@Retention(value=RUNTIME) @Target(value={TYPE,METHOD}) @Repeatable(value=AssertMatchesDatasets.class) public @interface AssertMatchesDataset
AssertMatchesDataset
annotation specifies location(s) of a JSON-based file(s) with data
to match the state of the database with.
It can be used on a class, on a method, or in combination of the two, which would have a
cumulative effect, i.e., the data pointed to by the method annotation would be added to the data
pointed to by the class annotation.
There is also an option to trigger assertion with no additional dataset by setting this
annotation's 'additionalDataset' to 'false'. In such a case, previously set assertion datasets
(if any at all) are used. This can be used to verify that nothing exists in the database or if
the dataset is coming from SeedWithDataset
annotation with the 'reuseForAssertion' set to
'true'.
If neither the target test method nor its containing target test class is annotated with this
annotation, no automatic assertion takes place.| Modifier and Type | Optional Element and Description |
|---|---|
boolean |
additionalDataset
Returns true to indicate additional assertion dataset should be used for this assertion based
on the 'value' or 'locations' pointed to by this annotation, false if no additional dataset
should be loaded.
|
String[] |
locations
List of locations of JSON files that contain the dataset to match the state the database with.
|
LocationType |
locationType |
String[] |
value
List of locations of JSON files that contain the dataset to match the state the database with.
|
@AliasFor(value="locations") public abstract String[] value
MongoUnitTest
), i.e.,
if the fully qualified name of the test class is 'com.mytest.MyIT' and this value is selected,
the file will be searched for in the '/com/mytest/MyIT' folder relative to the root of the
classpath. If 'com.mytest.MyIT' class' MongoUnitTest
annotation specifies 'name' of
'test1', the file will be searched for in the '/com/mytest/test1' folder relative to the root
of the classpath.
If the 'locationType' is 'ABSOLUTE', the file will be looked for in the root directory, i.e .,
'/'.
If the MongoUnit framework fails to find any of the dataset file(s), an exception will be
thrown.
If the desired assertion state is an empty dataset, set 'additionalDataset = false' and omit
setting this property.
Multiple locations have a cumulative effect on the data, i.e., the data is sequentially
combined in the order specified in the 'locations' array before a match assertion is made.@AliasFor(value="value") public abstract String[] locations
MongoUnitTest
), i.e.,
if the fully qualified name of the test class is 'com.mytest.MyIT' and this value is selected,
the file will be searched for in the '/com/mytest/MyIT' folder relative to the root of the
classpath. If 'com.mytest.MyIT' class' MongoUnitTest
annotation specifies 'name' of
'test1', the file will be searched for in the '/com/mytest/test1' folder relative to the root
of the classpath.
If the 'locationType' is 'ABSOLUTE', the file will be looked for in the root directory, i.e .,
'/'.
If the MongoUnit framework fails to find any of the dataset file(s), an exception will be
thrown.
If the desired assertion state is an empty dataset, set 'additionalDataset = false' and omit
setting this property.
Multiple locations have a cumulative effect on the data, i.e., the data is sequentially
combined in the order specified in the 'locations' array before a match assertion is made.public abstract LocationType locationType
public abstract boolean additionalDataset
Copyright © 2021. All rights reserved.


A Selection of calming videos.
A collection of humorous videos
A selection of classic TV episodes.
A selection of travel videos.
A selection of Movies.
A selection of exercise videos.
Various Interest Videos


Class Trees for Package View
Root class BaseUrlHelper
Root class ClassNotFoundException
Root class ContextSandbox
Root class EngineNotFoundException
Root class MethodNotFoundException
Root class NoEngine
Root class SimpleEngine
Root class TemplateNotFoundException
Root class View
|


Next: slot-missing, Previous: slot-exists-p, Up: Objects Dictionary
slot-makunbound
instance slot-name ⇒ instance
instance – instance.
Slot-name—a symbol.
The function slot-makunbound restores a slot of the name slot-name in an instance to the unbound state.
If no slot of the name slot-name exists in the instance, slot-missing is called as follows:
(slot-missing (class-of instance) instance slot-name 'slot-makunbound)
(Any values returned by slot-missing in this case are ignored by slot-makunbound.)
The specific behavior depends on instance’s metaclass. An error is never signaled if instance has metaclass standard-class. An error is always signaled if instance has metaclass built-in-class. The consequences are undefined if instance has any other metaclass–an error might or might not be signaled in this situation. Note in particular that the behavior for conditions and structures is not specified.
Although no implementation is required to do so, implementors are strongly encouraged to implement the function slot-makunbound using the function slot-makunbound-using-class described in the Metaobject Protocol.


mages are not technically inserted into a web page; images are linked to web pages.
mages are not technically inserted into a web page; images are linked to web pages.images are not technically inserted into a web page;
images are not technically inserted into a web page; images are linked to web pages.images are not technically inserted into a web page; images are linked to web
mages are not technically inserted into a web page; images are linked to web pages.images are not technically inserted into a web page; images are linked to web


