dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments We thank  Clare Burrage ,  Jeremy Sakstein , and  Leong Khim Wong  for helpful discussions. We particularly thank  Brendan Casey  for his help in understanding the experimental details. P. B. acknowledges support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skodowska-Curie  grant agreement No  860881-HIDDeN. A. C. D. acknowledges partial support from the  STFC  Consolidated Grants No. ST/P000673/1  and No. ST/P000681/1.
Its targets can be individuals, economy, or politics  [8]. Manipulated videos have already been used to create political tensions, and the technology enabling their creation is being considered as a threat by various governments  [9]. Motivated by those facts, researchers have proposed various techniques to detect digital face manipulations in the recent past  [10, 11]. In addition, public databases have been made available and first benchmarks have been conducted by different research groups  [12] [13] [14] [15] [16] [17] , proving the high potential of the latest manipulation detectors. Nonetheless, a reliable detection of manipulated face images and videos is still considered an unsolved problem. It is generally conceded that digital face manipulation detection is still a nascent field of research in which numerous issues and challenges have to be addressed in order to reliably deploy such methods in realworld applications.
This chapter concludes the book providing an overview of open issues and challenges in the field of digital face manipulation and detection. Limitations of state-ofthe-art methods are pointed out and potential future research direction toward advancing both fields are summarized, including promising application areas as well as novel use-cases. Moreover, legal and societal aspects of digital face manipulation and detection are discussed, such as the legality and legitimacy of the use of the manipulation detection or the potentially conflicting right to "one's own image", among others. Listing currently unsolved problems in the field, this chapter is intended to serve as a starting point for new researchers in the field. The remainder of this chapter is organized as follows: Sect. 21. 2 briefly describes the current state of the art in face manipulation together with public available databases.
Missing Resources Although new public databases are being released recently, these generally lack diversity and include low-quality videos. Specifically, for image databases, GANbased models have been proved to be very effective in creating realistic faces, but these are usually generated with one model (e. g. , StyleGAN2  [24] ) with one specific set of parameters  [28] [29] [30]. Video databases, on the other hand, are plagued with low-quality synthesis results, exhibiting visible blurring or boundary artifacts. As a result, it is easy for current fake detectors to over-adapt to the specific characteristics of the generation method and artifacts. In addition, high-accuracy fake detection performances on databases containing significant fraction of low-quality videos will not be representative for the performance in real life.
4 To make the detection more challenging, databases need to improve on the types and variants of the generation models, post-processing steps, codecs, and compression methods, as well as adversarial attacks. Furthermore, current databases are still small and monotonic compared with those in other areas like image classification or speech, e. g. , ImageNet  [31]  or Vox-Celeb2  [32]. One of the largest databases, the DFDC dataset  [12] , only has 128,154 videos with less than 20 types of manipulation methods. Moreover, most databases only contain one or two subjects in an image or video except the recently released Face Forensics in the Wild (FFIW) database  [33] , and they are easily perceived (not a small subject in a crowd). It is also interesting to highlight that none of the databases contain manipulated or synthesized speech except the DFDC, but its manipulation is simple and is hard to be considered as "fake".
Generalizability The vast majority of existing face manipulation detection techniques have been evaluated only on known types of face manipulations or on a single database  [10, 11]. In other words, the published empirical results showed performances of detectors under same train and test manipulation type/database. However, their performances usually drop significantly when evaluated under cross-manipulation setting (where train and test sets are not from the same manipulation type or database)  [12, 19, 37, 38]. Therefore, reported detection performance rates are over-optimistic. Tackling the unknown emerging face manipulations is still a key challenge  [30, 39]. In fact, generalization of detection techniques is crucial in attaining dependable accuracy in real-life scenarios. It is agreed upon researchers that face manipulation and detection is well described as a cat and mouse game, where improvements in one area trigger improvements in the other.
In fact, media assets often undergo a number of not malicious operations, such as compression and resizing  [41] , that occurs every time they are uploaded over a social network or made available on a website. Now these operations tend to weaken the forensic traces and above all cause a misalignment between training and test data that can make the learning-based detectors not properly work  [11]. Similar problems are also seen in other related areas, for example, Presentation Attack Detection (PAD)  [42] [43] [44] [45] , which despite decades of research, issues with cross-dataset performance and robustness to unseen attacks is still a major issue of even the most advanced solutions.
g. , sudden deaths. • Computer vision: due to the nature of contemporary machine learning models (which are notoriously data hungry), larger and larger datasets are needed for training and ensuring competitive performance. A common, but questionably practice established by the computer vision community in recent years, is to address this demand for data collecting large-scale datasets from the web. However, no consent is obtained for such collections and the generated datasets are often associated with privacy concerns. Using synthetic data, generated from images of a small number of consenting subject and state-of-the-art manipulation techniques, may be a possibility to address the need for the enormous amount of data required by modern machine learning models and comply with existing data protection regulations, such as General Data Protection Regulation (GDPR) of the European Union.
