dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This project has received funding from the  European Union 's  Seventh Framework Programme  for research, technological development and demonstration under grant agreement No. 606692  (HELCATS). We thank  Yi-Ming Wang  for sending us the PFSS computations of the source surface magnetic field and expansion factors used in this paper. We acknowledge usage of the tools made available by the  French plasma physics data center (Centre de Données de la Physique des Plasmas ; CDPP; http://cdpp. eu/), CNES  and the space weather service in Toulouse (Solar-Terrestrial Observations and Modelling Service; STORMS). This includes the data mining tools AMDA (http://amda. cdpp. eu/), the CLWEB tool (clweb. cesr. fr/)and the propagation tool (http://propagationtool. cdpp. eu). We acknowledge in particular the SOLA: Plotnikov-et-al_CDS_catalogue_16.
The model represents the life cycle of a single Mycoplasma genitalium bacterial cell and predicts the dynamics of every molecular species. The model is composed of 28 pathway sub-models that are represented using multiple mathematical formalisms including stochastic simulation, ordinary differential equations (ODEs), flux balance analysis (FBA), and Boolean rules (BRs). The model was implemented in MATLAB. The M. genitalium model has been used to gain novel insights into non-genetic cell cycle regulation mechanisms  [11] , learn unknown kinetic rate parameters from phenotypic data  [12] , calculate the metabolic costs of synthetic circuits  [13] , and repurpose antibiotics  [14]. Karr et al. extensively documented the model; developed the WholeCellKB  [15] , WholeCellSimDB  [16] , and Whole-CellViz  [17]  software tools to provide user-friendly interfaces to the model; and published the model open-source.
This has enabled other researchers to reuse the model  [12] [13] [14]. However, significant domain expertise is still needed to reuse the model or to develop new WC models. The multi-algorithm modeling methodology is complex. The model is difficult to understand, reuse, and extend because it is described directly in terms of its numerical simulation rather than in a software-independent format. The model code is difficult to learn and reuse because it is large, complex, and intertwined with the details of the M. genitalium model. The simulation code is also slow. Furthermore, the simulation code requires the proprietary MATLAB software package. New standards and software tools are needed to help researchers build and simulate WC models. They would help researchers reuse, reproduce, and compare models, as well as share models through repositories such as BioModels  [18].
The school focused on creating a reusable WC model by recoding the M. genitalium model in SBML. We focused on SBML because SBML is the most widely used systems biology standard and there was insufficient time to evaluate multiple standards. The school also aimed to improve numerous details of the model, visualize the model with SBGN, and describe model simulations with SED-ML. The latest versions of our SBML-encoded submodels and SBGN diagrams are available at https://github. com/whole-cell-tutors/wholecell/releases/tag/ meeting-report. Most importantly, the school generated extensive community discussion on how to best build and simulate WC models. This report describes the outcome of these discussions, including our recommendations for new standards and software to accelerate WC modeling.
A. Submodel encoding We pursued several strategies to encode submodels in SBML. Several groups encoded submodels by (1) reading the original documentation of the model, (2) drawing pathway diagrams using software tools such as CellDesigner  [30]  and VANTED  [31] , and (3) writing scripts to generate SBML from the diagrams. Other groups used model design tools such as Antimony  [32] , BioUML  [33] , COBRApy  [34] , COPASI  [35] , iBioSim  [36] , and libRoadRunner  [37]  to recode submodels based on the original documentation. A few of the groups encoded submodels by converting the MATLAB code to SBML. As an example, Fig. 1  and File S1 illustrate how we recoded the transcription submodel. We encountered several challenges to encoding the submodels in SBML. First, understanding the submodels was time-consuming because many students were not familiar with the modeled biology, many of the submodel details are described only in the MATLAB code, and the model documentation only summarizes the model.
