Blakes 7 RPG - Cast
Cast - Rebellion
Avon
Dayna
Orac
Scorpio
Soolin
Tarrant
Veron
Vila
Cast - Federation
Mutoids
Pursuit Ship
Pursuit Ship-X2 (The Librarian @ scifi-meshes)
Servalan
Blakes 7 RPG - Character Sheet -
https://github.com/ledlogic/blakes7-rpg


- safeDelete(File) - Static method in class io.github.privacystreams.utils.StorageUtils
-
Delete a file without throw any exception.
- sampleByCount(int) - Method in class io.github.privacystreams.core.PStream
-
Sample the items based on a given step count.
- sampleByCount(int) - Static method in class io.github.privacystreams.core.transformations.filter.Filters
-
Sample the items based on a given step count.
- sampleByInterval(long) - Method in class io.github.privacystreams.core.PStream
-
Sample the items based on a given interval.
- sampleByInterval(long) - Static method in class io.github.privacystreams.core.transformations.filter.Filters
-
Sample the items based on a given interval.
- SCALAR - Static variable in class io.github.privacystreams.sensor.RotationVector
-
(Optional) Scalar component of the rotation vector ((cos(θ/2)).
- SCREEN_STATE - Static variable in class io.github.privacystreams.device.DeviceState.Masks
-
- seconds(long) - Static method in class io.github.privacystreams.utils.Duration
-
- send(String...) - Method in class io.github.privacystreams.utils.PSDebugSocketServer
-
- serialize(AccessibilityNodeInfo) - Static method in class io.github.privacystreams.utils.AccessibilityUtils
-
- serialize(Object) - Static method in class io.github.privacystreams.utils.IOUtils
-
- SerializedAccessibilityNodeInfo() - Constructor for class io.github.privacystreams.utils.AccessibilityUtils.SerializedAccessibilityNodeInfo
-
- setContext(Context) - Method in class io.github.privacystreams.core.UQI
-
- setField(String, Function<Item, TValue>) - Static method in class io.github.privacystreams.commons.item.ItemOperators
-
Set the value of a new field with a function.
- setField(String, TValue) - Static method in class io.github.privacystreams.commons.item.ItemOperators
-
Set the value of a new field to a given value.
- setField(String, Function<Item, TValue>) - Method in class io.github.privacystreams.core.PStream
-
Set a field to a new value for each item in the stream.
- setFieldValue(String, TValue) - Method in class io.github.privacystreams.core.Item
-
Set a value to a field in the item
- setGroupField(String, Function<List<Item>, TValue>) - Static method in class io.github.privacystreams.commons.item.ItemOperators
-
Set a field to a new value for each item in the stream.
- setGroupField(String, Function<List<Item>, TValue>) - Method in class io.github.privacystreams.core.PStream
-
Set a field to a new value for each item in the stream.
- setId(int) - Method in class io.github.privacystreams.core.items.TestObject
-
- setIndependentField(String, Function<Void, TValue>) - Static method in class io.github.privacystreams.commons.item.ItemOperators
-
Set the value of a new field with a value generator function.
- setIndependentField(String, Function<Void, TValue>) - Method in class io.github.privacystreams.core.PStream
-
Set the value of a new field with a value generator function.
- sha1(String) - Static method in class io.github.privacystreams.commons.string.StringOperators
-
Get the SHA1-hashed value of the string value of a field.
- SHA1 - Static variable in class io.github.privacystreams.utils.HashUtils
-
- sha256(String) - Static method in class io.github.privacystreams.commons.string.StringOperators
-
Get the SHA256-hashed value of the string value of a field.
- SHA256 - Static variable in class io.github.privacystreams.utils.HashUtils
-
- SHA512 - Static variable in class io.github.privacystreams.utils.HashUtils
-
- shuffle() - Method in class io.github.privacystreams.core.PStream
-
Shuffle the items.
- shuffle() - Static method in class io.github.privacystreams.core.transformations.reorder.Reorders
-
Shuffle the order of the items in stream.
- since(String, Long) - Static method in class io.github.privacystreams.commons.time.TimeOperators
-
Check whether the timestamp value of a field is after a given timestamp.
- SOCIAL(String) - Static method in class io.github.privacystreams.core.purposes.Purpose
-
The purpose for social interaction (analyzing social relationship, recommending friends, etc.).
- socketPort - Static variable in class io.github.privacystreams.utils.Globals.DebugConfig
-
- sortBy(String) - Method in class io.github.privacystreams.core.PStream
-
Sort the items according to the value of a field, in ascending order.
- sortBy(String) - Static method in class io.github.privacystreams.core.transformations.reorder.Reorders
-
Sort the items in stream by the value of a field.
- SOURCE_NODE - Static variable in class io.github.privacystreams.accessibility.AccEvent
-
The source node of the UI action, only available when using `asUIActions` provider.
- SPEED - Static variable in class io.github.privacystreams.location.Geolocation
-
The speed at the location, in meters/second.
- SSID - Static variable in class io.github.privacystreams.device.WifiAp
-
The SSID.
- START_TIME - Static variable in class io.github.privacystreams.calendar.CalendarEvent
-
Event start time.
- StatisticOperators - Class in io.github.privacystreams.commons.statistic
-
A helper class to access statistic-related functions
- StatisticOperators() - Constructor for class io.github.privacystreams.commons.statistic.StatisticOperators
-
- StatisticUtils - Class in io.github.privacystreams.utils
-
A helper class to access statistic-related functions.
- StatisticUtils() - Constructor for class io.github.privacystreams.utils.StatisticUtils
-
- StepCounter - Class in io.github.privacystreams.sensor
-
Step counter.
- STEPS - Static variable in class io.github.privacystreams.sensor.StepCounter
-
Rotation vector component along the x axis (x * sin(θ/2)).
- stopAll() - Method in class io.github.privacystreams.core.UQI
-
Stop all query in this UQI.
- StorageConfig() - Constructor for class io.github.privacystreams.utils.Globals.StorageConfig
-
- StorageUtils - Class in io.github.privacystreams.utils
-
A helper class to access storage-related functions.
- StorageUtils() - Constructor for class io.github.privacystreams.utils.StorageUtils
-
- Stream - Class in io.github.privacystreams.core
-
Stream is one of the essential classes used in PrivacyStreams.
- StringOperators - Class in io.github.privacystreams.commons.string
-
A helper class to access string-related functions
- StringOperators() - Constructor for class io.github.privacystreams.commons.string.StringOperators
-
- sub(String, String) - Static method in class io.github.privacystreams.commons.arithmetic.ArithmeticOperators
-
Subtract the values of two fields in an item.
- SUB_TEXT - Static variable in class io.github.privacystreams.notification.Notification
-
The subtext of the notification.
- SUBJECT - Static variable in class io.github.privacystreams.communication.Email
-
The subject of the email
- subString(String, int, int) - Static method in class io.github.privacystreams.commons.string.StringOperators
-
Get a substring of the string value of a field.
- sum(String) - Static method in class io.github.privacystreams.commons.list.ListOperators
-
Calculate the sum of the numbers specified by a field.
- sum(String) - Static method in class io.github.privacystreams.commons.statistic.StatisticOperators
-
Calculate the sum of the number values of a field in the stream.
- sum(List<? extends Number>) - Static method in class io.github.privacystreams.utils.StatisticUtils
-
Get the sum of all numbers in the list.
- syncFiles(UQI, boolean) - Static method in class io.github.privacystreams.utils.DropboxUtils
-


QWebEngineScriptCollection Class
The QWebEngineScriptCollection class represents a collection of user scripts. More...
| Header: | #include <QWebEngineScriptCollection> |
| qmake: | QT += webenginewidgets |
| Since: | Qt 5.5 |
Public Functions
| ~QWebEngineScriptCollection() | |
| void | clear() |
| bool | contains(const QWebEngineScript &value) const |
| int | count() const |
| QWebEngineScript | findScript(const QString &name) const |
| QList<QWebEngineScript> | findScripts(const QString &name) const |
| void | insert(const QWebEngineScript &s) |
| void | insert(const QList<QWebEngineScript> &list) |
| bool | isEmpty() const |
| bool | remove(const QWebEngineScript &script) |
| int | size() const |
| QList<QWebEngineScript> | toList() const |
Detailed Description
The QWebEngineScriptCollection class represents a collection of user scripts.
QWebEngineScriptCollection manages a set of user scripts.
Use QWebEnginePage::scripts() and QWebEngineProfile::scripts() to access the collection of scripts associated with a single page or a number of pages sharing the same profile.
Member Function Documentation
QWebEngineScriptCollection::~QWebEngineScriptCollection()
Destroys the collection.
void QWebEngineScriptCollection::clear()
Removes all scripts from this collection.
bool QWebEngineScriptCollection::contains(const QWebEngineScript &value) const
Returns true
if the collection contains an occurrence of value; otherwise returns false
.
int QWebEngineScriptCollection::count() const
Returns the number of elements in the collection.
QWebEngineScript QWebEngineScriptCollection::findScript(const QString &name) const
Returns the first script found in the collection with the name name, or a null QWebEngineScript if none was found.
Note: The order in which the script collection is traversed is undefined, which means this should be used when the unicity is guaranteed at the application level.
See also findScripts().
QList<QWebEngineScript> QWebEngineScriptCollection::findScripts(const QString &name) const
Returns the list of scripts in the collection with the name name, or an empty list if none was found.
void QWebEngineScriptCollection::insert(const QWebEngineScript &s)
Inserts the script s into the collection.
void QWebEngineScriptCollection::insert(const QList<QWebEngineScript> &list)
Inserts scripts from the list list into the collection.
bool QWebEngineScriptCollection::isEmpty() const
Returns true
if the collection is empty; otherwise returns false
.
bool QWebEngineScriptCollection::remove(const QWebEngineScript &script)
Removes script from the collection.
Returns true
if the script was found and successfully removed from the collection; otherwise returns false
.
int QWebEngineScriptCollection::size() const
Returns the number of elements in the collection.
QList<QWebEngineScript> QWebEngineScriptCollection::toList() const
Returns a list with the values of the scripts used in this collection.