The objective of the Semantic Publishing (SemPub) challenge series is to bootstrap a value chain for scientific data to enable services, such as assessing the quality of scientific output with respect to novel metrics. The key idea was to involve participants in extracting data from heterogeneous resources and producing datasets on scholarly publications, which can be exploited by the community itself. Differently from other challenges in the semantic publishing domain, whose focus is on exploiting semantically enriched data, SemPub focuses on producing Linked Open Datasets. The goal of this paper is to review both (i) the overall organization of the Challenge, and (ii) the results that the participants have produced in the first two challenges of 2014 and 2015 – in terms of data, ontological models and tools – in order to better shape future editions of the challenge, and to better serve the needs of the semantic publishing community.
Semantic publishing – defined as the use of Semantic Web technologies to make scholarly publications and data easier to discover, browse and interact with – is a lively research area in which a big number of projects and events have emerged and showcase the potential of Linked Data technology. Extracting, annotating and sharing scientific data (by which, here, we mean standalone research datasets, data inside documents, as well as metadata about datasets and documents), up to building new research on them, will lead to a data value chain producing value for the scientific community .
Bootstrapping and enabling such value chains is not easy. A solution that has proved to be successful in other communities is to run challenges, i.e. competitions in which participants are asked to complete tasks and have their results ranked, often in objective way, to determine the winner. Even a number of projects have been launched to accelerate this process, for instance LinkedUp or Apps for Europe. The success of the LAK or Linked Up Challenges is worth mentioning here. However, these challenges focus on exploiting scholarly linked data for different purposes (for instance, to monitor progress) but less on actually producing such datasets.
To this end, we started a series of Semantic Publishing Challenges (SemPub), aiming at the production of datasets on scholarly publications. To the best of our knowledge, this was the first challenge of its kind. Now, in 2016, we are running the 3rd edition of SemPub and we believe it is good time to review the challenge and share some lessons learned with the community. On the other hand, community feedback can help us shape the future of SemPub. Continuous refinement is in fact a key aspect of our vision.
of this paper reviews the background of SemPub, its history, structure and evaluation methods. presents lessons learned from the challenge organization and from the overall approaches of the submitted solutions. concludes and provides an outlook to how future SemPub challenges will take these lessons into account.
We draw a brief history of the SemPub Challenge to provide the necessary background for the following discussion. More detailed reports have been published separately for the 2014 and 2015 challenges.
We started in 2014, reasoning about a challenge in the semantic publishing domain that could be measured in an objective way. This was difficult because of the tension between finding appealing and novel tasks and measuring them. We thus asked participants to extract data from scholarly papers and to produce an RDF dataset that could be used to answer some relevant queries: concretely, queries about the quality of scientific output. We were aware of other topics of interest for the community – nanopublications, research objects, etc. – but focused on papers only to bootstrap the initiative and to start collaboratively producing initial data.
We designed different tasks, sharing the same organization, rules and evaluation procedure. For each task, we published a set of queries in natural language and asked participants to translate them into SPARQL and to submit a dataset on top of which these queries would run. In line with the general rules for the new Semantic Web Evaluation Challenge track at ESWC, we also published a training dataset (TD) on which the participants could test and train their extraction tools. A few days before the submission deadline, we published an evaluation dataset (ED): the input for the final evaluation.
The evaluation consisted of comparing the output of these queries, given as CSV, against a gold standard and measuring precision and recall. All three editions used the same evaluation procedure, but the tasks were refined over time. summarizes all tasks, their data sources and queries.
| 2014 | 2015 | 2016 | |
|---|---|---|---|
Task1 |
Extracting data on workshops’ quality indicators; Source: CEUR-WS.org |
||
Format: HTML+PDF |
Format: HTML |
Format: HTML |
|
Task2 |
Extracting data on citations Source: PubMed Format: XML |
Extracting data on affiliations, citations, funding Source: CEUR-WS.org Format: PDF |
Extracting data on affiliations, internal structure, fundings Source: CEUR-WS.org Format: PDF |
Task3 |
Open tasks: showcase semantic publishing applications |
Interlinking Sources: CEUR-WS.org, Colinda, DBLP, Lancet, Semantic Web Dog Food (SWDF), Springer LD) |
Interlinking Sources: CEUR-WS, Colinda, DBLP, Springer LD. |
Two tasks have been defined at the very beginning (see for full details and statistics):
Task 1: participants were asked to extract information from selected CEUR-WS.org workshop proceedings volumes (HTML tables of content using different levels of semantic markup, plus PDF full text) to enable the computation of indicators for the workshops’ quality assessment. They were asked to answer 20 different queries.
Task 2: participants were asked to extract data about citations, to enable precise assessment of linking, sharing and evaluating research through citations. The dataset included a set of XML-encoded research papers, taken from PubMedCentral and Pensoft Open Access archives, and heterogeneous in terms of internal structure, styles and numbers. Both dataset and queries were completely disjoint from Task 1.
Having called for submissions, we received feedback from the community that mere information extraction, even if motivated by quality assessment, was not the most exciting task related to the future of scholarly publishing, as it assumed a traditional publishing model. Furthermore, to address the primary target of the challenge, i.e. “publishing” rather than just “metadata extraction”, we widened the scope by adding an open task, whose participants were asked to showcase data-driven applications that would eventually support publishing. We received a good number of submissions; winners were selected by a jury.
In 2015 we were asked to include only tasks that could be evaluated in a fully objective manner, and thus discarded the open task. We reduced the distance between Tasks 1 and Task 2 by using the same dataset for both. We transformed Task 2 into a PDF mining task and thus moved all PDF-related queries there. The rationale was to differentiate tasks on the basis of the competencies and tools required to solve them, but to make tasks interplay on the same dataset.
CEUR-WS.org data has become the central focus of the whole Challenge, for two reasons: on the one hand, the data provider (CEUR-WS.org) takes advantage of a broader community that builds on its data, which, before the SemPub Challenges, had not been available as linked data. On the other hand, data consumers gain the opportunity to assess the quality of scientific venues by taking a deeper look into their history, as well as the quality of the publications. While Task 1 queries remained largely stable from 2014 to 2015, the queries for Task 2 changed, mainly because the setting was completely new (PDF rather than XML sources), and we wanted to explore participants’ interest and available solutions. We asked them to extract data not only on citations but also on affiliations and fundings.
In 2015 we added a new Task 3, focusing on interlinking the dataset the winners of the first Challenge had extracted from a single source to further relevant datasets. Participants had to make such links explicit and exploit them to answer comprehensive queries about events and persons. CEUR-WS.org on its own provides incomplete information about conferences and persons, which can be complemented by interlinking with other datasets to broaden the context and to allow for more reliable conclusions about the quality of scientific events and the qualification of researchers.
Continuity is the key aspect of 2016 edition. The tasks are unchanged (allowing previous participants to use and refine their tools), except for details: Task 2, in particular, is extended to structural components of papers and does not include citations anymore.
In this section we discuss lessons learned from our experience in organizing the challenge. Our goal is to distill some generic guidelines that could be applied to similar events, starting from the identification of critical issues, errors and strengths of our initiative. The primary focus of the paper is on (even unexpected) aspects that emerged while running the challenge. This section will also present the lessons learned by looking at the solutions and data produced by the participants. We have grouped the lessons in four categories for clarity, even though there is some overlap between them.
The definition of the tasks is the most critical part of organizing a challenge. In our case, it was difficult to define appealing tasks that bridge the gap between building up initial datasets and exploring possibilities for innovative semantic publishing. As discussed in , we refined the tasks over the years according to the participants' and organizers' feedback. Overall, we think that tasks could have been improved in some parts – and undeniably other interesting ones could have been defined – but they were successful. There are other less evident issues which are worth discussing here.
L1.1. Continuity: allow users to re-submit the improved version of their tool over different editions. One of the goals of the first edition of the challenge was also to explore the interest of the participants. Exploiting such feedback and creating a direct link between different editions is a success key factor. In 2015, in fact, the Challenge was re-organized aiming to commit participants to re-submit overall improved versions of their first year submissions. Results were very good, as the majority of first year’s participants competed for the second year too. Continuity is also a key aspect of SemPub2016, whose tasks are the same as last year’s edition, allowing participants to reuse their tools to adapt to the new call after some tuning.
L1.2. Split tasks with a clear distinction of the competencies required to complete them. One of the main problems we faced was that some tasks were too difficult. In particular the Task 2 – extraction from XML and PDF – showed unexpectedly low performance. The main reason, in our opinion, is that the task was actually composed of two sub-tasks that required very different tools and technologies: some queries required participants to basically map data from XML/PDF to RDF, while the others required additional processing on the content. Some people were discouraged to participate as they only felt competitive for the one and not for the other. Our initial goal was to explore a larger amount of information and to give participants more options but, in retrospect, such heterogeneity was a limitation. A sharper distinction between tasks would have been more appropriate. In particular, it is important to separate tasks on plain data extraction from those on natural language processing and semantic analysis.
L1.3. Involve participants in advance in the task definition. Though we collected some feedback when designing the tasks, we noticed that such preliminary phase was not given enough relevance. The participants’ early feedback can help to identify practical needs of researchers and to shape tasks. Talking with participants, in fact, we envisioned alternative tasks, such as finding high-profile venues for publishing a work, summarizing publications, or helping early career researchers to find relevant papers. Proposing tasks emerged from the community can be a winning incentive to participate.
The continuity between tasks (L1.1) can be applied to the datasets as well:
L2.1. Use the same data source for multiple editions. We noticed benefits of using the same data sources across multiple editions of the Challenge. From the task 1 of the 2014 edition, in fact, we obtained an RDF dataset that served as the foundation to build the same task in 2015 and 2016. Participants were able to reuse their existing tools and to extend the previously-created knowledge-bases with limited effort. For the other tasks, which were not equally stable, we had to rebuild the competition every year without being able to exploit the past experience.
L2.2. Design all three tasks around the same dataset. Similarly, it is valuable to use the same dataset for multiple tasks. First of all, for the participants: they could extend their existing tools to compete for different tasks, with a quite limited effort. This also opens new perspectives for future collaboration: participants’ work could be extended and integrated in a shared effort for producing useful data. It is also worth highlighting the importance of such uniformity for the organizers. It reduces the time needed to prepare and validate data, as well as the risk of errors and imperfections. Last but not least, it enables designing interconnected tasks and producing richer output.
L2.3. Provide an exhaustive description of the expected output on the training dataset. An aspect that we underestimated in the first editions of the Challenge was the description of the training dataset. While we completely listed all papers we did not provide enough information on the expected output: we went into details for the most relevant and critical examples but we did not provide the exact expected output for all papers in the training dataset. Such information should instead be provided as it impacts directly the quality of the submissions and help participants to refine their tools.
All three editions of the Challenge shared the same evaluation procedure (see for more details). The workflow presented some weaknesses, especially in the first two years, which we subsequently addressed. Three main guidelines can be derived from these issues.
L3.1. Consider all papers in the final evaluation. Even though we asked participants to run their tools on the whole evaluation dataset, we considered only some exemplary papers for the final evaluation. These papers have been randomly selected from clusters representing different cases, which participants were required to address. Since these papers were representative of these cases we received a fair indication of the capabilities of each tool. On the other hand, some participants were penalized as their tool could have worked well on other values, which were not taken into account for the evaluation. In the third edition, we will radically increase the coverage of the evaluation queries and their number in order to assure that greatest part of the dataset (or the whole dataset) is covered.
L3.2. Make evaluation tool available during the training phase. The evaluation was totally transparent and all participants received detailed feedback about their scores, together with links to the open source tool used for the final evaluation. However we were able to release the tool only after the Challenge. It is instead more helpful to make it available during the training phase, as participants can refine their tool and improve the overall quality of the output. Such an approach reduces the (negative) impact of output imperfections. Though the content under evaluation was normalized and minor differences were not considered as errors, some imperfections were not expected and were not handled in advance. Some participants, for instance, produced CSV files with columns in a different order or with minor differences in the IRI structure. These all could have been avoided if participants received feedback during the training phase, with the evaluation tool available as a downloadable stand-alone application or as a service.
L3.3. Use disjoint training and evaluation datasets. A 2015 participant raised the issue that we underestimated when designing the evaluation process: the evaluation dataset was a superset of the training one. This resulted in some over-training of the tools, and caused imbalance in the evaluation. It is more appropriate to use completely disjoint datasets, a solution we are implementing for the last edition.
Further suggestions can also be derived from the Challenge’s organizational aspects, in particular regarding the expected outcome:
L4.1. Define clearly the license of produced output. Some attention should be given to the licensing of the output produced by the participants. We did not explicitly say which license they should use: we just required them to use an open license on data (at least permissive as the source of data) and we encouraged open-source licenses on the tools (but not mandatory). Most of the participants did not declare which exact license applies to their data. This is an obstacle for the reusability: especially when data come from heterogeneous sources and are heterogeneous in content and format, as in the case of CEUR-WS papers, it is very important to provide an explicit representation of the licensing information.
L4.2. Define clearly how the output of the challenge will be used. The previous observation can be generalized into a wider guideline about reusability. It is in fact critical to state how the results of the challenge will be eventually used, in order to encourage and motivate participants. The basic idea of the Challenge was to identify the best performing tool on a limited number of papers and to use the winning tool – or a refined version – to extract the same data on the whole CEUR-WS corpus . The production of the CEUR-WS Linked Open Dataset was actually slower than expected and we are finalizing it in these days. This is a critical issue: participants’ work should not target the challenge only, but it should produce an output that is directly reusable by the community.
