dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analysis The meta-analyses were performed using Review Manager Software version 5. 1 (The Nordic Cochrane Centre, The Cochrane Collaboration, Copenhagen, Denmark). The analyses were carried out using a random-effect model, and pooled-effect estimates were obtained by comparing the standardized mean difference between CFU/mL values obtained when a disinfection agent was used; against a control group when tap water was used. The standardized mean difference was performed since this statistic in meta-analysis is used when all the studies assess the same outcome but measure it in a variety of ways; for this to be appropriate, it must be assumed that between-study variation reflects only differences in measurement scales, such as the different scientific notation used among the studies included.
For the alginate materials, the use of disinfection agents such as chlorhexidine, alcohol, glutaraldehyde, and sodium hypochlorite significantly reduced the CFU/mL count (p < 0. 001). Irreversible hydrocolloids, the frequent material used in dentistry, tend to absorb both blood and saliva  [23]. Thus, research was focused on a solution to inhibit the colonization of microbe on the surface of these materials  [11]. Collected data were established on the CFU in a media culture. These were recorded by using a colony counter, and the counts were expressed by a standard technique of estimating microbial colony count known as the CFU count. The bacteriological examination evidently exhibited that the CFU recorded after disinfection were fewer than before disinfection  [23] , thus making the disinfection process an important issue to solve after taking an impression in the dental world.
Non-resorbable membranes There are four main types of non-resorbable membranes used in dentistry for periodontal regeneration procedures  (Soldatos et al. , 2017). These include: • expanded-polytetrafluoroethylene (e-PTFE); • dense-polytetrafluoroethylene (d-PTFE); • Titanium mesh; • Titanium-reinforced PTFE. 2. This review collected literature data concerning several membrane types. It intends to provide an overview on the major cellular events in the surgical wound and membrane surface. • Benefits of the non-resorbable membranes Biocompatibility and inertness are the main characteristics of these non-resorbable membranes  (Bottino and Thomas, 2015; Elgali et al. , 2017). These membranes are rigid and characterized by high biomechanical stability and the capability of retaining their shape  (Iviglia et al.
Conclusions Barrier membranes have been introduced into the oral and maxillofacial surgery field to support GBR and GTR plans. This review collected literature data concerning several membrane types. Recent advances in resorbable membranes have been discussed, such as the crosslinking technique which provides longer resorption time and optimal biomechanical properties, especially when compared to membranes from noncrosslinked collagen. Resorbable collagen membranes have drawn much attention, as they ensure a high success rate and cause fewer postoperative complications. Collagen membranes are now widely used in regenerative dentistry. In particular, bioactive collagen membranes are a popular choice in tissue engineering applications  (Allan et al. , 2021; Lee et al. , 2020). The use of regenerative therapies to treat periodontal damage is now popular.
Background: Bacterial biofilm formation (BBF) proves itself to be in the spotlight of microbiology research due to the wide variety of infections that it can be associated with, the involvement in food spoilage, industrial biofouling and perhaps sewage treatment. However, BBF remains difficult to study due to the lack of standardization of the existing methods and the expensive equipment needed. We aim to describe a new inexpensive and easy to reproduce protocol for a 3Dprinted microfluidic device that can be used to study BBF in a dynamic manner. Methods: We used the SolidWorks 3D CAD Software (EducationEdition 2019-2020, Dassault Systèmes, Vélizy-Villacoublay, France) to design the device and the Creality3D Ender 5 printer (Shenzhen Creality 3D Technology Co. , Ltd. , Shenzhen, China) for its manufacture.
3D-Printing of the Devices Two device types were printed-one plate type device for static BBF testing (Figure  1 ) and one microfluidic device for dynamic BBF testing (Figure  2 ). In order to properly describe the characteristics of BBF inside the device, we initially grew, analyzed and quantified BBF on the 3D-printed plate so that the interaction of bacteria with the polylactic acid (PLA) surface becomes clear. We chose a relatively simple design for the plates since our main interest was to observe the way bacteria interact with the surface and how much biofilm they produce. We have developed an "H" type microfluidic device for drug delivery, cell selection and dynamic study of biofilm growth using additive manufacturing (AM) as a manufacturing method. The system was designed using the SolidWorks 3D CAD Software (Edu-cationEdition 2019-2020, Dassault Systèmes, Vélizy-Villacoublay, France), having the overall dimensions of 54 mm × 24 mm × 4 mm (L × W × H).
