dataesr/scientific-paragraphs-categorization huggingface dataset:
Using a geohistoric approach (i. e. : presentation of the past floods events by requesting ORRION dataset, presentation of the chronology of the PPRI procedures and implementation on a regional scale, etc. ) allows to provide enough information to legitimize the procedure, to favor its acceptance and its appropriation  [40]  while fighting straightaway any idea of disparity between territories. Implementing risk policies or managing floods at the local scale is complex. Funding, strain between the local stakeholders, lack of citizens' interest, complexity of all the administrative procedures that can be used to prevent the risk etc. , are some parameters that make it more complex  [41]. It is also striking to see that the implementation of preventive information campaign is triggered by the necessity to obtain funding and is not seen as an essential prevention action.
But, it is worth noticing that two main administrative procedures (PAPI -"Programme d'Action de Prévention des Inondations") were funded because they propose concrete actions to maintain a memory of past events  [42]. They both considered the implementation of flood marks as a mean to increase the "risk culture". Unfortunately their budget draft concerned only the purchase of flood marks and no other reflections had been made as the best way to reach the goal. We need then to implement a strong method to find out all the necessary information on the historical floods for laying the flood marks, by requesting both the ORRION database and the local stakeholders, these latter contributing to the enrichment of the database. All the contradiction in floods management is revealed here: some stakeholders are involved in a process of strengthening the "risk culture" but they did not really have this culture themselves.
e ONRN (Observatoire National des Risques Naturels): http://www. onrn. fr/ f BDHI (Base de Données sur les Inondations Historiques): http://bdhi. fr/appli/web/welcome g http://rtm-onf. ifn. fr/ h ORRM (Observatoire Régional des Risques Majeurs): http://observatoire-regional-risques-paca. fr/.
Memory optimization, energy conservation, over-the-air reprogramming, and fault tolerance are the critical challenges for the proliferation of the wireless sensor network. To address these challenges, a new wireless sensor network platform termed LiveWSN is presented in this article. In LiveWSN, several new design concepts are implemented, such as the hierarchical shared-stack scheduling and the pre-linking native-code reprogramming. By doing so, the data memory cost of the LiveWSN scheduling system can be optimized by 25% if compared with that of the traditional multithreaded MANTIS OS. Moreover, the application reprogramming code size can be decreased by 72. 6% if compared with that of the Contiki dynamic-linking reprogramming. In addition to the new design concepts, the new research approach which addresses the energy conservation and fault tolerance challenges by combining the software technique and the multicore hardware technique is applied in LiveWSN.
With these mechanisms, the memory cost of the WSN platform can be decreased significantly. Moreover, the reprogramming performance of the WSN nodes can be improved soundly. On the other hand, the new research approach which addresses the WSN challenges by combining both the software technique and the multi-core hardware technique (rather than only the software technique) is realized in LiveWSN. By means of the multi-core hardware infrastructure, the energy conservation and the fault-tolerant performance of the WSN nodes can be optimized efficiently. Due to the above mechanisms, LiveWSN becomes the WSN platform which is memory efficient, energy efficient, reprogrammable, and fault tolerant. The structure of this article is organized as follows: In section ''LiveWSN memory-efficient multithreaded scheduling mechanism,'' the new LiveWSN hierarchical shared-stack scheduling mechanism is presented.
As a result, the data memory cost of the multithreaded OS is high. Since the data memory resources of the WSN nodes are constrained, the multithreaded OS is not feasible to run on the high memoryconstrained WSN nodes. To address this challenge, the optimization to the stack memory cost of the multithreaded WSN systems becomes significant. In LiveWSN, this objective is achieved by the implementation of the hierarchical-scheduling mechanism (section ''LiveWSN hierarchical scheduling'') and the sharedstack scheduling mechanism (section ''LiveWSN shared-stack scheduling''). With these mechanisms, the stack number and the average stack size of the multithreaded systems can be reduced, respectively. As a result, the stack memory cost of the multithreading system can be decreased significantly.
