Skyrmion-Bubble Bundles in an X-type Sr2Co2Fe28O46 Hexaferrite above Room Temperature

Magnetic skyrmions are spin swirls that possess topological nontriviality and are considered particle-like entities. They are distinguished by an integer topological charge Q. The presence of skyrmion bundles provides an opportunity to explore the range of values for Q, which is crucial for the advancement of topological spintronic devices with multi-Q properties. In this study, we present a new material candidate, Sr2Co2Fe28O46 hexaferrite of the X-type, which hosts small dipolar skyrmions at room temperature and above. By exploiting reversed magnetic fields from metastable skyrmion bubbles at zero fields, we can incorporate skyrmion-bubble bundles with different interior skyrmion/bubble numbers, topological charges, and morphologies at room temperature. Our experimental findings are consistently supported by micromagnetic simulations. Our results highlight the versatility of topological spin textures in centrosymmetric uniaxial magnets, thereby paving the way for the development of room-temperature topological spintronic devices with multi-Q characteristics.Comment: https://doi.org/10.1002/adma.20230611

White-Box Transformers via Sparse Rate Reduction

In this paper, we contend that the objective of representation learning is to compress and transform the distribution of the data, say sets of tokens, towards a mixture of low-dimensional Gaussian distributions supported on incoherent subspaces. The quality of the final representation can be measured by a unified objective function called sparse rate reduction. From this perspective, popular deep networks such as transformers can be naturally viewed as realizing iterative schemes to optimize this objective incrementally. Particularly, we show that the standard transformer block can be derived from alternating optimization on complementary parts of this objective: the multi-head self-attention operator can be viewed as a gradient descent step to compress the token sets by minimizing their lossy coding rate, and the subsequent multi-layer perceptron can be viewed as attempting to sparsify the representation of the tokens. This leads to a family of white-box transformer-like deep network architectures which are mathematically fully interpretable. Despite their simplicity, experiments show that these networks indeed learn to optimize the designed objective: they compress and sparsify representations of large-scale real-world vision datasets such as ImageNet, and achieve performance very close to thoroughly engineered transformers such as ViT. Code is at \url{https://github.com/Ma-Lab-Berkeley/CRATE}.Comment: 33 pages, 11 figure

DPPMask: Masked Image Modeling with Determinantal Point Processes

Masked Image Modeling (MIM) has achieved impressive representative performance with the aim of reconstructing randomly masked images. Despite the empirical success, most previous works have neglected the important fact that it is unreasonable to force the model to reconstruct something beyond recovery, such as those masked objects. In this work, we show that uniformly random masking widely used in previous works unavoidably loses some key objects and changes original semantic information, resulting in a misalignment problem and hurting the representative learning eventually. To address this issue, we augment MIM with a new masking strategy namely the DPPMask by substituting the random process with Determinantal Point Process (DPPs) to reduce the semantic change of the image after masking. Our method is simple yet effective and requires no extra learnable parameters when implemented within various frameworks. In particular, we evaluate our method on two representative MIM frameworks, MAE and iBOT. We show that DPPMask surpassed random sampling under both lower and higher masking ratios, indicating that DPPMask makes the reconstruction task more reasonable. We further test our method on the background challenge and multi-class classification tasks, showing that our method is more robust at various tasks

Learning in Nonzero-Sum Stochastic Games with Potentials

Multi-agent reinforcement learning (MARL) has become effective in tackling discrete cooperative game scenarios. However, MARL has yet to penetrate settings beyond those modelled by team and zero-sum games, confining it to a small subset of multi-agent systems. In this paper, we introduce a new generation of MARL learners that can handle nonzero-sum payoff structures and continuous settings. In particular, we study the MARL problem in a class of games known as stochastic potential games (SPGs) with continuous state-action spaces. Unlike cooperative games, in which all agents share a common reward, SPGs are capable of modelling real-world scenarios where agents seek to fulfil their individual goals. We prove theoretically our learning method, SPot-AC, enables independent agents to learn Nash equilibrium strategies in polynomial time. We demonstrate our framework tackles previously unsolvable tasks such as Coordination Navigation and large selfish routing games and that it outperforms the state of the art MARL baselines such as MADDPG and COMIX in such scenarios.Comment: ICML 202

Observation of Hybrid Magnetic Skyrmion Bubbles in Fe3Sn2 Nanodisks

It is well known that there are two types of magnetic bubbles in uniaxial magnets. Here, using Lorentz-transimission electronic microscopy magnetic imaging, we report the direct experimental observation of 3D type-III hybrid bubbles, which comprise N\'eel-twisted skyrmion bubbles with topological charge Q = -1 in near-surface layers and type-II bubbles with Q = 0 in interior layers, in Fe3Sn2 nanodisks. Using the tilted magnetic field, we further show the controlled topological magnetic transformations of three types of bubbles in a confined ferromagnetic nanodisk. Our observations are well reproduced using micromagnetic simulations based on measured magnetic parameters. Our results advance fundamental classification and understanding of magnetic bubbles, which could propel the applications of three-dimensional magnetism.Comment: https://doi.org/10.1103/PhysRevB.107.17442

