Changelog
3.0.0 (2019-04-20)
2.6.0 (2019-04-19)
- Feat
- Added new compilation options jsonx.compile, jsonx.outputJSON, jsonx.outputHTML, jsonx.outputJSX
- Fix
- pass functions in jsonx object values
- Chore
- added new benchmark tests
2.2.0 (2019-04-01)
- Feat
- Added React.lazy support
- Added React.Suspense support
- Fix
- Named class support via options
- passing state and props to class render component via options
- Chore
2.1.0 (2019-04-01)
- Feat
- Fix
- Improved inline function support
- Improved debugging support
- Automated parameter detection
- Chore
2.0.0 (2019-03-29)
- Feat
- Added support for Function Components
- Added support for Hooks
- Fix
- Supports refs on __functionProps
- Chore


Consider two worlds and how Meg's Minions might resonate in each...
Locally crafted, unique toys they'll love forever.
Customers at craft fairs expect to spend money: voting with their dollars to support local craftspeople. Minions are unique gifts typically puchased for the children of the customer's friends or family; being machine washable seals the deal.
Points of resonance in this world might include:
Creating minions to capitalize on public domain iconography, especially at church fairs (similar to the resonance achieved with Lucky Cat).
Customers who buy children's gifts want to know they're "giving a smile" and, ideally, will be noted and remembered for the gift. Obtain and display photos of children holding on to Minions.
Show yourself off with uniquely geeky (you-geekly?) toys.
Minion buyers on the Web are younger than those at craft fairs and are buying for themselves rather than loved ones. Most identify with a geek culture, which presents a special challenge: the geek demographic often buys to demonstrate an affinity for their favorite franchise (Star Wars, Dr. Who, etc.), however Minions are original creations.
Meg's Minions should consider appealing to the value of standing out and being unique without the crutch of a franchise by:
Soliciting production of original creations, for example taking someone's world-building project and making a part of it "real" through a Minion. Making your customers feel god-like is a great appeal to ego.
Appealing to emotion rather than fandom, for example by producing pieces related to customers' pets.
Building resonance means finding the right niche and ensuring
that your product and your message strikes a chord with that
niche. With limited producation capability, customization
is a good way to squeeze out additional dollars.
Roll over or click the images below for some thoughts
on how we might hit the right notes. Also, if you click "Craft
Fairs & Events" your photo lines up right next to
Jon Hamm!
Given the popularity of amigurumi, one limit on Meg's Minion's profit is time: you can only make so many Minions.
Therefore, I propose profit per hour be the key metric
that every effort is made to increase. Several scenarios below demonstrate the importance
of this number: Meg's Minions can achieve double-digit percentage increases in profit
simply with changes to production. All data represents a one year period. Assumptions used are available in
this
spreadsheet.
These are platforms that can be used for brand websites, storefronts, blogs, etc. The links below direct either to examples of sites that are hosted using that platform or that provide additional insight for comparison.
Some platforms (e.g. WordPress.com) were excluded either because they did not meet your needs or because the price was right out. Please feel free to explore or inquire about other options not listed here.
So you have this report - now what?
I can help Meg's Minions set up a spreadsheet(s) to more effectively monitor profit and ensure maximization of profit per hour. The Resources section includes a link to a similar sheet.
I can help Meg's Minions design and build a website that includes the features and resonance points I've gone over, including portrayal of brand, requests for custom orders, fair schedules, and more. Here's a barebones site I did for my mother in about an hour.
Even if a blog is not your style, we can use a Meg's Minions brand website to start putting together a newsletter. As audience grows, we have a readymade base for validating new ideas and clearing excess inventory. The Platforms section goes over several hosting options. Here's a blog I made in Wix from a template.
Let's pick what to do next from the Features section, I'll knock together a quick website to talk about it, then we'll put a post on Reddit to bring people in and buy!
This report has focused on optimizing existing lines of business (i.e. Minion Creation)
With the current product line (i.e. Minions only), there is a limit on
potential income: if Meg's Minions crafted only the currently most profitable item
(Lucky Cat) full time, profit would still top out at roughly $5,100.
Planet
June receives 80,000+ unique
visitors per month. I estimate their potential annual income
to between $24,000 to $96,000 per year, with a likely average
range of around $60,000.
There are options for diversification and similar income,
however as discussed the focus remains on Minions only.


@* * Copyright 2021 HM Revenue & Customs * * Licensed under the Apache License, Version 2.0 (the "License"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * http://www.apache.org/licenses/LICENSE-2.0 * * Unless required by applicable law or agreed to in writing, software * distributed under the License is distributed on an "AS IS" BASIS, * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. * See the License for the specific language governing permissions and * limitations under the License. *@ @import config.ApplicationConfig @import model.Crumb @import helpers.DateFormatter.formatDateWithShortPattern @import java.util.UUID @import domain.models.applications.ClientSecret @import domain.models.developers.DeveloperSession @import domain.models.applications.Application @import views.html.include.DevMain @import views.html.include.appEnvironment @this(devMain: DevMain) @(app: Application, secret: ClientSecret)(implicit request: play.api.mvc.Request[Any], loggedIn: DeveloperSession, messages: Messages, applicationConfig: ApplicationConfig) @devMain( title = "Are you sure you want to delete this client secret?", userFullName = loggedIn.loggedInName, breadcrumbs = Seq( Crumb("Delete client secret"), Crumb.application(app), Crumb.viewAllApplications, Crumb.home ), developerSession = Some(loggedIn) ) {
Before you delete this client secret, add a new client secret to your software.
| Client secret | Created | Last used |
|---|---|---|
| @{secret.name} | @{formatDateWithShortPattern(secret.createdOn)} | @{secret.lastAccess.map(formatDateWithShortPattern).getOrElse("Not used")} |
After you have deleted this client secret it cannot be restored or used to refresh expired tokens.
@helper.form(action = controllers.routes.Credentials.deleteClientSecretAction(app.id, secret.id), 'class -> "form") { @helper.CSRF.formField Cancel } }@import views.html.include.DevMain


|
libcbor
0.5.0
libcbor is a C library for parsing and generating CBOR, the general-purpose schema-less binary data format.
|
#include "cbor/common.h"
Go to the source code of this file.
Functions | |
| size_t | cbor_encode_uint8 (uint8_t, unsigned char *, size_t) |
| size_t | cbor_encode_uint16 (uint16_t, unsigned char *, size_t) |
| size_t | cbor_encode_uint32 (uint32_t, unsigned char *, size_t) |
| size_t | cbor_encode_uint64 (uint64_t, unsigned char *, size_t) |
| size_t | cbor_encode_uint (uint64_t, unsigned char *, size_t) |
| size_t | cbor_encode_negint8 (uint8_t, unsigned char *, size_t) |
| size_t | cbor_encode_negint16 (uint16_t, unsigned char *, size_t) |
| size_t | cbor_encode_negint32 (uint32_t, unsigned char *, size_t) |
| size_t | cbor_encode_negint64 (uint64_t, unsigned char *, size_t) |
| size_t | cbor_encode_negint (uint64_t, unsigned char *, size_t) |
| size_t | cbor_encode_bytestring_start (size_t, unsigned char *, size_t) |
| size_t | cbor_encode_indef_bytestring_start (unsigned char *, size_t) |
| size_t | cbor_encode_string_start (size_t, unsigned char *, size_t) |
| size_t | cbor_encode_indef_string_start (unsigned char *, size_t) |
| size_t | cbor_encode_array_start (size_t, unsigned char *, size_t) |
| size_t | cbor_encode_indef_array_start (unsigned char *, size_t) |
| size_t | cbor_encode_map_start (size_t, unsigned char *, size_t) |
| size_t | cbor_encode_indef_map_start (unsigned char *, size_t) |
| size_t | cbor_encode_tag (uint64_t, unsigned char *, size_t) |
| size_t | cbor_encode_bool (bool, unsigned char *, size_t) |
| size_t | cbor_encode_null (unsigned char *, size_t) |
| size_t | cbor_encode_undef (unsigned char *, size_t) |
| size_t | cbor_encode_half (float, unsigned char *, size_t) |
| Encodes a half-precision float. More... | |
| size_t | cbor_encode_single (float, unsigned char *, size_t) |
| size_t | cbor_encode_double (double, unsigned char *, size_t) |
| size_t | cbor_encode_break (unsigned char *, size_t) |
| size_t | cbor_encode_ctrl (uint8_t, unsigned char *, size_t) |
| size_t cbor_encode_array_start | ( | size_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 91 of file encoding.c.
| size_t cbor_encode_bool | ( | bool | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 116 of file encoding.c.
| size_t cbor_encode_break | ( | unsigned char * | , |
| size_t | |||
| ) |
Definition at line 178 of file encoding.c.
| size_t cbor_encode_bytestring_start | ( | size_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 62 of file encoding.c.
| size_t cbor_encode_ctrl | ( | uint8_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 183 of file encoding.c.
| size_t cbor_encode_double | ( | double | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 173 of file encoding.c.
| size_t cbor_encode_half | ( | float | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Encodes a half-precision float.
Since there is no native representation or semantics for half floats in the language, we use single-precision floats, as every value that can be expressed as a half-float can also be expressed as a float.
This however means that not all floats passed to this function can be unambiguously encoded. The behavior is as follows:
| value | |
| buffer | Target buffer |
| buffer_size | Available space in the buffer |
Definition at line 131 of file encoding.c.
| size_t cbor_encode_indef_array_start | ( | unsigned char * | , |
| size_t | |||
| ) |
Definition at line 96 of file encoding.c.
| size_t cbor_encode_indef_bytestring_start | ( | unsigned char * | , |
| size_t | |||
| ) |
Definition at line 76 of file encoding.c.
| size_t cbor_encode_indef_map_start | ( | unsigned char * | , |
| size_t | |||
| ) |
Definition at line 106 of file encoding.c.
| size_t cbor_encode_indef_string_start | ( | unsigned char * | , |
| size_t | |||
| ) |
Definition at line 86 of file encoding.c.
| size_t cbor_encode_map_start | ( | size_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 101 of file encoding.c.
| size_t cbor_encode_negint | ( | uint64_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 57 of file encoding.c.
| size_t cbor_encode_negint16 | ( | uint16_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 42 of file encoding.c.
| size_t cbor_encode_negint32 | ( | uint32_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 47 of file encoding.c.
| size_t cbor_encode_negint64 | ( | uint64_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 52 of file encoding.c.
| size_t cbor_encode_negint8 | ( | uint8_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 37 of file encoding.c.
| size_t cbor_encode_null | ( | unsigned char * | , |
| size_t | |||
| ) |
Definition at line 121 of file encoding.c.
| size_t cbor_encode_single | ( | float | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 167 of file encoding.c.
| size_t cbor_encode_string_start | ( | size_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 81 of file encoding.c.
| size_t cbor_encode_tag | ( | uint64_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 111 of file encoding.c.
| size_t cbor_encode_uint | ( | uint64_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 31 of file encoding.c.
| size_t cbor_encode_uint16 | ( | uint16_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 16 of file encoding.c.
| size_t cbor_encode_uint32 | ( | uint32_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 21 of file encoding.c.
| size_t cbor_encode_uint64 | ( | uint64_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 26 of file encoding.c.
| size_t cbor_encode_uint8 | ( | uint8_t | , |
| unsigned char * | , | ||
| size_t | |||
| ) |
Definition at line 11 of file encoding.c.
| size_t cbor_encode_undef | ( | unsigned char * | , |
| size_t | |||
| ) |
Definition at line 126 of file encoding.c.


