dataesr/scientific-paragraphs-categorization huggingface dataset:
Their results show that a fine-tuned MobileNet model can achieve 98. 2% precision, 58. 5% recall and 73. 4% harmonic mean, making it a potential candidate for a real time traffic monitoring application with big data due to its fast performance. A fine-tuned Faster R-CNN model provides a better harmonic mean (80. 4%), better recall (68. 8%) and more accurate estimations of car units, and could be used for traffic analysis applications that demand higher accuracy than speed. Acharya et al. [27]  used a Faster R-CNN neural network architecture for object detection and tracking. They evaluated their algorithm on the town center dataset  [28]. The tracking algorithm is able to track pedestrians with 71. 13% accuracy at a rate of four frames per seconds. To improve the performance, they proposed the usage of Kalman filters for overcoming the problem of unpredictable pedestrian movements.
3. Tracking the objects by matching the detections with the ones in the previous frame. 4. Updating the trajectories of objects already stored in the device database or creating records for the newly detected objects. In parallel to those tasks, the sensor periodically transmits the results from the video processing to the IoT Core, detailed in Section 4, either via LoRaWAN or Ethernet. The time interval between two successive transmissions can be set by the user, but can not be less than 5 min if LoRaWAN is used, due to the constraints of the protocol. After each transmission, the local database is emptied to prevent the saturation of the local storage (32 Gb). The activity flowchart of the sensor is represented in Figure  4. When LoRaWAN is not needed and the devices access the video feed of IP cameras (a typical situation for CCTV infrastructures), a 3U rack version has been designed and is shown in Figure  5.
Tracking Objects: SORT Once the detection task is done, the next step is to match the detected objects in the current frame with the ones from the previous frame. To perform this multiple object tracking (MOT) task, the Simple Online and Real-time Tracking (SORT) algorithm, fully detailed and benchmarked in  [38] , is used (A Python 3 implementation is freely available here: https://github. com/abewley/sort). SORT has been developed with a major focus on efficiency in order to be used in real-time application, a critical requirement for our application, while still being offering excellent tracking performances. This is achieved by coupling two methods well known for their computational efficiency and their accuracy: • a Kalman filter  [39]  to estimate the position of the bounding boxes in current frame from their previous locations; and • the Hungarian algorithm  [40]  for optimally solving the problem of association between the predictions and the bounding boxes.
The Agnosticity Infrastructure Agnosticity is the software framework developed for the Liverpool project. Its core idea is to rely on open source software and technologies as much as possible without making any assumptions on the type of sensors and communication protocols being used. The function of Agnosticity is to support the collection, storage and access of IoT data and to enable interoperability between different technologies thanks to the use of the OneM2M standard  [41, 42]. As the IoT quickly scales up, interoperability is becoming a crucial challenge to any Smart City application  [43]. The multiplication of sensors, network protocols and usage delegates to the infrastructure level the capacity to ingest data from different sources and to provide a common access point. The Eclipse OM2M project is an open source implementation of oneM2M and SmartM2M standard.
It provides a horizontal M2M service platform for developing services independently of the underlying network, with the aim to facilitate the deployment of vertical applications and heterogeneous devices. OM2M exposes a RESTful API providing primitive procedures for machine authentication, resource discovery, application registration, container management, synchronous and asynchronous communications, access rights authorization, groups organization, and re-targeting. Figure  7  introduces the general architecture of the Agnosticity framework implemented in this project. Fifteen fixed visual sensors are connected to the CCTV network and push data to the OM2M platform through HTTP Post. Five Mobile visual sensors and 20 air quality sensors rely on LoRaWAN and The Things Network to publish data.
A specific plugin automatically recovers the data from The Things Network using the MQTT broker and re-publishes the data to the OM2M platform through HTTP Post. Thus, all data are directly available on the OM2M platform under a specific container. A subscription mechanism allows it to automatically store each into a dedicated database for long-term storage. Different applications can be built on top of the Agnosticity framework, by either directly accessing data using the OM2M RESTful API, or by requesting data from the dedicated database. For example, the web-based dashboard shown in Figure  8  has been designed to visually explore the collected data from the different sensors. Now that the sensor and the Agnosticity architecture have been introduced, validation experiments are illustrated in the following section.
