I'm A Celebrity new arrival Cliff Parisi's plan to smuggle in contraband rumbled
Former EastEnders star Cliff Parisi had planned a unique way of making friends with the current I'm A Celebrity campmates as he fears they have all already bonded
by Irishmirror.ieNew I'm A Celebrity arrival Cliff Parisi had planned a genius way to make friends in the jungle by bringing in contraband.
But his cunning scheme has been rumbled - so he will have to get the current campmates on side in the traditional way.
The former EastEnders actor, who is filling up on a big breakfast before he enters the jungle, fears it will be a struggle to form friendships as a newbie.
Cliff said: "It is going to be tricky going in late though because they will have bonded and made friendships.
"How will I make friends with them? I am going to try and bring in a bag of sweets!"
The Call the Midwife is feeling guilty going into the camp late with a full stomach when everyone else will be starving.
He had a long think about taking part in the show - but realised he needs the money.
Cliff said: "I had always said ‘no’ to anything with the word ‘celebrity’ tagged to it,” he explains. “And I have avoided those shows like the plague!
"But everyone on last year’s series was so much fun and I loved watching it. I realised it would be great fun to do. And now I am going to do it, I am hoping it will be cathartic."
The Londoner jokes that "we don't have animals" and he is allergic to cats so may struggle with the critters.
He added: "I do think the trials will be scary but I will still give them a go. I will try my best to win the stars."
Cliff is a seasoned camper but think he may bore everyone with all of his stories.
He admits: "I will be the camp bore! I am looking forward to doing it now but I know it is going to be tough.
"When you have a crucial time in your life, you always have a cup of tea. To go without tea, no telly and not much food will be tough. It’s going to be hard being away from my creature comforts."
As for what his colleagues on Call the Midwife will think when they find out he is swapping the popular drama for snakes and rats, he says: "They will be screaming with delight once they find out. I know they won’t be disappointed and they will be rooting for me.
"In fact, I know they will be phoning up for me to do the trials!  It is going to be the best year!"
* I'm A Celebrity...Get Me Out Of Here! airs on ITV at 9pm


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Lucasfilm and director J.J. Abrams join forces once again to take viewers on an epic journey to a galaxy far, far away with Star Wars: The Rise of Skywalker, the riveting conclusion of the seminal Skywalker saga, where new legends will be born and the final battle for freedom is yet to come.
A force-sensitive human female, was an Alderaanian politician and military general whose career spanned several decades, from the reign of the Galactic Empire to the rise of the New Republic. Born under the name Leia Amidala Skywalker, she was the daughter of Jedi Knight Anakin Skywalker and Senator Padmé Amidala of Naboo.
A force-sensitive human male, was a legendary Jedi Master who fought in the Galactic Civil War during the reign of the Galactic Empire. Along with his companions, Princess Leia Organa and Captain Han Solo, Skywalker served on the side of the Alliance to Restore the Republic—an organization committed to the downfall of Emperor Palpatine and the restoration of democracy. Following the war, Skywalker became a living legend, and was remembered as one of the greatest Jedi in galactic history.
A force-sensitive human male who, after falling to the dark side of the Force, eventually renounced his adopted persona of Kylo Ren and was redeemed. The master of the Knights of Ren, he conquered much of the galaxy during his reign as Supreme Leader of the First Order. Ren was extraordinarily strong with the Force.
A Force-sensitive human female who, after years of living as a scavenger, became a Jedi during the war between the First Order and the Resistance. Unaware of her lineage as the granddaughter of Emperor Sheev Palpatine, Rey was the only name she went by prior to assuming the Skywalker surname.


The focus of this module is on instruments that measure pressure, so this exploration of “pressure altitude” and “geopotential height” is a diversion from the main theme. The material is included here because measurements of pressure often are used to construct weather maps for analysis or to provide input to numerical models, and these pressure fields are often expressed in geopotential height. Consider, for example, a representative “500 mb” map such as that shown at this URL (provided by the NCAR Research Applications Laboratory). The solid black lines show the heights (in decameters) where the pressure is 500 hPa. They are labeled with numbers like “548”, which would indicate that at this location the height of the 500 hPa level is 5480 m. These heights are geopotential heights, so learning about geopotential height benefits those providing data for constructing these maps and those using them. In the course of this discussion we will also review the concepts of pressure altitude and altimeter setting, as used in aviation, and the International Standard Atmosphere.
The starting point for this discussion is the hydrostatic equation (4) from the main module, repeated here in slightly modified form:
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(1) |
where p1 and p2 are the pressures at the bottom and top of a column of air, z1 and z2 are the corresponding altitudes, R* is the universal gas constant with units J Kmol-1K-1, g(z′) is the acceleration of gravity at altitude z′ (which may in general depend on position on the Earth, esp. on the latitude λ), Ma(z′) is the molecular weight of the air, which may vary with altitude, for example if there are variable amounts of water vapor in the air, and T (z′) is the temperature (in units of K).
To avoid the need for including the variation of gravity with altitude and latitude, the equation can be rewritten in terms of geopotential height (Z), defined as the height at which, if gravity did not vary, the potential energy of a unit mass relative to the potential energy at sea level would be the same as the actual parcel. By convention, the fixed value for gravity is taken to be g0 = 9.80665ms-2. The defining equation for geopotential height is then
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(2) |
The variations in temperature and molecular weight can then be expressed in terms of the variable Z, leading to the following form of the hydrostatic equation:
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(3) |
Some further simplifications lead to the form used for the International Standard Atmosphere (ISA), also described as the U. S. Standard Atmosphere, 1976. For a complete description, see this URL. It is important, and often not appreciated, that the standard atmosphere (hereafter denoted the ISA) relates pressure to geopotential height and the temperature profile is specified in terms of geopotential height. The additional simplifications are:
Consider only dry air. In that case, for levels in the atmosphere below 50 km, Ma(Z) can be assumed to be a constant that will be taken as Md = 28.9644 kg Kmol-1. This exact number is specified in the definition of the ISA, even though the value for the real atmosphere is changing as CO2 increases.
Below 50 km, use the following temperature profile. (The ISA is defined for heights above 50 km also, but additional complications arise from the changing molecular weight of dry air and ionization of molecules, so those levels won’t be treated here.) The "lapse rate" (the change in temperature with altitude) is specified by constant values in each of four intervals in geopotential altitude. The lapse rate has units of ∘C/km in geopotential height. The intervals are:
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troposphere |
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stratosphere |
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mesosphere (partial) |
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For a constant lapse rate, the hydrostatic equation specified in the form (3) is integrable analytically. The result is shown under the next tab.


"TRACHONITIS "a rugged region, corresponds to the Hebrews Argob(q.v.), the Greek name of a region on the east of Jordan (Luke 3:1); one of the five Roman provinces into which that district was divided. It was in the tetrarchy of Philip, and is now called the Lejah.
"TRACHONITIS "a rugged region, corresponds to the Hebrews Argob(q.v.), the Greek name of a region on the east of Jordan (Luke 3:1); one of the five Roman provinces into which that district was divided. It was in the tetrarchy of Philip, and is now called the Lejah.


Verification certificate key settings
View or edit the verification certificate signing keys for {{$realm.Name}} below.
{{if not $realm.UseRealmCertificateKey}} {{template "systemkeys" .}} {{end}}
Realm signing key configuration
{{if .supportsPerRealmSigning}}
{{if not $realm.UseRealmCertificateKey}}
{{if and $realm.CanUpgradeToRealmSigningKeys .activePublicKey}}
{{else}}
You can enable realm-specific verification certificates after configuring your realm's issuer and audience settings, as well as creating a signing key below.
{{end}}
{{end}}
{{else}}
This server is not configured to support per-realm certificate
signing keys. Please ask your operator about enabling this
functionality.
{{end}}