|
Walls And Holes
1
|
#include "groundblockypolygontilemesher.h"
Functions | |
| QVector< Triplet< BetterPolygon, QVector< float >, QVector< bool > > > | makeDiagonal (QPointF a, QPointF b, QPointF c, QPointF d) |
| QVector<Triplet<BetterPolygon, QVector<float>, QVector<bool> > > makeDiagonal | ( | QPointF | a, |
| QPointF | b, | ||
| QPointF | c, | ||
| QPointF | d | ||
| ) |


NCGC - functional annotation of somatic DNA variation¶
The NCGC functional annotation workflow contains a set of software tools and comprehensive genomic datasets for annotation and interpretation of single nucleotide variants (SNVs), short insertions/deletions (InDels), and copy number segments discovered from high-throughput sequencing. Currently, it builds largely upon Variant Effect Predictor (VEP) and vcfanno. It has the following focus:
- Cancer relevance
- Coding sequence variants (i.e. exome-type studies)
- insilico predictions of effect of coding variants
- Overlap with existing variant databases, both somatic (e.g. hotspots in different tumor types) and germline
- Clinical utility (i.e. clinical variant associations, drug-gene interactions, drug sensitivity markers etc.)
Data sources included¶
- VEP v85 - Variant Effect Predictor release 85 (GENCODE v19)
- COSMIC v78 - catalogue of somatic mutations in cancer (Sep 2016)
- dBNSFP v3.2 - database of non-synonymous functional predictions (March 2016)
- ICGC v22 - Somatic mutations discovered in all ICGC (International Cancer Genomics Consortium) tumor cohorts (Aug 2016)
- ExAC r0.3.1 - germline variant frequencies exome-wide (March 2016)
- dbSNP b147 - database of short genetic variants (April 2016)
- 1000Genomes phase3 - germline variant frequencies genome-wide (May 2013)
- ClinVar - database of clinically related variants (Nov 2016)
- DoCM - database of curated mutations (v3.2, April 2016)
- CIViC - clinical interpretations of variants in cancer (Nov 12th 2016)
- CBMDB - Cancer BioMarkers database (Oct 28th 2016)
- IntOGen catalogs of driver mutations/genes - (May 2016)
- Cancer Hotspots - a resource for statistically significant mutations in cancer (2016)
- UniProt/SwissProt KnowledgeBase 2016_09 - resource on protein sequence and functional information (Sep 2016)
- Pfam v30 - database of protein families and domains (June 2016)
- Appris 2016_03.v15 - annotation of prinicipal human transcript isoforms database (March 2016)
- DGIdb - database of interactions betweeen antineoplastic drugs and human proteins (v2.22, February 2016)
- TSGene v2.0 - tumor suppressor/oncogene database (November 2015)
Output¶
Somatic SNVs/InDels¶
1. Variant call format - VCF¶
Each tumor-normal sample pair is provided with an annotated VCF file with the following naming convention:
<sample_pair_identifier>.pass.all.annotated.vcf.gz
Following common standards, all VCF files are compressed with bgzip and indexed with tabix.
Below follows a description of all annotations/tags present in the VCF INFO column after processing with the NCGC annotation pipeline:
- VEP output
- CSQ - Complete consequence annotations from VEP. Format: Allele|Consequence|IMPACT|SYMBOL|Gene|Feature_type|Feature|BIOTYPE| EXON|INTRON|HGVSc|HGVSp|cDNA_position|CDS_position|Protein_position|Amino_acids| Codons|Existing_variation|ALLELE_NUM|DISTANCE|STRAND|FLAGS|PICK|VARIANT_CLASS| SYMBOL_SOURCE|HGNC_ID|CANONICAL|APPRIS|CCDS|ENSP|SWISSPROT|TREMBL| UNIPARC|RefSeq|DOMAINS|HGVS_OFFSET|CLIN_SIG|SOMATIC|PHENO|MOTIF_NAME| MOTIF_POS|HIGH_INF_POS|MOTIF_SCORE_CHANGE
- Consequence - Impact modifier for the consequence type (picked by VEP’s –flag_pick_allele option)
- Gene - Ensembl stable ID of affected gene (picked by VEP’s –flag_pick_allele option)
- Feature_type - Type of feature. Currently one of Transcript, RegulatoryFeature, MotifFeature (picked by VEP’s –flag_pick_allele option)
- Feature - Ensembl stable ID of feature (picked by VEP’s –flag_pick_allele option)
- cDNA_position - Relative position of base pair in cDNA sequence (picked by VEP’s –flag_pick_allele option)
- CDS_position - Relative position of base pair in coding sequence (picked by VEP’s –flag_pick_allele option)
- CDS_CHANGE - Coding, transcript-specific sequence annotation (picked by VEP’s –flag_pick_allele option)
- Protein_position - Relative position of amino acid in protein (picked by VEP’s –flag_pick_allele option)
- Amino_acids - Only given if the variant affects the protein-coding sequence (picked by VEP’s –flag_pick_allele option)
- Codons - The alternative codons with the variant base in upper case (picked by VEP’s –flag_pick_allele option)
- IMPACT - Impact modifier for the consequence type (picked by VEP’s –flag_pick_allele option)
- VARIANT_CLASS - Sequence Ontology variant class (picked by VEP’s –flag_pick_allele option)
- SYMBOL - Gene symbol (picked by VEP’s –flag_pick_allele option)
- SYMBOL_SOURCE - The source of the gene symbol (picked by VEP’s –flag_pick_allele option)
- STRAND - The DNA strand (1 or -1) on which the transcript/feature lies (picked by VEP’s –flag_pick_allele option)
- ENSP - The Ensembl protein identifier of the affected transcript (picked by VEP’s –flag_pick_allele option)
- FLAGS - Transcript quality flags: cds_start_NF: CDS 5’, incomplete cds_end_NF: CDS 3’ incomplete (picked by VEP’s –flag_pick_allele option)
- SWISSPROT - Best match UniProtKB/Swiss-Prot accession of protein product (picked by VEP’s –flag_pick_allele option)
- TREMBL - Best match UniProtKB/TrEMBL accession of protein product (picked by VEP’s –flag_pick_allele option)
- UNIPARC - Best match UniParc accession of protein product (picked by VEP’s –flag_pick_allele option)
- HGVSc - The HGVS coding sequence name (picked by VEP’s –flag_pick_allele option)
- HGVSp - The HGVS protein sequence name (picked by VEP’s –flag_pick_allele option)
- HGVSp_short - The HGVS protein sequence name, short version (picked by VEP’s –flag_pick_allele option)
- HGVS_OFFSET - Indicates by how many bases the HGVS notations for this variant have been shifted (picked by VEP’s –flag_pick_allele option)
- MOTIF_NAME - The source and identifier of a transcription factor binding profile aligned at this position (picked by VEP’s –flag_pick_allele option)
- MOTIF_POS - The relative position of the variation in the aligned TFBP (picked by VEP’s –flag_pick_allele option)
- HIGH_INF_POS - A flag indicating if the variant falls in a high information position of a transcription factor binding profile (TFBP) (picked by VEP’s –flag_pick_allele option)
- MOTIF_SCORE_CHANGE - The difference in motif score of the reference and variant sequences for the TFBP (picked by VEP’s –flag_pick_allele option)
- CELL_TYPE - List of cell types and classifications for regulatory feature (picked by VEP’s –flag_pick_allele option)
- CANONICAL - A flag indicating if the transcript is denoted as the canonical transcript for this gene (picked by VEP’s –flag_pick_allele option)
- CCDS - The CCDS identifier for this transcript, where applicable (picked by VEP’s –flag_pick_allele option)
- INTRON - The intron number (out of total number) (picked by VEP’s –flag_pick_allele option)
- EXON - The exon number (out of total number) (picked by VEP’s –flag_pick_allele option)
- DOMAINS - The source and identifier of any overlapping protein domains (picked by VEP’s –flag_pick_allele option)
- DISTANCE - Shortest distance from variant to transcript (picked by VEP’s –flag_pick_allele option)
- BIOTYPE - Biotype of transcript or regulatory feature (picked by VEP’s –flag_pick_allele option)
- TSL - Transcript support level (picked by VEP’s –flag_pick_allele option)>
- PUBMED - PubMed ID(s) of publications that cite existing variant - VEP
- SOMATIC - Somatic status of existing variant(s) - VEP
- PHENO - Indicates if existing variant is associated with a phenotype, disease or trait - VEP
- GENE_PHENO - Indicates if overlapped gene is associated with a phenotype, disease or trait - VEP
- ALLELE_NUM - Allele number from input; 0 is reference, 1 is first alternate etc - VEP
- REFSEQ_MATCH - The RefSeq transcript match status; contains a number of flags indicating whether this RefSeq transcript matches the underlying reference sequence and/or an Ensembl transcript (picked by VEP’s –flag_pick_allele option)
- PICK - Indicates if this block of consequence data was picked by VEP’s –flag_pick_allele option
- VEP_ALL_CONSEQUENCE - All transcript consequences (Consequence:SYMBOL:Feature_type:Feature:BIOTYPE) - VEP
- Gene information
- ENTREZ_ID - Entrez gene identifier
- APPRIS - Principal isoform flags according to the APPRIS principal isoform database
- UNIPROT_ID - UniProt identifier
- CANCER_CENSUS_SOMATIC - Gene with known cancer association - Cancer Gene Census, WTSI
- CANCER_CENSUS_GERMLINE - Gene with known cancer association - Cancer Gene Census, WTSI
- TUMOR_SUPPRESSOR - Gene is predicted as tumor suppressor candidate according to (TSGene v2.0)
- ONCOGENE - Gene is curated as an oncogene according to (TSGene v2.0)
- ONCOSCORE - Literature-derived score for cancer gene relevance Bioconductor/OncoScore, range from 0 (low oncogenic potential) to 1 (high oncogenic potential)
- INTOGEN_DRIVER - Gene is predicted as a cancer driver in the IntoGen Cancer Drivers Database - 2014.12
- Variant effect and protein-coding information
- CANCER_MUTATION_HOTSPOT - mutation hotspot codon in cancerhotspots.org. Format: gene_symbol | codon | q-value
- UNIPROT_FEATURE - Overlapping protein annotations from UniProt KB
- INTOGEN_DRIVER_MUT - Indicates if existing variant is predicted as driver mutation from IntoGen Catalog of Driver Mutations
- EFFECT_PREDICTIONS - Predictions of effect of variant on protein
function and pre-mRNA splicing from database of non-synonymous
functional predictions - dbNSFP
v3.2. Predicted
effects are provided by 14 different sources/algorithms (separated by
‘&’):
- SIFT (Jan 2015)
- PolyPhen2 (v 2.2.2, predictions based on HumDiv)
- LRT (2009)
- MutationTaster (data release Nov 2015)
- MutationAssessor (release 3)
- [FATHMM] (http://fathmm.biocompute.org.uk) (v2.3)
- PROVEAN (v1.1 Jan 2015)
- FATHMM_MKL
- CADD (v1.3)
- DBNSFP_CONSENSUS_SVM (Ensembl/consensus prediction, based on support vector machines)
- DBNSFP_CONSENSUS_LR (Ensembl/consensus prediction, logistic regression based)
- SPLICE_SITE_EFFECT_ADA (Ensembl/consensus prediction of splice-altering SNVs, based on adaptive boosting)
- SPLICE_SITE_EFFECT_RF (Ensembl/consensus prediction of splice-altering SNVs, based on adaptive boosting)
- SPLICE_SITE_EFFECT_BOOL (predicts ‘affected_splicing’ if SPLICE_SITE_EFFECT_ADA or SPLICE_SITE_EFFECT_RF predicts ‘affected_splicing’
- Variant overlap and frequencies in germline/somatic databases
- AFR_AF_EXAC - African/American germline allele frequency (Exome Aggregation Consortium release 0.3.1)
- AMR_AF_EXAC - American germline allele frequency (Exome Aggregation Consortium release 0.3.1)
- GLOBAL_AF_EXAC - Adjusted global germline allele frequency (Exome Aggregation Consortium release 0.3.1)
- EAS_AF_EXAC - East Asian germline allele frequency (Exome Aggregation Consortium release 0.3.1)
- FIN_AF_EXAC - Finnish germline allele frequency (Exome Aggregation Consortium release 0.3.1)
- NFE_AF_EXAC - Non-Finnish European germline allele frequency (Exome Aggregation Consortium release 0.3.1)
- OTH_AF_EXAC - Other germline allele frequency (Exome Aggregation Consortium release 0.3.1)
