dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015). FCD has been observed in task and resting conditions of the human brain  (Allen et al. , 2014; Hansen et al. , 2015). Subsequent work may include cases when functional connectivity fluctuates over time. All MULAN procedures are written in Matlab/Python code and are available freely with submitted code, which will be pushed on github once this paper is published. the fuzzy if-then rules with conditions, i. e. in case of C, IF A THEN B. The definition of MIS rules depends on the chosen BMs. Here we give an example of 5 MIS rules, which we use in this paper (Supplementary Fig. 2D ). BM1 and BM2 are two undirected methods, whereas BM3 and BM4 are two directed methods. H (high) corresponds to high likelihood for existence of a link, whereas L (low) corresponds to low likelihood. The value "-1" represents an unnecessary condition, and "1" a necessary one.
We use these conservative ranges of r r values (instead of just 0 or 1) to cover all possibilities. When analyzing data, the r r values are determined during the optimization process, as detailed below. These values can vary each time when a new dataset is analyzed. Step 2: Membership functions Membership functions map the normalized input value from each BM into a degree of non-existence/existence of this link; i. e. , L or H in our case. We use generalized bell-shaped membership function μðxÞ ¼ 1= 1 þ xÀc a 2b , where a ¼ θ i for L and 1 À θ i for H; b ¼ 2k⋅a, c ¼ 0 for L and 1 for H (Supplementary Fig. 1C ). Here θ i ; i ¼ 1; 2; 3; 4 determines the half width of the bell shape of the membership functions for each of the four BMs. The examples in Supplementary Fig. 1C  show that θ i strongly influences the membership functions; in particular, for values close to the steep parts of the sigmoid functions.
B. Basic methodsfunctional connectivity measures In this study, we consider 40 functional connectivity measures from 7 families as candidates of basic methods: correlation, h 2 , mutual information, coherence, Granger, transfer entropy and A H (MVAR-frequency domain based techniques, see below for details). The mathematical form of these measures of functional connectivity can be found in Supplementary Table  1  and note 1. We select these methods because they have low computational cost and do not require prior knowledge of the underlying generative model. We have demonstrated the way to select the best sets of parameters for the basic methods according to their AUC values on the simulation datasets with known ground truth and the number of nodes is 5  (Wang et al. , 2014). The important parameters are length of windows, the length of signals for all basic methods; the frequency ranges for the frequency domain methods and time delay for the time-domain methods.
In this paper we extended to the cases with more number of nodes, detailed the parameters in Supplementary Table  2. The type and property of datasets are important when simulation datasets considered. In this paper, we use neural mass models and fMRI models. In addition, three mathematical models such as linear and nonlinear models with linear/nonlinear couples can refer to  (Wang et al. , 2014). Users are encouraged to find the suitable models for their datasets. Many models are available from open source software toolkits such as Statistical Parametric Mapping and The Virtual Brain.
AUC value For each simulated dataset, a particular basic method computes a connectivity matrix, which assigns a connection value between 0 and 1 between any two nodes. The connection strength can be thresholded to produce a network graph. For example, in the connection matrix 2 in Supplementary Fig. 5A , choosing a connection threshold of 0. 4 will produce a graph with many more links (those in orange color) than the ground truth structure. However, with the threshold of 0. 5, the method identifies the ground truth structure. Based on these ground truth structures, we can calculate the number of false positive and true positive edges as a function of the threshold. This enables us to compute receiver operating characteristic (ROC) curves  (Zweig and Campbell, 1993)  and evaluate the area under the curve (AUC) (Supplementary Fig.
E. Genetic algorithm for parameter optimization The genetic algorithm is a method for solving optimization problems based on natural evolution, such as inheritance, mutation, selection and crossover. This type of algorithm is well suited for optimization problems in which the objective function is discontinuous, non-differentiable, or highly nonlinear. We use this algorithm for optimizing 10 MULAN parameters ½θ 1 ; θ 2 ; θ 3 ; θ 4 ; r 1 ; r 2 ; r 3 ; r 4 ; r 5 ; θ m (Supplementary Fig. 6 ). Using training datasets of known structure containing the same number of nodes as the empirical data to analyze, we start with 16 sets of MULAN randomly generated parameters within a given range, θ 1;2 2 ½0:2; 0:9, θ 3;4 2 ½0:3; 0:9, r 1;2;3;4 2 ½0; 0:5, r 5 2 ½0:8; 1, and θ m 2 ½0:5; 0:9 (we used a large range that was empirically determined during preliminary simulations).
