dataesr/scientific-paragraphs-categorization huggingface dataset:
In practice, there are two difficulties to address. First, the current functionals allow simulating generic electrolytes because they are mainly based on the primitive model. In order to gain more insight into specific effects linked to the chemical nature of the ions, additional molecular ingredients need to be included in the functionals. Second, from a technical point of view, the main difficulty for simulating extended systems (such as the realistic structures of nanoporous carbons shown above) will be the reduction of the memory. In contrast to MD, which involves large CPU resources but for which software is now tuned to reduce the memory footprint, cDFT is a grid-based method, so this is currently a strong bottleneck. In the case of MD, even though significant effort has been devoted to accurately simulating the electrodes, some limitations remain.
Versatile video coding (VVC) is the next generation video coding standard released in July 2020. VVC introduces new coding tools enhancing the coding efficiency compared to its predecessor high efficiency video coding (HEVC). These new tools have a significant impact on the VVC software decoder with a complexity estimated to two times HEVC decoder complexity. In particular, the adaptive loop filter (ALF) introduced in VVC as an in-loop filter increases both the decoding complexity and memory usage. These concerns need to be carefully addressed regarding the design of an efficient hardware implementation of a VVC decoder. In this paper, we present an efficient hardware implementation of the ALF tool for VVC decoder. The proposed solution establishes a novel scanning order between Luma and Chroma components that reduces significantly the ALF memory.
IV. PROPOSED DESIGN This paper proposes a novel hardware-architecture for the VVC ALF filter, considering its three filters: 7 × 7, 5 × 5 and 3 × 4 CCALF. This design is based on the Working Draft 10  [24]  and the test model VTM10 of the VVC standard. The proposed design sustains a fixed throughput of 2 pixels/cycle with a fixed system latency that reaches processing a 4K video at 30 frames per second in 4:2:2 Chroma subsampling. The proposed design is integrated into a professional video decoder chip supporting multiple video coding standards. This section is composed of three subsections. Subsection IV-A describes the top level design of the ALF module, then subsection IV-B describes the different memory configurations. The proposed scanning order for the optimal memory usage is presented in subsection IV-B, and finally subsection IV-D describes the unified architecture for the Luma and Chroma filters.
A. ALF top level design Fig. 6  shows the top level design of the ALF module. The proposed design is decomposed of three main parts: the memory management, the gradients computation and the filtering module. The memory module (MEM-ORY ACCESS MODULE in Fig. 6 ) is the most critical part of the ALF module, and this design aims to reduce the cost of the memory. To find the lowest memory cost, we investigated multiple scanning orders for Luma and Chroma components. The lowest cost for memory is found when the scanning of Luma and Chroma samples is interlaced. This design uses two memories: the first stores Luma and Chroma macro blocks where the second one stores only Luma macro blocks. The memory handler takes care of memory accesses and selects the appropriate group of macro blocks to send to core module.
Compared to the proposed scanning order, it consumes 74% more memory. To conclude, the lowest memory can only be achieved when the Luma and Chroma macro blocks are interlaced, which led us to define our own scanning order which is an alternation between color components of the same CTU line. The ALF module alternates between Luma and Chroma macro blocks by reading for each CTU line one 4 × 2 Chroma Cr macro block followed by one 4 × 2 Chroma Cb macro block followed by two 4 × 4 Luma macro blocks. The proposed scanning order is described in details in the next subsection.
D. Luma/Chroma unified filtering architecture After selecting the right group of macro blocks from the memory, the next stage of the ALF depicted by the ALF CORE MODULE in Fig. 6 : (a) is executed. This module is composed of two sub-modules: the gradient module and the unified filtering module. The gradient module, applied only when Luma samples are processed, computes the sum of gradients and determines the classification value used to select the appropriate Luma filter coefficients which are then delivered to the filtering module. The filtering module is a unified design that uses 26 multipliers to apply the three Luma/Chroma filters : 7×7 DSF, 5×5 DSF and 3×4 CCALF. The gradient module is actually formed by three submodules: the gradient sum, the read-only memory (ROM), and the kernel transpose.
