dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020). There, we proposed a specific criterion comparing different maps that provided incomparable costs. In this work, we generalize the criterion into the methodology to create different criteria from a chosen cost function that suits the studied attributes of maps. We created an extensive set of generalized approaches to represent the spatiotemporal phenomena covering a large part of the current methods. We applied them to the real-world dataset of 1 month of human detections and compared them using the two criteria following the proposed methodology. The straightforward interpretability of the results provides us with exceptional insight into the strengths and weaknesses of different approaches.
In the following work  (Pabón et al. , 2020) , kernel warping was applied to the homicide prediction in Bogota. The authors explain the inefficiency of the model in several ways: the relatively low amount of homicides, changes of positions of problematic groups across historical data, and the direct impact of patrolling strategy of police known as "runaway feedback loops. " They solve the known issues by enriching the data with street fights, which are very connected to the homicides, and including temporal decay components in the studied models. Temporal decay helped all the models, and the kernel warping model provided the best predictions. In the study by  (Zhou and Matteson, 2015) , the spatio-temporal ambulance demand is modeled in a way that the dataset is divided into spatio-temporal cells.
The robot is then supposed to plan its path through the environment for all these tasks minimizing the number of people it expects would cross its path. Path planning tests the method's ability to model the phenomena in the spatial domain. Another principal idea is introduced to test the temporal domain: the servicing ratio. The robot is asked to select a specific percentage of times out of all the times presented where it will execute the navigational task, again to minimize the expected number of encounters with people. All the selected plans are then executed and compared with the positions and movement of people in the testing dataset. The testing environment calculates the encounters between the 'blind' robot and 'blind' people-the system does not provide any artificial reaction of any element involved.
To formally define the original EE criterion, we first define the service disturbance function SD(r) with the parameter r ∈ [0, 1], which we interpret as the servicing ratio, that is, the ratio of navigational tasks the robot is required to perform. The robot is given a set of navigational tasks at times {t i } N i 1 , and for each one of them, it plans a path with cost c i defined by the spatio-temporal model of human presence to be evaluated. The testing dataset then, for a given trajectory, gives the number of weighted encounters e i (see Section 4. 2. 1) that would occur for the given planned trajectory. The service disturbance function is then given as follows: SD r ( ) rN k 1 e π k ( ) , (1) where π is a permutation which orders times t i so that ∀k: c π(k) ≤ c π(k+1) , where c i corresponds to t i , as defined above.
Robotic Platform As shown in Figure  1 , a Toyota HSR robot  (Yamamoto et al. , 2019)  was employed for experiments, which is equipped with an Xtion RGB-D camera (for obstacle avoidance only), a Hokuyo UST-20LX 2D LiDAR (for obstacle avoidance and global path planning), and other sensors that were not used in the experiment. The human-aware navigation system was deployed on a laptop with an Intel i7-7700HQ processor and 32GB RAM, wired to the robot, and carried on its back. All the software was implemented into the Robot Operating System (ROS)  (Quigley et al. , 2009)  with high modularity using C++ and Python mixed coding, running in real time on Linux Ubuntu 18. 04 LTS (64-bit) and ROS Melodic.
Dataset Collection The dataset collection was performed in a hall of approximately 500m 2 on the UTBM university campus. As shown in Figure  4 , a Velodyne HDL-32E 3D LiDAR was placed in the reception near the building door to ensure safe 24-h operation. The spatial placement of the LiDAR was carefully determined to ensure maximum field-of-view (i. e. , approximately 200m 2 ) of the hall beyond the glass windows. The raw data of the LiDAR was recorded to ROS rosbags 24 h a day. Our dataset consists of 1 month, March 2019, of continuous human detection used as the training set. The testing dataset consisted of 7 days from 1 week in December. More than 6 million human detections were generated from the LiDAR recordings. They were extracted using the FLOBOT human detection and tracking system  Yan et al.
