dataesr/scientific-paragraphs-categorization huggingface dataset:
Related Work Grammar-based testing has been used in compiler testing for many years  [5, 20]. Compilers are a specific kind of meta programs and could be tested using similar techniques. The YouGen tool  [14]  generates test data based on grammar definitions, controlling depth of derivation trees based on annotations. The major difference is about the algorithm based on enumeration and filtering of the derivation trees. This also reduces the number of test data generated, but the filtering is guided only using information from the grammar. Our approach also uses information from the program under test. In the same direction, the XTextGen  [13]  generates test-data based from grammars. This tool has a different approach to generate the data. It uses Cantor pairing and mandatory multiplicity control.
Electrical -level Transient currents issued from physical model can be injected on each illuminated collection node (transistor drains or sources). Doing so, the electrical model of the transient pulses can be associated with the circuit netlist. The link between the layout and the netlist is performed in our flow thanks to the "Calibre" tool from Mentor Graphics. According to this physical-level model, the laser effect is modeled at electrical level as a plug-in current source for each illuminated junction. The model is depicted in Fig. 9. In order to link the physical-level models and the electrical-level models for simulation purpose, a database was developed; each file corresponds to a standard cell in a given library and to a laser configuration data (energy, spot size). In each file, the transient current pulses I(t) are enumerated for each collection zone according to the position of the laser (dpn_d, dpn_s) for each logic state of the standard cell.
CAD Tools The proposed security-evaluation flow is supported by several tools dealing with different abstraction levels. First, the databases of induced currents is generated using the MUSCA SEP3 tool on every standard cell, then the Calibre tool is used to transfer this information on the netlist of the circuit under evaluation as presented in section 5. 1 and 5. 2. Developed on the basis of the 0-delay simulator LIFTING  [19] , tLIFTING (timing LIFTING)  [20]  is an open-source fault simulator for single/multiple stuck-at faults, single/multiple upsets and single/multiple transients faults. The tool allows 0delay/delay-annotated logic-level simulations and transistor-level fault simulation for digital circuit described in Verilog. Cooperating with a set of sub-tools, this simulator is able to perform transistor-level simulations based on the laser-induced fault model (current curves) and then further logic-level simulation for the whole circuit in order to analyze propagations of transient misbehaviors.
As an open-source tool, it was expanded to read the database generated by MUSCA SEP3. Fig. 13  shows how these tools interact with each other to produce simulation reports of laser-induced faults. The simulation process is illustrated in Fig. 14 : starting from the laser's parameters (size, position, power) and circuit layout information, affected PN junctions are located as sub-circuit in the design, and corresponding I(t) curves are extracted from the database. The corresponding electrical fault models are injected into the affected sub-circuit at transistor-level description. Then the whole system is simulated at logiclevel in order to compute the sub-circuit input waveforms during the whole external perturbation. This information is then provided to the electrical-level simulator in charge of the simulation of the sub-circuit in order to simulate the electrical perturbation.
Acknowledgment. This work has been supported by the  French National Research Agency  project " LIESSE"  ( ANR-2012-INSE-0008 ). The counter-measure development has also been partially supported by the contract  FUI CALISSON 2 (DGCIS AAP10 ). TIMA is Partner of the  Labex PERSYVAL Lab  ( ANR-11-LABX-0025 ). We would like to thank  Verific Design Automation Inc. for providing the Sys-temVerilog and VHDL front-end used for the implementation of our RTL methodology.
Domain adapted system Before the human translator starts working, our DA system is trained using an extracted subset of bilingual training data that is mostly relevant to our specific domain. The extraction process, widely known as data selection, is applied using cross-entropy difference algorithm proposed by  (Axelrod et al. , 2011)   2. In order to augment the amount of training data 3  (about 22M words) we also select a bilingual subset from Europarl, JRC-Acquis, news commentary, software manuals of the OPUS corpus, translation memories and the United Nations corpus. About 700M additional newspaper monolingual data selected from WMT evaluation campaign are also used for language modeling.
