dataesr/scientific-paragraphs-categorization huggingface dataset:
In this context, we remind practitioners that they should always acknowledge the USGS for making the Landsat data freely available. This review illustrates how recent advances in AI (e. g. , cloud masking, classification, and regression) have been used to analyze Landsat archive data automatically. Nevertheless, two points of progress must be discussed. The first concerns the use of reference data, which remain challenging to collect in some regions and/or for the past. This may explain why the accuracy of indicators derived from earlier Landsat images was rarely assessed. Future studies could make greater use of archive field databases, such as the Global Biodiversity Information Facility  [194]  for flora and fauna or the GlobalSoilMap  [195]  for soils, to further calibrate and validate models.
We calculated the normalized difference vegetation index (NDVI) time series in the Iraqi Hammar Marsh (Ramsar site ID 2242) from the Landsat archive for the period 1984-2022 as an example (Figure  8 ). Filtering Landsat data contributes to maintaining a high-quality time series, because it reduces the redundancy of information, residual noises in the images, and processing time and space. This review also shows the relevance of cloud computing for processing the Landsat archive over large areas  [14]. Cloud computing is an opportunity to reduce the technology gaps between countries in the Global North and South. All studies that used cloud computing chose the GEE for its easy access and use and for the availability of the entire Landsat archive pre-processed for surface reflectance (level 2).
Bridging the Gap between Remote Sensing and Wetland Monitoring This review emphasizes that the analysis and interpretation of the Landsat archive differs among scientific disciplines. Studies published in environmental journals used field observations to provide new insights on wetland ecosystem functioning but rarely used the most advanced AI or cloud-computing tools. Conversely, studies published in remote sensing journals used innovative methods to analyze large datasets from the Landsat archive, but the changes detected were rarely interpreted or validated using the visual interpretation of high-spatial-resolution images. Thus, as described by Pettorelli et al. [199] , scientists in environmental and remote sensing communities need to share skills and knowledge even more to facilitate and improve ecosystem monitoring using methods known to be effective.
Conclusions This review highlighted that the Landsat archive was used in hundreds of scientific studies for the global long-term monitoring of wetlands, especially in developing countries where managers have limited resources and funds for monitoring highly threatened wet-lands. The continuity of Landsat missions since 1972 enables the monitoring of wetland areas, structure, and functions including fine-grained changes in patterns, which would have been difficult to observe over shorter periods and/or at a broader spatial resolution. This review also emphasizes that new cloud-computing tools enable dense Landsat times series to be processed over large areas. Future research should study more extensively certain areas or wetland types that are still poorly explored, extend the monitoring period backwards and forwards, make greater use of archive field databases to further calibrate and validate models, focus on modeling the temporal profile using most of the images available in the Landsat archive, crosswalk to common operational frameworks such as functional assessment procedure or essential biodiversity variables, and bridge the gap between environmental and remote sensing scientific communities.
Much of the effort in obtaining the proposed framework was devoted designing such meta-level heuristics and fully implementing them in Maude  [8] , a high-performance reflective language and system supporting rewriting logic. The case studies presented in this paper comprise several propositional sequent systems, encompassing different proof-theoretical aspects. The chosen systems include: single conclusion (G3ip) and multi-conclusion intuitionistic logic (mLJ), classical logic (G3cp), classical linear logic (LL) and its dyadic system (DyLL), intuitionistic linear logic (ILL), and normal modal logics (K and S4). Beyond advocating for the use of rewriting logic as a meta-logical framework, the novel algorithms presented here are able to automatically discharge many proof obligations and ultimately obtain the expected results.
