arXiv subjects

Hazrat Ali

Publications and source records attributed to Hazrat Ali.

At least 19 recordsLinked to original sources

arXiv2026

Reservoir-controlled electromagnetically induced gratings in a weakly driven two-level medium

We theoretically investigate the transmission and diffraction of a weak probe field from an electromagnetically induced grating formed in a weakly driven two-level medium coupled to engineered quantum reservoirs. Using a perturbative solution of the optical Bloch equations in the weak-driving regime, we analyze how normal-vacuum, thermal, and broadband squeezed-vacuum environments modify the probe susceptibility and consequently reshape both the spatial transmission function and the far-field diffraction patterns. We show that reservoir statistics have a pronounced impact on the diffraction response by altering the amplitude and phase of the induced grating. Thermal reservoirs enhance the transmission modulation and increase the intensity of the dominant diffraction orders, whereas squeezed-vacuum reservoirs generate strongly phase-sensitive modifications that selectively redistribute optical power among diffraction channels. We further demonstrate that the detuning between the squeezed reservoir and the driving field provides an efficient mechanism for controlling diffraction directionality, leading to substantial amplification of selected angular orders. In two-dimensional geometries, squeezed-vacuum correlations produce highly structured phase landscapes and strongly anisotropic diffraction patterns, enabling directional enhancement of specific diffraction channels while suppressing others. These results establish reservoir engineering as a versatile approach for controlling transmission, diffraction efficiency, and angular selectivity in minimal two-level systems, with potential applications in programmable photonic devices, beam steering, and quantum optical platforms.

physics.optics↗

arXiv2026

Fine-Grained Cat Breed Recognition with Global Context Vision Transformer

Accurate identification of cat breeds from images is a challenging task due to subtle differences in fur patterns, facial structure, and color. In this paper, we present a deep learning-based approach for classifying cat breeds using a subset of the Oxford-IIIT Pet Dataset, which contains high-resolution images of various domestic breeds. We employed the Global Context Vision Transformer (GCViT) architecture-tiny for cat breed recognition. To improve model generalization, we used extensive data augmentation, including rotation, horizontal flipping, and brightness adjustment. Experimental results show that the GCViT-Tiny model achieved a test accuracy of 92.00% and validation accuracy of 94.54%. These findings highlight the effectiveness of transformer-based architectures for fine-grained image classification tasks. Potential applications include veterinary diagnostics, animal shelter management, and mobile-based breed recognition systems. We also provide a hugging face demo at https://huggingface.co/spaces/bfarhad/cat-breed-classifier.

cs.CV↗

arXiv2025

Foundation Models in Biomedical Imaging: Turning Hype into Reality

Foundation models (FMs) are driving a prominent shift in biomedical imaging from task-specific models to unified backbone models for diverse tasks. This opens an avenue to integrate imaging, pathology, clinical records, and genomics data into a composite system. However, this vision contrasts sharply with modern medicine's trajectory toward more granular sub-specialization. This tension, coupled with data scarcity, domain heterogeneity, and limited interpretability, creates a gap between benchmark success and real-world clinical value. We argue that the immediate role of FMs lies in augmenting, not replacing, clinical expertise. To separate hype from reality, we introduce REAL-FM (Real-world Evaluation and Assessment of Foundation Models), a multi-dimensional framework for assessing data, technical readiness, clinical value, workflow integration, and responsible AI. Using REAL-FM, we find that while FMs excel in pattern recognition, they fall short in causal reasoning, domain robustness, and safety. Clinical translation is hindered by scarce representative data for model training, unverified generalization beyond oversimplified benchmark settings, and a lack of prospective outcome-based validation. We further examine FM reasoning paradigms, including sequential logic, spatial understanding, and symbolic domain knowledge. We envision that the path forward lies not in a monolithic medical oracle, but in coordinated subspecialist AI systems that are transparent, safe, and clinically grounded.

q-bio.QM↗

arXiv2025

Enhancing Diabetic Retinopathy Classification Accuracy through Dual Attention Mechanism in Deep Learning

