dataesr/scientific-paragraphs-categorization huggingface dataset:
2) other sources, for example AwA2-bobcat, are identified reliably by some tests, but not at all by others. 3) for some sources, here AwA2-blue-whale, tests show different sensitivities, i. e. some tests work only for high mixture proportions. Figure  9a  illustrates the above examples for the ResNet50 classifier. Interestingly, the results differ substantially not only between data sources but also between networks. For example, ResNet50 allows for perfect detection at lower mixture proportions than MobileNet25. For NASNetAlarge on blue whale data, the symmetric mean test works as least as well as KS(conf), while the same test on the same images fails completely for VGG19. An illustration of these examples is provided in Fig. 9b. A possible explanation of the observed effects lies in the fact that score distributions differ not only strongly between ConvNets, as we had observed in Fig.
5 , but also between different data sources for the same ConvNets. For example, AwA2-bat exhibits a pattern as one would ideally expect from an unknown class: confidences are overall much lower, so the difference in distribution is easy to detect for all tests. The distribution for AwA2-blue-whale data differs much less from the within-specs situation, making it harder to detect. Finally, AwA2-bobcat shows quite unexpected behavior: even though no images of this class were used at training time the networks make overall more confident predictions than for withinspecs data. This is also the reason why the single-sided meanbased tests fail for this out-of-specs situation. For space reasons, we do not include illustrations of all discussed score distributions. They can be found on the accompanying website.
MobileNet25 -bat NASNetAlarge -blue whale VGG19 -blue whale (b) Examples of variability between different networks. Left: MobileNet25 requires larger batches to reliably detection an out-of-specs situation (here: AwA2 bat) than, e. g. , ResNet50 (upper row, left). Middle/right: on the same image data (here: AwA2 blue-whale), a test (here: sym. mean) might work well for one ConvNet (here: NASNetAlarge) but not at all for another ConvNet (here: VGG19).
Geometry and Color Preprocessing To simulate incorrect camera installations or geometry preprocessing, we benchmark KS(conf) with horizontally and vertically flipped images, as well as images that were rotated by 90, 180 or 270 degrees. To simulate incorrect color preprocessing, we use images in which the R and B channel have been swapped. For each transformation, we create 100,000 perturbed test images. Table  9  summarizes the characteristics of the score distribution. One can see that all of the network outputs are unaffected by horizontal flips of the training set. Presumably, the networks either learned a horizontal symmetry of the visual world, or this behavior was enforced by a data augmentation step during training. Vertical flips and rotations do have an impact on the score distribution that can be detected reliably using batches of 100 to 300 samples, while singlesample threshold classifiers would not be able to achieve high TPR or low FPR at the same time.
On the other hand, we see it as a call for caution that experimental studies in this field must be thorough and broad in order to avoid the risk of overfitting to individual datasets or network architectures. In conclusion, we hope that our work leads to more research on how to make automatic decision systems more trustworthy. To support this effort, our code and data are publicly available via the authors' homepage. unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecomm ons. org/licenses/by/4. 0/.
A Philips X'pert Pro diffractometer (PW 1130/00, Philips, The Netherlands) was used with Ni-ltered Cu Ka radiation (l ¼ 1. 541 Å). The data were obtained with geometry in which the sample was kept in place and the tube and counter were in motion. The X-ray beam was generated at 45 kV and 30 mA. Diffraction intensities were recorded with 2q ranging from 4 to 60 at a step size of 0. 079 and a scan step time of 390 s. The data was processed using X'Pert and OriginPro 2017 soware. Deconvolution of the peaks was carried out using OriginPro 2017 soware (Multiple Peak Fit) and Gaussian functions. Deconvolution was performed based on the study of Cao & Billows. 35 10 Dissolution and regeneration of feather keratin Regenerated keratin samples were obtained by dissolving the feathers in the aqueous DES aer which the dissolved keratin was regenerated in water.
