<%=h1%>
<%figure<-1%>
This is an un-interpreted and automatically-generated report on emerging, rising and falling terms from the <%=doc.pseudonym%>s of the
<%if(length(set.title)==1){%>
<%=set.title%>.
<%}else{
for(i in 1:length(set.title)){
if(i==length(set.title)){%>
and <%=set.title[i]%>.
<%}else if (i==length(set.title)-1){%>
<%=set.title[i]%>
<%}else{%>
<%=set.title[i]%>,
<%}#end if
}#end loop
}#end if%>
It is one part of the "Weak Signals Analysis" work of the TEL-Map Project. The same analytical method is also applied to different kind of source data, hence the term "document" is used in plots; this may be equated to "<%=doc.pseudonym%>" in this report.
All plots will open in a new window/tab as 1000x1000 pixel images if clicked on.
The Method
The analysis involves the application of text mining to the <%=docs.pseudonym.long%> divided into two sets according to time:
<%if(source.type=="b"){%>
- Target: <%=doc.pseudonym%>s published during the <%=recent.months%> months between <%=format(key.date, date.format)%> and <%=format(last.date, date.format)%>.
- Previous: <%=doc.pseudonym%>s published during the previous <%=prior.months%> months (back to <%=format(start.date, date.format)%>).
<%}else{%>
- Target: <%=doc.pseudonym%>s from <%=conf.year.recent%> conferences
- Previous: <%=doc.pseudonym%>s from the previous <%=conf.years.in_past%> years
<%}%>
Pearson's "Chi Squared" test is applied to determine the probability that the frequency of terms in both of these two samples could be from the same hypothetical distribution. The probability that the difference in term frequency could be ascribed to chance - assuming this hypothesis is true - must be less than <%=100*thresh.pval%>% for a term to be presented in this report. This is referred to as the "significance threshold" below.
Further informaton about the text mining process being used is available elsewhere but for the purpose of interpretation it should be noted that "stemming" and "stop-word" removal has been used. "Stop-word" removal means that common words are removed before beginning the analysis. "Stemming" involves the conversion of similar words that differ only by their ending into a truncated form that is used as the "term" in the analysis. For the purpose of readability the shortest word that produces the term is generally given, although sometimes the algorithm fails and the stemmed version is shown. This should be born in mind when examining the results.
Four views on the differences between the Target and Previous sets of <%=doc.pseudonym%>s are presented:
- New Terms, where this report highlights terms that meet the significance threshold and appear in at least <%=doc_count.thresh%> different Target <%=doc.pseudonym%>s but not at all in the Previous set.
- Rising Terms, where term frequency rises by a statistically-significant amount and has an average frequency of less than <%=100*max.past.freq%>% in the Previous set. A term must also appear in at least <%=doc_count.thresh%> different Target <%=doc.pseudonym%>s. The Rising Terms view focusses on possible weak signals, possible new trends.
- Established Terms are similar to "Rising Terms" but appear with a frequency of <%=100*max.past.freq%>% or more on average in the Previous set and hence are considered to be established upwards trends rather than indicators of a possible weak signal. Since this report is primarily concerned with Weak Signal detection, relatively little emphasis is given to Established Terms.
- Falling Terms, are those terms showing a decrease between the Previous set and the Target <%=doc.pseudonym%>s that exceeds a significance threshold of <%=100*thresh.pval.falling%>% (a more lenient condition is used for falling terms).
For Rising, Established and Falling Terms, the % change is calculated on the proportion of all stemmed words that are the term in question. For example if 2.00% of stemmed words in the previous set was "learn" and 2.12% pertains to the current set, then the rise is 6%.
Summary Statistics and Overall Pattern
Corpus Statistics
There are <%=sum(!past.doc_ids.bool)%> <%=doc.pseudonym%>s in the Target set and <%=sum(past.doc_ids.bool)%> <%=doc.pseudonym%>s in the Previous set. Within these <%=doc.pseudonym%>s, which define the corpus, there are <%=length(Terms(dtm.tf))%> distinct terms after removing stop words and applying stemming. The summary statistics for the number of times each term appears in the corpus are: mean=<%=summary(dtm.tf.sums)[["Mean"]]%>, median=<%=summary(dtm.tf.sums)[["Median"]]%>, maximum=<%=summary(dtm.tf.sums)[["Max."]]%>.
For the set of New Terms:
- the number of times each term appears in at least <%=doc_count.thresh%> different <%=doc.pseudonym%>s the Target set but before applying the significance test has: mean=<%=raw.summary.new[["Mean"]]%>, median=<%=raw.summary.new[["Median"]]%>, maximum=<%=raw.summary.new[["Max."]]%>.
- after selecting terms above the significance threshold, there are <%=length(Terms(dtm.tf.new))%> terms in scope which appear in <%=length(Docs(dtm.tf.new))%> out of the <%=sum(!past.doc_ids.bool)%> Target set <%=doc.pseudonym%>s.
For the set of Rising Terms:
- the % rise in frequency from the Previous set to the Target set before applying the significance test has: mean=<%=raw.summary.rise[["Mean"]]%>, median=<%=raw.summary.rise[["Median"]]%>, maximum=<%=raw.summary.rise[["Max."]]%>.
- after selecting terms above the significance threshold, there are <%=length(Terms(dtm.tf.rising))%> terms in scope which appear in <%=length(Docs(dtm.tf.rising))%> out of the <%=sum(!past.doc_ids.bool)%> Target set <%=doc.pseudonym%>s.
The set of Falling Terms (the % fall in frequency from the Previous to the Target set) before applying the singificance test has summary statistics of: mean=<%=raw.summary.fall[["Mean"]]%>, median=<%=raw.summary.fall[["Median"]]%>, maximum=<%=raw.summary.fall[["Max."]]%>.
New Terms
Figure <%=figure%><%figure<-figure+1%>: The number of terms that appear 1,2,3... times in the target set set not at all in the previous set. In this case there are <%=term.sums.new.t[doc_count.thresh+2]%> new terms that appear <%=doc_count.thresh+2%> times. NB: the low end is suppressed due to the requirement that terms appear in at lest <%=doc_count.thresh%> documents.
Rising and Falling Terms
Figure <%=figure%><%figure<-figure+1%>: Number of terms with a given % rise before applying the significance test.
Figure <%=figure%><%figure<-figure+1%>: Number of terms with a given % fall before filtering out those terms that do not meet the criterion of the significance test. A 100% fall indicates that the term does not appear in the target set's <%=doc.pseudonym%>s.
Auxillary Measures of Interest
In addition to the primary measures of interest - statistical significance and the percentage change - two more are calculated as an aid to judging the actual, as opposed to statistical, significance of differences between the Previous set of <%=doc.pseudonym%>s and the Target set. The auxillary measures apply to the <%=doc.pseudonym%>s and are:
- Novelty, which is a measure of how unusual the <%=doc.pseudonym%> is, based on the terms appearing in it, compared to all other <%=doc.pseudonym%>s in the corpus.
- Subjectivity, which is a measure of the fraction of the words used that are listed as generally having either positive or negative sentiment associated with their occurrence (the Harvard General Inquirer lexicon is used). A high Subjectivity is assumed to indicate the author's active interest rather than passive observation; active interest may correlate with weak signals. A subjectivity of 0.1 indicates 10% of the words (after removel of stopwords) have connotations of positive or negative sentiment.
Figure <%=figure%><%figure<-figure+1%>: Distribution of novelty measure among <%=doc.pseudonym%>s.
Novelty= 1 - the maximum similarity (specifically the
cosine similarity) between the <%=doc.pseudonym%> and any other <%=doc.pseudonym%> in the corpus.
Figure <%=figure%><%figure<-figure+1%>: Distribution of standardised novelty measure among <%=doc.pseudonym%>s.
A document with median novelty will have a standardised novelty of 0 and a unique <%=doc.pseudonym%>s a value of 1. Negative values indicate a document that has a closer "neighbour" than the median.
The Novelty Measure is particularly unreliable when <%=doc.pseudonym%>s are quite short, hence contain few terms after stopword removal, and if the corpus size is modest. In this case, the distribution of novelty scores is typically skewed towards a high value. In this case it is not realistic to identity truely novel <%=doc.pseudonym%>s, whereas a high negative value for the Standardised Novely is a reasonalble indicator of similarity.
The <%=doc.pseudonym%>s containing the highest novelty scores are listed below.
Figure <%=figure%><%figure<-figure+1%>: Comparative distribution of positive and negative sentiment and subjectivity acore among <%=doc.pseudonym%>s.
The central box has a range defined by the 1st and 3rd quartiles and the limits of the "whiskers" are no more than 1.5x the interquartile range away from the box. <%=doc.Pseudonym%>s outside the range of the whiskers are individually plotted as circles and are deemed to be "outliers". The central bar indicates the median and the cross indicates the mean.
Figure <%=figure%><%figure<-figure+1%>: Balance of sentiment among the 10% most-subjective <%=doc.pseudonym%>s.
The <%=doc.pseudonym%>s containing the highest subjectivity scores are listed below.
Significant New Terms
Significant New Terms appear for the first time in at least <%=doc_count.thresh%> different <%=doc.pseudonym%>s in the Target set and meet the <%=100*thresh.pval%>% "significance threshold". <%=in_themes.msg.new%>
Figure <%=figure%><%figure<-figure+1%>: Frequencies of occurrence of the New Terms in the target set.
Figure <%=figure%><%figure<-figure+1%>: Wordcloud of frequencies of occurrence of the New Terms in the target set, coloured and sized according to term frequency.
Uses
TagCanvas by "goat 1000".
The <%=doc.pseudonym%>s containing the significant new terms are listed below.
Figure <%=figure%><%figure<-figure+1%>: The number of <%=doc.pseudonym%>s containing each New Term. A New Term that appears in a large number of <%=doc.pseudonym%>s is likely to be a stronger indication of a new trend that a New Term that is concentrated in a single <%=doc.pseudonym%>.
Figure <%=figure%><%figure<-figure+1%>: Heatmap to show the distribution of the New Terms among <%=doc.pseudonym%>s. Red cells indicate the term does not appear. Orange cells indicate a relatively less-frequent term and yellow cells indicate terms that are relatively more frequent
among the New Terms in a given document. This also shows how terms may be clustered according to their occurrence and how <%=doc.pseudonym%>s may be clustered according to the Terms within them. The numbers are the identifiers assigned to each paper.
