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We study the evolution of a newly formed magnetized neutron-star (NS) as a power source of gamma-ray bursts (GRBs) in the light of both gravitational wave (GW) and electromagnetic (EM) radiations. The compressible and incompressible fluids are employed in order to model the secular evolution of Maclaurian spheroids. It is shown that the GW and EM light curves evolve as a function of eccentricity and rotational frequency with time. We find that the light curve characteristics crucially depend on NS parameters such as magnitude and structure of magnetic field, ellipticity and the equation of state (EOS) of the fluid. The presence of X-ray flares, whose origins are not yet well understood, can be captured in our model regarding some specific nuclear EOSs. Our model allowing us to explain flares that occur within the wide range of $ 10$ to $10^4$ seconds and the peak luminosity in the order of $10^{46}$ - $10^{51}$ $\rm \text{erg}/s$ by using a reasonable set of parameters such as magnetic field strength around $10^{14}-10^{16}$ Gauss, the quadrupole to dipole ratio of magnetic field up to 500. By applying our model to a sample of GRB X-ray flares observed by Swift/XRT, we try to constraint the crucial parameters of a deformed magnetar via MCMC fitting method. Our analysis shows that ongoing and upcoming joint multi-messenger detections can be used to understand the nature of GRB's central engine and its evolution at the early times of the burst formation.

In this paper, we address a physical layer security (PLS) framework for the integrated sensing and semantic communication (ISASC) system, where a multi-antenna dual-function semantic base station serves multiple single-antenna semantic communication users (SCUs) and monitors a malicious sensing target (MST), in the presence of a single-antenna eavesdropper (EVE), with both the MST and EVE aiming to wiretap information from the SCUs' signals. To enhance PLS, we employ joint artificial noise (AN) and dedicated sensing signal (DSS) in addition to wiretap coding. To evaluate the sensing accuracy, we derive the Cramer-Rao bound (CRB) as a function of the communication, sensing, and AN beamforming (BF) vectors. Subsequently, to assess the PLS level of the ISASC system, we determine a closed-form expression for the semantic secrecy rate (SSR). To achieve an optimal trade-off region between these two competing objectives, we formulate a multi-objective optimization problem for the joint design of the BF vectors. We apply semi-definite programming, Gaussian randomization method, and golden-section search techniques to address this problem. Simulation results demonstrate that the proposed scheme outperforms baseline schemes, achieving a superior trade-off between SSR and CRB., Comment: 4 pages

Reducing the atmospheric haze and enhancing image clarity is crucial for computer vision applications. The lack of real-life hazy ground truth images necessitates synthetic datasets, which often lack diverse haze types, impeding effective haze type classification and dehazing algorithm selection. This research introduces the HazeSpace2M dataset, a collection of over 2 million images designed to enhance dehazing through haze type classification. HazeSpace2M includes diverse scenes with 10 haze intensity levels, featuring Fog, Cloud, and Environmental Haze (EH). Using the dataset, we introduce a technique of haze type classification followed by specialized dehazers to clear hazy images. Unlike conventional methods, our approach classifies haze types before applying type-specific dehazing, improving clarity in real-life hazy images. Benchmarking with state-of-the-art (SOTA) models, ResNet50 and AlexNet achieve 92.75\% and 92.50\% accuracy, respectively, against existing synthetic datasets. However, these models achieve only 80% and 70% accuracy, respectively, against our Real Hazy Testset (RHT), highlighting the challenging nature of our HazeSpace2M dataset. Additional experiments show that haze type classification followed by specialized dehazing improves results by 2.41% in PSNR, 17.14% in SSIM, and 10.2\% in MSE over general dehazers. Moreover, when testing with SOTA dehazing models, we found that applying our proposed framework significantly improves their performance. These results underscore the significance of HazeSpace2M and our proposed framework in addressing atmospheric haze in multimedia processing. Complete code and dataset is available on \href{https://github.com/tanvirnwu/HazeSpace2M} {\textcolor{blue}{\textbf{GitHub}}}., Comment: Accepted by ACM Multimedia 2024

Emerging dual-functional radar communication(RadCom) systems promise to revolutionize wireless systems by enabling radar sensing and communication on a shared platform, thereby enhancing spectral efficiency. However, the high transmit power required for efficient radar operation poses risks by potentially exceeding the electromagnetic field (EMF) exposure limits enforced by the regulations. To address this challenge, we propose an EMF-aware signalling design that enhances RadCom system performance while complying with EMF constraints. Our approach considers exposure levels not only experienced by network users but also in sensitive areas such as schools and hospitals, where the exposure must be further reduced. First, we model the exposure metric for the users and the sectors that encounter sensitive areas. Then, we design the waveform by exploiting the trade-off between radar and communication while satisfying the exposure constraints. We reformulate the problem as a convex optimization program and solve it in closed form using Karush Kuhn Tucker (KKT) conditions. The numerical results demonstrate the feasibility of developing a robust RadCom system with low electromagnetic (EM) radiations., Comment: Presented at IEEE PIMRC 2024, Valencia, Spain

Vehicle-to-everything (V2X) communication supports numerous tasks, from driving safety to entertainment services. To achieve a holistic view, vehicles are typically equipped with multiple sensors to compensate for undetectable blind spots. However, processing large volumes of multi-modal data increases transmission load, while the dynamic nature of vehicular networks adds to transmission instability. To address these challenges, we propose a novel framework, Generative Artificial intelligence (GAI)-enhanced multi-modal semantic communication (SemCom), referred to as G-MSC, designed to handle various vehicular network tasks by employing suitable analog or digital transmission. GAI presents a promising opportunity to transform the SemCom framework by significantly enhancing semantic encoding to facilitate the optimized integration of multi-modal information, enhancing channel robustness, and fortifying semantic decoding against noise interference. To validate the effectiveness of the G-MSC framework, we conduct a case study showcasing its performance in vehicular communication networks for predictive tasks. The experimental results show that the design achieves reliable and efficient communication in V2X networks. In the end, we present future research directions on G-MSC.

