dataesr/scientific-paragraphs-categorization huggingface dataset:
96% achieved using LPQ with an SVM classifier. Moreover, the majority of the published approaches reached between 90% and 94%. In the JAFFE database, the accuracies of the other state-of-the-art methods are low compared to that of CK+, where the maximum reported accuracy is 76. 46% scored by the CFER-based framework, which was outperformed by our FER framework (77. 62%). The proposed framework managed to surpass with a significant margin all other approaches on the KDEF dataset, except for the WCFN (89. 55%) and AlexNet (89. 33%) based systems, where the margin is small (0. 65% and 0. 87% of our framework performance,    [39]  Deep 69. 4 C-classLDA-NN  [18]  Deep 74. 73 LBP based LDA  [32]  Handcrafted 73. 4 CFER  [38]  Handcrafted 76. 46 Features fusion  [29]  Handcrafted 70.
Proposed Handcrafted 77. 62   [34]  Handcrafted 78 VGG-Face Deep  [46]  Deep 72. 55 SCAE  [30]  Deep 86. 73 DFD  [40]  Handcrafted 82. 24 WCFN  [44]  Deep 89. 55 MobileNet  [11]  Deep 73. 74 EDR-PCANet  [39]  Deep 80. 61 Proposed Handcrafted 90. 2  respectively). On the OuluCasia dataset, the proposed framework (77. 32%) outperformed all other state-of-the-art methods, where the top accuracy was limited to 75. 52%, which was achieved by Atlases. On the RaFD database, our proposed framework obtained 97. 2%. Many studies described in the literature achieved an accuracy of nearly 97%. However, the majority applied only seven emotion classes. Overall, we conclude that the proposed facial expression recognition framework managed to outperform all of the state-of-the-art methods tested.
Confusion matrix-based analysis for the FER The confusion matrix allows the performance of the recognition according to each label (each emotion in our case). Through this chart, we are capable of analyzing the recognition rate of each emotion, as well as which are the easiest and most difficult emotions to recognize. In addition, this analysis allows us to identify which emotions affect the others. Figures 12, 13   In the CK+ database, the Happy and Surprise classes were perfectly recognized, whereas Fear and Sad experienced the highest misclassification rate (16. 0% and 14. 3%, respectively). Fear was confused three times with Happy and once with Sad. A Neutral emotion was the most affective emotion (with a 12. 5% false-negative rate) and was predicted four times in the case of  Sad and twice for Angry.
For the JAFFE database, Happy and Neutral were the easiest to identify with an accuracy of 86. 7%, and the hardest was Fear (60%), which was confused with all emotions, i. e. , three times with Disgust, Neutral, and Surprise, and once with the remaining emotions. The Sad class presented the highest false-negative rate (34. 4%). The females composing this database express Angry and Sad emotions in similar ways because five Angry samples were identified as Sad. For the KDEF dataset, the errors were less than those of JAFFE. Happy and Neutral once again were highly recognized emotions (98. 6%), but Angry was a challenging class, with a rate of only 78%. Our FER framework confused Disgust seven times with Fear and confused Fear six times with Sad. Happy and Disgust did not affect any other emotion except once each for Angry.
As expected from OuluCasia, the misclassification errors were extremely high. The Happiness emotion is the only one to be highly recognized with an accuracy of 96. 3%, followed by Surprise (91. 3%). The remaining accuracies were between 66. 3% and 80%. In addition, Angry and Neutral dramatically influenced the other emotions by 34. 5% and 36. 6%, respectively. In the RaFD database, all rates were high with perfect recognition rates for three classes (i. e. , Contempt, Fear, and Sad) in addition to Angry and Disgust, which were misclassified only once. However, there is a mutual confusion between Happy and Surprise because three Happy samples were identified as Surprise and Surprise samples were identified as Happy four times. For all databases, we determined that Happy and Neutral are the most recognized emotions, and that Neutral is an extremely perturbing framework, presenting high falsenegative rates on many benchmarks.
