dataesr/scientific-paragraphs-categorization huggingface dataset:
The DN Design Tool for an Embedded Implementation To design dedicated Decision Networks incorporating diagnosis or mitigation, we can extend the F2F BN design tool as shown in Figure  16. In this version, we have the three main layers: 1. In the first layer, a friendly front-end interface to generate the Bayesian Network model from FMEA-like analysis and incorporate to decision process for a DN definition. The DN specifications can be expressed in. net format or. m format in order to be compatible with other tools such as Genie  [26] , BNT  [27]  tool for Matlab.
3. In the second layer, a refinement of C-code is proposed by introduction of HLS directives for code parallelization, memory and interface management. This latter C-HLS compliant code is tailored for a complete FPGA implementation on ZedBoard. The second layer proposes high-level transformations and provides parallel opportunities for the code independent of the target platform. These high-level transformations are mainly detailed in Section 3. 2. The third layer gives an more practical guide for a parallel implementation on a SoC/FPGA platform. This latter one is platform dependent.
HW Exploration for DN In this part, we propose to explore a co-design implementation of the decision-making modules. To achieve a co-design implementation, we propose to target a ZedBoard incorporating a hybrid Zynq processor with processing system and programmable logic on chip. We couple our design tool to the commercial tool from Xilinx (Vivado-HLS, Vivado and SDK) such as in Figure  12  to target the Zynq processor. As shown in Figure  24 , the target architecture is built around the ARM Cortex-A9 processor (Zynq processing system PS; the SW part). The processor is communicating with dedicated HW accelerators (from the Programmable logic part) using the programmable logic through an Advanced eXtensible Interface (AXI) bus. We consider for the co-design implementation on the Zynq processor, the BRAM architecture for the communication between the HW and the SW parts (see  [20]  for more details).
For Bayesian networks based on FMEA, we propose an architecture where the parameters of the BN are stored in BRAM and can be modified using an AXI_BRAM Controller, which manages the data transfer between the BRAM blocks and the DDR memory. This architecture allows fast data transmission. Note that the probability tables can be sent when they are fixed and can be modified on-line via the AXI_BRAM controller. In practice, the update rate is low and the probability tables can often be considered static during a mission. The evidence indicators (Boolean values) are provided by the sensors and are updated for each computation according to the sensor frequency. To minimize time and optimize resources, they are concatenated by 32 and sent via AXI_BRAM Controller. The health status or decision results are sent via an AXI lite bus.
BN Parameter Issue One of the problems when dealing with a Bayesian Network (BN) is to define its parameters (the probability tables). Generally, these probabilities are defined by an expert of the system. However, this expertise may be incomplete or impossible to achieve in some domains and/or systems. In this case, it is necessary to use a learning method for the BN parameters  [12]. There are different learning methods for BN parameters in the context of complete data  [13]  or incomplete data  [14]. In the real application like the evaluation of autonomous system health status, the expert databases are often incomplete. The missing data can be provided by sensor failures, inaccurate measurements of sensors data, etc. In this case, it is possible to learn the BN parameters from the databases using the Expectation and Maximization (EM) algorithm  [38].
As this algorithm can consider the structure of the BN, the learning phase can be manageable. In  [39] , we show that we can reduce the timing in the learning phase if the structure of the BN is known in advance. Before applying the EM algorithm and learning the BN parameters, we need a learning database which can be built by simulation, mission history report, etc. For the GPS case study, we collect some information about a user position around an area (example: building) at different times. We consider a few known positions as a reference path. The difference between the computed position (using smartphone for example) and the reference one represent the GPS position deviation. The information was collected at almost the same time over several days. The information recovered for each position located with GPSData application are: the latitude, the longitude and the altitude, the number of visible satellites and the accuracy of the position given by the GPS.
