dataesr/scientific-paragraphs-categorization huggingface dataset:
It increases robustness to new data sets that have not been used to train the algorithm  [28]. It was only applied to subset of the database. 4) Data normalisation: Normalisation is used to mitigate the impact of the variation between training and testing and maintain good prediction. Its use for real-time implementation is sometimes difficult. Furthermore, although quite good in practice, this technique is less effective for database whose variation is small, as is the case for high-frequency acceleration data. Standard normalization and Z-score are common normalization techniques in machine learning. Our data being recorded every 0. 00625s, which gives a rather small variation between two records, we did not apply normalization.
C. Partitioning of the data Efficient learning requires adopting a good data partitioning strategy. Indeed, a too large training database can lead the algorithm to over-fitting  [29] ,  [30]. In this case, the algorithm produces very good results on the training data but the prediction error on new data is quite high. Also, a weak database is a source of under-fitting  [30]  where the prediction error strongly varies from one set of data to another. The balance between under-fitting and over-fitting is the optimum for data partitioning. Another source of problem is the nonrepresentative aspect, in the statistical sense, between training and testing data. Several techniques based on cross-validation exist to mitigate this. Considering the size of our database, we considered 70% of the data for learning and 30% for testing.
V. MODEL CONSTRUCTION FOR ZUPT DETECTION Machine learning and deep learning are the most widely used methods in artificial intelligence (AI). They aim at predicting observations on new data sets that have not been used to train the algorithm. But their computation cost differs depending on the type of method used. In this section, we present two machine learning algorithms whose performance approaches or surpasses that of deep learning (LSTM) with a lower computational cost. The search for reduced computation costs is essential for real-time implementation on smartdevices. Let's note that the training of these two models was done with the same dataset previously mentioned. To better detect ZUPT instants, the process is divided into two main steps. The first step classifies the human dynamics into the two classes: "single support" and "double float".
We implemented the RF with the Scikit-learn package whose parameters are given in Table  I. 2) Histogram-based Gradient boosting training: The gradient boosting classifier is a supervised machine learning technique based on two parts: gradient descent and boosting. Its loss function is the logarithm of the mean squared error (MSE). It is a variant of light GBM, which is inspired by the Gradient Boosting classifier. These histogram-based estimators are faster than Gradient Boosting Classifier when the number of samples is greater than ten of thousands of samples, as in our case. Similarly to light GBM, HGB can handle large amount of data and requires less memory to run. Also, it emphasizes the accuracy of the results. We chose this algorithm because of its good performance on machine learning competition platforms such as Kaggle.
The prediction of ZUPT faces the same long-term dependency phenomenon. This explains the growth of recent research targeting pedestrian inertial navigation for this deep learning approach  [34]. We trained an LSTM to compare the performance of our machine learning based methods with the later. Python Keras was used to train the LSTM models for both single support and double float classes. Cross-entropy was used in both types of movement as a loss function and Adam as an optimizer. The different models converged for both classes, respectively, after 50 and 5 epochs with a batch size of 100 for each.
B. Computation of two benchmarking solutions Two foot-mounted inertial navigation benchmarking solutions are used to assess the performance of our approach. The first one is the recently published AI based ZUPT detection method in  [13] , labeled LSTM utiasSTARS in the rest of the paper. The second solution is the Extended Kalman Filter based software suite  [14] , labeled PERSY. To run LSTM utiasSTARS on our evaluation dataset, we used the model provided by  [13]  in the github. Since all training parameters are not provided (e. g. learning rate), the model was directly applied this paper's dataset. Training with several learning rates was attempted without success leading to overfitting. We transformed the evaluation dataset into arrays following the instruction of the demo code. Finally, we used the LSTM class to predict the ZUPT before exporting them to estimate the coordinates of the scenarios.
