dataesr/scientific-paragraphs-categorization huggingface dataset:
An alternative paradigm is instance-based learning, also known as lazy learning. It avoids the costly computation of a global model by delaying the learning effort until a new instance -called the query instance -comes in. In this way, only a specialized local model needs to be built in order to perform the desired inference on the query instance. Another advantage of this paradigm is that it supports frequent updates in the training dataset. Indeed, there is no training phase, which is usually long and energy-consuming. The most famous example of instance-based learning algorithm is the k-Nearest-Neighbors (kNN) algorithm  [13]. It represents both training instances and query instances as points in a vector space. The class of a query instance is predicted from the class of the training instances that are the nearest neighbors of the query instance in the vector space.
Related work The notion of concepts of neighbors and its computation methods can be seen under three aspects. First, it is a machine learning approach on relational data for tasks such as classification. More precisely, it is an instance-based learning approach, conceptually similar to kNN algorithms. Second, this method is part of the FCA theory, and especially of Graph-FCA, a generalized framework for handling relational data under the FCA theory. Third, concepts of neighbors can be seen as a graph pattern mining method, searching for similarities in the form of graph patterns in graph datasets. This section presents successively existing work related to those three aspects, highlighting the main similarities and differences with concepts of neighbors.
Instance-based learning As far as we know, instance-based learning on relational data has rarely been studied. The most known approach on this subject is Relational Instance-Based Learning (RIBL)  [20] : it consists in defining a numerical distance between items in a relational dataset and then applying an algorithm similar to the kNN algorithm to classify query instances. A similar approach has been used in  [21] , this time in the context of the instance-based guided edition of RDF graphs. However, as far as we know, all existing approaches use a numerical distance between objects, whereas Concepts of Neighbors use a symbolic distance based on formal concepts. Formal concept analysis Because of its relatively low computational cost, the idea of using instance-based learning to perform classification has been considered in FCA-based machine learning  [22] : instead of computing the whole concept lattice of a context, it consists into computing only the concepts related to the object to be classified.
Implementation The main implementation of those algorithms is CONNOR, a Java library for the computation of concepts of neighbors. This library is a free and open-source software,  4  based on Apache Jena, 5 a well-known Java library for representing and reasoning on the RDF graphs of the Semantic Web. As presented in Section 3. 2, there is a correspondence between RDF graphs and Graph-FCA contexts with only unary and binary relations. This is the case handled by CONNOR: graph contexts are represented as RDF graphs and handled with the Jena library. This library gives a comprehensive interface for the handling of graph contexts (with the class ConnorModel) and concepts of neighbors (class ConceptOfNeighbour), and provides classes encapsulating the algorithms for the efficient computation of concepts of neighbors (classes ConnorPartition and ConnorThread).
For experiments, we use sets of queries on a few datasets of different size: 7 queries having 5 to 21 elements in their pattern on a geographical dataset (MONDIAL  [49] , 12k triples), 7 queries having 3 to 7 elements in their pattern  [44]  on two synthetic datasets about universities (LUBM10, 1. 3M triples; LUBM100, 13M triples  [50] ). Results in NoLimit mode Fig. 5  compares the runtime (log scale) of all algorithms on all queries of MONDIAL and LUBM10 in NOLIMIT mode. A few runtimes are missing for NODEENUM on LUBM10, and for RELAXENUM and PARTITION on Q6 of MONDIAL because they are much higher than other runtimes. RELAXENUM is sensitive to the query complexity, in particular to multivalued properties. NODEENUM looks insensitive to the query complexity, but does not scale well with the number of nodes.
PARTITION and PARTITIONLJ are always more efficient -or equally efficient -and the use of lazy joins generally makes it even more efficient. On LUBM10, PARTITIONLJ is typically one order of magnitude more efficient than RELAXENUM. It can also be observed that PARTITIONLJ scales well with data size because from MONDIAL to LUBM10, a 100-fold increase in number of triples, the median runtime also follows a 100-fold increase, from 0. 01-0. 1 s to 1-10 s. This linear scaling is verified on LUBM100 (not shown) where the runtimes are all 10 times higher. We want to emphasize that it is encouraging that the full relaxation of a query over a 1. 3M-triples dataset can be done in 4 s on average.
