Background Templates Research Articles

Measurement of γ-rays generated by neutron interaction with 16O at 30 MeV and 250 MeV

Abstract Deep understanding of γ-ray production from the fast neutron reaction in water is crucial for various physics studies at large-scale water Cherenkov detectors. We performed test experiments using quasi-mono energetic neutron beams (En = 30 and 250 MeV) at Osaka University’s Research Center for Nuclear Physics to measure γ-rays originating from the neutron-oxygen reaction with a high-purity germanium detector. Multiple γ-ray peaks which are expected to be from excited nuclei after the neutron-oxygen reaction were successfully observed. We measured the neutron beam flux using an organic liquid scintillator for the cross section measurement. With a spectral fitting analysis based on the tailored γ-ray signal and background templates, we measured cross sections for each observed γ-ray component. The results will be useful to validate neutron models employed in the on-going and future water Cherenkov experiments.

Improving new physics searches with diffusion models for event observables and jet constituents

We introduce a new technique called Drapes to enhance the sensitivity in searches for new physics at the LHC. By training diffusion models on side-band data, we show how background templates for the signal region can be generated either directly from noise, or by partially applying the diffusion process to existing data. In the partial diffusion case, data can be drawn from side-band regions, with the inverse diffusion performed for new target conditional values, or from the signal region, preserving the distribution over the conditional property that defines the signal region. We apply this technique to the hunt for resonances using the LHCO di-jet dataset, and achieve state-of-the-art performance for background template generation using high level input features. We also show how Drapes can be applied to low level inputs with jet constituents, reducing the model dependence on the choice of input observables. Using jet constituents we can further improve sensitivity to the signal process, but observe a loss in performance where the signal significance before applying any selection is below 4σ.

FieldPrism: A system for creating snapshot vouchers from field images using photogrammetric markers and QR codes.

Field images are important sources of information for research in the natural sciences. However, images that lack photogrammetric scale bars, including most iNaturalist observations, cannot yield accurate trait measurements. We introduce FieldPrism, a novel system of photogrammetric markers, QR codes, and software to automate the curation of snapshot vouchers. Our photogrammetric background templates (FieldSheets) increase the utility of field images by providing machine-readable scale bars and photogrammetric reference points to automatically correct image distortion and calculate a pixel-to-metric conversion ratio. Users can generate a QR code flipbook derived from a specimen identifier naming hierarchy, enabling machine-readable specimen identification for automatic file renaming. We also developed FieldStation, a Raspberry Pi-based mobile imaging apparatus that records images, GPS location, and metadata redundantly on up to four USB storage devices and can be monitored and controlled from any Wi-Fi connected device. FieldPrism is a flexible software tool designed to standardize and improve the utility of images captured in the field. When paired with the optional FieldStation, researchers can create a self-contained mobile imaging apparatus for quantitative trait data collection.

Flow-enhanced transportation for anomaly detection

Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions. We propose a means to generate this background template that uses a flow-based model to create a mapping between high-fidelity SM simulations and the data. The flow is trained in sideband regions with the signal region blinded, and the flow is conditioned on the resonant feature (mass) such that it can be interpolated into the signal region. To illustrate this approach, we use simulated collisions from the Large Hadron Collider (LHC) Olympics Dataset. We find that our flow-constructed background method has competitive sensitivity with other recent proposals and can therefore provide complementary information to improve future searches.

CURTAINs for your sliding window: Constructing unobserved regions by transforming adjacent intervals.

We propose a new model independent technique for constructing background data templates for use in searches for new physics processes at the LHC. This method, called Curtains, uses invertible neural networks to parameterise the distribution of side band data as a function of the resonant observable. The network learns a transformation to map any data point from its value of the resonant observable to another chosen value. Using Curtains, a template for the background data in the signal window is constructed by mapping the data from the side-bands into the signal region. We perform anomaly detection using the Curtains background template to enhance the sensitivity to new physics in a bump hunt. We demonstrate its performance in a sliding window search across a wide range of mass values. Using the LHC Olympics dataset, we demonstrate that Curtains matches the performance of other leading approaches which aim to improve the sensitivity of bump hunts, can be trained on a much smaller range of the invariant mass, and is fully data driven.