L4.3. Study conflicts and synergies with other events. The last guideline is not surprising and was confirmed by our experience as well. In 2015, in fact, we introduced a task on interlinking. The community has been studying interlinking for many years and a lot of research groups could have participated in the task (and produced very good results). However we did not receive enough submissions. One of the issues – not the only one, communication might be another – is the conflict with events like OAEI (Ontology Alignment Evaluation Initiative). Even though Task 3 of SemPub2015 did not intend to cover the specialized scope of OAEI, but rather put the interlinking task in a certain use case scope that merely serves in aligning the tasks output among each other and with the rest LOD cloud. The study of overlapping and similar events should always be kept in mind. Not only to identify potential conflicts but also to generate interest: the fact that the SePublica workshop was at ESWC 2014, for instance, was positive since we had fruitful discussions with the participants and the two events could benefit each other.
In this section we discuss lessons learned from the participants' solution. We start with an overview of the solutions; next, we group the lessons into four categories: lessons on submitted tools, used ontologies, submitted data and evaluation process; even though there is some overlap between these aspects.
There were four different solutions proposed to address Task 1 in 2014 and 2015. Three participated in both editions, whereas the fourth solution participated only in the second edition.
Solution 1.1. presented a case-specific crawling based approach for addressing Task 1. It relies on an extensible template-dependent crawler that uses sets of special predefined templates based on XPath and regular expressions to extract the content from HTML and to convert it in RDF. The RDF is then processed to merge resources using fuzzy-matching. The use of the crawler turns the system tolerant to invalid HTML pages. This solution improved its precision in 2015 as well the richness of the data model.
Solution 1.2. exploited a generic tool for generating RDF data from heterogeneous data. It uses the RDF Mapping Language (RML) to define how data extracted from CEUR-WS Web pages should be semantically annotated. RML extends R2RML to express mapping rules from heterogeneous data to RDF. CSS3 selectors are considered to extract the data from the HTML pages. The RML mapping rules are parsed and executed by the RML Processor. In 2015 the solution reconsidered its data model and was extended to validate both the mapping documents and the final RDF, resulting in an overall improved quality dataset.
Solution 1.3. designed a case-specific solution based on a linguistic and structural analyzer. It uses a pipeline based on the GATE Text Engineering Framework. To produce annotations, it relies on chunk-based and sentence-based support vector machine (SVM) classifiers which are trained using the CEUR-WS proceedings with microformat annotations. The annotation sanitizer has a set of heuristics which are applied to fix imperfections and interlink annotations. The produced dataset is also extended with information retrieved from external resources.
Solution 1.4. presented an application of the FITLayout framework This solution participated in the Semantic Publishing Challenge only in 2015. It combines different page analysis methods, i.e. layout analysis and visual and textual feature classification to analyze the rendered pages, rather than their code. The solution is quite generic but needs to be domain/case-specific at certain phases (model building step).
All solutions are summarised in , which also add details about the languages and technologies exploited by the participants.
| Solution 1 | Solution 2 | Solution 3 | Solution 4 | |
|---|---|---|---|---|
Primary Method |
Crawling |
Generic solution for abstracted mappings |
Linguistic and structural analysis |
Visual layout Multi-aspect content analysis |
Case-specific |
YES |
NO |
YES |
NO |
Template-based |
YES |
YES |
NO |
~NO |
Implementation basis |
– |
RML tools |
– |
FITLayout |
Implementation Language |
Python |
Java |
– |
Java/HTML |
Mappings/Rules |
XPath (embedded in the code) |
– |
RML/CSS (abstracted from the code) |
Hard coded |
RegEx |
YES |
YES |
– |
YES |
Solution 2.1. CERMINE is an open source system for extracting structured metadata and references from scientific publications published as PDF files. It has a loosely captured architecture and a modular workflow which is based on supervised and unsupervised machine-learning techniques which simplifies the system’s adoption to new document layouts and styles.
Solution 2.2. implemented a processing pipeline that analyzes the structure of a PDF document incorporating a diverse set of machine learning techniques, unsupervised to extract text blocks and supervised to classify blocks into different meta-data categories. Heuristic are applied to detect the reference section and sequence classification to categorize the tokens of individual references strings. Finally, named entity recognition (NER) are used to extract references to grants, funding agencies and EU projects.
Solution 2.3. presented Metadata And Citations Jailbreaker (MACJa – IPA), a tool that integrates hybrid techniques based on Natural Language Processing (NLP) and incorporating FRED, a novel machine reader. It also includes modules to query external services to enhance and validate data.
Solution 2.4. presented a system composed by two modules: a text mining pipeline based on the GATE framework to extract structural and semantic entities, leveraging also existing NER tools, and a LOD exporter, to translate the document annotations into RDF according to custom rules.
Solution 2.5. relies on a rule-based and pattern matching approach, implemented in Python and some external services for improving the quality of the results (for instance, DBLP for validating author’s data). It also relies on an external tool to extract the plain text from PDFs.
Solution 2.6. extended their framework used for Task 1 (and indicated as Solution 1.3 before) to extract data from PDF as well. Their pipeline includes text processing and entity recognition modules and employs external services for mining PDF articles. represents tools and its components:
L5.1. There are both generic and ad hoc solutions. All solutions were methodologically different among each other. For Task 1, for instance, two solutions (1.1 and 1.3) primarily consisted of a tool developed specific to this task, whereas the other two solutions wrote task-specific templates in the otherwise generic implementations (adaptive to other domains). In the later case, Solution 1.2 abstracted the case-specific aspects from the implementation, whereas Solution 1.4 kept them inline with the implementation. It becomes, therefore, clear that there are alternative approaches which can be used to produce RDF datasets.
L5.2. There are HTML code and content-based approaches to information extraction. Even though solutions were methodologically different, two main approaches for dealing with the HTML pages prevailed: HTML-code-based and content-based.
L6.1. All solutions used almost the same data model (Task 1). All solutions of Task 1 tend to converge regarding the model of the data. The same occurs but on a higher level in the case of Task 2. In particular for Task 1, Solution 1.4 domain modeling was inspired by the model used in Solution 1.2, with some simplifications. Note also that Solution 1.2 was the winner solution in 2014. Based on the aforementioned, we observe a trend of converging regarding the model the CEUR-WS data set should have, as most of the solutions converge on the main identified concepts in the data (Conference, Workshop, Proceedings, Paper and Person).
L6.2. All solutions used almost the same vocabularies for the same data (Task 1). There is a wide range of vocabularies and ontologies that can be used to annotate scholarly data. However, most of the solutions preferred to (re)use almost the same existing ontologies and vocabularies (see for Task 1). This is a good evidence that the spirit of vocabulary reuse gains traction. However, it is interesting that different solutions used the same ontologies to annotate the same data differently (see L6.3).
L6.3. Different solutions used different annotations (Task 1). Even though all solutions used almost the same vocabularies, not all solutions used the same classes to annotate same entities. To be more precise, all solutions only converged on annotating persons using foaf:Person. For the other main concepts the situation was heterogeneous, as reported in .
| Task 1 / 2015 | Solution 1 | Solution 2 | Solution 3 | Solution 4 |
|---|---|---|---|---|
Person |
foaf:Person |
foaf:Person |
foaf:Person |
foaf:Person |
Paper |
bibo:Article |
swrc:InProceedings |
swrc:Publication |
swc:Paper |
Conference |
swc:OrganizedEvent |
bibo:Conference |
swrc:Conference |
swc:ConferenceEvent |
Proceeding |
bibo:Proceeding |
bibo:Proceedings |
swrc:Proceedings |
swc:Proceedings |
Proceeding |
bibo:Workshop |
bibo:Workshop |
swrc:Workshop |
swc:section |
L6.4. Different solutions used different vocabularies for the same data (Task 2). In contrast to Task 1 solutions which all intuitively converged on using the same vocabularies and ontologies, Task 2 solutions use relatively different vocabularies and ontologies, but again pre-existing ones, to annotate same entities appearing in the same data. However, most of the Task 2 solutions use sub-ontologies of the family of SPAR ontologies. It is interesting to observe if the Task 2 solutions of 2016 will converge towards using same ontologies, being inspired one from the other, or if solutions will keep using different vocabularies.
L7.1. Overall dataset improved over successive challenges.
From the first edition to the second edition of the Semantic Publishing Challenge, we expected that participants who re-submit their solutions would have improve the overall dataset, rather than optimize it for answering the queries. All three solutions of Task 1 both in 2014 and 2015 edition modified the way they represented their model in 2014 for their submissions in 2015 which resulted in corresponding improvements to the overall dataset. Although this happens to a certain extend and indeed the results were more satisfying, we still see that there is room for overall improvement.
L7.2. Participants preferred custom solutions. Custom solutions for a particular task, such as publishing CEUR-WS.org proceedings, may obviously result in more accurate output in terms of answers to queries, however they lack repurposeability, as they cannot be reused for other input data. Moreover, despite the fact that there are generic tools for extracting RDF datasets, challenge participants preferred to develop custom solutions. This can be interpreted as a lack of alternatives of HTML specific tools to address the task.
L7.3. Striking differences in coverage. We further observed that solutions rarely agree upon the extracted information. Overall, we observe significant differences in respect to the number of identified entities per category. The results for Task 1 are summarized in the .
| – | Solution 1.1 | Solution 1.2 | Solution 1.3 | Solution 1.4 |
|---|---|---|---|---|
| #Conferences | 46 |
46 |
51 |
47 |
| #Workshops | 252 |
1393 |
127 |
198 |
| #Proceedings | 243 |
1392 |
202 |
1353 |
| #Papers | 3801 |
2452 |
720 |
2470 |
| #Persons | 6700 |
6414 |
3402 |
11034 |
Let us consider the proceedings for example. Apparently, Solution 1.1 and Solution 1.3 used the individual pages to identify the proceedings, whereas Solution 2 and Solution 4 used the index page to identify the proceedings, this is the reason that there is so big difference in the numbers. The number of identified papers is also significantly different among the different solutions, but in the case of persons we observe the most variation in terms of numbers. However, the more the solutions improve, the more we expect to find solutions that converge at least regarding the number of retrieved and/or distinctly identified entities.
L7.4. RDF datasets differed significantly w.r.t. statistics. Produced datasets were also very heterogeneous in term of size, number of triples, entities and so on. summarizes the statistics for Task 1.
| – | Solution 1.1 | Solution 1.2 | Solution 1.3 | Solution 1.4 |
|---|---|---|---|---|
| dataset size | 9.6M |
6.6M |
3.8M |
5.1M |
| #triples | 177,752 |
95,015 |
62,231 |
79,444 |
| #entities | 11,208 |
11,719 |
11,589 |
19,090 |
| #properties | 46 |
23 |
42 |
23 |
| #classes | 10 |
10 |
10 |
6 |
Note that the size of the largest dataset is almost double the size (9.6M) of the smallest (5.1M). Similarly, the largest dataset in terms of triples (~180,000), contains three times more triples compared to the smallest (~63,000) to model the same data set. Solution 4 is the only one which required significant larger number of entities to represent the same data. Considering that Solution 4 presents a very large number of persons, the correspondingly high number of entities is not so surprising.
L7.5. No provenance or other metadata. Unfortunately, no team intuitively provided any provenance or other metadata information. In particular licensing metadata information is of crucial importance for subsequent use of datasets.
L8.1. Performance ranking of the tools evolved but not as expected. In 2015 the performance ranking of the three tools evolved from 2014 has not changed but their performance has improved except for Kolchin et al., who improved precision but not recall. Disregarding the two queries that were new in 2015, the tool by Kolchin et al., which had won the best performance award in 2014, performs almost as well as Milicka’s/Burget’s.
L8.2. New and legacy solutions were both valuable. Task 1 participants both in 2014 and 2015 and they all had an improved version of different aspects of their solution which resulted in correspondingly improved versions of the final dataset. The new solution which introduced a fundamentally new approach and participated in Task 1 achieved equally good results as the best solution of 2014. In conclusion, legacy solutions might be able to improve and bring stable and good results, however there is still room for improvement and mainly for fundamentally new ideas that surpass problems that legacy solutions can not deal with.
L8.3. Newly introduced approaches have equal chances in winning the challenge. The winners of Task1 in 2014 participated in 2015 with an improved version of their tool but they did not win. The 2015 winner was a new tool with a brand new approach. The winners were not the same in the two versions of the challenge, creativity won.
One of the objectives of the SemPub Challenge series is to produce Linked Data that contribute to improving the scholarly communication. This includes supporting researchers finding relevant and high-quality papers by exploiting the information available in these datasets. Semantic Web technologies in this context do not only solve isolated problems, but generates further value in that data can be shared, linked to each other, and reasoned on.
The goal of this work is to shade light on the first editions of the Challenge and to distill some lessons learned from our experience. In particular, we were interested in both organizational aspects and evidences from the solutions proposed by the participants. Our conclusion is that we are moving in the right direction but the goal has not been fully reached yet. There are several positive aspects, among which the high participation and the quality of the produced results. The possibility of sharing knowledge and solutions among participants was another key factor of SemPub. The Challenge allowed us to share experience on semantifying scholarly data, using some ontological models, refining and extending existing datasets. On the other hand, our analysis showed that some other aspects have to be necessarily improved. In particular, we have to make the produced output well integrated in the Linked Open Data ecosystem and exploited by the community.
The next step in fact is to investigate what are the services that we can build on top of the produced data and how they can be offered. Some natural (and challenging) questions arise: what services can already be delivered based on the data we currently have? How do we need to extend these data to provide novel services? What would be the interface of such services look like? Which functionalities should be implemented first? The challenge will also be to turn all these questions into new material for a new Challenge, even better if measurable in an objective way.
Acknowledgments: We would like to thank our peer reviewers for their constructive feedback. This work has been partially funded by the European Commission under grant agreement no. 643410.
Milicka, M., Burget, R.: Information extraction from web sources based on multi-aspect content analysis. SemWebEval. CCIS 548. Springer, 2015.
Kovriguina, L. et al. Metadata Extraction From Conference Proceedings Using Template-Based Approach. SemWebEval. CCIS 548. Springer, 2015.
Nuzzolese, A. G., Peroni, S., Reforgiato Recupero, D. MACJa: Metadata And Citations Jailbreaker. SemWebEval. CCIS 548. Springer, 2015.
Klampfl, S., Kern, R. Machine Learning Techniques for Automatically Extracting Contextual Information from Scientific Publications. SemWebEval. CCIS 548. Springer, 2015.
Tkaczyk, D., Bolikowski, L. Extracting contextual information from scientific literature using CERMINE system. SemWebEval. CCIS 548. Springer, 2015.
Sateli, B., Witte, R. Automatic Construction of a Semantic Knowledge Base from CEUR Workshop Proceedings. SemWebEval. CCIS 548. Springer, 2015.
Ronzano, F. et al. On the automated generation of scholarly publishing Linked Datasets: the case of CEUR-WS Proceedings. SemWebEval. CCIS 548. Springer, 2015.
Ronzano, F., Casamayor Del Bosque, G., Saggion, H. Semantify CEUR-WS Proceedings: towards the automatic generation of highly descriptive scholarly publishing Linked Datasets. SemWebEval. CCIS 475. Springer, 2014.
Shotton, D. (2013). Publishing: Open citations. Nature, 502(7471).
Miller, G. & Mork, P., (2013). From Data to Decisions: A Value Chain for Big Data, Spart IT, ITPro.
Lange, C., & Di Iorio, A., Semantic publishing challenge – assessing the quality of scientific output. SemWebEval. CCIS 475. Springer, 2014.
Di Iorio, A., Lange, C., Dimou, A., & Vahdati, S. Semantic Publishing Challenge – Assessing the Quality of Scientific Output by Information Extraction and Interlinking. SemWebEval. CCIS 548. Springer, 2015.
Kolchin, M., et al.: CEUR-WS-LOD: conversion of CEUR-WS workshops to linked data. SemWebEval. CCIS 548. Springer, 2015
Kolchin, M., Kozlov, F.: A template-based information extraction from web sites with unstable markup. SemWebEval. CCIS 475. Springer 2014
Heyvaert, P., et al.: Semantically annotating CEUR-WS workshop proceedings with RML. SemWebEval. CCIS 548. Springer, 2015
Dimou, A., Vander Sande, M., Colpaert, P., De Vocht, L., Verborgh, R., Mannens, E., Van de Walle, R.: Extraction and semantic annotation of workshop proceedings in HTML using RML. SemWebEval. CCIS 475. Springer 2014
Learning Analytics and Knowledge; see http://meco.l3s.uni-hannover.de:9080/wp2/?page_id=18
At least, on the subset of CEUR-WS.org whose license scheme allowed us to republish metadata.
We abbreviate well-known ontologies by their prefixes according to prefix.cc.


