Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, primarily due to the lack of high-quality, balanced, and diverse datasets with expert annotations. In this work, we address this gap by introducing BRISC, a dataset designed for brain tumor segmentation and classification tasks, featuring high-resolution segmentation masks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans, which were collated from multiple public datasets that lacked segmentation labels. Our primary contribution is the subsequent expert annotation of these images, performed by certified radiologists and physicians. It includes three major tumor types, namely glioma, meningioma, and pituitary, as well as non-tumorous cases. Each sample includes high-resolution labels and is categorized across axial, sagittal, and coronal imaging planes to facilitate robust model development and cross-view generalization. To demonstrate the utility of the dataset, we provide benchmark results for both tasks using standard deep learning models. The BRISC dataset is made publicly available. datasetlink: Kaggle (this https URL), Figshare (this https URL), Zenodo (this https URL)

Physicians spend significant time documenting clinical encounters, a burden that contributes to professional burnout. To address this, robust automation tools for medical documentation are crucial. We introduce MedSynth -- a novel dataset of synthetic medical dialogues and notes designed to advance the Dialogue-to-Note (Dial-2-Note) and Note-to-Dialogue (Note-2-Dial) tasks. Informed by an extensive analysis of disease distributions, this dataset includes over 10,000 dialogue-note pairs covering over 2000 ICD-10 codes. We demonstrate that our dataset markedly enhances the performance of models in generating medical notes from dialogues, and dialogues from medical notes. The dataset provides a valuable resource in a field where open-access, privacy-compliant, and diverse training data are scarce. Code is available at this https URL and the dataset is available at this https URL.

Pneumonia, a prevalent respiratory infection, remains a leading cause of morbidity and mortality worldwide, particularly among vulnerable populations. Chest X-rays serve as a primary tool for pneumonia detection; however, variations in imaging conditions and subtle visual indicators complicate consistent interpretation. Automated tools can enhance traditional methods by improving diagnostic reliability and supporting clinical decision-making. In this study, we propose a novel multi-scale transformer approach for pneumonia detection that integrates lung segmentation and classification into a unified framework. Our method introduces a lightweight transformer-enhanced TransUNet for precise lung segmentation, achieving a Dice score of 95.68% on the "Chest X-ray Masks and Labels" dataset with fewer parameters than traditional transformers. For classification, we employ pre-trained ResNet models (ResNet-50 and ResNet-101) to extract multi-scale feature maps, which are then processed through a modified transformer module to enhance pneumonia detection. This integration of multi-scale feature extraction and lightweight transformer modules ensures robust performance, making our method suitable for resource-constrained clinical environments. Our approach achieves 93.75% accuracy on the "Kermany" dataset and 96.04% accuracy on the "Cohen" dataset, outperforming existing methods while maintaining computational efficiency. This work demonstrates the potential of multi-scale transformer architectures to improve pneumonia diagnosis, offering a scalable and accurate solution to global healthcare challenges."this https URL"

The Swampland program, which looks for low energy theories consistent with quantum gravity, has led to the introduction of a dark dimension stemming from the cosmological constant. We show that the same argument leads to the emergence of the electroweak scale, once the dark dimension is realised in a warped background. A second warped extra dimension at the TeV scale is, therefore, postulated, where the long-standing problem of the hierarchy between the electroweak and the Planck scales can be addressed. Furthermore, standard model contributions to the cosmological constant are tamed, together with the gravitational ones. In the emergent holistic picture of gravity and gauge interactions, both Planck and the electroweak scales are emergent from a theory with two fundamental scales: $10^{-2}$ eV and $10^{10}$ GeV, which are of geometric origin and, following the Distance Conjecture, natural. Hence, a bridge is established between the two standard models of particle physics and cosmology.

We consider a model of inflation consisting a triplet of $U(1)$ vector fields with the parity violating interaction which is non-minimally coupled to inflaton. The vector field sector enjoys global $O(3)$ symmetry which admits isotropic configuration and provides not only vector modes but also scalar and tensor modes. We decompose the scalar perturbations into the adiabatic, entropy and isocurvature perturbations and compute all power spectra and cross correlations of the scalar and the tensor sectors. The tensor modes associated with the vector fields contribute to the power spectrum of gravitational waves while the parity violating term generates chirality in gravitational power spectra and bispectra. We study nonlinear scalar and tensor perturbations and compute all bispectra and three-point cross-correlations. In particular, it is shown that the non-Gaussianity of curvature perturbations and gravitational waves are enhanced by the vector field perturbations. We show that non-Gaussianities put strong constraints on the model parameters such as the parity violating coupling and the fractional energy of the vector fields.