Saliency Detection via Fusing Color Contrast and Hash Fingerprint

Saliency detection is a technique for automatically extracting regions of interest from the background and has been widely used in the computer vision field. This study proposes a simple and effective saliency detection method combining color contrast and hash fingerprint. In our solution, the input image is segmented into nonoverlapping superpixels, so as to perform the saliency detection at the region level to reduce computational complexity. A background optimization selection is used to construct an accurate background template. Based on this, a saliency map that highlights the whole salient region is obtained by estimating color contrast. Besides, another saliency map that enhances the salient region while restraining the background is also generated through hash fingerprint matching. Ultimately, the final saliency map can be obtained by fusing the two saliency maps. Comparing the performance with other methods, the proposed algorithm works better even in the presence of complex background or very large salient regions.

An enhancement algorithm based on adaptive updating template with Gaussian model for Si3N4 ceramic bearing roller surface defects detection

The method based on machine vision image processing is used to detect the surface defects of Si3N4 bearing roller. Owing to the variety of defects, small area and low contrast, it is easy to miss or error detection. In this paper, an adaptive update template defect enhancement algorithm based on Gaussian model is proposed. First, a large number of surface images of Si3N4 bearing roller are collected to obtain the non-defect background statistical feature, and the background characteristic curve is fitted by Gaussian model. Further, the initial background template is gained according to the Gaussian curve. Then, combined with the gray distribute of defect images and initial background template, unique adaptive update template can be established. Finally, subtraction operation and nonlinear enhancement are used to improve the comparison of defect information and background. Through inverse sorting, adaptive threshold segmentation and Canny operation, the precise positioning of defects is realized. The enhancement algorithm can effectively enhance the contrast and eliminate the influence of noise. The average detection time is 0.84s, and the detection accuracy is 96.2%.

Rail surface defect inspection via a self-reference template and similarity evaluation

Although railway transportation has rapidly evolved in recent times, the existing manual maintenance of rail infrastructure presents several challenges, such as labor intensity and tediousness. Computer vision, as one of the automatic inspection methods, shows promising prospects for railway detection tasks. In this study, we proposed a novel rail surface defect detection method based on a self-reference template and similarity evaluation. Firstly, a self-reference template was generated by extracting the domain information together with the longitudinal direction of the rail image. We further defined an image structural similarity index, and subsequently compared all the transversal rows with the background template. A coarse-to-fine segmentation method was further proposed to locate the defect. In the first step of the segmentation procedure, rows with large differences were selected using the Otsu algorithm adaptively. The exact position of the defect was then determined by utilizing both gradient magnitude and grayscale information. Our method was evaluated on a public rail surface defects dataset, which included two types of data. The experiment results showed that our method detected type-I and type-II defects with 89.04% and 89.61% recall, and 98.46% and 97.87% precision, respectively. This shows that our method achieved higher accuracy than the established detection algorithms.

Graph weighting scheme for skin lesion segmentation in macroscopic images

Melanoma is the least common skin cancer but the most severe and lethal. Due to the expensive cost of medical screening, there is a need to develop automated computer-aided diagnosis (CAD) of melanoma in macroscopic images. Segmentation of skin lesions is then required as an initial processing step in these systems. In this paper, we propose a graph-based skin lesion segmentation algorithm. We start by roughly determining lesion and background templates. We then develop a graph that ranks regional similarities to both lesion and skin cues involving several features. The contribution of each feature is controlled according to its regional discriminative power. Finally, we segment the lesion using a strategy that combines the two ranking operations. A comparative evaluation is performed on both melanoma and benign lesions selected from three challenging databases. Our ranking process can suppress skin regions and highlights effectively the complete lesion. Quantitative evaluation using several metrics shows that our algorithm achieves consistently better segmentation performance compared to ten state-of-the-art methods. It yields accurate segmentation and enhances applicability on complicated images with different artifacts. Its high performance indicates that it is suitable for practical use as part of CAD systems.

