dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Real time HEVC encoder The proposed SHVC encoder (SHVC-AE) relies on the core HEVC software encoder (HEVC-AE) developed by ATEME. As for the SHVC decoder, SHVC-AE instantiates multiple instances of the core HEVC-AE to encode the SHVC layers. The software HEVC-AE is also optimized in SSE2 instructions to speed-up, on Intel platform, the main HEVC coding operations including Intra prediction, motion compensation filters, DCT/DST transforms and in-loop filters. The encoding steps in the HEVC-AE involving video acquisition, preprocessing, Group of Pictures (GOP) construction and coding decision are pipelined as illustrated in Fig. 5. The first step manages the video acquisition from a file or from an external device (camera, Serial Digital Interface (SDI) card). Then, the pre-processing step adapts the input source video format to the encoder input format including color conversion and bitdepth adaptation.
The second optimization consists in the definition of restricted sets of  The complexity reduction of HEVC encoders has been widely investigated in the literature  [33] , the derivation of these setups is not investigated in this paper, which focuses more on the parallel and optimized software implementation of the SHVC codec. The third optimization considers parallel processing at different levels of the encoder to take advantage of multi-core platforms. The core HEVC-AE supports the Tile parallel processing defined in the HEVC standard. HEVC-AE can process in parallel multiple independent rectangular regions (Tiles) of one frame. This will speed-up the coding decision step at the expense of slight coding performance loss caused by Tile partitioning. The Tile parallel processing will be activated only in LIVE setups.
VI. CONCLUSION In this paper we have proposed a complete software implementation solution of the scalable extension of the HEVC standard. This solution includes both SHVC encoder and decoder based, respectively, on the core professional HEVC encoder (HEVC-AE) and the open source real time HEVC decoder (OpenHEVC ). Several optimizations have been integrated into the proposed scalable HEVC encoder (SHVC), resulting in three setups of the encoder FILE , LIVE and LIVE+. The SHVC-AE in FILE setup enables to reach a high ratedistortion performance close to the reference SHM with a speed-up of 37 and 66 in Intra and Inter coding configurations. The SHVC-AE in LIVE and LIVE+ setups enables real time encoding performance of 3840×2160p 30 fps video with an efficient inter-layer prediction. The complete solution, including the SHVC-AE and scalable OpenHEVC decoder, enables a real time encoding/decoding of 3840×2160p30 videos on multi-core Intel Xeon platform.
Moreover, the scalable OpenHEVC decoder enables to decode the BL in HD resolution on ARM mobile platform. Several improvements on the SHVC-AE can be investigated as future works. First, the proposed encoder can be extended to support the encoding of more than two layers (N layers). In addition, it would be interesting to investigate the performance of the encoder with other types of scalability including quality, bit-depth, color and codec. Finally, more algorithmic optimizations can be performed to improve the coding efficiency of the HEVC-AE encoder, especially in Inter coding configuration.
G-quadruplexes (G4) are non-canonical DNA and/or RNA secondary structures formed in guanine-rich regions. Given their over-representation in specific regions in the genome such as promoters and telomeres, they are likely to play important roles in key processes such as transcription, replication or RNA maturation. Putative G4-forming sequences (G4FS) have been reported in humans, yeast, bacteria, viruses and many organisms. Here we present the first mapping of G-quadruplex sequences in Dictyostelium discoideum, the social amoeba. 'Dicty' is an ame- boid protozoan with a small (34 Mb) and extremely AT rich genome (78%). As a consequence, very few G4-prone motifs are expected. An in silico analysis of the Dictyostelium genome with the G4Hunter software detected 249-1055 G4-prone motifs, depending on G4Hunter chosen threshold.
