Models
What are models?
Whenever data needs to be retrieved, manipulated or deleted this should always be done by a model. A Model is a representation of some kind of data and has the methods to change them. For example: you never put SQL queries in a Controller, those are put in the Model and the Controller will call upon the Model to execute the queries. This way if your database changes you won't need to change all your Controllers but just the Model that acts upon it.
How are Models used?
In Fuel a Model is essentially just a Class like any other. They do nothing more than a library, but the Model_ prefix helps to differentiate it from other classes. To do something useful with a Model you will need to use other classes.
Creating a model
namespace Model;
class Welcome extends \Model {
public static function get_results()
{
// Database interactions
}
}
Remember to prefix a backslash to classes you're using from global, outside the namespace you're in. If you don't understand why, read up on namespaces on PHP.net.
Accessing a model
PHP has the use
keyword for importing classes into the current namespace, this allows
you to shorten the Model's classname from Model\Welcome
to just Welcome
within the file of a specific class:
use \Model\Welcome;
class Controller_Welcome extends Controller
{
public function action_index()
{
$results = Welcome::get_results();
}
}
Writing your own models
While models can be used with any type of data storage we'll focus here on usage with SQL because that's the most common usage. Almost always your models will have at least all CRUD methods: create, read, update, delete (or variations on those). In Fuel your models don't need to extend anything by default, though you can of course create your own base model or use Fuel's Orm package.
Writing SQL Queries
You can use the DB class to build queries with native SQL like so:
DB::query('SELECT * FROM users WHERE id = 5');
Using the Query Builder
When it comes to escaping data, working with data in arrays or making an application portable over multiple database engines, you might find native SQL to be rather cumbersome. Using the Query Builder methods in the DB Class you can abstract writing native SQL:
DB::select('title','content')->from('articles')->execute()->get('title');
See more about native SQL queries and using the Query Builder in the DB class documentation.
Using Model_Crud to create models
One possible way of creating models is by using Model_Crud, which adds a commonly used functionality to your models for interacting with a database table. There's an example of its usage below.
// find all articles
$entry = Model_Article::find_all();
// find all articles from category 1 order descending by date
$entry = Model_Article::find(array(
'where' => array('category_id', 1),
'order_by' => array('date' => 'desc')
));
Using the Orm to create models
For models with more functionality, like support for relations, you can use the Orm Package, which adds a lot of functionality out-of-the-box to your models. There's an example of its usage below.
// find all articles
$entry = Model_Article::find('all');
// find all articles from category 1 order descending by date
$entry = Model_Article::find('all', array(
'where' => array('category_id', 1),
'order_by' => array('date', 'desc')
));
Model_Crud and the ORM package use similar syntax, which makes it simple to migrate once you require more functionality than Model_Crud can provide.


beep
OS/161 Reference Manual
Name
beep - console beep device
Synopsis
device beep0 at ltimer*
Description
The beep device is an abstract entry point for in-kernel beeping. The
beep() function is provided, and redirected to the first attached
device. If no beep device is available, beeping will cause a warning
to be printed to the system console.
See Also
ltimer


|
Wave Recorder Private API
|
#include <tinycthread.h>
#include "wave_recorder.h"
#include "level_meter.h"
#include "analyzer.h"
#include "lock_free_fifo.h"
#include "wave_settings.h"
#include "encoder.h"
#include "level_advisor.h"
Go to the source code of this file.
Data Structures | |
| struct | WaveRecordingSession |
| struct | WaveRecordedChunk |
| struct | WaveControlEvent |
| struct | WaveAnalyzerSlot |
| struct | WaveInstance |
Macros | |
| #define | MAX_NUM_ANALYZER_SLOTS 16 |
| #define | MAX_NUM_INPUT_CHANNELS 1 |
| #define | MAX_NUM_OUTPUT_CHANNELS 2 |
| #define | WAVE_MAX_PATH_LEN 1024 |
| #define | MAX_RECORDED_CHUNK_SIZE 1024 |
Typedefs | |
| typedef struct WaveRecordingSession | WaveRecordingSession |
| typedef struct WaveRecordedChunk | WaveRecordedChunk |
| typedef enum WaveControlEventType | WaveControlEventType |
| typedef struct WaveControlEvent | WaveControlEvent |
| typedef enum WaveAudioThreadState | WaveState |
| typedef struct WaveAnalyzerSlot | WaveAnalyzerSlot |
| typedef struct WaveInstance | WaveInstance |
Enumerations | |
| enum | WaveControlEventType { WAVE_START_RECORDING = 0, WAVE_PAUSE_RECORDING, WAVE_RESUME_RECORDING, WAVE_STOP_RECORDING } |
| enum | WaveAudioThreadState { WAVE_STATE_IDLE = 0, WAVE_STATE_RECORDING, WAVE_STATE_RECORDING_PAUSED } |
Functions | |
| WaveError | wave_instance_init (WaveInstance *instance, WaveNotificationCallback notificationCallback, WaveErrorCallback errorCallback, WaveAudioWrittenCallback audioWrittenCallback, void *callbackUserData, WaveSettings *settings) |
| WaveError | wave_instance_host_specific_init (WaveInstance *instance) |
| WaveError | wave_instance_deinit (WaveInstance *instance) |
| WaveError | wave_instance_host_specific_deinit (WaveInstance *instance) |
| void | wave_instance_update (WaveInstance *instance, float timeStep) |
| void | wave_instance_audio_input_callback (WaveInstance *in, const float *inBuffer, int numChannels, int numFrames) |
| void | wave_instance_audio_output_callback (WaveInstance *in, float *inBuffer, int numChannels, int numFrames) |
| int | wave_instance_is_on_main_thread (WaveInstance *instance) |
| void | wave_instance_get_realtime_info (WaveInstance *instance, int channel, int logLevels, WaveRealtimeInfo *result) |
| void | wave_instance_on_audio_thread_control_event (WaveInstance *instance, const WaveControlEvent *event) |
| void | wave_instance_on_main_thread_notification (WaveInstance *instance, const WaveNotification *notification) |
| void | wave_instance_on_main_thread_error (WaveInstance *instance, WaveError error) |
| int | wave_instance_add_input_analyzer (WaveInstance *instance, void *analyzerData, WaveAnalyzerAudioCallback audioCallback, WaveAnalyzerDeinitCallback deinitCallback) |
| void | wave_instance_request_start_recording (WaveInstance *instance, const char *filePath) |
| void | wave_nstance_initiate_recording (WaveInstance *instance) |
| void | wave_instance_stop_recording (WaveInstance *instance) |
| void | wave_instance_invoke_error_callback (WaveInstance *instance, WaveError errorCode) |
| void | wave_instance_invoke_notification_callback (WaveInstance *instance, const WaveNotification *notification) |
| void | wave_instance_enqueue_error (WaveInstance *instance, WaveError error) |
| void | wave_instance_enqueue_notification (WaveInstance *instance, const WaveNotification *notification) |
| void | wave_instance_enqueue_notification_of_type (WaveInstance *instance, WaveNotificationType type) |
| void | wave_instance_enqueue_control_event (WaveInstance *instance, const WaveControlEvent *event) |
| void | wave_instance_enqueue_control_event_of_type (WaveInstance *instance, WaveControlEventType type) |
| void | wave_instance_get_dev_info (WaveInstance *instance, WaveDevInfo *devInfo) |
| typedef struct WaveAnalyzerSlot WaveAnalyzerSlot |
An analyzer slot.
| typedef struct WaveControlEvent WaveControlEvent |
A control event, i.e an event sent form the main thread to the audio thread.
| typedef enum WaveControlEventType WaveControlEventType |
Valid control event types
| typedef struct WaveInstance WaveInstance |
A recorder instance.
| typedef enum WaveAudioThreadState WaveState |
Possible recorder instance states.
| enum WaveAudioThreadState |
Possible recorder instance states.
| enum WaveControlEventType |
Valid control event types
| int wave_instance_add_input_analyzer | ( | WaveInstance * | instance, |
| void * | analyzerData, | ||
| WaveAnalyzerAudioCallback | audioCallback, | ||
| WaveAnalyzerDeinitCallback | deinitCallback | ||
| ) |
Returns 0 on success, or non-zero if there is no free analyzer slot.
| void wave_instance_enqueue_control_event | ( | WaveInstance * | instance, |
| const WaveControlEvent * | event | ||
| ) |
Must be called only from the main thread!
| void wave_instance_enqueue_control_event_of_type | ( | WaveInstance * | instance, |
| WaveControlEventType | type | ||
| ) |
Must be called only from the main thread!
| void wave_instance_enqueue_error | ( | WaveInstance * | instance, |
| WaveError | error | ||
| ) |
Must be called only from the audio thread!
| void wave_instance_enqueue_notification | ( | WaveInstance * | instance, |
| const WaveNotification * | notification | ||
| ) |
Must be called only from the audio thread!
| void wave_instance_enqueue_notification_of_type | ( | WaveInstance * | instance, |
| WaveNotificationType | type | ||
| ) |
Must be called only from the audio thread!
| WaveError wave_instance_host_specific_deinit | ( | WaveInstance * | instance | ) |
Implemented for each host.
Shuts down PortAudio.
| engine |
| WaveError wave_instance_host_specific_init | ( | WaveInstance * | instance | ) |
Implemented for each host.
Initializes PortAudio.
| engine | |
| sampleRate | |
| numChannels | |
| bufferSize |
| int wave_instance_is_on_main_thread | ( | WaveInstance * | instance | ) |
Returns a non-zero value if called from the same thread that called wave_instance_init.
| void wave_instance_on_audio_thread_control_event | ( | WaveInstance * | instance, |
| const WaveControlEvent * | event | ||
| ) |
Invoked when a control event reaches the audio thread. Invoked on the audio thread.
| void wave_instance_on_main_thread_notification | ( | WaveInstance * | instance, |
| const WaveNotification * | notification | ||
| ) |
Invoked when a notification reaches the main thread. Invoked on the main thread.


