dataesr/scientific-paragraphs-categorization huggingface dataset:
The NFV system supporting load balancing both selects the routing paths and splits traffic among them under constraints on NFVI resources and the requirement of the service demand. With the incorporation of the SFC feature of NFV, every data flow of the demand is required to go through a sequence of VNFs dynamically allocated on NFVI nodes according to the demand requirement. We aim to solve the optimization problem of load balancing using ECMP in order to minimize the maximum link utilization of data flows in NFV. NFVI is modeled as a directed graph G = (V, E), which consists of a set of nodes V and directed links E. A node represents a commodity hardware device that can instantiate a VNF flexibly according to dynamic service demands. Let C 1,e and C 2,v denote respectively the bandwidth capacity of link e ∈ E and the computing capacity of node v ∈ V.
We denote by i e and j e the starting node and terminating node of link e, respectively. Our objective is to find a link metric vector w and a flow allocation vector x(w) in order to minimize the maximum link utilization of data flows and satisfy all requirements of service demands under constraints on NFVI resources. x(w) is determined according to ECMP routing for each w. We formulate the problem of load balancing using ECMP as a MILP model that allows us to obtain the optimal solution. For convenience, we summarize our notations in Table  I. The variables of our model are as follows: • w e is a non-negative integer variable that represents the metric of link e. • l vt is a non-negative integer variable that is the length of the shortest-path from v to t (v = t). • x epd is a non-negative continuous variable that represents the traffic on link e of flow p associated with a demand d.
d:i∈Q(d) ζ d log (3κ + 1) (1 + π i ε) = π i O (ε log κ). Proof: From the cost function (  16 ) of the primal program and the adjustment function  (15) , we have ∆P o = i:qi∈Q(d) π i z i π i ε + 1 π i |Q(d)| = i:qi∈Q(d) z i ε + 1 |Q(d)| (26) Since constraint (  17 ) is infeasible when ORBIT updates the primal and dual solutions, we find ∆P o 2. In addition, the change in the dual cost is 1. Thus, we obtain ∆P o 2∆D o , which proves the claim. We are almost ready to derive the competitive ratio of the ORBIT algorithm that is introduced in the following proposition. Proposition 1: ORBIT produces an online load balancing solution which is O(ε log κ)-competitive. Proof: First, by Lemma 1 the online solution produced by the ORBIT algorithm is feasible. From Lemma 3 and the fact that initially P o = D o = 0, we find that the ORBIT algorithm produces online primal and dual solutions such that P o 2D o.
V. EVALUATION In this section, we describe the setting of our experiments using two real-world datasets. We then analyze the experimental results including the impact of the algorithm parameters and the evaluation of algorithm performance in comparison with the optimal and offline solutions. In our experiments, we use two real-world datasets that contain the network topology and traffic matrices measured at different times. The first dataset is the Internet2 research network including a topology of 12 nodes, 30 links and traffic matrices with 130 demands  [19]. The traffic traces are recorded for a duration of 30 minutes. The second dataset is the Geant dataset that contains a topology of 22 nodes and 72 links and traffic matrices with 250 demands. The traffic volume of demands in our experiments is a half of total traffic traces over four months in the Geant network.
In our experiments, we consider four VNFs available on NFVI. The computing capacity, the resource requirements of a VNF, and the SFC of each demand are randomly generated. In the experiments,   4. Impact of parameter κ on the algorithm performance using Geant we solve the MILP formulation for 10 service demands to compute a weight system for ORBIT. In the preparation phase of ORBIT, we use a multilevel graph partitioning algorithm for dividing NFVI into κ partitions  [20]. We first evaluate the performance of ORBIT when varying the value of the algorithm parameters in order to gain insight into the parameter setting of ORBIT in practice. Fig. 2  shows the maximum link utilization and the demand acceptance ratio obtained by ORBIT for various ε when κ = 2 and κ = 3 in the experiments using the two datasets.
