dataesr/scientific-paragraphs-categorization huggingface dataset:
• submit the form by clicking on the button "submit". After few seconds, depending on the number of smart-contracts that meet the requirements specified by the user, the smartcontracts' addresses and the metrics values will be displayed in a table layout format ready to be downloaded. The Smart Corpus has been in use for 10 months, since December 2019 and 100K smart contracts have been downloaded via the user interface. Until the paper was written (October 2020), Smart Corpus is a curated corpus of 30K smart-contracts's source codes, ABI and byte-codes with related metadata and software metrics. As the time passes, the Smart Corpus is continuously increasing at a rate of 100 smart contracts per day. Figure  7  shows the number of smart-contracts' source codes, ABI and byte-codes retrieved per day since the Smart Corpus was deployed for the first time.
For each smart contract Smart Corpus computed extrinsic and intrinsic metrics, as described in Sections III-C2 and III-C1. Summing up, the Smart Corpus has two GUIs to access data, the HTML GUI and the GraphQL interface. The HTML GUI is described in Section III-E1, while the GraphQL interface is described in Section III-E2. The GraphQL interface gives blockchain researchers the ability to request for exactly what they need. The user can directly access the results via GraphQL interface, as shown in Figure  5. Unlike the existing repositories (see Section II-C2) which make available the source code in a laborious way, Smart Corpus instead made this task easier and faster. Indeed, one of the advantages of using Smart Corpus lies in the fact that it can reduce the costs in performing the smart contract static analysis.
V. CONCLUSIONS AND FUTURE WORKS In this paper, we described the Smart Corpus project, an effort to bring smart contracts data (source codes, ABIs and byte-codes) to the hands of the research community, providing help for reproducible research and a less time-consuming way to gather data and perform static analysis. The project has already stored several megabytes of data, which correspond to about thirty thousand of smart contracts. This work corresponds to 10 months of data retrieving that are made available to the blockchain scientific community and the blockchain developers in a few seconds. The Smart Corpus data-set has strong potential to provide an interesting venue for research in many software engineering areas including, but not limited to, the best practices for Solidity software development, distributed collaboration, and code paternity and attribution.
The Smart Corpus project is in its initial stage of development, but it can already provide useful insight for researchers on smart contracts' coding and everyday use in the blockchain. The corpus will continue to be expanded in content and in the provision of intrinsic and extrinsic metrics, this becoming more and more representative of the Solidity code actually used in the blockchain community. APPENDIX Listing 1 displays a GQL query that returns the smart-contracts's address having more than 20 methods defined in a contract. Listing 2 displays the query results in the JSON format. The query output, in addition to the smart-contract's address, contains various information (intrinsic metrics) such as the number of events, the number of functions, the number of modifiers and the number of payables, as specified by the query 1,.
We present ADONIS, which stands for Aggregator/Disaggregator for Optical Network equIpmentS, a novel open device agent able to construct logical network devices from (dis)aggregation of physical components in order to expose meaningful network devices to the SDN controller. We experimentally assess it by means of a control closed-loop involving ADONIS, a Software Defined Network controller, a Monitoring and Data Analytics system, and a novel reconfiguration tool, SMART-A.
ARCHITECTURE The architecture of our testbed network control and management plane is depicted in Figure  1a. The ADONIS open device agent logically (dis)aggregates the physical devices into logical devices and interfaces them with ONOS. ADONIS is implemented in Python by Nokia Bell Labs. The NorthBound Interface (NBI) relies on NETCONF/YANG offering OpenROADM  [8]  interfaces for ROADMs and OpenConfig  [9]  ones for transponders. We illustrate the use of ADONIS in Figure  1b  showing the physical (commercial) data plane and in Figure  1c  depicting the logical devices composing the 4-node network topology. The physical data plane consists of one Nokia 1830 PSI-2T 4-lineport transponder (Figure  1b , upper right) disaggregated by ADONIS into 4 independent logical transponders (labelled TP1 to TP4 in Figure  1c ); two Lumentum RDM20 degree boxes (Figure  1b , upper right) are aggregated into one virtual 2-degree ROADM (labelled R4); and one Nokia 1830 PSS-32 shelf (Figure  1b , bottom left), holding 7 iROADM9R cards, is aggregated into 3 logical 2-degree ROADMs (labelled R1, R2 & R3), 1 card is not used.
