dataesr/scientific-paragraphs-categorization huggingface dataset:
The flow rate used was 1ml/min. SonoVue ® microbubbles (Bracco S. p. A, Milan, Italy) ultrasound contrast agent was reconstituted following the manufacturer guidelines. The concentration in microbubbles of such a solution is  [1] [2] [3] [4] [5]  x 10 8 microbubbles per mL  [30]. This solution was then mixed with water to obtain the desired range of concentrations VSonovue/Vwater= [1/10000; 1/5000; 1/3000; 1/2000; 1/1000; 1/800; 1/600; 1/400; 1/200; 1/100]. In number of microbubbles per mL, this range covers  [1] [2] [3] [4] [5]  x [10 3 -10 6 ] microbubbles per mL. For each infusion, we acquired 500 volumes at a volume-rate of 500 Hz with pauses between imaging blocks to allow for data transfer. These pauses took most of the time : 89 s for transfer after 1 second of acquisition.
C. Beamforming and localization process Delay-and-sum beamforming  [Provost et al, 2014 ] was done a posteriori on a computer equipped with an Intel ® Xeon Haswell 10-core @ 2. 60 GHz, 128 GB of RAM and a NVIDIA ® Tesla ® K40 GPU with 12Go of RAM. To reconstruct each voxel, the conventional delay and sum beamforming was applied to every element of the 2D array, thus making it a 3D DAS beamforming. The process was performed using Matlab 2017b with CUDA Each block (500 volumes), was beamformed at half wavelength in all directions. It took 45 seconds for each block including saving time. The same computer was used to implement the localization algorithm. Additional filtering was needed to prepare the images for localization. The time averaged intensity of each block was substracted from volume data to remove non or slow moving structures from the image.
3D ULM was then applied to these images. The localization process used here is based on QuickPALM  [26]  and has been entirely re-implemented to fit 3D data processing in Matlab. This new method determines the center of mass of the low-resolved bubble through its intensity profile: the ratio of the intensities of the pixels surrounding the local maximum over the intensity of the maximum defines the shift of the center of mass towards one direction. Analyzing this shift and fitting a model for the point spread function on a grid with higher resolution yields the microbubble's center position in the super-resolved basis. The local maxima were computed for each volume of each sequence in a parallelized manner. This yielded (z,x,y) triplets, along with time, corresponding to the position of particles.
0 V V moy  ) and gives a maximum velocity of 158 mm/s. s mm D D Q S Q V lmain Lmain main main / 79 2 * 2 *           (3) If we assume conservation of flow rate in the bifurcation we can write: D D V D D V D D V Q Q Q      (4) where L D and l D are respectively the large diameter and the small diameter for the canals. In addition to velocimetry, the trajectories were interpolated with a spline model to achieve better spatial sampling of the bifurcation. The original points given by the trajectories served as knots to construct the break sequence according to the centripetal scheme  [34] , by using the cscvn function packaged by Matlab. For each trajectory 1 million points were calculated on its respective spline and were then resampled to obtain a uniform spatial sampling of 1 µm in all three directions.
They were taken respectively at y=1310 µm and y=3600 µm (y=0 µm being the edge of the probe). The boundaries were automatically defined using the boundary function in Matlab. Such graphs were plotted for 54 consecutive slices ranging from y=1500 µm to y= 2040 µm and the distance between two points belonging to each side canal was recorded. When such points could not be found, the distance was noted to be 0. The distances measured are plotted on figure  8. c). The minimal distance measured between two distinct microbubbles was 55 µm. IV. DISCUSSION In this study we introduced and demonstrated the possibility to use 4D ultrafast ultrasound scanners to implement volumetric ultrafast Ultrasound Localization Microscopy. We developed an in-vitro setup that allowed us to image flow in three dimensions and implemented a 3D algorithm for localization, tracking and velocimetry of microbubbles at subwavelength resolution.
