Application of Multi-Frequency Synthetic Aperture Radar (SAR) in Crop Classification

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Microstructure-Empowered Stock Factor Extraction and Utilization

High-frequency quantitative investment is a crucial aspect of stock investment. Notably, order flow data plays a critical role as it provides the most detailed level of information among high-frequency trading data, including comprehensive data from the order book and transaction records at the tick level. The order flow data is extremely valuable for market analysis as it equips traders with essential insights for making informed decisions. However, extracting and effectively utilizing order flow data present challenges due to the large volume of data involved and the limitations of traditional factor mining techniques, which are primarily designed for coarser-level stock data. To address these challenges, we propose a novel framework that aims to effectively extract essential factors from order flow data for diverse downstream tasks across different granularities and scenarios. Our method consists of a Context Encoder and an Factor Extractor. The Context Encoder learns an embedding for the current order flow data segment's context by considering both the expected and actual market state. In addition, the Factor Extractor uses unsupervised learning methods to select such important signals that are most distinct from the majority within the given context. The extracted factors are then utilized for downstream tasks. In empirical studies, our proposed framework efficiently handles an entire year of stock order flow data across diverse scenarios, offering a broader range of applications compared to existing tick-level approaches that are limited to only a few days of stock data. We demonstrate that our method extracts superior factors from order flow data, enabling significant improvement for stock trend prediction and order execution tasks at the second and minute level

Comparison between Dense L-Band and C-Band Synthetic Aperture Radar (SAR) Time Series for Crop Area Mapping over a NISAR Calibration-Validation Site

Crop area mapping is important for tracking agricultural production and supporting food security. Spaceborne approaches using synthetic aperture radar (SAR) now allow for mapping crop area at moderate spatial and temporal resolutions. Multi-frequency SAR data is highly useful for crop monitoring because backscatter response from vegetation canopies is wavelength dependent. This study evaluates the utility of C-band Sentinel-1B (Sentinel-1) and L-band ALOS-2 (PALSAR) data, collected during the 2019 growing season, for generating accurate active crop extent (crop vs. non-crop) classifications over an agricultural region in western Canada. Evaluations were performed against the Agriculture and Agri-Food Canada satellite-based Annual Cropland Inventory (ACI), an open data product that maps land cover across the extent of Canada’s agricultural land. Classifications were performed using the temporal coefficient of variation (CV) approach, where an optimal crop/non-crop delineating CV threshold (CVthr) is selected according to Youden’s J-statistic. Results show that crop area mapping agreed better with the ACI when using Sentinel-1 data (83.5%) compared to PALSAR (73.2%). Analysis of performance by crop reveals that PALSAR’s poorer performance can be attributed to soybean, urban, grassland, and pasture ACI classes. This study also compared CV values to in situ wet biomass data for canola and soybeans, showing that crops with lower biomass (soybean) had correspondingly lower CV value

An AT-hook gene is required for palea formation and floral organ number control in rice

AbstractGrasses have highly specialized flowers and their outer floral organ identity remains unclear. In this study, we identified and characterized rice mutants that specifically disrupted the development of palea, one of the outer whorl floral organs. The depressed palea1 (dp1) mutants show a primary defect in the main structure of palea, implying that palea is a fusion between the main structure and marginal tissues on both sides. The sterile lemma at the palea side is occasionally elongated in dp1 mutants. In addition, we found a floral organ number increase in dp1 mutants at low penetration. Both the sterile lemma elongation and the floral organ number increase phenotype are enhanced by the mutation of an independent gene SMALL DEGENERATIVE PALEA1 (SDP1), whose single mutation causes reduced palea size. E function and presumable A function floral homeotic genes were found suppressed in the dp1–2 mutant. We identified the DP1 gene by map-based cloning and found it encodes a nuclear-localized AT-hook DNA binding protein, suggesting a grass-specific role of chromatin architecture modification in flower development. The DP1 enhancer SDP1 was also positional cloned, and was found identical to the recently reported RETARDED PALEA1 (REP1) gene encoding a TCP family transcription factor. We further found that SDP1/REP1 is downstreamly regulated by DP1

Mortality Prediction with Adaptive Feature Importance Recalibration for Peritoneal Dialysis Patients

The study aims to develop AICare, an interpretable mortality prediction model, using Electronic Medical Records (EMR) from follow-up visits for End-Stage Renal Disease (ESRD) patients. AICare includes a multi-channel feature extraction module and an adaptive feature importance recalibration module. It integrates dynamic records and static features to perform a personalized health context representation learning. The dataset encompasses 13,091 visits and demographic data of 656 peritoneal dialysis (PD) patients spanning 12 years. An additional public dataset of 4,789 visits from 1,363 hemodialysis (HD) patients is also considered. AI Care outperforms traditional deep learning models in mortality prediction while retaining interpretability. It uncovers mortality-feature relationships, variations in feature importance, and provides reference values. An AI-Doctor interaction system is developed for visualizing patients’ health trajectories and risk indicators

Numerical simulation for generation of stable single mode-locked pulses from QML laser with EO modulator and V3+:YAG

The instability of mode-locked pulses underneath Q-switched envelop limits the applications of Q-switched and mode-locked (QML) lasers. In this paper we present a developed theoretical model for QML laser at 1.3 µm. By adjusting the characteristics of the electro-optic modulator, V3+:YAG crystal, and laser resonator, stable single mode-locked pulse at 1.3 µm can be obtained at the pump power of 9 W. So far as we know, it is the first time to obtain single mode-locked pulses in QML laser at 1.3 µm by numerical simulation. Keywords: Single mode-locked pulse, Electro-optic modulator, Rate equations, Q-switched and mode-locke

Multi-Temporal Polarimetric RADARSAT-2 for Land Cover Monitoring in Northeastern Ontario, Canada

For successful applications of microwave remote sensing endeavors it is essential to understand how surface targets respond to changing synthetic aperture radar (SAR) parameters. The purpose of the study is to examine how two particular parameters, acquisition time and incidence angle, influences the response from various land use/land cover types (forests, urban infrastructure, surface water and marsh wetland targets) using nine RADARSAT-2 C-band fine-beam (FQ7 and FQ21) fully polarimetric SAR data acquired during the 2011 growing season over northern Ontario, Canada. The results indicate that backscatter from steep incidence angle acquisitions was typically higher than shallow angles. Wetlands showed an increase in HH and HV intensity due to the growth of emergent vegetation over the course of the summer. The forest and urban targets displayed little variation in backscatter over time. The surface water target showed the greatest difference with respect to incidence angle, but was also determined to be the most affected by wind conditions. Analysis of the co-polarized phase difference revealed the urban target as greatly influenced by the incidence angle. The observed phase differences of the wetland target for all acquisitions also suggested evidence of double-bounce interactions, while the forest and surface water targets showed little to no phase difference. In addition, Cloude-Pottier and Freeman-Durden decompositions, when analyzed in conjunction with polarimetric response plots, provided supporting information to confidently identify the various targets and their scattering mechanisms