dataesr/scientific-paragraphs-categorization huggingface dataset:
Form and derivation of the SimRNA energy function The energy function of SimRNA is composed of statistical potential terms, derived from the observed frequencies of occurrence of various proximate structural patterns (basebase contacts, short backbone fragments, etc. ). To compute the statistical potential, a manually curated set of RNA 3D structures was selected from the Protein Data Bank. We intentionally separated the set of structures used for the derivation of the potential from those used for testing SimRNA (see below). During the initial step, we selected RNA structures obtained by X-ray diffraction (of resolution higher than 3. 2 Å and more than 20 residues long). We analyzed all these structures in detail, and excluded ones that contained large gaps or where the conformation of the RNA molecule was significantly influenced by interactions with other molecules (e.
g. , proteins). In order to remove sequences that were closely similar to each other, we used the BlastClust tool (ftp://ftp. ncbi. nih. gov/blast/documents/ blastclust. html) from the NCBI-BLAST package  (34) , with a 50% sequence identity threshold. From each cluster, we selected only one structure solved at the highest resolution. For ribosomal RNA, we manually selected five structures solved at highest resolution (PDB ids: 1n32, 3i1m, 3cc2, 3kni, 3i1p). From the resulting data set, we also removed sequences with 50% or more identity to RNA molecules in the two previously published test sets  (8, 10) , which we used for testing of our method (see below). The resulting data set contained 150 structures in total (21238 residues in total) and the data set was used to extract the statistical prefer-ences for base-base and base-backbone interactions, which in turn were used to infer the corresponding terms of the statistical potential.
Conformational sampling method Sampling of the conformational space is accomplished in SimRNA by the use of an asymmetric Metropolis algorithm  (30) , which is executed by calling either of two schemes: single thread simulations or replica exchange Monte Carlo. The single thread variant allows for performing isothermal simulations and simulations with a gradual increase or decrease of temperature; e. g. , to study RNA unfolding. Conformational changes are accomplished via a specific set of moves (Figure  2 ). There are two basic types of moves. First, there is an exchange of a single nucleoside conformer by another one (from an internal database of conformers), which changes the orientation of the base with respect to the backbone. Second, there is an alteration of the backbone conformation, associated with maintaining the conformations of the base moieties in their local backbone coordinates.
Secondary structure restraints can be specified using the multiline dots-and-brackets format, which allows for defining RNA pseudoknots. The dots-and-brackets input is parsed and internally converted into the dedicated list of restraints. The output of a simulation is recorded as a trajectory file (or set of files) comprising the lowest-energy conformations selected from a consecutive series of simulation steps. Sim-RNA is accompanied by a software package for the processing of trajectory files. The content of the trajectory files (in the form of individual frames or a series of such frames) can be visualized, converted to PDB files, searched for structures with desired properties (lowest global energy, lowest RMSD to a reference structure), or subjected to clustering. The trajectory can be converted to a series of files in PDB format containing models in either the reduced SimRNA representation or models rebuilt to an all-atom representation.
RESULTS The ability of SimRNA to fold RNA sequences into nativelike 3D structures has been tested on five benchmark sets of experimentally determined RNA structures. The first data set  (10) , hereafter referred to as 'Ding et al. data set', comprises 153 structures of single-chain RNAs. The length of sequences in this test set varies from 20 to 100 nucleotide residues; however, the majority of sequences are shorter than 50 nt, and some of the sequences are redundant (e. g. , 1cq5 and 1cql). In this data set, 145 structures were obtained from nuclear magnetic resonance (NMR) spectroscopy, and only eight were obtained from X-ray crystallography. Most of these structures are relatively simple; nonetheless, they contain a variety of structural motifs such as three-and four-way junctions, kink-turns and pseudo-knots.