You may be wondering, "Why did you do this?" I moved to San Francisco with the hopes of becoming a full stack engineer at a meaningful, cultural company like Course Hero. As a fairly new developer, I knew I was up against some very experienced candidates and thought of how I can show my potential as a Hero. I decided to learn AngularJS, which is a part of CH's tech stack, and apply it to this site. Please look around!
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Default demo
A few ajax includes, using
data-after
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T-Pain Proves Your Theories Wrong On "Think It's A Game"
T-Pain drops a level-up anthem.
by Mitch FindlayThink It's A Game
T-Pain
Editor Rating: HOTTTTT
User Rating: meh - 58% (3)
Not only has T-Pain been a cultural mainstay in the hip-hop scene, but he's also been going heavy on the gaming for a minute now. In fact, he once described his "ideal day" as including a sixteen-hour video game binge. Not only that, but T-Pizzle has dipped his toe in the Lets Play pool, streaming games like Overwatch on his own personal gaming channel. Suffice it to say, the man is into the art of gaming, and has taken to the booth to voice his adoration to the world. Enter "Think Its A Game," a track built on iconic sound effects from various classics.
Anyone who ever played Mario will recognize the nostalgic samples, and T-Pain tears straight through the flaming mushroom. The rapper slash singer toes the line between his dueling styles, laying down an exuberant and spirited performance. "Turn like an amp on it," he raps. "I come and dance on it, you catch a cramp on it." Check this one out right now, and sound off if you got love for T. Pain.
Quotable Lyrics
You ain’t never gonna get them bags
If all you think about is concentrating on your swag


