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Note: only
variants in active path for Lasso are included in th coefficient plot.
Note: only
variants in active path for Lasso are included in th coefficient plot.
Odds ratio plot
Prevalence plot
Survival curves
Forest plot
{% if icd %}
{% set chrom = icd[0].chrom %}
{% include 'variant_table_icd.html' %}
{% else %}
No significantly associated variants.
{% endif %}


Please select a parameter down below and click on the federal state you want to retrieve the additional information from. By double clicking a federal state the map zooms to it automatically.


Diabate scores 18 to carry Portland past Maine 71-62
PORTLAND, Ore. (AP) - Tahirou Diabate had 18 points as Portland topped Maine 71-62 on Saturday night.
Diabate hit 9 of 11 shots.
JoJo Walker had 13 points for Portland (3-1). Malcolm Porter added 12 points and six rebounds. Chase Adams had seven assists .
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'There will be blood': Maher sees civil war breaking out if we don't 'learn to live with each other'
Company co-founded by Nancy Pelosi's son charged with securities fraud
Sudden expansion of 2020 Democratic presidential field reveals unease with Biden, Warren, Sanders
Sergio El Darwich had 21 points for the Black Bears (1-2). Andrew Fleming added 14 points and six assists. Nedeljko Prijovic had 10 points and seven rebounds.
Portland plays Portland State on the road on Wednesday. Maine plays Washington on the road on Tuesday.
For more AP college basketball coverage: https://apnews.com/Collegebasketball and http://twitter.com/AP_Top25
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<quiz> <metadata> <title>Information Retrieval</title> <open_time>2012-02-07 2201</open_time> <soft_close_time>2012-05-15 2359</soft_close_time> <hard_close_time>2012-05-22 2359</hard_close_time> <duration>0</duration> <retry_delay>10</retry_delay> <maximum_submissions>5</maximum_submissions> <modified_time>1336933132604</modified_time> <parameters> <show_explanations> <question>after_hard_close_time</question> <option>before_soft_close_time</option> <score>before_soft_close_time</score> </show_explanations> </parameters> <maximum_score>4</maximum_score> </metadata> <preamble><![CDATA[]]></preamble> <data> <question_groups> <question_group select="1"> <preamble><![CDATA[]]></preamble> <question id="564d6a72003c63a41e737f32436e23c5" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[Given the two documents:

Sam is Sam and is a ham.

Sam likes me, ham and Sam!

 What is the Jaccard similarity between them (ignoring punctuation)?]]></text> <explanation><![CDATA[The Jaccard coefficient between two documents is given by $$\frac{|D_1 \cap D_1 | }{ | D_1 \cup D_2 |}$$, where $$D_1$$ is the set of words occurring in document $$1$$ and $$D_2$$ is the set of words occurring in document $$2$$ - i.e. it is the number of distinct word types occurring in both documents divided by the number of distinct word types occurring in either of the documents.]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="10101648be75a9f48fe98a38bbdda33a" selected_score="1" unselected_score="0"> <text><![CDATA[$$\frac{3}{7}$$]]></text> <explanation><![CDATA[Correct. Only Sam, and, and ham occur in both documents, and there are $$7$$ distinct word types, so we get the Jaccard coefficient is $$3 / 7$$.]]></explanation> </option> <option id="b5993476062b48009072b30cfcaec501" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{3}{13}$$]]></text> <explanation><![CDATA[You may have thought that the denominator of the Jaccard coefficient is the total number of words in the two documents ($$13$$). However, it is actually the total number of distinct word types in the two sentences together ($$7$$).]]></explanation> </option> <option id="90b6955fbd7309a9ab1530114e4d5569" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{4}{13}$$]]></text> <explanation><![CDATA[You may have thought the numerator is the number of total word tokens that are shared between the two documents ($$4$$) and the denominator is the total number of word tokens in the documents together ($$13$$). However, the numerator is actually the number of distinct word types shared by both documents ($$3$$) and the denominator is the total number of distinct word types in either document ($$7$$).]]></explanation> </option> <option id="e05f54d3010e2c3d81a5350bd5ca387b" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{4}{7}$$]]></text> <explanation><![CDATA[You may have thought to include Sam twice in the numerator - however, it should only be included once because with the Jaccard coefficient we care only about whether a word occurs in a document, not how many times.]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="0f95aeade29bfda895da95b1fcd63299" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[Given the two documents:

To be or not to be.

To think and therefore to be.

 What is the Jaccard similarity between them (ignoring punctuation)?]]></text> <explanation><![CDATA[The Jaccard coefficient between two documents is given by $$\frac{|D_1 \cap D_1 | }{ | D_1 \cup D_2 |}$$, where $$D_1$$ is the set of words occurring in document $$1$$ and $$D_2$$ is the set of words occurring in document $$2$$ - i.e. it is the number of distinct word types occurring in both documents divided by the number of distinct word types occurring in either of the documents.]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="a30819fb60d1c2ab1bf92c37bed9caa9" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{1}{6}$$]]></text> <explanation><![CDATA[You may have thought that the denominator of the Jaccard coefficient is the total number of words in the two documents ($$12$$). However, it is actually the total number of distinct word types in the two sentences together ($$7$$).]]></explanation> </option> <option id="34919b170984cc2d6346c7c8625966da" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{1}{4}$$]]></text> <explanation><![CDATA[You may have thought that the denominator is the total number of words in the two documents ($$12$$), but it is actually the total number of distinct word tokens in the documents ($$7$$). You may have also thought that the numerator is the number of one-to-one matches of words in one document to words in the other document ($$3$$). However, it is actually the number of distinct word types that are shared between the documents ($$2$$).]]></explanation> </option> <option id="8db582e5867f602eb697f169ce5c95a7" selected_score="1" unselected_score="0"> <text><![CDATA[$$\frac{2}{7}$$]]></text> <explanation><![CDATA[Correct. Only to and be occur in both documents, and there are $$7$$ distinct word types, so we get the Jaccard coefficient is $$2 / 7$$.]]></explanation> </option> <option id="fa36b397f90a149a780a280ad146392b" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{7}{12}$$]]></text> <explanation><![CDATA[You may have thought the numerator is the number of total word tokens that are shared between the two documents ($$7$$) and the denominator is the total number of word types in the documents together ($$12$$). However, the numerator is actually the number of distinct word types shared by both documents ($$2$$) and the denominator is the total number of distinct word types in either document ($$7$$).]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="d9b12e0ff3e973ed01fe2b9f2c5c2785" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[Given the two documents:

We all live in that yellow submarine.

The yellow mustard in that submarine sandwich was not yellow!

 What is the Jaccard similarity between them (ignoring punctuation)?]]></text> <explanation><![CDATA[The Jaccard coefficient between two documents is given by $$\frac{|D_1 \cap D_1 | }{ | D_1 \cup D_2 |}$$, where $$D_1$$ is the set of words occurring in document $$1$$ and $$D_2$$ is the set of words occurring in document $$2$$ - i.e. it is the number of distinct word types occurring in both documents divided by the number of distinct word types occurring in either of the documents.]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="38979ca494ee7fd1af23b6c4b548306f" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{4}{17}$$]]></text> <explanation><![CDATA[The denominator should be the total number of distinct word types that occur in the two documents together, not the total number of words that occur in them.]]></explanation> </option> <option id="123b146362011a7e1586a5238b75da0c" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{9}{17}$$]]></text> <explanation><![CDATA[You probably thought that the numerator is the number of words that occur in both documents ($$9$$) and the denominator is the number of total words in the two documents combined ($$17$$). However, the numerator is actually the total number of distinct word types that are shared by the two documents ($$4$$) and the denominator is the total number of distinct word types in either of the documents ($$12$$).]]></explanation> </option> <option id="23d6eb24fe633a04392be50926ed9083" selected_score="1" unselected_score="0"> <text><![CDATA[$$\frac{1}{3}$$]]></text> <explanation><![CDATA[Correct. Exactly $$4$$ distinct words (in, that, yellow, and submarine) occur in both documents, and there are $$12$$ distinct word types, so we get the Jaccard coefficient is $$4 / 12 = 1 / 3$$.]]></explanation> </option> <option id="2088a9e2a68c652925d3a9075bd582ca" selected_score="0" unselected_score="0"> <text><![CDATA[$$\frac{8}{17}$$]]></text> <explanation><![CDATA[You may have thought that the denominator is the total number of words in the two documents ($$17$$), but it is actually the total number of distinct word types in the documents ($$17$$). You may have also thought that the numerator is the number of words in a document that match one-to-one to a word in the other document ($$8$$). However, it is actually the number of distinct word types that are shared between the documents ($$4$$).]]></explanation> </option> </option_group> </option_groups> </data> </question> </question_group> <question_group select="1"> <preamble><![CDATA[]]></preamble> <question id="546ddef8f0ce62caf20111141b7547b8" type="GS_Short_Answer_Question_Simple"> <metadata> <parameters> <rescale_score>1</rescale_score> <type>numeric</type> </parameters> </metadata> <data> <text><![CDATA[In a set of $$806,791$$ documents, we get the following data on a few terms and a few documents:

term	document frequency	Doc 1	Doc 2	Doc 3

car	$$18,165$$	$$27$$	$$4$$	$$24$$

auto	$$6723$$	$$3$$	$$33$$	$$0$$

insurance	$$19,241$$	$$0$$	$$39$$	$$29$$

best	$$25,235$$	$$14$$	$$0$$	$$17$$

 What is the tf-idf value for the term insurance in Document 2?

