HERCULES A film review by Andrew Hicks Copyright 1997 Andrew Hicks / Fatboy Productions
(1997) *** (out of four)
HERCULES is the first animated Disney film in a long time that doesn't seem like it came from Disney. It's entertaining, yes, and has the usual Disney trappings -- a story, adapted from classic literature, about stumbling hero who strikes it big, has a cute animal sidekick and a beautiful cartoon woman, but has to fight a very-evil villain with a cute animal sidekick of his own -- but it's got the cheap laughs of a Warner Bros. effort. It's more SPACE JAM than HUNCHBACK OF NOTRE DAME.
Visually, HERCULES is on the Warner Bros. level, or maybe half a notch above the Disney afternoon cartoon shows. Its characters all look like caricatures and the backgrounds are generally unimpressive, but there are a few beautifully-animated scenes, all of which seem to take place in the evil underworld. The other scenes are pretty autopilot, which makes me wonder where Disney's priorities lie. Maybe these people _are_ as aligned with the devil as folks at the American Family Association say.
I've pretty much forgotten all the Greek mythology stuff I learned in freshman English, so forgive me if I don't rattle off the names of the gods with the greatest of ease. I remember Hades, Hermes, Herpes, Sneezy and Doc, and at the head of it all -- the gods of the gods -- are Zeus and Hera. They have a baby at the beginning and name him (what else?) Hercules. Zeus gives him a playpen companion, a winged horse baby named Pegasus. I'm all for pets but a baby and a horse sleeping together is a little odd, even for mythology.
Things are peaceful on Mount Olympus, but as we all know, when a baby is born in the Disney world, there's a villain there to sabotage. This time it's Hades, lord of the underworld, who is so evil he has blue fire for hair. Voiced by James Woods, Hades is one of the best things about this movie, an enemy worthy of Disney. His two cute animal sidekicks, Pain and Panic (voices of Bobcat Goldthwait and Matt Frewer, both of whom needed the paycheck), can change themselves into whatever form they wish.
Old Hades kidnaps Hercules and strips him of his god status, but since he didn't drink all the evil potion, he still has the strength of a god. So he's trapped on earth, raised by an elderly couple who think he fell from heaven, just like Superman. There are a couple unintentionally amusing scenes that follow, where we see a 12-year-old Hercules called Jercules by his insensitive peers, who just can't accept that he's different.  By the way, Shirley MacLaine, that's a rhetorical question...
Hercules grows up and finds out he can't return to the table of the gods until he proves himself a true hero, which is cartoon code for "the end of the movie." That means he has to save the world from centaurs, giants, Limbaughs and all sorts of mythological beasts with the aid of his half-goat personal trainer (voice of Danny DeVito). There's also the femme fatale, Meg, who tries not to fall in love with Herc but can't resist.
HERCULES is good fun, but it's definitely one of the lesser movies of the Disney revolution that began with THE LITTLE MERMAID. It's the only one besides POCAHONTAS in which the attempts at drama fall completely flat, leaving only the surface-level jokes and songs to entertain. You get the feeling the Disney people are reaching with a lot of that present-past humor too -- once Hercules becomes a hero, all the cool Athens kids are wearing Air-Herc shoes. Flintstones, meet the Flintstones...
--
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SIANN: Strain Identification by Alignment to Near Neighbors
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Samuel S. Minot,
Stephen D. Turner,
Krista L. Ternus,
Dana R. Kadavy
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Abstract
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Next-generation sequencing is increasingly being used to study samples composed of mixtures of organisms, such as in clinical applications where the presence of a pathogen at very low abundance may be highly important. We present an
analytical method (SIANN: Strain Identification by Alignment to Near Neighbors) specifically designed to rapidly detect a set of target organisms in mixed samples that achieves a high degree of species- and strain-specificity by
aligning short sequence reads to the genomes of near neighbor organisms, as well as that of the target. Empirical benchmarking alongside the current state-of-the-art methods shows an extremely high Positive Predictive Value, even at
very low abundances of the target organism in a mixed sample. SIANN is available as an Illumina BaseSpace app, as well as through Signature Science, LLC. SIANN results are presented in a streamlined report designed to be
comprehensible to the non-specialist user, providing a powerful tool for rapid species detection in a mixed sample. By focusing on a set of (customizable) target organisms and their near neighbors, SIANN can operate quickly and with
low computational requirements while delivering highly accurate results.
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Introduction
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There are many different methods that characterize the mixture of organisms present within a metagenomic dataset. Such datasets are generated when a complex environmental sample is processed by a “next-generation” high-throughput
genome sequencing protocol, and they consist of large numbers of short nucleotide sequences. Each sequence represents a small fragment of a randomly selected genome from the very large collection of genomes present in the source
sample. Those sequences indicate the presence of one organism or another according to their similarity to a set of known reference genomes. While a given sequence may be unique to one species, it also may be found in diverse organisms
across the tree of life. Therefore, one analytical challenge (among many) is to take that collection of sequences (likely numbering in the millions) and accurately determine what species are present in the sample. Here we describe a
novel method (SIANN: Strain Identification by Alignment to Near Neighbors) that is specifically designed to rapidly detect a set of targeted organisms from a metagenomic dataset by aligning reads to genomic regions that are unique at
the strain or species level.
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The analytical question motivating a particular piece of metagenomic bioinformatic analysis may vary widely by user and sample type ([ref-11,"Segata, et al., 2013"]). For example, the function of the human gut microbiome may depend
on the relative abundance of hundreds of species of bacteria and the types of metabolic genes they contain ([ref-15,"Wu, et al., 2011"]; [ref-10,"Schloissnig, et al., 2013"]). In contrast, the clinical treatment of a patient may
depend on whether or not a particular virus, or a consortium of co-infecting pathogens, is/are detected in their blood. It is this second class of presence/absence questions that SIANN is designed to address. SIANN is appropriate for
situations in which a user wants to know whether a particular organism or set of organisms is present in a sample, but isn’t interested in the functions encoded in their genomes, the relative abundance of each organism, or any other
more in-depth analysis.
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Methods
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Approach
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Metagenomic classification methods are based on a wide variety of theoretical underpinnings. The basic varieties include alignment of reads to various nucleotide databases or exact matching to nucleotide or protein signature
sequences (or kmers). A representative set of recent methods are described in [T1,"Table 1"] (also see [ref-2,"Bazinet & Cummings 2012"]).
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Overall, these methods are designed to either classify individual reads to, and/or predict the total abundance of, clades (e.g. genus or species) across the entire tree of life. They generally require reference databases that
are very large and/or require a large amount of processing to generate. The gap SIANN is designed to fill is when the entire tree of life is irrelevant, and only predefined subsets of organisms need to be detected. For an
underlying method we chose read alignment to diagnostic genomic regions because the algorithms for read alignment are highly parallelizable and have been optimized heavily by the community at large (the current implementation of
SIANN uses bowtie2 [[ref-7,"Langmead & Salzberg, 2012"]] for the alignment function, but can be adapted to any alignment algorithm). This approach is distinct from using clade-specific marker genes ([ref-12,"Segata, et al.,
2012"]) because unique regions that are larger, smaller, or outside of genes can also be used. Furthermore, this approach supports the rapid construction of custom databases using reference genome sets that require only minimal
user-supplied structure.
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To understand the principle at work, consider a set of reads that have been aligned to the genomes of several strains belonging to two species. Some regions of those genomes are species-specific, some are strain-specific, and
some are shared ([F1,"Figure 1a"]). When a set of reads is aligned to those genomes such that each read is placed in as many locations as it has a match (at a reasonably stringent threshold), visual inspection of the
distribution of reads yields an intuitive understanding of the true source organism as Species I/Strain B ([F1,"Figure 1b"]). If Strain B were not present in the reference database, it would still be clear that the organism was
an unknown strain of Species I.
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The unique identification of a species or strain is quantified by the proportion of the genome that is determined to be species- or strain-specific (defined as reads that are aligned to regions that are species- or
strain-specific). Each species and strain is then assigned a numerical measure of the proportion that is covered by these diagnostic reads, and that proportional measure is compared to the ideal case, where sequences from a
single organism (generated in silico) are aligned against the database in an identical manner. After that normalization factor is applied, the resulting score indicates whether the source sample contained any of the organisms in
the reference database. The analysis is conducted independently on both the species and the strain level, so that if the true strain is not present in the database, the species of origin will still be identified. While many
methods consider the complete taxonomic tree and assign reads to the least common ancestor, SIANN considers only two taxonomic levels: species and strain, throwing out anything that is not unique at one of those levels and thus
obviating many of the confounding factors introduced by manually curated taxonomies.
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The example shown in [F1,"Figure 1b"] indicates that species-specific reads are identified as reads that align to one species (Species I, in that case) but not the other. If Species II were not present in the example shown in
[F1,"Figure 1b"], a much larger number of reads would be assigned as “species-specific,” when in fact those regions are shared with other species. Therefore, the ability of this method to identify strain- and species-specific
sequences is a direct function of the inclusion of near neighbors in the reference database. This characteristic is shared among many classification algorithms, but it is of particular note for this method when users have an
opportunity to construct their own database. In order to detect a target species with a high degree of specificity (reducing false positives), it is necessary to include other related species in the reference database. Only by
parallel alignment to those near neighbors can the redundant sequences be separated from the species-specific ones. For example, in order to detect Bacillus anthracis in a sample, it would be necessary to include other species
of Bacilli in the reference database so that the presence of B. cereus or B. thuringiensis in a sample does not lead to a false call for B. anthracis.
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The nomenclature of genus, species, and strain is potentially problematic because it does not correspond to a consistent degree of evolutionary distance or genomic distinctiveness. The ability to distinguish two organisms by
any method using genomic sequence data is proportional to the amount of each genome that is shared or unique. One might assume that any two organisms of the same species will have a relatively predictable amount of shared
genomic identity. However, some pairs of organisms from the same species may have less in common than other pairs of organisms from different species or even genera. This ambiguity impacts SIANN in two ways. If two organisms
have very little genomic sequence to distinguish them, the sensitivity of SIANN to detect either one will diminish (the rate of false negatives will increase as the likelihood of sequencing unique regions decreases). Conversely,
if an organism is extremely dissimilar to the near neighbors selected for the database, the specificity with which SIANN detects that organism will decline (the rate of false positives will increase as the number of related
genomes available in the database decreases). For example, if a database contained only E. coli and B.anthracis, a sample containing B. cereus would be misidentified as contraining B. anthracis. In the intended use case, a
database targeting B. anthracis would contain B. cereus and a number of other near neighbors to prevent that kind of misidentification. It would be convenient to say that an ideal database can be made by calculating the ideal
genetic distance between all references and then finding an ideal set of organisms to make up that database, but the behavior of any database will be governed by the particular genomes of the organisms it encounters in the wild.
Because not all organisms evolve in the same manner (differences in mutation rate, horizontal gene transfer, recombination, etc), the suitability of a database and method to detect a given organism can only be determined by
thorough validation and benchmarking, as well as updating the reference database as needed. Users of SIANN may construct their own custom databases to include newly identified genomes or specific subsets of genomes that best
suit their research interests.
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Steps to construct a custom database:\n
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The files contained within each SIANN database are a compressed genomic index and a list containing the proportion of each reference genome that was found to be strain- or species-specific during database construction.
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To run SIANN:\n
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Benchmarking
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The performance of SIANN (version 1.6) was tested in comparison to the following state-of-the-art metagenomic classification programs: LMAT (version 1.2), MetaPhlAn (version 1.7.7), and Kraken (version 0.9.1b). All of the
programs in
[T1,"Table 1"] were investigated for this effort, and three were chosen based on their ability to run on our high-performance computing cluster with an execution time and memory requirement that would be suitable to a clinical
lab.
Each program was run on a set of 600 simulated datasets generated by MetaSim ([ref-9,"Richter, et al., 2008"]). Each dataset consisted of 15,000,000 reads (100bp single-ended) with Illumina-simulated error (fourth-degree
polynomial)
([ref-6,"Korbel, et al., 2009"]). The 600 datasets were broken into 12 sets of 50 replicates. Each of the 12 sets contained organisms at different levels of abundance as shown in [T2,"Table 2"]. Organisms were specifically
chosen in
pairs so that the ability to distinguish these near neighbors could be determined. The abundances were staggered at 4-fold intervals so that a wide range could be evaluated. All known species of near neighbors for each of the 12
target organisms were included in the reference database used by SIANN for this benchmarking (“Target Pathogen Database”) and are shown in [app-1,"Appendix 1"].
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Each program outputs a distinct measure. Kraken and LMAT both count the reads assigned to each taxon, MetaPhlAn calculates the abundance, and SIANN outputs a measure of the proportion of diagnostic genomic regions present. To
put
these measures on an even footing, we empirically calculated the false positive rate for each method over all 600 samples, at each possible measure of output. Because each dataset is made up of known organisms, any result can be
classified as true or false. Therefore, for any possible result (say, 513 reads classified by LMAT or 1.6% abundance assigned by MetaPhlAn), one can calculate the proportion of calls with at least the same amount of support that
were
correct (True Positives/[True Positives + False Positives]), over all of the 600 datasets. That measure is commonly given as Positive Predictive Value (PPV). For each program, the results can be translated from the raw value
into a
PPV that is based on this empirical measure of error. The key item of interest is the PPV value for the results that we know to be true positives, the defined spike organisms. Another way of describing this approach is to say
that the
results of each program have been normalized to the false positive error rate that was empirically observed. If another set of samples were generated, the PPV vs. raw value curve ([F2,"Figure 2"]) would likely fall differently,
but in
this case it gives us a means of comparing a diverse set of methods against the same ground truth. If method 1 detects an organism with a higher PPV than method 2 does, it means that method 1 has fewer false positives in the
range
that it reports true positives, which is the definition of utility in this setting.
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For each method, PPV was calculated as a function of raw output value. Briefly, this was done by compiling the output for all 600 samples, labeling each result as false or true based on the sample set that it came from, and
then
calculating (at each possible value of output) what the proportion of TP/[TP + FP] was for results with at least that level of raw output. Some simplification steps were taken, such as focusing on the species-level assignments
(for
comparison with methods that do not perform strain assignment), and only taking the top hit for each species from each dataset. Custom R and BASH scripts were used for the data compilation and analysis.
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Results
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The relationship of raw output value to PPV is shown for each of the four methods in [F2,"Figure 2"]. The point at which PPV is very close to 1 (where 95% of results are true positives) is ∼41,000 reads for Kraken, ∼2,800 reads for
LMAT, ∼38% abundance for MetaPhlAn, and 0.21 for SIANN. For SIANN this means that having 38% of the species-unique genome covered by reads resulted in the vast majority of calls being accurate.
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For read-assignment methods (such as LMAT and Kraken), manual inspection of the results may yield a different understanding of confidence than is presented here, or in any automated analysis. For example, while each read that is
assigned by LMAT and Kraken fall above a certain cutoff for species-specificity, some individual reads may be much more specific than others. One could identify a read that aligns to a single species of bacteria with 100% accuracy
over its 300bp length, with the next closest match being only 90% similar. It is extremely unlikely that a 300bp exact match would arise due to random chance, and so the user could say with confidence that the organism of interest is
found within the sequence data (not considering contamination, horizontal gene transfer, etc). However, such an approach is not currently implemented in an automated method, and many of the steps needed to make that assertion are
performed manually by a domain expert, including alignment to near neighbors and ensuring that the read does not fall within a transposon, plasmid, etc. Therefore, while one could say that a single read is all that is needed to state
with high PPV that an organism is present, the amount of reads assigned in an automated manner needed to achieve that level of PPV will number in the thousands ([F2,"Fig 2"]).
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The next phase of benchmarking was to determine how many raw input reads were needed to achieve the threshold for high PPV. To demonstrate this we plotted the known abundance of each spike organism against the PPV value generated by
each method ([F3,"Figure 3"]). Each point (an organism at a known level of abundance) is comprised of a maximum of 50 replicates, where the diameter of each point increases with an increasing number of replicates. For demonstration
purposes we are showing two pairs of bacteria and three viruses. Recall that for each of the pairs of bacteria (and the two poxviruses) any sample containing one did not contain the other (as shown in [T1,"Table 1"]). The empty boxes
result from the organisms not being called at any abundance. For MetaPhlAn, that is a result of no viruses being included in the version of the reference database available for this analysis. Kraken assigned no reads to Hanta virus
because viral RNA genomes were not included in this version of the reference database (personal communication with D. Wood). This emphasizes the point that a) the ability to create custom databases targeting organisms of interest can
be valuable, and b) the performance of any method must be benchmarked against each potential target of interest.
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All methods were able identify the bulk of organisms in their databases at high abundances (75% and 18%, [F3,"Figure 3"]), however performance varied considerably at lower abundances and depended on the particular organism and method
used. SIANN detected each organism at high confidence, even at levels as low as 0.3% and 0.07% of the total.
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Discussion
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The process of detecting trace amounts of a specific organism in a complex mixture of DNA is challenging enough for an expert, but that pales in comparison to the difficulty of accomplishing the same certainty of detection in an
automated manner. The results presented here show that SIANN rapidly detects the presence of a given set of organisms with a high degree of specificity and sensitivity. For example, at the 95% confidence (PPV) cutoff of 0.2, SIANN
reliably detects all of the organisms tested here at as low as 0.3% abundance. This strong performance is likely due to the fact that SIANN is able to use a method (read alignment to whole genomes) that would be far too
computationally costly if it were applied to the entire collection of known genomes. By focusing on a set of (customizable) target organisms and their near neighbors, SIANN can operate quickly and with low computational requirements
while delivering highly accurate results.
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SIANN is available on Illumina’s BaseSpace ([http://www.basespace.illumina.com,"www.basespace.illumina.com"]) as a NativeApp, with the database tested here ([app-1,"Appendix 1"]), as well as a database made from the NCBI
representative set of prokaryotic genomes ([ftp://ftp.ncbi.nlm.nih.gov/genomes/genome_reports/,"ftp://ftp.ncbi.nlm.nih.gov/genomes/genome_reports/"]) ([app-2,"Appendix 2"]) and the complete set of NCBI viral genomes
([ftp://ftp.ncbi.nlm.nih.gov/refseq/release/viral/,"ftp://ftp.ncbi.nlm.nih.gov/refseq/release/viral/"]) ([app-3,"Appendix 3"]).
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BaseSpace was chosen as an appropriate release platform because while the entire set of software and dependencies can be deployed by the user from within a graphical user interface, the actual computation takes place in a controlled
‘cloud’ environment. Such a distribution strategy obviates the need to satisfy the multiple software or OS dependencies that often arises with academic computational methods. Results for SIANN are compiled into a report format,
showing both the organisms that surpass 95% confidence, as well as the closest strain match for each species. The default view masks the raw data output, so that the results are human-readable and do not present extraneous
information. While the code for execution and database-construction on a users system is available from Signature Science, LLC, additional databases on the BaseSpace platform can be made available upon request.
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There is a neverending list of questions that one could ask of metagenomic sequencing data generated from important samples. Instead of answering them all, we demonstrate a technique with a very narrow focus that is able to report
with a high degree of confidence whether a given set of organisms is present in a sample. These results are presented to the user in a comprehensible format, and accessible on a commonly-used web platform. The world of bioinformatics
will continue to progress and develop more sophisticated tools for metagenomic analysis, and we hope that the utility of SIANN will convince others to package and benchmark their tools in a way that they can be used with confidence by
the larger public, as well as the research community.
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References
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| Scalable metagenomic taxonomy classification using a reference genome database, Ames SK, Hysom DA, Gardner SN, Lloyd GS, Gokhale MB, Allen JE, 2013, CrossRef, PubMed |
| A comparative evaluation of sequence classification programs, Bazinet AL, Cummings MP, 2012, CrossRef, PubMed
|
| Rapid phylogenetic and functional classification of short genomic fragments with signature peptides, Berendzen J, Bruno WJ, Cohn
JD, Hengartner NW, Kuske CR, McMahon BH, Wolinsky MA, Xie G, 2012, CrossRef, PubMed
|
| Phymm and PhymmBL: metagenomic phylogenetic classification with interpolated Markov models, Brady A, Salzberg SL, 2009, CrossRef, PubMed, Web of Science |
| Integrative analysis of environmental sequences using MEGAN4, Huson DH, Mitra S, Weber N, Ruscheweyh
H, Schuster SC, 2011, Abstract/FREE
Full Text |
| PEMer: a computational framework with simulation-based error models for inferring genomic structural variants from massive paired-end sequencing data, Korbel JO, Abyzov A, Mu XJ, Carriero N, Cayting P, Zhang Z, Snyder M, Gerstein MB, 2009, CrossRef, PubMed |
| Fast gapped-read alignment with Bowtie 2, Langmead B, Salzberg S, 2012, CrossRef, PubMed |
| Accurate and fast estimation of taxonomic profiles from metagenomic shotgun sequences, Liu B, Gibbons T, Ghodsi M, Treangen T, Pop M, 2011, CrossRef |
| MetaSim: a sequencing simulator for genomics and metagenomics, Richter DC, Ott F, Auch AF, Schmid
R, Huson DH, 2008, CrossRef, PubMed |
| Genomic variation landscape of the human gut microbiome, Schloissnig S, Arumugam M, Sunagawa S, Mitreva
M, Tap J, Zhu A, Waller A, Mende DR, Kultima JR, Martin
J, Kota K, Sunyaev SR, Weinstock GM, Bork P, 2013, CrossRef, PubMed, Web of Science |
| Computational meta’omics for microbial community studies, Segata N, Boernigen D, Tickle TL, Morgan
XC, Garrett WS, Huttenhower C, 2013, CrossRef |
| Metagenomic microbial community profiling using unique clade-specific marker genes, Segata N, Waldron L, Ballarini A, Narasimhan V, Jousson O, Huttenhower C, 2012, CrossRef, PubMed, Web of Science |
| Metagenomic species profiling using universal phylogenetic marker genes, Sunagawa S, Mende DR, Zeller G, Izquierdo-Carrasco F, Berger SA, Kultima JR, Coelho LP, Arumugam M, Tap
J, Nielsen HB, Rasmussen S, Brunak S, Pedersen O, Guarner F, de
Vos WM, Wang J, Li J, Doré J, Ehrlich SD, Stamatakis A, Bork
P, 2013, CrossRef, PubMed, Web of Science |
| Ultrafast metagenomic sequence classification using exact alignments, Wood DE, Salzberg SL, |
| Linking long-term dietary patterns with gut microbial enterotypes, Wu GD, Chen J, Hoffmann C, Bittinger
K, Chen YY, Keilbaugh SA, Bewtra M, Knights D, Walters WA, Knight
R, Sinha R, Gilroy E, Gupta K, Baldassano R, Nessel L, Li
H, Bushman FD, Lewis JD, 2011, Abstract/FREE
Full Text |
|


