Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-varying hyperspectral characteristics of typical terrains

In order to make the visible and infrared characteristic of a camouflaged target closer to real background environment such that an effective fusion of the target and the background is achieved, it is necessary to study the hyperspectral signatures and analyze the performance for both target and background under various sun illumination at different time intervals by using imaging spectroscopy techniques. In this work, through outdoor field hyperspectral observations of six kinds of typical terrains such as grasslands, bare soils, concrete road and trees, the spectral image sequences are acquired under different solar conditions from 7:00 A.M to 15:00 P.M daily. It is found from the experiment results that the positions of the characteristic peak and valley kept stably fixed, while the reflectivity varies with noticeably different trends. The matching among the spectral data for different terrains are implemented and analyzed. It is seen that in visible band, the spectral matching values of wet soil, dry soil and bare soil are similar, while the spectral angle of wet soil is quite different from that of bare soil. On the other side, the matching values of trees and grassland are similar in both visible and nearinfrared bands. The results obtained are of great importance as a reference for designs of optical camouflage and stealth materials as well as for camouflage performance evaluation.
An intelligent garbage classifier based on deep learning models

Waste recycling is very important for economy and climate balance of the world. For this reason, intelligent classifying recyclable garbage is an important goal for humanity and Deep Learning models can be used for this purpose. In this paper, a deep learning framework with different architectures, such as Densenet, Inception- Resnet-V2, MobileNet, and Xception, is tested on Trashnet dataset to provide the most efficient approach. Meanwhile, Adam is selected for optimizing neural network models. Experimental results validate that Deep learning models with the Adam optimizer could provide better a test accuracy rate compared to the Adadelta optimizer. With comparison of quantitative results obtained by those architectures in the deep learning frame- work, we can find that the DenseNet using fine-tuning can get the best result (a test accuracy rate of 95%) and the Inception-ResNet-V2 using fine-tuning is the second best (a test accuracy of 94%).
An fMRI Study of Affective Congruence across Visual and Auditory Modalities

Abstract Evaluating multisensory emotional content is a part of normal day-to-day interactions. We used fMRI to examine brain areas sensitive to congruence of audiovisual valence and their overlap with areas sensitive to valence. Twenty-one participants watched audiovisual clips with either congruent or incongruent valence across visual and auditory modalities. We showed that affective congruence versus incongruence across visual and auditory modalities is identifiable on a trial-by-trial basis across participants. Representations of affective congruence were widely distributed with some overlap with the areas sensitive to valence. Regions of overlap included bilateral superior temporal cortex and right pregenual anterior cingulate. The overlap between the regions identified here and in the emotion congruence literature lends support to the idea that valence may be a key determinant of affective congruence processing across a variety of discrete emotions.
A lightweight deep learning model for mobile eye fundus image quality assessment

Image acquisition and automatic quality analysis are fundamental stages and tasks to support an accurate ocular diagnosis. In particular, when eye fundus image quality is not appropriate, it can hinder the diagnosis task performed by experts. Portable, smart-phone-based eye fundus image acquisition devices have the advantage of their low cost and easy deployment, however, their main disadvantage is the sacrifice of image quality. This paper presents a deep-learning-based model to assess the eye fundus image quality which is small enough to be deployed in a smart phone. The model was evaluated in a public eye fundus dataset with two sets of annotations. The proposed method obtained an accuracy of 0.911 and 0.856, in the binary classification task and the three-classes classification task respectively. Besides, the presented method has a small number of parameters compared to other state-of-the-art models, being an alternative for a mobile-based eye fundus quality classification system.
A novel spatial-frequency filtering scheme for x-ray angiograms with simultaneous denoising and enhancement