D. Annotation, documentation, and visualization The documentation group was responsible for annotating the model. The group aimed to define every model element independently from external databases and to provide cross references to databases where possible to help users interpret the model. For example, they used InChI  [40]  to define small molecule species in terms of structures. They defined DNA, RNA, and protein species as polymers of small molecules. The group wrote scripts to identify cross references for each model entity. However, many entities are not represented by any database. The group contributed the missing metabolite structures to ChEBI  [39]  and concluded that the biological databases must be expanded to help aggregate data for models. The group also helped the other groups visualize submodels by providing advice on SBGN and diagramming tools such as SBGN-ED  [41] , a VANTED add-on for creating, editing and validating SBGN diagrams.
E. Progress and future work We produced draft SBML and SBGN versions of the submodels. However, significant work remains to combine, identify, and verify the submodels. Using the lessons learned, a subgroup of the participants are continuing to recode the sub-models and integrate the submodels into a single model. We expect that the final model will be more scalable, extensible, and easy to use than the original model. We also plan to build an SBMLcompatible multi-algorithm simulator by expanding analysis tools, such as iBioSim and BioUML. After recoding the model, we plan to identify and validate the new model. We will validate the model in two steps. (1)  We will use the experimental data that was used to validate the original model. (2) To more rigorously validate the new model, we will compare the model to newly published single-gene deletion strain growth rates  [12]  that were not available when the original model was developed.
B. New software tools and databases Several new software tools and databases are needed to accelerate WC modeling (Table  II ). A high-performance simulator must be developed. This simulator should be parallelized to enable the simulation of vastly larger models that require more computing and memory than are available on a single machine. This requires research to determine how to concurrently integrate mathematically heterogeneous submodels that share state. The simulator should leverage recent advances in parallel discrete event simulation  [42]. The simulator must also implement the SBML Multistate and Multicomponent Species package  [43]  to support rule-based modeling. This will enable more succinct model descriptions, making models easier to understand and edit. For example, translation could be described using a single reaction pattern parameterized by mRNA-specific translation initiation rates rather than by enumerating each individual reaction.
By separating mathematical descriptions from parameter values, reaction patterns will also clarify the connection between dynamical models and their underlying data. Implementing this package would also enable modelers to efficiently simulate models with combinatorial state spaces, which, in turn, will enable the encoding of more complex models. Ultimately, to accurately predict phenotypes, WC models must also represent spatiallydependent processes. Currently, researchers are independently pursuing WC and spatial modeling. For example, the M. genitalium model only represents three compartments, and the most advanced spatial models only represent individual pathways. WC and spatial modeling should be combined by adding support for the SBML Spatial Processes package  [29]  to the new WC simulator.
New model design software must be developed to help researchers quickly build WC models. This software should help researchers systematically build WC models from experimental data organized into pathway/genome database. In turn, this software will help researchers build bigger models. New data curation tools are needed to aggregate data to build more comprehensive models. The software should automatically aggregate data from public databases, as well as accelerate manual curation from individual publications. This software will also make WC models more reproducible by automatically recording each data source. Natural language processing  [44] , crowdsourcing  [45] , and machine learning should also be explored to accelerate data curation. New pathway/genome database software is needed to organize the data required to build WC models.
C. Systematic WC modeling pipeline The new standards and software tools will enable a five step approach to WC model-driven discovery (Fig. 2 ). (1) Researchers will use data curation tools to aggregate heterogeneous data into pathway/genome databases. These databases will use semantic annotations to describe the connection between models and their underlying data. (2) Researchers will use design tools to build WC models from pathway/genome databases. These tools will export models to software-independent formats such as SBML. (3) Model identification and verification tools will be used to estimate parameters and test models. (4) A multi-algorithm simulator will be used to conduct in silico experiments. (5) Simulation databases and visualization software such as WholeCellSimDB and WholeCellViz will be used to discover new biology by visualizing and analyzing in silico experiments.