Deep learning (DL)-based channel state information (CSI) feedback has shown great potential in improving spectrum efficiency in massive MIMO systems. However, DL models optimized for specific environments often experience performance degradation in others due to model mismatch. To overcome this barrier in the practical deployment, we propose UniversalNet, an ID-photo-inspired universal CSI feedback framework that enhances model generalizability by standardizing the input format across diverse data distributions. Specifically, UniversalNet employs a standardized input format to mitigate the influence of environmental variability, coupled with a lightweight sparsity-aligning operation in the transformed sparse domain and marginal control bits for original format recovery. This enables seamless integration with existing CSI feedback models, requiring minimal modifications in preprocessing and postprocessing without updating neural network weights. Furthermore, we propose an efficient eigenvector joint optimization method to enhance the sparsity of the precoding matrix by projecting the channel correlation into the eigenspace, thus improving the implicit CSI compression efficiency. Test results demonstrate that UniversalNet effectively improves generalization performance and ensures precise CSI feedback, even in scenarios with limited training diversity and previously unseen CSI environments.

This paper develops an edge-device collaborative Generative Semantic Communications (Gen SemCom) framework leveraging pre-trained Multi-modal/Vision Language Models (M/VLMs) for ultra-low-rate semantic communication via textual prompts. The proposed framework optimizes the use of M/VLMs on the wireless edge/device to generate high-fidelity textual prompts through visual captioning/question answering, which are then transmitted over a wireless channel for SemCom. Specifically, we develop a multi-user Gen SemCom framework using pre-trained M/VLMs, and formulate a joint optimization problem of prompt generation offloading, communication and computation resource allocation to minimize the latency and maximize the resulting semantic quality. Due to the nonconvex nature of the problem with highly coupled discrete and continuous variables, we decompose it as a two-level problem and propose a low-complexity swap/leaving/joining (SLJ)-based matching algorithm. Simulation results demonstrate significant performance improvements over the conventional semanticunaware/non-collaborative offloading benchmarks.

Data preprocessing is a critical part of time series data analysis. Data from connected medical devices often have missing or abnormal values during acquisition. Handling such situations requires additional assumptions and domain knowledge. This can be time-consuming, and can introduce a significant bias affecting predictive model accuracy and thus, medical interpretation. To overcome this issue, we propose a new deep learning model to mitigate the preprocessing assumptions. The model architecture relies on a variational auto-encoder (VAE) to produce a preprocessing latent space, and a recurrent VAE to preserve the temporal dynamics of the data. We demonstrate the effectiveness of such an architecture on telemonitoring data to forecast glucose-level of diabetic patients. Our results show an improvement in terms of accuracy with respect of existing state-of-the-art methods and architectures.

We investigate a magnetic dipolar system influenced by Zeeman splitting, DM interaction, and KSEA exchange interaction, with an initial focus on quantum resource dynamics and a final application in modeling a quantum battery (QB). We analyze the effects of dephasing noise and thermal equilibrium on quantum resources, such as the $l_1$-norm of coherence, quantum discord, and concurrence, by solving the Lindblad master equation and evaluating the Gibbs state. Our findings indicate that increased Zeeman splitting diminishes quantum resources under dephasing and thermal equilibrium conditions. However, when we use the Hamiltonian of this system to realize our QB, Zeeman splitting boosts performance metrics such as ergotropy, instantaneous power, capacity, and quantum coherence during cyclic charging. We observe that the axial parameter improves QB performance, with coherence reaching a saturation point, beyond which ergotropy continues to rise, introducing the concept of incoherent ergotropy and highlighting the need to understand its true origin. Both KSEA interaction and the rhombic parameter consistently enhance quantum resources across the dephasing and thermal equilibrium regimes, and thus improve QB performance. The DM interaction improves QB metrics and shields quantum resources against temperature variations in the Gibbs state but remains insensitive during dephasing dynamics. Our work uncovers complex trends, including ergotropy enhancement without quantum coherence, the preferential role of QB capacity over quantum coherence, and the phenomenon of no-work extraction despite the presence of quantum coherence. These findings facilitate a robust foundation for future research on magnetic dipolar QBs, emphasizing non-unitary charging processes, environmental effects, and practical implementations. We show that the NMR platform could be a promising testbed for simulating such QBs., Comment: 17 pages, 10 figures. All comments are welcome

In this paper, we propose a new word embedding based corpus consisting of more than 61 million words crawled from multiple web resources. We design a preprocessing pipeline for the filtration of unwanted text from crawled data. Afterwards, the cleaned vocabulary is fed to state-of-the-art continuous-bag-of-words, skip-gram, and GloVe word embedding algorithms. For the evaluation of pretrained embeddings, we use popular intrinsic and extrinsic evaluation approaches. The evaluation results reveal that continuous-bag-of-words and skip-gram perform better than GloVe and existing Sindhi fastText word embedding on both intrinsic and extrinsic evaluation approaches, Comment: arXiv admin note: substantial text overlap with arXiv:1911.12579

Extremely large aperture array (ELAA) is a promising multiple-input multiple-output (MIMO) technique for next generation mobile networks. In this paper, we propose two novel approaches to accelerate the convergence of current iterative MIMO detectors in ELAA channels. Our approaches exploit the static components of the ELAA channel, which include line of sight (LoS) paths and deterministic non-LoS (NLoS) components due to channel hardening effects. This paper proposes novel convergence acceleration techniques for fast iterative ELAA-MIMO detection by leveraging the static channel component, including the LoS paths and deterministic NLoS components that arise due to channel hardening. Specifically, these static channel components are utilized in two ways: as preconditioning matrices for general iterative algorithms, and as initialization for quasi-Newton (QN) methods. Simulation results show that the proposed approaches converge significantly faster compared to current iterative MIMO detectors, especially under strong LoS conditions with high Rician K-factor. Furthermore, QN methods with the proposed initialization matrix consistently achieve the best convergence performance while maintaining low complexity., Comment: This work has been accepted by the IEEE Information Theory Workshop (ITW) 2024. Copyright may be transferred without notice, after which this version may no longer be accessible

