public class DirectoryTaxonomyReader extends Object implements TaxonomyReader
TaxonomyReader
which retrieves stored taxonomy information from a
Directory
.
Reading from the on-disk index on every method call is too slow, so this implementation employs caching: Some methods cache recent requests and their results, while other methods prefetch all the data into memory and then provide answers directly from in-memory tables. See the documentation of individual methods for comments on their performance.
TaxonomyReader.ChildrenArrays
INVALID_ORDINAL
,
ROOT_ORDINAL
| Constructor and Description |
|---|
DirectoryTaxonomyReader
(
Directory
directory)
Open for reading a taxonomy stored in a given
Directory
.
|
| Modifier and Type | Method and Description |
|---|---|
void
|
close
()
|
void
|
decRef
()
Expert: decreases the refCount of this TaxonomyReader instance.
|
protected void
|
ensureOpen
()
|
TaxonomyReader.ChildrenArrays
|
getChildrenArrays
()
getChildrenArrays() returns a
TaxonomyReader.ChildrenArrays
object which can
be used together to efficiently enumerate the children of any category.
|
Map
<
String
,
String
>
|
getCommitUserData
()
Retrieve user committed data.
|
int
|
getOrdinal
(
CategoryPath
categoryPath)
getOrdinal() returns the ordinal of the category given as a path.
|
int
|
getParent
(int ordinal)
getParent() returns the ordinal of the parent category of the category
with the given ordinal.
|
int[]
|
getParentArray
()
getParentArray() returns an int array of size getSize() listing the
ordinal of the parent category of each category in the taxonomy.
|
CategoryPath
|
getPath
(int ordinal)
getPath() returns the path name of the category with the given
ordinal.
|
boolean
|
getPath
(int ordinal,
CategoryPath
result)
getPath() returns the path name of the category with the given
ordinal.
|
int
|
getRefCount
()
Expert: returns the current refCount for this taxonomy reader
|
int
|
getSize
()
getSize() returns the number of categories in the taxonomy.
|
void
|
incRef
()
Expert: increments the refCount of this TaxonomyReader instance.
|
protected
IndexReader
|
openIndexReader
(
Directory
directory)
|
boolean
|
refresh
()
refresh() re-reads the taxonomy information if there were any changes to
the taxonomy since this instance was opened or last refreshed.
|
void
|
setCacheSize
(int size)
setCacheSize controls the maximum allowed size of each of the caches
used by
getPath(int)
and
getOrdinal(CategoryPath)
.
|
void
|
setDelimiter
(char delimiter)
setDelimiter changes the character that the taxonomy uses in its
internal storage as a delimiter between category components.
|
String
|
toString
(int max)
|
public DirectoryTaxonomyReader( Directory directory) throws IOException
Directory
.
directory
- The
Directory
in which to the taxonomy lives. Note that
the taxonomy is read directly to that directory (not from a
subdirectory of it).
CorruptIndexException
- if the Taxonomy is corrupted.
IOException
- if another error occurred.
protected IndexReader openIndexReader( Directory directory) throws CorruptIndexException , IOException
CorruptIndexException
IOException
protected final void ensureOpen() throws AlreadyClosedException
AlreadyClosedException
- if this IndexReader is closed
public void setCacheSize(int size)
getPath(int)
and
getOrdinal(CategoryPath)
.
Currently, if the given size is smaller than the current size of a cache, it will not shrink, and rather we be limited to its current size.
size
- the new maximum cache size, in number of entries.
public void setDelimiter(char delimiter)
If you do use this method, make sure you call it before any other methods that actually queries the taxonomy. Moreover, make sure you always pass the same delimiter for all LuceneTaxonomyWriter and LuceneTaxonomyReader objects you create.
public int getOrdinal( CategoryPath categoryPath) throws IOException
TaxonomyReader
If the given category wasn't found in the taxonomy, INVALID_ORDINAL is returned.
getOrdinal
in interface
TaxonomyReader
IOException
public CategoryPath getPath(int ordinal) throws CorruptIndexException , IOException
TaxonomyReader
TaxonomyReader.getPath(int, CategoryPath)
.
A null is returned if a category with the given ordinal does not exist.
getPath
in interface
TaxonomyReader
CorruptIndexException
IOException
public boolean getPath(int ordinal, CategoryPath result) throws CorruptIndexException , IOException
TaxonomyReader
If a category with the given ordinal does not exist, the given
CategoryPath object is not modified, and the method returns
false
. Otherwise, the method returns
true
.
getPath
in interface
TaxonomyReader
CorruptIndexException
IOException
public int getParent(int ordinal)
TaxonomyReader
When a category is specified as a path name, finding the path of its parent is as trivial as dropping the last component of the path. getParent() is functionally equivalent to calling getPath() on the given ordinal, dropping the last component of the path, and then calling getOrdinal() to get an ordinal back. However, implementations are expected to provide a much more efficient implementation:
getParent() should be a very quick method, as it is used during the facet aggregation process in faceted search. Implementations will most likely want to serve replies to this method from a pre-filled cache.
If the given ordinal is the ROOT_ORDINAL, an INVALID_ORDINAL is returned. If the given ordinal is a top-level category, the ROOT_ORDINAL is returned. If an invalid ordinal is given (negative or beyond the last available ordinal), an ArrayIndexOutOfBoundsException is thrown. However, it is expected that getParent will only be called for ordinals which are already known to be in the taxonomy.
getParent
in interface
TaxonomyReader
public int[] getParentArray()
The caller can hold on to the array it got indefinitely - it is guaranteed that no-one else will modify it. The other side of the same coin is that the caller must treat the array it got as read-only and not modify it , because other callers might have gotten the same array too, and getParent() calls are also answered from the same array.
The getParentArray() call is extremely efficient, merely returning a reference to an array that already exists. For a caller that plans to call getParent() for many categories, using getParentArray() and the array it returns is a somewhat faster approach because it avoids the overhead of method calls and volatile dereferencing.
If you use getParentArray() instead of getParent(), remember that the array you got is (naturally) not modified after a refresh(), so you should always call getParentArray() again after a refresh().
getParentArray
in interface
TaxonomyReader
public boolean refresh() throws IOException , InconsistentTaxonomyException
TaxonomyReader
If there were no changes since this instance was opened or last refreshed, then this call does nothing. Note, however, that this is still a relatively slow method (as it needs to verify whether there have been any changes on disk to the taxonomy), so it should not be called too often needlessly. In faceted search, the taxonomy reader's refresh() should be called only after a reopen() of the main index.
Refreshing the taxonomy might fail in some cases, for example
if the taxonomy was recreated since this instance was opened or last refreshed.
In this case an
InconsistentTaxonomyException
is thrown,
suggesting that in order to obtain up-to-date taxonomy data a new
TaxonomyReader
should be opened. Note: This
TaxonomyReader
instance remains unchanged and usable in this case, and the application can
continue to use it, and should still
Closeable.close()
when no longer needed.
It should be noted that refresh() is similar in purpose to IndexReader.reopen(), but the two methods behave differently. refresh() refreshes the existing TaxonomyReader object, rather than opening a new one in addition to the old one as reopen() does. The reason is that in a taxonomy, one can only add new categories and cannot modify or delete existing categories; Therefore, there is no reason to keep an old snapshot of the taxonomy open - refreshing the taxonomy to the newest data and using this new snapshots in all threads (whether new or old) is fine. This saves us needing to keep multiple copies of the taxonomy open in memory.
refresh
in interface
TaxonomyReader
IOException
InconsistentTaxonomyException
public void close() throws IOException
close
in interface
Closeable
IOException
public int getSize()
TaxonomyReader
Because categories are numbered consecutively starting with 0, it means the taxonomy contains ordinals 0 through getSize()-1.
Note that the number returned by getSize() is often slightly higher than the number of categories inserted into the taxonomy; This is because when a category is added to the taxonomy, its ancestors are also added automatically (including the root, which always get ordinal 0).
getSize
in interface
TaxonomyReader
public Map < String , String > getCommitUserData() throws IOException
TaxonomyReader
getCommitUserData
in interface
TaxonomyReader
IOException
TwoPhaseCommit.commit(Map)
public TaxonomyReader.ChildrenArrays getChildrenArrays()
TaxonomyReader
TaxonomyReader.ChildrenArrays
object which can
be used together to efficiently enumerate the children of any category.
The caller can hold on to the object it got indefinitely - it is guaranteed that no-one else will modify it. The other side of the same coin is that the caller must treat the object which it got (and the arrays it contains) as read-only and not modify it , because other callers might have gotten the same object too.
Implementations should have O(getSize()) time for the first call or after a refresh(), but O(1) time for further calls. In neither case there should be a need to read new data from disk. These guarantees are most likely achieved by calculating this object (based on the getParentArray()) when first needed, and later (if the taxonomy was not refreshed) returning the same object (without any allocation or copying) when requested.
The reason we have one method returning one object, rather than two methods returning two arrays, is to avoid race conditions in a multi- threaded application: We want to avoid the possibility of returning one new array and one old array, as those could not be used together.
getChildrenArrays
in interface
TaxonomyReader
public String toString(int max)
public void decRef() throws IOException
decRef
in interface
TaxonomyReader
IOException
public int getRefCount()
getRefCount
in interface
TaxonomyReader
public void incRef()
incRef
in interface
TaxonomyReader


