dataesr/scientific-paragraphs-categorization huggingface dataset:
However, this choice is made at the cost of a more complex management. A second option is to centralize all CN functions. This has the advantage of reducing the complexity and effort of managing and orchestrating the network at the different sites. Finally, a third intermediate stage is possible, called the hybrid model. In this model, a central CN is accompanied by replication of some critical control plane functions to specific distributed sites, such as a production plant, to enable services such as ultra-reliable low-latency communications. These network functions may include the application function, which, for example, must interact with the 5G Core and manufacturing applications; or, for privacy reasons, the user plane function in its role as a gateway to a central database. In contrast to intraenterprise scenarios, inter-enterprise scenarios include business cases where user plane data are shared between different enterprises.
In such a setting, communication protocols and network architectural choices must obey even stricter security mechanisms. It is possible that in such scenarios, different private 5G networks come from different vendors and are operated by different MNOs or service providers. The standard solution for interoperability and intercommunication is then to establish secure connections between endpoints or between services that are part of the data networks of the enterprises involved, such as VPN connections. Secure external databases or a database hosted by a party can be used to exchange information between enterprises. As in intraenterprise case, some critical network functions in the control plane must be replicated for reliability and security purposes.
Before selecting an operator model for an architecture, some KPIs must also be considered. Latency depends on the data path in a certain operator model. In fact, traffic may be sent only to private edge servers or to both the cloud server and the edge server, which may be physically or logically located on-site, in a local data center or in cloud centers. Security considerations are also important and depend heavily on the data path and control path in a certain operator model. The deployment strategy of private 5G networks may deploy a single operator model or multiple operator models based on coverage and KPI requirements or certain constraints imposed by regulators. For example, the enterprise may deploy a fully private model or a private MNO model. The enterprise can also deploy the MVNO model and the hybrid model at different sites.
Service placement In the MEC paradigm, resource-intensive and delay-sensitive applications are handled directly in the edge cloud, avoiding as much as possible the access to cloud computing available in possibly distant data centers to prevent high service delays, which are not tolerable in the Industry 4. 0 scenario. Of course the edge cloud offers less storage and computational resources than the large data centers. To overcome this issue, virtualization techniques such as virtual machines and containers help in creating an open edge computing environment, in which storage and computational resources are distributed across the edge cloud when and where needed. To properly exploit this possibility, it is necessary to optimize resource placement and scheduling, in particular, it is necessary to choose the nodes that are the most suitable to store the data and to run the applications offloaded from the querying sensors.
We consider a wireless edge network consisting of a set N of edge nodes, each one endowed with a MEC server to enable virtualizing net- work services/applications (VNFs), i. e. , equipped with storage, computation and communication capabilities, and endowed with a wireless access point, covering local areas, possibly overlapping. There is a set S of network services, usually consisting of virtual machines and/or containers, able to run such sophisticated applications. Services can be stored on the edge clouds in order to satisfy a set K of sensor devices, each demanding for network services, if there is an active connection link. The aim of our work is to minimize the total system delay occurring for transmitting data from each sensor to an edge server and for processing them. Thus, latency depends on the amount of information data needed to run the desired application, apart from the radio capacity of the transmission link and the computation capacity at the edge cloud.
Sensors can offload their data if connected to an access point, then, if none of the neighbor edge nodes cached the demanded service, the device can be routed to a data center, generically indicated as a core cloud C , storing the entire set S of services but placed at a much longer distance. The same occurs if none of the edge server has enough capacity to serve the request. To tackle the problem to solve an Integer Linear Program, we leverage an approximation algorithm based on a randomized rounding  [40] , with provably guarantees to satisfy the imposed communication and computation constraints on expectation. To prove the effectiveness of the proposed algorithm, we compare the results with a matching algorithm, which aims to route each device to a node with the best SNR on the communication link, an admission occurs if the node has enough storage and computation capabilities to cache and run the requested service.
Our algorithm performs better because the service placement allows the system to amortize the cost of storing a lot of data, reusing the same virtual machine for more users. Of course this can be done for shareable resource as for analytic data cached at the server, otherwise computation and communication resource are dedicated to the specific querying device. The more storage capacity is available at each server, the less requests are routed to the core cloud, and then the system experiences less delay in delivering all the applications. For delay-sensitive services, it is very important to have access at the edge, and the proposed algorithm satisfies the request for such applications in a very fast and efficient way.
MEC The implementation of Multi-access Edge Computing (MEC) can be based on User Plane Function (UPF)  [43]  or bump-in-the-wire MEC, which can meet the requirements of high bandwidth and low latency service, and enhance security. In 5G-CONNI project, the UPF for the European testbed is designed according to 3GPP standard, and the MEC platform leverages the 5G network architecture and performs the traffic routing and steering function in the UPF. An UL classifier of the UPF can be used to steer the UP traffic matching the filters controlled by the Session Management Function (SMF) to the local data network, where it can be consumed by the MEC application. The Policy Control Function (PCF) and the SMF can set the policy to influence such traffic routing in the UPF. Also, the application function can influence the traffic routing and steering via the PCF.
