dataesr/scientific-paragraphs-categorization huggingface dataset:
The hybrid modeling process is performed with satellite-derived irradiance at one hour ahead. The paper is organized as follows. First, the context of the study and dataset are described (section 2). In the following section, section 3, several model blending equations combining both models are proposed and evaluated, leading to a proposed blended model. After that, in section 4, a benchmarking between CMV, STVAR, scaled persistence, and blended CMV/STVAR, is performed at specific locations, namely at sea, and inland with low and high orography, as well as regionally integrated over the entire study area. An analysis of the results leads us to propose a coefficients quantification of the blended model as a function of a geographical parameter reflecting microclimatological/topographical nature of the considered location.
Experimental dataset The experimental data used in the study is made up of spatially homogeneous satellite-derived irradiances obtained from the SUNY model  [24, 30]  and is the same dataset used in the study by  André et al. in 2019. The data has a spatial resolution of 0. 1 degree in both latitude and longitude and a temporal resolution of one hour, covering a period of two years  (2008 and 2009). The measurement zone is a 20×20 cell area located between latitudes 14. 85N and 16. 75N, and longitudes 62. 35W and 60. 45W. The study zone, shown in Figure  1 , encompasses the Island of Guadeloupe and surrounding West Indies Islands and consists of 400 individual satellite pixels with an approximate resolution of 11×11 km. Figure  1  displays the coverage area. Perez et al. [31, 32]  have thoroughly tested and confirmed the precision of the SUNY model for estimating irradiance in locations across the United States.
The altitude data were obtained from GEBCO's gridded bathymetric data set. We propose here a modeling approach based on a look-up conversion table. Note that look-up based models have proven effective both in the case of solar irradiation transposition models  [33]  and solar irradiation fluctuations models  [10, 34]. The lookup table is given in Table  3. The uncertainty of means is zero when there is only one location in the considered altitude among the study area. There are no locations for altitudes between 600 and 1100 meters for our study area. The orographic context of the study area does not exhibit these values of altitude. In future work, it would be interesting to assess the model's generalizability across a broader range of elevations, by considering other non-interconnected zone insular regions such as our study case or a continental zone.
We developed a detector signal characterization model based on a Bayesian network trained on the waveform attributes generated by a dual-phase xenon time projection chamber. By performing inference on the model, we produced a quantitative metric of signal characterization and demonstrate that this metric can be used to determine whether a detector signal is sourced from a scintillation or an ionization process. We describe the method and its performance on electronic-recoil (ER) data taken during the first science run of the XENONnT dark matter experiment. We demonstrate the first use of a Bayesian network in a waveformbased analysis of detector signals. This method resulted in a 3% increase in ER event-selection efficiency with a simultaneously effective rejection of events outside of the region of interest.
I. INTRODUCTION XENONnT is a dark matter direct-detection experiment currently operating at INFN Laboratori Nazionali del Gran Sasso in Italy. The experiment has a wide range of (astro)particle physics capabilities, including the search for weakly interacting massive particles (WIMPs), solar axions, and coherent electron-neutrino nucleus scattering from 8 B solar neutrinos  [1]. XENONnT's physics program aims to make substantial progress on sensitivities through improvements in hardware, software, and analysis methods. The XENON Collaboration recently reported a search for WIMPs  [2]  and an analysis of electronic-recoil (ER) data  [3]  using the data collected during Science Run 0 (SR0). These new results relied on XENONnT's unprecedentedly low background radioactivity rates and its keV-scale energy threshold.
We evaluated how the model performs in classifying S1 and S2 signals, respectively, and compared the model performance to that of the baseline method of signal classification described in  [3]. Then, we further applied this quality metric beyond signal classification as the primary detector signal quality feature in event selection. We present the first application of a waveform-based analysis of detector signals using a Bayesian network by analyzing the electronic-recoil data reported in  [3]  and obtain results that are in agreement with the original work. The paper is organized as follows. In Sec. II we provide an overview of the XENONnT experiment. In Sec. III we describe the use of a Bayesian network to study S1 and S2 signal classification. In Sec. IV we apply the quantitative scores from the Bayesian network for selecting ER events, and we further present the results of this application.
