dataesr/scientific-paragraphs-categorization huggingface dataset:
We reach, from σ sim equivalent to σ par , a state σ ′ sim equivalent to σ ′ par , with a trace tr equivalent to [(t, a par )]. Lemma 3 (Backward simulation on a single step). Let prog sim be the simulating program of a safe parallel program prog par , σ sim a sequential state that reach σ ′ sim with a trace tr = (call select [l ptid ; l pct ]); tr ′ , such that tr ′ does not contain call action to select, σ par equivalent to σ sim , there exists an action (t, a par ) such that [(t, a par )] is equivalent to tr that allows to reach σ ′ par equivalent to σ ′ sim. Sketch of proof. Starting from ((. , ρ sim , {while ¬terminated do (c select + + c sim + + c termination )}), η sim ), the simulation builds a trace tr = (call select [l ptid , l pct ]); tr ′ so the condition is evaluated to true (else we would not execute the loop, and the first action of the trace would not be realized).
We also know that there exists t such that η sim sim (pct) = 0. We know that in the original program, there exists t such that stacks(t) is not empty, and we know the instruction instr that has to be performed by t. As prog par is safe, it does not block, the instruction instr of t can be executed, so there exists a new parallel state σ ′ par , reached with an action (t, a par ). By lemma 2, we know that there exists a simulated state σ ′ sim? equivalent to σ ′ par reached from σ sim with a trace tr f equivalent to [(t, a par )]. This trace tr f starts with an action call select [l ptid ] equivalent to the one produced for tr and represents the execution of instr by t, that is also simulated by our program prog sim. We can deduce that σ ′ sim? = σ ′ sim. As σ ′ par is equivalent to σ ′ sim? , it is also equivalent to σ ′ sim.
Towards a Mechanized Proof of Correctness We aim at mechanizing the proof of correctness using the proof assistant COQ. A first step to do so is to formalize both languages and their semantics, as well as the transformation. The current state of the development  1  includes this first step (about 3,000 lines of COQ, one third being proofs). We have roughly 20% devoted to supporting definitions and results (about quite general data types and data structures used in the rest of the formalization), 50% to the syntax and semantics of the two programming languages (about half of it comes from another project with only slight modifications), the remaining 30% focusing on the formalization of the transformation and the statement of the correctness theorem. The syntax and semantics of the languages have a rather usual formalization.
To ensure that all procedures of a program are non-recursive, if is sufficient to require that ≺ is well-founded. This property is necessary for two reasons. First, our simplified way to simulate the call stack in the transformed code requires it. Second, COQ requires that all functions are terminating. It automatically checks the termination of recursive functions when the recursive calls are done on syntactical sub-terms of one of the arguments of the function. In other cases, a proof of termination should be given. The next step of the mechanization is to define the equivalence between states: the properties about the uniqueness of procedure names and correct labelling are very important in this regard. The final step will be to prove the correctness.
[8]  propose a lazy version of these tools called LazySeq that shows high performances on known benchmarks. Other authors choose to bound memory accesses instead of context switching  [18]. While efficient to find bugs, these approaches are not suited to prove safety, which is the main reason we aimed at supporting the WP plugin of FRAMA-C. In  [14] , Nguyen et al. further generalize LazySeq to unbounded concurrent programs allowing safety checking. The approach for code generation is somehow dual to ours: instead of splitting original functions into smaller functions for each statement and adding the context switching management in an interleaving loop, context switching is modeled inside each function to obtain a behavior where each call to the function will execute a step of execution and then return (and where local variables become static).
All these approaches consider a sequentially consistent memory model, as we do, while other work is aimed at supporting weaker behaviors as well, e. g. [19]. Why3 is a deductive verification tool that proposes Why-ML, a language for writing programs and assertions, and a verification condition generator as well as translations of these conditions as input to a wide variety of automated provers. Fortin and Gava  [7]  used program transformation to perform deductive proof of bulk synchronous parallel (BSP) programs. In this work, the original annotated parallel program is compiled into an equivalent sequential Why-ML program. The deductive proof is then performed using the original Why-ML VCGen, designed for sequential programs. The transformation is written and proved using the COQ proof assistant.
The proof relies on the fact that in a way the simulating code mimics the operational semantics of the concurrent program with its own sequential instructions, but in a simplified version (in particular because we do not really need to simulate a call stack). Moreover all the simulating code is deterministic but the code that simulates thread switching. We aim at the mechanization of this proof in the interactive theorem prover COQ. A non-trivial first step was to formalize the languages and their semantics, as well as the transformation. The next step will be to write the correctness proof itself with COQ. The CONC2SEQ plugin does not only transform the code to verify. CONC2SEQ provides extensions to the ACSL behavioral specification language in order to write contracts for concurrent C programs.
