Miracle-dz/UniFAR-FTUs huggingface dataset:
A Neural Network Sea-Ice Cloud Classification Algorithm for Copernicus Sentinel-3 Sea and Land Surface Temperature Radiometer

A Neural Network approach to classify Sentinel-3 sea and land surface temperature radiometer (SLSTR) pixels over polar regions is presented. The proposed approach is based on a careful preliminary analysis aimed to simulate SLSTR observation by means of MODIS data. The latter have been considered because of the long available time series and the quality of cloud mask products. A large set of MODIS AQUA and TERRA products has been applied to develop the training set of the Neural Network classificator that has been tuned to discriminate clear ocean, clouds and sea-ice surfaces on the scene.
On Polinsar System Requirements for Forest Height Mapping

Polarimetric interferometric SAR (PolinSAR) data are contaminated by cross-talk and channel imbalance. To ensure the successful estimation of forest heights from forthcoming PolinSAR campaigns, a critical study on the polarimetric system requirements of PolinSAR for forest height mapping must be carried out. In this paper, a triple-factor analysis of cross-talk, channel imbalance and noise of PolinSAR system is conducted to understand the polarimetric system requirements for PolinSAR forest height mapping. A model relationship between forest height estimation error and polarimetric system parameters is established through theoretical analysis. Meanwhile, the numerical relationship between the two is obtained by artificially adding different system errors to simulated SAR images. The experiment results well validate the correctness of our established model relationship. The polarimetric system requirements of PolinSAR for forest height mapping can be provided for system designers according to our established relationship.
Multi-Feature-Based Decision Fusion Framework for Hyperspectral Imagery Classification

Classification of a hyperspectral image (HSI) is a very active topic in remote sensing, which has practical applications in many fields, In this paper, a multitask sparse logistic regression method based on multi-feature and decision fusion approach (MTSLR-MPGF) is proposed for cross-scene hyperspectral image classification. Specifically, the Gabor Features with certain orientations and morphology feature are utilized and used for hyperspectral imagery classification. Next, we used feature fusion methods in order to produce an accurate thematic map based on the remote sensed hyperspectral image classification. The extensive experiments on two real hyperspectral data sets have demonstrated superior performance of the proposed MTSLR-MPGF approach over the state-of-the-art methods in terms of overall accuracy and average accuracy.
Polarimetric Coherence Optimization as a Multidimensional Polarimetric SAR Signal Processing Tool

This paper summarizes a set of studies led on the topic of polarimetric coherence optimization for the coherent processing of stacks of polarimetric SAR images. It is shown that coherence maximization may be understood differently depending on the application at hand. Extracting polarimetric coherent signals embedded in noise or in a severe background requires to use polarimetric diversity as a supplementary mean for discovering organized speckles patterns, whereas classical MB-PolinSAR coherence optimization gives more importance to the polarimetric scattering mechanisms that extremize coherence values. This paper reviews different techniques able to cope with an arbitrary number of images and that are characterized by their low degree of computational complexity, conferred by the favored use of analytical solutions. The usefulness of these techniques is demonstrated using various kinds of applications to real spaceborne and airborne data sets.
The Capella Synthetic Aperture Radar Constellation

The Capella constellation will consist of 36 agile synthetic aperture radars. Each radar will operate at an altitude of around 500 km in an approximately 90-minute polar orbit, providing average imaging revisit times of less than one hour. The radars will be single-pol X-band systems, capable of operating over a 500 MHz bandwidth in stripmap and spotlight imaging modes.
The Relationship between Vegetable Intake and Weight Outcomes: A Systematic Review of Cohort Studies

The relationship between vegetable consumption and measures of adiposity was assessed in cohort studies. Seven databases were searched from inception until October 2018. The quality of individual studies was assessed using the Joanna Briggs Institute Critical Appraisal of Cohort Studies tool. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) system was applied to determine the quality of the body of evidence. Ten studies were included. Six measured change in vegetable intake over time. Two showed that increasing vegetable consumption resulted in weight loss of 0.09–0.1 kg over four years (p < 0.001). Increased vegetable intake was also associated with a reduced risk of weight gain and overweight or obesity (Odds ratios (ORs) ranged from 0.18 to 0.88) in other studies. Four studies measured vegetable intake at the baseline only. One showed that intakes >4 servings/day reduced the risk of weight gain (OR 0.27 (95% confidence interval (CI) 0.08–0.99) and another found an inverse association with waist circumference in women (−0.36 cm per vegetable serving/day). This review provides moderate quality evidence for an inverse association between vegetable intake and weight-related outcomes in adults. When these findings are coupled with no apparent harm from vegetable consumption, the evidence-base can be used with acceptable confidence to guide practice and policy.
Stable Isotope-Labeled Lipidomics to Unravel the Heterogeneous Development Lipotoxicity

