dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This research was supported by  NIBIB/NIH  Grant  R01EB007542  to IG,  American Cancer Society   Research Scholar Grant   RSG-09-174-01-CCE  to IG,  NIBIB/NIH  Grant  K99EB017723  to KPQ, Avon Grant  02-2009-093  and  02-2011-095  to AMS,  NIEHS/NIH  Grant  R01ES08314  to AMS, and Tufts Collaborates Seed Grant Program.
In the model below, we also use the global cardinality constraint global_cardinality_constr(X ,Y ,m,M) that allows to bound the number of times some items appear in a list, X being that list, Y the set of sought values, m the array of minimum number of occurrences for each sought value, M the array of maximum number of occurrences for each sought value. Finally, the Gecode convexity global constraint convex(X ) is used to force the integers of an integer set X to be a convex sequence ({1, 2, 3} is one while {1, 2, 4} is not). These global constraints are implemented in the open source solver Gecode  (Schulte et al. , 2013)  that we chose to use. ∀i ∈ {2, • • • , n b }, t ind(b,1) = t ind(b,i) (1) p ind(b,1) = p ind(b,i) (2) f ind(b,i1) = f ind(b,i) 1 (3) -As discussed in section 2. 1, channel reuse bounds are a tunable parameter in input used to limit hardware needs for the downconversions.
EXPERIMENTAL RESULTS Experiments were needed to assess the performances of the two following approaches: -Global Approach (GA): The global constraint program of section 3. 1 solved with a CP solver (Gecode) -Decomposition Method (DM) : Sequential solving of (S 1 ) of section 3. 2 with a CP solver (Gecode) and then of (S 2 ) of section 3. 3 with an ILP solver (Gurobi) A first detailed example is presented in Fig. 6   This is one of the instances for which GA solved with Gecode is unacceptably long to find a solution. On the other hand, with DM, the scheduling part and the subsequent binary linear program are both solved extremely efficiently respectively by Gecode and Gurobi. On Fig. 6a , the regular layout is reprensented with a ring color for each polarization, and on Fig. 6b , the frequencies of the carriers found in the scheduling part can be read on the horizontal axis, and each color for the carriers represents one TWT.
Note that the design of Fig. 6  obtained for that example is optimal since the number of TWTs used is exactly equal to the number of carriers divided by the maximum number of carriers in a TWT. When instances are randomly generated, note that there is no guarantee that they will be feasible. Even if this is true for both approaches, in the case of DM, this risk of infeasibility is even increased since some of the path-covering problem constraints are currently not anticipated in the preceding scheduling problem (the frequency ranges of the TWTs for instance). In practice, infeasibility is significantly harder to detect than actual solutions for feasible instances, at least when Gecode is used, that is in GA and in (S 1 ) of DM. In the results of this section, the statistic values presented only consider the instances that turned out to be feasible.
For each instance tested with the DM approach, the corresponding (S 1 ) scheduling problem is solved with Gecode using the corresponding subset of constraints in the global model of section 3. 1. Then, the solutions of (S 1 ) are transformed into (S 2 ) pathcovering instances that are solved with Gurobi thanks to the ILP model we derived in section 3. 3. With GA, let us remind that the problem is entirely solved with Gecode. For the first phase of our series of experiments, we generated FAP instances with similar characteristics as the example detailed before, with the following few changes :  Fig. 8  and Fig. 7  allow to compare GA and DM in terms of objective values and execution times. As expected, we can observe that in the the case of a joint assignment of TWT, frequency and polarization to the carriers (GA), the execution times are greater than those of DM but the objective values are better in average.
That remark is even more important when we consider the fact that infeasible instances are also re-ally hard to detect for Gecode in the scheduling part. (S 1 ) is therefore the subproblem that deserves more attention for future work, the goal being to solve the highest realistic instances. -Each beam b ∈ {1, • • • , 12} of Our not yet exploited analysis of the cliques in the interference graphs could certainly be an interesting direction.