--- title: The Splendor of December date: 2016-12-15 00:00:00 -07:00 categories: - whats-blooming layout: post blog-banner: whats-blooming-now-winter.jpg post-date: December 15, 2016 post-time: 2:17 PM blog-image: "2016-12-15-wbn.jpg" ---
We are now half way through December. Are you wondering if there is any color left here at Red Butte Garden? Well wonder no more. Take a look at the photos below, better yet, come to the Garden to see for yourself!
Nannyberry Viburnum Viburnum lentago
You better hurry to see these vibrant fruits, they won't be able to hang on for much longer.
Orangerie Living Wall
While at the Garden, take a break from the cold and stop inside the Orangerie to see the Living Wall, you won't be sorry.
Korean Barberry Berberis koreana
This bush is raining red berries!
European Cranberrybush Viburnum Viburnum opulus
You can't miss these bright berries in the Medicinal Garden!


Skip to main content
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Warning
Modifying the settings on this page can impact the ability of devices to access your private network.
DHCP Configuration
DHCP Network Range
If you change the IP address range, you must renew the DHCP lease for all devices on the network.
<IP_ADDRESS> / <IP_ADDRESS> (default)
<IP_ADDRESS> / <IP_ADDRESS>
Configure manually
Note: Use address and mask format - xxx.xxx.xxx.xxx, where xxx represents the number (0-255).Also, Router address, first and last DHCP address should not start with number 10.
Router Address
Subnet Mask
First DHCP Address
Last DHCP Address
DHCP Lease Time
hours (24 hours default)
Select Default Address Allocation Pool for the DHCP Server
Warning: This selection modifies the default LAN network used by the DHCP server for address assignments to new devices. The default setting is Private Network. Change it only to Public Network when you want every new device getting a public address assigned. The recommendation is Private Network. You can change the setting for each individual on the 'IP Address Allocation' page.
New Device DHCP Pool
Private Network
Guest DHCP Configuration
DHCP Network Range
If you change the IP address range, you must renew the DHCP lease for all devices on the network.
<IP_ADDRESS> / <IP_ADDRESS> (default)
Configure manually
Note: Use address and mask format - xxx.xxx.xxx.xxx, where xxx represents the number (0-255).Also, Router address, first and last DHCP address should not start with number 10.
Router Address
Subnet Mask
First DHCP Address
Last DHCP Address
DHCP Lease Time
hours (24 hours default)
DHCP6 Configuration
Prefix Delegation
Enabled
Address Assignment
Enabled


| lesson 27 Nothing to sell and nothing to buy |
| It has been said that everyone lives by selling something. |
| In the light of this statement, teachers live by selling knowledge, |
| philosophers by selling wisdom and priests by selling spiritual comfort. |
| Though it may be possible to measure the value of material goods in terms of money, |
| it is extremely difficult to estimate the true value of the services which people perform for us. |
| There are times when we would willingly give everything we possess to save our lives, |
| yet we might grudge paying a surgeon a high fee for offering us precisely this service. |
| The conditions of society are such that skills have to be paid for in the same way that goods are paid for at a shop. |
| Everyone has something to sell. |
| Tramps seem to be the only exception to this general rule. |
| Beggars almost sell themselves as human beings to arouse the pity of passers-by. |
| But real tramps are not beggars. |
| They have nothing to sell and require nothing from others. |
| In seeking independence, they do not sacrifice their human dignity. |
| A tramp may ask you for money, but he will never ask you to feel sorry for him. |
| He has deliberately chosen to lead the life he leads and is fully aware of the consequences. |
| He may never be sure where the next meal is coming from, but he is free from the thousands of anxieties which afflict other people. |
| His few material possessions make it possible for him to move from place to place with ease. |
| By having to sleep in the open, he gets far closer to the world of nature than most of us ever do. |
| He may hunt, beg, or steal occasionally to keep himself alive; |
| he may even, in times of real need, do a little work; but he will never sacrifice his freedom. |
| We often speak of tramps with contempt and put them in the same class as beggars, |
| but how many of us can honestly say that we have not felt a little envious of their simple way of life and their freedom from care? |


25 October 2004
The Ministry of Public Health in Thailand has today confirmed an additional fatal case of human infection with H5N1 avian influenza. The patient was a 14-year-old girl from Sukhothai Province. She developed symptoms on 8 October and died on 19 October. Chickens at her household died suddenly in late September.
The girl’s death brings the total in Thailand to 17 cases, of which 12 have been fatal.


{% extends "layouts/govuk-registers.html" %} {% set section = "index" %} {% block page_title %} GOV.UK prototype kit {% endblock %} {% block content %} {% include "includes/phase_banner_alpha.html" %} Tasks Edit service details Manage collaborators Sign out Details about your service Service name Use the name of the service as it appears to users The organisation that delivers the service The purpose of the service Why it was built, its policy objectives and the user need it meets How the service works Describe the processes and back-office operations that allow the service to function How frequently the service is used How often does the average user access this service? Time it takes for transactions to end in an outcome The average time it takes for a user to get what they applied for Save changes {% endblock %}


La^~^La^~^La~~~ ! Hm~Hm-Hm^ ! Woooo~~~ !
Humming the songs you taught me when I'm walking home alone. Seeing the crowd get on the bus, I always feel as if you were there saying this sentence to me: "You are the geek who is always hesitant to do something"!  Friends~~Friends~~Friends~~Mo~ni~~ca!! Do you believe in someone's quote that it says: "We met by chance, and we became friends by choice."? Perhaps you don't because you are science student, but I do. Moving back to our school time, we're those crazy students who talked and showed off our epic equipments in DNF day and night. Saying goodbye couldn't come with sadness; on the contrary, you said it with joy and smile before we parted.
Life is like puzzle, it takes you the whole life to seek those pieces to complete your map. To enlarge the map, we need people as our comrades, including you, my friends. Ah!!! Since that time I borrowed a guitar from you, I truly and deeply understood what kind of pain and sorrow you're having. As you strum the guitar with your tough fingers, I sense that you're strumming it with your pain. Just only by doing it, so then can you feel a bit better. I remember you used to say this to me: "Come sit, read the book quietly, but not being a fool all the time."
 Through HTML, through writing content on each page, I felt something unforgettable would properly come to me again. Touching words, I've already forgotten what our English teacher taught; good grammar points, I haven't used any of them since I graduated from high school and we chose our own path to go forwards; writing methods, ha~~, I did hard to find all the words to format the content; but I still remember what you strongly taught me by that push. You always come to see me after coming home from your university; you always accompany me to be foolish; you guffaw not for seeing me, but for making dirty jokes with me. I love nostalgic things, old friends, old books and old times. We know friends cannot be around in our whole life, they somehow leave you alone. But truly, merely, I want to have a long talk with you. Friends are what we cannot use any words to describe about, but by strong friendships, can we understand their feelings without a letter.


The carousel allows the user to browse through a set of items. It can display one or several items at a time.
From the displayed item or items, the user can navigate to either the next or the previous item. Optionally, a
paging indicator displays the user’s current position inside the set of items.


basic_usage.Rmd
There are two ways to use the topic model diagnostics included topicdoc
. You can calculate all the topic diagnostics at once using topic_diagnostics
or use the other functions to calculate the diagnostics individually.
The only prerequisite for using topicdoc
is that your topic model is fit using the topicmodels
package and that your document-term matrix (DTM) is slam
coercible. This includes DTMs created through popular text mining packages like tm
and quanteda
.
For this example, the Associated Press Dataset from topicmodels is used. It contains a DTM created a series of AP articles from 1988.
library(topicdoc)
library(topicmodels)
data("AssociatedPress")
lda_ap4 <- LDA(AssociatedPress,
control = list(seed = 33), k = 4)
# See the top 10 terms associated with each of the topics
terms(lda_ap4, 10)
#> Topic 1 Topic 2 Topic 3 Topic 4
#> [1,] "i" "percent" "bush" "soviet"
#> [2,] "people" "million" "i" "government"
#> [3,] "two" "year" "president" "united"
#> [4,] "police" "billion" "court" "president"
#> [5,] "years" "new" "federal" "people"
#> [6,] "new" "market" "new" "police"
#> [7,] "city" "company" "house" "military"
#> [8,] "time" "prices" "state" "states"
#> [9,] "three" "stock" "dukakis" "party"
#> [10,] "like" "last" "campaign" "two"
Here’s how you would run all the diagnostics at once.
topic_diagnostics(lda_ap4, AssociatedPress)
#> topic_num topic_size mean_token_length dist_from_corpus tf_df_dist
#> 1 1 3476.377 4.1 0.3899012 24.08191
#> 2 2 1910.153 5.6 0.5044673 26.67523
#> 3 3 2504.622 5.4 0.3830014 26.46131
#> 4 4 2581.848 6.5 0.3988826 25.52163
#> doc_prominence topic_coherence topic_exclusivity
#> 1 1053 -81.83339 7.813034
#> 2 598 -79.50691 9.560433
#> 3<PHONE_NUMBER>2 9.162590
#> 4 775 -84.46149 9.058854
Here’s how you would run a few of them individually.
A full list of the diagnostics included are provided below.
| Diagnostic/Metric | Function | Description |
|---|---|---|
| topic size | topic_size |
Total (weighted) number of tokens per topic |
| mean token length | mean_token_length |
Average number of characters for the top tokens per topic |
| distance from corpus distribution | dist_from_corpus |
Distance of a topic’s token distribution from the overall corpus token distribution |
| distance between token and document frequencies | tf_df_dist |
Distance between a topic’s token and document distributions |
| document prominence | doc_prominence |
Number of unique documents where a topic appears |
| topic coherence | topic_coherence |
Measure of how often the top tokens in each topic appear together in the same document |
| topic exclusivity | topic_exclusivity |
Measure of how unique the top tokens in each topic are compared to the other topics |


