dataesr/scientific-paragraphs-categorization huggingface dataset:
We developed a web application in order to collect and label an age estimation dataset online by the community. The application uses Facebook's API to facilitate the access and hence reaching more people with a broader background. It also allows us to easily collect data from the participants, such as gender, nationality and age. We show some panels of the application in Figures  1(a ), 1(b) and 1(c). The web application was developed in a gamified way, i. e. the users or players get points for uploading and labelling images, the closer the age guess was to the apparent age (average labelled age) the more points the player obtained. In order to increase the engagement of the players, we added a global and friends leader board where the users could see their position in the ranking. We asked users to upload images of a single person and we gave them tools to crop the image if necessary.
We also asked users to provide the real age for images they uploaded themselves (or a good approximation), allowing more analysis and comparisons between real age and apparent age. Table  2  shows the main characteristics of the previous dataset and the extended one. Some of the features of the database and its associated challenge are: • Thousands of faces labelled by many users. • Images with diverse backgrounds. • Images taken in non-controlled environments. • Non-labelled face bounding boxes, neither face landmarks, making the estimation problem even harder. • One of the first datasets in the literature including estimated age as labelled by many users to define the ground truth, with the explicit objective of estimating the age. • The evaluation metric is weighted by the mean and variance of the labelling by the participants.
Apparent age challenge results Nearly 100 participants registered for the competition. Finally, at the test step, the teams that submitted their predictions are shown in Table  4. Each prediction is evaluated as = 1 -e -(x-μ) 2 2σ 2 , where x is the prediction, μ and σ are the mean and standard deviation of the human labels. The summary of the methods is shown in Table  3. The summary of the first top ranked methods is shown next. First place (OrangeLabs). The winners of the apparent age competition used the VGG-16 Convolutional Neural Network (CNN) trained for face recognition on 2 million faces  [34]  as the starting point. They fine-tune this CNN on the IMDB-Wiki dataset  [40]  for the age estimation task using the label distribution encoding  [20]. Then authors fine-tune 2 separate CNNs from the obtained CNN on the competition data: the first type CNN is used for age estimation of all ages (this model is trained using label distribution encoding) and the second type of CNN is used for age estimation of children between 0 and 12 years old (this model is trained using a simple 0/1 classification encoding).
During the test phase, if the first type CNN output is above 12, the output is kept as the final one. In the opposite case the output of the second type CNN is kept as the final one. In both cases, the final output is calculated as an expected value of normalized neurons in the output layer. The final age estimation system consists of 11 type one CNN, which are trained by 11-fold crossvalidation on the training and validation datasets and 3 second type CNN which are trained using all available children images in training and validation datasets. For both types of CNN, the combination of age estimations is performed on the level of output neurons.
Second place (palm seu). The second ranked team of the apparent age estima-tion challenge preprocessed the images of the IMDB-WIKI  [40]  and the competition, including face detection  [44] , key point detection  [44]  and face alignment. They divided IMDB-WIKI data set into three parts randomly (each part has about 80,000 pictures) and generated the age distribution of each picture by using a normal distribution function (the assigned age is the mean value and the standard deviation is assumed to be 3). Then they fine-tuned three new deep net models on each IMDB-WIKI data set by using a pretrained vggface model (VGG-16)  [34]. The loss function of the net models the is KL-divergence which can be used to measure the difference between the ground truth distribution and the predicted age distribution.
This method can be called Deep Label Distribution Learning  [46]. Next, the authors fine-tuned these models on the competition data set (including previous  [11]  and current competition data) by using the same loss function. The authors also fine-tuned a pre-trained vggface model (VGG-16) directly on 2015 and 2016 competition data without pre-training on IMDB-WIKI data set. Therefore there are four nets in total. The final models were employed to extract the last full connected features of four net models, which were used by an ensemble method to generate the final result  [46].