Early detection of lung cancer is crucial as it increases the chances of successful treatment. Automatic lung image segmentation assists doctors in identifying diseases such as lung cancer, COVID-19, and respiratory disorders. However, lung segmentation is challenging due to overlapping features like vascular and bronchial structures, along with pixel-level fusion of brightness, color, and texture. New lung segmentation methods face difficulties in identifying long-range relationships between image components, reliance on convolution operations that may not capture all critical features, and the complex structures of the lungs. Furthermore, semantic gaps between feature maps can hinder the integration of relevant information, reducing model accuracy. Skip connections can also limit the decoder's access to complete information, resulting in partial information loss during encoding. To overcome these challenges, we propose a hybrid approach using the FusionLungNet network, which has a multi-level structure with key components, including the ResNet-50 encoder, Channel-wise Aggregation Attention (CAA) module, Multi-scale Feature Fusion (MFF) block, self refinement (SR) module, and multiple decoders. The refinement sub-network uses convolutional neural networks for image post-processing to improve quality. Our method employs a combination of loss functions, including SSIM, IOU, and focal loss, to optimize image reconstruction quality. We created and publicly released a new dataset for lung segmentation called LungSegDB, including 1800 CT images from the LIDC-IDRI dataset (dataset version 1) and 700 images from the Chest CT Cancer Images from Kaggle dataset (dataset version 2). Our method achieved an IOU score of 98.04, outperforming existing methods and demonstrating significant improvements in segmentation accuracy. this https URL

An integral domain is said to have the IDF property when every non-zero element of it has only a finite number of non-associate irreducible divisors. A counterexample has already been found showing that IDF property does not necessarily ascend in polynomial extensions. In this paper, we introduce a new class of integral domains, called MCD-finite domains, and show that for any domain $D$, $D[X]$ is an IDF domain if and only if $D$ is both IDF and MCD-finite. This result entails all the previously known sufficient conditions for the ascent of the IDF property. Our new characterization of polynomial domains with the IDF property enables us to use a different construction and build another counterexample which strengthen the previously known result on this matter.

A new estimation scheme based on the split-step quantum walk (SSQW) revealed that by just setting a single parameter, SSQW can potentially achieve quantum Crame\'r-Rao bound in multiparameter estimation. This parameter even does not involve the parameterization but the initial state and unlike ordinary Quantum walk (OQW) there is no necessity for an entangled initial states or even a parameter dependent initial state. The rigorous analytic equations derived in this study revealed that SSQW surpasses OQW in achievable precision of multiparameter estimation in almost all possible scenarios. Furthermore, in single parameter estimation, the extra parameter can be used to tune the dynamics of the walk in such a way to enhance the precision of the estimation through maximizing the elements of quantum Fisher information matrix. The results of this study indicate that SSQW can remarkably improve the estimation schemes through its rich topological properties.

This work examines the implications of a black hole featuring a de Sitter core. We begin by analyzing the spacetime and event horizon in the presence of de Sitter core. Then the partial wave equation necessary for calculating quasinormal modes is derived and the relation of scalar quasinormal modes with the de Sitter core parameter is explored. Subsequently, we explore the greybody factors and their correspondence with the gravitational quasinormal modes. We also analyze the emission rate. Finally, variations in the thin accretion disks and the influence of de Sitter core spacetime on the optical appearance of the black hole are discussed as well.

Parkinson's disease (PD) is a debilitating neurological disorder that necessitates precise and early diagnosis for effective patient care. This study aims to develop a diagnostic model capable of achieving both high accuracy and minimizing false negatives, a critical factor in clinical practice. Given the limited training data, a feature selection strategy utilizing ANOVA is employed to identify the most informative features. Subsequently, various machine learning methods, including Echo State Networks (ESN), Random Forest, k-nearest Neighbors, Support Vector Classifier, Extreme Gradient Boosting, and Decision Tree, are employed and thoroughly evaluated. The statistical analyses of the results highlight ESN's exceptional performance, showcasing not only superior accuracy but also the lowest false negative rate among all methods. Consistently, statistical data indicates that the ESN method consistently maintains a false negative rate of less than 8% in 83% of cases. ESN's capacity to strike a delicate balance between diagnostic precision and minimizing misclassifications positions it as an exemplary choice for PD diagnosis, especially in scenarios characterized by limited data. This research marks a significant step towards more efficient and reliable PD diagnosis, with potential implications for enhanced patient outcomes and healthcare dynamics.

