dataesr/scientific-paragraphs-categorization huggingface dataset:
The presence of such large reversals is a practical and visible indication that the constraint is not relevant for the data and that OCHAC should not be used. Dendrograms and clusterings. The same type of conclusion can be drawn when comparing not just the heights of the dendrograms but the dendrograms themselves or the clusterings induced by these dendrograms. Figure  8  shows the distribution of a measure of similarity between the order of fusion in the dendrogram. More precisely, the cophenetic distances have been computed for all pairs of objects in the dendrograms induced by standard HAC and OCHAC at different levels of perturbation and the Spearman correlation betwen these two vectors of cophenetic distances (coming from the constrained and the unconstrained version of the algorithm) has been obtained.
Actually, for small perturbation levels, these reversals in m t values help improve the quality of further clusterings by choosing a solution that is less efficient than that of standard HAC but more consistent with the data (as already discussed in the example of Figure  2 ). Hence, when the data structure is consistent with the constraint, (m t ) t typically provides an interpretable dendrogram. This nice property is, of course, lost when the constraint is no more consistent with the data structure (above a perturbation level of 60%), which is explained by the fact that the OCHAC has a poor performance in that context, as already discussed in the previous section. On the contrary, (I t ) t and ( Īt ) t exhibit larger numbers of reversals. This is particularly the case for the last mergers, even for small levels of perturbation and even in the unconstrained case: 40-60% of the simulations have reversals for both OCHAC and standard HAC at a number of clusters corresponding to the selected clustering.
We also observe that the percentage of simulations showing a reversal for standard HAC tends to decrease when the perturbation level in the data increases for the first steps of the hierarchical process (the same can be observed, to a much lesser extent, for OCHAC). This phenomenon is explained below. Figure  10  displays the evolution of the merged cluster size thorough the hierarchical clustering and provides an explanation for this fact. For standard HAC, the number of clusters with a size equal to 2 during the first steps of the algorithm is strongly increasing when the perturbation level increases. For a permutation level of 90%, most of the mergers have a size equal to 2 during half of the clustering process (for fusion numbers ranging from 1 to at least 2,000). However, for clusters with a size equal to 2, I t is equal to m t which explains the similiarities between m t and I t curves during the first steps of the clustering process, as the perturbation level increases.
Introduction AlterBBN is a public C program for the calculation of the abundance of the elements generated during Big-Bang nucleosynthesis (BBN), released under the GPL version 3 license. The first version was released in 2011  [1]  and could be considered as a spiritual successor of NUC123  [2]. Contrary to other public BBN codes such as the Fortran program PArthENoPE  [3, 4]  or the Mathematica code PRIMAT  [5] , the main purpose of AlterBBN is to provide a fast and reliable calculation of the abundance of the elements in the standard model of cosmology as well as in alternative scenarios. AlterBBN is also included in the SuperIso Relic package  [6] [7] [8]. AlterBBN can be downloaded from its new website: https://alterbbn. hepforge. org/ and involves an enlarged development team. In AlterBBN v2, automatic calculation of errors and correlations has been implemented using methods similar to the ones described in Refs.
[9, 10]. To improve the speed of the calculations, parallel processing is possible through the OpenMP library. In addition, the units throughout the code have been unified to have GeV as the main unit, in order to stay consistent with SuperIso Relic. The nuclear reaction network has also been extended, and the code has been scrutinised and improved for precision and speed. Moreover, new cosmological scenarios have been implemented in AlterBBN, such as reheating, decaying primordial scalar field, and WIMPs. The rest of this paper is organised as follows. Section 2 provides a review of BBN physics and the cosmological modifications implemented in AlterBBN. Section 3 describes the content of the AlterBBN package. Section 4 gives usage instructions. Section 5 describes the input and output of AlterBBN.
