dataesr/scientific-paragraphs-categorization huggingface dataset:
The observed bias in the MEGAN 2. 1 simulated isoprene fluxes is driven by two factors: (i) the fact that the average flux for the set of defined conditions is based on a limited number of data points (which induces a larger random error for both algorithms), ranging between n = 4 and n = 19, which may be a poor representation of the typical flux footprint and canopy heterogeneity, and (ii) the defined conditions are based on current PPFD and temperature, with larger uncertainty on the remaining gamma terms such as past PPFD and temperature. Therefore, we conclude that this approach succeeds in simulating emissions at "typical" conditions encountered at each site, but less reliably reproduces the average emission. While the reporting of fluxes at a set of defined reference conditions offers some clear advantages (e.
g. the avoidance of two different algorithms being used for the forwards and backwards calculations), our analysis shows that there are also drawbacks that need to be considered. For example, here, we chose only to bin the measured flux data by the two major drivers of isoprene emissions, current light and temperature, meaning that the corresponding average isoprene emission is only suited for algorithms that use only these two variables (e. g. G93 algorithm). The more complex algorithms have many more reference parameters, which means the measurement space becomes increasingly stratified, yielding far fewer flux averaging periods and resulting in larger uncertainties. In addition, with increasing bin width, additional uncertainty is introduced by averaging highly nonlinear responses. We recommend future studies report both an emission potential for a set of defined conditions and an emission potential derived using the whole dataset in conjunction with the weighted average method, providing a detailed description of exactly how the emission potential was calculated.
We have also clearly demonstrated that for any given dataset a very wide range of emission potentials can be calculated, the values of which depend upon both the specific algorithm used and how it is implemented to backout the emission potentials. Some of the variation between algorithms relates to changes in the standard light conditions from 1000 µmol m -2 s -1 PPFD in leaf-level models to 1500 µmol m -2 s -1 PPFD in canopy-scale algorithms. However, a comparison of the leaf-level extrapolated emission potentials which were harmonised to a similar set of standard conditions across all algorithms (e. g. 1000 µmol m -2 s -1 PPFD) demonstrated that these algorithms do not always yield similar emission potentials, with up to a factor of 2. 7 difference. Clearly, different emission algorithms and algorithm implementations result in different emission predictions even if the same emission potentials are used, with the variability stated here.
1 (e. g. Pocket MEGAN, Excel beta 3), which can be used by experimentalists to more easily determine emission potentials from their observational data. Furthermore, we recommend that all processed canopy-scale flux data from which emission potentials are to be derived should be stored on a common community database. The VOCsNET database (http://vocsnetdata. ceh. ac. uk/) enables others to recalculate emission potentials in a fashion that is compatible for their model application and to enable re-calculation in the future to keep pace with the evolution of models such as MEGAN. All five datasets used in this study can be accessed via the VOCsNET database. In addition to the approaches of how to derive emission potentials from canopy-scale flux measurements, further standardisation is also required for the micrometeorological flux measurement itself, including selection of instrumentation, instrument setup and operation, relative height of measurements above the canopy, data processing, and reporting of results and uncertainties.
Figure2. Panels (a, c) show the average diurnal cycle in the isoprene emission potential (e. g. IEP = F iso γ ) calculated for the Ispra forest site, Italy, using the G93 (a) and MEGAN 2. 1 (b) algorithms. Superimposed on top of these are the isoprene emission potentials calculated using the least-squares regression, orthogonal distance regression and average (with several averaging lengths) methods -see text for detailed description. Panels (b, d) show the average diurnal cycle of the measured fluxes and the average diurnal cycle of the fluxes modelled using the seven different isoprene emission potentials calculated for this dataset.