We have produced preliminary SBML versions of all of the submodels of the M. genitalium model and we are working to develop a software program to simulate the combined model. We plan to publish the new SBML-encoded model to BioModels. Most importantly, our community discussions generated clear goals for new WC modeling software and standards. We recommend that researchers develop a new SBML-compatible simulator that supports both model composition and sequence-and rule-based modeling, as well as develop new model design, parameter estimation, model testing, and visualization tools. We also recommend expanding the biological databases to facilitate model building and annotation. Furthermore, we believe that SBGN should be extended to support hybrid diagrams, advanced graph layout, and contextual zooming.
Acknowledgments The Rostock and Utah meetings were supported by the  Volkswagen Foundation  (Grant  88495  to D. W. and F. S. ). J. R. K was supported the  James S. McDonnell Foundation   Postdoctoral Fellowship Award in Studying Complex Systems  and  National Science Foundation  grant  1548123. J. C. was supported by the  Australian Research Council Centre of Excellence in Convergent Bio-Nano Science and Technology  (project  CE140100036 ).
X being the outer product of two vectors, this matrix must satisfy: X X X 0, rank(X X X) = 1, y i = Tr(a a a i a a a † i X X X) for i = 1,. , m (4) where X X X 0 means that X X X is positive semidefinite and † stands for the conjugate-transpose operator. Because rank minimization is computationally hard, a relaxation was proposed  [8]  by dropping the rank constraint and replacing it by a trace minimization, accounting for the sum of the singular values of X X X. The semidefinite program PhaseLift is hence defined as: minimize X X X tr(X X X) subject to X X X 0, Tr(a a a k a a a † k X) = y k , k = 1,. , m (5) The convexity of this formulation makes it solvable with the help of optimization software such as CVX  [13]. Among the numerous applications of this approach, the retrieval from phaseless far-field data of a microwave array has recently been demonstrated in numerical studies  [14, 15].
, 1998; Itti and Koch, 2001). There are many methods of constructing saliency maps  (Borji et al. , 2013) , but in this study we adopted the Itti-Koch implementation from  Harel et al. (2006)  because it consistently outperforms other versions and because it is part of the widely accessible GBVS (Graph-Based Visual Saliency) Matlab package. This saliency map (Fig. 1c ) was used in Experiment 1 by both MASC and the Itti-Koch model to prioritize the selection of saccades during the free viewing of naturalistic scenes. Experiment 2 used a search task, and in search the prioritization of visual information is captured by a target map  (Zelinsky, 2008; Zelinsky and Bisley, 2015). A target map is a top-down and task-dependent (i. e. , cortical) biasing of information to reflect visual similarity to a target goal.
2a, c ). Therefore, only general methods will be provided, with task-specific methods included as they pertain to the target cue manipulation. Experiment 2 used images of common objects (Hemera Technologies). Targets were from a dataset provided by  Konkle et al. (2010) , where subsets of Hemera objects were organized into categories consisting of 17 exemplars. From these, we selected five high-typicality exemplars to be used as targets from each of 25 target categories. Figure  3  shows the target categories and the specific exemplars presented to participants. Distractors were 3770 objects selected at random and without replacement from the Hemera collection, and no distractor was an exemplar of any of the 25 target categories. Objects averaged 3. 5 ϫ 3. 5°in size and were arranged into 5, 10, 15, 20, or 25 object displays.
Free viewing Experiment 1 tested MASC against the scene-viewing dataset from  Judd et al. (2009). For clarity in making model comparisons, we will hereafter refer to this version of MASC as MASC-S, reflecting the use of only saliency information in its priority map. The 1003 scenes shown to participants were input to MASC-S in their original sizes. For each scene, MASC-S made a series of "free viewing" saccades based on the corresponding visual-saliency map projected onto the SC (see Materials and Methods, Model methods), thereby generating the data for the following analyses. Figure  4  shows representative scanpaths, each truncated to the first six saccades, from MASC-S and 6 (of 15) participants, superimposed over the scenes that were viewed. Note that only a subset of the behavioral data is visualized for each scene so as to reduce clutter and make individual scanpaths more discernable; participants were selected randomly for visualization, not to maximize agreement with the model.