We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311-2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data was processed with the Shortwave Infrared CO Retrieval algorithm SICOR that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA's Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites.
For Ny-Ålesund, Lauder, Mauna Loa, and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. This improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6. 0 ppb and a strong correlation between the validation data set and the SCIAMACHY data sets with a mean Pearson correlation coefficient r = 0. 7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set, which is less sensitive to the spatial representativeness of the satellite and validation measurement. For example, at the cities Teheran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171.
2 ppb and 57. 9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. The validation improves significantly for cloudy-sky retrievals with b = 52. 3 ppb and 5. 0 ppb, respectively. This is due to a reduced retrieval sensitivity to CO below the cloud and so to the altitude range, which is mostly affected by strong local surface emissions. At the less urbanised region around the airport Windhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15. 5 ppb, but can be even further improved considering cloudy SCIAMACHY observations with a mean CO bias of b = 0. 2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short wave infrared measurements present a valuable addition to the clear-sky only data set.
, 2004) , monitor the transport of atmospheric pollutants  (Gloudemans et al. , 2006) , and detect pollution from mega cities  (Buchwitz et al. , 2007). The major limitation of the SCIAMACHY CO data set is its high retrieval noise, which can exceed 100 percent of the retrieved value for individual columns. Hence, in practice the data needs to be averaged spatially and temporally to reduce the noise contribution  (de Laat et al. , 2007; Gloudemans et al. , 2006). The SCIAMACHY instrument has had issues with degradation and calibration in the SWIR spectral range, and efforts to retrieve CO are hampered by the failure of a significant number of detector pixels and the growth of a layer of ice on the detector array  (Gloudemans et al. , 2005 (Gloudemans et al. , , 2008)). Borsdorff et al. (2016)  discussed a re-calibration of the measurements and presented a full mission CO data set, which is restricted to clear-sky scenes over land.
This SCIAMACHY CO data product for clear skies was validated with ground-based Fourier Transform Spectrometer (FTS) measurements that were provided by the Network for the Detection of Atmospheric Composition Change (NDACC) and the Total Carbon Column Observing Network (TCCON)  (de Laat et al. , 2010; Borsdorff et al. , 2016) , and it was also validated with airborne measurements provided by the MOZAIC/IAGOS project  (de Laat et al. , 2012; Borsdorff et al. , 2016). The successful validation of this recalibrated data set showed that SCIAMACHY observations reveal meaningful information about atmospheric CO over the full mission period. SCIAMACHY's SWIR measurements of clear skies over land have a good sensitivity for the vertical column density of CO. However, the clear-sky CO data product requires a strict cloud filter over land and the wholesale rejection of all observations over oceans -due to the low reflectivity of the ocean surface in the SWIR.
Hence, a retrieval that works for cloudy scenes is necessary in order to extend spatial coverage beyond the small fraction of SCIAMACHY observations which are made of clear skies over land. Gloudemans et al. (2009)  and  Buchwitz et al. (2006)  demonstrated that cloud contaminated measurements can be useful for CO retrievals, because the high reflectivity of clouds decreases retrieval noise. By using SCIAMACHY observations under cloudy conditions,  Gloudemans et al. (2009)  investigated the outflow of CO from Asia and Indonesia over the oceans. However, to properly interpret these measurements, one has to account for the shielding and scattering effect of clouds. For this purpose,  Vidot et al. (2012)  and  Landgraf et al. (2016b)  proposed the Shortwave Infrared CO retrieval (SICOR) algorithm for the operational processing of CO data for the S5P mission.
(2016). Whereas that CO data set was limited to clear-sky retrievals over land, here we provide SCIAMACHY CO data for cloudy scenes over the oceans and both clear and cloudy scenes over land. The new data product includes the column averaging kernels for individual measurements, and we use these to help validate the data product with vertical concentration profiles of CO that were measured by the MOZAIC/IAGOS project. We also validate the data product with TCCON and NDACC ground-based FTS measurements. The paper is structured as follows. Section 2. 1 introduces the inversion of vertical column densities by profile scaling, Sect. 2. 2 explains the retrieval setup for the SCIAMACHY measurements, and Sect. 2. 3 analyses the quality of the retrieved cloud parameters. In Sect. 3. 1 we present the data validation using MOZAIC/IAGOS aircraft measurements while Sect.
