Miracle-dz/UniFAR-FTUs huggingface dataset:
Locality-Constrained and Class-Specific Sparse Representation for Sar Target Recognition

Recently, sparse representation has achieved the impressive performance on target recognition in synthetic aperture radar (SAR) image. However, the unstable and unsupervised optimization of the sparse representation may lead to undesired recognition result. In this paper, a locality-constrained and class-specific sparse representation (LCSR) framework is presented to alleviate these problems. Instead of the sparse constraint, the locality constraint is designed to utilize the local structure information of the training samples. It provides stable representation for the samples with minor variations, which is beneficial to classification. To further improve the recognition performance, the query sample is represented as a linear combination of class-specific galleries based on the supervision of class information. The inference is reached corresponding to the class with the minimum reconstruction error. The experimental results demonstrate the effectiveness and robustness of the proposed method.
Robust Object Estimation using Generative-Discriminative Inference for Secure Robotics Applications

Convolutional neural networks (CNNs) are of increasing widespread use in robotics, especially for object recognition. However, such CNNs still lack several critical properties necessary for robots to properly perceive and function autonomously in uncertain, and potentially adversarial, environments. In this paper, we investigate factors for accurate, reliable, and resource-efficient object and pose recognition suitable for robotic manipulation in adversarial clutter. Our exploration is in the context of a three-stage pipeline of discriminative CNN-based recognition, generative probabilistic estimation, and robot manipulation. This pipeline proposes using a SAmpling Network Density filter, or SAND filter, to recover from potentially erroneous decisions produced by a CNN through generative probabilistic inference. We present experimental results from SAND filter perception for robotic manipulation in tabletop scenes with both benign and adversarial clutter. These experiments vary CNN model complexity for object recognition and evaluate levels of inaccuracy that can be recovered by generative pose inference. This scenario is extended to consider adversarial environmental modifications with varied lighting, occlusions, and surface modifications.
Humanitarian Microwave Detection of Improvised Explosive Devices in Colombia

In this contribution, we present an overview and the latest research results regarding ground penetrating radar operation for humanitarian demining in Colombia. Next to the description of the investigated and manufactured radar equipment, we present a ground penetrating synthetic aperture radar (GPSAR) imaging approach. Moreover, the design of non-hazardous test scenarios and measurement results with the mentioned equipment are shown and discussed in detail.
The Generic Ecosystem and Innovation Patterns of the Digital Transformation in the Financial Industry

The emergence of financial technology companies (Fintechs) through the easy access of digital technologies is transforming the entire financial industry, heralding a new era of business models. With digital technologies like mobile payments, robo advisors, and distributed ledgers or blockchain, Fintechs are challenging the prevailing position of traditional financial institutions. However, literature does not provide a structured overview of the digital transformation in the financial industry, including inter-organizational innovation patterns. By analyzing 792 Fintechs, this paper visualizes the 22 generic roles and value streams within the financial ecosystem using the e3-value method. Moreover, we identify and discuss seven inter-organizational innovation patterns of the digital transformation in the financial industry. We contribute to literature by examining digital transformation in the financial industry from an inter-organizational perspective. Practitioners may apply the model to position themselves and to identify disruptive actors or potential business opportunities. We also analyze the influence of blockchain technology.
SMOS-IC Vegetation Optical Depth Index in Monitoring Aboveground Carbon Changes in the Tropical Continents During 2010–2016

Tropical aboveground carbon changes during 2010–2016 were estimated by a newly developed vegetation optical depth (VOD) product retrieved from the low-frequency L-band (1.4 GHz) passive microwave observations from the Soil Moisture and Ocean salinity (SMOS) satellite. The aboveground carbon changes estimated by VOD in the tropical region during 2010–2016 indicate the tropical region acts as a net carbon source of 111 Tg C yr-1 during 2010–2016. The declines in tropical aboveground carbon were found mainly in eastern America, African drylands and Indonesia.
Landscape Structure Estimation using Fourier-Based Textural Ordination of High Resolution Airborne Optical Image

Landscape heterogeneity is a key factor for understanding ecosystems function and is often associated to high biodiversity. Yet, its characterization for biodiversity monitoring still remains challenging, especially for very heterogeneous landscapes mosaics. In this work, we study the potential of Fourier-based Textural Ordination (FOTO) applied on high resolution airborne optical images to provide synthetic information about landscape fragmentation of Mediterranean heterogeneous landscapes. The ability of texture indices resulting from FOTO combined with vegetation indices to predict landscape metrics was also tested using non linear SVM regression. Our research showed that FOTO methods had great potential to finely characterize different vegetation strata organization at large scale with only few synthetic indices. Best regression results were obtained for ligneous and ground strata fragmentation with coefficient of determination greater than 0.7.
A reservoir activation kernel for trees