Acknowledgements. This work was supported by the  European FP7  project  ECLAIRE  (no. 282910 ) and  NERC National Capability  funding and inspired by discussions within the  ECLAIRE community  and in particular with  David Simpson ,  Chalmers University Gothenburg. The authors thank  Matthew Wilkinson  at  Forest Research  for facilitating our measurements at the  Alice Holt  field site. For the O3HP dataset, we thank the  ANR- CANOPEE  project ( ANR 2010 JCJC 603 01 ),  CNRS ,  CEA  and the  Oak observatory  staff. We also thank  Alex Guenther ,  Rüdiger Grote  and two anonymous reviewers for their helpful comments and suggestions. Edited by: Georg Wohlfahrt Reviewed by:  Alex B. Guenther  and two anonymous referees.
One of the main enablers of this vision is the IoT (Internet of Things)  1. The Internet of Things is eating up the world. Nowadays, be it in academia, industry or government, Internet of Things is considered by many as the latest revolution. This is mostly due to the potential impact of IoT on the way we interact with the real world. The idea revolves around making "things" (e. g. cars, refrigerators, lamps, etc. ) smarter, providing them with relatively small sensory, computational and memory power in order to be able of some level of autonomy in the decision making process. In addition to smart things, IoT can also benefit from the widespread of mobile devices (e. g. smart phones, tablets, PDAs, wearables, etc. ), that are already equipped with numerous sensors and an ever increasing computing and memory capabilities, as a source of rich, multi-modal data or consumable services that can help understand the user's environment and enrich his user experience (e.
". The life cycle of context management systems spins generally across four stages, (1) Collecting and Aggregating Phase, (2) Modeling and Storing Phase, (3) Reasoning and Processing Phase and (4) Dissemination and Integration Phase. The first phase concerns the ways of acquiring the context data around an entity and aggregating it to provide more accurate data. The second phase deals with the representation of the collected data (Key-value, ontology, etc. ) and how to store it (Relative Databases, NoSQL Databases, XML files, etc. ). The third phase is responsible for processing the data stored to harvest better knowledge and more meaningful information. The fourth phase specifies the way the context information gathered is provided to the interested parties (e. g. services, events, reactions, etc.
We base our view on Cloud Computing's service models (Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS)), as they provide all the necessary flexibility and effectiveness to deliver services dealing with the challenges of smart applications (e. g. scalability, heterogeneity, resource consumption, etc. ). To better understand the Cloud service models, consider the following analogy :"Imagine the Interstate transportation system in the U. S. Even with all these roads built, they wouldn't be useful without cars and trucks to transport people and goods. In this analogy, the roads are the infrastructure and the cars and trucks are the platform that sits on top of the infrastructure and transports the people and goods. These goods and people might be considered the software and information in the technical realm.
"  4. In the SaaS model, the provider offers software functionality as a service, that can be accessed either through a web browser or an API. In the PaaS model, the service vendor provides the tools required to manage, develop, test, deploy applications. In the IaaS service model, the cloud provider offers resources (e. g. , processing, network and storage) as services according to the consumer's needs. In the following, we discuss the synergies between the enablers at every cloud level (service model) and how context information can benefit the development process.
Conclusion and Future Works Through this paper, our aim was providing a framework that would serve us as a stepping stone for the development of smart city application. We presented our vision for a smart city, empowered by four enablers namely, Internet of Things; Cloud Computing; Context-Awareness and Big Data, while highlighting the necessity for their integra-tion. To address the lack of a clearly defined view for the integration in literature, we proposed a framework called C2IoT for Cloud-based Context-aware Internet of Things services in smart cities, leveraging and encompassing the necessary techniques and components to ease the deployment, development and offering of new smart services for the future smart city. We've argued that context-awareness can bring intelligence to the data in IoT, to the services in the Cloud and to decision making in Big Data, while Cloud computing provides all the necessary flexibility and effectiveness to deliver services dealing with the challenges of smart applications (e.
