Miracle-dz/UniFAR-FTUs huggingface dataset:
Anti-Atherosclerotic Effect of a Polyphenol-Rich Ingredient, Oleactiv®, in a Hypercholesterolemia-Induced Golden Syrian Hamster Model

The development of nutraceutical ingredients has risen as a nutritional solution for health prevention. This study evaluated the effects of Oleactiv®, an ingredient developed for the prevention of atherogenesis, in hypercholesterolemic hamsters. Oleactiv® is a polyphenol-rich ingredient obtained from artichoke, olive and grape extracts as part of fruit and vegetables commonly consumed within the Mediterranean diet. A total of 21 Golden Syrian hamsters were divided into three groups. The standard group (STD) was fed a normolipidemic diet for 12 weeks, while the control group (CTRL) and Oleactiv® goup (OLE) were fed a high-fat diet. After sacrifice, the aortic fatty streak area (AFSA), plasmatic total cholesterol (TC), high-density lipoproteins (HDL-C), non-HDL-C and triglycerides (TG), were assessed. The cholesterol efflux capacity (CEC) of hamster plasma was quantified using a radiolabeled technique in murine macrophages J774. OLE administration induced a significant reduction of AFSA (−69%, p < 0.0001). Hamsters of the OLE group showed a significant decrease of both non-HDL-C (−173 mmol/L, p < 0.05) and TG (−154 mmol/L, p < 0.05). Interestingly, OLE induced a significant increase of total CEC (+17,33%, p < 0,05). Oleactiv® supplementation prevented atheroma development and had positive effects on the lipid profile of hypercholesterolemic hamsters. The increased CEC underlines the anti-atherosclerotic mechanism at the root of the atheroma reduction observed.
Monitoring Topography of Coastal Lagoons Using Satellite Radar Altimetry

Satellite radar altimetry was initially designed to measure the marine geoid. Thanks to the improvement in the orbit determination from the meter to the centimeter level, this technique has been providing accurate measurements of the sea surface topography over the open ocean since the launch of Topex/Poseidon in 1992. In spite of a decrease in the performance over land and coastal areas, it is now commonly used over these surfaces. This study analyzes the ability of satellite radar altimetry to retrieve for the very first time the topography of the intertidal zone in coastal lagoons.
Optimal Sampling of BRDF's of Varying Complexity

The goal of this paper is to develop a sampling grid with a minimum number of measurements to interpolate a reflectance function, $R(\theta, \varphi)$, of a maximum complexity to within a desired error bound. Our approach is: (i) Specify a correlation function over the angles representing the complexity of $R(\theta, \varphi)$, (ii) use this correlation function to perform Gaussian process regression on a given sampling grid to determine the variance of the estimated function, and (iii) find the optimal sampling grid by adjusting the parameters of the experimental design to minimize the computed variance of the regression. The optimal sampling grid or, equivalently, uniform sampling grid is a significant improvement over the commonly used equal-angle sampling grid as being able to reduce number of sampling points by 30% for the same accuracy while reducing data acquisition and processing time.
Highly Photoluminescent and Stable N-Doped Carbon Dots as Nanoprobes for Hg2+ Detection

We developed a microreactor with porous copper fibers for synthesizing nitrogen-doped carbon dots (N-CDs) with a high stability and photoluminescence (PL) quantum yield (QY). By optimizing synthesis conditions, including the reaction temperature, flow rate, ethylenediamine dosage, and porosity of copper fibers, the N-CDs with a high PL QY of 73% were achieved. The PL QY of N-CDs was two times higher with copper fibers than without. The interrelations between the copper fibers with different porosities and the N-CDs were investigated using X-ray photoelectron spectroscopy (XPS) and Fourier Transform infrared spectroscopy (FTIR). The results demonstrate that the elemental contents and surface functional groups of N-CDs are significantly influenced by the porosity of copper fibers. The N-CDs can be used to effectively and selectively detect Hg2+ ions with a good linear response in the 0~50 μM Hg2+ ions concentration range, and the lowest limit of detection (LOD) is 2.54 nM, suggesting that the N-CDs have great potential for applications in the fields of environmental and hazard detection. Further studies reveal that the different d orbital energy levels of Hg2+ compared to those of other metal ions can affect the efficiency of electron transfer and thereby result in their different response in fluorescence quenching towards N-CDs.
MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis

With an aging and growing population, the number of women requiring either screening or symptomatic mammograms is increasing. To reduce the number of mammograms that need to be read by a radiologist while keeping the diagnostic accuracy the same or better than current clinical practice, we develop Man and Machine Mammography Oracle (MAMMO) - a clinical decision support system capable of triaging mammograms into those that can be confidently classified by a machine and those that cannot be, thus requiring the reading of a radiologist. The first component of MAMMO is a novel multi-view convolutional neural network (CNN) with multi-task learning (MTL). MTL enables the CNN to learn the radiological assessments known to be associated with cancer, such as breast density, conspicuity, suspicion, etc., in addition to learning the primary task of cancer diagnosis. We show that MTL has two advantages: 1) learning refined feature representations associated with cancer improves the classification performance of the diagnosis task and 2) issuing radiological assessments provides an additional layer of model interpretability that a radiologist can use to debug and scrutinize the diagnoses provided by the CNN. The second component of MAMMO is a triage network, which takes as input the radiological assessment and diagnostic predictions of the first network's MTL outputs and determines which mammograms can be correctly and confidently diagnosed by the CNN and which mammograms cannot, thus needing to be read by a radiologist. Results obtained on a private dataset of 8,162 patients show that MAMMO reduced the number of radiologist readings by 42.8% while improving the overall diagnostic accuracy in comparison to readings done by radiologists alone. We analyze the triage of patients decided by MAMMO to gain a better understanding of what unique mammogram characteristics require radiologists' expertise.
High Resolution Soil Moisture Product Based on Smap Active-Passive Approach Using Copernicus Sentinel 1 Data

SMAP project released a new enhanced high-resolution (3km) soil moisture active-passive product. This product is obtained by combining the SMAP radiometer data and the Sentinel-IA and -IB Synthetic Aperture Radar (SAR) data. The approach used for this product draws heavily from the heritage SMAP active-passive algorithm. Modifications in the SMAP active-passive algorithm are done to accommodate the Copernicus Program's Sentinel-IA and -IB multi-angular C-band SAR data. Assessment of the SMAP and Sentinel active-passive algorithm has been conducted and results show feasibility of estimating surface soil moisture at high-resolution in regions with low vegetation density $(< 3\ \mathrm{kg}\ \mathrm{m}^{-2})$. The beta version of this product is released to public on Nov 1st, 2017. This high resolution (3 km) soil moisture product is useful for agriculture, flood mapping, watershed/rangeland management, and ecological/hydrological applications.
A new penalisation term for image retrieval in clique neural networks

Neural networks that are able to retrieve store and retrieve information constitue an old but still active area of research. Among the different existing architectures, recurrent networks that combine as-sociative memory with error correcting properties based on cliques have recently shown good performances on storing arbitrary random messages. However, they fail in scaling up to large dimensions data such as images, mostly because the distribution of activated neurons is not uniform in the network. We propose in this paper a new penalization term that alleviates this problem, and shows its efficiency on partially erased images reconstruction problem.
Efficacy of second-look endoscopy in preventing delayed bleeding after endoscopic submucosal dissection of early gastric cancer