As a result, they are expected to generalize better for the detection of novel (unseen) face manipulation techniques. Of course, such detection techniques come with their own set of problems that range from data representation and model design to learning objectives, among others. • Online learning is also one promising way to deal with generalization  [60]. Unfortunately, current databases are not optimal to conduct online learning research. Therefore, focusing on making better databases and applying online learning can be done together to improve face manipulation detection in the future. • Recent studies suggest that no single feature/characteristic is adequate to build effective and robust detectors of face manipulations. On the other hand, many successful real-life machine learning solutions are based on ensemble models that fuse results from individual types of features or detectors and are calibrated for stronger collective performance  [61, 62].
This research work has been funded by • The German Federal Ministry of Education and Research and the Hessian Ministry of Higher Education, Research, Science and the Arts within their joint support of the National Research Center for Applied Cybersecurity ATHENE. • The ARRS Research Project J2-1734 "Face deidentification with generative deep models", and ARRS Research Programs P2-0250 (B) "Metrology and Biometric Systems" and P2-0214 (A) "Computer Vision". • PRIMA (H2020-MSCA-ITN-2019-860315), TRESPASS-ETN (H2020-MSCA-ITN-2019-860813), BIBECA (MINECO/FEDER RTI2018-101248-B-I00), REAVIPERO (RED2018-102511-T), and COST CA16101 (MULTI-FORESEE). • The Defense Advanced Research Projects Agency (DARPA) and the Air Force Research Laboratory (AFRL) under agreement number FA8750-20-2-1004. The U. S.
Acknowledgements We are grateful to many of our colleagues and students for the fascinating discussions that allowed us to fuel the present manuscript. The final version of this manuscript greatly benefitted from the inputs of three reviewers, namely  E. Dumonteil ,  N. L. Gottdenker  and a third anonymous contributor. Preprint version 3 of this article has been peer-reviewed and recommended by  Peer Community  in Infections (https://doi. org/10. 24072/pci. infections. 100005).
Methodology used in ToR 2 To estimate the time lag between infection and reporting of a glanders suspicion (ToR 2), an extensive literature search (ELS) was outsourced by EFSA (OC/EFSA/ALPHA/2020/02 -LOT 2). The aim of this ELS was to answer the epidemiological question of: 'what is the average, shortest and longest period of time for an outbreak of glanders to be reported (measured as the number of days from the earliest point of infection with B. mallei to the time of declaration of a suspicion by the competent authority after the clinical investigation by an official veterinarian)?'. To answer this question, an ELS on case reports, papers describing outbreaks or epidemics of glanders and any other relevant grey literature or data was carried out. For the inclusion in the ELS, the earliest point of infection had to be estimated by carrying out an epidemiological investigation.
Results A database search was carried out, identifying 121 unique references. As no references were available for outbreak data from the EU/EEA, the search was extended to data from the rest of the world and to simulation data. Among the 121 references, three were selected to be included in the qualitative review. The full selection process is displayed in Figure  3. One of the three references reported dates instead of periods, therefore, the dates were used to calculate the different periods of interest (as described in Section 2. 1 -PICOS table ). Table  1  provides an overview of the data that were extracted for the main outcome of interest, i. e. the period between the earliest point of infection and the suspicion report, for which a single reference was retrieved: As described in Table  1 , the only available period between the earliest point of infection and the suspicion report was 82 days.
Seroconversion in animals To help with the assessment of Scenario 5, the literature regarding challenge studies with B. mallei was reviewed. The search revealed a lack of data on the time to seroconversion in glanderous animals. Based on agglutination, horses showed positive reactions 4-5 days post infection (dpi) with a   (1)  maximum on day 11 pi  (Miessner, 1909). Such antibody levels may decrease as the disease becomes chronic. Based on CFT after mallein injection, complement-fixing activity was observed in horses 7 dpi  (Rice et al. , 1951). Also  Ackerman et al. (1913)  reported positive results in CFT starting from 7 to 10 dpi for the entire course of the disease. Based on this information and expert opinion, it is assumed that horses seroconvert 7-14 dpi. There is no information available about the time of seroconversion in Capra ssp.
No specific guidelines described in legislation Note: OIE Terrestrial Code (OIE, 2021): General provisions: Equids are the major hosts and reservoirs of glanders although scientific data are not available on the occurrence of infection in zebras. Camelids, goats and various carnivores including bears, canids and felids can also be infected but play no significant role in the epidemiology of the disease. Glanders in humans is a rare but potentially fatal disease. 13th To assess the effectiveness of diseasespecific sampling procedures based on clinical and/or laboratory examinations of the animals of listed species in order to grant derogation from prohibitions and allow for these animals to be moved: a) from an establishment in a surveillance zone to a slaughterhouse located within or outside the restricted zone, b)from an establishment outside the surveillance zone to a slaughterhouse situated in the surveillance zone.