Similarly, the bottom left panel shows predicted SC motor map activity after averaging visual map activity over the motor point images. The cross indicates the center of the most active point image, which corresponds to the landing position of the subsequent saccade (the new fixation appearing in the top left panel). To evaluate MASC-S more quantitatively, we compared its behavior with the behavior generated from two saliency models applied to the dataset of  Judd et al. (2009). One of these was an Itti-Koch model  (Itti and Koch, 2001) , implemented by  Harel et al. (2006). The other was the Adaptive Whitening Saliency (AWS) model  (Garcia-Diaz et al. , 2012) , a top performer in a saliency model evaluation by  Borji et al. (2013). These models used WTA to select points from their saliency maps for fixation, with each fixated region then tagged with inhibition to generate scanpaths.
Exemplar and categorical search Experiment 2 tested MASC against behavioral data from exemplar (Fig. 2a, b ) and categorical (Fig. 2c, d ) search tasks. Extending MASC to a search task involved simply replacing the Itti-Koch saliency map used in Experiment 1 with either an exemplar or categorical target map. We will refer to this version of the model as MASC-T, reflecting its sole use of a target map to estimate priority. Except for the type of target map that it computed and used, MASC-T's underlying operations were identical in the exemplar and categorical search tasks, as were the search displays used as input (Fig. 2 , compare b, d). Movies 2 and 3 show MASC-T's behavior and key operations for exemplar and categorical search, respectively. In the two illustrated trials the model is searching for a leaf target embedded in the same array of nine random-category distractors (top left).
Behavioral eye movement data from both the exemplar and categorical search tasks were analyzed using two measures of search guidance: distance traveled, computed by summing saccade amplitudes until gaze reached the target, and target-first-fixated, the proportion of trials in which the target was the first fixated object during search. The distance-traveled measure captures weak guidance effects that accumulate over multiple search saccades, while the targetfirst-fixated measure captures strong and immediate guidance to a target. Note that these are not the same measures used in Experiment 1, and the reason for this is because the tasks themselves differ. Fundamental to a search task is the concept of a target, requiring that dependent measures of performance be relative to the target goal.
51 ( p Ͻ 0. 05) for MASC-T. S, indicating that MASC captured the same positive trend. Figure  6c  plots the proportion of trials in which the target was the first fixated object, again as a function of set size. Replicating a previously reported pattern  (Zelinsky et al. , 2013a) , here we found a negative set size effect; immediate guidance to the target decreased with increasing set size. Moreover, this trend existed in the behavioral data ( p Ͻ 0. 001), for MASC-T (r ϭ 0. 64; p Ͻ 0. 01), and for MASC-T. S (r ϭ 0. 78; p Ͻ 0. 01), based again on an averaged correlation after Fisher z-transformation. Comparing the two versions of MASC, MASC-T better captured the behavioral trend in saccade distance traveled. Both models successfully predicted target-first-fixated, although MASC-T. S predicted a slightly lower proportion of immediate target fixations.
05; MASC-Tv, p Ͻ 0. 01). Collectively, these data suggest a predictive advantage for two stages of population averaging in the SC rather than just one. Specifically, a version of MASC implementing the architecture suggested by  Vokoun et al. (2014) , one using a single averaging window that best predicted the saccade averaging observed in a behavioral dataset, cannot be preferred over dual-averaging versions of MASC. However, the small differences observed make the results from this computational experiment inconclusive; additional ex- perimental work is needed to gain clarity on how rodents and primates differ with respect to integration and competition operations occurring within the SC circuit.
