dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work based on the PERFORMER project (FP7, N°609154), we focus on studies based on statistical and machine learning approaches (black boxes) that make use only of available data to learn relationships, correlations and dependencies between data. These studies often introduce exogenous information such as weather information to help models to converge to better predictions. The main drawbacks of such methods is to apply the suited preprocessing on data, find the best exogenous information to add to models inputs and make use of a sufficiently large and reliable dataset. The present work proposes to use a well-known class of models in signal processing, namely: an ARMA model to generate predictions from the signal past values  (Box, Jenkins, & Reinsel, 1994). In that way, signal is considered as a time series and the objective is to predict future values of this series without any other information as input, a so called 'data-driven' approach.
This work both proposes to give useful forecasting information to time series data producers, further looking for anomalies comparing forecasting and actual values, and to give an insight of the ability of DNNs in time series prediction. This is part of a work that aims at generating online forecasting for each signal of instrumented building that upload their recordings to a database. This allow automatic forecasting production of several hundreds of signals without human interaction at each building. Each prediction model generates new forecasting at regular periods of time from the data uploaded by the building sensors. The provided forecasting for each individual signal is then used as a criterion for anomaly detection (not detailed here). Furthermore, such massive forecasting can then be used as input to other indicators such as expert systems, for part of the control for local energy storage/consumption, or to allow anticipation with regard to cooling/heating materials inertia ….
Data time series As a part of the PERFORMER Eurpoean project this work benefits from the project's pilot sites buildings settings. The project is based on four pilot sites: In the scope of the project are the instrumentations of each building. Buildings' sensors transmit their recordings to an online data warehouse that can be queried via a REST API. Datasets considered in this paper come from Woopa building (Lyon, France) and Saint Teilo's High Scool (Cardif, UK). This section presents some of the data used to achieve analyses presented in the following sections. Datasets are presented in two subsets each: a training set and a test set. The Saint Teilo's high school data available for analysis are temperature values coming from a block of classrooms in the building. Figure  1  shows a resampled set of data.
Woopa The first set of data in Woopa's building is the global gas consumption of the building (Figure  2 ). However, in this dataset, some slippage offset is also clearly visible in test set. Test set starts in October 2013 until the end of the period (see Figure  3 ). This may/will have an impact on models performances as it is not included in models training set. A huge part of the training set is also composed of zero values. It seems that consumption has either not been correctly recorded during the acquisition period or greatly changed over time. The third dataset used in this work is an electrical consumption one (Figure  4 ). We chose to use the global lighting consumption of a subzone in the first level of the Woopa building. This third dataset allows us to test the models on an occupancy signal of the building which constitutes a third category of data.
The order can also be a list enumerating indices of past values to be used in the combination. An MA moving average model is expressed as follows: 𝑋𝑋 𝑡𝑡 = 𝜇𝜇 + � 𝛽𝛽 𝑖𝑖 𝑝𝑝 𝑖𝑖=1 ε 𝑡𝑡-𝑖𝑖 + 𝜀𝜀 𝑡𝑡 Where  is mean of the series modelled and  is random noise. From these definitions, an ARMA model is the combination leading to the following expression; 𝑋𝑋 𝑡𝑡 = 𝐶𝐶 + 𝜇𝜇 + � ∝ 𝑖𝑖 𝑋𝑋 𝑡𝑡-𝑖𝑖 𝑛𝑛 𝑖𝑖=1 + � 𝛽𝛽 𝑖𝑖 𝑝𝑝 𝑖𝑖=1 ε 𝑡𝑡-𝑖𝑖 + 𝜀𝜀 𝑡𝑡 The model implementation used in the present work comes from  (Perktold, Seabold, & Taylor, 2016). The model is encapsulated in a python library for online data acquisition and predictions updates.
In this work, a simplified model is used as proposed in the original work where  = 1 -. The regular hidden layers are populated with Rectifier Linear Units (ReLu)  (Glorot, Bordes, & Bengio, 2011)  and gate layers use a sigmoidal activation function. The model used in the present work is adapted from  (Dieleman, 2015)  and encapsulated in a python library to allow online data acquisition and predictions updates.
Networks outputs consist in only 1 value predicted for the next step. To achieve several steps ahead forecasting predictions are reused as input for next step prediction. Thus the same model is used, as for ARMA models, to generate forecasted values whatever the the forecast horizon is. The first observation concerns one of the three datasets; namely the water consumption one. In the Figure  5  presented in previous sections a clear offset arises during the test set. This offset shifts the average consumption values until the end of the dataset. Such an offset is not observable in the training set. When we focus on models performances on this dataset, the weak performance of DHN models (worse than naïve, see Table  2 ) can be explained by this difference of behaviour in training and test sets.
