dataesr/scientific-paragraphs-categorization huggingface dataset:
We see great prospects for interpolated soil factors or, even better, measured in situ, to complement the topo-climatic factors hitherto favoured. In nature conservation, it is commonly advocated to optimise ecological similarity between source and target habitats in translocation efforts  [6]. With increased robustness of landscape genomic approaches, we see vast opportunities for deepening our understanding of how particular genotypes may respond to a given environment in their present or new location (e. g. , climate change), both now and under predicted conditions in the future  [3]. Incorporating more accurate and species-specific comprehensive environmental datasets will allow us to better grasp polygenic responses and, hence, improve our knowledge of selective pressures to support conservation decisions.
Wait-free algorithms can be further classified as retry-based approaches, e. g. , sequence locks in Linux  [58] , or use multiple buffers, e. g. , Simpson's four-slot algorithm  [74] , to exploit parallelism. In either case, data sharing overheads refer to the maximum time a job segment is delayed by other job segments accessing data concurrently. This corresponds for instance to blocking in lock-based approaches and the latency due to retries in retry-based approaches (more in Sect. 4).
If the unknown variables are all real, i. e. , X = R m , then the problem can be solved using the classical Simplex method  [66]. The LP problem becomes an ILP problem if the variables are all required to be integers, i. e. , X = Z m (binary variables 2  are considered as integers in the domain {0, 1}). Though ILP solving is NP-hard  [45] , many algorithms (e. g. , branch-and-bound and branch-and-cut) are mature and well integrated in powerful solvers, such as CPLEX and Gurobi.
In brief, if channel c has a higher priority than channel c, then at any state of the underlying TS, all discrete transitions involving c are disabled as long as there exists an enabled transition involving c. Data variables and functions. UPPAAL supports integer and Boolean data variables, whose values may be used in guards, and updated in discrete transitions (together with clock resets). Variables can be local (to a TA) or global. A small subset of C-like functions is also supported to ease writing complex updates. Committed locations. Committed locations implement both an urgency and a priority. That is, in the underlying TS, if at some state (L, v ) there exists L i ∈ L such that L i is committed, then a discrete transition starting from a committed location must be taken immediately, i.
Timed Computational Tree Logic (TCTL) TCTL is a logic that is both temporal and timed, allowing to reason on the order of, and the amount of time separating the satisfaction of some formulae, respectively. We focus on the fragment of TCTL that UPPAAL supports. A state formula is a propositional formula over locations, clocks and data variables, to be evaluated at a TS state. For instance, A i. x < 3 holds in all states of the TS where the valuation of clock x in TA A i is strictly less than 3. Path formulae enable quantifying over TS traces. Path formulae in UPPAAL use the operators A (for all paths) and E (there exists a path) combined with the modalities (necessity, all states) and (possibility, some state). For instance, the property A φ (where φ is a state formula) translates to "for each path, there exists a state that satisfies φ" whereas E φ reads "there exists a path in which all states satisfy φ".
Justifying the Scheduling Choices Say that we have an ERTS modeled correctly as an UPPAAL network of TA (Sect. 2. 3) on which we want to verify a TCTL property (Sect. 2. 4). Even if the property is local (e. g. , bounded response within one task), the verification must be carried out on the whole UPPAAL model since tasks behaviors depend on one another due to (i) data sharing and (ii) the possibility for a task to migrate between cores. Our goal is to break this bi-dimensional dependency as to enable compositional verification while preserving the correctness of the model. This goal is achievable under P-FP scheduling with limited preemption. First, partitioned (P) schedulers prevent tasks migration and thus eliminate the second dimension of the dependency. The good news is, P scheduling is widely used, it is even the de facto choice in ERTSs  [80, 81].
Then, given the input model, the affinity, and the formal TA model of ERTS that we define in Sect. 6, an UPPAAL model is automatically generated (Step 3). Both the ILP algorithm and the UPPAAL model take into account all information from the input model and the semantics of P-FP scheduling with limited preemption, and integrate data sharing overheads based on the bounds we compute in Sect. 4. In Step 4, important properties are verified compositionally on the generated UPPAAL model, including a tight computation of tasks WCRTs that enables the possibility to counterbalance, a posteriori, the pessimism of the ILP pass (Sect. 6, Sect. 7).
