dataesr/scientific-paragraphs-categorization huggingface dataset:
Photometric Depths The S-PLUS DR1 Stripe 82 data were used to estimate the average photometric depth of the S-PLUS images. As summarised in Table  8 , the photometric depths were calculated using five different definitions for sources detected in a given filter with a signal-to-noise ratio ≥3. Here, m peak corresponds to the Petrosian magnitude at which detections start declining rapidly (i. e. , the derivative is zero); m 50% , m 80% , and m 95% correspond to the magnitudes at which it includes 50%, 80%, and 95% of the total detected sources and m3arcs corresponds to the integrated magnitude within circular apertures of 3 arc-second diameter. As can be seen in Fig. 15 , where the estimated photometric depths of r and g-band images at different signal-to-noise ratios are shown, the S-PLUS images are expected to be complete down to a magnitude g < 21.
Photometric Redshift Depth S-PLUS DR1 Stripe 82 data were used to characterise the performance of the photo-z estimates for different magnitude and redshift ranges. This dataset is ideal because of the availability of a high number of spectroscopic redshifts for galaxies and quasars. For the present exercise, we compiled a sample of galaxies in S-PLUS DR1 of Stripe 82 with magnitudes r <21 and redshifts z < 1. 0. Our photometric redshift determinations were tested against a sample of galaxies with spectroscopic information taken from the literature. The following datasets were used for constructing Table  8. Photometric depth of images. The table shows the estimated photometric depth of the S-PLUS images using five different definitions, and selecting only sources detected with a minimum signal-to-noise of S/N≥3 on individual filters: m peak corresponds to the Petrosian (i.
01, for ∼16 million galaxies with δz/(1 + z) = 0. 02, and for ∼32 million galaxies with δz/(1 + z) = 0. 025, down to a magnitude r = 21. In terms of photo-z precision, the benefit of extending classical 5-filter broad-band surveys (such as SDSS;  York et al. 2000)  can be assessed directly using the S-PLUS data. Molino et al. (2019, submitted)  uses the S-PLUS DR1 Stripe 82 data and compare photo-zs obtained with 5 bands with those obtained with 12 bands, using the SED-fitting code BPZ  (Benítez 2000). As shown in their paper, the 12-band system leads to an improvement in photo-z over the 5-band system of a factor of 4, for galaxies with magnitudes r < 15, a factor of 2. 5 for magnitudes 15 < r < 17, and/or a factor of 1. 7 for magnitudes 17 < r < 19. As a function of redshift, the 12-band system leads to a factor of 2 improvement for galaxies with z < 0.
RESULTS FROM THE SCIENCE VERIFICATION DATA This section first summarizes some key features of the S-PLUS DR1 in Table  9. DR1 is composed of 170 contiguous pointings, adding up to ∼336 deg 2 of the Stripe 82 area, observed in 12 filters. The main characteristics of DR1 including a description of the reduction and calibration methods used, an analysis of the spatial distribution of the PSF along the images, as well as the photometric and photo-z depths attained in the DR1 dataset, have been described in §4. As described in §3. 1, files from the MS are generally dithered by 10 arc sec along the RA direction. However, in the case of the fields of Stripe 82 in the DR1, only those fields observed in 2018 were dithered, the ones in 2016 were not. DR1 fields had no overlapping area. These were decisions made early in the project that were then changed (to include dithering and overlapping areas for the remaining of the survey).
-------------  galaxies, nearly 35% are classified as early/quiescent galaxies and 65% as late/star-forming galaxies. In absolute numbers, S-PLUS DR1 includes ∼350k early and ∼650k latetype galaxies. S-PLUS DR1 catalogues released with this paper have a magnitude cut of r = 21 mag. This section also presents preliminary results obtained using DR1, which will be detailed in future papers. MS data on Stripe 82 are used to exemplify the usefulness of S-PLUS at improving star/galaxy classification ( §5. 1), at the determination of galaxy cluster/group membership ( §5. 2), in deriving environment density indicators ( §5. 3), in quasar searches ( §5. 4), in morphological studies ( §5. 5), and for IFUlike science projects ( §5. 6).