Additional neutrino species The AlterBBN program leaves the possibility to modify the number of Standard Model neutrino species through the value of N ν (see Eq. (2. 10)). It is possible to add equivalent neutrinos to this number through a contribution ∆N ν : N tot = N ν + ∆N ν. In addition, there could be a neutrino degeneracy between the 3 neutrino species of the Standard Model, which leads to non-zero adimensioned chemical potentials ξ ν 1 , ξ ν 2 and ξ ν 3 , which are not necessarily equal. This will have two consequences. The first one is a modification of Eq. (2. 10) for the computation of the neutrino density. The exact statistical mechanics formula is, for each species i = 1, 2, 3: ρ ν i ,ν i = 1 2π 2 T 4 ν +∞ 0 dx x 3 1 + exp(x ∓ ξ ν i ) , ( 2. 35) which for small degeneracies (ξ ν i < 0.
Content of the AlterBBN package The folder alterbbn_v2. X/ contains the 9 main programs of AlterBBN: • stand_cosmo. c, • alter_eta. c, • alter_neutrinos. c, • alter_neutron. c, • alter_etannutau. c, • alter_standmod. c, • alter_reheating. c, • alter_phi. c, • alter_wimps. c, together with a README file, a Makefile file and a folder alterbbn_v2. X/src/. The folder alterbbn_v2. X/src/ contains the source files: • bbn. c, • bbnrate. c, • general. c, • cosmodel. c, together with the files include. h, numbers. h, bbn. h, bbnrate. h -containing the headers of all the program routines -and Makefile. There is also a folder alterBBN_v2. X/src/sgStar containing numerically computed tables for h eff (T ) and g eff (T ), mentioned in Sections 2. 2. 1 and 2. 2. 3, and a folder alterBBN_v2. X/src/ contrib/newreac/ which contains routines to include reactions and isotopes from the REACLIB database  [21]  into AlterBBN, as explained in Section 3.
Parameter structure There are two main parameter structures in the program AlterBBN, defined in the file include. h. The first one is:  and it contains all the parameters necessary to compute the BBN abundances of the elements, both in standard cosmology and in alternative cosmologies. This structure is common with SuperIso Relic  [6] [7] [8]  and some of its parameters are not used in AlterBBN. typedef struct relicparam /* /*---------------------*/ /* AlterBBN parameters */ /*---------------------* The second one is: typedef struct errorparam { int failsafe; int errnumber; double random[]; double life_neutron; } errorparam; and it contains the parameters needed to give the estimated errors linked to the computed abundances. AlterBBN has different modes to compute the abundance of the elements, de-termined by the failsafe variable of the relicparam structure: 0 corresponds to a fast but less precise calculation, and positive values to more precise but slower calculations.
REACLIB reactions By default, AlterBBN incorporates 26 elements and 100 nuclear reactions. A new module has been added in the latest version to easily include more elements and reactions from the JINA REACLIB database  [21]. The routines are contained in the directory src/contrib/newreac/. To generate a new set of isotopes and reactions, the program has to be compiled with make, which creates create_network. x, and run with three parameters and one optional one: name of the WINVN file, name of the REACLIB database file, maximal atomic mass to be kept, and the optional parameter can be 0, 1 or 2, which selects all the isotopes with an atomic mass smaller than the maximal value, removes the isotopes with very small abundances after BBN, or removes the isotopes with very small abundances during BBN, respectively.
The program generates three files, numbers. h, bbn. h and bbnrate. h, which contain the REACLIB parameters and are symbolically linked into src/. In the packages, the latest WINVN and READLIB files from http://reaclib. jinaweb. org/ are included. Running. /create_network. x winvn_v2. 0. dat results02200820. dat 30 1 adds 19 elements and 101 nuclear reactions. This set of new reactions is included by default in AlterBBN, but can be easily modified by running create_network. x.
