Miracle-dz/UniFAR-FTUs huggingface dataset:
Endmember Learning with K-Means through SCD Model in Hyperspectral Scene Reconstructions

This paper proposes a simple yet effective method for improving the efficiency of sparse coding dictionary learning (DL) with an implication of enhancing the ultimate usefulness of compressive sensing (CS) technology for practical applications, such as in hyperspectral imaging (HSI) scene reconstruction. CS is the technique which allows sparse signals to be decomposed into a sparse representation “a” of a dictionary Du. The goodness of the learnt dictionary has direct impacts on the quality of the end results, e.g., in the HSI scene reconstructions. This paper proposes the construction of a concise and comprehensive dictionary by using the cluster centres of the input dataset, and then a greedy approach is adopted to learn all elements within this dictionary. The proposed method consists of an unsupervised clustering algorithm (K-Means), and it is then coupled with an advanced sparse coding dictionary (SCD) method such as the basis pursuit algorithm (orthogonal matching pursuit, OMP) for the dictionary learning. The effectiveness of the proposed K-Means Sparse Coding Dictionary (KMSCD) is illustrated through the reconstructions of several publicly available HSI scenes. The results have shown that the proposed KMSCD achieves ~40% greater accuracy, 5 times faster convergence and is twice as robust as that of the classic Spare Coding Dictionary (C-SCD) method that adopts random sampling of data for the dictionary learning. Over the five data sets that have been employed in this study, it is seen that the proposed KMSCD is capable of reconstructing these scenes with mean accuracies of approximately 20–500% better than all competing algorithms adopted in this work. Furthermore, the reconstruction efficiency of trace materials in the scene has been assessed: it is shown that the KMSCD is capable of recovering ~12% better than that of the C-SCD. These results suggest that the proposed DL using a simple clustering method for the construction of the dictionary has been shown to enhance the scene reconstruction substantially. When the proposed KMSCD is incorporated with the Fast non-negative orthogonal matching pursuit (FNNOMP) to constrain the maximum number of materials to coexist in a pixel to four, experiments have shown that it achieves approximately ten times better than that constrained by using the widely employed TMM algorithm. This may suggest that the proposed DL method using KMSCD and together with the FNNOMP will be more suitable to be the material allocation module of HSI scene simulators like the CameoSim package.
RealPoint3D: Generating 3D Point Clouds from a Single Image of Complex Scenarios

Generating 3D point clouds from a single image has attracted full attention from researchers in the field of multimedia, remote sensing and computer vision. With the recent proliferation of deep learning, various deep models have been proposed for the 3D point cloud generation. However, they require objects to be captured with absolutely clean backgrounds and fixed viewpoints, which highly limits their application in the real environment. To guide 3D point cloud generation, we propose a novel network, RealPoint3D, to integrate prior 3D shape knowledge into the network. Taking additional 3D information, RealPoint3D can handle 3D object generation from a single real image captured from any viewpoint and complex background. Specifically, provided a query image, we retrieve the nearest shape model from a pre-prepared 3D model database. Then, the image, together with the retrieved shape model, is fed into RealPoint3D to generate a fine-grained 3D point cloud. We evaluated the proposed RealPoint3D on the ShapeNet dataset and ObjectNet3D dataset for the 3D point cloud generation. Experimental results and comparisons with state-of-the-art methods demonstrate that our framework achieves superior performance. Furthermore, our proposed framework works well for real images in complex backgrounds (the image has the remaining objects in addition to the reconstructed object, and the reconstructed object may be occluded or truncated) with various viewing angles.
The Case for a Single Channel Composite Arctic Sea Surface Temperature Algorithm

Surface temperatures derived from satellite thermal infrared (TIR) data are critical inputs for assessing climate change in polar environments. Sea and ice surface temperature (SST, IST) are commonly determined with split window algorithms that use the brightness temperature from the 11 μm channel (BT11) as the main estimator and the difference between BT11 and the 12 μm channel (BTD11–12) to correct for atmospheric water vapor absorption. An issue with this paradigm in the Arctic maritime environment is the occurrence of high BTD11–12 that is not indicative of atmospheric absorption of BT11 energy. The Composite Arctic Sea Surface Temperature Algorithm (CASSTA) considers three regimes based on BT11 pixel value: seawater, ice, and marginal ice zones. A single channel (BT11) estimator is used for SST and a split window algorithm for IST. Marginal ice zone temperature is determined with a weighted average between the SST and IST. This study replaces the CASSTA split window IST with a single channel (BT11) estimator to reduce errors associated with BTD11–12 in the split window algorithm. The single channel IST returned improved results in the CASSTA dataset with a mean average error for ice and marginal ice zones of 0.142 K and 0.128 K, respectively.
Estimating Pasture Biomass and Canopy Height in Brazilian Savanna Using UAV Photogrammetry

