Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic Breast Cancer Survivor Detection from Social Media for Studying Latent Factors Affecting Treatment Success

Breast cancer patients often discontinue their long-term treatments, such as hormone therapy, increasing the risk of cancer recurrence. These discontinuations are often caused by adverse patient-centered outcomes (PCOs) due to hormonal drug side effects or other factors. PCOs are not detectable through laboratory tests and are sparsely documented in electronic health records. Thus, there is a need to explore other sources of information for PCOs associated with breast cancer treatments. Social media is a promising resource, but extracting true PCOs from it first requires the accurate detection of breast cancer patients. We describe a natural language processing (NLP) architecture for automatically detecting breast cancer patients from Twitter based on their self-reports. The architecture employs breast cancer-related keywords to collect streaming data from Twitter, applies NLP patterns to pre-filter noisy posts, and then employs a machine learning classifier trained using manually-annotated data (n=5019) for distinguishing firsthand self-reports of breast cancer from other tweets. A classifier based on bidirectional encoder representations from transformers (BERT) showed human-like performance and achieved F1-score of 0.857 (inter-annotator agreement: 0.845; Cohen's kappa) for the positive class, considerably outperforming the next best classifier--a deep neural network (F1-score: 0.665). Qualitative analyses of posts from automatically-detected users revealed discussions about side effects, non-adherence, and mental health conditions, illustrating the feasibility of our social media-based approach for studying breast cancer-related PCOs from a large population.
Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.
Increasing Summer Rainfall and Asymmetrical Diurnal and Seasonal Warming Enhanced Vegetation Greenness in Temperate Deciduous Forests and Grasslands of Northern China

Temperate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001–2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are 3.97 ± 0.062 × 10−3 and 4.8 ± 0.116 × 10−3 yr−1, respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of 0.72 ± 0.01. While wet-year (0.57 ± 0.01) and nongrowing-season precipitation (0.68 ± 0.01) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of 0.66 ± 0.01, but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of −0.70 ± 0.01. Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition.
Deep similarity metric learning for multiple object tracking. (Apprentissage profond de métrique de similarité pour le suivi d'objets multiples)

Multiple object tracking, i.e. simultaneously tracking multiple objects in the scene, is an important but challenging visual task. Objects should be accurately detected and distinguished from each other to avoid erroneous trajectories. Since remarkable progress has been made in object detection field, “tracking-by-detection” approaches are widely adopted in multiple object tracking research. Objects are detected in advance and tracking reduces to an association problem: linking detections of the same object through frames into trajectories. Most tracking algorithms employ both motion and appearance models for data association. For multiple object tracking problems where exist many objects of the same category, a fine-grained discriminant appearance model is paramount and indispensable. Therefore, we propose an appearance-based re-identification model using deep similarity metric learning to deal with multiple object tracking in mono-camera videos. Two main contributions are reported in this dissertation: First, a deep Siamese network is employed to learn an end-to-end mapping from input images to a discriminant embedding space. Different metric learning configurations using various metrics, loss functions, deep network structures, etc., are investigated, in order to determine the best re-identification model for tracking. In addition, with an intuitive and simple classification design, the proposed model achieves satisfactory re-identification results, which are comparable to state-of-the-art approaches using triplet losses. Our approach is easy and fast to train and the learned embedding can be readily transferred onto the domain of tracking tasks. Second, we integrate our proposed re-identification model in multiple object tracking as appearance guidance for detection association. For each object to be tracked in a video, we establish an identity-related appearance model based on the learned embedding for re-identification. Similarities among detected object instances are exploited for identity classification. The collaboration and interference between appearance and motion models are also investigated. An online appearance-motion model coupling is proposed to further improve the tracking performance. Experiments on Multiple Object Tracking Challenge benchmark prove the effectiveness of our modifications, with a state-of-the-art tracking accuracy. Cette thèse est accessible à l'adresse : http://theses.insa-lyon.fr/publication/2019LYSEI065/these.pdf © [B. Cuan], [2019], INSA Lyon, tous droits réservés Cette thèse est accessible à l'adresse : http://theses.insa-lyon.fr/publication/2019LYSEI065/these.pdf © [B. Cuan], [2019], INSA Lyon, tous droits réservés
Landsat-8 and Sentinel-2 Canopy Water Content Estimation in Croplands through Radiative Transfer Model Inversion

