dataesr/scientific-paragraphs-categorization huggingface dataset:
These creep mechanisms are consistent with the grain sizes characterized in  [6]  on the post-mortem samples. On the contrary, the activation energies identified in simulated post-DNB conditions are much higher than the usual values found in LOCA conditions. Additional microstructure analysis should be carried out to better understand the underlying mechanisms of such a difference. The model of this work is currently being implemented in the SCANAIR software developed by the French Institute for Radiological Protection and Nuclear Safety (IRSN), which simulates a reactivity initiated accident  [34, 35]. An extension of the creep law to the first 100 seconds has also been proposed for 8 experiments performed in the two-phase domain to reproduce the non linearity of the creep response at higher time scale.
Data & methodology The methodology to evaluate marine temperature extreme event's physical importance within a predefined domain and predefined time range (such as a season, a month, etc. ) is provided in three steps: (i) detection of events, (ii) calculation of the activity metric within the time range for each year and each grid point location of the domain, (iii) ranking according to the total activity (sum of the activity of each grid point over the domain). First, daily sea-surface temperature (SST) from NOAA OISSTV2  (Reynolds et al. , 2007; Huang et al. , 2020)  from 1st January 1982 to 23rd November 2021 at a regular resolution of 0. 25°x0. 25°(1440x720 number of cells) was used to detect MEEs. Both MHWs and MCSs in the Mediterranean over the period 1982-2021 were identified following the approach from  Hobday et al.
We suggest that the method can also be applied to other ocean regions, in particular for applications where there is a benefit to considering activity or impacts spatially aggregated. Thus we provide the code developed here, which could help to assess the evolution of these MHWs and MCSs metrics elsewhere in recent decades. Additionally, the code can be easily applied to the output of the most recent GCMs to further evaluate how more active and longer hot extreme events, and conversely less active and shorter cold extreme events, will affect different marine ecosystems or atmospheric circulation in the future.
Funding This work was partially supported by national funds through  FCT  ( Fundacão para a Ciencia e a Tecnologia, Portugal ) through project  ROADMAP  ( JPIOCEANS/0001/2019 ). AS, SP, AR, AT-M, RT are grateful for the FCT funding UIDB/50019/2020 - Instituto Dom Luiz. Supporting information that may be useful in reproducing the authors' work is available from the authors upon request (ajsimon@fc. ul. pt). AT-M was funded by the Project  SARDINHA2020  ( MAR-01. 04. 02-FEAMP-0009 ), funded by the  Operational Program Mar2020.
Data availability statement The code generated for this study is available for download at https://github. com/amelie-simon-pro/marine-extreme-event. It includes two python scripts. A script that loops over any domain the detection script of Hobday et al. (2016) (step (i)  of the protocol of this study) and a second script that calculates the activity and ranks the years according to the total activity (step (ii) and step (iii) of the protocol of this study).
This shows that the question of learning affordances from local features using exploration has not been extensively studied yet. This section reviews different groups of works addressing this question. A first group aims at learning several kinds of affordances with supervised learning on annotated datasets, a second one focuses on the object grasping issue and finally, works that do not fit in these two categories are mentioned. Some studies use an annotated dataset to train a model of affordance classification and then integrate this model in a robotic framework, as a tool for planning, task solving or manipulation. Myers et al. [17]  study tool use affordances. They train a classifier on superpixels using SLIC. Ashanta et al. [18]  have extended it to work on RGB-D images, with features related to shape.
e. small planar surfaces which compose a 3D shape, they can use a simple linear classifier, especially if trained offline on an annotated dataset. In the same idea, Kim and Sukhatme  [21]  proposed a method to detect affordances of surfaces based on a geometrical analysis of the pointcloud, K-means clustering, and logistic regression. Those methods proposed efficient tools for robotic systems to detect affordances, but they are all based on supervised learning on datasets annotated by a human expert. Annotating is a costly process that naturally limits the learned model to the datasets produced by the expert. Moreover, affordances in ecological psychology depend on the agent body structure and on the actions it is capable of. Another approach is thus to let the robot explores its environment with one or several actions and collects information about the affordances in its surrounding and discovers by itself the affordances.
The robot is equipped with a 3D sensor and collects data after each action labeled with the success of going through the objects. The sample data are extracted thanks to a simulated RGB-D camera. Then, an online SVM (  [30] ) is trained based on the collected data. The resulting model predicts the "traversability" of objects based on local features. To drive the exploration, an uncertainty measure is computed based on the soft margin of the model decision hyperplane. Finally, they tested their method on a navigation problem, on real robots and in a realistic environment. They demonstrate, by using the model learned in simulation, that the robot is able to navigate through a room full of boxes, spherical objects and cylindrical objects like trash bins without colliding with nontraversable objects.
