dataesr/scientific-paragraphs-categorization huggingface dataset:
Testing results grouped for such EEG types would not be representative. Therefore, metric values for the Dreams dataset were obtained for the entire recordings. Recordings in the InSleep dataset contain activity of a single stage. That allowed us to access the method performance on separate sleep stages and wakefulness. Table  2  reports the obtained results for the benchmark, and Table  3  contains values for the proposed method. Cohen's kappa for both methods on all datasets is shown in Figure  7. The proposed method, RPs, performed better than the benchmark in terms of FDR for the Dreams dataset (p-value < 0. 05) and InSleep REM and non-REM 2 data (p-value < 0. 05 and p-value < 0. 01, respectively). With respect to sensitivity, RPs did not outperform the benchmark. Finally, RPs significantly outperformed the benchmark method on non-REM 2 (p-value < 0.
01) and non-REM 3 (p-value < 0. 05) in terms of agreement with the expert scoring. A similar trend is observed for the InSleep dataset, as shown in Figure  7  (p-value < 0. 1). Visual inspection in the Dreams dataset revealed that the benchmark method tended to detect incorrectly as artifacts characteristic EEG patterns of non-dominant sleep stages such as high amplitude delta-waves corresponding to non-REM 3 activity in recordings with a dominant non-REM 2 sleep stage, as illustrated in Figure  8. FDR values obtained on InSleep non-REM 2 indicated a big proportion of false positive events. Both methods incorrectly detected injections of non-REM 3 sleep in non-REM 2. This activity is defined as high amplitude slow waves lasting 1-5 s. An example is provided in Figure  9a. Such events account for a small proportion of the recording for which the methods do not appear adapted.
Furthermore, the non-REM 2 sleep data also contained K-complexes and sleep spindles characterized as a single high-amplitude delta wave and sigma bursts, respectively. An example is depicted in Figure  9b. A wide range of patterns of such events complicates the detection for both methods. Moreover, artifacts in stages non-REM 2 and non-REM 3 occurred rarely according to Table  1 ; this made the metrics values for these datasets more sensitive to false detections. As we have mentioned, RPs utilizes 1-s epochs, which is a standard window length for Riemannian geometry analysis. We have also applied RPs using 2-s and 4-s epoch lengths to all datasets to test whether longer epoch lengths are more suitable for capturing the dynamics of slow waves. The obtained results are presented in Tables  4  and 5  respectively.
The results showed no significant increase in K in all datasets. In fact, a significant decrease in K was observed for wakefulness and REM InSleep (p-value < 0. 01). Indeed, the number of events corresponding to normal slow wave activity and incorrectly determined as artifacts in non-REM 2 and 3 decreased. However, using such modifications decreased the number of detected events in general and increased the number of false results. This is caused by an increased number of missed small artifacts. Moreover, the detected events were too long, which led to an increase of the FP value and, consequently, FDR.
time, s Additionally, we have applied FASTER  [25]  to all datasets. This method rejects epochs of a single-channel EEG based on calculated thresholds. The process is repeated for each channel. Mean, variance, amplitude range, and mean gradient are used for decision making. The results are presented in Table  6. The proposed RPs method significantly outperformed FASTER in K for all datasets (p-value < 0. 05 for InSleep non-REM 3, p-value < 0. 01 for others). Moreover, RPs achieves significantly better Se in InSleep wakefulness (p-value < 0. 01), REM, and non-REM 2 (p-value < 0. 05 for both datasets) and significantly smaller FDR for Dreams, InSleep non-REM 2 and 3 (p-value < 0. 01, p-value < 0. 05, and p-value < 0. 01, respectively). The method performed well in the detection of movement artifacts.
However, often, it incorrectly detected a high amplitude delta in non-REM 2 (Figure  9a ) and non-REM 3 (Figure  9c ) as an artifact. Furthermore, it missed many short and small-amplitude artifacts (Figure  9d ). For the estimation of time consumption, we ran FASTER and RPs on 15-min 19-channel recordings. A Windows 10 Pro computer with an Intel Core i7 2. 40-GHz CPU and 16 GB of RAM was used. Testing of FASTER, excluding the ICA decomposition, which was very time consuming, was performed using MATLAB R2014b. The processing time was 5. 44 ± 0. 01 s per recording. Testing of RPs was performed using Python 2. 7. The processing time of RPs was 43. 86-112. 63 s per recording depending on the number of constructed clusters. Cluster construction took 0. 03 s for a single cluster and 68. 72 s for the construction of six clusters.
