Royal Navy carrier HMS Queen Elizabeth joined by missile cruiser in the Atlantic
"Welcome to the Queen Elizabeth Carrier Strike Group"
by Rom Preston-EllisRoyal Navy aircraft carrier HMS Queen Elizabeth has welcomed two new ships to its 'strike group'.
USS Philippine Sea and USS Truxtun, the Ticonderoga-class guided missile cruiser and Arleigh Burke-class destroyer have joined us as UKF35 jet trials continue in the Atlantic off the coast of the USA.
The group assembled as part of Westlant 19 - a three-month exercise where aircraft carrier HMS Queen Elizabeth, F-35s and supporting units will be rigorously tested under realistic warfighting scenarios, while visiting Canada and the USA.
HMS QE also brought its renewed Carrier Strike Group capability online with a refuel at sea involving bridge work with HMS Northumberland.
HMS Queen Elizabeth's official Twitter account: "Welcome to the Queen Elizabeth Carrier Strike Group, USS Philippine Sea and USS Truxtun USNavy.
"The Ticonderoga-class guided missile cruiser and Arleigh Burke-class destroyer have joined us as #UKF35 jet trials continue in the USA #WESTLANT19 #jointcapability."
"A great shot to add and another first for WESTLANT19 as we bring our renewed Carrier Strike Group capability online.
"A double Replenishment at Sea (RAS), with some fantastic bridge work from FATideforce, HMSNORT and our own great team here."


