Miracle-dz/UniFAR-FTUs huggingface dataset:
ASSESSMENT OF THE SPATIAL VARIABILITY OF CWSI WITHIN ALMOND TREE CROWNS AND ITS EFFECTS ON THE RELATIONSHIP WITH STOMATAL CONDUCTANCE

This work focuses on understanding the effects caused by the within-tree structural heterogeneity on the Crop Water Stress Index (CWSI). We present an assessment of the CWSI variability and its relationship with stomatal conductance (Gs) using different automatic object-based tree-crown detection algorithms based on temperature quartile thresholds. The study was carried out in an almond orchard cultivated under three irrigated regimes. Highresolution (25 cm) thermal imagery was acquired by an aircraft on summer 2015. The tree crowns were segmented into 4 classes using the 25th, 50th and 75th percentiles via automatic object-based methods. Results showed that CWSI was linearly and inversely correlated with Gs in all thermal classes. However, the relationship with Gs was heavily affected by the crown segmentation levels applied, and improved remarkably when CWSI values where those corresponding to the coldest and purest vegetation pixels (r =0.78 from pure vegetation pixels vs. r=0.52 when warmer pixels were used).
MRF-Based Decision Fusion for Hyperspectral Image Classification

The high dimensionality of hyperspectral images, the limited availability of ground-truth data as well as the low spatial resolution (causing pixels to contain mixtures of materials) hinder hyperspectral image classification. In this work we propose a novel hyperspectral classification method where we combine the outcome of spectral unmixing with the outcome of a supervised classifier. In particular, we consider fractional abundances obtained from a Sparse Unmixing method along with posterior probabilities acquired from a Multinomial Logistic Regression classifier. Both sources of information are fused using a Markov Random Field framework. We conducted experiments on publicly available real hyperspectral images: Indian Pines and University of Pavia using a very limited number of training samples. Our results indicate that the proposed decision fusion approach significantly improves the classification result over using the individual sources and outperforms the state of the art methods.
Analyzing Land use Land Cover and Deforestation in the REDD+AREA, Central Kalimantan Province, Indonesia

A mitigation named reducing emission from deforestation and forest degradation (REDD+) has been developed as one of the most cost-effective strategies to tackle climate change due to forest loss. Timely and accurate forest data are required for REDD+ programs to succeed. A series of multitemporal Landsat imageries obtained during the period from 1995 to 2015 were used to evaluate deforestation in Central Kalimantan REDD+ boundaries. By using support vector machine (SVM) classification method, the LULC types was mapped with a kappa coefficient greater than 0.8 facilitated a reliable interpretation of LULC changes. In 1995, the forest occupied the total area at around 72.6% while it decreased dramatically to a level of 59.5% and 53.8% in 2005 and 2015. The deforestation in the REDD+ project area in the two decades from 1995–2005 and 2005–2015 was 321.2 and 131.1 thousand hectares.
ACCELERATING THE TRAINING PROCESS OF CONVOLUTIONAL NEURAL NETWORKS FOR CLASSIFICATION BY DROPPING TRAINING SAMPLES OUT

Stochastic gradient descent and other adaptive optimization methods such as RMSprop, and Adam have been proved effective for training deep neural networks [1], [2]. Within each epoch of these methods, the whole training set is involved. In general, large training datasets have data redundancy. In this paper, we investigate an algorithm that reduce the training time of CNN by dropping certain samples out. Thus, it is called DropSample. This method can be viewed as a special type of truncated cross-entropy loss with a finite margin. We design experiments on several datasets to demonstrate the effects of acceleration. The results show that this method could decrease the training time of multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) significantly. Despite reduced number of training samples, the accuracies of networks are similar, or even better.
AIS Electronic

