Miracle-dz/UniFAR-FTUs huggingface dataset:
A Stacked Dense Denoising-Segmentation Network for low-projection CT Volumes

Recently there have been a number of approaches which develop a semantic segmentation of X-ray Computed Tomography (CT) scans. In most of these cases, high-quality (HQ) tomography reconstructions are available as input; however, in practice this is not always available. When the X-ray exposure time of the imaged object must be limited to reduce the radiation exposure, or to capture time-critical events, noisy, low-quality (LQ) tomograms will be attained, and as a result these can be more difﬁcult to segment. Here, we address the case of time-resolved volumetric tomography data collection (4D datasets), where multiple LQ tomograms with small number of projections are collected, speciﬁcally to investigate the process of industrial corrosion at the Diamond Light Source (DLS) synchrotron. Fortunately however, it is common practice to collect HQ tomograms with a large number of projections before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the reconstructions of the LQ tomograms, using the representative fully segmented HQ tomogram to train the network. Our single network is able to offer two different desired outputs while only training once, with the denoised output improving the accuracy of the ﬁnal segmentation. Our method is able to outperform state-of-the-art methods in both tasks of segmentation and denoising. We also make our datasets as well as the code publicly available.
Investigating the differences in calculating global mean surface CO2 abundance: the impact of analysis methodologies and site selection

Abstract. The World Meteorological Organization (WMO) Global Atmosphere Watch (GAW) coordinates high-quality atmospheric greenhouse gas observations globally and provides these observations through the WMO World Data Centre for Greenhouse Gases (WDCGG) supported by Japan Meteorological Agency. The WDCGG and the National Oceanic and Atmospheric Administration (NOAA) analyse these measurements using different methodologies and site selection to calculate global annual mean surface CO2 and its growth rate as a headline climate indicator. This study introduces a third hybrid method named GFIT, which serves as an independent validation and open-source alternative to the methods described by NOAA and WDCGG. We apply GFIT to incorporate observations from most WMO GAW stations and 3D modelled CO2 fields from CarbonTracker Europe (CTE). We find that different observational networks (i.e. NOAA, GAW, and CTE networks) and analysis methods result in differences in the calculated global surface CO2 mole fractions equivalent to the current atmospheric growth rate over a 3-month period. However, the CO2 growth rate derived from these networks and the CTE model output shows good agreement. Over the long-term period (40 years), both networks with and without continental sites exhibit the same trend in the growth rate (0.030 ± 0.002 ppm yr−1 each year). However, a clear difference emerges in the short-term (1-month) change in the growth rate. The network that includes continental sites improves the early detection of changes in biogenic emissions.
The mode and the measurement of inequality with single and multiple ordinal variables

The extensive use of multivariate ordered categorical data in the social sciences presents challenges for the measurement of socioeconomic inequities. The ambiguities inherently associated with artificial attribution of scale to ordinal categories, preclude the use of standard distance-based inequality and polarization measures. These issues have been surmounted in the univariate world by employing notions of likelihood distance (the increasing likelihood that an outcome between two categories will occur the bigger is their categorical gap) and aggregating outcome distances from the median category as a reference point, adapting the transfer principle using Hammond transfers, or with probability-based notions of status. Unfortunately, the median category is not always the ideal reference point of complete commonality and is not uniquely defined in multivariate environments. However, as the most frequently observed outcome, the modal category provides a natural measure of the extent of commonality or equity in the population, thus providing a readymade reference point from which to measure likelihood outcome distance. We provide axiomatic foundations and characterise classes of modally focused inequality measures for univariate and multivariate ordered categorical environments together with their asymptotic distributions for inference purposes. We also identify the partial ordering induced by our proposed mode-clustering transfers which provides a useful robustness test for inequality indices in the spirit of stochastic dominance conditions. In an empirical illustration we study the evolution of inequality in educational attainment and experience among men and women in Canada.
A Statistical Learning Approach to Evidence the Acoustic Miracles in the Holy Quran Using Audio Features