Therefore, we expect to find improved results with a more robust and less strict analysis based on machine learning and these will be fully discussed in  Nakazono et al. (in prep. ). In the analysis described above we found about one previously unidentified quasar candidate for each known quasar selected using the SDSS dataset in the Stripe 82 field. Follow-up spectroscopy of these new candidates (r < 19 mag) will allow us to further test and calibrate our selection methods. Future tests with S-PLUS data in the GAMA fields, for which the spectroscopic samples are complete down to the limiting magnitude of our study, will furthermore allow us to assess quasar selection completeness and the contamination rate. In  Queiroz et al. (in prep. ) we perform the object classification without any near-infrared data, by employing a machine learning technique which provides the probabilities that any given point-like source detected with S-PLUS is a quasar, a star, or a galaxy.
ACKNOWLEDGMENTS The S-PLUS project, including the T80S robotic telescope and the S-PLUS scientific survey, was founded as a partnership between the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP), the Observatório Nacional (ON), the Federal University of Sergipe (UFS), and the Federal University of Santa Catarina (UFSC), with important financial and practical contributions from other collaborating institutes in Brazil, Chile (Universidad de La Serena), and Spain (Centro de Estudios de Física del Cosmos de Aragón, CEFCA). The members of the collaboration are grateful for the support received from the Conselho Nacional de Desenvolvimento Científico e Tecnológico  (CNPq; grants 312333/2014 -5, 306968/2014 -2, 142436/2014 -3, 459553/2014 -3, 400738/2014 -7, 302037/2015 -2, 312307/2015 -2, 300336/2016 -0, 304184/2016 -0, 304971/2016 -2, 401669/2016 -5, 308968/2016 -6, 309456/2016 -9, 421687/2016 -9, 150237/2017 -0, 311331/2017 -3, 304819/2017 -4, and 200289/2017-9) , FAPESP  (grants 2009/54202-8, 2011/51680-6, 2014/07684-5, 2014/11806-9, 2014/13723-3, 2014/18632-6, 2016/17119-9, 2016/12331-0, 2016/21532-9, 2016/21664-2, 2016/23567-4, 2017/01461-2, 2017/23766-0, and 2018/02444-7) , the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES; grants 88881.
Thus, they characterize in our opinion first-generation telemedicine projects and studies. Using PubMed database and Google Scholar, we have identified more than 20 reports of first-generation telemonitoring studies in the field of diabetes, including type 1 and type 2 diabetic patients, involving the upload and direct transmission of BG data by diabetic patients to providers via cellular telephone, telephone landline, or a Web-based program. The results of these studies were mixed, perhaps because many studies did not target diabetic patients with poor baseline BG control or the interval between glucose transmission and follow-up was delayed or unspecified or mainly with no therapeutic intervention (therapeutic inertia). None of these reports evaluated the intensity of intervention required to sustain achieved reductions in HbA1c after the implementation of home telemonitoring.
Patients used their own glucose meters to measure BG and were provided with an electronic digital scale to measure their weight. The device was programmed to sound an alarm at a pre-specified patient-referred time to prompt the patient to initiate a telemonitoring session. Patients were asked to enter data several times during the week. The device was programmed to ask how patients were feeling that day and whether they had taken their medications and then receive a prompt to take the measures. After, the patient received a series of education messages, focused on teaching patients about their diseases (diabetes, arterial hypertension) and associated comorbidities. The second telemonitoring delivery method is the use of an interactive voice response (IVR) system. Patients were provided with a BP monitor and electronic digital scales, but they used their own BG meter.
