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In this article, we present an experimental study supported by numerical modeling showing the possibility of exciting Symmetry-Protected Bound states In the Continuum (SP-BICs) in a 1D silicon grating fabricated on a lithium niobate substrate in both transverse electric and transverse magnetic polarization states of the incident illumination. This leads to different resonances in the transmission spectra with large quality factors up to $10^6$ and a significant electric/magnetic field enhancement up to $10^5$ opening the way to the exploitation of this structure for different sensing applications (biological, electromagnetic, thermal...) but also to nonlinear applications such as the generation of second harmonic, in addition to electro- and acousto-optic modulation.

Machine learning (ML) can process large sets of data generated from complex systems, which is ideal for classification tasks as often appeared in critical phenomena. Meanwhile ML techniques have been found effective in detecting critical points, or in a broader sense phase separation, and extracting critical exponents. But there are still many unsolved issues with the ML, one of which is the meaning of hidden variables of unsupervised learning. Some say that the hidden variables and the principal component may contain basic information regarding the order parameter of the system of interest, which sounds plausible but lacks evidence. This study aims at searching for evidence supporting the conjecture that the autoencoder's (AE) single latent variable and PCA's first principal component can only serve as signals related to particle density, which happens to be the order parameter of the non-equilibrium DP model. Indeed, in some phase transition (PT) models the order parameter is the particle density, whereas in some PT models it is not. Having conducted a certain degree of random shuffling on the DP configurations, which are then fed to the neural networks as input, we find that AE's single latent variable and PCA's first principal component can indeed represent particle density. It is found that shuffling does affect the size of maximum cluster in the system, which suggests that the second principal component of the PCA is related to the maximal cluster. This has been supported by changes in the correlation length of the transition system with variations in the shuffle ratio., Comment: 18 pages, 15 figures

Few-shot object detection, learning to adapt to the novel classes with a few labeled data, is an imperative and long-lasting problem due to the inherent long-tail distribution of real-world data and the urgent demands to cut costs of data collection and annotation. Recently, some studies have explored how to use implicit cues in extra datasets without target-domain supervision to help few-shot detectors refine robust task notions. This survey provides a comprehensive overview from current classic and latest achievements for few-shot object detection to future research expectations from manifold perspectives. In particular, we first propose a data-based taxonomy of the training data and the form of corresponding supervision which are accessed during the training stage. Following this taxonomy, we present a significant review of the formal definition, main challenges, benchmark datasets, evaluation metrics, and learning strategies. In addition, we present a detailed investigation of how to interplay the object detection methods to develop this issue systematically. Finally, we conclude with the current status of few-shot object detection, along with potential research directions for this field.

Crop phenology regulates seasonal agroecosystem carbon, water, and energy exchanges, and is a key component in empirical and process-based crop models for simulating biogeochemical cycles of farmlands, assessing gross and net primary production, and forecasting the crop yield. The advances in phenology matching models provide a feasible means to monitor crop phenological progress using remote sensing observations, with a priori information of reference shapes and reference phenological transition dates. Yet the underlying geometrical scaling assumption of models, together with the challenge in defining phenological references, hinders the applicability of phenology matching in crop phenological studies. The objective of this study is to develop a novel hybrid phenology matching model to robustly retrieve a diverse spectrum of crop phenological stages using satellite time series. The devised hybrid model leverages the complementary strengths of phenometric extraction methods and phenology matching models. It relaxes the geometrical scaling assumption and can characterize key phenological stages of crop cycles, ranging from farming practice-relevant stages (e.g., planted and harvested) to crop development stages (e.g., emerged and mature). To systematically evaluate the influence of phenological references on phenology matching, four representative phenological reference scenarios under varying levels of phenological calibrations in terms of time and space are further designed with publicly accessible phenological information. The results indicate that the hybrid phenology matching model can achieve high accuracies for estimating corn and soybean phenological growth stages in Illinois, particularly with the year- and region-adjusted phenological reference (R-squared higher than 0.9 and RMSE less than 5 days for most phenological stages). The inter-annual and regional phenological patterns characterized by the hybrid model correspond well with those in the crop progress reports (CPRs) from the USDA National Agricultural Statistics Service (NASS). Compared to the benchmark phenology matching model, the hybrid model is more robust to the decreasing levels of phenological reference calibrations, and is particularly advantageous in retrieving crop early phenological stages (e.g., planted and emerged stages) when the phenological reference information is limited. This innovative hybrid phenology matching model, together with CPR-enabled phenological reference calibrations, holds 3 considerable promise in revealing spatio-temporal patterns of crop phenology over extended geographical regions.