: This paper presents a novel approach for exploring the intrinsic acoustic properties of the Holy Quran, in an attempt to provide yet one more evidence of the miraculous nature of the Quran. The study uses a dataset composed of recitations made by seven prominent reciters and three chapters of the Quran. A novel statistical approach is used to detect the correlation between the recitations of the reciters for three different Chapters (Quranic Surah). The study utilizes the Mel-Frequency Cepstral Coefficients (MFCCs) feature to detect certain common patterns among the recitations. The main measurement indexes used in this study are the correlation and the Euclidian Distance (ED) between the mean of the MFCCs Cepstral Coefficients, and deltadelta MFCCs. The study reveals a strong correlation and short distance between all recitations for one verse at a time, and relatively high correlation and short distance for two or more verses. Furthermore, the study lays down a foundation to detect and formulate acoustic clusters for sequential verses in the Holy Quran.
Understanding the Perception and Awareness of Education Technologies’ Privacy and Security Issues

The rapid growth of education technologies (EdTech), accompanied by large-scale data collection with obscure policies for use and sharing, poses numerous privacy and security risks to users. While policymakers and the research community struggle to catch up with EdTech’s fast development, adequate risk awareness among the users and other stakeholders can go a long way in ensuring proper use and initiating movements against invasive technologies. Past studies surveyed users to understand their risk perceptions, but they predominantly focused on students, who may have little understanding of potential privacy risks (e.g., young students) or control over EdTech’s deployment. Moreover, while past studies repeatedly identified a general concern about surveillance and data collection, studies examining users’ perceptions of and experiences with specific EdTech apps remain scarce. Toward a broader and more nuanced understanding of risk perception, experience, and awareness involving a larger population, this paper reports findings based on large-scale online datasets: 9M Twitter posts and 0.5M Reddit posts related to EdTech. We implemented a novel pipeline to automatically identify posts re-lated to EdTech’s privacy and security issues; overall, we detected a low level of awareness about these issues among the examined population. Through manual annotation of more than 7,000 tweets and qualitative analysis of 186 Reddit posts, we surfaced concerns people expressed about specific technologies. Moreover, we identified several EdTech stakeholder groups (e.g., educators) from online profiles to examine their perspectives. Most educators cared for students’ privacy—affirming past studies; but importantly, we also found educators to be concerned about their own privacy. In contrast, some educators regarded students’ concerns for privacy as unwarranted and advocated for EdTech’s increased use. We discovered power asymmetry and tensions between stake-holders (e.g., instructors and administrators) regarding EdTech’s deployment and use, which may exacerbate the risks for students.
A nalysis of the Small Oscillations of a Pendulum Partially Filled by a Heavy Capillary Liquid

The authors study the small oscillations of a compound pendulum partially filled by a heavy liquid, in presence of surface tensions. Using the variational formulation of the problem, they prove that, under a very simple geometrical condition, it is a classical vibration problem. On the other hand, introducing the operatorial equations of motion, they prove that the eigenvalues equation can be obtained by equaling to zero an absolutely convergent infinite determinant.
Robust and Pseudo-Robust Solutions to Lord’s Paradox

ANCOVA-type analyses of residualized-change scores and difference-score analyses of simple change scores often produce discrepant and even contradictory results in non-randomized pre-post studies, a problem known as Lord’s Paradox (Lord, 1967). Duncan et al. (2014) called for developmental science to emulate econometrics in prioritizing robust results across different types of analyses and datasets. Robustness is especially impressive when the two+ analyses have contrasting biases, as is often the case for the two types of change-score analyses. Unfortunately, consistency across analyses of residualized and simple change scores can often occur without reducing the bias when Lord’s Paradox applies, a consistency of results we call pseudo-robustness . This paper identifies four pseudo-robust solutions to Lord’s Paradox. We limit ourselves to 2-occasion 2-group cases, consistent with our building-block approach. We need to understand simple building blocks more thoroughly to interpret more complex longitudinal analysis appropriately.
DESIGN OF A NEW TEST RIG TO INVESTIGATE TRANSONIC EXTERNAL FAN COWL SEPARATION

