Miracle-dz/UniFAR-FTUs huggingface dataset:
Operational Large-Area Land-Cover Mapping: An Ethiopia Case Study

Knowledge of land cover and land use nationally is a prerequisite of many studies on drivers of land change, impacts on climate, carbon storage and other ecosystem services, and allows for sufficient planning and management. Despite this, many regions globally do not have accurate and consistent coverage at the national scale. This is certainly true for Ethiopia. Large-area land-cover characterization (LALCC), at a national scale is thus an essential first step in many studies of land-cover change, and yet is itself problematic. Such LALCC based on remote-sensing image classification is associated with a spectrum of technical challenges such as data availability, radiometric inconsistencies within/between images, and big data processing. Radiometric inconsistencies could be exacerbated for areas, such as Ethiopia, with a high frequency of cloud cover, diverse ecosystem and climate patterns, and large variations in elevation and topography. Obtaining explanatory variables that are more robust can improve classification accuracy. To create a base map for the future study of large-scale agricultural land transactions, we produced a recent land-cover map of Ethiopia. Of key importance was the creation of a methodology that was accurate and repeatable and, as such, could be used to create earlier, comparable land-cover classifications in the future for the same region. We examined the effects of band normalization and different time-series image compositing methods on classification accuracy. Both top of atmosphere and surface reflectance products from the Landsat 8 Operational Land Imager (OLI) were tested for single-time classification independently, where the latter resulted in 1.1% greater classification overall accuracy. Substitution of the original spectral bands with normalized difference spectral indices resulted in an additional improvement of 1.0% in overall accuracy. Three approaches for multi-temporal image compositing, using Landsat 8 OLI and Moderate Resolution Imaging Spectroradiometer (MODIS) data, were tested including sequential compositing, i.e., per-pixel summary measures based on predefined periods, probability density function compositing, i.e., per-pixel characterization of distribution of spectral values, and per-pixel sinusoidal models. Multi-temporal composites improved classification overall accuracy up to 4.1%, with respect to single-time classification with an advantage of the Landsat OLI-driven composites over MODIS-driven composites. Additionally, night-time light and elevation data were used to improve the classification. The elevation data and its derivatives improved classification accuracy by 1.7%. The night-time light data improve producer’s accuracy of the Urban/Built class with the cost of decreasing its user’s accuracy. Results from this research can aid map producers with decisions related to operational large-area land-cover mapping, especially with selecting input explanatory variables and multi-temporal image compositing, to allow for the creation of accurate and repeatable national-level land-cover products in a timely fashion.
Association of head circumference with cognitive decline and symptoms of depression in elderly: a 3-year prospective study

Background Brain volume is associated with dementia and depression in the elderly. An easy way to predict relative brain volume is to measure head circumference. In this study, we investigated the relationship between head circumference and cognition as well as depression in a non-demented elderly community. Methods Baseline and follow-up surveys were conducted in 2007 and 2010. At baseline, community residents aged 65 years or over (n=382) within a rural area of South Korea were screened for dementia and symptoms of depression and were followed using the same screening battery after 3 years (n=279). Data from anthropometric measurements (head circumference, height, and body weight), demographics, and blood tests were gathered. Neuropsychological tests, including the Korean version of mini-mental state examination (K-MMSE), clinical dementia rating (CDR) including the CDR-sum of boxes, the Korean version of instrumental activities of daily living, and geriatric depression scale (GDS), were performed. None of the 279 subjects followed were demented. Results Baseline performance on the K-MMSE and GDS was poorer for participants with smaller head circumferences. Follow-up performance on the MMSE was also poorer for participants with smaller head circumferences. Interestingly, participants with smaller head circumference showed worse GDS scores at baseline but on follow-up examination, participants with larger head circumference showed rapid worsening than those with smaller head circumference with marginal significance by ANOVA test. In regression coefficient analysis, GDS decline showed significant difference. Conclusion Head circumference was not associated with cognitive change but was associated with symptoms of depression in non-demented community residents.
Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework

The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.
Preventing Spin Relaxation of Optically Pumped Alkali Metal Atoms in Magnetometer by Atomically Thin Film Coating