The party gave the rejection in a statement issued by its National Publicity Secretary, Kola Ologbondiyan on Wednesday in Abuja.
He said that in as much as the PDP did not condone hate speech under any guise, it regarded the bill as “unconstitutional, undemocratic and a barbarous”.
He said that the provisions of the bill were deliberately hazy and nebulous with the malicious intention to victimise innocent Nigerians.
Ologbondiyan said that the bill was targeted at official extermination of voice of dissent and perceived opposition.
He said that Nigeria already had enough constitutional provisions and extant laws to safeguard a sane and healthy public expression space, adding that such law could not find its way in Nigeria.
He recalled that the bill was rejected in the Senate in 2018, only for it to be re-introduced.
He said: “Our party holds that if allowed to pass, the hate speech prohibition bill, with its savage provisions, would destroy our democratic order, strip our constitutional provisions, the rights of citizens and usher in a full-blown despotism in Nigeria.”
Ologbondiyan advised sponsors of the bill to retrace their steps and withdraw the bill.
He said: “The PDP also charges the National Assembly to protect our nation by
throwing away the bill.
“If anything, NASS should use its legislative instruments to strengthen our institutions and authorities vested with regulation of public expression in line with the provisions of the 1999 constitution (as amended).”
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Browse The
Leaderboard
Find the best funds to invest in or start a fund of your own. Research top
funds, invest in these funds, and compare your leaderboard rank with
friends.
Whether you are running your own fund or investing in others,
see
how you stack up against the best!
Invest in Funds,
Led by People
Don’t invest in stocks, invest in the people who pick the stocks. Find the
profiles that you like, see
detailed analytics about that fund (historical
returns, AUM, number of
investors in fund, etc.) and invest any
amount
of money that you wish with that Fund Manager!
Manager your
Portfolio and
Track Returns
Invest in multiple Fund Managers to create a portfolio. Track your most
lucrative investments and watch as your wealth grows. Invest in Fund
Managers and watch them grow your money for you!
Compare with
Friends and Share
your Thoughts
Follow your friends, Fund Managers, and other investors to keep up with
whats going on in the world of stocks! Talk shop about investing, speak
your mind about the next big thing, and grow your following!


# rza2mfacedetection_demo
Face Detection Demo with RZ/A2M
In the demo, we connect the Sony IMX219 CMOS sensor through MIPI interface, input a 1280x720 resolution image, and use DRP in RZ/A2M to perform Simple ISP processing and image scaling processing on the input image, and then run a light and efficient mask face detection model. It can achieve detect speed of 30FPS in the face detection mode, and a detection speed of 20FPS in the mode of distinguishing whether to wear a mask.
Finally, the result is shown on a display (HDMI monitor).
RZ/A2M Evaluation Board Kit
e²studio project name: “rza2mfacedetectionfreertosgcc_evk”
(e²studio User’s Manual: Getting Started Guide / RZ Family)
Gadget Renesas board “GR-MANGO”
e²studio project name: “rza2mfacedetectionfreertosgcc_grmango”
(e²studio for GR-MANGO; GR-MANGO 'hands-on')
MIPI 1280x720 camera input.
Only use RZ/A2M internal 4MB memory.
Support face detection & mask detection.
Face Detection performance: 30fps
Face + Mask Detection performance: 15fps.
(DRP).
Please check the Application Note for more details.
Please contact your local Renesas Sales representative in case you like to get more information about RZ/A2M, DRP
Please contact us below if you have any questions. Q&A Forum


|
||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |
java.lang.Object java.util.ResourceBundle java.util.ListResourceBundle interbase.interclient.Resources
public class Resources
Default resource bundle for InterClient error messages and hardwired text within the driver.
This is the default (English) resource bundle.
You can provide your own locale-specific bundle
with an ISO language suffix appended to the class name for
your language. Additional or replacement locale-specific
resource bundles must reside in the interbase.interclient
package.
The driver locates the appropriate resource bundle for your locale by using
java.util.ResourceBundle.getBundle("interbase.interclient.Resources");Using the ClassLoader, this call in turn looks for the appropriate bundle as a .class file in the following order based on your locale:
Instructions for customizing resources for your locale:
Follow these instructions only if a resource bundle for your locale is not already
included in the InterClient distribution.
Having the Resources.java source file will save you from having to call
getContents()
as described below.
getContents()
. Set aside for translation;
write the contents, including keys and text, to a file or printer for translation.
java.util.ListResourceBundle
and
and is in the package interbase.interclient
. For example:
// A French language resource bundle for InterClient. package interbase.interclient; public class Resources_fr extends java.util.ListResourceBundle { // Contains the translated text for each resource key from default Resources contents. static private final Object[][] contents = { {"1", "Mot de passe incorrect"}, // The password is incorrect {"2", "Saisie du mot de passe"}, // Enter your password ... {"98", "Quitter"} // Exit } public Object[][] getContents() { return contents; } }
jar xvf interclient.jar
to extract the InterClient class files,
and include your new Resources_fr.class
in the interbase/interclient
directory.
Use jar cvf0 interclient.jar interbase borland
to archive all the
files back into a new interclient.jar
file.
Class directories interbase/interclient
,
interbase/interclient/utils
, borland/jdbc
,
and com/inprise/jdbc
should all be archived by this command.
Alternatively, modify the separate resource archive interclient-res.jar,
and include both interclient-core.jar
, interclient-res.jar
,
and optionally interclient-utils.jar
in your
classpath.
For more information on jar, refer to the documentation for your Java Development Kit.
A good article on using resources for localization may be found at http://developer.java.sun.com/developer/technicalArticles/ResourceBundles.html
Note for bundle designers: For details see the Sun javadocs for class { java.text.MessageFormat}.
Locale
,
ResourceBundle
| Constructor Summary | |
|---|---|
Resources()
|
| Method Summary | |
|---|---|
java.lang.Object[][] |
getContents()
Extracts an array of key, resource pairs for this bundle. |
| Methods inherited from class java.util.ListResourceBundle |
|---|
getKeys, handleGetObject |
| Methods inherited from class java.util.ResourceBundle |
|---|
getBundle, getBundle, getBundle, getLocale, getObject, getString, getStringArray |
| Methods inherited from class java.lang.Object |
|---|
equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public Resources()
| Method Detail |
|---|
public java.lang.Object[][] getContents()
getContents
in class java.util.ListResourceBundle
|
||||||||
| PREV CLASS NEXT CLASS | FRAMES NO FRAMES | |||||||
| SUMMARY: NESTED | FIELD | CONSTR | METHOD | DETAIL: FIELD | CONSTR | METHOD |


Exploration 3.1.2.
For each matrix in this activity, your job is to find both
- The minimal set of equations which cuts out the nullspace of the given matrix as an intersection of hyperplanes; and
- the size of the nullspace, expressed through the number “independent directions” it contains.
\begin{equation*}
A = \begin{pmatrix}
1 & 2 & 3 \\
4 & 8 & 5 \\
-1 & -2 & 0
\end{pmatrix},
B = \begin{pmatrix}
2 & 1 & 3 & 7 \\
1 & 1 & 1 & -3
\end{pmatrix},
C= \begin{pmatrix}
2 & 3 & 5 & 6 \\
1 & 1 & 2 & 3 \\
0 & 1 & 1 & 0 \\
-1 & -1 & -2 & -3 \\
1 & 1 & 2 & 3
\end{pmatrix}
\end{equation*}


Stoke set to appoint O'Neill as manager - report
Stoke City are set to appoint Northern Ireland manager Michael O’Neill as their head coach, Sky Sports reported on Thursday.
REUTERS: Stoke City are set to appoint Northern Ireland manager Michael O’Neill as their head coach, Sky Sports reported on Thursday.
Stoke, who are bottom of the English Championship, sacked Nathan Jones last week and the next manager will be their fifth in two years since relegation from the Premier League.
The 50-year-old O'Neill, who took charge of Northern Ireland in 2011, led them to a surprise last-16 berth at Euro 2016 and they reached the playoffs for last year's World Cup.
They are third, behind Germany and the Netherlands, in their Euro 2020 qualifying group and face an uphill battle to qualify for next year’s tournament.
(Writing by Joseph Cassinelli, editing by Ed Osmond)


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Tom Brady Trolled Cowboys With “Bowl 96” Cadence At Line of Scrimmage (VIDEO)
by Darrelle LincolnThe Dallas Cowboys offense could never quite get it going during the rain soaked evening at Gillette Stadium on Sunday Night, and it resulted in just 9 points scored another loss to a an above .500 team.
On top of that, the team seemed to get trolled by Tom Brady late in the 4th quarter with his snap count. The Patriots QB could be heard yelling ‘Bowl 96’ at the line of scrimmage, which seems to be a shot at the last time they won a Super Bowl back in 1996.
The Cowboys haven’t been back to the NFC Title game, let alone the Super Bowl since the mid 90’s, something Cowboys fans and certainly Jerry Jones would love to end soon.
If the team keep going about their losing ways to better competition, it won’t be happening this season.