To optimize the data memory cost M STK , the optimization to the stack number N STK and the stack average size S STK is needed. LiveWSN hierarchical scheduling. In LiveWSN, the optimization to the stack number N STK is realized by performing the hierarchical scheduling. Stack number N STK can be optimized since the thread stacks can be redundant in the conventional multithreaded systems. Typically, each thread in the conventional multithreaded system runs in its independent stack. Independent stacks are allocated since a thread's context needs to be stored when it is preempted. Yet, not all threads need the preemption operation during the run time. For the nonpreemptive threads, for example, the threads which have loose requirement to the responsive time, the preemptions among them need not be performed.
By doing so, the stack number N STK can be optimized. It is assumed that the number of the preemptive tasks in the WSN system is N p , while the number of the non-preemptive tasks is N n , and then, the data memory cost of the conventional multithreaded system M and the data memory cost of the hierarchical-scheduling LiveWSN M H can be denoted as, respectively M = M DATA + S TCB + N TCB + (N p + N n ) 3 S STK M H = M DATA + S TCB + N TCB + (N p + 1) 3 S STK ð2Þ Since M H is smaller than or equal to M, LiveWSN consumes less data memory than the conventional multithreaded system. And the larger the N n is, the more the memory cost will be reduced. LiveWSN shared-stack scheduling. With the hierarchical scheduling, the stack number N STK is optimized. As a result, the stack memory cost M STK can be decreased.
Instead of running the threads in the independent pre-reserved stacks, all the threads in the shared-stack LiveWSN run within a shared memory area, and the thread stacks are allocated dynamically as required (Figure  2(b) ). Compared to the conventional static pre-reservation mechanism, the LiveWSN shared-stack dynamic allocation will cost less memory resources. For example, in Figure  2 (a), 384-byte memory is needed for the allocation of the thread stacks (384 = 128 3 3). Yet, only 220-byte memory will be used in the shared-stack multithreading LiveWSN (220 = 72 + 66 + 82). After being allocated within a shared memory area, the thread stacks will be adjacent to each other. Therefore, the execution of one thread can cause the stack data of the others to be corrupted, for example, in Figure  2(b) , the stack data of the thread T3 will be corrupted if the thread T2 resumes its execution.
To solve this problem, the stack shifting technique is applied in the LiveWSN. Once a thread resumes its execution, its run-time context data will be shifted to the free memory area (Figure  2(c) ). By this means, the data corruption problem can be avoided. Yet, after the contexts are shifted for a set of times, the memory fragments will be produced. In case that the left free memory space is not sufficient for the next thread to run, the memory defragmentation needs to be performed. In LiveWSN, the defragmentation of the shared-stack area is performed in the same way as the defragmentation of the memory garbage in the Java virtual machine (JVM). 11 By coalescing the objects and updating the access references, all the fragments can be defragmented. It is assumed that the thread number is K; the prereserved stack size of each thread in the conventional multithreaded system is S STK ; the dynamic-allocated stack sizes of all the threads in the shared-stack LiveWSN are S 1 , S 2 , S 3 ,.
, S K , respectively; and the average stack size of all the threads in LiveWSN is S AVR_STK. Then, the data memory cost of the conventional multithreaded system M and the data memory cost of the shared-stack LiveWSN M S can be denoted, respectively, as follows M = M DATA + S TCB + N TCB + K 3 S STK M S = M DATA + S TCB + N TCB + X K i = 1 S i = M DATA + S TCB + N TCB + K 3 S AVGÀSTK ð3Þ Since the pre-reserved S STK needs to be large enough to meet the worst-case scenario, it will be larger than any of the dynamic-allocated stack sizes. Namely, S i \ S STK (i = 1, 2,. , K). Thus S AVGÀSTK = 1 K X K i = 1 S i \S STK ð4Þ In terms of equations (  3 ) and (  4 ), the LiveWSN shared-stack scheduling system will cost less data memory than the conventional multithreaded system. Integration of the hierarchical scheduling and the shared-stack scheduling.
