Search

We introduce the Aleppo spin ice geometry, another variation of decimated square ice patterns, which in contrast to similar systems previously studied, does not exhibit vertex frustration. Using synchrotron-based photoemission electron microscopy, we directly visualize low-energy states achieved after thermal annealing, in addition to temperature-dependent moment fluctuations. The results reveal the observation of ground state patterns and the absence of ergodicity-breaking dynamics. Our observations further confirm vertex frustration to be an important criterion for the emergence of ergodicity transitions., Comment: 7 pages; double column; 6 figures

Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early diagnosis remains a challenge, especially in underdeveloped regions where agriculture is crucial and agricultural experts are scarce. However, adopting Deep Learning applications can assist in accurately identifying diseases without needing plant pathologists. In this study, the effectiveness of various computer vision models for detecting paddy diseases is evaluated and proposed the best deep learning-based disease detection system. Both classification and detection using the Paddy Doctor dataset, which contains over 20,000 annotated images of paddy leaves for disease diagnosis are tested and evaluated. For detection, we utilized the YOLOv8 model-based model were used for paddy disease detection and CNN models and the Vision Transformer were used for disease classification. The average mAP50 of 69% for detection tasks was achieved and the Vision Transformer classification accuracy was 99.38%. It was found that detection models are effective at identifying multiple diseases simultaneously with less computing power, whereas classification models, though computationally expensive, exhibit better performance for classifying single diseases. Additionally, a mobile application was developed to enable farmers to identify paddy diseases instantly. Experiments with the app showed encouraging results in utilizing the trained models for both disease classification and treatment guidance., Comment: 21 pages,12 figures and 2 tables

Vibrational and magnetic properties of single-crystal uranium-thorium dioxide (UxTh1-xO2) with a full range of 0

Altermagnets are distinguished by their unique spin group symmetries, where spin and spatial symmetries are fully decoupled, resulting in nonrelativistic spin splitting (NRSS) of electronic bands. This phenomenon, unlike conventional spin splitting driven by relativistic spin-orbit coupling, has transformative potential in fields such as spintronics, superconductivity and energy-efficient electronics. However, direct observation of NRSS is challenging due to presence of competing phases, low N\'eel temperatures, and the limitations of existing experimental probes to unambiguously capture the associated properties. Here, we integrate theoretical and experimental approaches to uncover NRSS in the intercalated transition metal dichalcogenide CoNb$_4$Se$_8$. Symmetry analysis, density functional theory (DFT), a novel Symmetry-Constrained Adaptive Basis (SCAB), and tight-binding modeling predict the presence of symmetry-enforced spin splitting, which we directly confirm using spin-ARPES for the occupied band structure and a newly developed technique, spin- and angle-resolved electron reflection spectroscopy (spin-ARRES), for the unoccupied states. Together, these complementary tools reveal alternating spin textures consistent with our predicted g-wave altermagnetic order and demonstrate the persistence of NRSS across a broad energy range. Crucially, temperature-dependent measurements show the suppression of NRSS at the N\'eel temperature ($T_N$), providing the first direct evidence of an altermagnetic phase transition. Residual spin splitting above $T_N$ suggests the coexistence of altermagnetic fluctuations and spin-orbit coupling effects, underscoring a complex interplay of mechanisms. By establishing CoNb$_4$Se$_8$ as a prototypical g-wave altermagnet, this work offers a robust framework for understanding NRSS, and lays the foundation for designing energy-efficient spin-based technologies., Comment: 26 pages, 15 figures (5 main, 10 supplementary)

