Miracle-dz/UniFAR-FTUs huggingface dataset:
A Simplified Framework for High-Resolution Urban Vegetation Classification with Optical Imagery in the Los Angeles Megacity

High spatial resolution maps of Los Angeles, California are needed to capture the heterogeneity of urban land cover while spanning the regional domain used in carbon and water cycle models. We present a simplified framework for developing a high spatial resolution map of urban vegetation cover in the Southern California Air Basin (SoCAB) with publicly available satellite imagery. This method uses Sentinel-2 (10–60 × 10–60 m) and National Agriculture Imagery Program (NAIP) (0.6 × 0.6 m) optical imagery to classify urban and non-urban areas of impervious surface, tree, grass, shrub, bare soil/non-photosynthetic vegetation, and water. Our approach was designed for Los Angeles, a geographically complex megacity characterized by diverse Mediterranean land cover and a mix of high-rise buildings and topographic features that produce strong shadow effects. We show that a combined NAIP and Sentinel-2 classification reduces misclassified shadow pixels and resolves spatially heterogeneous vegetation gradients across urban and non-urban regions in SoCAB at 0.6–10 m resolution with 85% overall accuracy and 88% weighted overall accuracy. Results from this study will enable the long-term monitoring of land cover change associated with urbanization and quantification of biospheric contributions to carbon and water cycling in cities.
Is Polymorphism in the Apoptosis and Inflammatory Pathway Genes Associated With a Primary Response to Anti-TNF Therapy in Crohn’s Disease Patients?

Anti-tumor necrosis factor (TNF) therapy is used for the induction and maintenance of remission in Crohn’s disease (CD) patients. However, primary nonresponders to initial treatment constitute 20%–40% of cases. The causes of this phenomenon are still unknown. In this study, we aimed to determine the genetic predictors of the variable reactions of CD patients to anti-TNF therapy. Using long-range PCR libraries and the next-generation sequencing (NGS) method, we performed broad pharmacogenetic studies including a panel of 23 genes (TNFRSF1A, TNFRSF1B, CASP9, FCGR3A, LTA, TNF, FAS, ADAM17, IL17A, IL6, MMP1, MMP3, S100A8, S100A9, S100A12, TLR2, TLR4, TLR9, CD14, IL23R, IL23, IL1R, and IL1B) in a group of 107 diagnosed and clinically characterized CD patients following anti-TNF therapy. In the studied group, we indicated, in total, 598 single nucleotide variants for all analyzed genomic targets. Twelve patients (11.2%) did not respond to the induction therapy, which was associated with alleles in 11 loci located in FCGR3A (rs7539036, rs6672453, rs373184583, and rs12128686), IL1R (rs2041747), TNFRSF1B (rs5746053), IL1B (rs1071676, rs1143639, rs1143637, and rs1143634), and FAS (rs7896789) genes. After multiple comparison corrections, the results were not statistically significant, however for nonresponders the alleles distribution for those loci presented large differences and specified scheme compared to responders and populations. These findings require further investigation in an independent larger cohort before introducing them for a clinical setting, however, we identified an interesting direction. Polymorphism of the FCGR3A, IL1R, TNFRSF1B, IL1B, and FAS genes could be a predictor of the primary response to anti-TNF therapy in CD patients.
Proof of Concept for Sea Ice Stage of Development Classification Using Deep Learning

Accurate maps of ice concentration and ice type are needed to address increased interest in commercial marine transportation through the Arctic. RADARSAT-2 SAR imagery is the primary source of data used by expert ice analysts at the Canadian Ice Service (CIS) to produce sea ice maps over the Canadian territory. This study serves as a proof of concept that neural networks can be used to accurately predict ice type from SAR data. Datasets of SAR images served as inputs, and CIS ice charts served as labelled outputs to train a neural network to classify sea ice type. Our results show that DenseNet achieves the highest overall classification accuracy of 94.0% including water and the highest ice classification accuracy of 91.8% on a three class dataset using a fusion of HH and HV SAR polarizations for the input samples. The 91.8% ice classification accuracy validates the premise that a neural network can be used to effectively categorize different ice types based on SAR data.
CoUAS

In the past decade, unmanned aircraft systems (UASs) have been widely used in various civilian applications, most of which involve only a single unmanned aerial vehicle (UAV). In the near future, more and more UAS applications will be facilitated by the cooperation of multiple UAVs. In such applications, it is desirable to utilize a general control platform for cooperative UAVs. However, existing open-source control platforms cannot fulfill such a demand because (1) they only support the leader-follower mode, which limits the design options for fleet control, (2) existing platforms can support only certain type of UAVs and thus lack compatibility, and (3) these platforms cannot accurately simulate a flight mission, which may cause a big gap between simulation and real-world flight. To address these issues, we propose a general control and monitoring platform for cooperative UAS, namely, CoUAS, which provides a set of core cooperation services of UAVs, including synchronization, connectivity management, path planning, energy simulation, and so on. To verify the applicability of CoUAS, we design and develop a prototype in which an embedded path planning service is provided to complete any task with the minimum flying time while considering the network connectivity and coverage. Experimental results by both simulation and field test demonstrate that the proposed system is viable.
iSleep

