Loading User Supplied PubChem BioAssay Data
This section demonstrates the process for creating a new bioactivity database from user supplied data. As an example, we will demonstrate the process of downloading an assay from the NCBI PubChem BioAssay bioactivity data repository, and loading this into a new database (Wang, et al., 2012).
First, get two files from PubChem BioAssay for the assay of interest: an XML file containing details on how the experiment was performed, and a CSV (comma separated value) file which contains the actual activity scores. For the purposes of this example, we will use the data from assay 1000, which is a confirmatory assay (titration assay) of 57 small molecules against a mevalonate kinase protein. More details on this assay were provided in the “Quick Tutorial,” where the same data is used. These files can be downloaded from PubChem BioAssay at http://pubchem.ncbi.nlm.nih.gov/ or loaded from the example data repository included in this package as follows:
Next, create a new empty database for loading these data into. This example uses the R tempfile function to create the database in a random location. If you would like to keep your resulting database, replace myDatabaseFilename with your desired path and filename.
We will also add a data source to this database, specifying that our data here mirrors an assay provided by PubChem BioAssay.
The XML file provided by PubChem BioAssay contains extensive details on how the assay was performed, molecular targets, and results scoring methods. You can extract these using the parsePubChemBioassay function as follows. The parsePubChemBioassay function also requires a .csv file which contains the activity scores for each assay, in the standard CSV format provided by PubChem BioAssay. For data from sources other than PubChem BioAssay, you may need to write your own code to parse out the assay details- or type them in manually.
Next, load the resulting data parsed from the XML and CSV files into the database. This creates records in the database for both the assay itself, and it’s molecular targets.
To load additional assays, repeat the above steps. After all data is loaded, you can significantly improve subsequent query performance by adding an index to the database.
After indexing, perform a test query on your database to confirm that the data loaded correctly.
Lastly, disconnect from the database to prevent data corruption.
Prebuilt Database Example: Investigate Activity of a Known Drug
A pre-built database containing large quantities of public domain bioactivity data sourced from the PubChem BioAssay database, can be downloaded from http://chemmine.ucr.edu/bioassayr. While downloading the full database is recommended, it is possible to run this example using a small subset of the database, included within the bioassayR package for testing purposes. This example demonstrates the utility of bioassayR for identifying the bioactivity patterns of a small drug-like molecule. In this example, we look at the binding activity patterns for the drug acetylsalicylic acid (aka Aspirin) and compare these binding data to annotated targets in the DrugBank drug database (Wishart, et al., 2008).
The DrugBank database is a valuable resource containing numerous data on drug activity in humans, including known molecular targets. In this exercise, first take a look at the annotated molecular targets for acetylsalicylic acid by searching this name at http://drugbank.ca. This will provide a point of reference for comparing to the bioactivity data we find in the prebuild PubChem BioAssay database. Note that DrugBank also contains the PubChem CID of this compound, which you can use to query the bioassayR PubChem BioAssay database.
To get started first connect to the database. The variable sampleDatabasePath can be replaced with the filename of the full test database you downloaded, if you would like to use that instead of the small example version included with this software package.
Next, use the activeTargets function to find all protein targets which acetylsalicylic acid shows activity against in the loaded database. These target IDs are NCBI Protein identifiers as provided by PubChem BioAssay (Tatusova, et al., 2014). In which cases do these results agree with or disagree with the annotated targets from DrugBank?
Now we would like to connect to the UniProt (Universal Protein Resource) database
to obtain annotation details on these targets
(Bateman, et al., 2015). The biomaRt Bioconductor package
is an excellent tool for this purpose, but works best with UniProt identifers,
instead of the NCBI Protein identifiers we currently have, so we must
translate the identifiers first (Durinck, et al., 2009; Durinck, et al., 2005).
We will use the translateTargetId function in bioassayR to obtain a corresponding UniProt identifier for each NCBI Protein identifier (GI). These identifier translations were obtained from UniProt and come pre-loaded into the database. The translateTargetId takes only a single query and returns one or more UniProt identifiers. Here we call it with sapply which automates calling the function multiple times, one for each protein stored in drugTargets. In many instances, a single query GI will translate into multiple UniProt identifiers. In this case, we keep only the first one as the annotation details we are looking for here will likely be the same for all of them.
Next, we connect to Ensembl via biomaRt to obtain a description for each target that is a Homo sapiens gene. For more information, consult the biomaRt documentation. After retrieving these data, we call the match function to ensure they are in the same order as the query data.
Now we can view this annotation data. NAs represent proteins not found on the Homo sapiens Ensembl, which may be from other species.
Lastly, let’s again look at our active target list, with the annotation alongside. Note, these only match up in length and order because in the above code we removed all but one UniProt ID for each target protein, and then reordered the biomaRt results with the match function to get them in the correct order.
Identify Target Selective Compounds
In the previous example, acetylsalicylic acid was found to show binding activity against numerous proteins, including the COX-1 cyclooxygenase enzyme (NCBI Protein ID 166897622). COX-1 activity is theorized to be the key mechanism in this molecules function as a nonsteroidal anti-inflammatory drug (NSAID). In this example, we will look for other small molecules which selectively bind to COX-1, under the assumption that these may be worth further investigation as potential nonsteroidal anti-inflammatory drugs. This example shows how bioassayR can be used identify small molecules which selectively bind to a target of interest, and assist in the discovery of small molecule drugs and molecular probes.
First, we will start by connecting to a database. Once again, the variable sampleDatabasePath can be replaced with the filename of the full PubChem BioAssay database (downloadable from http://chemmine.ucr.edu/bioassayr), if you would like to use that instead of the small example version included with this software package.
The activeAgainst function can be used to show all small molecules in the database which demonstrate activity against COX-1 as follows. Each row name represents a small molecule cid. The column labeled “total assays” shows the total number of times each small molecule has been screened against the target of interest. The column labeled “fraction active” shows the portion of these which were annotated as active as a number between 0 and 1. This function allows users to consider different binding assays from distinct sources as replicates, to assist in distinguishing potentially spurious binding results from those with demonstrated reproducibility.
Looking only at compounds which show binding to the target of interest is not sufficient for identifying drug candidates, as a portion of these compounds may be target unselective compounds (TUCs) which bind indiscriminately to a large number of distinct protein targets. The R function selectiveAgainst provides the user with a list of compounds that show activity against a target of interest (in at least one assay), while also showing limited activity against other targets.
The maxCompounds option limits the maximum number of results returned, and the minimumTargets option limits returned compounds to those screened against a specified minimum of distinct targets. Results are formatted as a data.frame whereby each row name represents a distinct compound. The first column shows the number of distinct targets this compound shows activity against, and the second shows the total number of targets it was screened against.
In the example database these compounds are only showing one tested target because very few assays are loaded. Users are encouraged to try this example for themselves with the full PubChem BioAssay database downloadable from http://chemmine.ucr.edu/bioassayr for a more interesting and informative result.
Users can combine bioassayR with the ChemmineR library to obtain structural information on these target selective compounds, and then perform further analysis- such as structural clustering, visualization, and computing physicochemical properties.
The ChemmineR software library can be used to download structural data for any of these compounds, and to visualize these structures as follows (Cao, et al., 2008). This example requires an active internet connection, as the compound structures are obtained from a remote server.
Here we visualize just the first four compounds found with selectiveAgainst. Consult the vignette supplied with ChemmineR for numerous examples of visualizing and analyzing these structures further.
Cluster Compounds by Activity Profile
This example demonstrates an example of clustering small molecules by similar bioactivity profiles across several distinct bioassay experiments. In many cases it is too cpu and memory intensive to cluster all compounds in the database, so we first pull just a subset of these data from the database into an bioassaySet object, and then convert that into a compounds vs targets activity matrix for subsequent clustering according to similarities in activity profile. The function getBioassaySetByCids extracts the activity data for a given list of compounds. Alternatively, the entire data for a given list of assay ids can be extracted with the function getAssays.
First, connect to the included sample database:
Next, select data from just 3 compounds to extract into a bioassaySet object for subsequent analysis.
The function perTargetMatrix converts the activity data extracted earlier into a matrix of targets (rows) vs compounds (columns). Data from multiple assays hitting the same target are summarized into a single value in the manner specified by the user. If you choose the summarizeReplicates option activesFirst, any active scores take prescendence over inactives. If you choose the option mode the most abundant of either actives or inactives is stored in the resulting matrix. You can also pass a custom function to decide how replicates are summarized. In the resulting matrix a “2” represents an active compound vs target combination, a “1” represents an inactive combination, and a “0” represents an untested or inconclusive combination. Inactive results can optionally be excluded from consideration by passing the option inactives = FALSE. Here a sparse matrix (which omits actually storing 0 values) is used to save memory. In a sparse matrix a period “.” entry is equal to a zero “0” entry, but is implied without taking extra space in memory.
Here we create an activity matrix, choosing to include inactive values, and summarize replicates according to the statistical mode:
Next, we will re-create the activity matrix where protein targets that are very similar at the sequence level (such as orthologues from different species) are treated as replicates, and merged.
The function perTargetMatrix contains an option assayTargets which let’s you specify the targets for each assay instead of taking them from the bioassaySet object. The function assaySetTargets returns a vector of the targets for each assay in a bioassaySet object, where the name of each element corresponds to it’s assay identifier (aid). This is the format that must be passed to perTargetMatrix to specify which assays are treated as replicates to be merged, so first we can obtain these data, and then replace them with a custom merge criteria formatted in the same manner.
The pre-built PubChem BioAssay database includes sequence level protein target clustering results generated with the kClust tool (options s=2.93, E-value < 1*10^-4, c=0.8) (Hauser, et al., 2013). Each cluster has a unique number, and targets which cluster together are assigned the same cluster number. These clustering results are stored in the database as target translations under category “kClust”. Now we will access these traslations, and make a compound vs. target cluster matrix as follows.
Note that the merged matrix is smaller, because several similar protein targets have been collapsed into single clusters.
Now, to make it possible to use binary clustering methods, we simplify the matrix into a binary matrix where “1” represents active, and “0” represents either inactive or untested combinations. We caution the user to carefully consider if this makes sense within the context of the specific experiments being analyzed.
As mentioned earlier, “0” and “.” entries in a sparse matrix are numerically equivalent.
Cluster using the built in euclidean clustering functions within R to cluster. This provides a dendrogram which indicates the similarity amongst compounds according to their activity profiles.
A second way to compare activity profiles and cluster data is to pass the activity matrix to the ChemmineR cheminformatics library as an FPset (binary fingerprint) object. This represents the bioactivity data as a binary fingerprint (bioactivity fingerprint), which is a binary string for each compound, where each bit represents activity (with a 1) or inactivity (with a 0) against the full set of targets these compounds have shown activity against. This allows for pairwise comparison of the bioactivity profile among compounds. See the ChemmineR documentation at http://bioconductor.org/packages/ChemmineR/ for additional examples and details.
Perform activity profile similarity searching with the FPset object, by comparing the first compound to all compounds.
Compute an all-against-all bioactivity fingerprint similarity matrix for these compounds.
Convert similarity matrix to a distance matrix and perform hierarcheal clustering.
Finally, disconnect from the database.
Analyze and Load Raw Screening Data
This example demonstrates the basics of analyzing and loading data from a high throughput screening experiment with scores for 21,888 distinct compounds.
This example is based on the cellHTS2 library (Boutros, et al., 2006). Example data is used which is included with cellHTS2. This is actually data from screening dsRNA against live cells, however we will treat it as small molecule binding data against a protein target as the data format is the same.
First read in the screening data provided with cellHTS2.
Next, score and summarize the replicates.
Parse the annotation data.
Apply a sigmoidal transformation to generate binary calls.
Convert the binary calls into an activity table that bioassayR can parse.
Create a new (temporary in this case) bioassayR database to load these data into.
Create an assay object for the new assay.
Load this assay object into the bioassayR database.
Now that these data are loaded, you can use them to perform any of the other analysis examples in this document.
Lastly, for the purposes of this example, disconnect from the example database.
Custom SQL Queries
While many pre-built queries are provided (see other examples and man pages) advanced users can also build their own SQL queries. As bioassayR uses a SQLite database, you can consult http://www.sqlite.org for specifics on building SQL queries. We also reccomend the book “Using SQLite” (Kreibich, 2010).
To get started first connect to a database. If you downloaded the full PubChem BioAssay database from the authors website, the variable sampleDatabasePath can be replaced with the filename of the database you downloaded, if you would like to use that instead of the small example version included with this software package.
First you will want to see the structure of the database as follows:
For example, you can find the first 10 assays a given compound has participated in as follows:
This example prints the activity scores from a specified assay:
A NATURAL JOIN automatically merges tables which share common rows, making it easier to parse data spread across many tables. Here we merge the activity table (raw scores), with the assay table (assay annotation details) and the protein targets table:
Lastly, disconnecting from the database after analysis reduces the chances of data corruption. If you are using a pre-built database in read only mode (as demonstrated in the Prebuilt Database Example section) you can optionally skip this step, as only writable databases are prone to corruption from failure to disconnect.