Sequence Locks (Seqlocks) Seqlocks are a state-of-the-art locking mechanism for read-heavy consistent access to shared data in Linux  [42, 50, 58]. Seqlocks were conceived to solve starvation issues with reader-writer locks in certain use cases, such as updating the current system time from an interrupt handler  [42]. Alg. 1 shows an implementation. Seqlocks use a sequence counter for reader-writer synchronization and a spinlock for synchronization of multiple writers  9. The sequence counter, e. g. , a 32-bit unsigned integer initialized to zero, implements the following protocol. At the start of a write operation, the writer first increments the counter, then writes or updates the data, and finally increments the counter again. With this, an odd counter value indicates an ongoing write operation, and the upper bits of the counter define a generation counter.
e. , α(ζ, c) = 0). However, the constraints defined in (4) must be verified only if task τ is allocated to core c. In other words, they are true if τ is not allocated to c, i. e. , α(τ, c) = 0. A classical way to deal with this problem is to introduce a term to guarantee that the above condition is always true. This can be achieved by adding (α(τ, c) -1) • M to the left-hand operand of the constraints (from (4)) with a huge value M (known as big-M constraints). So if α(τ, c) is equal to 0, the constraint is always verified, and if α(τ, c) is equal to 1, the effect of M is null. Here, we do not use this technique. Indeed, modern ILP solvers have efficient approaches to model this kind of behavior. As we use Gurobi 10  , we can simply add a condition to activate a constraint; here the condition is simply α(τ, c) = 1.
ILP Formulation & Implementation Considerations To model the problem with shared data as an ILP, we start by defining the binary variables β(s, l, c), for each segment s ∈ JS τ and each data l such that cf (s, l ) = ∅. Then, β(s, l , c) = 1 if and only if the task τ that contains s and all tasks in cf (s, l ) are allocated to the same core c: β(s, l , c) = α(τ, c) ∧ τ ∈cf (s,l) α(τ , c) (9) This means that if β(s, l , c) = 1 then the data l does not induce additional overhead, i. e. , B s (l ) should not be considered 11. We also introduce new continuous variables γ(s, c) to represent the WCET of a segment s ∈ JS τ on a core c. With Gurobi, we can use conditions to activate a constraint, and so γ(s, c) is constrained by: γ(s, c) = wcs(s) + {l|cf (s,l) =∅} (1 -β(s, l, c)) • B s (l), if α(τ, c) = 1 0, otherwise (10) For each core c, to model wj (ι) (the worst-case execution time of a job ι on core c) and final (ι) (its final segment execution time), we introduce the continuous variables (ι, c) (resp.
λ(ι, c)) constrained by: (ι, c) = s∈Sι γ(s, c) (11) λ(ι, c) = γ(s |Sι| , c) (12) 11 Remark that logical operators can be easily translated into linear constraints, e. g. , a = b ∧ c is equivalent to the system 0 ≤ b + c -2a ≤ 1 and in general y = x 1 ∧ x 2 ∧. ∧ xn is equivalent to the constraint 0 ≤ n i=1 x i -y ≤ n -1. As we use the Gurobi solver, there exists a general constraint that facilitates the writing of logical operators. For instance, the constraint r = and_{x 1 ,. , xn} states that the binary variable r equals 1 iff all of the binary variables x 1 ,. , xn are equal to 1. Thus, for s ∈ JS τ and for all l such that cf (s, l) = ∅, we can simply rewrite in Gurobi the constraint on β(s, l, c) as β(s, l, c) = and_ { α(τ, c), α(τ , c) | τ ∈ cf (s, l) }. For modeling blocking (τ ) and wt (τ ), we introduce the intermediate continuous variables ν(τ, c), resp.
δ(τ, c), that represent the blocking time (resp. the worst-case execution time) of a task τ on a core c. These new variables are constrained by: ν(τ, c) = max _ { γ(s, c) | s ∈ JS τ , τ ∈ lp(τ ) } (13) δ(τ, c) = max _ { (ι, c) | ι ∈ J τ } (14) where the global constraint max _, defined in Gurobi, sets a decision variable to the max of a list of decision variables. With all these new intermediate variables, Inequality (8) becomes (under the conditional activation α(τ, c) = 1): ν(τ , c) + (j, c)+ τ ∈sp(τ ) δ(τ , c)+ τ ∈hp(τ ) δ(τ , c) • (1 + p(τ ) p(τ ) ) -τ ∈hp(τ ) u(τ ) • (λ(j, c) + δ(τ , c)) ≤ p(τ ) (15).