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https://forum.hackthebox.eu/discussion/612/oscp-practice
The following is a quick list but should help:
Lame
Beep
Bastard
Grandpa/Granny
Mirai
Solidstate
Jeeves
Tally (Much harder than anything on OSCP, but you've gotta get used to windows)
Bashed
Nibbles
Sense
Valentine
Bart (Again, same issue but really get used to windows)
Chatterbox
Popcorn
Haircut
Nineveh
Shocker
Cronos
Arctic
Optimum
Jerry
TartarSauce


So, I’ve been doing 1k Sp to grow my account and 1k Sp to power down every week to help pay for bills. It’s not always mentioned but I subsidize half the cost of the minnowsupport rpc node. I think it’s an important part of being a top 20 witness. I’ve also been using the powerdown to buy some of the steem monsters cards off the market, I’ve given some to some folks and projects in need, and helped fund the kriptojobs project.
While I’ve been doing that I’ve been steadily delegating out to various projects and people within the Minnow Support Project. Here are the major things I delegate to-
If you divide by 2 you get a pretty close estimate of how may thousands of SP I’ve delegated to each project.
msp-waves is 16ksp- this is the radio station and it’s auto-curating djs
lovejuice 15ksp- this is a bidbot and the incoming funds pay new authors to post content there
msp-active 15ksp- this is to support the members of the Minnow Support Community and reward people that are active chatters and hang out with us
msp-mods 15ksp- this is to reward the moderators with some minimal curation rewards for helping to run PALnet and the Minnow Support Project
msp-curation 12ksp- this is an initiative where community members manually curate great content
msp-lovebot 9ksp- this is an account that autovotes people that delegate to minnowsupport
msp-bidbot 5ksp- this is a small bid bot account where 5% of all proceeds go to minnowsupport
The rest goes to a variety of people and smaller projects and accounts that I want to help and support.
This is a major milestone for me. This week I hit 100k steem power delegated. This place is my home. This is a way I can support it!
Moving forward I’m planning on leaving my delegations as they are for a while (If I pull any back it’s only because prices are insane, I’ll sell, and buy back when it comes back down). I’m interested in growing the @aggroed steem power now so that my personal vote is worth more and I can curate people and projects I like better. I’m also going to power down more of my weekly witness rewards and use it to fund a variety of projects on the block. This will be sorta like an angel investment fund in some ways. We’ll see where it goes. So, I’m going to stop delegating out new steem as much, and try a little less general support and focus what I’m doing on a few stand out projects.
Anyway, I’m proud of myself and I thought I’d share! thanks all for your support of my witness and I continue to do my best to spread Peace, Abundance, and Liberty, help the Steem ecosystem grow, and train and retain minnows on the platform. I do this through actively leading the Minnow Support Project and the Peace, Abundance, and Liberty Network (the largest off chain community for Steem), Steem Monsters which is a collectible trading card drafting game progressing from alpha to beta with fighting, and msp-waves the radio station that we listen to.
Witness Stuff
I’m rocking 19.12. I was a little slower than my colleagues at this, but I was a little nervous after some of the bugs that were found in .11. But I’m here, it’s working well, and I’m excited to be switching over to HF20 about a month from now. GOOD TIMES! I support app-base and think it’s a great step in the right direction to reduce the hardware necessary to run the Steem blockchain.
General Thanks
I couldn’t do this without the people who support my witness, fantastic moderators who help run the day to day business of the Minnow Support Project, my business partner Yaba, or all the community members that hang out with us on air and in Discord.
Best Regards,
Aggy
This page is synchronized from the post: ‘Witness Update- 100k Sp delegated’


