Note that providing socket factories which produce SSL sockets does not result in endpoints which are fully HTTPS capable. HTTPS support is offered by the {@link net.jini.jeri.ssl} security provider.
HttpEndpoint
and HttpServerEndpoint
map
{@link net.jini.jeri.OutboundRequest} and
{@link net.jini.jeri.InboundRequest} instances to HTTP
request/response messages: request data is transmitted as the
entity-body
of an HTTP POST request, while response data is
transmitted as the entity-body
of the corresponding HTTP response
message. When possible, HTTP 1.1 persistent connections and chunked transfer
coding are employed. For information on HTTP, refer to
RFC 2616.
HttpEndpoint
consults the same set of system properties used by
{@link java.net.HttpURLConnection}:
http.proxyHost
: if specified, the value of this property is
interpreted as the host name of the HTTP proxy to use. If
http.proxyHost
is not specified, then the proxyHost
system property is consulted as a fallback; if it is not specified either,
then HTTP proxying is disabled (i.e., all HTTP messages are sent directly to
the target host of the given HttpEndpoint
).
http.proxyPort
: if http.proxyHost
or
proxyHost
is set, then the (integer) value of
http.proxyPort
system property is used as the port number on
which to connect to the HTTP proxy; if unspecified, then the
proxyPort
property is consulted as a fallback. If neither
http.proxyPort
nor proxyPort
is specified (but
http.proxyHost
or proxyHost
is), then the HTTP
proxy port number defaults to 80.
http.nonProxyHosts
: if specified, the value of this property
is interpreted as a list of host name patterns, each delimited by the '|'
character. Individual host name patterns may be literal host
names/addresses, or partial host names/addresses containing the '*' character
as a wildcard. The '*' wildcard matches arbitrary (including zero-length)
spans of characters. If http.nonProxyHosts
is specified and the
name of the target host for a given outbound message matches at least one of
the patterns, then messages to that host will not be sent through the HTTP
proxy.
HttpEndpoint
supports both basic and digest HTTP authentication
methods, specified in RFC
2617. Like HttpURLConnection
, it obtains authentication
parameters (e.g., username, password) from the {@link java.net.Authenticator}
class.
HttpEndpoint
and HttpServerEndpoint
support
the transport layer aspects of at least the following constraints:
ClientMaxPrincipal
,
ClientMaxPrincipalType
, ClientMinPrincipal
,
ClientMinPrincipalType
, ServerMinPrincipal
,
DelegationAbsoluteTime
, and
DelegationRelativeTime
constraints are trivially
supported if ClientAuthentication.YES
,
ServerAuthentication.YES
, and Delegation.YES
are not supported.)
@since 2.0
@version 2.0
@org.apache.river.impl
net.jini.jeri.http.client
to log information at the
following levels:
| Level | Description |
|---|---|
| {@link org.apache.river.logging.Levels#FAILED FAILED} | exceptions thrown attempting to initiate a request |
| {@link org.apache.river.logging.Levels#HANDLED HANDLED} | exceptions caught attempting to connect a socket, set options on a client-side socket, or reuse a connection |
| {@link java.util.logging.Level#FINE FINE} | client-side socket operations (create, connect, reuse, and close) |
This implementation uses the {@link java.util.logging.Logger} named
net.jini.jeri.http.server
to log information at the
following levels:
| Level | Description |
|---|---|
| {@link java.util.logging.Level#WARNING WARNING} | exceptions accepting on a server socket |
| {@link org.apache.river.logging.Levels#HANDLED HANDLED} | exceptions caught setting options on a server-side socket |
| {@link java.util.logging.Level#FINE FINE} | server-side socket operations (create, accept, and close) |
HttpEndpoint
and HttpServerEndpoint
recognizes the following system properties:
org.apache.river.jeri.http.idleConnectionTimeout
- The number of
milliseconds to retain idle client-side HTTP connections before closing
them. The default is 15000
.
org.apache.river.jeri.http.idleServerConnectionTimeout
- The
number of milliseconds to retain idle server-side HTTP connections before
closing them. The default is the idle client-side connection timeout (as
specified by the org.apache.river.jeri.http.idleConnectionTimeout
system property) plus 10000
.
org.apache.river.jeri.http.responseAckTimeout
- The number of
milliseconds to wait for acknowledgments from
{@link net.jini.io.context.AcknowledgmentSource} instances. The default is
15000
.
org.apache.river.jeri.http.disableProxyPersistentConnections
- If
the value is case-insensitive equal to true
, client-side
connections through the HTTP proxy are not reused across requests;
otherwise, persistent connections are maintained and reused if possible.
org.apache.river.jeri.http.pingProxyConnections
- If
the value is case-insensitive equal to true
, then if an
HTTP proxy is being used, ping the server endpoint to verify whether
it is alive and reachable. The ping occurs before the first request
and before each subsequent request which follows the expiration of
the ping proxy timeout period (below) following the previous ping.
When using an HTTP proxy it is often impossible to distinguish
between inability to reach the server endpoint (such as because the
server process refused a connection by the HTTP proxy) and the lack
of response from a delivered request (which might result in an
UnmarshalException). The ping increases the likelihood that the
inability to reach the server endpoint can be explicitly identified.
The default value is false
, and no pings are done.
org.apache.river.jeri.http.pingProxyConnectionTimeout
- The
number of milliseconds from the time a server endpoint was last
pinged before a ping will precede the next request. The default is
Long.MAX_VALUE
(essentially meaning, ping only before
the first request).


Every 40 seconds someone dies of suicide. According to the World Health Organization, depression will affect 1 out of 5 people during their lifetime. Explore the data below and find out what countries have the highest rates of depression and suicide, how depression rates have changed over the past ten years, and which groups are most at risk.
(For an enhanced experience, press F11 to enter fullscreen mode)
Countries with the highest rates of depression don't necessarily have the highest suicide rates. If you hover over the map you can see the rate of depression in % of the population, as well as the rate of suicide per 100,000 people in a country. Clicking on the map will take you to the next screen, where you can explore trends and demographics of depression. *
Data on suicides and depression may be deficient. People who live in countries
with easier access to mental health services, are naturally going to be diagnosed at a
higher rate. Taboos against mental health disorders may also lower diagnosis rates. The data made available by the World Health Organization (WHO)
therefore might be not be entirely accurate especially for lower-income countries.
(click to dismiss)
Globally, about 4.5% of the world population is depressed. This rate can vary greatly by country. Find out how depression rates in different regions of the world compare by selecting buttons on the left. If you want to know more about the demographics in a specific country, you can click on that country's line in the line graph.


Main Page
Namespaces
Data Structures
Files
Directories
Data Structures
Class Hierarchy
Data Fields
UVM-ML Class Hierarchy
This inheritance list is sorted roughly, but not completely, alphabetically:
uvm::delete_object
uvm::uvm_barrier
uvm::uvm_callback_agent< Arg >
uvm::uvm_event
uvm::uvm_callback_agent< Arg >::abstract_function
uvm::uvm_callback_agent< Arg >::callback< Fn >
uvm::uvm_callback_agent< uvm::uvm_event >
uvm::uvm_commandline
uvm::uvm_commandline_plugin
uvm::uvm_creator
uvm::uvm_component_creator
uvm::uvm_object_creator
uvm::uvm_object_creator_uvm_pool< T >
uvm::uvm_factory
uvm::uvm_factory_rep
uvm::uvm_manager
uvm::uvm_packer
uvm::uvm_packer_rep
uvm::uvm_phase
uvm::uvm_common_phase
uvm::uvm_build_phase
uvm::uvm_connect_phase
uvm::uvm_end_of_elaboration_phase
uvm::uvm_run_phase
uvm::uvm_start_of_simulation_phase
uvm::uvm_phase_controller
uvm::uvm_resource_base
uvm::uvm_resource< T >
uvm::uvm_resource< sc_bv_base >
uvm::uvm_resource_db< T >
uvm::uvm_config_db< T >
uvm::uvm_resource_pool
uvm::uvm_schedule
uvm::uvm_common_schedule
uvm::uvm_void
uvm::uvm_typed
uvm::uvm_component
uvm::uvm_object
uvm::uvm_objection
uvm::uvm_test_done_objection
uvm::uvm_pool< T >
Generated on Sun Aug 23 11:20:50 2015 for UVM-ML by
1.4.7


Right... recently there has been a plague of Paladin/Bard/Pale Master Builds on the server I play on...
you guys know what counters those types of builds?
Yeah...Start playing on a serious RP server
Perhaps try spamming with non save spells like IGMS from a hasted epic warded sorc, or a monk based run'n gun AA. _________________ The curve is more powerful than the sword -- BG courtesan
Edited By Mick Dagger on 08/21/06 10:11
I might start looking into the monk AA.
However, the missle storms spells... along with quite a few of the other good offensive spells were nerfed into useless-ness.
Time to change servers! I have heard this type of questin over the years and I cannot understand why people continue to play on servers that nerf everything. The best counter is a good offense. THe best offense is to vote elsewhere.. if tha makes any sense
Try a cleric/bard/PM that should be tougher than a paladin/bard/PM and harm should cut through that AC. If devastating critical has been disabled, you could try to out melee them with a cleric/bard/RDD, bard/fighter/WM, paladin/bard/RDD or bard/fighter/RDD. _________________ Two wrongs don't make a right but three lefts do.
TBH... I prolly should change servers, but I've been playing on this one server since I started playing nwn 2 years ago, so I'm kinda attached to the server i geuss...
Another reason is cuz there aren't alot of ppl that know the difference between Noob and Newbie, and I really don't feel like starting on another server and being branded a "noob"...
Noob: Someone that plays the game only to aggravate other players, and therefore should crawl under a rock and die somewhere.
Newbie: New player that wants to become better and plays the game to have fun.
Though, if any of you guys know any good servers that might be worth checking out?
Depends upon your preferences, but I know of 2 great ones, and they are veeeery different from each other.


