| java.lang.Object | |
| ↳ | android.Manifest.permission |
| Constants | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| String | ACCESS_CHECKIN_PROPERTIES | Allows read/write access to the "properties" table in the checkin database, to change values that get uploaded. | |||||||||
| String | ACCESS_COARSE_LOCATION | Allows an app to access approximate location derived from network location sources such as cell towers and Wi-Fi. | |||||||||
| String | ACCESS_FINE_LOCATION | Allows an app to access precise location from location sources such as GPS, cell towers, and Wi-Fi. | |||||||||
| String | ACCESS_LOCATION_EXTRA_COMMANDS | Allows an application to access extra location provider commands | |||||||||
| String | ACCESS_MOCK_LOCATION | Allows an application to create mock location providers for testing | |||||||||
| String | ACCESS_NETWORK_STATE | Allows applications to access information about networks | |||||||||
| String | ACCESS_SURFACE_FLINGER | Allows an application to use SurfaceFlinger's low level features | |||||||||
| String | ACCESS_WIFI_STATE | Allows applications to access information about Wi-Fi networks | |||||||||
| String | ACCOUNT_MANAGER | Allows applications to call into AccountAuthenticators. | |||||||||
| String | ADD_VOICEMAIL | Allows an application to add voicemails into the system. | |||||||||
| String | AUTHENTICATE_ACCOUNTS | Allows an application to act as an AccountAuthenticator for the AccountManager | |||||||||
| String | BATTERY_STATS | Allows an application to collect battery statistics | |||||||||
| String | BIND_ACCESSIBILITY_SERVICE | Must be required by an AccessibilityService ,
to ensure that only the system can bind to it. |
|||||||||
| String | BIND_APPWIDGET | Allows an application to tell the AppWidget service which application can access AppWidget's data. | |||||||||
| String | BIND_DEVICE_ADMIN | Must be required by device administration receiver, to ensure that only the system can interact with it. | |||||||||
| String | BIND_INPUT_METHOD | Must be required by an InputMethodService ,
to ensure that only the system can bind to it. |
|||||||||
| String | BIND_REMOTEVIEWS | Must be required by a RemoteViewsService ,
to ensure that only the system can bind to it. |
|||||||||
| String | BIND_TEXT_SERVICE | Must be required by a TextService (e.g. | |||||||||
| String | BIND_VPN_SERVICE | Must be required by an VpnService ,
to ensure that only the system can bind to it. |
|||||||||
| String | BIND_WALLPAPER | Must be required by a WallpaperService ,
to ensure that only the system can bind to it. |
|||||||||
| String | BLUETOOTH | Allows applications to connect to paired bluetooth devices | |||||||||
| String | BLUETOOTH_ADMIN | Allows applications to discover and pair bluetooth devices | |||||||||
| String | BRICK | Required to be able to disable the device (very dangerous!). | |||||||||
| String | BROADCAST_PACKAGE_REMOVED | Allows an application to broadcast a notification that an application package has been removed. | |||||||||
| String | BROADCAST_SMS | Allows an application to broadcast an SMS receipt notification | |||||||||
| String | BROADCAST_STICKY | Allows an application to broadcast sticky intents. | |||||||||
| String | BROADCAST_WAP_PUSH | Allows an application to broadcast a WAP PUSH receipt notification | |||||||||
| String | CALL_PHONE | Allows an application to initiate a phone call without going through the Dialer user interface for the user to confirm the call being placed. | |||||||||
| String | CALL_PRIVILEGED | Allows an application to call any phone number, including emergency numbers, without going through the Dialer user interface for the user to confirm the call being placed. | |||||||||
| String | CAMERA | Required to be able to access the camera device. | |||||||||
| String | CHANGE_COMPONENT_ENABLED_STATE | Allows an application to change whether an application component (other than its own) is enabled or not. | |||||||||
| String | CHANGE_CONFIGURATION | Allows an application to modify the current configuration, such as locale. | |||||||||
| String | CHANGE_NETWORK_STATE | Allows applications to change network connectivity state | |||||||||
| String | CHANGE_WIFI_MULTICAST_STATE | Allows applications to enter Wi-Fi Multicast mode | |||||||||
| String | CHANGE_WIFI_STATE | Allows applications to change Wi-Fi connectivity state | |||||||||
| String | CLEAR_APP_CACHE | Allows an application to clear the caches of all installed applications on the device. | |||||||||
| String | CLEAR_APP_USER_DATA | Allows an application to clear user data | |||||||||
| String | CONTROL_LOCATION_UPDATES | Allows enabling/disabling location update notifications from the radio. | |||||||||
| String | DELETE_CACHE_FILES | Allows an application to delete cache files. | |||||||||
| String | DELETE_PACKAGES | Allows an application to delete packages. | |||||||||
| String | DEVICE_POWER | Allows low-level access to power management | |||||||||
| String | DIAGNOSTIC | Allows applications to RW to diagnostic resources. | |||||||||
| String | DISABLE_KEYGUARD | Allows applications to disable the keyguard | |||||||||
| String | DUMP | Allows an application to retrieve state dump information from system services. | |||||||||
| String | EXPAND_STATUS_BAR | Allows an application to expand or collapse the status bar. | |||||||||
| String | FACTORY_TEST | Run as a manufacturer test application, running as the root user. | |||||||||
| String | FLASHLIGHT | Allows access to the flashlight | |||||||||
| String | FORCE_BACK | Allows an application to force a BACK operation on whatever is the top activity. | |||||||||
| String | GET_ACCOUNTS | Allows access to the list of accounts in the Accounts Service | |||||||||
| String | GET_PACKAGE_SIZE | Allows an application to find out the space used by any package. | |||||||||
| String | GET_TASKS | Allows an application to get information about the currently or recently running tasks. | |||||||||
| String | GLOBAL_SEARCH | This permission can be used on content providers to allow the global search system to access their data. | |||||||||
| String | HARDWARE_TEST | Allows access to hardware peripherals. | |||||||||
| String | INJECT_EVENTS | Allows an application to inject user events (keys, touch, trackball) into the event stream and deliver them to ANY window. | |||||||||
| String | INSTALL_LOCATION_PROVIDER | Allows an application to install a location provider into the Location Manager | |||||||||
| String | INSTALL_PACKAGES | Allows an application to install packages. | |||||||||
| String | INTERNAL_SYSTEM_WINDOW | Allows an application to open windows that are for use by parts of the system user interface. | |||||||||
| String | INTERNET | Allows applications to open network sockets. | |||||||||
| String | KILL_BACKGROUND_PROCESSES | Allows an application to call
killBackgroundProcesses(String) . |
|||||||||
| String | MANAGE_ACCOUNTS | Allows an application to manage the list of accounts in the AccountManager | |||||||||
| String | MANAGE_APP_TOKENS | Allows an application to manage (create, destroy, Z-order) application tokens in the window manager. | |||||||||
| String | MASTER_CLEAR | ||||||||||
| String | MODIFY_AUDIO_SETTINGS | Allows an application to modify global audio settings | |||||||||
| String | MODIFY_PHONE_STATE | Allows modification of the telephony state - power on, mmi, etc. | |||||||||
| String | MOUNT_FORMAT_FILESYSTEMS | Allows formatting file systems for removable storage. | |||||||||
| String | MOUNT_UNMOUNT_FILESYSTEMS | Allows mounting and unmounting file systems for removable storage. | |||||||||
| String | NFC | Allows applications to perform I/O operations over NFC | |||||||||
| String | PERSISTENT_ACTIVITY | This constant was deprecated in API level 9. This functionality will be removed in the future; please do not use. Allow an application to make its activities persistent. | |||||||||
| String | PROCESS_OUTGOING_CALLS | Allows an application to monitor, modify, or abort outgoing calls. | |||||||||
| String | READ_CALENDAR | Allows an application to read the user's calendar data. | |||||||||
| String | READ_CALL_LOG | Allows an application to read the user's call log. | |||||||||
| String | READ_CONTACTS | Allows an application to read the user's contacts data. | |||||||||
| String | READ_EXTERNAL_STORAGE | Allows an application to read from external storage. | |||||||||
| String | READ_FRAME_BUFFER | Allows an application to take screen shots and more generally get access to the frame buffer data | |||||||||
| String | READ_HISTORY_BOOKMARKS | Allows an application to read (but not write) the user's browsing history and bookmarks. | |||||||||
| String | READ_INPUT_STATE | This constant was deprecated in API level 16. The API that used this permission has been removed. | |||||||||
| String | READ_LOGS | Allows an application to read the low-level system log files. | |||||||||
| String | READ_PHONE_STATE | Allows read only access to phone state. | |||||||||
| String | READ_PROFILE | Allows an application to read the user's personal profile data. | |||||||||
| String | READ_SMS | Allows an application to read SMS messages. | |||||||||
| String | READ_SOCIAL_STREAM | Allows an application to read from the user's social stream. | |||||||||
| String | READ_SYNC_SETTINGS | Allows applications to read the sync settings | |||||||||
| String | READ_SYNC_STATS | Allows applications to read the sync stats | |||||||||
| String | READ_USER_DICTIONARY | Allows an application to read the user dictionary. | |||||||||
| String | REBOOT | Required to be able to reboot the device. | |||||||||
| String | RECEIVE_BOOT_COMPLETED | Allows an application to receive the
ACTION_BOOT_COMPLETED that is
broadcast after the system finishes booting. |
|||||||||
| String | RECEIVE_MMS | Allows an application to monitor incoming MMS messages, to record or perform processing on them. | |||||||||
| String | RECEIVE_SMS | Allows an application to monitor incoming SMS messages, to record or perform processing on them. | |||||||||
| String | RECEIVE_WAP_PUSH | Allows an application to monitor incoming WAP push messages. | |||||||||
| String | RECORD_AUDIO | Allows an application to record audio | |||||||||
| String | REORDER_TASKS | Allows an application to change the Z-order of tasks | |||||||||
| String | RESTART_PACKAGES |
This constant was deprecated
in API level 8.
The restartPackage(String)
API is no longer supported.
|
|||||||||
| String | SEND_SMS | Allows an application to send SMS messages. | |||||||||
| String | SET_ACTIVITY_WATCHER | Allows an application to watch and control how activities are started globally in the system. | |||||||||
| String | SET_ALARM | Allows an application to broadcast an Intent to set an alarm for the user. | |||||||||
| String | SET_ALWAYS_FINISH | Allows an application to control whether activities are immediately finished when put in the background. | |||||||||
| String | SET_ANIMATION_SCALE | Modify the global animation scaling factor. | |||||||||
| String | SET_DEBUG_APP | Configure an application for debugging. | |||||||||
| String | SET_ORIENTATION | Allows low-level access to setting the orientation (actually rotation) of the screen. | |||||||||
| String | SET_POINTER_SPEED | Allows low-level access to setting the pointer speed. | |||||||||
| String | SET_PREFERRED_APPLICATIONS |
This constant was deprecated
in API level 7.
No longer useful, see
addPackageToPreferred(String)
for details.
|
|||||||||
| String | SET_PROCESS_LIMIT | Allows an application to set the maximum number of (not needed) application processes that can be running. | |||||||||
| String | SET_TIME | Allows applications to set the system time | |||||||||
| String | SET_TIME_ZONE | Allows applications to set the system time zone | |||||||||
| String | SET_WALLPAPER | Allows applications to set the wallpaper | |||||||||
| String | SET_WALLPAPER_HINTS | Allows applications to set the wallpaper hints | |||||||||
| String | SIGNAL_PERSISTENT_PROCESSES | Allow an application to request that a signal be sent to all persistent processes | |||||||||
| String | STATUS_BAR | Allows an application to open, close, or disable the status bar and its icons. | |||||||||
| String | SUBSCRIBED_FEEDS_READ | Allows an application to allow access the subscribed feeds ContentProvider. | |||||||||
| String | SUBSCRIBED_FEEDS_WRITE | ||||||||||
| String | SYSTEM_ALERT_WINDOW | Allows an application to open windows using the type
TYPE_SYSTEM_ALERT ,
shown on top of all other applications. |
|||||||||
| String | UPDATE_DEVICE_STATS | Allows an application to update device statistics. | |||||||||
| String | USE_CREDENTIALS | Allows an application to request authtokens from the AccountManager | |||||||||
| String | USE_SIP | Allows an application to use SIP service | |||||||||
| String | VIBRATE | Allows access to the vibrator | |||||||||
| String | WAKE_LOCK | Allows using PowerManager WakeLocks to keep processor from sleeping or screen from dimming | |||||||||
| String | WRITE_APN_SETTINGS | Allows applications to write the apn settings | |||||||||
| String | WRITE_CALENDAR | Allows an application to write (but not read) the user's calendar data. | |||||||||
| String | WRITE_CALL_LOG | Allows an application to write (but not read) the user's contacts data. | |||||||||
| String | WRITE_CONTACTS | Allows an application to write (but not read) the user's contacts data. | |||||||||
| String | WRITE_EXTERNAL_STORAGE | Allows an application to write to external storage. | |||||||||
| String | WRITE_GSERVICES | Allows an application to modify the Google service map. | |||||||||
| String | WRITE_HISTORY_BOOKMARKS | Allows an application to write (but not read) the user's browsing history and bookmarks. | |||||||||
| String | WRITE_PROFILE | Allows an application to write (but not read) the user's personal profile data. | |||||||||
| String | WRITE_SECURE_SETTINGS | Allows an application to read or write the secure system settings. | |||||||||
| String | WRITE_SETTINGS | Allows an application to read or write the system settings. | |||||||||
| String | WRITE_SMS | Allows an application to write SMS messages. | |||||||||