Experiments The approach based on Concepts of Neighbors has been compared to state-of-the-art approaches -both latent-based and rulebased -on classical benchmarks for link prediction: WN18, WN18RR, FB15k, FB15k-237. The main results are shown in Table  2 , and all details about the experiments are available in  [16]. ComplEx-N3 (latent-based) clearly outperforms other approaches on all datasets except WN18 where C-N outperforms other approaches on the three measures (MRR, Hits@1, Hits@10). C-N comes second on FB15k, and remains close to the best approaches on WN18RR. On FB15k-237, the MRR delta is -0. 074 with ComplEx-N3, but only -0. 004 with AnyBURL, the best rule-based approach. It is noteworthy that C-N is competitive with AnyBURL because, whereas AnyBURL rules are computed in a supervised manner (knowing the target relation 𝑟), C-N concepts are computed in an unsupervised manner (i.
e. , only knowing the head entity 𝑢). This implies that the concepts of neighbors of an entity can be computed once, and used for many different inference tasks, e. g. predicting links for several target relations. The MONDIAL geographical dataset  [49]  was used to evaluate how complementary by-copy and by-analogy rules are. Although by-analogy rules appear more powerful than by-copy rules with an MRR equal to 0. 424 vs 0. 286, it is beneficial to combine the two as this raises the MRR to 0. 455. We report on an example of inference in the MONDIAL geographical dataset  [49]. The Islay island is correctly predicted to belong to Inner Hebrides (score=0. 34 0. 34 0. 31) by two by-analogy rules (supp=54, conf=0. 34) and one by-copy rule (supp=11, conf=0. 31). The five subsequent predictions are other UK islands that were inferred by the same first and second by-analogy rules, and by another third rule with lower confidence.
Second, we explain how concepts of neighbors can be used for relation extraction. Finally, we summarize the experiments conducted and the results obtained, pointing its results both on the quantitative aspect and the advantages in terms of interpretability. Note that the dataset used in the experimental part is in English, but this work is relatively independent of the language, and could be applied to other languages, as long as the language structure allows for tools similar to those used to exist, and as long as those tools have been developed.
Most relation extraction datasets and approaches use a negative class, or no_relation class, collecting all the task instances (i. e. , pairs of named entities) for which no relation exists between the subject and the object. Therefore, the relation extraction task can be divided in two steps: a relation detection step, charged to determine if a given instance expresses a relation, and a relation classification step, in which the instances expressing a relation are classified among different relation types. Such a two-step approach is introduced in  [65]. As the Concepts of Neighbors approach is based on the computation of similarities between instances, and as the instances of the negative class have no reason to be similar one to each other, the method developed below is suited for relation classification, and should be coupled with a relation detection module to perform proper relation extraction, as presented in  [18].
Application of concepts of neighbors to relation classification The task of performing relation classification on natural text sentences with concepts of neighbors can be divided into three steps. First, the textual data have to be transformed into relational data. Then, Concepts of Neighbors have to be computed for each instance to be classified. Finally, a ranking method has to be defined to infer a prediction from the set of concepts of neighbors. Sentence modeling The first step is to model each instance of the relation extraction dataset as a graph. An instance is a sentence that is annotated with the position and type of two entities (subject and object), and that is labeled with the expected relation type between the two entities. The modeling presented in this section has both syntactic and semantic features, and relies on an NLP toolkit (such as Stanford CoreNLP  [66] ) and on WordNet  [67] , a hierarchical lexical database for English.
By combining the syntactic modeling of sentences and the semantic relaxation mechanism over the lemmas, we obtain a syntactic and semantic modeling of natural language sentences as RDF graphs in which each token and named entity is identified by a unique RDF entity for which the computation of concepts of neighbors is possible. Use of concepts of neighbors For a given instance, the objective is to discover which are the other instances of the training dataset that are the more similar, and then predict a relation type from them. As an instance is a (subject, object) couple, we use binary concepts of neighbors, whose extensions are sets of similar (subject, object) couples. In addition, a mechanism similar to the RECENT paradigm presented in  [64]  is used: before computing the concepts of neighbors of a (subject, object) couple, we deduce from its subject and object types the relation types that are compatible with this query instance.
Dataset The following experiments were conducted on TACRED  [60] , a standard widely-used dataset for Relation Extraction proposed by Stanford University and composed of 100,000 instances (split between train, development and test instances) classified 67. 8 Ours 66. 9 C-GCN  [60]  66. 4 GCN  [60]  64. 0   [63]  and BERT-LSTM-Base  [68] ), while GCN and C-GCN use graph convolution networks over the dependency tree  [60]. Results On the relation classification task, several experiments were conducted. As explained in Section 5. 1, our algorithm is anytime, and therefore experiments were made using different timeouts. Table  3  presents the accuracy scores for both our approach (with a timeout range from 10 seconds to 20 minutes) and the baseline. It can be seen that, whatever is the timeout, our approach outperforms the baseline.
| The sample To better understand the relationship between innovation and economic growth, we used a sample of 21 African countries from 2009 to 2018. 1 The sample used in this study is presented in Table  1. 1 Initially, the sample was made up of 48 countries. However, owing to the availability and the continuity of country-level information, several countries have been excluded. For example, we excluded countries for which data on patents, either applications or grants, were missing or with information not available for at least 5 years. Hence, only 36 countries have been retained. However, we eliminated 15 countries for which data on imports of high technology were missing. The final sample was then reduced to only 21 countries. These data assess how to grow output: to innovate (patents applications and patents grants) or to import innovation (importing of high technology).
