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We characterize the intrinsic properties of any FRB using its redshift z , spectral index α and energy E 33 in units of |$10^{33} \, {\rm J}$| emitted across 2128–2848 MHz in the FRB's rest frame. Provided that z is inferred from the measured extra-galactic dispersion measure DM Ex, the fluence F of the observed event defines a track in (α, E 33) space which we refer to as the 'energy track'. Here, we consider the energy tracks for a sample of 254 non-repeating low dispersion measure FRBs from the CHIME/FRB catalogue-1, and use these to determine the conditional energy distribution, n (E 33∣α), i.e. the number of FRBs in the interval Δ E 33 given a value of α. For all values of α, we can identify two distinct energy ranges indicating that there are possibly two distinct FRB populations. At high energies, the distribution is well fitted by a modified Schechter function whose slope and characteristic energy both increase with α. At low energies, the number of FRBs are in excess of the predictions of the modified Schechter function indicating that we may have a distinctly different population of low-energy FRBs. We have checked that our main findings are reasonably robust to the assumptions regarding the Galactic Halo and Host galaxy contributions to the dispersion measure. [ABSTRACT FROM AUTHOR]

In real-world scenarios, identifying the optimal number of clusters in a dataset is a difficult task due to insufficient knowledge. Therefore, the indispensability of sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several automatic clustering algorithms assisted by quantum-inspired metaheuristics have been developed in recent years. However, the literature lacks definitive documentation of the state-of-the-art quantum-inspired metaheuristic algorithms for automatically clustering datasets. This article presents a brief overview of the automatic clustering process to establish the importance of making the clustering process automatic. The fundamental concepts of the quantum computing paradigm are also presented to highlight the utility of quantum-inspired algorithms. This article thoroughly analyses some algorithms employed to address the automatic clustering of various datasets. The reviewed algorithms were classified according to their main sources of inspiration. In addition, some representative works of each classification were chosen from the existing works. Thirty-six such prominent algorithms were further critically analysed based on their aims, used mechanisms, data specifications, merits and demerits. Comparative results based on the performance and optimal computational time are also presented to critically analyse the reviewed algorithms. As such, this article promises to provide a detailed analysis of the state-of-the-art quantum-inspired metaheuristic algorithms, while highlighting their merits and demerits. [ABSTRACT FROM AUTHOR]

We consider a sample of 82 non-repeating fast radio bursts (FRBs) detected at Parkes, ASKAP, CHIME, and UTMOST, each of which operates over a different frequency range and has a different detection criterion. Using simulations, we perform a maximum-likelihood analysis to determine the FRB population model that best fits these data. Our analysis shows that models where the pulse scatter broadening increases moderately with redshift (z) are preferred to those where this increases very sharply or where scattering is absent. Further, models where the comoving event rate density is constant over z are preferred to those where it follows the cosmological star formation rate. Two models for the host dispersion measure (DMHost) distribution (a fixed and a random DMHost) are found to predict comparable results. We obtain the best-fitting parameter values |$\alpha =-1.53^{+0.29}_{-0.19}$|⁠ , |$\overline{E}_{33}=1.55^{+0.26}_{-0.22}$|⁠ , and γ = 0.77 ± 0.24. Here α is the spectral index, γ is the exponent of the Schechter luminosity function, and |$\overline{E}_{33}$| is the mean FRB energy in units of |$10^{33} \, {\rm J}$| across 2128–2848 MHz in the FRB rest frame. [ABSTRACT FROM AUTHOR]

Sentiment analysis (SA) is the study of human perception in any subject of practice. It retrieves data from datasets using natural language processing (NLP) methodologies and algorithms that are either regulation-based, blended, or rely on machine learning approaches. SA is garnering fame for its capacity to fit in a large chunk of data with user evaluations, uncover a trend, and come to a consensus derived from real facts rather than hypotheses established on a limited number of observations. The flexible nature of sentiment gathering has helped in playing a critical role in both commercial and research applications in the last few years. This study presents new sentiment analysis models based on Bidirectional Encoder Representations from Transformers (BERT) for both labeled and unlabeled datasets. The labeled datasets using supervised learning are modeled in a hybrid architecture of fine-tuned BERT and interval Type—2 fuzzy sets. The inclusion of interval Type-2 fuzzy logic for handling reluctance or inaccuracy in data shows commendable results for the labeled datasets. For the prediction of sentiments in unlabeled datasets, they are embedded through a BERT tokenizer with the help of a threshold and activation functions. The coupling of a multi-layer perceptron with the BERT parser substantially decreases the time and complexity compared to supervised learning. Both the models have been implemented on multiple datasets and have outperformed existing state-of-the-art techniques in this field. [ABSTRACT FROM AUTHOR]

