dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge support by  MIUR  grant  PRIN 2017L5W2PT. DM is also partially supported by the  INFN  grant SESAMO and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , grant agreement  833280  (FLAY). Acknowledgements The authors acknowledge support by  MIUR  grant  PRIN 2017L5W2PT. DM is also partially supported by the  INFN  grant SESAMO and by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme , grant agreement  833280  (FLAY). Acknowledgements The authors acknowledge support by  MIU   PRIN 2017L5W2PT. DM is also partially supp the INFN grant SESAMO and by the  European Council (ERC)  under the European Union's 2020 research and innovation programme, gra ment 833280 (FLAY).
Acknowledgements AMT thanks the Organisers of EPS-HEP2021 for the invitation; the speaker is grateful for the support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No. 860881  ( HIDDe network ) and from the  IN2P3 (CNRS)   Master  Project, " Flavour probes: lepton sector and beyond " ( 16-PH-169 ).
Finally, the spectrally dispersed beams are projected on a C-Red2 InGaAs camera  35  with <30 e -read noise. Characterisation of the photonic chip. The characterisation of the chip informs the parameters required for data processing and the required configuration of the instrument for a night of observation. Therefore, the optical propagation lengths of the waveguides and the characteristics of the splitters and couplers were studied. The first phase of characterisation included the measurement of optical path length differences between all pairs of waveguides. It was performed by scanning the phase of each baseline (by incrementally pistoning the associated MEMS segment) while the null-channel output intensities (integrated over a 200 nm bandwidth) were recorded. The position of the centre of the fringe envelope was then recovered by fitting with a simple model (a more comprehensive multi-wavelength model is detailed in the subsection "Data processing").
Consequently, spectral dispersion reduces bias on null depth measurements and improves precision over non-dispersed detection, as illustrated with on-sky data in the following sections. Astrophysical exploitation of the spectral diversity of null depths is also possible, but not yet done. Whereas fluctuations of injection were directly measured in each spectral channel and used in the data processing, the version of GLINT discussed here was not able to measure the instantaneous phase of the fringes. Thus, the fluctuations of phases were assumed not to be wavelength-dependent to first order; an approximation that had been tested and validated with the results in the following sections. Recovery of instantaneous fringe phase is planned for the next instrument upgrade, however even just correcting for chromatic injection, spectral dispersion delivers enhanced null depth measurements with less systematic error compared to broadband light.
Even so, the null depth is not quite zero, which may be due to factors not included in the model such as blurring of the fringes by vibrations faster than the frame rate. Performance on sky: measurement of two stellar diameters. GLINT was deployed on-sky to determine the apparent angular diameters of α Bootis and δ Virgo. α Bootis is a red giant branch star, with magnitude in H band of -2. 81 mag and with published angular diameter measurements between 19. 1 and 20. 4 mas 38 in K band. This is around 40% of the λ/D diffraction limit of the telescope in our band. The average seeing during the night of the 20th of June 2020 ranged between 0. 3 and 0. 5 arcseconds at 1600 nm, but with some significant wavefront error resulting from low-wind-effect and telescope vibrations. Data were acquired for 15 min at a frame rate of 1400 Hz for the pairs of baselines N1, N4 and N5, N6.
The result of this fit of null depth as a function of the baseline is given in Fig. 8a. The four source null depth points are an excellent match to the form of the expected curve and yield parameters within the range of expected literature values. The angular diameter found is 19. 7 ± 0. 1 mas with a reduced χ 2 of 52. 0. The uncertainty has been rescaled by the square root of the reduced χ 2. The χ 2 is high because the error bars are derived only from statistical diversity in the data and do not account for systematics (for example blurring due to seeing variations faster than the frame rate. ) To evaluate the efficacy of spectral dispersion, the stellar diameter was also obtained from spectrally binned null depth data over the same bandwidth. The found diameter is 24. 2 ± 1. 7 mas, which is systematically larger than the established value (between 19.
The real-time software of GLINT automatically performs this operation by mapping and interpolating the injection with respect to the tip-tilt of the segments. The second and last step consisted in finding the appropriate delay to be added by the MEMS mirror to achieve the deepest null depth for each baseline. Each baseline was scanned by pistoning the appropriate MEMS segment while the total flux of the null output was monitored, and fitted with a sine function to find the point corresponding to maximum contrast and to determine the actual OPD of the dark fringe for data processing. The optimal set of values for tip/tilt and piston were then stored. Once on-sky, these optimisation scans were repeated with starlight (in a smaller domain about the optimum found in the lab, to save time). This accounted for any mechanical drifts in the instrument between the lab and on-sky measurements, different static aberrations and atmospheric angular dispersion.
