Appendix
This section contains a few diagrams, that explain the architecutre and the work-flow of the UnifiedPush Server
Global Architecture
This diagram show the different components that compose the UnifiedPush Server:
The UPS Core contains a DAO layer, talking to the (MySQL/Postgres) database. The DAO layer is invoked by different service classes. The message delivery to the different Push Networks is handled by JMS (see here for more details). The access to the server is handled via two different categories of RESTful APIs.
REST APIs
Core APIs
The UnifiedPush Server is not limited to be accessed only via the UI for creating PushApplications or Variants. The RESTful Core APIs (e.g. Management/Metric APIs) offer access to the server for integration cases like discussed in the UPS Integration section or below:
Developer APIs
The Developer APIs are more commonly used. They offer public endpoints for device registration and sending Push Notification delivery requests.
Device Registration
Below is a diagram that explain the flow of the Device Registration Process:
When the mobile device obtains a device token (or registrationID) from its PushNetwork (e.g. GCM or APNs), the AeroGear powered application calls our Registration SDK with the given token. The SDK itself authenticates itself against the UnifiedPush Server and sends the token to the server, so that it can be used for future Push Notification sends.
Sending a Push Notification
The UnifiedPush Server comes with APIs for Java and Node.js. Due to its RESTful architecture any backend, written in any language that supports HTTP(S), can send Push Notification requests to it:
If the backend needs to send a push notification, e.g. after catching an event, the AeroGear Push Sender SDK is invoked. SDK itself authenticates itself against the UnifiedPush Server and sends the payload to it. This results in several JMS jobs, that are than delivering the payload to the 3rd party Push Networks. Eventually the message may hit the device, delivered from the 3rd party Push Network.
JMS internals
Currently the delivery of Push Notifications is handled with CDI events and JMS.
CDI events are translated into JMS messages so that they can be processed asynchronously and transactionally (if needed).
Token Loading and Notification Dispatching
There is architectural diagram describing how different processors works internally:
Once the payload hits the RESTful endpoint, a NotificationRouter
is invoked, which queues a JMS message into {Network}PushMessageQueue
, one for each Variant in the given Application. Once these messages are queued, the REST interface replies 202 Accepted, which indicates that the Push Message was accepted for later processing.
Messages from {Network}PushMessageQueue
are then processed by TokenLoader
transactionally. TokenLoader
streams device tokens from database and pushes token batches of configured size (aerogear.{network}.batchSize
system property) to {Network}TokenBatchQueue
. The TokenLoader
loads number of batches to load in one transaction (configurable by aerogear.{network}.batchesToLoad
system property). After one token loading transaction, TokenLoader
sends a message back to same {Network}PushMessageQueue
with information from which token should next transaction continue. The TokenLoader
transaction can also fail when memory limits of {Network}TokenBatchQueue
are exceeded. In that case, the token loading transaction will be repeated (the batches are de-duplicated by their serial ID, so in that case there will be no duplicate batches).
The tokens from {Network}TokenBatchQueue
are received by NotificationDispatcher
that opens connection/-s to a given Push Network and submits Push Messages for the given batch of tokens. The number of NotificationDispatcher
workers can be limited by configuring maxSession
activation config properties of Message-Driven Beans.
maxSession activation config property for MDBs influences how many workers can be instantiated on given node, so this does not limit number of workers in a cluster of nodes.
|
Once NotificationDispatches
finishes processing for given batch of tokens (no matter if it succeeds or fails), it triggers MetricsCollector
that writes metric into database.
Metric Collection
The further details on metric collection process can be seen in the following diagram:
Once TokenLoader
loads a batch and sends it for processing, it also queues messages to BatchLoadedQueue
. Once it loads last token batch, it also queues message to AllBatchesLoadedQueue
. These events are important for MetricCollector
to decide whether token loading process ended.
NotificationDispatches
sends metrics for each processed message to MetricQueue
.
Both, TokenLoader
and NotificationDispatcher
sends message to TriggerVariantMetricQueue
. That messages are processed by MetricCollectionTrigger
that receives them, deduplicates and sends exactly one message to TriggerMetricCollectionQueue
.
