dataesr/scientific-paragraphs-categorization huggingface dataset:
INTRODUCTION Multihop networks have attracted researchers attention since the DARPA packet radio networks project in the 1970's. Research has fairly accelerated in the past decade and sensor, mesh and vehicular networks autonomous applications are progressively getting out of the labs. If several algorithms and protocols mostly target static networks or low mobility conditions, the interest in supporting more ambitious mobility models has increased lately. For research in multihop networks, we developed and released under an LGPL license two tools: SILUMOD  [5]  and VIRMANEL  [6]. SILUMOD is a domain specific language that allows to describe complex mobility models, i. e. how the various nodes in a network move around an environment that can include obstacles. SILUMOD is expressive and easy to use and to understand, making it perfectly suited for user than do not have advanced programming knowledge.
And VIRMANEL is a virtualization tool based on OpenVZ  [14]  whose role is to manages Linux-based virtual machines corresponding to the nodes of a mobile network and emulates their movements by tearing up and down links dynamically, according to the mobility pattern defined in SILUMOD. The main advantage of the virtualization is that it make possible to conduct experiments in a virtual environment even if financial and human resources are limited. In fact, a unique physical machine is often sufficient for the realisation of experiments. But one of the main difficulties in implementing a simulator for mobile multi-hop networks is the identification for a mobile node to nodes that are sufficiently close, for the establishment of a connection to transfer data. The nodes are frequently on movement so the simulator must at all times be able to evaluate the distance between the mobile nodes and update the status of virtual links between mobile nodes.
The SILUMOD/VIRMANEL couple combine the following advantages, which makes them really convenient for the testing of multihop wireless networks: An easy language: SILUMOD language is dedicated to the definition of mobility models used to simulate the movement of mobile nodes in the case of multihop wireless networks. Its keywords are specific to mobility models, which makes it a language easy to understand. A user can define mobility models easily, understand models built by others and create and maintain a models database. Use of real code: As VIRMANEL uses virtualization, a user is able to test the real software implementation in a wide range of configurations. Similarly, the installation of a given application of protocol is as easy as on a real machine. graphical user interface: VIRMANEL provides a graphical user interface that allows experimenters to monitor nodes mobility in real time.
scaling: VIRMANEL uses a very efficient virtualization solution based on containers. It makes possible to run multiple virtual machine instances on a single physical machine. In addition, regulation of the connectivity between the virtual nodes is provided by a controller using an optimized algorithm. In the rest of this paper, we first presents in section 2 the VIRMANEL key aspects and the SILUMOD language. We then present in section 3 the algorithm that we implemented to reduce the search operations of the mobile nodes present in a coverage radius and evaluate this algorithm in section 4. Section 5 presents the related work. We then conclude the paper in section 6 and present our future research directions.
ABOUT VIRMANEL AND SILUMOD VIRMANEL and SILUMOD are tools that facilitate the research in multihop networks, with a special focus on mobility. These tools were built with ease of use and flexibility in mind, as the user is able to evaluate a classical program running on Linux as well as real code running on an embedded operating system (Contiki, FreeRTOS,. ). We believe that VIRMANEL and SILUMOD provide a way to hide unnecessary technical aspects, as any programming language and operating system can be used, allowing users to focus on the mobility-related aspects.
VIRMANEL key aspects VIRMANEL is a tool responsible for managing the states links between virtual machines that model the network nodes and for updating and displaying the network topology. Each mobile node is instantiated as a virtual machine based on OpenVZ 1  , an efficient virtualization solution using containers to ensure an isolation of virtual machines. With a prepared template, it is possible to virtualize the network nodes running any Linux distribution or an embedded operating system running on an emulated MSP430 micro-controller. Besides the virtualization and the mobility management engines, VIRMANEL includes a graphical user interface that allows to monitor nodes positions and connections, as represented on Figure  2. The GUI allows users to see rough mobility properties such as the repartition of the nodes on the playground, or the network disconnections.