If we assume that this value holds true for most Dictyostelium genes, one can consider that upstream regions of 500 or 1000 bp may be correctly defined as promoter regions for most genes. The mean of the scored nucleic acid sequence is computed for a sliding window length arbitrarily set at 25 nucleotides (for G4Hunter principle see the reference  (25) ). Enrichment for G4FS in genomic features was computed with an in-house script using GenomicFeatures package tools. All bioinformatics procedures are described in a Rscript provided as a supplementary file. Regarding G4Hunter analysis, we advocate the use of different thresholds depending on the application or target (RNA, DNA). Rather than giving a Yes/No answer, G4Hunter provides a propensity score. In other words, some sequences may be more or less G4-prone, and a number of parameters may facilitate or hamper G4 formation.
Determination of molecularity by non-denaturing PAGE We then aimed at determining the molecularity of the selected D. discoideum sequences by gel electrophoresis. Sequences were already prepared and folded in 20 mM potassium phosphate buffer (KPi) at pH 6. 9 containing 70 mM potassium chloride (KCl) and 10% of deuterated water (D 2 O) for NMR. The same samples were used for gel electrophoresis. The oligonucleotides were loaded at two different concentrations (100 and 30 M) on native polyacrylamide gels supplemented with 10 mM KCl. Single stranded oligothymidylate dT n oligonucleotides were used for comparison. The bands were revealed first by UV shadowing (data not shown) and then with 'Stains-All' staining (Supplementary Figure  S6 ). The intensities of the bands correlate with the concentration of the oligonucleotide used.
AuMA affects the expression of D. discoideum genes containing G4-prone motifs in their promoters We then investigated the ability of AuMA to bind and affect the transcription of the selected D. discoideum genes. The effect of AuMA was tested using real time qRT-PCR. The D. discoideum strain HMX44A was treated by three different concentrations (10, 20 and 30 M) of AuMA and the total RNA was isolated after 24h of treatment. The IC50 (in- hibitory concentration) of AuMA in HMX44A was 30 M after 24 h of treatment (data not shown). The expression of histone H3a gene served as a control. We already verified that these selected sequences were present in the HMX44A strain by PCR amplification followed by sequencing (data not shown). AuMA decreased the expression of the different genes. The effect seemed low at 10 M, however, at 30 M, the effect of AuMA increased.
9%) and 1066/1522 (70. 0%) nucleotide sequences and a predicted targeting/ non-targeting status. Seek & Blastn correctly predicted the targeting/ non-targeting status of 293/304 (96. 4%) and 988/1066 (92. 7%) of the correctly extracted nucleotide sequences. A total of 38/39 (97. 4%) or 31/79 (39. 2%) Seek & Blastn predictions of incorrect nucleotide sequence reagent use were correct in the two literature corpora. Combined Seek & Blastn and manual analyses identified a list of 91 misidentified nucleotide sequence reagents, which could be built upon through future studies. In summary, incorrect nucleotide sequence reagents represent an under-recognized source of error within the biomedical literature, and fact checking tools such as Seek & Blastn may help to identify papers and manuscripts affected by these errors.
Of these 91 independent reagents, 26 (28. 6%) represented incorrectly identified sh/siRNA targeting reagents and the remaining 65 (71. 4%) sequences were incorrectly identified PCR primers, including one mutagenesis primer. All PCR primer sequences were cross-checked against the PrimerBank database  [53] , and a partial overlap was identified for one PCR primer only (S4 Table ). The described T/NT status of 48/91 (52. 7%) sequences was found to be incorrect, either as "non-targeting" sh/siRNA sequences that were identified to be targeting reagents, or as "targeting" reagents (sh/siRNA sequences, PCR and mutagenesis primers), for which no target for the claimed species could be identified. The remaining 43/91 (47. 3%) sequences were indicated to target a gene or sequence other than that described within the text.
Blastn component The blastn algorithm is widely used to verify whether a nucleotide sequence may target a particular gene or genomic sequence  [49]. To determine if the extracted T/NT claim reflects the corresponding sequence's verified identity, a blastn query is created for each extracted nucleotide sequence. Blastn queries analyze the human genomic and transcript database, as we have previously reported incorrect nucleotide sequences in human studies  [16]. The criteria for a targeting sequence require either (i) 100% sequence identity over at least 15 consecutive nucleotides including the 3' end nucleotide of the extracted sequence, or (ii) two different subsequences of the sequence query matching a single target with inverted homology or (iii) 100% sequence identity over at least 17 consecutive nucleotides.