Automatic classification of Diabetic Retinopathy (DR) can assist ophthalmologists in devising personalized treatment plans, making it a critical component of clinical practice. However, imbalanced data distribution in the dataset becomes a bottleneck in the generalization of deep learning models trained for DR classification. In this work, we combine global attention block (GAB) and category attention block (CAB) into the deep learning model, thus effectively overcoming the imbalanced data distribution problem in DR classification. Our proposed approach is based on an attention mechanism-based deep learning model that employs three pre-trained networks, namely, MobileNetV3-small, Efficientnet-b0, and DenseNet-169 as the backbone architecture. We evaluate the proposed method on two publicly available datasets of retinal fundoscopy images for DR. Experimental results show that on the APTOS dataset, the DenseNet-169 yielded 83.20% mean accuracy, followed by the MobileNetV3-small and EfficientNet-b0, which yielded 82% and 80% accuracies, respectively. On the EYEPACS dataset, the EfficientNet-b0 yielded a mean accuracy of 80%, while the DenseNet-169 and MobileNetV3-small yielded 75.43% and 76.68% accuracies, respectively. In addition, we also compute the F1-score of 82.0%, precision of 82.1%, sensitivity of 83.0%, specificity of 95.5%, and a kappa score of 88.2% for the experiments. Moreover, in our work, the MobileNetV3-small has 1.6 million parameters on the APTOS dataset and 0.90 million parameters on the EYEPACS dataset, which is comparatively less than other methods. The proposed approach achieves competitive performance that is at par with recently reported works on DR classification.

eess.IV↗

arXiv2025

Redefining Elderly Care with Agentic AI: Challenges and Opportunities

The global ageing population necessitates new and emerging strategies for caring for older adults. In this article, we explore the potential for transformation in elderly care through Agentic Artificial Intelligence (AI), powered by Large Language Models (LLMs). We discuss the proactive and autonomous decision-making facilitated by Agentic AI in elderly care. Personalized tracking of health, cognitive care, and environmental management, all aimed at enhancing independence and high-level living for older adults, represents important areas of application. With a potential for significant transformation of elderly care, Agentic AI also raises profound concerns about data privacy and security, decision independence, and access. We share key insights to emphasize the need for ethical safeguards, privacy protections, and transparent decision-making. Our goal in this article is to provide a balanced discussion of both the potential and the challenges associated with Agentic AI, and to provide insights into its responsible use in elderly care, to bring Agentic AI into harmony with the requirements and vulnerabilities specific to the elderly. Finally, we identify the priorities for the academic research communities, to achieve human-centered advancements and integration of Agentic AI in elderly care. To the best of our knowledge, this is no existing study that reviews the role of Agentic AI in elderly care. Hence, we address the literature gap by analyzing the unique capabilities, applications, and limitations of LLM-based Agentic AI in elderly care. We also provide a companion interactive dashboard at https://hazratali.github.io/agenticai/.

cs.AI↗

arXiv2025

A Deep Convolutional Neural Network-Based Novel Class Balancing for Imbalance Data Segmentation

Retinal fundus images provide valuable insights into the human eye's interior structure and crucial features, such as blood vessels, optic disk, macula, and fovea. However, accurate segmentation of retinal blood vessels can be challenging due to imbalanced data distribution and varying vessel thickness. In this paper, we propose BLCB-CNN, a novel pipeline based on deep learning and bi-level class balancing scheme to achieve vessel segmentation in retinal fundus images. The BLCB-CNN scheme uses a Convolutional Neural Network (CNN) architecture and an empirical approach to balance the distribution of pixels across vessel and non-vessel classes and within thin and thick vessels. Level-I is used for vessel/non-vessel balancing and Level-II is used for thick/thin vessel balancing. Additionally, pre-processing of the input retinal fundus image is performed by Global Contrast Normalization (GCN), Contrast Limited Adaptive Histogram Equalization (CLAHE), and gamma corrections to increase intensity uniformity as well as to enhance the contrast between vessels and background pixels. The resulting balanced dataset is used for classification-based segmentation of the retinal vascular tree. We evaluate the proposed scheme on standard retinal fundus images and achieve superior performance measures, including an area under the ROC curve of 98.23%, Accuracy of 96.22%, Sensitivity of 81.57%, and Specificity of 97.65%. We also demonstrate the method's efficacy through external cross-validation on STARE images, confirming its generalization ability.

eess.IV↗

arXiv2025

Light Drag in a Cavity Magnomechanics

The term "light dragging" describes how the trajectory of light changes as it travels through a moving medium. This phenomenon facilitates the precise detection of incredibly slow speeds of light, which is widely used in quantum gate operations, state transfer, and quantum memory implementations, etc. To the best of our knowledge, this is the first time we have proposed the existence of a light-dragging effect in a magnomechanical system (MMS). The origin of this crucial element stems from nonlinear dipole and magnetostrictive interactions in MMS. Magnomechanical characteristics such as magnon-photon and magnon-phonon couplings have a strong impact on both refractive and group index profile spectra. We also explore that lateral light drag shows a strong dependence on detuning by altering the amplitude and direction of the translational velocity. This enabled us to alter the light's propagation within the magnomechanical system from superluminal to subluminal and vice versa by adjusting the probe's detuning. The ability to control and manipulate the light drag through the MMS could be helpful in designing novel devices with improved functionality at the microscopic scale.