{% load staticfiles %}
| Prof. Russell Gray Website
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Dr. Simon Greenhill Website
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Dr. Quentin Atkinson Website
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Dr. Joseph Bulbulia Website
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Joseph Watts Website
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Oliver Sheehan
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Stephanie Ng
Description of person goes here.
|
|
| Dr. Angeline Song
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Joanne Ong
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Kelsey Buntain
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|
|
| Alex Thomson Website
Description of person goes here. Description of person goes here. Description of person goes here. Description of person goes here.
Description of person goes here. Description of person goes here.
|


You can manage items in this view.
| Item Id | Guide | Date Created | Description | Status |
|---|---|---|---|---|
| No Data | No Data | No Data | No Data | No Data |
| Item Id | Guide | Date Created | Description | Status |
You can modify items.
|
Options: |
|
|
Select Manager: |
|


Provides utility classes to handle rights using Spring-Security ACL module. Classes in this package provides methods to set rights over any domain object within the application.


--- layout: page title: Lean Prover Zulip Chat Archive permalink: archive/113488general/14422suppressingwarningsinleanpkgbuild.html ---
How do I suppress warnings (or more specifically sorries, or importing a file with sorries) whilst running leanpkg build
? I just want to check for errors.
I do leanpkg build | grep error
Fair do's
{% endraw %}


T′-MoTe hydrogen Fig. nanocrystalline evolution potential using electrode activation activity surface {border-style current density px;} #ffffff;} (Supplementary MoTe catalytic electrochemical performed reaction Supplementary catalyst material overpotential structure electrocatalytic enhanced obtained adsorption basal changes electron cycling measurements metallic phase mV Chem. Raman carbon cause cycles plane reductive sites (Fig. (vs. XPS analysis change electronic energy metal process shows spectra step studies . V University calculated glassy line morphology oxidation px; reduction reference {font-family applied catalysts data doping edge electrocatalysts experimental font-size improvement material. materials performance scan spectroscopy transition value working EPSRC Energy H-MoTe However, Mater. Nat. Nature Table Tafel XAFS Zhang active area carried cathodic cycling. directly double; gradual high http://orcid.org/--- lattice mechanism proposed range used values Ag/AgCl National PXRD Phys. Research [], activated border-color border-width crystal cycles. displayed distortion effect enhancement following improved instrument layer level monolayer operando required resulting reversible role single solid; suggested unit {font-style {font-weight × Commons Creative ECSA EDX Electrochemical Facility MoS Rev. Sci. Soc. Therefore, alternative atmosphere behaviour bias bold bond calculations cell computational constant counter courier; double edges efficient em; exchange functional gradient increase information irreversible lower measurements. molybdenum monoclinic observed order origin original point potentials production pt;} rate remains results samples structural substantial suggesting synthesised tellurium time underlying work °C ± mV −. V − mA


Treebank Statistics: UD_Xibe-XDT: Features: Number
This feature is universal.
It occurs with 2 different values: Plur
, Sing
.
455 tokens (3%) have a non-empty value of Number
.
57 types (2%) occur at least once with a non-empty value of Number
.
55 lemmas (3%) occur at least once with a non-empty value of Number
.
The feature is used with 4 part-of-speech tags: PRON (417; 3% instances), NOUN (34; 0% instances), DET (3; 0% instances), PART (1; 0% instances).
PRON
417 PRON tokens (66% of all PRON
tokens) have a non-empty value of Number
.
The most frequent other feature values with which PRON
and Number
co-occurred: PronType=Prs (405; 97%), Case=EMPTY (342; 82%), Poss=EMPTY (315; 76%).
PRON
tokens may have the following values of Number
:
Plur
(122; 29% of non-emptyNumber
): ᡨᡝᠰᡝ, ᠮᡠᠰᡝ, ᠪᡝ, ᠮᡝᠨᡞ, ᠮᡠᠰᡝᡞ, ᠰᡠᠸᡝ, ᡤᡝᠷᡝᠨ, ᡨᡝᠰᡝᡞ, ᠰᡠᠸᡝᠨᡞ, ᠰᡠᠸᡝᠮᠪᡝSing
(295; 71% of non-emptyNumber
): ᠪᡞ, ᠮᡞᠨᡞ, ᠰᡞ, ᡨᡝᠷᡝ, ᠰᡞᠨᡞ, ᡞᠨᡞ, ᡞ, ᠮᡞᠨᡩᡝ, ᠰᡞᠮᠪᡝ, ᡨᡝᠷᡝᡞEMPTY
(212): ᡤᡠᠪᠴᡞ, ᡤᡝᠷᡝᠨ, ᠠᡞ, ᠪᡝᠶᡝ, ᠪᡝᠶᡝᡞ, ᠸᡝ, ᡨᡠᡨᡨᡠ, ᠶᠠᠶᠠᠮᡠ, ᡝᠮᡝᠮᡠ, ᡨᠣᠮᡝ
| Paradigm ᡨᡝᠷᡝ | Sing | Plur |
|---|---|---|
| Case=Acc | ᡨᡝᠷᡝᠪᡝ | |
| Case=Gen | ᡨᡝᠷᡝᡞ | ᡨᡝᠷᡝᡞ |
| Polite=Elev | ᡨᡝᠷᡝ | |
| ᡨᡝᠷᡝ, ᡨᡝᠷᡝᡠ | ᡨᡝᠷᡝ |
Number
seems to be lexical feature of PRON
. 94% lemmas (32) occur only with one value of Number
.
NOUN
34 NOUN tokens (1% of all NOUN
tokens) have a non-empty value of Number
.
NOUN
tokens may have the following values of Number
:
Plur
(34; 100% of non-emptyNumber
): ᡤᡠᠴᡠᠰᠠ, ᡤᡠᠴᡠᠰᡝ, ᡥᡝᠷᡤᡝᠰᡞᠰᠠ, ᠠᠰᡞᡥᠠᡨᠠᠰᠠ, ᠨᡞᠶᠠᠯᠮᠠᠰᠠ, ᡞᠷᡤᡝᠰᡝ, ᡠᠰᡞᠰᡞ, ᡨᠠᠴᡞᠰᡞᠰᠠ, ᡫᡠᠨᡩᡝᠰᡞᠰᠠ, ᠠᠨᡨᠠᡥᠠᠰᠠEMPTY
(4572): ᠠᠨᡞᠶᠠ, ᠨᡞᠶᠠᠯᠮᠠ, ᡞᠷᡤᡝᠨ, ᡞᠰᠠᠨ, ᡤᡠᠷᡠᠨ, ᠪᠠᡞᡨᠠ, ᡥᡝᡨᡥᡝ, ᠰᡞᠶᠠᠨ, ᡠᠰᡞᠨ, ᠸᡝᡞᠯᡝᠷᠠᠨ
Number
seems to be lexical feature of NOUN
. 100% lemmas (18) occur only with one value of Number
.
DET
3 DET tokens (3% of all DET
tokens) have a non-empty value of Number
.
The most frequent other feature values with which DET
and Number
co-occurred: PronType=Dem (3; 100%).
DET
tokens may have the following values of Number
:
Sing
(3; 100% of non-emptyNumber
): ᡝᠷᡝ, ᡝᠷᡝᠪᡝ, ᡨᡝᠷᡝᠪᡝEMPTY
(103): ᡝᠷᡝ, ᡨᡝᠷᡝ, ᡤᡝᠷᡝᠨ, ᡠᡩᡠ, ᡝᠷᡝᠮᡠ, ᡝᠷᡝᠴᡞ, ᡝᠷᡝᠪᡝ, ᡝᡞᡨᡝᠨ
PART
1 PART tokens (1% of all PART
tokens) have a non-empty value of Number
.
PART
tokens may have the following values of Number
:
Plur
(1; 100% of non-emptyNumber
): ᠰᠠᡞEMPTY
(132): ᡞ, ᠨᡞ, ᡞᠨᡠ, ᡞ, ᡣᠠᡞ, ᠠᡞᠰᡝ, ᠨᡞᡠ, ᡩᡝᠷᡝ, ᠪᠠᡞ, ᠰᠠ


DevHub is a innovative platform that aims to bring developers and businesses in contact with each other to create the perfect match. It helps developers to find job opportunities and it tracks the perfect candidate for businesses. Perfect if you're looking for a job or a future employee.
There are a lot of benefits of being a member of the DevHub family. You will get to connect with a huge network
of developpers and businesses and expand your network worldwide. Also, you will find opportunities regarding your interests and
whishes and maybe more!
DevHubs mission is to improve and to keep it up to date. Also, its goal is to always develop in order to get better and fullfill its users recommondations.
Gijs is a LL.M Graduate currently enrolled in a coding bootcamp by New York Code + Design Academy. Eventually he hopes to build a bridge between the world of Law and the world of Start-Ups and Web Development. Gijs's life motto is: Inhale the future, exhale the past.
After finishing high school at Fons Vitae Lyceum, Amsterdam, The Netherlands, Kyle primaraly worked in different restaurants. Although he really enjoyed it, he couldn't resist applying for NYCDA's excellent programm. Ever since me an my compangons are pushing the boundries of coding. Kyle always follows his gut feeling and is motto in life is: Trust the vibes you get. Energy is'nt a lie.
Babette obtained a bachelors degree in musicology and found out that she was very interested in technology during her master in music business. That is why she diced to persue an intensive web development course at the NYCDA and is happy to contribute her input to the site of DevHub. Babette strives for a change and therefore her motto is: Be the change you want to see in the world.


