dataesr/scientific-paragraphs-categorization huggingface dataset:
(3) Grain size plays a fundamental role in the fatigue overload behaviour of the material. Grain refinement that strengthens the material reduces the plasticity that is introduced by the overload. The primary retardation mechanisms, plasticity-induced crack closure and residual stress, are weakened. Due to smaller grain size, the crack surfaces get less rough and the crack becomes straighter, which also diminishes the retardation effects of crack tortuosity and roughness-induced crack closure. As is described in Section 2. 3, the displacement field in the crack opening direction (u yy ) and along the crack propagation direction (u xx ) is estimated using the commercial software LaVision DaVis ver. 8. 4. Therefore, the displacement matrices in both directions are obtained as shown in Fig. 3.
Acknowledgement We acknowledge the  ESRF  for time at beamline ID15A under proposal MA-3405; we acknowledge  Diamond Light Source  for time at beamline I12-JEEP under proposal  EE16647  for the UFG experiment. WZ is grateful for the financial support from the  China Scholarship Council (CSC). PJW acknowledges support from the  European Research Council  grant No. 695638 CORREL-CT. Financial support from  Programa Opeativo FEDER  ( Junta de Andalucia, Spain ) through grant reference  UMA18-FEDERJA-250 ,  Ministerio de Economía y Competitividad  through grant reference  MAT2016-76951-C2-2-P  and from  Ministerio de Educación, Cultura y Deporte  through mobility grant  Jose Castillejo  for  PLC  to visit the University of Manchester in 2017 are also acknowledged.
This paper focuses on the conception and use of machine-learning algorithms for marketing. In the last years, specialized service providers as well as in-house data scientists have been increasingly using machine learning to predict consumer behavior for large companies. Predictive marketing thus revives the old dream of one-to-one, perfectly adjusted selling techniques, now at an unprecedented scale. How do predictive marketing devices change the way corporations know and model their customers? Drawing from STS and the sociology of quantification, I propose to study the original ambivalence that characterizes the promise of a mass personalization, i. e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets. By studying algorithms in practice, I show how the active embedding of local preexisting consumer knowledge and punctual de-personalization mechanisms are keys to the epistemic and organizational success of predictive marketing.
A customer's interest in a given product, their risk of churn, or fraud, is predicted through algorithmic models that assign probabilistic scores to individual customers. These scores rely on data describing their past behaviors, whether collected from the web, or already held and stored in the companies' internal databases (Alemany  Oliver and Vayre, 2015). These methods, which I here refer to under the generic term of predictive marketing, are defined by their promise of individualized customer knowledge, much more granular than knowledge produced by traditional market research (questionnaires, focus groups, etc. ). They are part of the longstanding project of personalizing market strategies, which traces back to the 1920s  (Lauer, 2012) , but has been given new prominence since the 1990s  (Turow, 2006).
e. the material settings in which they are conceived, experimented and interpreted. In this paper, I specifically focus on two distinct cases: the data lab of the banking group, which I will call "The Bank", and a predictive marketing startup, which I will call Predicto. While these two organizational contexts differ, data scientists from both the Bank's data lab and Predicto find themselves in a position where they strive to articulate the requests addressed to them, and the pre-existing data infrastructures of their (internal or external) clients: customer databases, purchase records, data management platforms, etc. The cases presented, set in the worlds of banking and insurance, make it possible to observe how predictive marketing takes place in universes that have historically been populated (or even saturated) with all kinds of calculations  (Lazarus, 2012; Porter, 1995).
Despite this specificity, the results developed in this paper have a more general reach, as the practices studied here are rather similar to what I observed in other domains where predictive marketing was used (namely retail and telecommunications). I show that there is more to algorithmic predictive marketing than the issues of surveillance and control raised by many critical studies of Big Data. In contrast, this paper aims to understand the paradox posed by contemporary mass personalization, i. e. algorithmic processes in which the precise adjustment of prediction to unique individuals involves the computation of massive datasets, compiling the behaviors of very large populations. As noted by Lury and Day, "Personalization is not only personal: it is never about only one person, just me or just you, but always involves generalization" (2019: 2).
So we ask them: for this output, this variable that we want to predict, in your opinion, what could be the relevant variables? What are the indicators that you are used to build? Even beyond existing data, we often ask the question of the interest for a product, for example, why a customer might be interested? So we make lists. And then we're going to prospect: does this type of information exist in internal databases? Can we look for it in external databases? Scientific Director of the Data Lab  (interview, July 2018)  This was the case in a project designed to identify at an early stage, among the Bank's "professional clients", 7 those who are going to experience financial difficulties in the coming months, to contact them in advance and possibly take facilitating measures (debt spreading, etc.