We introduce an eﬃcient tree kernel for reservoir computing models exploiting the recursive encoding of the structure in the state activations of the untrained recurrent layer. We discuss how the contractive property of the reservoir induces a topographic organization of the state space that can be used to compute structural matches in terms of pairwise distances between points in the state space. The experimental analysis shows that the proposed kernel is capable of achieving competitive classi-ﬁcation results by relying on very small reservoirs comprising as little as 10 sparsely connected recurrent neurons.
Class-conditional Embeddings for Music Source Separation

Isolating individual instruments in a musical mixture has a myriad of potential applications, and seems imminently achievable given the levels of performance reached by recent deep learning methods. While most musical source separation techniques learn an independent model for each instrument, we propose using a common embedding space for the time-frequency bins of all instruments in a mixture inspired by deep clustering and deep attractor networks. Additionally, an auxiliary network is used to generate parameters of a Gaussian mixture model (GMM) where the posterior distribution over GMM components in the embedding space can be used to create a mask that separates individual sources from a mixture. In addition to outperforming a mask-inference baseline on the MUSDB-18 dataset, our embedding space is easily interpretable and can be used for query-based separation.
Spectrally Adjusted Surface Reflectance and its Dependence with NDVI for pAssive Optical Sensors

Cross-calibration between sensors is necessary to bring measurements to a common radiometric scale; it allows a more complete monitoring of land surface processes and enhances data continuity and harmonization. However, differences in the Relative Spectral Response (RSR) of sensors generate uncertainties in the process [1]. For this reason, compensating for these differences is of great importance and can be achieved by using a spectral band adjustment factor (SBAF), which establishes a relationship between two spectrally adjusted bands. Nonetheless, this relationship has been shown to depend greatly on the surface type [2] and therefore needs to be corrected. In this work, we compute the SBAF between the historical Landsat and Sentinel 2 sensors by using the RSRs of different passive optical sensors in the Green, Red and NIR bands and the surface reflectance spectral libraries (ASTER, AVIRIS, IGCP) with a wide variety of classes. We produce a quadratic fit of the SBAF vs the surface's NDVI $(\rho_{nir}-\rho_{red})/(\rho_{nir}+\rho_{red})$ and propose an exponential correction equation dependent on the NDVI value for both bands. A comparison between Landsat 8 and Sentinel 2 images using the HLS product shows that this method improves the red band and NDVI accuracy by 46.4% and 63.9% respectively when the difference between the Relative Spectral Responses (RSR) is significant, but is inaccurate for the green band, where the atmospheric correction is likely to introduce same order errors.
A Novel High-Order Image Formation Algorithm for Gnss-Based Bistatic Sar

Global Navigation Satellite System (GNSS)-based bistatic Synthetic Aperture Radar (SAR) recently plays a more and more significant role in remote sensing applications for its low-cost, flexibility and real-time global coverage capability. In this paper, a novel high-order imaging algorithm is presented for GNSS-based bistatic SAR. Firstly, the modified equivalent squint range model (MESRM) is introduced to describe range history of the GNSS-based bistatic SAR, and the accuracy of the MESRM is analysed. Secondly, a high-order image formation algorithm based on the MESRM is derived, where the range cell migration (RCM) of the echo signal is corrected by two-step range cell migration correction (TSRCMC), and the azimuth-variance of the echo signal is solved by azimuth block hybrid correlation. Finally, simulation and experiment are performed to validate the presented algorithm.
Using Metaheuristics for Hyper-Parameter Optimization of Convolutional Neural Networks

Convolutional neural networks (CNNs) have attracted researchers’ increasing attention for almost three decades now, achieving superior results in such domains as computer vision, signal processing etc. Their success can be mainly attributed to a specific network architecture, which is conceived by assigning values to a large number of hyper-parameters, each influencing the resulting error rate. Yet a search for good hyper-parameter values is a challenging task, being usually done manually and taking a considerable amount of work. This paper is dedicated to the problem of designing automated hyper-parameter search algorithms for convolutional architectures. We propose two algorithms based on such meta-heuristics as evolutionary computation and local search. To our knowledge, they have never been applied to the case of CNN architectures before. Using image recognition datasets, we compare the algorithms and show that they can produce CNNs with nearly state of the art performance without any user interference, saving much tedious effort.
Rain Radar Studies of Boundary Layer Dynamics at a Tropical Location

Boundary layer dynamics play an important role in determining precipitation structure during convective processes. The radar reflectivity profiles which depend on the sixth moment of rain drop size distribution (DSD) can be employed to sense the precipitation processes. The height profiles of DSD significantly depend on the vertical wind velocity. The observations with a micro rain radar (MRR), an FMCW radar, over Kolkata, have been utilized to study the drop size distribution and its relationship with radar reflectivity at Ka-band. A sharp decrease of radar reflectivity in the height range 1–2 km corresponds to an increase of downdraft velocity observed around the same height range. This indicates that the increased downdraft causes breakup of rain drops into smaller sizes. The split in the Doppler spectrum, known as aliasing effect, is most prominent near the boundary layer height and has been employed to estimate downward wind velocity.
Radiation Resistance of Resonant Impedance Monopole Placed on Metal Square Screen

