Intelligent Light (United States)

Rib waveguides are one of the most widely used optical waveguide structures in integrated photonics, primarily owing to their low propagation loss and ease of control. A unique and often highly desirable feature of the rib waveguides is that even when the overall cross-sectional dimension of the waveguide is large, single-mode conditions can still be achieved with specific structural configurations. In this work, we revisit and analyze the single-mode conditions for the rib waveguides using an insightful mode-matching method. Based on the theoretically exact single-mode condition, a set of highly accurate empirical single-mode conditions are established for silicon-on-insulator (SOI) rib waveguides with different cross-sections: a true single-mode condition, ensuring exclusive propagation of the HE11 mode, and a quasi-single-mode condition, which also allows the EH11 mode. These conditions are verified using both Si and silicon nitride waveguides in SOI platforms. The proposed single-mode condition is of great significance for achieving high-density, low loss, and highly reliable silicon photonic integrated circuits.

Abstract Brain-machine interfaces (BMIs) predominantly rely on static digital architectures to decode biological neuronal networks, a paradigm that is incompatible with natural neural coding in the human brain1–4. Bridging this gap is a critical step in combating neuronal dysfunction, enhancing brain functionality, and refining the precision of neuroprosthetics5. The integration of brain organoids with microelectrode array (MEA), as a class of BMIs, offers a humanized in vitro platform with unique biological compatibility advantages for dynamic neuronal decoding. This study resolves the biological-electronic encoding incompatibility of brain organoid-MEA Integration through three progressive breakthroughs. First, a human-machine hybrid agent is developed as a newly proposed bioengineered platform that couples brain organoids together with high-density MEAs and computational chips, enabling closed-loop perturbation of biological neuronal networks via exogenous signals. Second, through plasticity-driven real-time tracking of neuronal activity, we establish dynamically reconfigurable stimulation nodes that self-align with the electrophysiological states of the organoids. This resolves the exogenous-endogenous encoding mismatch by implementing plasticity-driven adaptation principles that ensure biological compatibility through spatially adaptive coordination. Finally, through shared plasticity rules rather than centralized control, we construct the first scalable multi-agent interactive system (MAIS) and demonstrate its real-world applications. Through designed scenarios of pathological/normal neuronal network interaction, we validate that MAIS achieves stable cross-network coordination. MAIS embodies a self-evolving neural coding sandbox in which plasticity-driven dynamic decoding bridges the compatibility gaps between biological and digital systems, providing a scalable and foundational infrastructure for human-centered neural interfaces.

This poster presents a collaborative activity among R-CCS and JAXA towards the development of a common in-situ visualization infrastructure on the Arm A64FX supercomputers at both sites, which includes the Fugaku and Fujitsu FX700 on the R-CCS, and Fujitsu FX1000, named as TOKI-SORA, on the JAXA side. This large data visualization-related activity is just a part of a wide-ranging collaboration among the operational groups of the Japanese HPC sites running those Arm A64FX supercomputers. Although each HPC site has its own missions and targets, the visualization and analysis of extreme-scale simulation results still remain a common challenging problem, and thus, we have been evaluating the use of commercial and open-source software aiming to provide a useful in-situ visualization environment for the HPC users. In this poster, we will present the current development stage of this environment on the Fugaku.

In 2014, NASA released a report outlining a future state for aerospace computational fluid dynamics (CFD), the CFD Vision 2030 Study (the Study).1 Developed by experts from industry, government, and academia, the Study provided a forecast of CFD capabilities required for turbulent, transitional, and reacting flow simulations across a broad Mach number regime. In addition, the Study provided an aspirational role for future CFD as part of a routine, efficient, and physics-based aerospace design and development process. This future role of CFD was summarized in the Study as follows: “A single engineer/scientist must be able to conceive, create, analyze, and interpret a large ensemble of related simulations in a time-critical period (e.g., 24 hours), without individually managing each simulation, to a prespecified level of accuracy.”

