Search

Gastrointestinal cancer is a leading cause of cancer-related incidence and death, making it crucial to develop novel computer-aided diagnosis systems for early detection and enhanced treatment. Traditional approaches rely on the expertise of gastroenterologists to identify diseases; however, this process is subjective, and interpretation can vary even among expert clinicians. Considering recent advancements in classifying gastrointestinal anomalies and landmarks in endoscopic and video capsule endoscopy images, this study proposes a hybrid model that combines the advantages of Transformers and Convolutional Neural Networks (CNNs) to enhance classification performance. Our model utilizes DenseNet201 as a CNN branch to extract local features and integrates a Swin Transformer branch for global feature understanding, combining both to perform the classification task. For the GastroVision dataset, our proposed model demonstrates excellent performance with Precision, Recall, F1 score, Accuracy, and Matthews Correlation Coefficient (MCC) of 0.8320, 0.8386, 0.8324, 0.8386, and 0.8191, respectively, showcasing its robustness against class imbalance and surpassing other CNNs as well as the Swin Transformer model. Similarly, for the Kvasir-Capsule, a large video capsule endoscopy dataset, our model outperforms all others, achieving overall Precision, Recall, F1 score, Accuracy, and MCC of 0.7007, 0.7239, 0.6900, 0.7239, and 0.3871. Moreover, we generated saliency maps to explain our model's focus areas, demonstrating its reliable decision-making process. The results underscore the potential of our hybrid CNN-Transformer model in aiding the early and accurate detection of gastrointestinal (GI) anomalies.

EuM2As2 (M = Zn, Cd, In, Sn etc.) is an excellent material system for studying topological properties, which can be easily tuned by magnetism involved. Theoretical calculations predict gapped and flat bands in EuZn2As2 but gapless structure in EuCd2As2. In this work, low-temperature (77 K) cleaved EuZn2As2 crystals are studied using scanning tunneling microscopy/spectroscopy (STM/S) and density functional theory (DFT) calculations. Defects-induced local density of states (LDOS) modification with a triangular shape helps identify the surface terminations: Eu versus AsZn surface. While large bandgaps (~1.5 eV at 77 K) are observed on both pristine surfaces, the bandgap width is found to be very sensitive to local heterogeneity, such as defects and step edges, with the tendency of reduction. Combining experimental data with DFT simulations, we conclude that the modified bandgap in the heterogeneous area arises from Zn vacancies and/or substitution by As atoms. Our investigation offers important information for reevaluating the electron topology of the EuM2As2 family., Comment: 17 pages, 4 figures

Altermagnets (AMs) are a new class of magnetic materials that combine the beneficial spintronics properties of ferromagnets and antiferromagnets, garnering significant attention recently. Here, we have identified altermagnetism in a layered intercalated transition metal diselenide, CoNb$_4$Se$_8$, which crystallizes with an ordered sublattice of intercalated Co atoms between NbSe$_2$ layers. Single crystals are synthesized, and the structural characterizations are performed using single crystal diffraction and scanning tunneling microscopy. Magnetic measurements reveal easy-axis antiferromagnetism below 168 K. Density functional theory (DFT) calculations indicate that A-type antiferromagnetic ordering with easy-axis spin direction is the ground state, which is verified through single crystal neutron diffraction experiments. Electronic band structure calculations in this magnetic state display spin-split bands, confirming altermagnetism in this compound. The layered structure of CoNb$_4$Se$_8$ presents a promising platform for testing various predicted properties associated with altermagnetism.

