Why rare-earth ferromagnets are so rare: insights from the p-wave Kondo model

Magnetic exchange in Kondo lattice systems is of the Ruderman-Kittel-Kasuya-Yosida type, whose sign depends on the Fermi wave vector, $k_F$ . In the simplest setting, for small $k_F$ , the interaction is predominately ferromagnetic, whereas it turns more antiferromagnetic with growing $k_F$. It is remarkable that even though $k_F$ varies vastly among the rare-earth systems, an overwhelming majority of lanthanide magnets are in fact antiferromagnets. To address this puzzle, we investigate the effects of a p-wave form factor for the Kondo coupling pertinent to nearly all rare-earth intermetallics. We show that this leads to interference effects which for small kF are destructive, greatly reducing the size of the RKKY interaction in the cases where ferromagnetism would otherwise be strongest. By contrast, for large $k_F$, constructive interference can enhance antiferromagnetic exchange. Based on this, we propose a new route for designing ferromagnetic rare-earth magnets.Comment: 9 pages, 4 figure

Generalized Dice Focal Loss trained 3D Residual UNet for Automated Lesion Segmentation in Whole-Body FDG PET/CT Images

Automated segmentation of cancerous lesions in PET/CT images is a vital initial task for quantitative analysis. However, it is often challenging to train deep learning-based segmentation methods to high degree of accuracy due to the diversity of lesions in terms of their shapes, sizes, and radiotracer uptake levels. These lesions can be found in various parts of the body, often close to healthy organs that also show significant uptake. Consequently, developing a comprehensive PET/CT lesion segmentation model is a demanding endeavor for routine quantitative image analysis. In this work, we train a 3D Residual UNet using Generalized Dice Focal Loss function on the AutoPET challenge 2023 training dataset. We develop our models in a 5-fold cross-validation setting and ensemble the five models via average and weighted-average ensembling. On the preliminary test phase, the average ensemble achieved a Dice similarity coefficient (DSC), false-positive volume (FPV) and false negative volume (FNV) of 0.5417, 0.8261 ml, and 0.2538 ml, respectively, while the weighted-average ensemble achieved 0.5417, 0.8186 ml, and 0.2538 ml, respectively. Our algorithm can be accessed via this link: https://github.com/ahxmeds/autosegnet.Comment: AutoPET-II challenge (2023

IgCONDA-PET: Implicitly-Guided Counterfactual Diffusion for Detecting Anomalies in PET Images

Minimizing the need for pixel-level annotated data for training PET anomaly segmentation networks is crucial, particularly due to time and cost constraints related to expert annotations. Current un-/weakly-supervised anomaly detection methods rely on autoencoder or generative adversarial networks trained only on healthy data, although these are more challenging to train. In this work, we present a weakly supervised and Implicitly guided COuNterfactual diffusion model for Detecting Anomalies in PET images, branded as IgCONDA-PET. The training is conditioned on image class labels (healthy vs. unhealthy) along with implicit guidance to generate counterfactuals for an unhealthy image with anomalies. The counterfactual generation process synthesizes the healthy counterpart for a given unhealthy image, and the difference between the two facilitates the identification of anomaly locations. The code is available at: https://github.com/igcondapet/IgCONDA-PET.gitComment: 12 pages, 6 figures, 1 tabl

PyTomography: A Python Library for Quantitative Medical Image Reconstruction

Background: There is a scarcity of open-source libraries in medical imaging dedicated to both (i) the development and deployment of novel reconstruction algorithms and (ii) support for clinical data. Purpose: To create and evaluate a GPU-accelerated, open-source, and user-friendly image reconstruction library, designed to serve as a central platform for the development, validation, and deployment of novel tomographic reconstruction algorithms. Methods: PyTomography was developed using Python and inherits the GPU-accelerated functionality of PyTorch for fast computations. The software uses a modular design that decouples the system matrix from reconstruction algorithms, simplifying the process of integrating new imaging modalities or developing novel reconstruction techniques. As example developments, SPECT reconstruction in PyTomography is validated against both vendor-specific software and alternative open-source libraries. Bayesian reconstruction algorithms are implemented and validated. Results: PyTomography is consistent with both vendor-software and alternative open source libraries for standard SPECT clinical reconstruction, while providing significant computational advantages. As example applications, Bayesian reconstruction algorithms incorporating anatomical information are shown to outperform the traditional ordered subset expectation maximum (OSEM) algorithm in quantitative image analysis. PSF modeling in PET imaging is shown to reduce blurring artifacts. Conclusions: We have developed and publicly shared PyTomography, a highly optimized and user-friendly software for quantitative image reconstruction of medical images, with a class hierarchy that fosters the development of novel imaging applications.Comment: 26 pages, 7 figure

A cascaded deep network for automated tumor detection and segmentation in clinical PET imaging of diffuse large B-cell lymphoma

Accurate detection and segmentation of diffuse large B-cell lymphoma (DLBCL) from PET images has important implications for estimation of total metabolic tumor volume, radiomics analysis, surgical intervention and radiotherapy. Manual segmentation of tumors in whole-body PET images is time-consuming, labor-intensive and operator-dependent. In this work, we develop and validate a fast and efficient three-step cascaded deep learning model for automated detection and segmentation of DLBCL tumors from PET images. As compared to a single end-to-end network for segmentation of tumors in whole-body PET images, our three-step model is more effective (improves 3D Dice score from 58.9% to 78.1%) since each of its specialized modules, namely the slice classifier, the tumor detector and the tumor segmentor, can be trained independently to a high degree of skill to carry out a specific task, rather than a single network with suboptimal performance on overall segmentation.Comment: 8 pages, 3 figures, 3 table