Testing on the Dreams dataset resulted in 1-4 clusters. The number of clusters in the InSleep dataset varied in the range of 1-6 for awake and REM EEG and in the range of 1-4 for non-REM 2 and 3 activity. The number of clusters did not depend on the number of channels, but only on the presence of different activities, which could be distinguished in terms of the Riemannian geometry of the data.
Furthermore, the proposed method is scalable and may be applied for any number of channels. Nevertheless, using highly-correlated signals makes the application of the method cumbersome because the estimated covariance matrices may be badly conditioned. Therefore, in the case of a very large number of EEG channels, a dimensionality-reduction pre-processing step is recommended. To do this, methods inspired by the Riemannian geometry of SPD matrices may be used; see  [32, 33]. In comparison to FASTER, which relies on statistical properties in separate channels, the methods based on Riemannian geometry achieved better performance on all datasets. FASTER missed too many artifacts, mostly small ocular and short electrode artifacts. Nevertheless, all tested methods equally performed well on the detection of high frequency/amplitude artifacts.
Such artifacts prevailed in wakefulness, which allowed FASTER to achieve better performance among all datasets therein. For sleep, FASTER overestimated artifact activity, which lad to low performance. In non-REM 2, it detected only injections of non-REM 3 activity, which noticeably affected the metric values. All methods detected injections of non-REM 3 sleep into non-REM 2 as an artifact. Even if such events were not labeled by the expert as artifacts, their elimination led to improvement of the spectral characteristics of non-REM 2 activity. As for computational efforts, FASTER significantly outperformed RPs, since it utilized simple signal statistics, whereas RPs was based on eigenvalue estimations for computing Riemannian distances. The time required by the RPs method, however, is low in absolute terms and very acceptable for any practical purpose.
The method was not tested on high-density EEG. In this case, the problem of highly-correlated signals in high-density EEG must be carefully considered. Future work will also be focused on improvements of the method in order to eliminate false detections of K-complexes and sleep spindles. The application of the method is currently under consideration for semi-automatic artifact rejection at the National Institute of Mental Health of Czech Republic. At this stage, probability scoring will be provided along with data to support manual investigation. Additionally, the method will be extended to multimodal PSG recordings. Other directions of future research include application of the proposed method and analysis of the obtained clusters for automatic sleep stage identification using supervised machine learning approaches.
Conclusions The study presents an unsupervised multichannel artifact detection method for sleep EEG based on Riemannian geometry. The presented method forms clusters of artifact-free EEG data considering their spatial patterns. Application of the proposed method to sleep recordings brings significant benefits. The method is scalable, fully unsupervised and adaptive, independent of artifact types, and the outcome is easily interpreted. In comparison to the Riemannian potato artifact detector, it demonstrates better performance on complex sleep data in terms of agreement with human scoring and reduces the number of events incorrectly identified as artifacts. The RPs toolbox is available at https://gitlab. ciirc. cvut. cz/open-source/rps.
Dataset Sleep Stage Number of Subjects Length (minutes) Artifact Rate (%) Dreams 12 15 9. 38 ± 9. 84 Wakefulness 14 15. 38 ± 2. 26 18 ± 9. 9 InSleep REM Non-REM 2 12 10 13. 5 ± 1. 88 15. 3 ± 1. 77 7. 43 ± 5. 82 4. 07 ± 5. 33 Non-REM 3 8 14. 75 ± 2. 76 1. 97 ± 1. 43.
Thickness measurement The film thickness was measured on the silicon wafers using spectroscopic ellipsometry. On glass coverslips, the measurements were carried out using profilometry on scratched films. The consistency of the results obtained by ellipsometry or using profilometer profiles independently of the substrate used for a given chitosan solution permitted to use the measurement by ellipsometry as reference. The ellipsometer (SOPRA GES-5E) was set at an incident angle of 70°, very close to the silicon Brewster angle. At least three measurements were done on each film at different positions in order to verify the film homogeneity. Data were then processed using WINELLI (Sopra-SA) software. A Cauchy model was used to fit experimental data (cos Δ, tan Ψ), in the spectral range of 2. 0-4.