However, this capacity is very specific to the dataset since the chaining of the sequences remains the same from the learning to the application dataset. Therefore, while algorithms that were not designed to include time dependence can be trained with a random sampling of data samples, this is not possible for LSTM. In the case of an LSTM, sampling must be performed using sets of records that are arranged in a specific order. This has a direct impact on the use of AI-based methods for pedestrian navigation. Since human gait can change rapidly but following specific sequencing dictated by physical and/or surrounding constraints, when LSTM is trained with a specific motion sequencing, it will produce fewer mistakes for the same type of sequencing. Whereas, a sudden change in this sequencing will affect the LSTM predictions.
G. Computation costs The computation time was estimated with a HP-ZBook computer with a 16 GB RAM and an intel i7 2. 90 GHz processor on the two databases containing respectively 90950 rows, 96998 rows and 13 features. Table  V  gives the time for both evaluation datasets. A significant difference between the four learning methods is observed. HGB has the lowest computation time and is 3 times smaller than our LSTM model. LSTM utiasSTARS has the highest computation time. HGB is found to be suitable for real-time implementation.
Optimizing compilers for high performance computing only support IEEE 754 floating-point (FP) types and applications needing higher precision involve cumbersome memory management and calls to external libraries. We introduce an extension of the C type system to represent variable-precision FP arithmetic, supporting both static and dynamically variable precision. We design and implement a compilation flow bridging the abstraction gap between this type system and hardware FP instructions or software libraries. We demonstrate the effectiveness of our solution by enabling the full range of LLVM optimizations and leveraging two backend code generators: one for the ISA of a variable precision FP arithmetic coprocessor, and one for the MPFR multi-precision FP library. Both targets support the static and dynamically adaptable precision of our type system.
We address this challenge through a variable precision FP type system and language extension of standard C through the introduction of a template type named vpfloat. The syntax for this type system captures most of the expressiveness needed by numerical analysts while enabling highly efficient in-place execution, stack allocation and the full range of compiler optimizations expected for the C language. This is made possible by extending the LLVM intermediate representation  [7] , allowing classical compiler optimizations to operate on multi-precision FP types with few modifications. In particular, we enable multiple FP formats to coexist in a single numerical kernel with full procedural abstraction. For each format that supports it, we also enable computations over multiple variables of different precision and memory footprints, including dynamically-varying precision and footprints.
This allows to explore multiple numerical configurations within a single program source or even across successive iterations of a tuning or convergence loop. Every VP variable declaration must provide a type attribute which defines if subsequent attributes are needed and which information they carry. Attributes are specified in the following order: type, exponent, precision, and size. With the exception of type, vpfloat attributes can all be defined with integral constant literals or identifiers. This generic type supports not only constant-size but also dynamically-sized types, which is always a technical hurdle in languages with unmanaged memory like C, and its associated intermediate representations and ABIs. We designed and implemented a full compilation flow in LLVM supporting (1) mpfr types, which hold the number of bits of exponent and mantissa in the second and third fields, respectively; and (2) unum  [5]  types with second and third fields, ess and fss, respectively, with an optional size field that holds the maximum number of bytes used to represent the number.
EXPERIMENTAL RESULTS We demonstrate the effectiveness of our type system and compiler implementation by comparing the variable-precision MPFR type vpfloat<mpfr,. > with the Boost library for multi-precision. Both approaches rely on the MPFR library and execute code with identical precision  [3]. We compiled the PolyBench suite version 4. 1  [9]  at optimization level -O3, and enabling or disabling Polly's polyhedral loop nest optimizations  [4]. The nussinov benchmark results are missing due to erroneous computations (NaN) in the Boost baseline, we are investigating the issue. Overall, results show an average speedup of 1. 70× for the Intel Xeon E5-2637 with 128GB of RAM. To evaluate the portability of our approach, we also demonstrate our LLVM implementation targeting a coprocessor for a RISC-V Rocket core accelerating FP arithmetic in the UNUM format  [1].
