Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dynamic Wavelet Filtering for Anterior Chamber Optical Coherence Tomographic Images

Speckle noise is a common phenomenon in Optical Coherence Tomography (OCT) images. This paper describes a dynamic filtering approach for anterior chamber OCT images to reduce the speckle noise in wavelet domain. The approach proposed segments the OCT image into some parts and identifies if the parts have region of interest (ROI), which includes the anterior chamber tissues. Then, it suppresses noise with three different suppression strategies in part with ROI. For the part without ROI, it sets the discrete wavelet transform coefficients of this part to zero. Here, the sampling-based sub-band adaptive algorithm is used to distinguish the ROI; and the correlations of neighboring wavelet coefficients and the coefficients of the corresponding locations in adjacent decomposition levels are used to suppress the noise. The performance improvement over the previously published method is quantified in terms of noise suppression, image structural preservation and visual quality. The numerical values of the image quality metrics along with the qualitative analysis results indicated that the approach proposed has better performance.
A new approach for sensitivity improvement of retinal blood vessel segmentation in high-resolution fundus images based on phase stretch transform

The eye-fundus photograph is widely used for eye examinations. Accurate identification of retinal blood vessels could reveal information that is helpful for clinical diagnoses of many health disorders. Although several methods have been proposed to segment images of retinal blood vessels, the sensitivity of these methods is plausible to be improved. The algorithm’s sensitivity refers to the algorithm’s ability to identify retinal vessel pixels correctly. Furthermore, the resolution and quality of retinal images are improving rapidly. Consequently, new segmentation methods are in demand to overcome issues from high-resolution images. This study presented improved performance of retinal vessel segmentation using a novel edge detection scheme based on the phase stretch transform (PST) function as its kernel. Before applying the edge detection stage, the input retinal images were pre-processed. During the pre-processing step, non-local means filtering on the green channel image, followed by contrast limited adaptive histogram equalization (CLAHE) and median filtering, were applied to enhance the retinal image. After applying the edge detection stage, the post-processing steps, including the CLAHE, median filtering, thresholding, morphological opening, and closing, were implemented to obtain the segmented image. The proposed method was evaluated using images from the high-resolution fundus (HRF) public database and yielded promising results for sensitivity improvement of retinal blood vessel detection. The proposed approach contributes to a better segmentation performance with an average sensitivity of 0.813, representing a clear improvement over several benchmark techniques
Potential Neuroprotective Role of Calretinin-N18 and Calbindin-D28k in the Retina of Adult Zebrafish Exposed to Different Wavelength Lights

The incidence rates of light-induced retinopathies have increased significantly in the last decades because of continuous exposure to light from different electronic devices. Recent studies showed that exposure to blue light had been related to the pathogenesis of light-induced retinopathies. However, the pathophysiological mechanisms underlying changes induced by light exposure are not fully known yet. In the present study, the effects of exposure to light at different wavelengths with emission peaks in the blue light range (400–500 nm) on the localization of Calretinin-N18 (CaR-N18) and Calbindin-D28K (CaB-D28K) in adult zebrafish retina are studied using double immunofluorescence with confocal laser microscopy. CaB-D28K and CaR-N18 are two homologous cytosolic calcium-binding proteins (CaBPs) implicated in essential process regulation in central and peripheral nervous systems. CaB-D28K and CaR-N18 distributions are investigated to elucidate their potential role in maintaining retinal homeostasis under distinct light conditions and darkness. The results showed that light influences CaB-D28K and CaR-N18 distribution in the retina of adult zebrafish, suggesting that these CaBPs could be involved in the pathophysiology of retinal damage induced by the short-wavelength visible light spectrum.
Social Movement Sustainability on Social Media: An Analysis of the Women's March Movement on Twitter

