dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements Part of this work has been conducted in the context of the  French National Agency  project  ANNONA , and the first ideas and developments were started during the first author's former position as Research Engineer in data production and analysis at the  French National Center of Research and Laboratory of Transport Economics. The authors also thank  Christian Ambrosini , former senior lecturer at  Lumière University Lyon 2 and Laboratory of Transport Economics , who helped the authors with his valuable comments at an early stage of the research. Moreover, the data used for the analysis are extracted from the  French Urban Goods Transport Surveys  steered by the  French Ministry in charge of Transport (Direction of Research and Innovation, DRI)  and co-financed with the  French Agency of the Energy and the local authorities of Bordeaux, Marseille and Dijon.
The Laboratory of Transport Economics  was the scientific lead and methodology developer. Various exchanges with the data collection team during the first author's stay at the Laboratory of Transport Economics were necessary to understand the hypotheses and methods behind the dataset. The authors thank  Michel Julien  from  DRI , who was in charge of maintaining and disseminating those surveys and provided pertinent advice and comments to the authors. The authors would also like to acknowledge the support from the  Volvo Research and Educational Foundations (VREF) , which fund the  Gothenburg Urban Freight Platform (UFP)  through its  Future Urban Transport research program. Last but not least, the authors would like to acknowledge the valuable comments and suggestions from the reviewers that helped to substantially improve the paper.
Those relations can also be A third group of works, which are focused on specific types of activities, do not use extensive non-linear, including exponential, logarithmic and potential relations (Sanchez-Diaz et category classifications but use a dataset containing one or two categories: Demetsky (1975) al. , 2015). An alternative to those functions is that of utility functions, which are focuses on stores, whereas Maejima's (1978) work addresses construction. More recently, generally used for distribution and choice purposes but have also been applied at the Alho and de Abreu e Silva (2014), de Oliveira et al. (2017) and Sanchez-Diaz (2018) propose generation level (Russo and Comi, 2010; Nuzzolo and Comi, 2014). Finally, and models for the Hotels, Restaurants and Catering (Ho. Re. Ca. ) sector, and Aditjandra et al.
The surveys have been conducted on the basis of the Freturb model (Aubert and Routhier, 1999 ; Bonnafous et al. , 2014 ), but they have been also used to propose alternative FTG (Patier, 1999 (Patier, , 2001;; Deprez and Bourcier, 2002; CERTU, 2013;  Gonzalez-Feliu et al. , 2014a; Ducret and Gonzalez-Feliu, 2016)  and FG models (Henriot and Routhier, 2010; Abdelhai, 2013; Gonzalez-Feliu, 2018). Thus, this work uses the establishment-based data of the survey and the category classifications used in the data collection and analysis process (which do not exactly coincide with the Freturb categorization) to conduct the aggregation analysis, independently of the Freturb modeling choice and hypotheses. Indeed, in this work, raw data in terms of freight trips and frequencies issued directly from the surveys source databases have been used instead of the Freturb-calibrated data.
Serverless computing is an emerging paradigm that greatly simplifies the usage of cloud resources and suits well to many tasks. Most notably, Function-as-a-Service (FaaS) enables programmers to develop cloud applications as individual functions that can run and scale independently. Yet, due to the disaggregation of storage and compute resources in FaaS, applications that require fine-grained support for mutable state and synchronization, such as machine learning and scientific computing, are hard to build. In this work, we present Crucial, a system to program highly-concurrent stateful applications with serverless architectures. Its programming model keeps the simplicity of FaaS and allows to port effortlessly multi-threaded algorithms to this new environment. Crucial is built upon the key insight that FaaS resembles to concurrent programming at the scale of a data center.
While functions can initiate outgoing network connections, they cannot directly communicate between each other, and have little bandwidth compared to a regular virtual machine  [9, 53]. This is because this model was originally designed to execute event-driven, stateless functions in response to user actions or changes in the storage tier (e. g. , uploading a photo to Amazon S3 [2]). Despite these constraints, recent works have shown how this model can be exploited to process and transform large amounts of data  [25, 42, 44] , encode videos  [15] , execute linear algebra tasks  [46] , and perform Monte Carlo simulations with large amounts of parallelism  [23]. The above research projects, such as PyWren  [25, 44]  and ExCamera  [15] , prove that FaaS platforms can be programmed to perform a wide variety of embarrassingly parallel computations.
Moreover, the programming model of Crucial keeps the simplicity offered by current serverless architectures. These properties make Crucial a great tool to easily move multi-threaded applications to the cloud. • Using extensive evaluation of 𝑘-means and logistic regression over a 100GB dataset, we show that Crucial can lead to 18 -40% performance improvement over Spark running on dedicated instances at similar cost. Crucial is also within 8% of the completion time of the Santa Claus problem running on a local machine. The remaining of the paper is structured as follows: Section 2 puts this work in perspective with a general background. We explain Crucial's programming model in Section 3, and describe its design in Section 4. Section 5 covers some implementation details. The evaluation is presented in Section 6, where we validate Crucial through microbenchmarks and assess its effectiveness for fine-grained state management and coordination in serverless environments.