(2017). The falsepositive detections were removed by searching for extraordinarily stable and immovable human detections. Later, we filtered out reflections of people by manual localization of places producing those reflections. Compared to the dataset used in our previous work  (Vintr et al. , 2020) , the training dataset was twice as long, and the testing dataset did not immediately follow the training period. We also utilized our experience in filtering out the false-positive detections, which led to more effective scripts. Due to the temporal and spatial continuity of data, the dataset reflects people's regular activities, like students entering the building lobby at the beginning of class, leaving after class, and eating and chatting in the lobby during lunch break.
Continuous Models in the Grid Cells Every aforementioned forecasting method can be employed in the spatial grid forming spatio-temporal models similar to the original idea of the 'map enhancement' in the study by  (Krajnik et al. , 2014b). We included FreMEnGrid in the comparison as a representative of continuous ones in the temporal domain and HistWeekGrid as a fully discrete spatiotemporal model. We omitted HyTGrid and WHyTeGrid as their computational demand in the stage of prediction was considerably higher than that of the previous two, and the straightforward optimization of code would violate the design of the testing environment. The third not fully continuous model included is time_window_GMM (Kubiš, 2020) which expands HistWeek with GMM connected to each cell. Its prediction then consists of GMM predictions in each cell weighted by HistWeek forecast.
Testing Environment Setup The experiments were conducted in a general testing framework with a unified interface available for individual methods. The framework consisted of four stages: training, prediction, pathfinder, and simulation. The predictors were trained over spatio-temporal and spatiotemporal-directional data during the training stage. There are approximately six million detections of people in the training data. We also included scenarios where the training dataset includes only one per mile randomly chosen detections to provide insight into how robust different methods are to data sparsity. During the prediction stage, the framework created a spatio-temporal grid over the testing week. The week was broken into 40-s windows. For each of these windows, the area of approximately 200 m 2 was broken into a grid of 0.
The right columns of graphs depict traveled distances, the quantile functions of lengths of paths. The labels in graphs consist of names of models and the expected values. First row: models trained over the sparse, spatio-temporal data. Second row: models trained over the spatio-temporal-directional data. Third row: models trained over the sparse, spatio-temporal-directional data. We choose the three best-performing methods for the comparison in the subsequent scenarios, HyT_X_GMM, HyT_X_MeanGrid, and HyTted_GMM, together with HistWeekGrid which provided the best ability to lead the robot by the shortest path while performing exceptionally in evading encounters. On the top row of Figure  7 , we provided a comparison of chosen methods in the spatio-temporal scenario when we randomly selected only one per mile of human detections from the training dataset to train the models.
Acknowledgements. 0 References. 0 Acknowledgements This article was written as the result of a roundtable meeting of the coauthors at the  University of Barcelona in February 2020 , organized by the  Esteve Foundation  and the  Institute of Neurosciences of the University of Barcelona. S. F. is supported with the intramural funds of the  National Institute on Drug Abuse. C. W. D. is supported by the  National Institute of General Medicine  ( GM60419 ). J. G. -M. is supported by the  National Institute of Mental Health  ( R01MH084894  and  R01MH111940 ). D. E. L. is supported by the  National Institute of Heart, Lung and Blood Institute  ( HL09549-24 ). T. E. H. holds the  Canadian Pacific Chair in Biotechnology. F. C. and L. P. are supported by  FEDER-EU/"Ministerio de Ciencia, Innovación y Universidades-Agencia Estatal de Investigación " ( PID2020-118511RB-I00 ).
NASA's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (InSight)    seismometer has been recording Martian seismicity since early 2019, and to date, over 1,300 marsquakes have been cataloged by the Marsquake Service (MQS). Due to typically low signal-to-noise ratios (SNR) of marsquakes, their detection and analysis remain challenging: while event amplitudes are relatively low, the background noise has large diurnal and seasonal variations and contains various signals originating from the interactions of the local atmosphere with the lander and seismometer system. Since noise can resemble marsquakes in a number of ways, the use of conventional detection methods for catalog curation is limited. Instead, MQS finds events through manual data inspection. Here, we present MarsQuakeNet (MQNet), a deep convolutional neural network for the detection of marsquakes and the removal of noise contamination.