Note: In plots such as the above, where terms are shown they will usually be a short word but may occasionally be truncated or have an unusual ending. This is a consequence of the stemming process, where related words are rendered into a single form that is used in the processing. The word that actually occurs in a given <%=doc.pseudonym%>s may be one of the related forms. The unusual endings occur when the reverse-lookup algorithm fails. It should also be noted that punctuation is removed so hyphenated forms become concatenated. An unfamiliar term may be an abbreviation or project/product acronym.
Figure <%=figure%><%figure<-figure+1%>: Spread of standardised novelty among <%=doc.pseudonym%>s containing each New Term.
Figure <%=figure%><%figure<-figure+1%>: Spread of subjectivity score among <%=doc.pseudonym%>s containing each New Term.
Note: care should be exercised in interpreting these two plots since the number of <%=doc.pseudonym%>s containing each term is very small.
The <%=doc.pseudonym%>s containing the significant new terms are listed below.
Significant Rising Terms
Significant Rising Terms are those that meet the significance threshold criterion and appear in at least <%=doc_count.thresh%> different <%=doc.pseudonym%>s in the Target set but have an average frequency of less than <%=100*max.past.freq%>% (the "Established Term" threshold) in the Previous set. <%=in_themes.msg.rising%>
Figure <%=figure%><%figure<-figure+1%>: Significant rising terms and their level of significance (-log
10(p), where p is the probability that the rise is due to chance only). Colour coding matches the significance value.
Figure <%=figure%><%figure<-figure+1%>: Wordcloud coloured and sized according to significance level of the Rising Terms in the target set.
Uses
TagCanvas by "goat 1000".
The <%=doc.pseudonym%>s containing the significant rising terms are listed below.
Figure <%=figure%><%figure<-figure+1%>: Significant rising terms and their percentage of rise. Colour coding matches the % rise. <%if(max(rise.ratio)>800){%>The plot is cut off at a ceiling of <%=rising.plot.max%>%.<%}%>
Figure <%=figure%><%figure<-figure+1%>: The number of occurrences of the Rising Terms in the Previous and Target sets.
Figure <%=figure%><%figure<-figure+1%>: Comparison of the proportion of all stemmed words that are the term in question in both the previous and target set.
Figure <%=figure%><%figure<-figure+1%>: The number of <%=doc.pseudonym%>s containing each Significant Rising Term. The colour coding matches the % rise in the proportion of term occurrence. A Term that appears in a large number of <%=doc.pseudonym%>s is likely to be a stronger indication of a new trend that a Term that is concentrated in a single <%=doc.pseudonym%>.
Figure <%=figure%><%figure<-figure+1%>: Heatmap to show the distribution of the Rising Terms among <%=doc.pseudonym%>s. Red cells indicate the term does not appear. Orange cells indicate a relatively less-frequent term and yellow cells indicate terms that are relatively more frequent
in a given document. This also shows how terms may be clustered according to their occurrence and how <%=doc.pseudonym%>s may be clustered according to the Terms within them. The numbers are the identifiers assigned to each <%=doc.pseudonym%>.
Figure <%=figure%><%figure<-figure+1%>: Spread of standardised novelty among <%=doc.pseudonym%>s containing each Rising Term.
Figure <%=figure%><%figure<-figure+1%>: Spread of subjectivity score among <%=doc.pseudonym%>s containing each Rising Term.
Note: care should be exercised in interpreting these two plots since the number of <%=doc.pseudonym%>s for some terms is very small.
Correlation Between New and Rising Terms
This shows how significant terms from either the "New" and "Rising" sets appear together in <%=doc.pseudonym%>s. The visualisation is created using Gephi (v0.8 alpha). Using Gephi provides for good interactivity; the RisingTerm-Co-occurrence.gephi file can be downloaded and used in Gephi.
Figure <%=figure%><%figure<-figure+1%>: Graph of Co-occurrence of New and Rising Terms.
Interpreting the graph: terms are colour coded according to clustering aparrent in the results; the nodes are scaled according to the statistical significance; the lines joining the nodes are scaled according to the number of co-occurrences.
The <%=doc.pseudonym%>s containing the significant rising terms are listed below.
Significant Established Terms
These are terms with a statistically-significant increase but with a freqency of <%=100*max.past.freq%>% or more on average in the Previous set and hence are considered to be established upwards trends rather than indicators of a possible weak signal. Since this report is primarily concerned with Weak Signal detection, relatively little emphasis is given to Established Terms.
Figure <%=figure%><%figure<-figure+1%>: Significant rising terms and their percentage of rise. Colour coding is identical to the Rising Term plot. <%if(max(established.rise.ratio)>800){%>The plot is cut off at a ceiling of <%=rising.plot.max%>%.<%}%>
Figure <%=figure%><%figure<-figure+1%>: Significant rising terms and their level of significance. Colour coding matches the significance value as in the Rising Term plot.
Significant Falling Terms
Figure <%=figure%><%figure<-figure+1%>: Significant falling terms and their percentage of fall. Colour coding matches the % fall.
Figure <%=figure%><%figure<-figure+1%>: Significant falling terms. Colour coding matches the statistical significance.
Figure <%=figure%><%figure<-figure+1%>: The number of occurrences of the Falling Terms in the previous and target sets of <%=doc.pseudonym%>s.
Figure <%=figure%><%figure<-figure+1%>: Comparison of the proportion of all stemmed words that are the term in question in both the previous and target sets.
<%=doc.Pseudonym%>s Highlighted by the Analysis
<%=doc.Pseudonym%>s marked "** Top 10 **" are those where the <%=doc.pseudonym%> contains the greatest number of distinct terms that are either Rising, Established or New.
<%=doc.Pseudonym%>s Containing the Significant New Terms
See the end of this report for information about the source of <%=doc.pseudonym%>s.
<%for (i in 1:length(new.doclist)){%>
<%=doc.Pseudonym%>s Containing "<%=names(new.doclist[i])%>"
<%df<-new.doclist[[i]]%>
<%for (j in 1:length(df[,1])){%>
- "><%=df[j,"heading"]%>
-
<%=df[j,"authors"]%>, <%=paste(df[j,"origin"],df[j,"date"])%> (id=<%=df[j,"id"]%>
<%if(!is.na(top10[df[j,"id"]])){%>
** Top 10 **<%}-%>)
<%=paste("Ancillary Measures: Novelty=",round(as.numeric(df[j,"std.novelty"]),digits=3),", Subjectivity=",round(as.numeric(df[j,"subjectivity"]),digits=3), "Max Author Betweenness=",signif(as.numeric(df[j,"max.betweenness"]),digits=3))-%>
<%if(show.abstracts){%>
<%=Head.Text(df[j,"abstract"])%>
<%}#endif%>
<%}#j-loop%>
<%}#i-loop%>
<%=doc.Pseudonym%>s Containing the Significant Rising Terms
See the end of this report for information about the source of <%=doc.pseudonym%>s.
<%for (i in 1:length(rising.doclist)){%>
<%=doc.Pseudonym%>s Containing "<%=names(rising.doclist[i])%>"
<%df<-rising.doclist[[i]]%>
<%for (j in 1:length(df[,1])){%>
- "><%=df[j,"heading"]%>
-
<%=df[j,"authors"]%>, <%=paste(df[j,"origin"],df[j,"date"])%> (id=<%=df[j,"id"]%>
<%if(!is.na(top10[df[j,"id"]])){%>
** Top 10 **<%}-%>)
<%=paste("Ancillary Measures: Novelty=",round(as.numeric(df[j,"std.novelty"]),digits=3),", Subjectivity=",round(as.numeric(df[j,"subjectivity"]),digits=3), "Max Author Betweenness=",signif(as.numeric(df[j,"max.betweenness"]),digits=3))-%>
<%if(show.abstracts){%>
<%=Head.Text(df[j,"abstract"])%>
<%}#endif%>
<%}#j-loop%>
<%}#i-loop%>
<%if(length(df.std.nov.extreme[,1])>0){%>
<%=doc.Pseudonym%>s with a Standardised Novelty Score Exceeding <%=std.novelty.min%>
See the end of this report for information about the source of <%=doc.pseudonym%>s.
<%for (j in 1:length(df.std.nov.extreme[,1])){%>
- "><%=df.std.nov.extreme[j,"heading"]%>
-
<%=df.std.nov.extreme[j,"authors"]%>, <%=paste(df.std.nov.extreme[j,"origin"],df.std.nov.extreme[j,"date"])%> (id=<%=df.std.nov.extreme[j,"id"]%>
<%if(!is.na(top10[df.std.nov.extreme[j,"id"]])){%>
** Top 10 **<%}-%>)
<%=paste("Ancillary Measures: Novelty=",round(as.numeric(df.std.nov.extreme[j,"std.novelty"]),digits=3),", Subjectivity=",round(as.numeric(df.std.nov.extreme[j,"subjectivity"]),digits=3), "Max Author Betweenness=",signif(as.numeric(df.std.nov.extreme[j,"max.betweenness"]),digits=3))-%>
<%if(show.abstracts){%>
<%=Head.Text(df.std.nov.extreme[j,"abstract"])%>
<%}%>
<%}#j-loop%>
<%}%>
<%if(length(df.subj.outliers[,1])>0){%>
Subjectivity Upper Outlier <%=doc.Pseudonym%>s
See the end of this report for information about the source of <%=doc.pseudonym%>s.
<%=doc.Pseudonym%>s are listed that have a subjectivity score greater than the 3rd quartile subjectivity + 1.5x the interquartile range.
<%for (j in 1:length(df.subj.outliers[,1])){%>
- "><%=df.subj.outliers[j,"heading"]%>
-
<%=df.subj.outliers[j,"authors"]%>, <%=paste(df.subj.outliers[j,"origin"],df.subj.outliers[j,"date"])%> (id=<%=df.subj.outliers[j,"id"]%><%if(!is.na(top10[df.subj.outliers[j,"id"]])){%>
** Top 10 **<%}-%>)
<%=paste("Ancillary Measures: Novelty=",round(as.numeric(df.subj.outliers[j,"std.novelty"]),digits=3),", Subjectivity=",round(as.numeric(df.subj.outliers[j,"subjectivity"]),digits=3), "Max Author Betweenness=",signif(as.numeric(df.subj.outliers[j,"max.betweenness"]),digits=3))-%>
<%if(show.abstracts){%>
<%=Head.Text(df.subj.outliers[j,"abstract"])%>
<%}%>
<%}#j-loop%>
<%}%>
Copyright, Licence and Credits
<%if(source.type=="b"){%>
The blog content analysed was crawled by the Mediabase, for which thanks to Michael Derntl and colleagues at RWTH Aachen DBIS (and TELMap partners).
<%}else{%>
<%if(length(set.name)==1){%>
<%if(show.abstracts){-%>Quoted abstracts are © the publisher, <%=publisher.series%>.<%}%> Papers and a full set of abstracts may be found on the publisher's website and other information may be found on the conference website.
<%}else{%>
<%if(show.abstracts){-%>Quoted abstracts are © the respective publisher, as follows:<%}else{%>Conference proceedings and other information may be found at:<%}%>
<%for(conf in 1:length(set.name)){%>
- <%=set.name[conf]%> - publisher website<%if(last.conference.url[conf]!=""){%>, <%=conf.year.recent%> conference website<%}%>.
<%}#end loop%>
<%}#end if%>
<%}%>
This work was undertaken as part of the TEL-Map Project; TEL-Map is a support and coordination action within EC IST FP7 Technology Enhanced Learning.
Generated using Brew with R
Source code for processing and formatting is available on GitHub.
<%if(source.type=="c"){%>
Thanks to Michael Ley for maintaining DBLP which has greatly helped to stream-line the data collection and to Michael Derntl and colleagues at RWTH Aachen DBIS (and TELMap partners) for the author betweenness centrality data.
<%}%>
Data: the raw results are available as a CSV download under the same licence terms as this report.