- SAS_AF_EXAC - South Asian germline allele frequency (Exome Aggregation Consortium release 0.3)
- AFR_AF_1KG - 1000G Project - phase 3 germline allele frequency for samples from AFR (African)
- AMR_AF_1KG - 1000G Project - phase 3 germline allele frequency for samples from AMR (Ad Mixed American)
- EAS_AF_1KG - 1000G Project - phase 3 germline allele frequency for samples from EAS (East Asian)
- EUR_AF_1KG - 1000G Project - phase 3 germline allele frequency for samples from EUR (European)
- SAS_AF_1KG - 1000G Project - phase 3 germline allele frequency for samples from SAS (South Asian)
- GLOBAL_AF_1KG - 1000G Project - phase 3 germline allele frequency for all 1000G project samples (global)
- DBSNPRSID - dbSNP reference ID
- DBSNPBUILDID - Initial dbSNP build ID for rsID
- DBSNP_MAPPINGSTATUS - Status with respect to the genomic mappability of the flanking sequence of the rsID
- DBSNP_VALIDATION - Categories of evidence that support the variant in dbSNP
- DBSNP_SUBMISSIONS - Number of individual submissions to rsID
- GWAS_CATALOG_PMID - Variant is linked to phenotype through the GWAS Catalog, literature in PMID list
- GWAS_CATALOG_TRAIT_URI - List of trait URIs for GWAS-associated variant
- COSMIC_MUTATION_ID - Mutation identifier in Catalog of somatic mutations in cancer - COSMIC v78 database
- COSMIC_CODON_FRAC_GW - For different tumor types, number of samples mutated at associated codon position (format: codon_number:tumor_type:fraction_mutated). Samples subject to exome/genome-wide screens only Catalog of somatic mutations in cancer - COSMIC v78.
- COSMIC_CODON_COUNT_GW - For different tumor types, number of samples mutated at associated codon position (format: codon_number:tumor_type:frequency). Samples subject to exome/genome-wide screens only Catalog of somatic mutations in cancer - COSMIC v78
- COSMIC_COUNT_GW - Global frequency of variant in Catalog of somatic mutations in cancer - COSMIC v78.
- COSMIC_SITE_HISTOLOGY - Primary site/histology distribution across tumor types in Catalog of somatic mutations in cancer - COSMIC v78.
- COSMIC_CANCER_TYPE_GW - Frequency of variant across different tumor types in Catalog of somatic mutations in cancer - COSMIC v78 - samples subject to exome/genome-wide screens only
- COSMIC_CANCER_TYPE_ALL - Frequency of variant across different tumor types in Catalog of somatic mutations in cancer - COSMIC v78
- COSMIC_SAMPLE_SOURCE - Sample source distribution for variant in Catalog of somatic mutations in cancer - COSMIC v78.
- COSMIC_DRUG_RESISTANCE - Targeted drugs/therapies subject to resistance in tumors that carry the mutation. Catalog of somatic mutations in cancer - COSMIC v78.
- COSMIC_FATHMM_PRED - Variant effect prediction from COSMIC’s FATHMM algorithm (COSMIC variants only) Catalog of somatic mutations in cancer - COSMIC v78.
- COSMIC_VARTYPE - COSMIC variant type Catalog of somatic mutations in cancer - COSMIC v78.
- COSMIC_CONSEQUENCE - COSMIC consequence type Catalog of somatic mutations in cancer - COSMIC v78.
- ICGC_PROJECTS - Variant frequency count in different ICGC Project IDs
- Clinical variant associations
- CLINVAR_MSID - ClinVar Measure Set/Variant ID
- CLINVAR_PMIDS - Associated Pubmed IDs for variant in ClinVar
- CLINVAR_SIG - Clinical significance for variant in ClinVar
- CLINVAR_VARIANT_ORIGIN - Origin of variant (somatic, germline, de novo etc.) for variant in ClinVar
- DOCM_DISEASE - Associated disease types for variant in Database of Curated Mutations
- DOCM_PMID - Associated Pubmed IDs for variant in Database of Curated Mutations
- Other
- VT - Variant type (snv, INdel, inDEL, block_substitution)
- ANTINEOPLASTIC_DRUG_INTERACTION - Approved and experimental antineoplastic drugs interacting with the mutated gene, as retrieved from the Drug-Gene Interaction Database
- CIVIC_ID, CIVIC_ID_2 - Variant identifiers in the CIViC database
- CBMDB_ID - Variant identifier in the Cancer bioMarkers database
- CALL_CONFIDENCE - STRONG, WEAK_MUTECT, WEAK_STRELKA
2. Tab-separated values (TSV)¶
Annotated List of all SNVs/InDEls¶
We provide a tab-separated values file with most important annotations for SNVs/InDels. The file has the following naming convention:
<sample_pair_identifier>.snvs_indels.tiers.tsv
The SNVs/InDels are organized into different tiers that reflect relevance for therapeutics/tumorigenesis:
- Tier 1 constitute variants recorded as prognostic/diagnostic/drug sensitivity biomarkers in the CIViC database and the Cancer Biomarkers Database
- Tier 2 includes other coding variants that are found in known mutational hotspots, predicted as cancer driver mutations, or curated as disease-causing
- Tier 3 includes other coding variants found in oncogenes, tumor suppressor genes, or cancer census genes
- Tier 4 includes other coding variants
- Tier 5 includes non-coding variants
Note: ‘coding variants‘ refer to the set of variants with the following consequences: - missense variant - splice donor/splice acceptor alteration - stop gained/stop lost - frameshift/non-frameshift variants
The following variables are included in the tiered TSV file:
1. GENOMIC_CHANGE - Identifier for genomic variant, e.g. g.chr1:152382569:A>G
2. GENOME_VERSION - Assembly version, e.g. GRCh37
3. VCF_SAMPLE_ID - Sample identifier
4. VARIANT_CLASS - Variant type, e.g. SNV/insertion/deletion
5. SYMBOL - Gene symbol
6. GENE_NAME - Gene description
7. CCDS - CCDS identifier
8. ENTREZ_ID - Entrez gene identifier
9. UNIPROT_ID - UniProt protein identifier
10. ONCOSCORE - Literature-derived score for cancer gene relevance
11. ONCOGENE - Gene is curated as an oncogene according to TSGene
12. TUMOR_SUPPRESSOR - Gene is predicted as tumor suppressor
candidate according to TSGene
13. INTOGEN_DRIVER - Gene is predicted as a cancer driver in the
IntoGen Cancer Drivers Database - 2014.12
14. CANCER_CENSUS_SOMATIC - Gene with known cancer association -
Cancer Gene Census, WTSI
15. CANCER_CENSUS_GERMLINE - Gene with known cancer association -
Cancer Gene Census, WTSI
16. CONSEQUENCE - Variant consequence (as defined above for VCF output:
Consequence)
17. PROTEIN_CHANGE - Protein change (as defined above for VCF output:
HGVSp_short)
18. PROTEIN_DOMAIN - Protein domain
19. CDS_CHANGE - composite variable for coding change, format:
Consequence:Feature:cDNA_position:EXON:HGVSp_short
20. EFFECT_PREDICTIONS - as defined above for VCF
21. CANCER_MUTATION_HOTSPOT - mutation hotspot codon in
cancerhotspots.org. Format: gene_symbol | codon | q-value
22. INTOGEN_DRIVER_MUT - Indicates if existing variant is predicted as
driver mutation from IntoGen Catalog of Driver Mutations
23. VEP_ALL_CONSEQUENCE - all VEP consequences
24. DBSNP - dbSNP reference cluster ID
25. COSMIC - COSMIC mutation ID
26. COSMIC_SITE_HISTOLOGY - distribution of tumor sites/histology types
for COSMIC mutation
27. COSMIC_DRUG_RESISTANCE - variant associated with resistance to a
particular antineoplastic drug
28. CLINVAR - variant origin and associated traits associated with variant
29. CLINVAR_SIG - clinical significance of CLINVAR variant
30. GLOBAL_AF_EXAC - adjusted global germline allele frequency in ExAC release 0.3.1
31. GLOBAL_AF_1KG - 1000G Project - phase 3, germline allele frequency
for all 1000G project samples (global)
32. CALL_CONFIDENCE - _STRONG / WEAK_MUTECT / WEAK_STRELKA
33. DP_TUMOR - tumor depth
34. AF_TUMOR - allelic fraction tumor
35. DP_NORMAL - normal depth
36. AF_NORMAL - allelic fraction normal
37. TIER
38. TIER_DESCRIPTION
Biomarkers among SNVs/InDEls¶
For tumor samples that have hits in Tier 1 we provide an additional file with all underlying clinical evidence items. The file has the following naming convention:
<sample_pair_identifier>.snvs_indels.biomarkers.tsv
The format of the biomarker TSV file is as follows:
1. GENOMIC_CHANGE - Identifier for genomic variant, e.g. g.chr1:152382569:A>G
2. GENOME_VERSION - Assembly version, e.g. GRCh37
3. VCF_SAMPLE_ID - Sample identifier
4. SYMBOL - Gene symbol
5. CONSEQUENCE - Variant consequence
6. BM_CLINICAL_SIGNIFICANCE - The association with diagnostic/prognostic end point or treatment
7. BM_EVIDENCE_LEVEL - The type of experiment from which the evidence is curated
(validated, clinical, pre-clinical, case study, and inferential)
8. BM_EVIDENCE_TYPE - Category of clinical action/relevance implicated by event
(Predictive, Prognostic, Predisposing and Diagnostic)
9. BM_EVIDENCE_DIRECTION - An indicator of whether the evidence statement supports or
refutes the clinical significance of an event
10. BM_DISEASE_NAME - Specific disease or disease subtype that is associated with
this event and its clinical implication
11. BM_DRUG_NAMES - For predictive evidence, indicates the therapy for which sensitivity
or resistance is indicated
12. BM_RATING - A rating on a 5-star scale, portraying the curators trust in the
experiments from which the evidence is curated
13. BM_CITATION - Publication(s) where the event was described/explored/guidelines/trials
14. TIER
15. TIER_DESCRIPTION
Mutational signatures¶
For each tumor sample, we apply the deconstructSigs package to delineate the known mutational signatures. The inferred, weighted contributions by each signature and their underlying, proposed etiologies are given in a TSV file with the following naming convention:
<sample_pair_identifier>.mutational_signatures.tsv
The format of the mutational sigantures TSV file is as follows:
1. Signature_ID - ID of signature from COSMIC's 30 reference signatures
2. Weight - inferred weight of signature in the tumor sample
3. Cancer_types - cancer types in which the signature has been observed
4. Proposed_aetiology - proposed underlying etiology
5. SampleID - Sample identifier
Somatic copy number abberations¶
1. Tab-separated values (TSV)¶
Each tumor-normal sample pair is provided with a gzipped, tab-separated values (TSV) file of somatic copy number segments determined by FACETS. Copy number segments have been intersected with the genomic coordinates of all transcripts from ENSEMBL/GENCODE. In adddition, we attach cancer-relevant annotations for the affected transcripts. The naming convention of the compressed TSV file is as follows:
<sample_pair_identifier>.facets.cnv.annotated.gz
The format of the TSV file is the following:
1.chrom - chromosome
2.segment_start - start of copy number segment determined by FACETS
3.segment_end - end of copy number segment determined by FACETS
4.segment_length - length of segment in Mb
5.cf - estimated cellular fraction
6.LogR - Copy log-ratio
7.cnTotal - Estimated total copy number
8.cnMinor - Estimated minor copy number
9.ensembl_gene_id
10.symbol - gene symbol
11.ensembl_transcript_id
12.transcript_start
13.transcript_end
14.transcript_overlap_percent - percent of transcript length covered by CN segment
15.name - gene name description
16.gene_biotype - type of gene
17.cancer_census_germline - gene implicated with germline
predisposition to various cancer subtypes
18.cancer_census_somatic - gene for which somatic mutations
have been causally implicated in tumor development
19.tsgene - tumor suppressor gene status (TSgene 2.0 database)
20.tsgene_oncogene - oncogene status (TSgene 2.0 database)
21.intogen_drivers - predicted driver gene status (IntoGen Cancer Drivers Database)
22.antineoplastic_drugs_dgidb - validated and experimental
antineoplastic drugs interacting with gene
23.gencode_v19 - transcript is part of GENCODE V19


