dataesr/scientific-paragraphs-categorization huggingface dataset:
This latter point is ensured by the following constraints: 𝑥 𝑖𝑘 𝑛 𝑉 𝑘=1 ≤ 1, ∀𝑖 = 1. 𝑛 𝑄. ( 19 ) Decision variables y k are also introduced to determine whether view V k is materialized: y k = 1 if view V k is materialized, and y k = 0 otherwise. A view V k is materialized if it is used by at least one query (i. e. , when at least one query Q i exists such that x ik = 1), which is expressed by the following constraints:   ≤   , ∀ = 1. , ∀ = 1. (20) Moreover, there is no need to materialize V k if it is not used at all, which is expressed by the following constraints: 𝑦 𝑘 ≤ 𝑥 𝑖𝑘 𝑛 𝑄 𝑖=1 , ∀𝑘 = 1. 𝑛 𝑉. (21) As for the gains on response time, materialization and maintenance times (t mat P (V k ) and t maint P (V k )) have been estimated upstream. These times must be considered for a view V k only if this view is materialized: ( ) 22 As long as we assume that no data are inserted in the dataset during operation, we can consider a single period of length t(D) for storage.
Querying We have tested optimizationsMV 1 , MV 2 andMV 3 described in Section 5. 1 on a 5. 5 GB database and Amazon S3 and EC2 prices. Measures have been performedwith variable parameters. The first parameter is experiment duration, from 1 to 24 months. The second parameter is workload frequency, i. e. , the number of times the workload is executed during the considered period, from 1 to 5executions per week. The third parameter is the number of nodes used to process queries, from 5 to 20. A typical experiment fixes two parameters and varies the last. Fixed values used for experiment duration, workload frequency and node number are 12, 4 and 10, respectively. The experimental results we achieved clearly showthat our approach allows selecting views that significantly improveboth response time and cost (Figure  3 ).
Related works We discuss in this section previous research related to the main domains addressed in this paper, i. e. , cloud data management, data access optimization through materialized views, and cost models for large-scale distributed systems. Cloud data management brought about a lot of research and various operational systems. The most popular include so-called NoSQL systems, such as Amazon  DynamoDB (DeCandia et al. , 2007) , or Cassandra  (Lakshman & Malik, 2009) , which scale up very efficiently but only proposes eventual consistency, in contrast to traditional ACID (Atomicity, Consistency, Isolation, Durability) guarantees. Full cloud relational systems enforcing ACID constraints are also available, e. g. , Microsoft SQL Azure  (Campbell, Kakivaya, & Ellis, 2010) , Amazon RDS  (Amazon, 2013)  and Oracle Database Cloud Service  (Oracle, 2013) , but they currently operate on a smaller scale.
Finally, there exist large-scale data analytics systems that are specifically tailored for the cloud, such as Pig  (Gates et al. , 2009)  and Hive  (Thusoo et al. , 2010). The solution we propose in this paper is generic and can be applied within any of these systems. In the cloud, performance is mainly managed by exploiting computing power elasticity. Nevertheless, well-known performance optimization techniques from the database domain, such as indexing, view materialization or caching, may be used to decrease the global monetary cost of querying data in the cloud. In this paper, we particularly focus onto view materialization. Numerous approaches help select  (Agrawal, Silberstein, Cooper, Srivastava, & Ramakrishnan, 2009; Ceri & Widom, 1991; Luo, Naughton, Ellmann, & Watzke, 2003; Mami & Bellahsene, 2012; Yang, Karlapalem, & Li, 1997; Zhou, Larson, & Elmongui, 2007)  materialized views, whether in transactional databases, in decision-support databases (i.
e. , data warehouses) or even on the Web. In the view selection problem we address, all combinations of attributes in a database constitute a lattice of candidate materialized views. Cost models then help determine the materialized views that allow the best global performance improvement, usually under disk space constraints. Various optimization techniques are used to exploit these cost models, ranging from simple greedy algorithms (Vijay  Kumar & Ghoshal, 2009)  to simulated annealing or genetic algorithms  (Bellatreche et al. , 2006). Finally, to reduce the dimensionality of the input candidate view set, materialized views may also be pre-filtered with respect to the query workload, e. g. , with data mining techniques such as frequent itemset mining or clustering  (Aouiche & Darmont, 2009).
