dataesr/scientific-paragraphs-categorization huggingface dataset:
Feature Engineering Scenario The third series of experiments checks the usage of our REST service in a predictive analytics application of a retail bank. The decision task consists in predicting whether a client without a credit card will obtain a card in the 3 months following a given reference date. The training and prediction process uses tables from a publicly available database  [36]  related through PK-FK relationships as shown in Figure  17. Each account is described by static characteristics (e. g. date of creation, address of the branch) given in table ACCOUNT and dynamic characteristics (e. g. payments debited or credited, balances) given in table TRANSACTION. The two tables CLIENT and CREDIT CARD contain information about persons and credit cards respectively. Clients and accounts are linked by a many-to-many relationship stored in the associative table DISPOSITION.
Tables  GEOCODE  and TIME  provide detailed geographical and temporal information. The development of the application includes a feature engineering step to build a training dataset. Starting from a table describing a CLIENT entity and from a reference date, the developer augments this table through a sequence of database queries with as many new features (attributes) as possible. The goal is to obtain more detailed information about the past behaviour of the clients like, for example, the monthly amount and balance of their credit cards during the past 12 months, the delivery of a new.
Experiments The goal of our experiment is to verify if a data scientist can semi-automatically generate an view GV that is equivalent to FEAT by iteratively extending an initial dimension table CLIENT through several schema augmentation steps each of which consists of a sequence of CSA -RQG -MSA API calls. For running our experiment, we first created 6 dimension tables, denoted by dACCOUNT, dGEO, dCLIENT, dCARD, dTIME and dTRANSACTION, and 3 fact tables, denoted by TRANS, ACC DISPO, and CARD DISPO over the bank retail database tables. TRANS is defined over database table TRANSACTION and dimensions dTIME, dACCOUNT and dTRANSACTION and has two measures AMOUNT and BALANCE. ACC DISPO is defined over database table DISPOSITION and dimensions dCLIENT and dACCOUNT. Finally, CARD DISPO is defined as a join of tables CREDIT CARD and DISPOSITION over dimensions dCLIENT, dTIME and dCARD.
Third, the preparatory definition of formal aggregable properties for all measures in fact table TRANS help the expert to choose the correct aggregate functions for measures AMOUNT and BALANCE in Step 4. For instance, function SUM cannot be applied to BALANCE. Finally, our method for propagating aggregable properties controls the dimensions with respect to which measure COUNT(CARD ID) can be aggregated after Step 5 since the measure only depends on CLIENT ID. Note that our REST service could be directly applied to an SC graph consisting only of the retail database tables (i. e. , without creating any dimension or fact table) and would still produce table FEAT as result. However, working directly with database tables has two main drawbacks. First, the user must understand the operational data model of the database that carries many attributes that are irrelevant for business data analysis.
Comparison with related work We can assess other related work with respect to their ability to generate table FEAT. As we did in Section 8. 2. 2, we first consider methods that find schema complements. Starting from table dCLIENT, the previous works of  [8] ,  [10]  and  [34]  are only able to detect natural schema complements and suggest the merge of step 1 in Figure  18. The other steps require reduction queries which are not supported by these methods. Note that the ABA operator of  [9]  can be applied on dataset dCLIENT since its identifier consists of a single attribute CLIENT ID. However, this is again only useful for step 1. Concerning the use of drill-across queries  [2] , we can show that no step in Figure  18  can be defined by a drill-across operation between two fact tables: steps 1 and 3 merge two dimension tables and steps 2, 4 and 5 merge a dimension with a fact table.
Conclusions and Future Work In this paper, we propose a solution to the problem of discovering and merging schema augmentations for analytic datasets. We formally define efficient algorithms for building reduction-based schema complements which generalize previously defined natural schema complements. We discuss various quality issues and their solution for generating semantically correct schema augmentations and merged tables annotated with proper metadata enabling their correct usage in future analytic queries. We also present the implementation of our solution as a REST service in SAP HANA which, to the best of our knowledge, is the first implementation of that sort. This service assists business users and data scientists to explore a large space of possible schema augmentations and, as illustrated in our experiments, to generate complex views in a controlled way with formally defined quality guarantees concerning attribute ambiguity and data completeness.
