Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and Preliminary Results of Small-Size Uav-Borne Fmcw Sar

The small-size unmanned aerial vehicle (UAV) is being used widely in many fields, especially in surveying, mapping and remote sensing. Due to the platform's size and load ability, its payload is strictly limited. Synthetic aperture radar(SAR) is a kind of microwave imaging instrument with high performance under all weather, day and night conditions. Highly miniaturized SAR can satisfy the requirement of observation on small UAV. Frequency modulation continuous wave (FMCW) technology provides a way for small volume, low cost, high reliability and high resolution SAR. FMCW SAR transmits frequency modulated signal continuously. This reduces the peak power, which is the important factor determining the size and cost of SAR. Meanwhile, due to transmitting and receiving signals simultaneously, increasing the isolation between the transmitter and receiver becomes associated problem. Two separated antennas solve the problem partly. There is another problem in processing algorithm. Long duration of signal makes the assumption of stop and go model ineffective. Frequency scaling algorithm has to be chosen to correct the phase variation during one period of frequency sweep. Procedure of development for prototype and practical systems and verification experiments are shown in this paper. The results of experiments with such radars prove the expected performance.
Cross Documents Concept Augmentation

The current paper proposes a strategy for exploring and integrating related information extracted from unstructured documents with different degree of confidence, standardization and representation. The strategy was instantiated on the medical domain and designed for the English language. The goal of the proposed strategy was of augmenting the therapeutic information from patient leaflets with information extracted from clinical records. The approach proved to be a sound one as the information from the clinical records aligns with the information in the standardized sources. It confirmed the assumption that we can derive drug repositioning from clinical records and thus augmenting the existing medical knowledge. The reported metrics <95.14% precision, 83.3% recall>for patient leaflets and <94.07% precision, 87.27% recall >for EHRs measured for the concept extraction strategy, further support a good performance for the entities correlation approach. The degree of correlation between the extracted information from the two data sources reported as matches is of 85%.
MuLoG: A Generic Variance-Stabilization Approach for Speckle Reduction in SAR Interferometry and SAR Polarimetry

Speckle reduction is a long-standing topic in SAR data processing. Continuous progress made in the field of image denoising fuels the development of methods dedicated to speckle in SAR images. Adaptation of a denoising technique to the specific statistical nature of speckle presents variable levels of difficulty. It is well known that the logarithm transform maps the intrinsically multiplicative speckle into an additive and stationary component, thereby paving the way to the application of general-purpose image denoising methods to SAR intensity images. Multi-channel SAR images such as obtained in interferometric (InSAR) or polarimetric (PolSAR) configurations are much more challenging. This paper describes MuLoG, a generic approach for mapping a multi-channel SAR image into real-valued images with an additive speckle component that has a variance approximately constant. With this approach, general-purpose image denoising algorithms can be readily applied to restore InSAR or PolSAR data. In particular, we show how recent denoising methods based on deep convolutional neural networks lead to state-of-the art results when embedded with MuLoG framework.
Reconfigurable Hemispherical Dielectric Lens Antennas in MM-Waves

The objective of this paper is presenting a broadband and directive radiator system with reconfigurable radiation pattern for millimeter wave applications. Numerical validation of the structure was performed in the Ansys HFSS® software There are some suitable features for operation in high-data rate communication technologies, since the antenna bandwidth has a 40-80 GHz with gain reaching 18.3 dBi.
Deep Semantic Segmentation of Aerial Imagery Based on Multi-Modal Data

In this paper, we focus on the use of multi-modal data to achieve a semantic segmentation of aerial imagery. Thereby, the multi-modal data is composed of a true orthophoto, the Digital Surface Model (DSM) and further representations derived from these. Taking data of different modalities separately and in combination as input to a Residual Shuffling Convolutional Neural Network (RSCNN), we analyze their value for the classification task given with a benchmark dataset. The derived results reveal an improvement if different types of geometric features extracted from the DSM are used in addition to the true orthophoto.
Exploring Users' Motivation to Contribute in Online Platforms