Faces of Accessories Many works in mid-level face analysis, such as face recognition or facial expression analysis, consider the lower-level face analysis components to be solved. Indeed, problems such as face detection and facial point localisation have seen remarkable progress over the last five years  [41]. However, the real reason why people working in mid-or high-level face analysis have come to believe this is because they consistently work with over-simplified datasets, which are often designed to focus on mid-level problems (e. g. expression analysis) and to avoid low-level difficulties. This is not to say these are not realistic scenarios. For example, the SEMAINE database  [30]  contains recordings of realistic interactions between two people who communicate through a set of teleprompters in an office setting, which has constant and abundant illumination.
Participants naturally face the camera while discussing a topic of their choosing. Similarly, the CelebA dataset  [27]  contains over 200K photographs of celebrities, annotated with 40 attributes. However, CelebA mainly contains frontal face images of celebrities with posed expression (e. g. smile). The majority of the most popular face databases  [41, 38, 35, 27]  feature only mild non-frontal views with decent and constant illumination and little to no occlusion. Additionally, very often, these photographs contain only one face to be analysed, which greatly simplifies the analysis. Another issue with these dataset involves the diversity of the source material. A prime example of this is the MORPH database  [38] , introduced in Section 2. This dataset, widely used in the fields of face recognition and age estimation  [21]  is extremely skewed.
It contains 156,313 images of men and women from five different ethnic groups (Black, White, Asian, Hispanic and Other). However, out of the total, 115K images are Black subjects, but only 740 Asian subjects. Furthermore, there are only 26,171 images from women, versus the 130,142 images collected from men. As a consequence, we postulate that analysis made on such skewed datasets cannot be representative enough. The Faces of the World dataset has been created with the aim of overcoming these issues. We have collected over 25,000 publicly-available images from the Internet, with special emphasis in collecting diverse and balanced data. FotW comprises 25% Asian, Black, Hispanic and White subjects, 50% of which are men and 50% are women. Additionally, we made sure to include people from all ages, from newborn babies to elderly people.
As a consequence, FotW is a very diverse and technically challenging dataset. A full description of the Faces of the World dataset, including all the additional labels, can be found in  [28]. The creation process of the Faces of the World (FotW) dataset, from collection to annotation and post-processing, can be summarised as follows: 1. Selection: Over 25,000 images were downloaded from Flickr using Extreme Picture Finder  [1]. All of these images are licensed under Creative Commons 4. 0, which allows users to use copy and redistribute the material in any medium or format and to remix, transform, and build upon the material. A random selection of the dataset can be seen in Figure  2. 2. Annotation: We used Zooniverse  [6] , a citizenscience platform, to annotate the images collected in the previous step.
First place (SIAT MMLAB). The winner of the Accessories Classification Challenge used Cascaded Convolutional Neural Networks (CNNs), implemented in Caffe  [23]. This method used the celebA dataset  [27]  as additional training data. Three levels of cascade were trained: first, they fine-tuned pre-trained VGG-Faces  [33]  with CelebA (with their original 40 attributes), then they fine-tuned the first model with CelebA and only 5 attributes (earrings, hat, glasses, necklace and necktie). Finally, they find-tuned the previous model with the FotW dataset and 7 attributes (earrings, hat, glasses, necklace, necktie, headband and scarf).
Conclusion We presented the 2016 ChaLearn Looking at People and Faces of the World competitions on automatic face analysis. Three competitions were formulated, in apparent age estimation, accessories classification, and gender and smile prediction. Two new datasets were presented to support the three competitions; ChaLearn-AgeGuess and Faces of the World, totalling almost 30,000 labelled images. Perhaps unsurprisingly, all three competitions were won by teams employing Convolutional Neural Networks.
Acknowledgements We thank our sponsors:  Microsoft Research, University of Barcelona ,  Amazon ,  INAOE ,  Google , NVIDIA corporation, Facebook, and  Disney Research. This research has been partially supported by the  Spanish  projects  TIN2013-43478-P ,  TIN2015-66951-C2-2-R , and  European Union   Horizon 2020 research and innovation programme  under grant agreement No  645378.