This polyphonic arrangement guaranteed the representation of different modes of relationships between customers and the Bank, and brought out the ordinary forms of client knowledge, by asking participants questions such as: which product might be of interest to which category of customer? What indicators already exist? etc. It also made it possible to gather the opinion of professionals on the content and properties of future algorithmic models, the variables deemed relevant, or the data they would like to use -and of course, the legal and technical constraints regarding the use of said data. We can thus observe the relative epistemic modesty of data scientists, who actively solicit the expertise of the usual custodians of the problem they seek to address through computation. What concrete variables should then be taken into account to define the "financial difficulties" whose occurrence they were trying to predict? For data scientists, the obvious thing to do was to define these difficulties based on the variables contained in the litigation databases: late payment, overdraft, etc.
According to them, such events, faithfully recorded in a dedicated database, were a rather plausible indicator of a customer's difficulty. However, this first version of the predictive model, which was submitted to the assessment of their colleagues, was widely criticized by customer advisor representatives. We started with that, because it's something that is traced in our information systems. But what we want to do is to avoid getting to that point, so we're discussing with the advisers, because this was the first deliverable they received after the various workshops. And now we think we're getting in rather late. [. ] What the advisers told us is: we want to avoid being in front of a client who is already in default, etc. This client, I can't help anymore: it becomes a litigation, a debt recovery.
The first challenge for them is to build the training environment in which machine learning algorithms will explore the most significant correlations, between a set of descriptive variables of customer behavior, and one (or several) "target variables" (for example: the customer's future financial difficulties). To this end, they need to unify multiple and heterogeneous data sources, often stored in variously accessible databases: transaction records, purchase histories, calls to technical support, litigation, current contracts, etc. In this way, data scientists seek to constitute what they call a "master file", or "ground truth"  (Jaton, 2017) , a table in which each customer is described by one single line, with its multiple descriptive variables in columns. This table materializes unique, calculable and therefore predictable individual behaviors.
e. its ability to be transposed and used outside the data laboratory. This is where data scientists deliberately organize the blindness of their algorithms. First, it means hide part of the data available for learning from the algorithm, in order to be able to "regularize" the predictions a posteriori. The master file is thus divided into two parts: the first one -the most important -is the training database for the algorithm; the second part will then be used to test the predictive potential of the model on new "unknown" data, which are then treated as if they were really new and unknown cases. The aim is then to obtain a comparable prediction rate for the training set and the regularization set. If the gap is too large, then a new work of selection and construction of new variables begins.
It thus enables it to build predictive models that can be transposed to future populations, not only valid for the training dataset. There are mathematical techniques that allow data to be randomly and artificially dragged down. So the algorithm doesn't learn well. So that it doesn't give too much weight to certain values. So that he doesn't learn everything by heart. Randomly, I'm going to introduce some error. When it tries to minimize the error, it basically tries to minimize the weights so that the error is as small as possible. So it adjusts the weights less finely. It does something coarser, less efficient, but more robust. You take off its glasses, so its vision becomes blurry: it can't recognize John Doe any more. Data scientist, Predicto (interview, May 2018)  This "dragging down" may include deliberately eliminating variables (or groups of variables) that are most closely correlated with the variable to be predicted, to prevent the algorithm from learning strong but punctual correlations, unlikely to occur in the real world with future data.
As illustrated schematically in Figure  1 , from the data points learned by the algorithm (in red), this trick prevents the algorithm from inferring a complex function linking all the points (the blue curve) but too specific to detect the general movement of the observed behaviors (the green one). The construction of predictions, far from relying on the mechanical "ingestion" of large datasets, involves complex work in which the data are centralized, aggregated and tested by data scientists. Paradoxically, the promise of personalization of data marketing implies during this phase to make the algorithms blind to some specificities of the customers, and sometimes to the most significant data points of their behaviors. The algorithm must not know too precisely the unique individuals included in its training environment, otherwise an overly personalized calculation will subsequently prevent it from effectively predicting the behaviors of new individuals.