The first module is the gradient sum module. In this module the sum of gradients is computed following Equations (3), (  2 ), (  4 ) and  (5). Using the gradient result, the classification value is determined and the filter coefficients to be used for the Luma component are selected. When one of the Chroma components is chosen, the gradient sum module is bypassed and the it delivers directly the Chroma coefficients to the filtering module. These coefficients (whether Luma or Chroma) are synchronized with the required data and delivered to the filtering module. The second module is the ROM memory which is used to store the fixed Luma coefficients defined by the VVC standard. Luma components have also coefficients that are signaled in the APS. In all cases, from the ROM or from the APS, one Luma filter is selected.
B. ALF coding gain and software complexity The coding gain and software complexity of the ALF and CCALF modules under the VVC common Test conditions in three main coding configurations including all intra (AI), random access (RA) and low delay B are analysed. Table  III  gives the coding losses and the complexity reductions at both encoder and decoder when the ALF and CCALF tools are turned off. It can be noticed that disabling ALF and CCALF tools introduces a high coding losses for Chroma components for all configurations with a maximum of 25. 06% and 18. 58% BD-BR (U component) for low delay configuration when these two tools are disabled, respectively. Compared to SAO, disabling the ALF tools introduces larger coding loss specifically for the Chroma components. This is due to the fact that the ALF uses two filters for the Chroma components: the 5 × 5 DSF and the CCALF.
He was a postdoctoral researcher from Apr. 2013 to Aug. 2015 with VAADER team of IETR where he worked under collaborative project on HEVC video standardisation. Since Sept. 2015 he is an Associate Professor at INSA Rennes and a member of the VAADER team of IETR Lab. He has joined the Advanced Media Content Lab of b<>com IRT Research Institute as an academic member in Sept. 2017. His research interests focus on video coding and multimedia security. He is the author/coauthor of more than one hundred and forty papers at journals and conferences in image processing, two MPEG standards, three patents, several MPEG contributions, public datasets and open source software projects. Adrien Grill was born in 1988 in Aix-en-Provence, France. After receiving his engineering degree at Supelec in 2010, he specialized in hardware algorithm implementation.
4-Methylumbelliferyl phosphate was used as a substrate (Sigma, Saint-Quentin Fallavier, France). Measurements were run in triplicate. The fluorescence was measured at 440 nm (excitation 360 nm) using a Synergy HT reader (Bio-tek instruments, Colmar, France). Data from indirect ELISA were analysed using a repeated measures ANOVA with a post-hoc multiple comparison Tukey (Fig. 2A ) or a Dunnett's test (Fig. 2C ). A statistical cut-off of p < 0. 05 was used.
Unmodified peptide identification Identification of OVA peptides without modifications (except the oxidation of methionine Δm = +15. 99) and other proteins than OVA, was done against a databank restricted to proteins belonging to Taxon 9031 (Gallus_gallus) extracted from SwissProt (2,297 entries, updated on 2021/02/02) by using X!Tandem pipeline v. 0. 4. 34  (Langella et al. , 2017). Methionine oxidation was set as a variable modification and trypsin enzymatic cleavage was specified for the search with up to 3 miss-cleavage tolerance. Mass tolerance for precursors and fragments mass was set at +/-5 ppm or 5 ppm, respectively. Peptide identifications were validated by filtering the Peptide-Spectrum-Matches (PSM) with an e-value below 10 -3. Proteins were identified using at least two specific peptides and an e-value score below 10 -4.
Glycated peptides identification and localization of glycations Databank search was done using X!Tandem against a databank restricted to proteins belonging to Taxon 9031 (Gallus_gallus) extracted from SwissProt (2,297 entries, updated on 2021/02/02). Five types of adducts targeting Lys (K) and Arg (R) residues were searched as variable modifications. Names, structures, targeted residues and mass delta induced by these adducts are listed in Supplemental Fig. 2C. This first step allowed for the identification of peptides with a correct mass shift (depending on the type of glycation) and with a fragmentation profile sufficiently informative to confirm the peptide sequence. Glycated peptides (and residues) were filtered (and localized) using PTMProphet  (Shteynberg et al. , 2019)  which is included in the Trans-ProteomicPipeline (v6.