We observe that the best value of ε for ORBIT is not affected by the number of partitions κ. Specifically, ORBIT obtains better results when ε = 3 and ε = 1 in the experiments using dataset Internet2 and Geant respectively. Thus, we use these values of ε for other experiments. Fig. 3  and 4  show the maximum link utilization and the demand acceptance ratio obtained by ORBIT for various κ and the two datasets. We observe that ORBIT achieves better results when κ increases until a threshold is reached, beyond which further increase in κ degrades performance. Particularly, the algorithm obtains the best performance in both the maximum link utilization and the demand acceptance ratio when κ = 3 in the experiment using dataset Internet2 and κ = 2 in the experiment using dataset Geant. The reason is that ORBIT balances traffic over the partitions according to not only the number of partitions, but also the resource capacity of a partition.
Under a high value of κ, ORBIT is likely prevented from distributing a flow through a small partition due to the lack of resource, resulting in worse performance. When we choose a different set of traffic demands in both datasets, the performance improvement also occurs at values of κ between 2 and 3. It suggests that we can obtain an appropriate value of κ for a specific NFVI by observing the algorithm behaviour in a time duration. Second, we analyze the performance of the ORBIT algorithm in comparison with the optimal solution. We use the IBM ILOG CPLEX Optimizer to solve the MILP model in order to obtain the optimal results. Fig. 5  provides a comparison of the maximum link utilization obtained by ORBIT and the optimal solution. In both datasets, the results provided by ORBIT are close to the optimal solution.
Specially, we observe that ORBIT obtains slightly better results in the experiments using the Geant dataset. This occurs because when NFVI is divided into several partitions, ORBIT can use both the shortest paths and many near-optimal paths. Finally, in order to evaluate the performance of ORBIT in a large scenario we develop an offline solution based on a basic heuristic method called simulated annealing  [21]  for comparison purposes. As shown in Fig. 6  and 7 , ORBIT outperforms the offline solution in term of both the maximum link utilization and demand acceptance ratio when the number of service demands is large. We argue that ORBIT is more efficient in this case due to the support of near-optimal paths The above results demonstrate that we have designed an efficient solution for online load balancing in NFV.
VI. CONCLUSION We addressed the optimization problem of load balancing in NFV, which is important for guaranteeing the high performance requirements of a network function in a virtualized environment. We formulated the problem as a MILP model to compute the optimal solution. We developed the ORBIT algorithm that provides an efficient solution for online load balancing. As demonstrated through theoretical analyses, the performance of ORBIT is O(ε log κ)-competitive where ε and κ are the algorithm parameters associated to graph partitioning on NFVI. Using two real-world datasets, we performed several experiments in which the result provided by ORBIT is close to the optimal solution. Importantly, the experiment results show that ORBIT works very well in comparison with an offline solution when the network load is high.
Meteorological model: The SOLENE-microclimat model The SOLENE-microclimat model is described with details in MALYS L. (2012)  [20]. SOLENE-microclimat is the new version of solene software that includes new sub-models that provide it with many additional possibilities (see Figure  3 ). Generally, SOLENE-microclimat has three main models: the thermo-radiative model, the thermoradiative model of a building, and the thermo-radiative model coupling with other codes e. g. Code_Saturne (CFDSimulation), (see Figure  2 )  [20]. This study uses the thermo-radiative model of a building to investigate the indoor air temperature, external surface temperature (building facade, roof, and urban open spaces), and amount of energy needed to maintain the thermal comfort level. To find the outdoor air temperature, the energy balance equation described in MALYS L.
It also enables an estimation of the total causal effects between the gene of interest and the remaining genes. Our method performed very similarly to a classical differential analysis for experiments with a relatively large number of biological replicates, but has the advantage of providing a formal causal interpretation. Our proposed marginal causal approach is computationally efficient and may be applied to several thousands of genes simultaneously. In addition, it may help highlight subsets of genes of interest for a more thorough subsequent causal network inference. The method is implemented in an R package called MarginalCausality (available on GitHub).
