dataesr/scientific-paragraphs-categorization huggingface dataset:
In second phase, a weight is assigned to each node in the tree representing the storage probability. The hop distance of the node from the root is used to calculate this weight/ probability as detailed by Viana et al. 60  Finally, source nodes send their data to the root which then transmits each data throughout the tree structure. Each node in the tree stores the data according to its storage probability. Advantages and drawbacks: Supple introduces an important improvement for reducing the number of transmissions in the WSN during data dissemination. Supple was evaluated and compared to RaWMS, DEEP, and USEE in the study of Mekki et al. 47, 49  Authors show that Supple offers the lowest communication overhead and ensures low energy consumption. However, no counter measure against hotspots is proposed around the root node.
The importance factor distribution phase is responsible for calculating the  number of storage nodes in each tree. The third phase is responsible for calculating the number of replicas to be stored in each node of each tree. In this phase, data messages are finally forwarded from roots toward storage nodes. Advantages and drawbacks: ProFlex achieves good collection efficiency results. It performs data correlation for reducing the communication overhead. Unlike Supple, the use of different root nodes (H-sensors) mitigates the hotspot problem. Maria et al. 61  show that nodes around the roots in ProFlex relay about 91% less messages when compared to Supple. In addition, authors show that ProFlex offers a lowest communication overhead when compared to Supple, RaWMS, and DEEP. Z-DaSt: Z-DaSt 62 exploits the routing and addressing scheme of ZigBee to replicate data in WSN.
For a given source node i with address adr, Z-DaSt looks for existing nodes with addresses greater or equal to adr. Then, Z-DaSt uses the ZigBee routing protocol to transmit message to these nodes and stores data in all of them. Advantages and drawbacks: Z-DaSt benefits from the efficiency of ZigBee protocol. Thus, Z-DaSt is applicable only for WSN composed of ZigBee motes. In addition, it is better suited to low or moderate network density,  62  and no counter measure against hotspots is proposed around the root node. The data dissemination delay of Z-DaSt could be further reduced by employing an improvement scheme of ZigBee protocol as discussed in the work of Nefzi and Song. 63 uster-based technique. In this class, the protocols use clustering mechanism and usually cluster-heads are used as storage nodes.
In fact, data storage using clusters is more efficient than tree structure as discussed in literature. 64, 65 SDS, CBDS, and EEMSRA are three cluster-based storage protocols in WSN as shown in Figure  4. Each protocol is presented as follows. SDS: SDS 66 provides efficient spatial-temporal correlation of sensor data for reducing the number of transmissions in WSN. SDS divides the deployment area horizontally and vertically into small rectangular zones as shown in Figure  14. A node is selected as cluster-head in each zone. The head maintains the link to the neighbor nodes within its zone and to the heads of its neighbor zones. To this end, each node has both short-range and long-range radio interfaces for communicating with neighbors and adjacent cluster-heads, respectively. In each zone, all nodes except the head are in sleep mode.
Synthesis Table  1  summarizes all the above-mentioned in-network data storage protocols in this article. Existing protocols are classified into three categories (reactive, structured proactive, and unstructured proactive) based on topology, load balancing, storage strategy, and transmission mechanism. In addition, Table  2  compares these protocols regarding in-network storage requirements, performance metrics used by authors, simulation tool, existence of testbed experimentation, and existence of a radio propagation model description. Reactive protocols are executed only if an event is detected in WSN (e. g. mobile sink appearance) which reduces the storage rate during the network lifetime as well as the energy consumption. The structured proactive approaches exploit a virtual structure (grid, hexagon, border, rail, line, quadtree, and ring) serving as a rendezvous zone for data and sink queries.
Tree-based unstructured proactive approaches simplify the storage process by employing the root node as a relay to disseminate data messages. Nevertheless, this technique increases the severity of hotspots on nodes around the root. Random-walk-based unstructured proactive approaches are very simple to implement. However, a high communication overhead is generated to achieve a well-distributed data result leading to short network lifetime. Finally, cluster-based unstructured proactive approaches can highly improve the energy efficiency and the overall network scalability. Clustering is an efficient way to minimize the energy consumption in WSN, accomplishing data aggregation and fusion to decrease the number of transmitted messages between nodes. On the contrary, these approaches can cause the cluster-heads to overload leading to high latency and hotspot problem in these nodes.
Opportunity and open issues for efficient in-network data storage in WSN The issues presented in this section provide perspectives which could enhance the in-network storage in WSN, making it more applicable and effective. Among the above-mentioned storage classes, the cluster-based approach improves the energy efficiency and the storage process by electing cluster-heads (i. e. each cluster-head is responsible for storing data in its zone) and scheduling the communication of clustermembers (i. e. cluster-members are in sleep mode most of the time). Moreover, the clustering is a popular technique in WSN community  70  to efficiently reduce the energy consumption and accomplishing data aggregation/fusion to reduce the amount of transmitted messages between nodes. 71, 72 Despite that, in the literature, few works used the clustering mechanism for innetwork data storage in WSN.