Seasonal vegetation phenology can significantly alter surface albedo which in turn affects the global energy balance and the albedo warmingcooling feedbacks that impact climate change. To monitor and quantify the surface dynamics of heterogeneous landscapes, high temporal and spatial resolution synthetic time series of albedo and the enhanced vegetation index (EVI) were generated from the 500-meter Moderate Resolution Imaging Spectroradiometer (MODIS) operational Collection V006 daily BRDF (Bidirectional Reflectance Distribution Function) / NBAR (Nadir BRDF-Adjusted Reflectance) / albedo products and 30-meter Landsat 5 albedo and near-nadir reflectance data through the use of the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). The traditional Landsat Albedo (Shuai et al., 2011) makes use of the MODIS BRDFAlbedo products (MCD43) by assigning appropriate BRDFs from coincident MODIS products to each Landsat image to generate a 30-meter Landsat albedo product for that acquisition date. The available cloud free Landsat 5 albedos (due to clouds, generated every 16 days at best) were used in conjunction with the daily MODIS albedos to determine the appropriate 30-meter albedos for the intervening daily time steps in this study. These enhanced daily 30-meter spatial resolution synthetic time series were then used to track albedo and vegetation phenology dynamics over three Ameriflux tower sites (Harvard Forest in 2007, Santa Rita in 2011 and Walker Branch in 2005). These Ameriflux sites were chosen as they are all quite nearby new towers coming on line for the National Ecological Observatory Network (NEON), and thus represent locations which will be served by spatially paired albedo measures in the near future. The availability of data from the NEON towers will greatly expand the sources of tower albedometer data available for evaluation of satellite products. At these three Ameriflux tower sites the synthetic time series of broadband shortwave albedos were evaluated using the tower albedo measurements with a Root Mean Square Error (RMSE) less than 0.013 and a bias within the range of 0.006. These synthetic time series provide much greater spatial detail than the 500 meter gridded MODIS data, especially over more heterogeneous surfaces, which improves the efforts to characterize and monitor the spatial variation across species and communities. The mean of the difference between maximum and minimum synthetic time series of albedo within the MODIS pixels over a subset of satellite data of Harvard Forest (16 kilometers by 14 kilometers) was as high as 0.2 during the snow-covered period and reduced to around 0.1 during the snow-free period. Similarly, we have used STARFM to also couple MODIS Nadir BRDF-Adjusted Reflectances (NBAR) values with Landsat 5 reflectances to generate daily synthetic times series of NBAR and thus Enhanced Vegetation Index (NBAR-EVI) at a 30-meter resolution. While normally STARFM is used with directional reflectances, the use of the view angle corrected daily MODIS NBAR values will provide more consistent time series. These synthetic times series of EVI are shown to capture seasonal vegetation dynamics with finer spatial and temporal details, especially over heterogeneous land surfaces.

Photosynthesis (PSN) is a pigment level process in which antenna pigments (predominately chlorophylls) in chloroplasts absorb photosynthetically active radiation (PAR) for the photochemical process. PAR absorbed by foliar non-photosynthetic components is not used for PSN. The fraction of PAR absorbed (fAPAR) by a canopy/vegetation (i.e., fAPAR(sub canopy)) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images, referred to as MOD15A2 FPAR, has been used to compute absorbed PAR (APAR) for PSN (APARPSN) which is utilized to produce the standard MODIS gross primary production (GPP) product, referred to as MOD17A2 GPP. In this study, the fraction of PAR absorbed by chlorophyll throughout the canopy (fAPAR(sub chl)) was retrieved from MODIS images for three AmeriFlux crop fields in Nebraska. There are few studies in the literature that compare the performance of MOD15A2 FPAR versus fAPAR(sub chl) in GPP estimation. In our study MOD15A2 FPAR and the retrieved fAPAR(sub chl) were compared with field fAPAR(sub canopy) and the fraction of PAR absorbed by green leaves of the vegetation (fAPAR(sub green)). MOD15A2 FPAR overestimated field fAPAR(sub canopy) in spring and in fall, and underestimated field fAPAR(sub canopy) in midsummer whereas fAPAR(sub chl) correctly captured the seasonal phenology. The retrieved fAPAR(sub chl) agreed well with field fAPAR(sub green) at early crop growth stage in June, and was less than field fAPAR(sub green) in late July, August and September. GPP estimates with fAPAR(sub chl) and with MOD15A2 FPAR were compared to tower flux GPP. GPP simulated with fAPAR(sub chl) was corroborated with tower flux GPP. Improvements in crop GPP estimation were achieved by replacing MOD15A2 FPAR with fAPAR(sub chl) which also reduced uncertainties of crop GPP estimates by 1.12-2.37 g C m(sup −2) d(sup −1).