To gain first experience with the SICOR cloud parameters, we compared the retrieved cloud height h cld with the SCIA-MACHY cloud top height product, processed with the Fast Retrieval Scheme for Clouds from the Oxygen A Band (FRESCO) algorithm. In contrast to SICOR, this algorithm infers cloud top height and cloud fraction from the SCIAMACHY the Oxygen A band around 760 nm assuming an elevated Lambertian reflector as a cloud model  (Wang et al. , 2008). This comparison is particular worthwhile since SCIAMACHY's NIR measurements do not suffer from the formation of an ice layer on the detector module like the SWIR measurements  (Gloudemans et al. , 2008). Figure  1   In the following, we use the SICOR cloud parameters to classify the retrievals with respect to cloudiness. We distinguish between three CO retrieval conditions: (1) clear-sky observations over land with τ cld < 0.
full-mission SCIAMACHY CO data set of this study including clear-sky and cloudy-sky observations is available for download at ftp://ftp. sron. nl/pub/pub/DataProducts/SCIAMACHY_CO/. The underlying data of the figures presented in this publication can be found at ftp://ftp. sron. nl/open-access-data/. TCCON data is accessible from http://tccon. ornl. gov/ and the database, the participating airlines (Lufthansa, Air France, Austrian, China Airlines, Iberia, Cathay Pacific) for the transport free of charge of the MOZAIC/IAGOS instrumentation. MACC data were obtained from http://www. copernicus-atmosphere. eu. We acknowledge the NDACC-IRWG and TCCON ground-based FTS networks for providing data. This work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative.
Acknowledgements. SCIAMACHY is a joint project of the  German Space Agency DLR  and the  Dutch Space Agency NSO  with contribution of the  Belgian Space Agency. The work performed is (partly) financed by  NSO  through the  SCIAvisie  project and in collaboration with the SCIAMACHY Quality Working Group (SQWG) by  ESA. We acknowledge the  European Commission  for the support to the  MOZAIC  project (1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003) and the preparatory phase of IAGOS ( 2005IAGOS ( -2012 )), the partner institutions of the  IAGOS Research Infrastructure (FZJ, DLR ,  MPI ,  KIT in Germany ,  CNRS ,  CNES, Météo-France in France  and the  University of Manchester in UK) ,  ETHER  ( CNES-CNRS/INSU ) for.
32 meters in front of him (a short pass situation). We observed a linear age-related increase in the CMP: the passing accuracy, speed and reactiveness of players improved by 4 centimeters, 2. 3 km/h and 30 milliseconds per year of age, respectively. These data were converted into 5 point-scales and compared to the judgement of expert coaches, who also used a 5 point-scale to evaluate the same CMP parameters but based on their experience with the players during games and training. The objectively-measured age-related CMP changes were also observed in expert coaches' judgments although these were more variable across coaches and age categories. This demonstrates that high-technology systems like COGNIFOOT can be used in complement to traditional approaches of talent identification and to objectively monitor the progress of soccer players throughout a cognitive-motor training cycle.
Real-time ball motion tracking and screen calibration The ball motion was tracked in real-time with 8 Optitrack Prime 17W cameras (NaturalPoint OR, USA). Small infra-red light reflective soft markers were fixed on the ball (standard ball size diameter equal to 22 cm) and after a calibration procedure, the 3D ball position (X, Y and Z spatial coordinates according to a reference frame centered on the initial ball position) was streamed in real-time at a frequency of 360 Hz to the laptop. Ball coordinates were then processed on-line using a self-written "main program" in Matlab (Mathworks Inc. , Natick, MA, USA) to compute several parameters related to CMP (see next section). The calibration of the screen was performed using 4 markers located at the corners of the projected image. This allowed a conversion of the target position from pixels to (Y, Z) coordinates (same reference frame used for ball motion tracking).
Statistical analysis The mean CMP parameters reflect the global passing performance of a player across all trials and were computed as the mean value (PSE, PS or RT) across all recorded passes of this particular player. The effects of age (continuous predictor) on the mean CMP parameters (dependent variables) were assessed using a linear regression model which included 46 data points (players). The normality of each of the computed distributions was verified using Kolgomorov-Smirnov tests. In a second stage, the same linear regression model was performed with soccer experience as a predictor. This was tested to determine whether the accumulated soccer experience, in addition to age, could also predict the mean CMP. The strength of the correlation was measured using the correlation coefficient r and the parameters of the regression line equation (intercept and slope).
In particular, the slope was used to measure the age-related and soccer experience-related change in performance over years. Another linear regression model (with age as a continuous predictor variable) was then used for individual CMP parameters (PSE, PS and RT, dependent variables): this model allowed discriminating between the tested conditions. In particular, we assessed the potential effects of the target position (3 eccentricities x 3 heights), the number of distractors (N = 3), the stimulus duration (N = 4) and the interaction effects on the CMP, in addition to the effect of age. The normality of each of the computed distributions was here also verified using Kolgomorov-Smirnov tests prior to performing linear regressions. A total of 4879 data points was used for this model: 108 stimuli x 46 players = 4968 minus 9 incorrect trials minus 80 missing passes for one player of the U14 group (for whom we met hardware problems with the laptop after 28 passesdata of this player were not included in the model).