MetricCollector
is triggered by message from TriggerMetricCollectionQueue
. It receives it transactionally, and if it detects that Push Messaging processing is still in progress, it will roll that transaction back, which means that it will be redelivered (the time after it is redelivered can be configured in JMS queue properties).
MetricCollector
internally collects messages from MetricQueue
and BatchLoadedQueue
, updates PushMessageInformation
object and writes it into database. Once MetricCollector
receives message from AllBatchesLoadedQueue
and it detects, that metrics were processed for all loaded batches, it ends Push Message metrics collection process by committing its transaction.
Limiting Number of ApnsServices
APNs limits how many connections can be created for one iOS variant. In order to make sure that the number of opened connection won’t exceed that limit, UnifiedPush Server manages distributed counter of available (free) service slots. The counter is stored as messages in FreeServiceSlotQueue
, so each message in that queue represents one available slot for a new service that can be used to open connection to APNs.
FreeServiceSlotQueue
is populated in NotificationRouter
with number of messages representing limit of connections that can be created.
Once NotificationDispatcher
wants to push messages to APNs leveraging ApnsService
, it will first ask ApnsServiceHolder
for an instance of service. ApnsServiceHolder
provides a cached instance or it asks for a slot for a new service instance, then it can create it.
When the notifications are dispatched, NotificationDispatches
returns instance of ApnsService
to ApnsServiceHolder
that stores it to a cache for reuse by another dispatcher, and if not reused within time limits, it closes allocated connection and sends a messages to FreeServiceSlotQueue
indicating that a new instance can be created.


| Module Name | Last Release Tag Date | Commits Since Then | Number of downloads | ||||
|---|---|---|---|---|---|---|---|
| puppetlabs/cisco_ios | 2020-08-06 09:47:35 UTC | 11 | 3 | 2 | 0 | 1 | Error number of downloads cisco_ios |
| puppetlabs/device_manager | 2020-12-02 11:34:36 UTC | 7 | 3 | 0 | 0 | 0 | Error number of downloads device_manager |
| puppetlabs/puppetlabs-accounts | 2021-03-29 11:47:44 UTC | 1 | 1 | 0 | 0 | 0 | 133 |
| puppetlabs/puppetlabs-acl | 2021-03-01 13:34:36 UTC | 4 | 2 | 0 | 0 | 0 | 2188 |
| puppetlabs/puppetlabs-apache | 2021-03-02 10:51:20 UTC | 11 | 4 | 0 | 2 | 0 | 6475 |
| puppetlabs/puppetlabs-apt | 2021-03-29 12:00:33 UTC | 1 | 0 | 0 | 0 | 0 | 1425 |
| puppetlabs/puppetlabs-chocolatey | 2021-03-03 12:55:29 UTC | 8 | 1 | 0 | 1 | 0 | 4498 |
| puppetlabs/puppetlabs-concat | 2021-03-29 11:41:19 UTC | 1 | 1 | 0 | 0 | 0 | 837 |
| puppetlabs/puppetlabs-docker | 2021-03-04 10:34:02 UTC | 4 | 1 | 0 | 1 | 0 | 3467 |
| puppetlabs/puppetlabs-dsc | 2019-12-11 11:39:31 UTC | 22 | 10 | 2 | 0 | 0 | 23681 |
| puppetlabs/puppetlabs-dsc_lite | 2020-01-16 08:23:33 UTC | 30 | 10 | 1 | 1 | 0 | 9315 |
| puppetlabs/puppetlabs-exec | 2021-03-01 16:12:43 UTC | 4 | 2 | 0 | 0 | 0 | 512 |
