44,428 research outputs found
Deep Red: The Quintessential Giallo
No full textEssay exploring the giallo genre using Dario Argento's Profondo Rosso/Deep Red as example. Produced for Arrow Video's Blu-ray of Deep Red, and included as part of the full colour booklet
[Letter from Alex Bradford to Lieutenant and Mrs. Ray Starner - November 4, 1940]
No full textLetter from Alex Bradford to Lieutenant and Mrs. Ray Starner describing the the current state of affairs that the author was experiencing, including: the London blitz, the moral of the troops on the ground, and the collective company of men opposing the Nazi regime
The evolution of galaxies and AGN from deep x-ray and optical surveys
No full textTwo complementary new surveys of the x-ray background (XRB), the WHDF and the 10 X 10 ks, are presented. 140 serendipitous x-ray hard and soft sources (S(_2)-10 keV 3. 10(^15); S0.5-2 keV 4 . 10(^16) ergcm(^2)s(^1) have been identified and characterised by conducting concurrent optical and x-ray observations. A principal aim of this work has been to establish whether x-ray luminous narrow-emission line galaxies (NELGs) are the sources that are the major contributors to the hard XRB, along with finding an explanation for their emission mechanisms. We build a case for a hidden AGN as the most likely explanation for such emission and, while NELGs are indeed found to be major contributors to the hard XRB, they are so as the nearby representatives of a major class of obscured AGN, most of which are too faint for probing with current spectroscopic facilities and appear either as "normal" galaxies or as blank fields in optical observations. In particular, we find no evidence of significant contribution from starbursts to the XRB intensity. We also explore the high-redshift population of luminous absorbed AGN and report on the discovery of a type QSO candidate at z = 2.12. But the number of such sources observed is found to be significantly below the predictions from obscured AGN models of the XRB and, inspired by the discovery of several broad-line quasars amongst the hardest sources in the WHDF and also in other surveys, we suggest that x-ray luminous absorbed AGN show optical broad lines more often than not. This affects the relationship between gas and dust in AGN and has direct consequences for the basic unification schemes for AGN. In a parallel program, not only to study how the stellar content of the present universe was assembled over time but also to understand the photometric properties of the galaxies that host an AGN, we perform detailed analysis of the evolutionary properties of early-type galaxies. We find that a significant fraction of colour-selected elliptical and lenticular galaxies in the direction of the WHDF show colours that are too blue to be consistent with the predictions of a simple mono-littiic collapse at high-redshift and passive evolution thereafter. Their large scatter in photometric observables seems to imply divergent histories and indicate that the early-type populations are rather heterogeneous. In particular, significant low-redshift star formation is deduced from the large scatter in their colour-magnitude relation and from the presence of [OII]ƛ3727 emission lines
Gamma-ray Trajectory Reconstruction using Deep Learning Methods
No full textThe Dark Matter Particle Explorer experiment allows for γ-ray detection up to TeV energies, with an unprecedented energy resolution of about 1%, which makes it a unique instrument for γ-ray physics at these energies. Diffuse γ-ray analysis requires a precise tracking method in order to identify the incoming particle through the different detectors, as well as defining its origin in space. A deep-learning tool for track reconstruction has already been developed for electrons and ions. In this analysis we used this tool trained on electron samples on γ-ray samples to assess its efficiency on trajectory reconstruction up to 10 TeV. Preliminary results show very promising tracking precision and bring the prospect of this new tool in high energy γ-ray study. The efficiency of the γ selection by the deep-learning method is compared to the original track of simulated events.EPF
Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images
No full textCOVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application
Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models
No full textScreening programs for early lung cancer diagnosis are uncommon, primarily due to the challenge of reaching at-risk patients located in rural areas far from medical facilities. To overcome this obstacle, a comprehensive approach is needed that combines mobility, low cost, speed, accuracy, and privacy. One potential solution lies in combining the chest X-ray imaging mode with federated deep learning, ensuring that no single data source can bias the model adversely. This study presents a pre-processing pipeline designed to debias chest X-ray images, thereby enhancing internal classification and external generalization. The pipeline employs a pruning mechanism to train a deep learning model for nodule detection, utilizing the most informative images from a publicly available lung nodule X-ray dataset. Histogram equalization is used to remove systematic differences in image brightness and contrast. Model training is then performed using combinations of lung field segmentation, close cropping, and rib/bone suppression. The resulting deep learning models, generated through this pre-processing pipeline, demonstrate successful generalization on an independent lung nodule dataset. By eliminating confounding variables in chest X-ray images and suppressing signal noise from the bone structures, the proposed deep learning lung nodule detection algorithm achieves an external generalization accuracy of 89%. This approach paves the way for the development of a low-cost and accessible deep learning-based clinical system for lung cancer screening
