dataesr/scientific-paragraphs-categorization huggingface dataset:
Experimental Evaluation The experimentation is carried out on a real world dataset gathered in a smarthome equipped with 54 IoT devices offering up to 228 sensors and actuators (Figure  4 ). The associated infrastructure is depicted in the Figure  5. Several IoT protocols are used to communicate with all devices (ZWave, Wifi, etc. ). Computational resources (Arduino Uno/Nano, Raspberry Pi) are made available at the edge of the infrastructure where software components are deployed through docker containers. These software components define the control logic for actuators (e. g. , roller shutters) or extract features from sensor observations (e. g. , the sound characteristics of the microphone signal). Observations are recorded into a database (InfluxDB) on a regular basis. For instance, ZWave devices provide one observation per minute, Netatmo devices provide observations each 10 min, the status of the Neato smart vacuum cleaner is updated each minute, etc.
The dataset used for the experiment reproduces this legitimate behavior as depicted in Figure  6. Echo smart speaker is OFF. Finally, observations are colored in magenta when the TV is OFF and the Amazon Echo smart speaker is OFF. Output observations (i. e. , ZCR and MFCC sound features) are also segmented into three clusters. The blue one corresponds to the silence; the orange one characterizes the sound emitted by the TV and the green one characterizes the sound emitted by the Amazon Echo smart player (here, playing music). As expected, none of the devices operate simultaneously. The dataset is applied to the Algorithm 1 implemented with HDBSCAN* with min_samples parameter set to 40. The IOHMM model of the legitimate behavior learned from the dataset is depicted in Figure  7  where distributions are fitted into Gaussian Mixture Models (GMMs), characterized by the mean and the standard deviation of each feature (i.
Experimental Evaluation The dataset used in the Section 4. 3, corresponding to the legitimate behavior of an IoT-based system is complemented with longer run observations gathered from the same physical environment (Figure  8 ). This dataset highlights two unforeseen behaviors (denoted in magenta in Figure  8 ) that will have to be highlighted by the dissimilarity graph algorithm described in the previous section. A first behavior concerns a configuration on the TV and the Amazon Echo smart speaker, both operating simultaneously (Figure  8 , input features from observation#80 to #230). This behavior is illegitimate, both devices must not operate simultaneously. The second behavior is related to a new device that has appeared in the environment. This device is an autonomous smart vacuum cleaner the inhabitants have recently acquired (Figure  8 , output features from observation#280 to #430, then from observation#520 to #610 and, finally, from observation#800 to #1000).
This behavior is legitimate, the model of the legitimate behavior has to be updated according to this new device. The dataset representing the legitimate behavior (Figure  6 ) and the dataset representing the behavior observed from the field (Figure  8 ) are applied to the Algorithm A2. The resulting dissimilarity graph is depicted in Figure  9. Observations from the field whose behavior corresponds to the legitimate behavior lead blue states and state transitions to appear, while those whose behavior is not legitimate or legitimate but unforeseen in the model, lead red states and state transitions to appear. For instance, the state 0 in Figure  9  corresponds to the smart vacuum cleaner emitting noise while being in operation. This state is not part of the model of the legitimate behavior, i.
For heavier DM, on the other hand, thermal production of DM in the early universe would exceed the observed abundance whenever Λ is more than one order of magnitude larger than m χ (up to the unitarity bound at a few hundred TeV  [65] , where the maximum possible value of Λ approaches m χ ). This work is organised as follows. We introduce the DM EFT description in Sec. 2. In Sec. 3, we discuss the constraints used in this study, and our methods for computing likelihoods and observables. We present our results in Sec. 4. Finally, we present our conclusions in Sec. 5. The samples from our scans and the corresponding GAMBIT input files, and plotting scripts can be downloaded from Zenodo  [66].