The present study aimed to evaluate whether second-look endoscopy (SLE) is able to prevent delayed bleeding after endoscopic submucosal dissection (ESD) of gastric carcinoma and to identify which types of lesion require SLE. ESD of gastric cancer at the early stage was performed on 210 patients between October 2014 and September 2016. Mucosal damage-associated bleeding within 24 h after ESD was considered as delayed bleeding. The association of the characteristics of patients and lesions, as well as surgical factors, with the incidence of bleeding as a measure of outcome was analyzed. A total of 110 patients with melena and/or hematemesis underwent SLE on the second day following gastric ESD. Within the entire cohort (n=210), late delayed bleeding (LDB) was defined as hematemesis or melena occurring following second-look endoscopy. Early delayed bleeding (EDB) was defined as hematemesis or melena occurring from the end of ESD to second-look endoscopy, or as active or possible bleeding at the time of the second-look endoscopy was reported in 17 (8.1%) and 20 patients(9.5%), respectively. The median interval between late delayed bleeding and ESD was one day (range, 1–10 days). The incidence of late delayed bleeding was significantly decreased in the SLE group compared with that in the non-SLE group (4.5 vs. 12%, P=0.028). Multivariate analyses revealed that ulcer, flat gross type, lesion diameter (>2 cm), the resected tumor size of >40 mm and Helicobacter pylori infection were independently associated with late delayed bleeding after ESD, while flat gross type, ulcer, the resected tumor size of >40 mm and artificial ulcer diameter >3 cm were independently associated with early delayed bleeding. Thus, the data of the present study indicates that second-look endoscopy following gastric ESD may be useful in preventing post-ESD delayed bleeding and should be performed on the second day.
Characterization of the First OXA-10 Natural Variant with Increased Carbapenemase Activity

While carbapenem resistance in Gram-negative bacteria is mainly due to the production of efficient carbapenemases, β-lactamases with a narrower spectrum may also contribute to resistance when combined with additional mechanisms. OXA-10-type class D β-lactamases, previously shown to be weak carbapenemases, could represent such a case. ABSTRACT While carbapenem resistance in Gram-negative bacteria is mainly due to the production of efficient carbapenemases, β-lactamases with a narrower spectrum may also contribute to resistance when combined with additional mechanisms. OXA-10-type class D β-lactamases, previously shown to be weak carbapenemases, could represent such a case. In this study, two novel OXA-10 variants were identified as the sole carbapenem-hydrolyzing enzymes in meropenem-resistant enterobacteria isolated from hospital wastewater and found by next-generation sequencing to express additional β-lactam resistance mechanisms. The new variants, OXA-655 and OXA-656, were carried by two related IncQ1 broad-host-range plasmids. Compared to the sequence of OXA-10, they both harbored a Thr26Met substitution, with OXA-655 also bearing a leucine instead of a valine in position 117 of the SAV catalytic motif. Susceptibility profiling of laboratory strains replicating the natural blaOXA plasmids and of recombinant clones expressing OXA-10 and the novel variants in an isogenic background indicated that OXA-655 is a more efficient carbapenemase. The carbapenemase activity of OXA-655 is due to the Val117Leu substitution, as shown by steady-state kinetic experiments, where the kcat of meropenem hydrolysis was increased 4-fold. In contrast, OXA-655 had no activity toward oxyimino-β-lactams, while its catalytic efficiency against oxacillin was significantly reduced. Moreover, the Val117Leu variant was more efficient against temocillin and cefoxitin. Molecular dynamics indicated that Val117Leu affects the position 117-Leu155 interaction, leading to structural shifts in the active site that may alter carbapenem alignment. The evolutionary potential of OXA-10 enzymes toward carbapenem hydrolysis combined with their spread by promiscuous plasmids indicates that they may pose a future clinical threat.
Near Real-Time Modis-Detected Flood Monitoring Coupling with Composite White-Object Index

Floods is the greatest natural disasters that affect human society than any other type of natural disaster. With the availability of satellite rainfall analyses at fine time and space resolution, it has also become possible to mitigate such hazards on a near-global basis. In this research, we construct a real-time flood monitoring system using White Object Index (WOI) composite. The efficiency of the WOI was tested by comparing the MVC and MOD35.
Downscaling Land Surface Temperature by Using Random Forest Regression Algorithm