Bacterial Cultivation and Biofilm Formation Since biofilm formation is a characteristic of many bacteria, it was challenging choosing the most relevant bacterial species. For the initial evaluation, we settled for some of the most common bacteria involved in biofilm-related infections: two Gram positive  We have developed an "H" type microfluidic device for drug delivery, cell selection and dynamic study of biofilm growth using additive manufacturing (AM) as a manufacturing method. The system was designed using the SolidWorks 3D CAD Software (Edu-cationEdition 2019-2020, Dassault Systèmes, Vélizy-Villacoublay, France), having the overall dimensions of 54 mm × 24 mm × 4 mm (L × W × H). The microfluidic device was printed using the Creality3D Ender 5 printer (Shenzhen Creality 3D Technology Co.
Bacterial Cultivation and Biofilm Formation Since biofilm formation is a characteristic of many bacteria, it was challenging choosing the most relevant bacterial species. For the initial evaluation, we settled for some of the most common bacteria involved in biofilm-related infections: two Gram positive We have developed an "H" type microfluidic device for drug delivery, cell selection and dynamic study of biofilm growth using additive manufacturing (AM) as a manufacturing method. The system was designed using the SolidWorks 3D CAD Software (EducationEdition 2019-2020, Dassault Systèmes, Vélizy-Villacoublay, France), having the overall dimensions of 54 mm × 24 mm × 4 mm (L × W × H). The microfluidic device was printed using the Creality3D Ender 5 printer (Shenzhen Creality 3D Technology Co.
Statistical Analysis For the analytical statistics of the collected data, we used SPSS Statistics version 25 (IBM, New York, NY, USA). We measured the biofilm concentration (expressed in mol/L) and we collected the data in a spreadsheet. Given that biofilm concentration represents a continuous variable, we tested the distribution of the data using the Shapiro-Wilk test in order to categorize it as either normal or non-normal, and we applied the relevant statistical tests (parametric tests-either t-test for comparison between two sets of data or ANOVA for comparison between multiple sets of data-for the former category and non-parametric tests-either Mann-Whitney U test for comparison between two sets of data or Kruskal-Wallis test for comparison between multiple sets of data-for the latter).
ACKNOWLEDGMENTS We thank  T. Northup ,  V. M. Stojanović  and  E. Demler  for discussions. TF acknowledges support for this work from the  Okinawa Institute of Science and Technology Graduate University. HL acknowledges support by a  Marie Curie Intra European Fellowship  within the 7th European Community Framework Programme. TF and GM acknowledge support from the  German Research Foundation  ( DFG, DACH project  " Quantum crystals of matter and light ") and  BMBF (Qu. com).
X-Ray Single Crystal Diffraction (XRSCD) XRSCD data were recorded on a Bruker Smart Apex single crystal diffractometer. A molybdenum IµS microfocus X-ray source was used, running at 50 kV and 0. 6 mA, emitting Mo-Κα radiation (= 0. 710731 Å). A Charge-Coupled Device (CCD chip: 4K, 62 mm) area detector was positioned at 6. 0 cm. The crystals were each mounted from a Paratone N oil drop onto a low background mylar MiTeGen loop. A full Ewald sphere of reflections was collected (3 omega scans of 680 frames with a frame width of 0. 3°). Accumulation time for Form 1 and Form 2 was respectively 100 and 80 seconds for each frame. For Form 1 -a non-merohedral twin-the orientation matrix and unit cell were established using CELL_NOW (v2008/4) program. The 3D reflection profile and the integration of all reflections were carried out with the SAINT (v8.
