dataesr/scientific-paragraphs-categorization huggingface dataset:
However, as many exposome technologies are used, some of which are still under development, and data distributions in a normal population are only partially known, sufficient numbers of data points cannot be guaranteed for all parameters. We, therefore, use a combination of targeted and agnostic potential risk factors and internal markers. Also, we have focused the case studies on either one exposure situation or one health outcome to increase power. Also, for the internal exposome, a tiered approach will be followed, enhancing the chances of pathway and biomarker discovery. Participation in the European Human Exposome Network (EHEN) will enable exchange of experience with similar methods and technologies with concurrent EU exposome projects. EPHOR will contribute uniquely to EHEN by characterizing the working life exposome as an essential factor in the development of NCDs.
ACKNOWLEDGMENTS The  Exposome Project for Health and Occupational Research (EPHOR)  is funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement number  874703. We would like to acknowledge  Dr. Debbie Jarvis  and  Dr. Cecilie Svanes  for their leading and coordinating role in European Community Respiratory Health Survey. M. C. T. is funded by a  Ramón y Cajal fellowship  ( RYC-2017-01892 ) from the  Spanish Ministry of Science, Innovation and Universities  and cofunded by the  European Social Fund. ISGlobal acknowledges support from the  Spanish Ministry of Science and Innovation through the "Centro de Excelencia Severo Ochoa   2019-2023 "  Program  ( CEX2018-000806-S ) and support from the  Generalitat de Catalunya  through the  CERCA Program.
Freud most significantly brought narcissism under the lens of psychology, and proposed that narcissists publicly portray an improved and perfected image of themselves, oftentimes as a form of compensation of low self-esteem and self-worth  (March and McBean 2018). Thus, the public display of one's own image or portrait implies extra care in choosing the most favourable profile side, hair styling, and some individuals may even go as far as always using make-up products, either physical or virtual, with popular software tools like Photoshop and other smartphone apps, such as Instagram, Snapchat and FaceApp ranking among the most widely used. The "self" represented in the photo is clearly understood as carefully crafted and aestheticized, and presenting visually in a particular moment (i. e. , in this particular light, from this angle, with this arrangement of facial features, body parts) as afforded through a particular technological arrangement (i.
Within this system, the digital masks can act as a double-layer mediatory interface, as they are generated from faces mostly donated by a participating audience, through an open and on-going call. Up to this moment a total of 63 contributions (selfies) from different audience members, sometimes including their pets' faces, from eleven different countries across three continents were received, and are continuously incorporated into the artwork's database. Human and animal faces are thus iteratively and generatively integrated into all exhibitions of the artefact. Each donated face is carefully pre-processed and split into three horizontal rectangles: eyes, nose and mouth. These slices are then recombined in a mask-totem composition, as it uses vertically stacked different beings, clearly identifiable as individual elements, and (vertically) symmetrical in composition.
Unlike the oracle in Alchimia, this new state is the life-death cycle, common to all beings, human and non-human alike. At this point the screen is filled with words describing body parts and actions performed by living organisms and the system begins to slow down in pace and intensity. Finally death marks the end of the cycle, only for the whole generative process to start anew. Also like Alchimia, SAR uses a real-time generated soundtrack to achieve synchronicity with the visual animation, thus reinforcing SAR's cinematic experience. This soundtrack relies on ambient noises of wind, rain, insects, birds, and also on distinct tribal rhythms and instruments from all over the world, modulated with digital sound synthesis and effects. Over the soundtrack different human voices utter "I am" in various languages, recorded through Google Translate voice synthesis function.
Principal outcome The mean visual acuity using the Rossano-Weiss test was -0. 22 logMAR or 6. 2/10 for the right eye and -0. 24 logMAR or 6. 1/10 for the left eye. When assessed with the eMOVA test, mean visual acuity was -0. 28 logMAR or 5. 9/10 for the right eye and -0. 24 logMAR or 6. 1/10 for the left eye. The mean difference in visual acuity measured between both tests was -0. 06 logMAR or 0. 3/10 for the right eye and -0. 01 logMAR or 0/10 for the left eye. These data related to the principal study outcome are presented in Table  2. The maximum visual acuity achieved with both tests was -0. 18 logMAR. The minimum visual acuity assessed was -0. 67 logMAR and -1. 2 logMAR, respectively, for the Rossano-Weiss test and the eMOVA test. The rst quartile, median, third quartile and mode were -0.
These trips are new sources of external costs. Our model shows that it is then important to increase the number of charging stations, and the geographical locations of these stations should be optimized to improve their accessibility. Otherwise, our model shows that congestion increases and the emissions of other vehicles also increase. To find the optimal locations of charging stations we formulate and solve a simplified network optimization problem using Cplex, a solver developed by IBM that implements the Simplex and the branch and bound method in the C language (See Anand  [3] ) The objective of the problem is to minimize the total distance to charging stations and the constraints reflects realistic requirements in the process (each car should find a charging station, the number of charging stations is limited).