By Shaunak Pagnis
The OAuth 2.0 authorization framework enables a third-party application to obtain limited access to an HTTP service, either on behalf of a resource owner by orchestrating an approval interaction between the resource owner and the HTTP service, or by allowing the third-party application to obtain access on its own behalf. This specification replaces and obsoletes the OAuth 1.0 protocol described in RFC 5849.
+--------+ +---------------+ | |--(A)- Authorization Request ->| Resource | | | | Owner | | |<-(B)-- Authorization Grant ---| | | | +---------------+ | | | | +---------------+ | |--(C)-- Authorization Grant -->| Authorization | | Client | | Server | | |<-(D)----- Access Token -------| | | | +---------------+ | | | | +---------------+ | |--(E)----- Access Token ------>| Resource | | | | Server | | |<-(F)--- Protected Resource ---| | +--------+ +---------------+
Add this to your devise config
config.omniauth :my_provider_name, APP_ID, APP_SECRET,:provider_ignores_state => true
Write your OmniAuth Callbacks Controller to handle authorization
Add this to your Rails middleware
Rails.application.config.middleware.use OmniAuth::Builder do
provider :my_provider_name, APP_ID, APP_SECRET
end
Problem with managing different sessions
Add this to your session_store.rb
MyApp::Application.config.session_store :cookie_store, :key => "_my_app_key", :domain => :all
And Override You Sessions Controller
class Users::SessionsController < Devise::SessionsController def destroy super cookies.clear(:domain => :all) end end


This password would take to crack, according to zxcvbn by Dropbox. (But over a throttled internet connection it would take .)
FAQ
This is a web app for generating passphrases that form grammatically correct sentences.
What's a passphrase?
Passphrases are short sequences of common words that can be used as passwords. Because they are longer and more memorable than the sorts of passwords most people choose, they are generally considered to be more secure.
Why grammatically correct?
Sequences of words are easier to remember when they form sentences, even if those sentences are nonsensical.
But doesn't that make them less secure?
Well, yes... but it turns out not by much. In this case, knowing that an adjective is likely to be followed by a noun only reduces the possible number of words by half, or by one bit of entropy. This is offset by having grammatical variation, such as singulars and plurals, or past or present tenses.
Wait, what's entropy?
Entropy is a measure of the amount of information contained in a password. Each additional bit of entropy means it would take an attacker twice as long to crack your password. Based on the number of words used by this generator, passphrases with 4 or 5 words have an entropy of around 40-50 bits.
How can it "take centuries to crack" if it's only 40-50 bits of entropy?
The calculations provided by zxcvbn are based on blind entropy; in other words, they're not assuming any knowledge of the type of password that is being used. The 40-50 bits of entropy, on the other hand, are calculated based on the number of possible phrases generated by this site - this is a measure of how secure the passwords are if you're being targeted by an attacker who knows that you get your passwords from this site. In most cases, this is an extremely conservative estimate, and the longer estimate provided by zxcvbn is more reasonable.
So, how does it work?
For simplicity, the passphrases all follow the same grammatical construction: noun phrase, verb, noun phrase. The noun phrases may contain adjectives and may have definite or indefinite articles, and the verb can be in either present or past tense. The words are selected at random from a list of over 2000 common nouns, adjectives, and verbs.
I still can't remember a million different passwords, even if they're sentences...
The best solution to this problem is to use a password manager, such as Bitwarden or 1Password. These are apps that will remember most of your passwords for you, and automatically fill them in on websites. This page is meant for the few occasions that you can't use a password generator and actually have to remember your passwords, such as for computer logins.
The worst solution is to reuse the same password over and over again for different services. If you do this, the moment one website you use gets hacked because it has poor security, all of your accounts are vulnerable. Never reuse passwords.
Hang on, should I really be getting my passwords from a website?
Well, ideally not. But the phrases from this site are generated locally on your computer, and are never sent to a server, so it should be safe enough. This project is open-source and published on GitHub, so you can review it to make sure it does what I claim.
I've found a bug!
Great! Let me know by opening an issue here and I'll fix it.


2 Kings 17:20
And the LORD rejected all the seed of Israel, and afflicted them, and delivered them into the hand of spoilers, until he had cast them out of his sight.


Quickvoxel Core provides some events such as
startAddingVolume
and
volumeReady
, giving the possibility to show and hide a spinner based on the loading and processing stage or the data •
source


4th International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications.
Introduction to the ADAMS framework, including live demos.
Taking Machine Learning research online.


You need to have javascript enabled to view this site.
Alternatively, an error occurred that caused the site to fail, please try refreshing the page.


