Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential of SWOT for Monitoring Water Volumes in Sahelian Ponds and Lakes

Small water bodies play a pivotal role in the Sahel since they provide a critical water resource to livestock and people and they also offer important ecosystems services. Monitoring, modelling and better understanding the hydrological behaviour of this small water bodies is therefore a key issue. The SWOT mission will bring a resolution and spatial coverage breakthrough allowing monitoring water level and volume in small water bodies worldwide. This study assesses the potential of SWOT for monitoring water volumes in Sahelian ponds and lakes. This is done by analysing synthetic data produced using the SWOT HR simulator developed by NASA-JPL. In addition, data from the Global Precipitation Mission have been also employed to investigate the backscatter coefficient variability over land and water in the Sahel. Overall, the first application of SWOT _ HR simulator over the Sahel has shown the good potential of SWOT for monitoring the seasonal variability of water levels in this region
Random Forest Data Cube Based Algorithm for Land Cover Classification: A Colombian Case

The potential of discrimination and grouping of digital levels of image classification methods based on machine-learning algorithms allow obtaining good results in the classification of land coverages. The machine-learning algorithm Random Forest is considered a robust algorithm for classification and regression, presenting good performance for data of high dimensionality, as is the case of the satellite imagery stored in the Colombian Data Cube (CDCol). This paper aims to present the implementation of Radom Forest on the CDCol infrastructure for land cover classification, on the Orinoquía Natural Region in Colombia. We used Landsat 8 OLI imagery data for 2016 at surface reflectance level and seven thematic land cover classes for the supervised classification. The overall thematic accuracy assessment was 86% and Kappa index of agreement was 79%. The results suggest that this method produces an accurate sub-pixel characterization of the land cover classes that is acceptable for practical applications.
Learning acoustic word embeddings with phonetically associated triplet network

Previous researches on acoustic word embeddings used in query-by-example spoken term detection have shown remarkable performance improvements when using a triplet network. However, the triplet network is trained using only a limited information about acoustic similarity between words. In this paper, we propose a novel architecture, phonetically associated triplet network (PATN), which aims at increasing discriminative power of acoustic word embeddings by utilizing phonetic information as well as word identity. The proposed model is learned to minimize a combined loss function that was made by introducing a cross entropy loss to the lower layer of LSTM-based triplet network. We observed that the proposed method performs significantly better than the baseline triplet network on a word discrimination task with the WSJ dataset resulting in over 20% relative improvement in recall rate at 1.0 false alarm per hour. Finally, we examined the generalization ability by conducting the out-of-domain test on the RM dataset.
Airport Detection Based on Superpixel Segmentation and Saliency Analysis for Remote Sensing Images

Traditional target detection methods are usually based on prior knowledge by template matching and classification. Nowadays, remote sensing images contain richer and richer information. It will cause high computation complexity if we still apply traditional target detection methods to remote sensing images. This paper proposes an airport detection model based on superpixel segmentation and saliency analysis. First, the input image is segmented into superpixels. Then saliency analysis is performed by calculating differences between superpixels and corresponding weights in R, G and B color channels to get the saliency map. Finally we utilize the limitation in the ratio of perimeter and area and morphology operation to eliminate the interference. Experiments compare the proposed model with three saliency analysis models qualitatively and quantitatively. Results show that the proposed model is better than the three comparative models in keeping clear boundaries, eliminating interference and maintaining intact targets.
Neuroticism and Somatic Complaints: Concomitant Effects of Rumination and Worry

Background: Neuroticism is associated with inflated somatic symptom reporting. Worry and rumination are a cognitive concomitant of neuroticism and potentially mediate the neuroticism–somatic complaint relationship. Aims: The present study examined the degree to which worry and rumination mediated the relationship between neuroticism and somatic complaints. Method: A sample of 170 volunteers, recruited via convenience sampling, took part. Participants completed a series of self-report measures: the Eysenck Personality Questionnaire Revised-Short Form, Penn State Worry Questionnaire, the Ruminative Response Scale and the Somatic Symptom Scale-8. Results: Analysis revealed significant positive correlations between neuroticism, rumination and worry. Neuroticism, rumination and worry also correlated positively with somatic complaints. Using structural equation modelling, a mediational model indicated that rumination fully mediated the relationship between neuroticism and somatic complaints. Conclusions: Findings are consistent with the symptom perception hypothesis and have implications for healthcare in terms of managing individuals who present with multiple somatic complaints. Future research would benefit from adopting a longitudinal approach to test how rumination interacts with neuroticism and somatic complaints over time.
Genetic Resistance to Avian Leukosis Viruses Induced by CRISPR/Cas9 Editing of Specific Receptor Genes in Chicken Cells

