dataesr/scientific-paragraphs-categorization huggingface dataset:
Three different transformations (those that generate the space) relate each triangle (i. e. , each triad) with each contiguous neighbour. As the standard Tonnetz is a cyclic and connected space (the aforementioned torus), any pair of triangles can be related by means of a finite series of combinations of these only three transformations generating the space. As highlighted above, how music evolves over time is a key issue in transformational music theory. Consequently, these kinds of transformations have been sometimes described as metaphors of motion by music theorists  (Attas, 2009). Musical software like Tonnetz allows to perceptually reify this metaphor, and therefore to observe and potentially describe the way a user conceives them.
Adapting Tonnetz for Empirical Psychology Experiences We are currently developing a variant of the standard Tonnetz, in order to collect quantitative and qualitative data for discussing the cognitive features of human interaction with its environment. Several chord sequences will be submitted to listeners of different expertise, in the double format of sounds and coloured patterns. All these sequences are parsimonious (i. e. any pair of contiguous triads share two of their three respective pitches), but the last triad does not necessarily follow a parsimonious movement, as it happens for instance in the sequence of Figure  13. There, pitch names on the vertices will be erased to avoid potential bias, and the first six chords are associated with red (for major chords) and blue triangles (for minor chords).
In our study, we are going adapt the ME-MS scales to the participants' mother languages  (Schubert et al. , 2014)  for a population of trained musicians and scientific professionals to explore some potential specificities in achieving a "mathemusical" goal. While no significant difference between these groups is awaited at a first stage without training, we expect to disclose a number of correlations between the preferred cognitive style and the amount and specificity of the improvements, after some training at a second stage. In a follow-on experiment, a group of university students will be involved, adhering to a very similar protocol. In the future, we plan to consider the dual representation of the Tonnetz, as well as musical sequences related to non-standard Tonnetze or even more general related structures  (Tymoczko, 2012).
ACKNOWLEDGEMENTS This research has been partially carried on within the  ProAppMaMu  project that receives financial support from the  CNRS  through the  MITI interdisciplinary programs. In addition  José L. Besada  is currently supported by the Atracción del talento investigador grant (ref. 2019-T2/HUM-14477 ) from the Madrid region, and  Corentin Guichaoua  by the  European Union 's  Horizon 2020 research and innovation program  (ref. 788960) under the  ERC   ADG  project  COSMOS.
Indeed, the availability of High Performance Computing (HPC) implementations of such models within popular packages such as NAMD  18  or GROMACS  19  for Drude or Tinker-HP  20  for AMOEBA fosters the diffusion of these new generation techniques within the research community. This paper is dedicated to the evolution of the Tinker-HP package. 20 The software, which is part of the Tinker distribution,  21  was initially introduced as a double precision massively parallel MPI addition to Tinker dedicated to the acceleration of the various PFFs and non-polarizable force fields (n-PFFs) present within the Tinker package. The code was shown to be really efficient, being able to scale on up to tens of thousand cores on modern petascale supercomputers. 20, 22 Recently, it has been optimized on various platforms taking advantage of vectorization and of the evolution of the recent CPUs (Central Processing Units).
22 However, in the last 15 years, the field has been increasingly using GPUs (Graphic Processor Unit)  [23] [24] [25]  taking advantage of low precision arithmetic. Indeed, such platforms offer important computing capabilities at both low cost and high energy efficiency allowing for reaching routine microsecond simulations on standard GPUs cards with pair potentials. 24, 26 garding the AMOEBA polarizable force field, the OpenMM package  27  was the first to propose an AMOEBA-GPU library that was extensively used within Tinker through the Tinker-OpenMM GPU interface. 28 The present contribution aims to address two goals: i) the design of an efficient native Tinker-HP GPU implementation; ii) the HPC optimization in a massively parallel context to address both the use of research laboratories clusters and modern multi-GPUs pre-exascale supercomputer systems.