The ARMA models on the contrary handles this difficulty with an interesting performance (0. 22 as RMSE for an average dataset value between 0. 0 and 2. 0  3 , see Table  2  and Figure  5 ). This difference in performances is explainable by the fact that DHN focuses on training set to build a model of the data when ARMA considers a moving average and an auto-regressive part, thus whatever the changes in dataset, ARMA will adapt its predictions in this way. A second observation is that, if we exclude the water dataset for the reasons explained above, DHN models perform better than ARMA models. This is particularly true on short term prediction as shown in table 1 and 3. Figure  7  also shows that it remains true in the light consumption dataset even for longer forecast horizons. However, the gas consumption dataset shows that long horizons benefits to ARMA models (Figure  7 ).
The fact that, in this study, DHN models are built with only one output value for next step can be a limitating property in this case (Models with n output values could have been created for each of the forecast horizons studied, with n the considered horizon). Output function of DHN models shows great influence on model performance depending on dataset. Across datasets and forecast horizons, linear and very leaky rectified linear units (vlReLu) show better performance than sigmoid and rectified linear units (ReLu). The complexity of the models often being used as a criterion against the use of deep networks models, it is interesting to have an idea of the computational time needed for each model type. In the present work, the the models implementations used lead to a faster execution time for DHN models training and test w.
Conclusions This work as part of the Performer European project has focused on comparing performances of ARMA and Deep Highway Networks models for the forecasting of monodimensional signals solely based on past values. Data used have been produced by the project pilot sites, mainly Woopa building, and represent different types of real data acquired over a long period of time. Models parameters have been chosen based on preliminary anylses conducted on project's buildings data. Models performances have been compared across data types and different forecast horizons. Performance results show that both model types achieve accurate predictions with regard to a naïve prediction, even for uncleaned datasets. Results also show that Deep Networks can improve forecast accuracy. As a counter part they are less adaptative to changes in data behaviors.
As is known, networks need to be trained on highly representative datasets to improve accuracy. It has also been shown that computation load is heavier for the ARMA models than for the DHNs when considering the implmentations used in this work. Thus, even in a regression scheme with unclean datasets, Deep Networks seem to be able to perform beyond ARMA models, for the present data tested, with less computational load. The main drawback lie in the need for a representative training set and an adapted output dimension. The work presented in this paper was supported by the European Community's Seventh Framework Programme under Grant Agreement N° 609154 (Project PERFORMER). Special thanks also goes to Sylvain Robert (CEA) and Frederic Suard (CEA) for their valuable comments and to the other partners of the project for their help in data gathering.
This research was supported by the PERFORMER project. Project funding from the European Union's Seventh Programme for research, technological development and demonstration under grant agreement No 609154. The article reflects the views only of the authors, and the Commission cannot be held responsible for any use which may be made of the information contained therein. The information in this document is provided as is and no guarantee or warranty is given that the information is fit for any particular purpose. The user thereof uses the information at its sole risk and liability.
Causally dynamic hybrid bond graphs are generally considered unsuitable for simulation, and causality is therefore often constrained in hybrid bond graph models. This paper demonstrates how a causally dynamic model can be simulated, using a buck converter as a case study. A causally dynamic hybrid bond graph (utilizing controlled junctions) is used to derive a mixed-Boolean state equation. This state equation is transferred to MATLAB R , where a simple routine assigns values to the Boolean parameters and then solves the model. Where storage elements are in dynamic causality, the model takes descriptor state form and an implicit solver is used. Solver choice and event detection are discussed. MATLAB R was selected as an accessible environment which allows this type of model to be coded and solved, but the technique could be used in an environment of the practitioners choice.
's switching bond graph of a power converter. This was selected as a case study which often manifests in the literature on hybrid models, and can be challenging to simulate. The schematic is shown in figure  1  and the hybrid bond graph in figure  2. MATLAB R scripts were written to simulate this model under a range of conditions. The general script defined simulation time and initial conditions, and then solved the model and plotted the results. The mathematical model for this buck converter system is presented in Figure  3  without derivation. Some roughly representative figures are taken for the converter: inductance of 10mH, resistance of 1. 73Ω, motor shaft inertia of 2. 25 × 10 -7 kg. m 2 , load inertia of 1 × 10 -5 kg. m 2 and a damping coefficient of 2 × 10 -5 N m/ (rad/s). The DC motor is modelled as a gyrator element with a modulation constant of 0.
Study 1: Normal Operation, Load Disconnected (Explicit Model) In normal operation, switches 1 and 2 alternate between 'ON' or 'OFF. ' In this study, switch 3 remains 'OFF' (i. e. the load is disconnected) to yield an explicit model. The model can be simplified to a system of Ordinary Differential Equations (ODEs) and solved with any solver. In this case, ODE15s was selected as it is a potentially stiff model.   ṗ1 ṗ2 ṗ3   =   1 L1R1 -a L2 0 a L1 0 0 0 0 1 L3R2     p 1 p 2 p 3   +   λ 1 λ 2 0 0 0 0   V G (3) The switches operate at a set frequency, in this case 100kHz. Appendix 1 shows an example MATLAB R code for this model. Essentially, the Boolean parameters are defined symbolically, and a short loop assigns them a '1' or '0' depending on the time. Note that the final results for this case were gathered using the same script as the other [implicit] models for consistency, but using a stiff solver to accommodate the different form of equation and highfrequency switching, as shown in Appendix 2.