• Altitude correspond to 'altitude' node. According to the value of the collected data and the fixed threshold for each node status of the GPS monitor. Thus, the data correspond to one of status of the corresponding BN node (interpreted as evidence). Table  8  shows how to interpret the real data into BN data. Once the database of the observed data for each node is built, we can perform the EM algorithm to learn the BN parameters which is the CPT probability values. The idea here is to compare the accuracy of the GPS position obtained with different collected database, with the reference one. Figure  26  shows the reference path (known position in red) around a building. Figure  27  shows the different paths (resulting from GPS positions) obtained by applying EM with different databases built from he collected data.
In comparison with the reference path (in red on the figure), we can show that the accuracy of the GPS position varies according to the variation of the collected raw data and the rate of missing values in the database. In this case study, the parameters of all the BN nodes are learned with EM algorithm. The complexity of this approach varies according to the number of nodes and the conditional dependence. To deal with this, we can just learn the parameters of the pertinent nodes like the sensors nodes and the appearance context nodes using EM with another kind of inference mechanism  [39].
HW Exploration of the MDP Model To implement the BFM module, we can use a design flow very similar to the one defined in Figure  16  where the decision part is now expressed using an MDP model. The MDP part can be easily described in C-code and be mapped using HLS directives onto FPGA. Table  9  shows the speed up obtained by executing the different MDPs describing the tracking mission phases (navigation, landing and tracking) on ZedBoard Xilinx and also the SW and HW execution time. We can see that the execution time increases according to the MDP size in terms of states and actions number. With a more complex mission than the tracking mission (with a hundred states and actions), the execution time and speed up increase significantly. For our case study, a response time lower than 10 ms is enough to apply the new mission plan generated by the decision module of BFM model.
Zoned hydrothermal monazite-(Ce) from Alpinetype fissures and clefts is used to gain new insights into the tectonic history of the Lepontine Dome in the Central Alps and the timing of deformation along the Rhone-Simplon Fault zone on the dome's western end. Hydrothermal monazites-(Ce) (re)crystallization ages directly date deformation that induces changes in physicochemical conditions of the fissure or cleft fluid. A total of 480 secondary ion mass spectrometry (SIMS) spot analyses from 20 individual crystals, including co-type material of the monazite-(Nd) type locality, record ages for the time of ∼ 19 to 2. 7 Ma, with individual grains recording age ranges of 2 to 7. 5 Myr. The combination of these age data with geometric considerations and spatial distribution across the Lepontine region gives a more precise young exhumation history for the area.
At the northeastern and southwestern edges of the Lepontine Dome, units underwent hydrothermal monazite-(Ce) growth at 19-12. 5 and 16. 5-10. 5 Ma, respectively, while crystallization of monazite-(Ce) in the eastern Lepontine Dome started later, at 15-10 Ma. Fissure monazite-(Ce) along the western limit of the dome reports younger ages of 13-7 Ma. A younger age group around 8-5 Ma is limited to fissures and clefts associated with the Simplon normal fault and related strike-slip faults such as the Rhone Fault. The data set shows that the monazite-(Ce) age record directly links the fluid-induced interaction between fissure mineral and host rock to the Lepontine Dome's evolution in space and time. A comparison between hydrothermal monazite-(Ce) and thermochronometric data suggest that hydrothermal monazite-(Ce) dating may allow us to identify areas of slow exhumation or cooling rates during ongoing tectonic activity.
The mass spectrometer was operated at +10 kV and a mass resolution of ca. 4300 (M/ M, at 10 % peak height), with data collected in peak hopping mode using an ion-counting electron multiplier. Unknowns were calibrated against monazite-(Ce) standard 44069  (Aleinikoff et al. , 2006). Lead isotope signals were corrected for common Pb contribution using measured 204 Pb and an assumed present-day Pb isotope composition according to the model of  Stacey and Kramers (1975). The measurement of 204 Pb is subject to an unresolvable molecular interference by 232 Th 143 Nd 16 O ++ 2 , also affecting 206 Pb and 207 Pb to a lesser degree through replacement of 16 O by heavier O isotopes, which may result in an overestimation of common Pb concentrations. A correction was applied whenever the 232 Th 143 Nd 16 O ++ 2 signal at mass 203.
