dataesr/scientific-paragraphs-categorization huggingface dataset:
2/1. 3, 300 mesh) using a Vitrobot (FEI). With 110,000-fold nominal magnification 855 micrographs have been recorded on a Tecnai Arctica electron microscope operating at 200 kV with a field emission gun using a Falcon III (FEI) direct electron detector in integrating mode directed by EPU data collection software (version 1. 5). Each movie was composed of 20 fractions. Each fraction contained six frames, i. e. , a total of 120 frames were recorded per micrograph. The sample was exposed for 3 s to a total dose of 70 e -/Å 2. Applied underfocus values ranged between 0. 2 and 1. 7 µm. The pixel size was calibrated to 0. 935 Å as calibrated using gold diffraction rings within the powerspectra of a cross grating grid (EMS, Hatfield). Details of data acquisition are summarized in Table  S8. Cryo-EM image processing and helical reconstruction.
MotionCor2 44 was used for movie correction and CTF parameters were fitted with Gctf  45. All image processing was done using RELION 2. 1  46. Fibrils were manually picked, and segments were extracted with an interbox distance of 10% of the box sizes, which yielded 69,282 segments. Box sizes were chosen as 200 pixels. Segments from micrographs for which Gctf estimated a resolution worse than 5 Å were discarded, which left 64,760 segments. 2D classification was used to select those classes that show sufficient detail in the class averages (Fig. S13 ). The initial model for 3D reconstruction was built with the Relion relion_helical_toolbox program with the -simulate helix option, which places spheres along a helix, using a rise of 40 Å and a twist of 21°. The initial model was refined using 3D classification (K = 3).
To obtain a robust resolution estimate, the FSC curve was fitted using 1/[e ((x-A)/B) + 1] C , yielding A = 0. 122, B = 0. 015, and C = 0. 228. The 0. 143 criterion then yields a resolution of 4. 0 Å (Fig. S14 ). Image processing and reconstruction details can be found in Table  S8. The final map was sharpened with the EMAN2 tool e2proc3d. py with a B-factor of -150 Å 2 , locally normalized and filtered to 3. 5 Å. The local resolution as shown in Fig. 2b-d  0. 468 Å). The (local) FSC calculation was done with the EMAN2 program e2fsc. py, using a FSC cutoff of 0. 5 for defining the resolution. Structure calculation. The hybrid structure calculation aims to combine the experimental data from NMR and cryo-EM. Distance restraints were derived from NMR peak lists and incorporated using a logistic restraint potential similar as described before  47.
To determine the structural variance in the dataset a bootstrapping analysis was performed. For this, 300 density maps were reconstructed (with fixed orientations and shifts) from randomly resampled (with replacement) sets of segment images, using the relion_reconstruct command with C6 and helical symmetry. The density variance calculated directly over such resampled density maps often leads to artifacts (strong noisy variance outside the particle and strong variance at symmetry axes). We therefore computed instead the isosurface variance map, which yields a clearer view of the structural variance. To compute the isosurface variance, all 300 density maps were first low-pass filtered, and then masks were computed using a density threshold of 0. 162. Then a Gaussian filter was applied to all 300 masks.
Data availability Solid-state NMR chemical shift assignments are deposited at the Biological Magnetic Resonance Data Bank under the accession code 27468. The cryo EM electron density map is deposited at the Electron Microscopy Data Bank under the accession code EMD-10792. Protein structures are deposited at the Protein Data Bank under the accession codes 6YEG (hybrid structure of the SPP1 tail tube by solid-state NMR and cryo EM; final EM refinement) and 6YQ5 (hybrid structure of the SPP1 tail tube by solid-state NMR and cryo EM; NMR ensemble). The authors declare that any other data supporting the findings of this study are available within the article and in its Supplementary Information, or from the authors upon request. Source data are provided with this paper.
and Phenix 50. MDFF simulations for 5 ns were used on intermediate models to guide model building in Coot. Finally, two different models were generated for final interpretation (and were also deposited to the PDB): one (ensemble of) models represents the ensemble from the final ISD refinement (PDB ID: 6YQ5). The other model (PDB ID: 6YEG) represents a standard refinement against the EM density (including cycles of Coot and Phenix) starting from the hybrid NMR-EM model obtained from ISD. The statistics of the solid-state NMR long-range distance restraints, the violations and the RMSDs of the final ensemble are summarized in Tables S9-11.