Taken together, time-reversal and spatial inversion symmetries impose a two-fold spin degeneracy of the electronic states in crystals. In centrosymmetric materials, this degeneracy can be lifted by introducing magnetism, either via an externally applied field or through internal magnetization. However, a correlated alignment of spins, even in the paramagnetic phase, can lift the spin degeneracy of electronic states. Here, we report an in-depth study of the electronic band structure of the Eu-ternary pnictide EuZn2Sb2 through a combination of high-resolution angle-resolved photoemission spectroscopy measurements and first principles calculations. An analysis of the photoemission lineshapes over a range of incident photon energies and sample temperatures is shown to reveal the presence of band spin degeneracy-lifting in the paramagnetic phase. Our ARPES results are in good agreement with theoretical ferromagnetic-phase calculations, which indicates the importance of ferromagnetic fluctuations in the system. Through our calculations, we predict that spin-polarized bands in EuZn2Sb2 generate a single pair of Weyl nodes. Our observation of band-splitting in EuZn2Sb2 provides a key step toward realizing time-reversal symmetry breaking physics in the absence of long-range magnetic order., Comment: 13 pages, 7 figures

Kidney cancer is a global health concern, and accurate assessment of patient frailty is crucial for optimizing surgical outcomes. This paper introduces AI Age Discrepancy, a novel metric derived from machine learning analysis of preoperative abdominal CT scans, as a potential indicator of frailty and postoperative risk in kidney cancer patients. This retrospective study of 599 patients from the 2023 Kidney Tumor Segmentation (KiTS) challenge dataset found that a higher AI Age Discrepancy is significantly associated with longer hospital stays and lower overall survival rates, independent of established factors. This suggests that AI Age Discrepancy may provide valuable insights into patient frailty and could thus inform clinical decision-making in kidney cancer treatment., Comment: 10 pages, 3 figures, 2 tables

The LnSbTe (Ln = Lanthanides) family, like isostructural ZrSiS type compounds, has emerged as a fertile playground for exploring the interaction of electronic correlations and magnetic ordering with the nodal line band topology. Here, we report a detailed electronic band structure investigation of TbSbTe, corroborated by electrical transport, thermodynamic, and magnetic studies. Temperature-dependent magnetic susceptibility and thermodynamic transport studies indicate the onset of antiferromagnetic ordering below TN = 5.1 K. The electronic band structure study, carried out with high-resolution angle-resolved photoemission spectroscopy (ARPES) measurements aided with density functional theory based first-principles calculations reveals presence of nodal lines in the GammaX high symmetry direction, forming a diamond-shaped nodal plane around Gamma high symmetry point. A strongly photon energy dependent nodal feature located at the X point of the surface Brillouin zone, indicating an extended nodal line along X R direction, is also observed. This study elucidates the intricate interplay among symmetry-protected band characteristics, the influence of spin orbit coupling, magnetism, and topological properties., Comment: 21 pages, 4 figures

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. The local structures are conventionally probed using spatially resolved studies and the property correlations are usually deciphered by a researcher based on sequential explorations and auxiliary information, thus limiting the throughput efficiency. Here we demonstrate a Bayesian deep learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This autonomous method is deployed on the low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn2As2, one of the promising candidates for studying the magnetism-driven topological properties. The framework employs a sparse sampling approach to efficiently construct the scalar-property space using a minimal number of measurements, about 1 - 10 % of the data required in standard hyperspectral imaging methods. We further demonstrate a target-property-guided active learning of structures within a multiscale framework. This is implemented across length scales in a hierarchical fashion for the autonomous discovery of structural origins for an observed material property. This framework offers the choice to select and derive a suitable scalar property from the spectroscopic data to steer exploration across the sample space. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

The persistent battle to decrease childhood mortality serves as a commonly employed benchmark for gauging advancements in the field of medicine. Globally, the under-5 mortality rate stands at approximately 5 million, with a significant portion of these deaths being avoidable. Given the significance of this problem, Machine learning-based techniques have emerged as a prominent tool for assessing fetal health. In this work, we have analyzed the classification performance of various machine learning models for fetal health analysis. Classification performance of various machine learning models, such as support vector machine (SVM), random forest(RF), and attentive interpretable tabular learning (TabNet) have been assessed on fetal health. Moreover, dimensionality reduction techniques, such as Principal component analysis (PCA) and Linear discriminant analysis (LDA) have been implemented to obtain better classification performance with less number of features. A TabNet model on a fetal health dataset provides a classification accuracy of 94.36%. In general, this technology empowers doctors and healthcare experts to achieve precise fetal health classification and identify the most influential features in the process.