 (Please answer to $$3$$ decimal places - e.g. $$5.18254 \rightarrow 5.183$$ or $$1.128333 \rightarrow 1.128$$).
 Note: Be sure to use $$\log_{10}$$ (log base 10) in your calculations.]]></text> <explanation><![CDATA[TF-IDF for a term $$t$$ in a document $$d$$ is given by
 $$w_{t, d} = (1 + \log_{10} \textrm{tf}_{t, d} ) \times \log_{10} ( N / \textrm{df}_t )$$]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="d70b6a22c464cab25e531fa28e1a6fe2" selected_score="1" unselected_score="0"> <text><![CDATA[[4.201,4.207]]]></text> <explanation><![CDATA[Correct. The TF-IDF is given by
 $$(1 + \log_{10} 39 ) \times \log_{10} ( 806,791/19,241 )$$
 $$\approx 2.591 \times 1.623$$]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="3e689a8fdf7e25c882ed07261fc0d6db" type="GS_Short_Answer_Question_Simple"> <metadata> <parameters> <rescale_score>1</rescale_score> <type>numeric</type> </parameters> </metadata> <data> <text><![CDATA[In a set of $$806,791$$ documents, we get the following data on a few terms and a few documents:

term	document frequency	Doc 1	Doc 2	Doc 3

car	$$18,165$$	$$27$$	$$4$$	$$24$$

auto	$$6723$$	$$3$$	$$33$$	$$0$$

insurance	$$19,241$$	$$0$$	$$39$$	$$29$$

best	$$25,235$$	$$14$$	$$0$$	$$17$$

 What is the tf-idf value for the term car in Document 1?

 (Please answer to $$3$$ decimal places - e.g. $$5.18254 \rightarrow 5.183$$ or $$1.128333 \rightarrow 1.128$$).
 Note: Be sure to use $$\log_{10}$$ (log base 10) in your calculations.]]></text> <explanation><![CDATA[TF-IDF for a term $$t$$ in a document $$d$$ is given by
 $$w_{t, d} = (1 + \log_{10} \textrm{tf}_{t, d} ) \times \log_{10} ( N / \textrm{df}_t )$$]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="19c90051081169f2c9cd6466af39fead" selected_score="1" unselected_score="0"> <text><![CDATA[[4.003,4.009]]]></text> <explanation><![CDATA[Correct. The TF-IDF is given by
 $$(1 + \log_{10} 27 ) \times \log_{10} ( 806,791/18,165 )$$
 $$\approx 2.431 \times 1.648$$]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="e36d3d71d2e3a563b0a39fe045ed6d6d" type="GS_Short_Answer_Question_Simple"> <metadata> <parameters> <rescale_score>1</rescale_score> <type>numeric</type> </parameters> </metadata> <data> <text><![CDATA[In a set of $$806,791$$ documents, we get the following data on a few terms and a few documents:

term	document frequency	Doc 1	Doc 2	Doc 3

car	$$18,165$$	$$27$$	$$4$$	$$24$$

auto	$$6723$$	$$3$$	$$33$$	$$0$$

insurance	$$19,241$$	$$0$$	$$39$$	$$29$$

best	$$25,235$$	$$14$$	$$0$$	$$17$$

 What is the tf-idf value for the term best in Document 3?

 (Please answer to $$3$$ decimal places - e.g. $$5.18254 \rightarrow 5.183$$ or $$1.128333 \rightarrow 1.128$$).
 Note: Be sure to use $$\log_{10}$$ (log base 10) in your calculations.]]></text> <explanation><![CDATA[TF-IDF for a term $$t$$ in a document $$d$$ is given by
 $$w_{t, d} = (1 + \log_{10} \textrm{tf}_{t, d} ) \times \log_{10} ( N / \textrm{df}_t )$$]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="e03531120487e4a96114820631771450" selected_score="1" unselected_score="0"> <text><![CDATA[[3.353,3.359]]]></text> <explanation><![CDATA[Correct. The TF-IDF is given by
 $$(1 + \log_{10} 17 ) \times \log_{10} ( 806,791/25,235 )$$
 $$\approx 2.230 \times 1.505$$]]></explanation> </option> </option_group> </option_groups> </data> </question> </question_group> <question_group select="1"> <preamble><![CDATA[]]></preamble> <question id="ded4139672e43df2c752faa47864876a" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[Given the following term frequencies (counts) for a few words in a collection of $$4$$ documents,

term	Dawn	Beatrice	She	Regeneration

happiness	37	30	0	3

surprise	40	10	6	0

family	31	0	12	17

adventure	0	5	13	0

 What is the cosine similarity between Dawn and Beatrice? Use tf-idf weighting and assume that these are the only documents and words in the collection.]]></text> <explanation><![CDATA[]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="70efc7e3b1354759c7bc42d815ef14e9" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.78$$]]></text> <explanation><![CDATA[You may have made the mistake of thinking the idf term is $$\log_{10} (\textrm{df}_t)$$. Remember, it is the inverse document frequency, and is given by $$\log_{10} (N / \textrm{df}_t)$$, where $$N$$ is the number of documents (here $$4$$).]]></explanation> </option> <option id="d20ca9e2920c2538cff020b90ba9a9d0" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.72$$]]></text> <explanation><![CDATA[Don't forget the idf term! You may have forgotten to multiply by $$\log_{10} (N / \textrm{df}_t)$$ (where $$N$$ here is $$4$$).]]></explanation> </option> <option id="0ac06d6927f292abe6388a2f0285b3a1" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.59$$]]></text> <explanation><![CDATA[Don't forget that you must add $$1$$ to the tf term in TF-IDF - i.e. the term frequency contribution to TF-IDF is $$1 + \log_{10} \textrm{tf}_{t, d}$$.]]></explanation> </option> <option id="e14883cb8cb4d7837ec67b868ce3e921" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.50$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="5cf458545ad54a337265386ad3158bc5" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[Given the following term frequencies (counts) for a few words in a collection of $$4$$ documents,

term	Dawn	Beatrice	She	Regeneration

happiness	37	30	0	3

surprise	40	10	6	0

family	31	0	12	17

adventure	0	5	13	0

 What is the cosine similarity between Beatrice and She? Use tf-idf weighting and assume that these are the only documents and words in the collection.]]></text> <explanation><![CDATA[]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="6290d9e3e0aa3cacc0e65027fc3ef114" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.72$$]]></text> <explanation><![CDATA[Don't forget that you must add $$1$$ to the tf term in TF-IDF - i.e. the term frequency contribution to TF-IDF is $$1 + \log_{10} \textrm{tf}_{t, d}$$.]]></explanation> </option> <option id="2395e2ab9a63a162063a22ff13abeb18" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.81$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> <option id="cb484de512d12c28ec7defb405f282c7" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.49$$]]></text> <explanation><![CDATA[You may have made the mistake of thinking the idf term is $$\log_{10} (\textrm{df}_t)$$. Remember, it is the inverse document frequency, and is given by $$\log_{10} (N / \textrm{df}_t)$$, where $$N$$ is the number of documents (here $$4$$).]]></explanation> </option> <option id="cbe5a20e67416c6875c5f235d2f545d5" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.57$$]]></text> <explanation><![CDATA[Don't forget the idf term! You may have forgotten to multiply by $$\log_{10} (N / \textrm{df}_t)$$ (where $$N$$ here is $$4$$).]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="ffd9853444dd50059b6d3d7240d33057" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[Given the following term frequencies (counts) for a few words in a collection of $$4$$ documents,

term	Dawn	Beatrice	She	Regeneration

happiness	37	30	0	3

surprise	40	10	6	0

family	31	0	12	17

adventure	0	5	13	0

 What is the cosine similarity between Beatrice and Regeneration? Use tf-idf weighting and assume that these are the only documents and words in the collection.]]></text> <explanation><![CDATA[]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="c53783637ae33289e03520bab2e5db52" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.41$$]]></text> <explanation><![CDATA[You may have made the mistake of thinking the idf term is $$\log_{10} (\textrm{df}_t)$$. Remember, it is the inverse document frequency, and is given by $$\log_{10} (N / \textrm{df}_t)$$, where $$N$$ is the number of documents (here $$4$$).]]></explanation> </option> <option id="5373ef9d98e82ba253ddd71d7286e1f4" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.38$$]]></text> <explanation><![CDATA[Don't forget the idf term! You may have forgotten to multiply by $$\log_{10} (N / \textrm{df}_t)$$ (where $$N$$ here is $$4$$).]]></explanation> </option> <option id="e87c051a0d3827d89b71a8e1e3d5c611" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.26$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> <option id="b7c70aa4ba06df997328846087b2c5b6" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.22$$]]></text> <explanation><![CDATA[Don't forget that you must add $$1$$ to the tf term in TF-IDF - i.e. the term frequency contribution to TF-IDF is $$1 + \log_{10} \textrm{tf}_{t, d}$$.]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="94bf71a362443b61ac08cf35d4c84265" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[Given the following term frequencies (counts) for a few words in a collection of $$4$$ documents,

term	Dawn	Beatrice	She	Regeneration

happiness	37	30	0	3

surprise	40	10	6	0

family	31	0	12	17

adventure	0	5	13	0

 What is the cosine similarity between Dawn and She? Use tf-idf weighting (ltc variation) and assume that these are the only documents and words in the collection.]]></text> <explanation><![CDATA[]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="cec394c70f9a2aea67fe4555f76be02e" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.38$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> <option id="1024669b7faef9b6323956c3db11b4b7" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.35$$]]></text> <explanation><![CDATA[Don't forget that you must add $$1$$ to the tf term in TF-IDF - i.e. the term frequency contribution to TF-IDF is $$1 + \log_{10} \textrm{tf}_{t, d}$$.]]></explanation> </option> <option id="d16a18557c8b853a2383d8038e517394" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.64$$]]></text> <explanation><![CDATA[Don't forget the idf term! You may have forgotten to multiply by $$\log_{10} (N / \textrm{df}_t)$$ (where $$N$$ here is $$4$$).]]></explanation> </option> <option id="893efd38574722800d452b41be1a58e7" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.73$$]]></text> <explanation><![CDATA[You may have made the mistake of thinking the idf term is $$\log_{10} (\textrm{df}_t)$$. Remember, it is the inverse document frequency, and is given by $$\log_{10} (N / \textrm{df}_t)$$, where $$N$$ is the number of documents (here $$4$$).]]></explanation> </option> </option_group> </option_groups> </data> </question> </question_group> <question_group select="1"> <preamble><![CDATA[]]></preamble> <question id="46f82a577d421a7dfb9bb4148f15087a" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[What is the average precision for the following sequence of retrieved documents, where R denotes a relevant document and N denotes an irrelevant document?

