Miracle-dz/UniFAR-FTUs huggingface dataset:
Coronavirus Role of hospital environmental surfaces in the transmission of the severe acute respiratory syndrome - Coronavirus-2

Introduction: Severe acute respiratory syndrome - Coronavirus-2 (SARS-CoV-2) is mainly transmitted via respiratory secretions through coughing, sneezing, or contact with contaminated surfaces. This virus can be present in feces and many body fluids. The study aimed to screen the hospital environment as a potential source for SARS-CoV-2 transmission and identify the hospital zones with the highest contamination levels. Methodology: Swabs were collected from different sites in the hospital before and after routine cleaning/disinfection, transported in vials containing 1-3 mL of viral transport medium, and stored at - 80 ℃ as soon as possible until the time of testing. The real -time reverse-transcription PCR (rRT-PCR) system targeting RNA-dependent RNA polymerase and E genes was used to detect the SARS-CoV-2 RNA. Results: Moderate environmental contamination by SARS-CoV-2 RNA was detected by rRT-PCR before routine cleaning/disinfection (52% of the swabs were positive). The hospital surfaces with the highest contamination levels were elevators’ buttons, sinks and faucets’ handles at the waiting rooms, patient’s room and bathroom, call buttons and telephones in the patient’s room, toilet bowl surface, the doorknob and light switches at the X-ray room, and the computer keyboard at the staffroom. All the swabs collected after routine cleaning/disinfection were negative for SARS-CoV-2 RNA by rRT-PCR. Conclusions: The hospital environment is a high-risk area that can be contaminated by SARS-CoV-2 through contact, respiratory, and maybe fecal shedding of the virus. To limit this fatal virus transmission, strict adherence to proper hand hygiene with frequent optimal decontamination of hospital environmental surfaces is essential.
Lamb shift of energy levels in quantum rings

We study the vacuum radiative corrections to energy levels of a confined electron in quantum rings. The calculations are provided for the Lamb shift of energy levels in a low-momentum region of virtual photons and for both one-dimensional and two-dimensional quantum rings. We show that contrary to the well known case of a hydrogen atom the value of the Lamb shift increases with the magnetic momentum quantum number m. We also investigate the dependence of the Lamb shift on magnetic flux piercing the ring and demonstrate a presence of magnetic-flux-dependent oscillations. For a one-dimensional ring the value of the shift strongly depends on the radius of the ring. It is small for semiconductor rings but can attain measurable quantities in natural organic ring-shape molecules, such as benzene, cycloalcanes and porphyrins.
Performance Analysis of 2x2 MIMO 16-PSK with and without Applying TCM Coding Over Different Communication Channels

A high data rate wireless communication is required in many data communication systems. In most wireless communications, most of the research is focused on evaluating and improving service systems with higher data rates and spectral efficiency that satisfy more users and better channel capacity requirements. Modulation schemes and coding techniques are the key parameters for improving system performance to an optimized data rate without increasing the channel bandwidth achievable for a wireless system using orthogonal space-time block code (OSTBC) with modern multiinput multi-outputs (MIMO) systems. This work concentrates on evaluating bit error rate (BER) performance of16-PSK modulation technique with and without applying trellis-coded modulation (TCM) coding over three different communication channels, additive white gaussian noise (AWGN), Rayleigh, and Rician channels, using MIMO system to obtain a sensible BER performance while a 2x2 MIMO system is proposed. The achieved simulation results in both cases of 16-PSK with and without applying TCM showed that more Eb/No energy is required for the 16-PSK without TCM to achieve the same BER compared to 16-PSK with TCM coding technique. Therefore, applying TCM coding technique improved the performance on average by about 3 dB. Also, in both cases with and without TCM coding over AWGN channel gives better performance compared to the Rician and Rayleigh channels.
Link protection for the signal degradation of the SR-BE tunnels in the SPN

With the increase of business scenarios in 5G communication, the 5G bearer network puts forward higher requirements in terms of bandwidth, time delay, pan-connection and reliability, and also puts forward extremely high requirements for network-level protection technology. The introduction of segment routing tunnel technology, the innovative establishment of 5G efficient carrying network. Due to the working mechanism of the SR tunnel, the traditional signal degradation protection technology is no longer suitable. This paper proposes a protection scheme for the existence of signal degradation in the Segment Routing - Best Effort tunnel through the OAM message transmission alarm. The signal degradation detection and ISIS protocol, can realize link protection, in order to further improve the user experience, design method trigger TI-LFA protection business protection reverse, improve the reliability and viability of 5G carrying network, confirmed that the scheme has the ability to detect and protect the business flow signal, can be protected in the east-west eX2 traffic business.
Twopm: Two-Part Models