Recent text-to-image models like Stable Diffusion produce photo-realistic images but often show demographic biases. Previous debiasing methods focused on training-based approaches, failing to explore the root causes of bias and overlooking Stable Diffusion's potential for unbiased image generation. In this paper, we demonstrate that Stable Diffusion inherently possesses fairness, which can be unlocked to achieve debiased outputs. Through carefully designed experiments, we identify the excessive bonding between text prompts and the diffusion process as a key source of bias. To address this, we propose a novel approach that perturbs text conditions to unleash Stable Diffusion's intrinsic fairness. Our method effectively mitigates bias without additional tuning, while preserving image-text alignment and image quality., Comment: 21 pages, 20 figures; First two authors contributed equally

The Bengali language is the 5th most spoken native and 7th most spoken language in the world, and Bengali handwritten character recognition has attracted researchers for decades. However, other languages such as English, Arabic, Turkey, and Chinese character recognition have contributed significantly to developing handwriting recognition systems. Still, little research has been done on Bengali character recognition because of the similarity of the character, curvature and other complexities. However, many researchers have used traditional machine learning and deep learning models to conduct Bengali hand-written recognition. The study employed a convolutional neural network (CNN) with ensemble transfer learning and a multichannel attention network. We generated the feature from the two branches of the CNN, including Inception Net and ResNet and then produced an ensemble feature fusion by concatenating them. After that, we applied the attention module to produce the contextual information from the ensemble features. Finally, we applied a classification module to refine the features and classification. We evaluated the proposed model using the CAMTERdb 3.1.2 data set and achieved 92\% accuracy for the raw dataset and 98.00\% for the preprocessed dataset. We believe that our contribution to the Bengali handwritten character recognition domain will be considered a great development.

In recent years, Graph Neural Networks (GNNs) have been utilized for various applications ranging from drug discovery to network design and social networks. In many applications, it is impossible to observe some properties of the graph directly; instead, noisy and indirect measurements of these properties are available. These scenarios are coined as Graph Inverse Problems (GRIP). In this work, we introduce a framework leveraging GNNs to solve GRIPs. The framework is based on a combination of likelihood and prior terms, which are used to find a solution that fits the data while adhering to learned prior information. Specifically, we propose to combine recent deep learning techniques that were developed for inverse problems, together with GNN architectures, to formulate and solve GRIP. We study our approach on a number of representative problems that demonstrate the effectiveness of the framework.

The surge in connected devices in 6G with typical massive access scenarios, such as smart agriculture, and smart cities, poses significant challenges to unsustainable traditional communication with limited radio resources and already high system complexity. Fortunately, the booming artificial intelligence technology and the growing computational power of devices offer a promising 6G enabler: semantic communication (SemCom). However, existing deep learning-based SemCom paradigms struggle to extend to multi-user scenarios due to their rigid end-to-end training approach. Consequently, to truly empower 6G networks with this critical technology, this article rethinks generative SemCom for multi-user system with multi-modal large language model (MLLM), and propose a novel framework called "M2GSC". In this framework, the MLLM, which serves as shared knowledge base (SKB), plays three critical roles for complex tasks, spawning a series of benefits such as semantic encoding standardization and semantic decoding personalization. Meanwhile, to enhance the performance of M2GSC framework and to advance its implementation in 6G, we highlight three research directions on M2GSC framework, namely, upgrading SKB to closed loop agent, adaptive semantic encoding offloading, and streamlined semantic decoding offloading. Finally, a case study is conducted to demonstrate the preliminary validation on the effectiveness of the M2GSC framework in terms of streamlined decoding offloading.

Current iterative multiple-input multiple-output (MIMO) detectors suffer from slow convergence when the wireless channel is ill-conditioned. The ill-conditioning is mainly caused by spatial correlation between channel columns corresponding to the same user equipment, known as intra-user interference. In addition, in the emerging MIMO systems using an extremely large aperture array (ELAA), spatial non-stationarity can make the channel even more ill-conditioned. In this paper, user-wise singular value decomposition (UW-SVD) is proposed to accelerate the convergence of iterative MIMO detectors. Its basic principle is to perform SVD on each user's sub-channel matrix to eliminate intra-user interference. Then, the MIMO signal model is effectively transformed into an equivalent signal (e-signal) model, comprising an e-channel matrix and an e-signal vector. Existing iterative algorithms can be used to recover the e-signal vector, which undergoes post-processing to obtain the signal vector. It is proven that the e-channel matrix is better conditioned than the original MIMO channel for spatially correlated (ELAA-)MIMO channels. This implies that UW-SVD can accelerate current iterative algorithms, which is confirmed by our simulation results. Specifically, it can speed up convergence by up to 10 times in both uncoded and coded systems., Comment: This work has been accepted by IEEE Transactions on Wireless Communications. Copyright may be transferred without notice, after which this version may no longer be accessible

It is shown that the space of finite-to-finite holomorphic correspondences on an OT-manifold is discrete. When the OT-manifold has no proper infinite complex-analytic subsets, it then follows by known model-theoretic results that its cartesian powers have no interesting complex-analytic families of subvarieties. The methods of proof, which are similar to [Moosa, Moraru, and Toma ``An essentially saturated surface not of K\"ahler-type", {\em Bull. of the LMS}, 40(5):845--854, 2008], require studying finite unramified covers of OT-manifolds., Comment: The paper is published

In the modern context, hand gesture recognition has emerged as a focal point. This is due to its wide range of applications, which include comprehending sign language, factories, hands-free devices, and guiding robots. Many researchers have attempted to develop more effective techniques for recognizing these hand gestures. However, there are challenges like dataset limitations, variations in hand forms, external environments, and inconsistent lighting conditions. To address these challenges, we proposed a novel three-stream hybrid model that combines RGB pixel and skeleton-based features to recognize hand gestures. In the procedure, we preprocessed the dataset, including augmentation, to make rotation, translation, and scaling independent systems. We employed a three-stream hybrid model to extract the multi-feature fusion using the power of the deep learning module. In the first stream, we extracted the initial feature using the pre-trained Imagenet module and then enhanced this feature by using a multi-layer of the GRU and LSTM modules. In the second stream, we extracted the initial feature with the pre-trained ReseNet module and enhanced it with the various combinations of the GRU and LSTM modules. In the third stream, we extracted the hand pose key points using the media pipe and then enhanced them using the stacked LSTM to produce the hierarchical feature. After that, we concatenated the three features to produce the final. Finally, we employed a classification module to produce the probabilistic map to generate predicted output. We mainly produced a powerful feature vector by taking advantage of the pixel-based deep learning feature and pos-estimation-based stacked deep learning feature, including a pre-trained model with a scratched deep learning model for unequalled gesture detection capabilities.