|
|||||||||||
| PREV NEXT | FRAMES NO FRAMES |
| Packages that use COSName | |
| org.pdfbox | This package holds executable classes that interact with the PDFBox application. |
| org.pdfbox.cos | These are the low level objects that make up a PDF document. |
| org.pdfbox.encoding | This package contains the implementations for all of the encodings that are used in PDF documents. |
| org.pdfbox.filter | This package will hold the PDFBox implementations of the filters that are used in PDF documents. |
| org.pdfbox.pdfparser | The pdfparser package contains classes to parse PDF documents and objects within the document. |
| org.pdfbox.pdfwriter | This is the persistence layer used to write the PDFBox documents to a stream. |
| org.pdfbox.pdmodel.common | High level PD classes that are used throughout several packages are placed in the PDModel common package. |
| org.pdfbox.pdmodel.interactive.documentnavigation.destination | The destination package allows destinations into a pdf document to be specified. |
| Uses of COSName in org.pdfbox |
| Fields in org.pdfbox declared as COSName | |
static COSName |
Overlay.XOBJECT
COSName constant. |
static COSName |
Overlay.PROC_SET
COSName constant. |
static COSName |
Overlay.EXT_G_STATE
COSName constant. |
| Uses of COSName in org.pdfbox.cos |
| Fields in org.pdfbox.cos declared as COSName | |
static COSName |
COSName.A
A common COSName value. |
static COSName |
COSName.AA
A common COSName value. |
static COSName |
COSName.ACRO_FORM
A common COSName value. |
static COSName |
COSName.ANNOTS
A common COSName value. |
static COSName |
COSName.ART_BOX
A common COSName value. |
static COSName |
COSName.ASCII85_DECODE
A common COSName value. |
static COSName |
COSName.ASCII85_DECODE_ABBREVIATION
A common COSName value. |
static COSName |
COSName.ASCII_HEX_DECODE
A common COSName value. |
static COSName |
COSName.ASCII_HEX_DECODE_ABBREVIATION
A common COSName value. |
static COSName |
COSName.AP
A common COSName value. |
static COSName |
COSName.B
A common COSName value. |
static COSName |
COSName.BASE_ENCODING
A common COSName value. |
static COSName |
COSName.BASE_FONT
A common COSName value. |
static COSName |
COSName.BBOX
A common COSName value. |
static COSName |
COSName.BLEED_BOX
A common COSName value. |
static COSName |
COSName.CATALOG
A common COSName value. |
static COSName |
COSName.CALGRAY
A common COSName value. |
static COSName |
COSName.CALRGB
A common COSName value. |
static COSName |
COSName.CCITTFAX_DECODE
A common COSName value. |
static COSName |
COSName.CCITTFAX_DECODE_ABBREVIATION
A common COSName value. |
static COSName |
COSName.COLORSPACE
A common COSName value. |
static COSName |
COSName.CONTENTS
A common COSName value. |
static COSName |
COSName.COUNT
A common COSName value. |
static COSName |
COSName.CROP_BOX
A common COSName value. |
static COSName |
COSName.DCT_DECODE
A common COSName value. |
static COSName |
COSName.DCT_DECODE_ABBREVIATION
A common COSName value. |
static COSName |
COSName.DESCENDANT_FONTS
A common COSName value. |
static COSName |
COSName.DEST
A common COSName value. |
static COSName |
COSName.DEVICECMYK
A common COSName value. |
static COSName |
COSName.DEVICEGRAY
A common COSName value. |
static COSName |
COSName.DEVICEN
A common COSName value. |
static COSName |
COSName.DEVICERGB
A common COSName value. |
static COSName |
COSName.DIFFERENCES
A common COSName value. |
static COSName |
COSName.DV
A common COSName value. |
static COSName |
COSName.ENCODING
A common COSName value. |
static COSName |
COSName.ENCODING_90MS_RKSJ_H
A common COSName value. |
static COSName |
COSName.ENCODING_90MS_RKSJ_V
A common COSName value. |
static COSName |
COSName.ENCODING_ETEN_B5_H
A common COSName value. |
static COSName |
COSName.ENCODING_ETEN_B5_V
A common COSName value. |
static COSName |
COSName.FIELDS
A common COSName value. |
static COSName |
COSName.FILTER
A common COSName value. |
static COSName |
COSName.FIRST_CHAR
A common COSName value. |
static COSName |
COSName.FLATE_DECODE
A common COSName value. |
static COSName |
COSName.FLATE_DECODE_ABBREVIATION
A common COSName value. |
static COSName |
COSName.FONT
A common COSName value. |
static COSName |
COSName.FONT_FILE
A common COSName value. |
static COSName |
COSName.FONT_FILE2
A common COSName value. |
static COSName |
COSName.FONT_FILE3
A common COSName value. |
static COSName |
COSName.FONT_DESC
A common COSName value. |
static COSName |
COSName.FONT_MATRIX
A common COSName value. |
static COSName |
COSName.FORMTYPE
A common COSName value. |
static COSName |
COSName.FRM
A common COSName value. |
static COSName |
COSName.H
A common COSName value. |
static COSName |
COSName.HEIGHT
A common COSName value. |
static COSName |
COSName.ICCBASED
A common COSName value. |
static COSName |
COSName.IDENTITY_H
A common COSName value. |
static COSName |
COSName.IMAGE
A common COSName value. |
static COSName |
COSName.INDEXED
A common COSName value. |
static COSName |
COSName.INFO
A common COSName value. |
static COSName |
COSName.JPX_DECODE
A common COSName value. |
static COSName |
COSName.KIDS
A common COSName value. |
static COSName |
COSName.LAB
A common COSName value. |
static COSName |
COSName.LAST_CHAR
A common COSName value. |
static COSName |
COSName.LENGTH
A common COSName value. |
static COSName |
COSName.LENGTH1
A common COSName value. |
static COSName |
COSName.LZW_DECODE
A common COSName value. |
static COSName |
COSName.LZW_DECODE_ABBREVIATION
A common COSName value. |
static COSName |
COSName.MAC_ROMAN_ENCODING
A common COSName value. |
static COSName |
COSName.MATRIX
A common COSName value. |
static COSName |
COSName.MEDIA_BOX
A common COSName value. |
static COSName |
COSName.METADATA
A common COSName value. |
static COSName |
COSName.N
A common COSName value. |
static COSName |
COSName.NAME
A common COSName value. |
static COSName |
COSName.P
A common COSName value. |
static COSName |
COSName.PAGE
A common COSName value. |
static COSName |
COSName.PAGES
A common COSName value. |
static COSName |
COSName.PARENT
A common COSName value. |
static COSName |
COSName.PATTERN
A common COSName value. |
static COSName |
COSName.PDF_DOC_ENCODING
A common COSName value. |
static COSName |
COSName.PREV
A common COSName value. |
static COSName |
COSName.R
A common COSName value. |
static COSName |
COSName.RESOURCES
A common COSName value. |
static COSName |
COSName.ROOT
A common COSName value. |
static COSName |
COSName.ROTATE
A common COSName value. |
static COSName |
COSName.RUN_LENGTH_DECODE
A common COSName value. |
static COSName |
COSName.RUN_LENGTH_DECODE_ABBREVIATION
A common COSName value. |
static COSName |
COSName.SEPARATION
A common COSName value. |
static COSName |
COSName.STANDARD_ENCODING
A common COSName value. |
static COSName |
COSName.SUBTYPE
A common COSName value. |
static COSName |
COSName.TRIM_BOX
A common COSName value. |
static COSName |
COSName.TRUE_TYPE
A common COSName value. |
static COSName |
COSName.TO_UNICODE
A common COSName value. |
static COSName |
COSName.TYPE
A common COSName value. |
static COSName |
COSName.TYPE0
A common COSName value. |
static COSName |
COSName.V
A common COSName value. |
static COSName |
COSName.VERSION
A common COSName value. |
static COSName |
COSName.WIDTHS
A common COSName value. |
static COSName |
COSName.WIN_ANSI_ENCODING
A common COSName value. |
static COSName |
COSName.XOBJECT
A common COSName value. |
| Methods in org.pdfbox.cos that return COSName | |
static COSName |
COSName.getPDFName(String aName)
This will get a COSName object with that name. |
COSName |
COSDictionary.getKeyForValue(Object value)
Search in the map for the value that matches the parameter and return the first key that maps to that value. |
| Methods in org.pdfbox.cos with parameters of type COSName | |
Object |
ICOSVisitor.visitFromName(COSName obj)
Notification of visit to name object. |
COSBase |
COSObject.getDictionaryObject(COSName key)
This will get the dictionary object in this object that has the name key and if it is a pdfobjref then it will dereference that and return it. |
COSBase |
COSObject.getItem(COSName key)
This will get the dictionary object in this object that has the name key. |
COSObject |
COSDocument.getObjectByType(COSName type)
This will get the first dictionary object by type. |
List |
COSDocument.getObjectsByType(COSName type)
This will get a dictionary object by type. |
COSBase |
COSDictionary.getDictionaryObject(COSName key)
This will get an object from this dictionary. |
void |
COSDictionary.setItem(COSName key,
COSBase value)
This will set an item in the dictionary. |
void |
COSDictionary.setItem(COSName key,
COSObjectable value)
This will set an item in the dictionary. |
void |
COSDictionary.setBoolean(COSName key,
boolean value)
This will set an item in the dictionary. |
void |
COSDictionary.setName(COSName key,
String value)
This is a convenience method that will convert the value to a COSName object. |
void |
COSDictionary.setDate(COSName key,
Calendar date)
Set the date object. |
void |
COSDictionary.setEmbeddedDate(String embedded,
COSName key,
Calendar date)
Set the date object. |
void |
COSDictionary.setString(COSName key,
String value)
This is a convenience method that will convert the value to a COSString object. |
void |
COSDictionary.setEmbeddedString(String embedded,
COSName key,
String value)
This is a convenience method that will convert the value to a COSString object. |
void |
COSDictionary.setInt(COSName key,
int value)
This is a convenience method that will convert the value to a COSInteger object. |
void |
COSDictionary.setLong(COSName key,
long value)
This is a convenience method that will convert the value to a COSInteger object. |
void |
COSDictionary.setEmbeddedInt(String embeddedDictionary,
COSName key,
int value)
This is a convenience method that will convert the value to a COSInteger object. |
void |
COSDictionary.setFloat(COSName key,
float value)
This is a convenience method that will convert the value to a COSFloat object. |
String |
COSDictionary.getNameAsString(COSName key)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
String |
COSDictionary.getNameAsString(COSName key,
String defaultValue)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
String |
COSDictionary.getString(COSName key)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
String |
COSDictionary.getString(COSName key,
String defaultValue)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
String |
COSDictionary.getEmbeddedString(String embedded,
COSName key)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
String |
COSDictionary.getEmbeddedString(String embedded,
COSName key,
String defaultValue)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
Calendar |
COSDictionary.getDate(COSName key)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
Calendar |
COSDictionary.getDate(COSName key,
Calendar defaultValue)
This is a convenience method that will get the dictionary object that is expected to be a date. |
Calendar |
COSDictionary.getEmbeddedDate(String embedded,
COSName key)
This is a convenience method that will get the dictionary object that is expected to be a name and convert it to a string. |
Calendar |
COSDictionary.getEmbeddedDate(String embedded,
COSName key,
Calendar defaultValue)
This is a convenience method that will get the dictionary object that is expected to be a date. |
boolean |
COSDictionary.getBoolean(COSName key,
boolean defaultValue)
This is a convenience method that will get the dictionary object that is expected to be a COSBoolean and convert it to a primitive boolean. |
int |
COSDictionary.getEmbeddedInt(String embeddedDictionary,
COSName key)
Get an integer from an embedded dictionary. |
int |
COSDictionary.getEmbeddedInt(String embeddedDictionary,
COSName key,
int defaultValue)
Get an integer from an embedded dictionary. |
int |
COSDictionary.getInt(COSName key)
This is a convenience method that will get the dictionary object that is expected to be an int |
int |
COSDictionary.getInt(COSName key,
int defaultValue)
This is a convenience method that will get the dictionary object that is expected to be an integer. |
long |
COSDictionary.getLong(COSName key)
This is a convenience method that will get the dictionary object that is expected to be an long |
long |
COSDictionary.getLong(COSName key,
long defaultValue)
This is a convenience method that will get the dictionary object that is expected to be an integer. |
float |
COSDictionary.getFloat(COSName key)
This is a convenience method that will get the dictionary object that is expected to be an float |
float |
COSDictionary.getFloat(COSName key,
float defaultValue)
This is a convenience method that will get the dictionary object that is expected to be an float. |
void |
COSDictionary.removeItem(COSName key)
This will remove an item for the dictionary. |
COSBase |
COSDictionary.getItem(COSName key)
This will do a lookup into the dictionary. |
| Uses of COSName in org.pdfbox.encoding |
| Methods in org.pdfbox.encoding that return COSName | |
COSName |
Encoding.getName(int code)
This will take a character code and get the name from the code. |
COSName |
Encoding.getNameFromCharacter(char c)
This will take a character code and get the name from the code. |
| Methods in org.pdfbox.encoding with parameters of type COSName | |
Encoding |
EncodingManager.getEncoding(COSName name)
This will get an encoding by name. |
protected void |
Encoding.addCharacterEncoding(int code,
COSName name)
This will add a character encoding. |
int |
Encoding.getCode(COSName name)
This will get the character code for the name. |
static String |
Encoding.getCharacter(COSName name)
This will get the character from the name. |
| Uses of COSName in org.pdfbox.filter |
| Methods in org.pdfbox.filter with parameters of type COSName | |
void |
FilterManager.addFilter(COSName filterName,
Filter filter)
This will add an available filter. |
Filter |
FilterManager.getFilter(COSName filterName)
This will get a filter by name. |
| Uses of COSName in org.pdfbox.pdfparser |
| Methods in org.pdfbox.pdfparser that return COSName | |
protected COSName |
BaseParser.parseCOSName()
This will parse a PDF name from the stream. |
| Uses of COSName in org.pdfbox.pdfwriter |
| Methods in org.pdfbox.pdfwriter with parameters of type COSName | |
Object |
COSWriter.visitFromName(COSName obj)
visitFromName method comment. |
| Uses of COSName in org.pdfbox.pdmodel.common |
| Methods in org.pdfbox.pdmodel.common with parameters of type COSName | |
COSBase |
COSStreamArray.getItem(COSName key)
This will get an object from this streams dictionary. |
COSBase |
COSStreamArray.getDictionaryObject(COSName key)
This will get an object from this streams dictionary and dereference it if necessary. |
| Constructors in org.pdfbox.pdmodel.common with parameters of type COSName | |
PDNamedTextStream(COSName name,
COSBase str)
Constructor. |
| Uses of COSName in org.pdfbox.pdmodel.interactive.documentnavigation.destination |
| Constructors in org.pdfbox.pdmodel.interactive.documentnavigation.destination with parameters of type COSName | |
PDNamedDestination(COSName dest)
Constructor. |
|
|||||||||||
| PREV NEXT | FRAMES NO FRAMES |