In this article, we describe twopm, a command for fitting two-part models for mixed discrete-continuous outcomes. In the two-part model, a binary choice model is fit for the probability of observing a positive-versus-zero outcome. Then, conditional on a positive outcome, an appropriate regression model is fit for the positive outcome. The twopm command allows the user to leverage the capabilities of predict and margins to calculate predictions and marginal effects and their standard errors from the combined first- and second-part models.
Compressed sensing reconstruction using expectation propagation

Many interesting problems in fields ranging from telecommunications to computational biology can be formalized in terms of large underdetermined systems of linear equations with additional constraints or regularizers. One of the most studied, the compressed sensing problem, consists in finding the solution with the smallest number of non-zero components of a given system of linear equations for known measurement vector and sensing matrix . Here, we will address the compressed sensing problem within a Bayesian inference framework where the sparsity constraint is remapped into a singular prior distribution (called Spike-and-Slab or Bernoulli–Gauss). A solution to the problem is attempted through the computation of marginal distributions via expectation propagation, an iterative computational scheme originally developed in statistical physics. We will show that this strategy is more accurate for statistically correlated measurement matrices. For computational strategies based on the Bayesian framework such as variants of belief propagation, this is to be expected, as they implicitly rely on the hypothesis of statistical independence among the entries of the sensing matrix. Perhaps surprisingly, the method outperforms uniformly all the other state-of-the-art methods in our tests.
Event-Based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of $\mu$μs), very high dynamic range (140 dB versus 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world.
Angular Momentum across the Hubble sequence from the CALIFA survey

Abstract We investigate the stellar angular momentum of galaxies across the Hubble sequence from the CALIFA survey. The distribution of CALIFA elliptical and lenticular galaxies in the λRe - εe diagram is consistent with that shown by the Atlas3D survey. Our data, however, show that the location of spiral galaxies in this diagram is significantly different. We have found two families of spiral galaxies with particularly peculiar properties: (a) spiral galaxies with much higher λRe values than any elliptical and lenticular galaxy; (b) low-mass spiral galaxies with observed λRe values much lower than expected for their apparent flattening. We use these two families of objects to argue that (1) fading alone cannot explain the transformation of spiral to lenticular galaxies, and (2) that those low-mass spiral galaxies are in fact dark matter dominated, which explains the unusually low angular momentum.
Symplectic topology on algebraic 3-folds

In [18], the author introduced two Donaldson-type invariants, Φ and Φ, based on Gromov's remarkable theory of pseudoholomorphic curves in a symplectic manifold V. Roughly speaking, φ is based on counting the number of holomorphic spheres in V, and φ is based on counting the perturbed holomorphic maps from S to V. A major difference between the two invariants is that Φ takes into account multiple cover maps [8], but Φ does not. It turns out that Φ is the invariant used in topological σ models in mathematical physics. There is a remarkable mirror symmetry phenomenon among Calabi-Yau 3-folds relating this invariant to the variation of Hodge structures of its mirror. But we shall not say anything more about this phenomenon here. Instead, we refer the reader to [24], [14]. Here we deal with a different type of application, primarily for Φ . We should point out that various simple forms of these two invariants have been used by Gromov [5] and McDuff [10], [12]. Before we give the definition of Φ, recall that a symplectic manifold ( F , ω) is semipositive if for any A in the image of Hurewicz map π2(V) -> H2(V, Z), ω(A) > 0 implies that c{(V)A > 0. Now we give the definition of Φ, following the notation in [18]. Let Ω(F) be the oriented bordism group of V. Definition. Let ( F , ω) be a symplectic manifold and A e H2(V9 Z) with c{(V)A > 0. Furthermore, if d i m F > 8, suppose that ( F , ω ) is semipositive. Choose a generic tamed almost complex structure / on V. For any aχ, ••• , ak e Ω(F) such that degα^ < 2n 2 and Σjiln degα, 2) = 2cx(V)A + 2n 6, choose a representative of at, still denoted by α z . We can define an integer Φ^A 3 ω ) ( α j , , ak) as follows. First notice the following:
Deep Filtering: Signal Extraction and Reconstruction Using Complex Time-Frequency Filters