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| Precision Consulting -- Statistical consulting firm specializing in applying advanced modeling and big data techniques to real world problems. Past clients include Fortune 500 corporations as well as universities and academics. If you run into statistical problems, they may be able to help. |


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Researched and compared reverse engineering technics including network traffic inspection, HTTPS decryption, certificate pinning cracking, Tor proxy and social engineering. Implemented map scanner function using Google S2 Algorithm. Parsed mock Pokémon Go API to achieve nearby wild Pokémon.


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| Opcode/Instruction | Op /En | 64/32 bit Mode Support | CPUID Feature Flag | Description |
|---|---|---|---|---|
|
EVEX.512.66.0F38.W1 CC /r VRSQRT28PD zmm1 {k1}{z}, zmm2/m512/m64bcst {sae} |
FV | V/V | AVX512ER | Computes approximations to the Reciprocal square root (<2^-28 relative error) of the packed double-precision floating-point values from zmm2/m512/m64bcst and stores result in zmm1with writemask k1. |
| Op/En | Operand 1 | Operand 2 | Operand 3 | Operand 4 |
| FV | ModRM:reg (w) | ModRM:r/m (r) | NA | NA |
Description
Computes the reciprocal square root of the float64 values in the source operand (the second operand) and store the results to the destination operand (the first operand). The approximate reciprocal is evaluated with less than 2^-28 of maximum relative error.
If any source element is NaN, the quietized NaN source value is returned for that element. Negative (non-zero) source numbers, as well as -∞, return the canonical NaN and set the Invalid Flag (#I).
A value of -0 must return -∞ and set the DivByZero flags (#Z). Negative numbers should return NaN and set the Invalid flag (#I). Note however that the instruction flush input denormals to zero of the same sign, so negative denormals return -∞ and set the DivByZero flag.
The source operand is a ZMM register, a 512-bit memory location or a 512-bit vector broadcasted from a 64-bit memory location. The destination operand is a ZMM register, conditionally updated using writemask k1.
EVEX.vvvv is reserved and must be 1111b otherwise instructions will #UD.
A numerically exact implementation of VRSQRT28xx can be found at https://software.intel.com/en-us/arti-cles/reference-implementations-for-IA-approximation-instructions-vrcp14-vrsqrt14-vrcp28-vrsqrt28-vexp2.
Operation
VRSQRT28PD (EVEX encoded versions)
(KL, VL) = (8, 512)
FOR j (cid:197) 0 TO KL-1
i (cid:197) j * 64
IF k1[j] OR *no writemask* THEN
IF (EVEX.b = 1) AND (SRC *is memory*)
THEN DEST[i+63:i] (cid:197) (1.0/ SQRT(SRC[63:0]));
ELSE DEST[i+63:i] (cid:197) (1.0/ SQRT(SRC[i+63:i]));
FI;
ELSE
IF *merging-masking*
; merging-masking
THEN *DEST[i+63:i] remains unchanged*
ELSE
; zeroing-masking
DEST[i+63:i] (cid:197) 0
FI;
FI;
ENDFOR;
| Input value | Result value | Comments |
|---|---|---|
| NAN | QNAN(input) | If (SRC = SNaN) then #I |
| X = 2-2n | 2n | |
| X < 0 | QNaN_Indefinite | Including -INF |
| X = -0 or negative denormal | -INF | #Z |
| X = +0 or positive denormal | +INF | #Z |
| X = +INF | +0 |
Intel C/C++ Compiler Intrinsic Equivalent
VRSQRT28PD __m512d _mm512_rsqrt28_round_pd(__m512d a, int sae);
VRSQRT28PD __m512d _mm512_mask_rsqrt28_round_pd(__m512d s, __mmask8 m,__m512d a, int sae);
VRSQRT28PD __m512d _mm512_maskz_rsqrt28_round_pd(__mmask8 m,__m512d a, int sae);
SIMD Floating-Point Exceptions
Invalid (if SNaN input), Divide-by-zero
Other Exceptions
See Exceptions Type E2.


In this text here is a span with content "test" that has visibility: hidden -
test. You don't see the span, but it takes space
In this text here is a span with content "test" that has visibility: visible -
test. Now you see the span, but the surrounding text is the same
In this text here is a span with content "test" that has display:none -
test. You don't see the span and it doesnt take space


Perform the indicated binary op on the indicated register (first argument) and literal value (second argument), storing the result in the destination register.
| Op & Format | Mnemonic / Syntax | Arguments |
|---|---|---|
| d8..e2 22b | binop/lit8 vAA, vBB, #+CC d8: add-int/lit8 d9: rsub-int/lit8 da: mul-int/lit8 db: div-int/lit8 dc: rem-int/lit8 dd: and-int/lit8 de: or-int/lit8 df: xor-int/lit8 e0: shl-int/lit8 e1: shr-int/lit8 e2: ushr-int/lit8 |
A: destination register (8 bits)B: source register (8 bits)C: signed int constant (8 bits) |


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When I was a small boy,my father told me
never to recommend a church or a woman
to anyone.And I have found it wise never to
recommend a restraunt either.Something
always goes wrong with the cheese souffle'
There is something profoundly satisfying
about sharing a meal.Eating together,
breaking bread together,is one of the
oldest and most fundamentally unifying of
human experiences.
There is something profoundly satisfying
about sharing a meal.Eating together
breaking bread together,is one of the
oldest and most fundamentally unifying of
human experiences.
There is something profoundly satisfying
about sharing a meal.Eating together
breaking bread together,is one of the
oldest and most fundamentally unifying of
human experiences.


{% extends 'all-pictures/base.html' %} {% block content %} {{block.super}}
Images are coming up soon


Shape of 哇!
Github Pages is where people build software. After registration, create a new repository. The name of repo should corresponding with Gighub account, like ***.github.io
Jekyll is a website generator that’s designed for building minimal, static blogs to be hosted on Github Pages. Jekyll中文网站
Jekyll Themes
Mine is here
Go to the root of your blog’s repo and edit the CNAME file to include your domain name (e.g. yinengxiong.cn
)
Go to your domain name registrar and add CNAME DNS record pointing your domian to Github Pages
Feel free to checkout Markdown files in the _posts/
The front-matter of a post should like:
---
layout: post
title: "Hello"
subtitle: "Hello"
date: 2017-08-22 14:00:00
author: "Yineng Xiong"
header-img: "img/your-mum.jpg"
tags:
- Alibaba
---
Just git push
Aha!