Non-alcoholic fatty liver disease (NAFLD) as a global health problem has clinical manifestations ranging from simple non-alcoholic fatty liver (NAFL) to non-alcoholic steatohepatitis (NASH), cirrhosis, and cancer. The role of different types of fatty acids in driving the early progression of NAFL to NASH is not understood. Lipid overload causing lipotoxicity and inflammation has been considered as an essential pathogenic factor. To correlate the lipid profiles with cellular lipotoxicity, we utilized palmitic acid (C16:0)- and especially unprecedented palmitoleic acid (C16:1)-induced lipid overload HepG2 cell models coupled with lipidomic technology involving labeling with stable isotopes. C16:0 induced inflammation and cell death, whereas C16:1 induced significant lipid droplet accumulation. Moreover, inhibition of de novo sphingolipid synthesis by myriocin (Myr) aggravated C16:0 induced lipoapoptosis. Lipid profiles are different in C16:0 and C16:1-treated cells. Stable isotope-labeled lipidomics elucidates the roles of specific fatty acids that affect lipid metabolism and cause lipotoxicity or lipid droplet formation. It indicates that not only saturation or monounsaturation of fatty acids plays a role in hepatic lipotoxicity but also Myr inhibition exasperates lipoapoptosis through ceramide in-direct pathway. Using the techniques presented in this study, we can potentially investigate the mechanism of lipid metabolism and the heterogeneous development of NAFLD.
Circadian disruption: What do we actually mean?

The circadian system regulates physiology and behavior. Acute challenges to the system, such as those experienced when traveling across time zones, will eventually result in re‐synchronization to local environmental time cues, but this re‐synchronization is oftentimes accompanied by adverse short‐term consequences. When such challenges are experienced chronically, adaptation may not be achieved, as for example in the case of rotating night shift workers. The transient and chronic disturbance of the circadian system is most frequently referred to as “circadian disruption”, but many other terms have been proposed and used to refer to similar situations. It is now beyond doubt that the circadian system contributes to health and disease, emphasizing the need for clear terminology when describing challenges to the circadian system and their consequences. The goal of this review is to provide an overview of the terms used to describe disruption of the circadian system, discuss proposed quantifications of disruption in experimental and observational settings with a focus on human research, and highlight limitations and challenges of currently available tools. For circadian research to advance as a translational science, clear, operationalizable, and scalable quantifications of circadian disruption are key, as they will enable improved assessment and reproducibility of results, ideally ranging from mechanistic settings, including animal research, to large‐scale randomized clinical trials.
Joint Feature Extraction for Multispectral and Panchromatic Images Based on Convolutional Neural Network

Along with very high-resolution satellites were launched frequently, such as the satellite WorldView-3, panchromatic and multispectral remote-sensing images can be acquired easily. However, it is still an interesting and challenging task to fuse and classify these images. In general, panchromatic image has a high spatial resolution, but with only one spectral band. Multispectral image usually has four or eight bands, but the spatial resolution is four times smaller than panchromatic image. In this paper, an unsupervised feature extraction framework is proposed, which combines multispectral (MS) image and panchromatic (PAN) image into convolution neural network (CNN). There is an image-to-image mapping, learning from the input source (i.e., MS) to the output source (i.e., PAN). Then, by integrating the hidden layer of deep CNN, the extracted features represent MS and PAN data. The experimental results of two practical remote sensing data sets show the validity of the framework.
A Modified Dual-Wavelength Technique for Ku- and Ka-Band Radar Rain Retrieval

A modified dual-wavelength radar technique is described in an attempt to eliminate double solutions of DSD that the standard dual-wavelength technique faces for small-to-moderate rain rates. Assessment of the methods is made from the simulated hydrometeor profiles comprised of measured DSD. Preliminary results reveal that the modified radar technique has potential to improve accuracy of DSD and rain retrieval over the standard dual-wavelength radar technique.
Spatial Chirp-Z Transformer Networks

Convolutional Neural Networks are often used for computer vision solutions, because of their inherent modeling of the translation invariance in images. In this paper, we propose a new module to model rotation and scaling invariances in images. To do this, we rely on the chirp-Z transform to perform the desired translation, rotation and scaling in the frequency domain. This approach has the benefit that it scales well and that it is differentiable because of the computationally cheap sincinterpolation.
Interchangeability of Kinect and Orbbec Sensors for Gesture Recognition