We ported the QuickPALM algorithm from Java to Matlab in 2D at first and then extended it into three dimensions. The drift correction filter based on fiduciary landmarks was abandoned in our method because the in vitro technique is not subject to motion and also because no fiduciary landmarks were present in the gel. We worked in Matlab to make this code as fast as possible using parallelization of the mathematical operations to take advantage of multiple cores CPUs, and we added the convolution step mentioned above. We decided to use the method based on the calculation of the center of mass of the low-resolved bubble through its intensity profile. It is a straightforward method that minimizes computational requirements and do not need to interpolate the data. Sage et al  [38]  recently published an extensive paper about the different techniques available to perform Single Molecule Localization Microscopy and rated the different algorithms.
As we want to lower computational cost as much as possible and have an easy to package tool, it seems that QuickPALM is a good candidate for translation to 3D ULM. The new localization process was implemented on each volume. As pictured on figure  4 , the higher the concentration, the more particles are detected pre-tracking for the same acquisition duration. This means that higher concentration of microbubbles lead to faster reconstruction of the canals. The high concentration of microbubbles makes it very easy to distinguish the bifurcation compared to the tissue surroundings but requires a less discriminative tissue/bubble filtering to cope with microbubble cluttering. However after tracking, a lot of microbubbles detected are filtered out because the tracking is unable to perform well.
This technique allows to manipulate smaller dataset: a 1 second acquisition is about 23 GBytes for volumetric images (Radiofrequency and beamformed images) alone whereas a matrix containing intensities, positions of the microbubbles weighs only 800 kBytes. After filtering the positions by tracking, this scales down to 75 kBytes. This represents a compression factor of about 300 000. In a case where the uULM algorithm would take around the same amount of time than the acquisition and beamforming of the data (as is already the case in 2D), it would save a considerable amount of space and could allow for faster visualization. Currently, volumetric ultrafast ULM retains some drawbacks. An important aspect is that the size of the data produced with 1024 channels limits the speed at which we can transfer the data from the ultrasound machine to the computer memory.
ACKNOWLEDGEMENT This work was supported principally by  Agence Nationale de la Recherche , within the project  ANR Tremplin Resolve Stroke. This work was supported by  LABEX WIFI (Laboratory of Excellence   ANR -10 -LABX -24 ) within the  French Program  " Investments for the Future"  under reference  ANR -10 -IDEX -0001 -02 PSL*. Thanks  to  Hicham Serroune  for constant support and  Laura Zamfirov ,  Kailiang Xu  for insightful discussions.
We present QAnswer, a Question Answering system which queries at the same time 3 core datasets of the Semantic Web, that are relevant for end-users. These datasets are Wikidata with Lexemes, LinkedGeodata and Musicbrainz. Additionally, it is possible to query these datasets in English, German, French, Italian, Spanish, Pourtuguese, Arabic and Chinese. Moreover, QAnswer includes a fallback option to the search engine Qwant when the answer to a question cannot be found in the datasets mentioned above. These features make QAnswer as the first prototype of a Question Answering System over a considerable part of the LOD cloud.
by looking at the Linked Open Data Cloud 1  (LOD cloud), which collects datasets that have been published using Semantic Web Technologies. Though in 2007, the LOD cloud contained 12 datasets, it now contains 1,231 2  datasets. The LOD cloud does not only contain a lot of datasets, but at the same time some of these datasets are very large. A dump of the LOD cloud called LOD-a-lot  3  contains around 5 TB of structured data in uncompressed N-triples format. While Semantic Web standards are designed to make data to be machine comprehensible, they do not allow, at the same time, easy accessibility of the data to non experts and in particular to end-users. Question Answering (QA) is seen as the technology that can allow to bridge this gap between Semantic Web data and end-users. While the industry presents QA solutions on their proprietary Knowledge Base (such as Google and Baidu), no system reached a point which allows querying a substantial part of the LOD cloud.