The Brazilian territory contains approximately 160 million hectares of pastures, and it is necessary to develop techniques to automate their management and increase their production. This technical note has two objectives: First, to estimate the canopy height using unmanned aerial vehicle (UAV) photogrammetry; second, to propose an equation for the estimation of biomass of Brazilian savanna (Cerrado) pastures based on UAV canopy height. Four experimental units of Panicum maximum cv. BRS Tamani were evaluated. Herbage mass sampling, height measurements, and UAV image collection were simultaneously performed. The UAVs were flown at a height of 50 m, and images were generated with a mean ground sample distance (GSD) of approximately 1.55 cm. The forage canopy height estimated by UAVs was calculated as the difference between the digital surface model (DSM) and the digital terrain model (DTM). The R2 between ruler height and UAV height was 0.80; between biomass (kg ha−1 GB—green biomass) and ruler height, 0.81; and between biomass (kg ha−1 GB) and UAV height, 0.74. UAV photogrammetry proved to be a potential technique to estimate height and biomass in Brazilian Panicum maximum cv. BRS Tamani pastures located in the endangered Brazilian savanna (Cerrado) biome.
Relationships between serum and meconium biomarkers of prenatal tobacco smoke exposure and their association with infant and early childhood growth

JOE BRAUN: Relationship between Serum and Meconium Biomarkers of Prenatal Tobacco Smoke Exposure and their Association with Infant and Child Growth (Under the direction of Julie Daniels) Prenatal active and secondhand tobacco smoke exposures remain a prevalent and preventable risk factor for adverse infant and childhood health outcomes. I used a prospective birth cohort of 389 mothers and their infants who were followed from early pregnancy to three years of age to address two specific aims. First, I validated the utility of meconium as a biological matrix to quantify prenatal tobacco smoke exposure. Second, I examined the association between prenatal tobacco smoke exposure and early childhood body mass index (BMI). I validated the utility of meconium tobacco smoke metabolites as biomarkers of prenatal tobacco smoke exposure against self-report and serum cotinine biomarkers of tobacco smoke exposure. I also estimated and compared associations between meconium and serum metabolite concentrations and infant birth weight. Nicotine, cotinine, and trans-3’hydroxycotinine were detected in the majority of meconium samples (57-80%). Meconium tobacco smoke metabolite concentrations were positively associated with self-report and serum cotinine biomarkers of tobacco smoke exposure. The association between meconium metabolite concentrations and infant birth weight was similar to serum cotinine associations. Meconium is a promising biological matrix to quantify prenatal environmental toxicant exposure; however, meconium tobacco smoke metabolite concentrations did not provide additional information that could be obtained from a single serum cotinine measurement.
Climatic Drivers of Greening Trends in the Alps

<p>Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Spatial patterns and mechanisms of this phenomenon were extensively investigated, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We investigated the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000&#8211;2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. We had two objectives&#160;:</p><p>(i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. </p><p>Sixty-three percent of vegetated areas experienced significant trends during the 2000&#8211;2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. </p><p>Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts ( > 1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps. </p>
Integration of a Landsat Time-Series of NBR and Hydrological Modeling to Assess Pinus Pinaster Aiton. Forest Defoliation in South-Eastern Spain

Climate change is increasing the vulnerability of Mediterranean coniferous plantations. Here, we integrate a Landsat time series with a physically-based distributed hydrological model (Watershed Integrated Management in Mediterranean Environments—WiMMed) to examine spatially-explicit relationships between the mortality processes of Pinus pinaster plantations and the hydrological regime, using different spectral indices of vegetation and machine learning algorithms. The Normalized Burn Ratio (NBR) and Moisture Stress Index (MSI) show the highest correlations with defoliation rates. Random Forest was the most accurate model (R2 = 0.79; RMSE = 0.059), showing a high model performance and prediction. Support vector machines and neural networks also demonstrated a high performance (R2 > 0.7). The main hydrological variables selected by the model to explain defoliation were potential evapotranspiration, winter precipitation and maximum summer temperature (lower Out-of-bag error). These results show the importance of hydrological variables involved in evaporation processes, and on the change in the spatial distribution of seasonal rainfall upon the defoliation processes of P. pinaster. These results underpin the importance of integrating temporal remote sensing data and hydrological models to analyze the drivers of forest defoliation and mortality processes in the Mediterranean climate.
European Cooperative Trial in Operable Breast Cancer (ECTO): Improved freedom from progression (FFP) from adding paclitaxel (T) to doxorubicin (A) followed by cyclophosphamide methotrexate and fluorouracil (CMF)