 R N R R N N N R R N N R]]></text> <explanation><![CDATA[Recall that average precision is calculated by scanning through the list of retrieved documents (in the order that they are reported) and computing the precision at every point that we retrieve a relevant document. We then get the Average Precision by averaging over those precisions. Also recall that precision in this context is the number of relevant documents retrieved so far divided by the number of documents retrieved so far.]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="7bd914ba2c150d0574aadda06d33eb7b" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.500$$]]></text> <explanation><![CDATA[You may have calculated the precision just at the last relevant document retrieved. You have to calculate the precision at every point that you retrieve a relevant document and then take the average of those precisions.]]></explanation> </option> <option id="8ae61cb26f95a13733de86af019ef281" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.662$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> <option id="79df8fc8cdf8e03a40ae450fec2a9165" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.580$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that a document is retrieved. However, you should only calculate the precision at the points when a relevant document is retrieved, and then report the average of those precisions.]]></explanation> </option> <option id="dabed3a9d6fb0c3fa909279462064433" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.497$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that an irrelevant document is retrieved and taken the average of those precisions. You are supposed to calculate the precision at every point that a relevant document is retrieved and report the mean of those precisions.]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="12d70e4dedcae7107fa5f0d506c9b67d" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[What is the average precision for the following sequence of retrieved documents, where R denotes a relevant document and N denotes an irrelevant document?

 N N N R R N R R N R R N R]]></text> <explanation><![CDATA[Recall that average precision is calculated by scanning through the list of retrieved documents (in the order that they are reported) and computing the precision at every point that we retrieve a relevant document. We then get the Average Precision by averaging over those precisions. Also recall that precision in this context is the number of relevant documents retrieved so far divided by the number of documents retrieved so far.]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="bec515f8fefe8a1a91fa04a8031761de" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.342$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that a document is retrieved. However, you should only calculate the precision at the points when a relevant document is retrieved, and then report the average of those precisions.]]></explanation> </option> <option id="c84b324003f9f7801375888f7b056dab" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.538$$]]></text> <explanation><![CDATA[You may have calculated the precision just at the last relevant document retrieved. You have to calculate the precision at every point that you retrieve a relevant document and then take the average of those precisions.]]></explanation> </option> <option id="d2f65407fb385fb4122434aa0d63dedd" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.213$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that an irrelevant document is retrieved and taken the average of those precisions. You are supposed to calculate the precision at every point that a relevant document is retrieved and report the mean of those precisions.]]></explanation> </option> <option id="6da389df568cfe6e9b651a71357e0b21" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.452$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="134f84227cbf342831b883baf3be3e5d" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[What is the average precision for the following sequence of retrieved documents, where R denotes a relevant document and N denotes an irrelevant document?

 R R R R N N N N N N R N R]]></text> <explanation><![CDATA[Recall that average precision is calculated by scanning through the list of retrieved documents (in the order that they are reported) and computing the precision at every point that we retrieve a relevant document. We then get the Average Precision by averaging over those precisions. Also recall that precision in this context is the number of relevant documents retrieved so far divided by the number of documents retrieved so far.]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="a36c8bc9bf0bafb5e9dafa600f11168d" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.819$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> <option id="7c644dbf22db4610c4a81b16a383764c" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.670$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that a document is retrieved. However, you should only calculate the precision at the points when a relevant document is retrieved, and then report the average of those precisions.]]></explanation> </option> <option id="ba36bfabcce2f8da377b7bb2362f5f33" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.543$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that an irrelevant document is retrieved and taken the average of those precisions. You are supposed to calculate the precision at every point that a relevant document is retrieved and report the mean of those precisions.]]></explanation> </option> <option id="fbe1498ae906070c83b37a17d60d168d" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.462$$]]></text> <explanation><![CDATA[You may have calculated the precision just at the last relevant document retrieved. You have to calculate the precision at every point that you retrieve a relevant document and then take the average of those precisions.]]></explanation> </option> </option_group> </option_groups> </data> </question> <question id="585046969589bbd1b97191c0565004f4" type="GS_Choice_Answer_Question"> <metadata> <parameters> <rescale_score>1</rescale_score> <choice_type>radio</choice_type> </parameters> </metadata> <data> <text><![CDATA[What is the average precision for the following sequence of retrieved documents, where R denotes a relevant document and N denotes an irrelevant document?