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--- layout: base title: Starting a Computer Anonymous ---
If you aren't familiar with git and/or Github, contact the following people to do it for you:
Starting local groups is excellent and highly encouraged, but in order to maintain the integrity of the groups, there are a few things you should keep in mind.
Venues should be chosen with accessibility in mind - be it physical accessibility, financial accessibility, or generally safe places for all people.
Many groups will likely be meeting in pubs and bars, but this certainly isn't mandatory and might not even be desired. It's worth exploring alternatives.
Finding a good time for everyone is difficult, if not impossible, but you should make an attempt to use tools like doodle to find times that work best.
GitHub has worked well for us in planning meetups and answering questions. We've avoided tradtional mailing lists, because of spam, and general lack of moderation. With GitHub, discussions tend to stay on topic, we can close, and reference issues. We've found that creating an account, setting up notices, is just as annoying as a traditional mailing list. We also don't get email on the first of the month.
Git and IRC are great tools for collaboration, but not everyone is familiar with them. We want to include and encourage participation from people with all backgrounds, so knowledge of these systems shouldn't be a prerequisite.
Be available in other mediums so people can get help if they need it.
Remember, everyones safety is a top priority. In order for these groups to survive, people need to be held accountable. Someone should be available to handle problems within the group.
Try to have more than one point of contact, and if possible try to get people from different groups and backgrounds to volunteer. One person may burn out, and the more diverse the volunteers, the better.
We should aim to handle all incidents in a consistent fashion. Let the person know in plain language what they did wrong and what action is being taken to deal with the situation. It is important to make this about the behaviour/words, not the person.
After this point, if possible do not engage with the person who was causing the problem and give them time to cool off. If you do need to engage with them, offer them information without jokes or attacks, and disengage respectfully if it is a lost cause.
Ask the person/s who suffered if they would like further support. Listen to what they say and make sure their needs are feedback to the group so we can all learn and improve. It is vital to let the person reporting know the methods of action you intend to take before taking them. Sometimes they have a different context with the person who has violated the code of conduct - they may worry about their physical/professional/personal safety with the person, so we want to make sure that we respond in a way that does the least harm to the reporter.
If someone engages in behaviour that violates the code of conduct, organisers or designated volunteers may take action. Make sure that people attending the meeting are aware of who to contact (this should be on meeting pages; and made clear at the event).
Our expectation is that if someone is asked to stop a harassing or otherwise inappropriate behaviour, they will do so immediately. If not the organisers may ask them to leave.
We can expect to get some questions such as "can cis white straight guys come to this group without feeling uncomfortable?". Channel mods/tweeters should deal with these by referring to the 101 pages. It is understandable that people will get annoyed by such questions when they have to deal with them on a daily basis so please avoid tone policing people's responses and instead state that our purpose is not education on these issues and redirect to factual information.
Similarly nobody should feel obligated to continually carry out 101-style education–such requests should be referred to our statement of purpose and the 101 links pages.
If an incident occurs such that an individual is banned, this should be logged. If they tweet/email asking about this, reiterate what they did, post the logs and (briefly) why this was problematic.