Model file for Ishikawa et al. "Automatic Detection of Occulted Hard X-ray Flares Using Deep-Learning Methods" in Sol. Phys. (2021)
No full textThe file named "model_occulted_flare_classifier.h5" is a Keras model file to detect occulated hard X-ray flares by RHESSI spectrogram data described in Ishikawa et al. 2021. The model file was created with Python 3.6.8, Tensorflow 1.14.0 and Keras 2.2.4.Deep-learning model for occulted hard X-ray flare detection was published in association with the publication Ishikawa et al. "Automatic Detection of Occulted Hard X-ray Flares Using Deep-Learning Methods" in Sol. Phys. (2021). We checked the model file with the Google Colaboratory environment (Python 3.6.9 and Tensorflow 2.4.0).Ishikawa, Shin-nosuke; Matsumura, Hideaki; Uchiyama, Yasunobu; Glesener, Lindsay. (2021). Model file for Ishikawa et al. "Automatic Detection of Occulted Hard X-ray Flares Using Deep-Learning Methods" in Sol. Phys. (2021). Retrieved from the University Digital Conservancy, https://doi.org/10.13020/wtbm-2258
A deep X-ray view of the bare nucleus Seyfert Ark120: unveiling the core of AGN
No full textInternational audienceThe physical characteristics of the matter around supermassive black hole (SMBH) are currently determined thanks to X-ray observations. However, the origins of the main X-ray spectral components such as the soft X-ray excess, the FeK line complex and the hard X-ray excess are still hotly debated. This is difficult to investigate in AGN which show a significant warm absorber that severely distort the continuum. Therefore, AGN which show no (or very weak) warm absorption on the line-of-sight, so-called "bare AGN'' are the best targets to probe the processes at work very close to the SMBH. We will present the first results from an extensive observation campaign (XMM-Newton Large Program, Chandra/HETG, NuSTAR) of Ark120 that is the brightest and cleanest bare AGN known so far. We will focus on the analysis of the deep RGS spectrum, as well as on the X-ray broad band spectrum
A deep learning approach for quantum dots sizing from wide-angle X-ray scattering data
No full textDisclosing the full potential of functional nanomaterials requires the optimization of synthethic protocols and an effective size screening tool, aiming at efficiently triggering their size-dependent properties. Here we demonstrate the successful combination of a wide-angle X-ray total scattering approach with a deep learning classifier for directly sizing quantum dots in both colloidal and dry states. This work offers a compelling alternative to the lengthy process of deriving sizing curves from transmission electron microscopy coupled with spectroscopic measurements, especially in the ultra-small size regime, where traditional empirical functions exhibit larger discrepancies.
The core of our algorithm is an all-convolutional neural network trained on Debye scattering equation X-ray simulations, incorporating atomistic models to capture structural and morphological features, and augmented with physics-informed perturbations to account for different predictable experimental conditions. The model performances are evaluated using both wide-angle X-ray total scattering simulations and experimental datasets collected on lead sulfide quantum dots, resulting in size classification accuracies surpassing 97%. With the developed deep learning size classifier, we overcome the need for calibration curves for quantum dots sizing and thanks to the unified modeling approach at the basis of the total scattering method implemented, we include simultaneously structural and microstructural aspects in the classification process,
This algorithm can be complemented by incorporating input information from other experimental observations (e.g. small angle X-ray scattering data) and can be easily extended to other classes of nanocrystals, providing the nanoscience community with a powerful and broad tool to accelerate the development of novel functional (nano)materials
The student's guide to completing an author study
No full textThe 'Student's guide to completing an author study' emerged during the early development of the school library resource center program at Glen Stewart Elementary School in Stratford Canada on Prince Edward Island. This research process centered on an author study, with direct teaching and clear assignment. The resulting model has been adapted to various grade levels and subject areas in different schools.Source type: Electronic(1)http://proquest.umi.com/pqdweb?did=49237063&Fmt=7&clientId=65345&RQT=309&VName=PQ
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