The quality of sleep is an important factor in maintaining a healthy life style. A great deal of work has been done for designing sleep monitoring systems. However, most of existing solutions bring invasion to users more or less due to the exploration of the accelerometer sensor inside the device. This article presents iSleep—a practical system to monitor people’s sleep quality using off-the-shelf smartphone. iSleep uses the built-in microphone of the smartphone to detect the events that are closely related to sleep quality, and infers quantitative measures of sleep quality. iSleep adopts a lightweight decision-tree-based algorithm to classify various events. For two-user scenario, iSleep differentiates the events of two users either when two phones can collaborate with each other or when two phones cannot communicate with each other. The experimental results show that iSleep achieves consistently above 90% accuracy for event classification in a variety of different settings in one-user scenario and above 92% accuracy for distinguishing users in two-user scenario. By providing a fine-grained sleep profile that depicts details of sleep-related events, iSleep allows the user to track the sleep efficiency over time and relate irregular sleep patterns to possible causes.
Seasonal Changes in the Diameter of Secondarily Thickened Roots of Fruit Trees in Relation to Growth of Other Parts of the Tree

SummaryThe diameters of apple and cherry tree roots, between 1·5 ft. and 6·0 ft. from the base of the tree trunk, were measured with a microscope through glass observation panels at intervals during 4 years. The rate of root thickening was low in June and was most rapid from mid-July to mid-September, whereas rapid trunk thickening occurred in June and continued until September. The period of maximum root thickening coincided with a second peak in the production of new white extension roots when the intensity of shoot growth was decreasing.Some root thickening was detected one week after bud burst but many roots showed no thickening until July or August. At this distance from the trunk there was no progression of thickening along the root from the proximal to the distal end.
The Effect of Candling on the Hatchability of Eggs from Broiler Breeder Hens

Abstract In many commercial hatcheries candling is maintained as a routine operation, assuming that the removal of “clear” eggs at transfer can improve hatchability and chick quality. However, in the published literature, few data exist to support the benefits of this routine. A series of experiments was conducted to evaluate the influence of candling on hatchability and chick quality. Eggs from breeder flocks at seven different ages were used. Candling did not alter average hatchability when calculations were based on total number of eggs set (84.53 vs 84.07%, respectively, for candled and control eggs), as well as on the number of eggs with live embryos at 18 days of incubation (95.65 vs 95.89%, respectively, for candled and control eggs). There were no significant differences observed due to flock age. Average chick quality was slightly improved by candling when culls were expressed as a percentage of total settable eggs (0.84 vs 0.97% of culls). For flocks 63 weeks of age, a positive effect was also observed (0.93 vs 1.49% of culls).
Resilient fault diagnosis under imperfect observations–A need for Industry 4.0 era

In smart industrial systems, in many cases, a fault can be captured as an event to represent the distinct nature of subsequent changes. Event-based fault diagnosis techniques are capable model-based methods for diagnosing faults from a sequence of observable events executed by the system under diagnosis. Most event-based diagnosis techniques rely on perfect observations of observable events. However, in practice, it is common to miss an observable event due to a problem in sensor-readings or communication / transmission channels. This paper develops a fault diagnosis tool, referred to as diagnoser, which can robustly detect, locate, and isolate occurred faults. The developed diagnoser is resilient against missed observations. A missed observation is detected from its successive sequence of events. Upon detecting a missed observation, the developed diagnoser automatically resets and then, asynchronously resumes the diagnosis process. This is achieved solely based on post-reset / activation observations and without interrupting the performance of the system under diagnosis. New concepts of asynchronous detectability and asynchronous diagnosability are introduced. It is shown that if asynchronous detectability and asynchronous diagnosability hold, the proposed diagnoser is capable of diagnosing occurred faults under imperfect observations. The proposed technique is applied to diagnose faults in a manufacturing process. Illustrative examples are provided to explain the details of the proposed algorithm. The result paves the way towards fostering resilient cyber-physical systems in Industry 4.0 context.
Ship-Iceberg Classification in SAR and Multispectral Satellite Images with Neural Networks