TA Model & Compositional Verification In this section, we develop a TA model in UPPAAL of a realistic ERTS (following the definitions given in Sect. 3) on which various real-time properties can be verified compositionally. In Sect. 6. 1, we detail our UPPAAL model taking data sharing overheads (Sect. 4, 5) and an affinity (Sect. 5) into account. Then, we show how properties can be verified on our model compositionally (Sect. 6. 2). In the remainder of this paper, we simply write wcs(s) to denote the WCET of segment s in which data sharing overheads (Sect. 4, 5) have been integrated. In other words, we use wcs(s) to denote wcs (s) defined in Sect. 5.
We want however to go further and quantify the effect of fine-grained data sharing overheads on WCRTs and schedulability (we recall that these overheads are the tightest that can be obtained analytically, Sect. 5). For this, we also compute the optimistic WCRT of τ , denoted optW (τ ), corresponding to the most optimistic scenario of data sharing, i. e. , when there are no overheads at all. For this, we rely on compositional model checking, using UPPAAL, on a model where segments' WCETs are the original ones (Sect. 3. 1). Accordingly, the exact WCRT of task τ is comprised between its optimistic WCRT and its tight WCRT, that is in the interval [optW (τ ), tW (τ )]. We will see that, thanks to the careful fine-grained computation of data sharing overheads, tW (τ ) is typically very close to optW (τ ), with no effect on schedulability in our case studies, whereas the purely analytical pesW (τ ) leads to a lower schedulability ratio, notably in the drone case study under Sect.
The results are given in Table  2. As we can see under the column pesW , the schedulability ratio using Inequality 8 is 6 9 with tasks control, plan and exec deemed unschedulable because their (pessimistic) WCRTs are larger than their periods (marked in bold). This actually explains why the ILP algorithm refrained from e. g. , assigning both control and plan to the same core: in doing so, according to the ILP's computations, none of them would be schedulable  17. On the other hand, thanks to the scalable and tight computations via compositional model checking (column tW ), the schedulability ratio is equal to 1. To further tune the WCRTs, we compute, using UPPAAL, the optimistic WCRT of each task (where data sharing overheads are assumed null, column optW ). Accordingly, we can situate each task's exact WCRT within the interval [optW , tW ].
Notice how these intervals are typically tight due to the fine-grained computation of data sharing overheads. In Sect. 7. 2. 2, we will see how these intervals become even tighter when WCETs and data penalties estimation is more elaborate. Finally, we report on the scalability of our approach on this case study. Both the ILP pass and the UPPAAL verification of properties (including WCRTs computation) were extremely fast, making the whole process last barely a few seconds per property. For model checking alone, and thanks to our compositional approach, the verification time was upper-bounded by 0. 01s and the memory consumption by merely 15MB. Terrestrial Robot. For the terrestrial robot, Step 1 succeeds, and the schedulability is confirmed with UPPAAL. We now try to quantify the maximum time separating perceiving the environment (reading the laser sensor, task scan) and using that latest perception to compute a new speed (task plan).
Artefacts The experiments described in this section are publicly available and fully reproducible. The source code is available at the public gitlab repository https: //gitlab. math. univ-paris-diderot. fr/foughali/fhz_aeic-jsa. The README file contains details on how to reproduce the experiments and/or use the toolchain on the user's own examples. The user has the choice between downloading the sources and using the toolchain on their own machine (in which case they should install the dependencies themselves), or download a ready virtual machine. Also, we provide a WATCHME video, lasting roughly twenty minutes, that demonstrates the experiments and provides a walk-through to reproduce them in a fully automatic manner. Links to both the video (that can be directly played without prior download) and the VM, hosted permanently on our institutional cloud, are provided within the README.
For this, we strive to keep the generic expressiveness of TA and their model checking tools instead of tailoring constructions specific to schedulability. Compositional verification. Compositional verification is well-grounded for untimed systems  [7, 14]. However, for ERTS, compositional reasoning on a for-malism (e. g. , TA) in a generic sense is a hard research problem, leading to frameworks supporting small fragments of the underlying formalisms. For instance, ECDAR  [26]  is restricted to deterministic I/O automata, and only a subset of the RT-BIP language  [1]  (with e. g. , no priorities, no broadcast channels and no data variables) is supported by the RTD-Finder tool  [12]. As we have shown in  [32] , such restrictions may render compositional verification even less scalable than classical model checking in real ERTS.