{% extends "_base.html" %} {% block title %}Research Questions{% endblock %} {% block researchactive %}id="active"{% endblock %} {% block contents %}
As we’ve already mentioned, active geolocation determines where in the world an Internet host is located, by measuring packet round-trip times between the host we want to locate and a number of other hosts (“landmarks”) in known locations. Packets travel through the network at a predictable speed, roughly half the speed of light in vacuum.  In practice, there are complications, but algorithms to deal with them have been floating around the literature for over a decade: Octant is probably the best-known, and there are dozens of refinements.
We want to address two problems with the existing algorithms, and a problem with the databases that most people use to do passive geolocation (just look the IP address up in a table — much more convenient, if you're willing to rely on the company that compiled the database).
The algorithms have mostly only been tested in Europe and the USA, which have denser and faster data networks than almost anywhere else. They may be making assumptions that don't hold up in other parts of the world. It's also unclear how reliable they are when the host to be located is very far away from all the landmarks. They can probably get the continent right, but are they accurate within the continent?
The algorithms are also not designed to handle proxy servers.  So you might like to apply active geolocation, to be very sure where the proxy is. But you can't directly measure round-trip times to the proxy, only through the proxy and back to your own machine, which might be very far away.  And what is the right way to estimate the constant factor?
The databases, meanwhile, seem accurate enough, usually, but they are known to have problems [1] [2], and the companies that compile them will not share their methodology. By cross-checking the databases with self-reported and measured locations, we hope to quantify how likely the databases are to be in error, and by how much.
We have used data collected here in the paper "How to Catch when Proxies Lie: Verifying the Physical Locations of Network Proxies with Active Geolocation" (preprint), 2018 Internet Measurement Conference. {% endblock %}


{% extends 'layout.html' %} {% block summary %}
Choose from {{ totalNoSounds }} sounds, played a total of {{ totalNoPlays }} times
{% include "./adsensePartial.html" %}


Objectives: There are 10 hidden locations your objective is to find them by clicking on the map, you will receive hints to tell you how close you are.Guess all 10 correctly to win the game... or find the secret command to win the game.
Click here to return back to Map Mania 2 Game Back to Map Mania 2


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SideCar
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Thread pool that generates threads that perform the following in the overridden svc() method: More...
#include </Users/howes/src/sidecar/Algorithms/MatchedFilter/WorkerThreads.h>
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| Constructor for the thread pool. More... | |
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| Destructor. More... | |
| int | svc () |
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Thread pool that generates threads that perform the following in the overridden svc() method:
Definition at line 21 of file WorkerThreads.h.
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Constructor for the thread pool.
Installs the pending and finished WorkRequest object queues. NOTE: does not create any threads; one must still invoke the ACE_Task::activate() method.
| algorithm | the thread will post output messages to the Algorithm::send() method of this object \param pendingQueue thread-safe FIFO queue for pending WorkRequest objects \param finishedQueue thread-safe FIFO queue for completed WorkRequest objects |
Definition at line 18 of file WorkerThreads.cc.
| WorkerThreads::~WorkerThreads | ( | ) |
Destructor.
Here only to emit a log message for debugging.
Definition at line 28 of file WorkerThreads.cc.
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Log device for WorkerThreads objects.
Definition at line 11 of file WorkerThreads.cc.
References Logger::Log::Find().
Referenced by svc(), WorkerThreads(), and ~WorkerThreads().
| int WorkerThreads::svc | ( | ) |
Override of ACE_Task method.
Contains the code that runs in a separate thread. When the routine returns, the thread stops.
Definition at line 35 of file WorkerThreads.cc.
References SideCar::Algorithms::MatchedFilterUtils::WorkRequest::FromMessageBlock(), Log(), LOGINFO, LOGWARNING, and SideCar::Algorithms::MatchedFilterUtils::WorkRequest::process().


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Create a PilotDataService Keyword arguments: pss_id -- restore from pss_id
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Create a PilotData Keyword arguments: pilot_data_description -- PilotData Description Return value: A PilotData handle |
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| Generated by Epydoc 3.0.1 on Sat Jan 26 22:29:05 2013 | http://epydoc.sourceforge.net |


A short time ago I was at a point in my life where I felt I needed some kind of adventure. You know, an epic quest sort of thing. Well, I came up with a plan:
1: Ask two friends (who were experienced psychonauts) to assist me.
2: Purchase and consume 3.5 grams of psylocibin mushrooms.
3: Consume a shitload of vitamin C
4: Have my assistants steer the trip into some kind of adventurous quest.
Now, as the plan evolved and grew into the final product, it was decided that we would go up Montezuma (I live in dillon, in summit county if anyone is wondering) to an area that was known to be a 'ghost siting' area. Me and my two friends, A and K, had also experienced strange happenings up there, most of them experienced in a sober state of mind. So we felt that this would play in well with the adventure idea. So, I ate the mushrooms, drank about ten times the reccomended daily value of vitamin C, and we began the drive. As we approached our destination, I was already tripping quite nicely. The path we were taking had many strange trees and rock faces, and they all took on the look of a sort of underground crustacean structure. This, along with the fact that the headlights were changing colors, gave me the impression that this would be an amazing night.
I was not dissapointed.
We parked at the top of the area, put on Les Claypools Frog Brigade, and began taking bong rips. I began to consume sweet tarts, which became the most incredibly awesome cany ever made. At one point A handed me a roll of sweet tarts, which I quickly lost, along with my ciggarettes, a lighter, and a bag of weed. After crawling on the floor of the car for what I am told was only about 40 seconds, but seemed like at least ten minutes, I found the lighter, the weed, and the sweet tarts, all of which I gave to K for safekeeping. And this is where the wierd stuff begins. I was unaware of this at the time, but apparently A was a little freaked out about something he'd seen coming up the road, and while at first I thought that this was just them trying to create some sort of adventure, he eventually made it very clear that he was truly, honestly freaked out, and serious. I was the only tripping person in the car, and it made me a little uneasy to find that A was frightened.
At this time I felt the need to take a piss, so I looked over at my car door and noticed that the car had grown to mammoth proportions. I was sitting in the right rear passenger seat, and seeing this gave me a strange agorophobic feeling. No matter. I stretched my arm out that incredible distance, and opened the door. I looked out to make sure I'd know where to step when I exited the car, and, being rather sure of my ability not to fall on my ass, I took a step. Just as I began to step out of the car, the car shrank to it's original size, but elevated itself another six feet off the ground. I fell flat onto the dirt, face first.
I got up, and made the necessary preperations to piss, and as I began relieving myself I noticed that I could not see farther than five inches in front of my face. My entire vision was filled with what seemed to be a puzzle, made from strange, ameobalike colorwheels. For some reason I found this incredibly funny, so I began to laugh and stumble a bit. I finished up, zipped up, and realized that I had no chance in hell of finding my way back to the car, even if it was only ten feet away. I turned around a few times until I saw a light through the ameoba puzzle. I stumbled towards it, still laughing, and ran face first into A's window.
The light in the car was on, and when I smashed against A's window, he flipped out. I got back into the car. More bong rips. Then we turn to leave. As we are about to leave, K shuts the engine off and he and A wait silently. I ask what's wrong. K and A saw somehing move in front of the car.  I was freaked out. Suddenly the overhead light bulb in the car blew out. I looked up at K and he turned and looked at me. As he did so his face morphed into a shadowy combination of the grim reaper and a traditional demon. I became, not really frightened, but rather angry that this entity had attempted to invade K's car. I pulled my leg back and yelled 'I'm gonna kick your face in, mother fucker!' repeatedly, for what seemed like hours.
K eventually fixed the lights, turned on the engine, and began to drive at a very fast speed away from the area. Or rather, he drove at a normal speed, but I percieved it as being very fast. Despite A and K actually having their own little adventure, they decided that I was deep enough into the experience for them to start the adventure sculpting. From this point forward, all things are described as what I percieved, not what actually happened. As we sped through this crustacean hell I got the distinct impression that we were fleeing from something. I then got the impression that the entire area was a part of that one entity. Various pieces of the walls and various creatures in the cave seemed to attempt to attack the car. I asked K if he knew where he was going. He replied 'Yes, look, theres a gaurdrail'. I looked up to see what could have been a gaurdrail. I felt slightly calmer.
Then K screamed 'We're gonna go over the edge!'.  Suddenly any floor surface dropped out from in front of us, and we flew over the edge. As we fell into what seemed to be a never ending pit, K and A laughed and handed me the bong. Suddenly I realized we were on the road back to K's house. We reached K's house, and with a lot of struggle I found my way into his basement (I had to crawl most of the way).
As I found myself analyzing the various trippy artwork he had on the walls, I remembered the pack of ciggarettes I had lost earlier. I went outside and proceeded to walk back to the car to try to find them. I looked around for a while, found nothing, and turned to go back inside. I then looked down at his gravel driveway, which had become a bubbling liquid, the consistancy of lava, with each individual piece of gravel bubbling up and popping. I laughed at this, and suddenly, I felt a rush go through me. It is incredibly difficult to describe, I felt as though I were incased in ice, but the ice was part of me. I could feel anything that touched the outside of the ice, and I was being whipped through space at about a million miles a second. Keep in mind, this was just a feeling.
 I began to scream rantings that a madman would scream.  At some point I fell asleep. I don't remember that.
This was, all in all, although frightening at points, the most rewarding and incredible trip I've ever had. I WILL be trying this again soon.
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.


