dataesr/scientific-paragraphs-categorization huggingface dataset:
Inspecting the detailed results in Table  A. 1 shows that while SaBTS performs better on more instances of the Clustering, Delaunay and Walshaw datasets, MQI performs remarkably better on the 42 Redistricting  graphs and relatively better on some Social graphs. Very interestingly, the results of Table  4  indicate that SaBTS can further improve the results of MQI for all five datasets. In terms of Φ best (Φ avg resp. ), the number of improved results is 8  (12)  for the 17 Clustering graphs, 8  (9)  for the 9 Delaunay graphs, 16  (42)  for the 42 Redistricting graphs, 16  (28)  for the 30 Walshaw graphs, and 4 (8) for the 12 Social graphs. The average improvement percentage achieved by SaBTS over MQI varies according to the datasets. The improvements are more important for the Clustering, Delaunay, Walshaw and Social datasets than for the Redistricting dataset.
search procedure and the self-adaptive and multi-strategy perturbation procedure. We performed a large scale computational study to assess the performance of the proposed algorithm and investigated its key components (parameters, constrained neighborhood, perturbation strategy) to gain insights on the functioning of the algorithm. The computational study on five datasets of 110 benchmark graphs with up to around 500 000 vertices allows us to draw the following conclusions. • SaBTS with greedy initial solutions (Greedy+SaBTS) dominates the recent StS-AMA algorithm dedicated to MC-GPP  [12]  both in terms of the best and average results. • SaBTS with greedy initial solutions (Greedy+SaBTS) and the popular graph partitioning tool Metis (version 5. 1. 0) [24]  perform well on different datasets.
Globally there is no clear dominance of one method over the other. When SaBTS is used to refine the results of Metis, it consistently improves the partitions provided by Metis for 108 out of the 110 benchmark graphs. • When SaBTS is used as a post-processing procedure of Metis and is compared to the state-of-the-art max-flow based method MQI  [25] , SaBTS improves the result of Metis on slightly more graphs than MQI, but MQI achieves much more important quality improvement. • When SaBTS is used as a post-processing procedure of MQI, it consistently improves the partitions provided by MQI, raising the partition quality for the tested datasets. • SaBTS and existing methods like Metis and MQI can be advantageously used in a combined way, helping to find high quality partitions for graphs with very different structures and characteristics.
(39)bserve that Greedy+SaBTS performs remarkably well on all five datasets compared to StS-AMA in terms of Φ best (Φ avg resp. ) by reporting 87 (89) wins, 16(10)ties and 7(11)losses for the 110 graphs. First, Greedy+SaBTS performs marginally better on the Clustering and Social network datasets. For the 17 Clustering graphs, Greedy+SaBTS reports 5(7)better, 9 (7) equal and 3 (3) worse results for Φ best (Φ avg resp. ), while it wins 6 (6), ties 4 (3) and losses 2 (3) instances in terms of Φ best and Φ avg for the 12 Social graphs. Second, Greedy+SaBTS shows a clear dominance over StS-AMA for the Delaunay, Redistricting and Walshaw datasets. For the 9 Delaunay graphs, Greedy+SaBTS wins all 9 instances for both Φ best and Φ avg. Similarly, for the 42 Redistricting graphs, Greedy+SaBTS wins 41(39), and looses 1 (3) instances for Φ best (Φ avg resp.
). For the 30 Walshaw's graphs, Greedy+SaBTS reports 26 (28) better, 3 (0) equal and 1 (2) worse values for Φ best (Φ avg resp. ). When we inspect the results of Greedy+SaBTS and Metis in Table2, we observe that Greedy+SaBTS performs significantly better for the Clustering dataset with 11 (11) wins, 3 (2) ties and 3 (4) losses, and the Social dataset with 10 (10) wins, 0 (0) ties and 2 (2) losses for Φ best (Φ avg resp. ).
within the context of solving MC-GPP. Finally, the literature offers few exact approaches for MC-GPP. It is thus worth investigating general approaches such as integer linear programming and dedicated branch and bound algorithms. values of each compared algorithm (G best and G avg ) on five datasets. The best 1 values of G best are highlighted in boldface while the best values of G avg are 2 indicated in italic. Table A. 1 Detailed computational results of Metis, Metis+MQI, Metis+SaBTS and (Metis+MQI)+SaBTS on four datasets from the 10th DIMACS Challenge (including 17 Clustering, 9 Delaunay, 42 Redistricting, 30 Walshaw graphs) and 12 Social network graphs from the SNAP network dataset (a total of 110 graphs). As explained in Section 4. 3, these results are based on 20 independent runs of each algorithm to solve each instance with a cutoff limit of 60 minutes per run and per instance.