CONCLUSION The models we proposed for this particular frequency assignment problem applied to the design of multibeam satellite systems allowed to algorithmically solve instances that could not be solved by satellite telecommunications engineers. We showed that the decomposition method we devised could produce solutions and even optimal solutions in reasonable computational times especially compared to the perfor-mances of the global constraint program for that prob-lem. We also showed that relying on the cliques of the interference graphs was an acceptable direction and most likely a way to improve our current algorithms for the scheduling subproblem of our decomposition method. Concerning the pathcovering problem, a series of experiments showed that realistic instances where solved almost instantaneously by the solver Gurobi, which tells us that we extracted an interesting subproblem, and we will definitely try to take advantage of this in some way in the next algorithms we will implement.
We perform multi-plane ray-tracing using the GLAMER gravitational lensing code within high-resolution light-cones extracted from the CoDECS simulations: a suite of cosmological runs featuring a coupling between Dark Energy and Cold Dark Matter. We show that the presence of the coupling is evident not only in the redshift evolution of the normalisation of the convergence power spectrum, but also in differences in non-linear structure formation with respect to ΛCDM. Using a tomographic approach under the assumption of a ΛCDM cosmology, we demonstrate that weak lensing measurements would result in a σ 8 value that changes with the source redshift if the true underlying cosmology is a coupled Dark Energy one. This provides a generic null test for these types of models. We also find that different models of coupled Dark Energy can show either an enhanced or a suppressed correlation between convergence maps with differing source redshifts as compared to ΛCDM.
COSMOLOGICAL SIMULATION For our investigation we rely on the publicly available CoDECS simulations  (Baldi 2012c ) that represent the largest suite of cosmological and hydrodynamical simulations of coupled Dark Energy (cDE) models to date. The simulations have been performed by means of a modified version developed by  Baldi et al. (2010) , of the widely used TreePM/SPH N-body code GADGET  (Springel 2005) , and self-consistently include all the effects associated with the non-minimal inter-Table  1. The list of cosmological models considered in the present work and their specific parameters. All the models have the same amplitude of scalar perturbations at z CMB ≈ 1100, but have different values of σ 8 at z = 0. In short, α is a parameter in the inflation potential as shown, β(φ) is the coefficient of the coupling term with dark matter density and w φ (z = 0) is the effective equation of state parameter (p/ρ).
Ray-Tracing through the planes Once the lens planes are created as described in the previous section the lensing calculation itself is done using the GLAMER lensing code  (Metcalf & Petkova 2013; Petkova et al. 2013 ). The multiplane ray-tracing method is described in detail in Petkova et al. (  2013 ) so we will only outline the procedure here. A few definitions are required. If the angular position on the sky is θ θ θ and the position on the source plane expressed as an angle (the unlensed position) is β β β, then a distortion matrix A can be defined as A ≡ ∂β β β ∂θ θ θ = 1 -κ -γ1 γ2 -γ3 γ2 + γ3 1 -κ + γ1. ( 1 ) The traditional decomposition of this matrix is shown, where κ is called the convergence and γ γ γ represents the shear. The torsion, γ3, represents a rotation which can occur when there are multiple deflection planes.
While for SUGRA003 model the relative deviation is of the order of 1 -2%, for both EXP003 and EXP008e3 it manifests larger values. These results indicate that, if sufficient accuracy is attained, measuring σ8 from shear maps as a function of redshift would lead to inconsistencies were one of these coupled dark energy models the correct one. To observe these effects will require large amounts of very accurate data of the kind that will be provided by future wide surveys, like the Euclid space mission  (Laureijs et al. 2011 ). An additional complication not taken into account here is that weak lensing studies are generally sensitive to a combination of the power spectrum normalisation, σ8, and the total density of matter, Ωm, in the combination σ8 (Ωm/0. 25) α where α is dependent on the weak lensing statistic used: α = 0.