Conclusion As we have seen from the cases presented here, mass personalization involves an iterative, back-and-forth process between the world and the data lab. Contrary to a widespread representation  (Darmody and Zwick, 2020; Steiner, 2012; Zuboff, 2018) , algorithms do not "manipulate" the social world from the outside, at the end of a linear process of learning, calculation and prediction. The world and the algorithms define each other, through the articulation work carried out not only by the "little hands" of the information society  (Bowker and Star, 1999; Dagiral and Peerbaye, 2012) , but also by data scientists. They centralize customer knowledge, harmonize the available data, interpret the results and reintegrate them into explanatory schemes specific to the predicted universes. As  Grosman and Reigeluth (2019)  point out, both learning objectives and training datasets are constructed, negotiated and adjusted by humans, themselves embedded in local knowledge regimes, organizations and cultures.
), it can be called upon by group entities, such as its regional branches. It can also take on subjects deemed innovative and important on its own. 4. Interview, Jul-18. All interviews are translated from French by the author. 5. Idem. 6. Idem. 7. At the Bank, "professional clients" are very small businesses, usually artisans. 8. The French National Institute for Statistics and Economic Studies. 9. A crucial issue in this harmonization work is the existence or absence of a unique identification key that can be used to circulate between multiple databases (customer service, litigation, sales, etc. ). 10. It does not mean by any way that data scientists cannot, if needed, produce mathematical estimations of gender or race based on stored data. See  Sweeney (2002). 11. In French : "plomber". 12.
We show that, as well as correction for motion and radiation damage, the use of the information within movies allows substantially improved direct estimation of the remaining key image parameters required for accurate 3D reconstruction: the image CTF and spectral SNR. We are developing "CARYON" {insert contrived acronym here}, as a LAFTER-family filter for cryo-EM movies based upon such measurements. CARYON is intended to provide the best parameter estimation and filtration possible for a single complete, or large sub-section from a, movie micrograph without the use of a previously refined density. We demonstrate its utility in both single-particle and tomographic cryo-EM data processing.
We therefore must assume that the astigmatism is negligible from the point of view of the resolution bands. This is unlikely to be a serious issue for the majority of applications as large amounts of astigmatism are considered unwise and typically avoided in cryo-EM data collection, however it is something that must considered when using CARYON. Thirdly, a further assumption is that the bandwidth over which the information is calculated is sufficiently smaller than the width of the CTF waveforms to allow them to be adequately represented. This issue can be avoided by up-sampling; however, it is worth noting that this is computationally intensive and has not been done in our reference implementation. Fourthly, we assume that the signal accrues linearly at each resolution with increasing exposure.
Reference implementation details We note that the CARYON process is computationally intensive. We believe that further improvements and wider adoption will eventually necessitate the incorporation of elements of the CARYON process into single particle refinement programs and other pre-processing software, e. g. CTF estimation or motion correction. {CARYON is currently prototyped in Python using NumPy and SciPy, and code is available for CTF estimation, motion correction and filtering on request}. Before publication we intend to provide a reference implementation of CARYON as an optimised C99 program using FFTW3 for Fourier transformation  (Frigo & Johnson, 2005) , and the GNU scientific library implementation of simplex minimisation  (Nelder & Mead, 1965 )), to maximise speed and portability.
CARYON operates on MRC mode 2 format image stacks  (Cheng et al, 2015) , i. e. C float or FORTRAN real. Source code for the CARYON reference implementation will be made available from the Imperial College Section for Structural Biology GitHub (github. com/StructuralBiology-ICLMedicine) under the GPL open source licence. CARYON can be run on any POSIX-compatible operating system, and will also eventually be made available in pre-compiled binary format for both Linux and Mac OS X as part of the CCP-EM suite  (Burnley et al, 2017).
Data used for testing For single particle analysis, we have used the Relion 3. 1 test-dataset. For tomographic testing we used tilt series collected as dose-fractionated multi-frame micrographs of the E. coli mini-cell strain WM4196  (Burt et al. 2020). CC-BY-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted November 26, 2020. ; https://doi. org/10. 1101/2020. 11. 25. 398891 doi: bioRxiv preprint.
Images filtered with CARYON are superior for particle picking, and particles extracted from CARYON images can be classified more readily Both CTF-deconvoluted and raw SNR filtered images can be generated with CARYON. Both types of CARYON output image provide improved visualisation of the features within the field of view and aid manual particle picking (Fig. 3a ). When automated blob particle picking software (BOXER) is applied to original and CARYON-filtered images, the CARYON images demonstrate substantially improved particle picking (Fig. 3a ). In contrast to neural network-based filters, CARYON filtered images can be used in singleparticle refinement software because they retain information to high resolution. Refinements from CARYON SNR-filtered images reach the same, or close to the same, final resolution as unfiltered data.
