dataesr/scientific-paragraphs-categorization huggingface dataset:
The VSE model outperformed the 5 most prevalent models previously used to fractionate the cognitive processes engaged by the IGT, in terms of log-likelihood (penalized for model complexity), prediction accuracy, as well as model and parameter recovery rates. We further demonstrated the potential of this architecture to capture fine-grained differences in IGT behavior between young and old participants. Last but not least, we published the scripts used to generate our results under the form of a user-friendly Matlab toolbox which shall enable the community of researchers and clinicians relying on the IGT as a routine assessment of risky decision-making to report more informative and detailed results with minimal programming and mathematical skills. In the field of reinforcement-learning, most algorithms are oriented towards normative utility-maximization goals.
Combined with the model-free analysis of the SE index, it expands the heuristic value of the IGT beyond the study of exploitation and reward-seeking behaviors. Moreover, the fact that VSE parameters were on average more recoverable than parameters of previous models will facilitate the interpretation of inter-individual and inter-group differences. Numerous studies which had used the IGT to characterize clinical populations may thus benefit from re-analyzing their data using the toolbox associated with the current paper. Once the trial-by-trial IGT data is converted to the appropriate Matlab format, this toolbox make such re-analysis extremely simple and intuitive, thanks to its compact but informative documentation and its densely commented scripts. With minimal programming knowledge, the six models described hereinabove can be fitted to any standard IGT dataset, compared and evaluated with respect to recoverability and prediction accuracy.
Beyond the study of cognitive aging, the good recoverability of the VSE model itself and the excellent recoverability of its free parameters constitute two useful features with respect to the development of computational psychiatry. Indeed, the Variational Bayes approach adopted here can be readily combined with the advanced clustering techniques underlying this growing field of research (https://mbb-team. github. io/VBA-toolbox/), which aims at redefining the dimensionality of behavioral impairments across clinical labels in the hope of promoting drug discovery and personalized medicine  [36]. To which extent the decomposition of IGT-related behaviors will contribute to this effort remain uncertain, but it has a high potential which could be realized if more clinical teams subscribe to the open-science philosophy by sharing their raw data.
Following the initiative of Ahn and colleagues who provided data of stimulant and opiate users  [37] , addiction research appears as a timely candidate: indeed, large datasets exist for alcohol use  [38, 39] , cannabis use  [40] , as well as for behavioral addictions such as gambling and eating disorders  [41, 42]. To conclude, our study leveraged the power of an open "many labs" dataset in order to demonstrate the existence-and characterize the influence-of an overlooked behavior in the IGT. Building on previous work and more particularly on the Prospect Valence Learning (PVL) model  [43] , the VSE architecture represents not only a quantitative but also a qualitative improvement upon alternative models by shedding light on directed exploration. Besides enabling any experimenter to fit the VSE and its ancestors (EV, PVL, PVL-Delta, VPP, ORL) on IGT data, the toolbox accompanying this paper might be used as an environment to develop even better models in the future.
It must be acknowledged that this tool relies heavily on two other open-source packages for Matlab: modeling analyses largely depend on the VBA toolbox by Daunizeau and colleagues  [18]  whereas visualizations take advantage on the Gramm toolbox by Morel  [44]. Last but not least, this study is fully aligned with the ideals of reproducibility and transparency in science: the dataset used is both large and freely available, while the scripts used to generate figures and statistics are available online alongside a clear documentation (https://github. com/romainligneul/igttoolbox).
Dataset and participants The dataset comes from a 'many labs' initiative grouping 10 studies and containing data from 617 healthy participants  [13]. Here, we restricted the analysis to the subset of 7 studies which used the classical 100 trials version of the IGT, resulting in 504 participants (age range: 18-88 years; for the 5 studies with available information about sex: 54% of females). Within this dataset, 153 participants come from a single study on aging  [15]. Among these participants, 63 are older adults (61-88 years old; 17 males) and 90 are younger adults (18-35 years old; 22 males) matched in terms of education level and intelligence (WASI vocabulary).