enca(1) enca(1) NAME enca -- detect and convert encoding of text files SYNOPSIS enca [-L LANGUAGE] [OPTION]... [FILE]... enconv [-L LANGUAGE] [OPTION]... [FILE]... INTRODUCTION AND EXAMPLES If you are lucky enough, the only two things you will ever need to know are: command enca FILE will tell you which encoding file FILE uses (without changing it), and enconv FILE will convert file FILE to your locale native encoding. To convert the file to some other encoding use the -x option (see -x entry in section OPTIONS and sections CONVERSION and ENCODINGS for details). Both work with multiple files and standard input (output) too. E.g. enca -x latin2 <sometext | lpr assures file `sometext' is in ISO Latin 2 when it's sent to printer. The main reason why these command will fail and turn your files into garbage is that Enca needs to know their language to detect the encoding. It tries to determine your language and preferred charset from locale settings, which might not be what you want. You can (or have to) use -L option to tell it the right language. Sup‐ pose, you downloaded some Russian HTML file, `file.htm', it claims it's windows-1251 but it isn't.  Be warned, currently there are not many supported languages (see section LANGUAGES). Another warning concerns the fact several Enca's features, namely its charset conversion capabilities, strongly depend on what other tools are installed on your system (see section CONVERSION)--run enca --version to get list of features (see section FEATURES). Also try enca --help to get description of all other Enca options (and to find the rest of this manual page redundant). DESCRIPTION Enca reads given text files, or standard input when none are given, and uses knowledge about their language (must be supported by you) and a mix‐ ture of parsing, statistical analysis, guessing and black magic to deter‐ mine their encodings, which it then prints to standard output (or it con‐ fesses it doesn't have any idea what the encoding could be). By default, Enca presents results as a multiline human-readable descriptions, several other formats are available--see Output type selectors below. Enca can also convert files to some other encoding ENC when you ask for it--either using a built-in converter, some conversion library, or by calling an external converter. Enca's primary goal is to be usable unattended, as an automatic conver‐ sion tool, though it perhaps have not reached this point yet (please see section SECURITY). Please note except rare cases Enca really has to know the language of input files to give you a reliable answer. On the other hand, it can then cope quite well with files that are not purely textual or even detect charset of text strings inside some binary file; of course, it depends on the character of the non-text component. Enca doesn't care about structure of input files, it views them as a uni‐ form piece of text/data. In case of multipart files (e.g. mailboxes), you have to use some tool knowing the structure to extract the individual parts first. It's the cost of ability to detect encodings of any dam‐ aged, incomplete or otherwise incorrect files. OPTIONS There are several categories of options: operation mode options, output type selectors, guessing parameters, conversion parameters, general options and listings. All long options can be abbreviated as long as they are unambiguous, mandatory parameters of long options are mandatory for short options too. Operation modes are following: -c, --auto-convert Equivalent to calling Enca as enconv. If no output type selector is specified, detect file encodings, guess your preferred charset from locales, and convert files to it (only available with +target-charset-auto feature). -g, --guess Equivalent to calling Enca as enca. If no output type selector is specified, detect file encodings and report them. Output type selectors select what action Enca will take when it determines the encoding; most of them just choose between different names, formats and conventions how encodings can be printed, but one of them (-x) is special: it tells Enca to recode files to some other encoding ENC. These options are mutually exclusive; if you specify more than one output type selector the last one takes precedence. Several output types represent charset name used by some other program, but not all these programs know all the charsets which Enca recognises. Be warned, Enca makes no difference between unrecognised charset and charset having no name in given namespace in such situations. -d, --details It used to print a few pages of details about the guessing process, but since Enca is just a program linked against Enca library, this is not possible and this option is roughly equiva‐ lent to --human-readable, except it reports failure reason when Enca doesn't recoginize the encoding. -e, --enca-name Prints Enca's nice name of the charset, i.e., perhaps the most generally accepted and more or less human-readable charset identi‐ fier, with surfaces appended. This name is used when calling an external converter, too. -f, --human-readable Prints verbal description of the detected charset and sur‐ faces--something a human understands best. This is the default behaviour. The precise format is following: the first line contains charset name alone, and it's followed by zero or more indented lines con‐ taining names of detected surfaces. This format is not, however, suitable or intended for further machine-processing, and the ver‐ bal charset descriptions are like to change in the future. -i, --iconv-name Prints how iconv(3) (and/or iconv(1)) calls the detected charset. More precisely, it prints one, more or less arbitrarily chosen, alias accepted by iconv. A charset unknown to iconv counts as unknown. This output type makes sense only when Enca is compiled with iconv support (feature +iconv-interface). -r, --rfc1345-name Prints RFC 1345 charset name. When such a name doesn't exist because RFC 1345 doesn't define given encoding, some other name defined in some other RFC or just the name which author consideres `the most canonical', is printed. Since RFC 1345 doesn't define surfaces, no surface info is appended. -m, --mime-name Prints preferred MIME name of detected charset. This is the name you should normally use when fixing e-mails or web pages. A charset not present in http://www.iana.org/assignments/charac‐ ter-sets counts as unknown. -s, --cstocs-name Prints how cstocs(1) calls the detected charset. A charset unknown to cstocs counts as unknown. -n, --name=WORD Prints charset (encoding) name selected by WORD (can be abbrevi‐ ated as long as is unambiguous). For names listed above, --name=WORD is equivalent to --WORD. Using aliases as the output type causes Enca to print list of all accepted aliases of detected charset. -x, --convert-to=[..]ENC Converts file to encoding ENC. The optional `..' before encoding name has no special meaning, except you can use it to remind yourself that, unlike in recode(1), you should specify desired encoding, instead of cur‐ rent. You can use recode(1) recoding chains or any other kind of brain‐ dead recoding specification for ENC, provided that you tell Enca to use some tool understanding it for conversion (see section CON‐ VERSION). When Enca fails to determine the encoding, it prints a warning and leaves the the file as is; when it is run as a filter it tries to do its best to copy standard input to standard output unchanged. Nevertheless, you should not rely on it and do backup. Guessing parameters There's only one: -L setting language of input files. This option is mandatory (but see below). -L, --language=LANG Sets language of input files to LANG. More precisely, LANG can be any valid locale name (or alias with +locale-alias feature) of some supported language. You can also specify `none' as language name, only multibyte encodings are recognised then. Run enca --list languages to get list of supported languages. When you don't specify any language Enca tries to guess your language from locale settings and assumes input files use this language. See section LANGUAGES for details. Conversion parameters give you finer control of how charset conversion will be performed. They don't affect anything when -x is not specified as output type. Please see section CONVERSION for the gory conversion details. -C, --try-converters=LIST Appends comma separated LIST to the list of converters that will be tried when you ask for conversion. Their names can be abbrevi‐ ated as long as they are unambiguous. Run enca --list converters to get list of all valid converter names (and see section CONVER‐ SION for their description). The default list depends on how Enca has been compiled, run enca --help to find out default converter list. Note the default list is used only when you don't specify -C at all. Otherwise, the list is built as if it were initially empty and every -C adds new converter(s) to it. Moreover, specifying none as converter name causes clearing the converter list. -E, --external-converter-program=PATH Sets external converter program name to PATH. Default external converter depends on how enca has been complied, and the possibil‐ ity to use external converters may not be available at all. Run enca --help to find out default converter program in your enca build. General options don't fit to other option categories... -p, --with-filename Forces Enca to prefix each result with corresponding file name. By default, Enca prefixes results with filenames when run on mul‐ tiple files. Standard input is printed as STDIN and standard output as STDOUT (the latter can be probably seen in error messages only). -P, --no-filename Forces Enca to not prefix results with file names. By default, Enca doesn't prefix result with file name when run on a single file (including standard input). -V, --verbose Increases verbosity level (each use increases it by one). Currently this option in not very useful because different parts of Enca respond differently to the same verbosity level, mostly not at all. Listings are all terminal, i.e. when Enca encounters some of them it prints the required listing and terminates without processing any following options. -h, --help Prints brief usage help. -G, --license Prints full Enca license (through a pager, if possible). -l, --list=WORD Prints list specified by WORD (can be abbreviated as long as it is unambiguous). Available lists include: built-in-charsets. All encodings convertible by built-in con‐ verter, by group (both input and output encoding must be from this list and belong to the same group for internal conversion). built-in-encodings. Equivalent to built-in-charsets, but consid‐ ered obsolete; will be accepted with a warning, for a while. converters. All valid converter names (to be used with -C). charsets. All encodings (charsets). You can select what names will be printed with --name or any name output type selector (of course, only encodings having a name in given namespace will be printed then), the selector must be specified before --list. encodings. Equivalent to charsets, but considered obsolete; will be accepted with a warning, for a while. languages. All supported languages together with charsets belong‐ ing to them. Note output type selects language name style, not charset name style here. names. All possible values of --name option. lists. All possible values of this option. (Crazy?) surfaces. All surfaces Enca recognises. -v, --version Prints program version and list of features (see section FEA‐ TURES). CONVERSION Though Enca has been originally designed as a tool for guessing encoding only, it now features several methods of charset conversion. You can control which of them will be used with -C. Enca sequentially tries converters from the list specified by -C until it finds some that is able to perform required conversion or until it exhausts the list. You should specify preferred converters first, less preferred later. External converter (extern) should be always specified last, only as last resort, since it's usually not possible to recover when it fails. The default list of converters always starts with built-in and then continues with the first one available from: librecode, iconv, nothing. It should be noted when Enca says it is not able to perform the conver‐ sion it only means none of the converters is able to perform it. It can be still possible to perform the required conversion in several steps, using several converters, but to figure out how, human intelligence is probably needed. Built-in converter is the simplest and far the fastest of all, can perform only a few byte- to-byte conversions and modifies files directly in place (may be consid‐ ered dangerous, but is pretty efficient). You can get list of all encod‐ ings it can convert with enca --list built-in Beside speed, its main advantage (and also disadvantage) is that it doesn't care: it simply converts characters having a representation in target encoding, doesn't touch anything else and never prints any error message. This converter can be specified as built-in with -C. Librecode converter is an interface to GNU recode library, that does the actual recoding job. It may or may not be compiled in; run enca --version to find out its availability in your enca build (feature +libre‐ code-interface). You should be familiar with recode(1) before using it, since recode is a quite sophisticated and powerful charset conversion tool. You may run into problems using it together with Enca particularly because Enca's support for surfaces not 100% compatible, because recode tries too hard to make the transformation reversible, because it sometimes silently ignores I/O errors, and because it's incredibily buggy. Please see GNU recode info pages for details about recode library. This converter can be specified as librecode with -C. Iconv converter is an interface to the UNIX98 iconv(3) conversion functions, that do the actual recoding job. It may or may not be compiled in; run enca --version to find out its availability in your enca build (feature +iconv-inter‐ face). While iconv is present on most today systems it only rarely offer some useful set of available conversions, the only notable exception being iconv from GNU libc. It is usually quite picky about surfaces, too (while, at the same time, not implementing surface conversion). It how‐ ever probably represents the only standard(ized) tool able to perform conversion from/to Unicode. Please see iconv documentation about for details about its capabilities on your particular system. This converter can be specified as iconv with -C. External converter is an arbitrary external conversion tool that can be specified with -E option (at most one can be defined simultaneously). There are some stan‐ dard, provided together with enca: cstocs, recode, map, umap, and piconv. All are wrapper scripts: for cstocs(1), recode(1), map(1), umap(1), and piconv(1). Please note enca has little control what the external converter really does. If you set it to /bin/rm you are fully responsible for the conse‐ quences. If you want to make your own converter to use with enca, you should know it is always called CONVERTER ENC_CURRENT ENC FILE [-] where CONVERTER is what has been set by -E, ENC_CURRENT is detected encoding, ENC is what has been specified with -x, and FILE is the file to convert, i.e. it is called for each file separately. The optional fourth parameter, -, should cause (when present) sending result of conversion to standard output instead of overwriting the file FILE. The converter should also take care of not changing file permissions, returning error code 1 when it fails and cleaning its temporary files. Please see the standard external converters for examples. This converter can be specified as extern with -C. Default target charset The starightforward way of specifying target charset is the -x option, which overrides any defaults. When Enca is called as enconv, default target charset is selected exactly the same way as recode(1) does it. If the DEFAULT_CHARSET environment variable is set, it's used as the tar‐ get charset. Otherwise, if you system provides the nl_langinfo(3) function, current locale's native charset is used as the target charset. When both methods fail, Enca complains and terminates. Reversibility notes If reversibility is crucial for you, you shouldn't use enca as converter at all (or maybe you can, with very specifically designed recode(1) wrap‐ per). Otherwise you should at least know that there four basic means of handling inconvertible character entities: fail--this is a possibility, too, and incidentally it's exactly what cur‐ rent GNU libc iconv implementation does (recode can be also told to do it) don't touch them--this is what enca internal converter always does and recode can do; though it is not reversible, a human being is usually able to reconstruct the original (at least in principle) approximate them--this is what cstocs can do, and recode too, though dif‐ ferently; and the best choice if you just want to make the accursed text readable drop them out--this is what both recode and cstocs can do (cstocs can also replace these characters by some fixed character instead of mere ignoring); useful when the to-be-omitted characters contain only noise. Please consult your favourite converter manual for details of this issue. Generally, if you are not lucky enough to have all convertible characters in you file, manual intervention is needed anyway. Performance notes Poor performance of available converters has been one of main reasons for including built-in converter in enca. Try to use it whenever possible, i.e. when files in consideration are charset-clean enough or charset- messy enough so that its zero built-in intelligence doesn't matter. It requires no extra disk space nor extra memory and can outperform recode(1) more than 10 times on large files and Perl version (i.e. the faster one) of cstocs(1) more than 400 times on small files (in fact it's almost as fast as mere cp(1)). Try to avoid external converters when it's not absolutely necessary since all the forking and moving stuff around is incredibily slow. ENCODINGS You can get list of recognised character sets with enca --list charsets and using --name parameter you can select any name you want to be used in the listing. You can also list all surfaces with enca --list surfaces Encoding and surface names are case insensitive and non-alphanumeric characters are not taken into account. However, non-alphanumeric charac‐ ters are mostly not allowed at all. The only allowed are: `-', `_', `.', `:', and `/' (as charset/surface separator). So `ibm852' and `IBM-852' are the same, while `IBM 852' is not accepted. Charsets Following list of recognised charsets uses Enca's names (-e) and verbal descriptions as reported by Enca (-f): tab (@); l l. ASCII@7bit ASCII characters ISO-8859-2@ISO 8859-2 stan‐ dard; ISO Latin 2 ISO-8859-4@ISO 8859-4 standard; Latin 4 ISO-8859-5@ISO 8859-5 standard; ISO Cyrillic ISO-8859-13@ISO 8859-13 standard; ISO Baltic; Latin 7 ISO-8859-16@ISO 8859-16 standard CP1125@MS-Windows code page 1125 CP1250@MS-Windows code page 1250 CP1251@MS-Windows code page 1251 CP1257@MS-Windows code page 1257; WinBaltRim IBM852@IBM/MS code page 852; PC (DOS) Latin 2 IBM855@IBM/MS code page 855 IBM775@IBM/MS code page 775 IBM866@IBM/MS code page 866 baltic@ISO-IR-179; Baltic KEY‐ BCS2@Kamenicky encoding; KEYBCS2 macce@Macintosh Central European mac‐ cyr@Macintosh Cyrillic ECMA-113@Ecma Cyrillic; ECMA-113 KOI-8_CS_2@KOI8-CS2 code (`T602') KOI8-R@KOI8-R Cyrillic KOI8-U@KOI8-U Cyrillic KOI8-UNI@KOI8-Unified Cyrillic TeX@(La)TeX control sequences UCS-2@Universal character set 2 bytes; UCS-2; BMP UCS-4@Universal charac‐ ter set 4 bytes; UCS-4; ISO-10646 UTF-7@Universal transformation format 7 bits; UTF-7 UTF-8@Universal transformation format 8 bits; UTF-8 CORK@Cork encoding; T1 GBK@Simplified Chinese National Standard; GB2312 BIG5@Tradi‐ tional Chinese Industrial Standard; Big5 HZ@HZ encoded GB2312 unknown@Unrecognized encoding where unknown is not any real encoding, it's reported when Enca is not able to give a reliable answer. Surfaces Enca has some experimental support for so-called surfaces (see below). It detects following surfaces (not all can be applied to all charsets): tab (@); l l. /CR@CR line terminators /LF@LF line terminators /CRLF@CRLF line terminators N.A.@Mixed line terminators N.A.@Surrounded by/inter‐ mixed with non-text data /21@Byte order reversed in pairs (1,2 -> 2,1) /4321@Byte order reversed in quadruples (1,2,3,4 -> 4,3,2,1) N.A.@Both little and big endian chunks, concatenated /qp@Quoted-printable encoded Note some surfaces have N.A. in place of identifier--they cannot be spec‐ ified on command line, they can only be reported by Enca. This is inten‐ tional because they only inform you why the file cannot be considered surface-consistent instead of representing a real surface. Each charset has its natural surface (called `implied' in recode) which is not reported, e.g., for IBM 852 charset it's `CRLF line terminators'. For UCS encodings, big endian is considered as natural surface; unusual byte orders are constructed from 21 and 4321 permutations: 2143 is reported simply as 21, while 3412 is reported as combination of 4321 and 21. Doubly-encoded UTF-8 is neither charset nor surface, it's just reported. About charsets, encodings and surfaces Charset is a set of character entities while encoding is its representa‐ tion in the terms of bytes and bits. In Enca, the word encoding means the same as `representation of text', i.e. the relation between sequence of character entities constituting the text and sequence of bytes (bits) constituting the file. So, encoding is both character set and so-called surface (line termina‐ tors, byte order, combining, Base64 transformation, etc.). Nevertheless, it proves convenient to work with some {charset,surface} pairs as with genuine charsets. So, as in recode(1), all UCS- and UTF- encodings of Universal character set are called charsets. Please see recode documen‐ tation for more details of this issue. The only good thing about surfaces is: when you don't start playing with them, neither Enca won't start and it will try to behave as much as pos‐ sible as a surface-unaware program, even when talking to recode. LANGUAGES Enca needs to know the language of input files to work reliably, at least in case of regular 8bit encoding. Multibyte encodings should be recog‐ nised for any Latin, Cyrillic or Greek language. You can (or have to) use -L option to tell Enca the language. Since peo‐ ple most often work with files in the same language for which they have configured locales, Enca tries tries to guess the language by examining value of LC_CTYPE and other locale categories (please see locale(7)) and using it for the language when you don't specify any. Of course, it may be completely wrong and will give you nonsense answers and damage your files, so please don't forget to use the -L option. You can also use ENCAOPT environment variable to set a default language (see section ENVI‐ RONMENT). Following languages are supported by Enca (each language is listed together with supported 8bit encodings). tab (@); l l. Belarussian@CP1251 IBM866 ISO-8859-5 KOI8-UNI maccyr IBM855 Bulgarian @CP1251 ISO-8859-5 IBM855 maccyr ECMA-113 Czech @ISO-8859-2 CP1250 IBM852 KEYBCS2 macce KOI-8_CS_2 CORK Estonian @ISO-8859-4 CP1257 IBM775 ISO-8859-13 macce baltic Croatian @CP1250 ISO-8859-2 IBM852 macce CORK Hungarian @ISO-8859-2 CP1250 IBM852 macce CORK Lithuanian @CP1257 ISO-8859-4 IBM775 ISO-8859-13 macce baltic Lat‐ vian @CP1257 ISO-8859-4 IBM775 ISO-8859-13 macce baltic Polish @ISO-8859-2 CP1250 IBM852 macce ISO-8859-13 ISO-8859-16 baltic CORK Rus‐ sian @KOI8-R CP1251 ISO-8859-5 IBM866 maccyr Slovak @CP1250 ISO-8859-2 IBM852 KEYBCS2 macce KOI-8_CS_2 CORK Slovene @ISO-8859-2 CP1250 IBM852 macce CORK Ukrainian @CP1251 IBM855 ISO-8859-5 CP1125 KOI8-U maccyr Chinese @GBK BIG5 HZ none @ The special language none can be shortened to __, it contains no 8bit encodings, so only multibyte encodings are detected. FEATURES Several Enca's features depend on what is available on your system and how it was compiled. You can get their list with enca --version Plus sign before a feature name means it's available, minus sign means this build lacks the particular feature. librecode-interface. Enca has interface to GNU recode library charset conversion functions. iconv-interface. Enca has interface to UNIX98 iconv charset conversion functions. external-converter. Enca can use external conversion programs (if you have some suitable installed). language-detection. Enca tries to guess language (-L) from locales. You don't need the --language option, at least in principle. locale-alias. Enca is able to decrypt locale aliases used for language names. target-charset-auto. Enca tries to detect your preferred charset from locales. Option --auto-convert and calling Enca as enconv works, at least in principle. ENCAOPT. Enca is able to correctly parse this environment variable before command line parameters. Simple stuff like ENCAOPT="-L uk" will work even without this feature. ENVIRONMENT The variable ENCAOPT can hold set of default Enca options. Its content is interpreted before command line arguments. Unfortunately, this doesn't work everywhere (must have +ENCAOPT feature). LC_CTYPE, LC_COLLATE, LC_MESSAGES (possibly inherited from LC_ALL or LANG) is used for guessing your language (must have +language-detection feature). The variable DEFAULT_CHARSET can be used by enconv as the default target charset. DIAGNOSTICS Enca returns exit code 0 when all input files were successfully proceeded (i.e. all encodings were detected and all files were converted to required encoding, if conversion was asked for). Exit code 1 is returned when Enca wasn't able to either guess encoding or perform conversion on any input file because it's not clever enough. Exit code 2 is returned in case of serious (e.g. I/O) troubles. SECURITY It should be possible to let Enca work unattended, it's its goal. How‐ ever: There's no warranty the detection works 100%. Don't bet on it, you can easily lose valuable data. Don't use enca (the program), link to libenca instead if you want any‐ thing resembling security. You have to perform the eventual conversion yourself then. Don't use external converters. Ideally, disable them compile-time. Be aware of ENCAOPT and all the built-in automagic guessing various things from environment, namely locales. SEE ALSO autoconvert(1), cstocs(1), file(1), iconv(1), iconv(3), nl_langinfo(3), map(1), piconv(1), recode(1), locale(5), locale(7), ltt(1), umap(1), uni‐ code(7), utf-8(7), xcode(1) KNOWN BUGS It has too many unknown bugs. The idea of using LC_* value for language is certainly braindead. How‐ ever I like it. It can't backup files before mangling them. In certain situations, it may behave incorrectly on >31bit file systems and/or over NFS (both untested but shouldn't cause problems in practice). Built-in converter does not convert character `ch' from KOI8-CS2, and possibly some other characters you've probably never heard about anyway. EOL type recognition works poorly on Quoted-printable encoded files. This should be fixed someday. There are no command line options to tune libenca parameters. This is intentional (Enca should DWIM) but sometimes this is a nuisance. The manual page is too long, especially this section. This doesn't mat‐ ter since nobody does read it. Send bug reports to <http://bugs.cihar.com/>. TRIVIA Enca is Extremely Naive Charset Analyser. Nevertheless, the `enc' origi‐ nally comes from `encoding' so the leading `e' should be read as in `encoding' not as in `extreme'. AUTHORS David Necas (Yeti<EMAIL_ADDRESS>Michal Cihar<EMAIL_ADDRESS>Unicode data has been generated from various (free) on-line resources or using GNU recode. Statistical data has been generated from various texts on the Net, I hope character counting doesn't break anyone's copyright. ACKNOWLEDGEMENTS Please see the file THANKS in distribution. COPYRIGHT Copyright (C) 2000-2003 David Necas (Yeti). Copyright (C) 2009 Michal Cihar<EMAIL_ADDRESS>Enca is free software; you can redistribute it and/or modify it under the terms of version 2 of the GNU General Public License as published by the Free Software Foundation. Enca is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with Enca; if not, write to the Free Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA. enca 1.11 Sep 2009 enca(1)


