Identification of Free and Bound Exciton States and Their Phase-Dependent Trapping Behavior in Lead Halide Perovskites

In this work we probe the sub-gap energy states within polycrystalline and single crystal lead halide perovskites to better understand their intrinsic photophysics behaviors. Through combined temperature and intensity-dependent optical measurements, we reveal the existence of both free and bound exciton contributions within the sub-gap energy state manifold. The trapping and recombination dynamics of these excitons is shown to be strongly dependent on the structural phase of the perovskite. The orthorhombic phase exhibits ultrafast exciton trapping and distinct trap emission, while the tetragonal phase gives low monomolecular recombination velocity and capture cross-sections (~10-18 cm2). Within the multiphonon transition scenario, this suppression in charge trapping is caused by the increase in the charge capture activation energy due to the reduction in electron-lattice interactions, which can be the origin for the unexpected long carrier lifetime in these material systems.Comment: 5 figure

Hybrid edge-cloud collaborator resource scheduling approach based on deep reinforcement learning and multi-objective optimization

Collaborative resource scheduling between edge ter- minals and cloud centers is regarded as a promising means of effectively completing computing tasks and enhancing quality of service. In this paper, to further improve the achievable perfor- mance, the edge cloud resource scheduling (ECRS) problem is transformed into a multi-objective Markov decision process based on task dependency and features extraction. A multi-objective ECRS model is proposed by considering the task completion time, cost, energy consumption and system reliability as the four objectives. Furthermore, a hybrid approach based on deep reinforcement learning (DRL) and multi-objective optimization are employed in our work. Specifically, DRL preprocesses the workflow, and a multi-objective optimization method strives to find the Pareto-optimal workflow scheduling decision. Various experiments are performed on three real data sets with different numbers of tasks. The results obtained demonstrate that the proposed hybrid DRL and multi-objective optimization design outperforms existing design approaches

Non-line-of-sight imaging over 1.43 km

Non-line-of-sight (NLOS) imaging has the ability to reconstruct hidden objects from indirect light paths that scatter multiple times in the surrounding environment, which is of considerable interest in a wide range of applications. Whereas conventional imaging involves direct line-of-sight light transport to recover the visible objects, NLOS imaging aims to reconstruct the hidden objects from the indirect light paths that scatter multiple times, typically using the information encoded in the time-of-flight of scattered photons. Despite recent advances, NLOS imaging has remained at short-range realizations, limited by the heavy loss and the spatial mixing due to the multiple diffuse reflections. Here, both experimental and conceptual innovations yield hardware and software solutions to increase the standoff distance of NLOS imaging from meter to kilometer range, which is about three orders of magnitude longer than previous experiments. In hardware, we develop a high-efficiency, low-noise NLOS imaging system at near-infrared wavelength based on a dual-telescope confocal optical design. In software, we adopt a convex optimizer, equipped with a tailored spatial-temporal kernel expressed using three-dimensional matrix, to mitigate the effect of the spatial-temporal broadening over long standoffs. Together, these enable our demonstration of NLOS imaging and real-time tracking of hidden objects over a distance of 1.43 km. The results will open venues for the development of NLOS imaging techniques and relevant applications to real-world conditions.https://www.pnas.org/content/pnas/118/10/e2024468118.full.pdfPublished versio

Motion Layer Based Object Removal In Videos

This paper proposes a novel method to generate plausible video sequences after removing relatively large objects from the original videos. In order to maintain temporal coherence among the frames, a motion layer segmentation method is applied. Then, a set of synthesized layers are generated by applying motion compensation and region completion algorithm. Finally, a new video, in which the selected object is removed, is plausibly rendered given the synthesized layers and the motion parameters. A number of example videos are shown in the results to demonstrate the effectiveness of our method