A. Input data The training and evaluation data for this work consist of a combination of simulated and measured data, primarily simulated ER interactions ranging from [0. 75, 200] keV. The simulated data were generated using XENONnT's waveform simulator package, WFSim  [27] , and eventbuilding software, STRAXEN  [28]. Light and charge yields used in WFSim are computed using NEST  [29, 30]. In WFSim, the light yield for an interaction with a given energy is used to generate S1 signals before computing the photons' arrival times at the PMTs. The light yield accounts for scintillation of the Xe atoms, electron-ion recombination, and the singlet-to-triplet fraction of excited states. The observed light-yield probability distribution is computed for each PMT based on an S1 light-collection efficiency (LCE) map.
PMT hits are grouped iteratively into clusters with adjacent hits within a 700-ns time window, forming a waveform that can subsequently be classified as an S1, S2, or "unknown" by STRAXEN. Hits with no neighbors in the time window are treated as isolated hits. In XENONnT simulations and SR0 data taking, every waveform is saved with a fixed number of 200 samples. Sample duration in XENONnT can vary from a few tens of nanoseconds for an S1 signal and up to few hundreds of nanoseconds for an S2 signal, to accommodate the full waveform within the fixed 200-sample interval. We augmented the training data in the low-area range where signal classification is particularly challenging in SR0. To this end, we simulated a set of S2-only signals with  [1, 10]  electrons (at very low ER interaction energies, S1 signals are rarely observable and are below the energy threshold of 1 keV used in the low-energy ER analysis).
Additionally, a very pure (> 99. 9%) measured sample of single-electron S2 signals, produced from a highly emitting electron source near the gas region, was added to ensure that experimental effects at these low areas were represented in the training sample despite any limitations in modeling at these low areas. The measured single-electron data represented 2. 5% of the training data, the cause of which was a short between the bottom screening and cathode electrodes which produced intermittent, localized bursts of single electrons that could be tagged via their position to the location of their source. In total, the simulation and measured data used in training and evaluation consisted of 10 6 waveforms with ground-truth labels. The training dataset was composed of true S1 or S2 waveforms from single-scatter interactions within the detector's 4-tonne fiducial volume, which is defined in  [3].
IV. EVENT SELECTION USING THE NBC SCORES In this section we describe the application of signal characterization in event selection based on NBC scores to the XENONnT SR0 ER dataset. Most recorded signals in a dark matter detector science run, as in XENONnT's SR0 campaign, are not derived from single-scatter recoil interactions in the fiducial volume. They are primarily due to mislabeled detector signals, detector signals from interactions in the gas which are misplaced within the fiducial volume, multiple detector signals which are merged together in processing, and detector signals grouped from lone hits from multiple PMTs, such as dark counts. These signals can contribute to events that are defined as being outside of the ER ROI. Conversely, events within the ER ROI are defined as single-scatter ER events within the energy range of interest and occurring within the fiducial volume.
The 220 Rn decay chain produces 212 Pb, a uniform β-emitting source of ER events across the 1-140-keV energy region in this analysis. The 220 Rn data were used to define signal selection efficiency across the ER energy spectrum  [41]. 37 Ar decays by electron capture into 37 Cl, producing an electron vacancy in either the K, L, or M shell. In particular, the K-shell vacancy (90. 2% branching ratio) being filled by an electron rearrangement deposits a total energy of 2. 82 keV  [42] , which was useful in this study for calibrating ER event acceptance near the threshold of detection with high statistics and contamination. We calculated NBC scores for both the S1 signal and S2 signal of each event in the calibration data. Each event's S1 (S2) signal selection depends upon a decision boundary between the NBC score of S1 (S2) signals and the S1 (S2) observed signal area.