Five structures (1esy, 1kka, 1qwa, 28sp, 2f88) are common to both the Ding et al. and Das&Baker sets, and the structure 1evv is common to the Ding et al. and Seetin&Mathews sets). The fourth data set consists of short 3D motifs used to test the FARFAR method (39), which will be referred to as the 'motifs data set', and the fifth set is taken from the RNA Puzzles challenge (Puzzles 1-6, 8, 10 and 12  (13, 40 )) and will be called the 'RNA Puzzles data set'. For all sequences in the benchmark sets, we carried out tertiary structure prediction by de novo folding with SimRNA (folding using sequence alone) as well as folding with restraints on the secondary structure, obtained from the target structures using RNAView  (36). For the Seetin&Mathews and RNA Puzzles data sets we also predicted structures using restraints on both secondary structure and tertiary contacts, to mimic the predictions reported in these original works  (13, 38, 40).
The motifs data set contained only short segments of RNA 3D structures, so the structures could only be tested with the end parts of the structure restrained. For each prediction, we carried out eight independent runs of the Replica Exchange Monte Carlo simulation, each employing 10 replicas. Each run comprised 1000 simulation intervals (16000 steps each) and the lowest energy frame from each interval was recorded. The resulting eight trajectories were combined with each other to yield 80000 conformations per target (1000 conformations from each of the 10 replicas in each of the 8 simulation runs) and the best 1% scored conformations from the set were retrieved and clustered (see Methods for details). The assessment of RNA structures requires analysis of both the global conformation, and the local features such as interaction patterns  (41).
RNA 3D structure prediction without any restraints The results of the tests clearly show that SimRNA performed well in predicting both simple and complex RNA structures from sequence information alone, without restraints on the secondary or tertiary structure. Predictions generated by SimRNA (see Supplementary As expected, the results from de novo folding of the Seetin&Mathews and RNA Puzzles data sets were predictably lower: INF roughly 60% for all contacts including stacking, 50% for canonical pairs and 20-30% for noncanonical pairs. These structures are generally very difficult to model, which is why the RNA Puzzles challenge is so important for the community of researchers working on RNA 3D structure prediction  (13, 40). For the Das&Baker data set, in 7 cases out of 10, Sim-RNA generated more accurate predictions (medoids of largest clusters) than the ones reported by Das&Baker.
If only one best-scored model is considered per target, 138/153 (90%) and 9/10 (90%) significantly correct predictions were obtained for single-chain RNAs in Ding et al. and Das&Baker data sets, respectively. Only in one case (2a9l structure), the energy criterion alone allowed us to ob-tain a significantly correct prediction in the absence of a correct prediction in the first cluster; however, in this case the second cluster medoid was significantly correct. On average, models selected by clustering were more accurate than models selected based on energy alone (110/153 cases, and 13/20 cases in Ding et al. and Das&Baker data sets, respectively). In one case where a single-chain RNA was folded without any restraints (2evy in the Ding et al. data set), Sim-RNA failed to produce any conformations that could be evaluated as significantly correct.
Folding of RNA 3D structure motifs Finally, we analyzed the ability of SimRNA to predict the structure of short RNA 3D motifs from the data set used by Das&Baker to test FARFAR. In this data set, the structures were relatively small, but many of them comprised multiple chains, and were often derived from larger structures that did not correspond to autonomously folded structural units. For this data set, we performed simulations with the base-paired termini of all chains restrained to reproduce the context of each motif, scrambled the structure (including the base-pair termini) using a very high initial temperature and allowed SimRNA to predict the internal structure of the motif. Here, the main question was the ability of the program to predict non-canonical interactions. Data from Supplementary Table  S1  demonstrate that, in general, models generated by SimRNA had low RMSDs relative to the native structures, and ideal or nearly ideal inferred canonical base pairs.