CONCLUSION We propose an extension to the C type system to operate on variable precision FP formats. Our extension supports FP arithmetic of arbitrary representation whose precision and exponent size can be configured at compilation time or runtime. We demonstrate the productivity benefits of our programming model and its ability to leverage the full range of optimizations of LLVM. Experiments on the PolyBench suite yield strong speedups at all optimization levels in comparison to the Boost Multi-precision library for MPFR. This work was partially funded by the French Agence nationale de la recherche (ANR) for project IMPRENUM under grant n°ANR-18-CE46-0011.
Introduction ] [6] [7]  GBD 2019 provides an opportunity to incorporate newly available datasets, enhance method performance and standardisation, and reflect changes in scientific understanding. Since GBD 2017, 1-3 no comprehensive update of descriptive epidemiology levels and trends has been released, to our knowledge. In this study, we summarise GBD methods and present integrated results on fatal and non-fatal outcomes for the GBD disease and injury hierarchical cause list. GBD 2019 includes estimation of numerous different models for disease and injury outcomes. This Article provides a high-level overview of our findings. Results are presented both broadly and in detail for a selection of diseases, injuries, and impairments in two-page summaries with a standard set of tables and figures.
Overview The general approach to estimating causes of death and disease incidence and prevalence for GBD 2019 is the same as for GBD 2017. 2, 3 Appendix 1 provides details on the methods used to model each disease and injury. Here, we provide an overview of the methods, with an emphasis on the main methodology changes since GBD 2017. For each iteration of GBD, the estimates for the whole time series are updated on the basis of addition of new data and change in methods where appropriate. Thus, the GBD 2019 results supersede those from previous rounds of GBD. GBD 2019 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement (appendix 1 section 1. 4). 8 Analyses were completed with Python version 3. 6. 2, Stata version 13, and R version 3. 5. 0.
Data The GBD estimation process is based on identifying multiple relevant data sources for each disease or injury including censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Each of these types of data are identified from systematic review of published studies, searches of government and international organisation websites, published reports, primary data sources such as the Demographic and Health Surveys, and contributions of datasets by GBD collaborators. 86 249 sources were used in this analysis, including 19 354 sources reporting deaths, 31 499 reporting incidence, 19 773 reporting prevalence, and 26 631 reporting other metrics. Each newly identified and obtained data source is given a unique identifier by a team of librarians and included in the Global Health Data Exchange (GHDx).
Cause of death redistribution Vital registration with medical certification of cause of death is a crucial resource for the GBD cause of death analysis in many countries. Cause of death data obtained using various revisions of the International Classification of Diseases and Injuries (ICD) 9 were mapped to the GBD cause list. Many deaths, however, are assigned to causes that cannot be the underlying cause of death (eg, cardiopulmonary failure) or are inadequately specified (eg, injury from undetermined intent). These deaths were reassigned to the most probable underlying causes of death as part of the data processing for GBD. Redistribution algorithms can be divided into three categories: proportionate redistribution, fixed proportion redistribution based on published studies or expert ("exposure to unspecified factor") and Y34 ("unspecified event, undetermined intent") and GBD injury causes were the underlying cause of death.
Clinical informatics Clinical informatics data include inpatient admissions, outpatient (including general practitioner) visits, and health insurance claims. Several data processing steps were undertaken. Inpatient hospital data with a single diagnosis only were adjusted to account for nonprimary diagnoses as well as outpatient care. For each GBD cause that used clinical data, ratios of non-primary to primary diagnosis rates were extracted from claims in the USA, Taiwan (province of China), New Zealand, and the Philippines, as well as USA Healthcare Cost and Utilization Project inpatient data. Ratios of outpatient to inpatient care for each cause were extracted from claims data from the USA and Taiwan (province of China). The log of the ratios for each cause were modelled by age and sex using MR-BRT (Meta-Regression-Bayesian Regularised Trimmed), the Bayesian meta-regression tool.