Data today is regarded as a new type of fuel of platform ecosystems. It enables sustainable growth of platform ecosystems through users’ data contribution. Data governance is necessary to safely manage platform data and succeed in business. Platform ecosystems, however, encounter a complicated business context and environment since there are multiple participating groups, different strategies, goals, and different levels of market regulations. How to adopt appropriate data governance dealing with the concerns has been neglected, and therefore there is little research on this topic. To respond to the challenge, we introduce a novel design approach which can address the various contingencies, characteristics and governance goals of platform ecosystems. We present a case study and use case to illustrate the implications and support the implementation of the approach in practice.
Scale‐free collaboration networks: An author name disambiguation perspective

Several studies have found that collaboration networks are scale‐free, proposing that such networks can be modeled by specific network evolution mechanisms like preferential attachment. This study argues that collaboration networks can look more or less scale‐free depending on the methods for resolving author name ambiguity in bibliographic data. Analyzing networks constructed from multiple datasets containing 3.4 M ∼ 9.6 M publication records, this study shows that collaboration networks in which author names are disambiguated by the commonly used heuristic, i.e., forename‐initial‐based name matching, tend to produce degree distributions better fitted to power‐law slopes with the typical scaling parameter (2 < α < 3) than networks disambiguated by more accurate algorithm‐based methods. Such tendency is observed across collaboration networks generated under various conditions such as cumulative years, 5‐ and 1‐year sliding windows, and random sampling, and through simulation, found to arise due mainly to artefactual entities created by inaccurate disambiguation. This cautionary study calls for special attention from scholars analyzing network data in which entities such as people, organization, and gene can be merged or split by improper disambiguation.
Prisma: The Italian Hyperspectral Mission

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is one the most important investments of Italian Space Agency (ASI) in the field of Optical Remote Sensing for Earth Observation. The PRISMA Space Segment consists of a single spacecraft embarking a state-of-the-art hyperspectral/panchromatic payload using pushbroom scanning technique. The PRISMA Ground Segment inlcudes the Fucino facilities for satellite/mission control and Matera CNM (Multimission National Center) systems, mainly devoted to data acquisition, products archive/delivery and user management. The IDHS facilty processes the payload data downloaded using the CNM X-Band antenna. The launch is scheduled in 2018 (VEGA Launcher) for a five years operational lifetime. This paper reports an overview of the mission and program development.
Inhibition, shifting and updating in relation to psychometric intelligence across ability groups in the psychiatric population

Abstract Background Assessment of intelligence and executive function (EF) is common in complex neuropsychiatric practice. Although previous studies have shown that EF and intelligence are related, it is unknown whether these constructs relate to one another in a similar manner across different ability groups (mild intellectual disability, borderline intellectual disability and normal/high intelligence). This study therefore examines the relation between three EFs (inhibition, shifting and updating) and intelligence in a heterogeneous psychiatric sample. It is hypothesised that the strength of the relation between intelligence and the three EFs decreases when the level of intelligence increases, in accordance with Spearman's Law of Diminishing Returns. Methods In a cross‐sectional, between and within subject design, one of the three intelligence tests (Kaufman Adolescent and Adult Intelligence Test and Wechsler Adult Intelligence Scale – third and fourth editions) and several EF tests (Stroop Colour–Word Test, Trail Making Test and Spatial Working Memory task) were administered to 250 neuropsychiatric inpatients and outpatients (Mage = 39.8, standard deviation = 14.3, 52.8% male). Based upon their full‐scale IQ score, patients were divided into three ability groups (mild intellectual disability, borderline intellectual disability or normal/high intelligence). The relation between EF and intelligence was assessed through analyses of the correlation pattern; groups were compared using analysis of covariance. Results Analyses showed significant correlations between the constructs of EF and intelligence. A significant interaction effect was found for shifting, with highest correlations in the normal to high intelligence group, but not for inhibition and updating. Conclusions Results support a specific role for shifting in this EF–intelligence relation. The correlational pattern of updating and intelligence, as well as the differential relation of shifting and intelligence across ability groups, suggests that EF tasks may not measure distinct EFs in lower intellectual ability but rely on cognitive primitives such as processing speed. EF tasks can be considered less valid indicators of EF ability. Implications in terms of the need for development of specific tasks to measure cognition in low intellectual ability are discussed.
Discovering Temporal Patterns of Air Quality in Different Parts of Europe with Data Driven Feature Extraction

