for the observed distributions of particles and photons. Here we analyze the aspect of binning with respect to different machine learning methods (Support Vector Machine (SVM), Gradient Boosting Trees (GBT), Fully-Connected Neural Network (FCNN), Convolutional Neural Network (CNN)) using numerical simulations that mimic expected properties of upcoming experiments. We see that binning can crucially affect the performance of SVM and GBT, and, to a less extent, FCNN and CNN. This can be interpreted as the latter methods being able to effectively learn the optimal binning, discarding unnecessary information. Nevertheless, given limited training sets, the results indicate that the efficiency can be increased by optimizing the binning scale along with other hyperparameters. We present specific measurements of accuracy that can be useful for planning of experiments in the specified research area.     

Machine Learning Algorithms for Prediction of the Quality of Transmission in Optical Networks

Increasing demand in the backbone Dense Wavelength Division (DWDM) Multiplexing network traffic prompts an introduction of new solutions that allow increasing the transmission speed without significant increase of the service cost. In order to achieve this objective simpler and faster, DWDM network reconfiguration procedures are needed. A key problem that is intrinsically related to network reconfiguration is that of the quality of transmission assessment. Thus, in this contribution a Machine Learning (ML) based method for an assessment of the quality of transmission is proposed. The proposed ML methods use a database, which was created only on the basis of information that is available to a DWDM network operator via the DWDM network control plane. Several types of ML classifiers are proposed and their performance is tested and compared for two real DWDM network topologies. The results obtained are promising and motivate further research.     

Active Inference: Applicability to Different Types of Social Organization Explained through Reference to Industrial Engineering and Quality Management

Active inference is a physics of life process theory of perception, action and learning that is applicable to natural and artificial agents. In this paper, active inference theory is related to different types of practice in social organization. Here, the term social organization is used to clarify that this paper does not encompass organization in biological systems. Rather, the paper addresses active inference in social organization that utilizes industrial engineering, quality management, and artificial intelligence alongside human intelligence. Social organization referred to in this paper can be in private companies, public institutions, other for-profit or not-for-profit organizations, and any combination of them. The relevance of active inference theory is explained in terms of variational free energy, prediction errors, generative models, and Markov blankets. Active inference theory is most relevant to the social organization of work that is highly repetitive. By contrast, there are more challenges involved in applying active inference theory for social organization of less repetitive endeavors such as one-of-a-kind projects. These challenges need to be addressed in order for active inference to provide a unifying framework for different types of social organization employing human and artificial intelligence.     

Ultrasound-assisted of alkali chloride separation using bulk ionic liquid membrane

An ultrasonic-assisted separation of alkali chloride (LiCl, NaCl, and KCl) salts have been carried out using of an hydrophobic ionic liquid membrane (ILM). The ionic liquid 1-butyl-3-methylimidazolium hexafluorophosphate and tributyl phosphate mixture have been used as ILM. An ultrasonic probe with different frequencies (25, 100, and 250) kHz have been applied as source of ultrasound generator with different times of sonication (2, 5, and 10) min in three phases system containing feed, ILM, and receiver in osmotic U-shaped tube. Also, 250, 500, and 1000 ppm of the feed (alkali chloride) concentration have been used to separate. The frequency of 250 kHz with higher sonication time provides optimum condition for separation of LiCl with lower feed concentration. The thermodynamic properties such as density and speed of sound and the related thermodynamic properties have been calculated to optimize ILM composition (x_IL = 0.45) for ultrasound-separation.     