Thus, in the future, in-network storage protocols could benefit from these improved LEACH algorithms for more efficient data replication/storage solution. Other perspectives have to be considered for innetwork storage in WSN including heterogeneous networks and security. Nowadays, WSN applications tend to employ heterogeneous network cooperation approaches which must be considered for performing in-network storage. In fact, storage in such networks is weakly studied in the literature. For example, Peng et al. 79  analyzed the problem of data replication in heterogeneous WSN. The problem is presented as minimum non-leaf node Steiner tree. They also proposed a protocol called HSR to face this issue. However, they considered heterogeneous networks as different storage zones. Nodes within each zone are first separated, and then data are replicated across each network (i.
e. different strategies are used in each zone). This approach is inefficient in terms of communication overhead and energy consumption as discussed by Peng et al. 79  Thus, to improve data storage in heterogeneous networks, a research opportunity consists of developing protocols while avoiding the overhead of separating these networks. Furthermore, security and integrity are vital in WSN such as supervising and monitoring in military applications. In such context, there is high probability that a malicious device plays the sink role, queries or negatively changes the stored data identity. 80, 81 Therefore, a secure communication for in-network storage needs to be addressed. The classic encryption methods are inefficient for WSN regarding its complexity and the limited resource feature of nodes.
Conclusion In this article, a survey of existing in-network storage protocols for WSN is presented. These protocols are first classified, and then benefits and disadvantages of each approach are discussed in terms of in-network storage requirements and challenges. We also provide several opportunities and remaining challenges to guide future researchers. In-network storage has major advantages for improving data collection performances in WSN and to overcome the problem of node failure. Therefore, developing an efficient in-network storage solution is still a promising research field.
current study, we sought to use the power of state of the art machine learning algorithms applied to a large-scale, diverse and clinically detailed database of ALS patients to uncover and characterize clinically relevant subpopulations of ALS patients. Two complementary data sources were used in this challenge. The first was data from ALS national or regional registers from Ireland and the Piemonte and Valle d' Aosta region in Italy, representing ALS community data collected as part of standard clinical visits. The second dataset was ALS clinical trial data, from the Pooled Resource Open-Access ALS Clinical Trials platform (PRO-ACT, www. ALSdatabase. org, developed by Prize4Life and Massachusetts General Hospital), an open-access database containing harmonized and de-identified data of over 10,000 ALS patients from 23 completed clinical trials  15.
The PRO-ACT database was previously used for a crowdsourcing computational challenge: The 2012 DREAM-Phil Bowen ALS Prediction Prize4Life challenge (The ALS Prediction Challenge)  16. This Challenge invited participants to develop computational algorithms that could predict ALS disease progression, with the best performing algorithms achieving a prediction accuracy that would allow a 20% reduction in the number of patients needed for a trial  16  and are currently being tested in real-world clinical trial applications  17, 18. Building upon the success of the earlier prediction challenge, the 2015 DREAM ALS Stratification Prize4Life Challenge (The ALS Stratification Challenge) sought to extend the scope of prediction algorithms by inviting participants to stratify the ALS patient population into distinct clusters and develop separate predictive models for each subpopulation.
The ALS Stratification Challenge included both disease progression and survival as predicted outcome measures and used both clinical trial and community-based registries data. A prize of $28,000, collected through a crowdfunding effort, was offered to best performing algorithms (see Supplementary material part 1 and 2 for detailed description of both datasets and the challenge description as given to participants, respectively). In this publication we describe the results of the challenge including analysis of the best performing algorithms' performance and methods, as well as novel methods to uncover the patient sub-populations, their statistical significance and relative importance of different predictive features obtained from cross-model assessment.
Results Challenge outline. The ALS Stratification Challenge was developed and ran through a collaboration between the nonprofit organizations Dialogue for Reverse Engineering Assessments and Methods initiative (DREAM, http://dreamchallenges. org/) and Prize4Life (www. prize4life. org. il) using the Sage Bionetworks Synapse platform (www. synapse. org). The challenge ran between June and October 2015 and drew 288 registrants, eventually leading to final submissions by 30 teams (88 individual participants) from 15 countries (see Supplementary material part 3 for participant survey). The challenge was based on two datasets: (1) ALS clinical trials data collected through the PRO-ACT database, and (2) community-based ALS clinical data collected through ALS registries. Both datasets contained longitudinally sampled demographic and clinical information with some additional genetic (specific mutation) and family history data in the registries and detailed laboratory tests in PRO-ACT (See Supplementary material part 1 and 2).