Linked peptides identification Cross-linked peptide identifications were done against a databank containing only the sequence of ovalbumin from SwissProt (P01012, OVAL_CHICK) using two dedicated databank search algorithms: pLink v. 2. 3. 9  (Chen et al. , 2019)  and Merox v. 2. 0. 1. 4 (Götze et al. , 2015). Briefly, mass tolerances for precursors and fragments were set at 3 ppm. Inbuilt FDR evaluation was used to filter identified peptides under a threshold of 1%. More detailed information about search parameters are available in Supplemental Table  2. A manual curation of identified MS/ MS spectra was performed on all peptide sequences identified by the two software, and only unambiguous spectra (for linker positions on sequences) were conserved after this step. To be considered as unambiguous, a MS/MS spectrum needed to present at least one signature fragment of the cross-link position on both peptides (e.
1A , compiling the knowledge from SDS-PAGE and DLS analyses regarding the structural changes of OVA at the protein level. Further information about the structural modifications of OVA induced by heat was provided by a global analysis of tandem mass spectrometry metadata collected from the tryptic peptides generated from the different OVA samples. These data can be represented in the form of a "heatmap", in which each spot represents the mass and the chromatographic retention time of a precursor peptide selected to be fragmented by MS/MS. Heatmaps are provided for all OVA model samples in Supplemental Fig. 4. In line with the data obtained using SDS-PAGE and DLS, the heatmaps showed a clear distinction of samples heated at 80 • C (OVA-W-H-80-6 h and OVA-W-HG-80-6 h) compared to OVA-N or to samples dry-heated at 55 • C (OVA-D-H-55-24 h, OVA-D-H-55-72 h, OVA-D-HG-55-24 h and OVA-D-HG-55-72 h).
rcsb. org/3d-sequ ence/1OVA). This confirms the limited level of OVA tertiary structure denaturation in samples moderately heated at 55 • C, and a much stronger impact on OVA structure in samples heated at 80 • C, the latter condition probably allowing the exposition of residues localized in hydrophobic (and buried) areas of the native structure. We also identified more AGEs in this sample, although only glucose and pyrraline adducts were unambiguously identified in mass spectrometry data, with most peptides substituted by one glucose unit. This indicates that the Maillard's reaction was not sufficiently advanced in all samples to cause the apparition of other AGEs. In this study, all MS/MS spectra were carefully and manually curated to list the glycated peptides. This manual curation of glycation data yields results with high confidence, as compared to unreviewed software-based assignments.
For example, we rejected all proposed hits that had no fragments on the spectrum clearly attesting the position of glycation. Consequently, the number of glycated residues remains modest as compared to other studies  (Liao et al. , 2018; Yang et al. , 2019) , which are otherwise comparable regarding the preparation of OVA samples. To allow the reader to examine our data, raw MS files have been placed in a public repository (PRIDE, PXD030366) Table  1.
, 2019). In the present work, fragments produced by HCD were measured at a resolving power of 30k in an orbitrap mass analyzer, with an extended m/z range (up to 4000 m/z). The combination of both technical features (i. e. , HCD and orbitrap) led to MS/MS spectra containing helpful information for the identification of bridged peptides, including peptides with a high m/z ratio and/or high charges, and the presence of highly charged fragments attributed to both peptides involved in the bridge. Dedicated software, such as pLink and MEROX, have been developed to decipher complex HCD fragmentation spectra of cross-linked peptides and were used in this study. However, these tools suffer from a high rate of false positive identifications as evidenced by a recent study  (Beveridge et al. , 2020).
