dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The study of landscape structure has been steadily growing in recent years (e. g. ,  Lichstein et al. , 2002; Saravia, 2015; Marcantonio et al. , 2013)  with the development of several methodologies and approaches, which have been tested ecosystems and supported in the scientific literature (see  Bar-Massada and Wood, 2014). In particular, the use and availability of remote sensing data have made it possible to assess specific heterogeneity patterns over various ecosystems, with increasing performance in terms of spectral/spatial/temporal characteristics, opening up new possibilities for exploring complex ecological processes. Using our algorithm, environmental heterogeneity is estimated by the range of spectral values associated to the spatial variability within a given habitat.
Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The rasterdiv package that contains the new function proposed can be downloaded at https://github. com/mattmar/rasterdiv or directly from the CRAN (https://CRAN. R-project. org/package=rasterdiv). The hyperspectral images of Santa Barbara of June 2009 and 2011 can be respectively retrieved from https://popo. jpl. nasa. gov/avcl/y09_data/ f090826t01p00r08. tar. gz and https://popo. jpl. nasa. gov/avcl/y11_data /f110719t01p00r19. tar. gz. The images of Kangaroo Island can be retrieved from https://scihub. copernicus. eu/dhus/#/home.
Acknowledgements This study has received funding from the project  SHOWCASE  ( SHOW-CASingsynergies between agriculture, biodiversity and ecosystems services tohelp farmers capitalising on native biodiversity) within the EuropeanUnion 's  Horizon 2020 Researcher and Innovation Programme  under grantagreement No. 862480. DR was also supported by the  Horizon Europe project Earthbridge. PZ was supported by  LifeWatch Italy  through the project  LifeWatch-PLUS  ( CIR01_00028 ). RF is supported by the  Swiss National Science Foundation, Switzerland   SNSF175529.
When the patient was properly set-up on the Chair (as shown in Fig. 2 ), anterior and lateral optical images were acquired as references. Patient positioning characteristics including setup parameters (seat height coordinate, seat pan inclination, shin rest and heel stop coordinates) were recorded in the database. No tattoo or landmarks were made on the patient' skin. The patients were repositioned at the second and third appointments using the patient positioning characteristics. Then, the repositioning accuracy was verified based on the optical image reference system. After applying shifts, when necessary, a simulated treatment session was performed with several helical movements for 20 min, with positioning checks using optical images, performed every 4 min.
Data collection The patient characteristics, including age, weight recorded at each upright positioning, tumor site, Karnovsky index, and surgical history, were collected. Positioning characteristics, including backrest angle, seat height coordinate, seat pan inclination, shin rest and heel stop coordinates, belt and arms support position, and head rest shape, were also recorded. The patient's cumulative radiotherapy dose on the day of the upright positionings test was collected as well. The duration of the various steps was calculated as followed: • First positioning: interval between the time when the patient was ready to be positioned on the Chair and the time the first optical acquisition was performed. • Repositioning time: interval between the time when the patient was ready to be repositioned on the Chair and the time when the positioning had been checked using the optical system.
• Uninstallation time: interval between the end of the Chair rotation and the time when the patient had left the Chair. The inter-fraction positioning shifts were calculated after manual registration between the reference optical images and the images taken during the repositioning. Image registration was performed using the skin surface at the level of the thighs and pelvis. After image matching, right-left deviations (x-direction), anterior-posterior deviations (y-direction), and cranio-caudal deviations (z-direction) were calculated by the OGTS software; deviations in pixels were converted into deviation in millimeter after calibration of the system. Negative values were set for left, posterior and caudal displacements in the x-, y-and z-direction, respectively. Deviations were collected before they were applied to the Chair.
Data analyses The mean, standard deviations (SD), minimal and maximal values of positioning and repositioning durations, and the mean, SD, minimal and maximal values of inter-fraction position deviations were calculated. A Mann Whitney test was used to compare the first positioning duration, the repositioning duration and the time to get out of the device, with one versus two RTTs. For inter-fraction position deviations, the proportion of values larger than or equal to 5 mm and 3 mm were also calculated. Lastly for the intra-fraction shift, means, SD, and minimal and maximal values at 4, 8, 12, 16 and 20 min after repositioning were calculated. The proportion of values larger than or equal to 3 mm was also calculated. Regarding the questionnaire, the mean scores were calculated for the global comfort, the easiness to get in and get out of the device, and the patients' score proportion for each answer and for each sub-site were reported.