New-generation projects and studies in diabetes Over the last 5 years, new-generation telemedicine projects and studies have emerged in the setting of chronic diseases setting, especially in the setting of chronic heart failure, chronic obstructive pulmonary diseases, and type 1 and type 2 diabetes  [29, [39] [40] [41] [42]. They support transmission and remote interpretation of patients' data for follow-up and preventive interventions. These projects and studies have for main objectives to evaluate the use of technology to implement medical and cost-effective healthcare management on a large scale for diabetes management. Using PubMed database and Google Scholar, we have identified three of such projects and studies in the field of diabetes management: Telemonitoring and Health Counseling for Self-Management Support from Lindberg et al.
These new-generation telemedicine projects in diabetes (Telemonitoring and Health Counseling for Self-Management Support from Lindberg et al. , TELESAGE, DIABETe)  [39] [40] [41] [42]  are often known as telemedicine 2. 0 projects, given that they all utilize new information and communication technologies (ICT) and the Web (tools for the e-Health 2. 0) (as defined in Table  1 )  [44]. Most projects and studies rely on the standard connected tools for monitoring type 1 and type 2 diabetes, such as glucose meters, BP, heart rate monitors, weighing scales, and pulse oximeters, which relay the collected information via Bluetooth, 3G, or 4G  [29, [39] [40] [41] [42]. Several projects also include continuous glycemic monitoring solution and often a video-call  [29, 30]. Several of these telemedicine projects use machine learning, also called artificial intelligence (AI), in order to be able to: • Adjust the BG level to the patient's activity (software Diabeo™ [see below])  [40, 41].
• Predict patient risks of diabetes decompensation  [42, 45]. In this later situation, the cloud-based software aggregates, cleans, and analyzes patient data to allow for identifying patterns that may indicate potential risks and provide predictive insights on healthcare outcomes, as the software MyPredi™ (see below)  [29, 42]. In the setting of chronic diseases, as in chronic heart disease or in diabetes, several informatics solutions or tools have been developed and used, such as artificial neural network (ANN) algorithms, data mining software, and ontology  [45, 46]. In this context of AI, three clinical datasets are of particular interest: (1) patients' phenotype; (2) patients' electronic medical records containing physicians' notes, laboratory test results, as well as other information on diseases, treatments, and epidemiology that may be of interest for association studies and predictive modeling on prognosis and drug responses; and (3) literature knowledge including rules on diabetes management  [46].
To date, several patients have been included. The results of this project are expected in late 2019-early 2020. The DIABETe project is based on an intelligent platform that likely assists healthcare professionals by automatically processing the information obtained from nonintrusive medical sensors (BG meter, BP monitor, actimeter, connected scale, etc. ) as well as the subjective information provided by the patient himself (questionnaires) and his/her behavior (compliance), enabling it to detect and report, at an early time, these situations at risk of hospitalization  [29, 42]. Patientand situation-adapted therapeutic education tools will be made available to the individual, and communication with the subject will likely occur via a touch pad. Alerts indicating a deterioration of the patient's condition will be generated by AI (new software version of MyPredi™ adapted for the management of diabetes) and transmitted to the health professionals in charge of the patient.
2. 3. Research method The objective of the research method is to specify a PSS modelling language while building on case studies and literature In case studies researchers develop an in-depth analysis of a case, often a program, event, activity, process, or one or more individuals  (Creswell, 1994). This choice is due to the lack of integrated models covering the various PSS dimensions, also illustrated in the literature review findings. The aim is thus to explore use cases in order to derive concepts relevant to the current study, following an iterative process defined in section 3. A multiple case study was chosen for the sake of robustness of the results. Data from the case studies are collected through interviews, work meetings and reports. These data support a progressive specification of the modelling language.
The domain-specific knowledge is basically built upon data collected from use case reports and information system software tools and through interviews with personnel from the use cases. The reference models refer to PSS models existing in the literature which may be used to extend the meta-model (e. g. Lim et al. , 2012). In the event that no explicit models are discussed, relevant PSS-related concepts may be derived from conceptual or review papers (Cavalieri and Pezzotta, 2012). The extension is enabled by an iterative transformation of the meta-models supported by the evolution patterns. These patterns can be defined as a set of rules for adapting a given (meta-) model towards more generic ones with extended scopes. Three main patterns are used namely generalisation, specialisation and grouping.