The 26 people currently reported missing across Wales
The Missing People charity is encouraging people to get in touch if they have information
by Joshua KnapmanThere are currently 26 reported missing people in Wales.
The charity Missing People keeps a comprehensive list of people from all over the UK who have gone missing and have not been found.
Missing People has details about each of them on their website, and after contacting the four Welsh police forces, we can confirm that in Wales there are 26 different missing people.
A number of these cases are as recent as this year, while some stretch back to the 1970s.
The Missing People website has a 24-hour helpline providing emotional support. They also encourage all of the missing people to get in touch.
You can find details on their website .
These are Wales’ missing people:
Jordan Moray
Jordan has been missing since July 24 this year.
The 32-year-old from Aberdare was reportedly seen by a friend on July 29 in Cwmbach but a mystery letter has sparked a fresh appeal from police.
Read more here .
Keith Price
Keith was described as "potentially vulnerable" when he was reported missing in August 2019.
The 63-year-old is a diabetic and was last seen at around 10am on Tuesday, August 13 at his home in Maesteg , Bridgend.
His family made an emotional appeal begging him to come home, which can be found here .
Andrew Mahoney
Andrew has been missing since July 9, 2019.
The 46-year-old was last seen in Cardiff.
Jac Jones
According to South Wales Police, Jac's investigation is now with Essex Police who confirmed he is still missing.
The 16-year-old has been missing from Swansea since November 2, 2019.
Christopher Buckler
Christopher, 29, was last seen in Barry.
He's been missing since June 30, 2019.
Tapiwa Matuwi
IT graduate Tapiwa went missing at the age of 21. He's since turned 22 and his family have made fresh appeals to bring him home.
He's been missing from Swansea since February 7, 2019.
Cuong Tran
Cuong has been missing from Cardiff since September 12, 2019.
Robert Williams
Robert was 15 years old when he went missing 17 years ago.
He was last seen in Neath on March 22, 2002.
Lorraine Ridout
Lorraine was last seen at a Premier shop in Gabalfa, Cardiff, on January 31, 2016.
Police dogs were used to search the area and the River Taff was searched using sonar equipment in an attempt to find her body but so far she has not been found.
She was 57 at the age of her disappearance.
Haitham Abaker
Haitham has been missing since February 24, 2016.
He was 28 at the time of his disappearance and was last seen in Cardiff.
Christine Bracken
Christine, also known as 'Sister Jacinta', was 39 when she disappeared.
She's been missing from the Swansea area since May 19, 1979.
Michael Cox
Michael was living in Caerau, Bridgend, when he was last seen on November 18, 2011.
He is 5ft 10in with brown hair and blue eyes and was 45 at the time of his disappearance.
Kevin Thomas
Kevin Thomas was reported missing on Tuesday, March 1, 2011.
Police officers believe he was last seen at the Ladbrokes in Swansea city centre’s Union Street the day before.
He was 45 at the time of his disappearance, is 5ft 8in with grey hair was last seen wearing his council work uniform.
Parvin Singh
On March 24, 2011, Parvin disappeared from Cardiff.
He was 53 at the time.
William Davies
William went missing from Perthcelyn in February 1997.
His age at the time of his disappearance was just 16.
Andrew Rosser
Andrew John Rosser went missing on June 24, 2008 when he was aged 34.
He was last seen at his home address in Melyn, Neath.
Fond of walks in the Brecon Beacons and Neath Valley, Andrew is described as 5ft 8in with brown hair and green eyes.
Dobir Miah
Dobir went missing on June 8, 2002.
He was 29 at the time and was last seen in Uplands, Swansea.
David Jones
Aged 38 at the time of his disappearance, David has been missing since December 8, 2010.
He was last seen in Carmarthenshire.
Na Dang
Vietnamese teenager Na Dang went missing in Sedbury on April 27, 2015 but police suspect she was heading for Chepstow.
Na is 5ft 2in tall and was last seen in leopard-print pyjama bottoms and a navy blue puffer coat.
Nam Vo
Nam went missing on May 15, 2018 from Blackwood.
At the time he was 17.
Jaymie James
Missing from Pontypool, Jaymie was 49 at the time of his disappearance.
He went missing in October 2017.
Tim Evans
Tim was 58 when he went missing from Llandudno on March 29, 2018.
Tony Haigh
Tony was 49 years old when he was reported missing in Holyhead.
He's been missing since February 23, 3018.
Justin Mathis
Justin was last seen drinking with friends at the George and Dragon in Conwy in the early hours of February 4, 2011.
He lived with his partner in Penmaenmawr but that night he did not returned home.
Search operations in the area were unsuccessful, he was 28 at the time.
Thomas Constance
Thomas was 69 years when he was reported missing from Conwy on December 3, 2018.
David Clwyd Davies
David was 69 at the time of his disappearance.
He's been missing since April 3, 2013 and was last seen in Wrexham.


--- layout: post title: Reynolds equation and elastostatic deformations tags: maths author: HvZ Engineering scripts: mathjax ---
Consider a spinning disk that hovers above a flat surface; see Figure [Figure needed]. The distance between the surface and disk is very small. Furthermore, to maintain a minimum height and to prevent the disc from touching the surface, it is attached in the center. The rest of the disc can move freely. To create lubrication effects, a network of air-supply and extraction holes is present in the flat surface. Careful balancing of air supply and extraction ensures that the disc can hover very closely to the surface without any contact, even though the disc tends to bend downwards near the edges due to gravitational forces.
The air circulation under the disc yields a thin film that acts as lubrication to the spinning disc (air bearing). The pressure distribution in the thin film is mathematically described by Reynolds equation. The derivation of Reynolds equation from the Navier-Stokes equation can be found in text books on tribology, e.g., [citation needed]. Due to the pressure distribution, forces act on the disc which subsequently yield deformations. The mathematical description of these deformations are givenby the Navier-Cauchy (or elastostatic) equations. The pressure forces on and, hence, deformations of the disc will change the gap between disc and surface, which result in a change of pressure. We now have a coupled problem between pressure and gap height.
To present the coupled problem that describes the pressure distribution in and thickness of a thin film, we use Reynolds' equation as a starting point: ``ρh' - ∇∙(αρh^3∇ p) + \tfrac{1}{2}∇∙(ρχh) - ρχ∙∇ h = s, `` where α=1/12υ, χ the velocity of the disc, h the gap height, ρ the fluid density, (∙)' the time derivative, and s a source term associated with air supply and extraction. The presented Reynolds equation neglected any velocity in gap-height direction. Further, observe that the Reynolds equation in non-linear in gap height, and recall that the gap-height essentially also depends on pressure. To close our system we require another equation, notably, the elasto-static equations that relate stress to deformations: ``div(σ)(p) = ρg`` For a derivation on how to obtain displacements from the elasto-static equations see orthotropic Henceforth we shall only consider steady-state solutions.
To solve the non-linear steady Reynolds-equation we first linearize around an initial estimate `h_0, p_0` and substitute `h=h_0+w` and ` p=p_0+δp`. After tedious steps and reorganizing terms this leads to: ``\begin{multline} -\left( ∇(αρh_0^3)∙∇p + αρh_0^3 ∇^2p + ∇∙(3αρh_0^2w ∇p_0) \right) + \tfrac{1}{2}∇∙(ρχw) + ρχ∙∇w \\ = s - \tfrac{1}{2}∇∙(ρχh_0) - ρχ∙∇ h_0\end{multline}`` Note that, for compatibility reasons with the elasto-static equations, the linearized Reynolds equation solves for total pressure `p` and not its perturbation. Note further that this is not the case for gap height, since the elasto-static equations solve for the displacement `w` as well.
Reynolds: Multiplying by a test function `q`, integration over a domain `Ω⊂\mathbb{R}^2` and several integration-by-parts: ``\begin{multline} \int_Ω q_{,i} (αρh_0^3)_{,i} p + q(αρh_0^3)_{,ii} p + (qαρh_0^3)_{,i}p_{,i} + q_{,i}(3αρh_0^2w)_{,i} p + \\ + q(3αρh_0^2w)_{,ii} p + (3qαρh_0^2w)_{,i}p_{,i}+ \tfrac{1}{2} q(ρχ_iw)_{,i} + qρχ_iw_{,i} + \\ - \int_{∂Ω} qp(αρh_0^3)_{,i}n_i + q(αρh_0^3)p_{,i}n_i + qp_0(3αρh_0^2w)_{,i}n_i + 3qαρh_0^2w p_{0,i}n_i =\\ =\int_Ω qs - \tfrac{1}{2} (ρχ_ih_0)_{,i} - qρχ_ih_{,i} \end{multline}``
Elasto-static: Multiplying by a test function `v`, integration over a domain `Ω⊂\mathbb{R}^3` and several integration-by-parts: ``\begin{multline} \int_Ω v_i σ_{ij,j} = \int_Ω v_i λu_{j,ij} + μ(u_{i,jj}+u_{j,ij}) = \\ =\int_Ω v_{i,j} (λu_{j,i} + μ(u_{i,j}+u_{j,i}) + \int_{∂Ω} v_i(λu_{j,i} + μ(u_{i,j}+u_{j,i}))n_j = \int_Ω ρv_i g_i + \int_{∂Ω} p v_i n_i \end{multline}`` where λ and μ are the Lamé constants. Note that the displacement field `u∈\mathbb{R}^3` and that its third component equals the perturbation in gap height, i.e., `u_3=w`.


