dataesr/scientific-paragraphs-categorization huggingface dataset:
These data were analyzed to identify the areas that could not be used for the competition: without Wi-Fi or BLE coverage or white coverage areas. The analysis showed that all regions were sufficiently covered, except for some parts of the car parking areas where the Wi-Fi signal was particularly weak. Data provided to the competitors for this challenge were registered with the same ''GetSensorData'' Android application in three phases: training, validation and evaluation. For the training and validation phases, some short trajectories were defined in the evaluation area to record the required data. Training data is usually used for calibrating the IPS, whereas validation data can be used by the teams to estimate the accuracy of the developed solution. However, competitors can use both datasets at their own discretion.
2) TRACK 4: FOOT-MOUNTED IMU AND GNSS BASED CHALLENGE The second off-site track was dedicated to foot-mounted inertial and GNSS navigation. The data were collected with the unit PERSY (Personal Reference SYstem, Fig. 2  right part) developed by GEOLOC at IFSTTAR. It comprises a 9 degrees of freedom inertial unit and a GNSS receiver and antenna  [16] ,  [17]. Two datasets were prepared. The first one was used for sensor calibration and the second one for the calculation of the coordinates of the waypoints. The usage of maps was completely forbidden in this track. First, the magnetometer calibration dataset was recorded in an undisturbed geomagnetic environment by performing rotations around the different sensor axes. It is a standard procedure for ellipsoid fitting based magnetometer calibration algorithms  [18].
It was complemented with data for conducting an Allan variance analysis  [19] ,  [20]  of signals recorded during several hours in a static location to determine  VOLUME 7, 2019  noise and measurement bias of inertial sensors. These data were provided to the participants to enable them to calibrate the device with their algorithms. Finally, a dataset was recorded over 1 km of walking distance on three floors and provided to the competitors for the computation of the coordinates of the waypoint. The purpose of this challenge was to compare pedestrian navigation algorithms based on inertial, GNSS and magnetic measurements on similar data. The dataset provided to the competitors for Track 4  [21]  (raw signals and reference points coordinates) can be downloaded for further bench-marking.
50 cm up to 1 m deviations were observed in the dataset. Known coordinates on the corners of walls at the ends of the gallery were needed for the global point clouds adjustment in a post-processing mode with PIMMS software  [25]. They were surveyed with a theodolite and used to georeference the SLAM acquisition. The backpack survey started on a known reference point allowing connecting with iMS3D point cloud. A 5 per 1000 deviation was observed on the global adjustment due to dynamic obstacles (movements of persons) and due to the reflections on the mirrors and the windows of shops through the gallery. The start and end positions of the acquisition were identical to close the loop and bond the drifting errors. Finally, the point clouds were colorized using the photos to produce more realistic restitution of the scenes.
c: VISUAL LANDMARKS MAPPING The process of visual landmark mapping follows the following steps: 1) preprocess the visual measured data and extract the visual landmark vectors v w that correspond to each landmark (Section IV-A. 1. a); 2) the visual landmarks are then registered (mapped) in real-world coordinates relative to the environmental map (note that the task of mapping a complex building such as a shopping mall that may have hundreds of light sources might be challenging and timeconsuming); and 3), a light source is spotted and tracked along a path, the position of the light source is then estimated as the weighted average of all the intersections of the 3D vectors to that source.
The main implementation was not completed in time for the IPIN2018 competition and was ended up using the PoseNet based relocalization with a filter over the estimated positions. The implementation ran on a notebook computer receiving images from a smartphone at about 5 frames per second. On the day before the competition, images were collected from reference points (spaced about three meters apart) along a set of likely trajectories in the mall. The dataset preparation and model training were performed using cloud servers. Looking back at the results, the relocalization approach performance was affected by the sparseness of the collected dataset and the limited time for model training/tuning. For example, several sections of the testing trajectory were outside of the areas covered by the dataset.
a: STEP ESTIMATION To separate the sequential data into the steps,an approach based on filtering and thresholding for the norm of the acceleration was ''used. This process is the same as that in other PDR systems. The norm of the 3D acceleration in the sequence is first computed, and a low pass filter is applied to it. Finally, the peeks are extracted because they correspond to the moments when the foot hits the ground. In other words, one step is defined as the interval from a peek to the next one. The next process is to compute the step length and direction. Fig. 11  illustrates the conceptual figure on the 1 http://x-io. co. uk/ngimu/ 2 https://github. com/rairyuu/PDR-with-Map-Matching velocity changes of foot and chest while walking. For the foot-mounted IMU, zero velocity update is useful because the foot can be fixed while it is attached on the ground.