Methods The architecture of the cGAN-based CS-DTI de-aliasing is illustrated in ). The generator is composed of a nested-UNet model, in which the long and short connections coexist, which not only allows concatenating the deep and shallow features belonging to the same layer in the topology, but also let the features from different layers to be fused. Fig  1(a)  shows how the feature map travels through the top skip pathway of generator. Formally, it can be formulated as follows: x ij = S (x i-1 , j) , j = 0 S (C (x i,j , L (x i+1,j-1 ))) , j > 0, k = 0, 1,. , j -1 (1) where x ij is the feature map represented by the pellet at the positions i and j , S(•) is a convolution operation followed by an activation function. L(•) denotes an up-sampling layer and C(•) the concatenation layer. Each standard unit embed a squeeze and excitation (SE) module, which can calculate the importance of each feature channel through learning, and then use this importance to enhance useful features and suppress features that are not very useful for the current task.
Dataset Description and Experimental Setup To effectively evaluate the superiority of the proposed model, two kinds of diffusion MRI datasets were used in the present work, including DW images of 11 human neonatal/infant hearts and those of 11 human adult hearts. For neonatal/infant hearts, the DW images were acquired with a 3T clinical MRI scanner (MAGNE-TOM Verio, Siemens AG) using MDDW (Multi-Directional Diffusion Weighting) sequence with the following imaging parameters: slice thickness and spacing=1. 4mm, number of slice=30 to 40, in-plane spatial resolution=1. 38×1. 38mm, TE=70ms , TR=5500ms to 7900ms, matrix size=104×104, number of diffusion gradient di-rections=192, and diffusion sensitivity (b value=700s/mm 2 ). All the DW images were acquired for six times and averaging them allows increasing SNR.
For adult hearts, the DW images were acquired with a 1. 5 T clinical MRI scanner with the parameters as follows: slice thickness and spacing=5 or 2mm , number of slice=25 to 62, in-plane spatial resolution=0. 5×0. 5mm 2 or 2×2mm 2 , TE=70ms to 102ms , TR=5270ms to 8600ms, matrix size=128×128 or 336×512, number of diffusion gradient directions=12 or 64, and diffusion sensitivity (b value=1000s/mm 2 ). In our experiments, DW images of seven neonatal/infant hearts were selected randomly for training the reconstruction model, two hearts were selected for validation, and the images of the remaining two hearts were taken as the testing dataset. In summary, we have 38851 samples for training, 5018 samples for testing, and 12352 samples for validation. To speed up the training, the original slices were cropped to a size of 64×64 and the region of interest (ROI) was extracted to avoid the effects of image background.
A few slices with small ROI were disregarded. The intensity of the selected DW images was normalized to the range of (-1, 1). For the human adult hearts, we fine-tuned the well-trained model obtained with the neonatal/infant hearts. Specifically, the network parameters of the well trained model were taken as the initial, then we used the DW images of 7 adult hearts to fine-tune the parameters. The DW images of two hearts were used to validate, and those of remaining two hearts were used to test. The proposed reconstruction method was also compared with other CS reconstruction methods including ADMM and DAGAN. For these two existing models, the hyperparameters in their loss function were adjusted based on our own dataset to achieve their best performance. To be adapted to image size and maintain the same deep architecture simultaneously, the stride length that was used in the first two convolutional layers of the DAGAN model was changed from 2 to 1.
All the training strategies for ADMM  [28]  and DAGAN  [33]  were identical to those given in the initial works, which can be downloaded from the following links, https://github. com/yangyan92/Deep-ADMM-Net and https://github. com/nebulaV/DAGAN. The initial learning rate was 0. 0001 and dropped every 5 epochs with a decay rate of 0. 75 to get the best performance; epoch number was set as 30-40. For our proposed model CSGAN, we used nested-UNet as the generator instead of UNet. The initial learning rates for both generator and discriminator were set as 0. 0001 and they were dropped every 5 epochs with a decay rate of 0. 85; epoch number was set as 20-30. ADMM-net was implemented with Matlab 2016a, DAGAN and the proposed CSGAN was implemented with Tensorflow on a NV-Linked V100 GPU. The original full k-space was firstly sampled with a randomly perturbed goldenangle sampling mask with ratios of 50%, 40%, 30%, 20% and 10%.