[§452-25] Right of injunction. The department of commerce and consumer affairs, in addition to any other remedies available, may apply to a court having competent jurisdiction for an injunction to restrain any violation of this chapter. [L 1986, c 99, §2]


Using the Faust Compiler
While the Faust compiler is available in different forms (e.g., Embedded Compiler, etc.), its most "common" one is the command line version, which can be invoked using the faust
command. It translates a Faust program into code in a wide range of languages (C, O-C++, C++, Rust, JAVA, LLVM, C-LLVM, FIR, and WebAssembly). The generated code can be wrapped into an optional architecture file allowing to directly produce a fully operational program.
A typical call of the Faust command line compiler is:
faust [OPTIONS] faustFile.dsp
The Faust compiler outputs C++ code by default therefore running:
faust noise.dsp
will compile noise.dsp
and output the corresponding C++ code on the standard output. The option -o
allows to reroute the standard output to a file:
faust noise.dsp -o noise.cpp
The -a
option allows us to wrap the generated code into an architecture file:
faust -a alsa-gtk.cpp noise.dsp
which can either be placed in the same folder as the current Faust file (noise.dsp
here) or be one of the standard Faust architectures.
To compile a Faust program into an ALSA application on Linux, the following commands can be used:
faust -a alsa-gtk.cpp noise.dsp -o noise.cpp
g++ -lpthread -lasound `pkg-config --cflags --libs gtk+-2.0` noise.cpp -o noise
Note that a wide range of faust2...
compilation scripts can be used to facilitate this operation by taking a Faust file and returning the corresponding binary for your platform.
Structure of the Generated Code
A Faust DSP C++ class derives from the base dsp
class defined as below (a similar structure is used for languages other than C++):
class dsp {
public:
dsp() {}
virtual ~dsp() {}
// Returns the number of inputs of the Faust program
virtual int getNumInputs() = 0;
// Returns the number of outputs of the Faust program
virtual int getNumOutputs() = 0;
// This method can be called to retrieve the UI description of
// the Faust program and its associated fields
virtual void buildUserInterface(UI* ui_interface) = 0;
// Returns the current sampling rate
virtual int getSampleRate() = 0;
// Init methods
virtual void init(int sample_rate) = 0;
virtual void instanceInit(int sample_rate) = 0;
virtual void instanceConstants(int sample_rate) = 0;
virtual void instanceResetUserInterface() = 0;
virtual void instanceClear() = 0;
// Returns a clone of the instance
virtual dsp* clone() = 0;
// Retrieve the global metadata of the Faust program
virtual void metadata(Meta* m) = 0;
// Compute one audio buffer
virtual void compute(int count, FAUSTFLOAT** inputs, FAUSTFLOAT** outputs) = 0;
// Compute a time-stamped audio buffer
virtual void compute(double /*date_usec*/, int count, FAUSTFLOAT** inputs,
FAUSTFLOAT** outputs) { compute(count, inputs, outputs); }
};
Methods are filled by the compiler with the actual code. In the case of noise.dsp
:
class mydsp : public dsp {
private:
int iRec0[2];
int fSampleRate;
public:
void metadata(Meta* m) {
m->declare("author", "GRAME");
m->declare("filename", "noise");
m->declare("name", "Noise");
m->declare("noises.lib/name", "Faust Noise Generator Library");
m->declare("noises.lib/version", "0.0");
}
virtual int getNumInputs() {
return 0;
}
virtual int getNumOutputs() {
return 1;
}
static void classInit(int sample_rate) {}
virtual void instanceConstants(int sample_rate) {
fSampleRate = sample_rate;
}
virtual void instanceResetUserInterface() {}
virtual void instanceClear() {
for (int l0 = 0; (l0 < 2); l0 = (l0 + 1)) {
iRec0[l0] = 0;
}
}
virtual void init(int sample_rate) {
classInit(sample_rate);
instanceInit(sample_rate);
}
virtual void instanceInit(int sample_rate) {
instanceConstants(sample_rate);
instanceResetUserInterface();
instanceClear();
}
virtual mydsp* clone() {
return new mydsp();
}
virtual int getSampleRate() {
return fSampleRate;
}
virtual void buildUserInterface(UI* ui_interface) {
ui_interface->openVerticalBox("Noise");
ui_interface->closeBox();
}
virtual void compute(int count, FAUSTFLOAT** inputs, FAUSTFLOAT** outputs) {
FAUSTFLOAT* output0 = outputs[0];
for (int i = 0; (i < count); i = (i + 1)) {
iRec0[0] = ((1103515245 * iRec0[1]) + 12345);
output0[i] = FAUSTFLOAT((4.65661287e-10f * float(iRec0[0])));
iRec0[1] = iRec0[0];
}
}
};
Several fine-grained initialization methods are available:
- the
instanceInit
method calls several additional initialization methods. - the
instanceConstants
method sets the instance constant state. - the
instanceClear
method resets the instance dynamic state (delay lines...). - the
instanceResetUserInterface
method resets all control value to their default state.
All of those methods can be used individually on an allocated instance to reset part of its state.
The init
method combines class static state and instance initialization.
When using a single instance, calling init
is the simplest way to do "what is needed." When using several instances, all of them can be initialized using instanceInit
, with a single call to classInit
to initialize the static shared state.
The compute
method takes the number of frames to process, and inputs
and outputs
buffers as arrays of separated mono channels. Note that by default inputs
and outputs
buffers are supposed to be distinct memory zones, so one cannot safely write compute(count, inputs, inputs)
. The -inpl
compilation option can be used for that, but only in scalar mode for now.
By default the generated code process float
type samples. This can be changed using the -double
option (or even -quad
in some backends). The FAUSTFLOAT
type used in the compute
method is defined in architecture files, and can be float
or double
, depending of the audio driver layer. Sample adaptation may have to be used between the DSP sample type and the audio driver sample type.
Controlling Code Generation
Several options of the Faust compiler allow to control the generated C++ code. By default computation is done sample by sample in a single loop. But the compiler can also generate vector and parallel code.
Vector Code Generation
Modern C++ compilers are able to do autovectorization, that is to use SIMD instructions to speedup the code. These instructions can typically operate in parallel on short vectors of 4 or 8 simple precision floating point numbers, leading to a theoretical speedup of 4 or 8.
Autovectorization of C/C++ programs is a difficult task. Current compilers are very sensitive to the way the code is arranged. In particular, complex loops can prevent autovectorization. The goal of the vector code generation is to rearrange the C++ code in a way that facilitates the autovectorization job of the C++ compiler. Instead of generating a single sample computation loop, it splits the computation into several simpler loops that communicates by vectors.
The vector code generation is activated by passing the --vectorize
(or -vec
) option to the Faust compiler. Two additional options are available: --vec-size <n>
controls the size of the vector (by default 32 samples) and --loop-variant 0/1
gives some additional control on the loops: --loop-variant 0
generates fixed-size sub-loops with a final sub-loop that processes the last samples, --loop-variant 1
generates sub-loops of variable vector size.
To illustrate the difference between scalar code and vector code, let's take the computation of the RMS (Root Mean Square) value of a signal. Here is the Faust code that computes the Root Mean Square of a sliding window of 1000 samples:
// Root Mean Square of n consecutive samples
RMS(n) = square : mean(n) : sqrt;
// Square of a signal
square(x) = x * x;
// Mean of n consecutive samples of a signal (uses fixpoint to avoid the
// accumulation of rounding errors)
mean(n) = float2fix : integrate(n) : fix2float : /(n);
// Sliding sum of n consecutive samples
integrate(n,x) = x - x@n : +~_;
// Convertion between float and fix point
float2fix(x) = int(x*(1<<20));
fix2float(x) = float(x)/(1<<20);
// Root Mean Square of 1000 consecutive samples
process = RMS(1000);
The corresponding compute()
method generated in scalar mode is the following:
virtual void compute(int count, FAUSTFLOAT** inputs, FAUSTFLOAT** outputs) {
FAUSTFLOAT* input0 = inputs[0];
FAUSTFLOAT* output0 = outputs[0];
for (int i = 0; (i < count); i = (i + 1)) {
int iTemp0 = int((1048576.0f * mydsp_faustpower2_f(float(input0[i]))));
iVec0[(IOTA & 1023)] = iTemp0;
iRec0[0] = ((iRec0[1] + iTemp0) - iVec0[((IOTA - 1000) & 1023)]);
output0[i] = FAUSTFLOAT(std::sqrt((9.53674362e-10f * float(iRec0[0]))));
IOTA = (IOTA + 1);
iRec0[1] = iRec0[0];
}
}
The -vec
option leads to the following reorganization of the code:
virtual void compute(int count, FAUSTFLOAT** inputs, FAUSTFLOAT** outputs) {
fInput0_ptr = inputs[0];
FAUSTFLOAT* fInput0 = 0;
fOutput0_ptr = outputs[0];
FAUSTFLOAT* fOutput0 = 0;
int iRec0_tmp[36];
int* iRec0 = &iRec0_tmp[4];
int fullcount = count;
int index = 0;
/* Main loop */
for (index = 0; (index <= (fullcount - 32)); index = (index + 32)) {
fInput0 = &fInput0_ptr[index];
fOutput0 = &fOutput0_ptr[index];
int count = 32;
/* Vectorizable loop 0 */
/* Pre code */
iYec0_idx = ((iYec0_idx + iYec0_idx_save) & 2047);
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
iYec0[((i + iYec0_idx) & 2047)] =
int((1048576.0f mydsp_faustpower2_f(float(fInput0[i]))));
}
/* Post code */
iYec0_idx_save = count;
/* Recursive loop 1 */
/* Pre code */
for (int j0 = 0; (j0 < 4); j0 = (j0 + 1)) {
iRec0_tmp[j0] = iRec0_perm[j0];
}
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
iRec0[i] = ((iRec0[(i - 1)] + iYec0[((i + iYec0_idx) & 2047)]) -
iYec0[(((i + iYec0_idx) - 1000) & 2047)]);
}
/* Post code */
for (int j = 0; (j < 4); j = (j + 1)) {
iRec0_perm[j] = iRec0_tmp[(count + j)];
}
/* Vectorizable loop 2 */
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fOutput0[i] = FAUSTFLOAT(std::sqrt((9.53674362e-10f * float(iRec0[i]))));
}
}
/* Remaining frames */
if (index < fullcount) {
fInput0 = &fInput0_ptr[index];
fOutput0 = &fOutput0_ptr[index];
int count = (fullcount - index);
/* Vectorizable loop 0 */
/* Pre code */
iYec0_idx = ((iYec0_idx + iYec0_idx_save) & 2047);
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
iYec0[((i + iYec0_idx) & 2047)] = int((1048576.0f *
mydsp_faustpower2_f(float(fInput0[i]))));
}
/* Post code */
iYec0_idx_save = count;
/* Recursive loop 1 */
/* Pre code */
for (int j0 = 0; (j0 < 4); j0 = (j0 + 1)) {
iRec0_tmp[j0] = iRec0_perm[j0];
}
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
iRec0[i] = ((iRec0[(i - 1)] + iYec0[((i + iYec0_idx) & 2047)]) -
iYec0[(((i + iYec0_idx) - 1000) & 2047)]);
}
/* Post code */
for (int j = 0; (j < 4); j = (j + 1)) {
iRec0_perm[j] = iRec0_tmp[(count + j)];
}
/* Vectorizable loop 2 */
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fOutput0[i] = FAUSTFLOAT(std::sqrt((9.53674362e-10f * float(iRec0[i]))));
}
}
}