| String | WRITE_SOCIAL_STREAM | Allows an application to write (but not read) the user's social stream data. | |||||||||
| String | WRITE_SYNC_SETTINGS | Allows applications to write the sync settings | |||||||||
| String | WRITE_USER_DICTIONARY | Allows an application to write to the user dictionary. |
| Public Constructors | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
[Expand]
Inherited Methods | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|
From class
java.lang.Object
|
Allows read/write access to the "properties" table in the checkin database, to change values that get uploaded.
Allows an app to access approximate location derived from network location sources such as cell towers and Wi-Fi.
Allows an app to access precise location from location sources such as GPS, cell towers, and Wi-Fi.
Allows an application to access extra location provider commands
Allows an application to create mock location providers for testing
Allows applications to access information about networks
Allows an application to use SurfaceFlinger's low level features
Allows applications to access information about Wi-Fi networks
Allows applications to call into AccountAuthenticators. Only the system can get this permission.
Allows an application to add voicemails into the system.
Allows an application to act as an AccountAuthenticator for the AccountManager
Allows an application to collect battery statistics
Must be required by an AccessibilityService
,
to ensure that only the system can bind to it.
Allows an application to tell the AppWidget service which application can access AppWidget's data. The normal user flow is that a user picks an AppWidget to go into a particular host, thereby giving that host application access to the private data from the AppWidget app. An application that has this permission should honor that contract. Very few applications should need to use this permission.
Must be required by device administration receiver, to ensure that only the system can interact with it.
Must be required by an InputMethodService
,
to ensure that only the system can bind to it.
Must be required by a RemoteViewsService
,
to ensure that only the system can bind to it.
Must be required by a TextService (e.g. SpellCheckerService) to ensure that only the system can bind to it.
Must be required by an VpnService
,
to ensure that only the system can bind to it.
Must be required by a WallpaperService
,
to ensure that only the system can bind to it.
Allows applications to connect to paired bluetooth devices
Allows applications to discover and pair bluetooth devices
Required to be able to disable the device (very dangerous!).
Allows an application to broadcast a notification that an application package has been removed.
Allows an application to broadcast an SMS receipt notification
Allows an application to broadcast sticky intents. These are broadcasts whose data is held by the system after being finished, so that clients can quickly retrieve that data without having to wait for the next broadcast.
Allows an application to broadcast a WAP PUSH receipt notification
Allows an application to initiate a phone call without going through the Dialer user interface for the user to confirm the call being placed.
Allows an application to call any phone number, including emergency numbers, without going through the Dialer user interface for the user to confirm the call being placed.
Required to be able to access the camera device.
This will automatically enforce the <uses-feature>
manifest element for all camera features.
If you do not require all camera features or can properly operate if a camera
is not available, then you must modify your manifest as appropriate in order to
install on devices that don't support all camera features.
Allows an application to change whether an application component (other than its own) is enabled or not.
Allows an application to modify the current configuration, such as locale. Allows an application to modify the current configuration, such as locale.
Allows applications to change network connectivity state
Allows applications to enter Wi-Fi Multicast mode
Allows applications to change Wi-Fi connectivity state
Allows an application to clear the caches of all installed applications on the device.
Allows an application to clear user data
Allows enabling/disabling location update notifications from the radio. Not for use by normal applications.
Allows an application to delete cache files.
Allows an application to delete packages.
Allows low-level access to power management
Allows applications to RW to diagnostic resources.
Allows applications to disable the keyguard
Allows an application to retrieve state dump information from system services.
Allows an application to expand or collapse the status bar.
Run as a manufacturer test application, running as the root user. Only available when the device is running in manufacturer test mode.
Allows access to the flashlight
Allows an application to force a BACK operation on whatever is the top activity.
Allows access to the list of accounts in the Accounts Service
Allows an application to find out the space used by any package.
Allows an application to get information about the currently or recently running tasks.
This permission can be used on content providers to allow the global search system to access their data. Typically it used when the provider has some permissions protecting it (which global search would not be expected to hold), and added as a read-only permission to the path in the provider where global search queries are performed. This permission can not be held by regular applications; it is used by applications to protect themselves from everyone else besides global search.
Allows access to hardware peripherals. Intended only for hardware testing
Allows an application to inject user events (keys, touch, trackball) into the event stream and deliver them to ANY window. Without this permission, you can only deliver events to windows in your own process. Very few applications should need to use this permission.
Allows an application to install a location provider into the Location Manager
Allows an application to install packages.
Allows an application to open windows that are for use by parts of the system user interface. Not for use by third party apps.
Allows applications to open network sockets.
Allows an application to call
killBackgroundProcesses(String)
.
Allows an application to manage the list of accounts in the AccountManager
Allows an application to manage (create, destroy, Z-order) application tokens in the window manager. This is only for use by the system.
Allows an application to modify global audio settings
Allows modification of the telephony state - power on, mmi, etc. Does not include placing calls.
Allows formatting file systems for removable storage.
Allows mounting and unmounting file systems for removable storage.
Allows applications to perform I/O operations over NFC
This constant was deprecated
in API level 9.
This functionality will be removed in the future; please do
not use. Allow an application to make its activities persistent.
Allows an application to monitor, modify, or abort outgoing calls.
Allows an application to read the user's calendar data.
Allows an application to read the user's call log.
Note: If your app uses the
READ_CONTACTS
permission and both your minSdkVersion
and targetSdkVersion
values are set to 15 or lower, the system implicitly
grants your app this permission. If you don't need this permission, be sure your targetSdkVersion
is 16 or higher.
Allows an application to read the user's contacts data.
Allows an application to read from external storage.
Any app that declares the WRITE_EXTERNAL_STORAGE
permission is implicitly
granted this permission.
Currently, this permission is not enforced and all apps still have access to read from external storage without this permission. That will change in a future release and apps will require this permission to read from external storage. So if your app reads from the external storage, you should add this permission to your app now to ensure that it continues to work on future versions of Android.
You can test your app with the permission enforced by either running your app on the Android Emulator when running Android 4.1 or higher, or enabling Protect USB storage under Developer options in the Settings app on a device running Android 4.1 or higher.
Note: If both your minSdkVersion
and targetSdkVersion
values are set to 3 or lower, the system implicitly
grants your app this permission. If you don't need this permission, be sure your targetSdkVersion
is 4 or higher.
Allows an application to take screen shots and more generally get access to the frame buffer data
Allows an application to read (but not write) the user's browsing history and bookmarks.
This constant was deprecated
in API level 16.
The API that used this permission has been removed.
Allows an application to retrieve the current state of keys and switches. This is only for use by the system.
Allows an application to read the low-level system log files. Log entries can contain the user's private information, which is why this permission is not available to normal apps.
Allows read only access to phone state.
Note: If both your minSdkVersion
and targetSdkVersion
values are set to 3 or lower, the system implicitly
grants your app this permission. If you don't need this permission, be sure your targetSdkVersion
is 4 or higher.
Allows an application to read the user's personal profile data.
Allows an application to read SMS messages.
Allows an application to read from the user's social stream.
Allows applications to read the sync settings
Allows applications to read the sync stats
Allows an application to read the user dictionary. This should really only be required by an IME, or a dictionary editor like the Settings app.
Required to be able to reboot the device.
Allows an application to receive the
ACTION_BOOT_COMPLETED
that is
broadcast after the system finishes booting. If you don't
request this permission, you will not receive the broadcast at
that time. Though holding this permission does not have any
security implications, it can have a negative impact on the
user experience by increasing the amount of time it takes the
system to start and allowing applications to have themselves
running without the user being aware of them. As such, you must
explicitly declare your use of this facility to make that visible
to the user.
Allows an application to monitor incoming MMS messages, to record or perform processing on them.
Allows an application to monitor incoming SMS messages, to record or perform processing on them.
Allows an application to monitor incoming WAP push messages.
Allows an application to record audio
Allows an application to change the Z-order of tasks
This constant was deprecated
in API level 8.
The restartPackage(String)
API is no longer supported.
Allows an application to send SMS messages.
Allows an application to watch and control how activities are started globally in the system. Only for is in debugging (usually the monkey command).
Allows an application to broadcast an Intent to set an alarm for the user.
Allows an application to control whether activities are immediately finished when put in the background.
Modify the global animation scaling factor.
Configure an application for debugging.
Allows low-level access to setting the orientation (actually rotation) of the screen. Not for use by normal applications.
Allows low-level access to setting the pointer speed. Not for use by normal applications.
This constant was deprecated
in API level 7.
No longer useful, see
addPackageToPreferred(String)
for details.
Allows an application to set the maximum number of (not needed) application processes that can be running.
Allows applications to set the system time
Allows applications to set the system time zone
Allows applications to set the wallpaper
Allows applications to set the wallpaper hints
Allow an application to request that a signal be sent to all persistent processes
Allows an application to open, close, or disable the status bar and its icons.
Allows an application to allow access the subscribed feeds ContentProvider.
Allows an application to open windows using the type
TYPE_SYSTEM_ALERT
,
shown on top of all other applications. Very few applications
should use this permission; these windows are intended for
system-level interaction with the user.
Allows an application to update device statistics. Not for use by third party apps.
Allows an application to request authtokens from the AccountManager
Allows an application to use SIP service
Allows access to the vibrator
Allows using PowerManager WakeLocks to keep processor from sleeping or screen from dimming
Allows applications to write the apn settings
Allows an application to write (but not read) the user's calendar data.
Allows an application to write (but not read) the user's contacts data.
Note: If your app uses the
WRITE_CONTACTS
permission and both your minSdkVersion
and targetSdkVersion
values are set to 15 or lower, the system implicitly
grants your app this permission. If you don't need this permission, be sure your targetSdkVersion
is 16 or higher.
Allows an application to write (but not read) the user's contacts data.
Allows an application to write to external storage.
Note: If both your minSdkVersion
and targetSdkVersion
values are set to 3 or lower, the system implicitly
grants your app this permission. If you don't need this permission, be sure your targetSdkVersion
is 4 or higher.
Allows an application to modify the Google service map.
Allows an application to write (but not read) the user's browsing history and bookmarks.
Allows an application to write (but not read) the user's personal profile data.
Allows an application to read or write the secure system settings.
Allows an application to read or write the system settings.
Allows an application to write SMS messages.
Allows an application to write (but not read) the user's social stream data.
Allows applications to write the sync settings
Allows an application to write to the user dictionary.