Other Seek & Blastn outputs We previously used Google Scholar to identify other instances of misidentified nucleotide sequence reagents within the literature, recognizing that Google Scholar did not identify all instances of these sequences, possibly because of formatting limitations  [16]. For each sequence submitted to blastn analysis, a hyperlink is provided to the Google Scholar search results. Gene names, contaminated cell lines and species are automatically extracted from the text using named entity recognition techniques  [58, 71]. Named entity recognition was achieved using lists of known entities or gazetteers. The gazetteer for contaminated or misidentified cell line recognition was built on the Database of Cross-contaminated or Misidentified Cell Lines version 7. 2 established by the International Cell Line Authentication Committee  [52].
Acknowledgments We gratefully acknowledge the assistance of  Ms Rachael West  ( Children's Cancer Research Unit) , and funding from the  Post-Truth Initiative , a  Sydney University Research Excellence Initiative  ( SREI 2020 ) (to JAB), and from the  US Office of Research Integrity  grant  ORIIR180038-01-00  (to JAB and CL). This work was supported by donations to the  Children's Cancer Research Unit of the Children's Hospital at Westmead. The authors thank 2 anonymous reviewers for their helpful comments during the peer review process.
We propose here a method where sources are modulated sinusoidally in time using signal generators (no mechanical chopper) and projected in the form of single-frame patterns for processing using the SSOP method (no need for sequential frames). Sources are modulated in time to encode wavelengths and combined in a multifiber bundle connected to a spatial-light modulator. Light is then projected with specific patterns onto the field-of-view and images captured using a fast camera. The obtained threedimensional (3-D) cube of data (two-dimensions + time) is then processed in the temporal frequency domain to separate wavelengths, and the resulting images are processed in the spatial frequency domain using the SSOP methodology to extract optical properties for each wavelength and then calculate blood parameters (oxyhemoglobin, deoxyhemoglobin, and oxygenation).
Acquisition and Processing In this work, we performed acquisitions at a sampling frequency Fe ¼ 100 Hz, processing N ¼ 10 images, and modulated two wavelengths to determine oxygenation,  21  The number of images (N ¼ 10) was chosen such that all parameters have been adjusted to satisfy the conditions for proper DFT processing (Sec. 2. 1) while maintaining a proper signal-to-noise ratio (SNR) in the acquired images. We implemented a custom rolling window algorithm of the DFT method onto Matlab with the parallel computing toolbox (using a graphics processing unit, GPU) by adding the N þ 1 term of the DFT sum and subtracting the first term of the DFT sum in real time. More details regarding the GPU implementation of the processing method are available elsewhere. 20 ll acquisitions have been performed on the imaging system described in the next section.
Acquisition protocol SFDI and spatiotemporal modulation of light experiments were performed sequentially. SFDI was used as a reference by acquiring a total of six images for each wavelength (with no temporal modulation) at spatial frequencies of 0 and 0. 3 mm -1 with three evenly spaced phases each to extract the amplitude modulation and the calibrated diffuse reflectance. A lookup table (LUT) generated from Monte-Carlo was used to extract the optical properties for each wavelength following acquisition. Extensive details regarding the SFDI acquisition and LUT processing method can be found elsewhere. 7, 23, 24 patiotemporal acquisitions were performed at 30 Hz for 665 nm and 20 Hz for 860 nm. Acquisition rate of the camera was set at 100 fps. The SSOP projection frequency was 0. 3 mm -1 and the acquired data postprocessed, first in time to extract contributions from the sources, and then in space to extract the optical properties for each wavelength.
