dataesr/scientific-paragraphs-categorization huggingface dataset:
Scope and Challenges Given the very large investment in particle physics experiments, it is incumbent upon physicists to preserve the data and the knowledge that leads to scientific results in a manner such that this investment is not lost to future generations of scientists. For preserving "data", at whatever stage of production, many of the aspects of the low level bit-wise preservation have been covered by the Data Preservation for HEP group  [133]. "Knowledge" preservation encompasses the more challenging aspects of retaining processing and analysis software, documentation, and other components necessary for reusing a given dataset. Preservation of this type can enable new analyses on older data, as well as a way to revisit the details of a result after publication. The latter can be especially important in resolving conflicts between published results, applying new theoretical assumptions, evaluating different theoretical models, or tuning new modeling techniques.
Current Practices Each of the LHC experiments has adopted a data access and/or data preservation policy, all of which can be found on the CERN Open Data Portal  [137]. All of the LHC experiments support public access to some subset of the data in a highly reduced data format for the purposes of outreach and education. CMS has gone one step further, releasing substantial datasets in an Analysis Object Data (AOD) format that can be used for new analyses. The current data release includes simulated data, virtual machines that can instantiate the added analysis examples, and extensive documentation  [138]. ALICE has promised to release 10% of their processed data after a five-year embargo and has released 2010 data at this time  [139]. LHCb is willing to make access to reconstructed data available, but is unable to commit to a specific timescale due to resource limitations.
Online material can be shared by a student and a teacher to provide the exchange of real examples and practical exercises. For example, notebook technologies, such as Jupyter, support embedding of runnable code and comments into the same document. The initial material can be easily enriched by allowing other students and experts to add comments and more examples in a collaborative way. The HSF started to experiment with this approach with WikiToLearn  [165] , a platform developed in Italy outside HEP that promotes this kind of training and collaborative enrichment of the training material. Projects such as ROOT have also started to provide some training material based on notebooks. A lot of initiatives have been undertaken by the software community that HEP can benefit from, and materials have been made available in the form of online tutorials, active training, and Massive Open Online Courses (MOOCs).
Conclusions Future challenges for High Energy Physics in the domain of software and computing are not simply an extrapolation of the challenges faced today. The needs of the HEP programme in the high luminosity era far exceed those that can be met by simply making incremental changes to today's code and scaling up computing facilities within the anticipated budget. At the same time, the limitation in single core CPU performance is making the landscape of computing hardware far more diverse and challenging to exploit, whilst offering huge performance boosts for suitable code. Exploiting parallelism and other new techniques, such as modern machine learning, offer great promise, but will require substantial work from the community to adapt to our problems. If there were any lingering notion that software or computing could be done cheaply by a few junior people for modern experimental programmes, it should now be thoroughly dispelled.
COOL LHC Conditions Database Project, a subproject of the POOL persistency framework. Concurrency Forum Software engineering is moving towards a paradigm shift in order to accommodate new CPU architectures with many cores, in which concurrency will play a more fundamental role in programming languages and libraries. The forum on concurrent programming models and frameworks aims to share knowledge among interested parties that work together to develop 'demonstrators' and agree on technology so that they can share code and compare results. CRSG Computing Resources Scrutiny Group, a WLCG committee in charge of scrutinizing and assessing LHC experiment yearly resource requests to prepare funding agency decisions. CSIRT Computer Security Incident Response Team. A CSIRT provides a reliable and trusted single point of contact for reporting computer security incidents and taking the appropriate measures in response tothem.
CVMFS The CERN Virtual Machine File System is a network file system based on HTTP and optimised to deliver experiment software in a fast, scalable, and reliable way through sophisticated caching strategies. CWP The Community White Paper (this document) is the result of an organised effort to describe the community strategy and a roadmap for software and computing R&D in HEP for the 2020s. This activity is organised under the umbrella of the HSF. Deep Learning (DL) one class of Machine Learning algorithms, based on a high number of neural network layers. DNN Deep Neural Network, class of neural networks with typically a large number of hidden layers through which data is processed. DPHEP The Data Preservation in HEP project is a collaboration for data preservation and long term analysis. EGI European Grid Initiative.