The optimal decision boundaries used in this selection were determined using the 220 Rn calibration dataset and chosen to isolate noncanonical detector signals. It is noteworthy that the selection can be made more strict or loose depending on the analysis application. We then evaluated both the efficiency of accepting ER-like events, and the effectiveness at rejecting events outside of the ER ROI, using the NBC score boundaries that were imposed on S1 and S2 signals contributing to events reconstructed within the fiducial volume. The selection of ER events in this Bayesian networkbased method differs from the previous work in  [3]  by reducing several signal-quality selection criteria applied in  [3]  to only two: one selection on the S1 signal's NBC score, and one selection on the S2 signal's NBC score.
The increase in total efficiency relative to the previous work is 3%. The improvement arises from fewer ER events being removed from the data than in the previous method, which relied on several more sequential selection criteria. The effectiveness of this method at removing events outside of the ER ROI was calculated using the SR0 ER dataset. Events outside of the ER ROI were tagged using the set of signal-quality selection criteria developed in  [3]. These tagged populations were then used to evaluate the relative effectiveness of the S1 and S2 NBC selections at removing events outside of the ER ROI. The population overlap.
A. Analysis of electronic-recoil spectrum The low-energy electronic-recoil spectrum of XENONnT SR0 was measured in a total exposure of 1. 16 tonne-years; it mainly consists of radiogenic ER events from 222 Rn contamination in the LXe target. The spectrum is shown in Fig. 6  and was fit by a detailed background model, with all components as described in  [3] , with a step-function approximation to account for the electron binding energies in the solar neutrino spectrum as suggested in  [43] , and by using an unbinned maximum-likelihood framework. The goodness-of-fit measurement for the spectrum is χ 2 =N dof ¼ 128. 64=128 ¼ 1. 004 (p value of 0. 467). As in  [3] , the ER data are consistent with the background-only hypothesis. The individual background components contributing to the SR0 ER dataset are summarized in Table  II.
V. CONCLUSION AND FUTURE WORK We demonstrated the first use of a Bayesian network to perform detector signal classification, waveform-based event selection, and subsequent analysis in a full-scale dual-phase Xe TPC. Applying the NBC-based metric in event selection reduces significantly detector signals outside of the region of interest and solves the need for additional selection criteria based on detector signal quality. The method can be used both as an independent analysis framework and as a valuable cross-check for analyses that use lower-dimensional features in subsequent event selection steps. The development of this method relies on simulated data, and the selection criteria rely on calibration data, without optimizing on ER search data. Therefore, due to the NBC being developed blinded to the ER dataset, it is agnostic to any new interactions present in the XENONnT SR0 ER search data, allowing us to demonstrate that the Bayesian network-based method of signal selection corroborates the ER background-only hypothesis of the lowenergy ER data from XENONnT SR0  [3].
In the future, developing data selection criteria primarily using Bayesian network-based methods could help to increase signal-to-background ratios in dark matter detector experiments, thereby increasing experimental sensitivity to new physical processes. Relaxing the threefold PMT coincidence requirement for a valid S1 signal, which is the primary reason for efficiency loss below 2 keV, could be viable without a significant increase in the accidental coincidence rate by using a Bayesian network specially trained for classifying these events. With this additional improvement on efficiency in a future study dedicated to reducing accidental coincidence background rates, low-energy physics phenomena could be probed to new sensitivities. Other future directions of this work in XENONnT's physics program include the use of dynamic Bayesian networks  [44] , which incorporate temporal dependencies between the attribute nodes.