Acknowledgements We thank  Dr. Paulo Tavares  for valuable discussions and  Dr. Sascha Lange  for help with the sample production. This work was supported by the  Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP)  and the  European Research Council  ( ERC Starting Grant  to A. L. ). C. Ö. was supported by the  Human Frontier Science Program   LT000303/2019 -L. M. H. was supported by  Deutsche Forschungsgemeinschaft (DFG)  via project  B09  ( SFB 860 ). Molecular graphics and analyses were performed with  UCSF ChimeraX , developed by the  Resource for Biocomputing, Visualization, and Informatics at the University of California, San Francisco , with support from  National Institutes of Health   R01-GM129325  and the  Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases.
In the next phase, we force the discrete points in the curve tree to move towards the original point cloud by treating each point in the curve tree as a center of Gaussian, and points in the input cloud data as observations from the Gaussians. The task is to find the correct locations of the Gaussian centroids by maximizing a likelihood. The optimization technique is iterative and is based on the Expectation Maximization (EM) algorithm. The E-step estimates which Gaussian the observed point cloud was sampled from, and the M-step maximizes the negative log-likelihood that the observed points were sampled from the Gaussian Mixture Model (GMM) with respect to the model parameters. We experiment with several real world and synthetic datasets and demonstrate the robustness of the approach over the state-of-the-art.
The vertex v j is the child of v i denoted as, child (v i ) = {v j ∈ V |(v i , v j ) ∈ E}, and the vertex v i is the parent of v j denoted as v i = parent(v j ). A rooted tree graph is a graph G that contains no cycle, only one connected component, and there exists a unique vertex in V , called the root that has no parent. The descendants of a vertex v i ∈ V is defined as the set of vertices that belong to the branching system starting from v i excluding itself. Definition 1: Axial Tree, adapted from  [30] : An Axial Tree T is defined as a tree graph along with a successor function succ associated with each vertex in the graph defined as, succ : V → V, v i → v i , where v i ∈ child(v i ), 1 https://github. com/fredboudon/plantscan3d where v i is called the successor of v i. From the definition, we see that all the vertices v i ∈ V in an axial tree, there exists at most one successor v i ∈ V.
Given the fact that β splines are 1 st and 2 nd order continuous  [32] , we can approximate the branches as smooth enough. The blending function of a β-spline denoted as β(v), parameterized by v is given by, β(v) =                                  2 δ (2 + v) 3 , -2 ≤ v ≤ -1 1 δ ((t + 4s + 4s 2 ) -6(1 -s 2 )v -3v 2 (2 + t + 2s) -2v 3 (1 + t + s + s 2 )), -1 ≤ v ≤ 0 1 δ ((t + 4s + 4s 2 ) -6v(s -s 3 ) -3v 2 (t + 2s 2 + 2s 3 ) + 2v 3 (t + s + s 2 + s 3 )), 0 ≤ v ≤ 1 2 δ s 3 (2 -v) 3 , 1 ≤ v ≤ 2, (4) where v = nu -i, t is the tension parameter and s is the skew parameter (  [32] ,  [33] ), which are kept constant. We used t = 10 and s = 1 for all of our experiments. While the skew parameter is always set to 1 to ensure the smoothness of the curve, we have tested different values of t on several datasets.
IV. EXPERIMENTS AND ANALYSES We performed several experiments with real world and synthetic datasets in both qualitative and quantitative manners. More specifically, we have experimented with the data from 3 datasets. The first dataset we have used is the PlantScan3D dataset  [37] , which contains different varieties of plants including Cherry, and Apple tree point cloud data. Next, we used the dataset from Tabb et al. [26]. The dataset contains 'Fuji' apple tree data in voxel format. We have extracted the raw point cloud from the data, and performed experiments without using any connectivity information from the voxel structure. Finally, we have generated point cloud data by scanning real Arabidopsis thaliana plants. For all the datasets, we obtained the initial coarse skeleton using the method of Xu et al.
