dataesr/scientific-paragraphs-categorization huggingface dataset:
Then we tested the statistical procedure on an extensive RFID dataset covering the life-long monitoring of 2,100 individual honey bees. We further compared our AOF outputs with the more conventional approaches based on arbitrary thresholds, evaluated the robustness of the procedure with different types of datasets, and compared the foraging timeactivity budget allocations between the predicted foragers and observed foragers. We also explored the relationship between AOF ontogenetic plasticity and foraging performance in honey bees, and we tracked the potential risk of early ontogenetic transition over time. Finally, we provide guidelines (the aof R-package) on how using the procedure to detect life-history transitions of free-ranging insects from time series data, as a generic approach towards measuring ontogenetic shifts in central-place foraging insects.
The inner reader row (I) and outer row (O) are identified along with each hit, making it possible to distinguish between incoming and outgoing flights (O-I vs. I-O hit successions, respectively, Figure  1c ). The sequences (seq) of incoming and outgoing flights delineate the time periods spent in-nest or outside of the hive, providing the opportunity to investigate time-activity budgets. We relied on the "Track a forager" method (Van Geystelen, Benaets, de Graaf, Larmuseau, & Wenseleers, 2016) to infer time-activity budgets from the raw RFID dataset. We modified the analytical process (Section S1) to adapt the method to the common 10-frame Dadant hive by establishing two rows of five RFID readers over the entire width of the hive entrance, and free-walking room between the two rows of readers.
We tested the parsimony of the breakpoint prediction using the 'GetModels' function. Following  Gurarie (2013) , we considered the predicted breakpoint as parsimonious whenever ∆ Bayesian information criterion (BIC) > 2 between the simple model (without breakpoint) and the complete model (Figure  2a ). Successful detections were pooled in the "detected" group, and unsuccessful ones in the "undetected" group. Individuals with no trip recorded (i. e. only one hit) were discarded from the analysis as well as the individuals with less than five days with recorded trips because breakpoints could not be established; both were pooled in the "uncomputed" group. All statistical analyses were performed using the R Project for Statistical Computing version 3. 6. 2 (R Development Core Team, 2020). To facilitate the use of this procedure by other researchers, we provide an aof R-package with examples using both simulated and empirical dataset.
Documentation and source code are freely available on CRAN (https://cran. r-project. org/package=aof) and GitHub (https://github. com/frareb/aof). We then averaged the values of breakpoint age obtained from two candidate time budget variables, Trip duration and Trip time, to provide the AOF estimates. Trip number was discarded because of the non-parsimonious estimation of its AOF value (Trip time was considered due to marginally significant effect, see results). We a posteriori assessed the overall difference in time budget allocations between the two life stages, learning and foraging, using Generalized Linear Mixed Models (GLMMs) with the lme function in the nlme R-package. We specified a Gaussian error structure and the origin of each honey bee (crossed cohort × site grouping) as a context random effect.
ONTOGENETIC SHIFTS IN HONEY BEES From the 2,100 tagged honey bees, 11. 1 % (233 bees, on average 15. 3 ± 8. 1 per cohort) did not provide any data and were discarded from the analysis. For the rest, we collected a total of 402,471 hits, yielding 256,462 seq and 21,104 trips from 1,867 bees, over a non-stop monitoring record of 193 days. Time-activity budgets were used to estimate the age at onset of foraging, AOF. Excluding 31. 1 % of bees which provided only one hit, and 6. 4 % providing less than 5 trips, which were both pooled in the "uncomputed" group, a breakpoint could be identified in 42. 2 to 88. 5 % of the 1,167 remaining bees (depending of the time budget variable considered, Supporting Information Section S3), either from daily trip numbers, trip durations or average time of trips in the day (Figure  2a ).
