dataesr/scientific-paragraphs-categorization huggingface dataset:
For that reason, we do not use an ensemble of networks and we cite the compatible results in other related works. MNIST is by far the most studied dataset in deep learning literature. The state-of-the-art is already down to 21 erroneous classifications (0. 21%) which is extremely difficult to obtain without extensive data augmentation. TNN's error rate on MNIST is 1. 67% with a single 3-layer MLP with 750 neurons   [15]  1. 29 9. 90 2. 30 TC  [9]  1. 15 12. 01 2. 42 TWN  [8]  0. 65 7. 44 EBP  [24]  2. 20 XNOR-Net  [16]  9. 88 DoReFa-Net  [7]  2. 40 in each layer. Bitwise NNs  [10]  with 1024 neurons in 3 layers achieves a slightly better performance. TNN with an architecture that has similar size to Bitwise NN is worse due to over-fitting. Since TNN selects a different sparsity level for each neuron, it can perform better on smaller networks, and larger networks cause over-fitting on MNIST.
Bitwise NN's global sparsity parameter has a better regularization effect on MNIST for relatively bigger networks. Its performance with smaller networks or on other datasets is unknown. TrueNorth  [11]  with a single network achieves only 7. 30% error rate. To alleviate the limitations of single network performance, a committee of networks can be used, reducing the error rate to 0. 58% with 64 networks. The error rate of TNN on CIFAR10 is 12. 11%. When compared to partially discretized alternatives, a fully discretized TNN is obtained at the cost a few points in the accuracy and exceeds the performance of TrueNorth by more than 4%. On SVHN, it has a similar achievement with lower margins. For CIFAR100, on the other hand, it does not perform better than TrueNorth. Given the relatively lower number of related works that report results on CIFAR100 as opposed to CIFAR10, we can conclude that this is a more challenging dataset for resourceefficient deep learning with a lot of room for improvement.
TNN has the most remarkable performance on GTSRB dataset. With 0. 98% error rate, CNN-Big model exceeds the human performance which is at 1. 16%. Partially discretized approaches use real-valued input which contains more information. Therefore, it is expected that they are able to get higher classification accuracy. When compared to partially discretized studies, TNNs only lose a small percentage of accuracy and in return they provide better energy efficiency. Next, we describe the unique hardware design for TNNS and investigate to which extent TNNs are area and energy efficient.
B. Hardware Performance For the preliminary measurements, we used the dataset MNIST and the FPGA board Sakura-X  [27]  because it features precise power measurements capabilities. It can accommodate a 3-layer fully connected NN with 1024 neurons per layer (for a total of 3082 neurons), using 81% of the Kintex-7 160T FPGA. The performance of our FPGA design in terms of latency, throughput and energy efficiency is given in Table  V. With a 200 MHz clock frequency, the throughput (here limited by the number of neurons) is 195 K images/s with a power consumption of 3. 8 W and a classification latency of 20. 5 µs. We know that TrueNorth  [11]  can operate at the two extremes of power consumption and accuracy. It consumes 0. 268 µJ with a network of low accuracy (92. 7%), and consumes as high as 108 µJ with a committee of 64 networks that achieves 99.
For ASIC technology, we took into account perdataset zero-activations to reduce power consumption, similar to what was done in EIE works. We compare with TrueNorth because only paper  [12]  gives figures of merit related to CNNs on ASIC. The TrueNorth area is calculated according to the number of cores used. Using different CNN models than TrueNorth's, we achieve better accuracy on three datasets out of four, while having higher throughput, better energy efficiency and much better area efficiency.
VI. DISCUSSIONS AND FUTURE WORK In this study, we propose TNNs for resource-efficient applications of deep learning. Energy efficiency and area efficiency are brought by not using any multiplication nor any floating-point operation. We develop a student-teacher approach to train the TNNs and devise a purpose-built hardware for making them available for embedded applications with resource constraints. Through experimental evaluation, we demonstrate the performance of TNNs both in terms of accuracy and resource-efficiency, with CNNs as well as MLPs. The only other related work that has these two features is TrueNorth  [12] , since Bitwise NNs do not support CNNs  [10]. In terms of accuracy, TNNs perform better than TrueNorth with relatively smaller networks in all of the benchmark datasets except one.