Such reasons makes MNOs rely on well-understood, traditional approaches when building and delivering their core services. This includes the use of statically planned and provisioned resources and expensive tailor-made infrastructures. This specialized hardware is traditionally provided for vertical scalability and reliability but is less flexible in capacity planning. Nevertheless, as cloud technologies evolve and mature, many of the reasons for not adopting cloud technologies (such as OpenStack 1  or CloudStack 2  ) on the MNO core network are decreasing. The evolution of cloud technologies, including orchestration mechanisms and programmatic approaches such as Software Defined Networks (SDN) are reaching a point where such approaches, leveraging cloud technologies, become viable  [3] -  [5].
Besides the DSSaaS service, the deployment of the DSS system requires other "X-as-a-Service" components from the cloud infrastructure and network provider, such as AAAaaS for authentication and authorization, performance monitoring of services and instances (MaaS) and SLA assurance (SLAaaS). Information Centric Networking (ICNaaS) is also required to enhanced content distribution (e. g. , based on the location of mobile users, or according to their preferences). The RCBaaS allows the charging for consumed resources (e. g. , storage and compute resources). Once appropriately placed and instantiated, these service components are managed during runtime by appropriate and suitable algorithms (including horizontal scaling operations). DSSaaS also employs Load Balancing (LBaaS) to enable scaling and load sharing between Content Management Servers (CMS) that provide the content to DSS Players, according to the user profile and preferences that are managed by the Main Content Repository (MCR) interfacing with Databases (DBaaS) to store content and user data.
B. Testbeds The evaluation work uses five testbeds, as illustrated in Fig. 8. Testbeds are interconnected through the deployment of The RegionOne (Bart) is located in Winterthur, being operated by ICCLab, and includes OpenStack Kilo, which has the role of Identity Provider, based on keystone. The installation of OpenStack runs on CentOS and is based on the packstack tool. 6 This region also hosts the OpenShift v3. The region EURE (EURECOM) is located at Sophia Antipolis, and is managed by Eurecom. It includes OpenStack Juno in a single-host all-in-one setup, which is connected to a NI/ETTUS USRB B210 radio frontend (2x2 MIMO). This region uses Linux Containers (LXCs) to provide appropriate performance to RANaaS with OpenAirInterface support. The host runs a Linux low-latency kernel with the SCHED_DEADLINE real-time scheduler.
The Infrastructure common to most of the testbeds is OpenStack, with different versions and specific configurations (e. g. , in software defined networking components). The several IaaS are optimized for supporting configuration and updates of the Service Instance Components at runtime. OpenShift v3 has been employed as the PaaS to enable effective means to run the workload of service orchestrators. The support for CloudFoundry has also been considered during the project but has complexity issues associated when compared to OpenShift, namely on small scale deployments. OpenShift v3 is based on docker images managed by Kubernetes runtime and offer great performance improvements in comparison to OpenShift v2, as presented in Section V-A, and in D6. 2  [17].
VI. RELATED WORK This section analyzes related work, focusing on five of the large scale projects with most relevance for MCN: T-NOVA, iJOIN, MODAClouds, Coco Cloud and CloudScale. We also describe the most relevant software-based RAN implementations that may be leveraged to provide a fully cloudified RAN. T-NOVA is probably the project with more similarities with MCN. It aims at designing and integrating an end-to-end architecture for NFV services, targeting all the layers including applications and infrastructure components  [24]. The evaluation methodology includes several validation areas, such as functional and performance of NFVs, reliability of network services, portability and stability, and monitoring of virtual network services. The evaluation methodology also encompasses system and service-level metrics, such as time to deploy, scale-out/scale-in delay, data plane performance such as maximum achievable throughput.