public_key
Module
Module Summary
Description
Provides functions to handle public-key infrastructure, for details see public_key(6).
DATA TYPES
All records used in this Reference Manual are generated from ASN.1 specifications and are documented in the User's Guide. See Public-key Records.
Use the following include directive to get access to the records and constant macros described here and in the User's Guide:
-include_lib("public_key/include/public_key.hrl").
The following data types are used in the functions for public_key:
- oid()
Object identifier, a tuple of integers as generated by the ASN.1 compiler.
- boolean() =
true | false
- string() =
[bytes()]
- der_encoded() =
binary()
- pki_asn1_type() =
-
'Certificate'
| 'RSAPrivateKey'
| 'RSAPublicKey'
| 'DSAPrivateKey'
| 'DSAPublicKey'
| 'DHParameter'
| 'SubjectPublicKeyInfo'
| 'PrivateKeyInfo'
| 'CertificationRequest'
| 'CertificateList'
| 'ECPrivateKey'
| 'EcpkParameters'
- pem_entry () =
-
{pki_asn1_type(), binary(), %% DER or encrypted DER
not_encrypted | cipher_info()}
- cipher_info() =
-
{"RC2-CBC" | "DES-CBC" | "DES-EDE3-CBC", crypto:strong_rand_bytes(8)
| {#'PBEParameter{}, digest_type()} | #'PBES2-params'{}}
- public_key() =
rsa_public_key() | dsa_public_key() | ec_public_key()
- private_key() =
rsa_private_key() | dsa_private_key() | ec_private_key()
- rsa_public_key() =
#'RSAPublicKey'{}
- rsa_private_key() =
#'RSAPrivateKey'{}
- dsa_public_key() =
{integer(), #'Dss-Parms'{}}
- dsa_private_key() =
#'DSAPrivateKey'{}
- ec_public_key()
= {#'ECPoint'{}, #'ECParameters'{} | {namedCurve, oid()}}
- ec_private_key() =
#'ECPrivateKey'{}
- public_crypt_options() =
[{rsa_pad, rsa_padding()}]
- rsa_padding() =
-
'rsa_pkcs1_padding'
| 'rsa_pkcs1_oaep_padding'
| 'rsa_no_padding'
- digest_type() =
Union of rsa_digest_type(), dss_digest_type(), and ecdsa_digest_type().
- rsa_digest_type() =
'md5' | 'sha' | 'sha224' | 'sha256' | 'sha384' | 'sha512'
- dss_digest_type() =
'sha'
- ecdsa_digest_type() =
'sha'| 'sha224' | 'sha256' | 'sha384' | 'sha512'
- crl_reason() =
-
unspecified
| keyCompromise
| cACompromise
| affiliationChanged
| superseded
| cessationOfOperation
| certificateHold
| privilegeWithdrawn
| aACompromise
- issuer_name() =
-
{rdnSequence,[#'AttributeTypeAndValue'{}]}
- ssh_file() =
-
openssh_public_key
| rfc4716_public_key
| known_hosts
| auth_keys
Exports
compute_key(OthersKey, MyKey)->
compute_key(OthersKey, MyKey, Params)->
Types
Computes shared secret.
decrypt_private(CipherText, Key) -> binary()
decrypt_private(CipherText, Key, Options) -> binary()
Types
Public-key decryption using the private key. See also crypto:private_decrypt/4
decrypt_public(CipherText, Key) - > binary()
decrypt_public(CipherText, Key, Options) - > binary()
Types
Public-key decryption using the public key. See also crypto:public_decrypt/4
der_decode(Asn1type, Der) -> term()
Types
Decodes a public-key ASN.1 DER encoded entity.
der_encode(Asn1Type, Entity) -> der_encoded()
Types
Encodes a public-key entity with ASN.1 DER encoding.
dh_gex_group(MinSize, SuggestedSize, MaxSize, Groups) -> {ok, {Size,Group}} | {error,Error}
Types
Selects a group for Diffie-Hellman key exchange with the key size in the range MinSize...MaxSize and as close to SuggestedSize as possible. If Groups == undefined a default set will be used, otherwise the group is selected from Groups.
First a size, as close as possible to SuggestedSize, is selected. Then one group with that key size is randomly selected from the specified set of groups. If no size within the limits of MinSize and MaxSize is available, {error,no_group_found} is returned.
The default set of groups is listed in lib/public_key/priv/moduli. This file may be regenerated like this:
$> cd $ERL_TOP/lib/public_key/priv/ $> generate ---- wait until all background jobs has finished. It may take several days ! $> cat moduli-* > moduli $> cd ..; make
encrypt_private(PlainText, Key) -> binary()
Types
Public-key encryption using the private key. See also crypto:private_encrypt/4.
encrypt_public(PlainText, Key) -> binary()
Types
Public-key encryption using the public key. See also crypto:public_encrypt/4.
generate_key(Params) -> {Public::binary(), Private::binary()} | #'ECPrivateKey'{} | #'RSAPrivateKey'{}
Types
Generates a new keypair. Note that except for Diffie-Hellman the public key is included in the private key structure. See also crypto:generate_key/2
pem_decode(PemBin) -> [pem_entry()]
Types
Decodes PEM binary data and returns entries as ASN.1 DER encoded entities.
pem_encode(PemEntries) -> binary()
Types
Creates a PEM binary.
pem_entry_decode(PemEntry) -> term()
pem_entry_decode(PemEntry, Password) -> term()
Types
Decodes a PEM entry. pem_decode/1 returns a list of PEM entries. Notice that if the PEM entry is of type 'SubjectPublickeyInfo', it is further decoded to an rsa_public_key() or dsa_public_key().
pem_entry_encode(Asn1Type, Entity) -> pem_entry()
pem_entry_encode(Asn1Type, Entity, {CipherInfo, Password}) -> pem_entry()
Types
Creates a PEM entry that can be feed to pem_encode/1.
pkix_decode_cert(Cert, otp|plain) -> #'Certificate'{} | #'OTPCertificate'{}
Types
Decodes an ASN.1 DER-encoded PKIX certificate. Option otp uses the customized ASN.1 specification OTP-PKIX.asn1 for decoding and also recursively decode most of the standard parts.
pkix_encode(Asn1Type, Entity, otp | plain) -> der_encoded()
Types
DER encodes a PKIX x509 certificate or part of such a certificate. This function must be used for encoding certificates or parts of certificates that are decoded/created in the otp format, whereas for the plain format this function directly calls der_encode/2.
pkix_is_issuer(Cert, IssuerCert) -> boolean()
Types
Checks if IssuerCert issued Cert.
pkix_is_fixed_dh_cert(Cert) -> boolean()
Types
Checks if a certificate is a fixed Diffie-Hellman certificate.
pkix_is_self_signed(Cert) -> boolean()
Types
Checks if a certificate is self-signed.
pkix_issuer_id(Cert, IssuedBy) -> {ok, IssuerID} | {error, Reason}
Types
Returns the issuer id.
pkix_normalize_name(Issuer) -> Normalized
Types
Normalizes an issuer name so that it can be easily compared to another issuer name.
pkix_path_validation(TrustedCert, CertChain, Options) -> {ok, {PublicKeyInfo, PolicyTree}} | {error, {bad_cert, Reason}}
Types
Performs a basic path validation according to RFC 5280. However, CRL validation is done separately by pkix_crls_validate/3 and is to be called from the supplied verify_fun.
Available options:
- {verify_fun, fun()}
-
The fun must be defined as:
fun(OtpCert :: #'OTPCertificate'{}, Event :: {bad_cert, Reason :: atom() | {revoked, atom()}} | {extension, #'Extension'{}}, InitialUserState :: term()) -> {valid, UserState :: term()} | {valid_peer, UserState :: term()} | {fail, Reason :: term()} | {unknown, UserState :: term()}.
If the verify callback fun returns {fail, Reason}, the verification process is immediately stopped. If the verify callback fun returns {valid, UserState}, the verification process is continued. This can be used to accept specific path validation errors, such as selfsigned_peer, as well as verifying application-specific extensions. If called with an extension unknown to the user application, the return value {unknown, UserState} is to be used.
- {max_path_length, integer()}
- The max_path_length is the maximum number of non-self-issued intermediate certificates that can follow the peer certificate in a valid certification path. So, if max_path_length is 0, the PEER must be signed by the trusted ROOT-CA directly, if it is 1, the path can be PEER, CA, ROOT-CA, if it is 2, the path can be PEER, CA, CA, ROOT-CA, and so on.
Possible reasons for a bad certificate:
- cert_expired
Certificate is no longer valid as its expiration date has passed.
- invalid_issuer
Certificate issuer name does not match the name of the issuer certificate in the chain.
- invalid_signature
Certificate was not signed by its issuer certificate in the chain.
- name_not_permitted
Invalid Subject Alternative Name extension.
- missing_basic_constraint
Certificate, required to have the basic constraints extension, does not have a basic constraints extension.
- invalid_key_usage
Certificate key is used in an invalid way according to the key-usage extension.
- {revoked, crl_reason()}
Certificate has been revoked.
- atom()
Application-specific error reason that is to be checked by the verify_fun.
pkix_crl_issuer(CRL) -> issuer_name()
Types
Returns the issuer of the CRL.
pkix_crls_validate(OTPCertificate, DPAndCRLs, Options) -> CRLStatus()
Types
Performs CRL validation. It is intended to be called from the verify fun of pkix_path_validation/3.
Available options:
- {update_crl, fun()}
-
The fun has the following type specification:
fun(#'DistributionPoint'{}, #'CertificateList'{}) -> #'CertificateList'{}
The fun uses the information in the distribution point to access the latest possible version of the CRL. If this fun is not specified, Public Key uses the default implementation:
fun(_DP, CRL) -> CRL end
- {issuer_fun, fun()}
-
The fun has the following type specification:
fun(#'DistributionPoint'{}, #'CertificateList'{}, {rdnSequence,[#'AttributeTypeAndValue'{}]}, term()) -> {ok, #'OTPCertificate'{}, [der_encoded]}
The fun returns the root certificate and certificate chain that has signed the CRL.
fun(DP, CRL, Issuer, UserState) -> {ok, RootCert, CertChain}
pkix_crl_verify(CRL, Cert) -> boolean()
Types
Verify that Cert is the CRL signer.
pkix_dist_point(Cert) -> DistPoint
Types
Creates a distribution point for CRLs issued by the same issuer as Cert. Can be used as input to pkix_crls_validate/3
pkix_dist_points(Cert) -> DistPoints
Types
Extracts distribution points from the certificates extensions.
pkix_match_dist_point(CRL, DistPoint) -> boolean()
Types
Checks whether the given distribution point matches the Issuing Distribution Point of the CRL, as described in RFC 5280. If the CRL doesn't have an Issuing Distribution Point extension, the distribution point always matches.
pkix_sign(#'OTPTBSCertificate'{}, Key) -> der_encoded()
Types
Signs an 'OTPTBSCertificate'. Returns the corresponding DER-encoded certificate.
pkix_sign_types(AlgorithmId) -> {DigestType, SignatureType}
Types
Translates signature algorithm OID to Erlang digest and signature types.
pkix_verify(Cert, Key) -> boolean()
Types
Verifies PKIX x.509 certificate signature.
pkix_verify_hostname(Cert, ReferenceIDs) -> boolean()
pkix_verify_hostname(Cert, ReferenceIDs, Opts) -> boolean()
Types
This function checks that the Presented Identifier (e.g hostname) in a peer certificate conforms with the Expected Identifier that the client wants to connect to. This functions is intended to be added as an extra client check to the peer certificate when performing public_key:pkix_path_validation/3
See RFC 6125 for detailed information about hostname verification. The User's Manual and code examples describes this function more detailed.
sign(Msg, DigestType, Key) -> binary()
Types
Creates a digital signature.
ssh_decode(SshBin, Type) -> [{public_key(), Attributes::list()}]
Types
Decodes an SSH file-binary. In the case of known_hosts or auth_keys, the binary can include one or more lines of the file. Returns a list of public keys and their attributes, possible attribute values depends on the file type represented by the binary.
- RFC4716 attributes - see RFC 4716.
{headers, [{string(), utf8_string()}]}
- auth_key attributes - see manual page for sshd.
- {comment, string()}
- {options, [string()]}
{bits, integer()} - In SSH version 1 files.
- known_host attributes - see manual page for sshd.
- {hostnames, [string()]}
- {comment, string()}
{bits, integer()} - In SSH version 1 files.
ssh_encode([{Key, Attributes}], Type) -> binary()
Types
Encodes a list of SSH file entries (public keys and attributes) to a binary. Possible attributes depend on the file type, see ssh_decode/2.
ssh_hostkey_fingerprint(HostKey) -> string()
ssh_hostkey_fingerprint(DigestType, HostKey) -> string()
ssh_hostkey_fingerprint([DigestType], HostKey) -> [string()]
Types
Calculates a ssh fingerprint from a public host key as openssh does.
The algorithm in ssh_hostkey_fingerprint/1 is md5 to be compatible with older ssh-keygen commands. The string from the second variant is prepended by the algorithm name in uppercase as in newer ssh-keygen commands.
Examples:
2> public_key:ssh_hostkey_fingerprint(Key). "f5:64:a6:c1:5a:cb:9f:0a:10:46:a2:5c:3e:2f:57:84" 3> public_key:ssh_hostkey_fingerprint(md5,Key). "MD5:f5:64:a6:c1:5a:cb:9f:0a:10:46:a2:5c:3e:2f:57:84" 4> public_key:ssh_hostkey_fingerprint(sha,Key). "SHA1:bSLY/C4QXLDL/Iwmhyg0PGW9UbY" 5> public_key:ssh_hostkey_fingerprint(sha256,Key). "SHA256:aZGXhabfbf4oxglxltItWeHU7ub3Dc31NcNw2cMJePQ" 6> public_key:ssh_hostkey_fingerprint([sha,sha256],Key). ["SHA1:bSLY/C4QXLDL/Iwmhyg0PGW9UbY", "SHA256:aZGXhabfbf4oxglxltItWeHU7ub3Dc31NcNw2cMJePQ"]
verify(Msg, DigestType, Signature, Key) -> boolean()
Types
Verifies a digital signature.
short_name_hash(Name) -> string()
Types
Generates a short hash of an issuer name. The hash is returned as a string containing eight hexadecimal digits.
The return value of this function is the same as the result of the commands openssl crl -hash and openssl x509 -issuer_hash, when passed the issuer name of a CRL or a certificate, respectively. This hash is used by the c_rehash tool to maintain a directory of symlinks to CRL files, in order to facilitate looking up a CRL by its issuer name.