The family of transition metal dipnictides (TMDs) has been of theoretical and experimental interest because this family hosts topological states and extremely large magnetoresistance (MR). Recently, TaAs2, a member of this family, has been predicted to support a topological crystalline insulating state. Here, by using high resolution. Angle resolved photoemission spectroscopy (ARPES), we reveal both closed and open pockets in the metallic Fermi surface and linearly dispersive bands on the (201) surface, along with the presence of extreme MR observed from magneto-transport measurements. A comparison of the ARPES results with first-principles computations show that the linearly dispersive bands on the measured surface of TaAs2 are trivial bulk bands. The absence of symmetry-protected surface state on the (201) surface indicates its topologically dark nature. The presence of open Fermi surface features suggests that the open orbit fermiology could contribute to the extremely large MR of TaAs., Comment: 13 pages, 5 figures

We studied the electronic structure of a layered antiferromagnetic metal, EuSnP, in the paramagnetic and in the antiferromagnetic phase using angle resolved photoemission spectroscopy (ARPES) alongside density functional theory (DFT) based first principles calculations. The temperature dependence of the magnetic susceptibility measurements exhibits an antiferromagnetic transition at a Neel temperature of 21 K. Employing high resolution ARPES, the valence band structure was measured at several temperatures above and below the Neel temperature, which produced identical spectra independent of temperature. Through analysis of the ARPES results presented here, we attribute the temperature independent spectra to the weak coupling of the Sn, and P conduction electrons with Eu 4f states., Comment: 8 pages, 4 figures

Nodal line semimetals feature topologically protected band crossings between the bulk valence and conduction bands that extend along a finite dimension in the form of a line or a loop. While ZrSiS and similar materials have attracted extensive research as hosts for the nodal line semimetallic phase, an alternative avenue has emerged in the form of isostructural rare-earth (RE) based RESbTe materials. Such systems possess intriguing potentialities for harboring elements of magnetic ordering and electronic correlations owing to the presence of 4f electrons intrinsic to the RE elements. In this study, we have carried out angle resolved photoemission spectroscopy (ARPES) and thermodynamic measurements in conjunction with first principles computations on PrSbTe to elucidate its electronic structure and topological characteristics. Magnetic and thermal characterizations indicate the presence of well-localized 4f states with the absence of any discernible phase transition down to 2 K. The ARPES results reveal the presence of gapless Dirac crossings that correspond to a nodal-line along the XR direction in the three-dimensional Brillouin zone. Furthermore, Dirac crossing that makes up nodal line, which forms a diamond-shaped nodal plane centered at the center of the Brillouin zone is also identified within the experimental resolution. This study on the electronic structure of PrSbTe contributes to the understanding of the pivotal role played by spin-orbit coupling in the context of the RESbTe family of materials, Comment: 26 pages, 11 figures; SUPPLEMENTAL MATERIAL included

This work presents electronic properties of the kagome metal ScV6Sn6 using de Haas-van Alphen (dHvA) oscillations and density functional theory (DFT) calculations. The torque signal with the applied fields up to 43 T shows clear dHvA oscillations with six major frequencies, five of them are below 400 T (low frequencies) and one is nearly 2800 T (high frequency). The Berry phase calculated using the Landau level fan diagram near the quantum limit is approximately {\pi}, which suggests the non-trivial band topology in ScV6Sn6. To explain the experimental data, we computed the electronic band structure and Fermi surface using DFT in both the pristine and charge density wave (CDW) phases. Our results confirm that the CDW phase is energetically favorable, and the Fermi surface undergoes a severe reconstruction in the CDW state. Furthermore, the angular dependence of the dHvA frequencies are consistent with the DFT calculations. The detailed electronic properties presented here are invaluable for understanding the electronic structure and CDWorder in ScV6Sn6, as well as in other vanadium-based kagome systems., Comment: 13 pages, 4 figures