THE REMAINS OF THE DAY A film review by Scott Renshaw Copyright 1993 Scott Renshaw
Starring: Anthony Hopkins, Emma Thompson, James Fox, Christopher Reeve. Screenplay: Ruth Prawer Jhabvala. Director: James Ivory.
I was not among those who considered HOWARDS END one of the top ten films of 1992. As beautiful and well-acted as it was, I felt it offered a fragmented and somewhat unsatisfying treatment of E. M. Forster's novel. Therefore I did not approach THE REMAINS OF THE DAY, the new film from the Merchant/Ivory production stable, as a blessing dropped directly from heaven. It appears I should have. THE REMAINS OF THE DAY will draw much the same audience as HOWARDS END and THE AGE OF INNOCENCE, but it corrects nearly every flaw in each of those films, focusing on one character played to near perfection by Anthony Hopkins.
Hopkins stars as Mr. Stevens, the head butler of Darlington Hall, who reflects in flashback on a few pivotal years in his service to Lord Darlington (James Fox). In 1935, Stevens welcomes a new housekeeper, Miss Kenton (Emma Thompson) as Darlington prepares to welcome some important guests. These guests include several English gentlemen, representatives from Germany and France, and an outspoken American congressman (Christopher Reeve), all gathered to discuss the rearming of Germany. All this, however, is irrelevant to Stevens, whose entire life is focused on being the best possible servant. His focus is so complete that he is unaware of the growing affections of Miss Kenton, and unable to express his own emotions. Stevens' relfection, interspersed through the flashbacks, becomes an attempt to justify his life as loyal servant to a man of sometimes questionable character.
THE REMAINS OF THE DAY is an exceptional adaptation of the award-winning novel by Kazuo Ishiguro. Rather than inflate the romance between Stevens and Miss Kenton out of proportion, screenwriter Ruth Prawer Jhabvala wisely keeps the focus on Stevens' universe as a butler. He is a character committed to a fault to his ideals of servanthood, expressed in one pithy line: "I don't believe a man can conisder himself fully content until he has done all he can to be of service to his employer." So deeply does Stevens' belief in the innate superiority of his social betters run that he blinds himself completely to Lord Darlington's politics and his behavior towards a pair of Jewish maids. There is also a crucial relationship between Stevens and his elderly father, himself a veteran butler whom Stevens respects so devoutly that he ignores signs that the elder Stevens is losing his faculties. It is only because these relationships with Darlington and his father are so fully developed that the relationship between Stevens and Miss Kenton can be convincingly poignant.
It also doesn't hurt that Anthony Hopkins turns in the single best male performance of the year as Stevens. His career has experienced an astonishing surge in the last three years, and REMAINS shows why. It is not so much his ability to deliver a line which raises him above the pack, although he is brilliant there as well; rather, it is his precise command of shrugs, turns of the head and almost imperceptible eye movements that is incomparable. The scenes where he is in command, such as a lecture to Miss Kenton for referring to his father by his first name, are fine. Those where he is uneasy are masterful. In one such scene, he attempts to fulfill Lord Darlington's request that he explain the facts of life to his newly engaged godson. It's wonderfully funny because Stevens is trying so desperately to maintain his dignity in an inherently undignified situation. This is one of the finest physical performances in many years, beginning with one of the first moments he appears on screen, walking with stooped shoulders through an empty parlor.
The technical end of THE REMAINS OF THE DAY is, as expected, glorious as well. Director James Ivory creates some marvelous shots, many framed through windows to lend a sense of voyeurism. The photography is sharp, capturing the vastness of Darlington Hall expertly, and a beautiful score by Richard Robbins lends the perfect air of gentility. THE REMAINS OF THE DAY is just as visually striking as HOWARDS END, and the performances are just as solid. What gives REMAINS an edge over its much-lauded cousin is a more focused narrative which is riveting where HOWARDS END wandered. The world of Stevens the butler is one well worth visiting for a thoroughly magnificent film experience.
On the Renshaw scale of 0 to 10 servants: 10.
-- Scott Renshaw Stanford University Office of the General Counsel
.
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


[gecode-users] Memory issues with repeated execution of the script
Guido Tack
tack at gecode.org
Thu Jun 16 10:31:19 CEST 2011
On 15 Jun 2011, at 17:33, Jan Kelbel wrote:
> Hi,
>
> I found the following strange behavior of Gecode 3.5.0 when using 32bit windows binaries in Win XP and MS Visual Studio 2008:
>
> When the problem is infeasible and the total number of used nodes is 0, some memory is not released, and when the script is executed repeatedly, the consumed memory increases.
There are two memory leaks in the Driver module of Gecode 3.5.0 that become noticeable when searching a failed problem repeatedly . The first one was already fixed in the svn trunk (and was responsible for most of the leaked memory you saw), and I found a second small leak just now and also fixed it. Thanks for the report!
Cheers,
Guido
--
Guido Tack, http://people.cs.kuleuven.be/~guido.tack/
More information about the users
mailing list


An Empty Name
Avg. Score
Avg. Grade
Reviews
No information found
Avg. Score
Avg. Grade
n/a
n/a
Reviews


-
valBoard
int[][] valBoard
2D array representing the current status of the board populated by
values in range [-1,8] inclusive representing how many mines a location
is touching or -1 if the location is a mine.
-
visibleBoard
int[][] visibleBoard
2D array representing the current status of the board populated by
0 for invisible, 1 for visible, and -2 for flag.
-
visibles
java.util.HashSet<E> visibles
HashSet containing OrderedPair objects for all coordinates on the grid
visible to the player.
-
dnes
java.util.HashSet<E> dnes
A set of all the "Does Not Exist" Ordered Pair locations on the board.
-
rows
int rows
The number of rows in the current board.
-
cols
int cols
The number of columns in the current board.
-
mineLocs
java.util.HashSet<E> mineLocs
HashSet containing OrderedPair objects for all coordinates on the grid
containing mines.
-
adjacencies
java.util.HashMap<K,V> adjacencies
HashMap that contains an adjacency list for each coordinate
in the grid. Each key has a list of all neighboring positions on the grid.
-
initialized
boolean initialized
Flag to determine if mines and proximity locations have been placed on the valBoard.
-
score
int score
The current runtime of the current game.
This is stored here to be serialized.
-
difficulty
java.lang.String difficulty
Difficulty mode the current board.
This is stored here to be serialized.
-
shape
java.lang.String shape
Current shape of the board. It will either be rectangle, diamond, hexagon,
triangle or custom.
-
flags
int flags
Tracks the number of flags available for use.


Taco Bell wants you to put its chicken tacos in a blender and serve as bisque this Thanksgiving
by Kitty Capelle(Gray News) - Taco about a unique Thanksgiving side.
Taco Bell published a recipe for chicken taco bisque on its blog, and it promises this recipe will “be the star of your Friendsgiving.”
The recipe starts normal enough by sauteing onions and garlic in a cast iron stockpot, and then adding spices, broth and tomatoes. That’s where it stops feeling so conventional.
Then the recipe asks you to put six rolled chicken tacos, cut into pieces, into the pot.

It’s suggested to serve the soup with a side of more rolled chicken tacos, for dipping.
If you do end up making the soup, the fast food chain suggests you use #RolledChickenTacosBisque on social media.
You can view the entire recipe here.


Content-Type: text/html Content-Transfer-Encoding: 8bit ATTENTION, SIR, I AM OF THE VIEW THAT YOU WILL BE OF TREMENDEOUS VALUE, ASSISTANCE, CONSENTING/ AGREEING TO BAIL ME AND ALSO, HELP ME SECURE A GREAT FORETUNE (MONEY) WHICH MY PARENTS ACQUIRED OVER PERIOD OF YEARS OF HARDWORK. I HAVE NO FAINTST DOUBT IN MIND THAT YOU WILL NOT ASSIST ME THIS DIFFICULT AND CRYING TIME. TO BE CANDID, I AM VANDE, THE SON OF THE LATE COTE'D HOLBORG HEAD OF STATE, GENERAL , MY PARENTS WERE KILLED IN THE HANDS OF REBELS LAST YEAR 2002 CIVIL WAR IN THE COUNTRY, WHICH STILL RAGES. I AND OTHER OF OUR SIBILINGS WERE FORTUNATE TO HAVE SURVIVED THE WAR, THANKS WITH THE ASSISTANCE OF FRENCH TROOPS EVACUATED US TO NIGERIA WHERE WE ARE ON TEMPORARY ASSYLUM, WITH THE SHOCK, KILLINGS, WITCH-HUNTING OF FAMILY MENBERS BY THE CIVILLIAN GOVERNMENT MAKES IT UNSAFE TO RETURN BACK. DURING MY FATHER'S REGIME AS HEAD OF STATE OF COTE'D IVORE, HE AMAZED GREAT WEALTH/FORTUNES TO THE TUNE OF EURO 10 MILLION (TWENTY MILLION EUROS ) WHICH HE PERSONALLY CONCEALED IN A DIPLOMATIC CASE AWAITING TO BE TRANSFERED TO A FOREIGN ACCOUNT IN EUROPE. BUT THE REBELS CAUGHTUP WITH HIM AND DIED WITHOUT TRANSFERING THE MONEY. I METICOUSLY CARRIED THE 10 MILLION EURO IN THE DIPLOMATIC CASE DOWN TO IRELAND, DEPOSITED THE MONEY WITH A SECURITY FIRM .    I ANTICIPATE YOUR CO-OPERATION. REGARDS, VANDER