{% extends 'home/frame.html' %} {% block content %}
Openstack components and there interactions
Openstack (pike) has been instlled on top of a CENTOS 7 with the help of packstack
Django has been installed on top of Ubuntu 14.04 server with the help of PIP3
Requests library has been used to make API calls to Openstack server
In order to send any API request a token has to be generated which has to be sent in subsequent API calls/requests


--- layout: post title: Capistrano and Leopard post_id: 88 categories: - projects date: 2007-06-12 09:16:00.000000000 -06:00 author: Jamis permalink: 2007/6/12/capistrano-and-leopard.html ---
So, this was totally news to me, but apparently Mac OS X Leopard will bundle Capistrano. How about that! It’s probably too late for them to bundle cap2, even if I were to release it tomorrow, but at the very least this is motivating me to get things rolling again. I’ll be releasing the third (and final) preview release of Capistrano “soon”, and then the focus will be on getting the capify.org site polished enough for the final release on cap2. I’d expect to see cap2 final out late this month, or early July.
Someone, hold me to that!


Text with1 reference footnote1. Text with inline footnote2.
This is a cat Fig. 1
This is a second cat Fig. 2
And here we reuse the footnote1. Also a use of a Fig refernce Fig. 1.
↑ 1. This is the named reference
↑ 2. here the inline reference
Reference from a group Fig. 1 ⇡
Reference from a different group Fig. 2 ⇡


The return statement stops the execution of a function and returns a value from that function.
The return statement ends function execution and specifies a value to be returned to the function caller.
When
you have a return
in your function, this will be the
last line of code executed in that function.
Which means if you have a console.log
on the line after the return
, it will not be executed.
Thus you will not see its result.
So the take away is that console.log does NOT replace return
.
You can have both, the console.log is optional and useful for debugging,
but you really need to have the return
Forgetting return = result is undefined. Even though you will see 8 printed in the Console, the function double is missing a return this it will return nothing (more specifically undefined).
This also does not work, even though you will see 8
printed
in the Console.
That's because return console.log
will return undefined because the result of console.log is undefined
.
The function WORKS but we don't see the console.log becayse it is
placed after the return
Needs to be re-written. You need to swap the return and console.log like this:
With every function you write, there are many ways on how to call that function.
We have sum(3, 5) as a sample usage but that does NOT mean that sum should
always return 8. No! Beware of hardcoding your answer.
You should still use variables and not rely on the actual values of these
variables.
This way the function is reusable over and over again.
Hardcoding is bad because it is inefficient. We want the function to be reusable over and over again with different arguments. Thus parameters should be symbolic rather than hardcoded values.
Here we ask for the character length of the different arguments from the
"name" parameter.
It should return 3 for first name and 6 for second:


The Codurance Colour Palette offers a sense of our bold and flexible personality plus brings an element of stability and consistency to all our communications.


Reward Center
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Password
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Powering discoveries that change the world
Texas Advanced Computing Center
https://www.tacc.utexas.edu/Jay Boisseau
Director of TACCKelly Gaither
Director of VisualizationPaul Navratil
Manager of ScalableVis TechnologiesLuis Revilla
Manager of VisualizationInterfaces & ApplicationsHeri Nieto
Research EngineerRob Turknett
Digital MediaArts & HumanitiesGreg Johnson
Large DataVisualizationGreg Abram
Large Data Visualization & Scientific ComputingGreg Foss
Presentation GraphicsAnne Bowen
Chemical/Biological DataAnalysis & VisualizationAaron Knoll
Volume Visualization &Ray TracingCarson Brownlee
Visualiztion & LargeScale Ray TracingDon Koehler
SystemsAdministratorJoao Barbosa
Computer ScienceDivya Thakur
Aerospace EngineeringAndrew Solis
Computer ScienceMichelle Huddleston
Mechanical Engineering


In recent years the image of an urban “underclass” has become the central representation of poverty in American society. In less than a decade the underclass took hold of the public imagination, and came to shape policymakers’ agendas concerning issues of race, urban decay, and social welfare. But what is the underclass? What is so compelling about that image? What is its significance in American political life?
The underclass idea rests on fuzzy and disturbing assumptions about poor people, poverty, and the world in which both are reproduced. Those assumptions amount to tacit—and sometimes explicit—claims regarding the defective nature of poor people’s motivations, moral character, and behavior. They appeal to hoary prejudices of race, gender, and class which give the upper-class image instant acceptance and verisimilitude even though it is ambiguous and inconsistent on its own terms.
Right-wing, mean-spirited beliefs about poor people have come to suffuse even self-consciously liberal, technocratic policy discussion. Such supposed “friends of the poor” as Charles Murray, Lawrence Mead, Nicholas Lemann, Mickey Kaus, Thomas Sowell, Walter Williams, Robert Woodson, and Glen Loury assume the need to correct, or at least to take into account, poor people’s defective tendencies as an essential limit on social policy. The reactionary, purely ideological foundation of the underclass idea becomes clear on close examination.
Although the term has been around for longer, it caught fire in popular and academic circles after Ken Auletta canonized it in 1982 in The Underclass, a journalistic, mock-ethnographic essay originally serialized in the New Yorker.
Auletta began by joining “poverty” and “antisocial behavior” as equivalent qualifications for underclass status. “The underclass need not be poor—street criminals, for instance, usually are not,” he wrote. “The underclass usually operates outside the generally accepted boundaries of society. They are often set apart by their ‘deviant’ or antisocial behavior, by their bad habits, not just their poverty.”
Auletta mused that we might not want to include those who “actually earn a living in the underground economy” and maybe “add illegal, or undocumented aliens,” and he wondered whether “those with serious mental illness [should] be counted.” In a pinch, for a quick, quasi-empirical referent, however, he called up the holy trinity of “welfare mothers, ex-convicts, and drug addicts.”
For Mickey Kaus, writing in the New Republic, the underclass is the “black lower class” for whom “the work ethic has evaporated and the entrepreneurial drive is channeled into gangs and drug-pushing.” Culture figures prominently in Kaus’s outlook. In addition to the culture of poverty, he asserts the existence of a “single-parent culture,” a “welfare culture,” a “culture of single motherhood” (presumably a more specific articulation of the single-parent culture), a “working, taxpaying culture,” and a “work-ethic culture.”
Nicholas Lemann never tells us exactly how we can identify a member of his underclass. In his nearest attempt at definition, he simply announces that “blacks are splitting into a middle class and an underclass that seems likely never to make it.” He tells us that the underclass suffers from a “strongly self-defeating culture” which has its roots in the sharecrop system and whose centerpiece seems to be out-of-wedlock birth. That, however, is as precise as Lemann gets. He does volunteer, though, that this ghetto culture is “venerable” and “disorganized” and that its members need training in some equally vague “bourgeois values.”
Within four months of publication of Kaus’s and Lemann’s articles, Richard Nathan, then of Princeton’s prestigious Woodrow Wilson School, declared it was time to shut off debate about the usefulness, empirical soundness, or implications of the underclass notion. We should, he suggested, follow the media in using the term “as a shorthand expression of the concentration of economic and behavioral problems among racial minorities (mainly black and Hispanic) in large, older cities.” This underclass is “not just a function of being poor. It involves geography and behavior.”
Isabel Sawhill, senior fellow at the Urban Institute, similarly proceeds from the authoritative imagery of “television and newspaper stories” which document the existence of the underclass. Again, the underclass’s behavior is its “most distinctive, most interesting, and most troubling” characteristic.
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The underclass appeals entirely as a powerful metaphor; its resonance has far outpaced its empirical content, and it has thrived as a concept in search of its object. Most who find the notion attractive agree that the underclass is mainly urban and largely nonwhite. They typically agree as well that it constitutes approximately 10 to 20 percent of the poverty population. That percentage, however, is produced by sleight of hand. The number, glibly repeated by journalists and professional statisticians of poverty, has become the consensual estimate without justificatory argument.
The ultimate source of this estimate, instructively, may be Oscar Lewis, auteur of the “culture of poverty,” the last generation’s effort to ground a behavioral focus on poverty in the authority of social science. Lewis speculated that “about 20 percent of the population below the poverty line” fell into his poverty culture. But his speculation had no sounder basis than contemporary punditry. He characterized it as a belief and a “rough guess” and offered no supporting evidence or argument.
Despite the consensus on the size of the underclass, it is not clear exactly what joins the various aggregations of people said to constitute the underclass. What makes street crime and teen pregnancy signifiers of a common population? Does participation in an underground economy not suggest just the opposite of an evaporated work ethic? How exactly does out-of-wedlock birth become an instance of social pathology?
If a thirty-five-year-old lawyer decides to have a baby without seal of approval from church or state or enduring male affiliation, we do not consider her to be acting pathologically; we may even laud her independence and refusal to knuckle under to patriarchal conventions. Why does such a birth become pathological when it occurs in the maternity ward in Lincoln Hospital in the South Bronx, say, rather than within the pastel walls of an alternative birthing center?
If a woman’s decision expresses pathology because she makes it in poverty, then we have fallen into a tautology: she is poor because she is pathological because she is poor.
Part of the problem stems from reliance on mystical assumptions about class, attitudes, behavior, values, and culture. Underclass constructions revise the old nature/culture dichotomy, in which “culture” stood for the principle of human plasticity and adaptation—in the old, Enlightenment view, the agency of progress. Instead, the power of the underclass idea derives from its naturalization of “culture” as an independent force that undermines adaptability and retards progress.
Culture-of-poverty ideology resuscitates the idea of cultural lag, itself a vestige of antique notions of racial temperament.
The underclass image proceeds from a view of class in general that strikingly resembles Victorian convention. Victorians often used “class” and “race” interchangeably; each category was seen as innate. Class and race essences generally were thought to include—in addition to distinctive physiognomy—values, attitudes, and behavior. Thus, Victorian fiction commonly featured characters in humble circumstances who, though unaware of their true, genteel natal origins, always felt ill at ease or out of place among their coarse fellows, as well as other characters whose base derivations, unknown even to themselves, nonetheless brought them low in polite society.
The Victorian resonances come out in the rhetorical moves and chains of inference current culture-of-poverty thinkers employ as they seek to construct meticulous, apparently social-scientific descriptions of an objectively existing underclass.
Richard Nathan, for example, might be able to adduce evidence supporting his contentions about the prevalence of crime, prostitution, drugs, long-term welfare use, and homelessness, but “lack of will and commitment to get an education or a regular job” cannot be ascertained from the data at his disposal. On what does he base that inference? He avoids justifying it by slipping it into a list of categories for which he could marshal plausible evidence. Isabel Sawhill suggests that as dysfunctional behaviors “become commonplace, they are likely to become more acceptable.” Both scholars presume to know people’s motivations without interviewing them or taking account of their self-understandings.
The problem is compounded by another subtle but critical elision: the assumption that census tracts are synonymous with neighborhoods. Analysis of aggregate socioeconomic and demographic characteristics from census tracts does not inform our understanding of patterns of interaction or the character of norms, values, and aspirations in a group of people who live together. Nevertheless, the elision has become institutionalized in the poverty-research industry, and debate occurs now only over which batches of indicators and what thresholds of them most satisfactorily mark an underclass area.
Those laundry lists of characteristics also entail a questionable extension of the category “behavior” to phenomena that might only indirectly be products of human agency. Female household-heading, for example, can result from a number of circumstances entirely beyond the control of women whose lives are compressed into that label. The same applies to unemployment or underemployment, and even long-term status as a welfare recipient can stem completely from impersonal forces. Characterizing those phenomena as behavior reveals a zeal for validating the underclass concept, and a fundamental inclination to seek the sources of poverty in deficiencies of individuals.
All versions of the underclass notion center on the behavior of its categorical members, though liberals typically hedge with genuflections toward the ultimate weight of historical or structural forces. (The differences on that score, however, are not great. Conservatives also frequently genuflect toward structural pressures and past oppression before enunciating one or another brand of tough-love remedy for the present.)
Why, though, does the underclass idea appeal so powerfully to people—including disciplined scholars—even as they must perform elaborate, dubious maneuvers to define it?
Some of the notion’s popularity is driven by the sociology of the policy research community. Technocratic discourse and methods nurture ideas that depoliticize the frame for examining social problems. The underclass formulation is attractive precisely because it does not exist as anyone’s self-description. It is purely a statistical artifact, and therefore exclusively the creation and property of its chroniclers.
The underclass notion also appeals to several ideological dispositions. Most immediately it resonates with the ahistorical individualism rampant in the Reagan/Bush/Clinton era. As a corollary, it is attractive to many petit bourgeois blacks because it flatters their success by comparison and, through the insipid role model rhetoric, allows fawning over the allegedly special, tutelary role of the black middle class.
The idea of a behaviorally defined underclass also affirms an ensemble of racial and class prejudices that lurk beneath an apparently innocuous, certainly stupid tendency to reduce the social world to aggregates of good people and bad people. Simply, good people are people like “us”; bad people are not, and the same behavior is assessed differently depending on the category into which the perpetrator falls.
An eighteen-year-old drug courier with a monogrammed BMW is pathological; an arbitrageur who strays too far onto the wrong side of legality is too clever for his own good—the stuff of tragedy. Dependency on AFDC breeds sloth and pathology; dependency on military contracts, tax abatements, or FHA loans does a patriotic service for the country, incubates family values, and so forth.
Finally, the underclass notion may receive the greatest ideological boost from pure sexism. For drug-crazed, lawless black and Hispanic men, the companion image is the so-called “cycle of poverty,” which focuses on women’s living and reproductive practices as the transmission belt that drives the cycle.
The rhetoric of “family values,” and of “disorganization,” “deterioration,” and “disintegration” stigmatizes female-headed households, which now are home to a majority of American children, and applies a hierarchy of propriety to the conjugal arrangements within which women might give birth. Of the master list of empirical indicators of pathology, most are observable only in women.
We are already seeing the policy fruit that this imagery bears. A judge in Kansas City has ordered children to use their absent fathers’ names, presumably to strengthen obligation by establishing ownership. A Kansas state legislator has argued that impoverished women should be induced to accept Norplant birth-control implants as a way to hold down welfare costs and cut the size of the recipient population.
 These moves demonstrate unambiguously the repressive, antifeminist outlook lurking beneath the focus on family. (In 1996, this repressive impetus culminated in Congress’s passage of the Personal Responsibility and Work Opportunity Reconciliation Act, which eliminated AFDC. The Act was signed by President Clinton, who boasted that it was the fulfillment of his 1992 campaign pledge to “end welfare as we know it.”)
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How, then, should we talk about those who are stigmatized as the “underclass”? First, it is imperative to reject all assumptions that poor people are behaviorally or attitudinally different from the rest of American society. Some percentage of all Americans take drugs, fight in families, and abuse or neglect children. If the behavior exists across lines of class, race, and opportunity, then it cannot reasonably be held to produce poverty. If it does not cause poverty, therefore, we do not need to focus on it at all in the context of policy-oriented discussion about poverty.
We should also fight against lurid, exploitative journalism that reproduces obnoxious class and racial prejudices. And we should be prepared to recognize the extent to which such prejudices infiltrate even ostensibly more careful, allegedly sympathetic depictions and expose them for what they are.
Affirmatively, we should insist on returning the focus of the discussion of the production and reproduction of poverty to examination of its sources in the operations of the American political and economic system. Specifically, the discussion should focus on such phenomena as the logic of deindustrialization, models of urban redevelopment driven by real-estate speculation, the general intensification of polarization of wealth, income, and opportunity in American society, the ways in which race and gender figure into those dynamics, and, not least, the role of public policy in reproducing and legitimating them.
Moreover, we should fight for policy changes that will open opportunity structures: support for improving access to jobs, housing, schooling, real drug rehabilitation of the sort available to the relatively well-off. A focus on behavior, after all, leads into a blind alley in policy terms. If we say that poor people are poor because they have bad values, we let government off the hook, even though conscious government policy—for example, in the relations between support for metropolitan real estate speculation and increasing homelessness, malnutrition, and infant mortality—is directly implicated in causing poverty.
Finally, with respect to the litany of moral repressiveness that seems to be obligatory these days, I want the record to show that I do not want to hear another word about drugs or crime without hearing in the same breath about decent jobs, adequate housing, and egalitarian education.