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Comments
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Greg Hurrell
This is not a bug but a feature. Of album art is not available in iTunes and Synergy has not yet found the art by searching at Amazon then it will show the question mark.
If it is successful in finding and downloading the art from Amazon then you will see the art the next time you play the track. Note that there may be some delay before the downloading finishes because Synergy places downloads in a queue and only downloads them after an interval of time has passed to minimize the number of requests sent to the Amazon servers.
You should be able to try some other tracks in your library and you'll see that those which have art in iTunes do indeed show art in the Floater, likewise for those for which cover art has been downloaded. If you want to see the downloaded art in the Finder, navigate to "~/Library/Application Support/Synergy/ Album Covers/".
Make sure that you are really running 1.8.1 and that you don't have an older version on your system somewhere (have a look in ~/Library/PreferencePanes/ and /Library/PreferencePanes/).
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grammar::me::cpu::core - ME virtual machine state manipulation
This package provides an implementation of the ME virtual machine. Please go and read the document grammar::me_intro first if you do not know what a ME virtual machine is.
This implementation represents each ME virtual machine as a Tcl value and provides commands to manipulate and query such values to show the effects of executing instructions, adding tokens, retrieving state, etc.
The values fully follow the paradigm of Tcl that every value is a string and while also allowing C implementations for a proper Tcl_ObjType to keep all the important data in native data structures. Because of the latter it is recommended to access the state values only through the commands of this package to ensure that internal representation is not shimmered away.
The actual structure used by all state values is described in section CPU STATE.
The package directly provides only a single command, and all the functionality is made available through its methods.
This method returns a list containing a disassembly of the match instructions in asm. The format of asm is specified in the section MATCH PROGRAM REPRESENTATION.
Each element of the result contains instruction label, instruction name, and the instruction arguments, in this order. The label can be the empty string. Jump destinations are shown as labels, strings and tokens unencoded. Token names are prefixed with their numeric id, if, and only if a tokmap is defined. The two components are separated by a colon.
This method returns code in the format as specified in section MATCH PROGRAM REPRESENTATION generated from ME assembly code asm, which is in the format as returned by the method disasm.
This method creates state value for a ME virtual machine in its initial state and returns it as its result.
The argument matchcode contains a Tcl representation of the match instructions the machine has to execute while parsing the input stream. Its format is specified in the section MATCH PROGRAM REPRESENTATION.
The tokmap argument taken by the implementation provided by the package grammar::me::tcl is here hidden inside of the match instructions and therefore not needed.
This method takes the state value of a ME virtual machine and uses it to convert a location in the input stream (as offset) into a line number and column index. The result of the method is a 2-element list containing the two pieces in the order mentioned in the previous sentence.
Note that the method cannot convert locations which the machine has not yet read from the input stream. In other words, if the machine has read 7 characters so far it is possible to convert the offsets 0 to 6, but nothing beyond that. This also shows that it is not possible to convert offsets which refer to locations before the beginning of the stream.
This utility allows higher levels to convert the location offsets found in the error status and the AST into more human readable data.
This method takes the state value of a ME virtual machine and returns a Tcl list containing the part of the input stream between the locations from and to (both inclusive). If to is not specified it will default to the value of from. If from is not specified either the whole input stream is returned.
This method places the same restrictions on its location arguments as the method lc.
This method takes the state value of a ME virtual machine and returns the current value of the stored program counter.
This method takes the state value of a ME virtual machine and returns the current value of the stored eof flag.
This method takes the state value of a ME virtual machine and returns the current location in the input stream.
This method takes the state value of a ME virtual machine and returns the current token.
This method takes the state value of a ME virtual machine and returns the current semantic value stored in it. This is an abstract syntax tree as specified in the document grammar::me_ast, section AST VALUES.
This method takes the state value of a ME virtual machine and returns the match status stored in it.
This method takes the state value of a ME virtual machine and returns the current error status stored in it.
This method takes the state value of a ME virtual machine and returns the location stack.
This method takes the state value of a ME virtual machine and returns the AST stack.
This method takes the state value of a ME virtual machine and returns the AST marker stack.
This method takes the state value of a ME virtual machine and returns the error stack.
This method takes the state value of a ME virtual machine and returns the subroutine return stack.
This method takes the state value of a ME virtual machine and returns the nonterminal match cache as a dictionary.
This method takes the state value of a ME virtual machine and returns the abstract syntax tree currently at the top of the AST stack stored in it. This is an abstract syntax tree as specified in the document grammar::me_ast, section AST VALUES.
This method takes the state value of a ME virtual machine and returns the current halt status stored in it, i.e. if the machine has stopped or not.
This method takes the state value of a ME virtual machine and returns the code stored in it, i.e. the instructions executed by the machine.
This method takes the state value of a ME virtual machine as stored in the variable named by statevar and modifies it so that the eof flag inside is set. This signals to the machine that whatever token are in the input queue are the last to be processed. There will be no more.
This method takes the state value of a ME virtual machine as stored in the variable named by statevar and modifies it so that the token tok is added to the end of the input queue, with associated lexeme data lex and line/column information.
The operation will fail with an error if the eof flag of the machine has been set through the method eof.
This method takes the state value of a ME virtual machine as stored in the variable named by statevar, executes a number of instructions and stores the state resulting from their modifications back into the variable.
The execution loop will run until either
n instructions have been executed, or
a halt instruction was executed, or
the input queue is empty and the code is asking for more tokens to process.
If no limit n was set only the last two conditions are checked for.
A match program is represented by nested Tcl list. The first element, asm, is a list of integer numbers, the instructions to execute, and their arguments. The second element, pool, is a list of strings, referenced by the instructions, for error messages, token names, etc. The third element, tokmap, provides ordering information for the tokens, mapping their names to their numerical rank. This element can be empty, forcing lexicographic comparison when matching ranges.
All ME instructions are encoded as integer numbers, with the mapping given below. A number of the instructions, those which handle error messages, have been given an additional argument to supply that message explicitly instead of having it constructed from token names, etc. This allows the machine state to store only the message ids instead of the full strings.
Jump destination arguments are absolute indices into the asm element, refering to the instruction to jump to. Any string arguments are absolute indices into the pool element. Tokens, characters, messages, and token (actually character) classes to match are coded as references into the pool as well.
"ict_advance message"
"ict_match_token tok message"
"ict_match_tokrange tokbegin tokend message"
"ict_match_tokclass code message"
"inc_restore branchlabel nt"
"inc_save nt"
"icf_ntcall branchlabel"
"icf_ntreturn"
"iok_ok"
"iok_fail"
"iok_negate"
"icf_jalways branchlabel"
"icf_jok branchlabel"
"icf_jfail branchlabel"
"icf_halt"
"icl_push"
"icl_rewind"
"icl_pop"
"ier_push"
"ier_clear"
"ier_nonterminal message"
"ier_merge"
"isv_clear"
"isv_terminal"
"isv_nonterminal_leaf nt"
"isv_nonterminal_range nt"
"isv_nonterminal_reduce nt"
"ias_push"
"ias_mark"
"ias_mrewind"
"ias_mpop"
A state value is a list containing the following elements, in the order listed below:
code: Match instructions, see MATCH PROGRAM REPRESENTATION.
pc: Program counter, int.
halt: Halt flag, boolean.
eof: Eof flag, boolean
tc: Terminal cache, and input queue. Structure see below.
cl: Current location, int.
ct: Current token, string.
ok: Match status, boolean.
sv: Semantic value, list.
er: Error status, list.
ls: Location stack, list.
as: AST stack, list.
ms: AST marker stack, list.
es: Error stack, list.
rs: Return stack, list.
nc: Nonterminal cache, dictionary.
tc, the input queue of tokens waiting for processing and the terminal cache containing the tokens already processing are one unified data structure simply holding all tokens and their information, with the current location separating that which has been processed from that which is waiting. Each element of the queue/cache is a list containing the token, its lexeme information, line number, and column index, in this order.
All stacks have their top element aat the end, i.e. pushing an item is equivalent to appending to the list representing the stack, and popping it removes the last element.
er, the error status is either empty or a list of two elements, a location in the input, and a list of messages, encoded as references into the pool element of the code.
nc, the nonterminal cache is keyed by nonterminal name and location, each value a four-element list containing current location, match status, semantic value, and error status, in this order.
This document, and the package it describes, will undoubtedly contain bugs and other problems. Please report such in the category grammar_me of the Tcllib Trackers. Please also report any ideas for enhancements you may have for either package and/or documentation.
When proposing code changes, please provide unified diffs, i.e the output of diff -u.
Note further that attachments are strongly preferred over inlined patches. Attachments can be made by going to the Edit form of the ticket immediately after its creation, and then using the left-most button in the secondary navigation bar.
Grammars and finite automata
Copyright © 2005-2006 Andreas Kupries <andreas_kupries@users.sourceforge.net>