Hierarchical Rule Based Parser to Convert Voice Commands to Text for Human Computer Interaction
Carried out by:
Prashanth R (1RN12IS064)
Rohith P R (1RN12IS077)
Shreyas R (1RN12IS089)
Sushant K (1RN12IS101)
Under the guidance of:
Dr. M V Sudhamani
Professor and Head of Department
Dept. of Information Science and Engineering
RNS Institute of Technology
Agenda
- Abstract
- Introduction
- Literature survey
- Functional and non-functional requirements
- Software and hardware requirements
- System architecture and Sequence diagram
- Modules: Client, Server, Application and Parser
- Testing
- Comparison with a commercial tool
- Screenshots
- Future work and conclusion
Abstract
- Rule based parser for Voice based Human Computer Interaction
- Convert text to actionable data
- High accuracy and minimum setup time
- General Purpose Parser
Introduction
- Human Computer Interaction
- Types of Interfaces
- Command Line Interface
- Menu Driven Interface
- Graphical User Interface
- Voice User Interface
Introduction (continued)
- Predictive Parsing
- Training sets
- Parts of Speech tagging
- Rule Based Parsing
Introduction (continued)
- Issues
- Motivation
- General Purpose Parser
Literature Survey
[1] Human-Computer Interaction: Overview on State of the Art by Fakhreddine Karray et. al.
- Contents of the overview
- Existing HCI and recent advances in HCI
[2] The role of voice input for human-machine communication by Philip R. Cohen et. al.
- When speaking to computer is useful
[3] Speech Processing in Java-based PC Speech Commanding Application by Nguyen Chung et. al.
- Speech Recognition
- Development of Java based application
[4] Personalized Speech Recognition for Internet of Things by Mahnoosh Mehrabani et. al.
- Spoken Language Understanding
- Semantic analysis
[5] HiedelTime: High Quality Rule-based Extraction and Normalization of Tempoaral Expressions by Michael Gertz et. al.
- Rule based system using regular expressions
[6] A Rule-Based Approach to Aspect Extraction from Product Reviews by Soujanya Poria et. al.
- Opinion mining using a rule-based approach
- Exploits common-sense knowledge and sentence dependency trees
Non-functional Requirements
- Reliability
- Availability
- Robust
- Scalable
- Usability
- General Purpose
- Testability
Functional Requirements
- Client should capture voice input and have it converted to text
- Server side controller should act as a communication pathway between other modules
- Parser should convert unstructured text to a structured format
- Application handlers must register themselves with the parser
- External interface should display relevant information to the user
Software Requirements
- Operating System: Ubuntu 14.04, Debian 8
- Web Bowser: Google Chrome 49
- Front End: HTML, JavaScript, jQuery
- Back End: Python 3.4, Flask 0.10
- Server: Werkzeug 0.11
Hardware Requirements
- Processor: Intel core 2 duo
- RAM: 2GB
- Hard disk: 10GB
- Internet: Min upload speed of 500 Kbps
- Microphone
Other requirements: Vocabulary
- Reserved (4): start, stop, reset, quit, session
- Applications (11): media player, tweet, forecast
- Intents (65): play, fetch, temperature
- Arguments (6): song, topic, city, directory
- Modifiers (7): in, by, about, to, from, yes, no
System Architecture
Sequence Diagram
Client Side
- Call speech recognition API to convert user's voice input to text
- Look out for reserved keywords
- Sends commands to the server once a session is started
- Responsible for tracking state
- Render output on screen
- Control front end applications based on the parser's output
Implementation - Client Side
- Set language to English and accent to Indian
- Enable continuous recording and disable interim results
- Start listening
- if (isStartSession(command))
set session flag to true
display message indicating that session has started
else if (isStopSession(command))
reset session flag
quit running applications
display message indicating that session has stopped
else
send command to server for parsing
Server Side
- Always active
- Serves HTML pages and other resources
- Stateless
- Pathway between the other components
- Accepts and sends data in JSON format
Implementation - Server Side
- Send HTML, CSS and JavaScript
- Default template of 'output', to be sent to client
- try block
Retrieve parsed details from parser
Send command for parsing
if target application is not current application
return
if intent is not provided
set example as available intents
set message as example message
return
Implementation - Server Side
if operation requires confirmation
if response was cancel
return
set message as default intent message
return
else
Retrieve result from application execution handler
return
- except block
set message to 'Something went wrong'
return
Applications
- Has to be registered with the parser
- Each application has its own interface
- Independent execution handlers
- Can ask for additional information or confirmation
-
Supported applications:
- File explorer: Traverse the file system
- Totem: Play music locally
- SoundCloud: Stream music online
- Weather: Fetch reports from Open Weather
- Twitter: Get latest tweets
Implementation - Applications
- Register applications
- Call totem execution handler
- if intent is play
replace alias with 'totem'
clean up command
if argument is present in command
traverse through directory tree to find matching filenames
if no matching file found
return
if more than one file found
provide options
return
else
store duration of song
- Generate shell command and perform a system call
- return
Parser
- Number of rules grows at the rate of O(n)3
- Hierarchically organized collection of regular expressions
- Number of searches grows at the rate of O(n)
- Declaration of capabilities reduces resource wastage
Registering and Parsing
{
'alias': ['totem', 'video player', 'media player'],
'operations': {
'--play': {
'triggers': [r'play video', r'play songs?', r'play'],
'arguments': {
'name': ['{trigger}(?P<name>( .*)?)((in )?{alias})?'],
}
}
}
}
{
"application": "totem",
"intent": "--play",
"arguments": {
"name": "space bound"
}
}
Testing
| Id |
Input |
|
Expected Application |
Expected Intent |
Actual Application |
Actual Intent |
Status |
| 1 |
totem play space bound |
|
totem |
play |
totem |
play |
✓ |
| 2 |
get tweets about cricket |
|
tweet |
cricket |
tweet |
cricket |
✓ |
| 3 |
soundcloud list pursuit |
|
soundcloud |
list |
soundcloud |
list |
✓ |
| 4 |
forecast describe weather |
|
forecast |
weather |
forecast |
weather |
✓ |
| 5 |
play forecast in music player |
|
totem |
play |
forecast |
null |
✗ |
Comparison
Command: "play space bound in media player"
| Commercial software |
|
|
|
Proposed parser |
|
|
|
|
|
Screenshots
Confirm before performing critical operations
Provide options in case of conflict
File explorer
Tweets
Conclusion
- General Purpose Parser
- Actionable data extracted from unstructured text
- Scalable
- Modular
Future work
- Handle commands that require services of more than one application
- Detect negations
- Add support for multiple arguments of the same type
- Conflicts must be resolved correctly
- Record history to predict missing information


Direct Debits
| Account ID | Direct Debit Id | Mandate Identification | Direct Debit Status Code | Name | Previous Payment Date Time | Previous Payment Amount |
| No direct debit | ||||||
| Account ID | Direct Debit Id | Mandate Identification | Direct Debit Status Code | Name | Previous Payment Date Time | Previous Payment Amount |
| No direct debit | ||||||


|
binary_likelihood_profs()
|
Return likelihood profiles for binary covariates special case |
|
binary_loglike_wrapper()
|
Wrapper to optimize over a single variable for likelihood profiling |
|
bootstrap_clusters()
|
Resample clusters from an observed data set |
|
bp_loglike_binary_cov()
|
Calculate the likelihood for binary covariate MC trees |
|
calculate_transmission_prob()
|
Get the probability of transmission given a set of covariates |
|
compute_A_infects_B()
|
Compute probability from samples that person A infects person B |
|
covariate_df_to_mat()
|
Take data frame and turn it into matrix for logistic regression |
|
draw_covariate_rows()
|
Randomly sample covariate rows |
|
draw_features()
|
Draw the features for the tree people |
|
general_cluster_like()
|
Get the likelihood for a single cluster |
|
general_cluster_like.dt()
|
Get the likelihood for a single cluster |
|
general_generation_infection()
|
Infect the current generation given the previous generation |
|
general_loglike()
|
Calculate the estimated general loglikelihood |
|
general_loglike_mc_trees()
|
Calculate the log likelihood for a set of data, averaged by n |
|
general_tree_sampler()
|
Generate trees of certain size |
|
likelihood_profs()
|
Return likelihood profiles |
|
loglike_wrapper()
|
Wrapper to optimize over a single variable for likelihood profiling |
|
mc_trees_to_A_infects_B()
|
Looks at most likely transmissions from A to B for a cluster |
|
sample_connections()
|
Sample connections and form a tree |
|
sample_gen_out_cond()
|
Sample trees with same features as original but permuted through the trees |
|
sample_general_cond_trees()
|
Sample general conditional trees |
|
sample_general_tree_perm()
|
Sample the actual tree for given permutation |
|
sample_mc_binary_cov()
|
Sample binary covariate transmission trees |
|
sample_mc_binary_cov_inner()
|
Innfer function to sample MC trees with binary covariates |
|
sample_mc_trees()
|
Sample trees with same features as original but permuted through the trees |
|
sample_single_gen_out_cond()
|
Make the sampled clusters for a single cluster |
|
sample_trees_fixed_g()
|
Sample trees with given generation sizes |
|
sample_unif_trees_no_features()
|
Sample trees |
|
sample_unique_perms()
|
Sample unique permutations from a partition space |
|
simulate_bp()
|
Simulate the branching process of flipping until failure for K clusters |
|
simulate_general_outbreak_inner()
|
Generate a single cluster of infections |
|
summarize_binary_clusters()
|
Summarize binary covariate clusters |
|
summarize_binary_cov_trees()
|
Condense trees into a smaller format |
|
tb_clean
|
Subset of Tuberculosis data |


TravelMore Company Offers You 25% Discount On Every Hotel If You Have More Than 4 Kids
TravelMore Company Offers You 15% Discount on Every Hotel If You have Newbord Child
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Card information


Publications
Fischer, Joel E., Andy Crabtree, James A. Colley, Tom Rodden, and Enrico Costanza. "Data work: how energy advisors and clients make IoT data accountable." Computer Supported Cooperative Work (CSCW) 26, no. 4-6 (2017): 597-626.
Read moreEnrico Costanza, Benjamin Bedwell, Michael Jewell, James Colley, Tom Rodden, 'A bit like British Weather, I suppose' Design and Evaluation of the Temperature Calendar, In Proceedings ACM CHI 2016
Read moreJoel Fischer, Andy Crabtree, Tom Rodden, James Colley, Enrico Constanza, Michael Jewell, Sarvapali Ramchurn, None None, "Just whack it on until it gets hot": Working with IoT Data in the Home, In Proceedings ACM CHI 2016
Read moreAlper Alan, Enrico Costanza, J.Fisher, Sarvapali Ramchurn, T.Rodden, N.R.Jennings, "A field study of human-agent interaction for electricty tariff switching", In Proceedings AAMAS 2014
Read moreEnrico Constanza, Hoel E Fischer, James A Colley, Tom Rodden, Sarvapali Ramchurn, Nicholas R. Jennings, "Doing the laundry with agents: a field trial of a future smart evergy system in the home", In Proceedings ACM CHI 2014
Read moreJoel E.Fischer, Enrico Constanza, Sarvapali Ramchurn, James Colley, Tom Rodden, Energy advisors at work: charity work practices to support people in fuel poverty", In Proceedings ACM UbiComp '14'
Read moreJoel E.Fischer, Sarvapali D.Ramchurn, Mchael A. Osborne, Oliver Parson, Trung Dong Huynh, Muddasser Alam, Nadia Pantidi, Stuart Moran, Khaled Bachour, Steven Reece, Enrico Costanza, Tom Rodden, Nicholas R. Jennings, "Recommending Energy Tariffs and Load Shifting Based on Smart Household Usage Profiling", In Proceedings ACM IUI '13
Read moreEnrico Costanza, Sarvapali D.Ramchurn, Nicholas R.Jennings, "Understanding domestic energy consumption through interactive visualisation: a field study", In Proceedings ACM Ubicomp '12'
Read more


