Miracle-dz/UniFAR-FTUs huggingface dataset:
Change Detection between High-Resolution Airborne SAR and Multispectral Data with Dempster-Shafer Theory

Change detection from multi-sensoral SAR-MS datasets is useful for improving the performance by adding data from another modality or for increasing the number of available times. Few methods with two modalities have been investigated though, and often only for low-resolution data. Here, we approach change detection of decimeter-resolution data from an airborne platform. Geometric differences are mitigated by generating a point cloud from optical images, focusing the SAR on it and projecting this into camera view. A joint segmentation of the two datasets is followed by a change detection based on Iterated Conditional Modes (ICM) and Dempster-Shafer theory (DS) to mark changes. An experiment with moving cars is conducted to confirm the method’s applicability.
A Novel Radar Vegetation Index for Compact Polarimetric SAR Data

In this study, we propose a vegetation index for compact polarimetric (CP) SAR data (CpRVI) using a geodesic distance between two Kennaugh matrices projected on a unit sphere, as given in Ratha et. al. This distance is utilized to compute a similarity measure between the observed Kennaugh matrix and the Kennaugh matrix of an isotropic depolarizer. The proposed vegetation index is compared with the Radar Vegetation Index (RVI) obtained from RADARSAT-2 full-polarimetric SAR data. We use a time series of simulated compact-pol SAR data (RH-RV) obtained from the RADARSAT-2 data acquired during the SMAPVEX16-MB campaign over the Joint Experiment for Crop Assessment and Monitoring (JECAM) test site in Manitoba, Canada to assess the proposed vegetation index. Among the various crops grown in this region, only the growth stages of soybean are analyzed in this work. The temporal trend of CpRVI follows the growth stages of soybean. Regression analysis shows that CpRVI correlates better with the Plant Area Index (PAI) and Vegetation Water Content (VWC) than RVI.
Acupuncture for the treatment of ankle sprain

Abstract Background: Ankle sprain is one of the most common musculoskeletal injuries in our daily life, which may lead to chronic ankle instability, reducing the quality of patients’ life and imposing a heavy burden on social medical security system. There are many kinds of methods treating ankle sprain, which can be divided into the conservative treatments and surgical intervention. Acupuncture is one of the conservative treatments for ankle sprain, especially in China. Therefore, we perform a systematic review and meta-analysis to evaluate the evidence for acupuncture's effectiveness, safety and cost benefits for the treatment. Methods: For the acquisition of required data of eligible randomized controlled trials (RCTs), literature search will be undertaken from the following database: PubMed, Embase, Web of Science, The Cochrane Library, Cochrane Central Register of Controlled Trials (CENTRAL), and ClinicalTrials.gov, Chinese National Knowledge Infrastructure (CNKI), VIP Database, and Wanfang database. Quality assessment of the included studies will be independently performed according to the Cochrane Risk of Bias Tool by 2 investigators and the level of evidence for results will be assessed using the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) method. Statistical analysis will be conducted with Revman 5.3. Results: From the study we will assess the effectiveness, safety and cost benefit of acupuncture on pain relief and functional improvement in patients with ankle sprain. Conclusion: The conclusion of this study will provide evidence to ensure the effectiveness, safety and cost benefits of acupuncture on ankle sprain, which can further guide the selection of appropriate interventions. PROSPERO registration number: CRD42018116829.
Response to Comment on "Standardizing Hepatic Venous Pressure Gradient Measurements in Liver-transplant Recipients".