Introduction Cloud computing services (including public cloud services) hold the promise to deliver a host of benefits to both public sector organizations and enterprises, including reduced Capital Expenditure (CAPEX), improved performance and scalability, and enhanced reliability, as well as a reduced overall Total Cost of Ownership (TCO) for their ICT infrastructures and services. According to the European Union Agency for Network and Information Security (ENISA), public bodies can also benefit from the improvement of citizen interactions with government in respect of reducing information processing time, lowering the cost of government services and enhancing citizen data security  [1]. These benefits are particularly important for public bodies, especially for governmental agencies, municipalities and regions; the adoption of public cloud services as part of their numerous e-government interactions is expected to have a significant economic and social impact.
Composition of new value-added services: The ability to compose and integrate novel public cloud services based on other existing/legacy services could allow public administrations to streamline their processes thereby reducing overheads, alleviating bureaucracy and overall improve citizens' benefits and satisfaction  [6]. The primary incentive for an organization/public body to adopt a Cloud Orchestrator is to increase automation. The need to orchestrate really becomes clear as when using cloud environments as the automation of storage, network, performance and provisioning are in most cases handled by miscellaneous solutions that have been added incrementally over time. Even for organizations that take a transformational approach, jumping to an advanced cloud to optimize their data centers, the management of heterogeneous environments with disparate systems can be a challenge not simply addressed by automation alone  [5].
STRATEGIC Workload Metadata Model Workloads are a key concept and capability in the STRATEGIC Cloud Orchestrator, and are designed to make it possible to manage not just simple (single server) apps, but also much more complex multi-tier applications, as found in the enterprise or distributed applications consisting of many distinct server elements. The STRATEGIC Workload Metadata Model has been created in order to capture the semantics of any possible workload. The metadata model has several sections. The first section covers basic details about the <Name>, <Version>, <License> and <Category> of the application. These elements of the schema are indexableinstances of the model will be aggregated in a central repository in order for a customer (public body) to be able to search and deploy them.
Puppet uses <Recipes> as the basic notion of orchestration-bundle. The Workload Metadata Model contains placeholders for Recipes  [10]. The XSD schema is provided in the following figure: Table  1  provides a summary of the application and the expected usage of the STRATEGIC cloud orchestrator for each scenario. The primary incentive for an organization/public body to adopt the STRATEGIC Cloud Orchestrator is to increase automation. The need to orchestrate really becomes clear when various aspects of cloud management are brought together. The value of adopting an orchestrator derives from the convergence of multiple hypervisors, the need for efficient resource usage, availability, scalability, performance and more. Through the STRATEGIC Marketplace, the pieces are woven together and can be managed more effectively to ensure smooth and rapid service delivery -and delivered in a user-friendly catalogue of services accessible through a single pane.
As cloud environments become more complex and organizations seek greater benefit from their computing resources, the need for sophisticated management solutions that can orchestrate across the entire environment will become ever clearer. The rationale of this paper is to provide an overview of STATEGIC Cloud Orchestrator. For this reason a brief description of STRATEGIC project main goals and the identified requirements and desirable features of a Cloud Orchestrator are provided. For the conception of STRATEGIC Cloud Orchestrator, the high level architecture and component overview has been briefly described, along with the proposed Workload Metadata Model. Finally the pilot use cases overview and the value proposition of the STRATEGIC Cloud Orchestrator are provided.
With the increasing amount of -omics data available, a particular effort has to be made to provide suitable analysis tools. A major challenge is that of unraveling the molecular regulatory networks from massive and heterogeneous datasets. Here we describe RulNet, a weboriented platform dedicated to the inference and analysis of regulatory networks from qualitative and quantitative -omics data by means of rule discovery. Queries for rule discovery can be written in an extended form of the RQL query language, which has a syntax similar to SQL. RulNet also offers users interactive features that progressively adjust and refine the inferred networks. In this paper, we present a functional characterization of RulNet and compare inferred networks with correlation-based approaches. The performance of RulNet has been evaluated using the three benchmark datasets used for the transcriptional network inference challenge DREAM5.
