Miracle-dz/UniFAR-FTUs huggingface dataset:
BIR: A Time and a Place to Envision Concepts and Tools around Bibliometrics

The BIR and BIRNDL workshops are a unique place where people from several different domains, Bibliometrics, Information Retrieval and more broadly Natural Language Processing can exchange ideas and work together to develop new approaches for the processing of scientific corpora. During our PhD theses at Paris-Sorbonne university, we both worked on the linguistic analysis of corpora, in the framework of enunciative linguistics. Iana Atanassova focused on the semantic dimension of discourse in scientific corpora, and in particular for applications in Information Retrieval, and Marc Bertin focused on the discursive forms present in citation contexts. This work on citation acts, with a purely linguistic approach at the beginning, is the starting point of more than 10 years of work. The link between these two approaches and our collaboration started around the same period as the first BIR workshop in 2014. At that moment Iana worked on the development of a search engine at MyScienceWork and Marc was a post-doctoral fellow at Montreal working with Yves Gingras and Vincent Larivière [8]. The BIR and BIRNDL workshops have become regular events where we can discuss our research progress and share ideas around bibliometrics and the processing of scientific papers. Iana is proud to have been the keynote speaker of BIR 2019 [1]. Considering bibliographic references in papers, we can observe a great variety in the citation contexts but also many regularities that need to be studied an categorized in order to better understand the linguistic nature of citations and build solutions for ontology population. We have approached the study of citation contexts firstly by examining the regularities in their lexical distributions in the IMRaD structure [2] and by further characterizing the different sections of papers by Factorial Correspondance Analysis [3] and later by K-means and Hierarchical clustering methods [6]. We also studied phenomena around multiple references in the same sentence [4] and negative citations [5]. The InTeReC corpus was proposed [7] to address some of the problems in developing machine learning approaches for citation classification and we conducted a preliminary study to compare such approaches with rule-based ones [10]. Finally, we ques-
Evaluation of Seven Atmospheric Profiles from Reanalysis and Satellite-Derived Products: Implication for Single-Channel Land Surface Temperature Retrieval

Land surface temperature (LST) is vital for studies of hydrology, ecology, climatology, and environmental monitoring. The radiative-transfer-equation-based single-channel algorithm, in conjunction with the atmospheric profile, is regarded as the most suitable one with which to produce long-term time series LST products from Landsat thermal infrared (TIR) data. In this study, the performances of seven atmospheric profiles from different sources (the MODerate-resolution Imaging Spectroradiomete atmospheric profile product (MYD07), the Atmospheric Infrared Sounder atmospheric profile product (AIRS), the European Centre for Medium-range Weather Forecasts (ECMWF), the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), the National Centers for Environmental Prediction (NCEP)/Global Forecasting System (GFS), NCEP/Final Operational Global Analysis (FNL), and NCEP/Department of Energy (DOE)) were comprehensively evaluated in the single-channel algorithm for LST retrieval from Landsat 8 TIR data. Results showed that when compared with the radio sounding profile downloaded from the University of Wyoming (UWYO), the worst accuracies of atmospheric parameters were obtained for the MYD07 profile. Furthermore, the root-mean-square error (RMSE) values (approximately 0.5 K) of the retrieved LST when using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were smaller than those but greater than 0.8 K when the MYD07, AIRS, and NCEP/DOE profiles were used. Compared with the in situ LST measurements that were collected at the Hailar, Urad Front Banner, and Wuhai sites, the RMSE values of the LST that were retrieved by using the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles were approximately 1.0 K. The largest discrepancy between the retrieved and in situ LST was obtained for the NCEP/DOE profile, with an RMSE value of approximately 1.5 K. The results reveal that the ECMWF, MERRA2, NCEP/GFS, and NCEP/FNL profiles have great potential to perform accurate atmospheric correction and generate long-term time series LST products from Landsat TIR data by using a single-channel algorithm.
Satellite-Derived PM2.5 Composition and Its Differential Effect on Children's Lung Function