henryfikse
on 2011-12-26 18:24:18
mashikseli
on 2011-12-17 11:40:45
gunsilie
on 2011-12-16 14:12:43
mashikseli
on 2011-12-15 08:57:44
ganiselim
on 2011-12-14 08:24:54
sametikaresi
on 2011-12-14 02:44:13
ganiselim
on 2011-12-13 14:52:25
Toilbonsill
on 2011-12-08 15:15:45
Toilbonsill
on 2011-12-08 01:21:48
ZL@B
on 2011-10-10 01:43:17
glymninitty2005
on 2011-08-10 00:30:07
Rhiccadssaria1995
on 2011-08-09 03:39:56
Ralkekaltep1976
on 2011-08-07 23:22:49
Bemjuple2008
on 2011-08-04 07:53:40
Kevin van Zonneveld
on 2009-02-11 02:21:13
@ Brett Zamir: Yeah sounds good! I will need to do some work on the tester soon. But first the phpjs.org site! The online compiler is coming along nicely have you seen it?
Brett Zamir
on 2009-02-06 17:32:31
I don't know about the errors with Rhino, but maybe that is due to going through window properties?
I concur with your steps of waiting to build a module, and might suggest also waiting for all arguments to be implemented (unless impossible) for any completed functions.
Is it ok for me to move interface_exists() and get_declared_interfaces() (and to add class_implements()) to not porting? JavaScript has no built-in interfaces, so we could not implement these without adding our own language constructs to JavaScript.
Kevin van Zonneveld
on 2009-02-01 22:55:04
@ Brett Zamir: They keep giving me
Exception in thread "Thread-0" org.mozilla.javascript.EcmaError: TypeError: Cannot read property "prototype" from undefined
..errors, even when I check for prototype first. Rhino issue maybe.
Concidering the functions that are in unported/notporting, etc. That has never been a scientific process. It was the result of a sleepless night of crawling php.net. It has never been more than a rough guideline and has always been open to suggestions.
Bringing me to SPL. I think that may be taking it to another level. That can be a good or a bad thing, but I think for now we should focus on the normal functions & the site of the project. But that's my humble opinion, I'm open to ideas.
btw I think we should move deprecated functions to _notporting, so that's what I did for now.
As far as the Mozilla thing goes. I have actually never made an extension or anything but Yeah it would be cool if we're included at some point. I don't know if they're interested or anything, but no harm in asking, right?
I do think we should further professionalize PHP.JS before taking such steps though.
In my eyes mainly meaning:
- bugfixing
- porting more _unported
- making everything associative-aware
- move out the development process to phpjs.org
- deciding on a good 'default' package that doesn't contain any controversial functions
Brett Zamir
on 2009-01-31 13:09:33
Sorry, also realized the new one I just submitted now for get_class_vars() should have variable names changed to reflect the behavior of this function:
function get_class_vars (name) {
var constructor, retArr={};
if (typeof name === 'function') {
constructor = name;
}
else if (typeof name === 'string') {
constructor = window[name];
}
for (var prop in constructor) {
if (typeof constructor[prop] !== 'function' && prop !== 'prototype') {
retArr[prop] = constructor[prop];
}
}
// Comment out this block to behave as "class" is usually defined in JavaScript convention
for (var prop in constructor.prototype) {
if (typeof constructor.prototype[prop] !== 'function') {
retArr[prop] = constructor.prototype[prop];
}
}
return retArr;
}
Brett Zamir
on 2009-01-31 13:06:40
Sorry, just realized that get_class_method() returns an associative array. Here it is again, along with get_class_vars() and get_object_vars():
function Myclass () {this.privMethod = function(){}}
Myclass.classMethod = function () {}
Myclass.prototype.myfunc1 = function () {
return(true);
};
Myclass.prototype.myfunc2 = function () {
return(true);
}
whatIs(get_class_methods('Myclass'));
var myClassObj = new Myclass();
whatIs(get_class_methods(myClassObj));
function get_class_methods (name) {
var constructor, retArr={};
if (typeof name === 'function') {
constructor = name;
}
else if (typeof name === 'string') {
constructor = window[name];
}
else if (typeof name === 'object') {
constructor = name;
for (var method in constructor.constructor) { // Get class methods of object's constructor
if (typeof constructor.constructor[method] === 'function') {
retArr[method] = constructor.constructor[method];
}
}
// return retArr; // Uncomment to behave as "class" is usually defined in JavaScript convention (and see comment below)
}
for (var method in constructor) {
if (typeof constructor[method] === 'function') {
retArr[method] = constructor[method];
}
}
// Comment out this block to behave as "class" is usually defined in JavaScript convention (and see comment above)
for (var method in constructor.prototype) {
if (typeof constructor.prototype[method] === 'function') {
retArr[method] = constructor.prototype[method];
}
}
return retArr;
}
and:
function Myclass () {this.liveProp = 'prop';}
Myclass.classProp = 'classPropVal';
Myclass.prototype.myprop1 = 1;
Myclass.prototype.myprop2 = 2;
whatIs(get_class_vars('Myclass'));
var myClassObj = new Myclass();
myClassObj.prototype = {a:'b'};
function get_class_vars (name) {
var constructor, retArr={};
if (typeof name === 'function') {
constructor = name;
}
else if (typeof name === 'string') {
constructor = window[name];
}
for (var method in constructor) {
if (typeof constructor[method] !== 'function' && method !== 'prototype') {
retArr[method] = constructor[method];
}
}
// Comment out this block to behave as "class" is usually defined in JavaScript convention
for (var method in constructor.prototype) {
if (typeof constructor.prototype[method] !== 'function') {
retArr[method] = constructor.prototype[method];
}
}
return retArr;
}
function get_object_vars (obj) {
var retArr={};
for (var prop in obj) {
if (typeof obj[prop] !== 'function' && prop !== 'prototype') {
retArr[prop] = obj[prop];
}
}
for (var prop in obj.prototype) {
if (typeof obj.prototype[prop] !== 'function') {
retArr[prop] = obj.prototype[prop];
}
}
return retArr;
}
Brett Zamir
on 2009-01-31 11:44:54
Here's another.... get_class_method(). I've left comments on how to get only the "class methods" as typically used in JavaScript (as opposed to PHP) terminology (i.e., not privileged or prototype methods, but those in the class "namespace").
function Myclass () {this.privMethod = function(){}}
Myclass.classMethod = function () {}
Myclass.prototype.myfunc1 = function () {
return(true);
};
Myclass.prototype.myfunc2 = function () {
return(true);
}
alert(get_class_methods('Myclass')); // classMethod,myfunc1,myfunc2
var myClassObj = new Myclass();
alert(get_class_methods(myClassObj));
function get_class_methods (name) {
var constructor, retArr=[];
if (typeof name === 'function') {
constructor = name;
}
else if (typeof name === 'string') {
constructor = window[name];
}
else if (typeof name === 'object') {
constructor = name;
for (var method in constructor.constructor) { // Get class methods of object's constructor
if (typeof constructor.constructor[method] === 'function') {
retArr.push(method);
}
}
// return retArr; // Uncomment to behave as "class" is usually defined in JavaScript convention (and see comment below)
}
for (var method in constructor) {
if (typeof constructor[method] === 'function') {
retArr.push(method);
}
}
// Comment out this block to behave as "class" is usually defined in JavaScript convention (and see comment above)
for (var method in constructor.prototype) {
if (typeof constructor.prototype[method] === 'function') {
retArr.push(method);
}
}
return retArr;
}
Brett Zamir
on 2009-01-31 07:45:46
Also, for get_declared_interfaces() and interface_exists() (and class_implements() but you don't seem to have these in your imported list: http://cn.php.net/manual/en/ref.spl.php -- btw, is it easy to say which modules you included/excluded?), since JavaScript offers no formal way to implement interfaces, do you want to move these to not porting? We could implement our own interface system (which even checked the number of arguments, etc.) and which worked with these PHP-named functions, but I presume that is beyond your intended scope here (though it would be pretty cool to pseudo-standardize on an interface system for JavaScript, at least until JS 2.0).
Off the topic, as far as the php_js global, in the namespaced version of PHP.JS, you could put that at the top of the (function())() closure namespacing, so it wasn't actually global.
And another off-the-topic, https://wiki.mozilla.org/Labs/JS_Modules , they state there "If you would like to contribute a new module, get in touch with us at #labs!", so at whatever point you think we have something, we can submit a PHP-JS module for Mozilla so a bunch of extensions (and the same extension) could be able to reuse the module without any additional loading cost!
Brett Zamir
on 2009-01-31 06:57:30
Hi Kevin,
call_user_method() and call_user_method_array() are now deprecated in PHP in favor of call_user_function (with the array argument). Do you want to move to not porting, or use the following?
function call_user_method(method, obj) {
// http://kevin.vanzonneveld.net
// + original by: Brett Zamir
var func = eval(obj+"['"+method+"']");
if (typeof func != 'function') {
throw new Exception(func + ' is not a valid method');
}
return func.apply(null, Array.prototype.slice.call(arguments, 2));
}
function call_user_method_array(method, obj, params) {
// http://kevin.vanzonneveld.net
// + original by: Brett Zamir
var func = eval(obj+"['"+method+"']");
if (typeof func != 'function') {
throw new Exception(func + ' is not a valid method');
}
return func.apply(null, params);
}


Background Information about the Pig-Dice-Game
Pig-Dice is a simple and interesting game played mostly by children in South America and other parts of the world. The other name for the game is Ambition.The game can be played by two users playing agains each other.
Just like any other game pig dice also has several variants.The following represents the three types of variants
The rules of the Game
Enter your names to begin playing:
FIRST PLAYER
SECOND PLAYER
Dice Roll:
Turn Score:
Running Total:
Dice Roll:
Turn Score:
Running Total:


Rebuild America Act
$1,000,000,000,000
Putting at least 13 million Americans to work by investing $1 trillion over five years towards rebuilding our crumbling roads, bridges, railways, airports, public transit systems, ports, dams, wastewater plants, and other infrastructure needs.
Railways - 75B
Airports - 12.5B
Air Traffic Control - 17.5B
Inland Waterways & Shipping Channels - 15B
Dams - 12B
Drinking Water Systems - 6B
Wastewater Plants - 6B
Electrical Grid - 10B
High-Speed Broadband - 5B