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The number of members your organization can have depends on the organization's tier. In general, higher organization tiers can have more members within the organization to share resources. This design enables you to upgrade your organization as your trading business grows over time. The following table shows the number of members each organization tier can have:
| Tier | Minimum Members | Maximum Members |
|---|---|---|
| Free | 1 | 1 |
| Quant Researcher | 1 | 1 |
| Team | 2 | 10 |
| Trading Firm | 2 | Unlimited |
| Institution | 2 | Unlimited |


artvision_instanton::NCCMatcher Class Reference
Implements a mutual correspondence NCC matcher. More...
#include <ncc_matcher.h>
Classes | |
| struct | mutual_feature |
| Container to hold information about feature matches. More... | |
Public Types | |
| typedef std::vector< mutual_feature > | bucket_t |
| typedef std::vector< std::vector< bucket_t > > | bucket_grid_t |
Public Member Functions | |
| NCCMatcher () | |
| void | alloc (int width, int height, float disparity) |
| Allocate the matcher. More... | |
| size_t | match (const NCCTemplateStore &features1, const NCCTemplateStore &features2) |
| Match two feature stores. More... | |
| void | setMinMatchScore (float minScore) |
| Set/Get the min score between a pair of features. More... | |
| float | minMatchScore () const |
| const matches_t & | matches () const |
| Get matches. More... | |
| float | disparityRadius () const |
| int | bucketSize () const |
| int | numBuckets () const |
Detailed Description
Implements a mutual correspondence NCC matcher.
Member Typedef Documentation
| typedef std::vector<std::vector<bucket_t> > artvision_instanton::NCCMatcher::bucket_grid_t |
| typedef std::vector<mutual_feature> artvision_instanton::NCCMatcher::bucket_t |
Constructor & Destructor Documentation
| NCCMatcher::NCCMatcher | ( | ) |
Member Function Documentation
Allocate the matcher.
Here is the call graph for this function:
|
inline |
- Returns
- Size of each bucket.
|
inline |
- Returns
- Disparity radius in pixels.
| size_t NCCMatcher::match | ( | const NCCTemplateStore & | features1, |
| const NCCTemplateStore & | features2 | ||
| ) |
Match two feature stores.
- Returns
- Number of matches
Here is the call graph for this function:
|
inline |
Get matches.
|
inline |
|
inline |
- Returns
- Number of buckets in (X,Y).
Here is the call graph for this function:
|
inline |
Set/Get the min score between a pair of features.
The documentation for this class was generated from the following files:
- /bitrise/src/Source/AR6/InstantOn/include/AR6/InstantOn/cv/trackers/ncc_matcher.h
- /bitrise/src/Source/AR6/InstantOn/cv/trackers/ncc_matcher.cpp


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Licensed under the BSD license.
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cd /vagrant /vagrant/app/Console/cake test app Controller/Component/ /vagrant/app/Console/cake test app Controller/


{% extends "templates/login.html" %} {% set announcement = '' %} {% block main %}
Welcome to NSIDC's JupyterHub deployment. For more information about NSIDC, visit nsidc.org.


