The detection, extraction and parameter estimation of extreme-mass-ratio inspirals with deep learning

One of the primary goals of space-borne gravitational wave detectors is to detect and analyze extreme-mass-ratio inspirals (EMRIs). This endeavor presents a significant challenge due to the complex and lengthy EMRI signals, further compounded by their inherently faint nature. In this letter, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors, achieving a true positive rate (TPR) of 96.9 % at a 1 % false positive rate (FPR) for signal-to-noise ratio (SNR) from 50 to 100. Especially, the key intrinsic parameters of EMRIs such as mass and spin of the supermassive black hole (SMBH) and the initial eccentricity of the orbit can be inferred directly by employing a VGG network. The mass and spin of the SMBH can be determined at 99 % and 92 % respectively. This will greatly reduce the parameter spaces and computing cost for the following Bayesian parameter estimation. Our model also has a low dependency on the accuracy of the waveform model. This study underscores the potential of deep learning methods in EMRI data analysis, enabling the rapid detection of EMRI signals and efficient parameter estimation .Comment: 6 pages, 5 figure

Detecting extreme-mass-ratio inspirals for space-borne detectors with deep learning

One of the primary objectives for space-borne gravitational wave detectors is the detection of extreme-mass-ratio inspirals (EMRIs). This undertaking poses a substantial challenge because of the complex and long EMRI signals, further complicated by their inherently faint signal. In this research, we introduce a 2-layer Convolutional Neural Network (CNN) approach to detect EMRI signals for space-borne detectors. Our method employs the Q-transform for data preprocessing, effectively preserving EMRI signal characteristics while minimizing data size. By harnessing the robust capabilities of CNNs, we can reliably distinguish EMRI signals from noise, particularly when the signal-to-noise~(SNR) ratio reaches 50, a benchmark considered a ``golden'' EMRI. At the meantime, we incorporate time-delay interferometry (TDI) to ensure practical utility. We assess our model's performance using a 0.5-year dataset, achieving a true positive rate~(TPR) of 94.2\% at a 1\% false positive rate~(FPR) across various signal-to-noise ratio form 50-100, with 91\% TPR and 1\% FPR at an SNR of 50. This study underscores the promise of incorporating deep learning methods to advance EMRI data analysis, potentially leading to rapid EMRI signal detection.Comment: 12 pages, 8 figures, 2 table

Supporting nickel on vanadium nitride for comparable hydrogen evolution performance to platinum in alkaline solution

The hydrogen evolution reaction (HER) is an effective means to producing hydrogen from electrolytic water splitting. However the best-performing catalysts use expensive Pt-group metals. Cheaper non-precious metal alternatives have shown low activity as their mechanism of H-2 formation (Volmer-Heyrovsky) leads to high overpotentials. Here, we report an outstanding HER catalyst (Ni/VN) highly dispersed nickel supported on vanadium nitride that matches the turnover frequency of the platinum on carbon (Pt/C) benchmark material. It is more durable than Pt/C in alkaline solution. Ni/VN follows the low-overpotential (Volmer-Tafel) mechanism of H-2 formation, with a 43 mV overpotential at a current density of 10 mA cm(-2). This value is even below that of Pt/C (57 mV). The support of VN enhances the dispersion of nickel, weakens the surface oxidation, decreases the hydrogen binding energy, and therefore significantly improves the HER catalysis. This result removes one of the major barriers for scalability of electrolytic water-splitting by demonstrating that nitride-based materials can match and even surpass the efficiency and durability of precious metal catalysts

Surface Functionalized Sensors for Humidity-Independent Gas Detection

Semiconducting metal oxides (SMOXs) are used widely for gas sensors. However, the effect of ambient humidity on the baseline and sensitivity of the chemiresistors is still a largely unsolved problem, reducing sensor accuracy and causing complications for sensor calibrations. Presented here is a general strategy to overcome water-sensitivity issues by coating SMOXs with a hydrophobic polymer separated by a metal-organic framework (MOF) layer that preserves the SMOX surface and serves a gas-selective function. Sensor devices using these nanoparticles display near-constant responses even when humidity is varied across a wide range [0-90 % relative humidity (RH)]. Furthermore, the sensor delivers notable performance below 20 % RH whereas other water-resistance strategies typically fail. Selectivity enhancement and humidity-independent sensitivity are concomitantly achieved using this approach. The reported tandem coating strategy is expected to be relevant for a wide range of SMOXs, leading to a new generation of gas sensors with excellent humidity-resistant performance

DISCO: Achieving Low Latency and High Reliability in Scheduling of Graph-Structured Tasks over Mobile Vehicular Cloud

To effectively process data across a fleet of dynamic and distributed vehicles, it is crucial to implement resource provisioning techniques that provide reliable, cost-effective, and real-time computing services. This article explores resource provisioning for computation-intensive tasks over mobile vehicular clouds (MVCs). We use undirected weighted graphs (UWGs) to model both the execution of tasks and communication patterns among vehicles in a MVC. We then study low-latency and reliable scheduling of UWG asks through a novel methodology named double-plan-promoted isomorphic subgraph search and optimization (DISCO). In DISCO, two complementary plans are envisioned to ensure effective task completion: Plan A and Plan B.Plan A analyzes the past data to create an optimal mapping ($\alpha$) between tasks and the MVC in advance to the practical task scheduling. Plan B serves as a dependable backup, designed to find a feasible mapping ($\beta$) in case $\alpha$ fails during task scheduling due to unpredictable nature of the network.We delve into into DISCO's procedure and key factors that contribute to its success. Additionally, we provide a case study that includes comprehensive comparisons to demonstrate DISCO's exceptional performance in regards to time efficiency and overhead. We further discuss a series of open directions for future research