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

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.

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The CMS High Granularity Calorimeter for HL-LHC

International Journal of Modern Physics Conference Series ◽

10.1142/s2010194518600753 ◽

2018 ◽

Vol 46 ◽

pp. 1860075

Author(s):

L. Mastrolorenzo

Keyword(s):

Fine Structure ◽

Large Fraction ◽

Particle Identification ◽

Particle Flow ◽

Radiation Tolerance ◽

High Luminosity ◽

Good Energy Resolution ◽

Silicon Sensors ◽

Event Reconstruction ◽

Good Energy

The High Luminosity LHC (HL-LHC) will integrate 10 times more luminosity than the LHC, posing significant challenges for radiation tolerance and event pileup on detectors, especially for forward calorimetry, and hallmarks the issue for future colliders. As part of its HL-LHC upgrade program, the CMS Collaboration is designing a High Granularity Calorimeter (HGCAL) to replace the existing endcap calorimeters. It features unprecedented transverse and longitudinal segmentation for both electromagnetic (CE-E) and hadronic (CE-H) compartments. This will facilitate particle-flow (PF) calorimetry, where the fine structure of showers can be measured and used to enhance pileup rejection and particle identification, whilst still achieving good energy resolution. The CE-E and a large fraction of CE-H will be based on hexagonal silicon sensors of [Formula: see text] cell size, with the remainder of the CE-H based on highly-segmented scintillators with SiPM readout. The intrinsic high-precision timing capabilities of the silicon sensors will add an extra dimension to event reconstruction, especially in terms of pileup rejection. An overview of the HGCAL project is presented in this paper.

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Overview of Machine Learning Approaches for Wireless Communication

Research Anthology on Artificial Intelligence Applications in Security ◽

10.4018/978-1-7998-7705-9.ch069 ◽

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.

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Intelligent Neural Network Schemes for Multi-Class Classification

Applied Sciences ◽

10.3390/app9194036 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4036 ◽

Cited By ~ 1

Author(s):

You ◽

Wu ◽

Lee ◽

Liu

Keyword(s):

Neural Network ◽

Clustering Algorithm ◽

Classification Problem ◽

Machine Learning Techniques ◽

Training Dataset ◽

Reduction Techniques ◽

Learning Techniques ◽

Benchmark Datasets ◽

Dimensionality Reduction Techniques ◽

Multi Class Classification

Multi-class classification is a very important technique in engineering applications, e.g., mechanical systems, mechanics and design innovations, applied materials in nanotechnologies, etc. A large amount of research is done for single-label classification where objects are associated with a single category. However, in many application domains, an object can belong to two or more categories, and multi-label classification is needed. Traditionally, statistical methods were used; recently, machine learning techniques, in particular neural networks, have been proposed to solve the multi-class classification problem. In this paper, we develop radial basis function (RBF)-based neural network schemes for single-label and multi-label classification, respectively. The number of hidden nodes and the parameters involved with the basis functions are determined automatically by applying an iterative self-constructing clustering algorithm to the given training dataset, and biases and weights are derived optimally by least squares. Dimensionality reduction techniques are adopted and integrated to help reduce the overfitting problem associated with the RBF networks. Experimental results from benchmark datasets are presented to show the effectiveness of the proposed schemes.

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Study on Intrusion Detection System Based on Data Mining

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.713-715.2499 ◽

2015 ◽

Vol 713-715 ◽

pp. 2499-2502

Author(s):

Jiang Kun Mao ◽

Fan Zhan

Keyword(s):

Data Mining ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Clustering Algorithm ◽

Detection Efficiency ◽

Detection System ◽

Machine Learning Techniques ◽

Cluster Center ◽

Security Technology ◽

Learning Techniques

Intrusion detection system as a proactive network security technology, is necessary and reasonable to add a static defense. However, the traditional exceptions and errors detecting exist issues of leakage police, the false alarm rate or maintenance difficult. In this paper, The intrusion detection system based on data mining with statistics, machine learning techniques in the detection performance, robustness, self-adaptability has a great advantage. The system improves the K-means clustering algorithm, focus on solving two questions of the cluster center node selection and discriminating of clustering properties, the test shows that the system further enhance the detection efficiency of the system.

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Machine Learning in KM3NeT

EPJ Web of Conferences ◽

10.1051/epjconf/201920705004 ◽

2019 ◽

Vol 207 ◽

pp. 05004 ◽

Cited By ~ 1

Author(s):

Chiara De Sio

Keyword(s):

Machine Learning ◽

Network Models ◽

High Energy ◽

Particle Identification ◽

Machine Learning Techniques ◽

Neutrino Interaction ◽

Mass Hierarchy ◽

Reconstruction Algorithms ◽

Neural Network Models ◽

Neutrino Mass Hierarchy

The KM3NeT Collaboration is building a network of underwater Cherenkov telescopes at two sites in the Mediterranean Sea, with the main goals of investigating astrophysical sources of high-energy neutrinos (ARCA) and of determining the neutrino mass hierarchy (ORCA). Various Machine Learning techniques, such as Random Forests, BDTs, Shallow and Deep Networks are being used for diverse tasks, such as event-type and particle identification, energy/direction estimation, source identification, signal/background discrimination and data analysis, with sound results as well as promising research paths. The main focus of this work is the application of Convolutional Neural Network models to the tasks of neutrino interaction classification, as well as the estimation of energy and direction of the propagating particles. The performances are also compared to those of the standard reconstruction algorithms used in the Collaboration.