Signal extraction from a single-channel mixture with additional undesired signals is most commonly performed using time-frequency (TF) masks. Typically, the mask is estimated with a deep neural network (DNN), and element-wise applied to the complex mixture short-time Fourier transform (STFT) representation to perform the extraction. Ideal mask magnitudes are zero for solely undesired signals in a TF bin and undefined for total destructive interference. Usually, masks have an upper bound to provide well-defined DNN outputs at the cost of limited extraction capabilities. We propose to estimate with a DNN a complex TF filter for each mixture TF bin which maps an STFT area in the respective mixture to the desired TF bin to address destructive interference in mixture TF bins. The DNN is optimized by minimizing the error between the extracted and the ground-truth desired signal allowing to learn the TF filters without having to specify ground-truth TF filters. We compare our approach with complex and real-valued TF masks by separating speech from a variety of different sound and noise classes from the Google AudioSet corpus. We also process the mixture STFT with notch-filters and zero whole time-frames, to simulate packet-loss during transmission, to demonstrate the reconstruction capabilities of our approach. The proposed method outperformed the baselines, especially when notch-filters and time-frame zeroing were applied.
Qualified Types: Theory and Practice

1. Introduction 2. Predicates 3. Type inference for qualified types 4. Evidence 5. Semantics and coherence 6. Theory into practice 7. Type classes in Haskell 8. Type classes in Gofer 9. Summary and future work 10. Epilogue Appendix References Index.
PIV measurements in the draft tube of a down-scale propeller turbine: uncertainty analysis

In this study, the flow in the conical section of the draft tube of a propeller turbine has been investigated at the best efficiency point and part-load operating conditions using 2D and stereoscopic 3D particle image velocimetry. Since the flow in the turbine is periodic, it is necessary to study the mean flow field rather than the instantaneous one to identify the flow characteristics from a statistical standpoint. However, the statistical convergence of the obtained mean velocity is questionable. Thus, the current work proposes a methodology for investigating the convergence of mean velocity profiles based on the central limit theorem. The methodology is applied to the best efficiency point and part-load results. The results show that 3D PIV results have lower uncertainty than 2D PIV results because measuring the tangential velocity component affects uncertainty, only measured in 3D PIV. The uncertainty difference is more significant, especially in part-load operation, due to the presence of the rotating vortex rope, and therefore a more accurate measurement is necessary to produce a reliable mean flow field. Furthermore, the convergence of the mean velocity profile is faster, with lower uncertainty for best efficiency point results since, at the part-load condition, the tangential velocity component of the flow is higher. In addition, the converged mean velocity profiles show a backflow region with minor rotation in the center, surrounded by a high rotational axial flow during the part-load operation of the turbine.
A novel plasmonic sensor using solid core D-shaped negative curvature optical fiber with AU–TiO2 layer

Recently, unique structure fiber enacts a significant role in sensing applications. A novel D-shaped negative curvature fiber based surface plasmon resonance (SPR) sensor is proposed. The design consists of negative curvature solid core fiber surrounded by a cladding composed of double-layer adjoined airholes. An outside layer of the cladding contains semi-circular airholes, and the interior layer has small circular air holes to overwhelm the higher-order modes. The external sensing approach detects changes in the refractive index between 1.33 and 1.37. Employing gold as a plasmonic material, SPR is developed on top of the photonic crystal fiber (PCF). Between the gold layer and the PCF, a small layer of titanium dioxide is placed for binding the plasmonic material to the fiber. And it acts as an adhesive layer. The finite element method is used to perform numerical analyses. After improving the structural parameters, an enhanced sensitivity of 26 000 nm RIU−1 is achieved. Because of its enhanced sensitivity, this sensor is used in a bio-medical application to detect and prevent diseases in the early stage.
Semiparametric Methods for the Analysis of Progression-Related Endpoints

Use of progression-free survival in the evaluation of clinical interventions is hampered by a variety of issues, including censoring patterns not addressed in the usual methods for survival analysis. Progression can be right-censored before survival or interval-censored between inspection times. Current practice calls for imputing events to their time of detection. Such an approach is prone to bias, underestimates standard errors andmakes inefficient use of the data at hand. Moreover a composite outcome prevents inference about the actual treatment effect on the risk of progression. This thesis develops semiparametric and sievemaximum likelihood estimators to more formally analyze progression-related endpoints. For the special case where death rarely precedes progression, a Cox-Aalen model is proposed for regression analysis of time-to-progression under intermittent inspection. The general setting considering both progression and survival is examined with a Markov Cox-type illness-death model under various censoring schemes. All of the resulting estimators globally converge to the truth slower than the parametric rate, but their finitedimensional components are asymptotically efficient. Numerical studies suggest that the new methods perform better than their imputation-based alternatives under moderate to large samples having higher rates of censoring.
From Unruh temperature to the generalized Bousso bound

