Spectral fingerprint and terahertz(THz)field-induced carrier dynamics demands the exploration of broadband and intense THz signal *** THz emitters(STEs),with high stability,a low cost,and an ultrabroad bandwidth,have ...
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Spectral fingerprint and terahertz(THz)field-induced carrier dynamics demands the exploration of broadband and intense THz signal *** THz emitters(STEs),with high stability,a low cost,and an ultrabroad bandwidth,have been a hot topic in the field of THz *** of the main barriers to their practical application is lack of an STE with strong radiation ***,through the combination of optical physics and ultrafast photonics,the Tamm plasmon coupling(TPC)facilitating THz radiation is realized between spin THz thin films and photonic crystal *** results show that the spectral absorptance can be increased from 36.8%to 94.3%for spin THz thin films with *** coupling with narrowband resonance not only improves the optical-to-spin conversion efficiency,but also guarantees THz transmission with a negligible loss(~4%)for the photonic crystal *** to the simulation,we prepared this structure successfully and experimentally realized a 264%THz radiation ***,the spin THz thin films with TPC exhibited invariant absorptivity under different polarization modes of the pump beam and weakening confinement on an obliquely incident pump *** approach is easy to implement and offers possibilities to overcome compatibility issues between the optical structure design and low energy consumption for ultrafast THz optospintronics and other similar devices.
We introduce the first on-chip, microelectromechanical system for the in situ tuning of twisted 2D materials, enabling tunable interfacial properties, synthetic topological singularities, and adjustable-polarization l...
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Waste heat in fiber lasers poses major challenges in frequency, power, and pointing stabilities, as well as beam quality and power scaling. Using anti-Stokes-fluorescence cooling, we developed a radiation-balanced Yb-...
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Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usag...
ISBN:
(纸本)9798331314385
Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms.
Volumetric muscle loss (VML), a severe muscle tissue loss from trauma or surgery, results in scarring, limited regeneration, and significant fibrosis, leading to lasting reductions in muscle mass and function. A promi...
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The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative fea...
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The article describes a new method for malware classification,based on a Machine Learning(ML)model architecture specifically designed for malware detection,enabling real-time and accurate malware *** an innovative feature dimensionality reduction technique called the Interpolation-based Feature Dimensionality Reduction Technique(IFDRT),the authors have significantly reduced the feature space while retaining critical information necessary for malware *** technique optimizes the model’s performance and reduces computational *** proposed method is demonstrated by applying it to the BODMAS malware dataset,which contains 57,293 malware samples and 77,142 benign samples,each with a 2381-feature *** the IFDRT method,the dataset is transformed,reducing the number of features while maintaining essential data for accurate *** evaluation results show outstanding performance,with an F1 score of 0.984 and a high accuracy of 98.5%using only two reduced *** demonstrates the method’s ability to classify malware samples accurately while minimizing processing *** method allows for improving computational efficiency by reducing the feature space,which decreases the memory and time requirements for training and *** new method’s effectiveness is confirmed by the calculations,which indicate significant improvements in malware classification accuracy and *** research results enhance existing malware detection techniques and can be applied in various cybersecurity applications,including real-timemalware detection on resource-constrained *** and scientific contribution lie in the development of the IFDRT method,which provides a robust and efficient solution for feature reduction in ML-based malware classification,paving the way for more effective and scalable cybersecurity measures.
This article reports the fabrication, characterization, implementation, and microsystem integration of micromachined flexible silicon solar cells to supply electric power to smart contact lenses. Single silicon solar ...
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A vast number of technologies based on Artificial Intelligence (AI) have proliferated into various application domains. As part of its objectives to develop agents which can behave and think like humans, some branches...
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Crop Yield Analysis and Prediction is a fast-expanding discipline that is critical for optimizing agricultural methods. A lack of trustworthy data is one of the challenges in estimating crop yields. We develop predict...
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Two identical UVC light-emitting diode AlGaN epitaxy were grown on high-quality AlN buffer layers with different strain states. We characterized the AlGaN epi by high-resolution scan transmission electron microscopy (...
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