dataesr/scientific-paragraphs-categorization huggingface dataset:
Therefore, to reduce the number of possible solutions (i. e. images) that can equally reproduce the data, image reconstruction codes use priors to constrain the brightness distribution. Within MiRA we imposed both a positivity prior (i. e. flux must be ≥ 0) and the hyperbolic regularisation which is an edge-preserving smoothness prior. The hyperbolic regularisation favours solutions where the flux is smooth inside the structure but contains sharp edges. Two hyperparame-5 publicly available at https://github. com/emmt/MiRA ters can be tuned with this regularisation, µ and τ. µ is simply the weight given to the regularisation in determining the total likelihood, L = f data + µ f prior , where f data is a function comparing the model to the data and f prior is a function comparing the model to the constraints given by the prior.
This velocity is also the maximum velocity we would expect given a fully inclined orbit and the secondary currently at its maximum projected distance from the primary. We attempted to fit the normalised Brγ profiles with the addition of a second broad emission component but could not achieve any reasonable fit. Another potential signal of a binary SMBH would be a periodic light curve, however given the distance of the secondary and the mass of primary, the expected period of the orbit is 4400 years. Thus, the current data on NGC 3783 cannot fully rule out a SMBH binary as the origin of the offset cloud. A simpler explanation is scenario 2, where the offset cloud is a massive cloud of gas either inflowing towards the AGN from the circumnuclear disk or potentially outflowing away from it. The dust emission then would be heated, not internally, but externally by the central AGN.
Summary In this paper, we reported on our analysis of VLTI/GRAVITY observations of the nearby type 1 AGN NGC 3783. We investigated three distinct components of the nuclear region around the AGN: 1) the broad line region, 2) the hot dust, and 3) the coronal line region. Our main conclusions are as follows: -We detect and successfully model the BLR as a rotating, thick disk with a physical mean radius of 16 light days. Due to the centrally peaked but heavy tailed distribution of clouds, this leads to a cross-correlation function measured peak time lag of ∼ 10. 2 light days, fully consistent with reverberation mapping results. We find that the key parameters of interest for this Paper like M BH appear to be robustly determined but others, in particular R min , are not. In a future paper exploring joint modelling of GRAVITY and reverberation mapping data we will return to this issue.
-We reconstruct an image of the hot dust which reveals the presence of an offset cloud of gas and dust 0. 6 pc in projected distance away from the main central hot dust component. We measure a FWHM size of 0. 14 pc for the main component consistent with previous interferometric results and with the expected dust sublimation radius. -We measure a gas mass for the offset cloud of 20 M and interpret it primarily as an AGN heated cloud outflowing within the ionization cone. -We measure a FWHM size for the nuclear [Ca viii] emitting CLR of 0. 4 pc, firmly placing it at the very inner regions of the NLR. Combined with our VLT/SINFONI data, we show the CLR is composed of a bright compact nuclear component and a fainter extended component out to 100 pc with outflow kinematics. -We combine our results with past MIR interferometric and our VLT/SINFONI integral field unit data to establish a comprehensive view of the nuclear and circumnuclear region of NGC 3783 that includes an extended dusty outflow originating along the polar axis of the BLR.
Acknowledgements. J. D. was supported in part by  NSF  grant  AST 1909711  and an Alfred P. Sloan Research Fellowship. A. A. and P. G. were supported by  Fundação para a Ciência e a Tecnologia , with grants reference  UIDB/00099/2020  and  SFRH/BSAB/142940/2018. SH acknowledges support from the  European Research Council   via Starting Grant ERC-StG-677117 DUST-IN-THE-WIND. This research has made use of the NASA/IPAC Extragalactic Database (NED), which is operated by the  Jet Propulsion Laboratory, California Institute of Technology , under contract with the  National Aeronautics and Space Administration. This research made use of Astropy, 7 a community-developed core Python package for Astronomy (Astropy Collaboration et al. 2013, 2018), numpy ( Van Der Walt  et al. 2011), scipy (Jones et al.