Therefore, MEC in 5G is able to influence the UPF through the standardized Cloud Provider (CP) interface in the SMF. Furthermore, the MEC platform is completely virtualized environment in order to enable seamless application lifecycle management paired with seamless platform management. On the other hand, the bump-in-the-wire MEC for the Taiwanese testbed is implemented according to ETSI standard, where MEC cloud includes an NFV infrastructure and data plane functions. NFV infrastructure is named ECoreCloud. The data plane functions comprise Software-Defined Networking (SDN) switch and Mobile Edge Enabler (MEE) VNF developed, where SDN switch is used to route and mirror the traffic, and MEE VNF provides a traffic steering function so that selected data traffic can be offloaded locally. The MEE VNF is divided into two modules, including the Control Plane Analyzer module and Data Plane Processor.
The Control Plane Analyzer module takes charging in decoding and correlating signals, and the Data Plane Processor is to process data plane traffic and steering. The bump-in-thewire MEC standalone prototype is deployed between 5G New Radio and 5G standalone core network, which is a convenient deployment because it does not need the additional configurations for the core and RAN network. Furthermore, the MEC must handle N2 interface according to 3GPP TS38. 413  [44]  and process the GPRS tunneling protocol Fig. 16  Total delay of network system user plane extension header packet for N3 interface based on 3GPP TS29. 281 [45] so that the GPRS tunneling protocol user plane can be handled properly.
Joint optimization of enabling technologies In private industrial networks, devising computation offloading strategies to enable complex processing of data collected by mobile inspectors/sensors/machines is necessary in order to guarantee continuous monitoring and anomaly detection and control decisions during industrial processes. Thus, we now present a strategy for dynamic resource allocation for computation offloading of machine learning tasks at the edge, in the new framework of Edge Machine Learning  [46, 47]. The goal is to allocate radio (e. g. , transmit power, quantization) and computation resources (e. g. , CPU scheduling at the edge server), to explore the trade-off between network energy consumption, end-to-end (E2E) delay and accuracy of the learning task. Here, the E2E delay is intended as the time elapsed from the generation of a new data unit by a sensor/mobile device, until its computation is performed at the Edge Server (ES) to run the online learning task.
A stochastic gradient descent distributed machine learning algorithm at the edge is presented in  [51]. In particular, the authors consider the trade-off between local update and global aggregation. In  [52] , the authors present a data compression algorithm to reduce the communication burden and energy consumption of an IoT network, to enable machine learning with a desired target accuracy. Edge machine learning is a research topic at its infancy, so that the research community just started investigating the possible directions. Our goal is to jointly optimize radio and computation resources to minimize the network energy consumption, under constraints on the E2E delay and the accuracy of the learning tasks, which in this case is an estimation task based on least mean squares. The power of the method is that it does not require any prior knowledge of the statistics of data arrivals, radio channels, and data distributions.
These information networks can include local area networks, WANs, intranets and extranets. The enter- prise network IT plays an important role regarding the deployment and integration of a private 5G network, especially with respect to IT security Third-party Cloud Platform It is a third-party company platform proposing the delivery of computing services-including servers, storage, databases, networking, software, analytics, and intelligence-over the Internet to offer faster innova- tion, flexible resources, and economies of scale Enterprise operations and maintenance (OAM) Enterprise OAM systems plans and executes activities Systems such as operating the system, or monitoring system performance. Such systems become important, when existing network infrastructures converge with the private 5G infrastructure A WAN is a telecommunications network that extends over a large geographic area for the primary purpose of computer networking.
WAN infrastructure may be privately owned or leased as a service from a third-party service provider, such as a telecommunications carrier, internet service provider, private IP network operator or cable company. For operator models, in which multiple stakeholders are involved carrying out OAM tasks remotely, the WAN infrastructure plays a significant role, e. g. , regarding availability of the entire distributed system Enterprise Personnel and/or End Device Database It corresponds to the database of enterprise personnel to provide them access or to end device such as computers, robots, machines, cameras, etc.
The global population structure of P. falciparum has been observed previously using microsatellite loci  [30]  and subsequently single nucleotide polymorphisms (SNPs) and whole genome sequencing  [31] [32] [33] [34] [35]. Recent work has used whole genome sequencing to investigate a global dataset of var gene sequences but the high cost of such an approach makes it currently impractical for routine surveillance in malaria endemic countries  [5]. Nearly all var genes encode a single DBLα domain, making it a suitable marker for characterizing population structure. Not only is the DBLα domain highly prevalent, it is also very variable, with the average pairwise identity of the amino acid sequences encoding this domain being approximately 42%  [6]. For the purposes of this study, we were interested to determine if the 450bp var sequence encoding the DBLα domain could determine geographic population structure.