subtom_extract_subtomograms¶
Extract subtomograms on the cluster.
subtom_extract_subtomograms(
'tomogram_dir', tomogram_dir (''),
'tomo_row', tomo_row (7),
'subtomo_fn_prefix', subtom_fn_prefix ('subtomograms/subtomo'),
'subtomo_digits', subtomo_digits (1),
'all_motl_fn_prefix', all_motl_fn_prefix ('combinedmotl/allmotl'),
'stats_fn_prefix', stats_fn_prefix ('subtomograms/stats/tomo'),
'iteration', iteration (1),
'box_size', box_size (-1),
'process_idx', process_idx (1),
'reextract', reextract (0),
'preload_tomogram', preload_tomogram (1),
'use_tom_red', use_tom_red (0),
'use_memmap', use_memmap (0))
Takes the tomograms given in tomogram_dir
and extracts subtomograms
specified in all_motl_fn_prefix
_#.m where # corresponds to iteration
with size box_size
into scratch_dir
with the name formats
subtomo_fn_prefix
_#.em where # corresponds to the entry in field 4 in
all_motl_fn_prefix
_#.em zero-padded to have at least subtomo_digits
digits.
Tomograms are specified by the field tomo_row
in motive list
all_motl_fn_prefix
_#.em, and the tomogram that will be processed is
selected by process_idx
. A CSV-format file with the subtomogram ID-number,
average, min, max, standard deviation and variance for each subtomogram in the
tomogram is also written with the name format stats_fn_prefix
_#.em where #
corresponds to the tomogram from which subtomograms were extracted.
If reextract
evaluates to true as a boolean, than existing subtomograms will
be overwritten. Otherwise the program will do nothing and exit if
stats_fn_prefix
_#.em already exists, or will also skip any subtomogram it
is trying to extract that already exists. This is for in the case that the
processing crashed at some point in execution and then can just be re-run
without any alterations.
If preload_tomogram
evaluates to true as a boolean, then the whole tomogram
will be read into memory before extraction begins, otherwise the particles will
be read from disk or from a memory-mapped tomogram. If use_tom_red
evaluates
to true as a boolean the old particle extraction code will be used, but this is
only for legacy support and is not suggested for use. Finally if use_memmap
evaluates to true as a boolean then in place of reading each particle from disk
a memory-mapped version of the file of will be created to attempt faster access
in extraction.


| Black player | Cho Chikun | White player | Yoda Norimoto |
| Date | 1995-01-12 | Result | White wins for 0.5 points |
| Game 1 - Figure 1 (1-244) 128 at 74, 129 at 77, 194 at 188, 197 at 191, 200 at 188, 203 at 191, 206 at 188, 209 at 191, 214 at 188, 222 at 99, 227 at 191, 229 at 176, 230 at 188, 235 at 81, 237 at 170, 244 at 191 |


Time Triggered: ${alarmDate}
Rule Id: ${ruleId}
Rule Description: ${ruleDescription}
Severity: ${ruleSeverity}
Device Id: ${deviceId}
${notes}
See alert and device details here


A lot of computing power is wasted in many programs as most programs use only one core. If your program is computation intensive, you might want to put some extra effort in your program and make use of this wasted computing power.
There are two ways to execute your code on multiple cores: Multiprocessing and multithreading. Both, processes and threads, provide the possibility to execute code sequences independently and concurrently. But where is the difference?
- When you execute code in multiple processes, the operating system handles the scheduling. It decides when which process gets executed and tries to find an optimal order. Every process has its own memory segment. A process is sometimes also called "kernel level thread".
- When you execute code in multiple threads, all threads have one process they belong to (see Multi-Threading models, also from Java). Every set of threads that have the same process share their memory.
In Java, you will use multithreading most of the time. I will only write about multithreading, but you can create multiple processes wiht ProcessBuilder.
Java Basics for Multithreading
All important lessons you need to learn are covered in Java Concurrency Tutorial. If you're really interested in multithreading, you should read this.
You can put the part that can get executed concurrently in a separate class that implements the interface Runnable. This class has a method called run(). You can create a new Thread(Runnable) by calling start().
Here is an example:
Sum
public class Sum implements Runnable {
private final int UpperEnd;
public Sum(int upperEnd) {
UpperEnd = upperEnd;
}
@Override
public void run() {
for (int i = 0; i < UpperEnd; i++) {
RaceCondition.bigSum++;
}
}
}
Main
import java.util.ArrayList;
import java.util.List;
public class Main {
public static int BIG_NR;
public static int NR_THREADS;
public static long bigSum;
public static void main(String[] args) {
if (args.length < 2) {
System.err.println("You should specify the number "
+ "of threads and BIG_NR. Call me like this:\n"
+ "java -jar RaceCondition.jar 5 20000\n"
+ "This will create 5 Threads which try to count"
+ "up to 2000000.\n" + "-v will show status "
+ "information ");
return;
}
boolean verbose = false;
if (args.length > 2) {
verbose = true;
}
NR_THREADS = Integer.parseInt(args[0]);
BIG_NR = Integer.parseInt(args[1]);
List<Thread> threads = new ArrayList<Thread>();
for (int i = 0; i < NR_THREADS; i++) {
Runnable task = new Sum(BIG_NR);
Thread worker = new Thread(task);
worker.start();
threads.add(worker);
}
int running = 0;
do {
running = 0;
for (Thread thread : threads) {
if (thread.isAlive()) {
running++;
}
}
if (verbose) {
System.out.println("Remaining threads: " + running);
}
} while (running > 0);
System.out.println(RaceCondition.bigSum);
}
}
Call it like this:
java Main 5 2000
Race Conditions
When you execute the code above, the output will vary. Why is this the case?
In short: The execution order is not defined and RaceCondition.bigSum++
is not atomic.
Let's imagine that you call this with two threads only:
java Main 2 2000
Now you could get this execution order:
- Thread 1: Loads
RaceCondition.bigSum
. It is 0. - Thread 2: Executes completely. Now
RaceCondition.bigSum
is 2000 - Thread 1: Increases the loaded value of
RaceCondition.bigSum
by 1. Now it is 1. - Thread 1: Finishes it's execution. The Value of
RaceCondition.bigSum
is 2000 = BIG_NR.
Ok, we can obviously get values in \([\text{BIG}\_\text{NR}, \text{NR}\_\text{THREADS} \cdot \text{BIG}\_\text{NR}]\).
Can we get smaller values? Yes, we can!
Execute:
java Main 50 20000
- Thread 1 loads
bigSum
. It's 0. - Thread 2 loads
bigSum
. It's 0. - Thread 3 - 50 execute completely.
- Thread 2 executes until it is at the latest
bigSum++
. The latest doesn't execute. - Thread 1 increases the loaded value from 0 to 1 and writes 1 back
- Thread 2 loads
bigSum
= 1 - Thread 1 executes and finishes.
- Thread 2 increases the loaded value from 1 to 2 and writes 2 back.
You can get all values in \([2, \text{NR}\_\text{THREADS} \cdot \text{BIG}\_\text{NR}]\)!
Usually, you don't want to get different results when you give the same input to your program. How can you fix this? Take a look at AtomicLong and replace the long bigNr
by the AtomicLong bigNr
.
Playing with BASH
If you want to execute this more often, you could save it as a executable JAR and execute the following bash script. It takes three arguments:
- \(1: The number of times you execute a the program with a fixed number of THREADS</li> <li>\)2: The maximum number of THREADS you would like to use
- $3: BIG_NR
The script executes the program \($1 \cdot $2\) times. The output gets divided by the number of threads and the result is saved in raceCondition.tmp. Every line is one execution of the program. When the second number is BIG_NR, then no race conditions occured.
rm raceCondition.tmp
touch raceCondition.tmp
# Up to $2 threads
for (( threads=1;threads<=$2; threads++))
do
for (( c=1; c<=$1; c++ ))
do
# $threads threads, count up to $3 in each thread
thisExecutionSum=`java -jar RaceCondition.jar $threads $3`
# Normalize: thisExecutionSum / number of threads
normalizedSum=`awk -vsome=$thisExecutionSum -vtotal=$threads 'BEGIN { printf("%d\n", some/total); exit } '`
echo -e $threads"\t"$normalizedSum >> raceCondition.tmp
done
done


Perform Filter Options |
Top Previous Next |
|
-Select Saved Filters submodule -Select one filter at a time on left side to retrieve the selected filter results -Select Filter Options button: -Select one option in the left section -Bulk Close Projects - selecting this option will apply a close status for all projects appearing in the filter list -Bulk ReOpen Projects - selecting this option will apply a reopen status for all projects appearing in the filter list Note: These bulk operations are applied across the selected filter list. The user cannot select only a few rows to use the bulk operations. The action will only be applied across entire filter list. |


Vector
Vector is an array-like type that supports length/bounds checking at type-level. It also implements the dimensional interfaces and supports generic programming.
iota
(Fn [(Ref (Vector (Size a) b) c)] (Vector (Size a) (Index (Vector (Size a) Opaque) Int)))
(iota v)
map
(Fn [(Ref (Fn [a] b) c), (Ref (Vector (Size d) a) e)] (Vector (Size d) b))
(map f v)
Maps a function f
over all the elements of a Vector and returns a new
Vector containing the results.
nth
(Fn [(Ref (Vector (Size a) b) c), (Index (Vector (Size a) Opaque) Int)] b)
(nth v ix)
Retrieves the nth element of a Vector using a given Index
.
The given Index
must have a Size
equivalent to the Vector
's size in
its first position (the bound position).
If the Index
's Int value exceeds the length of the Vector, this
function will return the last element of the Vector.
push
(Fn [a, (Ref (Vector b a) c)] (Vector (Size b) a))
(push x v)
Adds an element to the end of a Vector, returning a new Vector.
tail
(Fn [(Ref (Vector (Size a) b) c)] (Vector a b))
(tail v)
Returns all but the first element of a Vector as a new Vector.
zip
(Fn [(Ref (Fn [a, b] c) d), (Ref (Vector e a) f), (Ref (Vector e b) g)] (Vector e c))
(zip f v v1)
Combines two Vectors using a binary function f
.


