dataesr/scientific-paragraphs-categorization huggingface dataset:
Offline phase and online phase of CB-DSSD From a set of cyclically-recorded raw data (cycle time is 16 ms), representing signals of various sensors and actuators of the sorting system, a regrouping of these data is made, so that each 2 instances will represent a case. Then, an expert accompanies the construction phase of initial cases and expresses his/her opinion with regard to each case (normal behavior or failing behavior). In this stage, we obtain 52 normal cases and 10 abnormal cases. During online phase and at every 60 ms, the CB-DSSD elaborates the problem part of the TC (new problem). After that, it searches the TC first at NCB. If the TC is already in NCB, then diagnosis result will be normal operation (case type=N). An example of this situation is shown in figure  7. Then, we trigger a failure which corresponds to an existing case in ACB.
MOHAMMAD-ALI KHALIGHI (M'97-SM'07) is currently an Associate Professor with the Ecole Centrale Marseille and a Head of the Telecommunications and Antenna Processing Team, Fresnel Institute Research Laboratory. His main research areas of interest include signal processing for wireless communication systems with an emphasis on the physical layer aspects of free space, underwater, and indoor visible-light optical communications. He has served as the Technical Program Committee Member for over 20 international conferences and workshops in the communications area. He was the Vice Chair of WG2 of the FP7 IC1101 COST Action on optical wireless communications. He is currently the Project Coordinator of the H2020 ITN MSCA ''VISION'' on visiblelight communications. He received the Scientific Excellence Award from the French Ministry of Research and Higher Education for the period of 2009-2017.
We present CheSS, the "Chebyshev Sparse Solvers" library, which has been designed to solve typical problems arising in large scale electronic structure calculations using localized basis sets. The library is based on a flexible and efficient expansion in terms of Chebyshev polynomials, and presently features the calculation of the density matrix, the calculation of matrix powers for arbitrary powers, and the extraction of eigenvalues in a selected interval. CheSS is able to exploit the sparsity of the matrices and scales linearly with respect to the number of non-zero entries, making it well suited for large scale calculations. The approach is particularly adapted for setups leading to small spectral widths of the involved matrices, and outperforms alternative methods in this regime. By coupling CheSS to the DFT code BigDFT we show that such a favorable setup is indeed possible in practice.
The so-called Fermi Operator Expansion (FOE)  20, 21  , which gave the inspiration to the creation of CheSS, calculates the density matrix as a direct expansion of the Hamiltonian matrix in terms of Chebyshev polynomials. Another method, which is similar in spirit, writes the density matrix as a rational expansion  22, 23  , exploiting Cauchy's integral theorem in the complex plane. This method has the advantage that -unlike FOE -it only has to cope with the occupied states, which makes it advantageous for large basis sets containing many high energetic virtual states. One particular implementation is the PEXSI package  24  , which we will use later on for a comparison with CheSS. Other popular approaches to calculate the density matrix are the density-matrix minimization approach  25  , which calculates the density matrix by minimizing a target function with respect to F , and the divide-and-conquer method  26  , which is based on a partitioning of the density matrix into small subblocks.
We will show later that such a favorable regime can indeed be reached within the context of DFT calculations. In addition CheSS exploits the sparsity of the involved matrices and hence only calculates those elements that are non-zero. Obviously, this requires that the solution f (M) can reasonably well be represented within the predefined sparsity pattern; however, since the sparsity pattern is defined by the underlying physical or mathematical problem, we leave the responsibility of well defining this pattern to the code interfacing CheSS. If the cost of calculating one matrix element can be considered as constant (which is the case for a high degree of sparsity), we consequently reach an approach that scales linearly with respect to the number of non-zero elements. Hence, CheSS is an ideal library for linear scaling calculations, which are crucial for the treatment of large systems.
