Dark Background Research Articles

Research on Calculation Method of Radiation Response Eigenvalue of a Single-Chip Active Pixel Sensor.

In this paper, we present a calculation method for the radiation response eigenvalue based on a monolithic active pixel sensor. By comparing the statistical eigenvalues of different regions of a pixel array in bright and dark environments, the linear relationship between the statistical eigenvalues obtained by different algorithms and the radiation dose rate was studied. Additionally, a dose rate characterization method based on the analysis of the eigenvalues of the MAPS response signal was proposed. The experimental results show that in the dark background environment, the eigenvalues had a good linear response in the region of any gray value in the range of 10–30. In the color images, due to the difference in the background gray values in adjacent color regions, the radiation response signal in dark regions was confused with the image information in bright regions, resulting in the loss of response signal and affecting the analysis results of the radiation response signal. For the low dose rate radiation field, as the radiation response signal was too weak and there was background dark noise, it was necessary to accumulate frame images to obtain a sufficient response signal. For the intense radiation field, the number of response events in a single image was very high, and only two consecutive frames of image data needed to be accumulated to meet the statistical requirements. The binarization method had a good characterization effect for the radiation at a low dose rate, and the binarization processing and the total gray value statistics of the response data at a high dose rate could better characterize the radiation dose rate. The calibration experiment results show that the binarization processing method can meet the requirements of using a MAPS for wide-range detection.

Gravitational instability with a dark matter background: exploring the different scenarios

We study the Jeans-type gravitational instability for a self-gravitating medium composed of two species, baryonic (bright) and dark matter particles, using a hybrid quantum-classical fluid approach. Baryonic matter is treated classically, which is appropriate for most astrophysical environments, e.g., Bok globules, while dark matter is treated through a quantum hydrodynamic approach allowing for possible nonlinearities. These nonlinearities may arise in bosonic dark matter due to attractive or repulsive short-range self-interaction (attractive interaction being more relevant for axions) or from the Pauli exclusion principle for fermionic dark matter, e.g., massive neutrinos. This allows us to explore, in a very broad context, the impact of a dark matter background on the Jeans process for different scenarios discussed in the literature. We confront the established stability criterion with Bok globule stability observations and show that the model adequately accounts for the data with dark matter parameters close to those predicted independently from numerical simulations.

COMPARISON OF UNCERTAINTY QUANTIFICATION METHODS FOR CNN-BASED REGRESSION

Abstract. The evaluation of reliability is not only of high importance for safety-critical deep learning applications but for object pose estimation as well. The uncertainty of the result is one way to express its reliability. In order to better understand existing uncertainty quantification (UQ) methods and their performance on image-based regression tasks, we use a small CNN and various scenarios to evaluate the estimated uncertainties. The evaluation is done on different simplistic synthetic datasets, consisting of gray-scale images of squares on a darker background. We train the CNN to predict the square center position of the square in the image. We compare how different UQ methods perform under dataset shift, rotation, occlusion, noise changes in the images.

Detection of marine aerosols using ocean colour sensors

Ocean colour sensors are equipped with atmospheric correction bands operating in the near infrared region of the spectrum. At these wavelengths, the ocean surface, because of high infrared absorption by water, acts as a dark background and the sensor detected radiance is due to solar radiation backscattered by the atmospheric air molecules and aerosols called Rayleigh and aerosol path radiances respectively. From the radiances in the atmospheric correction bands, after accounting for Rayleigh path radiance, it is possible to determine aerosol parameters like aerosol optical depth (AOD) and particle size distribution index. 
 
 While sensors like IRS P3 MOS-B, IRS P4 OCM, NOAA-AVHRR, etc detect AOD in the total atmospheric column, IRS P3 MOS-A sensor can detect AOD in two atmospheric layers by exploiting the differential absorption property of oxygen at four narrow band channels at the O2 A band around 760nm. The method of determination from ocean colour sensors and the results from IRS P3 MOS-B, IRS P4-OCM and IRS P3 MOS-A radiance data are presented and discussed.

Colour detection using Enhanced K-Means Clustering

Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. Carrying out image enhancement understanding under poor quality image could be a difficult downside. Due to low distinction, we cannot clearly extract objects from the dark background. Most colour based methods will fail on this matter if the colour of the objects and that of the background are similar. Our algorithm improves the overall image quality. Keywords - Segmentation, Histogram Equalization, Enhanced K-means Clustering, PSNR, MSE.

Rapid Campimetry-A Novel Screening Method for Glaucoma Diagnosis.

