dataesr/scientific-paragraphs-categorization huggingface dataset:
Datasets For the training part, we used the Common Object in Context (COCO) dataset  [53] , a large-scale object detection, segmentation, and captioning dataset that is the benchmark for most state-of-the-art methods in various computer vision tasks. Since our task is not a general-purpose detection/segmentation task, we sub-sampled the COCO-stuff dataset into 15,402 images containing the road and person categories. We found that the average height and width of images are 477. 23 and 587. 85, respectively (Figure  7 ). The 587. 85 aspect ratio is 1. 23, which is fine to use with a non-destructive resize and with a padding approach to resize our images to the desired size suitable for our backbone input. For the test dataset, we opted to mix (1) the previously presented COCO dataset, excluding the part we used for the training, and (2) a set of images that we filmed by ourselves on the roads of Tunis city, Tunisia.
Finally, we filtered the collected data, and we selected only the best clips from the recorded videos. We obtained about 3 h of good-quality clips that we used as a test dataset. More data are being collected to build a complete dataset that can be used for training, validation, and testing. Figure  9  shows a collection of images that we captured: Figure  9a  shows an area with high taxi demand (Beb Alioua, Tunis) with many people and cars; Figure  9b  shows the same area but at a different location with fewer cars and people; Figure  9c  shows an area with high demand (Monplaisir, Tunis) but in a secondary road such that we have many cars and no people; and Figure  9d  shows very visible people and far away cars. The objective is to capture different situations that will help us evaluate our model in extracting many anchor sizes for persons and roads and to make sure it will work in various scenarios.
As illustrated in Figure  10 , the precision is 68%, and the recall value is 60%. These values are acceptable with respect to the COCO dataset  [57]. Figure  11  illustrates the results of two different examples of the road-segmentation tasks. The image highlights the pixels of the road and manages to distinguish the road from the sidewalk. However, the segmentation does not extend to the far-away road, which will not affect our detection since the proposed method works on video streams and the far-away road will eventually become the up-front road for the recording camera. Lastly, Figure  12  illustrates an example of human keypoints detection results. Our model succeeds in detecting the keypoints of a crowded area (people very close to each other) and since the keypoints detection plays a major role in the second stage, we used a higher confidence level of 70%.
Detection of Street Hailing In our proposed architecture, we used an unsupervised method for the detection of street hailing, which does not require a training process. However, given that we used a dataset that we collected as a test set, we evaluated the performance of the overall streethailing detection process (including both explicit and implicit) using true positive (TP), true negative (TN), false negative (FN) and false positive (FN) metrics. Once a street-hailing detection was carried out, we evaluated the ability of the proposed method to discern the type of hailing (explicit or implicit). In this case, we used only the false negative and true positive metrics since the existence of a street-hailing action was already confirmed. Table  1  reports the accuracy, precision, recall and specificity scores of the obtained results.
Conclusions In this paper, we proposed a new method for street-hailing detection based on computer-vision techniques augmented with contextual information. Besides the originality of its deep-learning-based pipeline, our method allows for the detection of both explicit and implicit street-hailing scenes by the integration of visual and contextual information in a logic flow based on a defined scoring scheme. The design of the pipeline was inspired by the feedback collected from 50 experienced taxi drivers that we interviewed in order to understand the intuitive way of identifying prospect street-hailing passengers. We experimentally studied our proposed method using a dataset of urban road video scenes that we collected from a real taxi in the city of Tunis, Tunisia. The study showed that the proposed method achieves satisfactory results with respect to the detection of explicit and implicit street-hailing cases.
Nevertheless, there is still room for future improvement. First, we are currently working on further developing the concept of context. A formal definition of the context is required in order to better model, represent and use contextual information for the detection of both explicit and implicit street-hailing behaviors. Second, the context is also fundamental for the implementation of a more extended street-hailing service stage as a social interaction between Robotaxis and passengers. As we mentioned earlier, hailing recognition is only one part of the hailing interaction, and there is a gap in the state-ofthe-art of human-autonomous taxi interaction (HATI) with respect to this aspect. Third, we are collecting more data in order to build a complete dataset dedicated to taxi streethailing scenes in urban environments that we will use to test and improve the performance of our approach.
