dataesr/scientific-paragraphs-categorization huggingface dataset:
The first eight programs are open source, whereas the last three are commercial products. We next identified the main features of each program analyzed along five major axes: general information, licensing, information content supported, visualization and data exploration. This information was based on publicly available resources (i. e. original articles published in PubMed describing the systems and dedicated Web sites) and direct correspondence with authors of the original papers or representatives for commercial products. In addition, we also include our personal experience using the program where available (based on using the five in-use open-source programs cBioPortal, Igloo-Plot, G-DOC Plus, tranSMART and Caleydo Domino as well as demo versions of Qlucore Omics and OmicsOffice).
Overview of multi-visualization tools Our search results identified several flexible analytic tools or software programs with easy-to-use front-end graphic user interfaces (GUI) that have been developed to help researchers visualize complex data without needing deep data analytics or programming backgrounds. Tables  1  and 2  summarize general information, licensing, information content supported, visualization and data exploration features for each system (Tables  1  and 2 ). The text below summarizes the systems in general with particular focus on visualization. cBioPortal cBioPortal, originally developed at the Memorial Sloan-Kettering Cancer Center, provides an interactive platform to visualize the data for over 120 different cancer studies  [47, 48]. In a typical workflow, a researcher will accept a cancer study, select data type priority such as mutation and copy number alteration data, enter a list of genes of interest and then visualize various graphics summarizing the data slice.
While the public online version is based on TCGA data sets, users can customize their instances by editing the code available through GitHub  [50]. iGPSe iGPSe is a proof of concept visual analytic system designed to allow users to perform complicated feature selection, clustering and subgroup comparison of genomic and clinical data without the need of deep programming or scripting knowledge  [8]. Users begin by loading mRNA, microRNA (miRNA) and clinical data, as well as lists of genes of interest. The clustering analysis section allows patients to select clustering parameters and visualization results with heatmaps, silhouette plots and interactivity sparsity graphs. The final, integrative patient stratification, section contains interactive parallel sets based on clustering analysis linked to survival plots that allow real-time survival comparison of mRNA or miRNA clusters  [51].
G-DOC Plus G-DOC Plus is an updated version of the original G-DOC data management platform designed in 2011 to integrate structured clinical research with high-throughput data to advance precision medicine, translational research and population genetics  [55, 56]. General visualization features include survival curves, Venn diagrams and heatmaps as well as those more specific for high-throughput analyses such as tools to visualize copy number instability, interaction networks and 3D representations of molecular targets. A principle feature of G-DOC Plus is its inherent comprehensive structure based on plug-ins to further its commitment to stay up-to-date with emerging omic technologies; the current version supports a wide variety of formats to accept mRNA, copy number variation, metabolite mass spectrometry and whole genome sequencing data.
TranSMART TranSMART is a rapidly growing web-based robust research management and analysis platform based on N-tier (data, business, presentation tiers in this case) architecture and Java schema designed to integrate disparate data sources to close the gap between basic science and clinical practice currently used by >100 organizations around the world. It features a simple user interface involving drag-and-drop movements that allows for an interactive analysis of a wide variety of data (demographic, diagnosis, medication, genetic, etc. )  [57, 58]  (Figure  2 ). The default installation provides a wide variety of basic, noninteractive, R-based plotting options such as scatterplots, bar charts, histograms, as well as more complex waterfall plots, Manhattan plots and frequency plots for genomic analysis.
Papilio Papilio is another interactive tool that leverages visual analytics developed to explore heterogeneous medical cohort data to guide medical researchers and facilitate hypothesis generation, especially when no evident hypotheses are initially favored. After loading data, a first module called PrePap prepares the data. Next, the visualization module, VisPap, offers an interactive data exploration environment where users interact with a dashboard showing scatterplots, parallel coordinates and line diagrams all coordinated so as to maintain relationships and dependencies of data. Users also have the ability to visualize statistical analyses such as confidence-weighted principal component ellipses overlaid onto the data. Its principle features include a thorough image-processing pipeline that prepares raw images for downstream analysis as well as its robust conceptual framework based on domains, features and mappers that enhance the flexibility of the database while maintaining relationships between data.
