dataesr/scientific-paragraphs-categorization huggingface dataset:
A mild excess of HE events over the expected background is observed in both track and cascade samples, compatible with the IceCube signal (see Fig. (2) ). The null cosmic flux assumption is rejected with a 1. 6σ significance. ANTARES data are being scrutinized for signals of neutrino emission from known extended structures, such as the Fermi Bubble region, the Galactic Plane, and Galactic Superbubbles. The tensions observed between different IceCube data samples have indeed raised speculations on a potential galactic component in the detected events  [5]. ANTARES (and, in the future, KM3NeT) is particularly well-suited to probe such scenarios and help constrain the origin of the IceCube signal. A recent all-flavour analysis spanning 9 years of data has been performed targeting the neutrino flux expected from interactions of cosmic rays with the interstellar medium as predicted in the "KRA γ " model, which accounts for local cosmic ray as well as Galactic γ-ray observations  [6].
No significant excess was found, supporting the exclusion of the diffuse Galactic neutrino emission as the major cause of the "spectral anomaly" between the two hemispheres measured by IceCube  [7]. ANTARES has also developed a rich multi-messenger program aiming at correlating neutrino events -in space and/or time -with electromagnetic, cosmic ray and gravitational waves counterparts. Specific low-latency pipelines have been implemented that send alerts to optical robotic telescopes (TAROT, ROTSE and MASTER), radio (MWA), X-ray (Swift, INTEGRAL) and γ-ray (H. E. S. S. , HAWC) telescopes. Dedicated analyses in ANTARES also follow-up on external alerts distributed through the Gamma-ray Coordinated Network to look for transient events. Several on-and offline studies have also been conducted to search for neutrinos in coincidence with the gravitational wave events recently detected by LIGO and Virgo  [8].
The KM3NeT design builds upon the experience gained with ANTARES, while introducing significant technological improvements such as the use of digital optical modules (DOMs) comprising 31 small (3-inch) photomultipliers inside a 17-inch glass sphere. This multi-PMT design provides a factor of 3 increase in photocathode area with respect to ANTARES. It also allows a more accurate measurement of the light intensity (photon counting) and offers directional information with an almost isotropic field of view. Every DOM of KM3NET is also equipped with an internal piezo-acoustic sensor, glued directly on the inside of the glass sphere, which is part of the positioning system of the detector (along with a long-baseline array of several acoustic emitters and hydrophones installed in fixed positions on the seabed).
1 • for track events (limited below 10 TeV by the intrinsic scattering angle between the muon and the neutrino) and will be below 2 • for showers in the whole range of relevant energies. This pointing accuracy, coupled to its location in the Northern Hemisphere, make ARCA particularly well suited for studying potential neutrino emission from Galactic sources. The latest projections show that with 6 years of operation of the full ARCA (2 blocks), an observation at 3σ significance level is possible for promising sources such as Supernovae Remnants RX J1713. 7-3946 and Vela Jr  [10]. Within this time span, the absence of neutrino signal from these two objects would constrain the fraction of their gamma-ray flux coming from hadronic processes to be below 50%. The first two ARCA-DUs were deployed in December 2015 and May 2016, and data were taken continuously from December 2015 up to April 2017.
The AMADEUS (ANTARES Modules for the Acoustic Detection Under the Sea)  [12]  system comprises six acoustic storeys installed on the 12th line of ANTARES and on the IL07, as shown in Fig. (1). Each storey supports 6 piezoelectric hydrophones with a frequency range 1 -50 kHz. Dedicated electronics is used for the digitization and pre-processing of the analogue signals; data filtering is performed onshore. The selection algorithms use coincidence tests based on threshold and/or pulse shape recognition. The use of local clusters proved to be quite advantageous for fast processing and event selection, and also allowed directional reconstruction of transient events. AMADEUS showed that the ambient noise (broadband, mainly caused by sea surface agitation) is low and stable at the ANTARES site, while transient background, due e.