The drift in terms of OPD between the lab optimisation and on-sky was found to vary by between λ/8 and λ/2. Once the pair of baselines was optimised, data was acquired for 15 min with a frame rate of 1400 Hz. The same procedure was repeated for the second pair of baselines. Finally, dark frames were acquired for 15 min by closing the shutter (Fig. 1a ). Modelling the null depth. Analysis of nulling interferometry data by way of statistical distributions has been described by various authors  24, 29  ; here the model used is detailed with specific reference to extensions in the method for the richer set of observables delivered by GLINT. The interference between two beams within the chip is performed in a codirectional coupler, with the relationship between the inputs and the outputs of the coupler written as  48  : a out;i a out;j ! ¼ cosðκ ij L ij Þ Àj sinðκ ij L ij Þ Àj sinðκ ij L ij Þ cosðκ ij L ij Þ ! a in;i a in;j ! ; ð8Þ where a in,i/j (respectively a out,i/j ) is the complex amplitude of the incoming monochromatic wavefront of modulus |a in,i/j | and phase ϕ i/j (resp.
Acknowledgements This work was supported by the  Australian Research Council Discovery  Project  DP180103413. Critical fabrication was performed in part at the OptoFab node of the  Australian National Fabrication Facility utilising Commonwealth as well as NSW state government funding. S. G. acknowledges funding through a  Macquarie University Research Fellowship  ( 9201300682 ) and the  Australian Research Council Discovery Program  ( DE160100714 ). N. C. acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement  CoG -683029 ). The authors acknowledge support from the  JSPS  ( Grant-in-Aid for Research #   23340051 , #  26220704  #  23103002 ). This work was supported by the  Astrobiology Center (ABC)  of the  National Institutes of Natural Sciences, Japan  and the director's contingency fund at Subaru Telescope.
For the machine learning training phase, two main processes are proposed: The first proposed process consists of a direct learning using all the selected features in only one training phase, named direct learning blind visual quality assessment DLBQA. The second process is an indirect learning and consists of two training phases, named indirect learning blind visual quality assessment ILBQA. This second process includes an additional phase of construction of intermediary metrics used for the construction of the prediction model. The produced models are evaluated on many benchmarks image databases as TID2013, LIVE, and LIVE in the wild image quality challenge. The experimental results demonstrate that the proposed models produce the best visual perception quality prediction, compared to the state-of-theart models.
• extracting the features from images taken in the IQA databases, • removing the superfluous features according to the correlation between the extracted features, • grouping the linearly independent features to construct some intermediate metrics, and • using the produced metrics to construct the estimator model for perceived image quality assessment. Finally, we compare the designed models with the state-of-the-art models using the features extraction, but also the process using deep learning with convolutional neural network; as shown in Section 4. 1. To evaluate the performances of the produced models, we measure the correlation between objective and subjective quality scores using three correlation coefficients: 1. Pearson's linear correlation coefficient (PLCC), which is used to measure the degree of the relationship between linear related variables.
Direct Process (DLBQA) In the direct process, the whole set of 46 selected features is used by only one training phase to produce the final quality assessment model. The produced models have been called Direct Learning Blind visual Quality Assessment index based on Machine Learning (DLBQA-ML). Different machine learning methods have been used to construct assessment models. These include methods based on artificial neural networks (DLBQA-ANN), polynomial Nonlinear Regression (DLBQA-NLR), Support Vector Machine (DLBQA-SV M), Decision Tree (DLBQA-DT), and Fuzzy Logic (DLBQA-FL). This different machine learning methods have been presented in  [11, 40]  Table  4  resumes the results using different correlation scores on the test data by the DLBQA-ML models produced using different machine learning methods, on the TID2013  [41, 42] , LIVE  [8, 43, 44] , and LIVE challenge  [45, 46]  image databases.
Construction of Intermediate Models Having removed superfluous features, the next step consists of grouping the selected features into a set of classes. The produced classes are based on four principal axes: Each produced class is used to construct one intermediary metric. In each produced class, the features have been merged to generate the appropriate intermediary metrics. The pool process has been carried out, using different machine learning methods. Table  5  compares the result of applying four ML methods (fuzzy logic, support vector machine, decision tree, and artificial neural network) in order to build the intermediary metrics, using the feature classes. The comparison is represented by Spearman's correlation score between the produced intermediary metrics and the subjective scores give in different image databases.
Construction of Final Indirect Prediction Model Once the intermediary metrics are built, the next step in the indirect process is the construction of final prediction model using these intermediary metrics. The final evaluation index constructed consists of several models: four models of construction of the intermediary metrics, and a model of evaluation of the final score using the intermediary metrics. As the intermediary metrics have been constructed in the previous section, all that remains is the construction of the final prediction model, taking as inputs these intermediary metrics previously evaluated. These models are built using different machine learning methods. Table  7  resumes the prediction performance of ILBQA-based image quality models trained on TID213, LIVE, and Live challenge image databases.