The unique connectivity of kagome lattices gives rise to topological properties, such as flat bands and Dirac cones. When combined with ferromagnetism and a chemical potential near the 2D Dirac points, this structure offers the potential to realize the highly sought-after topological Chern magnetotransport. Recently, there was considerable excitement surrounding this possibility in the ferrimagnetic kagome metal TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. However, density functional theory (DFT) calculations reveal that the 2D Chern gap lies well above the Fermi energy, challenging its relevance in the observed anomalous Hall conductivity. Here, we investigate YMn$_\mathbf{6}$Sn$_\mathbf{5.45}$Ga$_\mathbf{0.55}$, a compound with similar crystallographic, magnetic, and electronic properties to TbMn$_\mathbf{6}$Sn$_\mathbf{6}$. Our findings show that the intrinsic anomalous Hall conductivity in this material, while comparable in magnitude to that in TbMn$_\mathbf{6}$Sn$_\mathbf{6}$, is fully three-dimensional, thus providing experimental evidence that Hall conductivity in this class of materials does not originate from 2D Chern gaps. Additionally, we confirm that the newly proposed empirical scaling relation for extrinsic Hall conductivity is universally governed by spin fluctuations.

Out-of-distribution (OOD) detection is critical to ensure the safe deployment of deep learning models in critical applications. Deep learning models can often misidentify OOD samples as in-distribution (ID) samples. This vulnerability worsens in the presence of spurious correlation in the training set. Likewise, in fine-grained classification settings, detection of fine-grained OOD samples becomes inherently challenging due to their high similarity to ID samples. However, current research on OOD detection has largely ignored these challenging scenarios, focusing instead on relatively easier (conventional) cases. In this work, we present a unified Approach to Spurious, fine-grained, and Conventional OOD Detection (ASCOOD). First, we propose synthesizing virtual outliers from ID data by approximating the destruction of invariant features. We identify invariant features with the pixel attribution method using the model being learned. This approach eliminates the burden of curating external OOD datasets. Then, we simultaneously incentivize ID classification and predictive uncertainty towards the virtual outliers leveraging standardized feature representation. Our approach effectively mitigates the impact of spurious correlations and encourages capturing fine-grained attributes. Extensive experiments across six datasets demonstrate the merit of ASCOOD in spurious, fine-grained, and conventional settings. The code is available at: https://github.com/sudarshanregmi/ASCOOD/

Large Language Models (LLMs), such as GPT, have revolutionized artificial intelligence by enabling nuanced understanding and generation of human-like text across a wide range of applications. However, the high computational and financial costs associated with frequent API calls to these models present a substantial bottleneck, especially for applications like customer service chatbots that handle repetitive queries. In this paper, we introduce GPT Semantic Cache, a method that leverages semantic caching of query embeddings in in-memory storage (Redis). By storing embeddings of user queries, our approach efficiently identifies semantically similar questions, allowing for the retrieval of pre-generated responses without redundant API calls to the LLM. This technique achieves a notable reduction in operational costs while significantly enhancing response times, making it a robust solution for optimizing LLM-powered applications. Our experiments demonstrate that GPT Semantic Cache reduces API calls by up to 68.8% across various query categories, with cache hit rates ranging from 61.6% to 68.8%. Additionally, the system achieves high accuracy, with positive hit rates exceeding 97%, confirming the reliability of cached responses. This technique not only reduces operational costs, but also improves response times, enhancing the efficiency of LLM-powered applications.

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.

The kagome lattice, known for its strong frustration in two dimensions, hosts a variety of exotic magnetic and electronic states. A variation of this geometry, where the triangular motifs are twisted to further reduce symmetry, has recently revealed even more complex physics. HoAgGe exemplifies such a structure, with magnetic and electronic properties believed to be driven by strong in-plane anisotropy of the Ho spins, effectively acting as a two-dimensional spin ice. In this study, using a combination of magnetization, Hall conductivity measurements, and density functional theory calculations, we demonstrate how various spin-ice states, stabilized by external magnetic fields, influence the Fermi surface topology. More interestingly, we observe sharp transitions in Hall conductivity without concurrent changes in magnetization when an external magnetic field is applied along a particular crystallographic direction, underscoring the role of strong magnetic frustration and providing a new platform for exploring the interplay between magnetic frustration, electronic topology, and crystalline symmetry. These results also highlight the limitations of a simple spin-ice model, suggesting that a more sophisticated framework is necessary to capture the subtle experimental nuances observed., Comment: 17 pages, 11 figures

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