dataesr/scientific-paragraphs-categorization huggingface dataset:
However, as here the response is conditionally independent from the covariates, we do not encounter such issues, but we can explore in depth the potentially complex relationship between response and covariates. The prior model for the mixture weights is given by the stick-breaking priors (constructive definition of the Dirichlet process), that is, ψ c = V c l<c (1 -V l ) for all c, ψ 1 = V 1 , V c ∼ Beta(1, α) i. i. d. The parameter α can be fixed or can have a Gamma(s α , r α ) distribution with s α and r α as shape and rate parameters respectively. Other prior models for the mixture weights are possible, and, for example, the Pitman-Yor construction is also available in the R package PReMiuM. The covariate model f (x i |z i = c, φ c ) can be defined as continuous or discrete. In the continuous case X assumes a mixture of Gaussian distributions.
It is often useful to characterise the partition which is most supported by the data. However, as at each iteration of the sampler individual profiles are assigned to clusters, the MCMC output is very rich. Molitor et al. (2010)  developed methods to process this output to make useful and interpretable inference. Several methods for this are available in the R package PReMiuM but we find the most robust method is to process the similarity matrix using partitioning around medoids (PAM), which is available in the R package cluster. First of all, a score matrix is constructed, where each element of the matrix is set equal to 1 if individuals i and j belong to the same cluster and 0 otherwise. Then a similarity matrix S is computed by dividing each element of the score matrix by the number of iterations, so that S ij denotes the probability that individuals i and j are assigned to the same cluster.
Results We have fit profile regression to the data using the R package PReMiuM  (Liverani et al. , 2015). The pollution data are modelled with a Gaussian distribution including a spatial ICAR term. The covariate profiles, given by the selected IMD domains, are modelled with a discrete distribution, as we have transformed each IMD domain into quintiles. We have not included any additional fixed effects. The results were very robust on several MCMC runs using a range of initial values and different chain lengths. We present here the results obtained with 5,000 iterations after a burn in of 5,000 with the following hyperparameter settings. s α = 2, r α = 1, a 1 =. = a 6 = 1, µ θ = 0, σ θ = 2. 5, µ β = 0, σ β = 2. 5, s τ -Y = 2. 5, r τ -Y = 2. 5. In this section we aim to illustrate how profile regression can help shed light on complex patterns between highly covariates covariates and response.
David Pollock (University of Colorado School of Medicine, Denver, USA) presented AnTE, software implementing a Bayesian method to predict the ancestral sequence of highly duplicated TEs. This eliminates major problems encountered with the consensus sequence approaches. Remarkably, it improves the analysis of substitution rates and replication timing, introduces simultaneous alignment, distribution of replication ages, and allows the analysis of one million sequences simultaneously. The analysis of ~800 k Alu sequences showed that replicating sequences are highly constrained, and that the identity of functional sites fluctuates over time. Inference of replication time indicated that ancestral replicating sequences exhibit a much narrower range of activity than previously realized. A much more accurate estimation of timing suggested the coevolution of Alu with LINEs and/ or the primate host.
These elements can attach to exposed G-rich overhangs at telomeres via short stretches of telomeric repeats, and can form long head-to-tail chains extending up to 100 kb. The principal polymerizing components are intron-containing reverse transcriptases from the PLE (Penelope-like elements) class. Terminons contain multiple co-oriented ORFs, which code for a variety of enzymatic and structural functions and their replication follows the master copy model, with fixed 3′-ends and frequent 5′-truncations. Amongst plants, the Arabidopsis mobilome is particularly rich at the species level. In his talk, Leandro Quadrana (IBENS, Ecole Normale Supérieure, Paris, France) indicated that the composition and activity of the mobilome varies greatly between accession numbers in the public databases. Natural variation of the mobilome is associated with sequence variants, notably in the MET2a gene, which encodes a poorly characterized DNA methyltransferase.
