dataesr/scientific-paragraphs-categorization huggingface dataset:
6 373 6 ConclusionThe presented demonstrator holds a lot of potential for 374 teachers and researchers combining dynamical systems 375 with audio engineering or music. In terms of teaching, 376 it illustrates transient effects in direct and entertaining 377 ways. It can also be useful for proof of concepts, to 378 quickly assess the behavior of a dynamical system, or 379 to compare two slightly different versions (parameter 380 values, physical hypotheses or integration scheme). 381 Because it relies solely on Matlab, any researcher 382 that has mainly been coding in Matlab can reuse their 383 usual tools. In particular, we show that a continuous-384 time formulation of a system can be sufficient to pro-385 duce sound, simply leaving the integration to a built-in 386 method such as ode45. There is also reasonable head-LabCom LIAMFI (ANR-16-LCV2-007-01).
This Letter is organized as follows. Section 2 presents our data set, the NIRCam parallel of the MIRI Deep Survey. Section 3 describes our method to select galaxies at z > 8. The sample is used to construct UV luminosity functions and derive the early evolution of the luminosity density in the universe, presented in Section 4. Section 5 summarizes our findings. Throughout this Letter, we assume a flat cosmology with Ω M = 0. 3, Ω Λ = 0. 7, and a Hubble constant H 0 = 70 km s -1 Mpc -1. We use AB magnitudes  (Oke & Gunn 1983). All stellar mass and star formation rate (SFR) estimations assume a universal  Chabrier (2003)  IMF.
Description of the Data This Letter is based on the analysis of the NIRCam data taken in parallel with the Guaranteed Time Observations of the Hubble Ultra Deep Field (HUDF) carried out in 2022 December by the MIRI European Consortium Team, Program ID 1283 (PIs Hans Ulrik Nørgaard-Nielsen R. I. P. , and Göran Östlin). While MIRI was observing the HUDF with the F560W filter during 61 hr (including overheads) in our MIRI Deep Survey (MDS; G. Östlin et al. 2023, in preparation) , parallel data were also being acquired by the NIRISS (20 hr) and NIRCam (40 hr) instruments. The NIRCam observations were carried out with the F115W, F150W, F277W, and F356W filters. Nominal exposure times were 55,187 s in each filter, achieved with 2 visits per filter using 20 integrations with NGROUP = 7, DEEP8 readout pattern, and 10 dithering positions following the MIRIoptimized CYCLING medium-size pattern with different starting points in each visit.
Photometric Catalog We selected galaxies in the F277W filter (the deepest) and measured photometry in all bands following a methodology especially developed to deal with ultra-deep data and crowded fields. First, we masked the four bright stars present in one of the modules and the very bright spikes coming from two other stars located to the east of our pointing (outside the field of view, but the bright diffraction spikes protrude our observations). This resulted in the loss of 9% of the field of view. The final MDS-NIRCam-par survey area is 7. 3 arcmin 2. We then detected sources in several passes, starting from the most extended ones and progressing to smaller and fainter galaxies in a number of levels. The galaxies detected in a given level were masked completely down to the isophote corresponding to 5σ of the sky (∼26.
Once all galaxies in a given level were fitted, the residual image was constructed by removing the core and the outskirts fitted with the isophotal analysis, and the new frame was fed to the next level of detection. We implemented a total of 10 levels for our final catalog in this data set, starting from galaxies covering more than 10,000 pixels (10 arcsec 2 ) in the first pass to the faintest objects with just 30 pixels (∼0. 03 arcsec 2 ). Once the procedure was completed for the selection band, the photometry was measured in all other filters by fixing centers, shapes, and sizes. The final catalog is composed of 40,526 sources, of which 33,905 were detected at the 5σ level or higher in F277W. The photometric catalog included color measurements in the  Kron (1980)  aperture as well as fixed- Note.