Materials. The experiment was a variation on an original paradigm used in several instances  31, 32  and conducted in a dimly lit room. The background sound level in the room was 45 dB, which is expected for a quiet room with a running computer. Participants sat 110 cm away from an extra-large screen (OLED SAMSUNG Smart TV), 125 cm large by 71 cm tall. Screen resolution was 1920 × 1080 px and refresh rate was 60 Hz. Luminance of the screen background was at around 18. 2 cd/m² on average. Loudspeakers were mounted on the sides of the screen, their center measured at 35° from the fixation point at the center of the screen. Stimuli were generated and responses recorded using the Psychophysics Toolbox for Matlab  48. Eye fixation was monitored using an Eyetribe tracking device (The Eye Tribe, Copenhagen).
We used their methods and adapted their analysis script to fit our needs. In the analysis pipeline used, we fit a separate SW distribution to each experimental condition for each participant, and considered the global RT distribution to be a mixture of these. We only modeled response time distributions for experimental conditions where we observed an effect of the SOA or congruence, which were the short pre-and post-cued SOAs (-150 ms and +150 ms). With 20 participants, this led us to fit 2 (SOAs) × 2 (congruence) × 20 (participants) individual SW distributions, leading to as many estimations for each separate parameter. 2 × 2 ANOVAs and Bonferroni-corrected 10,000 bootstrapped confidence intervals of the mean difference were then conducted for significance testing.
In order to check the precision of our numerical methods, we show in Fig. 40a  the values of the first two moments of the spectral function, ρ 0 and ρ 1 defined in Eq. (3. 17), which are to be compared with the exact results (3. 20) and (3. 21), i. e. ρ 0 = 1 and ρ 1 = m. Another check is provided in Fig. 40b , where we plot the energy computed from the real time data via Eq. (3. 103), and compare with the results obtained from the Euclidean formula (3. 102). Similar results are obtained for the U(n) 2 model at temperatures other than T = T c , used here for illustrative purposes, as well as for other thermodynamic quantities such as the fermion number (computed from Euclidean or real time data through (3. 96) and (3. 97), respectively). 12  for the solution at T = 0. 01). Plot (c) corresponds to the HO-like phase, where the low-temperature limit ρ(ω) → δ(ω -m) is already evident for T = 0.
Acknowledgements We would like to thank  Igor Klebanov ,  Juan Maldacena ,  Subir Sachdev ,  Douglas Stanford  and  Herman Verlinde  for useful discussions and comments. F. S. M. acknowledges affiliation to the  IBS Center for Theoretical Physics of the Universe (Seoul, South Korea ) during the initial stages of this project. This research is supported in part by the  Belgian Fonds National de la Recherche Scientifique FNRS  (convention IISN  4. 4503. 15 )and the  Fédération Wallonie-Bruxelles  ( Advanced ARC  project " Holography, Gauge Theories and Quantum Gravity "). This work was performed in part at the  Aspen Center for Physics , which is supported by the  National Science Foundation  grant  PHY-1607611 , and was partially supported by a grant from the  Simons Foundation.
This general philosophy of exploiting experimental data to empirically improve the model has been applied successfully to optimize the van der Waals parameters in the case of additive nonpolarizable force fields. 228 On the basis of a large database of experimental densities ρ and refractive indices n D of a range of structurally diverse ILs averaged molecular volumes ⟨V i ⟩ and molecular polarizabilities ⟨α i ⟩ of the cations and anions can be determined assuming no particular correlation between the molecules (see Figure  12 ). 145, 229 As the molecular composition of the ion pairs is known, a "designed regression"-analysis yields atomic volumes ⟨V iβ ⟩ and polarizabilities ⟨α iβ ⟩ of each chemical element involved, assuming again that there is no particular correlation between the atoms with respect to these properties.
These atomic values can be used to predict molecular polarizabilities of ion pairs which were not part of the initial database. Notably, these predictions match QM calculations at the MP2/aug-cc-pVTZ level  229, 230  despite the crude approximations made. Furthermore, the predicted density can be used as a starting point for the simulation box length in a constant pressure equilibration run. In addition, this approach is superior compared to classical quantitative structure-property relationship models  231  as physically mean-.