Caleydo domino Domino is a flexible data-visualization tool that improves the extraction, manipulation and comparison of interconnected heterogeneous subsets of multidimensional data sets in general  [60, 61]. Users position draggable blocks in a workspace to rapidly assemble complex coordinated graphical schema representing the data and relationships between subsets. The software features a wide variety of simple and complex visualizations to incorporate into the schema ranging from histograms and scatterplots to parallel coordinate plots, mosaic plots and Sankey diagrams  [62]  (Figure  3 ). Two principle features include an intuitive GUI featuring placeholders and live previews that indicate possible drop locations and possible visualization to use as well as its library of innovative visualization techniques such as flexible linked axis ('Flexible linked axes for multivariate data visualization') and StratomeX, used for interactive visualization in cancer subtype analysis  [64]  (Figure  4 ).
Oracle health sciences translational research center Oracle Health Sciences translational research center (TRC) provides a standardized industrial architecture that helps store, integrate and analyze multi-omic and clinical data and is specifically designed to facilitate biomarker discoveries, validation and application to clinical care  [67]. The software's top layer component is a cohort explorer used to identify and stratify clinical cohorts based on various normalization and filtering criteria. A principle advantage of the system is that it contains a rich omics data bank compiled from a large number of public studies that helps fit the project at hand into the context of upto-date literature as well as promote cross-study omics data analysis. Of note, while the TRC supports direct integration with statistical and visualization software or even natural language processing functionality for test reports, these features are not included in the basic system package.
For example, NetGestalt  [70]  allows for multiomic exploration of the colorectal cancer TCGA data set and canEvolve  [71]  allows for integrated exploration of multiple TCGA studies. Note that while the current version of cBioPortal is dedicated primarily for the TCGA cancer data sets, we decided to keep this platform in our review because of its code availability and its strong presence in the translational research community. In addition, SysBioCube is an integrative data analysis platform designed by the US Army Medical Research group to study posttraumatic stress disorder  [72] , and Data Portal is a tool for interactive exploration of cognitive and radiological data for pediatric patients  [73]. These tools allow researchers to intuitively explore rich data sets to uncover important biological pathways, regulation networks or drug targets.
Statistical power In our study, it was important not only to group or cluster patients but also to understand or measure the strength of the clusters or the differences between them. It is, thus, important that any program we have would be backed by a powerful statistics package. Much progress has been made in this domain in the past few years allowing statistics packages such as R be easily integrated into third-party software such as Web sites ('embedded scientific computing'-see OpenCPU  [94] , rApache  [95] ).
Input Generation. The input generation module of AdaptCont collects or generates the required parameters for making decisions about efficient resource allocation. These parameters are as follows: (i) The input workload size. (ii) The input split size enforced by the application. (iii) The total number of available containers per each user. (iv) The wave size in which these containers may be run. (v) The constraints introduced by users. Some of these parameters are collected directly from the application. For instance, the input workload size comes in every job request. Other parameters are more complex to be generated. For instance, the number of waves  depends on the number of input splits   and the number of available containers per user   , being calculated as  =   /.
Predefined Containers. A possible improvement of AdaptCont is enabling the use of predefined containers with different configurations (e. g. , small, medium, and large). This means that a cloud has a pool of static containers that can be used for different user request. In this way, it will not be necessary to trigger a new container, but a predefined one ready to be used. This reduces the overhead of the resource allocation process during the job submission. This feature should be part of the decision-making module. How can the framework define this pool of containers? First, it should be able to identify the typical user requests in the system. These requests may be evaluated from (i) previous (stored) monitoring values or from (ii) other monitoring variables measured at the same time, according to  [9].
Design. In order to optimize containers for the application master, we will follow the same pattern of the general framework, that is, AdaptCont. The input generation module divides the workload input size into splits. The YARN scheduler provides containers to users, according to the number of available containers of the infrastructure each instant of time. As we mentioned above, the input generation module calculates the number of waves from the number of input splits and the number of available containers per user. Figure  3  shows how the application master manages these waves. Many constraints can be raised from the scheduler. An example of this is the phase priority. It is well known that the map phase input is by default bigger than or equal to the reduce phase input  [15]. This is one of the reasons why the number of mappers is higher than the number of reducers.
Master. To compare YARN with AdaptCont, we use the following application master configurations: (i) YARN Application Master (YARN). This is the default implementation of the application master in YARN. (ii) Dynamic Master (Dynamic AdaptCont). This master container is adjusted in accordance with AdaptCont. Namely, it calculates the memory and CPU, according to the decision-making module and only after this does it initiate the master. (iii) Predefined Containers-Based Master (Pool Adapt-Cont). As defined in Section 2. 4, the resource manager has a pool of master containers, which can be allocated depending on the workload size. This is an optional optimization of AdaptCont. Workload. According to the job arrival time, we consider two additional sets of experiments: (i) Set-All. In this scenario, all the jobs are already in the queue of the scheduler.