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Copyright 2002-2006 by The BioBike Team (contact<EMAIL_ADDRESS>Blast is the most important tool (at the moment) in the computational
biologist's kit. But most people don't actually know how it works!
Blast finds all the sequences that match a particular given sequence
from a database. But what do we mean by "match"? The answer to this
has a long and interesting history. Although we're going to survey
some of this history here, and look at some of the algorithms in
detail, there's a huge literature in sequence matching (more
generally: 'string matching') -- most of which is interesting only to
computer scientists. But a basic understanding of how Blast and other
related algorithms work is useful. (All of this is discussed in great
detail in D. Gusfield: Algorithms on Strings, Trees, and
Sequences, 1997, Cambridge University Press. So if you want more
info, get that book.)
We'll approach BLAST in three stages. First, we'll look at a
relatively simply string-matching problem known as edit distance, and
develop the code for it. Then we'll discuss a set of general
string-matching algorithms known in the BioInformatics literature as
"Needleman-Wunsch" and "Smith-Waterman". Finally, with this as
background, we'll look at how the inner kernel of BLAST works.
Let's stop using the word 'matching,' which is vague, and
instead use the word 'alignment', or, in our case: 'sequence
alignment'. The sequence alignment problem is this:
Given two sequences (i.e, in our case: strings of letters), arrange
them with respect to one another such that places in each subsequence
that are similar to subsequences in the other are maximized. Let's
take, for example, the two sentences:
Now, the first thing to notice about alignments is that there are
many ways to make an alignment. Here's another one:
It's pretty easy to see how you would do that: Just try every possible
way to align the two sequences, score each one, and then select the
best one. This is pretty much what the sequence alignment algorithms
we're going to look at do, but they do it in a fancy way that is very
computationally efficient.
A form of this problem is called the 'edit distance' problem. That
is, figuring out the minimum number of insertions, deletions and
replacements necessary to turn string A into string B.
For instance, here's one way to turn "FIREMAN" into "POLICEMAN":
In general, a dynamic programming algorithm specifies initial
conditions on this array and a recursion relationship defining the
value of S(i,j) in terms of one or more previously computed elements
S(k<=i,l<=j) of the array.
For the edit distance problem, the initial conditions are
Here's what we'd get if we do that for FIREMAN -> POLICEMAN:
Here's Lisp code that does this for us:
Okay, so that's looks right, but we're after the actual steps of the
transformation, not just the edit distance! It turns out that we're
most of the way to having that with what we've already done. Take a
look at the final array again, just above. Let's see if we can read
the actual edits off of this array. To do this, we start at the lower
right corner (4), and try to find the cheapest path back to the upper
left. From the lower righthand corner, there are three possibile ways
to move: straight left to 5, up and to the left to 4, or straight up
to 5. Only one of those possibilities is minimal: up and left to 4.
Everytime we go up-and-left we will replace the corresponding letter
of FIREMAN with a letter of POLICEMAN, but in this case, we're just
replacing N with N, so there's no change. Now what? Well, we have to
go up-and-left three more times in order to keep our score at 4, so we
take another three letters without change to get EMAN (remember, we're
going backwards through the word -- follow along in the array!)
Now we're at the R/C row and column in the array (the R and C are not
for Row and Column; they're the letters -- just a coincidence!) In
order to keep along the cheapest path we need to go up-and-left again,
which means replacing C for R: CEMAN, and our score is 3.
So far so good, but now we're in trouble: We have several ways that we
could go and retain a score of 3: up-left, left, or down-left (but
we're not allowed to go down or right, so this last one isn't really
an option). These have different implications. Let's keep going
up-left until we run out of room: I->I (ICEMAN = 3), F->L (LICEMAN =
2), and now we're at the top of the array and the only way to go is to
the left -- each time we go left, we insert a letter: Insert O
(OLICEMAN = 1), Insert P (POLICEMAN = 0), and we're done!
So reversing our steps, and ignoring exact match steps, our
transformation sequence is:
(By the way, we never went straight up; if we did go straight up, the
operation would be to delete a letter. Exercise for the reader:
Figure out some other paths through this array that give different
edit sequences. For fun, try not being constrained by finding the
cheapest path. For example, first go straight up to the top, and then
straight over to the left on the top line. What's the sequence of
edits for that path?!)
That's the basic idea behind the principal sequence alignment
algorithm, called "Needleman-Wunsch" (after the folks who invented
it). Here's the
original Needleman-Wunsch paper. We'll refer to the algorithm as
NW from this point on.
NW is nearly the same as the edit distance algorithm. The main
differences are: a) In the edit distance problem, inserting, deleting
and replacing characters all cost 1, while an exact match costs 0; NW
generalizes this to let you specify the costs of insertion, deletion,
replacement and exact matches; Furthermore, gaps can be scored as an
arbitrary function of gap length, not just a cost times the number of
spaces in the gap, as in edit distance. b) As the matrix is filled
in, NW keeps track of the optimal backpaths, whereas our edit distance
code didn't do that and we later computed backpaths from the
lower-right of the matrix by hand.
You can find the NW algorithm coded up in Lisp in the Biolisp release
in the file "matching/needleman-wunsch.lisp". We will not develop the
code itself here, but we will give a detailed description of how it
works.
We're going to change notation just slightly now in order to add some
flexibility to the method. We're still aligning two sequences (A and
B), given by: A[0...N], and B[0...M], and we're looking for a solution
set consisting of:
For example:
Here's one solution where there is a single gap in B, while A is
shifted by one position to the right with respect to B:
What constitutes a 'best match' solution depends on how one defines what
constitutes a good match. There are three parameters generally
employed to determine a good match:
It is possible to specify different rewards for different matching
alignments (e.g., a match of T on top with T on bottom could be given
a higher value than matching G on top with G on bottom). It is also
possible to reward aligments that don't 'match': e.g., having T on top
and T on the bottom could generate a very high score, while T on top
and A on the bottom a somewhat lesser score, and T on top and either C
or G on the bottom a poor score; that is: TT > TA > TC = TG.
(It's also theoretically possible to specify different penalties for
gaps and gap lengths in the first sequence vs in the second sequence,
although we don't provide that option in the code.)
Determining the reward for element alignment is done by constructing
what is known in the literature as a SUBSTITUTION MATRIX, and, in the
code, the corresponding SUBSTITUTION FUNCTION (SUBF). For any two
sequence elements X and Y, (SUBF X Y) is used to obtain the reward for
X and Y being aligned. Similarly, determining the penalties for a gap
is done by providing a GAP-PENALTY-FUNCTION or providing parameters
that allow construction of a function which increases linearly with
increasing gap length.
Typical gap functions are one of:
This last expression is referred to as the 'affine gap penalty
function', and is commonly used.
The NW algorithm to find the set of best alignments consists of
three parts:
The first two steps are done concurrently.
The first step of this algorithm is conceptually nearly
identical to the edit distance algorithm we went through above,
although there are some important differences (aside from the keeping
of the backpointer matrix). We'll go through this carefully so that
you can see these differences clearly.
The SCORE matrix initially looks like this, assuming it's elements
have been initialized to zero.
(One difference from the edit distance array example: Here the first
two characters are at (0,0) of the array and we index the sequences
starting, in normal Lisp fashion, at 0. If you intend to look at the
NW code, note that in it the SCORE matrix is always indexed using (i =
ROW, j = COLUMN). Further, I is always the first index and J the
second index. In the code sequence A is called 'SEQ-DOWN-ROWS' and
sequence B is named 'SEQ-ACROSS-COLS', for clarity.)
The substitution matrix, SUB, for our sequences might look like this:
Now to the details of the algorithm.
For any (i,j), A[I] and B[J] give us the RESIDUES (as above) for
position (i,j). Given the residues, we can (conceptually) index into
the substitution matrix to determine the score for alignment of the
two sequences at position (i,j). (Of course to index into an actual
matrix one needs numerical indices, so we really need the mapping A ->
0, C -> 1, G -> 2, T -> 3, given our example SUB matrix. (This
mapping is provided transparently in the code by the function
CREATE-SUBSTITUTION-FUNCTION-AND-MATRIX, which returns a function
which is called with the two residues.)
The algorithm must first fill in values for all of the elements of the
SCORE matrix according to the following recursion relationship:
For any given element SCORE(i,j), assuming we have already filled in all
elements SCORE(k<i,l<j), the value of SCORE(i,j) will be:
Given that we are careful to avoid screwing up at the leftmost column
and uppermost row (since there is no J-1 column or I-1 row in these
cases) this algorithm can then be used to sequentially fill in the
rest of the array.
(The recursion relationship above is one of two such global alignment
recursion formulas found in the biology literature. We'll talk about
these differences a little later on. A few minor implementation
details: When we get to the edge, we use a value of 0.0 rather than
indexing outside of the array bounds; This consitutes our initial
condition, instead of using an additional row and column as we did
with the edit distance array. Also, in edit distance as we implmented
it above, we were doing MIN, and looking for the path with the lowest
numbers. Here we're doing MAX, and we'll be looking for the paths
with the highest numbers. This is not actually an important
difference; If we did the scoring functions the other way around
(replacing pluses with minuses and vice versa), the algorithms could
both use MIN or MAX.)
The BACKPOINTER matrix is filled in by determining which of the cases
from above, 1, 2, or 3, had the maximum score, and setting the
backpointer to point from cell (I,J) back to the cell which held the
maximum score.
So for the cell labelled '*' in the below matrix, the recursion formula
looks at all cells labelled '+'
(In the code, the SCORE matrix (*SCOREMAT*), and the BACKPOINTER
matrix (*POINTERMAT*) are computed by the
GENERATE-NEEDLEMAN-WUNSCH-SCORES function.)
What do the numbers in the SCORE matrix mean once we compute them?
Just as in the edit distance algorithm, the numbers tend to increase
as we go down and right, but in this case, the more positive an
element (I,J) is, the better it is to align the two sequences such
that SEQA[I] is over SEQB[J].
Once the SCORE and BACKPOINTER matrices have been filled in, to
determine a best alignment all we need to do is follow the
backpointers we have set up. We start from a position with the
highest score in the bottom row or rightmost column, and work our way
up and to the left (towards the origin) via our backpointers, just as
we did by eye in the edit distance case! As we do this, we create
paths through the matrix from this point back to the origin. The
geometry of these paths determine the alignment. (Since there may be
more than one such maximal point along the bottom row or rightmost
column, and there may be more than one backpointer from a given cell,
there may be numerous best alignments).
Given a path through the matrix, we can easily determine what sequence
alignment it specifies.
To make this concrete, suppose we have the following path back through the matrix:
So there are two points along the bottom row or rightmost column from
which optimal backpaths originate (i.e., those points with the highest
scores). The first of those points is used, and it has 3 optimal
paths ending at it. The demo shows the alignments dictated by these
three optimal paths.
Notice that while the number of matches in the first alignment is
less than the number of matches in alignments 2 and 3, there are no
gaps in the first alignment. The two competing factors (matches good,