Overall, patients felt comfortable especially on their back and their arms, but the overall score was in favor of the supine position. In future work we will investigate a new backrest for our system that has been optimally designed to position the head and the neck. There are a few shortcomings in our study that need to be outlined. Firstly, only 16 patients were enrolled in the study, and for each of them a maximum of 3 set-ups were tested. As patients were not treated in the upright position, we could reasonably not ask them to be tested more often. But care was taken to test the patients at early, mid-, and late phases of their treatment. This being acknowledged, the data were consistent even with only 16 patients, and set-up recommendations could already be drawn for future studies. Secondly, we could only assess the positioning accuracy based on the skin match.
A direction determination, if required. However, the scope of the study was limited to the networks with high penetration of PV systems. As the PV fault behaviour is dictated by their inverter control strategies and introduces potential impacts on the protection scheme, a comprehensive PV fault model with appropriate control algorithm for achieving FRT and DVS requirements was thoroughly evaluated and utilized. The islanding transition due to faults and the operation in islanded mode of LV-MGs was responsible by Battery Energy Storage Systems (BESS), whose model was also simulated in detail. It should be stated that, high impedance faults which are characterized by fault current levels similar to normal load currents and unrecognisable voltage drop, are beyond the study scope. The effectiveness of the developed protection scheme was validated by numerous simulations on a real MV network incorporating various LV-MGs within the DIgSILENT/Power-Factory software environment.
Conclusion In this paper, the authors developed an advanced scheme that is capable of ensuring comprehensive selective protection for MV networks and incorporated LV-MGs in a high PV penetration context by appropriately coordinating all deployed overcurrent and under/overvoltage elements across MV and LV levels. Various fault scenarios were simulated in the software environment DIgSILENT/PowerFactory with PV and BESS systems modelled in great detail taking into account the requirements of new grid codes, including FRT and DVS capabilities, to evaluate the effectiveness of the proposed system. The further step is to validate the performance of the protection scheme using a combined Control-and Power-Hardware-In-the-Loop testing platform (C/PHIL). The platform will include a real-time simulator OPAL-RT, various solar inverters with the proposed PV control algorithm being implemented and many numerical feeder protection relays and MCCBs.
CONTRIBUTION Our main contribution is providing an abstraction layer between realwalking experiences and the physical space they use, by drawing from the field of operating systems. At the core of this abstraction is a tracking volume-independent representation of virtual reality experiences that offer real-walking or passive haptics, as expressed by creators. This representation can be interpreted through our software systems to be instantiated in any tracking volume, specifically tracking volumes that can be shared with other people (VirtualSpace), that can have any size and shape  (Scenograph) , that contain limited sets of physical props (Mise Unseen) or arbitrary sets of physical props or room geometry (Stuff-Haptics). Making real-walking experiences independent of the user's physical space will have substantial commercial impact for virtual reality systems, as the problem of requiring specific physical spaces has all but stopped the proliferation of real-walking due to developers' reluctance to require users to have access to that space.
IMPLEMENTATION AND HARDWARE The applications were developed in Unity3D. The backend uses C# with the libraries Clipper  [18] , Protobuf  [73]  and Gurobi  [35]. To allow researchers to replicate our work, we provided the full source code of VirtualSpace and the apps in C#/Unity3D  [99]. The space is tracked using the Vive Lighthouse system with eight trackers  [100]. The tracking information is forwarded via UDP to head-mounted displays (four GearVRs with Samsung S6 running Android) as shown in Figure  17.