| puppetlabs/puppetlabs-facter_task | 2021-02-27 03:06:11 UTC | 8 | 4 | 0 | 0 | 0 | 577 |
| puppetlabs/puppetlabs-firewall | 2021-03-01 14:57:38 UTC | 2 | 1 | 0 | 0 | 0 | 8924 |
| puppetlabs/puppetlabs-haproxy | 2021-03-29 11:36:25 UTC | 1 | 0 | 0 | 0 | 0 | 104 |
| puppetlabs/puppetlabs-helm | 2020-03-18 18:38:54 UTC | 55 | 15 | 1 | 4 | 1 | 9503 |
| puppetlabs/puppetlabs-ibm_installation_manager | 2021-03-03 16:36:26 UTC | 4 | 2 | 0 | 0 | 0 | 798 |
| puppetlabs/puppetlabs-iis | 2021-03-02 12:48:22 UTC | 7 | 1 | 0 | 1 | 0 | 867 |
| puppetlabs/puppetlabs-inifile | 2021-03-29 11:27:17 UTC | 1 | 0 | 0 | 0 | 0 | 591 |
| puppetlabs/puppetlabs-java | 2021-03-01 16:08:50 UTC | 4 | 2 | 0 | 0 | 0 | 125554 |
| puppetlabs/puppetlabs-java_ks | 2021-03-01 15:28:40 UTC | 4 | 2 | 0 | 0 | 0 | 7199 |
| puppetlabs/puppetlabs-kubernetes | 2021-02-27 03:09:13 UTC | 14 | 4 | 0 | 3 | 0 | 689 |
| puppetlabs/puppetlabs-motd | 2021-03-01 15:03:00 UTC | 4 | 1 | 0 | 1 | 0 | 1683 |
| puppetlabs/puppetlabs-mysql | 2021-03-01 14:47:40 UTC | 6 | 1 | 0 | 1 | 0 | 5893 |
| puppetlabs/puppetlabs-ntp | 2021-03-29 12:20:14 UTC | 1 | 0 | 0 | 0 | 0 | 1269 |
| puppetlabs/puppetlabs-package | 2021-03-01 16:17:05 UTC | 4 | 2 | 0 | 0 | 0 | 1929 |
| puppetlabs/puppetlabs-panos | 2019-07-29 16:56:30 UTC | 33 | 11 | 1 | 1 | 1 | 11148 |
| puppetlabs/puppetlabs-postgresql | 2021-03-15 14:00:13 UTC | 4 | 2 | 0 | 0 | 0 | 4371 |
| puppetlabs/puppetlabs-powershell | 2021-02-27 03:19:28 UTC | 5 | 2 | 0 | 0 | 0 | 10454 |
| puppetlabs/puppetlabs-puppet_conf | 2021-03-13 03:23:32 UTC | 4 | 2 | 0 | 0 | 0 | 1050 |
| puppetlabs/puppetlabs-reboot | 2021-03-19 11:03:15 UTC | 1 | 0 | 0 | 0 | 0 | 2999 |
| puppetlabs/puppetlabs-registry | 2021-02-27 03:23:19 UTC | 4 | 2 | 0 | 0 | 0 | 10718 |
| puppetlabs/puppetlabs-rook | 2021-02-27 03:13:16 UTC | 6 | 2 | 0 | 1 | 0 | 1087 |
| puppetlabs/puppetlabs-satellite_pe_tools | 2021-03-29 08:46:10 UTC | 1 | 0 | 0 | 0 | 0 | 78 |
| puppetlabs/puppetlabs-scheduled_task | 2021-03-03 07:35:27 UTC | 2 | 1 | 0 | 0 | 0 | 2507 |
| puppetlabs/puppetlabs-service | 2021-03-04 09:36:04 UTC | 6 | 4 | 0 | 0 | 0 | 2263 |
| puppetlabs/puppetlabs-sqlserver | 2021-02-27 03:19:48 UTC | 4 | 2 | 0 | 0 | 0 | 623 |
| puppetlabs/puppetlabs-stdlib | 2021-03-01 13:33:33 UTC | 6 | 2 | 0 | 1 | 0 | 149743 |
| puppetlabs/puppetlabs-tagmail | 2021-03-01 10:45:42 UTC | 3 | 2 | 0 | 0 | 0 | 419 |
| puppetlabs/puppetlabs-testing | 2018-04-17 15:20:33 UTC | 94 | 9 | 1 | 1 | 0 | Error number of downloads puppetlabs-testing |
| puppetlabs/puppetlabs-tomcat | 2021-02-27 03:04:24 UTC | 5 | 2 | 0 | 0 | 0 | 4514 |
| puppetlabs/puppetlabs-vcsrepo | 2021-03-15 12:43:31 UTC | 1 | 0 | 0 | 0 | 0 | 6083 |
| puppetlabs/puppetlabs-vsphere | 2020-12-16 14:22:34 UTC | 9 | 2 | 0 | 0 | 1 | 1796 |
| puppetlabs/puppetlabs-websphere_application_server | 2020-07-28 11:53:11 UTC | 33 | 10 | 2 | 0 | 1 | 1430 |
| puppetlabs/puppetlabs-wsus_client | 2021-03-01 11:00:57 UTC | 3 | 2 | 0 | 0 | 0 | 1451 |


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|
|
CIFXFileReference Class ReferenceInheritance diagram for CIFXFileReference:
List of all members.