Positive or negative sign to the values is assigned to indicate whether there is a right or left turn respectively. Step Detection. In the step detection module a peak detection module is applied, which detects peaks caused by the action when the heel strikes the ground. Visualize Location. After calculating the new positions of the particles using the dead reckoning algorithm, weighted through a fusion with the map and the user context, the weighted average of the particles position is displayed as the final position to the user. Context Extraction. This module is responsible for collecting data, filtering noise, segmenting sensor values, extracting features and for the classification of these values in a series of activities.
After computing kRMSE of each candidate, the average location of the top ordered N RPs relative to the minimum kRMSE value is selected as centroid point. In the proposed algorithm, the position of the centroid is calculated by taking an average of the coordinate values (X , Y ) of each selected candidate position. The Euclidean distance of a candidate RP from the calculated centroid is called the centroid distance. This distance from the centroid is used to define the RP priorities that are closer to the centroid point. • Next, the entropy error is another important measure after kRMSE to estimate the similarities of each candidate location RSSI list in the database against the RSSI list received online. The proposed algorithm calculates Vji for each corresponding AP in both RSSI lists, first by sorting the two RSSI lists relative to the RSSI level, then measuring the difference of AP index in both lists (Eq.
Then the FACT is calculated as a product of kRMSE with the sum of Entropy and D cntr , as detailed in Eq. 14. The candidate RP with the least value of FACT is selected as the final position estimated by the proposed algorithm. FACT = kRMSE • ( Entropy + D cntr ) (14) 3) EGEC TEAM The algorithm used by EGEC Team is based on PDR and map matching. WiFi RSSI information and magnetic field strength information are also used to build the fingerprint databases, to provide the initial position and improve the algorithm's precision and stability. The sensor information used in this algorithm includes the inertial measurements (accelerometer and gyroscope), WiFi RSSI, magnetic field strength and air pressure. In particular, the map information provided by the competition organizers is fully utilized in the map matching algorithm to correct the pedestrian's position and direction.
4) HFTS TEAM The HFTS algorithm used in the 2018 competition is based on WiFi and pedestrian dead reckoning (PDR), and a particle filter (PF) (Fig. 17 ). It is an evolution of the algorithm deployed at the 2016 competition  [48]  and the 2017 Competition  [49]. For the dead reckoning, heading estimation is performed using compass and gyroscope data. The gyroscope is able to detect heading changes quite accurately on a short timescale, but will drift in the long-term. In contrast, compass heading is subject to strong local magnetic perturbations but shows no drift on a long-term scale. Therefore, gyroscope data is used to detect immediate heading changes. The heading relaxes to the compass direction with a certain time constant of some seconds. Step detection is performed by peak detection of the accelerometer data.
WiFi positioning is performed using the scalar product correlation fingerprinting algorithm  [50]  which is based on knn and the cosine similarity. Prior to the estimation of the unknown competition track, a radio map is obtained by evaluating the reference data provided by the competition organizers. These data are enriched with true ground positions and RSS readings. Therefore obtained RSS vectors can be related to a position. As the true ground readings occur only rarely, the actual reference position between two reference points is interpolated by PDR. The actual positioning process comprises 2 phases. In the first phase RSS positioning is performed with the radio map. RSS readings are obtained at a rate of one reading every 4 seconds. For each reading a position is estimated. The floor is also estimated by RSS.
b: THE LANDMARK SELECTION MODULE The Landmark Selection module (Fig. 19 ) is responsible for extracting landmarks from raw sensor data that have been extracted while the user was localized from the localization module and for localizing the user once a model is available. Its components are: Classification. In this module sensed values from WiFi, geomagnetic and GSM sensors are imported to a support vector machine classifier responsible for predicting the location of the client. Raw Data. The raw streamed sensor values are then labeled based on their spatial coordinates, estimated from the localization module, and are stored in a database. Cluster Analysis. A cluster analysis is performed on the collected data and the optimum number of clusters is been estimated. Fusion. Once the number of clusters has been identified, weights are assigned to the clustered values and the data are aggregated based on their proximity.
Clustering. Once the data are aggregated, they are clustered based on their characteristics, forming rectangular shapes whose edges are tangent to the walls in the map. Labeling. The new created clusters are now labeled based on coordinates of the rectangular that they include. The size of the rectangular, in the localization phase, will indicate the localization confidence. Clustered. The clustered and labeled data are now stored in a database. Training. Once there are enough data and with a regular interval, a classifier is trained on them and more precise localization is enabled.
These N paths are updated each time new WiFi measurements become available. The next candidate positions, starting from the current last points of the paths in memory, are determined based on a maximum walking speed (derived from a step counting algorithm using the available accelerometer data), and the walls and obstacles of the building (people cannot move through walls). Each path consists of a chain of grid points and a cost that indicates the probability of this path at this time step. The path with the lowest cost after processing all sensor data is the most likely trajectory. The cost of a path is the sum of costs based on various sensor measurements: WiFi RSS measurements, barometer, accelerometer and gyroscope data: WiFi. The RSS values are compared to the corresponding reference values in a fingerprint map and are weighed based on the estimated distance to the WiFi access points.
