- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Hierarchical Bayesian Inverse Lighting of Portraits with a Virtual Light Stage
摘要： From a single RGB image of an unknown face, taken under unknown conditions, we estimate a physically plausible lighting model. First, the 3D geometry and texture of the face are estimated by fitting a 3D Morphable Model to the 2D input. With this estimated 3D model and a Virtual Light Stage (VLS), we generate a gallery of images of the face with all the same conditions, but different lighting. We consider non-lambertian reflectance and non-convex geometry to handle more realistic illumination effects in complex lighting conditions. Our hierarchical Bayesian approach automatically suppresses inconsistencies between the model and the input. It estimates the RGB values for the light sources of a VLS to reconstruct the input face with the estimated 3D face model. We discuss the relevance of the hierarchical approach to this minimally constrained inverse rendering problem and show how the hyperparameters can be controlled to improve the results of the algorithm for complex effects, such as cast shadows. Our algorithm is a contribution to single image face modeling and analysis, provides information about the imaging condition and facilitates realistic reconstruction of the input image, relighting, lighting transfer and lighting design.
关键词： 3D Morphable Model,Hyperparameters,Generative Model,Hierarchical Bayesian Optimization,Inverse Lighting,Virtual Light Stage,Single Face Image
更新于2025-09-23 15:22:29
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Bayesian Optimization of Terahertz Quantum Cascade Lasers
摘要： We use Bayesian optimization algorithms in combination with a nonequilibrium Green’s function transport model to increase the maximum operating temperature of terahertz quantum cascade lasers. This procedure lead to the recent temperature record of 210 K in terahertz quantum cascade lasers, and here we provide even-further-improved structures. The Bayesian optimization algorithm, which takes into account all the available history of the optimization, converges much faster and more securely than the commonly used genetic algorithm. Designs based on two and three wells per period are considered, and using the large amount of data generated, we systematically evaluate the studied schemes in terms of optimal extraction energy and relevance of electron-electron correlations. This analysis shows that the two-well scheme is superior for reaching high operating temperatures, while the three-well scheme is more robust to variations in layer thicknesses. Furthermore, we study the sensitivity to ?ux-rate ?uctuations during growth and simulation-model inaccuracies, showing the period thickness needs to be controlled to within a few percent, which is challenging but achievable with present-day molecular-beam epitaxy. These limits to the growth accuracy can be a guiding principle for experimentalists, along with the suggestion to fabricate devices across the wafer radius so as to ?nd the optimal period thickness.
关键词： Bayesian optimization,electron-electron correlations,nonequilibrium Green’s function,terahertz quantum cascade lasers,molecular-beam epitaxy
更新于2025-09-23 15:19:57
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Bayesian Optimization of a Free-Electron Laser
摘要： The Linac coherent light source x-ray free-electron laser is a complex scientific apparatus which changes configurations multiple times per day, necessitating fast tuning strategies to reduce setup time for successive experiments. To this end, we employ a Bayesian approach to maximizing x-ray laser pulse energy by controlling groups of quadrupole magnets. A Gaussian process model provides probabilistic predictions for the machine response with respect to control parameters, enabling a balance of exploration and exploitation in the search for the global optimum. We show that the model parameters can be learned from archived scans, and correlations between devices can be extracted from the beam transport. The result is a sample-efficient optimization routine, combining both historical data and knowledge of accelerator physics to significantly outperform existing optimizers.
关键词： Bayesian optimization,accelerator physics,free-electron laser,Gaussian process
更新于2025-09-23 15:19:57
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Machine Learning Accelerates Discovery of Optimal Colloidal Quantum Dot Synthesis
摘要： Colloidal quantum dots (CQDs) allow broad tuning of the bandgap across the visible and near-infrared spectral regions. Recent advances in applying CQDs in light sensing, photovoltaics, and light emission have heightened interest in achieving further synthetic improvements. In particular, improving monodispersity remains a key priority in order to improve solar cells’ open-circuit voltage, decrease lasing thresholds, and improve photodetectors’ noise-equivalent power. Here we utilize machine-learning-in-the-loop to learn from available experimental data, propose experimental parameters to try, and, ultimately, point to regions of synthetic parameter space that will enable record-monodispersity PbS quantum dots. The resultant studies reveal that adding a growth-slowing precursor (oleylamine) allows nucleation to prevail over growth, a strategy that enables record-large-bandgap (611 nm exciton) PbS nanoparticles with a well-defined excitonic absorption peak (half width at half max (HWHM) of 145 meV). At longer wavelengths, we also achieve improved monodispersity, with HWHM of 55 meV at 950 nm and 24 meV at 1500 nm, compared to the best published to date values of 75 meV and 26 meV, respectively.
关键词： machine learning,PbS,Bayesian optimization,synthesis,nanocrystals,colloidal quantum dots
更新于2025-09-12 10:27:22
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LSTM-Attention-Embedding Model-Based Day-Ahead Prediction of Photovoltaic Power Output Using Bayesian Optimization
摘要： Photovoltaic (PV) output is susceptible to meteorological factors, resulting in intermittency and randomness of power generation. Accurate prediction of PV power output can not only reduce the impact of PV power generation on the grid but also provide a reference for grid dispatching. Therefore, this paper proposes an LSTM-attention-embedding model based on Bayesian optimization to predict the day-ahead PV power output. The statistical features at multiple time scales, combined features, time features and wind speed categorical features are explored for PV related meteorological factors. A deep learning model is constructed based on an LSTM block and an embedding block with the connection of a merge layer. The LSTM block is used to memorize and attend the historical information, and the embedding block is used to encode the categorical features. Then, an output block is used to output the prediction results, and a residual connection is also included in the model to mitigate the gradient transfer. Bayesian optimization is used to select the optimal combined features. The effectiveness of the proposed model is verified on two actual PV power plants in one area of China. The comparative experimental results show that the performance of the proposed model has been significantly improved compared to LSTM neural networks, BPNN, SVR model and persistence model.
关键词： residual connection,LSTM-attention-embedding model,Bayesian optimization,deep learning,features extraction
更新于2025-09-11 14:15:04
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[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Human Gait Recognition with Micro-Doppler Radar and Deep Autoencoder
摘要： The micro-Doppler signals from moving objects contain useful information about their motions. This paper introduces a novel approach for human gait recognition based on backscattered signals from a micro-Doppler radar. Three different signal techniques are utilized for the extraction of micro-Doppler features via time-frequency and time-scale representations. To classify the human motions into various types, this paper presents a deep autoencoder with the use of local patches extracted along the spectrogram and scalogram. The network configuration and the learning parameters of the deep autoencoder, which are considered as hyperparameters, are optimized by a Bayesian optimization algorithm. Experimental results produced by the proposed technique on real radar data show a significant improvement compared to several existing approaches.
关键词： Short-time Fourier Transform,micro-Doppler radar,deep autoencoder,S-method,wavelet transform,Bayesian optimization
更新于2025-09-09 09:28:46