QA system Lang KBs Url Qakis  [2]  en DBpedia http://qakis. org/ Quint  [1]  en Freebase https: //gate. d5. mpi-inf. mpg. de/quint/quint AskNow  [5]  en DBpedia http://asknowdemo. sda. tech Frankenstein  [8]  en DBpedia http://frankenstein. qanary-qa. com gAnswer  [10]  en • Robust, users ask questions using keywords, natural language questions and even malformed questions, i. e. , syntactically wrong questions. The algorithm is robust enough to deal with all these scenarios, but not to spelling mistakes. • Real-time, the algorithm can answer questions in real-time, i. e. , on existing benchmarks like QALD and SimpleQuestions an average run-time of 2 seconds per query is realistic. • Low hardware footprint, the algorithm uses specific indexes that guarantee low disk and memory footprint. Our demo will show that QAnswer can query 700Gb of n-triples and can be run on a (standard) laptop having 4 cores and 16 Gb of RAM.
• Portable, making question answering over a new dataset can be difficult. Some approaches need a lot of training data, other are not designed to be portable at all. Our system is designed such that any new dataset can be used as a base for a new QA system. • Multi-Knowledge-base, the algorithm allows to query multiple Knowledge-bases at the same time. • Precision and Recall, the algorithm was tested on multiple benchmarks and can compete with most of the existing approaches  [3]. We are using the algorithm described in  [3]  to query what we believe are the three more significant datasets in the LOD cloud for end-users: Wikidata, LinkedGeoData and MusicBrainz. While, DBpedia and Freebase can also be queried, we consider these datasets as outdated since both are not maintained anymore. We now describe the improvements over previous works brought by QAnswer.
• Non-european languages: While it was shown that the algorithm in  [3]  can be used to answer questions in multiple languages, it was only tested over European languages. -"巴黎的博物馆", i. e. , searching in Chinese for the museums in Paris. A screenshot of an example can be seen in Figure  1. [7]. Some example requests that can be answered using this dataset are: -"Steinwenden", i. e. searching for a city -"Worms Renzstraße", i. e. searching for a street in a city -"give me fountains in Saarbrücken", i. e. searching for a point of interest in a city A screenshot of an example can be seen in Figure  2. • Lexemes: In the previous work, the algorithm presented in  [3]  was used to query Wikidata. When Wikidata was new, it contained only Q-items, i. e. , resources describing a thing or an idea, but not the word describing it.
Since 2018, Wikidata was extended to also store new information such as words, phrases and sentences, in many languages. This information is stored in new types of entities, called Lexemes (L), Forms (F) and Senses (S). The Lexeme extension can be seen as a structured representation of the Wiktionary 4. There exists an earlier attempt to semantify wiktionary by DBpedia project  5. This project is unfortunately not maintained anymore and the extraction framework does not work anymore due to changes in the structure of Wiktionary. While the Lexemes in Wikidata still represent a small portion of the information contained in Wiktionary, we believe that the active Wikidata community will be able to fill this gap. Example questions that can be answered using this data are: -"what is the pronunciation of magic?", i.
• Musicbrainz: Musicbrainz is one of largest music databases that is open and available online. The data is natively stored in a relational database (i. e. a PostgreSQL database). In previous work, it was already possible to query MusicBrainz, but it relied on the dump provided by the following RML mappings: https://github. com/LinkedBrainz/MusicBrainz-R2RML. We improved the mappings in different ways. The most radical change was related to how some information is organized. For example in the original LinkedBrainz dump every album appears multiple times (every time for each publication year and country where it was released). The same problem appears for songs. In particular the dump didn't allow to recognize that the different publications of the albums and songs were in fact referring to the same album and song.
This means that when asking for albums or songs, many duplicates appeared, a behaviour that is not expected by users and also not explicable for them. We therefore restructured the export to avoid this. Other changes represent a better coverage to external links like to Wikipedia, Wikidata and Social Media. Moreover, the dump is enriched with the links to the covers of albums from http://coverartarchive. org. Finally, we enriched the artists, albums and songs with links to youtube so that users can effectively hear the pieces they are querying. Example requests where Musicbrainz is used to answer some queries are: -"albums by green day", i. e. , searching for albums of a band -"record label from turin", i. e. searching for record labels in a city, -"songs blink-182", i. e. , searching for songs of a band A screenshot of an example can be seen in Figure  4.