In this proposal, two studies on users’ motivations for contribution in online platforms are presented. The first study aims to investigate users’ dynamic contribution pattern in reward-based crowdfunding. We construct our unique dataset based on bi-hour crawling of project-related information from Kickstarter. By analyzing backers’ contribution and referral patterns before, during, and after the threshold, we show that users are more likely to help rather than to pre-order the projects. The second study focuses on the effect of monetary incentive on users’ voluntary contributions in Q&A platform. We find monetary intervention can create a positive spillover effect on users’ free content creation. Our results provide support that users contribute in order to improve their reputation. Overall, these two studies contribute to a better understanding of users’ motivations for contribution in crowdfunding and Q&A platforms. They have direct managerial implications to platform owners. one A set of robustness analyses are conducted: 1) three and live are excluded, Propensity Score Matching (PSM) and CEM matching methods are used to further corroborate the results, a quarter-year level analysis is also examined, and 4) the most active users are excluded for analysis. Our results are robust under these checks.
Fusion of Hyperspectral and Panchromatic Images Based on Matting Model

In this paper, a novel hyperspectral (HS) image fusion method using matting model is presented. Matting model refers to each band of an HS image that can be decomposed into three components, i.e., alpha channel, spectral foreground, and spectral background. First, panchromatic (PAN) image is sharpened to enhance details, and the spatial information of each band of HS image is obtained by weighted least squares filtering. Different from traditional matting model based methods that PAN image is served as the alpha channel, we do the PCA transformation to PAN image and spatial information of each band to obtain the first principal component channel which is selected for the alpha channel. This processing reduces spatial distortion. Finally, HS foreground and HS background are estimated by the alpha channel, and the fused HS image is reconstructed nearly perfectly. Experiments reveal that the proposed method is superior to the state-of-the-art methods.
Nutritional Composition and Bioactive Content of Legumes: Characterization of Pulses Frequently Consumed in France and Effect of the Cooking Method

Pulses display nutritional benefits and are recommended in sustainable diets. Indeed, they are rich in proteins and fibers, and can contain variable amounts of micronutrients. However, pulses also contain bioactive compounds such as phytates, saponins, or polyphenols/tannins that can exhibit ambivalent nutritional properties depending on their amount in the diet. We characterized the nutritional composition and bioactive compound content of five types of prepared pulses frequently consumed in France (kidney beans, white beans, chickpeas, brown and green lentils, flageolets), and specifically compared the effects of household cooking vs. canning on the composition of pulses that can be consumed one way or the other. The contents in macro-, micronutrients, and bioactive compounds highly varied from one pulse to another (i.e., 6.9 to 9.7 g/100 g of cooked product for proteins, 4.6 to 818.9 µg/100 g for lutein or 15.0 to 284.3 mg/100 g for polyphenols). The preparation method was a key factor governing pulse final nutritional composition in hydrophilic compounds, depending on pulse species. Canning led to a greater decrease in proteins, total dietary fibers, magnesium or phytate contents compared to household cooking (i.e., −30%, −44%, −33% and −38%, p < 0.05, respectively, in kidney beans). As canned pulses are easy to use for consumers, additional research is needed to improve their transformation process to further optimize their nutritional quality.
Mass spectrometry imaging of the in situ drug release from nanocarriers