Collider physics The effective operators defined in Sec. 2 allow for the pair production of WIMPs in the proton-proton collisions at the LHC. If one of the incoming partons radiates a jet through initial state radiation (ISR), one can observe the process pp → χχj as a single jet associated with missing transverse energy ( / E T ). In this study, we include the CMS  [95]  and ATLAS  [94]  monojet analyses based on 36 fb -1 and 139 fb -1 of data from Run II, respectively. ATLAS and CMS have performed a number of further searches for other types of ISR, leading for example to mono-photon signatures, but these are known to give weaker bounds on DM EFTs than monojet searches  [24, 161, 162]. The expected number of events in a given bin of the / E T distribution is N = L × σ × ( A) , where L = 36 fb -1 or 139 fb -1 is the total integrated luminosity, σ the total production cross-section and the factor ( A) is the efficiency times acceptance for passing the kinematic selection requirements for the analysis.
While this is perfectly reasonable, given that the comparison is to the best-fit DM EFT point and not to the SM expectation, it is also interesting to study the global fit results under the assumption that mild excesses in the collider data indeed do not originate from a true new physics signal. A simple and pragmatic approach is then to replace ∆ ln L LHC with a capped version, ∆ ln L cap LHC (s) = min[∆ ln L LHC (s), ∆ ln L LHC (s = 0)]. (43) This will assign the same log-likelihood value, ∆ ln L cap LHC = 0, for all DM EFT parameter points whose prediction fit the collider data as well as, or better than, the SM prediction (s = 0) does. Thus, analogous to how exclusion limits from LHC searches are constructed to only exclude new physics scenarios that predict too many signal events, the capped likelihood only penalizes parameter points for performing worse than the background-only scenario.
g. Refs. [180] [181] [182] ). Moreover, the effects of electroweak operator mixing on the direct detection bounds are expected to be much more pronounced in such scenarios. Of course, the most important outstanding task is to collect more data that may shed light on the nature of DM. Upcoming LHC analyses will improve the sensitivity to missing energy signatures of DM, the next generation of direct detection experiments  [174, 183, 184]  will be able to probe substantially smaller scattering cross-sections, and ongoing  [185] [186] [187]  and planned  [136]  indirect detection experiments will probe the freeze-out paradigm with unprecedented precision. Our present work has shown that this effort is highly worthwhile given the wide regions of parameter space that cannot currently be excluded in a model-independent way.
Reducing the vast number of viable possibilities to explain DM therefore remains a key challenge for years to come. to interface a new model to DirectDM. For more background on the technical aspects of the GAMBIT framework, please refer to the original GAMBIT manual  [48] , and the GUM paper  [55, 56]. DirectDM matches Wilson coefficients of a relativistic EFT onto a non-relativistic EFT valid at the nuclear scale. The GAMBIT implementation interfaces with the Python version of this package. Relativistic Wilson coefficients can be defined at the 3-, 4-or 5-quark scale, with the capability DD_rel_WCs_flavscheme. For a given model, a new module function providing this capability should be written, returning the type map_str_dbl (std::map<std::string,double>). Once this capability has been fulfilled, GAMBIT uses the module function DD_nonrel_WCs_flavscheme to call the DirectDM backend via the convenience function get_NR_WCs_flav.
This provides the capability DD_nonrel_WCs which can be connected to the DDCalc backend. This module function providing the capability DD_nonrel_WCs depends on the capability WIMP_properties, of native GAMBIT type WIMPprops. WIMP_properties supplies the particle information about the WIMP candidate, such as its spin, mass, and whether or not it is self-conjugate, extracted from the particle database and either the spectrum or model parameters. As an example, consider a simplified model where a vector mediator governs the interaction between dtype quarks and a fermionic DM candidate χ, with the following interaction Lagrangian, L int ⊃ g χ χγ µ χV µ + g b q=d,s,b qγ µ qV µ. ( ) 45 The model implementation within GAMBIT will contain four free parameters: the couplings g χ and g b , the DM mass m χ , and the mediator mass m V.
Acknowledgements We thank all members of the GAMBIT community as well as  Fady Bishara  for discussions and checks. For computing, we thank  PRACE  for awarding us access to Marconi at CINECA and  Joliot-Curie  at  CEA. This project was also undertaken with the assistance of resources and services from the  National Computational Infrastructure , which is supported by the  Australian Government. We thank  Astronomy Australia Limited  for financial support of computing resources, and the  Astronomy Supercomputer Time Allocation Committee  for its generous grant of computing time. We thank  Juan Fuster ,  Adrián Irles ,  Davide Melini  and  Marcel Vos  for clarifications regarding Ref. [172]. PA is supported by the  Australian Research Council   Future Fellowship  grant  FT160100274 , and PA, CB, TEG and MW also acknowledge support from  ARC   Discovery  Project  DP180102209.
