dataesr/scientific-paragraphs-categorization huggingface dataset:
ESI Fig. 4. † As such, the algorithm is also readily applicable to samples on a thin-film support, for example, nanoparticles. Only if the sample extends beyond the field of view, problems may arise, similar to other reconstruction algorithms such as SIRT. The algorithm can be readily applied to any type of signal fulfilling the projection requirement; in particular, its application to EELS data or to combined EDXS/EELS datasets is straightforward. For the material, the observed gradual transition at the interface between Yb-rich and Si-rich precipitates indicates that a side by side nucleation of the Yb and eutectic Si particles might happen. The diffusion interface between Yb and Si leads to stoichiometry changes (i. e. gradual transitions) of both phases depending on the solute and/or solid diffusion of Yb atoms.
52 To calculate the local density, we weighted the elemental densities of Yb (ρ Yb = 6. 9 g cm -3 ), Al (ρ Al = 2. 7 g cm -3 ) and Si (ρ Si = 2. 3 g cm -3 ) by their local atomic fraction. The thickness t equals the phantom pixel size of 0. 1 nm. The dose D e was calculated for an electron current of I e = 2 nA, a pixel dwell time of t px = 2 ms and the elemental charge of q = 1. 602 × 10 Si and Al were measured for our microscope; the zeta-factor for Yb is an estimation based on theoretical k-factors for the investigated elements. To obtain an estimate of the HAADF signal, we used tabulated values of the differential cross section @σ @Ω for elastic scattering from the NIST database,  53  integrating them over the angular detector range [θ min ,θ max ] with  54, 55  σ θmin;θmax ½ ¼ 2π ð θmax θmin @σ @Ω sin θdθ: ð5Þ For the inner and outer detector angles, we used θ min = 94.
For alignment in the direction of the tilt axis, common-line alignment is applied,  58  in the direction perpendicular to the tilt axis the center of mass of each projection is used for alignment and for tilt axis alignment, rotational centers are calculated based on the center of mass. 59 The alignment parameters calculated for the HAADF STEM data are applied to all datasets. Drift correction and alignment are done using MATLAB. Further pre-processing steps can be done using the Python software presented here. Intensity fluctuations can appear between different tilt angles. There are two main sources for these effects: partial shadowing of the detector and residual diffraction contrast. To reduce intensity variations between different projections, we rescale all projections to be non-negative and to have a common mean.
Note that for Ew we store each off-diagonal entry only once and, to account for that, count off-diagonal entries twice in the evaluation of the Frobenius norm of Ew. Discrete single-channel model. With the TGV functional in (10), the forward operator in (  8 ) and the discrepancy term in (9), we formulate the reconstruction of a single channel volumetric image as the following convex minimization problem: min u!0 μD KL Tu; f ð ÞþTGV α 2 u ð Þ: ð11Þ Problem (11) balances the discrepancy between the Radon transform Tu and the data f with the TGV α 2 (u) regularization, weighted by the regularization parameter μ > 0. Discrete multi-channel model. In order to jointly reconstruct density information and elemental maps and to take advantage of complementary information in these datasets, we propose a joint TGV regularization method for multichannel data.
The operators T c model the measurement operation for each channel and comprise the Radon transform as described above. In this context, we also note that in our experiments all data sets were acquired and reconstructed with the same resolution. Since acquisition of HAADF data is typically much faster than EDXS, an alternative would be to acquire the HAADF signal with a higher resolution and reconstruct all channels at this high resolution. This can be realized in our model by including an additional subsampling operator in the EDXS channels after the Radon transform and would allow a joint reconstruction and zooming of EDXS data. It is worth mentioning that our method can, of course, also be used to include more information such as EELS data, from which one can expect a further improvement due to additional information present in the data.
This will be highly beneficial in cases where elemental maps from EELS data have a better signal-to-noise ratio, or if, for example, valence states are mapped by EELS. We note, however, that assuming the forward operators for EDXS and EELS signals to coincide, such a coupled reconstruction is equivalent to first taking the (weighted) average of the EDXS and EELS signal for each material and then carrying out a reconstruction using the averaged data for the different materials together with the HAADF data.
Numerical solution In this section we present the derivation and implementation of a solution algorithm for (13), of which the single-channel setting (  11 ) is a special case. For further reference, we note that Graptorour Python/OpenCL  38, 39  implementation of the algorithmas well as a script reproducing all experiments of the paper is available online. 37, 68 Due to the non-differentiability of the objective functional, we employ a first order primaldual algorithm  66  to solve (13). In order to do so, the problem needs to be reformulated as a saddle-point problem to which the primal-dual algorithm is applicable. To achieve this aim, we first note that TGV α 2 can be re-written as TGV α 2 u ð Þ ¼ min w R ∇u À w; Ew ð Þ ; with R(φ,ψ): = α 1 k|ϕ| frob k 1 + α 0 k|ψ| frob k 1. Also, the constraint u c ≥ 0 for all c can be replaced by adding the functional I D u ð Þ to the energy in (13), where D ¼ fu ¼ ðu 1 ;.