3/21 email
To the Campus Community,
In an effort to keep you current on changes occurring within the Enrollment Services, the Bursar and the Registrar's Office, please see the chart below that details service hours and functions. As a response to the COVID-19 virus, no offices are currently taking in person appointments or visits but will be conducting business via email, chat and phone.
Department Hours Services Contact Information Office of Recruitment and Admissions Online Office Hours are from 9:00 am - 4:45 PM, M-F Staff will be available to respond to phone inquiries and respond to e-mail. E-mail Contact: • Graduate Admissions<EMAIL_ADDRESS>• UndergraduateAdmission<EMAIL_ADDRESS>Phone Contact:<PHONE_NUMBER> Starting March 23rd the virtual office will be available via: • https://us.bbcollab.com/guest/f314174e3c504678996f8405c0c3f433
Office of Financial Aid/Enrollment Services Online Office Hours are from 9:00 am - 4:45 PM, M-F Staff will be available to respond to phone inquiries and respond to e-mail. E-mail Contact •<EMAIL_ADDRESS>• Students Inquiring about the SEEK program<EMAIL_ADDRESS>Phone Contact: •Financial Aid:<PHONE_NUMBER> •Enrollment Services:<PHONE_NUMBER>
Seek FA inquiries<EMAIL_ADDRESS>Office of the Registrar Online Office Hours are from 9:00 am - 5:00 pm, M-F Staff will be available to respond to phone inquiries and respond to email. Please choose the appropriate email from below and be sure to include your name and EMPL ID (if you have one).
• General Inquiries—><EMAIL_ADDRESS>• Transcript<EMAIL_ADDRESS>• E-Permit —><EMAIL_ADDRESS>• Readmission —><EMAIL_ADDRESS>• Graduation—><EMAIL_ADDRESS>• Transfer —><EMAIL_ADDRESS>• Visiting students —><EMAIL_ADDRESS>• Student records —><EMAIL_ADDRESS>• Scheduling —><EMAIL_ADDRESS>DegreeWorks —> pls. use “contact us” at the top of your DegreeWorks page.
St. George Location Building closed. Online Office Hours are from 9-5 M to F. Staff will be available to respond to phone inquiries and respond to email. Phone Contact:<PHONE_NUMBER> Email<EMAIL_ADDRESS>Office of the Bursar Online Office Hours are from 9:00 am - 5:00 pm, M-F.
Staff will be available to respond to phone inquiries and respond to e-mail during office hours. The Bursar's Window will also be open collect payment, distribute Metro and Shop Rite cards and distribute paychecks. Phone Contact:<PHONE_NUMBER>.
E-Mail Inquiries. Please choose the appropriate e mail from below. Be sure to include your name and EMPLID.
• General Inquries<EMAIL_ADDRESS>• If you have a Bursar Hold on your account<EMAIL_ADDRESS>• If you have a question regarding your Spring or Summer charges<EMAIL_ADDRESS>• Fall billing questions Fallbilling@csi.cuny.edu


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[gecode-users] IntVar class container as parameter for branchers
Christian Schulte
cschulte at kth.se
Tue Apr 23 15:33:43 CEST 2013
Hi Christine,
If the data structure is small then you do not have to share it by use of a
LocalHandle. If it is maybe not so small then the use of LocalHandle might
be useful.
I personally always use LocalHandle when I can use it (that is, I have a
data structure shared between several entities in a space).
Best
Christian
--
Christian Schulte, Professor of Computer Science, KTH,
www.ict.kth.se/~cschulte/
From: users-bounces at gecode.org [mailto:users-bounces at gecode.org] On Behalf
Of christine bouzant
Sent: Tuesday, April 23, 2013 3:10 PM
To: users at gecode.org
Subject: [gecode-users] IntVar class container as parameter for branchers
Hello all,
In my script class, i have an array of CarDeparture pointer objects which
class contains several IntVar variables and some data information.
Inside the script copy constructor, i create a copy of the array of
CarDeparture objects using a CarDeparture copy constructor which updates
each of its IntVar variables and data.
I am thinking to set a CarDeparture object as a parameter for a brancher.
So, i should again do a copy of this object inside the brancher copy
constructor.
According to you, is it judicious to proceed like this concerning
performance ?
Or maybe, can i make the CarDeparture class inherit of LocalHandle (in order
to be space dependant ?).
Thank you all.
Cheers,
Christine
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Program started at Mon-07-May-2007 18:08.
Analysed requests from Mon-01-Jan-2007 00:00 to Wed-31-Jan-2007 23:59 (31.00 days).
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report contains overall statistics.
Successful requests: 18,725,023
Average successful requests per day: 604,046
Successful requests for pages: 6,143,636
Average successful requests for pages per day: 198,186
Failed requests: 4,850
Redirected requests: 367,665
Distinct files requested: 771
Distinct hosts served: 233,250
Corrupt logfile lines: 212
Data transferred: 322.46 gigabytes
Average data transferred per day: 10.40 gigabytes
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the activity in each month.
Each unit () represents 200,000 requests for pages or part thereof.
| month | reqs | pages | |
|---|---|---|---|
| Jan 2007 | 18725023 | 6143636 |
Busiest month: Jan 2007 (6,143,636 requests for pages).
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the total activity for each day of the week, summed over all the weeks in the report.
Each unit () represents 40,000 requests for pages or part thereof.
| day | reqs | pages | |
|---|---|---|---|
| Sun | 3215986 | 1023165 | |
| Mon | 4058601 | 1387642 | |
| Tue | 3084981 | 1057234 | |
| Wed | 2635534 | 842036 | |
| Thu | 1779670 | 584868 | |
| Fri | 1686465 | 549775 | |
| Sat | 2263786 | 698916 |
(Go To: Top | General Summary | Monthly Report | Daily Summary | Hourly Summary)
This report lists the total activity for each hour of the day, summed over all the days in the report.
Each unit () represents 15,000 requests for pages or part thereof.
| hour | reqs | pages | |
|---|---|---|---|
| 0 | 252728 | 83852 | |
| 1 | 119958 | 44019 | |
| 2 | 70845 | 28979 | |
| 3 | 46999 | 21530 | |
| 4 | 41525 | 19294 | |
| 5 | 46966 | 20851 | |
| 6 | 98380 | 37940 | |
| 7 | 277453 | 93980 | |
| 8 | 682594 | 216276 | |
| 9 | 1017954 | 321328 | |
| 10 | 1055618 | 334813 | |
| 11 | 1105957 | 353251 | |
| 12 | 1261890 | 413736 | |
| 13 | 1298640 | 421719 | |
| 14 | 1039792 | 349377 | |
| 15 | 1053608 | 356274 | |
| 16 | 1245798 | 404626 | |
| 17 | 1368422 | 437004 | |
| 18 | 1425066 | 465046 | |
| 19 | 1412159 | 464850 | |
| 20 | 1285832 | 427299 | |
| 21 | 1123812 | 371464 | |
| 22 | 889854 | 290146 | |
| 23 | 503173 | 165982 |


CASINO A film review by James Berardinelli Copyright 1995 James Berardinelli
RATING (0 TO 10): 8.6
United States, 1995 U.S. Availability: 11/22/95 (wide) Running Length: 2:58 MPAA Classification: R (Violence, profanity, drug use, sexual situations) Theatrical Aspect Ratio: 2.35:1
Cast: Robert De Niro, Joe Pesci, Sharon Stone, Don Rickles, James Woods, Alan King, Kevin Pollak, Frank Vincent, L. Q. Jones Director: Martin Scorsese Producer: Barbara De Fina Screenplay: Nicholas Pileggi & Martin Scorsese based on the book by Nicholas Pileggi Cinematography: Robert Richardson Music: various artists U.S. Distributor: Universal Pictures
After viewing CASINO, you may never look at Las Vegas in quite the same way. While this film, adapted from Nicholas Pileggi's nonfiction book, CASINO: LOVE AND HONOR IN LAS VEGAS, doesn't offer much in the way of startling revelations, it presents a fascinating insider's perspective of what goes on behind-the-scenes in the country's gambling mecca. As is stated several times, Vegas isn't about fun, glitz, or glamour. Those things are just the surface gloss.
CASINO opens with short sequence in 1983 before moving on to the meat of the story, which is related through flashbacks. Director Martin Scorsese makes heavy use of voiceovers, employing disembodied monologues by both lead actors--Robert De Niro and Joe Pesci--to fill in gaps. The sheer volume of words sporadically detracts from character development, but it is integrated successfully enough not to seem overly intrusive. While the intelligence and wit of the voiceovers makes them palatable, such nonstop talking isn't always the best way to convey a story--the temptation to tell something, rather than show it, is too great. CASINO doesn't always avoid that trap.
In every way--from the fantastic sets, rich dialogue, and unapologetic violence to the well-portrayed characters and themes of loyalty and betrayal--CASINO is pure Scorsese. Although not the director's top work, this is nevertheless compelling film making. Scorsese has never pulled punches in breathing life into his ideas, and he doesn't start here. If there's an obvious weakness in CASINO, it's that it occasionally seems derivative of GOODFELLAS.
During its three-hour running time, CASINO tells the story of two men's intermingled lives. "Back home years ago" (as an on-screen caption reads), they were friends and co-workers. Sam "Ace" Rothstein (Robert De Niro, in his eighth acting collaboration with Scorsese) was a gambler who never lost. He researched all his bets carefully, and rarely made a bad pick. His winning tendencies gained him popularity and favor with the local mob, who used Nicky Santoro (Joe Pesci) to shadow and protect him. Now that Ace has moved to Vegas to manage the Tangiers Casino, Nicky isn't far behind. And, while the two gravitate to opposite sides of the law, with Ace keeping his fingers clean and Nicky taking over the local crime scene, their paths continue to cross, and their encounters become increasingly less friendly. Stirred into the mix is Ace's girlfriend, Ginger (Sharon Stone), an expert hustler who attracts men like flies. Although she agrees to marry Ace, she continues a liaison with her former pimp (James Woods) while encouraging Nicky's affections.
As usual, Scorsese obtains excellent performances from his leads. Joe Pesci, essentially reprising his GOODFELLAS performance, will probably get all the attention, since this sort of flamboyance attracts raves. Actually, though, it's De Niro's more subtle, better-contained acting that's riveting. CASINO is supposed to focus on both Ace and Nicky, but, despite nearly equal screen time for each, our sympathy is drawn towards the former. For this, De Niro's portrayal shares equal responsibility with the screenplay.
In his most recent four films, Scorsese has shown the ability to take a mediocre actress and cull an impressive performance from her. In GOODFELLAS, it was Lorraine Bracco. In CAPE FEAR, Juliette Lewis. In THE AGE OF INNOCENCE, Michelle Pfeiffer (arguably the best of the bunch). And now, in CASINO, it's Sharon Stone, whose resume (BASIC INSTINCT, SLIVER, THE SPECIALIST) is enough to make any serious movie- goer wince. Surprisingly, however, she's fine--not Oscar material, but strong enough not to drag down the film. Stone doesn't stick out like a sore thumb, and, based on Scorsese's track record, a lion's share of the credit for this should be given to him.
Supporting players include a restrained James Woods, comedian Don Rickles in a serious role, Alan King (also playing it straight), and Kevin Pollak. Scorsese's mother has a brief (and very funny) appearance as the no-nonsense parent of a bumbling, small-time gangster. And faces associated with Vegas, like Steve Allen, Frankie Avalon, Jayne Meadows, and Jerry Vale, have cameos.
CASINO was filmed in Las Vegas, and this shows in the splashiness and energy of nearly every scene. By using gaudy costumes and a 70's soundtrack, Scorsese takes us back some two decades. Starting with the vivid opening credits (designed by Elaine and Saul Bass, whose amazing work on THE AGE OF INNOCENCE was one of that film's highlights), CASINO sparkles like its fake diamond of a host city. The cinematography, which uses surprisingly few tricks other than freeze-framing to emphasize key moments, is crisp and clean.
By now, audiences have come to expect forceful films from Martin Scorsese. With CASINO, he doesn't disappoint. The movie is long, but, with a fast-moving storyline, escalating tension, and surprisingly robust humor, the three hours move quickly. Several flaws, mostly minor, keep CASINO on a plateau slightly below that of the director's best (MEAN STREETS, RAGING BULL, TAXI DRIVER, GOODFELLAS), but, in this relatively-bland Thanksgiving movie season, this is one of the few entries worth making an effort to see.
- James Berardinelli<EMAIL_ADDRESS>The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