Execution lifecycle The execution lifecycle of a Crucial application is similar to that of a multi-threaded Java application. Every time a CloudThread is started, a standard Java thread (i. e. , instance of java. lang. Thread) is spawned in the client application with some extra logic. The basic role of this logic lies in calling a generic serverless function to execute the Runnable code attached to the CloudThread. During the execution of a cloud thread, each access to a shared object is mediated by a proxy. This proxy is created when a constructor is encountered in the code, and either the newly created object belongs to Crucial's library, or it is tagged @Shared. The Java thread remains blocked until the call to the serverless function terminates. Such behavior gives cloud threads the appearance of conventional threads; minimizing code changes and allowing the use of the join() method in the application's master thread to establish synchronization points (e.
Fault tolerance As detailed next, fault tolerance in Crucial is based on the disaggregation of the compute and storage layers. On the one hand, writes to the shared object layer can be made durable with the help of data replication. In such a case, Crucial tolerates the joint failure of up to rf -1 servers. 2 On the other hand, Crucial offers the same fault-tolerance semantics in the compute layer as the underlying FaaS platform. In AWS Lambda, this means that any failed cloud thread can be re-started and re-executed with the exact same input. Thanks to the cloud thread abstraction, Crucial allows full control over the retry system. For instance, the user may configure how many retries are allowed and/or the time between them. If retries are permitted, the programmer should ensure that the re-execution is sound (e.
Since our prototype only accepts Runnable, the return payload is empty unless an error occurs. In case of error, the system interprets it and re-throws an exception. The DSO layer is written atop the Infinispan in-memory data grid  [35]  as a partial rewrite of the Creson project  [49]. The code of the client and the server of this layer weigh 2. 5k and 9. 2k SLOC, respectively. The client prototype includes a small library of shared objects and the proxies to access them. To wave proxies in the code of the client application and the cloud functions, both are compiled with the help of AspectJ  [29]. In the case of user-defined shared objects, the aspects are applied to annotated instance fields (see Section 3. 1). Such objects must be serializable and contain an empty constructor for marshalling purposes.
Evaluation This section presents experimental results assessing that our approach is not only feasible but also desirable for certain types of applications, e. g. , machine learning (ML). We first validate the general design of Crucial with a series of micro-benchmarks. Next, we show that our system based on fine-grained updates to shared mutable data outperforms Spark at comparable cost in two instances of ML problems. Further, we outline the benefits of Crucial when coordinating serverless functions The end of this section explains quantitatively how Crucial simplifies the programming of multi-threaded stateful applications over a serverless infrastructure. Evaluation setup All the experiments are conducted in Amazon Web Services (AWS), within in a Virtual Private Cloud (VPC) located in the us-east-1 region.
These values are chosen to have the optimal performance at the lowest cost (see Section 6. 2. 3). 7  Spark experiments are run in an Amazon EMR cluster with 1 master node and 10 m5. 2xlarge worker nodes (Core nodes in EMR's terminology), each having 8 cores. The Spark executors are configured to utilize the maximum resources possible on Dataset The input is a 100GB dataset generated with sparkperf [11] that contains 55. 6M elements. For the logistic regression use case, each element is labeled and contains 100 numeric features. For 𝑘-means, each element corresponds to a 100-dimensional point. The dataset has been split into 80 equal-size partitions to ensure that all partitions are small enough to fit into the function's memory. Each partition been stored as an independent in Amazon S3. Logistic We evaluate a Crucial implementation of logistic regression its equivalent counterpart in Spark's MLlib  [37] : LogisticRegressionWithSGD.
The first solution uses plain old Java objects (POJO), where objects are monitors and the entities are threads. Our second solution is a refinement of this base approach, where shared objects are stored in the DSO layer. The conversion is straightforward using the API presented in Section 3. In particular, the code of the objects used in the POJO solution is not changed. Only the @Shared annotation is required. The last refinement consists in using cloud threads instead of Java ones-leveraging the CloudThread abstraction. We consider an instance of the problem with 10 elves, 9 reindeer, and Santa, and run the simulation for 15 toy deliveries (epochs of the problem). We take the average time to complete the problem for each solution and plot the results in Figure  7c. Storing the objects in Crucial induces an overhead of 8%.
Programming Simplicity Table  4  details the modifications that were necessary to port each application to Crucial. The difference between single-machine, parallel code and its serverless counterpart is small. Even for complex programs, such as the logistic regression detailed in Section 6. 2. 2, changes account for less than 3%. Starting from a conventional object-oriented program, Crucial requires a handful of changes to port it to a FaaS platform. We believe that this smooth transitioning can help everyday programmers to start leveraging the benefits of serverless computing.