Block Detection and Tracking Algorithm of Foreign Objects Debris in Airport Runway Based on Background Alignment and Difference

<p indent=0mm>Aiming at the problems of unmanned detection and recognition of foreign object debris (FOD) in the autonomous detection and recognition of airport runways, the detection results are affected by the environment, the detection of small targets is difficult, and the rate of missed detection is high. An airport runway foreign object debris sub-block detection and tracking method based on background difference is proposed in this article. First, the linear interpolation of position information assisted by the speed is used to obtain the most relevant frame of the image in background image template library and the image sequence to be identified. Then, the image is divided into different sub-blocks, the adaptive weights are set using the image texture complexity in each sub-block, and the feature point extraction is combined with the oriented fast and rotated brief (ORB) method to normalize the sub-block feature points to the original image. Align the image with the most relevant frame of the background template library using the feature points result before to obtain the foreign object detection result. Finally, the kernelized correlation filter is introduced to track the detection results for multiple targets. Local tracking methods of each sub-block are used to reduce the computation time and the tracking results are tested for reliability. According to the experiment results in three different scenarios, compared with another five common image-based airport FOD detection algorithms, this method can reduce the overall false detection rate of foreign objects by more than 70%, and the overall missed detection rate to 0 when the size of foreign body is larger than <sc>1 cm<inline-formula id=inline1><mml:math xmlns:mml=http://www.w3.org/1998/Math/MathML display=inline><mml:mo>×</mml:mo></mml:math></inline-formula>1 cm,</sc> while ensuring the algorithm processing speed, which achieved good results.

Heterogeneity of Graphite Lithiation in State‐of‐the‐Art Cylinder‐Type Li‐Ion Cells

AbstractInvited for this month's cover picture is the group of Anatoliy Senyshyn. The cover picture displays the lithium distribution in the graphite anode of an 18650‐type Li‐ion battery. A look into the cell's interior illustrates a non‐destructive character of the performed studies. The background template corresponds to the structure of lithiated graphite, referring to the diffraction technique as a tool for spatially resolved quantification of lithium concentration within the cell. Read the full text of the Article at 10.1002/batt.202000178.

Evaluation of Deceptive Jamming Effect on SAR Based on Visual Consistency

In complicated electromagnetic environments, synthetic aperture radar (SAR) can be threatened by various kinds of malicious interference, of which deceptive jamming is the intentional and efficient one. For jamming effect evaluation on SAR images, traditional methods are mostly based on the change of image quality, while not suitable for evaluating the confusion caused by high-fidelity false targets. In this paper, a novel framework to evaluate the effect of deceptive jamming on SAR is proposed based on visual consistency (VC). Three levels of vision, namely detection, recognition and semantics, are fused for efficient deception evaluation along with the corresponding metrics system. Fully considering the imaging characters of deceptive jamming, specifically designed detection and recognition flows are introduced to quantitatively evaluate the deception. Furthermore, to evaluate whether the generated false targets are with reasonable context, a unprecedent concept named semantic accuracy is proposed via considerations of statistical differences compared with that of the background template. Besides, the cases that deceptive jamming with several common non-ideal issues are considered when evaluating the effects. Sufficient experiments have proved the practicality and superiority of the proposed evaluation framework under different deceptive jamming with various non-ideal factors.