About the NTEAA
How We Started
The North Texas – Electric Auto Association (NTEAA) was established in November of 1995 as an affiliate chapter of the Electric Auto Association (EAA). NTEAA was Incorporated in 2008 and became 501C in 2009.
The North Texas – Electric Auto Association’s goal is to support local EV enthusiasts, and to promote and educate the public regarding the benefits of electric vehicles.
Membership
Even if you’re not a National EAA member, you are always welcome to attend our meetings. Anyone who’s at a meeting is always a member in our eyes…
We do accept a voluntary “local” dues to help fund our non-profit events but it is still fine to come to a few meetings to decide if you wish to join/support our local or national club. The suggested local fee would be $20 for the year.
If you do wish to join the National EAA, please join using this link: EAA Membership page on the official National Association web site.
Indicate “North Texas” as the local club you wish to support.
Our local (North Texas) club will receive a portion of your National EAA membership fee which we use for shows, fliers, cards, meetings, events and teaching tools.
Donations
If you wish to make a Donation to NTEAA, please contact us by emailing<EMAIL_ADDRESS>We use the money for shows, fliers, cards, meetings, events, teaching tools, and etc.
For physical donations (car, motors, cable, etc.) please contact us by email or in person.For donations received, you will receive a donation letter for your donation. The North Texas Electric Auto Association does not set the value of your gift. This is the privilege and responsibility of the donor. No goods or services are provided to the donor other than intangible benefits.
Monthly Meetings
Monthly meetings are FREE and held on the 2nd Saturday of each month. Be sure you check out details about upcoming meetings and events by visiting the Upcoming Events/Meeting page. We do occasionally move to a different date because of conflicts or convenience from another event. So be sure to check the upcoming meetings and events page.
NTEAA Officers
Ron Swanson (President)
Ron is a retired nuclear engineer who is sure the cars of the future must be electric powered. He has been a follower of the electric car industry since 1980, and now driving his 3rd Nissan Leaf. He is also interested in converting existing ICE cars to run on electricity, as are many members of the club. He uses all electric lawn equipment. He has an extensive background in computers and software from his days at IBM. He also has a global perspective after living and working in Johannesburg South Africa for several years. He has served NTEAA as VP since 2009 till 2012 and is now President.
Neal Farris (Vice President)
Neal built is first EV in 2009 which has been very popular build. It is a 1999 Volkswagen Golf. It was so well built that it shows every part of the EV. Even his wife now drives a Nissan Leaf. He also has a riding electric mower he mows his yard with which he restored and looks very sharp. Neal has been a big help for the EV Community for the last several years with shows and events.
Paul Schaffer (Secretary)
Paul’s been an avid EV person since the mid 80’s when he owned his first EV. That EV is long gone but he is now driving a Nissan Leaf every day. He also has an electric mower to keep his gas usage down. He has served NTEAA wearing many different hats as he has held each of the main officer positions through out the years.
Stanton Zeff (Treasurer)
Stanton’s life-long dream of owning an electric car (BEV) was finally realized with the commercial availability of the Nissan Leaf in 2011. Since then, he has promoted EVs at every opportunity, eventually leading to his involvement in the NTEAA. He enjoys passing gas stations (without stopping) and giving presentations on his EV experience.
Stanton holds a BSEE from the University of Texas at Austin and an MS in Telecommunications from SMU. He is a registered Professional Engineer in the State of Texas with over twenty years experience in the telecommunications field. In addition, he is a Senior Member of IEEE and Past Chairman of the Dallas Section of the IEEE Communications and Vehicular Technology Society, where he received an award for Outstanding Service to the society.
Jared Leverington (Web Developer)
Jared is a computer programmer; primary doing Web Development with API interfaces into Legacy systems. He joined NTEAA in 2006, when he first learned about EVs from watching the movie “Who Killed The Electric Car” (also known as WKTEC in the EV Community). He has had his EV grin since 2007 when he bought his first EV conversion. He has sold his first EV conversion but now he drives a Nissan Leaf as his daily driver. He also has other electric devices, using an electric mower and weed eater for his yard and an electric scooter for around the neighborhood. Other notables about Jared; he was Vice President in 2009 and then became President of NTEAA from 2009 thru 2012. So he has been very active in the EV community since 2006.
William Louis Bohan "Bill" (Board of Directors)
Bill’s earliest involvement in EV’s could be dated back to his Junior Year in college. For a Lab Project, he designed a Motor Speed Control for an Electric Car. Since then, he always wanted one. In about 1984, when the first EV was produced by GM, called the EV-1, he called the R&D Group in Detroit and asked to buy an EV-1 off the assembly line. Back then he was making about $24K a year, gross pay. The R&D Group quoted the price of $120K. Gross adjusted dollars, present value, that is the same as asking $500K for the EV-1 now. One of the earliest EV meetings was held in the newly constructed Richardson Square Mall, in the Community Room, hosted by Paul Schaffer. Those were our humble beginnings as a group.


Page Alerts
Potential security issues on the page you are viewing will be shown as 'Page Alerts',
which by default are shown on the left-hand side. This allows you to just focus on the potential issues on
your current page without being distracted by issues on other pages.
Each alert has an associated risk, and all alerts with the same level of risk are shown together:
- High-Level Page Alerts
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The Page Alert tools show the number of alerts for each level of risk. You can click on any of them to
see the list of alerts, the URLs that have each type of alert, and the full details for any specific alert.


I've been thinking about
paster
(the
PasteScript
frontend) and setuptools and whatnot, and thinking about how to
simplify them.
Here's my current thinking (note "application" =
"web application"; this stuff doesn't entirely apply
to other models of applications):
-
Each "application" will usually get a script. This
script manages instances of the application.
-
Some little framework for this script creation is created,
probably similar to PasteScript. But it's optional to use
this; it's just that using such a framework you can get
near-instant access to commands inherited from other
frameworks, like a database-setup routine.
-
Inherited commands that require configuration will typically
look in
.egg-info/
for configuration files. These configure the
project/application in relation to the
frameworks that it uses. Not to be confused with
configuring application instances.
-
"Development" tasks go into
setup.py
extensions. These are opt-in new commands that refer to the
development process, not to application instances.
For instance,
python
setup.py
make-servlet. (This requires
an addition to setuptools)
-
The
Buildutils
pbu
script gets slightly nicer, so you can do
pbu
make-servlet
and it will find the nearest parent
setup.py
and call it, and add on Setuptools features implicitly (so
pbu
develop
will work for non-setuptools packages).
-
Probably a number of features of Buildutils will be moved
elsewhere, like the
pudge
command into the
Pudge
package.
-
Some of those features will also be opt-in (but not all),
like
py.test
support. But, for instance,
flakes
(the
pyflakes
runner) would be global available (but still locally
configurable).
-
setup.cfg
contains project-specific development-task configuration.
-
A new command -- maybe
nest
or
paster
create
refactored or something -- is for bootstrapping new
projects. A project that doesn't yet exist has no
setup.py
of course, and no script, so those options don't apply here.
I'm not sure where the current
paster
make-config/setup-app
goes.
setup-app
(which takes a configuration file and does any system setup --
like creating a database) can be a specific command in the
app-specific script, of course. But there's no enforced
uniformity then, and you can't be sure how any particular
application is setup. And
make-config
actually implies installation of a package, in addition to the
uniform way of creating a blank(ish) configuration file. Maybe
these will become commands in a new
paste-deploy
script of some sort.
Anyway, my thoughts at the moment.


A DDD example of container truck management system
This is a consulting DDD case for a shanghai transportation company(shanghai Yangshan harbor which is maximum harbour in the world) ,this paper shows some processes and ideas of analysis and design.
Requirement
I. Background
II. functional requirements Dispatch center manages all tasks, monitor task execution status and improve the rationality of distributed tasks , reduce human error rates and unreasonable truck cost. Can be embedded GPS monitoring, according to the vehicle transport tasks, you can display information corresponding vehicle GPS monitoring system, SMS can achieve vehicle tasks distributed before each task execution task messages can be sent to the driver's cell phone . The driver can reply different information indicates that the task execution , the system promptly modify the current state of the driver and the vehicle based on the replies received
Understand requirement
According to the requirements document , the first project from the beginning to understand the flow chart positioning throughout a large system , Route planning is so important for transportation company, it has set up a two-stage designer:
Modeling
The use case diagram can be used to find out the basic requirements, the company's managers have their own understanding for the requirements, they will mislead our analysis. in this system , "Task" may be our focus, because task is managers's focus, but it is not domain melody, not a aggregate root entity. At first we regarded a task as a root entity, a entity should have a unique identifier, what is unique identifier of task? task's time or task's no.? what is important for a task? after discussion we found the task main core is planning a vehicle running route. Next step, we according below method to check if "Task " is fit for requirement functions:
Task is the Entity, it should has a state that can be changed by user's command, in a dreiver arrived container inventory, he send loading a container event to system, the loading event will modify the state of the task, but Task's state should be planning or running or finished, the loading event change which state of them? At last, we realize business cohesion is the "truck" entity, it should be aggregate root, truck's route specification is main core of a task. task only a explicit output for tracing truck's route planning and transport process.
ImplementionAfter we get the domain model, we can use jdonframework to implements it. programmer's codes include: repository implemention (such as mysql), and UI implemention(Angular.js and mobile app.)
|
|