Niobium halides, Nb3X8 (X = Cl,Br,I), which are predicted two-dimensional magnets, have recently gotten attention due to their breathing kagome geometry. Here, we have studied the electronic structure of Nb3Br8 by using angle-resolved photoemission spectroscopy (ARPES) and first-principles calculations. ARPES results depict the presence of multiple flat and weakly dispersing bands. These bands are well explained by the theoretical calculations, which show they have Nb d character indicating their origination from the Nb atoms forming the breathing kagome plane. This van der Waals material can be easily thinned down via mechanical exfoliation to the ultrathin limit and such ultrathin samples are stable as depicted from the time-dependent Raman spectroscopy measurements at room temperature. These results demonstrate that Nb3Br8 is an excellent material not only for studying breathing kagome induced flat band physics and its connection with magnetism, but also for heterostructure fabrication for application purposes., Comment: 24 pages, 12 figures, Supplemental Material included

Cocktail party problem is the scenario where it is difficult to separate or distinguish individual speaker from a mixed speech from several speakers. There have been several researches going on in this field but the size and complexity of the model is being traded off with the accuracy and robustness of speech separation. "Monaural multi-speaker speech separation" presents a speech-separation model based on the Transformer architecture and its efficient forms. The model has been trained with the LibriMix dataset containing diverse speakers' utterances. The model separates 2 distinct speaker sources from a mixed audio input. The developed model approaches the reduction in computational complexity of the speech separation model, with minimum tradeoff with the performance of prevalent speech separation model and it has shown significant movement towards that goal. This project foresees, a rise in contribution towards the ongoing research in the field of speech separation with computational efficiency at its core., Comment: 5 pages, 6 figures, 2 tables, study conducted as major project for B.E. (Computer Engineering), IOE Tribhuvan University 2023

This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed, in addition to a larger dataset. A novel annotation method was used to collect three separate annotations for each region of interest, and these annotations were performed in a fully transparent setting using a web-based annotation tool. Further, the KiTS21 test set was collected from an outside institution, challenging participants to develop methods that generalize well to new populations. Nonetheless, the top-performing teams achieved a significant improvement over the state of the art set in 2019, and this performance is shown to inch ever closer to human-level performance. An in-depth meta-analysis is presented describing which methods were used and how they faired on the leaderboard, as well as the characteristics of which cases generally saw good performance, and which did not. Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole., Comment: 34 pages, 12 figures

Computer-assisted treatment has emerged as a viable application of medical imaging, owing to the efficacy of deep learning models. Real-time inference speed remains a key requirement for such applications to help medical personnel. Even though there generally exists a trade-off between performance and model size, impressive efforts have been made to retain near-original performance by compromising model size. Neural network pruning has emerged as an exciting area that aims to eliminate redundant parameters to make the inference faster. In this study, we show an application of neural network pruning in polyp segmentation. We compute the importance score of convolutional filters and remove the filters having the least scores, which to some value of pruning does not degrade the performance. For computing the importance score, we use the Taylor First Order (TaylorFO) approximation of the change in network output for the removal of certain filters. Specifically, we employ a gradient-normalized backpropagation for the computation of the importance score. Through experiments in the polyp datasets, we validate that our approach can significantly reduce the parameter count and FLOPs retaining similar performance.

