We propose a theoretical investigation of the photonic spin Hall effect (PSHE) in a mid-infrared probe field by employing an asymmetric double AlGaAsGaAs quantum well as the intracavity medium. The system is designed such that an external control beam together with tunable tunneling barriers regulates the quantum interference of the probe tunneling process. This configuration enables precise manipulation of the PSHE for both horizontally and vertically polarized components of light. Our analysis reveals the emergence of a giant horizontal PSHE in the quantum well based cavity system. Moreover, by incorporating absorptive and gain-assisted cavity slabs, the horizontal PSHE is further amplified, leading to an even more pronounced photonic spin separation. The results provide novel insights into light matter interactions in semiconductor quantum wells and suggest an effective route for enhancing and controlling the PSHE in mid-infrared photonic devices.

We investigate the thermodynamic and observational implications for the charged torus-like black holes, a class of solutions distinct from the classical Schwarzschild black holes. We explicitly derive the fundamental thermodynamic properties, such as heat capacity, P-V diagram, isothermal compressibility, Helmholtz free energy, and Gibbs free energy, under different entropy models. We find that only the exponential corrected entropy demonstrates multiple phase transitions, which we validate with the Ricci Scalar divergence obtained from the Ruppeiner formalism. This indicates that exponential corrected entropy is more sensitive to BH's microstructure as compared to the Hawking-Bekenstein and Rènyi entropy models. In addition, we study the sparsity and emission rates of Hawking radiation, demonstrating that exponential correction entropy yields more consistent and stable behavior. In our observational analysis, we graphically demonstrate the behavior of redshift, blueshift, and gravitational shift, and identify specific conditions where the photon sphere radius exceeds the innermost stable circular orbit radius, which depends on the values of parameters such as electric charge and cosmological constant. The novel insight of this work is that despite this violation, our computed redshift, blueshift, and gravitational shifts fall within the range of the observational data of NGC 4258 and UGC 3789.

Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a review of the contemporary automatic image aesthetics assessment techniques. Many traditional hand-crafted and deep learning-based approaches are reviewed, and critical problem aspects are discussed, including why some features or models perform better than others and the limitations. A comparison of the quantitative results of different methods is also provided.

Based on $10.64~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data taken at center-of-mass energies between 4.237 and 4.699 GeV with the BESIII detector, we study the leptonic $D^+_s$ decays using the $e^+e^-\to D^{*+}_{s} D^{*-}_{s}$ process. The branching fractions of $D_s^+\to\ell^+\nu_{\ell}\,(\ell=\mu,\tau)$ are measured to be $\mathcal{B}(D_s^+\to\mu^+\nu_\mu)=(0.547\pm0.026_{\rm stat}\pm0.016_{\rm syst})\%$ and $\mathcal{B}(D_s^+\to\tau^+\nu_\tau)=(5.60\pm0.16_{\rm stat}\pm0.20_{\rm syst})\%$, respectively. The product of the decay constant and Cabibbo-Kobayashi-Maskawa matrix element $|V_{cs}|$ is determined to be $f_{D_s^+}|V_{cs}|=(246.5\pm5.9_{\rm stat}\pm3.6_{\rm syst}\pm0.5_{\rm input})_{\mu\nu}~\mathrm{MeV}$ and $f_{D_s^+}|V_{cs}|=(252.7\pm3.6_{\rm stat}\pm4.5_{\rm syst}\pm0.6_{\rm input}))_{\tau \nu}~\mathrm{MeV}$, respectively. Taking the value of $|V_{cs}|$ from a global fit in the Standard Model, we obtain ${f_{D^+_s}}=(252.8\pm6.0_{\rm stat}\pm3.7_{\rm syst}\pm0.6_{\rm input})_{\mu\nu}$ MeV and ${f_{D^+_s}}=(259.2\pm3.6_{\rm stat}\pm4.5_{\rm syst}\pm0.6_{\rm input})_{\tau \nu}$ MeV, respectively. Conversely, taking the value for $f_{D_s^+}$ from the latest lattice quantum chromodynamics calculation, we obtain $|V_{cs}| =(0.986\pm0.023_{\rm stat}\pm0.014_{\rm syst}\pm0.003_{\rm input})_{\mu\nu}$ and $|V_{cs}| = (1.011\pm0.014_{\rm stat}\pm0.018_{\rm syst}\pm0.003_{\rm input})_{\tau \nu}$, respectively.