Accuracy and Performance We evaluated the accuracy and performance of the sensor on the Oxford Town Center Dataset  [28]. The video gives a high definition view (1920 × 1080 @ 25 fps) of a busy town center street from a CCTV perspective. The validation experiment was done on the first 4500 frames of the video corresponding to 3 min. In this sequence, 230 pedestrians and their position were annotated in the video by the authors of the dataset. Therefore, we assessed the performance of our sensor by comparing its results to the ground truth. Table  3  summarizes the performance results with basic statistics computed over the 4500 frames for the following variables: • detection: the number of objects detected by the sensor; • true: the number of object annotated in the dataset, i. e. , the ground truth; • error: the difference between detection and true; • |relative error|: the relative error computed as: |error| true ; • accuracy: the accuracy defined by: detection true ; and • f ps: the inverse of the time required to process a frame of the video, i.
Conclusions and Future Work This paper details an original visual sensor, part of an IoT solution for monitoring the traffic flow of bicycles, vehicles and pedestrians. The complete solution proposes to deploy more visual sensors, and to collect data through the Agnosticity framework. The main objective of this framework, also introduced in this work, is to enable interoperability by using the OM2M open source software, implementing the oneM2M standard. Since its hardware is based on the NVIDIA Jetson TX2 embedded computing platform and all computation is made onboard, the sensor is an edge-computing device. Its software couples YOLO V3, a popular convolutional deep neural network, with SORT, a real-time tracking algorithm. Meta-data are then extracted and transmitted using either Ethernet or LoRaWAN protocols.
As the solution relies on open-source software, it is extensible, offers a high level of maintainability and can be easily replicated. As only meta-data are transmitted, and no raw or processed images, the sensor offers a privacy compliant tracking solution. This also means that the sensor can be paired with the existing CCTV infrastructure typically owned by city councils, thus optimizing their use and adding new value to the network as it is now possible to exploit the vast amount of collected video data. In addition, thanks to the long-range LoRaWAN radio protocol, additional camera can be easily deployed in the field where no conventional Internet connectivity is available. The use of the sensor in two different environments (indoor and outdoor) has shown that the proposed approach is promising and is able to detect different types of traffic flows and trends.
The solution was tested in the city of Liverpool (NSW, Australia), with 20 sensors deployed. This allows the city council to monitor in real-time the traffic conditions, as well as to explore the traffic flows through a dashboard. An extended analysis of the collected data by urban planners over a longer period of time will assess how these data might be used daily to better plan mobility within the city. Future work includes the improvement of the detection and tracking algorithms performance and accuracy. For instance, being able to quantify the error made by the detection algorithm can help improve and correct the counts of detected object. Using the NVIDIA Xavier platform instead of the Jetson TX2 is a good candidate for improving the performance. Preliminary experiences suggests that, thanks to the double number of CUDA cores compared to the TX2, the Xavier is able to perform the detection and tracking twice as quickly.
Thus, sequestration of cells dedicated to the task of reproduction absolves them of 'dirty work' which keeps their genomes as pristine as possible, and thus leads to the germ-soma dichotomy. Despite being elegant and powerful in its simplicity, the 'dirty work' hypothesis is nevertheless tricky to test since it would require capturing the transition to differentiated multicellularity in the act. In recent years, experimental evolution models have made significant advances in our understanding of transitions to undifferentiated multicellularity in many unicellular organisms (yeast, Volvox, Chlamydomonas, choanoflagellates) but reproductive differentiation remains elusive. Enter AVIDA, a powerful artificial life software platform designed by Charles Ofria, Chris Adami, and C. Titus Brown, to study the evolutionary biology of self-replicating and evolving digital cells, self-replicating computer programs that evolve in an open-ended fashion.
