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Context.High-resolution observations of the Sun reveal a multitude of small-scale striations throughout the photosphere. While these features are well observed in broad-band intensity images, spectropolarimetric observations remain rare. Aims.In this study, we characterize small dark striations at the pore-granulation boundary and bright grains moving along them. We seek to describe their magneto-convective nature. Methods.We analyzed restored context images and many-line Stokes inversions of a restored spectropolarimetric scan from GST/FISS-SP with a spatial resolution of 0.068″. In the inversion, we used 85 solar absorption lines within a 33 Å wide spectral window in the 5250 Å region. We compare the observations with a MURaM simulation to discern the magneto-convective nature of striations and grains. Results.We find multiple dark striations in the vicinity of pores or active region intergranular lanes with a typical width of 0.09″ and moving bright grains that migrate along some of those striations toward the adjacent pore. Grains forming in a high-resolution MURaM simulation of a pore show similar lifetimes of about 70 s. A comparison of the atmospheric configurations of simulated and observed grains reveals good qualitative agreement in structure, dynamics, thermal, and magnetic stratification. The simulation shows that the dark striations form at the top of a convective plume confined by the surrounding field, and that their dark appearance is caused by plasma trapped in the field cusp at optical depth unity. The moving bright grains are composed of hot plasma pulled upwards by turbulent flows at the tip of the striation. Conclusions.By combining high resolution spectro-polarimetry, many-line inversions, and MURaM simulations, we present the first analysis of the 3D fine structure of small-scale striations and moving bright grains in the vicinity of a pore and describe their magneto-convective nature. 

Context.For the analysis of highly resolved solar spectra the simultaneous observation and interpretation (inversion) of only a few (often only one) spectral lines is still the norm. With modern instruments spatially highly resolved spectropolarimetric data covering many lines are available. Aims.For the first time we combine the information from 85 simultaneously observed absorption lines in spatially highly resolved data to test a proposed solar many-line inversion strategy. Methods.We inverted full Stokes spectra recorded with the FISS spectro-polarimeter (FISS-SP) at the 1.6-m Goode Solar Telescope in California, using the SPINOR code. We contrasted two different setups: one following the traditional approach of using a line doublet, and a new method inverting many-lines simultaneously. Results.Compared to results from an inversion using two lines of a line doublet, we discovered more fine-structure and better constrained values using the many-line technique. An average quiet Sun spectrum was successfully reproduced using a model atmosphere, but when inverting spatially resolved data, uncertainties in line parameters and blend configurations did not average out. Thus, a deliberate selection process of lines and line blends was required, in order to make the many-line case converge to a physically expected and coherent atmosphere. We successfully developed and tested such a selection method. Conclusions.Our results highlight that the many-line inversions method delivers more coherent results with superior line of sight (LOS) resolution of the atmospheric structure. Moreover, it effectively detects and utilizes even weak polarimetric signals in noisy data and thereby partly circumvents low noise requirements. It reveals uncertainties in atomic parameters of individual spectral lines and models, as the degree of freedom to compensate for these uncertainties by compromising the inferred atmospheric parameters is considerably reduced. It is thereby pointing to a need for improved atomic data, including log(gf) values, of many lines in the solar spectrum. The many-line method presents significant potential for solar physics and may become the preferred option for future observations with upcoming spectrographs. 

Context.Small-scale events measuring only tens of kilometres can have significant implications for the overall energy state of various layers of the solar atmosphere. Current spectro-polarimetric observations lack either spatial or spectral resolution for a comprehensive study of these small-scale events. Aims.The slit-scanning spectro-polarimetric instrument described here is designed for spectral image reconstruction and, in combination with the excellent optical performance of the 1.6 metre Goode Solar Telescope, yields spectral hypercubes of the highest spatial and spectral resolution. Additionally, the instrument offers a huge spectral window of more than 30 Å, allowing many solar absorption lines to be observed simultaneously. Methods.We extended the existing Fast Imaging Solar Spectrograph (FISS) instrument with polarimetric capabilities, new customized cameras, and a context imager. We applied numerical methods to measure and correct for field-dependent instrumental and atmospheric degradations, to obtain diffraction-limited spectro-polarimetric scans. Results.In this work we present the instrument design, the data reduction workflow, and the first-light results. Compared to a typical HINODE/SP dataset, we find a higher signal-to-noise ratio in our data within the resolution limits of the respective telescopes when utilizing the signal of all simultaneously observed spectral lines. Conclusions.We have obtained the first diffraction-limited full Stokes spectro-polarimetric datasets recorded with a slit-scanning spectrograph on a telescope with an aperture exceeding 1.5 metres. 