Researchers have been developing Hand Gesture Recognition (HGR) systems to enhance natural, efficient, and authentic human-computer interaction, especially benefiting those who rely solely on hand gestures for communication. Despite significant progress, the automatic and precise identification of hand gestures remains a considerable challenge in computer vision. Recent studies have focused on specific modalities like RGB images, skeleton data, and spatiotemporal interest points. This paper provides a comprehensive review of HGR techniques and data modalities from 2014 to 2024, exploring advancements in sensor technology and computer vision. We highlight accomplishments using various modalities, including RGB, Skeleton, Depth, Audio, EMG, EEG, and Multimodal approaches and identify areas needing further research. We reviewed over 200 articles from prominent databases, focusing on data collection, data settings, and gesture representation. Our review assesses the efficacy of HGR systems through their recognition accuracy and identifies a gap in research on continuous gesture recognition, indicating the need for improved vision-based gesture systems. The field has experienced steady research progress, including advancements in hand-crafted features and deep learning (DL) techniques. Additionally, we report on the promising developments in HGR methods and the area of multimodal approaches. We hope this survey will serve as a potential guideline for diverse data modality-based HGR research.

This study examines the performance of finite spin quantum batteries (QBs) using Heisenberg spin models with Dzyaloshinsky-Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions. The QBs are modeled as interacting quantum spins in local inhomogeneous magnetic fields, inducing variable Zeeman splitting. We derive analytical expressions for the maximal extractable work, ergotropy and the capacity of QBs, as recently examined by Yang et al. [Phys. Rev. Lett. 131, 030402 (2023)]. These quantities are analytically linked through certain quantum correlations, as posited in the aforementioned study. Different Heisenberg spin chain models exhibit distinct behaviors under varying conditions, emphasizing the importance of model selection for optimizing QB performance. In antiferromagnetic (AFM) systems, maximum ergotropy occurs with a Zeeman splitting field applied to either spin, while ferromagnetic (FM) systems benefit from a uniform Zeeman field. Temperature significantly impacts QB performance, with ergotropy in the AFM case being generally more robust against temperature increases compared to the FM case. Incorporating DM and KSEA couplings can significantly enhance the capacity and ergotropy extraction of QBs. However, there exists a threshold beyond which additional increases in these interactions cause a sharp decline in capacity and ergotropy. This behavior is influenced by temperature and quantum coherence, which signal the occurrence of a sudden phase transition. The resource theory of quantum coherence proposed by Baumgratz et al. [Phys. Rev. Lett. 113, 140401 (2014)] plays a crucial role in enhancing ergotropy and capacity. However, ergotropy is limited by both the system's capacity and the amount of coherence. These findings support the theoretical framework of spin-based QBs and may benefit future research on quantum energy storage devices., Comment: 13 pages, 11 figures

Semantic Communication (SEM-COM) has emerged as one of the disruptive technologies facilitating the evolution towards sixth-generation (6G) wireless networks. This article presents the potential of SEM-COM to transform Vehicle-to-Everything (V2X) communications, with a particular emphasis on its ability to enhance communication efficiency and intelligence. We discuss the core components and metrics that characterize SEM-COM, providing insights into its operational framework within the context of V2X communications. We illustrate the applicability and practicality of SEM-COM through real-world vehicular use cases, demonstrate the potential of SEM-COM to enhance aspects of intelligent mobility, such as communication efficiency and decision-making. Finally, the article identifies key open research questions for SEM-COM V2X, pointing to areas that require further exploration and thus setting a foundation for future work in this evolving domain., Comment: 8 pages, 4 figures

Wi-Fi sensing has become an attractive option for non-invasive monitoring of human activities and vital signs. This paper explores the feasibility of using state-of-the-art commercial off-the-shelf (COTS) devices for Wi-Fi sensing applications, particularly respiration monitoring and motion detection. We utilize the Intel AX210 network interface card (NIC) to transmit Wi-Fi signals in both 2.4 GHz and 6 GHz frequency bands. Our experiments rely on channel frequency response (CFR) and received signal strength indicator (RSSI) data, which are processed using a moving average algorithm to extract human behavior patterns. The experimental results demonstrate the effectiveness of our approach in capturing and representing human respiration and motion patterns. Furthermore, we compare the performance of Wi-Fi sensing across different frequency bands, highlighting the advantages of using higher frequencies for improved sensitivity and clarity. Our findings showcase the practicality of using COTS devices for Wi-Fi sensing and lay the groundwork for the development of non-invasive, contactless sensing systems. These systems have potential applications in various fields, including healthcare, smart homes, and Metaverse., Comment: This work has been accepted by IEEE ICCC Workshop 2024. Copyright may be transferred without notice, after which this version may no longer be accessible

Recent advancements in diffusion models have made a significant breakthrough in generative modeling. The combination of the generative model and semantic communication (SemCom) enables high-fidelity semantic information exchange at ultra-low rates. A novel generative SemCom framework for image tasks is proposed, wherein pre-trained foundation models serve as semantic encoders and decoders for semantic feature extractions and image regenerations, respectively. The mathematical relationship between the transmission reliability and the perceptual quality of the regenerated image and the semantic values of semantic features are modeled, which are obtained by conducting numerical simulations on the Kodak dataset. We also investigate the semantic-aware power allocation problem, with the objective of minimizing the total power consumption while guaranteeing semantic performance. To solve this problem, two semanticaware power allocation methods are proposed by constraint decoupling and bisection search, respectively. Numerical results show that the proposed semantic-aware methods demonstrate superior performance compared to the conventional one in terms of total power consumption., Comment: Accepted at IEEE GLOBECOM 2024