MobiPag is not specifically conceived for NFC, and most of its infrastructure could be used to support other technologies through the addition of new specific modules. However, in this work, we clearly assume the use of NFC technology as central to the payment process. The MobiPag Project was conducted by a large consortium of Universities, technology companies, mobile operators, banks and other financial institutions. The most distinctive characteristic of Mobipag is its open architectural model that allows multiple partners to become part of the payment value-chain and create integrated solutions that complement payments in many new and unexpected ways. This openness is an important step towards payment solutions that support flexible business models. In particular, it does not impose any particular collaboration pattern on many different stakeholders that are normally involved in payment solutions, allowing them to adjust their role according to the specificities of particular markets.
The merchant's APT may be a specific device built for that purpose, similar to current APTs, but with NFC capabilities, or another mobile device, similar to the one carried by the customer, in which case, the transactions between customer and merchant can be framed as person-to-person payments. Similarly, the POS may also be a common merchant's POS or specific POS software running on the merchant's mobile device. This solution targets payments to small merchants, where the actual business logic of the merchant (e. g. , stock, price list, etc. ) can be inside its mobile device. Financial institutions own financial account services, which are outside of MobiPag's scope, but interconnected with the system. Financial institutions manage the accounts for both merchants and customers, which are identified by virtual cards (vCards), i.
e. , tamper-proof and running only verified and audited software. The difference between customer and merchant is that there is no POS equivalent on the customer side. Customer interaction is done through the merchant's POS, i. e. , it checks the payment value and receives the receipt from the merchant POS. In the MobiPag system, the customers use a mobile device with a display and keyboard of their own and NFC communication capabilities, and the merchant can use a traditional APT equipped with an NFC reader. In the absence of these specialized terminals, the functions of the APT and the POS can be performed by another mobile device. This generic messaging protocol between the customer and the merchant may be described in three steps (see Figure  2 ), separated by two tapping moments, in which both peers explicitly communicate with each other.
Payment Terminal's Architecture The MobiPag software was designed in a modular and expandable way in order to support multiple payment applications. Besides the interface, these applications, customized for each merchant organization, also incorporate, in the user's point-of-view, the loyalty logic and services. These applications are deployed in the final customer devices and also in the shopkeepers' POSs, providing their users with a personalized experience. All of these applications use the same middleware, already present in devices and POSs, exposing the bare minimum of the API needed for starting and performing a payment transaction, including the loyalty elements supported in the protocol. See Figure  3. The main functionality of the middleware component comprises the function to perform a vCard registration in the secure element, the management of all security related information on the secure element, such as the security codes, secure payments that involve the transfer of money between user and merchant's accounts and token redemption.
Automated segmenting and labeling of individual brain anatomical regions, in MRI are challenging, due to the issue of individual structural variability. Although atlas-based segmentation has shown its potential for both tissue and structure segmentation, due to the inherent natural variability as well as disease-related changes in MR appearance, a single atlas image is often inappropriate to represent the full population of datasets processed in a given neuroimaging study. As an alternative for the case of single atlas segmentation, the use of multiple atlases alongside label fusion techniques has been introduced using a set of individual "atlases" that encompasses the expected variability in the studied population. In our study, we proposed a multi-atlas segmentation scheme with a novel graph-based atlas selection technique.
We first paired and co-registered all atlases and the subject MR scans. A directed graph with edge weights based on intensity and shape similarity between all MR scans is then computed. The set of neighboring templates is selected via clustering of the graph. Finally, weighted majority voting is employed to create the final segmentation over the selected atlases. This multi-atlas segmentation scheme is used to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline employing BatchMake for its pipeline scripting, developed at the Neuro Image Research and Analysis Laboratories of the University of North Carolina at Chapel Hill. AutoSeg performs N4 intensity inhomogeneity correction, rigid registration to a common template space, automated brain tissue classification based skull-stripping, and the multi-atlas segmentation.