| build |
Builds the project for the target platform and produces an application package or a QR code for deployment. |
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Builds the project for Android platform and produces an application package or a QR code for deployment. |
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Builds the project for iOS platform and produces an application package or a QR code for deployment. |
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Builds the project for Windows Phone 8 platform and produces an application package or a QR code for deployment. |
| debug |
Shows the debug tools to let you debug applications on connected devices. |
| debug ios |
Lets you debug applications on connected iOS devices. |
| debug android |
Lets you debug applications on connected Android devices. |
| deploy |
Builds the project for the selected platform and deploys it to connected physical devices. |
| deploy android |
Builds the project for android platform and deploys it to connected physical devices. |
| deploy ios |
Builds the project for ios platform and deploys it to connected physical devices. |
| emulate |
Builds the specified project in the cloud and runs it in a native emulator. |
| emulate android |
Builds the specified project in the cloud and runs it in a native Android emulator. |
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Builds the specified project in the cloud and runs it in the native iOS Simulator. |
| emulate wp8 |
Builds the specified project in the cloud and runs it in the native emulator from the Windows Phone 8. |
| livesync |
Synchronizes the latest changes in your project to connected devices. |
| livesync android |
Synchronizes the latest changes in your project to connected Android devices. |
| livesync ios |
Synchronizes the latest changes in your project to connected iOS devices. |
| livesync cloud |
Synchronizes the project with the cloud to enable LiveSync via wireless connection. |
| simulate |
Runs the current project in the device simulator. |