6. 2). We changed the strategy of modelling deaths for acute hepatitis A, B, C, and E from a natural history model relying on inpatient case fatality rates to CODEm models after predicting type-specific acute hepatitis deaths from vital registration data with specified hepatitis type. DisMod-MR was used to estimate deaths from three outcomes (dementia, Parkinson's, and atrial fibrillation), and to determine the proportions of deaths by underlying aetiologies of cirrhosis, liver cancer, and chronic kidney disease deaths.
J J Jozwiak reports personal fees from Amgen, ALAB Laboratoria, Teva, Synexus, and Boehringer Ingelheim, outside of the submitted work. S V Katikireddi reports grants from NRS Senior Clinical Fellowship, Scottish Government Chief Scientist Office, and the UK Medical Research Council, during the conduct of the study. S Lewington reports grants from the UK Medical Research Council and the CDC Foundation (with support from Amgen), outside of the submitted work. K J Looker reports grants from WHO and GlaxoSmithKline, outside of the submitted work. S Lorkowski reports personal fees from Akcea Therapeutics, Amedes, Amgen, Berlin-Chemie, Boehringer Ingelheim Pharma, Daiichi Sankyo, Merck Sharp & Dohme, Novo Nordisk, Sanofi-Aventis, Synlab, Unilever, and Upfield and non-financial support from Preventicus, outside of the submitted work.
Acknowledgments Research reported in this publication was supported by the  Bill & Melinda Gates Foundation ; the  University of Melbourne ; Queensland www. thelancet. comVol 396 October 17, 2020 from  Add Health , a program project designed by  J Richard Udry ,  Peter S Bearman , and  Kathleen Mullan Harris , and funded by a grant  P01-HD31921  from the  Eunice Kennedy Shriver National Institute of Child Health and Human Development , with cooperative funding from 17 other agencies. Special acknowledgment is due to  Ronald R Rindfuss  and  Barbara Entwisle  for assistance in the original design. Information on how to obtain the Add Health data files is available on the Add Health website. No direct support was received from grant  P01-HD31921  for this analysis. The data reported here have been supplied by the United States Renal Data System.
M L Ferreira  holds an  NHMRC   Research Fellowship. C Flohr  was supported by the  NIHR Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust. M Freitas  acknowledges financial support from the  EU (European Regional Development Fund [FEDER ] funds through  COMPETE POCI-01-0145-FEDER-029248 ) and  National Funds (Fundação para a Ciência e Tecnologia)  through project  PTDC/NAN-MAT/29248/2017. A L S Guimaraes acknowledges support from  CNPq. C Herteliu  was partially supported by a grant co-funded by  FEDER through Operational Competitiveness Program  (project  ID   P_40_382 ). P Hoogar  acknowledges  Centre for Bio Cultural Studies, Directorate of Research, Manipal Academy of Higher Education and Centre for Holistic Development and Research, Kalaghatagi. F N Hugo  acknowledges the  Visiting Professorship, PRINT Program ,  CAPES Foundation ,  Brazil.
B-F Hwang  was supported by  China Medical University  ( CMU107-Z-04 ), Taichung,  Taiwan. S M S Islam  was funded by a  National Heart Foundation Senior Research Fellowship  and supported by  Deakin University. R Q Ivers was supported by a research fellowship from the  National Health and Medical Research Council of Australia. M Jakovljevic  acknowledges the Serbian part of this GBD-related contribution was co-funded through Grant  OI175014  of the  Ministry of Education Science and Technological Development of the Republic of Serbia. P Jeemon  was supported by a Clinical and  Public Health intermediate fellowship  (grant number  IA/CPHI/14/1/501497 ) from the  Wellcome Trust-Department of Biotechnology, India Alliance  ( 2015-20 ). O John  is a recipient of UIPA scholarship from  University of New South Wales ,  Sydney.