Based on three-component seismic data, MQNet predicts segmentation masks that identify and separate event and noise energy in time-frequency domain. As the number of cataloged MQS events is small, we combine synthetic event waveforms with recorded noise to generate a training data set. We apply MQNet to the entire continuous 20 samples-per-second waveform data set available to date (>1,000 Martian days), for automatic event detection and for retrieving denoised amplitudes. The algorithm reproduces all high quality, as well as majority of low quality events in the manual, carefully curated MQS catalog. Furthermore, MQNet detects ∼60% additional events that were previously unknown with mostly low SNR, that are verified in manual review. Our analysis on the event rate confirms seasonal trends and shows a substantial increase in the second Martian year.
Introduction Seismology allows us to explore the interior of the Earth by studying the numerous earthquakes that occur each year and are recorded by a worldwide network of thousands of seismometers. In contrast, extraterrestrial seismology has been relying on the limited data sets collected during the Apollo  (Latham et al. , 1970) , Viking  (Anderson et al. , 1977)  or Venera  (Ksanfomaliti et al. , 1982)  missions to the Moon, Mars, and Venus. While numerous moonquakes were found in the Apollo data set, no quake was unambiguously identified on Mars or Venus due to the high seismic noise level or the short mission duration. On Mars, this has changed with the arrival of National Aeronautics and Space Administration's Interior Exploration using Seismic Investigations, Geodesy and Heat Transport (NASA's InSight) mission in 2018  (Banerdt et al.
, , 2020;; Ross et al. , 2018; Zhu & Beroza, 2019 )-we train MQNet on synthetic data. We generate event waveforms with characteristics following the different marsquake types and combine them with recorded noise, which allows the network to learn the idiosyncrasies of the InSight data. The efficacy of this approach is demonstrated on existing cataloged MQS events and then used for event detection on the complete 20 samples-per-second (sps) InSight data set, spanning 1,043 sols. We compare the performance to the MQS event catalog and review and report our new detections. The product of this study is the first automatically generated marsquake catalog, which extends the manually curated MQS catalog from 1,297 to 2,078 events in the investigated time period (+60%). Our extended catalog is consistently produced across the mission and allows us to study the temporal distribution of marsquakes.
Two longer gaps were caused by the loss of communication to the lander during the solar conjunction between Sol 267-288, and power constraints during Sol 1107-1129. Data could be stored on the lander during the solar conjunction in the second year, and were transmitted afterward. We focus on the continuous 20 sps data set from Sol 182 (June 2020) to 1224 (May 2022) (InSight Marsquake Service, 2022), after which recordings are only sporadically available due to increasingly severe power constraints. Next to the seismic data streams, atmospheric conditions are recorded by a pressure sensor, and two sets of temperature and wind sensors, all mounted on the lander deck. The atmospheric sensors had to be turned off during large portions of the later mission (starting from around Sol 700 for wind, and Sol 850 for pressure) due to power restrictions.
Seismic Data Throughout the manuscript, we show the VBB seismic data in velocity, following the instrument response deconvolution (and a frequency taper) and the rotation to vertical (Z), north (N), and east (E) components using the ObsPy package (See Data Availability Statement). The seismic data contain various noise signals that might resemble seismic events in duration, frequency content and amplitude and are potentially challenging to differentiate from seismic events with automated detection algorithms. We explain them by taking the example of Sol 923, shown in Figure  1. The VBB instrument records extremely small ground motions  (Lognonné et al. , 2020)  and is consequently also sensitive to the local environment. The main source of background noise in the bandwidths of interest for seismic events (0-10 Hz) is the wind that predominantly couples to the ground through lander induced vibrations  (Murdoch et al.