Avian leukosis viruses (ALVs), which are pathogens of concern in domestic poultry, utilize specific receptor proteins for cell entry that are both necessary and sufficient for host susceptibility to a given ALV subgroup. This unequivocal relationship offers receptors as suitable targets of selection and biotechnological manipulation with the aim of obtaining virus-resistant poultry. This approach is further supported by the existence of natural knock-outs of receptor genes that segregate in inbred lines of chickens. We used CRISPR/Cas9 genome editing tools to introduce frame-shifting indel mutations into tva, tvc, and tvj loci encoding receptors for the A, C, and J ALV subgroups, respectively. For all three loci, the homozygous frame-shifting indels generating premature stop codons induced phenotypes which were fully resistant to the virus of respective subgroup. In the tvj locus, we also obtained in-frame deletions corroborating the importance of W38 and the four amino-acids preceding it. We demonstrate that CRISPR/Cas9-mediated knock-out or the fine editing of ALV receptor genes might be the first step in the development of virus-resistant chickens.
Selenate Prevents Adipogenesis through Induction of Selenoprotein S and Attenuation of Endoplasmic Reticulum Stress

The conversion of preadipocytes to adipocytes (adipogenesis) is a potential target to treat or prevent obesity. Selenate, an inorganic form of selenium, elicits diverse health benefits, mainly through its incorporation into selenoproteins. The individual roles of selenium and certain selenoproteins have been reported. However, the effects of selenate treatment on selenoproteins in adipocytes are unclear. In this study, the effects of selenate pretreatment on selenoprotein and endoplasmic reticulum (ER) stress during adipogenesis were examined in vitro. The selenate pretreatment dose-dependently suppressed the adipogenesis of 3T3-L1 preadipocytes. The selenate pretreatment at 50 μM for 24 h almost completely suppressed adipogenesis without cytotoxic effects. The expression of the adipogenic genes peroxisome proliferator-activated receptor gamma, CCAAT-enhancer binding protein alpha, and leptin was suppressed by selenate. This pretreatment also upregulated selenoprotein S (SEPS1), an ER resident selenoprotein that reduces ER stress, and prevented dexamethasone-induced SEPS1 degradation during the early stage of adipogenesis. The selenate-inhibited adipogenesis was associated with an attenuation of ER stress. The expression of the ER stress marker genes was upregulated during the early stage of differentiation, whereas the selenate pretreatment suppressed the mRNA expression of the XBP1 and C/EBP homologous protein. The collective data suggest a preventive role of selenate and SEPS1 in adipogenesis, and support a novel dietary approach to prevent obesity.
A Fast Ransac Based Approach for Computing the Orientation of Obstacles in Traffic Scenes

A low complexity approach for computing the orientation of 3D obstacles, detected from lidar data, is proposed in this paper. The proposed method takes as input obstacles represented as cuboids without orientation (aligned with the reference frame). Each cuboid contains a cluster of obstacle locations (discrete grid cells). First, for each obstacle, the boundaries that are visible for the perception system are selected. A model consisting of two perpendicular lines is fitted to the set of boundary cells, one for each presumed visible side. The main dominant line is computed with a RANSAC approach. Then, the second line is searched, using a constraint of perpendicularity on the dominant line. The existence of the second line is used to validate the orientation. Finally, additional criteria are proposed to select the best orientation based on the free area of the cuboid (on top view) that is visible to the perception system.
Differentiated storage services

This article presents a Differentiated Storage Services architecture for file and storage systems. By classifying data at the block-level, a filesystem can request that different classes of data (e.g., file, directory, executable, text) be handled with different policies (e.g., low-latency versus highbandwidth), and it is left to the storage system to enforce these policies. Our approach assumes that an I/O classifier can be included in-band with each I/O request (e.g., using a field in the SCSI block command set) and that the policy for each class can be specified out-of-band through the management interface of the storage system. We describe our prototypes based on Linux Ext3, Windows NTFS, and hybrid storage systems composed of rotating and solid-state disks. With very little modification, filesystems can identify latency sensitive I/O classes (e.g., small files, directories, metadata, and the journal) and request that the storage system provision the solid-state storage for just these classes; and this is simply one of many possibilities. As part of our ongoing work, across a variety of file and storage systems, we are exploring other policies and mechanisms that can be used to improve application performance, reliability, and security.
Gaussian Attractive Force-Based Alternative Parametric Active Contour Model for 3D Lunar Crater Detection

In this paper, we present an alternative parametric active contour (APAC) model for 3D lunar crater detection with shadow and overexposure problem. Compared with the traditional parametric active contour model, the main difference is that we construct a Gaussian attractive force field between two initial curves for each crater proposal, which enables the two initial curves to mutually convey message to each other and avoids local optimum. In addition, we introduce the elevation information estimated from CCD stereo images into the external energy term of the APAC model, which helps to remove false craters by providing the geometric properties of craters. The proposed method is evaluated on eight pairs of stereo images with different numbers, scales and illuminations captured by Chang'E-I satellite, which demonstrates its effectiveness and efficiency.
Evaluation of different cerebrospinal fluid and white matter fMRI filtering strategies—Quantifying noise removal and neural signal preservation