The rise of the amateur photographer, coupled with the popularity of portable camera devices calls for the introduction of new motion timelapse products.
This product was chosen for development to overcome three main issues with existing motion timelapse devices, namely:
Motion Lapse therefore aims to deliver high quality, portable and customiseable motion time lapse solutions to the modern amateur photographer.
Motion Lapse relies on a number of key features to provide its revolutionary functionality. These innovations enable Motion Lapse to perform above the capabilities of current motion time lapse products.
Three preloaded wheels are used to grip the camera track, ensuring stable shots and maneuverability. The DC motor and gearbox allow up to full vertical movement, while in built control systems ensure steady speed throughout the timelapse.
Provides key information such as battery life an time remaining throughout the taking of the timelapse, while control is provided via smartphone.
Flexible yet strong, the track allows a vast range of shapes to be created, determining the path of the motion time lapse. The lightweight construction allows for easy transportation and quick setup.
The Motion lapse is of similar size and weight to the most popular action cameras, while boasting over 6 hours battery life and being ready for the outdoors. The future of motion time lapse photography is here.
Atom was used as the main text editor. Atom benefits from being a free, open source development tool. It also allows cross platform editing. Atom's main advantage is it's ability to produce results without the need to get 'under the hood' with config files, for example.
Github was used as a repository hosting service, to allow for easy version control and backing up of files.
Bootstrap was used as a webpage template source, which allowed for the advanced layout. Bootstrap code was the basis of the webpage which was then modified and added to. Bootstrap benefits from allowing particularly high development speeds through ready-made code, as well as a fluid grid system that is great for creating mobile-friendly webpages.
A domain name must be registered through a domain name registrar. The registrar searches wether the requested domain already exists, and if it does not, they can place the domain name at a website created for the individual. This saves the domain name until the next steps can be taken.
Github pages can be used to create a website. First, a repository is created. From here, the repository is cloned onto an individual computer. The website can then be created and edited through an index file, which can be commited and published. The webpage will be stored at https://username.github.io.


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mortality RCP. ( ± .) exposure premature SSP future burden baseline scenario change projected India climate population ambient scenarios decade air expected GDP period models CMIP estimated using (). Environ. estimate increase relative study {border-style Fig. \documentclass[pt]{minimal} \setlength{\oddsidemargin}{-pt} \usepackage{amsbsy} \usepackage{amsfonts} \usepackage{amsmath} \usepackage{amssymb} \usepackage{mathrsfs} \usepackage{upgreek} \usepackage{wasysym} model Change px;} #ffffff;} Indian demographic Supplementary decrease health pollution used approach changes year (–) Table estimates future. grid socioeconomic aerosol century emissions pentad scenario. RCP \hfill decades meteorology scenarios. PPP distant far pt} sensitivity Glob. Global Methods considered data disease future, mitigation obtained policy project range use (see Health age combination concentration constant fine function greenhouse impact particulate period. risk total values ,–, Climate Premature analysis changing difference emission end following gas global level meteorological px; scenario, transition {font-family (Fig. (Supplementary AOD Clim. \times average burden. combined deaths distribution font-size human national near new pop projections rate satellite-derived till world Atmos. Eq. GDP-PPP Lancet London Model Projected Res. assume attributed available averted based contribution diseases double; epidemiologic exposed exposure. group higher modulating prices report representative represents respective simulated targets {\sum (. ± .) ( µg m Ambient Burden Data Geophys. IIASA SOA Socioeconomic assessment bold border-color border-width drivers effect factor forcing given high highest important indicate matter mean meets present previous provide respectively. shared solid; studies systematic thank warming {font-style {font-weight }^N (Table (–), ./j.gloenvcha... .° × .° . ± . Change./j.gloenvcha... Commons Creative Dey Disease IER India, India. LINK Nature OECD Pathways Project Sci. Shared USD \begin{document}$${\mathrm{PM}}_{._{{\mathrm{pentad}} adaptation apply assumptions attributable averaged challenge challenges change. control courier; decadal depends depth discussed effects em; enforced factors followed fraction household landmass material measures meet non-linear optical organic pathway pathways policies pollution: pt;} quality radiative reduce relation resolution results risks role summarized terms w}$$\end{document} w}}$$\end{document} z,{\mathrm{SSP}}


dprod(3f) - [FORTRAN:INTRINSIC:NUMERIC] Double product function
result = dprod(x, y)
DPROD(X,Y) produces a higher DOUBLEPRECISION product of default REAL numbers X and Y.The result has a value equal to a processor-dependent approximation to the product of X and Y. It is recommended that the processor compute the product in double precision, rather than in single precision and then converted to double precision.
The setting of compiler options specifying REAL size can affect this function.
X shall be default real. Y shall be default real.
X Must be of default REAL(kind=kind(0.0)) type Y Must have the same type and kind parameters as X
The return value is of type real(kind=kind(0.0d0)).
Sample program:
program demo_dprod use, intrinsic :: iso_fortran_env, only : real_kinds, & & real32, real64, real128 implicit none integer,parameter :: dp=kind(0.0d0) real :: x = 5.2 real :: y = 2.3 real(kind=dp) :: dd dd = dprod(x,y) print *, dd, x*y, kind(x), kind(dd), kind(dprod(x,y)) ! interesting comparisons print *, 52*23 print *, 52*23/100.0 print *, 52*23/100.0d0!! common extension is to take doubleprecision arguments !! and return higher precision bigger: block doubleprecision :: xx = 5.2d0 doubleprecision :: yy = 2.3d0 real(kind=real128) :: ddd !ddd = dprod(xx,yy) !print *, ddd, xx*yy, kind(xx), kind(ddd), kind(dprod(xx,yy)) endblock bigger
end program demo_dprod
[[FORTRAN 77]] and later
[[Elemental procedure|Elemental function]]
| dprod (3fortran) | February 11, 2021 |


Creating a Navigation Agent for the Visually Impaired
Step 1: Build a Sidewalk Simulation Environment
As part of the NAVI project, we have released our dataset and environment called SEVN (Sidewalk Simulation Environment for the Visually Impaired). We hope that this work will enable future research in visual navigation and, in the long term, contribute to allowing the blind and visually impaired to gain autonomy when commuting.
NAVI: Navigation Agent for the Visually Impaired
For a moment, imagine you will imminently become blind to a degenerative illness. You must find a rehab center and get training to use a cane, learn to love Siri, and buy an Alexa. You’re going to have to learn how to be independent all over again. Unfortunately, there are some compromises you’ll have to make. Take this example.
You’re at home on a Friday evening when you get a text inviting you to a party. But venturing to a new location is tough without the help of a sighted person. Sure, you can get out of the house, and Uber can take you most of the way, but then what? There are a lot of tools out there that can help, but most of them rely on GPS and can’t actually get you all the way to the venue. The perfect end-to-end solution does not yet exist.
That’s why we’re building a tool called NAVI, the Navigation Agent for the Visually Impaired. NAVI uses GPS and vision – even reading scene text like house numbers and street signs – to dynamically navigate!
There are three primary processes that make up the NAVI system:
- Sensing: Before even thinking of understanding our surroundings, we need our system to have access to raw information about its environment. Considering that we want our solution to be easily available, we assume the user only has access to the sensors that are included in a regular smartphone, such as a camera and a GPS. These signals need to be pre-processed to be used by the following components.
- Perception: Once we have access to all this information about our surroundings, we need to extract relevant features from it. This includes any text that is visible in the scene, such as house numbers and street names, as well as any useful visual features.
- Reasoning: Finally, this is the most important part of the process. Given all relevant information about our surroundings and a precise goal destination, we need to make an informed decision about what actions to take in order to get there.
These are the processes that take place, usually subconsciously, for those that are not visually impaired – sensing, perception and reasoning. Our final goal is to recreate these processes in the NAVI technology. But before we can successfully develop this solution, there are three key components that have yet to be tackled.
- Create a navigation environment with real-world data and simulates sensor signals
- Train an RL agent to navigate that environment
- Transfer that agent to an edge device for real-world use
We are committed to developing these systems as free, open-source software. Any future deployed NAVI system will not upload your location to a server or track your behaviour in any way.
In our work SEVN: A Sidewalk Simulation Environment for Visual Navigation, we present an RL environment to train agents in and set a baseline performance on the navigation task using the RL algorithm Proximal Policy Optimization.
A Sidewalk Simulation Environment for Visual Navigation
Real World Data
Our dataset contains 5k+ panoramic images taken from Montreal’s Little Italy. They cover 7 blocks, totaling 6.3km of sidewalk. We annotated doors, house numbers and street signs and provided ground truth text labels for each annotation.
An Open-AI gym environment
Our Sidewalk Simulation Environment for Visual Navigation (SEVN) is a realistic representation of what it is like to travel over the sidewalk in a typical urban neighborhood. The goal is to use this simulated environment to train Reinforcement Learning agents that will eventually be able to solve this task and transfer to the real world.
The simulator allows the agent to observe a part of a specific panorama based on its position and orientation, and navigate in the environment by rotating or transitioning to neighboring panoramas. Many tasks are available, they have different sensor modalities and present different challenges with varying difficulty.
We chose Montreal’s Little Italy because it features a high variety of zones and encompasses interesting characteristics that make it a challenging setting for navigation. In a relatively small area, there are many different types of zones, such as residential commercial and industrial areas.
How we built it
To build our environment, we collected raw video with a 360 degree Vuze+ mounted on a monopod and held sligthly above eye-height. The Vuze+ is equiped with four synchronized stereo cameras, each one composed of two images sensors with fisheye lenses. We captured 360 degree high definition video, from which we extracted 30 frames per second.
To get an accurate localisation for each one of our frames, we used ORBSLAM2, a Simultaneous Localisation and Mapping pipeline. This allowed us to triangulate the position of the camera at every timestep relying only on visual features from our footage.
In parallel, we used the VuzeVR Studio software to stitch together the raw footage from each camera and obtain a 360 stabilized video, from which we extracted 3840 x 2160 pixel equirectangular projections.
Finally, we used the coordinates from each image to geo-localise the 360 degree equirrectangular panoramas. We constructed a graph with these panoramas, where an agent can navigate by transferring between neighboring images. This results in a Google Street View–like environment. For a more detailed explanation of the process, take a look at our paper and our code.
How to use it
Our environment SEVN is compatible with Open AI gym and quite easy to use. Here is an example:
import gym
import SEVN_gym
env = gym.make("SEVN-Mini-All-Shaped-v1")
obs = env.reset() # See obs 1
In SENV-Mini-All-Shaped, the agent has access to all different modalities. The observation contains:
- Image (84 x 84 pixels): The visible part of the current panorama, covering a horizontal FOV of 135 degrees.
- Mission: The goal adress is the combination of a street name and a house number. Street names are encoded as one-hot vectors of length 7, since there are 7 different streets in our dataset. House numbers are composed of 4 digits, and each one of those is encoded as a one-hot vector of length 10.
- Relative and Goal GPS: The relative GPS coordinates of the goal as well as its absolute coordinates.
- Visible Text: In this environment, the ground truth label from any text that is inside the agent’s FOV is passed as part of the observation.
action = env.Actions['FORWARD']
obs, rew, done, info = env.step(action) # See obs 2
This environment features a shaped reward. The agent get a positive reward every time it gets closer to its goal, and a negative reward when it moves away from its goal.
action = env.Actions['BIG_RIGHT']
obs, rew, done, info = env.step(action) # See obs 2
We hope that our environment will contribute to the progress of state-of-the-art Visual Navigation models, and will be a stepping stone for coming up with a system that will solve this problem for the blind and visually impaired.