{% extends "base.html" %} {% block content %}
Name: {{ FACILITY_NAME }}
Address: {{ FACILITY_ADDRESS }}
Email: {{ FACILITY_EMAIL }}
Phone: {{ FACILITY_PHONE }}


- initial copy-construction expensive for large Obj
+ easy memory: when out of scope, goes away
+ more explicit/self-contained in terms of ownership
+ read a bit faster since no indirection needed
- may require a default constructor
=====================================================================
map< string, Obj* >
+ insertion/copying is cheap: only a pointer
- have to think about ownership issues for new/delete
+ more flexible: actual Obj can live else-where
- requires extra indirection
+ can take-advantage of memory pools if you create a gazillion Obj (requires new)
=====================================================================
unique_ptr is... somewhat thread-safe, because of its "uniqueness". Unfortunately, there is a way to get around it...
Something you would need to watch out for is that std::unique_ptr is a C++0x feature, so... be sure you use the appropriate compiler and flags. ;) Also, if you're relying on the unique_ properties of a unique_ptr, do note that there are ways to accidentally (or deliberately) circumvent these properties, such as unique_ptr::get().
Also...
[It] stores a pointer to an owned object. The object is owned by no other unique_ptr.
Finally, I don't know how valgrind works with unique_ptr. Sorry. :(
It doesn't matter whether or not unique_ptr is thread-safe (it isn't) because std::map<> isn't thread-safe.
For me, the answer is very simple: store pointers only when polymorphism is required or the object to be stored is non-copyable, and then, use boost::ptr_map instead of std::map.
valgrind knows nothing about the source language of the executable; it will work just fine.


Homework: Bus Tracking
Overview - see video
Overview
1.- Create a map of Cambridge, center on MIT campus - see video
Hello World Map
2.- Create a map marker for MIT, center above the lobby 7 (mass ave entrance) - see video
Map Markers
3.- Animate marker for a bus route of your choosing (not route 1) - see video
Animate Markers
4.- Track buses for MBTA route 1 - see video
Track Buses
5.- Working solution - see video
Bus Tracking Demo


{% load static %}
{{editProd.itemName}}
Log Out
Dashboard
Shops
Categories
Products
Click to assign to your Shop
{% csrf_token %}
Chose the Shop:
{% for shop in ownerShops %}
{{shop.shopName}}
{% endfor %}
Add Shop


Number with
min="5"
and
value="6"
Number with
max="10"
Range with
min="5"
,
max="10"
Value:
Number with
step="2"


MBTI© is Junk Science
Here’s a question I get asked from time to time:
Why does it matter what my Myers-Briggs type is?
It’s often followed by this, usually in a hushed voice:
You know it’s bullshit, right?
 You’ll find plenty of people fuming over the MBTI© for a myriad of reasons. The most common of these are…
- Weak factual basis
- Neither Isabel Briggs Myers nor Katherine Briggs had a formal background in psychology
- Based on binary characteristics, which is too restrictive for personality
- Jung’s analytic psychology is based on anecdotal rather than empirical evidence, and is therefore unscientific
- The tests don’t work
- Most people get a different result upon re-taking the test 1
-
- Reinforces stereotypes of personality
- Seems like a scam
- MBTI Trust® has trademarked Myers-Briggs Type Indicator®, Myers Briggs®, and MBTI®. Here are their guidelines for usage.
- Moreover, the bulk of scientific literature pertaining to MBTI is published for conferences of the Center for the Application of Psychological Type or as papers in the Journal of Psychological Type, both of which are funded by sales of MBTI-related tests and services.
When stacked up, these arguments are pretty convincing.
The Importance of Helpful Bullshit
While I totally agree that MBTI is not legitimate science, I disagree with
And that is precisely what should happen to MBTI – it should be demoted. The scientific community and Fortune 500 companies alike should respect the evidence.
In fact, the majority of the population has little need for personality categorization.
There is a small minority for whom MBTI serves a valuable purpose. In the Big 5, these are called BLANK and BLANK. MBTI classifies them as INTPs and INTJs.
- Classifying people’s personalities is inherently ambiguous
-
Fuels navel-gazing-and-elitism2
- It’s an interesting system of classification
- Most importantly, MBTI crudely describes human behavior in a way that makes sense to awkward people (INTP and INTJ then INFs, ENT, ENF, everyone else) (Dylan 2016)
I am an INTP. INTPs constitute 3-5% of the U.S. population.3 INTPs share some bizarre quirks like a fondness for discordant music and a strong suspicion towards their own emotions. For more information, see An INTP Profile by Paul James.
Here are some INTP sites I’m familiar with:
- INTP subreddit
- intp.org has a few cool resources
- intpexperience.com goes into depth about the INTP’s relationship with emotion
Most online personality forums seem to share the masturbatory “nobody understands us” mentality. These are no exception – you’ve been warned. Unlike other forums, however, many commenters express sincere gratitude for the typology. INTPs often have a hell of a time finding others that they can relate to.
Bibliography
- 13 Reasons Why The Myers-Briggs Test Is Absolute Nonsense
- The Skeptic’s Dictionary
- Adam Grant’s critique of MBTI as well as CPP’s response
-
See this paper for more information ↩
-
Then again, those sorts of people will use any means they can to assert superiority – be it IQ scores, taste in music, what have you. ↩
-
This figure comes from the official Myers-Briggs page ↩


A web app that allow a user to post a project
he/she has created and get it reviewed by others.
A web app where users can post their pitches
And other users will vote on them and leave comments to give their feedback on them.
I have worked as an intern
@ICT-Consultants Limited. Where
i worked as a techinician.
I have always had passion in coding. Thats
why i decided
to take a class in programming.