Answer set programming In this section, we briefly recapitulate the basic elements of ASP  [18] , a declarative language that proved efficient to address highly computational problems. An answer set program is a finite set of rules of the form: where n ≥ m ≥ 0, a 0 is an atom or ⊥, all a 1 ,. , a n are atoms, and the symbol "not" denotes negation as failure. The intuitive reading of such a rule is that whenever a 1 ,. , a m are known to be true and there is no evidence A = (�a 0 , b 2 , c 1 , d 0 �; �a 0 , b 2 , c 1 , d 1 �; �a 1 , b 2 , c 1 , d 1 �; �a 1 , b 2 , c 1 , d 0 �; �a 0 , b 2 , c 1 , d 0 �) C = (�a 1 , b 2 , c 0 , d 1 �; �a 1 , b 2 , c 0 , d 0 �; �a 1 , b 2 , c 0 , d 1 �). (1) a 0 ← a 1 ,. , a m , not a m+1 ,. , not a n. for any of the negated atoms a m+1 ,. , a n to be true, then a 0 has to be true as well.
An atom or a negated atom is also called a literal. Some special rules are noteworthy. A rule where m = n = 0 is called a fact and is useful to represent data because the left-hand atom a 0 is thus always true. It is often written without the central arrow [see rule  (2) ]. On the other hand, a rule where n > 0 and a 0 = ⊥ is called a constraint. As ⊥ can never become true, if the right-hand side of a constraint is true, this invalidates the whole solution. Constraints are thus useful to filter out unwanted solutions. The symbol ⊥ is usually omitted in a constraint [see rule  (3) ]. In the ASP paradigm, the search of solutions consists in computing the answer sets of a given program. An answer set for a program is defined by Gelfond and Lifschitz  [26]  as follows. An interpretation I is a finite set of propositional atoms.
Furthermore, if such a cardinality is found in the body of a rule, then it is true if the above condition is satisfied. Atoms in ASP are expressed as predicates with an arity, that is, they can apply to terms (also called arguments). For instance, let us take the following program: The intuitive meaning of this program is that if fish do not fly (which is the case) and that something is a fish, then this thing lives in water. Here, fishesCannotFly is a predicate with arity zero (no terms), fish has arity one (one term, defining something that is a fish), and livesIn has arity two (the first term lives in the second term). On the other hand, the terms shark and water are constants while X is a variable, which can stand for any atom. By convention, constant names start with a low letter or are written in quotes, and variable names start with a capital letter.
However, solving an ASP program as explained above requires that it contains no variable; for this, a grounding step is first required, consisting in the removal of all free variables by replacing them by possible constants while preserving the meaning of the program. In the example above, the grounding step produces the following variable-free program, where X is replaced by the only suitable constant shark: After solving, the only answer set corresponding to this program is: For the present work, we used Clingo 1  [27]  which is a combination of a grounder and a solver. In the rest of this paper, we use ASP to tackle the problems of enumerating all fixed points and attractors of a given AN model.
and a 0 , b 0 , c 0 , d 1. All of them are represented in both Figs. 2  and 3. In this model, no other state verifies this property. We recall that the fixed points are identical for the synchronous and asynchronous schemes  [24]. If we execute the ASP program detailed above (lines 23-33) alongside with the the AN model given in Example (lines 1-21), we 3 answer that match the expected result. The output of Clingo is the following:.
Therefore, we define an ASP predicate unPlayable(T,S) in line 30 stating that a transition T is not playable at a step S. More precisely, T cannot be played in the corresponding global state of the system at step S, which is the case when at least one of its conditions is not satisfied. Obviously, each local transition that is not unplayable, is a playable. From this, we will be able to flag the actually played local transitions with played(T,S) (see later in lines 33 and 39). following how to compute the evolution of the model through the asynchronous and the synchronous update schemes, as presented in "Update schemes and dynamics of automata networks". The piece of program that computes the attractors, given afterwards, is common to whatever update schemes. All possible evolutions of the network (that is, the resulting paths after playing a set of global transitions) can be enumerated with a cardinality rule (explained in "Answer set programming") such as the ones in line 33 for the asynchronous update scheme, and line 39 for the synchronous update scheme.