One of the most important functions of the retina—the enabling of perception of fast movements—is largely suppressed in standard automated perimetry (SAP) and kinetic perimetry (Goldmann) due to slow motion and low contrast between test points and environment. Rapid campimetry integrates fast motion (=10°/4.7 s at 40 cm patient–monitor distance) and high contrast into the visual field (VF) examination in order to facilitate the detection of absolute scotomas. A bright test point moves on a dark background through the central 10° VF. Depending on the distance to the fixation point, the test point automatically changes diameter (≈0.16° to ≈0.39°). This method was compared to SAP (10-2 program) for six subjects with glaucoma. Rapid campimetry proved to be comparable and possibly better than 10-2 SAP in identifying macular arcuate scotomas. In four subjects, rapid campimetry detected a narrow arcuate absolute scotoma corresponding to the nerve fiber course, which was not identified as such with SAP. Rapid campimetry promises a fast screening method for the detection of absolute scotomas in the central 10° visual field, with a potential for cloud technologies and telemedical applications. Our proof-of-concept study motivates systematic testing of this novel method in a larger cohort.

Counteracting Dark Web Text-Based CAPTCHA with Generative Adversarial Learning for Proactive Cyber Threat Intelligence

Automated monitoring of dark web (DW) platforms on a large scale is the first step toward developing proactive Cyber Threat Intelligence (CTI). While there are efficient methods for collecting data from the surface web, large-scale dark web data collection is often hindered by anti-crawling measures. In particular, text-based CAPTCHA serves as the most prevalent and prohibiting type of these measures in the dark web. Text-based CAPTCHA identifies and blocks automated crawlers by forcing the user to enter a combination of hard-to-recognize alphanumeric characters. In the dark web, CAPTCHA images are meticulously designed with additional background noise and variable character length to prevent automated CAPTCHA breaking. Existing automated CAPTCHA breaking methods have difficulties in overcoming these dark web challenges. As such, solving dark web text-based CAPTCHA has been relying heavily on human involvement, which is labor-intensive and time-consuming. In this study, we propose a novel framework for automated breaking of dark web CAPTCHA to facilitate dark web data collection. This framework encompasses a novel generative method to recognize dark web text-based CAPTCHA with noisy background and variable character length. To eliminate the need for human involvement, the proposed framework utilizes Generative Adversarial Network (GAN) to counteract dark web background noise and leverages an enhanced character segmentation algorithm to handle CAPTCHA images with variable character length. Our proposed framework, DW-GAN, was systematically evaluated on multiple dark web CAPTCHA testbeds. DW-GAN significantly outperformed the state-of-the-art benchmark methods on all datasets, achieving over 94.4% success rate on a carefully collected real-world dark web dataset. We further conducted a case study on an emergent Dark Net Marketplace (DNM) to demonstrate that DW-GAN eliminated human involvement by automatically solving CAPTCHA challenges with no more than three attempts. Our research enables the CTI community to develop advanced, large-scale dark web monitoring. We make DW-GAN code available to the community as an open-source tool in GitHub.

Enhanced scanning electron microscopy images using muscovite mica, an example with Rhizaria.

Muscovite mica sheets were used as a support to capture scanning electron microscopy pictures of marine biological samples. The physical properties of the cleaved muscovite mica provide a clean background, which greatly reduces the postprocessing of images, thereby enhancing them and resulting in impressive images. We chose siliceous Rhizaria for this investigation due to their morphological diversity and elaborate skeletons. RESEARCH HIGHLIGHTS: Muscovite mica sheet was used as a SEM support for biological sample Physical properties of muscovite mica sheet can provide native dark, gray, and white background of SEM images Muscovite mica sheet should be considered for image production in the context of art and science intersection.

Cosmic Queries: StarTalk's Guide to Who We Are, How We Got Here, and Where We’re Going

Cosmic Queries: StarTalk's Guide to Who We Are, How We Got Here, and Where We’re Going

A Regional SAR Dark Spot Detection Method Based on GMTRJ Algorithm

Generalized Multiple-Try Reversible Jump (GMTRJ) is a recent algorithm which makes it possible to improve the accurate tuning of the jump proposals in the Reversible Jump Markov Chain Monte Carlo algorithm. In this paper, the GMTRJ algorithm is combined the regular tessellation and the Bayesian paradigm to propose a regional synthetic aperture radar (SAR) dark spot detection method. First, the regular tessellation and the Bayesian paradigm are applied to build a regional SAR dark spot detection model; then the GMTRJ algorithm is designed to simulate from the detection model to obtain the optimal regional detection result. As the blocks may cross the boundaries between dark spot and ocean background regions, a refined operation is designed to improve the detection accuracy of boundaries. We present test results demonstrating the feasibility and advantages of the GMTRJ algorithm on simulating from the detection model, and illustrating the feasibility of the refined SAR dark spot detection operation on improving the detection accuracy of boundaries.