Social media has emerged as a powerful medium to organize and mobilize social movements. In particular, the connective action of social media builds associations and allows for the continuity of social movements. Yet there is a lack of research on how connective action emergent from social media messages sustains long-term social movements. Accordingly, in this study, we concentrate on Twitter messages related to Women’s March protests held in 2017, 2018, and 2019. Using an interpretive analysis followed by the topic modeling approach, we analyzed the tweets to identify the different types of messages associated with the movement. These messages were classified using a set of categories and subcategories. Furthermore, we conducted a temporal analysis of the message (sub)categories to understand how distinct messages allow movement continuity beyond a specific protest march. Results suggest that while most of the messages are used to motivate and mobilize individuals, the connective action tactics employed through messages sent before, during, and after the marches allowed Women’s March to become a broader and more persistent movement. We advance theoretical propositions to explain the sustainability of a long-term social movement on social media, exemplified through large-scale connective action that persists over time. In doing so, this study contributes to connective action research by providing message categorization that synthesizes the meaning of message content. The findings could help social movement organizers learn different ways to frame messages that resonate with broader social media users. Moreover, our approach to analyzing a large set of tweets might interest other qualitative researchers.
Energy relaxation in quantum dots

We present the results of energy relaxation calculations for electron-hole pairs confined in a quantum dot and in strong coupling with LO phonons (excitonic polarons). We show that despite the robustness of the polaron entity, its disintegration induced by the anharmonic decay of its LO phonon component can lead to an efficient energy relaxation. We compute the time-dependent photoluminescence signals associated with the ground-state excitonic polaron as well as the photoluminescence excitation signal and the energy loss rate of an excitonic polaron in a quantum dot. Our calculations clearly evidence the existence of dot parameters where the energy relaxation is inhibited.
A cosmological redshift-distance square law

We directly examine the claims of Segal that the (m,z) Hubble diagram is fitted best by a square law z=Kr/sup 2/ rather than by the traditional Hubble law z=Hr in the low-redshift range z< or approx. =0.01, corresponding to galaxies brighter than 14th mag. Segal attempts to fit a distance relation to the (m,z) scatter diagram in which each individual galaxy is plotted. We calculate the exact relation between the mean redshift for all galaxies in a small magnitude interval m +- deltam/2 and the apparent magnitude m. Our relation is independent of luminosity function and peculiar velocity distribution about the general expansion. Also, it is not affected by sample incompleteness as a function of apparent magnitude or the clustering of galaxies in the sample. Segal's method is affected by all of these and requires a highly sophisticated statistical analysis to deal with the non-Gaussian pointwise scatter. Our analysis favors the Hubble law and conclusively rules out the square law for the small redshift region.
Unified Active Damping Strategy Based on Generalized Virtual Impedance in LCL-Type Grid-Connected Inverter

Extensive solutions have been proposed to damp LCL filter resonance. However, the time delay caused by computation and modulation process aggravates the complexity of current loop analysis. In this article, a generalized virtual impedance model for six kinds of commonly used active damping methods is proposed, and the physical meaning is established by introducing the metric of “damping factor. ” To ensure system robustness in a wide range of grid conditions, a hybrid active damping strategy that combines inverter current feedback and capacitor voltage feedforward is proposed. Importantly, this method only requires inverter current and capacitor voltage sensors, which are the basic variables for over-current protection, power control, and synchronization. The superposition theorem is also utilized to analyze the damping capability provided by parasitic resistances, current feedback loop, and voltage feedforward loop. Finally, experimental results verify the feasibility and robustness of the proposed damping method.
Measurement of thermal-mechanical noise in microelectromechanical systems

We report absolute measurements of thermal-mechanical noise in microelectromechanical systems. The devices are studied with an optical microcavity technique that has a resolution on the order of tens of femtometers per root hertz. The measured noise spectrum agrees with the calculated noise level to within 25%, a discrepancy most likely due to uncertainty in the effective dynamic mass of the vibrating bridge. These measurements demonstrate that thermal-mechanical noise can be the dominant noise source in actuated microelectromechanical devices.
Analysis and Design of Multi-Stacked FET Power Amplifier With Phase-Compensation Inductors in Millimeter-Wave Band

