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Abstract Precomputed Radiance Transfer (PRT) remains an attractive solution for real‐time rendering of complex light transport effects such as glossy global illumination. After precomputation, we can relight the scene with new environment maps while changing viewpoint in real‐time. However, practical PRT methods are usually limited to low‐frequency spherical harmonic lighting. All‐frequency techniques using wavelets are promising but have so far had little practical impact. The curse of dimensionality and much higher data requirements have typically limited them to relighting with fixed view or only direct lighting with triple product integrals. In this paper, we demonstrate a hybrid neural‐wavelet PRT solution to high‐frequency indirect illumination, including glossy reflection, for relighting with changing view. Specifically, we seek to represent the light transport function in the Haar wavelet basis. For global illumination, we learn the wavelet transport using a small multi‐layer perceptron (MLP) applied to a feature field as a function of spatial location and wavelet index, with reflected direction and material parameters being other MLP inputs. We optimize/learn the feature field (compactly represented by a tensor decomposition) and MLP parameters from multiple images of the scene under different lighting and viewing conditions. We demonstrate real‐time (512 x 512 at 24 FPS, 800 x 600 at 13 FPS) precomputed rendering of challenging scenes involving view‐dependent reflections and even caustics. 

Scientific data, its analysis, accuracy, completeness, and reproducibility play a vital role in advancing science and engineering. Open Science Chain (OSC) is a cyberinfrastructure platform built using the Hyperledger Fabric (HLF) blockchain technology to address issues related to data reproducibility and accountability in scientific research. OSC preserves the integrity of research datasets and enables different research groups to share datasets with the integrity information. Additionally, it enables quick verification of the exact datasets that were used for a particular published research and tracks its provenance. In this paper, we describe OSC’s command line utility that will preserve the integrity of research datasets from within the researchers’ environment or from remote systems such as HPC resources or campus clusters used for research. The Python-based command line utility can be seamlessly integrated within research workflows and provides an easy way to preserve the integrity of research data in OSC blockchain platform. 

Scientific data, along with its analysis, accuracy, completeness, and reproducibility, plays a vital role in advancing science and engineering. Open Science Chain (OSC) provides a Cyberinfrastructure platform, built using distributed ledger technologies, where verification information about scientific dataset is stored and managed in a consortium blockchain. Researchers have the ability to independently verify the authenticity of scientific results using the information stored with OSC. Researchers can also build research workflows by linking data entries in the ledger and external repositories such as GitHub that will allow for detailed provenance tracking. OSC enables answers to questions such as: how can we ensure research integrity when different research groups share and work on the same datasets across the world? Is it possible to enable quick verification of the exact data sets that were used for particular published research? Can we check the provenance of the data used in the research? In this poster, we highlight our work in building a secure, scalable architecture for OSC including developing a security module for storing identities that can be used by the researchers of science gateways communities to increase the confidence of their scientific results. 

Abstract Monolayer transition-metal dichalcogenides (TMDCs) show a wealth of exciton physics. Here, we report the existence of a new excitonic species, the high-lying exciton (HX), in single-layer WSe 2 with an energy of ~3.4 eV, almost twice the band-edge A-exciton energy, with a linewidth as narrow as 5.8 meV. The HX is populated through momentum-selective optical excitation in the K -valleys and is identified in upconverted photoluminescence (UPL) in the UV spectral region. Strong electron-phonon coupling results in a cascaded phonon progression with equidistant peaks in the luminescence spectrum, resolvable to ninth order. Ab initio GW -BSE calculations with full electron-hole correlations explain HX formation and unmask the admixture of upper conduction-band states to this complex many-body excitation. These calculations suggest that the HX is comprised of electrons of negative mass. The coincidence of such high-lying excitonic species at around twice the energy of band-edge excitons rationalizes the excitonic quantum-interference phenomenon recently discovered in optical second-harmonic generation (SHG) and explains the efficient Auger-like annihilation of band-edge excitons. 

Data sharing is an integral component of research and academic publications, allowing for independent verification of results. Researchers have the ability to extend and build upon prior research when they are able to efficiently access, validate, and verify the data referenced in publications. Despite the well known benefits of making research data more open, data withholding rates have remained constant. Some disincentives to sharing research data include lack of credit, and fear of misrepresentation of data in the absence of context and provenance. While there are several research data sharing repositories that focus on making research data available, there are no cyberinfrastructure platforms that enable researchers to efficiently validate the authenticity of datasets, track the provenance, view the lineage of the data and verify ownership information. In this paper, we introduce and provide an overview of the NSF funded Open Science Chain, a cyberinfrastructure platform built using blockchain technologies that securely stores metadata and verification information about research data and tracks changes to that data in an auditable manner in order to address issues related to reproducibility and accountability in scientific research.