dataesr/scientific-paragraphs-categorization huggingface dataset:
Research funding Several financial issues have been overlooked by advocates of PDA. Archiving mutualizes the benefits, but not the costs of long-term studies, because there is no cost to the person accessing the data. This might be a sustainable model when recurrent funding is available, but not when funding is granted on a per project basis. Also, PDA could incur some new costs for long-term studies since Dryad, for example, has required extra payment for large data sets. Researchers with scarce funding might not be able to absorb this additional cost. Maintaining constant funding is a critical issue for long-term studies to avoid fatal gaps in the data  [11, 59] , contrasting once again with short-term studies that can be restarted at a later time. Long-term studies of all durations experienced difficulties with funding (Figure  2 ) as only 33% were fully funded in all years with the remainder having funding gaps varying in duration from 1 to 19 years (Figure  2 ).
Providing primary data on a confidential basis A solution that would satisfy most PIs would be to provide tabulated summary data initially, and if that data were insufficient for editors to evaluate a submitted paper, primary data could be provided on a confidential basis. After the review process, the data could be destroyed and would not be available to be used for any other purpose. Once the paper is published, people who want to use the data could contact the Pls of the long-term study for additional data. As the survey has shown, 93% of the respondents have indicated that they have supplied data on request. For example, researchers have used summary data from the 40-year study on Darwin's finches  [45, 60]  by Peter and Rosemary Grant which was deposited in Dryad, and raw data have been supplied to four others upon request.
Depositing data on institutional servers Centralizing the data in a single database in one location will prevent fragmentation of data on different archiving sites and ensures that the data are completely secure and up to date. Data could be archived on institutional servers and the institution and its staff could control access and determine if collaboration is appropriate. An example of an effective approach to the management of archived data held by institutions is practiced by The Netherlands Institute of Ecology where people can request the data and data extraction is done by members of the Institute, provided that the applicant will use the data for a well described project, commit to not sharing the data with others, and offer co-authorship to the collector if the data forms an essential part of the publication.
A possibility would be to implement data tracking, allowing data collectors to obtain information on who is using the data and why. For example, any request for data to the Climate Change, Agriculture and Food Security Data Portal, triggers an email to be sent to the PI who deposited the data. Journals should have a rule that no paper is considered where the data users have not corresponded with the data owners and included appropriate acknowledgement of the source of the data within the paper. A rule set by journals would have a lot of clout with data users. Data tracking would also allow the PI to be systematically asked to review papers based on their data. Another option would be to send an e-mail to the PI every year asking whether they wish the data to be private or open access.
This paper describes the results of the Call Centre Conversation Summarization task at Multiling'15. The CCCS task consists in generating abstractive synopses from call centre conversations between a caller and an agent. Synopses are summaries of the problem of the caller, and how it is solved by the agent. Generating them is a very challenging task given that deep analysis of the dialogs and text generation are necessary. Three languages were addressed: French, Italian and English translations of conversations from those two languages. The official evaluation metric was ROUGE-2. Two participants submitted a total of four systems which had trouble beating the extractive baselines. The datasets released for the task will allow more research on abstractive dialog summarization.
Introduction Speech summarization has been of great interest to the community because speech is the principal modality of human communications, and it is not as easy to skim, search or browse speech transcripts as it is for textual messages. Speech recorded from call centres offers a great opportunity to study goal-oriented and focused conversations between an agent and a caller. The Call Centre Conversation Summarization (CCCS) task consists in automatically generating summaries of spoken conversations in the form of textual synopses that shall inform on the content of a conversation and might be used for browsing a large database of recordings. Compared to news summarization where extractive approaches have been very successful, the CCCS task's objective is to foster work on abstractive summarization in order to depict what happened in a conversation instead of what people actually said.
