dataesr/scientific-paragraphs-categorization huggingface dataset:
The mismatch between the duration of an age class and the time interval would, for instance, lead to an overestimation of the first-year survival probability if the sampling interval were greater than one year. The fundamental problem is that a given age-dependent parameter applies to only a part of the time interval. This can be solved by specifying the length of the interval for each parameter considered. We outline the solution and provide a step-by-step illustration of how this can be implemented in freely available software for CMR analysis (e. g. MARK, RMark or E-SURGE, see details in the Supplemental Information S1, S2 and S3). Note that the two implementations below are simply two practical approaches to solve the problem (see supplementary material S1, S2 and S3).
The survival probability over the Table  1. Not separately identifiable parameters in presence of missing occasions: ϕ' juvenile survival; ϕ adult survival; p, recapture; ' t ' time effect; ' cov ' covariate effect; '. ' constant parameter; Np, number of separately identifiable parameters. Note that no individuals were marked during missing occasions and consequently juvenile survival parameters do not exist in the model for cohorts without released juveniles (i. e. ϕ' 3 , ϕ' 4 for dataset 1 and ϕ' 3 for dataset 2). Similarly, adults were not marked on the first occasion and, consequently, ϕ 1 do not exist in the model. Tabla 1. Parámetros no identificables por separado en presencia de ocasiones sin muestreo: ϕ' supervivencia juvenil; ϕ supervivencia en adultos; p, recaptura; ' t ' efecto del tiempo; ' cov ' efecto de una covariable; '.
Simulated cases To demonstrate the problem generated by unequal time interval in combination with age-dependent models, we considered a simple scenario with a model assuming two age classes and constant survival and recapture parameters. Note that this simple scenario is only for illustrative purposes. We simulated 100 datasets with five sampling occasions during a 7-years period (k = 7). A hundred new juvenile animals were released at each occasion. The time span elapsed between occasions was as l = 1, 3, 1, 1. We assumed constant yearly survival of newly marked juvenile individuals (ϕ' = 0. 4) and constant yearly survival of adult individuals (ϕ = 0. 8). We first analysed these datasets using unequal time interval (the incorrect approach) to illustrate the biases. We subsequently analysed them adding dummy columns to 'fill' the years without monitoring (implementation 1) and using the log-link implementation with r (= 1) and s (= 2) values for newly marked individuals in the second cohort to constrain the corresponding survival parameters appropriately (implementation 2).
For each analysis, maximum likelihood estimates of ϕ' and ϕ were obtained using RMark  (Laake, 2013). The code to simulate the data and run the analysis is provided in supplementary material S1. Supplementary material on S2 and S3 illustrate how to implement the solution in MARK (White and  Burnham, 1999 ) and E-SURGE, respectively  (Choquet et al. , 2009). Parameter identifiability in time-dependent models In the example above we have assumed constant survival and recapture probabilities to illustrate the problem and its solution. However, in many cases, parameters are time dependent. To show the applicability of the solution to more complex parameter structures and to study parameter identifiability, we simulated two different datasets with seven intervals (k = 7, supplementary material S4).
Both datasets considered temporal variation on juvenile and adult survival parameters as a function of a hypothetical temporal covariate D (adult survival was modelled as 1/(1 + exp(-(1. 386 + 0. 55 * D))) and juvenile survival as one half of adult survival at each occasion) and a constant recapture probability of 0. 7. Datasets differed in the length of the period with no CMR information: dataset 1 considered a gap of two years (data from sessions 3 and 4 are missing), while dataset 2 considered a gap of one year only (data from session 3 are missing) so that l = 1, 3, 1, 1 and l = 1, 2, 1, 1, 1, respectively. To avoid identifiability problems associated with sample size (which is not within the scope of this note) a thousand new juvenile animals were released on each occasion. To explore parameter identifiability, we implemented 18 models to each simulated dataset, considering different combinations of temporal, covariate and constant effects on juvenile and adult survival probabilities, and temporal vs.