Xmake is a lightweight cross-platform build utility based on Lua.
It is very lightweight and has no dependencies because it has a built-in Lua runtime.
It uses xmake.lua to maintain project builds and its configuration syntax is very simple and readable.
We can use it to build project directly like Make/Ninja, or generate project files like CMake/Meson, and it also has a built-in package management system to help users solve the integrated use of C/C++ dependent libraries.
Xmake = Build backend + Project Generator + Package Manager + [Remote|Distributed] Build + Cache
Although not very precise, we can still understand Xmake in the following way:
Xmake ~= Make/Ninja + CMake/Meson + Vcpkg/Conan + distcc + ccache/sccache
Introduction of new features
In this release, we have refactored and improved the C++20 Modules implementation, improved the dependency graph parsing of module files, added support for STL and User HeaderUnits, and made the CMakelists/compile_commands generator support C++ Modules.
In addition, we’ve added an xmake watch
plugin that can monitor current project file updates in real time, automatically trigger incremental builds, or run some custom commands.
C++ Modules Improvements
Xmake has long supported C++ Modules build support, and can automatically analyze dependencies between modules to maximize parallel compilation.
In addition, Xmake uses .mpp
as the default module extension, but also supports .ixxx
, .cppm
, .mxx
and so on.
For example
set_languages("c++20")
target("class")
set_kind("binary")
add_files("src/*.cpp", "src/*.mpp")
For more examples see: C++ Modules
However, the previous implementation has many shortcomings.
- no support for HeaderUnits, so you can’t use modules like stl
- the module dependency graph parsing is done by scanning the source code itself, and does not support the dependency scanning provided by the compiler, so it is not fully reliable
- do not support CMakelists generation
- compile_commands.json generation is not supported
In the new version, we have refactored and upgraded the implementation of C++20 module, and we support all the points mentioned above, and added support for Headerunits, so we can introduce STL and user header modules in the module.
Also, since higher versions of msvc and gcc have built-in scan analysis of module dependency graphs, Xmake prioritizes module dependency graph analysis with the compiler, and if the compiler does not support it (clang), then Xmake will degrade to its own source code scan implementation.
A related patch is available at #2641, with many thanks to @Arthapz for their contribution.
Here is an example of a module that uses STL HeaderUnits, e.g.
stl_headerunit$ xmake
[ 0%]: generating.cxx.module.deps src/main.cpp
[ 0%]: generating.cxx.module.deps src/hello.mpp
[ 20%]: generating.cxx.headerunit.bmi iostream
[ 60%]: generating.cxx.module.bmi hello
[ 70%]: cache compiling.release src/main.cpp
[ 80%]: linking.release stl_headerunit
[100%]: build ok!
For the first compilation, we scan the module code for dependencies and then precompile stl libraries like iostream as headerunit.
Any subsequent recompilation will directly reuse them for compilation acceleration.
Note: Usually we need to add at least one .mpp
file to enable C++20 modules compilation, if there is only a cpp file, module compilation will not be enabled by default.
However, if we just want to use the module’s Headerunits feature in the cpp file, for example, by introducing some STL Headerunits in the cpp, then we can also set the .mpp
to .mpp
.
then we can also force C++ Modules compilation by setting set_policy("build.c++.modules", true)
, e.g.
add_rules("mode.debug", "mode.release")
target("test")
set_kind("binary")
add_files("src/*.cpp")
set_languages("c++20")
set_policy("build.c++.modules", true)
Project file monitoring and auto-build
In this release, we have added the xmake watch
plugin command to automatically monitor project files for updates and then trigger automatic builds or run some custom commands.
This is often used for personal development to enable fast real-time incremental builds without the need to manually execute the build command each time, improving development efficiency.
Auto-build after project update
The default behavior is to monitor the entire project root, and any file changes will trigger an incremental build of the project.
$ xmake watch
watching /private/tmp/test/src/** .
watching /private/tmp/test/* ...
/private/tmp/test/src/main.cpp modified
[ 25%]: ccache compiling.release src/main.cpp
[ 50%]: linking.release test
[ 100%]: build ok!
Monitoring a specific directory
We can also monitor specific code directories to narrow down the scope of monitoring and improve performance.
$ xmake watch -d src
$ xmake watch -d "src;tests/*"
The above command will recursively watch all subdirectories. If you want to only watch the files in the current directory without recursive monitoring, you can use the following command.
$ xmake watch -p src
$ xmake watch -p "src;tests/*"
Watch and run the specified command
If you want to run the build automatically even after the automatic build, we can use a custom command set.
$ xmake watch -c "xmake; xmake run"
The above command list is passed as a string, which is not flexible enough for complex command arguments that require escaping to be more cumbersome, so we can use the following for arbitrary commands.
$ xmake watch -- echo hello xmake!
$ xmake watch -- xmake run --help
Watching and running the target program
Although we can automate the running of the target program with custom commands, we also provide more convenient arguments to achieve this behavior.
$ xmake watch -r
$ xmake watch --run
[100%]: build ok!
hello world!
Watching and running lua scripts
We can also watch for file updates and run the specified lua script for more flexible and complex command customization.
$ xmake watch -s /tmp/test.lua
We can also get a list of all updated file paths and events in the script again.
function main(events)
-- TODO handle events
end
Mac Catalyst Support
MAc Catalyst is Apple’s new solution for bringing iPad apps to the Mac. Mac apps built with Mac Catalyst share code with your iPad apps, and you can add more features to your Mac separately.
With this new version, we’ve added support for building Mac Catalyst targets, and on macOS platforms, we just need to add the -appledev=catalyst
configuration option to support compiling existing iOS code and getting it up and running on macOS without making any changes.
$ xmake f --appledev=catalyst
$ xmake
We can experience the Mac in the test project iosapp_with_framework Catalyst program compile and run.
$ xmake
[ 36%]: processing.xcode.release src/framework/Info.plist
[ 40%]: cache compiling.release src/framework/test.m
[ 44%]: linking.release test
[ 48%]: generating.xcode.release test.framework
[ 56%]: compiling.xcode.release src/app/Assets.xcassets
[ 56%]: processing.xcode.release src/app/Info.plist
[ 60%]: cache compiling.release src/app/ViewController.m
[ 60%]: cache compiling.release src/app/SceneDelegate.m
[ 60%]: cache compiling.release src/app/main.m
[ 60%]: cache compiling.release src/app/AppDelegate.m
[ 60%]: compiling.xcode.release src/app/Base.lproj/LaunchScreen.storyboard
[ 60%]: compiling.xcode.release src/app/Base.lproj/Main.storyboard
[ 88%]: linking.release demo
[ 92%]: generating.xcode.release demo.app
[100%]: build ok!
$ xmake run
2022-08-26 15:11:03.581 demo[86248:9087199] add(1, 2): 3
2022-08-26 15:11:03.581 demo[86248:9087199] hello xmake!
Improving remote builds
Pulling remote build files
For remote builds, we have added a new pull remote file command, which can usually be used to download remote target build files, library files locally after the remote build is complete.
$ xmake service --pull 'build/**' outputdir
We can match the files to be downloaded with the -pull 'build/**'
pattern, either build files or other files.
Note: Files are segregated by project, only files under the current project can be specified for download, and will not let users download files from other directories on the server to avoid some security risks.
Real-time echo output
In the previous version, when using remote compilation, the client could not output the compilation information of the server in real time, because the cache existed, the compilation progress information seen locally was refreshed piece by piece, which was not a good experience.
Therefore, we added line buffer refresh support to improve the real-time output display and make the user experience closer to the local compilation when compiling remotely.
Improve distributed compile scheduling algorithm
We have also further improved the server node scheduling for xmake’s distributed compilation by adding weight to cpu load and memory resources, rather than just assigning tasks by the number of cpu cores.
Thus, if some nodes are overloaded, we will prioritize the compilation tasks to the nodes that are quite free and take advantage of all compilation resources.
More flexible cmake package lookup
Specify links
For cmake packages, we have added the link_libraries
configuration option to allow users to customize the configuration of package dependencies and even support for target links when searching for packages to use with cmake.
add_requires("cmake::xxx", {configs = {link_libraries = {"abc::lib1", "abc::lib2"}}})
xmake automatically appends the following configuration when looking for cmake packages, improving the extraction of links libraries.
target_link_libraries(test PRIVATE ABC::lib1 ABC::lib2)
Specify the search mode
In addition, we add the following search mode configuration.
add_requires("cmake::xxx", {configs = {search_mode = "config"}})
add_requires("cmake::xxx", {configs = {search_mode = "module"}})
add_requires("cmake::xxx") -- both
Specify config search mode, for example, to tell cmake to look for packages from XXXConfig.cmake
.
xmake will automatically append the following configuration internally when it looks for cmake packages.
find_package(ABC CONFIG REQUIRED)
armcc/armclang/rc incremental compilation support
In the new version, we also perform header dependency analysis for keil’s armcc/armclang compiler to support incremental compilation.
In addition, msvc’s rc.exe resource compiler itself cannot provide header dependency analysis, but cl.exe’s preprocessor can handle resource files.
Therefore, we can use cl.exe /E test.rc
to preprocess resource files and extract dependency information from them to achieve incremental compilation support for resource files.
So far, it works pretty well, and we also have support for internal ICON/BITMAP resource reference dependencies.
Other issue fixes
We’ve also made a number of fixes to the build cache, which will be more stable than the previous version. We have also streamlined the generation of CMakelists.
More detailed improvements can be found in the following changelog.
Changelog
New features
- #2555: Add fwatcher module and
xmake watch
plugin command - Add
xmake service --pull 'build/**' outputdir
to pull the given files in remote server - #2641: Improve C++20 modules, support headerunits and project generators
- #2679: Support Mac Catalyst
Changes
- #2576: More flexible package fetching from cmake
- #2577: Improve add_headerfiles(), add
{install = false}
support - #2603: Disable
-fdirectives-only
for ccache by default - #2580: Set stdout to line buffering
- #2571: Improve task scheduling for parallel and distributed compilation based on memory/cpu usage
- #2410: Improve cmakelists generator
- #2690: Improve to pass toolchains to packages
- #2686: Support for incremental compilation and parse header file deps for keil/armcc/armclang
- #2562: Improve include deps for rc.exe
- Improve the default parallel building jobs number


Black to play 11
b6-w4/7
pips Black=71 White=139
(CorrectAnswer= 0/0, ErrorScore= 0)
1. 8/6(2) eq:-0.615 31.82% (G:18.74% B:0.99%) 68.18% (G:12.57% B:0.78%) 2. 18/14 eq:-0.708 (-0.093) 32.07% (G:13.94% B:0.40%) 67.93% (G:17.46% B:1.44%) 3. 18/16 8/7(2) eq:-0.710 (-0.095) 31.10% (G:15.50% B:0.58%) 68.90% (G:16.61% B:1.34%) 4. 18/17 8/7 8/6 eq:-1.046 (-0.431) 21.77% (G: 7.41% B:0.15%) 78.23% (G:24.05% B:1.02%) 5. 18/16 8/6 eq:-1.271 (-0.656) 15.09% (G: 4.16% B:0.09%) 84.91% (G:28.64% B:1.26%)


CrimeDB is an easy-to-use database of crime incidents from around the USA.
Citizens can use CrimeDB to view information about crime activity in their area, including which areas are more affected than others. Developers can use CrimeDB data in their applications with its easy-to-use API. See the developers page for more information.
We collect crime incident data from individual police departments around the country. Check out the list here to see detailed information from each department and see if yours is included.
More questions? Check out the FAQ.