Compilation and installation instructions AlterBBN has been written in C respecting the C99 standard, and it has been tested with the GNU C and the Intel C compilers on Linux, Windows and Mac. The package can be downloaded at the address: https://alterbbn. hepforge. org/ The package should be unpacked in the desired directory, creating the main directory alterbbn_vX. X/ containing the material described in Section 3. If needed, the user's C compiler information and flags can be specified in the file Makefile in this main directory. In particular, some of the computations are made in parallel using the OpenMP library. The user should comment the corresponding lines if this library is not installed. More information is provided in the README file. To activate the extra REACLIB reactions in AlterBBN (see Section 3.
Standard cosmology The program stand_cosmo. x computes the BBN abundances of the nuclei as well as the associated errors and correlation matrix in the standard cosmological model. It takes one integer as argument; if it is 0 a fast calculation is done, larger values provide slower but more precise calculations. A description of the possible integration methods is given in Sections B and C. The values of the baryon-to-photon ratio, the neutron lifetime and the number of neutrinos species are those fixed by the Init_cosmomodel routine. Running the program with:. /stand_cosmo. x 3 returns: Yp H2/H He3/H Li7/H Li6/H Be7/H low: 2. 473e-01 2. 522e-05 1. 024e-05 5. 034e-10 1. 685e-15 4. 751e-10 cent: 2. 472e-01 2. 459e-05 1. 033e-05 5. 382e-10 1. 083e-14 5. 094e-10 high: 2. 471e-01 2. 399e-05 1. 044e-05 5.
WIMP scenario The program alter_wimps. x computes the BBN abundances, errors and correlations in a cosmological scenario where WIMPs are added to the Standard Model particles. It takes 3 input arguments, which are the type of WIMP particle (1: real scalar, 2: complex scalar, 3: Majorana fermion, 4: Dirac fermion), the type of coupling to the Standard Model particles (1: neutrinos, 2: EM, 3: equivalent neutrinos) and m χ (to be given in MeV) (see Section 2. 2. 5). An additional parameter can be given to specify the integration method. Running the program with:. /alter_wimp. x 2 2 15. returns: Yp H2/H He3/H Li7/H Li6/H Be7/H value: 2. 473e-01 2. 421e-05 1. 028e-05 5. 496e-10 1. 067e-14 5. 212e-10 +/-: 3. 183e-04 5. 985e-07 1. 712e-07 3. 839e-11 1. 065e-14 3. 800e-11 Compatible with BBN constraints (conservative limits) Compatible with BBN constraints (chi2 including correlations).
With this hardware in any given physical equipment, the virtualization process allows one to share and encapsulate the physical resources among a set of VMs by following a pattern similar to the master-slave model  [26] , in which the (real) server hosts different VMs. This capability, made possible by the virtualization technology, allows the computational resources in the virtualization layer to be considered unlimited resources, while the physical resources or hardware infrastructure are limited to the real infrastructure. The only disadvantage of this technology is that the management of the virtual environment requires a highly-specialized software called hypervisor  [27]. This component is responsible for managing the hardware abstraction in each physical node, and this also consumes resources  [28].
Since this memory can grow considerably as a maintenance strategy, a high-speed, schema-free database, based on MongoDB, is used to provide rapid access to the stored data. The reallocation process is initiated by the SSA associated with the service that is experiencing difficulties in responding to demand, as shown in Figure  4. This figure shows a hypothetical case of communication among the agents presented in Figure  3 , where service k is deployed on virtual nodes hosted by three servers (PR1,2 and i) that have been represented as vertical lines. The servers host their agents and processes as described. The communication between agents and other processes is indicated by arrows. The aim of the communication is to find a solution-S(P)-to service demand. When a service k is experiencing difficulties, the associated SSA alerts all physical machines (that host the nodes of the service) that a new allocation of resources is required to handle the demand, as shown in the messages m2 sent from the SSA to the remaining agents of the service in Figure  4.