Sensitivity of the Cherenkov Telescope Array to a dark matter signal from the Galactic centre

We provide an updated assessment of the power of the Cherenkov Telescope Array (CTA) to search for thermally produced dark matter at the TeV scale, via the associated gamma-ray signal from pair-annihilating dark matter particles in the region around the Galactic centre. We find that CTA will open a new window of discovery potential, significantly extending the range of robustly testable models given a standard cuspy profile of the dark matter density distribution. Importantly, even for a cored profile, the projected sensitivity of CTA will be sufficient to probe various well-motivated models of thermally produced dark matter at the TeV scale. This is due to CTA's unprecedented sensitivity, angular and energy resolutions, and the planned observational strategy. The survey of the inner Galaxy will cover a much larger region than corresponding previous observational campaigns with imaging atmospheric Cherenkov telescopes. CTA will map with unprecedented precision the large-scale diffuse emission in high-energy gamma rays, constituting a background for dark matter searches for which we adopt state-of-the-art models based on current data. Throughout our analysis, we use up-to-date event reconstruction Monte Carlo tools developed by the CTA consortium, and pay special attention to quantifying the level of instrumental systematic uncertainties, as well as background template systematic errors, required to probe thermally produced dark matter at these energies.

Galactic Center Excess in a New Light: Disentangling the γ-Ray Sky with Bayesian Graph Convolutional Neural Networks.

A fundamental question regarding the Galactic Center excess (GCE) is whether the underlying structure is pointlike or smooth, often framed in terms of a millisecond pulsar or annihilating dark matter (DM) origin for the emission. We show that Bayesian neural networks (NNs) have the potential to resolve this debate. In simulated data, the method is able to predict the flux fractions from inner Galaxy emission components to on average ∼0.5%. When applied to the Fermi photon-count map, the NN identifies a smooth GCE in the data, suggestive of the presence of DM, with the estimates for the background templates being consistent with existing results.

Measurement of the jet mass in high transverse momentum [formula omitted] production at [formula omitted] using the ATLAS detector

The integrated fiducial cross-section and unfolded differential jet mass spectrum of high transverse momentum Z→bb‾ decays are measured in Zγ events in proton–proton collisions at s=13TeV. The data analysed were collected between 2015 and 2016 with the ATLAS detector at the Large Hadron Collider and correspond to an integrated luminosity of 36.1fb−1. Photons are required to have a transverse momentum pT>175GeV. The Z→bb‾ decay is reconstructed using a jet with pT>200GeV, found with the anti-ktR=1.0 jet algorithm, and groomed to remove soft and wide-angle radiation and to mitigate contributions from the underlying event and additional proton–proton collisions. Two different but related measurements are performed using two jet grooming definitions for reconstructing the Z→bb‾ decay: trimming and soft drop. These algorithms differ in their experimental and phenomenological implications regarding jet mass reconstruction and theoretical precision. To identify Z bosons, b-tagged R=0.2 track-jets matched to the groomed large-R calorimeter jet are used as a proxy for the b-quarks. The signal yield is determined from fits of the data-driven background templates to the different jet mass distributions for the two grooming methods. Integrated fiducial cross-sections and unfolded jet mass spectra for each grooming method are compared with leading-order theoretical predictions. The results are found to be in good agreement with Standard Model expectations within the current statistical and systematic uncertainties.

A visual detection method for PCI blockage and coke size distribution in tuyere raceway

ABSTRACT Accurate detection for pulverized coal injection (PCI) blockage and coke size distribution are effective for blast furnace (BF) operation. Nowadays, tuyere cameras have been applied in BF. However, the appearance of captured raceway images changes, which requests the detection method to be adaptive. Therefore, an intelligent detection method for PCI blockage and coke size distribution is proposed. An adaptive pre-processing algorithm is firstly developed to improve image quality. Secondly, the fitting circle is used to locate tuyere region and the improved U-net network is investigated for lance segmentation to construct the background template. Then, target regions can be obtained by background subtraction algorithm. Finally, the PCI blockage can be detected according to the area information, and the size distribution of cokes can be calculated by multi-directional Feret diameter. Massive raceway videos are used to evaluate the method. Experiment results show the method can detect PCI blockage and cokes size distribution.