Notice that using this termination conditions, the process dose not always converge to 0 false positive link as SEEG datasets do. For example, in the case of fMRI data, the optimization process terminates around 21 false positive links. The last parameter group is for generating the MULAN graph. We consider two scenarios. When the MULAN graph has one parameter, θ ffor strong connections here shown in greenor to parameters θ f 1 , θ f 2 for two-color mapping (green links for the strong connection and pink ones for the week). These parameters can be determined easily when comparing the MULAN output with ground truth structures (using 50 training datasets).
F. Statistical results Although MULAN returns connectivity matrices with AUC values close to 1, some links may be incorrectly assigned. To further improve the performance, we use the bootstrapping procedure to define the final connection graph based on combination of N bt matrices, each from on bootstrapping on N sliding windows (Supplementary Fig. 7A ). We vary the number of nodes (20, 30 and 50) and the density of links (5*, 10* and 15*; a * indicating the number of links within each sub-network), using 10 datasets for each case. We use the cosine similarity (COS) to compare the final connection graphs with the ground truth. After testing N bt from 10 to 300, we find that N bt ¼ 20 is sufficient to recover the ground truth structure (Supplementary Fig. 7B ).
G. MULAN application on strong connection strengths Here we demonstrate the examples with only strong connection strengths for better understanding MULAN algorithm and evaluation system. We simulate the data with 30 number of nodes with 50 different small-world ground truth structures (i. e. links with connection strengths varying between 0. 8 and 1. 0 to simplicity). We then apply a genetic algorithm  (Mitchell, 1998)  to determine the optimal values for MULAN parameters. For each simulated dataset, the genetic algorithm (methods and Supplementary Fig. 7 ) returns an optimal set of 11 values. Supplementary Fig. 8A  shows one example from 50 simulated datasets with the ground truth network in the left and the MULAN graph in the right. The performance based on all 50 simulated datasets (Supplementary Fig.
8B ) illustrates that MULAN produces neither false positives nor false negatives when using optimal parameters. Since 50 simulated datasets generate a set of optimal parameters S s , we obtain a distribution of values for each parameter (Supplementary Fig. 8C ). To evaluate MULAN using this optimal distribution, we generate 30 new datasets with the same generative parameters (numbers of nodes etc. ) but with ground truth structures differing from the 50 training datasets. We randomly pick 20 groups of parameters from the set S s of optimal parameters. For each dataset, we obtain a matrix as the median value of 20 MULAN matrices (Supplementary Fig. 8D ). We thus obtain the final MULAN graph in which strong and weak links are shown in green and pink, respectively. When we evaluated MULAN on the 30 test datasets, we find that all links were correctly identified, although some strong CS ¼ 0.
Selection of optimal parameters To select a particular value from each distribution (Supplementary Fig. 8C ) of optimal parameters obtained from 50 training datasets, we generate other 30 new datasets with the same generative parameters (the numbers of nodes etc. ) but with ground truth structures differing from the 50 training datasets. We evaluate 4 alternative ways to choose the optimal parameters using bootstrapping (N bt ¼ 20) (Supplementary Fig. 8D ). In the first three, we use 20 sets of optimal parameters randomly generated from normal distributions having their expectation as 'mean', 'median' or 'highest peak' (hp) of the optimal parameter set S s , and half standard deviation. The fourth approach is to randomly pick 20 groups of parameters from the set S s of optimal parameters. In each case, we obtain 20 MULAN matrices.