Moreover, he presented some recent results on the assessment of transposition in planta in rice, using next-generation sequencing technologies, showing the impact of the down-regulation of RdDm pathway on transposition activation. The analysis of variation in plants has revealed that their genomes are characterized by high levels of structural variation, consisting of both smaller insertion/ deletions, mostly due to recent insertions of TEs, and of larger insertion/deletions similar to the human Copy Number Variants (CNVs). Michele Morgante (Universita di Udine, Udine, Italy) described the sequencing to high coverage of more than 100 grapevine entries in the public databases. He used a variety of approaches to detect structural variants of different size and origin, including de novo assembly of a selected set of genotypes, and proposed different mechanisms that could have generated and maintained the dispensable portion of the genomes and the genetic and epigenetic effects of the structural variants caused by TE movement.
Acknowledgments The congress was mainly funded by  Alliance pour les sciences de la vie et de la santé (Aviesan) ,  Centre national de la recherche scientifique (CNRS research group  "Mobile genetic elements: from mechanism to populations, an integrative approach",  GDR3546 ),  Institut national de la recherche agronomique (INRA) ,  Université de Rennes 1 and Région Bretagne , and  Mobile DNA  (BioMed central). Other sponsors were the Society for molecular biology and evolution (SMBE),  Institute of Human Genetics, IRCAN, GReD, GATC biotech, Hybrigenics and Beckman Coulter genomics. Work in MB's lab  is supported by intramural funding from the  Centre National de la Recherche Scientifique (CNRS)  and by grants from the  Agence Nationale de la Recherche  ( ANR-12-BSV6-0017  and  ANR-14-CE10-0005-01 ) and the  ARC Foundation for Cancer Research (ARC)  ( PJA 20151203521 ).
Work in SC's lab is supported by the  Fondation pour la Recherche Médicale (FRM)  ( DEP20131128518 ) and the  ARC  ( PJA2015-1203231 ). Work in the laboratory of GC is supported by the  ARC  ( PJA 20141201838 ), the  ANR  through the  'Investments for the Future ' (LABEX  SIGNALIFE , # ANR-11-LABX-0028-01 ) and the  FRM  ( DEP20131128533 ). Work in CV's lab is supported by intramural funding from the  CNRS , the  Université Clermont Auvergne  and the  Institut National de la Santé et de la Recherche Médicale (INSERM) , and by a grant from the  ANR (ANR-PlasTISIPI ). Work in HQ's lab is supported by intramural funding from the  Institut National de la Recherche Agronomique (INRA). Work in J-NV's lab is supported by a grant from the  ENS Lyon. NG's lab is supported by intramural funding from the  INSERM  and by grants from the  ANR  ( ANR-12-BSV6-0003 , RETROGENO).
Based on our previous experience, we finally decided to employ the fibers of the manufacturer Fiberguide Industries shown in Figure  5 , where R1 = 300 µ m, R2 = 700 µ m, and R3 = 1070 µ m. In the same figure, a microscope picture of the cross section of the manufactured bundle is also depicted. The final distances in our manufactured bundle were R1 = 324 µ m, R2 = 686 µm, and R3 = 1125 µ m, somewhat different from the design radius. Before manufacturing the bundle, we developed a MATLAB program in order to verify the behavior of the sensor. This software was developed according to the theoretical models described in literature  [23, [31] [32] [33]. As all these papers assume that the target is a mirror instead of a blade, we adjusted the parameters of the reflected light in order to get more realistic results.
This parameter optimization was achieved by performing several experimental measurements using the trifurcated bundles we have in our laboratory. Figure  6  depicts the experimentally obtained calibration curve for the sensor employing the tetrafurcated bundle in the region I, together with the simulation. Table  1  shows the distance differences between the two curves for the same values of V 2 /V 1 obtained along the measurement range. We can see that the simulation provides good results except for the last part of the measurement range, where the distance difference increases significantly. Sensors 2016, 16, 1897 5 of 14 Before manufacturing the bundle, we developed a MATLAB program in order to verify the behavior of the sensor. This software was developed according to the theoretical models described in literature  [23, [31] [32] [33].