513 Background: In the mid 1990’s data indicated that paclitaxel had marked antitumor activity in metastatic breast cancer, justifying the attempt at investigating whether the taxane could improve the therapeutic benefit of established regimens in early breast cancer Methods: From 1996 to 2002 a total of 1355 women with operable breast cancer (T>2cm) were randomized to adjuvant A (75 mg/m2 q21d x 4) followed by i.v. CMF (day 1&8 q28d x 4), or adjuvant A (60 mg/m2) and T (200 mg/m2 over 3 hrs q21d x 4) followed by CMF (AT→CMF), or AT→CMF as primary systemic therapy (PST). Results: Main patient characteristics (T-size, ER/PgR status, grade and age) were evenly distributed among arms. After median follow-up of 43 months, freedom from progression (FFP) was significantly better for women receiving adjuvant AT→CMF than A→CMF (HR 0.65, range 0.48–0.90, P=0.01). In a multivariate analysis, treatment inclusive of paclitaxel stood out as significantly associated with FFP (HR 0.66, P=0.012), together with clinical d.
Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach

We explore and compare the capabilities and limitations of different optical sensors (Sentinel-2/ESA, Landsat 7/8/USGS, Ven μ s/CNES-ISA, Pléiades/AirbusD&S and Planet Labs images) for mapping the surface speeds of mountain glaciers on a regional scale. We present here our automated workflow designed to download data from institutional or commercial servers, prepare images, launch the feature tracking algorithm, calibrate glacier surface speeds, and our post-processing treatment to obtain filtered and time-averaged velocity maps. We applied our methodology to three regions: (1) the European alps; (2) the Peruvian Cordillera Blanca; and (3) the Southern Alps of New Zealand for years 2017 and 2018 and quantified ice velocity for every possible repeat cycle from few days up to 400 days. For these regions, we demonstrate the ability of our processing chain to derive precise time-averaged ice flow maps. The statistical analysis of the results provided by each individual repeat cycles shows that velocity mapping from Sentinel-2 is about twice more precise than that from Landsat 7/8. If Sentinel-2 captures more details than Landsat, some of the smallest glaciers (<250 m wide) remain challenging. Given the estimated precision for Sentinel-2, we also conclude that velocity fluctuations of the order of 10 m/yr can only be captured with repeat cycles longer than 60 days. Comparing Sentinel-2 with Pléiades, Planet and Ven μ s imagery, we finally highlight the advantages of high-resolution sensors to map glacier surface speed with finer details in space and time.
Assessment of IMERG Precipitation Estimates over Europe

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014–2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data.
Disentangling Information in Artificial Images of Plant Seedlings Using Semi-Supervised GAN

Lack of annotated data for training of deep learning systems is a challenge for many visual recognition tasks. This is especially true for domain-specific applications, such as plant detection and recognition, where the annotation process can be both time-consuming and error-prone. Generative models can be used to alleviate this issue by producing artificial data that mimic properties of real data. This work presents a semi-supervised generative adversarial network (GAN) model to produce artificial samples of plant seedlings. By applying the semi-supervised approach, we are able to produce visually distinct samples for nine unique plant species using a single GAN model, while still maintaining a relatively high visual variance in the produced samples for each species. Additionally, we are able to control the appearance of the generated samples with respect to rotation and size through a set of latent variables, despite these not being annotated features in the training data. The generated samples resemble the intended species with an average recognition accuracy of ∼64.3%, evaluated using an external state-of-the-art plant seedling classification model. Additionally, we explore the potential of using the GAN model’s discriminator as a quality assessment tool to remove poor representations of plant seedlings from the artificial samples.
Change in the Recent Warming Trend of Sea Surface Temperature in the East Sea (Sea of Japan) over Decades (1982-2018)