Data Set Description This work is made in the context of developing an automated bladder cancer diagnosis system based on the analysis of urinary cytology digital slides. Given a digital slide, the diagnosis produced by the screening system depends mainly on the detection of cells coming from the urothelium called urothelial cells and on the distinction between healthy and atypical ones. A particular focus will be put on this class of cells in this article. The exploited urinary cytology digital slides are produced via the following protocol: first, urine sample is obtained from a patient, filtered, and spread onto a glass slide. Then, the glass slide is colored with a Papanicolaou stain (  16 ) by immersing it into several dyes baths. The Papanicolaou staining enhances contrast between nucleus and cytoplasm and accentuates morphological and textural differences between healthy and atypical urothelial cells.
Results The MM mutational signature landscape. To comprehensively assess the catalog of mutational processes involved in MM pathogenesis, we interrogated WGS data from 52 patients (Supplementary Data 1). Twenty-six patients (50%) had >1 sample collected at different time points for a total of 89 tumor samples (Supplementary Table  1 ). To improve the accuracy of de novo mutational signatures extraction, we integrated two independent approaches: SigProfiler and the hierarchical Dirichlet process (hdp; "Methods")  9, 18. Eight single-base substitution (SBS) signatures were identified, seven of which were compatible with one included in the most recent mutational signature catalog: SBS1, SBS2, SBS5, SBS8, SBS9, SBS13, and SBS18 (Fig. 1a ; https:// cancer. sanger. ac. uk/cosmic/signatures/SBS/)  9.
e. , SMM, newly diagnosed MM, or relapsed MM; Bonferroni-Holm adjusted p = 1 by pairwise Wilcoxon test; Supplementary Fig. 11 , Supplementary Data 9). Taken together, our data suggest that SBS5 mutations accumulate at a constant rate in each MM tumor, similar to other cancers and normal tissues  [4] [5] [6] [7] 38, 39. Considering the clock-like behavior of SBS5 in MM, we would expect a similar pattern in other B cell-derived lymphoproliferative disorders. To confirm this, we interrogated 89 CLL and 107 B cell lymphoma genomes included in the ICGC/PCAWG consortium. The association between SBS5 mutational burden and patient age was confirmed by linear regression analysis in both non-hypermutated B cell lymphomas [slope 28. 21 (standard error 4. 87); p < 0. 0001] and CLL [slope 18. 6 (standard error 4.
MRCA timing was estimated based on early clonal SBS5 mutation burden for patients with multiple samples available. The median time lag between the age at MRCA and at the first sample collection varied greatly (14. 2 years; range 0-45) (Supplementary Fig. 13  and Supplementary Data 9). In some patients, the estimated age when the MRCA emerged was very close to the sample collection, which could also reflect an overestimation of clonal mutation burden. This may occur when a branch of the tumor phylogenetic tree is inappropriately assigned as the trunk because of missed spatial and genomic heterogeneity and sampling bias  40. To estimate the patient age when the first MM driver event was acquired, we leveraged the high prevalence of chromosomal gains in MM and their involvement in cancer initiation.
Twenty-eight patients (54%) had at least one multi-gain event, and in six of these patients, there was evidence of gains acquired in two distinct time windows (Fig. 7 , "Methods," and Supplementary Data 9)  2, 15. Using the patient-specific SBS5 mutation rate, we estimated the patient age when these events occurred, similar to what was done for the MRCA. Intriguingly, the first chromosomal duplication was acquired on average 37. 5 years (range 8-66) before sample collection. In 21/27 (78%) cases, the first multi-gain event occurred before 30 years of age and in 12/27 (44%) before 20 years (Fig. 7b  and Supplementary Data 9). Overall, the second multi-gain event tended to occur closer to the first sample collection (median 8, range 0-37), reflecting a protracted evolutionary process where drivers accumulate and undergo selection over time.