Studies of the association between air pollution and children’s health typically rely on fixed-site monitors to determine exposures, which have spatial and temporal limitations. Satellite observations of aerosols provide the coverage that fixed-site monitors lack, enabling more refined exposure assessments. Using aerosol optical depth (AOD) data from the Multiangle Imaging SpectroRadiometer (MISR) instrument, we predicted fine particulate matter, PM 2.5 , and PM 2.5 speciation concentrations and linked them to the residential locations of 1206 children enrolled in the Southern California Children’s Health Study. We fitted mixed-effects models to examine the relationship between the MISR-derived exposure estimates and lung function, measured as forced expiratory volume in 1 second (FEV 1 ) and forced vital capacity (FVC), adjusting for study community and biological factors. Gradient Boosting and Support Vector Machines showed excellent predictive performance for PM 2.5 (test R 2 = 0.68 ) and its chemical components (test R 2 = –0.71). In single-pollutant models, FEV 1 decreased by 131 mL (95% CI: − 232 , − 35 ) per 10.7-µg/m 3 increase in PM 2.5 , by 158 mL (95% CI: − 273 , − 43 ) per 1.2-µg/m 3 in sulfates (SO 4 2 − ), and by 177 mL (95% CI: − 306 , − 56 ) per 1.6-µg/m 3 increase in dust; FVC decreased by 175 mL (95% CI: − 310 , − 29 ) per 1.2-µg/m 3 increase in SO 4 2 − and by 212 mL (95% CI: − 391 , − 28 ) per 2.5-µg/m 3 increase in nitrates (NO 3 − ). These results demonstrate that satellite observations can strengthen epidemiological studies investigating air pollution health effects by providing spatially and temporally resolved exposure estimates.
The Role of Sea Surface Temperature Forcing in the Life-Cycle of Mediterranean Cyclones

Air–sea interface processes are highly associated with the evolution and intensity of marine-developed storms. Specifically, in the Mediterranean Sea, the air–ocean temperature deviations have a profound role during the several stages of Mediterranean cyclonic events. Subsequently, this enhances the need for better knowledge and representation of the sea surface temperature (SST). In this work, an analysis of the impact and uncertainty of the SST from different well-known datasets on the life-cycle of Mediterranean cyclones is attempted. Daily SST from the Real Time Global SST (RTG_SST) and hourly SST fields from the Operational SST and Sea Ice Ocean Analysis (OSTIA) and the NEMO ocean circulation model are implemented in the RAMS/ICLAMS-WAM coupled modeling system. For the needs of the study, the Mediterranean cyclones Trixi, Numa, and Zorbas were selected. Numerical experiments covered all stages of their life-cycles (five to seven days). Model results have been analyzed in terms of storm tracks and intensities, cyclonic structural characteristics, and derived heat fluxes. Remote sensing data from the Integrated Multi-satellitE Retrievals (IMERG) for Global Precipitation Measurements (GPM), Blended Sea Winds, and JASON altimetry missions were employed for a qualitative and quantitative comparison of modeled results in precipitation, maximum surface wind speed, and wave height. Spatiotemporal deviations in the SST forcing rather than significant differences in the maximum/minimum SST values, seem to mainly contribute to the differences between the model results. Considerable deviations emerged in the resulting heat fluxes, while the most important differences were found in precipitation exhibiting spatial and intensity variations reaching 100 mm. The employment of widely used products is shown to result in different outcomes and this point should be taken into consideration in forecasting and early warning systems.
Coherence-Factor-Based Rough Surface Clutter Suppression for Forward-Looking GPR Imaging

We present an enhanced imaging procedure for suppression of the rough surface clutter arising in forward-looking ground-penetrating radar (FL-GPR) applications. The procedure is based on a matched filtering formulation of microwave tomographic imaging, and employs coherence factor (CF) for clutter suppression. After tomographic reconstruction, the CF is first applied to generate a “coherence map” of the region in front of the FL-GPR system illuminated by the transmitting antennas. A pixel-by-pixel multiplication of the tomographic image with the coherence map is then performed to generate the clutter-suppressed image. The effectiveness of the CF approach is demonstrated both qualitatively and quantitatively using electromagnetic modeled data of metallic and plastic shallow-buried targets.
Food Recognition: Can Deep Learning or Bag-of-Words Match Humans?

: Automated smartphone-based food recognition is a useful basis for applications targeted at dietary assessment. Dish recognition is a necessary step in that process. One of the possible approaches to use is deep learning-based recognition, another one is bag-of-words based classiﬁcation. Deep learning has increasingly become the preferred approach to use in either this or other image classiﬁcation tasks. Additionally, if humans are better recognizing the dish, the automated approach is useless (it will be less error-prone for the user to identify the dish instead of capturing the photo). We compare the alternatives of Deep Learning (DL), Bag-of-words (BoW) and Humans (H). The best deep learner beats humans when on few food categories, but looses if it has to learn many more food categories, which is expected in real contexts. We describe the approaches, analyze the results, draw conclusions and design further work to evaluate further and improve the approaches.
Procedural Reconstruction of 3D Indoor Models from Lidar Data Using Reversible Jump Markov Chain Monte Carlo