8. 1  [113] , which is used to generate the HIP pairs and to calculate the production cross sections. The events were interfaced to Pythia 8. 244  [114, 115]  to model the parton showering and hadronization, with parameter values set according to the A14 tune  [116] , and the NNPDF2. 3LO  [117]  and LUX QED  [118]  parton distribution functions were used for DY and PF production, respectively. To determine selection efficiencies, samples of 60 000 MC events of Drell-Yan spin-½ HIPs of a given mass and charge were simulated using Geant4  [119]  and processed with the ATLAS reconstruction software. The Geant4 simulation describes the HIP ionization energy loss in the ATLAS detector, the 𝛿-ray production and secondary ionization, and the monopole acceleration in the magnetic field. The standard Bethe-Bloch formula is used for ionization by HECOs, whereas a modified formula accounting for the velocity-dependent Lorentz force  [120]  is used for monopoles.
Single-particle events have less detector activity than events with pair production from proton-proton collisions, and consequently can be abundantly produced at a reasonable cost of computational resources. Hence, the selection efficiencies of DY spin-0 HIPs and PF pair-produced HIPs of both spins are extrapolated by sampling model-independent efficiency maps based on the HIP kinematic properties, transverse kinetic energy 𝐸 kin T = 𝐸 kin sin 𝜃 and pseudorapidity |𝜂|. The efficiency maps of a given mass and charge are derived from samples of 120 000 events comprising single monopoles or HECOs simulated using Geant4, overlaid with pileup, and processed with the ATLAS reconstruction software. Given the statistics available in the single-particle samples, the bin size of the maps was optimized to ensure that most bins are populated.
7, since the forward regions contain more multĳet background events. Events collected by the trigger are processed by the standard ATLAS reconstruction software, which performs a sophisticated topological clustering algorithm  [123]  in the calorimeter. A cluster is a group of cells, neighbouring in 3D, in which significant energy deposition is recorded. EM clusters with 𝐸 𝑇 > 22 GeV and |𝜂| < 1. 375 are considered as a starting point for HIP signal selection. The |𝜂| < 1. 375 requirement is needed to exclude the EM calorimeter end-caps, where the correlation between the final discriminating variables, described below, is enhanced. This analysis does not use ATLAS track reconstruction for two main reasons: first, the trajectories of magnetic monopoles moving in a solenoidal magnetic field bend in the 𝑟 -𝑧 plane (unlike charged particles, which bend in the 𝑟 -𝜙 plane), and second, the presence of multiple 𝛿-rays confuses the track fitting algorithm.
Conclusions This paper presents a search for spin-0 and spin-½ magnetic monopoles and HECOs produced via the Drell-Yan and photon-fusion mechanisms using a dataset of 138 fb -1 of 13 TeV proton-proton collisions collected by the ATLAS detector between 2015 and 2018 during Run 2. Upper cross-section limits and lower mass limits computed at 95% confidence level are presented. The search improves by approximately a factor of three the cross-section limits on the Drell-Yan production of magnetic monopoles with magnetic charges 1𝑔 D and 2𝑔 D and HECOs in the range 20 ≤ |𝑧| ≤ 100 attained in the 2015/16 dataset alone. Also, the first ATLAS limits on the photon-fusion pair production mechanism of magnetic monopoles and HECOs are obtained. The crucial computing support from all WLCG partners is acknowledged gratefully, in particular from CERN, the ATLAS Tier-1 facilities at TRIUMF (Canada), NDGF (Denmark, Norway, Sweden), CC-IN2P3 (France), KIT/GridKA (Germany), INFN-CNAF (Italy), NL-T1 (Netherlands), PIC (Spain), ASGC (Taiwan), RAL (UK) and BNL (USA), the Tier-2 facilities worldwide and large non-WLCG resource providers.
Acknowledgements We thank  CERN  for the very successful operation of the LHC, as well as the support staff from our institutions without whom ATLAS could not be operated efficiently. We acknowledge the support of  ANPCyT, Argentina ;  YerPhI, Armenia ;  ARC, Australia ;  BMWFW  and  FWF, Austria ;  ANAS ,  Azerbaĳan ;  CNPq  and  FAPESP ,  Brazil ;  NSERC ,  NRC  and  CFI ,  Canada ;  CERN ;  ANID, Chile ;  CAS ,  MOST  and  NSFC, China ;  Minciencias, Colombia ;  MEYS CR, Czech Republic ;  DNRF  and  DNSRC ,  Denmark ;  IN2P3-CNRS  and  CEA-DRF/IRFU, France ;  SRNSFG, Georgia ;  BMBF ,  HGF  and  MPG, Germany ;  GSRI, Greece ;  RGC  and  Hong Kong SAR, China ;  ISF  and  Benoziyo Center, Israel ;  INFN, Italy ;  MEXT  and  JSPS, Japan ;  CNRST, Morocco ;  NWO, Netherlands ;  RCN, Norway ;  MEiN, Poland ;  FCT ,.