• Qwant: While the Semantic Web grew very fast in the last years, it contains only a fraction of the information contained in the web. Whereas, most of the information is stored in a non-structured format, i. e. , in the form of HTML pages. This information is typically accessed with traditional information retrieval techniques  [6]. We therefore integrated, a fall back option, which is a traditional search engine, namely Qwant  6. This means that when we cannot find the information requested by the users in one of the underlying datasets, we search for an answer using Qwant. Note that this represents a new problem in the area of QA over Knowledge Bases. Current benchmarks only contain questions whose answer can be found in the underlying knowledge base. There are a few exceptions in the QALD benchmark where less than 1% of the questions are not answerable.
CONCLUSION We presented QAnswer a QA system which queries 3 key Semantic Web datasets at the same time, namely, Wikidata with lexemes, LinkedGeoData and Musicbrainz. These datasets contain a huge amount of information like books, films, persons, music, streets, points of interest and many more. The data can be queried using natural language in 8 different languages, namely English, German, French, Italian, Spanish, Pourtuguese, Arabic and Chinese. In particular two non-European languages, Arabic and Chinese, are included. All together this represents a first prototype of a QA system querying a considerable part of the Semantic Web. This represents also a step towards new challenges in this area like: correctly selecting which Knowledge Base to query, new scalability scenarios, making use of links between the datasets to deal with redundant information, studying the impact of the dataset quality on the QA performance, and studying the problem of not answering a question.
LinkedGeoData is the RDF extract of OSM data. The last public extraction was performed in 2015. We extracted a new export, covering entire Europe, and made the dataset queriable by QAnswer. Note that there is no online demo querying geo-spatial data and we are aware of only one work tackling this problem, which is presented in • LinkedGeoData: One of the largest open databases for geographical information is OpenStreetMap (OSM). It collects geographical information thanks to more than 2 million registered users. It contains information about streets, buildings, points of interest (like shops, restaurants, museums, fountains), cities, regions and many more. The data is natively stored in a PostgreSQL database with PostGIS extension.
Exponentially increasing amounts of unprocessed bacterial and viral genomic sequence data are stored in the global archives. The ability to query these data for sequence search-terms would facilitate both basic research and applications such as real-time genomic epidemiology and surveillance. However, this is not possible with current methods. To solve this problem, we combine knowledge of microbial population genomics with computational methods devised for web-search to produce a searchable data structure named Bitsliced Genomic Signature Index (BIGSI). We indexed the entire global corpus of 447,833 bacterial and viral whole genome sequence datasets using 4 orders of magnitude less storage than previous methods. We applied our BIGSI search function to rapidly find resistance genes MCR-1/2/3, determine the host-range of 2827 plasmids, and quantify antibiotic resistance in archived datasets.
resistance, identifying zoonoses and investigating the biology of infectious diseases. Genome sequence data is deposited in public archives such as the European Nucleotide Archive (ENA) and the Sequence Read Archive (SRA), which mirror their underlying data. The volume of archived microbial sequence data has doubled in size every two years, and is likely to grow at an increasing pace1-6. However, it is currently impossible to search these archives for DNA sequence of interest, such as mutations or genes. This functionality would transform infectious disease management, by enabling rapid identification of alreadysequenced organisms that are highly similar to an outbreak strain, or by enabling studies of antibiotic resistance mutations, genes or plasmids. At first sight BLAST7 and its successor8,9 algorithms seem to enable these searches, by performing alignment of query sequences against large databases.
There are two reasons why these tools do not suffice. First, they would not scale to databases the size of the ENA or beyond. Second, they require assembled genomes as input -if applied to raw sequence data they would only find matches completely contained within a single read. However assembly is fundamentally lossy when the input data contains multiple strains, and the highly heterogeneous historical data in the ENA would result in very variable assemblies, particularly of plasmids 10-12. The first scalable method for searching raw sequences, named Sequence Bloom Tree (SBT)13, comprised a k-mer (fixed-length DNA word) index. SBT enabled detection of specific transcripts in RNA-seq data, and allowed users to search archived datasets for the first time. SBT, methods based on SBTs14-16 and methods such as Cortex, vari and McCortex17-19 are all constrained by a scaling dependence on the total number of k-mers in the union of datasets indexed.