gaps bad) happen to balance out exactly and the scores turn out to
be the same for all three alignments!
By monkeying with the substitution matrix and the gap penalty function
you can get all sorts of different behavior out of this algorithm.
Try
Also look at the documentation in the code and especially the
arguments to RUN-SW if you'd like to play with different substitution
matrices, gap functions, and sequences. You might want to try a few
runs in order to get a feel for how it works before pushing onward!
Now, there is a variant of NW that you'll find a lot in discussion of
sequence matching algorithms. It is called "Smith-Waterman" (SW) and
is used extensively, particularly within the Blast algorithm as we
will see shortly. SW is nearly identical algorithmically to NW, but
the small changes introduced result is substantially different behavior.
The algorithmic changes from NW to SM are:
Essentially, while NW finds best global alignments, reporting
the best overall match, SW finds best local alignments. When
sequences are very similar in a sub-region, but not overall, global
alignment algorithms will tend to miss this fact, whereas local
alignment enable us to pick up sub-region similarity. (One can intuit
this because in NW the sequence match starts at the start of both
sequences and ends at the end of both sequences. In SW, the match can
start anywhere (because the backpaths terminate when 0 is found) and
end anywhere (because the backpaths can start at any maximal point in
the whole array), so it can pick out a small region of high
similarity).
Here's the same sequence set as above, run with Smith-Waterman:
Although it's a little hard to see, the SW alignment of this segment
of the sequences is better than either of the NW global alignments!
(There are ten matches and two gaps, while in NW above, the final two
alignments have nine matches and two gaps)
As with the RUN-NW function, the RUN-SW function takes various
arguments that allow you to vary the sequences, substitution matrix
and gap penalty function. There's also a DEMO-SW function that you
can run. Take a look at the code (it's in
"matching/smith-waterman.lisp") and try some things out yourself if
you'd like.
We've taken a look at the Needleman-Wunsch and Smith-Waterman
algorithms. There's just one problem with attempting to apply them to
DNA and protein matching -- they're too slow and they take too much
space! If you happen to be comparing a DNA sequence of 500 base
pairs against a relatively small database of, say, half a billion base
pairs, then you'd have to fill in a 500 x 500,000,000 matrix. Most
PC's don't (yet!) have a terabyte (a trillion bytes) of main memory...
To deal with this the famous Blast algorithm was invented! Blast
attempts to find the best match(es) for a (relatively small)
query sequence in a (very large) database sequence.
It's actually a complex combination of two algorithms: First Blast
uses a set of 'words' -- that is, small subsequences (usually just a
few letters long) that appear in the target sequence -- to locate
regions of the database that are likely to have high sequence
similarity with the query (or to think about it another way, to
eliminate huge regions of the database that almost certainly do
not match the query). Once these regions of possible
similarity have been identified Blast uses a Smith-Waterman-type
local alignment algorithm to extend the match around each region of
these matching words, scoring the aligments in each region and
returning to the user the best such scores.
Here's an little example: Suppose we have the query sequence "CAT" and
the database sequence "THE MAD HATTER AND THE WHITE RABBIT SAW A CARD
BATTLE". We'll divide up the query into all substrings of length 2,
i.e., "CA" and "AT". Now we'll find occurences of those substrings in
our database sequence:
We've implemented a simplified Blast (in the file
matching/blast.lisp). Just to mix things up a little, this version of
Blast compares amino acid (AA) sequences instead of boring DNA
sequences. Recall that there are 20 AAs (whereas there are only 4 DNA
bases). The standard substitution matrix for AAs is called the
BLOSUM-62 matrix, and it represents certain complex probabilities of
substitutions among amino acids. Here it is:
Where do these numbers come from? And why are the amino acids not
named for the first 20 characters of the alphabet, instead of using an
apparently random collection? And why aren't they at least in
alphabetical order? Good questions. We won't answer these questions
here, but a Google search will give you a lot of information on the
first question. The other two are probably the result of history and
convention.
The code in matching/blast.lisp deals with three-letter words, that
is, triples, (unlike the doublets in our little example above) and
forms every combination of the 20 AAs composed of three letters. A
quick calculation will show that there are 20x20x20 of these, or 8,000
combinations.
We need to be able to figure out the substitution score for any pair
of sequences (of the same length); That is, the sum of the elements in
the matrix above, indexed by the respective letters in each sequence.
Here's some (inefficient) code that does this (a more efficient version
can be found in matching/blast.lisp):
sub(a,a) = (subs(a,a) = 4) + (sub(s,s) = 4) + (sub(d,d) = 6) + (sub(f,f) = 6) = 20.
One more example, using two difference sequences:
The idea now is to find all the triples in the database sequence
which, when scored against any triple in the query sequence,
result in at least a threshold SUBSTITUTION-SCORE result (this
threshold can be increased or decreased as a Blast user wishes, to
decrease or increase, respectively, the number of such matches).
The code at this point becomes involved, due to complications involving
duplications of triples in both the query and database sequences.
We won't develop the code further, as the general idea should be clear,
but if you are interested in the details have a look at the comments
and code in matching/blast.lisp.
Instead, just follow along as we show a sample run.
First let's create a database:
Now find all the places that any triple in the database matches any
triple in the query using a high substitution score as the match
criterion. There are fast and slow ways to do this, our way isn't the
fastest, but it's okay for the moderately-sized database that we'll
be using here.
All that is left to do is examine each seed area and determine the
best match of the query to the database region containing that seed.
Here's a run of the entire algorithm, showing how it does this:
In our code we use a very simple-minded maximizing algorithm that
keeps extending the seed in both directions until the score goes
negative, without permitting any gapping or deletion. But you can see
how a local alignment algorithm like SW would fit in nicely here,
permitting gaps and deletions, and finally producing a good local
alignment between the query and matching portions of a possibly very
large database. Also note how we have reduced the initial problem of
having to fill in a 500 x 500,000,000 matrix to one of filling in
several 500 x 1000 or so matrices and some simple score evaluations of
short sequences.
So that's how Blast works!
A technical note on slight differences in the recursion formulations found in
the computational biology literature
Browsing Biolinformatics web sites and books that discuss
Needleman-Wunsch and Smith-Waterman algorithms, one comes across two
apparently different recursion formulas that claim to implement these
alignment algorithms.
Below we provide the two different formulas, discuss what they mean,
and why they are in fact different but in some cases equivalent.
Consider the alignment problem
But given a relatively large mismatch penalty and a relatively small
gap penalty, then:
Using the recursion relationship found in the original Needleman-Wunsch
algorithm (Here's
the orginal Needleman-Wunsch paper!), which is:
The reason finding these alignments is not possible using the
recursion formula above is because it forces the backpointers from
(I,J) to point to some point K<I AND L<J. A backpointer from (I,J)
cannot go to (I-1,J) or (I,J-1) and then to (I-1,J-1), and these paths
are the only ways to produce such an alternating gap.
Recall that backpointer(s) from (I,J) point back to the cell(s) selected
by the MAX operations in the recursion formula that contribute the
highest score(s). No cell with I or J as a coordinate is ever considered.
Graphically, for the '*' point below, the algorithm allows
backpointers to the '+' points.
The fact that SEQA[I] and SEQB[J] must be exactly aligned here is
enforced by the addition of SUBST(I,J) (the score for this exact
match) regardless of which term is selected by the outer MAX
operation. This addition is done for all I and J, so every
backpointer points back to an exact alignment.
Now consider the alternate recursion formula, found, for instance, in
Gusfield, "Algorithms of Strings, Trees and Sequences", Chapter 11.
(It is presented there in a somewhat different format. It has been
converted to the format used above):
Graphically, as above,
This recursion relationship says that the optimal alignment for
the subsequences SEQA[0...I] and SEQB[0...J] is either:
There is no presumption in (2) that SEQA[I] must exactly align with
any character in SEQB, and similarly for (3).
Illustrated graphically, the first recursion relation can only produce
the following backpath, regardless of the penalty for mismatches:
Now, in some kinds of DNA matching it may be the case that one never
encounters a situation where it would be appropriate to have a large
mismatch penalty and small gap penalty. Then the two recursion
formulas would, I believe, produce the same alignments, all other
things equivalent. Also note that the first algorithm is slightly
more efficient since less cells need to be searched (although in the
standard case, with a linear gap penalty function, this is no longer
true if one implements the optimization that allows the algorithm to
run in O(NM) instead of cubic time; both algorithms need to search the
same number of cells).
But presumably there are other scenarios where a gap would be
acceptable while a mismatch highly unlikely (See Gusfield, p 253, for
a discussion of when adjacent gaps in opposite strings might be
appropriate in a biological context).
As noted, the original Needleman-Wunsch paper uses the first algorithm,
and the paper specifically notes the constraint that both I and J must
decrease as the path matrix is traversed backwards. They do not,
however, talk about the implications/restrictions of this contraint
on what alignments are possible.
The Gribskov,
Devereux paper also uses the first algorithm. However, most texts
and websites seem to use the second, more general, recursion formula.
Introduction
(This tutorial is based upon lisp sequence matching code originally
written and contributed by Russ Altman, although it has been almost
entirely rewritten for this presentation.)
I like peanut butter and jelly sandwiches.
I don't like to put peanut butter on ham sandwiches.
Here's a plausible alignment of these sentences:
I like peanut butter a nd jelly sandwiches.
I don't like to put peanut butter on ham sandwiches.
Notice that in doing the alignment we added a number of 'gaps'
(spaces) to each sequence, and that there are no mismatches -- that
is, there are no letters that are aligned with the wrong letter in the
other sentence. (Except for spaces, which we'll consider the same as
gaps for this example. Once we get to doing DNA sequences, this won't
matter because they don't have spaces.)
I like peanut butter a nd jelly sa ndwiches.
I don't like to p ut peanu t butter on h am sandwiches.
There are still no mismatches, but there are lot more broken up words.
Here's another, this time allowing mismatches among letters that sound
sort of alike:
It's hard to rec ognize speech.
It's hard to wreck a nice beach.
The point of these examples is just that sequence alignment isn't
something that has one 'correct' answer; instead there are many ways
to put two sequences into alignment with one another. Given this
flexibility, how are we to decide which alignment to report as 'the'
alignment? One way to do this is to 'score' each alignment. That is,
give it a numerical score based on the amount of mismatch allowed and
the amount of gapping that you do, and then report the alignment with
the best score.
1. Insert P: -> PFIREMAN
2. Insert O: -> POFIREMAN
2. Replace F with L: -> POLIREMAN
3. Replace R with C: -> POLICEMAN
This (and many other solutions) can be conveniently found through a
method called 'Dynamic Programming.' For two sequences that we'll call
A and B, dynamic programming typically uses an array (S) of dimensions
LENGTH(A)+1 by LENGTH(B)+1. That is, it's one longer on each side
than the strings we're comparing. One side of the array represents
the letters in sequence A and the other side represents the letters in
sequence B, like this:
P O L I C E M A N
. . . . . . . . . .