The paper is organized as follows. First, we will describe our OpenACC port and its efficiency in double precision. After observing the limitations of this implementation regarding the use of single precision, we introduce a new CUDA approach and detail the various parts of the code it concerns after a careful study of the precision. In both cases, we present benchmarks of the new code on illustrative large biosystems of increasing size on various NVIDIA platforms (including RTX 2080Ti, 3090, Tesla V100 and A100 cards). Then, we explore how to run on even larger systems and optimize memory management by making use of latest tools such as NVSHMEM. 29 enACC Approach.
Then, within each timestep, positions of the atoms and forces are exchanged between processes before and after the computation of the forces. Additionally, small communications are required after the update of the positions to deal with the fact that an atom can change of subdomain during a timestep. This workflow is described in detail in reference. 31  recent years a new paradigm has emerged to facilitate computation and programming on GPU devices. In the rest of the text, we will denote as kernel the smallest piece of code made of instructions designed for a unique purpose. Thus, a succession of kernels might constitute a routine and a program can be seen as a collection of routines designed for a specific purpose. There are two types of kernels • Serial kernels, mostly used for variable configuration • Loops kernels, operating on multiple data sets This programming style, named OpenACC ,  32, 33  is a directive-based language similar to the multi-threading OpenMP paradigm with an additional complexity level.
Since a target kernel is destined to be executed on GPUs , it becomes crucial to manage data between both GPU and CPU platforms. At the most elementary level, OpenACC compiler interacts on a standard host (CPU ) kernel and generates a device (GPU ) kernel using directives implemented to describe its parallelism along with clauses to manage global data behaviour at both entry and exit point and/or kernel launch configuration [Figure  1 ]. This method offers two majors benefits. Unlike the low-level CUDA programming language,  34  it takes only a few directives to generate a device kernel. Secondly, the same kernel is compatible with both platforms -CPUs and GPUs -. The portability along with all the associated benefits such as host debug is therefore ensured. However, there are some immediate drawbacks mainly because CPUs and GPUs do not share the same architecture, specifications and features.
In summary, we should expect at least a performance or precision improvement from FP. A practical verification is shown in [Figure  10 ]. In all cases, both MP and FP behave similarly. Forces being the driving components of MD, the trajectories generated by our mixed precision implementation are accurate. However, as one can see, if errors remain very low for forces even for large systems, a larger error exists for energies, a phenomenon observed in all previous MD GPUs implementations. Some specific post-treatment computations, like in a BAR free energy computation or NPT Simulations with a Monte-Carlo barostat, require accurate energies. In such a situation, one could use the DP capabilities of the code for this post-processing step as Tinker-HP remains exceptionally efficient in DP even for large systems.
Available Features The main features of Tinker-HP have been offloaded to GPU such as its various integrators like the multi-timestep integrators: RESPA1 and BAOAB-RESPA1  49  which allow up to a 10 fs timestep with PFF (this required to create new neighbor lists to perform short-range non-bonded interactions computations for both van der Waals and electrostatics). Aside from Langevin integrators, we ported the Bussi  43  (which is the default) and the Berendsen thermostats, as well as the Monte-Carlo and the Berendsen barostats. We also ported free energy methods such as the Steered Molecular Dynamics 50 and van der Waals soft cores for alchemical transformations, as well as the enhanced sampling method Gaussian Accelerated Molecular Dynamics. 51 Even if it is not the main goal of our implementation as well optimized software suited to such simulations exist, we also ported the routines necessary to use standard non-polarizable force fields such as CHARMM, 4 Amber 5 or OPLS.
To do that, we rely on a NVHSMEM feature which consists in storing a symmetric memory allocation address in a pointer on which arithmetic operations can be done. Then, depending on the address returned by the pointer, either a global memory access (GBA) or a remote memory access (RMA) is instructed to fetch the data. The implementation requires a Fortran interface to be operational since NVSHMEM source code is written in the C language. Moreover, an additional operation is required for every allocation performed by the NVSHMEM specific allocator to make the data allocated accessible through OpenACC kernels. Such a singular approach affects performances since additional communications have to be made inside kernels. Furthermore, all communications do not follow a special pattern that would leave room for optimizations, meaning that each device accesses data randomly from the others depending on the atoms involved in the interactions it needs to compute.