Automated reconstruction of Building Information Models (BIMs) from point clouds has been an intensive and challenging research topic for decades. Traditionally, 3D models of indoor environments are reconstructed purely by data-driven methods, which are susceptible to erroneous and incomplete data. Procedural-based methods such as the shape grammar are more robust to uncertainty and incompleteness of the data as they exploit the regularity and repetition of structural elements and architectural design principles in the reconstruction. Nevertheless, these methods are often limited to simple architectural styles: the so-called Manhattan design. In this paper, we propose a new method based on a combination of a shape grammar and a data-driven process for procedural modelling of indoor environments from a point cloud. The core idea behind the integration is to apply a stochastic process based on reversible jump Markov Chain Monte Carlo (rjMCMC) to guide the automated application of grammar rules in the derivation of a 3D indoor model. Experiments on synthetic and real data sets show the applicability of the method to efficiently generate 3D indoor models of both Manhattan and non-Manhattan environments with high accuracy, completeness, and correctness.
Learning to Rank Research Articles: A Case Study of Collaborative Filtering and Learning to Rank in ScienceDirect

Online academic repositories help millions of researchers discover relevant articles, a domain in which there are many potential signals of relevance, including text, citation links, and how recently an article was published. In this paper we present a case study of prodictionizing learning to rank for large scale recommendation, which utilises these diverse feature sets to increase user engagement. We ﬁrst introduce item-to-item collaborative ﬁltering (CF), then how these recommendations are rescored with a LtR model. We then describe oﬄine and online evaluation, which are essential for productionizing any recommender. The online results show that learning to rank signiﬁcantly increased user engagement with the recommender. Finally we show through post-hoc analysis that the original CF solution tended to promote older articles with lower traﬃc. However, by learning from subjective user interactions with the recommender system, our relevance model reversed those trends.
Measuring Individual Tree Diameter and Height Using GatorEye High-Density UAV-Lidar in an Integrated Crop-Livestock-Forest System

Accurate forest parameters are essential for forest inventory. Traditionally, parameters such as diameter at breast height (DBH) and total height are measured in the field by level gauges and hypsometers. However, field inventories are usually based on sample plots, which, despite providing valuable and necessary information, are laborious, expensive, and spatially limited. Most of the work developed for remote measurement of DBH has used terrestrial laser scanning (TLS), which has high density point clouds, being an advantage for the accurate forest inventory. However, TLS still has a spatial limitation to application because it needs to be manually carried to reach the area of interest, requires sometimes challenging field access, and often requires a field team. UAV-borne (unmanned aerial vehicle) lidar has great potential to measure DBH as it provides much higher density point cloud data as compared to aircraft-borne systems. Here, we explore the potential of a UAV-lidar system (GatorEye) to measure individual-tree DBH and total height using an automatic approach in an integrated crop-livestock-forest system with seminal forest plantations of Eucalyptus benthamii. A total of 63 trees were georeferenced and had their DBH and total height measured in the field. In the high-density (>1400 points per meter squared) UAV-lidar point cloud, we applied algorithms (usually used for TLS) for individual tree detection and direct measurement of tree height and DBH. The correlation coefficients (r) between the field-observed and UAV lidar-derived measurements were 0.77 and 0.91 for DBH and total tree height, respectively. The corresponding root mean square errors (RMSE) were 11.3% and 7.9%, respectively. UAV-lidar systems have the potential for measuring relatively broad-scale (thousands of hectares) forest plantations, reducing field effort, and providing an important tool to aid decision making for efficient forest management. We recommend that this potential be explored in other tree plantations and forest environments.
GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification

Deep learning architectures are the most effective methods for analyzing and classifying Ultra-Spectral Images (USI). However, effective training of a Deep Learning (DL) gradient classifier aiming to achieve high classification accuracy, is extremely costly and time-consuming. It requires huge datasets with hundreds or thousands of labeled specimens from expert scientists. This research exploits the MAML++ algorithm in order to introduce the Model-Agnostic Meta-Ensemble Zero-shot Learning (MAME-ZsL) approach. The MAME-ZsL overcomes the above difficulties, and it can be used as a powerful model to perform Hyperspectral Image Analysis (HIA). It is a novel optimization-based Meta-Ensemble Learning architecture, following a Zero-shot Learning (ZsL) prototype. To the best of our knowledge it is introduced to the literature for the first time. It facilitates learning of specialized techniques for the extraction of user-mediated representations, in complex Deep Learning architectures. Moreover, it leverages the use of first and second-order derivatives as pre-training methods. It enhances learning of features which do not cause issues of exploding or diminishing gradients; thus, it avoids potential overfitting. Moreover, it significantly reduces computational cost and training time, and it offers an improved training stability, high generalization performance and remarkable classification accuracy.
Data-Driven Insights towards Risk Assessment of Postpartum Depression