Memory Usage. Figure  6  shows for the 4 scenarios the total memory used by the three approaches: YARN, Dynamic AdaptCont, and Pool AdaptCont. In the case of YARN, we have deployed the default configuration, choosing the minimum memory allocation for the application master (1024 MB). The Dynamic AdaptCont-based application master memory is dependent on the waves size. If the wave size is under 100, the decision-making module allocates a minimum recommended memory of 256 MB. For each increase of 100 in the wave size, the memory is doubled. The reasons behind this are as follows: (1) A normal Hadoop task does not need more than 200 MB  [12] , and this is even clearer in the case of the application master. (2) As most of the jobs are small  [12] [13] [14] , consequently, the maximum number of mappers is also small and, therefore, the application master requires less memory.
CPU Usage. The CPU usage is another relevant parameter to take into account. In order to measure it, we have correlated memory and CPU, considering that we need higher CPU power to process a larger amount of data, stored in memory. In YARN, you can assign a value ranging from 1 up to 32 of virtual cores for the application master. This is also the possible interval allocation for every other container. According to  [16] , 32 is the maximum value. In our experiments, we use the minimum value for the YARN master (1 virtual core for its container) per 1024 MB. For the Dynamic AdaptCont, the decision-making module increases the number of virtual cores after two successive increments of 256 MB of memory. This decision is based on the abovementioned methodology, which states that a physical CPU core is capable of giving optimal performance of the container, if it simultaneously processes 2 containers at most  [16] [17] [18].
Related Work As far as we know, this paper is the first contribution that proposes a MapReduce optimization through container management. In particular, linked to our use case, it is the first contribution that aims to create reliable masters, by means of the allocation of sufficient resources to their containers. There are many contributions on MapReduce whose goal is optimizing the framework from different viewpoints. An automatic optimization of the MapReduce programs has been proposed in  [19]. In this work, authors provide out-of-the-box performance for MapReduce programs that need to be run using as input large datasets. In  [20] , an optimization system called Manimal was introduced, which analyzes MapReduce programs by applying appropriate dataaware optimizations. The benefit of this best-effort system is that it speeds up these programs in an autonomic way, without human intervention.
In  [21] , a new classifications algorithm is introduced with the aim of improving the data locality of mappers and the task execution time. All these contributions differ from our contribution since they are only software-oriented optimizations for the MapReduce pipeline, and they do not take into account the resource allocation or the CPU and memory efficiency. FlexSlot  [22]  is an approach that resizes map slots and changes the number of slots of Hadoop in order to accelerate the job execution. With the same aim, DynamicMR  [23]  tries to relax the slot allocation constraint between mappers and reducers. Unlike our approach, FlexSlot is only focused on the map stage and both FlexSlot and DynamicMR do not consider the containers as resource allocation facility. In  [24] , authors introduce MRONLINE, which is able to configure relevant parameters of MapReduce online, by collecting previous statistics and predicting the task configuration in fine-grain level.
The fMRI-Neurofeedback Protocol After completing data acquisition, the effective signal change measured within the functional localizer tasks is analyzed with univariate fMRI methods, such as the general linear model. The difference between the average BOLD signal of the activation block and the baseline block should be used to create the ROI mask. Several programs offer tools for online analysis, e. g. , the AFNI software (http://afni. nimh. nih. gov/afni/). Here, the mask is created by eyeballing the resulting 3D cluster and manually specifying the statistical thresholds until a cluster of the required size/shape is present in the target ROIs. Ideally, the cluster size choice should be informed by previous meta-analytic studies. The mask should also include a control region to serve the averaging out of non-specific brain activation.
A training session allows the optimal classifier to be built on the basis of a training dataset, for which the periods of interest (e. g. , symptomatic vs. asymptomatic) have been identified and provided  (122). A validation session is then needed to test the performance and possible generalization of this classifier to new data based on an independent sample. Several interesting results for diagnosis or therapeutic response prediction purposes have been published, notably in bipolar disorder  (123)  or schizophrenia  (124). However, this is not the only way to use such tools in psychiatry. Classifiers can quickly detect the emergence of subjective symptoms by detecting specific patterns of brain activity identified during symptomatic periods (see Figure  2 , Strategy 3).