Long-term trends of sea surface temperature (SST) of the East Sea (Sea of Japan, EJS) were estimated by using 37-year-long satellite data, for the observation period from 1982 to 2018. Overall, the SST tended to increase with time, for all analyzed regions. However, the warming trend was steeper in the earlier decades since the 1980s and slowed down during the recent two decades. Based on the analysis of the occurrence of events with extreme SST (high in the summertime and low in the wintertime), a shift toward the more frequent occurrence of events with extremely high SST and the less frequent occurrence of events with extremely low SST has been observed. This supports the observations of the consistent warming of the EJS. However, seasonal trends revealed continuous SST warming in the summertime, but frequent extreme SST cooling in the wintertime, in recent decades. The observed reduction in the warming rates occurred more frequently in specific regions of the EJS, where the occurrence frequency of events with extremely low SST was unusually high in the recent decade. The recent tendency toward the SST cooling was distinctively connected with variations in the Arctic Oscillation index. This suggests that changes in the Arctic Ocean environment likely affect the recently observed SST changes in the EJS, as one of the marginal seas in the mid-latitude region far from the polar region.
Phase III trial of linifanib versus sorafenib in patients with advanced hepatocellular carcinoma (HCC).

249 Background: Linifanib (ABT-869; Lin) is a potent and selective inhibitor of the vascular endothelial growth factor (VEGF) and platelet-derived growth factor (PDGF) receptor tyrosine kinase families. In a phase II trial in patients (pts) with advanced HCC, Lin showed clinical activity (objective response rate [ORR] 10.5% in Child-Pugh A [CPA] pts). This open-label, global phase 3 trial evaluated Lin versus sorafenib (Sor) as first-line therapy in pts with advanced CPA HCC (NCT01009593). Methods: Pts were randomized 1:1 to Lin 17.5 mg QD or Sor 400 mg BID and stratified by region (non-Asia/Japan/rest of Asia), ECOG performance status (0/1), vascular invasion or extrahepatic spread (yes/no) and HBV infection (yes/no). The primary efficacy endpoint was overall survival (OS); both non-inferiority (margin 1.0491) and superiority hypotheses were to be tested. Secondary efficacy endpoints included time to progression (TTP) and ORR, using RECIST v1.1. AE severity was graded using NCI-CTCAE v4.0. Results: 1035 .
Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds

Electric power transmission and maintenance is essential for the power industry. This paper proposes a method for the efficient extraction and classification of three-dimensional (3D) targets of electric power transmission facilities based on regularized grid characteristics computed from point cloud data acquired by unmanned aerial vehicles (UAVs). First, a special hashing matrix was constructed to store the point cloud after noise removal by a statistical method, which calculated the local distribution characteristics of the points within each sparse grid. Secondly, power lines were extracted by neighboring grids’ height similarity estimation and linear feature clustering. Thirdly, by analyzing features of the grid in the horizontal and vertical directions, the transmission towers in candidate tower areas were identified. The pylon center was then determined by a vertical slicing analysis. Finally, optimization was carried out, considering the topological relationship between the line segments and pylons to refine the extraction. Experimental results showed that the proposed method was able to efficiently obtain accurate coordinates of pylon and attachments in the massive point data and to produce a reliable segmentation with an overall precision of 97%. The optimized algorithm was capable of eliminating interference from isolated tall trees and communication signal poles. The 3D geo-information of high-voltage (HV) power lines, pylons, conductors thus extracted, and of further reconstructed 3D models can provide valuable foundations for UAV remote-sensing inspection and corridor safety maintenance.
Bi-Directional Reflectance Factor Determination of the Railroad Valley Playa

Vicarious calibration is the determination of an on-orbit sensor’s radiometric response using measurements over test sites such as Railroad Valley (RRV), Nevada. It has the highest accuracy when a remote sensor’s view angle is aligned with that of the surface measurements, namely at a nadir view. For view angles greater than 10, the dominant error is the uncertainty in the off-nadir correction factor. The factor is largest in the back-scatter principal plane and can reach 20%. The Orbiting-Carbon Observatory has access to a number of datasets to determine this deviation. These include measurements from field instruments such as the Portable Apparatus for Rapid Acquisition of Bidirectional Observation of the Land and Atmosphere (PARABOLA), as well as satellite measurements from Multi-angle Imaging SpectroRadiometer (MISR) and MODerate resolution Imaging Spectroradiometer (MODIS). The correction factor derived from PARABOLA is consistent in time and space to within 2% for view angles as large as 30. Field spectrometer data show that the correction term is spectrally invariant. For this reason, a time-invariant model of RRV surface reflectance, along with empirically derived coefficients, is sufficient to use in the calibration of off-nadir sensors, provided there has been no recent rainfall. With this off-nadir correction, calibrations can be expected to have uncertainties within 5%.