Rewriting Logic Preliminaries This section briefly explains order-sorted rewriting logic  [24]  and its main features as a logical framework. Maude  [8]  is a language and tool supporting the formal specification and analysis of rewrite theories, which are the specification units of rewriting logic. An order-sorted signature Σ is a tuple Σ =(S, ≤, F) with a finite poset of sorts (S, ≤) and a set of function symbols F typed with sorts in S, which can be subsortoverloaded. For X = {X s } s∈S an S-indexed family of disjoint variable sets with each X s countably infinite, the set of terms of sort s and the set of ground terms of sort s are denoted, respectively, by T Σ (X) s and T Σ ,s ; similarly, T Σ (X) and T Σ denote the set of terms and the set of ground terms. A substitution is an S-indexed mapping θ : X -→ T Σ (X) that is different from the identity only for a finite subset of X and such that θ (x) ∈ T Σ (X) s if x ∈ X s , for any x ∈ X and s ∈ S.
Modulo the axioms B S , a term proved | G is structurally equal to G, thus making the goal G automatically active for proof-search under the rewrite strategy declared for goals. As illustrated with the running example, the syntax of the object logic in R S , as well as its inference rules, is straightforward. This is usually called in rewriting logic the ε-representational distance  [25] , where the system being specified mathematically as a rewrite theory and its codification in Maude as a system module are basically the same. This is certainly an appealing feature that can widen the adoption of the framework proposed here for implementing and analyzing sequent systems. In face of the ε-representational distance, the adequacy of R S with respect to S follows from the soundness of rewriting logic itself.
Sequent System Specification The starting point for a sequent system specification is the definition of its syntax and inference rules. The file syntax. maude contains the functional module (i. e. , an equational theory and no rewriting rules) SYNTAX-BASE. Such a module defines the sorts Prop and Formula, as well as the subsort relation Prop < Formula. No constructors for these sorts are given since those depend on the OL and hence must be provided. The sort MSFormula, for multiset of formulas, is pre-defined here with the constructors presented in Section 4: op * denoting the empty multiset and op _;_ for multiset union. Some auxiliary functions, needed to produce L A T E X outputs, are declared in this module. In particular, the OL may define a mapping to replace symbols of the syntax with L A T E X macros, e.
OLs are also allowed to define equations to complete the definition of the mapping TEXReplacementSeq that replaces the name of the rules with suitable L A T E X symbols: eq TEXReplacementSeq = ('AndL |-> '\wedge_L), ('AndR |-> '\wedge_R). The sort Goal, the subsort relation Sequent < Goal, the constructors op _|_, and proved (for building list of sequents) are also defined in the module SEQUENT. The sequent. maude file also specifies the module SEQUENT-SOLVING with auxiliary procedures for building derivation trees and outputting L A T E X code. This module uses reflection heavily (more on this in the next section) in order to deal, in a general and uniform way, with the representation of any sequent regardless of its specific syntax. Moreover, Maude's module LEXICAL is used for converting between strings and lists of Maude's quoted identifiers (terms of the form 'identifier with sort Qid).
Reflection and the Core System A reflective logic is a logic in which important aspects of its meta-theory can be represented consistently at the object level. In a nutshell, a reflective logic is a logic that can be faithfully interpreted in itself. In this way, the object-level representation can correctly simulate the relevant deductive aspects of its meta-theory. Maude's language design and implementation make systematic use of the fact that rewriting logic is reflective, making the meta-theory of rewriting logic accessible to the user as a programming module  [7]. For the purpose of this paper, the focus is on two meta-theoretic notions, namely, those of theory/module and the deductive entailment relation. Formally, there is an universal rewrite theory U in which any finitely represented rewrite theory R can be represented as a term R, including U itself, any terms t, u in R as terms t, u, respectively, and a pair (R,t) as a term R, t in such a way that the equivalence R t → u ⇔ U R, t → R, u holds.
Since U is representable in itself, a "reflective tower" can be achieved with an arbitrary number of levels of reflection. In general, simulating a single step of rewriting at one level involves many rewriting steps one level up. Therefore, in naive implementations, each step up the reflective tower comes at considerable computational cost. In Maude, key functionalities of the universal theory U have been efficiently implemented in the functional module META-LEVEL, providing ways to perform reflective computations. Additional utilities for manipulating modules and terms in the L-Framework are implemented in the meta-level-ext. maude file. For instance, all the operations on theories described in Section 5 are contained there (some of them are detailed below). The module META-LEVEL implements the so called descent functions that manipulate (meta-) terms.