1. Troisi RI, Vanlander A, Giglio MC, et al. Somatostatin as inflow modulator in liver-transplant recipients with severe portal hypertension: a randomized trial. Ann Surg. 2018. 10.1097/SLA.0000000000003062 [Epub ahead of print]. 2. Garcia-Tsao G, Abraldes JG, Berzigotti A, et al. Portal hypertensive bleeding in cirrhosis: Risk stratification, diagnosis, and management: 2016 practice guidance by the American Association for the study of liver diseases. Hepatology. 2017;65:310–335. 3. Groszmann RJ, Wongcharatrawee S. The hepatic venous pressure gradient: anything worth doing should be done right. Hepatology. 2004;39: 280–282. 4. Abraldes JG, Sarlieve P, Tandon P. Measurement of portal pressure. Clin Liver Dis. 2014;18:779–792. 5. Qi X, Berzigotti A, Cardenas A, et al. Emerging non-invasive approaches for diagnosis and monitoring of portal hypertension. Lancet Gastroenterol Hepatol. 2018;3:708–719. 6. Qi X, An W, Liu F, et al. Virtual hepatic venous pressure gradient with CT angiography (CHESS1601): a prospective multicenter study for the noninvasive diagnosis of portal hypertension. Radiology. 2019;290:370–377. 7. Liu F, Ning Z, Liu Y, et al. Development and validation of a radiomics signature for clinically significant portal hypertension in cirrhosis (CHESS1701): a prospective multicenter study. EBioMedicine. 2018;36:151–158. 8. Silva-Junior G, Turon F, Baiges A, et al. Timing affects measurement of portal pressure gradient after placement of transjugular intrahepatic portosystemic shunts in patients with portal hypertension. Gastroenterology. 2017;152:1358–1365. 9. Reverter E, Blasi A, Abraldes JG, et al. Impact of deep sedation on the accuracy of hepatic and portal venous pressure measurements in patients with cirrhosis. Liver Int. 2014;34:16–25.
Trajectory Uncertainty in Repeat-Pass SAR Interferometry: A Case Study

In the context of differential synthetic aperture radar interferometry (DInSAR), precise trajectory estimation of the SAR platform is necessary to minimize residual phase errors induced by inaccurate knowledge of the 3D acquisition geometry.Inertial navigation systems (INS) and global navigation satellite system (GNSS) are usually employed to track the position of the platform. However, their unavoidable inaccuracies lead to motion estimation errors that negatively affect the quality of the processed radar data.To assess the positioning performance in a repeat-pass scenario, we used a navigation-grade INS/GNSS system to preciselytrack the position and the attitude of a platform moving along a rail and carrying a SAR sensor. We analyse the performance of the positioning solution for different scenarios relevant to repeat-pass DInSAR. Since the position of the platform is nearly perfectly repeated at every pass (zero interferometric baseline), the precision of the estimated position can be assessed and the interferometric performance evaluated.
Cyber Insurance and Time-to-Compromise: An Integrated Approach

Fast-growing numbers of technologies and devices make cyber security landscape more complicated and require more accurate models. This complexity challenges cyber security experts to devise a better solution to manage cyber risks. One of the promising methods is to find the best distribution of security expenditure for risk mitigation and transfer (i.e. cyber insurance) options. In this work, we propose a solution to find the optimal security investments when there is a cyber insurance option by applying a time-to-compromise metric to the probability of attack computation. In particular, we find the best set of countermeasures which decreases the maximum number of vulnerabilities to increase the required time to compromise a system. Our approach is based on a multiple-objective knapsack problem for the selection of countermeasures and we find the best distribution of security expenditure by computing the time-to-compromise metric which eventually defines the probability of attack.
Compressive Sensing Based Reconstruction and Pixel-Level Classification of Very High-Resolution Disaster Satellite Imagery Using Deep Learning

Disasters such as earthquakes, floods, landslides etc. create great economic and social loss by destroying the balance of life and property and create chaos. In the wake of a disaster, it becomes very significant to take real-time and on-the-fly actions to minimize the effects of the event. Remote Sensing data acquired through airborne or spaceborne platforms is usually huge in size and requires huge time in generating actionable insights during the disaster scenario. In this work, we propose a two-fold analysis of the Very High Resolution (VHR) satellite imagery based on Compressive Sensing (CS) and Deep Learning. We propose employing a deep learning approach for inferencing over compressed sensing satellite imagery. We hypothesize that this could be beneficial in generating real-time actionable insights during a catastrophe. In our work, we are using the satellite imagery from GeoEye-1 of Haiti Earthquake. Our objectives are: (1) To generate CS images for 75%, 50%, and, 25% sampling on the sparse space and (2) To develop a deep learning pixel-level classification model based on the UNet architecture using the original and reconstructed images. The UNet architecture has shown promising results for pixel-level classification in the recent literature. We envisage to combine both the objectives into an end-to-end learning framework for on-board processing which we foresee would be of great significance in various applications for rapid disaster management response.
Downscaling Ocean Surface Net Radiation at Global Scales with Random Forest