--- title: Title keywords: fastai sidebar: home_sidebar summary: "summary" ---
path = untar_data(URLs.m4_daily)
path
# df_train = pd.read_csv(path/'train.csv')
df_test = pd.read_csv(path/'train.csv')
print(df_test.shape)
df_test.head()
data_arr = df_test.iloc[:, 1:].values
data_arr = list(data_arr)
for i, ts in enumerate(data_arr):
data_arr[i] = ts[~np.isnan(ts)][None, :]
[d.shape for d in data_arr[:10]]
horizon = 14
lookback = 128
import random
data_train = []
for time_series in data_arr:
data_train.append(time_series[:, :-horizon],)
data_train = random.sample(data_train, int(len(data_train) * 0.1))
[d.shape for d in data_train[:10]]
# note this is still the https://github.com/MSRDL/Deep4Cast/blob/master/deep4cast/datasets.py needs some tweaks to work with fastai
import numpy as np
from torch.utils.data import Dataset
# from deep4cast import transforms
class TimeSeriesDataset(Dataset):
"""Takes a list of time series and provides access to windowed subseries for
training.
Arguments:
* time_series (list): List of time series ``numpy`` arrays.
* lookback (int): Number of time steps used as input for forecasting.
* horizon (int): Number of time steps to forecast.
* step (int): Time step size between consecutive examples.
* transform (``transforms.Compose``): Specific transformations to apply to time series examples.
* static_covs (list): Static covariates for each item in ``time_series`` list.
* thinning (float): Fraction of examples to include.
"""
def __init__(self,
time_series,
lookback,
horizon,
step,
transform,
static_covs=None,
thinning=1.0):
self.time_series = time_series
self.lookback = lookback
self.horizon = horizon
self.step = step
self.transform = transform
self.static_covs = static_covs
# Slice each time series into examples, assigning IDs to each
last_id = 0
n_dropped = 0
self.example_ids = {}
for i, ts in enumerate(self.time_series):
num_examples = (ts.shape[-1] - self.lookback - self.horizon + self.step) // self.step
# Time series shorter than the forecast horizon need to be dropped.
if ts.shape[-1] < self.horizon:
n_dropped += 1
continue
# For short time series zero pad the input
if ts.shape[-1] < self.lookback + self.horizon:
num_examples = 1
for j in range(num_examples):
self.example_ids[last_id + j] = (i, j * self.step)
last_id += num_examples
# Inform user about time series that were too short
if n_dropped > 0:
print("Dropped {}/{} time series due to length.".format(
n_dropped, len(self.time_series)
)
)
# Store the number of training examples
self._len = int(self.example_ids.__len__() * thinning)
def __len__(self):
return self._len
def __getitem__(self, idx):
# Get time series
ts_id, lookback_id = self.example_ids[idx]
ts = self.time_series[ts_id]
# Prepare input and target. Zero pad if necessary.
if ts.shape[-1] < self.lookback + self.horizon:
# If the time series is too short, we zero pad
X = ts[:, :-self.horizon]
X = np.pad(
X,
pad_width=((0, 0), (self.lookback - X.shape[-1], 0)),
mode='constant',
constant_values=0
)
y = ts[:, -self.horizon:]
else:
X = ts[:, lookback_id:lookback_id + self.lookback]
y = ts[:, lookback_id + self.lookback:lookback_id + self.lookback + self.horizon]
# Create the input and output for the sample
sample = {'X': X, 'y': y}
sample = self.transform(sample)
# Static covariates can be attached
if self.static_covs is not None:
sample['X_stat'] = self.static_covs[ts_id]
return sample


This visualization uses a version of kd-tree with points placed only in tree leaves that can have partition out of points coordinates set (when we calculate median of even points set) that is described in "Computational Geometry" by Berg. This version is a little easier to understand but may be less efficient compared to the version with one point for each node.
This algorithm computes ranges for each node during kd-tree creation. It is not required as we can compute it on-demand when performing the search but in this case is easier to write and understand the search algorithm.
function kdTreeNode() {
return {
left: null, // left child
right: null, // right child
axis: null, // axis that we used to divide range [0 -> x, 1 -> y]
value: null, // division value (median)
point: null, // point (only in leaves)
range: null // range the node is responsible for
};
}
function createKDTree(points, axis, range, describer) {
var node = kdTreeNode();
node.axis = axis;
node.range = range;
if (points.length == 1) { // if there is only 1 point left create a leaf
node.value = points[0][axis];
node.point = points[0];
return node;
}
// otherwise split points by median on current axis
var medianValue = d3.median(points, function(point) {
return +point[axis];
});
node.value = medianValue;
var left = points.filter(function(point) {
return +point[axis] <= medianValue;
});
var leftRange = [range[0].slice(), range[1].slice()];
leftRange[1][axis] = medianValue;
var right = points.filter(function(point) {
return +point[axis] > medianValue;
});
var rightRange = [range[0].slice(), range[1].slice()];
rightRange[0][axis] = medianValue;
// call recursively with next axis and both parts of range
var nextAxis = (axis + 1) % 2;
node.left = createKDTree(left, nextAxis, leftRange, describer);
node.right = createKDTree(right, nextAxis, rightRange, describer);
return node;
}
function search(kdTree, range, describer) {
if(kdTree.point) { // is it a leaf?
// check only the point's adherence to the range
if(isInRange(kdTree.point, range)) {
return [kdTree.point];
} else {
return [];
}
}
var result = []
if(fullyContains(range, kdTree.left.range)) {
// add all points from the subtree
var leftResult = reportSubtree(kdTree.left);
result = result.concat(leftResult);
}
else if(intersects(range, kdTree.left.range)) {
//call recursively
var leftResult = search(kdTree.left, range, describer);
result = result.concat(leftResult);
}
if(fullyContains(range, kdTree.right.range)) {
// add all points from the subtree
var rightResult = reportSubtree(kdTree.right);
result = result.concat(rightResult);
}
else if(intersects(range, kdTree.right.range)) {
// call recursively
var rightResult = search(kdTree.right, range, describer);
result = result.concat(rightResult);
}
return result;
}
Kd-tree is extremely easy to scale on higher dimensions. The only change you need to make is to change this line from tree creation algorithm:
var nextAxis = (axis + 1) % 2;
and substitute 2 to 3 or different dimension. Of course you have to modify
also some primitives like intersection checking if you don't have them already.
Unfortunately kd-tree is susceptible to "curse of dimensionality" and becomes quite inefficient in higher dimensions. The range query time complexity equals to $$O(n^{1-\frac{1}{d}})$$ where $d$ is the dimension of your input points. It becomes more and more linear as the dimension increases. You can find thorough time complexity analysis in book "Computational Geometry" by Berg.