Previous studies of the DBLα diversity have so far been restricted to specific geographic regions or small sample cohorts. In these studies, we have designated sequences encoding DBLα with less than 96% sequence identity as unique DBLα types. When dealing with field isolates often containing multi-genome infections, DBLα alleles of each member of the var multigene family cannot be assigned due to the absence of known chromosomal positions and inability to assign to a specific genome. Given these constraints, DBLα types of an isolate rather than alleles are used for population genetic analyses in regional and local datasets  [36]. Barry et al. (2007)   [7]  investigated the DBLα type diversity found in 89 global isolates from both field and laboratory clones, including 30 isolates from Amele, Papua New Guinea (PNG).
They found an extreme diversity of DBLα types at the global level, with a higher level of conservation in the PNG population compared to the global isolates. Due to the limited size of their global dataset, it was not possible to investigate the DBLα type population structure at a global level. Chen et al. (2011)   [37]  analyzed 160 field isolates from a global collection, confirming the higher DBLα type diversity observed in Africa, and observed that Bakoumba, Gabon appeared to have a higher conservation than the other African countries sampled. Tessema et al. (2015)  [38]  further investigated the DBLα type population structure in PNG, identifying fine-scale population structure of the DBLα type repertoires at the village level. Rougeron et al. (2017)   [39]  analyzed a collection of isolates from South America, observing a smaller population size of DBLα types than has been reported in other regions, and suggested that the DBLα type population structure mirrored that found in the SNP and microsatellite analysis of  Yalcindag et al.
Here, by leveraging an approach we designed specifically for the analysis of recombination among var genes  [43]  and applying it to a global dataset of DBLα types from 23 locations in 10 countries, we describe DBLα population structure at a global scale. The sensitivity of this approach also allows for a comparison of DBLα sequences isolated from ape samples of other Plasmodium species to a global dataset of translated P. falciparum DBLα types. Indeed, we identify strong DBLα population structure both globally and within Africa. This contrasts with previous studies, which have struggled to distinguish population structure within Africa using entire var genes  [5]. The population structure we identified was then related to distant ape species as well as previous population studies of P. falciparum.
Jumping hidden Markov model To extend previous approaches that focused on comparing DBLα types between isolates at 96% pairwise sequence identity  [7] , we adapted the approach of  Zilversmit et al. (2013)   [43] , which reconstructs the translated sequence of each DBLα type in the dataset as an imperfect mosaic of donor sequences using a jumping hidden Markov model (JHMM). For the purposes of this study, this improves upon prior approaches that ignore recombination, as previously, var repertoires that share a significant amount of homology could be mis-classified as very distant due to the presence of a recombination event (Fig  1A ). We used the JHMM to infer the posterior probability that each location in an isolate's DBLα type amino acid sequence is most closely related to every other DBLα type in our dataset.
Global population structure of DBLα types The global dataset of the DBLα types used in this study were sequenced from 1,248 P. falciparum isolates (obtained from individuals with asymptomatic infections or individuals presenting with uncomplicated malaria at the time of sample collection, see Table  1 ) across 23 locations in Colombia, French Guiana, Gabon, Ghana, Iran, Papua New Guinea (PNG), Peru, Thailand, Uganda, and Venezuela (i. e. , 10 countries worldwide, Fig  2A )  [31, 36, [38] [39] [40] [41] 46, 47]. Clustering of all the DBLα types at 96% identity resulted in 32,682 unique DBLα types used for subsequent analyses. The median number of DBLα types per isolate varied significantly between countries (range = 19-75), with African countries having the highest median and maximum number of types, consistent with a higher multiplicity of infection, that is a higher number of isolates having more than one distinct P.
Despite this, clustering by country within each disease category is still evident although the distance between the major groups is reduced and the clusters are less clearly defined (S3 and S4 Figs). Some of this reduction in definition is likely to be the result of the reduced number of isolates leading to a weaker signal for the t-SNE algorithm. The t-SNE algorithm is also not guaranteed to preserve large distances and thus changes in distances between clusters cannot be easily interpreted. In addition, the JHMM inferred proportions were found to have a significant and larger effect size than disease status in the multinomial logistic regression. This coupled with the observation that the inferred proportions for Ghanaian isolates more closely resembled Ugandan isolates than the other asymptomatic isolates, suggests that the overriding signal in this dataset is due to geography.