For the sake of clarity, emulated components have not been depicted in Figure  1b, c. The CASTOR MDA system  [2]  collects and processes monitored data from the network equipment and issues appropriate recommendations to the SDN controller. CASTOR is implemented in Python by UPC following a micro-services architecture for the backend. The MDA is extended with an application implementing a degradation detection algorithm that triggers the control closed-loop presented in section 3. The M-COM interface  [10]  is used to populate the MDA operational databases, in particular, the Traffic Engineering Database (TED) and the Label Switching Path Database (LSPDB), and to issue recommendations to the SDN controller. To build a monitoring system scalable and performant, given the heterogeneity of network equipment and the volume of monitored samples, we choose to use the standardized IPFIX protocol  [11]  to convey data from ADONIS to CASTOR.
WORKFLOWS Three workflows, reproduced in Figure  2 , are used in this work. The first workflow (a), is manually triggered by the network operator when the network controller is initialized. It consists in an initial synchronization of the operational databases, i. e. TED and LSPDB, belonging to ONOS and CASTOR. Then, ONOS discovers logical network devices; for each device, it retrieves its details by means of ADONIS and issues an operational databases update notification to CASTOR. The second workflow (b), is manually triggered by the network operator every time a new connection is created in ONOS; it configures the appropriate rules in the logical devices along connection's path and issues appropriate update notifications to CASTOR to synchronize its operational databases. The third workflow (c) implements the control closed-loop.
Periodically, ADONIS retrieves monitoring data samples from the physical devices, associates them to the corresponding logical devices and forwards collected data to CASTOR by means of IPFIX messages (label 1 in Figure  2c ); CASTOR stores the samples on its internal repository and forwards them to the degradation detection algorithm  (2). The algorithm correlates the samples with the network components and analyses trends to identify and localize the origin of degradations by comparing measurements on transmitted and received power. In case of detecting a degradation, the algorithm issues a reconfiguration recommendation to ONOS (3) including the component(s) to be avoided and the affected optical connections. When ONOS receives the recommendation, it forwards a request to SMART-A with similar content (4); the latter, retrieves from ONOS an up-to-date copy of the operational databases  (5)  and solves the optimization problem to reroute the affected connections taking into account optical layer constraints and aiming at minimizing their reconfiguration downtime  (6).
Upon reception of the message, ONOS forwards a request with similar content to SMART-A to reroute the affected connections (4). Next, SMART-A retrieves the operational databases from ONOS (5) and solves the optimization problem  (6)  and implements the solution found (7). Eventually, ONOS installs the appropriate rules in each logical device supported by ADONIS  (8). The resulting paths are illustrated in Figure  3b. Figure  3d  shows the workflow timeline of the execution. The complete workflow took around 9 seconds; mainly driven by the planning tool. Note that a 38-node 76-link is retrieved by SMART-A (real and emulated logical hardware) and used as input to solve the optimization problem. The operations performed in that time frame includes construction of internal data structures and validation of topology, compute the optimal routing solution for the paths, and validate the solutions.
The time contributed by the MDA was 847 ms, including the storage of samples on its internal repository and execution of the degradation detection algorithm. The content of IPFIX and recommendation messages are illustrated in Figure  4. Figure  4a  details the custom IPFIX template record message (id=701); it contains 4 fields: i) standard field "Observation Point Id" used to encode the device port identifier, ii) custom 32-bit unsigned integer field "TimeStamp Nanos" carries the fractional part of the timestamp to improve accuracy of IPFIX header's timestamp field from seconds to nanoseconds, and two custom 32-bit floating point fields iii) "Port Input Power dBm" and iv) "Port Input Power dBm" carrying, respectively, the input and output optical power of ROADM degree line ports. Figure  4b  depicts an example of IPFIX message containing one sample belonging to port 26 in logical device with unique identifier 739198.