Algorithms¶
QISKit Algorithms and Circuits for QUantum Applications (QISKit ACQUA) is an extensible collection of algorithms and utilities for use with quantum computers to carry out research and investigate how to solve problems using near-term quantum applications on short depth circuits. The applications can span different domains QISKit ACQUA uses QISKit for its quantum computation.
The following Quantum Algorithms are part of QISKit ACQUA:
- Variational Quantum Eigensolver (VQE)
- Quantum Dynamics
- Quantum Phase Estimation (QPE)
- Iterative Quantum Phase Estimation (IQPE)
- Quantum Grover Search
- Support Vector Machine Quantum Kernel (SVM Q Kernel)
- Support Vector Machine Variational (SVM Variational)
QISKit ACQUA includes also some Classical Algorithms, which may be useful to compare and contrast results in the near term while experimenting with, developing and testing quantum algorithms:
Extending the Algorithm Library
Algorithms and many of the components they used have been designed to be pluggable. A new algorithm may be developed according to the specific API provided by QISKit ACQUA, and by simply adding its code to the collection of existing algorithms, that new algorithm will be immediately recognized via dynamic lookup, and made available for use within the framework of QISKit ACQUA.
To develop and deploy any new algorithm, the new algorithm class should derive from the QuantumAlgorithm
class.
Along with any supporting module, the new algorithm class
should be installed under its own folder in the qiskit_acqua
directory, just like the
existing algorithms.
Quantum Algorithms¶
In this section, we describe the quantum algorithms currently available in QISKit ACQUA.
Note
QISKit ACQUA requires associating a quantum device or simulator to any experiment that uses a quantum
algorithm. This is done by configuring the backend
section of the experiment to be run.
Variational Quantum Eigensolver (VQE)¶
VQE uses a variational approach to find the minimum eigenvalue of a Hamiltonian energy problem. It is configured with a trial wavefunction, supplied by a variational form, and an optimizer. An initial state may be supplied too.
Additionally, VQE can be configured with the following parameters:
A
string
indicating the mode used by theOperator
class for the computation:operator_mode : "matrix" | "paulis" | "group_paulis"
If no value for
operator_mode
ia specified, the default is"matrix"
.The initial point for the search of the minimum eigenvalue:
initial_point : [float, float, ... , float]
An optional list of
float
values may be provided as the starting point for the variational form. The length of this list must match the number of the parameters expected by the variational form being used. If such list is not provided, VQE will create a random starting point for the optimizer, with values randomly chosen to lie within the bounds of the variational form. If the variational form provides no lower bound, the VQE will default it to \(-2\pi\); if the upper bound is missing, the default value is \(2\pi\).
Declarative Name
When referring to VQE declaratively inside QISKit ACQUA, its code name
, by which QISKit ACQUA dynamically discovers and loads it,
is VQE
.
Problems Supported
In QISKit ACQUA, VQE supports the energy
and ising
problema.
Quantum Dynamics¶
Dynamics provides the lower-level building blocks for simulating universal quantum systems. For any given quantum system that can be decomposed into local interactions (for example, a global hamiltonian H as the weighted sum of several pauli spin operators), the local interactions can then be used to approximate the global quantum system via, for example, Lloyd’s method or Trotter-Suzuki decomposition.
Note
This algorithm only supports the local_state_vector
simulator.
Dynamics can be configured with the following parameter settings:
Evolution time:
evo_time : float
A number is expected. The minimum value is
0.0
. The default value is1.0
.The evolution mode of the computation:
evo_mode = "matrix" | "circuit"
Two
string
values are permitted:"matrix"
or"circuit"
, with"circuit"
being the default.The number of time slices:
num_time_slices = 0 | 1 | ...
This has to be a non-negative
int
value. The default is1
.Paulis grouping mode:
paulis_grouping = "default" | "random"
Two
string
values are permitted:"default"
or"random"
, with"default"
being the default and indicating that the paulis should be grouped.The expansion mode:
expansion_mode = "trotter" | "suzuki"
Two
string
values are permitted:"trotter"
(Lloyd’s method) or"suzuki"
(for Trotter-Suzuki expansion), with"trotter"
being the default one.The expansion order:
expansion_order = 1 | 2 | ...
This parameter sets the Trotter-Suzuki expansion order. A positive
int
value is expected. The default value is2
.
Declarative Name
When referring to Quantum Dynamics declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is Dynamics
.
Problems Supported
In QISKit ACQUA, Quantum Dynamics supports the dynamics
problem.
Quantum Phase Estimation (QPE)¶
QPE (also sometimes abbreviated as PEA, for Phase Estimation Algorithm), takes two quantum registers, control and target, where the control consists of several qubits initially put in uniform superposition, and the target a set of qubits prepared in an eigenstate (or, oftentimes, a guess of the eigenstate) of the unitary operator of a quantum system. QPE then evolves the target under the control using Dynamics of the unitary operator. The information of the corresponding eigenvalue is then kicked-back into the phases of the control register, which can then be deconvoluted by an Inverse Quantum Fourier Transform (IQFT), and measured for read-out in binary decimal format.
Note
This algorithm does not support the local_state_vector
simulator.
QPE is configured with an initial state and an IQFT.
QPE is also configured with the following parameter settings:
The number of time slices:
num_time_slices = 0 | 1 | ...
This has to be a non-negative
int
value. The default value is1
.Paulis grouping mode:
paulis_grouping = "default" | "random"
Two string values are permitted:
"default"
or"random"
, with"default"
being the default and indicating that the paulis should be grouped.The expansion mode:
expansion_mode = "trotter" | "suzuki"
Two
string
values are permitted:"trotter"
(Lloyd’s method) or"suzuki"
(for Trotter-Suzuki expansion), with"trotter"
being the default one.The expansion order:
expansion_order = 1 | 2 | ...
This parameter sets the Trotter-Suzuki expansion order. A positive
int
value is expected. The default value is2
.The number of ancillae:
num_ancillae = 1 | 2 | ...
This parameter sets the number of ancillary qubits to be used by QPE. A positive
int
value is expected. The default value is1
.
Declarative Name
When referring to QPE declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is QPE
.
Problems Supported
In QISKit ACQUA, QPE supports the energy
problem.
Iterative Quantum Phase Estimation (IQPE)¶
IQPE, as its name
suggests, iteratively computes the phase so as to require less qubits.
It takes in the same set of parameters as Quantum Phase Estimation (QPE), except for the number of
ancillary qubits num_ancillae
, which is replaced by
num_iterations
(a positive int
, also defaulted to 1
), and for the fact that an IQFT is not
used for IQPE.
Note
This algorithm does not support the local_state_vector
simulator.
For more details, please see arXiv:quant-ph/0610214.
Declarative Name
When referring to IQPE declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is IQPE
.
Problems Supported
In QISKit ACQUA, IQPE supports the energy
problem.
Quantum Grover Search¶
Grover’s Search is a well known quantum algorithm for searching through unstructured collection of records for particular targets with quadratic speedups. All that is needed for carrying out a search is an oracle for specifying the search criterion, which basically indicates a hit or miss for any given record. Currently the satisfiability (SAT) oracle implementation is provided, which takes as input a SAT problem in DIMACS CNF format and constructs the corresponding quantum circuit.
Declarative Name
When referring to Quantum Grover Search declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is Grover
.
Problems Supported
In QISKit ACQUA, Grover supports the search
problem.
Support Vector Machine Quantum Kernel (SVM Q Kernel)¶
Classification algorithms and methods for machine learning are essential for pattern recognition and data mining applications. Well known techniques, such as support vector machines or neural networks, have blossomed over the last two decades as a result of the spectacular advances in classical hardware computational capabilities and speed. This progress in computer power made it possible to apply techniques theoretically developed towards the middle of the XX century on classification problems that soon became increasingly challenging.
A key concept in classification methods is that of a kernel. Data cannot typically be separated by a hyperplane in its original space. A common technique used to find such a hyperplane consists on applying a non-linear transformation function to the data. This function is called a feature map, as it transforms the raw features, or measurable properties, of the phenomenon or subject under study. Classifying in this new feature space – and, as a matter of fact, also in any other space, including the raw original one – is nothing more than seeing how close data points are to each other. This is the same as computing the inner product for each pair of data in the set. In fact we do not need to compute the non-linear feature map for each datum, but only the inner product of each pair of data points in the new feature space. This collection of inner products is called the kernel and it is perfectly possible to have feature maps that are hard to compute but whose kernels are not.
The SVM Q Kernel algorithm applies to classification problems that require a feature map for which computing the kernel is not efficient classically. This means that the required computational resources are expected to scale exponentially with the size of the problem. SVM_QKernel uses a Quantum processor to solve this problem by a direct estimation of the kernel in the feature space. The method used falls in the category of what is called supervised learning, consisting of a training phase (where the kernel is calculated and the support vectors obtained) and a test or classification phase (where new labeless data is classified according to the solution found in the training phase).
SVM Q Kernel can be configured with the following parameter:
A Boolean indicating whether or not to print additional information when the algorithm is running:
print_info : bool
A Boolean value is expected. The default is
False
.
Declarative Name
When referring to SVM Q Kernel declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is SVM_QKernel
.
Problems Supported
In QISKit ACQUA, SVM Q Kernel supports the svm_classification
problem.
Support Vector Machine Variational (SVM Variational)¶
Just like SVM_Kernel, the SVM_Variational algorithm applies to classification problems that require a feature map for which computing the kernel is not efficient classically. SVM_Variational solves such problems in a quantum processor by variational method that optimizes a parameterized quantum circuit to provide a solution that cleanly separates the data.
SVM_Variational can be configured with the following parameters:
The depth of the variational circuit to be optimized:
circuit_depth = 3 | 4 | ...
An integer value greater than or equal to
3
is expected. The default is3
.A Boolean indicating whether or not to print additional information when the algorithm is running:
print_info : bool
A Boolean value is expected. The default is
False
.
Declarative Name
When referring to SVM Variational declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is SVM_Variational
.
Problems Supported
In QISKit ACQUA, SVM Variational supports the svm_classification
problem.
Classical Algorithms¶
In this section, we describe the classical algorithms currently available in QISKit ACQUA. While these algorithm do not use a quantum device or simulator, and rely on purely classical approaches, they may be useful in the near term while experimenting with, developing and testing quantum algorithms.
Note
QISKit ACQUA prevents associating a quantum device or simulator to any experiment that uses a classical
algorithm. The backend
section of an experiment to be conducted via a classical algorithm is
disabled.
Exact Eigensolver¶
Exact Eigensolver computes up to the first k
eigenvalues of a complex square matrix of dimension n x n
,
with k
\(leq\) n
.
It can be configured with the following parameter:
The number of eigenvalues to compute:
k = 1 | 2 | ... | n
An
int
valuek
in the range \([1,n]\) is expected. The default is1
.
Declarative Name
When referring to Exact Eigensolver declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is ExactEigensolver
.
Problems Supported
In QISKit ACQUA, Exact Eigensolver supports the energy
, ising
and excited_states
problems.
CPLEX¶
This algorithm uses the IBM ILOG CPLEX Optimization Studio which should be installed along with its Python API setup, for this algorithm to be operational. This algorithm currently supports computing the energy of an Ising model Hamiltonian.
CPLEX can be configured with the following parameters:
A time limit in seconds for the execution:
timelimit = 1 | 2 | ...
A positive
int
val;ue is expected. The default value is 600.The number of threads that CPLEX uses:
thread = 0 | 1 | 2 | ...
A non-negative
int
value is expected. Settingthread
to0
lets CPLEX decide the number of threads to allocate, but this may not be ideal for small problems. Any value greater than0
specifically sets the thread count. The default value is1
, which is ideal for small problems.Decides what CPLEX reports to the screen and records in a log during mixed integer optimization (MIP).
display = 0 | 1 | 2 | 3 | 4 | 5
An
int
value between0
and5
is expected. The amount of information displayed increases with increasing values of this parameter. By default, this value is set to2
.
Declarative Name
When referring to CPLEX declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is CPLEX
.
Problems Supported
In QISKit ACQUA, CPLEX supports the ising
problem.
Support Vector Machine Radial Basis Function Kernel (SVM RBF Kernel)¶
This algorithm uses a classical approach to experiment with feature map classification problems. SVM RBF Kernel can be configured with the following parameter:
A Boolean indicating whether or not to print additional information when the algorithm is running:
print_info : bool
A Boolean value is expected. The default is
False
.
Declarative Name
When referring to SVM RBF Kernel declaratively inside QISKit ACQUA, its code name
, by which
QISKit ACQUA dynamically discovers and loads it, is SVM_RBF_Kernel
.
Problems Supported
In QISKit ACQUA, SVM RBF Kernel supports the svm_classification
problem.