HEPData The Durham High Energy Physics Database is an open access repository for scattering data from experimental particle physics. HERWIG This is an event generator containing a wide range of Standard Model, Higgs and supersymmetric processes. It uses the parton-shower approach for initial-and final-state QCD radiation, including colour coherence effects and azimuthal correlations both within and between jets. HL-LHC The High Luminosity Large Hadron Collider is a proposed upgrade to the Large Hadron Collider to be made in 2026. The upgrade aims at increasing the luminosity of the machine by a factor of 10, up to 10 35 cm -2 s -1 , providing a better chance to see rare processes and improving statistically marginal measurements. HLT High Level Trigger. The computing resources, generally a large farm, close to the detector which process the events in real-time and select those who must be stored for further analysis.
LHCOPN LHC Optical Private Network. It is the private physical and IP network that connects the Tier0 and the Tier1 sites of the WLCG. MADEVENT This is a multi-purpose tree-level event generator. It is powered by the matrix element event generator MADGRAPH, which generates the amplitudes for all relevant sub-processes and produces the mappings for the integration over the phase space. Matplotlib This is a Python 2D plotting library that provides publication quality figures in a variety of hardcopy formats and interactive environments across platforms. ML Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. It focuses on prediction making through the use of computers and emcompasses a lot of algorithm classes (boosted decision trees, neural networks.
). MONARC A model of large scale distributed computing based on many regional centers, with a focus on LHC experiments at CERN. As part of the MONARC project, a simulation framework was developed that provides a design and optimisation tool. The MONARC model has been the initial reference for building the WLCG infrastructure and to organise the data transfers around it. OpenGL Open Graphics Library is a cross-language, cross-platform application programming interface(API) for rendering 2D and 3D vector graphics. The API is typically used to interact with a graphics processing unit(GPU), to achieve hardware-accelerated rendering. Openlab CERN openlab is a public-private partnership that accelerates the development of cutting-edge solutions for the worldwide LHC community and wider scientific research.
P5 The Particle Physics Project Prioritization Panel is a scientific advisory panel tasked with recommending plans for U. S. investment in particle physics research over the next ten years. PRNG A PseudoRandom Number Generator is an algorithm for generating a sequence of numbers whose properties approximate the properties of sequences of random numbers. PyROOT A Python extension module that allows the user to interact with any ROOT class from the Python interpreter. PYTHIA A program for the generation of high-energy physics events, i. e. , for the description of collisions at high energies between elementary particles such as e+, e-, p and pbar in various combinations. It contains theory and models for a number of physics aspects, including hard and soft interactions, parton distributions, initial-and final-state parton showers, multiparton interactions, fragmentation and decay.
It lets you easily write parallel C++ programs that take full advantage of multicore performance. TMVA The Toolkit for Multivariate Data Analysis with ROOT is a standalone project that provides a ROOT-integrated machine learning environment for the processing and parallel evaluation of sophisticated multivariate classification techniques. VecGeom The vectorised geometry library for particle-detector simulation. VO Virtual Organisation. A group of users sharing a common interest (for example, each LHC experiment is a VO), centrally managed, and used in particular as the basis for authorisations in the WLCG infrastructure. WebGL The Web Graphics Library is a JavaScript API for rendering interactive 2D and 3D graphics within any compatible web browser without the use of plug-ins. WLCG The Worldwide LHC Computing Grid project is a global collaboration of more than 170 computing centres in 42 countries, linking up national and international grid infrastructures.
The mission of the WLCG project is to provide global computing resources to store, distribute and analyse data generated by the Large Hadron Collider (LHC) at CERN. X. 509 A cryptographic standard which defines how to implement service security using electronic certificates, based on the use of a private and public key combination. It is widely used on web servers accessed using the https protocol and is the main authentication mechanism on the WLCG infrastructure. XRootD Software framework that is a fully generic suite for fast, low latency and scalable data access.