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Maharashtra CM post: Amid deadlock with BJP, Raut stresses Sena's claim
Raut, executive editor of the Shiv Sena mouthpiece Saamana, has been the most vocal among Sena leaders in pressing his party's claim on the top post
by Press Trust of IndiaShiv Sena MP Sanjay Raut said on Saturday that his party's demand for Maharashtra chief minister's post was valid, and the basis for power sharing with the BJP must be the pre-poll agreement and not the number of seats won.
Raut, executive editor of the Shiv Sena mouthpiece Saamana, has been the most vocal among Sena leaders in pressing his party's claim on the top post.
The political impasse in the state continues after Assembly election results were announced on October 24, with the victorious allies BJP and Shiv Sena bickering.
"Government has to be formed as per what was agreed on (between the BJP and Shiv Sena before the polls) and not on the basis of which is the single largest party," Raut told a news channel.
His party has won 56 seats, against the BJP's tally of 105 in the 288-member Maharashtra Assembly.
"Shiv Sena president Uddhav Thackeray had resolved to make a Shiv Sainik (a Sena member) chief minister, and the time for that has come now," the Rajya Sabha MP said.
"Shiv Sena has moved beyond portfolios in the state and at the Central level,or additional Legislative Council berths (as bargaining chips). We are talking about the chief minister's post," he said.


Adjust the hue of RGB images by a random factor.
tf.compat.v1.image.random_hue
tf.compat.v2.image.random_hue
tf.image.random_hue( image, max_delta, seed=None )
Equivalent to adjust_hue()
but uses a delta
randomly
picked in the interval [-max_delta, max_delta]
.
max_delta
must be in the interval [0, 0.5]
.
image
: RGB image or images. Size of the last dimension must be 3.max_delta
: float. Maximum value for the random delta.seed
: An operation-specific seed. It will be used in conjunction with the
graph-level seed to determine the real seeds that will be used in this
operation. Please see the documentation of set_random_seed for its
interaction with the graph-level random seed.Adjusted image(s), same shape and DType as image
.
ValueError
: if max_delta
is invalid.


Covid-19
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Toggle Menu
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Dropdown
Homepage
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Impacts of Covid-19
Unemployment


✨To celebrate Spense's launch, we will be waiving all fees for the entire month of April. You will be receiving 100% of the earnings
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Our text editor pulls you into focus mode with it's simplistic
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Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.
Using banner stand to increase trade show traffic
There is a lot of exciting stuff going on in the stars above us the make astronomy so mouch fun.


This example shows how to use the ngeoCreatePopup service factory to display all sorts of modals. One can display plain text or display an external web page within an iframe.
ngeoCreatePopup


Adjective:
Plural Noun:
Adjective:
Fill
Ninth grade can be
, but if you find good
it will be a
time.


E-waste
by Manny PalmeroResidents of Barangay West Triangle in Quezon City bring their broken or discarded equipment to a collection event organized by the barangay council to promote awareness and support for the ‘Safe PCB and E-Waste Management’ project by the Department of Environment and Natural Resources-Environmental Management Bureau to protect public health and the environment against harmful chemicals in electronic waste.


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Friday December 10, 2021 | 01:14:33 AM
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Blacksmith of Wind Rooney:
What interesting research!
I tried various things with the fragments, and was able to make a totem containing the power of the dragons.
The effect differs, depending on how the fragments are combined. There is also a problem with range of output, so it can only be used where the power of the dragons can be felt. But still, the effect is... well... startling.
There are four types of totems.
[Quest _901_HowLavasaurusesAreMade blacksmith_rooney_q901_08.htm|"Totem of Body"]
[Quest _901_HowLavasaurusesAreMade blacksmith_rooney_q901_09.htm|"Totem of Spirit"]
[Quest _901_HowLavasaurusesAreMade blacksmith_rooney_q901_10.htm|"Totem of Fortitude"]
[Quest _901_HowLavasaurusesAreMade blacksmith_rooney_q901_11.htm|"Totem of Courage"]


{% extends "main_template.html" %} {% load static %} {% block title %} 1XPredict.com - VipTips {% endblock %} {% block header %} {% include "header.html" %} {% endblock %} {% block content %}
Make payment to the following Bank Account number:
2061281040
Account name
ANIEBIET BASSEY
Upon payment kindly send the following details to us at<EMAIL_ADDRESS>a) Your full names as reflected in that payment
b) Your phone number with your country's code e.g. +23483736
Image of the payslip
We will subsequently activate your account promptly
Kindly make your monthly subscription fee through Skrill to this email
<EMAIL_ADDRESS>Upon payment kindly send the following details to us at<EMAIL_ADDRESS>a) Your full names as reflected in that Skrill payment
b) Your phone number with your country's code e.g. +23483736
We will subsequently activate your account promptly
a) Lipa na M-PESA
b) Pay Bill
c) Enter Business Number 247247
d) Enter Account Number<PHONE_NUMBER>770
e) Enter Amount
f) Press OK
We will subsequently activate your account promptly


Welcome to Nick's Website!
Hi, I'm Nick a junior studying Computer Science at Cornell in the very sunny city of Ithaca!
I was born in Japan but I am from Colombia and the Bronx.
I like to watch sports such as Formula 1, Basketball (go Knicks!), and Soccer.
Click the button to see a fact about me.
Contact Me!
See sentiment after sending your message.
Click here to see content created by server code.