Microsoft Trinity and later Azure SQL DB provided an early distributed database-oriented approach to big graph management. The diversity of models and systems led initially to the fragmentation of the market and a lack of clear direction for the community. Opposing this trend, we see promising efforts to bring together the programming languages, the ecosystem structure, and the performance benchmarks. As we have argued, there is no single most-common ("killer") application, so the community cannot be brought together around it. Co-authored by a representative sample of the community, this article addresses the questions: How do the nextdecade big graph processing systems look like from the perspectives of the data management and the large scale systems communities 6 ? What can we say today about the guiding design principles of these systems in the next 10 years? Figure  1  outlines the complex pipeline of future big graph processing systems.
Considerable complexity stems from (intermediate) results being fed back into early process steps, as indicated by the blue arrows. 1 As indicated by a user survey  [12]  and by a systematic literature survey of 18 application domains, including biology, security, logistics and planning, social sciences, chemistry, and finance, see http://arxiv. org/abs/1807. 00382 2 Cf. https://app. databox. com/datawall/551f309602080e2b2522f7446a20adb705cabbde8 As an example, to study coronaviruses and their impact on the human and animal populations (e. g. , the COVID-19 disease), the pipeline depicted in Figure  1  could be purposed for two major kinds of analysis: network-based 'omics' and drug-related search, and network-based epidemiology and spread-prevention. For the former, the pipeline could have the following steps: the initial genome sequencing leads to identifying similar diseases, then text (non-graph data) and structured (database) searches help identify genes related to the disease, then a network treatment coupled with various kinds of simulations could reveal various drug targets and valid inhibitors, could lead to effective prioritization of usable drugs and treatments.
For the latter, social media and location data, and data from other privacy-sensitive sources, could be combined into social interaction graphs, which could be traversed to establish super-spreaders and super-spreading events related to them, leading to prevention policies and containment actions. However, the current generation of graph processing technology cannot support such a complex pipeline. For instance, on the COVID-19 knowledge graph,  7  even though useful queries can be posed against individual graphs 8  inspecting the papers, patents, genes, and most influential authors related to COVID-19, a full-fledged graph processing pipeline across multiple (graph-)datasets as illustrated in Figure  1  inspecting several data sources raises many challenges for the current graph database technology.
g. , in HILDA workshops at Sigmod; however, these are not exploring the search space of graph data models. Studying (  2 ) and (3) can build on existing work in database theory, but can also leverage findings from neighboring computer science communities on comparison, featurization, graph summarization, visualization and transformation of models. As an example, graph summarization  [26]  has been widely exploited in order to provide succinct representations of graph properties in graph mining  [27]  but they have seldom been used by graph processing systems to make processing more efficient, more effective, and more user-centered. For instance, approximate query processing for property graphs cannot rely on sampling as done by its relational counterpart and might need to use quotient summaries for query answering.
Logic-based and declarative formalisms Logic provides a unifying formalism for expressing queries, optimizations, integrity constraints, and integration rules. Starting from Codd's seminal insight relating logical formulae to relational queries  [3] , many First Order (FO) logic fragments have been used to formally define query languages with desirable properties such as decidable evaluation. Graph query languages are essentially a syntactic variant of FO augmented with recursive capabilities. Logic provides a yardstick for reasoning about graph queries and graph constraints. Indeed, a promising line of research is the application of formal tools, such as model checking, theorem proving  [4] , and testing, to establishing the functional correctness of complex graph-processing systems, in general, and of graph database systems, in particular.
The influence of logic is pivotal not only to database languages, but also as a foundation for combining logical reasoning with statistical learning in AI. Logical reasoning derives categorical notions about a piece of data by logical deduction. Statistical learning derives categorical notions by learning statistical models on known data and applying it to new data. Both leverage the topological structure of graphs (ontologies and knowledge graphs,  10  graph embeddings (e. g. Node2vec) 3 to produce better insights than on non-connected data). However, both happen to be isolated. Combining both techniques can provide crucial advancements. As an example, deep learning (unsupervised feature learning) applied to graphs allows us to infer structural regularities and obtain meaningful representations for graphs that can be further leveraged by indexing and querying mechanisms in graph databases and exploited for logical reasoning.
