Miracle-dz/UniFAR-FTUs huggingface dataset:
A Unified Spatial-Temporal-Spectral Learning Framework for Reconstructing Missing Data in Remote Sensing Images

In this paper, a unified spatial-temporal-spectral framework of missing information reconstruction in remote sensing images is proposed. Based on an end-to-end non-linear mapping structure, the proposed method employs a unified deep convolutional neural network combined with joint spatial-temporal-spectral supplementary information. It should be noted that the proposed model can use multi-source data (spatial, spectral, and temporal) as the input of the unified framework. The results of real-data experiments demonstrate that the proposed model exhibits high effectiveness in missing information reconstruction tasks like dead lines in Aqua MODIS band 6, Landsat ETM+ SLC-off and thick cloud removal.
Monitoring of Soil Moisture Dynamics in the Semi-Arid Tropics by Means of ALOS-2/PALSAR-2 Dual-Polarization ScanSAR Data

In the dry tropical forest and semi-arid savanna information about the soil moisture dynamics and distribution are vital to better understand and mitigate damages caused by drought. To estimate the soil moisture over large areas from 50-m resolution ALOS-2 ScanSAR data, we adopt dual-polarization L-band algorithms originally developed for high resolution ALOS data. Based on the partial-polarimetric eigenvector decomposition, the scattering entropy and alpha angles are employed to correct for the above ground vegetation effects. The large incidence angle variation along the 350-km ScanSAR swath requires the use of an incidence angle correction factor. However, due to the relative poor data quality of the ScanSAR products suffering from scalloping and near-range/far-range striping along the subswathes, we observe lower accuracies than Stripmap mode. While in the dry season soil moisture retrieval in the open forest is well possible, in the rainy season the entropy exceeds the validity range due to the larger amounts of canopy biomass resulting in unreliable soil moisture estimates. Savanna vegetation allows a biomass-corrected soil moisture estimation throughout the whole year. Due to the lack of distributed in-situ data, we use coarse scale SMOS and SMAP soil moisture data to validate the accuracy of dual-polarization ScanSAR soil moisture estimates. Comparisons between the kilometer-scale radiometer derived soil moisture estimates and 50-m ALOS-2 results show a good agreement in the dry season with RMSEs smaller 5 Vol.-%, while in the rainy season the RMSEs can be as high as 15 Vol.-%.
Profiling Supercooled Liquid Water Clouds with Multi-Frequency Radar

An optimal estimation scheme is employed to demonstrate the utility of using multi-band radar observations for estimating supercooled liquid profiles. Qualitative comparisons with microphysical probe images show that the retrievals are capable of producing supercooled liquid consistent with in situ data. Finally, a path forward for quantifying performance and extending the study to a more robust measurement suite is given.
Potential of Satellite Remote Sensing to Monitor Vulnerablity of Buildings to Earthquakes Within a Semi-Empirical Macroseismic Approach

An essential step in earthquake risk assessment consists in collecting information regarding buildings at risks in order to evaluate their vulnerability, and, ultimately, their expected damages for events of specific intensity. A number of building features can be retrieved by means of remote sensing, either directly (through direct observations of features such as their size or footprints) or indirectly (by appraising the expected building types, given features such as the shape of roads or roofs, and an a-priori knowledge of the urban landscape of interest). Here, we evaluate to which extent the accuracy of mean damage grade can be improved by means of remote sensing using the RISK-EU (2004) [1] assessment grid. The results show that if nothing is known regarding the building stocks in the city of interest, the uncertainty in average vulnerability can be reduced by half using remote sensing observations if adequate indirect proxies can be found.
Typhoon Cloud Prediction Via Generative Adversarial Networks