quant-ph↗

arXiv2025

Light drag in an Optomechanical system

Light dragging refers to the change in the path of light passing through a moving medium. This effect enables accurate detection of very slow speeds of light, which have prominent applications in state transfer, quantum gate operations, and quantum memory implementations. Here, to the best of our knowledge, we demonstrate the existence of the light-dragging effect in an optomechanical system (OMS) for the first time. The origin of this key factor arises from the nonlinear effects linked to optomechanical-induced transparency (OMIT). Hence, we observe prominent effects in the group and refractive indices profile spectra related to optomechanical parameters such as the decay rate of the cavity field, the mirror's damping momentum rate, and mechanical frequency. We find out that lateral light drag depends on the detuning by altering the amplitude and direction of the translational velocity. This allowed us to change the light's propagation through the optomechanical cavity from superluminal to subluminal and vice versa by modifying the probe's detuning. The ability to manipulate and control the light drag through an optomechanical system might be useful in designing novel optical devices and systems with enhanced performance.

quant-ph↗

arXiv2025

Cross Modality Medical Image Synthesis for Improving Liver Segmentation

Deep learning-based computer-aided diagnosis (CAD) of medical images requires large datasets. However, the lack of large publicly available labeled datasets limits the development of deep learning-based CAD systems. Generative Adversarial Networks (GANs), in particular, CycleGAN, can be used to generate new cross-domain images without paired training data. However, most CycleGAN-based synthesis methods lack the potential to overcome alignment and asymmetry between the input and generated data. We propose a two-stage technique for the synthesis of abdominal MRI using cross-modality translation of abdominal CT. We show that the synthetic data can help improve the performance of the liver segmentation network. We increase the number of abdominal MRI images through cross-modality image transformation of unpaired CT images using a CycleGAN inspired deformation invariant network called EssNet. Subsequently, we combine the synthetic MRI images with the original MRI images and use them to improve the accuracy of the U-Net on a liver segmentation task. We train the U-Net on real MRI images and then on real and synthetic MRI images. Consequently, by comparing both scenarios, we achieve an improvement in the performance of U-Net. In summary, the improvement achieved in the Intersection over Union (IoU) is 1.17%. The results show potential to address the data scarcity challenge in medical imaging.

eess.IV↗

arXiv2025

Perfect Transfer of Entanglement and One-Way Quantum Steering via Parametric Frequency Converter in a Two-mode Cavity Magnomechanical System

We study the effects of a parametric frequency converter in a two-mode cavity system where one of the cavity mode is coupled with yttrium iron garnet (YIG) via magnetic dipole interaction. Parametric frequency converter acts as a nonlinear source for enhanced entanglement among all bipartitions and asymmetrical quantum steering. The behavior of the two types of quantum correlations are shown to be dependent on parametric coupling and the associated phase factor. We show that cavity-cavity entanglement and cavity-phonon entanglement (cavity-magnon entanglement) decreases (increases) with the increase of the parametric phase factor {\phi}. In addition, generated entanglements in the present system have shown to be more robust against the thermal effects, with the inclusion of the parametric converter as compared with the bare cavity case. Another intriguing finding is the asymmetric one-way steering, where we notice that magnon and phonon modes can steer the indirectly coupled cavity modes, yet the steering in swapped direction is not observed. It is of great interest that the perfect transfer of entanglement and quantum steering is achieved among different modes by adjusting the system's parameters. In fact, our protocol for these transferring processes suggests a different approach to the processing and storage of quantum information.

quant-ph↗

arXiv2025

Phase-Sensitive Enhanced Absorption, Transmission and Slow Light in a Cross-cavity Magnomechanical System