Ultra high-bypass ratio engines, which show considerable promise in reducing the environmental impact of commercial aviation, generally adopt slim fan cowl profiles. These geometries can be more sensitive to separation on the external surfaces in engine windmilling conditions during take-off climb out or during cruise. This paper describes the development of a two-dimensional wind tunnel rig which can accurately replicate the separation mechanisms experienced by real aero-engine na-celles. This design process highlights the importance of considering factors such as Reynolds-number effects, tunnel-wall effects, the two-dimensional nature of the rig, and the tunnel boundary layers.
GDPRxiv: Establishing the State of the Art in GDPR Enforcement

Though European Union's General Data Protection Regulation (GDPR) is hailed as a model privacy regulation, details about its enforcement are not well understood. To address this gap, we propose establishing the state of the art (SOTA) in GDPR enforcement, and present the design and implementation of GDPRxiv: an information archival system that collects and curates GDPR rulings, judgements, reports, and official guidances. GDPRxiv consists of 8000+ official precedents and guidances, the largest such collection. To demonstrate the usefulness of this corpora, we share insights gleaned at the aggregate-level (say, how is the GDPR being enforced in the field) and at the article-level (say, what are the common failures observed in the field while implementing article-17 Right to be Forgotten). We release all of our software artifacts and datasets at https://GDPRxiv.org.
Thermodynamic evidence of a second skyrmion lattice phase and tilted conical phase in 
Cu2OSeO3

M. Halder, A. Chacon, A. Bauer, W. Simeth, S. Mühlbauer, H. Berger, L. Heinen, M. Garst, A. Rosch, and C. Pfleiderer Physik Department, Technische Universität München, D-85748 Garching, Germany Heinz Maier-Leibnitz Zentrum (MLZ), Technische Universität München, D-85748 Garching, Germany École Polytechnique Federale de Lausanne, CH-1015 Lausanne, Switzerland Institut für Theoretische Physik, Universität zu Köln, D-50937 Köln, Germany Institut für Theoretische Physik, Technische Universität Dresden, D-01062 Dresden, Germany (Dated: March 31, 2021)
An Automatic Tool Performing Functional Analysis in MR Urography in Children

Magnetic Resonance urography (MRU) can be used to evaluate abnormalities of the urinary tract in children, with the advantage of being a non-invasive technique and allowing both morphologic and functional assessments. Today, MRU analysis is usually performed using semi-automatic software that typically requires manual segmentation of kidney and pelvis and other time-consuming interactions. In this work, we propose a deep learning approach to automatize the functional MRU analysis. Our pipeline first employs an Attention U-Net for kidney and pelvis segmentation on morphological magnetic resonance and then an image registration process to align the segmentations on the functional MR. The automatic segmentation of morphological MR has been tested on 107 patients using cross validation, achieving a dice score of $\mathbf{0.87}\pm \mathbf{0.15}$ and $\mathbf{0.91}\pm \mathbf{0.11}$ for left and right kidney, and a dice score of $\mathbf{0.75}\pm \mathbf{0.24}$ and $\mathbf{0.71}\pm \mathbf{0.25}$ for the left and right pelvis respectively. These segmentations are used to extract morphological and functional parameters to assess the urinary-tract function in children undergoing analysis. The proposed approach has been integrated into a commercial web viewer (DicomVision 0.18.3) so that it can be used easily by clinical staff. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of children MRU, thus laying the pave for its wider exploitation in clinical routines.
Research Status and Prospect of Medical Material Scheduling

: When sudden public health events broke out, the demand for medical materials surged, so there were some problems such as lack of materials and low efficiency of medical materials transportation. This paper collects the literature related to medical material scheduling at home and abroad, mainly summarizes the location of medical emergency material warehouse and emergency medical material scheduling model, points out the areas that can be improved in the process of medical material scheduling in China at present and the direction that can be studied in the future, and puts forward several measures to improve it, so as to enhance the national emergency support capacity and better deal with major public health emergencies
Programmable Metasurface-Based Beam Forming by Physics-Driven Deep Learning Network