Discussion Changes in the temporal pattern of time-activity budgets are indicative of a transition stage in ontogenetic development  (Lambin & Yoccoz, 2001; Gopukumar, 2003; Benton et al. , 2008; Fay et al. , 2016). Life-long individual monitoring that measures time-activity budgets provides a way to quantify a shift in the temporal pattern in timeactivity budgets of insects. In this study, we develop a simple statistical procedure, derived from a breakpoint-based method, to detect such shifts in central-place foraging insects when using individual life-long monitoring (Figure  5a ). The automated method is freely available through the aof R-package that includes documentation, source code and examples using both simulated and empirical dataset (see Section 2). The AOF ontogenetic shift developed here is consistent with previous published estimates or observations in honey bees, e.
While developed for the study on honey bees, the present method could propose new ecological applications of individual life-long monitoring devices to detect ontogenetic shifts in the behaviour of other central-place foraging insects, such as bumble bees  (Gill & Raine, 2014; Crall et al. , 2018; Minahan & Brunet, 2018) , wasps, hornets  (Poidatz, Monceau, Bonnard, & Thiéry, 2018)  and ants  (Pinter-Wollman et al. , 2012). The sensitivity analysis showed a higher performance of the aof method to detect behavioural changes when the number of data points in time series increased, and an additional performance gain whenever the data variance decreased. These GitHub (https://github. com/frareb/aof). The aof R-package can be installed in R with the following command: install. packages("aof"). Moreover, data on life-history traits of honey bees are publically available through the figshare repository (doi:.
Acknowledgments This work was supported by grants from the  French Ministry of Agriculture  ( CASDAR ,  POLINOV  project  n°9535 ), the  Poitou-Charentes Region  and the  European Community program  ( 797/2004 ) for French beekeeping coordinated by the  French Ministry of Agriculture  (TECHBEE and RISQAPI projects). Special thanks to  Jean-François Odoux  and  Clovis Toullet  for logistical help in fieldwork. We are grateful to  Cynthia McDonnell  for critical reading of English used in the text, as well as the associate editor and three anonymous reviewers for constructive comments on the manuscript.
These requirements can be organized according to following four perspectives: (i) environment per- Fig. 1  The key ingredients characterizing a cognitive architecture for assistive robots spective; (ii) autonomy perspective; (iii) interaction perspective; (iv) adaptation perspective. -Environment perspective. Pursuing the idea of GiraffPlus different IoT devices are used to gather information about the environment and the health status of an assisted person. Broadly speaking, there are two categories of sensing devices that are relevant in domestic assistance scenarios: (i) environmental sensors; (ii) physiological sensors. Environmental sensors (or ambient sensors) produce data about the state of a particular area of the house like e. g. , the kitchen, the living-room and so on. Physiological sensors produce data about physiological parameters of a person like e.
A tight integration between these two technologies and related capabilities is crucial to achieve a continuous and personalized interaction between a user and an intelligent system (either a robot or an intelligent software). Relevant examples are the works  (Bacon et al. 2013; Cesta et al. 2014; Cortellessa et al. 2013 ) that integrate knowledge reasoning and planning for realizing personalized crisis management training. Specifically, KOaLa relies on the tight integration of an ontology-based knowledge processing module and a timeline-based planning and execution module within a sense-reason-act cycle. These modules are called respectively the KOaLa Semantic Module and the KOaLa Acting Module. Figure  3  describes the architecture of the prototype highlighting the phases that compose the control flow and the relationships among components.
The Semantic Module The KOaLa Semantic Module is responsible for the interpretation of sensor data and the management of the resulting knowledge of the robot. This module relies on the KOaLa Ontology to provide gathered data with semantics and incrementally build an abstract representation of the application context i. e. , the Knowledge Base (KB). A data processing mechanism uses standard semantic technologies based on the Web Ontology Language (OWL), defined by  (Bechhofer 2009) , to build and continuously refine the KB. Then, a goal recognition process analyzes the KB in order to identify specific situations that require a proactive "intervention" of the robot and dynamically generates related goals for the acting module. The KOaLa ontology has been defined by leveraging SSN, defined by  (Compton et al.