NAK  and ACV are supported by the  Arthur B. McDonald Canadian Astroparticle Physics Research Institute  and  NSERC , with equipment funded by the  Canada Foundation for Innovation  and the  Province of Ontario , and supported by the  Queen's Centre for Advanced Computing. Research at Perimeter Institute  is supported by the  Government of Canada  through the  Department of Innovation, Science , and Economic Development, and by the  Province of Ontario. AB acknowledges support by F. N. R. S. through the F. 6001. 19 convention, JB and JZ by  DOE  grant  DE-SC0011784 , BF by the  Horizon 2020 Marie Skłodowska-Curie  actions (EU;  H2020-MSCA-IF-2016-752162 ), WH by a  Royal Society University Research Fellowship , FK, TEG and PSt from the  DFG Emmy Noether  Grant No. KA 4662/1-1  and Grant  396021762 -TRR 257 , JJR by  Katherine Freese  through a grant from the  Swedish Research Council  (Contract No.
into the GUM pipeline is the comparison mode, which compares two directories containing. ufo and. mdlfilesgeneratedbyFeynRules:pythonufo_to_mdl. py<UFODir><MDLDir>Thisensures that all vertices in the MadGraph files are present in the CalcHEP files. ufo_to_mdldoes not explicitly check that the vertex functions and Lorentz indices are in agreement; it solely checks the particle content of the vertices. If there are vertices missing from the CalcHEP files,19ufo_to_mdl generates these vertices and writes a set of corrected CalcHEP files to a new directory <MDLDir>_ufo2mdl. define MODEL NewModel START_MODEL // Standard model definition in Gambit DEFINEPARS(mchi, mV, gchi, gb) #undef MODEL The information about the WIMP properties should be added to the particle database, if it does not exist already, in the following format -name: chi conjugate: chi∼ chargex3: 0 color: 1 spinx2: 1 description: Dirac DM Singlet PDG_context: [62, 0].
Turbulence is still one of the main challenges in accurate prediction of reactive flows. Therefore, the development of new turbulence closures that can be applied to combustion problems is essential. Over the last few years, data-driven modeling has become popular in many fields as large, often extensively labeled datasets are now available and training of large neural networks has become possible on graphics processing units (GPUs) that speed up the learning process tremendously. However, the successful application of deep neural networks in fluid dynamics, such as in subfilter modeling in the context of large-eddy simulations (LESs), is still challenging. Reasons for this are the large number of degrees of freedom in natural flows, high requirements of accuracy and error robustness, and open questions, for example, regarding the generalization capability of trained neural networks in such high-dimensional, physics-constrained scenarios.
Introduction Machine learning (ML) and deep learning (DL) have gained widespread use and have impacted many research communities and industries. The availability of exceptionally large, often extensively labeled datasets and the possibility to train large networks on graphics processing units (GPUs), reducing the training time tremendously, are two reasons for this success. Prominent applications of DL include image processing  [1] [2] [3]  , speech recognition  [4]  , or learning of optimal complex control  [5]. These data-driven approaches have also been applied to fluid dynamics problems  [6] [7] [8] [9] [10]  , including works on subfilter modeling for large-eddy simulation (LES)  [11] [12] [13]  based on direct numerical simulation (DNS) data. Recently, the idea of physics-informed networks  [14]  has emerged, where architecture or loss functions are designed to support known properties of underlying physical problems.
Neural networks have also been applied successfully to reactive flows. Some examples are the adaptive reduction scheme for modeling reactive flows by Banerjee et al. [15]  , artificial neural network (ANN)-based storage of flamelet solutions  [16, 17]  , and direct mapping of LES resolved scales to filtered-flame generated manifolds using customized convolutional neural networks (CNNs), as shown by Seltz et al. [18]. Additionally, regularized deconvolution methods, such as those published by Wang and Ihme  [19]  , are closely related ideas. Often, the applications regarding flow data are limited owing to the use of either simple networks or small, artificial datasets. Thus, many questions still remain open, such as the determination of proper network architectures for flow problems, search for hyperparameters, or improvement of the generalization ability of networks.