5 eV, depending on fits and regression qualities, to evaluate the thickness. The UV parameters A and B were respectively set to 1. 53 and 0. 002. A mechanical profilometer (Veeco Instruments) equipped with a cantilever of 2. 5 µm in diameter was used to measure film thickness on glass coverslips. For this purpose, the samples were previously scratched by tweezer to locally remove CS film. Data analysis was performed with VISION V4. 10 software from Veeco Instruments.
Image segmentation Image segmentation procedures were developed in Python. In order to provide a streamlined analysis procedure, we used the parameter-free threshold setting algorithm "iso_data" from the scikit-image python package  (29)  to extract the contours of the bacterial cells. For each contour, we then perform a singular value decomposition from the numpy library  (30)  to retrieve aligned and centered contours for each bacteria. We use defect analysis (provided by the opencv library) to detect the septum and split the contours. If the defects attributed to the septum are distant of less than 0. 5 µm and their center is less than 0. 3 µm from the cell center, the contour is considered to be composed of two cells, and is therefore split. From the detected and split contours, we then extract relevant morphometric data: • the contour area, • the contour length or perimeter, • the longer of the min area rectangle, • the width of the min area rectangle, • the circularity defined as 4/ℓ '.
Shown is an outlier detection obtained with the Scikit-learn one class scalable vector machine from the annotated dataset. For clarity, we only display the data corresponding to two dimensions, circularity and rectangular fill (see Methods for other parameters). Blue symbols: classified as normal; red symbols: classified as abnormal. Overall, there is a 10% rate of false negatives (normal contours labeled as abnormal) in both the training and test sets. This performance induces a noise is the growth curves and globally results in a 10% uncertainty in the computed growth rate. This uncertainty (which could be reduced with enhanced segmentation procedures) does not significantly affect the accuracy of MIC measurements.
In our proposed framework, the AR objects are available at different Levels-Of-Detail (LODs) and can be streamed independently from each other. An LOD adaptation heuristic is in charge of dynamically deciding what object should be fetched from the server and at what LOD level. Our proposed heuristic prioritizes content that is more likely to be viewed by the user and selects the best LOD to maximize the object's perceived visual quality. Moreover, the adaptation takes into account the available bandwidth resources to ensure a timely delivery of the AR objects. Experiments carried out over the Internet using an AR streaming prototype developed on an iOS device allow us to show the gains brought by the proposed framework. Particularly, our approach can decrease start-up latency up to 90% with respect to a download-and-play baseline, and decrease the amount of data needed to deliver the AR experience up to 79%, without sacrificing on the visual quality of the AR objects.
I. INTRODUCTION Augmented Reality (AR) represents the blending of digital content with the real world. The release of software development kits such as ARKit and ARCore  [1, 2]  has enabled more than 500 million devices to feature AR capabilities as of 2019  [3]. As headsets become cheaper and more widespread, business studies forecast an increased demand for AR content in the near future  [3]. To provide an immersive experience that resembles (as closely as possible) our interaction with the real world, AR applications should ideally feature both the highest possible visual quality and the lowest possible start-up latency. A *Worked performed while the author was in Adobe Research visual quality up to photorealism is extremely bandwidthconsuming, with data sizes ranging from tens  1  to hundreds 2  of megabytes, for a single AR object.
Streaming the AR scene can consistently reduce this amount, from a minimum of 14 % to a maximum of 79 %. The utility value proposed in Section III-B effectively captures when the quality of an object would need to be increased. Therefore, a higher LOD is requested only if it would result in an actual increase in perceived quality. Sessions with fewer data fetched from the server correspond to cases where the user did not get close enough to an object to trigger an LOD increase. We also measure the amount of unused data, e. g. , LODs that have been downloaded but never displayed to the user. This can happen because the user can quickly switch her focus to a different object, making the old LOD request not relevant anymore. As we discuss in Section V, FOV prediction algorithms can be used to reduce this behavior and also to ensure the user can always visualize the AR content at the highest possible visual quality, by anticipating the user's movements.