Numerical wave functions (WFs), root mean square (RMS) radii, $E1$ and $M1$ radiative transitions, and branching ratios of $S, P, D$ and $F$ states of toponium mesons ($t\overline{t}$) are calculated using a non-relativistic quark potential model (NRQPM). Shooting method is used to solve the radial Schrodinger equation to find the radial wave functions which are used in the calculation of properties of mesons. Calculated masses are compared with the theoretical available data.

Quantum Machine Learning (QML) has shown promise in diverse applications such as environmental monitoring, healthcare diagnostics, and financial modeling. However, its practical implementation faces challenges, including limited quantum hardware and the complexity of integrating quantum algorithms with classical systems. One critical challenge is handling imbalanced datasets, where rare events are often misclassified due to skewed data distributions. Quantum Bayesian Networks (QBNs) address this issue by enhancing feature extraction and improving the classification of rare events such as oil spills. This paper introduces a Bayesian approach utilizing QBNs to classify satellite-derived imbalanced datasets, distinguishing ``oil-spill'' from ``non-spill'' regions. QBNs leverage probabilistic reasoning and quantum state preparation to integrate quantum enhancements into classical machine learning architectures. Our approach achieves a 0.99 AUC score, demonstrating its efficacy in anomaly detection and advancing precise environmental monitoring and management. While integration enhances classification performance, dataset-specific challenges require further optimization.

We propose an automatic framework for toll collection, consisting of three steps: vehicle type recognition, license plate localization, and reading. However, each of the three steps becomes non-trivial due to image variations caused by several factors. The traditional vehicle decorations on the front cause variations among vehicles of the same type. These decorations make license plate localization and recognition difficult due to severe background clutter and partial occlusions. Likewise, on most vehicles, specifically trucks, the position of the license plate is not consistent. Lastly, for license plate reading, the variations are induced by non-uniform font styles, sizes, and partially occluded letters and numbers. Our proposed framework takes advantage of both data availability and performance evaluation of the backbone deep learning architectures. We gather a novel dataset, \emph{Diverse Vehicle and License Plates Dataset (DVLPD)}, consisting of 10k images belonging to six vehicle types. Each image is then manually annotated for vehicle type, license plate, and its characters and digits. For each of the three tasks, we evaluate You Only Look Once (YOLO)v2, YOLOv3, YOLOv4, and FasterRCNN. For real-time implementation on a Raspberry Pi, we evaluate the lighter versions of YOLO named Tiny YOLOv3 and Tiny YOLOv4. The best Mean Average Precision (mAP@0.5) of 98.8% for vehicle type recognition, 98.5% for license plate detection, and 98.3% for license plate reading is achieved by YOLOv4, while its lighter version, i.e., Tiny YOLOv4 obtained a mAP of 97.1%, 97.4%, and 93.7% on vehicle type recognition, license plate detection, and license plate reading, respectively. The dataset and the training codes are available at this https URL

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.

By analyzing $6.32~\mathrm{fb}^{-1}$ of $e^+e^-$ annihilation data collected at the center-of-mass energies between 4.178 and 4.226\,GeV with the BESIII detector, we determine the branching fraction of the leptonic decay $D_s^+\to\tau^+\nu_\tau$ with $\tau^+\to\pi^+\pi^0\bar \nu_\tau$, to be $\mathcal{B}_{D_s^+\to\tau^+\nu_\tau}=(5.29\pm0.25_{\rm stat}\pm0.20_{\rm syst})\%$. We estimate the product of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cs}|$ and the $D_s^+$ decay constant $f_{D^+_s}$ to be $f_{D_s^+}|V_{cs}|=(244.8\pm5.8_{\rm stat}\pm4.8_{\rm syst})~\mathrm{MeV}$ using the known values of the $\tau^+$ and $D_s^+$ masses as well as the $D_s^+$ lifetime, together with our branching fraction measurement. Combining with the value of $|V_{cs}|$ obtained from a global fit in the standard model and $f_{D_s^+}$ from lattice quantum chromodynamics, we obtain $f_{D_s^+}=(251.6\pm5.9_{\rm stat}\pm4.9_{\rm syst})$\,MeV and $|V_{cs}| = 0.980\pm0.023_{\rm stat}\pm0.019_{\rm syst}$.