Each floor is represented by a polygon with the floor id as a feature value and likewise, the escalator and elevator are represented by a polygon with the start and end floor as a feature value (Fig. 20(b) ). The route mapping filter uses this shapefile as input to construct a grid database with a certain grid size, e. g. , 1 m, and a reachable database, consisting of the reachable grid points, for each grid point (Fig. 20(a) ). The reachable grid points are the neighboring grid points that do not cross walls or obstacles, and the above or below grid points if they are located within the polygon of an escalator or elevator (and correct floor level). This grid and reachable database are used to select the next candidate positions in the route mapping filter, given the current last points of the paths in memory and the time that has passed since the last location update.
7) WHU TEAM The method used by Wuhan University team utilizes data of many sensors available in a smartphone. The main method combines PDR, Wi-Fi fingerprinting and magnetic matching. The PDR algorithm processes accelerometer, compass, gyroscope and barometer data. Similarly to PDR algorithm, compass and gyroscope signals are used to estimate heading and accelerometer signal is used for step detection. In contrast to traditional PDR, barometer data is used as an important source for the altitude estimation to detect much more precisely the floor. Since the competition organizers have provided a training set, the team took full advantage of the WiFi signals to construct a fingerprinting database. WiFi fingerprinting method is an attractive indoor positioning method but the training data was limited.
a: STEP DETECTION The training dataset gives the coordinates (longitude and latitude) of some known points, i. e. POSI, but these POSI points are sparse in the space domain. To build a WiFi fingerprint, coordinates of many other points than the known POSI points have to be estimated. Therefore, first, the system assumes that the walking path between two consecutive POSI points follows a straight line. Then, the sensed data of the three-axis accelerometer is used to estimate the number of steps between two known POSI positions. This can be done by finding the local maximum points of the magnitude of the output of a three-axis accelerometer between two POSI points. The magnitude can be expressed as: Mi = M i -E {M i } (15) where M i = x 2 i + y 2 i + z 2 i ; x i , y i , and z i are the values of a three-axis accelerometer at epoch i.
3) WHU TEAM In the last several years, WHU team has been working on developing an accurate and reliable pedestrian navigation system (PNS), which will significantly increase the safety of military personnel and first responders. The data sources used in WHU solution are IMU data and GNSS raw data. Magnetometer is not included due to its vulnerability to disturbance (especially indoors). The basic navigation algorithm adopted is quite simple and commonly consists of INS mechanization, zero-velocity detector, and a complementary Kalman filter (KF), as shown in Fig. 25. The Foot-Mounted INS signals are integrated with GNSS signals in a loosely-coupled manner. To get rid of gross error as much as possible, satellites with small elevation should be discarded. Some measurements with poor quality based on the magnitude and covariance of the innovation vector can also be rejected.
If a team decided to run the course a second time, only this second course would be evaluated. For the off-site tracks, Track 3 and Track 4, competitors had to provide the position estimates the latest on September 14 th , several days before the conference and competition. The evaluation trajectory was recorded by the track chairs in advance following a similar approach than the one reported for the on-site tracks. i. e. , the competition organizers surveyed the area and collected the required data for the system calibration under realistic conditions (similar than the ones for the on-site tracks) and, after that, they recorded the data for positioning algorithms on the evaluation path simulating a real-time on-site evaluation. The same format was expected for all the competition tracks. For each trial, the teams had to submit a CSV file, where each row represented a single estimated position.
For the process of dataset anonymization and georeferenciation, also two people checked that the resulting logfiles with anonymized WiFi data and georeferenced landmarks were coherent with the manual logs and the raw log files collected in January. • Screening of contestants by previous analysis of their methodological approach to indoor positioning. The deadline to send the technical descriptions was set to the second week of July. All proposals were evaluated by two independent track chairs, who analyzed the proposal feasibility. Although 34 teams participated in the competition, we reviewed more than 40 proposals. • Commitment to the competing teams. During the Setup day, three chairs were in the shopping mall to act as the main interface with the shopping mall and to give some clarifications to the competitors about the competition rules.
This work was supported in part by the  French Pays de la Loire Regional Council through Etoiles Montantes  under the organization of IPIN2018 international competition under Grant  2017-10671 , in part by the  Atlantis Shopping Mall , in part by the  Project REPNIN Plus  under Grant  TEC2017-90808-REDT , in part by the  ICT Research and Development Program of MSIT/IITP ,  South Korea , ( Development of Precise Positioning Technology for the Enhancement of Pedestrian Position, Spatial Cognition and Sports Competition Analysis ), through the  ETRI Team  under Grant  2017-0-00543 , in part by the  JSPS KAKENHI  through the  Kyushu Team  under Grant  JP18H04125 , in part by the  Slovak Grant Agency, Ministry of Education and Academy of Science , Slovak, through  UPJS Team , under Grant  1/0056/18 , in part by the  Slovak Research and Development Agency through UPJS Team  under Contract  APVV-15-0091 , in part by the  German federal Ministry of Education and Research Programme  '' FH-Impuls 2016 ''through  HFTS Team  under Contract  13FH9I01IA , in part by the  Ministry of Science and Technology (MOST) ,  Taiwan , through the  YAI Team  under Grant  MOST 108-2634-F-155-001 , and in part by the  Strategic Priority Research Program  of the  Chinese Academy Sciences  under Grant  XDA17040202.
