scholarly journals Symmetry and Combinatorial Concepts for Cyclopolyarenes, Nanotubes and 2D-Sheets: Enumerations, Isomers, Structures Spectra & Properties

Symmetry ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 34
Author(s):  
Krishnan Balasubramanian
Keyword(s):  
Generating Functions ◽  
Machine Learning Techniques ◽  
Machine Construction ◽  
Multiple Quantum ◽  
Spectral Techniques ◽  
Learning Techniques ◽  
Recent Developments ◽  
Topological Measures ◽  
Metal Organic ◽  
Vertex Partitions

This review article highlights recent developments in symmetry, combinatorics, topology, entropy, chirality, spectroscopy and thermochemistry pertinent to 2D and 1D nanomaterials such as circumscribed-cyclopolyarenes and their heterocyclic analogs, carbon and heteronanotubes and heteronano wires, as well as tessellations of cyclopolyarenes, for example, kekulenes, septulenes and octulenes. We establish that the generalization of Sheehan’s modification of Pólya’s theorem to all irreducible representations of point groups yields robust generating functions for the enumeration of chiral, achiral, position isomers, NMR, multiple quantum NMR and ESR hyperfine patterns. We also show distance, degree and graph entropy based topological measures combined with techniques for distance degree vector sequences, edge and vertex partitions of nanomaterials yield robust and powerful techniques for thermochemistry, bond energies and spectroscopic computations of these species. We have demonstrated the existence of isentropic tessellations of kekulenes which were further studied using combinatorial, topological and spectral techniques. The combinatorial generating functions obtained not only enumerate the chiral and achiral isomers but also aid in the machine construction of various spectroscopic and ESR hyperfine patterns of the nanomaterials that were considered in this review. Combinatorial and topological tools can become an integral part of robust machine learning techniques for rapid computation of the combinatorial library of isomers and their properties of nanomaterials. Future applications to metal organic frameworks and fullerene polymers are pointed out.

scholarly journals A novel reconstruction framework for an imaging calorimeter for HL-LHC

2021 ◽  
Vol 251 ◽  
pp. 03013
Author(s):  
Leonardo Cristella ◽  
Keyword(s):  
Clustering Algorithm ◽  
Particle Identification ◽  
Machine Learning Techniques ◽  
Radiation Tolerance ◽  
Reconstruction Algorithms ◽  
High Luminosity ◽  
Silicon Sensors ◽  
Learning Techniques ◽  
Recent Developments ◽  
Speed And Accuracy

To sustain the harsher conditions of the high-luminosity LHC, the CMS collaboration is designing a novel endcap calorimeter system. The new calorimeter will predominantly use silicon sensors to achieve sufficient radiation tolerance and will maintain highly-granular information in the readout to help mitigate the effects of pileup. In regions characterised by lower radiation levels, small scintillator tiles with individual on-tile SiPM readout are employed. A unique reconstruction framework (TICL: The Iterative CLustering) is being developed to fully exploit the granularity and other significant detector features, such as particle identification and precision timing, with a view to mitigate pileup in the very dense environment of HL-LHC. The inputs to the framework are clusters of energy deposited in individual calorimeter layers. Clusters are formed by a density-based algorithm. Recent developments and tunes of the clustering algorithm will be presented. To help reduce the expected pressure on the computing resources in the HL-LHC era, the algorithms and their data structures are designed to be executed on GPUs. Preliminary results will be presented on decreases in clustering time when using GPUs versus CPUs. Ideas for machine-learning techniques to further improve the speed and accuracy of reconstruction algorithms will be presented.

Overview of Machine Learning Approaches for Wireless Communication

2021 ◽  
pp. 1579-1597
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

scholarly journals Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics

2019 ◽  
Vol 28 (01) ◽  
pp. 115-117
Author(s):  
William Hsu ◽  
Christian Baumgartner ◽  
Thomas Deserno ◽  
Keyword(s):  
Artificial Intelligence ◽  
Imaging Informatics ◽  
Machine Learning Techniques ◽  
Mesh Terms ◽  
Learning Techniques ◽  
Different Types ◽  
Recent Developments ◽  
Hidden Patterns

Objective: To identify research works that exemplify recent developments in the field of sensors, signals, and imaging informatics. Method: A broad literature search was conducted using PubMed and Web of Science, supplemented with individual papers that were nominated by section editors. A predefined query made from a combination of Medical Subject Heading (MeSH) terms and keywords were used to search both sources. Section editors then filtered the entire set of retrieved papers with each paper having been reviewed by two section editors. Papers were assessed on a three-point Likert scale by two section editors, rated from 0 (do not include) to 2 (should be included). Only papers with a combined score of 2 or above were considered. Results: A search for papers was executed at the start of January 2019, resulting in a combined set of 1,459 records published in 2018 in 119 unique journals. Section editors jointly filtered the list of candidates down to 14 nominations. The 14 candidate best papers were then ranked by a group of eight external reviewers. Four papers, representing different international groups and journals, were selected as the best papers by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Conclusions: The fields of sensors, signals, and imaging informatics have rapidly evolved with the application of novel artificial intelligence/machine learning techniques. Studies have been able to discover hidden patterns and integrate different types of data towards improving diagnostic accuracy and patient outcomes. However, the quality of papers varied widely without clear reporting standards for these types of models. Nevertheless, a number of papers have demonstrated useful techniques to improve the generalizability, interpretability, and reproducibility of increasingly sophisticated models.