Ngeshlew
Learning the minimalist CSS framework
Milligram| Name | Company | Name | Company |
|---|---|---|---|
| Roy Mugera | Graduates Corner | Evans Kithuku | Binary, Triplex |
| Olina Bishop | Asiatic Rhythms | Lorna Onduu | ZosiWorld |
| Shaniece McGill | Signature Occasion Events | Robert Maoga | Maoga, Taafrija |


by Leah Wagner // @leahamwagner
from The Jibe // @thejibe
I ♥ talking about...
Frontend development, Drupal, modular SASS/CSS & components, base styles, calc(), JS (only because I need your help keeping up), Jekyll, craft beer, red wine and rock & roll.
THEN: We only had 2x retina screens.
THEN: We only had 2x retina screens.
NOW: Today we also have 3x and 4x so our 2x images weren’t looking their best on those devices.
THEN: IE8 traffic was significant.
THEN: IE8 traffic was significant.
NOW: Today's traffic is predominantly iOS. With IE8 now being less important, we can optimize our CSS, JS and animations techniques.
THEN: Being a luxury brand, design was the highest priority.
THEN: Being a luxury brand, design was the highest priority.
NOW: Esthetics remain important, but today, we are better at considering performance implications for these decisions.
THEN: We typically remain agile when developing a new site.
THEN: We typically remain agile when developing a new site.
NOW: Today, working with a relatively stable build, we now have an improved understanding of the code base and an opportunity to refine our work.
Stable, predictable and easy to update code that removed the fragile feeling we were currently experiencing.
Simplification and optimization that would give us not only greater confidence but also increase our productivity and more reliable outcomes.
THEN: We only had 2x retina screens and we used media queries to apply 2x images.
CHANGE: With recent advancements, we would now require 3x and 4x images as well.
NOW: Instead of managing 4 assets (1x, 2x, 3x and 4x), we refactored to use a vector based SVG. We still have a PNG fallback for IE8 and older Androids and we use Modernizr.js to apply an .svg class to our html element when it is supported.
THEN: We implemented the navigation to fit with breakpoints we had defined for the page layout: 0px, 480px and 768px.
CHANGE: This led us to adjusting font sizes in our menu to fit within these layout breakpoints. Not how we should be make font size decisions.
NOW: We defined custom breakpoints specifically for the navigation component. We serve the ideal font size for legibility and, when the layout no longer accommodates this font size, we simply change it.
CHANGE: As we started to refactor the code used in for the primary display, the code for the override was not considered.
NOW: Recognizing the difference between two was colour, it became evident our code could be optimized.
THEN: We used JS SlideToggle to power the hide/show functionality in our mobile navigation.
CHANGE: With CSS transitions/animations fully supported in modern browsers, it was time to transition away from JS and provide a graceful downgrade for older browsers.
NOW: We are still lean on JS to add/remove an 'is-visible' class but the transition has been moved to CSS.
As a result of our changes, we were able to make our JavaScript more concise and rely on CSS to do more.
We were able to cut:
by Leah Wagner // @leahamwagner
from The Jibe // @thejibe
slides.leahwagner.ca/frontend-refactoring


--- layout: post title: Incorrect Data Type for IUCR Dataset date: '2014-05-21T16:01:52-05:00' author: Open Data Portal Team categories: - Open Data - Data Portal tags: - c7ck-438e ---
The column containing IUCR codes on the IUCR lookup table was incorrectly categorized as a numeric value instead of plain text. As a result, while the data appeared on the portal correctly, someone attempting to download it would have seen 22 null values in the IUCR column.
We have correct the issue by changed the data type to “plain text”. We apologize for any inconvenience.


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What happens when we die
Title
What happens when we die
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The relations of wind, moon, and cloud to human beings after death, etc. (L V.-15. 5147-5158). What happens when the |xam die.


Component: tos.lib.serial.SerialAMSenderC
generic configuration SerialAMSenderC(am_id_t AMId)
Author:
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@import gospeak.core.domain.{Talk, UserRequest}
@import gospeak.libs.scala.domain.Markdown
@import gospeak.web.pages.user.routes.UserCtrl
@import gospeak.web.utils._
@(invite: UserRequest.TalkInvite, talk: Talk, message: Markdown)(implicit req: UserReq[AnyContent])
@if(message.nonEmpty) { @message.render
} else {
Hi,
<EMAIL_ADDRESS>has invited you on the @talk.title.value talk.
You can accept or deny this invitation.
We wish you the best in your public speaking adventure!
The Gospeak team