Install and Configure from Cloud App Catalog
About
This topic walks you through installing and configuring Visual Update 1 for HCL Connections from the Cloud App Catalog. You can enable the application for your entire organization in a few clicks. You can also enable the app for a specific sub-set of users.
Prerequisites
Customizer is available on HCL Connections Cloud for tenants of the North America and Europe data centers. Before using Customizer, your organization must first register for the service by sending an email with some simple information. Refer to section 3 of the HCL Connections Customizer documentation for more details.
- As an administrator go to Manage Organization.
- Go to Apps and then App Catalog.
- Locate Visual Update 1 for HCL Connections and click Get.
- When configuring the application, on the Users step, select to enable it for the Entire Organization or Individuals.
- Click Next past Extensions. There is not support for individual extensions at this time.
- Click Next until you get to the last page and click Finish.
Because this is a new visual style, it is recommended that you enable the application for a sub-set of users first. This lets you review and get feedback about the application before turning it on for your entire organization.
To enable for a sub-set of users:
- From the App Catalog, Configure Visual Update 1.
- On the Users step select Individuals and click through to Finish.
- When the configuration is finished go to Organization Extensions.
- At the top of the page, follow the new Apps Manager link.
- Click on the name of the app to edit the extension and select Code Editor.
- In the .json, in each extension playload look for "match", "user-email", and a sample email address. Replace the sample email address with the email address of the users you want to enable.
Here's and example of how multiple email addresses would appear.
"payload": {
"include-files":[
"periscope.js"
],
"include-repo": {
"name":"periscope"
},
"match": {
"user-email": [
<EMAIL_ADDRESS><EMAIL_ADDRESS>]
}
},
If you previously enabled for a set of users, follow these steps to enable for the entire organization.
- From the App Catalog, Configure Visual Update 1.
- On the Users step select Entire Organization. This will result in the email addresses being removed from the payload section of the .json file.
- Click through to Finish.


(static) Helper class. Helps build urls (sanitize urls, filter double slashes etc.).
UrlHelper
Builds an url by the given parts. All parts that are entered are first split with a '/' character. Those segments are then processed (leading / removed, etc) and after that joined with slashes.
string
(static) Helper class. Helps build urls (sanitize urls, filter double slashes etc.).
UrlHelper


{% extends "base.html" %} {% block body %}
It seems like activation link has been already used or it is invalid. Please make sure to use complete activation url. You can always try to activate your acount second time.
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Choose between Upload and Download
goal
- Upload: Uploads source files from Base Directory
filtered by Include and Exclude rules to your instance of
Globalization Pipeline in form of bundles. Machine Translation engine
automatically translates those bundles.
- Download: Downloads bundles from your instance of IBM
Globalization Pipeline to Jenkins job workspace. The download format
and configuration would be executed based on your choices for options
in this form.


Escaping Social Dilemmas
Cooperator
Defector
Grudger
TitForTat
WinStay-LoseShift
Stochastic Cooperator
Bully
Grumpy
Collective Strategy
Adaptive Pavlov
games
strategies
interaction
cooperate rate
Tit-For-Tat
Copies partner's last behavior.
See strategy play out


Brother Act: Wisconsin signs Davis twins
La Crosse duo added to class of 2020
by Kevin LewisLa Crosse - The Wisconsin men's basketball program got a big boost Wednesday with the signing of twin brothers Johnny and Jordan Davis for the class of 2020. The La Crosse Central seniors made it official at a ceremony held at the school.
Johnny, a 6'5" wing player, averaged 23 points and nine rebounds per game last season. He had scholarship offers from Marquette, Iowa, Minnesota, and DePaul among others. Jordan, 6'4," is known more as a football standout, but he also received some Division 1 hoops offers.
"I wasn't going to go anywhere else without my brother," Johnny said after the signing ceremony. "He's my go-to man. He's been by my side my whole entire life. Just to be able to go to college with him and play the sport that we both love is just incredible."
Jordan thinks he and his brother can both be impact players in a physical Big Ten Conference. "We'll be hustle players and contributing to the Badgers right off the bat and doing our thing," Jordan, the younger twin (by eight minutes) said. "I think this is one of the best classes Wisconsin's had in a while. I think we're going to bring a lot to the table."


You have the option to contribute activity data collected through:
You can choose not to provide this data and still participate in the study.
We will NOT access your personal contacts, other applications, personal photos, text or email messages.


A set \( C \) is \( convex \) if for all
\(x,y \in C \) and for all
\( \alpha \in [0,1] \) the point
\( \alpha x + (1-\alpha) y \in C \).
There are no natural numbers
\(N = (1, , 3, \ldots)\)
\(x\), \(y\), and \(z\) such that
\(x^n + y^n = z^n\), in which \(n\)
is a natural number greater than 2.


You can share photos and start discussions. This is called a salt.
Alright, that's cool, how do I get started?
Simple, register and start posting!


The Great
RGB
Color Game
Databases
Test out the input below and hit the "Enter" key.
This information will be temporarily saved to a local database.
Databases are complex and I am no master, but I can provide your website with reliable solutions.
Authentication
Below I have provided a link to my Github PassportJS outline. This is a template I created to begin the process of building out user authentication.
PassportJS Outline


This demo will show record-keeping for the collection and transfer of biological samples. It will also show managed access to the information relating to the sample.
The terms of the MTA are established in /Dx/MIT/01-MaterialsTransfer/CoverPage.md.
The CoverPage is used to create a specific MTA for each sample, as /Dx/MIT/01-MaterialsTransfer/Sample_1.md, et seq.
Each specific MTA references a file of metadata associated with the sample by a GUID. In /U/Asset/Tissue/ABCD5E/*.
The example can be deepened by reflecting the stages of the negotiation and signature, and by a succession of communications following transfer.
These materials can be hypothetically interrogated by a third party authorized by the Donor or the Scientist.


public class MainController
extends java.lang.Object
implements javafx.fxml.Initializable
Initializable
.| Modifier and Type | Field and Description |
|---|---|
private javafx.animation.AnimationTimer |
at |
private GameBoard |
board |
private javafx.scene.canvas.Canvas |
canvas |
private javafx.scene.control.Label |
cellsAliveCnt |
private javafx.scene.control.Label |
cellsDeadCnt |
private javafx.scene.control.ColorPicker |
colorPicker |
private long |
delayNanoseconds |
private java.util.Map<java.lang.String,java.lang.Integer> |
hashMapRulesMenu |
private javafx.scene.control.Label |
labelDelay |
private javafx.scene.control.Label |
labelGen |
private MouseController |
mc |
private javafx.scene.control.Menu |
mnuRuleList |
private javafx.scene.layout.Pane |
pane |
private long |
previousNanotime |
private javafx.scene.layout.VBox |
root |
private RuleController |
ruleController |
private javafx.scene.control.CheckMenuItem |
soundMute |
private javafx.scene.control.Slider |
speedSlider |
private javafx.scene.control.Button |
startPauseButton |
private boolean |
userDrawActivity |
private Viewer |
viewer |
private javafx.scene.control.Slider |
zoomSlider |
| Constructor and Description |
|---|
MainController() |
| Modifier and Type | Method and Description |
|---|---|
void |
aboutMenu()
aboutMenu
public void aboutMenu()
opens and displays a window with information about the game and the authors. |
void |
cellColor()
cellColor
public void cellColor()
Lets you choose the cellColor, and updates the canvas. |
void |
closeGame()
closeGame
public void closeGame()
Stops the sound played, and exists the platform the game is running on. |
void |
controllersInfo()
controllersInfo
public void controllersInfo()
Opens and displays a window with information about the controllers of the game. |
void |
handleResetBtn()
handleResetBtn
public void handleResetBtn()
Resets the board, and sets all element in the array to dead, and the dead cells have the same color
as the background color. |
void |
handleStartBtn()
handleStartBtn
public void handleStartBtn()
Starts and pauses the animation of the cells on the board. |
void |
howToPlay()
howToPlay
public void howtoPlay()
opens and displays a window with information about how to play the game. |
void |
initialize(java.net.URL location,
java.util.ResourceBundle resources)
initialize
public void initialize({@link URL} location,{@link ResourceBundle} resources)
initializes needed methods and objects to run Game of Life. |
void |
loadRLE()
loadRLE
public void loadRle()
opens a FileChooser which lets you select and load a RLE file to the game board. |
void |
loadRuleMenu()
loadRuleMenu
public void loadRuleMenu()
Loads the game rules, and displays them in a horizontal row
|
void |
loadURL() |
void |
menuClicked(javafx.event.ActionEvent event)
menuClicked
public void menuCicked({@link ActionEvent} event)
Gets the text, and content of the given rules. |
void |
menuCreateRule(javafx.event.ActionEvent event)
menuCreateRule
public void menuCreateRule({@link ActionEvent} event)
Opens a new window, where you can create and save new game rules. |
private javafx.scene.layout.VBox root
private javafx.scene.layout.Pane pane
private javafx.scene.canvas.Canvas canvas
private javafx.scene.control.Label cellsAliveCnt
private javafx.scene.control.Label cellsDeadCnt
private javafx.scene.control.Button startPauseButton
private javafx.scene.control.Slider speedSlider
private javafx.scene.control.Slider zoomSlider
private javafx.scene.control.CheckMenuItem soundMute
private javafx.scene.control.ColorPicker colorPicker
private javafx.scene.control.Menu mnuRuleList
private javafx.scene.control.Label labelDelay
private javafx.scene.control.Label labelGen
private Viewer viewer
private GameBoard board
private MouseController mc
private RuleController ruleController
private javafx.animation.AnimationTimer at
private long delayNanoseconds
private long previousNanotime
private boolean userDrawActivity
private java.util.Map<java.lang.String,java.lang.Integer> hashMapRulesMenu
public void initialize(java.net.URL location, java.util.ResourceBundle resources)
initializepublic void initialize({@link URL} location,{@link ResourceBundle} resources)
initializes needed methods and objects to run Game of Life. The board,rules and view is initialized. see
initialize
for more information.
initialize
in interface javafx.fxml.Initializable
location
- - location of the root object.resources
- - resources needed to localize the root object.
public void loadRuleMenu()
loadRuleMenupublic void loadRuleMenu()
Loads the game rules, and displays them in a horizontal row
public void menuClicked(javafx.event.ActionEvent event)
menuClickedpublic void menuCicked({@link ActionEvent} event)
Gets the text, and content of the given rules.
event
- - The menu item pressed.
public void menuCreateRule(javafx.event.ActionEvent event)
menuCreateRulepublic void menuCreateRule({@link ActionEvent} event)
Opens a new window, where you can create and save new game rules.
event
- - The event of the menu item pressed.
public void handleStartBtn()
handleStartBtnpublic void handleStartBtn()
Starts and pauses the animation of the cells on the board.
public void handleResetBtn()
handleResetBtnpublic void handleResetBtn()
Resets the board, and sets all element in the array to dead, and the dead cells have the same color as the background color.
public void loadRLE() throws PatternFormatException
loadRLEpublic void loadRle()
opens a
FileChooser
which lets you select and load a RLE file to the game board.
PatternFormatException
- - throws an exception if the pattern in the specified
file is incorrect.
public void loadURL()
public void closeGame()
closeGamepublic void closeGame()
Stops the sound played, and exists the platform the game is running on.
public void cellColor()
cellColorpublic void cellColor()
Lets you choose the cellColor, and updates the canvas.
public void howToPlay()
howToPlaypublic void howtoPlay()
opens and displays a window with information about how to play the game.
public void aboutMenu()
aboutMenupublic void aboutMenu()
opens and displays a window with information about the game and the authors.
public void controllersInfo()
controllersInfopublic void controllersInfo()
Opens and displays a window with information about the controllers of the game.


jQuery Assignment 4
Constant Random Number
Add Random number
Score
Wins :
Losses :
Heart
Diamond
Spade
Clubs


Kandra Wilko
I'm not a web developer. I just love to learn about web. Blogger was my first web activity then later I used wordpress was my choice of web framework. Today I'm using Jekyll and github pages.
You could find me at these social media
Experience
Travel & Sharia Insurance
My last involvement in Asuransi Umum Mega was to help the company developing digital work flow of umra travel insurance. A travel insurance product specially made for umra business. In this last action as Mega Insurance employee I had to coordinate with Religion Ministry especially their IT department and Mega Insurance IT department in order to sync database system between the company and the Ministry.
My last position in Asuransi Umum Mega, PT also made me responsible to get new clients, especially umra travel agencies. I had several responses from my canvassing. Unfortunately since covid-19 outbreak made Saudi Arabia government restricted new umrah participants and my personal reason, I had to take my leave from the company.