The LiveWSN hierarchical scheduling and shared-stack scheduling can decrease the data memory cost of the conventional multithreaded OS by optimizing the stack number N STK and the average stack size S STK , respectively. Indeed, these two kinds of mechanisms can be integrated into the comprehensivescheduling LiveWSN. By doing so, both the N STK and the S STK can be optimized, and the data memory cost of the multithreaded system can be decreased significantly. In Figure  3 , the stack structure of the comprehensive-scheduling LiveWSN is shown. The preemptive tasks are scheduled by the RMS scheduler and run within the dynamic-allocated stack area, whereas the non-preemptive tasks are executed one by one by the FIFO scheduler and run within the sharedstack area. It is assumed that the number of the preemptive tasks is P, the number of the non-preemptive tasks is N, the static-reserved stack size in the conventional multithreaded system is S STK , and the dynamic-allocated stack sizes of all the preemptive tasks in LiveWSN are S 1 , S 2 , S 3 ,.
, S P , respectively. Then, the data memory cost of the conventional multithreaded system M and the data memory cost of the comprehensive-scheduling LiveWSN M C can be denoted, respectively, as follows M = M DATA + S TCB + N TCB + P 3 S STK + N 3 S STK M C = M DATA + S TCB + N TCB + X P i = 1 S i + 1 3 S STK ð5Þ In terms of equations (  4 ) and (  5 ), the memory cost of the comprehensive-scheduling LiveWSN is much lower than that of the conventional multithreaded WSN system.
In the SIS project, nine tasks are created. Three tasks are the real-time preemptive ones, while the others are the non-preemptive ones (namely, P = 3 and N = 6). In the MANTIS OS, the stacks are reserved statically and the stack size S STK is set to 128 bytes (S STK = 128). In the LiveWSN, the stacks are allocated dynamically and the average stack size is actually 96 bytes (S AVG_STK = 96). Then, the data memory cost and the thread scheduling cycles of the different mechanisms can be calculated as shown in Table  1. This result shows that the hierarchical-scheduling LiveWSN, the shared-stack LiveWSN, and the comprehensive-scheduling LiveWSN can optimize the stack memory cost by 55. 6%, 25%, and 66. 7%, respectively, if compared with the conventional multithreaded OS MANTIS OS.
Performance evaluation In this section, the reprogramming performance of the LiveWSN is evaluated by comparing with that of the Darjeeling JVM,  11  the Contiki dynamic reprogramming,  16, 17  and the Atmel OTAU (over-the-air upgrade)  18  from the perspectives of the memory cost and the reprogramming code size. The memory cost of the different mechanisms is shown in Figure  7. The JVM reprogramming and the dynamic-loading reprogramming mechanisms have the high memory cost since the bytecode interpreters and the dynamic linker need to be implemented. The LiveWSN reprogramming system has the memory cost much lower than the others since it uses the pre-linked machine-code mechanism. With this mechanism, most of the linking operations are performed on the personal computer rather than on the WSN nodes.
The LiveWSN generates the prelinked machine-code application binary. Since no redundant data are contained in the application code, the reprogramming code size of the LiveWSN is rather small. From the results in Figure  7 , it can be seen that the code size of the LiveWSN can be optimized by 92. 7%, 72. 6%, and 99. 8% if compared with that of the EJVM Darjeeling, the Contiki dynamic-loading reprogramming, and the Atmel OTAU mechanisms, respectively. This result proves that LiveWSN reprogramming mechanism keeps low memory cost and high reprogramming performance, and it is typically appropriate for the memory-constrained, energy-limited, and bandwidth-limited WSN platforms.
LiveWSN multi-core fault-recovery mechanism After the faults are validated, the fault recovery to the working microcontroller can be performed. Currently in LiveWSN, two granularities of recovery mechanisms have been implemented. One is the state-granularity recovery and the other is the microcontrollergranularity recovery. The state-granularity recovery recovers the faults by re-executing the code of the state. The microcontroller-granularity recovery recovers the faults by restarting the working microcontroller through the power supply unit. If the state-granularity recovery is performed, the run-time context of each state needs to be stored at the beginning of the state, and the context data should be restored when the recovery mechanism is launched. Compared with the state-granularity recovery mechanism, the microcontroller-granularity recovery mechanism is easier to be implemented.