The ocean surface net radiation (Rn) characterizing the ocean surface radiation budget, is a key variable of interest in ocean climate modeling and analysis, which determines the climate model of ocean surface with heat, freshwater and momentum flux. Most available ocean surface Rn data face various issues in accuracy, spatiotemporal patterns, and coarse spatial resolutions. J-OFURO3 (The third generation of Japanese Ocean Flux Datasets with Use of Remote Sensing Observations) released recently is considered to be one of the best datasets in ocean Rn, but its spatial resolution is also at 0.25°. In this study, we downscaled ocean surface Rn from J-OFURO3 using AVHRR TOA observations with Random Forest (RF) method. The accuracy of the downscaling daily surface ocean Rn is satisfactory with a root mean square error (RMSE) of 29Wm−2, which is better than other reanalysis data and comparable to that of CERES-SYN.
Pixelwise Time Series Retrieval in Phenological Studies

The support of time series similarity searches might be crucial in phenology studies, in which long-term time series analysis based on the identification of similar and different phenological patterns shared by individuals belonging to different species is a widely common task. In this paper, we introduce the use of well-established Information Retrieval (IR) technologies in the search of time series. The solution comprises four main steps: extraction of an image-based time series representation; image content description to encode time series properties and patterns; textual signature extraction based on image content descriptions; and textual signature indexing using off-the-shelf IR approaches. In this paper, we demonstrate both the effectiveness and the efficiency of the proposed solution in time series retrieval problems related to the management of phenological data associated with near-surface vegetation images.
Electrocardiogram-Derived Respiratory Signal in Sleep Apnea by Segmented Beat Modulation Method

The most common sleep disorder is sleep apnea, whose manifestations are long breathing pauses. Sleep apnea assessment is usually performed by polysomnography. During this long-term monitoring, patient respiration and other biosignals are recorded by many sensors, causing a high level of discomfort. Thus, methods able to indirectly estimate the biosignal of interest from the others measured should be preferred. Respiration indirectly measured from electrocardiogram (ECG) is called ECG-derived respiratory (EDR) signal. Recently, Segmented Beat Modulation Method (SBMM) was proposed as a good method for EDR signal estimation in normal breathing. Thus, the aim of this study was to assess the quality of EDR signal estimation by SBMM in pathological events of sleep apnea. With this purpose, sixteen long term polysomnographic recordings from MITBIH Polysomnographic Database were considered. After standard preprocessing, respiration and ECG signals were divided in 30s windows and, in order to match to provided annotations, each window was classified into Normal or Apnea. EDR signal was estimated by SBMM procedure from each ECG window. Respiration and EDR signals were then processed by Fourier analysis to extract respiration frequencies. Respiration frequencies computed from respiration and EDR signals were compared in term of error. Results confirmed the good quality of the estimated EDR signal. Respiration frequency extracted from EDR signal in both Normal (16[13;19]cpm) and Apnea windows (18[15;21]cpm) are equal to those extracted from respiration signal (Normal: 16 [13;19]cpm and Apnea: 18 [15;21]cpm), providing null error distributions. In conclusion, SBMM proved to be a promising tool for EDR signal estimation.
SAR Tomographic Imaging Demonstration Using GF-3 Data