VII. Conclusion In this paper, an SC-SSB-FSK with a CC and its iterative decoding based on PAM decomposition is presented. Furthermore, using this system implementation, an extensive MATLAB-based simulation is carried out to analyze the feasibility of the SC schemes using this new SSB-FSK CPM family. To do so, an EXIT-chart analysis is used to obtain the convergence capabilities of the iterative decoder. The reported Pareto frontier parameters of SSB-FSK as well as other interesting configurations close to the Pareto frontier, are used to analyze the EXIT chart performance. The performance of the iterative decoder in the presence of ACI is analyzed. The optimization results are given in terms of the minimum number of iterations required to converge under given environmental conditions, assuming a fixed amount of ACI.
These studies used observational proxies for temperature and salinity from the Northeastern subpolar North Atlantic, which are used as indirect AMOC fingerprints rather than direct measurements of AMOC strength. This gives long enough data to analyse for early warning signals, but using indirect proxies adds uncertainty. The model used by  Ditlevsen & Ditlevsen (2023)  to project collapse is also highly simplified with a tipping point assumed, and does not take into account the low-frequency variability of the AMOC, nor the presence of external forcings such as increasing greenhouse gases. So while signals in this dataset are consistent with approaching a tipping point, there are substantial uncertainties with this methodology (see also  Michel et al. , 2023  highlighting potential false warnings).
The latest AR6 assessment states that we have only medium confidence that an AMOC collapse will not happen before 2100 (IPCC AR6 WG1 Ch9). This uncertainty is due to models having strong ocean salinity biases, absence of meltwater release from the Greenland Ice Sheet in climate change scenarios, and the possible impact of eddies and other unresolved ocean processes on freshwater pathways. However, a recent study with the PAGES2K database of climate reconstructions of the past 2,000 years suggests, using statistical methods based on dynamical systems theory, that we may be close to an AMOC tipping point  (Michel et al. , 2022) , as do the studies of  Boers (2021)  and  Ditlevsen and Ditlevsen (2023)  cited above. AR6 also concluded that reported recent weakening in both historical model simulations and observation-based reconstructions of the AMOC have low confidence.
Acknowledgements. This review article has been carried out within the framework of the  Global Tipping Points  Report 2023. We would like to thank  Norman Steinert  for developing the original concept of the tipping systems overview figure (Fig 1) and for providing the basemap. S. L. and D. I. A. M. acknowledge financial support from the  Earth Commission. The work of the  Earth Commission , a program of  Future Earth , is made possible through the support of the  Global Commons Alliance , a sponsored project of  Rockefeller Philanthropy Advisors. Furthermore, D. I. A. M. acknowledges financial support via the  Global Tipping   Points Report  project, funded by the  Bezos Earth Fund. Y. A. , S. R. and B. S. acknowledge funding from the project  COMFORT  (grant agreement no. 820989 ) under the  European Union 's  Horizon 2020 research and innovation programme , and from the  EC Horizon Europe  project  OptimESM "Optimal High Resolution Earth System Models for Exploring Future Climate Changes ", grant  101081193  and  UKRI  grant  10039429 ,  CANARI  ( NE/W004984/1 ), and.
C. M. C. acknowledges the financial support from  FAPESP  (grants  2018/15123-4  and  2019/24349-9 ) and  CNPq  (grant  312458/2020-7 ). HD acknowledges financial support from the  European Research Council  through the  ERC-AdG  project  TAOC  (project  101055096 ). J. F. D. acknowledges financial support from the  European Research Council Advanced Grant  project  ERA (Earth Resilience in the Anthropocene ,  ERC-2016-ADG-743080 ) and by the project  CHANGES  funded by the  German Federal Ministry for Education and Research (BMBF)  within the framework ' PIK_Change ' under grant  01LS2001A. MHE is supported by the  Australian Research Council (ARC) , Grant Nos. SR200100008  and  DP190100494. A. V. F. acknowledges support from the  ARCHANGE  project of the " Make our planet great   again" program  ( ANR-18-MPGA-0001 ),  France , and from  National Oceanic and Atmospheric Administration  ( NA20OAR4310377 ),  National Science Foundation  ( AGS-205309 ), and  Department of Energy  ( DE-SC0023134 ,  DE-SC0024186 ),  USA.