The asymptotical solution of the 3-D vector problem of diffraction of the field of the monopole with the uniform distributed surface impedance, placed on the ideally conducting rectangular screen with the sides sizes L and $W$ is obtained. Within the frame of the uniform geometrical theory of diffraction the radiation field in a far zone is represented as the sum of the geometric optics field from an impedance monopole placed on infinite screen, and the diffracted fields from the rectangular screen edges. The radiation resistances are calculated by the Poynting vector method as the functions of the monopole's surface impedance and the sizes of monopole and screen. The input impedances of the same monopoles on the square screens have calculated using commercial software too. It is shown that at all surface impedances and monopole's lengths the radiation and input resistances reach maximum at L=W=0.9of wavelength.
Keeping the User in the Cloud: A Cognitive Social Capital Antecedent to Use Continuance and Service Commitment

Online services are characterized by a separation between service initiation, execution and consumption. This separation presents a problem for retaining users because users with high intention to continue online service use are not necessarily committed to the provider of that service. We use a cognitive social capital explanation to unify two literature streams of use continuance and service commitment. We apply our research model to 245 cloud storage users. We find a significant relationship between mutual understanding, use continuance and relationship commitment. Implications for online service providers are offered.
Effect of case volume on mortality after pediatric liver transplantation in Korea.

BACKGROUND The purpose of this study was to evaluate whether institutional case volume affects clinical outcomes after pediatric liver transplantation. METHODS We conducted a nationwide retrospective cohort study using the database of Korean National Healthcare Insurance Service. Between January 2007 and December 2016, 521 pediatric liver transplantations were performed at 22 centers in Korea. Centers were categorized according to the average annual number of liver transplantations: >10, 1-10, and <1. RESULTS In-hospital mortality rates in the high-, medium-, and low-volume centers were 5.8%, 12.5%, and 32.1%, respectively. After adjustment, in-hospital mortality was significantly higher in low-volume centers (adjusted odds ratio, 9.693; 95% CI, 4.636-20.268; P<0.001) and medium-volume centers (adjusted odds ratio, 3.393; 95% CI, 1.980-5.813; P<0.001) compared to high-volume centers. Long-term survival for up to 9 years was better in high-volume centers. CONCLUSIONS centers with higher case volume (>10 pediatric liver transplantations/year) had better outcomes after pediatric liver transplantation, including in-hospital mortality and long-term mortality, compared to centers with lower case volume (≤10 liver transplantations/year).
Global Monitoring of Fault Zones and Volcanoes with Sentinel-1

Sentinel-1 represents a major step forward in enabling us to monitor the Earth's hazardous tectonic and volcanic zones. Here, we present the latest progress from the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET), where we provide deformation results to the community for volcanoes and the tectonic belts. For the estimation of seismic hazard, we require relative accuracy on the order of 1 mm/yr between points 100 km apart. This requires mass processing of long time series of radar acquisitions. As of January 2018, we are producing in-terferograms systematically for the entire Alpine-Himalayan belt (~9000 × 2000 km) and the majority of subaerial volcanoes. Currently we make interferograms and coherence products available to the community, but we plan to also provide average deformation rates and displacement time series, in the future. The results are made available through a dedicated COMET portal, and we are in the process of linking them to the ESA G-TEP and EPOS. COMET also responds routinely to significant continental earthquakes, larger than ~Mw 6.0. The short repeat interval of Sentinel-1, together with the rapid availability of the data, allows us to do this within a few days for most earthquakes. For example, after the Mw 7.8 Kaikoura earthquake we supplied a processed interferogram to the community just 5 hours and 37 minutes after the Sentinel-1 acquisition. In this paper we provide an overview of some of the latest results for tectonics and volcanism and discuss how the accuracy of these products will improve as the number of data products acquired by Sentinel-1 increases.
Visualizing stacked autoencoder language learning

Visualizing the features of unsupervised deep networks is an important part of understanding what a network has learned. In this paper, we present a method for visualizing a deep autoencoder’s hidden layers when trained on natural language data. Our method provides researchers insight into the semantic language features the network has extracted from the dataset. It can also show a big picture view of what a network has learned and how the various features the network has extracted relate to one another in semantic hierarchies. We hope that these visualizations will aid human understanding of deep networks and can help guide future experiments.
Demand and Supply Side Barriers that Limit the Uptake of Nutrition Services among Pregnant Women from Rural Ethiopia: An Exploratory Qualitative Study

