Detecting small infrared (IR) targets against low-altitude complex background is always a challenge for IR search and tracking (IRST) system due to limited small target characteristics, the moving ...background caused by camera motion, and extremely cluttered backgrounds. The existing methods usually cause high false alarm or do not work against the chaotic low-altitude complex background. In this article, a novel spatial-temporal tensor model with saliency filter regularization (STTM-SFR) is developed to detect small IR targets. First, the small target detection task is transformed into a sparse and low-rank tensor optimization problem using the spatial-temporal prior knowledge of background and target. The construction of the holistic STTM can retain the complete spatial-temporal information of the original IR image sequence. Then, the SFR term limited between background and foreground aims to promote target saliency learning. That is to say, the SFR term can avoid the offset approximation of the low-rank tensor, so as to recover a clean target image from the original IR tensor. Finally, an effective alternating direction method of multipliers (ADMM) algorithm framework is designed to solve the proposed STTM-SFR model. The effectiveness and robustness of the STTM-SFR model are verified in six real IR scenes. Experimental results show that our method outperforms other baseline methods. Moreover, the proposed STTM-SFR method is more robust than the existing state-of-the-art STTMs against low-altitude moving backgrounds.
The nature of the TRAPPIST-1 exoplanets Grimm, Simon L.; Demory, Brice-Olivier; Gillon, Michaël ...
Astronomy and astrophysics (Berlin),
05/2018, Volume:
613
Journal Article, Web Resource
Peer reviewed
Open access
Context. The TRAPPIST-1 system hosts seven Earth-sized, temperate exoplanets orbiting an ultra-cool dwarf star. As such, it represents a remarkable setting to study the formation and evolution of ...terrestrial planets that formed in the same protoplanetary disk. While the sizes of the TRAPPIST-1 planets are all known to better than 5% precision, their densities have significant uncertainties (between 28% and 95%) because of poor constraints on the planet’s masses. Aims. The goal of this paper is to improve our knowledge of the TRAPPIST-1 planetary masses and densities using transit-timing variations (TTVs). The complexity of the TTV inversion problem is known to be particularly acute in multi-planetary systems (convergence issues, degeneracies and size of the parameter space), especially for resonant chain systems such as TRAPPIST-1. Methods. To overcome these challenges, we have used a novel method that employs a genetic algorithm coupled to a full N-body integrator that we applied to a set of 284 individual transit timings. This approach enables us to efficiently explore the parameter space and to derive reliable masses and densities from TTVs for all seven planets. Results. Our new masses result in a five- to eight-fold improvement on the planetary density uncertainties, with precisions ranging from 5% to 12%. These updated values provide new insights into the bulk structure of the TRAPPIST-1 planets. We find that TRAPPIST-1 c and e likely have largely rocky interiors, while planets b, d, f, g, and h require envelopes of volatiles in the form of thick atmospheres, oceans, or ice, in most cases with water mass fractions less than 5%.
Network anomaly detection is an important and dynamic research area. Many network intrusion detection methods and systems (NIDS) have been proposed in the literature. In this paper, we provide a ...structured and comprehensive overview of various facets of network anomaly detection so that a researcher can become quickly familiar with every aspect of network anomaly detection. We present attacks normally encountered by network intrusion detection systems. We categorize existing network anomaly detection methods and systems based on the underlying computational techniques used. Within this framework, we briefly describe and compare a large number of network anomaly detection methods and systems. In addition, we also discuss tools that can be used by network defenders and datasets that researchers in network anomaly detection can use. We also highlight research directions in network anomaly detection.
With the rapid development of spaceborne imaging techniques, object detection in optical remote sensing imagery has drawn much attention in recent decades. While many advanced works have been ...developed with powerful learning algorithms, the incomplete feature representation still cannot meet the demand for effectively and efficiently handling image deformations, particularly objective scaling and rotation. To this end, we propose a novel object detection framework, called Optical Remote Sensing Imagery detector (ORSIm detector), integrating diverse channel features extraction, feature learning, fast image pyramid matching, and boosting strategy. An ORSIm detector adopts a novel spatial-frequency channel feature (SFCF) by jointly considering the rotation-invariant channel features constructed in the frequency domain and the original spatial channel features (e.g., color channel and gradient magnitude). Subsequently, we refine SFCF using learning-based strategy in order to obtain the high-level or semantically meaningful features. In the test phase, we achieve a fast and coarsely scaled channel computation by mathematically estimating a scaling factor in the image domain. Extensive experimental results conducted on the two different airborne data sets are performed to demonstrate the superiority and effectiveness in comparison with the previous state-of-the-art methods.