We are currently witnessing an ongoing technological revolution. The longstanding promise of artificial intelligent systems realized in neural networks is beginning to materialize 1. Significant milestones have been overcome such as, for instance, the deep neural network algorithm AlphaGo beating the world champion of the board game go. A neural network is a system of interconnected units, which is inspired by the mammalian brain. The units, called neurons, perform a simple basic non-linear process, and their inter-connections are called synapses  2. Neural network systems are implemented by either running software on a conventional (super) computer, as AlphaGo  3  , or directly in hardware by dedicated integrated CMOS (VLSI) circuits  4. A notable example of the latter is the chip TrueNorth, whose circuits emulate both, synaptic and neuronal functionalities  5.
We developed an ad-hoc acquisition software based on LabVIEW. The generation of the input voltages was at a rate of 100ksps. The acquisition of the input voltages was done synchronously. The oscillograms in Fig. 2  were acquired using an Agilent digital oscilloscope (DSO6104A) with standard (non-attenuated) probes. strongly correlated insulators, such as VO 2 , V 2 O 3 , NdNiO 3 , etc. , display qualitatively similar I-V characteristics to that of the SCRs. The key physical phenomenon in those systems is an unusual thermally driven first-order insulator-metal transition, which may also be induced by a strong electric field  [14] [15] [16]. An important and attractive feature is that while the Mott materials are challenging to control and fabricate, they may eventually enable the replacement of the whole SCR + RC block of the "soma" with a single two-terminal Mott insulator device  17, 18.
1) AVISPA DESCRIPTION AVISPA  [32]  (Automated Validation of Internet Security Protocols and Applications) is a push-button tool that takes as input HLPSL specifications. It permits the analysis of large-scale Internet security-sensitive protocols and applications. And SPAN, the Security Protocol ANimator for AVISPA, helps in interactively buiding Message Sequence Charts (MSC) of the protocol execution. We can see on the Figure  10  the architecture of this modular tool. A protocol in HLPSL is automatically translated into the rewrite based formalism IF (Intermediate Format). The IF language will be analysed by the back-ends of AVISPA. Each back-end implements one specific analysis technique. In fact, the AVISPA toolkit offers several backends for analyzing a HLPSL specification: OFMC (On-the-Fly Model-Checker), ATSE (Constraint-Logic-based Attack Searcher), SATMC (SAT-based Model-Checker) and TA4SP (Tree Automata tool based on Automatic Approximations for the Analysis of Security Protocols).
ACKNOWLEDGMENT The authors would like to thank the French road operator SANEF and the  laboratory IRSEEM-ESIGELEC  which closely support this work. Also, it is under contract with the  National Agency for Research and Technology (ANRT)  under the contract number  2015/1248 ; it has connections with the SCOOP@F project, which is co-funded by the  European Commission  under the  CEF  programme.
Indeed, experimental mapping of the HAS positions on the X chromosome by roX ChIRP-seq revealed that the individual HAS positions undergo rapid evolutionary turnover  (Quinn et al. 2016 ). However, it remained unclear how this would impact their interactions and the 3D conformation of the X chromosome, particularly in light of the extensive genomic rearrangements occurring in these species. We developed HiCAssembler, a Hi-C scaffolding tool allowing the assembly of genomes using Hi-C data combined with scaffolds obtained from shortand long-read sequencing that is compatible with our previously published package for Hi-C data processing, HiCExplorer  (Ramírez et al. 2018). Using these data and tools, we find extensive rearrangements within chromosomes, whereas higher-order genome topology (A/B compartments) and a subset of TADs appear to be maintained as conserved units.
busckii gDNA (Supplemental Fig. S1 ). The Illumina reads were combined with the error-corrected PacBio read data using DBG2OLC  (Ye et al. 2016 ) to obtain a total of 245 longer contigs with an N50 of 1. 4 Mb (Table  1 ). We then used the Hi-C data to produce chromosomelength assemblies of the D. busckii and D. virilis genomes. For this, we developed the algorithm "HiCAssembler," which uses strategies derived from LACHESIS  (Korbel and Lee 2013)  and 3D-DNA  (Dudchenko et al. 2017)  and is freely available at https://github. com/maxplanck-ie/ HiCAssembler. HiCAssembler uses the linking information from Hi-C contacts to order preassembled contigs/scaffolds depending on their contact frequency. This is possible because the Hi-C contact frequency follows a power-law decay by distance  (Lieberman-Aiden et al.