Novel label-free MSI strategy was developed for evaluation of the in situ drug release of nanocarriers in sub-organ level. It is crucial but of a great challenge to study in vivo and in situ drug release of nanocarriers when developing a nanomaterial-based drug delivery platform. We developed a new label-free laser desorption/ionization mass spectrometry (MS) imaging strategy that enabled visualization and quantification of the in situ drug release in tissues by monitoring intrinsic MS signal intensity ratio of loaded drug over the nanocarriers. The proof of concept was demonstrated by investigating the doxorubicin (DOX)/polyethylene glycol–MoS2 nanosheets drug delivery system in tumor mouse models. The results revealed a tissue-dependent release behavior of DOX during circulation with the highest dissociation in tumor and lowest dissociation in liver tissues. The drug-loaded MoS2 nanocarriers are predominantly distributed in lung, spleen, and liver tissues, whereas the accumulation in the tumor was unexpectedly lower than in normal tissues. This new strategy could also be extended to other drug-carrier systems, such as carbon nanotubes and black phosphorus nanosheets, and opened a new path to evaluate the drug release of nanocarriers in the suborgan level.
MemorIES: a programmable, real-time hardware emulation tool for multiprocessor server design

Modern system design often requires multiple levels of simulation for design validation and performance debugging. However, while machines have gotten faster, and simulators have become more detailed, simulation speeds have not tracked machine speeds. As a result, it is difficult to simulate realistic problem sizes and hardware configurations for a target machine. Instead, researchers have focussed on developing scaling methodologies and running smaller problem sizes and configurations that attempt to represent the behavior of the real problem. Given the increasing size of problems today, it is unclear whether such an approach yields accurate results. Moreover, although commercial workloads are prevalent and important in today's marketplace, many simulation tools are unable to adequately profile such applications, let alone for realistic sizes.In this paper we present a hardware-based emulation tool that can be used to aid memory system designers. Our focus is on the memory system because the ever-widening gap between processor and memory speeds means that optimizing the memory subsystem is critical for performance. We present the design of the Memory Instrumentation and Emulation System (MemorIES). MemorIES is a programmable tool designed using FPGAs and SDRAMs. It plugs into an SMP bus to perform on-line emulation of several cache configurations, structures and protocols while the system is running real-life workloads in real-time, without any slowdown in application execution speed. We demonstrate its usefulness in several case studies, and find several important results. First, using traces to perform system evaluation can lead to incorrect results (off by 100% or more in some cases) if the trace size is not sufficiently large. Second, MemorIES is able to detect performance problems by profiling miss behavior over the entire course of a run, rather than relying on a small interval of time. Finally, we observe that previous studies of SPLASH2 applications using scaled application sizes can result in optimistic miss rates relative to real sizes on real machines, providing potentially misleading data when used for design evaluation.
Surface Deformation Detection by Small Baseline Sar Interferometry in Cangzhou Coastal Zone

We collaborate with Department Of Land And Resources of Hebei Province and detect the Surface deformation by InSAR SBAS processing. SAR data covered the coast of Hebei Cangzhou (ENVISAT IS2 & RADARSAT-2 W1 beam,2003-2013) are acquired. Image interpretation and change detection of buildings base on middle and high resolution optical images, provides prior background knowledge for InSAR processing in this case. InSAR results and fieldwork show there is a strong relationship between the effect of industrial expansion and the location of land subsidence.
Deep Learning for Ground-Level PM2.5 Prediction from Satellite Remote Sensing Data

Satellite remote sensing is a promising approach for the estimation of ground-level $\mathrm{PM}_{2.5}$. In this paper, a deep learning framework for satellite-based $\mathrm{PM}_{2.5}$ estimation is presented. Taking advantage of multi-layer learning and layer-by-layer pre-training, deep learning has the great potential to mine nonlinear relationship between $\mathrm{PM}_{2.5}$ and satellite observations. Firstly, the presented deep learning framework can be employed to estimate ground $\mathrm{PM}_{2.5}$ using satellite-derived aerosol optical depth (AOD). Secondly, the AOD products are retrieved from satellite top-of-atmosphere (TOA) reflectance. The deep learning framework can further be adopted to estimate ground $\mathrm{PM}_{2.5}$ directly from satellite TOA reflectance. The presented framework was tested for AOD-based $\mathrm{PM}_{2.5}$ estimation in China and reflectance-based $\mathrm{PM}_{2.5}$ estimation across Wuhan Metropolitan Area, respectively. The results show that the deep learning framework achieves an outstanding performance for both AOD-based and reflectance-based $\mathrm{PM}_{2.5}$ estimation. This study provides an effective way for the satellite-based estimation of $\mathrm{PM}_{2.5}$.
Enterprise Architecture in Healthcare and Underlying Institutional Logics: a Systematic Literature Review of IS Research