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Improving user experience : AB Testing changes
We will be using statistical tools to validate improvements in the retention rate of our app
In this post we will find out how to choose the best location for multiple capacitated facilities given a set of points of interest
Code and data used: https://github.com/davbom97/source
The capacitated multi-facility location problem is an extension of the single facility problem to n facilities simultaneously. In this project, the objective is to find the optimal location of multiple facilities at the same time, given a maximum output capacity, in Italy.
The hypotheses are quite the same as the single facility location problem but we dropped the hypothesis that a facility can supply infinite demand.
First we load the data about all the italian cities with more than 5000 population (ISTAT 2013 data)
import pandas as pd
from geopy.geocoders import Nominatim
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from sklearn.cluster import KMeans
from IPython.display import display
import time
import pickle
data = pd.read_csv("cities_data_fromMax_to5mil.csv")
display(data.head())
display(data.tail())
| city_name | pop_size | lat | long | |
|---|---|---|---|---|
| 0 | Varallo Pombia | 5004 | 45.665904 | 8.632693 |
| 1 | Certosa di Pavia | 5004 | 45.257015 | 9.148114 |
| 2 | Mistretta | 5014 | 37.929885 | 14.362873 |
| 3 | Santa Teresa Gallura | 5018 | 41.241377 | 9.188518 |
| 4 | Alzate Brianza | 5019 | 45.769778 | 9.182041 |
| city_name | pop_size | lat | long | |
|---|---|---|---|---|
| 2370 | Palermo | 657561 | 38.111227 | 13.352443 |
| 2371 | Torino | 872367 | 45.067755 | 7.682489 |
| 2372 | Napoli | 962003 | 40.835934 | 14.248783 |
| 2373 | Milano | 1242123 | 45.466800 | 9.190500 |
| 2374 | Roma | 2617175 | 41.894802 | 12.485338 |
The latitude and longitude are retrieved the same way as explained in the single facility location problem.
Next it is important to check if the data is representative, we sum all the population of the cities we are considering and divide them by the total population of Italy at that time.
np.round(data.loc[:,"pop_size"].sum()/59433744,3)
0.822
The cities represent the 82.2% of all the population of italy which is representative enough.
Suppose this time that the number of people buying our product is the 10% of the population, equally distributed across all the cities.
Then we must calculate the weight of each city by dividing the number of potential clients in each city by the total number of clients
pt_clients_perc = 0.1
clients = np.round(data.loc[:,"pop_size"].values*pt_clients_perc)
clients_perc = pd.DataFrame(clients/clients.sum(),columns = ["weight"])
data = data.join(clients_perc)
data = data.join(pd.DataFrame(clients,columns = ["number_of_clients"]))
#data = data.join(clients)
display(data.head())
| city_name | pop_size | lat | long | weight | number_of_clients | |
|---|---|---|---|---|---|---|
| 0 | Varallo Pombia | 5004 | 45.665904 | 8.632693 | 0.000102 | 500.0 |
| 1 | Certosa di Pavia | 5004 | 45.257015 | 9.148114 | 0.000102 | 500.0 |
| 2 | Mistretta | 5014 | 37.929885 | 14.362873 | 0.000103 | 501.0 |
| 3 | Santa Teresa Gallura | 5018 | 41.241377 | 9.188518 | 0.000103 | 502.0 |
| 4 | Alzate Brianza | 5019 | 45.769778 | 9.182041 | 0.000103 | 502.0 |
The way we will find the solution is by using a clustering method, in particular the K-Means algorithm, which seeks to minimize the distance between each point in a cluster (the cities assigned to a particular facility) and the centre of the cluster (the facility itself)
First we initialize the clustering process by putting all the cities in one cluster
coordinates_df = data.loc[:,["lat","long"]]
n = 1
#solver.fit(coordinates_df,sample_weight = data.loc[:,"%clients"] )
#location = solver.cluster_centers_
#labels = data.set_index(solver.labels_)
Now we introduce a constraint max_capacity, defined as the maximum amount of product units the facility can output in one year, divided by the total demand of the output in one year, for a single product
suppose the maximum output is 1500 units/day the maximum capacity is:
units_day = 1500
units_year = units_day*365
max_capacity = np.round(units_year/clients.sum(),6)
print(f"The maximum capacity for each facility is {max_capacity}")
print(f"Which equals to a maximum yearly output of {units_year} units/year ")
The maximum capacity for each facility is 0.112119
Which equals to a maximum yearly output of 547500 units/year
The total sum of the demand (or weights) for i-th facility must be less or equal than the units_year (or max_capacity).
Next we define the function find_optimum that takes in the minimum amount of facilities we want to locate and find an optimal amount of facilities with the aforementioned contraint. Then, among the possible solutions we calculate the the average saturation rate, defined as :
In this case, given a maximum daily output of 1500 units/day:
In the end we pick the combination of facilities with the maximum saturation rate.
def find_optimum(starting_n):
n = starting_n
optimal_solution = None
optimal_cluster_n = 10000 #initializes the variables that will hold the optimum
optimal_array = [] #optimal array will contain the all the possible optimum solutions
optimal_capacity = [] #optimal capacity will contain the output of the faclities in a given solution
while True:
print (f"Trying number of facilities = {n}")
labels = data.set_index(np.zeros(len(data.index)))
#These lines will initialize the labels array which will hold the clusters
for j in range(200):
solver = KMeans(n,n_init = 1,max_iter = 1000,tol = 1e-9,init = "k-means++")
solver.fit(coordinates_df,sample_weight = data.loc[:,"weight"])
labels = pd.DataFrame(solver.labels_,columns = ["label"])
labels = data.set_index(solver.labels_)
#fits the kmeans algorithm to the data points
for i in range(0,n):
used_capacity = labels.groupby(level=0)["weight"].sum().values
#computes used_capacity which measures how much the capacity of each facility is saturated
if all([i <= max_capacity for i in used_capacity]):
#if the capacity of all the facilities is not saturated, it is considered the minimum amount of facilities
#that satisfy the capacity contraint,and will search an optimum among them,
#otherwise the loop continues
optimal_cluster_n = n
optimal_array.append(solver)
optimal_capacity.append(used_capacity)
if optimal_array:
#calculates the average output of each found solution, then picks the one with the maximum avg output
optimal_capacity_avg = [facility_used_capacity.mean() for facility_used_capacity in optimal_capacity]
optimal_saturation = max(optimal_capacity_avg)
indx = optimal_capacity_avg.index(optimal_saturation)
used_capacity = optimal_capacity[indx]
optimal_solution = optimal_array[indx]
print("Done!")
return optimal_cluster_n,optimal_solution,used_capacity
if optimal_solution == None:
n+=1
continue
else:
break
We run the algorithm starting from one facility :
cluster_n,solver,used_capacity = find_optimum(9)
location = solver.cluster_centers_
labels = data.set_index(solver.labels_)
Trying number of facilities = 9
Trying number of facilities = 10
Trying number of facilities = 11
Trying number of facilities = 12
Trying number of facilities = 13
Trying number of facilities = 14
Trying number of facilities = 15
Trying number of facilities = 16
Trying number of facilities = 17
Done!
We might run the algorithm multiple times, becouse it does not guarantee that it outputs a feasible location for each facility.
Then we plot our found facilities on a map to check feasibility of the results:
Next we locate the facilities from the coordinates
geoloc = Nominatim(user_agent="Facility_loc_problem")
addresses = []
i = 0
while True :
try:
if i == len(location):
break
else:
loc = geoloc.reverse(location[i],exactly_one = True)
addresses.append(loc[0])
time.sleep(2)
i+=1
except:
time.sleep(20)
facilities = pd.DataFrame(location,index = addresses,columns = ["Latitude","Longitude"])
facilities = facilities.join(pd.DataFrame(np.arange(cluster_n),index = addresses, columns = ["Cluster number"]))
The last step is to calculate the saturation rate and display the results
print(f"The optimal number of facilities is = {cluster_n}")
saturation_avg = np.around(sum(used_capacity*100)/(cluster_n*max_capacity),2)
saturation_pctg = np.around(used_capacity*100/max_capacity,2)
facility_output = np.round(saturation_pctg/100*units_day,0)
print(f"The average saturation rate is = {saturation_avg}%")
facilities = facilities.join(pd.DataFrame(saturation_pctg,
index = facilities.index,
columns = ["Saturation rate(%)"]))
facilities = facilities.join(pd.DataFrame(facility_output,
index = facilities.index,
columns = ["Daily facility output(pieces/day)"]))
facilities = facilities.join(pd.DataFrame(facility_output*365,
index = facilities.index,
columns = ["Yearly facility output(pieces/year)"]))
The optimal number of facilities is = 17
The average saturation rate is = 52.47%
labels.index = labels.index.set_names("Cluster number")
display(facilities)
display(labels.head())
| Latitude | Longitude | Cluster number | Saturation rate(%) | Daily facility output(pieces/day) | Yearly facility output(pieces/year) | |
|---|---|---|---|---|---|---|
| Poggio del Lupo, Misterbianco, Catania, Sicilia, 95045, Italia | 37.545541 | 15.038274 | 0 | 48.32 | 725.0 | 264625.0 |
| Via Simoncini, Ville, Serravalle Pistoiese, Pistoia, Toscana, 51130, Italia | 43.889483 | 10.859511 | 1 | 70.31 | 1055.0 | 385075.0 |
| Cartiera, Loreto Aprutino, Pescara, Abruzzo, 65014, Italia | 42.405641 | 13.996831 | 2 | 22.45 | 337.0 | 123005.0 |
| Santa Maria in Prato, San Zenone al Lambro, Milano, Lombardia, 26852, Italia | 45.316446 | 9.363823 | 3 | 95.78 | 1437.0 | 524505.0 |
| Erchie, Brindisi, Puglia, 72028, Italia | 40.451648 | 17.710318 | 4 | 32.12 | 482.0 | 175930.0 |
| Circonvallazione sud, Asuni, Aristanis/Oristano, Sardegna, Italia | 39.861941 | 8.945599 | 5 | 19.93 | 299.0 | 109135.0 |
| Via Brigata Emilia, Pero, Breda di Piave, Treviso, Veneto, 31048, Italia | 45.700320 | 12.342201 | 6 | 70.84 | 1063.0 | 387995.0 |
| Via Prenestina, Quartiere XIX Prenestino-Centocelle, Roma, Roma Capitale, Lazio, 00171, Italia | 41.894788 | 12.560785 | 7 | 91.55 | 1373.0 | 501145.0 |
| Parcheggio Stadio, Via Palermo, San Bernardino, Oltrestazione, Legnano, Milano, Lombardia, 20025, Italia | 45.588261 | 8.910030 | 8 | 55.69 | 835.0 | 304775.0 |
| Mulino Drago, Strada statale Corleonese Agrigentina, Casa Sant'Ippolito, Corleone, Palermo, Sicilia, 90030, Italia | 37.860989 | 13.297069 | 9 | 36.99 | 555.0 | 202575.0 |
| Via Tetti Laghi, Cascina Reggenza, Carmagnola, Torino, Piemonte, 10022, Italia | 44.885312 | 7.767223 | 10 | 50.88 | 763.0 | 278495.0 |
| Via Paradiso, Maldritto, Castelbelforte, Mantova, Lombardia, 46032, Italia | 45.223828 | 10.887862 | 11 | 77.83 | 1167.0 | 425955.0 |
| Valcarecce, Cingoli, Macerata, Marche, 60039, Italia | 43.402066 | 13.177101 | 12 | 22.85 | 343.0 | 125195.0 |
| strada Liardi Iunci, Iunci Soprano, Catanzaro, Calabria, 88049, Italia | 39.096233 | 16.353780 | 13 | 20.05 | 301.0 | 109865.0 |
| Masseria Friuli, Corato, Bari, Puglia, Italia | 41.106532 | 16.329850 | 14 | 44.45 | 667.0 | 243455.0 |
| Cutinelli, Sant'Anastasia, Napoli, Campania, 80038, Italia | 40.889406 | 14.389779 | 15 | 95.82 | 1437.0 | 524505.0 |
| Turrito, Sarsina, Unione dei comuni Valle del Savio, Forlì-Cesena, Emilia-Romagna, Italia | 43.907781 | 12.121604 | 16 | 36.05 | 541.0 | 197465.0 |
| city_name | pop_size | lat | long | weight | number_of_clients | |
|---|---|---|---|---|---|---|
| Cluster number | ||||||
| 8 | Varallo Pombia | 5004 | 45.665904 | 8.632693 | 0.000102 | 500.0 |
| 3 | Certosa di Pavia | 5004 | 45.257015 | 9.148114 | 0.000102 | 500.0 |
| 0 | Mistretta | 5014 | 37.929885 | 14.362873 | 0.000103 | 501.0 |
| 5 | Santa Teresa Gallura | 5018 | 41.241377 | 9.188518 | 0.000103 | 502.0 |
| 8 | Alzate Brianza | 5019 | 45.769778 | 9.182041 | 0.000103 | 502.0 |
Then we save the facilities and the clusters for use or for further analysis using pickle
save = [labels,facilities]
pickle.dump(save,open("Multi_facility_clients_and_facilities.pkl","wb"))
We will be using statistical tools to validate improvements in the retention rate of our app
We will analyze the sales data of the videogames market using the data about games with more than 100000 copies sold
In this post we will find out how to choose the best location for multiple capacitated facilities given a set of points of interest
In this post we will find out how to choose the best location for a single uncapacitated facility given a set of points of interest


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Methods: include() & exclude()
By default, the result.rules object retains the sub-phrases matched by all named rules. Often there are rule names that are of no interest. The include() and exclude() methods can be used to limit the list of rule names retained in the results.
Syntax
var exp = new ApgExp(pattern[, flags]); exp.include(array); exp.exclude(array);
Parameters
• array: An array of rule names (strings) to include/exclude.
Return
none
Example 1
By default, all matches to all rules are retained in the result.rules object.
var pattern, str, exp, result; pattern = 'word = alpha *(alpha / num)\n'; pattern += 'alpha = %d65-90 / %d97-122\n'; pattern += 'num = %d48-57\n'; str = "ab12"; exp = new ApgExp(pattern); result = exp.exec(str); console.log(result.toText()); /* returns */ result: [0]: ab12 input: ab12 index: 0 length: 4 tree depth: 7 node hits: 26 rules: word : 0: ab12 : alpha : 0: a : alpha : 1: b : num : 2: 1 : num : 3: 2
Example 2
By using exclude(["alpha", "num"]) "alpha" and "num" are removed from the result.rules object.
/* same as Example 1 except */ exp = new ApgExp(pattern); exp.exclude(["alpha", "num"]) result = exp.exec(str); console.log(result.toText()); /* returns */ result: [0]: ab12 input: ab12 index: 0 length: 4 tree depth: 7 node hits: 26 rules: word : 0: ab12
Example 3
By using include(["word"]) all rules except "word" are removed from the result.rules object.
/* same as Example 1 except */ exp = new ApgExp(pattern); exp.include(["word"]) result = exp.exec(str); console.log(result.toText()); /* returns */ result: [0]: ab12 input: ab12 index: 0 length: 4 tree depth: 7 node hits: 26 rules: word : 0: ab12


In the summer of 97', I received quite a sum of money via a settlement. Me being 18 you know I partied it away. I spent the majority of $12,000 on Meth (crank) in less than two months. I would snort it or smoke it and stay up until my body collapsed, which would take about two weeks. During the first few days of every speed trip, I was the MVP of anything I did. I did it quicker,and better than anybody else. I also started making funny noises. After a few days, the energy turned to paranoia and the funny noises turned to growls. Every one was out to get me. Including family and friends.
I remember in one instance, after about 10 days of being wide awake, I was partying with my cousin, another friend, and a few girls. All of the sudden, I tottaly wigged out, all of the people that were with me were trying to steal my dope. I got really mad and finally asked my cousin if I could borrow his car. He didn't realize that I was wigging and told me I could. I got in the car and drove a couple of blocks to a gas station to get a drink.
I came out and took off, next I started hearing my cousin and my friend coming from the trunk of the car. In my mind they had jumped in the trunk while I was in the store. I thought, I'll get these sonsabitches. So I started swerving trying to make them roll around in the trunk.  After about two hours of this I returned to my apartment to find the same people there as when I left. I immediately called them out for being in the trunk and trying to wig me out. They thought I was absolutely out of my mind, and I was. Soon after, I collapsed for a day or two, When I awoke I was still convinced that they were in the trunk. After a couple of days, I had dismissed the Idea alltogether.
Wigging out is the singlemost scariest thing the first few times it occurs.
I mean, really beleiving that everyone has ulterior motives against you. It will drive you crazy. But after a while, when I almost figured out what was real and what was not, I started to enjoy wigging out. In fact, when I do crank now I have to stay up long enough to see the 'Shadow Monsters', think everyone is talking about me, and look into the mirror and not recognize myself. To someone who has never done crank this may be farfetched, but to the geekers out there you know it's pretty easy to call up the Shadow World A.K.A. Geekerville.
P.S. If you've never done meth don't.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