In the context of recent advances and high market dynamics of the 3D motion camera products widely used in physical rehabilitation systems, we evaluate in this study the similarity and interchangeability of a first generation motion sensor, Microsoft Kinect, with one of the latest generations sensors for skeletal tracking, Orbbec. We have analyzed several body postures represented by skeletal points with each sensor, and the possibility to mix or interchange data from the two sensors to train machine learning algorithms and accurately classify these body postures. We have analyzed the performance of 23 of the best performing classifiers with skeletal data, using for testing our own data collected with Kinect and Orbbec. Results show there is a similarity between the two sensors and the possibility to interchange the sensor data, with no impact on accuracy for the majority of the classifiers used (16 out of 23), having minor adaptations applied on the data. The accuracy for the two sensors is similar when used independently. This proves it is possible to drive continuity between these two different generations of 3D motion cameras in systems performing gesture recognition.
Isolation and identification of nucleosides/nucleotides raising testosterone and NO levels of mice serum from Chinese chive (Allium tuberosum) leaves

Our previous study found that Chinese chive could significantly (p < 0.01) raise testosterone and nitric oxide (NO) levels in mice serum. However, the specific functional components of this traditional remedy are still unknown. In order to isolate and identify the active constituents from Chinese chive for enhancing testosterone and NO levels, the Chinese chive leaves were extracted by petroleum ether, ethyl acetate, n‐butanol and water respectively. Results indicated that the n‐butanol extract had a significant effect on NO and testosterone blood levels. Subsequently, n‐butanol extract was further isolated by D101 macroporous adsorption and eluted with 50% ethanol and then isolated by Sephadex LH‐20 and preparative high‐performance liquid chromatography to obtain nucleosides. The fraction eluted with 70% ethanol was further isolated by RP‐18 and pre‐HPLC to obtain nucleotides. Four novel compounds were identified, and their effects on testosterone and NO levels of male mice were evaluated. Results showed that nucleotides, especially the adenosine in Chinese chive leaves, increased serum testosterone and NO levels in male mice, which had not been reported before. This finding might bring into perspective the treatment strategy for those doctors who treat hormone deficiencies, and might be suitable for using in functional food.
Cbcbeat: an Adjoint-enabled Framework for Computational Cardiac Electrophysiology

cbcbeat (cbcbeat 2017) is a Python-based software collection targeting computational cardiac electrophysiology problems. cbcbeat contains solvers of varying complexity and performance for the classical monodomain and bidomain equations coupled with cardiac cell models. The cbcbeat solvers are based on algorithms described in (Sundnes et al. 2006) and the core FEniCS Project software (Logg et al. 2012). All solvers allow for automated derivation and computation of adjoint and tangent linear solutions, functional derivatives and Hessians via the dolfin-adjoint software (Farrell et al. 2013). The computation of functional derivatives in turn allows for automated and efficient solution of optimization problems such as those encountered in data assimillation or other inverse problems.
Deep Learning Vector Quantization

While deep neural nets (DNN’s) achieve impressive performance on image recognition tasks, previous studies have reported that DNN’s give high conﬁdence predictions for unrecognizable images. Motivated by the observation that such fooling examples might be caused by the extrapolating nature of the log-softmax, we propose to combine neural networks with Learning Vector Quantization (LVQ). Our proposed method, called Deep LVQ (DLVQ), achieves comparable performance on MNIST while being more robust against fooling and adversarial examples.
Blocking analysis of spin locks under partitioned fixed-priority scheduling

Partitioned fixed-priority scheduling is widely used in embedded multicore real-time systems. In multicore systems, spin locks are one well-known technique used to synchronize conflicting accesses from different processor cores to shared resources (e.g., data structures). The use of spin locks can cause blocking. Accounting for blocking is a crucial part of static analysis techniques to establish correct temporal behavior. In this thesis, we consider two aspects inherent to the partitioned fixedpriority scheduling of tasks sharing resources protected by spin locks: (1) the assignment of tasks to processor cores to ensure correct timing, and (2) the blocking analysis required to derive bounds on the blocking. Heuristics commonly used for task assignment fail to produce assignments that ensure correct timing when shared resources protected by spin locks are used. We present an optimal approach that is guaranteed to find such an assignment if it exists (under the original MSRP analysis). Further, we present a well-performing and inexpensive heuristic. For most spin lock types, no blocking analysis is available in prior work, which renders them unusable in real-time systems. We present a blocking analysis approach that supports eight different types and is less pessimistic than prior analyses, where available. Further, we show that allowing nested requests for FIFOand priority-ordered locks renders the blocking analysis problem NP -hard.
An Efficient Image Formation Algorithm for Spaceborne Video SAR

To avoid unnecessary duplication of processing and meet the real-time requirement of video SAR. An image formation algorithm of spaceborne video SAR based on sub-aperture extended chirp scaling processing was proposed in this paper. Firstly the SAR raw data segmentation approach was presented to realize the SAR video frames continuous output. Then the raw data of a video frame was divided to several sub-apertures, after the extended CS processing in the sub-apertures, the frequency sub-bands of sub-apertures were combined to achieve the desired high azimuth resolution video SAR frames. This innovative image formation algorithm can avoid the duplication of processing, so as to improve computation efficiency. Finally, experiments on simulated data show that the proposed algorithm is effective.
Early Winner Takes All: Exploring the Impact of Initial Herd on Overfunding in Crowdfunding Context