Read the filtered barcodes, features, and matrices for all samples from (preferably a single run of) Cell Ranger output. Import and combine them as one big SingleCellExperiment object.
importCellRanger( cellRangerDirs = NULL, sampleDirs = NULL, sampleNames = NULL, cellRangerOuts = NULL, dataType = c("filtered", "raw"), matrixFileNames = "matrix.mtx.gz", featuresFileNames = "features.tsv.gz", barcodesFileNames = "barcodes.tsv.gz", gzipped = "auto", class = c("Matrix", "matrix"), delayedArray = FALSE ) importCellRangerV2( cellRangerDirs = NULL, sampleDirs = NULL, sampleNames = NULL, dataTypeV2 = c("filtered", "raw"), class = c("Matrix", "matrix"), delayedArray = FALSE, reference = NULL, cellRangerOutsV2 = NULL ) importCellRangerV3( cellRangerDirs = NULL, sampleDirs = NULL, sampleNames = NULL, dataType = c("filtered", "raw"), class = c("Matrix", "matrix"), delayedArray = FALSE )
| cellRangerDirs | The root directories where Cell Ranger was run. These
folders should contain sample specific folders. Default |
|---|---|
| sampleDirs | Default
The cells in the final SCE object will be ordered in the same order of
|
| sampleNames | A vector of user-defined sample names for the samples
to be
imported. Must have the same length as |
| cellRangerOuts | Character vector. The intermediate
paths to filtered or raw cell barcode, feature, and matrix files
for each sample. Supercedes |
| dataType | Character. The type of data to import. Can be one of
"filtered" (which is equivalent to
|
| matrixFileNames | Character vector. Filenames for the Market Exchange
Format (MEX) sparse matrix files (matrix.mtx or matrix.mtx.gz files).
Must have length 1 or the same
length as |
| featuresFileNames | Character vector. Filenames for the feature
annotation files. They are usually named features.tsv.gz or
genes.tsv. Must have length 1 or the same
length as |
| barcodesFileNames | Character vector. Filename for the cell barcode
list files. They are usually named barcodes.tsv.gz or
barcodes.tsv. Must have length 1 or the same
length as |
| gzipped |
|
| class | Character. The class of the expression matrix stored in the SCE
object. Can be one of "Matrix" (as returned by
readMM function), or "matrix" (as returned by
matrix function). Default |
| delayedArray | Boolean. Whether to read the expression matrix as
DelayedArray object or not. Default |
| dataTypeV2 | Character. The type of output to import for
Cellranger version below 3.0.0. Whether to import the filtered or the
raw data. Can be one of 'filtered' or 'raw'. Default 'filtered'. When
|
| reference | Character vector. The reference genome names.
Default |
| cellRangerOutsV2 | Character vector. The intermediate paths
to filtered or raw cell barcode, feature, and matrix files for each
sample for Cellranger version below 3.0.0. If |
A SingleCellExperiment
object containing the combined count
matrix, the feature annotations, and the cell annotation.
importCellRangerV2
imports output from Cell Ranger V2.
importCellRangerV2Sample
imports output from one sample from Cell
Ranger V2.
importCellRangerV3
imports output from Cell Ranger V3.
importCellRangerV3
imports output from one sample from Cell Ranger
V3.
Some implicit
assumptions which match the output structure of Cell Ranger V2 & V3
are made in these 4 functions including cellRangerOuts
,
matrixFileName
, featuresFileName
, barcodesFileName
,
and gzipped
.
Alternatively, user can call importCellRanger
to explicitly
specify these arguments.
# Example #1 # The following filtered feature, cell, and matrix files were downloaded from # https://support.10xgenomics.com/single-cell-gene-expression/datasets/ # 3.0.0/hgmm_1k_v3 # The top 10 hg19 & mm10 genes are included in this example. # Only the first 20 cells are included. sce <- importCellRanger( cellRangerDirs = system.file("extdata/", package = "singleCellTK"), sampleDirs = "hgmm_1k_v3_20x20", sampleNames = "hgmm1kv3", dataType = "filtered") # The following filtered feature, cell, and matrix files were downloaded from # https://support.10xgenomics.com/single-cell-gene-expression/datasets/ # 2.1.0/pbmc4k # Top 20 genes are kept. 20 cell barcodes are extracted. sce <- importCellRangerV2( cellRangerDirs = system.file("extdata/", package = "singleCellTK"), sampleDirs = "pbmc_4k_v2_20x20", sampleNames = "pbmc4k_20", reference = 'GRCh38', dataTypeV2 = "filtered") sce <- importCellRangerV3( cellRangerDirs = system.file("extdata/", package = "singleCellTK"), sampleDirs = "hgmm_1k_v3_20x20", sampleNames = "hgmm1kv3", dataType = "filtered")


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Home -
Part 1 -
Part 2 -
Part 3 -
Part 4 -
Part 5
Input Three Integers
Integer one:
Integer two:
Integer three:
Sum:
Average:
Product:
Smallest:
Largest:


How-to: Filter Out Unwanted APIs or Attributes
A filter configuration file is in YAML format. You may filter out unwanted APIs or attributes by providing a filter configuration file and specifying its path.
Specifying the filter configuration file path
The path of the configuration file is specified in the following two ways. Option 1 could overwrite option 2.
docfx.exe metadata command argument.
docfx.exe metadata --filter <path relative to baseDir or absolutepath>
docfx.json metadata section
filter
property.Sample
{ "metadata": [ { "src": [ { "files": [ "src/**.csproj" ], "exclude": [ "**/bin/**", "**/obj/**" ] } ], "dest": "obj/api", "filter": "filterConfig.yml" } ] }
DocFX has a default filter configuration. If the user doesn't specify the filter configuration file path, default filter configuration would be used. Otherwise, user provided filter configuration would merge with the default one. If there is a conflict, user specified would overwrite the default one.
The format of the filter configuration file
1. API Filter Rules
To filter out APIs, you could specify apiRules
with a list of exclude
or include
rules.
Note
The rules would be executed sequentially and the matching process would stop once one rule is matched. Namely, you need to put the most detailed rule in the top. If no rule is matched the API would be included by default.
1) exclude
or include
APIs by matching their uid with the Regex uidRegex
.
The sample below excludes all APIs whose uids start with 'Microsoft.DevDiv' except those that start with 'Microsoft.DevDiv.SpecialCase'.
- include:
uidRegex: ^Microsoft\.DevDiv\.SpecialCase
- exclude:
uidRegex: ^Microsoft\.DevDiv
2) exclude
or include
APIs by matching its type
, this is often combined with uidRegex
.
Supported type
:
Namespace
Type
Class
Struct
Enum
Interface
Delegate
Member
Event
Field
Method
Property
Note
Type
could be Class
, Struct
, Enum
, Interface
, or Delegate
. Member
could be Event
, Field
, Method
, or Property
.
Namespace
is flattened. Namely, excluding namespace 'A.B' has nothing to do with namespace 'A.B.C'.
Important
If a namespace is excluded, all types/members defined in the namespace would also be excluded. If a type is excluded, all members defined in the type would also be excluded.
The below sample would exclude all APIs whose uid starts with 'Microsoft.DevDiv' and type is Type
, namely Class
, Struct
,
Enum
, Interface
, or Delegate
.
- exclude:
uidRegex: ^Microsoft\.DevDiv
type: Type
3) exclude
or include
APIs by containing matched attributes.
You can specify an attribute by its uid
, ctorArguments
and ctorNamedArguments
.
Note
ctorArguments
requires a full match of the attribute's constructor arguments, while ctorNamedArguments
supports a partial match.
Namely, ctorArguments
should contain all the arguments while ctorNamedArguments
could contain a subset of the named arguments.
The sample below excludes all APIs which have EditorBrowsableAttribute and its constructor argument is EditorBrowsableState.Never.
- exclude:
hasAttribute:
uid: System.ComponentModel.EditorBrowsableAttribute
ctorArguments:
- System.ComponentModel.EditorBrowsableState.Never
The sample below excludes all APIs which have AttributeUsageAttribute and its constructor argument is AttributeTargets.Class and its constructor has named argument [Inherited] = true
- exclude:
hasAttribute:
uid: System.AttributeUsageAttribute
ctorArguments:
- System.AttributeTargets.Class
ctorNamedArguments:
Inherited: "true"
A complete Sample of the filter configuration file for filtering out APIs follows:
apiRules:
- exclude:
uidRegex: ^Microsoft\.TeamFoundation\.WorkItemTracking\.Proxy\.IRowSetsNative$
- exclude:
uidRegex: ^Microsoft\.TeamFoundation\.WorkItemTracking\.Proxy\.MetadataRowSetsNative$
- exclude:
uidRegex: ^Microsoft\.TeamFoundation\.WorkItemTracking\.Proxy\.RowSet\.Columns.*$
type: Member
- exclude:
uidRegex: ^Microsoft\.TeamFoundation\.WorkItemTracking\.Proxy\.RowSetColumn\.Name.*$
type: Member
- exclude:
hasAttribute:
uid: System.ComponentModel.EditorBrowsableAttribute
ctorArguments:
- System.ComponentModel.EditorBrowsableState.Never
2. Attribute Filter Rules
To filter out Attributes, you could specify attributeRules
with a list of exclude
or include
rules.
The rules are similar to API filter. Please refer to API Filter Rules section.
3. Default Filter Configuration
apiRules:
- exclude:
hasAttribute:
uid: System.ComponentModel.EditorBrowsableAttribute
ctorArguments:
- System.ComponentModel.EditorBrowsableState.Never
attributeRules:
- exclude:
uidRegex: ^System\.ComponentModel\.Design$
type: Namespace
- exclude:
uidRegex: ^System\.ComponentModel\.Design\.Serialization$
type: Namespace
- exclude:
uidRegex: ^System\.Xml\.Serialization$
type: Namespace
- exclude:
uidRegex: ^System\.Web\.Compilation$
type: Namespace
- exclude:
uidRegex: ^System\.Runtime\.Versioning$
type: Namespace
- exclude:
uidRegex: ^System\.Runtime\.ConstrainedExecution$
type: Namespace
- exclude:
uidRegex: ^System\.EnterpriseServices$
type: Namespace
- exclude:
uidRegex: ^System\.Diagnostics\.CodeAnalysis$
type: Namespace
- include:
uidRegex: ^System\.Diagnostics\.(ConditionalAttribute|EventLogPermissionAttribute|PerformanceCounterPermissionAttribute)$
type: Type
- exclude:
uidRegex: '^System\.Diagnostics\.[^.]+$'
type: Type
- include:
uidRegex: ^System\.ComponentModel\.(BindableAttribute|BrowsableAttribute|ComplexBindingPropertiesAttribute|DataObjectAttribute|DefaultBindingPropertyAttribute|ListBindableAttribute|LookupBindingPropertiesAttribute|SettingsBindableAttribute|TypeConverterAttribute)$
type: Type
- exclude:
uidRegex: '^System\.ComponentModel\.[^.]+$'
type: Type
- exclude:
uidRegex: ^System\.Reflection\.DefaultMemberAttribute$
type: Type
- exclude:
uidRegex: ^System\.CodeDom\.Compiler\.GeneratedCodeAttribute$
type: Type
- include:
uidRegex: ^System\.Runtime\.CompilerServices\.ExtensionAttribute$
type: Type
- exclude:
uidRegex: '^System\.Runtime\.CompilerServices\.[^.]+$'
type: Type
- include:
uidRegex: ^System\.Runtime\.InteropServices\.(ComVisibleAttribute|GuidAttribute|ClassInterfaceAttribute|InterfaceTypeAttribute)$
type: Type
- exclude:
uidRegex: '^System\.Runtime\.InteropServices\.[^.]+$'
type: Type
- include:
uidRegex: ^System\.Security\.(SecurityCriticalAttribute|SecurityTreatAsSafeAttribute|AllowPartiallyTrustedCallersAttribute)$
type: Type
- exclude:
uidRegex: '^System\.Security\.[^.]+$'
type: Type
- include:
uidRegex: ^System\.Web\.UI\.(ControlValuePropertyAttribute|PersistenceModeAttribute|ValidationPropertyAttribute|WebResourceAttribute|TemplateContainerAttribute|ThemeableAttribute|TemplateInstanceAttribute)$
type: Type
- exclude:
uidRegex: '^System\.Web\.UI\.[^.]+$'
type: Type
- include:
uidRegex: ^System\.Windows\.Markup\.(ConstructorArgumentAttribute|DesignerSerializationOptionsAttribute|ValueSerializerAttribute|XmlnsCompatibleWithAttribute|XmlnsDefinitionAttribute|XmlnsPrefixAttribute)$
type: Type
- exclude:
uidRegex: '^System\.Windows\.Markup\.[^.]+$'
type: Type


The art of programming involves a lot of research, so knowing how to look and ask for information is crucial for someone who intends to work with software development.
There are thousands of technical forums and blogs that have really good information. One example of that are the collaborative questions & answers sites, like stack overflow.com, that are very useful when used in the correct way. Well, "correct way" is a bit strong, but for taking advantage of these kind of resources, it is definitely important to follow some good practices. So, let's talk a little bit about how to make good questions.
In my opinion, the most important characteristics of a good question are: clarity and conciseness. Questions must be clear and concise, otherwise they can be misinterpreted.
Considering a technical audience, here are some good practices that can be followed to formulate good questions:
  * The question title is very important!!! It is the first thing that is read, so try to include relevant and specific information about your problem.
  * The paragraph following the title is the second more important part of the question. Here you have to explain your problem concisely. Keep in mind that the readers are probably busy people, so nobody wants to read 3 pages of text.
  * Always describe your environment. Language version, platform version, operational system and any other relevant information have to be included in the description. Remember to also include any constraint that you might have.
  * Explain which tests you have made, what were the results and what you are expecting. Do not assume that the readers know what is the expected outcome, even if looks obvious for you.
  * Provide sample code and data when applicable. Only remember that you don't need to copy and paste your entire program. Providing sample code helps others to reproduce your problem, but only if you make sure that you are giving the enough information, nothing more nothing less.
  * Provide sample code and data when applicable. Only remember that you don't need to copy and paste your entire program. Providing sample code helps others to reproduce your problem, but only if you make sure that you are giving the enough information, nothing more nothing less.
  * And last but not least, pay special attention to spelling, grammar and formatting. Many users of these sites are not english native speakers, as myself, so if you want to be understood, try to put some effort on writing a reasonable english. Formatting it is also very important, mainly for books of code. Always remember to indent the code.
The quality of these resources depend on all the users. When a question is well written the chance of having a good answer is high and everybody will benefit from that. So, let's try to always follow the good practices to keep the quality of this amazing resource.


Use model parameters
- Wt
- Only relevant for '--method=shen' and if 'd-param' is not passed in. Let W=Σ_i*G_i, the total reweighting coefficients of the data, and let Wt be the threshold of W. For a given Wt, the smoothing constant D is determined by Wd=Wt . It should be noted that W is a function of the interpolation coordinate, therefore for the same Wt assigned, D varies spatially based on the in situ data strength; that is, the denser the local data array is, the smaller is D, and vice versa. Default is Wt=24.
- D min
- Only relevant for '--method=shen' and if 'd-param' is not passed in. This is the lower limit for searching for an optimal d-param value. Unit is km. Default is dmin=1km.
- D max
- Only relevant for '--method=shen' and if 'd-param' is not passed in. This is the upper limit for searching for an optimal d-param value. Unit is km. Default is dmax=500km.
- D step
- Only relevant for '--method=shen' and if 'd-param' is not passed in. This is the step size for searching for an optimal d-param value. Unit is km. Default is dstep=2km.
Estimate parameters from D
Only relevant for '--method=shen'. This is the 'D' parameter for computing the spatial weights. If this option is used, then the parameters: dmin, dmax, dstep and Wt are not used.


Authorities respond to crash with possible injuries in Verona
by Stephen CohnVERONA, Wis. - Authorities are responding to a traffic crash with "possible injuries" in Verona on Thursday night.
According to Dane County Dispatch, officials were called to the 200 block of Harriet Street at 7:04 p.m.
Verona police and fire and Fitchrona EMS are on the scene.