The design of IQA models consists here of two training steps. The first step uses the decision tree approach in order to merge the quality features into four intermediary metrics. Moreover, the next training step is used different ML approaches (DT, FL, SV M, and ANN) for predicting quality score from the corresponding set of intermediary metrics. These results show that the ILBQA based on decision tree (DT) methods provide the best models for predicting image quality from the different image quality databases.
Experimental Results Having extracted and removed the superfluous features, the next step is to construct machine learning models, which automatically predict the perceived quality of an input image without a need to reference image. The construction process of the objective NR-IQA block essentially consists of training (with α% of data) and validation phases. During the training phase, machine learning algorithms are applied to create an appropriate mapping model that relates the features extracted from an image to subjective score provided in the image database. While the validation phase is used to estimate how well the trained ML models predict the perceived image quality, and to compare the accuracy and robustness of the different ML methods. The Monte Carlo Cross-Validation (MCCV) method  [47]  is also applied to compare and confirm the accuracy of the obtained results, as follows: • First, split the dataset with a fixed fraction (α%) into training and validation set, where α% of the samples are randomly assigned to the training process in order to design a model, and the remaining samples are used for assessing the predictive accuracy of the model.
Results Comparison Having built our evaluation models, the next step is the validation of these models, and the comparison of the results produced with those produced by the state-of-the-art models. The validation process is done using Monte Carlo Cross-Validation (MCCV)  [47]. In Figures  4 5 6 , different state-of-the-art no-reference IQA methods have been studied and compared with the two models design in this paper (DLBQA and ILBQA). These blind IQA methods consist of BRISQUE  [28]  that uses NSS in the spatial domain, GMLOGQA  [29]  that uses the gradient magnitude (GM) and the Laplacian of Gaussian (LOG) to make a predictor for image quality, and SSEQ  [30]  based on the spatial and spectral entropies. These figures depict the box-plot of Brownian's distance in the MCCV phase, of different noreference IQA methods, trained on TID2013, LIVE, and LIVE Challenge image databases, respectively.
They also show that the BIQA model that obtains the best prediction accuracy (with the highest mean and the smallest standard deviation) is designed by the proposed method ILBQA with two training steps. We compare the proposed models (DLBQA, ILBQA, and CNN-BQA) with six feature extraction based BIQA methods, and five CNN-based methods, on three popular benchmark IQA databases. The results are listed in Table  8. In the table, the best two SROCC and PLCC are highlighted in bold. The performances are the correlations between the subjective score (MOS/DMOS) take in image databases, and the evaluated objective scores given by the different studied NR-IQA methods in cross-validation phase with k = 1000. The main remark is that for every image databases (TID2013, LIVE, and LIVE Challenge), the proposed DLBQA and ILBQA methods perform the best results.
Computational Complexity In many image quality assessment applications, the computational complexity is an important factor when evaluating the produced BIQA models. In this paper, the feature extraction is based on four principal axes. Table  9  presents the percentage of time spent by each feature extraction axes. The run-time of the produced model is compared to the run-times of five other good NR-IQA indexes: BLIINDS-II  [50] , DIIVINE  [26] , BRISQUE  [28, 36] , GMLOGQA  [29] , and SSEQ  [30]. Table  10  presents the estimated run-time for an execution on CPU, in second(s), of the different methods, for a BMP image with resolution 512 × 768, on MATLAB, using a computer with 16 GB RAM and Intel Xeon dual-core CPU, 1. 70 GHz for each core. We observe that the run-time of the proposed method (ILBQA) is significantly inferior to DI IV I NE and BLI I ND-I I run-time, approximately equal to SSEQ run-time, and superior to BRISQUE and GMLOGQA run-time.
Implementation Architectures and Results After the construction of the no-reference image quality evaluation models, through the MATLAB tool, the next step in our process is the implementation of the produced models on an FPGA platform. The produced IP-Core can be integrated into the output of an image sensor to evaluate the quality of the produced images. The implemented index is the indirect process (ILBQA). This implementation is done with the Xilinx Vivado and Vivado HLS and is implemented on the Xilinx Virtex 7 (VC707) and Zynq 7000 (XC7Z020) platforms. The work in  [11]  introduces this phase of implementation of our proposed process on the objective no-reference image quality assessment. The general implementation process consists of the following.