In summary, we present with CheSS a flexible and powerful framework to compute sparse matrix functions required in the context of large scale electronic structure calculations. If the above mentioned requirement -namely a small eigenvalue spectrum of the matrices -is fulfilled, CheSS can yield considerable performance boosts compared to other similar approaches. Hence, this library represents an interesting tool for any code working with localized basis functions that aims to perform such large scale calculations. The remainder of the paper is structured as follows: In Sec. II we first show the basic theory behind CheSS, starting with the applicability of CheSS for electronic structure calculations (Sec. II A), detailing the basic algorithm (Sec. II B) and available operations (Sec. II C), giving a brief discussion about sparsity (Sec.
However, there is also a dependence on the physical model that is used, for instance whether the low energetic cores states are absorbed into a pseudopotential or not. Since H has in general both negative and positive eigenvalues, the condition number is not a good measurement any more, and we therefore rather consider the total spectral width λ = maxmin. To see whether optimal conditions for CheSS -i. e. a small spectral width for both H and S -can be satisfied in practice, we investigated the properties of these two matrices for some typical calculations with BigDFT. This code  8, 9  uses a minimal set of quasi-orthogonal and in-situ optimized basis functions. The first property allows to keep the condition number of the overlap matrix small, whereas the second property -together with the fact that BigDFT uses pseudopotentials to handle the core electrons  42, 43  -allows to operate with a Hamiltonian exhibiting a small spectrum.
Thus, the fact that such low values can be reached within a DFT code illustrates the need for an algorithm that can exploit this feature and thus lead to very efficient calculations, and indeed CheSS is used with great success together with BigDFT. Moreover a low condition number of the overlap matrix is also crucial in the context of linear scaling algorithms, since it can be shown  48  that a localized and well-conditioned overlap matrix leads to an inverse with similar decay properties and finally also to an equally localized density matrix.
III. PERFORMANCE In the following we will present various benchmarks in order to evaluate the accuracy and performance of CheSS. The sparsity patterns of all matrices used for these tests are coming from calculations of small water droplets with the BigDFT 8,9 code. The buffer regions mentioned in Sec. II D are based on simple geometrical criteria; nevertheless this does not decrease the validity of the following tests, as the sparsity pattern is always something that depends on the specific application and is therefore determined by the code interfacing with CheSS. Moreover, we took -unless otherwise statedfrom the BigDFT runs only the sparsity pattern, but not the content of the matrices; rather they were filled with random numbers in order get the desired properties, like for instance the spectral width.
For the calculation of the inverse, there exist more specific routines within (Sca)LAPACK; however we nevertheless use the aforementioned approach as it is the most general one and allows -as does CheSSto calculate any desired power. With respect to PEXSI, we can invert a matrix using the Selected Inversion algorithm, which this package contains as well as it uses it for the pole expansion within the density kernel calculation. In Fig. 13  we show the timings that we obtain for the calculation of the inverse as a function of the condition number κ. As can be seen, CheSS is indeed the most efficient method for matrices with small values of κ. The only method that is competitive is the Selected Inversion, which does not show any dependence on the condition number and will therefore be faster for large values.
This depends, obviously, on the function itself, but also on the spectral width of the matrices. Whereas the first dependence is imposed by the specific application, the second one is as well related to the physical model and basis set that is employed. CheSS has been designed for matrices exhibiting a small eigenvalue spectrum, since this reduces the number of polynomials required to represent the function that shall be calculated. We used the library together with the DFT code BigDFT, which uses a minimal set of quasi-orthogonal in-situ optimized basis functions, leading to the required small eigenvalue spectra of the matrices. We showed that in such a favorable setup CheSS is able to clearly outperform other comparable approaches, and hence can considerably boost large scale DFT calculations.
Finally, the algorithm on which CheSS is built can be parallelized in a very efficient way, allowing the library to scale up to thousands of cores. In addition, the parallelism can already be well exploited for relatively small matrices, and consequently good speedups and low runtimes can be obtained for such systems. The initial performance of CheSS was evaluated within an performance audit by the Performance Optimization and Productivity center of excellence (POP), which helped to understand performance issues and gave recommendations and performance improvements  71. In addition, we continue to cooperate with POP to further analyze and optimize the parallel efficiency and scalability of the library. CheSS is been used intensively within the BigDFT code  8, 9  and is about to being coupled with the SIESTA code  10, 11.