Conclusion This paper presents Crucial, a system to program highlyconcurrent stateful applications on top of a Function-as-a-Service platform. Crucial is built using an efficient disaggregated in-memory data store, and it can be used to construct demanding serverless applications that require fine-grained support for mutable state and synchronization. In particular, we show that Crucial achieves superior or comparable performance to Spark for two common machine learning algorithms. In both cases, less than 3% of our code differs from a conventional solution using plain old Java objects. We also assess experimentally that Crucial rivals in performance with a single-machine, multi-threaded implementation to solve a complex coordination problem, despite the unavoidable overhead imposed by the serverless architectural constraints.
In their thorough review article on Nature Biomedical Engineering, Yu, Beam, and Kohane  [4]  survey the literature on AIM, explain the advanced techniques and their applications, and point out the breakthroughs and challenges for the field. The paper, though among the most recent attempts to draw out clinical integration of medical AI at various stages, has yet to dig into the entirety of the literature on AIM over a certain period of time. Thus, in order to identify research gaps and facilitate the clear, on-point translation of knowledge that would better inform policy development, this study presents the use of scientometric analysis in exploring research trends in the subject of AI in health and medicine. Scientometrics uses databases of published literature to objectively assess the impact of research knowledge on health issues and provide substantial empirical evidence.
It shows the way of changing concerned research topics in national and international contexts with the increasing number of published articles over time, and reflects the visual collaborations of researcher networks within different topics  [16] [17] [18]. Scientometric methods are particularly useful in the evaluation of global scientific production and development trends, such as in the cases of health systems research  [19] , administrative healthcare database  [20] , or diabetes research in Middle East countries from 1992 to 2012  [21] , to name a few. Through an extensive review of the scholarship on AIM, this paper aims to present a better understanding of publications and research trends, and suggests potential directions toward solving this ongoing challenge. Specifically, we reviewed the global growth of research production in medical AI and analyzed patterns of research areas and trends in this field.
Materials and Methods The search used the Web of Science (WOS) database from Clarivate  [22]  and Scopus from Elsevier  [23]. WOS was chosen because it covers (i) more research fields compared with PubMed, and (ii) research dated from 1900 to the present. For the Scopus database, due to the restriction for not downloading completed data larger than 2000 papers, we could only download papers by year. This analysis focuses on articles published from 1971 to 31 December 2018 in peer-reviewed journals. The bibliometric study does not include grey literature, conference proceedings, or books/book chapters. Articles written in any languages other than English are excluded.
Data Extraction As a restriction from Scopus, we applied this step and the following only for the dataset downloaded from WOS. Retrieved data were exported from the WOS database under text format and applied STATA version 15 (STATA Corp. , College Station, TX, USA) to merge data files and extract others. Based on the Global burden of disease 2017 study, we identified 25 diseases with the highest Disability-Adjusted Life Years (DALYs) and used STATA to extract the number of papers related to AI types. The. dta file then was stored in Excel. The data exported include: (1) Title name, (2) Names of journals, (3) Authors' name with the Web of Science affiliation, (4) Number of citations, (5) The types of documents,  (6)  The year of publication for each publication,  (8)  Author and Web of Science keywords, and (9) Abstracts.
Data Analysis We applied STATA to perform a regression model for the growth of world publication in AI in healthcare and medicine. In terms of coauthorship analysis, the study examined the most productive countries based on the number of papers, total citations, citations per paper, the number of downloaded papers, collaborative country, and international collaborative papers. VOSviewer software was used to create visualization maps (http://www. vosviewer. com/). For the most prolific countries, we applied the cutoff point of 5 papers, and there were 93 countries in the mapping analysis. A network graph illustrates the connection among the 568 most common authors' keywords by applying the specific threshold of 15 appearances for each keyword. Based on this graph, the team identified the main topic of AIM.
Global Collaboration Table  2  illustrates the productivity ranking of the top 20 countries in this dataset. In this list, the top five countries were from North America (the United States and Canada), Europe (Italy, and Germany), and China. The United States ranked at the top of all indices: total papers (10,623 papers, 30. 8%), total citations (232,669 citations), and number of downloads  (25, 384). Countries in Europe had the strong co-operation in AIM, confirmed by the high number of institutes (>7. 7), countries in collaboration (>2. 0), and collaborative papers (>50%). Meanwhile, in Asia, China was ranked second with 2671 papers (7. 6%) and 15,995 downloads. It is remarkable that Israel and Singapore had the highest level of international collaboration (nearly 70%), while the figure in other Asian countries was only one-third of this.