 N R R N N N R R N N N N N N R]]></text> <explanation><![CDATA[Recall that average precision is calculated by scanning through the list of retrieved documents (in the order that they are reported) and computing the precision at every point that we retrieve a relevant document. We then get the Average Precision by averaging over those precisions. Also recall that precision in this context is the number of relevant documents retrieved so far divided by the number of documents retrieved so far.]]></explanation> <option_groups randomize="true"> <option_group select="all"> <option id="70526c030f2990aac7f7fb798f558a4e" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.333$$]]></text> <explanation><![CDATA[You may have calculated the precision just at the last relevant document retrieved. You have to calculate the precision at every point that you retrieve a relevant document and then take the average of those precisions.]]></explanation> </option> <option id="76dfe4010d7b82ef98e2a74fc73e2661" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.386$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that a document is retrieved. However, you should only calculate the precision at the points when a relevant document is retrieved, and then report the average of those precisions.]]></explanation> </option> <option id="5f816f1c96fcb14a8af716ac5f5b3e01" selected_score="1" unselected_score="0"> <text><![CDATA[$$0.486$$]]></text> <explanation><![CDATA[Correct!]]></explanation> </option> <option id="4a72cdd21553b140c000cf7cd4a5963a" selected_score="0" unselected_score="0"> <text><![CDATA[$$0.337$$]]></text> <explanation><![CDATA[You may have calculated the precision at every point that an irrelevant document is retrieved and taken the average of those precisions. You are supposed to calculate the precision at every point that a relevant document is retrieved and report the mean of those precisions.]]></explanation> </option> </option_group> </option_groups> </data> </question> </question_group> </question_groups> </data> </quiz>
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The Clinical Brain Lab
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Brain Entropy, Fractal Dimensions and Predictability: A Review of Complexity Measures for EEG in Healthy and Neuropsychiatric Populations
While electroencephalography (EEG) signals are commonly examined using conventional linear methods, there has been an increasing trend …
Lau Zen Juen
,
Tam Pham
,
Annabel Chen
,
Dominique Makowski
Project
DOI
A Parametric Framework to Generate Visual Illusions using Python
Visual illusions have had considerable importance in the history of psychological science, leading to important scientific …
Lau Zen Juen
,
Dominique Makowski
,
Tam Pham
,
Annabel Chen
PDF
Heart Rate Variability in Psychology: A Review of HRV Indices and an Analysis Tutorial
The use of heart rate variability (HRV) in research has been greatly popularized over the past decades due to the ease and …
Tam Pham
,
Lau Zen Juen
,
Annabel Chen
,
Dominique Makowski
PDF
Cite
Project
DOI
The Structure of Deception: Validation of the Lying Profile Questionnaire
While deception is considered as a common phenomenon with important implications, its conceptualization and study as a dispositional …
Dominique Makowski
,
Tam Pham
,
Lau Zen Juen
,
Adrian Raine
,
Annabel Chen
PDF
Cite
Project
DOI
NeuroKit2: A Python toolbox for neurophysiological signal processing
NeuroKit2 is an open-source, community-driven, and user-centered Python package for neurophysiological signal processing. It provides a …
Dominique Makowski
,
Tam Pham
,
Lau Zen Juen
,
Jan C. Brammer
,
François Lespinasse
,
Hung Pham
,
3Christopher Schölzel
,
Annabel Chen
PDF
Cite
Project
DOI
Individual-fMRI-approaches reveal cerebellum and visual communities to be functionally connected in obsessive compulsive disorder
There is significant interest in understanding the pathophysiology of Obsessive–Compulsive Disorder (OCD) using resting-state fMRI …
Rajan Kashyap
,
Eng Goi Khia
,
Sagarika Bhattacharjee
,
Bhanu Gupta
,
Roger Ho
,
Cyrus S. H. Ho
,
Melvyn Zhang
,
Rathi Mahendran
,
Kang Sim
,
Annabel Chen
Cite
Project
DOI
Aging patterns of Japanese auditory semantic processing: an fMRI study
The efficacy of listening comprehension is presumably sustained over the life span, contradicting the stereotype of universal cognitive …
Hengshuang Liu
,
Makoto Miyakoshi
,
Toshiharu Naka
,
Annabel Chen
Cite
Project
DOI
Reading proficiency influences the effects of transcranial direct current stimulation: Evidence from selective modulation of dorsal and ventral pathways of reading in bilinguals
Introduction: tDCS can modulate reading which is processed by lexical (ventral) and sub-lexical (dorsal) pathways. Previous research …
Sagarika Bhattacharjee
,
Rajan Kashyap
,
Beth Ann O’Brien
,
Michael McCloskey
,
Kenichi Oishi
,
John E. Desmond
,
Brenda Rapp
,
Annabel Chen
Cite
Project
DOI
Psychological Determinants of the Susceptibility to Fake News amidst the COVID-19 Pandemic
In this study, we seek to examine whether the belief in conspiracy theories and one’s ability to regulate emotions are related to one’s …
Dominique Makowski
,
Lau Zen Juen
,
Tam Pham
,
Annabel Chen
PDF
Project
i-SATA: A MATLAB based toolbox to estimate current density generated by transcranial direct current stimulation in an individual brain
Background: Transcranial Direct Current Stimulation (tDCS) is a technique where a weak current is passed through the electrodes placed …
Rajan Kashyap
,
Sagarika Bhattacharjee
,
Ramaswamy Arumugam
,
Kenichi Oishi
,
John E. Desmond
,
Annabel Chen
Cite
Project
DOI
The role of primary motor cortex: More than movement execution
The neuroscience community has widely acknowledged primary motor cortex as a region important for execution of voluntary motor …
Sagarika Bhattacharjee
,
Rajan Kashyap
,
Turki Abualait
,
Annabel Chen
,
Woo-Kyoung Yoo
,
Shahid Bashir
Cite
DOI
Indices of effect existence and significance in the Bayesian framework
The Bayesian framework is increasingly believed to address some issues with the frequentist framework, especially the misuse and …
Dominique Makowski
,
Mattan S. Ben-Shachar
,
Annabel Chen
,
Daniel Lüdecke
PDF
Cite
DOI
Maximizing dissimilarity in resting state detects heterogeneous subtypes in healthy population associated with high substance use and problems in antisocial personality
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to study the differences in brain activation pattern …
Rajan Kashyap
,
Sagarika Bhattacharjee
,
B. T. Thomas Yeo
,
Annabel Chen
PDF
Cite
DOI
Translating education neuroscience for teachers
Tham and colleagues (2019) examined if translating neuroscience abstracts into simple language could help teachers better understand …
Rachel Tham
,
Zachary Walker
,
Shi Hui Desiree Tan
,
Li Tong Low
,
Annabel Chen
Cite
Project
DOI
Brain literacy empowers educators to meet diverse learner needs
Walker and colleagues (2019) posited that brain literacy has the potential to help educators meet the needs of diverse learners. They …
Zachary Walker
,
James B. Hale
,
Annabel Chen
,
Kenneth Poon
Cite
Project
DOI
Simulation analyses of tDcS montages for the investigation of dorsal and ventral pathways
Modulating higher cognitive functions like reading with transcranial direct current stimulation (tDCS) can be challenging as reading …
Sagarika Bhattacharjee
,
Rajan Kashyap
,
Brenda Rapp
,
Kenichi Oishi
,
John E. Desmond
,
Annabel Chen
PDF
Cite
Project
DOI
Cerebro-cerebellar pathways for verbal working memory
Verbal working memory involves holding verbal information in short-term memory. Two cerebro-cerebellar networks for verbal working …
Monika Sobczak-Edmans
,
Yu-Chun Lo
,
Yung-Chin Hsu
,
Yu-Jen Chen
,
Fu Yu Kwok
,
Kai-Hsiang Chuang
,
Wen-Yih Isaac Tseng
,
Annabel Chen
PDF
Cite
Project
DOI
Working memory, age and education: A lifespan fMRI study
Our brain changes in terms of mental functions and its structures as we age. In this study, we examined age-related differences in …
Jo A. Archer
,
Annie Lee
,
Anqi Qiu
,
Annabel Chen
PDF
Cite
Project
DOI
Functional connectivity of resting-state, working memory and inhibition networks in perceived stress
In this study, we examined the relationship between perceived stress and brain functioning in women and men. Using functional magnetic …
Jo A. Archer
,
Annie Lee
,
Anqi Qiu
,
Annabel Chen
PDF
Cite
DOI
The role of regional heterogeneity in age-related differences in functional hemispheric asymmetry: an fMRI study
Some cognitive functions, such as language processing, utilize one side of the brain more than the other. This pattern of brain …
Gladys Heng
,
Chiao-Yi Wu
,
Jo A. Archer
,
Makoto Miyakoshi
,
Toshiharu Nakai
,
Annabel Chen
Cite
Project
DOI
Frontal-subcortical circuitry in social attachment and relationships: A cross-sectional fMRI ALE meta-analysis
Researchers have explored the concept of attachment in multiple ways, from animal studies examining imprinting to abnormal attachment …
Shu-Hui Lee
,
Zachary Walker
,
James B. Hale
,
Annabel Chen
Cite
Project
DOI
Reconciling individual differences with collective needs: The juxtaposition of sociopolitical and neuroscience perspectives on remediation and compensation of student skill deficits
The increasingly diverse student population within classrooms potentially stretches the competencies of every teacher. Differentiated …
James B. Hale
,
Annabel Chen
,
Seng Chee Tan
,
Kenneth Poon
,
Kim R. Fitzer
,
Lara A. Boyd
Cite
Project
DOI
Modality specificity in the cerebro-cerebellar neurocircuitry during working memory
Previous studies have suggested cerebro-cerebellar circuitry in working memory. The present fMRI study aims to distinguish differential …
H. B. Tommy Ng
,
K. L. Cathy Kao
,
Yeecheun Chan
,
Effie Chew
,
Kai-Hsiang Chuang
,
Annabel Chen
Cite
Project
DOI
A comprehensive analysis of connectivity and aging over the adult life span
Aging has been associated with decreased intra- and internetwork connectivity during rest and task. Recent work has shown the …
Jo A. Archer
,
Annie Lee
,
Anqi Qiu
,
Annabel Chen
Cite
Project
DOI
Temporal dynamics of visual working memory
The involvement of the human cerebellum in working memory has been well established in the last decade. However, the cerebro-cerebellar …
Monika Sobczak-Edmans
,
H. B. Tommy Ng
,
Yeecheun Chan
,
Effie Chew
,
Kai-Hsiang Chuang
,
Annabel Chen
Cite
Project
DOI
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Named one of the top 25 most influential people in government and technology, Fed 50’s Disruptor of the Year, and described by the US Chief Technology Officer as one of “the baddest of the badass innovators,” Ben Balter is the Government Evangelist at GitHub.
Rich has been described as “an engineer turned artist turned activist” and is one of the co-founders of Loomio.
He has a background in open source hardware and community organising.
He believes in the jujitsu power of open source to flip things upside down. Hwooaaah!
GitHub has changed the way open source is built, and Brandon is changing the way GitHub builds open source.
An engineer by trade, he believes open source is a fundamental to building great products, and great products sustain healthy open source projects.
He shares his endeavours on twitter as @bkeepers and on his blog at opensoul.org.
Doug Kirkpatrick is the author of BEYOND EMPOWERMENT: THE AGE OF THE SELF-MANAGED ORGANIZATION, available on Amazon.com.
He is an organizational change consultant, TEDx and Featured Speaker, executive coach, writer and educator. He holds degrees in economics and law, and has a Senior Professional in Human Resources (SPHR®) designation with the Society for Human Resource Management.
He currently serves as a director for the Association for Talent Development (ATD, formerly ASTD), the world’s largest talent development association, with over 40,000 members in 120+ countries.
He played the first season of his business career in the manufacturing sector, principally with The Morning Star Company of Sacramento, California, a world leader in the food industry, as a financial controller and administrator.
He now engages with the Morning Star Self-Management Institute, the Center for Innovative Cultures, Great Work Cultures and other vibrant organisations and leaders to co-create the future of management.
He enjoys travel in rough parts of the world and appreciates the perspective he gains from it.
Craig is passionate about ecovillages, permaculture and hand crafts.
During the day he builds software with Enspiral Craftworks, a non hierarchical collective of software professionals with a focus on social enterprise clients.
By night he builds furniture with hand tools, and helps with facilitation and community processes at Atamai Ecovillage.
Bene Anderson is Senior Product Manager in Government Information Services at Internal Affairs. Bene manages the New Zealand Government’s Common Web Platform (CWP), which is an open source platform-as-a-service offering for the creation and hosting of government websites. Bene comes from a web and digital background, and prior to joining Internal Affairs, spent five years as a web project manager and information architect in the private sector.
Jessica is a former urban designer turned open source developer by way of a fellowship at Code for America.
She fell hard for open source and now works at GitHub and spends her free time working on open source projects to teach or enable others to get hacking.
Jeremy Palmer led the development of the Land Information New Zealand (LINZ) Data Service – New Zealand’s first standard’s compliant data service, providing free access to government-held open data.
Embracing the vision for the LINZ Data Service, Jeremy has played a big part in championing the service across the geospatial and data communities to support uptake.
Taking the LINZ Data Service from strength to strength, Jeremy now heads the Data Service’s group and is responsible for leading the strategic direction and operational effectiveness of data services within LINZ.
A leading figure in the on-line Māori renaissance of the Internet since 1997; Karaitiana Taiuru has been a long-time advocate of the need for Māori to adapt to Open Source technologies in order to protect and enhance their Intellectual Property Rights.
Joshua is a programmer and entrepreneur.
In 2010 he launched Enspiral and turned his personal consulting company into a collective vehicle to help more people work on stuff that matters.
In 2013 he helped launch Enspiral Dev Academy which is an intensive 9 week programming course and has been exploring the intersection of tech, startups and education.
Dave Lane is President of the NZ Open Source Society, and an advocate of Free and Open Source Software (FOSS) adoption in business, education and government.
He works for Catalyst IT, which in 2012 acquired Egressive, the FOSS company he founded in 1998.
Among many other interests, he is passionate about his adopted home, NZ, and would love to see the government and education become far more open for the inherent social, economic, and pedagogical advantages openness offers.
He goes to great lengths to use FOSS exclusively himself, he's a fan of anyone who knows what "Commons-based peer production" and "permissionless innovation" are, and he has been advocating for "open" since long before it was cool.
Michelle is all about the Big Shift and moving from passive observers and consumers to active participants reshaping our world. Michelle is currently writing the book Participation is the new Consumption which she successfully crowdfunded for last year.
Driven by our own individual empowerment, a renewal of our sense of purpose and beliefs, and our regeneration of community, Michelle's vision in this book gives a roadmap to a world whose roads are yet to be built.
Michelle founded Ideaction after co-founding social innovation Sydney in 2010. She loves innovation and how to spread that as a digital strategist, communicator, community organiser, educator, public speaker and event producer.
Previously Michelle spent many years empowering businesses with the right technology through ERP systems, network system integration and Asia Pacific's largest IT Distributor.
Marianne Elliott is National Director for ActionStation - a movement to reignite participation in our democracy and restore 'people power' by building a community of active citizens, and facilitating collective social action for progressive change.
Trained as a human rights lawyer, Marianne worked in Timor-Leste, the Gaza Strip and Afghanistan before returning to New Zealand. She believes in the power of open democracy to help solve the wicked problems of our age.
Nathan Sobo is the co-founder of the Atom open-source text editor at GitHub. He is also the author of Treetop, a Ruby-based parser generator based on parsing expression grammars.
Josh fell into the web at the raw age of 16, building websites for all his long-haired friends who played loud music.
Since then, Josh has applied his astounding capacity to making music of his own, studying design at Massey Uni (which went ok - he spent 6 months teaching there too), before scooting off to London to exhibit his final-year project for Neville Brody’s Anti-Design Festival.
Josh still found time to work at a leading branding agency, shaping such projects as in-flight entertainment systems for airlines like Air New Zealand and Cathay Pacific. You might’ve heard of them.
Picking up 2 BeST Awards for Typography in 2009 and running an environmentally sustainable manufacturing business on the side helped to keep him entertained on ‘slow’ days.
Naturally, with all that going on, Josh has a bit of spare time. He plays guitar, bass and keyboards, and has his own home studio to prove it. He also lists running UP hills and planning weddings as his hobbies. Nobody’s perfect.
Transparency International Chair, Suzanne Snively, is a joint Co-Director with Murray Petrie of the Integrity Plus 2013 NZ National Integrity System Assessment. Suzanne was previously a Partner in Public Sector Advisory working as an economic strategist at PricewaterhouseCoopers.
She has been a company director for over 27 years and has been a regular analyst and commentator on New Zealand's comparative economic position for over 30 years, including commentary on its competitive positioning.
Suzanne achieved her Masters with Distinction at Victoria University of Wellington as a Fulbright and Reserve Bank Scholar, having completed a BA with thesis in Economics at Reed in the USA. She has extensive governance experience as a former Director of the Reserve Bank of New Zealand, of R A Hannah and Co and of Wellington City Council's Capital Holdings and as a Ministerial appointee to a number of task forces including the Whanau Ora Task Force.
Amongst other roles, she is currently on the Health Research Council, a Director of the Whanau Ora Commissioning Agency, Te Pou Matakana. and an Independent Director of the Army Leadership Board. Suzanne is also an external advisor on the internal audit / risk management committees of several central government agencies.
Mike's 3 blobs of life underly his passion in connecting people, explaining stuff and getting things done.
Together with his popular Kiwi tech podcast, Access Granted, and his years of experience of business mentorship he is a driving force behind the highly successful barcamp/hackathon scene in New Zealand.
Tying this together with a focus on ensuring people work towards"open government" and specifically "finding the value in open data" he is organising the July HackGovNZ and specifically the Wellington event to be held in the MBIE Stout Street venue.
This event will be the perfect 'do-y' to the OS/OS 'hui'.
Cam looks after the SilverStripe open source community ensuring it is a valuable, collaborative environment where participants can share knowledge and create great open source software.
He has a cross-discipline background with over a decade working as a web developer and more recently completing a business degree in information systems, with a focus on communities as social knowledge systems.
Communities are of great interest to Cam, both in practice and academically.
He has interest in the ideas of social learning and how communities act as an informal, living store of knowledge often co-existing alongside commercial organisations.
Having worked in private organisations, advising public sector and as an independent consultant, he brings a wide range of experience technically, and on the people side of the IT industry to the SilverStripe community.
Alanna has a passion for Open Business meets Open Tech meets Open Collaboration. She loves innovating new tools and processes to help people work in radically transparent and participatory ways.
Alanna is an anarchist management hacker, internet geek, feminist, and community builder - and can't wait to get amongst it with the OS//OS community!
Chris is a developer, open source advocate, and community organizer. At GitHub, his team connects, celebrates, and amplifies the amazing open source community on GitHub. He is a co-founder of TODO, a group of companies pragmatically approaching open source problems. His things live in San Francisco, while he spends much of his time on an airplane.
Chelsea is a member of the Loomio Cooperative, a social enterprise building an online tool for collaborative decision-making.
As one of the founders of New Zealand youth climate movement Generation Zero and a youth delegate to the United Nations climate negotiations in Copenhagen and Cancun, Chelsea is passionate about building inclusive organisations which create positive change.
In Loomio, Chelsea helps organisations & communities to learn the cultural skills required to democratise decisions and collaborate effectively at every scale.
Sascha Meinrath has been described as a “community Internet pioneer” and an “entrepreneurial visionary.”
He is Director of X-Lab, focusing on thought-provoking, bold tech policy interventions.
He also founded the Open Technology Institute and was named to the “TIME Tech 40” in 2013 as one of the most influential figures in technology.
Billy is an education technologist supporting collaboration and knowledge sharing across open education initiatives on the web.
He formerly worked at Creative Commons, supporting stakeholders in the world’s largest OER project in higher education and workforce training and now spends much of his time looking for new ways to leverage the web for global good.
Keitha Booth manages the NZ Open Government Information and Data Programme, based at Land Information New Zealand.
She has led this programme since 2009 and driven the development of the Declaration on Open and Transparent Government, the New Zealand Government Open Access and Licensing framework (NZGOAL) and the New Zealand Data and Information Management Principles (NZDIMP).
Her earlier roles include Communications and Relationships Manager, E-government Unit, State Services Commission; Information Manager, National Library of New Zealand; Information Manager, Ministry for Economic Development and Lead Consultant, Beca Group, providing information related advice to clients.
Keitha is on the Advisory Boards of Creative Commons Aotearoa NZ and Digital New Zealand. She has post graduate qualifications in librarianship and information systems.
possibilities. challenges - bespoke open. systems guy - kiwi.
Christopher Cormack has a BSc in Computer Science and a BA in Mathematics and Māori Studies.
While working for Katipo Communications he was the lead developer of the original version of Koha, which went live at Horowhenua Library Trust on January 5, 2000.
Since then he has served various roles in the community: Release manager, QA manager and Translation manager. He is the elected release maintainer for 3.18.
Christopher currently works for Catalyst IT in Wellington and believes in Free Software and allowing users the freedom to innovate.
Lillian Grace brings a new perspective on data, one where data is a language in which everyone can be fluent.
She believes societies and individuals will benefit when anyone can use data to inform their thinking and insights without requiring intermediaries.
Lillian is the CEO and Founder of Wiki New Zealand, the first organisation to designate everyone as a user of data and to build systems and software to deliver to this standard.
Wiki New Zealand puts data in simple charts using consistent standards so that people can easily play with and explore data. Critical to this solution was the development of Grace, a backend application that can extract data from a variety of formats and apply a transparent standardisation process that results in the data being consistent, clean and easy to find and use.
Collaboration is central to Wiki New Zealand, and Lillian believes the most comprehensive collection of data about New Zealand will only come from working together with domain and data experts.
Lillian engages with those in Government, private sector, academia, media, schools, and the general public to involve them in the effort and inform the evolution of Wiki New Zealand and its offerings.
Vik Olliver is a longhair Open enthusiast who does actually make a living out of Open hardware. He is best know for his work in the RepRap 3D printer core team but enjoys diddling with all manner of Open projects, from Alcohol to Zeroemission transport.
He spends a lot of his time expounding the virtues of Open to the world, and encouraging local groups to collaborate in an ethical manner. He has a Green philosophy but eats meat and doesn't do any of the airyfairy crystal wavey stuff.
With his wife Suz, daughters Tamara and Kate, and a few lesser mortals he runs a factory and open workshop in Henderson.
This notionally produces Open 3D printers and printer filament, but in practice gets used as a brewery, metal foundry, CNC emporium, hydroponic garden and zombie apocalypse refuge. Delegates are welcome to drop by.
He also raises a pride of arrogant cats.
Gina Rembe is a member of Enspiral, where she works, amongst others, on Lifehack, the Smart Energy Challenge, and Enspiral itself.
Passionated about bringing more good things into the world, she's interested in creating safe spaces where ideas, collaboration and relationship can thrive on trust, honesty and integrity.
Project manager, collaborator, maker, pioneer, furniture designer, Wendy Neale manages Fab Lab Wgtn. With a digital and craft-based practice, Wendy designs and creates meaningful objects from waste and obsolete furniture, and also develops furniture with modified traditional joints using digital techniques.


