Coherence properties of infrared thermal emission from heated metallic nanowires

Coherence properties of the infrared thermal radiation from individual heated nanowires are investigated as function of nanowire dimensions. Interfering the thermally induced radiation from a heated nanowire with its image in a nearby moveable mirror, well-defined fringes are observed. From the fringe visibility, the coherence length of the thermal emission radiation from the narrowest nanowires was estimated to be at least 20 um which is much larger than expected from a classical blackbody radiator. A significant increase in coherence and emission efficiency is observed for smaller nanowires.Comment: 4 pages,figures include

Learning and Recognizing Archeological Features from LiDAR Data

We present a remote sensing pipeline that processes LiDAR (Light Detection And Ranging) data through machine & deep learning for the application of archeological feature detection on big geo-spatial data platforms such as e.g. IBM PAIRS Geoscope. Today, archeologists get overwhelmed by the task of visually surveying huge amounts of (raw) LiDAR data in order to identify areas of interest for inspection on the ground. We showcase a software system pipeline that results in significant savings in terms of expert productivity while missing only a small fraction of the artifacts. Our work employs artificial neural networks in conjunction with an efficient spatial segmentation procedure based on domain knowledge. Data processing is constraint by a limited amount of training labels and noisy LiDAR signals due to vegetation cover and decay of ancient structures. We aim at identifying geo-spatial areas with archeological artifacts in a supervised fashion allowing the domain expert to flexibly tune parameters based on her needs

TensorBank:Tensor Lakehouse for Foundation Model Training

Storing and streaming high dimensional data for foundation model training became a critical requirement with the rise of foundation models beyond natural language. In this paper we introduce TensorBank, a petabyte scale tensor lakehouse capable of streaming tensors from Cloud Object Store (COS) to GPU memory at wire speed based on complex relational queries. We use Hierarchical Statistical Indices (HSI) for query acceleration. Our architecture allows to directly address tensors on block level using HTTP range reads. Once in GPU memory, data can be transformed using PyTorch transforms. We provide a generic PyTorch dataset type with a corresponding dataset factory translating relational queries and requested transformations as an instance. By making use of the HSI, irrelevant blocks can be skipped without reading them as those indices contain statistics on their content at different hierarchical resolution levels. This is an opinionated architecture powered by open standards and making heavy use of open-source technology. Although, hardened for production use using geospatial-temporal data, this architecture generalizes to other use case like computer vision, computational neuroscience, biological sequence analysis and more

Integrin αE(CD103) Is Involved in Regulatory T-Cell Function in Allergic Contact Hypersensitivity

Murine contact hypersensitivity (CHS) is a dendritic cell (DC)-dependent T-cell-mediated inflammation with CD8+ T cells as effectors and CD4+ T cells as regulators (Treg cells) that models human allergic contact dermatitis. The integrin αE(CD103) is expressed by some T-cell and DC subsets and has been implicated in epithelial lymphocyte localization, but its role in immune regulation remains enigmatic. We have identified a function for CD103 in the development of cutaneous allergic immune responses. CHS responses, but not irritant contact dermatitis, were significantly augmented in CD103-deficient mice in hapten-challenged skin. Phenotype and function of skin DCs during sensitization were normal, whereas adoptive transfer experiments revealed that the elevated CHS response in CD103-deficient mice is transferred by primed T cells and is independent of resident cells in recipient mice. While T-cell counts were elevated in challenged skin of CD103-deficient mice, the FoxP3 expression level of CD4+CD25+ Treg cells was significantly reduced, indicating impaired functionality. Indeed, Treg cells from CD103-deficient mice were not able to suppress CHS reactions during the elicitation phase. Further, CD103 on FoxP3+ Treg cells was involved in Treg retention to inflamed skin. These findings indicate an unexpected dichotomous functional role for CD103 on Treg cells by modulating FoxP3 expression

AI Foundation Models for Weather and Climate: Applications, Design, and Implementation

Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, and meteorological agencies in building digital twins of the Earth. Recent approaches using transformers, physics-informed machine learning, and graph neural networks have demonstrated state-of-the-art performance on relatively narrow spatiotemporal scales and specific tasks. With the recent success of generative artificial intelligence (AI) using pre-trained transformers for language modeling and vision with prompt engineering and fine-tuning, we are now moving towards generalizable AI. In particular, we are witnessing the rise of AI foundation models that can perform competitively on multiple domain-specific downstream tasks. Despite this progress, we are still in the nascent stages of a generalizable AI model for global Earth system models, regional climate models, and mesoscale weather models. Here, we review current state-of-the-art AI approaches, primarily from transformer and operator learning literature in the context of meteorology. We provide our perspective on criteria for success towards a family of foundation models for nowcasting and forecasting weather and climate predictions. We also discuss how such models can perform competitively on downstream tasks such as downscaling (super-resolution), identifying conditions conducive to the occurrence of wildfires, and predicting consequential meteorological phenomena across various spatiotemporal scales such as hurricanes and atmospheric rivers. In particular, we examine current AI methodologies and contend they have matured enough to design and implement a weather foundation model.Comment: 44 pages, 1 figure, updated Fig.

Report from the conference, ‘identifying obstacles to applying big data in agriculture’

Data-centric technology has not undergone widespread adoption in production agriculture but could address global needs for food security and farm profitability. Participants in the U.S. Department of Agriculture (USDA) National Institute for Food and Agriculture (NIFA) funded conference, “Identifying Obstacles to Applying Big Data in Agriculture,” held in Houston, TX, in August 2018, defined detailed scenarios in which on-farm decisions could benefit from the application of Big Data. The participants came from multiple academic fields, agricultural industries and government organizations and, in addition to defining the scenarios, they identified obstacles to implementing Big Data in these scenarios as well as potential solutions. This communication is a report on the conference and its outcomes. Two scenarios are included to represent the overall key findings in commonly identified obstacles and solutions: “In-season yield prediction for real-time decision-making”, and “Sow lameness.” Common obstacles identified at the conference included error in the data, inaccessibility of the data, unusability of the data, incompatibility of data generation and processing systems, the inconvenience of handling the data, the lack of a clear return on investment (ROI) and unclear ownership. Less common but valuable solutions to common obstacles are also noted

Tissue-resident memory T cells invade the brain parenchyma in multiple sclerosis white matter lesions

Multiple sclerosis is a chronic inflammatory, demyelinating disease, although it has been suggested that in the progressive late phase, inflammatory lesion activity declines. We recently showed in the Netherlands Brain Bank multiple sclerosis-autopsy cohort considerable ongoing inflammatory lesion activity also at the end stage of the disease, based on microglia/macrophage activity. We have now studied the role of T cells in this ongoing inflammatory lesion activity in chronic multiple sclerosis autopsy cases. We quantified T cells and perivascular T-cell cuffing at a standardized location in the medulla oblongata in 146 multiple sclerosis, 20 neurodegenerative control and 20 non-neurological control brain donors. In addition, we quantified CD3+, CD4+, and CD8+ T cells in 140 subcortical white matter lesions. The location of CD8+ T cells in either the perivascular space or the brain parenchyma was determined using CD8/laminin staining and confocal imaging. Finally, we analysed CD8+ T cells, isolated from fresh autopsy tissues from subcortical multiple sclerosis white matter lesions (n = 8), multiple sclerosis normal-ap