In this project, you'll use generative adversarial networks to generate new images of faces.
You'll be using two datasets in this project:
Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.
If you're using FloydHub, set data_dir
to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images
.
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

You'll build the components necessary to build a GANs by implementing the following functions below:
model_inputs
discriminator
generator
model_loss
model_opt
train
This will check to make sure you have the correct version of TensorFlow and access to a GPU
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
Implement the model_inputs
function to create TF Placeholders for the Neural Network. It should create the following placeholders:
image_width
, image_height
, and image_channels
.z_dim
.Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
real_input_images = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='real_images')
z_inputs = tf.placeholder(tf.float32, (None, z_dim), name='z_input')
learning_rate = tf.placeholder(tf.float32, name='learning_rate')
return real_input_images, z_inputs, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Implement discriminator
to create a discriminator neural network that discriminates on images
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).
def discriminator(images, reuse=False, alpha=0.2, keep_prob=0.8):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
with tf.variable_scope('discriminator', reuse=reuse):
# Input layer is 28x28x3
x = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
x = tf.maximum(x, alpha * x)
x = tf.nn.dropout(x, keep_prob)
# 14*14*64
x = tf.layers.conv2d(x, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
x = tf.layers.batch_normalization(x, training=True)
x = tf.maximum(x, alpha * x)
x = tf.nn.dropout(x, keep_prob)
# 7*7*128
x = tf.layers.conv2d(x, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer_conv2d())
x = tf.layers.batch_normalization(x, training=True)
x = tf.maximum(x, alpha * x)
x = tf.nn.dropout(x, keep_prob)
# 4*4*256
x = tf.reshape(x, (-1, 4 * 4 * 256))
logits = tf.layers.dense(x, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Implement generator
to generate an image using z
. This function should be able to reuse the variables in the neural network. Use tf.variable_scope
with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim
images.
def generator(z, out_channel_dim, is_train=True, alpha=0.2, keep_prob=0.5):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
with tf.variable_scope('generator', reuse=not is_train):
# First fully connected layer
x = tf.layers.dense(z, 7 * 7 * 512)
x = tf.reshape(x, (-1, 7, 7, 512))
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(x, alpha * x)
x = tf.nn.dropout(x, keep_prob)
# 7x7x512
x = tf.layers.conv2d_transpose(x, 256, 5, strides=2, padding='same')
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(x, alpha * x)
x = tf.nn.dropout(x, keep_prob)
# 14x14x256
x = tf.layers.conv2d_transpose(x, 128, 5, strides=2, padding='same')
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(x, alpha * x)
x = tf.nn.dropout(x, keep_prob)
# 28x28x128
x = tf.layers.conv2d_transpose(x, 64, 5, strides=1, padding='same')
x = tf.layers.batch_normalization(x, training=is_train)
x = tf.maximum(x, alpha * x)
x = tf.nn.dropout(x, keep_prob)
# 28x28x128
# Output layer, 28x28x3
logits = tf.layers.conv2d_transpose(x, out_channel_dim, 4, strides=1, padding='same')
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Implement model_loss
to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:
discriminator(images, reuse=False)
generator(z, out_channel_dim, is_train=True)
def model_loss(input_real, input_z, out_channel_dim, alpha=0.2, discriminator_keep_prob=0.8, generator_keep_prob=0.5):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
g_model = generator(input_z, out_channel_dim, alpha=alpha, keep_prob=generator_keep_prob)
d_model_real, d_logits_real = discriminator(input_real, alpha=alpha, keep_prob=discriminator_keep_prob)
d_model_fake, d_logits_fake = discriminator(g_model, reuse=True, alpha=alpha, keep_prob=discriminator_keep_prob)
d_loss_real = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real) * 0.9))
d_loss_fake = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(
tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return d_loss, g_loss
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Implement model_opt
to create the optimization operations for the GANs. Use tf.trainable_variables
to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False, keep_prob = 1.0),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Implement train
to build and train the GANs. Use the following functions you implemented:
model_inputs(image_width, image_height, image_channels, z_dim)
model_loss(input_real, input_z, out_channel_dim)
model_opt(d_loss, g_loss, learning_rate, beta1)
Use the show_generator_output
to show generator
output while you train. Running show_generator_output
for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator
output every 100 batches.
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode, alpha=0.2,
print_every=10, show_every=100):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data (n_images, width, height, image_channels)
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
total_images, image_width, image_height, image_channels = data_shape
# Build network
real_inputs, z_inputs, lr = model_inputs(image_width, image_height, image_channels, z_dim)
discriminator_keep_prob = tf.placeholder(tf.float32, name='keep_prob')
generator_keep_prob = tf.placeholder(tf.float32, name='keep_prob')
d_loss, g_loss = model_loss(real_inputs, z_inputs, image_channels, alpha, discriminator_keep_prob, generator_keep_prob)
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
n_batches = 0
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
# Multiply value by 2 to change value range from [-0.5, 0.5] to [-1.0, 1.0]
images_input = np.dot(batch_images, 2)
n_batches += 1
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
# Run optimizers
_ = sess.run(d_opt,
feed_dict={real_inputs: images_input, z_inputs: batch_z, lr: learning_rate, discriminator_keep_prob:0.8, generator_keep_prob:0.5})
# Run the optimization for the generator twice to make sure that the discriminator
# loss does not go to zero.
_ = sess.run(g_opt,
feed_dict={z_inputs: batch_z, real_inputs: images_input, lr: learning_rate, discriminator_keep_prob:0.8, generator_keep_prob:0.5})
_ = sess.run(g_opt,
feed_dict={z_inputs: batch_z, real_inputs: images_input, lr: learning_rate, discriminator_keep_prob:0.8, generator_keep_prob:0.5})
if n_batches % print_every == 0:
train_loss_d = d_loss.eval({z_inputs: batch_z, real_inputs: images_input, discriminator_keep_prob:1.0, generator_keep_prob:1.0})
train_loss_g = g_loss.eval({z_inputs: batch_z, discriminator_keep_prob:1.0, generator_keep_prob:1.0})
print("Epoch {}/{}...".format(epoch_i + 1, epochs),
"Discriminator Loss: {:.4f}...".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
if n_batches % show_every == 0:
show_generator_output(sess, 25, z_inputs, image_channels, data_image_mode)
print("Result for trained generator:")
show_generator_output(sess, 25, z_inputs, image_channels, data_image_mode)
Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.
batch_size = 32
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.
batch_size = 128
z_dim = 128
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.


ugrep
Ultra fast search tool with query TUI.
More information: home page
- Start a query TUI to search files in the current directory recursively (CTRL-Z for help):
ugrep --query
- Search the current directory recursively for files containing a regex search pattern:
ugrep "search_pattern"
- Search in a specific file or in all files in a specific directory, showing line numbers of matches:
ugrep --line-number "search_pattern" path/to/file_or_directory
- Search in all files in the current directory recursively and print the name of each matching file:
ugrep --files-with-matches "search_pattern"
- Fuzzy search files with up to 3 extra, missing or mismatching characters in the pattern:
ugrep --fuzzy=3 "search_pattern"
- Also search compressed files, `zip` and `tar` archives recursively:
ugrep --decompress "search_pattern"
- Search only files whose filenames match a specific glob pattern:
ugrep --glob="glob_pattern" "search_pattern"
- Search only C++ source files (use `--file-type=list` to list all file types):
ugrep --file-type=cpp "search_pattern"


So, what is femtoJS? femtoJS is a JavaScript library for basic DOM manipulation. It has a jQuery-inspired syntax and supports chaining.
femtoJS is about 100 lines of code in size, and the entire library weighs in at just under 0.9kB compressed and gzipped.
Because FemtoJS is so small and simple, you can add or remove methods directly in the library, meaning it's very customizable. All methods are unrelated, so removing one will not affect any others in any way.
This library is designed to be personalized to meet your needs. It's easy to modify the code to add new methods or change existing ones.
Here are some code examples:
$('.someClass').css('background-color: green;').html('Hello World');
$('a').on('click', function() {
$('#someDiv').css('background-color: green; color: #fff;');
})
It works in ES6-supporting browsers. For Internet Explorer -56346 support, see NanoJS.