This paper deals with the problem of modelling and controlling a multi-zone office building to ensure its thermal comfort by an optimal and cost effective management of the energy consumption. A thermal behaviour analysis of the building is carried out using the Simscape TM library in MATLAB/Simulink R environment that leads to a multi-model representation. Based on this modelling, the Yalmip toolbox in the MATLAB programming environment or an iterative optimization algorithm can be used to solve the control optimisation problem. The design of a model predictive control associated with a wise choice of the cost function makes it possible to obtain in simulation substantial energy benefits.
This type of control is favoured because it has the advantage of taking into account the disturbance predictions that greatly influence the interior comfort of a building (outside temperature, sunshine, occupations of parts) while anticipating their effects  (Ma et al. , 2011) ,  (Oldewurtel et al. , 2012) ,  (Privara et al. , 2011). Knowing that each control action is associated with a different state-space model of the system, the predictive control used is considered as a hybrid type  (Le et al. , 2014). In view of the control objectives, the associated optimization criterion is defined to integrate the energy cost of each actuator in kW h, the resolution of the optimization problem is executed using the Yalmip tool of Matlab or an iterative algorithm for important prediction horizon. The first part of this work is focused on the thermal modelling of a set of building parts because few works have been interested in this issue.
Optimization problem To minimize criterion J 1 , the Yalmip software (in the Matlab environment) can be used  (Lofberg, 2004). It turns out however that the design load increases with the horizon of prediction N. Also, due to the combinatorial explosion in computation time, for a number of control scenarios equal to 9 (i j ∈ 0,. , 8 ), the value of N may not exceed 6. For larger values of N, using the Yalmip software no longer makes it possible to obtain a digital solution to the optimization problem. Yet, it can be very interesting to consider a more important prediction horizon. Indeed, a scenario of optimal control on a low prediction horizon may become inappropriate on a larger horizon. This happens especially when the heating and/or cooling capabilities of the building are modest or y min and y max are time-dependent.
Introduction The most (~80%) of ITER diagnostic systems are hosted by the Port Plugs which are arranged at 3 levels (Upper, Equatorial, and Lower) depicted in Fig. 1  as UPP, EPP, and LP. The plugs have dual purpose: provide the access for diagnostic instruments to measure the plasma characteristics (density, energy, etc. ), and at the same way plug the ports against the neutron and gamma radiation and vacuum leakage. Therefore, the designing of the port diagnostics must be supported by the radiation shielding analysis for each of port system. At ITER the design developing process is performing in the Computer Aided Design (CAD) models with ENOVIA software platform. In order to follow all the important from neutronic point of view changes in the CAD models, the nuclear analyst should be capable to use the CAD model in creation of the neutronics models in the CAD-based shielding analysis.
Several shielding approaches to bring these values down and to minimize the dose to a worker have been proposed in the paper. This paper presents new results of CAD-based neutronics analyses performed with the Monte Carlo MCNP5 code  [1]  and the Rigorous 2-Step mesh (R2Smesh) method  [2]  for SDDR-calculations. The ITER tokamak geometry complexity and multiple diagnostic instruments inside the Upper and Equatorial Port Plugs (UPP and EPP) shown in Fig. 2 , make the MCNP simulation challenging. Some of challenges are discussed in work  [3].
Problem also arises due to free-form and splines surfaces of complex CAD models. The MCNP5 code still supports only limited number of the geometry types, as defined in the MCNP User's Guide  [1]. Therefore unsupported surfaces (mostly splines) must be approximated to series of planes and cylinders. The SpaceClaim 2015 CAD-modeling software (http://www. spaceclaim. com) has been used for these pre-processing operations. Then, after the CAD models simplification and surface approximations, in order to use CAD geometry for MC particle transport in the Constructive Solid Geometry (CSG) format one has to convert a solid given by a boundary representation into a cell given by a semi-algebraic description. The conversion process could be performed automatically by means of the CAD-to-MCNP converter programs MCAM  [5]  and McCad  [6].