These effects thus appear to be generally well-estimated with the proposed method. In order to compare the results of our marginal causal approach with another marginal (but non-causal) approach routinely used in practice for comparisons between two groups (here, observational and interventional), we also performed a classical differential analysis on the same set of simulated data using the R/Bioconductor limma package  [23]. Briefly, limma makes use of a robust moderated two-sample t-test between the observational and interventional samples for each gene, where an empirical Bayes method is used to shrink per-gene sample variances towards a common value. We calculated the area under the receiver operating characteristic (ROC) curve (AUC) over 100 simulations to compare the sensitivity and specificity of the marginal causal approach and the differential analysis to detect downstream causal relationships.
For this comparison, we focus only on the downstream causal relationships from the KO gene of interest. We use the graph structure in  Fig 4  to simulate data as above (100 datasets, with 24 replicates in each of the WT and KO groups), and we define an F-score to assess the performance of each algorithm: R ¼ TP TP þ FN P ¼ TP TP þ FP FÀ score ¼ 2 R þ P RP ; where "TP" corresponds to nodes that were simulated to be downstream of the KO gene and were correctly identified by a given method, "FP" corresponds to nodes that were not simulated to be downstream of the KO gene but were incorrectly identified by a given method, and "FN" corresponds to nodes that were simulated to be correlated/upstream of the KO gene but were incorrectly missed by a given method. The boxplot of F-scores for each method is shown in  Fig 7,  as well as the same simulation for upstream/correlation links.
Real data analysis We applied our marginal causal method to a set of transcriptomic data in the context of gene regulatory networks  [25]. These data were produced at the French National Institute for Agricultural Research (INRA), in a study investigating gene expression differences between wildtype chickens and their siblings carrying a functionally inactive growth hormone receptor (GHR) gene, leading to a dwarf phenotype; in this case, a functional KO refers to a mutation for which the associated protein is generated but can no longer fulfill its role  [22]. We considered the mutation of the GHR gene to be an experimental knock-out, and the expression level of the GHR gene was set to a value close to zero for the dwarf chickens. Customized Agilent microarrays were used to measure gene expression from liver samples taken from 24 wild type and 24 knock-out chickens for 18,855 genes.
These data are available at GEO under the GEO accession number GSE91084. After standard preprocessing and normalization steps, we aimed to identify genes that are downstream causally related to the GHR gene using the marginal causal method presented in this work. A classical per-gene differential analysis was also performed between wild-type and dwarf chickens using limma  [23]. Fig 8 compares  the Bayes factors obtained with the marginal causal method and the p-values of the differential analysis. In this case, the results are very similar for the two analyses, with a clear linear trend in the scatter plot. This follows the results obtained in the simulation study for more than 10 biological replicates; the added value provided by the marginal causal method, however, is that it provides a formal interpretation of the differential analysis in terms of downstream causal relationships.
RNA-Puzzles is a CASP-like  (Moult et al. 2016 ) collective blind experiment for three-dimensional (3D) RNA structure prediction evaluation. The aims are to identify the capacities and bottlenecks in the RNA prediction problem. The structures to be predicted are unknown to the databases and to the modelers and, thus, biases due to prior knowledge are avoided. The prediction methods result from various approaches, but often combine fragment assemblies from known structures present in databases and energy minimizations through various types of force fields adapted to the granularity of the models and the stages of the modeling (for a recent review on the commonly applied algorithms, see  Dawson and Bujnicki 2016). Experimental data, newly collected  (Cordero et al. 2014) , can be also used as constraints during the prediction process.
95 Å with PDB ID 4L81. Before this crystal structure of the SAM-I/IV riboswitch aptamer was published, the SAM-I structure was known and available during RNA-Puzzle modeling. Nevertheless, the SAM-I/IV riboswitch aptamer exhibits distinct peripheral tertiary domains and pseudoknots. The prediction of SAM-I/IV is an appropriate application of RNA structure prediction programs for understanding biological problems with unknown RNA structures. The known clues could be explored, such as the structural topologies of SAM-I and SAM-IV, to predict parts of the unknown structure, while other parts required de novo modeling. Puzzle 12: ydaO riboswitch Two cylclic diadenosine monophosphate (c-di-AMP) molecules bind the ydaO riboswitch, which is involved in sporulation, osmotic stress responses, and cell wall metabolism, in two pseudo-symmetry-related pockets  (Ren and Patel 2014).
