dataesr/scientific-paragraphs-categorization huggingface dataset:
SENSORNETS2015-4thInternationalConferenceonSensorNetworks 80 TinyDB because it is the most widely used system by researchers and it has shown its effectiveness in maintaining energy and for data managing. The network is based on Mica2 sensors randomly scattered in the field. We have varied the number of sensors from 10 to 100 by step of 10. The sensor are queried on demand by the user via the base station. Table  3  summarizes the simulation parameters. TinyDB uses a tree-Based Routing Protocol  (Gehrke and Madden, 2004)  to provide the query dissemination and the result collection. Each node forwards the query to other nodes in the network, to form the routing tree. This process ends when all nodes will receive the query. TinyDB uses CSMA protocol, which is the already existing default MAC protocol used by TOSSIM.
First Scenario Each sender node uses the UDP sender algorithm to send its data to the base station. We have defined a transmission period of 60 seconds. We have chosen a random waiting time (SEND TIME), before sending packets to avoid that the senders sending packets at the same time. We have activated the digital sht11 sensor, relative to sensor Humidity and temperature. A sender node listens to its neighbors us-ing tcpip handler() function and continues to send data currently detected to the base station using the "send packet" function. end if 13: end while.
Impact of Number of Nodes on Network Convergence Time The network convergence time is defined as the time needed for the sensors to connect to the base station and to build routing tables. This step comes just before the process of data collection. It can affect the arrival time of data. The shorter is the convergence time, the quicker is the availability of the data. In Figure  3 , the network convergence time increases when the number of nodes increases. The network convergence time depends on the DODAG building process, which begins at the root (base station). The root starts the dissemination of information about the structure using DIO (DODAG Information Object) message. The nodes not connected to the tree (without communication with the root) receive the message DIO and process it. Then they decide to join or not the structure.
Once the node has joined the structure, it has a route to the root of the DODAG structure. Building the final routing table depends on the number of nodes and the path cost between nodes. We note also  in Figure  4  that the convergence time for TinyDB (acquisition on demand) is less than that of data collection (periodically). TinyDB constitutes a routing tree or Directed Acyclic Graph (DAG) with all sensors in which the root is the sink (gateway). TinyDB uses a selecting multi-hop tree based on efficiency of routing which depends on energy consumption. Sensors make intelligent routing decisions, where every node selects a parent based on link quality to the base station. The nodes keep both a short list of neighbors which have a recently heard transmit, and some routing information about the connectivity of those neighbors to the rest of the network.
Impact of Number of Nodes on Data Collection Time Data collection time represents the time taken to get the responses from all sensors connected to the base station. It can affects the data validity time and then not reflecting the current state of the environment. The time required to send the data from sensor to the base station depends on the sensors capacity, its po-sition and the path quality depend on the number of hops to the base station. Generally, the sender uses multi-hop paths to send its data when the base station is far located, which leads to transmission delay and can cause a failure, i. e. to not respect the data validity time. Figure  5  shows the time spent by the nodes to complete the sending of their data, using both data collection technique and query processing technique.
We observe that the curve increases when the number of sensors increases for the two techniques. The time required to send the data depends of the number of hops and the availability of parent nodes to send data received from children. Their availability is not guaranteed all time because the choice of parent node depends on the node decision to join or not the structure. We also notice in Figure  5  that the time required to obtain results from all sensors by using the technique of processing request is less than that of data collection. With TinyDB, the data is regularly reported and aggregated by a tree or a directed acyclic graph from the nodes to an access point network. It includes aggregation and filtering operations inside the sensor network to maintain all routing information. The parent nodes near the root put agreements with their children on a time interval for the data listened to them.
Impact of the MAC Layer Protocols The goal of this test is to observe the Complete Cycle Time (CCT) vs Duplicate Packets Sending (DPS) with either a periodic data collection, or a query processing system (TinyDB). We have varied the number of sensors in each time. We note in Table  5  that the number of transmission packets with TinyDB is greater than that with peri- odic data collection. The MAC-layer protocol can be considered as one of the causes to explain these results. Tossim implements CSMA 1 based MAC protocols by default. The basic principles of this protocol are "listen before talk and contention". Each node listens on the channel and checks if it is clear before each sending. If the channel is not clear, it waits for a randomly chosen period of time called the backoff, until the channel becomes clear.