Sunday Siesta
Menu
Home
Upcoming Events
My Events
Past events
Q&A
Created Events
Joined Events
Personal information
×
Tel. number:
010-3798-7829
Email:
<EMAIL_ADDRESS>Kakao ID:
sweet99


JULY 28 St Victor I, Pope and Martyr
The
"Liber Pontificalis" makes him a native of Africa and gives his father the name of Felix.
This authority, taking the "Liberian Catalogue" as its basis, gives
the years 186-197 as the period of Victor's episcopate. The Armenian text of
the "Chronicle" of Eusebius (Leipzig, 1911, p. 223) places the
beginning of Victor's pontificate in the seventh year of the reign of the Emperor
Commodus (180-87) and gives it a duration of twelve years; in his "Church
History" (V, xxxii, ed. Schwarts,
Leipzig, 1902, p. 486) Eusebius transfers the beginning of the pontificate to
the tenth year of the reign of Commodus and makes it last ten years. Jerome
calls Pope Victor the first Latin
writer in the Church.
Common
of Pastors (for Popes) or For One Martyr
COLLECT
Almighty
ever-living God, who chose Saint Victor to preside over your whole people and
benefit them by word and example, keep safe, we pray, by his intercession, the
shepherds of your Church along with the flocks entrusted to their care, and
direct them in the way of eternal salvation. Through our Lord Jesus Christ,
your Son, who lives and reigns with you in the unity of the Holy Spirit, one
God, for ever and ever.
Readings
Preface of Pastors


Trip
Planner
Home
Sign in
Sign up
My trips
Sign out
City - Type
Name of Hotel
Description
Info:
Service options
Add a new trip
List name
Add
Cancel
Choose a trip
Add to trip
Cancel


I am constantly checking in with the kids: How was your day? Who’d you play with? Don’t do drugs! (ok that last part’s a little early for my family but you get the gist).
I hate to say it but I’m less focused on the academics at this point and more looking to bolster their sense of self. The emotional well being part - the what makes you YOU! is one of the biggest gifts you can give your kiddos. If being a mother’s taught me anything, it’s that we all just want to be heard and cherished for ourselves.
Anywho, this all started to come to a head as I’m trying to discern how to design a nice inexpensive, theme-y room decor that matches each kiddos personality. My 2 and 4 year olds share a room and have yet to graduate from the cute baby bunnies and giraffes of their nursery but my bigs! OHHHHH I need some help!
My oldest daughter, Imagen, is so outwardly bright and bubbly was easy. We mixed pink with gold accents to come up with a shiny sparkly - somewhat funky - look.
 He is quickly outgrowing his race car bed and the 30 different art projects he has taped to the walls, have definitely started to see better days. I’m at a complete loss for what to!! Anyone have a good theme that can grow with the ages!?!


Simple DOM Model Example
Qt provides two complementary sets of classes for reading XML files: The classes based around QXmlReader provide a SAX-style API for incremental reading of large files, and the classes based around QDomDocument enable developers to access the contents of XML files using a Document Object Model (DOM) API.
In this example, we create a model that uses the DOM API to expose the structure and contents of XML documents to views via the standard QAbstractModel interface.
Design and Concepts
Reading an XML document with Qt's DOM classes is a straightforward process. Typically, the contents of a file are supplied to QDomDocument, and nodes are accessed using the functions provided by QDomNode and its subclasses.
The aim is to use the structure provided by QDomDocument by wrapping QDomNode objects in item objects similar to the TreeItem
objects used in the Simple Tree Model example.
DomModel Class Definition
Let us begin by examining the DomModel
class:
class DomModel : public QAbstractItemModel { Q_OBJECT public: explicit DomModel(QDomDocument document, QObject *parent = 0); ~DomModel(); QVariant data(const QModelIndex &index, int role) const override; Qt::ItemFlags flags(const QModelIndex &index) const override; QVariant headerData(int section, Qt::Orientation orientation, int role = Qt::DisplayRole) const override; QModelIndex index(int row, int column, const QModelIndex &parent = QModelIndex()) const override; QModelIndex parent(const QModelIndex &child) const override; int rowCount(const QModelIndex &parent = QModelIndex()) const override; int columnCount(const QModelIndex &parent = QModelIndex()) const override; private: QDomDocument domDocument; DomItem *rootItem; };
The class definition contains all the basic functions that are needed for a read-only model. Only the constructor and document()
function are specific to this model. The private domDocument
variable is used to hold the document that is exposed by the model; the rootItem
variable contains a pointer to the root item in the model.
DomItem Class Definition
The DomItem
class is used to hold information about a specific QDomNode in the document:
class DomItem { public: DomItem(QDomNode &node, int row, DomItem *parent = 0); ~DomItem(); DomItem *child(int i); DomItem *parent(); QDomNode node() const; int row(); private: QDomNode domNode; QHash<int,DomItem*> childItems; DomItem *parentItem; int rowNumber; };
Each DomItem
provides a wrapper for a QDomNode obtained from the underlying document which contains a reference to the node, it's location in the parent node's list of child nodes, and a pointer to a parent wrapper item.
The parent()
, child()
, and row()
functions are convenience functions for the DomModel
to use that provide basic information about the item to be discovered quickly. The node() function provides access to the underlying QDomNode object.
As well as the information supplied in the constructor, the class maintains a cache of information about any child items. This is used to provide a collection of persistent item objects that the model can identify consistently and improve the performance of the model when accessing child items.
DomItem Class Implementation
Since the DomItem
class is only a thin wrapper around QDomNode objects, with a few additional features to help improve performance and memory usage, we can provide a brief outline of the class before discussing the model itself.
The constructor simply records details of the QDomNode that needs to be wrapped:
DomItem::DomItem(QDomNode &node, int row, DomItem *parent) { domNode = node; rowNumber = row; parentItem = parent; }
As a result, functions to provide the parent wrapper, the row number occupied by the item in its parent's list of children, and the underlying QDomNode for each item are straightforward to write:
DomItem *DomItem::parent() { return parentItem; } int DomItem::row() { return rowNumber; } QDomNode DomItem::node() const { return domNode; }
It is necessary to maintain a collection of items which can be consistently identified by the model. For that reason, we maintain a hash of child wrapper items that, to minimize memory usage, is initially empty. The model uses the item's child()
function to help create model indexes, and this constructs wrappers for the children of the item's QDomNode, relating the row number of each child to the newly-constructed wrapper:
DomItem *DomItem::child(int i) { if (childItems.contains(i)) return childItems[i]; if (i >= 0 && i < domNode.childNodes().count()) { QDomNode childNode = domNode.childNodes().item(i); DomItem *childItem = new DomItem(childNode, i, this); childItems[i] = childItem; return childItem; } return 0; }
If a QDomNode was previously wrapped, the cached wrapper is returned; otherwise, a new wrapper is constructed and stored for valid children, and zero is returned for invalid ones.
The class's destructor deletes all the child items of the wrapper:
DomItem::~DomItem() { QHash<int,DomItem*>::iterator it; for (it = childItems.begin(); it != childItems.end(); ++it) delete it.value(); }
These, in turn, will delete their children and free any QDomNode objects in use.
DomModel Class Implementation
The structure provided by the DomItem
class makes the implementation of DomModel
similar to the TreeModel
shown in the Simple Tree Model example.
The constructor accepts an existing document and a parent object for the model:
DomModel::DomModel(QDomDocument document, QObject *parent) : QAbstractItemModel(parent), domDocument(document) { rootItem = new DomItem(domDocument, 0); }
A shallow copy of the document is stored for future reference, and a root item is created to provide a wrapper around the document. We assign the root item a row number of zero only to be consistent since the root item will have no siblings.
Since the model only contains information about the root item, the destructor only needs to delete this one item:
DomModel::~DomModel() { delete rootItem; }
All of the child items in the tree will be deleted by the DomItem
destructor as their parent items are deleted.
Basic Properties of The Model
Some aspects of the model do not depend on the structure of the underlying document, and these are simple to implement.
The number of columns exposed by the model is returned by the columnCount()
function:
int DomModel::columnCount(const QModelIndex &/*parent*/) const { return 3; }
This value is fixed, and does not depend on the location or type of the underlying node in the document. We will use these three columns to display different kinds of data from the underlying document.
Since we only implement a read-only model, the flags()
function is straightforward to write:
Qt::ItemFlags DomModel::flags(const QModelIndex &index) const { if (!index.isValid()) return 0; return QAbstractItemModel::flags(index); }
Since the model is intended for use in a tree view, the headerData()
function only provides a horizontal header:
QVariant DomModel::headerData(int section, Qt::Orientation orientation, int role) const { if (orientation == Qt::Horizontal && role == Qt::DisplayRole) { switch (section) { case 0: return tr("Name"); case 1: return tr("Attributes"); case 2: return tr("Value"); default: return QVariant(); } } return QVariant(); }
The model presents the names of nodes in the first column, element attributes in the second, and any node values in the third.
Navigating The Document
The index() function creates a model index for the item with the given row, column, and parent in the model:
QModelIndex DomModel::index(int row, int column, const QModelIndex &parent) const { if (!hasIndex(row, column, parent)) return QModelIndex(); DomItem *parentItem; if (!parent.isValid()) parentItem = rootItem; else parentItem = static_cast<DomItem*>(parent.internalPointer());
The function first has to relate the parent index to an item that contains a node from the underlying document. If the parent index is invalid, it refers to the root node in the document, so we retrieve the root item that wraps it; otherwise, we obtain a pointer to the relevant item using the QModelIndex::internalPointer() function. We are able to extract a pointer in this way because any valid model index will have been created by this function, and we store pointers to item objects in any new indexes that we create with QAbstractItemModel::createIndex():
DomItem *childItem = parentItem->child(row); if (childItem) return createIndex(row, column, childItem); else return QModelIndex(); }
A child item for the given row is provided by the parent item's child()
function. If a suitable child item was found then we call createIndex() to produce a model index for the requested row and column, passing a pointer to the child item for it to store internally. If no suitable child item is found, an invalid model index is returned.
Note that the items themselves maintain ownership of their child items. This means that the model does not need to keep track of the child items that have been created, and can let the items themselves tidy up when they are deleted.
The number of rows beneath a given item in the model is returned by the rowCount()
function, and is the number of child nodes contained by the node that corresponds to the specified model index:
int DomModel::rowCount(const QModelIndex &parent) const { if (parent.column() > 0) return 0; DomItem *parentItem; if (!parent.isValid()) parentItem = rootItem; else parentItem = static_cast<DomItem*>(parent.internalPointer()); return parentItem->node().childNodes().count(); }
To obtain the relevant node in the underlying document, we access the item via the internal pointer stored in the model index. If an invalid index is supplied, the root item is used instead. We use the item's node()
function to access the node itself, and simply count the number of child nodes it contains.
Since the model is used to represent a hierarchical data structure, it needs to provide an implementation for the parent()
function. This returns a model index that corresponds to the parent of a child model index supplied as its argument:
QModelIndex DomModel::parent(const QModelIndex &child) const { if (!child.isValid()) return QModelIndex(); DomItem *childItem = static_cast<DomItem*>(child.internalPointer()); DomItem *parentItem = childItem->parent(); if (!parentItem || parentItem == rootItem) return QModelIndex(); return createIndex(parentItem->row(), 0, parentItem); }
For valid indexes other than the index corresponding to the root item, we obtain a pointer to the relevant item using the method described in the index()
function, and use the item's parent()
function to obtain a pointer to the parent item.
If no valid parent item exists, or if the parent item is the root item, we can simply follow convention and return an invalid model index. For all other parent items, we create a model index containing the appropriate row and column numbers, and a pointer to the parent item we just obtained.
Data is provided by the data()
function. For simplicity, we only provide data for the display role, returning an invalid variant for all other requests:
QVariant DomModel::data(const QModelIndex &index, int role) const { if (!index.isValid()) return QVariant(); if (role != Qt::DisplayRole) return QVariant(); DomItem *item = static_cast<DomItem*>(index.internalPointer()); QDomNode node = item->node();
As before, we obtain an item pointer for the index supplied, and use it to obtain the underlying document node. Depending on the column specified, the data we return is obtained in different ways:
QStringList attributes; QDomNamedNodeMap attributeMap = node.attributes(); switch (index.column()) { case 0: return node.nodeName(); case 1: for (int i = 0; i < attributeMap.count(); ++i) { QDomNode attribute = attributeMap.item(i); attributes << attribute.nodeName() + "=\"" +attribute.nodeValue() + '"'; } return attributes.join(' '); case 2: return node.nodeValue().split("\n").join(' '); default: return QVariant(); } }
For the first column, we return the node's name. For the second column, we read any attributes that the node may have, and return a string that contains a space-separated list of attribute-value assignments. For the third column, we return any value that the node may have; this allows the contents of text nodes to be displayed in a view.
If data from any other column is requested, an invalid variant is returned.
Implementation Notes
Ideally, we would rely on the structure provided by QDomDocument to help us write the parent() and index() functions that are required when subclassing QAbstractItemModel. However, since Qt's DOM classes use their own system for dynamically allocating memory for DOM nodes, we cannot guarantee that the QDomNode objects returned for a given piece of information will be the same for subsequent accesses to the document.
We use item wrappers for each QDomNode to provide consistent pointers that the model can use to navigate the document structure.
Files:
- itemviews/simpledommodel/domitem.cpp
- itemviews/simpledommodel/domitem.h
- itemviews/simpledommodel/dommodel.cpp
- itemviews/simpledommodel/dommodel.h
- itemviews/simpledommodel/mainwindow.cpp
- itemviews/simpledommodel/mainwindow.h
- itemviews/simpledommodel/main.cpp
- itemviews/simpledommodel/simpledommodel.pro