Notable features include edge orientation, properties/timestamps for edges and nodes; graph methods (neighborhood statistics, pathfinding and traversal, and subgraph mining); programming models (thinklike-a-vertex, think-like-an-edge, and think-like-a-subgraph); diverse graph sizes, including trillion-edge graphs  [15] ; and query and process selectivities  [1]. (2) Graph workloads can be highly irregular, mixing (short-term) data-intensive and computeintensive phases  [15]. The source of irregularity, e. g. , different datasets and algorithms, and computing platforms, affects performance greatly. Their interdependency forms the Hardware-Platform-Algorithm-Dataset (HPAD) Law  [13]. (3) Graph processing uses a complex pipeline, combining a variety of tasks other than querying and algorithms  [12, 27].
Workloads in graph processing ecosystems Workloads affect both the functional requirements (what a graph processing ecosystem will be capable of doing) and the non-functional (how well). Survey data  [12]  point to pipelines as in Figure  1 : complex workflows, combining heterogeneous queries and algorithms, managing and processing diverse datasets, with characteristics summarized by Sidebar B. In Figure  1 , graph processing links to domain-specific processing ecosystems, including simulation and numerical methods in science and engineering, aggregation and modeling in business analytics, and ranking and recommendation in social media; and to general processing, including machine learning.
Standards for data models and query languages Graph-processing ecosystem standards can provide a common technical foundation, thereby increasing the mobility of applications, tooling, developers, users, and stakeholders. Standards for both OLTP and OLAP workloads should standardize the data model, the data manipulation and data definition language, and the exchange formats. They should be easily adoptable by existing implementations, and also enable new implementations in the SQL-based technological landscape. It is important that standards reflect existing industry practices, by following widely used graph query languages. To this end, in 2019, ISO/IEC started a project to define a new graph query language, GQL. GQL is backed by backing of ten national standards bodies with representatives from major vendors in the industry and supported by the property graph community as represented by the Linked Data Benchmarks Council (LDBC).
Another research challenge is to identify the graph query processing operators that can be evaluated on dynamic and streaming graphs while taking into account recursive operators  [10, 11]  and path-oriented semantics, as needed for standard query languages such as GQL and G-Core  [19]. The graph processing platforms are also dynamic: Discovering, understanding, and controlling the dynamic phenomena that occur in complex graph processing ecosystems is an open challenge. As graph processing ecosystems become more mainstream and are embedded in larger data-processing pipelines, we expect to increasingly observe known systems phenomena such as performance variability, the presence of cascading failures, and autoscaling resources. What new phenomena will emerge? What programming abstractions  [16]  and systems techniques can respond to them?.
CONCLUSION Graphs are a mainstay abstraction in today's data processing pipelines. How can future big graph processing and database systems provide highly scalable, efficient and diversified querying and analytical capabilities, as demanded by real-world requirements? To tackle this question, we have undertaken a community approach. We started through a Dagstuhl Seminar and, shortly after, shaped the structured connections presented here. We have focused in this article on three interrelated elements: abstractions, ecosystems, and performance. For each of these elements, and across them, we have provided a view at what's next. Only time can tell if our predictions provided worthwhile directions to the community. In the meantime, join us in solving the problems of big graph processing. The future is big graphs!.
Graph OLTP Operations Graph OLAP Operations 3https://www. oreilly. com/library/view/graph-algorithms/9781492047674/4Manyhighlycitedarticles support this statement, e. g, Hamilton, W. , Zhitao, Y. , and Leskovec, J. Inductive representation learning on large graphs. NIPS (2017); Perozzi, B. , Al-Rfou, R. , Skiena S. DeepWalk: Online Learning of Social Representations. https://arxiv. org/pdf/1403. 6652. pdf 5 https://neo4j. com/graphs4good/covid-19/6Thesummary of the Dagstuhl seminar: https://www. dagstuhl. de/19491 Processing Non-Graph Formalism Data Sources Graph Extraction Data Model Graph Database Machine Learning Business Intelligence Relational Database Extracted Graphs Scientific Computing Augmented Reality & Visualization Processed Output Graph Graph-Based Engines Algorithm Graph Data ETL for Graph Data Graph Workflow Graph Analytics Engine.