SignificanceThree-photon (3P) microscopy significantly increases the depth and resolution of in vivo imaging due to decreased scattering and nonlinear optical sectioning. Simultaneous excitation of multiple fluorescent proteins is essential to study multicellular interactions and dynamics in the intact brain.AimWe characterized the excitation laser pulses at a range of wavelengths for 3P microscopy, and then explored the application of tdTomato or mScarlet and EGFP for dual-color single-excitation structural 3P imaging deep in the living mouse brain.ApproachWe used frequency-resolved optical gating to measure the spectral intensity, phase, and retrieved pulse widths at a range of wavelengths. Then, we performed in vivo single wavelength-excitation 3P imaging in the 1225 - 1360 nm range deep in the mouse cerebral cortex to evaluate the performance of tdTomato or mScarlet in combination with EGFP.ResultsWe find that tdTomato and mScarlet, expressed in oligodendrocytes and neurons respectively, have high signal-to-background in the 1300–1360 nm range, consistent with enhanced 3P cross sections.ConclusionsThese results suggest that a single excitation wavelength source is advantageous for multiple applications of dual-color brain imaging and highlight the importance of empirical characterization of individual fluorophores for 3P microscopy.

In 2014, the CFD Vision 2030 Report proposed a desired status for aerospace CFD by 2030 that included several challenge problems and a Roadmap describing how to reach this status. Since then, the Report has been used to identify research topics and support funded activities. Further, the AIAA CFD Vision 2030 Integration Committee has been established to further progress toward this vision. A detailed review of the progress made on the milestones identified on the Roadmap was completed in 2020 by this committee and was used to assess the technology readiness level (TRL) of the different milestones. An interpretation of the TRL scale based on publications and level of technology usage was developed and used for assessment consistency. With this information, the Roadmap has been adjusted to reflect the present status of key CFD technology items. Emerging and additional key technologies have been incorporated into the Roadmap. With these modifications, an updated Roadmap is provided that reflects present status of technology development and steps necessary to attain the CFD Vision 2030.

Abstract We address the “plane of satellites problem” by studying planar configurations around two disk galaxies with no late major mergers, formed in zoom-in hydro-simulations. Due to the current lack of good-quality kinematic data for M31 satellites, we use only positional information. So far, positional analyses of simulations are unable to find planes as thin and populated as the observed ones. We follow a novel systematic and detailed plane searching technique to study the properties and quality of planes of satellites, in both simulations or real data. In particular, (i) we extend the four-galaxy-normal density plot method (Pawlowski et al. 2013) in a way designed to efficiently identify high-quality planes (i.e., thin and populated) without imposing extra constraints on their properties, and (ii), we apply it for the first time to simulations. Using zoom-in simulations allows us to mimic Milky Way/M31-like systems regarding the number of satellites involved as well as galactic disk effects. In both simulations, we find satellite planar configurations that are compatible, along given time intervals, with all of the spatial characteristics of observed planes identified using the same methodology. During most of these periods, planes are approximately perpendicular to the galactic disk. However, the fraction of co-orbiting satellites within them is, in general, low, suggesting time-varying satellite membership. We conclude that high-quality positional planes of satellites could be not infrequent in ΛCDM-formed disk galaxies with a quiet assembly history. Detecting kinematically coherent, time-persistent planes demands considering the full six-dimensional phase-space information of satellites.

JMSI Inc DBA Intelligent Light has completed Phase I SBIR research leading to the development of AMT FEVER. AMT FEVER is a visualization library that enables in situ data extraction and visualization for Asynchronous Many Task (AMT) simulations written for Legion. The goals of the Phase I research were to gain experience writing AMT algorithms by porting the LULESH shock hydrodynamics mini-app to Legion and to create a library of AMT visualization algorithms to generate in situ geometric and image extract databases. The research performed in Phase I successfully produced a port of LULESH to Legion’s Regent language that generates comparable output to the original MPI-based code. AMT FEVER consists of a library of AMT tasks that can create isosurface extracts for FieldView XDB format and scalar images for Cinema databases. Cinema images were produced using both external rendering libraries such as Ascent and OSPRay as well as a native ray-tracer written in Regent. In addition, AMT FEVER includes implementations of image compositing algorithms based on binary-tree and Radix-K that have been adapted to Legion. This project demonstrates how crucial visualization algorithms can operate in situ as tasks within an AMT host simulation.