Stacked-FET topology is analyzed to increase the output power of a power amplifier (PA) in the millimeter-wave (mm-wave) band. In the mm-wave band, parasitic capacitances of the transistor severely degrade stacking efficiency due to the phase mismatch between stacked FETs. The phase-compensation (PC) inductances, including the losses of the inductor for the best stacking efficiency, are presented in both series and shunt connections. From this analysis, a triple-stacked-FET PA is designed in the <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>-band. Proper PC series or shunt inductor types are used between the first and second stacked FETs and between the second and third stacked FETs in consideration of the core layout and inductor size. The PA is fabricated in the 28-nm CMOS fully depleted silicon-on-insulator (FD-SOI) process. With a compact core area of 0.054 mm2, the PA achieves peak <inline-formula> <tex-math notation="LaTeX">$P_{\mathrm {SAT}}$ </tex-math></inline-formula> and PAEMAX of 15.1 dBm and 18.6%, respectively.
Observations of fast anisotropic ion heating, ion cooling, and ion recycling in large-amplitude drift waves

Large-amplitude drift wave fluctuations are observed to cause severe ion temperature oscillations in plasmas of the Caltech Encore tokamak [J. M. McChesney, P. M. Bellan, and R. A. Stern, Phys. Fluids B 3, 3370 (1991)]. Experimental investigations of the complete ion dynamical behavior in these waves are presented. The wave electric field excites stochastic ion orbits in the plane normal (perpendicular to) to B, resulting in rapid perpendicular to heating. Ion-ion collisions impart energy along (parallel to) B, relaxing the perpendicular to-parallel to temperature anisotropy. Hot ions with large orbit radii escape confinement, reaching the chamber wall and cooling the distribution. Cold ions from the plasma edge convect back into the plasma (i.e., recycle), causing further cooling and significantly replenishing the density depleted by orbit losses. The ion-ion collision period tau(ii)similar to Tau(3/2)/n fluctuates strongly with the drift wave phase, due to intense (approximate to 50%) fluctuations in n and Tau. Evidence for particle recycling is given by observations of bimodal ion velocity distributions near the plasma edge, indicating the presence of cold ions (0.4 eV) superposed atop the hot (4-8 eV) plasma background. These appear periodically, synchronous with the drift wave phase at which ion fluid flow from the wall toward the plasma center peaks. Evidence is presented that such a periodic heat/loss/recycle/cool process is expected in plasmas with strong stochastic heating.
D Region Electron Density Derived From Sprite Observations

Four upward propagating streamers in two carrot sprites over northwest Texas were recorded at 100,000 frames per second at 07:46:35 UT, 2 June 2019, from the Langmuir Laboratory near Socorro, New Mexico. The streamers reached velocities of 50–80 × 106 m/s with accelerations up to 25 × 1010 m/s/s. The upward motion ended with the top of the streamers near 90 km altitude. At this time, the streamers reached maximum brightness. The streamer head then dissolved and the brightness decayed exponentially with time constants between 0.078 and 0.097 ms. We interpret the dissolution and decay to be the result of interaction with the bottom of the ionosphere. Assuming the decay to be dictated by electric field relaxation, the ambient D region electron density would be 107 to 108 m−3. The analysis suggests that sprite observations can provide multipoint measurements of the D region ionosphere impacted by powerful lightning capable of triggering sprites.
Maximum studentized score tests for the detection of outliers in time series regression models