Image captioning by the encoder-decoder framework has shown tremendous advancement in the last decade where CNN is mainly used as encoder and LSTM is used as a decoder. Despite such an impressive achievement in terms of accuracy in simple images, it lacks in terms of time complexity and space complexity efficiency. In addition to this, in case of complex images with a lot of information and objects, the performance of this CNN-LSTM pair downgraded exponentially due to the lack of semantic understanding of the scenes presented in the images. Thus, to take these issues into consideration, we present CNN-GRU encoder decode framework for caption-to-image reconstructor to handle the semantic context into consideration as well as the time complexity. By taking the hidden states of the decoder into consideration, the input image and its similar semantic representations is reconstructed and reconstruction scores from a semantic reconstructor are used in conjunction with likelihood during model training to assess the quality of the generated caption. As a result, the decoder receives improved semantic information, enhancing the caption production process. During model testing, combining the reconstruction score and the log-likelihood is also feasible to choose the most appropriate caption. The suggested model outperforms the state-of-the-art LSTM-A5 model for picture captioning in terms of time complexity and accuracy.

Using $e^+e^-$ collision data collected with the BESIII detector operating at the Beijing Electron Positron Collider, the cross section of $e^+e^-\to \pi^+\pi^- h_c$ is measured at 59 points with center-of-mass energy $\sqrt{s}$ ranging from $4.009$ to $4.950~\mathrm{GeV}$ with a total integrated luminosity of $22.2~\mathrm{fb}^{-1}$. The cross section between $4.3$ and $4.45~\mathrm{GeV}$ exhibits a plateau-like shape and drops sharply around $4.5~\mathrm{GeV}$, which cannot be described by two resonances only. Three coherent Breit-Wigner functions are used to parameterize the $\sqrt{s}$-dependent cross section line shape. The masses and widths are determined to be $M_1=(4223.6_{-3.7-2.9}^{+3.6+2.6})~\mathrm{MeV}/c^2$, $\Gamma_1=(58.5_{-11.4-6.5}^{+10.8+6.7})~\mathrm{MeV}$, $M_2=(4327.4_{-18.8-9.3}^{+20.1+10.7})~\mathrm{MeV}/c^2$, $\Gamma_2=(244.1_{-27.1-18.3}^{+34.0+24.2})~\mathrm{MeV}$, and $M_3=(4467.4_{-5.4-2.7}^{+7.2+3.2})~\mathrm{MeV}/c^2$, $\Gamma_3=(62.8_{-14.4-7.0}^{+19.2+9.9})~\mathrm{MeV}$. The first uncertainties are statistical and the second are systematic. The inclusion of the relatively narrower third component proves crucial for reproducing the drop at around 4.5~GeV. The statistical significance of the three-resonance assumption over the two-resonance assumption is greater than $5\sigma$.

The potato is a widely grown crop in many regions of the world. In recent decades, potato farming has gained incredible traction in the world. Potatoes are susceptible to several illnesses that stunt their development. This plant seems to have significant leaf disease. Early Blight and Late Blight are two prevalent leaf diseases that affect potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is to use image processing to identify and analyze these disorders. Here, we present an autonomous method based on image processing and machine learning to detect late blight disease affecting potato leaves. The proposed method comprises four different phases: (1) Histogram Equalization is used to improve the quality of the input image; (2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated; (3) feature selection is performed using wrapper-based feature selection; (4) classification is performed using an SVM classifier and its variants. This proposed method achieves the highest accuracy of 99% using SVM by selecting 550 features.

We study the phenomenological implications of the minimal supersymmetric standard model (MSSM) augmented by a non-abelian flavor symmetry labeled as sMSSM. Incorporating this flavor symmetry allows for a significant reduction in the original plethora of free parameters present in the MSSM, ultimately reducing them down to just seven in sMSSM. This reduction of free parameters is not achieved through ad hoc assumptions like in the constrained MSSM (CMSSM); rather, it is grounded in theoretical considerations. Our work focuses on exploring the interplay between the $W$ boson mass ($M_W$) predictions, the cold dark matter (CDM) relic abundance ($\Omega_{\rm CDM} h^2$), and the $(g-2)_{\mu}$ anomaly. We identified correlations among the theoretical parameters arising from this interplay, which can be complemented by experimental constraints such as the Higgs boson mass, B-physics observables, and charge and color breaking minima. Additionally, our investigations show that the $(g-2)_{\mu}$ discrepancy and the Planck bounds on $\Omega_{\rm CDM} h^2$ can be addressed within the sMSSM, but only in a very narrow region of the parameter space.