Portugal; MNE/IFA, Romania; MESTD, Serbia; MSSR, Slovakia; ARRS and MIZŠ, Slovenia; DSI/NRF, South Africa; MICINN, Spain; SRC and Wallenberg Foundation, Sweden; SERI, SNSF and Cantons of Bern and Geneva, Switzerland; MOST, Taiwan; TENMAK, Türkiye; STFC, United Kingdom; DOE and NSF, United States of America. In addition, individual groups and members have received support from BCKDF, CANARIE, Compute Canada and CRC, Canada; PRIMUS 21/SCI/017 and UNCE SCI/013, Czech Republic; COST, ERC, ERDF, Horizon 2020 and Marie Skłodowska-Curie Actions, European Union; Investissements d'Avenir Labex, Investissements d'Avenir Idex and ANR, France; DFG and AvH Foundation, Germany; Herakleitos, Thales and Aristeia programmes co-financed by EU-ESF and the Greek NSRF, Greece; BSF-NSF and MINERVA, Israel; Norwegian Financial Mechanism 2014-2021, Norway; NCN and NAWA, Poland; La Caixa Banking Foundation, CERCA Programme Generalitat de Catalunya and PROMETEO and GenT Programmes Generalitat Valenciana, Spain; Göran Gustafssons Stiftelse, Sweden; The Royal Society and Leverhulme Trust, United Kingdom.
Context. GRB 220627A is a rare burst with two distinct γ-ray emission episodes separated by almost 1000 s that triggered the Fermi Gamma-ray Burst Monitor twice. High-energy GeV emission was detected by the Fermi Large Area Telescope coincident with the first emission episode but not the second. The discovery of the optical afterglow with MeerLICHT led to MUSE observations which secured the burst redshift to z = 3. 08, making this the most distant ultra-long gamma-ray burst (GRB) detected to date. Aims. The progenitors of some ultra-long GRBs have been suggested in the literature to be different to those of normal long GRBs. Our aim is to determine whether the afterglow and host properties of GRB 220627A agree with this interpretation. Methods. We performed empirical and theoretical modelling of the afterglow data within the external forward shock framework, and determined the metallicity of the GRB environment through modelling the absorption lines in the MUSE spectrum.
Results. Our optical data show evidence for a jet break in the light curve at ∼1. 2 days, while our theoretical modelling shows a preference for a homogeneous circumburst medium. Our forward shock parameters are typical for the wider GRB population, and we find that the environment of the burst is characterised by a sub-solar metallicity. Conclusions. Our observations and modelling of GRB 220627A do not suggest that a different progenitor compared to the progenitor of normal long GRBs is required. We find that more observations of ultra-long GRBs are needed to determine if they form a separate population with distinct prompt and afterglow features, and possibly distinct progenitors.
X-rays The Swift X-Ray Telescope (XRT;  Burrows et al. 2005 ) conducted ToO observations of the LAT error box in order to search for X-ray afterglow candidates  (Evans & Swift Team 2022). The MeerLICHT identification of an optical source coincident with one of the potential X-ray afterglow candidates (see Sect. 2. 1) confirmed this source as the X-ray afterglow, along with evidence of X-ray fading. A total of three orbits of Photon Counting mode (PC) observations of the afterglow were acquired at mid-times of 0. 52, 1. 59 and 4. 62 days post-trigger. We obtained the X-ray count-rate light curve and spectrum from the online Swift/XRT GRB Catalogue hosted on the UK Swift Science Data Centre (UKSSDC) website  (Evans et al. 2007 (Evans et al. , 2009)). We fitted the photon spectrum with a photoelectrically absorbed power law model (tbabs*ztbabs*pow) in Xspec version 12.
Optical spectroscopy Following the detection of the optical afterglow by MeerLICHT, we observed the GRB afterglow region with the Multi-Unit Spectroscopic Explorer (MUSE;  Bacon et al. 2010)  mounted on UT4 at the ESO VLT. A set of four exposures of 600 s were obtained at 1. 17 days post-trigger covering the wavelength range 4800-9300 Å with the corresponding spectral resolution ranging from R ∼ 1800-3500. Data from each exposure was reduced and stacked using standard esorex recipes. Finally, we subtracted the sky background in the reduced data cube using zap  (Soto et al. 2016). We extracted a spectrum from the MUSE data cube using a circular aperture of 0. 6. The spectrum is characterised by the presence of a broad damped Lyα absorption feature and the pres-  (Izzo et al. 2022 ). An intervening absorber at z = 2.