25 ns/day on a complete node of Jean Zay on the same system. We also had the opportunity to test our implementation on the latest generation NVIDIA GPU Ampere architecture: the Selene supercomputer which is made of nodes consisting in DGX-A100 servers. A DGX-A100 server contains eight A100 graphic cards with 40 GB of memory each and with latest generation inter-connection NVIDIA Switches. The results we obtained on such a node with the same systems as above in the same RESPA and BAOAB-RESPA1 framework are listed in [Table  8 ] and [Figure  19 ]. We observe an average of 50% of performance gain for systems larger than 100 000 atoms on a single A100 compared to a single V100 card. Also, the more efficient interconnection between cards (NV-switch compared to NV-link) allows to scale better on several GPUs with the best performances ever obtained with our code on all the benchmark systems, the larger ones making use of all the 8 cards of the node.
Conclusion We presented the native Tinker-HP multi-GPUs -multi-precision acceleration platform. The new code is shown to be accurate and scalable across multiple GPUs cards offering unprecedented performances and new capabilities to deal with long timescale simulations on large realistic systems using polarizable force fields such as AMOEBA. The approach strongly reduces the time to solution offering to achieve routine simulations that would have require thousands of CPUs on a single GPU card. Overall, the GPU -accelerated Tinker-HP reaches the best performances ever obtained for AMOEBA simulations and extends the applicability of polarizable force fields. The package is shown to be compatible with various computer GPU system architectures ranging from research laboratories to modern supercomputers.
Acknowledgements This work was made possible thanks to funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  810367 ), project  EMC2. This project was initiated in 2019 with a " Contrat de Progrès " grant from  GENCI (France)  in collaboration with HPE and NVIDIA to port Tinker-HP on the Jean Zay HPE SGI 8600 GPUs system (IDRIS supercomputer center,  GENCI-CNRS, Orsay, France ) using OpenACC. FC acknowledges funding from the  French  state funds managed by the  CalSimLab LABEX  and the  ANR  within the  Investissements d'Avenir program  (reference  ANR11-IDEX-0004-02 ) and support from the  Direction Génerale de l'Armement (DGA) Maîtrise NRBC of the French Ministry of Defense. Computations have been performed at  GENCI  on the Jean Zay machine (IDRIS) on grant no  A0070707671  and on the  Irène Joliot Curie ATOS Sequana X1000 supercomputer  ( TGCC, Bruyères le Chatel, CEA, France ) thanks to PRACE COVID-19 special allocation (projet COVID-HP).
Currently, errors and incomplete data exist in distribution system operator (DSO) databases  [11] ; these uncertainties impact the accuracy of the voltage profile. DSOs consider a margin of error (Figure  1 ) to avoid voltage violations. Enedis, the main French DSO, mentions, for instance, a margin of 1. 5% in  [12]. This margin is considered to be 2% in this paper. Due to model errors, the voltage profile must be within a nominal voltage of ±8%. With a more accurate network model, it is possible to develop new management tools (e. g. , state estimation and demand response) to limit reinforcements and to use LV networks as close to their limits as possible (i. e. , suppressing error margins). Moreover, the surrounding environment also has an impact on the network operation. Considering the.
Finite Element Method on the French LV Cable To compute LV cable impedance, the Finite Element Method Magnetics (FEMM) software  [27] , a two-dimension finite element software that solves electrical, magnetic, and thermal problems was used. The magnetic module was used to determine the cable impedance matrix. Standard values for external parameters were chosen for the model, such as a ground electrical resistivity of 100 Ωm, a cable temperature of 20 • C (i. e. , an aluminum electrical resistivity of 3. 068 × 10 -8 Ωm), a frequency of 50 Hz, an insulator (PVC) electrical resistivity of 10 16  Ωm, and a cable length of 1000 m. A fine mesh was used around and inside the cable (Figure  5a ) to get an accurate impedance value. Ωm, and a cable length of 1000 m. A fine mesh was used around and inside the cable (Figure  5a ) to get an accurate impedance value.