CONCLUSION O N CONCURRENT USE OF VIRTUALIZED SPACE VirtualSpace is a novel software system that makes real-walking more space-efficient by having users immersed in different virtual reality experiences share the same physical space. VirtualSpace achieves this by containing each app in a smaller tile. Frequent maneuvers allow apps to incentivize their users to walk across the entire physical space, thereby allowing each app to progress its narrative and to prevent users from noticing that they are confined to a tile. This strategy enables VirtualSpace to achieve packings of the unprecedented density of four users in 16m 2 , as we demonstrated in our user study. This technique of overloading physical space will help real-walking applications expand to different scenarios such as mobile virtual reality, where space needs to be shared with other people, or application domains requiring space with high interaction rates, such as e-sports, as demonstrated with our demo apps.
MULTI-PURPOSING OF PROPS THROUGH COVERT SCENE CHANGES Another technique for having multiple virtual objects share one physical prop is through unnoticeable manipulation of the virtual scene. We implemented a software tool called Mise-Unseen that applies such unnoticeable runtime changes to any virtual environment by means of eye tracking. Mise-Unseen thereby makes efficient use of physical objects by mapping multiple virtual objects to them. This technique is similar to impossible spaces  [88] , which achieves reuse of physical space by re-routing the user to new rooms, so that changes to the virtual environment happen outside the user's view. Mise-Unseen applies these changes also inside the user's field of view without them noticing. Creators who want to tell a story with a limited physical prop set would require such a software tool.
CONCLUSION ON SPACE-INDEPENDENT EXPERIEN CES We have presented Scenograph, a novel software system that supports the design of real-walking experiences, which can adapt to any tracking volume's size and shape. For creators, who generally design experiences to fit a specific tracking volume, this allows them to define experiences with complex storylines independent of the users physical space, and it allows users to run these experiences in whatever space they have available. The core of Scenograph is a tracking volume-independent representation of real-walking experiences. This representation is not spatial until instantiated. Scenograph instantiates the experience after splitting the locations into smaller ones while maintaining narrative structure. In our user study, participants' ratings of realism decreased significantly when existing techniques were used to map a 25m 2 experience to 9m 2 and an L-shaped 8m 2 tracking volume.
VIRTUALIZING SETS OF PROPS WITHIN PHYSICAL SPACE We now have gathered almost all attributes for a full virtualization of physical space. We allow for concurrent use of tracking volumes, concurrent use of one prop, and we allow complex experiences to run in tracking volumes of arbitrary size and shape. We now also want experiences to run in physical spaces that contain arbitrary sets of physical props, so that experiences can truly run anywhere. This chapter directly builds on the last as it extends our abstraction layer between virtual reality experience and the arbitrary physical spaces they can occupy, so that these spaces can contain any set of physical props. This is the goal of Stuff-Haptics, the software system that extends Scenograph and which we describe and evaluate in the following.
TECHNICAL EVALUATION , ASSESSING PARAMETRIC DESIGNS Creators might not be able to intuit the correct parameters for spatial placement. We implemented an approach based on example models to a b allow for quick evaluation of the design, inspired by related work such as Project MARS  [61]  and others  [31]. We included a subset of 206 3D models from Lim et al. 's library of IKEA furniture  [46, 54] , removing redundant models, and imported them into Stuff-Haptics with simplified 3D meshes. By passing a virtual object through this database its parametric design can be accessed by the amount of furniture fitting to the virtual object. Figure  46    Participants enjoyed Goldilocks (mean rating 6. 4/7 ± 0. 9 sd). They found the experience only somewhat realistic (mean rating 3. 8/7 ± 0. 8 sd), but found it to fit well into their home environment (mean rating 5.
We then revisit our examples in §11, specifying them through signatures of our registers TrModMnd(P, S) for transition monads. Finally, we conclude in §12 by summing up our contributions, assessing the scope of our registers TrModMnd(P, S), and giving some perspectives. 1. 2. Related work. As mentioned above, our work refines a recent work  [7] , allowing to cover many new applications with the same approach. This approach differs from the bialgebaic one introduced long ago by Plotkin and Turi  [51] : the positive difference is that it covers higher-order languages like the -calculus, while a negative difference is that it does not recover congruence of bisimilarity. The present work is largely about signatures and relies heavily on previous work on equational systems  [23] , pointed strong endofunctors  [24] , and modules over monads  [6].