I .

The idea is to fill in the array with numbers representing the edit
distance between the substrings upward and to the left of the location
in the array. That is, more formally: For all i and j, S(i,j)
represents the edit distance of the two substrings A[1...i], B[1...j].
Therefore, when we are done, the lower righthand corner cell of the
array will be our answer: The final edit distance between the two
strings.

The recursion relationship is:
S(i,j) = MIN (S(i-1,j)+1, S(i,j-1)+1, S(i-1,j-1) + mismatch(A[i],B[j]))
where mismatch(c1,c2) = (if (= c1 c2) 0 1)
Or, in English: To find the edit distance for A[1...I] and B[1...J],
we can use the edit score of A[1...(I-1)] and B[1...J] and insert a
character between A[I-1] and A[I], increasing the edit distance by 1;
or, alternatively, we can use the edit score of A[1...I] and
B[1...J-1] and delete A[I], also increasing the edit distance by 1;
finally we use the edit score of A[1...I-1] and B[1...J-1], and see if
A[I] and B[J] match. If they match, the edit distance is not
increased, while a mismatch means we substitute B[I] for A[I] and
increase the edit distance by 1.
P O L I C E M A N
0 1 2 3 4 5 6 7 8 9
F 1 1 2 3 4 5 6 7 8 9
I 2 2 2 3 3 4 5 6 7 8
R 3 3 3 3 4 4 5 6 7 8
E 4 4 4 4 4 5 4 5 6 7
M 5 5 5 5 5 5 5 4 5 6
A 6 6 6 6 6 6 6 5 4 5
N 7 7 7 7 7 7 7 6 5 4
Notice that the upper lefthand is a zero (initial condition!), and
that as we proceed downward and to the left in the array, the numbers
generally increase, because each time we find a mismatch the edit
distance increases, and it cannot descrease. Notice also that the
lower righthand corner has the value of the edit distance that we
predicted above! In fact, all we need to do to get the edit distance
after filling the array is to read off the lower righthand entry!
>> (defvar *edt* nil)
;;; Main routine.
>> (defun find-edit-distance (s1 s2)
>> (let ((s1len (length s1)) (s2len (length s2)))
>> ;; Create the dynamic programming array.
>> (setq *edt* (make-array (list (1+ s1len) (1+ s2len)) :initial-element 0))
>> (set-initial-conditions)
>> (run-recurrence s1 s2 s1len s2len)
>> (aref *edt* s1len s2len) ; Read off the edit distance!
>> ))
;;; Fill in the dynamic programming array, using the initial conditions.
>> (defun set-initial-conditions ()
>> (let ((edt *edt*))
>> (dotimes (i (array-dimension edt 0)) (setf (aref edt i 0) i))
>> (dotimes (j (array-dimension edt 1)) (setf (aref edt 0 j) j))
>> ))
;;; Fill in the array, using the recurrence relationship above.
>> (defun run-recurrence (s1 s2 s1len s2len)
>> (let ((edt *edt*))
>> (loop for i fixnum from 1 to s1len do
>> (loop for j fixnum from 1 to s2len do
>> (let ((prev-i (the fixnum (1- i)))
>> (prev-j (the fixnum (1- j))))
>> (setf (aref edt i j)
>> ;; Here's the MIN just as in the formula.
>> (min
>> (the fixnum (1+ (aref edt prev-i j)))
>> (the fixnum (1+ (aref edt i prev-j)))
>> (the fixnum
>> (+ (aref edt prev-i prev-j)
>> (if (eql (schar s1 prev-i) (schar s2 prev-j)) 0 1)
>> )))))))))
;;; This is just a convenience to show the resulting array.
>> (defun pp-edt (s1 s2)
>> (terpri) (terpri)
>> (format t " ")
>> (loop for ch across s2 do (format t "~A " ch)) (terpri)
>> (loop for i from 0 to (length s1) do
>> (if (zerop i)
>> (format t " ")
>> (format t "~A " (aref s1 (1- i))))
>> (loop for j from 0 to (length s2) do
>> (format t "~A " (aref *edt* i j)))
>> (terpri)
>> )
>> (terpri)
>> )
Let's try it:
>> (find-edit-distance "FIREMAN" "POLICEMAN")
:: 4
Looks good. Let's look at the array:
>> (pp-edt "FIREMAN" "POLICEMAN")
P O L I C E M A N
0 1 2 3 4 5 6 7 8 9
F 1 1 2 3 4 5 6 7 8 9
I 2 2 2 3 3 4 5 6 7 8
R 3 3 3 3 4 4 5 6 7 8
E 4 4 4 4 4 5 4 5 6 7
M 5 5 5 5 5 5 5 4 5 6
A 6 6 6 6 6 6 6 5 4 5
N 7 7 7 7 7 7 7 6 5 4
1. Insert P: -> PFIREMAN
2. Insert O: -> POFIREMAN
2. F->L: -> POLIREMAN
3. R->C: -> POLICEMAN
And here's the path we followed (starting from the lower right):
P O L I C E M A N
0 1 2 . . . . . . .
F . . . 3 . . . . . .
I . . . . 3 . . . . .
 4 . . . .
E . . . . . . 4 . . .
 4 . .
 4 .
 Some of these are just as inexpensive as the
one we followed, while many are much more expensive. In fact, there
are a huge number of possible solutions implicit in the array: i.e,
every possible path from the lower righthand corner to the upper left
that does not go down or right. And each one of them is a solution to
the alignment, although most of them are much more complex than the
one that we found above, and only a small number of them minimize the
edit distance.
A = CGAGGAA
B = ACGTGA
(From here on in we'll be using the DNA alphabet, {A,C,G,T}.)
A: CGAGGAA
|| | |
B: ACGTG*A
Another solution is:
A: AC-GTCA
| | |
B: CGA*GG**AA
This actually has more gaps and less matches, so it's not likely to
rank as high as the first solution using any kind of reasonable
metric, and therefore probably would not be a 'best match'.
B -->
A C G T G A
C 0.0 0.0 0.0
G 0.0 0.0 0.0
A 0.0 0.0 0.0 . . .
A G .
.
| G .
|
\ / A
.
A
Given an index (i,j) into this matrix, we can compute what are called
the RESIDUES, (the symbols above the jth column and the symbols
to the left of the ith row) So, for example, the RESIDUES for position
(3,5) are G and A, while the RESIDUES for (0,0) are C and A. (These
are just the elements of our sequences, A[I] and B[J].)
A C G T
A 1 0 0 0
C 0 1 0 0
G 0 0 1 0
T 0 0 0 1
In this case, all matches are rewarded equally, and all non-matches
are neutral (neither rewarded nor penalized). (If we changed the
entry for (A,A) to 2 from 1, then AA matches would score higher than
CC, GG and TT matches. And if we changed the entries for GC and CG
to -1, then GC/CG alignments would be penalized, while other mismatches
would be neutral.)
W is our penalty function for gaps: W(x) is the penalty value for
having a gap of length x. The value of k determines how big the gap
is.  (The code handles the case where two or more cells hold maximal
values).
(5,5) -> (3,4) -> (2,3) -> (1,2) -> (0,0),
or graphically:
A C G A C T
A *
G *
A *
A *
A
T *
We start at the bottom right, at (5,5), with T aligned with T as the
last characters of both sequences. The next point is not at (4,4),
but rather at (3,4) which is above (4,4), so there is a gap in the
sequence that was indexed across the columns, meaning that the element
of the sequence indexed down the rows at position 4, A, is paired with
the gap. So we have (remember, we are going backwards):
AAT
C*T
From (3,4) we go to (2,3), so there is no gap, and once more with no gap,
to (1,2):
GAAAT
GAC*T
Finally, we go to (0,0) which is to the left of (0,1), so there is a gap
in the sequence indexed down the rows. Our end result is therefore:
A-GAAAT
ACGAC*T
We can produce such alignments for every backpath if we choose to do
so, but generally we're only looking for one solution.
Let's try it!
;; Best points: ((19 14) (19 12))
;; Using first endpoint (19 14)
Number of best paths: 3
;; Alignment 1 (of 3):
TTGACACCCTCCCAATTGTA
|||| || |
ACCCCAGGCTTTACACAT
;; Alignment 2 (of 3):
TTGACACCCTCCCAATTGTA
|| ||| | || |
AC*CCCAGGC*TTTACACAT
;; Alignment 3 (of 3):
TTGACACCCTCCCAATTGTA
|| ||| | || |
AC*CCCAGGCT*TTACACAT
Cool!
>> (user::demo-nw)
:: -- various alignments using different parameters --
as an example of how varying parameters affects the optimal alignments.
>> (run-sw)
;; Sequence A: T T G A C A C C C T C C C A A T T G T A
;; Sequence B: A C C C C A G G C T T T A C A C A T
;; Gap penalty formula = P(n) = 1.0 + ((n-1) * 0.25))
Substitution matrix =
C G T A
C : 1.0 -0.25 -0.25 -0.25
G : -0.25 1.0 -0.25 -0.25
T : -0.25 -0.25 1.0 -0.25
A : -0.25 -0.25 -0.25 1.0
Score matrix =
<< Another big, hairy SCORE matrix which need not be shown here! >>
;; 1 Best endpoints.
;; Alignment ending at point (19,12):
;; Local alignment starting at: SEQA[5], SEQB[0]
;; and ending at: SEQA[19], SEQB[12]
ACCCTCCCAATTGTA
|||| | || ||
ACCC*CAGGCTT*TA
Notice that we only have a portion of the sequence aligned. Some of
sequence A's initial bases (the first five) and some of sequence B's
final bases (the last five) are not shown in the alignment (nor do
they contribute to the score for the alignment, unlike NW, where they
could be scored as gaps in the other sequence).
THE MAD HATTER AND THE WHITE RABBIT SAW A CARD BATTLE
^^ ^^ ^^
We've found three regions of possible high similarity, and a large
region (in the middle) where we don't expect any matching algorithm to
generate a high score for "CAT". So we need only run the matching
algorithm over the regions we located.
>> (defvar *amino-acid-letters* '(a r n d c q e g h i l k m f p s t w y v))
>> (defvar *blosum-62*
>> (make-array '(20 20)
>> :initial-contents
>> ;;A R N D C Q E G H I L K M F P S T W Y V
>> '(
>> ( 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0) ;; A
>> (-1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3) ;; R
>> (-2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3) ;; N
>> (-2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3) ;; D
>> ( 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1) ;; C
>> (-1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2) ;; Q
>> (-1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2) ;; E
>> ( 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3) ;; G
>> (-2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3) ;; H
>> (-1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3) ;; I
>> (-1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1) ;; L
>> (-1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2) ;; K
>> (-1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1) ;; M
>> (-2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1) ;; F
>> (-1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2) ;; P
>> ( 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2) ;; S
>> ( 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0) ;; T
>> (-3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3) ;; W
>> (-2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1) ;; Y
>> ( 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4) ;; V
>> )))
So, for example, aligning C with C scores 9, which is quite high.
Aligning D with N scores 1, and aligning D with C scores -3. All
matches are good, while some mismatches are ok, and others bad.
>> (defun substitution-score (seqa seqb)
>> (let ((letters *amino-acid-letters*))
>> (apply #'+
>> (mapcar
>> #'(lambda (x y)
>> (aref *blosum-62* (position x letters) (position y letters)))
>> seqa seqb))))
>> (substitution-score '(a s d f) '(a s d f))
:: 20
i.e. this calculates:
>> (substitution-score '(a s d f) '(f d s a))
:: -4
Okay, next we need to be able to extract all the triples from the
query sequence:
>> (defun contiguous-subsequences (sequence len)
>> (let ((seqlen (length sequence)))
>> (if (> len seqlen)
>> nil
>> (let ((subseqs nil) (stop (1+ (- seqlen len))))
>> (dotimes (j stop)
>> (push (subseq sequence j (+ j len)) subseqs))
>> (nreverse subseqs)
>> ))))
>> (contiguous-subsequences (setq query '(a r n d c q e g h i l k m f p s t w y v)) 3)
:: ((A R N) (R N D) (N D C) (D C Q) (C Q E) (Q E G) (E G H) (G H I) (H I L) (I L K) ...)
>> (defun random-amino-acid-chain (n)
>> (let ((chain nil) (naa (length *amino-acid-letters*)))
>> (dotimes (j n) (push (elt *amino-acid-letters* (random naa)) chain))
>> chain))
>> (setq db (coerce (random-amino-acid-chain 2000) 'simple-vector))
:: #(R H P A P S S R Q M ...)
(When you do this, and all of the subsequent examples, you will get
somewhat different results because your database will start out
differently, since it's random!)
>> (user::find-seeds query db 18)
;; Seed (W Y I) at db position 617 matches query triple(s)
;; (W Y V) at query position 17
;; Seed (W Y A) at db position 976 matches query triple(s)
;; (W Y V) at query position 17
;; Seed (S W Y) at db position 1619 matches query triple(s)
;; (T W Y) at query position 16
;; Seed (W Y L) at db position 1620 matches query triple(s)
;; (W Y V) at query position 17
;; Seed (T W Y) at db position 1970 matches query triple(s)
;; (T W Y) at query position 16
::((617 (17) 21) (976 (17) 18) (1619 (16) 19) (1620 (17) 19) (1970 (16) 23))
So it found five places in the database sequence which match some
query triple at or above our substitution score threshold of 18, and
the output shows which triple in the query sequence was matched.
Notice the important point that the database triple is not necessarily
an exact match to the query triple. Only the substitution score is
relevant!
So these five database triple locations constitute the seeds for the
local alignment part of the Blast algorithm. (If we wanted nearly
perfect matches, we could raise the substitution score limit, and if
we wanted less perfect matches, but more of them, we could reduce it.)
>> (user::run-test-blast)
Query sequence: (G P Y H A L N F E T)
;; Seed (P F H) at db position 150 matches query triple(s)
;; (P Y H) at query position 1
;; Seed (P H H) at db position 1288 matches query triple(s)
;; (P Y H) at query position 1
;; Found 2 seeds
;; A maximal alignment starts at position 149
;; of the database, and at position 0 in the query.
;; The alignment has a score of 29.
DB MATCH: G P F H A V V D T C
QUERY : G P Y H A L N F E T
^ ^
;; A lesser alignment starts at position 1288
;; of the database, and at position 1 in the query.
;; The alignment has a score of 21.
DB MATCH: K P H H A F K A H G
QUERY : G P Y H A L N F E T
^ ^
Note that in the first alignment the match was expanded in both
directions; initially the triple at positions 1-3 in the query was
matched to positions 150-152 in the database, but the local alignment
code expanded this match to include the G to the left and the A and L
to the right (the L is included because the VL match is a positive
score in the substitution matrix, even though the characters are not
identical). In the second alignment the match was not extended to the
left but was extended three to the right.
SEQA = ACGTA
SEQB = ACATA
One would normally think that the alignment
ACGTA
|| ||
ACATA
was optimal.
AC-GTA
|| ||
ACA*TA
or
ACG-TA
|| ||
AC*ATA
could be optimal.
 That is, one MUST go back to a previous point
where two sequence elements are exactly aligned.
 They only align exactly if the
first term is chosen (because only then do we add SUBST(I,J), the
score for exact alignment).
A C A T A
A 0
C 1
G 2
T 3
A 4
while the latter recursion relation can produce the same as above,
but can also produce
A C A T A
A 0
C 1 2
G 3
T 4
A 5
or
A C A T A
A 0
C 1
G 2 3
T 4
A 5
given suitable mismatch and penalty functions.