We solicited predictions across four sub-challenges, namely of (1) disease progression or (2) survival probability using PRO-ACT data and (3) disease progression or (4) survival probability using ALS registries data. Challenge participants were asked to use patients' data from the first 3 months of records to predict disease progression at 12 months or probability of survival at 12, 18 & 24 months. Disease progression was defined as the slope of the ALS Functional Rating Scale (ALSFRS or ALSFRS-R) between 3 and 12 months (see online methods). To avoid overfitting of algorithms  19  , data from the publicly available PRO-ACT database was used as the training set, and a separate set of data from six additional clinical trials, which were not previously publicly available, was used for validation.
In the scoring of the challenge, the algorithms made predictions for patients that were not part of the original datasets available for algorithms training, and the accuracy of these predictions was assessed. is restricted, and this effect plateaued at around 6 features (see Supplementary material part 4). Thus, participants were asked to write algorithms that first selected the most informative features (up to 6 features), and in a second step used only the data from these selected features to make progression or survival predictions (Fig. 1 ). To enforce the separation between these steps we required participants to implement their algorithms in Docker containers (https://docs. docker. com) executed on the IBM Z cloud (https://www. ibm. com/ it-infrastructure/z/capabilities/enterprise-security).
Indeed, the strategy outperformed a standard Cox model by 20% accuracy  22  , and outperformed the respective second best team in 98. 4% and 99. 4% of the bootstrap samples (in the PROACT and registry subchallenges, respectively, compare Fig. 2 ). Notably, for the sub-challenges running on the PRO-ACT database, the best performing algorithms significantly outperformed the winning method from the first challenge  16. This is even more noteworthy given that the validation set was not randomly divided from the training set, but actually included the more difficult and realistic criteria of application of the algorithms to a data comprising of six new never used before trials. Predictive clinical features. The challenge's requirement of using only up to six features for prediction encouraged participants to identify the most informative features for prediction of disease progression or survival (Fig.
Interestingly, ALSFRS question Q2 (salivation) was rarely used, in line with previous works indicating this question might be less well correlated with total ALSFRS scores and/or disease progression, due to effective available treatment options  29. Data availability was another important factor for feature selection. For example, while weight or BMI, known to correlate with ALS prognosis  30, 31  , was frequently selected for predictions for the PRO-ACT dataset, it was recorded in only < 10% of the cases in the registry data, making it unusable for predictions (Fig. 3 ). Similar potential predictive benefit of features was observed with respect to features evaluating breathing capacity, which is the main cause of death for ALS patients  32  , but not routinely collected for registry patients.
Features that are recommended to be assessed by clinicians more often to aid prognosis are marked in bold. observe any consistent advantage (in terms of improved prediction accuracy) for clustering in any of the evaluation metrics. Instead, as described in the following, we developed novel strategies utilizing this crowdsourcing approach to reveal, across a large variety of methods, consistent clusters of patients, to support understanding the ALS pathology, improving clinical care and planning better, more efficient clinical trials. This sort of analysis can only be accomplished in the context of a large communal effort that allows comparison of independently designed algorithms working on sufficiently large datasets, as was the case in this challenge. In comparison, individual clustering methods led to heterogeneous results and the numbers of clusters per method varied from 2 to more than 100.
Thirty teams submitted algorithms to the challenge, with the winning solutions outperforming currently available prediction algorithms (adapted from the previous ALS Prediction challenge)  16. Compared to the ALS Prediction Challenge  16  , the current challenge included additional data and design features that made its resulting algorithms more robust and more relevant for clinical application, including the use of community-based data, the limitation on the number of features used for prediction, and the prediction of survival as well as disease progression. Another requirement highly relevant to the application for future clinical trials: the validation of algorithms on a dataset derived from completely independent clinical trials. The current challenge invited participants to develop prediction algorithms either based on clinical trial data or from ALS registries containing data collected through ALS clinics.
Novel differentiating features such as creatinine or SVC can also help shed light on mechanisms related to disease progression, as well as mechanisms related specifically to end of life in ALS, a topic of critical clinical importance. Ideally, in the future, clinical data such as described here would be further integrated with data obtained from different types of high-throughput technologies (such as transcriptomic, genomics, metabolomics), allowing for the identification of predictive biomarkers for early diagnosis and treatments. Several such highly needed large scale initiatives are being developed now  42. Given the covert nature of ALS patient stratification, only a large-scale crowdsourcing effort, where different and independent teams apply diverse methods on a similar and large enough dataset can uncover such an underlying population structure free from a-priory assumptions.