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In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides.corr() # Maybe some freatures strongly correlate and can be removed from the model
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save data for approximately the last 21 days
test_data = data[-21*24:]
# Now remove the test data from the data set
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
#print("Train_freatures shape: {}\nTrain_targets shape:{}".format(np.shape(train_features),np.shape(train_targets)))
Below you'll build your network. We've built out the structure and the backwards pass. You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
The network has two layers, a hidden layer and an output layer. The hidden layer will use the sigmoid function for activations. The output layer has only one node and is used for the regression, the output of the node is the same as the input of the node. That is, the activation function is $f(x)=x$. A function that takes the input signal and generates an output signal, but takes into account the threshold, is called an activation function. We work through each layer of our network calculating the outputs for each neuron. All of the outputs from one layer become inputs to the neurons on the next layer. This process is called forward propagation.
We use the weights to propagate signals forward from the input to the output layers in a neural network. We use the weights to also propagate error backwards from the output back into the network to update our weights. This is called backpropagation.
Hint: You'll need the derivative of the output activation function ($f(x) = x$) for the backpropagation implementation. If you aren't familiar with calculus, this function is equivalent to the equation $y = x$. What is the slope of that equation? That is the derivative of $f(x)$.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
(self.input_nodes, self.hidden_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.output_nodes))
self.lr = learning_rate
#### TODO: Set self.activation_function to your implemented sigmoid function ####
#
# Note: in Python, you can define a function with a lambda expression,
# as shown below.
self.activation_function = lambda x : 1/(1+np.exp(-x)) # Replace 0 with your sigmoid calculation.
self.activation_prime = lambda x: x*(1-x) # Not exactly the derivative but it's arithematic operation
# to save computing time since we calculated the sigmoid earlier
def train(self, features, targets):
''' Train the network on batch of features and targets.
Arguments
---------
features: 2D array, each row is one data record, each column is a feature
targets: 1D array of target values
'''
n_records = features.shape[0]
delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
for X, y in zip(features, targets):
#### Implement the forward pass here ####
hidden_inputs = np.dot(X,self.weights_input_to_hidden) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer
# Since the activation function on the output node is f(x) = x --> input = output
final_outputs = final_inputs # signals from final output layer
### Backward pass ###
error = y-final_outputs # Output layer error is the difference between desired target and actual output.
# Error gradient in the output unit
output_error_term = error#*(final_outputs) # final_output is the derivative
hidden_error_term = np.dot(self.weights_hidden_to_output, output_error_term)*self.activation_prime(hidden_outputs)
# Weight step (hidden to output)
delta_weights_h_o += output_error_term*hidden_outputs[:, None] # reshaping
delta_weights_i_h += hidden_error_term*X[:,None] # reshaping
#Update the weights - Replace these values with your calculations.
self.weights_hidden_to_output += self.lr*delta_weights_h_o/n_records # update hidden-to-output weights with gradient descent step
self.weights_input_to_hidden += self.lr*delta_weights_i_h/n_records # update input-to-hidden weights with gradient descent step
def run(self, features):
''' Run a forward pass through the network with input features
Arguments
---------
features: 1D array of feature values
'''
#### Implement the forward pass here ####
# TODO: Hidden layer - replace these values with the appropriate calculations.
hidden_inputs = np.dot(features, self.weights_input_to_hidden) # signals into hidden layer
hidden_outputs = self.activation_function(hidden_inputs) # signals from hidden layer
# TODO: Output layer - Replace these values with the appropriate calculations.
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output) # signals into final output layer
# Since the output activation function is f(x) = x
final_outputs = final_inputs # signals from final output layer
return final_outputs
def MSE(y, Y):
return np.mean((y-Y)**2)
Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly befor you starting trying to train it. These tests must all be successful to pass the project.
import unittest
inputs = np.array([[0.5, -0.2, 0.1]])
targets = np.array([[0.4]])
test_w_i_h = np.array([[0.1, -0.2],
[0.4, 0.5],
[-0.3, 0.2]])
test_w_h_o = np.array([[0.3],
[-0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
network.train(inputs, targets)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328],
[-0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, -0.20185996],
[0.39775194, 0.50074398],
[-0.29887597, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
### Set the hyperparameters here ###
iterations = 12000
learning_rate = 0.4
hidden_nodes = 19
output_nodes = 1
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
train_loss = var_loss = 0
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=128)
X, y = train_features.ix[batch].values, train_targets.ix[batch]['cnt']
network.train(X, y)
# Printing out the training progress
train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
#print('train loss:{}, val_loss:{}'.format(train_loss, val_loss))
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
_ = plt.ylim()
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(8,4))
mean, std = scaled_features['cnt']
predictions = network.run(test_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.ix[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
data = (test_targets['cnt']*std+mean).reshape(504,1)
pred = predictions.T
acc = MSE(data,pred)
print(acc)
Answer these questions about your results. How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
The model does a pretty good job at predict the data. It fails at key dates within the month, example christmas time. The model seems to over shoot the amount needed.


|
IAIK PKCS#11 Wrapper version 1.3 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object | +--iaik.pkcs.pkcs11.SessionInfo
An object of this class provides information about a session. The information provided is just a snapshot at the time this information object was created; it does not retrieve the information from the session on demand.
| Field Summary | |
protected long |
deviceError_
An token specific error-code. |
protected boolean |
rwSession_
True, if this is a read-write session. |
protected boolean |
serialSession_
True, if this a serial session. |
protected long |
slotID_
The identifier of the slot in which the token resides this session is bound to. |
protected State |
state_
The current session state. |
| Constructor Summary | |
protected |
SessionInfo(CK_SESSION_INFO ckSessionInfo)
Constructor taking a CK_SESSION_INFO object that provides the infromation. |
| Method Summary | |
java.lang.Object |
clone()
Create a (deep) clone of this object. |
boolean |
equals(java.lang.Object otherObject)
Compares all member variables of this object with the other object. |
long |
getDeviceError()
Get the current device error-code of the token. |
State |
getState()
Get the current state of this session. |
int |
hashCode()
The overriding of this method should ensure that the objects of this class work correctly in a hashtable. |
boolean |
isRwSession()
Check, if this is a read-write session. |
boolean |
isSerialSession()
Check, if this is a serial session. |
java.lang.String |
toString()
Returns the string representation of this object. |
| Methods inherited from class java.lang.Object |
finalize, getClass, notify, notifyAll, wait, wait, wait |
| Field Detail |
protected long slotID_
protected State state_
protected long deviceError_
protected boolean rwSession_
protected boolean serialSession_
| Constructor Detail |
protected SessionInfo(CK_SESSION_INFO ckSessionInfo)
ckSessionInfo
- The object providing the session information.| Method Detail |
public java.lang.Object clone()
clone
in class java.lang.Object
public State getState()
public long getDeviceError()
public boolean isRwSession()
public boolean isSerialSession()
public java.lang.String toString()
toString
in class java.lang.Object
public boolean equals(java.lang.Object otherObject)
equals
in class java.lang.Object
otherObject
- The other SessionInfo object.public int hashCode()
hashCode
in class java.lang.Object
|
IAIK PKCS#11 Wrapper version 1.3 |
|||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||||
| SUMMARY: INNER | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Play
Options
Reverse
Reverse
Alternate
Alternate
Progress
Delay
Duration
Rate
Easing
linear
easeInQuad
easeOutQuad
easeInOutQuad
easeInCubic
easeOutCubic
easeInOutCubic
easeInQuart
easeOutQuart
easeInOutQuart
easeInQuint
easeOutQuint
easeInOutQuint
easeInSine
easeOutSine
easeInOutSine
easeInExpo
easeOutExpo
easeInOutExpo
easeInCirc
easeOutCirc
easeInOutCirc
easeInElastic
easeOutElastic
easeInOutElastic
easeInBack
easeOutBack
easeInOutBack
easeInBounce
easeOutBounce
easeInOutBounce
Iterations
Play with new options


{% extends "layout_task-list.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block home_link %} ../../task-list {% endblock %} {% block proposition_header %} {% set my_account="1" %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} {% if data.return_page and data.return_page.includes('check-and-send') or data.return_page and data.return_page.includes('check-your-answers')%} {% else %} {% endif %} {{formData | safe}} Timeline of events If you do not know the exact date, tell us the month and year. Example timeline 12 January 2021 - John Smith gave me a quote to replace the roof. 14 February 2021 - We agreed and signed a contract for the work. 21 March 2021 - I noticed a leak on the landing and told Mr Smith about this. Explain what happened This cannot be moved up Date For example, March 2021 What happened For example, you might have signed a contract Actions Remove {{data['cd-timeline-1']}} Move up Move down This cannot be moved down Add new {% if data.return_page and data.return_page.includes('check-and-send') or data.return_page and data.return_page.includes('check-your-answers')%} Cancel {% else %} {% endif %} {% block page_scripts %} {% endblock %} {% include 'prototypes/prototype-oct-2019/includes/contact.html' %} {% endblock %}
If you do not know the exact date, tell us the month and year.
Example timeline