This paper reports on a systematic literature review of empirical studies in the information systems literature focusing on Enterprise Architecture (EA) in healthcare. 30 papers were selected for extended analysis. We utilized institutional logics as a theoretical lens and focused on the logics of IT professionalism, medical professionalism and managerialism. According to this lens, we identified three foci of interests. In addition, we utilized the institutional level as an analytical dimension. The logics of IT professionals and the purpose of organizational implications of EA were dominating. Generally, there is a need for more in-depth understanding for all logics, however, the logics of managerialism and medical professionalism need particularly more attention. Moreover, there is a need for more empirical research to understand how institutional logics for similar professions may differ across institutions. Finally, few studies on EA apply theoretical lenses, and EA research is immature in sense of theoretical contributions.
A Hybrid Approach to Planning and Execution in Dynamic Environments Through Hierarchical Task Networks and Behavior Trees

Intelligent autonomous agents that are acting in dynamic environmentsin real-time are often required to follow long-termstrategies while also remaining reactive and being able to actdeliberately. In order to create intelligent behaviors for videogame characters, there are two common approaches – plannersare used for long-term strategical planning, whereas BehaviorTrees allow for reactive acting. Although both methodologieshave their advantages, when used on their own, theyfail to fully achieve both requirements described above. Inthis work, we propose a hybrid approach combining a HierarchicalTask Network planner for high-level planning whiledelegating low-level decision making and acting to BehaviorTrees. Furthermore, we compare this approach with a pureplanner in a multi-agent environment.
Sebal Based Evapotranspiration Estimation for Upper Tapi Basin (India)

Evapotranspiration is one of the important component in hydrological cycle and plays a major role in water balance studies of tropical and subtropical areas. Estimation of actual evapotranspiration using remote sensing is a step towards the real time spatial mapping of evapotranspiration. However, this estimation being a tedious process requires determination of heat and radiation fluxes with the help of field observations. Real time spatio-temporal mapping of ET will go a long way in solving problems of water use, land use and water allocation. In this paper we used Surface Energy Balance Algorithm for Land (SEBAL) estimating the actual evapotranspiration (ET) for upper Tapi basin (India). Study emphasises on the methodology of the model and energy flux components it operates on.
AIS Electronic Library (AISeL)

Programme management is increasingly employed by governments and industry leaders as a mechanism for achieving strategic changes of significant complexity. Yet, research on programme management in general, and information technology (IT) enabled programmes specifically, is scarce. A common justification of programme management approach is its ability to coordinate across multiple projects and operational activities which are interrelated or interdependent, thus making coordination a central concept in programme management. Despite its importance, the process of coordination in programme management has remained unexplored. Based on a review of IS literature on coordination, a research study has been designed which aims to develop a process model of coordination in IT enabled programmes. This study will make research contribution to the understudied area of programme management and to programme management practice by explaining how coordination concretely occurs in IT enabled programmes. By taking the neglected whole-of-process approach, this study will also contribute to coordination research.
Discriminative Learning of Point Cloud Feature Descriptors Based on Siamese Network