Methods Datasets. The challenge made use of two datasets: data collected during clinical trials (PRO-ACT) and data collected during routine visits to ALS clinics (ALS registries). Both datasets included both static and time resolved measurements covering a wide range of data types and clinical measurements (full data dictionaries for both datasets can be found in the supplementary material). The time in which measurements were taken was noted in days relative to clinical trial onset or to first clinic visit available on records (time "delta"). Data was provided to challenge participants in tabular form. Each line represented the measurement of a single feature for a single patient at a particular time point Outcome measures (ALSFRS slope for progression and survival) for the training datasets were provided to challenge participants in separate tables.
PRO-ACT. The Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) Database was formed in 2011 by Prize4Life in collaboration with the Neurological Clinical Research Institute (NCRI) of Massachusetts General Hospital, the Northeast ALS Consortium, and with funding from the ALS Therapy Alliance. PRO-ACT contains data collected during phase II/III ALS clinical trial, volunteered by PRO-ACT Consortium members. Data from 17 clinical trials  15  was used for the previous prediction challenge  16  and later made publicly available via the PRO-ACT web platform (www. ALSdatabase. org). This data was used for algorithms' training in the current challenge. Data from 6 additional clinical trials  [50] [51] [52] [53] [54] [55]  , never before made publicly available, was homogenized to comply with PRO-ACT data format standards and used for algorithms' validation and assessment.
ALS registries data. Community-based data, never before publicly released, used in this challenge was collected through two ALS registries: 1) The Irish National ALS Register including data collected from ALS clinics in Ireland. 2) The Piemonte and Valle d' Aosta Register for ALS, including data collected from ALS clinics in Piemonte and Valle d' Aosta region of Italy. Data from the two registries was merged, harmonized and converted to the same format as the PRO-ACT data. Data was divided into a training (986 patients) and a validation (493) set. Stratification of data ensured a similarly proportional representation of patients from the Irish and Italian cohorts but was otherwise done randomly. Due to the small number of patients available in this dataset there was no test set (leaderboard data) available to challenge participants.
Definition of predicted outcome measures -"ground truth" calculation. Disease progression. ALS disease progression was defined as the slope of the total ALSFRS score, similar to the definitions used in the 2012 prediction challenge  16. Briefly, ALSFRS was calculated as: - - ALSFRS t ALSFRS t t t ( ) ( ) 2 1 2 1 where t 1 and t 2 were the time points at the first and last clinic records in the relevant time period 92-365 days in which total ALSFRS scores were recorded. Whereas time data in the both challenge datasets was given in days, it was converted to months for the calculation of ALSFRS slope, according to the following: t (months) = (t (days) /365) *12). Patients had to have at least two clinical records in the relevant time period for their data to be used for validation. Participants were required to write algorithms that would predict ALSFRS slope between 3 and 12 months, based on data collected in the first three months of clinical records.
All methods for performance assessment were based on evaluating how close the algorithms' predictions were compared to the respective ground truth, averaged across all patients. We used three different evaluation metrics to assess submitted algorithms' performance in the disease progression sub-challenges. Two methods, the root mean square deviation (RMSD) and Pearson's correlation (PCC) were used and described in the previous ALS prediction challenge  16. In the current challenge we added a third evaluation metric: the concordance index (CI), which evaluates the similarity of ranks between predicted and actual ordered lists of measurements. The concordance index was the only metric used to evaluate performance in the survival prediction sub-challenge, since it is commonly used in survival analysis and is best suited for assessing censored data  56.
These mean and SD values were used to calculate the Z-scores for the three assessment metrics for each of the submitted algorithms, given by: Outcomes of the survival sub-challenges were further validated by a receiver operating characteristic analysis (time ROC analysis). For the purpose of the challenge, time ROC was calculated using the R-package timeROC (https://cran. r-project. org/web/packages/timeROC/timeROC. pdf). On the plus side, the timeROC analysis incorporated the three survival time frames to be predicted (12, 18, 24 months), and performance is thus specific to the time frame in contrast to the analysis via CI, where the same set of predictions would evaluate the same irrespective to the time frame. However, the CI is better suited to represent the right censured nature of the survival data.
In addition, each feature vector (containing the values for that feature across the p patients) was randomly permuted a hundred times and statistical tests were applied to the 100 randomized datasets to compute FDRs (analogous to the description above) separately for the results from Fisher's exact test and ANOVA. We then regard the relative ranks from the ANOVA and Fisher as new test values. From these, we compute FDRs now across discrete and continuous features together. By this integrated statistical assessment, we obtained FDR values for all features. Features were deemed statistically significant across all clusters if they exhibited FDR values of <5%. See below pseudo code (Fig. 5 ) for details. Subsequently, t-tests were applied to identify all pairs of clusters where ANOVA determined that continuous features exhibited significantly differential values (FDR < 5%).