Unlike the previous studies that used patents in force  (Hakimi & Inglesi-Lotz, 2019)  or patents count  (Su & Moanib, 2017) , we use this study on patent grants and patents applications  (Dosi & Marengo, 2015; Josheski & Koteski, 2011; Sinha, 2008). The number of patent applications and grants is collected from the World Intellectual Property Organization (WIPO) database. To measure the import of high technology, we used statistics relative to the import of high and medium-high R&D intensive activities. These values are in US dollars in the thousands. Data on the imports of high technology are collected from the OECD statistics. All the rest of the data are derived from the World Development Indicators database.
| CONCLUSION AND POLICY RECOMMENDATIONS This paper aimed to assess what African countries needed to do to boost their growth: innovate or import innovation. To better understand this relationship, we used data from 21 African countries from 2009 to 2018. We used the number of patent applications and patent grants as measures for local innovation and the import of high technology as a measure of imported innovation. Results of the SUR model indicate that the effect of regional innovation is negative but not significant. Although most of the literature has suggested a positive impact of innovation on economic growth (see  Law et al. , 2020; Mansfield, 1980; Griliches & Mairesse, 1984) , the particular source of innovation (whether local or imported) was not considered. innovation with economic growth but without separately considering the source of innovation.
In distributed systems, however, a consistent and highly-available state machine is not a safe assumption  [13] , which has forced OSM and its descendants to use dedicated message brokers  [14] , whereas ATHENA solely utilizes K8s APIs to handle communication between its components asynchronously. When it comes to deployments with K8s, the former practice not only involves substantial overhead, but also means the pre-existing cloud native tools need to be reinvented, especially on lifecycle management and resource control. O-RAN OAM has the tendency to follow the same path and use the same implementations for reference. Despite the admiration for virtualized and software-defined RANs, the O-RAN OAM design is more in favor of PNFs  [21]. The defined services and interfaces become less and less relevant and compelling as one moves from PNFs to VNFs to CNFs and finally cloud native RANs.
In this context, K8s is analogous to an Operating System (OS) and Helm is analogous to a package manager for it; none of them carry on the intended logic for OAM or MANO. We do not perform further comparisons with the mentioned as they seem irrelevant and unfair. Workload Types: ATHENA orients itself around supporting CNFs that are packaged with the particular wireframe of ATHENA to (a) make it compatible with the cloud native models  [16] , (b) improve its lifecycle, and (c) to align itself most-efficiently with Private5G use cases where CNFs would be the dominant workload type. By preserving the support for VMs, solutions like OSM [14] are condemned to lag behind the advanced CNF capabilities or otherwise make the platform heavy or over-complicated. On the other hand, O-RAN's specifications define inapplicable interfaces and services for CNFs which are rather useful for PNFs.
ATHENA Manager performs agile sub-lifecycle operations, observability proxying, configuration management, and dependency resolution. It should be noted that unlike network proxy sidecars, the sidecar model for the Manager in ATHENA is not abolished by the advent and adaptation of the extended Berkeley Packet Filter (eBPF) and Xpress Data Plane (XDP)  [25]  technologies. However, if the functionalities of the Manager are implemented otherwise, it could be discarded for a particular NF. ATHENA remains agnostic to the way the Manager functionalities are provided, and indeed, the Manager is intended to aid rather traditional and non-cloud native applications to be aligned with the cloud native paradigms.
IV. DESIGN AND IMPLEMENTATION We section ATHENA's design into four distinct planes that impact different aspects of the workloads' lifecycle, namely the Operator Plane, Orchestration Plane, Management Plane, and Functional Plane. This concept of plane separation might be viewed as an extension of the Control and User Plane Separation (CUPS) utilized in 4G/5G networks  [19]  into MANO/OAM. The Functional Plane itself is decomposed into the Control Plane and the Data Plane, according to the CUPS.
);  (2)  The external mode where it is just a presentation of a handset outside the cluster and no container would be deployed in this mode; (3) The internal mode where a container is deployed attached to a physical UE module on the cluster; (4) The backhaul mode to support external network formations that shall use this terminal as a backhaul. The identity of the UE is automatically injected into the databases of the corresponding core networks, given their identification.
B. Pod Design and Management Plane The pod in the proposed sidecar design depicted in the Fig. 2  is composed of up to four containers, two of which are mandatory and two are optional. (1)  The Workload container that has the application with some frontend helper scripts that are application dependent with a generic API server that implements the Workload Manager Interface (WMI); (2) The Manager sidecar; (3) An optional logging agent that is responsible for collecting the logs from the application and forwarding processed events to the Observatory in the Manager; (4) An optional test suite container not shown in the figure that provides some testing utilities such as ping, traceroute, or iperf. Bosun is the name of the default implementation of the WMI server in the Workload container. The Workload and Manager containers share a common volume where the Manager would place the configuration files for the application to consume.