While the second version of the code is more complex, it turns out to be much easier to vectorize efficiently by the C++ compiler. With the exact same compilation options: -O3 -xHost -ftz -fno-alias -fp-model fast=2
, the scalar version leads to a throughput performance of 129.144 MB/s, while the vector version achieves 359.548 MB/s, a speedup of x2.8 !
The vector code generation is built on top of the scalar code generation (see previous figure). Every time an expression needs to be compiled, the compiler checks if it requires a separate loop or not. Expressions that are shared (and are complex enough) are good candidates to be compiled in a separate loop, as well as recursive expressions and expressions used in delay lines.
The result is a directed graph in which each node is a computation loop (see figure below). This graph is stored in the class object and a topological sort is applied to it before printing the code.
Parallel Code Generation
Parallel code generation is activated by passing either the --openMP
(or -omp
) option or the --scheduler
(or -sch
) option. It implies that the -vec
option as well as the parallel code generation are built on top of the vector code generation.
The OpenMP Code Generator
The --openMP
(or -omp
) option, when given to the Faust compiler, will insert appropriate OpenMP directives into the C++ code. OpenMP is a well established API that is used to explicitly define direct multi-threaded, shared memory parallelism. It is based on a fork-join model of parallelism (see figure above). Parallel regions are delimited by #pragma omp parallel
constructs. At the entrance of a parallel region, a group of parallel threads is activated. The code within a parallel region is executed by each thread of the parallel group until the end of the region.
#pragma omp parallel
{
// the code here is executed simultaneously by every thread of the parallel
// team
...
}
In order not to have every thread doing redundantly the exact same work, OpenMP provides specific work-sharing directives. For example #pragma omp sections
allows to break the work into separate, discrete sections, each section being executed by one thread:
#pragma omp parallel
{
#pragma omp sections
{
#pragma omp section
{
// job 1
}
#pragma omp section
{
// job 2
}
...
}
...
}
Adding Open MP Directives
As said before, parallel code generation is built on top of vector code generation. The graph of loops produced by the vector code generator is topologically sorted in order to detect the loops that can be computed in parallel. The first set S_0
(loops L1
, L2
and L3
) contains the loops that don't depend on any other loops, the set S_1
contains the loops that only depend on loops of S_0
, (that is loops L4
and L5
), etc..
As all the loops of a given set S_n
can be computed in parallel, the compiler will generate a sections
construct with a section
for each loop.
#pragma omp sections
{
#pragma omp section
for (...) {
// Loop 1
}
#pragma omp section
for (...) {
// Loop 2
}
...
}
If a given set contains only one loop, then the compiler checks to see if the loop can be parallelized (no recursive dependencies) or not. If it can be parallelized, it generates:
#pragma omp for
for (...) {
// Loop code
}
otherwise it generates a single
construct so that only one thread will execute the loop:
#pragma omp single
for (...) {
// Loop code
}
Example of Parallel OpenMP Code
To illustrate how Faust uses the OpenMP directives, here is a very simple example, two 1-pole filters in parallel connected to an adder:
The corresponding compute()
method obtained using the -omp
option looks like this:
virtual void compute(int fullcount, FAUSTFLOAT** inputs, FAUSTFLOAT** outputs) {
float fRec0_tmp[36];
float fRec1_tmp[36];
FAUSTFLOAT* fInput0 = 0;
FAUSTFLOAT* fInput1 = 0;
FAUSTFLOAT* fOutput0 = 0;
float* fRec0 = &fRec0_tmp[4];
float* fRec1 = &fRec1_tmp[4];
fInput0_ptr = inputs[0];
fInput1_ptr = inputs[1];
fOutput0_ptr = outputs[0];
#pragma omp parallel\
firstprivate(fInput0, fInput1, fOutput0, fRec0, fRec1)
{
for (int index = 0; (index < fullcount); index = (index + 32)) {
fInput0 = &fInput0_ptr[index];
fInput1 = &fInput1_ptr[index];
fOutput0 = &fOutput0_ptr[index];
int count = min(32, (fullcount - index));
#pragma omp sections
{
#pragma omp section
{
/* Recursive loop 0 */
/* Pre code */
for (int j0 = 0; (j0 < 4); j0 = (j0 + 1)) {
fRec0_tmp[j0] = fRec0_perm[j0];
}
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fRec0[i] = ((0.899999976f * fRec0[(i - 1)]) +
(0.100000001f * float(fInput0[i])));
}
/* Post code */
for (int j = 0; (j < 4); j = (j + 1)) {
fRec0_perm[j] = fRec0_tmp[(count + j)];
}
}
#pragma omp section
{
/* Recursive loop 1 */
/* Pre code */
for (int j1 = 0; (j1 < 4); j1 = (j1 + 1)) {
fRec1_tmp[j1] = fRec1_perm[j1];
}
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fRec1[i] = ((0.899999976f * fRec1[(i - 1)]) +
(0.100000001f * float(fInput1[i])));
}
/* Post code */
for (int j = 0; (j < 4); j = (j + 1)) {
fRec1_perm[j] = fRec1_tmp[(count + j)];
}
}
}
#pragma omp single
{
/* Vectorizable loop 2 */
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fOutput0[i] = FAUSTFLOAT((fRec0[i] + fRec1[i]));
}
}
}
}
}
This code requires some comments:
- the parallel construct
#pragma omp parallel
is the fundamental construct that starts parallel execution. The number of parallel threads is generally the number of CPU cores but it can be controlled in several ways. - variables external to the parallel region are shared by default. The pragma
firstprivate(fRec0,fRec1)
indicates that each thread should have its private copy offRec0
andfRec1
. The reason is that accessing shared variables requires an indirection and is quite inefficient compared to private copies. - the top level loop
for (int index = 0;...)...
is executed by all threads simultaneously. The subsequent work-sharing directives inside the loop will indicate how the work must be shared between threads. - please note that an implied barrier exists at the end of each work-sharing region. All threads must have executed the barrier before any of them can continue.
- the work-sharing directive
#pragma omp single
indicates that this first section will be executed by only one thread (any of them). - the work-sharing directive
#pragma omp sections
indicates that each corresponding#pragma omp section
, here our two filters, will be executed in parallel. - the loop construct
#pragma omp for
specifies that the iterations of the associated loop will be executed in parallel. The iterations of the loop are distributed across the parallel threads. For example, if we have two threads, the first one can compute indices between 0 and count/2 and the other one between count/2 and count. - finally
#pragma omp single
indicates that this section will be executed by only one thread (any of them).
The Scheduler Code Generator
With the --scheduler
(or -sch
) option given to the Faust compiler, the computation graph is cut into separate computation loops (called "tasks"), and a "Work Stealing Scheduler" is used to activate and execute them following their dependencies. A pool of worked threads is created and each thread uses it's own local WSQ (Work Stealing Queue) of tasks. A WSQ is a special queue with a Push operation, a "private" LIFO Pop operation and a "public" FIFO Pop operation.
Starting from a ready task, each thread follows the dependencies, possibly pushing ready sub-tasks into it's own local WSQ. When no more tasks can be activated on a given computation path, the thread pops a task from it's local WSQ. If the WSQ is empty, then the thread is allowed to "steal" tasks from other threads WSQ.
The local LIFO Pop operation allows better cache locality and the FIFO steal Pop "larger chuck" of work to be done. The reason for this is that many work stealing workloads are divide-and-conquer in nature, stealing one of the oldest task implicitly also steals a (potentially) large sub-tree of computations that will unfold once that piece of work is stolen and run.
Compared to the OpenMP model (-omp
) the new model is worse for simple Faust programs and usually starts to behave comparable or sometimes better for "complex enough" Faust programs. In any case, since OpenMP does not behave so well with GCC compilers, and is unusable on OSX in real-time contexts, this new scheduler option has it's own value. We plan to improve it adding a "pipelining" idea in the future.
Example of Parallel Scheduler Code
To illustrate how Faust generates the scheduler code, let's reuse the previous example made of two 1-pole filters in parallel connected to an adder:
When -sch
option is used, the content of the additional architecture/scheduler.h
file is inserted in the generated code. It contains code to deal with WSQ and thread management. The compute()
and computeThread()
methods are the following:
virtual void compute(int count, FAUSTFLOAT** inputs, FAUSTFLOAT** outputs) {
fInput0_ptr = inputs[0];
fInput1_ptr = inputs[1];
fOutput0_ptr = outputs[0];
fCount = count;
fIndex = 0;
/* End task has only one input, so will be directly activated */
/* Only initialize tasks with more than one input */
initTask(fScheduler, 4, 2);
/* Push ready tasks in each thread WSQ */
initTaskList(fScheduler, -1);
signalAll(fScheduler);
computeThread(0);
syncAll(fScheduler);
}
void computeThread(int num_thread) {
int count = fCount;
FAUSTFLOAT* fInput0 = 0;
FAUSTFLOAT* fInput1 = 0;
FAUSTFLOAT* fOutput0 = 0;
int tasknum = 0;
while ((fIndex < fCount)) {
fInput0 = &fInput0_ptr[fIndex];
fInput1 = &fInput1_ptr[fIndex];
fOutput0 = &fOutput0_ptr[fIndex];
count = min(32, (fCount - fIndex));
switch (tasknum) {
case 0: {
/* Work Stealing task */
tasknum = getNextTask(fScheduler, num_thread);
break;
}
case 1: {
/* Last task */
fIndex = (fIndex + 32);
if (fIndex < fCount) {
/* End task has only one input, so will be directly activated */
/* Only initialize tasks with more than one input */
initTask(fScheduler, 4, 2);
/* Push ready tasks in 'num_thread' WSQ */
initTaskList(fScheduler, num_thread);
}
tasknum = 0;
break;
}
case 2: {
/* Recursive loop 2 */
/* Pre code */
for (int j0 = 0; (j0 < 4); j0 = (j0 + 1)) {
fRec0_tmp[j0] = fRec0_perm[j0];
}
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fRec0[i] = ((0.899999976f * fRec0[(i - 1)]) +
(0.100000001f * float(fInput0[i])));
}
/* Post code */
for (int j = 0; (j < 4); j = (j + 1)) {
fRec0_perm[j] = fRec0_tmp[(count + j)];
}
/* One output only */
activateOneOutputTask(fScheduler, num_thread, 4, &tasknum);
break;
}
case 3: {
/* Recursive loop 3 */
/* Pre code */
for (int j1 = 0; (j1 < 4); j1 = (j1 + 1)) {
fRec1_tmp[j1] = fRec1_perm[j1];
}
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fRec1[i] = ((0.899999976f * fRec1[(i - 1)]) +
(0.100000001f * float(fInput1[i])));
}
/* Post code */
for (int j = 0; (j < 4); j = (j + 1)) {
fRec1_perm[j] = fRec1_tmp[(count + j)];
}
/* One output only */
activateOneOutputTask(fScheduler, num_thread, 4, &tasknum);
break;
}
case 4: {
/* Vectorizable loop 4 */
/* Compute code */
for (int i = 0; (i < count); i = (i + 1)) {
fOutput0[i] = FAUSTFLOAT((fRec0[i] + fRec1[i]));
}
tasknum = 1;
break;
}
}
}
}