Experimental data description The Das group provided "fast-track" experimental data to all the modelers for puzzles 7, 8, 12, 13, and 14. One-dimensional chemical mapping using SHAPE, CMCT, DMS, and hydroxyl radical footprinting and multidimensional chemical mapping measurements based on mutate-and-map (M 2 ) and multiplexed •OH cleavage analysis (MOHCA-seq, for targets 12-14) were acquired as described in  Cordero et al. (2014) ,  Kladwang et al. (2014), and Cheng et al. (2015a). Data were distributed via the private RNA-Puzzles website or via entries with anonymized sequences in the RNA Mapping Database  (Cordero et al. 2012) ; deposition IDs are summarized in Supplemental Table  S8.
The prediction of the Loop E structure is compared in Supplemental Figure  S14  for both free and bound states. In the free state, only Das group post-experiment model 2 gave the right prediction. In the bound state, Bujnicki group pre-experiment model 2 and Das group pre-experiment model 6 are the best predicted ones. The well-predicted loop E modules are always detected in the top ranking predictions, which underscores the importance of predicting correctly any well-known structural modules for the final resulting model. The RNA-Puzzle on a ribozyme As the only ribozyme in this round of RNA-Puzzles and the largest of the small nucleolytic ribozymes, the VS ribozyme was very difficult to predict de novo, although much structural data and many models were previously available  (Wilson and Lilley 2011).
The Adamiak group also applied Rfam  (Gardner et al. 2009)  to identify conserved base pairs (Puzzle 13). Information about pseudoknots was obtained by manual analysis of the experimental data (Puzzles 7, 8, and 13) and/or based on the literature review (Puzzles 12 and 13). Supplemental Table  S9  presents input RNA secondary structure topologies applied to the considered puzzles. Information about pairing patterns within the pseudoknots was introduced into the RNAComposer dot-bracket annotated input using square brackets. The structure of each potential pseudoknot was additionally refined using distance restraints derived from canonical A-RNA structure (Supplemental Table  S9 ) applying the described procedure  (Huang et al. 2013). In some cases, the generated 3D structures did not correlate with long-distance interactions or relative positioning of the helixes deduced from the provided experimental data (i.
e. , Puzzle 8). This prompted us to develop new functionality of the RNAComposer system  (Antczak et al. 2016 ) that allows the user to introduce a particular, user-defined 3D element for the structure assembly. This functionality was subsequently utilized in solving Puzzles 13 and 14. Difficulties in predicting some interactions from the experimental data resulted in another improvement of the RNAComposer system. New algorithms are being developed that will increase the pool of promising 3D elements that can be applied for 3D structure assembly by allowing the user to explore RNA FRABASE  (Popenda et al. 2008 (Popenda et al. , 2012) )  using new wild card characters. Such characters will be introduced in the definition of the query patterns. All 3D models were evaluated using the criteria of total energy and right geometry to exclude knotted structures, and those that did not fulfilled experimental restraints criterion.
Puzzle 4 The Adamiak group focused on the prediction of the RNA-protein complex (Fig. 1 ). The high level of homology for B. subtilis YbxF protein (sequence: GSYDKVSQAKSIIIGTKQTVKALKRGSVKEV VVAKDADPILTSSVVSLAEDQGISVSMVESMKKLGKACGIEVGA AAVAIIL) allowed us to obtain a protein 3D model based on homologous modeling with I-TASSER  (Yang et al. 2015). RNA secondary structure was extracted from the SAM-I riboswitch of B. subtilis (PDB id: 3NPB) and used to predict RNA 3D structure in the fully automated mode of RNAComposer. Three-dimensional models of RNA/protein complexes were obtained using HADDOCK  (Dominguez et al. 2003). The Adamiak group defined protein-RNA interfaces based on the structures of L7Ae bound to the K-turn motif in box C/D RNA (1RLG-A. fulgidus L7Ae, 1SDS-M. jannaschii L7Ae, 3PLA-S.