Implementation and execution time The FER experiments presented were conducted on an Alienware Aurora R8 with a 4. 6 GHz Core i7-8th Processor Boost, 12 threads, and 48 GB of RAM, running with the Ubuntu 18. 04. 2 LTS (Bionic Beaver) operating system and equipped with two GTX1080Ti GPUs. The developed framework is coded using Python 3. 7 and MATLAB 2019b environments. The authors are willing to share all codes used by the community once this paper is published. The computational cost is one of the key performance indicators considered in machine learning applications. Therefore, we ran an experiment that calculates the elapsed time required to predict the label of a given input image with a pixel resolution of 762 × 562, highlighting the execution time of each step of the proposed framework: • Dlib landmark detection.
Conclusion and Future works In this paper, we proposed a new handcrafted descriptor, OPD-GQMBP, along with a framework for facial expression recognition. We considered using individual people as the most challenging task acquiring the intention of many computer vision researchers, as can be seen from the number of studies published in this context. The OPD-GQMBP method applies orthogonality and parallelism geometrics to cover the most prominent pixels within a neighborhood. OPD-GQMBP is a generic descriptor with the neighborhood size as a parameter, which can be adjusted to obtain the most discriminative feature extraction regarding the intended application. For FER, we found that the neighborhood size of n = 7 is the best configuration of the proposed OPD-GQMBP descriptor. With regard to the developed FER framework, we combined the shape and texture feature extraction techniques applied considering 49 landmarks detected by the Dlib package on each input image.
Although it is true that our system managed to outperform many of the state-ofthe-art systems, it needs improvement on databases containing Asian individuals (e. g. , JAFFE and Oulu CASIA) because they tend to present similar facial features, which confuse the classifier in the case of the LOSO protocol. We believe that the ultimate solution is to reduce the number of extracted appearance features and focus on the binary patch calculated based on the detected landmarks. This patch should incorporate more information, and not only the landmark location, and therefore handcraft a shape descriptor to obtain the most prominent information. Moreover, this proposal will help in the development of generic person-independent FER systems because the input images will be coded into a common patch. Even though our framework, along with the proposed descriptor, performs efficiently in terms of computational time, we think that there is room for improvement using other dimension reduction strategies and/or landmark selections.
We are also investigating an enhancement of the performance of our framework by utilizing other sophisticated classifiers than an SVM and combining learnable features with the proposed OPD-GQMBP descriptor. In addition, we intend to extend the set of the studied emotion classes with the compound classes, reaching 20 different classes. We also considered creating a mixed database from existing databases to gather all challenges in a unique benchmark, which will offer more challenging testing and evaluation. 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9 15. 9.
Experimental datasets • The Japanese Female Facial Expression (JAFFE) dataset has 213 facial expression images, representing the 7 basic emotions: Anger (30 images), Disgust (29 images), Fear (32 images), Happiness (31 images), Sadness (31 images), Surprise (30 images), and Neutral (30 images). Figure6illustrates some examples of facial expressions. This database is extremely challenging regarding the particularity of Japanese females that have similar face features, generating more inter-class visual features• The Karolinska Directed Emotional Faces (KDEF) is a widely used dataset for evaluating FER methods. It includes 70 individuals (50% men, 50% women) that uniformly express basic emotions over two sessions, leading to a total of 980 images. In our experiments, we considered only one session to have only one observation per emotion for each person (490 images), which resulted in.
4. 1. 1 Experimental datasets • The Japanese Female Facial Expression (JAFFE) dataset has 213 facial expression images, representing the 7 basic emotions: Anger (30 images), Disgust (29 images), Fear (32 images), Happiness (31 images), Sadness (31 images), Surprise (30 images), and Neutral (30 images). Figure6illustrates some examples of facial expressions. This database is extremely challenging regarding the particularity of Japanese females that have similar face features, generating more inter-class visual features• The Karolinska Directed Emotional Faces (KDEF) is a widely used dataset for evaluating FER methods. It includes 70 individuals (50% men, 50% women) that uniformly express basic emotions over two sessions, leading to a total of 980 images. In our experiments, we considered only one session to have only one observation per emotion for each person (490 images), which resulted in.