Versatile monarch butterflies can switch navigation strategy

While the logistics of international travel remain off-putting as the Covid-19 pandemic ebbs and flows, creatures that routinely circumnavigate oceans and continents have continued undaunted. Arctic terns are still smashing long distance records while wildebeest move en masse in colossal herds. But the sovereign of the multigenerational migration has to be the monarch butterfly. Each spring, the original pioneers depart their overwintering grounds in Mexico for their Canadian summer home, yet it is their great-great-great-grandchildren that return ∼8 months later. ‘To keep a constant direction over this enormous distance, these animals rely on the sun for orientation’, says Myriam Franzke from the University of Wuerzburg, Germany. However, when the insects near their final destination, they may switch to other forms of guidance to select their ultimate landing site. Yet, no one had tested whether monarch butterflies can use other forms of navigation, beyond being directed by the sun, so Franzke, Christian Kraus, Maria Gayler, Keram Pfeiffer and Basil el Jundi, from the University of Wuerzburg, with David Dreyer from Lund University, Sweden, teamed up to test out the insect's homing instinct.‘We ordered the butterfly pupae from Costa Rica where they were raised in captivity’ says el Jundi, recalling that the adults began emerging from their chrysalises 3 days after their arrival in Germany. ‘It is always a remarkable moment when the small chrysalises turn into colourful butterflies with large wings’, says Franzke. Next, the team gently tethered individual butterflies at the centre of a cylindrical flight arena, allowing the insects to swivel in any direction as they took to the wing. Then, the scientists tested which directions the insects selected when they illuminated LEDs lining the wall of the arena, providing the insects with different views.Switching off three LED columns to show a 4 cm wide vertical dark band against a bright background, the team filmed the butterflies’ manoeuvres for 8 min, switching the position of the bar to the opposite side of the arena from time to time to make sure that the insects were still paying attention. Although the butterflies took hardly any notice of the bar initially, swivelling continually in different directions, by the end they were occasionally holding a steady direction toward one edge of the bar, before veering off again.In contrast, the monarchs seemed far more fixated by a bright vertical bar against a dark background. Some flew directly toward the light for the full 8 min, only switching direction when the bar shifted spontaneously to the other side of the arena: they were definitely attracted to the bright object. But how would the insects fare when the team presented them with a single illuminated green LED, which the butterflies mistake for the sun?This time, the insects set a bearing relative to the fake sun and tended to keep to that path, rotating through 180 deg each time the simulated sun jumped to the other side of the arena. They were using the bright light as a compass guiding their choice of direction, as if they were dispersing toward Canada or returning to Mexico to overwinter. And when the researchers alternated the bright vertical bar with the simulated sun, the butterflies flew persistently toward the bar, but switched direction and set a bearing when the fake sun flashed into view.‘[Monarch] butterflies can switch between compass orientation and attraction’, says el Jundi, and the team suspects that the insects depend on specialised guidance circuits in the brain when navigating over long distances by the sun, but have the ability to switch with ease and simply gravitate toward an object when close to home.

UNet-based model for crack detection integrating visual explanations

Although Convolutional Neural Network (CNN) can show the progressive nature of crack detection, it still mainly acts as a black box and interpreting CNN has yet become a difficult task. This paper presents a more accurate, efficient, and ‘transparent’ UNet-based model for crack detection and it integrates visual explanations to interpret the model. This UNet-based model changes the encoder of UNet by using typical CNN models to have different computational and model complexity. Instead of the only accuracy metrics, three evaluation metrics, including accuracy, time (computational complexity), and memory (model complexity), are used to estimate the overall performance of CNN for crack detection. An open benchmark dataset for crack detection is used to train and evaluate the models. The results show that the type and depth of the encoder have a significant influence on the accuracy of UNet-based models. UNet-VGG19, UNet-InceptionResNetv2, and UNet-EfficientNetb3 are ranked the top three by considering all evaluation metrics and thus selected to show the prediction and visual explanation under different conditions and in different training periods. The background of images, especially rough or dark background, has a significant influence on the performance of UNet-based models, while different types of cracks (single and multi, thin and thick) don’t. All these three UNet-based models perform well for large-scale images but they don’t fully depict the shape of cracks for small-scale images. It is discovered from this limited study that among all these conditions, UNet-VGG19 performs the best in terms of prediction and visual explanation.