Conclusion and ongoing works In this article, a new memory-efficient, energy-efficient, reprogrammable, and fault-tolerant WSN platform LiveWSN is designed and implemented. In LiveWSN, the new hierarchical scheduling, the shared-stack scheduling, the comprehensive scheduling, and the prelinked native-code reprogramming mechanisms are implemented. By doing so, the stack memory cost of LiveWSN can be optimized by more than 25% if compared to that of the multithreaded MANTIS OS, and the reprogramming code size can be decreased by 72. 6% if compared to that of the Contiki dynamiclinking reprogramming. In addition to the above new design concepts, the new multi-core energy conservation and multi-core fault tolerance mechanisms are also applied in LiveWSN. By means of the multi-core hardware infrastructure, the lifetime of the WSN node can be prolonged by more than 34% if compared with the traditional single-core AVR node.
Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been sponsored by the  French Government research program ''Investissements d'avenir'' through the IMobS3 Laboratory of Excellence  ( ANR-10-LABX-16-01 ), by the  European Union  through the  program Regional Competitiveness and Employment (ERDF-Auvergne region ). This work also received financial support from the  National High-tech R&D 863 Program of China  ( 2015AA015403 ), the  Sci-Tech ; pillar program of  Hubei Province  ( 2014BHE024 ); the  National Key Technology Support Program  ( 2012BAH45B01 ); and the  Innovative Project of Wuhan University of Technology  (No. 163110003 ).
In particular, we propose to use network centrality measures to constrain the model in terms of significant genes. Results: We applied DEGREECOX to three datasets of ovarian cancer carcinoma and tested several centrality measures such as weighted degree, betweenness and closeness centrality. The a priori network information was retrieved from Gene Co-Expression Networks and Gene Functional Maps. When compared with RIDGE and LASSO, DEGREECOX shows an improvement in the classification of high and low risk patients in a par with NET-COX. The use of network information is especially relevant with datasets that are not easily separated. In terms of RMSE and C-index, DEGREECOX gives results that are similar to those of the best performing methods, in a few cases slightly better. Conclusions: Network-based regularization seems a promising framework to deal with the dimensionality problem.
This fact severely hampers the modeling task, usually leading to a degradation in the classifier accuracy and a greater difficulty in extracting knowledge from data  [2, 3]. Furthermore, datasets suffering from this curse of dimensionality often lead to over-fitted models which, although they represent the training data, exhibit a significant decrease in their accuracy on new observations  [4]. This problem may persist even when feature selection and validation schemes are used. One possible solution to tackle this problem is to impose further constraints on the solution space. This can be accomplished through regularization methods, that penalize more complex structures of the solution space. The goal is to penalize the cost function (e. g. quadratic error, log-likelihood) with additional functions in order to impose a structure on the parameter space.
For linear regression, a regularization method that is widely used is LASSO -Least Absolute Shrinkage and Selection Operator  [5] , which penalizes the error function with the L1 norm of the regression parameters, leading to a sparse solution. Other possible regularizers include feature or group sparsity, smoothness of the features' coefficients, or a graph representing how the features are connected  [5] [6] [7] [8] [9] [10] [11]. These techniques have led to models that are partially capable of dealing with the dimensionality problem and, additionally, are able to improve model interpretability  [12] [13] [14]. In this context, survival analysis in oncology research represents one of the most challenging areas of application, with the recent development of public databases such as TCGA -The Cancer Genome Atlas  [15].
Survival analysis involves modeling the time to an event of interest, by uncovering relationships between the given covariates and time distributions  [16] , and allowing for censored observations (for which the event does not occur). The Cox proportional hazard model  [16]  is used to model these relationships and has been widely applied in this context. However, it also exhibits problems for datasets with more covariates than observations. For example, using genomic data to determine the relationship of the expression levels of thousands of genes to a death event leads to an under-determined problem that can have multiple solutions. Recent efforts to combine Cox modeling with regularization techniques have already shown promising results  [11, 17, 18]. In particular, sparse models have been developed to identify a small set of gene signatures related to high or low risk patients.