g. to ships and marine fauna is higher than expected and will require sophisticated methods of separation from a potential neutrino signal  [13]. KM3NeT appears as an excellent framework for a second generation acoustic detection test setup through the exploitation of the built-in piezoelectric sensors used for the detector positioning  [14]. The design of the KM3NeT positioning system is based on experience with the systems developed for ANTARES and the former NEMO neutrino telescope prototype, which conducted several acoustic experiment campaigns off the coast of Sicily (see  [15]  for further detail). Acoustic data will be acquired continuously at a rate of about 200 kHz, in the frequency range 10 -70 kHz. All data will be transmitted to shore and can be subsequently filtered and used for additional analyses, ranging from neutrino detection to vessel tracking sea mammal studies, or development of early detection of tsunami events.
Similarly, we are also studying scs interpretations of other fundamental operations from mathematical morphology such as opening and closing. We conclude with some applications of scs in the development of ccp tools and languages. In  [14, 13]  we described D-SPACES, an implementation of scs that provides property-checking methods as well as an implementation of a specific type of constraint systems (boolean algebras). In  [21]  we used rewriting logic for specifying and analyzing ccp processes combining spatial and real-time behavior. These processes can run processes in different computational spaces while subject to real-time requirements. The real-time rewriting logic semantics is fully executable in Maude with the help of rewriting modulo SMT: partial information (i. e. , constraints) in the specification is represented by quantifier-free formulas on the shared variables of the system that are under the control of SMT decision procedures.
The approach is used to symbolically analyze existential real-time reachability properties of process calculi in the presence of spatial hierarchies for sharing information and knowledge. We also developed dspacenet, a multi-agent spatial and reactive ccp language for programming academic forums 2. The fundamental structure of dspacenet is that of space: A space may contain spatial and reactive ccp programs or other spaces. The fundamental operation of dspacenet is that of program posting: In each time unit, agents (users) can post spatial reactive ccp programs in the spaces they are allowed to do so. Currently dspacenet is used at Univ. Javeriana Cali for teaching spatial reactive declarative programming.
The Damped Bundle Adjustment Toolbox (DBAT) is a free, open-source, toolbox for bundle adjustment. The purpose of DBAT is to provide an independent, open-source toolkit for statistically rigorous bundle adjustment computations. The capabilities include bundle adjustment, network analysis, point filtering, forward intersection, spatial intersection, plotting functions, and computations of quality indicators such as posterior covariance estimates and parameter correlations. DBAT is written in the high-level Matlab language and includes several processing example files. The input formats have so far been restricted to PhotoModeler export files and Photoscan (Metashape) native files. Fine-tuning of the processing has so far required knowledge of the Matlab language. This paper describes the development of a scripting language based on the XML (eXtensible Markup Language) language that allow the user a fine-grained control over what operations are applied to the input data, while keeping the needed programming skills at a minimum.
INTRODUCTION 1. 1 Background Bundle adjustment is a crucial part of photogrammetry and computer vision. Several commercial photogrammetric software include a dedicated module for the computation of bundle adjustment, e. g. , Trimble Inpho and ERDAS  (Lumban-Gaol et al. , 2018). In other software, the bundle adjustment might be hidden in a more black-box manner. Open source solutions to the bundle adjustment problem also exist in the form of toolboxes and/or dedicated functions. Examples include the Apero module from the Apero-MicMac photogrammetric suite  (Rupnik et al. , 2017)  or the DGAP developed by the University of Stuttgart  (Cramer, 2006). From the computer vision domain, other open source options also exist, for example Sparse Bundle Adjustment (SBA)  (Lourakis, Argyros, 2009) , multi-core bundle adjustment (MCBA)  (Wu et al.
, 2011) , or the Bundler software  (Snavely et al. , 2008). Most of these solutions are written in a low-level language such as C or C++. The Damped Bundle Adjustment Toolbox in Matlab (DBAT) is written in the high-level Matlab language  (Börlin, Grussenmeyer, 2013a). One goal is to provide open-source, easy-to-use implementations of core photogrammetric computation, including computation of posterior statistical quality estimates. Several demo examples and data sets are included in DBAT. However, unless only minor modifications of the examples are needed to suit the user needs, programming skills have been * Corresponding author required to use DBAT on the user's own data. Furthermore, the user has so far been restricted to either Photomodeler or Metashape (Photoscan) formats as input.