Φ best of an algorithm for an instance indicates the lowest conductance achieved for the instance among the conductance values of the 20 runs, while Φ avg is the average of the 20 Φ best values. The best of the Φ best values for each instance is highlighted in boldface, while the best of the Φ avg values for each instance is indicated in italic. t(s) is the average CPU time in seconds of 20 runs to attain the 20 best results, and a time less than one second is indicated as 0. Table A. 2 The statistical results (p-values) from the Wilcoxon signed-rank test with a confidence level of 99% of different pairwise comparisons for the five datasets. Table A. 3 The geometric mean of the best and average conductance values (G best and G avg ) of Metis, Metis+MQI, Metis+SaBTS and (Metis+MQI)+SaBTS on five datasets.
). For the 30 Walshaw's graphs, Greedy+SaBTS reports 26 (28) better, 3 (0) equal and 1 (2) worse values for Φ best (Φ avg resp. ). When we inspect the results of Greedy+SaBTS and Metis in Table2, we observe that Greedy+SaBTS performs significantly better for the Clustering dataset with 11 (11) wins, 3 (2) ties and 3 (4) losses, and the Social dataset with 10 (10) wins, 0 (0) ties and 2 (2) losses for Φ best (Φ avg resp. ). Greedy+SaBTS performs only marginally better than Metis for the Delaunay dataset (#Wins/#Ties/#Looses=5/0/4 for Φ best and Φ avg ), and the Walshaw dataset (#Wins/#Ties/#Looses=15/1/14 for Φ best and 14/0/16 for Φ avg ). On the other hand, Greedy+SaBTS performs much worse than Metis for the Redistricting graphs (#Wins/#Ties/#Looses=0/0/42 for Φ best and Φ avg ).
A small value indicates a better performance. The best of the G best values for each dataset is highlighted in boldface, while the best of the G avg values for each dataset is indicated in italic. Instance Metis [24] Metis+MQI [25] Metis+SaBTS (Metis+MQI)+SaBTS Graph |V | Φ best Φavg t(s) Φ best Φavg t(s) Φ best Φavg t(s) Φ best Φavg t(s) karate 34. 12820512. 12820512 0. 12820512. 12820512 0. 12820512. 12820512 0. 12820512. 12820512 chesapeake dolphins Algorithm pair 39 62. 33823529. 33823529 0. 11428571. 11428571 0 Clustering Delaunay Redistricting Walshaw. 31666666. 31666666 0. 27810650. 27810650. 11428571. 11428571 0. 06382978. 06382978 Indicator p-value p-value p-value p-value 0 0. 27810650. 27810650 Social. 06382978. 06382978 p-value lesmis no 77. 13829787. 13829787 0. 13043478. 13043478 0.
12252964. 12252964 0. 12252964. 12252964 polbooks adjnoun football Greedy+SaBTS vs. 105 112 115 Dataset StS-AMA [12] G best. 04347826. 04347826 0. 29047619. 36504418 0. 10116086. 10607000 0 Φ best 4. 61e-01. 04347826. 04347826 0. 29047619. 36371867 0. 10116086. 10568471 0 3. 90e-03 Metis [24] Metis+MQI [25] Φavg 4. 32e-01 3. 90e-03 Gavg G best Gavg. 04347826. 04347826. 27830188. 27830188. 10116086. 10116086 2. 73e-08 1. 10e-05 Metis+SaBTS 4. 07e-06 2. 88e-06 G best Gavg 0 2 0. 04347826. 04347826. 27830188. 27830188. 10116086. 10116086 2. 50e-01 (Metis+MQI)+SaBTS 7. 34e-01 G best Gavg jazz celegansneural no celegans metabolic Greedy+SaBTS vs. 198 297 453 Clustering. 06627171. 07595360. 04158546. 04814845. 05982594. 06583945. 03902536. 20062942. 23112145 0. 12292358. 15637114 0. 12292358. 13773036 1180.
The rest of this article is organized as follows. Section II first discusses the related work on several situational awareness topics, such as search and rescue, surveillance, data collection and disaster response. Then we report on other works on human detection and action recognition. Section III describes the architecture and main modules of our PAL system: Meta Camera, Deep Learning, Pixel2GPS converter, and User Interface. This section provides standalone results from our work on the Deep Learning models and the Pixel2GPS converter. Section IV reports on the results of field testing the fully integrated PAL system. Section V summarizes the paper and highlights its main contributions.