gen_fsm is a generic finite state machine behaviour in erlang. And as the name suggests gen_fsm allows us to model state machines.
"What is a finite state machine?"
A finite state machine is a logical machine. Much like a bicycle which is a mechanical machine. A bicycle exists in two states:- a stationary state (where it just stands) and in motion (when the tires are moving and it's doing the work that it's supposed to do, fulfilling it's life's purpose so to speak. When I think about it bicycles have it easy. All they have to do is move those tires sigh). A force is needed to transform the state from stationary to motion and from motion to stationary. Let's try to model the logical machine for the bicycle.
Stationary--cycling-->Motion
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Force
Simple right? This is what in essence a finite state machine is. But we can do better. Let us try to derive a formal definition:-
A finite state machine is a logical machine that can exist in one of the many states. But only one at a time. A cycle can either be moving or stationary. An event is needed to transform from one state to another. Like how a force must be applied on a stationary cycle to make it move.
State1--Acton-->State2
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|
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Event1
Fsm(s) can be used to model the states of our applications for the purpose of testing it's behaviour. Though an fsm can be created in any programming language, erlang gives us the upper hand by abstracting out all the generic stuff into a reusable library. So all we have to do is write the specifics of our fsm.
"Cool but I don't know any erlang"
No worries. Erlang is easy. So easy in fact that all you need is just 15 minutes of free time. And after reading this post you will have a very good reason to use it in your application. Ready?
Consider that We have a web application. Very basic. Our application starts, creating the database and doing some other housekeeping stuff. A landlord logs into our application. He adds a tenant. The application sends an email to the tenant seeking permission to approve the landlord. When the due date of rent draws another email is sent. The tenant then pays the rent. When the tenant leaves the house the contract between the landlord and the tenant is terminated.
So if we make a state diagram of our application it should look something like this:-
prepare_database<---start_event
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add_tenant<--- start_event
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approve_landlord<--- start_event
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prepare_for_notifiaction<--- start_event
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notify_due_rent<--- start_event
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pay_rent<--- start_event
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end_contract
This diagram is not much different from other state diagrams that we discussed before. For our machine we have only one event-- the start_event. Whenever start_event is called on a state it moves the machinery forward by transforming the existing state to next one. For example when start_event is called on prepare_database state the resultant state would be add_tenant. When the event is called on add_tenant the resultant state would be approve_landlord and so on. Compare this state machine to the pedalling motion of the bicycle. When stationary the pedals put the bicycle in motion. When in motion the pedals move the bicycle forward.
Now this is just an abstraction. The "real" states are exposed to us in the form of http endpoints. For example the add_tenant
state is reachable in our application from /add_ten
url. What we will do is to call the endpoints of each of these states one by one and run some tests against our database to see if our records in the database reflect the state of our application given by the url.
So let's get right to it. The entire code is available online so if you can open it in a new tab it will be a lot easier to follow along. Though I will be quoting the relevant parts of the code while discussing them. First we will discuss the code and then see how it is executed. One more thing, the code is only for demonstrative purposes. There are some parts missing like the "hrl" file due to which it won't compile. It was very application specific anyway. My idea was to show how gen_fsm can be used for testing applications in a stateful way.
-module(wrinq_state_machine).
-behaviour(gen_fsm).
-include("wrinq_headers.hrl").
-export([prepare_database/2,add_tenant/2,prepare_for_notification/2,
notify_due_rent/2,pay_rent/2,approve_landlord/2,
end_contract/2,next_state/0,get_status/0]).
-export([init/1, handle_event/3,start_link/0,
handle_sync_event/4, handle_info/3, terminate/3,
code_change/4]).
The first few lines define the name of the module (wrinq_state_machine), behaviour(which is gen_fsm), the header files to be included("wrinq_headers.hrl") and the functions to be exported. Pretty straightforward. You must be asking yourself why two different export functions. Well you could have a single export declaration but I kept them separate to show the difference between the generic functions of the gen_fsm behaviour and the functions specific to our application.
-export([prepare_database/2,add_tenant/2,prepare_for_notification/2,
notify_due_rent/2,pay_rent/2,approve_landlord/2,
end_contract/2,next_state/0,get_status/0]).
The first export declaration "exports (that is making them available to other modules)" the functions specific to our application. If you will notice the functions with /2 behind them (actually /2 just informs about the airity of the function which in this case is 2) represent the states that we discussed above. The functions with /0 behind them are helper functions that we use to query the state of the application(get_status) and fire the "start_event" event in the fsm (next_state).
-export([init/1, handle_event/3,start_link/0,
handle_sync_event/4, handle_info/3, terminate/3,
code_change/4]).
The other export definition exports the functions that are mandatory in every gen_fsm behaviour.
"What is the difference between the two?"
Well only that you must export mandatory functions of a gen_fsm where as you have a choice to not export the functions that are specific to your application. Moving on
start_link() ->
gen_fsm:start_link({local, ?MODULE}, ?MODULE, [],[]).
init(_Args) ->
erlang:display("Initialized the state machine"),
{ok, prepare_database, initial_state}.
next_state() ->
gen_fsm:send_event(?MODULE,start_event).
get_status()->
sys:get_state(?MODULE).
The start_link() function provides an entry point into our fsm. Calling this function starts our machine. When the start_link function is called it immediately executes the init() function. The init function writes on to the console and then moves the application to it's first state "prepare_database".
The next_state() function simply sends events to our fsm which as we discussed above executes the current state and makes it move to the next one. The get_status is a helper function that returns the state which fsm is currently in.
prepare_database(_Event,State)->
httpc:request(delete,{?wrinq_db,[]},[],[]),
os:cmd(?couchapp_push),
{next_state,add_tenant,State}.
The prepare database is the first state of our fsm. When the callback for this state is executed we delete our existing database and create a new one with the built in erlang HTTP library (httpc). When the callback for this state finishes our fsm changes it's state to "add_tenant". Changing states is as simple as returning a tuple of the form {next_state,Next_State_Name,State_Data}
where next_state is a predefined atom that tells the fsm to change the state of the machine. The second element of the tuple is another atom which is the name of the actual state to which the fsm must transform. The atom must match the name of the callback function for the state. For example if the name of the state is add_tenant there must be a callback function for add_tenant in the fsm. And so here is our add_tenant callback function
add_tenant(_Event,State)->
{{_,_,D},_} = erlang:localtime(),
{ok,{_,Headers,_}} = httpc:request(
post,
{"http://localhost:5984/_session",
[],
"application/x-www-form-urlencoded",
"name=user1012&password=apple"},
[],[]),
Cookie = proplists:get_value("set-cookie",Headers),
And = <<"&">>,
Equal = <<"=">>,
F = fun({A, B},C) -> <<A/binary,Equal/binary,B/binary,And/binary,C/binary>> end,
Form_D = lists:foldl(F, <<>>,?con_approve(integer_to_binary(D))),
httpc:request(post,
{?add_tenant_url,
[{"Cookie",Cookie}],
"application/x-www-form-urlencoded",
Form_D},
[],[]),
{ok,{_,_,Body}}= httpc:request(?get_con_ids),
try proplists:get_value(<<"rows">>,jsx:decode(list_to_binary(Body))) of
[A|_T]->
ID = proplists:get_value(<<"id">>,A),
{next_state,approve_landlord,
[binary_to_list(ID),Cookie]};
_ ->
{stop,tenant_not_added,State}
catch
_:_->{stop,tenant_not_added,State}
end.
This piece of code is a bit more interesting. Here is what is happening. First we send a url-encoded data using the erlang http library to ?add_tenant_url. We then query the database using the endpoint ?gen_con_ids. We pattern match the returned body to see if the tenant was actually added or not. If the tenant was added a non empty array list will be returned by the database and the state advances to "approve_landlord" phase with the contract id as the state data. If not we terminate the fsm by calling the returning the stop atom.
approve_landlord(_Event,State)->
[H|_T] = State,
httpc:request(?aproove_by_tenant(H)),
{ok,{_,_,Body}} = httpc:request(?wrinq_db++H),
Decoded = jsx:decode(list_to_binary(Body)),