The function op upTerm : Universal -> Term. returns the (meta) representation t of a term t. For example, constants are represented as Id. Type (e. g. , 'proved. Goal), variables as Id:Type (e. g. , 'F:Formula), and functions as Id[Params] (e. g. , the term '_;_[Gamma:MSFormula,Delta:MSFormula] represents the multiset of formulas Gamma ; Delta). From a well-formed (meta-) term t it is possible to recover the term t (one level down). Note, however, that not all meta-terms have a suitable representation in the theory in the level below, for instance, '_|_[S:Sequent, F:Formula] is not the image of any valid sequent or formula in the module specifying the system G3ip. The function op downTerm : Term Universal -> Universal takes as parameter the (meta) representation of a term t and a term t ′.
It returns the canonical form of t if it is a term having the same sort of t ′ ; otherwise, it returns t ′. Usually, t ′ is an error term used to denote that the descent translation was not possible. For instance, if t is expected to be the meta-representation of a formula, then t ′ can be the constant op error : -> Formula. At the meta-level, modules in Maude (i. e. , rewrite theories) are represented as terms with sort Module. The function upModule can be used to obtain such a term. All the components of a module (sorts, functions, equations and rules) have a suitable sort and representation in the meta-level, thus making them first-class citizens. For instance, most of the analyses require to extend the sequent theory with new rules. The function below adds to the module M a set of rules RS: op newModuleRls : Module RuleSet -> Module.
eq newModuleRls(M, RS) = (mod 'NEW-MOD is getImports(M) ---Sorts, imports, equations, etc remain the same sorts getSorts(M). getSubsorts(M) getOps(M) getMbs(M) getEqs(M) (getRls(M) RS) ---Union of the rules of M and RS endm ). Rules at the meta-level are terms of sort Rule built from the constructors The parameter of sort AttrSet is a set of attributes of the rule, including, e. g. , the label used as identification ([label('AndR)]). This representation opens the possibility of manipulating rules in a simple way. For instance, consider a sequent rule r = (S -→ S ′ ), for some conclusion S and premise S ′ , to be proved height-preserving admissible. Consider also that the current goal is to show that a given ground sequent is provable with height at most s(h). A call to the function below with parameters h and r produces a restricted version of r where it can be applied only on sequents annotated with (the constant height) h: The module META-LEVEL offers functions to perform Maude's operations at the meta-level.
For instance, the function metaRewrite allows for rewriting the metarepresentation of a term in a given module and metaVariantDisjointUnify to solve unification problems. Moreover, given a theory R and two terms t, u in that theory, metaSearchPath allows for checking the entailment R t → u by testing whether U R, t → R, u. For instance, in the following assignment: Ans := metaSearchPath(M, SGoal, upTerm(proved), nil, '*, bound-spec, 0). the term M is the meta-representation of a sequent system; SGoal is a term representing the goal/sequent to be proved; nil specifies that there are no additional conditions to be satisfied by the solution; '* means that the reduction may take 0 or more steps; bound-spec indicates the maximum depth of the search; and the final 0 is the solution number. The term Ans is a term of type Trace?.
The General Approach for Implementing the Algorithms All the analyses implemented in L-Framework are instances of the same template: 1. A module interface (called theory in Maude) specifies the input for the analysis. This theory includes parameters such as: the name of the module implementing the OL; the specification, as a rewrite rule, of the theorem to be proved; the extra hypotheses (set of rewrite rules) corresponding to the already proved theorem; the bound for the search depth; etc. 2. A module implementing the decision procedures proposed in Sections 5 and 6. All algorithms follow the same principles: (a) A function generate-cases uses unification to generate all the proof obligations to be discharged. In each kind of analysis, there are suitable sorts and constructors to represent the proof obligations.