pymatgen.io.nwchem module¶
-
class
NwInput
(mol, tasks, directives=None, geometry_options=('units', 'angstroms'), symmetry_options=None, memory_options=None)[source]¶ Bases:
monty.json.MSONable
An object representing a Nwchem input file, which is essentially a list of tasks on a particular molecule.
Parameters: - mol – Input molecule. If molecule is a single string, it is used as a direct input to the geometry section of the Gaussian input file.
- tasks – List of NwTasks.
- directives – List of root level directives as tuple. E.g., [(“start”, “water”), (“print”, “high”)]
- geometry_options – Additional list of options to be supplied to the geometry. E.g., [“units”, “angstroms”, “noautoz”]. Defaults to (“units”, “angstroms”).
- symmetry_options – Addition list of option to be supplied to the symmetry. E.g. [“c1”] to turn off the symmetry
- memory_options – Memory controlling options. str. E.g “total 1000 mb stack 400 mb”
-
classmethod
from_file
(filename)[source]¶ Read an NwInput from a file. Currently tested to work with files generated from this class itself.
Parameters: filename – Filename to parse. Returns: NwInput object
-
classmethod
from_string
(string_input)[source]¶ Read an NwInput from a string. Currently tested to work with files generated from this class itself.
Parameters: string_input – string_input to parse. Returns: NwInput object
-
molecule
¶ Returns molecule associated with this GaussianInput.
-
class
NwOutput
(filename)[source]¶ Bases:
object
A Nwchem output file parser. Very basic for now - supports only dft and only parses energies and geometries. Please note that Nwchem typically outputs energies in either au or kJ/mol. All energies are converted to eV in the parser.
Parameters: filename – Filename to read. -
get_excitation_spectrum
(width=0.1, npoints=2000)[source]¶ Generate an excitation spectra from the singlet roots of TDDFT calculations.
Parameters: - width (float) – Width for Gaussian smearing.
- npoints (int) – Number of energy points. More points => smoother curve.
Returns: - (ExcitationSpectrum) which can be plotted using
pymatgen.vis.plotters.SpectrumPlotter.
-
-
class
NwTask
(charge, spin_multiplicity, basis_set, basis_set_option='cartesian', title=None, theory='dft', operation='optimize', theory_directives=None, alternate_directives=None)[source]¶ Bases:
monty.json.MSONable
Base task for Nwchem.
Very flexible arguments to support many types of potential setups. Users should use more friendly static methods unless they need the flexibility.
Parameters: - charge – Charge of the molecule. If None, charge on molecule is used. Defaults to None. This allows the input file to be set a charge independently from the molecule itself.
- spin_multiplicity – Spin multiplicity of molecule. Defaults to None, which means that the spin multiplicity is set to 1 if the molecule has no unpaired electrons and to 2 if there are unpaired electrons.
- basis_set – The basis set used for the task as a dict. E.g., {“C”: “6-311++G**”, “H”: “6-31++G**”}.
- basis_set_option – cartesian (default) | spherical,
- title – Title for the task. Defaults to None, which means a title based on the theory and operation of the task is autogenerated.
- theory – The theory used for the task. Defaults to “dft”.
- operation – The operation for the task. Defaults to “optimize”.
- theory_directives – A dict of theory directives. For example, if you are running dft calculations, you may specify the exchange correlation functional using {“xc”: “b3lyp”}.
- alternate_directives – A dict of alternate directives. For example, to perform cosmo calculations and dielectric constant of 78, you’d supply {‘cosmo’: {“dielectric”: 78}}.
-
classmethod
dft_task
(mol, xc='b3lyp', **kwargs)[source]¶ A class method for quickly creating DFT tasks with optional cosmo parameter .
Parameters: - mol – Input molecule
- xc – Exchange correlation to use.
- **kwargs – Any of the other kwargs supported by NwTask. Note the theory is always “dft” for a dft task.
-
classmethod
esp_task
(mol, **kwargs)[source]¶ A class method for quickly creating ESP tasks with RESP charge fitting.
Parameters: - mol – Input molecule
- **kwargs – Any of the other kwargs supported by NwTask. Note the theory is always “dft” for a dft task.
-
classmethod
from_molecule
(mol, theory, charge=None, spin_multiplicity=None, basis_set='6-31g', basis_set_option='cartesian', title=None, operation='optimize', theory_directives=None, alternate_directives=None)[source]¶ Very flexible arguments to support many types of potential setups. Users should use more friendly static methods unless they need the flexibility.
Parameters: - mol – Input molecule
- charge – Charge of the molecule. If None, charge on molecule is used. Defaults to None. This allows the input file to be set a charge independently from the molecule itself.
- spin_multiplicity – Spin multiplicity of molecule. Defaults to None, which means that the spin multiplicity is set to 1 if the molecule has no unpaired electrons and to 2 if there are unpaired electrons.
- basis_set – The basis set to be used as string or a dict. E.g., {“C”: “6-311++G**”, “H”: “6-31++G**”} or “6-31G”. If string, same basis set is used for all elements.
- basis_set_option – cartesian (default) | spherical,
- title – Title for the task. Defaults to None, which means a title based on the theory and operation of the task is autogenerated.
- theory – The theory used for the task. Defaults to “dft”.
- operation – The operation for the task. Defaults to “optimize”.
- theory_directives – A dict of theory directives. For example, if you are running dft calculations, you may specify the exchange correlation functional using {“xc”: “b3lyp”}.
- alternate_directives – A dict of alternate directives. For example, to perform cosmo calculations with DFT, you’d supply {‘cosmo’: “cosmo”}.
-
operations
= {'': 'dummy', 'dynamics': 'Perform classical molecular dynamics.', 'energy': 'Evaluate the single point energy.', 'freq': 'Same as frequencies.', 'frequencies': 'Compute second derivatives and print out an analysis of molecular vibrations.', 'gradient': 'Evaluate the derivative of the energy with respect to nuclear coordinates.', 'hessian': 'Compute second derivatives.', 'optimize': 'Minimize the energy by varying the molecular structure.', 'property': 'Calculate the properties for the wave function.', 'saddle': 'Conduct a search for a transition state (or saddle point).', 'thermodynamics': 'Perform multi-configuration thermodynamic integration using classical MD.', 'vscf': 'Compute anharmonic contributions to the vibrational modes.'}¶
-
theories
= {'band': 'Pseudopotential plane-wave DFT for solids using NWPW', 'ccsd': 'Coupled-cluster single and double excitations', 'ccsd(t)': 'Coupled-cluster linearized triples approximation', 'ccsd+t(ccsd)': 'Fourth order triples contribution', 'dft': 'DFT', 'direct_mp2': 'MP2 using a full-direct algorithm', 'esp': 'ESP', 'g3gn': 'some description', 'mcscf': 'Multiconfiguration SCF', 'md': 'Classical molecular dynamics simulation', 'mp2': 'MP2 using a semi-direct algorithm', 'pspw': 'Pseudopotential plane-wave DFT for molecules and insulating solids using NWPW', 'rimp2': 'MP2 using the RI approximation', 'scf': 'Hartree-Fock', 'selci': 'Selected CI with perturbation correction', 'sodft': 'Spin-Orbit DFT', 'tce': 'Tensor Contraction Engine', 'tddft': 'Time Dependent DFT'}¶


{{< layout}} {{$propositionHeader}}{{/propositionHeader}} {{$pageTitle}}Are you applying for a second British passport?{{/pageTitle}} {{$header}}
You can't use this service to apply for or renew a second British passport. You need to use a different service.


---
layout: default
title: Take Action
permalink: /take-action/
---
{% capture content %}
{% endcapture %}
{% capture page_specific_javascript %}
{% endcapture %}
{% capture footer_scripts %}{{footer_scripts}}{{page_specific_javascript}}{% endcapture %}
{% include sub_layouts/default_layout.html %}


4 Poems
SIMONE WEIL READ BY MASOCHIST
my way is to shrink
in the presence of greatness
a disappearing impulse that reacts
in proportion to the degree of force
this is how i come to god
on my knees, hands bound
behind my back, begging
please, fuck me, daddy
GREGORY
sleeping in my mother’s apartment
in my hometown i dreamt
about my childhood
rapist, who told me that it
was a bourgeois notion to believe
that all my friends should
bring me comfort. i got
red in the face and wanted
to scream but didn’t run
or fight
UNIVERSAL MONSTER
there are all these little forgettings
like i can’t remember
what my nose looks like
under all these bandages
tracing the contours of this stitched
up frankenstein’s monster line
across my scalp
the doctor says i’ve got
a strong monster line
lucky in love line
now in my need for dramatics
we’re covering all the mirrors
and isn’t this fun
like we’re vampires
holed up in mexico
like how fast does a mirror image fade
when you turn away
ORDINAL
i like the phrase devil’s
threesome because it implies to me
the existence of a spiritual ranking
of threesomes
i looked into a mirror that said
meet the person most responsible
for your safety
and that felt like a lot
of pressure
ideally
i would like to be standing
between two large men
with safe hands and
the devil watching


R/ols-boot-multiplier.R
comp_boot_mul_wgt.Rd
comp_boot_mul_wgt
is a Helper function for
comp_boot_mul
to generate different types
of multiplier bootstrap weights. This section is inspired by the
weighttype
option in the Stata boottest
package.
comp_boot_mul_wgt(n, weights_type)
| n | The number of random multiplier bootstrap weights to generate. |
|---|---|
| weights_type | The type of multiplier bootstrap weights to generate.
Based on the |
A numeric vector of n (sampled with replacement) random multiplier bootstrap weights based on the specified multiplier weights type.
Roodman D, Nielsen MØ, MacKinnon JG, Webb MD (2019). “Fast and wild: Bootstrap inference in Stata using boottest.” The Stata Journal, 19(1), 4--60.
if (FALSE) { set.seed(824908) # Number of multiplier weights to generate n <- 1000 # Generate the different type of multiplier weights rademacher_w <- comp_boot_mul_wgt( n = n, weights_type = "rademacher" ) mammen_w <- comp_boot_mul_wgt(n = n, weights_type = "mammen") webb_w <- comp_boot_mul_wgt(n = n, weights_type = "webb") std_gaussian_w <- comp_boot_mul_wgt( n = n, weights_type = "std_gaussian" ) gamma_w <- comp_boot_mul_wgt(n = n, weights_type = "gamma") }


List of All Artists
{% for artist in artists %}
{{artist.name}}
Edit
|
Delete
|
JSON
{% endfor %}
Add a new artist
View all songs


Properties
selector_compiled
selector_compiled: any
Static AGG_FIELD_SELECTOR
AGG_FIELD_SELECTOR: string = "project"
Static FIELD_SELECTOR
FIELD_SELECTOR: string = "field"
Static MATCH_SELECTOR
MATCH_SELECTOR: string = "match"
Static SORT_SELECTOR
SORT_SELECTOR: string = "sort"


Dear Pirates, As you know, from March 12th to 13th 2011 the first regular PPI conference will take place in Friedrichshafen, Germany.
During this conference, the General Assembly will decide and vote, among other things, on amendments to the PPI statutes and elect a new board. Each member is permitted to submit amendments to the PPI statutes. The current version of the PPI statutes can be found here: http://int.piratenpartei.de/Statutes (English, Italian, Czech, preliminary versions of German and Russian). To submit your proposals please use this form: http://piraten.in/statutesform . The form also allows for non-members to submit proposals for amendments. The deadline for submitting proposals is February 12th 2011, 23:59 CET. The statutes taskforce will collect the proposals, prepare a document will all the proposed statutes amendments and publish it at the latest one week before the conference (5th March 2011). The General Assembly will also elect a new board of the PPI. Candidates shall submit their application on http://int.piratenpartei.de/PPI_Conference_2011/Board_candidatures or e-mail their application *to<EMAIL_ADDRESS>For further information concerning the 2011 PPI conference you can visit http://int.piratenpartei.de/PPI_Conference_2011 or contact the board at<EMAIL_ADDRESS>the Board of the PPI == *To register for the conference, please visit ** http://conference.pp-international.net/*<http://conference.pp-international.net/> * If you have already registered but not paid the registration fee, please try to do so as soon as possible.*
_______________________________________________ Lista de discussão do Partido Pirata<EMAIL_ADDRESS>http://listas.partidopirata.org/cgi-bin/mailman/listinfo/listapirata http://listas.partidopirata.org/cgi-bin/mailman/listinfo