The single cable had an average temperature of 13 • C in the winter and 42 • C in the summer. The four cables had an average temperature of 41 • C in the winter and 66 • C in the summer. The daily variations in the summer were lower than in the winter, mainly because of the winter consumption peaks, often dimensioning for LV networks. Networks with four feeders had a higher temperature all year round than networks with only one feeder, with a four-fold increase in the number of thermal sources. This innovative model realized under COMSOL software has identified the impact of external factors on the external parameters in several cases. By defining the network simulation cases, it was possible to obtain the value of the cable temperature without having to create an analytical model. Hence, the external parameters were not directly considered, and network configurations were preferred to obtain the cable temperature.
The initial soil temperature at a depth of one meter was 50 °C in the summer and 20 °C in the winter. With all these assumptions, the temperature of cables in different configurations is presented in Figure  11. After ten days of simulation, a steady regime was reached for the four simulations. The single cable had an average temperature of 13 °C in the winter and 42 °C in the summer. The four cables had an average temperature of 41 °C in the winter and 66 °C in the summer. The daily variations in the summer were lower than in the winter, mainly because of the winter consumption peaks, often dimensioning for LV networks. Networks with four feeders had a higher temperature all year round than networks with only one feeder, with a four-fold increase in the number of thermal sources. This innovative model realized under COMSOL software has identified the impact of external factors on the external parameters in several cases.
By defining the network simulation cases, it was possible to obtain the value of the cable temperature without having to create an analytical model. Hence, the external parameters were not directly considered, and network configurations were preferred to obtain the cable temperature. Nevertheless, the previously developed model had some limitations. Ref. [33]  experimentally measured the temperature of an MV cable. The authors observed that the temperature was not uniform along the cable. To determine more accurately the temperature of the cable, and thus the proper resistance of the cable, a three-dimensional model under COMSOL was necessary. Nevertheless, this model reached the limits of time and precision of the software, with the dimensions of the cable being tiny compared with the size Nevertheless, the previously developed model had some limitations.
Test Cases In order to make the study results easily transferable, IEEE test feeders are the most relevant networks to take. However, the cable impedance of these test feeders is defined with the S CM and not the 3 CM, 4 CM or 4 CMext. The transformation of the S CM matrix to 3 CM, 4 CM or 4 CMext is excluded. Therefore, the use of these feeders is impossible to compare cable models. Different LV networks, part of the distribution dataset used in the CGMES 2. 4. 1 interoperability tests performed in 2016  [34] , are considered for this paper. This dataset gathered urban, semi-rural, and rural networks. A network of each type was selected to examine the impact of the cable model and the external parameters. Figure  14  describes the topology of the rural 8-bus, semi-rural 49-bus, and the urban 139-bus network.
These three networks are adaptations of dataset networks, including service cables. Network characteristics are provided in Table  9. No producer was plugged to the semi-rural network; the two others included PV production. Different LV networks, part of the distribution dataset used in the CGMES 2. 4. 1 interoperability tests performed in 2016  [34] , are considered for this paper. This dataset gathered urban, semi-rural, and rural networks. A network of each type was selected to examine the impact of the cable model and the external parameters. Figure  14  describes the topology of the rural 8-bus, semi-rural 49-bus, and the urban 139-bus network. These three networks are adaptations of dataset networks, including service cables. Network characteristics are provided in Table  9. No producer was plugged to the semi-rural network; the two others included PV production.
Concerning external parameters, 3 CM and 4 CM in the winter and summer defined common values for external parameters: earthing resistances of 1 Ω at each node, a constant cable temperature of 20 °C, and ground electrical resistivity of 100 Ωm. The 4 CMext in the winter and summer defined more realistic external parameters: earthing resistances between 10 and 160 Ω at some network nodes, a cable temperature between 15 and 65 °C, and ground electrical resistivity between 30 and 500 Ωm. The cable temperature was deduced from the COMSOL model in Section 2. 3. 2. Data selection for 4 CMext in the winter and summer was also dependent on the network type. These ranges for the external parameters are taken from Section 2. 3. 3 T cable = 20 • C T cable = 20 • C T cable = 20 • C ρ g = 100 Ωm ρ g = 100 Ωm ρ g = 100 Ωm winter profile winter profile winter profile.