One of the most elaborated case studies that has considered electric mobility in Sweden is reported in Bischoff et al. [8]. The model considers freight and person transport. The data are collected from GTFS (General Transit Feed Specification) and OSM (OpenStreetMap) data files. In general, the automotive industry is expanding to satisfy the human addiction to the private car. As far as electric vehicles are concerned, there has been several major innovations in the recent years, even the progress in the main challenge for more efficient batteries has progressed at slower pace. Issues with respect the autonomy of the batteries (autonomy and charging time) has been for a long time blocking points. A large research effort has been deployed with respect to these issues (See Rouhana  [2] ). There exist three main charging technologies (See Xi et al  [4] ).
To obtain realistic traffic flows we need to add cross-border trips and freight transport. Unfortunately, there are no comprehensive datasets available to use for this purpose. Cross-border trips are important since the study region is adjacent to Belgium and cross-border commuting is central to several regional policies focusing on the development of European metropolitan areas. To overcome the lack of available information on cross-border traffic flows, we use census data for home-to-work trips, which is available at the scale of the "commune". The database reports only flows departing from France to Belgium and not trips in the opposite direction. We assume a symmetric distribution of the trips and generate a random flow from Belgium to the study region in France. At the destination, these trips are directed towards most important agglomerations, i.
e. activity zones. Freight transport is not yet fully implemented in our model. The main reason is again the lack of an available database for these trips. Freight transport remains an important part of traffic flows since several logistic activities are taking place in the North of France. This region is crossed by the A1 motorway that connects the countries in the North (Netherlands, Belgium) to Paris and southern regions in Europe. To include representative traffic flows we produce freight transport, along the motorways crossing the study region, that matches counting data for trucks running on the corresponding links. We check that the calibrated model produces realistic patterns of congestion. To run large-scale simulations, it is difficult to include the whole population. Each iteration would need a long time, and the calibration, which requires a large number of iterations, becomes unpractical.
For the road network, we have used the open database provided by Institut Géographique National 2 , which is derived from OpenStreetMap databases with some corrections and standard formatting. This database provides the attributes of each link (speed limit, capacity, number of lanes, etc. ) and is globally satisfactory for the road network. The road network has a large number of links that are grouped with respect to their importance (motorways, main links, secondary roads, etc. ). The main limitation in this database is the incomplete rail network which is composed of a dozen of service lines. We have then manually added the train lines, and the corresponding stations, directly in the MATSim format. Public transport includes several urban bus networks, the TER (and TERGV) network, metro and tramway in Lille.
Acknowledgments. We are thankful to the participants in the conference of the  International Transport Economics Association  ( ITEA, June 2022, Toulouse ), session "Automobile market", who formulated several useful comments. The revised version of the paper has benefited from comments of two anonymous referees. The authors acknowledge financial support from the  Région des Hauts-de-France  and  ANR (Agence Nationale de la Recherche)  under project fund  ANR-21-HDF1-0014. Experiments presented in this paper were carried out by using the CALCULCO computing platform and supported by  SCoSI/ULCO (Service COmmun du Systme dInformation de lUniversit du Littoral Cte dOpale).
FARMYARD: A Generic GPU-based Pipeline for Feature Discovery from Massive Planetary LiDAR Data Shen Liang, Antoine Lucas, Stephen Porder, Gregory Sainton, Alexandre Fournier, Themis Palpanas Abstract-In recent decades, with the placement of LiDAR remote sensing instruments in orbit, we now have global coverage of the bare ground elevation on the Earth, Mars and beyond. Encoded in such planetary LiDAR data are interesting landscape features that promise to further our knowledge of planetary topography. However, discovery of such features comes with 3 major challenges: First, the volume of planetary LiDAR data can be massive, calling for analytical algorithms with great efficiency. Second, interesting features can often repeat themselves in multiple scales in local regions, thus it is vital to enable multiscale feature discovery.
Third, planetary LiDAR data can be heterogeneous, and evaluation of the quality of the extracted features can often be hampered by a variety of interfering factors. In response to these challenges, we propose FARMYARD, a generic pipeline for Feature Discovery From Planetary LiDAR Data. To the best of our knowledge, this is the first time such a pipeline has been proposed, which provides a brand new methodology for comparative studies of planetary topography. Specifically, drawing on the parallel computing power of the Graphics Processing Unit (GPU), we propose a novel pseudoon-pass sweep (POPS) framework for fast and memory-efficient feature extraction for massive planetary LiDAR data, a twolevel division scheme for local regions with support for multiscale features, and a Domain-Shifted Partition (DSP) scheme for feature evaluation that is robust against interfering factors.