Genrose Pizza
Place your order:
How hungry are you?
Small 12" -- $12
Medium 14" -- $14
Large 16" --$16
Choose your toppings:
The meats:
- $2 each -
sausage
prosciutto
pepperoni
canadian bacon
The veggies:
- $1 each -
roasted garlic
arugula
Spanish olives
artichoke hearts
red onions
jalepenos
pineapple
mushrooms
Place order
Delivery Info:
First name
Last name
Street
City
State
Submit
Your Order:
Order Cost:


Import data written in the JCAMP-DX format. This is an instrument-independent format used in the field of spectroscopy. Examples include IR, NMR, and Raman spectroscopy. See the vignette for background and supported formats. The official JCAMP-DX site is <http://www.jcamp-dx.org/>.
| Version: | 0.3.250 |
| Depends: | R (≥ 3.0) |
| Imports: | stringr |
| Suggests: | knitr |
| Published: | 2018-10-15 |
| Author: | Bryan A. Hanson [aut, cre] (ORCID 0000-0003-3536-8246) |
| Maintainer: | Bryan A. Hanson <hanson at depauw.edu> |
| BugReports: | https://github.com/bryanhanson/readJDX/issues |
| License: | GPL (≥ 3) |
| URL: | https://github.com/bryanhanson/readJDX |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | readJDX results |
| Reference manual: | readJDX.pdf |
| Vignettes: |
readJDX |
| Package source: | readJDX_0.3.250.tar.gz |
| Windows binaries: | r-devel: readJDX_0.3.250.zip, r-release: readJDX_0.3.250.zip, r-oldrel: readJDX_0.3.250.zip |
| OS X binaries: | r-release: readJDX_0.3.250.tgz, r-oldrel: readJDX_0.3.250.tgz |
| Old sources: | readJDX archive |
| Reverse imports: | specmine |
| Reverse suggests: | ChemoSpec |
Please use the canonical form https://CRAN.R-project.org/package=readJDX to link to this page.


001// *************************************************************************************************************************** 002// * Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file * 003// * distributed with this work for additional information regarding copyright ownership. The ASF licenses this file * 004// * to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance * 005// * with the License. You may obtain a copy of the License at * 006// * * 007// * http://www.apache.org/licenses/LICENSE-2.0 * 008// * * 009// * Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an * 010// * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the * 011// * specific language governing permissions and limitations under the License. * 012// *************************************************************************************************************************** 013package org.apache.juneau.http; 014 015import org.apache.juneau.http.annotation.*; 016 017/** 018 * Represents a parsed <l>Range</l> HTTP request header. 019 * 020 *
 021 * Request only part of an entity. Bytes are numbered from 0. 022 * 023 *
Example
 024 *
 025 * Range: bytes=500-999 026 *
 027 * 028 *
RFC2616 Specification
 029 * 030 * Since all HTTP entities are represented in HTTP messages as sequences of bytes, the concept of a byte range is 031 * meaningful for any HTTP entity. 032 * (However, not all clients and servers need to support byte- range operations.) 033 * 034 *
 035 * Byte range specifications in HTTP apply to the sequence of bytes in the entity-body (not necessarily the same as the 036 * message-body). 037 * 038 *
 039 * A byte range operation MAY specify a single range of bytes, or a set of ranges within a single entity. 040 *
 041 * ranges-specifier = byte-ranges-specifier 042 * byte-ranges-specifier = bytes-unit "=" byte-range-set 043 * byte-range-set = 1#( byte-range-spec | suffix-byte-range-spec ) 044 * byte-range-spec = first-byte-pos "-" [last-byte-pos] 045 * first-byte-pos = 1*DIGIT 046 * last-byte-pos = 1*DIGIT 047 *
 048 * 049 *
 050 * The first-byte-pos value in a byte-range-spec gives the byte-offset of the first byte in a range. 051 * The last-byte-pos value gives the byte-offset of the last byte in the range; that is, the byte positions specified 052 * are inclusive. 053 * Byte offsets start at zero. 054 * 055 *
 056 * If the last-byte-pos value is present, it MUST be greater than or equal to the first-byte-pos in that 057 * byte-range-spec, or the byte- range-spec is syntactically invalid. 058 * The recipient of a byte-range- set that includes one or more syntactically invalid byte-range-spec values MUST 059 * ignore the header field that includes that byte-range-set. 060 * 061 *
 062 * If the last-byte-pos value is absent, or if the value is greater than or equal to the current length of the 063 * entity-body, last-byte-pos is taken to be equal to one less than the current length of the entity-body in bytes. 064 * 065 *
 066 * By its choice of last-byte-pos, a client can limit the number of bytes retrieved without knowing the size of the 067 * entity. 068 *
 069 * suffix-byte-range-spec = "-" suffix-length 070 * suffix-length = 1*DIGIT 071 *
 072 * 073 *
 074 * A suffix-byte-range-spec is used to specify the suffix of the entity-body, of a length given by the suffix-length 075 * value. 076 * (That is, this form specifies the last N bytes of an entity-body.) 077 * If the entity is shorter than the specified suffix-length, the entire entity-body is used. 078 * 079 *
 080 * If a syntactically valid byte-range-set includes at least one byte- range-spec whose first-byte-pos is less than the 081 * current length of the entity-body, or at least one suffix-byte-range-spec with a non-zero suffix-length, then the 082 * byte-range-set is satisfiable. 083 * Otherwise, the byte-range-set is unsatisfiable. 084 * If the byte-range-set is unsatisfiable, the server SHOULD return a response with a status of 416 (Requested range 085 * not satisfiable). 086 * Otherwise, the server SHOULD return a response with a status of 206 (Partial Content) containing the satisfiable 087 * ranges of the entity-body. 088 * 089 *
 090 * Examples of byte-ranges-specifier values (assuming an entity-body of length 10000): 091 *
 092 * - The first 500 bytes (byte offsets 0-499, inclusive): bytes=0-499 093 * - The second 500 bytes (byte offsets 500-999, inclusive): bytes=500-999 094 * - The final 500 bytes (byte offsets 9500-9999, inclusive): bytes=-500 095 * - Or bytes=9500- 096 * - The first and last bytes only (bytes 0 and 9999): bytes=0-0,-1 097 * - Several legal but not canonical specifications of the second 500 bytes (byte offsets 500-999, inclusive): 098 * bytes=500-600,601-999 099 * bytes=500-700,601-999 100 *
 101 * 102 *
 103 * HTTP retrieval requests using conditional or unconditional GET methods MAY request one or more sub-ranges of the 104 * entity, instead of the entire entity, using the Range request header, which applies to the entity returned as the 105 * result of the request: 106 * 107 *
 108 * Range = "Range" ":" ranges-specifier 109 *
 110 * 111 *
 112 * A server MAY ignore the Range header. 113 * However, HTTP/1.1 origin servers and intermediate caches ought to support byte ranges when possible, since Range 114 * supports efficient recovery from partially failed transfers, and supports efficient partial retrieval of large 115 * entities. 116 * 117 *
 118 * If the server supports the Range header and the specified range or ranges are appropriate for the entity: 119 *
 120 *
The presence of a Range header in an unconditional GET modifies what is returned if the GET is otherwise 121 * successful. 122 * In other words, the response carries a status code of 206 (Partial Content) instead of 200 (OK). 123 *
The presence of a Range header in a conditional GET (a request using one or both of If-Modified-Since and 124 * If-None-Match, or one or both of If-Unmodified-Since and If-Match) modifies what is returned if the GET is 125 * otherwise successful and the condition is true. It does not affect the 304 (Not Modified) response returned if 126 * the conditional is false. 127 *
 128 * 129 *
 130 * In some cases, it might be more appropriate to use the If-Range header (see section 14.27) in addition to the Range 131 * header. 132 * 133 *
 134 * If a proxy that supports ranges receives a Range request, forwards the request to an inbound server, and receives an 135 * entire entity in reply, it SHOULD only return the requested range to its client. 136 * It SHOULD store the entire received response in its cache if that is consistent with its cache allocation policies. 137 * 138 *
See Also:
 139 *
 140 *
{@doc RFC2616} 141 *
 142 */ 143@Header("Range") 144public final class Range extends HeaderString { 145 146 /** 147 * Returns a parsed Range header. 148 * 149 * @param value The Range header string. 150 * @return The parsed Range header, or <jk>null</jk> if the string was null. 151 */ 152 public static Range forString(String value) { 153 if (value == null) 154 return null; 155 return new Range(value); 156 } 157 158 private Range(String value) { 159 super(value); 160 } 161}


Hannibal's Wars - Battlefield
ADD ARMY
INFANTRY
CAVALRY
SKIRMISHERS
INFANTRY
CAVALRY
SKIRMISHERS
DESTROY
_________


a message
The following formats can be used to import data into to system. Format describes each line in the CSV data that must be written in the text area above.
Import progress format: username, author, title, section, position, finished.
Import users format: username, encoded password.
Add users format: username, password.
Delete users format: username.