The evaluation focuses mainly an e-commerce application, according to multiple areas (capacity, elasticity and efficiency) in OpenStack and Amazon Web Services. In addition multiple stakeholders are included, such as service providers and system developers. The CloudScale evaluation methodology has the advantage of supporting new systems, not yet deployed and already running. Table VI summarizes the evaluation scope of each of these projects. Functional aspects include assessment on the functionalities of service(s), while non-functional aspects include Considering the cloud RAN, the feasibility of LTE RAN software implementations over General Purpose Processors (GPPs), which are radically different from traditional approaches provided through Application-Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), or Field-Programmable Gate Arrays), was successfully demonstrated in the recent works  [34].
The existing software based implementations of the LTE evolved Node B (eNB) include: a) Amarisoft LTE solution, which is a puresoftware fully-functional LTE eNB  [35] , b) Intel solutions featuring energy efficiency and high computing performance using a hybrid GPP-accelerator architecture and load-balance algorithms among a flexible IT platform  [36]  and c) OpenAirInterface (OAI) developed by EURECOM implementing eNB (a Software Defined Radio (SRD)) and EPC as open source  [37].
Open Baton has been further extended for supporting OCCI as northbound interface and can be integrated within MCN as an additional service orchestrator. Open-O [42] represents a new initiative under the Linux Foundation providing an open source solution for service orchestration. Nonetheless, Open-O did not have any available releases. Juju is a very mature VNF Manager supported by Canonical. It provides management and orchestration of services which has to be defined and stored as charms in the Juju repository. Very recently AT&T announced the launch of their open source initiative Enhanced Control, Orchestration, Management & Policy (ECOMP)  [43]. ECOMP aims to provide a comprehensive framework for the management of SDN and NFV functionalities. Concluding, the MCN project encompasses a fully cloudified mobile network infrastructure proposal that considers OAI in RANaaS and Open5GCore  [22]  for EPCaaS differentiating with all the solutions presented in terms of features provided.
Paulo Simões received the Ph. D. degree with the Department of Informatics Engineering, University of Coimbra, Portugal, in 2002, where he is a Tenured Assistant Professor. He has been involved in several FP6, FP7, and H2020 European research projects and he regularly leads industry-funded technology transfer projects for telecommunications operators and energy utilities. Andy Edmonds received the M. Sc. degree in distributed systems from Trinity College Dublin. He is a Senior Researcher with the Zürich University for Applied Sciences. He was in industrial and academic positions in Siemens, Infineon, Intel, and the Distributed Systems Group, Trinity College in Dublin. He has been involved in several European FP6 and FP7 projects. He is currently on the FP7 MCN project, as Deputy Technical Coordinator.
Year 2009 country and global totals (by fuel type) were extrapolated from the 2007 Carbon Dioxide Information Analysis Center (CDIAC,  Boden et al. 2013 ) used for the CT2010 fossil fuel fluxes  (CarbonTracker, 2010). Open-source Data Inventory for Anthropogenic CO 2 (ODIAC,  Oda and Maksyutov, 2011)  emissions are spatially distributed using many available "proxy data" that explain spatial extent of emissions according to emission types (emissions over  Geosci. Model Dev. Discuss. , doi:10. 5194/gmd-2016 -213, 2016  Manuscript under review for journal Geosci. Model Dev. Published: 8 November 2016 c Author(s) 2016. CC-BY 3. 0 License. land, gas flaring, aviation and marine bunker). CarbonTracker combined the ODIAC emissions with CDIAC emissions to generate CT2011 fossil fuel fluxes  (Andres et al.
Transport The European Centre for Medium-range Weather Forecasts (ECMWF) operational wind fields at T799 spectral resolution were used to drive the Lagrangian particle dispersion model FLEXPART  (Stohl et al. , 2005). The ECMWF modelled data were retrieved with a temporal resolution of 3-h (analyses at 0000, 0600, 1200, and 1800 UTC; forecasts at 0300, 0900, 1500, and 2100 UTC) for two domains. The inner domain has a horizontal resolution of 0. 2° x 0. 2° on the Gaussian grid over Canada and the US (180°W to 0°E and 20°N to 90°N). The outer domain is a global grid with resolution of 1° x 1°. Both grids have 91 vertical levels. The FLEXPART model was used to simulate the 5-day transport history (retroplume) of the fossil fuel CO 2 mole fractions at each station location. The model calculated the trajectories of 5,000 particles from the intake height at each station location daily at 21:00 UTC (14:00 to 16:00 LST depending on time zones) representing afternoon well-mixed condition near the surface.