The scaling parameter λ varies between zero (disregarding ϵ iβ for the atom with the highest polarizability) and one (no scaling at all). It can be determined by comparison of computational and experimental data of the mass density and conductivity. 140 (4) The molecular dispersion coefficients C 6 can also be determined via the isotropic dynamic polarizabilities: 232,250 C i i 3 ( ) ( ) d 6 0 ∫ π α ν α ν ν = ∞ (2. 5. 16) and then scaled to the atomic property by the respective volumes:  232, 251, 252 C V V C 4 i i i i i 6 6 6 σ = ϵ = β β β β (2. 5. 17) Another possibility to obtain C 6 coefficients is from maximally localized Wannier functions. 253  choice of combining rules for the repulsion-dispersion interactions is also important but is often hard to assess due to the presence of electrostatic and polarizable interactions in polar or charged molecules.
334 In the context of the Drude force field neutron scattering data on methanol (MeOH) and tetrahydrofuran (THF), along with water, was used to validate the associated force field parameters. 335 Comparison of simulated and experimental partial structure factors yielded improved agreement with the Drude model for both MeOH and THF over the additive CHARMM36 force field. The Kirkwood-Buff theory represents another approach to obtain atomistic details from condensed phase simulations that may be directly compared to experimental data. 336 The Kirkwood-Buff theory has been used for a number of years as a guide for force field development  337  and can be used to understand the interactions of solutes with macromolecules. 338 n the context of the Drude force field, the Kirkwood-Buff theory was used to validate the amide and alcohol parameters.
Acknowledgments 7984 References 7984 ACKNOWLEDGMENTS We acknowledge support from the following grants: C. S. from the  Austrian Science Fund  ( FWF P28556-N34 ), D. B. from the  Army Research Laboratory  under cooperative agreement number ( W911NF-12-2-0023 ), and A. D. M. and B. R. from  National Institutes of Health  ( GM131710  and  GM072558 ). O. B. was supported as part of the  Joint Center for Energy Storage Research , an  Energy Innovations Hub  funded by the  U. S. Department of Energy, Office of Science, Basic Energy Sciences  through  IAA SN2020957.
The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists.
In the wake of this growth, different hotels, resorts and other building were erected to host these people in the exotic nature of the Pyrenees, known for its hot springs, mild weather, interesting local culture, and in the 20th century for its snow and white-water rivers too. These important activities greatly enriched the tangible cultural heritage (architecture, furniture, etc. ) in the Pyrenees and also the intangible part of it (traditions, social events, cuisine, etc. ) As of today, an important part of this inheritance has been catalogued and registered in several different databases across the south-west of France, but not all of it. We take part in a multidisciplinary European Project (FEDER TCVPyr) that aims to create a comprehensive catalogue of this cultural heritage related to tourism in the French Pyrenees.
This catalogue is yet to be finished, but at the end a database containing all of the most important points of interest (POIs) georeferenced for tourists in the region should be available. Examples of POIs include natural hot springs, casinos, hotels, villas, winter sport stations, other natural wonders, and even cultural events and traditions related to specific locations. In fact, this data has an enormous potential to be used not only by tourists, but also by experts (e. g. , historians and anthropologists), tourism providers, and the general public (tourists, local population, etc. ) In this context, we present two main contributions related to leveraging this cultural heritage data: (i) an open-source framework capable of exporting cultural heritage from our database to well-known open data providers such as Wikipedia and OpenStreetMap; and (ii) an open-source algorithm and framework capable of recommending a sequence of cultural heritage POIs to be visited by tourists according to their preferences, as well as the context and strong links between POIs.
Currently, the French government employs at least two different software (RenablLP2 and Gertrude), each one having different data models (format) to manage and store cultural heritage data. In addition, different (widely unknown) systems are also used to make this information available to the general public and experts. In our first main contribution, we aim to reduce this heterogeneity, especially for the general public, by unifying this data into a single model capable of being published in already established and widely accessed open data providers. We present an application that adopts this model and automatically publishes cultural heritage POIs into Wikipedia according to the principles of publication and reuse of machine-readable data proposed in  [1]. The second main contribution of this paper is the contextualized recommendation of itineraries (i.