We present a novel typhoon cloud prediction method via generative adversarial networks (GANs). Specifically, we develop a adversarial prediction model consisting of a generator and a discriminator. The generator generates the continuous future cloud images by learning the evolution trend of typhoon clouds from multiple continuous historical typhoon cloud images. In this way, the generator completes the visual predictions for typhoon clouds. On the other hand, the discriminator distinguishes the generated future cloud images from the real ones. Furthermore, we adopt a gradient difference loss function and a total variation loss function to improve the quality of the generated cloud images. The proposed method effectively predicts the whole spatial-temporal evolution of the typhoon clouds, resulting in a visual complement for classic typhoon prediction methods. The effectiveness of the proposed method has been evaluated in the real satellite cloud images.
Localized discriminative Gaussian process latent variable model for text-dependent speaker verification

The duration of utterances is one of the effective factors on the performance of speaker verification systems. Text dependent speaker verification suffers from both short duration and unmatched content between enrollment and test segments. In this paper, we use Discriminative Gaussian Process Latent Variable Model (DGPLVM) to deal with the uncertainty caused by short duration. This is the first attempt to utilize Gaussian Process for speaker verification. Also, to manage the unmatched content between enrollment and test segments we proposed the localized-DGPLVM that trains DGPLVM for each phrase in dataset. Experiments show the relative improvement of 27.4% in EER on RSR2015.
Clustering from two data sources using a kernel-based approach with weight coupling

In many clustering problems there are multiple data sources which are available. Although each one could individually be used for clustering, exploiting information from all data sources together can be relevant to ﬁnd a clustering that is more accurate. Here a new model is proposed for clustering when two data sources are available. This model is called Binary View Kernel Spectral Clustering (BVKSC) and is based on a constrained optimization formulation typical to Least Squares Support Vector Machines (LS-SVM). The model includes a coupling term, where the weights of the two diﬀerent data sources are coupled in the primal model. This coupling term makes it possible to exploit the additional information from each other data source. Experimental comparisons with a number of similar methods show that using two data sources can improve the clustering results and that the proposed method is competitive in performance to other state-of-the-art methods.
Segmentation of Imbalanced Classes in Satellite Imagery using Adaptive Uncertainty Weighted Class Loss

We propose a novel loss function for the training of deep Convolutional Neural Networks (CNNs) focusing on land use and land cover classification in remote sensed data. In satellite imagery, object classes are often highly imbalanced leading to poor pixel-wise classification results when using standard training methods only. In this work, we introduce a loss function which leverages the per class uncertainty of the model during training together with median frequency balancing of the class pixels. We evaluate our result on aerial images of the state-of-the-art dataset Vaihingen. We obtain a significant improvement of the F1-Score and pixel accuracy against the standard cross entropy loss on the small car class. The overall Fl-Score using a single CNN achieves 89.35% resulting in an error reduction of 21.22% against the baseline.
Analyzing network coding gossip made easy

We introduce projection analysis - a new technique to analyze the stopping time of gossip protocols that are based on random linear network coding (RLNC). Projection analysis drastically simplifies, extends and strengthens previous results. We analyze RLNC gossip in a general framework for network and communication models that encompasses and unifies the models used previously in this context. We show, in most settings for the first time, that the RLNC gossip converges with high probability in optimal time. Most stopping times are of the form O(k + T), where k is the number of messages to be distributed and T is the time it takes to disseminate one message. This means RLNC gossip achieves "perfect pipelining". Our analysis directly extends to highly dynamic networks in which the topology can change completely at any time. This remains true, even if the network dynamics are controlled by a fully adaptive adversary that knows the complete network state. Virtually nothing besides simple O(kT) sequential flooding protocols was previously known for such a setting. While RLNC gossip works in this wide variety of networks our analysis remains the same and extremely simple. This contrasts with more complex proofs that were put forward to give less strong results for various special cases.
A Geospatial Ontological Model for Remote Sensing Scene Semantic Knowledge Mining for the Flood Disaster