Then, spatial images were reconstructed using respectively ADMM, DAGAN, and CSGAN. The reconstruction results and quantitative analysis are given in the following subsections. Note that the above initial DW images are spatial magnitude data (so, their corresponding k-space data are complex but Hermitian symmetric). Such Hermitian symmetry was however rendered non-Hermitian symmetric by the following calculation procedure: a) take the Fourier transform of spatial magnitude DW images, yielding complex but Hermitian-symmetric k-space data; b) randomly undersample the complex k-space data, making the k-space data asymmetric; c) obtain complex spatial DW image containing phase information after estimating the nonlinear function G using CNN; d) take the magnitude of the complex spatial DW image as the final reconstructed DW image.
Quantitative Analysis To quantitatively compare the proposed model and existing ones, the average PSNR and SSIM for DW images were given in Table  1. It is clearly seen that PSNR and SSIM of DW images reconstructed with our method are the highest, and this for all the sampling ratios. PSNR and SSIM only reflect overall reconstruction quality. To get insights into the difference in image intensity distribution, the violin plots (drawn with python package seaborn) of DW images, FA, MD, and fiber orientation errors reconstructed with different methods for sampling ratio of 20% are illustrated in Fig 6. In the violin plot, the middle vertical black bar indicates the interquartile range. The thin black line extended from the middle black bar represents the 95% confidence interval. The white point is the median.
Funding This work is funded partially by the  National Nature Science Foundations of China  (Grant No. 61661010 ,  61701105 ,  61671049 ), the  National Key R&D Plan of China  under Grant  2017YFB1400100 , the Program  PHC-Cai Yuanpei 2018 (N • 41400TC ), the  Guizhou Science and Technology Plan Project  under  Qinkehe  ([2018]5301, [2020] 1Y255 ), and the  Committee of Science and Technology of Shanghai  (No. 19510711200 ).
a b s t r a c t There is a growing interest in understanding functional brain decline with aging. The dataset provides raw anatomical and functional images recorded in a group of 20 young volunteers and in another group of 19 older volunteers during a 10-minute period of resting state followed by four consecutive task-related runs. During each task-related run, the participants were exposed to two types of sensory stimulation: a tactile stimulation consisting in a textured-disk rotation under the palm of their right hand or a muscle proprioceptive stimulation consisting in a mechanical vibration applied to the muscle tendon of their wrist abductor. These two stimulations are known to evoke illusory sensations of hand movement, while the hand remains actually still. Therefore, the dataset is meant to be used to assess age-related functional brain changes during the perception of hand movements based on muscle proprioception or touch individually.
It also allows to explore any structural changes or functional resting connectivity alteration with aging. The dataset is a supplement to the research findings in the paper 'Functional brain changes in the elderly for the perception of hand movements: a greater impairment occurs in proprioception than touch published in NeuroImage. © 2020 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license. ( http://creativecommons. org/licenses/by-nc-nd/4. 0/ ).
Parameters for data collection Fieldmap images were acquired with a spin-echo EPI (Echo-planar imaging) sequence twice with opposite phase encode directions along the anterior-posterior: TR/TE = 7060/59 ms, voxel size = 2. 5 × 2. 5 × 2. 5 mm 3 , slices = 54, FOV (Field of View) = 210 × 210 mm 2 , flip angle = 90/180 °BOLD images were acquired using a gradient-echo EPI with a multi-band gradient sequence with a factor of 3 that cover the whole brain and cerebellum: TR = 1224 ms, TE = 30 ms, flip angle = 65 °, slices = 54, voxel size = 2. 5 × 2. 5 × 2. 5 mm 3  -T1-weighted anatomical image is available for each participant in this dataset. -A total of 284 T2 * -weighted images were recorded during the 10 min of resting-state acquisition, while participants were asked to keep their eyes open and fix a foveal cross.
Data description The released dataset contains one directory for each participant called sub-XX where XX is the participant number. Each subject's directory is composed of 3 subdirectories named 'anat', 'fmap' and 'func' that respectively contains the anatomical, fieldmap and functional raw images recorded for each of the 39 participants who underwent the experiment (20 young and 19 older adults). The 'anat' folder contains T1-weighted ('sub-XX_T1w. nii. gz') anatomical image with skull stripped and the corresponding descriptive files with '. json' extension. The folder 'fmap' is composed of 6 fieldmap images and the corresponding descriptive files with '. json' extension. The 'func' directory regroups 4D functional images including the resting-state run ('XX_task-rest_bold. nii. gz') and the four task-related run acquisitions ('XX_task-illusion_runYY_bold.