Fitting procedures A validated toolbox (http://mbb-team. github. io/VBA-toolbox) was used to optimize model parameters  [18]. This toolbox relies on a Variational Bayesian (VB) scheme. Compared to non-Bayesian methods, this approach has the advantage of accounting for the uncertainty related to estimated model parameters and of informing the optimization algorithm about prior distributions of parameters' values. All priors were natively defined as Gaussian distributions of mean 0 and variance 3, which approximates the uniform distribution over the [0-1] interval after a sigmoid transformation. Depending on the range of values in which each parameter was allowed to vary, the sigmoid-transformed parameters were further stretched or shifted to cover different intervals while preserving the flatness of their prior distribution (e.
g. "multiplied by 2, minus 1", to obtain the interval [-1,1]). Model comparison results were replicated using a non-Bayesian model fitting procedure which relied on the standard fminunc function of Matlab (line-search algorithm). All hidden states (i. e values) were initialized at 0 except for exploration weights which were initialized at 1 (since no deck has been sampled at the beginning of the task). The VB algorithm was not allowed to update the initial values for hidden states.
SGS16/235/OHK3/3T/13, and FR-8J18FR018, PHC Barrande programme under grant agreement No. 40682ZH (3L4AV), and European Union's Horizon 2020 research and innovation programme under grant agreement No. 732737 (ILIAD). We thank Dr. Sergi Molina for the long-term dataset we use in our experiments. Fig. 1. Example of the warped hypertime projection of 1D data (number of humans in a given area). The numbers x i observed at t i are projected into a 3d vector space as (x i , cos(2 π t i /T ), sin(2 π t i /T )), where they form clusters because they exhibit a periodic behaviour with a period T. Here, the warped hypertime dimensions define a base of a cylinder, while the numbers x i define a cylinder side. Obtained from  [11]. would be useful to allow the robot to choose the locations as well according to the typical areas of people presence.
III. EVALUATION To evaluate our method, we utilised a dataset of people presence collected at the University of Lincoln. The data were collected by a mobile robot equipped with a Velodyne 3D laser rangefinder. The robot was driven to a location which provided a good overview of one of the T-shaped corridor junctions (Fig. 2 ). To localize people in the measurements provided by the laser 3D scanner, we used a reliable and efficient person detection and localisation method  [29]. Since the robot batteries need to be recharged on a daily basis, we could not collect the data in a continuous, 24/7 manner, but had to remove the robot from the observation spot every night when the building was vacant, and there were no people at the corridors. The collected dataset contains detections from early mornings to late evening from weekdays over several weeks.
A typical day contains approximately 32000 people detection measurements, which correspond to a large number of people walking or standing in the monitored corridors. The method  [29]  provides human detection result as a single vector, e. g. , a point in space. However, for the purpose of mobile robot path planning, a human represents an obstacle with a particular spatial volume. For this reason, we preprocessed the dataset by substituting every detection vector by a set of vectors in its spatial neighborhood with a diameter of 0. 5m. The primary purpose of the evaluation is to estimate the predictive capability of the models created by the proposed method. Therefore we split the gathered data into training and test sets and learn the model from the training set only. The training dataset consists of two weeks of measurements and the test dataset consists of two days of measurements from another week (Wednesday and Thursday).
Then, we partition the timeline of the test data into a spatio-temporal 3D grid G and create a 3D histogram H. In particular, for each bin, we count the number of detections in the test data. Then, we predict a histogram M for the same grid G using models created from the training data. The overall error of the method is calculated as a root-mean-square deviation between histograms H and M, similar to equation (  6 ). For our evaluation, we calculated this error for different spatial and temporal resolutions of the grid G,, i. e. , for the different basic volumes b, see Fig. 3. To evaluate our method, we compare it to three other spatio-temporal models. These three models represent the space in a discrete way. In particular, they associate each cell of the spatial 2d grid with a temporal model: Mean, which is simply an average of the all past measurements occurring within a given cell, Hist, which splits each day into h intervals and predicts the number of occurrences as an average for the relevant time of a day, and FreMEn, which extracts f spectral components from the people occurrence history and uses these periodic components for future predictions.