Ellie Brown responds to rumours that she is dating Love Island’s Michael Griffiths
"When your man's giving you a migraine."
by Sarah MaglioccoEx on the Beach star Ellie Brown has responded to rumours that she is dating Love Islander Michale Griffiths.
Ellie was linked to the firefighter after she took to social media to share a pretty selfie, writing in the caption:
“When your man’s giving you a migraine.”
Fans thought that the star was referring to Michael, after the pair reportedly hit it off while both starring on Ex On The Beach.
A source told The Sun: “Ellie and Michael hit it off during filming on Ex On The Beach and now they’ve started dating.”
“They were supposed to keep the relationship under wraps until the show had aired of course, but it seems they just couldn’t keep quiet.”
However, Ellie claims that she is still very much single.
In the comments section of the photo, her fellow Ex On The Beach star Sophie Kasaei commented, asking her who her “man” was.
Ellie replied, “I couldn’t be anymore single just thought it was a funny caption.”


--- permalink: /26073165 layout: post title: An Experiment in Simplicity published: true categories: [] ---
Our inventions are wont to be pretty toys, which distract our attention from serious things. They are but improved means to an unimproved end. - Henry David Thoreau, Walden
Like Thoreau, I've decided to leave behind the trappings of modernity— in my web apps, at least. For my next project, I am setting out to build the simplest idea that will actually make me money. For once (and it pains me to say this), I won't focus on building innovative technology. My goal is to push out a minimum viable product that will generate revenue. I'm giving myself no more than 4 hours to make this happen
Let's get down to business.


Enter your email to generate a unique identification string for the bookmarklet.
Your email is not stored, only the hash is used to keep track of submissions.
Stucco Bookmarklet ← Drag this to your bookmarks bar.
Oak Ridge National Laboratory is leading a research project for the Department of Homeland Security Cyber Security Division. As part of the project we are building a cyber security knowledge graph to facilitate analyzing security events and incident handling. Part of the data used to build the knowledge graph will be blog posts, news stories, and other online information.
We are collecting anonymous data that will help us label security information that will be used in extracting information into the knowledge graph.  To make this possible, we have made a bookmarklet that we'd like you to click when you find an interesting security article.
This sends the url along with a short form (and your optional anonymous hash) to us that we will then use to build models to automatically parse and prioritize security information.
As usual, we're making these tools open source and we will publish our findings in academic venues.
For more information on stucco, see the project page or contact John Goodall at jgoodall at ornl dot gov


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You must complete the survey, although consenting to participate in the research is optional (indicate this on the survey).