Efficient score tests exist among others, for testing the presence of additive and/or innovative outliers that are the result of the shifted mean of the error process under the regression model. A sample influence function of autocorrelation-based diagnostic technique also exists for the detection of outliers that are the result of the shifted autocorrelations. The later diagnostic technique is however not useful if the outlying observation does not affect the autocorrelation structure but is generated due to an inflation in the variance of the error process under the regression model. In this paper, we develop a unified maximum studentized type test which is applicable for testing the additive and innovative outliers as well as variance shifted outliers that may or may not affect the autocorrelation structure of the outlier free time series observations. Since the computation of the p-values for the maximum studentized type test is not easy in general, we propose a Satterthwaite type approximation based on suitable doubly non-central F-distributions for finding such p-values [F.E. Satterthwaite, An approximate distribution of estimates of variance components, Biometrics 2 (1946), pp. 110–114]. The approximations are evaluated through a simulation study, for example, for the detection of additive and innovative outliers as well as variance shifted outliers that do not affect the autocorrelation structure of the outlier free time series observations. Some simulation results on model misspecification effects on outlier detection are also provided.
Analysis of the 1 December 2011 Wasatch Downslope Windstorm

AbstractA downslope windstorm on 1 December 2011 led to considerable damage along a narrow 50-km swath at the western base of the Wasatch Mountains in northern Utah. The strongest surface winds began suddenly at 0900 UTC, primarily in the southern portion of the damage zone. Surface winds reached their peak intensity with gusts to 45 m s−1 at ~1600 UTC, while the strongest winds shifted later to the northern end of the damage swath. The northward shift in strong surface winds relates to the rotation of synoptic-scale flow from northeasterly to easterly at crest level, controlled by an evolving anticyclonic Rossby wave breaking event. A rawinsonde released at ~1100 UTC in the midst of strong (>35 m s−1) easterly surface wind intersected a rotor and sampled the strong inversion that surmounted it. The windstorm’s evolution was further examined via Weather Research and Forecasting Model simulations initialized from North American Mesoscale Model analyses ~54 h before the windstorm onset. The Control model si.
Towards an Active Foveated Approach to Computer Vision

In this paper, a series of experimental methods are presented explaining a new approach towards active foveated Computer Vision (CV). This is a collaborative effort between researchers at CONICET Mendoza Technological Scientiﬁc Center from Argentina, Argonne National Laboratory (ANL), and Loyola University Chicago from the US. The aim is to advance new CV approaches more in line with those found in biological agents in order to bring novel solutions to the main problems faced by current CV applications. Basically this work enhance Self-supervised (SS) learning, incorporating foveated vision plus saccadic behavior in order to improve training and computational efﬁciency without reducing performance signiﬁcantly. This paper includes a compendium of methods’ explanations, and since this is a work that is currently in progress, only preliminary results are provided. We also make our code fully available. 1
Effect of intrinsic growth stress on the Raman spectra of vacuum‐arc‐deposited amorphous carbon films

Amorphous carbon (a‐C) films grown by vacuum‐arc deposition techniques contain substantial amounts of compressive, residual, growth stress (over 10 GPa). The magnitude of the stress can be controlled by changing the incoming energy of the ions. It is observed that stress shifts the Raman scattering feature in these films to higher frequency by as much as 20 cm−1. The Raman spectra of adhering and delaminated films are used to measure the magnitude of the stress‐induced shift, which is −1.9 cm−1/GPa for compressive biaxial stress. The observed value is compared to that expected from disordered diamond and graphite structures.
Comparative Analysis of Data Mining Techniques to Predict Cardiovascular Disease

Cardiovascular disease is the leading cause of death. In recent days, most people are living with cardiovascular disease because of their unhealthy lifestyle and the most alarming issue is the majority of them do not get any symptoms in the early stage. This is why this disease is becoming more deadly. However, medical science has a large amount of data regarding cardiovascular disease, so this data can be used to apply data mining techniques to predict cardiovascular disease at the early stage to reduce its deadly effect. Here, five data mining classification techniques, such as: Naïve Bayes, K-Nearest Neighbors, Support Vector Machine, Random Forest and Decision Tree were implemented in the WEKA tool to get the best accuracy rate and a dataset of 12 attributes with more than 300 instances was used to apply all the data mining techniques to get the best accuracy rate. After doing this research people who are at the early stage of cardiovascular disease or probably going to be a victim can be identified more accurately.
Determinants of the Gender Pay Gap in the Czech Republic and Selected European Countries