Operation of Merge – sort on the array
A = < 3, 41, 52, 26, 38, 57, 9, 49 >
The operation of merge sort can be shown as follows:
The unsorted elements which are stored in the array are first segregated into individual elements using the divide and conquer technique. The merge sort algorithm splits the array into half. When a single element is left, it is sorted. If the array has more than one element, the array is split recursively and the merge sort is invoked on both halves.
When the halves are sorted, the merge operation is performed in which the elements are merged by taking the two smaller elements together and combining them into a sorted array which can be shown below:
Modified version of MERGE procedure that does not use sentinels:
MERGE (A, p, q, r)
// Calculate the length (n1) of the sub array A[p..q]
1 n1 = q - p+1
// Calculate the length (n2) of the sub array A[q+1..r]
2 n2 = r – q
// Create the left array L and right array R
3 L [1..n1] and R [1..n2]
// Copy the elements of the subarray A[p..q] into the array L [1..n1]
4 for i =1 to n1
5 L[i] = A[p+i-1]
// Copy the elements of the subarray A[q+1..r] into the array R [1..n2]
6 for j =1 to n2
7 R[j] = A[q + j]
// Set the values of variable i and j to 1
8 i=1
9 j=1
// merge the lists
10 for k = p to r
// check if the index i is greater than n1. If true, then it indicates that the array
// L is empty. Then copy the remaining elements of array R into array A.
11 if (i > n1)
12 A[k] = R[j]
13 j = j + 1
// check if the index j is greater than n2. If true, then it indicates that the array
// R is empty. Then copy the remaining elements of array L into array A.
14 else if (j > n2)
15 A[k] = L[i]
16 i = i + 1
// Compare L[i] and R[j]. If L[i]<= R[j], then copy the element L[i] into array
// A and increment the index i
17 else if L[i] <= R[j]
18 A[k] = L[i]
19 i = i + 1
// If L[i]> R[j], then copy the element R[j] into array A and increment the
// index j
20 else
21 A[k] = R[j]
22 j = j + 1
Explanation:
In the above algorithm, the use of sentinels has been removed. Instead of using the sentinels, the indexes i and j are used.
• The index i is used to keep track of the number of elements of the array L copied into array A.
• The index j is used to keep track of the number of elements of the array R copied into array A.
• In each iteration of the loop, the value of the index i is compared with n1 and index j is compared with n2 respectively.
• n1 is the length of the array L and n2 is the length of the array R.
• If all the elements of the array L are copied, then the value of i will be greater than n1.
• If all the elements of the array R are copied, then the value of j will be greater than n2.
The line 11 checks whether the index i is greater than n1, which is the size of the array L.
• If true, then it indicates that all the elements of the array L are copied back to array A and the array L is empty.
• The lines 12 and 13 will copy the remaining elements of array R into array A.
The line 14 checks whether the index j is greater than n2, which is the size of the array R.
• If true, then it indicates that all the elements of the array R are copied back to array A and the array R is empty.
• The lines 15 and 16 will copy the remaining elements of array L into array A.
The recurrence relation is used to calculate the nth element in the given sequence. The recurrence relation is an equation which is recursively defines a sequence.
Consider the recurrence relation:
Base case:
This base case is used to prove that the statement holds the natural number n.
, when n=2
The Mathematical induction Hypothesis is as follows:
Substituting the n value then we get
Hence,
RECURSIVE-INSERTION-SORT (A,n)
1 if n>1
2 RECURSIVE-INSERTION-SORT (A,n-1)
3 INSERT(A,n)
INSERT(A,k)
// Insert A[k] to the sorted A[1..k-1]
// Use Insertion-Sort without outside loop
1 key=A[k]
2 i=k-1
3 while i>0 and A[i]>key
4 A[i+1] = A[i]
5 i--
6 A[i+1] = key
Let T(n) be the total time taken for insertion sort to sort a list with n elements.
The recurrence relation for the recursive version of the insertion sort is,
The running time for the recursive version of the insertion sort can be calculated using substitution method as shown below:
The recurrence is
Substitute n=n-1 in equation (1)
Substitute n=n-2 in equation (1)
Substitute n=n-3 in equation (1).
Substitute n=1 in equation (1).
Substitute equation (2) in equation (1).
Substitute equation (3) in equation (6).
Substitute equation (4) in equation (7).
When n=1, substitute equation (5) in the previous equation.
Therefore, the running time for the recursive version of the insertion sort is approximately equal to .
Iterative version of binary search:
Binary Search (A, v)
1. , ,
2. while and found
//calculate the mid
3.
//check whether A[mid] is equal to the search element v
// if A[mid] is equal to the search element v, element is found
4. if
5.
// if A[mid] is less than the search element v, then assign
6. else if
7.
// if A[mid] is greater than the search element v, then assign
8. else
9.
// if found is true, then the element is found at location mid
10. if (found==true)
11. return mid
12. else
13. return NIL
Worst case running time of binary search:
Worst case refers to the case when the element to be searched is not found in the list.
Let denote the length of the sub array being searched on the iteration of loop 2-9.
The length of the subarray is halved in each iteration.
In the first iteration,
In the second iteration
In the third iteration
In the fourth iteration
In the kth iteration,
Iteration stops when the length of the subarray is 1.
In the kth iteration,
From equation (1) and (2),
Apply on both sides.
Therefore, the running time of Binary Search is in worst case.
The INSERTION-SORT procedure uses a linear search to scan through the sorted sub array A[1…..j-1].
INSERTION-SORT (A)
1 for j = 2 to A. length
2 key =A[j]
3 //Insert A[j] into the sorted sequence A[1…j – 1].
4 i = j – 1
5 while i >0 and A [ i ] > key
6 A [ i+1 ] = A [ i ]
7 i = i -1
8 A[i+1] = key
To locate the insertion position (A[i]>key) among the sorted sub-array A[1…j – 1], the procedure INSERTION–SORT uses linear search technique.
Because the array is already sorted using loop invariant, it can select the middle of A[1, j-1], check to see if value is greater or lower value, and recursively repeat this procedure until left with one element.
The pseudo code for such an algorithm is:
Binary-Insertion-Sort (A)
1 for i = 0 to A. length do
2 temp = A[i]
3 left = 0
4 right = i
5 while left < right do
6 middle = (left + right)/2
7 if temp A[middle]
8 left = middle + 1
9 else right = middle
10 for j = i to left
11 do
12 stemp = A[j1 ]
13 A[j1 ] = A[j]
14 A[j ] = stemp
15 j = j 1
In binary search, the length of the subarray is halved in the iterations.
In the first iteration,
In the second iteration
In the third iteration
In the fourth iteration
In the kth iteration,
Iteration stops when the length of the subarray is 1.
In the kth iteration,
From equation (1) and (2),
Apply on both sides.
Therefore, binary search required comparisons where
The first for loop iterates n times and the algorithm takes times.
Therefore, the running time of the algorithm is .
The time required to find the element to be replaced is. In order to insert the element in the required position, remaining elements need to be shifted. The time required to shift the elements is .
The total time required to find and insert the element is .
Therefore, it is not possible to improve the overall worst-case running of insertion sort to time even binary search is used instead of linear search.
It is given that the set S contains n integers. It is required to find two elements in S such that their sum is exactly x. This can be achieved using the binary-search algorithm.
• Assume that there exist two elements such that their sum is x. Also assume y be the second element of those two elements. Then, obviously, the first element be the difference between x and y.
• Thus, it is enough to find whether the first element (difference) exists in the set S or not using binary-search.
Assume that the set S is an array and contains positive integers. Then the required algorithm is as follows:
Algorithm running time:
• The for loop in the step 2 runs n times. In each iteration, one element(y) from S is taken and applied binary search to find whether the difference between x and y (i.e., second element), is exist in the S or not.
• Obviously, the worst case run time of binary-search is .
• In each iteration of the for loop, binary-search algorithm will be executed. That is, binary-search algorithm runs n times in the worst case.
• Since the run time of binary search is and the binary-search algorithm runs n times, the overall running time of the given algorithm is n*=.
Therefore the total running time of the algorithm is


Myanmar Font Converter
From
Win Innwa (ASCII Encoded)
Unicode (Myanmar3, Paudauk etc.)
ZawGyi-One (Partial Unicode)
Convert
To
Win Innwa (ASCII Encoded)
Unicode (Myanmar3, Paudauk etc.)
ZawGyi-One (Partial Unicode)