Hence, various simplifications must be employed. A particularly successful simplification used for protein structure prediction has been coarse-graining, in which an atomistic description of a molecular system is replaced with a less complex model, where groups of atoms are treated as single interaction centers  (50). The development of a coarse-grained model is challenging, because the reduction in detail of the representation must be accompanied by modifications of the energy function to capture the key interactions that are responsible for folding: kinetics and thermodynamics. SimRNA is a new coarse-grained RNA model, in which the explicit representation has been reduced to five atoms per ribonucleotide residue, and in which the physical energy function has been approximated by a statistical potential derived from a database of experimentally determined structures.
Second, because the energy function is rooted in statistics, SimRNA best recapitulates the structural motifs that are most frequent; i. e. , canonical base pairs and stacking. Non-canonical interactions, especially the rare ones, are not scored as highly favorable, and they are very difficult to capture. Both of these issues can be addressed by introducing a high-resolution refinement of SimRNA-generated models, with an energy function that takes into account the true strength of the interactions and does not penalize interactions that are statistically rare, but physically strong. We have already developed an independent computer program QRNAS dedicated to such refinement (J. M. B. and Juliusz Stasiewicz, unpublished data) and we demonstrated its applicability in the context of the RNA Puzzles experiment  (40).
Another solution to be tested and potentially implemented in the future would be to rescale the relative as well as the absolute strengths of interactions represented in SimRNA according to values determined experimentally as well as values that could be obtained from simulations of RNA molecules with fine-grained methods and high-end physical force fields. Finally, folding of large RNA molecules with SimRNA is computationally demanding, as the program has to sample many different 3D architectures. Thus, modeling of large RNA structures with Sim-RNA may be jump-started by using starting models generated by other modeling methods developed to predict the global architecture; e. g. , by comparative modeling  (6, 54)  or by sampling of helical topologies  (55, 56) , and the conformational space to be sampled may be restricted by the use of additional restraints  (57).
AVAILABILITY SimRNA is written in C++ and currently is only available for the Linux and MacOSX operating systems. A Windows version is also planned. The source code of SimRNA is not distributed due to intellectual property restrictions. Compiled Linux binaries for Intel and AMD (32 bit and 64 bit) are available from http://genesilico. pl/simrna/. The multiprocessor code requires openmp. MacOSX binaries are compiled with OSX. 6/7 support and can run on most Mac-Book Pro and Air distributions. Users interested in obtaining compiled binaries in some other distribution must contact the authors. The use of a compiled version of SimRNA is free for non-commercial use by academic users. Nonacademic users and those interested in commercial use contact J. M. B. to obtain a commercial license.
ACKNOWLEDGEMENTS We thank  Nikolay Dokholyan  for providing software to calculate HCS, and  Francois Major  for providing software for INF calculations. We would also like to thank  Albert Bogdanowicz ,  Lukasz Kozlowski ,  Marcin Magnus ,  Piotr Pokarowski ,  Claus Seidel  and  Juliusz Stasiewicz  for stimulating discussions and/or for critical reading of the manuscript. We also thank  Jan Kogut  and  Tomasz Jarzynka  for maintaining computational facilities in IIMCB and  Lukasz Munio  for the SimRNA web page.
FUNDING Polish Ministry of Science  [ HISZPANIA/152/2006  to J. M. B. ;  PBZ/MNiSW/07/2006  to M. B. ]; European Commission  [ 6FP GA No LSHG-CT-2005-518238  to  Reinhard L ührmann ,  7FP GA No 316125  to  Jacek Kuźnicki ];  German Research Foundation (DFG)  [GA No SPP  1258  to Claus Seidel];  European Research Council (ERC)  [ StG grant   RNA+P = 123D  to J. M. B;  Foundation for Polish Science (FNP)  [ TEAM/2009-4/2  to J. M. B. and 'Ideas for Poland' fellowships to J. M. B. ]. Computing power was provided by  IIMCB , funded by  EU structural funds  [ POIG. 02. 03. 00-00-003/09  to  J. M. B. ]. Funding  for open access charge:  European Commission  [7FP grant Fishmed, GA No  316125  to  Jacek Kuźnicki ].
illustrates the case of a pseudoknotted RNA: gene 32 messenger RNA pseudoknot of bacteriophage T2, (PDB id: 2tpk, 36 residues). SimRNA was able to identify a nativelike 3D structure (largest cluster of solutions), with secondary structure identical to that of the experimentally determined reference. It is noteworthy that alternative nonpseudoknotted hairpin-loop structures also emerged as well (clusters 2 and 3), which exhibited low energies, but could be successfully discriminated from the correct solution. The analysis of the folding trajectories provided useful insight not only into the final folded 3D structure, but also into the structures of potential folding intermediates. In the Sim-RNA simulations of.