Study 2: Normal Operation, Load Connected (Implicit Model) In normal operation, the buck converter rapidly alternates between two modes: Sw1 'ON' Sw2 'OFF' and Sw1 'OFF' Sw2 'ON,' as already illustrated in Study 1. In this study, the load is connected (i. e. Sw3 remaining 'ON' throughout). This model is implicit, due to a causal path (representing a real kinematic constraint) between the rigidlyconnected motor and load. The implicit solver ODE15i is used, as it is the only implicit solver available in Matlab.   1 0 0 0 1 1 0 0 0     ṗ1 ṗ2 ṗ3 d   =   1 L1R1 -a L2 0 a L1 1 L2R2 0 0 1 L2 1 L3     p 1 p 2 p 3 d   +   λ 1 λ 2 0 0 0 0   V G (4) As with Study 1, a switching frequency of 100kHz is used. The first few cycles are simulated assuming zero initial conditions. Appendix 2 shows the code for this study.
Conclusion This work demonstrates how a mixed-Boolean state model, derived from a hybrid bond graph, can be solved using MATLAB R. Once the Boolean parameters have been assigned a numeric value, the model can be solved using standard ODE solvers. Since hybrid models are often implicit (where commutation places a storage element in derivative causality) the ode15i solver is suggested for use. Where switching occurs during a simulation, a simple routine assigns Boolean parameters at given times and simplifies the model (to prevent nonsingularities due to unused rows and columns). In these studies, switching occurred at known times. Matlab can also handle event-driven commutation using the 'event' command, as demonstrated using the example of a bouncing ball  [15]. A programme of future work is proposed including more in-depth studies of power converters.
This paper discusses challenges that are related to the different methods of data collection and presents potential solutions where possible. No one-size-fits-all recommendation can be given for the collection of data in pragmatic trials, although in general the application of existing routinely used data-collection systems and processes seems to best suit the pragmatic approach. However, data access and privacy, the time points of data collection, the level of detail in the data, and the lack of a clear understanding of the data-collection process were identified as main challenges for the usage of routinely collected data in pragmatic trials. A first step should be to determine to what extent existing health care databases provide the necessary study data and can accommodate data collection and management.
Introduction Clinical trials that evaluate new drug treatments are required to underpin market authorization and are generally conducted under highly controlled circumstances. Such traditional, explanatory trials deliver data on efficacy and safety of treatments, yet they often insufficiently inform physicians, policy makers, and other stakeholders how treatments will actually perform in real-world clinical practice  [1]. Pragmatic trials, however, evaluate relative effectiveness under conditions routinely encountered in clinical practice and can yield evidence of the added value of new treatments in real life  [2]. Clinical trials commonly require the collection of highly detailed patient data. This necessitates onsite staff training regarding data collection and management, which presents a burden to study sites, interferes with usual care, and often Funding: The work leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement no.
What this adds to what was known? This paper addresses challenges and solutions for data collection and management in pragmatic trials, where a high level of accuracy and completeness of data needs to be balanced with a low level of interference with clinical practice. Data access and privacy, the time points of data collection, the level of detail in the data, and the lack of a clear understanding of the data-collection process were identified as main challenges for the usage of routinely collected data, for example, electronic health records, in pragmatic trials. Current international initiatives, that might facilitate a more pragmatic approach for data collection and management, are discussed.
What is the implication and what should change now? The quality of routinely collected data can be optimized by, for example, automated query generation and pop-ups that are embedded in the electronic health record or other electronic health care systems. Pragmatic trials should interfere with routine care as little as possible while ensuring high-quality data. Successful existing pragmatic trials typically use a hybrid approach for data collection: dedicated eCRFs combined with routinely collected data. excludes research-na€ ıve practitioners and sites from participation in such trials  [3, 4]. This does not affect the aim of more explanatory trials, where the interest is in the pharmacological effect of the drug. However, it may limit the generalizability of the study results and thus the value of evidence of a pragmatic trial.
Data-collection methods In general, three approaches for data collection in trials can be identified: i. Collecting all necessary data using electronic case report forms (eCRFs) that are specifically created for the study. This is the usual method for traditional more explanatory trials. This approach is the most likely to interfere with routine practice, as additional labor and expertise is often required to implement the data-collection process. ii. Extracting routinely collected data from existing sources, such as electronic health records (EHRs: systems into which practitioners enter routine clinical and laboratory data during usual practice), registries (any system that collects uniform data), or insurance claims (databases maintained by payers for reimbursement purposes or other routine health care databases).