This study proposes a land surface temperature (LST) downscaling method to downscale the LST of Moderate Resolution Imaging Spectroradiometer (MODIS) from 990m to 90m by using random forest (RF) regression algorithm. The LST product of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), with 90m resolution, serves as the validation reference at the finer scale. The proposed method is based on the relationship between LST and a variety of surface parameters including band reflectance, spectral indices, land cover types and terrain factors. The proposed downscaling method is evaluated in Segovia, Spain, the Pefiarora mountain region. Comparison between downscaled LST and referenced LST proves that the proposed method shows a great accuracy in downscaling LST. Furthermore, another downscaling method, an algorithm for sharpening thermal imagery (TsHARP), is also implied to get finer resolution LST to make a more complicated comparison with the proposed method. The results are evaluated by root mean squared error (RMSE) and bias, which demonstrate that the accuracy and robustness of RF downscaling method compared with TsHARP.
An Automatic Deployment Support for Processing Remote Sensing Data in the Cloud

Master/Worker distributed programming model enables huge remote sensing data processing by assigning tasks to Workers in which data is stored. Cloud computing features include the deployment of Workers by using virtualized technologies such as virtual machines and containers. These features allow programmers to configure, create, and start virtual resources for instance. In order to develop remote sensing applications by taking advantage of high-level programming languages (e.g., R, Matlab, and Julia), users have to manually address Cloud resource deployment. This paper presents the design, implementation, and evaluation of the Infra. jl research prototype. Infra. jl takes advantage of Julia Master/Worker programming simplicity for providing automatic deployment of Julia Workers in the Cloud. The assessment of Infra. jl automatic deployment is only ~2.8 s in two different Azure Cloud data centers.
Gcom Science Overview and the Initial Results of GCOM-C

JAXA is now planning GCOM mission which is composed of a series of satellites. They are called GCOM- W and GCOM-C satellites. Both satellites are composed of 3 satellites with 5 year lifetime. Hence, 13 years of continuous observation can be assured with 1 year overlaps. The first satellite of GCOM-W was launched on 18, May, 2012 while the first one of GCOM-C was launched on 23, Dec. 2017. GCOM-Wl carries AMSR-2. AMSR-2 is very similar to AMSR on ADEOS II and AMSR-E on EOS-Aqua with some modifications. GCOM-Cl will carry SGLI. The SGLI will be rather different from GLI. The main targets of SGLI are atmospheric aerosols, coastal zone and land. In order to measure aerosols over both ocean and land, it will have an near ultra violet channel, as well as polarization and bidirectional observation capability. For, coastal zone and land observation, the IFOV of SGLI for these targets is around 250m. The instrument will be composed of several components. The shorter wavelength region adopts push broom scanners, while long wave region uses a conventional whisk broom scanner. The orbit of GCOM-Wl is A-train, while the orbit of GCOM-Cl will be similar to ADEOS II GCOM-C L1B product will be distributed from June 2018, and the initial results from GCOM-C will be presented at the Symposium.
Multiple Scatterers Detection Based on Signal Correlation Eploitation in Urban Sar Tomography

This paper addresses the problem of SAR Tomographic (TomoSAR) imaging, allowing the detection of multiple scatterers in presence of partially correlated Gaussian clutter. TomoSAR is a multidimensional imaging technique that has proven its ability in localizing the scatterers, reconstructing the elevation profile of the structures on the ground (3D reconstruction) and estimating the temporal deformations and thermal dilations of the scene (5D reconstruction). In the literature statistical based TomoSAR reconstruction refers to a signal model where in each range-azimuth resolution cell one or more scatterers are interfering in presence of noise and clutter signals, modeled as zero-mean complex circular white Gaussian random vectors. In this paper, we propose to extend a generalized likelihood ratio test (GLRT) detector, proposed by the authors and denoted Fast-Sup-GLRT, to a different signal model, where a correlated clutter model is considered. Results on TerraSAR-X real data are presented.
Imaging Spectrometry and Fluorometry in Support of Flex: What Can We Learn from Multi-Scale Experiments?