2022 ), a dynamic nested sampler based on  Higson et al. (2019). Finally, we used the template-fitting code LePhare  (Arnouts et al. 2002; Ilbert et al. 2006)  to help with the selection of our candidates. We adopted two configurations of this program. For the first one, we followed  Rinaldi et al. (2022 Rinaldi et al. ( , 2023)). Briefly, we made use of galaxy templates with the following set of SFHs: a standard exponentially declining ( t SFR exp t t 0 ( ) ( ) µ t -- ) and an instantaneous burst by adopting a simple stellar population model (SFR(t) ∝ δ(t)). In particular, for the standard exponentially declining models, we used the following e-folding timescales (τ) in Gyr: 0. 01, 0. 1, 0. 3, 1, 3, 5, 10, 15. We employed the stellar population synthesis models of  Bruzual & Charlot (2003)  based on a Chabrier (2003) IMF, considering two different values of the metallicity: solar metallicity and one-fifth of solar metallicity.
In this execution, we also adopted the recipes of  Saito et al. (2020)  to include the emission lines, allowing for an additional free rescaling by a factor two of all line fluxes. We note that LePhareʼs fits are performed based on the fluxes and not the magnitudes, which do not require the need of introducing upper limits, considering that the flux uncertainties at low signal-to-noise are still meaningful (following  Laigle et al. 2016). We rejected sources with χ 2 > 100, which corresponds to an unreliable fit and sources that are best fitted by a brown dwarf stellar template. Given that we had two executions of eazy and two of LePhare, in order to avoid a bias toward any of these codes, we considered the highest redshift among the two executions for each code to select galaxies as explained in the next subsection.
Selection of Candidates We constructed a sample of z  8 galaxy candidates by searching for F115W and F150W dropouts in our NIRCam data and applying a similar methodology to that presented in  Finkelstein et al. (2023). Distinctively, we explored the effect of the photometric aperture and the peculiarities of different photometric redshift estimation codes. Indeed, for each galaxy, we used five SEDs derived from different aperture sizes and three redshift estimation codes, as outlined in the previous section, working with weighted averages for all relevant quantities used in the selection. We applied twice the weight of any other SED to that corresponding to 0 3 diameter aperture (typically used in this type of studies, see  Naidu et al. 2022a; Adams et al. 2023; Bouwens et al. 2023a; Donnan et al.
This was tested and confirmed with JAGUAR simulations similar to those mentioned in Section 3. We compared results using our NIRCam filter set with those achieved when adding F200W fluxes (with depths similar to the F115W filter). The inclusion of the F200W filter decreased the typical photometric redshift errors by a factor of >3 and also provided larger completeness levels, reaching values beyond 90% (compared to 80% without that filter); the interloper fraction decreased from 3% to 1% when adding F200W to the simulations. Examples of F115W and F150W dropout galaxies in our sample; in this case, for sources fainter than F277W = 30 mag (distinctively probed by our data set). Same information as in Figure  1  given.
Summary and Conclusions We present a sample of 44 z > 8 galaxy candidates identified in the NIRCam parallel observations of the MDS (centered in the HUDF and parallel pointing 2, respectively). These data reach around 2 mag deeper than those from previous surveys carried out during Early Release Observations, Early Release Science, and cycle (1) programs (e. g. , the SMACS0723 ERO survey, CEERS, and GLASS), reaching 5σ depths around 30. 8 mag. The median and quartiles of the magnitude distribution of our sample is F277W 30. 2 29. 8 30. 7 á ñ = mag. Aiming to reduce the effects of photometric uncertainties and a priori assumptions in the determination of redshifts, the selection of our z > 8 galaxy candidates is based on the analysis of SEDs measured in a variety of apertures and with different photometric redshift codes.
Acknowledgments The authors dedicate this Letter to the memory of the members of the  European Consortium MIRI  Team  Hans Ulrik Nørgaard-Nielsen  and  Olivier Le Fèvre , R. I. P. We thank the anonymous referee for their constructive comments that certainly helped to the robustness of this work. This work has made use of the Rainbow Cosmological Surveys Database, which is operated by the  Centro de Astrobiología (CAB) ,  CSIC-INTA , partnered with the  University of California Observatories  at Santa Cruz (UCO/Lick, UCSC). P. G. P. -G. and L. C. acknowledge support from  Spanish Ministerio de Ciencia e Innovación MCIN /AEI/10. 13039/501100011033  through grant  PGC2018-093499-B-I00. L. C. acknowledges financial support from  Comunidad de Madrid  under  Atracción de Talento  grant  2018-T2/TIC-11612.