To investigate the structural and magnetic properties of Cr-doped M-type strontium hexaferrite (SrFe$_{12}$O$_{19}$) with x = (0.0, 0.5, 1.0), we perform first-principles total-energy calculations relied on density functional theory. Based on the calculation of the substitution energy of Cr in strontium hexaferrite and formation probability analysis, we conclude that the doped Cr atoms prefer to occupy the 2a, 12k, and 4f$_{2}$ sites which is in good agreement with the experimental findings. Due to Cr$^{3+}$ ion moment, 3 {$\mu_B$}, smaller than that of Fe$^{3+}$ ion, 5 {$\mu_B$}, saturation magnetization (M$_{s}$) reduce rapidly as the concentration of Cr increases in strontium hexaferrite. The magnetic anisotropic field $\left(H_{a}\right)$ rises with an increasing fraction of Cr despite a significant reduction of magnetization and a slight increase of magnetocrystalline anisotropy $\left(K_{1}\right)$.The cause for the rise in magnetic anisotropy field $\left(H_{a}\right)$ with an increasing fraction of Cr is further emphasized by our formation probability study. Cr$^{3+}$ ions prefer to occupy the 2a sites at lower temperatures, but as the temperature rises, it is more likely that they will occupy the 12k site. Cr$^{3+}$ ions are more likely to occupy the 12k site than the 2a site at a specific annealing temperature (>700{\deg}C)., Comment: arXiv admin note: text overlap with arXiv:1811.04101

Niobium chloride (Nb3Cl8) is a layered 2D semiconducting material with many exotic properties including a breathing kagome lattice, a topological flat band in its band structure, and a crystal structure that undergoes a structural and magnetic phase transition at temperatures below 90 K. Despite being a remarkable material with fascinating new physics, the understanding of its phonon properties is at its infancy. In this study, we investigate the phonon dynamics of Nb3Cl8 in bulk and few layer flakes using polarized Raman spectroscopy and density functional theory (DFT) analysis to determine the material's vibrational modes, as well as their symmetrical representations and atomic displacements. We experimentally resolved 12 phonon modes, 5 of which are A1g modes while the remaining 7 are Eg modes, which is in strong agreement with our DFT calculation. Layer-dependent results suggest that the Raman peak positions are mostly insensitive to changes in layer thickness, while peak intensity and FWHM are affected. Raman measurements as a function of excitation wavelength (473-785 nm) show a significant increase of the peak intensities when using a 473 nm excitation source, suggesting a near resonant condition. Temperature-dependent Raman experiments carried out above and below the transition temperature did not show any change in the symmetries of the phonon modes, suggesting that the structural phase transition is likely from the high temperature P3m1 phase to the low-temperature R3m phase. Magneto-Raman measurements carried out at 140 and 2 K between -2 to 2 T show that the Raman modes are not magnetically coupled. Overall, our study presented here significantly advances the fundamental understanding of layered Nb3Cl8 material which can be further exploited for future applications., Comment: 18 pages, 8 figures, 1 table

Charge density wave (CDW) ordering has been an important topic of study for a long time owing to its connection with other exotic phases such as superconductivity and magnetism. The RTe3 (R = rare-earth elements) family of materials provides a fertile ground to study the dynamics of CDW in van der Waals layered materials, and the presence of magnetism in these materials allows to explore the interplay among CDW and long range magnetic ordering. Here, we have carried out a high-resolution angle-resolved photoemission spectroscopy (ARPES) study of a CDW material GdTe3, which is antiferromagnetic below 12 K, along with thermodynamic, electrical transport, magnetic, and Raman measurements. Our Raman spectroscopy measurements show the presence of CDW amplitude mode at room temperature, which remains prominent when the sample is thinned down to 4-layers by exfoliation. Our ARPES data show a two-fold symmetric Fermi surface with both gapped and ungapped regions indicative of the partial nesting. The gap is momentum dependent, maximum along G-Z and gradually decreases going towards G - M. Our study provides a platform to study the dynamics of CDW and its interaction with other physical orders in two- and three-dimensions., Comment: 30 pages, 12 figures, Supplementary Information included