1. Mu2e estimates 4 year running starts mid-FY22 after 18 months commissioning 2. DUNE without beam operates in  FY25-FY26 FY26  ν µ p  FY26 DUNE FY25 FTBF FTBF FY24 FTBF FTBF FTBF FTBF FTBF FTBF FTBF FTBF FY16 FY17 FY18 FY19 SY 120   FTBF FTBF FTBF FTBF FTBF FTBF FTBF FTBF SeaQuest SeaQuest Muon Campus  g-2 g-Version 7a  FY23 FY24 FY25 OPEN OPEN LBNF LBNF / PIP II Mu2e Mu2e  b Vladimir V. Gligorov acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  724777  "RECEPT" c Supported by the  US-DOE ,  DE-AC02-07CH11359  d Supported by  FCT-Portugal ,  IF/00050/2013/CP1172/CT0002  e Supported by the  US-NSF ,  ACI-1450319  f Supported by the  US-NSF ,  PHY-1607578  g Supported by  ES-MINECO ,  FPA2016-80994-c2-1-R & MDM-2015-0509  h  Caterina Doglioni  acknowledges funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  under grant agreement No  679305  "DARKJETS" i Supported by the  US-DOE ,  DE-SC0009973  j Supported by the  ES-MINECO FPA2016-80994-C2-2 -R k Supported by the  US-DOE ,  DE-SC0018098  and  US-NSF   ACI-1558233  l Supported by the  US-DOE ,  DE-SC0007859  and  US-NSF ,  76749/1136652/2  m Supported by funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No.
Results We collected data from 14 subjects aged between 22 and 54 (mean = 27. 9, std = 7. 9). The illumination conditions sometimes disturbed the vision-based tracking performed by the Tango device, and we discarded these iterations (∼ 15%) from the analysed results. Figures  17(a)(b ) and (c) illustrate the results of ANOVA tests. The completion time (in seconds) was significantly lower in mode 1 (M = 22. 09, std = 8. 39) than in mode 2 (M = 27. 11, std = 11. 99): F (1, 13) = 3. 76, p = 0. 07, small effect size = 0. 22. In terms of positional precision, we found no significant differences between the two modes. However, orientation precision was better for mode 1 than mode 2. In the approach phase (i. e. the time in which Figure  15. The two guiding techniques compared in this study. The squared guiding pointer is shown on the left, and the pointer with compass on the right (the compass is the small cylindrical pointer at the bottom of the viewport).
, 1999; Gupta et al. , 1999; Masson et al. , 2003; Sargsyan et al. , 2014). Many of these parameters can be estimated directly in the field, but others are difficult or impossible to measure due to various complications such as the abstract representation of processes, technological limitations, or spatial/temporal aggregation  (Entekhabi and Eagleson, 1989; Kumar et al. , 2006). Parameters that are observable in the field are also often subject to large natural variability, including changes through space and time  (Wood et al. , 1992; Masson et al. , 2003; Fisher et al. , 2015). For example, vegetation parameters can be used to describe different root profiles  (Vrugt et al. , 2001; Zeng, 2001; Massoud et al. , 2019a)  or photosynthetic capacities  (Leuning, 2002; Rogers, 2014) ; however, model parameter values are often taken from literature publications or databases, and may not represent local variation or capture seasonal or ontogenetic changes.
5(FATES). To conduct the sensitivity analysis, we have extracted many parameters in the model that were "hard-wired" in CLM4. 5(FATES). The FAST algorithm requires valid ranges to be chosen for each parameter, which creates the possible parameter space to sample from. In theory, each parameter has a corresponding observational distribution that produces the ideal space for sampling  (LeBauer et al. , 2013). However, in this study, there are both a large parameter set and a scarcity of appropriate data sources for Amazonian forests for many of the relevant quantities; therefore, obtaining a robust data-supported distribution for each parameter was difficult. Because we only aim to understand the baseline model structure, the parameter ranges in this study were generated by applying a uniform distribution over a range that spans ±15 % of the default parameter values of CLM4.