Synthetic aperture radar (SAR) tomography (TomoSAR) is well established for three-dimensional (3D) reconstruction in urban buildings. There are many methods usually require a large data stacks for a reliable reconstruction and very few methods to handle small data stacks. This paper proposes a new processing method for 3D reconstruction of tall buildings using extremely small data stacks with large baseline interval. In this method, it is necessary to select persistent scatterers (PSs) with high signal-to-noise ratio (SNR) as reference points and estimate the elevation ambiguity issue by using the geometric relationship of SAR imaging. This paper presents the experimental results and shows a well performance of the proposed method in 3D reconstruction of tall buildings with simulated data and SAR data acquired by Chinese spaceborne SAR GF-3 satellite.
SAR Image Rectification Based on Vector Map

The automatic geometric rectification of space-borne SAR remote sensing images is generally achieved by automatic matching with the optical control base map. However, the optical map has the problem that its coverage is limited and the data cannot be updated in time. With the continuous development of digital maps, the coverage and accuracy of vector maps continue to increase. In order to expand the source of data for correction processing of SAR remote sensing images, an automatic SAR image correction method based on vector map is proposed. Application experiments show that the geometric positioning accuracy of SAR images can be improved significantly based on vector map.
Early Intrathecal T Helper 17.1 Cell Activity in Huntington Disease

Huntington disease (HD) is an autosomal dominantly inherited neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin (HTT) gene. No disease‐modifying therapy exists for the treatment of patients with HD. The purpose of this study was therefore to investigate early disease mechanisms that potentially could be used as a target therapeutically.
Complete Genome Sequence of Klebsiella pneumoniae Myophage Mulock

Klebsiella pneumoniae is an opportunistic pathogen associated with hospital-acquired infections. This report describes the complete genome of the K. pneumoniae myophage Mulock, which appears to be a temperate myophage distantly related to other Klebsiella myophages in morphogenesis genes and is partially syntenic with the canonical Escherichia phage lambda in genes encoding lambda-like functions. ABSTRACT Klebsiella pneumoniae is an opportunistic pathogen associated with hospital-acquired infections. This report describes the complete genome of the K. pneumoniae myophage Mulock, which appears to be a temperate myophage distantly related to other Klebsiella myophages in morphogenesis genes and is partially syntenic with the canonical Escherichia phage lambda in genes encoding lambda-like functions.
Comment on “A Particle Tracking Algorithm for Arbitrary Unstructured Grids”

This is a comment on the article by Craig et al. (2020). The paper presents a particle tracking algorithm for unstructured finite volume grids. There are some issues in the paper that need certain clarifications. The statements “irregular three-dimensional grid cells” and “arbitrary unstructured grids” are not properly used because, as in the explanation of the algorithm (“The method is restricted to cells whose bottom and top are parallel to the x-y plane”), and in the figures, the kind of grids used is more commonly named in literature as “2.5 Voronoi grids. ” The introduction concerning particle tracking algorithms and velocity estimation methods on unstructured grids is limited to methods developed by Cordes and Kinzelbach (1992). There are a series of remarkable more recent works in the literature and, among others, it is worth mentioning Jimenez et al. (2007); Klausen et al. (2012); Matringe et al. (2006); Matringe et al. (2008); Prevost et al. (2002); Rasmussen (2010); and Zhang et al. (2012). Among them, the method by Klausen et al. (2012) and improved by Rasmussen and Lie (2014) allows the computation of the velocity field for three-dimensional (3D) Voronoi unstructured grids in an analytical way, and it is a mass conservation method. More specifically, the presented method of Craig et al., named in the paper as the Waterloo method, is claimed to be a “mass conservation method,” but in the simple case study shown in Figure 4 of the paper, the computed path appears to have a bigger deviation compared to the one computed using the Pollock method (Pollock 1988). The estimation of the particle path deviation between the Waterloo and the Pollock method is declared to be
Numerical Evaluation of the Maximum Heatings of Solid Rotor of the Synchronous Machine in Transient Mode