constant effect on recapture probability. Datasets were analysed using program E-SURGE  (Choquet et al. , 2009) , which provides detailed results on parameter identifiability using the explicit method proposed by  Catchpole and Morgan (1997)  to detect parameter redundancy (supplementary material S4). 8 (ϕ' 2 * ϕ 3 * ϕ 4 ) ; (ϕ 2 * ϕ 3 * ϕ 4 ) 10 (ϕ' 2 * ϕ 3 ) ; (ϕ 2 * ϕ 3 ) ϕ' t ϕ t p t 9 (ϕ 3 * ϕ 4 ) ϕ'. ϕ t p. 6 (ϕ 3 * ϕ 4 ) ϕ' cov ϕ t p t 10 (ϕ 3 * ϕ 4 ) ϕ' cov ϕ t p. 7 (ϕ 3 * ϕ 4 ) Application to real case We considered a dataset of capture-mark-recapture data of adult Mediterranean storm petrels (Hydrobates pelagicus melitensis) from Palomas Island (Eastern Spain). Birds were captured using mist-nets from 1996-2000 and from 2004-2006. The vector l j was 1,1,1,1,4,1,1 where the '4' stands for the 3-year gap between 2000 and 2004.
A full analysis of this dataset can be found in Sanz-Aguilar et al. (  2010 ). Here we report results obtained by using the unequal time interval option and the proposed solution for comparative purpose. We only present implementation 1 as results of both implementations are equivalent (see results). The goodness of fit test of a model assuming all parameters time dependent  (Pradel et al. , 2005)  indicated a surplus of animals seen only at marking, i. e. , transients (ϕ 2 6 = 29. 96, p < 0. 05). As a consequence, survival during the first year after marking, ϕ', was considered separately from the subsequent survival, noted ϕ, in 2-age class models  (Sanz-Aguilar et al. , 2010).
However, there might be practical limitations because in some cases this solution would lead to add a great number of dummy occasions and would 'push' estimate of survival probability close to the upper boundary value of 1. In these cases, a log-link function can be especially useful to accommodate heterogeneity in the duration of encounter occasions when uneven periods are of small duration and too many dummy occasions should otherwise be incorporated to account for the unequal time period. However, not all problems can be solved using the approaches suggested. For example, the log-link function works well in rich datasets but might cause numerical problems when data are sparse  (Tavecchia et al. , 2001). Moreover, by using the log-link function, the effect of temporal covariates cannot be modelled.
, 2017)  and it is licensed under the MIT license. EGG can be installed from https://github. com/facebookresearch/EGG. Notable features of EGG include: (a) Primitives for implementing single-symbol or variable-length communication (with vanilla RNNs  (Elman, 1990) , GRUs  (Cho et al. , 2014) , LSTMs  (Hochreiter and Schmidhuber, 1997).
The Trainer and Game modules are preimplemented in EGG. In a typical scenario, the communication method (single or multiple symbol messages) will be implemented by EGG-provided wrappers. As a result, what is left for the user to implement consists of: (a) the data stream, (b) core (non-communication-related) parts of the agents, (c) the loss. The data interface that is expected by Trainer is an instance of the standard PyTorch data loader utils. data. DataLoader. To implement Sender, the user must define a module that consumes the data and outputs a tensor. On Receiver's side, the user has to implement a module that takes an input consisting of a message embedding and possibly further data, and generates Receiver's output. Section 4 below provides examples of how to implement agents, choose communication and optimization type, and train a game.
activity of the synthetic peptide. Taken together, these results show that cysteine residues and disulfide bonds are not involved in the mechanism of action of MgtR-S17I peptide. We next designed and synthesized a peptide that contained the amino-acids of MgtR in a scrambled order (Scr), thus retaining the hydrophobicity (73% hydrophobic residues), but lacking the predicted α-helix, as shown by the TMHMM program (Fig. 7A ). As expected, the scrambled peptide exhibited a circular dichroism spectra that differs from that of MgtR and MgtR-S17I (Fig. 7B ), indicative of a strong reduction of the α-helix structure. The α-helical content in the absence or presence of SDS was calculated with the CDPro Software package (CDSSTR, CONTINLL and SELCON3) as 93. 7%/88. 4%, 70. 7%/70. 8% and 37%/37% for MgtR, MgtR-S17I and scrambled MgtR, respectively (Fig.