The observation context characterizes the features that can actually produce information in a give configuration as well as the events and the activities that can be observed through them. This context identifies the observable features of a domestic environment as the physical elements that are actually capable of producing information through the deployed sensors. Similarly, it identifies the observable properties as the properties of the observable features that can be actually observed through the deployed sensors. In this way, the KB is capable of representing observations and processing/interpreting received data by taking into account the associated environmental information like e. g. , the are of the house data comes from or the type of object data refers to. A knowledge processing mechanism elaborates sensor data following the semantics defined by the KOaLa ontology.
The Acting Module The KOaLa Acting Module is responsible for planning and executing operations according to the events or activities inferred by the semantic module. These events are inferred by the Goal Recognition module (GR) of the knowledge processing pipeline show in Fig. 5. GR is a key enabling feature of KOaLa for dynamically linking the semantic and the acting modules as shown in  (Umbrico et al. 2018). It can be seen as a background process that monitors the updated KB and generates assistive task signals every time particular situations are recognized. GR is a key enabling feature for proactivity. Assistive task signals are modeled as planning goals the problem formulation process encodes into a planning problem specification. The resulting problem specification is then given to a planner which synthesizes a temporal plan describing the sequences of operations needed to support the user.
Planning and execution capabilities of KOaLa rely on a timeline-based framework called PLATINUm, which is integrated into the cognitive architecture. The set of goals the Acting Module deals with depends on the particular configuration of the environment and the available capabilities of the deployed sensors. A planning model encapsulates the knowledge about the capabilities of the controllable elements that compose the environment and how such capabilities can be coordinated to realize the desired assistive functionalities. A planning model must describe the primitive capabilities of an assistive robot like e. g. , make a call, send a message or move to a particular location, as well as the primitive capabilities of the available sensors like e. g. , turn on, turn off a sensor or set a particular configuration on a sensor.
A sensor close to the door of the kitchen is activated by John's motions and Giraff receives this signal. The semantic module elaborates this data by applying the inference rules of the data processing mechanism. KOaLa knows that the received data must be interpreted as "something or someone is moving here". Also, KOaLa knows the configuration of the home environment and therefore it also knows that this sensor is installed into the kitchen of the apartment. Then, it can further elaborate the data and conclude that "someone is moving inside the kitchen". We are assuming here that only one person is being monitored/assisted. Therefore, it is possible to infer that "John is moving inside the kitchen" and add this fact/belief to the KB. Similar processing mechanisms elaborate data about the luminosity of the kitchen.
The goal reasoner detects this activity and triggers a high-level planning goal which is internally sent/dispatched to the acting module. KOaLa dynamically formulates a timeline-based planning problem by taking into account the known (physical) capabilities of the robot, the known state of the environment, the known state of John and his expected behavior within the rest of the day as well. The acting module synthesizes a timeline-based plan which integrates the sequence of operations needed to carry out the assistive task (support meal preparation -i. e. , the goal triggered by the goal reasoner) with the (previously generated) daily plan. Figure  8  above shows the synthesized timeline-based plan and the outcome of its execution. Following the plan, the assistive robot starts moving from the  bedroom towards John who is cooking.
Conclusions and Future Works This paper presented an AI-based cognitive architecture called KOaLa which integrates sensing, knowledge representation and automated planning techniques to constitute a high-level control loop to enhance proactivity features of an assistive robot designed to support an older persons living at home in her daily routine. A semantic module leverages a dedicated ontology to build a KB by properly processing data collected by means of a sensor network installed in the environment. An acting module takes advantage of the timeline-based planning approach to control robot behaviors. A goal triggering process acts as a bridge between the two modules and provides the key enabling feature to endow the robot with suitable proactivity levels. KOaLa and the related long-term research objectives have been placed into a conceptual cognitive architecture called AI 3 , designed according to general cognitive capabilities  Langley et al.
Acknowledgements This research is partially supported by  Italian "Ministero dell'Istruzione, dell'Università e della Ricerca"  under project " SI-ROBOTICS: SocIal ROBOTICS for active and healthy ageing " ( PON 676 -Ricerca e Innovazione 2014-2020 -G. A. ARS01 01120 ) and by  European Commission  under project  ShareWork  ( H2020 -Factories  of the Future -G. A. # 820807 ).