This study examines the impact of using different cerebrospinal fluid (CSF) and white matter (WM) nuisance signals for data‐driven filtering of functional magnetic resonance imaging (fMRI) data as a cleanup method before analyzing intrinsic brain fluctuations. The routinely used temporal signal‐to‐noise ratio metric is inappropriate for assessing fMRI filtering suitability, as it evaluates only the reduction of data variability and does not assess the preservation of signals of interest. We defined a new metric that evaluates the preservation of selected neural signal correlates, and we compared its performance with a recently published signal–noise separation metric. These two methods provided converging evidence of the unfavorable impact of commonly used filtering approaches that exploit higher numbers of principal components from CSF and WM compartments (typically 5 + 5 for CSF and WM, respectively). When using only the principal components as nuisance signals, using a lower number of signals results in a better performance (i.e., 1 + 1 performed best). However, there was evidence that this routinely used approach consisting of 1 + 1 principal components may not be optimal for filtering resting‐state (RS) fMRI data, especially when RETROICOR filtering is applied during the data preprocessing. The evaluation of task data indicated the appropriateness of 1 + 1 principal components, but when RETROICOR was applied, there was a change in the optimal filtering strategy. The suggested change for extracting WM (and also CSF in RETROICOR‐corrected RS data) is using local signals instead of extracting signals from a large mask using principal component analysis.
Research Into Sar Imaging of Moving Ship Target Based on Isar Technology

The ship targets affected by sea conditions and other environment factors have complex non-cooperation movement, which cause serious defocusing phenomena in high resolution space-borne SAR images. In this paper, a novel ISAR-based imaging algorithm for moving ship target is presented. Firstly, the spatial geometric model of spaceborne SAR is given, and the motorial characteristics of moving ship target are analyzed. Then, a novel imaging algorithm is derived, where the range alignment is solved by minimum information entropy criterion, and the accurate focusing is achieved through inverse SAR (ISAR) processing. Experiments based on the simulation and real data of TerraSAR-X are performed to validate the presented algorithm.
The Moderating Effect of Workload in Determining the Continuous Usage of Virtual Learning Environment amongst School Teachers

This paper empirically tests the effect of teachers’ workload to the Virtual Learning Environment (VLE) usage. The population of the study is 97,503 Malaysian teachers. Eight hundred and fifty questionnaires were distributed using simple random sampling procedure and 719 were returned. However, after the data cleaning, only 643 of them are usable for the main analysis, which was done using PLS-SEM. The result shows that Workload has positively moderated the relationships of Information Quality to Intention to Use, and Intention to Use to Use. However, the similar moderating role in the relationships of System Quality and Service Quality to Intention to Use were not supported. Therefore, this would imply that teachers who suffer heavier workload might use more VLE system, so long as the provided information is good and useful to them. This indicates that the VLE system is helpful for teachers in dealing with the heavy workload.
Comparative Study of Classification Algorithms to Detect Interlayer Debondings within Pavement Structures from Step-Frequency Radar Data

In the field of civil engineering, Ground Penetrating Radar (GPR) is widely used to monitor structural integrity. With the help of Step-frequency Radar (SFR) for data acquisition and proper data processing algorithms, it is possible to detect small sub-surface defects within the pavement. In this paper, we discuss a conventional method based on the signal's amplitude, a supervised machine learning method (namely SVM) and a semi-supervised clustering based algorithm to detect said defects. The data are collected using an SFR at IFSTTAR's fatigue carousel where debondings are artificially introduced.
Gemini: Bidirectional Generation and Analysis of Games via ASP

Current approaches to game generation don't understand the games they generate. As a result, even the most sophisticated systems in this regard, e.g., Game-o-Matic, betray this problem — generating games with goals that are at odds with their mechanics. We describe Gemini, the first bidirectional game generation and analysis system. Gemini is able to take games as input, perform a proceduralist reading of them, and produce possible interpretations that the games might afford. By utilizing the declarative nature of Answer Set Programming (ASP), this analysis pathway opens up generation of games targeting specific interpretations and makes it possible to ensure the generated games are consistent with the desired reading. For Gemini, we developed a game specification language capable of expressing a larger domain of games than is possible with VGDL, the most widespread representation. We demonstrate the generality of our approach by generating games in a series of domains. These domains are based on prototypes hand-created by a team without knowledge of the constraints and capabilities of Gemini.
Demystifying Neural Network Filter Pruning