Lung injuries, such as ventilator-induced lung injury and radiation-induced lung injury, can lead to heterogeneous alterations in the biomechanical behavior of the lungs. While imaging methods, e.g., X-ray and static computed tomography (CT), can point to regional alterations in lung structure between healthy and diseased tissue, they fall short of delineating timewise kinematic variations between the former and the latter. Image registration has gained recent interest as a tool to estimate the displacement experienced by the lungs during respiration via regional deformation metrics such as volumetric expansion and distortion. However, successful image registration commonly relies on a temporal series of image stacks with small displacements in the lungs across succeeding image stacks, which remains limited in static imaging. In this study, we have presented a finite element (FE) method to estimate strains from static images acquired at the end-expiration (EE) and end-inspiration (EI) timepoints, i.e., images with a large deformation between the two distant timepoints. Physiologically realistic loads were applied to the geometry obtained at EE to deform this geometry to match the geometry obtained at EI. The results indicated that the simulation could minimize the error between the two geometries. Using four-dimensional (4D) dynamic CT in a rat, the strain at an isolated transverse plane estimated by our method showed sufficient agreement with that estimated through non-rigid image registration that used all the timepoints. Through the proposed method, we can estimate the lung deformation at any timepoint between EE and EI. The proposed method offers a tool to estimate timewise regional deformation in the lungs using only static images acquired at EE and EI., Comment: 4 pages, 4 figures, accepted in the IEEE EMBC 2024 conference

Type I gamma-ray bursts (GRBs) are believed to originate from compact binary merger usually with duration less than 2 seconds for the main emission. However, recent observations of GRB 211211A and GRB 230307A indicate that some merger-origin GRBs could last much longer. Since they show strikingly similar properties (indicating a common mechanism) which are different from the classic "long"-short burst (e.g. GRB 060614), forming an interesting subclass of type I GRBs, we suggest to name them as type IL GRBs. By identifying the first peak of GRB 230307A as a quasi-thermal precursor, we find that the prompt emission of type IL GRB is composed of three episodes: (1) a precursor followed by a short quiescent (or weak emission) period, (2) a long-duration main emission, and (3) an extended emission. With this burst pattern, a good candidate, GRB 170228A, was found in the Fermi/GBM archive data, and subsequent temporal and spectral analyses indeed show that GRB 170228A falls in the same cluster with GRB 211211A and GRB 230307A in many diagnostic figures. Thus this burst pattern could be a good reference for rapidly identifying type IL GRB and conducting low-latency follow-up observation. We estimated the occurrence rate and discussed the physical origins and implications for the three emission episodes of type IL GRBs. Our analysis suggests the pre-merger precursor model, especially the super flare model, is more favored for type IL GRBs.

Recently, capacitive micromachined ultrasound transducers (CMUTs) with long rectangular membranes have demonstrated performance advantages over conventional piezoelectric transducers; however, modeling these CMUT geometries has been limited to computationally burdensome numerical methods. Improved fast modeling methods such as equivalent circuit models could help achieve designs with even better performance. The primary obstacle in developing such methods is the lack of tractable methods for computing the radiation impedance of clamped rectangular radiators. This paper presents a method which approximates the velocity profile using a polynomial shape model to rapidly and accurately estimate radiation impedance. The validity of the approximate velocity profile and corresponding radiation impedance calculation was assessed using finite element simulations for a variety of membrane aspect ratios and bias voltages. Our method was evaluated for rectangular radiators with width:length ratios from 1:1 up to 1:25. At all aspect ratios, the radiation resistance was closely modeled. However, when calculating the radiation reactance, our initial approach was only accurate for low aspect ratios. This motivated us to consider an alternative shape model for high aspect ratios, which was more accurate when compared with FEM. To facilitate development of future rectangular CMUTs, we provide a MATLAB script which quickly calculates radiation impedance using both methods., Comment: 18 pages, 10 figures, submitted to Sensors

Advancements in satellite technology have made direct-to-device connectivity a viable solution for ensuring global access. This method is designed to provide internet connectivity to remote, rural, or underserved areas where traditional cellular or broadband networks are lacking or insufficient. This paper is a survey providing an in-depth review of multi-satellite Multiple Input Multiple Output (MIMO) systems as a potential solution for addressing the link budget challenge in direct user-satellite communication. Special attention is given to works considering multi-satellite MIMO systems, both with and without satellite collaboration. In this context, collaboration refers to sharing data between satellites to improve the performance of the system. This survey begins by explaining several fundamental aspects of satellite communications (SatComs), which are vital prerequisites before investigating the multi-satellite MIMO systems. These aspects encompass satellite orbits, the structure of satellite systems, SatCom links, including the inter-satellite links (ISL) which facilitate satellite cooperation, satellite frequency bands, satellite antenna design, and satellite channel models, which should be known or estimated for effective data transmission to and from multiple satellites. Furthermore, this survey distinguishes itself by providing more comprehensive insights in comparison to other surveys. It specifically delves into the Orthogonal Time Frequency Space (OTFS) within the channel model section. It goes into detail about ISL noise and channel models, and it extends the ISL section by thoroughly investigating hybrid FSO/RF ISLs. Furthermore, analytical comparisons of simulation results from these works are presented to highlight the advantages of employing multi-satellite MIMO systems., Comment: 29 pages, 11 figures, 6 tables, IEEE Communication Survey and Tutorials

Optimal experimental design is a well studied field in applied science and engineering. Techniques for estimating such a design are commonly used within the framework of parameter estimation. Nonetheless, in recent years parameter estimation techniques are changing rapidly with the introduction of deep learning techniques to replace traditional estimation methods. This in turn requires the adaptation of optimal experimental design that is associated with these new techniques. In this paper we investigate a new experimental design methodology that uses deep learning. We show that the training of a network as a Likelihood Free Estimator can be used to significantly simplify the design process and circumvent the need for the computationally expensive bi-level optimization problem that is inherent in optimal experimental design for non-linear systems. Furthermore, deep design improves the quality of the recovery process for parameter estimation problems. As proof of concept we apply our methodology to two different systems of Ordinary Differential Equations.