Note
Click here to download the full example code
Importing data from MEG devices¶
This section describes how to read data for various MEG manufacturers.
Page contents
Elekta NeuroMag (.fif)¶
Neuromag Raw FIF files can be loaded using mne.io.read_raw_fif()
.
If the data were recorded with MaxShield on and have not been processed
with MaxFilter, they may need to be loaded with
mne.io.read_raw_fif(..., allow_maxshield=True)
.
Artemis123 (.bin)¶
MEG data from the Artemis123 system can be read withmne.io.read_raw_artemis123()
.
4-D Neuroimaging / BTI data (dir)¶
MNE-Python provides mne.io.read_raw_bti()
to read and convert 4D / BTI
data. This reader function will by default replace the original channel names,
typically composed of the letter A
and the channel number with Neuromag.
To import the data, the following input files are mandatory:
A data file (typically c,rfDC) containing the recorded MEG time series.
A hs_file containing the digitizer data.
A config file containing acquisition information and metadata.
By default mne.io.read_raw_bti()
assumes that these three files are located
in the same folder.
Note
While reading the reference or compensation channels,
the compensation weights are currently not processed.
As a result, the mne.io.Raw
object and the corresponding fif
file does not include information about the compensation channels
and the weights to be applied to realize software gradient
compensation. If the data are saved in the Magnes system are already
compensated, there will be a small error in the forward calculations,
whose significance has not been evaluated carefully at this time.
CTF data (dir)¶
The function mne.io.read_raw_ctf()
can be used to read CTF data.
CTF Polhemus data¶
The function mne.channels.read_dig_polhemus_isotrak()
can be used to read
Polhemus data.
Applying software gradient compensation¶
Since the software gradient compensation employed in CTF
systems is a reversible operation, it is possible to change the
compensation status of CTF data in the data files as desired. This
section contains information about the technical details of the
compensation procedure and a description of
mne.io.Raw.apply_gradient_compensation()
.
The raw instances returned by mne.io.read_raw_ctf()
contain several
compensation matrices which are employed to suppress external disturbances
with help of the reference channel data. The reference sensors are
located further away from the brain than the helmet sensors and
are thus measuring mainly the external disturbances rather than magnetic
fields originating in the brain. Most often, a compensation matrix
corresponding to a scheme nicknamed Third-order gradient
compensation is employed.
Let us assume that the data contain \(n_1\) MEG sensor channels, \(n_2\) reference sensor channels, and \(n_3\) other channels. The data from all channels can be concatenated into a single vector
where \(x_1\), \(x_2\), and \(x_3\) are the data vectors corresponding to the MEG sensor channels, reference sensor channels, and other channels, respectively. The data before and after compensation, denoted here by \(x_{(0)}\) and \(x_{(k)}\), respectively, are related by
where the composite compensation matrix is
In the above, \(C_{(k)}\) is a \(n_1\) by \(n_2\) compensation data matrix corresponding to compensation “grade” \(k\). It is easy to see that
To convert from compensation grade \(k\) to \(p\) one can simply multiply the inverse of one compensate compensation matrix by another and apply the product to the data:
This operation is performed by mne.io.Raw.apply_gradient_compensation()
.
Ricoh/KIT MEG system data (.con/.sqd)¶
MNE-Python includes the mne.io.read_raw_kit()
and
mne.read_epochs_kit()
to read and convert Ricoh/KIT MEG data.
To import continuous data, only the input .sqd or .con file is needed. For epochs, an Nx3 matrix containing the event number/corresponding trigger value in the third column is needed.
The following input files are optional:
A KIT marker file (mrk file) or an array-like containing the locations of the HPI coils in the MEG device coordinate system. These data are used together with the elp file to establish the coordinate transformation between the head and device coordinate systems.
A Polhemus points file (elp file) or an array-like containing the locations of the fiducials and the head-position indicator (HPI) coils. These data are usually given in the Polhemus head coordinate system.
A Polhemus head shape data file (hsp file) or an array-like containing locations of additional points from the head surface. These points must be given in the same coordinate system as that used for the elp file.
Modern Ricoh systems may encode this information it the file itself, in which
case mrk
, elp
, and hsp
can all be None
and the data will be
read from the file itself.
Note
The output fif file will use the Neuromag head coordinate system convention, see MEG/EEG and MRI coordinate systems. A coordinate transformation between the Polhemus head coordinates and the Neuromag head coordinates is included.
By default, KIT-157 systems assume the first 157 channels are the MEG channels, the next 3 channels are the reference compensation channels, and channels 160 onwards are designated as miscellaneous input channels (MISC 001, MISC 002, etc.). By default, KIT-208 systems assume the first 208 channels are the MEG channels, the next 16 channels are the reference compensation channels, and channels 224 onwards are designated as miscellaneous input channels (MISC 001, MISC 002, etc.).
In addition, it is possible to synthesize the digital trigger channel (STI 014) from available analog trigger channel data by specifying the following parameters:
A list of trigger channels (stim) or default triggers with order: ‘<’ | ‘>’ Channel-value correspondence when converting KIT trigger channels to a Neuromag-style stim channel. By default, we assume the first eight miscellaneous channels are trigger channels. For ‘<’, the largest values are assigned to the first channel (little endian; default). For ‘>’, the largest values are assigned to the last channel (big endian). Can also be specified as a list of trigger channel indexes.
The trigger channel slope (slope) : ‘+’ | ‘-‘ How to interpret values on KIT trigger channels when synthesizing a Neuromag-style stim channel. With ‘+’, a positive slope (low-to-high) is interpreted as an event. With ‘-‘, a negative slope (high-to-low) is interpreted as an event.
A stimulus threshold (stimthresh) : float The threshold level for accepting voltage changes in KIT trigger channels as a trigger event.
The synthesized trigger channel data value at sample \(k\) will be:
where \(t_p(k)\) are the thresholded from the input channel data d_p(k):
The threshold value \(t\) can
be adjusted with the stimthresh
parameter.
FieldTrip MEG/EEG data (.mat)¶
MNE-Python includes mne.io.read_raw_fieldtrip()
, mne.read_epochs_fieldtrip()
and mne.read_evoked_fieldtrip()
to read data coming from FieldTrip.
The data is imported directly from a .mat
file.
The info
parameter can be explicitly set to None
. The import functions will still work but:
All channel locations will be in head coordinates.
Channel orientations cannot be guaranteed to be accurate.
All channel types will be set to generic types.
This is probably fine for anything that does not need that information, but if you intent to do things like interpolation of missing channels, source analysis or look at the RMS pairs of planar gradiometers, you most likely run into problems.
It is highly recommended to provide the info
parameter as well. The info
dictionary can be extracted by loading the original raw data file with the corresponding MNE-Python functions:
original_data = mne.io.read_raw_fiff('original_data.fif', preload=False)
original_info = original_data.info
data_from_ft = mne.read_evoked_fieldtrip('evoked_data.mat', original_info)
The imported data can have less channels than the original data. Only the information for the present ones is extracted from the info
dictionary.
As of version 0.17, importing FieldTrip data has been tested on a variety of systems with the following results:
System |
Read Raw Data |
Read Epoched Data |
Read Evoked Data |
|---|---|---|---|
BTI |
Works |
Untested |
Untested |
CNT |
Data imported as microvolts. Otherwise fine. |
Data imported as microvolts. Otherwise fine. |
Data imported as microvolts. Otherwise fine. |
CTF |
Works |
Works |
Works |
EGI |
Mostly Ok. Data imported as microvolts. FieldTrip does not apply calibration. |
Mostly Ok. Data imported as microvolts. FieldTrip does not apply calibration. |
Mostly Ok. Data imported as microvolts. FieldTrip does not apply calibration. |
KIT |
Does not work. Channel names are different in MNE-Python and FieldTrip. |
Does not work. Channel names are different in MNE-Python and FieldTrip. |
Does not work. Channel names are different in MNE-Python and FieldTrip. |
Neuromag |
Works |
Works |
Works |
eximia |
Works |
Untested |
Untested |
Creating MNE data structures from arbitrary data (from memory)¶
Arbitrary (e.g., simulated or manually read in) raw data can be constructed
from memory by making use of mne.io.RawArray
, mne.EpochsArray
or mne.EvokedArray
in combination with mne.create_info()
.
This functionality is illustrated in Creating MNE objects from data arrays. Using 3rd party libraries such as NEO in combination with these functions abundant electrophysiological file formats can be easily loaded into MNE.
Estimated memory usage: 8 MB


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Summary stats
| Count | Instability |
|---|
Parameter weights
MIGs
| Instability | Size |
|---|
Clustered tree
Display options
Export data
Export instability values for every gene from the indicated species to a comma-separated file.
Usage details
This program analyzes the phylogenetic tree of a multi-species gene family, reconciles it with the given species tree, and then clusters it into minimum instability groups (MIGs) based on the most parsimonious reconciliation. More details can be found on the Documentation page.
Definitions
Duplication: The cost associated with the duplication of some locus in an ancestral species.
Loss: The cost associated with the loss of some locus in an ancestral species.
Incongruence: The cost of an event that has caused the gene tree to differ from the species tree. The event itself is not specified, and may be due to a lineage- or gene-specific change in selective pressure, horizontal gene transfer, incomplete lineage sorting, or something else.
Spread: The cost associated with including dissimilar sequences into a MIG. This is measured by the standard deviation of the MIG; to calculate this the tree is transformed into a set of points in Euclidean space that respect the pairwise phylogenetic distances.
Relative spread: The spread of a MIG divided by the median spread of all MIGs, translated such that it is centered around zero. A relative spread of -0.3 indicates that the MIG has a spread 30% smaller than the median for that tree, 1.0 indicates that it is double the median, and 0.0 is exactly equal to the median.
Features
Click on any sequence name, entry in the MIGs list, or MIG in the tree to view more information about it.
The MIGs list can be sorted by Instability score or MIG size by clicking on the appropriate header.
The tree can be panned using the mouse, or zoomed in by scrolling or by the - and + buttons in the top-right corner of the tree. The Reset button returns both to their defaults. Sequence names can be searched using the input field.
The Previous button allows two paramater configurations to be easily compared.