We developed molecular layer deposition method of atomically thin hybrid polymer film for the first time by developing atomic layer deposition method with sequential surface chemical reactions in order to minimize the effect of the dipole-dipole interaction between the electron spin of alkali metal atoms and the nuclear spin of the atoms in the glass of the cell. We controlled film thickness of polymer thin film precisely and finally aimed at improving the sensitivity of the optically pumped atomic magnetometer. In the presentation, we report on the relaxation time of spin polarization by atomically thin hybrid polymer film with laser pumpprobe method.
Deep-learning in Identification of Vocal Pathologies

The work consists in a classification problem of four classes of vocal pathologies using one Deep Neural Network. Three groups of features extracted from speech of subjects with Dysphonia, Vocal Fold Paralysis, Laryngitis Chronica and controls were experimented. The best group of features are related with the source: relative jitter, relative shimmer, and HNR. A Deep Neural Network architecture with two levels were experimented. The first level consists in 7 estimators and second level a decision maker. In second level of the Deep Neural Network an accuracy of 39,5% is reached for a diagnosis among the 4 classes under analysis.
Browsing Citation Clusters for Academic Literature Search: A Simulation Study with Systematic Reviews

Our aim is to test if citation clusters can be useful in academic literature search for systematic reviews. We performed an initial offline evaluation using simulated user behaviour on a browsing tool for academic literature search over a set of 17 systematic reviews. To perform the evaluation, we clustered papers in a citation network obtained from the Web of Science database. The clustering solution was a system of seven hierarchical levels of clusters that allowed the simulated user to navigate from larger to smaller clusters. We simulated five user models with different emphasis on precision and recall. We found that citation clusters are more helpful for tasks focused on recall than for precision-oriented tasks. Our future research includes evaluation on a larger set, and a comparison to query search, followed by a study with real users.
Review: Cost-Effective Unmanned Aerial Vehicle (UAV) Platform for Field Plant Breeding Application

Utilization of remote sensing is a new wave of modern agriculture that accelerates plant breeding and research, and the performance of farming practices and farm management. High-throughput phenotyping is a key advanced agricultural technology and has been rapidly adopted in plant research. However, technology adoption is not easy due to cost limitations in academia. This article reviews various commercial unmanned aerial vehicle (UAV) platforms as a high-throughput phenotyping technology for plant breeding. It compares known commercial UAV platforms that are cost-effective and manageable in field settings and demonstrates a general workflow for high-throughput phenotyping, including data analysis. The authors expect this article to create opportunities for academics to access new technologies and utilize the information for their research and breeding programs in more workable ways.
Assessment of Gallbladder Wall Vascularity from Laparoscopic Images using Deep Learning

: Despite the significant progress in content-based video analysis of surgical procedures, methods on analyzing still images acquired during the operation are limited. In this paper we elaborate on a novel idea for computer vision-based assessment of the vascularity of the gallbladder (GB) wall, using frames extracted from videos of laparoscopic cholecystectomy. The motivation was based on the fact that the wall’s vascular pattern provides an indirect indication of the GB condition (e.g. fat coverage, wall thickening, inflammation), which in turn is usually related to the operation complexity. As the GB wall vascularity may appear irregular, in this study we focus on the classification of rectangular sub-regions (patches). A convolutional neural network (CNN) is proposed for patch classification based on two ground-truth annotation schemes: 3-classes (Low, Medium and High vascularity) and 2-classes (Low vs. High). Moreover, we employed three popular classifiers with a rich set of hand-crafted descriptors. The CNN achieved the best performance with accuracy: 98% and 83.1%, and mean F1-score: 98% and 80.4%, for 2-class and 3-class classification, respectively. The other methods’ performance was lower by 2%-6% (2-classes) and 6%-17% (3 classes). Our results indicate that CNN-based patch classification is promising for intraoperative assessment of the GB wall vascularity.
Woody Cover Fractions in African Savannas From Landsat and High-Resolution Imagery