Reports sealed class which permits list doesn't contain some of the inheritors of this class from the same file.
New in 2020.3


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THE PORTRAIT OF A LADY A film review by Laura & Robin Clifford Copyright 1997 Laura & Robin Clifford
(This review is an excerpt from Reeling, a movie review show hosted by Laura and Robin Clifford, running on Boston Cable TV. Note that the film descriptions vary in completeness due to the fact that they're actually intros for running film clips.)
THE PORTRAIT OF A LADY ----------------------
Continuing with the 19th century literature onslought begun with 3 Jane Austen film adaptations in the past year, Australian director Jane Campion ("The Piano") brings Henry James' "The Portrait of a Lady" to the screen. Nicole Kidman stars as American Isabel Archer an independent young woman with a large inheritance who's moved to Europe to experience more of the world. Her curiousity allows her to be led by a manipulative friend with a shady agenda, Barbara Hershey's Madame Merle, into the arms of a dark dilettante, John Malkovich's Gilbert Osmond.
LAURA:
"The Portrait of a Lady" is a complex story, gorgeously shot, moodily symbolic and erotic. Jane Campion is proving that she can stand easily in the pantheon of world class directors, and I, for one, would love to see her nab a second Best Director Oscar nod.
I'm a bit perplexed why this film hasn't received more accolades - I've seem some negative response based on comparison with the source material. I'll state right here that I've never read the book (although my appetite to do so has now been whetted), so I'm judging the film simply on its own merits.
The film begins oddly, with an opening credit sequence of modern women shot in black and white, as well as color, in sisterly groups of staring into the camera - there's almost a hint of lesbian overtones. Here Campion announces that her interpretation of the story will bear her distinctive mark.
Isabel Archer, in an attempt to mark her independence, makes a perverse choice that will bring her nothing but unhappiness.
Nicole Kidman is fine as Isabel, but I doubt this role will garner her an Oscar nomination even though she was unjustly overlooked last year. James Malkovich is typecast as the perversely dark charmer who revels in emotional abuse, but he is good at these types of roles and plays this one quietly.
The standout in the cast is Martin Donovan as Isabel's sickly cousin Ralph, one of her three, more upstanding admirers. He's a Victorian embodiment of the 'if you love them, set them free' school of thought. He reminded me of Jeff Bridges at his most haunted best - maybe even better!
Barbara Hershey does some fine work as the duplicitous Madame Merle, who finally sinks into despair as she witnesses the pain brought about by her actions.
John Geilgud is also outstanding - he makes a strong impression in two small scenes. When left alone with his son Ralph, he immediately indulges himself in a forbidden cigarette (Gielgud's pale face and white hair framed in smoke - a beautiful, haunting image).
Another brilliant scene takes place at a ball. While the black-clad matron Isabel politically juggles her stepdaughter's two suitors, plump Italian women in lavish costumes pass out to her left and right - the coldness of an arranged marriage juxataposed against the fever of passion.
The film wraps with triumphantly, if obliquely, in a gothically bleak snowy England after the false sunshine of Italy. The film's beautiful cinematography and locations are complimented by a hauntingly romantic score.
A
ROBIN:
Normally, as a film critic, I can divorce the guy in me and just watch a film on the critic level. Sometime, as in "Portrait of a Lady", I watch the film as both a critic and a guy, which causes me great confusion.
As Robin, the film critic, I watch "Portrait..." and note the detail and attention given to costume, set design, photography, writing, acting and direction, and see a finely crafted work with solid credits all around.
The acting is uniformly first rate from top to bottom - especially the performance given by Martin Donovan as Nicole Kidman's cousin and admirer, Ralph. Very subtly, he builds on the viewer's and cousin Isabel's (Kidman) affections so that, by the end, you're rooting for him to get the girl. Too bad he dies. Blame the Henry James novel for that.
The key actors, Nicole Kidman, John Malkovich (making a career as a slimy sex symbol) and Barbara Hershey, are very good. Not great, through no fault of their own, but still very good.
My only problem with the film, as Robin, the film critic, is that the story is too involved for the acting. The actors are trying so hard to do justice to the story that I never was able to get too deep into the motivations of the characters they portray. I think its the wealth of information being conveyed that overshadows the actors efforts.
Technically, especially the photography alluded to in the title - some of the shots are, in fact, as visually stunning as genuine portraits - this is a top notch affair.
Again, as Robin, the film critic, I find Portrait of a Lady to be one of the years best and give it an A-.
Now, to Robin, the guy: its over two hours long, theres no gratuitous violence or nudity (nudity is symbolic here, at best - guys don't do symbolic). No guns. No good guys/bad guys. No explosions. No shootouts. Just a lot of talking. Hey, this isn't the kind of movie for A Guy to see, is it? Decidedly not.
Robin, the guy, gives it a C+, but that doesn't count. Robin, the critic, still gives "Portrait of a Lady" an A-.
Visit Reeling at http://www.neponset.com/reeling.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