, 2016). In addition, activations impart non-linear expression functionality to the model, and down-sampling operations gradually reduce the dimensions of the feature maps and allow the model to find meaningful features across different scales. The UNet  (Ronneberger et al. , 2015)  builds on the fully convolutional architecture, but is extended by an expansive path that leads to high-resolution localization of features. The network typically consumes 2D data in form of images or time-frequency representations of time series. The output is not a single label but a segmentation mask that pixel-wise classifies 2D data into regions with common class labels. In this study, we use the UNet architecture to detect marsquakes by casting the problem as a segmentation task. When working with the InSight seismic data, we are primarily dealing with low SNR events that are hidden in highly fluctuating noise conditions.
Training Data Set Deep learning models usually perform best when trained on large data sets. For example,  Zhu et al. (2019)  built a data set by combining more than 56k earthquake and 179k noise waveforms. When labeled training samples are sparse, data sets are commonly augmented by generating copies with slightly altered properties (e. g. ,  Ronneberger et al. , 2015). Similarly, the MQS catalog currently contains only about 1,300 events that are very unevenly distributed among the different event types, and most are highly noise-contaminated. In order to train a robust model, we build a data set based on synthetic event waveforms replicating features of real marsquake waveforms that are combined with noise recorded by the VBB sensor. We create synthetic event waveforms using a modified version of the stochastic waveform simulation presented by  Boore (2003).
2. LF event family: we compute smoothed envelopes from Instaseis (See Data Availability Statement) by generating events with random source parameters at different distances and back azimuths. The event spectra are approximated by narrow to wide peaks at low frequencies, and partly include a Lorentz peak at 2. 4 Hz to represent BB events. The three component amplitudes are controlled by the relative amplitude of the Instaseis envelopes. We build a synthetic event waveform catalog consisting of 40% LF/BB (MQS catalog: 7%), 30% VF (5%), and 30% HF/2. 4 Hz events (88%). The different distribution compared to the MQS catalog, accounts for the much higher variability of LF, BB, and VF events in terms of frequency content, duration, and S-P time. Since InSight's background noise is complex and highly fluctuating, we use real VBB noise to capture all types of environmental and instrumental signals in the training data set.
Event Detection on Continuous Data We now apply MQNet to the full 20 sps waveform data set, spanning 1,043 sols. For each sol, we split the data into 110 time windows of 27 min length each, with 50% overlap. The event masks are then predicted for each of these time windows. Figure  7  shows the procedure for Sol 923 (also shown in Figure  1 ). The MQS catalog includes six events on this sol (S0923a, QC, BB; S0923b, QD, 2. 4; S0923c, QB, HF; S0923d, QC, VF; S0923e, QD, 2. 4; and S0923f, QC, VF), which are all clearly visible in the predicted event masks. The high-amplitude HF and the two VF events are detected in both overlapping rows, whereas the weaker events are not (fully) found when they are close to the window edge. The start and end of the event masks approximately match the MQS start and end times, which are defined as the earliest and latest possible event energy  (Clinton et al.
Detection Statistics On the 1,002 sols with data between Sol 182-1224 we collect 2,187 detections with D scores above the selected thresholds. Of these, 2,075 belong to the HF family and 112 to the LF family. First, we compare the detections to the MQS catalog (InSight Marsquake Service, 2022) in Figure  9a , while distinguishing event types and qualities. Across all quality classes, the recall is ∼88% for HF family events, and ∼92% for LF family events. We detect all high quality (QA&B) and the majority of low quality events (QC&D). Further, we find all but five BB (1), HF (1) and VF events (3, where S1143a, QC, VF was originally detected but removed as it occurred during lander activity, Figure  S25  in Supporting Information S1). The recall of MQS events is lower for LF (two QC, four QD undetected) and 2.
Acknowledgments The authors thank  Editor-in-Chief Laurent Montesi ,  Sheng Wang , and one anonymous reviewer for their careful and critical review of the manuscript. We acknowledge  NASA ,  CNES , their partner agencies and institutions (UKSA,  SSO ,  DLR ,  JPL ,  IPGP-CNRS, ETHZ, IC , and  MPS-MPG)  and the flight operations team at  JPL , SISMOC, MSDS, IRIS-DMC, and  PDS  for providing SEED SEIS data. We acknowledge funding from (a) Swiss  State Secretariat for Education, Research and Innovation  ( SEFRI  project " MarsQuake Service-Preparatory Phase "), (b)  ETH Research Grant   ETH-06 17-02 , and (c) ETH+02 19-1: Planet MARS. The Swiss contribution in implementation of the SEIS electronics was made possible through funding from the  federal Swiss Space Office (SSO) , the contractual and technical support of the  ESA-PRODEX  office, (d) A.