Background As the name suggests, the Damped Bundled Adjustment Toolbox started out as a toolbox to study different damping strategies known in non-linear optimisation  (Börlin, Grussenmeyer, 2013a , Börlin, Grussenmeyer, 2013b). The source code has been available on GitHub since February 2014 1. An early application was camera calibration  (Börlin, Grussenmeyer, 2014). Later, the focus shifted into validating other, closed-source, Photogrammetric software, such as Photomodeler and Metashape (then Photoscan)  (Börlin, Grussenmeyer, 2016 , Murtiyoso et al. , 2017 , Murtiyoso et al. , 2018). A recent development was to modularise the bundle projection model to allow users to develop novel projection models for, e. g. , non-standard lenses or underwater applications  (Börlin et al. , 2018 , Menna et al.
2. 2. 5 Output and report generating DBAT can present the results graphically in 2D and 3D and in text files. The available 2D plots present the raw data (Figure  1 ) or image and point quality statistics, such as image coverage, ray count, ray intersection angles, posterior standard deviations, etc. , (figures 2-3). Other 2D plots illustrate the evolution of the bundle process (Figure  4 ). The 3D plots either show the final camera network or the evolution of the camera network and OP parameters during the bundle process (Figure  5 ). The text output is mainly a PhotoModeler-style text summary report file that contain the processing input and setup, any detected problems, the estimated values and posterior covariances. Any detected high correlations among the estimated parameters are also listed.
SCRIPT-BASED PROCESSING Script-based processing uses a single DBAT function rundbatscript to load and execute a script. The script is written in the XML language (extensible markup language). Advantages of the XML file format include that they are human-readable, structured, and can easily be extended. Furthermore, since they follow the XML standard, other software may be used for searching or organising the files. The DBAT XML file contain four major sections: metadata, input, processing, and output.
Language extensions The combination of the XML language and the Matlab language makes it easy to extend the DBAT scripting language. An extension with a new operation requires adding two things: A Matlab function that performs the operation and adding the name of the operation to the list of known operations of the script parser. Any parameters to the function can be specified as XML attributes to the operation. Adding a parameter to an existing function would only require the latter. Similarly, extending the input capabilities may simply require adding a keyword to the format string and support for the keyword in the corresponding Matlab function.
Conclusions The modularity provided by the scripting allows a simple setup of the DBAT processing with little to no programming knowledge. At the same time, the language provides flexibility of what parameters to estimate or what operations to perform and in what order. Furthermore, the ability to read text files with a general format enables input from many sources. The combination of the XML and Matlab languages makes it relatively easy to add new function or file format to the DBAT scripting language. These qualities suggest that DBAT XML scripting should be useful both for users that want to independently verify a black-box computation by another software, or teach students the inner workings of the bundle adjustment process. The modularity concept is particularly interesting for teaching purposes, as it enables a step-by-step learning of the photogrammetric workflow in lieu of a one-click black-box solution.
The projection onto the manifold paradigm We use the strategy developed in  [4, 5]  to provide localized anomaly detection, when a normal control database is available. In essence the paradigm consists in "projecting" any new subject image Y onto a learned manifold representing the normal controls. The "projection" µ(Y) will correspond to the image closest to that of the tested subject Y, while belonging to the normal controls manifold. The residual Y -µ(Y), which is representative of any abnormalities present in Y, is computed and converted into a p-value map (or a z-score map) that can be thresholded for detection purposes. The p-value conversion requires at first the estimation of the probability density function (pdf ) of the residual value at each component (voxel) under H 0 (when the subject belongs to the control group).