Cognition & Education Lab, Department of Education, Dartmouth College
Study Title: Memory and Reasoning
You are being asked to participate in a research study. Your participation is entirely voluntary. If you are a student, your decision as to whether or not to participate in this study will have no effect on your academic standing or job status. Please feel free to ask questions at any time if there is anything that you do not understand or if there is anything about which you want to know more.
The purpose of this study is to investigate whether reasoning can be promoted with certain study strategies.
You will not personally benefit from being in this research study. Your participation helps us to learn more about the factors that influence making inferences. The data obtained from your participation will be included with data from other participants as part of a scientific study that may be published in a peer-reviewed journal. These results will inform future studies of memory and reasoning.
Your participation in this study will involve up to 2 sessions. The second session will occur 48 hours after the first session. The first session should not last more than 60 min, and the second session should not last more than 30 min.
During this experiment, you will be presented with sentences to read. You will study the material and be tested on it. You will also be asked to fill out a questionnaire with questions about your race/ethnicity, age, sex, and language background.
The risks of participating in this research are minimal. There are no known physical, psychological, legal, social or other risks associated with this study. You might find the task slightly boring. All paperwork and computer files related to this experiment will use a participant code to protect your confidentiality, and will be stored on password-protected computers.
a. Withdrawal from this study: You may choose to stop taking part in this study at any time.
b. Number of Participants: We expect about 200 participants to enroll in this study.
c. Funding: Dartmouth College had provided funding for this research.
a. Data Collection
The information collected as data in this study includes: your responses to the questionnaire and your responses on the tests of your learning/understanding of the material. The data collected in this study will be maintained indefinitely or as required by federal or state regulations.
b. Confidentiality
Every effort will be made to protect the identities of the participants and the confidentiality of the research data used in this study.
Each participant will be assigned a code number, and only these code numbers will be linked to any data collected. All of the other study materials will identify you only with a code. These materials will also be stored in a secure lab, and data will be entered and analyzed on password-protected computers by trained members of the Cognition & Education Lab.
Raw data will be available only to Dr. Sean Kang and trained research assistants in the Cognition & Education Lab. The data obtained from your participation will be included with data from other participants; no data will be reported on an individual basis.
The information collected for this study will be used only for purposes of research. However, the Dartmouth College Office of the Committee for the Protection of Human Subjects may request access to this form to ensure that procedures designed to protect research participants are being followed properly.
Despite these safeguards, it is possible for a court or government official to order the release of study data including information about you.
Yes: you will receive up to $7 per hour of time you spend during participation in the study.
Questions about this study may be directed to the researcher in charge of this study: Dr. Sean Kang at<PHONE_NUMBER> or<EMAIL_ADDRESS>If you have general questions, concerns, complaints, or suggestions about human research at Dartmouth, you may call the Office of the Committee for the Protection of Human Subjects at Dartmouth College at<PHONE_NUMBER> during normal business hours.
I have read the above information about Memory and reasoning and have been given time to ask questions. I agree to take part in this study and I have been given a copy of this signed consent form if so desired.
Check this box to indicate your consent.


--- layout: page title: How to parent well permalink: /parents/how-to-parent-well/ main_image_path: main_image_caption: frontpage: false homepage_description_markdown: frontpagetitle: frontpageorder_number: aside: true _options: layout: hidden: true permalink: hidden: true image_path: uploads_dir: "assets/images/:year" width: 1200 height: 1200 resize_style: contain mime_type: image/jpeg main_image_path: uploads_dir: "assets/images/:year" width: 1200 height: 1200 resize_style: contain mime_type: image/jpeg content: uploads_dir: "assets/:year" _comments: title: Page title main_image_path: Image used to represent your page frontpage: Show this page on the homepage homepage_description_markdown: Text used on homepage if shown frontpagetitle: Alternative title for home page use ---


Before Change
x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.25, random_state=42)
pickle.dump(self, open(os.path.join(self.path, "classifier"), "wb"))
self.model_id = self.searcher.search(x_train, y_train, x_test, y_test)
def predict(self, x_test):
Return predict result for the testing data.
After Change
break
p = multiprocessing.Process(target=run_searcher_once, args=(x_train, y_train, x_test, y_test, self.path))
p.start()
p.join()
def predict(self, x_test):
Return predict result for the testing data.


My name is Artur Radziszewski, I'm passionate about full-stack web development and currently studying Computer Science at KEA in Copenhagen.
I am looking for opportunities to strengthen my skills in web development and learn new technologies. Through my studies I learned programming languages like JavaScript, C#, Python and Java, and became familiar with frameworks like Express, ASP.Net, and Mongoose.
Currently I am looking for an internship for my 5th and final semester this fall. If you would like to know more about me, don't hesitate to contact me or check my LinkedIn profile.


Home
About SEDS
Clusters
Extras
Contact
Send us a WOW! Signal. We are ready to approach you!
Name
Phone number
Email
Rishikesh
949572152


Reshma saujani founded Girls Who Code to help close the gender gap in Tech. This aims to increase the innovation and global competitiveness in US economy. She initiated the campaign that addresses cultural and social stereotypes that girls have towards the Tech Industry. After partnering up with companies like Facebook, Twitter and Google, she set a goal to teach a million women to code by 2020. She is galvanising the industry to change hiring practices one woman at at time. She is an activist that aims to arms young women with the coding tools necessary to compete in the fast growing tech industry.
Reshma came up with the idea of creating Girls Who Code during her run for the United States Congress when she noticed that schools along her campaign route lacked girl in computer science classrooms. Girls Who Code run programs during the academic year teaching high school girls computing skills like programming, robotics and web design with sessions including projects and trips to companies like Twitter and Facebook where they take part in Industry Immersions.
She continues in her quest to educate young girls around America through Girls Who Code.
Aside from being the founder of the tech organisation Girls who code, Reshma Saujani is an American lawyer and politician. She was previously the Deputy Public Advocate at the Office of the New York City Public Advocate. She did, however, loose the 2010 Democratic Primary for the US House of Representative against Congresswoman Carolyn B Maloney. She was the first Indian-American woman (and the first South Asian American woman) to run for Congress. She came third when she ran for Democratic New York City Public Advocate.
Through her non-profit, Girls Who Code, Reshma Saujani initiates young women into the tech world by encouraging young women to be brave instead of perfect.
There are now over 150 Girls Who Code clubs across America. By December 2014, three thousand students had completed a Girls Who Code program, 95% of whom went on to major in computer science at university.
65% of Clubs participants say they are considering a major or minor in Computer Science because of Girls Who Code. 90% of Girls Who Code Summer Immersion Program participants said they were planning to major or minor in CS or a closely-related field.
"What started as an experiment has grown into a national movement
Girls Who Code has gone from 20 girls in New York to 10,000 girls in 42 states. That’s the same number of girls who graduate each year with a degree in computer science. That’s progress! I’m proud to say we’re not just aiming to close the gender gap in tech — we’re actually doing it.
When girls learn to code, they become change agents in their communities. Whether it’s a game to illustrate the experience of an undocumented immigrant or a website to provide free college prep, our girls create technology that makes the world a better place. Like us, you believed in girls’ unlimited potential. Thanks to your support and contributions, together we’ve inspired thousands of girls to see a future in tech."
“I want the next generation of Mark Zuckerbergs and Jack Dorseys to be women.”
“Fail hard, fail fast, fail often. It's the key to success. This one I've learned from experience.”


MonGyi
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{% macro tail_banners(header) %} {% if invalid_postcode is defined and invalid_postcode %} {% set invalidPostcode = "I" %} {% else %} {% set invalidPostcode = "V" %} {% endif %}
View data for a specific area within the UK.
Not all data are available for every area – we will show the most local information in each category for your selected area.
{% include "components/postcode_search.html" %}


---
layout: default
title: "Design of KAIST HOUND, a Quadruped Robot Platform for Fast and Efficient Locomotion with Mixed-Integer Nonlinear Optimization of a Gear Train"
---
{{ page.title }}
IEEE International Conference on Robotics and Automation (ICRA), 2022
Young-Ha Shin (1) Seungwoo Hong (1) Sangyoung Woo (1) JongHun Choe (1) Harim Son (1) Gijeong Kim (1) Joon-Ha Kim (1) KangKyu Lee (1) Jemin Hwangbo (1) Hae-won Park (1)
(1) Korea Advanced Institute of Science and Technology
Abstract
This paper introduces a design method for an
efficient and agile quadruped robot. A mixed-integer opti-
mization formulation including the number of gear teeth is
derived to obtain the optimal gear ratio that minimizes cost
for a running-trot with the target speed of 3 m/s. With
the inclusion of integer constraints related to the number of
gear teeth, detailed design considerations of gear trains can
be included in the optimization process. Thermal dissipation
of the motor controller is also taken into account in the
optimization to consider heat generation during high-speed
running. KAIST Hound, a 45 kg robot, designed with the
obtained design parameters has successfully demonstrated a 3
m/s running-trot using a nonlinear model predictive controller
(NMPC). Furthermore, the robot has proved its robustness by
the demonstration of additional experiments such as 22 ◦ slope
climbing, 3.2 km walking, and traversing a 35 cm obstacle.
@INPROCEEDINGS{HOUNDShin2022,
author={Shin, Young-Ha and Hong, Seungwoo and Woo, Sangyoung and Choe, JongHun and Son, Harim and Kim, Gijeong and Kim, Joon-Ha and Lee, KangKyu and Hwangbo, Jemin and Park, Hae-Won},
booktitle={2022 International Conference on Robotics and Automation (ICRA)},
title={Design of KAIST HOUND, a Quadruped Robot Platform for Fast and Efficient Locomotion with Mixed-Integer Nonlinear Optimization of a Gear Train},
year={2022},
volume={},
number={},
pages={6614-6620},
doi={10.1109/ICRA46639.2022.9811755}
}