24 (20. 04) [54. 13] 27. 48 (20. 40) [54. 16] 27. 42 (20. 99)   19 (22. 34) [53. 16] 30. 46 (23. 71) [54. 31] Table  1 : TER scores for English-French data of the Legal domain for the three translators over 5 days. Parenthesized scores are calculated using the references of all three translators, while scores in brackets are calculated using a generic reference provided by the European Commission. analyzing translation quality in terms of BLEU score (results not presented here). Like for the prior TER results, the BLEU scores for translator 3 are much worse than the scores of the two other ones. After the third day, the scores reach the same level. Again, this could indicate that the adaptation process has learned his particular style.
We introduce in this paper LocFaults, a new flow-driven and constraint-based approach for error localization. The input is a faulty program for which a counter-example and a postcondition are provided. To identify helpful information for error location, we generate a constraint system for the paths of the control flow graph for which at most k conditional statements may be erroneous. Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. The removal of one of these sets of constraints yields a maximal satisfiable subset, in other words, a maximal subset of constraints satisfying the post condition. To compute the MCS, we extend the algorithm proposed by Liffiton and Sakallah [21]  in order to handle programs with numerical statements more efficiently. The main advantage of this flow-driven approach is that the computed sets of suspicious instructions are small, each of them being associated with an identified path.
The process starts with a faulty program and a counter-example violating the postcondition. To provide helpful information for finding potential errors, we generate a constraint system for the paths of the CFG for which at most k conditional statements may be erroneous. Then, we calculate Minimal Correction Sets (MCS) of bounded size for each of these paths. In other words, we bound both the number of suspected assignments on the initial path, and the number of deviations from that path. To compute MCS, we extend the algorithms introduced by Liffiton and Sakallah  [21, 20]  to be able to handle programs with numerical statements more efficiently. Note that LocFaults may miss some errors since the number of deviations as well as the size of the MCS are bounded to limit combinatorial explosion.
MOTIVATING EXAMPLE Consider program AbsMinus (see fig. 1 ). The inputs are integers {i, j} and the expected output is the absolute value of i -j. An error has been introduced in line 10, thus for the input data {i = 0, j = 1}, program AbsMinus returns -1. The postcondition here is just result = |i -j| 1. 1 class AbsMinus { 2 / * returns | i-j|,the absolute value of i minus j * / 3 / * @ ensures 4 @ ((i < j) ==> (\result == j-i)) && 5 @ ((i >= j) ==> (\result == i-j)); * / 6 int AbsMinus (int i, int j) { 7 int result; 8 int k = 0; 9 if (i <= j) { 10 k = k+2; } // error : k = k+2 instead of k=k+1 11 if (k == 1 && i != j) { 12 result = j-i; } 13 else { 14 result = i-j; } 15 return result; } } Figure 1: Program AbsMinus The CFG of program AbsMinus and a faulty path are depicted in figure  2. This faulty path corresponds to the input data : {i = 0, j = 1}.
(b), blue path) produces a path that satisfies the postcondition. So, LocFaults collects the constraints on the part of path 3. (b) which precedes the deviated condition, that is C2 = {i = 0, j = 1, k0 = 0, k1 = k0 + 2}. Then LocFaults searches for an MCS of C2 ∪ ¬(k = 1 ∧ i = j). That is to say, one tries to identify the assignments which must be modified to force the program to follow a path that satisfies the postcondition. Therefore, for this second deviation two suspicious statements are identified: • The conditional statement in line 11; • The assignment in line 10 since the corresponding constraint is the only MCS of C2 ∪ ¬(k = 1 ∧ i = j). Then, LocFaults goes on and tries to deviate a second condition. The only possible path is the one where both conditions of program AbsMinus are deviated.
EXPERIMENTS To evaluate the capabilities of our approach we experimented with two sets of Benchmarks : the well known TCAS suite from Siemens  [25] , and a set of variations of the Tritype program. We compared the results of LocFaults with the one of BugAssist. TCAS is an aircraft collision avoidance system. The program contains 173 lines of C code with almost no arithmetic operations. The suite contains 41 faulty versions. The Tritype program is a standard benchmark in test case generation and program verification since it contains numerous non-feasible paths because of complex conditional statements in the program. The program takes three positive integers as inputs (i, j, k) the triangle sides, and returns the value 2 if the inputs correspond to an isosceles triangle, the value 3 if they correspond to an equilateral triangle, the value 1 if they correspond to some other triangle, and the value 4 otherwise.