The muscle proprioception stimulation (P) was delivered by pneumatic vibrators (cylinder 3 cm in diameter and 4 cm long) applied to the abductor muscle tendon of the participant's right wrist. Two constant vibration frequencies (P Low : 30 Hz or P High : 60 Hz) were applied using a SMC ITV2050 air pressure regulator, driven by a 0-10 V analog command. A specific software developed for the current study in the LabVIEW environment (National Instruments) was implemented for the stimulation protocol. The software was synchronized with the MR acquisition using a NI-PXI 6289 multifunction I/O device. The hardware clock source of the whole protocol was defined by a digital input line connected to the TTL (Transistor-Transistor Logic) MR pulses and was described in a sequential text script, which included the analog commands of both tactile and proprioceptive stimulation devices.
Data acquisition MR scans were acquired using a 3-Tesla MRI Scanner (Magnetom-Prisma, Siemens) with an in-built body-coil for radio frequency excitation and the manufacturer's 64-channel phasearray head coil for reception. To estimate a map of the magnetic field, we first acquired a pair of phase-reversed fieldmap images with opposite phase encode directions along the anterior-posterior axis with the following parameters: TR (Repetition time) = 7060 ms, TE (Echo time) = 59 ms, voxel size = 2. 5 × 2. 5 × 2. 5 mm 3 , slices = 54, flip angle = 90/180 °, FOV (Field of View) = 210 × 210 mm 2. Then, BOLD images were acquired using a multi-band gradient-echo EPI sequence with a factor of 3 to cover the whole brain and cerebellum. 54 slices were acquired during the TR of 1224 ms, a of TE = 30 ms, an isotropic resolution 2.
Acknowledgments This research was supported by  Agence Nationale de la Recherche  Grant (#  ANR12-JSH2-0 0 05-01 -Project :  MULTISENSE ) to AK. CL was funded by a grant  A * MIDEX  ( ICN PhD Program ,  ANR-11-IDEX-0 0 01-02  grant) funded by the  French Government «Investissements d'Avenir » program. This work was performed in the  Center IRM-INT@CERIMED  ( UMR 7289 ,  AMU-CNRS ), platform member of  France Life Imaging network  (grant  ANR-11-INBS-0 0 06 ).
The algorithm runs until the selection of the N vlpz (t) roadside infrastructures is done. An example allows illustrating the RIs selection process is given as follows. As shown in Figure  10a , we assume a cell that contains 10 RIs candidates and 70 vehicles. The positions of both vehicles and RIs are randomly generated using Matlab and the capacity of each RI (K opt ) is equal to 10. The number of RIs that should then be selected is equal to 7. Figure  10b  illustrates the 7 clusters that have been created using the k-means modified version proposed by ELKI Framework and their centroids. Figure  10c  illustrates the selected RIs after running the selection algorithm.
It may be difficult to distinguish species of individual parasites on the thick film, especially at low densities, and the parasite counts in mixed infections can be combined for reporting but each detected species should be clearly indicated. Depending on the requirements of the study, gametocytes may need to be reported and counted, whereas schizonts are counted along with other asexual parasite forms (Box 4). In all cases however, parasite counts must be reported as raw data for conversion into parasite densities (see Table  4  for an example of a reporting format compliant with the Clinical Data Acquisition Standards Harmonization format from the Clinical Data Interchange Standards Consortium)  [28].
Introduction Currently, Software-Defined Networks (SDNs) present a revolution in the field of computer networks since they have reshaped several concepts of IP networks  [1]. An important concept is that the network control logic is decoupled from forwarding devices. Indeed, the control logic is moved from forwarding devices, and it is encapsulated into an external entity called the controller (control plane). Through the latter, and by leveraging a logically centralized network view and standardized communication protocols (e. g. ,  OpenFlow [2]  and ForCES  [3] ), forwarding devices (data plane) become programmable. Despite the concept of a logically centralized controller, SDN remains a distributed system wherein the control and data planes cooperate and communicate via an asynchronous communication (asynchronous communication implies arbitrary and unpredictable transmission delays) interface to establish networking.