For Java Web Start applications and applets, you can use most of techniques available to debug and profile standalone applications, except you will need to use the attach mechanism instead of direct launch.
Note: Both plugin and Java Web Start will spawn additional java
or javaw
processes that will actually run the JVM executing the application or applet. You need to attach to those processes to be able to collect information about your applet. For example, if you want to get a memory dump of your applet, then first you need to figure out the process id for the java
process executing the PluginMain
class. For example, use theThe jps Utility utility from the JDK and then use The jmap Utility to get a memory dump.
The following are development tips for debugging applets.
Debug Java applets and Web Start applications:
Just as with standalone Java applications, any JPDA-based debugger can be used to debug your applet or Web Start application; for example, see The jdb Utilityor the NetBeans debugger.
You will need to enable the JDWP agent for the JVM running your application and specify the port number. Once the JVM is started, you can use your favorite IDE or tools to attach to it.
For details on how to pass arguments to the JVM running applet or Java Web Start application, see Pass Parameters to the JRE. Example 16-1 shows how you can pass details to the Java Web Start application from the command line.
Example 16-1 Command-line Option to Debug Java Web Start Application
bash$ javaws -J-agentlib:jdwp=transport=dt_socket,address=4000,server=y,suspend=y http://acme.com/my/webstart.jnlp
This instructs the agent to suspend after the JVM is initialized and wait for a debugger to connect on port 4000. Please consult jdpa
documentation for invocation options.
For FAQs on Web Start Applications, see Java Web Start.
Profile Java applets and Java Web Start applications:
When you profile a standalone Java application, your favorite IDE is likely to be using the JVMTI agent to collect details on program execution. You can do the same for applets and Java Web Start applications but you may need to configure the JVMTI agent explicitly by passing the -agentpath
option to the JVM as shown in Example 16-2. For details on how to pass options to the JVM, see Pass Parameters to the JRE.
Example 16-2 Configure JVMTI Agent
set _JPI_VM_OPTIONS="-agentpath:C:\Tools\NetBeans\profiler\lib\deployed\jdk16\windows\profilerinterface.dll=C:\Tools\NetBeans\profiler\lib,5140"
Now launch your browser. The NetBeans profile agent is enabled for any applet you will be running in this browser session. You can use NetBeans IDE to attach to the java
process. Please consult your profiler documentation for exact details on what agent to use and how to configure it.
Debug memory leaks:
See Troubleshoot Memory Leaks for any techniques available for standalone applications on the process running your applet or application. For example, use jmap
to obtain a heap dump, VisualVM or jconsole
to observe threads, or pass -XX:+HeapDumpOnOutOfMemoryError
to the JVM (see Pass Parameters to the JRE) to get a memory dump on error. Use the jps
utility to find the process id for the process running your application.


DocStore CloudSearch is a managed service in the AWS Cloud that makes it simple and cost-effective to set up, manage, and scale a custom search solution for your scanned documents.
CloudSearch supports powerful search features such as: Free text, Boolean, and Faceted search Autocomplete suggestions Customizable relevance ranking and query-time rank expressions Field weighting
CloudSearch offers powerful autoscaling for all search domains. As your data or query volume changes, CloudSearch can scale your search domain's resources up or down as needed. You can control scaling if you know that you need more capacity for bulk uploads or are expecting a surge in search traffic.
AngularJS is web app framework. Angular's website offers a lot. The Thinkster Popular Guide and Egghead Videos are great resources.
CloudSearch ensures low latency and high throughput performance, even at large scale.


{% include head.html %}
For the last 16+ years, I've designed digital products between Madrid, London and now, from San Francisco. I also co-founded and later sold my company Erasmusu.
My career started as a Software Developer, but it soon became clear that my true passion was Design. I love combining both skillsets to create products that fulfill a clear purpose.


the and for viral are that {background SARS-CoV- The margin {border-style px;} #ffffff;} inhibitors (), helicase this with â€". COVID- RNA protease have host targets against polymerase been clinical proteins px; {font-family COVID-. Coronavirus SARS-CoV complex current design drug font-size from fusion has into other proteases structure CLpro Viral cell coronavirus domain double; membrane replication C.; RdRP additional approved binding border-color border-width essential human infection inhibitor known not nsp solid; treatment two virus which {font-style {font-weight A.; Nat. RTC Respiratory S.; Syndrome This bold can compounds courier; currently cycle development drugs em; expressed form future host. inhibit inhibition life main multiple novel nsps predicted proposed protein provides pt;} recent sequence shown spike structural therapeutic these treat trials


u64
A value that represents a 64-bit Integer
This is an array of exactly 2 values, as described below.
u64
Type:
Array<number>
Properties
1 (number)
: High value of u64
Example
[ 0x00000000, 0xffff0000 ]
SploitCore
Represents an instance of SploitCore
new SploitCore(exploitMe:
object)
Parameters
exploitMe (object)
Reference to object used for leaking data
Properties
base (u64)
: Base address
name (string)
: Returns "browser"
Instance Members
Returns address of function
Returns
u64
:
Address of function
Reads 4 bytes from address
Parameters
addr (u64)
Address to read value from
offset (number
= 0
)
Offset to add to addr before read
Returns
number
:
▸
write4(val, addr, offset)
Writes 4 bytes to address
Parameters
addr (u64)
Address to write value to
offset (number
= 0
)
Offset to add to addr before write
Reads 8 bytes from address
Parameters
addr (u64)
Address to read value from
offset (number
= 0
)
Offset to add to addr before read
Returns
number
:
▸
write8(val, addr, offset)
Writes 8 bytes to address
Parameters
addr (u64)
Address to write value to
offset (number
= 0
)
Offset to add to addr before write
▸
memview(addr, size, func)
Calls callback with an ArrayBuffer pointing to the view of memory requested.
If you return a value from within the callback it will be returned by SploitCore#memview
Warning: If you keep that view or any object using it around; you will tank the GC and your Switch will crash.
Parameters
addr (u64)
Base address for view
size (number)
Number of bytes to view
func (function)
Function which is called with ArrayBuffer.
Returns
any
:
Value returned by func
Returns address of object
Parameters
obj (object)
Object to get address of
Returns
u64
:
Address of object
Calculate address relative to main address
Parameters
Returns
u64
:
Relative address
Finds a gadget that matches required pattern
Parameters
Returns
(u64 | null)
:
Address of gadget, or null if not found.
Disables the browser watchdog
disableWatchdog()
Returns the address of SP
Returns
u64
:
Address of SP
Allocates a region of memory to use
Parameters
Returns
u64
:
Address of region
TODO DOCS
free(addr: any)
Parameters
TODO DOCS
getArrayBufferAddr(ab: any)
Parameters
▸
call(funcptr, args, fargs, registers, dump_regs)
TODO DOCS
call(funcptr: any, args: any, fargs: any, registers: any, dump_regs: any)
Parameters
▸
svc(id, args, dump_regs)
Call a specific svc with arguments
Parameters
args (Array<any>)
Arguments to pass
TODO DOCS
str2buf(inp: any)
Parameters
▸
memdump(offset, size, fileName)
Initiate a memory dump over HTTP
Parameters
offset (u64)
Memory address to start from
size (number)
Number of bytes you wish to dump
fileName (string)
Name of file, used to set Content-Disposition
Forces the garbage collector to run
gc()
▸
readString(addr, length)
Reads a string from memory
Parameters
addr (u64)
Address to start from
length (number)
Number of bytes to read
Returns
string
:
Copy memory from one region to another
Parameters
dst (u64)
Base destination address
src (u64)
Base source address
size (number)
Number of bytes to copy


What are GHOSTS? (cont'd)
Many people have seen ghosts over time, yet science today has yet to answer the question, “What are ghosts?” So, what exactly are ghosts? Could some ghosts possibly be the power of the human mind projecting forth apparitions of loved ones? Are ghosts, people trapped in our earthly world, wandering spirits who are struggling in a temporary hell, being unable to move beyond the past? Or, are ghosts simply rare glimpses of the past that replay within the physical environment, being seen only sparingly?
The reason we struggle with identifying the exact nature of ghosts is due to a lack of conclusive evidence. No one has yet been able to capture scientific, definitive proof that ghosts do indeed exist, or put forth a provable explanation as to why ghosts exist. Until this occurs, ghosts will remain the stuff of legend and lore.


I promise a "total reform" of the NYPD! I will work for New Yorkers to bring the NYPD into the 21st century. Time to put an end to a practise that treat New Yorkers in all the five boroughs as if living in the Jewish gettho of the Venetian republic.
Stop and frisk or "Terry stop" is unconstitutional! And since more than 40% city residents are immigrants, it is my duty as Mayor to offer equal protection to citizens and non-citizens under the law. I will therefore issue special cards with the 4th, the 5th and the 14th amendment printed on it for all New York city residents' ID's.
Blue collar crime is more damaging to the city than petty crimes. I will leave no stone unturned tackling these high minded crimes, cyber crimes and other forms of crimes and criminal activities in all the five boroughs and the tri-states.
I will put a cap on rent hike and property tax. Why? It is one of many ways to create a sustainable economy and create jobs for New Yorkers. It is also the best way to bring back research institutes, educational facilities and businesses into city properties.
I will enable affordable housing development by adjusting zone and spatial arrangements in all the five boroughs - so developers and landlords could either build upwards or build grand basement dwellings. And for security sake, I will make special provisions for digging deep and building basement dwellings throughout the city, because it is the best way to protect the city from EMP attack.
I will tackle obesity, solid waste management, carbon footprint and set up a Children's Cabinet in the City hall, as way to boost prekindergarten education in all the five boroughs.
Fortune favors the bold. Join up with the party of the bold. Let's ReBuildNewYork today!
Since 2014, I have been actively campaigning for the office of the President of the United States. As a Radical Republican and an Independent, my campaign focus was on total infrastructure update, embrace technology and the new age of American energy.
http://smithieforpresident.com https://veteranstalkback.quora.comIn 2013, I promised to use technology as a means for running a better and more effective government in the city of New York. I promised to create a system where the arts (artists and artistes) flourish under city care, patronage, agreement to teach and serve the public.
https://smithiesnewyork2013.com https://smithieformayor.quora.comKnown as La Allianza in the Latino communities of New York, New Jersey, California, Florida and Arizona, my own political party, The PILGRIMS Alliance Party of America is a nationally recognized "third" political entity.
http://pilgrimsalliance.us http://chattieme.comReBuildUSAtoday is a socio-political hybrid PAC. Like the reconsruction acts, I use ReBuildUSAtoday to push the governmnet to provide a better means for uplifting the poor and the middle class out of misery by focusing on challenges posed by emerging trends in technology, robotics, mining and globalization. I have sister bodies in ReBuildUK, ReBuildCanada and ReBuildAustralia.
http://rebuildusa.today http://chattieme.comThanks a million for supporting Friends of Smithies New York (FoSNY)
I understand State and Federal laws requires that a contribution be in my name and be from my own funds. I hereby affirm that this contribution is being made from my personal credit card account, billed to and paid by me for my personal use, and having no corporate or business affiliation: and is not being made as a loan.


