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We report improved wave function for mesons in QCD potential model approach using multiplicative method for solution of Schr\"{o}dinger equation for the extreme cases of inter-quark separations ($r\rightarrow0$ and $r\rightarrow\infty$). Using the wave function we find the range of the scale factor c of the Cornell potential with reference to the mass scale of B and D sectors heavy-flavoured mesons. With our computed range of c, we also explore the critical inter-quark separation which should govern the choice of parent-child terms in perturbation method out of the linear and Coulombic terms of the Cornell potential.

In this study we explore the astrophysical implications of pressure anisotropy on the physical characteristics of millisecond pulsars within the framework of $f(Q)$ gravity. Starting off with the field equations for anisotropic matter distributions, we adopt the physically salient Durgapal-Fuloria ansatz together with a well-motivated anisotropic factor for the interior matter distribution. This leads to a nonlinear second order differential equation which is integrated to give the complete gravitational and thermodynamical properties of the stellar object. The resulting model is subjected to rigorous tests to ensure that it qualifies as a physically viable compact object within the $f(Q)$-gravity framework. We study in detail two factors, one being non-metricity (gravitational interaction) and the other the pressure anisotropy (matter variation) and their contributions to the mass, radius and stability of the star. Our analyses indicate that our models are well-behaved, singularity-free and can account for the existence of a wide range of observed pulsars with masses ranging from 2.08 to 2.67 $M_{\odot}$, with the upper value being in the so-called {\em mass gap} regime observed in gravitational events such as GW190814. A novel finding of this work is two-fold: an increase in the non-metricity factor (fixed anisotropy) leads to a spectrum of pulsars with radii ranging from a minimum of 10 km whereas a variation in anisotropy (fixed non-metricity) restricts the radii to a narrow window between ($12 - 13.2$) km for a 450 Hz pulsar. In addition, contributions from anisotropy outweigh the impact of nonmetricity in predicting the masses of pulsars with the ratio, $\left(\frac{M_\Delta}{M_Q}\right)_{max} \approx 2.43 $ for low mass pulsars., Comment: 6 figures and 2 tables (Submssion for JCAP)

Epilepsy is one of the most common neurological diseases globally, affecting around 50 million people worldwide. Fortunately, up to 70 percent of people with epilepsy could live seizure-free if properly diagnosed and treated, and a reliable technique to monitor the onset of seizures could improve the quality of life of patients who are constantly facing the fear of random seizure attacks. The scalp-based EEG test, despite being the gold standard for diagnosing epilepsy, is costly, necessitates hospitalization, demands skilled professionals for operation, and is discomforting for users. In this paper, we propose EarSD, a novel lightweight, unobtrusive, and socially acceptable ear-worn system to detect epileptic seizure onsets by measuring the physiological signals from behind the user's ears. EarSD includes an integrated custom-built sensing, computing, and communication PCB to collect and amplify the signals of interest, remove the noises caused by motion artifacts and environmental impacts, and stream the data wirelessly to the computer or mobile phone nearby, where data are uploaded to the host computer for further processing. We conducted both in-lab and in-hospital experiments with epileptic seizure patients who were hospitalized for seizure studies. The preliminary results confirm that EarSD can detect seizures with up to 95.3 percent accuracy by just using classical machine learning algorithms.

In this paper, we review the theoretical basis for generation of gravitational waves and the detection techniques used to detect a gravitational wave. To materialize this goal in a thorough way we first start with a mathematical background for general relativity from which a clue for gravitational wave was conceived by Einstein. Thereafter we give the classification scheme of gravitational waves such as (i) continuous gravitational waves, (ii) compact binary inspiral gravitational waves and (iii) stochastic gravitational wave. Necessary mathematical insight into gravitational waves from binaries are also dealt with which follows detection of gravitational waves based on the frequency classification. Ground based observatories as well as space borne gravitational wave detectors are discussed in a length. We have provided an overview on the inflationary gravitational waves. In connection to data analysis by matched filtering there are a few highlights on the techniques, e.g. (i) Random noise, (ii) power spectrum, (iii) shot noise, and (iv) Gaussian noise. Optimal detection statistics for a gravitational wave detection is also in the pipeline of the discussion along with detailed necessity of the matched filter and deep learning., Comment: 23 pages, 9 figures, Accepted in International Journal of Modern Physics D (Special Issue)

Magnetic reconnection is a fundamental mechanism through which energy stored in magnetic fields is released explosively on a massive scale, they could be presented as eruptive or confined flares, depending on their association with coronal mass ejections (CMEs). Several previous works have concluded that there is no correlation between flare duration and flare class, however, their sample sizes are skewed towards B and C classes; they hardly represent the higher classes. Therefore, we studied a sample without extreme events in order to determine the correlation between flare duration and flare type (confined and eruptive). We examined $33$ flares with classes between M5 to X5 within $45^{\circ}$ of the disk centres, using data from the Atmospheric Imaging Assembly (AIA) and the Helioseismic and Magnetic Imager (HMI). We find that the linear correlation between flare class against flare duration by full width half maximum (FWHM) in general is weak ($r=0.19$); however, confined flares have a significant correlation ($r=0.58$) compared to eruptive types ($r=0.08$). Also, the confined M class flares' average duration is less than half of the eruptive flares. Similarly, confined flares have a higher correlation ($r=0.89$) than eruptive flares ($r=0.60$) between flare classes against magnetic reconnection flux. In this work, a balanced sample size between flare types is an important strategy for obtaining a reliable quantitative comparison., Comment: 26 pages, 11 figures