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The Phoenix suite of products offers world class restoration tools for the most demanding jobs, and features Digital Vision's award-winning DVO technology. Phoenix is designed to produce great results fast with less manual intervention. With a familiar timeline based interface, the artist and archivist can easily review and retouch defects in context as required.
Using the Phoenix, means you never have to compromise on image quality. With automated and semi-automated file-based restoration features for greater operator efficiency, the only decision to be made is which Phoenix version provides the right level of functionality for the task in hand.
Learn More →An end-to-end solution that includes a highly automated restoration and enhancement solution for handling video content stored on tape formats such as 1¨, U-Matic and Betacam SP. The system handles common image defects such as dropouts, severe noise, cross colour artifacts, line sync problems and more.
A cost effective image restoration software, aimed at film based material and for cleaning up VFX plates and DI scans. Phoenix Touch contains powerful automatic dust-busting capability alongside a high quality manual repair toolset that can turn around intensive repair tasks quickly. Phoenix Touch can also act as assist station to Phoenix Refine and Phoenix Finish.
The best high-end restoration toolset for repairing damage to film material. Combining the best in automatic, semi-automatic and manual restoration tools, Phoenix Refine adds editorial and effects processing capability, including:
Brings all the power of both Phoenix Refine and Phoenix Video together in a unique combination, allowing users to fully correct and deliver projects of mixed film and video media within a single timeline, including:
ColorScene is a full-service Digital Vision reseller and approved systems integrator for turn-key Phoenix Restoration systems. Please contact us to design the perfect system and tools to meet your restoration needs.
Contact Us →The normal approach for film restoration is to address global issues (i.e. problems affecting the whole image such as unsteadiness or flicker), then tackling localized defects and finally and optionally enhancing the image.
Unsteadiness is first removed using the automated DVO Steady, in combination with correcting any splice bumps. Then flicker (including chroma breathing) is reduced with DVO Flicker, also automated. Once the global issues have been addressed, tram lines can be removed using DVO Scratch. The typical time-consuming dust-busting process is done swiftly with the motion compensated DVO Dust, optionally complemented with manual DVO Fix.
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Time stretching can be achieved with DVO Twister, using multiple layers/tracks and compositing tools. Colour/density matching can be performed using the advanced grading tools available in Phoenix Finish.
The negative, parts of a print or even material previously put on tape can be used to match to the negative by image enhancement such as using DVO Sharpen, DVO Clarity or DVO Grain. Image resize/positioning is achieved using Pan & Scan or DVO Upscale.
Digital camera noise is the most common issue, but other sensor problems, if present, like patterning need to be addressed first.
Patterning can usually be resolved by sharp cut-off low pass filtering using the generic DVO Brickwall or DVO Alias tools. Digital dropouts can be removed using DVO Dust – a very versatile tool that can remove almost any type of random artefacts, both field or frame based.
Digital noise is finally reduced by DVO Clarity. It is not unusual that different camera types are used in the same production, with different types of noise being induced. With DVO Clarity these different shots can easily be matched.
Start by correcting any “line-start” problems using DVO Line Sync and then composite artefacts can be eliminated with DVO Cross Colour.
One of the biggest issues with material on analogue tapes is dropouts. Use DVO Dropout to take care of this. Larger defects can be removed manually with DVO Fix using its motion compensated fill-in.
Video tape noise is typically also a big problem, but can be dramatically reduced with DVO Noise. This has separate luma/chroma controls, to influence the chroma channel more strongly. To make the final product look pristine, sharpening (DVO Aperture) can be added.
Are there any mixed film/video related defects? No problem, film and video restoration tools can be mixed to restore the content, matching it to the original capture.


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Maps a Cloud object to a compute device.
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#include <pcps_device_cloud.h>
Maps a Cloud object to a compute device.
◆ DeviceCloud() [1/3]
| pcps::DeviceCloud::DeviceCloud |
( |
const Cloud & |
hostCloud, |
|
|
Context & |
context |
|
) |
| |
Class contructor.
- Parameters
-
| hostCloud | Read-only host Cloud object. |
| context | Compute context. |
◆ DeviceCloud() [2/3]
| pcps::DeviceCloud::DeviceCloud |
( |
Cloud & |
hostCloud, |
|
|
Context & |
context |
|
) |
| |
Class contructor.
- Parameters
-
| hostCloud | Writeable host Cloud object. |
| context | Compute context. |
◆ DeviceCloud() [3/3]
Move constructor.
- Parameters
-
◆ getDeviceData() [1/2]
| const void* pcps::DeviceCloud::getDeviceData |
( |
| ) |
const |
|
inlinenoexcept |
Retrieves a read-only reference to the points stored in the compute device.
It can never be null.
◆ getDeviceData() [2/2]
| void* pcps::DeviceCloud::getDeviceData |
( |
| ) |
|
|
noexcept |
Retrieves a writeable reference to the points stored in the compute device.
It can be null if the source host cloud is read-only.
◆ operator=()
Move assignment operator.
- Parameters
-
◆ updateHostCloud()
| bool pcps::DeviceCloud::updateHostCloud |
( |
Context & |
context | ) |
|
Updates the host cloud with the content of the device cloud.
This operation only works if the source host cloud is writeable.
- Parameters
-
- Returns
- true if the operation was completed successfully; false otherwise.
The documentation for this class was generated from the following file:


{% blocktrans %}Welcome to The Virtual Stock Market{% endblocktrans %}
{% if not user.is_authenticated %}
{% url "account_login" as login_url %}
{% url "account_signup" as signup_url %}
{% blocktrans %}Please Log In or Sign Up to join us!{% endblocktrans %}
{% endif %}
{% blocktrans %}What is The Virtual Stock Market?{% endblocktrans %}
{% blocktrans %}
VSM is an free, open-source game based on Django and intended to provide a real world look into the exciting world of trading in the stock market.
{% endblocktrans %}
{% blocktrans %}
The money isn't real but the trading and experience is!
{% endblocktrans %}
{% blocktrans %}
Trading is show with real time stock quotes and information so you are as close to the markets as the experts.
{% endblocktrans %}
{% blocktrans %}
Watch as you compete for best broker against other people around the world!
{% endblocktrans %}
{% blocktrans %}
See help
if you are stuck.
{% endblocktrans %}


Form
Documents to help schools converting to academy status submit their land information to the Department for Education.
MS Word Document , 58.5KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
MS Word Document , 65.5KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
MS Word Document , 59.5KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
MS Word Document , 62KB
If you use assistive technology (such as a screen reader) and need a version of this document in a more accessible format, please email<EMAIL_ADDRESS>. Please tell us what format you need. It will help us if you say what assistive technology you use.
Schools converting to academy status should ask their solicitor or legal adviser to fill in the land questionnaire that corresponds to their type of school.
Before completing the questionnaire, we suggest that your solicitor or legal adviser reads the land transfer advice document .
You should send the completed questionnaire to your Department for Education project lead.
Read more guidance on the process for converting to academy status .
Published: 23 July 2012


HXL is a different kind of data standard, designed to improve information sharing during a humanitarian crisis without adding extra reporting burdens.
The 30-second HXL tutorial
-
Grab a spreadsheet of humanitarian data.
Cluster District People affected People reached WASH Coast 9000 9000 WASH Mountains 1000 200 Education Coast 15500 8000 Education Mountains 750 600 Health Coast 20000 3500 Health Mountains 3500 1500 -
Insert a new row between the headers and the data.
Cluster District People affected People reached WASH Coast 9000 9000 WASH Mountains 1000 200 Education Coast 15500 8000 Education Mountains 750 600 Health Coast 20000 3500 Health Mountains 3500 1500 -
Add some HXL hashtags.
Cluster District People affected People reached #sector #adm1 #aff_num #reached_num WASH Coast 9000 9000 WASH Mountains 1000 200 Education Coast 15500 8000 Education Mountains 750 600 Health Coast 20000 3500 Health Mountains 3500 1500


{{ forms.panel_fields( 'Mail.Ru', (form.mailru_app_id, form.mailru_app_secret), ''' Base domain for Mail.Ru application: %s ''' % request.host ) }}


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Hinxton


One of our goals is to spread the game of Vintage American Football. To do this everyone must use the same rules and equipment.
The purpose of this page is to share our rules and equipment as well as to educate on football in the early 1900's through the 1920's.
While researching vintage football we have found that football started much like baseball. It was played in small city parks by neighborhood teams. One neighborhood would challenge another and the better team would live on. Often these teams took street names, neighborhood names, city names or even business names depending on the manager. Some cities had several teams each year, some of these teams were different weight classes and some were at different levels.
Our goal is to revive those old neighborhood, city and business teams and help them find games with their old rivals. Using this approach we will be growing the game exactly as they did over 100 years ago.
Field of Play: 80 by 48.33 yards
Length of Game: Four - fifteen minute quarters, running clock (or less if agreed upon by both managers)
Rosters: 12-18 players
Lineups: 9 on both sides of the ball
Scoring Points: TD = 6 | Drop Kick = 4 | FG = 3 | Safety = 2 | Drop Goal Kick = 2 | Place Goal Kick = 1
Yard Lines: Kickoff = 30 | Touchback = 10 | First Downs 20-40-20 | Free Kick from Safety = 10 | Extra Point = 20 | Drop Kick = placed 20+ yards out
The Ball: Replica Spalding J5 Melon Football - By VAF
Tee: 1 inch flat (black)
Referee Whistle: Acme Thunderer by Gundog Supply Co.
Tackle Flags: Belt by Triple Flag Threat flag football belts
Down Marker Pole: Custom made - See images
Yard Markers: Custom made - See images
Score Board: Custom made - Images to come
Jersey Top: 12 oz cotton Jersey Top by Vintage American Football
Socks/Hose: Full length by Vintage American Football
Helmet: Replica Leather Helmet by Various Suppliers
Pants: Replica Pants by Various Suppliers
Announcing our newest team - Des Moines Comets!!!
Team Name - City - State - Owner