SARS-CoV-2, the highly contagious pathogen responsible for the COVID-19 pandemic, has persistent effects that begin four weeks after initial infection and last for an undetermined duration. These chronic effects are more harmful than acute ones. This review explores the long-term impact of the virus on various human organs, including the pulmonary, cardiovascular, neurological, reproductive, gastrointestinal, musculoskeletal, endocrine, and lymphoid systems, particularly in older adults. Regarding diagnosis, RT-PCR is the gold standard for detecting COVID-19, though it requires specialized equipment, skilled personnel, and considerable time to produce results. To address these limitations, artificial intelligence in imaging and microfluidics technologies offers promising alternatives for diagnosing COVID-19 efficiently. Pharmacological and non-pharmacological strategies are effective in mitigating the persistent impacts of COVID-19. These strategies enhance immunity in post-COVID-19 patients by reducing cytokine release syndrome, improving T cell response, and increasing the circulation of activated natural killer and CD8 T cells in blood and tissues. This, in turn, alleviates symptoms such as fever, nausea, fatigue, muscle weakness, and pain. Vaccines, including inactivated viral, live attenuated viral, protein subunit, viral vectored, mRNA, DNA, and nanoparticle vaccines, significantly reduce the adverse long-term effects of the virus. However, no vaccine has been reported to provide lifetime protection against COVID-19. Consequently, protective measures such as physical distancing, mask usage, and hand hygiene remain essential strategies. This review offers a comprehensive understanding of the persistent effects of COVID-19 on individuals of varying ages, along with insights into diagnosis, treatment, vaccination, and future preventative measures against the spread of SARS-CoV-2.

This research presents a comprehensive assessment of pan-sharpening techniques for satellite imagery, focusing on the critical aspects of spectral fidelity and spatial enhancement. Motivated by the need for informed algorithm selection in remote sensing, A novel cascaded and structured evaluation framework has been proposed with a detailed comparative analysis of existing methodologies. The research findings underscore the intricate trade-offs between spectral accuracy of about 88\% with spatial resolution enhancement. The research sheds light on the practical implications of pan-sharpening and emphasizes the significance of both spectral and spatial aspects in remote sensing applications. Various pan-sharpening algorithms were systematically employed to provide a holistic view of their performance, contributing to a deeper understanding of their capabilities and limitations.

Recent strides in multimodal model development have ignited a paradigm shift in the realm of text-to-image generation. Among these advancements, CLIP stands out as a remarkable achievement which is a sophisticated autoencoder adept at encoding both textual and visual information within a unified latent space. This paper delves into a comparative analysis between CLIP and its recent counterpart, CLOOB. To unravel the intricate distinctions within the embedding spaces crafted by these models, we employ topological data analysis. Our approach encompasses a comprehensive examination of the modality gap drivers, the clustering structures existing across both high and low dimensions, and the pivotal role that dimension collapse plays in shaping their respective embedding spaces. Empirical experiments substantiate the implications of our analyses on downstream performance across various contextual scenarios. Through this investigation, we aim to shed light on the nuanced intricacies that underlie the comparative efficacy of CLIP and CLOOB, offering insights into their respective strengths and weaknesses, and providing a foundation for further refinement and advancement in multimodal model research., Comment: 10 pages, 17 figures, 2 tables

We investigate the behavior of various measures of quantum coherence and quantum correlation in the spin-1/2 Heisenberg XYZ model with added Dzyaloshinsky-Moriya (DM) and Kaplan--Shekhtman--Entin-Wohlman--Aharony (KSEA) interactions at a thermal regime described by a Gibbs density operator. We aim to understand the restricted hierarchical classification of different quantum resources, where Bell nonlocality $\subseteq$ quantum steering $\subseteq$ quantum entanglement $\subseteq$ quantum discord $\subseteq$ quantum coherence. This hierarchy highlights the increasingly stringent conditions required as we move from quantum coherence to more specific quantum phenomena. In order to enhance quantum coherence, quantum correlation, and fidelity of teleportation, our analysis encompasses the effects of independently provided sinusoidal magnetic field control as well as DM and KSEA interactions on the considered system. The results reveal that enhancing the entanglement or quantum correlation of the channel does not always guarantee successful teleportation or even an improvement in teleportation fidelity. Thus, the relationship between teleportation fidelity and the channel's underlying quantum properties is intricate. Our study provides valuable insights into the complex interplay of quantum coherence and correlation hierarchy, offering potential applications for quantum communication and information processing technologies., Comment: 12 pages, 4 figures. All comments are welcome

The Open Radio Access Network (Open RAN) framework, emerging as the cornerstone for Artificial Intelligence (AI)-enabled Sixth-Generation (6G) mobile networks, heralds a transformative shift in radio access network architecture. As the adoption of Open RAN accelerates, ensuring its security becomes critical. The RAN Intelligent Controller (RIC) plays a central role in Open RAN by improving network efficiency and flexibility. Nevertheless, it also brings about potential security risks that need careful scrutiny. Therefore, it is imperative to evaluate the current state of RIC security comprehensively. This assessment is essential to gain a profound understanding of the security considerations associated with RIC. This survey combines a comprehensive analysis of RAN security, tracing its evolution from 2G to 5G, with an in-depth exploration of RIC security, marking the first comprehensive examination of its kind in the literature. Real-world security incidents involving RIC are vividly illustrated, providing practical insights. The study evaluates the security implications of the RIC within the 6G Open RAN context, addressing security vulnerabilities, mitigation strategies, and potential enhancements. It aims to guide stakeholders in the telecom industry toward a secure and dependable telecommunications infrastructure. The article serves as a valuable reference, shedding light on the RIC's crucial role within the broader network infrastructure and emphasizing security's paramount importance. This survey also explores the promising security opportunities that the RIC presents for enhancing network security and resilience in the context of 6G mobile networks. It outlines open issues, lessons learned, and future research directions in the domain of intelligent control in 6G open RAN, facilitating a comprehensive understanding of this dynamic landscape., Comment: 36 pages, 14 figures, IEEE COMST (in review)