A more elaborate stopping criterion that, in some cases, allows to compute the difference of the value of the objective functional to the optimal value could be based on the primal-dual gap of the problem. 44 However, this would require some additional computations during the algorithm and for the sake of simplicity and speed, we hence used a fixed number of iterations. In order to further accelerate the computations, we developed an implementation in Python  38  via OpenCL 39 for multicore CPUs and Graphical Processing Units (GPUs), which allows for highly parallel execution of all major computations. This is of particular relevance for the forward and backward projections, which are major contributors to the computational effort needed for Algorithm 1 and can be executed completely in parallel, leading to significant speedups.
Tables  2  and 3  illustrate the time required for reconstruction on a computer with an Nvidia(R) GeForce(R) GTX 980 graphic card and an Intel(R) Xeon(R) CPU E5-2690 processor in order to show the computational effort. Regarding the memory requirement, we note that our method in 3D essentially requires to hold, for each channel, 13 variables of image size and two variables of the data size in memory (ignoring temporary storage for computations). In comparison, TV regularization using the same algorithm essentially requires a memory that is 4 times the image size and two times the data size. Parameter choice. Here we comment on our parameter choices for the presented experiments and provide a guideline on how to choose parameters in practice. For the TGV parameters α = (α 0 , α 1 ), we note that only the ratio α 0 /α 1 is relevant and balances between the first and second order smoothness.
Acknowledgements The  Institute of Mathematics  and  Scientific Computing  is a member of NAWI Graz (http://www. nawigraz. at)and  BioTechMed Graz  (http://www. biotechmed. at). The Institute for Electron Microscopy and Nanoanalysis is also a member of NAWI Graz. KB, MH and RH acknowledge support from the  Austrian Science Fund (FWF)  (KB, RH: Grant No. P 29192, MH: Grant No. J 4112 ). RH acknowledges support from the  International Research Training Group IGDK 1754 Optimization  and  Numerical Analysis  for Partial Differential Equations with Nonsmooth Structures, funded by the  German Research Council (DFG)  and the  Austrian Science Fund (FWF)  [W  1244-N18 ]. This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No.
Simulation setup To implement these changes in the code, we started using FabFlee. The FabFlee tool  [12]  is an automation tool that facilitates the study of the effects of policy decisions on the refugees' situation, and relies on the Flee code for underlying simulations. FabFlee allows for easy changes in simulation settings, such as border closures, forced redirection, speed changes, etc. The basic workflow of the tool consists of (i) loading the conflict we want to work on, including locations, routes and conflicts information, (ii) performing desired changes to the simulation settings and (iii) instantiating the modified version of the conflict and run it to obtain the results. The advantage of performing multiple different simulations for the same conflict without having to manually edit every part of the code each time makes FabFlee well suited for studying the effects of food security on refugee migrations: instead of having to construct a complete simulation from scratch, we can just add a few modules to FabFlee to account for these modifications.
In this way we also ensure that both simulations (with the above-described modifications and without) run under the same general conditions. To include the proposed modifications themselves we incorporated a separate submodel in the original FLEE code  [1] , that either updates the move-chances of neutral locations or modifies the spawning probabilities of refugees over time. Starting from an appropriate CSV file containing the IPC index of the different regions at different times, The data-driven food security submodel updates the affected locations' parameters when a change occurs in the IPC index of their region as the simulation evolves. Lastly, we implemented new commands in FabFlee: one to run the simulation including the food security aspects, and one to generate side-by-side comparison graphs of key metrics between both types of simulations.
Cloud Storage To date, cloud storage remains restrictive in terms of confidentiality. In fact, the best-known provider (e. g GoogleDrive, Dropbox, iCloud, pCloud, OVH. ) do not ensure total confidentiality of their customers data. Either the data are encrypted under a key known by the cloud or stored in plaintext. Because these CSPs are considered as an all-trusted part. Several works exist that aim to protect privacy such as CryptDB  (Popa et al. , 2011)  but still have some limitations. For example: it cannot be used for no-sql databases or file systems, and key management complexity increase with the number of users. ESPRESSO  (Kang et al. , 2014)  delivers an encryption service for CSP to maintain confidentiality. It uses only symmetric encryption and requires to trust a third party or the CSPs to ensure the encryption and key management.