Boasting a spectacular display of color, the titanium Aurora ring stands out as a beautiful anomaly contemporary fashion. Be light-years ahead of the rest!
Alexandra's passion is for fashion, photography and travel, which she established in her blog Lovely Pepa back in 2009, and has since become one of the most influential blogs in the world, one of the only Spanish bloggers to achieve such tremendous success. Her passion for photography and exotic, far away places come to life in her newest collection.
Boasting a spectacular display of color, the titanium Aurora ring stands out as a beautiful anomaly contemporary fashion. Be light-years ahead of the rest!


In June, members of the American Red Cross GIS team traveled to Hoi An, Viet Nam for a Missing Maps training with the Viet Nam Red Cross Society (VNRC) and the American Red Cross Viet Nam Delegation. The purpose of the training was twofold: (1) to train Red Cross staff on how to conduct community mapping, with a focus on mobile data collection, and (2) to demonstrate how to use open-source GIS platforms to produce maps, with the objective of improving preparedness for natural disasters. Using a training of trainers model, participants – and future trainers – learn best practices for conducting community mapping projects in medium- to large-scale teams and how to help their mappers succeed.
Each year, Central Viet Nam is affected by several major flood events. In recent a recent assessment, The United Nations Institute for Training and Research (UNITAR) published a map that analyzed how many people were affected by floodwaters in this coastal area following tropical cyclone DAMREY-17 (highlighted in red).
This flood analysis indicates that approximately 190,000 people were affected by Cyclone DAMREY-17 in Quang Nam Province. Having access to maps like these and being able to analysis how far residents live from coastal fronts allow community planners and the VNRC to prepare flood strategies and mitigate large impacts from major storm surges and landslides.
One way the VNRC prepares for annual flood events is by routinely completing Vulnerability Capacity Assessments (VCAs). These were traditionally done by hand, making results hard to share and refer back to. When the teams incorporate OpenStreetMap (OSM) into a workflow to complete the VCA, this can result in a more detailed and accurate spatial layout of communities. We can use OSM data layers (like historic flood levels, transportation routes, population densities, etc.) to enhance VCAs so they are more informative to decision-makers. Additionally, understanding where communities are most vulnerable allows us to identify and resolve gaps in our data, ensuring that future plans improve over time. Lastly, using OSM allows us to digitize our results and share the data we created with the wider community for further discussions and improvements.
Day 1: Remote Mapping Topic: Introduction to Missing Maps and OpenStreetMap Activity: Remote mapping and validation practice Goal: Understand best practices for community mapping
Day 2: Plan Topic: Utilizing Field Papers for effective planning Activity: Plan for groups to conduct mobile data collection, gathering points-of-interests in the community and upload data into OSM Goal: Learn how to effectively plan and conduct mobile data collection in teams
Day 3: Execute Topic: Using open source tools for mobile data collection Activity: Collect data using Maps.me Goal: Combine everything learned from Day 1 and 2 and collect Points of Interest (POIs)
Day 4: Analyze Topic: Integrating open data and conducting a Vulnerability and Capacity Assessment Activity: Participants create VCAs using the OSM base layer to inform where there are heavy residential areas and major POIs that are valuable during a storm surge Goal: Integrate data created through mobile data collection with pre-existing data in OSM to help inform the VCA and ultimately improve preparedness plans
Day 5: Implement Topic: Map production Activity: Learn how to use Overpass Turbo to extract mapping data and QGIS to produce maps Goal: Create individual maps from VCAs, complete with traditional mapping markers (including a title, north arrow, and legend)
After the training, participants are able to:
Explore data in OSM and “validate” data if it was incorrect or poorly drawn
Collect missing data on their own, combining two tools: Field Papers and Maps.me
Scan their field maps into a digital format and draw their vulnerable community layers on top, indicating where neighborhoods or areas are most vulnerable to major floods and storm surges
Take their new strategies learned and data collected into QGIS to produce a professional map that can be used in meetings with partners and stakeholders


Spurs set sights on former United winger Depay
Cape Town - Tottenham Hotspur have reportedly made Lyon winger Memphis Depay their top transfer target in the January transfer window.
Depay joined Manchester United from PSV in 2015, struggling to impress before leaving for Lyon in January 2017.
United retain a buy-back clause but have yet to make their interest in re-signing the Dutchman known, with Tottenham seeking to capitalise on the situation.
The Sunday Mirror say they are 'ready to make a move' to bring him back to the Premier League for £50m, the player having 'matured and gone from strength to strength' in France.
Tottenham are among 'a number of leading clubs' keeping tabs on Depay's progress at Lyon, having scouted him extensively in the Champions League in particular.
That has given Spurs the opportunity to watch Moussa Dembele, a player who attracted their interest at Celtic, but Depay has become their 'main target'.


Example that demontrates that the ticket 25 is fixed. Here is demonstrated how to style in HTML individually, and have your own layout of headers, board, moves, buttons, ...: Ticket 25 at GitHub


I need to add the chance to add as many documents as needed for several models of a Rails 3 app.
Each model is something that can have associated drawings, administrative acts and so forth.
To avoid duplication as far as possible I opted for an acts_as like feature for documents.
It will be called acts as documentable.
The acts as coding pattern rely on the polymorphic association with which a model can belong to more than one other model, on a single association.
Let’s see what this means.
Let’s imagine we have three models: User, Project and Tasks.
We want each model to be able to store n documents uploaded by a user.
With a polymorphic association we can obtain this result with the following Rails code:
The polymorphic reference in the migration automatically creates two columns:
- documentable_id: the id of the object to which the document will be added
- documentable_type: the class name of the object to which the document will be added
In this way Rails will be able to add n documents to each model which will be documentable.
We thus need a way to extend each model with the Documentable module.
This is achieved extending ActiveRecord::Base and is the “standard” way of structuring a gem extension for ActiveRecord.
Here is the structure:
The documentable.rb file should be saved in the lib folder.
In Rails 3 because of the assets pipeline files included in the lib folder are no longer loaded by default.
You both need to explicitly load files in the lib AND require them! I know it sounds strange but it’s the only way I managed to extend ActiveRecord with my Documentable module.
Now I can extend my models with the is_documentable class method and let them be able to have many documents:
Now let’s throw in a couple more things:
- dragonfly to upload files
- activeadmin to manage documents
For dragonfly nothing interesting, just follow the rails 3 quick start guide and use the file_accessor instead of the image_accessor.
Something more interesting for activeadmin. Here you can add a nested form for your documents using the wonderful has_many form method:
What this code does is simply to add a nested form for documents with buttons to add and remove each document!
If you want some more info on the has_many method check this page on active_admin.
Maybe I’ll set up a gem …. sometime ….


| Country Facts |
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Open to foreign entities with no economic activity in jurisdiction
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| Industry Facts |
| Rank as Oil Producer1 |
#{{ d.raw_oil_producing_rank || '—' }}
|
Barrels per day (thousands) |
{{ d.raw_thousand_barrels_per_day || '—' }} |
| Rank as Maritime Flag1 |
#{{ d.raw_global_rank_as_maritime_flag_cia_factbook || '—' }} |
Total Vessels1 |
{{ d.raw_total_vessel_cia_factbook_20102012 || '—' }} |
| Oil Tankers1 |
{{ d.raw_oil_tankers_20102012 || '—' }} |
Foreign Owned Vessels1 |
{{ d.raw_foreign_owned_cia_factbook_20102012 || '—' }}
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| Rank |
#{{ d.raw_fsi_rank || '—' }}
of 82 assessed
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(method)
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|
| Company Accounts |
{{ d.raw_public_company_accounts_fsi }}
|
| Record Company Ownership |
{{ d.raw_information_exchange_fsi }}
|
| Publish Company Ownership |
{{ d.raw_public_company_ownership_fsi }}
|
| Publish Trusts Ownership |
{{ d.raw_trust_and_foundations_register_fsi }}
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The last element of a list
Define the inductive predicate (last A a l) meaning
l is a list of terms of type A and a is the
last item of l
Define a function last_fun of type
(list A -> option A) which returns the last element of a
list (if it exists).
Then show the consistency between both definitions.
Solution
Look at this file .


I am a junior in the information systems program at Brigham Young University with a passion for financial analysis. In my career, I aim to leverage technology in a financial role in the telecoms industry. To that end, I have accepted an offer from Comcast NBCUniversal to intern in their finance department throughout summer 2017 in Los Angeles, CA. I will apply the technical experience I have garnered in my classes and as a digital media developer for BYU Broadcasting to complete analytical tasks efficiently and discover methods to impact the bottom line.
I am a firm believer that the whole is greater than the sum of its parts and I believe that my technically savvy yet socially conscious point of view will increase the synergy of any team. I have experience accomplishing objectives with individuals from cultures around the globe and believe that my fluency in both spoken and technical languages will be an asset to my employer.
BYU
BYU Broadcasting
Insert Portfolio here.
Feel free to reach out about employment opportunities or to just say hello!