Cross-domain ship detection tries to identify synthetic aperture radar (SAR) ships by adapting knowledge from labeled optical images, without labor-intensive annotations. In practical applications, a ...few (e.g., one or three samples) labeled SAR samples are available, which provides additional supervision for SAR ships. However, the existing cross-domain methods ignore the SAR supervision (a few labeled and unlabeled SAR images), which limits their performances in a practical and under-investigated task: semisupervised cross-domain ship detection (SCSD). In this article, a dual-teacher framework is proposed to address the mutual interference between optical supervision and SAR supervision. First, both optical and SAR supervision are decomposed into two subtasks: cross-domain task and semisupervised task. Then, both cross-domain tasks and semisupervised tasks can be learned interactively in two individual teacher–student models. The teacher–student models generate pseudo-labels on unlabeled SAR images by a teacher network and fine-tune the student network. Finally, the dual-teacher framework retrains two teacher–student models in cotraining strategies. Both cross-domain datasets and semisupervised datasets are exploited to jointly improve the pseudo-label quality. The effectiveness of the dual-teacher framework has been fully experimentally demonstrated. The code is available at https://github.com/XiangtaoZheng/DualTeacher .
IMPORTANCE: In the United States, the lifetime risk of being diagnosed with prostate cancer is approximately 11%, and the lifetime risk of dying of prostate cancer is 2.5%. The median age of death ...from prostate cancer is 80 years. Many men with prostate cancer never experience symptoms and, without screening, would never know they have the disease. African American men and men with a family history of prostate cancer have an increased risk of prostate cancer compared with other men. OBJECTIVE: To update the 2012 US Preventive Services Task Force (USPSTF) recommendation on prostate-specific antigen (PSA)–based screening for prostate cancer. EVIDENCE REVIEW: The USPSTF reviewed the evidence on the benefits and harms of PSA-based screening for prostate cancer and subsequent treatment of screen-detected prostate cancer. The USPSTF also commissioned a review of existing decision analysis models and the overdiagnosis rate of PSA-based screening. The reviews also examined the benefits and harms of PSA-based screening in patient subpopulations at higher risk of prostate cancer, including older men, African American men, and men with a family history of prostate cancer. FINDINGS: Adequate evidence from randomized clinical trials shows that PSA-based screening programs in men aged 55 to 69 years may prevent approximately 1.3 deaths from prostate cancer over approximately 13 years per 1000 men screened. Screening programs may also prevent approximately 3 cases of metastatic prostate cancer per 1000 men screened. Potential harms of screening include frequent false-positive results and psychological harms. Harms of prostate cancer treatment include erectile dysfunction, urinary incontinence, and bowel symptoms. About 1 in 5 men who undergo radical prostatectomy develop long-term urinary incontinence, and 2 in 3 men will experience long-term erectile dysfunction. Adequate evidence shows that the harms of screening in men older than 70 years are at least moderate and greater than in younger men because of increased risk of false-positive results, diagnostic harms from biopsies, and harms from treatment. The USPSTF concludes with moderate certainty that the net benefit of PSA-based screening for prostate cancer in men aged 55 to 69 years is small for some men. How each man weighs specific benefits and harms will determine whether the overall net benefit is small. The USPSTF concludes with moderate certainty that the potential benefits of PSA-based screening for prostate cancer in men 70 years and older do not outweigh the expected harms. CONCLUSIONS AND RECOMMENDATION: For men aged 55 to 69 years, the decision to undergo periodic PSA-based screening for prostate cancer should be an individual one and should include discussion of the potential benefits and harms of screening with their clinician. Screening offers a small potential benefit of reducing the chance of death from prostate cancer in some men. However, many men will experience potential harms of screening, including false-positive results that require additional testing and possible prostate biopsy; overdiagnosis and overtreatment; and treatment complications, such as incontinence and erectile dysfunction. In determining whether this service is appropriate in individual cases, patients and clinicians should consider the balance of benefits and harms on the basis of family history, race/ethnicity, comorbid medical conditions, patient values about the benefits and harms of screening and treatment-specific outcomes, and other health needs. Clinicians should not screen men who do not express a preference for screening. (C recommendation) The USPSTF recommends against PSA-based screening for prostate cancer in men 70 years and older. (D recommendation)
Stationary marine targets, such as oil rigs and offshore wind turbines, usually show up like bright spots without or with trivial position shifts in multitemporal synthetic aperture radar (SAR) ...imagery. They will trigger considerable false alarms in target detection applications tasked with ship detection, if they are not identified. An algorithm for stationary marine target detection, developed based on the assumption that the apparent positions of a stationary target in multitemporal imagery are also stationary or nearly so, is proposed in this article. This algorithm requires a strict time-series of SAR images in temporal sequence as input. For each input SAR image, all targets on sea surface are initially detected with an iterative cell-averaging constant false-alarm rate detection algorithm, and their longitude and latitude positions are then used to identify whether they are stationary targets. Under this algorithm, a stationariness index with five levels ("unknown target," "suspected new stationary target," "stationary target," "suspected removed stationary target," and "removed target") is defined for each target and must be iteratively updated with the latest level of identification. The proposed algorithm is promising for monitoring the status of stationary marine targets over a large sea area, because the processing of all input SAR images follows the same procedure and meanwhile the stationariness index is generated and kept updated. Two examples with GF-3 and RADARSAT-2 images are presented to illustrate the effectiveness of the proposed algorithm in detecting both offshore wind turbines and oil rigs.