This work formulates antiviral repositioning as a matrix completion problem where the antiviral drugs are along the rows and the viruses along the columns. The input matrix is partially filled, with ones in positions where the antiviral has been known to be effective against a virus. The curated metadata for antivirals (chemical structure and pathways) and viruses (genomic structure and symptoms) is encoded into our matrix completion framework as graph Laplacian regularization. We then frame the resulting multiple graph regularized matrix completion problem as deep matrix factorization. This is solved by using a novel optimization method called HyPALM (Hybrid Proximal Alternating Linearized Minimization). Results on our curated RNA drug virus association (DVA) dataset shows that the proposed approach excels over state-of-the-art graph regularized matrix completion techniques.
The information about a drug being effective for a disease is encoded with a one into the matrix and zero, otherwise. The matrix is only partially filled since information about all drug disease associations are not known. Matrix completion amounts to filling the missing entries by relying and interpolating over the available knowledge. Our recent work  [52]  introduced a dataset of antiviral drug virus association (DVA). It shows how the matrix completion framework can be used to computationally predict the drugs that could be effective against SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2), the virus responsible for the ongoing pandemic, COVID-19 (COrona VIrus Disease-2019). In the aforesaid dataset, we manually curated drug-virus associations from drugbank. ca. We collected all the anti-viral drugs proved to be effective against viruses infecting humans along with the similarity information between the viruses (genomic structure-based similarity) and drugs (chemical structurebased similarity).
The dataset contains both DNA and RNA viruses known to infect human beings. State-of-the-art off-the-shelf matrix completion techniques were then employed to solve the association prediction problem. It was found that graph regularised techniques, including similarities based on genomic structure of viruses and chemical structure of antiviral drugs, performed better than the non-regularised competitors. The most successful techniques were graph regularized versions of matrix completion  [51] , binary matrix completion  [48]  and (shallow) matrix factorization  [25]. In this present work, we propose to solve the problem of drug-virus association prediction via graph regularized deep matrix factorization. We propose a novel theoretically sounded algorithm for this task. Moreover, since our final goal is to repurpose drugs for COVID-19, an RNA virus, we have pruned our DVA dataset to consists only RNA viruses.
Evaluation We compare the performance of our method with other recent graph regularized matrix completion techniques such has graph regularized matrix completion (GRMC)  [51] , graph regularized binary matrix completion (GRBMC)  [52]  and graph regularized techniques like graph regularized matrix factorization (GRMF)  [25]. The authors of  [52]  show that GRMF and GRBMC perform well in predicting drug-virus association and hence estimating drugs for SARS-CoV-2. We evaluate the performance of 2-layers and 3-layers graph regularized deep matrix factorization model (GRDMF) with the similarities provided by the method from  [52]  and by the newly proposed models GRDMF-2L and GRDMF-3L. To quantitatively measure and compare the performance of the proposed and other benchmark algorithms, we carry out 10-fold cross validation on the drug-virus association dataset in 3 settings and observe the standard metrics such as mean AUC and AUPR.
Hence, we use the 2-layers GRDMF for further experiments, as it presents lower complexity. Let us analyse deeper the results in Table  2. The dataset contains more drugs (86) than viruses  (23). In CV1 settings, when entries are randomly omitted, without skipping drugs and viruses our method performs the best. There is enough data and hence our method does not overfit. The results improve from one (GRMF) to two and three layers. In the CV2 setting, when viruses are omitted, the size of the dataset reduces. The proposed deeper method overfits and hence performs poorly compared to shallower techniques. CV3 also reduces the size of the dataset by omitting drugs, but since the number of drugs are significantly larger than the number of viruses, the effect is not pronounced on our proposed algorithm and we see an improved prediction.