Air quality is strongly affecting human lifestyle all over the world, and its impact is apparent on healthcare, sustainable development, welfare and public administration policies. Accurate understanding of the polluting processes requires to analyze huge volumes of records, so that significant patterns and regularities can be detected. In this paper, we introduce a framework to explore the air pollution dynamics over all Europe by means of a data driven feature extraction approach. Taking advantage of MODIS records, we are able to investigate daily trends of air quality from 2003 to 2016. By means of an automatic learning scheme based on mutual information maximization, we extract the most significant patterns in the dataset. Experimental results show that the proposed approach is able to identify relevant air pollution trends that can be associated with specific physical phenomena on ground.
Hy-Demosaicing: Hyperspectral Blind Reconstruction from Spectral Subsampling

This paper proposes a very light hyperspectral sensing strategy, implemented in the spectral domain, conceived to spaceborne sensor systems, where physical space, storage resources, and communication bandwidth are extremely scarce and expensive. Instead of acquiring all samples in spectral domain, we propose to randomly select a few samples per pixel. This subsampling sensing strategy yields a demosaicing problem. We propose a blind hyperspectral reconstruction technique termed hyperspectral demosaicing (Hy-demosaicing) exploiting low-rank and self-similarity properties of hyperspectral images. It is blind in sense that the signal subspace is learned from measured subsamples. The subspace basis is data adaptive and provides a more compact representation than other non-adaptive representations. This adaptiveness leads to improved image recovery as illustrated in experiments with real data.
UWB/LiDAR Fusion For Cooperative Range-Only SLAM

We equip an ultra-wideband (UWB) node and a 2D LiDAR sensor a.k. a. 2D laser rangefinder on a mobile robot, and place UWB beacon nodes at unknown locations in an unknown environment. All UWB nodes can do ranging with each other thus forming a cooperative sensor network. We propose to fuse the peer-to-peer ranges measured between UWB nodes and laser scanning information, i.e., range measured between robot and nearby objects/obstacles, for simultaneous localization of the robot, all UWB beacons and LiDAR mapping. The fusion is inspired by two facts: 1) LiDAR may improve UWB-only localization accuracy as it gives a more precise and comprehensive picture of the surrounding environment; 2) on the other hand, UWB ranging measurements may remove the error accumulated in the LiDAR-based SLAM algorithm. Our experiments demonstrate that UWBLiDAR fusion enables drift-free SLAM in real-time based on ranging measurements only.
Engineering selectivity into RGK GTPase inhibition of voltage-dependent calcium channels