A General Overview of the IAM Framework The DPSIR analytical concept (Fig. 2. 1) is the causal framework for describing the interactions between society and environment, adopted by the European Environment Agency. The building blocks of this scheme are: -DRIVING FORCES, -PRESSURES, -STATE, -IMPACT, -RESPONSES, and represent an extension of the PSR model developed by OECD (definitions from EEA glossary, available at http://glossary. eea. europa. eu). The DPSIR scheme helps "to structure thinking about the interplay between the environment and socioeconomic activities", and "support in designing assessments, identifying indicators, and communicating results" (EEA 2012). Furthermore, a set of DPSIR indicators has been proposed, that helps to reduce efforts for collecting data and information by focusing on a few elements, and to make data comparable between institutions and countries.
g. changes in population, economical activities, transport needs etc. ) realistically translate into output (i. e. activities and technologies). Therefore the assessment of future developments of the DRIVERS block typically requires a more sophisticated framework than what would be needed for the base year inventory. In the following, the main emission source sectors are discussed in addition to the general three-level approach presented above. Road traffic activities and projections are typically relatively well known at city level because these data are of interest also for other bodies than environmental assessment. In addition to factors affecting tail-pipe emissions, non-exhaust road dust emissions are an important impairer of air quality. Important parameters for non-exhaust emission factors, in addition to vehicle types, are tire type, road surface type and climate conditions.
Input An emission is computed for a specific pollutant, an emission source, a spatial and temporal resolution. An emission inventory is a database combining emissions with a specific geographical area and time period (usually yearly-based) containing: -The activity of the emission sources. For instance: the volume and the type of fuel burned, the number of kilometres travelled by the vehicles, etc. The activity data could be derived from (economic) statistics, including energy statistics and balances, economic production rates, population data, etc. ; -The amount of pollutant emitted by these sources per unit of activity, i. e. the emission factors. The emission inventory may have different level of details depending on the availability of the data and their uncertainties. Data could be given per each activity sector, technology and fuel.
Functionality The functionality of the PRESSURES box of an IAM aims at producing emission data or/and emission projections. The PRESSURES can be estimated through three different levels of complexity, depending on their further uses and the available data: -LEVEL 1: emissions are estimated for rough sectors on a coarse grid (spatialization), using a top-down methodology. Uncertainties are not necessarily estimated. This level does not allow to perform detailed emissions projections. -LEVEL 2: a combination of bottom-up and top-down methodology is used to calculate the emissions with the SNAP-NAPFUE classifications. Emissions factors and activity data representative of the area of study are used when available. Uncertainties are not necessarily estimated. -LEVEL 3: emissions are calculated with the finest space and time resolution available, with the bottom-up method with all the SNAP-NAPFUE classifications details.
Emission factors and activity data have to correspond to the specific activities of the studied area. The processes have to be detailed so that it is possible to attribute the most representative emissions. In case of lack of data, the top-down approach can be used but with the help of complementary data to take into account the regional specificities. The uncertainties may be quantitatively calculated, e. g. by a Monte Carlo method, whenever possible. This level is the best one to allow the generation of all kinds of scenarios provided that the emission changes are higher enough compared to the uncertainties of the SEI emission values. Emission scenarios may be built directly from the SEIs by reducing the total emissions per grid box. These scenarios are then used in the STATE block to give general indications of the possible evolution of the air quality, or identify simplified equations that represent the links between emissions and concentrations in a complex IAM.
It is obvious that some relations exist between these different types of uncertainties and it is sometimes difficult to distinguish them. The uncertainties of an emission inventory can be evaluated in a qualitative or quantitative way. The qualitative evaluation is mainly performed by experts  (IPCC 2000; EPA 1996) , while the quantitative one is based on error propagation methods and Monte Carlo methods. There is also a semi-quantitative method that can be used to evaluate the uncertainties, which consists in the rating of the data quality. Some numerical or alphabetical scores are attributed by experts to emission factors and activity data to describe the uncertainties of these data. There are two main classifications for these methods (see: EPA 1996): (1) the DARS method (Data Attribute Rating System) that attributes to each dataset a score ranging between 1 and 10 (the most accurate); (2) the AP-42 emission factor rate system that is the main reference in the USA but only for emission factors evaluation.
Therefore, such calculations are generally limited to estimate large emission contribution over an area (e. g. , industry, traffic, etc. ). For some RESPONSES module implementations (as in the case of optimization approaches) thousands of model runs would be required, for example to minimize the cost of emission reduction measures. In such cases, the AQ model may be substituted by a more computational efficient source/receptor model (also called surrogate model or meta-model) based on simplifications of the AQ model. This model directly links the activity levels or the emissions to an AQ index calculated from targeted pollutant concentrations. The level of complexity of the surrogate model depends on the objectives of the IAM, on the nature of the pollutant (nonlinearities, chemical reactivity, etc.