Review of: THE AMBUSHERS (1968)
Review written: 1/24/98 Copyright 1998 Afterburner Email me at<EMAIL_ADDRESS>Rating (out of 10): N/A. See review for further details.
-----
Of the four Matt Helm movies made, THE AMBUSHERS (1968) is the most "MST3K-able" of the bunch. (Meaning that it's ripe for parody by the folks at Mystery Science Theater 3000, for those who may not get the reference.)
If you haven't seen any of them, the Matt Helm movies answer the question "What if Hugh Hefner (founder and publisher of Playboy Magazine) had been a secret agent on the side?" Dean Martin plays Matt Helm, an affable, boozing photographer of scantily-clad women who works for "Slaymate" magazine. But that's just a cover! The truth is that Helm is *really* an affable, boozing, super-competent secret agent who works for the Intelligence Counter Espionage (ICE) organization.
Before we go further, I should point out that the Matt Helm movies are all pretty bad. But they're wonderfully bad, and they're great, cheezy, campy fun. I tend to speak glowingly of the Matt Helm movies because I enjoy them for their over-the-top campiness, but more serious-minded viewers will probably be turned off.
And let me tell you: THE AMBUSHERS spares *no* expense in the "campiness" department. Try this plot on for size:
The ICE organization is testing out a new, super-secret flying saucer. That's right, a flying saucer. As MacDonald (James Gregory), head of ICE points out, if it works it will put the other planets "right next door," and the rest of the universe will be "just around the corner." It's never sufficiently explained why a counter-espionage organization would need to go to other planets (much less the rest of the universe), but no matter. They've made it and they're testing it.
The test works fine, until the saucer is forced to land by Mysterious Bad Guys. The leader of the Mysterious Bad Guys enters the saucer, and the pilot (a female) screams. Fade to black. This all takes place within the first 10 minutes of the movie.
Cut to Matt Helm, in Matt Helm heaven. He's helping to train a cadre of new ICE agents who, coincidentally enough, all happen to be gorgeous, scantily-clad babes. Go figure. But he runs into a white-haired, crazed woman who thinks all men are out to kill her. It's his old partner! It's also the woman who piloted the flying saucer! Will wonders never cease? (Answer: Of course not! This is Matt Helm!)
Naturally, Helm is assigned to find the saucer, and he has to take his former partner (who's now recovered) because only females can fly the saucer. The reason is because the saucer uses "electromagnetic fields" to propel itself around the neighborhood. And, according to the movie, electromagnetic fields are lethal to males. No, I never learned that in my basic physics classes either, but there you are.
You can guess the rest. Typical spy-movie stuff with the usual Matt Helm twist. Helm still wanders from place to place as though he's looking for the nearest bar. During one fight scene he gets knocked into a huge vat of beer, much to his obvious delight. And in a send-up of his singing career, the very final scene shows him trying to teach an attractive new recruit how to make love while on the job. The recruit is cold and unresponsive, even after Helm puts on a Dean Martin tune. But when he puts on Perry Como, she responds amorously, much to his chagrin.
High points of the movie:
* The whole "electromagnetic fields are lethal to men" bit, which had me rolling on the floor. * A couple of male bad guys dying from, as near as I can tell, turning completely red all over. Even their clothes. This is, apparently, the inevitable consequence of exposing men to electromagnetic fields. (Now you know why your Mom always told you not to sit so close to the TV...) * The obvious set pieces when Helm is supposed to be outdoors. * A set of railroad tracks which leads right up to the very edge of a cliff, apparently for no other reason than to allow a railroad flatcar to careen dramatically off said cliff. * The *incredibly* cheesy effects, which include ray guns that emit sparks, and radar towers that emit obvious "radar" noises. * Helm turning his belt into a sword by the simple expedient of getting it wet.
It's true that none of the other Matt Helm movies are pinnacles of the film-maker's craft. But the plot and cheesy special effects make THE AMBUSHERS a cut below the others. In ranking the four Matt Helm movies in terms of overall quality, this would be #4 on my list. But in ranking them in terms of laughs (both intentional and otherwise), this is easily #1.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Miah's house catering is a self contained property offering clean, comfortable, furnished accommodation for visitors who are also provided with the means to prepare and cook their own meals.


R/create_provenance_metadata.R
create_provenance_metadata.Rd
Create provenance metadata for a data source external to PASTA+.
create_provenance_metadata(title, creator, online.description, url, contact, path, file.name)
| title | (character) Title of external data source. |
|---|---|
| creator | (list) Named list of EML creator elements and values. Supprted elements are: givenName, surName, organizationName, electronicMailAddress |
| online.description | (character) Description of online resource. |
| url | (character) URL of online resource. |
| contact | (list) Named list of EML contact elements and values. Supprted elements are: givenName, surName, organizationName, electronicMailAddress |
| path | (character) Where the XML will be written. |
| file.name | (character) Name of file to be written. |
('xml_document' 'xml_node') EML provenance metadata
# Define arguments ds_title <- 'Some dataset title' ds_online_description <- 'Description of online resourceURL' ds_url <- 'https://url.of.data.resource' ds_creator = list( list(givenName = 'Jane', surName = 'Scientist', organizationName = 'Some organization name', electronicMailAddress =<EMAIL_ADDRESS>), list(givenName = 'Bill', surName = 'Sciguy', electronicMailAddress =<EMAIL_ADDRESS>) ) ds_contact = list( list(givenName = 'Jane', surName = 'Scientist', organizationName = 'Some organization name', electronicMailAddress =<EMAIL_ADDRESS>) ) # Run function create_provenance_metadata( title = ds_title, creator = ds_creator, online.description = ds_online_description, url = ds_url, contact = ds_contact )#>#> {xml_document} #> <methodStep> #> [1] <description/> #> [2] <dataSource>\n <title>Some dataset title</title>\n <creator>\n <indi ...


Eyewitness This: LA holding streetlight design competition with cash prize offered
LOS ANGELES (KABC) -- Calling all designers: A competition is underway to create a new streetlight design that is "emblematic of 21st-century Los Angeles."
In additional to the historic streetlights that line L.A. roadways and sidewalks, Mayor Eric Garcetti wants to install new streetlights across the city.
The project, L.A. Lights the Way, aims to connect "the rich design tradition of the Los Angeles streetlight while also incorporating new requirements related to technology and climate and making room for text or other elements reflecting community input."
There are three categories to enter: Professional designers, undergraduates and graduate students in design schools, and L.A. high school students.
The winning team will receive a cash prize. Participants must register by Jan. 10, and proposals are due March 6.
Watch Eyewitness This for more stories making headlines.


The purpose of the attribute is to sorts the child elements from parent element that contains the attribute.
See the following chapters:
Attribute
data-wd-sort=order
Target
Sensitivity
- After loading the document;
- After defining the attribute on the element with the method WDdom.data;
- After external data loading procedures using the WD Attributes Tools library.
Example
HTML code
Result
- Item 01/10
- Item 02/10
- Item 03/10
- Item 04/10
- Item 05/10
- Item 06/10
- Item 07/10
- Item 08/10
- Item 09/10
- Item 10/10


At Interloop, we want to create harmonious experiences for our users. We solve challenging interactions at the outset, and then apply the solutions consistently across our application - making sure people only have to learn them once.
Our guidelines have space for flexibility, and are continuously revised and expanded to suit changing product needs.