The early infantile epileptic encephalopathies (EIEE) are a group of rare, severe neurodevelopmental disorders, where even the most thorough sequencing studies leave 60-65% of patients without a molecular diagnosis. Here, we explore the incompleteness of transcript models used for exome and genome analysis as one potential explanation for lack of current diagnoses. Therefore, we have updated the GENCODE gene annotation for 191 epilepsy-associated genes, using human brain-derived transcriptomic libraries and other data to build 3,550 novel putative transcript models. The extended transcriptional footprint of these genes allowed for 294 intronic or intergenic variants, found in human mutation databases, to be reclassified as exonic, while a further 70 intronic variants were reclassified as splice-site proximal.
Improvements in diagnostics can, in part, be achieved through improvements in gene annotation. Currently, most GENCODE (i. e. Ensembl)  7  and RefSeq 8 gene annotations are based on cDNA and EST libraries produced alongside the initial experimental phase of the human genome project. Such datasets were limited by a lack of human-specific sequence and relied on computer algorithms that used cross-species homology to identify, for example, putative open reading frames  [9] [10] [11]  However, large datasets produced by more recent RNA-seq and long-read sequencing-based projects from human tissue remain largely unincorporated. Such datasets have the potential to add novel exons via transcript models to existing gene annotations, and these novel features can provide new insights into genetic disease.
For example, 'expanded' exomes offer the potential to capture additional disease-linked mutations beyond the reach of previous studies. Additional sequences can be added to existing exome 'panels' used for diagnostics in the clinic and used to select novel regions for resequencing in patients without a molecular genetic diagnosis. Indeed, this has already been demonstrated for DLG2, where newly-identified exons were observed to be deleted in patients with neurodevelopmental disorders  12. Furthermore, the resequencing of novel annotated regions identified previously-missed pathogenic variants linked to epilepsy in SCN8A  13. Meanwhile, additional transcript features can also be used to reappraise existing variation datasets; providing, for example, new mechanistic explanations for known disease-associated variants, whilst also allowing for the reconsideration of variants from whole-genome studies that had previously been de-prioritised due to an apparent lack of transcription.
However, although modern transcriptomics datasets have the potential to add an enormous amount of novel transcribed features to gene annotation catalogues, the exact proportion of this 'transcriptional complexity' that is linked to gene function, and therefore to phenotype, remains hard to fathom. Thus, if a portion of observed transcription events for a given gene lack functional relevance, then expanded gene annotations could unknowingly be a source of misleading variant interpretations. In practice, transcript functionality is confirmed in the laboratory, although important insights can be gained from bioinformatics. Evolutionary conservation has long been regarded as a strong proxy for functionality, for example in the observation that coding sequences (CDS) have been subjected to purifying selection, or that splice sites are constrained and consistently transcribed in multiple species  14.
e. according to a 'gain of function' model  16. Here, we explore the potential of expanded gene annotation to improve the diagnostics of epilepsy, utilising a manual annotation workflow that is initially agnostic with regards to the potential functionality of the novel transcript sequences. Overall, this work has added 3,550 GENCODE transcript models to 191 genes associated with epilepsy via the utilisation of publicly available short and long read transcriptomics datasets. We subsequently create an expanded exome panel for eight genes associated with Dravet syndrome (an EIEE subtype), incorporating 125 newly-identified transcript regions, and, after resequencing 122 patients with a clinical diagnosis of Dravet syndrome or a similar phenotype, we discover two de novo variants within novel SCN1A exonic sequences.
Manual re-annotation substantially increases the number of novel transcripts We manually re-annotated 191 genes associated with epilepsy as part of the GENCODE project, primarily using publicly available long-read transcriptional datasets, including brainderived SLR-Seq and PacBio Iso-Seq reads (see Methods). In total, 3550 novel alternatively spliced transcripts were annotated across the 191 genes, increasing the number of transcripts for these genes from 1807 to 5357. All novel transcripts are included in GENCODE v28; contemporary RefSeq annotation contains 1397 NM and XM transcripts. The majority of added transcripts contain either completely novel exons (37%) or utilize newly-identified alternative splice sites within existing exons (21%). In total, more than 674 kb of additional exonic sequence was added across the 191 genes.