Direct detection experiments obtain 90% upper limits on the elastic scattering cross sections of dark matter with nucleons assuming point-like interactions and standard astrophysical and cosmological parameters. In this paper we provide a recasting of the limits from XENON1T, PICO-60, CRESST-III and DarkSide-50 and include them in micrOMEGAs. The code can then be used to directly impose constraints from these experiments on generic dark matter models under different assumptions about the DM velocity distribution or on the nucleus form factors. Moreover, new limits on the elastic scattering cross sections can be obtained in the presence of a light t-channel mediator or of millicharged particles.
The code is developed as a module of micrOMEGAs  [27, 28]. In this first version, a recast of the limits from XENON1T  [1] , DarkSide-50  [3] , PICO-60  [5]  and CRESST-III  [10]  are provided. These thus provide the best limits for the cases of spin independent and spin dependent interactions in neutrons and protons for DM masses above 1 GeV. Based on this recast, we give typical examples on how the code can be used to set limits on new models. The models considered include the case of a light mediator, in particular a Z , as well as millicharged particles. Moreover the impact of alternate velocity distributions is analysed. Recasting of these limits as well as other recent direct detection experiments are also included in DDCalc  [29] [30] [31]  and in SuperIso  [32]. Note that both these recasting reproduce well the XENON1T exclusions for DM masses at the weak scale or above, however they feature significant differences for masses near the sensitivity threshold when events are expected in the region at low nuclear recoil energy which is particularly challenging for experiments.
For 4 ≤ n e -< 7 there is a large difference between the data and the estimated background, hence, following the DS-50 analysis, we treat the additional background as a nuisance parameter when constructing the likelihood function  [42]. It means that we include the contribution of the 4 ≤ n e -< 7 only if the DM signal plus known background is larger than the experimental data. The average number of ionizations is determined by quenching. The ionization quenching depends on the recoil energy and suffers from a large uncertainty  [43]. We use the minimal quenching. 4 We have checked that making a linear interpolation between the minimal and maximal values of the quenching for each energy and treating the parameter of interpolation as a nuisance parameter leads to very similar results. Moreover, the distribution of the number of ionizations around the average is not known.
Applications In this section we show how to exploit our reconstruction of DD experimental limits to obtain limits on specific DM models while taking into account uncertainties from astrophysical and nuclear physics parameters. All numerical results presented below can Figure  7 : Left : Comparison of the recasted 90% limit on σ SD χp from micrOMEGAs (red) with the PICO-60 limit  [4, 5]  (black) using the EFT form factors from Ref. [39]. The impact of the choice of form factor is illustrated for the SHELL (green-dot) and SHELL-min form factors (blue-dot-dash). Right: Comparison of the recasted 90% limit on σ SD χn from micrOMEGAs (red) with the CRESST-III limit  [47]  (black) with zero momentum form factors. be easily reproduced with the micrOMEGAs code. The corresponding code is stored in mdlIndep/dd_exp.
For XENON1T one can chose between p q ef f with q = 0, 1, 2, see Section 3. 1. The flag Xe1TnEvents=q allows to choose the corresponding recasting, otherwise and by default the code uses p 1 ef f. For PICO-60, the user can choose between the recasting based on Feldman-Cousins statistics, PICO60Flag=0 which is the default value, or the one based on Neyman one side belt exclusion, PICO60Flag=1. • DD_factorCS(expCode, α, f v , σ SI P , σ SI N , σ SD P , σ SD N ,&expName) returns the overall factor which should be applied to the cross sections, σ SI P , σ SI N , σ SD P , σ SD N to reach the exclusion level α. All parameters are the same as in DD pvalCS above. • *dNdEFact(Enr_kev, A) is the address of the function which modifies the nucleus recoil distribution for DD pvalCS and DD factorCS to take into account a t-channel propagator with small or zero mass.