We believe that this will only have substantial benefits for single particle projects in extremely heterogenous cases, however, as refinement software can generate Wiener-deconvolution filters specific to the object of interest once refinement is convergent, which would be expected to perform better than filters estimated from the raw images alone. CARYON-filtered images demonstrate improved classification in 2D in comparison to raw input images (Fig. 3b ). {Comparisons in 3D classification remain ongoing}.
4b ). CARYON filtered tomograms, like single-particle images, will be suitable for sub-tomogram averaging, although we believe that this will once again only have substantial benefits in certain cases, as refinement software can generate Wienerdeconvolution filters specific to the object of interest from the current best estimate of the reconstructed density. CC-BY-ND 4. 0 International license perpetuity. It is made available under a preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in The copyright holder for this this version posted November 26, 2020. ; https://doi. org/10. 1101/2020. 11. 25. 398891 doi: bioRxiv preprint.
Here we show that CARYON filtered images can be used for both 2D and 3D refinement, as the high-resolution components of the images remain accurately represented, even while the representation of the low-resolution components has been improved for visualisation and particle selection. In particular, this could mitigate the need for production of a separate tomogram for visualisation and particle picking purposes in cryo-ET. {CTF deconvolution across tilted images has also been shown to be practicable and will be implemented before publication}. Furthermore, we show improvement for 2D classification of problematic data, in the case shown due to low particle number. This has potential benefits in cases where the data display compositional or conformational heterogeneity, both of which require resolution before a high-resolution structure, and therefore appropriate filter, can be ascertained.
Funding This work was supported by the  Wellcome Trust  and the  Royal Society  through a  Sir Henry Dale Fellowship  ( 206212/Z/17/Z ) to CHSA. AB was supported by a  European Union   Horizon 2020 research and innovation programme  under grant agreement No  647784  to  Irina Gutsche. CMP was supported by  Medical Research Council funding  ( MR/N009614/1 ).
OpenModelica is a unique large-scale integrated open-source Modelica-and FMI-based modeling, simulation, optimization, model-based analysis and development environment. Moreover, the OpenModelica environment provides a number of facilities such as debugging; optimization; visualization and 3D animation; web-based model editing and simulation; scripting from Modelica, Python, Julia, and Matlab; efficient simulation and co-simulation of FMI-based models; compilation for embedded systems; Modelica-UML integration; requirement verification; and generation of parallel code for multi-core architectures. The environment is based on the equation-based object-oriented Modelica language and currently uses the MetaModelica extended version of Modelica for its model compiler implementation. This overview paper gives an up-to-date description of the capabilities of the system, short overviews of used open source symbolic and numeric algorithms with pointers to published literature, tool integration aspects, some lessons learned, and the main vision behind its development.
Introduction The OpenModelica environment was the first open source Modelica environment supporting equationbased object-oriented modeling and simulation using the Modelica modeling language  (Fritzson and Engelson, 1998; Modelica Association, 2017; Fritzson, 2014). Its development started in 1997 resulting in the release of a flattening frontend for a core subset of Modelica 1. 0 in 1998  (Fritzson and Kågedal, 1998). After a pause of four years, the open source development resumed in 2002. A very early version of OpenModelica is described in  (Fritzson et al. , 2005). Since then the capabilities of OpenModelica have expanded enormously. The Open Source Modelica Consortium which supports the long-term development of OpenModelica was created in 2007, initially with seven founding organizations.
Goals for OpenModelica Providing a complete open source Modelica-based industrial-strength implementation of the Modelica language, including modeling and simulation of equation-based models, system optimization, and additional facilities in the programming/modeling environment. Providing an interactive computational environment for the Modelica language. It turns out that with support of appropriate tools and libraries, Modelica is very well suited as a computational language for development and execution of numerical algorithms, e. g. for control system design and for solving nonlinear equation systems. The research related goals and issues of the Open-Modelica open source implementation of a Modelica environment include, but are not limited to, the following: Development of a complete formal specification and reference implementation of Modelica, including both static and dynamic semantics.
Such a specification can be used to assist current and future Modelica implementers by providing a semantic reference, as a kind of reference implementation. Language design, e. g. to further extend the scope of the language, e. g. for use in diagnosis, structural analysis, system identification, integrated product development with requirement verification, etc. , as well as modeling problems that require partial differential equations. Language design to improve abstract properties such as expressiveness, orthogonality, declarativity, reuse, configurability, architectural properties, etc. Improved implementation techniques, e. g. to enhance the performance of compiled Modelica code by generating code for parallel hardware. Improved debugging support for equation-based languages such as Modelica, to make them even easier to use.