It is challenging to direct extract the feature descriptors of the object in the point cloud, although deep learning has been widely used with the classification and detection in the point cloud, those methods hidden feature presentation in the network. Since the point cloud scanned by the Laser Scanner usually have different point density, unordered and even the different occlusion, which go beyond the reach of handcrafted descriptors, e.g. FPH, FPFH, VFH, ROPS. In this paper, we aim to direct extract the feature descriptors of the point cloud object through the raw point cloud. Inspired by the recent success of the Siamese networks[6], PointNet[7] and PointNet++[8], we propose a novel network to direct extract the feature descriptors of the whole point cloud object. We train our network with the Euclidean distance as the loss function which reflects feature descriptors similarity. The experiment object datasets were acquired by Mobile Laser Scanning (MLS) system which contains 6 categories. Experiment result shows that our network has a robust generalization, which can well direct extract the feature descriptors of the whole point cloud object.
The Role of Diet, Micronutrients and the Gut Microbiota in Age-Related Macular Degeneration: New Perspectives from the Gut–Retina Axis

Age-related macular degeneration (AMD) is a complex multifactorial disease and the primary cause of legal and irreversible blindness among individuals aged ≥65 years in developed countries. Globally, it affects 30–50 million individuals, with an estimated increase of approximately 200 million by 2020 and approximately 300 million by 2040. Currently, the neovascular form may be able to be treated with the use of anti-VEGF drugs, while no effective treatments are available for the dry form. Many studies, such as the randomized controlled trials (RCTs) Age-Related Eye Disease Study (AREDS) and AREDS 2, have shown a potential role of micronutrient supplementation in lowering the risk of progression of the early stages of AMD. Recently, low-grade inflammation, sustained by dysbiosis and a leaky gut, has been shown to contribute to the development of AMD. Given the ascertained influence of the gut microbiota in systemic low-grade inflammation and its potential modulation by macro- and micro-nutrients, a potential role of diet in AMD has been proposed. This review discusses the role of the gut microbiota in the development of AMD. Using PubMed, Web of Science and Scopus, we searched for recent scientific evidence discussing the impact of dietary habits (high-fat and high-glucose or -fructose diets), micronutrients (vitamins C, E, and D, zinc, beta-carotene, lutein and zeaxanthin) and omega-3 fatty acids on the modulation of the gut microbiota and their relationship with AMD risk and progression.
A Two-Step Approach for the Design and Generation of Nanobodies

Nanobodies, the smallest possible antibody format, have become of considerable interest for biotechnological and immunotherapeutic applications. They show excellent robustness, are non-immunogenic in humans, and can easily be engineered and produced in prokaryotic hosts. Traditionally, nanobodies are selected from camelid immune libraries involving the maintenance and treatment of animals. Recent advances have involved the generation of nanobodies from naïve or synthetic libraries. However, such approaches demand large library sizes and sophisticated selection procedures. Here, we propose an alternative, two-step approach for the design and generation of nanobodies. In a first step, complementarity-determining regions (CDRs) are grafted from conventional antibody formats onto nanobody frameworks, generating weak antigen binders. In a second step, the weak binders serve as templates to design focused synthetic phage libraries for affinity maturation. We validated this approach by grafting toxin- and hapten-specific CDRs onto frameworks derived from variable domains of camelid heavy-chain-only antibodies (VHH). We then affinity matured the hapten binder via panning of a synthetic phage library. We suggest that this strategy can complement existing immune, naïve, and synthetic library based methods, requiring neither animal experiments, nor large libraries, nor sophisticated selection protocols.
Evaluation of Heterogeneous Landscape Environments Based on Information Capacity

Remote sensing image is a kind of structured data and its adjacent spatial domain pixels are highly correlated. The value of information capacity of image has a high correlation with the meaningful gray level of remote sensing image, which reflected and characterized the complexity of land surface landscape. Therefore, this paper introduces the information capacity to describe the land surface spatial pattern in the heterogeneous landscape environment. The experimental areas with the same size and different landscapes include a variety of landforms, such as plains, hills and mountains, and so on. The relationship between information capacity and the fractal dimension, and that between information capacity and Moran's are discussed. Finally, the paper proposes that information capacity can be used as a new index for spatial pattern analysis of heterogeneous landscape environments.