case proplists:get_value(<<"valid">>,Decoded) of
true->
{next_state,prepare_for_notification,State};
_ ->
{stop,tenant_not_approved,State}
end.
The approve landlord callback functions mocks the landlord approval in our application and then checks against the database to see if state matches. Once more we first call the http endpoint of our application and then query our database. Finally we pattern match for property (valid in this case). If valid matches to true we advance to the next state otherwise we stop the fsm.
prepare_for_notification(_Event,State)->
case unit_tests:add_rent() of
no_rent_added->
{stop,could_not_prepare_for_notifiaction,State};
{ok,Id}->
{next_state,notify_due_rent,lists:append(State,[{notify,Id}])}
end.
and here is the code for unit_tests:add_rent()
add_rent()->
{ok,{_,_,Con_Body}} = httpc:request(?get_con_ids),
Contract = jsx:decode(list_to_binary(Con_Body)),
[Row|_T] = proplists:get_value(<<"rows">>,Contract),
Con_ID = proplists:get_value(<<"id">>,Row),
{Mega, Secs, _} = erlang:now(),
JS_Timestamp = Mega*1000000*1000 + Secs*1000,
{ok,{{_,Status,_},_,Body}} = httpc:request(
post,
{?add_rent,
[],
"application/json",
jsx:encode(?rent_template(JS_Timestamp,Con_ID))},
[],[]),
case Status == 201 of
true-> {ok,Body};
_-> no_rent_added
end.
In prepare_for_notification state we run two checks at once. First we run a unit test that confirms that the add_tenant operation is indeed working as expected. If it is the state of the database changes and an Id of a newly added rent is returned. If the rent was successfully added the state machines changes state and the Id of the rent is passed on as state data .Now this rent can be used in the next state to send notifications.
notify_due_rent(_Event,State)->
httpc:request(?notify),
Notify = proplists:get_value(notify,State),
{ok,{_,_,Body}} = httpc:request(?wrinq_db++Notify),
Decoded = jsx:decode(list_to_binary(Body)),
Sent = proplists:get_value(<<"sent">>,Decoded),
case Sent of
undefined->
{stop,notificaton_not_sent,State};
_->
{next_state,pay_rent,State}
end.
The notify_due rent works in exactly the same way except we pattern match the <<"sent">> property. All we are checking here that the document returned contains a sent property in it.
pay_rent(_Event,State)->
[H|_T] = State,
{ok,{_,_,Body}}= httpc:request(?get_rent(H)),
[A|_]= proplists:get_value(<<"rows">>,jsx:decode(list_to_binary(Body))),
ID = proplists:get_value(<<"id">>,A),
{ok,_} = httpc:request(?rent_paid(binary_to_list(ID))),
{ok,{_,_,Rent}} = httpc:request(?wrinq_db++binary_to_list(ID)),
Decoded = jsx:decode(list_to_binary(Rent)),
case proplists:get_value(<<"paid">>,Decoded) of
true ->
{next_state,end_contract,State};
_->
{stop,rent_not_paid,State}
end.
The pay_rent state state also works in exactly the same except we pattern match the <<"paid">> property. See a pattern here. We are simply invoking an action in our application and then checking how the data in our database is changed.
end_contract(_Event,State)->
[H,Cookie|_T] = State,
httpc:request(
get,
{
?end_contract(H),
[{"Cookie",Cookie}]
},
[],[]),
{ok,{_,_,Body}} = httpc:request(?wrinq_db++H),
Decoded = jsx:decode(list_to_binary(Body)),
case proplists:get_value(<<"valid">>,Decoded) of
false->
{stop,fsm_done,State};
_ ->
{stop,contract_not_ended,State}
end.
The final state. No more state changes and we stop the fsm here.
This was all easy, right? We do some stuff inside a callback function. Perform some checks and either advance the state or stop the fsm. Erlangs gen_fsm behaviour allows us to focus our energy on writing our states rather than worrying about the details of how to move it forwards or backwards. But it is not over. There is still some stuff left for us to discuss.
The state of failure
Remember how we halted the fsm if the callback functions failed our matching criteria? When you return a tuple of the form {stop,NextState,StateData}
the gen_fsm behaviour calls a special terminate function.
terminate(fsm_done,_R,_S)->
erlang:display("The state machine is finished");
terminate(_Reason, _StateName, _State) ->
erlang:display(_Reason).
Here I've defined the terminate function in two clauses. The first clause is for when the fsm completes in a natural manner--that is after simulating all the states that we've defined. The second terminate clause is the generic clause which handles all the other kinds of termination. Regardless of which clause is invoked when the gen_fsm terminates it stops moving. It stands still.
Which is not something that Captain Haddock would approve. I remember quite well that scene. Tintin was sitting down defeated. All gloomy and despondent when he let the villain escape. He thought that he was a failure. Here's how the Captain responded:-
"Failed. There are plenty of others willing to call you a failure.   A hopeless souse. Don't you ever say it of yourself.   You care about something, you fight for it. You hit a wall, you push through it. There's something you need to know about failure, Tintin. You can never let it defeat you."
Right now our application is like pre "Captain Haddock Speech" Tintin. Smallest of failures sends it right down from where it refuses to get up. This attitude is of no use to us. We must do something about it. Our application needs some one like Captian Haddock to cheer it up when the chips are down. To lift the defeated spirit. To... you get the idea I suppose?
Supervising our behaviour
What we are looking for are supervisors. A type of erlang behaviour which is responsible for starting, stopping and monitoring it's child behaviour. Here's how we actually want our application to behave:-
When the gen_fsm terminates either naturally or unnaturally we want to restart it from it's in initial state ready to run the tests again. For example after end_contract
state which terminates the execution we want our gen_fsm to be restored to prepare database state so it can run the tests again.
"What is the benefit of this behaviour?"
Well it is very handy during the development phase. When you are working on your application it is quite natural that the tests will fail. So instead of manually running the same steps over and over we circle within a predefined set of states. In terms of this particular application we always remain between the prepare_database and end_contract states. So we don't have to worry about restarting our fsm when our tests fail.
This is what our supervisor looks like
-module(wrinq_state_sup).
-behaviour(supervisor).
-export([start_link/0,start_in_shell/0]).
-export([init/1]).
start_link() ->
supervisor:start_link({local,?MODULE},?MODULE, []).
start_in_shell() ->
{ok, Pid} =supervisor:start_link({local,?MODULE},?MODULE, []),
unlink(Pid).
init(_Args) ->
{ok, {{one_for_one, 100, 60},
[{wrinq_state_machine, {wrinq_state_machine, start_link, []},
permanent, brutal_kill, worker, [wrinq_state_machine]}]}}.
There is very little logic to supervisors. All the terms inside the init function are predefined. Here we have a restart strategy of one_for_one
that is if a child process dies then restart only that process no more than 100 times in 60 seconds.
Then we give the supervisor the id of wrinq_state_machine
and define the start function in a module {wrinq_state_machine, start_link, []} (wrinq_state_machine is the name of the module and start_link is the function that starts this machine).
The keyword permanent
indicates that this child process is to be always started. The brutal_kill indicates that the child process is to be unconditionally terminated should it be required. And the keyword worker indicates that the child process is a worker.
There is hardly anything that we have done here. Writing a supervisor is more like choosing a restart strategy and then configuring the "keywords" to implement that strategy.
Any way with this supervisor in place whenever our machine halts it is restarted with initial state.
"Are you happy now captain?"
"Ten thousand thundering typhoons"
"What?"
"Yes that means I am happy."
So how is this machine executed?
Glad you asked. Remember our definition of an fsm. Event happens on a state which transforms it to another one. That gives us a clue. To make the application jump through the states we've just got to call our next_state() function that sends start_event events to our fsm. I created a module for starting the fsm and making it go through the states:-
-module(wrinq_start_app).
-export([start/0,test_states/0]).
-include("wrinq_headers.hrl").
start()->
inets:start(),
wrinq_state_sup:start_in_shell().
test_states()->
next_state().
next_state()->
wrinq_state_machine:next_state(),
erlang:display(wrinq_state_machine:get_status()),
next_state().
It is pretty straightforward. The start function initializes the fsm and the test_states() function calls next_state recursively until the fsm terminates. Once terminated the supervisor restarts our function and we can call test_states again to redo the steps. And don't worry the gen_fsm breaks the recursion when an appropriate state is reached so it's not infinite. :-). Though be careful if your fsm is circular or if it never terminates then a code like this will create a never ending loop.
How do I create fsm for my application?
If you have followed the post up to this point then it should not be too hard. Here are the steps that you need to follow:-
The TDD approach
Writing states of the application in a TDD like manner can also be pretty effective. You start by writing the mock states of your application and simulating their behaviour in your callbacks. Then for each state you create and each behaviour you write, you implement the corresponding state and the behaviour in your application. Repeat this for every state and you should have a thoroughly tested application with pieces working well individually and together.