Additionally, in our dataset, when searching "ethics" on both keyword field and abstracts there are only 204 papers (0. 7%) related to ethics. The first paper was published in 1994 "Ethical considerations in the management of individuals with severe neuromuscular disorders". The application of AI has brought many benefits to the healthcare system and improve medicine. However, the use of AI technology unethically may be dangerous to patients and physicians. Thus, we need an ethical standard to apply to all the actors not only in healthcare services, but also in health-related fields  [29]. Table  3  provides the number of publications of most common AI tools and types of clinical application using AI (diagnosis/prediction or treatment) for each of the top 25 diseases in terms of the burden of disease measured in DALYs  [30].
Robotics is transforming health care with its diverse applications, such as early detection, training future doctors, or treatment. The highest number of papers were about robotic surgery. Machine learning was the second most popular AI type in which one would use for large and complex data analysis fields, such as genetics  [31]. AI clinicians are higher than medical clinicians  [32] , however, in our dataset, the number of papers using AI for prediction of disease or its consequences was higher than that of treatment. The burdens of disease shift from infectious diseases to noncommunicational diseases (NCDs)  [33] , thus the research on AI application for heart disease, stroke, or respiratory disease were higher than for other diseases. The increasing burden due to cancer has attracted scientific concern, with the highest total number of papers in AI tools (Robotic, Machine leaning, or Artificial neural network) and in treatment and diagnosis.
g. , genomics, metabolomics)  [35]  and state-of-the-art imaging methods to predict treatment responses  [36, 37] , especially in areas where there is a lack of objective diagnostic methods, e. g. , psychiatric disorders  [38]. Finally, to accelerate application and expansion of AI in health and medicine, it is critical to develop global and national protocols and regulations to frequently review and justify the validity of AIM products in clinical and practical environments. An important challenge in applying AI for health and medicine is the lack of large clinical datasets for training AI models. This is especially true for datasets with labels, which require doctors/medical expert annotation and therefore are very costly and time-consuming to collect  [39] [40] [41]. In general, AI techniques that are applicable to situations with limited amounts of labeled training data are of great interest for many applications  [42] , in addition to health and medicine.
This is an important fundamental problem with active AI research and several promising AI directions: First, more sophisticated data augmentation techniques have recently been proposed to enrich the training datasets to better characterize data distribution. These techniques include feature space data augmentation  [42, 43]  and data synthesis using complex deep neural networks models, notably generative adversarial networks (GAN)  [44] [45] [46] [47]. Second, there are substantial interests to improve semisupervised learning: learning methods that typically use a small amount of labeled data and unlabeled data  [48]. Third, self-supervised learning  [49, 50]  has attracted a lot of attention. Self-supervised learning methods automatically identify and extract supervisory signals without using any labeled data.
e MELiveOV consists of an omnidirectional video generation module, a streaming module, and a viewpoint prediction module. A prototype system of MELiveOV is implemented to prove its complete end-to-end OV live streaming service. e evaluation result demonstrates that compared with the traditional solution, the MELiveOV can reduce the network bandwidth requirement by about 50% and the transmission delay by more than 70% while ensuring the quality of the user's experience. e article "An End-to-End Automation Framework for Mobile Network Testbeds" by A. D. Zayas et al. describes the end-to-end automation framework developed as part of the TRIANGLE project. e TRIANGLE project is devoted to the benchmarking of apps and devices in mobile networks. For that purpose, it is needed to ensure the repeatability in the behaviour of all the components of the mobile network during the execution of the same test.
e article "Expanding G ÉANT Testbeds Service to Support Pan-European 5G Network Slices for Research in the EuWireless Project" by Á. Rios et al. presents the design options for creating a Pan-European mobile network for research in the context of the European Horizon 2020 EuWireless project. e most likely direction is a platform that makes it easier to create network slices for research. In this context, we identify one promising technology to implement network slicing in 5G networks: the framework G ÉANT Testbeds Service (GTS). GTS is currently a production service by G ÉANT that offers remote construction and use of virtual testbeds for wired networks mapped to the real G ÉANT infrastructure. ese GTS virtualized testbed environments conform to software-defined network (SDN) principles and offer computing, storing, and switching resources, at scale and with line rate performance.
Polygenic Risk Scores (PRS) combine genotype information across many single-nucleotide polymorphisms (SNPs) to give a score reflecting the genetic risk of developing a disease. PRS might have a major impact on public health, possibly allowing for screening campaigns to identify high-genetic risk individuals for a given disease. The "Clumping+Thresholding" (C+T) approach is the most common method to derive PRS. C+T uses only univariate genome-wide association studies (GWAS) summary statistics, which makes it fast and easy to use. However, previous work showed that jointly estimating SNP effects for computing PRS has the potential to significantly improve the predictive performance of PRS as compared to C+T. In this paper, we present an efficient method for the joint estimation of SNP effects using individual-level data, allowing for practical application of penalized logistic regression (PLR) on modern datasets including hundreds of thousands of individuals.