The SILUMOD language SILUMOD is an open source (LGPL) internal domain-specific language that allows an easy and expressive description of the nodes movement patterns. Based on the Scala language  [17] , SILUMOD uses the same syntax and the same libraries as this language and adds a series of keywords and operators specific to the description of mobility. SCALA provides the structure of a classical languages, including iterative and conditional constructs, which allows to develop fairly complex models. As example of a SILUMOD program, the Random Waypoint Mobility Model is represented in Listing 1. import silumod. language. _ virmanelNode value "saturne" virtualization value "no" name value "Mobile" playground define(0,0,600,400,"PG","blue","no") speed value 50 positionX value 100 positionY value 200 distance value 150 coverageRadius value 100 var x = 0 while ( x < 100 ) { toPositionX value ( 1 <-> 599 ); toPositionY value ( 1 <-> 399 ); while ( MOBILE calculateNewPosition ) { if ( MOBILE ->| LIMITS ) { angle reflexive; } else { MOBILE Move } } x = x + 1 } Listing 1: Specification of the Random Waypoint Mobility Model in SILUMOD With SILUMOD, it is possible to define the environment in which mobile nodes evolve, specifying the playground dimensions, positioning obstacles, and to define each node's movement characteristics through the specification of its movement speed, angle, etc.
The user can describe the behavior of a node when it encounters an obstacle such as a wall, form groups of nodes and define zones in the scene so that mobiles change their behavior when they cross these zones limits. This set of keywords allows to describe most classical mobility models, from the classical random movement patterns (random waypoint, random direction, etc. ) to more complex and adaptive mobility patterns. For example, using zones, it is possible to vary speed according to different types of grounds, but also to define attractors and to model the movements of humans in a city, traveling between home and work. SILUMOD is intuitive so that users can easily make movement parameters vary, implement new mobility models, or simply analyze a provided mobility description. We are currently working on creating a repository for mobility models that should be available online and distributed with future versions of the software.
The mobility model described in the SILUMOD language is then passed to a simulation engine that computes nodes successive positions and communicates these positions to VIRMANEL through classical inter-process communication. The user may easily keep track of the successive positions taken by the nodes by accessing the positionX current and positionY current variables and writing their values to a file. The nodes effective mobility can then be compared to the algorithm results, replayed and analyzed. Writing a small procedure, it is easy to log the successive positions of the nodes in a file and, consequently, to perform offline analysis of various properties. The successive network topologies can also be saved to a file and input into Another use case of VIRMANEL allows real-time display of a mobile nodes network moving and the states links between mobile nodes (Fig.
Definition of the critical zone In fact, let M1 and M2 be two mobile nodes separated by a distance DM1-M2. Assuming M1 and M2 move at a maximum speed Vmax such that at each movement of M1 and M2 the distance between them tends to be the smallest possible. And given that M1 and M2 can communicate with another mobile node if the distance to the mobile node is less than Dpropagation, then it is possible to define for how long T, no connection is possible between M1 and M2. V1 = D1 / T1 V2 = D2 / T2 Speed, distance and travel time are parameters calculated according to the mobility model selected in advance. It is also not necessary that M1 and M2 move according to the same mobility model. Thus, according to the limits set by the user in the specific SILUMOD script of each mobile node, these parameters can vary considerably from one limit to the other during the movement of a mobile node.
4 ). VIRMANEL being implemented in Erlang, a language that natively manage parallelism, connectivity management between different mobile nodes is done by processes running in parallel which significantly reduces the computation time required for the processing by each node of all mobile nodes under its management. When the process P rocX receives the first position of the mobile X, it evaluates the number of steps StepsN umberX -Y needed before an evaluation of the connectivity status with the Y mobile nodes under its management. The complexity of this initial stage is O( n 2 ). A table T abX-Y whose size maxNumberOfSteps is the number of steps between the two most distant points of the simulation area (the ends of the diagonal), contains the evaluation order of the mobile nodes Y by the mobile node X (Fig.