: The goal of the article is to analyse determinants of the gender pay gap in selected European countries. For my analysis, I used different types of data published by Eurostat, OECD, the Czech Statistical Office and the Ministry of Labour and Social Affairs of the Czech Republic. Determinants have been estimated using multiple regression analysis in selected European countries including the Czech Republic. The article is also focused on the Czech Republic data on the gross average salary in private and public sectors according to gender and administrative regions. Results show that the maternity benefits and a complicated situation in the labour market, where full-time jobs are preferred, and moreover the parental allowances are strong drivers of the gender pay gap in the Czech Republic. This implies that the issue of the gender pay gap in the Czech Republic is rather structural, influenced by the social system and the labour market features. Future research should take into consideration the tax allowances as well (specific tax credits on children and a spouse).
Effect of oxygen on the optical properties of small silicon pyramidal clusters

Optical absorption and light emission of oxygen-incorporated small silicon (Si 3 0 H 4 0 O i ) pyramidal clusters as a function of oxygen content were theoretically studied using the self-consistent semiempirical molecular orbital method (modified neglect of diatomic overlap-parametric method 3). In the absolute majority of the cluster configurations with low oxygen content (i 4) structures, a wide spread of the optical transition energies ranging from 1.60 to 3.00 eV is observed, due to the competition between two opposite tendencies. According to the first one, connected with the increasing of the quantum confinement effects due to oxidation, the optical transition energies tend to increase, whereas the enhanced possibility of involvement of oxygen or oxygen-adjacent silicon atoms in the process tends to decrease the energy transitions.
Predictions of Milk Fatty Acid Contents by Mid-Infrared Spectroscopy in Chinese Holstein Cows

Genetic improvement of milk fatty acid content traits in dairy cattle is of great significance. However, chromatography-based methods to measure milk fatty acid content have several disadvantages. Thus, quick and accurate predictions of various milk fatty acid contents based on the mid-infrared spectrum (MIRS) from dairy herd improvement (DHI) data are essential and meaningful to expand the amount of phenotypic data available. In this study, 24 kinds of milk fatty acid concentrations were measured from the milk samples of 336 Holstein cows in Shandong Province, China, using the gas chromatography (GC) technique, which simultaneously produced MIRS values for the prediction of fatty acids. After quantification by the GC technique, milk fatty acid contents expressed as g/100 g of milk (milk-basis) and g/100 g of fat (fat-basis) were processed by five spectral pre-processing algorithms: first-order derivative (DER1), second-order derivative (DER2), multiple scattering correction (MSC), standard normal transform (SNV), and Savitzky–Golsy convolution smoothing (SG), and four regression models: random forest regression (RFR), partial least square regression (PLSR), least absolute shrinkage and selection operator regression (LassoR), and ridge regression (RidgeR). Two ranges of wavebands (4000~400 cm−1 and 3017~2823 cm−1/1805~1734 cm−1) were also used in the above analysis. The prediction accuracy was evaluated using a 10-fold cross validation procedure, with the ratio of the training set and the test set as 3:1, where the determination coefficient (R2) and residual predictive deviation (RPD) were used for evaluations. The results showed that 17 out of 31 milk fatty acids were accurately predicted using MIRS, with RPD values higher than 2 and R2 values higher than 0.75. In addition, 16 out of 31 fatty acids were accurately predicted by RFR, indicating that the ensemble learning model potentially resulted in a higher prediction accuracy. Meanwhile, DER1, DER2 and SG pre-processing algorithms led to high prediction accuracy for most fatty acids. In summary, these results imply that the application of MIRS to predict the fatty acid contents of milk is feasible.