- Black Forest : Mix of blueberry , cherry , sugar and 5% of coffein it is a good starter for your gym session
- Ocean : Without sugary , real taste of the peach and grapes. You Can feel your energy again on your diet
- No Pain : No Pain , No Gain , Everything You want for post work out is here . good and delicious taste and the best availbale drink in the market for recovery after a good gym session.


- The distance between your phone and the other Bluetooth device
may be over 10 meters.
- The other Bluetooth device may not have responded to your file
transfer request in time.
- The other Bluetooth device may not support the format
of the file you are trying to send.


Last updated: 2021-01-21
Checks: 7 0
Knit directory: invitroOA_pilot_repository/
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The command set.seed(20210119)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
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) files. If you’ve configured a remote Git repository (see ?wflow_git_remote
), click on the hyperlinks in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e3fbd0f | Anthony Hung | 2021-01-21 | add ward et al data files to data |
| html | e3fbd0f | Anthony Hung | 2021-01-21 | add ward et al data files to data |
| Rmd | 0711682 | Anthony Hung | 2021-01-21 | start normalization file |
| html | 0711682 | Anthony Hung | 2021-01-21 | start normalization file |
| Rmd | 28f57fa | Anthony Hung | 2021-01-19 | Add files for analysis |
This code takes the raw count data from bulk RNA sequencing and performs upper quartile normalziation on the data, filtering out lowly expressed genes. It then saves the resulting data and filtered count data (unnormalized) as R objects.
Here we also plot PCA plots to visualize the structure of the full data set (PCA plots for the data set excluding the sample that failed QC are located in the Modeling and removing unwanted technical variation page).
Here we are loading both the full data and the data without the one sample that failed QC.
library("gplots")
Attaching package: 'gplots'
The following object is masked from 'package:stats':
lowess
library("ggplot2")
library("reshape")
library("edgeR")
Loading required package: limma
library("RColorBrewer")
library("scales")
library("cowplot")
Attaching package: 'cowplot'
The following object is masked from 'package:reshape':
stamp
theme_set(theme_cowplot())
library("dplyr")
Attaching package: 'dplyr'
The following object is masked from 'package:reshape':
rename
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
# Load colors
colors <- colorRampPalette(c(brewer.pal(9, "Blues")[1],brewer.pal(9, "Blues")[9]))(100)
pal <- c(brewer.pal(9, "Set1"), brewer.pal(8, "Set2"), brewer.pal(12, "Set3"))
# load in relabeled counts
raw_counts <- readRDS("data/raw_counts_relabeled.rds")
raw_counts_includeNA19160 <- readRDS("data/raw_counts_relabeled_includeNA19160.rds")
# Create DGEList object to allow for easier application of different normalization methods
raw_counts <- DGEList(raw_counts, group = colnames(raw_counts))
raw_counts_includeNA19160 <- DGEList(raw_counts_includeNA19160, group = colnames(raw_counts_includeNA19160))
# load in reordered sample information
sampleinfo <- readRDS("data/Sample.info.RNAseq.reordered.rds")
sampleinfo_includeNA19160 <- readRDS("data/Sample.info.RNAseq.reordered_includeNA19160.rds")
Apply upperquartile normalization to the data.
upperquartile <- calcNormFactors(raw_counts, method = "upperquartile")
upperquartile <- cpm(upperquartile, log=TRUE, normalized.lib.sizes = T)
head(upperquartile)
18855_3_S 19160_3_S 18856_3_U 18856_1_U 18855_2_S 18856_2_S
ENSG00000223972 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000227232 -1.376624 -2.046788 -2.154778 -2.585632 -2.545595 -1.167335
ENSG00000278267 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000243485 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000284332 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000237613 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
19160_3_U 18855_2_U 19160_2_S 18855_1_S 18856_1_S 19160_1_S
ENSG00000223972 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000227232 -3.129986 -2.527469 -1.317333 -1.415729 -3.129986 -2.313104
ENSG00000278267 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000243485 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000284332 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000237613 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
19160_2_U 18855_1_U 18856_3_S 18856_2_U 18855_3_U
ENSG00000223972 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000227232 -2.601918 -1.247154 -2.084127 -1.609909 -3.129986
ENSG00000278267 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000243485 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000284332 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
ENSG00000237613 -3.129986 -3.129986 -3.129986 -3.129986 -3.129986
strained <- sampleinfo$treatment == "Strain"
unstrained <- sampleinfo$treatment == "Unstrain"
ind_1 <- sampleinfo$Individual == "NA18855 "
ind_2 <- sampleinfo$Individual == "NA18856 "
ind_3 <- sampleinfo$Individual == "NA19160 "
# Look at density plots for all individuals broken down by each treatment type
col = as.data.frame(pal[as.numeric(sampleinfo$Individual)])
plotDensities(upperquartile[,strained], col=col[strained, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
plotDensities(upperquartile[,unstrained], col=col[unstrained, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
# Look at density plots broken down by individual
col = as.data.frame(pal[as.numeric(sampleinfo$treatment)])
plotDensities(upperquartile[,ind_1], col=col[ind_1, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
plotDensities(upperquartile[,ind_2], col=col[ind_2, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
plotDensities(upperquartile[,ind_3], col=col[ind_3, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
meltupperquartile <- melt(upperquartile)
names(meltupperquartile) <- c("gene", "sampleID", "upperquartile")
p <- ggplot(meltupperquartile, aes(factor(sampleID), upperquartile))
p + geom_boxplot() + theme(axis.text.x = element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
The criteria we are using is an average log2upperquartile > 2.5 in at least 4 samples. After this filtering step, 10486 genes remain.
cutoff <- 2.5
keep <- rowSums( upperquartile > cutoff ) >=4
counts_upperquartile <- raw_counts[keep,]
filtered_upperquartile <- upperquartile[keep,]
dim(filtered_upperquartile)
[1] 10486 17
After normalization, the data do indeed look more normal than when unnormalized.
melt_filt_upperquartile <- melt(filtered_upperquartile)
names(melt_filt_upperquartile) <- c("gene", "sampleID", "log2upperquartile")
p1 <- ggplot(melt_filt_upperquartile, aes(factor(sampleID), log2upperquartile))
p1 + geom_boxplot() + theme(axis.text.x = element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
plotDensities(filtered_upperquartile, legend = F)
| Version | Author | Date |
|---|---|---|
| 0711682 | Anthony Hung | 2021-01-21 |
saveRDS(counts_upperquartile, "data/filtered_counts.rds")
saveRDS(filtered_upperquartile, "data/norm_filtered_counts.rds")
upperquartile_includeNA19160 <- calcNormFactors(raw_counts_includeNA19160, method = "upperquartile")
upperquartile_includeNA19160 <- cpm(upperquartile_includeNA19160, log=TRUE, normalized.lib.sizes = T)
head(upperquartile_includeNA19160)
18855_3_S 19160_3_S 18856_3_U 18856_1_U 18855_2_S 18856_2_S
ENSG00000223972 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000227232 -1.371277 -2.027812 -2.134933 -2.565487 -2.526602 -1.157511
ENSG00000278267 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000243485 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000284332 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000237613 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
19160_3_U 18855_2_U 19160_2_S 18855_1_S 18856_1_S 19160_1_S
ENSG00000223972 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000227232 -3.101145 -2.508875 -1.294699 -1.401586 -3.101145 -2.296780
ENSG00000278267 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000243485 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000284332 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000237613 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
19160_2_U 19160_1_U 18855_1_U 18856_3_S 18856_2_U 18855_3_U
ENSG00000223972 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000227232 -2.577970 -2.336134 -1.239235 -2.062052 -1.601659 -3.101145
ENSG00000278267 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000243485 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000284332 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
ENSG00000237613 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145 -3.101145
strained <- sampleinfo_includeNA19160$treatment == "Strain"
unstrained <- sampleinfo_includeNA19160$treatment == "Unstrain"
ind_1 <- sampleinfo_includeNA19160$Individual == "NA18855 "
ind_2 <- sampleinfo_includeNA19160$Individual == "NA18856 "
ind_3 <- sampleinfo_includeNA19160$Individual == "NA19160 "
# Look at density plots for all individuals broken down by each treatment type
col = as.data.frame(pal[as.numeric(sampleinfo_includeNA19160$Individual)])
plotDensities(upperquartile_includeNA19160[,strained], col=col[strained, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
plotDensities(upperquartile_includeNA19160[,unstrained], col=col[unstrained, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
# Look at density plots broken down by individual
col = as.data.frame(pal[as.numeric(sampleinfo_includeNA19160$treatment)])
plotDensities(upperquartile_includeNA19160[,ind_1], col=col[ind_1, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
plotDensities(upperquartile_includeNA19160[,ind_2], col=col[ind_2, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
plotDensities(upperquartile_includeNA19160[,ind_3], col=col[ind_3, ], legend="topright")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
meltupperquartile_includeNA19160 <- melt(upperquartile_includeNA19160)
names(meltupperquartile_includeNA19160) <- c("gene", "sampleID", "upperquartile")
p <- ggplot(meltupperquartile_includeNA19160, aes(factor(sampleID), upperquartile_includeNA19160))
p + geom_boxplot() + theme(axis.text.x = element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
cutoff <- 2.5
keep <- rowSums( upperquartile_includeNA19160 > cutoff ) >=4
counts_upperquartile_includeNA19160 <- raw_counts_includeNA19160[keep,]
filtered_upperquartile_includeNA19160 <- upperquartile_includeNA19160[keep,]
dim(filtered_upperquartile_includeNA19160)
[1] 10517 18
melt_filt_upperquartile_includeNA19160 <- melt(filtered_upperquartile_includeNA19160)
names(melt_filt_upperquartile_includeNA19160) <- c("gene", "sampleID", "log2upperquartile")
p1 <- ggplot(melt_filt_upperquartile_includeNA19160, aes(factor(sampleID), log2upperquartile))
p1 + geom_boxplot() + theme(axis.text.x = element_text(angle = 90))
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
plotDensities(filtered_upperquartile_includeNA19160, legend = F)
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
Reassuringly, the sample that was removed for low numbers of mapped reads still clusters as expected with its technical replicates.
#Load PCA plotting Function
source("code/PCA_fn.R")
# Perform PCA
pca_genes <- prcomp(t(filtered_upperquartile_includeNA19160), scale = T)
scores <- pca_genes$x
variances <- pca_genes$sdev^2
explained <- variances / sum(variances)
plot(pca_genes, main = "Variance per PC")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
#Make PCA plots with the factors colored by Individual
### PCA norm+filt Data
for (n in 1:5){
col.v <- pal[as.integer(sampleinfo_includeNA19160$Individual)]
plot_scores(pca_genes, scores, n, n+1, col.v)
}
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
#make PCA plots with symbols as treatment status and colors as individuals for figure
library(ggfortify)
autoplot(pca_genes, data = sampleinfo_includeNA19160, colour = "Individual", shape = "treatment", size = 3) +
theme_cowplot() +
theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
autoplot(pca_genes, data = sampleinfo_includeNA19160, colour = "Individual", shape = "treatment") +
theme_cowplot()
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
autoplot(pca_genes, data = sampleinfo_includeNA19160, colour = "Individual", shape = "treatment", size = 3, x = 3, y = 4) +
theme_cowplot() +
theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
autoplot(pca_genes, data = sampleinfo_includeNA19160, colour = "Individual", shape = "treatment", size = 3, x = 5, y = 6) +
theme_cowplot() +
theme(legend.position = "none")
| Version | Author | Date |
|---|---|---|
| e3fbd0f | Anthony Hung | 2021-01-21 |
sessionInfo()
R version 3.6.3 (2020-02-29)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Catalina 10.15.7
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggfortify_0.4.10 dplyr_1.0.2 cowplot_1.1.1 scales_1.1.1
[5] RColorBrewer_1.1-2 edgeR_3.26.8 limma_3.40.6 reshape_0.8.8
[9] ggplot2_3.3.2 gplots_3.0.4
loaded via a namespace (and not attached):
[1] gtools_3.8.2 tidyselect_1.1.0 locfit_1.5-9.4
[4] xfun_0.16 purrr_0.3.4 lattice_0.20-41
[7] colorspace_1.4-1 vctrs_0.3.4 generics_0.0.2
[10] htmltools_0.5.0 yaml_2.2.1 rlang_0.4.7
[13] later_<IP_ADDRESS> pillar_1.4.6 glue_1.4.2
[16] withr_2.2.0 lifecycle_0.2.0 plyr_1.8.6
[19] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0
[22] workflowr_1.6.2 caTools_1.18.0 evaluate_0.14
[25] labeling_0.3 knitr_1.29 httpuv_1.5.4
[28] Rcpp_1.0.5 KernSmooth_2.23-17 promises_1.1.1
[31] backports_1.1.9 gdata_2.18.0 farver_2.0.3
[34] fs_1.5.0 gridExtra_2.3 digest_0.6.25
[37] stringi_1.4.6 grid_3.6.3 rprojroot_1.3-2
[40] tools_3.6.3 bitops_1.0-6 magrittr_1.5
[43] tibble_3.0.3 tidyr_1.1.2 crayon_1.3.4
[46] whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.1
[49] rmarkdown_2.3 rstudioapi_0.11.0-9000 R6_2.4.1
[52] git2r_0.27.1 compiler_3.6.3