Comparison of methods on synthetic data We first create a synthetic dataset in which we control the location of abnormalities. As it can be quite cumbersome to sample directly from a high-dimensional space, we embed a half-sphere (to avoid any closed manifold issue) of dimension d into a space of dimension D with D >> d. To this end, an orthonormal transformation that maps the low-to the highdimensional space is randomly generated. The embedding mapping is thus linear, but the data in the latent space is distributed non linearly. Control data are generated as follows: we first sample N points on the half-sphere. The orthonormal transformation is then applied to each point, producing N points of dimension D. Finally, a zero-mean Gaussian noise of variance σ 2 is added independently on each component of the points.
Application on a database of AD patients We applied the previous two best methods (RNGPCA and RLLE) and the GLM approach, that is widely used in neuroimaging, to a real dataset comprising MRI samples of both healthy subjects and Alzheimer's disease (AD) patients. The healthy subjects serve as a control group over which AD patients are projected to localize the anomalies. We used three public databases OASIS  [16] , ADNI  [17]  and IXI (http://brain-development. org/ixi-dataset/), as well as an internal database. This extended dataset contains a total of 1507 structural MRI images (around 1000 of which are healthy subjects scans).
CONCLUSION In this paper, we have compared several linear and non-linear methods for performing anomaly detection in images in the single subject versus group setup. The proposed methods were based on manifold learning methods such as PCA, LLE or Isomap. We confronte these methods to the most widely used algorithm for anomaly detection in medical images: the GLM. We presented results over both a non-linear, synthetic dataset and a real MRI dataset with focus on Alzheimer's Disease. The two methods that clearly stand out (RNGPCA and RLLE) both rely on non-linear subspace modeling and implement robustness to large anomalies. The results obtained on the AD data set must still be analyzed in depth by a medical expert, based on the history of each patient.
PoS(ICRC2019)655 reach the maximal sensitivity of detection and lower the detection threshold in energy, we would use all the available mini-arrays analogically phased individually on the source to build the trigger, by reading their signals in real time and decide whether or not there has been a transient in their phased combination. This however is at that time out of reach, due to the huge amount of data to be processed online: indeed, each MA 19 delivers 400 MB/s per polarization, which should be taken at the level of the TBB, where the raw waveforms are still recorded and should be read and then combined again to sum all the signals. We then chose a different approach, based on the experience gained on CODALEMA. It is possible to split the signal from individual MA 19 before they enter the acquisition chain, but after analog phasing at the level of one MA 19.
As soon as the trigger electronics mentioned PoS(ICRC2019)655 in section 2. 3 is ready, we will observe them during dedicated observing time with NenuFAR and CODALEMA. Several exploratory studies are still to do, notably the extrapolation of the number of expected photons above a given energy from the data obtained by Cerenkov experiments. In the direction of any gamma ray source, one can also expect to detect a certain number of cosmic rays above the NenuFAR threshold energy, whose signal should mimic the gamma shower signals. In principle, it should be quite easy to disentangle between gamma and UHECR, thanks to many factors: the electric field footprint will have a different extent and a different shape; for showers around 100 TeV, the muon contents of the UHECR shower is large, offering the possibility to trigger the nearby CODALEMA particle detector and use it as a veto; the use of simulations should also allow to recover the X max , thus the nature, of the primary.
We acknowledge the  Région Pays de la Loire  for its financial support to the CODALEMA and EXTASIS programs. This work was supported by the  Programme National des Hautes Energies of CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. This paper is based on data obtained using the  NenuFAR radiotelescope. NenuFAR  has benefitted from the following funding sources:  CNRS-INSU, Observatoire de Paris, Station de Radioastronomie de Nançay, Observatoire des Sciences de l'Univers de la Région Centre, Région Centre-Val de Loire, Université d'Orléans, DIM-ACAV  and  DIM-ACAV+ de la Région Ile de France ,  Agence Nationale de la Recherche.