We study neutral Dirac particles confined to a family of $(1+2)$-dimensional wormholes arising from surfaces of revolution with a constant negative Gaussian curvature, in the framework of a comprehensive effective field theory allowing deviations from Lorentz symmetry: the gravitational standard-model extension (SME). The Dirac particles are described with a fixed background tensor field that rules the Lorentz symmetry violation in the CPT-even gauge sector of SME. We implement this geometrical approach by incorporating non-minimal couplings that possibly induce a Lorentz-symmetry violating term in the modified Dirac equation. We also analyze the exact analytical solutions of the corresponding modified Dirac equation in the presence of a peculiar external magnetic field.

Deep learning has proven to be a highly effective tool for a wide range of applications, significantly when leveraging the power of multi-loss functions to optimize performance on multiple criteria simultaneously. However, optimal selection and weighting loss functions in deep learning tasks can significantly influence model performance, yet manual tuning of these functions is often inefficient and inflexible. We propose a novel framework called dynamic memory fusion for adaptive multi-loss function penalizing in real-time to address this. This framework leverages historical loss values data to dynamically adjust the weighting of multiple loss functions throughout the training process. Additionally, this framework integrates an auxiliary loss function to enhance model performance in the early stages. To further research horizons, we introduce the class-balanced dice loss function, designed to address class imbalance by prioritizing underrepresented classes. Experiments on breast ultrasound datasets demonstrate that the framework improves segmentation performance across various metrics. These results demonstrate the effectiveness of our proposed framework in ensuring that the model dynamically adjusts its focus to prioritize the most relevant criteria, leading to improved performance in evolving environments. The source code for our proposed methodology is publicly available on GitHub.

The Event Horizon Telescope (EHT) imaging of the supermassive black holes at the centers of Messier 87 galaxy and the Milky Way galaxy marks a significant step in observing the photon rings and central brightness depression that define the optical appearance of black holes with an accretion disk scenario. Inspired by this, we take into account a static and spherically symmetric magnetically charged regular black hole (MCRBH) metric characterized by its mass and an additional parameter q, which arises from the coupling of Einstein gravity and nonlinear electrodynamics (NLED) in the weak field approximation. This parameterized model offers a robust foundation for testing the coupling of Einstein gravity and NLED in the weak-field approximation, using the EHT observational results. In this study, we investigate the geodesic motion of particles around the solution, followed by a discussion of its fundamental geometrical characteristics such as scalar invariants. Using null geodesics, we examine how the model parameter influences the behavior of the photon sphere radius and the associated shadow silhouette. We seek constraints on q by applying the EHT results for supermassive black holes M87* and Sgr A*. Furthermore, it is observed that the geodesics of time-like particles are susceptible to variations in q, which can have an impact on the traits of the innermost stable circular orbit and the marginally bounded orbit. Our primary objective is to probe how the free parameter q affects various aspects of the accretion disk surrounding the MCRBH using the thin-disk approximation. Next, we discuss the physical characteristics of the thin accretion disk as well as the observed shadows and rings of the MCRBH, along with its luminosity, across various accretion models. Ultimately, variations in accretion models and the parameter q yield distinct shadow images and optical appearances of the MCRBH.

We construct a top-down holographic model of Weyl semimetal states using $(3+1)$-dimensional $\mathcal{N}=4$ supersymmetric $SU(N_c)$ Yang-Mills theory, at large $N_c$ and strong coupling, coupled to a number $N_f \ll N_c$ of $\mathcal{N}=2$ hypermultiplets with mass $m$. A $U(1)$ subgroup of the R-symmetry acts on the hypermultiplet fermions as an axial symmetry. In the presence of a constant external axial gauge field in a spatial direction, $b$, we find the defining characteristic of a Weyl semi-metal: a quantum phase transition as $m/b$ increases, from a topological state with non-zero anomalous Hall conductivity to a trivial insulator. The transition is first order. Remarkably, the anomalous Hall conductivity is independent of the hypermultiplet mass, taking the value dictated by the axial anomaly. At non-zero temperature the transition remains first order, and the anomalous Hall conductivity acquires non-trivial dependence on the hypermultiplet mass and temperature.