ACKNOWLEDGMENTS We gratefully acknowledge support from the  National Science Foundation ,  Swiss National Science Foundation ,  German Ministry for Education and Research ,  Max Planck Gesellschaft, Deutsche Forschungsgemeinschaft, Helmholtz Association, Dutch Research Council (NWO) ,  Weizmann Institute of Science, Israeli Science Foundation ,  Binational Science Foundation, Fundacao para a Ciencia e a Tecnologia ,  Région des Pays de la Loire, Knut and Alice Wallenberg Foundation ,  Kavli Foundation ,  JSPS Kakenhi and JST FOREST Program in Japan ,  Tsinghua University Initiative Scientific Research Program , and  Istituto Nazionale di Fisica Nucleare. This project has received funding/support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie Grant  Agreement No.
Search on ImageNet When using data augmentation and architecture tricks, namely Squeeze-and-excitation and AutoAugment, in the optimal architecture, we achieve 78. 6% accuracy on Imagenet. In addition, we compare our methodology to OFA  [5]. Using the proxylessNAS supernetwork, we can find a 1. 39x faster architecture with a drop of 0. 56%. Using our training methodology on OFA's supernetwork directly allows us to find a +2. 2% accurate and 1. 5x faster architecture on Pixel 3 and a +1. 4% accurate and 2. 3x faster architecture on Jetson Nano. These results can be attributed to the fact that our methodology searches for architectures that are optimized for each specific hardware platform, whereas OFA optimizes for a variety of hardware platforms simultaneously. By focusing on specific hardware platforms, our methodology can tailor the architecture to the specific computational resources, and latency requirements of each platform, leading to superior results on those platforms.
Offline reinforcement learning (RL) has emerged as a promising paradigm for real-world applications since it aims to train policies directly from datasets of past interactions with the environment. The past few years, algorithms have been introduced to learn from high-dimensional observational states in offline settings. The general idea of these methods is to encode the environment into a latent space and train policies on top of this smaller representation. In this paper, we extend this general method to stochastic environments (i. e. , where the reward function is stochastic) and consider a risk measure instead of the classical expected return. First, we show that, under some assumptions, it is equivalent to minimizing a risk measure in the latent space and in the natural space. Based on this result, we present Latent Offline Distributional Actor-Critic (LODAC), an algorithm which is able to train policies in high-dimensional stochastic and offline settings to minimize a given risk measure.
Introduction Often, human decisions are stored and this builds interesting datasets. With the success of modern machine learning tools, comes the hope of exploiting them to construct useful decision helpers. To achieve this, we can use an imitation learning approach  [1]. However, in this case, we will at best be as good as humans. Moreover, the performance of this approach depends heavily on the quality of the training dataset. In this work, we would like to avoid these limitations and thus, we consider another framework: reinforcement learning (RL). RL has achieved impressive results in a number of challenging areas, including games  [2, 3] , robotic control  [4, 5]  and even health care  [6, 7]. In particular, offline RL seems to be really interesting for real-world applications as it aims to train agents from a dataset of past interactions with the environment.
Due to this inherent randomness, stochastic environments demand a larger number of samples to accurately estimate value functions and policies. On the other hand, offline learning has limited data available for training. The combination of these factors makes offline RL in stochastic environments particularly challenging. Furthermore, the performance of classical RL methods in such contexts remains unclear. Moreover, and as presented in Sect. 5. 2, our experiments suggest that adding stochasticity to the environment decreases significantly the performance of these algorithms. Based on these observations, we aim to develop a method which is able to train policies in offline settings with high-dimensional states while being robust to stochasticity. In this paper, we present Latent Offline Distributional Actor-Critic (LODAC), an algorithm designed to train policies in high-dimensional, stochastic environments within offline settings.
The main idea is to encode the natural environment space into a smaller representation and train the agent directly in this latent space. But instead of considering the expected return, we use a risk measure. First, assuming some hypotheses, we show that minimizing this risk measure in the latent space is equivalent to minimizing the risk measure directly in the natural state. This theoretical result provides a natural framework: employ a latent variable model to encode the natural state into a latent space, and subsequently train a policy atop this latent space using a risk-sensitive RL algorithm. In the experimental part, we evaluate our algorithm on high-dimensional stochastic and deterministic datasets. In the best of our knowledge, we are the first authors to propose an algorithm to train policies in highdimensional, stochastic and offline settings.