The results obtained can corroborate the displacement trends due to the characteristics of the soil and the geological environments. With Sentinel-1 data, we were able to obtain the general deformation overview of the overall highways network, followed by a selection and classification of the PSI content for each section. With Cosmo-SkyMed data, we were able to increase the precision in the analysis for one sample infrastructure for which high-resolution data from CSK were available. Both datasets were demonstrated to be valuable for collecting data useful to understand the safety condition of the infrastructure and to support the maintenance actions.
Although many studies have been carried out using DInSAR in the field of monitoring individual infrastructures, such as bridges  [18] [19] [20] [21] , railways  [22, 23] , and subways  [24] [25] [26] , few documents use DInSAR for infrastructure network monitoring and maintaining  [27, 28]. In this study, the movements of line-of-sight (LOS) of the urban highways in Rome (namely A24, A12, A90, A91, and the A1 with its branches D18-D19) were analyzed. The sections of the highways with the highest subsidence rates were selected and classified on the basis of PSI results. Each section was divided in samples 100 m long, and for each sample, the average velocity (mm/year) with the internal PSI was calculated. In addition, by means of the hydrogeological map of Rome (La  Vigna et al. , 2015) , it was possible to relate the subsidence to the soil type and use some rainfall data in certain areas.
The intention of applying Sentinel-1 data was to obtain an overview of the displacements observed along the infrastructure network and to analyze the performance of Sentinel-1 data in three areas showing major subsidence, one of which was also investigated by using Cosmo-SkyMed to deepen the analysis. Two processing techniques were used: StaMPS integrated with SNAP  [29]  and the SBAS technique  [30]. Two types of SAR images were also used, the first obtained from the Sentinel-1 sensor of ESA (European Space Agency) and the second images obtained from Cosmo-SkyMed of ASI (Agency Italian Space).
Dataset The Copernicus Sentinel-1 SAR data used covered the period 30 March 2015-13 April 2018 using descending geometries. We limited our analysis to Sentinel-1A data only (12-day repeat cycle) (Table  1 ), which is considered sufficient given the expected magnitude of ground displacements and the availability of a large archive over the area of interest. It should be noted that since Sentinel-1 products are not spatially synchronized, meaning that their start and end times can vary within each orbit, more than one scene is often required to fully cover our area of interest  [43]. The advantages of Sentinel-1 come from its wide range coverage (250-km swath in the interferometric wide mode) and sufficient spatial resolution (5 m × 20 m in range vs. azimuth). In addition, the TOPS (Terrain Observation with Progressive Scans) imaging technique guarantees homogeneous image quality throughout the range  [44].
The high-resolution SAR data used are from the Cosmo-SkyMed mission, and covered the period 29 July 2011-7 March 2017. The Constellation of the Italian Space Agency (ASI) allows a 1-6-11 day repeat cycles for the same area of Earth with the same acquisition mode with each satellite. The products used were Stripmap-HIMAGE (3 m × 3 m in range vs. azimuth). One of the advantages of the X-band is the high spatial resolution, and also the nominal (full size) constellation orbit configuration was designed to achieve the best balance between cost and performance, providing global access to Earth at a constellation level of a few hours, with at least two opportunities in one day to access the same target site on Earth under different observation conditions  [45]. The area of interest (AOI) and the extent of the ascending and descending Sentinel-1 orbits and the descending Cosmo-SkyMed orbit are illustrated in Figure  2  and Table  1  indicates the observation periods for each sensor.
Interferometric Processing In this work, two interferometry techniques were used, the first based on a completely open source SNAP-StaMPS workflow  [46] , with open SAR data on the Copernicus Open Access Hub of the European Space Agency (ESA)  [47]. The second was based on a Small Baseline Subset (SBAS) technique  [8, 30]  using a Cosmo-SkyMed dataset obtained by a License Agreement with the Italian Space Agency (ASI).