Data Availability Statement The InSight seismic event catalog version 12 (InSight Marsquake Service, 2022), waveform and station metadata data are available from IRIS-DMC and IPGP Datacenter (InSight Mars SEIS Data Service, 2019), as are previous catalog versions. The Supporting Information S1 contains additional material on the MQS catalog, details on the network selection and optimization, examples from the review process of newly identified events, and temporal distribution of MQNet detections and LF family events across the mission. Our extended event catalog, sol-wise solution for the complete processed data set, as well as a snapshot of the MQNet implementation are available from https://doi. org/10. 5281/zenodo. 7157843 (Dahmen, 2022). The UNet architecture was implemented in TensorFlow  (Abadi et al.
, 2015)  and the network figure (Figure  3 ) was created with  (Iqbal, 2018). Synthetic envelopes were computed from the Instaseis  (van Driel et al. , 2015)  based on Mars model InSight_KKS21GP  (Stähler et al. , 2021) , accessible at http://instaseis. ethz. ch/marssynthetics/InSight_KKS21_GP/ (last accessed June 2022). The data were processed with ObsPy  (Beyreuther et al. , 2010 ), NumPy (Harris et al. , 2020)  and Scipy  (Jones et al. , 2001) , and visualizations were created with Matplotlib  (Hunter, 2007).
These include an expert's interface that describes a knowledge class diagram; a practitioner's interface that instantiates domain objects and their attribute constraints; and an end-user interface to input attribute values of the real cases stored in a domain case database. This paper illustrates resultant knowledge bases in different domains, with examples of pulmonary embolism diagnosis in medicine and decision making in French municipal territorial recomposition. The paper concludes with the current limitations of the proposed model, its future perspectives and possible platform enhancements.
Design and implementation of a platform The novelty of the tool is a platform that supports the method and provides three interfaces: an expert interface that allows the expert to describe the class diagram of the domain knowledge, and an attribute language that is used to define the attribute descriptors that are made available in a repository to the practitioners. A practitioners' interface allows stakeholders in the field to choose object types and to instantiate the necessary knowledge objects and their attribute descriptors that define and control the characteristics and range of attribute values of knowledge objects. The third interface is for end users, who simply enter the attribute values during their professional practice as they are solving real cases, where they are then stored in an experience case database.
The Case Based Reasoning Cycle Experience allows the advancement and enhancement of knowledge in a specific area of science. Case-based reasoning (CBR) is a model of experience that is particularly used in medicine to answer questions such as the following: What diagnosis methods were used? What treatments were chosen? What results were observed? Was the patient satisfied? All of these make a case that can be indexed and stored in an object-oriented database. These cases can then be reused in later consultations, where the additional knowledge gained from a past case can be used to provide additional benefit to enhance the relevance and the quality of care. The CBR approach uses a distance measure of the present case to past, similar cases, to retrieve cases from the database. It applies the most similar past cases to the new problem case in order to apply the most appropriate methods to solve the new problem  [31] [32] [33].
Several machine learning models are used to apply the CBR approach in different knowledge domains: -Object-oriented approaches were used to implement the CBR approach  [34] [35] [36]. - The fuzzy-object relational system aims to model imprecise requirements with fuzzy objects implemented in relational databases  [37]. The literature has described the comparison problem and the recursivity of assessing the relevant attributes in the objects to be compared with a distance  [38]. -In health care, CBR was used to implement clinical reasoning and case modeling  [39] [40] [41] [42]. -Fuzzy logic, or stochastic approaches, were proposed to account for a set of attributes of various types from many objects, regardless of their structure. The problem becomes complex when it comes to dealing with a large number of objects  [43, 44].