Head-based signals such as EEG, EMG, EOG, and ECG collected by wearable systems will play a pivotal role in clinical diagnosis, monitoring, and treatment of important brain disorder diseases. However, the real-time transmission of the significant corpus physiological signals over extended periods consumes substantial power and time, limiting the viability of battery-dependent physiological monitoring wearables. This paper presents a novel deep-learning framework employing a variational autoencoder (VAE) for physiological signal compression to reduce wearables' computational complexity and energy consumption. Our approach achieves an impressive compression ratio of 1:293 specifically for spectrogram data, surpassing state-of-the-art compression techniques such as JPEG2000, H.264, Direct Cosine Transform (DCT), and Huffman Encoding, which do not excel in handling physiological signals. We validate the efficacy of the compressed algorithms using collected physiological signals from real patients in the Hospital and deploy the solution on commonly used embedded AI chips (i.e., ARM Cortex V8 and Jetson Nano). The proposed framework achieves a 91% seizure detection accuracy using XGBoost, confirming the approach's reliability, practicality, and scalability.

Desire is a set of human aspirations and wishes that comprise verbal and cognitive aspects that drive human feelings and behaviors, distinguishing humans from other animals. Understanding human desire has the potential to be one of the most fascinating and challenging research domains. It is tightly coupled with sentiment analysis and emotion recognition tasks. It is beneficial for increasing human-computer interactions, recognizing human emotional intelligence, understanding interpersonal relationships, and making decisions. However, understanding human desire is challenging and under-explored because ways of eliciting desire might be different among humans. The task gets more difficult due to the diverse cultures, countries, and languages. Prior studies overlooked the use of image-text pairwise feature representation, which is crucial for the task of human desire understanding. In this research, we have proposed a unified multimodal transformer-based framework with image-text pair settings to identify human desire, sentiment, and emotion. The core of our proposed method lies in the encoder module, which is built using two state-of-the-art multimodal transformer models. These models allow us to extract diverse features. To effectively extract visual and contextualized embedding features from social media image and text pairs, we conducted joint fine-tuning of two pre-trained multimodal transformer models: Vision-and-Language Transformer (ViLT) and Vision-and-Augmented-Language Transformer (VAuLT). Subsequently, we use an early fusion strategy on these embedding features to obtain combined diverse feature representations of the image-text pair. This consolidation incorporates diverse information about this task, enabling us to robustly perceive the context and image pair from multiple perspectives., Comment: 28 pages, 4 figures

We report the masses of $B$ and $D$ sectors heavy-flavoured mesons obtained by using our recently developed meson wave function employing potential model approach with linear confinement term in potential as parent in the perturbation method. As the wave-function involves infinite Airy's polynomial series, in carrying out the mass calculation, to avoid divergences, we have introduced some cut-off parameter for inter-quark separation. Our results for ground state masses of heavy-flavoured $B$ and $D$ sector mesons are reasonably closer to the PDG masses., Comment: 8 pages, 4 tables, 1 figure

The rapid growth of edge data generated by mobile devices and applications deployed at the edge of the network has exacerbated the problem of information overload. As an effective way to alleviate information overload, recommender system can improve the quality of various services by adding application data generated by users on edge devices, such as visual and textual information, on the basis of sparse rating data. The visual information in the movie trailer is a significant part of the movie recommender system. However, due to the complexity of visual information extraction, data sparsity cannot be remarkably alleviated by merely using the rough visual features to improve the rating prediction accuracy. Fortunately, the convolutional neural network can be used to extract the visual features precisely. Therefore, the end-to-end neural image caption (NIC) model can be utilized to obtain the textual information describing the visual features of movie trailers. This paper proposes a trailer inception probabilistic matrix factorization model called Ti-PMF, which combines NIC, recurrent convolutional neural network, and probabilistic matrix factorization models as the rating prediction model. We implement the proposed Ti-PMF model with extensive experiments on three real-world datasets to validate its effectiveness. The experimental results illustrate that the proposed Ti-PMF outperforms the existing ones.

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field., Comment: 19 pages, 6 figures

It is generally believed that wormholes are supported by exotic matter violating Null Energy Condition (NEC). However, various studies of wormhole geometries under Lovelock theories of gravity have reported existence of wormhole supported by matter satisfying NEC. Being inspired by these results, we explore the possibility of the existence of wormhole supported by normal quark matter in third order Lovelock gravity theory. Well known MIT Bag Model Equation of state is chosen for describing the quark matter. Taking physically acceptable approximations, we solve the field equations for shape function which satisfies flare out condition. The residual of the approximate solution is studied for accuracy and found to be acceptable., Comment: 15 pages, 6 figures