Classification of ships and icebergs in the Arctic in satellite images is an important problem. We study how to train deep neural networks for improving the discrimination of ships and icebergs in multispectral satellite images. We also analyze synthetic-aperture radar (SAR) images for comparison. The annotated datasets of ships and icebergs are collected from multispectral Sentinel-2 data and taken from the C-CORE dataset of Sentinel-1 SAR images. Convolutional Neural Networks with a range of hyperparameters are tested and optimized. Classification accuracies are considerably better for deep neural networks than for support vector machines. Deeper neural nets improve the accuracy per epoch but at the cost of longer processing time. Extending the datasets with semi-supervised data from Greenland improves the accuracy considerably whereas data augmentation by rotating and flipping the images has little effect. The resulting classification accuracies for ships and icebergs are 86% for the SAR data and 96% for the MSI data due to the better resolution and more multispectral bands. The size and quality of the datasets are essential for training the deep neural networks, and methods to improve them are discussed. The reduced false alarm rates and exploitation of multisensory data are important for Arctic search and rescue services.
UAV Photogrammetry Accuracy Assessment for Corridor Mapping Based on the Number and Distribution of Ground Control Points

Unmanned aerial vehicle (UAV) photogrammetry has recently emerged as a popular solution to obtain certain products necessary in linear projects, such as orthoimages or digital surface models. This is mainly due to its ability to provide these topographic products in a fast and economical way. In order to guarantee a certain degree of accuracy, it is important to know how many ground control points (GCPs) are necessary and how to distribute them across the study site. The purpose of this work consists of determining the number of GCPs and how to distribute them in a way that yields higher accuracy for a corridor-shaped study area. To do so, several photogrammetric projects have been carried out in which the number of GCPs used in the bundle adjustment and their distribution vary. The different projects were assessed taking into account two different parameters: the root mean square error (RMSE) and the Multiscale Model to Model Cloud Comparison (M3C2). From the different configurations tested, the projects using 9 and 11 GCPs (4.3 and 5.2 GCPs km−1, respectively) distributed alternatively on both sides of the road in an offset or zigzagging pattern, with a pair of GCPs at each end of the road, yielded optimal results regarding fieldwork cost, compared to projects using similar or more GCPs placed according to other distributions.
An Overview of the Risk Posed by Thermal Neutrons to the Reliability of Computing Devices

The high performance, high efficiency, and low cost of Commercial Off-The-Shelf (COTS) devices make them attractive also for applications with strict reliability constraints. Today, COTS devices are adopted in HPC and safety-critical applications such as autonomous driving. Unfortunately, we cannot assume that COTS chips manufacturing process does not include the cheap natural Boron, that could makes them highly susceptible to thermal (low energy) neutrons. Through radiation beam experiments, using high-energy and low-energy neutrons, it has been shown that thermal neutrons are a significant threat to COTS device reliability. The evaluation includes AMD APU, three different NVIDIA GPUs, an Intel accelerator, and an FPGA executing a relevant set of algorithms. Besides the sensitivity of the devices to thermal neutrons it is also fundamental to consider the thermal neutron flux in different scenarios such as weather, concrete walls and floors, or even HPC liquid cooling systems. Correlating beam experiments and neutron detector data, it is shown that thermal neutrons FIT rate could be comparable or even higher than the high energy neutron FIT rate.
Towards a Large-Scale 3D Modeling of the Built Environment - Joint Analysis of TanDEM-X, Sentinel-2 and Open Street Map Data

Continental to global scale mapping of the human settlement extent based on earth observation satellite data has made considerable progress. Nevertheless, the current approaches only provide a two-dimensional representation of the built environment. Therewith, a full characterization is restricted in terms of the urban morphology and built-up density, which can only be gained by a detailed examination of the vertical settlement extent. This paper introduces a methodology for the extraction of three-dimensional (3D) information on human settlements by analyzing the digital elevation and radar intensity data collected by the German TanDEM-X satellite mission in combination with multispectral Sentinel-2 imagery and data from the Open Street Map initiative and the Global Urban Footprint human settlement mask. The first module of the underlying processor generates a normalized digital surface model from the TanDEM-X digital elevation model for all regions marked as a built-up area by the Global Urban Footprint. The second module generates a building mask based on a joint processing of Open Street Map, TanDEM-X/TerraSAR-X radar images, the calculated normalized digital surface model and Sentinel-2 imagery. Finally, a third module allocates the local relative heights of the normalized digital surface model to the building structures provided by the building mask. The outcome of the procedure is a 3D map of the built environment showing the estimated local height for all identified vertical building structures at 12 m spatial resolution. The results of a first validation campaign based on reference data collected for the seven cities of Amsterdam (NL), Indianapolis (US), Kigali (RW), Munich (DE), New York (US), Vienna (AT), and Washington (US) indicate the potential of the proposed methodology to accurately estimate the distribution of building heights within the built-up area.
Light emitting diode (LED) as a source of monochromatic light: a novel lighting approach for immunity and meat quality of poultry