Magnus Gudmundsson
CyberDaDa
cli.angular.io
npm install -g angular-cli
npm install -g angular-cli@latest
ng new my-new-project
ng new my-new-project
ng generate component bongocomp
Generation of components generates:
ng serve
ng build
ng test (Karma/Jasmine/Istanbul coverage)
ng e2e (Protractor)
ng github-pages:deploy
+ build creates the /dist/ folder copy deploy anywhere :)
A detailed overview
A more biased overview
Based upon the Web-Animation API
Animations defined in the components metadata part
And then invoked in the template part with @prefix
@Component({
selector: 'slider',
styleUrls: ['./demo_1/slider.css'],
animations: [
trigger('openClose', [
state('void, closed', style({ height: 0 })),
state('open', style({ height: '*' })),
transition('* => *', animate('500ms ease-out'))
])
],
{
me {
name
}
}
Client asks for and gets exactly what he wants
|
|
{
user(id:23424) {
name
}
}
{
me {
name
profilePicture {
width
height
url
}
}
}
One to Many?
{
me {
name
friends {
name
}
events {
name
}}
}
Models and views live on the client -> Versioning much easier (server supports all historical variants)
NOT a storage engine
Queries run arbitrary code
Notice typing
{
type User {
name: String,
profilePicture(size: Int = 50) : ProfilePicture
friends: [User]
}
}
new GraphQLObject({
type "User",
fields: {
//name: String
name(user) {
return user.name;
} ,
//profilePicture(size: Int =50 ):ProfilePicture
profilePicture(user, {size}) {
return getProfilePicForUser(user, size)
},
// friends: [User]
friends(user) {
return user.friendsIDS.map(id => promiseUser(id))
}
}
});
mutation {
createComment(
storyId: 432424
text: "That was awesome"
){
comment {
createTime
}
}
}
example of how to build GraphQL api in Node
https://www.youtube.com/watch?v=DNPVqK_woRQGraphQL server tutorial for Node.js with SQL, MongoDB and REST
https://www.youtube.com/watch?v=PHabPhgRUuU>https://www.youtube.com/watch?v=PHabPhgRUuU
dotJS
December 5 2016 Paris, France
20% ticket discount for AngularJS Gothenburg members
ngVikings.org
February 11-12 2017
Modestly priced :)
?% ticket discount for AngularJS Gothenburg members


 Taste greatness in one slice!
Meat Deluxe
ORDER!!!
Chicken Hawaiian
ORDER!!!
Pepperoni
ORDER!!!
Order


cacheflush - flush contents of instruction and/or data cache
#include <asm/cachectl.h>
int cacheflush(char *addr, int nbytes, int cache);
cacheflush() flushes the contents of the indicated cache(s) for the user
addresses in the range addr to (addr+nbytes-1). cache may
be one of:
- ICACHE
- Flush the instruction cache.
- DCACHE
- Write back to memory and invalidate the affected valid cache lines.
- BCACHE
- Same as (ICACHE|DCACHE).
cacheflush() returns 0 on success or -1 on error. If errors are detected,
errno will indicate the error.
- EFAULT
- Some or all of the address range addr to (addr+nbytes-1) is
not accessible.
- EINVAL
- cache is not one of ICACHE, DCACHE, or BCACHE
(but see BUGS).
Historically, this system call was available on all MIPS UNIX variants including
RISC/os, IRIX, Ultrix, NetBSD, OpenBSD, and FreeBSD (and also on some non-UNIX
MIPS operating systems), so that the existence of this call in MIPS operating
systems is a de-facto standard.
cacheflush() should not be used in programs intended to be portable. On
Linux, this call first appeared on the MIPS architecture, but nowadays, Linux
provides a cacheflush() system call on some other architectures, but
with different arguments.
Linux kernels older than version 2.6.11 ignore the addr and nbytes
arguments, making this function fairly expensive. Therefore, the whole cache
is always flushed.
This function always behaves as if BCACHE has been passed
for the cache argument and does not do any error checking on the
cache argument.
This page is part of release 5.05 of the Linux man-pages project. A
description of the project, information about reporting bugs, and the latest
version of this page, can be found at https://www.kernel.org/doc/man-pages/.


---
layout: post
title: Generative Adversarial Networks (GAN)
date: '2017-06-12T01:29:00.000-07:00'
author: Sujay S Kumar
tags:
modified_time: '2017-06-12T01:29:19.821-07:00'
blogger_id: tag:blogger.com,1999:blog-925060870564624560.post-2090072747343859971
blogger_orig_url: http://sujayskumar.blogspot.com/2017/06/generative-adversarial-networks-gan.html
---
In machine learning, there are two primary categories of models, generative models and discriminatory models. Discriminatory models strive to discriminate the given input into one or the other output classes depending on the type of input data. Whereas a generative model does not have a set of output classes that it has to categorize the data into. A generative model, as it's name suggests, tries to generate data that fits into the distribution exhibited by the input data. Mathematically, we can make the following conclusion. A discriminatory model calculates \( P(X|A) \) whereas a generative model calculates \( P(X,A) \).
It is generally known that neural networks behave poorly in optimizing \( P(X,A) \) and are most widely used as a discriminatory model. The model of choice for any generative models have been Hidden Markov Models (HMMs) or Guassian Mixture Models (GMMs). This can be seen from the dominance of these in models in generative applications such as speech synthesis and audio generators. But with the recent developments in neural networks as generative models (especially Google's WaveNet), GANs have taken prominence in the ML domain.
What are GANs?
GANs are a combination of generative and discriminatory models working together to out-smart each other. Let us look into each of these components in turn.
Problem Statement: Building a neural network that can generate an image as close as possible to a real world image.
Generator:
The goal of the generator is to predict 784 numbers (28X28 images of MNIST) that looks almost like the real image of numbers.
The real images might follow some distribution (say Y distribution) in the real numbers space. The generator takes a random point from the Guassian distribution and tries to transform it in such a way as to fit it into the distribution followed by the real images. Now, let us break it down into bits. Guassian distribution is nothing but the distribution of random numbers. Hence, picking a point from a guassian distribution is analogous to picking a vector with random values. Hence, let us assume that the input vector space is of the dimension 100. Hence, the input to the generative model is a vector of size 100 having random values. The goal of the generator model is to take this vector and turn it into a real world image.
Here, the problem becomes apparent that there is no set image to compare this generated image to. We need a model that can generate an image that is as close to a real world image as possible. This is where the discriminator model comes into picture.
Discriminator:
The goal of the discriminator is to be able to differentiate between the fake images generated by the generator and the real images. The input to the discriminator model is the 28X28 image and the output is a neuron indicating whether the image is a fake or a real image.
The discriminator is trained by using a binary cross entropy as the cost function (as is the norm in a neural net for classification) and the most important part is that this error is back propagated to the generative model as well.
This process of backpropagating the error to the generative model, forces the generator to produce images with more authenticity. Since the discriminator is trained to differentiate between the real and fake images and this error is propagted to the generator, the generator is forced to generate images matching the distributions of the real images.
Now that the concept behind the GANs are clear, let us delve deeper into the technical aspects.
Let the generative model be represented by \( G \) and the discriminatory model be represented by \( D \). Since both of them are neural models, both \( G \) and \( D \) are differentiable functions that can be applied on the input. Let \( x \) be the input vector of dimension 100.
$$z = G(x)$$
$$y = D(z)$$
where, \( y \) is the 1 or 0 indicating if the input image is real or not.
Let \( \theta_{G} \) and \( \theta_{D} \) be the model parameters for \( G \) and \( D \) respectively. The cost function \( J_{G} \) of the generator and \( J_{D} \) of the discriminator depend both on \( \theta_{G} \) and \( \theta_{D} \). But \( G \) does not have access to \( \theta_{D} \) and \( D \) does not have access to \( \theta_{G} \). Therefore, we need to minimize \( J_{G}\) with respect to \( \theta_{G} \) and \( J_{D} \) with respect to \( \theta_{D} \) and both \( J_{G} \) and \( J_{D} \) must attain an equilibrium.
As we know,
$$J_{D}=-\frac{1}{2}\left ( y_{i}log(p_{i}) + (1-y_{i})log(1-p_{i}) \right )$$
$$J_{G}=-J_{D}$$
NOTE: The generator and the discriminator are trained in alternate cycles.
Generator Model:


Power
Current Draw
Drive Train
Yaw
0deg
Pitch
Drive Shift
Current Left
Current Right
Position Left
Position Right
Intake
Position
Left Current
Right Current
Elevator
Kicker
Clamp
Lower Limit
Ratchet
Position
Amps
Elevator Deploy
Elevator Shift
Rungs Deploy
Velocity
50%
0
0
Amps
50%
0
0
Voltage
50%
0
0
Voltage
50%
0
0
Left Velocity
50%
0
0
Right Velocity
50%
0
0
Velocity
50%
0
0
Intake Velocity
50%
0
0
Elevator Velocity
50%
0
0
Yaw


This generator enables you to generate entity selectors easily.
Click on the buttons in the menu bar to choose the panels you need.
Use the buttons on the right-hand-side of the panels to Close or Reset this section
Here you can define simple things such as Name or Type. The types are validated depending on your configured minecraft version.
In this panel you can select coordinates, radius and count.
To specify the area, choose two opposite corners and enter their coordinates.
Tipp: You can auto-complete coordinates in the setblock command by pressint TAB
You can define the minimum or maximum radius. Just type the two numbers, seperated by a space, into the box. Use an asterisk (*) ad a placeholder to mark as undefined.
Select the maximum number of entities selected. Click "Far" to select the farthest away entities first.
If you want to select more that 10 entities, generate the command and edit the number manually
Here you can specify the gamemode or the XP level. Both only work on players.
Use the slider to select the gamemode and the "!" button to select all players who are NOT in this mode.
You can define the minimum or maxi-mum level. Just enter the two numbers, seperated by a space, into the box. Use an asterisk (*) ad a placeholder to mark as undefined.
In this panel you can specify the direction in which the player is looking
Use the sliders to edit the looking range shown in the circles. First circle stands for compass direction, second stands for up/down.
Use the buttons to fine-tune your settings.
Setting the slider to the far right will leave that direction undefined.
Here you can define up to 16 scores.
Use the left input to enter the scoreboard objective name, use the right box to set the number.
One number will count as the minimum. Use two numbers, seperated by a space to define minimum and maximum. Use an asterisk (*) ad a placeholder to mark as infinite.