The proposed method explores the use of multiple sequence alignment information and parallel modeling of RNA homologs to improve 3D structure prediction over modeling of single RNA sequences. To build a structural model of the target sequence, a multistep modeling process is performed. First, for the target sequence, a subset of homologous sequences is selected using the Rfam database. Subsequently, independent folding simulations are carried out for these homologs; in this series of RNA-Puzzles experiments the Bujnicki group used ROSETTA/FARNA  (Das and Baker 2007) , but in principle any template-free folding method can be used. Structural fragments corresponding to the evolutionarily conserved regions (in particular helices)-determined from the alignment-are extracted from all obtained models and clustered to identify the most common structural arrangement.
The Bujnicki group also explored a way to constrain the simulation by keeping the conserved residues identified by the alignment as being in close spatial proximity. The approach was used in modeling of Puzzles 13 and 14. In a blind prediction of Puzzle 13, a model obtained with this methodology was second, and in a Puzzle 14 (bound form), one model was best in terms of the RMSD to the reference structure. This approach is now under further systematic tests in preparation to make it automated and available for the community. The current version of the program, documentation, and input files to solve Puzzles 13 and 14 can be accessed under a github repository at https://github. com/mmagnus/ EvoClustRNA.
Puzzle 4 For Puzzle 4, the Bujnicki group submitted the structure of the complete RNA-protein complex. The models of both RNA and protein components have been constructed using homology modeling, as suitable templates could be found in both cases. The RNA part was based on a homologous Thermoanaerobacter tengcongensis SAM-I riboswitch structure (PDB ID: 3IQP) used as a template. The structure of the YbxF protein was predicted by fold-recognition using the GeneSilico metaserver  (Kurowski and Bujnicki 2003) , followed by template-based modeling using the L7Ae protein structure (PDB ID: 2FC3) as a template, with MODELLER  (Sali and Blundell 1993). RNA-protein docking was performed using a procedure that in the meantime has been automated and now is available via the NPDock web server  (Tuszynska et al.
Puzzle 14 This structure was modeled using a hybrid approach, by combining template-based modeling for individual structural fragments, followed by global folding with restraints. As templates, the Bujnicki group used the E-loop motif from the structure of H. marismortui ribosome (PDB ID: 1JJ2) and of the sarcin-ricin loop motif (PDB ID: 1Q9A). Models of these fragments were used as a source of distance restraints for SimRNA, derived using an in-house-developed tool. The Bujnicki group also used restraints on the secondary structure of the whole molecule derived from an Rfam alignment (GlnA family). Further, they used the CARTAJ method  (Lamiable et al. 2012)  to predict the architecture of the junction and generated restraints to enforce coaxial stacking of helical regions. Further, restraints on individual base pairs were added based on our interpretation of additional information available for the free and bound forms.
For the 3D template-based structure prediction algorithms, such as Vfold3D, a critical limitation is that not all motifs have proper templates in the template database built from the known PDB database. They used the Rosetta (assembly) package to sample the allatom motif 3D structures for the motifs without any templates. The top five cluster centers (centroid structures) were selected for further structure assembly in the Vfold3D to build 3D structures of the whole RNA. For example, the all-atom structures of one of the three-way junctions in Puzzle 12 were generated by the Rosetta package. They used the A-form helix to build the all-atom helix structures. The treatment of the A-form helices, which have small differences from the real (slightly distorted) all-atom 3D helix structures, could also result in notable structural differences in the global fold  (Xu and Chen 2015).
In summary, the computation involved two steps: the prediction of the 2D structure and the construction of the 3D structure, followed by the AMBER minimization. They manually incorporated the constraints from the Rfam results into the Vfold2D model for 2D structure predictions. In general, the prediction can be completed within 5 h. However, if the Rosetta package is used to generate the centroid motif structures, it may take 2 d to sample 50,000 structures using 8 CPUs (Intel Core i7-2600 CPU at 3. 40 GHz). Their predictions did not take into account the effect of the ligand molecules on the structure of RNA.