34A) program. The TWINABS (v2012/1) program [5-3] was used to correct for Lorentz and polarization effects and for absorption due to the sample; In addition both HKLF4 and HKLF5 data were generated to respectively solve and refine with SHELXTL (v2014/7) program suite. For Form 2 the orientation matrix and unit cell were established using the Apex2 (v2014. 11-0) program suite. The 3D reflection profile and the integration of all reflections were carried out with the SAINT (v8. 34A) program. The SADABS (v2014/5) program  was used to correct for Lorentz and polarization effects and for absorption due to the sample. For both forms the tentative space group was determined with the XPREP (v2014/2) program. The SHELXTL (v2014/7) program suite  was used to solve the structure by intrinsic phasing method and to refine the solution by full-matrix least-squares calculations on F².
Temperature High resolution X-ray powder diffraction (T-HR-XRPD) XRPD diagrams as a function of temperature were carried óut with a PANalytical X'Pert Pro MPD powder diffractometer using the Bragg-Brentano (vertical -2 configuration) parafócusing geómetry cóupled with an X'Celeratór detectór and an Antón-Paar TTK450 temperature chamber. A sealed copper anode X-ray tube was used, running at 45 kV and 40 mA levels. An incident beam monochromator (Johansson type) produces pure Cu Κ1 radiation (= 1. 54060 Ǻ). A thin layer óf the próduct was depósited ón a singlecrystal silicon wafer, cut out according to Si (510) crystallographic orientation that, by systematic extinction, impedes any Bragg reflection. Data were acquired isothermally from 278 to 423 K with a heating rate of 3 K/min between data collections.
Crystal structures Form 1: Several plate-like crystals were chosen and all were found to be twinned. The selected crystal was indexed as a two-component non merohedral twin using the CELL_NOW program [3-1]. The twin operator is a 180° rotation about c*. The reflections from the two domains were simultaneously integrated with the SAINT program. The TWINABS program [5-3] was used to carry out adsorption corrections and to produce the HKLF 5 format file for final refinement of both twin domains. The space group determined using XPREP was P21/c. All hydrogen atoms were located on a Fourier difference map and subsequently included in the refinement with one overall isotropic thermal parameter. The final least-squares refinement of 330 parameters against 4590 data resulted in residuals R1 (for 4169 Fo > 4(Fo)) and wR2 (for all data) of 0.
026 and 0. 066, respectively. The structure converged with nearly equal mass twin components (BASF = 0. 54), see Table  2  for details. One hydrogen atom of fumaric acid is found located on the external N atom of the diazabicyclo-nonane group of the ssr180711 molecule (dN-H = 0. 96 Å). This indicates the salt nature of SSR180711c compound in form I solid state. Form 2: A plate-like crystal was selected and indexed using APEX2 suite. The reflections were integrated with the SAINT program. The SADABS program was used to carry out adsorption corrections. The space group determined using XPREP was P21/n. All hydrogen atoms were located on a Fourier difference map and subsequently included in the refinement with one overall isotropic thermal parameter. The final least-squares refinement of 329 parameters against 4519 data resulted in residuals R1 (for 4335 Fo > 4(Fo)) and wR2 (for all data) of 0.
Introduction Dense Gaussian elimination over a finite field is a main building block in computational linear algebra. Driven by a large range of applications in computational sciences, parallel numerical dense LU factorization has been intensively studied for several decades which results in software of great maturity (e. g. , LINPACK is used for benchmarking the efficiency of the top 500 supercomputers). As in numerical linear algebra, exact dense Gaussian elimination is a key building block for problems that are dense by nature but also for large sparse problems, where some intermediate computations also involve dense linear algebra: -sparse direct methods, may switch to dense Gaussian elimination when the fill-in becomes too large  [30, §10. 3] ; -block iterative methods (like the block-Wiedemann or block-Lanczos algorithms) also require dense linear algebra to handle the block projections.