Evaluation of the Proposed Model In order to evaluate and validate the model proposed in this article, a CC platform designed and developed by the BISITE research group (http://bisite. usal. es. ) has been used. This CC platform was deployed in the HPC (High Performance Computing) environment, which offers numerous services and is composed of 15 machines that support virtualization in hardware with the use of Intel-VT technology and the KVM (Kernel-based Virtual Machine) virtualization system. MAS is implemented using the Python programming language because of its power, ease of maintenance and flexibility in handling data structures. In this regard, the data exchange format is based on JSON (JavaScript Object Notation). The web service layer has been implemented using the Tornado web development framework because of its ability to manage a large number of client connections.
However, the proposed model simplifies the search for an adequate solution to the problem, as (i) the computational needs are distributed among different nodes; (ii) the value space to be considered is smaller since each node must only consider the data regarding its own resources and, furthermore, it is not necessary to have global knowledge of the platform; and, finally, (iii) each node can autonomously apply a partial solution to the problem, eliminating the coordination needs at the global level of the platform. Finally, the clear advantage of the proposed model with respect to other works is its capacity for learning. In the macro-level infrastructure distribution model, it has been used as an adaptation algorithm based on a case-based reasoning system that is integrated into a specialized agent in the +Cloud organization.
However, in this case, the MAS architecture overcomes these challenges; thanks to its adaptive design, it is possible to modify the individual agents that carry out specific actions in the MAS. This approach ensures independence between the software layers in which decisions are made and those in which the decisions are executed. In a CC environment, such separation of responsibilities is particularly important because today's platforms are highly dependent on the technological environment (virtualization tools, load balancers, distributed file systems, etc. ). This dependence constitutes a great limitation and hinders the platform's evolution, since a change in some of the hardware or software components also makes it necessary to alter the algorithms and techniques that make the system elastic.
The Dementias Platform UK Data Portal is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal is an end-to-end data management solution providing a secure, fully auditable, remote access environment for the analysis of cohort data. All projects utilising the data are by default collaborations with the cohort research teams generating the data. The Data Portal uses UK Secure eResearch Platform infrastructure to provide three core utilities: data discovery, access, and analysis. These are delivered using a 7 layered architecture comprising: data ingestion, data curation, platform interoperability, data discovery, access brokerage, data analysis and knowledge preservation. Automated, streamlined, and standardised procedures reduce the administrative burden for all stakeholders, particularly for requests involving multiple independent datasets, where a single request may be forwarded to multiple data controllers.
Introduction Here we describe the Dementias Platform UK (DPUK) Data Portal (https ://porta l. demen tiasp latfo rm. uk/)  [1]. The Data Portal is a collaboration between DPUK and a growing number of cohort research teams who wish to make their data globally accessible. DPUK was established by the Medical Research Council (MRC) to accelerate the development of new treatments for dementia. The Data Portal is a component infrastructure of DPUK, designed to exploit the opportunities provided by cohort data to inform experimental medicine and to improve access to cohort data more widely. It is a data repository facilitating access to data for 3 370 929 individuals in 42 cohorts. The Data Portal provides a secure, fully auditable, remote access environment for the analysis of cohort data. The Data Portal supports the FAIR principles (Findability, Accessibility, Interoperability and Reusability) to improve the infrastructure supporting the re-use of data  [2].
Arguments for multi-cohort-focused data repositories include:  (1)  as research questions focus on smaller effect sizes, access is required to data at-scale to achieve statistical purchase, (2) as emerging research questions become more complex, access to diverse multi-modal data is needed for rigorous hypothesis testing, (3) as scientific rigour increases there is growing recognition of the value of triangulation and replication using independent datasets, (4) as cohort datasets increase in size the transfer of large datasets is decreasingly feasible, (5) as cohort datasets become more complex the mastering of bespoke data models for survey, omics (genomics, proteomics and metabolomics), imaging and device data becomes burdensome,  (6)  as cohort datasets become more sensitive the non-auditable use of data is decreasingly acceptable.