In advance of the FLEX mission, experimental studies are needed to better understand the factors driving Solar-Induced Fluorescence (SIF) emission from vegetation across different temporal and spatial scales. Here, we present findings from boreal (evergreen and deciduous) forest trees and Midwestern (annual) crops, illustrating effects of seasonal downregulation and drought on the fluorescence signals. Further work is needed to develop defensible, quantitative fluorescence measurements, and to partition the drivers of the fluorescence signals into effects of structure and physiology.
First -Order SSA Modeling of the Anisotropic Rough-Soil Bistatic Scattering

In this contribution, we analyze the bistatic scattering generated by agricultural soils, which can be characterized by a strong anisotropic component of the rough surface profile. A novel spectral representation is proposed in conjunction with a numerical solution of the scattering based on the well-known small-slope approximation. Comparisons are given with different modeling. The investigation provides interesting and novel information on the phenomenology of the bistatic scattering from the anisotropic soil. This is of great interest for the next generation of satellite missions, which foresees the possibility of launching companion satellites carrying aboard a passive receiver collecting the signal transmitted by an active SAR-based platform.
Synthesizing realistic facial expressions from photographs

We present new techniques for creating photorealistic textured 3D facial models from photographs of a human subject, and for creating smooth transitions between different facial expressions by morphing between these different models. Starting from several uncalibrated views of a human subject, we employ a user-assisted technique to recover the camera poses corresponding to the views as well as the 3D coordinates of a sparse set of chosen locations on the subject's face. A scattered data interpolation technique is then used to deform a generic face mesh to fit the particular geometry of the subject's face. Having recovered the camera poses and the facial geometry, we extract from the input images one or more texture maps for the model. This process is repeated for several facial expressions of a particular subject. To generate transitions between these facial expressions we use 3D shape morphing between the corresponding face models, while at the same time blending the corresponding textures. Using our technique, we have been able to generate highly realistic face models and natural looking animations.
An early fusion approach for multimodal emotion recognition using deep recurrent networks

In this paper we compare different strategies for handling incomplete data and different classification architectures for emotion recognition from multimodal data, using an early fusion approach. In order to allow the different modalities to complement each other at feature level, the initial task was to align the data at the same frame rate. The source data possessed a high degree of incompleteness, which we addressed by different imputation approaches. Since the data was missing in blocks, we found that the best performing approach was to replace missing values with zeros. For the classification model, we experimented with LSTM and GRU networks, in both unidirectional and bidirectional flavors, and various hyper-parameter settings. We found that a bidirectional GRU model trained using a smaller batch size and more aggressive dropout produced the best classification performance.
Amalgamating Knowledge towards Comprehensive Classification

With the rapid development of deep learning, there have been an unprecedentedly large number of trained deep network models available online. Reusing such trained models can significantly reduce the cost of training the new models from scratch, if not infeasible at all as the annotations used for the training original networks are often unavailable to public. We propose in this paper to study a new model-reusing task, which we term as knowledge amalgamation. Given multiple trained teacher networks, each of which specializes in a different classification problem, the goal of knowledge amalgamation is to learn a lightweight student model capable of handling the comprehensive classification. We assume no other annotations except the outputs from the teacher models are available, and thus focus on extracting and amalgamating knowledge from the multiple teachers. To this end, we propose a pilot two-step strategy to tackle the knowledge amalgamation task, by learning first the compact feature representations from teachers and then the network parameters in a layer-wise manner so as to build the student model. We apply this approach to four public datasets and obtain very encouraging results: even without any human annotation, the obtained student model is competent to handle the comprehensive classification task and in most cases outperforms the teachers in individual sub-tasks.
A Net Diffusion Model of Mobile Application: Considering Self-referencing, Within-category Dependency, and Drop-out Process

The diffusion process of mobile application has several distinctive features. First, unlike other products, there is little cost to acquire and abandon mobile applications. This easy-come-easy-go makes the issue of assimilation gap most prevalent in the mobile application diffusion. Second, many applications are tested on a popular platform before the market introduction or promoted from already popular applications, which can form the early market and accelerate the diffusion of such applications. This self-referencing can make the unique diffusion pattern, particularly in the early stage of the market introduction. Lastly, the diffusion process of applications, especially for those in the same category, can be interrelated with each other. This within-category dependency might require the diffusion of applications in the same category to be modeled together. We develop and empirically validate a flexible diffusion model to accommodate aforementioned distinctive features in the diffusion process of mobile applications.