Despite poverty reduction and increased promotion of improved nutrition practices in the community, undernutrition in Ethiopia remains a concern. The present study aimed to explore the demand and supply side barriers that limit the uptake of nutrition services among pregnant women from the rural communities of the Tigray Region, Northern Ethiopia. A community-based qualitative study was conducted in December through January 2017. A total of 90 key informant in-depth interviews and 14 focus group discussions were undertaken. Study participants were purposively selected for specific characteristics, along with health professionals deployed at various levels of the health system, including health posts, health centers, woreda health offices, and the regional health bureau. Study participants were asked to identify the barriers and implementation challenges that limit access to nutrition services for pregnant women. Participants’ responses were transcribed verbatim, without editing the grammar, to avoid losing meaning. The data were imported to ATLAS.ti 7 (qualitative data analysis software) for coding and analyzed using a thematic content analysis approach. The study findings indicated that the dietary quality of pregnant women in the study area remains poor and in some cases, poorer quality than pre-pregnancy. Across study sites, heavy workloads, food taboos and avoidances, low husband support, lack of economic resources, lack of awareness, low educational level of women, poor dietary habits, increased expenditure for cultural and religious festivities, “dependency syndrome”, low physical access to health facilities, poorly equipped health facilities, focus on child health and nutrition, poor coordination among nutrition specific and sensitive sectors, and limited sources of nutrition information were identified as the demand and supply side barriers limiting the uptake of nutrition services during pregnancy. In conclusion, the community would benefit from improved social behavior change communication on nutrition during pregnancy and multi-sectoral coordination among nutrition-specific and nutrition-sensitive sectors.
Blockchain Standard: Can We Reach Consensus?

In this article, we study ongoing blockchain and distributed ledger technologies (DLT) standardization. To this end, we list standards organizations and the efforts they devote to standardise blockchain and DLT. We then identify a lack of terminology that can hamper communication on this topic and propose clarifications to address these ambiguities. Finally, we propose a high-level description of blockchain and DLT by describing three elements of their functional architecture.
Association between admission lactate levels and mortality in patients with acute coronary syndrome: a retrospective cohort study

Objective Hyperlactatemia at admission is associated with poor outcome in critically ill patients. However, data on the prognostic value of blood lactate level in patients with acute coronary syndrome (ACS) are limited. The present study assessed the prognostic effect of admission lactate level in a large population of patients with ACS. Materials and methods This was a retrospective observational study including patients with ACS who were admitted to the Coronary Care Unit of the First Affiliated Hospital of Wenzhou Medical University between 2014 and 2017. Patients were divided into tertiles of lactate level (T1: <1.8; T2: 1.8–2.6; T3: ≥2.7 mmol/l). The clinical outcomes were 30-day and 180-day mortality from hospital admission. Cox proportional hazards models were used to evaluate the association between lactate level and survival. Results A total of 1865 consecutive patients with ACS were enrolled. Significant positive associations were observed between admission lactate level and both 180-day and 30-day mortality, with highest risk for lactate greater than or equal to 2.7 mmol/l. The adjusted hazard ratio for 180-day mortality was 2.09 [95% confidence interval (CI): 1.18–3.71, P=0.011] for T3 and 1.53 (95% CI: 0.86–2.72, P=0.147) for T2 compared with T1 (P for trend=0.006), and 1.10 (95% CI: 1.02–1.18, P=0.010) for each unit increase in lactate level. Similar trends were observed for 30-day mortality. The association was highly consistent across all subgroups studied (all P for interaction >0.05). Conclusion In patients with ACS, elevated admission lactate level is an independent predictor of 30-day and 180-day all-cause mortality.
Magneto-Electric Effect on Guided Waves in Functionally Graded Piezoelectric–Piezomagnetic Fan-Shaped Cylindrical Structures

Functionally graded piezoelectric–piezomagnetic (FGPP) material simultaneously consists of piezomagnetic and piezoelectric phases, which are able to convert energy among mechanical, electric, and magnetic fields. The magneto-electric effect on waves in FGPP fan-shaped cylindrical structures is studied by exploiting the double Legendre orthogonal polynomial method. By means of the Heaviside function, the initial conditions are brought into wave motion equations. Dispersion properties, electric and magnetic potential, and the Poynting vector are calculated. Subsequently, the effect of the graded variation and geometric size on wave characteristics is analyzed. The FGPP fan-shaped cylindrical structures are of complex geometrical shape and material inhomogeneity, so their influences on the magneto-electric effect are the focus of discussion. Results reveal that the cut-off frequencies have a negative relationship with the cross-section area of the structure. The magneto-electric effect could be adjusted via altering the geometric size of the cross-section. These results can be utilized to design and optimize piezoelectric–piezomagnetic fan-shaped transducers.