Overall, RulNet performed as well as the best methods that participated in this challenge and it was shown to behave more consistently when compared across the three datasets. Finally, we assessed the suitability of RulNet to analyze experimental -omics data and to infer regulatory networks involved in the response to nitrogen and sulfur supply in wheat (Triticum aestivum L. ) grains. The results highlight putative actors governing the response to nitrogen and sulfur supply in wheat grains. We evaluate the main characteristics and features of RulNet as an all-in-one solution for RN inference, visualization and editing. Using simple yet powerful RulNet queries allowed RNs involved in the adaptation of wheat grain to N and S supply to be discovered. We demonstrate the effectiveness and suitability of RulNet as a platform for the analysis of RNs involving different types of -omics data.
Query driven approaches allow prioritizing predictions likely to be valuable in the context of a research. These approaches can be further divided according to the data mining or statistical methods they rely on to infer interactions. The most widely used approaches are Boolean networks  [9, 10] , Bayesian networks  [11] , Petri nets  [12, 13]  and association rule discovery  [14] [15] [16]. Several tools based on association rule discovery have been developed. For instance, GenMiner  [16]  allows the simultaneous analysis of biological datasets such as gene expression or annotation, but quantitative data are preprocessed and discretized in the workflow. Georgi et al. [15]  has presented an approach for association rule discovery that uses quantitative data and allows users to specify parameters to discover rules matching their personal interest.
[14]. It is implemented as a web-oriented platform dedicated to the inference of RNs suitable for qualitative and quantitative-omics data. It provides users with options to specify entities (genes, metabolites,. ) of interest to discover rules involving these entities. A special query language has been integrated into RulNet which allows users to perform custom queries. Each type of rule has its own biological interpretation, which provides flexibility in the type of networks that can be inferred. RulNet can therefore be used to infer very distinct networks e. g. between traits and genetic markers in association studies. This all-in-one platform allows experimental data to be uploaded or data already in a database management system (DBMS) can be interrogated. The RulNet platform has several functionalities such as validation or invalidation of interactions, network generation from one or two lists of components and parameterization of the number of intermediate components.
The ultimate aim of RulNet is to provide biological researchers with more control over their search for interactions during the network inference process. In order to assess the effectiveness and suitability of RulNet to infer RNs, we first benchmarked it using the standard datasets used for the transcriptional network inference challenge DREAM5  [19]. We then used RulNet to analyze the changes in gene expression, grain storage protein (GSP) composition, free amino acids and key metabolites concentrations in response to nitrogen (N) and sulfur (S) supply for developing wheat (Triticum aestivum L. ) grain. Wheat GSPs accumulate during the effective filling phase of grain development and account for 60% to 80% of the total protein content of mature grains  [20]. Increased N supply via the use of fertilizers increases grain yield and protein concentration  [21] , but also modifies the balance between GSP fractions, i.
Multipredicate rules and RQL language The notion of rules or implications is widely used in the database, data mining and artificial intelligence communities. A rule reflects an observation on the data. Examples of rules are functional dependencies  [26] , implications  [27]  or association rules  [28]. A rule is written X!Y and is read X implies Y, where X and Y are two sets of attributes (i. e. entities). A rule expresses a new relation when a property (called a predicate) is observed on X (the left hand side or antecedent) then a property is observed on Y (the right hand side or consequent) in the data. The two predicates can be of the same type but not necessarily. The principle of a rule-based learning approach is to indicate which type of rules (semantics) to discover then to apply algorithms to infer all the rules satisfying this semantics in the data.