Results The ion-probe measurement data set is given in the data table in the Supplement and can be found in the PANGAEA database (https://doi. org/10. 1594/PANGAEA. 898689). Only those few measurements that did not yield an age were discarded during the initial data reduction (their positions are included in Fig. 5 ). The age data of the individual samples and the whole data set cover a large range of ca. 16 Myr, between ∼ 19 and 2. 7 Ma. Individual grains record ages over a lifetime of 2 to 7. 5 Myr. An overview over the individual age ranges and the weighted mean domain ages that could be calculated for the individual samples is shown in Table  2. Figure  5  shows the measurement positions, an age plot, and a chemical plot for each sample. Sample GRAESER 4 (Appendix Fig. A1 ; Supplement table) is a grain (co-type) from the monazite-(Nd) type locality  (Graeser and Schwander, 1987).
Summary Hydrothermal fissure monazite-(Ce) offers the possibility to date tectonic activity in the brittle domain for extended time periods, as it provides a record of the shifting tectonic activity within the Lepontine Dome likely associated with the regional exhumation and shear zone history. The fluid and likely often also host rock temperature range of roughly 350 • C to at least 200 • C, as indicated by previous studies, likely extends to significantly lower temperatures as suggested by a comparison with thermochronometers. This comparison between hydrothermal monazite-(Ce) samples from different parts of the Lepontine Dome area and the Eastern Alps with thermochronometric data suggests that hydrothermal monazite-(Ce) dating might help with identifying areas of slow exhumation or cooling rates during ongoing tectonic activity.
The 232 Th-208 Pb monazite-(Ce) crystallization data record prolonged hydrothermal activity during tectonics between ∼ 19 and 2. 7 Ma and thereby contributed to the understanding of the tectonic evolution of the Central Alps. Overall, the monazite-(Ce) age record reveals a relatively smooth east-west age trend within the Lepontine Dome. The record starts in the eastern (and southern) parts of the study area, with the recorded activity then moving through the central to the western area, where it progressively concentrates on the large fault systems of the western Lepontine Dome.
Acknowledgements. Fraukje Brouwer  and  Meinert Rahn  are thanked for their very helpful and detailed reviews that greatly improved this article. We would also like to thank  Bernhard Grasemann  and  Anna Wenzel  for their editorial handling and helpful contact during manuscript revision. Axel Schmitt  is thanked for discussions during the revision process. We greatly appreciate the help of  Martin Andres ,  Rudolf Duthaler ,  Michael Flepp ,  Stefan Graeser ,  Leo Klemm ,  Beda Hofmann ,  Alexandre Salzmann ,  Francesco Vanini , and  Manfred Walter  by providing monazite-(Ce) samples for this study. Financial support. This research has been supported by the  Swiss National Science Foundation  (grant nos. 200021-143972  and  200020-165513 ). Review statement. This paper was edited by  Bernhard Grasemann  and reviewed by  Fraukje Brouwer  and  Meinert Rahn.
Acknowledging this particularly significant problem, Braginsky et al. [8]  ingeniously suggested the use of two pendulums attached to the same support but running out of phase and so cancelling each other's seismically induced precessions, (vii) Atmospheric refraction whereby variations in the measured position of the reference star will be caused by changes in azimuthal atmospheric refraction both near the telescope and far from it. South Pole location would help, and effects could be reduced further by tracking two stars on opposite sides of the sky. They observed that a further correction could be made by two-colour refractometry, (viii) Distortion of the telescope which manifests as any instability in the azimuthal optics due to gravitational stresses, temperature fluctuations and ageing.