Collider searches for SUSY particles First we impose generic constraints from LEP on neutralinos, charginos, sleptons and squarks. For the latter we ignore the possibility of very compressed spectra and use the generic limit at 103 GeV. Lighter compressed squarks can in any case be constrained from LHC monophoton searches  [70, 71]  and monojet analyses  [72]. The most powerful and comprehensive constraints on supersymmetric partners have been obtained by ATLAS and CMS using the data collected at 7 and 8 TeV. Searches were performed for a wide variety of channels and results were presented both in the framework of specific models, such as the MSSM, and in the context of simplified model spectra (SMS). Here we use the SMS results to find the main constraints on the UMSSM. We base our analysis on SModelS v1.
0. 1  [39, 40] , a tool designed to decompose the signal of an arbitrary BSM model into simplified model topologies and to test it against LHC bounds. The version used includes more than 60 SMS results from both ATLAS and CMS. The input to SModelS are SLHA files that contain the full mass spectrum, decay tables as well as production cross sections. The input files, including tree-level production cross sections, are generated using micrOMEGAs 4. 1. 5 [73] , for strongly produced particles, SModelS then calls NLL-fast  [74] [75] [76] [77] [78] [79] [80]  to compute the k-factor at NLO+NLL order. Subsequently the code decomposes the full model into simplified model components, and calculates the weight (production cross section times branching ratio, σ × B) for each topology. To limit computing time, topologies with small weights are not considered in the decomposition.
As a minimum weight we have used a cutoff σ cut of 0. 03 fb. The resulting list is then confronted with the SModelS database, for any matching result σ × B is compared against the experimental upper limit. If no matching results are found the point is labeled as not tested. This may happen for several reasons, either all cross sections are below σ cut in which case the decomposition will not return any entries, there is no matching simplified model result in the database, or the mass vector of the new particles lies outside the experimental grids for all applicable SMS results. Since soft decay products cannot be detected, in this analysis we disregard vertices where the mass splitting between the mother and daughter SUSY particles is less than 5 GeV. A fully invisible decay at the end of a decay chain is compressed, the corresponding mother SUSY particle is then treated as an effective LSP.
Dark matter The value of the dark matter relic density has recently been measured precisely by the Planck collaboration and a combination of Planck power spectra, Planck lensing and other external data leads to  [97]  Ωh 2 = 0. 1188 ± 0. 0010. (4. 2) We will impose only the 2σ upper bound from eq. (4. 2) on the value of the relic density. That is we assume that either there is another component of dark matter or that there exists some regeneration mechanism that can bring the dark matter within the range favoured by Planck  [98, 99]. This measurement puts a strong constraint on the parameter space of the UMSSM whether the dark matter candidate is the lightest neutralino or the supersymmetric partner of the right-handed neutrino. Since the three RH sneutrinos have the same coupling to all other particles in the model we assume for simplicity that the third generation sneutrino is the lightest.
Summary after LHC constraints 7. 1 Exclusion potential of current LHC searches on the UMSSM To summarize the impact of the LHC constraints on the sfermion spectrum we display in figure  21  the excluded/non-excluded points in the plane θ E 6 -m f for f ∈ { t1 , b1 , dR } as well as f = μL for the sample where the muon anomalous magnetic moment constraint is imposed. Among the non excluded points those that satisfy all constraints have a different colour code than those that are associated with a long-lived NLSP or that are not tested by SModelS v1. 0. 1. In all cases the excluded points are scattered and represent only a fraction of all points. It should be stressed again that many scenarios with squark masses well below 1 TeV are allowed. When the agreement with ∆a µ is not required we found that 45% (41%) of the points that were confronted with the LHC limits had a long-lived sparticle in the case of a neutralino (RH sneutrino) LSP, 16% (17%) were tested by SModelS of which 10% (11%) were excluded.
Suggestions for future LHC searches Conventional searches for a new Z provide the most distinctive signature of the UMSSM. In addition we have pointed out in previous sections many additional signatures that are still unconstrained by current SMS results. Here we summarize the main missing topologies found for each scenario. As expected, the most distinctive SUSY signatures in the UMSSM are found in the case of a RH sneutrino LSP. We have already stressed that long-lived gluinos or squarks are fairly common in such scenarios and could therefore provide further constraints on the model. We have also found that the following topologies could be used to probe the model either by reinterpreting current LHC data or by exploiting Run II data. • mono-W , "(inv)(W ν)", from chargino neutralino production with χ0 1 → ν R νR and χ± 1 → W ± ν R νR.