LAZY GENIE
JOĆO
STATUS
AVAILABLE
BUSY
AWAY
RATING
REQUEST WORLD
WALL
MY JOBS
FAVORITES
NEAREST USERS


Builds a feed-forward multilayer artificial neural network on an H2OFrame.
h2o.deeplearning(x, y, training_frame, model_id = NULL, validation_frame = NULL, nfolds = 0, keep_cross_validation_predictions = FALSE, keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"), fold_column = NULL, ignore_const_cols = TRUE, score_each_iteration = FALSE, weights_column = NULL, offset_column = NULL, balance_classes = FALSE, class_sampling_factors = NULL, max_after_balance_size = 5, max_hit_ratio_k = 0, checkpoint = NULL, pretrained_autoencoder = NULL, overwrite_with_best_model = TRUE, use_all_factor_levels = TRUE, standardize = TRUE, activation = c("Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout", "MaxoutWithDropout"), hidden = c(200, 200), epochs = 10, train_samples_per_iteration = -2, target_ratio_comm_to_comp = 0.05, seed = -1, adaptive_rate = TRUE, rho = 0.99, epsilon = 1e-08, rate = 0.005, rate_annealing = 1e-06, rate_decay = 1, momentum_start = 0, momentum_ramp = 1e+06, momentum_stable = 0, nesterov_accelerated_gradient = TRUE, input_dropout_ratio = 0, hidden_dropout_ratios = NULL, l1 = 0, l2 = 0, max_w2 = 3.4028235e+38, initial_weight_distribution = c("UniformAdaptive", "Uniform", "Normal"), initial_weight_scale = 1, initial_weights = NULL, initial_biases = NULL, loss = c("Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile"), distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber"), quantile_alpha = 0.5, tweedie_power = 1.5, huber_alpha = 0.9, score_interval = 5, score_training_samples = 10000, score_validation_samples = 0, score_duty_cycle = 0.1, classification_stop = 0, regression_stop = 1e-06, stopping_rounds = 5, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error", "r2"), stopping_tolerance = 0, max_runtime_secs = 0, score_validation_sampling = c("Uniform", "Stratified"), diagnostics = TRUE, fast_mode = TRUE, force_load_balance = TRUE, variable_importances = TRUE, replicate_training_data = TRUE, single_node_mode = FALSE, shuffle_training_data = FALSE, missing_values_handling = c("MeanImputation", "Skip"), quiet_mode = FALSE, autoencoder = FALSE, sparse = FALSE, col_major = FALSE, average_activation = 0, sparsity_beta = 0, max_categorical_features =<PHONE_NUMBER>, reproducible = FALSE, export_weights_and_biases = FALSE, mini_batch_size = 1, categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"), elastic_averaging = FALSE, elastic_averaging_moving_rate = 0.9, elastic_averaging_regularization = 0.001, verbose = FALSE)
| x | (Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used. |
|---|---|
| y | The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model. |
| training_frame | Id of the training data frame. |
| model_id | Destination id for this model; auto-generated if not specified. |
| validation_frame | Id of the validation data frame. |
| nfolds | Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0. |
| keep_cross_validation_predictions |
|
| keep_cross_validation_fold_assignment |
|
| fold_assignment | Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO. |
| fold_column | Column with cross-validation fold index assignment per observation. |
| ignore_const_cols |
|
| score_each_iteration |
|
| weights_column | Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. |
| offset_column | Offset column. This will be added to the combination of columns before applying the link function. |
| balance_classes |
|
| class_sampling_factors | Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes. |
| max_after_balance_size | Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0. |
| max_hit_ratio_k | Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable). Defaults to 0. |
| checkpoint | Model checkpoint to resume training with. |
| pretrained_autoencoder | Pretrained autoencoder model to initialize this model with. |
| overwrite_with_best_model |
|
| use_all_factor_levels |
|
| standardize |
|
| activation | Activation function. Must be one of: "Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout", "MaxoutWithDropout". Defaults to Rectifier. |
| hidden | Hidden layer sizes (e.g. [100, 100]). Defaults to [200, 200]. |
| epochs | How many times the dataset should be iterated (streamed), can be fractional. Defaults to 10. |
| train_samples_per_iteration | Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic. Defaults to -2. |
| target_ratio_comm_to_comp | Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning). Defaults to 0.05. |
| seed | Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default) Note: only reproducible when running single threaded. Defaults to -1 (time-based random number). |
| adaptive_rate |
|
| rho | Adaptive learning rate time decay factor (similarity to prior updates). Defaults to 0.99. |
| epsilon | Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress). Defaults to 1e-08. |
| rate | Learning rate (higher => less stable, lower => slower convergence). Defaults to 0.005. |
| rate_annealing | Learning rate annealing: rate / (1 + rate_annealing * samples). Defaults to 1e-06. |
| rate_decay | Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1). Defaults to 1. |
| momentum_start | Initial momentum at the beginning of training (try 0.5). Defaults to 0. |
| momentum_ramp | Number of training samples for which momentum increases. Defaults to 1000000. |
| momentum_stable | Final momentum after the ramp is over (try 0.99). Defaults to 0. |
| nesterov_accelerated_gradient |
|
| input_dropout_ratio | Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). Defaults to 0. |
| hidden_dropout_ratios | Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5. |
| l1 | L1 regularization (can add stability and improve generalization, causes many weights to become 0). Defaults to 0. |
| l2 | L2 regularization (can add stability and improve generalization, causes many weights to be small. Defaults to 0. |
| max_w2 | Constraint for squared sum of incoming weights per unit (e.g. for Rectifier). Defaults to 3.4028235e+38. |
| initial_weight_distribution | Initial weight distribution. Must be one of: "UniformAdaptive", "Uniform", "Normal". Defaults to UniformAdaptive. |
| initial_weight_scale | Uniform: -value...value, Normal: stddev. Defaults to 1. |
| initial_weights | A list of H2OFrame ids to initialize the weight matrices of this model with. |
| initial_biases | A list of H2OFrame ids to initialize the bias vectors of this model with. |
| loss | Loss function. Must be one of: "Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile". Defaults to Automatic. |
| distribution | Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO. |
| quantile_alpha | Desired quantile for Quantile regression, must be between 0 and 1. Defaults to 0.5. |
| tweedie_power | Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5. |
| huber_alpha | Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). Defaults to 0.9. |
| score_interval | Shortest time interval (in seconds) between model scoring. Defaults to 5. |
| score_training_samples | Number of training set samples for scoring (0 for all). Defaults to 10000. |
| score_validation_samples | Number of validation set samples for scoring (0 for all). Defaults to 0. |
| score_duty_cycle | Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring). Defaults to 0.1. |
| classification_stop | Stopping criterion for classification error fraction on training data (-1 to disable). Defaults to 0. |
| regression_stop | Stopping criterion for regression error (MSE) on training data (-1 to disable). Defaults to 1e-06. |
| stopping_rounds | Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 5. |
| stopping_metric | Metric to use for early stopping (AUTO: logloss for classification, deviance for regression) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error", "r2". Defaults to AUTO. |
| stopping_tolerance | Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0. |
| max_runtime_secs | Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0. |
| score_validation_sampling | Method used to sample validation dataset for scoring. Must be one of: "Uniform", "Stratified". Defaults to Uniform. |
| diagnostics |
|
| fast_mode |
|
| force_load_balance |
|
| variable_importances |
|
| replicate_training_data |
|
| single_node_mode |
|
| shuffle_training_data |
|
| missing_values_handling | Handling of missing values. Either MeanImputation or Skip. Must be one of: "MeanImputation", "Skip". Defaults to MeanImputation. |
| quiet_mode |
|
| autoencoder |
|
| sparse |
|
| col_major |
|
| average_activation | Average activation for sparse auto-encoder. #Experimental Defaults to 0. |
| sparsity_beta | Sparsity regularization. #Experimental Defaults to 0. |
| max_categorical_features | Max. number of categorical features, enforced via hashing. #Experimental Defaults to<PHONE_NUMBER>. |
| reproducible |
|
| export_weights_and_biases |
|
| mini_batch_size | Mini-batch size (smaller leads to better fit, larger can speed up and generalize better). Defaults to 1. |
| categorical_encoding | Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO. |
| elastic_averaging |
|
| elastic_averaging_moving_rate | Elastic averaging moving rate (only if elastic averaging is enabled). Defaults to 0.9. |
| elastic_averaging_regularization | Elastic averaging regularization strength (only if elastic averaging is enabled). Defaults to 0.001. |
| verbose |
|
predict.H2OModel
for prediction
# NOT RUN { library(h2o) h2o.init() iris.hex <- as.h2o(iris) iris.dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris.hex, seed=123456) # now make a prediction predictions <- h2o.predict(iris.dl, iris.hex) # }


MADE Written and Directed by Jon Favreau With Vince Vaughn, Jon Favreau Jean Cocteau R 95 min.
 The gap between who Ricky is and who he thinks he is, between his self-confidence and his self-awareness, between his level of intelligence and his level of hubris, is blinding. And Vaughn handles the role with the sure-footedness and bursts of improvisational inspiration of a Kobe Bryant going coast-to-coast for a layup. It's a gutsy, antagonizing performance sewn with just enough seeds of endearment to keep it from ricocheting off the screen.
Still, it wouldn't work without the low-key finesse of costar Jon Favreau as Bobby, a down-to-earth plug who balances his buddy's spectacular asininity with his own plodding good sense. Favreau, who also wrote and directed Made, is a sort of latter-day William Bendix (of the ‘50s sitcom Life of Reilly.)
Vaughn and Favreau made an impact five years ago in Swingers, which was also written by Favreau. Here he doesn't try to extend the same characters, but they're clearly related.  They're boxers, laborers, petty criminals. Neither of them are too bright, but Bobby at least has a solid head on his solid shoulders. He's in love with Jess (Famke Janssen), a lap-dancer in the stable of a West Coast crime boss named Max (Peter Falk). He `drives' (bodyguards) her until his jealous, protective heart gets him canned by Max for decking a customer who gets out of line.
Max likes Bobby. When he has to fire him from one job, he offers him another – a trip to New York to deliver a package. Max doesn't like Ricky, who has managed to lose a van from his carpet-cleaning business, but he agrees to let Bobby take him along on the operation, which is first class all the way. Why a canny guy like Max would send two shmoes first class to New York and put them up in a ritzy hotel and give them a chauffeured limo and a hefty per diem in order to do a drop that could perfectly well be done by his efficient New York people, headed by Ruiz (Sean Combs), is a question best not examined too closely. It's simply a vehicle, in the tradition of the classic comedy team movies of Laurel and Hardy or Abbott and Costello, designed to get these mismatched louts, this Yin and Yang of ineptitude, into a set of situations where they can do their worst, and best.
The cleverness of Made is certainly not in the plotting. There's no `wow!' twist, no layers and levels peeled away for a socko revelation. This movie works in its dialogue, and in the rapport of its characters. Vaughn's performance is dazzling, an edgy, obnoxious tour-de-force that makes you wince while it makes you laugh. Favreau, from his director's and writer's seat, is content to cede the limelight to his buddy, and to let his own character provide a cautious touchstone that gives the audience a place to park its sympathies. Combs lends a savvy New York cool, and stares incredulously as Ricky tries to impress with his gangsta hipness; `Man, what is all this 21 Jump Street stuff?' he asks. Falk is wise and wizened, a made veteran who can shift from compassionate to threatening on a dime. The beautiful Famke Janssen doesn't get much to do outside of her lap-dancing, but little Mackenzie Vega as her daughter is adorable, and the rapport between her and Favreau provides one of the movie's satisfying emotional chords.
========== X-RAMR-ID: 29126 X-Language: en X-RT-ReviewID: 242806 X-RT-TitleID: 1108720 X-RT-AuthorID: 2779
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.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


1. Introduce the Main Idea of Transparency and Interaction in Machine Learning
Transparency
Transparency aims to understand what did the model learn, what do the weights tell us about features and their importance, when an object is classified by this model, which feature values contributed to each class and how much.
Interaction
If we want to label more objects, which one should we label next? Can we provide any rationales into the learning process?
Why Transparency?
A perfect example comes from my professor Mustafa. He said that sometimes the machine instead provides a suggestion and gives a reason than deciding for the human. In medical recommender system, the doctor prefers the system provides suspected symptoms to the percentage of the diagnosis. If a cancer detection system only gives “This patient has a 79% chance gets cancer”. Such a result will make confuse the doctor and the patient. In contrast, if this system said: “This patient has a 79% chance gets cancer because I detected the patient has high compression in the spinal cord and pleural effusion”. This diagonal report will be of great help to doctors and dramatically reduce diagnostic time. Moreover, transparency also used in Trustworthy fraud detection{target:blank}. We should tell the customer this product evaluation is fake and why at the same time.
2. Concept Learning
Motivation
Different classification task has different purposes such as emails labeled as spam/~spam, patient records labeled as flu/~flu or reviews labeled as positive/negative.
Our goal is inducing a general function that fits the training data well and generalized well to unseen or future data.
General-to-Specific Ordering of Hypothese
Example
Suppose we have two features: Weight: (Light, Heavy) and Color: (Red, Green, Blue. The class is YES and NO. Therefore, we have six objects/instances . and we have 64 hypotheses/functions .
| Weight | Color | F1 | … | F64 |
|---|---|---|---|---|
| Light | Red | NO | YES | |
| Light | Green | NO | YES | |
| Light | Blue | NO | YES | |
| Heavy | Red | NO | YES | |
| Heavy | Green | NO | YES | |
| Heavy | Blue | NO | YES |
If we know that <Light, Red> is ‘Yes’, can you guess how many hypothese left? The answer is .
The number of hypotheses of <Light, Blue > is ‘Yes,’ or ‘No’ is 16.
For now, we know how to calculate the hypothesis. Let us assume two particular assumptions:
- ‘No’ to everything
- ‘Yes’ to everything
- In particular:
- : ‘No’ to everything
- : ‘Yes’ to everything
- <Light, Red>: ‘Yes’ to <Light, Red> and ‘No’ to everthing else
- <Light, >: ‘Yes’ to anything that is Light; ignore Color; everything else is ‘No’
- Combination of one or more of with other feature values is still equivalen to ‘No’ to everything. E.g., Red is ‘No’ to everything
- Most-general hypothesis is where :
- Most-specific hypothesis is where :
- These called hypothesis space
But, how do we search this space efficiently?
Algorithms
1.Find-S
Initialize to the most specific hypothesis in For each positive training instance
- For each attribute constraint in :
- If the constraint in is satisfied by
- Then do nothing
- Else replace in by the next more general constraint that is satisfied by Output hypothesis
- Warning!!!: In that case, Find-S always return the most specific one, but we do not know whether we need the most specific one or the most general one or interval between them. Moreover, what if our training hata has errors or what if the most specific hypothesis in not unique?
2.List-Then-Eliminate
VS a list of containing every hypothesis in For each training example,
- Remove from VS any hypothesis for which Output VS
Though this algorithm can output all consistent hypotheses, we need to list all possible explanation. It is possible in small predictions space. However, it is impossible for infinite hypothesis spaces.
3.Candidate-Elimination
Initialize to the set of maximally-general hypotheses in Initialize to the set of maximally-specific hypotheses in For each training example , do
- If is a positive example
- Remove from any hypothesis that is inconsistent with
- For each hypothesis in that is not consistent with
- Remove from
- Add to to all minimal gegneralizations of such that
- is consistent with , and some member of is more general than
- Remove from any hypothesis that is more general than another hypothesis in