III. METHOD The goal of this work is, for a robot, to learn which part of an environment affords a given effect to a specific action through an autonomous exploration. The robot is interacting with the environment thanks to an action primitive in order to collect data. The method is tested with three affordances: pushable objects, activable push-buttons and liftable objects. These affordances are respectively linked to a push primitive, a push-button primitive and a lift primitive. The general approach, summarized in figure  1 , is to separately build the relevance map relative to each considered affordance. Each relevance map is built by collecting data thanks to the interactions of the robot and then by training online a classifier on the data. The classifier is used to build the relevance map by attributing weights to segments extracted from the current scene (see sections III-D and III-B2).
• Step 2: The classifier is updated with the training dataset extended with a new labeled sample. Then, the classifier weights are attributed to each supervoxel. The outcome is the relevance map of the current scene. This step is explained in section III-B4. • Step 3: The next supervoxel to interact with is chosen as a target for the action primitive. Section III-B5 explains how the target is chosen. • Step 4: An action primitive is applied on the center of the chosen supervoxel. Each action primitive is explained in section III-B6. • Step 5: To check if an effect is produced by the action primitive, an effect detector is applied. The visual feature of the selected supervoxel is added to the training dataset with a label indicating if there was an effect. The different effect detectors are described in section III-B6.
The classifier named collaborative mixture models is described in section III-D. 5) Choice of the Next Area to Explore: From the predictions of the classifier, a choice distribution map is computed. The choice distribution map is also a set of weighted supervoxels, but a weight represents the probability for the supervoxel to be chosen by the system as the next target of the interaction. A weight is the combination of the uncertainty and the confidence of the classifier and the diversity of the dataset. The higher the uncertainty and the lower the confidence and the diversity, the higher is the probability for a supervoxel to be chosen. This step is described in section III-D3. 6) Action Primitives and Effect Detectors: Pushable Objects: The pushable affordance is associated to a push primitive and a change detector as effect detector.
1 is added to the numerator and 2 is added to the denominator to obtain a default probability of 1  2 if both mixtures are empty. The parameters of CMMs are the following : • K E : number of components of the mixture models encoding the class ∆ = (a, E) with E ∈ {e, e}. This number is estimated during the training. • S = {s i , ∆ i } i<I : database of samples and their corresponding label constituted during the exploration. A component is a cluster of samples of the feature space modeled by a multivariate normal distribution. Naturally, all the samples of a component have the same label. So formally, we write C k (X) = (w k , G(µ k , Σ k , X)), S k , ∆) a component k, where S k is the dataset used to estimate µ k , Σ k , and w k , and G is a Gaussian function. The probability of a sample with feature X to belong to a component k is given by the equation 8.
3. A split operation is applied on a random component of each class, if they are not split then the merge operation is applied. The split operation increases the number of components to adapt the distribution to the dataset and the merge operation reduces the number of components to avoid overfitting and reduce computational time. Split and merge operations are described respectively in algorithm 1 and 2. A split operation occurs if two components of different classes are overlapping, as illustrated in figure  6a. A merge operation occurs if two components of the same class are overlapping as illustrated in figure  6b. The number of components is limited to a maximum number. If this number is reached only the merge operation is applied. To decide if two components are overlapping, hyperellipsoids of tolerance of the multivariate normal distributions are estimated.
Also, the exploration gives priority to class with less samples collected over other classes (see figure  12 ). This Fig. 8. Function used for uncertainty estimation. This function gives a higher probability of choice to uncertain classification, but also to certain classification to the chosen class, i. e the one with fewest samples. pushes the system to build a balanced dataset between the classes. Confidence: The classification of CMMs relies on a mapping of the feature space of normal distributions. The border of these distributions can give an insight on the least dense areas in the feature space. The confidence of the classifier for a sample is its probability of membership in its closest component. This probability is computed thanks to equation 8. By choosing areas with the lowest confidence, the exploration gives a focus to areas in which the system has less information.
Therefore, this metric could be interpreted as an approximation of entropy. For each of the three affordances, 4 experiments have been conducted. An initialization step has been added in the experiments of liftable and activable push-button in which the system is forced to gather at least 10 samples of each class. With a uniform random sampling, the chance to gather positive samples in these experiments is very low, thus at the beginning of the experiment, the robot collects only negative samples. This initial step allows the system to start from a balanced dataset. Adding this step was not useful for the experiments with the push affordance as the probability to gather positive samples is higher.
For too long, many refined transportation models have focused solely on private and public transportation, assuming that bicycles only require simple models, such as bird flight distance or trips on horizontal tracks at a constant speed. This paper aims to study the impact of the road characteristics, such as road gradient, type of road and pavement surface of the road, on cyclists' behavior using dedicated modules of MATSim. For that, we compare two approaches: a standard approach which does not consider the road characteristics, and a second approach that uses MATSim bicycle extension of Ziemke et al. The two approaches are analyzed over a sub-regional area around a district, focusing on a suburban city with an undulating relief made of average-to-steep hills. The focus is on the bicycle transportation model because the catchment area has a particularly challenging altitude profile and a large variety of roads, whether in type-from residential to national highwayor in pavement surface due to the number of green areas, such as parks and forests.