The values of the maximum temperatures and the influence of their deviations from the experimental data on the assessment of the actual thermal stability of the structure have been analyzed on the basis of a comparison of the experimental and calculated values of the heating of the elements of the solid rotor of a synchronous machine. The low adequacy of experimental methods of the assessment of solid toothed rotors thermal stability has been proved. A model and methods for the essential improvement of the accuracy of determination of the rotor surface temperature with the use of a combined calculation-experimental approach have been developed. The calculation efficiency depending on the depth and position of placing thermocouples has been researched.
A New Method for Urban Subsidence Monitoring Using Time Series InSAR Combining Tandem: A Case Study of Pazhou Island

With the rapid development of Guangzhou's economy and urbanization and the gradual deepening of the urban planning and development system, various types of engineering construction activities have become increasingly frequent, and different degrees of ground subsidence have appeared in some areas of the city.Traditional InSAR technology is greatly affected by terrain residuals when monitoring surface deformation.To improve the accuracy and resolution of the digital elevation model and remove the terrain residual phase is the key to improve the monitoring accuracy of temporally interferometric SAR deformation.Based on the TerraSAR-X and tandem X acquisition of high-precision DEM, this paper proposes a Tandem-assisted sequential interference SAR (permanent scatterer) technology, 21 views of ENVISAT/ASAR data and 56 views sentinels on Pazhou Island in Guangzhou City - 1 The data was processed to obtain high-precision monitoring results of surface subsidence and spatio-temporal evolution of urban areas in the study area.The experimental results show that the DEM extracted by TSX/TDX can significantly improve the accuracy of temporally interferometric synthetic aperture radar, and proves that this method has a good effect and application potential in the monitoring of urban surface subsidence.
Population Incidence and Mortality of Sepsis in an Urban African Setting, 2013–2016

Abstract Background Sepsis is an important cause of mortality globally, although population incidence estimates from low-income settings, including sub-Saharan Africa, are absent. We aimed to estimate sepsis incidence burden using routinely available data from a large urban hospital in Malawi. Methods We linked routine-care databases at Queen Elizabeth Central Hospital, Blantyre, Malawi, to provide admission and discharge data for 217 149 adults from 2013–2016. Using a definition of sepsis based on systemic inflammatory response syndrome criteria and Blantyre census population data, we calculated population incidence estimates of sepsis and severe sepsis and used negative binomial regression to assess for trends over time. Missing data were multiply imputed with chained equations. Results We estimate that the incidence rate of emergency department–attending sepsis and severe sepsis in adults was 1772 per 100 000 person-years (95% confidence interval [CI], 1754–1789) and 303 per 100 000 person-years (95% CI, 295–310), respectively, between 2013 and 2016, with a year-on-year decrease in incidence. In-hospital mortality for patients admitted to the hospital with sepsis and severe sepsis was 23.7% (95% CI, 22.7–24.7%) and 28.1% (95% CI, 26.1 – 30.0%), respectively, with no clear change over time. Conclusions Sepsis incidence is higher in Blantyre, Malawi, than in high-income settings, from where the majority of sepsis incidence data are derived. Worldwide sepsis burden is likely to be underestimated, and data from low-income countries are needed to inform the public health response.
Modified Gradient Spectral Variance Smoothing Adaptive Filter Control for Grid Connected PV System

The modified gradient spectral variance smoothing (MGSVS) based control is used for a grid integrated two stage solar photovoltaic (PV) system. The boost converter is used as the first stage for tracking maximum power through the perturb and observe (P & O) control technique. A voltage source converter (VSC) is placed at the next stage, which handshakes between the PV array and the grid. MGSVS based control is used to provide gating pulses for the VSC. This PV system operates at unity power factor, compensates reactive power, reduces the grid harmonics current. The fast convergence is achieved by providing an optimal step size in the algorithm. This system is modeled and its performance is simulated in MATLAB-SIMULINK platform under different operating conditions and these simulated results are validated on an experimental prototype. Test results obtained, are reasonable with enhanced steady state and dynamic performances. The total harmonic distortion (THD) of grid current follows the IEEE-519 standard.