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{{ site.author.bio }}
I'm currently pursuing my Master's in Computer Science from Courant Institute of Mathematical Sciences - New York University graduating December 2022. I have around 3 years of previous work experience, at companies like Meta, Barclays,. I am passionate about Backend Engineering, and Machine Learning.


| Introduction to Neural Networks |
By the end of this practical you will know how to:
Open your neuralnets
R project.
Open a new R script. Save it as a new file called representation_practical.R
in the 2_Code
folder.
Using library()
load the the packages tidyverse
and keras
# install.packages("tidyverse")
# install.packages("keras")
# Load packages necessary for this exercise
library(tidyverse)
library(keras)
source()
the helper_2.R
file in your 2_Code
folder.# Load helper.R
source("2_Code/helper.R")
Part 1: Fashion
fashion.RDS
dataset as a new object.# MNIST fashion data
fashion <- readRDS(file = "1_Data/fashion.RDS")
fashion
object using str()
.# Inspect contents
str(digit)
# PREPARATIONS -------
# fashion items
fashion_labels <- c('T-shirt/top','Trouser','Pullover','Dress','Coat',
'Sandal','Shirt','Sneaker','Bag','Ankle boot')
# split digit train
c(fashion_train_images, fashion_train_items) %<-% fashion$train
# split digit test
c(fashion_test_images, fashion_test_items) %<-% fashion$test
# reshape images
fashion_train_images_serialized <- array_reshape(fashion_train_images, c(nrow(fashion_train_images), 784))
fashion_test_images_serialized <- array_reshape(fashion_test_images, c(nrow(fashion_test_images), 784))
# rescale images
fashion_train_images_serialized <- fashion_train_images_serialized / 255
fashion_test_images_serialized <- fashion_test_images_serialized / 255
# expand criterion
fashion_train_items_onehot <- to_categorical(fashion_train_items, 10)
fashion_test_items_onehot <- to_categorical(fashion_test_items, 10)
# MODELING -------
# initialize deepnet
net <- keras_model_sequential()
# add layer
net %>%
layer_dense(input_shape = 784, units = 256, activation = "relu") %>%
layer_dense(units = 144, activation = "relu") %>%
layer_dense(units = 10, activation = "softmax")
# model information
summary(net)
# loss, optimizers, & metrics
net %>% compile(
optimizer = 'adam',
loss = 'categorical_crossentropy',
metrics = c('accuracy')
)
# fit network
net %>% fit(
x = fashion_train_images_serialized,
y = fashion_train_items_onehot,
epochs = 10
)
# extract weights
weights <- get_weights(net)
str()
inspect the structure of the weights
object. Do the contents line up with your expectations?# inspect weights
str(weights)
There are six elements. Three containing the weights (elements 1, 3, 5) and three containing the biases (elements 2, 4, 6).
Use the first elements in weights
to calculate the activation patterns, aka the embeddings, at the first layers for the first 1,000
fashion items, ignoring the bias and the activation function. You’ll see, this can be easily done using matrix multiplication %*%
.
# inspect weights
embedding <- fashion_train_images_serialized[1:1000, ] %*% weights[[1]]
Assess the dimensionality of embedding
using dim()
. Correct numbers of rows and columns?
Use the plot_embedding()
function, which you loaded earlier when you sourced the helper_2.R
file, to visualize the activations. Rows in the plot will be the 1,000
fashion items and columns the 256
nodes of the embedding at the first hidden layer. Looks a bit messy right?
# plot activation
plot_embedding(embedding)
1000
fashion items from fashion_train_items
and then use those to order the rows in embedding
.# extract fashon items
items <- fashion_train_items[1:1000]
# order activations
embedding <- embedding[order(items), ]
plot_embedding()
to plot the embedding. Things should look a lot clearer. The bands correspond to the different items, with the 0
-item ("T-shirt/top"
) at the bottom and the 9
-item ("Angle boot"
) at the top.cosine()
function from the helper_2.R
file to determine the similarities between the fashion item vectors in the embedding. Cosine determines the angle between the locations of two fashion items in the 256
dimensional space that is the embedding. Cosine is algebraically close to the standard correlation coefficient.# calculate cosine similarities
fashion_cosines <- cosine(embedding)
plot_cosine(fashion_cosines)
plot_cosine
function (also from the helper_2.R
file) to plot the matrix of cosine values. The categories 0
to 9
go from top to bottom and from left to right. Light grey values indicate high cosine similarity, darker ones low cosine similarity. Try to make sense of the plot.# Plot cosine similarities
plot_cosine(fashion_cosines)
plot_cosine_mds()
. Try to make sense of the plot.# calculate cosine similarities
plot_cosine_mds(fashion_cosines, fashion_labels[items[order(items)]+1])
# prediction confusion matrix
pred = net %>% predict_classes(fashion_test_images_serialized)
table(fashion_labels[fashion_test_items+1], fashion_labels[pred+1])
Coat
, Pullover
, and Shirt
, which in the cosine mds pretty much sit on top of each other.# second layer embedding
relu = function(z) {z[z < 0] = 0; z}
z_1 <- cbind(img_train[1:1000,],1) %*% rbind(weights[[1]], weights[[2]])
a_1 <- t(apply(z_1, 1, relu))
embedding <- a_1 %*% weights[[3]]
Part 2: Words
capital.RDS
dataset as a new object.# load embeddings
capital <- readRDS(file = "1_Data/capital.RDS")
rownames()
inspect to words for which embeddings are present.# rownames of capital
rownames(capital)
plot_embedding()
to plot the capital embeddings.# plot capital embedding
plot_embedding(capital)
# plot capital
capital_cosine = cosine(capital)
plot_cosine(capital_cosine)
# plot capital
capital_cosine = cosine(capital)
plot_cosine_mds(capital_cosine, rownames(capital_cosine), col = F)


{% extends "layout.html" %} {% block title %} Documentation- {% endblock %} {% block head %} {{ super() }}{% endblock %} {% block content %}
Secure TLS communication between client and database server requires the client be configured to use certs. Configure your MySQL client to use TLS certificates. Edit the my.cnf configuration file on the client. The MariaDB/MyDB client configuration file is typicaly located here: /etc/mysql/my.cnf. Append the followin in [client] section as follows:
## MySQL Client Configuration ##
[client]
ssl-verify-server-cert
ssl-ca=/etc/mysql/ssl/ca-cert.pem
ssl-cert=/etc/mysql/ssl/client-cert.pem
ssl-key=/etc/mysql/ssl/client-key.pem
Download all of the following client certs.
By default user connections are not protected by TLS. It is best practice to enforce TLS connections by creating accounts that only permit encrypted TLS connections. Example:
CREATE USER redcap@'%' REQUIRE SSL;
SET PASSWORD FOR redcap = PASSWORD('secretword');
GRANT ALL PRIVILEGES ON *.* TO 'redcap'@'%' WITH GRANT OPTION;
SHOW GRANTS FOR 'redcap'@'%';
Verify your client connections by using the status command from an interactive session. The SSL field needs to show a Cipher.
MariaDB [(none)]> status
--------------
mysql Ver 15.1 Distrib 5.5.58-MariaDB, for debian-linux-gnu (x86_64) using readline 5.2
Connection id: 9
Current database:
Current user<EMAIL_ADDRESS>SSL: Cipher in use is DHE-RSA-AES256-GCM-SHA384
Current pager: stdout
Using outfile: ''
Using delimiter: ;
Server: MariaDB
Server version: 10.3.4-MariaDB-10.3.4+maria~jessie mariadb.org binary distribution
Protocol version: 10
Connection: mydb-dev via TCP/IP
Server characterset: latin1
Db characterset: latin1
Client characterset: utf8
Conn. characterset: utf8
TCP port: 32023
Uptime: 21 sec
Secure connections will no longer be available after the certificates expire. The expirate dates are set to five years. These certificates will expire on notAfter=Mar 13 21:27:42 2023 GMT Certificates should only be regenerated after they expire. Run the script TLS-create.sh to regenerate keys. Copy the new keys to all running MariaDB containers. All clients need to download new keys and be updated. Remove ca-cert.pem before running the script. The DBaas container needs to be rebuilt to update the keys.
rm TLS/ca-cert.pem
./TLS-create.sh
docker cp TLS/ca-cert.pem [container_name]:/keys
docker cp TLS/server-cert.pem [container_name]:/keys
docker cp TLS/server-cert.pem [container_name]:/keys
docker cp TLS/server-key.pem [container_name]:/keys
docker cp TLS/server-req.pem [container_name]:/keys


--- title: MA 241 ---
Ordinary Differential Equations
Basics concepts: Introduction and examples through physical models, First and second order equations, general and particular solutions, linear and nonlinear systems, linear independence, solution techniques.
Existence and Uniqueness Theorems :Peano's and Picard's theorems, Grownwall's inequality, Dependence on initial conditions and associated flows.
Linear system: The fundamental matrix, stability of equilibrium points, Phase-plane analysis, Sturm-Liouvile theory .
Nonlinear system and their stability: Lyapunov's method, Non-linear Perturbation of linear systems, Periodic solutions and Poincare- Bendixson theorem.
Recommended books
Hartman, P., Ordinary Differential Equations, Birkhaeuser, 1982.
Coddington, E. A. and Levinson, N., Theory of Ordinary Differential Equations, Tata McGraw-Hill 1972.
Perko, L., Differential Equations and Dynamical Systems, Springer-Verlag, 1991.


|
Sensor Fusion Library
0.4.0
Orientation sensing for Espressif (ESP32, ESP8266) processors
|
StatusSubsystem() provides an object-like interface for communicating status to the user. More...
#include <status.h>
Public Attributes | |
| fusion_status_t | previous |
| Previous status state - fusion_status_t is defined in sensor_fusion.h. | |
| fusion_status_t | status |
| Current status. | |
| fusion_status_t | next |
| Pending status change. | |
| ssSetStatus_t * | set |
| change status immediately - no delay | |
| ssGetStatus_t * | get |
| return status | |
| ssSetStatus_t * | queue |
| queue status change for next regular interval | |
| ssUpdateStatus_t * | update |
| make pending status active/visible | |
| ssUpdateStatus_t * | test |
| unit test which simply increments to next state | |
| uint8_t | toggle |
| This implementation can change LED color and have either solid/toggle. | |
StatusSubsystem() provides an object-like interface for communicating status to the user.