[15] , and performed the proposed optimization technique. Although other types of skeletonization methods can be used to obtain the initial skeleton, the result of the optimization algorithm depends on the quality of the initial skeleton. We have performed the optimization by using Space Colonization algorithm (  [20] ,  [21] ) as the initial skeleton, where we selected the set of parameters which produced best results. We noticed that in many cases, the results suffer from problems where the skeleton points are too far from the centerline. Especially the branch junction points are located out of the boundary of the point cloud in many cases. The optimization performs poorly in these cases than using the method of Xu et al. [15]  as the initial skeleton. We used Python 2. 7 version along with PlantScan3D library for the implementation.
The experiments were performed in a MacBook Pro with 2. 2 GHz Intel Core i7 processor and 32GB DDR4 RAM. The datasets in Figure  4  contain about 124k, 30k, and 18k points in the Apple tree, Cherry tree and Arabidopsis data, respectively. The respective computation times are 68 minutes, 81 seconds, and 34 seconds. First we demonstrate some qualitative results in Figure  3 , where we show the skeletonization results on Arabidopsis plant and Cherry tree point cloud data. For visual clarity, we show part of the original data for the Arabidopsis plant. Similarly for the Cherry tree data, we removed the noise and truncated part of a branch to demonstrate the quality of the skeleton. In Figure  4 , we show the full skeleton structures along with the original point cloud data for a 'Fuji' apple tree  [26] , cherry tree, and arabidopsis plant, respectively.
V. DISCUSSION In this work, we have proposed a technique to improve an existing skeleton by a stochastic optimization technique. The idea of exploiting the original point cloud data in the optimization process allows the skeleton to retain biological relevance with respect to the input point cloud data. The method is tested on different types of data, and improvement over the existing technique is demonstrated. We have demonstrated the effectiveness of the approach by quantifying the junction point location and branch length segment errors, where the branch segment is considered as the length between two consecutive junction points. While the performance varies among the type of the datasets, the optimization clearly improves over the initial skeleton obtained by state-of-the-art (  [15] ,  [20] ,  [21] ) in the general case.
Daly et al. [26]  have developed a software plugin GUI, called the Fully Online and Automated Artifact Removal for Brain-Computer Interfacing (FORCe). This plugin works based on the combination of Wavelet Decomposition, Independent Component Analysis and thresholding. FORCe runs in MATLAB and it is stated that it can be used for online BCI applications, making it the only software plugin that is able to perform significantly faster. Most recently, Tonachini et al. in  [27]  has developed an online automatic artifact rejection using artifact subspace reconstruction (ASR), online recursive independent component analysis (ORICA) and an IC classifier. However, the author has stated that ASR had negligible effect on eyeblink artifact removal, and the time it took for ORICA to converge well enough on the blink-related IC for the artifact to be removed is 26 seconds, which is a significant amount of time.
Optimal Use of EMD in Eyeblink Artifact Template Extraction In applications that require online monitoring of EEG signals, the applications could not wait until the entire EEG signal is recorded for analysis, it may take from few hours to days for an EEG recording to be completed. The eyeblink artifacts have to be removed instantaneously from the EEG recording as well. EMD algorithm is not recommended to be applied after the recording is complete, as it may cause a delay in interpreting an EEG signal and applying EMD on the entire EEG recording will cause the application to get computationally heavy. As an option, EMD can be applied repetitively on short segments, whenever an eyeblink artifact event is captured, provided the occurrence of eyeblinks are known. Unfortunately, EMD gets computationally inefficient and slow on repetitive application to a huge dataset especially during online recording and analysis, which may even disrupt the recording task.