In this paper, the most conversions have been carried out with MCAM. The methodology of SDDR calculations consisted in use of recently updated Rigorous 2-Step mesh-based (R2Smesh) method  [2]  which allows studying decay gamma sources in particular materials with estimations of isotopic contributions to SDDR. The maximal decay sources were produced in steel of Diagnostic First Wall (DFW) and steel structure of Diagnostic Shield Module (DSM). The following nuclides were found to dominate in SDDR at 12 days inside the port interspace: 60 Co, 182 Ta, 58 Co, 54 Mn, and 59 Fe. These radioactive isotopes are generated in the steel structure of the port rear side, mostly its closure plate, rear flange and port walls. The visualization capabilities are based on the ParaView code http://www. paraview.
For the GUPP design, the 3D B-lite MCNP model with more detailed description of the blanket modules around the UP has been used in the state of GUPP Final Design Review (FDR) 2013  [8]  with only one modificationintroduction of single labyrinth at the bottom gap of GUPP. The UPP apertures of Cases #1 and #2 were inserted in the bulk shield of the GUPP Case #3. The CAD model files of CXRS-core and Glow Discharge Cleaning (GDC) system were prepared for neutronics analysis by means of simplification and substitution of all spline surfaces with cylinders or planes keeping the original volumes of the CXRS components unchanged. The shape of the GDC model was updated to the newest design and its definition was simplified to the two-part geometry (front-end electrode and transmission line) with homogeneous material compositions.
Diagnostic Generic Upper Port Plug (DGUPP) of ITER The result of MCNP 3D model development of Diagnostic Generic Upper Port Plug (DGUPP) is shown in Fig. 17. That model will be used as standard neutronics model for ITER-related neutronics calculations and distributed among the Domestic Agencies and ITER contactors. Its geometry was converted from the actual CAD models of ITER Upper Port, inter-space structures (ISS), and bioshield port plug. The model was created in format of the MCNP code which is suitable for fusion neutronics to perform radiation transport calculations with the Monte Carlo stochastic methodology. The model will be used for analysing any other ITER systems and ports such as Equatorial or Lower ports and it is ready for integration of any particular Diagnostic system inside its Diagnostic Shielding Module (DSM).
Acknowledgment This work has been funded by the  ITER Organization  under the ITER service contract Nr. IO/4300000896 using an adaptation of the C-lite MCNP models which were developed as a collaborative effort between the  FDS team of ASIPP China, University of Wisconsin-Madison, ENEA Frascati, CCFE UK, JAEA Naka , and  ITER Organization. The views and opinions expressed herein do not necessarily reflect those of the  ITER Organization. This work was carried out using an adaption of the Diagnostics Generic Equatorial Port Plug MCNP model which was developed as a collaborative effort between  Universidad Nacional de Educacion a Distancia (UNED)  and  ITER Organization.
The IsoGeometric Analysis concept, introduced by Hughes et al  (2005)   [1]  in the context of Finite Element Method, was extended to the Boundary Element Method by various authors, see, e. g. ,  Politis et al (2009)   [29] ,  Simpson et al (2012)   [30] ,  Scott et al (2013)   [31] ,  Belibassakis et al (2013)   [32] , Peake et al (2013)  [33] ,  Simpson et al (2014)   [34] ,  Ginnis et al (2014)   [35]. The developed Isogeometric-BEM is based on a parametric NURBS representation of the hydrofoil and employs the very same basis of the geometry for representing the velocity potential. The Boundary Integral Equation is numerically solved by collocating at the Greville abscissas of the knot vector of the hydrofoil's parametric representation. Numerical error analysis of the Isogeometric-BEM, using h-refinement technique (knot insertion), is performed and compared with classical low-order panel methods.