Registers for monads In this section, we design a register for monads on a category of the form Set P for some set P. Because monads are monoids in the category of endofunctors, the register MES [Set P ,Set P ] could allow us to specify the monads of §4. However, we choose to define below a dedicated subregister, ModMnd(P) which we hope more user-friendly, especially in the simply-typed case. The name ModMnd(P) is chosen because signatures are based on modules over monads. This register generalises straightforwardly Ahrens et al. 's approach  [6]  from the untyped to the simply-typed setting. The basic idea for specifying operations is that the arity of an operation consists of • an "input" (Set-valued) parametric module, in the sense of module that is definable for any monad on Set P , together with • an "output" placetaker type ∈ P.
Semantics: The abstract signature associated to any signature is the forgetful functor -alg → Mnd (Set P ). Validity proof: By Corollary 10. 19 below. 6. 3. The register ModMnd(P). We now define our register ModMnd(P), where a signature will consist of a signature of SModMnd(P), plus a family of "equations". An equation is essentially a pair of "metaterms", which may have "metavariables". The idea is that metavariables are given by a parametric module. Example 6. 12. Consider associativity of parallel composition in -calculus, |( | ) ≡ ( | )| : the metavariables are , , and. The corresponding parametric module is Θ 3 p. Intuitively, a metaterm will be a parametric module morphism from metavariables to some Θ. However, it should potentially rely on constructions from the considered signature of SModMnd(P), as in Example 6.
Registers for (state) functors In this section, we define a register PModFun(P, S), which is an adaptation of ModMnd(P) to the case of state functors. As in the previous section, we first introduce a register SPModFun(P, S) for operations, and then a refinement PModFun(P, S) with equations. Again, all validity proofs are deferred to §10. the constant functor mapping anything to the identity endofunctor. Post-composing with evaluation at any ∈ P, resp. ∈ S, we get parametric premodules Θ and I.
Registers for slice categories In this section, we provide registers for slice categories C/ , where C is a locally finitely presentable category and an object of C. In §8, we first present a basic register SSlice(C, ) that would work for the untyped case without variable binding. Then, in §8. 2, we extend SSlice(C, ) to a more powerful register MSlice(C, ) that deals with variable binding. Mimicking the passage from endofunctors to parametric modules (more precisely, say, from MES to ModMnd), we then provide a variant adapted to the typed setting, called Slice(C, ). Finally, in §8. 4, we specialise these abstract definitions to categories of the form C = -Mod /( 1 • × 2 • ), for which we introduce special notation. 8. 1. Small register for slice categories. In this section, we introduce a first, limited register for slice categories.
Applications In this section, we recast the announced languages as transition monads, and for each of them we design a signature in TrModMnd. One exception is Positive GSOS systems: for them, we go further, by recasting them as the signatures of a subregister of TrModMnd. 11. 1. The call-by-value, simply-typed, big-step -calculus. Recall from §4. 1, that the simply-typed, call-by-value, big-step -calculus forms a transition monad, where we take P = S to be the set of types (generated from some fixed set of type constants). The monad over Set P is given by values, the source state functor 1 is given by application binary trees, and the second state functor 2 is the identity. Let us now design a signature for this transition monad. Let : Set P → Set S be specified by the signature of PModFun(P, S) consisting of two families of operations app , ′ : Θ → ′ × Θ → Θ ′ and : I → Θ.
Let us now define a register GSOS + for specifying positive GSOS systems  [15]. This is a subregister of our register TrModMnd, for untyped (P = S = 1) transition monads. Let us recall that signatures in this register consist of tuples (Σ , Σ 1 , Σ 2 , Σ Trans ), where = spec (Σ ), 1 = spec (Σ 1 ), 2 = spec (Σ 2 ), and Σ Trans is a signature in Rule * ( , 1 , 2 ). In order to describe this subregister, we have to describe its class of signatures, and then assign to each such signature a tuple (Σ , Σ 1 , Σ 2 , Σ Trans ) as above. is generally defined on closed programs. A less trivial treatment might be obtained, however, by designing a reduction relation for open programs. E. g. , the reduction relation could naturally include steps like if = + 1 then 1 else 2 -→ 2. Languages whose transition rules involve some kind of evaluation contexts, like ML [45, Chapter 10], -calculi with let rec  [10] , or the substitution calculus  [1] , should also fit into the framework, although with a bit more work.