3. 3. 2 Molecular weight. MALDI-TOF MS was used to study changes in the molecular weights of the regenerated keratin samples. In the literature, it is proposed that the molecular weights of feather keratin is around 10 kDa. 51 The native feathers could not be dissolved completely and therefore no reliable MALDI-TOF spectra could be measured. Fig. 5 , which represents the data obtained from MALDI-TOF MS, shows that with longer dissolution time (Fig. 5a-c ) and with higher dissolution temperatures (Fig. 5d ), the range of molecular weights became wider. Aer 4 h dissolution time, a wide peak can be observed around 10 000 m/z and a smaller wide peak at approximately 5000 m/z. When the dissolution time and temperature increased, wide peaks started to appear at approximately 7000 m/z and 8000 m/z, and peak at 10 000 m/z started to decrease.
S4 † ). The results obtained from band tting are summarized in Fig. 6a. The data obtained from the deconvolution shows that when the dissolution time was increased from 0 h to 16 h, the disordered structure, random coil, increased from 55. 4% to 71. 6% and the portion of a-helix & random coil increased from 2. 6% to 3. 9%, while the percentage of b-sheet and turns decreased from 30. 5% and 11. 5% to 19. 7% and 4. 8%, respectively. The data and results obtained from the deconvolution should be treated with caution, as the parameter choice is rather subjective. However, although the values obtained should not be interpreted as absolute, a common trend can be concluded. In addition to the ATR-FTIR, NMR was also used to investigate the molecular conformations and dynamics in the feather keratin.
Acknowledgements Funding for this work was received from the  European Union 's  Horizon 2020 Research and Innovation program  under Grant Agreement 723268 on a project KaRMA2020. The authors thank  Eino Sivonen  for the compactor grinding,  Atte Mikkelson  for carrying out the elemental analysis,  Päivi Matikainen  for performing MALDI-TOF MS and the direct colorimetric solid phase assay,  Ritva Heinonen  for the ATR-FTIR measurements and  Mirja Nygård  for the TGA measurements.
Acknowledgments This work was partly performed within the framework of the  EUROfusion Consortium  and has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  633053. Furthermore, funding from the  EUROfusion Enabling Research work-package   AWP15-ENR-09/IPP-02  is acknowledged. The views and opinions expressed herein do not necessarily reflect those of the  European Commission. This work is supported by the  US DOE  under grants  DE-SC0006419  and  DE-SC0017381.
Simulation Results for the HB-5L-NPC Converter Controlled by LS-PWM Some simulations in the Matlab/Simulink environment using the SimPowerSystem library were performed to study the system depicted in Figure  2. The simulation results illustrate the possibility of reducing the number of commutations. The system parameters are introduced in Table  1 , where fpwm is the PWM switching frequency and m is the modulation index. The simulation results for the output voltage are shown in Figure  4. As mentioned before, the goal of this paper is to propose an additional advanced functionality for the modulation strategy that provides the minimal number of switch commutations. For this, let us first analyze the output voltage generated by the classical LS-PWM, as given in Figure  4.
Simulation Results for the HB-5L-NPC Converter Controlled by LS-PWM Some simulations in the Matlab/Simulink environment using the SimPowerSystem library were performed to study the system depicted in Figure  2. The simulation results illustrate the possibility of reducing the number of commutations. The system parameters are introduced in Table  1 , where f pwm is the PWM switching frequency and m is the modulation index. The simulation results for the output voltage are shown in Figure  4. As mentioned before, the goal of this paper is to propose an additional advanced functionality for the modulation strategy that provides the minimal number of switch commutations. For this, let us first analyze the output voltage generated by the classical LS-PWM, as given in Figure  4.
Simulation Results for the HB-5L-NPC Converter Controlled by LS-PWM Some simulations in the Matlab/Simulink environment using the SimPowerSystem library were performed to study the system depicted in Figure  2. The simulation results illustrate the possibility of reducing the number of commutations. The system parameters are introduced in Table  1 , where fpwm is the PWM switching frequency and m is the modulation index. The simulation results for the output voltage are shown in Figure  4. As mentioned before, the goal of this paper is to propose an additional advanced functionality for the modulation strategy that provides the minimal number of switch commutations. For this, let us first analyze the output voltage generated by the classical LS-PWM, as given in Figure  4. Car_Neg. Ref_Neg.