fMRI analysis FSL software packages (FMRIB Software Library v6. 0), Oxford, UK  (Smith et al. , 2004; Jenkinson et al. , 2012)  were used to process the images. We adapted a method described by  Vahdat et al. (2011)  to pre-process the resting-state fMRI data using the first-level FEAT toolbox  (Woolrich et al. , 2001). The pre-processing pipeline was performed in the following order: five volumes in each scan were removed (to ensure equilibrium magnetization had been reached); non-brain removal using BET (Brain Extraction Tool); motion correction using the 6 DOF rigid body transformation implemented in FLIRT (FMRIB Linear Image Registration Tool); spatial smoothing (Gaussian kernel of FWHM, 5. 5 mm); temporal high-pass filtering (Gaussian-weighted least-squares straight-line fitting with σ = 100 s, equivalent to a cut-off frequency of 0.
ROI identification To determine the coordinates of ROIs for later seed-based analysis, we analyzed the task-based fMRI scans. The task-based data were preprocessed as follows: non-brain removal using BET (Brain Extraction Tool); motion correction using the 6 DOF rigid body transformation implemented in FLIRT (FMRIB Linear Image Registration Tool); motion outliers detection; spatial smoothing (Gaussian kernel of FWHM, 5 mm); temporal high-pass filtering (Gaussian-weighted least-squares straightline fitting with σ = 85 s); global intensity normalization. A regressor for activity versus the rest condition was modeled with a boxcar function as well as a regressor for the temporal derivative of the task timing, then the two regressors were convolved with a gamma hemodynamic response function. Z-score images were obtained from participant-level GLM analysis in FEAT and were then input to the higher-level FEAT for group-level analysis  (Woolrich et al.
Functional connectivity analysis associated with behavioral performance The objective of the functional connectivity (FC) analysis was to investigate the brain networks that were modulated by perceptual learning during a haptic task. We sought to identify the connections between brain areas for which the change in the strength of FC from the pre-to post-training scans, ΔFC, was correlated with BP. The average time series of all voxels in each of the seed ROIs from the pre-processed resting-state data were calculated on a per-participant basis and the correlation between this regressor and any other voxels in the brain was assessed in a participant-level GLM. Since the hemodynamic response function may vary among different subjects, sessions and regions of the brain, the temporal derivative of each seed ROI's regressor was also included in the GLM to take into account possible variations in the hemodynamic delay.
Machine learning algorithms are growing increasingly popular in particle physics analyses, where they are used for their ability to solve difficult classification and regression problems. While the tools are very powerful, they may often be under-or mis-utilised. In the following, we investigate the use of gradient boosting techniques as applicable to a generic particle physics problem. We use as an example a Beyond the Standard Model smuon collider analysis which applies to both current and future hadron colliders, and we compare our results to a traditional cut-and-count approach. In particular, we interrogate the use of metrics in imbalanced datasets which are characteristic of high energy physics problems, offering an alternative to the widely used area under the curve (auc) metric through a novel use of the F-score metric.
This offers an analysis of the utility of BDT models across a variety of scenarios, allowing for inclusive scans across parameter spaces. A high energy physics analysis at colliders traditionally consists of comparing real data obtained from a collider, such as the Large Hadron Collider (LHC) situated at CERN, Geneva, with simulated signal and background processes, to gauge the understanding of the results. The goal is to separate signal from background. The datasets are often largely imbalanced, and rare signal processes may mimic the distributions of relevant backgrounds. Additionally regions of feature space, which is a collection of features which characterise the data, often exhibit overlap from both distributions. A foundational method of analysis, 'cut-and-count', applies thresholds on discriminating variables (cuts) on both signal and background, and counts the number of signal s and background b events that pass the selection.
Once the optimal cuts have been obtained, the significance of the signal over the background can be measured using s and b, where there is some uncertainty, ∆b, associated to the background which includes statistical and systematic uncertainties. However, cutand-count analyses are not sensitive to the regions of significant overlap, where either large amounts of signal must be cut, or background kept and counted. While the number of signal events s also has associated to it some uncertainty, this is generically included in a quoted error band and is thus not explicitly accounted for here. Instead of relying on cut-and-count techniques, one may employ machine learning to classify signal and background events. A generic supervised machine learning algorithm takes as input the observables of a given dataset and performs a regression (where the model learns a continuous function) or classification task.