Numerous remote sensing applications - flood monitoring, forest fires monitoring, earthquake analysis etc. require users to query satellite images based on their content. Such requirements have led to the evolution of Content-based Image Information Mining Systems over the last decade. Recent developments in the area of Image Information Mining(IIM) are geared towards bridging the gap between low level image features and higher-level semantics. This research focuses on improving the semantic understanding of a remote sensing scene during the flood disaster from a spatio-contextual standpoint. During a flood occurrence, it is crucial to understand the flood inundation and receding patterns in context to the spatial configurations of the land-use/land-cover in the flooded regions. This study focuses on bridging the spatio-contextual semantic gap in understanding of the remote sensing imagery during a flood, thereby attempting to improve the machine interpretability of a flood remote sensing imagery. In this regard, the Flood Scene Ontology (FSO) has been developed to mine the topological and directional knowledge in context to the flood disaster phenomenon. The FSO is envisaged to form the basis for developing applications that would utilize the spatio-contextual semantics of the flood disaster to aid in the Disaster Assessment and Management process. This paper describes the conceptual framework that was developed to address the same.
Seven Years of Tandem-X: Volume Loss of Grosser Aletschgletscher, Switzerland

Grosser Aletschgletscher, the largest glacier in the European Alps, contains 20% of the entire Swiss ice mass. However, mass balance simulations based on current climate models predict 90% mass loss by 2100. The glacier is a super-testsite for the TanDEM-X satellite mission and data series starting from 2011 are available. In this study we analyzed almost 100 digital elevation models computed from bistatic radar in-terferograms and compare the derived height loss with model predictions. Additionally, we used patch-based offset tracking to determine a new velocity map of the complete glacier. The results show that the current height loss of 3.3 meters per year is close to that mass balance simulation which uses the ensemble median of common European climate models which predict, relative to 1999, a warming of 4.3°C by 2100.
KraljicMatrix: An R package for implementing the Kraljic Matrix to strategically analyze a firm's purchasing portfolio

KraljicMatrix is an R package (R Core Team (2016)) that implements a quantified approach to the Kraljic Matrix (Kraljic (1983)) introduced by Montgomery et al. (Montgomery, Ogden, and Boehmke (2017)). It allows a firm to strategically analyze its purchasing portfolio with singleand multi-attribute value analysis to measure purchasing characteristics. In addition KraljicMatrix also provides useful functions to identify the preferred single utility slope based on subject matter expert inputs, assign and place purchases within the Kraljic Matrix, and perform sensitivity analysis.
AIS Electronic Library (AISeL) Organizational Adoption of Social Media in Ethical Fashion

This study investigates how microenterprises in Ethical Fashion (EF) retail adopt and make use of social media. Drawing on an explorative case study, supplemented by an action research approach, we first examine the antecedents of a successful adoption based on the Technology-Organization-Environment (TOE) framework. Subsequently, we shed light on the benefits and drawbacks of social media adoption for the three microenterprises of our case study on the operational and the strategical level. More particularly, we present how they improve value creation through the employment of social media in their value networks and how they overcome the lack of a sophisticated IT infrastructure. Finally, we investigate the reluctance of the three microenterprises to adopt inter-organizational information systems. The findings of our study also reveal a halo effect in the adoption process that may mislead the adopting organization.
Automatic Roaming Consortium Discovery and Routing for Large-Scale Wireless LAN Roaming Systems

The Next Generation Hotspot (NGH), based on the Hotspot 2.0 specification, is being introduced as a new conceptual standard that brings higher security and usability to Public Wireless LAN. There has been a movement in recent years for connecting some Roaming Consortia (RC) by NGH to develop a Large-Scale Wireless LAN roaming system. However, a routing problem has been identified in the identity federation system when an RC consisting of multiple operators is being connected. A typical example is eduroam, the roaming system for research and education institutions, having thousands of different realms. It is hard for wireless Service Providers (SPs) to find which RC the authentication requests should be sent to. To deal with this problem, this paper develops a Hub Proxy with learning-based automatic RC discovery and routing. The effectiveness of the system has been confirmed through some experiments using a virtual inter-roaming federation network.
Crop Lodging Analysis from Uas Orthophoto Mosaic, Sentinel-2 Image and Crop Yield Monitor Data