This extension contains two main components: a speed calculator, which calculates the travel speed of the cyclist by link and depends on the quality of the road and its slope; and a cycling itinerary calculator, which selects the trip route based on the travel distance, travel time, type and gradient of the route. Table  1  gives an overview of the main characteristics of the bicycle models in use in MATSim. In this paper, we propose to integrate cyclists in MATSim in a realistic way-with trips on the road network instead of teleportation, with or without the additional Ziemke module. We provide an analysis of the impact of road characteristics on the cyclists' behavior and the accuracy of simulations integrating this information.
The planned tramway line is also a structuring element in the future mobility pattern; the catchment area is spread around it. A total of 126,000 people were selected to constitute the agents of a synthetic population representing the resident of this area. This study analyzes the agents using bicycles for their activity in this challenging area. It is a study on a recent MATSim module developed to better model cyclists' transportation: it considers road grades, road type and pavement surface. The model is assessed and sometimes criticized in light of the modification in cyclists route choices it outputs, compared to a standard 2D model that does not include the infrastructure characteristics.
Scoring Functions in a Daily Travel A MATSim network consists of a set of nodes and links. A node stores the geographical coordinates, while a link represents a section of the road between two nodes. We collected a 2D network from OpenStreetMap  [22] , which contains information about roads, including length, speed limit and flow capacity. The elevation of nodes and the slope of links are extracted from an elevation database (European Digital Elevation Model), which is public and has a resolution of  25 m [23]. The daily plans of travelers are generated using a synthetic population process; in this study, the plans are generated using the model that we described in  [20, 21]  and are used to create the simulation scenario. The MATSim  [3]  process consists of three modules: mobsim, scoring and replanning.
Elevation Imprecision: Sensitivity to Gradient Estimation Nodes and links in OpenStreeMap data do not include the altitude or slope information. In absence of a terrain survey done specifically for an area of study, one has to use a digital elevation model. As pointed out in [26], there are two types of digital elevation model: digital surface model created from satellite imagery, and digital terrain model from aerial photogrammetry. In an urban environment, a digital surface model is not filtered with the altitude of the bare road: its includes buildings and trees elevation. A digital terrain model represents the ground surface of the Earth with no objects, but this kind of data is usually not publicly available. The European Digital Elevation Model EU-DEM used by default in the extended bicycle model of MATSim and in the study of Ziemke et al.
[4]  is a post-processing of a combination of high resolution digital surface models from the NASA, where a broad range of artifacts have been removed. The EU-DEM database resolution is 25 m, meaning that the continental area is divided 25 × 25 m cells: all points within a cell have the same estimated altitude from the sea level. By using this public database to estimate the elevation of a road network, the nodes located in the same mesh have the same Z-coordinate. It induces an error in the slopes estimation: a link connects two nodes located in the same cell has an estimated slope of 0%, which can be unrealistic. On the other hand, the slope of a link crossing the frontier of a cell may be overestimated as the altitude of its two terminal nodes are from two different cells. It can generate unrealistic behavior of the cyclist: for instance, the velocity can be suddenly decreased from 20 km/h to 3-4 km/h for a few meters then back to 20 km/h in.
That region includes all inhabitants able to access the district in 15 min, whatever the mode of transportation, considering restrictions related to the built environment. Elements structuring the population mobility are integrated, such as the presence of a highway in the south, which acts as a barrier to the possibility of trips toward or out of the city. The planned tramway line is also a structuring element in the future mobility pattern; the catchment area is spread around it. A total of 126,000 people were selected to constitute the agents of a synthetic population representing the resident of this area. This study analyzes the agents using bicycles for their activity in this challenging area. It is a study on a recent MATSim module developed to better model cyclists' transportation: it considers road grades, road type and pavement surface.
Funding: This research was funded by the  E3S  project, a partnership between Eiffage and the  I-SITE FUTURE consortium. FUTURE bénéficie d'une aide de l'État gérée par l'Agence Nationale de la Recherche (ANR) au titre du programme d'Investissements d'Avenir  (référence  ANR-16-IDEX-0003 ) en complément des apports des établissements et partenaires impliqués. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Introduction The ATLAS and CMS experiments at the Large Hadron Collider (LHC) produce data rates around 40 terabytes per second and per experiment  [1, 2] , a number that will increase further for the high-luminosity upgrades  [3, 4]. These rates are far too large to record all events, so these experiments use triggers to quickly select potentially interesting collisions, while discarding the rest  [5] [6] [7] [8]. The first two trigger stages are a hardware-based low-level trigger, selecting events with µs-level latency, and a software-based high-level trigger with 100 ms-level latency. After these two trigger stages, some interesting event classes, such as events with one highlyenergetic jet, still have too high rates to be stored. They are recorded using prescale factors, essentially a random selection of events to be saved.