Test platform description The testbed on which we install VIRMANEL and run our evaluation is composed of a single server running the full software suite (SILUMOD, VIRMANEL and the OpenVZ virtualized machines). The server is equipped with 4GB of RAM and a 2. 6 GHz QuadCore Intel CPU. It is running Ubuntu Linux 10. 04 (The Lucid Lynx), kernel version 2. 6. 32 patched with the OpenVZ patch (2. 6. 32-feoktistov. 1).
CONCLUSION VIRMANEL allows to easily study real applications for mobile multi-hop networks and the SILUMOD languages allows to define well-know of new mobility models to obtain the successive positions of a moving node. The use of containers for virtualization ensures the deployment of a large number of virtual machines, each with its own Linux environment (network stack, routing tables, disk space,. ). And the algorithm defined to adapt the nearest neighbor search problem in mobility simulation, allows experiments with a large number of mobile nodes. Indeed, we have observed during our evaluations that the average time needed to evaluate states links between mobile nodes is very low and evolved linearly. In addition, the number of operations necessary to this evaluation evolves according to a logarithmic law.
Indeed, many large-scale phenotypic screens have been published previously, shedding light on the regulation of such diverse cellular processes as protein secretion  (Simpson et al. , 2012) , endocytosis  (Collinet et al. , 2010)  or cell division  (Neumann et al. , 2010). Such screens do not only provide lists of candidate genes for further follow-up studies and for computational modeling approaches, they also generate large image databases. Those can turn out to be a precious scientific resource-as a collection of experimental data for punctual queries or as a basis for systematic and potentially integrative computational analysis. Although it has always been a strength in bioinformatics to rely on rich publicly available data sources, the use and re-use of image data is not straightforward.
The contribution of this article is to present MotIW (Motility study Integrated Workflow), and its application to motility gene discovery. A generic methodological framework, MotIW enables to quantitatively study cell motility at single cell resolution in HT timelapse data in an unsupervised way. It consists of cell tracking, cell trajectory mapping to an original feature space and outlier experiment detection according to a new statistical procedure (Fig. 1 ). We show the power of our method by applying MotIW to simulated data, which allows us to estimate recall and precision to be expected on real data. We then apply this workflow to a previously published genome-wide screen by RNA interference (RNAi) and live cell imaging, the Mitocheck dataset. Analysis of the screening data reveals the existence of a cell trajectory ontology in the dataset.
Without any prior assumption on cell motion, we are able to identify eight types of cell trajectories. The remainder of this article is organized as follows: after a short description of the data and the implementation details in Section 2, we detail MotIW in Sections 3. 1-3. 3, as well as its application to simulated data in Section 3. 4 and the Mitocheck screen in Section 3. 5. Section 4 briefly discusses broader perspectives of this workflow.
A genome-wide time-resolved dataset We used a previously published genome-wide dataset of timeresolved records of cellular phenotype responses to gene silencing, which were generated for virtually all protein-coding genes  (Neumann et al. , 2010). It is publicly available at mitocheck. org. For this, arrays of transfection cocktails containing small interfering RNAs (siRNAs) were spotted directly in live cell-imaging chambers in a 384 format. HeLa cells stably expressing the core histone 2B tagged with green fluorescent protein (GFP) were seeded on top of the arrays, and imaged 18 h after the transfection for 48 h with a time lapse of 30 min (Plan10x, NA 0. 4; Olympus). Each microarray contained 8 negative controls (scrambled: not targeting any gene) and 12 positive controls showing different phenotypes.
A total of 22 612 protein-coding genes have been targeted by at least 2 siRNAs each, in total 51 767 siRNAs. For each siRNA, there are data from at least 3 technical replicates, which created 182 191 quality controlled time-lapse experiments in total. Because of updates in the genome annotation, some reagents could not be mapped to the current ENSEMBL version. In total, the dataset contains data for 17 816 protein-coding genes in144 909 quality controlled timelapse experiments.