Adaptive Region-Based Video Registration

Video registration without meta data (camera location, viewing angles, and reference DEMs) is still a challenging problem. With the aim of handling this kind of problem, this paper presents an adaptive region expansion approach to propagate the alignment process from high confidence areas (reliable salient features) to low confidence areas and to simultaneously remove outlier regions. Hence, we re-cast the image registration problem as a partitioning problem such that we determine the optimal supporting regions and their corresponding motion parameters for the registration. First, we determine sparse robust correspondences between mission and reference images by using our wide baseline algorithm. Next, starting from the seed regions, the aligned areas are expanded to the whole overlapping areas using the graph cut algorithm, which is controlled by the level set representation of the previous region shape. Consequently, a robust video registration is achieved if the scene can be represented by one homography. Furthermore, we extend this approach to multi-homography video registration problem for 3D scenes, which cannot be directly solved by the current alignment methods. Using our motion layer extraction algorithm, the mission video first is segmented into several layers, then each layer is respectively aligned to the reference image by employing the region expansion algorithm. Several examples are demonstrated in the experiments to show that our approach is effective and robust

Bird Species Identification Using Spectrogram Based on Multi-Channel Fusion of DCNNs

Deep convolutional neural networks (DCNNs) have achieved breakthrough performance on bird species identification using a spectrogram of bird vocalization. Aiming at the imbalance of the bird vocalization dataset, a single feature identification model (SFIM) with residual blocks and modified, weighted, cross-entropy function was proposed. To further improve the identification accuracy, two multi-channel fusion methods were built with three SFIMs. One of these fused the outputs of the feature extraction parts of three SFIMs (feature fusion mode), the other fused the outputs of the classifiers of three SFIMs (result fusion mode). The SFIMs were trained with three different kinds of spectrograms, which were calculated through short-time Fourier transform, mel-frequency cepstrum transform and chirplet transform, respectively. To overcome the shortage of the huge number of trainable model parameters, transfer learning was used in the multi-channel models. Using our own vocalization dataset as a sample set, it is found that the result fusion mode model outperforms the other proposed models, the best mean average precision (MAP) reaches 0.914. Choosing three durations of spectrograms, 100 ms, 300 ms and 500 ms for comparison, the results reveal that the 300 ms duration is the best for our own dataset. The duration is suggested to be determined based on the duration distribution of bird syllables. As for the performance with the training dataset of BirdCLEF2019, the highest classification mean average precision (cmAP) reached 0.135, which means the proposed model has certain generalization ability

Cross-corpus open set bird species recognition by vocalization

In the wild, bird vocalizations of the same species across different populations may be different (e.g., so called dialect). Besides, the number of species is unknown in advance. These two facts make the task of bird species recognition based on vocalization a challenging one. This study treats this task as an open set recognition (OSR) cross-corpus scenario. We propose Instance Frequency Normalization (IFN) to remove instance-specific differences across different corpora. Furthermore, an x-vector feature extraction model integrated Time Delay Neural Network (TDNN) and Long Short-Term Memory (LSTM) are designed to better capture sequence information. Finally, the threshold-based Probabilistic Linear Discriminant Analysis (PLDA) is introduced to discriminate the extracted x-vector features to discover the unknown classes. When compared to the best results of the existing method, the average ACCs for the single-corpus and cross-corpus experiments are improved, implying that our method can provide a potential solution and improve performance for cross-corpus bird species recognition based on vocalization in open set condition

Retrieval of Live Fuel Moisture Content Based on Multi-Source Remote Sensing Data and Ensemble Deep Learning Model

Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with R2  = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method

Depth-Based Dynamic Sampling of Neural Radiation Fields

Although the NeRF approach can achieve outstanding view synthesis, it is limited in practical use because it requires many views (hundreds) for training. With only a few input views, the Depth-DYN NeRF that we propose can accurately match the shape. First, we adopted the ip_basic depth-completion method, which can recover the complete depth map from sparse radar depth data. Then, we further designed the Depth-DYN MLP network architecture, which uses a dense depth prior to constraining the NeRF optimization and combines the depthloss to supervise the Depth-DYN MLP network. When compared to the color-only supervised-based NeRF, the Depth-DYN MLP network can better recover the geometric structure of the model and reduce the appearance of shadows. To further ensure that the depth depicted along the rays intersecting these 3D points is close to the measured depth, we dynamically modified the sample space based on the depth of each pixel point. Depth-DYN NeRF considerably outperforms depth NeRF and other sparse view versions when there are a few input views. Using only 10–20 photos to render high-quality images on the new view, our strategy was tested and confirmed on a variety of benchmark datasets. Compared with NeRF, we obtained better image quality (NeRF average at 22.47 dB vs. our 27.296 dB)