Crowdfunding endows entrepreneurs with a means of kickstarting their businesses. The amount of investment that can be elicited through crowdfunding campaigns often surpasses fundraisers’ expectation, leading to market inefficiencies. Yet, this phenomenon of overfunding has garnered relatively little attention in past studies. Subscribing to signaling theory, we strive to shed light on the causes of overfunding by positing crowdfunding platforms as fertile breeding grounds for herding behavior. Specifically, we attempt to capture the dynamic nature of herding by decomposing herds on crowdfunding platforms into their constituent dimensions of maturity, intensity, and persistency. We further endeavor to explore how these constituent dimensions shape overfunding. This study advances a model to testify to the instrumental role of initial herds in amplifying overfunding on the crowdfunding platforms, which in turn could offer insights into remedying the problem of overfunding.
End to End Simulation Study of Geostaionary Passive Microwave Atmospheric Sounding

A numerical simulator for analysis of the geostationary orbit multispectral passive microwave (Geo-MW) mapping and sounding is described, which mainly includes upwelling brightness temperatures forward, microwave radiometer payload simulation and atmospheric profile retrieval. This simulator allows evaluation and optimization of the ability of microwave sensors to retrieve atmospheric temperature and humidity profile. In this paper, end to end simulation study of a Geo-MW radiometer observation at selected frequencies from 50 to 425 GHz with a real-aperture antenna is performed and the accuracy of the brightness temperature measurement and the atmospheric temperature and humidity profile sounding are quantitatively analyzed and evaluated.
PGC-1α as a Pivotal Factor in Lipid and Metabolic Regulation

Traditionally, peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), a 91 kDa transcription factor, regulates lipid metabolism and long-chain fatty acid oxidation by upregulating the expression of several genes of the tricarboxylic acid cycle and the mitochondrial fatty acid oxidation pathway. In addition, PGC-1α regulates the expression of mitochondrial genes to control mitochondria DNA replication and cellular oxidative metabolism. Recently, new insights showed that several myokines such as irisin and myostatin are epigenetically regulated by PGC-1α in skeletal muscles, thereby modulating systemic energy balance, with marked expansion of mitochondrial volume density and oxidative capacity in healthy or diseased myocardia. In addition, in our studies evaluating whether PGC-1α overexpression in epicardial adipose tissue can act as a paracrine organ to improve or repair cardiac function, we found that overexpression of hepatic PGC-1α increased hepatic fatty acid oxidation and decreased triacylglycerol storage and secretion in vivo and in vitro. In this review, we discuss recent studies showing that PGC-1α may regulate mitochondrial fusion–fission homeostasis and affect the renal function in acute or chronic kidney injury. Furthermore, PGC-1α is an emerging protein with a biphasic role in cancer, acting both as a tumor suppressor and a tumor promoter and thus representing a new and unresolved topic for cancer biology studies. In summary, this review paper demonstrates that PGC-1α plays a central role in coordinating the gene expression of key components of mitochondrial biogenesis and as a critical metabolic regulator in many vital organs, including white and brown adipose tissue, skeletal muscle, heart, liver, and kidney.
Information Privacy Paradox: A Neural Science Study

Most people indicate disclosure of private information as a concern but keep revealing personal information in actual behavior. This inconsistency is called “privacy paradox”. Previous literature has proposed a few interpretations of this paradoxical behavior from the rational perspective- privacy calculus. However, individuals are not purely rational decision-makers. They might be bounded by psychological limitations which are often unconscious. Drawing on the dual-process model, this study suggests that the occurrence of contradictory behavior can be explained by the decision-making process people adopted in their actual behavior different from they adopted in their self-report. In many cases, people tend to state their choices after the rational information process even when their behavior indicates they are following their intuition. An electroencephalographic experiment is conducted to capture direct responses when an individual makes privacy-related decisions, further demonstrate that individual’s privacy decision-making process is not totally rational.
Axial Guiding Magnetic Field Influence on Gain Characteristics of Plasma-Beam Superheterodyne Free-Electron Laser of Dopplertron Type with Helical Electron Beam

We have analyzed the effect of axial guiding magnetic field on the wave dynamics in plasma-beam superheterodyne free electron laser (SFEL) of the Dopplertron type with helical relativistic electron beam in the framework of cubic nonlinear theory. We have found the magnetic field critical values and the saturation levels. We found out that the saturation level of electromagnetic waves is maximal at a certain value of the longitudinal magnetic field strength (the magnetic field critical value). We demonstrated the possibility of a plasma-beam SFEL to generate powerful electromagnetic radiation in the millimeter wavelength range.