The figures also show sensitivities of the remaining parameters in light grey (first-order sensitivity index for all other parameters) as well as the sensitivity of parameter interactions in dark grey (higher-order sensitivity index for all parameters). In this analysis, we assume the majority of CLM4. 5(FATES) parameters to be non-correlated with uniform probability because our study is focused on the model parametric sensitivity for model behaviors and there is a limitation of data for estimating covariance among the 80+ parameters. However, we do need to take care of the correlation among parameters in the temperature response functions (Appendix B) in order to generate realistic temperature response curves. These parameters are tested for correlation using a published dataset  (Leuning, 2002) , which showed that the photosynthetic parameters for activation energy (e.
2 ), we expected an improved parameterization of both allometry coefficient c and target carbon storage could help fit the model predictions to data. We compare our mortality simulations with an extensive dataset of observed mortality of 1781 species from 14 pantropical large-area ForestGEO forest dynamics plots  (Johnson et al. , 2018). For this study, the forest plots ranged from 2 to 52 ha each, with 371 ha in total, in which all recorded stems are ≥ 1 cm diameter at breast height. Our comparison shows that the CLM4. 5(FATES) simulations of medium and large tree mortality (Fig. 4c, d   0. 5 %-5. 7 % per year. However, for small and medium trees (Fig. 4b, c ), the simulated mortality rate of ∼ 15 %-30 % and ∼ 1 %-10 % is high when compared to the observed 95 % confidence interval of mortality rate of ∼ 0.
, 2011a, b) , there are still limited datasets with observations of multiple traits for the same species. Future uncertainty analysis studies that explicitly consider the prior distributions and correlations for all the parameters can build on this analysis and gain further insights on where the uncertainty in the model predictions comes from. Third, it is possible that the parameter sensitivity could be different if we use different model inputs, different sites, and different structures of subcomponents within the model. For example, using site-level climate drivers, instead of the reanalysis meteorological drivers used in this study  (Qian et al. , 2006) , could lead to different sensitivity values since our analysis showed that parameter importance is quite sensitive to different climate conditions.
Acknowledgements. Model simulations were made possible thanks to the Conejo supercomputing system at the  Los Alamos National Laboratory (LANL). We thank the four reviewers for their very helpful comments that substantially improved our manuscript. Financial support. This work was supported by the  United States Department of Energy (US DOE)   Office of Science Next Generation Ecosystem Experiment at Tropics (NGEE-T) project , the  DOE Graduate Student Researcher (SCGSR)  Fellowship, and the  UC-Lab Fees Research Program  (grant nos. 237285  and  LFR-18-542511 ). Shawn P. Serbin  was also partially supported by the  United States Department of Energy  contract no. DE-SC0012704  to  Brookhaven National Laboratory. A portion of  Elias C. Massoud 's contribution to this research was carried out at the  Jet Propulsion Laboratory, California Institute of Technology , under a contract with the  National Aeronautics and Space Administration, Copyright 2019.
Code and data availability. To access the FATES source code, visit https://github. com/NGEET/fates (last access: 15 May 2018). The FAST methodology described herein is available at https://sites. google. com/site/xuchongang/uasatoolbox (last access: 15 May 2018). The version of the model codes used in this paper and the corresponding model simulations from all 5000 parameter combinations as well as simulation of the default parameter set are available at the NGEE tropics data archive (https://doi. org/10. 15486/ngt/1497413, Massoud et al. , 2019b)  and also upon request from the corresponding authors.
Tabu Search Method of the First Search Stage The first search stage is ensured by a solution-based tabu search algorithm (denoted by T abuSearch 1 ()), whose pseudo-code is given in Algorithm 3. After initiating its tabu lists (lines 3-5), T abuSearch 1 () performs a number of iterations to improve the current solution until 1) a feasible solution is found and no improvement can be observed during α consecutive iterations, where α is a parameter called the depth of tabu search, or 2) the allowed maximum time limit t max is reached (lines 8-17 The ingredients of this tabu search algorithm, including the search space, the neighborhood structures and the tabu strategy, are respectively described in the following subsections.