Simulation Results To validate the performance of the proposed OPT-PWM applied to the HB-5L-NPC topology (Figure  2 ), some simulations were performed in the Matlab/Simulink environment using the SimPowerSystem library developed by MathWorks (Natick, MA, USA). The simulated system was the same as that illustrated in Figure  1  with an RL load and the OPT-PWM control. The parameters are presented in Table  1. Figure  14  shows the output voltage when the converter is controlled by the proposed OPT-PWM. By comparing Figures  4  and 14 , it can be seen that the output voltage waveforms are the same. To verify the elimination of the unnecessary commutations on the V dc /2 (and also -V dc /2) level, the same zooms as in Figure  4  were performed. Figures 15-18 can be compared with Figures  6 7 8 9.
Experimental Results To verify the validity of the proposed OPT-PWM control applied to the HB-5L-NPC, several experimental tests were carried out. The same parameters as those used in the simulations were considered. The test bench (Figure  19 ) was based on IGBT modules commercialized by the SEMIKRON Company (Nuremberg, Germany), reference SKM50GB123D. These IGBTs were driven by SKHI 22A drivers, distributed by the same company. The DC bus capacity value was 2200 µF. The control method was applied by using a dSPACE system (Paderborn, Germany) containing a DS1005 control card as well as a DS2004 for high-resolution analog conversion (16 bit-0. 8 µs) and a DS5101 PWM card with 12 outputs. The hardware implementation on the DS1005 was based on the modeling of the control algorithm carried out in the Matlab environment with classical blocks from the Simulink toolbox.
Experimental Results with LS-PWM Control Figure  20  shows the output voltage of the converter with LS-PWM control. These results show clearly that there are the same small variations (peaks) at the same intervals as in the simulations. To confirm this, two zooms, one when the voltage remains a long time at Vdc/2 and the other at -Vdc/2, were made and are presented in Figures  21  and 22   The control method was applied by using a dSPACE system (Paderborn, Germany) containing a DS1005 control card as well as a DS2004 for high-resolution analog conversion (16 bit-0. 8 µs) and a DS5101 PWM card with 12 outputs. The hardware implementation on the DS1005 was based on the modeling of the control algorithm carried out in the Matlab environment with classical blocks from the Simulink toolbox. Some tests with a LS-PWM control were first performed and then the proposed OPT-PWM was applied to control the system.
Size Exclusion Chromatography (SEC) The number average molar mass (M n,TD-SEC ) and dispersity (Đ M ,TD-SEC) were measured on a Triple Detection Size Exclusion Chromatography (TD-SEC) with OmniSEC software (Viscotek, Vénissieux, France). The instrument is composed of a GPC Max (comprising a degazer, a pump and an autosampler) with TDA-302 (RI detector, right and low angle light scattering detector at 670 nm and viscometer) and UV detector (Knauer, Berlin, Germany). The following Viscotek columns were used: a HHR-H precolumn, a GMHHR-H and a GMHHR-L ViscoGel columns. THF was used as the eluent with a flow rate of 1. 0 mL/min at 30 • C. Purified polymers were dissolved in THF at ca. 10 mg/mL and filtered on a 0. 2 µm PTFE filter. Molar masses were calculated using the value of the refractive index increment dn/dc for PMATM2 (dn/dc= 0.
044 mL/g in THF) and PCL (dn/dc= 0. 071 mL/g in THF) using the OmniSEC software  [38, 47]. The analysis of the PCL-b-PDMS-b-PCL triblock copolymer was not possible by TD-SEC in THF due to its refractive index similar to n THF , resulting in no signal with the RI and light scattering detectors. This triblock copolymer was thus analyzed on a SEC apparatus (Waters, Milford, CT, USA) equipped with a refractive index detector, with toluene as eluent. The columns used were Styragel HR1, HR4, and HR5. Toluene was used as the eluent at a flow rate of 1. 0 mL/min. Polystyrene standards (M n values varying from 400 to 20,000 g/mol) were used to generate a conventional calibration curve. Data were analyzed using Breeze software (Waters, Milford, CT, USA).