Saliency detection via image sparse representation and color features combination

Saliency detection is a technique to analyze image surroundings to extract relevant regions from the background. In this paper, we propose a simple and effective saliency detection method based on image sparse representation and color features combination. First, the input image is segmented into non-overlapping super-pixels, so as to perform the saliency detection at the region level to reduce computational complexity. Then, a background optimization selection scheme is used to construct an appropriate background template. Based on this, a primary saliency map is obtained by using image sparse representation. Next, through the linear combination of color coefficients we generate an improved saliency map with more prominent salient regions. Finally, the two saliency maps are integrated within Bayesian framework to obtain the final saliency map. Experimental results show that the proposed method has desirable detection performance in terms of detection accuracy and running time.

More Than Meets the Eye: A Canadian Comparative Study on PowerPoint Use Among Post-Secondary Students With and Without Disabilities

The present study set out to explore effective teaching techniques using PowerPoint for post-secondary students with disabilities by comparing their views to those of students without disabilities. 284 Canadian post-secondary social science students, 75 of whom self-reported a disability, were surveyed about what aspects of PowerPoint use helps them learn. The good news is that the results indicate many similarities between the views of students with and without disabilities. Although all students felt that having PowerPoint available online was highly desirable, for students with disabilities this was perceived as an important disability accommodation. All students preferred that PowerPoint slides be made available before class in both PowerPoint and PDF formats; however, this was particularly important to students with disabilities. All students preferred that professors walk around some of the time rather than simply stand beside the lectern, that they select slides with good contrast rather than an interesting but busy background template, that they write concepts in full sentences as opposed to key words only, and that PowerPoint images be accompanied by text rather than presenting images only. Overall, our findings show that well-designed PowerPoint slides which incorporate accessibility features (easily included by using the Accessibility Checker feature of PowerPoint) can benefit everyone. Notably, fewer than half (41%) of the students with disabilities had registered with the college to receive disability-related accommodations; therefore, it is important that PowerPoint, as used by professors, be accessible to this large segment of the population of students with disabilities. Informative slides with a clear template and good contrast, along with an engaging presentation style, are likely to benefit everyone. Our findings also show that, contrary to the fears of many educators, students are unlikely to miss class if slides are posted online. Moreover, this is equally true for academically stronger as well as weaker students.

Visual object tracking via a manifold regularized discriminative dual dictionary model

Real-time object tracking plays an important role in many computer vision systems, yet in complex scenarios, it is still a very challenging problem. In this paper, we propose a new visual tracking algorithm via a manifold regularized discriminative dual dictionary (MD3) model. First, a dual dictionary is introduced to avoid the calculation of representation coefficient in distance function construction. Second, the local background templates are utilized to keep the learned dictionaries discriminative. Third, the manifold regularization on representation coefficient is proposed to ensure that MD3 model has a bit error tolerance on the object update. We formulate object tracking in a particle filter framework, in which the observation model is calculated as the reconstruction error between learned dictionaries and the candidate template. Extensive experiments in various tracking scenarios are performed to evaluate the proposed method, and the results interpret that the tracking accuracy as well as the computational cost can be improved as compared with the state-of-the-art approaches.

A target detection algorithm for SAR images based on regional probability statistics and saliency analysis

ABSTRACT In comparison with optical images, a Synthetic Aperture Radar (SAR) image has many defects, such as low resolution, strong noise interference and random distribution of the target, which increases the false alarm rate of traditional detection methods. To improve the detection accuracy of the SAR image, a novel detection method is proposed based on regional probability statistics and saliency analysis. A saliency analysis model based on dense and sparse reconstruction (DSR) is reconstructed to locate the target precisely. Firstly, the regional probability of the SAR image is estimated to extract the background region. And then, the extracted background sub-blocks are clustered and employed to replace the corresponding background template set of the DSR model. Subsequently, the reconstructed DSR model is used to extract the target, and the detection accuracy of the proposed method is enhanced greatly. Compared with the constant false alarm rate (CFAR)-based detection method, the proposed method can achieve a high detection accuracy and protect the edges of the SAR image.