LJ was supported by the  Met Office Hadley Centre Climate Programme  funded by  DSIT  and by  TiPES. This project is  TiPES  contribution no. 262, and this project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 820970. GM acknowledges funding from the  Swedish Research Council Vetenskapsrådet  (proj. No. 2022-06599 ). T. T acknowledges funding from the  Department of Science and Technology, India  through the ' INSPIRE Faculty Fellowship '. References Abernathey, R. P. , Cerovecki, I. , Holland, P. R. , Newsom, E. , Mazloff, M. , & Talley, L. D. (2016). Water-mass transformation by sea ice in the upper branch of the Southern Ocean overturning. Nature Geoscience, 9(8), Article 8. https://doi. org/10. 1038/ngeo2749 Ali, H.
Study population The present study was a follow-up extension of a retrospective observational multicenter case-control study conducted in  17  French and Belgian academic centers affiliated with the Groupe d'Etude Therapeutique des Affections Inflammatoires du tube Digestif (GETAID). In this study, 482 patients with IBD who were healthcare workers and 482 controls patients were included from the MICISTA registry, a tertiary monocentric clinical database of all consecutive patients with IBD at Saint-Antoine Hospital (Paris, France)  [7, 8]. Patients were identified from personal databases and/or a standardized hospital inpatient diagnosis datasets. Occupational status of patients with IBD was collected in IBD databases upstream of this study. From January 2021 to October 2021, investigators were asked to report patients previously included in their center who were still followed up until December 31 st 2020.
Data collection Previously collected data were retrieved and updated until December 31 st 2020. The recorded data included a detailed account of the IBD diagnosis and history, smoking status, IBD phenotype according to the Montreal classification, medical and surgical treatment history and any serious infection history. For each of the patients, year 2020 was divided into semesters. For each semester, which was independently analyzed, the occurrence of COVID-19 and severe COVID-19, smoking status, physician global assessment of IBD activity (active or not), weight and immunosuppressive therapy (e. g. , steroids, aminosalicylates, thiopurines, methotrexate, anti-TNF therapy, ustekinumab, anti-integrin therapy and tofacitinib) were assessed  [9]. A semester was considered as a treatment semester if the patient received steroids, immunomodulator and/or anti-integrine therapy during at least 3 months within the studied semester.
Case-control study In the previous study, controls were selected randomly within the MICISTA registry to match to the healthcare worker cases (1:1). MICISTA is an electronic database of the gastroenterology department of Saint-Antoine Hospital. All patients seen in the institution from 1994 are included in the database. Data regarding medical and IBD history and follow-up are prospectively coded in the system. Case-control matching was based on gender, birth year (AE2. 5 years), type of IBD and IBD diagnosis calendar (AE2. 5 years). After exclusion of 156 cases and 143 controls (Figure  1 ), 221 case-control couples were still available. One-hundred and five cases were rematched with 105 controls without redundancy. In total, 326 cases and 326 controls (one control for one case) were included in the present study.
The shear measurement from the Dark Energy Camera Le gac y Surv e y (DECaLS) pro vides an e xcellent opportunity for galaxygalaxy lensing study with the Dark Energy Spectroscopic Instrument (DESI) galaxies, given the large ( ∼9000 de g 2 ) sk y o v erlap. We explore this potential by combining the DESI 1 per cent surv e y and DECaLS Data Release 8 (DR8). With ∼106 de g 2 sk y o v erlap, we achiev e significant detection of g alaxy-g alaxy lensing for Bright Galaxy Surv e y (BGS) and luminous red galaxy (LRG) as lenses. Scaled to the full BGS sample, we expect the statistical errors to impro v e from 18(12) per cent to a promising level of 2(1. 3) per cent at θ > 8 arcmin ( < 8 arcmin ). This brings stronger requirements for future systematics control. To fully realize such potential, we need to control the residual multiplicative shear bias | m | < 0.
2022 ) , Kilo-Degree Survey (KiDS;  Heymans et al. 2021 ) , and Hyper-Suprime Cam (HSC;  Hikage et al. 2019 )  do not offer extremely large overlap with DESI, while the Stage IV surv e ys mentioned previously will require many years of observations before reaching their full o v erlap with DESI. In short, the sky o v erlap will limit the cross-correlation studies with DESI in the near future. In this work, we study the cross-correlations between galaxy shear measured from the Dark Energy Camera Le gac y Surv e y (DECaLS) Data Release 8 (DR8) and galaxies from the DESI 1 per cent (SV3) surv e y, and compare those with the o v erlapped data from KiDS and HSC. We measure the g-g lensing signals of the different weak lensing surv e ys with DESI 1 per cent surv e y and estimate their signal-to-noise ratio (S/N) that can be achieved with full DESI in the future.