dataesr/scientific-paragraphs-categorization huggingface dataset:
The detected fronts show significant nonzonal orientation, primarily due to the steering of the flow by subsurface topography (e. g. , between 1508E and 1808 at the Campbell Plateau) or high-frequency variability induced by Rossby waves moving along the fronts, as described by  Hughes (1996) , mostly clearly seen in the Agulhas region, south of Africa (between 208 and 508E). Animations of frontal location indicate substantial highfrequency variability, including the aforementioned Rossby wave propagation as well as the drift, splitting, and merging identified in the idealized experiments conducted by  Thompson (2010). In contrast to  Shao et al. (2015) , who uses the alongtrack AVISO product, we employ the gridded product in this study. Since the use of optimal interpolation for gridding the along-track data does modify the statistics of the ADT field, including the kurtosis (Sura and Gille 2010), we have repeated the analysis on the AVISO along-track ADT for a single year: 2010.
c. Connection with fronts defined by contours of sea surface height We now compare the fronts detected by the WHOSE method with those defined by altimetrically derived dynamic height contours  (Sokolov and Rintoul 2007). To proceed, we determine the value of the MDT from the  Rio et al. (2014)  dataset at each of the time-mean frontal locations in Fig. 4b. The normalized histogram of the MDT at the mean frontal locations, computed with an MDT bin spacing of 0. 07 dyn m, is shown in Fig. 7a. Peaks in the histogram (dashed lines in Fig. 7a ) are used to define a MDT value with which we can identify a front. Four distinct peaks in the MDT histogram are identified. Based on comparisons with  Sokolov and Rintoul (2007) , we label the detected fronts the SAF and PF, both of which have a northern and a southern branch (denoted SAF-N and SAF-S in the case of the Subantarctic Fronts and PF-N and PF-S for the Polar Fronts).
a. Long-term trends in frontal positions Using our methodology we now estimate the trends in frontal locations to determine where, if any, changes in the positions of these fronts have occurred over the satellite period. To accomplish this, we form annual occurrence maps and fit the skew normal function, yielding annual-mean frontal locations for each year from 1993 to 2014. We then associated each frontal location with one of the SAF-N, SAF-S, PF-N, or PF-S using the MDT values described in section 3 and find the mean latitude for each of the labeled fronts at every longitude and for every year in the database. Trends in the annual mean position of the fronts are then obtained at each longitude using standard linear regression and are shown in Fig. 9 , along with regions where the trends are statistically significant at the 90% level (i.
58, which is approaching the effective resolution of the gridded dataset. The largest movements of the fronts appear to be along the northern boundary of the ACC, although these shifts are neither spatially coherent nor large in magnitude. Any shifts in frontal position detected here are generally limited to less than 1. 08 of latitude and have a coherent spatial extent of no more than 108 of longitude. We also find no changes in the frontal occurrence maps driven by the ENSO significant at the 90% level and only a few regions that show statistically significant frontal shifts with changes in the SAM. In fact, statistical significance is often lacking even in regions with relatively coherent changes in occurrence frequency. However, it seems likely that the shift in front position to the south near the Pacific-Antarctic Rise during negative SAM events is both spatially coherent and statistically significant.
Discussion and conclusions In this paper, we have applied the WHOSE method, described in  Chapman (2014) , to 21 yr of gridded sea surface height altimetry in order to investigate the spatial and temporal variability of mesoscale fronts in the Southern Ocean. We have attempted to tease out concrete conclusions from a complex dataset by forming occurrence maps of the frontal occurrence frequency and then approximating these maps by fitting a superposition of skew normal curves. This approach allows us to quasi objectively determine the number of fronts at each latitude and their time-mean position. With these data, we are able to investigate in detail their spatial and temporal variability of the frontal structure. The principle result of this study is the identification of marked spatial variation in the frontal structure throughout the Southern Ocean latitudes.