V. ACKNOWLEDGMENTS We gratefully acknowledge the support of the MaX (SM) and POP (MW) projects, which have received funding from the  European Union 's  Horizon 2020 re-search and innovation programme  under grant agreement No. 676598  and  676553 , respectively. This work was also supported by the  Energy oriented Centre of Excellence (EoCoE) , grant agreement number  676629 , funded within the Horizon2020 framework of the  European Union , as well as by the  Next-Generation Supercomputer  project (the K computer project) and the FLAGSHIP2020 within the priority study5 (Development of new fundamental technologies for high-efficiency energy creation, conversion/storage and use) from the  Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. We (LG, DC, WD, TN) gratefully acknowledge the joint CEA-RIKEN collaboration action.
Acknowledgments The authors thank  Dr. D. Aouali  for his strong involvement in the present work and  Professor K. E. Cooksey  ( Environmental Biotechnology Consultants, Manhattan, USA ) for fruitful discussion and acknowledge  SAPC Division (UTC, France)  for surface characterizations. Caroline Richard  and  Jessem Landoulsi  acknowledge the support of the  "Mission Interministérielle et Interrégionale de l' Aménagement du Territoire du Bassin Parisien (MIIAT-BP) " and the  "Conseil Régional de Picardie " in the framework of  BIOCORYS  Project;  Smita Mitbavkar  acknowledges the support of  CSIR-NIO  (no. 6039 ).
Discussion of the finite element method developed for anisotropic Sobolev's spaces is given in  [26]. We are also confident that the same tools will allow us to study non-linear deformations problems. Finally we want to stress once more that the study of apparent pathologies can be a fruitful source of understanding in both mathematics and in the mathematical modelling of physical phenomena. Acknowledgement V. A. E. acknowledges the financial support by the Russian Science Foundation under grant "Methods of microstructural nonlinear analysis, wave dynamics and mechanics of composites for research and design of modern metamaterials and elements of structures made on its base" (No. 15-19-10008). Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http://creativecommons.
Injection molded part quality can be improved by precise process adjustment, which could rely on in-situ measurements of part quality. Geometrical and appearance quality (visually and sensory) requirements are increasing. However, direct measurement is often not feasible industrially. Therefore, process control must rely on a prediction of parts quality attributes. This study compares prediction performances of diverse neural networks architectures with "classical" regression algorithms. Dataset comes from inline industrial measurements. Regression was performed on 97 scalar statistical features extracted from multiple acquisitions sources: thermographic images and analog signals. Haralick features were extracted. Convolutional Neural Networks were trained on thermographic images and Long Short Term Memory networks were trained on raw signals.
A. Experimental data acquisition on inline industrial process Plastic injection process has more than thirty factors, from material property to external humidity, all of which are time dependent. An experimental trial was held at the IPC Center (Bellignat, France) in industrial conditions. We produce 204 100x100 millimeters box with different machine adjustments. The injection process has a small capability window to achieve completion of the injected part. Thus, we choose adjustments to maximize variance across the process capability window. A pressure sensor and a temperature sensor were placed inside the mold. Additionally, we acquired on the machine the hydraulic injection pressure and the screw position. Production cycles of 30 seconds were records for each signal at 100 Hertz (Fig. 2 ).
B. Model comparaisons We compare different regressive models using the Scikit-Learn library  [6]. Hyper-parameters were optimized with cross validation. We compare Mean Square Error (MSE) and R squared (R 2 ) regression scores using extracted signals  Authors version -accepted for publication in the Proceedings of the 2017 CIVEMSA conference -Annecy, June 2017 descriptors, thermographic images (Thermo) descriptors and both. Dataset was randomly split in 177 parts for training and 27 parts for testing from the 204 images. This dataset is small and this work can certainly be improved by using a larger set. Selected algorithms are : Least Absolute Shrinkage And Selection Operator (LASSO)  [11] , Elastic-Net  [6] , Support Vector Regression  [8] , Random Forest  [9] [10], K Nearest Neighbor  [11] , Stochastic Gradient Descent  [12] , Stochastic Gradient Boosting  [13] , Gradient Boosting regression  [14] , Decision Tree  [15] , Bagging Decision Tree  [16] , Ada Boosting Decision Tree (AdaBoost.