SNAP-StaMPS PSI The Sentinel-1 PSI dataset was processed in  [43] , and it is openly available  [48]. Interferometric processing was carried out using the Sentinel Application Platform (SNAP)  [49] , developed by ESA under a GPL license v3. We employed the snap2stamps package, which  [46]  provides a series of scripts to automate the processing of entire stacks of SLC Sentinel-1 images in batch mode. The Stanford Method for PS (StaMPS), is a MATLAB-based software developed at Stanford University and subsequently updated at the University of Iceland, Delft University of Technology, and the University of Leeds. The package is equipped with persistent scatterer interferometry and small baseline methods and with an option to combine them. It is developed to make PSI processing work well on arid terrain in non-urban areas that undergo non-linear deformation without artificial structures.
It uses amplitude and phase information from individual pixels to determine the probability of being a persistent scatterer (PS). To automate the interferogram formation process, the "snap2stamps" package was used, which, depending on the configuration of some parameters (Sentinel-1 images sub-figure to be processed, bounding box of the area of interest, path to the data folder, the path and name of the master image, the parameters related to the computational resources to be used, etc. ), the interferograms are calculated automatically. These scripts are Python wrappers that use SNAP as an InSAR processor and provide StaMPS compliant results for PSI analysis. Those sets of scripts are available as open source on GitHub  [46]. Multi-temporal PSI processing was based on the StaMPS interferometric processing scheme  [50].
Furthermore, distribute scatters (DSs) are created, thus increasing the coherence and simplifying the investigation of extended areas. It is worth noting that the multitemporal SBAS technique has been extensively evaluated in various deformation contexts, recovering an accuracy of approximately 5 mm for single deformation measurements and 1 mm/year for information on mean deformation velocity  [7, 53, 54]. Such an approach was applied to process the CSK dataset collected from 2011 to 2017 over the A91 highway, providing the results described in Section 4.
Area (a): D18-GRA Highways A deformation pattern was identified from the data obtained from Sentinel-1 and an area of intersection of two highways GRA and D18 was analyzed. The subsidence zones for this case are indicated in boxes 1 and 2 of Figure  4a. We analyzed bridges and embankments located along the highways, superimposing the routes on the hydrogeological map of Rome  [39] , and it was found that the greatest displacement occurs in alluvial deposits and mainly in embankments near water courses (shown in Figure  4b ). For the section of the D18 highway (Box 1, Figure  4a ), ∼180 PSI were obtained with a standard deviation of 1. 22, and PSI reaching -7. 00 mm/year of displacement, and we proceeded to section every 100 mt and classify the PSI contained in each section of the road (see Figure  5a ), obtaining the velocity mean of each section; in this case, we obtained a maximum velocity of -6.
Conclusions The DInSAR satellite interferometry techniques are useful to manage road operability on a large scale. Two interferometry techniques were used: the first based on a completely open-source SNAP-StaMPS workflow, with open SAR data on the Copernicus Open Access Hub the European Space Agency (ESA), and the second based on a Small Baseline Subset (SBAS) technique using a Cosmo-SkyMed dataset obtained by a License Agreement with the Italian Space Agency (ASI). The amount of data available from Sentinel-1 allowed investigation of the overall structural behavior of a transport network, in this case the highways network of Metropolitan Rome. With Cosmo-SkyMed, we were able to confirm the displacement trends along the alluvial deposits of the Tiber Valley, as identified by the Sentinel-1 dataset, providing at the same time more spatial details.
Acknowledgments: We acknowledge  Maria Rosaria Manzo e Manuela Bonano  of  IREA-CNR  for CSK data SBAS processing performed thanks to the I. MODI  Project funded by  Horizon   2020-SME INSTRUMENT PHASE 2 , Grant agreement No  720121. For Cosmo-SkyMed data: "Project carried out using  COSMO-SkyMed ® Product © ASI (Italian Space Agency)  provided under license of ASI.