When the number of sensor nodes increases, the nodes compete for the channel and make the channel occupied. This competition causes a transmission delays and a backoff time accumulation, which increases the time needed to collect data from all sensors. On the other hand, each sender repeatedly sends its packet, during the full wake-up, until it receives a link layer acknowledgment from the receiver (sink node), meaning that the receiver has correctly received the packet. In periodic data collection, the sky motes implement a radio duty cycling protocol, named ContikiMac using a wake-up mechanism to listen for packet transmissions from neighbors and to precise timing between transmissions  (Dunkels, 2011) , which explains the decrease in duplicate Packets Sending.
CONCLUSION In this work, we have studied and compared the timing properties of the data collection from a sensor network using a static database versus an abstract in-network database, named TinyDB. We have evaluated temporal constraints such as data collection time, database response time, and network convergence time in both approaches. We have found, according to the tests performed, that many factors can affect the temporal constraints in a WSN. We have determined that the network topology and the routing protocol together may play an important role on data collection time. The convergence time also has an impact on the process of data collection. We have shown clearly the timing-response advantage of using a TinyDB approach compared to accessing the data stored in an external database.
a b s t r a c t We propose a framework for the robust and fully-automatic segmentation of magnetic resonance (MR) brain images called ''Multi-Atlas Label Propagation with Expectation-Maximisation based refinement'' (MALP-EM). The presented approach is based on a robust registration approach (MAPER), highly performant label fusion (joint label fusion) and intensity-based label refinement using EM. We further adapt this framework to be applicable for the segmentation of brain images with gross changes in anatomy. We propose to account for consistent registration errors by relaxing anatomical priors obtained by multi-atlas propagation and a weighting scheme to locally combine anatomical atlas priors and intensityrefined posterior probabilities. The method is evaluated on a benchmark dataset used in a recent MICCAI segmentation challenge.
Traumatic brain injury (TBI) database We obtained T 1 -weighted MR brain images of 101 TBI patients provided through the University Division of Anaesthesia, Cambridge University, UK. The images were acquired using an MPRAGE sequence on a Siemens MAGNETOM TrioTim Syngo with parameters: TR 2300 ms, TE 2. 98 ms, TI 900 ms, flip angle 9°, matrix size 256 Â 240 Â 176 and an isotropic voxel size of 1. 0 mm Â 1. 0 mm Â 1. 0 mm. Patients underwent MRI in the acute-phase, as part of the follow-up, or both. As only 24 patients had MR scans at both time points, we focus on a cross-sectional analysis in this work. In total we had 125 datasets available, including 61 acute and 64 follow-up MR brain scans. Information about the patients' gender and age distributions, the elapsed time between scanning date and injury and the Glasgow Coma Score (GCS) is summarised in Table  1.
The GCS is a clinical score that quantifies a patient's level of consciousness in the acute stage of the injury  (Teasdale and Jennett, 1974). The datasets were further grouped by clinical scores using Marshall Classification (MC,  Marshall et al. (1991) ) and the Glasgow Outcome Score (GOS,  Jennett and Bond (1975) ), as shown in Table  2. MC is a score based on the worst acute computed tomography (CT) image within 24 h of injury. MC takes into account brain pathology such as lesion load, the presence of oedema and midline shift caused by the injury. In contrast, the GOS is a clinical measure categorising the outcome of TBI and is assessed 6 months after injury or once the TBI outcome is considered stable. In Section 3. 2. 3 we describe correlations of these clinical scores with asymmetry biomarkers derived from brain MR scans.