In 2012, Tsuji et al. 16  successfully used the program LAST  25  with an optimized scoring scheme to search for NUMTs, and this methodology has been the choice for some subsequent NUMT studies. In cattle, the mitochondrial genome has about 16,340 bp and has been the subject of several studies  26. However, the only comprehensive research on NUMTs was carried out by Liu and Zhao  5  , based on a Bos taurus genome reference sequence released in August 2006. A total of 303 NUMTs were identified, with a total length of about 75 kbp. A study with a more recent and improved assembly of the Bos taurus nuclear genome could give further information on the mitochondrial sequences present in the nuclear genome. In this work, we identified and characterized the regions with mitochondrial-like sequences in the nuclear genome using LAST and two assemblies of the bovine genome (ARS-UCD1.
Materials and Methods DnA sequences. Unmasked Bos taurus reference genome sequences from the UMD_3. 1. 1 and ARS-UCD1. 2 assemblies were retrieved from Genbank (Assemblies accessions: GCA_000003055. 5, 25 th November 2014, and GCA_002263795. 2, 11 th April 2018). As mitochondrial reference sequence, CM008198. 1 (Genbank INSDC) was used for all analyses. These sequences were obtained by sequencing the whole genome of L1 Dominette 01449, a Hereford cow  27. Genome-wide search for NUMTs using LAST. Since mitochondrial DNA is circular, it was linearized for alignments. To study the effect of linearization on NUMTs search and to avoid losing NUMTs at the beginning or at the end of the linearized mitochondrial genome sequence, three alternative linearization configurations were tested: (1) a standard linearization starting at position 1 and ending at position 16,340, (2) a standard linearization duplicated in tandem, and (3) a shifted linearization starting arbitrarily at position 8,340 and ending at position 8,339.
These three linearized mitogenome configurations were aligned to the ARS-UCD1. 2 and UMD_3. 1. 1 genome assemblies using version 980 of the program LAST  25. Following Tsuji et al. 16  , the scoring scheme for the alignments was set as: +1 for matches, -1 for mismatches, 7 for gap opening cost and 1 for gap extension cost. We therefore empirically tested which e-value threshold would fit our dataset for the identification of a high number of NUMTs, while being conservative to minimize acceptance of misleading false positives. At first, a set of 1,000 random sequences of 16,340 nucleotides was aligned to both assemblies using LAST with the same NUMT search used above. These random sequences, with specific nucleotide content, were generated using the software Unipro UGENE v1. 26. 1  28  , based on windows of 1,000 bp of the cattle mitochondrial genome.
2 genome assembly. Repetitive regions located in the mitogenome and in the NUMT regions and their flanking positions, 1 kbp up-and downstream, were retrieved from BovineMine, version 1. 6  32  , based on the position of the repetitive regions in the ARS_UCD1. 2 genome assembly. Open Reading Frames (ORFs) were predicted for all NUMT regions using the program ORF Finder from an NCBI website  33. For ORFs larger than 300 amino acids and located outside of gene regions, a BLAST alignment with Expressed Sequenced Tags (ESTs) (EST Divisions from Database of GenBank + EMBL + DDBJ sequences) and protein databases (All non-redundant GenBank CDS translations + PDB + SwissProt + PIR + PRF, excluding environmental samples from WGS projects) of Bos taurus was performed to verify whether there was evidence that these regions were expressed.
a Information about the number of alignments, total alignment length, total length of mitochondrial genome aligned, total length of nuclear genome aligned, alignment gap and average identity between mitochondrial and nuclear sequences. An e-value threshold of 10 -4 was applied to select data to be included in the calculations. For the duplicated linearization, duplicated alignments were excluded for calculations. than 300 bp comprise only about 2. 5% of the total NUMT regions length, but represent almost half of the number of NUMT regions (Table  2 ). A total of 8 NUMT regions detected on UMD3. 1. 1 were not present in ARS-UCD1. 2, while conversely one NUMT region recorded for the ARS-UCD1. 2 could not be detected in UMD3. 1. 1. Although the total alignment length reached around 240 kbp for both assemblies, the total length of the NUMT regions reached 430-440 kbp, representing 0.
Acknowledgements We are grateful to  Tara McDaneld  ( USDA, ARS, US Meat Animal Center, USA ) who provided the DNA sample of Dominette. We are also grateful to  Graham Plastow  ( University of Edmonton, Canada ) for his help. The authors of this study would also like to thank the reviewers for their useful comments in improving the overall quality of the manuscript. The work was supported by the  French National Institute for Agricultural Research (INRA).