The second step consisted in the assignment of each extracted process to one specific mutational signature for SBS included in the recently updated COSMIC catalogs (https://cancer. sanger. ac. uk/cosmic/signatures/SBS/)  9. The mutational signature assignment approach was comprised of two steps  18. In the first, all mutational signatures extracted by hdp and SigProfiler were assigned to one or a combination of two COSMIC signatures 9. To do so, cosine similarities between the extracted mutational signatures and each COSMIC signature, or a linear combination of two COSMIC signatures (using non-negative least squares R package NNLS), were computed. In the second, we determined whether the exclusion of each extracted mutational signature would affect the reconstruction error, i. e. , the difference between the original catalogs and the fitted linear combination of mutational signatures for each sample.
Recent years saw a growing interest in predicting antibiotic resistance from wholegenome sequencing data, with promising results obtained for Staphylococcus aureus and Mycobacterium tuberculosis. In this work, we gathered 6,574 sequencing read datasets of M. tuberculosis public genomes with associated antibiotic resistance profiles for both first and second-line antibiotics. We performed a systematic evaluation of TBProfiler and Mykrobe, two widely recognized softwares allowing to predict resistance in M. tuberculosis. The size of the dataset allowed us to obtain confident estimations of their overall predictive performance, to assess precisely the individual predictive power of the markers they rely on, and to study in addition how these softwares behave across the major M. tuberculosis lineages.
Dataset constitution A dataset of 6,616 genomes with reads available on the sequence read archive (SRA) and resistance phenotypes was gathered from several studies  (Coll et al. , 2018; Bradley et al. , 2015; Schleusener et al. , 2017; Vincent et al. , 2012)  and from the ReSeqTB website (https://platform. reseqtb. org/), as of June 2017. After removal of duplicated genomes, for which the raw reads had the same nucleotidic content, the final dataset included 6,574 genomes. All sequencing experiments were based on the Illumina technology and their great majority involved Illumina HiSeq or Miseq instruments. The resulting dataset contains a variable number of phenotypes for 20 antibiotics. The current study focused on the 10 antibiotics addressed by TBProfiler and/or Mykrobe, namely amikacin, capreomycin, ethambutol, ethionamide, fluoroquinolones, isoniazid, kanamycin, pyrazinamide, rifampicin, and streptomycin.
The numbers of candidate mutations considered by Mykrobe shown between brackets were taken directly from the catalog embedded in the software. Since many mutations are defined in terms of genomic or proteomic positions instead of specific alternative alleles, these numbers were translated into theoretical number of alternative alleles by multiplying genomic and proteomic positions by 3 and 19, respectively. To estimate the predictive performance of the Lasso-penalized logistic regression model, we relied on a nested 10-fold cross-validation procedure, that is, a 10-fold cross-validation procedure with an inner optimization of the regularization parameter, based on the AUC criterion. The final models were similarly obtained by applying a standard cross-validation procedure, designed to maximize the AUC, on the entire dataset.
Data availability All data used and obtained in this study are available as Supplementary Materials. They contain in particular, (i) a table containing the reference phenotypes and the SRA accessions, (ii) the resistance genotype matrices obtained by TBProfiler and Mykrobe, (iii) the inferred lineages, (iv) the predictive power of individual markers, and (v) the final models by the Lasso-penalized logistic regression models (i. e. , mutations selected and beta coefficients in the corresponding multivariate logistic model). Note that accessing and using data originating from the ReseqTB platform is subject to the following terms and conditions: https://platform. reseqtb. org/main/acceptTerms. html.
Inferred lineages A very high level of consistency between lineages inferred by both softwares was observed, consistently with a previous study  (Schleusener et al. , 2017). We noted however that TBProfiler provided "mixed" lineage calls for 228 samples out of the 6,571 successfully processed. These ambiguities were especially observed among the West-African, bovis and bovis/African lineages, for which TBProfiler systematically provided mixed results. Still, between 1. 8% and 5. 8% of ambiguous results were observed among the four major lineages (lineages 1-4). Further analyzes revealed however that in many cases one of the lineages was called from a marker detected at a low frequency, and that including a minimum frequency threshold to detect a lineage allowed to significantly decrease the number of ambiguous results observed among main lineages.