Acknowledgements We have benefited from exchanges with members of various direct detection collaborations, in particular  Masayuki Wada  and  Davide Franco  (DarkSide),  Victor Zacek  and  Scott Fallows  (PICO) and  Florian Reindl (CRESST). We also acknowledge useful discussions with  Bryan Zaldívar. This work was funded by  RFBR  and  CNRS , project number  20-52-15005. The work of A. Pukhov was supported in part by a grant  AAP-USMB  and by the  Interdisciplinary Scientific and Educational School of Moscow University "Fundamental and Applied Space Research". The authors would like to thank the  Mainz Institute for Theoretical Physics , the  ICTP-SAIFR in Sao Paulo , and the  Paris-Saclay Particle Symposium 2019  with the support of the  P2I  and  SPU  research departments and of the  P2IO Laboratory of Excellence (program  " Investissements d'avenir "  ANR-11-IDEX-0003-01   Paris-Saclay  and  ANR-10-LABX-0038 ) for their hospitality and support during the completion of this work.
I. INTRODUCTION HE MONACO system   [1]   [2]   (Multichannel Online Neutron Acquisition in Campbell mOde) was developed at CEA during the past 10 years until the TRL7 level and demonstrated its efficiency as a functional prototype for Fission Chamber (FC) signal acquisition. The main feature of the system is to make possible to acquire simultaneously three different FC working mode: pulse, Campbelling and, current mode. Within the commissioning of the new Jules Horowitz nuclear reactor, this system will be integrated into the experimental part of the data acquisition system. The industrialization of the MONACO system is a result of a mutual efforts between CEA who developed the prototype and Instrumentation Technologies. The industrialized version of the MONACO system will be available on the market from 2023 and will be supported for at least 10 years aftermarket.
One of the key parts of the MONACO system is the frontend amplifier originally developed by CEA and based on analog components. Instrumentation Technologies redesigned the preamplifier to meet CEA performance requirements using new discrete and high-performance components. The entire redevelopment process was divided into different phases, including a CEA specific PO for the first phase and a bilateral collaboration agreement for the remaining phases, to lower the risk connected to every step of industrialization: -Phase 1: Redevelopment of the integrated preamplifier unit. This phase will consist of the redevelopment of the preamplifier frontend and of the modules that are critical for the MONACO system performance: the HV module, and the current measurement unit. This phase was successfully concluded and will be described in the next paragraphs of this paper.
-Phase 2: Integration of a 4-channel system based on the hardware prototype developed in phase 1. This phase will expand the 1-channel integrated frontend to a fully functional 4-channel system with the integration of a digitizer unit aimed to collect and process the digitalized data. The hardware developed within this phase will consist in the finalized version of the MONACO system First testing at a research reactor facility to confirm the hardware performance are planned in August 2021. -Phase 3: Development of the Digital Signal Processing (DSP) interface on the digitizer module integrated into the 4-channel system. -Phase 4: Development of the user Graphical User Interface (GUI) software and final testing of the device. This paper will cover the first phase of the process where a functional pre-industrialization 1-channel version of the preamplifier was developed and tested.
II. INTEGRATED PREAMPLIFIER REQUIREMENTS The MONACO system developed by CEA[MOL] is based on different building blocks that fulfill the final user application requirements. Each of the 4 channels is composed of a processing chain with the following blocks: -Preamplifier frontend: aimed to collect the signal from the FC. -High voltage source (independent for all the 4 channels) that polarizes the FC. -Current measurement unit (independent for all the 4 channels) that measures the current provided to the MFC. -Digital acquisition board and FPGA processing. -Host PC with GUI. The block system is well described in Fig. 1. In the following paragraphs, we will refer to the "preamplifier frontend" as the frontend unit which is converting and amplifying the signals from the FC, and to "integrated preamplifier" as the unit that was developed by Instrumentation Technologies that integrates the preamplifier frontend, the HV module, and the current meter.