IV. CONVOLUTIONAL NEURAL NETWORKS ON THERMOGRAPHIC IMAGES A. Raw datasets preprocessing Contrary to the previous step where we extracted descriptors from signals and images, neural networks infer these descriptors with iterative backpropagation learning. Dataset used the same validation split: 177 parts for training and 27 parts for testing. This is a small dataset for training neural networks. Raw images of 156x156 pixels were reduced to 28x28 for training and standardized. This helps gradient convergence on our size limited training set. Furthermore, we could have use whitening or principal component analysis to decorrelate images. Images are single pixel valued, thus equivalent to grayscale: input tensor has a 28x28x1 shape.
B. Multi Layers Perceptron networks architectures Previous works  [22]  used a three layers MLP architectures (9-21-2) trained on 162 training parts. This achieved a 0. 80 R 2 score on parts weight prediction and a 0. 882 R 2 score on parts dimension prediction. All of our work was done using the TensorFlow library  [23]. Firstly, a simple MLP  [24]  of 2 Fully Connected layers (2 FC) was trained. First layer transforms the 28x28x1 vector input into a 128x1 vector; then the second layer computes the final continuous value which predict the standardized width of the produced part. We obtained a 0. 26 MSE and 0. 70 R 2 on the test dataset. These are better scores than our previous regressions on descriptors and this is improvable.
C. Convolutional Neural Networks architectures A successful architecture for image processing are Convolutional Neural Networks  [25]  (CNN). We evaluated multiple architectures with one to three convolutional layers, one and two fully connected (architecture is shown in Fig. 6  and results are shown in Fig. 5 ). Hyper-parameters were tuned for each network. State-of-the-art networks are going deeper by stacking layers, but a larger dataset is then clearly needed for training. Two 5 by 5 pixels' convolution layer are successively applied to the 28x28x1 image input vector. Then 2 Fully Connected layers transform the 3136x1 vector into a 1024x1 vector after FC1 and to the final continuous predicted value after FC2. Network architecture is shown in Fig. 3  using Tensorboard. Maxpooling (2x2 width and 2 stride) after each convolutional layers and Rectified Linear Unit (ReLu)  [26]  before the Fully Connected layer were evaluated and validated.
D. Hyperparameters tuning Batch size was chosen to fit with dataset. Best results were obtained with 17 images randomly chosen for each batch. The common L1 distance for regression problem was used as a loss function. Mean Error, Mean Square Error, Root Mean Square and Huber loss were also evaluated but convergence was slower. To reduce overfitting, a 0. 01 L2 penalty was set on backpropagation and a 0. 90 dropout probability was set before the Fully Connected layers. Learning rate was set to 0. 001 per iterations which is lower enough to guaranty gradient descent convergence. Stochastic gradient descent and RMSprop optimizer  [27]  algorithms were evaluated but best results were achieved with Adaptive Momentum stochastic gradient descent optimizer (Adam)  [28]  with suggested defaults parameters: 0.
E. Results analysis In comparison with regression on multiple descriptors, we obtained a 0. 13 Mean Squared Error on the test set and 0. 85 R 2 correlation. Best results were obtained with a 2 CNN 2 FC networks (Fig. 7 ). Using shorthand notation, architecture is C(32, 5, 1)-P-C(64, 5, 1)-P-D-N-FC(3136)-FC(1024), where C(d, f, s) indicates a convolutional layer with d filters of spatial size f×f, applied to the input with stride s. D is a dropout layer. N is a non-linear ReLu layer. FC(n) is a fully connected layer with n nodes. The trained weights of convolutional filters were extracted for visualization (inspired by  [30]  the first layer is shown in Fig. 9 ). Filters started to react to real patterns, but generalization is not achieved because of the small training dataset. L2 regularization and ReLu did not limit overfitting on this small architecture.