Other important missing topologies include dijets + MET, "(inv)(j j)", from chargino neutralino production with the same decays as the mono-W above except that the W is off-shell leading to soft final states as well as W pairs + MET, "(W )(W )", from chargino pair production with χ± 1 → W ± χ0 1 → W ± ν R νR. These topologies also arise in the MSSM with a neutralino LSP and are poorly constrained from searches at Run I partly due to the small production cross section. The situation should however improve after accumulating more data in Run II. When the neutralino is the LSP, most SUSY signatures are the same as found in the MSSM. However we stress that having imposed only the upper limit on the dark matter relic density, most of our scenario have a wino/higgsino-like LSP. Thus, the SUSY signatures can differ from the bino LSP assumed in several SMS results.
We find this when the mass splitting between the squark and the LSP is large. Note that typically there would be similar channels with Z 1 or h 1 in the final state instead of a W reducing the cross section for each single channel. • 2 jets + W W + MET, "(j, W )(j, W )", from squark pair production, here both squarks can decay to chargino, followed by χ± 1 → W ± χ0 1 as above. This channel has been considered at the LHC but is not yet included in the SModelS database, moreover results are available only for specific mass relations. It would be preferable to provide SMS results that allow for interpolation over wide range of masses in different mass planes. In addition the signature W W + MET from chargino pair production will be useful in constraining the model, although it currently gives only weak limits  [121].
Acknowledgements We are grateful to  Sabine Kraml  for useful comments on the manuscript and to  Suchita Kulkarni ,  Andre Lessa ,  Veronika Magerl ,  Wolfgang Magerl ,  Michael Traub  and  Wolfgang Waltenberger  for useful discussions. We thank  Vincent Tisserand  and  Diego Guadagnoli  for useful discussions on the current determination of the CKM matrix elements. We also thank  Tomaso Lari ,  Jamie Boyd  and  Shlomit Tarem  for exchanges on ATLAS results for stable particles. G. B. thanks the LPSC where part of this work was done for its hospitality. This work was supported in part by the  LIA-TCAP of CNRS , by the  French ANR , Project  DMAstro-LHC ,  ANR-12-BS05-0006 , by the  Investissements d'avenir, Labex ENIGMASS  and by the  European Union  as part of the  FP7 Marie Curie Initial Training Network MC-netITN  ( PITN-GA-2012-315877 ).
Example 3. 4. Consider the signature Σ = {cons 2 , succ 1 , zero 0 , nil 0 } and the predicates List(-), Nat(-). The program NatList, encoding the definition of lists of natural numbers, will be our running example of a non-ground logic program. List(cons(x 1 , x 2 )) ← Nat(x 1 ), List(x 2 ) List(nil) ← Nat(succ(x 1 )) ← Nat(x 1 ) Nat(zero) ← Let A be the atom List(cons(x 1 , cons(x 2 , x 1 ))). It is intuitively clear that there is no substitution of variables making A represent a list of natural numbers: we should replace x 1 with a "number" (for instance zero) in its first occurrence and with a "list" (for instance nil) in its second occurrence. Consequently, there is no SLD-refutation for {A} in NatList. However, consider the ∧∨-tree of A in NatList, for which we provide a partial representation as follows.
allDifferent The constraint allDifferent(Vars) is a global constraint that enforces all the variables of the set Vars to be different. This constraint is very useful, it occurs in many practical problems. Finding a solution for a CSP is performed by a program called a CSP solver. There are many solvers in the literature, such as Abscon, Choco and ECL i PS e solver. They essentially differ by their implementation language (Prolog for ECL i PS e , Java for the two others), the expressivity of the supported constraints and the complexity of the propagation algorithms implemented in the solver. To perform our experiments, we choose a solver allowing the use of a generic language for defining CSP such as XCSP  (Lecoutre and Roussel, 2009) , thus, we use Abscon solver  (Merchez et al. , 2001).