Total-body photography (TBP) has the potential to revolutionize early detection of skin cancers by monitoring minute changes in lesions over time. However, there is no standardized Digital Imaging and Communications in Medicine (DICOM) format for TBP. In order to accommodate various TBP data types and sophisticated data preprocessing pipelines, we propose three TBP Extended Information Object Definitions (IODs) for 2D regional images, dermoscopy images, and 3D surface meshes. We introduce a comprehensive pipeline integrating advanced image processing techniques, including 3D DICOM representation, super-resolution enhancement, and style transfer for dermoscopic-like visualization. Our framework tracks individual lesions across multiple TBP scans from different imaging systems and provides cloud-based storage with a customized DICOM viewer. To demonstrate the effectiveness of our approach, we validate our framework using TBP datasets from multiple imaging systems. Our framework and proposed IODs enhance TBP interoperability and clinical utility in dermatological practice, potentially improving early skin cancer detection. 

Travel-time computation with large transportation networks is often computationally intensive for two main reasons: 1) large computer memory is required to handle large networks; and 2) calculating shortest-distance paths over large networks is computing intensive. Therefore, previous research tends to limit their spatial extent to reduce computational intensity or resolve computational intensity with advanced cyberinfrastructure. In this context, this article describes a new Spatial Partitioning Algorithm for Scalable Travel-time Computation (SPASTC) that is designed based on spatial domain decomposition with computer memory limit explicitly considered. SPASTC preserves spatial relationships required for travel-time computation and respects a user-specified memory limit, which allows efficient and large-scale travel-time computation within the given memory limit. We demonstrate SPASTC by computing spatial accessibility to hospital beds across the conterminous United States. Our case study shows that SPASTC achieves significant efficiency and scalability making the travel-time computation tens of times faster. 

Abstract We provide data on daily social contact intensity of clusters of people at different types of Points of Interest (POI) by zip code in Florida and California. This data is obtained by aggregating fine-scaled details of interactions of people at the spatial resolution of 10 m, which is then normalized as a social contact index. We also provide the distribution of cluster sizes and average time spent in a cluster by POI type. This data will help researchers perform fine-scaled, privacy-preserving analysis of human interaction patterns to understand the drivers of the COVID-19 epidemic spread and mitigation. Current mobility datasets either provide coarse-level metrics of social distancing, such as radius of gyration at the county or province level, or traffic at a finer scale, neither of which is a direct measure of contacts between people. We use anonymized, de-identified, and privacy-enhanced location-based services (LBS) data from opted-in cell phone apps, suitably reweighted to correct for geographic heterogeneities, and identify clusters of people at non-sensitive public areas to estimate fine-scaled contacts. 

Abstract MotivationDriven by technological advances, the throughput and cost of mass spectrometry (MS) proteomics experiments have improved by orders of magnitude in recent decades. Spectral library searching is a common approach to annotating experimental mass spectra by matching them against large libraries of reference spectra corresponding to known peptides. An important disadvantage, however, is that only peptides included in the spectral library can be found, whereas novel peptides, such as those with unexpected post-translational modifications (PTMs), will remain unknown. Open modification searching (OMS) is an increasingly popular approach to annotate modified peptides based on partial matches against their unmodified counterparts. Unfortunately, this leads to very large search spaces and excessive runtimes, which is especially problematic considering the continuously increasing sizes of MS proteomics datasets. ResultsWe propose an OMS algorithm, called HOMS-TC, that fully exploits parallelism in the entire pipeline of spectral library searching. We designed a new highly parallel encoding method based on the principle of hyperdimensional computing to encode mass spectral data to hypervectors while minimizing information loss. This process can be easily parallelized since each dimension is calculated independently. HOMS-TC processes two stages of existing cascade search in parallel and selects the most similar spectra while considering PTMs. We accelerate HOMS-TC on NVIDIA’s tensor core units, which is emerging and readily available in the recent graphics processing unit (GPU). Our evaluation shows that HOMS-TC is 31× faster on average than alternative search engines and provides comparable accuracy to competing search tools. Availability and implementationHOMS-TC is freely available under the Apache 2.0 license as an open-source software project at https://github.com/tycheyoung/homs-tc.