X-ray angiograms, which suffer from low-contrast and noise, need to be improved by both the image enhancement and denoising techniques. However, the goals of these two tasks usually conflict, which makes it difficult to efficiently combine the enhancement and denoising in one scheme. To solve this problem, we propose a novel spatial-frequency filtering (SFF) scheme to simultaneously enhance and denoise low-quality X-ray cardiovascular angiogram images. The proposed scheme includes three key components: Firstly, a relative total variation method is employed as a guide filter to separate an input image into two parts, including the base layer with strong structures and the detail layer with weak structures and noise. Then the base layer is enhanced by a proposed improved histogram equalization (IHE) method while the detail layer is extracted by a short-time Fourier transform and is further enhanced by using a proposed adaptive correction parameter. Finally, the improved image is the combination of results obtained by the two components. Both quantitative and qualitative results of experiments on real-world low-quality X-ray angiogram images demonstrate that the proposed method outperforms the state-of-the-arts in terms of contrast enhancement, structure preservation, and noise reduction.
Methods of automatic vegetation encroachment detection for high voltage power lines

Overhead inspections of high voltage power lines are very expensive undertaking. Power lines require systematic inspections to avoid failures and power outages. In each growing season vegetation encroachment is one of the most severe threat. Presently overhead inspections use helicopters and are carried out manually at least once a year. More cost effective systems, using UAV, are being researched. In this paper selected methods from all studied solutions were compared. A cost effective system was proposed using consumer grade UAV with standard camera and deep neural networks for detecting vegetation encroachment.
Comparison of single channel indices for U-Net based segmentation of vegetation in satellite images

Hyper-spectral satellite imagery, consisting of multiple visible or infrared bands, is extremely dense and weighty for deep operations. Regarding problems related to vegetation as, more specifically, tree segmentation, it is difficult to train deep architectures due to lack of large-scale satellite imagery. In this paper, we compare the success of different single channel indices, which are constructed from multiple bands, for the purpose of tree segmentation in a deep convolutional neural network (CNN) architecture. The utilized indices are either hand-crafted such as excess green index (ExG) and normalized difference vegetation index (NDVI) or reconstructed from the visible bands using feature space transformation methods such as principle component analysis (PCA). For comparison, these features are fed to an identical CNN architecture, which is a standard U-Net-based symmetric encoder-decoder design with hierarchical skip connections and the segmentation success for each single index is recorded. Experimental results show that single bands, which are constructed from the vegetation indices and space transformations, can achieve similar segmentation performances as compared to that of the original multi-channel case.
The air object tracking in 3D space using distance measurements

The widespread availability of global navigation satellite signals has become so obvious that it is difficult to imagine their absence. However, under conditions of severe intentional interference, these signals can be degraded (spoofing) or blocked (jamming). It becomes necessary to search for solutions that allow to create your own location system operating in the local coordinate system. The ultrawideband modules that measure distances (that are on the institute's equipment) can be used for this purpose. Therefore, an attempt was made to analyze algorithms based on the measurement of this parameter that allow the location and tracking of the air objects (drone) in 3D space. Although the use of radio waves is also assumed here, the spectrum of the signal is dispersed. This allows you to avoid detecting signals and thereby avoiding interference. The adopted method also gives potential opportunities to build an indoor positioning system where satellite system signals are unavailable. Studies have been carried out for various air object trajectories in open space. They were analysed using the ordinary least squares method, the extended Kalman filter and its modification. Graphic imagery of trajectory and calculated values of traced trajectory errors for different system configurations are shown. On this basis, the implemented algorithms were evaluated.
Spectral distortion correction of photon counting detector based on neural network