The following dropout layer serves as regularization, reducing the risk of overfitting by ignoring network units during training with the probability β dropout. A relativistic adversarial loss proposed by Jolicoeur-Martineau  [26]  is employed. Using relativistic loss as adversarial loss was shown to stabilize GAN training in different scenarios  [26]. It also presumably aids learning of sharper edges and more detailed textures in SISR cases, which should also help to learn very high-frequency details in the turbulence context. The perceptual loss proposed for the ESRGAN based on the VGG-feature space pre-trained with the ImageNet dataset is less suitable for turbulence data, as the natural image features from VGG19 may be not representative of turbulent flows. Instead, physics-informed constraints are incorporated into the loss function, guided by laws governing the physics of turbulence.
Training strategy Many industrially relevant applications are operated at very high Reynolds numbers that are not accessible by DNS. Thus, training the network only with the DNS data of the relevant Reynolds number range is not possible. This raises the question whether a network trained with DNS data of lower Reynolds numbers is general enough to give also good results at higher Reynolds numbers, i. e. , whether it has an extrapolation capability. In the a priori test (cf. Subsection 2. 4 ), it was seen that training the network only with DNS data leads to a lack of accuracy for Reynolds numbers falling outside the training range. Therefore, in this work, the training was extended by introducing a second step. After training the generator and discriminator simultaneously with DNS data ("H") and corresponding filtered data ("F") only, the generator was further trained and updated using filtered data (" ˜ F "), which were generated for a wider range of Reynolds numbers with LES without subfilter closing, which can be computed at low computational cost.
Corresponding "H" data did not exist, and the discriminator was not further updated. Note that, as mentioned before, the loss function reduces for this second learning phase, as the evaluation of loss terms related to DNS data is no longer possible. Thus, the loss is driven mainly by the part of the adversarial loss that corresponded to correctly recognizing generated flow samples and by the physics-informed continuity constraint. Another critical challenge during the training is the search for optimal hyperparameters. The training was started with the hyperparameters given by Wang et al. [2]. However, the weighting coefficients in the loss function were initially chosen such that all loss terms had a similar order of magnitude after a few numbers of epochs. Afterward, the weighting coefficient β 4 was increased until the error in the non-dimensionalized continuity equation based on the zero mean-centered and rescaled velocity field was comparable to the order of magnitude of the error introduced by the numerical approximation of the divergence operator.
A priori testing In this work, one of the largest existing decaying turbulence DNS datasets  [27]  was used for training and testing the PIESRGAN. The dataset features periodic boxes of homogeneous isotropic turbulence with Reynolds numbers based on the Taylor microscale Re τ of up to 88, simulated on 4096 3 mesh points. The first data time step investigated was defined to lie in the self-similar range of the flow, as indicated with t * start in Fig. 4. Before the training, the data was filtered to obtain combinations of "H" and "F". The PIESRGAN could reconstruct the data within the trained Reynolds number range well. To test the extrapolation capability of the network, the first time step of the DNS data was used only for testing, not for training. As the Reynolds number reduces over time for the decaying turbulence case, skipping the first time step of the data resulted in a highest Reynolds number of about 75 for the training, while testing was performed with a Reynolds number of 88.
The results are shown in Fig. 2  as "R " for the dimensionless passive scalar z * and one dimensionless velocity component u *. It is obvious that the network adds insufficient small-scale structures to the flow -maybe because it had never seen such a high Reynolds number before, i. e. , earlier, it never needed to add such small structures to the flow. The column labeled "R" shows the results of a network additionally trained with " ˜ F " data, featuring Re τ of up to 250. The reconstruction results are much better, and the visual agreement with the DNS data is almost perfect. One reason for this could be that the " ˜ F " data merely modified all weights in the network, which results in higher subfilter contributions for all Reynolds numbers, randomly leading to good reproduction for the target Reynolds number but worse results for the others.