Euclidean division by 2
Define a recursive function restecting the following specification:
Definition div2_mod2 :
forall n:nat, {q:nat & {r:nat | n = 2*q + r /\ r <= 1}}.
Remark
We all know that there exists in Coq's standard library such a function
for euclidean division by any positive integer, but we just ask for a simpler
development.
Solution
Look at This file


Coding languages
700+
First Computer Virus Created
1983
New Jobs in STEM which are in computing
67%
First Mouse Prototype
1964
Welcome!
Today, we will be learning about a key component of software development: APIs.
API is the acronym for Application Programming Interface, which is a software intermediary that allows two applications to talk to each other.
Each time you use an app like Facebook, send an instant message, or check the weather on your phone, you're using an API.


DOMProcessingInstruction
interface represents a "processing instruction", used in XML as a way to keep processor-specific information in the text of the document.
More...
Public Member Functions | |
Destructor | |
| virtual | ~DOMProcessingInstruction () |
| Destructor. | |
Functions introduced in DOM Level 1 | |
| virtual const XMLCh * | getTarget () const =0 |
| The target of this processing instruction. | |
| virtual const XMLCh * | getData () const =0 |
| The content of this processing instruction. | |
| virtual void | setData (const XMLCh *data)=0 |
| Sets the content of this processing instruction. | |
Protected Member Functions | |
Hidden constructors | |
| DOMProcessingInstruction () | |
| DOMProcessingInstruction (const DOMProcessingInstruction &other) |
DOMProcessingInstruction
interface represents a "processing instruction", used in XML as a way to keep processor-specific information in the text of the document.
| DOMProcessingInstruction::DOMProcessingInstruction | ( | ) | [protected] |
| DOMProcessingInstruction::DOMProcessingInstruction | ( | const DOMProcessingInstruction & | other | ) | [protected] |
| virtual DOMProcessingInstruction::~DOMProcessingInstruction | ( | ) | [virtual] |
Destructor.
| virtual const XMLCh* DOMProcessingInstruction::getTarget | ( | ) | const [pure virtual] |
The target of this processing instruction.
XML defines this as being the first token following the markup that begins the processing instruction.
| virtual const XMLCh* DOMProcessingInstruction::getData | ( | ) | const [pure virtual] |
The content of this processing instruction.
This is from the first non white space character after the target to the character immediately preceding the ?>
.
| DOMException | NO_MODIFICATION_ALLOWED_ERR: Raised when the node is readonly. |
| virtual void DOMProcessingInstruction::setData | ( | const XMLCh * | data | ) | [pure virtual] |
Sets the content of this processing instruction.
This is from the first non white space character after the target to the character immediately preceding the ?>
.
| data | The string containing the processing instruction |


PlantUMLCompile
Extends:
Constructs a PlantUML version of all classes fed to the compiler and puts them into one diagram. Only supports a very specific subset of ES6 functionality;
- Every module included in the graph contains one class.
- Class names are unique (for now).
Constructor Summary
| Public Constructor | ||
| public |
constructor(options: {}) |
Member Summary
| Public Members | ||
| public |
classes: * |
Method Summary
| Public Methods | ||
| public |
addClass(name: *) Adds a class to the classes.
|
|
| public |
addClassParent(className: *, classParent: *) Adds the parent class as parent to this one.
|
|
| public |
compileAllFiles(cb: *) |
|
| public |
compileChunk(sourceFilePath: *, chunk: *, cb: *): * |
|
| public |
compileFile(sourceFilePath: *, cb: *) |
|
| public |
convert(chunk: *) Converts a chunk of ES6 code - containing a complete module - to a class.
|
|
| public |
doesClassExtend(ast: *): boolean Returns true if the class extends another, otherwise it returns false.
|
|
| public |
getClassName(ast: *) Returns the name of the class in the AST of a module.
|
|
| public |
getClassParent(ast: *) Returns the name of the parent class.
|
|
| public |
Returns true if the AST contains a class token, otherwise it returns false.
|
|
| public |
removeClass(name: *) Removes a class from the classes.
|
|
| public |
run(cb: *) |
|
| public |
setClassMethods(className: *, ast: *) Sets the methods of the class, if any.
|
|
| public |
setClassParent(className: *, ast: *) Sets the parent of the class, if any.
|
Public Constructors
public constructor(options: {}) source
Params:
| Name | Type | Attribute | Description |
| options | {} |
|
Public Members
public classes: * source
Public Methods
public addClass(name: *) source
Adds a class to the classes.
Params:
| Name | Type | Attribute | Description |
| name | * |
public addClassParent(className: *, classParent: *) source
Adds the parent class as parent to this one.
Params:
| Name | Type | Attribute | Description |
| className | * | ||
| classParent | * |
public compileAllFiles(cb: *) source
Params:
| Name | Type | Attribute | Description |
| cb | * |
public compileChunk(sourceFilePath: *, chunk: *, cb: *): * source
Params:
| Name | Type | Attribute | Description |
| sourceFilePath | * | ||
| chunk | * | ||
| cb | * |
Return:
| * |
public compileFile(sourceFilePath: *, cb: *) source
Params:
| Name | Type | Attribute | Description |
| sourceFilePath | * | ||
| cb | * |
public convert(chunk: *) source
Converts a chunk of ES6 code - containing a complete module - to a class. in-memory model inside the instance.
Params:
| Name | Type | Attribute | Description |
| chunk | * |
public doesClassExtend(ast: *): boolean source
Returns true if the class extends another, otherwise it returns false.
Params:
| Name | Type | Attribute | Description |
| ast | * |
public getClassName(ast: *) source
Returns the name of the class in the AST of a module.
Params:
| Name | Type | Attribute | Description |
| ast | * |
public getClassParent(ast: *) source
Returns the name of the parent class.
Params:
| Name | Type | Attribute | Description |
| ast | * |
public hasClass(ast: Object): Boolean source
Returns true if the AST contains a class token, otherwise it returns false.
Params:
| Name | Type | Attribute | Description |
| ast | Object |
public removeClass(name: *) source
Removes a class from the classes.
Params:
| Name | Type | Attribute | Description |
| name | * |
public run(cb: *) source
Params:
| Name | Type | Attribute | Description |
| cb | * |
public setClassMethods(className: *, ast: *) source
Sets the methods of the class, if any.
Params:
| Name | Type | Attribute | Description |
| className | * | ||
| ast | * |
public setClassParent(className: *, ast: *) source
Sets the parent of the class, if any.
Params:
| Name | Type | Attribute | Description |
| className | * | ||
| ast | * |


SCOPE
Scope refers to where you are able to access a
variable. Basically variables cannot be accessed from outside the
scope they are created in. Look at the following:
A = 5
While A < 10
B = 1
'----- B is created inside this loop and cannot be accessed outside of
this loop
A = A + 1 '----- A was created
before this loop started so it will be able to be used after this loop ends
Wend
Print B
The above program WILL NOT COMPILE. That is because
the variable B was created inside the scope of the WHILE loop and nothing
outside the WHILE loop will be able to access it. The variable A was
created outside the WHILE loop so the variable A could be used anywhere in
this example. So a variable can go into a deeper scope than where it
was created but it cannot go to a lesser scope than where it was
created. A scope deepens every time a new block is started.
Blocks refer to the code within a loop, within a Function, or within a Sub
Routine.