This approach does not interfere with routine practice as it does not require additional data collection. iii. Combining routinely collected data with additional data collected specifically for the study in a hybrid approach. Which data are required, the level of detail needed, as well as the frequency and timing of collection drive the choice for the method of data collection. Although large numbers of variables are usually collected at predefined time points in an explanatory trial, the data collection in a pragmatic trial would ideally follow real-life clinical practice: that is, data, with the level of detail needed for treating the patient, are collected when patients have contact with their health care provider, who then routinely enters it into his/her health-record system. To find the right balance between the highly controlled data collection in traditional more explanatory trials and routinely executed data collection (eg, EHRs) remains a challenge when designing a pragmatic trial (Fig.
Routinely collected data are not meant for research purposes Systems that collect patient data in routine daily care include EHRs, insurance claims, as well as other health care or vital statistics databases and registries. EHRs are systems in which practitioners enter routine clinical and laboratory data of their patients over the course of their usual practice. Because the primary focus of such data is the support of clinical care rather than research, data may lack detailed information on indications, patient characteristics, treatments, and events and may be less structured, for example only provided as free text  [5, 12, 13]. Claims databases are run by insurers or other payers for reimbursement purposes and may contain information on the diagnoses, procedures, national drug codes, service provider, prescribing physicians, or health plans.
Generation of valid, accurate, and complete data may be challenging A major challenge when using routinely collected data is to produce valid, accurate results  [12, 16]. As data collection happens under real-life conditions, measurement may be more variable and higher levels of missing data and entry errors may be expected, possibly resulting in bias especially, but not only, when measurement error is not random  [17, 18]. Such bias can call into question the validity of a trial, especially if this ascertainment differs across comparison groups, for example, when physicians suspect an effect on blood pressure of a new treatment, data on blood pressure may be collected more frequent and uniform for that trial arm. In case data are likely to be missing at random, the problem may be overcome by appropriate statistical methods such as multiple imputation  [19].
Random errors in data collection and missing data can reduce the power of the study, with implications for the calculation of the required sample size. Gauging the pros and cons of using statistical approaches to deal with the limitations of routinely collected data vs. the implementation of additional data collection is difficult to evaluate Box 2 eLung/Retroprodpragmatic trials using EHRs in combination with add-on systems Aim: To develop and evaluate methods to implement simple pragmatic trials using routinely collected electronic health records (EHRs) and recruiting patients at the point of care; to identify the barriers and facilitators for general practitioners (GPs) and patients and the experiences of trial participants. Outcomes: Successful trial completion with implementation of information technology (IT) system for flagging and data processing and documentation of operational and scientific experiences Approach for data collection: Trials were conducted in English and Scottish general practices that contributed their EHRs to a research database (including a total of 459 practices).
Additional software was developed that allowed for instantaneous monitoring of EHR activities, flagging and clinician's notification during consultation of trial eligibility, complex eligibility assessments using the EHR database, daily eligibility review, confirmation of eligibility and randomization on the study web site, daily monitoring of side effects, and long-term follow-up of major clinical outcomes. Recruitment of patients was done by general practitioners (GPs) via invitations (i. e. , cold recruitment) and/or immediate flagging (through the HER system) while unscheduled consultation (i. e. , hot recruitment). GPs were required to complete Web-based protocol and good clinical practice training and required governance approvals were obtained for both trials. Potentially eligible patients were identified through the EHR; however, GPs had to confirm eligibility and patients were then randomized using a concealed allocation schedule.
Box 4 TASTEda pragmatic trial nested in an existing registry Aim: To evaluate the effects of thrombus aspiration in patients with ST-elevation myocardial infarction undergoing a primary percutaneous coronary intervention Outcomes: All-cause mortality (primary), coronary re-interventions, and outcomes related to the intervention Approach for data collection: The trial was embedded in the Swedish Coronary Angiography and Angioplasty Registry (SCAAR).
Box 3 Salford Lung Studyda pragmatic trial using EHRs in combination with add-on systems Aim: The Salford Lung Study (SLS) is the world's first pragmatic RCT (pRCT) of an investigational medication. SLS will evaluate the effectiveness and safety of a new inhaled medicine combination compared with patients' usual care in a broad group of COPD patients in an everyday clinical practice setting. The study is being conducted in and around Salford, UK. Approach for data collection: The SLS uses Salford's electronic health records (EHRs) systems and is delivered in collaboration with local health care providers to allow patients on the study to be closely monitored in real time, but with minimal intrusion into their everyday lives. The study's principal investigators are the GPs. They are ideally placed to facilitate recruitment, identify, and report SAEs or serious ADRs and report study end points.