Related work Before going further into our work, we present some related research. As discussed and motivated in the previous section, this paper focuses on offline RL  [9]. Offline RL is a specific approach of RL that aims to learn from past interactions with the environment. This framework holds promise for real-world applications as it enables the deployment of trained policies. Therefore, it is not surprising that offline RL has received a lot of attention in recent years  [10] [11] [12] [13] [14] [15]. One of the main problems in offline RL is that the Q-function tends to be overly optimistic in the case of outof-distribution (OOD) states-actions pairs, where observational data is limited  [16]. Several approaches have been introduced to address this issue. While some studies extend importance sampling methods  [17, 18]  other propose model-based techniques  [19, 20]  or even incorporate dataset re-weight sampling  [21].
Equivalently, we can look for the policy that maximizes the Q-function, which is defined as Q p : S Â A ! R, Q p ðs t ; a t Þ :¼ E p ½ P H t 0 ¼t c t 0 Àt r t 0. Instead of this classical objective function, other choices are possible. For example, the maximum entropy RL objective function E p ½ P H t¼0 r t þ HðpðÁjs t ÞÞ, where H denotes the entropy. This function has an interesting connection with variational inference  [64]  and it has shown impressive results in recent years  [4, 19, 65]. In offline RL, we have access to a fixed dataset D ¼ fðs t ; a t ; r t ; s tþ1 Þg, where s t 2 S, a t 2 A, r t $ rðs t ; a t Þ and s tþ1 $ pðÁjs t ; a t Þ, and we aim to train policies without interaction with the environment. A such dataset comes with the empirical behavior policy p b ðajsÞ :¼  P ðs t ;a t Þ2D.
r h contains one hidden dense layer with 128 units and uses Exponential Linear Unit as activation function. r h is trained using maximum log-likelihood. After the training of these models, the dataset D is encoded into the latent space and stored in a replay buffer B latent. More precisely, B latent contains transitions of the form ðz 1:H ; r 1:H ; a 1:H Þ where z 1:H $ / h ðÁjs 1:H ; a HÀ1 Þ and s 1:H ; r 1:HÀ1 ; a 1:HÀ1 $ D. Next, we introduce a latent buffer B synthetic which contains rollouts transitions performed using the policy p h , the latent model q h and the reward estimator r h. The policy p h comprises three dense layers. The first two use ReLU as activation function and 256 hidden units. The final layer does not use any activation function and yields two distinct numbers. The first number is interpreted as the mean of a normal distribution and the second one as a standard deviation.
Experimental setup In this section, we evaluate the performance of LODAC. First, our method is compared with LOMPO  [19] , an offline high-dimensional risk-free algorithm. Then, we build a version of LODAC where the actor and the critic are trained using O-RAAC  [54]. We denote it LODAC-O. Moreover, it is also possible to use a risk-free offline RL in the latent space. Thus, a risk-free policy is also trained in the latent space using COMBO  [23]. These algorithms are evaluated on the standard walker walk task from the DeepMind Control suite  [8] , but here we learn directly from the pixels of dimensions 3 Â 64 Â 64. As a standard practice, each action is repeated two times on the ground environment and episodes of length 1000 are used. These algorithms are tested on three different datasets: expert, medium and expert-replay.
Each dataset consists of 100K transitions steps. More precisely, the following datasets are built. • Expert For the expert dataset, actions are chosen according to an expert policy that has been trained online, in a risk-free environment using SAC for 500K training steps. The states used for this training are the classical states provided by DeepMind Control suite. • Medium In this dataset, actions are chosen according to a policy that has been trained using the same method as above. However, here, the training was stopped when the policy achieves about half the performance of the expert policy. • Expert_replay The expert_replay dataset consists of episode that were sampled from the expert policy during the training. The setup presented above allows to test our approach on deterministic environments.