industry systems. How to extend this comparative understanding across multiple data model families, such as graph, relational, document? Which costs and benefits of choosing one model over another? W3C RDF Dataset ISO/IEC GQL Property Graph Model Hyper vertices Multiple edge labels Neo4j / Oracle PGQL Property Graph Model Multiple vertex labels Gremlin / MS Cosmos Property Graph Model Single vertex labels, vertex properties, edge properties W3C RDF Graph Pregel / Giraph Graph (used for e. g. Node2vec) Multiple edges between a pair of nodes, single edge label Data Graph Weighted Graph (used for e. g. Page rank, Connected (used for e. g. Shortest path, Louvain component) modularity) Vertex data Edge data Directed Graph Direction Simple Graph.
In this paper, we do automatic correctness assessment for patches generated by program repair systems. We consider the human-written patch as ground truth oracle and randomly generate tests based on it, a technique proposed by Shamshiri et al. , called Random testing with Ground Truth (RGT) in this paper. We build a curated dataset of 638 patches for Defects4J generated by 14 state-of-the-art repair systems, we evaluate automated patch assessment on this dataset. The results of this study are novel and significant: First, we improve the state of the art performance of automatic patch assessment with RGT by 190% by improving the oracle; Second, we show that RGT is reliable enough to help scientists to do overfitting analysis when they evaluate program repair systems; Third, we improve the external validity of the program repair knowledge with the largest study ever.
First, it allows researchers to assess the correctness of program repair patches at scale, and this would enable them to compute the correctness labels of large datasets of patches. Second, having this large amount of annotated patches is a prerequisite to conducting wide analyses of the characteristics of overfitting patches, and to applying machine learning techniques that require a large amount of data. Consequently, there is an important need for an automated patch assessment. In this paper, we consider automated patch assessment given a ground truth reference patch written by humans, as done by  Xin and Reiss (2017a) ,  Le et al. (2018)  and  Yu et al. (2018). Having a ground truth reference patch is in line with the manual assessment based on the human-written patch, and enables us to compare them.
Notably, there exist other works such as those by  Xiong et al. (2018)  and  Yang et al. (2017)  based on the opposite premise: the absence of a reference patch. We perform automated patch assessment technique using test generation: we generate tests based on the human-written patch, which encodes the correct program behavior considered as the oracles. If any automatically generated test fails on a machine patch, it means its behavior is different from the human-written patch's behavior and it is considered to be overfitting. In this paper, we call this procedure RGT, standing for Random testing based on Ground Truth. Our implementation uses Evosuite  (Fraser and Arcuri, 2011)  and Randoop  (Pacheco and Ernst, 2007)  as test generators and the collected 638 patches in our dataset are automatically assessed with 4,477,707 RGT tests generated (to our knowledge, the largest number of tests ever reported in this context).
We measure the false positive rate of automated patch assessment which has never been done before; 5. Our study is at the largest scale ever, hence has a bigger external validity than the related work: we analyze 638 patches (versus 79 in  Xin and Reiss (2017a)  and 189 in  (Le et al. , 2019) ) from 14 repairs systems (versus 6 considered in  Xin and Reiss (2017a)  and 8 considered in  Le et al. (2019) ). Furthermore, we share with the program repair community: 1) a curated dataset of 638 patches generated from 14 program repair tools for the Defects4J benchmark, together with their correctness metadata. Those patches are given in canonical format so that they provide a foundation for future program repair research. All the data presented in this paper are publicly-available 2. 2) a curated dataset of 4,477,707 generated tests for Defects4J based on the ground truth human patch.