For the data examined, the safe upper limit to the integration time was found to be restricted to 3600 s or below. It should be noted that this is a specific result for the data used here and that further work remains to be done to generalize this properly, preferably through a comprehensive non-dimensionalization strategy. This will be undertaken in the next phase of the research, along with a perturbation analysis using the method of multiple scales. 5. A parametrically excited experimental pendulum was constructed in the laboratory and several tests were performed using a monitoring system based on calibrated laser instrumentation. The nominal length of the pendulum was 4685. 5 mm and the peak amplitude of the parametric excitation was 50 mm, thereby slightly exceeding 1% of the nominal length, and maintaining that relationship (as applied previously in the numerical analyses).
Acknowledgements. The authors wish to thank the following colleagues for many useful and insightful discussions during the course of this initial research programme:  Prof. Sheila Rowan ,  Prof. Sir James Hough  and  Prof. Martin Hendry  of the  Institute for Gravitational Research in the School of Physics and Astronomy at the University of Glasgow. The authors would also like to thank  Mr Lee Stewart ,  Mr Alastair Kerr ,  Mr John Redgate  and  Mr Drew Irvine  of the  Department of Mechanical and Aerospace Engineering at the University of Strathclyde  for manufacturing parts and installing the experimental pendulum in the laboratory. We also want to thank the anonymous reviewers for their excellent suggestions for improvements to the paper.
Identifying fakes According to Jones (1992, 9), 'questions of motive are perhaps less important than the quality and impact of the result'. While important progress has been made in detecting fakes by scientific analysis, counterfeiters' ability to make fakes that look like authentic artefacts has been growing at the same time  (Lowenthal 1990). They now have a better understanding of the physical and chemical processes of corrosion and natural ageing, for instance  (Craddock 2009, 10-12). Forgers may also be helped if they have access to radiometric data  (Ira Rabin and Oliver Hahn). This explains the endless debates on certain emblematic artefacts. The Silwan ossuary, for example, which was discovered in 2002, bears an inscription suggesting that it belonged to 'James, son of Joseph, brother of Jesus'.
Acknowledgements Most of the contributions to this volume were presented as preliminary versions at the 2018 conference on Fakes and  Written Artefacts  ( 28 February-1 March 2018 ), which was organised by  Sonderforschungsbereich 950 'Manuscript Cultures in Asia, Africa and Europe'  and funded by the  German Research Foundation (DFG)  as part of the work conducted by the  Centre for the Study of Manuscript Cultures (CSMC) at Hamburg University. Claudia Colini ,  Dan Shapira  and  Jost Gippert 's articles have been included because of their direct relevance to the topics discussed here. We wish to thank everyone who participated in the conference, in particular the authors of the different chapters of this book, as well as  Caroline Macé ,  Carl Carter  and  Laurence Tuerlinckx  for their help during the editing process.
There is an increasingly pressing need, by several applications in diverse domains, for developing techniques able to analyze very large collections of static and streaming sequences (a. k. a. data series), predominantly in real-time. Examples of such applications come from Internet of Things installations, neuroscience, astrophysics, and a multitude of other scientic and application domains that need to apply machine learning techniques for knowledge extraction. It is not unusual for these applications, for which similarity search is a core operation, to involve numbers of data series in the order of hundreds of millions to billions, which are seldom analyzed in their full detail due to their sheer size. Such application requirements have driven the development of novel similarity search methods that can facilitate scalable analytics in this context.
At the same time, a host of other methods have been developed for similarity search of highdimensional vectors in general. All these methods are now becoming increasingly important, because of the growing popularity and size of sequence collections, as well as the growing use of high-dimensional vector representations of a large variety of objects (such as text, multimedia, images, audio and video recordings, graphs, database tables, and others) thanks to deep network embeddings. In this work, we review recent eorts in designing techniques for indexing and analyzing massive collections of data series, and argue that they are the methods of choice even for general highdimensional vectors. Finally, we discuss the challenges and open research problems in this area.