A binding group theorem is proved in the context of quantifier-free internality to the fixed field in difference-closed fields of characteristic zero. This is articulated as a statement about the birational geometry of isotrivial algebraic dynamical systems, and more generally isotrivial $\sigma$-varieties. It asserts that if $(V,\phi)$ is an isotrivial $\sigma$-variety then a certain subgroup of the group of birational transformations of $V$, namely those that preserve all the relations between $(V,\phi)$ and the trivial dynamics on the affine line, is in fact an algebraic group. Several application are given including new special cases of the Zariski Dense Orbit Conjecture and the Dixmier-Moeglin Equivalence Problem in algebraic dynamics, as well as finiteness results about the existence of nonconstant invariant rational functions on cartesian powers of $\sigma$-varieties. These applications give algebraic-dynamical analogues of recent results in differential-algebraic geometry., Comment: 40 pages

This paper aims to analyze the stochastic performance of a multiple input multiple output (MIMO) integrated sensing and communication (ISAC) system in a downlink scenario, where a base station (BS) transmits a dual-functional radar-communication (DFRC) signal matrix, serving the purpose of transmitting communication data to the user while simultaneously sensing the angular location of a target. The channel between the BS and the user is modeled as a random channel with Rayleigh fading distribution, and the azimuth angle of the target is assumed to follow a uniform distribution. Due to the randomness inherent in the network, the challenge is to consider suitable performance metrics for this randomness. To address this issue, for users, we employ the user's rate outage probability (OP) and ergodic rate, while for target, we propose using the OP of the Cram\'er-Rao lower bound (CRLB) for the angle of arrival and the ergodic CRLB. We have obtained the expressions of these metrics for scenarios where the BS employs two different beamforming methods. Our approach to deriving these metrics involves computing the probability density function (PDF) of the signal-to-noise ratio for users and the CRLB for the target. We have demonstrated that the central limit theorem provides a viable approach for deriving these PDFs. In our numerical results, we demonstrate the trade-off between sensing and communication (S \& C) by characterizing the region of S \& C metrics and by obtaining the Pareto optimal boundary points, confirmed with simulations., Comment: arXiv admin note: text overlap with arXiv:2305.15388

Semantic communication is an emerging feature for future networks like 6G, which emphasizes the meaning of the message in contrast to the traditional approach where the meaning of the message is irrelevant. Yet, the open nature of a wireless channel poses security risks for semantic communications. In this paper, we derive information-theoretic limits for the secure transmission of semantic source over wiretap channel. Under separate equivocation and distortion conditions for semantics and observed data, we present the general outer and inner bounds on the rate-distortion-equivocation region. We also reduce the general region to a case of Gaussian source and channel and provide numerical evaluations.

This study delves into the innovative landscape of metasurfaces, with a particular focus on their role in achieving EM illusion (EMI) a facet of paramount significance. The control of EM waves assumes a pivotal role in mitigating issues such as signal degradation, interference, and reduced communication range. Furthermore, the engineering of waves serves as a foundational element in achieving invisibility or minimized detectability. This survey unravels the theoretical underpinnings and practical designs of EMI coatings, which have been harnessed to develop functional metasurfaces. EMI, practically achieved through engineered coatings, confers a strategic advantage by either reducing the radar cross-section of objects or creating misleading footprints. In addition to illustrating the outstanding achievements in reconfigurable cloaking, this study culminates in the proposal of a novel approach, suggesting the emergence of EMI without the need for physically coating the device to conceal and thus proposing the concept of a smart EMI environment. This groundbreaking work opens a new way for engineers and researchers to unlock exotic and versatile designs that build on reconfigurable intelligent surfaces (RIS). Crucially the designs enabled by the proposed approach, present a wide array of applications, encompassing camouflaging, deceptive sensing, radar cognition control, and defence security, among others. In essence, this research stands as a beacon guiding the exploration of uncharted territories in wave control through smart EMI environments, with profound implications spanning basic academic research in RIS through advanced security technologies and communication systems.

When granular materials of shape-anisotropic grains are sheared in a split-bottom shear cell, a localized shear band is formed with a depression at its center. This effect is closely related to the alignment of the particles with aspect ratio (AR), which, in turn, influences the local packing density, the stress distribution, and the system's overall bulk rheology. Particles with large AR tend to align with the shear direction, which increases the packing density in the shear band and affects rheological properties like stress, macroscopic friction coefficient, and effective viscosity. A scaling law correlates particle AR to macroscopic friction and effective viscosity, revealing shear-thinning behavior in bulk and near the surface., Comment: 11 pages, 7 figures

In a previous paper, we have shown that a recurrent neural network (RNN) can be used to detect cellular network radio signal degradations accurately. We unexpectedly found, though, that accuracy gains diminished as we added layers to the RNN. To investigate this, in this paper, we build a parallel model to illuminate and understand the internal operation of neural networks, such as the RNN, which store their internal state in order to process sequential inputs. This model is widely applicable in that it can be used with any input domain where the inputs can be represented by a Gaussian mixture. By looking at the RNN processing from a probability density function perspective, we are able to show how each layer of the RNN transforms the input distributions to increase detection accuracy. At the same time we also discover a side effect acting to limit the improvement in accuracy. To demonstrate the fidelity of the model we validate it against each stage of RNN processing as well as the output predictions. As a result, we have been able to explain the reasons for the RNN performance limits with useful insights for future designs for RNNs and similar types of neural network.

