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This dataset includes 30 hyperspectral cloud images captured during the Summer and Fall of 2022 at Auburn University at Montgomery, Alabama, USA (Latitude N, Longitude W) using aResonon Pika XC2 Hyperspectral Imaging Camera. Utilizing the Spectronon software, the images were recorded with integration times between 9.0-12.0 ms, a frame rate of approximately 45 Hz, and a scan rate of 0.93 degrees per second. The images are calibrated to give spectral radiance in microflicks at 462 spectral bands in the 400 – 1000 nm wavelength region with a spectral resolution of 1.9 nm. A 17 m focal length objective lens was used giving a field of view equal to 30.8 degrees and an integration field of view of 0.71 mrad. These settings enable detailed spectral analysis of both dynamic cloud formations and clear sky conditions. Funded by NSF grant 2003740, this dataset is designed to advance understanding of diffuse solar radiation as influenced by cloud coverage.  The dataset is organized into 30 folders, each containing a hyperspectral image file (.bip), a header file (.hdr) with metadata, and an RGB render for visual inspection. Additional metadata, including date, time, central pixel azimuth, and altitude, are cataloged in an accompanying MS Excel file. A custom Python program is also provided to facilitate the reading and display of the HSI files.  The images can also be read and analyzed using the free version of the Spectron software available at https://resonon.com/software. To enrich this dataset, we have added a supplementary ZIP file containing multispectral (4-channel) image versions of the original hyperspectral scenes, together with the corresponding per-pixel photon flux and spectral radiance values computed from the full spectrum. These additions extend the dataset’s utility for machine learning and data fusion research by enabling comparative analysis between reduced-band multispectral imagery and full-spectrum hyperspectral data. The ExpandAI Challenge task is to develop models capable of predicting photon flux and radiance—derived from all 462 hyperspectral bands—using only the four multispectral channels. This benchmark aims to stimulate innovation in spectral information recovery, spectral-spatial inference, and physically informed deep learning for atmospheric imaging applications. 

Neurons throughout the neocortex exhibit selective sensitivity to particular features of sensory input patterns. According to the prevailing views, cortical strategy is to choose features that exhibit predictable relationship to their spatial and/or temporal context. Such contextually predictable features likely make explicit the causal factors operating in the environment and thus they are likely to have perceptual/behavioral utility. The known details of functional architecture of cortical columns suggest that cortical extraction of such features is a modular nonlinear operation, in which the input layer, layer 4, performs initial nonlinear input transform generating proto-features, followed by their linear integration into output features by the basal dendrites of pyramidal cells in the upper layers. Tuning of pyramidal cells to contextually predictable features is guided by the contextual inputs their apical dendrites receive from other cortical columns via long-range horizontal or feedback connections. Our implementation of this strategy in a model of prototypical V1 cortical column, trained on natural images, reveals the presence of a limited number of contextually predictable orthogonal basis features in the image patterns appearing in the column’s receptive field. Upper-layer cells generate an overcomplete Hadamard-like representation of these basis features: i.e., each cell carries information about all basis features, but with each basis feature contributing either positively or negatively in the pattern unique to that cell. In tuning selectively to contextually predictable features, upper layers perform selective filtering of the information they receive from layer 4, emphasizing information about orderly aspects of the sensed environment and downplaying local, likely to be insignificant or distracting, information. Altogether, the upper-layer output preserves fine discrimination capabilities while acquiring novel higher-order categorization abilities to cluster together input patterns that are different but, in some way, environmentally related. We find that to be fully effective, our feature tuning operation requires collective participation of cells across 7 minicolumns, together making up a functionally defined 150 μm diameter “mesocolumn.” Similarly to real V1 cortex, 80% of model upper-layer cells acquire complex-cell receptive field properties while 20% acquire simple-cell properties. Overall, the design of the model and its emergent properties are fully consistent with the known properties of cortical organization. Thus, in conclusion, our feature-extracting circuit might capture the core operation performed by cortical columns in their feedforward extraction of perceptually and behaviorally significant information. 

Accurate detection of protein sequence homology is essential for understanding evolutionary relationships and predicting protein functions, particularly for detecting remote homology in the “twilight zone” (20-35% sequence similarity), where traditional sequence alignment methods often fail. Recent studies show that embeddings from protein language models (pLM) can improve remote homology detection over traditional methods. Alignment-based approaches, such as those combining pLMs with dynamic programming alignment, further improve performance but often suffer from noise in the resulting similarity matrices. To address this, we evaluate a newly developed embedding-based sequence alignment approach that refines residue-level embedding similarity using K-means clustering and double dynamic programming (DDP). We show that the incorporation of clustering and DDP contributes substantially to the improved performance in detecting remote homology. Experimental results demonstrate that our approach outperforms both traditional and state-of-the-art approaches based on pLMs on several benchmarks. Our study illustrates embedding-based alignment refined with clustering and DDP offers a powerful approach for identifying remote homology, with potential to evolve further as pLMs continue to advance.