Instead, a decision tree may not reject events when they fail one criterion, but will consider whether other criteria may help classify them better. In this approach, which differs from the strict 'cut-and-count' of traditional analyses, we may see improvements in hard-to-reach signal parameter space. A generic machine learning algorithm is particularly useful in applications to large datasets, such as those produced at particle colliders, where it can be utilised to find new features in data. In particular, this work focuses on the use of machine learning tools, and in particular BDTs, to separate signal processes of interest from background processes, where the signal is almost always far more scarce. We employ here the XGBoost package  [25]  in addressing a classification problem typical of a particle physics analysis.
Machine learning in physics In the following, we describe the functioning of a machine learning algorithm from an optimisation perspective, and outline a number of key definitions. A central goal in this analysis is to compare the utility of three of metrics, which are used during the training and testing stages of the multivariate selection, and to outline the capabilities of BDT algorithms. A supervised learning problem assumes there exists a mapping f , which may be stochastic or deterministic, relating elements of dataset D, y = f (x), D = {x i , y i }, (2. 1) where x i are the input data, which have associated to them some features, and y i the final outcome to be predicted, or the target. The supervised learning goal for a classification problem is to approximate f given D using the characteristics of the objects and the classification as the target, returning an approximation to f : f = A(D) (2.
Boosted decision trees BDT multivariate techniques are a family of supervised machine learning tools whose robust classification power  [4]  makes them a popular choice in high energy physics applications. While state of the art deep learning with neural networks perform well in particle physics scenarios, they often require large amounts of data for training and are CPUintensive  [35]. In contrast, decision trees are robust machine learning algorithms, favoured by many due to their ability to deal well with missing values, imbalanced datasets, and redundant attributes  [36]. These characteristics, while signal-dependent, often appear in a generic dataset produced by the particle collider such as the LHC. For instance, features such as the angular separation ∆R ( 1 , 2 ) between two leptons 1 and 2 would be missing in an event where only one good quality lepton was detected.
At each step, the events which are misclassified (signal as background, or vice versa), are given a larger weight (not to be confused with the weights present in high energy physics simulations), or boosted, and the a new tree is built on the new weights. As such, learners which are added to the model later are better at classifying samples which were previously misclassified. In this way, the power of boosting lies not with the strength of the final tree (which would be expected to have very undesirable performance on the full dataset) but rather on the ensemble of all the week learners at once. In this work, we will consider binary trees, which output only two possible classes: 1 or 0. A boosting algorithm exploits a large number of weak learners h k where each improves the mistakes of the previous learner, creating a general meta-algorithm which can create a strong hypothesis from sum of weak learners with associated weights α k  [23] F (x) = Ntree k=1 α k h k (x), (2.
The splitting at each node should maximise the purity of the split or the separability of signal from background. A general boosting process with a generic loss function L(y i , f (x i )) proceeds by iteratively finding the next learner h i (x) to be added to the ensemble of decision trees for a dataset of size N. This is done through minimisation of the loss function N i=1 L n (y i , F n-1 (x i ) + α n h n (x i )) → min α,h , (2. 4) by finding appropriate values for the weight α n associated to the new learner h n (x i ), where F N -1 (x) is the function built up to that point. Gradient boosting machine learning algorithms utilise gradient descent  [39]  as a loss function, adding a learner on each iteration, and is a general approach to boosting, adding weak learners that approximate the gradient of the loss function.
Metrics and class imbalance A characteristic of high energy physics datasets, including real data from colliders and simulated events, is a large class imbalance. Typical datasets feature many more background events than signal events, where the background events may come from a number of processes which mimic the signal of interest. The signal can have many incarnations according to the model parameters, but which finally results in the same 'signature' which is characteristic of the process of interest. In order to measure the success of a given machine learning model, a metric should be introduced which quantifies the model's ability to correctly classify the signal and background events. The measure of classification performance is needed both for assessing the final result and for tuning the hyperparameters during training.