Synthetic Data Experiments To have a measure of the ability and limitations of the proposed inversion approaches, four components were examined in this study: 1) the magnitude and spatial distribution of the prior fluxes, 2) modelled transport, 3) number of sub-regions (parameters to estimate) and 4) inversion methods to estimate the parameters (scaling factors) for the purpose of assessing the sensitivity introduced by each component and their interactions. We conducted a series of inversion experiments presented in Table  3 using   Table  3a  shows the first (I) set of experiments E1-E7 and E22-E28 used the CT2010 fossil fuel CO 2 fluxes to simulate the prior mole fractions for each station. The target modelled mole fractions were simulated using CT2011 fossil fuel CO 2 fluxes. The same FLEXPART transport was used to simulate the prior and target mole fractions.
Data preparation For the observed data, we denote an observed genotype as 1 and an undetected or absent genotype as 0. For the 9 time-points, there are 512 possible patterns which were numbered from 0 to 511 using their binary value (eg 000000100 is pattern 4). This yielded a frequency distribution of binary patterns for each child, to which statistical models could be fitted (Fig  1 ). We restricted the analysis to participants who provided a blood sample on at least one day at each of six or more routine time-points. Where treatment was given following the first visit of a routine 24 hour pair, the second visit was excluded. Blood samples were also collected and genotyped if a child was ill at non-routine visits. In the analysis, we assigned genotypes from visits prompted by illness to the next routine timepoint (we define month intervals to start just after the routine time-point).
For this reason the primary analysis (including estimates of the force of infection) of P vivax focuses on the marker MS16. For P vivax msp1F3 we report the sensitivity of the estimates to the exclusion of the four Example of the structure of the data. The diagram represents the data for a child who had no missing visits. The time of the routine visits is shown by red triangles: the timepoints were spaced two months apart with two visits 24 hours apart for all but the first and last timepoints. The child had had a sample taken at each routine visit. Eighteen distinct genotypes were detected, each is shown with blue rectangles representing the visits where the genotypes where detected. The timing of an illness visit (light blue rectangle) and recorded treatments (dark blue rectangle) is shown below.
The column for observed patterns shows how the data are represented for the analysis: 0 indicates that the genotype was not detected at that routine time-point and 1 that it was detected. The pairs of routine visits 24 hours apart were combined, and the detectability was taken into account. The observed pattern ID is the distinct number assigned to the specific pattern. doi:10. 1371/journal. pntd. 0004582. g001 most common alleles We omit alleles with frequencies greater than 5% from analyses of the seasonality of P falciparum msp2. The seasonality of P vivax primary infections is estimated from the seasonality of P falciparum infections We assume that the seasonal pattern of P falciparum infections provides approximate estimates of the seasonal pattern of P vivax primary infections since (i) P vivax and P falciparum are transmitted by the same vectors and (ii) we assume that seasonality is driven primarily by vector densities and that parasites in the human host are less important.
Descriptive summary of the Ilaita I cohort data A total of 264 children were enrolled into the cohort, 189 at the time of the first survey. Routine survey follow-up was high: on average 95% of those enrolled were present at each of the routine visits  [13]. Of the 264 enrolled, 243 (92%) were seen at least once at six or more routine study time-points and 143 (54%) children were seen at least once at all nine routine time-points. The children seen at least once at 6 or more routine time-points contributed a total of 2003 routine time-points, 656 (33%) with one visit and 1347 (67%) with two visits 24 hours apart. There were 1038 intervals which included visits made due to illness. Treatment was given during a median of 4 intervals per child (range = 0,9). There were 114 P vivax MS16 alleles detected, the most common had a frequency of 5% in the baseline survey.