--- layout: page title: "Real World Haskell, 1st Edition" prev: gui_split_000.html next: gui_install.html book_path: books/real-world-haskell-_oeb/ --- {% include JB/setup %} {% raw %}
Throughout this book, we have been developing simple text-based tools. While these are often ideal interfaces, sometimes a graphical user interface (GUI) is required. There are several GUI toolkits available for Haskell. In this chapter, we will look at one of them, gtk2hs.[55]
[55] Several alternatives also exist. Alongside gtk2hs, wxHaskell is also a prominent cross-platform GUI toolkit.


RCMP warns of AirBnb fraud after guest poses as owner in Okanagan
Police search for a male suspect after reported frauds in Kelowna
by Mackenzie BrittonRCMP are investigating a string of rental scams that have been targeting would-be renters in Kelowna.
The fraudsters are reportedly listing property for rent in the Kelowna area in which police believe they are currently renting through sites like AirBnb.
Posing as the property’s owner, the scammer then lists the site online and shows the property to renters and then secures damage deposits. Come move-in day, the scam victims find the property is occupied. They are then unable to track down the scammer and are out their deposits.
READ MORE: Fireworks allegedly shot at Kelowna homeless encampment
Cpl. Meghan Foster with the Kelowna RCMP believe the scammer used a fake name during the rental frauds.
“The suspect male has allegedly provided the name of Tim Halyhart to the renters but we do not believe this to be the suspect male’s true identity,” said Foster.
“This investigation is on-going and our priority is to identify the suspect male in an effort to advance the investigation.”
Halyhart is described as a black male between the ages of 23 and 30, five-foot-eleven and with a larger body build.
Anyone with information are asked to cal Kelowna RCMP at<PHONE_NUMBER> or Crime Stoppers at 1-800-222-8477.
To report a typo, email:
newstips@kelownacapnews.com.


BlockFetcherIterator
storage
blockId
FetchResult
blockManager
SparkEnv
BlockManagerSlaveActor
storage
blocks
FetchRequest
blocksByAddress
BlockFetcherIterator
booleanWritableConverter
SparkContext
boolToBoolWritable
SparkContext
BoundedDouble
partial
broadcast
JavaSparkContext
spark
broadcast
SparkContext
broadcastManager
SparkEnv
bufferSize
KryoSerializer
bytesInFlight
BlockFetcherIterator
bytesToBytesWritable
SparkContext
bytesWritableConverter
SparkContext
bytesWrittenSinceSync
RateLimitedOutputStream


QVCS Enterprise Tutorial: Client Application Basics
Checking In a File
Now that you've made some edits to your file, you can check them in as a new revision in the associated archive. First, go ahead and press F5 to refresh the display; you'll notice that the lock icon has changed to show a delta symbol , and the file status now displays "Your copy changed" to show that your work file is different from the latest revision in the archive. Now, select the file and click on the Check-in File toolbar icon. Doing so will bring up the following dialog:
As you can see, you have a number of options as to how you want Enterprise to handle the file you check in, but for now just enter a fitting description of the changes you made and click OK. Enterprise will now create the the 1.1 revision from the information in the work file. You'll also notice that the file is now unlocked and displays a status of "Current." Since your work file created the latest revision, there is no longer any difference between the two.
When you checked in the file, Enterprise will have updated the keywords you just embedded. Open up the file to take a look at how they've changed. Rather than just the one-word strings you entered, you should now see something like this:
Since you only have two revisions, the $Log3$ keyword can only expand to show those two. However, as you make further revisions, $Log3$ will keep track of the most recent three. Enterprise will also keep the other keywords up-to-date whenever it fetches a revision.
You'll also notice that the copyright line is just initialized to display a formulaic message. In the next step, we'll change it to display the correct copyright information.
Changing the Copyright Message
To change the copyright message, you'll have to edit the qvcs.keywords.properties file located in the qvcsBehaviorProperties directory. This directory is located within the directory where you installed the client.
Open the qvcs.keywords.proeprties file with your preferred text editor and find the line where the copyright message is defined; this should be the thirteenth line or so. Then, change the message to the right of the = sign to whatever string you want to use as your copyright message. For example:
CopyrightMessage=Quma Software, Inc. ALL RIGHTS RESERVED WORLDWIDE.
You don't have to worry about entering any dates; Enterprise will automatically determine the year range of the copyright from the oldest and newest revisions within a particular QVCS archive.
 The change won't take effect until you next start the application, so go ahead and close it.
Start the client again and log in. Finally, do a get on the file you edited to add the $Copyright$ keyword, and then open it with your preferred editor. The $Copyright$ keyword will have changed to display whatever message you defined.
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{% block title %}Title goes here, this is on the browser tab {% endblock title%}
Hi there, the header would go here
{% block content %} {% endblock %}


When this page is displayed with the Percival browser, only the adversarial Jack Ryan advertisement is shown.
|
Jack Ryan 225x225 |
Pepsi 225x225 |
|
Jack Ryan 225x225 |
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FP white 225x225 |
FP gray 225x225 |


How to Play
Play Game
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asdasd
asd
asd
asd
asd
asd
asd
asd
asd
asd
as
asd
asd
asd
asd
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asd
Next Image
Play Game
Single Player
Multiplayer
and
asdasd
Hit
0
Remain
7
Shot
12
Molly
Hit Target
12
Remain
22
Shot
12
Place Your Ship
Nusret
Yavuz
Mahmudiye
Deneme
Game ID
Validity Time
01:00
minutes!
Search ID
Create ID


As soon as some filter will be created, the frequency of matching will be shown. Also as soon as some chart term will be created, the chart will be rendered.


{% import 'partials/section.html' as section %} {% extends "layouts/_twocol.html" %} {% block page_title %}Error - Not signed in - ONS Business Surveys{% endblock %} {% block main %}
Please sign in to see this page.


{%extends "layout.html"%} {%block title%} Setting|Dashboard {%endblock%} {%block main%}
{{message}}
Your site's name can be anything
Your Twitter name. eg: lepture
You can use your feed burned by feedburner or feedsky.
HTML in the head.
HTML in the sidebar. example:


Dashboard
System information
Processors
0
Producers
0
Preferred utilization
20
0
20
40
60
80
100
Actual utilization
Number of Producers
Cluster information


Traveller 5 Projects | 41 Stocks
Professional Photographer at Adobe 12 Projects | 419 Stocks
Designer at Yahoo 89 Projects | 1203 Stocks
Nature Lover 32 Projects | 590 Stocks
Content Growth Specialist at Envato 90 Projects | 439 Stocks
Motion Designer at Facebook 11 Projects | 119 Stocks


Lennie Cottrell Software Developer
>>>I am a developer with AppWorks
>>>I am the artistic director of a professional choir
>>>I have cute dogs
Have a look around my GitHub for more


| glssoln {ouch} | R Documentation |
Solves the generalized least squares problem.
glssoln(a, x, v, tol = sqrt(.Machine$double.eps))
Given matrices a
, x
, v
, glssoln
computes y
such that
(x-ay)^T v^{-1} (x-ay)
is minimized.
This is accomplished by first computing the Choleski decomposition of v
:
v=LL^T.
One then solves for y
in the equation
L^{-1}ay=L^{-1}x.
This is accomplished by means of a singular-value decomposition of L^{-1} a
.
The resulting y
then satisfies
x=ay+e,
where the entries of e
are the residuals.
glssoln
returns a list of two named components:
coeff
is y
as above.
residuals
is e
as above.