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public class IotProvider extends java.lang.Object implements TopologyProvider, DirectSubmitter<Topology,Job>
IotDevice
to communicate
with an IoT scale message hub.
Publish-subscribe
is
used to allow multiple topologies to communicate through the single
connection.
This provider registers these services:
control
- An instance of JsonControlService
.application
- An instance of AppService
.publish-subscribe
- An instance of ProviderPubSub
IotDevice
using IotDevicePubSub
.
Applications using this provider that want to connect
to the message hub for device events and commands must create an instance of
IotDevice
using IotDevicePubSub.addIotDevice(quarks.topology.TopologyElement)
quarksControl
.
An IotProvider
is created with a provider and submitter that it delegates
the creation and submission of topologies to.
IotDevice
,
IotDevicePubSub
| Modifier and Type | Field and Description |
|---|---|
static java.lang.String |
CONTROL_APP_NAME
IoT control using device commands application name.
|
| Constructor and Description |
|---|
IotProvider(DirectProvider provider,
Function<Topology,IotDevice> iotDeviceCreator)
Create an
IotProvider that uses the passed in DirectProvider . |
IotProvider(Function<Topology,IotDevice> iotDeviceCreator)
Create an
IotProvider that uses its own DirectProvider . |
IotProvider(TopologyProvider provider,
DirectSubmitter<Topology,Job> submitter,
Function<Topology,IotDevice> iotDeviceCreator)
Create an
IotProvider . |
| Modifier and Type | Method and Description |
|---|---|
protected void |
createIotCommandToControlApp()
Create application connects
quarksControl device commands
to the control service. |
protected void |
createIotDeviceApp()
Create application that connects to the message hub.
|
protected IotDevice |
createMessageHubDevice(Topology topology)
Create the connection to the message hub.
|
ApplicationService |
getApplicationService()
Get the application service.
|
protected JsonControlService |
getControlService() |
ServiceContainer |
getServices()
Access to services.
|
Topology |
newTopology()
Create a new topology with a generated name.
|
Topology |
newTopology(java.lang.String name)
Create a new topology with a given name.
|
protected void |
registerApplicationService() |
protected void |
registerControlService() |
protected void |
registerPublishSubscribeService() |
void |
registerTopology(java.lang.String applicationName,
BiConsumer<IotDevice,com.google.gson.JsonObject> builder)
Register an application that uses an
IotDevice . |
void |
start()
Start this provider by starting its system applications.
|
java.util.concurrent.Future<Job> |
submit(Topology topology)
Submit an executable.
|
java.util.concurrent.Future<Job> |
submit(Topology topology,
com.google.gson.JsonObject config)
Submit an executable.
|
public static final java.lang.String CONTROL_APP_NAME
public IotProvider(Function<Topology,IotDevice> iotDeviceCreator)
IotProvider
that uses its own DirectProvider
.iotDeviceCreator
- How the IotDevice
is created.DirectProvider
public IotProvider(DirectProvider provider, Function<Topology,IotDevice> iotDeviceCreator)
IotProvider
that uses the passed in DirectProvider
.provider
- DirectProvider
to use for topology creation and submission.iotDeviceCreator
- How the IotDevice
is created.DirectProvider
public IotProvider(TopologyProvider provider, DirectSubmitter<Topology,Job> submitter, Function<Topology,IotDevice> iotDeviceCreator)
IotProvider
.provider
- How topologies are created.submitter
- How topologies will be submitted.iotDeviceCreator
- How the IotDevice
is created.public ApplicationService getApplicationService()
public ServiceContainer getServices()
getServices
in interface DirectSubmitter<Topology,Job>
public final Topology newTopology()
newTopology
in interface TopologyProvider
public final Topology newTopology(java.lang.String name)
newTopology
in interface TopologyProvider
public final java.util.concurrent.Future<Job> submit(Topology topology)
submit(executable, new JsonObject())
.public final java.util.concurrent.Future<Job> submit(Topology topology, com.google.gson.JsonObject config)
submit
in interface Submitter<Topology,Job>
topology
- executable to submitconfig
- context information for the submissionprotected void registerControlService()
protected void registerApplicationService()
protected void registerPublishSubscribeService()
protected JsonControlService getControlService()
protected void createIotDeviceApp()
IotDevicePubSub
,
createMessageHubDevice(Topology)
protected void createIotCommandToControlApp()
quarksControl
device commands
to the control service.
Subscribes to device
commands of type Commands.CONTROL_SERVICE
and sends the payload into the JSON control service
to invoke the control operation.public void start() throws java.lang.Exception
java.util.concurrent.ExecutionException
- Exception starting applications.java.lang.Exception
protected IotDevice createMessageHubDevice(Topology topology)
IotDevice
used to communicate with the message hub. This
provider creates and submits an application
that subscribes to published events to send
as device events and publishes device commands.
IotDevicePubSub.createApplication(IotDevice)
.
IotDevice
is created using the function
passed into the constructor.topology
- Topology the IotDevice
will be contained in.IotDevice
,
IotDevicePubSub
public void registerTopology(java.lang.String applicationName, BiConsumer<IotDevice,com.google.gson.JsonObject> builder)
IotDevice
.
ApplicationService.registerTopology(String, BiConsumer)
that passes in an IotDevice
and configuration to the supplied
function builder
that builds the application. The passed
in IotDevice
is created using IotDevicePubSub.addIotDevice(quarks.topology.TopologyElement)
.
builder
obtains a reference to its topology using
TopologyElement.topology()
.
When the application is
submitted
builder.accept(iotDevice, config)
is called to build the application's graph.
applicationName
- Application namebuilder
- Function that builds the topology.Copyright © 2016 The Apache Software Foundation. All Rights Reserved - d6cdfc6-20160517-0028


Button Wrapper
Block One
The HTML (figure) It is a long established fact that a reader will be distracted by the readable content of a page when looking at its layout. The point of using Lorem Ipsum is that it has a more-or-less normal distribution of letters, as opposed to using 'Content here, content here', making it look like readable English. Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).sometimes on purpose (injected humour and the like).Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).Many desktop publishing packages and web page editors now use Lorem Ipsum as their default model text, and a search for 'lorem ipsum' will uncover many web sites still in their infancy. Various versions have evolved over the years, sometimes by accident, sometimes on purpose (injected humour and the like).sometimes on purpose (injected humour and the like)


Innocuous Value
Safe Because of Runtime Type
JS URL of Unknown Provenance
Authored By Trusted Author


chasepeck.com
/films
latest video
guaranteed fresh
featured films
alleged award-winning films
morgz i
a hilarious comedy, full of surprises and laughs
morgz ii
a very ambitious sequel (notice: mild language)
ytp - it's meat hour!
a brilliant twist on the conventional ytp format
some gmod tomfoolery
tense action that keeps you on the edge of your seat
python tutorial
a totally unsatirical educational program
sitar dance
a short but sweet roller coaster of emotion


Select image
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Upload
Select audio
select audio...
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write your answer
write your answer
predict Similarity
{{ prediction_voice }}
{{ prediction_text }}
{{ prediction_similarity}}


Devesh Patel
Conflict
How to Resolve
August 8, 2015
I tend to avoid conflict if I can. This is probably not a good thing because it requires me to sacrifice my feelings or needs in favor of others.
If something is bothering me I usually let it go. One instance where I did get upset was when my roommate was giving out my internet password to anybody who would come over even if I did not know them. This I thought was unacceptable and let it go the first few times but got upset when it happened for the fifth time. That time I spoke up and said to stop giving out the password. I felt bad about speaking up and did not do so angrily, but felt as if my roommate should have known better.In hindsight, I should have mentioned this the first time and been clear that the password was something that should not be given out to people I do not know. And I should have not assumed that my roomate knew why this would be a source of conflict.
In the future, I should address the issue when it first arises instead of waiting as this could lead to confusion and me seeming arbitrary as to when I am upset about something.


Galvanize Delivers
Home
menu
Order Delivery
Menu
Royale with Cheese
$8.99
ADD TO ORDER
Arugula Pie
$11.99
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Smoked Swine
$14.99
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Ice Cream Biscuit
$7.99
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Order
Item
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Subtotal
$0.00
Tax
$0.00
Total
$0.00
account_circle
phone
home
PLACE ORDER


{% extends "base.html" %} {% block body %} this is my link {% for appointment in appointments %}
Name of Patient: {{appointment.name}}