Significance Influx of calcium ions through surface membrane calcium channels that open in response to electrical signals is important for vital biological processes including generation of the heartbeat and nerve cell communication. Blocking such calcium channels in a tissue- and isoform-specific manner is a sought-after treatment strategy for diseases including chronic pain and Parkinson’s disease. Proteins that can be expressed in cells to selectively block different calcium channel types have particular advantages over conventional small-molecule blockers. A four-member family of proteins known as RGK proteins strongly inhibit calcium channels, but do so in a non-selective manner, limiting their potential usefulness. Here we identified mutated RGK proteins that perform as isoform-selective calcium channel blockers, advancing the therapeutic potential of these proteins. Genetically encoded inhibitors for voltage-dependent Ca2+ (CaV) channels (GECCIs) are useful research tools and potential therapeutics. Rad/Rem/Rem2/Gem (RGK) proteins are Ras-like G proteins that potently inhibit high voltage-activated (HVA) Ca2+ (CaV1/CaV2 family) channels, but their nonselectivity limits their potential applications. We hypothesized that nonselectivity of RGK inhibition derives from their binding to auxiliary CaVβ-subunits. To investigate latent CaVβ-independent components of inhibition, we coexpressed each RGK individually with CaV1 (CaV1.2/CaV1.3) or CaV2 (CaV2.1/CaV2.2) channels reconstituted in HEK293 cells with either wild-type (WT) β2a or a mutant version (β2a,TM) that does not bind RGKs. All four RGKs strongly inhibited CaV1/CaV2 channels reconstituted with WT β2a. By contrast, when channels were reconstituted with β2a,TM, Rem inhibited only CaV1.2, Rad selectively inhibited CaV1.2 and CaV2.2, while Gem and Rem2 were ineffective. We generated mutant RGKs (Rem[R200A/L227A] and Rad[R208A/L235A]) unable to bind WT CaVβ, as confirmed by fluorescence resonance energy transfer. Rem[R200A/L227A] selectively blocked reconstituted CaV1.2 while Rad[R208A/L235A] inhibited CaV1.2/CaV2.2 but not CaV1.3/CaV2.1. Rem[R200A/L227A] and Rad[R208A/L235A] both suppressed endogenous CaV1.2 channels in ventricular cardiomyocytes and selectively blocked 25 and 62%, respectively, of HVA currents in somatosensory neurons of the dorsal root ganglion, corresponding to their distinctive selectivity for CaV1.2 and CaV1.2/CaV2.2 channels. Thus, we have exploited latent β-binding–independent Rem and Rad inhibition of specific CaV1/CaV2 channels to develop selective GECCIs with properties unmatched by current small-molecule CaV channel blockers.
Deformation Monitoring Using Persistent Scatterer Interferometry and Sentinel-1 Data in Urban Areas

In this paper, an approach to Persistent Scatterer Interferometry (PSI) is used to derive deformation measurements over the Catalonia region (Northern Spain). A set of tools to control the quality of the 2+1d phase unwrapping, one of the key steps of the proposed procedure, are described and applied over a set of Sentinel-1A (S-1A) images. The results, derived using 64 S1-A images comprising the period from March 2015 to May 2017, are analyzed in the last section of the paper. Finally, the deformation velocity map and the time series are described, in particular over the urban areas, where the proposed approach yields the best results.
On the Characterization of Frequency-Persistent Scatterers in Split-Band Interferometry

Split-Band Interferometry (SBInSAR) requires the presence of reliable frequency-persistent scatterers within the studied scene. However, the physical nature of such reflectors is still poorly known. In this work, we perform a temporal analysis over the region of the Virunga Volcanic Province (Democratic Republic of Congo - DRC) and we show that frequency-persistent scatterers are not necessarily stable over time. We study and quantify the effect of the range resolution loss due to the bandwidth degradation in the SBInSAR process. Finally, radiometric information and spectral coherence are used to show that frequency-persistent scatterers cannot be associated to a specific scattering mechanism.
Multi-view Network Embedding via Graph Factorization Clustering and Co-regularized Multi-view Agreement

Real-world social networks and digital platforms are comprised of individuals (nodes) that are linked to other individuals or entities through multiple types of relationships (links). Sub-networks of such a network based on each type of link correspond to distinct views of the underlying network. In real-world applications each node is typically linked to only a small subset of other nodes. Hence, practical approaches to problems such as node labeling have to cope with the resulting sparse networks. While low-dimensional network embeddings offer a promising approach to this problem, most of the current network embedding methods focus primarily on single view networks. We introduce a novel multi-view network embedding (MVNE) algorithm for constructing low-dimensional node embeddings from multi-view networks. MVNE adapts and extends an approach to single view node embedding using graph factorization clustering (GFC) to the multi-view setting using an objective function that maximizes the agreement between views based on both the local and global structure of the underlying multi-view graph. Our experiments with several benchmark real-world single view networks show that SVNE yields network embeddings that are competitive with or superior to those produced by the state-of-the-art single view network embedding methods when the embeddings are used for labeling unlabeled nodes in the networks. Our experiments with several multi-view networks show that MVNE substantially outperforms the single view methods on integrated view and the state-of-the-art multi-view methods. We further show that even when the goal is to predict labels of nodes within a single target view, MVNE outperforms its single-view counterpart suggesting that the MVNE is able to extract the information that is useful for labeling nodes in the target view from the all of the views.
Data Augmentation with Gabor Filter in Deep Convolutional Neural Networks for Sar Target Recognition