Postpartum depression is defined as depressive episodes that occur during pregnancy or within 12 months of parturition. The goal of this study is the exploration of the birth features and maternal traits which affect the risk of postpartum depression for mothers with preterm neonates. We analysed data from 144 women (63 mothers of term and 81 mothers of preterm infants) who completed the Edinburgh Postnatal Depression Scale (EPDS) in the postpartum period. We used three feature selection algorithms: ReliefF, Random Forests (RF) variable importance, and Boruta, in order to select the most predictive feature subsets, which were subsequently mapped onto the binarized EPDS total score (a threshold of 10 was used to binarize the EPDS total scores) using RF. We found that positive affectivity (rs=-0.467, p<0.001), and the Apgar score at 5 minutes (rs=-0.430, p<0.001) are the most statistically strongly associated features with the risk of postpartum depression. We used 10-fold cross-validation with 100 iterations and report out-of-sample balanced accuracy (median±IQR): 75.0±16.7, sensitivity: 66.7±16.7, specificity: 100±16.7, and F1 score: 0.8±0.2. Collectively, these findings highlight the potential of using a data-driven process to automate risk prediction using standard clinical characteristics and motivate the deployment of the developed tool using larger-scale datasets. a https://orcid.org/0000-0003-0625-9454 b https://orcid.org/0000-0001-6699-9476 c https://orcid.org/0000-0001-9197-4924 d https://orcid.org/0000-0002-7256-3027 e https://orcid.org/0000-0001-6963-1335 f https://orcid.org/0000-0003-2688-1734 g https://orcid.org/0000-0003-3904-8960 h https://orcid.org/0000-0002-0994-8100
PySpot: A Python based Framework for the Assessment of Laser-modified 3D Microstructures for Windows and Raspbian

: Biocompatible 3D microstructures created with laser lithography and modiﬁed for optimal cell growth with laser grafting, can imitate the 3D structure cells naturally grow in. For the evaluation of the quality and success of those 3D microstructures, specialized software is required. Our software PySpot can load 2D and 3D images and analyze those through image segmentation, edge detection, and surface plots. Additionally, the creation and modiﬁcation of regions of interest (ROI) allow for the quality evaluation of speciﬁc areas in an image by intensity analysis. 3D rendering allows for identifying complex geometrical properties. Furthermore, PySpot is executable on Windows as well as on Raspbian, which makes it ﬂexible to use.
Survey on Various Big Data Analytics Platform

The primary purpose of this paper is to provide an in-depth analysis of different platforms available for performing big data analytics. This paper surveys different hardware platforms available for big data analytics and assesses the advantages and drawbacks of each of these platforms based on various metrics such as scalability, data I/O rate, fault tolerance, real-time processing, data size supported and iterative task support. In addition to the hardware, a detailed description of the software frameworks used within each of these platforms is also discussed along with their strengths and drawbacks. Some of the critical characteristics described here can potentially aid the readers in making an informed decision about the right choice of platforms depending on their computational needs.
Solving Multiple Isolated, Interleaved, and Blended Tasks through Modular Neuroevolution

Many challenging sequential decision-making problems require agents to master multiple tasks. For instance, game agents may need to gather resources, attack opponents, and defend against attacks. Learning algorithms can thus benefit from having separate policies for these tasks, and from knowing when each one is appropriate. How well this approach works depends on how tightly coupled the tasks are. Three cases are identified: Isolated tasks have distinct semantics and do not interact, interleaved tasks have distinct semantics but do interact, and blended tasks have regions where semantics from multiple tasks overlap. Learning across multiple tasks is studied in this article with Modular Multiobjective NEAT, a neuroevolution framework applied to three variants of the challenging Ms. Pac-Man video game. In the standard blended version of the game, a surprising, highly effective machine-discovered task division surpasses human-specified divisions, achieving the best scores to date in this game. In isolated and interleaved versions of the game, human-specified task divisions are also successful, though the best scores are surprisingly still achieved by machine discovery. Modular neuroevolution is thus shown to be capable of finding useful, unexpected task divisions better than those apparent to a human designer.
Fast and Scalable Solvers for the Fluid Pressure Equations with Separating Solid Boundary Conditions

In this paper, we propose and evaluate fast, scalable approaches for solving the linear complementarity problems (LCP) arising from the fluid pressure equations with separating solid boundary conditions. Specifically, we present a policy iteration method, a penalty method, and a modified multigrid method, and demonstrate that each is able to properly handle the desired boundary conditions. Moreover, we compare our proposed methods against existing approaches and show that our solvers are more efficient and exhibit better scaling behavior; that is, the number of iterations required for convergence is essentially independent of grid resolution, and thus they are faster at larger grid resolutions. For example, on a 2563 grid our multigrid method was 30 times faster than the prior multigrid method in the literature.