Despite the potential implications of a cropland canopy water content (CCWC) thematic product, no global remotely sensed CCWC product is currently generated. The successful launch of the Landsat-8 Operational Land Imager (OLI) in 2012, Sentinel-2A Multispectral Instrument (MSI) in 2015, followed by Sentinel-2B in 2017, make possible the opportunity for CCWC estimation at a spatial and temporal scale that can meet the demands of potential operational users. In this study, we designed and tested a novel radiative transfer model (RTM) inversion technique to combine multiple sources of a priori data in a look-up table (LUT) for inverting the NASA Harmonized Landsat Sentinel-2 (HLS) product for CCWC estimation. This study directly builds on previous research for testing the constraint of the leaf parameter (Ns) in PROSPECT, by applying those constraints in PRO4SAIL in an agricultural setting where the variability of canopy parameters are relatively minimal. In total, 225 independent leaf measurements were used to train the LUTs, and 102 field data points were collected over the 2015–2017 growing seasons for validating the inversions. The results confirm increasing a priori information and regularization yielded the best performance for CCWC estimation. Despite the relatively low variable canopy conditions, the inclusion of Ns constraints did not improve the LUT inversion. Finally, the inversion of Sentinel-2 data outperformed the inversion of Landsat-8 in the HLS product. The method demonstrated ability for HLS inversion for CCWC estimation, resulting in the first HLS-based CCWC product generated through RTM inversion.
Laboratory and UAV-Based Identification and Classification of Tomato Yellow Leaf Curl, Bacterial Spot, and Target Spot Diseases in Tomato Utilizing Hyperspectral Imaging and Machine Learning

Tomato crops are susceptible to multiple diseases, several of which may be present during the same season. Therefore, rapid disease identification could enhance crop management consequently increasing the yield. In this study, nondestructive methods were developed to detect diseases that affect tomato crops, such as bacterial spot (BS), target spot (TS), and tomato yellow leaf curl (TYLC) for two varieties of tomato (susceptible and tolerant to TYLC only) by using hyperspectral sensing in two conditions: a) laboratory (benchtop scanning), and b) in field using an unmanned aerial vehicle (UAV-based). The stepwise discriminant analysis (STDA) and the radial basis function were applied to classify the infected plants and distinguish them from noninfected or healthy (H) plants. Multiple vegetation indices (VIs) and the M statistic method were utilized to distinguish and classify the diseased plants. In general, the classification results between healthy and diseased plants were highly accurate for all diseases; for instance, when comparing H vs. BS, TS, and TYLC in the asymptomatic stage and laboratory conditions, the classification rates were 94%, 95%, and 100%, respectively. Similarly, in the symptomatic stage, the classification rates between healthy and infected plants were 98% for BS, and 99–100% for TS and TYLC diseases. The classification results in the field conditions also showed high values of 98%, 96%, and 100%, for BS, TS, and TYLC, respectively. The VIs that could best identify these diseases were the renormalized difference vegetation index (RDVI), and the modified triangular vegetation index 1 (MTVI 1) in both laboratory and field. The results were promising and suggest the possibility to identify these diseases using remote sensing.
Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16

The tassel development status and its branch number in maize flowering stage are the key phenotypic traits to determine the growth process, pollen quantity of different maize varieties, and detasseling arrangement for seed maize production fields. Rapid and accurate detection of tassels is of great significance for maize breeding and seed production. However, due to the complex planting environment in the field, such as unsynchronized growth stage and tassels vary in size and shape caused by varieties, the detection of maize tassel remains challenging problem, and the existing methods also cannot distinguish the early tassels. In this study, based on the time series unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we proposed an algorithm for automatic detection of maize tassels which is suitable for complex scenes by using random forest (RF) and VGG16. First, the RF was used to segment UAV images into tassel regions and non-tassel regions, and then extracted the potential tassel region proposals by morphological method; afterwards, false positives were removed through VGG16 network with the ratio of training set to validation set was 7:3. To demonstrate the performance of the proposed method, 50 plots were selected from UAV images randomly. The precision, recall rate and F1-score were 0.904, 0.979 and 0.94 respectively; 50 plots were divided into early, middle and late tasseling stages according to the proportion of tasseling plants and the morphology of tassels. The result of tassels detection was late tasseling stage > middle tasseling stage > early tasseling stage, and the corresponding F1-score were 0.962, 0.914 and 0.863, respectively. It was found that the model error mainly comes from the recognition of leaves vein and reflective leaves as tassels. Finally, to show the morphological characteristics of tassel directly, we proposed an endpoint detection method based on the tassel skeleton, and further extracted the tassel branch number. The method proposed in this paper can well detect tassels of different development stages, and support large scale tassels detection and branch number extraction.
Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review

Recent development of non-destructive optical techniques, such as spectroscopy and machine vision technologies, have laid a good foundation for real-time monitoring and precise management of crop N status. However, their advantages and disadvantages have not been systematically summarized and evaluated. Here, we reviewed the state-of-the-art of non-destructive optical methods for monitoring the N status of crops, and summarized their advantages and disadvantages. We mainly focused on the contribution of spectral and machine vision technology to the accurate diagnosis of crop N status from three aspects: system selection, data processing, and estimation methods. Finally, we discussed the opportunities and challenges of the application of these technologies, followed by recommendations for future work to address the challenges.
Factor Structure of the Mayo Older Americans Normative Sample (MOANS) Core Battery: Replication in a Clinical Sample