South America: Out of Africa hypothesis Our comparisons to a global DBLα type dataset and JHMM analysis allowed us to further investigate the "Out of Africa" hypothesis and build upon previous work  [31, 39]. We compared each South American isolate to all non-South American isolates from Africa and Asia/ Oceania to determine the proportion of DBLα type matching proportions among isolates. These comparisons revealed a higher matching proportion between the South American and African isolates than with the Asian or PNG isolates (S6 Fig). These results are in line with the hypothesis that P. falciparum was introduced into South America from Africa through the trans-Atlantic slave trade  [31].
Evolutionary relationships to P. praefalciparum To investigate the emergence and adaptation of P. falciparum in human populations, we used the JHMM to compare a P. praefalciparum isolate  [5]  against all other P. falciparum isolates in our DBLα type dataset. The JHMM estimates the likelihood of a match between every base in each P. praefalciparum DBLα domain and every other DBLα domain sequence including the other P. praefalciparum domains. After controlling for the number of isolates and DBLα types sampled in each country, the estimated base levels probabilities were aggregated to give the estimated matching proportions at the country level. Unlike in the previous comparisons, where we normalized these proportions at the isolate level, here we normalized at the level of each DBLα type in the P.
Insights into the recombination structure of the var DBLα domain We sought evidence for recombination "hot spots" in the sequences encoding the DBLα domain given our unique dataset of over 30,000 DBLα types collected from over 1,200 field isolates worldwide. As was also found previously  [43] , there did not appear to be any regions of very high or low recombination along the region of the var gene encoding the DBLα domain. S7 Fig illustrates the homogeneity of the recombination rate by plotting the number of recombinations at all locations inferred using the JHMM approach on the multiple sequence alignment built using Gismo  [49] , versus the occupancy of the alignment at that column. A sliding window of 15 columns was applied to the ratio of recombination count to alignment column occupancy but only one region (alignment columns 355-358) was found to be a mild outlier with a ratio between 1.
Highly conserved DBLα types at the global scale We and others have previously shown that a number of DBLα types are conserved spatially, at the scale of countries and regions  [7, [37] [38] [39] [40]. We were therefore interested in exploring whether certain DBLα types were conserved at the global scale. By examining the frequency of all 32,682 DBLα types identified in our dataset, we found that 56. 8% of the DBLα types were rare (i. e. , seen in only one isolate) after clustering at 96% pairwise sequence identity (Fig  4A ). This agrees with previous findings from a number of studies, and is consistent with the impact of immune selection on a diverse population  [7, [37] [38] [39] [40] [41] [42]. This pattern was largely driven by the DBLα types found among the African isolates, consistent with the overall higher diversity of P.
Comparative analyses of highly conserved DBLα types to previously published data In an attempt to annotate these high-frequency types, we also searched each sequence against the NCBI nucleotide reference database  [50, 54, 55] , as well as the known conserved var types that encode DBLα domains: var1 and var3  [6, 56, 57]. Any matches greater than 96% pairwise sequence identity are reported in S1 Data. The high conservation of our 100 high-frequency DBLα types was also confirmed by searching for them in a recent independent global assembly of var genes conducted by Otto   [5]. This global analysis included countries from around the world except for South America. We found matches to six countries in Asia (Bangladesh, Cambodia, Laos, Myanmar, Thailand and Vietnam) and eight countries in Africa (Democratic Republic of Congo, Gambia, Ghana, Guinea, Kenya, Malawi, Mali and Nigeria) (S16 Fig).
The types observed at the highest frequency in our dataset were also observed in a high number of countries across the world, with the median number of countries with matches being 11 and ranging from 1 to 14 (S16 Fig). Of the 100 high-frequency DBLα types, 81 were seen in both Asia and Africa, with 19 types that were found only in African isolates. For the complete list of countries with matches to each high-frequency type, along with the corresponding BLAST results, see S2 and S3 Data, respectively. The most conserved DBLα type (seen in 521 isolates, 46. 2% of our global dataset) was seen in every one of our study countries except PNG. Additionally, BLAST hits to this type were found in other published var gene sequences from Brazil, Kenya, Tanzania and Malawi, further demonstrating its conservation in P.
Discussion By analyzing the geographic relatedness of a large dataset of P. falciparum field isolates from 23 locations in 10 countries, we demonstrate that the evolution of the parasite population emerging "Out of Africa" underlies current patterns of DBLα type diversity. This evolutionary result presents an opportunity for contemporary malaria surveillance in these times of globalization. Human migration due to conflict, food security, economic opportunity, will undoubtedly perturb the observed geographic patterns. Specifically, in identifying geographic population structure in DBLα types of the major variant surface antigen of P. falciparum blood stages, we provide compelling evidence for use of these types in malaria surveillance. Based on the JHMM output, we can track DBLα types to specific localities, countries and continents to monitor changing patterns of malaria transmission.