Here again, the isogeometric context may be helpful because it allows to avoid geometrical approximations, which reduces the error level, and permits to construct high-order solutions yielding a better computational efficiency. In our case, the optimization loop comprises three components: the optimization algorithm, the IGA solver presented in §2 and the hydrofoil parametric model which will be presented in the following subsection. Hydrofoil shape optimization examples using the developed framework are presented in §3. 3. Both the optimizer and IGA solver are implemented in matlab while the hydrofoil parametric model is implemented in Rhinoceros 3D modeling package. The selected optimization algorithm belongs to the category of evolutionary ones, as experimentation with gradient and hessian-based algorithms has indicated the existence of multiple local minima that makes their usage problematic.
Parametric Model for a Hydrofoil The parametric model for a general hydrofoil has been materialized within Rhinoceros 3D modeling software package with the aid of its VBscript-based programming language, Rhinoscript. Our model generates a closed cubic B-Spline curve that represents a hydrofoil, using a set of 8 parameters; see Table  1. The geometrical definition of these parameters is depicted in Figure  5. All parameters, with the exception of chord's length (L), are defined using appropriate nondimensional ratios so that their values always lie in [0, 1]. This approach eliminates the need of implementing complex interdependent constraints while guaranteeing the robustness of the procedure which is of significant importance in an optimization procedure. The dimensionalized range of the parameters is appearing in the "actual range" column of Table  1.
05 around those corresponding to the approximation of NACA-4412 by the parametric model (see Figure  6 ). Specifically, the min-max allowed values of the free parameters are given in Table  2. The multiobjective optimization method gamultiobj, offered by Matlab, is employed to calculate the Pareto front of the above optimization problem, which is depicted in Figure  8. The Pareto front is defined to be the set of design points that are Pareto efficient, i. e. , it is impossible to make any one of the objective function values better off without making the other (or more generally, at least one of the others) worse off. Obviously, the optimum shapes lie on the Pareto front; see green line depicted in Figure  8. If, for example, we choose to accept a 0. 52% deviation of the target area, the optimum solution would be the hydrofoil instance whose parameter values are depicted in Table  2.
Numerical Modeling The alignment method of FOS is studied in the composite material bonding zone by the numerical method. The FOS is considered with both acrylate and polyimide coating materials as they are used generally for industrial application. Our study focuses on providing a clear overview about the fiber alignment. There are some finite element analyses that are realized for the mechanical and thermal stress study between FOS and host materials  [25] [26] [27]. The model is developed using Abaqus commercial software. The numerical model of the specimen is optimized with a dimension of (10 × 2 × 0. 66) mm for the finite element analysis. It is made of three main parts, including optical fiber sub-parts. The FOSs are designed with the significant coating material properties mentioned in Table  1.
Results of Numerical Simulation The model is simulated using the static loading condition with the standard increment proposed by the Abaqus software. The results were mainly concentrated in epoxy, which surrounds the FOS, because the stress concentration is high in this area. Initially, the model is simulated with acrylate fiber optic coating. Figure  8  shows the linear, shear and lateral strains in the bonding area. The unit of strain in the numerical part is mm/mm. In the longitudinal direction (E 11 ), there is no strain observed in the interesting zone, as the maximum strain reaches 0. 772 at the loading point. Shear strain inside the embedded space of the coating is E 12 , which has effects only on the left lower corner and right upper corner of the part. The red spots seem to have the max value of 0.
Introduction: The current model of medical knowledge production, transfer, and application suffers from serious shortcomings. Learning health systems (LHS) have recently emerged as a potential solution-systems in which health information generated from patients is continuously analyzed to improve knowledge that will be transferred to patient care. Method: Various approaches of data integration already exist and could be considered for the implementation of a LHS. We discuss what are the possible informatics approaches to address the functional requirements of LHS, in the specific context of primary care, and present the experience and lessons learned from the TRANSFoRm project. Result: Implemented in 4 countries around 5 systems, TRANSFoRm is based on a local-asview data mediation approach integrating the structural and terminological models in the same framework.