It is generally recognized that the electromagnetic multipolar emission from magnetars can be used to explain radiation from Soft Gamma Repeaters (SGRs) or Anomalous X-ray Pulsars (AXPs), but they have little impact on the spindown of magnetars. We here present a comprehensive analytical solution for the neutron star multipolar electromagnetic fields and their associated expected luminosities. We find that for newborn millisecond magnetars, the spin-down luminosity from higher multipolar components can match or even exceed that from the dipole component. Such high-intensity radiation will undoubtedly affect related astrophysical phenomena at the birth of a magnetar. We show that the spin-down luminosity from multipoles can well explain the majority of Gamma-Ray Bursts (GRBs) afterglows, from the plateau starting at several hundred seconds till the normal decay phase lasting for many years. The fitted magnetar parameters for GRB afterglows are all typical values, with spins in the millisecond range and magnetic field strengths in the order of $10^{14} - 10^{15}$ Gauss. Our results in turn, provide support for the hypothesis that GRBs originate from the birth of magnetars with a few millisecond period, thus deepening our understanding of the complex magnetic field structure and the equation of state of magnetars., Comment: Accepted for publication in The Astrophysical Journal (ApJ)

ChatGPT has been the most talked-about concept in recent months, captivating both professionals and the general public alike, and has sparked discussions about the changes that artificial intelligence (AI) will bring to the world. As physicists and astrophysicists, we are curious about if scientific data can be correctly analyzed by large language models (LLMs) and yield accurate physics. In this article, we fine-tune the generative pre-trained transformer (GPT) model by the astronomical data from the observations of galaxies, quasars, stars, gamma-ray bursts (GRBs), and the simulations of black holes (BHs), the fine-tuned model demonstrates its capability to classify astrophysical phenomena, distinguish between two types of GRBs, deduce the redshift of quasars, and estimate BH parameters. We regard this as a successful test, marking the LLM's proven efficacy in scientific research. With the ever-growing volume of multidisciplinary data and the advancement of AI technology, we look forward to the emergence of a more fundamental and comprehensive understanding of our universe. This article also shares some interesting thoughts on data collection and AI design. Using the approach of understanding the universe - looking outward at data and inward for fundamental building blocks - as a guideline, we propose a method of series expansion for AI, suggesting ways to train and control AI that is smarter than humans., Comment: 27 pages, 7 figures. Comments welcome

Rapid urbanization and improved living standards have led to a substantial increase in the number of vehicles on the road, consequently resulting in a rise in the frequency of accidents. Among these accidents, motorbike accidents pose a particularly high risk, often resulting in serious injuries or deaths. A significant number of these fatalities occur due to delayed or inadequate medical attention. To this end, we propose a novel automatic detection and notification system specifically designed for motorbike accidents. The proposed system comprises two key components: a detection system and a physiological signal monitoring system. The detection system is integrated into the helmet and consists of a microcontroller, accelerometer, GPS, GSM, and Wi-Fi modules. The physio-monitoring system incorporates a sensor for monitoring pulse rate and SpO$_{2}$ saturation. All collected data are presented on an LCD display and wirelessly transmitted to the detection system through the microcontroller of the physiological signal monitoring system. If the accelerometer readings consistently deviate from the specified threshold decided through extensive experimentation, the system identifies the event as an accident and transmits the victim's information -- including the GPS location, pulse rate, and SpO$_{2}$ saturation rate -- to the designated emergency contacts. Preliminary results demonstrate the efficacy of the proposed system in accurately detecting motorbike accidents and promptly alerting emergency contacts. We firmly believe that the proposed system has the potential to significantly mitigate the risks associated with motorbike accidents and save lives.

Diffusion models create data from noise by inverting the forward paths of data towards noise and have emerged as a powerful generative modeling technique for high-dimensional, perceptual data such as images and videos. Rectified flow is a recent generative model formulation that connects data and noise in a straight line. Despite its better theoretical properties and conceptual simplicity, it is not yet decisively established as standard practice. In this work, we improve existing noise sampling techniques for training rectified flow models by biasing them towards perceptually relevant scales. Through a large-scale study, we demonstrate the superior performance of this approach compared to established diffusion formulations for high-resolution text-to-image synthesis. Additionally, we present a novel transformer-based architecture for text-to-image generation that uses separate weights for the two modalities and enables a bidirectional flow of information between image and text tokens, improving text comprehension, typography, and human preference ratings. We demonstrate that this architecture follows predictable scaling trends and correlates lower validation loss to improved text-to-image synthesis as measured by various metrics and human evaluations. Our largest models outperform state-of-the-art models, and we will make our experimental data, code, and model weights publicly available.

Federated Learning (FL) is a machine learning (ML) approach that enables multiple decentralized devices or edge servers to collaboratively train a shared model without exchanging raw data. During the training and sharing of model updates between clients and servers, data and models are susceptible to different data-poisoning attacks. In this study, our motivation is to explore the severity of data poisoning attacks in the computer network domain because they are easy to implement but difficult to detect. We considered two types of data-poisoning attacks, label flipping (LF) and feature poisoning (FP), and applied them with a novel approach. In LF, we randomly flipped the labels of benign data and trained the model on the manipulated data. For FP, we randomly manipulated the highly contributing features determined using the Random Forest algorithm. The datasets used in this experiment were CIC and UNSW related to computer networks. We generated adversarial samples using the two attacks mentioned above, which were applied to a small percentage of datasets. Subsequently, we trained and tested the accuracy of the model on adversarial datasets. We recorded the results for both benign and manipulated datasets and observed significant differences between the accuracy of the models on different datasets. From the experimental results, it is evident that the LF attack failed, whereas the FP attack showed effective results, which proved its significance in fooling a server. With a 1% LF attack on the CIC, the accuracy was approximately 0.0428 and the ASR was 0.9564; hence, the attack is easily detectable, while with a 1% FP attack, the accuracy and ASR were both approximately 0.9600, hence, FP attacks are difficult to detect. We repeated the experiment with different poisoning percentages.