Crop lodging is surveyed from different image sources and from the crop yield map. Crop lodging has effect on remotely sensed and field level measurements when indirectly measuring, for example, soil variation, nutrients, or crop condition. The interpretation of results may be incorrect especially in the case when it is not possible to detect lodging from the analyzed dataset. Such situation may arise, for example, when analyzing Sentinel-2 (S2) images or crop yield monitor data. In this study crop lodging is inspected from UAS-based or-thophoto mosaic taken 50 meters above the ground level, S2 image and crop yield monitor connected to a combine harvester.
Two-Dimensional Local Sample Directional Discriminant Projection for SAR Automatic Target Recognition

Synthetic aperture radar and its application in remote sensing have been an international focus in recent years. Feature extraction is a key step in SAR automatic target recognition. In this paper, 2DLSDDP is proposed for dimensional reduction and feature extraction, which is based on manifold learning theory. 2DLSDDP preserves not only local and class information of the original dataset, but also intrinsic geometric information of the image. In addition, it seeks a proper clustering direction in the neighborhood for each sample in feature extraction, which enhances the discriminative capability of the method. Compared to other SAR imagery feature extraction methods, the experiments based on MSTAR database show that the proposed method improves the recognition performance.
Rigorous Assessment of Mission Impact on Sea Ice Forecast Quality

We present the prototype of a a flexible modelling system for Arctic Mission Benefit Analysis (ArcMBA) that evaluates in a mathematically rigorous fashion the observational constraints imposed by individual and groups of EO data products in using the quantitative network design (QND) approach. The assessment of the observation impact (added value) is performed in terms of the uncertainty reduction in a four-week forecast of sea ice and snow volumes (SIV and SNV) for three regions along the Northern Sea Route (NSR) by a coupled model of the sea ice-ocean system, which is extended by observation operators that link the simulated variables to equivalents of sea ice thickness (SIT), sea ice freeboard (SIFB), radar freeboard (RFB), laser freeboard (LFB), and snow depth (SND) products.
Pressing questions in the study of psychological and behavioral diversity

Extreme biased sampling of research participants and the neglect of their cultural context are increasingly recognized as threats to the generalizability of much of what we know about human thought and behavior (1, 2). In addition to reinforcing narrow views of what it means to be human, these parochial research practices have also shaped the methodological core of the human sciences by favoring tasks that are tailored to the skills, motivations, and social expectations of a very rarefied set of humanity (3). Despite decades of calls for reform, there is little evidence that increasing awareness of this threat has led to changes in practice or publishing (2, 4). This special issue stems from the Sackler Colloquium on “Pressing Questions in the Study of Psychological and Behavioral Diversity” (September 7–9, 2017) designed to address these issues. Building on discussions emerging from two National Science Foundation-funded workshops held in 2016 and 2017, the colloquium convened scholars from a wide range of disciplines who have conducted important research with diverse populations. The colloquium participants identified barriers that discourage researchers from harnessing the full breadth of human diversity for understanding human thought and behavior. These included concerns about current peer review practices, methodological narrowness, lack of researcher diversity, and the need for theoretical frameworks that are sensitive to cultural, social, and ecological variation. Participants also shared innovations and insights from their own research that can foster and inform future investigations of human diversity. In this introduction, we highlight three threads that weave together the 10 papers in this special issue. The first thread documents the theoretical and practical payoffs of engaging with a broader range of participants (and the pitfalls of failing to do so). The second thread identifies barriers to pursuing such work and proposes potential solutions to overcome them. … [↵][1]1To whom correspondence should be addressed. Email: dhruschk{at}asu.edu. [1]: #xref-corresp-1-1
Mapping of Plant Functional Type from Satellite-Derived Land Cover Datasets for Climate Models