The use of light emitting diodes (LEDs) as a source of monochromatic light to determine its impact on immunity and meat quality in poultry is investigated in the current review. The use of LEDs in poultry housing is preferred over other conventional light sources. LEDs emitting blue, green and yellow wavelength have been shown to improve the immunity and meat quality responses in broilers. Blue and green light help to promote greater antibody production and immune function, compared to red light. Keeping birds under yellow and green-blue mixed lighting has resulted in softer breast and drumstick muscles while white light improved lean meat and amino acid content of meat. From the available research it can be inferred that birds perform better in terms of meat quality and immunity if reared under the environment of monochromatic light produced by LED for cost-effective and advanced poultry production.
Generation of realistic signal strength measurements for a 5G Rogue Base Station attack scenario

The detection and prevention of cyber-attacks is one of the main challenges in Vehicle-to-Everything (V2X) autonomous platooning scenarios. A key tool in this activity is the measurement report that is generated by User Equipment (UE), containing received signal strength and location information. Such data is effective in techniques to detect Rogue Base Stations (RBS) or Subscription Permanent Identifier SUPI/5G-GUTI catchers. An undetected RBS could result in unwanted consequences such as Denial of Service (DoS) attacks and subscriber privacy attacks on the network and UE. Motivated by this, this paper presents the novel simulation of a 5G cellular system to generate a realistic dataset of signal strength measurements that can later be used in the development of techniques to identify and prevent RBS interventions. The results show that the tool can create a large dataset of realistic measurement reports which can be used to develop and validate RBS detection techniques.
Auto-transform

Data Transformation is a long-standing problem in data management. Recent work adopts a "transform-by-example" (TBE) paradigm to infer transformation programs based on user-provided input/output examples, which greatly improves usability, and brought such features into mainstream software like Microsoft Excel, Power BI, and Trifacta. While TBE is great progress, the need for users to provide paired input/output examples still poses limits on its applicability. In this work, we study an alternative that transforms data based on input/output data patterns only (without paired examples). We term this new paradigm transform-by-patterns (TBP). Specifically, we demonstrate that there is a rich class of transformations in TBP that can be "learned" from large collections of paired table columns. We show the proposed method can harvest such transformations across diverse domains and corpora (e.g., in different languages such as English, Chinese, Spanish, etc.). TBP transformations so obtained can be used in scenarios such as suggesting data-repairs in tables, or automating transformations in ETL pipelines. Extensive experiments on real data suggest that TBP outperforms existing methods on tasks such as data repairs, and is a promising direction for future research.
Baran

Traditional error correction solutions leverage handmaid rules or master data to find the correct values. Both are often amiss in real-world scenarios. Therefore, it is desirable to additionally learn corrections from a limited number of example repairs. To effectively generalize example repairs, it is necessary to capture the entire context of each erroneous value. A context comprises the value itself, the co-occurring values inside the same tuple, and all values that define the attribute type. Typically, an error corrector based on any of these context information undergoes an individual process of operations that is not always easy to integrate with other types of error correctors. In this paper, we present a new error correction system, Baran, which provides a unifying abstraction for integrating multiple error corrector models that can be pretrained and updated in the same way. Because of the holistic nature of our approach, we generate more correction candidates than state of the art and, because of the underlying context-aware data representation, we achieve high precision. We show that, by pretraining our models based on Wikipedia revisions, our system can further improve its overall precision and recall. In our experiments, Baran significantly outperforms state-of-the-art error correction systems in terms of effectiveness and human involvement requiring only 20 labeled tuples.
Water Stress Estimation in Vineyards from Aerial SWIR and Multispectral UAV Data

Mapping water stress in vineyards, at the parcel level, is of significant importance for supporting crop management decisions and applying precision agriculture practices. In this paper, a novel methodology based on aerial Shortwave Infrared (SWIR) data is presented, towards the estimation of water stress in vineyards at canopy scale for entire parcels. In particular, aerial broadband spectral data were collected from an integrated SWIR and multispectral instrumentation, onboard an unmanned aerial vehicle (UAV). Concurrently, in-situ leaf stomatal conductance measurements and supplementary data for radiometric and geometric corrections were acquired. A processing pipeline has been designed, developed, and validated, able to execute the required analysis, including data pre-processing, data co-registration, reflectance calibration, canopy extraction and water stress estimation. Experiments were performed at two viticultural regions in Greece, for several vine parcels of four different vine varieties, Sauvignon Blanc, Merlot, Syrah and Xinomavro. The performed qualitative and quantitative assessment indicated that a single model for the estimation of water stress across all studied vine varieties was not able to be established (r2 < 0.30). Relatively high correlation rates (r2 > 0.80) were achieved per variety and per individual variety clone. The overall root mean square error (RMSE) for the estimated canopy water stress was less than 29 mmol m−2 s−1, spanning from no-stress to severe canopy stress levels. Overall, experimental results and validation indicated the quite high potentials of the proposed instrumentation and methodology.