Concept-focused literature review A targeted review of published literature was conducted to identify relevant pediatric EoE-related signs and symptoms. Articles were identified using a search strategy including terms related to EoE (eg, eosinophilic, esophagitis, allergic); signs, symptoms, and impacts; the target patient population (eg, child, pediatric); and qualitative research (eg, interview, patient experience, caregiver report). The search was run via OvidSP on July 31, 2018 in the MEDLINE, Embase, and PsycINFO databases and was limited to peer-reviewed, English-language articles that were published within 15 years of the search being conducted. Additional articles were identified through a supplemental search via Google Scholar and input from 2 medical experts consulting on the study (MC and JMS).
Articles were selected for full-text review and data extraction only if they focused on the signs, symptoms, and/or health-related quality of life impacts of EoE among children younger than 12 years of age. Articles were excluded if they focused only on aspects of EoE unrelated to the sign, symptom, and/or impact experience (eg, biology, treatment efficacy), reported only data relevant to adult patients with EoE, were not peer-reviewed, and/or were not available in English.
It was anticipated that the primary respondent during interviews with patients 8 years and older would be from the child, whereas the primary respondent during interviews with patients younger than 8 years was anticipated to be the caregiver. Questions were targeted toward the primary respondent, but the other respondent was asked questions as well to collect additional information. Each interview was audio-recorded, transcribed, and anonymized for formal qualitative data coding and analysis. 19, 20, 24 aturation of concept (ie, the point at which no new relevant information would be gained from the conduct of additional CE interviews) was also analyzed to evaluate the adequacy of the sample size. 19, 20 Interviews were ordered chronologically into transcript groups and coded data were used to determine the first transcript group in which a concept was first mentioned.
• Analysis 3 (described in subsection 4. 3) is based on a largely model-independent two-point correlation analysis of the arrival directions of UHECR and neutrinos with a high probability of astrophysical origin. With these three approaches, we follow up on previous searches performed by the Pierre Auger, Telescope Array and IceCube collaborations (The IceCube Collaboration et al. 2016), which showed an interesting correlation at a significance level close to three standard deviations. Since 2015, the analyses have been improved with substantially enlarged data sets, extending the Auger data from 10 yr to 13 yr (see subsection 2. 3) and TA data from 6 yr to 9 yr (see subsection 2. 4). The different data sets from IceCube are enlarged from 4 yr to 10. 5 yr, from 4 yr to 7. 5 yr, and from 2 yr to 8 yr, depending on the specific detection channel as described in subsection 2.
1. Newly included are two data sets of 9 yr and 11 yr from ANTARES, as further specified in subsection 2. 2. Due to the updated data sets by Auger and TA, their respective shift of the energy scale is updated from a sole shift of TA energies by -13% to a shift of +14% for Auger and -14% TA energies above the energy threshold of ∼50 EeV  (Biteau et al. 2019). Furthermore, we include an improved magnetic deflection model that distinguishes between the Northern and Southern hemisphere for analysis 2. We report the results from the three improved correlation searches, which update the preliminary reported results in  Schumacher (2019) ;  Aublin et al. (2019) ;  Barbano (2019). In addition, we report upper limits on the correlated fluxes of UHECRs and neutrinos based on benchmark models for the magnetic deflections.
2018b (Aartsen et al. , 2017c,d),d). In all cases, the remaining event rate is dominated by atmospheric neutrinos. The selection of astrophysical neutrinos can be achieved on a statistical basis by selecting very energetic events or, in case of the very down-going region, by vetoing events where an atmospheric shower is observed in IceTop, IceCube's surface detector for cosmic rays  (Abbasi et al. 2013). For the three analyses, data from multiple detection channels are used, which are: (i) a data set of through-going tracks from the full sky optimized for point-source searches (PS), (ii) a data set of high-energy starting events (HESE) of both topologies from the full sky, (iii) a data set of high-energy neutrinos (HENU) selected from through-going tracks with horizontal and up-going directions, and (iv) a data set of tracks from a selection of extremely high-energy events (EHE).
The PS data set is used for analysis 1, while the HESE, HENU and EHE data sets are used for analyses 2 and 3. For analyses 2 and 3, track-like events from the HESE, HENU, and EHE data sets are combined, while multiple instances of identical events are removed. This results in a data set of 81 track-like events. In analyses 2 and 3, the 76 cascade-like events from the HESE data set are analyzed separately due to their larger directional uncertainty. The sky distribution of selected events is shown in Figure  1  and an overview of the nomenclature is presented in Table  1. The PS data set consists of a combination of data collected from 7 years of operation between 2008 and 2015 that was used for point-source searches  (Aartsen et al. 2017d ) and data from 3. 5 years of operation between 2015 and 2018 that was selected for the real-time gamma-ray follow-up (GFU) program of IceCube  (Aartsen et al.