Recent studies show that there are large differences in the dominant plant functional types (PFT) used in the CMIP5 models, which contributes to the large spread in surface albedo and snow albedo feedback strength among these models. While several global land cover datasets are commonly used to derive PFTs for use in climate models, methods for mapping PFTs tend to be subjective and there has been very limited evaluation of the uncertainties in the PFT datasets currently used in the models. In this study, we propose a method to link field observed canopy parameters to land cover categories in a high resolution land cover map over Canada, and attempt to some extent to map PFTs objectively. We present some preliminary results from offline simulations of the latest version Canadian Land Surface Scheme over North America.
Nonlinear Hyperspectral Unmixing Via Modelling Band Dependent Nonlinearity

Wavelength dependent nonlinearity is an essential issue in hyperspectral unmixing, which was overlooked in the past. In this paper, a band-wise nonlinear unmixing method is presented. An extended multilinear mixing model is adopted for interpreting different degrees of nonlinear contributions per band. Moreover, regularizers including abundances' sparsity and nonlinear parameters' smoothness are exploited to formulate the optimization problem and obtain better unmixing results. Finally, unmixing is implemented in the scheme of alternating direction method of multipliers. Experimental results on both simulated and real hyperspectral data validate that the proposed method can improve the unmixing accuracy and reveal the change of nonlinearity at each band as well.
Enhancing digital libraries with TechLens

The number of research papers available is growing at a staggering rate. Researchers need tools to help them find the papers they should read among all the papers published each year. In this paper, we present and experiment with hybrid recommender algorithms that combine collaborative filtering and content-based filtering to recommend research papers to users. Our hybrid algorithms combine the strengths of each filtering approach to address their individual weaknesses. We evaluated our algorithms through offline experiments on a database of 102,000 research papers, and through an online experiment with 110 users. For both experiments we used a dataset created from the CiteSeer repository of computer science research papers. We developed separate English and Portuguese versions of the interface and specifically recruited American and Brazilian users to test for cross-cultural effects. Our results show that users value paper recommendations, that the hybrid algorithms can be successfully combined, that different algorithms are more suitable for recommending different kinds of papers, and that users with different levels of experience perceive recommendations differently. These results can be applied to develop recommender systems for other types of digital libraries.
Efficacy of Lactobacillus Administration in School-Age Children with Asthma: A Randomized, Placebo-Controlled Trial

Probiotics may have immunomodulatory effects. However, these effects in asthma remain unclear and warrant clinical trials. Here, we evaluated the effects of Lactobacillus paracasei (LP), Lactobacillus fermentum (LF), and their combination (LP + LF) on the clinical severity, immune biomarkers, and quality of life in children with asthma. This double-blind, prospective, randomized, placebo-controlled trial included 160 children with asthma aged 6–18 years (trial number: NCT01635738), randomized to receive LP, LF, LP + LF, or a placebo for 3 months. Their Global Initiative for Asthma–based asthma severity, Childhood Asthma Control Test (C-ACT) scores, Pediatric Asthma Severity Scores, Pediatric Asthma Quality of Life Questionnaire scores, peak expiratory flow rates (PEFRs), medication use, the levels of immune biomarkers (immunoglobulin E (IgE), interferon γ, interleukin 4, and tumor necrosis factor α) at different visits, and the associated changes were evaluated. Compared with the placebo group by generalized estimating equation model, children receiving LP, LF, and LP + LF had lower asthma severity (p = 0.024, 0.038, and 0.007, respectively) but higher C-ACT scores (p = 0.005, < 0.001, and < 0.001, respectively). The LP + LF group demonstrated increased PEFR (p < 0.01) and decreased IgE levels (p < 0.05). LP, LF, or their combination (LP + LF) can aid clinical improvement in children with asthma.