For species such as humans, where there is considerable kmer sharing between datasets, SBTs work very well; efficiencies can also be gained by leveraging patterns of sharing between datasets20. However, bacterial and viral genomes encapsulate billions of years of evolution; indeed even within one bacterial species there can be enormous diversity due to horizontal transfer of DNA (Supplementary Figure  1 ). We show this diversity renders previous methods unable to scale. We solve the problem with an alternative type of k-mer index, using a fixed-length binary "signature" of each dataset. We call our data structure a Bitsliced Genomic Signature Index (BIGSI). Searching the DNA archive is one example of a "document retrieval" problem, a subject which has been intensely studied and successfully implemented at massive scale by internet search engines.
Our "search terms" are k-mers from SNPs/alleles, and our "web-pages" are raw read datasets or assemblies. Bitsliced signatures were once used for text search21,22, but were largely abandoned after Zobel et al showed in 1998 that an alternative method (inverted indexes) performed better for natural language23. One notable exception in 2017 was the Microsoft Bing search engine24, which revived their use. For our use case, where each new microbial dataset brings new variation, bitsliced signatures provide much better scaling than inverted indexes. Web and (microbial) DNA search have different dimensionality, as the language of microbial genomes is vastly more complex than English. Our ENA/SRA dataset was only 10 6 documents but contained 10 10 unique terms (here, kmers), and this would continue to increase with more data, whereas Google indexes 10  12  documents containing (we estimate) 10 8 terms with a much more slowly growing lexicon.
Results We developed a data structure suitable for storing microbial genomic data, the bitsliced genomic signature index (BIGSI). We use the generic term "dataset" to refer to either an assembled genome or unassembled sequence read-files from clonal or non-clonal samples. BIGSI combines a k-mer index with constraints on sequence queries, described below. A bloom filter is a data structure25 which stores data (here, k-mers) in a bit-vector (array of zeroes and ones) and answers set-membership queries ("is this k-mer contained in the set?") probabilistically. The false negative rate is zero, and the false positive rate is controlled by 2 parameters (size of bit-vector and the number of (hash) functions used to generate the binary encoding, see Figure  2  and Online Methods), creating a trade-off between false-positive rate and compression.
We describe how we set the bloom filter parameters below. BIGSI encodes data as a matrix in which each column is a bloom filter of the k-mers in a dataset. Querying for presence of a k-mer involves applying the Bloom filter hash functions to this k-mer, which each return an integer, and taking the rows of the matrix ("bitslices") indexed by these integers; datasets (columns) containing the k-mer have a 1 in all those rows. Fast (O(1)) access to the rows is achieved using a hash mapping from row index to the corresponding bitarray. Figure  2  shows how data is processed and stored, and how queries are made; details are in Online Methods. Incorporating a new dataset simply requires the addition of a new column, without needing to rebuild the index. However, since appending a new column to a BIGSI requires modifying every key in the index this is an expensive operation.
We measured query speed by first building a BIGSI of 3,480 datasets of Mycobacterium tuberculosis obtained from27, and genotyping 68,269 SNPs. Searching all of these datasets for these SNPs took less than 90 minutes on a single CPU core which translates to a genotyping rate of more than 46,000 genotypes per second. We validated SNP genotyping accuracy using a subset of 100 of the M. tuberculosis datasets for which we had high quality SNP calls using samtools28 (see Methods). The concordance between methods was 99. 997% with a total of only 286/682,690 discrepancies. We measured accuracy of longer allele detection by searching (with T = 70% match) for a catalogue of E. coli Multi Locus Sequence Type (MLST) alleles and choosing the best scored allele for each gene. We then compared calls on a set of 954 datasets with the MLST allele calls from a high-quality caller: SRST229.