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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 The coupling between superconductors and oscillation cycles of light pulses, i.e., lightwave engineering, is an emerging control concept for superconducting quantum electronics. Although progress has been made towards terahertz-driven superconductivity and supercurrents, the interactions able to drive non-equilibrium pairing are still poorly understood, partially due to the lack of measurements of high-order correlation functions. In particular, the sensing of exotic collective modes that would uniquely characterize light-driven superconducting coherence, in a way analogous to the Meissner effect, is very challenging but much needed. Here we report the discovery of parametrically driven superconductivity by light-induced order-parameter collective oscillations in iron-based superconductors. The time-periodic relative phase dynamics between the coupled electron and hole bands drives the transition to a distinct parametric superconducting state out-of-equalibrium. This light-induced emergent coherence is characterized by a unique phase–amplitude collective mode with Floquet-like sidebands at twice the Higgs frequency. We measure non-perturbative, high-order correlations of this parametrically driven superconductivity by separating the terahertz-frequency multidimensional coherent spectra into pump–probe, Higgs mode and bi-Higgs frequency sideband peaks. We find that the higher-order bi-Higgs sidebands dominate above the critical field, which indicates the breakdown of susceptibility perturbative expansion in this parametric quantum matter. 

The BICEP/Keck (BK) series of cosmic microwave background (CMB) polarization experiments has, over the past decade and a half, produced a series of field-leading constraints on cosmic inflation via measurements of the “B-mode” polarization of the CMB. Primordial B modes are directly tied to the amplitude of primordial gravitational waves (PGW), their strength parameterized by the tensor-to-scalar ratio, r, and thus the energy scale of inflation. Having set the most sensitive constraints to-date on r, σ(r) = 0.009 (r0.05 < 0.036, 95% C.L.) using data through the 2018 observing season (“BK18”), the BICEP/Keck program has continued to improve its dataset in the years since. We give a brief overview of the BK program and the “BK18” result before discussing the program’s ongoing efforts, including the deployment and performance of the Keck Array’s successor instrument, BICEP Array, improvements to data processing and internal consistency testing, new techniques such as delensing, and how those will ultimately serve to allow BK reach σ(r) ≲ 0.003 using data through the 2027 observing season.