Deep Convolutional Neural Networks (DCNNs) have been widely used in target recognition due to the availability of large dataset. The DCNNs have the ability of learning highly hierarchical image feature, which provides great opportunity for synthetic aperture radar automatic target recognition (SAR-ATR). However, when the DCNNs were directly applied to the SAR target recognition, it will result in severe overfitting due to limited SAR image training data. To overcome this problem, we present a Gabor-Deep Convolutional Neural Networks (G-DCNNs). Instead of training a deep network with limited dataset of raw SAR images, Gabor features for multi -scale and multi -direction were used for data augmentation as training dataset at first. Then based on this data augmentation method, we designed a DCNNs for SAR image target recognition. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset prove the effectiveness of our method.
Gravitational Octree Code Performance Evaluation on Volta GPU

In this study, the gravitational octree code originally optimized for the Fermi, Kepler, and Maxwell GPU architectures is adapted to the Volta architecture. The Volta architecture introduces independent thread scheduling requiring either the insertion of the explicit synchronizations at appropriate locations or the enforcement of the same implicit synchronizations as do the Pascal or earlier architectures by specifying -gencode arch=compute_60,code=sm_70. The performance measurements on Tesla V100, the current flagship GPU by NVIDIA, revealed that the N-body simulations of the Andromeda galaxy model with 223 = 8 388 608 particles took 3.8 × 10-2 s or 3.3 × 10-2 s per step for cases without or with the implicit synchronizations, respectively. Tesla V100 achieves a 1.4 to 2.2-fold acceleration in comparison with Tesla P100, the flagship GPU in the previous generation. The observed speed-up of 2.2 is greater than 1.5, which is the ratio of the theoretical peak performance of the two GPUs. The independence of the units for integer operations from those for floating-point number operations enables the overlapped execution of integer and floating-point number operations. It hides the execution time of the integer operations leading to the speed-up rate above the theoretical peak performance ratio. Tesla V100 can execute N-body simulation with up to 25 × 220 = 26 214 400 particles, and it took 2.0 × 10-1 s per step. It corresponds to 3.5 TFlop/s, which is 22% of the single-precision theoretical peak performance.
Ococo: an online variant and consensus caller

Motivation: Identifying genomic variants is an essential step for connecting genotype and phenotype. The usual approach consists of statistical inference of variants from alignments of sequencing reads. State-of-the-art variant callers can resolve a wide range of different variant types with high accuracy. However, they require that all read alignments be available from the beginning of variant calling and be sorted by coordinates. Sorting is computationally expensive, both memory- and speed-wise, and the resulting pipelines suffer from storing and retrieving large alignments files from external memory. Therefore, there is interest in developing methods for resource-efficient variant calling. Results: We present Ococo, the first program capable of inferring variants in a real-time, as read alignments are fed in. Ococo inputs unsorted alignments from a stream and infers single-nucleotide variants, together with a genomic consensus, using statistics stored in compact several-bit counters. Ococo provides a fast and memory-efficient alternative to the usual variant calling. It is particularly advantageous when reads are sequenced or mapped progressively, or when available computational resources are at a premium.
Deep Domain Adaptation for Single-Shot Vehicle Detector in Satellite Images

In this paper, we designed unsupervised domain adaptation (DA) methods to vehicle detection in high-resolution satellite images. We applied two Single Shot MultiBox Detectors, which have advantages in handling image feature differences among various kinds of image data: Correlation Alignment DA (CORAL DA) and adversarial DA. These novel methods can much improve accuracy without annotated data by finding the common feature space of source and target domains and aligning the features. While a mean of average precision (AP) and F1 dropped from 84.1 % in the source domain to 66.3% in the target domain, the CORAL DA and adversarial DA improved it to 76.8% and 75.9% respectively. These improvements were over a half of the performance degradation, indicating the usability of our methods.