At the end, we use the averaged weights for generation. In Fig. 4  we show how ONLINEFLOW, trained on signal plus background attempt to reproduce the signal mass peak. While the width of the peak is not correctly described we do see a noticeable abundance. As the goal is in finding evidences of new physics in a subsequent offline analysis of the ONLINEFLOW data, this abundance may still, however, be sufficient for our purpose even if the peak width is mismodeled. To estimate the statistical uncertainty § (described in more detail below), needed to compute p-values, we train 20 flows in parallel. As training samples for the statistical uncertainty, we use bootstrapping. Creating classical bootstrapped ensembles is not possible because we do not have the full dataset to resample from with replacement.
Data, model, training The dataset comprises a background of dijet events including a new-physics signal. This signal, which we assume to contribute 1% of our events, originates from a W decaying into two heavy particles, which in turn decay into quarks, W → X (→ qq)Y (→ qq). ( 8 ) The respective particle masses are m W = 3. 5 TeV, m X = 500 GeV, and m Y = 100 GeV. All events are generated using PYTHIA8  [58]  and DELPHES3. 4. 1  [59] [60] [61]. The jets are clustered using FASTJET  [62]  with the anti-k T algorithm  [63]  using R = 1. Finally, all events are required to have at least one jet with p T > 1. 2 TeV. While this dataset features high mass resonances that are not perfectly in line with the intended application range of ONLINEFLOW, we feel that the proven and well known nature of the LHCO data, as well as its availability make up for this shortcoming.
The same input format used for the anomaly detection  [32, 40, 64]  is also used for the ONLINEFLOW. Specifically, there are five input features, the dijet mass, the mass of the leading jet, the mass difference between the leading and sub-leading jets, and the two n-subjettiness ratios  [65, 66] , m j j , m 1 , m 2 -m 1 , τ (1) 21 , τ (2) 21. (9) All observables except for m j j are subjet observables and at most weakly correlated with m j j. We show distributions of all observables in Fig. 8 , for the training data and the ONLINEFLOW output. We also show a 10-fold enhanced signal, relative to the 1% signal rate we will use for our actual analysis, to illustrate the narrow kinematic patterns of the W resonance. The LHCO version of the ONLINEFLOW network is slightly modified compared to the parametric setup to accommodate a 10-dimensional input.
These comprise five features and five additional noise dimensions, the additional noise was found to increase the performance, although no systematic scan over this hyperparameter was performed. The number of MADE blocks is now 10, and the number of nodes in the fully connected layers is quadrupled to 128. The buffer size is still 10,000, and the number of iterations per buffer is increased to 1,000. Learning rate and optimizer are identical to those used before. For an assumed signal rate of 1% we split a total of around 300k LHCO events into 80% training, 10% evaluation, and 10% validation data. The limited evaluation data is further supplemented with additional signal and background events, to smooth out the ROC and SIC curves. This gives us a new set of 330k evaluation events, roughly equally split between signal and background, but only to present our results.
CWoLa benchmark Just like for the parametric example, we need to determine how well the ONLINEFLOW captures the signal features for an anomaly detection setup. As a simple benchmark we choose the Classification Without Labels (CWoLa)  [55, 56, 67]  setup, implemented following Ref. [40]. In the CWoLa framework, a classifier is trained to distinguish between two samples with different relative amounts of signal. For the LHCO dataset these two samples are the signal region defined in terms of the dijet invariant mass and indicated in Fig. 8 , m j j = m W ± 200 GeV = 3. 3. 3. 7 TeV ,  (10)  and the control regions away from the W -peak. While this definition of a signal region has no effect on the training of the ONLINEFLOW, it implies that we cannot include m j j as a training    9 ). We show the original training data, with 1% signal contamination, and the data generated by the flow.
The training uses a binary cross entropy loss, the ADAM  [49]  optimizer with a learning rate of 10 -3 , and runs for 100 epochs. As the signal and control region contain an unequal amount of data, the two classes are reweighted to correct for this imbalance. The final classifier comprises an ensemble of the ten network states with the lowest validation loss during training. To benchmark the signal extraction of the ONLINEFLOW, we train CWoLa on the LHCO training data. It contains 240k events and is identical to the data used to train the ONLINE-FLOW. This training is aided by a 30k validation set. To determine how much information the ONLINEFLOW captures we repeat the CWoLa benchmark training with 50%, 20%, 10%, and 5% of the LHCO training and validation sets.