Software We use CellCognition  (Held et al. , 2010)  (cellcognition. org) for segmentation and feature extraction and CPlex (http://www-01. ibm. com/software/commerce/optimization/cplex-optimizer/) for optimization in the tracking procedure. To store, manage and access the screening data, we use a previously published data format CellH5  (Sommer et al. , 2013). All scripts are written in the programming language Python 2. 7 using SciPy  (Jones et al. 2001)  and NumPy, and all plots were generated by matplotlib  (Hunter 2007).
Cell tracking There exist two main approaches to cell tracking. The deformable model approach relies on identifying and modeling objects on the first frame, and linking them to objects in consecutive frame by updating the models  (Zimmer et al. , 2002). On the other hand, the object association approach associates preidentified objects in consecutive frames  (Lou and Hamprecht, 2011). Different datasets usually require different object identification approaches. For the sake of modularity, we therefore prefer to keep segmentation and tracking steps independent. Cell tracking faces several challenges in videos from high content screens like Mitocheck, including high population density in each picture, high phenotypic inter-cell variability and possibly low time resolution between successive images (30 min).
We also added three object division and fusion to E. This is important in a screening context, where aberrant cell divisions may occur. In the future, it could be interesting to couple object segmentation and tracking to correct for missing detections. To validate MotIW's cell tracking procedure, we compare it with CellCognition's constrained nearest-neighbor (CNN) tracking algorithm, and with  Jaqaman et al. (2008)  as implemented in CellProfiler  (Carpenter et al. , 2006). The latter approach views tracking as a linear assignment problem (LAP) and uses user-defined costs for performing merges, splits, appearances and disappearances. We have chosen these two approaches for benchmarking, as they are available in popular High Content Screening software. Our training set consists of $32 000 matches, among which 0.
5% appear, 0. 5% disappear, 1% merge and 2% split. They come from the Mitocheck dataset. Furthermore, they were taken from both control experiments and phenotypic experiments according to  Neumann et al. (2010)  in order to ensure that the algorithm also works in the presence of phenotypes. The training set was annotated using CellCognition's CNN tracking algorithm followed by manual correction. As shown in Table  1 , MotIW outperforms the other two methods as measured by the average accuracy on the five movement types. As can be seen in Figure  2 , they show similar performances on move events and have therefore similar overall (pooled) accuracies. The contribution of the learning approach is most important for the other events, such as cell division, when object matching is less trivial.
Trajectory features Once cell trajectories are captured, each trajectory is described by a set of 15 features. These features were partly taken from previous publications on quantitative motility analysis, partly newly designed. Robust and precise features are needed to account for the partial stochasticity of cell migratory behavior. We use three types of features, as detailed in Table  2. Prior to that, trajectories resulting from object fusion and trajectories which are shorter than 10 frames are discarded. This trajectory quality control ensures that cell clusters are not considered, and increases the dataset robustness.
Different radii may be relevant for different data (depending on, e. g. the experiment time lapse, the pixel size or the cell type). We chose to use two different radii, r 1 and r 2 with r 1 < r 2 , to incorporate information about cell trajectories on two different time scales. r 1 and r 2 were manually chosen, such that for the Mitocheck dataset, the corresponding features are neither constant nor too correlated. They respectively correspond to $2. 5 and 12 mm. In the following, the features Entropy i and Ball number i correspond to radius r i.
Feature set evaluation Track time length is an irrelevant random variable for studying single cell motility, which could bias some features. Therefore, we ensured that they are not significantly correlated with this parameter: the correlation between track time lengths and features is maximal for the Effective space length, where it is equal to $30% (on a subset of the Mitocheck dataset, data not shown). Figure  4  shows the correlation matrix for the extracted features. One group of highly correlated features is visible in the bottom left corner of the heatmap, which encompasses speed-related features. The existence of two feature subgroups within this group can be explained by the following observation: the first group of features, from Ball number 1 to MSD, is linked to cell instantaneous displacements, whereas the second group, from Effective speed to Entropy 2, is linked to its displacements on the whole trajectory.