Acknowledgements We thank  Teru Ogura  ( Kumamoto University, Japan ),  Yoshinori Akiyama  ( Kyoto University, Japan ) and  James Bardwell  ( University of Michigan, Ann harbor, Michigan ) for providing strains,  Daniel Ladant  ( Pasteur Institute, Paris, France ) for BACTH vectors and Zip plasmid,  Michael Maguire  ( Case Western Reserve University, Cleveland, Ohio ) and  Jean-Marie Pages  ( IRBA, Marseille, France ) for providing antibodies. We are grateful to  Pascal Verdié  from the SynBio3 platform for providing peptide synthesis facilities, to  Albert Meyer  for performing MALDI analysis, both from the  Institut des Biomolécules Max Mousseron, Montpellier. We also acknowledge the imaging facility MRI, member of the national infrastructure  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04 , "Investments for the future").
g. , Wi-Fi, BLE, UWB, etc. )  [3]. Our approach will be to extend the open-source Anyplace (anyplace. cs. ucy. ac. cy) architecture with components and specialized localization algorithms, which will enable fine-grain infrastructure-free localization using Internet of Things (IoT) -enabled devices. We will particularly redesign the algorithmic components necessary for indoor localization, indoor localization accuracy estimation  [4] , API channel optimization techniques  [3] , and visual analytic cues necessary to provide a rigorous Fingerprint Management Studio for crowd-sourced indoor signals. The infrastructure-less multi-robot navigation will be implemented by a software architecture that will combine ROS packages for navigation and path planning implemented onto the commercially available Robotnik (www.
C. Reconfigurable and modular hardware architectures One of the major innovations and tangible technical outcomes of the project will be the development and testing of the modular interfaces and of the e-diagnostic mobile station integrated on-board the mobile robot and tested in a healthcare environment. This will require research and development in both non-invasive sensors/devices (ECG, blood pressure, oximeter, temperature) and dedicated software applications. To this end, modular hardware interfaces (mechanical, electrical and data interfaces) mounted on the chassis of the Robotnik RB1-base robot will be developed (see Fig. 1 ), so that different hardware-based functional modules can be easily swapped. This would be the first time that a medical diagnostic functionality is integrated on a mobile robot.
D. Cloud based services Cloud computing delivers ubiquitous, 24/7 access to software-as-a-service (SaaS) solutions, capable of adapting to varying degrees of user load. Moreover, cloud services must guarantee what is known as a Service Level Agreement (SLA). Typically, SLAs have been customarily specified as some percentage time during which the functionality of the service is available. Guaranteeing the SLA of a service is, thus, intrinsically related to the management of the service Fig. 1. The commercially available Robotnik RB1-base robot navigating in a healthcare environment life cycle, and the events driving it: initial deployment, reconfigurations, upgrade deployment, fault handling, re-scaling. Automation makes it possible to manage the data centres so that they deliver portions of themselves (in the form of virtual machines and networks) in a cost-effective manner.
Cloudbased applications will offer to the enterprise users a high memory service, data sharing and security. Cloud servers will be able to connect with different individual applications and enterprise legacy systems. The cloud service proposed will contain a repository of algorithms, which will be fed by the data gathered through the cloud. The algorithms in the cloud will allow supporting the management of fleet, including the assignment of tasks to the robots, the queuing functionalities, the generation of dynamic routing and traffic control of the robots, and the supervision the fleets task execution. Developing a cloud oriented software platform will facilitate the development of APIs for integrating ENDORSE software with corporate ERP solutions and heath record systems that are often provided as a service in a Cloud environment.