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2020 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

scholarly journals Failure-Experiment-Supported Optimization of Poorly-Reproducible Synthetic Conditions for Novel Lanthanide Metal-Organic Frameworks

2021 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Impedance Measurements ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

scholarly journals Failure-experiment-supported optimization of poorly reproducible synthetic conditions for novel lanthanide metal-organic frameworks with two-dimensional secondary building units

2021 ◽  
Author(s):  
Yu Kitamura ◽  
Emi Terado ◽  
Zechen Zhang ◽  
Hirofumi Yoshikawa ◽  
Tomoko Inose ◽  
...  
Keyword(s):  
Machine Learning ◽  
Water Adsorption ◽  
Metal Organic Frameworks ◽  
Machine Learning Techniques ◽  
Initial Screening ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Secondary Building Units ◽  
Metal Organic ◽  
Lanthanide Metal

A series of novel metal organic frameworks with lanthanide double-layer-based inorganic subnetworks (KGF-3) was synthesized assisted by machine learning. Pure KGF-3 was difficult to isolate in the initial screening experiments. The synthetic conditions were successfully optimized by extracting the dominant factors for KGF-3 synthesis using two machine-learning techniques. Cluster analysis was used to classify the obtained PXRD patterns of the products and to decide automatically whether the experiments were successful or had failed. Decision tree analysis was used to visualize the experimental results, with the factors that mainly affected the synthetic reproducibility being extracted. The water adsorption isotherm revealed that KGF-3 possesses unique hydrophilic pores, and impedance measurements demonstrated good proton conductivities (σ = 5.2 × 10<sup>−4</sup> S cm<sup>−1</sup> for KGF-3(Y)) at a high temperature (363 K) and high relative humidity (95%).<br>

Overview of Machine Learning Approaches for Wireless Communication

2019 ◽  
pp. 123-140 ◽  
Author(s):  
Tolga Ensari ◽  
Melike Günay ◽  
Yağız Nalçakan ◽  
Eyyüp Yildiz
Keyword(s):  
Machine Learning ◽  
Wireless Communications ◽  
Smart Phones ◽  
Machine Learning Techniques ◽  
Learning Approaches ◽  
Research Areas ◽  
Learning Techniques ◽  
Recent Developments ◽  
Security And Reliability ◽  
Day By Day

Machine learning is one of the most popular research areas, and it is commonly used in wireless communications and networks. Security and fast communication are among of the key requirements for next generation wireless networks. Machine learning techniques are getting more important day-by-day since the types, amount, and structure of data is continuously changing. Recent developments in smart phones and other devices like drones, wearable devices, machines with sensors need reliable communication within internet of things (IoT) systems. For this purpose, artificial intelligence can increase the security and reliability and manage the data that is generated by the wireless systems. In this chapter, the authors investigate several machine learning techniques for wireless communications including deep learning, which represents a branch of artificial neural networks.

scholarly journals A Survey on Emotion Recognition for Human Robot Interaction

2021 ◽  
Vol 28 (2) ◽  
pp. 125-146
Keyword(s):  
Machine Learning ◽  
Emotion Recognition ◽  
Human Robot Interaction ◽  
Machine Learning Techniques ◽  
Emotional States ◽  
Robot Interaction ◽  
Artificial Intelligent ◽  
Learning Techniques ◽  
Human Counterpart ◽  
Recent Developments

With the recent developments of technology and the advances in artificial intelligent and machine learning techniques, it becomes possible for the robot to acquire and show the emotions as a part of Human-Robot Interaction (HRI). An emotional robot can recognize the emotional states of humans so that it will be able to interact more naturally with its human counterpart in different environments. In this article, a survey on emotion recognition for HRI systems has been presented. The survey aims to achieve two objectives. Firstly, it aims to discuss the main challenges that face researchers when building emotional HRI systems. Secondly, it seeks to identify sensing channels that can be used to detect emotions and provides a literature review about recent researches published within each channel, along with the used methodologies and achieved results. Finally, some of the existing emotion recognition issues and recommendations for future works have been outlined.

scholarly journals Videogame Analytics, Surveillance, and Memory

2019 ◽  
Vol 17 (1/2) ◽  
pp. 161-168 ◽  
Author(s):  
Ben Egliston
Keyword(s):  
Machine Learning ◽  
Human Experience ◽  
Machine Learning Techniques ◽  
Theoretical Argument ◽  
Learning Techniques ◽  
Recent Developments ◽  
Bernard Stiegler ◽  
Short Essay

There is a growing suite of commercially available analytics tools platforms use within the context of videogaming. Increasingly, these analytics tools are reliant on the capture of data pertaining to player activity—using machine learning techniques to parse these data and create subscription based “guide” services to assist players in their gameplay. This is often spruiked by marketers as a supplement to one’s gameplay and a method for improving one’s own performance. Looking at the example of DotaPlus, a recent kind of machine learning based analytics platform in the popular game Dota 2, this short essay argues that recent developments in gaming analytics might be understood as what Bernard Stiegler calls mnemotechnics—essentially technologies that shape human experience and perception in various, often commercially desirable ways. The theoretical argument I advance here is that the digital traces of player activity, captured and fed back to users in the form of DotaPlus guides, significantly alters the experience of playing Dota 2—done in a way that is economically desirable for the game’s developer.

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