Atlases The atlas cohort used in this study consisted of 35 manually annotated MR brain images of 30 subjects of the OASIS database  (Marcus et al. , 2007). The manual segmentation into 138 anatomical structures has been carried out by experts according to publicly available protocols 1  and were provided by Neuromorphometrics, Inc. (http://Neuromorphometrics. com/, last accessed: 8 December 2014) under academic subscription. The same atlas cohort was used in the recent ''MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling''  (Landman and Warfield, 2012). As suggested by  Landman and Warfield (2012) , the small regions (order of 100 voxels each) ''vessel'' and ''cerebral exterior'' were excluded in our experiments in both the left and the right hemisphere, so that we effectively investigated 134 structures.
Experiments and results The goal of this work was to devise a robust segmentation framework that can be employed to segment brain MRI with potentially highly abnormal brain configuration. Specifically we aimed to segment a database of traumatic brain injury patients and to extract biomarkers that can be correlated with clinical variables. However, before applying the proposed methodology to clinical data in Section 3. 2 we conducted quantitative experiments investigating our method's performance and characteristics on a wellstudied benchmark dataset. For this dataset reference labels, which were manually annotated by experts, are available. This allowed us to calculate label overlaps, to perform a test-retest analysis and to compare our method to other state-of-the-art approaches in Section 3.
Quantitative evaluation on a benchmark dataset using manual labels For evaluating MALP-EM, we used the dataset provided in the course of the ''MICCAI 2012 Grand Challenge and Workshop on Multi-Atlas Labeling''  (Landman and Warfield, 2012)   We consider this as the setup yielding the highest accuracy on this benchmark dataset. PICSL-BC (PICSL research group -Bias Correction): best performing method in the Grand Challenge  (Landman and Warfield, 2012)  using SyN registration from the Advanced Normalization Tools (ANTs) and employs joint label fusion  (Wang et al. , 2013)  and bias correction  (Wang et al. , 2011). A further overview over the compared methods is provided in Table  3. We segmented each of the 20 test images into 134 regions and calculated Dice overlaps (similarity indices, SI,  Dice (1945) ) with the available manual segmentations.
Evaluation of the influence of the weighting factor C As illustrated in Fig. 5 , a weighting factor of C ¼ 0:8 yields the best segmentation results on the training data set. This result shows that joint fusion yields accurate labels for the majority of voxels. For voxels with a high uncertainty (more than one label has a high non-zero probability) the EM-refined result should be considered as additional weighting. Using the more flexible model with a spatially varying C i , we observed comparable overlaps to C ¼ 0:8. This is encouraging, because a global and fixed C leads to a stricter model that is assumed to perform well on healthy, normal data while only a data-driven choice of C i allows the flexibility to cope with highly abnormal images of TBI subjects.
Segmentation and analysis of a traumatic brain injury (TBI) database We used MALP-EM C i (in the following referred to as MALP-EM) to automatically segment 125 MR brain scans of TBI subjects with potential pathology. This database is described in Section 2. 1. 1. For the segmentation we used all available atlas datasets, except the 5 repeat images, i. e. , a total of 30. All MR images were corrected for intensity inhomogeneities using the N4 algorithm  (Tustison et al. , 2010). The images were further brain extracted with a fully-automatic in-house brain extraction method called Extended Tissue Classification (ETC). In this method, first, an expectation-maximisation classifier based on  Van Leemput et al. (1999)  was applied for producing a coarse tissue segmentation, which is used as an initial brain mask.
Quantitative evaluation on TBI datasets using expert validation scores We devised a scoring protocol to semi-quantitatively assess the quality of automatically generated segmentations of TBI images. We selected four paired regions that frequently show morphological change in patients (hippocampus, thalamus, putamen, and occipital pole). We selected thalamus, putamen and occipital cortices because these structures are frequently implicated in TBI and its sequelae  (Warner et al. , 2010a; Strangman et al. , 2010; Ramlackhansingh et al. , 2011). We added the hippocampus because it is a challenging structure to segment (cf. Fig. 1 ). Consideration of the hippocampus is biologically justified, as it is typically involved in dementia, which in turn is a frequent long-term consequence of severe TBI.