C. Prediction scores analysis Results (Fig. 11 ) show better performance for the unique LSTM layer architecture. This is probably due to the small training dataset, which increased overfitting on a small network. Recurrent networks architecture is also complex. Specific dataset preprocessing must be study. However, scores are higher than all previously evaluated prediction algorithms and prediction systems from the literature. Results are encouraging. Furthers works will be held on recurrent network tuning for this specific industrial application.
However, setting up a neural network architecture takes probably the same time as developing application specific images and signals processing system. The most important work for neural network training is to build a large dataset. The size of the dataset is the main limit of our present study. An open source and collaborative dataset for solid mechanics engineering would help to develop and validate new neural networks architectures. Adaptive control could rely on a prediction of the produced part quality. In this paper, we compare algorithms to predict a unique continuous characteristic. Furthers works will study multiple quality characteristics prediction. We study geometrical quality which is crucial for technical parts but other sensorial qualities (haptic and visual) are also decisive industrial challenges.
Coordination rules The three operations rd(), get() and put() are used within production rules  [9]. A production rule is composed of a precondition phase and a performance phase. The precondition phase is a sequence of rd() operations which detect or wait for the presence of tuples in several given bags. The tuples are, for instance, values from sensors, external events, or results of service calls. The output fields of a rd() operation can be used to define input fields of subsequent rd() operations. The precondition builds an inference tree with right propagation. A rd() is blocked until at least one tuple corresponding to the input pattern is available. In addition to read in the tuple spaces it is possible to call ASSERT and COMPUTE functions. Both can execute any Python code. ASSERT functions return a boolean value that stops the inference tree if false and triggers the subsequent token (rd, ASSERT or COMPUTE) if true.
Applications developed with LINC More than a dozen of demonstrators have been developed with LINC, within European or national projects, with industrial partners, in several domains. LINC is now transferred to industry through the start-up Bag-Era 7  which is responsible for its support and commercialisation. This section details a few examples showing what LINC and its tools can be used for. In the building automation domain, several demonstrators have been built during the H2020 TOPAs and FP7 SCUBA projects. In these demonstrators buildings have been controlled automatically. Some experiments ran for several months. Data collection, on several buildings in Ireland and France, is ongoing to continuously analyse the buildings' behaviour (with ≈ 1000 data points updated every minutes or ever 10 minutes).
However, REO focuses only on the interaction between components whereas our approach provides developers with a complete model of the environment and the application. ReSpecT nets  [38]  is a language to map ReSpecT programs to Petri nets. ReSpecT is a reactive language based on TuCSoN  [29]  and tuple spaces. However, this fails to bridge the gap between (i) the large expression capabilities of tuple space coordination languages and (ii) the set of Petri nets on which interesting formal analysis can be done. Whereas, in our approach verification are done on the model and then the coordination code is generated. It is thus possible to limit in the model the expressiveness to provided formal guarantees. However this language could be a good target for our rule generation tools. Finally, in  [26]  the authors propose a software engineering methodology to develop multiagent systems in the SAPERE project which target self awareness in pervasive systems.
Participants were instructed to perform the movement as fast and accurate as possible but could initiate the movement at their convenience after the 'go' signal. The obstacles were 11 cuboid wooden stands (3 x 1 cm ground surface dimensions) varying in height from 5 to 15 cm in steps of 1 cm. Kinematic data were collected at a sampling frequency of 100 Hz with two Optotrak 3020 system sensors (Northern Digital, Waterloo, Canada). Six rigid plates, made of PVC, with each three markers were attached to the sternum, to the acromion, at the left side of the right upper arm below the insertion of the deltoid, proximal to the ulnar and radial styloids, to the dorsal surface of the hand  [29] , and to the index finger  [27]  with skin friendly tape. Following the procedure described by Van Andel et al.