'Goblin' co-star Gong Yoo to be Lee Dong Wook's first guest on upcoming talk show
by danisurstGong Yoo will be the first guest on Lee Dong Wook's SBS talk show, 'I Want To Talk'!
On November 4 KST, one media outlet reported that Gong Yoo joined Lee Dong Wook in Jeju, where they taped the first episode together.
In response, Lee Dong Wook's agency King Kong confirmed that Gong Yoo would be the show's first guest.
Meanwhile, the two actors famously appeared as the two main male roles in the 2017 tvN drama 'Goblin,' making their reunion on Lee Dong Wook's talk show all the more anticipatory.
The first episode of 'I Want To Talk' will air on December 4 KST.


Q3 Fintech Deals Near $9 Billion, Year to Date $24.6 Billion Has Been Raised Topping All of 2017
by JD AloisFintech funding for the 3rd quarter of 2019 surpassed $8.9 billion – a new quarterly record if you factor out Ant Financial’s whopping $14. billion investment in Q2 of 2018. Fintech, and its various subcategories remains a hot sector of venture financing as the year so far has registered $24.6 billion already beating the 2017 total. Fintech funding has been boosted by some super-rounds, 19 $100 million + funding worth about $4 billion during Q3 2019.
CB Insights has just published its Q3 report on Fintech funding and 2019 stands to be a solid year but probably not as strong as last year when 1967 deals were funded for a total of $40.6 billion. Deals picked up in Q3 but not enough to beat the gangbuster year of 2018.
The report covers the diverse universe of Fintech including:
- Payments
- Regtech
- Blockchain/Crypto
- Lending
- Insurtech
- Capital Markets
- Wealthtech
- Transfers & Remittances
- Real Estate & Mortgages
Regarding specific regions, the US declined to a multi-quarter low number of deals, while Asia rebounded. Southeast Asia hit a new annual record for $701 million for 87 deals through the 3rd quarter. The top deals in Southeast Asia included $100 million each for Singapore based Deserka and Vietnam based MoMo.
Europe bounced lower to with just 90 deals – also, a multiquarter low.
China, a hotbed of Fintech activity, delivered 55 deals in Q3 raising $661 million but India raised more at $674 million.
The Fintech unicorn herd continues to expand as 6 new Fintechs joined the elite group during Q3 and, according to the report, 3 more have been added in the first part of Q4.
In total, there are 58 Fintech unicorns valued at around $213.5 billion.
Challenger or digital banks remained a hot sector of Fintech as these asset-light, agile bank service providers build momentum to challenge established brick and mortar operations. Year to date, digital banks have raised $3 billion with Q3 delivering $1.3 billion – a quarterly high.
As Fintechs redefine banking, digital platforms continue to pummel the geriatric banks with services like high-interest rate savings and free brokerages. Do we need to visit a bank branch ever again? Soon enough. I promise.
The blending of services is exemplified by Fintechs like:
- Revolut – 8 million users (recently launched in the US)
- SoFi – 7.5 million users
- MoneyLion – 5.7 million users
- CashApp – 15 million users
- there are others….
(None of these platforms have a US bank charter, as far as we know).
So what is around the corner? Always a good question. Early stage deals have declined a bit while later stage funding has increased. So is innovation coming to end? Not a chance, it is just beginning.
The CB Insights Fintech Report for Q3 2019 is available here.


Writing down my thoughts feels good, so good, awesome thing!
___ ___ ___
{o,o} {o.o} {o,o}
|)__) |)_(| (__(|
--"-"------"-"-------"-"--
O RLY? YA RLY NO WAI!
This world is huge, I love it when someone writes what I’m thinking about writing. The same happens whenever any ideas come to my mind, I look up about it on the internet and find out that people have spent time on this figuring out the solutions and developing them. I used to consider this differently but now I think about this positively.
This blog will be the place to post links which I find relevant and interesting. It particularly boils down to two things.
The stuff what I do want to learn about and Insightful thoughts on what I already know.
I never felt the need to have a blog because of using Telegram channels to dump links, thoughts, and plans. But, In the past few days, I felt a desire to do this.
This will be a good start, I am excited, Yay!


RUTH ORKIN: FRAMES OF LIGHT A film review by Steve Rhodes Copyright 1997 Steve Rhodes
RATING (0 TO ****): **
RUTH ORKIN: FRAMES OF LIGHT is a short subject documentary by Mary Engel and narrated by Julie Harris. I saw the documentary at San Jose's Cinequest film festival. Ruth Orkin (1921-1985) was a famous photographer whose specialty was black and white images of people. Her most well known photograph is entitled "The American Girl."
Mary Engel approaches the documentary by concentrating on Orkin's life rather than her pictures. This could be considered either the strength or the weakness of the film.
Orkin was an interesting person. She got her first camera at the age of 10, and by 12 she considered herself a photographer. She looked at the pictures of the professional photographers of the time, and decided that she must get herself a 35mm camera if she was to be competitive. Given her financial situation, she could not afford one until she turned 24.
As she grew up, she decided that what she really wanted to be was a cinematographer, but the unions at the time would not let a woman join. Without her union credentials, no producer would dare hire her. She resigned herself to making a living as a photographer.
Later in life she was associated with the 1953 movie THE LITTLE FUGITIVE. This was one of the first films to be shot on location. It told the story of a 7-year-old boy who leaves Brooklyn and ends up on Ellis Island. She received an Academy Award nomination for the script.
Ruth Orkin was an interesting woman and a pace setter, but this documentary about her could have been better. It presented the basic facts of Orkin's life, but I left thirsting for more knowledge about all the photos we saw in the documentary. What was she trying to capture? What were her techniques? I left with little more than a collection of images and some random pieces of historical data.
RUTH ORKIN: FRAMES OF LIGHT runs about 15-20 minutes. It is not rated, but would be a G. It is a close call, but the documentary skimmed the surface too much for me to be able to recommend it. I give it **.
**** = A must see film. *** = Excellent show. Look for it. ** = Average movie. Kind of enjoyable. * = Poor show. Don't waste your money. 0 = Totally and painfully unbearable picture.
REVIEW WRITTEN ON: January 31, 1997
Opinions expressed are mine and not meant to reflect my employer's.
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


--- layout: page title: About Me description: This is what I do. background: '/images/bg-about.jpg' ---
I am a Business Analytics graduate student and Data Science enthusiast with a deep understanding of machine learning concepts and analytical practices. I have 10+ years of IT experience working with MNCs in telecommunication and CRM domain.