This would Fig. 3. Dimensionless spectra evaluated DNS data, filtered data, and reconstructed data for the dimensionless velocity vector u * and passive scalar z * for the time step with Reynolds number of about 88. contradict the idea that the neural network used the new data to "really learn" the results of the target Reynolds number by means of the adversarial loss. Therefore, the PIESRGAN was alternatively trained with the "H"/"F" dataset and additionally a dataset " ˜ F ", featuring only Re τ of up to 200. These results are shown in Fig. 2  with "R ", and the agreement is as good as before, which indicates that the network really learned to reproduce the higher Reynolds number data. This was also confirmed by analyzing the results for "R " and "R " within the Reynolds number training range, which did not differ (these results are not shown here).
The same result was also observed for the other two velocity components, which are not visualized in Fig. 2. Note that higher Reynolds number data can be chosen arbitrarily as long as the maximum Re τ is higher than the target Re τ , which was 88 for the a priori test. However, the large Re τ of 250 was selected to enable usage of the trained network for the ECN Spray A case (cf. Section 3 ). In addition to the visual evaluation, Fig. 3  presents the non-dimensionalized spectra (denoted by S * ) computed with "H", "F", and "R" data. It shows that also the statistical agreement be-tween DNS and reconstructed data is very good. Only for very high wavenumber magnitudes κ, the reconstructed flow field slightly differs from the DNS data. Note that the spectra based on the velocity use all three velocity components.
A posteriori testing Before using the trained network PIESRGAN R in a complex reactive turbulent flow, an a posteriori test was performed with respect to the decaying turbulence data. For the test, filtered data of the early time step t * start of a realization of the decaying turbulence DNS data, which was not used during the training, were used as the initial flow field and advanced over time t according to the steps outlined in the beginning of this section. To keep the filter width of the data consistent with the training data, the DNS data of size 4096 3 were filtered to a 64 3 mesh. The time step size of the LES was increased compared to the DNS, and approximated ensemble averages were computed over all grid points of a single time step. Fig. 4  compares the decay of the ensemble-averaged dimensionless turbulent kinetic energy k * and the ensembleaveraged dimensionless dissipation rate ε * evaluated during the DNS and the a posteriori test with PIESRGAN R as LES model.
Acknowledgments The authors acknowledge the funding received from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  under grant agreement No  695747. Moreover, the computing time grants for the project  JHPC55  by the  JARA-HPC Vergabegremium  provided on the JARA-HPC Partition part of the supercomputer JURECA at  Jülich Supercomputing Center, Forschungszentrum Jülich , and on the national supercomputer Cray XC40 at the  High-Performance Computing Center Stuttgart (HLRS)  under the grant number GCS-MRES are acknowledged.
Study Design Experiment 1: Long-Term Effects of HDBR With and Without Exercise as a Countermeasure (RSL) As part of the ESA sponsored bed rest study 'Reactive jumps in a Sledge jump system as a countermeasure during Long-term bed rest -RSL Study' (RSL) we acquired resting state EEG data once before, three times during, and once after 60 days of HDBR to identify the time course of electrocortical activity in response to prolonged HDBR with and without exercise as a countermeasure. The study was carried out at the :envihab facility of the German Aerospace Agency (DLR) in Cologne, Germany in 2015/2016. Details on the general study design and exercise program are described elsewhere  (Kramer et al. , 2017). Briefly, twenty-three young, healthy right-handed men [age: 29 ± 6 years, height: 181 ± 6 cm, body mass: 77 ± 7 kg (mean ± SD)] with no personal history of neurological or psychiatric illness, drug or alcohol abuse, and normal or corrected-to-normal vision were enrolled in the study.
Each training had a total duration of 20 min including preparation. Because of medical reasons two participants (one from each group) started their recovery after HDBR49 and HDBR50, respectively (instead of HDBR60). A comparison of the subgroup demographics is displayed in Table  1. There were no significant differences in any subject characteristics (all ps > 0. 35). Resting state eyes-closed EEG data were collected for 3 min seven days prior to bed rest (BDC-7), on the second day of HDBR (HDBR2), on the 28th day of HDBR (HDBR28), on the 56th day of HDBR (HDBR56), and after 11 days of recovery (R+10, the first day of recovery was R+0). For the two participants that started their recovery earlier, data collection was performed on the last day of their bed rest phase (i. e. , HDBR49 and HDBR50, respectively).