I. INTRODUCTION T OPOGRAPHY, the surface configuration of a planet, bears the imprint of the processes that shape it. Deciphering the "text" encoded in topography has been the goal of scholars for centuries (Darwin, Gilbert, others). In the past 15 years, the advent of orbital Light Detection And Ranging (LiDAR) technology has lead to global highresolution topographical data for Mars  [1]  and the Moon  [2] , and to some extent for others bodies such as Titan, the largest moon of Saturn  [3]. Moreover, December of 2018 saw the arrival of the Global Ecosystem Dynamics Investigation 1  (GEDI)  [4] , a full-waveform LiDAR installed onboard the International Space Station (ISS). Over the nominal two-year mission, GEDI provides an estimated 10 billion waveform LiDAR observations between 51 Degrees N and 51 Degrees S, producing readings at both ground and vegetation height.
This was previously impossible until the GEDI mission  [3]  which, as was mentioned, has enabled us to see through the terrestrial vegetation cover for the first time. While we now have the high-quality LiDAR data needed to conduct inter-planetary comparative studies, we still lack the proper methodology needed to carry out such comparisons. Indeed, there have been strong geophysical perspectives to justify global topography field surveys since the 1990 on terrestrial planets, yet existing works on comparison of planetary bodies has mainly focused on exploring deep earth issues with spherical harmonic  [7]  and/or spherical spline  [8]  approaches. Such a methodology leads to very smooth global signals that with limited high frequency components, losing information that is present in the raw records which may be significant to subsequent comparative analysis.
In view of the drawbacks in existing works, in this paper we present a brand new methodology for comparative studies with planetary LiDAR data. Specifically, the task we will undertake in this paper is defined as follows. Definition 1: Feature Discovery From Planetary LiDAR Data (FARMYARD): given a set of planetery LiDAR data, find interesting features that can be used to distinguish between two or more geo-objects of the same category T. For example, suppose T is planets, and we have LiDAR data for the Earth and Mars, FARMYARD aims to find features that can be used to distinguish between the two planets. For the rest of the paper, we refer to T as the target category (TC). It is worth noting that while the motivation of this work is to enable effective comparison of different planets, FARMYARD also supports comparison of other geo-objects by changing T from planets to other categories.
For instance, one can change T to landscapes to compare different landscapes, or to geolocations to compare different regions on the same planet. In our work, we draw on a machine learning based methodology and formulate FARMYARD as a classification problem. Specifically, we first extract certain candidate features from the dataset that can potentially distinguish between the geo-objects in question, and then conduct cross validation (CV) using some pre-defined classifiers upon these features, applying evaluation criteria for classification (e. g. Cohen's kappa coefficient  [9] , F1-score, etc. ) to quantify their distinguishing power in terms of T. However, this comes with the following challenges. • C1: Massive data. Planetary LiDAR data often cover vast areas, making them big data in their raw form.
For example, as was previously mentioned, the GEDI  [4]  planetary LiDAR is to conduct 10 billion measurements of terrestrial landscapes over its nominal twoyear mission. Worse still, raw planetary LiDAR data is often irregularly-sampled (namely not aligned to a uniform raster), which is likely to pose an inconvenience to further analysis. Hence, it is necessary to transform them into raster data using some interpolation algorithm such as Kriging  [10]  and Inverse Distance Weighting (IDW)  [11]. This can make the volume of the raster data far more massive than the already big raw data. In fact, the numbers of data points after interpolation can be several to several dozen times of those before interpolation. Like many other big data problems, the sheer volume of planetary LiDAR data calls for analytical algorithms with high efficiency.
• C3: Sensitivity to interfering factors. As is well-known to the machine learning community  [13] , due to the presence of interfering factors, a high classification score does not necessarily translate to high feature quality. For example, suppose we want to find features that can distinguish between the Earth and Mars, and we have a LiDAR dataset composed of two classes: one corresponds to volcanoes on the Earth, while the other corresponds to drainage systems on Mars. In this case, a high classification score does not necessarily mean that the extracted features can effectively distinguish between the Earth and Mars, for they may actually have been distinguishing between volcanoes and drainage systems. In other words, differences in landscapes may well interfere with feature discovery concerning the differences in planets.
Such interference must be minimized. To address these challenges, we propose the following 3 solutions that will be implemented in our FARMYARD pipeline. • S1: Memory-efficient Graphics Processing Unit (GPU) based parallelism. To address Challenge C1, namely the problem with massive data, we draw on the power of GPU-based parallelism  [14]   To address Challenge C3, namely the problem with interfering factors, we first ensure that the entire dataset is diverse enough for these factors to be taken into account. We then device a novel Domain-Shifted Partition (DSP) strategy for the dataset which can lead to train-test splits that maximize intra-class diversity in terms of the interfering factors. This can in turn make the classification scores better reflect the classifier's robustness to interference, thus better evaluating feature quality.