De novo D. busckii hybrid contig assembly To assemble the D. busckii genome we followed a hybrid approach (Fig. 1A ) in which we combined short Illumina and long PacBio reads. Illumina reads of female flies were obtained from  Vicoso and Bachtrog (2015)  and  Zhou and Bachtrog (2015)  accession codes SRR1795010, SRR1794619, SRR1794616, SRR1794617, SRR1794614, and SRR826809. These paired-end reads of whole-genome sequencing data were trimmed for adapters and sequencing quality using Trim Galore (v0. 4. 0). All reads (68 bp average length, 18. 8 Gb, 156× coverage) were merged like single-end data and assembled into contigs using SparseAssembler v20160205  (Ye et al. 2012)  with parameters "k 51 g 15 Node-CovTh 1 EdgeCovTh 0 TrimN 2 GS 240000000. " We did not include the paired-end information from Illumina reads using SparseAssembler to reduce errors introduced by heuristics usually applied in short-read assembly as gap closing or scaffolding.
Hi-C assembly algorithm To assemble the D. busckii and D. virilis genomes we used an iterative scaffolding strategy similar to 3D-DNA  (Dudchenko et al. 2017). To determine the order of scaffolds we used a maximum spanning tree as in LACHESIS  (Korbel and Lee 2013). Our algorithm is open source, easy to install, use, and is freely available at https://github. com/maxplanck-ie/HiCAssembler. Our assembly of chromosome-length Hi-C scaffolds can be done using preassembled short contigs or scaffolds and consists of the following steps (in this Materials and Methods section we will use "scaffolds" to refer to preassembled short contigs or already available scaffolds): Creation of corrected Hi-C contact matrix Read mapping: Reads are aligned to the preassembled scaffolds, each mate is aligned separately using BWA MEM  (Li 2013 ) with parameters -A1 -B4 -E50 -L0 (which promote a read to be split instead of adding a gap).
Merging initial matrix bins During each iteration, the Hi-C matrix is reduced by merging bins that belong to one Hi-C scaffold (Fig. 1C ). The internal bins of each Hi-C scaffold are merged into parts that are about the size of the smallest Hi-C scaffold. Thus, some Hi-C scaffolds' bins are all merged together into one new bin while other scaffolds may contain several new larger bins (Supplemental Fig. S3B , second panel). Merging bins allows more Hi-C data to be aggregated per bin and increases confidence in the analysis. HiCAssembler uses fast algorithms that efficiently merge matrix bins. Because each new bin is the result of merging a variable number of smaller bins, the new matrix is corrected using an optimized version of the iterative correction method  (Imakaev et al. 2012 ).
" In total, we sequenced 45. 7 M, 51. 8 M, and 64. 5 M useful Hi-C reads from stage 15-16 D. melanogaster, D. virilis, and D. busckii embryos, respectively, and 26. 8 M useful Hi-C reads from 21-to 23-h-old D. busckii embryos (Supplemental Table  S2 ). For validation of our TAD calling, we used Hi-C data sets from Kc167 cells  (Eagen et al. 2017 ), which we processed as described above. First eigenvector (PC1) corresponding to active (A) and inactive (B) compartments was computed using "hicPCA -noe1 -norm" from HiCExplorer v2. 2 after removal of heterochromatic chromosome ends. Corrected Hi-C matrices at restriction fragment resolution with 50 adjacent bins merged were used, resulting in matrices with a median bin size of ∼25 kb. The correct orientation of PC1, that is, positive values corresponding to the active compartment (A) and negative values corresponding to the inactive compartment (B), was verified for each chromosome using female H4K16ac ChIP-seq data (this study).
melanogaster TADs using BLASTn (blast+ v2. 6. 0) (Altschul et al. 1990; Camacho et al. 2009 ). First, a database of D. melanogaster TAD sequences was made using "makeblastdb -dbtype nucl. " Then, the sequences of D. virilis and D. busckii TADs were compared to this database and the best hit was retrieved using "blastn -outfmt 6 -evalue 1000 -max_target_ seqs 1. " Using this method, a BLASTn bitscore was assigned to each D. virilis or D. busckii TAD compared with D. melanogaster TADs, reflecting how well a given TAD in one species is conserved in the other species. Definition of conserved TADs D. melanogaster TADs giving above-median Jaccard similarity indices or bitscores from the comparison with D. busckii or D. virilis were overlapped using "bedtools intersect -f 0. 8 -r. " The intersect of both comparisons was overlapped and resulted in the definition of 175 conserved TADs covering 11 Mb of the D.