Constructor & Destructor Documentation
| CIFXFileReference::CIFXFileReference |
( |
|
) |
[protected] |
|
| CIFXFileReference::~CIFXFileReference |
( |
|
) |
[protected, virtual] |
|
Member Function Documentation
| U32 CIFXFileReference::AddRef |
( |
|
) |
[virtual] |
|
|
|
This method increments the reference count for an interface on a component.
- Note:
- It should be called for every new copy of a pointer to an interface on a given component.
- Returns:
- It returns a U32 that contains a value from 1 to 2^32 - 1 that defines the new reference count. The return value should only be used for debugging purposes.
Implements IFXUnknown. |
| void CIFXFileReference::GetCollisionPolicy |
( |
IFXCollisionPolicy & |
rCollisionPolicy |
) |
[virtual] |
|
| void CIFXFileReference::GetEncoderX |
( |
IFXEncoderX *& |
rpEncoderX |
) |
[virtual] |
|
|
|
Function that hands back the IFXEncoderX interface pointer to a component that can be used to encode this component.
- Parameters:
-
| rpEncoderX | Reference to the encoder's interface pointer that will be set to a valid value upon success. |
Implements IFXMarkerX. |
| void CIFXFileReference::GetFileURLs |
( |
IFXFileURLs & |
rFileURLs |
) |
[virtual] |
|
| void CIFXFileReference::GetObjectFilters |
( |
IFXObjectFilters & |
rObjectFilters |
) |
[virtual] |
|
| void CIFXFileReference::GetScopeName |
( |
IFXString & |
rScopeName |
) |
[virtual] |
|
| void CIFXFileReference::GetWorldAlias |
( |
IFXString & |
rWorldAlias |
) |
[virtual] |
|
| IFXRESULT CIFXFileReference::QueryInterface |
( |
IFXREFIID |
riid, |
|
|
void ** |
ppv |
|
) |
[virtual] |
|
|
|
This method provides access to the various interfaces supported by a component.
Upon success, it increments the component's reference count and hands back a pointer to the specified interface.
- Note:
- A number of rules must be adhered to by all implementations of IFXAPI QueryInterface . For a list of such rules, refer to the IFXCOM usage guidelines.
- Returns:
- Upon success, IFX_OK is returned. Otherwise, either IFX_E_INVALID_POINTER or IFX_E_UNSUPPORTED are returned.
Implements IFXUnknown. |
| U32 CIFXFileReference::Release |
( |
|
) |
[virtual] |
|
|
|
This method decrements the reference count for the calling interface on a component.
- Note:
- If the reference count on a component falls to zero, the component is destroyed.
- Returns:
- It returns a U32 that contains a value from 1 to 2^32 - 1 that defines the new reference count. The return value should only be used for debugging purposes.
Implements IFXUnknown. |
| void CIFXFileReference::SetCollisionPolicy |
( |
const IFXCollisionPolicy & |
CollisionPolicy |
) |
[virtual] |
|
| void CIFXFileReference::SetFileURLs |
( |
const IFXFileURLs & |
FileURLs |
) |
[virtual] |
|
| void CIFXFileReference::SetObjectFilters |
( |
const IFXObjectFilters & |
ObjectFilters |
) |
[virtual] |
|
| void CIFXFileReference::SetScopeName |
( |
const IFXString & |
ScopeName |
) |
[virtual] |
|
| void CIFXFileReference::SetWorldAlias |
( |
const IFXString & |
WorldAlias |
) |
[virtual] |
|
Friends And Related Function Documentation
| IFXRESULT IFXAPI_CALLTYPE CIFXFileReference_Factory |
( |
IFXREFIID |
iid, |
|
|
void ** |
ppv |
|
) |
[friend] |
|
Member Data Documentation
© 1999 - 2006 Intel Corporation. All rights reserved.