Smith et al. (1992) confirmed 5 factors to account for the covariance structure of a «core battery» of the Wechsler Adult Intelligence Scale-Revised, Wechsler Memory Scale-Revised, and Auditory Verbal Learning Test administered to the Mayo Older Americans Normative Studies (MOANS) sample. We attempted to replicate this model in a clinical sample. Data from 417 clinical cases were used in LISREL confirmatory factor analyses. Modifications of the original secondary variable assignments were necessary to obtain a 5-factor model with adequate fit. This 5-factor model was superior to alternative models. The results support the use of a 5-factor model in the development of factor-based summary indexes
Dynamic Remote Sensing Prediction for Wheat Fusarium Head Blight by Combining Host and Habitat Conditions

Remote sensing technology provides a feasible option for early prediction for wheat Fusarium head blight (FHB). This study presents a methodology for the dynamic prediction of this classic meteorological crop disease. Host and habitat conditions were comprehensively considered as inputs of the FHB prediction model, and the advantages, accuracy, and generalization ability of the model were evaluated. Firstly, multi-source satellite images were used to predict growth stages and to obtain remote sensing features, then weather features around the predicted stages were extracted. Then, with changes in the inputting features, the severity of FHB was dynamically predicted on February 18, March 6, April 23, and May 9, 2017. Compared to the results obtained by the Logistic model, the prediction with the Relevance Vector Machine performed better, with the overall accuracy on these four dates as 0.71, 0.78, 0.85, and 0.93, and with the area under the receiver operating characteristic curve as 0.66, 0.67, 0.72, and 0.75. Additionally, compared with the prediction with only one factor, the integration of multiple factors was more accurate. The results showed that when the date of the remote sensing features was closer to the heading or flowering stage, the prediction was more accurate, especially in severe areas. Though the habitat conditions were suitable for FHB, the infection can be inhibited when the host’s growth meets certain requirements.
Perennial Supraglacial Lakes in Northeast Greenland Observed by Polarimetric SAR

Supraglacial liquid water at the margins of ice sheets has an important impact on the surface energy balance and can also influence the ice flow when supraglacial lakes drain to the bed. Optical imagery is able to monitor supraglacial lakes during the summer season. Here we developed an alternative method using polarimetric SAR from Sentinel-1 during 2017–2020 to distinguish between liquid water and other surface types at the margin of the Northeast Greenland Ice Stream. This allows the supraglacial hydrology to be monitored during the winter months too. We found that the majority of supraglacial lakes persist over winter. When comparing our results to optical data, we found significantly more water. Even during summer, many lakes are partly or fully covered by a lid of ice and snow. We used our classification results to automatically map the outlines of supraglacial lakes, create time series of water area for each lake, and hence detect drainage events. We even found several winter time drainages, which might have an important effect on ice flow. Our method has problems during the peak of the melt season, but for the rest of the year it provides crucial information for better understanding the component of supraglacial hydrology in the glaciological system.
A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model.
RGB Image-Derived Indicators for Spatial Assessment of the Impact of Broadleaf Weeds on Wheat Biomass

In precision agriculture, the development of proximal imaging systems embedded in autonomous vehicles allows to explore new weed management strategies for site-specific plant application. Accurate monitoring of weeds while controlling wheat growth requires indirect measurements of leaf area index (LAI) and above-ground dry matter biomass (BM) at early growth stages. This article explores the potential of RGB images to assess crop-weed competition in a wheat (Triticum aestivum L.) crop by generating two new indicators, the weed pressure (WP) and the local wheat biomass production (δBMc). The fractional vegetation cover (FVC) of the crop and the weeds was automatically determined from the images with a SVM-RBF classifier, using bag of visual word vectors as inputs. It is based on a new vegetation index called MetaIndex, defined as a vote of six indices widely used in the literature. Beyond a simple map of weed infestation, the map of WP describes the crop-weed competition. The map of δBMc, meanwhile, evaluates the local wheat above-ground biomass production and informs us about a potential stress. It is generated from the wheat FVC because it is highly correlated with LAI (r2 = 0.99) and BM (r2 = 0.93) obtained by destructive methods. By combining these two indicators, we aim at determining whether the origin of the wheat stress is due to weeds or not. This approach opens up new perspectives for the monitoring of weeds and the monitoring of their competition during crop growth with non-destructive and proximal sensing technologies in the early stages of development.