| << Öops | Table of Contents | Përs >> |
Oütf Resource
Oütf resources store information on the items that you can buy when you choose "Outfit Ship" at a planet or station.
DispWeight The display weight of this item. Items with a higher display weight are shown closer to the top of the outfit dialog. This can be used to effectively rearrange the order in which items are displayed without rearranging the resources themselves. Mass The mass in tons of the item (0 = no appreciable mass) TechLevel What the technology level of the item is. This item will be available at all spaceports with a tech level of this value or higher. (The exception to this rule involves the SpecialTech fields of the spöb resource; see the section on spöb resources for more information.) The next two fields tell Nova what kind of modification this item performs:
If ModType is: Then it's: And ModVal refers to: 1 a weapon The ID number of the associated wëap resource 2 more cargo space The number of tons of cargo space to add 3 ammunition The ID number of the associated wëap resource 4 more shield capacity The number of shield points to add 5 faster shield recharge How much to speed up (1000 = one more shield point per frame) 6 armor The number of armor points to add 7 acceleration booster Amount of accel to add (see shïp for more info) 8 speed increase Amount of speed to add (see shïp for more info) 9 turn rate change Amount of turn change (100 = 30*/sec) 10 unused 11 escape pod ignored 12 fuel capacity increase Amount of extra fuel (100 = 1 jump) 13 density scanner ignored 14 IFF (colorized radar) ignored 15 afterburner How much fuel to use (units/sec) 16 map 1 and up How many jumps away from present system to explore -1
independent systemsExplore all inhabited -1000 Explore all systems of this & down
class 0, -1001 is govt
class 1, etc.)govt class (-1000 is govt 17 cloaking device 0x0001 - Faster fading
0x0002 - Visible on radar
0x0004 - Immediately drops shields on activation
0x0008 - Cloak deactivates when ship takes damage
0x0010 - Use 1 unit of fuel per second
0x0020 - Use 2 units of fuel per sec
0x0040 - Use 4 units of fuel per sec
0x0080 - Use 8 units of fuel per sec
0x0100 - Use 1 unit of shield per sec
0x0200 - Use 2 units of shield per sec
0x0400 - Use 4 units of shield per sec
0x0800 - Use 8 units of shield per sec
0x1000 - Area cloak - ships in formation with a shïp carrying this cloaking device will also be cloaked18 fuel scoop How many frames per 1 unit of fuel generated. Enter a negative value to perform the same function in "fuel sucking" mode 19 auto-refueller ignored 20 auto-eject ignored (requires escape pod to work) 21 clean legal record ID of gövt to clear legal record with, or -1 for all 22 hyperspace speed mod Number of days to increase or decrease ship's hyperspace travel time (still can't go below 1 day/jump) 23 hyperspace dist mod Amount to increase or decrease the no-jump zone's radius by (the standard radius is 1000) 24 interference mod Subtracts the value in ModVal from the current star system's Interference value when calculating how "fuzzy" to make the radar scanner 25 marines Adds the value in ModVal to your ship's effective crew complement when calculating capture odds 1 and up Add this number to the player's ship's effective crew size -1 to -100 Increase the player's capture odds by this amount (e.g. -5 is an increase of 5%) 26 (ignored) 27 increase maximum The ID number of another outfit item, (call it "B") whose maximum value is to be increased. Item B's standard maximum will be multiplied by the number of items the player has that have a ModType of 27 and point to B. If the player owns no items that modify the maximum of item B, its maximum will be unchanged. 28 murk modifier The amount by which to increase or decrease the current system's murkiness level 29 faster armor recharge How much to speed up (1000 = one more armor point per frame) 30 cloak scanner 0x0001 - reveal cloaked ships on radar
0x0002 - reveal cloaked ships on the screen
0x0004 - allow targeting of untargetable ships
0x0008 - allow targeting of cloaked ships31 mining scoop 32 multi-jump number of extra jumps to perform when the user initiates a hyperspace jump 33 Jamming Type 1 amount of jamming to add or subtract 34 Jamming Type 2 amount of jamming to add or subtract 35 Jamming Type 3 amount of jamming to add or subtract 36 Jamming Type 4 amount of jamming to add or subtract 37 fast jumping (grants carrying shïp the ability to enter hyperspace without slowing down) 38 inertial dampener (makes shïp inertialess) 39 ion dissipator amount of deionization to add. 100 equals 1 point of ion energy per 1/30th of a second. Higher values yield faster ion charge dissipation. 40 ion absorber amount of extra ionization capacity to add 41 gravity resistance 42 resist deadly stellars 43 paint the color to paint the player's shïp, encoded as a 15-bit color value, where the bits are: 0RRRRRGGGGGBBBBB (this is necessary because the ModVal field isn't big enough to hold a 32-bit HTML color value) 44 reinforcement inhibitor a gövt class value. any gövt with this value in its Class1-4 fields will be prevented from calling in reinforcements while the player is in the system and has this outfit. setting this field to -1 will inhibit reinforcements for all ships regardless of gövt. note that this outfit will only work when carried by the player. 45 modify max guns add/subtract from max number of guns 46 modify max turrets add/subtract from max number of turrets 47 bomb destroys the player in flight. set the modval to the ID of the dësc resource to show after the player is destroyed, or -1 for none. 48 IFF scrambler a gövt class value. any gövt with this value in its Class1-4 fields will fooled into thinking the player is a friendly shïp and will not attack without provocation. note that this outfit will only work when carried by the player. 49 repair system will occasionally repair the shïp when it's disabled 50 nonlethal bomb randomly destroys itself and damages the player (nonfatally) in flight. set this field to the ID of a bööm resource to display when an item of this type self-destructs The next two fields tell Nova how many of this item you can possibly have at once:
Max How many you can have (not counting weapon limitations) Flags Miscellaneous info:
0x0001 - This item is a fixed gun
0x0002 - This item is a turret
0x0004 - This item stays with you when you trade ships (persistent)
0x0008 - This item can't be sold
0x0010 - Remove any items of this type after purchase (useful for permits and other intangible purchases)
0x0020 - This item is persistent in the case where the player's shïp is changed by a mission set operator. The item's normal persistence for when the player buys or captures a new shïp is still controlled by the 0x0004 bit.
0x0100 - Don't show this item unless the player meets the Require bits, or already has at least one of it.
0x0200 - This item's total price is proportional to the player's ship's mass. (ship class Mass field is multiplied by this item's Cost field)
0x0400 - This item's total mass (at purchase) is proportional to the player's ship's mass. (ship class Mass field is multiplied by this item's Mass field and then divided by 100)
0x0800 - This item can be sold anywhere, regardless of tech level, requirements, or mission bits.
0x1000 - When this item is available for sale, it prevents all higher-numbered items with equal DispWeight from being made available for sale at the same time.
0x2000 - This outfit appears in the Ranks section of the player info dialog instead of in the Extras section.
0x4000 - Don't show this item unless its Availability evaluates to true, or if the player already has at least one of it.The next field, Cost, tells Nova how much to charge you for the item.
The next few fields (ModType2-4 and ModVal2-4) are for you to specify "alternate" functions for an outfit item - e.g., a weapon could also reduce the ship's turning speed. The only restriction on ModType2-4 is that you shouldn't use it for weapons or ammo (modtypes 1 or 3).
Availability Control bit test expression. Leave blank if unused. Note that depending on the configuration of other flags, the item might appear in the outfit window even if Availability is false (it will still not be able to be purchased) OnPurchase Control bit set expression. Leave blank if unused. Contribute
ContributeThese two Contribute fields together form a 64-bit flag that is subsequently combined with the Contribute fields from the player's shïp and the other outfit items in the player's possession, to be used with the Require fields in the oütf and mïsn resources. Require
RequireThese two Require fields together form a 64-bit flag that is logically and'ed with the Contribute fields from the player's current shïp and outfit items. If for each 1 bit in the Require fields there is a matching 1 bit in one or more of the Contribute fields, the item will be available. Leave these set to zero if unused. Note that depending on the configuration of other flags, the item might still appear in the outfit window even if the player doesn't meet the Require bits (it will still not be able to be purchased). OnSell Evaluated when the item is sold. ItemClass The item's classification, used in the përs resource for items that are given out by non-player characters' ships. Set to 0 or -1 if unused. ScanMask If this is an "illegal" outfit type, this is used in conjunction with the ScanMask field in the gövt resource. If any of the 1 bits in a ship's government's ScanMask field match any of the 1 bits in an oütf type's ScanMask field, that government will consider that outfit item illegal. Set to zero if unused. BuyRandom The percent chance that an item of this type will be available for purchase on a given day, from 1-100. Values less than 1 or greater than 100 are interpreted as 100. ShortName The short string that is displayed in the outfit dialog menu for this item type. If you want to split this name into two separate lines, put the characters "\n" into the name, e.g.: "Real Big\nScary Gun". When using this, lines that start with an alphanumeric character are drawn in white, while lines that start with other symbols are drawn in grey. LCName The lower-case, singular name that's displayed in the player-info dialog and other places, e.g. "real big scary gun". LCPlural The lower-case, plural name that's displayed in the player-info dialog and other places, e.g. "real big scary guns". RequireGovt Which governments the outfit item's Require bits pertain to:
-1 - Requirements apply in all outfit shops.
128-383 - Requirements apply only on stellars belonging to this gövt or its allies.
1128-1383 - Requirements apply only on independent stellars and stellars belonging to this gövt or its allies.
2128-2383 - Requirements apply on all stellars except those belonging to this gövt or its allies.
3128-3383 - Requirements apply on all stellars except independent stellars or stellars belonging to this gövt or its allies.
| << Öops | Table of Contents | Përs >> |
(©) 2000-2002 by Matt Burch and Ambrosia Software, Inc.


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#PROG: ALIGNMENT QUALITY METRICS
- Reads: Number of input reads.
- Uniquely Mapped: Percentage of uniquely mapped reads.
- Splices: Total number of splices.
- Mismatch Rate: Percentage mismatch rate per base.
- Deletion Rate: Percentage deletion rate per base.
- Insertion Rate: Insertion rate per base.
- Multiple Loci: Percentage of reads mapped to mulitple loci.
- Unmapped: Percentage of reads too short to map.


Post from Palm
Aha! Finally – Graffiti 1 on my Clie! This should improve it's usability, but now I have to unlearn G2... G1 is definitely faster.
As a test I've attached a picture I shot on my Clie (TJ-37).
(From Palm)
About this entry
You"re currently reading "Post from Palm," an entry on a crick in the net
- Published:
- 05.19.04 / 7pm
- Category:
- Circular File


§580-52 Marriage after divorce. Whenever a marriage is dissolved, either party to the divorce may marry again at any time. [CC 1859, §1334; am L 1866, p 3; am L 1870, c 10, §2; am L 1876, c 48, §2; RL 1925, §2982; RL 1935, §4479; RL 1945, §12230; RL 1955, §324-41; HRS §580-52]
Case Notes
Remarriage during pendency of appeal. 37 H. 34.


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