V. TECHNOLOGICAL SOLUTIONS A. Cloud-based services In the project, a creation of an API framework will be accomplished, which will integrate logistic robotics into the corporate IT environment, and enable end-to-end automation (automatic traceability of the transferred goods, update of the ERP databases when transactions are carried out, etc. ). Moreover, communication and security standards will be investigated in order to securely transfer data from the robot to the patients cloud-based EHR. To realize this task, ROS-based software integration will be pursued, where all system modules will be represented by ROS packages and ROS nodes. Non-ROS modules (i. e. Java or JavaScript applications) will have APIs that create objects that can be translated by ROS bridge package into ROS standard objects (topics, services, actions, etc.
Within the context of the ENDORSE project, the objective is to augment our existing EHR system  [11]  [12],  [13]  complying with the afore-described interoperability requirements with vital signs data coming from the e-diagnostic support module detailed below. For that purpose, the necessary APIs will be developed relying on the Fast Healthcare Interoperability Resources (FHIR -www. hl7. org/fhir) standard, the most widely used standard for developing smartphone-based mobile health applications today. Lightweight APIs will acquire data coming from different sensors, at preset sampling rates and using the suitable drivers residing in the e-diagnostic modules communication interface, and assemble them into JSON or XML -like format for communicating them to the cloudbased EHR. Naturally, the appropriate security and device authentication procedures will be installed, while the EHR system will facilitate appropriate auditing schemas for registering each communication and data update session.
There, the objective of the optimization problem is taking as a quasi-distribution function δ (x). New points are randomly sampled along δ (x) with the slice sampling routine  [56]. Accordingly, sample points with higher results for the optimization function have a higher possibility to be chosen. After identifying the new sample points with one of the two methods the corresponding responses have to be calculated with the original function. With the expanded training data set, a new surrogate can be constructed. In the following, we explain in more details how the adaptive sampling algorithm works and give a pseudo-code for a Matlab implementation. Implementation: In the main program, first, the LS-SVR is constructed with the initial training data set X = [x 1 ,. , x n ] T and their responses of the original function y.
Although, the CoD is a meaningful error criterion, it uses the training points also as test points and could result therefore in misleading outcomes. Instead, we use on the one hand the cross-validation approach and on the other hand an additional unused data set. By using f -i instead of f , we result in the CoD based on cross-validation (CoD CV ). In the second case -the validated CoD (CoD val )-the test points x i and their responses y i with i = 1,. , N come from a large data set (for example N = 100 000), which was not used before. In the investigation, the most meaningful is the CoD val , but it can be used only for selected examples where the amount of available data is large enough. In the following subsections, we observe different functions which represent cases that can appear in real-world problems and investigate the application of the adaptive sampling method to the sensitivity analysis.
For a better overview, the discussed examples are listed hereinafter: • One-dimensional model f (x) = x sin x (Subsection 3. 1) • Three-dimensional model f (x) = x 1 sin x 1 + x 2 sin x 2 + x 3 sin x 3 (Subsection 3. 2) • Model with Singularity H(ω) = 1/(-mω 2 + cωi + k) (Subsection 3. 3) • Model with decaying influence of the variables f (x) = k ∑ i=1 x i √ i sin √ ix i (Subsection 3. 4) • Noisy function (Subsection 3. 5) With the first example we show how the introduced adaptive sampling method works. Afterwards, the aim is to show the efficiency of the distance-based LOO error sampling method by comparing the results of different function types represented by the listed examples. There, we start for the application of the adaptive sampling method with a LHS data set of size n = 5 (if k = 1) or n = 25 (if k > 1) and add each step 1 or 5 points accordingly in the sense of the adaptive sampling method.
Based on this constructed training data set a LS-SVR is built and then tested with the CoD CV and the CoD val. We compare this results with the calculations based on full LHS data sets. To get an overview on the expected quality and the variance of the approximation, 100 independent simulations are performed and results are displayed in boxplots. The mean 50% of the data are represented by a box with the middle line as the median. With the whiskers, 95% of the results are depicted and the remaining are outliers.
CoD val Adaptive sampling method LHS Figure  5 : Boxplot of the CoD CV (left) and the CoD val (right) for the distance-based LOO error method (red) and LHS method (blue) depending on n for the approximation of x 1 sin x 1 + x 2 sin x 2 + x 3 sin x 3 can be improved. Therefore, the adaptive sampling method shows its improvement just for higher values of n and it would make sense to start later with this method. It should be mentioned that the results of the CoD CV do not show an improvement of the adaptive sampling method for any number of n. However, the CoD val uses an additional data set and is therefore more trustable.