The SVM uses Linux inotify to detect changes introduced in the filesystem. The reconfigure is the first mile towards the genuine Service Continuity in 4G/5G networks by enabling Day-2 actions that require no restarting of the network using specific plugins developed for each vendor. However, still restarting the application should take a considerably shorter time than restarting the container by avoiding the initialization for the containers (V-A). The plugins could assume that all the base files for compiling the configuration are already made Manager Extensions: As opposed to the global, longer term decisions of the Operators, ATHENA Manager enables execution of micro-decisions. A micro-decision represents a decision that is concluded and intended for a short period of lifecycle and could be invalidated and overwritten by another decision in a short time.
Implementation Details: Automated device management in ATHENA relies on systemd and K8s device plugins, see Fig. 3. Systemd is the most-commonly used init daemon across all the Linux distributions which comes with a device management mechanism called udev. Given specific rules to udev, we govern the naming, management, and initialization of the devices on the nodes to achieve a harmonized representation of the resources. Later the ATHENA device plugin automatically and dynamically detects the devices and advertises them as node resources in K8s. The physical devices in ATHENA are transformed to logical software devices by Linux systemd and udev in the smallest operational units for each device. These units are categorized by their capabilities into homogeneous sets of devices. The compatability of the devices with the NFs is left for the vendors to decide via the Composition Models.
Methods of performance or accounting management that like O-RAN O1-PM fully or partially delegate the task of the measurement to the NFs themselves are potentially biased, inaccurate, and prone to the side effects of the measurement itself. Moreover, operating legacy NETCONF-based interfaces at scale becomes inefficient and challenging in the medium to large scale cloudnative applications, due to limitations of its transport protocol and improper data models. ATHENA does not directly involve itself in key management, authentication, or authorization. For most cases, the orchestration plane, K8S in this context, provides the means to handle these issues. However, ATHENA's device management and secured, isolated, and rootless containers are the cornerstones of a secure network. Otherwise, any security barriers could be breached by a faulty or malicious workload.
B. Performance and Overhead To form a conclusive image of our platform, we demonstrate the E2E 5G network throughput UDP and TCP throughput achievable in ATHENA, compared to a fully PNF deployment on bare metal and a deployment with Snaps 13 , shown in the 13  Fig. 5  for OAI on B210 (SISO), AMR on SDR50 (MIMO 2x2), and AMR on SDR100 (MIMO 2x1). All the tests are performed on the same machine, running RedHat Enterprise Linux 8 with 12 cores of CPU of type Intel Core i9-10920X at 3. 50GHz base frequency and 64GB of RAM. The configuration of the RAN is the same over all the setups: 5G-SA FR1, 106 PRBs, 40 MHz bandwidth, TDD band 78 with the pattern of 7DL:2UL slots and 6DL:4UL symbols. For the FR2 setup we used 5G-NSA where the LTE cell is configured with 20 MHz bandwidth in FDD band 66, and the NR cell is configured with 100 MHz bandwidth, 120 kHz subcarrier spacing, and TDD band 261 with the pattern of 3DL:1UL for the slots 10DL:2UL for the symbols.
In this case, the results show the aggregated throughput of the two cells that are running with MIMO 2x1. The data shows negligible difference between the throughput on different setups, and the slight variation could only be caused by minor aerial differences. Gathered in the Tab. III, one finds a comparison between Charmed OSM and ATHENA in terms of the computing resources, as well as the corresponding total image sizes. Unfortunately, OSM does not define limits for its resource usage, so we relied on K8s top command. To make a fair comparison, we have grouped the components of both Charmed OSM and ATHENA into the three layers defined earlier in Sec. IV. For the OSM, we have considered the Model and Charmed Operators, LCM, PLA, and POL as the necessary parts of the Operator Plane, while taking into account an average for Charmed Operators for workloads as replacement for the Management Plane, whereas the RO, databases, messaging system (Kafka and ZooKeeper), and authentication management (Keystone) are counted as part of the Orchestration Plane.
ACKNOWLEDGMENT This work has been funded by  Davidson Consulting, France  and partially supported by the  European Commission  as part of the  H2020 program 5G-Victori project , under the grant agreement  857201 ; the  Horizon Europe 2022-6Green project , under grand agreement  101096925 ; the  ImagineB5G  project, under grand agreement  101096452 ; as well as the  LeadingEdge  project under the CHIST-ERA SDCDN topic. In addition, the results of this work have been supported by  BubbleRAN 's cloud-native  5G Open RAN  infrastructure.
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