Resistance genotyping agreement To assess the agreement of the softwares in terms of resistance, we first evaluated their agreement at the marker level, that is, their ability to detect the same markers within the same panel of genomes. For this purpose, we extracted a list of 116 mutations embedded in both catalogs and compared the number of calls made by both softwares. We noted that the number of calls differed quite significantly in some cases, as illustrated in Fig. 1. For instance, three markers detected in a single strain by TBProfiler are detected in 10 strains or more by Mykrobe, with one being detected in almost 30 strains. Likewise, a marker detected in around 20 strains by TBProfiler is detected in more than 70 strains by Mykrobe. Conversely, however, a marker detected in around 400 strains by TBProfiler is detected in around 35 strains less by Mykrobe.
It is worth noting that these results were obtained without considering any minimum frequency threshold for calling a marker present. Similar results were also obtained when considering a threshold of 0. 5 (see Fig. S4 ), indicating that the observed discrepancies did not result from calling present markers observed at a low frequency. This analysis therefore revealed that the discrepancies in R/S predictions made by both softwares may not only be due to differences in the catalogs of mutations they embed, but also to their underlying genotyping pipelines. We then compared their overall agreement in terms of R/S prediction. For this purpose, we worked from the 6,570 samples and the nine antibiotics for which both tools provided a prediction (Mykrobe does not predict resistance to ethionamide).
5%, meaning that 92. 5% of the S strains were recognized as such), but was simply a consequence of the fact that the dataset involved around five times more S than R strains. While the precision (or positive predictive value) is an indicator of great clinical interest, since it is helpful to interpret the results actually provided by the software, it intrinsically depends on the prevalence of R strains in the panel studied hence must be interpreted with care  (Zignol et al. , 2018). Strikingly, we observed major differences between our performance estimations and that reported in previous studies. For instance, streptomycin sensitivities differed by more than 20 points from the ones reported in  Schleusener et al. (2017) , 57% for both TBProfiler and Mykrobe, instead of 77. 4% and 81. 4%, respectively, in the current study.
Predictive performance by lineage The size of the dataset considered allowed then to evaluate how TBProfiler and Mykrobe behaved across lineages. For this purpose, we focused on the four main lineages, that is lineages 1-4, and computed the same performance indicators per lineage. Table  3  summarizes the results obtained for TBProfiler. The global entries correspond to the performance obtained from the 6,342 samples coming from these four major lineages, and excluding all samples with ambiguous lineage calls. These values can slightly differ from the values in Table  2 , obtained from the entire dataset of 6,571 samples, including samples of other lineages and ambiguous lineage calls. As before, the minimum frequency threshold to call a marker present was fine-tuned, for each drug, to maximize the macro-accuracy indicator on the entire dataset (i.
Note: For each software and antibiotic, the minimum frequency threshold considered to call a marker present was chosen to maximize the macro-accuracy. 95% confidence intervals are provided for sensitivity, specificity, and precision. that a lower performance could be due either to a lack of sensitivity, as it was the case for amikacin/lineage 4, capreomycin/lineage 4 and streptomycin/lineage 4, or to a lack of specificity, for ethambutol/lineage 2 and ethionamide/lineage 2. Similar observations could be made from the results obtained by Mykrobe, shown in Table  S2.
We first noted that a limited number of markers had a high level of sensitivity. Considering all drugs, 80. 6% and 70. 6% of the markers detected by TBProfiler and Mykrobe, respectively, had a sensitivity smaller than 1%. Only 19 and 14 of TBProfiler and Mykrobe markers had a sensitivity above 10%, and only seven above 40% in both cases. In contrast, the great majority of markers were highly specific: 98. 6% of the TBProfiler and all Mykrobe markers had a specificity greater than 95%. This is illustrated in Fig. S6. Figure  2  shows the individual markers performance obtained for amikacin and streptomycin to illustrate how they can help better understanding the trade-offs achieved by both softwares. For amikacin, we noted that the predictive power was mostly driven by a specific mutation in both cases.