INTRODUCTION Accurate segmentation of brain structures from magnetic resonance imaging (MRI)  (Khan et al. , 2011) , functional MRI (fMRI)  (Maldjian et al. , 2003)  and positron emission tomography (PET)  (Tohka et al. , 2007)  is essential for quantitative studies of the brain, such as disease progression and aging. In general, manual brain anatomical labeling (identification of anatomical brain structures and assignment of a unique label to each structure) is considered the most accurate means of giving the most accurate results closest to the true segmentation of brain structures. However, as the size and availability of large MRI databases increase, manual segmentation of brain structures is not realistic means of segmenting the brain because of the significant time-cost of human raters and unpredictable intra-and inter-rater variability.
Therefore, automated segmentation methods are highly desirable when the size of MRI databases is considerably large (e. g. , >50 cases). However, automated anatomical brain region segmentation (labeling) of subcortical regions in MRI data is challenging, since the contrast between tissues is often low for a variety of brain structures ("Subcortical regions" is included since folding/shape variation may be a bigger determining factor than contrast for labeling cortical structures. ). The commonly present shape and intensity variations in a number of diseases further complicate robust brain segmentation. Atlas-based segmentation is a simple method for automated segmentation as it is a compromise between manually driven and fully automated segmentation approaches  (Bajcsy et al. , 1983; Gee et al.
The atlases are also paired and co-registered with each other. A directed graph with edge weights based on intensity and shape similarity between all MRI scans is then computed. In contrast to the atlas selection strategies discussed above, we proposed a novel atlas selection method that separates the graph into several clusters and makes use of all clusters with each one only contributing a single exemplar atlas (neighboring template), the one closest to the subject image. Finally, weighted majority voting is employed to create the final segmentation over the selected neighboring templates. We use this multi-atlas-based segmentation scheme to extend a single-atlas-based segmentation toolkit entitled AutoSeg, which is an open-source, extensible C++ based software pipeline developed at the Neuro Image Research and Analysis Laboratories (NIRAL) of the University of North Carolina at Chapel Hill.
This software pipeline employs BatchMake pipeline scripts that call tools within the AutoSeg toolset based on the Insight Tool Kit (ITK). AutoSeg has been used and is in use in a number of studies, including Parkinson's disease  (Lewis et al. , 2009; Du et al. , 2011 Du et al. , , 2012;; Sterling et al. , 2013) , autism  (Hazlett et al. , 2009 , 2011 , 2012 ), schizophrenia (McClure et al. , 2013) , craniosynostosis  (Paniagua et al. , 2013) , and drug abuse  (Gerig et al. , 2011). AutoSeg entails intensity inhomogeneity correction, brain tissue classification based skull-stripping, rigid and non-rigid image registration, and multi-atlas-based segmentation with atlas selection. For the multi-atlas-based segmentation step, an atlas population consisting of multiple brain MRI scans and corresponding structural region of interest (ROI) (label files) and/or lobar subdivision definitions (parcellation files) is needed as input.
MATERIALS We evaluated the proposed multi-atlas segmentation within AutoSeg with a dataset of 35 defaced T 1 -weighted structural MRI scans. Fifteen scans (5 males and 10 females with an age range of 19-34) were used as the multi-atlas population and the remaining 20 scans (8 males and 12 females with an age range of 18-90) were used for testing. Thus, the 20 testing MRI scans were segmented one-by-one using the 15 atlases. These MRI scans were selected from the Open Access Series of Imaging Studies (OASIS) database (http://www. oasis-brains. org) (Asman and Landman, 2013). This dataset has been used in the MICCAI 2012 Multi-Atlas Labeling challenge, URL: https://masi. vuse. vanderbilt. edu/workshop2012/. This dataset was expertly labeled courtesy of Neuromorphometrics, Inc. (Somerville, MA) and provided under a non-disclosure agreement of the Creative Commons Attribution-NonCommercial (CC BY-NC).