The protocol calls for the raters to assign a score on a six point scale (0, worst to 5, best). Three experienced raters (JCL, 1 year of clinical service; RAH, 12 years of clinical service; AH, 16 years of clinical service) developed the protocol by consensus, using 9 images with corresponding segmentations calculated with both MALP-EM and joint label fusion (MALP-JF). All raters had basic (JCL) or advanced (RAH, AH) training in neuroanatomy, neuropathology, radiology, and neuroimaging. The 9 images had been selected from the TBI database using an ad hoc approach that ensured that the sample was broadly representative (MC 2-6; 4 baseline and 5 follow up scans). The detailed protocol is provided as supplementary material to this manuscript. Based on this protocol the three independent raters assessed 13 images using the tool rview from the Image Registration Toolkit (IRTK, https://github.
9% vs. 74. 1%) and MALP-EM (overall SI: 76. 4% vs. 75. 8%) than in the Grand Challenge. Additional application of a learning-based segmentation bias correction method  (Wang et al. , 2011)  further improves our segmentation results (overall SI: 77. 2%), yielding small but significant improvements over the best method (PICSL-BC; overall SI: 76. 5%) in the Grand Challenge. We acknowledge that in the development of MALP-EM we benefitted from the experience of participating in the Grand Challenge, where the timeframe for algorithm development and tuning was tight. However, we did not use the testing set from the Grand Challenge to tune MALP-EM. While the segmentation bias correction significantly improves label overlaps on the MICCAI Segmentation Challenge dataset, we did not employ this post processing technique to segment the TBI subjects.
We reason that it is difficult to justify the application of a correction classifier that was trained exclusively on a homoge-neous cohort of healthy subjects to a heterogeneous cohort of brain scans with severe pathology. To evaluate MALP-EM on a TBI database, we specifically developed a protocol for the expert assessment of segmentation quality of the hippocampus, thalamus, putamen and occipital pole. The protocol is publicly available as supplementary material to this work. Using the protocol-based ratings of three independent experts, we showed on 13 subjects of the TBI cohort that the proposed modifications based on image intensities improve on pure label fusion. The proposed method is shown to be robust: 120 out of 125 TBI images were segmented successfully into 134 regions. After manual intervention on the preprocessing step, the successful record increased to 122/125.
This is a solid basis for future research into image-based quantification of brain abnormality. Derived morphometric biomarkers, such as a structural asymmetry index, can serve as features for automatic classifiers, predicting how the image will be rated by an expert (MC) and prognosticating clinical outcome (GOS). Given that MC is assessed at the acute stage quantifying brain pathology (cf. Appendix B) it seems reasonable that structural asymmetry in acute MRIs correlates well with this score. In contrast to this, GOS is an outcome score assessed several months after the injury. It was expected that MRI features derived from follow up scans are more consistent with the outcome measure than features available at the acute stage. The segmentation setup for the TBI subjects inherently differs from the intra-atlas experiments (15 training, 20 test images) in that we used more atlases for the segmentation of the TBI subjects, the image source (scanner) differs from the atlas database, and, most importantly, we are segmenting subjects with potentially substantial pathology.
Visual inspection confirmed the robustness and advantages of the proposed method under these new challenges (cf. Fig. 9 ). The generated segmentations of the TBI data set are a valuable resource for investigating further potential biomarkers for TBI disease progress. This work also motivates further research and discussion about how meaningful or generalisable high Dice overlaps on a homogeneous cohort of healthy patients are. More informative similarity measures are desirable  (Ledig et al. , 2014). In many studies, e. g. Alzheimer's disease or TBI, the subjects of interest show high variability in both brain appearance and disease burden. More restrictive models might lead to a high labelling accuracy on subjects that are very similar to the atlas cohort. However, they are potentially too rigid to cope with images of subjects with significant pathology.
Here more flexible formulations might be desirable, even if they are slightly less performant in intra-atlas cohort validations. We conclude from our experiments that MALP-EM C i yields a segmentation accuracy that is on healthy subjects comparable to other state-of-the-art methods while offering sufficient flexibility to cope with gross pathology. Most inaccuracies, as visually confirmed in the segmented TBI datasets, were due to minor problems in the brain extraction. This highlights the necessity of further improvement of fully automatic brain extraction tools, which is a very challenging task for brains in the presence of pathology.