Consequently, such a platform requires a maximal power consumption of 0. 1W per core. In order to achieve this ambitious goal, alternative low power processor architectures are required. In this context, two main directions are currently explored: (1) relying on accelerators and co-processors (either General-Purpose Graphics Processing Unit (GPGPU) such as the Nvidia Tesla cards, or Many Integrated Coress (MICs) e. g. Intel Xeon Phi) or (2) using the low-power processors (ARM, Intel Atom etc. ) primarily designed for the mobile and embedded devices market. In parallel, the Cloud Computing (CC) paradigm has emerged as a promising approach to consolidate in a cost-effective way existing computing platforms so that many companies and researchers are engaged in efforts of scaling system software to meet the requirements of diversifying on-line cloud applications and services.
Intranode devices The intranode group is composed of highly customized hardware instrumentation tools, such as the PowerMon line of devices  [22] , placed between a node's power supply and mainboard; or the PowerInsight device  [57] , designed for component-level instrumentation of commodity hardware; the ARM Energy Probe  [15] , integrated with the ARM development toolchain; and the Linux Energy Attribution and Accounting Platform (LEA 2 P)  [73].
Hardware sensors Many recent components offer a number of built-in sensors able to directly report consumption data at runtime. These may be exposed as performance counters or through a vendorprovided API. For example, the Intel Running Average Power Limit (RAPL) interface reports per-package estimates of total energy consumed on Intel Sandy Bridge CPUs and later; the Nvidia Management Library (NVML) interface can query instant power draw values from recent Nvidia Tesla GPUs; some motherboards report power draw value through extensions to the Intelligent Platform Management Interface (IPMI).
Software interfaces The Performance API (PAPI)  [28]  recently added a number of components which can access a system's integrated energy and power consumption. Being a mature library, it is a compelling choice for hardware counter data acquisition. However, we feel that there is a place for a higherlevel abstraction with narrower scope and support for devices other than hardware counters (as in external devices), which may build upon any hardware counter interfaces (in fact, work integrating PAPI as a low-level provider to our library is underway). PowerAPI, from Sandia National Laboratories  [74] , is a recent attempt to standardize access to power measurement data and power control. It is comprised of an API specification and a reference implementation which already implements tightly coupled support for some energy data sources.
The platform and user role models defined in this specification, however, are aimed towards HPC rather than cloud systems. The Energy Measurement Library (EML)  [29, 30]  is a software library created to simplify analysis of energy consumption in heterogeneous systems. It provides a very simple interface for energy data acquisition and automatic run-time detection of available vendor interfaces and supported devices. These features abstract platform-specific details away from instrumentation code, greatly speeding up the measurement and experimentation process. Other software interfaces which provide directly measured energy or power related information are: the perf events  [53]  subsystem of the Linux kernel, which exports a variety of hardware counters to Linux userspace applications, the power measurement library pmlib  [18] , which implements a client/server architecture for out-of-band data collection from instrumented code; LIKWID, a performance-oriented library that accesses performance counters in x86 architectures  [84].