Within Phase 1 , the goal is to provide a 1-channel prototype that can replicate the preamplifier frontend, high voltage source, and current meter unit integrated into a standalone system that can lead to the same or better results than the existing MONACO prototype developed by CEA. The preamplifier development began with the review of the specifics of the actual MONACO system. As described in Fig. 1  the unit is composed of three separated modules, the preamplifier frontend, an HV unit, and a current measurement unit connected with wires in a common enclosure. All these blocks should work as an integrated subsystem, so the first choice was to integrate all the units on a common PCB to simplify the communication between the units and to improve EMC (noise immunity). The preamplifier frontend converts the current signal retrieved from the FC detector into a voltage signal and amplifies it with some specific performance parameters in terms of gain and noise.
B. I-Tech integrated preamplifier development Based on the requirements listed in paragraph A, the Instrumentation Technologies preamplifier was redesigned by using discrete components. As described before, since this module needs to integrate all the key components of the final MONACO system (preamplifier, HV source, and current meter), special attention was given to noise immunity and DC-DC converter shielding. All these blocks will find their place on a dual-side PCB with a strong amplifier stage shielding as reported in Fig. 6. The preamplifier input is retrieved by a HN connector and a decoupling condensators allows the further stages to get only the AC component without the HV bias that is provided to the detector. To investigate different amplification levels and to allow a quicker circuit debugging, the preamplifier frontend was split into two different gain outputs that were compared within the same pulse signal, both outputs are 50 Ohms matched.
IV. INTEGRATED SYSTEM PROTOTYPE All the units listed in the previous paragraphs (preamplifier frontend, HV module, current measurement module) were integrated on a single PCB board that also includes the power supply unit and part of the control logic. This proof of concept was also lately applied to the effective MONACO prototype by extending the setup to a 4-channel board and adding the ADC acquisition and control system. The hardware enclosure allows the testing of the integrated preamplifier by shielding the inner components which are very sensitive to external perturbations. The power supply was also integrated in the final design. The input signal from the detector is delivered to the HN connector and the HV can be adjusted with an external knob. The outputs, as mentioned before, allow the user to evaluate the signal from the preamplifier stages (1 and 2) and from the current measurement section.
Within the finalized prototype, the base noise level of the preamplifier was measured to be 40 mVpp but with some spikes coming from the DC/DC converters on the prototype board at approximately 100 kHz. With the use of ad-hoc low drop-out integrated into the DC supply module the total noise figure improved. The S21 parameters showed a high increase in the amplifier bandpass: By injecting white noise in the preamplifier frontend and recording the spectral response on an oscilloscope the bandwidth was confirmed. The addition of the 23m length high immunity mineral cable did not influence significatively the noise figure even if some reflections were noticeable. In any case, the cable reflections are not estimated as a showstopper for the implementation of the DSP algorithm that will be running on MONACO.
C. Current meter module performance The overall performance of the unit was measured to be in the range of 4nA and will be lowered in the next designs. The operative range from 0 to 1kV was measured by comparing the current value measured by the current meter unit and the Keithley 6517B Electrometer/Resistance meter. The results are reported in Fig. 11  where strong linearity is seen between the two systems. A 100 MOhm resistor was connected to the HV output and the relative current was measured. The data relate very well in the entire span from 0 to -350 V with a final difference at the highest voltage and current value due to the prototype amplifier saturation (green box). The bandpass of the current meter was measured using a sinusoidal signal from the Keithley current source, with a sweep from 50 Hz with measured amplitude 35.
VI. CONCLUSIONS The development of the integrated preamplifier prototype was the first milestone and the proof of capability for the redesign of the MONACO system's main building blocks. The results reported in the measurements allowed Instrumentation Technologies to evaluate all the critical points of the preamplifier, HV module, and current meter unit. Some of the main difficulties were due to the preamplifier design (cable capacitance…) and logistics due to the actual Covid19 pandemic (prototype testing, electronic components delivery). The integrated preamplifier was also tested at the CEA facility with a neutron source, the results will be reported in the next publications. The next steps of industrialization are already in progress with the foreseen delivery of the 4-channel integrated version of MONACO as a hardware prototype in July 2021.