METHOD SUMMARY In summary, we extended a single-atlas-based segmentation toolkit entitled AutoSeg, with an additional multi-atlas-based segmentation tool. The processing pipeline of the proposed method is shown in Figure  1. The AutoSeg software pipeline is publicly available under a BSD license on the NITRC website, at http://www. nitrc. org/projects/autoseg. AutoSeg starts with intensity inhomogeneity correction, followed by registration into a common MRI template space (such as standard MNI space). Next, the Atlas Based Classification (ABC) tool is applied to perform atlas moderated, Expectation Maximization based tissue classification  (van Leemput et al. , 1999b; Prastawa et al. , 2003)  for skull-stripping. AutoSeg then employs the symmetric diffeomorphic registration via the ANTS (Advanced Normalization ToolS) registration tool  (Avants et al.
Construction of graph We represent the registered dataset as a graph (Figure  3 ) whose vertices correspond to the atlases and target. Every edge between two vertices is assigned a cost (e ij ), which is defined by a weighted sum of an intensity similarity term M ij (mean squared voxel-wise intensity difference) and a shape similarity term H ij (harmonic energy) [Equation (  1   ) ]. e ij = w 1 M ij + w 2 H ij (1) where w 1 and w 2 represent the weighting factors for the intensity similarity term and shape similarity term, respectively. We empirically determined a combination of w 1 = 0. 2 and w 2 = 0. 8 to be the weighting factors. The weighting factors were determined by applying the AutoSeg to a training dataset, i. e. , a small group of MRI scans, and then selecting the combination of parameters that produced the best segmentation of the training dataset.
EXPERIMENTAL RESULTS We have applied the AutoSeg segmentation software pipeline to the brain MRI data set with 20 testing scans and 15 atlases. The parameter settings of this experiment are described in the Appendix. Table  1  summarizes the mean values of the DSC, MAD, and Hausdorff distance of the 28 subcortical structures for the 20 testing MRI scans in our database. The mean DSC, mean MAD and the mean Hausdorff distance for subcortical regions were 81. 73%, 0. 57 and 5. 68 mm, respectively. All structures showed a MAD below 1 mm, which indicates sub-millimeter accuracy on average (at a 1 mm isotropic image resolution). Smaller, skinnier structures showed DSC above 70% and larger structures were all above 80% DSC. Figure  5  shows the 3D segmentation results of subcortical structures on a selected example.
The segmentation results and the parameter settings of the testing data set used for our experiment are available at the NITRC web page of AutoSeg: http://www. nitrc. org/docman/view. php/ 421/1312/MICCAI_2012_Challenge_Data_Seg. zip. The most time consuming steps of our segmentation method are the ANTS based image registration step and the ABC based brain tissue classification. The average computational time of the ANTS registration of one pair of images on a standard workstation with 2. 6GHz CPU (running on as a single  core/thread) and 8GB RAM was 185 min; the average computational time of the ABC tissue classification including deformable, fluid image registration was 48 min. The remaining steps were performed with an average computational time less than 5 min.
DISCUSSION In this paper we present both a novel label fusion algorithm for multi-atlas-based brain segmentation, as well as a comprehensive, extendable brain segmentation software pipeline called AutoSeg. For the label fusion, we employ an approach that incorporates shape and intensity information of subject and atlases for both atlas selection and fusion weighting. The multi-atlas-based AutoSeg segmentation software pipeline was tested on a dataset with 15 atlases and 20 testing MRI scans for the segmentation of subcortical structures. As volumetric analysis of subcortical structures is a major aim in various neuroimaging studies, many automated segmentation methods, particularly atlas-based methods, have been developed as mentioned in the introduction section. Liu et al. (2007)  and  Gouttard et al.
(2007) 's single atlas-based methods achieved mean DSC of 74. 66 and 79%, respectively, over the major subcortical structures (caudate and putamen for Liu's study and amygdala, caudate, hippocampus, lateral ventricle, pallidus, and putamen for Gouttard's study) in different settings. In our multi-atlas-based method, we achieved a mean DSC of 81. 50% for caudate and putamen and 81. 18% for amygdala, caudate, hippocampus, lateral ventricle, pallidus, and putamen. Although the experiments of these studies were conducted on different datasets, the relatively large improvement in results with our method indicates the advantage of using multiple atlases [see also other multi-atlas segmentation papers such as  (Asman and Landman, 2013) ]. The dataset used in our experiment is from the MICCAI 2012 Grand Challenge on Multi-atlas Labeling.