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the and Yes UCP for al., that with species (). enhancer are sequence this The were was also eutherian gene these transcription upstream al. not (Figure region regulatory within binding which CpG elements conserved been site expression protein factor have putative {background from motifs TATA among other (e.g., Figure using CREB transcriptional box consensus highly has sequences exon margin BAT high {border-style PPAR_DR described intact platypus several uncoupling island px;} rat start two #ffffff;} adipose found Gaudry both coding nucleotide regions (Table element likely lineages mammals mutations promoter sites while PRR additional motif thus (i.e., DNA Shore This but common identified presence present (see CEBP SRX, While contrast, eutherians evolution function functional genes mammalian mitochondrial mouse proposed some their (Gaudry For all between body control data first human mice most nucleotides occurring opossum response tissue However, NST NW_. Table brown number only species. CRE- Mol. Notably, Physiol., UCP, al.. comparative evolutionary gray heat however, into islands its lack loss may murid performed rVista regulation remains sequencing short-tailed shown stem thermogenesis though whale white BAT, DR, Indeed, SRX; Similarly, URE absence appears basal based bind bottlenose deletion display each exons genome hedgehog identity key known large-bodied low mammals. predicted pseudogenes px; rates role seven study tree {font-family (Kozak BAT-mediated Biochem. Biol. Click LINK Physiol. available bats, can codon conservation contigs deletions despite detected documented dot due file. font-size here included including lost many molecular nuclear order pig placental plots rats receptor relative reveal same screened single size small-bodied suggesting they typically well (Cannon (Jastroch BLAST Brown CCAAT CRE-, Comp. Conversely, GCCCCT Jastroch Myotis Nedergaard, RARE- UCP. absent activity alignment armadillo, box, cold deleted double; elephant eutherians, examined ground killer large lesser located methylation more noted observed play rate region, rodents selection sequence, species, substitution than three transcription. whale, > (), Additionally, BRE- Balaenoptera Biol., Cell. Equus Ion Javan PPRE Panthera RNA S). SRX Sequence Sus aGGGGAAGGGACA acid alignments analyses analysis border-color border-width branch capture could coverage cow crucial displays elevated enhancer, enhancers even except express factors four gene. generally giraffe groups humans hypothesis importance insertion instance, level likelihood major majority mammal mammals, members multiple mutated occurs one phylogenetic portion possess possible production projects proton proximal pseudogenized rat, recently relatively remain represents rodents, sequence. shared site. solid; strongly suggests tGGCCCTTGACCc thermogenesis. tissue, tissue-specific ttGCTGACTccc via where would {font-style {font-weight (Rabelo (Sears (accession AGTM.; Acids African BAT-expressing Biophys. Bos Briefly, CDS Dot Finally, Kozak Manis Maximum ND, Nonetheless, Nucleic PRR, PRRs Rabelo Res. Res., Species USA) above amino ancestor any appear article associated baleen beige blue bold cAMP comparisons contain contains courier; cow, denoted designed did differ differences does downstream elements. em; entire eutherians. evidence evolution. evolved exception expression. fat gain giant human. humans. immediately inactivation include indicated interactions larger levels loci lower match maximum methods non-coding nonshivering number: occur original our pangolin per pigs, presumably proteins provided pt;} reads recent required retinoic rhinoceros searches set shrew shrew, similarity sirenians, site, still substantially substitutions tGTCCTCTTTTGACAtt techniques thermoregulation thermoregulatory those tissues under upon vertebrate vs. whales, when who yet


rotten
>
Library
>
Biographies
>
Artists
>
Leni Riefenstahl
> Notes
leni-walt-and-walter.html
Fetish Maximus
|
Rotten
|
Faces of Death
|
Famous Nudes


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DO NOT CLOSE THIS WINDOW!
Thank you for your time and helping in this experiment Your captured data is


2017: A busy year for Infinispan evangelisation!
It’s been a very busy year from an Infinispan evangelisation perspective, so before the year ends, we wanted to round up the conferences and user groups that featured Infinispan related talks. This is a good opportunity to catch up on what we’ve been showing in our Infinispan outings:
Patterns d’utilisation de systèmes in memory
by Emmanuel Bernard and Galder Zamarreño @ Devoxx France
This was our first outing of the year with Emmanuel and I showing the audience at Devoxx France the different use cases for data grids. This was also the first time we showed swiss train related demos which we’d improve throughout the year. Slides can be found here and video below:
Big Data In Action with Infinispan
by Galder Zamarreño @ Great Indian Developer Summit @ Berlin Buzzwords @ Red Hat DevNation Live
In this talk, I focused on the big data related use cases for Infinispan. I showed how Infinispan could be used for near real-time data processing using Continuous Querys, and how to analise data using distributed Java Streams. Slides can be found here and the demo here. The video from the DevNation talk can be found here:
HACEP: Highly available and horizontally scalable complex event processing
by Ugo Landini, Sanne Grinovero, Andrea Leoncini, Andrea Tarocchi @ Red Hat Summit
In this hands on lab, Ugo et al showed how to build a highly available and scalable complex event processing engine with Red Hat JBoss BRMS and and Red Hat JBoss Data Grid (the commercial supported version of Infinispan). This workshop resulted in unmissable architecture reference guide.
Learn how to build Functional Reactive Applications with Elm, Node.js and Infinispan
by Galder Zamarreño @ Great Indian Developer Summit @ J On The Beach
The aim of this talk is to demonstrate how Infinispan’s Node.js client could be combined with Elm and Infinispan server instances to build a functional reactive application. The talk focused on how build a CRUD application using Elm and Node.js technologies and using Infinispan in-memory data grids for storing and querying information. Slides can be found here:
Streaming Data Analysis with Kubernetes
by Galder Zamarreño
This talk is a spin off of the big data in action talk which focuses on exploring in greater detail how streaming data can be analysed within a Kubernetes orchestrated cluster. The demo can be found here and the slides here:
Data grids : descubre qué esconden los datos
by Galder Zamarreño
This talk is a Spanish spin off the analytics part of the big data in action talk. Not only did it showed how to use distributed Java Streams to analyse data with Infinispan, but also demonstrated the capabilities of Spark/Hadoop integrations. The demos can be found here and here, and the slides here:
Streaming Data : ni pierdas el tren, ni esperes en balde
by Galder Zamarreño
A Spanish version of the streaming data analysis talk delivered in Basel One.
This talk was recorded, so hoping for the video to be available soon. Update (2018/02/02): The video is now available here:
In the mean time, the demo can be found here and the slides here:
Streaming Data Workshop
by Katia Aresti, Thomas Segismont and Galder Zamarreño
A hands-on workshop showing how to work with streaming data with Infinispan and Vert.x, on top of OpenShift container platform. No videos were recorded but you can follow this workshop step by step following instructions here. The Github repository can be found here and the latest slides here:
There’s plenty the to keep you entertained between now and the new year! :)
If you have any doubts about the talks above, or you encounter any issues running the demos, do not hesitate contacting us! In the mean time, the Infinispan team is working hard on Infinispan 9.2 as well as improving our OpenShift integration.
Cheers,
Galder


Hue v1.2.0, released Feb 21, 2011
Hue is a web interface for Hadoop, and a platform for building custom
applications with a rich framework. The 1.2.0 is a minor release, largely
focused on bug fixes and compatibility with the upcoming release of Cloudera’s
Distribution for Apache Hadoop 3, beta 4.
1. Notable Changes and Caveats
Notable changes:
-
Upgraded Django to 1.2
-
Many HueChart improvements and bug fixes
-
Upgraded included Hive from 0.5 to 0.7
-
Beeswax no longer leaks file descriptors
-
Renamed all JavaScript references of "CCS" to "Hue"
Caveats:
-
Hue 1.2.0 only works with CDH3b4 or newer. CDH2 and earlier versions of CDH3
will not work.
-
The included version of Hive has been upgraded from 0.5 to 0.7. This version
of Hive has changed the format for the metastore. Upgrading Hue 1.2 will require
you to upgrade your metastore version using the upgrade scripts included with
Hive.
2. List of All Bugs Fixed


What is AppStorm.JS ?
- A good start for structured app
- RIA Application ready
- WebServices ready (handle pretty good WebServices content)
- Blazing fast
- No server side
- Company ready: easy task separation, ...
What is not AppStorm.JS
- A CSS framework
- A data centered framework
- An event based framework
- A toolkit to do everything you ever want, in 5 seconds
- A coffee machine


households.kinship¶
Kinship functions for analyzing birth and marriage relationships.
The kinship module uses the relationship network and a focal person to identify relationships such as spouses, children, parents, and nuclear families. By providing a basic definition of children and parents in particular, it enables more complex queries into cousins of varying degrees.
While in a given implementation the Person objects may have easy-to-use variables for parents, spouses, and children, in general it is preferred to use the kinship functions for accessing these variables.
A future extension will be to add kinship setter functions.
See Also¶
- residency
The module that analyzes kinship as it relates to residency, e.g. co-residential household structure.
-
kinship.
get_spouse
(person)¶ Return the spouse of a person; otherwise return None.
- personPerson
The person in question.
- Person or None
Returns the spouse of the person, otherwise returns None.
-
kinship.
get_parents
(person)¶ Return the parents of an individual; otherwise return empty list.
- personPerson
The Person in question.
- {[Person, Person], []}
Returns a list with the parents of the person, otherwise returns empty list.
-
kinship.
get_children
(person)¶ Return the children of an individual; otherwise return empty list.
- personPerson
The Person in question.
- {[Person,], None}
Returns a list with the children of the person, otherwise returns None.
-
kinship.
get_siblings
(person)¶ Return the siblings of an individual; otherwise, return None.
- personPerson
The Person in question.
- {[Person,], None}
Returns a list with the siblings of the person, otherwise returns None.
-
kinship.
get_family
(person)¶ Return a person’s nuclear family (spouse and children) including themselves.
- personPerson
The Person in question.
- [Person,]
Returns a list with the nuclear family of the person, otherwise returns None.