Type systems with parametric polymorphism can encode a significant proportion of the information contained in session types. This allows concurrent programming with session-type-like guarantees in languages like ML and Java. However, statically enforcing the linearity properties of session types, in a way that is also natural to program with, is more challenging. Haskell provides various language features that can capture concurrent programming with session types, with full linearity invariants and in a mostly idiomatic style. This chapter overviews various approaches in the literature for session typed programming in Haskell. As a starting point, we use polymorphic types and simple type-level functions to provide session-typed communication in Haskell without linearity. We then overview and compare the varying approaches to implementing session types with static linearity checks.
haskell. org/package/full-sessions) • Orchard and Yoshida  [11]  use an embedding of effect systems into Haskell via graded monads based on a formal encoding of session-typed π-calculus into PCF with an effect system; (https://github. com/dorchard/sessions-in-haskell) • Lindley and Morris  [8]  provide a finally tagless embedding of the GV session-typed functional calculus into Haskell, building on a linear λcalculus embedding due to Polakow  [12]. (https://github. com/jgbm/GVinHs) The following table summarises the various implementations' support for desirable session-type implementation features: recursion, delegation, multiple channels (for which we summarise how session contexts are modelled and its members are accessed), idiomatic Haskell code, and whether manual user-given specification of session types is feasible.
We characterise idiomatic Haskell as code which does not require interposing combinators to replace standard syntactic elements of functional languages, e. g. , λ-abstraction, application, let-binding, recursive bindings, and variables. In the above, for example, PT08 has one tick and IYA10 has two since PT08 must use specialised combinators for handling multiple channel variables whilst IYA10 does not require such combinators, instead using standard Haskell variables.
Branching follows a similar scheme. Recursive behaviour is mediated by combinators which provide the unrolling of a recursive session type (enter) and referencing a bound De-Bruijn-indexed recursive variable via zero and suc: The types of both can be inferred. For example, the type of server is: server :: Eq a => Chan t -> Session (Cap t e (Rec ((a :?: (a :?: (Bool :!: Var Z))) :&: Eps)), x) x () Dual endpoints of a channel are created by functions accept and request capturing the notion of shared channels  [18] , called a rendezvous here: accept :: Rendezvous r -> (forall t. Chan t -> (Cap t () r, x) y a) -> Session x y a request :: Dual r r' => Rendezvous r -> (forall t. Chan t -> Session (Cap t () r', x) y a) -> Session x y a Thus, for our example, the server and client processes can be composed by the following code which statically enforces duality through request: example = runSession $ do rv <-io newRendezvous forkSession (request rv client) accept rv server with forkSession :: Session x () () -> Session x () () enforcing a closed final state for the forked subcomputation (line 2).
Whilst the above code is fairly idiomatic Haskell (modulo the management of recursion variables), the example has only one channel. In the context of multiple channels, the capability of a channel may not be at the top of the session environment stack, thus context manipulating combinators must be used to rearrange the stack: swap :: Session (r, (s, x)) (s, (r, x)) () dig :: Session x x' a -> Session (s, x) (s, x') a where swap is akin to exchange and dig moves down one place in the stack. Thus, multi-channel code requires the user to understand the type-level stack representation and to manipulate it explicitly. Multi-channel code is therefore non-idiomatic, in the sense that we can't just use Haskell variables on their own. Example 2 cannot be captured as channels cannot be passed. Pucella and Tov provide a way to send and receive capabilities, however there is no primitive for sending channels along with an associated capability.
g. ssend) returning computations in the SessionChain parameterised monad. We elide the details, but show the implementation of the server from Example 2: The session type specification serverSpec is linked to computation to enforce linearity via fork. Above, client refers to the client code which is forked and given a channel whose behaviour is dual to that created locally by createSession, specified by serverSpec. Thesjump primitive provides the recursive behaviour but has no target which is implicitly provided by the specification. The withChannel primitive "focuses" the computation on a particular channel such that the communication primitives are not parameterised by channels, similar to Neubauer-Thiemann. This has some advantage over Pucella-Tov, which required manual session-context manipulation, though channel variables still cannot be used directly here.