Sensitive detection of copper ion (Cu2+), which is of great importance for environmental pollution and human health, is crucial. In this study, we present a highly sensitive method for measuring Cu2+ ...in an array of femtoliter wells. In brief, magnetic beads (MBs) modified with alkyne groups were bound to the azide groups of biotin-PEG3-azide (bio-PEG-N3) via Cu+-catalyzed click chemistry. Cu+ in the click chemistry reaction was generated by reducing Cu2+ with sodium ascorbate. Following the ligation, the surface of the MBs was modified with biotin, which could be labeled with streptavidin-β-galactosidase (SβG). The MBs complex was then suspended in β-galactosidase substrate fluorescein-di-β-d-galactopyranoside (FDG), and loaded into the array of femtoliter wells. The MBs sank into the wells due to gravity, and the resulting fluorescent product, generated from the reaction between SβG on the surface of the MBs and FDG, was confined within the wells. The number of fluorescent wells increased with higher Cu2+ concentrations. The bright-field and fluorescent images of the wells were acquired using an inverted fluorescent microscope. The detection limit of this assay for Cu2+ was 1 nM without signal amplification, which was 103 times lower than that of traditional fluorescence detection assays.
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•A digitized assay for Cu2+ detection based on click chemistry was developed.•The digitized assay detection volume is 109 times smaller than the bulk method.•The digitized assay detection limit for Cu2+ is 103 times lower than bulk method.
Space-based infrared tiny ship detection aims at separating tiny ships from the images captured by Earth-orbiting satellites. Due to the extremely large image coverage area (e.g., thousands of square ...kilometers), candidate targets in these images are much smaller, dimer, and more changeable than those targets observed by aerial- and land-based imaging devices. Existing short imaging distance-based infrared datasets and target detection methods cannot be well adopted to the space-based surveillance task. To address these problems, we develop a space-based infrared tiny ship detection dataset (namely, NUDT-SIRST-Sea) with 48 space-based infrared images and <inline-formula> <tex-math notation="LaTeX">17\,598 </tex-math></inline-formula> pixel-level tiny ship annotations. Each image covers about <inline-formula> <tex-math notation="LaTeX">10\,000 </tex-math></inline-formula> km 2 of area with <inline-formula> <tex-math notation="LaTeX">10 \ 000\,\, \times \ 10 \ 000 </tex-math></inline-formula> pixels. Considering the extreme characteristics (e.g., small, dim, and changeable) of those tiny ships in such challenging scenes, we propose a multilevel TransUNet (MTU-Net) in this article. Specifically, we design a vision Transformer (ViT) convolutional neural network (CNN) hybrid encoder to extract multilevel features. Local feature maps are first extracted by several convolution layers and then fed into the multilevel feature extraction module multilevel ViT module (MVTM) to capture long-distance dependency. We further propose a copy-rotate-resize-paste (CRRP) data augmentation approach to accelerate the training phase, which effectively alleviates the issue of sample imbalance between targets and background. Besides, we design a FocalIoU loss to achieve both target localization and shape description. Experimental results on the NUDT-SIRST-Sea dataset show that our MTU-Net outperforms traditional and existing deep learning-based single-frame infrared small target (SIRST) methods in terms of probability of detection, false alarm rate, and intersection over union. Our code is available at https://github.com/TianhaoWu16/Multi-level-TransUNet-for-Space-based-Infrared-Tiny-ship-Detection
Automatic crack detection from images of various scenes is a useful and challenging task in practice. In this paper, we propose a deep hierarchical convolutional neural network (CNN), called as ...DeepCrack, to predict pixel-wise crack segmentation in an end-to-end method. DeepCrack consists of the extended Fully Convolutional Networks (FCN) and the Deeply-Supervised Nets (DSN). During the training, the elaborately designed model learns and aggregates multi-scale and multi-level features from the low convolutional layers to the high-level convolutional layers, which is different from the standard approaches of only using the last convolutional layer. DSN provides integrated direct supervision for features of each convolutional stage. We apply both guided filtering and Conditional Random Fields (CRFs) methods to refine the final prediction results. A benchmark dataset consisting of 537 images with manual annotation maps are built to verify the effectiveness of our proposed method. Our method achieved state-of-the-art performances on the proposed dataset (mean I/U of 85.9, best F-score of 86.5, and 0.1 s per image).
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