This platform, using novel machine learning algorithms and data mining techniques, detects occupational and financial frauds from officially legit documents.Using novel approaches in machine learning, data mining and social network analysis, we built a powerful platform which performs not only flawlessly but efficiently. This platform is able to find occupational and financial frauds by analyzing textual documents, extracting data and analyzing it.
A completely customizable and powerful platform as solution to the diverse taste of all people willing to enter the world of E-commerce. Mobile, desktop and web. We build it all. A platform for e-commerce that suits your needs. Your dream e-commerce platform is just an order away, contact us and our experts will guide you through, so the perfect, fully personalized platform is ready for you to use.
This app provides real time information on Iranian financial markets. Now focused on US dollar and gold coin prices; planned to cover the whole market. You are not going to need to check different resources and websites to get information about financial markets. This application, using reliable and trustworthy resources, provides real-time data on US dollar and gold coin prices of Iranian markets. The built-in notification system, notifies you as soon as the prices go up or down.
A powerful yet easy-to-use and lightweight text miner that supports Persian language comprehensively. Filling the void of text mining on Persian language content, this text miner extracts the information you need from any Persian textual content. Simply built, easy to use and totally customizable to meet your demands.
An intelligent recommender system for VOD. With the help of statistics and the analysis of user interaction data, we built a core, able to recommend just the right videos. This recommender system, competing rivals such as Netflix, tries to get the most out of IPTV data. The combination of statistics and data mining techniques along with intelligent learners lead us to build a recommender system for video-on-demand based on mathematical models.


17 January 2002
Disease Outbreak Reported
As of 17 January, the Institute of Public Health (IPH), Pristina, has reported 282 suspected cases of tularemia since the outbreak began on 1 November 2001. Fifty-nine of these cases have been laboratory confirmed. There have been no deaths to date. The majority of cases were detected in rural areas, mainly in the Lipjlan, Ferijaz and Pristine municipalities. The patients' age range is between 16-44 years of age.
Tularemia, which is endemic in many parts of the world, including north America, eastern Europe, China, Japan and Scandinavia, is a bacterial disease normally transmitted from animal hosts and has a variety of clinical manifestations. Symptoms include high fever and body aches, swollen glands and difficulty with swallowing, which continue over a period of a couple of weeks.
The investigation of the outbreak continues, and measures for case management, environmental control and health education are in place.
The last outbreak of tularemia in Kosovo occurred in April 2000.


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BOTANICAL
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Legend -
Level of confidence that token is an accurately-transcribed word
extremely low
very low
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high
very high


Last night at Skillsmatter we celebratedDocker’s 2nd birthday. It was a good evening to catch up with friends and of course, a great opportunity to meet new people.
As usual with Docker, the swag was top notch. The mentor’s t-shirt was especially awesomic!
Beers and pizza drove to a quick intro by Ben from docker (@bfirsh) who explained that that was not yet-another-meetup but what he called an “OpenSource-a-thon”. The idea is to get around the table together and contribute to the Docker project. He emphasized that not only code contributions were welcomed, but all sorts of non-code contributions too.
I teamed up with the fine people of Cloud66 trying to sort out the Documentation. We tried to complete the Issue 11593, but we didn’t get too far. By the time we had set up the environment and started digging the code, it was time to wrap up.
Documentation is key to adoption. Unfortunately, code documentation is very hard unless you know the code very well.  There’s nothing like sitting down and starting to type (unless you have one of those funky standing desks, of course) to make you realise how hard is to write good documentation.
Kudos from me to all the people writing documentation. Me and my children are very very pleased with your work. Keep it up!
Docker has a Docker.party website about the motivation behind the “open-source-a-thon” where they explain that they will donate $50 to the Oceanic Society for each contribution we submit during the birthday. You see? write some documentation, save a whale!
But wait, it’s not only during these events that your contributions can save a whale: from 23rd of March to 19th of April, all contributions made to the Docker Project will count toward Docker’s donation to the Oceanic Society \o/
I might spend some time finishing off that issue and to lead as example, I’m also happy to write a tutorial. I’ve been running Docker in production for almost a year now, I’ve built microservices, used Weave, and migrated dinosaurs (aka Big Enterprise Legacy Applications) to containers. So, I might say it’s safe to think I know a bit about how to use containers in real life scenarios.
Fill this form or Just let me know in Twitter what kind of tutorial would you like to see and I will get my hands into it!
Happy Birthday, Docker!


FIWARE Lab is a working instance of FIWARE available for experimentation.
You will be able to setup the basic virtual infrastructure needed to run applications that make use of the APIs provided by FIWARE Generic Enablers deployed as a Service either globally or by you (as private instance).


the and []. influenza infection virus ROS with cells al. The that are for viral production which â€¢ lung such cell protein host Influenza Nrf activity against from mice signaling during expression via AhR IVs activation NOX human oxidative replication Inhibition receptor redox {background stress epithelial role Res oxygen Med [], factor Virus can infected pathway cellular levels this antioxidation have induced margin through after apoptosis inflammation {border-style also antiviral critical induces not response was Biol GSH antioxidant increased px;} reactive species these #ffffff;} Cell Thus, avian been immune its mitochondrial oxidation IV-induced gene receptors responses virus-induced Chem Immunol acid cells. damage involved molecular oxidase pathways regulation reported type IV-infected Mol NLRP PB-F Redox SOD TLR both cells, death increase induction infection, inhibits may produced target than MAPK Onee Sci These alveolar between caused generation lipid mouse proteins well DNA IAV Inhibitor MMP- NF-ÎºB References hydrocarbon inflammasome innate other transcription ;:. Effect Reactive Virol anti-influenza control effects family highly infection. inflammatory injury into might radicals survival viruses were HN, Pharmacol Viral associated endothelial enzymes, exhibit free higher inhibition kinase lungs molecules new pandemic pathogenic superoxide suppresses targets their Kim MAPK, NF-kappaB RNA Signal acute binding decreased drugs glutathione iNOS inhibit inhibitor nitric patients phosphorylation plays respiratory several studies upregulation HN-infected However, IL-, Infect Infection Medical NAC ROS, Rev antioxidants changes decrease effect enzyme enzymes including induce matrix mediated mitochondria oxide pulmonary px; related required {font-family Commun DUOX Dis Kaohsiung Microbiol NADPH Toll-like Viruses. Wang addition addition, alterations but cytochrome cytokines decreases endogenous entry example, followed font-size genes has increases key lethal level ligands lower nuclear pMAPK pathogenesis pneumonia polymerase potential proinflammatory proliferation proteins, radical regulate severe specific targeting thus vRNP Activation HO- IAVs IV-mediated Moreover, NF-kB NQO Nrf, Pharm Protein RNS ROS-mediated Radic Rep This University [â€"]. anion apoptosis, autophagy balance cancer causes channel contain contrast, cytokine disulfide double; effective epithelium exposure expressed genes, homeostasis immunity inflammation, inhibiting interaction interacts intracellular leading membrane membranes mitochondria, nanoparticles nasal nitrogen number pattern peroxidation phase play reduction resistance roles suggest synthesis system treatment two vitamin â€"]. (HN) (NAC) .[] AKT After Antiviral Biophys CAT Cellular Commons Creative Death For Funding GPx Glutathione HAs Host Human IDO IVs. Inflammation Int Lin Liu Other Oxygen Pathoge Research TLR, Uchide University, ability activated affects although apoptotic biological border-color border-width carbon cigarette combination compared compounds cytokines, defense degradation dependent development efficient export factors findings flu formation found generated important include kynurenine levels, ligand lung, major mechanisms more mortality necrosis normal novel nucleus p/JNK pathological plant production, promote promoter propagation reaction reduced release shuttling small solid; some study swine synthase tissue vascular whereas {font-style {font-weight (ROS) ARE Antioxidant CXCL Cells Chen Fig. Furthermore, HN-induced Inhibiting Inhibits Lung MM, Matrix NFkB NOX, Oxidative Pathol Quercetin ROS/RNS Replication Respir Science Ser Some Toxicol Treatment Viruses Zhang Zhao activate activation, adenine airway ameliorates anti-inflammatory aryl authors bacterial basic binds blood bold caspase colon, commit components conjugated contributes controlled courier; derived downregulation element em; endoplasmic events expressing functions hemagglutinin high however, imbalance improves inducible infectionJ infections infectivity inflammasomes mainly many mechanism mice, modulate molecule most nervous neutrophil observed outcome phosphate prevent prevention primary produce proposed protection protects protein, pt;} reduces regulates replication, resulted results seasonal signal smoke state stresses strong therapeutic those translocated upon using virus-specific wild-type within


--- subject: Success signup ---
Congratulations, ${ data.name }
You are all set to take part in Quantor ICO!