Integrated above 60 TeV, which corresponds to 60 events in total, the percentage of events of astrophysical origin, i. e. the astrophysical purity, is larger than 75 % (Wandkowsky 2018), while the percentage is lower below 60 TeV. The angular resolution is about 1°for track-like events and 15°for cascade-like events above 100 TeV. The resolution of the deposited energy for tracks and cascades is around 10 % (Aartsen et al. 2014a) without accounting for systematic uncertainties, but the cascades have a better correlation with the primary neutrino energy since they deposit most of their energy inside the detector, while tracks do not. The HENU data set consists of the 35 highest energy track-like events with a reconstructed declination ≥ -5 • , which have been collected between 2009 and 2016  (Haack & Wiebusch 2017)  for the measurement of the diffuse muon-neutrino flux  (Aartsen et al.
1). Particles above the Cherenkov threshold induce coherent radiation emitted in a cone with a characteristic angle of 42 • in water. The position, time, and collected charge of the signals induced in the photomultiplier tubes by detected photons are used to reconstruct the direction and energy of events induced by neutrino interactions and atmospheric muons. Trigger conditions based on combinations of local coincidences are applied to identify signals due to physics events over the environmental light background due to 40 K decays and bioluminescence  (Aguilar et al. 2007). ANTARES is thus also capable of detecting neutrino charged-and neutral-current interactions of all flavors. For analysis 1, we use all track-like events from the 11-year data set used for point-source searches (PS) recorded between  2007 and 2017 (Albert et al.
2018;; Illuminati et al. 2019). The high-energy events (HENU) for analyses 2 and 3 are selected from an earlier data set of track-like and cascade-like events collected between  2007 and 2015 (Albert et al. 2017)). In order to ensure a high probability of astrophysical origin for analyses 2 and 3, we require an astrophysical purity ≥ 40 % based on the same definition as used for the HENU data set of IceCube. This selection results in a total of three track-like events and no cascade-like events, of which the arrival directions are shown in Figure  1. An overview of the nomenclature is presented in Table  1. The track-like events are combined with the respective IceCube data sets. All events have a good angular resolution that is below 0. 4°above 10 TeV  (Albert et al. 2017). Overall, the angular resolution of ANTARES tracks is better than for IceCube tracks for energies below 100 TeV, and comparable around 100 TeV and above.
The median angular resolution, Ψ, of the 11-year data set compared to the IceCube PS data set is shown in Figure  2. Despite the smaller detection volume, the inclusion of ANTARES data significantly improves the allsky coverage of the neutrino data set used in analysis 1. This is shown in subsection 4. 1 and Figure  5  through the relative contribution to the expected number of signal events for the individual PS data sets of ANTARES and IceCube. Particularly in the Southern hemisphere, where the background from atmospheric muons results in a higher energy threshold in IceCube, the ANTARES data contributes substantially to the signal acceptance for soft source spectra.
2019). The celestial distribution of selected events is shown in Figure  1. The right-hand side of the figure is a histogram of the sine of declination of all UHECR events, showing that TA data substantially contributes to the full-sky exposure of the combined UHECR data set. Figure  3  shows the directional exposure as a function of declination, with an integrated total exposure of 11 600 km 2 yr sr  (Biteau et al. 2019). 3. MAGNETIC DEFLECTIONS Despite their extremely high rigidity, UHECRs are deflected in galactic and in extragalactic magnetic fields (GMF/EGMF) by a non-negligible amount. Neither the strength and correlation length of the extragalactic magnetic fields  (Durrer & Neronov 2013; Kronberg 1994)  nor the distance of the UHECR sources are known well. Measurements of the Faraday rotation of extragalactic sources indicate that the extragalactic magnetic fields are weaker than 1 nG  (Pshirkov et al.
ANALYSIS METHODS 4. 1. Unbinned neutrino point-source search with UHECR information The goal of analysis 1 is to find point-like neutrino sources that are spatially correlated with UHECR arrival directions within a region derived from their magnetic deflection estimate. The search for neutrino sources utilizes the unbinned maximum-likelihood analysis commonly used in IceCube  (Aartsen et al. 2017d (Aartsen et al. , 2019 (Aartsen et al. , 2020b)). This enables an easy combination of the high-statistics, full-sky neutrino data sets, which are the PS data sets of IceCube and ANTARES described in subsection 2. 1 and subsection 2. 2, respectively. In addition to this standard method, the magnetic deflection regions, defined by the UHECR arrival directions, energy, and scaling factor, are used for constraining the possible source regions.