Directive 2001/29/EC47 enables Member States to choose whether to implement in their national laws a limitation for the purpose of non-commercial scientific research. The open formulation of this (optional) provision allows for rather different implementations at Member States level.
Summary
Lorem ipsum dolor sit amet, consectetur adipisicing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.
{% for question_num in (47..49) %}
{% for question in site.data.questions %}
{% if question.num == question_num %}
{% include question.html %}
{% endif %}
{% endfor %}
{% endfor %}


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There will be three rounds in this event.
Round 1 (GD):
In first round, GD for group of ten persons will be conducted. Participants will have to discuss on the given topic to show their views for the respective topic. Judge will give marks to each participant. after completion of GD for all groups results will be declared. Judges decision will be final. Participants getting highest marks will go for next round.
Round 2(picture perception):
In this round a picture showing some situation or famous personality will be shown to the participant. participant has to describe it in 5 minutes. judge will give marks to each participant. judges decision will be final. participants getting highest marks will go for next round.
Round 3(it all comes to your speaking & marketing skills):
In this round participant has to sell defective things provided by us. with the help of that money they will have to invest it wisely & try to collect maximum amount. time limit for the round is 30 minutes. participant getting highest amount will be announced as winner.
1 member only
10 /3/17 Friday
9:00 am onwards
CF
Rs. 30
Kalind Mehta
Mo.9426427989
Email: kalindmehta2412@gmail.com


Guide¶
PySHAC has a very simple interface, which follows similar semantics to Scikit-Learn. Simply importing as below grants the majority of the functionality of the library.
import pyshac
There are three main processes that are followed when using PySHAC :
-
Declaration of hyper parameters:
- Hyper parameters can be discrete or continuous, and if continuous, be sampled from different distributions.
- All hyper parameters must be defined before being passed to the engine.
-
Training of the models:
- Once the parameters are defined, we create an instance of the engine.
- This engine is passed an evaluation function that the user must write, along with the hyper parameter declarations and other parameters as required (such as total budget and number of batches)
- The engine's
fit()
method is used to perform training of the classifiers. - Training can be stopped at any time between epochs. All models are serialized at the end of each epoch.
-
Sampling the best hyper parameters:
- Using the serialized models, we can sample the search space in order to obtain the best hyper parameters possible.
- This is done using the engine's
predict()
method.
Important Note for Windows Platform¶
Warning
The fit
and predict
methods must be called only from inside of a if __name__ == '__main__'
block. when using the multiprocessing backend.
This is no longer an issue when using the loky
backend (default).
This is a limitation of how Windows does not support forking, and so the engine definition and its methods must be called
inside of a __main__
block when using the multiprocessing
backend.
It is simpler to put this code inside of a function, and simply call this function from the __main__
block
to have better readability of code.
Declaration of Hyper Parameters¶
There are 3 available hyper parameters made available :
There are also 3 additional hyper parameters, which are useful when a parameter needs to be sampled multiple times for each evaluation :
- MultiDiscreteHyperParameter
- MultiUniformContinuousHyperParameter
- MultiNormalContinuousHyperParameter
These multi parameters have an additional argument sample_count
which can be used to sample multiple times
per step.
Note
The values will be concatenated linearly, so each multi parameter will have a list of values
returned in the resultant OrderedDict. If you wish to flatten the entire search space, you can
use pyshac.flatten_parameters
on this OrderedDict.
While these can declare most of the common hyper parameters, if there is a need for custom hyper parameters, then they can very easily be added as shown in Custom Hyper Parameters.
Naming hyper parameters¶
For the engine to work correctly, all hyper parameters must have unique names associated with them.
These names are the keyword arguments of the dictionary received by the evaluation function, so we can get the value of the sample by this name inside the evaluation function.
Declaring hyper parameters¶
Declaring hyper parameters is as easy as follows :
import pyshac # Discrete parameters dice_rolls = pyshac.DiscreteHyperParameter('dice', values=[1, 2, 3, 4, 5, 6]) coin_flip = pyshac.DiscreteHyperParameter('coin', values=[0, 1]) # Continuous Parameters classifier_threshold = pyshac.UniformContinuousHyperParameter('threshold', min_value=0.0, max_value=1.0) noise = pyshac.NormalContinuousHyperParameter('noise', mean=0.0, std=1.0)
Training of Models¶
There are multiple engines available for use, and their use case differs based on what work is being done. For this guide, we are considering our task will be accomplished with python classifiers (XGBoost, Scikit-Learn etc) which do not depend on GPU acceleration.
However, when working with Tensorflow, PyTorch or Keras models, it is advisable to use the appropriate engine. More information can be found in Managed Engines.
Setting up the engine¶
When setting up the SHAC engine, we need to define a few important parameters which will be used by the engine :
- Hyper Parameter list: A list of parameters that have been declared. This will constitute the search space.
- Total budget: The number of evaluations that will occur.
- Number of batches: The number of batches per epoch of evaluation.
- Objective: String value which can be either
max
ormin
. Defines whether the objective should be maximised or minimised. - Maximum number of classifiers: As it suggests, decides the upper limit of how many classifiers can be trained. This is optional, and usually not required to specify.
Warning
The total budget needs to be divisible by the number of batches, since all evaluations need to have the same number of samples. This eases the training of models as well, since we can be sure of how many samples are present after each epoch.
Evaluation Function receives 2 inputs :
- Worker ID: Integer id that can be left alone when executing only on CPU or used to determine the iteration number in the current epoch of evaluation.
- Parameter OrderedDict: An OrderedDict which contains the (name, value) pairs of the Parameters passed to the engine.
- Since it is an ordered dict, if only the values are required,
list(parameters.values())
can be used to get the list of values in the same order as when the Parameters were declared to the engine. - These are the values of the sampled hyper parameters which have passed through the current cascade of models.
- Since it is an ordered dict, if only the values are required,
These parameters can be easily defined as :
import numpy as np import pyshac # define the parameters param_x = pyshac.UniformContinuousHyperParameter('x', -5.0, 5.0) param_y = pyshac.UniformContinuousHyperParameter('y', -2.0, 2.0) parameters = [param_x, param_y] # define the total budget as 100 evaluations total_budget = 100 # 100 evaluations at maximum # define the number of batches num_batches = 10 # 10 samples per batch # define the objective objective = 'min' # minimize the squared loss shac = pyshac.SHAC(parameters, total_budget, num_batches, objective)
While this looks like a lot, these few lines are in essence all that is required to define the search space, the evaluation measure and the engine.
Training the classifiers¶
To train a classifier, the user must define an Evaluation function. This is a user defined function, that accepts 2 or more inputs as defined by the engine, and returns a python floating point value.
The Evaluation Function receives at least 2 inputs :
- Worker ID: Integer id that can be left alone when executing only on CPU or used to determine the iteration number in the current epoch of evaluation.
- Parameter OrderedDict: An OrderedDict which contains the (name, value) pairs of the Parameters passed to the engine.
- Since it is an ordered dict, if only the values are required,
list(parameters.values())
can be used to get the list of values in the same order as when the Parameters were declared to the engine. - These are the values of the sampled hyper parameters which have passed through the current cascade of models.
- Since it is an ordered dict, if only the values are required,
An example of a defined evaluation function :
# define the evaluation function def squared_error_loss(id, parameters): x = parameters['x'] y = parameters['y'] y_sample = 2 * x - y # assume best values of x and y and 2 and 0 respectively y_true = 4. return np.square(y_sample - y_true)
Once the engine has been created, then it is simply a matter of calling fit()
on the engine with the evaluation function
as the parameter. This will create the worker threads, generate the samples, test them if they pass all of the current
classifiers, prepare the dataset for that batch, test a cross validated classifier to see if it will train properly on
the dataset, finally train a classifier and then begin the next epoch.
# assuming shac here is from the above code inside __main__ shac.fit(squared_error_loss)
There are a few cases to consider:
- There can be cases where the search space is not large enough to train the maximum number of classifier (usually 18).
- There may be instances where we want to allow some relaxations of the constraint that the next batch must pass through all of the previous classifiers. This allows classifiers to train on the same search space repeatedly rather than divide the search space.
In these cases, we can utilize additional arguments to allow the training behaviour to better adapt to these circumstances. These parameters are :
- skip_cv_checks: As it suggests, if the number of samples per batch is too small, it is preferable to skip the cross validation check, as most classifiers will not pass them.
- early_stop: Determines whether training should halt as soon as an epoch of failed learning occurs. This is useful when evaluations are very costly.
- relax_checks: This will instead relax the constrain of having the sample pass through all classifiers to having the classifier past through most of the classifiers. In doing so, more samples can be obtained for the same search space.
# `early stopping` default is False, and it is preferred not to use it when using `relax checks` shac.fit(squared_error_loss, skip_cv_checks=True, early_stop=False, relax_checks=True)
Sampling the best hyper parameters¶
Once the models have been trained by the engine, it is as simple as calling predict()
to sample multiple samples or batches of parameters.
Samples can be obtained in a per instance or per batch (or even a combination) using the two parameters - num_samples
and num_batches
.
# sample a single instance of hyper parameters parameter_samples = shac.predict() # Gets 1 sample. # sample multiple instances of hyper parameters parameter_samples = shac.predict(10) # Gets 10 samples. # sample a batch of hyper parameters parameter_samples = shac.predict(num_batches=5) # Samples 5 batches, each containing 10 samples. # sample multiple batches and a few additional instances of hyper parameters parameter_samples = shac.predict(5, 5) # Samples 5 batches (each containing 10 samples) and an additional 5 samples.
Dealing with long sampling time¶
When using a very large number of classifiers (default of 18), it may take an enormous amount of time to sample a single hyper parameter list, let alone a batch. Therefore, it is more efficient to reduce the checks if necessary by using a few parameters.
-
num_workers_per_batch:
- When using a large number of samples or large number of classifiers, it is advisable to have many parallel workers sampling batches at the same time to reduce the amount of time taken to get samples that pass through all classifiers.
- When using a small number of samples or small number of classifiers, it is advisable to reduce the time taken to sample each batch by reducing the number of parallel workers.
- If left as
None
, will determine the value that was used during training.
-
relax_checks: Same as for training, relaxes the checks during evaluation to sample parameters faster.
- max_classfiers: Another way to reduce the time taken to sample parameters is to simply reduce the number of classifiers used to perform the check. This will use the first
K
classifier stages to perform the checks.
# number of sample is 16, so using 16 processes to sample in parallel is more efficient than the default memory conservative count. parameter_samples = shac.predict(16, num_workers_per_batch=16, relax_checks=True, max_classfiers=16)
Continuing Training¶
As mentioned at the beginning of this guide, training can be stopped at any time after an epoch has finished.
After this, training run can be resumed from the last epoch successfully trained with only slight changes to how we
define and use the engine before using fit()
.
There are a few steps, and a few important cautions that must be taken when continuing training :
- Create an instance of the engine in the same location as where the earlier model was trained.
- It must be provided the same evaluation measure as before.
- It is optional to provide the hyper parameter list. It is not required as it will be replaced by the parameters in the file.
- If training further, it is essential to use the same
total_budget
,batch_size
andobjective
as before. - If only evaluating the trained classifiers, using any value of
total_budget
is allowed (though the earlier value is better for consistency), however it must be divisible bybatch_size
and theobjective
must be same as before. - When restoring data, it must find the
shac
folder and all of the sub-folders in the same directory level.
- Call
restore_data()
on the engine. It will print out the number of samples and the number of classifiers it was able to find and load. - Call
fit()
orpredict()
as before.
In doing so, the engine instance will be restored to the exact state as in the previous training session, and the new classifiers can be trained as normal.
Example :
# Reduced batch_size allows faster sampling # None for the hyper parameter list as it will be loaded in the restoration step. new_shac = pyshac.SHAC(None, total_budget, batch_size=5, objective=objective) # restore the engine new_shac.restore_data() # predict or train like before new_shac.fit(squared_error_loss) or samples = new_shac.predict()