A Traditional Scientific Perspective on the Integrated Information Theory of Consciousness

This paper assesses two different theories for explaining consciousness, a phenomenon that is widely considered amenable to scientific investigation despite its puzzling subjective aspects. I focus on Integrated Information Theory (IIT), which says that consciousness is integrated information (as ϕ^Max) and says even simple systems with interacting parts possess some consciousness. First, I evaluate IIT on its own merits. Second, I compare it to a more traditionally derived theory called Neurobiological Naturalism (NN), which says consciousness is an evolved, emergent feature of complex brains. Comparing these theories is informative because it reveals strengths and weaknesses of each, thereby suggesting better ways to study consciousness in the future. IIT’s strengths are the reasonable axioms at its core; its strong logic and mathematical formalism; its creative “experience-first” approach to studying consciousness; the way it avoids the mind-body (“hard”) problem; its consistency with evolutionary theory; and its many scientifically testable predictions. The potential weakness of IIT is that it contains stretches of logic-based reasoning that were not checked against hard evidence when the theory was being constructed, whereas scientific arguments require such supporting evidence to keep the reasoning on course. This is less of a concern for the other theory, NN, because it incorporated evidence much earlier in its construction process. NN is a less mature theory than IIT, less formalized and quantitative, and less well tested. However, it has identified its own neural correlates of consciousness (NCC) and offers a roadmap through which these NNCs may answer the questions of consciousness using the hypothesize-test-hypothesize-test steps of the scientific method.     

Deep Learning for Walking Behaviour Detection in Elderly People Using Smart Footwear

The increase in the proportion of elderly in Europe brings with it certain challenges that society needs to address, such as custodial care. We propose a scalable, easily modulated and live assistive technology system, based on a comfortable smart footwear capable of detecting walking behaviour, in order to prevent possible health problems in the elderly, facilitating their urban life as independently and safety as possible. This brings with it the challenge of handling the large amounts of data generated, transmitting and pre-processing that information and analysing it with the aim of obtaining useful information in real/near-real time. This is the basis of information theory. This work presents a complete system aiming at elderly people that can detect different user behaviours/events (sitting, standing without imbalance, standing with imbalance, walking, running, tripping) through information acquired from 20 types of sensor measurements (16 piezoelectric pressure sensors, one accelerometer returning reading for the 3 axis and one temperature sensor) and warn the relatives about possible risks in near-real time. For the detection of these events, a hierarchical structure of cascading binary models is designed and applied using artificial neural network (ANN) algorithms and deep learning techniques. The best models are achieved with convolutional layered ANN and multilayer perceptrons. The overall event detection performance achieves an average accuracy and area under the ROC curve of 0.84 and 0.96, respectively.     

管流系统的可控扰动装置及其应用

阐述了具有可控人工扰动装置的低背景湍动液体管流系统的研制和评价要点在及在高分子减阻液研究中的应用，并指出了这种实验系统在液体内流研究中的重要意义。     

An efficient correlation for calculating compressibility factor of natural gases

Compressibility factor (z-factor) values of natural gases are necessary in most petroleum engineering calculations. Necessity arises when there are few available experimental data for the required composition, pressure and temperature conditions. One of the most common methods of calculating z-factor values is empirical correlation. Firstly, a new correlation based on the famous Standing-Katz (S-K) Chart is presented to predict z-factor values. The advantage of this correlation is that it is explicit inzand thus does not require an iterative solution as is required by other methods. Secondly, the comparison between new one and other correlations is carried out and the results indicate the superiority of the new correlation over the other correlations used to calculate z-factor.     

Formation of artificial micro‐pits on Al alloy with the photon rupture method and the localized corrosion behavior of the formed pits

An in situ artificial micro‐pit fabrication method with an area selective electrochemical measurement technique was applied to investigate the effect of the geometry of artificially formed pits on their localized corrosion behavior in anodized 1000 series aluminum. This technique enables the fabrication of artificial micro‐pits with different aspect ratios (pit depth/pit diameter) in solutions. The aspect ratios of the fabricated artificial micro‐pits in this experiment could be varied from 0.13 to 1.83 by controlling the irradiation time of the focused pulsed YAG laser beam. By applying a constant potential to the final laser‐beam‐irradiated spot in chloride environments, localized dissolution occurred only at the laser beam irradiated area, because the anodic oxide film acted as an insulator. The corrosion current and charge increase with increasing aspect ratio at any applied potential. Copyright © 2010 John Wiley & Sons, Ltd.