Status: Not started yet
| Unmatched participants |
|---|
| {{i.participant}} |
| Men | Women | Table | |
|---|---|---|---|
| Ingmar | Ida | 1 | |
| Lars-Erik | Helena | 2 | |
| Gabriel | Lejla | 3 | |
| Adam | Sara | 4 | |
| Bertil | Sandra | 5 | |
| Anders | Sofia | 6 | |
| Sven | Ann-sofie | 7 | |
| Jakob | Birgit | 8 | |
| Henrik | Anna | 9 | |
| 10 |
{{rating.user}}'s ratings
Did your date talk too much? 1 = didn't say much, 5 = talked way too much
{{target.fst}}Did you find your date funny? 1 = not funny at all, 5 = really funny
{{target.snd}}What was your overall impression of your date? 1 = really bad, 5 = really good
{{target.rd}}The algorithm isn't much of an algorithm. It only selects one male and one female randomly and assign them to a free table
Ida
Age: 35
Persona group: The romantist
Demographics: Tierp
Goals: Have fun and meet new people.
Personal information: Ida lives outside of Uppsala and likes art. Shes very creative, likes to draw and paint but also enjoys reading.
Ingmar
Age: 21
Persona group: Student
City: Uppsala
Goals: Have fun and meet new people.
Personal information: Ingmar lives in Uppsala and enjoys watching Netflix. He usually study exercises or hang around with his friends.
Lars-erik
Age: 79
Persona group: Pansionär
City: Uppsala
Goals: Lars-erik would like to meet someone to spend the rest of his remaining life with.
Personal information: Lars-erik has lived in västerfärnebo for 79 years. His favorite hobby is fishing
and the wildlife. He usualy went out and daped with his wife but since last year she died.
Helena
Age: 41
Persona group: Computer scientist
City: Stockholm
Goals: Helena would like to meet new people and hopefully the love of her life.
Personal information: Helena lives in stockholm and enjoys creating new programs for computers, hang around with her friends and cook food.

Personal information: Henrik lives outside of Stockholm and enjoys watching Netflix. He trains often and loves computer science.
Bertil
Age: 25
Persona group: Student
Demographics: Uppsala
Goals: Bertil would like someone with similar intrests and have fun.
Personal information: Bertil lives in Uppsala and studies law. He usually study exercises or hang around with his friends.
Anders
Age: 67
Persona group: Pensionär
City: Knivsta
Goals: Anders would like to meet someone to spend the rest of his remaining life with.
Personal information: Anders lives outside of Uppsala and is a former fotball player. He usually exercises or watch sports.
Gabriel
Age: 30
Persona group: The movie enthusiast
City: Uppsala
Goals: Gabriel would like to meet new, fun people that share similar intrests.
Personal information: Gabriel lives outside of Stockholm and enjoys watching Netflix. He usually watch movies or hang around with his friends.
Jakob
Age: 25
Persona group: The romantist
Demographics: Stockholm
Goals: Jakob would like to meet new people with similar intrests.
Personal information: Jakob lives in Uppsala and enjoys watching Netflix. He usually exercises and enjoys fishing.
Sven
Age: 30
Persona group: The chief
Demographics: Västerås
Goals: Sven would like to meet new people and have just have fun .
Personal information: Sven lives in Västerås and enjoys watching Netflix. He enjoys reading and cooking food .
Adam
Age: 38
Persona group: The basketball player
Demographics: Uppsala
Goals: After being a bachelor for a long time, adam now wants to find the right one and settle down.
Personal information: Adam lives in Uppsala and plays basketball . He usually exercises or hang around with his friends and family.
Sandra
Age: 33
Persona group: The athlete
Demographics: Uppsala
Goals: Sandra would like to meet someone with the same love for sports.
Personal information: Sandra lives outside of Uppsala and enjoys playing fotball. She now works as a fotball trainer and loves working with the youth.
Lejla
Age: 30
Persona group: The book enthusiast
Demographics: Västerås
Goals: Lejla would like to meet new people and have fun.
Personal information: Lejla lives outside of Uppsala and loves reading books and novels. She works at a gym as a personal trainer and likes to spend time with family and friend on her spare time.
Ann-sofie
Age: 41
Persona group: The romantist
Demographics: Stockholm
Goals: Ann-sofie would like to meet new people and find love.
Personal information: Ann-sofie works as a social-worker and enjoys working with young people. She usually exercises or spends time with her family on her spare time.
Birgit
Age: 66
Persona group: Pensionär
Demographics: Uppsala
Goals: Birgit would like to meet someone to spend the rest of her remaining life with.
Personal information: Birgit lives in Uppsala and loves to exercise. She is a former athlete and loves skiing.
Sara
Age: 37
Persona group: The movie enthusiast
Demographics: Uppsala
Goals: Sara is a middle aged women who would like to find a partner to spend her life with.
Personal information: Sara enjoys watching movies and is a aspiring movie director. A part from that she loves dancing and spending time wiht her family.
Sofia
Age: 21
Persona group: The romantist
Demographics: Uppsala
Goals: Sofia would like to meet new people with similar intrests and find a partner.
Personal information: Sofia lives in Uppsala and studies interior design. Sofia is a student that works part-time at a cafe and enjoys baking and interior design.
Anna
Age: 36
Persona group: The romantist
Demographics: Stockholm
Goals: Anna would like to meet the right one.
Personal information: Anna lives in Stockholm and enjoys dancing. she works at her own dance studio or hang around with her friends.
Age: {{person.age}}
E-mail: {{person.email}}
phone number: {{person.phone}}
gender: {{person.gender}}


| PNG files (converted from FITS): | FITS images and binary tables: |
Move circle catalog:
Catalogs are arrays of JavaScript objects having the following elements (as well any others that you wish to include):
JS9.NewShapeLayer(catalog_name, def_opts); JS9.AddShapes(catalog_name, catalog_array, opts);where catalog_name is an indentifying name for the catalog. and def_opts is a JavaScript object which can specify default values for any of the parameters above (except for the x and y coordinate). When all else fails, default values are taken from the JS9.Catalog.opts object in JS9.
JS9 Demos:


Smart Mall
notifications
Products
Add a product
User Path
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Product Description
Product Weight
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or
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send


A Masters student of Computer Science at New Jersey Institute of Technology. I am passionate about web development and have done various projects related to it using many technologies.
I am self motivated, quick learner, have leadership qualities, good written and communication skills, dedication towards work. With a belief that the above qualities will be useful if given a chance by an organization. I am actively seeking full time jobs.
Resume!Office Assistant
Dean of Students Office


# 22022-10-16T20:00:39+03:00
|
Lower Bound |
Estimate |
Upper Bound |
| Value: |
43.56ns |
43.74ns |
44.04ns |
| Throughput: |
65.68MiB/s |
65.41MiB/s |
64.96MiB/s |
| Change in Value: |
-12.434% |
-11.821% |
-10.961% |
| Change in Throughput: |
+14.199% |
+13.406% |
+12.310% |
No change in performance detected.
# 12022-10-16T17:21:05+03:00
|
Lower Bound |
Estimate |
Upper Bound |
| Value: |
49.52ns |
49.56ns |
49.61ns |
| Throughput: |
57.78MiB/s |
57.73MiB/s |
57.67MiB/s |
| Change in Value: |
-0.2505% |
+0.3038% |
+0.8413% |
| Change in Throughput: |
+0.2511% |
-0.3029% |
-0.8343% |
Change within noise threshold.
# 02022-10-15T17:13:12+03:00
|
Lower Bound |
Estimate |
Upper Bound |
| Value: |
49.40ns |
49.48ns |
49.58ns |
| Throughput: |
57.92MiB/s |
57.82MiB/s |
57.70MiB/s |


car loan payment calculator
Enter the details of your loan.
Click the
calculate
button to calculate the monthly payments.
Total Loan Amount:
$
Yearly Interest Rate:
%
Loan Term (Yrs.):
Number of Payments:
Payment Amount:
$
Total Paid:
$
Interest Paid:
$