SDN Model We developed the SDN model by adapting the sets P, M (also extended to the set MP), and E, presented in the distributed system model of Section 5. 1, to the SDN context and by adding the sets MATCH, MP, PFLOW, and FPATH, which correspond, respectively, to the set of matches, the set of messages and data packets, the set of packet flows, and the set of forwarding paths, which are specific to the SDN model.
-FwdP(pkt): denotes that an rp i ∈ P rp forwards a data packet pkt ∈ PKT to an rp j ∈ P rp (rp i = rp j ). E receive is composed of one event: -RecMP(mp): denotes that an rp j ∈ P rp receives a message or a data packet mp ∈ MP. Such an event only notifies the reception of an mp by rp j. Furthermore, E delivery is composed of a unique event: -DlvMP(mp): denotes that an rp j ∈ P rp delivered a message or a data packet mp ∈ MP. A delivery event identifies the execution performed or the consumption of an ingoing mp by rp j. The set of events associated with MP is the following: E(MP) = {SdM(m), FwdP(pkt)} ∪ {RecMP(mp)} ∪ {DlvMP(mp)} (1) The whole set of events in the system is the finite set: E = E internal ∪ E(MP) (2) The order of occurrence of events can be collected based on the causal dependencies between them.
, rp j } from rp i to rp j , such that intermediates rp r may exist where f path µ ∈ FPATH | f path µ = ∧ match λ. A TFL from rp i to rp j exists iff there is a data packet flow p f low τ = pkt 1 , pkt 2 ,. , pkt n (n ≥ 1), where p f low τ ∈ PFLOW | p f low τ = ∧ match λ , such that: 1. pkt 1 is sent by rp j after the delivery of m , 2. if pkt k (1 ≤ k ≤ n) is delivered by rp r (rp r = rp i ), then pkt k+1 is the next data packet sent by rp r , and 3. pkt n is delivered by rp i before the delivery of m. It can be interpreted that a TFL occurs due to the violation of the causal order delivery of the OpenFlow update message(s) of type deleteand matched packets/packet flows (see the three conditions in Definition 5). Based on the abstraction defined in Definition 5, we present how to capture and avoid the occurrence of the defined pattern during updates, ensuring the TFL-free property.
• Execution model: All OpenFlow update messages are asynchronously disseminated from P cp to P rp. No upper bound on messages/data packets transmission delay is required. P cp never waits for an acknowledgment message from P rp once a message is delivered, and clocks of all entities are not in synchronization with each other, that is the execution model is fully asynchronous. • Data structures: At a process-level, each process p i ∈ P maintains a vector of control information CI i to store direct dependency information between the set of messages/packets MP with respect to the corresponding match. Furthermore, each rp i ∈ P rp maintains a matrix Delivery i to track dependency information (see the data structures' subsection below for more details) when an rp receives an mp ∈ MP. We will also make reference to the state of the delivery matrix structure when a process p i sends an mp, denoted as ForDelivery i.
Data Structures In the algorithm, each process p i ∈ P maintains a vector of control information CI i of length N to store direct dependency information (N is the number of processes). Each element of CI i is a set of tuples of the form (process id , logical_clock). For instance, let CI i be the vector of a process p i such that (k, t) ∈ CI i [j] (i = j). This implies that any message sent by a process p i should be delivered to p j after the message mp of sequence number t sent by p k has been delivered to p j. Furthermore, each process rp i maintains an N × N integer matrix Delivery i to track dependency information. Each matrix Delivery i records the messages of the last sequence number delivered to other processes. For instance, Delivery i [j, k] = t denoted that p i knows that messages sent by process p j to p k , whose sequence numbers are less than or equal to t, have been delivered to p k.
Conclusions A model of the TFL pattern based on causal dependencies in SDNs was presented. We characterized the TFL phenomenon in an OpenFlow-based SDN, by identifying the relevant events and conditions under which it could occur during updates. In Definition 6, we specified that a TFL occurred due to a violation of the causal order delivery of a data packet during the process to update flow tables. This updating process was performed through FlowMod controller-to-switch messages of type add and delete. Based on this abstraction, we proposed a causal consistent update algorithm oriented to ensure the TFL-free property. To demonstrate this, a proof of correctness of the algorithm was provided. Based on these results, we propose as a future work to extend our solution to manage temporal constraints by including the principle of ∆-causal proposed by Pomares et al.