The similarity search problem has been studied heavily in the past 25 years and will continue to attract attention as massive collections of high-dimensional objects are becoming omnipresent in various domains  [7, 55]. Objects can be data series, text, images, audio and video recordings, graphs, database tables or deep network embeddings. Similarity search over high-dimensional objects is often reduced to a :-Nearest Neighbor (k-NN) problem such that the objects are represented using high-dimensional vectors and the (dis)-similarity between them is measured using a distance. In this work, we review recent eorts in designing techniques for indexing and analyzing massive collections of data series that will enable users to run complex analytics on their data in an interactive fashion, and we argue that they are the methods of choice even for general high-dimensional vector datasets.
The =6-approximate methods IMI and HNSW provide better eciency but they suer from three major limitations: (a) they have no guarantees; (b) they are very dicult to tune; and (c) their speed-accuracy tradeo is not determined only at query time, but also during index building. Choosing the best approach to answer an approximate similarity search query depends on a variety of factors including the accuracy desired, the dataset characteristics, the size of the query workload, the presence of an existing index and the hardware. technique to use for answering a query workload using an existing index. Overall, DSTree is the best performer, with the exceptions of ng-approximate queries, where iSAX2+ also exhibits excellent performance, and of in-memory datasets, where HNSW is the overall winner. Accounting for index construction time as well, DSTree becomes the method of choice across the board, except for small workloads, where iSAX2+ wins.
Short Series decision depends on dataset size decision depends on dataset size decision depends on dataset size decision depends on dataset size decision depends on dataset size decision depends on dataset size decision depends on dataset size and length decision depends on dataset size and length decision depends on dataset size and length decision depends on dataset size and length decision depends on dataset size and length decision depends on dataset size and length iSAX2+ DSTree Long Series Disk-Resident Long Series Disk-Resident Long Series Disk-Resident Long Series Disk-Resident Long Series Disk-Resident Long Series Disk-Resident Long Series SERIES LENGTH In-Memory Short Series In-Memory Short Series In-Memory Short Series In-Memory Short Series In-Memory Short Series In-Memory Short Series Disk-Resident VA+file Disk-Resident Short Series Disk-Resident Short Series Disk-Resident Short Series Disk-Resident Short Series Disk-Resident Short Series DSTree VA+file DSTree DATASET SIZE Figure 3: Exact search recommendations.
The main purpose on a meta-model is to give an approximation of the objective function values over unknown areas. The main steps to work with a meta-model are to (i) build the meta-model on an initial database of "black box" computations; (ii) use this meta-model to select an interesting area of investigation; and (iii) run a "black box" call in this interesting area. These three steps lead to the enrichment of the database. After several iterations of these three steps, a solution of the global optimization problem is obtained. This methodology can be classified in the field of analysis of computer experiments as defined by Santner et al. [17] , or in more recent work, such as  [18]. With nowadays computation environment, it seems appropriate to extend this kind of algorithms to parallel environments.
The algorithm presented here was integrated in the MOOPI (MOdular software for Optimization and Parameters Identification) software developed at Cemef-Mines ParisTech. It has shown its efficiency in forming applications  [20]  and model calibration using digital image correlation data  [21]. The software is developed in an oriented object framework (C++), which makes it easy to add extra optimization algorithms, without working on the management of the finite element computation. Moreover, all the functionalities are accessible through a dedicated user interface.
Kriging predictor construction Ordinary Kriging comes from Gaussian fields process theory, and was initially used in geostatistics  [25]. Ordinary Kriging assumes that the process is a sum of a constant trend m and a local deviation R x. We assume that the R x process results from a Gaussian random field process with a mean value equal to 0 and a variance of σ 2. Therefore, the "black box" function F x can be written as a stochastic process: F x = m + R x. (2) The estimator needs a master point database DBp, and some associated values of the objective function DBy in order to be computed. DBp : p = p 1 ,. , p n ∈ d×n. (3) The database DBp is a list of n sets of parameters. Each set of parameters is specified as a vector of d values. DBy is a vector obtained by the n evaluations of the objective function.