External forward shock framework We interpret our afterglow observations in the framework of the synchrotron external forward shock model  (Mészáros & Rees 1997; Sari et al. 1998; Chevalier & Li 2000). In this model, the GRB central engine -usually assumed to be a black hole or neutron star -powers an extremely relativistic, collimated outflow that sweeps up mass in the surrounding circumburst medium, Right: telluric-corrected spectrum (black) and error spectrum (grey) of GRB 220627A extracted from the MUSE data cube using an aperture with a radius of 0. 6. We show identified absorption lines at the GRB redshift (z = 3. 084) and the intervening system (z = 2. 665) in grey and red, respectively. The vertical shaded region is heavily affected by telluric lines. forming a shock front behind which electrons are accelerated to a power law distribution in energies with N(γ e ) ∝ γ -p e for γ e > γ m , where γ m is the minimum Lorentz factor of the electrons in the distribution and p is the electron spectral index.
(  D'Elia et al. 2007 ). The study of these absorption features can provide us with information on the metallicity of the immediate GRB region, while from the estimate of the column densities N X , with X being the element under consideration, one can also obtain a rough estimate of the extinction A V along our line of sight  (De Cia et al. 2016 ). Due to their large redshifts, GRB afterglows are therefore one of the most important tools for investigating the evolution of the properties of the ISM in long GRB host galaxies, and consequently, in star-forming galaxies over a large redshift range. The HI column density in GRB 220627A is derived by modelling the broad Lyα absorption trough with a Voigt profile using VoigtFit  (Krogager 2018). This code convolves the intrinsic model with the spectral resolution and determines the redshift z, broadening parameter b, and column density N, of the input lines.
Model We make use of the ScaleFit software package to perform theoretical modelling of our broadband afterglow dataset  (Ryan et al. 2015; Aksulu et al. 2020 Aksulu et al. , 2022)). ScaleFit is based on the BoxFit set of high-resolution hydrodynamic simulations of GRB explosions  (van Eerten et al. 2012). Boxfit interpolates between a series of compressed data sets of hydrodynamical jet simulations and employs scale invariance between the blast wave energies and circumburst medium densities to cover the space of afterglow parameters, rapidly computing light curves and spectra using a linear radiative transfer module. Scalefit builds on this by making use of simple scaling laws between a set of scale-invariant characteristic quantities to derive the full set of spectral parameters that determine the observed emission (van Eerten & MacFadyen 2012a).
We use the emcee Python package (Foreman-Mackey et al. 2013) to implement a Markov chain Monte Carlo (MCMC) exploration of our ScaleFit model parameter space. An MCMC analysis allows for correlations (and degeneracies) between parameters to be readily identified. Furthermore, the uncertainty on each parameter can be determined through marginalisation of the posterior distribution. emcee offers advantages over traditional MCMC samplers because its multiple 'walkers' moving through the parameter space make it affineinvariant, with the result that it is unaffected by covariances between parameters. For GRB afterglow modelling, if a large majority of the measured data points are in a single spectral regime (e. g. , at optical and X-ray frequencies) the location of the spectral breaks will not be well constrained, and so there are likely to be strong correlations between the E K,iso , n 0 , e and B parameters.
Values of p less than two have been suggested in the literature  (Panaitescu & Kumar 2002; Dai & Cheng 2001)  and have been found through modelling of actual GRB datasets  (Aksulu et al. 2022). As discussed in  Granot & Sari (2002) , using ¯ e is still valid when p < 2 as long as γ m is proportional to the shock Lorentz factor; a high-energy cutoff in the electron energy distribution is then needed so that the total energy in electrons is bounded. Wang et al. (2015)  found that the jet opening angle takes values of θ j = 2. 5 ± 1. 5 deg. Our value of 4. 5 +1. 2 -0. 3 deg is just outside their 1σ range, but well within the range of the sample considered by  Laskar et al. (2014)  having θ j = 7. 4 +11 -6. 6 deg. The afterglow properties derived from our theoretical modelling are fully consistent with long GRBs, and do not suggest a different or unusual progenitor.