Thus, the movements applied to the head were virtually reproduced by the avatar. The software was developed on the Unity3D platform (Unity technologies, San Francisco, USA). The tracker position was sampled at 60 Hz. The delay between the dummy head movement and avatar's movements was estimated at 58 ms. This delay includes: the transmission time between the movement of the tracker and its reception by the application (6 ms), the time for the application to calculate the position of the avatar (11 ms) and the time reaction of Unity3D (31 ms). The avatar movements were sampled at 120 Hz, for the calculation of the eye movements. The display (headset) refresh rate was 90 Hz. The system was run on a laptop computer (Intel ® CoreTMI7-8750H CPU at 2. 2 GHz, and 16. 0 GB RAM) with a graphic processor (GPU, NVIDIA GeForce RTX 2060) and 6 GB dedicated RAM.
Subjects with visual, motor, or cognitive pathology which could interfere with the test or those with a known intolerance to simulation were excluded. At inclusion, the above inclusion and exclusion criteria were verified and data regarding age, sex, resident's specialty, and seniority (semesters), hand laterality, prior knowledge of the HIT (yes/no), and prior practice of the test (yes/no) were collected. For the novices, 2 trials were conducted, before and after a training session. They were subjected to a questionnaire regarding the test difficulty and the confidence on the observations after each series. Experts were submitted to only one trial, did not undergo training, and answered the same questionnaire. The test was explained stereotypically as follows: "This is a clinical test to determine the presence and the side of a vestibular deficit.
Statistical analysis. Based on the hypothesis of an improvement in test sensitivity from 60 to 100% after training and considering a 40% dispersion, with an alpha error = 0. 05, for a bilateral paired t-test, the number of subjects was estimated at 7 individuals in each subgroup (expert, novice)  41. The number of subjects in the novice group was increased to 35 to insure the repeatability of the training effect. Graphpad Prism software (v. 6, Graphpad Inc. , San Diego, CA, United States) was used for statistics. The quantitative descriptive variables were described as means and standard error of the mean (SEM), and the qualitative descriptive variables by frequencies and percentages. A Fisher test was used to compare true positives and false positives. The comparison of VAS scores (maneuver and detection) was performed using a repeated-measures.
III. IP IMPAIRMENTS IN THE EMULRADIO4RAIL PLATFORM A. HIGH LEVEL VISION The Emulradio4Rail testing platform comprises two realtime LTE platforms built with Open Air Interface (OAI)  [25] , a Wi-Fi platform, and a satellite platform. Railway radio environments and their statistical behavior are emulated with a channel emulator. Two different channel emulators have been considered for the project, the Keysight (Propsim F32  [26] ) channel emulator and an FPGA-based channel emulator developed by IKERLAN  [27]  shown on Fig. 1  in the lab. Any Tapped-Delay Line (TDL) channel model, and more complex ones as geometrical ones, can be easily configured with respect to the performance limits of each channel emulator. The satellite platform is IP native and connected directly at the IP level. In this paper, we focus on the use of the LTE and Wi-Fi modules of the platform.
VII. ACKNOWLEDGMENT This work has been supported by the  H2020 Emulradio4Rail  project under Grant  826152. The authors also would like to thank  Keysight  warmly for allowing them to realize the tests with the powerful PROPSIM equipment. We also would like to thank  Nathalie Rolland  and  Rédha Kassi  for their help in the final integration of the platform at IRCICA (Research Institute on software and hardware devices for information and Advanced communication, a  Service and Research unit  ( USR-3380 ) associated with the  CNRS  and the  University of Lille ).
A public database such as the SRA (Sequence Read Archive) has reached 30 peta-bases of raw sequences and doubles its nucleotide content every two years. While BLAST-like methods can routinely search a sequence in a single genome or a small collection of genomes, making accessible such immense resources is out of reach for alignment-based strategies. In the last years, an abundant literature tackled the fundamental task of locating a sequence in an extensive dataset collection by converting the query and datasets to k-mers sets and computing their intersections. Among those methods, approximate membership query data structures conjugate the ability to query small signatures or variants while being scalable to collections of thousands of sequencing experiments. However, at present, more than 10,000 eukaryotic samples are not analyzable in reasonable time and space frames.