Audio Description, also known as Video Description, is an audio service to assist viewers who can not fully see a visual presentation to understand the content, usually achieved by mixing a ‘description’ audio track with the main programme audio, at moments when this does not clash with dialogue, to deliver an audio description mixed audio track. More information about what Audio Description is and how it works can be found at [[WHP051]].
Audio Description is usually delivered as audio, either pre-recorded or synthesised, but (until now) has not been deliverable as accessible text using an open standard. This report describes the requirements for text documents that can support audio description script exchange throughout the workflow from production (scripting) through to distribution (mixing either at the broadcaster or at the viewers device).
This document is a Community Group Report, including requirements and a proposed specification that meets the requirements. The proposed specification is a profile of the Timed Text Markup Language version 2.0 [[TTML2]].
This specification defines a single text-based profile of the Timed Text Markup Language version 2.0 [[TTML2]]. This profile is intended to support audio description workflows worldwide, including description creation, script delivery and exchange and generated audio description distribution. The proposed profile is a syntactic subset of the Timed Text Markup Language version 2.0 [[TTML2]], and a document can simultaneously conform to both the base standard and the proposed profile.
This document defines NO extensions to [[TTML2]].
This document is the first version of this proposal.
This report specifies a profile of [[TTML2]] that meets the requirements for documents needed to support audio description script exchange throughout the workflow from production (scripting) to distribution (mixing). This report also includes a proposed specification that meets those requirements, and serves as a basis for verifying that the proposed specification intended to support that process is suitable. It is anticipated that recent additions to Timed Text Markup Language version 2.0 [[TTML2]] are sufficient to meet all of the requirements. Furthermore, the requirements do not assume or require that a single document format or instance be used for every step of an Audio Description workflow, however that would appear to be desirable since it would reduce conversion step requirements.
The Timed Text Markup Language version 2.0 [[TTML2]] format is an xml based [[XML]] markup language that specifies timed text, so can be used for storing the audio description scripts. This recent version of the TTML standard introduces audio mixing and text to speech semantics into the previous versions of the TTML specification. These new semantics support the main requirements for audio descrition, synchronised audio playback, continuous animation, control of pan / gain for audio mixing and speech rate and pitch attributes for controlling text to speech. There is support for these new TTML features in browsers using [[WEBAUDIO]] and WebSpeech respectively.
A requirements document has been available, published and updated on github for a period of time (approximately 2 years) [[ADPTREQS]]. It is considered that the captured requirements are valid at this point. Those requirements are included in this document as a rationale to support the proposed profile.
This document proposes an Audio Description profile of TTML that will allow the delivery of an audio description script, pre-recorded audio and mixing data in a single file using an open standard format. The proposed profile should allow client implementations to provide real time mixing of the Audio Description, perhaps with some user customisation (like changing the relative volumes of the main programme audio and the AD audio). Additionally, the presentation of the Audio Desciption script text on a completely different device, like a braille display may be possible. A client that is hosted 'server side' would be able to create a “broadcaster mix”, whereas a “receiver mix” could be implemented by hosting the client at the viewers device.
The following example shows an audio description script, with times and text, before any audio has been added.
References to audio recordings of the voiced words can be added:
If the audio recording is long and just a snippet needs to be played,
that can be done using clipBegin
and clipEnd
.
If we just want to play the part of the audio from file from 5s to
8s it would look like:
Or audio attributes can be added to trigger the text to be spoken:
The gain of "received" audio can be changed before mixing in
the audio played from inside the span
, smoothly
animating the value on the way in and returning it on the way out:
In the above example, the div
element's
begin
time becomes the "syncbase" for its child,
so the times on the animate
and span
elements are relative to 25s here.
The first animate
element drops the gain from 1
to 0.39 over 0.3s, and the second one raises it back in the
final 0.3s of this description. Then the span
is
timed to begin only after the first audio dip has finished.
This document uses the same conventions as [[TTML2]] for the specification of parameter attributes, styling attributes and metadata elements. In particular:
Section 2.3 of [[TTML2]] specifies conventions used in the [[XML]] representation of elements; and Sections 6.2 and 8.2 of [[TTML2]] specify conventions used when specifying the syntax of attribute values.
All content of this specification that is not explicitly marked as non-normative is considered to be normative. If a section or appendix header contains the expression "non-normative", then the entirety of the section or appendix is considered non-normative.
This specification uses Feature designations as defined in Appendices E at [[TTML2]]: when making reference to content conformance, these designations refer to the syntactic expression or the semantic capability associated with each designated Feature; and when making reference to processor conformance, these designations refer to processing requirements associated with each designated Feature. If the name of an element referenced in this specification is not namespace qualified, then the TT namespace applies (see 9.3 Namespaces.)
The following terms are used in this proposal:
Audio description An audio rendition of a Description or a set of Descriptions.
Audio description mixed audio track The output of an audio mixer incorporating the main programme audio and the audio description.
Description A set of words that describe an aspect of the programme presentation, suitable for rendering into audio by means of vocalisation and recording or used as a Text Alternative source for speech to text translation.
Default Region See Section <IP_ADDRESS> at [][TTML2]].
Document Instance As defined by [[TTML2]].
Document Interchange Context As defined by [[TTML2]].
Document Processing Context See Section 2.2 at [[TTML2]].
Feature See Section 2.2 at [[TTML2]].
Intermediate Synchronic Document See Section <IP_ADDRESS> at [[TTML2]].
Linear White-Space See Section 2.3 at [[TTML2]].
Main programme audio The audio associated with the programme prior to any mixing with audio description.
Markup Language A human-readable computer language that uses tags to define elements within a document. Markup files contain standard words, rather than typical programming syntax.
Presentation processor See Section 2.2 at [[TTML2]].
Processor Either a Presentation processor or a Transformation processor.
Profile A TTML profile specification is a document that lists all the features of TTML that are required / optional / prohibited within “document instances” (files) and “processors” (things that process the files), and any extensions or constraints.
Related Media Object See Section 2.2 at [[TTML2]].
Related Video Object A Related Media Object that consists of a sequence of image frames, each a rectangular array of pixels.
Root Container Region See Section 2.2 at [[TTML2]].
Text Alternative As defined in [[WCAG20]].
Timed Text Text media that is presented in synchrony with other media, such as audio and video with (optional) specified text presentation styling information such as font, colour and position.
Transformation processor See Section 2.2 at [[TTML2]].
TTML A Markup Language designed for the storage and delivery of Timed Text, as defined in [[TTML2]] primarily used in the television industry. TTML is used for authoring, transcoding and exchanging timed text information and for delivering captions, subtitles, and other metadata for television material repurposed for the Web or, more generally, the Internet.
A Document Instance that conforms to the profile defined herein:
A Document Instance, by definition, satisfies the requirements of Section 3.1 at [[TTML2]], and hence a Document Instance that conforms to a profile defined herein is also a conforming TTML2 Document Instance.
A presentation processor that conforms to the profile defined in this specification:
A transformation processor that conforms to the profile defined in this specification:
Change DFXP to something more appropriate in the following note, or remove it altogether.
The use of the terms presentation processor and transformation processor within this document does not imply conformance per se to any of the Standard Profiles defined in [[TTML2]]. In other words, it is not considered an error for a presentation processor or transformation processor to conform to the profile defined in this document without also conforming to the TTML2 Presentation Profile or the TTML2 Transformation Profile.
This document does not specify presentation processor or transformation processor behavior when processing or transforming a non-conformant Document Instance.
The permitted and prohibited dispositions do not refer to the specification of a ttp:feature or ttp:extension element as being permitted or prohibited within a ttp:profile element.
The Profile consists of Constraints.
For the purpose of content processing, the determination of the resolved profile SHOULD take into account both the signaled profile, as defined in , and profile metadata, as designated by either (or both) the Document Interchange Context or (and) the Document Processing Context, which MAY entail inspecting document content.
If the resolved profile is not the Profile supported by the Processor but is feasibly interoperable with the Profile, then the resolved profile is the Profile. If the resolved profile is undetermined or not supported by the Processor, then the Processor SHOULD nevertheless process the Document Instance using the Profile; otherwise, processing MAY be aborted. If the resolved profile is not the proposed Profile, processing is outside the scope of this specification.
If the resolved profile is the profile supported by the Processor, then the Processor SHOULD process the Document Instance according to the Profile.
A Document Instance SHALL use UTF-8 character encoding as specified in [[UNICODE]].
A Document Instance MAY contain elements and attributes that are neither specifically permitted nor forbidden by a profile.
A transformation processor SHOULD preserve such elements or attributes whenever possible.
Document Instances remain subject to the content conformance requirements specified at Section 3.1 of [[TTML2]]. In particular, a Document Instance can contain elements and attributes not in any TT namespace, i.e. in foreign namespaces, since such elements and attributes are pruned by the algorithm at Section 4 of [[TTML2]] prior to evaluating content conformance.
For validation purposes it is good practice to define and use a content specification for all foreign namespace elements and attributes used within a Document Instance.
The following namespaces (see [[xml-names]]) are used in this specification:
| Name | Prefix | Value | Defining Specification |
|---|---|---|---|
| XML | xml |
http://www.w3.org/XML/1998/namespace |
[[xml-names]] |
| TT | tt |
http://www.w3.org/ns/ttml |
[[TTML2]] |
| TT Parameter | ttp |
http://www.w3.org/ns/ttml#parameter |
[[TTML2]] |
| TT Feature | none | http://www.w3.org/ns/ttml/feature/ |
[[TTML2]] |
| TT Audio Style | tta: | http://www.w3.org/ns/ttml#audio |
[[TTML2]] |
| ADPT 1.0 Profile Designator | none |
|
This specification |
The namespace prefix values defined above are for convenience and Document Instances MAY use any prefix value that conforms to [[xml-names]].
The namespaces defined by this proposal document are mutable [[namespaceState]]; all undefined names in these namespaces are reserved for future standardization by the W3C.
Each intermediate synchronic document of the Document Instance is intended to be presented (audible) starting on a specific frame and removed (inaudible) by a specific frame of the Related Video Object.
When mapping a media time expression M to a frame F of a Related Video Object (or Related Media Object), e.g. for the purpose of mixing audio sources signalled by a Document Instance into the main program audio of the Related Video Object, the presentation processor SHALL map M to the frame F with the presentation time that is the closest to, but not less, than M.
EXAMPLE 1 A media time expression of 00:00:05.1 corresponds to frame ceiling( 5.1 × ( 1000 / 1001 × 30) ) = 153 of a Related Video Object with a frame rate of 1000 / 1001 × 30 ≈ 29.97.
In typical scenario, the same video program (the Related Video Object) will be used for Document Instance authoring, delivery and user playback. The mapping from media time expression to Related Video Object above allows the author to precisely associate audio description content with video frames, e.g. around existing audio dialogue and sound effects. In circumstances where the video program is downsampled during delivery, the application can specify that, at playback, the relative video object be considered the delivered video program upsampled to is original rate, thereby allowing audio content to be presented at the same temporal locations it was authored.
The ttp:profile attribute SHOULD be present on the tt element and equal to the designator of the ADPT 1.0 profile to which the Document Instance conforms.
See Conformance for a definition of permitted, prohibited and optional.
| Feature | Disposition | Additional provision |
|---|---|---|
| Relative to the TT Feature namespace | ||
#animation-version-2 |
permitted | |
#audio |
permitted | |
#audio-description |
permitted | |
#audio-speech |
permitted | |
#backgroundColor-block |
prohibited | |
#backgroundColor-region |
prohibited | |
#cellResolution |
prohibited | |
#chunk |
permitted | |
#clockMode |
prohibited | |
#clockMode-gps |
prohibited | |
#clockMode-local |
prohibited | |
#clockMode-utc |
prohibited | |
#content |
permitted | |
#contentProfiles |
permitted | |
#core |
permitted | |
#data |
permitted | |
#display-block |
prohibited | |
#display-inline |
prohibited | |
#display-region |
prohibited | |
#display |
prohibited Consider display="none" in relation to AD content |
|
#dropMode |
prohibited | |
#dropMode-dropNTSC |
prohibited | |
#dropMode-dropPAL |
prohibited | |
#dropMode-nonDrop |
prohibited | |
#embedded-audio |
permitted | |
#embedded-data |
permitted | |
#extent-root |
prohibited | |
#extent |
prohibited | |
#frameRate |
permitted |
If the Document Instance includes any time expression that uses the frames term or any
offset time expression that uses the f metric, the ttp:frameRate attribute SHALL be present
on the tt element.
|
#frameRateMultiplier |
permitted | |
#gain |
permitted | |
#layout |
prohibited | |
#length-cell |
prohibited | |
#length-integer |
prohibited | |
#length-negative |
prohibited | |
#length-percentage |
prohibited | |
#length-pixel |
prohibited | |
#length-positive |
prohibited | |
#length-real |
prohibited | |
#length |
prohibited | |
#markerMode |
prohibited | |
#markerMode-continuous |
prohibited | |
#markerMode-discontinuous |
prohibited | |
#metadata |
permitted | |
#opacity |
prohibited | |
#origin |
prohibited | |
#overflow |
prohibited | |
#overflow-visible |
prohibited | |
#pan |
permitted | |
#pitch |
permitted | |
#pixelAspectRatio |
prohibited | |
#presentation |
prohibited | |
#processorProfiles |
permitted | |
#profile |
permitted | See . |
#region-timing |
prohibited | |
#resources |
permitted | |
#showBackground |
prohibited | |
#source |
permitted | |
#speak |
permitted | |
#speech |
permitted | |
#structure |
permitted | |
#styling |
permitted | |
#styling-chained |
permitted | |
#styling-inheritance-content |
permitted | |
#styling-inheritance-region |
prohibited | |
#styling-inline |
permitted | |
#styling-nested |
permitted | |
#styling-referential |
permitted | |
#subFrameRate |
permitted | |
#tickRate |
permitted | ttp:tickRate SHALL be present on the tt element if the document contains any time
expression that uses the t metric. |
#timeBase-clock |
prohibited | |
#timeBase-media |
permitted |
NOTE: [[TTML1]] specifies that the default timebase is |
#timeBase-smpte |
prohibited | |
#time-clock-with-frames |
permitted | |
#time-clock |
permitted | |
#time-offset-with-frames |
permitted | |
#time-offset-with-ticks |
permitted | |
#time-offset |
permitted | |
#timeContainer |
permitted | |
#timing |
permitted |
|
#transformation |
permitted | See constraints at #profile. |
#visibility-block |
prohibited | |
#visibility-region |
prohibited | |
#writingMode-horizontal-lr |
prohibited | |
#writingMode-horizontal-rl |
prohibited | |
#writingMode-horizontal |
prohibited | |
#zIndex |
prohibited |
The following diagram illustrates the workflow related to the proposed profile described by this document:
It is proposed that after each process in this workflow the output data may be either inserted into a manifest document such as a TTML document or referenced by it.
| Process step | Description |
|---|---|
| 1. Identify gaps in programme dialog | Automatically or manually process the programme audio track to identify intervals within which description audio may be inserted. |
| 2. Write script | Write a set of descriptions to fit within the identified gaps. |
| 3. 'Voice' the script or synthesise audio | Generate an audio rendition of the script either by using an actor or voicer and recording the speech or synthesise the audio description by using a text to speech system. This is typically a mono audio track that may be delivered as a single track that is the same duration as the programme or as a set of short audio tracks each beginning at a defined time. |
| 4. Define AD track left/right pan data | Select a horizontal pan position to apply to the audio rendition of the description when mixing with the main programme audio. This is typically a single value that applies to each description. |
| 5. Define main programme audio levels during AD | Select the amount by which to lower the main programme audio prior to mixing in the description audio. This is typically defined as a curve defined by a set of moments in time and fade levels to apply, with an interpolation algorithm to vary the levels between each moment in time. |
| 6. Mix programme audio with descriptions | Mix the programme audio with the rendered descriptions. This may be pre-mixed (also known as “broadcaster mix”) prior to delivery to the audience, or mixed in real time (also known as “receiver mix”) at playback time; mixing at playback time is a requirement to enable user customisation of the relative levels of main programme audio and descriptions. See [BBC_RD_WHP051] for the reference model for this. |
The following table lists the requirements at each stage of the workflow:
| Requirement ID | Process step | Requirement |
|---|---|---|
| ADR1 | 1 |
|
| ADR2 | 2 |
|
| ADR3 | 2 |
|
| ADR4 | 2 |
|
| ADR12 | 3 |
|
| ADR5 | 3 |
|
| ADR6 | 3 |
|
| ADR7 | 4 |
|
| ADR8 | 5 |
|
| ADR9 | 6 |
|
| ADR10 | 6 |
|
| ADR11 | 6 |
|
[[WEBAUDIO]] specifies three interpolation mechanisms for traversing from one parameter value to another: linear, exponential and linear interpolation between points on a curve, where the default ramp e.g. for setTargetAtTime uses an exponential interpolation.
Every content element in the audio tree creates a mixer that adds the audio from its parent and its audio element children, and optionally applies a pan and gain to the output.
Every audio element provides an audio input from some audio resource, with its own pan and gain.
The output of every content element’s audio is passed to each of its children.
The audio output of all the leaf content elements is mixed together on the “master bus” in Web Audio terms.
This section includes part of a real world audio description script generated by the BBC for its Eastenders television programme.
We have a div element that wraps all the other content. Crucially it includes two audio element children, which do a few things:
We need a convention to identify "tracks that are provided to us from somewhere else", and in this case we've defined ;track=n to do that.
Then there are bunch of child p elements that each have a begin and end time. They each represent a snippet of audio description and the time during which some stuff happens. The text of the audio description is contained in a child span element, which itself has begin and end times. The span's begin and end times are relative to the parent p element's begin time.
There is some metadata there too, which might be helpful during the authoring process, for example.
A few things need to happen for each snippet of audio description:
The fade up and down are both achieved by placing animate elements as children of the p element. They smoothly change ("continuously animate") the tta:gain value between values, in a semi-colon separated list, where the begin and end times of the animation are specified on the element and are relative to the parent p element's begin time. The audio that they modify is the audio that is available to that element, i.e. the programme audio that comes down to the p from the parent div (remember that specified some audio? This is where it goes).
Playing the audio description is done by adding a new audio child to the span. The playback begins in the presentation at the span's begin time, and the clipBegin and clipEnd mark the in and out points of the referenced audio resource to play, which is specified by the src attribute. If we wanted to specify a left/right pan value, we could do that by setting a tta:pan attribute on the audio element itself. Similarly we could vary the level of the audio by setting a tta:gain value.
This structure is implemented in the mixing code by constructing a Web Audio Graph, where the outputs of all the spans are, in the end, mixed together.
The editor acknowledges the current and former members of the W3C Timed Text Working Group (TTWG), the members of other W3C Working Groups, and industry experts in other forums who have contributed directly or indirectly to the process or content of this document.
The editors wish to especially acknowledge the contributions by:


As a Mechanical engineer interested in software development, the internship season put me into a bit of a quandary. Since software companies seldom open for non-CS students, weeks had passed by without me signing a single IAF (Internship Application Form). In mid-August, Microsoft India Development Centre (MSIDC) came along. I had stood runner-up in code.fun.do (the Microsoft university hackathon) the previous year. Consequently, Microsoft invited me to participate in the internship selection process (which is otherwise open only to CS students) and I cashed in on the opportunity. This was my first insight into how well thought-out the Microsoft internship program is.
Getting There:
The selection process involved two rounds of coding. Shortlisted candidates were then invited for multiple rounds of interviews. Knowledge of algorithms and data structures is a prerequisite, where having done CS-213 (or equivalent like EE-717) would come in handy. Being familiar in backend development, the interviewer asked me several questions related to my field of expertise in addition to the standard data structures ones. I also found this knowledge to be highly useful during the course of my internship.
MSIDC hires for two locations in India – Hyderabad and Bangalore. I was assigned to the Dynamics CRM team at Bangalore. Dynamics CRM is an enterprise CRM (Customer Relationship Management) software. Not being a mass-market product, I had never even heard of this arm of Microsoft before. I was stumped to know that it makes over $1Billion in revenues and is still growing at a massive rate. It has now taken the second position in the segment, right behind Salesforce.
About the Company
While I was sitting at my workstation with the source code, trying to figure out how this billion dollar application works, I knew that my work was the only thing I needed to worry about. Microsoft literally took care of everything else. It is rare, even among the best of employers to offer full provisions for accommodation, travel and food throughout the duration of the intern. The office was bang in the centre of the city. All interns were provided accommodation at a luxurious service apartment within a walking distance from the office. The food at the office was healthy and delicious. This is in addition to their well-stocked 24×7 pantry.
What impressed me the most about Microsoft was their work culture. All the rules (or lack thereof) are an extension of pure logic. There are no in-out time requirements. You can work from home, or from the office for as long as you want. All you have to ensure is to get the work done in the allotted timespan – it’s that simple! People who are used to working independently would feel at-ease working here. There is no dress code over here – you can wear anything you feel like wearing (You will, however, feel awkward in formal clothing when your manager walks in wearing a tee and jeans). The organisation is extremely agile for a company this size. The focus is on getting the work done, than on frivolous rules or procedures.
For my intern, my team gave me a completely new project to develop. I, and a fellow intern were the only people working on it. Due to the nature of the task, I am contractually bound to not disclose even a vague outline of the job description – at least until the feature is released to the public. The team was very open to inputs and we were given full freedom to suggest features, or methods to implement those features.
All my co-workers are extremely approachable.  The managers, even at the highest levels have a good idea about the specifics of what everyone is working on. You will often have co-workers randomly challenging you to a game of carrom or foosball when you need a break. Not to be missed are the facilities for gymming, badminton, basketball, cricket, in addition to the tons of Xboxs lying around at every corner of the office.
Bangalored!
I had been allotted two very helpful mentors who guided me throughout the intern. One of them was possibly the coolest mentor an intern at Microsoft ever got (PM for more). I was also blessed to have been allotted the team which was most prolific in outings. I went out with my team for several lunches, dinners, a movie, and even an IPL match!
The cherry on the cake was that since the intern is in Bangalore, where the largest chunk of students from IITB is interning, I went out with friends almost every weekend. There are several places to see around Bangalore – Coorg, Nandi Hills, Ooty; the list is endless. We managed to explore many of them. Bangalore is a trekker’s paradise. You can rent a bike/car and head out on the weekends on your own with traveller blogs and Google Bing Maps coming to your rescue. There are also quite a few tour operators who will provide you with guides (recommended for medium to difficult treks). The weather in Bangalore is pleasant like that of Mumbai. It rained frequently throughout my intern right from May beginning.
Overall, the internship has provided me a great insight into the software development industry. Knowing how the biggest and best companies pull off their work has been a valuable experience. Given the scale Microsoft provides, you know you’re working for something that is going to impact an enormous number of people (albeit, in an indirect, subconscious manner). Knowledge of this fact gives a very unique feeling of satisfaction that is difficult to describe.


EventWindow3D()
Syntax
Result = EventWindow3D()Description
After a WindowEvent3D() function, use this function to determine on which window the event has occurred.
Parameters
None.
Return value
The 3D window number associated with the last event.
Supported OS
All


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--- title: HTML5 For Web Designers excerpt: 'HTML5 For Web Designers: http://tinyurl.com/23v2cp5. There''s no mention about advanced JavaScript APIs, but I''m going to buy myself a copy!' date: '2010-05-04 19:28:08 +0200' date_gmt: '2010-05-04 17:28:08 +0200' categories: - Webdev tags: - tweets - web - design - development ---
HTML5 For Web Designers: http://tinyurl.com/23v2cp5. There's no mention about advanced JavaScript APIs, but I'm going to buy myself a copy!


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