The challenge of mapping savanna vegetation has limited our understanding of the factors that shape these ecosystems at large scales. We tested seven methods for mapping savanna woody cover (trees and shrubs; WC) across 12 protected areas (PAs) in eastern Africa using Landsat 8 imagery. Because we wanted a method viable for mapping across the often-limited Landsat historical archive, we limited ourselves to three images: one each from the wet, dry, and transition (halfway between wet and dry) seasons. Models were trained and tested using 1,330 WC reference points and the variance explained by cross validation (VEcv) accuracy metric. Of the methods we tested, RF significantly (p < 0.001) outperformed the others with the best models in nine PAs scoring over 75% (range of 34.5%–91.1%). RF models trained using data from all the PAs and tested in the individual PAs significantly (p < 0.001) outperformed their single-PA-derived counterparts (67.7 ± 23.3% versus 30.5 ± 27.4%). We also found that while the transition image appears to be critical to mapping WC and the wet season image should be avoided, no single season or seasonal combination significantly outperformed all the others, allowing some flexibility in image selection. Our findings show that with proper sampling of landscape heterogeneity, even with limited imagery, accurate maps of savanna WC are possible and could catalyze discoveries in this crucial biome.
A Public Dataset of Overground and Treadmill Walking in Healthy Individuals Captured by Wearable IMU and sEMG Sensors

: The paper presents our public Gait Analysis Data Base (http://gaitanalysis.th-brandenburg.de), which contains 3D walking kinematics and muscle activity data from healthy adults walking at normal, slow or fast pace on the flat ground or at an incremental speeds on treadmill. The acceleration, angular velocity and magnetic rate vectors are measured using XSens MTw sensors attached to both feet, shanks, thighs and the pelvis. EMG recordings are acquired using PLUX sEMG sensors applied at various leg muscles. The paper gives not only a detailed description of the data base, its webpage and the used terms (scenario, proband, experiment and trial), but also an overview about the experimental setup, the acquisition of data and the procedure of the experiments, the data processing and evaluation. Results of exemplary applications are described in the second part of the paper. Here the focus is set on the performance of walking: the individual ability to control, to repeat and to reproduce the pace or the dependence of gait parameters on the pre-set velocity.
Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades

Primary production by marine phytoplankton is one of the largest fluxes of carbon on our planet. In the past few decades, considerable progress has been made in estimating global primary production at high spatial and temporal scales by combining in situ measurements of primary production with remote-sensing observations of phytoplankton biomass. One of the major challenges in this approach lies in the assignment of the appropriate model parameters that define the photosynthetic response of phytoplankton to the light field. In the present study, a global database of in situ measurements of photosynthesis versus irradiance (P-I) parameters and a 20-year record of climate quality satellite observations were used to assess global primary production and its variability with seasons and locations as well as between years. In addition, the sensitivity of the computed primary production to potential changes in the photosynthetic response of phytoplankton cells under changing environmental conditions was investigated. Global annual primary production varied from 38.8 to 42.1 Gt C yr − 1 over the period of 1998–2018. Inter-annual changes in global primary production did not follow a linear trend, and regional differences in the magnitude and direction of change in primary production were observed. Trends in primary production followed directly from changes in chlorophyll-a and were related to changes in the physico-chemical conditions of the water column due to inter-annual and multidecadal climate oscillations. Moreover, the sensitivity analysis in which P-I parameters were adjusted by ±1 standard deviation showed the importance of accurately assigning photosynthetic parameters in global and regional calculations of primary production. The assimilation number of the P-I curve showed strong relationships with environmental variables such as temperature and had a practically one-to-one relationship with the magnitude of change in primary production. In the future, such empirical relationships could potentially be used for a more dynamic assignment of photosynthetic rates in the estimation of global primary production. Relationships between the initial slope of the P-I curve and environmental variables were more elusive.
Predictive Technologies and Biomedical Semantics: A Study of Endocytic Trafficking

: Predictive technologies with increased uptake of machine learning algorithms have changed the landscape of computational models across problem domains and research disciplines. With the abundance of data available for computations, we started looking at the efficiency of predictive inference as the answer to many problems we wish to address using computational power. However, the real picture of the effectiveness and suitability of predictive and learning technologies in particular is far from promising. This study addresses these concerns and illustrates them though biomedical experiments which evaluate Tf/TfR endosomal recycling as a part of cellular processes by which cells internalise substances from their environment. The outcome of the study is interesting. The observed data play an important role in answering biomedical research questions because it was feasible to perform ML classifications and feature selection using the semantic stored in the observed data set. However, the process of preparing the data set for ML classifications proved the opposite. Precise algorithmic predictions, which are ultimate goals when using learning technologies, are not the only criteria which measure the success of predictive inference. It is the semantic of the observed data set, which should become a training data set for ML, which becomes a weak link in the process. The recognised practices from data science do not secure any safety of preserving important semantics of the observed data set and experiments. They could be distorted and misinterpreted and might not contribute towards correct inference. The study can be seen as an illustration of hidden problems in using predictive technologies in biomedicine and is applicable to both: computer and biomedical scientists.
The association between psychosocial distress, pain and disability in patients with persistent low back pain —A cross-sectional study