The Tritype program is also a decision problem but we derived from the initial program two versions with more arithmetic operations. The first returns the product of the length of the sides, whereas the second one computes the square of the surface of the triangle by using Heron's formula. The results of these experiments are detailed in the next subsections. All experiments were done on an Intel Core Core i7-3720QM at 2. 6 GHz with 8 GB of memory running 64-bit Linux. LocFaults uses the IBM solvers CP OPTI-MIZER and CPLEX 4.
TCAS suite The results of the experiments are reported on Table  1. First column specifies the TCAS version number, the second one the number of errors in the program and the third column gives the number of the generated counter-examples. The two last columns provide the number of errors that have been found by LocFaults and BugAssist. Some of the versions are omitted because the errors correspond to array index out of bound and we still cannot handle this kind of overflow errors. We didn't report the computation times because there is no significant difference. The reported results for LocFaults have been obtained with at most one deviation; except for version V41 where two deviations were required. The size of the set of suspicious instructions identified by BugAssist is in general larger than the sum of the sizes of the sets of suspicious instructions generated by LocFaults but BugAssist identifies a bit more errors than LocFaults.
Variations on the Tritype program The results of the experiments on the different variations of the Tritype program 5  are reported in Table  2. In that table, the numbers are the line numbers and the red numbers are the injected errors that have been found by the tools. For LocFaults, the underlined numbers are conditions, one line corresponds to a path through the CFG, and contains either a condition alone, or condition and the assignments before that condition which allow to change the branch. For example, for TritypeV1 in column = 1 where one condition is diverted, LocFaults first locates the condition 26 as being erroneous. Then it locates condition 48 and locates the assignments 30 or 25 which can be responsible of the bad decision on 48. Versions 1 to 5 of Tritype correspond to the standard Tritype program where we injected different kinds of errors.
LocFaults needs to divert 3 conditions to find the two errors, while BugAssist only finds the first one. • TritypeV6 : is a variation that returns the perimeter of the triangle. LocFaults identified this error in the first step, without deviating any condition. Versions 7 and 8 of Tritype are some variations of the original program that return non linear expressions. They have the same control structure as Tritype. TritypeV7 computes the product of the three sides and TritypeV8 computes the square of the area of the triangle. The specification of TritypeV8 uses the Heron formula s(s -i)(s -j)(s -k) where s = (i + j + k)/2. To ensure that the returned value is an integer, we compute the square of the area and we assume as precondition that s is even. Moreover, the returned value varies according to the triangle type.
Contribution and future work Our flow-based and incremental approach is a good way to help the programmer with bug hunting since it locates the errors around the path of the counter-example. Further work concerns programs with loops where scalability may be an issue. We plan also to develop an interactive version of our tool that provides the localizations one after the others, and takes benefit from the user knowledge to select the condition that must be diverted. Furthermore, the constraint-based framework that we have introduced is well adapted for handling arithmetic operations. Moreover, it can be extended in straightforward way for error-localization in programs with floating-point numbers computations. Such an extension would be more difficult in SAT-based framework of BugAssist, or in SNIPER where the whole program is transformed in anLLVM intermediate representation.
* This work was partially supported by  ANR VAC-SIM  ( ANR-11-INSE-0004 ),  ANR AEOLUS  ( ANR-10-SEGI-0013 ), and OSEO ISI PAJERO projects. Part of this work was done while  Michel Rueher  was visiting professor at  NII  ( National Institute of Informatics ), Tokyo. contribution was authored or co-authored by an employee, contractor or affiliate of the national government. As such, the Government retains a nonexclusive, royalty-free.
Orthology blocks and phylogeny We chose twelve genomes completely assembled and present in a Pecan  [17]  multiple alignment of 20 amniote genomes available in the release 73 of the Ensembl Compara database  [18]. The phylogenetic tree was directly infered from the classifications of the species obtained from the National Center for Biotechnology Information Taxonomy database  [19]  (see Additional File 1). We constructed a set of synteny blocks using the multiple alignments as seeds. We used the block construction method described in  [20] , keeping only the seeds that had an occurrence in each of the twelve genomes, removing the seeds that spanned less than 100Kb, and joining seeds collinear in all genomes. This resulted in a set of 12 genomes composed of 689 blocks for species Homo sapiens (GRCh37), Pan troglodytes (CHIMP2.