(cf. Section 2. 1. 2). The dataset consists of 35 T 1 -weighted MR images with corresponding labels created manually by experts. As in the Grand Challenge, we divided the cohort into a training set of 15 subjects (10 female, 5 male, age: 23 AE 4. 3 (mean AE SD) years, minimum age 19, maximum age 34) and a test set of 15 subjects (10 female, 5 male, age 45. 7 AE 24. 4, minimum age 18, maximum age 90). Including the 5 repeat scans, the test set consists of 20 images.
Acknowledgements This work was partially funded under the 7th Framework Programme by the  European Commission  (http://cordis. europa. eu/ist/, TBIcare: http://www. tbicare. eu/,last accessed: 8 December 2014). The research was further supported by the  National Institute for Health Research (NIHR) Biomedical Research Centre (BRC)  based at  Imperial College Healthcare NHS Trust  and  Imperial College London. AH is supported by the  Department of Health  via the  NIHR comprehensive BRC  award to Guy's  & St Thomas' NHS Foundation Trust  in partnership with  King's College London and Kings College Hospital NHS Foundation Trust. This work was further supported by a  Medical Research Council (UK)  Program Grant (Acute brain injury: heterogeneity of mechanisms, therapeutic targets and outcome effects [ G9439390 ID 65883 ]), the  UK National Institute of Health Research Biomedical Research Centre at Cambridge , the  Technology Platform  funding provided by the  UK Department of Health  and an  EPSRC Pathways  to Impact award.
(cf. Section 2. 1. 2). The dataset consists of 35 T 1 -weighted MR images with corresponding labels created manually by experts. As in the Grand Challenge, we divided the cohort into a training set of 15 subjects (10 female, 5 male, age: 23 AE 4. 3 (mean AE SD) years, minimum age 19, maximum age 34) and a test set of 15 subjects (10 female, 5 male, age 45. 7 AE 24. 4, minimum age 18, maximum age 90). Including the 5 repeat scans, the test set consists of 20 images. 3. 1. 1. Label overlaps In total we compared five different approaches: MAPER (Multi-Atlas Propagation with Enhanced Registration): Standard MAPER as proposed by Heckemann et al. (2010). MALP-JF (Multi-Atlas Label Propagation with Joint label Fusion): Segmentations obtained through joint label fusion using the implementation of Wang et al.
Background: Image analysis is increasingly used in plant phenotyping. Among the various imaging techniques that can be used in plant phenotyping, chlorophyll fluorescence imaging allows imaging of the impact of biotic or abiotic stresses on leaves. Numerous chlorophyll fluorescence parameters may be measured or calculated, but only a few can produce a contrast in a given condition. Therefore, automated procedures that help screening chlorophyll fluorescence image datasets are needed, especially in the perspective of high-throughput plant phenotyping.
Moreover, the automation of image analysis and eventually of image acquisition allows high-throughput phenotyping  [1]. Several software or applications devoted to image analysis have been developed to answer specific questions such as the measuring of the area of leaves, plants and grains  [12] [13] [14] , the impact of pests and abiotic stresses  [5, 12, 13]  and the callose deposition  [15]. In practice, such methods have to be user-friendly and automated to match the needs of the community of biologists, especially when large datasets are treated. In plant science, imaging can be achieved both by visible imaging (e. g. photographs or scans) and by other types of imaging such as chlorophyll fluorescence, thermographic or hyperspectral imaging. Chlorophyll fluorescence (CF) imaging has been used to study the impact of both biotic and abiotic stresses on photosynthesis and hence on plant physiology  [5, 11, [16] [17] [18] [19] [20] [21] [22] [23] [24] [25].
thaliana non-inoculated vs inoculated with P. ramosa. For each plant, 55 images and their associated histograms were recorded. We developed a script in the R software aiming at facilitating the identification of CF parameters impacted during the infection. We implemented this R script as a web application at http://www. phenoplant. org. The R script described in the present paper was associated to a script previously described  [5]  in order to provide easy-to-use web resources for the analysis of CF imaging datasets.