Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood agg...
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ISBN:
(纸本)9798350324457
Graph-based data present unique challenges and opportunities for machine learning. Graph Neural Networks (GNNs), and especially those algorithms that capture graph topology through message passing for neighborhood aggregation, have been a leading solution. However, these networks often require substantial computational resources and may not optimally leverage the information contained in the graph's topology, particularly for large-scale or complex *** propose Topology Coordinate Neural Network (TCNN) and Directional Virtual Coordinate Neural Network (DVCNN) as novel and efficient alternatives to message passing GNNs, that directly leverage the graph's topology, sidestepping the computational challenges presented by competing algorithms. Our proposed methods can be viewed as a reprise of classic techniques for graph embedding for neural network feature engineering, but they are novel in that our embedding techniques leverage ideas in Graph Coordinates (GC) that are lacking in current *** results, benchmarked against the Open Graph Benchmark Leaderboard, demonstrate that TCNN and DVCNN achieve competitive or superior performance to message passing GNNs. For similar levels of accuracy and ROC-AUC, TCNN and DVCNN need far fewer trainable parameters than contenders of the OGBN Leaderboard. The proposed TCNN architecture requires fewer parameters than any neural network method currently listed in the OGBN Leaderboard for both OGBN-Proteins and OGBN-Products datasets. Conversely, our methods achieve higher performance for a similar number of trainable parameters. These results hold across diverse datasets and edge features, underscoring the robustness and generalizability of our methods. By providing an efficient and effective alternative to message passing GNNs, our work expands the toolbox of techniques for graph-based machine learning. A significantly lower number of tunable parameters for a given evaluation metric makes TCNN and DVCNN especiall
Soft(flexible and stretchable) biosensors have great potential in real-time and continuous health monitoring of various physiological factors, mainly due to their better conformability to soft human tissues and organs...
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Soft(flexible and stretchable) biosensors have great potential in real-time and continuous health monitoring of various physiological factors, mainly due to their better conformability to soft human tissues and organs, which maximizes data fidelity and minimizes biological *** of the early soft sensors focused on sensing physical signals. Recently, it is becoming a trend that novel soft sensors are developed to sense and monitor biochemical signals in situ in real biological environments, thus providing much more meaningful data for studying fundamental biology and diagnosing diverse health conditions. This is essential to decentralize the healthcare resources towards predictive medicine and better disease management. To meet the requirements of mechanical softness and complex biosensing, unconventional materials, and manufacturing process are demanded in developing biosensors. In this review, we summarize the fundamental approaches and the latest and representative design and fabrication to engineer soft electronics(flexible and stretchable) for wearable and implantable biochemical sensing. We will review the rational design and ingenious integration of stretchable materials, structures, and signal transducers in different application scenarios to fabricate high-performance soft biosensors. Focus is also given to how these novel biosensors can be integrated into diverse important physiological environments and scenarios in situ, such as sweat analysis, wound monitoring, and neurochemical sensing. We also rethink and discuss the current limitations,challenges, and prospects of soft biosensors. This review holds significant importance for researchers and engineers, as it assists in comprehending the overarching trends and pivotal issues within the realm of designing and manufacturing soft electronics for biochemical sensing.
Exceptional point (EP) is a special degeneracy of non-Hermitian systems. One-dimensional transmission systems operating at EPs are widely studied and applied to chiral conversion and sensing. Lately, two-dimensional s...
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Exceptional point (EP) is a special degeneracy of non-Hermitian systems. One-dimensional transmission systems operating at EPs are widely studied and applied to chiral conversion and sensing. Lately, two-dimensional systems at EPs have been exploited for their exotic scattering features, yet so far been limited to only the non-visible waveband. Here, we report a universal paradigm for achieving a high-efficiency EP in the visible by leveraging interlayer loss to accurately control the interplay between the lossy structure and scattering lightwaves. A bilayer framework is demonstrated to reflect back the incident light from the left side ( | r_(−1) | >0.999) and absorb the incident light from the right side ( | r_(+1) | < 10^(–4)). As a proof of concept, a bilayer metasurface is demonstrated to reflect and absorb the incident light with experimental efficiencies of 88% and 85%, respectively, at 532 nm. Our results open the way for a new class of nanoscale devices and power up new opportunities for EP physics.
The study of all-group-IV SiGeSn lasers has opened a new avenue to Si-based light sources. SiGeSn heterostructure and quantum well lasers have been successfully demonstrated in the past few years. It has been reported...
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Amorphous indium gallium zinc oxide (a-IGZO)-based thin film transistors (TFTs) are increasingly becoming popular because of their potential in futuristic applications, including CMOS technology. Given the demand for ...
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We report a split ring photonic crystal that demonstrates an order of magnitude larger peak energy density compared to traditional photonic crystals. The split ring offers highly focused optical energy in an accessibl...
The future deployment of terawatt-scale proton exchange membrane water electrolyzer (PEMWE) technology necessitates development of an efficient oxygen evolution catalyst with low cost and long lifetime. Currently, the...
The future deployment of terawatt-scale proton exchange membrane water electrolyzer (PEMWE) technology necessitates development of an efficient oxygen evolution catalyst with low cost and long lifetime. Currently, the stability of the most active iridium (Ir) catalysts is impaired by dissolution, redeposition, detachment, and agglomeration of Ir species. Here we present a ripening-induced embedding strategy that securely embeds the Ir catalyst in a cerium oxide support. Cryogenic electron tomography and all-atom kinetic Monte Carlo simulations reveal that synchronizing the growth rate of the support with the nucleation rate of Ir, regulated by sonication, is pivotal for successful synthesis. A PEMWE using this catalyst achieves a cell voltage of 1.72 volts at a current density of 3 amperes per square centimeter with an Ir loading of just 0.3 milligrams per square centimeter and a voltage degradation rate of 1.33 microvolts per hour, as demonstrated by a 6000-hour accelerated aging test.
Fantasy Sports has a current market size of ${\$}$27B and is expected to grow more than ${\$}$84B in less than a decade. The intent is to create virtual teams that somehow reflect what would happen if the constituent ...
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We present a dip testing setup, which employs a copper mold instrumented with high resolution distributed fiber-optic sensors, to precisely map closely spaced thermal features that are imparted to the mold during stee...
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