For the two periods and three clusters, it occupies 1. 7 KiB of memory space independently to the grid resolution. In contrast, Fre-MEn needs several numbers for each modelled periodicity of every cell of the grid-based representation. Thus, on our dataset, where the aforementioned grid has a resolution of 100, 50, 20, and 10 cm, the FreMEn model occupies 1. 1, 4. 4, 27. 2, and 108. 9 MiB of memory respectively, see Table  I. In addition, the resolution of the proposed method is derived from the cluster weights α j. Therefore it is possible to store different resolutions of one model at almost no memory cost. The downside of the proposed method is its computational complexity, which is caused primarily by the fact that the.
IV. CONCLUSION We proposed a novel approach to anticipate human presence for robots that are required to provide people-oriented services. The method uses projections of the time to several new dimensions derived from the natural human behavior. A projection of people detections into the created warped hypertime-space is analyzed using Gaussian Mixture Models. The obtained clusters are used to create spatio-temporal model of the human presence distribution. This model is then used to predict the occurrences in a given time and space. We evaluated the proposed method on a real dataset from the corridors at a university collected over several weeks. Then, we compared the accuracy of the prediction to stateof-the-art methods used in robotics, and showed that our approach outperforms them in terms of prediction accuracy and memory efficiency.
Moreover, the published works on magnetic space groups were not complete and the equivalent of the Wyckoff positions in crystallographic groups were never described; two kinds of notations (OG for Opechowski-Guccione and BNS for Belov-Neronova-Smirnova) were used by different authors. It was only in 2001 that Daniel B. Litvin provided for the first time a full description of all Shubnikov groups in an electronic book, freely accessible, similar to the International Tables, but too big to be printed on paper  [79]. Only in 2010, the data provided by D. B. Litvin were put available in a database compiled by H. T. Stokes and B. J. Campbell  [80] , in which both notations OG and BNS were used. Finally, the magnetic space groups can be classified into four types. 1. Type 1, or monochrome groups, corresponding to the 230 crystallographic group: M = G.
Structure factors in superspace The crystallographic (3 + d)D superspace groups, for dimensions d = 1, 2, and 3, have been tabulated recently  [90]  in form of a database, the number of superspace groups being 775, 3338, and 12 584 respectively. The magnetic superspace groups have not yet been tabulated and their number is still unknown. 9 The symbols used for designing a superspace group is based on the Hermann-Mauguin notation of the parent crystallographic group followed by the modulation vector and a set of letters indicating the translation in the internal space. In principle, one can use the same kind of notation for magnetic superspace groups using primed symbols. For instance, the crystallographic space group noted Pnma(00γ )0s0 can be easily constructed by calculating the augmented matrices of the generators n, m, a using the rules  (60)  and putting the internal translation of the ĝS (m) equal to s = 1/2, t S4 = 1/2 and zero for the other generators.
Its scope is very well summarised by the following words of the authors: The Cambridge Crystallography Subroutine Library (CCSL) is a set of subroutines which are used in the construction of Fortran crystallographic programs. CCSL has been written with the needs of the nonstandard crystallographer in mind. [. ]  the aim of CCSL is to allow each user to do something a little different, and to experiment with his own ideas using the subroutines in the library for the routine parts of the calculations. Together with many programs and utilities for crystallographic applications, the magnetic programs based on CCSL are able to treat single crystal diffraction data (polarised and unpolarised neutrons) and, to a limited extent, powder diffraction. For instance, the program MAGLSQ allows the refinement of magnetic structures (commensurable and incommensurable) as well as the treatment of flipping ratios for spin densities.