Using the latest version of gcc compiler we can also benefit from the feature of tasks with data-flow dependency of the OpenMP-4. 0 standard. In our experiments we use the depend clause of OpenMP-4. 0 to express dependencies between data produced and/or consumed by tasks which makes it possible to construct the DAG (directed acyclic graph) diagram that precomputes dependencies of all tasks before execution. This feature helps reduce the idle time of resources by removing unnecessary synchronizations. We will see in the next sections the im-pact of dataflow parallelization using the libkomp runtime that also implements the latest norms of OpenMP-4. 0. Data mapping on NUMA architecture. The efficiency of computations on a NUMA machine architecture can be disrupted due to remote accesses between different NUMA nodes.
But with the 3D-Inplace recursive variant we add synchronizations between tasks at each level of recursion. This can slow down the performance of this variant. The "#pragma omp taskwait" directive synchronizes all four tasks created before. So, the second task that rewrites in the block C 11 , for instance, needs to wait for all data of the matrix to be produced. Input: A = (a ij ) a m × k matrix over a field B = (b ij ) a k × n matrix Implementation 2 3D-Inplace recursive using OpenMP-4. 0 dependencies In this OpenMP Implementation 1, each task calls recursively the 3D-Inplace recursive routine. Using OpenMP 4. 0 directives helps specifying dependencies between tasks, indicating when to start the computations on a block once its data are produced. We show the OpenMP code of the 3D-Inplace recursive routine using the clause "depend" in Implementation 2.
The action descriptor is then defined as the collection of motion histograms from all the regions in a specific frame. Classic support vector machine (SVM) models are used to carry out the classification according to each time depth. The proposed approach is easy to implement, very fast and the representation is consistent to code a broad variety of actions thanks to a multi-level representation of motion primitives. The proposed approach was evaluated on different public action datasets showing competitive results (94% and 88. 7% of accuracy are achieved in KTH and UT datasets, respectively), and an efficient computation time.
Datasets The proposed approach has been evaluated on two well known public human action datasets presenting different levels of complexity, and different types of activity and human interaction. Here is a brief description of these datasets: KTH: contains six human action classes: walking, jogging, running, boxing, waving and clapping. Each action is performed by 25 subjects in four different scenarios with different scales, clothes and scene variations. This dataset contains a total of 2 391 video sequences. The proposed approach was evaluated following the original experimental setup which specifies training, validation and test groups  (Schuldt et al. , 2004)  as well as five-fold cross validation suggested in  (Liu et al. , 2009). UT-Interaction: contains six different human interactions between different people: shake-hands, point, hug, push, kick and punch  (Ryoo and Aggarwal, 2010).
Three different time depths ∆ i of 8, 16 and 32 frames were considered. The velocity components and the curvature of the trajectories were considered as kinematic features for the local representation of the trajectories. Firstly, the proposed motion descriptor was tested to classify actions in complete video-sequences. The label for a global video-sequence was defined following an occurrence criterion of the labels recovered in each frame, i. e. , the action that is predicted most often at the frame level is assigned as the sequence label. The motion histograms were computed for each kinematic feature individually. Table  1  shows the results for the different versions of trajectories and for every kinematic feature. In general, the kinematic histograms achieve an appropriate characterization of individual actions and interactions from the datasets, the orientation of the velocity θ providing the best results.
A concatenation of the three kinematic features was also tested, but the improvement in accuracy is only around 1%, while the size of the descriptor may turn prohibitive for online applications. The weak improvement provided by concatenating different kinematic histograms is probably due to a strong statistical dependency of these features. In Table  2  and Table  3  are summarized the average accuracies of different state-of-the-art approaches for automatic classification in KTH and UT-interaction datasets. The proposed approach achieves a good performance for different motion activities under very different scenarios, and a wide spectrum of human motion activities. Furthermore, it has the determining advantage to compute frame level action descriptors with fixed size, allowing the action label prediction of partial sequences in a computationally efficient way.
This representation is not convenient for actions that are more discriminant in peripheral regions, like waving or pointing. In average, for the orientation of velocity, the log-polar representation achieves an accuracy of 91. 19%, and 83% for KTH and UT datasets, respectively. Figure  3  illustrates the typical action recognition of the proposed method at the frame-level and for dif-   (Wang et al. , 2011)  94. 2 Proposed approach 94. 00 Laptev et. al. (Laptev et al. , 2008)  91. 8 Motion context  (Zhang et al. , 2008)  91. 33 Polar flow histogram  (Tabia et al. , 2012) 82. 33  Table  3 : Action recognition results on UT-interaction dataset for complete video sequences, compared to different state-of-the-art approaches.