Furthermore, the predictability of the model was tested with datasets from five geographically distant populations  [17]. Cox regularized models have also been used to predict a patient's risk of conversion from a mild cognitive impairment to Alzheimer's disease  [18]. Besides these sparsity methods, other techniques tried to embed network-based regularizers, following work on group sparsity  [19]. When the features can be connected through a graph, one can further explore this structure in order to improve the models. One example is to impose smoothness on the parameters associated with connected features (in the network). This technique provided good results for modeling survival of ovarian cancer patients where the features correspond to gene expression data  [14]. Since there is an underlying structure on the gene feature space given by the patterns of co-expression, these correlations can be applied as constraints to the Cox proportional hazards model.
Although the results are promising, there are still few studies that fully explore the network properties of the feature space beyond this connectivity. In this context, we propose and explore a novel networkdegree-constraint Cox proportional hazard model, that we called DEGREECOX, which uses a priori knowledge to leverage the correlation or functional information present in gene expression data. In this survival model, a graph degree constraint is introduced that expresses the importance of a gene by how highly connected it is in the overall network. We applied DEGREECOX to identify gene expression signatures associated with survival of ovarian carcinoma patients. This type of cancer is the fifth-leading cause of cancer death in US women  [20]. DEGREECOX was applied to three large-scale ovarian cancer gene expression datasets  [20] [21] [22]  to predict a patient's risk and to identify genes associated to death events.
Research on this topic emerged in the 1950s on the role of central vertices in social networks  [24] [25] [26]. Different metrics have been proposed in the literature. These typically use network topology to define a function that determines a measure for vertex y i. Among the proposed methods to classify important vertices are degree, betweenness and closeness centrality, briefly described below and illustrated in Fig. 1 , where the size and color of a vertex reflect the importance of the vertex for each method. In the Results Section, all these measures will be tested on real datasets in order to choose the best ones to be integrated in the proposed regularizer.
LASSO and RIDGE regression Strategies that can be used to minimize this problem include the application of L 1 and L 2 norms, in order to restrict the solution space, in particular imposing sparsity and small coefficients for the parameters  [5, 6, 33]. This can be done by penalizing the total log-likelihood with a weighted sum of the L1 and L2 norms, a method called elastic net  [6] : l L 1 L 2 (β, h 0 ) = n i=1 -exp(X i β)H 0 (t i )+ δ i log(h 0 (t i )) + X i β - 1 2 λ α|β| 1 + (1 -α)|β| 2 2 , ( 10 ) where λ is the parameter controlling the penalizing weight and α the balance between the two norms. In particular, α = 0 leads to the RIDGE regression and when α = 1, LASSO regression is obtained. The R package "glmnet"  [11]  was used to estimate the coefficients with this type of regularizer.
Results and discussion In the following experiments, the DEGREECOX, NET-COX, LASSO and RIDGE models were applied to ovarian cancer gene expression datasets. The experiments ran with multiple parameter values, which were selected using the same cross-validation technique as described in  [14]. The selected models were then evaluated by comparing the prognostic risk of each patient in the sample, the obtained clustering in high and low risk groups based on Kaplan-Meier estimators  [34]  and log-rank tests. Analysis of the deviance residues  [35]  and the concordance c-index of the selected models  [36]  is also presented for all combinations of datasets and methods.
Datasets and networks The three datasets used in these experiments, hereafter named Bonome, TCGA and Tothill, are publicly available from three independent ovarian cancer studies  [20] [21] [22]. All three contain gene expression data and survival follow-up times for each patient in the study. The Fig. 2  DEGREECOX network regularizer datasets were obtained from the HG-U133A platform and the raw files were normalized using the Robust Multichip Average (RMA) preprocessing methodology. The Bonome dataset comprises the follow-up time, survival status and microarray gene expressions for 185 patients. The microarray data contain 12,442 gene expression levels  [21]. The TCGA dataset comprises the followup time, survival status and microarray gene expression of 517 patients and the microarray data contain 12,042 gene expression levels  [20].