SDK Implementation and Results The global design containing our models has been implemented on an FPGA board, then a program is implemented in the micro-controller (MicroBlaze) to evaluate the quality of the image thus received through the model implemented on FPGA. This program also return the objective score evaluated by the FPGA implemented models. The images are retrieved in "AXI-Stream" format and transmitted on the FPGA board through an extension using the HDMI protocol. The program is written in C/C++ codes, and passed to micro-controller via the JTAG interface of the FPGA board, thanks to Xilinx SDK tool. 120 images distorted by 24 types of distortion, from the TID2013 image database  [41, 42]  were evaluated by the produced models on the FPGA platform. FPGA implementation results were compared to the experimental results under MATLAB and also to the subjective scores taken the image database.
• Spreading 32-bit floating-point truncation errors in the FPGA system; • The difference between the results of the basic functions of the MATLAB and Vivado HLS tools. For example, the conversion functions in gray-scale, or the resizing functions using the bi-cubic interpolation, give slightly different results on MATLAB and Vivado HLS. These small errors are propagated throughout the evaluations, to finally cause the differences between the estimated scores shown in the previous figures.
Different machine learning methods were used to assess image quality and their performance was compared. The proposed methods consisting of artificial neural network (ANN), nonlinear polynomial regression (NLR), decision tree (DT), support vector machine (SV M), and fuzzy logic (FL) are discussed and compared. Both the stability and the robustness of designed models are evaluated using a variant of Monte Carlo crossvalidation (MCCV) 1000 randomly chosen validation image subsets. The accuracy of the produced models has been compared to the results produced by other state-of-theart NR-IQA methods. Implementation results on MATLAB and on the FPGA platform demonstrated the best performances for ILBQA proposed in this paper, which uses two training phases in the modeling process. One of the future work based on this article to present how to use our image quality assessment indexes in the control loop process for self-healing of the image sensor.
The "Simulated Falls and Daily Living Activities Data Set" was chosen to verify the effectiveness of the proposed algorithm, which comes from the UCI database, and is a commonly used standard test dataset for machine learning proposed by the University of California Irvine. The experiment and simulation results show that the proposed method has good real-time performance and high accuracy. The remainder of this paper is organized as follows. In Section 2, we describe the methods of preprocessing, feature extraction, and classification. In Section 3, we introduce the raw data and the hardware platform, and two sets of experiments are presented to verify the effectiveness of the proposed method. Then, we discuss the limitation of our method and future work. We conclude this paper in Section 4.
Data Preprocessing Since a fall action generally lasts 1-3 s, and the original data collection's length is about 15 s, the original data must be intercepted to collect valid data information. The sampling frequency is 25 Hz. Thus, a sliding window with a length of 75 is selected. That is, the detection time is 3 s. In order to intercept effective information, the Algorithm 1 is adopted. Take the x axis acceleration signal as an example, and the signal before and after the window interception ass shown in Figure  1. As shown in Figure  1 , in the original signal with a length of 15 s, only the data within a period of about 5-8 s are valid data. Using our window interception algorithm, effective data were extracted, which is very important for subsequent feature extraction.
Random forest combines hundreds of decision trees, trains each decision tree on slightly different observation sets, and considers only a limited number of features in each tree to split nodes. The final prediction of random forest is through averaging the predictions of each tree. The introduction of two random trees makes mitigates the chances of the random forest falling into over-fitting and provides it with good anti-noise ability. It can evaluate each variable's importance and perform feature selection on high-dimensional data, thereby making the model more robust. The calculation method of the importance of a certain feature X in a Random Forest is shown as follows: 1. For each decision tree in the Random Forest, we use the corresponding out-of-bag (OOB) data to calculate its error, denoted as err oob 1; 2.
Randomly add noise interference to the feature X of all samples of OOB data (the value of the sample at feature X can be randomly changed), and calculate its out-of-bag data error again and record it as err oob 2; 3. Assuming that there are N trees in the random forest, then the importance of feature is expressed as X = ∑ (err oob 2err oob 1)/N. After a certain feature is randomly added to the noise, the accuracy rate outside the bag is greatly reduced, which means that this feature greatly influences the classification result of the sample. 2. 4. Classification 2. 4. 1. Use SVM to Deal with Two Classification Problems SVM is a generalized linear classifier that classifies binary or multivariate data according to the supervised learning method, and its decision boundary is the maximum margin hyperplane for solving learning samples.
The "one-against-all" algorithm sequentially classifies the samples of a specific category into one category and other remaining samples into another category. In this way, the sample of N categories constructs N second-class classifiers. When classifying, the samples to be tested are classified into the category with the largest classification function value. Although this algorithm has the advantages of a small number of classifiers and a fast classification speed, due to the large difference in sample size between the two types, it is easy to cause the problem of dataset skew. At the same time, the "one-against-one" algorithm uses the training samples of any two categories to train a two-class classifier without repetition. For a multi-class classification problem with N categories of training samples, a total of N * (N -1)/2 second-class classifiers is required.