Our TSTS algorithm employs four parameters, including γ 1 , γ 2 and γ 3 that are used to define the hash functions, and the depth α of tabu search used in the first search stage. The parameters γ 1 , γ 2 and γ 3 are set as in Table  1  according to the analysis shown in Section 4. 3 while α is empirically set to 5 × 10 3. In addition, our algorithm was implemented in C++ and compiled by g++ compiler with -O3 option 2. All computational experiments were carried out on a computer with an Intel E5-2670 processor (2. 5 GHz and 2G RAM), running the Linux operating system. Moreover, when running the DIMACS machine benchmark procedure dmclique 3 , our processor requires 0. 19, 1. 17, and 4. 54 seconds to solve the graphs r300. 5, r400. 5, and r500. 5, respectively. Finally, due to the stochastic nature of the algorithm, we independently ran the algorithm 30 times to solve each instance, where the time limit t max for each run was set to 60 seconds for instances with n ≤ 250 according to  [14].
The recent evolution of mobile communication systems toward a 5G network is associated with the search for new types of non-orthogonal modulations such as Sparse Code Multiple Access (SCMA). Such modulations are proposed in response to demands for increasing the number of connected users. SCMA is a non-orthogonal multiple access technique that offers improved Bit Error Rate (BER) performance and higher spectral efficiency than other comparable techniques, but these improvements come at the cost of complex decoders. There are many challenges in designing near-optimum high throughput SCMA decoders. This paper explores means to enhance the performance of SCMA decoders. To achieve this goal, various improvements to the MPA algorithms are proposed. They notably aim at adapting SCMA decoding to the Single Instruction Multiple Data (SIMD) paradigm.
A. OVERVIEW OF THE SCMA SYSTEM MODEL An SCMA encoder with J users (layers) and K physical resources is a function that maps a binary stream of data to K-dimensional complex constellations f : B log2(M ) → X, x = f (b) where X ⊂ C k. The K-dimensional complex codeword x is a sparse vector with N < K non-zero entries. Each layer j = 1,. , J has its own codebook to generate the desired codeword according to the binary input stream. Fig. 1  shows SCMA uplink chain with J = 6, K = 4 and N = 2. SCMA codewords are spread over K physical resources, such as OFDMA tones. Fig. 1a  shows that in the multiplexed scheme of SCMA, all chosen codewords of the J layers are added together after being multiplied by the channel coefficient h j. Then, the entire uplink chain is shown in Fig. 1b. The output of the SCMA encoder is affected by the white additive noise n.
B. CHARACTERIZING THROUGHPUT GAINS, ENERGY EFFICIENCY AND POWER CONSUMPTION Energy efficiency is of interest in the design of C-RAN servers. It is determined by the rate of computation that can be delivered by a processor. Joint optimization of the throughput and energy consumption is a main goal of system designers. Energy optimization can reduce the cost of cloud services significantly while it can contribute to decrease the emission of greenhouse gases. Power utilization is also important because improved performance per Watt is useful to limit power demands. This section explores the power,  energy efficiency and throughput of the various message passing algorithms suggested in this work. Tests have been conducted on three platforms running the Ubuntu Linux operating system. The three systems are : 1) an Intel TM Core-i7 6700HQ processor with AVX instructions (256bit SIMD) and four physical cores using 2-way Simultaneous Multi-Threading (SMT or Intel Hyper-Threading TM technology) running at nominal frequency of 2.
6 GHz, 2) an ARM TM Cortex-A57 with NEON instructions (128-bit SIMD) and four cores (no SMT) running at 2. 0 GHz and 3) an Intel TM Xeon-Phi Knight-Corner 7120P with KNCI instructions (512-bit SIMD) and 61 cores using 4-way SMT and running at 1. 2 GHz. Table  1  shows the comparison of throughput, latency, power consumption and energy of different decoding algorithms that are executed on the three platforms to decode 768 Million bits. The average power and energy consumption measured on the Core-i7 processor were obtained with the turbostat software  [26]  which exploits the Intel performance counters in Machine Specific Registers (MSRs) to monitor CPU and RAM utilizations. However, in the case of ARM and Xeon Phi platforms, external current sensors were used to measure the energy and power consumptions.