Photon-Counting-Detector (PCD) has a broad application prospect in medical X-ray computed tomography (CT) and Xray (XR) imaging, which can improve contrast and spatial resolution, optimize spectral imaging, and use energy-dependent attenuation coefficient for the great potential of material composition identification. However, the measurement provided by the photon-counting-detector causes spectral distortion due to physical phenomena such as pulse pileup effect, charge sharing, K-escape and Compton scattering occurring in the detector. Since the calculation of the physical phenomenon that causes distortion is very complicated, this paper proposes a method of using the neural network for spectral correction based on Monte Carlo simulation, that is, using the Monte Carlo method to simulate the particle transport process to obtain undistorted spectrum as the label of the neural network, the spectrum is used as the input data of the neural network, and the relationship between the distortion spectrum and the corrected spectrum is learned by training the neural network. After the training is completed, using the test set for model evaluation, the standard error between the predicted result and the label was only 25.1601ppm. This method can effectively correct the spectral distortion problem of the photon-countingdetector, and can more accurately invert the X-ray spectral data.
Dual efficient self-attention network for multi-target detection in aerial imagery

Aerial imagery target detection has been widely used in the military and economic fields. However, it still faces a variety of challenges. In this paper, we proposed several efficiency improvements based on YOLO v3 framework for getting a better small target detection precision. Firstly, a dual self-attention (DAN) block is embedded in Darknet-53’s ResNet units to refine the feature map adaptively. Furthermore, the deep semantic features are cascaded with the shallow outline features in a feedforward deconvolutional module to obtain context details of small targets. Finally, introducing online hard examples mining and combining Focal Loss to enhance the discriminating ability between classes. The experimental results on the VEDAI aerial dataset show that the proposed algorithm is significantly improved in accuracy compared to the original network and achieves better performance than two-stage algorithms.
Sustainable manufacturing of sensors onto soft systems using self-coagulating conductive Pickering emulsions

An ethanol-based Pickering emulsion that spontaneously forms conductive composites is used to sustainably manufacture compliant strain sensors. Compliant sensors based on composite materials are necessary components for geometrically complex systems such as wearable devices or soft robots. Composite materials consisting of polymer matrices and conductive fillers have facilitated the manufacture of compliant sensors due to their potential to be scaled in printing processes. Printing composite materials generally entails the use of solvents, such as toluene or cyclohexane, to dissolve the polymer resin and thin down the material to a printable viscosity. However, such solvents cause swelling and decomposition of most polymer substrates, limiting the utility of the composite materials. Moreover, many such conventional solvents are toxic or otherwise present health hazards. Here, sustainable manufacturing of sensors is reported, which uses an ethanol-based Pickering emulsion that spontaneously coagulates and forms a conductive composite. The Pickering emulsion consists of emulsified polymer precursors stabilized by conductive nanoparticles in an ethanol carrier. Upon evaporation of the ethanol, the precursors are released, which then coalesce amid nanoparticle networks and spontaneously polymerize in contact with the atmospheric moisture. We printed the self-coagulating conductive Pickering emulsion onto a variety of soft polymeric systems, including all-soft actuators and conventional textiles, to sensitize these systems. The resulting compliant sensors exhibit high strain sensitivity with negligible hysteresis, making them suitable for wearable and robotic applications.
Progress in NDT System Engineering Through Sensor Physics and Integrated Efficient Computing

Nondestructive testing (NDT) system engineering is comprised of different technologies, primarily measurement physics, computing, electronics, mechanical engineering and automation. Significant progress in sensor physics, microelectronics and computing in the last decade will change both the quality and application of NDT. We present examples of intelligent sensor technologies and integrated, efficient computing structures for signal processing. Keywords: Sensor-on-chip, Quantum Physical Sensing Effects and efficient scheduling of Optimized Algorithms including ill-posed problems. Our first systems designed on these principles allow for high-speed imaging with real-time quantitative assessment of inspection results. Multi-sensor systems, which communicate and combine their information for smarter processing, have also been developed. These principles will contribute to the current efforts in process integrated NDT, quantitative NDT and Structural Health Monitoring (SHM). We discuss ultrasonic inspection systems featuring distributed apertures, the use of optimized algorithms and computing structures for the processing of microwave and thermal data, and the health monitoring of lightweight structures with integrated ultrasonic sensor networks. Keywords
Detoxification Impacts of Ascorbic Acid and Clay on Laying Japanese Quail Fed Diets Polluted by Various Levels of Cadmium