Here we present PAC, a novel approximate membership query data structure for querying collections of sequence datasets. PAC presents several advantages over the state-of-the-art, enabling users to scale to the next order of magnitude. PAC index construction works in a streaming fashion without any disk footprint besides the index itself. It shows a 3 to 6 fold improvement in construction time compared to other compressed methods for comparable index size. Using inverted indexes and a novel data structure dubbed aggregative Bloom filters, a PAC query can need single random access and be performed in constant time in favorable instances. Thanks to efficient partitioning techniques, index construction and queries can be performed in parallel with a low memory footprint. Using limited computation resources and in five days, we built a PAC index that included more than 30,000 human RNA-seq samples (corresponding to more than 40 billion distinct k-mers) to assess PAC's scalability.
Introduction Public databases such as the SRA (Sequence Read Archive) or ENA (European Nucleotide Archive) overflow with sequencing data. The vast amount of sequences, experiments, and species allows in principle ubiquitous applications for biologists and clinicians. Such databases are becoming a fundamental shared community resource for daily sequence analysis. But concretely, we only witness the onset their exploitation, while their exponential growth poses serious scalability challenges (for instance, SRA has reached 30 Peta-bases of raw reads and roughly doubles every two years  [1] ). Today, searching a query sequence in a single genome/dataset or a restrained collection of genomes is considered routine through alignment-based tools as BWA-MEM  [2] , BLAST  [3] , BEETLfastq  [4] , population BWT  [5]  or SRA-BLAST  [6].
On the contrary, the scale of databases such as SRA makes BLAST and any alignment-based method ill-suited because of the prohibitive cost of the alignment phase. Broader usages of such resources necessitate algorithms allowing hyperscalable membership queries as a foundation. Therefore recently, abundant literature tackled the fundamental task of locating a sequence in an extensive dataset collection using alignment-free, k-mer based strategies. The known most scalable solutions are sketching methods, which are based on the principle of locality-sensitive hashing. However, the loss of resolution implied by these methods narrows the query possibilities to large queries of the order of magnitude of genomes or larger. The query size is significantly smaller for numerous applications, e. g. , variant calling of SNP/small indels, finding alternative splicing sites, or other small genomic signatures.
Thus, recent alignment-free literature has described novel methodologies to fill a dual need: smallsized queries in vast datasets collections. Mainly two paradigms cover this question. Both consider each dataset and the query itself as k-mer sets (the distinction between the different approaches are summarized in Figure  1 ). The first type of approach relies on exact k-mer set representations. These methods build an associative index (using hash tables or FM-indexes) where the key set corresponds to all k-mers of the collection. K-mers are associated with bit-vectors that indicate their presence/absence in the different datasets. Jointly, some methods implement a co-assembly graph over the datasets. These methods (that we denoted as color aggregative in a previous survey  [7] ) are interesting for queries with high precision requirements or when the graph structure is needed.
Since exact representation is costly, color aggregative methods proposed different optimizations to increase their scalability. The main directions have been: efficient representations for the key set (i. e. , k-mers)  [8, 9] , key set compression via self-indexing  [10] , optimization of the values space requirements using minimal perfect hashing or other hashing techniques  [11, 8, 9, 12] , compression of the presence/absence information  [13, 14]  (and combinations of these strategies). However, we observe that this approach hardly scales to extensive instances with very large k-mer cardinality, and the literature shows they are currently limited to ∼2,500 eukaryotic datasets. The second family of methods relies on probabilistic set representation. These approximate membership query (AMQ) structures trade false positives during the query for improved speed and memory performances in comparison to the previous category.
The methods discussed in this paper fall in the category of AMQ structures. Unlike colored-aggregative methods, most AMQ methods separately represent each dataset's k-mers using Bloom filters. Bloom filters combine a very space-efficient set representation with the ability to stream k-mers for their construction and constant time queries. K-mers are then aggregated in a data structure allowing queries on the totality of the Bloom filters (hence we named these methods k-mer aggregative in  [7] ). Finally, a unique approach was proposed in SeqOthello  [15]. While it is conceptually close to color aggregative methods, as it works on the entire set of distinct k-mers from the collection of datasets, SeqOthello is also an AMQ relying on a different hashing strategy than Bloom filters. AMQ approaches enabled indexing hundreds of thousands of microbial datasets  [16].