PEOPLE have been THINKING too long that ART is a PRIVILEGE of the MUSEUMS & the RICH. ART IS NOT BUSINESS! It does not belong to banks & fancy investors!
ART
X
$$$
???


I am experienced in leveraging agile frameworks to provide a robust synopsis for high level overviews. Iterative approaches to corporate strategy foster collaborative thinking to further the overall value proposition.
Apart from being a web developer, I enjoy most of my time being outdoors. In the winter, I am an avid skiier and novice ice climber. During the warmer months here in Colorado, I enjoy mountain biking, free climbing, and kayaking.
When forced indoors, I follow a number of sci-fi and fantasy genre movies and television shows, I am an aspiring chef, and I spend a large amount of my free time exploring the latest technolgy advancements in the front-end web development world.
Bring to the table win-win survival strategies to ensure proactive domination. At the end of the day, going forward, a new normal that has evolved from generation X is on the runway heading towards a streamlined cloud solution. User generated content in real-time will have multiple touchpoints for offshoring.
Baeldung is a technical site focused on the Java ecosystem, with a reach of about 4.5M page views per month.
My role:
Write tutorials and how-to articles – with a strong focus on practical, ready-to-run code.
Collaboratively administrate empowered markets via plug-and-play networks. Dynamically procrastinate B2C users after installed base benefits. Dramatically visualize customer directed convergence without revolutionary ROI.
CGPA: 9.8
CGPA: 9.51
Percentage: 87%
Percentage: 83.33%


The snake is controlled by the player and when it eats a red dot it gets bigger. Your goal is to eat as many red dots as possible without croosing itself or it will end in a gameover. Use the arrow keys to move the snake around.
Duck Hunt is a game where you have to hit these ducks and have certain amout of bullets to hit the two ducks or else it is game over. Use your mouse and click where to shoot.
You use your little spaceship to shoot the invaders out of the air. Use the left and right arrow key to move left and right. Also use the up arrow to shoot.
 Use your left and right to tuen your ship and the up arrow to move it in that direction. Also use space to shoot the asteroids.


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{% raw %}
There are two envelopes. In the envelope handed to you there is definitely {{amount | currency}}. Whether the other envelope contains {{amount * 2 | currency}} or {{amount / 2 | currency}} on any particular occasion is decided by flipping a fair coin, so half the time the other envelope will yield {{amount * 2 | currency}} and half of the time it will yield {{amount / 2 | currency}}.
Do you KEEP or SWAP?
{% endraw %} {% include buttons.html %} {% raw %}| # | Payout | Total | Average |
|---|---|---|---|
| {{game.count | number}} | {{game.payout | currency}} | {{game.total | currency}} | {{game.total / game.count | currency}} |


|
Connected Home over IP
672cf7684
|
Go to the source code of this file.
Macros | |
| #define | BLE_LAYER_NUM_BLE_ENDPOINTS 1 |
| This defines the number of BLEEndPoint objects allocated for use by the BleLayer subsystem. Value should be defined as the minimum of (max number of simultaneous BLE connections the system supports, max number of simultaneous BLE connections the application will establish). More... | |
| #define | BLE_CONNECTION_OBJECT void* |
| This defines the type of BLE_CONNECTION_OBJECT parameters passed between BLE platform code and the BleLayer subsystem. More... | |
| #define | BLE_CONFIG_BLUEZ_MTU_FEATURE 0 |
| This define if BLUEZ MTU FEATURE is enabled or not. More... | |
| #define | BLE_CONNECTION_UNINITIALIZED NULL |
| This defines the value of an uninitialized BLE_CONNECTION_OBJECT. More... | |
| #define | BLE_READ_REQUEST_CONTEXT void* |
| This defines the type of BLE_READ_REQUEST_CONTEXT parameters passed between BLE platform code and the BleLayer subsystem. More... | |
| #define | BLE_MAX_RECEIVE_WINDOW_SIZE 3 |
| This is the maximum allowed size of a BLE end point's receive window, defined as the number of fragments the end point may reliably receive without BTP-layer acknowledgement. This value should be no larger than the floor of ONE-HALF the total number of slots or buffers reserved for GATT operations at any point along a platform's BLE pipeline. The BLE layer reserves all of these buffers for its own use - one half for incoming GATT writes or indications, and the other half for incoming GATT confirmations. More... | |
| #define | BLE_CONFIG_ERROR_TYPE int32_t |
| This defines the data type used to represent errors for the BleLayer subsystem. More... | |
| #define | BLE_CONFIG_NO_ERROR 0 |
| This defines the BleLayer error code for no error or success. More... | |
| #define | BLE_CONFIG_ERROR_MIN 6000 |
| This defines the base or minimum BleLayer error number range. More... | |
| #define | BLE_CONFIG_ERROR_MAX 6999 |
| This defines the top or maximum BleLayer error number range. More... | |
| #define | _BLE_CONFIG_ERROR(e) (BLE_ERROR_MIN + (e)) |
| This defines a mapping function for BleLayer errors that allows mapping such errors into a platform- or system-specific range. More... | |
This file defines default compile-time configuration constants for the CHIP BleLayer, a Bluetooth Low Energy communications abstraction layer.
Package integrators that wish to override these values should either use preprocessor definitions or create a project- specific BleProjectConfig.h header and then assert HAVE_BLEPROJECTCONFIG_H via the package configuration tool via –with-chip-ble-project-includes=DIR where DIR is the directory that contains the header.
NOTE WELL: On some platforms, this header is included by C-language programs.
| #define _BLE_CONFIG_ERROR | ( | e | ) | (BLE_ERROR_MIN + (e)) |
This defines a mapping function for BleLayer errors that allows mapping such errors into a platform- or system-specific range.
| #define BLE_CONFIG_BLUEZ_MTU_FEATURE 0 |
This define if BLUEZ MTU FEATURE is enabled or not.
| #define BLE_CONFIG_ERROR_MAX 6999 |
This defines the top or maximum BleLayer error number range.
| #define BLE_CONFIG_ERROR_MIN 6000 |
This defines the base or minimum BleLayer error number range.
| #define BLE_CONFIG_ERROR_TYPE int32_t |
This defines the data type used to represent errors for the BleLayer subsystem.
| #define BLE_CONFIG_NO_ERROR 0 |
This defines the BleLayer error code for no error or success.
| #define BLE_CONNECTION_OBJECT void* |
This defines the type of BLE_CONNECTION_OBJECT parameters passed between BLE platform code and the BleLayer subsystem.
This type must support operator == such that BLE_CONNECTION_OBJECT instances which refer to the same BLE connection are considered equivalent.
Most platforms should be able to retain this type's default definition as (void *), and pass [pointers to] connection handles generated by their platform interface where BLE_CONNECTION_OBJECT arguments are required by BleLayer input functions.
| #define BLE_CONNECTION_UNINITIALIZED NULL |
This defines the value of an uninitialized BLE_CONNECTION_OBJECT.
| #define BLE_LAYER_NUM_BLE_ENDPOINTS 1 |
This defines the number of BLEEndPoint objects allocated for use by the BleLayer subsystem. Value should be defined as the minimum of (max number of simultaneous BLE connections the system supports, max number of simultaneous BLE connections the application will establish).
| #define BLE_MAX_RECEIVE_WINDOW_SIZE 3 |
This is the maximum allowed size of a BLE end point's receive window, defined as the number of fragments the end point may reliably receive without BTP-layer acknowledgement. This value should be no larger than the floor of ONE-HALF the total number of slots or buffers reserved for GATT operations at any point along a platform's BLE pipeline. The BLE layer reserves all of these buffers for its own use - one half for incoming GATT writes or indications, and the other half for incoming GATT confirmations.
This value must be greater than 1, or race condition avoidance logic will prevent send the on remote device. This logic prevents any send with no piggybacked ack when the receiver's window has only 1 slot open. Without this logic, simultaneous data transmissions could fill both receiver's windows, leaving no room for the acks required to re-open them. Both senders would wedge, and the BTP connection would stall.
This value must also exceed (BLE_CONFIG_IMMEDIATE_ACK_WINDOW_THRESHOLD + 1), or immediate stand-alone acks will forever be sent without delay in response to one another as each peer's window size dips below BLE_CONFIG_IMMEDIATE_ACK_WINDOW_THRESHOLD with receipt of any single message fragment.
Default value of 3 is absolute minimum for stable performance, and an attempt to ensure safe window sizes on new platforms.
| #define BLE_READ_REQUEST_CONTEXT void* |
This defines the type of BLE_READ_REQUEST_CONTEXT parameters passed between BLE platform code and the BleLayer subsystem.
BLE_READ_REQUEST_CONTEXT objects are handed to BleLayer when a read request is received by the BLE platform. BleLayer hands these objects back to the appropriate platform delegate function when sending the read response.


