Nonlocal magnon spin transport in yttrium iron garnet with tantalum and platinum spin injection/detection electrodes

We study the magnon spin transport in the magnetic insulator yttrium iron garnet (YIG) in a nonlocal experiment and compare the magnon spin excitation and detection for the heavy metal paramagnetic electrodes platinum (Pt|YIG|Pt) and tantalum (Ta|YIG|Ta). The electrical injection and detection processes rely on the (inverse) spin Hall effect in the heavy metals and the conversion between the electron spin and magnon spin at the heavy metal|YIG interface. Pt and Ta possess opposite signs of the spin Hall angle. Furthermore, their heterostructures with YIG have different interface properties, i.e. spin mixing conductances. By varying the distance between injector and detector, the magnon spin transport is studied. Using a circuit model based on the diffusion-relaxation transport theory, a similar magnon relaxation length of ~ 10 \mu m was extracted from both Pt and Ta devices. By changing the injector and detector material from Pt to Ta, the influence of interface properties on the magnon spin transport has been observed. For Ta devices on YIG the spin mixing conductance is reduced compared with Pt devices, which is quantitatively consistent when comparing the dependence of the nonlocal signal on the injector-detector distance with the prediction from the circuit model.Comment: 7 pages, 4 figure

Magnon spin transport in magnetic insulators

Magnonen of spin golven komen alleen voor in magnetische materialen. Een spin golf is een trilling in de magnetische eigenschap van het materiaal: De noord- en zuidpool van de magneet liggen, als je kijkt op het niveau van enkele atomen, niet helemaal vast maar kunnen bewegen rond hun evenwichtspositie. Dat zulke golven bestaan was al langer bekend, maar in dit proefschrift laten we zien dat we ze op een relatief eenvoudige manier kunnen opwekken en detecteren. Met behulp van nanofabricage maken we bovenop een magneet een hele kleine spin golf zender en ontvanger: Doordat we een stroompje door de zender sturen, gaan er spin golven door de magneet lopen. Als die bij de ontvanger aankomen, wekken ze daar een spanning op, die wij vervolgens meten. Met deze spin golf zend en ontvang techniek zijn we vervolgens veel te weten gekomen over het gedrag van de golven: Hoe beweegt zo’n golf eigenlijk door het materiaal? En wat gebeurt er als we ons apparaatje in een sterk magneetveld brengen, worden de golven daardoor verstoord? Kunnen we de spin golven niet alleen opwekken en detecteren, maar ook beïnvloeden op hun weg door het magnetische materiaal? Spin golven zijn mogelijk interessant voor gebruik in nieuwe computer technologie, omdat ze bepaalde logische operaties in principe zeer efficiënt kunnen uitvoeren en de mogelijkheid bieden om data opslag en data verwerking te combineren. Voor het zover is, is er echter nog veel onderzoek nodig. Dit proefschrift is een van de eerste stappen in die richting

The natural language processing of radiology requests and reports of chest imaging:Comparing five transformer models’ multilabel classification and a proof-of-concept study

Background: Radiology requests and reports contain valuable information about diagnostic findings and indications, and transformer-based language models are promising for more accurate text classification. Methods: In a retrospective study, 2256 radiologist-annotated radiology requests (8 classes) and reports (10 classes) were divided into training and testing datasets (90% and 10%, respectively) and used to train 32 models. Performance metrics were compared by model type (LSTM, Bertje, RobBERT, BERT-clinical, BERT-multilingual, BERT-base), text length, data prevalence, and training strategy. The best models were used to predict the remaining 40,873 cases’ categories of the datasets of requests and reports. Results: The RobBERT model performed the best after 4000 training iterations, resulting in AUC values ranging from 0.808 [95% CI (0.757–0.859)] to 0.976 [95% CI (0.956–0.996)] for the requests and 0.746 [95% CI (0.689–0.802)] to 1.0 [95% CI (1.0–1.0)] for the reports. The AUC for the classification of normal reports was 0.95 [95% CI (0.922–0.979)]. The predicted data demonstrated variability of both diagnostic yield for various request classes and request patterns related to COVID-19 hospital admission data. Conclusion: Transformer-based natural language processing is feasible for the multilabel classification of chest imaging request and report items. Diagnostic yield varies with the information in the requests

Influence of yttrium iron garnet thickness and heater opacity on the nonlocal transport of electrically and thermally excited magnons

We studied the nonlocal transport behavior of both electrically and thermally excited magnons in yttrium iron garnet (YIG) as a function of its thickness. For electrically injected magnons, the nonlocal signals decrease monotonically as the YIG thickness increases. For the nonlocal behavior of the thermally generated magnons, or the nonlocal spin Seebeck effect (SSE), we observed a sign reversal which occurs at a certain heater-detector distance, and it is influenced by both the opacity of the YIG/heater interface and the YIG thickness. Our nonlocal SSE results can be qualitatively explained by the bulk-driven SSE mechanism together with the magnon diffusion model. Using a two-dimensional finite element model (2D-FEM), we estimated the bulk spin Seebeck coefficient of YIG at room temperature. The quantitative disagreement between the experimental and modeled results indicates more complex processes going on in addition to magnon diffusion and relaxation, especially close to the contacts.Comment: 16 pages, 11 figure

COOD:Combined out-of-distribution detection using multiple measures for anomaly & novel class detection in large-scale hierarchical classification

High-performing out-of-distribution (OOD) detection, both anomaly and novel class, is an important prerequisite for the practical use of classification models. In this paper, we focus on the species recognition task in images concerned with large databases, a large number of fine-grained hierarchical classes, severe class imbalance, and varying image quality. We propose a framework for combining individual OOD measures into one combined OOD (COOD) measure using a supervised model. The individual measures are several existing state-of-the-art measures and several novel OOD measures developed with novel class detection and hierarchical class structure in mind. COOD was extensively evaluated on three large-scale (500k+ images) biodiversity datasets in the context of anomaly and novel class detection. We show that COOD outperforms individual, including state-of-the-art, OOD measures by a large margin in terms of TPR@1% FPR in the majority of experiments, e.g., improving detecting ImageNet images (OOD) from 54.3% to 85.4% for the iNaturalist 2018 dataset. SHAP (feature contribution) analysis shows that different individual OOD measures are essential for various tasks, indicating that multiple OOD measures and combinations are needed to generalize. Additionally, we show that explicitly considering ID images that are incorrectly classified for the original (species) recognition task is important for constructing high-performing OOD detection methods and for practical applicability. The framework can easily be extended or adapted to other tasks and media modalities