These signals were stored and post-processed off-line using a LabVIEW program designed specifically for the analysis of TC measurements using the sensor. The program divides the whole acquisition in individual revolutions making use of the OPR signal. In this way, it can evaluate the quotient V 2 /V 1 during one revolution and find the TC values for each of the 28 blades of the compressor. The minimum of such values is considered as the TC for that revolution. To calculate the TC value for each blade, the calibration curve in Figure  8  is employed to convert the value of the quotient V 2 /V 1 into distance. In Figure  13  the three signals (OPR, V 1 and V 2 ) are depicted at 7600 rpm and at 15,600 rpm. For the sake of clarity, the amplitude of the signal V 1 has been magnified by a factor of 10.
In this figure, it is clearly seen that the amplitude of the signals becomes higher as the rotational speed increases. This is due to the fact that the faster the engine is turning, the closer the blades are to the casing and the higher is the reflected light intensity that reaches the bundle of optical fibers. During each cycle, three signals were acquired: the OPR (Once Per Revolution) signal and the signals corresponding to the outputs of the photodetectors, V1 and V2. These signals were stored and post-processed off-line using a LabVIEW program designed specifically for the analysis of TC measurements using the sensor. The program divides the whole acquisition in individual revolutions making use of the OPR signal. In this way, it can evaluate the quotient V2/V1 during one In Table  2  the TC measurements during the acceleration of the cycles 1 and 3 are shown.
Acknowledgments: This work has been funded in part by the  Fondo Europeo de Desarrollo Regional (FEDER) ; by the  Ministerio de Economía y Competitividad  under project  TEC2015-638263-C03-1-R ; by the  Gobierno Vasco/Eusko Jaurlaritza  under projects  IT933-16  and ELKARTEK; and by the  University of the Basque Country UPV/EHU  under programmes  UFI11/16  and Euskampus.
Data collection consisted of 10 blocks of 10 cycles with 5 s integration time. An electronic baseline (10 s integration time) was measured prior to each block. A Faraday gain calibration was performed weekly. Instrumental mass bias was corrected for by the 130 Ba-135 Ba double spike. The measured raw data were reduced using the iterative solution of  Compston and Oversby (1969). However, the exponential fractionation law was used instead of the linear one. All data are reported relative to NIST SRM 3104a using the delta-notation in per mill (‰): (1) The external reproducibility of sample analyses was estimated by calculating the pooled standard deviation of all repeated measurements to be ±0. 09 ‰ (2 sp) on 137/134 Ba. This is in excellent agreement with repeated measurements of the Fluka Aldrich Ba(NO3)2 ICP-OES standard solution used in von  Allmen et al.
Acknowledgements We thank  Maria Hierz  and  Andrea Wolf  from the  TU Graz  for laboratory support with preparation of silica hydrogel and conduction of the diffusion experiments. Jennifer Druhan  and two anonymous reviewers are thanked for constructive comments that greatly improved the quality of the manuscript. This work was supported by the  Swiss National Science Foundation  (SNSF Grant  200021_140223  to TFN). The paper is a contribution to the collaborative research initiative CHARON, financed by the  German Research Foundation.
There is a large variety of SAT and SMT solvers, with each solver typically excelling at solving problems in some specific class of propositional or first-order problems. While the SAT and SMT communities have adopted standard languages for expressing input problems (namely the DIMACS standard for SAT and the SMT-LIB  [4]  standard for SMT), agreeing on a common output language for proof witnesses has proven to be more challenging. Several formats  [11, 21, 6]  have been proposed but none has emerged as a standard yet. Each proof-producing solver currently implements its own variant of these formats. To be able to combine the advantages of multiple SAT and SMT solvers despite the lack of common standards for representing proof certicates, SMTCoq has been designed to be modular along two dimensions:.
SMTCoq • supporting new theories: SMTCoq's main checker is an extendable combination of independent small checkers; • supporting new solvers: SMTCoq's kernel relies on a generic certificate format that can encode most SAT and SMT reasonings for supported theories; the encoding can be done during a preprocessing phase, which does not need to be certified. In this abstract, we emphasize the key ideas behind the modularity of SMTCoq, and validate this by reporting on work in progress on the integration of the SMT solver CVC4  [3]  and the theory of bit vectors. We simultaneously aim at: • offering to CVC4 users the possibility to formally check its answers in a trusted environment like Coq; • bringing the power of a versatile and widely used SMT solver like CVC4 to Coq; • providing in Coq a decision procedure for bit vectors, a theory widely used, for instance, for verifying circuits or programs using machine integers.