The massive number of research articles on the Web makes it troublesome for researchers to identify related works that could meet their preferences and interests. Consequently, various network representation learning-based models have been proposed to produce citation recommendations. Nevertheless, these models do not exploit semantic relations and contextual information between the objects of bibliographic papers' networks, which can result in inadequate citation recommendations. Moreover, existing citation recommendation methods face problems such as lack of personalization, cold-start, and network sparsity. To mitigate such problems and produce individualized citation recommendations, we propose a heterogeneous network embedding model that jointly learns node representations by exploiting semantics corresponding to the author, time, context, field of study, citations, and topics. Compared to baseline models, the results produced by the proposed model over the DBLP datasets prove 10% and 12% improvement on mean average precision (MAP) and normalized discounted cumulative gain (nDCG@10) metrics, respectively. Also, the effectiveness of our model is analyzed on the cold-start papers and network sparsity problems, where it gains 12% and 9% better MAP and recall@10 scores, respectively. [ABSTRACT FROM AUTHOR]

• A Qutrit Genetic Algorithm is proposed for thresholding of hyperspectral images. • A new quantum mutation procedure is implemented to bring diversity in the offsprings. • Interactive Information method and BS-NET-Conv Network reduce the number of bands. • Modified Otsu Criterion and Masi entropy are used as fitness functions. • A quantum disaster operation prevents the population to get stuck into local optima. Hyperspectral images contain rich spectral information about the captured area. Exploiting the vast and redundant information, makes segmentation a difficult task. In this paper, a Qutrit Genetic Algorithm is proposed which exploits qutrit based chromosomes for optimization. Ternary quantum logic based selection and crossover operators are introduced in this paper. A new qutrit based mutation operator is also introduced to bring diversity in the off-springs. In the preprocessing stage two methods, called Interactive Information method and Band Selection Convolutional Neural Network are used for band selection. The modified Otsu Criterion and Masi entropy are employed as the fitness functions to obtain optimum thresholds. A quantum based disaster operation is applied to prevent the quantum population from getting stuck in local optima. The proposed algorithm is applied on the Salinas Dataset, the Pavia Centre Dataset and the Indian Pines dataset for experimental purpose. It is compared with classical Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Gray Wolf Optimizer, Harris Hawk Optimization, Qubit Genetic Algorithm and Qubit Particle Swarm Optimization to establish its effectiveness. The peak signal-to-noise ratio and S ø rensen-Dice Similarity Index are applied to the thresholded images to determine the segmentation accuracy. The segmented images obtained from the proposed method are also compared with those obtained by two supervised methods, viz., U-Net and Hybrid Spectral Convolutional Neural Network. In addition to this, a statistical superiority test, called the one-way ANOVA test, is also conducted to judge the efficacy of the proposed algorithm. Finally, the proposed algorithm is also tested on various real life images to establish its diversity and efficiency. [ABSTRACT FROM AUTHOR]

• Smoke detection and localization in both clear and hazy outdoor environments. • Using a lightweight CNN architecture called EfficientNet for smoke detection. • Employing DeepLabv3+ semantic segmentation architecture for smoke localization. • Pixel-wise annotation of a new benchmark dataset for smoke semantic segmentation. • Outperformed existing smoke detection and segmentation methods. Fire disaster throughout the globe causes social, environmental, and economical damage, making its early detection and instant reporting essential for saving human lives and properties. Smoke detection plays a key role in early fire detection but majority of the existing methods are limited to either indoor or outdoor surveillance environments, with poor performance for hazy scenarios. In this paper, we present a Convolutional Neural Network (CNN)-based smoke detection and segmentation framework for both clear and hazy environments. Unlike existing methods, we employ an efficient CNN architecture, termed EfficientNet, for smoke detection with better accuracy. We also segment the smoke regions using DeepLabv3+, which is supported by effective encoders and decoders along with a pixel-wise classifier for optimum localization. Our smoke detection results evince a noticeable gain up to 3% in accuracy and a decrease of 0.46% in False Alarm Rate (FAR), while segmentation reports a significant increase of 2% and 1% in global accuracy and mean Intersection over Union (IoU) scores, respectively. This makes our method a best fit for smoke detection and segmentation in real-world surveillance settings. [ABSTRACT FROM AUTHOR]