The constraint Pickup above specifies that at the n th position in ss is the session type Send v a. The constraint Update then states that ss' is the list of session types produced by replacing the n th position in ss with the session type a. The rest of the communication primitives follow a similar scheme to the above, generalising Pucella-Tov primitives to work with the De Bruijn indices instead of just the capability at the top of the stack. A fresh channel can be created by the following combinator: where l is the length of the list ss as defined by the constraint SList,a n d thus is a fresh variable for the computation. Using this library leads to highly idiomatic Haskell code, with no additional combinators required for managing the context of session-typed channels. Both examples can be implemented, with code similar to that shown for Pucella-Tov in Section 10.
Furthermore, the use of multiple channels is idiomatic, using Haskell's normal variables. The major disadvantage of this approach is that the user must give their own explicit type-level names to the channels, e. g. , type signatures like Chan (Ch "c") above. For simple examples this is not a burden, but manually managing uniqueness of variables does not scale well. Furthermore, the approach is brittle due to complex type-level representation and manipulations of finite maps. For example, GHC has difficulty reasoning about the type-level union operation (used as Plus) when applied to types involving some polymorphism. combinators for application, λ-abstraction and term composition. Sackman and Eisenbach provide session types which are easily specified by-hand with a value witness, but with non-idiomatic code and hard to manipulate types.
We then use these variables to construct a robust 2D phase-space that is highly robust across mice and days. This phase space forms the basis of an innovative analytical methodology for the study of fast time scale transitions using kinematic modelling of trajectories. As an application, we provide strong evidence for a deep asymmetry between transitions from sleep to wake and wake to sleep. In particular, the awakening transition displays strongly driven, fast dynamics whereas the process of falling asleep is more stochastic and slower. Together, we propose a new sleep scoring method that has strong methodological (automatic, reproducible, no training data required) and conceptual (no reliance on motor activity) advantages over traditional methods and allows us to characterize the fine dynamics of state transitions in the brain.
Brain activity params. Manual calibration  In order to construct a sleep scoring method that relies on brain signals alone, we screened multiple brain regions to find a good predictor for discriminating between sleep and waking states. We recorded from multiple brain regions in 15 freely-moving mice: the olfactory bulb (OB, n=15), the hippocampus (HPC, n=15), the prefrontal cortex (PFCx, n=6) and the parietal cortex (PaCx, n=6) cortex. Mice were recorded for an average of 6. 6 ± 0. 58 hrs (minimal recording length: 2hrs) in their homecages in the light period and slept on average 58% of the time. We initially used a classical sleep scoring method based on movement and hippocampal activity to establish a database of recordings from different brain states using 10 of the 15 mice that either were implanted with an EMG wire in the nuchal muscles (n=6) or tracked using video (n=4).
In other words, the phase space used to define sleep states must be stable. This phase space was constructed so that the separation between wake and sleep on the one hand and REM and NREM on the other hand used orthogonal axis. This simple space is remarkably consistent among animals and across days as can be seen by the similar position of the clouds of points representing each state (Fig. 5A ). We first quantified this similarity in the same animals between days and between light and dark cycles. We used the thresholds defined for one animal on a given light-cycle to score test data from the same animal on a subsequent light-or dark-cycle. The scoring was then compared with that obtained using thresholds determined from the test data itself. Thresholds for one animal were calculated as the distance from the SWS peak, both for OB gamma activity and HPC  B.
Overlap of scoring using thresholds determined on the same mouse on one day and applied the following day (i) and during successive light and dark periods (ii). Each column gives the percent of the brain state (x label) identified using thresholds determined on the reference data set that is classified as NREM (blue), REM (red) and wake (grey) using thresholds determined using data from different days (i) or during the light period (ii). (n=10 mice were recorded on consecutive days and used for inter-day scoring, n=5 mice were recorded over a 24h period of light and dark periods) C. Heat map of point density averaged over all phase spaces for all mice (n=15). Circles show the 95% boundaries of NREM, REM and wake for each mouse. Histograms from all mice are shown along the relevant axis with automatically determined thresholds (*).