H. Evaluation thresholds In order to assess the validity of the final averaged graph from 20 sets of parameters, we include two new thresholds (used only for validation purposes, i. e. they are not part of the general algorithm, hence they do not need to be optimized). A strong (green) link is assigned if the median value of the link is larger than θ S and a weak (pink) link for values larger than θ W but smaller than θ S : For the example of 30 nodes NMM datasets, MULAN is robust for a wide range θ W 2 ½0:15; 0:45 and θ S 2 ½0:65; 0:95 to obtain zero false negative for CS! 0:9: Hence, any value can be used within these intervals. Because for each MULAN graph from one set of parameters, we set 0. 5 for weak links and 1 for strong links. We search θ W less than 0. 5 and θ S 2 ½0:5; 1: For comparison, the same evaluation procedure performed on two well performing directed basic methods, PDC and BCorrD.
For both BMs, we have to fix θ W (0. 1 for PDC and 0. 16 for BCorrD) and θ S (0. 14 for PDC and 0. 24 for BCorrD) to obtain zero false negative for CS! 0:9: While the MULAN algorithm leads 9 false positive pink link, PDC has 402 false links and BCorrD, has 364. Thus MULAN provides a better estimate of the connectivity as compared to individual BMs. Importantly, MULAN is more robust regarding threshold values as compared to BMs applied alone. For the evaluation on datasets based on 100 human connectome with 84 nodes, we apply MULAN algorithm on 50 subjects using the obtained optimal parameters from other 50 subjects. Then, we use two thresholds θ W and θ S to identify weak (pink) and strong (green) links (middle of Fig. 7D-F ). ) For one example in the text, MULAN is robust for a large range of threshold values (θ W ¼ ½0:1; 0:49; θ S ¼ ½0:5; 1Þ to the absence of false negatives for CS!0.
Acknowledgement We are very grateful to  Amanda Easson  for preprocessing fMRI datasets,  Michael Wibral ,  Pedro A. Vald  es -Sosa ,  Fabrice Wendling , Timoth ee Proix and  Mathieu Golos  for helpful discussions, and  Andrea Brovelli  for useful discussions and technical help. This work was supported by grants from the  Fondation pour la Recherche M edicale en France FRM : ING20121226265 ,  ANR-2010-BLAN-1520-01 ,  ANR-13-NEUC-0005 , and  Ligue Française Contre  l'Epilepsie. K. J. F is funded by a  Wellcome Trust   Principal Research Fellowship  (Ref:  088130/Z/09/Z ). Fibre-tracking was performed using the MRtrix package ( J-D Tournier ,  Brain Research Institute, Melbourne, Australia , https://github. com/MRtrix3/mrtrix3). Data were provided [in part] by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
For 14 additional tasks, 1 or more patients experienced goal or action failure, with an overall failure rate of 69% (18 of 26 tasks). Content analysis revealed 4 main categories: hardware usability, software usability, facilitators of therapy goals, and improvement suggestions. Conclusions: FITBoard hardware and software changes are needed to address goal and action failures to rectify identified usability issues. Results highlight potential FITBoard applications to address therapeutic goals and outline important practical considerations for product use by therapists. Subsequent research will evaluate therapist, parent, and child perspectives on FITBoard clinical utility when integrated within regular therapy interventions.
Software Usability Software usability included challenges interacting with the FITBoard app interface on the tablet. Participants found aspects of the app interface confusing, particularly the game selection screen where they were not able to complete the task of viewing the game description. When shown this task action following the CW, participants expressed concern that without a picture of the game, it was difficult to understand the description. For example, with respect to the Whack a Mole game, 1 participant said:.
Suggestions for Improvement There were multiple specific suggestions for improving the FITBoard hardware and software interfaces. Hardware suggestions included increasing the stability of the head and foot controls, covering the microcontroller to protect it from exposure to cleaning fluids, and increasing the mobility of the top and side panels so they could be positioned higher and surround the user. Software improvement suggestions included adding a pause feature to the games:. I get so many interruptions at random times in the session. work with kids that need that closure. they want to make sure you can pause and finish that game and get that score, or like they won't be able to listen to what a nurse has to say or take a medication, unless they can pause that game. One therapist suggested a more intuitive way to access and store the touch materials, saying:.