The last thirty years The original Rietveld  [94]  program was limited to commensurable magnetic structures, but his idea gave rise to a series of computing programs, using the Rietveld method, that extended the initial capabilities. At the end of the 1990s, there were several Rietveld programs and the way of using them, in the form of guidelines, was published in a paper of the Powder Diffraction Commission of the IUCr 10   [97]. Those able to handle magnetic structures were, from the beginning, the programs GSAS  [98]  and RIETAN  [99, 100]. The most recent versions of those programs are able to work with commensurable magnetic structures. In the case of RIETAN, there are strong limitations because magnetic groups are not implemented. In the latest versions of GSAS (GSAS-II)  [98] , the Shubnikov groups are fully implemented.
More recently, the commercial program TOPAS  [101]  has included the possibility to work with commensurable magnetic structures. For working with incommensurable magnetic structures, the program SIMREF  [102] , published in 1988, was developed later to include this option; however, its use has been only testimonial. The program FullProf was developed at ILL at the end of the 1980s by the first author of this paper. It was based on DBW  [103] , a popular application for working with X-ray powder diffraction distributed freely from 1981 onwards. The genesis of FullProf started in the middle of the 1980s with strong modifications of the DBW program and finally with the introduction of magnetic structures using the formalism of propagation vectors in 1990. The first published description of features available in FullProf for incommensurable magnetic structures was done in  [66].
In that article, there was also the first description of the program MagSan for determining commensurable magnetic structures using Simulated Annealing, later included in the FullProf Suite for general crystal and magnetic structures. By the middle-end of the 1990s, three programs  [104] [105] [106] , MODY, SARAh, and BasIreps (this last included within the FullProf Suite) were released for helping to determine magnetic structures. These programs use the representation analysis popularised by Bertaut  [40]  and were distributed and used by the community. The FullProf Suite is being developed permanently and it is currently the most used set of program for handling magnetic structures using powder or single crystal neutron diffraction data. A series of computing tools for crystallography, including magnetic structures, are available at the Bilbao Crystallographic Server (BCS)  [107].
This is a web application that is very intuitive, and the user can consult everything related with space crystal and magnetic groups. Programs for making transformation between different settings, determination of subgroups and coset decompositions are made available to non-specialists. They can output Crystallographic Information Files (CIF) that serve as exchange of crystallographic data and structures. The most important programs for magnetic structure determination and analysis are MAGMAGN and k-SUBGROUPS  [108] ; they are able to output magnetic CIF that can be read immediately by other programs like FullProf, JANA or GSAS-II. Moreover, they provide for the first time a database of determined magnetic structures  [109, 110]  that the user can inspect and download, which are accompanied by visualisation capabilities through the program JMol  [111].
For incommensurable crystal structures, two programs for single crystals were developed from the middle of the 1980s: REMOS  [112]  and JANA  [113]. JANA-2006 was the first program that incorporated the treatment of incommensurable magnetic structures using superspace groups. Whereas this formalism is not yet in the common culture of condensed matter physicists working on magnetism, a strong progress is being accomplished in the last years thanks to the contribution of web applications using data bases  [90, 114]. A huge amount of work on symmetry has been developed by H. Stokes, D. Hatch, and B. Campbell. Stokes developed the computing system ISOTROPY  [115] , in which many algorithms for working with space groups, representations and symmetry modes, are implemented. Together with the programs, they have made available databases to be used by external software.
In particular some applications of the BCS and the FullProf Suite use the databases of Shubnikov groups  [80]  and irreps  [116]. The program ISODISTORT is a web application interfacing ISOTROPY and works making an analysis of a paramagnetic group together with a series of propagation (modulation). The program calculates the irreps and isotropy subgroups, providing symmetry modes and the (super)space groups corresponding to different selections of the irreps and order parameter directions. For the case of commensurable structures, the program outputs directly a template file to be used by FullProf or by TOPAS. An account of the current state of methods and tools has been published recently by F. Damay of the LLB  [117]. A very recent program for analysing polarimetry and integrated intensity data on single crystals has been developed at ILL by N.