K J Looker  thanks the  NIHR Health Protection Research Unit in Evaluation of Interventions at the University of Bristol , in partnership with  Public Health England , for research support. S Lorkowski  was funded by the  German Federal Ministry of Education and Research  (nutriCARD, grant agreement number  01EA1808A ). R A Lyons is supported by  Health Data Research UK  ( HDR-9006 ), which is funded by the  UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, NIHR (England) ,  Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government ),  Public Health Agency (Northern Ireland) ,  British Heart Foundation , and  Wellcome Trust.
In this paper, we propose an accountable privacy preserving attribute-based framework, called Ins-PAbAC, that combines attribute based encryption and attribute based signature techniques for securely sharing outsourced data contents via public cloud servers. The proposed framework presents several advantages. First, it provides an encrypted access control feature, enforced at the data owner's side, while providing the desired expressiveness of access control policies. Second, Ins-PAbAC preserves users' privacy, relying on an anonymous authentication mechanism, derived from a privacy preserving attribute based signature scheme that hides the users' identifying information. Furthermore, our proposal introduces an accountable attribute based signature that enables an inspection authority to reveal the identity of the anonymously-authenticated user if needed.
ABE consists on encrypting data by the data owner w. r. t. a defined access policy over a set of attributes. Consequently, an authorised user who is able to decrypt and access data is an entity holding a set of attributes satisfying the access policy. In this paper, we present Ins-PAbAC, an accountable privacy preserving attribute-based framework, for an authenticated encrypted access to data outsourced to cloud servers. The proposed framework combines Attribute Based Encryption (ABE) and Attribute Based Signature (ABS) mechanisms, while considering a two-level access control model. That is, it introduces (i) fine-grained access control supporting comprehensive granularity for access rules, and anonymous data access, allowing the storage server to manage access requests without any need to learn the user's identity neither his attributes.
The originality of Ins-PAbAC is multifold. First, the Ins-PAbAC framework proposes a privacy preserving authentication feature. That is, based on a novel design of a multiauthority attribute-based signatures, we ensure anonymous authentication for requesting data users where the user's identity remains protected against the cloud service provider. In addition, as the attribute based signature scheme is used to authenticate requesting users, the cloud provider is able to control the bandwidth consumption, thus, maintaining the system's availability. Obviously, the authentication of requesting users permits to mitigate flooding attacks  [9]  as only authenticated users can download encrypted data contents. Second, as a decentralised multi-authority attribute based framework, Ins-PAbAC ensures that users' attributes are issued and managed by multiple authorities.
For instance, the disclosure of access patterns may reveal privacy-sensitive information about patients and/or doctors and consequently may leak confidential information about the medical data content. Furthermore, although ensuring anonymous access to medical data, designated authorities need to be able to reveal the identity of a user when required. For instance, if a unauthorised access or even modification of a medical record occurs, the user should be identified. Thus, our proposed Ins-PAbAC framework has to fulfill the following properties: • data confidentiality -Ins-PAbAC should ensure the secrecy of outsourced data contents against both curious cloud service providers and malicious users. • fine-grained access control -Ins-PAbAC should ensure flexible security policies among several dynamic groups of users with different access rights, belonging to diverse groups.
B. Attribute based Signature (ABS) Attribute-Based Signature (ABS) has been derived from ABE mechanism to provide a fine grained and privacy preserving authentication of users. Similar to the concept of ABE, ABS consists in enabling a signer to sign a message only if his set of attributes satisfies a defined access policy. A verification entity can verify the signature's correctness without accessing the signer's identity neither knowing the exact attributes used to generate the signature  [38]. In 2011, Maji et al. [38]  introduced the first construction for a multi-authority attribute based signature scheme. This scheme consists of using multiple attribute authorities to manage attributes and issue related secret keys. Several works rely on the attribute based signature to ensure data owners' authentication and fine grained access control over outsourced data to the cloud.