This study derives a novel family of charged black hole solutions featuring short- and long-range modifications. These ones are achieved through a Yukawa-like gravitational potential modification and a nonsingular electric potential incorporation. The short-range corrections encode quantum gravity effects, while the long-range adjustments simulate gravitational effects akin to those attributed to dark matter. Our investigation reveals that the total mass of the black hole undergoes corrections owing to the apparent presence of dark matter mass and the self-adjusted electric charge mass. Two distinct solutions are discussed: a regular black hole solution characterizing small black holes, where quantum effects play a crucial role, and a second solution portraying large black holes at considerable distances, where the significance of Yukawa corrections comes into play. Notably, these long-range corrections contribute to an increase in the total mass and hold particular interest as they can emulate the role of dark matter. Finally, we explore the phenomenological aspects of the black hole. Specifically, we examine the influence of electric charge and Yukawa parameters on thermodynamic quantities, the quasinormal modes for the charged scalar perturbations as well as for the vector perturbations, analysis of the geodesics of light/massive particles, and the accretion of matter onto the charged black hole solution.

Deep learning techniques, particularly convolutional neural networks, have shown great potential in computer vision and medical imaging applications. However, deep learning models are computationally demanding as they require enormous computational power and specialized processing hardware for model training. To make these models portable and compatible for prototyping, their implementation on low-power devices is imperative. In this work, we present the implementation of Modified U-Net on Intel Movidius Neural Compute Stick 2 (NCS-2) for the segmentation of medical images. We selected U-Net because, in medical image segmentation, U-Net is a prominent model that provides improved performance for medical image segmentation even if the dataset size is small. The modified U-Net model is evaluated for performance in terms of dice score. Experiments are reported for segmentation task on three medical imaging datasets: BraTs dataset of brain MRI, heart MRI dataset, and Ziehl-Neelsen sputum smear microscopy image (ZNSDB) dataset. For the proposed model, we reduced the number of parameters from 30 million in the U-Net model to 0.49 million in the proposed architecture. Experimental results show that the modified U-Net provides comparable performance while requiring significantly lower resources and provides inference on the NCS-2. The maximum dice scores recorded are 0.96 for the BraTs dataset, 0.94 for the heart MRI dataset, and 0.74 for the ZNSDB dataset.

Buses and heavy vehicles have more blind spots compared to cars and other road vehicles due to their large sizes. Therefore, accidents caused by these heavy vehicles are more fatal and result in severe injuries to other road users. These possible blind-spot collisions can be identified early using vision-based object detection approaches. Yet, the existing state-of-the-art vision-based object detection models rely heavily on a single feature descriptor for making decisions. In this research, the design of two convolutional neural networks (CNNs) based on high-level feature descriptors and their integration with faster R-CNN is proposed to detect blind-spot collisions for heavy vehicles. Moreover, a fusion approach is proposed to integrate two pre-trained networks (i.e., Resnet 50 and Resnet 101) for extracting high level features for blind-spot vehicle detection. The fusion of features significantly improves the performance of faster R-CNN and outperformed the existing state-of-the-art methods. Both approaches are validated on a self-recorded blind-spot vehicle detection dataset for buses and an online LISA dataset for vehicle detection. For both proposed approaches, a false detection rate (FDR) of 3.05% and 3.49% are obtained for the self recorded dataset, making these approaches suitable for real time applications.

The problems that tobacco workshops encounter include poor curing, inconsistencies in supplies, irregular scheduling, and a lack of oversight, all of which drive up expenses and worse quality. Large quantities make manual examination costly, sluggish, and unreliable. Deep convolutional neural networks have recently made strides in capabilities that transcend those of conventional methods. To effectively enhance them, nevertheless, extensive customization is needed to account for subtle variations in tobacco grade. This study introduces InspectionV3, an integrated solution for automated flue-cured tobacco grading that makes use of a customized deep convolutional neural network architecture. A scope that covers color, maturity, and curing subtleties is established via a labelled dataset consisting of 21,113 images spanning 20 quality classes. Expert annotators performed preprocessing on the tobacco leaf images, including cleaning, labelling, and augmentation. Multi-layer CNN factors use batch normalization to describe domain properties like as permeability and moisture spots, and so account for the subtleties of the workshop. Its expertise lies in converting visual patterns into useful information for enhancing workflow. Fast notifications are made possible by real-time, on-the-spot grading that matches human expertise. Images-powered analytics dashboards facilitate the tracking of yield projections, inventories, bottlenecks, and the optimization of data-driven choices. More labelled images are assimilated after further retraining, improving representational capacities and enabling adaptations for seasonal variability. Metrics demonstrate 97% accuracy, 95% precision and recall, 96% F1-score and AUC, 95% specificity; validating real-world viability.