A slice classification neural network for automated classification of axial PET/CT slices from a multi-centric lymphoma dataset

Automated slice classification is clinically relevant since it can be incorporated into medical image segmentation workflows as a preprocessing step that would flag slices with a higher probability of containing tumors, thereby directing physicians attention to the important slices. In this work, we train a ResNet-18 network to classify axial slices of lymphoma PET/CT images (collected from two institutions) depending on whether the slice intercepted a tumor (positive slice) in the 3D image or if the slice did not (negative slice). Various instances of the network were trained on 2D axial datasets created in different ways: (i) slice-level split and (ii) patient-level split; inputs of different types were used: (i) only PET slices and (ii) concatenated PET and CT slices; and different training strategies were employed: (i) center-aware (CAW) and (ii) center-agnostic (CAG). Model performances were compared using the area under the receiver operating characteristic curve (AUROC) and the area under the precision-recall curve (AUPRC), and various binary classification metrics. We observe and describe a performance overestimation in the case of slice-level split as compared to the patient-level split training. The model trained using patient-level split data with the network input containing only PET slices in the CAG training regime was the best performing/generalizing model on a majority of metrics. Our models were additionally more closely compared using the sensitivity metric on the positive slices from their respective test sets.Comment: 10 pages, 6 figures, 2 table

Towards engineering of the selective optical excitations in the topological insulator Bi₂Se₃

Topological insulators are widely studied class of materials with fascinating electronic properties and a great potential for application to spintronics. The property that makes these materials interesting is the existence of linearly dispersing electronic states on the surface, occurring due to strong spin-orbit coupling and time-reversal symmetry. The electrons in these topological surface states (TSSs) exhibit helical spin-texture and are forbidden from backscattering due to time-reversal symmetry. Bi₂Se₃ is one such topological insulator that hosts two TSSs: one near the Fermi level and another in the unoccupied states. The optical excitation of the electrons from the occupied TSS to the unoccupied TSS has been an interesting avenue for exciting spin-polarized surface currents in Bi₂Se₃. Several transport experiments have demonstrated the current generation by optical means, confirming the idea, and ARPES experiments have helped in understanding the mechanism of optical excitation. However, several questions remain unanswered including the involvement of the bulk states in the generation scheme, the symmetry of the optical excitation, and therefore the directionality of the injected currents and possibilities to improve the efficiency and selectivity of these excitations. Identifying and controlling these optical transitions, understanding their nature, and isolating the TSS-TSS transition to injected current while suppressing the contribution from the bulk is the essence of the presented work. In particular, we perform a pump-probe ARPES experiment on Bi₂Se₃ single crystals using a 1.55 eV circularly-polarized pump and 6.2eV linearly-polarized probe pulse with two different in-plane orientations of the sample. The pump-induced circular dichroism maps were analyzed at different binding energies to deduce the direction of injected currents and the type of underlying optical transitions associated with them: bulk-bulk, bulk-TSS, or TSS-TSS. We classify the optical transitions into three categories based on the symmetry of the bulk and the TSS alone. As an attempt to explain our findings and simulate our experimental results, we also build a tight-binding model using the Chinook software, starting from the density functional theory calculation on Bi₂Se₃. Finally, we give a prescription to calculate the pump-probe intensity and the pump-induced circular dichroism pattern using the resultant tight-binding model.Science, Faculty ofPhysics and Astronomy, Department ofGraduat

The relationship between physical self-concept and job satisfaction in the physiotherapists working in Saudi Arabia

Work satisfaction can be seen as a proxy for emotional or physiological health. Physiotherapists face high levels of occupational stress because their daily works puts considerable strain on the articular, skeletal and muscular systems, which are associated with excessive exertion that physiotherapists endure in their daily work. The purpose of present research was to find the relationship of Physical Self-Concept with the job satisfaction of the Physiotherapist working in Saudi Arabia. Study design was Convenient, descriptive-correlation type of study Design. In order to collect the data, the questionnaire on the physical self-concept and the job satisfaction survey were applied. Demographic data of subjects including gender, age, last educational degree, total working experience, the region in which they work, and duration of working on current job, were descriptively summarized to project the results. The dependents variables for the statistical analysis were analyzed using correlation. 189 physiotherapists were surveyed about their Level of PSPP score (M= 63.6, SD= 16.4) and their MSQ score (M= 69.2, SD= 14.7). The relationship was positive, weak in strength and statistically significant (r (189) = .34, p < 0.05. The current study described the relationship between physiotherapists' physical self-concept and job satisfaction

Effect of heavy-load eccentric calf muscle training as a rehabilitation protocol in soccer players with persistent Achilles tendinosis

Background: All of the studies included in the analysis developed scales to assess a variety of outcomes, including tenderness, patient experience, return to sport, degree of improvement, and physical activity engagement. Method: In terms of training principles such as assets, repetitions, and frequency of performance, differences in technique between researches implementing the Heavy Load Eccentric Calf Muscle (HLECM) training regimen were rather minor. Some studies implemented the HLECM routine gradually throughout the first few weeks or reduced the frequency from twice daily (180 repetitions) to once daily (90 repetitions). The HLECM training procedure progressions could have been more diverse. Results: HLECM has received a lot of attention as a therapy for Achilles Tendinosis (AT). Despite the fact that the results are difficult to comprehend, a significant decrease in agony and an increase in work was observed following HLECM training in all studies examined here.</jats:p