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Disaggregated memory architecture decouples computing and memory resources into separate pools connected via high-speed interconnect technologies, offering substantial advantages in scalability and resource utilization. However, this architecture also poses unique challenges in designing effective index structures and concurrency protocols due to increased remote memory access overhead and its shared-everything nature. In this paper, we present DART, a lock-free two-layer hashed Adaptive Radix Tree (ART) designed to minimize remote memory access while ensuring high concurrency and crash consistency in the disaggregated memory architecture. DART incorporates a hash-based Express Skip Table at its upper layer, which reduces the round trips of remote memory access during index traversal. In the base layer, DART employs an Adaptive Hashed Layout within ART nodes, confining remote memory accesses during in-node searches to small hash buckets. By further leveraging Decoupled Metadata Organization, DART achieves lock-free atomic updates, enabling high scalability and ensuring crash consistency. Our evaluation demonstrates that DART outperforms state-of-the-art counterparts by up to 5.8X in YCSB workloads. 

Background:Achieving optimal glycemic control for persons with diabetes remains difficult. Real-world continuous glucose monitoring (CGM) data can illuminate previously underrecognized glycemic fluctuations. We aimed to characterize glucose trajectories in individuals with Type 1 and Type 2 diabetes, and to examine how baseline glycemic control, CGM usage frequency, and regional differences shape these patterns. Methods:We linked Dexcom CGM data (2015–2020) with Veterans Health Administration electronic health records, identifying 892 Type 1 and 1716 Type 2 diabetes patients. Analyses focused on the first three years of CGM use, encompassing over 2.1 million glucose readings. We explored temporal trends in average daily glucose and time-in-range values. Results:Both Type 1 and Type 2 cohorts exhibited a gradual rise in mean daily glucose over time, although higher CGM usage frequency was associated with lower overall glucose or attenuated increases. Notable weekly patterns emerged: Sundays consistently showed the highest glucose values, whereas Wednesdays tended to have the lowest. Seasonally, glycemic control deteriorated from October to February and rebounded from April to August, with more pronounced fluctuations in the Northeast compared to the Southwest U.S. Conclusions:Our findings underscore the importance of recognizing day-of-week and seasonal glycemic variations in diabetes management. Tailoring interventions to account for these real-world fluctuations may enhance patient engagement, optimize glycemic control, and ultimately improve health outcomes. 

Two-dimensional (2D) transition metal carbides, nitrides and carbonitrides, known as MXenes, are of interest as electrocatalysts. Tungsten-based MXenes are predicted to have low overpotentials in the hydrogen evolution reaction but their synthesis has proven difficult due to the calculated instability of their hypothetical MAX precursors. In this study, we present a theory-guided synthesis of a tungsten-based MXene, W2TiC2Tx, derived from a non-MAX nanolaminated ternary carbide (W,Ti)4C4−y precursor by the selective etching of one of the covalently bonded tungsten layers. Our results indicate the importance of tungsten and titanium ordering, the presence of vacancy defects in the metal layers, and the lack of oxygen impurities in the carbon layers for the successful selective etching of the precursor. We confirm the atomistic out-of-plane ordering of tungsten and titanium using computational and experimental characterizations. The tungsten-rich basal plane endows W2TiC2Tx MXene with a high electrocatalytic hydrogen evolution reaction performance (∼144 mV overpotential at 10 mA cm−2). This study reports a tungsten-based MXene synthesized from a covalently bonded non-MAX precursor, adding to the synthetic strategies for 2D materials. 

Abstract The stellar atmospheric parameters and physical properties of stars in the Kepler Input Catalog (KIC) are of great significance for the study of exoplanets, stellar activity, and asteroseismology. However, despite extensive effort over the past decades, accurate spectroscopic estimates of these parameters are available for only about half of the stars in the full KIC. In our work, by training relationships between photometric colors and spectroscopic stellar parameters from Gaia DR3, the Kepler-INT Survey, Large Sky Area Multi-Object Fiber Spectroscopic Telescope DR10, and Galactic Evolution Experiment at Apache Point Observatory DR17, we have obtained atmospheric parameter estimates for over 195,000 stars, accounting for 97% of the total sample of KIC stars. We obtain 1σuncertainties of 0.1 dex on metallicity [Fe/H], 100 K on effective temperatureT_eff, and 0.2 dex on surface gravity logg. In addition, based on these atmospheric parameters, we estimated the ages, masses, radii, and surface gravities of these stars using the commonly adopted isochrone-fitting approach. External comparisons indicate that the resulting precision for turnoff stars is 20% in age; for dwarf stars, it is 0.07M_⊙in mass, 0.05R_⊙in radius, and 0.12 dex in surface gravity; and for giant stars, it is 0.14M_⊙in mass, 0.73R_⊙in radius, and 0.11 dex in surface gravity. 