We theoretically propose a scheme to explore the magnetically and magnomechanically induced transparency phenomena in a cross-cavity magnomechanical system, focusing on the role of relative phase and the intensity of the two probing fields in enhancing the absorption and transmission spectra and manipulating the group delay of the transmitted light. Interestingly, the relative phase of the two probe fields could have overwhelming effects on both the absorption spectrum and the group delay of the output field. Tuning the relative phase and amplitude of the probe fields can suppress or enhance the absorption and transmission spectra. The combined effect of the magnon-photon and magnon-phonon couplings, along with relative phase modulations, helps to switch the probe field's behavior from subluminal to superluminal in the current system. The current study offers a straightforward and practical approach, demonstrating the capability to employ the relative phase for the modulation of microwave signals within the cavity magnomechanical system, providing insights for the design of information transduction and quantum sensing.

quant-ph↗

arXiv2024

From Mesh to Neural Nets: A Multi-Method Evaluation of Physics-Informed Neural Networks and Galerkin Finite Element Method for Solving Nonlinear Convection-Reaction-Diffusion Equations

Non-linear convection-reaction-diffusion (CRD) partial differential equations (PDEs) are crucial for modeling complex phenomena in fields such as biology, ecology, population dynamics, physics, and engineering. Numerical approximation of these non-linear systems is essential due to the challenges of obtaining exact solutions. Traditionally, the Galerkin finite element method (GFEM) has been the standard computational tool for solving these PDEs. With the advancements in machine learning, Physics-Informed Neural Network (PINN) has emerged as a promising alternative for approximating non-linear PDEs. In this study, we compare the performance of PINN and GFEM by solving four distinct one-dimensional CRD problems with varying initial and boundary conditions and evaluate the performance of PINN over GFEM. This evaluation metrics includes error estimates, and visual representations of the solutions, supported by statistical methods such as the root mean squared error (RMSE), the standard deviation of error, the the Wilcoxon Signed-Rank Test and the coefficient of variation (CV) test. Our findings reveal that while both methods achieve solutions close to the analytical results, PINN demonstrate superior accuracy and efficiency. PINN achieved significantly lower RMSE values and smaller standard deviations for Burgers' equation, Fisher's equation, and Newell-Whitehead-Segel equation, indicating higher accuracy and greater consistency. While GFEM shows slightly better accuracy for the Burgers-Huxley equation, its performance was less consistent over time. In contrast, PINN exhibit more reliable and robust performance, highlighting their potential as a cutting-edge approach for solving non-linear PDEs.

physics.comp-ph↗

arXiv2024

Deep Learning for Surgical Instrument Recognition and Segmentation in Robotic-Assisted Surgeries: A Systematic Review

Applying deep learning (DL) for annotating surgical instruments in robot-assisted minimally invasive surgeries (MIS) represents a significant advancement in surgical technology. This systematic review examines 48 studies that and advanced DL methods and architectures. These sophisticated DL models have shown notable improvements in the precision and efficiency of detecting and segmenting surgical tools. The enhanced capabilities of these models support various clinical applications, including real-time intraoperative guidance, comprehensive postoperative evaluations, and objective assessments of surgical skills. By accurately identifying and segmenting surgical instruments in video data, DL models provide detailed feedback to surgeons, thereby improving surgical outcomes and reducing complication risks. Furthermore, the application of DL in surgical education is transformative. The review underscores the significant impact of DL on improving the accuracy of skill assessments and the overall quality of surgical training programs. However, implementing DL in surgical tool detection and segmentation faces challenges, such as the need for large, accurately annotated datasets to train these models effectively. The manual annotation process is labor-intensive and time-consuming, posing a significant bottleneck. Future research should focus on automating the detection and segmentation process and enhancing the robustness of DL models against environmental variations. Expanding the application of DL models across various surgical specialties will be essential to fully realize this technology's potential. Integrating DL with other emerging technologies, such as augmented reality (AR), also offers promising opportunities to further enhance the precision and efficacy of surgical procedures.

eess.IV↗

arXiv2024

An Early Investigation into the Utility of Multimodal Large Language Models in Medical Imaging

Recent developments in multimodal large language models (MLLMs) have spurred significant interest in their potential applications across various medical imaging domains. On the one hand, there is a temptation to use these generative models to synthesize realistic-looking medical image data, while on the other hand, the ability to identify synthetic image data in a pool of data is also significantly important. In this study, we explore the potential of the Gemini (\textit{gemini-1.0-pro-vision-latest}) and GPT-4V (gpt-4-vision-preview) models for medical image analysis using two modalities of medical image data. Utilizing synthetic and real imaging data, both Gemini AI and GPT-4V are first used to classify real versus synthetic images, followed by an interpretation and analysis of the input images. Experimental results demonstrate that both Gemini and GPT-4 could perform some interpretation of the input images. In this specific experiment, Gemini was able to perform slightly better than the GPT-4V on the classification task. In contrast, responses associated with GPT-4V were mostly generic in nature. Our early investigation presented in this work provides insights into the potential of MLLMs to assist with the classification and interpretation of retinal fundoscopy and lung X-ray images. We also identify key limitations associated with the early investigation study on MLLMs for specialized tasks in medical image analysis.