We develop a fast-deployed crop health monitoring system using state-of-the-art technologies to collect data from crop fields in order to take appropriate and timely actions. For the proposed resource optimized system, Saudi Arabian agriculture is taken as a case study. To achieve the desire goals, we harness IoT (Internet of Things) and drones in agriculture to establish rapid system deployment. This paper focuses on the data collection from crop field by organizing heterogeneous IoT devices in clusters and localise them for data harvesting. Clusters are formed by considering the path of UAVs, sensors heterogeneity, weather conditions, fluctuation of sensor nodes, and the communication cost of IoT devices. For localisation, carrying larger or heavier arrays of antennas and receivers with a small size UAV is also a major issue considered in this paper.
The selection of Cluster Head (CH) is an important task in our clustering scheme, which selects the node in good health, the best overall specification, and most suitable for UAV connectivity. 4. A localisation technique explores the ground sensing devices by an aerial UAV and connects them for collecting data. An important issue addressed in this research is data gathering from many sensors unable to establish an ad-hoc communication due to widespread deployment, geographical constraints, weather conditions or power constraints. An Affordable, broaD and Agile farMing (ADAM) system especially for rural and remote area is proposed in this research paper. This ADAM is composed of UAVs and field sensor nodes and is not limited to prerequisites, such as communication infrastructure, network updates to maintain routes and clusters, installation of special CH nodes for data gathering, and knowledge of UAV paths.
A speciallydesigned, energy efficient, lightweight, and small antenna is proposed to accomplish this task. 7. The data collection is performed in the final Step 7 during which the CH transmits the collected data to the UAV through a high-bandwidth dedicated link. This lifecycle continues until all data is harvested from the target area. The system spans in three distinct dimensions including dynamic runtime clustering, real-time CH selection, and localisation of ground sensors by the UAV. The remainder of the paper is organised as follows. The next section reviews the dynamic cluster formation and cluster head selection. In Section III, we elaborate on the architecture of the ADAM system in terms of layers and phases. Section IV discusses the localisation process for which we propose a lightweight virtual phase array antenna carried by the UAV to locate the position of the field sensors.
The assumptions for this system include the following: 1. All sensor nodes are indistinguishable with no predefined CH. The advantage of this assumption is that data will be collected only from active and available sensors. A node that is unavailable for any reason (e. g. , covered by sand, water, plant follicle or drained battery) does not affect the data gathering process. 2. No predefined data routes and clusters are considered so that periodic updates for route discovery and maintenance are not required, which saves energy of the sensor nodes. 3. The UAV and sensor nodes are equipped with dual frequency support, UHF (433 MHz) for localisation and Wi-Fi (4. 2 GHz) for data collection, to optimise the energy utilisation. 4. Independent UAV paths enable the UAV to move freely while scanning a target area and instruct the selected ground sensors to activate and aggregate required data at a suitable point.
D. PROPOSED SYSTEM ALGORITHM The functionality of the ADAM system is elaborated by a homological sequence of steps represented by the state diagram in Figure  6. Each vertical colour code represents a layer as green, yellow, and blue, which represent the bottom, middle, and top layers, respectively. UAV sends beacon message to the sensor nodes to start the process. Sensor nodes activate and calculate their probabilities and decide either they are candidate of cluster head or not and decide their role (CM or CCH) accordingly. CCHs starts negotiation with UAV with at a very energy economical frequency F 1. UAV decides the final CH and broadcasts a message to all to let them know. All the CCHs change their role again to CM accept one which has chosen as a CH. All the CMs send their data to CH, meanwhile UAV navigates to CH and adjusts its height and speed.