In a classical spacetime satisfying Einstein's equation and the null convergence condition, the same quantum mechanical effects that cause black holes to have a temperature are found to imply, if joined to the macroscopic nature of entropy, the covariant entropy bound in its generalized form. This is obtained from thermodynamics, as applied across the local Rindler causal horizon through every point p of the null hypersurfaces L the covariant entropy bound refers to, in the direction of the null geodesics generating L.
Serology Assays Used in SARS-CoV-2 Seroprevalence Surveys Worldwide: A Systematic Review and Meta-Analysis of Assay Features, Testing Algorithms, and Performance

Background: Many SARS-CoV-2 serological assays were rapidly developed during the COVID-19 pandemic. However, differences in detection mechanism limit the comparability of assay outputs. Methods: As part of the SeroTracker global living systematic review of SARS-CoV-2 seroprevalence studies, we collated serological assays used in serosurveys between January 1, 2020 and November 19, 2021. We mapped performance metrics to the manufacturer, third-party head-to-head, and independent group evaluations, comparing the assay performance data using a mixed-effect beta regression model. Results: Among 1807 serosurveys, 192 distinctive commercial assays and 380 self-developed assays were identified. According to manufacturers, 28.6% of all commercial assays met WHO criteria for emergency use (sensitivity [Sn.] >= 90.0%, specificity [Sp.] >= 97.0%). Third-party and independent evaluations indicated that manufacturers overstated the Sn. of their assays by 5.4% and 2.8%, and Sp. by 6.3% and 1.2%. We found in simulations that inaccurate Sn. and Sp. can substantially bias seroprevalence estimates corrected for assay performance. Conclusions: The Sn. and Sp. of the serological assay are not fixed properties, but varying features depending on testing population. To achieve precise population estimates and to ensure comparability, serosurveys should select assays with strong, independently validated performance and adjust seroprevalence estimates based on assured performance data.
Communism’s Lasting Effect? Former Communist States and COVID-19 Vaccinations

Historical cultural practices that no longer exist can have modern day effects. Because communism has been linked with distrust of government, it was hypothesized that (a) historical communism would be negatively associated with COVID-19 vaccination rates, and (b) trust in government would mediate the association. Two studies assessed these hypotheses. Study 1 tested the hypotheses among European, Asian, and African countries, while Study 2 focused on East and West Germany within Europe. All samples except Africa found support for an association between historical communism and lower COVID-19 vaccination rates. However, trust in government did not mediate the association in Study 1, though a significant indirect effect did emerge within Germany in Study 2. Associations held controlling for GDP and age of population. Together, the studies suggest that historical communism in Europe and Asia is associated with real-world behavior today, and that trust in government might be partly responsible for the effect within Germany but less likely within Europe as a whole.
What determines online rumour sharing on COVID-19? A stimulus–response framework

Based on the stimulus–response framework, this study examines the external environmental stimuli influencing online rumour sharing about COVID-19 and considers the contingent effect of fear. A large-scale online survey was used to test the proposed research model and hypotheses. The final data set comprised 2807 valid responses. The results indicate that perceptions of community safety and infection risk negatively affect online rumour sharing, while social influence positively affects online rumour sharing. Fear weakens the negative effects of community safety on online rumour sharing but strengthens the positive effect of social influence on online rumour sharing. This study provides a comprehensive analysis by applying the stimulus–response framework to explore the underlying drivers of online rumour sharing with regard to COVID-19 and the moderating effects of fear in the Chinese context.
Generating Minimal Adversarial Perturbations with Integrated Adaptive Gradients

We focus our attention on the problem of generating adversarial perturbations based on the gradient in image classification domain; substantial pixel perturbations make features learned by deep neural networks changed in clean images which fool deep neural models into making incorrect predictions. However, large-scale pixel modification in the image directly makes the changes visible although attack process success. To find the optimal perturbations which can quantify the boundary distance directly between clean images and adversarial examples in latent space, we propose a novel method for integrated adversarial perturbations generation, which formulate adversarial perturbations in the adaptive and integrated gradient level. Our approach calls few adaptive gradient operators to seek the decision boundary between original images and corresponding adversarial examples directly. We compare our proposed method for crafting adversarial perturbations with other state-of-the-art gradient-based attack methods. Experimental results suggest that adversarial samples generated by our approach show excellent efficiency in fooling deep neural classification networks with lower pixel modification and good transferability on image classification models.