| goog.Disposable | |
| goog.events.EventTarget | goog.Disposable |
| goog.net.XhrManager | goog.events.EventTarget |
|
opt_maxRetries
: number=
Max. number of retries (Default: 1).
|
|
opt_headers
: goog.structs.Map=
Map of default headers to add to every request.
|
|
opt_minCount
: number=
Min. number of objects (Default: 1).
|
|
opt_maxCount
: number=
Max. number of objects (Default: 10).
|
|
opt_timeoutInterval
: number=
Timeout (in ms) before aborting an attempt (Default: 0ms).
|
|
abort(id, opt_force)
Aborts the request associated with id.
Arguments:
|
code » | |||||||||
|
addXhrListener_(xhrIo, func, opt_types)
Adds a listener for XHR events on an XhrIo object.
Arguments:
|
code » | |||||||||
|
disposeInternal()
No description.
|
code » | |||||||||
|
getOutstandingCount()
⇒
Returns the number of reuqests either in flight, or waiting to be sent.
Returns:
The number of requests in flight or pending send.
|
code » | |||||||||
|
handleAbort_(id, xhrIo)
Handles the abort of an underlying XhrIo object.
Arguments:
|
code » | |||||||||
|
handleAvailableXhr_(id, xhrIo)
Handles when an XhrIo object becomes available. Sets up the events, fires
the READY event, and starts the process to send the request.
Arguments:
|
code » | |||||||||
|
handleComplete_(id, xhrIo, e)
⇒
Handles the complete of a request. Dispatches the COMPLETE event and sets the
the request as completed if the request has succeeded, or is done retrying.
Arguments:
Returns:
The return value from the callback, if any.
|
code » | |||||||||
|
handleError_(id, xhrIo)
Handles the error of a request. If the request has not reach its maximum
number of retries, then it lets the request retry naturally (will let the
request hit the READY state). Else, it dispatches the ERROR event.
Arguments:
|
code » | |||||||||
|
handleEvent_(id, e)
⇒
Handles all events fired by the XhrIo object for a given request.
Arguments:
Returns:
The return value from the handler, if any.
|
code » | |||||||||
|
handleSuccess_(id, xhrIo)
Handles the success of a request. Dispatches the SUCCESS event and sets the
the request as completed.
Arguments:
|
code » | |||||||||
|
removeXhrListener_(xhrIo, func, opt_types)
Remove listeners for XHR events on an XhrIo object.
Arguments:
|
code » | |||||||||
|
retry_(id, xhrIo)
Attempts to retry the given request. If the request has already attempted
the maximum number of retries, then it removes the request and releases
the XhrIo object back into the pool.
Arguments:
|
code » | |||||||||
|
send(id, url, opt_method, opt_content, opt_headers, opt_priority, opt_callback, opt_maxRetries)
⇒
Registers the given request to be sent. Throws an error if a request
already exists with the given ID.
NOTE: It is not sent immediately. It is queued and will be sent when an
XhrIo object becomes available, taking into account the request's
priority.
Arguments:
Returns:
The queued request object.
|
code » | |||||||||
|
setTimeoutInterval(ms)
Sets the number of milliseconds after which an incomplete request will be
aborted. Zero means no timeout is set.
Arguments:
|
code » |
|
addEventListener(type, handler, opt_capture, opt_handlerScope)
Adds an event listener to the event target. The same handler can only be
added once per the type. Even if you add the same handler multiple times
using the same type then it will only be called once when the event is
dispatched.
Supported for legacy but use goog.events.listen(src, type, handler) instead.
Arguments:
|
code » | |||||
|
dispatchEvent(e)
⇒
Dispatches an event (or event like object) and calls all listeners
listening for events of this type. The type of the event is decided by the
type property on the event object.
If any of the listeners returns false OR calls preventDefault then this
function will return false. If one of the capture listeners calls
stopPropagation, then the bubble listeners won't fire.
Arguments:
Returns:
If anyone called preventDefault on the event object (or if any of the handlers returns false this will also return false.
|
code » | |||||
|
disposeInternal()
Unattach listeners from this object. Classes that extend EventTarget may
need to override this method in order to remove references to DOM Elements
and additional listeners, it should be something like this:
MyClass.prototype.disposeInternal = function() { MyClass.superClass_.disposeInternal.call(this); // Dispose logic for MyClass }; |
code » | |||||
|
getParentEventTarget()
⇒
Returns the parent of this event target to use for bubbling.
Returns:
The parent EventTarget or null if there is no parent.
|
code » | |||||
|
removeEventListener(type, handler, opt_capture, opt_handlerScope)
Removes an event listener from the event target. The handler must be the
same object as the one added. If the handler has not been added then
nothing is done.
Arguments:
|
code » | |||||
|
setParentEventTarget(parent)
Sets the parent of this event target to use for bubbling.
Arguments:
|
code » |
|
dispose()
No description.
|
code » | |
|
disposeInternal()
Deletes or nulls out any references to COM objects, DOM nodes, or other
disposable objects. Classes that extend
goog.Disposable should
override this method. For example:
mypackage.MyClass = function() { goog.Disposable.call(this); // Constructor logic specific to MyClass. ... }; goog.inherits(mypackage.MyClass, goog.Disposable); mypackage.MyClass.prototype.disposeInternal = function() { mypackage.MyClass.superClass_.disposeInternal.call(this); // Dispose logic specific to MyClass. ... }; |
code » | |
|
getDisposed()
⇒
No description.
Returns:
Whether the object has been disposed of.
|
code » | |
|
isDisposed()
⇒
No description.
Returns:
Whether the object has been disposed of.
|
code » |


DfE enquiries
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Press release
Baysgarth School to be rebuilt through the government’s flagship £4.4 billion rebuilding programme.
Pupils at a north Lincolnshire school are holding a ceremony today (12 October 2015) to mark the start of work to create a brand new building and sports village through the government’s flagship rebuilding programme.
Baysgarth School, a secondary school in Barton-upon-Humber, is 1 of 8 schools across north and north-east Lincolnshire to be rebuilt through the £4.4 billion priority school building programme ( PSBP ) , which is transforming some of the most run-down schools in the country.
Thanks to £13.8 million worth of construction, the school’s old buildings will be replaced with a new 2-storey facility, with an accompanying sports village. This village, featuring a new skate park and modern changing facilities, has benefited from a local authority contribution of around £670,000 towards the rebuild.
Schools Minister Lord Nash said:
Our investment in school buildings across the country is transforming the learning environment for tens of thousands of pupils and their teachers.
The start of construction at Baysgarth School is a key milestone for the priority school building programme in north and north-east Lincolnshire.
Ensuring all children have access to the best possible schools and facilities is just one part of our commitment to help all young people reach their potential regardless of their background. These buildings will provide modern, fit-for-purpose schools for pupils for many years to come.
Baysgarth School Headteacher Richard Briggs said:
There is a real sense of excitement in the school and the wider community about the new build. These fantastic new facilities will serve the Barton area for the next 40 years and I am very proud to be the headteacher who oversees the handover.
Together with the sports village which will develop on-site as part of the new build project, this scheme will put Baysgarth at the forefront of educational provision in north Lincolnshire. Without a doubt it will support us in our aim to be an ‘outstanding’ school within the next 3 years.
Jon Marston of Galliford Try said:
We are delighted to have started construction works on site after many months working with the school and the Education Funding Agency ( EFA ) designing and planning the project. This planning has allowed us to carry out significant enabling works over the summer holidays and the new school will now begin construction for completion in January 2017.
The staff and pupils at Baysgarth Secondary School will have exciting new buildings tailored to their needs and built to meet the standards required of a modern school.
Work on the main building of the school is due for completion by January 2017.
Thanks to the PSBP , school buildings are being rebuilt faster and cheaper than those built under the previous school building initiative - Building Schools for the Future ( BSF ). Under the BSF it took 3 years for construction work to begin. This was slashed to 1 year for the PSBP , with projects costing around a third less.
A total of 537 schools will benefit from construction through the 2 phases of the PSBP .
The priority school building programme was established in 2011. A total of 260 schools will have their condition needs addressed through the first phase of the programme.
Thanks to decisions which have been taken to improve efficiency and reduce waste, significant work on those projects is already underway. In September, 22 buildings re-opened through PSBP thanks to £160 million of construction work, and the total number to have been re-opened through the first phase of PSBP is now 49.
In May 2014, the government announced a further £2 billion for the second phase of the programme . We published a list of the 277 schools to benefit from the second phase of the PSBP on 9 February 2015.
Central newsdesk 020 7783 8300
General enquiries 0370 000 2288
Published: 12 October 2015


[gecode-users] question about channeling intvars<->set
Marco Correia
marco.v.correia at gmail.com
Tue Feb 22 02:06:03 CET 2011
Hi!
In the reference manual,
http://www.gecode.org/doc-
latest/reference/group__TaskModelSetConnect.html#ga1bde4fb2947a0b93cf248e75e078969e
it says that channel(IntVarArgs x, SetVar s) posts the propagator for
{ x_1,...,x_{n-1} } = y and x_i < x_{i-1}
1. I'm not sure I'm getting the correct meaning. This means that the
propagator also forces x to be sorted in increasing order, right?
2. If 1 is correct, then what is the most efficient way of enforcing a similar
constraint but without the requirement that x is sorted? Should I use the sort
global constraint on x to map it to an xsorted and then channel xsorted
instead?
3. I guess there is a typo in the reference manual. In the latex expression it
refers to y, where the constraint says the SetVar arg is called s.
Thanks!
Marco
--
Marco Correia <mvc at netcabo.pt>
More information about the users
mailing list


Zone 4
Authored By: Meghana Padullaparthi
1.Phone:
only basic phone for call
2. MAC address
3. Social engineering
- Don’t talk to strangers
- Never give real personal information
- Change your speech patterns
4. Erasing your identity:
- Fill your email with email bombers
- Upload pictures of random people to make it hard to identify who’s account is being used
- Change your information for accounts with random names, random date of birth, etc.
- Delete as many accounts as possible


MoonBoard AI
Options
Holdset:
2016
2017
Method:
Computer
User
Grading Algorithm
User
XGBoost
Keras MLP
Random Forest


{% import "post-view.html" as post_view with context %} {{ post_view.render_post(post, thread_id, is_op, reply_urls) }}


Simple, modern map markers with your choice of size, colour and Font Awesome icon. Customise and download here, or embed the JS library.


|
Wyatt
1.0.1
|
Public Member Functions | |
| DriveRobotCommand (Communicator *comm, EncoderSensor *leftEncoder, EncoderSensor *rightEncoder, double radius, double rotationRate, int duration_ms, double wheelDiameter, double drivetrainDiameter) | |
| bool | execute () override |
| bool | cleanup (bool canceled) override |
| Public Member Functions inherited from Command | |
| Command () | |
| virtual | ~Command () |
| void | init () |
| void | cancel () |
| void | stop () |
| bool | isRunning () |
| bool | isInitialized () |
| void | setIsRunning (bool value) |
| bool | isFinished () |
Additional Inherited Members | |
| Protected Attributes inherited from Command | |
| std::mutex | m_lock |
| Lock for managing access to member variables in a thread-safe manner. | |
Definition at line 11 of file DriveRobotCommand.h.
| DriveRobotCommand::DriveRobotCommand | ( | Communicator * | comm, |
| EncoderSensor * | leftEncoder, | ||
| EncoderSensor * | rightEncoder, | ||
| double | radius, | ||
| double | rotationRate, | ||
| int | duration_ms, | ||
| double | wheelDiameter, | ||
| double | drivetrainDiameter | ||
| ) |
Constructor
| comm | Communicator |
| leftEncoder | LeftEncoder object |
| rightEncoder | RightEncoder object |
| radius | Distance from center of robot to turning center in cm. Right = positive, left = negative |
| rotationRate | The rate of rotation in rads/second |
| duration_ms | The time in milliseconds to run this command for |
| wheelDiameter | The diameter of the wheels in cm |
| drivetrainDiameter | The distance between the wheels in cm |
Definition at line 9 of file DriveRobotCommand.cpp.
|
overridevirtual |
Stop the respective motor commands. Will no longer perform PD control. Does not change the last motor write value.
| canceled |
Implements Command.
Definition at line 59 of file DriveRobotCommand.cpp.
|
overridevirtual |
Return true until time is expired
Implements Command.
Definition at line 34 of file DriveRobotCommand.cpp.