Complex attractors Up to this point, we managed to propose an ASP program that enumerates all the attractors in a given AN. Each attractor is the trace of a path of length n. In many cases, except for some complex attractors, this length n (which corresponds to the number of played global transitions in the path) is also equal to the number of visited states (i. e. , the size of the trace). This is a trivial case of a minimal path covering a given attractor, that is, no path of lesser length can cover it. Indeed, as in the examples of attractors in Figs. 2  and 3 , enumerating the paths of length 2 is enough to obtain all the attractors having two global states, and the same goes for the attractors of length 4. But without the constraint that we develop below (given in lines 70-93), when the program is asked to display the attractors covered by a path of length n, it will also return various paths of size lower than n by considering non-minimal paths, that is, containing unwanted repetitions inside the cycle, or even repetitions of the entire cycle.
To obtain the models that we have studied in this section, we first extracted them from the GINsim model repository 3  [38] , in GINML format. These models correspond to the discrete asynchronous networks given in the corresponding papers. Then, the conversion step towards an ASP program is automated using the following tools: • The existing GINsim tool allows to export its models into the SBML qual formalism; • The existing LogicalModel library  [39, 40]  can convert SBML qual models into AN models; • Finally, our script AN2ASP converts AN models into ASP programs, following the principles detailed in 3 http://ginsim. org/models_repository. "Translating automata networks into answer set programs". It is noteworthy that each step fully preserves the dynamics between models regarding the asynchronous update scheme  [41] ; thus, the final (asynchronous) ASP program is bisimilar to the original GINML model.
Regarding the T-helper model (the largest studied model with 101 automata), the search for all attractors of size n = 2 with the synchronous update scheme takes about 275 s (∼5 min) and returns 2,058,272 answers, while it takes only 57 s to return all the attractors of size n=12, (6144 answers). However, as explained before, these results mean that this model features strictly less than, for instance, 6144 attractors covered by a cycle of length 12, because each one is repeated several times. In order to filter out the remaining repetitions, it should be possible to use a script or a text editor in order to extract only the states of each answer set and thus.
Multivariate Hawkes process We consider a dataset with M maps of ChIP-Seq peaks, or more generally of mapped genomic features. We denote by X m the m-th map, such that X m " tT m 1 ,. , T m nm u, with T m i the genomic coordinate of the peak i in map m. In a first approximation, we reduce the ChIP-Seq intervals to single points. Then, we consider that these positions are random to account for biological and technical noise and we model pX 1 ,. , X M q as a multivariate point process. To model the spatial interactions between genomic features, we introduce the conditional intensity of the process, which models the probability of occurrence of points, such that: for 1 ď m ď M , for t P rT 1 , T 2 s a given genomic region, λ pmq ptq " ν pmq `M ÿ l"1 ÿ T PX l h pmq l pt ´T q (1) • h pmq l is a function defined on p0, `8q that characterizes the influence of each occurrence T P X l on T 1 P X m.
Input: λ, α Let h(W ) be a strongly convex function with modulus 1 Let W 1 = W 0 = arg min W h(W ) Let Ḡ0 = 0 for t = 1, 2, 3,. do (y t , x t ) ← observe data (W t+1 , Ḡt ) ← mDAGL-update(t, y t , x t , W t , Ḡt-1 , λ, α) end for function similar to the group lasso  [33]  which has been recently suggested for efficient feature selection among a very large set of features  [27]. Multinomial logistic regression is a simple yet effective classification method. Assuming K classes of d-dimensional vectors x ∈ R d , we represent each class k with a d-dimensional prototype vector W k. Classification of an input vector x is based on how "similar" it is to the prototype vectors. Similarity is measured with inner product W k , x = d i=1 W ki x i , where x i denotes feature i. The probability of a class is defined by P (y = k | x; W k ) ∝ e W k ,x.