This dataset is valuable for future automated patch assessment, as well as for sister fields such as fault localization and testing. Shamshiri et al. (2015)  and Deepened in This Empirical Study. Findings on Manual Versus RGT Assessment Implications #F1 The misclassification of patches by manual assessment is a common problem. Our experiment shows it happened for 6/14 repair systems manually assessed in previous research. (1) The research community of APR researchers needs better techniques for patch assessment to strengthen scientific validity. #F2 APR researchers confirm that the inputs sampled by random testing are valuable to assess patch correctness. (2) It helps APR researchers to have concrete inputs to analyze patch correctness, suggesting more research about automatic identification of interesting input points (e.
Findings on RGT Time Cost Implications #F10 Over 87% of the time cost of RGT patch assessment is spent in test case generation. (10) We encourage researchers to share the generated tests for behavioral assessment of APR patches. This is a big time saver and this improves scientific reproducibility. #F11 Using previous generated RGT tests from  (Shamshiri et al. , 2015)  is able to identify 219/381 (57. 5%) of overfitting patches without paying any test generation time. (11) Future APR experiments on Defects4J can reuse previously generated RGT tests. When researchers assess patch correctness of APR patches with the same dataset of tests, the community has a fair and unbiased comparison of program repair effectiveness. #F12 There is a trade-off between time spent in generating tests and effectiveness to discard overfitting patches.
Le et al. (2019)  employed 3 to 5 external software experts to evaluate the correctness of this patch and the result was overfitting. We performed several discussions of the correctness of this patch with the original authors of DeepRepair and JGenProg via email. Eventually, they achieved consensus on the correctness of this patch and confirmed that this patch is actually a correct patch. The motivating example shows that analysts may hold different opinions of the correctness even on the same patch. If manual patch correctness assessment gives too many erroneous results, it is a significant threat to the validity of the evaluation of program repair research. With unreliable correctness assessment, a technique A claimed as better than a technique B may actually.
In addition, we analyze the RGT test failures to determine (1) whether the observed behavioral difference is indeed triggered by an incorrect patch and (2) whether the sampled test data violates any program pre-condition. All results are discussed among at least two authors. The RGT assessment is considered as a true positive if: a) our manual analysis perceives the patch is 'no or partially fix' or 'a new bug introduced', b) the observed behavioral difference is related to the bug, and c) the sampled test data does not violate any program pre-condition. Last, for those true positive cases, we send our RGT assessment results and failing RGT tests to the original authors of the patch and ask them for feedback. In particular, we explore to what extent they agree with the RGT assessment results.
For RQ2, we record the number of the false positive cases by RGT assessment. This enables us to estimate a false positive rate of RGT assessment. RQ3. RQ3 focuses on the effectiveness of RGT assessment in identifying overfitting patches. We first collect a set of APR patches for Defects4J, that were manually assessed as overfitting by the corresponding researchers. This set of patches is denoted as D overf itting. We execute RGT tests over the whole D overf itting patches and record test failures. A test failure means that RGT succeeds in identifying a patch as overfitting, that RGT agrees with the manual analysis by researchers. Next, we also execute the state-of-the-art overfitting patch detection technique DiffTGen over the same dataset. We execute DiffTGen by the default mode which calls EvoSuite in 30 trials with the searching timeout being 60 seconds for each trial.
We do not execute Opad  (Yang et al. , 2017)  and PATCH-SIM  (Xiong et al. , 2018)  on this dataset for the following reasons: Opad is based on memory safety analysis in C which is not relevant in the context of the memory safe language Java. PATCH-SIM is not considered in our study for two reasons: (1) PATCH-SIM targets APR users who do not have any ground truth patch available. On the contrary, RGT targets APR researchers who have a ground truth patch at hand. As reported in  Xiong et al. (2018) , PATCH-SIM has a false positive rate of 8. 25% for assessing human-written patches, we aim at having a lower false positive rate. RQ4. We estimate the performance of RGT from a time cost perspective. We measure the time cost of RGT in three dimensions: the time cost of test case generation, the time cost of sanity checking and the time cost of executing the test cases over the APR patches.