Harnessing orbital Hall effect in spin-orbit torque MRAM.

Spin-Orbit Torque (SOT) Magnetic Random-Access Memory (MRAM) devices offer improved power efficiency, nonvolatility, and performance compared to static RAM, making them ideal, for instance, for cache memory applications. Efficient magnetization switching, long data retention, and high-density integration in SOT MRAM require ferromagnets (FM) with perpendicular magnetic anisotropy (PMA) combined with large torques enhanced by Orbital Hall Effect (OHE). We have engineered a PMA [Co/Ni] FM on selected OHE layers (Ru, Nb, Cr) and investigated the potential of theoretically predicted larger orbital Hall conductivity (OHC) to quantify the torque and switching current in OHE/[Co/Ni] stacks.

Electronic transparency of internal interfaces in metallic nanostructures comprising light, heavy and ferromagnetic metals measured by terahertz spectroscopy.

We investigate the electronic transport at the internal interface within a selection of metallic bilayer nanostructures using the contact-free, all-optical method of THz time-domain spectroscopy. The Ru/Co, Ru/Pt, and Ru/Al bilayer nanostructures and their individual constituent metals are studied, with Ru representing an archetypal -band metal, Co an archetypal ferromagnet, and Pt and Al archetypal heavy and light metals, respectively. The THz conductivity data were analyzed in terms of Drude and Bloch-Grüneisen models, and the interface current coefficient of the internal nanointerface was determined.

Treatment overview of axial spondyloarthritis in 2023.

The treatment of patients with axial spondyloarthritis (axSpA) is characterized by non-pharmacological and pharmacological treatment options. It may depend on the type and extent of musculoskeletal and extramusculoskeletal manifestations. Recent data on non-pharmacological treatment options, such as physical activity, physiotherapy, and modification of lifestyle factors, are summarized in this review.

Analysis of neuropsychological and neuroradiological features for diagnosis of Alzheimer's disease and mild cognitive impairment.

Background: Age-related neurodegenerative diseases are constantly increasing with prediction that in 2050 over 60 % of population will suffer from some level of cognitive impairment. A cure for the Alzheimer's disease (AD) does not exist, so early diagnosis is of a great importance. Machine learning techniques can help in early diagnosis with deep medical data processing, disease understanding, intervention analysis and knowledge discovery for achieving better medical decision making.

SuperFormer: Continual learning superposition method for text classification.

One of the biggest challenges in continual learning domains is the tendency of machine learning models to forget previously learned information over time. While overcoming this issue, the existing approaches often exploit large amounts of additional memory and apply model forgetting mitigation mechanisms which substantially prolong the training process. Therefore, we propose a novel SuperFormer method that alleviates model forgetting, while spending negligible additional memory and time.