Contact Angle Measurement and Surface Free Energy of Coatings Static contact angle measurements were performed using a DSA 30 apparatus (Krüss, Hambourg, Germany) by the sessile drop technique under ambient conditions. Five contact angle measurements were carried out with 2 µL-droplets of deionized water (θ w ) and diiodomethane (θ diiodo ), after 4 s of stabilization. The surface free energy of the coatings (γ S ) was determined by the Krüss Advance software using the Owens Wendt method  [6]. Both the dispersive (γ D S ) and the polar (γ P S ) components of the surface free energy were assessed (γ S = γ D S + γ P S ). Measurements were performed on pristine samples and samples immersed in deionized water for 5 weeks.
In the Knudsen layer close to the walls, however, local thermodynamic disequilibrium takes place and specific approaches are required. An effective mean free path model was implemented by Bhagat et al. [1]  in OpenFOAM, an open source computational fluid dynamics (CFD) code based on the Navier-Stokes equations. A hybrid Langmuir-Maxwell-Smoluchowski velocity slip and temperature jump boundary condition was used with a Knudsen layer formulation and tested on the backward facing step channel. Comparison with direct simulation Monte Carlo (DSMC) demonstrated a significant improvement over existing CFD solvers. Pressure drop in microchannels is a fundamental quantity to control for many engineering problems. In a number of devices, the entrance region is not negligible and should be taken into account.
Dataset of velocity cubes We created a series of velocity boxes with characteristics indicated in Table  1. The smooth and continuous velocity power-spectrum takes a form similar to that of ZuHone et al. (  2016 ), namely: P 3D (k) = C n e -(k/k diss ) 2 k α e -(kinj /k) 2 (14) with C n a normalization constant having units such that P 3D (k)dk = σ 2  turb and α = -11/3 typical of a Kolmogorov turbulence spectrum  (Kolmogorov 1941). Scales k diss = 1/L diss and k inj = 1/L inj represent the dissipation and injection frequencies, respectively. Moduli of the Fourier coefficients are drawn from a Rayleigh distribution. The computationally demanding Fast Fourier transforms (FFT) were distributed across 10 processors using 2DECOMP&FFT 2  (Li & Laizet 2010). The histograms of the (3-d) velocity standard deviation in each of the three configurations is shown in Fig.
Acknowledgements. The authors thank the referee for useful comments that helped to improve the quality and perspectives of this work. The authors thank  D. Barret  for fruitful discussions that led to improve this manuscript. NC thanks  A. Marin-Laflèche  for help in the selection of an FFT algorithm suited to this research,  A. Proag  for useful discussions on geometrical calculations and  L. Le Briquer  for commenting a preliminary version of this manuscript. This research made use of Astropy, 5 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013;Price-Whelan et al. 2018). This research made use of Matplotlib (Hunter 2007).
4 by adding 0. 1 M NaOH solution and the temperature was fixed at 60 °C. The electrodeposition was performed with a potentiostat/galvanostat instrument (Voltalab PGP 201 Radiometer Analytical -VOLTAMASTER software) by pulsing the current  [18]. A deposition time td = 1 min with a current density jd = 8 mA cm -2 was followed by a break time tb = 2 min (jb = 0 mA cm -2 ). Seven pulse-cycles were used for a total deposition time of 21 minutes. Then, the electrodeposited CaP coatings were thermally treated under controlled atmosphere (argon flow) inside a tubular furnace (Nabertherm RS 80/500/11) according to the THUCA protocol developed in our laboratory  [19]. A first plateau at 120 °C for 1 h to evaporate the solvents is followed by a second plateau at 1000 °C for 1 h. The controlled atmosphere is maintained until the complete cooling to room temperature.
X-ray diffraction (XRD) X-ray diffraction (Bruker D8 Advance) was used to investigate the phases of the CaP coatings. The XRD patterns were collected from a monochromatic CuKα radiation for 2θ ranging from 20° to 50° with the step of 0. 06° every 60 seconds. These experimental parameters were optimized to enhance the resolution for an accurate identification and quantification of the phases. The phases' identification was performed by using the Powder Diffraction Files (PDF) of the International Center for Diffraction Data (ICDD). The phases percentages and the Ca/P atomic ratio were calculated with the standard ISO 13779-3 for the biphasic calcium phosphate materials  [21]. This method consists in calculating the intensity ratio of the main peak for each phase and to compare its value to those of standard samples with known percentages.