Data No animals were used in this study, and ethical approval for the use of animals was thus deemed unnecessary. Data used in the official multibreed genomic evaluations for US dairy cattle breeds were provided by the Council on Dairy Cattle Breeding (Bowie, MD). Only purebred AY, BS, GU, HO, and JE animals were considered. It is important to note that animals are considered purebred if they have more than 90% pedigree-based ancestry from one breed. The data sets were analyzed in 4 different scenarios: (1) each single breed separately (single); (2) HO and JE together (HO_JE); (3) AY, BS, and GU together (AY_BS_GU); and (4) all the 5 breeds together (5_BREEDS). Number of phenotypic lactation records (and cows), genotyped animals, and total animals in the pedigree (traced back 3 generations) are shown in Table  1.
Validation of Breeding Values The (G)EBV were validated within each tested scenario. Two data sets were considered: (1) complete, with phenotypes for milk, fat, and protein recorded in cows born from 1992 to 2018; (2) reduced, with phenotypic records from cows born up to 2014. Genotyped cows born between 2015 and 2018 with phenotypes in the complete but not in the reduced data set were in the validation set. The numbers of validation cows were 181 for AY, 2,423 for BS, 750 for GU, 577,340 for HO, and 90,666 for JE. Genotyped bulls with no daughters with phenotypes in the reduced but at least 10 daugh-ters in the complete data set were in the validation group for AY (n = 17), BS (n = 107), and GU (n = 28), whereas 50 daughters were requested for HO (n = 3,278) and JE (n = 471) validation bulls.
In this study, theoretical accuracies were not analyzed, and models were compared based on validation reliabilities. The validation for bulls was carried out using daughter yield deviations (DYD), estimated in the complete data sets for all bulls following the method by  Liu et al. (2004)  and the algorithm by  Mrode and Swanson (2004). The validation reliability of predictions for bulls was measured using the coefficient of determination (R 2 ) from the regression of DYD on (G) EBV. The validation for cows was based on the estimates of phenotypes adjusted for fixed effects (Y adj ), computed based on the complete data set using PRE-DICTF90 (ver. 1. 5;  Misztal et al. , 2018). Note that the R 2 cannot be, strictly speaking, interpreted as a reliability (squared correlation between true and estimated BV) unless the number of daughters in the DYD is very high.
, 2019). Independent chromosome segments can also be shared among different breeds, especially if there was recent introgression from the use of a few sires across breeds. When combining multiple breeds in a joint ssGBLUP evaluation with APY, it is important to have a good representation of all breeds in the core  (Mäntysaari et al. , 2017; Vandenplas et al. , 2018). Additionally, modeling the breed effects in a proper way guarantees more accurate predictions in any multibreed evaluations. In this study we added breed code to all the fixed effects to ensure animals from different breeds were not mistakenly in the same group. Breed effects were also added to the model as a fixed UPG. Therefore, GEBV resulting from the multibreed models were adjusted for breed-specific fixed effects but contained genetic breed effects combined with selection pathway by sex and year of birth (i.
ACKNOWLEDGMENTS This study was partially funded by  Agriculture and Food Research Initiative Competitive  Grant no. 2020-67015-31030  from the  US Department of Agriculture's National Institute of Food and Agriculture. The authors thank the  Council on Dairy Cattle Breeding (CDCB, Bowie, MD)  for providing access to the data. The contribution of dairy producers who supplied data through their participation in the  Dairy Herd Improvement program  and the Dairy Records Processing Centers that edited and relayed information to the  Council of Dairy Cattle Breeding  are also acknowledged. Discussions with  Duane Norman  ( CDCB, Bowie, MD ) and  Yutaka Masuda  ( Rakuno Gakuen University, Ebetsu ,.