One fundamental task for NLP is to determine the similarity between two texts and evaluate the extent of their likeness. The previous methods for the Persian language have low accuracy and are unable to comprehend the structure and meaning of texts effectively. Additionally, these methods primarily focus on formal texts, but in real-world applications of text processing, there is a need for robust methods that can handle colloquial texts. This requires algorithms that consider the structure and significance of words based on context, rather than just the frequency of words. The lack of a proper dataset for this task in the Persian language makes it important to develop such algorithms and construct a dataset for Persian text. This paper introduces a new transformer-based model to measure semantic similarity between Persian informal short texts from social networks. In addition, a Persian dataset named FarSSiM has been constructed for this purpose, using real data from social networks and manually annotated and verified by a linguistic expert team. The proposed model involves training a large language model using the BERT architecture from scratch. This model, called FarSSiBERT, is pre-trained on approximately 104 million Persian informal short texts from social networks, making it one of a kind in the Persian language. Moreover, a novel specialized informal language tokenizer is provided that not only performs tokenization on formal texts well but also accurately identifies tokens that other Persian tokenizers are unable to recognize. It has been demonstrated that our proposed model outperforms ParsBERT, laBSE, and multilingual BERT in the Pearson and Spearman's coefficient criteria. Additionally, the pre-trained large language model has great potential for use in other NLP tasks on colloquial text and as a tokenizer for less-known informal words.

In this paper, we devise a novel radio resource block (RB) structure named dynamic resource block structure (D-RBS) which can handle low latency traffics and large fluctuations in data rates by exploiting smart time and frequency duplexing. In our framework, the main resource block with a predefined bandwidth and time duration is divided into several small blocks with the same bandwidth and time duration. Depending on the service requirements, e.g., data rate and latency, the users are assigned to some these small blocks which could be noncontiguous both in frequency and time. This is in contrast to the previously introduced static resource block structure (S-RBS) where the size of each RB is predetermined and fixed. We provide resource allocation frameworks for this RB structure and formulate the optimization problems whose solutions are obtained by alternate search method (ASM) based on successive convex approximation approach (SCA). We provide a global optimal solution by exploiting the monotonic optimization method. By simulation we study the performance of our proposed scheme with S-RBS scheme and show it has 26% gain compared to the S-RBS scheme.

This paper explores gravitational phenomena associated with a non-commutative black hole. Geodesic equations are derived, and a thin accretion disk is analyzed to model the black hole shadow image, considering an optically thin, radiating, and infalling gas. Retrolensing effects are examined to trace photon emission configurations, while gravitational lensing is investigated through weak and strong deflection limits, with lensing equations and observables applied to Sagittarius A*. The study also includes calculations of time delay, energy deposition rate from neutrino annihilation, phase and probability of neutrino oscillation, and neutrino gravitational lensing.

This paper considers the chattering problem of sliding mode control while delay in robot manipulator caused chaos in such electromechanical systems. Fractional calculus as a powerful theorem to produce a novel sliding mode; which has a dynamic essence is used for chattering elimination. To realize the control of a class of chaotic systems in master-slave configuration this novel fractional dynamic sliding mode control scheme is presented and examined on delay based chaotic robot in joint and work space. Also the stability of the closed-loop system is guaranteed by Lyapunov stability theory. Beside these, delayed robot motions are sorted out for qualitative and quantification study. Finally, numerical simulation example illustrates the feasibility of proposed control method.

The present work investigates the possible range of the spectral index $n_s$ and the tensor-to-scalar ratio $r$ for a sub-class of the generalized multi-scalar field inflation, which includes a linear coupling term between the multi-scalar field potential and the canonical Lagrangian. This coupling influences the slow-roll parameters and also alters our predictions for $n_{s}$ and $r$, which directly depend on those parameters. More precisely, compared to standard multi-field inflation, the values of $n_{s}$ and $r$ decrease to levels consistent with the recent Planck+BICEP/Keck constraint. Interestingly, this validates the chaotic-type potential $V=\sum_{i} \mu_{i} \phi_{i}^{p}$, which were previously ruled out in the light of the current observations.