From the users' and applications' point of view, a much more detailed view is required, since their goal and scale are different. Indeed, users need a higher measurement frequency (which may go below the second) and a more focused view: at a node, core or even thread level. Software-based tools are more suitable for such a fine-grained view in spite of their intrusive behavior. The challenge at ultrascale level consists in being able to combine both views and make them available through a usable API. For instance, energy monitoring information for a 150 nodes platform equipped with external wattmeters providing one measurement per second corresponds to approximately 70 GB of data annually  [35]. At ultrascale level, the amount of energy monitoring data becomes rapidly unmanageable, even with scalable monitoring tools such as Ganglia  [42].
Energy efficient cloud computing Cloud computing offers many benefits to users. However, large-scale virtualized data centers require large amounts of electrical power, resulting in high operating costs. Due to the variability of the workload, the VM placement in the servers should be optimized continuously in an online manner. In  [23]  Beloglazov and Buyya conduct competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Additionally, based on an analysis of historical data, they propose adaptive heuristics for dynamic consolidation of VMs. With simulation experiments using realworld workload traces the authors show the effectiveness of the proposed algorithms, as energy savings are gained and a high level of adherence to the SLAs is achieved.
Energy-aware modeling and simulation tools The growing importance of efficient computing systems and emergence of new computing paradigms caused gaining importance of modeling and simulation of various computing architectures and corresponding algorithms. In the recent years several simulation tools have been developed in order to address these issues. Among them, one should mention CloudSim  [31] , GreenCloud  [55] , BigSim  [95]  and DCworms  [56]. CloudSim enables modeling and simulation of Cloud computing data centers and focuses on evaluating different approaches for provisioning host resources to virtual machines. It provides basic means to model power consumption and network traffic. GreenCloud pays more attention to networks aspects of cloud environments with a fine-grained modeling of the energy consumed by the elements of the data center, such as servers, switches, and links.
However, a main focus is devoted to the packet-level simulation of communications in the data center networks. On the other hand, BigSim is a tool allowing simulation and performance prediction of machines with a very large number of processors. It provides the ability to evaluate the performance of specific applications on very large computer clusters. Finally, DCworms enables simulation of computing infrastructures to estimate their performance, energy consumption, and energy-efficiency metrics for diverse workloads and management policies. Compared to other tools, DCworms allows simulating a wide scope of physical and logical architectural patterns. In particular, it enables simulations of complex distributed architectures containing models of the whole data centers, containers, racks, nodes, etc.
with a detailed energy and thermal modeling of each component. Moreover, DCworms provides means for modeling application performance both in HPC and cloud environments. These tools benefit from the models that are widely present in the literature. A direction of such studies span from network systems  [89]  through storage systems  [13]  up to servers systems  [72]. Additionally, researchers considered also virtualized environments  [68]. In  [20]  authors propose models that present data center power usage in a comprehensive way.
Adaptation of more lightweight architectures for hypervisors, such as those based on microkernel  [16] , have still not take its full swing, and are mostly used in embedded systems, rather than Cloud Computing. However, most recently a technology based on Linux containers (e. g. , Docker  [85] ) has shown some worthwhile benefits due to its lightweight design, fast deployment and low resource consumption footprint  [39]. Furthermore, its performance is almost identical to bare-metal deployment as the applications running inside a container directly utilize hardware resources. On the one hand, due to its still relative immaturity the container technology offers a limited set of features, where adding more features could easily eliminate its benefits based on the lightweight design. On the other hand, hypervisor technology started with a heavyweight design, and due to optimization efforts it significantly increased its efficiency without losing its basic features.
In order to achieve this, we developed a kernel module, GNTADDR, which retrieves page identifiers of target memory zones. Identifiers are then transferred to the privileged VM which uses them to map pages and then access data. With a 4096 byte page size and 8 bytes per identifier, the transferred data volume is reduced by 512. The accelerator device managed by the privileged VM has therefore a direct access to data produced in an unprivileged VM without any expensive duplication. Shared pages are always owned by the unprivileged VM and still exist after the transfer. However, this mechanism cannot avoid the four duplications inherent of a ring buffer pages when identifiers need to be transferred. That is why we developed a second scheme. The second scheme improves transfer of "short time lived" data.