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Allowing an unimportant mistake to pass without comment is a wonderful social grace. Ideological differences are no excuse for rudeness.
Allowing an unimportant mistake to pass without comment is a wonderful social grace.
Ideological differences are no excuse for rudeness.


this week
one day i'll put some CONTENT here. but there's this BATHROOM BREAK i must attend so not now
events
- donation time7:00pm – 8:00pmpls donate we need money
- donation dayWednesday, December 10th, 2017u rich people dont donate enough
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- donation day #3Friday, December 12th, 2017there are kids in AFRICA that could've used that money more wisely!!
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This section assumes a general knowledge of playing D&D.
To allow players to join your session, just send them the share link (top of the screen). This is the same as the URL that shows up in your browser, so you could use that too. If you set a password for the session, make sure you send that to your players as well. Once they join, they will be prompted to enter a name, and a password if you set one. They can input any name they want, and they can change it later.
To load a map, click the Manage Maps icon at the bottom left of your screen, above the settings icon. This will open a window in which you can set the grid columns and rows of your map, as well as the size of each grid square in feet. Once you've set these numbers to your liking, click Load New Map to load a map image from your computer.
If this succeeds, the map image will appear above the input boxes in the map manager window. Hovering over this image will show you the grid dimensions and square size of the map. Clicking this map will show three buttons, in order from left to right:
Once a map is placed, you can right-click on it to display a few options, as shown below.
Close Map: This removes the map from the display area. However, it can still be accessed from the Map Manager, and any NPCs on it will retain their data unless you remove it altogether using the Map Manager.
Add NPC: This adds an NPC to the map at your cursor's position. See the NPCs section for more information.
Maps can be moved around on your screen by selecting the Move icon on the upper right side of your screen. Once you have selected that, your cursor will become a Move cursor over maps and NPCs, signifying that you can move them around.
To activate Fog of War editing, select the Cloud icon at the top right of your screen. Your cursor will then become a + shape over maps. If you drag your cursor across a map, it will create a transparent blue selection box, showing where the obscuration rectangle will be placed. When you're satisfied with the size of your obscuration rectangle, release your mouse and it will be placed on the map. To remove an obscuration rectangle, just click it while the Obscuration cursor is active.
Sessions can be saved with the Save Session button on the bottom left of your screen. Clicking this will download a file named session.json to your computer, which you can then use with the Load Session button on the home screen.


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