| DOSE: |
25 mg |
oral |
Pharms - Atenolol |
(pill / tablet) |
| |
|
smoked |
Salvia divinorum |
(extract - 5x) |
I ingested a tiny (.5cm) white pill that held 25mg of Tenormin in preparation for smoking Salvia divinorum. I had read around and was fairly certain that this was uncharted territory, but I felt I was safe. Tenormin is a pharmaceutical in a class called Beta-Blockers. Beta-Blockers DO NOT get you high. Beta-Blockers simply regulate and normalize body functions such as blood pressure and heart rate. They are prescribed to pianists who would shake horribly on stage or people afraid of heights getting on an airplane etc.
I was a little nervous about my experiment because, well, everyone I know gets nervous when Salvia is waiting. My goal was to have the strong (and generally unpleasant) physical rush of Salvia eliminated without hindering the psychoactive effects. I thought this might work because when people I know smoke Salvia, they feel a tremendous rush of adrenaline with symptoms ranging from tingling to being pulled in some direction or another. The rush of adrenaline and weird inconsistent physical sensations fit well into my mind as a good old-fashioned panic attack.
Since Tenormin is not a fun drug, I had a little trouble discerning when I thought it had reached its full potential. But then I felt a familiar settling of my heart rate and I knew I could begin when I wanted.
I loaded a small dose of good quality 5x Salvia divinorum extract into my water pipe (you heard me, water pipe, bongs are illegal). Then I prepared myself for a typical Salvia journey or a quick jaunt to the speed-dialed 911 on my phone. As soon as I cleared the first bowl I knew the experiment was a success. The psychedelic effects were not quite as strong as usual, but I had taken a small dose. Of course, this was easily remedied by smoking a couple more bowls of the bugaboo (loving monster).
I definitely achieved a Salvia experience that was among the strongest I have ever had. I could continue smoking unhindered by the immobilizing physical side effects. I had quite an intense experience and it had the exact same feel as I am used to Salvia having. I had all kinds of uncomfortable and extraordinarily odd thoughts, but along my usual path in the Salvia world. The only thing that I believe might have changed was that I may have had less auditory distortion. Though this happens with me sometimes without Beta-Blockers.
I will run this experiment again, and I intend to try a larger dose of Beta-Blockers at some point. Though I imagine that as one increases the amount of Beta-Blockers taken, Salvia will lose some of its characteristic feel. However, I do think a large amount of Beta-Blockers will allow me to push further into the strength of the psychotropic experience.
I believe, though I have absolutely no evidence, that the combination of Salvia and Beta-Blockers should be safe. If anyone is to try this, always remember the golden rule of experimenting with psychotropics: START SMALL. Huge doses of Beta-Blockers will eventually make the amount of Salvia needed astronomical, and the Beta-Blockers still won't be fun. Do not be careless with what you are trying to explore.
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


Kalrav'17
Kalrav'17
"Education is the kindling of flame,not the filling of a vessel."
Socrates
Behind all milestones in the world history,there was a thrust for the betterment of Mankind.Education is the most powerful weapon
that can be used to change the world.Encompassing this ideology,with its vision and mission as "Moulding managers for Mankind", our College has completed its glorious journey of 17 long years.
About Kalrav
We present to you ,RKGIT's college fest.Kalrav with its inception in the year 2009,is the most significant festival for RKGIT Ghaziabad. Spread across a span of two days,the fest provides students pursuing any course of all years and branch a chanbe to participate and explore.Be it an exposure into the different worlds of business and management,a chance to discuss world issues at an international model UN conference,participating and showcasing talent in a cultural extravaganza or showing their competitive spirit through sports,Kalrav has it all.With immense and growing national and international participation,Kalrav aims at being one of the premier fests of the country foran unforgettable experience for all.


WITNESS LIST Business & Industry Committee February 23, 1999-2:00P HB 260 For: Allen, Daniel (Texas Water Utilities Association) Bell, Diana L. (Self) HB 341 For: Criss, Eric (National Federation of Independent Business/TX) Dupree, Kathy A. (Self and AHRMA) Findeisen, Les (Texas Motor Transportation Association) Keffer, Jim (Self) Knepp, Christopher A. (Texas Employment Law Council) Obdyke, Louis K. (Human Resource Managers' Association) Pinkus, David (Small Business United of Texas) Pollard, Mike (Texas Association of Life & Health Insurers) Vasek, Heather (Texas Association for Home Care) Williams, Lt. Howard E. (Texas Police Chiefs' Association) Against: Hinojosa, Walter (Texas AFL-CIO) On: Douglas, Wanda (Texas Nurses Association) Elliott, Chris (Self and Texas Trial Lawyers) HB 1305 For: Graham, Ann (Texas Bankers' Association) Neeley, Karen (Independent Bankers' Association of Texas) On: Boggus, Wally (Secretary of State) HB 1306 For: Graham, Ann (Texas Bankers' Association) Neeley, Karen (Independent Bankers' Association of Texas) On: Boggus, Wally (Secretary of State) HB 1307 For: Graham, Ann (Texas Bankers' Association) Neeley, Karen (Independent Bankers' Association of Texas) On: Boggus, Wally (Secretary of State) HB 1351 On: Flores, Carmen (Corporations Section,Office of Tx. Sec. of State) HB 1405 For: Floyd, Maurice (TU Electric) On: Jordan, Paul (Texas Department of Transportation) Invited Testimony On: Murdock, Steve (Self) Perryman, Ray (Self)


| chipKIT Sketch Help |
The serial port can now be selected with Tools>Port:>COMxx, where xx is the COM port number for the board.Figure: Select the CoM Port
|
Microchip Support
Help Updated: 2017-04-13 14:25:13Z
Help Page: 8
|


Chats
Online Users
Thomas Bangalter
I was wondering...
Dog Woofson
I've forgotten how it felt before
Louis CK
But we’re probably gonna need a new carpet.
Bo Jackson
It’s not that bad...

Bo Jackson
It’s not that bad...

Hakan Aydın
I'm in California dreaming
... about who we used to be.

Hakan Aydın
When we were younger and free...
Hakan Aydın
I've forgotten how it felt before


Notebooks
Story: !nuin-|kui-ten (who was a sorcerer or magician)
!nuin-|kui-ten (who was a sorcerer or magician)
Title
!nuin-|kui-ten (who was a sorcerer or magician)
Collection
Summary
The sorcerer !nuin|kúïten. In the Katkop dialect, by Díä!kwãin (L V.-15. 5079-5101, 5104-5109).
Comments
1) p.5079 opp: this story was told to Dia!kwain by his father (Xaa-ttin) who was taught about '!khwa ka tiken-tiken' by !nuin-|kui-ten when a young man; !nuin-|kui-ten was a friend of Dia!kwain's paternal grandfather, 2) p.5083v: the |xam names for 'patient' and 'impatient' men, 3) See also The broken string and What Xaa-ttin used to sing (the broken string), 4) This story is found in Book V-15
Contributors
Date
26 July 1875 (started)
Categories
History (personal), Custom and daily life, Transformation, Relations with others
Keywords
!nuin-|kui-ten (who was a sorcerer or magician) , !nuin-|kui-ten (his personal history) , !nuin-|kui-ten (wants to be remembered) , !nuin-|kui-ten (his place) , !nuin-|kui-ten (feels the string is broken) , !nuin-|kui-ten (feels he is about to die) , !nuin-|kui-ten (what people say when they see his spoor) , !nuin-|kui-ten (was an angry man) , !nuin-|kui-ten (his character) , !nuin-|kui-ten (taught Xaa-ttin songs and magic) , !nuin-|kui-ten (his actions) , !nuin-|kui-ten (walked about at night) , !nuin-|kui-ten (and Dia!kwain's father Xaa-ttin) , !nuin-|kui-ten (who was a rainmaker) , !nuin-|kui-ten (his magical expeditions) , !nuin-|kui-ten (was killed by a 'Boer' commando) , !nuin-|kui-ten (concealed his lion's body) , !nuin-|kui-ten (killed a 'Boer's' ox when a lion) , !nuin-|kui-ten (his actions in the form of an animal) , !nuin-|kui-ten (became a lion) , !nuin-|kui-ten (his transformation) , sorcerers (!nuin-|kui-ten who was a sorcerer or magician) , sorcerers (rainmakers) , sorcerers (teach their magic and songs) , sorcerers (the death of) , sorcerers (and !nuin-|kui-ten) , sorcerers (their work and actions) , sorcerers (watch people at night) , sorcerers (travel at night) , sorcerers (the transformation of) , sorcerers (become lions) , sorcerers (become beasts of prey) , sorcerers (their magical expeditions) , sorcerers (protect their people from other sorcerers) , sorcerers (good and bad) , death (of the sorcerer !nuin-|kui-ten) , death (to 'stand') , death (and the broken string) , rain (magic or !khwa ka tiken-tiken) , Dutch ('Boers') , Dutch (and !nuin-|kui-ten who was a sorcerer or magician) , Dutch (the commando that killed !nuin-|kui-ten) , broken string (and !nuin-|kui-ten who was a sorcerer or magician) , broken string (or thong) , broken string (and !nuin-|kui-ten) , broken string (and the open or empty country) , string (broken) , string (and !nuin-|kui-ten who was a sorcerer or magician) , Dia!kwain (his personal history) , Dia!kwain (his father Xaa-ttin was taught magic by the sorcerer !nuin-|kui-ten) , magic power (and !nuin-|kui-ten who was a sorcerer or magician) , magic power (of sorcerers) , magic power (the teaching of) , magic power (and songs) , magic power (or !khwa ka tiken-tiken) , personal history (of Dia!kwain's father, Xaa-ttin, who was taught magic by the sorcerer !nuin-|kui-ten) , personal history (of !nuin-|kui-ten who was a sorcerer or magician) , teaching (of magic by sorcerers) , teaching (Xaa-ttin who was taught magic by the sorcerer !nuin-|kui-ten) , education (of sorcerers) , education (and !nuin-|kui-ten who was a sorcerer or magician) , education (Xaa-ttin who was taught magic by the sorcerer !nuin-|kui-ten) , songs (and magic taught by the sorcerer !nuin-|kui-ten) , songs (sorcerers teach their magic and songs) , commando ('Boer', that killed !nuin-|kui-ten) , commando (and !nuin-|kui-ten who was a sorcerer or magician) , name (used by the |xam for magic power is !khwa ka tiken-tiken) , magical expeditions (of sorcerers at night) , magical expeditions (and !nuin-|kui-ten who was a sorcerer or magician) , transformation (of sorcerers into lions) , transformation (of sorcerers into beasts of prey) , transformation (and !nuin-|kui-ten who was a sorcerer or magician) , transformation (!nuin-|kui-ten concealed his lion's body) , transformation (!nuin-|kui-ten killed a 'Boer's' ox when a lion) , transformation (!nuin-|kui-ten became a lion to watch over his people) , lion (sorcerers become) , lion (!nuin-|kui-ten became one to watch over his people) , lion (and !nuin-|kui-ten who was a sorcerer or magician) , lion (!nuin-|kui-ten killed a 'Boer's' ox when one) , beasts of prey (sorcerers become) , rainmaker (!nuin-|kui-ten who was a sorcerer or magician)
Story Pages
5079-5101


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