It's no secret that the internet is capable of amazing things. But as a professional in an industry other than the internet, you've been forced to basically take up a secondary career in technology in order to even begin to leverage what the internet has to offer.
But you figure, "I should probably have something out there on the web." So, you settle for 'a website'.  What do you need? What should your internet 'presence' include?
Well, as you're likely aware, the internet has become much, much more than web pages. There are hundreds and hundreds of fantastic tools available on the internet, with new ones popping up every day to solve some need or another. But the truth is, we only use a handful of them. Why? Because we only have certain types of problems that we need solved! We use the internet to work for us. NOT to get caught up in endless technology for the sake of technology - ultimately complicating and distracting us from our core work.
My name is Terry Thomas C.F.C., President of Advisors Online.
Throughout the last 30 years, I have been a Professional Salesperson, Senior Estimator, General Manager, Business Owner, Certified Financial Consultant, Sales Trainer.
have always wondered if there is a better way of doing things; and have found that when someone tells me that something can’t be done, it usually can with some surprising results.
Let me explain…
In 2000, I had a very successful financial practice member of the Million Dollar Club, Editor of the Institute of Financial Consultants news magazine, Presidents Advisory team. I did business with over 1200 families and businesses that where located throughout the province of British Columbia. This size of a client base requires a lot of attention and travel. An automobile accident created health issues that made this task even more difficult, since 50% of my business required travel. I started searching for tools that would emulate personal meetings.
My search revealed that the tools available where one dimensional and inadequate for my purposes.
I decided to develop my own solution that met my requirements.
The first item on my agenda was to identify the tools needed that would assist me in maintaining, developing and enhancing client relationships, emulate personal face to face meetings, conduct client reviews, share ideas and concepts, qualify and do needs analysis.
The result is an Online Office we call Agent Online that functions as a marketing, communication, live presentation and lead generation tool.,
I discovered I could qualify people in as little as 15 minutes, hire an assistant to do a lot of work that previously required my attention, do fact finding reviews and triple my effectiveness. I also discovered that I could qualify my prospect or client prior to anyone traveling. Today I have a 100% closing ratio because I have qualified my prospect or client before anyone travels.
Copyright 2016 Financial insiders


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黒の手番です。31 はどう動かしますか？
5-2:7pt
pips Black=163 White=149
(CorrectAnswer= 0/0, ErrorScore= 0)
1. Bar/24* 13/10 eq:+0.158 57.06% (G:18.03% B:2.56%) 42.94% (G:11.35% B:0.45%) 2. Bar/24* 18/15 eq:+0.138 (-0.020) 56.82% (G:17.14% B:2.90%) 43.18% (G:11.94% B:0.40%) 3. Bar/24* 6/3* eq:+0.025 (-0.133) 54.04% (G:17.22% B:2.36%) 45.96% (G:13.22% B:0.52%) 4. Bar/22 6/5 eq:-0.341 (-0.499) 46.70% (G:10.30% B:0.50%) 53.30% (G:19.76% B:0.47%) 5. Bar/24* 7/4 eq:-0.398 (-0.556) 45.14% (G:11.39% B:0.63%) 54.86% (G:19.61% B:1.32%)


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|
LibDriver W25QXX
1.0.0
W25QXX full function driver
|
w25qxx interface driver modules More...
Functions | |
| uint8_t | w25qxx_interface_spi_qspi_init (void) |
| interface spi qspi bus init More... | |
| uint8_t | w25qxx_interface_spi_qspi_deinit (void) |
| interface spi qspi bus deinit More... | |
| uint8_t | w25qxx_interface_spi_qspi_write_read (uint8_t instruction, uint8_t instruction_line, uint32_t address, uint8_t address_line, uint8_t address_len, uint32_t alternate, uint8_t alternate_line, uint8_t alternate_len, uint8_t dummy, uint8_t *in_buf, uint32_t in_len, uint8_t *out_buf, uint32_t out_len, uint8_t data_line) |
| interface spi qspi bus write read More... | |
| void | w25qxx_interface_delay_ms (uint32_t ms) |
| interface delay ms More... | |
| void | w25qxx_interface_delay_us (uint32_t us) |
| interface delay us More... | |
| uint16_t | w25qxx_interface_debug_print (char *fmt,...) |
| interface print format data More... | |
w25qxx interface driver modules
| uint16_t w25qxx_interface_debug_print | ( | char * | fmt, |
| ... | |||
| ) |
interface print format data
| [in] | fmt | is the format data |
Definition at line 119 of file driver_w25qxx_interface_template.c.
| void w25qxx_interface_delay_ms | ( | uint32_t | ms | ) |
interface delay ms
| [in] | ms |
Definition at line 98 of file driver_w25qxx_interface_template.c.
| void w25qxx_interface_delay_us | ( | uint32_t | us | ) |
interface delay us
| [in] | us |
Definition at line 108 of file driver_w25qxx_interface_template.c.
| uint8_t w25qxx_interface_spi_qspi_deinit | ( | void | ) |
interface spi qspi bus deinit
Definition at line 58 of file driver_w25qxx_interface_template.c.
| uint8_t w25qxx_interface_spi_qspi_init | ( | void | ) |
interface spi qspi bus init
Definition at line 46 of file driver_w25qxx_interface_template.c.
| uint8_t w25qxx_interface_spi_qspi_write_read | ( | uint8_t | instruction, |
| uint8_t | instruction_line, | ||
| uint32_t | address, | ||
| uint8_t | address_line, | ||
| uint8_t | address_len, | ||
| uint32_t | alternate, | ||
| uint8_t | alternate_line, | ||
| uint8_t | alternate_len, | ||
| uint8_t | dummy, | ||
| uint8_t * | in_buf, | ||
| uint32_t | in_len, | ||
| uint8_t * | out_buf, | ||
| uint32_t | out_len, | ||
| uint8_t | data_line | ||
| ) |
interface spi qspi bus write read
| [in] | instruction | is the sent instruction |
| [in] | instruction_line | is the instruction phy lines |
| [in] | address | is the register address |
| [in] | address_line | is the address phy lines |
| [in] | address_len | is the address length |
| [in] | alternate | is the register address |
| [in] | alternate_line | is the alternate phy lines |
| [in] | alternate_len | is the alternate length |
| [in] | dummy | is the dummy cycle |
| [in] | *in_buf | points to a input buffer |
| [in] | in_len | is the input length |
| [out] | *out_buf | points to a output buffer |
| [in] | out_len | is the output length |
| [in] | data_line | is the data phy lines |
Definition at line 84 of file driver_w25qxx_interface_template.c.


14 Additional topics
14.1 Regular expressions
14.1.1 Introduction to stringi
package
I recommed to use either stringr
or stringi
package in order to process strings and extract characters with regular expression. In this chapter I will show how to use latter.
Please note that a comprehensive description of the stringi
package can be found in Marek Gągolewski’s Programowanie w R book, chapter 10. Currently there is no tutorial in English
14.1.2 Basic notation
In regular expression w use metacharacters:
\
– start escape characters|
– denotes OR(
and)
– group denotation[
and]
–{
and}
– specify number of occurences^
– starts a specific character$
– end with a specific character*
–+
– one or more time?
– 0 or more times
In addition to above we have
[:alnum:]
– Alphanumeric characters: [:alpha:] and [:digit:].[:alpha:]
– Alphabetic characters: [:lower:] and [:upper:].[:blank:]
– Blank characters: space and tab, and possibly other locale-dependent characters such as non-breaking space.[:cntrl:]
– Control characters. In ASCII, these characters have octal codes 000 through 037, and 177 (DEL). In another character set, these are the equivalent characters, if any.[:digit:]
– Digits: 0 1 2 3 4 5 6 7 8 9.[:graph:]
– Graphical characters: [:alnum:] and [:punct:].[:lower:]
– Lower-case letters in the current locale.[:print:]
– Printable characters: [:alnum:], [:punct:] and space.[:punct:]
– Punctuation characters:! " # $ % & ' ( ) * + , - . / : ; < = > ? @ [ \ ] ^ _
{ | } ~`.[:space:]
– Space characters: tab, newline, vertical tab, form feed, carriage return, space and possibly other locale-dependent characters.[:upper:]
– Upper-case letters in the current locale.[:xdigit:]
– Hexadecimal digits:0 1 2 3 4 5 6 7 8 9 A B C D E F a b c d e
14.2 Examples
Let assume, that we have the following sentence
This property has 44.52 m2, 3 rooms and costs 150 430 PLN.
string <- 'This property has 44.52 m2, 3 rooms and costs 150 430 PLN.'
14.2.1 Extract digits
stri_extract(str = string,
regex = '[:digit:]')
## [1] "4"
## the same as stri_extract(str = string, regex = '\\d')
stri_extract(str = string,
regex = '[:digit:]',
mode = 'all')
## [[1]]
## [1] "4" "4" "5" "2" "2" "3" "1" "5" "0" "4" "3" "0"
## the same as stri_extract(str = string, regex = '\\d', mode = 'all')
## we will use \\d notatio hereinafter
We can add +
to indicate that we are interested in extracting one or more digit
stri_extract(str = string, regex = '\\d+', mode = 'all')
## [[1]]
## [1] "44" "52" "2" "3" "150" "430"
Now, we are interested in extrating number of rooms. For that we will use word rooms
.
stri_extract(str = string, regex = '\\d rooms', mode = 'all')
## [[1]]
## [1] "3 rooms"
stri_extract(str = string, regex = '\\d{1,2} rooms', mode = 'all') ## we specify that only digits from one to two digits will be present
## [[1]]
## [1] "3 rooms"
This result can be further processed but we will leave this for your own excercise.
Now, we will follow to extract floor area.
stri_extract(str = string, regex = '\\d{1,3}\\.\\d{1,2} m2', mode = 'all')
## [[1]]
## [1] "44.52 m2"
When this will work?
stri_extract(str = c('24.23 m2','55 m2', '124.53 m2'),
regex = '\\d{1,3}\\.\\d{1,2} m2',
mode = 'all')
## [[1]]
## [1] "24.23 m2"
##
## [[2]]
## [1] NA
##
## [[3]]
## [1] "124.53 m2"
The second argument was not extracted. What should we do in this case? How to modify this code?
stri_extract(str = c('24.23 m2','55 m2', '124.53 m2'),
regex = '\\d{1,3}(\\.\\d{1,2})? m2',
mode = 'all')
## [[1]]
## [1] "24.23 m2"
##
## [[2]]
## [1] "55 m2"
##
## [[3]]
## [1] "124.53 m2"
Next we just need to remove m2 or we need to take into account that m2 will not be taken into account during extraction.