eess.IV↗

arXiv2024

Nonreciprocal Multipartite Entanglement in a two-cavity magnomechanical system

We propose a theoretical scheme for the generation of nonreciprocal multipartite entanglement in a two-mode cavity magnomechanical system, consisting of two cross-microwave (MW) cavities having a yttrium iron garnet (YIG) sphere, which is coupled through magnetic dipole interaction. Our results show that the self-Kerr effect of magnon can significantly enhance multipartite entanglement, which turns out to be nonreciprocal when the magnetic field is tuned along the crystallographic axis [110]. This is due to the frequency shift on the magnons (YIG sphere), which depends on the direction of the magnetic field. Interestingly, the degree of nonreciprocity of bipartite entanglements depends upon a careful optimal choice of system parameters like normalized cavity detunings, bipartite nonlinear index, self-Kerr coefficient, and effective magnomechanical coupling rate G. In addition to bipartite entanglement, we also present the idea of a bidirectional contrast ratio, which quantifies the nonreciprocity in tripartite entanglements. Our present theoretical proposal for nonreciprocity in multipartite entanglement may find applications in diverse engineering nonreciprocal devices

quant-ph↗

arXiv2024

A Study on Thermal Quantum Resources and Probabilistic Teleportation in Spin-1/2 Heisenberg XYZ+DM+KSEA Model under Variable Zeeman Splitting

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.

quant-ph↗

arXiv2024

Nanomechanically induced transparency in $\mathcal{PT}$-symmetric optical cavities

In this paper, we analytically present the phenomena of nanomechanically induced transparency (NMIT) and transmission rate in a parity-time-symmetric ($\mathcal{PT}$-symmetric) opto-nanomechanical system (ONMS) where a levitated dielectric nanospheres is trapped near the antinodes closest to right mirror of passive cavity which further coupled to an active cavity via hoping factor. We find that the phenomenon of NMIT may be generated from the output probe field in the presence of an effective opto-nanomechanical coupling between the cavity field and the nanosphere, whose steady-state position is influenced by the Coulomb interaction between the cavity mirror and the nanosphere. In addition, the width and height of the transparency window can be controlled through the effective optomechanical coupling, which is readily adjusted by altering changing the nanosphere's radius and the Coulomb interaction. One of the most interesting result is the transition NMIT behavior in $\mathcal{PT}$-symmetric and broken $\mathcal{PT}$-symmetric regime. We show that the presence of nanosphere in the passive cavity enhances the width and transmission rate of NMIT window in passive-passive regime and in passive-active regime, a notable decrease of sideband amplification has been observed. These results show that our scheme may find some potential applications for optical signal processing an and quantum information processing.

quant-ph↗

arXiv2023

Improving diagnosis and prognosis of lung cancer using vision transformers: A scoping review

Vision transformer-based methods are advancing the field of medical artificial intelligence and cancer imaging, including lung cancer applications. Recently, many researchers have developed vision transformer-based AI methods for lung cancer diagnosis and prognosis. This scoping review aims to identify the recent developments on vision transformer-based AI methods for lung cancer imaging applications. It provides key insights into how vision transformers complemented the performance of AI and deep learning methods for lung cancer. Furthermore, the review also identifies the datasets that contributed to advancing the field. Of the 314 retrieved studies, this review included 34 studies published from 2020 to 2022. The most commonly addressed task in these studies was the classification of lung cancer types, such as lung squamous cell carcinoma versus lung adenocarcinoma, and identifying benign versus malignant pulmonary nodules. Other applications included survival prediction of lung cancer patients and segmentation of lungs. The studies lacked clear strategies for clinical transformation. SWIN transformer was a popular choice of the researchers; however, many other architectures were also reported where vision transformer was combined with convolutional neural networks or UNet model. It can be concluded that vision transformer-based models are increasingly in popularity for developing AI methods for lung cancer applications. However, their computational complexity and clinical relevance are important factors to be considered for future research work. This review provides valuable insights for researchers in the field of AI and healthcare to advance the state-of-the-art in lung cancer diagnosis and prognosis. We provide an interactive dashboard on lung-cancer.onrender.com/.

eess.IV↗