General Idea The heart of SMTCoq is a checker for a generic format of certificates (close to the format proposed by Besson et al. [6] ), implemented and proved correct inside Coq (see Figure  1a ). Taking advantage of Coq's computational capabilities the SMTCoq checker is fully executable, either inside Coq or after extraction to a general-purpose language  [18]. The Coq signature of this checker is the following: checker : formula → certificate → bool where the type formula represents the deep embedding in Coq of SMT formulas, and the type certificate represents SMTCoq's format of certificates. The checker's soundness is stated with respect to a translation function from the deep embedding of SMT formulas into Coq terms: • : formula → bool that interprets every SMT formula into its Coq Boolean counterpart.
The Checker We now provide more details on the checker of SMTCoq. As presented in Figure  2 , it consists of a main checker obtained as the combination of several small checkers, each specialized in one aspect of proof SMTCoq checking in SMT (e. g. , CNF conversion, propositional reasoning, reasoning in the theory of equality, linear arithmetic reasoning, and so on). The type certificate is actually the aggregation of specialized types, one for each small checker. The role of the main checker is thus to dispatch each piece of the certificate to its dedicated small checker, until the initial formula is proved. A small checker is a Coq program that, given a (possibly empty) list of formulas and a certificate associated with it (which may be just a piece of the input certificate), computes a new formula: small_checker : list formula → certificate_sc → formula The soundness of the checker comes from the soundness of each small checker, stated as follows: small_checker_sound : ∀ f 1.
To encode an LFSC proof into SMTCoq, our preprocessor thus linearizes nested proofs. The LFSC proofs generated by CVC4 are constructed in such a way that this does not cause a blow-up in practice; however, to support LFSC in general, we plan to extend SMTCoq certificates with nested proofs. Again, this extension should be made easier by the modularity inside the checker. It should impact only the main checker, and not the various small checkers already in SMTCoq.
Training Corpus To learn our sequence-to-sequence models for generation and to test our hypotheses, we build a synthetic training data-to-text corpus for generation which consists of 18 397 (data,text) pairs split into 11039 pairs for training, 7358 for development and 7358 for testing. We build this corpus by extracting data from DBPEdia using SPARQL queries and by generating text using an existing surface realiser. As a result, each training item associates a given input shape (the shape of the RDF tree from DBPedia) with several output shapes (the syntactic shapes of the sentences generated from the RDF data by our surface realiser). Figure  3  shows an example input data and the corresponding paraphrases.
Data RDF triples consist of (subject property object) tuples such as (Alan Bean occupation Test pilot). As illustrated in Figure  1 , RDF data can be represented by a graph in which edges are labelled with properties and vertices with subject and object resources. To construct a corpus of RDF data units which can serve as input for NLG, we retrieve sets of RDF triples from DBPedia SPARQL endpoint. Given a DBPedia category (e. g. , Astronaut), we define a SPARQL query that searches for all entities of this category which have a given set of properties. The query then returns all sets of RDF triples which satisfy this query. For instance, for the category Astronaut , we use the SPARQL query shown in Figure  4. Using this query, we extract sets of DBPedia triples corresponding to 634 entities (astronauts).
Text To associate data with text, we build lexical entries for DBPedia properties and use a small handwritten grammar to automatically generate text from sets of DBPedia triples using the GenI generator  (Gardent and Kow, 2007). Lexicon. The lexicon is constructed semiautomatically by tokenizing the RDF triples and creating a lexical entry for each RDF resource. Subject and Object RDF resources trigger the automatic creation of a noun phrase where the string is simply the name of the corresponding resource (e. g. , John E Blaha, San Antonio,. ). For properties, we manually create verb entries and assign each property a given lexicalisation. For instance, the property birthDate is mapped to the lexicalisation was born on. Grammar. We use a simple Feature-Based Lexicalised Tree Adjoining Grammar which captures canonical clauses (1a), subject relative clauses (1b), VP coordination (1c) and sentence coordination (1d).