5% of the actions evaluated in their sample of 11 therapists had some element of failure but did not classify failures into goal or action components  [28]. Our testing situation was unique because it focused on evaluating usability of both novel hardware and software interfaces, which differs, for example, from usability testing of a new website where users could be expected to be familiar with general layout and functioning of a keyboard, mouse, and monitor. The CW and TA processes illuminated usability problems and flaws in the process of using the FITBoard from the beginning to the end that led to errors for some or all participants. The primary usability problems included structural issues with the FITBoard that prevented users from being comfortable interacting with the device (ie, opening and closing panels and attaching foot and head controls).
Other problems were related to lack of clarity in FITBoard software interactions (eg, how to select keys to play the game and how to find game descriptions before selecting a game). The results from CW and TA identified problematic tasks that must be addressed before therapists are able to test the FITBoard with children and families. Despite these limitations, participants easily identified a client and functional goal that would be relevant to FITBoard use. In addition, they appreciated the diversity of options that the FITBoard provides to motivate and engage children in maintaining upright head control, which was identified as a priority in the informal needs assessment. Therapists appeared to view the FITBoard as relevant to the goals for the patients on their caseloads and provided valuable information to direct changes to the FITBoard before evaluation of its clinical utility.
Next Steps Study results are guiding changes to the FITBoard to address hardware and software usability issues. Our next steps are to reintroduce the revised FITBoard to the clinical sites and undertake a clinical utility study with therapists, children, and families to determine how FITBoard use addresses relevant therapeutic goals. Therapists will use the FITBoard on several occasions, recording their functional goals and perceptions of how FITBoard use was able to address the goal; therapists, parents, and children, as able, will complete standardized measures evaluating satisfaction, engagement, and motivation. Finally, we will conduct interviews with children, parents, and therapists to further identify barriers to and facilitators for FITBoard use. On the basis of the results, we can approach industry partners with respect to making changes to the FITBoard interface and app to support creation of additional, improved devices with a larger budget for construction and game development.
Conclusions The FITBoard is a newly developed, low-cost rehabilitation tool for movement skill practice that integrates the motivating attributes of video games with the functional, touch-based sensory input of traditional rehabilitation interventions. Usability testing methods (CW and TA) with a small sample of physical and occupational therapists revealed FITBoard hardware and software concerns, potential apps for therapy goals, and suggestions for improvement. FITBoard hardware and software changes are needed to address goal and action failures and respond to identified usability issues. Following these improvements, our goal is to produce an accessible, user-friendly, and low-cost product that can be integrated into school, home, or community programs to enhance practice dosage of functionally relevant movement skills for children and youth with disabilities.
Acknowledgments The authors are grateful to the  Deborah Munroe Noonan Memorial Research Fund , administered by the  Medical Foundation , for funding our feasibility and usability testing process. The funding organization played no role in study design, data analyses, or interpretation of the findings. They are also very grateful for funding from  Northeastern University's Undergraduate Provost Research and Creative Endeavors Award  and financial support from  Northeastern University 's Enabling Engineering Student Club. The authors thank undergraduate students  Raymond Huang ,  Emma Bobola ,  Brandon Nyguyen ,  Gino Jacob ,  Winston Ge ,  Todd Roberts ,  Nicholas Sullo ,  Monsurat Olaosobikan ,  Grant Ritter ,  Adam Perruzzi ,  Christopher Mohen ,  Cullen Lampazzo ,  Jose Castillo ,  David Yi , and  Immanual Mensah Ampomah  for their dedicated work constructing and programming the FITBoard.
II. STATE OF THE ART The research and development on fog computing has focused on adapting infrastructures conceived for cloud computing, such as the OpenStack, to fog computing. For instance, in  [4]  an OpenStack based infrastructure is proposed to serve as a fog layer for smart cities. In  [8] , the authors identify some of the limitations induced by OpenStack's centralized architecture and propose the use of a distributed key value store to eliminate the dependency on a centralized database. While these contributions are on the right track toward enabling fog computing, they are biased toward virtualizing the edge infrastructure as opposed to providing an end-to-end solution that can virtualize, manage and operate from the cloud to the thing while addressing the heterogeneity specific to fog computing.