Neural networks are notorious for being overconfident predictors, posing a significant challenge to their safe deployment in real-world applications. While feature normalization has garnered considerable attention within the deep learning literature, current train-time regularization methods for Out-of-Distribution(OOD) detection are yet to fully exploit this potential. Indeed, the naive incorporation of feature normalization within neural networks does not guarantee substantial improvement in OOD detection performance. In this work, we introduce T2FNorm, a novel approach to transforming features to hyperspherical space during training, while employing non-transformed space for OOD-scoring purposes. This method yields a surprising enhancement in OOD detection capabilities without compromising model accuracy in in-distribution(ID). Our investigation demonstrates that the proposed technique substantially diminishes the norm of the features of all samples, more so in the case of out-of-distribution samples, thereby addressing the prevalent concern of overconfidence in neural networks. The proposed method also significantly improves various post-hoc OOD detection methods.

Medical image analysis is a hot research topic because of its usefulness in different clinical applications, such as early disease diagnosis and treatment. Convolutional neural networks (CNNs) have become the de-facto standard in medical image analysis tasks because of their ability to learn complex features from the available datasets, which makes them surpass humans in many image-understanding tasks. In addition to CNNs, transformer architectures also have gained popularity for medical image analysis tasks. However, despite progress in the field, there are still potential areas for improvement. This study uses different CNNs and transformer-based methods with a wide range of data augmentation techniques. We evaluated their performance on three medical image datasets from different modalities. We evaluated and compared the performance of the vision transformer model with other state-of-the-art (SOTA) pre-trained CNN networks. For Chest X-ray, our vision transformer model achieved the highest F1 score of 0.9532, recall of 0.9533, Matthews correlation coefficient (MCC) of 0.9259, and ROC-AUC score of 0.97. Similarly, for the Kvasir dataset, we achieved an F1 score of 0.9436, recall of 0.9437, MCC of 0.9360, and ROC-AUC score of 0.97. For the Kvasir-Capsule (a large-scale VCE dataset), our ViT model achieved a weighted F1-score of 0.7156, recall of 0.7182, MCC of 0.3705, and ROC-AUC score of 0.57. We found that our transformer-based models were better or more effective than various CNN models for classifying different anatomical structures, findings, and abnormalities. Our model showed improvement over the CNN-based approaches and suggests that it could be used as a new benchmarking algorithm for algorithm development.

Kagome lattice magnets are an interesting class of materials as they can host topological properties in their magnetic and electronic structures. YMn6Sn6 is one such compound in which a series of competing magnetic phases is stabilized by an applied magnetic field, and both an enigmatic topological Hall effect and a Dirac crossing close to the Fermi energy have been realized. This material also shows a magnetization-induced Lifshitz transition and evidence of a unique charge spin coupling in one of the magnetic phases, namely the fan-like phase. Tuning the magnetism, and thus the interplay with the electronic states, opens new avenues for precise control of these novel properties. Here, we demonstrate the extreme sensitivity of the magnetic phases in YMn6Sn4Ge2 through the investigation of structural, magnetic, and transport properties. The high sensitivity to small doping provides great potential for engineering the magnetic phases and associated electronic properties in this family of rare-earth kagome magnets.

Condensed matter systems with flat bands have been the center of research interest in recent years as they provide a platform for the emergence of exotic many-body states, such as superconductivity, ferromagnetism, and the fractional quantum Hall effect. However, realization of materials possessing at bands near the Fermi level experimentally is very rare. Here, we report the experimental observation of flat bands in a weakly-correlated system YRu2Si2 employing angle-resolved photoemission spectroscopy (ARPES) which is supported by first-principles calculations. These flat bands originate from Ru d orbitals and are found to be sensitive to the polarization of light. In addition, ARPES data revealed surface and bulk Dirac-like bands. The observed ARPES data is in excellent agreement with the density functional theory results. The presence of both flat bands and Dirac-like bands in YRu2Si2 suggest a unique synergy of correlation and topology in this material belonging to the centrosymmetric tetragonal ThCr2Si2-type structure thus establishing a new platform to investigate flat band physics in combination with non-trivial topological states in a weakly correlated system., Comment: 9 pages, 5 figures