, 2012; Asman and Landman, 2013). There is no conflict between "atlas selection" and "local weighting" based label fusion. Rather, the atlas selection algorithm we proposed in this paper can be combined with local weighting to improve performance; such an implementation is currently being added to AutoSeg. Because of the large shape and intensity variations of the brain structures caused by disease, we need a database whose size is large enough to represent the variations of the data. Although the current atlas population (15 MRI scans) in our experiment is of limited size, our method has been shown to provide segmentation results at an acceptable performance level on the separate testing dataset (20 scans). In our current neuroimaging studies, we employ all training and testing datasets (totally 35 scans) for multi-atlas segmentation to improve the performance even further than reported here.
In the experiments of this study, ABC was iterated twice and thus the first iteration performed registration and tissue classification on a non-skull-stripped image, whereas the second iteration performed registration and tissue classification on the skull-stripped data from the first iteration. We employ several existing tools/algorithms in the AutoSeg framework. Decisions on algorithms are based on (i) performance in our own tests as well as in the literature, and (ii) availability as open source. In general, AutoSeg employs the state-of-the-art tools in the field and each component has been vetted in our studies. All tools employed by AutoSeg are currently up-to-date (N4, ABC) or continuously being improved (BRAINSfit, ANTS, etc. ). ANTS is the core registration tool employed and has consistently been shown to be the best current option for deformable registration  (Klein et al.
, 2009). It is noteworthy that AutoSeg has a very flexible computational scheme that allows a developer to efficiently replace one component with a new tool, i. e. , additional algorithms for different purposes can be easily added to AutoSeg. Most of the recently developed multi-atlas segmentation algorithms including the STAPLE-based algorithms and the ANTS joint fusion algorithm participated in the 2012 Grand Challenge. In this paper, AutoSeg was tested on the same dataset of the Grand Challenge. The segmentation results of the subcortical structures were compared to the segmentation results of other algorithms that participated in the Grand Challenge and we found that AutoSeg would be ranked 5 out of 25. This result indicates that AutoSeg can provide good segmentation results that are comparable to other widely used multi-atlas segmentation methods.
Furthermore, AutoSeg has a unique user friendly GUI. Thus, users without any computer science or technology background can also easily use AutoSeg in their studies. The pipeline scripting by AutoSeg employs BatchMake, which is a cross-platform tool for batch processing large amounts of data. BatchMake scripts can be easily edited with any text editor or a specified BatchMake script editor developed by Kitware Inc. BatchMake is easy to use, and allows straightforward integration of the scripts into condor and SGE grid environments. In datasets where large morphological or intensity changes are present [due to pathology, e. g. , in Parkinson's disease, MPS, ALD, Duchenne muscular dystrophy (DMD)], and where AutoSeg is being applied, atlas selection has significant advantages as AutoSeg reduces the influence of common disproportion of the normative vs.
pathology exhibiting atlases in such settings. On the other hand, selecting the best atlas in each cluster ensures that the label fusion is achieved from the atlases similar to the target, with the variability represented by the atlas population still being incorporated by the label fusion procedure. AutoSeg was tested on the same dataset from the MICCAI 2012 Grand Challenge on Multi-atlas Labeling, and it would be ranked 5th out of the 25 algorithms in the challenge. This result shows that AutoSeg can provide good segmentation results that are comparable to other widely used multi-atlas segmentation methods, with still room for improvement. The segmentation of brain structures, in general, includes the segmentation of subcortical structures and cortical regions/parcellations. The proposed AutoSeg software pipeline allows for the direct labeling of cortical regions via standard atlas/multi-atlas-based segmentation.
It further allows the combination of the tissue classification with the cortical regions for a joint classification/multi-atlas-based cortical parcellation. The experimental results presented here illustrate the power of our multi-atlas AutoSeg MRI segmentation software pipeline. This software pipeline is publicly disseminated as open source on the NIH Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) website with accompanying testing datasets (http://www. nitrc. org/projects/autoseg).