Based on filter magnitude ranking (e.g. L1 norm), conventional filter pruning methods for Convolutional Neural Networks (CNNs) have been proved with great effectiveness in computation load reduction. Although effective, these methods are rarely analyzed in a perspective of filter functionality. In this work, we explore the filter pruning and the retraining through qualitative filter functionality interpretation. We find that the filter magnitude based method fails to eliminate the filters with repetitive functionality. And the retraining phase is actually used to reconstruct the remained filters for functionality compensation for the wrongly-pruned critical filters. With a proposed functionality-oriented pruning method, we further testify that, by precisely addressing the filter functionality redundancy, a CNN can be pruned without considerable accuracy drop, and the retraining phase is unnecessary.
Information Systems Betrayal: When Cybersecurity Systems Shift from Agents of Protection to Agents of Harm

Cybersecurity systems provide a unique opportunity of study as they can be used as agents of protection and harm. Practice uses these systems of protection against employees through the use of red team and black hat tactics for perimeter testing as well as invasive, complex monitoring for defense of internal threats. It is important to understand the effects of these actions on end users. This study seeks to understand the effects of these cybersecurity practices on individuals through the perspectives of trust, betrayal, aversion, and resistance. An integrative model is built and employed to understand the formation and consequences of IS betrayal.
Percutaneous Cement Injection for the Palliative Treatment of Osseous Metastases: A Technical Review

Abstract The technical art to percutaneous injection of polymethyl methacrylate (PMMA) cement for the palliative treatment of osseous metastases is not without pitfalls. Pathologic fracture, cortical bone erosion, large lytic tumor, aggressive tumor biology, and tumor vascularity may increase the risk of cement leakage or limit complete consolidation. A calculated and determined approach is often necessary to achieve satisfactory patient-tailored results. This article reviews the challenges and potential complications during the consolidation of osseous metastases. Case examples are presented to facilitate early detection of impending cement leakage, minimize procedural risks, and provide management suggestions for complications. Technical pearls are provided to refine consolidative techniques and improve the comprehensive treatment of painful osseous metastases.
Modeling Player Experience with the N-Tuple Bandit Evolutionary Algorithm

Automatic game design is an increasingly popular area of research that consists of devising systems that create content or complete games autonomously. The interest in such systems is two-fold: games can be highly stochastic environments that allow presenting this task as a complex optimization problem and automatic play-testing, becoming benchmarks to advance the state of the art on AI methods. In this paper, we propose a general approach that employs the N-Tuple Bandit Evolutionary Algorithm (NTBEA) to tune parameters of three different games of the General Video Game AI (GVGAI) framework. The objective is to adjust the game experience of the players so the distribution of score events through the game approximates certain pre-defined target curves. We report satisfactory results for different target score trends and games, paving the path for future research in the area of automatically tuning player experience.
Deep Semi-Nonnegative Matrix Factorization Based Unsupervised Change Detection of Remote Sensing Images

In the paper, an unsupervised change detection method for remote sensing (RS) images based on deep semi-nonnegative matrix factorization (semi-NMF) is proposed. Firstly, the difference image is generated in different ways, depending on the types of input images. Then principal component analysis (PCA) is applied on the difference image to form the feature matrix $\boldsymbol{X}$ for improving the capability against various noise. In order to exploit more useful information from the resulting feature matrix, deep semi-NMF is introduced to factorize $\boldsymbol{X}$ into $L+1$ factors consisting of $L$ nonrestricted matrices $\{\boldsymbol{F}_{l}\}_{l=1}^{L}$ and nonnegative cluster indicator matrix $\boldsymbol{G}_{L}$. Finally, the binary change mask (CM) is generated by assigning the pixels into changed and unchanged classes according to maximum criterion. The experimental results on two pairs of multitemporal RS images demonstrate the effectiveness of the proposed method.
ENASearch: A Python library for interacting with ENA's API

The European Nucleotide Archive (ENA) (Leinonen et al. 2010) is a database with a comprehensive record of nucleotide sequencing information (raw sequencing data, sequence assembly information and functional annotation). The data contained in ENA can be accessed manually or programmatically via REST URLs. However, building HTTP-based REST requests is not always straightforward a user friendly, high-level access is needed to make it easier to interact with ENA programmatically.
Sar Image Despeckling Based on a Novel Total Variation Regularization Model and Gf-3 Data

We propose a novel total variation (TV) regularization model for synthetic aperture radar (SAR) image despeckling. A method combined with second-order and fourth-order TV sparse constraints is applied to reconstruct and despeckle SAR image. Experiments have been carried out in echo domain using GF-3 spaceborne SAR raw data. We introduce the target-to-clutter ratio (TCR) to quantitatively assess the SAR image quality and the effectiveness of target detection and multiplicative noise suppression. Experimental results show that the proposed second-order and fourth-order TV method can further effectively suppress SAR image speckles without compromising the details of image features according to both subjective visual assessment of image quality and objective evaluation using TCR.