Current-Controlled Skyrmion Number in Confined Ferromagnetic Nanostripes

Skyrmions are vortex-like localized magnetic structures that possess an integer-valued topological index known as the skyrmion number or topological charge. Skyrmion number determines the topology-related emergent magnetism, which is highly desirable for advanced storage and computing devices. In order to achieve device functions, it is necessary to manipulate the skyrmion number in confined nanostructured geometries using electrical methods. Here, we report the reliable current-controlled operations for manipulating the skyrmion number through reversible topological transformations between skyrmion chains and stripe domains in confined Fe3Sn2 nanostripes. The results of micromagnetic simulations are successful in numerically reproducing our experiments and explaining them through the combined effect of current-induced Joule heating and magnetic hysteresis. These findings hold the potential to advance the development of topological spintronic devices.Comment: https://doi.org/10.1002/adfm.20230404

Soluble Receptor for Advanced Glycation End Product Ameliorates Chronic Intermittent Hypoxia Induced Renal Injury, Inflammation, and Apoptosis via P38/JNK Signaling Pathways

Obstructive sleep apnea (OSA) associated chronic kidney disease is mainly caused by chronic intermittent hypoxia (CIH) triggered tissue damage. Receptor for advanced glycation end product (RAGE) and its ligand high mobility group box 1 (HMGB1) are expressed on renal cells and mediate inflammatory responses in OSA-related diseases. To determine their roles in CIH-induced renal injury, soluble RAGE (sRAGE), the RAGE neutralizing antibody, was intravenously administered in a CIH model. We also evaluated the effect of sRAGE on inflammation and apoptosis. Rats were divided into four groups: (1) normal air (NA), (2) CIH, (3) CIH+sRAGE, and (4) NA+sRAGE. Our results showed that CIH accelerated renal histological injury and upregulated RAGE-HMGB1 levels involving inflammatory (NF-κB, TNF-α, and IL-6), apoptotic (Bcl-2/Bax), and mitogen-activated protein kinases (phosphorylation of P38, ERK, and JNK) signal transduction pathways, which were abolished by sRAGE but p-ERK. Furthermore, sRAGE ameliorated renal dysfunction by attenuating tubular endothelial apoptosis determined by immunofluorescence staining of CD31 and TUNEL. These findings suggested that RAGE-HMGB1 activated chronic inflammatory transduction cascades that contributed to the pathogenesis of the CIH-induced renal injury. Inhibition of RAGE ligand interaction by sRAGE provided a therapeutic potential for CIH-induced renal injury, inflammation, and apoptosis through P38 and JNK pathways

Research and innovation identified to decarbonise the maritime sector

The maritime sector requires technically, environmentally, socially, and economically informed pathways to decarbonise and eliminate all emissions harmful to the environment and health. This is extremely challenging and complex, and a wide range of technologies and solutions are currently being explored. However, it is important to assess the state-of-the-art and identify further research and innovation required to accelerate decarbonisation. The UK National Clean Maritime Research Hub have identified key priority areas to drive this process, with particular focus on marine fuels, power and propulsion, vessel efficiency, port operations and infrastructure, digitalisation, finance, regulation, and policy

Precipitation seasonality and variability over the Tibetan plateau as resolved by the High Asia reanalysis

Because of the scarcity of meteorological observations, the precipitation climate on the Tibetan Plateau and surrounding regions (TP) has been insufficiently documented so far. In this study, the characteristics and basic features of precipitation on the TP during an 11-yr period (2001–11) are described on monthly-to-annual time scales. For this purpose, a new high-resolution atmospheric dataset is analyzed, the High Asia Reanalysis (HAR), generated by dynamical downscaling of global analysis data using the Weather Research and Forecasting (WRF) model. The HAR precipitation data at 30- and 10-km resolutions are compared with both rain gauge observations and satellite-based precipitation estimates from the Tropical Rainfall Measurement Mission (TRMM). It is found that the HAR reproduces previously reported spatial patterns and seasonality of precipitation and that the high-resolution data add value regarding snowfall retrieval, precipitation frequency, and orographic precipitation. It is demonstrated that this process-based approach, despite some unavoidable shortcomings, can improve the understanding of the processes that lead to precipitation on the TP. Analysis focuses on precipitation amounts, type, seasonality, and interannual variability. Special attention is given to the links between the observed patterns and regional atmospheric circulation. As an example of an application of the HAR, a new classification of glaciers on the TP according to their accumulation regimes is proposed, which illustrates the strong spatial variability of precipitation seasonality. Finally, directions for future research are identified based on the HAR, which has the potential to be a useful dataset for climate, glaciological, and hydrological impact studies