Normally, the cases include the terms denoting the premises (that can be assumed to be provable) as well as the goal to be proved. (b) Auxiliary definitions to extend the theory with new axioms and the right inductive hypothesis. Take for instance the function inductive-rule explained above. (c) A function holds? that receives as parameter one of the proof obligations, uses the functions in (b) to extend the theory and calls to metaSearchPath to check if the goal can be proved. 3. An extra module providing facilities to produce the L A T E X output. The following sections give some details about the components (1) and (  2 ) for each kind of analysis. Common facilities for all the analyses are implemented in the theorem-proving-base. maude file, including: converting the term t into the ground term t (i.
endfth Theories in Maude are module interfaces used for declaring parameterized modules. Such interfaces define the syntactic and semantic properties to be satisfied by the actual parameter modules used in an instantiation  [7]. The name of the theorem is specified with the string th-name (e. g. , "Admissibility of weakening. "). The identifier of the module mod-name (e. g. , 'G3ip) is used to obtain the meta-representation of the OL module defining the syntax and inference rules of the sequent system. The field file-name specifies the output (L A T E X) file. The theorem to be proved is specified via a rewriting rule (a term of sort Rule). As an example, the height-preserving admissibility of weakening for G3ip is: This term is the meta-representation of the rule rl [W] n : Gamma, F |--G => n : Gamma |--G.
This specification is nothing else that the meta-representation of the rule in Equation  (8). In all the analyses, the cut-formula is expected to be named as FCut$$ and the height of the two premises as h1$$ and h2$$. The module CUT-BASE offers mechanisms to generate, in a uniform way, the proof obligations for the cut-elimination procedures considered here. This is done in two steps. First, one of the rules of the system identified as Q1 is matched against the first premise of the cut-rule (lcut below): U := metaVariantDisjointUnify (module, lcut =? getHead(Q1, module), empty, 0, N) The unifier U must map the variables of lcut to some fresh variables. Hence, before unifying a second rule Q2 against the second premise of the cut-rule, denoted as rcut, the substitution U must be applied on rcut (see Definition 8): U' := metaVariantDisjointUnify(module, apply(rcut, U) =? getHead(Q2, module), empty, Nvars, M).
This work moves forward towards such a direction: it proposes a general, natural, and uniform way of proving key structural properties of sequent systems by using the rewriting logic logical and meta-logical framework  [2]. It ultimately enables modular and incremental proofs of meta-level properties of propositional sequent systems, both specified and mechanized in the language of Maude  [8]. The approach builds on top of core algorithms that are combined and used for proving admissibility and invertibility of rules, as well as cut-elimination and identity expansion of sequent systems. The choice of rewriting logic as a meta-logical framework brings key advantages. Indeed, as detailed below, while approaches using logical frameworks depend heavily on the specification method and/or the implicit properties of the meta and object logics, rewriting logic enables the specification of the rules as they are actually written in text and figures  [2, 23].
The recent emergence of imaging mass cytometry technology has led to the generation of an increasing amount of high-dimensional data and, with it, the need for suitable performant bioinformatics tools dedicated to specific multiparametric studies. The first and most important step in treating the acquired images is the ability to perform highly efficient cell segmentation for subsequent analyses. In this context, we developed YOUPI (Your Powerful and Intelligent tool) software. It combines advanced segmentation techniques based on deep learning algorithms with a friendly graphical user interface for non-bioinformatics users. In this article, we present the segmentation algorithm developed for YOUPI. We have set a benchmark with mathematics-based segmentation approaches to estimate its robustness in segmenting different tissue biopsies.
Segmentation consists of annotating the image to assign specific pixels to objects and thus gather pixels with already known criteria. When applied to biological data, segmentation is critical for working at a single-cell level. Thus, cell segmentation remains a complex exercise, primarily because of the cells' irregular shapes, heterogeneous density, and unevenly distributed membrane marking  (8). Few attempts have been made to handle the task with simple mathematical approaches, such as CellProfiler, which has been developed using threshold pixel intensity, the watershed method, or the propagation algorithm  (9). Pipelines based on the combination of Ilastik  (10)  and CellProfiler softwares (11) and, more recently, QuPath software  (12)  are some commonly used tools in research laboratories for the cell segmentation of IMC data.