CodeIgniter is a PHP full-stack web framework that is light, fast, flexible, and secure. More information can be found at the official site.
This repository holds the distributable version of the framework, including the user guide. It has been built from the development repository.
More information about the plans for version 4 can be found in the announcement on the forums.
The user guide corresponding to this version of the framework can be found here.
index.php
is no longer in the root of the project! It has been moved inside the public folder,
for better security and separation of components.
This means that you should configure your web server to "point" to your project's public folder, and not to the project root. A better practice would be to configure a virtual host to point there. A poor practice would be to point your web server to the project root and expect to enter public/..., as the rest of your logic and the framework are exposed.
Please read the user guide for a better explanation of how CI4 works! The user guide updating and deployment is a bit awkward at the moment, but we are working on it!
We use Github issues, in our main repository, to track BUGS and to track approved DEVELOPMENT work packages. We use our forum to provide SUPPORT and to discuss FEATURE REQUESTS.
This repository is a "distribution" one, built by our release preparation script. Problems with it can be raised on our forum, or as issues in the main repository.
We welcome contributions from the community.
Please read the Contributing to CodeIgniter section in the development repository.
PHP version 7.2 or higher is required, with the following extensions installed:
Additionally, make sure that the following extensions are enabled in your PHP:


SCENT OF A WOMAN A film review by Jon Webb Copyright 1993 Jon Webb
Al Pacino plays a blind former Lieutenant Colonel who, being depressed and irascible, has been left in the care of an impoverished prep school boy for Thanksgiving weekend. The Colonel plans a trip to New York City to experience the best life offers and brings the boy along.
I read a review that pointed out that this film was intended to be a sentimental holiday heart-warmer--both the old Colonel and the young boy learn from each other, etc.--but it turned into a much better film because of Al Pacino's performance. That is exactly right. Pacino refuses to give the Colonel the one-dimensional nature that would have made this a superficial film. His portrayal gets to the heart of the character of the Colonel, who is not all bad and not all good, and not completely healed at the end of the film. He mouths the lines given him, turning the good ones (about women) into poetry, and keeping the bad ones (concerning the subplot of the boy's problems at the prep school) from being totally sentimental trash. He makes this film entertaining and worth seeing.
The prep school boy (played by Chris O'Donnell) is also pretty good. Perhaps it is just in reaction to Pacino's performance, but I thought he'd captured the character of such a young man very well, particularly in the early scenes.
That said, there are numerous flaws in this film. The script is bad, and the editing often gives one no impression of time passing -- e.g., at the Thanksgiving Dinner they seem to sit down and have pie, and when the boy is waiting in the car Pacino seems to take about five minutes, which is something of a disappointing climax after such a big buildup.
Even worse, the direction of the Thanksgiving Dinner family is so bad you don't have any idea what's going on. Pacino shows up, and everyone seems constipated. What's going on there? The film is best when O'Donnell and Pacino are alone; at least then they seem to have some idea of what they're doing. Martin Brest, the director, doesn't have a clue.
-- J
.
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


Sign In
☰
×
Home
Learn
Login
Chat
My Progress
About us
Create Spontaneous Account
Personal Details
Verification Details
Select a picture
Submit


Convert the sugar in your recipes to maple syrup!
Sugar
Cups
Tablespoons
=
Maple Syrup
Cups
Tablespoons
And remove 1 tablespoon of liquid (e.g. milk, oil, etc.)


Getting started
Command line:
git clone https://github.com/marcobambini/gravity.git
cd gravity
make
You can also use the Xcode project to create the gravity or unittest executables.
Unitest usage:
./unittest path_to_test_folder
You should obtain an output like:
Gravity help:
./gravity --help
To compile a gravity file to a exec.json executable:
./gravity -c myfile.gravity -o exec.json
To execute a precompiled json executable file:
./gravity -x exec.json
To directly execute a gravity file (without first serialize it to json):
./gravity myfile.gravity


All Berkeley DB utility programs can be run on environments and
databases managed by Berkeley DB servers. For example, you can use
db_hotbackup
to backup databases, or
db_stat
to get database statistics.
The following constraints apply when running utility programs on managed environments and databases that are accessed simultaneously by other processes:
Constraints do not apply if the accessed environment or database has no client handles opened or if the server is offline.
No constraint applies.
If root.data and/or
root.log are different from
root.home, you need a
DB_CONFIG
file which sets the
proper paths using the add_data_dir
,
or set_lg_dir
configuration
parameters.
Options -d, -R
and
-r
cannot be used on databases
actively accessed by clients.
db_load
cannot be used on databases
actively accessed by clients.
If root.blob is different from
root.home, you need to set the
proper blob path using the -b
option.
No constraint applies.
If root.log is different from
root.log, you need a
DB_CONFIG
file which sets the
proper path using the set_lg_dir
configuration parameter.
No constraint applies.
If root.log is different from
root.log, you need a
DB_CONFIG
file which sets the
proper path using the set_lg_dir
configuration parameter.
db_recover
cannot be used on environments
actively accessed by clients.
db_recover
must have exclusive access to
the environment being recovered. An alternative way to perform
normal recovery of an environment is to open the environment
with the DB_RECOVER
flag from a client
application.
db_replicate
cannot be used on databases
managed by Berkeley DB servers.
Replication is not supported by Berkeley DB servers.
Therefore, even when the server is offline,
db_replicate
still cannot be used
on databases managed by Berkeley DB server.
db_sql_codegen
does not access
environments or databases, and is irrelevant.
dbsql
cannot be used on databases managed
by Berkeley DB servers because they are not created with the
SQL APIs.
Options -r
and -R
cannot be used on databases managed by Berkeley DB servers
because replication is not supported.
db_upgrade
cannot be used on databases
actively accessed by clients.
db_verify
cannot be used on databases
actively accessed by clients.
If root.blob is different from
root.home, you need to set the
proper blob path using the -b
option.
sqlite3
cannot be used on databases
managed by Berkeley DB servers.
To manage secondary keys created for secondary databases, an auxiliary database is maintained on the server for each secondary database. If the secondary database is file based, the auxiliary database is also file based. If the secondary database is in-memory, the auxiliary database is also in-memory.
For file-based auxiliary databases, its file name is
constructed by appending "__aux.db"
to the
file name of its corresponding secondary database. For example,
if the file name of a secondary database is
"secondary"
, the file name of its
corresponding auxiliary database is
"secondary__aux.db"
.
Make sure that the auxiliary database is in sync with its corresponding secondary databases. Otherwise, operations on the secondary database may fail. For example, when backing up or restoring databases, the secondary databases and their corresponding auxiliary databases must be backed up or restored together. When restoring databases, the databases must be restored together.


Voter 2020 Ballot Search 2020
Montgomery County, TX
Check Status of Absentee, Early and Voting Day Ballots
Enter your first and last names as on your voter registraion card to check ballot status!
First name:
Last Name:


tencyclopedia
Toggle navigation
Home
About
Contact Us
Newsletter
Top 10 Cars Under 20 Lakhs - March 2016
1. Hyundai Creta (SUV) Ex-Showroom Price : 10.16 Lakhs
Engine
Displacement
Max Power
1.4 CRDi Diesel
1591 CC
126 bhp @ 6400 rpm
Mileage
Fuel
Transmission
15 - 21 Kmpl
Diesel, Petrol
Manual, Automatic
2. Skoda Octavia (Sedan) Ex-Showroom Price : 16.03 Lakhs
Engine
Displacement
Max Power
turbocharged petrol engine
1968 CC
6032 bhp @ 6200 rpm
Mileage
Fuel
Transmission
15 - 21 Kmpl
Petrol, Diesel
Manual, Automatic
3. Renault Duster (SUV) Ex-Showroom Price : 10.03 Lakhs
Engine
Displacement
Max Power
1.5L Dci THP
1598 CC
108 bhp @ 5750 rpm
Mileage
Fuel
Transmission
13 - 20 Kmpl
Diesel, Petrol
Manual
4. Ford EcoSport (SUV) Ex-Showroom Price : 6.50 - 11 Lakhs
Engine
Displacement
Max Power
1.0P Ecoboost Titanium MT
1499 CC
123 bhp @ 6300 rpm
Mileage
Fuel
Transmission
16 - 23 Kmpl
Diesel, Petrol
Manual, Automatic
5. Volkswagen Jetta (Sedan) Ex-Showroom Price : 15 Lakhs
Engine
Displacement
Max Power
Jetta 1.4L TSI, 4-cylinder
1968 CC
138 bhp @ 5000 rpm
Mileage
Fuel
Transmission
15 - 19 Kmpl
Petrol, Diesel
Manual, Automatic
6. Hyundai Elantra (Sedan) Ex-Showroom Price : 14.68 Lakhs
Engine
Displacement
Max Power
1.6l
1797 CC
149 bhp @ 6500 rpm
Mileage
Fuel
Transmission
23 Kmpl
Diesel, Petrol
Manual, Automatic
7. Chevrolet Cruze (Sedan) Ex-Showroom Price : 14.57 Lakhs
Engine
Displacement
Max Power
vcdi 16v dohc
1998 CC
163 bhp @ 320 rpm
Mileage
Fuel
Transmission
13 - 16 Kmpl
Diesel
Manual, Automatic
8. Volkswagen Vento (Sedan) Ex-Showroom Price : 10 Lakhs
Engine
Displacement
Max Power
Vento 1.5L TDI, Turbocharged
N/A
103 bhp @ 5250 rpm
Mileage
Fuel
Transmission
16 - 21 Kmpl
Petrol, Diesel
Manual, Automatic
9. Toyota Innova (Minivan) Ex-Showroom Price : 10.62 Lakhs
Engine
Displacement
Max Power
2KD-FTV Diesel Engine
2494 CC
100 bhp @ 3600 rpm
Mileage
Fuel
Transmission
10 - 13 Kmpl
Diesel
Manual
10. Mahindra XUV500 (SUV) Ex-Showroom Price : 11.60 Lakhs
Engine
Displacement
Max Power
2.2-litre 140bhp 16V mHawk
2179 CC
140 bhp @ 3750 rpm
Mileage
Fuel
Transmission
13 - 16 Kmpl
Diesel
Manual, Automatic
Enter Your E-mail Id below to Subscribe for Newsletters every Month!!
Name
Email-id