5, 6 ird, knowledge transfer is a slow process: even in countries like the UK, where a national agency (the National Institute for Health and Care Excellence) is funded to carry out this process, guidelines might be updated only once in a decade. 6 Moreover, clinicians encounter an increasing problem of memorization and prioritization of the potential applicable guidelines to a given patient at a given point on his health care trajectory. 7 nally, the 3 problems identified above are seriously compounded by a common technological issue: health and research data can be expressed according to various semantics, which are often poorly interoperable. To understand the complexity of the issue, note that the semantics of a piece of data constituted by a terminological code in a database field is encapsulated in 2 elements: the terminological code itself, and the structure of the database-this is the so-called "binding" of structural and terminological information.
8 For example, a code such as "ICPC-X76" (from the International Classification of Primary Care) refers to breast cancer, but depending on the structure of the database in which it is located, an instance of this terminological code can denote various diagnoses of breast cancer, such as the diagnosis of a current condition of the patient, of a past condition of the patient, or of the current condition of a family member. A potential solution to those problems has emerged with the concept of learning health system (LHS)-a system in which health information generated from patients within that system is continuously analyzed to improve knowledge that will be transferred to patient care (Figure  1 ). Various sources characterize LHS differently, but the IOM 9,10 defines the LHS as a vision for an integrated health system ".
| THE TRANSFoRm EXPERIENCE As mentioned earlier, the TRANSFoRm project was a proof of concept of an international LHS based on PC, supporting 3 different kinds of applications: recruitment for a prospective study, analysis for a retrospective genomic-clinical study, and decision support. TRANSFoRm involved a variety of data sources in different computer formats (such as relational databases and XML data extract files), with different structures, and using different terminologies. Given this heterogeneity, TRANSFoRm was based on the data mediation with a local-as-view approach, as described earlier. Developing this LHS required to develop a unified structural/terminological interoperability framework to enable data integration from various sources. On the structural side, data source models had to be mapped with a central model.
On the terminological side, terminological codes pertaining to different terminologies but referring to the same real-world entity (eg, the same disease) had to be represented as synonyms. In this part, we will first explain how the central model was represented as an ontology; second, we will explain how LexEVS was used both to map the data source models to the central model, and to represent synonymy relations between terminological codes; third, we will present the applications of TRANSFoRm.
| Using LexEvs for structural/terminological binding Several tools such as LexEvs and Bioportal  27  have been developed to facilitate the management of semantic biomedical resources. LexEVS was chosen for 2 main reasons. First, LexEVS can be used locally, whereas Bioportal is based on a central server. This enables higher control over the information, with 2 noticeable advantages: there is no risk of losing access to our data when the central system is updated, and there is no need to store confidential information on an outside system (because of confidentiality concerns, EHR companies would not agree to put their EHR data model in a public server). Second, LexEVS enables more flexibility. While LexEVS was initially designed to accept terminology files, loader helper modules can be created to expand its use to load structural models too.
| Applications The LHS was applied on 3 use cases. First, a retrospective diabetes clinical-genomic study, which demonstrated that linked genomic and clinical data, could be used across several countries using the same platform. Second, a diagnostic decision support system in case of chest pain, abdominal pain, and shortness of breath,  28  which demonstrated that giving early prompts of diagnosis to clinicians was more favorable than giving late prompts. 29 Third, a prospective acid reflux clinical study, an international RCT evaluating dosing regimen of proton pumps inhibitors (medication used for gastric reflux) on multiple sites. 2  Note that the LHS supported existing standards such as the CDISC Operational Data Model, enabling prospective RCT to be developed using CDISC research standards (in the form of the Operational Data Model, ODM-cf.
Traditionally, the structural models of the data source have not been available in a standardized manner. This is important to enable coherent and efficient structural/terminological binding. For example, in Figure 2, a field in a database might be named Dx and contain the value ICPC-T90. Given that Dx represents a patient current diagnosis, and the term T-90 denotes non-insulin dependent diabetes in the International Classification of Primary Care 2 (ICPC-2), we can assert that this represents a current diagnosis of non-insulin dependent diabetes. To express this knowledge in a general fashion, Dx is mapped to the CDIM entity "Patient current diagnosis", and ICPC-T90 is mapped to synonym entities in other terminologies such as ICD10-E10.