Parametric Gaussian Elimination We will show in the next section that an algorithmic realisation of Definition 4 essentially boils down to analysing a parametric linear system of equations. Therefore we study now parametric Gaussian elimination in some detail and propose a corresponding algorithm that satisfies a number of particular requirements coming with our application to differential equations. While parametric Gaussian elimination has beed studied in theory and practice for more than 30 years, e. g. [5, 23, 43] , it is still not widely available in contemporary computer algebra systems. One reason might be that it calls for logic and decision procedures for an efficient heuristic processing of the potentially exponential number of cases to be considered. The algorithm proposed here is based on experiences with the PGAUSS package which was developed in REDUCE  [24, 25]  as an unpublished student's project under co-supervision of the third author in 1998.
The original motivation at that time was the investigation of possible integration and implicit use of the REDUCE package REDLOG for interpreted first-order logic  [18, 45, 46]  in core domains of computer algebra (see also  [17] ). For our proof-of-concept purposes here, we keep the algorithm quite basic from a linear algebra point of view. For instance, we do not perform Bareiss division  [6] , which is crucial for polynomial complexity bounds in the non-parametric case. On the other hand, we apply strong heuristic simplification techniques  [19]  and quantifier elimination-based decision procedures  [31, 40, 54, 55]  from REDLOG for pruning at an early stage the potentially exponential number of cases to be considered. In a rigorous mathematical language, we consider the following problem over a field K of characteristic 0.
This is of great importance for a possible further analysis of the found singularities (not discussed here). In a more formal language, our novel approach translates into Algorithm 2, the correctness of which follows from the above discussion. Note that for computational purposes we limit ourselves to input with integer coefficients. The critical steps are the parametric Gaussian elimination which may potentially lead to many case distinctions, but which represents otherwise a linear operation. For each obtained case, an existential closure must be studied to check whether the case actually occurs on J. The real quantifier elimination in REDLOG primarily uses virtual substitution techniques  [31, 47, 48, 54, 55]  and falls back into partial cylindrical algebraic decomposition  [10, 11, 40]  for subproblems where degree bounds are exceeded.
Computational Experiments We will now study the practical applicability and quality of results of the approach developed in this article on several examples. To this end, we have realised a prototype implementation of Algorithm 1 and Algorithm 2 in REDUCE  [24, 25] , which is not yet ready for publication. We chose REDUCE because on the one hand it is an open-source general purpose computer algebra system, and on the other hand its REDLOG package  [18, 45, 46]  provides a suitable infrastructure for computations in interpreted first-order logic as required by our approach. Although technically a "package", REDLOG establishes a quite comprehensive software system on top of REDUCE. Systematically developed and maintained since 1995, it has received more than 400 citations in the scientific literature, mostly for applications in the sciences and in engineering.
Acknowledgments This work was supported by the  bilateral  project  ANR-17-CE40-0036  and  DFG-391322026 SYM-BIONT. The first two authors thank  Marc Diesse  for useful discussions about singularities in real algebraic geometry. The third author is grateful to  Sarah Sturm  at the  University of Bonn  and  Marco Voigt  at  Max Planck Institute for Informatics  for inspiring and clarifying discussions around Propositions 6 and 7. Werner M. Seiler Institut für Mathematik ,  Universität Kassel , 34109 Kassel, Germany e-mail: seiler@mathematik. uni-kassel. de.
Open data platforms provide free access to datasets in key areas of urban development. Often managed by the local state, open data platforms constitute a part of smart city strategies worldwide, serving different, potentially contradictory aims, i. e. , fostering economic development, pursuing civic objectives or improving administrative efficiency. Reflecting these diverse orientations, our interest lies in understanding how open data platforms are coproduced by different actors based on their conceptions of open data. Taking a critical stance toward the often functionalistic interpretation of open data initiatives as ecosystems, we conceptualize the development of open data initiatives through a dynamic approach to agency, focusing on the strategies of different actor groups shaping open data initiatives and platforms in specific local governance contexts.