We evaluated our POINet model on the Okutama dataset. We have trained our model for more than 100 epochs until the training loss has converged. Our model achieves a Recall of 86%, Precision of 85%, and F1 score of 84%. Compared to previous works  [2] , which uses Single Shot MultiBox Detector (SSD)  [30]  and has a F1 score of 72. 3%, our model is able to more robustly detect persons regardless of the orientation of the camera angles.
2) DEEP LEARNING MODEL FOR MULTI-ACTION RECOGNITION The Okutama dataset contains the following types of actions. • Primary actions (non-interactive actions): Running, Walking, Lying, Sitting, Standing • Secondary actions are comprised of -Human-to-human interactions: Handshaking, Hugging -Human-to-object interactions: Reading, Drinking, Pushing/Pulling, Carrying, Calling ActivityNet uses a different approach to capture the features of the detected persons. Instead of purely relying on the spatial appearance of the persons, ActivityNet learns the temporal information using a modified Long Short-Term Memory (LSTM) network  [31]. LSTM is a type of recurrent neural network that can learn from a sequence of data, such as video feeds, to produce informed predictions based on the changes in features over time.
C. PIXEL2GPS CONVERTER Most UAVs come equipped with GPS receivers, so it is trivial to know where a UAV is positioned when it detects a person. But since the person is typically not directly below the UAV, the GPS position of the person is not the same as the UAV's position. The purpose of the Pixel2GPS converter is to locate detected people by using a spatial conversion of their pixel position in the frame (image) output given by the Deep Learning module to the corresponding GPS position. Our converter is based on  [35] , and takes as input the GPS coordinates of the UAV, its height above ground relative to takeoff position, the UAV's roll, pitch and yaw, the camera's pitch angle relative to the UAV, and the image 2D point to be converted. Additionally, it uses the camera's intrinsic matrix and its lens distortion parameters, obtained through calibration with OpenCV  [36] , which were not considered in  [35].
A. PAL SYSTEM PROTOTYPE The main purpose of this field test was to validate our system integration and interoperability. Therefore, we focused on a simple scenario, that allowed us to observe in near real-time the consistency between the information displayed on the PAL Web Application (originally reported by the onboard PAL system) and the real scenario. Additionally, we collected data such as the field of view of the camera, the number of people detected and their calculated position, as well their recognized actions. System specifications, such as the size of data sent to the server, the time of overall refresh rate of the system, and battery life, were also measured. We will start by explaining our prototype hardware configuration, before we explain the test scenario.
1) SURVEILLANCE TEST SCENARIO Three actors performed a subset of the primary and secondary actions (see Sect. IV). Each act lasted for around 150 seconds. After Act2 finished, they repeated the two Acts, for a total length of 10 minutes. PA abbreviates ''primary action'' and SA abbreviates ''secondary action''. Act1 Actor 1 sits (PA) and reads (SA) Actor 2 walks (PA) while pushing a wheelbarrow (SA) Actor 3 stands (PA) Act2 Actor 1 sits (PA) and reads (SA) Actor 2 stands (PA) Actor 3 walks (PA) Act1 (repetition) Act2 (repetition) During the entire test, when the UAV pilot is visible, he considered as standing (PA) and carrying (SA), as he is holding the UAV controller. Data was obtained as follows: • Perform the scenario one time; • Retrieve 10 minutes of video at 30 FPS using the Phantom 4; • Extract one frame every 1.
illustrates the information flow in our onboard system. Video feed frames captured from the UAV's camera are used as the input data for the model. The input is first passed to a DL model called ''Position and Orientation Invariant Neural Network'' (POINet), which extracts the point of interest and generates bounding boxes around the objects. The extracted bounding boxes are then passed to a second DL model called ''Activity Network'' (ActivityNet) to learn the persons' temporal information and predict their current actions. Our Deep Learning models were trained and evaluated on the Okutama dataset [2]. It is a dataset of images captured by two different UAVs with varying camera angles and altitudes (15 m, 20 m, 25 m, and 30 m), taken during morning and evening periods. There is no restriction on altitude except the legal ceiling of 150 m.