3. 2. Supporting modelling platforms The supporting platforms are represented by the mechanisms of the elementary process (bottom arrows). The process is supported by a UML modelling platform and a meta-modelling platform. The former allows for building and updating meta-models while the latter supports the implementation of the metamodels and the application to use cases. To illustrate the subsequent steps (i. e. details of the iterative process and model transformation), it is worthwhile providing an overview of the software platforms supporting meta-model implementation and application to use cases. To build and update meta-models, the Eclipse Luna platform was selected and a plug-in to build UML class diagrams consistently with OMG standards was installed. For meta-model implementation and application, an open meta-modelling platform was selected, namely ADOxx.
UML classes and associations are respectively represented by class objects and association class objects within the meta-modelling library. Additional attributes are available in the ADOxx library for the class object for graphical and calculation purposes. The different association types in UML can be modelled using constraints in the ADOxx library. The output of step A0-2 is an ADOxx library of the draft meta-model specifying a modelling language. The subsequent step, A0-3, consists in building an instance of use case i using the ADOxx platform. In other words, use case i is modelled consistently with the draft meta-model i using the ADOxx library. The instance resulting from step A0-3 undergoes a consistency check in step A0-4, in order to see whether the concepts used in modelling the instance are still consistent with meta-model i-1.
g. inventory). In addition, specific data on the operators within the PSS scenario led to the introduction of an operator concept associated to the activities and services. These extensions resulted in a more loaded model with additional concepts and associations. In order to keep the readability of the meta-model and subsequent instances, the meta-model was broken down into two views, namely demand and offer, and performance evaluation. These adaptations resulted in metamodel 2 which was implemented in an ADOxx library allowing to easily model PSS scenarios of both C2 and C1. The graphical notations are still basic at this point. Use case 3 boosted the development of the modelling language further due to its wealth and PSS scenario maturity. As mentioned in the use cases overview, the PSS project involves multiple actors as the PSS spans over a major part of the machining sludge value chain.
However, analysis of the APMV genome did not reveal any significant homology with enzymes of a CRISPR/Cas system. As a CRISPR/Cas system consists of enzymes but also of repeats and integration of the targeted bacteriophage sequence, we then searched for repeats and virophage sequences in the APMV genome. The genome of Zamilon was fragmented into short fragments of 40 nucleotides using a sliding window of 10 nucleotides (nts), and all fragments were compared using BLAST software against the respective APMV genomes. We were surprised to find, in the lineage A Mimiviruses, four repeats of 15 nts corresponding to Zamilon. Observation in the vicinity of these repeats of nuclease and helicase genes allowed us to suspect that this operon that we named MIMIVIRE for Mimivirus resistance element could be the defense mechanism of lineage A Mimivirus againt the Zamilon virophage.
It homogenizes the format of metagenomes and then performs automated reciprocal BLAST hits, including comparison with BLAST searches conducted in the NCBI GenBank nucleotide or protein sequence databases. We notably tested MG-Digger on metagenomes generated from marine water and sewage, but also human blood samples. We detected sequences most related to various giant viruses including Mimiviruses and Marseilleviruses, but also more recently discovered representative members such as Faustoviruses, Pithoviruses, and Pandoraviruses, as well as virophage-like sequences. In addition, we detected sequences from putative giant virus relatives in the NCBI GenBank environmental protein sequence database (env nr), using giant virus sequences encoding a DNA-dependent RNA polymerase subunit, as well as reconstructed putative ancestral sequences for this protein as baits  [157].