In vitro electrochemical corrosion studies The protective properties of the coatings were investigated using potentiodynamic polarization tests. The corrosion studies were performed in two different media: DMEM, used for ICP-AES analysis, and Ringer's solution (Table  1 ). This solution is typically used in research works about corrosion due to its saline composition and chemical aggressiveness. The potentiodynamic polarization curves were carried out by scanning the applied potential from -1. 5 V vs SCE to + 1. 5 V vs SCE with a scan rate of 10 mV s -1 using a potentiostat/galvanostat instrument (Voltalab PGP 201 Radiometer Analytical -VOLTAMASTER software). Before the polarization, the samples reach the open circuit potential EOCP for 15 min. Each test was repeated four times in order to reduce the margins of error and to verify the repeatability of the results.
Introduction High Voltage CMOS (HVCMOS) sensor chips could be the building blocks of the proposed CMOS quad module which is a low cost alternative to hybrid modules for ATLAS Inner Tracker (ITk) upgrade. The HVCMOS designs are targeted to meet the specifications  [1]  of ATLAS ITk pixel barrel layer 4. The sensors are tested to be radiation tolerant up to at least 100 Mrad and 1 × 10 15 n eq /cm 2  [2]. The principle of operation of HVCMOS sensors is explained in  [3]. The pixel electronics  [4]  include a charge sensitive amplifier and a discriminator that are placed inside the n-well which acts as the charge collection electrode. A high voltage is used to deplete the p-substrate underneath. The major blocks of ATLASpix chips are HVCMOS sensor array, hit buffers to store the hit information, Readout Control Unit (RCU) for scheduling the readout operation and several full custom blocks such as PLL, bias block, serializer etc.
Conclusions and future work ATLASpix1_M2 is the first large area and full height HVCMOS sensor prototype with triggered readout. The chip architecture includes several novel design concepts such as the Parallel Pixel to Buffer (PPtB) transfer, address compression of a super pixel, Content Addressable Buffer (CAB) readout, pipelined 8b/10b data encoding and serializer tree. The sensor is fully functional. The thresholds have been measured using the full readout chain. The threshold after tunning had a mean value of 1055 e -with a standard deviation of 35 e -. The mean value of noise was about 78 e -. These values are small compared to the average signal of about 5000 e -. The serial data link works at the required data rate of 1. 28 Gbps. Based on ATLASpix1 designs, a full size 2 cm×2 cm HVCMOS prototype will be developed.
These networks, named Brain and Spinal Cord Injury Center segmentation (BASICseg1-3), are fully automated and use axial image augmentation and a network architecture. Furthermore, we present preliminary data using the same network architecture to automatically segment intramedullary contusion in patients with acute SCI on T2WI. To assess the clinical validity of injury volumes segmented by this network, we correlated the contusion volumes segmented by BASICseg with lower extremity motor scores. Targeted 2D convolutional neural network training with a cohort of patients with acute SCI, in whom whole-cord and contusion-injury segmentation is particularly challenging, enhances the performance of automated spinal cord and contusion injury segmentation for this patient population compared with currently available state-of-the-art algorithms and also provides clinically relevant metrics of cord injury.
Inclusion criteria were the following: 1) blunt acute cervical or thoracic SCI, 2) 18 years of age or older, 3) presurgical cervical or thoracic spine MR imaging performed within 24 hours after injury, and 4) documented motor score assessment and American Spinal Injury Association Impairment Scale score obtained during first hospitalization. Exclusion criteria were the following: 1) penetrating SCI, 2) surgical decompression and/or fusion before MR imaging, 3) MR imaging that was too degraded by motion or other artifacts so that images were nondiagnostic, 4) pre-existing surgical hardware, and 5) incomplete clinical data for the outcome (American Spinal Injury Association Impairment Scale score) or relevant confounders. We identified 56 patients, of whom 47 met all inclusion criteria and were enrolled in the study.
In total, the network comprises 19 convolutional layers. Stochastic gradient descent was used with a learning rate of 1e-5 with an Adam optimizer. 23  All hyperparameters were the same for each of the BASICseg architectures. Programming was performed in Python with the TensorFlow framework (https://www. tensorflow. org/). Training was performed on 2 GeForce-1080ti GPUs (NVIDIA, Santa Clara, California) with a combined random-access memory of 22G.