28, 95% CI ¼ 1. 48 to 3. 50) and overall survival (HR ¼ 3. 95, 95% CI ¼ 2. 13 to 7. 32)  (34). Among CTCpositive patients, those with at least 5 CTC per 30 mL exhibited the worst prognosis. Janni et al. (12)  conducted a pooled analysis of individual data from 3173 patients and suggested that the presence of CTCs was an independent predictor of poor diseasefree survival (HR ¼ 1. 82, 95% CI ¼ 1. 47 to 2. 26), distant disease-free survival (HR ¼ 1. 89, 95% CI ¼ 1. 49 to 2. 40), breast cancerspecific survival (HR ¼ 2. 04, 95% CI ¼ 1. 52 to 2. 75), and overall survival (HR ¼ 1. 97, 95% CI ¼ 1. 51 to 2. 59). However, among the recruited population, 8% of patients received neoadjuvant chemotherapy. In addition to the prognostic impact of CTC detection, a recent retrospective analysis of the SUCCESS-A trial and of the Surveillance, Epidemiology, and End Results database suggested that, with statistically significant interaction tests, CTC-positive patients benefit from adjuvant radiation therapy in terms of relapse-free survival and/or overall survival, whereas CTC-negative patients do not  (36).
Another report from this same study found that among 206 subjects enrolled in the SUCCESS study with follow-up information and known CTC status at 5 years, 7. 8% were CTC-positive at 5 years (median ¼ 1 cell, range ¼ 1-53 cells per 7. 5 mL blood) and was associated with a 6-fold increase in recurrence  (45). Sparano et al. (  46 ) performed a per-protocol secondary analysis of the prospective NCT00433511 clinical trial, which accrued patients without clinical evidence of recurrence between 4. 5 and 7. 5 years after primary surgical treatment of HER2-negative stage II-III breast cancer followed by adjuvant systemic therapy  (47). In this late recurrence substudy, the results indicated that 26 of 547 patients (4. 8%, 95% CI ¼ 3. 1% to 6. 9%) had positive CTC assay results. Of 18 patients, 7 (38.
g. the diagnosticity of a single Mendelian marker is sufficient, and see approaches based on multilocus genotype developed below). Various methods (reviewed in  Carstens et al. , (2013) ) propose to delimit species using nucleotide sequences from several markers. Some of these methods are based on divergence levels or consider ratio of within-group and between-group diversity to delimit species (e. g. the automatic barcoding gap discovery ABGD,  (Puillandre et al. , 2012). Although they provide powerful tools to propose primary species hypotheses for large datasets  (Ratnasingham and Hebert, 2007) , such approaches cannot prove the absence of gene flow and often depend on arbitrary thresholds or assumptions such as constant effective sizes among ancestor and daughter species. The multispecies coalescent theory  (Fujita et al.
, 2012; Yang and Rannala, 2010)  provides a statistical framework to model the coalescence of multiple markers during genetic isolation of groups of individuals. Bayesian methods applied to the multispecies coalescent allow establishing the probability of species partitions and phylogenies for a sample of allele sequences from various individuals  (Rannala, 2015; Yang and Rannala, 2010)  and the most recent development of this method, implemented in the software BPP  (Yang, 2015) , allows the joint inference of species (or lineage) delimitation and species phylogeny  (Rannala and Yang, 2017; Yang and Rannala, 2014). However, despite these qualities and being able to handle some degree of Incomplete Lineage Sorting (ILS)  (Carstens et al. , 2007) , the efficiency of these methods depend for a large part on the accumulation of mutations since species separation, and thus can only delimit entities isolated long enough  (Rannala, 2015).
, 2013 (Abbott et al. , , 2016;; Lamichhaney et al. , 2017; Meier et al. , 2017). Current species discovery and delimitation methods do not allow for testing such cases, but Approximate Bayesian Computation (ABC) provides powerful methods allowing to do so  (Csilléry et al. , 2010; Lopes and Beaumont, 2010) , with the simultaneous testing of several divergence scenarios (with or without hybridization) and estimation of demographic parameters (e. g. divergence times, effective population sizes, migration rates). These methods are computationally efficient, especially when using a small (tens or hundreds) number of loci, as they use simulated datasets for which several summary statistics are compared to the original dataset (instead of likelihood computations). Thus, information rich datasets such as sequence genotypes at tens of loci in a hundred individuals can be exploited using ABC.
The population numbers refer to the number indicated in Fig. 1. C1-C6: genetic clusters or clusters found in DAPC analysis. L1-L6: Ophioderma longicauda mitochondrial lineages defined in  Boissin et al. (2011). Congruent: congruence between mitochondrial lineage and nuclear data (genetic cluster). technology limitations). In addition, seven already existing loci were used, namely a mitochondrial locus (COI), ribosomal loci (ITS1, ITS2) and four EPIC loci, introns i21, i36, i50 and i54b  (Chenuil et al. , 2010; Gérard et al. , 2013; Penant et al. , 2013). Of the 55 exon-based loci tested, 16 amplified correctly in each lineage of O. longicauda. Furthermore, six out of seven existing loci amplified correctly. Finally, 22 loci were PCR amplified in the 89 specimens.