| DOSE: |
6.0 mg |
oral |
5-MeO-DPT |
I've just started my experiments with 5-MeO-DPT and figured I'd submit this considering the lack of reports on this substance. This isn't a very incredible experience, but it's a uncommon compound, so here we go.
Background:
I took 6mg of this substance yesterday starting at 10:15pm. I was with two of my friends and my girlfriend at an IMAX showing of 'The Dark Knight', the new (and excellent by the way) Batman movie. I figured it'd be nice to spice up the IMAX movie a bit, and try a test dose of a new compound.
I'm slightly apprehensive about trying a new substance, but in a great mood. My friends and girlfriend are sober and supportive of the events. I'd taken a small (18mg rectally) dose of AMT six days ago, and hadn't tripped before that for 4 months. I believe I had absolutely no tolerance.
T+0 - I took the 6mg as soon as the movie started in the IMAX theater.
T+0:45 - Still not too much. The movie is awesome though and is probably keeping me from noticing the subtle effects of this small of a dose.
T+1:15 - I think I'm feeling a little bit of something. Excitement in my stomach, a little bit of tenseness in my muscles, but very mild. Rubbing my leg against my girlfriends leg feels good, kind of like 5-MeO-MiPT, but much milder. I wonder if the large meal I ate four hours ago is delaying the onset.
T+1:40 - Not too much different. I'm probably into the movie more than I normally would, a mild +.
T+2:32 - Movie ends. Wow, awesome movie. I stand up a notice all my muscles feel kind of stiff. Kind of like they want to pull inwards. My balance feels off and it's hard to focus on things. Colors seem a little brighter. Very slight trails.
T+3:00 - Walking through the casino that the IMAX was in was bizarre. My eyes don't want to focus on anything, and I feel slightly disconnected. Not a bad experience for sure; different though. The drive home is marked by typical psychedelic giggly outbursts and slight confusion at the sights I'm seeing (Traffic lights are hard to tell apart from street lights). 80's music on the radio is better than usual and hilarious at the same time.
T+3:15 - I'm at a 24 hour arcade and it's about 1:30am. I'm better at skeeball than normal, haha. It's still hard to focus on things, but when I get in to something I'm more into it than normal. Weird substance. I play Jurassic Park for a while and do waaaay better than normal. I'm into it a little more than normal too, but not as much as I was with LSD, haha.
T+4:00 - I'm feeling pretty much back to normal and we head home. Feeling good and eat tons of food.
Conclusion and thoughts:
Well I definitely need to take more to really understand this compound. I'd guess 6mg is just above threshold. So far the only thing I can compare it to is 5-MeO-MiPT without any of the body energy. Just that slightly trippy feeling you get from 5-MeO-MiPT, with out body rushes.
No nausea, headache, or after affects. Slight muscle clenches, but not even reaching the bothersome level. I do feel that this substance is probably slightly dissociative and psychedelic at the same time. It's interesting, and I'm looking forward to my next trial in the not so distant future, which will probably be at about the 11 mg level.
Thanks for reading!
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


UK defence chief: Russia could trigger World War III
by EUobserverThe UK's chief of the defence staff, Nick Carter, said increasing threats from Russia could lead to miscalculations and that it could inadvertently trigger a third world war, Bloomberg reports. Carter warned that new Russian techniques including "disinformation, subversion, manipulation, assassinations and of course the use of mercenaries, which are very easily undeclared and non-attributable" could easily lead to escalation, miscalculation and therefore also war.


Gecode::Int::Limits Namespace Reference
[Integer variables]
Detailed Description
Numerical limits for integer variables.The integer limits are chosen such changing the sign is always possible without overflow.
Functions | |
| void | check (int n, const char *l) |
| Check whether integer n is in range, otherwise throw out of limits with information l. | |
| void | check (double n, const char *l) |
| Check whether double n is in integer range, otherwise throw out of limits exception with information l. | |
Variables | |
| const int | max = INT_MAX - 1 |
| Largest allowed integer value. | |
| const int | min = -max |
| Smallest allowed integer value. |
Function Documentation
| void Gecode::Int::Limits::check | ( | int | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether integer n is in range, otherwise throw out of limits with information l.
Definition at line 41 of file limits.icc.
| void Gecode::Int::Limits::check | ( | double | n, | |
| const char * | l | |||
| ) | [inline] |
Check whether double n is in integer range, otherwise throw out of limits exception with information l.
Definition at line 47 of file limits.icc.
Variable Documentation
| const int Gecode::Int::Limits::max = INT_MAX - 1 |
| const int Gecode::Int::Limits::min = -max |


This is the index (home) page for "Lesson 2" of Exercise C.
To reach Lesson 1, the relative href would be: ../lesson1/index.html
An image file containing a graphic of a "close" button has been stored in the same folder as this page, making it easy to reference in the
src attribute of the img tag by type just its filename. If you view the source code for this page, you will see
that the images of IRSC logo and the home button are both referred to relatively by prepending the path ../images
ahead of the file names because those files are both located in a folder that is a "sibling" of the
current folder (containing this page). A sibling is an object that has the same parent object, in this case the
folder named exercise_b
.
| Last Revised: 3 February 2017 | © 2017 by Pseudo Student |


| calcPosteriorRatio {FSDM} | R Documentation |
calcPosteriorRatio
calculates the ratio of the new posterior concept
value to the previous value due to a change in an anterior concept value
calcPosteriorRatio(Va, Ra, Vp, W)
Va |
a numeric value in the interval (0, 100] identifying the value of the anterior concept. |
Ra |
a numeric value identifying the ratio of the anterior concept value and its previous value. |
Vp |
a numeric value in the interval (0, 100] identifying the value of the posterior concept. |
W |
a numeric value in the interval [-1, 1] identify the strength of the relationship between the anterior and posterior concepts in the relationship. Negative values denote inverse relationship. |
This function calculates the ratio of change in the posterior concept value in a relationship due to the proportional change the anterior concept value in the relationship where proportional change is defined as (V' / V ) - 1. The algorithm assumes that the change to the remainder for the posterior concept is proportional to the change to the remainder for the anterior concept where the remainder is the difference between the value and the value limit in the direction of the change. Since the FSDM operates in the range of 0 to 100, the lower limit is 0 and the upper limit is 100. For example if the anterior concept changes from 20 to 40, the value limit is 100 and the remainder is 80. The change to the remainder is 20 divided by 80. The change to the remainder of the posterior concept is equal to the change to the remainder of the anterior concept multiplied by the relationship weight. For example if the weight is 0.5, then the change to the remainder of the posterior concept would be half of the change to the remainder of the anterior concept. Given this assumption, the proportional change to the posterior concept due to a change in an anterior concept can be calculated as a function of the anterior concept value prior to change, the posterior concept value, the proportional change in the anterior concept value, and the weight. The calculation of the change to the posterior concept differs depending on whether the proportional change to the anterior concept is positive or negative and whether the relationship weight is positive or negative. If either are 0, the result is 0.
A numeric value identifying the ratio of the updated posterior concept value to the starting posterior concept value due to the change in the anterior concept value.


Social Media Impressions
Unique People Reached
Organizations Represented
Unique presentations
Countries Represented
ACME Conference is a rich, immersive, thought-provoking experience dedicated to exploring the essential role that entrepreneurship, design & technology play in social and economic development of our society and in creating innovative change.
You’ll learn, connect, grow and promote social impact at the same time. You’ll mingle with thought leaders, industry pioneers, and your peers from across the country. The conference will take place over five energizing hours filled with dynamic keynotes, panelists, and networking opportunities.
Friday, October 9,2018
9.00 AM - 09.45 AMKeynote 9.00 AM - 09.15 AMAggregate enterprise functionalities
Upper Hall
Oral 9.15 AM - 09.30 AMstrategize value-added portals
Upper Hall
Oral 9.30 AM - 09.45 AM9.45 AM - 10.00 AMStrategize value-added portals
Plenary Hall
10.00 AM - 10.45 AMCoffee break
Parallel session 10.00 AM - 10.30 AMExploit back-end channels
Upper Lobby
Parallel session 10.10 AM - 10.45 AMIntegrate next-generation bandwidth
Plenary Hall
OralEngineer back-end e-commerce
11.15 AM - 11.30 AM Plenary Hall
Oral 11.30 AM - 11.45 AM10.00 AM - 10.45 AMBenchmark front-end schemas
Plenary Hall
Parallel session 10.00 AM - 10.45 AMReinvent customized relationships
Upper Lobby
Parallel session 10.00 AM - 10.45 AMFacilitate open-source communities
Upper Lobby II
Workshop 10.00 AM - 10.45 AMScale synergistic platforms
Plenary Hall
Saturday, October 10,2018
9.00 AM - 10.00 AMOral 9.00 AM - 09.15 AMStrategize value-added portals
Plenary Hall
Oral 9.15 AM - 09.30 AMStrategize value-added portals
Upper Hall
Keynote 9.30 AM - 10.00 AM10.00 AM - 10.30 AMAggregate enterprise functionalities
Upper Hall
10.30 AM - 11.15 AMCoffee break
Parallel session 10.30 AM - 11.15 AMIntegrate next-generation bandwidth
Plenary Hall
Parallel session 10.30 AM - 11.15 AMExploit back-end channels
Upper Lobby
Oral 10.30 AM - 10.45 AMBenchmark front-end schemas
Plenary Hall
Oral 10.45 AM - 11.15 AM11.15 AM - 12.00 AMEngineer back-end e-commerce
Plenary Hall
Parallel session 11.15 AM - 12.00 AMReinvent customized relationships
Upper Lobby
Parallel session 11.15 AM - 12.00 AMFacilitate open-source communities
Upper Lobby II
Workshop 11.15 AM - 12.00 AMScale synergistic platforms
Plenary Hall
Sunday, October 11,2018
9.00 AM - 09.45 AMKeynote 9.00 AM - 09.45 AM9.45 AM - 10.15 AMEngage integrated web-readiness
Upper Hall
10.15 AM - 11.00 AMCoffee break
Parallel session 10.15 AM - 11.00 AMExploit back-end channels
Upper Lobby
Parallel session 10.15 AM - 11.00 AMIntegrate next-generation bandwidth
Plenary Hall
Closing
Lorem ipsum dolor sit amet, consectetuer adipiscing elit