Slides were cooled to room temperature (RT) and subsequently blocked using phosphatebuffered saline (PBS) with 3% BSA for 30 min at RT. Each slide was incubated with 100 ml of the metal-tagged antibody cocktail (Table  1 ) overnight at 4°C. Then, the slides were washed three times with PBS and labeled with a 1:500 dilution of Intercalator-Ir (Fluidigm) in TBS for 2 min at RT. Slides were briefly washed with H2O and air dried before IMC acquisition. Data were acquired on a Hyperion Imaging System ™ coupled to a Helios Mass Cytometer (Fluidigm) at a laser frequency of 200 Hz and a laser power of 3 dB. For each recorded region of interest (ROI), stacks of 16-bit singlechannel TIFF files were exported from MCD binary files using MCD ™ Viewer 1. 0 software (Fluidigm). To prepare for training the neural network, cell-based morphological segmentation was carried out using supervised pixel classification with the Ilastik toolkit  (22)  to identify nuclei, membranes, and backgrounds.
IMC training dataset pre-processing The dataset used to train the neural network consisted of 81 patches randomly selected, including 30 patches from salivary glands of a patient with Sjögren's syndrome, 20 patches from an intestine, five patches from a small cell lung cancer, and 26 patches from a nonsmall cell lung cancer. The heterogeneity of the tissues was expected to improve the capacity for detecting cells of various shapes. The trained model was evaluated on 90 new patches from salivary glands. Cell-based morphological segmentation was conducted using supervised pixel classification by Ilastik  (22)  to identify nuclei, membranes, and backgrounds, followed by CellProfiler analysis (11) to segment the resulting probability maps. Masks obtained from this segmentation were manually corrected, based on the codetection of the nuclei signals and membrane signals, and used as the ground truth for the network training.
The output is in the form of an image whose pixels have values between 0 and 1. To obtain a binary image, all pixels below 0. 5 must be 0 for black, and all pixels greater than 0. 5 must be 1 for white. The white part represents the cell, and the black part represents the background. For the training, we used a binary cross-entropy loss function defined as follows: loss(y, y) = - 1 N o N i=0 (y i * log(yî) + (1 -y i ) * log(1 -y i )) ; where N stands for the total number of pixels in an image, y i represents the corresponding target value, and yî is for the predicted pixel probability. The cross-entropy loss compares the predicted probabilities with the ground truth values, and the loss is minimized during the training process. The network runs with the Adam's optimizer, a stochastic gradient descent method, a batch size of 16, and 500 epochs with an early stop, using Keras/Tensorflow packages in Python 3.
YOUPI features 2. 6. 1 Cell segmentation mask generation For each ROI, stacks of 16-bit single-channel TIFF files were exported from MCD fi les using MCD ™ Viewer 1. 0 software (Fluidigm). A first cell-based morphological segmentation was conducted, as described in section 2. 4. A second cell segmentation method was performed using the OME-TIFF files of the markers stacked in a single TIFF file with ImageJ software (v. 1. 8. 0_172). The TIFF file was opened with QuPath software (v. 0. 3. 2), and the image type was set to fluorescence. The segmentation process was then run with the optimal parameters for each ROI (filter function, signal intensity threshold, etc. ), based on the iridium channel.
Elaboration of the preliminary segmentation mask The training dataset is the result of several steps. Once the preparation of the tissue slides is ready, IMC acquisition is performed (Figure  1 ). The acquisition produces a stack of images representing the same ROI with a different marker detected for each image. The MCD acquisition files from the Hyperion Imaging System ™ are converted into TIFF format using an IMC file conversion tool (https://github. com/BodenmillerGroup/imctools). The marker panel was previously designed according to the tissue from which the IMC images were obtained. The interesting membrane markers are selected (Table  1 ; Supplementary Figure  1 ) and summed. In addition, image with stained nuclei is also selected. A graphical interface allows for colorizing the summed membrane markers in green and the nuclei marker in red before summing the two colors.