For mammalian datasets, scaling to more samples is a timely challenge since the increase of k-mer/datasets content poses serious issues for the construction/footprint of current methods. Thus, to our knowledge, no published method has yet overcome the barrier set by SeqOthello  [15]  of indexing over 10,000 mammalian RNA samples. This manuscript proposes a novel AMQ method for querying biological sequences in collections of datasets dubbed PAC (Partitioned Aggregative Bloom Comb Trees). In our application case, queries are typically alternative splicing events or a short genomic context around a small variant or mutation. Our structure is designed to be highly scalable with the number of indexed samples and requires moderate resources.
Preliminary definitions A set of sequences R (also called dataset) is a set of finite strings over the alphabet Σ = {A, C, G, T }. In practice sets of sequences can be read sets or genome sets. K-mers are strings of fixed length k > 0 over these sets. An input data-base D = {R 1 ,. R n } is a set of datasets. Similarly to the datasets, a query is a finite string from which all distinct k-mers are extracted (we denote the query of a single k-mer a single query). Problem statement: The structures described hereafter compute the cardinality of the intersection of each dataset's k-mers with the query's k-mers. Then, according to a threshold parameter τ , the query is said to be in a dataset if their intersection is larger or equal to τ. A Bloom filter (BF) is a space-efficient data structure for inexact set representation.
For BFs, inexact means that queries to such a representation have a controlled rate of false positives and no false negatives. A BF has two parameters (b, h), which define the size of a bitvector and the number of hash functions of the filter. A Bloom filter maps each set element in the bitvector using h hash functions, and sets mapped bits to 1 (initially, all bits are 0's). The query follows the same principle and verifies that all hashed locations contain a 1. False positives on foreign keys happen because of hash collisions (which can be controlled by tuning (b, h)). Bloom filters' usage in this work: for fixed parameters (k, b, h), for each R i (0 < i ≤ n) in D, we build a Bloom filter BF i and populate BF i with R i 's k-mers. Thus, each Bloom filter represents the k-mers of a dataset from D.
Sequence sets of sets AMQ structures cheat-sheet We start by stating intuitive definitions for each type of structure. A sequence Bloom tree (SBT) is an approximate membership query structure introduced in  [19]. For a set of n Bloom filters built over an input database D, a SBT is a balanced binary tree that aggregates the k-mer contents in the different datasets of the input. We call a sequence Bloom matrix the matrix of BFs introduced in BIGSI  [20]. A Bloom filter is built for n datasets in an input database and becomes a matrix column. A SeqOthello is an AMQ structure relying on a different paradigm than sequence Bloom trees and matrices. It maps any distinct k-mer from the whole database to their presence/absence in datasets using compressed bit-vectors v. Pairs of (k-mer, vector) are associated using a static hashing strategy.
Operations Construction In sequence Bloom trees, the Bloom filters corresponding to the datasets are in the tree's leaves, and each internal node contains the union Bloom filter of its two children nodes (which is equivalent to a bitwise OR of the two children). All Bloom filters are built using the same parameters (b, h). Thus, the tree's root represents the union of all distinct k-mers in D. Construction of recent SBT's improvements  [21, 22]  (including the latest, HowDeSBT) involves a prior clustering of the leaves to obtain a tree's fill-up favorable to efficient compression. Sequence Bloom matrices use the property that all Bloom filters are constructed with the same parameters to stack them and obtain an inverted index on the BFs. Thus, a row accesses the same bit position in n Bloom filters.
In SeqOthello, an associative index handles (k-mer, presence/absence vector) pairs, built by combining two passes of Othello hashing. In short, Othello hashing is a static hashing strategy, which creates a surjection for sets of sets so that each key (here k-mer) is associated with a value corresponding to its origin set. SeqOthello hashes each k-mer to a bucket, then to an index in the bucket using this associativity. The index leads to a presence/absence vector. The static nature of the hashing makes it prone to false positives when querying foreign keys. All three methods require dataset processing (converting datasets to BFs for sequence Bloom trees and matrices, converting datasets to k-mer sets for SeqOthello). Once it is done, the index construction itself is in O(n) time.