Hence, the eyeblink artifacts and EEG signals are artificially generated as discussed in section 2. 4. 1. These artificial signals serve as ground truths in carrying out the performance evaluation, before applying the enhanced algorithm to real sets of EEG signals. In real EEG signal recordings, the EOG electrodes that capture eyeblinks are not recorded for convenience purposes. Additionally, there are no training data with blinking recorded so that the algorithm is fully automatic. Since EOG is not recorded, validation, if the eyeblink artifacts are removed turns out to be difficult. Thus, an expert's advice is sought to substantiate if the algorithm is able to remove eyeblink artifacts effectively through visual manual inspection (VMI).
The algorithm as a whole is an unsupervised and a fast approach that performs well in identifying and removing eyeblink artifacts while preserving the underlying neural information as revealed by the results. The computation environment for eyeblink artifact removal in EEG plays a vital role in online applications. Although eyeblink artifact removal is achievable using various methods, a medium or tool that supports and aid in speeding up the eyeblink artifact removal process has not been sufficiently studied. Currently, MATLAB is the most preferred tool used for research purposes, but for online applications MATLAB may not be a feasible platform. Hence, future work will be in the direction of executing the algorithm, FastEMD-CCA in an inexpensive computing environment such as in C++ and GPU based platform as to support online applications.
In this paper, we present a prospective data model and a query processing module for sensed data streams. Our approach offers a high-level logical view of Spatio-Temporal Data Series (STDS) as well as an internal physical model that combines aggressive indexing and partitioning over time and space to dissolve the heterogeneity and the variety of data. We introduce an incremental query processing approach within a distributed framework to take into account the real-time processing of continuous queries, the large volume, and the high velocity of data. Our contributions are as follows: -A high level logical view of STDS and a multi-granular physical data model that combines temporal and spatial partitioning. -An extension of a unified distributed framework for big stored and stream data. -A query optimizer within an incremental query processing model that offers a set of customized transformations rules for the optimization of spatiotemporal queries.
Such heterogeneous data sources should be taken into account in the model, and a harmonized view on the data is highly desirable in order to facilitate their processing and analytics. Multi-Granularity. Besides, one of the most fundamental characteristics of mobile sensor data is the diversity of their granularity, both under the temporal and spatial dimensions. The temporal domain is typically represented at different time granularities. The spatial entity can be represented using a hierarchical representation that describes the subdivision of the spatial domain into different regions or cells. Combining multiple datasets with several granularities or changing the granularity of a dataset are important analysis tasks that we intend to deal with. Thus, we need to define a multi-granularity framework that takes into account the definition of the spatial and temporal granularities.
). It builds a time series aware data structure (TimeSeriesRDD) that allows to associate a time range to each partition and preserves the temporal order of data. Flint does not consider the spatial dimension and the real time nature of queries. It also lacks optimization techniques for temporal queries. However, in real life applications, data collected from sensors is represented by multi-dimensional time series where one dimension corresponds to the spatial location traversed by moving objects over time. TimeScaleDB  [29]  extends PostgreSQL query planner, data model, and execution engine to support SQL queries on time series data. Internally, it splits tables into chunks, each chunk corresponds to a specific time interval and a region of the partition key's space (using hashing). The created partitions are disjoint, which allows the query planner to select only required chunks to resolve a query.
However, such an architecture inherits the limits of the Lambda architecture, as it needs to implement the transformation logic twice, once in the batch system and once in the stream processing system. In contrast to our proposal, PlanetSense does not propose to manipulate temporal operations specific to time series and lacks a representative data model for time series data taking into account the heterogeneity of data. Data Sources. There are two types of data ingestion in the system. The first is batch data ingestion (finite datasets) that consists of loading a previously acquired data (e. g. , loading the data from a previous campaign as a cold start of the data store). The second type corresponds to streaming data (infinite datasets) collected from current campaigns. Notice that we archive the acquired data for further batch analysis, when needed.