OB gamma power drops when the animal falls asleep but not when the animal freezes. EMG power shows similar changes during freezing and sleep onset (n=6, error bars : s. e. m) The different dynamics of waking up and falling asleep Our automated sleep scoring method classifies all data points into periods of wake, NREM and REM. Another essential aspect of sleep study concerns the intermediate transitory period between these two states. Here we show that these transitions can be studied by visualizing moment to moment variations in brain state as a moving point in the previously described phase space (Fig. 5C ). The trajectories that this point describes during transition periods can then be studied using classical tools from kinematic analysis. Shifting to this finer time scale, we replaced the previously used threshold with a transition zone, consistent with many experts arguing that there is no single time point corresponding to the onset or offset of sleep  (Ogilvie, 2001).
After implantation the method is full automated and therefore removes the time-consuming steps of scoring by hand the full data set or a training set to calibrate semiautomatic algorithms. We have shown that this method for sleep scoring is robust to slight changes in implantation site, across days and between different animals. It therefore allows easy comparison between mice and throughout time, and may be between data sets from different laboratories. Beyond the technical ease of use, this method also provides a promising framework for the study of the dynamics of brain states. Using activity recorded in the brain and not muscle activity allows to track sleep/wake activity independently of movement. This could provide the heretofore lacking methodology to study phenomena such as REM without atonia induced in lesion studies  (Lu et al.
Animal housing facility of the laboratory where experiments were made is fully accredited by the French Direction of Veterinary Services (B-75-05-24, 18 May 2010). Animal surgeries and experimentations were authorized by the French Direction of Veterinary Services for K. B. (14-43). Signals from all electrodes were recorded using an Intan Technologies amplifier chip (RHD2216, sampling rate 20 KHz). Local field potentials were sampled and stored at 1,250 Hz. Analyses were performed with custom made Matlab programs, based on generic code that can be downloaded at http://www. battaglia. nl/computing/ and http://fmatoolbox. sourceforge. net/.
Periods of sleep and wake shorter than 3s were merged into the surrounding periods to avoid artificially short epochs. Then, LFP recordings from the HPC restricted to the sleep periods defined above, were filtered in the theta (5-10Hz) and delta (2-5Hz) bands and instantaneous amplitude derived from the Hilbert Transform. The ratio of the theta and delta powers was smoothed using a 2s sliding window and the distribution of values was fit by a single normal distribution that accounted for the NREM data points (low theta/delta ratio). The REM/NREM threshold was placed at the point above which the residuals systematically explained more than 50% of the actual data (Fig2. Bii). Periods of NREM and REM shorter than 3s were merged into the surrounding periods to avoid artificially short epochs.
Automatic EMG scoring Automatic EMG scoring was performed in a similar fashion to automatic OB gamma power scoring. EMG data was filtered in the 50-300Hz band and instantaneous amplitude derived from the Hilbert Transform. This time-series was then smoothed using a 2s sliding window and the distribution of values could be fit with a mixture of two normal distributions. The intersection of these two distributions, once normalized provided the sleep-wake threshold. The theta/delta power ratio and period dropping procedures are the same as above.
Acknowledgements S. B. did the experiments and analysed the data. M. M. L. , G. d. L. and J. M. L. did several experiments included in the dataset. H. G. performed the histology. S. B. and K. B. wrote the manuscript with the help of M. M. L and J. M. L. This work was supported by the  Fondation pour la Recherche sur le Cerveau (FRC) , by the  French National Agency for Research   ANR-12-BSV4-0013-02  (AstroSleep), by the  CNRS: ATIP-Avenir  ( 2014 ) and by the  city of Paris  (Grant Emergence  2014 ). This work also received support under the program Investissements d'Avenir launched by the  French Government  and implemented by the  ANR , with the references:  ANR-10-LABX-54   MEMO LIFE  and  ANR-11-IDEX-0001-02   PSL* Research University. G. d. L. and M. M. L. were funded by the  Ministère de l'Enseignement Supérieur et de la Recherche, France.