Programmable metasurfaces have attracted much attention due to the ability of dynamical manipulation of electromagnetic (EM) waves. A large variety of EM beams can be constructed by metasurfaces once the digital codes are given properly. The traditional way of getting optimal codes is to use nonlinear optimization algorithms, which takes much time because of high computational complexity. In this work, a new physics-assisted deep learning (DL) model is proposed to compute the coding pattern in milliseconds, given only a simple illustration of the required beam. The physics-driven part would predict the radiation pattern from the designed coding. The difference between the predicted and target pattern will regularize the training process of DL model to give reasonable design in physics. The more appropriate cosine embedding loss (CEL) is utilized instead of the commonly used mean square error loss (MSE) in our model. We investigate both the single- and dual-beam cases, for which reliable design results are given by the proposed method.
A Systematic Review and Meta-Analysis of the Prevalence of Triplex Infections (Combined Human Immunodeficiency Virus, Hepatitis B Virus, and Hepatitis C Virus) among Pregnant Women in Nigeria

Objective We systematically identified the prevalence of triplex infections (combined human immunodeficiency virus (HIV), hepatitis B virus (HBV), and hepatitis C virus (HCV)) in pregnancy. Methods To gather information on the frequency of triplex infections, we searched the databases of PubMed, CINAHL, and Google Scholar. Without regard to language, we utilized search terms that covered HIV, HBV, HCV, and pregnancy. Pregnant women with triplex infections of HIV, HBV, and HCV were included in studies that also examined the prevalence of triplex infections. Review Manager 5.4. 1 was employed to conduct the meta-analysis. Critical appraisal and bias tool risk data were provided as percentages with 95% confidence intervals (95% CIs), and I2 was used as the statistical measure of heterogeneity. The checklist was created by Hoy and colleagues. The study protocol was registered on PROSPERO, under the registration number CRD42020202583. Results Eight studies involving 5314 women were included. We identified one ongoing study. Pooled prevalence of triplex infections was 0.03% (95% CI: 0.02–0.04%) according to meta-analysis. Subgroup analysis demonstrated a significantly high prevalence of 0.08% (95% CI: 0.06–0.10%; 3863 women) in HIV-positive population than 0.00% (95% CI:−0.00-0.00; 1451 women; P < 0.001) in general obstetric population. Moreover, there was a significant difference in the pooled prevalence between studies published between 2001 and 2010 and between 2011 and 2021 (0.14% (95% CI: 0.12 to 0.16 versus 0.03% (95% CI: 0.02 to 0.04%; P < 0.001))) and participants recruited in the period between 2001 and 2011 and between 2012 and 2021 (0.13% (95% CI: 0.05 to 0.21; p=0.002 versus 0.00% (95% CI: −0.00 to 0.00%; p=1.00))), respectively. Conclusion The combined prevalence of prenatal triplex infections was 0.03%, with rates notably higher among the group of pregnant women who were HIV-positive and during the recruitment period that took place before 2012. This prevalence still necessitates screening for these infections as necessary.
Investigation of Physiological Features by Age Groups in Children with Autism

This paper presents the computerized analysis of physiological signals collected from children with autism from different age groups in 4 different countries during a child-robot interaction project. While the child interacts with the KASPAR humanoid robot, physiological signals (blood volume pulse (BVP), skin conductance (EDA), and temperature (ST)) were recorded using an Empatica E4 wristband to explore the emotions or stress of children in future studies. In this study, parametric statistical tests and feature selection methods have been used to investigate whether there are significant differences between age groups. The statistical tests revealed that there are specific subsets of features derived from BVP, EDA, and ST signals causing a significant difference between the signals of children from different age groups. Additionally, the feature selection study showed the set of most distinctive features for each age group.
Developing and Building an Intelligent Inter-Symbol Interference Reduction System Using Deep Learning Techniques