We introduce Layered Self-Supervised Knowledge Distillation (LSSKD) framework for training compact deep learning models. Unlike traditional methods that rely on pre-trained teacher networks, our approach appends auxiliary classifiers to intermediate feature maps, generating diverse self-supervised knowledge and enabling one-to-one transfer across different network stages. Our method achieves an average improvement of 4.54\% over the state-of-the-art PS-KD method and a 1.14% gain over SSKD on CIFAR-100, with a 0.32% improvement on ImageNet compared to HASSKD. Experiments on Tiny ImageNet and CIFAR-100 under few-shot learning scenarios also achieve state-of-the-art results. These findings demonstrate the effectiveness of our approach in enhancing model generalization and performance without the need for large over-parameterized teacher networks. Importantly, at the inference stage, all auxiliary classifiers can be removed, yielding no extra computational cost. This makes our model suitable for deploying small language models on affordable low-computing devices. Owing to its lightweight design and adaptability, our framework is particularly suitable for multimodal sensing and cyber-physical environments that require efficient and responsive inference. LSSKD facilitates the development of intelligent agents capable of learning from limited sensory data under weak supervision.

U-Net is a widely adopted neural network in the domain of medical image segmentation. Despite its quick embracement by the medical imaging community, its performance suffers on complicated datasets. The problem can be ascribed to its simple feature extracting blocks: encoder/decoder, and the semantic gap between encoder and decoder. Variants of U-Net (such as R2U-Net) have been proposed to address the problem of simple feature extracting blocks by making the network deeper, but it does not deal with the semantic gap problem. On the other hand, another variant UNET++ deals with the semantic gap problem by introducing dense skip connections but has simple feature extraction blocks. To overcome these issues, we propose a new U-Net based medical image segmentation architecture R2U++. In the proposed architecture, the adapted changes from vanilla U-Net are: (1) the plain convolutional backbone is replaced by a deeper recurrent residual convolution block. The increased field of view with these blocks aids in extracting crucial features for segmentation which is proven by improvement in the overall performance of the network. (2) The semantic gap between encoder and decoder is reduced by dense skip pathways. These pathways accumulate features coming from multiple scales and apply concatenation accordingly. The modified architecture has embedded multi-depth models, and an ensemble of outputs taken from varying depths improves the performance on foreground objects appearing at various scales in the images. The performance of R2U++ is evaluated on four distinct medical imaging modalities: electron microscopy (EM), X-rays, fundus, and computed tomography (CT). The average gain achieved in IoU score is 1.5+-0.37% and in dice score is 0.9+-0.33% over UNET++, whereas, 4.21+-2.72 in IoU and 3.47+-1.89 in dice score over R2U-Net across different medical imaging segmentation datasets.

Using data samples of $(10087\pm 44)\times10^{6}$ $J/\psi$ events and $(2712.4\pm 14.3)\times10^{6}$ $\psi(3686)$ events collected with the BESIII detector at the BEPCII collider, we search for the CP violating decays $J/\psi\rightarrow K^{0}_{S}K^{0}_{S}$ and $\psi(3686)\rightarrow K^{0}_{S}K^{0}_{S}$. No significant signals are observed over the expected background yields. The upper limits on their branching fractions are set as \mathcal{B}(J/\psi\rightarrow K^{0}_{S}K^{0}_{S}) <4.7\times 10^{-9} and \mathcal{B}(\psi(3686)\rightarrow K^{0}_{S}K^{0}_{S}) <1.1\times 10^{-8} at the 90% confidence level. These results improve the previous limits by a factor of three for $J/\psi\rightarrow K^{0}_{S} K^{0}_{S}$ and two orders of magnitude for $\psi(3686)\rightarrow K^{0}_{S} K^{0}_{S}$.