Abstract Background: Psychological factors as depression and somatization are considered along with a high level of disability as risk factors for developing persistent low back pain (LBP). Furthermore, LBP and psychosocial distress are two of the most frequent reasons for seeking health care and sickness absence. However, it is not clear how these factors are intercorrelated. The aim of this study was to analyze how pain, fear-avoidance beliefs, depression and somatization were associated with disability in persistent LBP patients. Methods: In a cross-sectional design, 765 LBP patients filled in Roland Morris Disability Questionnaire, LBP Rating Scale, Fear-Avoidance Beliefs Questionnaire (physical activity/work) and Symptoms Checklist 90 (psychological distress). Results: In a multivariate regression analysis, disability was found to be significantly associated with pain, depression, fear-avoidance beliefs (physical activity), age and body mass index (BMI). Pain was significantly associated with disability, depression, somatization, sex and BMI. Disability, pain and the psychosocial variables were mutually correlated. Conclusion: The results of this study support earlier suggestions of an association between disability, pain and the psychosocial factors somatization, depression and fear-avoidance beliefs. The results support the importance of recognizing the mental health of LBP patients in the clinical setting.
Deep Relation Network for Hyperspectral Image Few-Shot Classification

Deep learning has achieved great success in hyperspectral image classification. However, when processing new hyperspectral images, the existing deep learning models must be retrained from scratch with sufficient samples, which is inefficient and undesirable in practical tasks. This paper aims to explore how to accurately classify new hyperspectral images with only a few labeled samples, i.e., the hyperspectral images few-shot classification. Specifically, we design a new deep classification model based on relational network and train it with the idea of meta-learning. Firstly, the feature learning module and the relation learning module of the model can make full use of the spatial–spectral information in hyperspectral images and carry out relation learning by comparing the similarity between samples. Secondly, the task-based learning strategy can enable the model to continuously enhance its ability to learn how to learn with a large number of tasks randomly generated from different data sets. Benefitting from the above two points, the proposed method has excellent generalization ability and can obtain satisfactory classification results with only a few labeled samples. In order to verify the performance of the proposed method, experiments were carried out on three public data sets. The results indicate that the proposed method can achieve better classification results than the traditional semisupervised support vector machine and semisupervised deep learning models.
Research on Post-Earthquake Landslide Extraction Algorithm Based on Improved U-Net Model

Seismic landslides are the most common and highly destructive earthquake-triggered geological hazards. They are large in scale and occur simultaneously in many places. Therefore, obtaining landslide information quickly after an earthquake is the key to disaster mitigation and relief. The survey results show that most of the landslide-information extraction methods involve too much manual participation, resulting in a low degree of automation and the inability to provide effective information for earthquake rescue in time. In order to solve the abovementioned problems and improve the efficiency of landslide identification, this paper proposes an automatic landslide identification method named improved U-Net model. The intelligent extraction of post-earthquake landslide information is realized through the automatic extraction of hierarchical features. The main innovations of this paper include the following: (1) On the basis of the three RGB bands, three new bands, DSM, slope, and aspect, with spatial information are added, and the number of feature parameters of the training samples is increased. (2) The U-Net model structure is rebuilt by adding residual learning units during the up-sampling and down-sampling processes, to solve the problem that the traditional U-Net model cannot fully extract the characteristics of the six-channel landslide for its shallow structure. At the end of the paper, the new method is used in Jiuzhaigou County, Sichuan Province, China. The results show that the accuracy of the new method is 91.3%, which is 13.8% higher than the traditional U-Net model. It is proved that the new method is effective and feasible for the automatic extraction of post-earthquake landslides.