Layer 1: Data ingestion The data journey begins with upload to the Data Portal. Datasets and data dictionaries are received from cohorts on an 'as-is' basis along with other supporting documentation. The Data Portal operates within the UK Secure eResearch Platform (UKSeRP) environment according to ISO 27001 [3] and operates exclusively as a data processor according to the UK Data Protection Act 2018  [4]  and EU General Data Protection Regulation 2016  [5]. DPUK facilitates the legal engagements necessary for data transfer into the Data Portal on behalf of data controllers, by ensuring robust contractual arrangements are in place as an overarching mechanism for data governance and use  [6]. In practice the data controller is considered to be the principal investigator for the dataset. A dataset is removed from the Data Portal upon receipt of a wet-ink signature request from the data controller.
Layer 2: Data curation Upon receipt, 'native' data are curated to a common data model (C-Surv). The C-Surv ontology is designed to simplify the analytic challenge of working across multiple datasets and multiple modalities by providing standard structure, variable naming and value labelling conventions. Other data models, CDISC  [7] , OMOP [8], or HPO  [9]  involve structural complexity that is rarely relevant to cohort-based analyses. An example variable name using the C-Surv model is given below: The cohort is identified using a three digit alphabetic character (GEN for Generation Scotland), and category-level data by a two digit numeric character (05 for physical health status). The measure is described by an alphanumeric acronym (PAINCHESTEVR for: Do you ever get pain or discomfort in your chest?).
Layer 3: Interoperability We anticipate a global mixed-model data access environment that respects sovereign boundaries and requires the highest levels of security. In practice, this involves the integration of individual participant data across datasets ('pooled' analyses) and the integration of summary data across datasets ('federated' analyses). Both models require interoperability between data platforms across national boundaries. DPUK supports the development of interoperability in several ways. In partnership with UKSeRP, we provide access to our software solutions to other data platforms to facilitate architectural compatibility. We also engage with other data platforms to develop interoperability across architectures. In collaboration with Gates.
Layer 4: Data discovery A tiered data discovery pathway begins with the Cohort Matrix  [11]  which provides a high-level comparison of data availability for each cohort. The Cohort Directory  [12]  enables detailed exploration across cohorts using a range of metadata categories. The Cohort Explorer [13] provides access to a set of 30 variables harmonised across cohorts to enable feasibility analysis. Adding cohorts to the Cohort Explorer is ongoing. As the Cohort Explorer provides access to harmonised data, login to the Data Portal is required. Data discovery is a dynamic area and the development of increasingly ergonomic and intuitive tools is anticipated. This is likely to include the identification of high priority variable sets and variable selection algorithms through the analysis of user-activity and the development of application programming interfaces (APIs) to rapidly reproduce data selection (see  Layer 7).
High Performance Computing and non-Windows operating systems are available upon request. The Data Portal also offers consortia-based workspace; providing an independent and transparent storage, access and analysis solution for use by multiple institutions. Two-factor authentication is required to access approved datasets. This involves the provision of a username with password creation, and an authentication code generated by an app on a mobile device of the applicant's choosing. Data may not be removed from the Data Portal. However, tables, graphs and scripts for export are submitted to the data export panel for approval. Manuscripts may be prepared in the Data Portal so that collaborators who are registered users may contribute without the need for manuscript download. A facility for import is also available, enabling researchers to upload scripts and additional datasets from outside the Data Portal to reside within their approved DPUK datasets.
Layer 7: Knowledge environment Cohort data grow in quantity and can be complex, but ultimately there is the need for the transition from data to knowledge and insight. In response to this challenge, the Data Portal is developing a Knowledge Hub to organise and make information readily accessible on the key activities of the Data Portal. The immediate purpose of the Knowledge Hub is to enable collaborative knowledge building; enabling researchers to understand what is available, what is known, and where there are knowledge gaps. It also serves as a knowledge preservation repository enabling the storage of linked datasets and analysis code with persistent identifiers for rapid access and replication. DPUK welcomes collaboration in the development of this hub. Application for access can be made through the Data Portal: https ://porta l.