8 references to StopComplete
System.Management.Automation (8)
engine\remoting\client\Job.cs (2)
2092
operationStateEventArgs.OperationState = OperationState.
StopComplete
;
2736
operationStateEventArgs.OperationState = OperationState.
StopComplete
;
engine\remoting\commands\ConnectPSSession.cs (1)
676
operationStateEventArgs.OperationState = OperationState.
StopComplete
;
engine\remoting\commands\DisconnectPSSession.cs (1)
476
operationStateEventArgs.OperationState = OperationState.
StopComplete
;
engine\remoting\commands\newrunspacecommand.cs (2)
1338
operationStateEventArgs.OperationState = OperationState.
StopComplete
;
1427
operationStateEventArgs.OperationState = OperationState.
StopComplete
;
engine\remoting\commands\PSRemotingCmdlet.cs (2)
3467
OperationState.
StopComplete
;
3711
OperationState.
StopComplete
;


| REALTECH VBE 2.0 CORE Library |
Prototype : VBE_RESULT VBE_Initialize();
Description : Initialize VBE engine. You should call RM_Initialize()
before. Success if returns VBEERR_NOERR.
Prototype:
void VBE_Release();
Description: Release VBE engine. Notes that it doesn't release RM
Manager.
Prototype: const char * VBE_GetStringErr(VBE_RESULT
err);
Description: Convert a error code to english message string.
Prototype: VBE_MODE VBE_FindDisplayMode(int width,
into height, int bitsPerPixel);
Description: Find a desired display mode according criteria
(width, height and screen depth). if returns 0, then there is no video
modes that match criteria.
Prototype: VBE_RESULT VBE_SetDisplayMode(VBE_MODE mode);
Description: Set desired display mode. Success if returns
VBEERR_NOERR.
Prototype : int VBE_EnumDisplayModes(VBE_ENUMDISPLAYCALLBACK callback, void *context);
Description: Enumerate all display modes. Each
valid, video modes a call to
VBE_ENUMDISPLAYCALLBACK function syntax is callback(const VBE_ModeInfoBlock *info, VBE_MODE mode, void *context)
Prototype : void VGA_SetDisplayMode(int vgamode);
Description: Set VGA mode thru INT10h. For example, passing 0x3 as
parameter, set video mode to 80x25 text mode, passing 0x13 set video to
320x200x256 colors.
Always successful.
Prototype :
void CALLING_C VBE_Flip(void);
Description: Flip backbuffer / front buffer. Call this function when
flipping between front and back buffer
Prototype :
uchar* VBE_LockSurface(void);
Description: Return the current frame buffer video address (where you can
read/write).
It's return the back buffer which is the buffer that it is no visible until
you do a VBE_Flip() call.
Prototype: void VBE_UnlockSurface(void);
Description: Not really required to unlock the surface (invalid current display video
pointer), but it is good to have this policy of Locking/Unlocking surface which
is used in some others OS.
Prototype :
VBE_RESULT VBE_SetScanlineLength(long new_length);
Description: Set the scanline
length. Useful for horizontal scrolling . For example, settings a
ScanlineLength to 1024 in 640x480 display mode enables you to scroll
horizontally with VBE_SetDisplayOffset.
Prototype :
VBE_RESULT VBE_SetDisplayOffset(long x,long y);
Description: Set scrolling position. Useful for horizontal/vertical scrolling.
x and y are absolute screen offset to apply.
There is an extended LockSurface function for advanced users
Prototype : void VBE_LockSurfaceAlt(uchar **front, uchar
**back, long *pitch);
Description: fill front buffer address, back buffer address, and
pitch (number of pixels per lines). Useful if you want to write directly on the
visible page (front page). Width VESA 2.0, the write on front buffer is guaranteed
to be same as the back buffer.
Prototype : VBE_RESULT VBE_SetDac(VBEDACMODE mode);
Description: Set VGA 8bit DAC precision (6bit or 8bit). This only
meaningful in 256 colors modes.
Prototype :
VBE_RESULT VBE_GetPalette(int start, int numEntries, void *pal);
Description: Get current 8bit palette. Only in 8bit display modes. start : first color entry (0), numEntries : number of
colors (256), pal
(buffer of
Prototype : void VBE_SetPalette(ulong start, ulong numEntries, const void * pal);
Description: Set 8bit palette. Only in 8bit display modes.
Prototype : void VBE_SetBank(int bank);
Description: Change video bank (just given if you want to manage VESA 1.2).
© 1995-1999 realtech VR. All rights reserved.
Microsoft, MS-DOS is a registered trademarks or trademarks of Microsoft
Corporation in the United States and/or other countries
VESA is a registered trademark or trademark of VESA Org. in the United States
and/or other countries


Punishment Settings
By forcing reasons, any moderation will require a reason to always be provided for it to be able to be executed
Enables logging of moderation in the main logging area. To make this work, ensure that logging has also been enabled in Logging > Manage
Exports your current punishment data as a .CSV file
Resets punishment data. Not recoverable once reset!. Please note that this will also lose any information on outstanding punishments so you will have to remove the warns, mutes or bans manually


Friends cast reunite to support Jennifer Aniston at swanky red carpet bash
Jennifer Aniston was pictured cuddling her former co-stars Courteney Cox and Lisa Kudrow as she was honoured for her successful acting career
by Tiffany WallisJennifer Aniston was reunited with her Friends co-stars Courteney Cox and Lisa Kudrow last night at a glamorous red carpet event in Beverly Hills, California.
The actress, 50, enjoyed a mini reunion with her former co-stars at the 2019 SAG-AFTRA Foundation's Patron of the Artists Awards, where she was honoured with two Artists Inspiration prizes.
Friends castmates Courteney and Lisa presented the actress with the accolades and gushed about their "beloved, generous and deserving" friend.
Courteney, 55, and Lisa, 56, went on to speak about Jennifer’s achievements, both in her charity work and her acting career.
During a proud acceptance speech, Aniston described her time in the beloved 90's sitcom as "lightening in a bottle" and said the cast went from "friends to a family".
Cox praised her friend of 25 years to Entertainment Tonight , saying: "She inspires me every day.
"She is incredible. I'm just so happy to be here to support her."
During a proud acceptance speech, Aniston described her time in the beloved 90's sitcom as "lightening in a bottle" and said the cast went from "friends to a family".
Aniston, Cox and Kudrow starred as Rachel Green, Monica Geller and Phoebe Buffay, on the popular sitcom from 1994-2004.
Paul Rudd, who played Kudrow’s onscreen husband Mike Hannigan, also joined them for the mini reunion and posed for a picture with his former co-stars.
Leonardo Dicaprio, Olivia Wilde and Director Ava DuVernay were also honoured during the evening.
Just yesterday, Courteney sent the Internet into meltdown after she reunited with her Friends co-star Matthew Perry.
Courtney, shared a picture of herself with Matthew, who played her on-screen husband Chandler Bing.
Cox captioned the selfie: "Guess who I had lunch with today....I KNOW!! Could I BE any happier? #realfriends."
Their reunion comes after Jennifer recently posted a rare photo of all six of the Friends cast together.
The actress, who rose to fame as Rachel Green, blew everyone away with her first ever Instagram post.
She joined Matthew, Courteney, Lisa, David Schwimmer and Matt LeBlanc for dinner at Courteney's house.
It prompted rumours that the cast were set to get the old gang back together once more, but Jen denied there would be a reunion.
Speaking on The Ellen DeGeneres Show, she said her and her fellow stars would love there to be "something" but they aren't yet sure what that "something" is.
However during an interview on Howard Stern's Sirius XM Radio, the actress confessed that while she missed the show, a reboot wasn't a good idea.
She said: "We all miss it every day. I would be nothing without it […] If there’s a reboot of the show, it won’t be as good as what it was, so why do it? It would ruin it."
Her words echoed those of Friends co-creator Marta Kauffman who has always insisted that a reboot would never happen.


|
Ray Tracing
Ray Tracing algorithm implementation with many functions
|
Files | |
| file | aabb.h [code] |
| .h file that contains Axis-Aligned Bounding Boxes class implementation | |
| file | aarect.h [code] |
| .h file that contains rectangle class implementation | |
| file | box.h [code] |
| .h file that contains box class implementation | |
| file | bvh.h [code] |
| .h file that contains Bounding Volume Hierarchies class implementation | |
| file | camera.h [code] |
| .h file that contains camera class implementation | |
| file | color.h [code] |
| .h file that contains color output function | |
| file | constant_medium.h [code] |
| .h file that contains constant medium implementation | |
| file | hittable.h [code] |
| .h file that contains all hittable objects | |
| file | hittable_list.h [code] |
| .h file that contains hittable objects class implementation | |
| file | material.h [code] |
| .h file that contains all possible materials | |
| file | moving_sphere.h [code] |
| .h file that contains moving sphere implementation | |
| file | perlin.h [code] |
| .h file that contains perlin class implementation and noise generator | |
| file | Ray Tracing.cpp |
| .cpp file of ray color function, demos and output | |
| file | ray.h [code] |
| .h file that contains ray class implementation | |
| file | sphere.h [code] |
| .h file that contains sphere class | |
| file | texture.h [code] |
| .h file that contains different textures | |
| file | utility.h [code] |
| .h file that contains utility functions | |
| file | vec3.h [code] |
| .h file that contains three-dimensional vector class implementation | |


Mohd Ahmed
When Mohd was a kid, he spent a lot of time by the sea in Mumbai, exploring and looking for sea creatures. When he wasn't adventuring, he was on his commodore computer, writing programs in basic which he would then save on audio tape. Sufficient to say, he got started on programming very early.
The stage was set for Mohd's exemplary career in technologies at the Bombay International School, St. Xaviers College in Mumbai, and Rensselaer Polytechnic Institute in Troy NY, where he specialized in computer systems engineering. His professional accomplishments are numerous. He built SCADA software for Department of Atomic Energy in Manuguru, India, acted as consultant to Securities and Exchange Board of India for their IT needs, created his own CMS called ezCMS which is now being used internationally to build many sites, and built countless unique and imaginative programs.
Mohd has over 18 years of experience in the IT industry. He is proficient in php, html, css, js, mysql, ms visual basic, and prides himself in learning new languages very quickly.
It's true Mohd spends a lot of his time in front of his computer, but he loves the natural world; organic farming, camping, and traveling are his passions. Someday, he hopes to travel the world without using google maps.