Screen simulation To evaluate the performance of our workflow on data for which the ground truth is known, we designed a process to simulate an HT screening experiment. In a first step, five types of single cell movements were designed, in agreement with qualitative observations from the dataset: random, fast random, curbed directed, flip directed and stop-and-go (Fig. 5 ). Let ðd t ; / t Þ be the polar coordinates of the difference vector m tÀ1 Àm t of any two consecutive points. For random movement, / t is chosen at random and the distance d t ¼ jjm t À m tÀ1 jj 2 is drawn from a normal distribution, whose parameters are estimated from the data. The same holds for fast random with increased distance d t. For the curbed-directed movement type, d t follows again a normal distribution as for random movement, but the angle is calculated as / t þ with / t ¼ / tÀ1 þ D/ t , where D/ t and follow normal distributions, whose parameters are set manually to visually match some observed trajectories.
Normal movies account both for control movies and experiments in which cell motility is similar to that of controls. They contain on average 80% of random trajectories, and a mix of the four other trajectory types. This reflects our observation that in real data, experiments and controls typically contain all possible types of cell trajectories and that phenotypes are characterized in a shift in percentage. All other movie types contain (on average) 50-65% random trajectories, the rest being completed according to the movie type. For example, movie type fast is composed of 30% of fast random trajectories, 60% of random trajectories and a mix of the three other trajectory types. The total number of trajectories in each movie was drawn at random from real data in the following way: first, a batch is randomly chosen in the dataset.
Application to a simulated screen Our workflow successfully recognized more than 98% of the experiments, as detailed in Table  3. Our simulation pipeline was also used to estimate how useful the trajectory feature set is to capture the differences between different types of trajectory motion. A total of 500 samples of each trajectory type were simulated, and their features extracted. A PCA was performed, after which we retained the eighth first principal components, which explain $95% of the dataset variance. Finally, kmeans was applied to the dataset with k ¼ 5. Many simulation parameters (e. g. each track length) are chosen at random, and k-means' results depend on its initialization: the procedure was therefore repeated 10 times. The results are presented in Table  3. Although distinguishing trajectory types is subject to some errors, it shows that the whole pipeline is robust enough to identify experiments in which cell motility is significantly different.
Application to the Mitocheck dataset After evaluating MotIW on simulated data, we then apply it to the whole genome-wide screen Mitocheck  (Neumann et al. , 2010) , which enables us to identify an ontology of 2D cell trajectories. In the context of the Mitocheck dataset, the identification of an experiment in which cell motility is significantly different from negative controls leads to the identification of genes which might be involved in its mechanisms. The application of MotIW to the Mitocheck dataset enabled the identification of the experiments which significantly deviate from controls (5%; 7 153 of 144 909). It amounts to 1 180 genes (out of 17 816), some of which are known to be involved in cellular motility, such as RhoA (Ras homolog family, member A) or CDK5 (cyclin-dependent kinase 5).
A related question to motility gene discovery is to know whether there exists an ontology of cell trajectories. The approach would be to apply unsupervised clustering methods on the whole trajectory dataset and try to identify a number of motility patterns for which the clustering is of good quality. This is measured by cluster quality indices, which depend on the clustering method  (Tan et al. , 2005, Chapter 8; Halkidi et al. 2001). As an example, two common indices to evaluate the output of k-means are the intra-cluster cohesion C(k) and the silhouette score S(k). They both compare intra-cluster distances with inter-cluster distances. A slope change in C(k) and a maximum in S(k) are expected at the appropriate number of clusters, if it exists. This approach did not prove to be successful when applied to pooled trajectories from all experiments, for a wide range of clustering techniques (k-means, Gaussian mixtures models, spectral clustering, fuzzy c-means, kernel k-means-data not shown).