Simple Summary The present study aimed to evaluate the impacts of ascorbic acid and clay supplementation on laying Japanese quail fed diets polluted by various levels of cadmium (Cd). Results revealed that consuming polluted diets with Cd causes harmful impacts on the productive performance of laying Japanese quail. The supplementation of ascorbic acid or natural clay to layer diets had beneficial effects on productive performance, improved egg quality and diminished the toxic effect of Cd. Abstract A total number of 360 laying Japanese quail (8 weeks of age) were randomly divided into 12 groups. Birds in all groups had nearly the same average initial body weight. A factorial arrangement (4 × 3) was performed including four levels of dietary cadmium (Cd) as cadmium chloride (0, 50, 100, and 150 mg/kg diet) and three levels of feed additives (without, 300 mg/kg ascorbic acid and 1.50% natural clay). Results revealed that Cd contaminated feed caused significant (p < 0.01) retardation in body weight, lower egg number and egg mass and worse feed conversion. On the other hand, the addition of ascorbic acid or natural clay to quail diets caused a significant (p < 0.01) improvement in all studied traits. With respect to the interaction among Cd and the experimental additives, results showed that within each Cd level, ascorbic acid or clay supplementation recorded the highest body weight, egg number, egg weight and mass in addition to improved feed conversion. Cadmium levels decreased (p < 0.05) blood total protein, albumen and A/G ratio. Both 300 mg ascorbic acid and 1.50% clay increased (p < 0.05) blood total protein and albumen compared to non-supplemented groups. It could be concluded that the consumption of polluted diets Cd causes deleterious effects on the productive performance of laying Japanese quail. The addition of ascorbic acid or natural clay to the diets causes beneficial effects on productive performance traits, improves egg quality criteria and diminishes the toxic effects of Cd.
Remote sensing: generation of long-term kelp bed data sets for evaluation of impacts of climatic variation.

A critical tool in assessing ecosystem change is the analysis of long-term data sets, yet such information is generally sparse and often unavailable for many habitats. Kelp forests are an example of a rapidly changing ecosystem that are in most cases are data-poor. Because kelp forests are highly dynamic and have high intrinsic interannual variability, understanding how regional-scale drivers are driving kelp populations-and particularly how kelp populations are responding to climate change-requires long-term data sets. However, much of the work on kelp responses to climate change has focused on just a few, relatively long-lived perennial, canopy-forming species. To better understand how kelp populations with different life history traits are responding to climate related variability, we leverage 35 years of Landsat satellite imagery to track the population size of an annual, ruderal kelp, Nereocystis luetkeana, across Oregon. We found high levels of interannual variability in Nereocystis canopy area and varying population trajectories over the last 35 years. Surprisingly, Oregon Nereocystis population sizes were unresponsive to a 2014 marine heatwave accompanied by increases in urchin densities that decimated northern California Nereocystis populations. Some Oregon Nereocystis populations have even increased in area relative to pre-2014 levels. Analysis of environmental drivers found that Nereocystis population size was negatively correlated with estimated nitrate levels and positively correlated with winter wave height. This pattern is the inverse of the predicted relationship based on extensive prior work on the perennial kelp Macrocystis pyrifera and may be related to the Nereocystis' annual life cycle. This paper demonstrates 1) the value of novel remote sensing tools to create long-term data sets that may challenge our understanding of nearshore marine species and 2) the need to incorporate life history traits into our theory on how climate change will shape the ocean of the future.
High-Entropy Perovskite Fluorides: A New Platform for Oxygen Evolution Catalysis.