Amplicon sequencing and dataset processing PCR products of different genes belonging to the same individual were pooled according to agarose gel quantification, i. e. quantities of PCR product were adjusted for pooling according to the band intensity on agarose gel. 96 Illumina libraries were then constructed using MiSeq Reagent Kits v2 (Illumina). Paired-end (2× 250 pb) sequencing of the 96 libraries was performed on a single run of MiSeq Sequencing System (Illumina). Library preparation and subsequent sequencing were performed by the genomic platform Genotoul (www. genotoul. fr). About thirty millions raw reads were obtained after sequencing. For each library, Illumina adapters were trimmed, forward and reverse reads were assembled and demultiplexed by locus using the program MOTHUR v 1.
Haplotype networks and mitochondrial distances For each analysis performed in this study, a summary of number of markers used and data type is displayed in Table  2. For each marker, haplotype networks were generated using the median-joining algorithm of Network, version 4. 6. 1. 1  (Bandelt et al. , 1999). Kimura 2-parameter (K2P) pairwise distances  (Kimura, 1980)  among mitochondrial lineages (or among species when considering outgroups) were calculated using MEGA v7  (Kumar et al. , 2016). The within-group K2P distances were calculated in the same way.
Species discovery: Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC) In order to determine the number of existing genetic groups without prior knowledge (i. e. mitochondrial lineage or geographic origin), we performed a Discriminant Analysis of Principal Components (DAPC) using the R software package adegenet 1. 4-1  (Jombart et al. , 2010). The DAPC is a clustering method that maximizes the between-group variance while minimizing the within-groups variance. This analysis uses genotypic information for each individual and each locus. Therefore, we converted our sequence data in genotype data using PGDspider v. 2. 1  (Lischer and Excoffier, 2012). Then, the minimum value of the Bayesian Information Criterion (BIC) was used to determine the optimal number of genetic clusters k, implemented in adegenet.
Lineage confirmation and divergence time estimates: The multi-species coalescent Since genetic clusters obtained after the first approach from multilocus genotypes appeared as separate genetic entities (see Results) we considered that their relationships could be described by tree-like topologies, possibly assuming gene flow events between some clusters. Based on the genetic clusters found with DAPC, we calculated the average distance between clusters using between-group K2P distances (based on the 30 sequence markers) implemented in MEGA 6. 0. 5. Then, we reconstructed a phylogenetic tree based on the K2P distances between clusters, using the distance-based Neighbor-Joining method, to define a starting tree for multi-species coalescent based analyses (Fig. 2 , scenario 1). Joint Bayesian species delimitation and species tree estimation was conducted using the program BPP v3.
Each analysis was run three times to confirm consistency between runs. Analyses were run using the full dataset and species were defined using the clusters found with DAPC. As it was recently suggested that 'species discovery' methods may eventually not be necessary due to improvements of algorithms and computational power  (Rannala, 2015) , we tested the accuracy of BPP alone to discover and delimit species, using a subset of our C3 dataset for computational reasons. Indeed, as each individual is defined as a putative species in BPP, the number of different topologies to test increases more than exponentially with each additional individual. Thus, we used C3 (9 individuals) & C5 (11 individuals) specimens from Greece (populations Agios Pavlos and Symi Island), known to represent two sympatric biological species (brooding and broadcast spawning individuals;  (Weber et al.
, 2015 (Weber et al. , , 2014)) ). By focusing on these sympatric populations, we ensured that approximately the same number of individuals per species was compared, across the same distributional range. A DAPC was first performed on this sub-dataset. Then, each individual was set as a single species in BPP, and three replicate analyses A11 (joint species delimitation and species tree estimation) were performed using the same parameters as previously mentioned.
For ABC analyses, data from the six Ophioderma longicauda clusters were used, excluding the outgroups. In addition, eight markers were excluded due to their low amplification success in some clusters (Table  S2 ). Three sequence groups were defined, each one with a different mutation model. The first group included 19 transcriptome-based markers, the second group included the mitochondrial marker COI and the third group included two introns (Table  S2 ). Default priors were used in preliminary analyses (800,000 simulated datasets) and were then adjusted using posterior distributions and pre-evaluation verifications. When each posterior probability of parameters fell in the prior range in preliminary analyses, 8,000,000 simulated datasets were used to estimate posterior probabilities of parameters and each scenario.