The meta-model and its OCL constraints are translated into alloy language, then the Alloy solver generates solutions. The translating operation from a meta-model to Alloy constraints is quite simple. However, the approach suffers from a lack of scalability, since Alloy is dedicated to model checking and does not allow solutions to be quickly generated. In addition, the OCL constraints of a given meta-model are manually translated. This is not suitable when there are a lot of constraints to treat. Cabot et al. use an approach based on CSP in  (González Pérez et al. , 2012) ,  (Cabot et al. , 2008). It consists in the modelling of a meta-model and its OCL constraints into a CSP instance. Then, a CSP solver (namely ECL i PS e ) generates solutions. The CSP paradigm is very flexible and allows a large part of OCL constraints as well as additional constraints to be processed to improve the relevance of the generated models.
Feature Modelling To model a feature of simple type into CSP, we create a variable whose type is the domain. A variable is created for each instance of each class having a feature. ∀c ∈ C l, ∀ f ∈ F feature of c, ∀i ∈ {1,. , size(c)} : create a variable F c,i, f. Notation 1. The data type of a feature f is denoted Type( f ). The domain of the variables F c,i, f is given by: D(F c,i, f ) = Type( f ). The most frequently used data types are: Integer, Char and String. Definition 8. The data type Enumeration is a set of values {v 1 , v 2 ,. , v l } of the same type. A value v i is called a literal of the enumeration. Enumerations are modelled as follows. Let f be a feature of type enum, where enum is an enumeration of l literals: Type( f ) = enum ⇒ D(F c,i, f ) = {1, 2,. , l}.
For binary classification problem, we use: P (s = 1|x 1 , y) = 0. 5 if x 1 > mean(x 1 ) and y = 1 1 if otherwise. For each data set, we then train 4 predictive models learned under the four weighting schemes discussed above and a model learned from the unbiased data (baseline) using SVM-light  [4]  which allows importance weighting to be fed directly to SVM. All classifiers are trained with the common Radial Basis Function (RBF), with a kernel size σ chosen through a 5-fold cross validation. This procedure is repeated 100 times for each data set. The results are reported in Tab. 1. As may be seen, all importance weighting schemes achieve lower prediction error with respect to the baseline unweighted scheme. The underlying probability weighting scheme performs pretty good. Curiously, on the Boston Housing data set, all three weighting schemes perform worse than the baseline unweighted method.
This linear model is often improved by auxiliary regularization terms. Recently, a few kernel-based NMF have been proposed to extent the linear NMF model to the nonlinear scope. By exploiting the framework offered by the kernel machines, these methods map the This work was supported by the French ANR, grand HYPANEMA: ANR-12BS03-0033. data with some nonlinear function from the input space to a feature space, and perform the existing linear techniques on the transformed data. Unfortunately, the curse of the preimage  [2] , inherent from kernel machines, remains the bottleneck in most kernel-based NMF, e. g. ,  [3, 4]. That is, the obtained bases lie in the feature space, and thus one cannot represent both low-rank matrices in the input space. Recently proposed in  [5] , the so-called KNMF overcomes this difficultly by minimizing an objective function, which is defined in the feature space, directly in the input space.
The experiments are conducted using the Gaussian kernel. By conducting a preliminary analysis with the batch KNMF on a small-size dataset, we choose the bandwidth in kernel as σ = 3. 3 for Moffett and σ = 3. 0 for Urban. Regarding the parameter-setting, the mini-batch size is empirically chosen as in  [8] , with p = min{⌈ k 10 ⌉, 30}. To ensure a fair comparison, the iteration number is equally set to be I = 100 in all the algorithms. As shown in Figure  1  and Figure  2 , the proposed KONMF with the Gaussian kernel outperforms ONMF and INMF in terms of the reconstruction error in the input space, and surpasses all the state-of-the-art methods in terms of the reconstruction error in the feature space.