We also examine the efficiency of our POPS framework, which can be hundreds or even thousands of time faster than its CPU-based counterpart. This paper is an extension to our work  [15]  which will appear in the 49th International Conference on Very Large Databases (VLDB 2023), a top data science venue. In the VLDB paper, we mainly focused on the POPS routine for GPU-based raw feature extraction as well as feature aggregation, with brief mentions of the two-level division scheme only to help the readers better understand the POPS algorithm. In this work, we extend our VLDB paper to a fullfledged FARMYARD pipeline, with complete and thorough description of how to conduct data collection, preprocessing, feature extraction, feature evaluation and further analysis using planetary LiDAR data, and how to apply GPU acceleration to break multiple computational bottlenecks of the pipeline.
Specifically, we present a much more detailed description of our two-level division scheme, and for the first time propose our DSP startegy for dataset split. Also, in our VLDB paper, the experiments were largely focused on showcasing the efficiency of POPS, while in this work, not only do we demonstrate the efficiency of our GPU-based algorithms, we also present a complete case study showcasing the utility of the entire FARMYARD framework. Finally, unlike our VLDB paper which is mainly oriented towards a data science audience, in this work, while maintaining all the technical merits in terms of data science of our VLDB paper, we add more discussions from the geoscience perspective. In particular, based upon our experimental results, we present several suggestions to possible future directions to pursue on planetary LiDAR analytics.
With the data preprocessed and features defined, we conduct feature extraction where we first extract raw features from the raster data and then aggregate them to obtain the final features. Afterwards, we perform feature evaluation in two ways: First, we partition the dataset into training and testing data for cross validation (CV) and obtain CV scores such as Cohen's kappa Fig. 2. The workflow of our FARMYARD pipeline, with its 3 design highlights shown in dark blue banners, and its GPU-accelerated procedures shown in pink boxes. Note that these GPU-accelerated procedures are the main efficiency bottlenecks of our pipeline, thus moving them onto the GPU makes our pipeline almost entirely GPU-based. coefficient  [9]  and F1-score which quantify the distinguishing power of the extracted features.
See Section III-D for more. 2) CRS unification: The raw data points in the dataset often follow different coordinate reference systems (CRS) as they originate from different data repositories produced for different planets. We need to unify the CRS so that all data points are comparable. Specifically, we recommend a Lambert Conformal Conic Alternative (LCCA) projection, as it can be well adapted to the variety of latitude's cover as well as extent of the ROIs. 3) Raster interpolation: After CRS transformation, we interpolate the raw data in each ROI to raster with uniform resolution. The reason for raster interpolation is two-fold: First, the raw cloud is irregularly sampled, thus the sampling protocol differs from ROI to ROI. Like the discrepancy with CRS, the discrepancy with sampling needs to be addressed to make all ROIs comparable with each other.
For the choice of classification score, we recommend using a combination of Cohen's kappa coefficient  [9]  (κ) and F1score, as these two metrics are both robust against imbalanced datasets, which is common in real-world scenarios. κ is a single-value metric that measures the agreement between the predicted labels and the groundtruth labels. The range of κ is [-1, 1], the higher it is, the better classification performance. Specifically, a κ of 1 indicates perfect agreement; a κ of 0 indicates no agreement other than those that are expected by chance; a κ that is negative indicates either no relationship between the predicted and groundtruth labels, or a tendency of the two disagreeing with each other. On the other hand, F1-score is a per-label metric (that is, there is one F1-score for each label) that can reflect the classifier's accuracy of identifying examples with each label.
The higher the F1-score is, the more accurate. In our case, we recommend using κ to quantify the overall quality of the features as defined by the feature template, and inspecting the F1-scores to gain deeper insight into their per-label distinguishing power. Apart from choosing CV scores that can better cope with imbalanced datasets, it is also advisable to balance the training set before training to actively counter this problem. Possible data balancing methods include oversampling methods such as SMOTE  [23]  and ADASYN  [24] , as well as undersampling methods such as random undersampling and undersampling with instance hardness threshold  [25] , which are implemented in the Imbalanced-learn 2   [26]  package. 3) Feature visualization: Aside from quantitative evaluation with CV, it is also important for domain experts to manually inspect the extracted features.
However, we cannot be certain of the cause of such a bias, which may be down to the feature template, the dataset composition (in particular, data imbalance), or the FARMYARD configurations, or a combination of some or all of the aforementioned factors. The user would probably need further investigation to pinpoint the cause, by means of scrutinizing the feature visualization results, etc. Again, we recommend conducting multiple runs on different configurations. This can at least help determine whether such a bias exists only for certain FARMYARD configurations, or are common to the majority of them, thus providing clues to the cause of the bias. Besides the aforementioned analyses, the user can perform other types of analysis as needed. It is also advisable to conduct multiple analyses for a more comprehensive insight.