These data suggested then that the contacts of Marseilleviruses with humans are frequent. Marseilleviruses were also detected in lymphoid organs in three reports. A Marseillevirus-like organism was detected in a lymph node of an 11-month-old boy who presented an adenitis of undetermined etiology  [45] , in the lymph node from a 30-year-old woman with Hodgkin lymphoma, and in the tonsils of a 20-year-old patient with neurologic disorders  [46, 47]. In addition, it was described, in this latter case, the first evidence of a prolonged carriage of Marseillevirus in humans. These reports may raise questions about the pathogenicity of these Marseilleviruses, particularly in the case of lymphomas. The prolonged carriage, the presence in the blood, the detection in pathologic lymph nodes are arguments for a possible causal link between the virus and the disease.
Funding: This work was supported by the  French Government  under the " Investissements d'avenir" (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR ,  French National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ), by  Région Provence-Alpes-Côte d'Azur and European funding FEDER PRIMI.
Introduction An important task of cryptographic research is to verify cryptographic implementations for security flaws, in particular to avoid so-called timing attacks. Such attacks consist in measuring the execution time of an implementation on its execution platform. For instance, Brumley and Boneh  [12]  showed that it was possible to mount remote timing attacks by against OpenSSL's implementation of the RSA decryption operation and to recover the key. Albrecht and Paterson  [3]  showed that the two levels of protection offered against the Lucky 13 attack from  [2]  in the first release of the new implementation of TLS were imperfect. A This work is supported by the French National Research Agency in the framework of the "Investissements d' avenir" program (ANR-15-IDEX-02) related class of attacks are cache-based attacks in which a malicious party is able to obtain memory-access addressses of the target program which may depend on secret data through observing cache accesses.
FlowTracker  [25]  allows to statically detect time-based side-channels in LLVM programs. Relying on the assumption that LLVM code is in SSA form, they compute control dependencies using a sparse analysis  [13]  without building the whole Program Dependency Graph. Leakage at assembly-level is also considered in  [6]. They propose a fine-grained information-flow analysis for checking that assembly programs generated by CompCert are constant-time. Moreover, they consider a stronger adversary which controls the scheduler and the cache. All the above works do not consider publicly observable outputs. The work that is closest to ours is  [4] , where the authors develop a formal model for constant-time programming policies. The novelty of their approach is that it is distinguishing not only between public and private input values, but also between private and publicly observable output values.
As they state, this distinction poses interesting technical and theoretical challenges. Moreover, constant-time implementations in cryptographic libraries like OpenSSL include optimizations for which paths and addresses can depend not only on public input values, but also on publicly observable output values. Considering only input values as nonsecret information would thus incorrectly characterize those implementations as non-constant-time. They also develop a verification technique based on the self-composition based approach  [7]. They reduce the constant time security of a program P to safety of a product program Q that simulates two parallel executions of P. The tool operates at the LLVM bytecode level. The obtained bytecode program is transformed into a product program which is verified by the Boogie verifier  [5]  and its SMT tool suite.
This may require to refine our notion of arrays and to take into account arrays and pointers as output variables. We could also consider applying a sparse analysis, as in FlowTracker  [25]. It may happen that such a pure static analysis would be too strict, rejecting too much "correct" implementations. To solve this issue, a solution would be to combine it with the dynamic verification technique proposed in  [4]. Thus, our analysis could be used to find automatically which branching conditions are benign in the output-sensitive sense, which could reduce the product construction of  [4]. Finally, another interesting direction would be to adapt our work in the context of quantitative analysis for program leakage, like in  [14].
On the other hand, DSM is the architecture to be exploited. As suggested by Ma et al. [8] , DSM can ensure the energy-efficient expected for future mobile architectures. Note that in this implementation only instructions get distributed across tiles participating to a cluster whereas shared data remain hosted on a host tile. This distinction is because mobile applications are typically user-interactive applications, which involve rich GUI display using shared libraries and system codes, leading to a large instruction working set  [2, 4, 5]. On the other hand, the data access patterns in mobile applications have small footprints and exhibit good locality  [4, 5]. Figure  3  illustrates a comparison between CSM and DSM systems when both make available the same global address space size. To create a global address space in DSM, part of the local memory range in each tile is reserved for global use.