The performance of mRM is investigated on two datasets: a highresolution German dataset and a slightly lower resolved European dataset. The adaptive time stepping scheme within mRM shows a remarkable scalability compared to its predecessor. Median Kling-Gupta efficiencies change less than 3 % when the model parameterisation is transferred from 3 to 48 km resolution. mRM also exhibits seamless scalability in time, providing similar results when forced with hourly and daily runoff. The streamflow calculated over the Danube catchment by the regional climate model REMO coupled to mRM reveals that the 50 km simulation shows a smaller bias with respect to observations than the simulation at 12 km resolution. The mRM source code is freely available and highly modular, facilitating easy internal coupling in existing Earth system models.
Data requirements and model setup Three different input sources are required to run the multiscale routing model mRM. First, mRM requires information about the river network. mRM uses a rectangular grid to represent the river network over a domain  (Yamazaki et al. , 2011). Water can only be transported from a specific grid cell to one of the eight neighbouring cells following the steepest slope direction (D8 method, O'Calaghan and  Mark, 1984). This procedure has to be carried at the highest possible resolution supported by the available dataset. For example, a high-resolution 100 m digital elevation model (DEM) can be used to calculate flow directions following the D8 method (Fig. 1  top left). It is worth mentioning that DEMs typically have to be adjusted to align with existing river networks using additional information about river locations  (Döll and Lehner, 2002).
High-resolution datasets such as HydroSHEDS  (Lehner et al. , 2008)  can be used alternatively. Once the high-resolution flow direction is obtained following the nomenclature of the D8 method (2 d-1 , d = 1,. , 8; O'  Calaghan and Mark, 1984) , it is internally upscaled in mRM (Fig. 1  centre top) to the routing resolution specified by the user, employing the method of  Döll and Lehner (2002). This upscaling technique has already been implemented in  Samaniego et al. (2010). The flow direction in a low-resolution grid cell (3 × 3 grid in Fig. 1 ) is equal to the flow direction in the underlying high-resolution grid cell with the highest flow accumulation. If this high-resolution grid cell is not on an edge of the low-resolution grid cell, then the low-resolution grid cell is an outflow of the domain.
Thus, the upscaled flow accumulation in each grid cell is given in an output NetCDF file, which allows comparison to the drainage area of a given gauge. Observed discharge data are not required for mRM when applied, for example, at ungauged locations. They are, however, mandatory when performing model optimisation. Different error measures such as Nash-Sutcliffe efficiency  (Nash and Sutcliffe, 1970)  and Kling-Gupta efficiency  (Gupta et al. , 2009)  can be calculated directly in mRM to inform the user about model performance. A test basin is provided alongside the model code to illustrate the different data required to run the model and their formatting. The model code also contains pre-processing scripts to calculate the flow direction from a given DEM or flow accumulation from given flow directions.
Experimental setup A total of 622 stream gauges are used in this study to assess the performance and scaling capabilities of mRM (Fig. 2 ). These contain 368 basins in the German dataset (Fig. 2a ) and 254 basins in the European dataset (Fig. 2b ). Input for mRM is derived from simulations carried out with the mesoscale hydrologic model mHM  (Samaniego et al. , 2010; Kumar et al. , 2013b). Two different model setups for mHM are used in this study. The setup for the German dataset is identical to  Samaniego et al. (2013)  and  Zink et al. (2016)  with details given in  Zink et al. (2017). The flow direction and ac-   2016 ). The DEM used to derive the river network has a 500 m resolution in this case. Runoff simulated by mHM was stored at hourly and daily resolutions in NetCDF files for both sets of basins.
mHM simulations for the German dataset are provided at 4 km resolution while European simulations are provided at 24 km spatial resolution. The difference originates from the available meteorological forcing datasets, which are derived from station data of the German weather service at 4 km resolution  (Zink et al. , 2017)  for the German dataset, while E-OBS data at 24 km resolution  (Haylock et al. , 2008)  were used for the European dataset. To study the spatial scalability of mRM, streamflow is routed at different spatial resolutions, which are 1, 2, 4, 8, and 16 km for the German dataset and 3, 6, 12, 24, and 48 km for the European dataset. The selected resolutions cover a range of hydrologic applications from small-to large-scale modelling studies  (Wood et al. , 2011; Samaniego et al.