Proposal The proposal is to design a new advanced controller in Matlab environment, to overcome the limits of the previous controllers and to provide a fully automatic controller which works even in presence of disturbance. First step to design the controller is to build a mathematical model of the plant. Since this step is not trivial, the way of the black box identification has been followed. Since the system is divided in different logical parts, Figure  7  shows the strategy used to identify the model of the plant. Proceeding from left to right, first block is a Function (a third order polynomial, Figure  8a ) which replies the mechanical characteristic of an isopercentage valve. It takes as input the valve opening percentage and gives as output the flow. This block was necessary due to a problem that afflicts the flow measure below a certain value.
e. , one word being replaced by another), both to keep the analysis tractable and because of missing information in our dataset. 2 While this limits the scope of our results to the particular dataset we use, the methodological point we also make is left intact. By characterizing words using six well-studied features, we identify what makes a substitution more likely, and how a word changes when it is substituted. This exploratory approach uncovers a number of transmission biases consistent with known effects in linguistics. While the transformations we describe are not the only ones at work in this dataset, our analysis also indicates that feature-specific attractors could exist because of the substitution process. This study can be viewed as analyzing part of the transmission step operating in transmission chains of artificial languages like those studied by  Kirby, Cornish, and Smith (2008) , yet with natural language out of the laboratory.
More complex features based on word networks built from free association or phonological data have also been analyzed:  Nelson, Kitto, Galea, McEvoy, and Bruza (2013) , for instance, show the importance of clustering coefficient in such a semantic network by studying the role it plays in a variety of recall and recognition tasks (extralist and intralist cuing, single item recognition, and primed free association). Chan and Vitevitch (2010)  show that pictures are named faster and with fewer mistakes when they have a lower clustering coefficient in an underlying phonological network. Griffiths, Steyvers, and Firl (2007)  analyze a task where subjects are asked to name the first word which comes to their mind when they are presented with a random letter from the alphabet. The authors show that there is a link between the ease of recall of words and their authority position (PageRank) in a language-wide semantic network built from external word association data  (Austerweil, Abbott, & Griffiths, 2012 , further develop this tool to give a parsimonious account of the fact that related words are often retrieved together from memory).
simulated) cultural evolution with tools from cognitive science. Aiming to exhibit such subtle biases in complex data is the main reason we chose to use aggregate measures that have a chance of smoothing out the possible variations of experimental conditions in the dataset. To keep the analysis tractable, we focused on quotation transformations consisting of the substitution of a word by another word (and only these cases) in order to unambiguously discuss single word replacements. This restriction also allows us to more reliably infer the information that is missing in our dataset, as explained in Section 3. 3. To quantify these substitutions, we decided to associate a number of features to each word, the variation of which we can statistically study. The next subsections describe the dataset and the measures we used to assess this cognitive bias.
Corpus-based utterances We used a quotation dataset collected by  Leskovec et al. (2009) , large enough to lend itself to statistical analysis. This dataset consists of the daily crawling of news stories and blog posts from around a million online sources, with an approximate publication rate of 900k texts per day, over a 9-month period of time from  August 2008 to April 2009 (Leskovec et al. , 2009). 4 The authors automatically extracted quotations from this corpus. Each quotation is a more or less faithful excerpt of an utterance (oral or written) by the quoted person; for instance: The Bank of England said, "these operations are designed to address funding pressures over quarter-end. " Then, the authors gathered quotations in a graph and connected each pair that differed by no more than one word or that shared at least 10 consecutive words (they tested this procedure with a number of different parameters; see  Leskovec et al.
Manual inspection of this dataset revealed that it contains a significant number of everyday language quotations (such as "it was much better than I expected," "did that just happen," as well as many simple expletive-based sentences). Their presence is largely due to random variations around casual expressions, while we are interested in transformations of news-related quotes causally linked to an original, identifiable utterance. To filter them out, we exclude quotes with fewer than five words or whose occurrences span more than 80 days (indicating causally unrelated occurrences), as well as quotes not written in English. Clusters that are emptied by this procedure are therefore excluded. If, after this screening, a cluster's occurrences still span more than 80 days (because of short-lived but unrelated quotes far apart in time), we also exclude it.