Given this grammar, the lexicon described in the previous section and the RDF triple shown in (1a), the GenI generator generates the five verbalisations shown in five (1b-f). (1) a. To learn a sequence-to-sequence model that can generate sentences from RDF data, we use the neural model described in  (Sutskever et al. , 2011)  and the code distributed by Google Inc 1. We experiment with different versions of the training corpus. Raw corpus (BL). This is our a baseline system. In this case, the model is trained on the corpus of (data,text) pairs as is. No explicit information about the structure of the output is added to the data. Raw Corpus+Structure Identifier (R+I). Each input data is associated with a structure identifier corresponding to one of the five syntactic shapes shown in Figure  3.
Raw corpus+Infix Connectors (R+C). The input data is enriched with infix connectors where & specifies conjunction, parentheses indicate a relative clause and ". " sentence segmentation. The last line in Figure  3  shows the R+C input for S1. 4 Evaluation and Results. We evaluate the results by computing the BLEU-4 score of the generated sentences against the reference sentence. Table  1  shows the results. The baseline and the R+I model have very low results. For the baseline model, this indicates that training on a corpus where the same input is associated with several distinct paraphrases make it difficult to learn a good data-to-text generation model. The marked difference between the R+I and the RI+C model shows that simply associating each input with an identifier labelling the syntactic structure of the associated sentence is not sufficient to learn a model that should predict different syntactic structures for differently labelled inputs.
INTRODUCTION Variability intensive systems, such as the Linux kernel, may include several thousands of features allowing for an enormous number of possible configurations. Among them, a lot of theoretically possible configurations are, however, not valid for a specific task (e. g. , the kernel does not compile). This may happen due to some hidden constraints (mutually exclusive features, incompatible values for constants, etc. ). It is then extremely painful for the engineer to dis-ACM ISBN 978-1-4503-2138-9. DOI: 10. 1145/1235 cover, late in the process (at compilation time or even worse at testing time), that her carefully chosen set of features is actually invalid. The space of actually possible products for a specific task can be seen as a subspace of the initial space of possible products given by a variability model.
A typical way to go from the initial space to the desired subspace is to add constraints to the variability model to restrict the initial space down to the desired subspace. It can be equally seen as a way to constrain the acceptable inputs of the variables of an automatic product derivator. The challenge addressed in this paper is to try to automatically synthesize a set of constraints that would be both precise (allow all correct configurations) and complete (never allow a wrong configuration with respect to some oracle). We propose the use of machine learning to infer such product-line constraints from an oracle that is able to assess whether a given product is correct (e. g. , the compiler in the case of the Linux kernel). We propose to randomly generate products from the product line, keeping for each of them its resolution model.
Case and Problem Given a configuration file, the MOTIV generator synthesizes a video variant with specific properties (luminance, trajectory of vehicles, noises, distractors, etc. ). The software generator is written in Lua and implements numerous complex, parameterized transformations for synthesizing variants of videos  [1, 12]. The motivation behind the generator is that the current practice for benchmarking tracking algorithms (e. g. , algorithms that track objects of interests in a scene) is limited to a manual collection of video sequences. Such manual effort is error-prone and time-consuming since it requires to (1) shoot video sequences in real environments and (2) annotate videos with a "ground truth". As a result, benchmarks with limited size and diversity are usually employed. In response, this MOTIV generator has been developed to automatically produce a large and diverse set of video sequences.
Without constraints, many configurations lead to the generation of unrealistic video variants, due to the incompatibility between features and attributes' values. Beyond usability problems, this is an issue for two major reasons. First, the production of videos has a cost (about half an hour of computation on average per video variant). As a result, the synthesis of large datasets with thousands of video variants (as originally planned by industrials) would exhibit a lot of irrelevant videos, thus wasting computation power. Second, tracking algorithms performed on the synthesized videos are computationally expensive, which, in case of irrelevant videos, is again a waste of time and resources. Our early effort  [1, 12]  for properly formalizing the variability was thus not sufficient; we need to enforce the generator with constraints to make it usable and useful for practitioners.