Indeed, our query optimizer injects optimization rules to avoid scanning all the data series and eliminates records that do not contribute to the query result. Our query processing module uses the Spark streaming DataFrames/Datasets API to combine batch and stream processing. This data structure allows to represent bounded data as well as streaming data. The advantage is that we can apply the same operations on static and streaming DataFrames. Indexing. We integrate the concept of indexing to achieve better query performances. We employ time slicing to divide the input data into multiple slices that are distributed on their time granularity. Data within each slice is further divided into sub-slices based on spatial indexing techniques. The key idea is to consider the temporal dimension first, which is important for time series analysis.
If the analysis needs to operate over raw time format, then we use the metadata to calculate the row number of the corresponding values. Our model allows to manage missing data, for example, if there is no value between two sequences, at the storage level nothing is stored, at the logical level, it is represented by an empty value denoted as '!'. To summarize the first phase of our proposed physical model, time series data is organized as sequences of lists where each list is represented by a granularity. This allows to structure data in a temporal hierarchy where each granularity is represented as a level in the hierarchy. In order to speed-up the access and the filtering on time series, and in particular to STDS data, we propose a model that alternates temporal and spatial indexing. We envision a two-level partitioning scheme, where the first level follows a global index, and the second depends on a second index.
Acknowledgements. This work benefited from the support of the project  POLLUS-COPE   ANR-15-CE22-0018  of the  French National Research Agency (ANR). It has been also supported by the  MASTER  project that has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie-Slodowska Curie grant  agreement  N. 777695.
A sensory fusion algorithm was proposed using Dempster Shaffer theory. This technique elaborated the combined relationship of changing site conditions and response of sensing units. Heat, wind and rain have been considered the main instrument for the most appropriate combination of sensors was selected using probability tables depending on different environmental and terrain conditions. Simulation has been performed in the MATLAB. The false alarm was investigated in MATLAB by considering an array of 100 × 100 which contained numbers that has been generated randomly. Decision theory has been applied to validate the presence and no show of target by comparing the signal to the threshold value. Dempster-Shaffer is a generalization of Bayesian inference. Dempster-Shaffer not only defined the probability of single variable but also to the mixture of the variables therefore it was considered appropriate for the sensor fusion  [17].
Sensor fusion algorithm are recommended for the higher accuracy, precision and resolution in investigation the false alarm and degradation in false alarm rate compared to the single transducer. Particularly if we take an example of PIR (Passive infrared sensors) in which many sensors output from PIR sensors were taken to investigate the presence of the real target and to identify the false alarm. Data was collected from shielded room to minimize the unwanted noise. Room was fitted with eight manually developed sensor modules on the walls. Time series issues were found to be the signal model for both the background noise signal and possible intrusion event signals were unknown. CDF (Cumulative distribution function) was applied and as soon as signal exceeded the threshold an intrusion was detected.
Frequency domain results were better than the time series. Sensor fusion algorithm has been developed to eliminate false alarm. The PCA dimension was selected to record at least 95 % of the variance and PCA coefficients were Gaussian distributed. The results were taken from post processing data that was recorded and observed by the test bed particularly analysis was performed on MATLAB  [15]. Nistara was a device interfaced with different sensors (thermal, fire, seismic) was installed at different points for sending signals to the main computer which implemented the fuzzy logic-based decisions. Images and other important information could also be propagated using cellular phones. The research included various sensors, master computer placed in a safest zone and in case of disaster there must be backup of master computer with others various functions like camera and voice recognition.
NEPTUNE, VENUS and CAMBRIDGE Bay have been administered by Ocean Networks Canada (ONC -oceannetworks. ca). Tremendous number of transducers was connected to the surface by undersea cables  [20]. Recently data can be accessed through more than 200 online equipment and can be preliminary seen on Oceans 2. 0. WARN received data as an input from pressure observer (lower surface of ocean), accelerometers and evaluated to identify the signs of seismic waves from earth. The practical example comprised of concrete software design to gather, analyze and discriminate the data from noise. Hydrographic radars were utilized to identify tsunami up to 150km. Dr. Carlos from University of British Columbia designed economical accelerator whose main motive was to discriminate the P wave (elastic wave). That observation also included the magnitude of earthquake and acceleration, orientation and height of the tornados and tsunamis  [20] ,  [21].