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UNTAMED HEART A film review by Jon Ridge Copyright 1993 by Jon Ridge
CAST: Christian Slater, Marisa Tomei, Rosie Perez DIRECTOR: Tony Bill PRODUCER: Helen Bartlett SCREENPLAY: Tom Sierchio SCORE: Cliff Eidelman
To begin this review, I must say that I did not want to see this film. The romantic-love-heart-ache genre in cinema has grown a bit outdated in recent years, and basically I've become quite disillusioned with the whole concept. But, a funny thing happened while I sat down to watch UNTAMED HEART--I found myself completely drawn into its world, and the private lives of its characters. I was pulled in, held--and as soon as the lights in the theater were turned on again I felt as if a part of me had been torn away from me.
The story is simple: Two lonely people fall in love. Sounds just like every other love story you've ever seen on film. Where this one differs, however, is that you really care about these people. His name is Adam. Adam is an odd, reclusive young man who doesn't like to talk a lot and isn't very good with people. It seems as though he has embodied a rather unhealthy heart since birth, and believes the story about it that a nun told him at the orphanage where he was raised. Adam has just one wish: To love and be loved by a pretty young waitress, with whom he works at an all-night diner in Minneapolis, named Caroline. Caroline is on a constant lookout for love, but she's been dumped more times than she or anyone else can count. Nobody wants to be with her... except for Adam. The only problem is that Caroline hasn't given Adam much thought. She thinks he's just plain weird. Then, one night as she is walking home alone through the park, Caroline is attacked by two men. At her rescue is, of course, Adam. He fights both men off, and then carries an unconscious Caroline home. He lays her down in the swing on her front porch, covers her with his overcoat and sits on the steps, keeping a constant watch on Caroline while she sleeps. She awakens ready to fight, but there's no need; no words are spoken between her and Adam, and he runs away.
They later meet at passing in a shopping mall. He touches her hand, and that's all. You see, Caroline doesn't know what to say to Adam or how to approach him. She's always thought of him as a strange part of life that she'd rather not get to know. Nonetheless, Caroline gets up the courage one night at work to finally thank Adam ... with a kiss. So begins their relationship.
As I have said, this is a cut above other romance movies--a huge cut above. The characters are real people, not cookie-cutter, factory outlet, assembly line characters. These characters live on the screen, and you feel all the time you spend watching this film that you are intruding on their lives, or listening in. That is the type realism most film- makers strive for and fail miserably at capturing. The per- formances are absolutely indelible. Christian Slater has never been any better than he is here. In the majority of his prior work, Slater has pretty much hidden behind some sort of mask-- whether it be Jack Nicholson, or whatever. Here, he has nothing to hide behind. As a result, his work in this film is touching, endearing, subtly eloquent and very moving. This is not just a pretty face anymore; this is the most competent actor working in film today.
I'm afraid, however, that all credit for the success of this film must go to Marisa Tomei. She makes up the heart and soul of this film with a performance that is as sweet and sensitive, as it is altogether sexy. Tomei is the most vibrant young face to grace the screen in years, and she brings a great deal of freshness to the role of the "love interest." These two actors have wonderful chemistry on-screen-- they really do look great together. All their scenes together can be class- ified as love scenes, and each of them is a small gem. Director Tony Bill does a good job of erasing most of the grit of Tom Sierchio's poignant script, and Cliff Eidelman skillfully captures the light, exuberantly sweet tone of the film. Put all these elements together, and you have a truly involving and engaging movie. UNTAMED HEART is the best film of this, or any other, year. See it, cry, and feel good about life.
1993 -- 1:42m -- Rated PG-13, for mild Violence
.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Noun, derogatory: She was being a real snatch waffle at the store today."
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I am a student at UC Davis studying Computer Science and planning on pursuing a minor in Technology Managament. I am passionate about how technology can be used to help society. I am a support programmer/analyst at Enterprise Student Applications which is a subset of UC Davis Information and Educational Technology (IET) and part of the HackDavis Sponsorship Team. I am currently looking for oppurtunities for summer 2020 that would help me futhur my career
With a focus on AI and tech management.
Created the backend and UI/UX of a Magic Mirror. Wrote code and worked with APIs to display time, date, weather, calendar, pictures from Instagram. Also impleted voice control features that lets users take seflies and send it to themselves. Used Amazon APIs to integrate Amazon Alexa into the Magic Mirror.
View ProjectAndroid Studio APP that pulls data from the UC Davis Dining Commons and lets users know what to eat based on if they want to lose or gain weight. It also lets users rate the meal.
View ProjectCreated a Battleship game with an UI and an AI system using C++ and the NCURESE Library.
View Project3D survival game with enemy AIs built using Unity and JavaScript. The envionrment and enemy placement changes each time, so each replay feels new!
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The pivot control can easily be used with a listView. The classes "win-pivot-slide[x]" can be used to smoothly delay entrance of elements in the pivot control. In this case the animation controlling css class is used on the individual lines of text.
Also shown in the 'lock' API which disables the users ability to pivot to another item during listview multi-selection.


filmcritic.com presents a review from staff member James Brundage. You can find the review with full credits at http://www.filmcritic.com/misc/emporium.nsf/2a460f93626cd4678625624c007f2b46/96080dfd22979835882568a50018c122?OpenDocument
Final Destination (2000, R)
Directed by James Wong
Written by James Wong, Glen Morgan, and Jeffrey Reddick
Story by Jeffrey Reddick
Produced by Craig Perry and Warren Zide
Starring Devon Sawa, Kristen Cloke, Amanda Detmer, Chad Donella, Ali Larter, Sean William Scott, and Brandon Fehr
As Reviewed by James Brundage
"I'm never going to die!" yells one character at a memorial for 39 fellow French-class students who died in a plane crash that, by all rights, he should have died on too. As incredibly laughable as this line is, it provides the big flaw in the basic premise behind Final Destination… making a horror movie about people cheating death does not have much potential.  No matter what is said or done, everyone ends up getting it.
This is not to give you a spoiler and to say that everyone gets it in the movie. This is simply to point out the fact that no matter which way they go, they're screwed. Death is a no-win scenario. If you're going to make a movie about escaping death, make it a la Fearless, where we focus on the human element. But since it's a horror film the ball is in the court of Director James Wong to provide us with something that will keep us interested for an hour and forty minutes of inevitability.
The film actually does a halfway-decent job of this, but only by going so far pass the realm of the believable to enter into the land of the ludicrous. How so? Well, virtually everyone who bites it in the movie does so by the means of a Rube Goldberg contraption.
Rube Goldberg, for those not familiar with his work, was drew comic which often depicted a scientist trying to find the most complicated way to do the most simple job. A classic example of this is the machine in which a cord is snapped, a mallet unlocks a cage, an anteater crawls out from the cage and licks the ants on stamps, thus preparing said stamps to be mailed. Final Destination features several of Rube Goldberg's guillotines… forms of death that end up being so drawn out that, about five minutes into the elaborate trap, you utter a groan and yell at the movie to get on with it.
The result is a movie that is only good for the target audience: the young college student who is looking for an excuse to have his girlfriend become utterly frightened and leap into his arms. Do not think in this film… you'll find plot holes. Do not pay attention to the dialogue in this film… you'll hate it. Do not expect this film to be up to par with half of the episodes of "The X-Files" that Wong has worked on… you'll be severely disappointed.
Final Destination is good for a horror film. This means that the acting is terrible, the script was probably written by monkeys, and somehow you find yourself enjoying the film anyway. So sit back, relax, and watch it… but only if you like cheese.
2.5 stars
-- Christopher Null -<EMAIL_ADDRESS>- http://www.filmcritic.com
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I had to do a wee bit of research as it has been a while since I asked myself these questions, and I am sure that getting older and experiencing different obstacles and things in life , my values either changed or just intensified. I initially proceeded by writing a list of my core values and then, I regrouped them as I believe some core values do work hand in hand. Lastly, I ranked them. And here is the end result. Et voilà!
1. Kindness - Love - Peace - Friendship.
2. Integrity - Honesty - Sincerity - Justice.
3. Understanding - Learning - Growth.
4. Courage - Determination - Independence - Success.
5. Creativity - Spontaneity - Fun.