Voltage Limits The different simulation data have been defined in the previous sections. The load flow is computed by EPRI's OpenDSS [37], and Matlab does data analysis. Figure  16  shows 24 ⋅ 3 voltage profiles as a histogram for each model (3 CM, 4 CM and 4 CMext) for a given case and network. For instance, in Figure  16a , the network is rural (8 nodes) and the case is summer. For this pair, 24 instants (blue curve of Figure  15 ) and 3 phases (line-to-neutral voltage magnitudes for 4 CM and 4 CMext and line-to-ground voltage magnitudes for 3 CM) are considered. Thus, the voltage magnitude of 3 phases for the 24 instants and the 8 nodes are represented in the histogram. In total, 8 ⋅ 3 ⋅ 24 = 576 instants for each model (bleu for 3 CM, red for 4 CM and yellow for 4 CMext) are presented in Figure  16a.
Consumption and production profiles were specified using the data from  [35, 36]. The maximum of   with respect to time, was used: Load flows were computed over a week with a time step of 30 min (336 points), to see the impact of earthing resistances on several consumption points. Consumption and production profiles were specified using the data from  [35, 36]. The maximum of MV ME a with respect to time, was used: 𝑀𝑀𝑉𝑀𝐸 𝑎 = max 𝑡 max 𝑘 |𝑣 𝑎𝑛 𝑘,𝑡 -𝑣 ̌𝑎𝑔 𝑘,𝑡 | (23) MMV ME a = max t max k v k,t an -vk,t ag (23) Table  12  gives MMV ME a for the 49-bus network. Between 3 CM and 4 CM/4 CMext, the deviation depends on the value of the earthing resistance. On the one hand, if the earthing resistance was small, the two models were very close. On the other hand, if the earthing resistance was about 100 Ω, the deviation was above 0.
, 3 CM and 4 CM in the winter and summer defined common values for external parameters: earthing resistances of 1 Ω at each node, a constant cable temperature of 20 • C, and ground electrical resistivity of 100 Ωm. The 4 CMext in the winter and summer defined more realistic external parameters: earthing resistances between 10 and 160 Ω at some network nodes, a cable temperature between 15 and 65 • C, and ground electrical resistivity between 30 and 500 Ωm. The cable temperature was deduced from the COMSOL model in Section 2. 3. 2. Data selection for 4 CMext in the.
Secondary Endpoints The percentage of re-epithelialization will be assessed through clinical inspection and computer-assisted image analysis (iMitoWound digital imaging software), at 1, 3, 6, 9, and 12 months by the Investigator and a trained Independent Assessor. The absence of sub-blisters in the treated areas will be evaluated by Optical Coherence Tomography (OCT) at the end of the study. Changes in quality of treated area(s), from baseline to 12 months follow-up, will be analyzed according to the Epidermolysis Bullosa Disease Activity and Scarring Index (EBDASI) validated questionnaire  (Loh et al. , 2014). The incidence of adverse events (TEAEs), serious adverse events (SAEs), event of special interest (AESI), and adverse drug reactions (ADRs), vital signs and laboratory parameters will also be analyzed as secondary safety endpoints.
All clinical and molecular data previously collected from the patients will be analyzed (Visit 1-2). Whenever possible, previous testing that is documented and satisfies screening requirements will be considered, in order to limit the number of skin biopsies and the quantity of blood required from the patient. At Visit 1, the complete medical and surgical history of the patient is recorded. Skin physical examination will be performed and digital skin photographs will be collected using digital imaging software (iMitoWound; Figure  3 ). All areas suitable for transplantation will be scored using EBDASI (Visit 1 to Visit 4). After ICF/IAF signature, AE will be registered and assessed through the study (up to Visit 11) as well as any changes in concomitant medications. A self-monitoring disease assessment has been introduced in this study in order to provide a fast track record and report every adverse event.