The biologist then needs to interpret the set of rules inferred and determine if they have a biological meaning. The intrinsic variability (from noise and error) of biological data means that learning approximate rules, i. e. rules that are not always satisfied in the data is also of interest. A generic SQL-like language called RQL (Rule Query Language) has been recently developed to specify rule semantics in a simple way  [29]. RQL allows association rules or functional dependencies on a relation to be defined but can also capture rules between attributes from different tables of a database, or conditional rules such as conditional functional dependencies  [30, 31]. As originally defined, RQL is however limited to rules with a single predicate (i. e. the same condition on all the attributes of the left hand side implies the same condition on all the attributes on the right hand side).
The advantage of MP-rules, is that we can capture different perspectives from the data. The advantage is also that we can address quantitative, categorical or binary attributes in the same inference process. To illustrate the use of MP-rules, let us consider a dataset composed of three tables (Fig  1 ). In this dataset, each time A1 = 1 in table ann then M2 > 0. 5 in table met. This observation can be represented by the MP-rule A1 1 !M2, which means that A1 = 1 guarantees M2 > 0. 5 (i. e. A1 = 1 is a sufficient condition to observe M2 > 0. 5). Another exact MP-rule in this dataset is G3!A1 0 (i. e. if G3 > 0. 5 then A1 = 0). Here A1 = 0 is a necessary condition to observed G3 > 0. 5. In RulNet, a query for MP-rules has the following template: where the "SCOPE" line defines the tuples (rows) of the datasets on which the "HAVING" clause is tested.
Datasets can be data files, database tables or SQL queries. The "HAVING" and "AND" lines define the predicates and the associated lists of attributes, respectively. The "WHERE" line is optional, it allows setting conditions on the tuples. For instance, in the dataset of Fig 1, the clause WHERE t2. time = t1. time + 1 means that all the couples of consecutive samples (tuples) will be tested. The theoretical basis for rules expressed in RQL and implemented in RulNet has been presented by Chardin et al. [29]  but these theoretical aspects are beyond the scope of this paper.
In RulNet, the set generation can be accomplished with only a single SQL query and thus benefit from the optimization features available in relational DBMS  [29, 34]. The second step for the generation of n-1 MP-rules, consists of computing the two sets of exact and approximate MP-rules  [35]. This is the most expensive step in terms of computation time. RulNet uses the algorithm and the code proposed by Murakami and Uno, which is recognized as the best solution for such problems  [36]. Once generated, rules can be objectively evaluated by calculating quality measures. Support, confidence, lift and leverage are four commonly used measures of significance and interestingness for association rules  [37, 38]. In RulNet, these metrics were adapted for MP-rules and are defined as follows. The support is a measure of significance (importance) of a rule and is given by: Support X ! Y ð Þ¼ count of ðX [ YÞ N ð1Þ where N is the number of possible tuples in the dataset verifying the specified condition if it exists (the "WHERE" clause) if only one tuple variable (t1) is defined.
If two or more tuple variables are specified, N is equal to the number of possible combinations of tuples in each dataset verifying the specified condition if it exists. The support is often used to reduce the search space (i. e. to retain only the rules with a support higher than a user defined threshold value). The confidence is a measure of the strength of a rule. It is defined as the probability of seeing the rule's consequent under the condition that the antecedent is satisfied. The confidence is directed and gives different values for the rules X!Y and Y!X. The confidence is given by: Confidence X ! Y ð Þ¼ count of ðX [ YÞ count of X ð2Þ The lift  [39]  and the leverage  [40]  compare the observed support of the rule and the expected support if X and Y are statistically independent. The lift compares how many times X and Y occur together with the number of times they would occur together it they were statistically independent: Lift X ! Y ð Þ¼ count of ðX [ YÞ Â N count of X Â count of Y ð3Þ The leverage measures the difference of X and Y appearing together in the dataset and what would be expected if X and Y were statistically dependent: LeverageðX ! YÞ ¼ count of ðX [ YÞ N À count of X N Â count of Y N ð4Þ A lift equal to 1 and a leverage equal to 0 means that X and Y are statistically independent.