Acknowledgements AP's engineer position is supported by a  Région Nord-Pas-de-Calais  funding (Projets Emergents). The participation of AP and AO to the conference ISCB-LA in October 2014 was funded by  Inria and Université de Sherbrooke. Inria  provided the funding for publication of the article. This article has been published as part of  BMC Genomics Volume 16 Supplement 5 , 2015: Proceedings of the 10th  International Conference of the Brazilian Association for Bioinformatics and Computational Biology  (X-Meeting 2014). The full contents of the supplement are available online at http://www. biomedcentral. com/bmcgenomics/supplements/ 16/S5.
Snow-covered albedo is affected by many factors including snow grain size, solar zenith angle, liquid water content, snow impurities, layer structure in the snowpack, and snow depth  [5] [6] [7] [8]. Among these factors, snow grain size is the most important variable controlling snowcovered albedo. However, snow grain size is difficult to predict and is crudely parameterized in terms of the snow age and its temperature history (e. g. [9] ). Thus, the parameterizations of snow-covered albedo are empirical in many land surface models (e. g. [10] ), including the multi-layer snow module that has recently been developed in ORCHIDEE (ORganizing Carbon and Hydrology In Dynamic EcosystEms,  [11] )  [12]. Although physically based snow albedo models (e. g. [8] ,  [13] ) have emerged in recent years, they are not sufficiently validated in global applications.
To date, only a few examples exist of the assimilation of albedo into land surface models on the continental scale (e. g. [21] ); let alone exploring impacts of snow albedo assimilation in the coupled land-atmosphere models. This is primarily because most albedo products have large spatial and temporal gaps that hamper their assimilation into such models. Our snow albedo assimilation framework not only uses in-situ albedo measurements at the site level but also capitalizes on a newly released global-covered albedo product (GlobAlbedo,  [22] ) with no data gap at the continental level. Use of this newly released snow albedo product and a recently developed ORCHIDEE snow module could enable us to quantify the simulation errors owing to model deficiency in snow albedo representation. The primary purpose of this paper is thus to demonstrate the importance of appropriately representing surface albedo during the snow season in both ORCHIDEE and the coupled ORCHIDEE-LMDZ (a general circulation model of Laboratoire de Météorologie Dynamique) model.
Study sites We use two sites (Weissfluhjoch and Col De Porte) to investigate the impacts of snow albedo assimilation on simulated snow depth and upward shortwave radiation in ORCHIDEE. Weissfluhjoch  (WFJ, 1992 (WFJ, -1993 ) is a high-elevation site at 2540 m with flat topography, located in the eastern Swiss Alps (46. 83°N, 9. 81°E) and managed by the Swiss Federal Institute for Snow and Avalanche Research. The average air temperature during the period of continuous snow cover is -2. 9°C. Rainfall does not occur from mid-October to mid-May. Snow continuously accumulates from mid-October until mid-April and then melts through May and June owing to temperatures above the melting temperature. On this site, the meteorological forcing used for driving ORCHIDEE is provided. Evaluation data comprise hourly observations of snow depth from an ultrasonic sensor and daily snow albedo.
Large-scale datasets Climate forcing. The meteorological forcing CRUNCEP is used to drive ORCHIDEE in an offline mode. CRUNCEP is a merged product of the Climate Research Unit (CRU) monthly 0. 5°climatology (v5. 3. 2, 1901-2012)  observations and the NCEP (National Centers for Environmental Prediction) reanalysis data with a high temporal resolution. This merged product has a six-hourly temporal and 2°spatial resolution. In addition, climatological variables (e. g. air temperature at 2 m and incoming shortwave radiation) in CRUNCEP are also used for evaluating the coupled ORCHIDEE-LMDZ simulations. GlobAlbedo product. The GlobAlbedo product is generated by employing multiple sensors (SPOT4-VEGETATION, SPOT5-VEGETATION2 and MERIS)  [22]. Both black-sky (Direct Hemispherical Reflectance) and white-sky (BiHemispherical Reflectance) albedo in three spectral broadband ranges (shortwave: 400-3000 nm, visible: 400-700 nm and near-and shortwave-infrared: 700-3000 nm) are provided.