Coaches' judgements vs Cognifoot measurements In this section, we focus on the scores provided by coaches in order to a) determine the extent to which they were also affected by the age of the players and b) to test how close they were to the objective measurements provided by the COGNIFOOT system. However, the difference between absolute/relative DCs was mainly induced by relative judgments data of coaches 1 and 9. No statistically significant difference between absolute/relative DCs was observed when excluding these coaches for all tested parameters (p>0. 05). Notably, only coaches 5 and 6 orally reported that relative judgments were easier to provide while coaches 1 and 9 declared that absolute judgments were easier to provide. Variability of coaches' judgments. The Table  1  was designed to complement the observation of a higher dispersion of data points of coaches' scores (see also supplementary material) compared to COGNIFOOT scores.
In line with our hypothesis, the mean relative coaching scores were closer to the objectively-measured scores by the COGNIFOOT (Figs 5B, 6B and 7B) and reflected better the progression with age and/or experience. This was also indicated by smaller variability of the data when using the relative compared to the absolute coaches' judgement (Table  1 ). However, irrespective of the way coaches judged the performance of their players (absolute or relative judgement) it has to be emphasized that only the mean score across all coaches for each individual player led to considerably reduced data dispersion and increased correlation coefficients. For the relative judgements, this procedure resulted in comparable regression line parameters as the ones obtained with COGNIFOOT (Fig  5B ). This questions the reliability of evaluations that are based on one coach/scout only-especially if they are made without a specific reference, which may be important for talent selection procedures.
Implications for talent selection The comparison of absolute and relative judgements in the present study has indicated that clear reference values, either the level of one specific player or a certain performance criterion may help to reduce variability across coaches' judgements. This may be an important aspect to improve talent identification programs. However, in any case, objective measurements of quantitative data like those provided by the COGNIFOOT system have an even greater discriminative power as it relies on physical measurements of clearly-defined CMP parameters. Furthermore, as only the mean of the coaches' score was comparable to the COGNIFOOT score, talent selection based on the judgement of one single coach may inherit serious limitations as mentioned earlier. At the same time, it has to be noted that the COGNIFOOT system only assessed CMP in short pass situations and does therefore not allow a more global assessment.
Cognitive-motor training in soccer The efficiency of any training program can only be assessed by having pre-training and posttraining measurements. For physiological assessments in soccer, reliable field tests like the Yo-Yo test  [32]  are widely used in elite soccer. The COGNIFOOT prototype used in this study offers an interesting possibility for providing such objective and quantitative measurements of the CMP. In our study, we noticed few interaction effects between motor factors (linked to the difficulty to hit laterally-high targets) and visual factors (stimulus duration and number of visual distractors) on the passing accuracy (see Figure A of S1 File) or on the response times (see Figure  C  of S1 File). Adding a textured background or a real image (or movie) may more significantly affect passing accuracy and response times.
It is important to stress that our model is suitable for integrating arrivals and departures of entities (corresponding either to species invasion/extinction or birth/arrival/death/departure of individuals) through the possible presence or absence of nodes at the different time steps. Moreover, it can also deal with quantitative edges (and is not restricted to binary interactions) which are often available in datasets. We first illustrate our approach through the analysis of the dynamic contact network in a colony of ants  (Mersch et al. , 2013). Contact networks represent a relevant proxy to study animal sociality  (Wey et al. , 2008). In the literature, these networks may be built from field observations of association between animals (e. g. giraffes in  Carter et al. , 2013) , trapping data (e.
g. field voles in  Davis et al. , 2015)  and more recently and predominantly from sensorsbased measurements (e. g. song birds in  Farine et al. , 2015). These data are now available for large time periods, ranging from days to years of observations for instance. It is therefore possible to investigate the (in-)stability of the social structure  (Pinter-Wollman et al. , 2013)  and potentially question the impact of other time-related factors (seasonal changes, response to stresses such as draught, arrival/departure of a peculiar individual,. ). We lastly present the study of a seasonal trophic network (or food web;  Woodward et al. , 2005). The structure of trophic relations has been intensively studied in the network framework (see  Thompson et al. , 2012 , for a review on food webs). Nowadays, following the seminal work of  Baird and Ulanowicz (1989) , new datasets allow for monitoring the variation of this structure along temporal gradients (seasons or years), spatial gradients (latitudinal or longitudinal for instance;  Kortsch et al.