SES are efficient, cost-effective, autonomous samplers of Southern Ocean environmental parameters. They have been widely recruited to fill a "blind spot" in Southern Ocean sampling coverage, having collected 90% of the oceanographic data (mainly vertical profiles of temperature and salinity) available south of 60°S since 2004  (Charrassin et al. , 2008; Guinet et al. , 2013). Classical weather buoys, for example, those that send data to the US National Oceanic and Atmospheric Administration's National Data Buoy Center (http://www. ndbc. noaa. gov) are very scarce south of 40°S. Various research programs such as the Marine Mammals Exploring the Oceans Pole to Pole (MEOP) consortium (http://meop. net; see also  Treasure et al. , 2017, in this issue)  and the French Mammifère marin Echantillonneur du Milieu Océanique (MEMO; in English, "Mammals as Samplers of the Ocean Environment") observatory and SO-MEMO ensure the ABSTRACT.
Biologging using Kerguelen Islands' southern elephant seals (SES; Mirounga leonina) has been widely used to collect environmental parameters of the Southern Ocean. This study evaluates whether raw accelerometer and magnetometer data from biologging of SES can be used to predict above-surface wind conditions and wave period and height. From these sensors, SES behavior during post-dive surfacing time has been modeled with the Euler angles pitch, roll, and yaw. Environmental variables (i. e. , wind orientation, wind speed, significant wave height, and mean wave period) have been extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF) database, which assimilates satellite images into models to provide high-resolution environmental data. SES behavioral and environmental variables are analyzed together to test two hypotheses: (1) SES behavior is constrained by wind speed and/or direction, and (2) SES can be used as weather buoys to estimate wave parameters.
g. , on SES behavior and physiology and/or oceanographic information (e. g. , physical and biogeochemical data on the marine environment). On the biological side,  Guinet et al. (2014)  investigated SES foraging behavior, relating seal diving depth and prey encounter events to water temperature at 200 m depth as well as to light level at the surface and at depth. Richard et al. (2014)  studied swimming effort and speed to monitor density variations of SES throughout their foraging excursions. More recently, acoustic data have been used to investigate behavioral and ecophysiological (breathing rate) parameters  (Génin et al. , 2015; Day et al. , 2017). On the oceanographic side, MEOP and MEMO/SO-MEMO are responsible for collection of considerable conductivitytemperature-depth (CTD) and timedepth recorder (TDR) data from the Southern Ocean.
The quality-controlled MEOP-CTD and MEOP-TDR databases are publicly available to the scientific and operational oceanographic communities. Through such data collection, new insights into water masses, oceanographic processes, and global ocean climate have been gained  (Roquet et al. , 2014). In particular, the seals have provided a marked increase in the number of hydrographic profiles from the sea ice zone, allowing mapping of major fronts south of 60°S and inference of sea ice formation rates from changes in upper-ocean salinity  (Charrassin et al. , 2008). Seals were also used to quantify phytoplankton concentration  (Guinet et al. , 2013)  and to collect data for the first complete assessment of dense shelf water formation in Prydz Bay, which improved understanding of the production of globally important Antarctic Bottom Water  (Williams et al.
, 1985). The biologging tags on the SES also include a triaxial accelerometer and magnetometer. The main objective of this current study is to explore whether ocean environmental parameters influence SES behavior during post-dive surfacing time, and whether the raw data can be converted into proxies to reliably estimate wind and wave parameters. The full threedimensional dynamical behavior of SES at the ocean surface has been modeled with the well-known Euler angles pitch, roll, and yaw, as well as with the horizontally projected vertical acceleration, which can be double-integrated to extract vertical displacement. The ocean environmental parameters selected for this study are wind direction and speed (measured 10 m above sea surface), significant wave height, and mean wave period. They have been extracted from the European Centre for Medium-Range Weather Forecasts (ECMWF) database, and will be used here to ground truth these parameters.