The complete algorithm evaluation, including an overview of the algorithms, reference datasets, and the results are presented in the GlobSnow design justification file  [31]. The product with mountainous regions unmasked is used in this study. Previous validation of the GlobSnow SWE retrievals demonstrated RMSE values of 30 to 40 mm for SWE values below 150 mm and acknowledged that further improvement is necessary to better account for land cover and forest properties and the effect of lakes  [31].
This simple linear interpolation may introduce the bias especially during the late snowmelt season since the decrease of spring snow cover can be non-linear, which highlights the use of high temporal resolution dataset (e. g. MODIS daily SCF) in the future study. To be consistent with the albedo data, we still use 8-day GlobAlbedo SCF in this study. At the continental scale, assimilation of surface albedo only occurs when GlobAlbedo SCF is larger than 10%. Although albedo from GlobAlbedo product was used for assimilation, it is also employed for evaluating the success degree of snow albedo assimilation in ORCHIDEE since GlobAlbedo has a much coarser time resolution (8-day period) than the model time step (30 min).
Coupled experiments. We use ORCHIDEE-LMDZ to quantify impacts of snow albedo assimilation on air temperature and SWE. LMDZ is a global atmospheric general circulation model designed for climate studies, and the coupling between LMDZ and ORCHIDEE is described in Hourdin et al. [38]. We performed two simulations using a uniform horizontal resolution of 3. 75 in longitude and 2. 5 in latitude and 19 vertical layers. The first simulation, referred to as CTRL-COU, is the control experiment. The second simulation, referred to as ASSALB-COU, is identical to the control, except that it assimilates SCF and surface albedo from GlobAlbedo. Multi-year (1998-2011) mean SCF and surface albedo from GlobAlbedo are used in ASSALB-COU. All experiments are driven by 14-year (1998-2011) climatological monthly mean sea surface temperature and sea ice concentration with a spatial resolution of 1 degree taken from PCMDI (Program for Climate Model Diagnosis and Intercomparison).
g. [39] ). However, these masking effects have been overestimated by CTRL. Mean upward shortwave radiation over the boreal zone from CTRL during autumn, winter and spring is 11. 3, 11. 4 and 41. 8 W m -2 , respectively, which is lower than the equivalent observed figures of 14. 0, 15. 4 and 53. 6 W m -2. The ASSSCF analysis only assimilated with GlobAlbedo SCF indicates that the improvements in the simulation of upward shortwave radiation are mainly found in the north of 50 degrees but the simulations are degraded in the southern boundary especially during the spring (Fig  3c ). Further analyses have shown that the region with degraded simulation has a lower SCF from ORCHIDEE than that from GlobAlbedo (data not shown). Since surface albedo is computed based on the combination of SCF, snow-covered and snow-free albedo (Eq 6) and snow-covered albedo is generally larger than the snow-free albedo, the over-estimation of upward shortwave radiation from ASSSCF in the southern boundary (Fig  3c ) can then be mainly attributed to over-estimation of snow-covered albedo in ORCHIDEE.
In addition, the improvements over the region north of 50 degrees are relatively small in ASSSCF compared to those from ASSALB (Fig  3d ). This highlights the necessity of improving snow-covered albedo simulations in ORCHIDEE. One potential alternative is to utilize satellite-based albedo products (e. g. GlobAlbedo, MODIS) along with SCF maps to re-parameterize the default snow albedo scheme in ORCHIDEE (e. g. [14, 40] ). Snow water equivalent. The gridded SWE dataset generated by combining satellite measurements with ground stations is used to assess the spatial distribution of simulated SWE. Fig  4  shows the spatial distribution of multi-year (1998-2011) mean seasonal (October throughout May) SWE from different simulations (CTRL, ASSSCF and ASSALB) over the region north of 40 degrees. The CTRL analysis roughly captures the spatial pattern of GlobSnow SWE with a spatial correlation, ME and RMSE of 0.