* Other names and brands may be claimed as the property of others.
|
|


--- layout: post title: Introducing ScriptCs.Rebus date: 2014-05-26 13:45:50.000000000 +02:00 categories: - scriptcs - scriptcs.rebus tags: [] status: publish type: post published: true meta: _edit_last: '1' author: login: admin email<EMAIL_ADDRESS>display_name: admin first_name: '' last_name: '' ---
In a recent blog post I introduced the reader to the world of scripting with C# using ScriptCs. I really love the idea of encapsulating boiler-plate code into a script pack, and be able to consume larger frameworks from simple scripts. One of my favorite frameworks is Rebus. Rebus is this lean and very simple service bus. It is founded and coordinated by my good friend Mogens Heller Grabe.
Well, why not? Rebus is a simple, yet powerful framework for messaging, it is obvious for me to make Rebus the messaging engine for ScriptCs. On the current work project we use Rebus extensively, and to provide some sample messages and for testing purposes, we’ve so far used small console applications. It is my intend to switch over and use ScriptCs.Rebus from now on. With scripting possibilities for messaging I can imagine larger test scenarios, and stress-testing.
I’d refer to the readme on GitHub for installation guidelines.
To enter the REPL, run scriptcs
. Send a message of type string to MyMessageQueue:
Require<RebusScriptBus>() .ConfigureBus("MyMessageQueue") .Send<string>("My first scripted message!")
Now open another command prompt or use the same, and enter the following to receive the message of type string from MyMessageQueue:
Require<RebusScriptBus>() .ConfigureBus("MyMessageQueue") .Receive<string>(x => Console.WriteLine(x)) .Start()
The command prompt should output the following:
From MyMessageQueue > My first scripted message!
The examples from above apply to scripts. Put the send and receive code into two .csx
files, lets call them send.csx
and receive.csx
, and execute them by typing the following from a command prompt:
scriptcs send.csx
and
scriptcs receive.csx
In the \samples
folder there is some samples to get you started.
At this point you should be able to apply these more advanced usages to both the REPL and from scripts.
It is possible to use custom types in your messages. Consider the following class:
public class Message { public string Content { get; set; } }
You enter this class definition in the REPL or create a message.csx
script file. If defined in a script file, load this class definition into your script by using #load
keyword,
#load "message.csx"
Now you can send and receive messages of this type by using the following syntax:
Require<RebusScriptBus>() .ConfigureBus("MyMessageQueue") .Send<Message>(new Message {Content = "Hello from custom type!"})
and
Require<RebusScriptBus>() .ConfigureBus("MyMessageQueue") .Receive<Messge>(x => Console.WriteLine(x.Content)) .Start()
``
This should output From MyMessageQueue > Hello from custom type
.
In some cases you might be expecting to handle different kinds of messages. This can be achieved by adding multiple receive commands fluently:
Require<RebusScriptBus>() .ConfigureBus("MyMessageQueue") .Receive<string>(x => Console.WriteLine(x)) .Receive<Message>(x => Console.WriteLine(x.Content)) .Start()
Applying multiple handlers of the same type, will give the possibility to handle message of the same type in different ways.
This is only the first revision of ScriptCs.Rebus, I’ll continue to explore new possibilities with building messaging infrastructure using scripts. I’ve got RabbitMQ and Azure Service Bus aligned in the pipeline.