CONCLUSIONS This paper presented a motion descriptor for the on-line recognition of human actions based on the spatio-temporal characterization of semi-dense trajectories. The proposed approach achieved competitive results on different public datasets while being intrinsically computationally efficient. A polar grid designed over each frame allows to code spatial regions of the activity. In each region, motion histograms are updated at each frame according to the dynamic of the trajectories. These histograms form a motion descriptor that is capable of recognition at each frame and then for partial video sequences. Integrated at different time depths, such descriptors represent multiscale statistics of kinematics features from the trajectories, and they are relatively independent on the visual appearance of the objects.
The saturation points can be extracted. This point corresponds to the saturation of the architecture. The saturation points are identical for the mono-FPGA and the platform in version1. Multiplexing data for the inter-FPGA communication (version2) changes the position of the saturation point. As the number of inter-FPGA crossed is extracted and specified for each packet, the timing performance can be extracted from case 1. Timing for transmitting packet through the aurora protocol (t aurora ) takes 24 cycles for initialization and 1 cycle/flit. FIFOs at the input (t FIFO_in ) and output (t FIFO_out ) take 2 cycles for controlling and 1 cycle/flit. Therefore it is possible to obtain the timing performance on the mono FPGA by removing these three latencies. For the experiment, the added latency for the inter-FPGA communication is 478 cycles.
Experimental Results We validate the performance of the CTS descriptor on two different datasets. The first dataset is an object-based scene: the 21-class UC Merced dataset  [55] , which was manually generated from large images from the USGS National Map Urban Area Imagery collection for various urban areas around the United States. The pixel resolution of this public domain imagery is approximately 0. 30 m. The second dataset contains two large HRS scenes: a large scene of Tongzhou (Scene-TZ)  [56]  and a large scene near the Tucson airport (Scene-AT)  [57] , which were both captured by the GeoEye-1 satellite sensor. The GeoEye-1 satellite includes a high-resolution CCD camera, which acquires images with a spatial resolution up to 0. 41 m in the panchromatic band and of up to 1. 65 m in the multi-spectral band.
In each experiment, we first introduce the dataset and experimental settings and then provide the results. We utilize the 21-class data set to test the coding method and color features. To generate more discriminative high-level descriptors, we compare the FV coding method  [54]  with the classical BOW  [58]  and LLC methods  [50]. To further demonstrate the efficiency of our method, we compare the CBPT with the Gray-BPT and the topographic map and subsequently compare color moments with color names. Next, we compare our CTS descriptor with other popular descriptors, such as BOVW (bag of SIFT), SC+ Pooling, and bag of colors. The two large satellite scene classification experiments first provide the direct visual effects of the classification result, which are then used for comparison with some popular satellite image classification methods.
Experiments on UC Merced Dataset The UC Merced dataset, which is a very challenging object-based HRS image dataset, has been widely used in HRS scene classification. A total of 100 images measuring 256 ˆ256 pixels were manually selected for each of the following 21 classes: agricultural, airplane, baseball, diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium-density residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts. Two typical examples of each class are shown in Figure  4.
Experiments on UC Merced Dataset The UC Merced dataset, which is a very challenging object-based HRS image dataset, has been widely used in HRS scene classification. A total of 100 images measuring 256 × 256 pixels were manually selected for each of the following 21 classes: agricultural, airplane, baseball, diamond, beach, buildings, chaparral, dense residential, forest, freeway, golf course, harbor, intersection, medium-density residential, mobile home park, overpass, parking lot, river, runway, sparse residential, storage tanks, and tennis courts. Two typical examples of each class are shown in Figure  4. To conduct the classification experiments, the number of randomly selected training samples per class is set to 80 images, and the remaining 20 samples are retained for testing. Once the popular descriptors' experimental settings on the UC Merced dataset are applied (BOVW, SPM, Bag of colors etc.