In this case, access privileges are not addressed to a set of users but only to users with a specific number of attributes. Unfortunately, this does not allow for a selective sharing. As an alternative to these solutions, we choose to use the proxy re-encryption(PRE). Thanks to this scheme, we will be able to delegate decryption rights to specific entities. We found out that it was used as a cloud based solution for file sharing called Skycryptor by  (Jivanyan et al. , 2015). Basically, the proposed solution uses a unique symmetric key for each file to be encrypted with AES and then encrypt the key with the asymmetric public key of the user generated thanks to the PRE algorithm. The solution is a dedicated device software and is now marketed under the name of BeSafe. Each user's device has its own key pair and re-encryption is used to share files between different devices or users.
But above all, the users must install the BeSafe software and use it to encrypt the data. The solution could be adapted to our problematic and used as a solution but a lot of changes need to be done. Instead, we propose the PREaaS which dosn't need any heavy client and which is more flexible, modular and transparent. We consider important to give definition and the state of art of the PRE so that the reader can better understand our choice of PRE as well as the rest of the paper.
Most importantly the scheme is unidirectional, collision resistant and CCA secure in the random oracle model. Two implementations were made. First we use a generic group with prime order length 3072-bit, and the second one using NIST Standard ECC p-256  (Gueron and Krasnov, 2015)  thanks to SJCL (Standford Javascript Crypto Library)  (Stark et al. , 2013). Both correspond to the same security level that is 128bit. We give a formal version of the Chow's algorithm with ECC below : • Setup(1 k ): for 128-bit, choose a prime p of 256-bit length and an elliptic curve E F p such that a = p -3. G = (x G , y G ), a point on the curve, known as the base point with order n. The elliptic curve equation is then : y 2 = x 3 -3x + b mod (p). (You can find the exact values of NIST p-256 curve parameters in  (Gueron and Krasnov, 2015) ).
[(H 4 (Pk b,2 ). Pk a,1 ) * Pk a,2 ]. -Compute F = H 2 (r. G) ⊕ (m||w). -Compute D = u. H 5 (Pk a,1 , Pk a,2 , D, E, F) & E = r. H 5 (Pk a,1 , Pk a,2 , D, E, F). -Compute s = u + r × H 3 (D, E, F) mod(p). -Return C a = (D, E, F, s). • ReEnc(C a , -Compute (h||π) = W ⊕ H 2 ( 1 Sk a,2. V ). - Extract (m||w) = F ⊕ H 2 ( 1 h. E ). -If : V =H 1 (h, π). Pk a,2 and E =(h × H 1 (m, w)). G return m , else return +. We chose to use JavaScript as a core technology. And thus to be executed in the client side directly by navigator or even mobile devices without any software and also in the server side, thanks to Nodejs. For the tests we used a 2,5 GHz intel core i7, with 16 GB RAM. Table  2. shows the time resources consumed by the different functions of Chow's algorithm for both  An API will be developed which will act as the gateway to our service functionalities.
Scientific innovation is increasingly reliant on data and computational resources. Much of today's life science research involves generating, processing, and reusing heterogeneous datasets that are growing exponentially in size. Demand for technical experts (data scientists and bioinformaticians) to process these data is at an all-time high, but these are not typically trained in good data management practices. That said, we have come a long way in the last decade, with funders, publishers, and researchers themselves making the case for open, interoperable data as a key component of an open science philosophy. In response, recognition of the FAIR Principles (that data should be Findable, Accessible, Interoperable and Reusable) has become commonplace. However, both technical and cultural challenges for the implementation of these principles still exist when storing, managing, analysing and disseminating both legacy and new data.
Introduction The importance of metadata Metadata, defined as "data about data", provides contextual information about data and is one of the central concepts required to allow the structuring of datasets in a way that allows automated search, query and retrieval. In the life sciences, this process is called biocuration  1. Naively, people tend to catalogue information within computer systems (and elsewhere) in ways that require subsequent interpretation. For example, naming a file "20190114_final-with-corrections. txt" reminds the author of certain properties of the data inside the file with very little overhead. Documents ending in the suffix ". txt", are generally written in plain text, but this structure is explicitly given, and must be assumed by the person wanting to reuse this data. Now consider a file called "dogs.
Providing structured metadata makes the context for a given piece of data explicit. Consider a table in a database called "dog_behaviour_observations" which keeps behavioural observations in tabular (row and column) form. Each row of the table represents a single observation, and each column represents a variable about that observation, such as "behaviour_observation_id", "behaviour_type", "breed", "date_observed", "video_link", "duration". Entries within this database now have a structure to the data, and researchers generating observation data have a (relatively) strict format to adhere to when providing data to the resource. However, this methodology still has issues. The names of the variables (column headers in the database) may not be standardised or self-describing, e. g. the variable "duration" might relate to an observation in a number of ways, but without further documentation, it is impossible to know this for sure.