Staggered Coprime Pulse Repetition Frequencies Synthetic Aperture Radar (SCopSAR)

High-resolution wide-swath synthetic aperture radar (HRWS-SAR) imaging is highly desirable since it allows one to produce high-resolution SAR images of large areas during a short visit time. In this article, staggered coprime pulse repetition frequencies synthetic aperture radar (SCopSAR) is proposed. It divides the time during which a scatterer is illuminated by the antenna beam pattern into two halves where, in each half, pulses are transmitted at the rate of one of two sub-Nyquist pulse repetition frequencies (PRFs). Such PRFs are related to the Nyquist PRF using two coprime subsampling factors. This allows extending the maximum range swath width that can be imaged by a number of times that equals the smaller subsampling factor at the expense of a reduction in the azimuth resolution by half. It further allows for a reduction in the amount of data to be stored and communicated. SCopSAR is an imaging modality suitable for scenes that contain a small number of bright scatterers over a dark background which, for instance, is the case when imaging ships in a calm sea background. Compared with the techniques recently proposed in the literature, SCopSAR simplifies the radar requirements since it uses only one carrier frequency, one waveform, and one channel. Simulations and real ERS-2 satellite raw data are used to validate the theoretical findings presented in this article.

Zymography and Reverse Zymography for Testing Proteases and Their Inhibitors.

Zymography is a powerful technique for the assay of different hydrolases that act upon any biological macromolecule. In particular, zymography is used to assay the activities of serine proteases, e.g., matrix metalloproteinases (MMPs), and reverse zymography is used for tissue inhibitors of metalloproteinases (TIMPs) in multifarious experimental samples. Zymography is a method of electrophoretic separation of proteases under non-reducing conditions in a polyacrylamide gel containing substrate. The resolved proteins are renatured by exchange of the anionic detergent with a nonionic one, and the gel is incubated in a specific buffer for the specific proteases. After staining the gel by Coomassie blue staining solution, the proteolytic activities are visualized as clear colorless bands against a dark background. In contrast, reverse zymography is a parallel technique to detect protease inhibitors. In addition to substrate gelatin, proteases (i.e., MMPs) are also incorporated in proper ratio into the polyacrylamide gel. After electrophoresis, during the developing step, the MMPs specifically digest the substrate in regions where TIMPs are absent. Thus, inhibitors/TIMP is represented as dark zones of inhibition against a transparent background after staining. In this chapter, common troubleshoots during sample preparation, running zymography, and data interpretation are discussed. Notes are specified to enhance the sensitivity of the methods. In conclusion, zymography could be crucial for enzyme assay at the nanogram level and for the improvement of new investigative techniques for diseases such as endometriosis, rheumatoid arthritis, osteoarthritis, tumor invasion, and inflammation.

Об атмосферной реализации квантового алгоритма сверхплотного кодирования

We consider the properties of a quantum communication channel in open space using the superdense coding algorithm as an example. We studied the theoretical model of the installation that implements this algorithm, and identified the main factors affecting the quality of the model. Among them is the atmospheric transmittance, the efficiency of detectors, and the average value of the number of noise counts caused by background radiation and dark counts. We made a complete calculation of the installation model and obtained explicit results. These results were analyzed using realistic parameters of detectors and the atmosphere. It was found that the atmospheric turbulence instability, detectors efficiency and the average value of noise counts have the greatest influence on the results.

The minimum thickness of a multilayer ceramic restoration required for masking dark background