Information-Centric Networking (ICN) advocates a cleanslate redesign of the Internet network layer through a major shift from a host-to-host communication model to a content-based one. Hence ideally, current ICN propositions and implementations, for instance NDN (Named Data Networking), should replace the IP layer. Indeed none of these already provide a full-fledged solution, due to the big challenges that their deployment at a global scale implies. Nevertheless ICN's ambitious goal could be a plausible short-term step for networks at the Internet's borders, where several layer-2 technologies populate the Internet of Things ecosystem. Those domains still need an in-depth analysis of the potential of the ICN solutions as well as of the concrete mechanisms to deploy them. Thus in the following, we've sketched out our research path to find out common layer-2 requirements for the future Internet's network-layer.
Background on Named Data Networking The NDN architecture  [3]  is the ICN instantiation targeted for our study. The NDN communication model is receiver-driven and it is governed by two different named network packets, the Interest and the Data, and by three data structures, the Forwarding Information Base (FIB), the Pending Interest Table (PIT) and the Content Store (CS). The communication is started by information consumers that issue Interests to request contents. Then the network routes by name the Interests (matching FIB entries held by the network devices) towards one or more content providers (either the original source or devices that have cached a copy in their CS). Finally the Data is routed back to the requester, following the trail left (the PIT entries) by the corresponding Interest into the traversed network devices.
NDN features several design choices that appeal to the IoT. It provides innetwork caching and content-based security (every packet carries a digital signature) that might reduce the duty cycle of constrained-resources devices, as long as the information they've disseminated will be valid. It provides flow balance mechanisms, matching an Interest to a single Data packet, that might reduce the overall traffic and so the demand for computation into the network devices. It allows to define human-readable naming schemes to address the network's contents, and this might ease the services and objects discovery which is crucial in IoT dense environments. Moreover NDN performs multi-path namebased forwarding of network packets, that is, a network device can forward a request to multiple next hops based both on some information it holds and on the request's name.
Methodology and preliminary results Our research roadmap mainly targets the testing and the extension of the NDN's platform codebase  [19]. However we use in parallel the network simulator ndnSim  [14]  based on the ns-3, a network simulator that offers modules modeling the most common layer-2 technologies. We've tested how the plain NDN performs in simple scenarios wherein some nodes exhibit both WiFi and Ethernet connectivity. We've run some rounds of simulation over a three-level tree network topology, where all the leaf nodes fetch content from a single producer at the root of the tree, see figure  2. We've run the experiments for three distinct scenarios, namely labeled as Caching, WiFi, WiFi & Caching. In the Caching scenario, all the tree nodes cache content, but none of them have WiFi connectivity.
Acknowledgements The authors acknowledge the  ANR agency  ( SPINNOVA ANR-11-NANO-0016 ) as well as  EU   FP7  grant (MOSAIC No. ICT-FP7-8. 317950 )for financial support. RFBR grant  10-02-01162  is acknowledged. A. D. B. acknowleges the  Dynasty Foundation  for the financial support. V. V. N. acknowledges support from program  Competitive Growth of KFU. F. A. A. acknowledges the Research Science Foundation of Belgium (FRS-FNRS)  for financial support ( FRIA grant ) and the  UCL  for an  FSR complement  ( Fonds Spécial de Recherche). A. D. B. , P. N. S. and K. A. Z. acknowledge 50  Labs Initiative of Moscow Institute of Physics and Technology. The authors acknowledge  Prof. Anatoly Zvezdin  for fruitful discussion.
Speciation modelling of uranium (as uranyl) and thorium, in four freshwaters impacted by mining activities, was used to evaluate (i) the influence of the co-contaminants present on the predicted speciation, and (ii) the influence of using nine different model/database combinations on the predictions. Generally, co-contaminants were found to have no significant effects on speciation, with the exception of Fe(III) in one system, where formation of hydrous ferric oxide and adsorption of uranyl to its surface impacted the predicted speciation. Model and database choice on the other hand clearly influenced speciation prediction. Complexes with dissolved organic matter, which could be simulated by three of the nine model/database combinations, were predicted to be important in a slightly acidic, soft water.