Abstract We search for an optimal filter design for the estimation of stellar metallicity, based on synthetic photometry from Gaia XP spectra convolved with a series of filter-transmission curves defined by different central wavelengths and bandwidths. Unlike previous designs based solely on maximizing metallicity sensitivity, we find that the optimal solution provides a balance between the sensitivity and uncertainty of the spectra. With this optimal filter design, the best precision of metallicity estimates for relatively bright (G∼ 11.5) stars is excellent,σ_[Fe/H]= 0.034 dex for FGK dwarf stars, superior to that obtained utilizing custom sensitivity-optimized filters (e.g., SkyMapperv). By selecting hundreds of high-probability member stars of the open cluster M67, our analysis reveals that the intrinsic photometric-metallicity scatter of these cluster members is only 0.036 dex, consistent with this level of precision. Our results clearly demonstrate that the internal precision of photometric-metallicity estimates can be extremely high, even providing the opportunity to perform chemical tagging for very large numbers of field stars in the Milky Way. This experiment shows that it is crucial to take into account uncertainty alongside the sensitivity when designing filters for measuring the stellar metallicity and other parameters. 

Two-dimensional transition metal carbides, nitrides, and carbonitrides, known as MXenes, hold potential in electrocatalytic applications. Tungsten (W) based-MXenes are of particular interest as they are predicted to have low overpotentials in hydrogen evolution reaction (HER). However, incorporating W into the MXene structure has proven difficult due to the calculated instability of its hypothetical MAX precursors. In this study, we present a theory-guided synthesis of a W-containing MXene, W2TiC2Tx, derived from a non-MAX nanolaminated ternary carbide (W,Ti)4C4-y precursor by selective etching of one of the covalently bonded tungsten layers. Our results indicate the importance of W and Ti ordering and the presence of vacancy defects for the successful selective etching of the precursor. We confirm the atomistic out-of-plane ordering of W and Ti using density functional theory, Rietveld refinement, and electron microscopy methods. Additionally, the W-rich basal plane endows W2TiC2Tx MXene with a remarkable HER overpotential (~144 mV at 10 mA/cm2). This study adds a tungsten-containing MXene made from a covalently bonded non-MAX phase opening more ways to synthesize novel 2D materials. 

Abstract Each year, underwater remotely operated vehicles (ROVs) collect thousands of hours of video of unexplored ocean habitats revealing a plethora of information regarding biodiversity on Earth. However, fully utilizing this information remains a challenge as proper annotations and analysis require trained scientists’ time, which is both limited and costly. To this end, we present a Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), a benchmark suite and growing large-scale dataset to train, validate, and test methods for temporally localizing four underwater substrates as well as temporally and spatially localizing 59 underwater invertebrate species. DUSIA currently includes over ten hours of footage across 25 videos captured in 1080p at 30 fps by an ROV following pre-planned transects across the ocean floor near the Channel Islands of California. Each video includes annotations indicating the start and end times of substrates across the video in addition to counts of species of interest. Some frames are annotated with precise bounding box locations for invertebrate species of interest, as seen in Fig. 1. To our knowledge, DUSIA is the first dataset of its kind for deep sea exploration, with video from a moving camera, that includes substrate annotations and invertebrate species that are present at significant depths where sunlight does not penetrate. Additionally, we present the novel context-driven object detector (CDD) where we use explicit substrate classification to influence an object detection network to simultaneously predict a substrate and species class influenced by that substrate. We also present a method for improving training on partially annotated bounding box frames. Finally, we offer a baseline method for automating the counting of invertebrate species of interest.