A. OpenStack Overview OpenStack adopts a modular service-oriented architecture, where each component provides a service to other components. Only a minimal set of services is required in order to run a minimal OpenStack deployment, these are: In summary, all of these components use (1) a RESTful API for external or inter-component communication,  (2)  an AMQP based RPC for inter-component communi-cation, and (3) a data-base for storing component related state. The minimal OpenStack deployment requires two servers hosting the following services: • Controller Node -SQL Server Database -RabbitMQ Server -HTTP Server -Keystone -Glance -Nova -Neutron -Horizon • Compute Node -Nova with nova-compute -Neutron.
B. Fog Computing Requirements From the list above, it emerges how OpenStack has a clear client-server architecture with dependencies toward centralized services such as the AMQP broker and the database. From the list of services required for the minimal deployment, it also follows that a minimal installation requires relatively resourceful hardware. This is a consequence of the fact that OpenStack was designed for resourceful, symmetric and relatively static environments such as data centers. A typical fog deployment is rather the opposite as (1) nodes may not be extremely resourceful, (2) the system is highly asymmetric w. r. t. computational and connectivity capabilities, and (3) nodes have a high degree of churn which beside from failures and intermittent connectivity stems from mobility -consider for instance autonomous driving vehicles in a factory.
A. fog?5 Agent The core component of fog?5 is the agent. The agent represents a manageable resource on the system, which from now on we'll call a fog?5 node. Each agent has two stores, one representing the actual state of the node and the other representing the desired state for the node. The actual state of a node can only be written by the agent running on that node, while the desired state can be written by anybody to cause state transitions, such as provisioning a VM, or a binary executable, and so on. Agents dynamically discover each other by leveraging the dynamic discovery provided by the distributed store, which in turn leverage the dynamic discovery provided by the data sharing layer which may be implemented via Data Distribution Service (DDS)  [1]  or zenoh  [2]. The set of functionalities supported by an agent is controlled by plug-ins.
fog?5 has a decentralized architecture as a consequence of the decentralized key/value store used at its foundation. • Low Latency. As the distributed key/value store is implemented using either DDS or zenoh, the latency for distributing data on a LAN, is around 50 microseconds when using DDS and around 20 microseconds when using zenoh. • Low Resource Utilization. The fog?5 agent takes around 3MB of memory on a 64-bit machine running Linux. This is for a fully functional agent with no additional external dependency. Notice also that, as the architecture is decentralized, there is nothing else beside the fog?5 agent that needs to be deployed. fog?5 is currently being used in R&D laboratories around the world, such as ITRI, UC3M, III and NCTU, for applications ranging from 5G VNF in heterogeneous environments to deployment and orchestration of complex services inside a smart factory to enable on-demand manufacturing.
Search strategy and inclusion criteria We compiled a large database of network meta-analyses of RCTs based on searches of Medline, Embase, and the Cochrane Database of Systematic Reviews from inception to 14 April 2015. 12 In the present study we included networks published after January 2012, as empirical evidence suggested that the quality of the systematic reviews and statistical rigor have considerably improved in recent years. 12 To ensure a critical mass of data, we included networks with at least one closed loop of evidence that compared at least five different treatments and included 20 or more RCTs published within at least 10 years.
Statistical information is defined as the inverse of the variance-that is, precision. We defined the maximum information as the precision of a single RCT that is adequately powered to detect the anticipated difference between the two interventions, given α and β. The α error is then distributed along the sequential tests, with smaller values "spent" for early tests and larger values spent at later stages. The monitoring boundaries correspond to the quantiles of the α levels and approximate the (1-α/2) % quantile of thestandard normal distribution as the statistical information approaches its maximum. We implemented the methods in a freely available R package (see appendix N). We defined a significance level α=5%, power of 90% (β=10%) and an anticipated treatment effect to detect equal to the final estimate from the network meta-analysis.