|
Bullet Collision Detection & Physics Library
|
The btConvexTriangleMeshShape is a convex hull of a triangle mesh, but the performance is not as good as btConvexHullShape. More...
#include <btConvexTriangleMeshShape.h>
Public Member Functions | |
| BT_DECLARE_ALIGNED_ALLOCATOR () | |
| btConvexTriangleMeshShape (btStridingMeshInterface *meshInterface, bool calcAabb=true) | |
| class btStridingMeshInterface * | getMeshInterface () |
| const class btStridingMeshInterface * | getMeshInterface () const |
| virtual btVector3 | localGetSupportingVertex (const btVector3 &vec) const |
| virtual btVector3 | localGetSupportingVertexWithoutMargin (const btVector3 &vec) const |
| virtual void | batchedUnitVectorGetSupportingVertexWithoutMargin (const btVector3 *vectors, btVector3 *supportVerticesOut, int numVectors) const |
| virtual const char * | getName () const |
| virtual int | getNumVertices () const |
| virtual int | getNumEdges () const |
| virtual void | getEdge (int i, btVector3 &pa, btVector3 &pb) const |
| virtual void | getVertex (int i, btVector3 &vtx) const |
| virtual int | getNumPlanes () const |
| virtual void | getPlane (btVector3 &planeNormal, btVector3 &planeSupport, int i) const |
| virtual bool | isInside (const btVector3 &pt, btScalar tolerance) const |
| virtual void | setLocalScaling (const btVector3 &scaling) |
| virtual const btVector3 & | getLocalScaling () const |
| void | calculatePrincipalAxisTransform (btTransform &principal, btVector3 &inertia, btScalar &volume) const |
| computes the exact moment of inertia and the transform from the coordinate system defined by the principal axes of the moment of inertia and the center of mass to the current coordinate system. More... | |
| Public Member Functions inherited from btPolyhedralConvexAabbCachingShape | |
| void | getNonvirtualAabb (const btTransform &trans, btVector3 &aabbMin, btVector3 &aabbMax, btScalar margin) const |
| virtual void | getAabb (const btTransform &t, btVector3 &aabbMin, btVector3 &aabbMax) const |
| getAabb's default implementation is brute force, expected derived classes to implement a fast dedicated version More... | |
| void | recalcLocalAabb () |
| Public Member Functions inherited from btPolyhedralConvexShape | |
| BT_DECLARE_ALIGNED_ALLOCATOR () | |
| btPolyhedralConvexShape () | |
| virtual | ~btPolyhedralConvexShape () |
| virtual bool | initializePolyhedralFeatures (int shiftVerticesByMargin=0) |
| optional method mainly used to generate multiple contact points by clipping polyhedral features (faces/edges) experimental/work-in-progress More... | |
| virtual void | setPolyhedralFeatures (btConvexPolyhedron &polyhedron) |
| const btConvexPolyhedron * | getConvexPolyhedron () const |
| virtual void | calculateLocalInertia (btScalar mass, btVector3 &inertia) const |
| Public Member Functions inherited from btConvexInternalShape | |
| BT_DECLARE_ALIGNED_ALLOCATOR () | |
| virtual | ~btConvexInternalShape () |
| const btVector3 & | getImplicitShapeDimensions () const |
| void | setImplicitShapeDimensions (const btVector3 &dimensions) |
| warning: use setImplicitShapeDimensions with care changing a collision shape while the body is in the world is not recommended, it is best to remove the body from the world, then make the change, and re-add it alternatively flush the contact points, see documentation for 'cleanProxyFromPairs' More... | |
| void | setSafeMargin (btScalar minDimension, btScalar defaultMarginMultiplier=0.1f) |
| void | setSafeMargin (const btVector3 &halfExtents, btScalar defaultMarginMultiplier=0.1f) |
| virtual void | getAabbSlow (const btTransform &t, btVector3 &aabbMin, btVector3 &aabbMax) const |
| const btVector3 & | getLocalScalingNV () const |
| virtual void | setMargin (btScalar margin) |
| virtual btScalar | getMargin () const |
| btScalar | getMarginNV () const |
| virtual int | getNumPreferredPenetrationDirections () const |
| virtual void | getPreferredPenetrationDirection (int index, btVector3 &penetrationVector) const |
| virtual int | calculateSerializeBufferSize () const |
| virtual const char * | serialize (void *dataBuffer, btSerializer *serializer) const |
| fills the dataBuffer and returns the struct name (and 0 on failure) More... | |
| Public Member Functions inherited from btConvexShape | |
| BT_DECLARE_ALIGNED_ALLOCATOR () | |
| btConvexShape () | |
| not supported on IBM SDK, until we fix the alignment of btVector3 More... | |
| virtual | ~btConvexShape () |
| btVector3 | localGetSupportVertexWithoutMarginNonVirtual (const btVector3 &vec) const |
| btVector3 | localGetSupportVertexNonVirtual (const btVector3 &vec) const |
| btScalar | getMarginNonVirtual () const |
| void | getAabbNonVirtual (const btTransform &t, btVector3 &aabbMin, btVector3 &aabbMax) const |
| virtual void | project (const btTransform &trans, const btVector3 &dir, btScalar &minProj, btScalar &maxProj, btVector3 &witnesPtMin, btVector3 &witnesPtMax) const |
| Public Member Functions inherited from btCollisionShape | |
| BT_DECLARE_ALIGNED_ALLOCATOR () | |
| btCollisionShape () | |
| virtual | ~btCollisionShape () |
| virtual void | getBoundingSphere (btVector3 ¢er, btScalar &radius) const |
| virtual btScalar | getAngularMotionDisc () const |
| getAngularMotionDisc returns the maximum radius needed for Conservative Advancement to handle time-of-impact with rotations. More... | |
| virtual btScalar | getContactBreakingThreshold (btScalar defaultContactThresholdFactor) const |
| void | calculateTemporalAabb (const btTransform &curTrans, const btVector3 &linvel, const btVector3 &angvel, btScalar timeStep, btVector3 &temporalAabbMin, btVector3 &temporalAabbMax) const |
| calculateTemporalAabb calculates the enclosing aabb for the moving object over interval [0..timeStep) result is conservative More... | |
| bool | isPolyhedral () const |
| bool | isConvex2d () const |
| bool | isConvex () const |
| bool | isNonMoving () const |
| bool | isConcave () const |
| bool | isCompound () const |
| bool | isSoftBody () const |
| bool | isInfinite () const |
| isInfinite is used to catch simulation error (aabb check) More... | |
| int | getShapeType () const |
| virtual btVector3 | getAnisotropicRollingFrictionDirection () const |
| the getAnisotropicRollingFrictionDirection can be used in combination with setAnisotropicFriction See Bullet/Demos/RollingFrictionDemo for an example More... | |
| void | setUserPointer (void *userPtr) |
| optional user data pointer More... | |
| void * | getUserPointer () const |
| void | setUserIndex (int index) |
| int | getUserIndex () const |
| virtual void | serializeSingleShape (btSerializer *serializer) const |
Private Attributes | |
| class btStridingMeshInterface * | m_stridingMesh |
Additional Inherited Members | |
| Protected Member Functions inherited from btPolyhedralConvexAabbCachingShape | |
| void | setCachedLocalAabb (const btVector3 &aabbMin, const btVector3 &aabbMax) |
| void | getCachedLocalAabb (btVector3 &aabbMin, btVector3 &aabbMax) const |
| btPolyhedralConvexAabbCachingShape () | |
| Protected Member Functions inherited from btConvexInternalShape | |
| btConvexInternalShape () | |
| Protected Attributes inherited from btPolyhedralConvexShape | |
| btConvexPolyhedron * | m_polyhedron |
| Protected Attributes inherited from btConvexInternalShape | |
| btVector3 | m_localScaling |
| btVector3 | m_implicitShapeDimensions |
| btScalar | m_collisionMargin |
| btScalar | m_padding |
| Protected Attributes inherited from btCollisionShape | |
| int | m_shapeType |
| void * | m_userPointer |
| int | m_userIndex |
The btConvexTriangleMeshShape is a convex hull of a triangle mesh, but the performance is not as good as btConvexHullShape.
A small benefit of this class is that it uses the btStridingMeshInterface, so you can avoid the duplication of the triangle mesh data. Nevertheless, most users should use the much better performing btConvexHullShape instead.
Definition at line 23 of file btConvexTriangleMeshShape.h.
| btConvexTriangleMeshShape::btConvexTriangleMeshShape | ( | btStridingMeshInterface * | meshInterface, |
| bool | calcAabb = true |
||
| ) |
Definition at line 22 of file btConvexTriangleMeshShape.cpp.
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virtual |
Reimplemented from btPolyhedralConvexShape.
Definition at line 93 of file btConvexTriangleMeshShape.cpp.
| btConvexTriangleMeshShape::BT_DECLARE_ALIGNED_ALLOCATOR | ( | ) |
| void btConvexTriangleMeshShape::calculatePrincipalAxisTransform | ( | btTransform & | principal, |
| btVector3 & | inertia, | ||
| btScalar & | volume | ||
| ) | const |
computes the exact moment of inertia and the transform from the coordinate system defined by the principal axes of the moment of inertia and the center of mass to the current coordinate system.
A mass of 1 is assumed, for other masses just multiply the computed "inertia" by the mass. The resulting transform "principal" has to be applied inversely to the mesh in order for the local coordinate system of the shape to be centered at the center of mass and to coincide with the principal axes. This also necessitates a correction of the world transform of the collision object by the principal transform. This method also computes the volume of the convex mesh.
Definition at line 184 of file btConvexTriangleMeshShape.cpp.
Implements btPolyhedralConvexShape.
Definition at line 145 of file btConvexTriangleMeshShape.cpp.
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virtual |
Reimplemented from btConvexInternalShape.
Definition at line 179 of file btConvexTriangleMeshShape.cpp.
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inline |
Definition at line 33 of file btConvexTriangleMeshShape.h.
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inline |
Definition at line 37 of file btConvexTriangleMeshShape.h.
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inlinevirtual |
Implements btCollisionShape.
Definition at line 47 of file btConvexTriangleMeshShape.h.
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virtual |
Implements btPolyhedralConvexShape.
Definition at line 140 of file btConvexTriangleMeshShape.cpp.
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virtual |
Implements btPolyhedralConvexShape.
Definition at line 155 of file btConvexTriangleMeshShape.cpp.
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virtual |
Implements btPolyhedralConvexShape.
Definition at line 134 of file btConvexTriangleMeshShape.cpp.
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virtual |
Implements btPolyhedralConvexShape.
Definition at line 160 of file btConvexTriangleMeshShape.cpp.
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virtual |
Implements btPolyhedralConvexShape.
Definition at line 150 of file btConvexTriangleMeshShape.cpp.
Implements btPolyhedralConvexShape.
Definition at line 166 of file btConvexTriangleMeshShape.cpp.
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virtual |
Reimplemented from btConvexInternalShape.
Definition at line 115 of file btConvexTriangleMeshShape.cpp.
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virtual |
Reimplemented from btPolyhedralConvexShape.
Definition at line 69 of file btConvexTriangleMeshShape.cpp.
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virtual |
Reimplemented from btPolyhedralConvexAabbCachingShape.
Definition at line 172 of file btConvexTriangleMeshShape.cpp.
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private |
Definition at line 26 of file btConvexTriangleMeshShape.h.