High-entropy perovskite fluorides (HEPFs) have great poten-tial in electrocatalysis that has not been realized because of the limitation of a high-temperature synthetic route and the limited understanding of high-entropy materials. The use of HEPFs in effective oxygen evolution catalysis and a feasible synthesis route for HEPFs in a boiled solution by combining a hydrothermal method with mechanochemistry are first reported here. These HEPFs consisting of cost-effective ele-ments dramatically gave excellent catalytic activity for the oxygen evolution reaction (OER) in an alkaline media. Par-ticularly, K0.8Na0. 2(MgMnFeCoNi)F3 exhibited excellent OER performance with a low overpotential of 314 mV at a current density of 10 mA/cm2, much lower than that of commercial IrO2.
Efficacy of Crizotinib, Ceritinib, and Alectinib in ALK-Positive Non-Small Cell Lung Cancer Treatment: A Meta-Analysis of Clinical Trials

This study aimed to evaluate the efficacy of anaplastic lymphoma kinase (ALK)-inhibitors in the treatment of ALK-positive non-small cell lung cancer (NSCLC) by using a meta-analysis of clinical trials. We searched PubMed, EMBASE, Cochrane Library, and Clinicaltrials.gov by using keywords related to the topic in August 2018. The pooled effect sizes were calculated based on a random-effects model. We also performed subgroup meta-analysis by types of ALK inhibitors (crizotinib, ceritinib, and alectinib). A total of 20 clinical trials with 10 single-arm trials and 10 double-arm trials were included in the final meta-analysis. The median overall survival (OS), progression-free survival (PFS), overall response rate (ORR), disease control rate (DCR), 1 year survival rate, and 2 year survival rate were 19.14 months, 8.47 months, 62%, 78%, 74%, and 62%, respectively. ALK inhibitors showed a significantly superior efficacy compared with chemotherapy (hazard ratio (HR) for OS, 0.83; HR for PFS, 0.43; rate difference (RD) for ORR, 0.23; and RD for DCR, 0.10). The current meta-analysis of clinical trials showed the significant efficacy of ALK inhibitors in the treatment of ALK-positive NSCLC. Further head-to-head trials are needed to compare their efficacy with other types of NSCLC treatment regimens. PROSPERO registration: CRD42018085987.
Childhood Extracorporeal Membrane Oxygenation Survivors: Parents Highlight Need for Structured Follow-Up and Support After Hospital Discharge

Objectives: To examine parental experiences of childhood extracorporeal membrane oxygenation survivors to understand: 1) the problems children faced and 2) the support received following hospital discharge. Design: Single-center descriptive study. Setting: Nationally commissioned center for neonatal and pediatric extracorporeal membrane oxygenation. Patients: All traceable survivors less than 18 years old who received extracorporeal membrane oxygenation from January 1998 to April 2013. Intervention: Anonymized postal questionnaire completed by parents of extracorporeal membrane oxygenation survivors. Measurements and Main Results: Parent-reported developmental problems, follow-up, and the degree of satisfaction with any follow-up experience. Parents of 89 of 366 extracorporeal membrane oxygenation survivors (24%) responded. Sixty-six (74%) reported having developmental concerns about their child, including speech and language (n = 32; 36%), concentration (n = 28; 31%), movement/physical difficulties (n = 26; 29%), and educational difficulties (n = 22; 25%); 46 (52%) indicated that their child had difficulties across multiple domains. Twenty-one (34%) of those with one or more reported developmental concerns were not receiving any follow-up. However, 57 (64%) attended our 1-year follow-up extracorporeal membrane oxygenation clinic and 54 of 57 (95%) found it very useful. Three themes related to perceived need were identified from parents’ free-text comments: the need for an expert point of contact and follow-up at the extracorporeal membrane oxygenation center; more information on extracorporeal membrane oxygenation and any long-term effects; and more support from, and easier access to, community specialist services. Conclusions: A proportion of children who have undergone extracorporeal membrane oxygenation treatment have needs that are not being met, with variable access to service provision. Structured follow-up after discharge would enable early identification of developmental concerns, permit early referral or intervention, and provide support to families. Education and sharing of information about extracorporeal membrane oxygenation with general practitioners/family physicians, community professionals, and schools are essential.