Multipath fading and inter-symbol interference are two common issues that can arise in digital communication systems. These issues can significantly affect the quality of signal transmission and can cause significant distortion of the original signal. In this paper, we propose an improved model to mitigate (cancel) the inter-symbols interference which is one of multipath fading problem effects by building a deep learning model using recurrent neural networks to predict the coefficient values that help in signal recovery. We compared the performance of four equalizers based on neural network, Least Mean Square (LMS) Equalizer, Recursive least squares (RLS), Decision Feedback Equalizer (DFE) based on Recursive least squares algorithm (RLS), and Decision Feedback Equalizer (DFE) based on Least Mean Square algorithm (LMS). In simulation, the new DFE RLS equalizer outperforms the other previously mentioned equalizers. The order from best to worst performance is: DFE RLS equalizer, DFE LMS equalizer, RLS then LMS. We used QAM (Quadrature Amplitude Modulation) for modulation.
A dynamic path planning method for terrain-aided navigation of autonomous underwater vehicles

Terrain-aided navigation (TAN) is one of the most effective approaches for solving the long-range navigation of autonomous underwater vehicles. However, the positioning accuracy of TAN can be greatly influenced by the terrain information of matching areas. A TAN system might also fail catastrophically when the seabed topography changes. To address these problems, a dynamic path planning method for TAN, which includes environment modelling, offline path planning, and online re-planning, is proposed in this paper. Terrain standard deviation is applied to represent terrain information in environment modelling, and the Mahalanobis distance and roulette approach are introduced in the offline path planning approach. Furthermore, the topographic changes are identified and handled via an online re-planning approach. Simulation experiments are conducted, and comparisons are made with the A* algorithm. According to the experimental results, the proposed method improves the positioning accuracy of TAN during the mission, and its ability to deal with seabed topographic changes is also verified.
Cellular, Molecular, Pharmacological, and Nano-Formulation Aspects of Thymoquinone—A Potent Natural Antiviral Agent

The goal of an antiviral agent research is to find an antiviral drug that reduces viral growth without harming healthy cells. Transformations of the virus, new viral strain developments, the resistance of viral pathogens, and side effects are the current challenges in terms of discovering antiviral drugs. The time has come and it is now essential to discover a natural antiviral agent that has the potential to destroy viruses without causing resistance or other unintended side effects. The pharmacological potency of thymoquinone (TQ) against different communicable and non-communicable diseases has been proven by various studies, and TQ is considered to be a safe antiviral substitute. Adjunctive immunomodulatory effects in addition to the antiviral potency of TQ makes it a major compound against viral infection through modulating the production of nitric oxide and reactive oxygen species, decreasing the cytokine storm, and inhibiting endothelial dysfunction. Nevertheless, TQ’s low oral bioavailability, short half-life, poor water solubility, and conventional formulation are barriers to achieving its optimal pharmacologic benefits. Nano-formulation proposes numerous ways to overcome these obstacles through a small particle size, a big surface area, and a variety of surface modifications. Nano-based pharmaceutical innovations to combat viral infections using TQ are a promising approach to treating surmounting viral infections.
A Compact Dual-Band Omnidirectional Circularly Polarized Filtering Antenna for UAV Communications

A compact, low-profile, dual-band (2.4/5.8 GHz) omnidirectional circularly polarized (CP) antenna for consumer Unmanned Aerial Vehicle (UAV) communications is proposed. Given the constraints of UAV installation, we incorporate complex miniaturization etching techniques into the antenna design. The low-frequency resonance is primarily realized by four short-terminated arcs, while the high-frequency resonance is predominantly induced by four open-terminated branches. In addition, a dual-band coupled strip-line filter is cascaded to the radiator, enhancing the design's bandwidths. A prototype of the antenna is simulated, fabricated, and measured, resulting in final dimensions of 0.2 × 0.2 × 0. 042λ03 (λ0 is the free-space wavelength at 2.4 GHz). The -10 dB |S11| and 3 dB Axial Ratio (AR) overlapped bandwidths reach 40 and 125 MHz for the low- and high-frequency bands, respectively. Lastly, we present an example of integrating the antenna into a quadcopter UAV and briefly discuss the potential of applying this antenna for a Multiple-Input, Multiple-Output (MIMO) system.