| Package | Description |
|---|---|
| edu.cmu.sphinx.decoder.search |
Provides classes and interfaces that are used to manage the search
through the search graph.
|
| edu.cmu.sphinx.frontend |
Provides a set of high level classes and interfaces that are used
to perform digital signal processing for speech recognition.
|
| edu.cmu.sphinx.frontend.denoise | |
| edu.cmu.sphinx.frontend.endpoint |
Provides classes and interfaces used for speech endpointing.
|
| edu.cmu.sphinx.frontend.filter |
Provides classes that implement frequency filters.
|
| edu.cmu.sphinx.frontend.frequencywarp |
Provides classes that perform frequency warping.
|
| edu.cmu.sphinx.frontend.util |
Provides classes that are generally useful to the various frontend
classes.
|
| edu.cmu.sphinx.frontend.window |
Provides classes that implement windowing functions.
|
| edu.cmu.sphinx.linguist |
Provides a set of interfaces and classes that are used to define the
search graph used by the decoder.
|
| edu.cmu.sphinx.linguist.acoustic.tiedstate |
Provides classes that represent acoustic model in terms of a set of
tied states.
|
| edu.cmu.sphinx.linguist.acoustic.tiedstate.trainer |
Provides classes that represent a trainable acoustic model.
|
| edu.cmu.sphinx.linguist.aflat | |
| edu.cmu.sphinx.linguist.allphone | |
| edu.cmu.sphinx.linguist.dflat | |
| edu.cmu.sphinx.linguist.flat |
Provides an implementation of the
Linguist that
statically represents the search space as a flat graph, where each
word in the vocabulary has its own branch. |
| edu.cmu.sphinx.linguist.language.ngram |
Provides classes and interfaces that represent a stochastic language model
|
| edu.cmu.sphinx.linguist.language.ngram.large |
Provides an implementation of the LanguageModel interface.
|
| edu.cmu.sphinx.linguist.lextree |
Provides an implementation of the
Linguist that
represents the search space as a lex tree. |
| edu.cmu.sphinx.trainer |
Provides a set of high level classes that can
be used to configure and initiate the speech recognition training
process.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
WordPruningBreadthFirstSearchManager.PROP_ACOUSTIC_LOOKAHEAD_FRAMES
The property that controls the amount of simple acoustic lookahead
performed.
|
static String |
WordPruningBreadthFirstLookaheadSearchManager.PROP_LOOKAHEAD_PENALTY_WEIGHT |
static String |
WordPruningBreadthFirstSearchManager.PROP_RELATIVE_BEAM_WIDTH
The property that specifies the relative beam width
|
static String |
ActiveListFactory.PROP_RELATIVE_BEAM_WIDTH
Property that sets the minimum score relative to the maximum score in the list for pruning.
|
static String |
ActiveList.PROP_RELATIVE_BEAM_WIDTH
Property that sets the minimum score relative to the maximum score in the list for pruning.
|
static String |
SimpleBreadthFirstSearchManager.PROP_RELATIVE_WORD_BEAM_WIDTH
The property that sets the minimum score relative to the maximum score in the word list for pruning.
|
static String |
ActiveListManager.PROP_RELATIVE_WORD_BEAM_WIDTH
The property that specifies the relative word beam width
|
| Modifier and Type | Field and Description |
|---|---|
static String |
GainControlProcessor.GAIN_FACTOR |
static String |
DataBlocker.PROP_BLOCK_SIZE_MS
The property for the block size of generated data-blocks in milliseconds.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
Denoise.LAMBDA_A |
static String |
Denoise.LAMBDA_B |
static String |
Denoise.LAMBDA_POWER |
static String |
Denoise.LAMBDA_T |
static String |
Denoise.MAX_GAIN |
static String |
Denoise.MU_T |
| Modifier and Type | Field and Description |
|---|---|
static String |
SpeechClassifier.PROP_ADJUSTMENT
The property specifying the adjustment.
|
static String |
SpeechClassifier.PROP_MIN_SIGNAL
The property specifying the minimum signal level used to update the background signal level.
|
static String |
SpeechClassifier.PROP_THRESHOLD
The property specifying the threshold.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
Dither.PROP_MAX_DITHER
The maximal value which could be added/subtracted to/from the signal
|
static String |
EnergyFilter.PROP_MAX_ENERGY
If energy is below this threshold frame is dropped
|
static String |
Dither.PROP_MAX_VAL
The maximal value of dithered values.
|
static String |
Dither.PROP_MIN_VAL
The minimal value of dithered values.
|
static String |
Preemphasizer.PROP_PREEMPHASIS_FACTOR
The property for preemphasis factor/alpha.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
PLPFrequencyFilterBank.PROP_MAX_FREQ
The property for the center frequency of the highest filter in the filterbank.
|
static String |
MelFrequencyFilterBank2.PROP_MAX_FREQ
The property for the maximum frequency covered by the filterbank.
|
static String |
MelFrequencyFilterBank.PROP_MAX_FREQ
The property for the maximum frequency covered by the filterbank.
|
static String |
PLPFrequencyFilterBank.PROP_MIN_FREQ
The property for the center frequency of the lowest filter in the filterbank.
|
static String |
MelFrequencyFilterBank2.PROP_MIN_FREQ
The property for the minimum frequency covered by the filterbank.
|
static String |
MelFrequencyFilterBank.PROP_MIN_FREQ
The property for the minimum frequency covered by the filterbank.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
StreamHTKCepstrum.PROP_FRAME_SHIFT_MS
The property name for frame shift in milliseconds, which has a default value of 10F.
|
static String |
StreamCepstrumSource.PROP_FRAME_SHIFT_MS
The property name for frame shift in milliseconds, which has a default value of 10F.
|
static String |
StreamHTKCepstrum.PROP_FRAME_SIZE_MS
The property name for frame size in milliseconds.
|
static String |
StreamCepstrumSource.PROP_FRAME_SIZE_MS
The property name for frame size in milliseconds.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
RaisedCosineWindower.PROP_ALPHA
The property for the alpha value of the Window, which is the value for the RaisedCosineWindow.
|
static String |
RaisedCosineWindower.PROP_WINDOW_SHIFT_MS
The property for window shift in milliseconds, which has a default value of 10F.
|
static String |
RaisedCosineWindower.PROP_WINDOW_SIZE_MS
The property for window size in milliseconds.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
Linguist.PROP_FILLER_INSERTION_PROBABILITY
Filler insertion probability property
|
static String |
Linguist.PROP_LANGUAGE_WEIGHT
The property that defines the language weight for the search
|
static String |
Linguist.PROP_SILENCE_INSERTION_PROBABILITY
Silence insertion probability property
|
static String |
Linguist.PROP_UNIT_INSERTION_PROBABILITY
Unit insertion probability property
|
static String |
Linguist.PROP_WORD_INSERTION_PROBABILITY
Word insertion probability property
|
| Modifier and Type | Field and Description |
|---|---|
static String |
Sphinx3Loader.PROP_MC_FLOOR
Mixture component score floor.
|
static String |
Saver.PROP_MC_FLOOR
Mixture component score floor.
|
static String |
HTKLoader.PROP_MC_FLOOR
Mixture component score floor.
|
static String |
Sphinx3Loader.PROP_MW_FLOOR
Mixture weight floor
|
static String |
Saver.PROP_MW_FLOOR
Mixture weight floor.
|
static String |
HTKLoader.PROP_MW_FLOOR
Mixture weight floor.
|
static String |
Sphinx3Loader.PROP_VARIANCE_FLOOR
Variance floor.
|
static String |
Saver.PROP_VARIANCE_FLOOR |
static String |
HTKLoader.PROP_VARIANCE_FLOOR
Variance floor.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
TrainerAcousticModel.PROP_MC_FLOOR
Mixture component score floor.
|
static String |
Sphinx3Saver.PROP_MC_FLOOR |
static String |
ModelInitializerLoader.PROP_MC_FLOOR
Mixture component score floor.
|
static String |
TrainerAcousticModel.PROP_MW_FLOOR
Mixture weight floor.
|
static String |
ModelInitializerLoader.PROP_MW_FLOOR
Mixture weight floor.
|
static String |
TrainerAcousticModel.PROP_VARIANCE_FLOOR |
static String |
ModelInitializerLoader.PROP_VARIANCE_FLOOR |
| Modifier and Type | Field and Description |
|---|---|
static String |
AFlatLinguist.OUT_OF_GRAMMAR_PROBABILITY
The property for the probability of entering the out-of-grammar branch.
|
static String |
AFlatLinguist.PHONE_INSERTION_PROBABILITY
The property for the probability of inserting a CI phone in the
out-of-grammar ci phone loop
|
| Modifier and Type | Field and Description |
|---|---|
static String |
AllphoneLinguist.PROP_PIP
The property that controls phone insertion probability.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
DynamicFlatLinguist.OUT_OF_GRAMMAR_PROBABILITY
The property for the probability of entering the out-of-grammar branch.
|
static String |
DynamicFlatLinguist.PHONE_INSERTION_PROBABILITY
The property for the probability of inserting a CI phone in the out-of-grammar ci phone loop
|
| Modifier and Type | Field and Description |
|---|---|
static String |
FlatLinguist.PROP_OUT_OF_GRAMMAR_PROBABILITY
The property for the probability of entering the out-of-grammar branch.
|
static String |
FlatLinguist.PROP_PHONE_INSERTION_PROBABILITY
The property for the probability of inserting a CI phone in the out-of-grammar ci phone loop
|
| Modifier and Type | Field and Description |
|---|---|
static String |
LanguageModel.PROP_UNIGRAM_WEIGHT
The property specifying the unigram weight
|
| Modifier and Type | Field and Description |
|---|---|
static String |
LargeNGramModel.PROP_LANGUAGE_WEIGHT
The property that defines the language weight for the search
|
static String |
LargeNGramModel.PROP_WORD_INSERTION_PROBABILITY
Word insertion probability property
|
| Modifier and Type | Field and Description |
|---|---|
static String |
LexTreeLinguist.PROP_UNIGRAM_SMEAR_WEIGHT
The property that determines the weight of the smear.
|
| Modifier and Type | Field and Description |
|---|---|
static String |
TrainManager.PROP_MINIMUM_IMPROVEMENT
The minimum relative improvement of the log likelihood associated with the training data.
|
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