Acknowledgements. The MeerLICHT consortium is a partnership between  Radboud University , the  University of Cape Town , the  South African Astronomical Observatory (SAAO) , the  University of Oxford , the  University of Manchester  and the  University of Amsterdam , in association with and, partly supported by, the  South African Radio Astronomy Observatory (SARAO) , the  European Research Council  and  The Netherlands Research School for Astronomy (NOVA). We acknowledge the use of the  Inter-University Institute  for Data Intensive Astronomy (IDIA) data intensive research cloud for data processing. IDIA is a South African university partnership involving the  University of Cape Town , the  University of Pretoria  and the  University of the Western Cape. S. dW. and P. J. G. are supported by  NRF   SARChI Grant   111692.
This gives m equations, m -1 of which have degree m -1 with the last one having degree m -2. We can see that solving this system is not hard when m is small, as is typically the case. Moreover, by fixing µ and τ 1 ,. , τ m , we get m independent univariate equations of degree m -1 (except for the last one which is of degree m -2). In practice, we can sample µ and distinct τ 1 ,. τ m randomly until the univariate solver natively present in SAGE [The20] finds a distinct solution for each equation such that for all 1 ≤ i ≤ m, 1 ≤ j ≤ m, σ i ̸ = τ j. We have found that sampling one tuple is usually enough. The code in SAGE used to generate such matrices is given in Appendix B. 1.
1, using the code in Appendix B. 1. We generated the A t pseudo-randomly using a built-in function of SAGE, namely F. random_element with F = GF(p). As the details of the A t do not matter for our purposes, they could be generated in any other way. Opening its Backdoor. Let X t be the internal state of Snare at the beginning of Round t, so that X t+1 = M • S t (X t ) + K t u + (r t + rt t )e 3 , with e 3 = (0, 0, 1) T , as indeed the key injection was chosen so as to be aligned with u. For the malicious tweak T ⋆ , the last term vanishes. Then, as M was chosen so as to satisfy Equation (3), we have that, for any X t = x t u, with x t ∈ F p , X t+1 = M • S t (x t u) + K t u = (µx d t + K t )u , ( 6 ) where d = α if i is even, d = 1/α otherwise. As we can see, X t+1 is then also a multiple of u.
We first applied an artificial intelligence (AI-)-driven iterative procedure to improve the generation of pathologist validated training datasets for NPs and NFTs. This procedure increased the annotation quality by more than 50%, especially for NPs when present in high density. Using this procedure, we obtained an expert validated annotation database with 5013 NPs and 5143 NFTs. As a second step, we trained two U-Net convolutional neural networks (CNNs) for accurate detection and segmentation of NPs or NFTs. The workflow achieved a high accuracy and consistency, with a mean Dice similarity coefficient of 0. 81 for NPs and 0. 77 for NFTs. The workflow also showed good generalization performance across different patients with different staining and tissue quality. Our study demonstrates that artificial intelligence can be used to correct and enhance annotation quality especially for complex objects, even when intermingled and present in high density, in brain tissue.
More recently it was demonstrated that different tau species are present in NFTs and NPs and are associated with distinct properties in terms of seeding activity and synaptotoxic effects  [30]. Interestingly, the debated "primary age-related tauopathy" (PART) entity is described as a NFT-predominant condition  [12] , separated from AD or, alternatively, classified as "part" of the AD continuum  [6]. It is hence tempting to consider that NFTs accumulation in the medial temporal lobe of aged mild-cognitively impaired individuals, when subsequently combined with emerging NPs, triggers a quantum leap in the neurodegenerative progression. All these data reveal an unmet need in modern AD neuropathology related to the detectionsegmentation and quantification of discrete lesions (such as tau lesions that rely on multiple heterogeneous morphologies).
g. , discrimination between NFTs vs NPs patches). Neltner and colleagues  [26]  used Aperio ScanScope XT image analysis software and pattern recognition tools to quantify tau burden; the authors reported a good correlation between automatic tau burden segmentation and manual estimation of overall tau load but individual object isolation was not possible. Based on Google AutoML algorithms, Koga and Dickson  [15]  performed automatic detection of tau-positive tufted astrocytes, astrocytic plaques and neuritic plaques but did not investigate the potential of their models to detect and segment NFTs. The same authors succeeded in another published work  [16]  to classify lesions occurring in different tauopathies including cytoplasmic NFTs and coiled bodies. However, their efforts were directed to detection and classification while no information was given on the segmentation of the different objects.