This paper provides a new intelligent technique for semisupervised data clustering problem that combines the Ant System (AS) algorithm with the fuzzy -means (FCM) clustering algorithm. Our proposed approach, called F-ASClass algorithm, is a distributed algorithm inspired by foraging behavior observed in ant colonyT. The ability of ants to find the shortest path forms the basis of our proposed approach. In the first step, several colonies of cooperating entities, called artificial ants, are used to find shortest paths in a complete graph that we called graph-data. The number of colonies used in F-ASClass is equal to the number of clusters in dataset. Hence, the partition matrix of dataset founded by artificial ants is given in the second step, to the fuzzy -means technique in order to assign unclassified objects generated in the first step.
Our proposed technique presented in this paper, which we call F-ASClass, belongs to fuzzy-semisupervised partitional clustering technique. It uses fuzzy rules and stochastic behavior to partition dataset into specified number of clusters. For the present paper, it suffices to note that the following techniques (-means, -medoid, and FCM) are used to improve F-ASClass algorithm. A comparative study between us will be presented in Section 4. -means algorithm is a hard-unsupervised learning algorithm that appears to partition dataset into a specified number of clusters. The technique presented in  [6]  consists of starting with  groups, each of which consists of a single randomly selected object, and thereafter adding each new object to its closest cluster center. After an object is added to a group, the mean of that group is adjusted in order to take account of the new added object.
The algorithm is deemed to have converged when the assignments no longer change. The -medoid algorithm described in  [7]  is based upon the search of representative objects of each cluster (called medoid), which should represent the various aspects of the structure of the data. -medoid algorithm is related to the -means algorithm. The main difference between -means and -medoid stands in calculating the cluster center. The medoid is a statistic which represents that data member of a dataset whose average dissimilarity to all the other members of the set is minimal. Therefore a medoid unlike mean is always a member of the dataset. It represents the most centrally located data item of the dataset. The fuzzy -means (FCM) algorithm firstly presented in  [8]  and improved in  [9, 10]  allows one samples to belong to two or more one clusters.
The first idea which aims to characterize an individual objects' similarity to all the clusters was introduced in  [11]. In this context, the similarity an object shares with each cluster is represented with a membership function whose values are between zero and one. Object in dataset will have a membership in every cluster; memberships close to unity indicate a high degree of similarity between the object and a cluster while memberships close to zero involve little similarity between the object and that cluster. FCM method differs from previously presented -means and -medoid algorithms by the fact that the centroid of a cluster is the mean of all samples in the dataset, weighted by their degree of belonging to the cluster. The degree of belonging is presented by a function of the distance of the sample from the centroid, which includes a parameter controlling for the highest weight given to the closest sample.
Based on this existing work, Lumer and Faieta  [22]  have extended it to clustering data problems. The idea is to define dissimilarity between objects in the space of object attributes. Each ant remembers a small number of locations where it has successfully picked up an object. And so, when deposing a new item this memory is used in order to bias the direction in which the ant will move: ant tends to move towards the location where it last dropped a similar item. From these basic models, in  [23]  Monmarché has proposed an ant based clustering algorithm, namely, AntClass which introduces clustering in a population of artificial ants capable to carry heaps of objects. Furthermore, this ant algorithm is hybridized with the -means algorithm. In  [24] , a number of modifications have been introduced on both LF and AntClass algorithm and authors have proposed AntClust, which is an ant based clustering algorithm for image segmentation.
The main idea here is to consider that each ant represents a data and is initially placed on a fixed point, called the support, which corresponds to the root of the tree. The behavior of an ant consists of moving on already fixed ants to fix itself to a convenient location in the tree. This behavior is directed by the local structure of the tree and by the similarity between data represented by ants. When all ants are fixed in the tree, this hierarchical can be interpreted as a partitioning of the data  [26]. Bird flocks and schools clearly display structural order and appear to move as single coherent entity  [27]. In  [28, 29] , it has been demonstrated that flying animal can be used to solve data clustering problem. The main idea is to consider that individuals represent data to cluster and that they move following local behavior rule in a way; after few movements, homogeneous individual clusters appear and move together.