"Duration" may also be measured in different ways with different units, and the processes of measuring the duration of an observation may not have been recorded or standardised. Furthermore, without a way of validating the input to this database, errors may find their way in. Whilst databases do have a minimal form of validation in the form of field data types (e. g. "behaviour_observation_id" might be required to be a non-negative whole number, "video_link" a string of fewer than 255 characters, etc,), these constraints provide almost no semantic insight into the meaning of a field. This type of validation also does not take broader context into account, adding sparse metadata to a dataset. By using well-defined vocabulary terms for field names and values, we can easily provide information such as: -What was the breed of the dog, based on an agreed set of dog breed names? -How were the researchers measuring the impact of a given behaviour on other dogs, based on an agreed measurement technique? -What was the weather like on the day of the test, based on standard meteorological descriptions? -Are the individuals observed related, based on a common vocabulary of familial relationships? The ability to ask and answer questions such as these may well affect the ability of another researcher to find and reuse this dataset.
The role of ontologies for interoperability Common vocabularies, standards, and dictionaries of agreed terms, or "facts", can be represented as ontologies, i. e. formal descriptions of a domain of knowledge 2. Ontologies define axioms, i. e. terms and the relationships between them, that can be used to construct more complex statements of knowledge. For example, we are interested in finding information on crop trials involving Chinese Spring wheat in sub-Saharan Africa. Our first problem is one of vocabulary. There is not a single shared name for this geographical region: we could search for "sub-saharan Africa", "Africa (sub-saharan)", or one of many other permutations. The name of the data field containing this information could also be varied e. g. "Location", "Region", or "Country". If trial data is not standardised across experiments, aggregating searches across multiple datasets quickly becomes a time-consuming manual task, if not impossible.
However, by labelling with unambiguous ontology terms, we can specify exactly what we mean, thus increasing the usefulness of our data to other researchers. Figure  1  shows an example of multiple datasets being aggregated in such a way. Three columns from three different spreadsheets are shown, where the field names "Location", "region" and "LOC" could be resolved by a curator to the same ontology term "LOCATION", telling us that they are labelling the same entity, i. e. a geographical area. The values "Great Rift Valley" and "GRV" also resolve to the same term, meaning that a common vocabulary is used, removing ambiguity. Similarly, incomplete and erroneous data can also be corrected to a degree. For instance, a curator would likely assume the value "centra;" to be both incomplete and misspelt.
However, resolving it to the term "CENTRAL AFRICA" overcomes these inconsistencies. Data is often messy and needs active curation to resolve issues, either through manual editing or through the use of specific software. Tools such as OpenRefine 3 are designed to facet human-readable terms and quickly highlight discrepancies. Ontology terms are typically arranged into a directed acyclic graph (DAG) structure based on a model of a domain, where the nodes are the entities and the edges are the properties or relations between the entities. Part of the model may include a hierarchy, which allows us to infer context even when it is not explicitly provided. To continue the example, although none of the data in Figure  1  are labelled with the term "Sub-Saharan Africa", through the contextual hierarchy of the ontology (the graph defines "Sub-Saharan Africa" as composed of three more specific terms "Central Africa", "West Africa", and "East Africa") a human or a machine is able to easily infer that all these data are part of "Sub-Saharan Africa".
As such, advancing FAIR requires not just technical, but also cultural, considerations. For example, as discussed above, the use of defined ontology terms to annotate data and data services helps to render data finadable and interoperable -computers can be used to search, aggregate, and integrate data based on the occurrence of compatible terms (e. g. overlapping concepts or synonyms) between datasets. However, life science datasets tend to be disparate, except for a few large, curated, public data repositories (e. g. INSDC data warehouse resources, FlyBase, WormBase, Ensembl Genomes, etc. ) The databases and data hosting services that house them may not mandate ontology terms for data description upon submission. Nor may publishers or funders, even those with mature Open Access policies that govern data as well as other research outputs.
New approaches, such as BioSchemas  17  , are attempting to exploit the ubiquity of internet search engines and their metadata harvesting and indexing capacity to expose data through standard search results. Once datasets are discoverable, interoperability then relies on exposure of data through standard application programming interfaces (APIs) of which there are active and notable examples in the life sciences  18, 19. However, data management tools, especially those used to annotate and describe data assets, tend not to be designed for typical biologists. Rather they are often designed for the small number of professional biocurators, and this may deter other researchers whose participation is essential if curation capacity is to scale to match the growth of data production. Scientific institutions often lack these biocuration experts who could advise researchers  20.
Researchers need help to biocurate Recording metadata for structured FAIR data remains difficult for researchers, and represents the "last mile" in scientific data publishing  21. We believe the solution is to help users to publish FAIR data through user-friendly systems and workflows explicitly designed for that purpose. Alongside these, training and guidance about the ontologies suitable to annotate their data, and repositories suitable to house it, should be provided. COPO is a platform for researchers to publish their research assets, providing metadata annotation and deposition capability. It allows researchers to describe their datasets according to community standards and broker the submission of such data to appropriate repositories whilst tracking the resulting accessions/identifiers.