9). The results are shown in Figure  9. The values of the CoD CV recommend to use only LHS data because the results of the distance-based LOO error sampling method are lower and have a higher variance. However, the CoD val show that the approximation quality with the use of the adaptive sampling method is better for n larger than 300. Because at the beginning the adaptive sampling method not necessarily provides better approximation quality, it is recommended to start later sampling new points with respect to that strategy, depending on the dimension of the observed problem. For this example, it is recommended to start with n = 200 LHS training points and increase then the quality by adding new points regarding the distance-based LOO error method.
1) and N (0, 0. 2). With the displayed results of the CoD val it is visible that with increasing noise the maximal possible approximation quality decreases, which is reasonable because the error in the training data set through the noise cannot be avoided. In the case with σ = 0. 1 the variance of the CoD val is higher for n = 18, 19 and 20 than for n = 14 to n = 17 if a LHS training data set is used. It shows that not always the use of more training points improves the approximation quality, rather the locations of new points are important. This effect is not visible for the higher noise level. From the results it can be concluded that the distance-based LOO error method provides better approximations even if the data are corrupted by noise. However, if the variance of the overlying noise gets higher, the variation of the noise and of the function cannot be distinguished and the results of both ways of approximation are more and more similar.
The convergence of the CoD CV and the CoD val to one is significantly faster in the case where the adaptive sampling method is applied. Also from n = 125 the variance of the results is lower. (x) = x 1 sin x 1 + x 2 2 sin x 2 + x 3 3 sin x 3 Now, we compare the sensitivity values calculated from the different surrogate models with the true sensitivity values. The results of all first-order and total-order effects are shown in Figure  15. Here, we come to the same conclusion, that the adaptive sampling method improves the result visibly. For the two low sensitivity values, the differences between the results of the two methods are visible particularly for the total sensitivity values. It requires more training points to detect a low total sensitivity value if only a LHS data set is used. The results of the third input parameter even show a clear improvement for the first sensitivity value.
Computation of the sensitivity indices of the model with decaying influence of the variables In this subsection, we have a look at the example from Subsection 3. 4. In the analysis of Expression 14 for k = 2 it was visible that the adaptive sampling methods improves the results of the approximation quality at the latest n = 100. The results for the sensitivity analysis, shown in Figure  16 , provide the same results: The surrogate approximations of the sensitivity values are all closer to the true sensitivity values (dashed line) from n = 100 and have a lower variance. Even though, the results do not differ extremely. For the three-dimensional case, the CoD val indicates a better approximation with the use of the adaptive sampling method from n = 400, while the results of the CoD CV advise to use a LHS-data set.
In general, it is advised to apply the observed sampling strategy to global sensitivity investigations. Finally, it should be mentioned that we compared in this paper only the adaptive sampling method with the LHS method because it gives the most suitable space-filling distribution. However, in an engineering application it does not make sense to sample new, larger LHS data sets if the previous number of sample points is not sufficient. In this situation an adaptive sampling method has to be considered, anyway. Therefore, further research will compare the distance-based LOO error method with other global adaptive sampling methods and will apply this method to different engineering examples.
As part of the development stage, focus groups were conducted with prospective users, and relevant literature was identified, which led to 3 design considerations  [9, 30]. First, physical activity should be supported by functional exercises that can be executed safely within a home environment. Second, to facilitate behavior change, the intervention should support self-regulation. Third, a blended approach allows the convenience of a home-based exercise program and the ability to tailor the intervention to individual needs to be combined with the effectiveness of rich feedback and social support. These design considerations were implemented in a tablet-based app called VITAMIN that delivered a home-based exercise program in conjunction with coaching. Key components were goal setting, the ability to tailor the program to individual needs, video demonstration of functional exercises, rating of exercises, and progress tracking and feedback of a personal coach that could remotely monitor performance.