Masking dark tooth structure or darkness of oral cavity with ceramic restorations is an important concern. The aim of this study was to determine the minimum thickness of a multilayer all-ceramic restoration (IPS-emax Press) required for a proper masking in these situations. In this experimental in vitro study, 36 multilayer ceramic disks of IPS e.max Press (IvoclarVivadent, Schaan, Liechtenstein) with the diameter of 13 mm were prepared and allocated in six groups of different core/veneer thicknesses: 0.4/0.4 mm (G1), 0.5/0.5 mm (G2), 0.6/0.6 mm (G3), 0.8/0.7 mm (G4), 1.0/0.8 mm (G5), and 1.1/0.9 mm (G6). For backgrounds, the standard black tile of spectrophotometer (B) was used to stimulate the darkness of oral cavity, and an opaque ceramic (OC) of IPS-emax Press (OC) was fabricated to determine the masking ability. CIELAB values of all disks on B and OC backgrounds were measured, and ΔE was calculated between two backings. One-way ANOVA and post hoc tukey test were used to analyze the data. ΔE ≤3.3 and P < 0.05 were considered, respectively, as the clinically acceptable limit and the level of statistical significancy. The mean ΔE between B and OC of groups 4 (2.83 ± 0.80) and 5 (1.46 ± 0.36) were within the range of the clinically acceptable color difference (ΔE ≤3.3); thus these groups could properly mask the black background. A trend was existed in the results as by increasing the thickness, ΔE was decreased. A thickness of 1.5 mm of a multilayer ceramic restoration (IPS e.max Press) is required to mask a dark discoloration.

Superconducting nanowire single photon detector under AC-bias with self-differential readout

Superconducting nanowire single photon detector (SNSPD) has been widely used in many fields such as quantum computing, quantum key distribution and laser radar, due to its high detection efficiency, low dark count rate, high counting rate, and low timing jitter. In most cases, the SNSPD works under the DC-bias mode that can detect single photons arrived at any time. In some cases such as satellite laser ranging and single-photon laser radar where the light pulses arrive regularly, the AC-bias mode enables the SNSPD to work with higher counting rates and lower background dark counts, which however requires complicated readout due to the low signal-to-noise ratio of the photon response. In this work, we report on an AC-biased SNSPD system with a self-differential readout circuit. The system includes a 2-pixel SNSPD consisting of two parallel nanowires, which are biased with 100 MHz sinusoidal current. The output signals of these two nanowires are amplified and combined for the differential readout of the photon response. The resulting response pulse possesses a signal-to-noise ratio ten times higher than that extracted before self-differential readout. In addition, the dark counts are reduced by a factor of 4, and the count rates are increased by a factor of 1.5, in comparison with those under the DC-bias mode. This work provides a specific method to read out the AC-biased SNSPD.

Human temporal learning with mixed signals

The influence of cue informativeness on human temporal discrimination was evaluated using a peak-interval (PI) procedure. A target moved across the computer monitor, reaching the center at 2 or 4 s. Key presses shot the center of the screen. Participants earned points when shots hit the target and lost points for misses. The target was masked during occasional, extended PI trials, allowing for measurement of temporal discrimination. During PI trials, the screen background color could exert stimulus control by providing information about target speed. Cue informativeness was represented as the correlation (φ) between light or dark green backgrounds and the 2- or 4-s target and was manipulated across 4 conditions: a multiple schedule (φ = 1), mixed signals (φ = 0.8, 0.4), and a mixed schedule (φ = 0). In Experiment 1, participants were randomly assigned to one of the 4 conditions. In Experiment 2, each participant experienced all 4 conditions. Participants learned to respond at both intervals in all conditions. Cue informativeness did not affect peak time or spread. For the most part, temporal distributions of responses for the two background colors suggested a cover-both-bases strategy in the presence of mixed signals. Participants incorporated probabilistic information from cues to allocate responding in time.

New Cosmological Solutions of a Nonlocal Gravity Model

A nonlocal gravity model (2) was introduced and considered recently, and two exact cosmological solutions in flat space were presented. The first solution is related to some radiation effects generated by nonlocal dynamics on dark energy background, while the second one is a nonsingular time symmetric bounce. In the present paper, we investigate other possible exact cosmological solutions and find some the new ones in nonflat space. Used nonlocal gravity dynamics can change the background topology. To solve the corresponding equations of motion, we first look for a solution of the eigenvalue problem □(R−4Λ)=q(R−4Λ). We also discuss possible extension of this model with a nonlocal operator, symmetric under □⟷□−1, and its connection with another interesting nonlocal gravity model.

The channel for detecting contrast modulation also responds to density modulation (or vice versa)

In textures composed of black and white dots, we modulated dot density and/or dot contrast in one direction of visual space. Just as Mulligan and MacLeod (Vision Research 28 (1988) 503–519) found a strong reciprocity between density and luminance for dots viewed against a darker background, we found a strong reciprocity between density and contrast: detection thresholds for in-phase modulations of density and contrast were 30% – 55% lower than detection thresholds for density and contrast modulations that were 180° out of phase. These findings support the existence of at least one psychophysical channel that is excited by both density modulations and contrast modulations. A good, quantitative fit to our data can be obtained with a two-channel model.