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Advancing Artificial Intelligence in Sensors, Signals, and Imaging Informatics

Yearbook of Medical Informatics ◽

10.1055/s-0039-1677943 ◽

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.

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A Novel Semi-Supervised Fuzzy C-Means Clustering Algorithm Using Multiple Fuzzification Coefficients

Algorithms ◽

10.3390/a14090258 ◽

2021 ◽

Vol 14 (9) ◽

pp. 258

Author(s):

Tran Dinh Khang ◽

Manh-Kien Tran ◽

Michael Fowler

Keyword(s):

Machine Learning ◽

Clustering Algorithm ◽

Machine Learning Techniques ◽

Unsupervised Machine Learning ◽

Practical Applications ◽

Fuzzy C Means ◽

Learning Techniques ◽

Fuzzy C Means Clustering ◽

Data Points ◽

Data Elements

Clustering is an unsupervised machine learning method with many practical applications that has gathered extensive research interest. It is a technique of dividing data elements into clusters such that elements in the same cluster are similar. Clustering belongs to the group of unsupervised machine learning techniques, meaning that there is no information about the labels of the elements. However, when knowledge of data points is known in advance, it will be beneficial to use a semi-supervised algorithm. Within many clustering techniques available, fuzzy C-means clustering (FCM) is a common one. To make the FCM algorithm a semi-supervised method, it was proposed in the literature to use an auxiliary matrix to adjust the membership grade of the elements to force them into certain clusters during the computation. In this study, instead of using the auxiliary matrix, we proposed to use multiple fuzzification coefficients to implement the semi-supervision component. After deriving the proposed semi-supervised fuzzy C-means clustering algorithm with multiple fuzzification coefficients (sSMC-FCM), we demonstrated the convergence of the algorithm and validated the efficiency of the method through a numerical example.

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Hierarchical Sparse Subspace Clustering (HESSC): An Automatic Approach for Hyperspectral Image Analysis

Remote Sensing ◽

10.3390/rs12152421 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2421

Author(s):

Kasra Rafiezadeh Shahi ◽

Mahdi Khodadadzadeh ◽

Laura Tusa ◽

Pedram Ghamisi ◽

Raimon Tolosana-Delgado ◽

...

Keyword(s):

Machine Learning ◽

Clustering Algorithm ◽

Hyperspectral Image ◽

Imaging Techniques ◽

Clustering Algorithms ◽

Machine Learning Techniques ◽

Mixed Data ◽

Number Of Clusters ◽

Hyperspectral Image Analysis ◽

Learning Techniques

Hyperspectral imaging techniques are becoming one of the most important tools to remotely acquire fine spectral information on different objects. However, hyperspectral images (HSIs) require dedicated processing for most applications. Therefore, several machine learning techniques were proposed in the last decades. Among the proposed machine learning techniques, unsupervised learning techniques have become popular as they do not need any prior knowledge. Specifically, sparse subspace-based clustering algorithms have drawn special attention to cluster the HSI into meaningful groups since such algorithms are able to handle high dimensional and highly mixed data, as is the case in real-world applications. Nonetheless, sparse subspace-based clustering algorithms usually tend to demand high computational power and can be time-consuming. In addition, the number of clusters is usually predefined. In this paper, we propose a new hierarchical sparse subspace-based clustering algorithm (HESSC), which handles the aforementioned problems in a robust and fast manner and estimates the number of clusters automatically. In the experiment, HESSC is applied to three real drill-core samples and one well-known rural benchmark (i.e., Trento) HSI datasets. In order to evaluate the performance of HESSC, the performance of the new proposed algorithm is quantitatively and qualitatively compared to the state-of-the-art sparse subspace-based algorithms. In addition, in order to have a comparison with conventional clustering algorithms, HESSC’s performance is compared with K-means and FCM. The obtained clustering results demonstrate that HESSC performs well when clustering HSIs compared to the other applied clustering algorithms.

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

Symmetry ◽

10.3390/sym14010034 ◽

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.

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GPU-based Clustering Algorithm for the CMS High Granularity Calorimeter

EPJ Web of Conferences ◽

10.1051/epjconf/202024505005 ◽

2020 ◽

Vol 245 ◽

pp. 05005

Author(s):

Ziheng Chen ◽

Antonio Di Pilato ◽

Felice Pantaleo ◽

Marco Rovere

Keyword(s):

Phase Ii ◽

Clustering Algorithm ◽

Software Framework ◽

High Luminosity ◽

Silicon Sensors ◽

Density Based Clustering ◽

Pile Up ◽

The Future

The future High Luminosity LHC (HL-LHC) is expected to deliver about 5 times higher instantaneous luminosity than the present LHC, resulting in pile-up up to 200 interactions per bunch crossing (PU200). As part of the phase-II upgrade program, the CMS collaboration is developing a new endcap calorimeter system, the High Granularity Calorimeter (HGCAL), featuring highly-segmented hexagonal silicon sensors and scintillators with more than 6 million channels. For each event, the HGCAL clustering algorithm needs to group more than 105 hits into clusters. As consequence of both high pile-up and the high granularity, the HGCAL clustering algorithm is confronted with an unprecedented computing load. CLUE (CLUsters of Energy) is a fast fullyparallelizable density-based clustering algorithm, optimized for high pile-up scenarios in high granularity calorimeters. In this paper, we present both CPU and GPU implementations of CLUE in the application of HGCAL clustering in the CMS Software framework (CMSSW). Comparing with the previous HGCAL clustering algorithm, CLUE on CPU (GPU) in CMSSW is 30x (180x) faster in processing PU200 events while outputting almost the same clustering results.

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