Macros | |
| #define | JOINT_CONTROL_CLIENT_SERVER_ADDRESS "/j2s7s300_driver/joints_action/joint_angles" |
| #define | JOINT_CONTROL_CLIENT_SERVER_TIMEOUT 3 |
| #define | POSE_CONTROL_CLIENT_SERVER_ADDRESS "/j2s7s300_driver/pose_action/tool_pose" |
| #define | POSE_CONTROL_CLIENT_SERVER_TIMEOUT 3 |
| #define | STANDART_VEL_CMD_TIMEOUT (0.5) |
Typedefs | |
| typedef enum State_ | State |
Enumerations | |
| enum | State_ { VELOCITY_CONTROL, POSITION_CONTROL, JOINT_CONTROL, STOPPED, NO_STATE } |
Functions | |
| void | goalDoneCallback (actionlib::SimpleClientGoalState state, kinova_msgs::ArmPoseResultConstPtr result) |
| Callback function the Pose Control Client sending pose goals. This function sets the current state to stopped if it was set to position control before. More... | |
| void | jointGoal_rawCallback (kinova_msgs::ArmJointAnglesActionGoal goal) |
| void | jointGoalDoneCallback (actionlib::SimpleClientGoalState state, kinova_msgs::ArmJointAnglesActionGoal result) |
| int | main (int argc, char **argv) |
| void | poseGoal_rawCallback (geometry_msgs::PoseStamped pose) |
| Callback function for position control subscriber. Clears latest velocity command and sends position command. More... | |
| ros::Duration | poseVel_timeout (STANDART_VEL_CMD_TIMEOUT) |
| void | poseVelocity_rawCallback (geometry_msgs::TwistStamped twist) |
| Callback function for velocity command subscriber. Stores the command, update latest command time and cancel a position controls. More... | |
| void | publishState (const ros::Publisher &status_pub) |
| Publishes the state of current control if it changed. Currently publishes via ROS_INFO. More... | |
| bool | velCmd_upToDate () |
| Checks if velocity command is up to date. More... | |
Variables | |
| State | currentState = STOPPED |
| actionlib::SimpleActionClient < kinova_msgs::ArmJointAnglesAction > * | jointControlClient_ptr |
| actionlib::SimpleActionClient < kinova_msgs::ArmPoseAction > * | poseControlClient_ptr |
| std::mutex | poseVel_lock |
| ros::Time | poseVel_time |
| kinova_msgs::PoseVelocity | poseVelCmd |
| #define JOINT_CONTROL_CLIENT_SERVER_ADDRESS "/j2s7s300_driver/joints_action/joint_angles" |
Definition at line 17 of file robot_control.cpp.
| #define JOINT_CONTROL_CLIENT_SERVER_TIMEOUT 3 |
Definition at line 19 of file robot_control.cpp.
| #define POSE_CONTROL_CLIENT_SERVER_ADDRESS "/j2s7s300_driver/pose_action/tool_pose" |
See README.md for more info.
Definition at line 16 of file robot_control.cpp.
| #define POSE_CONTROL_CLIENT_SERVER_TIMEOUT 3 |
Definition at line 18 of file robot_control.cpp.
| #define STANDART_VEL_CMD_TIMEOUT (0.5) |
Definition at line 20 of file robot_control.cpp.
| enum State_ |
| Enumerator | |
|---|---|
| VELOCITY_CONTROL | |
| POSITION_CONTROL | |
| JOINT_CONTROL | |
| STOPPED | |
| NO_STATE |
Definition at line 23 of file robot_control.cpp.
| void goalDoneCallback | ( | actionlib::SimpleClientGoalState | state, |
| kinova_msgs::ArmPoseResultConstPtr | result | ||
| ) |
Callback function the Pose Control Client sending pose goals. This function sets the current state to stopped if it was set to position control before.
Definition at line 76 of file robot_control.cpp.
| void jointGoal_rawCallback | ( | kinova_msgs::ArmJointAnglesActionGoal | goal | ) |
Definition at line 116 of file robot_control.cpp.
| void jointGoalDoneCallback | ( | actionlib::SimpleClientGoalState | state, |
| kinova_msgs::ArmJointAnglesActionGoal | result | ||
| ) |
Definition at line 83 of file robot_control.cpp.
| int main | ( | int | argc, |
| char ** | argv | ||
| ) |
Definition at line 147 of file robot_control.cpp.
| void poseGoal_rawCallback | ( | geometry_msgs::PoseStamped | pose | ) |
Callback function for position control subscriber. Clears latest velocity command and sends position command.
| pose | position command |
Definition at line 94 of file robot_control.cpp.
| ros::Duration poseVel_timeout | ( | STANDART_VEL_CMD_TIMEOUT | ) |
| void poseVelocity_rawCallback | ( | geometry_msgs::TwistStamped | twist | ) |
Callback function for velocity command subscriber. Stores the command, update latest command time and cancel a position controls.
| twist | velocity command |
Definition at line 52 of file robot_control.cpp.
| void publishState | ( | const ros::Publisher & | status_pub | ) |
Publishes the state of current control if it changed. Currently publishes via ROS_INFO.
| status_pub | The publisher to send data with |
Definition at line 131 of file robot_control.cpp.
|
inline |
Checks if velocity command is up to date.
Definition at line 44 of file robot_control.cpp.
Definition at line 32 of file robot_control.cpp.
| actionlib::SimpleActionClient<kinova_msgs::ArmJointAnglesAction>* jointControlClient_ptr |
Definition at line 37 of file robot_control.cpp.
| actionlib::SimpleActionClient<kinova_msgs::ArmPoseAction>* poseControlClient_ptr |
Definition at line 36 of file robot_control.cpp.
| std::mutex poseVel_lock |
Definition at line 35 of file robot_control.cpp.
| ros::Time poseVel_time |
Definition at line 33 of file robot_control.cpp.
| kinova_msgs::PoseVelocity poseVelCmd |
Definition at line 31 of file robot_control.cpp.


This demonstration application is dedicated to showcasing ngx-matomo features in an almost real application.
{{ configurationCode }}


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The part parameter identifies groups of properties that should be returned for a resource. It requires you to select the resource components that your application actually uses.


Preparation time
less than 30 mins
Cooking time
10 to 30 mins
Serves
Serves 4
The homemade garam masala paste is the key to this tasty curry sauce, served with juicy chunks of seared venison.
For the garam masala paste, blend or pound the ingredients for the garam masala with 50ml/2fl oz water to make a paste.
For the Bangladeshi venison curry, heat the oil in a large saucepan and fry the onions gently until lightly coloured. Add the ginger-garlic paste and cook for 2-3 minutes to cook out the raw flavours, then add the garam masala paste and salt to taste. Cook, stirring, for a further 3-4 minutes or until fragrant.
Add the ground spices, tomatoes and stir to mix. Pour in about 750ml/1¼ pints of water. Bring to the boil, then cook on a low heat until the sauce has thickened. Add the chopped coriander a few minutes from the end of the cooking time.
For the venison, preheat the oven to 240C/450F/Gas8. Heat a medium frying pan and add the oil, place the venison fillet in the pan and cook for a couple of minutes on each side then place in oven for eight minutes. Remove from the oven and leave to rest for 10 minutes before serving.
Serve the sauce on plates, and cut the venison into batons and arrange the venison pieces on top of the sauce and garnish with coriander cress. Serve with steamed basmati rice.


TIFF2PS(1) TIFF2PS(1) NAME tiff2ps - convert a TIFF image to PostScript™ SYNOPSIS tiff2ps [ options ] input.tif ... DESCRIPTION tiff2ps reads TIFF images and writes PostScript or Encapsulated Post‐ Script (EPS) on the standard output. By default, tiff2ps writes Encapsu‐ lated PostScript for the first image in the specified TIFF image file. By default, tiff2ps will generate PostScript that fills a printed area specified by the TIFF tags in the input file. If the file does not con‐ tain XResolution or YResolution tags, then the printed area is set according to the image dimensions. The -w and -h options (see below) can be used to set the dimensions of the printed area in inches; overriding any relevant TIFF tags. The PostScript generated for RGB, palette, and CMYK images uses the col‐ orimage operator. The PostScript generated for greyscale and bilevel images uses the image operator. When the colorimage operator is used, PostScript code to emulate this operator on older PostScript printers is also generated. Note that this emulation code can be very slow. Color images with associated alpha data are composited over a white back‐ ground. OPTIONS -1 Generate PostScript Level 1 (the default). -2 Generate PostScript Level 2. -3 Generate PostScript Level 3. It basically allows one to use the /flateDecode filter for ZIP compressed TIFF images. -a Generate output for all IFDs (pages) in the input file. -b Specify the bottom margin for the output (in inches). This does not affect the height of the printed image. -c Center the image in the output. This option only shows an effect if both the -w and the -h option are given. -d Set the initial TIFF directory to the specified directory number. (NB: Directories are numbered starting at zero.) This option is useful for selecting individual pages in a multi-page (e.g. fac‐ simile) file. -e Force the generation of Encapsulated PostScript (implies -z). -h Specify the vertical size of the printed area (in inches). -H Specify the maximum height of image (in inches). Images with larger sizes will be split in several pages. Option -L may be used for specifying size of split images overlapping. -W Specify the maximum width of image (in inches). Images with larger sizes will be split in several pages. Options -L and -W are mutu‐ ally exclusive. -i Enable/disable pixel interpolation. This option requires a single numeric value: zero to disable pixel interpolation and non-zero to enable. The default is enabled. -L Specify the size of overlapping for split images (in inches). Used in conjunction with -H option and -W option. -l Specify the left margin for the output (in inches). This does not affect the width of the printed image. -m Where possible render using the imagemask PostScript operator instead of the image operator. When this option is specified tiff2ps will use imagemask for rendering 1 bit deep images. If this option is not specified or if the image depth is greater than 1 then the image operator is used. -o Set the initial TIFF directory to the IFD at the specified file offset. This option is useful for selecting thumbnail images and the like which are hidden using the SubIFD tag. -p Force the generation of (non-Encapsulated) PostScript. -r 90|180|270|auto Rotate image by 90, 180, 270 degrees or auto. Auto picks the best fit for the image on the specified paper size (eg portrait or landscape) if -h or -w is specified. Rotation is in degrees coun‐ terclockwise. Auto rotates 90 degrees ccw to produce landscape. -s Generate output for a single IFD (page) in the input file. -w Specify the horizontal size of the printed area (in inches). -x Override resolution units specified in the TIFF as centimeters. -y Override resolution units specified in the TIFF as inches. -z When generating PostScript Level 2, data is scaled so that it does not image into the deadzone on a page (the outer margin that the printing device is unable to mark). This option suppresses this behavior. When PostScript Level 1 is generated, data is imaged to the entire printed page and this option has no affect. EXAMPLES The following generates PostScript Level 2 for all pages of a facsimile: tiff2ps -a2 fax.tif | lpr Note also that if you have version 2.6.1 or newer of Ghostscript then you can efficiently preview facsimile generated with the above command. To generate Encapsulated PostScript for a the image at directory 2 of an image use: tiff2ps -d 1 foo.tif (Notice that directories are numbered starting at zero.) If you have a long image, it may be split in several pages: tiff2ps -h11 -w8.5 -H14 -L.5 foo.tif > foo.ps The page size is set to 8.5x11 by -w and -h options. We will accept a small amount of vertical compression, so -H set to 14. Any pages between 11 and 14 inches will be fit onto one page. Pages longer than 14 inches are cut off at 11 and continued on the next page. The -L.5 option says to repeat a half inch on the next page (to improve readability). BUGS Because PostScript does not support the notion of a colormap, 8-bit pal‐ ette images produce 24-bit PostScript images. This conversion results in output that is six times bigger than the original image and which takes a long time to send to a printer over a serial line. Matters are even worse for 4-, 2-, and 1-bit palette images. Does not handle tiled images when generating PostScript Level I output. SEE ALSO pal2rgb(1), tiffinfo(1), tiffcp(1), tiffgt(1), tiffmedian(1), tiff2bw(1), tiffsv(1), libtiff(3) Libtiff library home page: http://www.remotesensing.org/libtiff/ libtiff November 2, 2005 TIFF2PS(1)


{% extends "base.html" %} {% block title %}Registration complete{% endblock %} {% block content %}
Within 5 minutes you'll be recieving an email with an activation link. Do the following:


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