Before Change
def make():
with lcd(path.dirname(__file__)):
with virtualenv(VENV_DIR) as venv_local:
venv_local("pip install wheel")
venv_local("pip install cython")
venv_local("pip install murmurhash")
venv_local("pip install -r requirements.txt")
venv_local("python setup.py build_ext --inplace")
def sdist():
with lcd(path.dirname(__file__)):
with virtualenv(VENV_DIR) as venv_local:
local("python setup.py sdist")
After Change
def make():
with virtualenv(VENV_DIR) as venv_local:
with lcd(path.dirname(__file__)):
venv_local("pip install cython")
venv_local("pip install -r requirements.txt")
venv_local("PYTHONPATH=`pwd` python setup.py build_ext --inplace")
def sdist():
with virtualenv(VENV_DIR) as venv_local:
with lcd(path.dirname(__file__)):
local("python setup.py sdist")


Before Change
if __name__ == "__main__":
import time, argparse
parser = argparse.ArgumentParser(description= "Checks if Title is present in Question Text")
parser.add_argument("--gender_db", default = "data/questions.db", help = \
"Name of db containing gender information of titles")
parser.add_argument("--gender_table", default = "questions", help = \
"Name of table in db containing gender information of titles")
flags = parser.parse_args()
tests = {}
tests[u"A"] = uCole sought out the pope to
seek forgiveness of her sins, only to be told that just as the pope"s staff
After Change
start = time.time()
print(gg, ge.vw_from_title(gg, tests[ii]))
elapsed = time.time() - start
print("%f secs" % (elapsed))


Simply copy the content from the text area. You can import it again by clicking the import button and pasting it in the text area there.
Simply paste the content to the text area.


OCTOBER 17 St Ignatius of
(+107). He was the
successor to St Peter as Bishop of
ENTRANCE ANTIPHON
I am crucified with Christ, yet I live; no longer I, but Christ lives in me. I live by faith in the Son of God, who has loved me and given himself up for me.
COLLECT
Almighty ever-living God, who adorn the sacred body of your Church with the confessions of holy Martyrs, grant, we pray, that, just as the glorious passion of Saint Ignatius of Antioch, which we celebrate today, brought him eternal splendour, so may it be for us unending protection. Through our Lord Jesus Christ, your Son, who lives and reigns with you in the unity of the Holy Spirit, one God, for ever and ever.
PRAYER OVER THE OFFERINGS
May this oblation and our homage be pleasing to you, O Lord, just as you accepted Saint Ignatius, the wheat of Christ, made pure bread through his martyrdom and passion. Through Christ our Lord.
COMMUNION ANTIPHON
I am the wheat of Christ to be ground by the teeth of beasts, that I may be found to be pure bread.
PRAYER AFTER COMMUNION
May the
heavenly Bread we have received, O Lord, on the feast day of Saint Ignatius
renew us, we pray, and make us Christians in name and in deed. Through Christ our
Lord.


https://www.mozilla.org/en-US/firefox/
sudo apt-get install firefox
Some people may be into other browsers but I like Firefox. It is partly because I am always using it even with Windows.
Firefox has the ability to extend its default features by adding add-on. Here are some cool add-ons I always use with Firefox (just Goolge their name):
* **Adblock plus**: block advertising on the websites you are visiting [https://addons.mozilla.org/en-US/firefox/addon/adblock-plus/](https://addons.mozilla.org/en-US/firefox/addon/adblock-plus/)
* **Flashgot**: integrate downloads in Firefox with favourite download manager (more on this later with Uget) [https://addons.mozilla.org/en-US/firefox/addon/flashgot/](https://addons.mozilla.org/en-US/firefox/addon/flashgot/)
https://www.mozilla.org/en-US/thunderbird/
Email manager help you to effective manage your email. If you are familiar with Microsoft Outlook then in Linux, a very competent alternative is Thunderbird.
sudo apt-get install thunderbird
Just add your email address and you are all set.
http://www.transmissionbt.com/
Transmission gives you the ability to download from both torrent file and magnet link. I have never had any more requirement for a torrent client beyond those two feature. Transmission just get the job done for me. To install:
sudo apt-get install transmission
I used to feel that all the download managers in Linux will never be as good as Internet Download Manager in Windows platform but changed my mind after finding Uget.
Uget is a java based app with a modern looking interface and does the job nicely.
apt-get install uget
In my own experience, of all cloud storage suppliers, Dropbox is the nicest regarding its fluently synchronization between multiple platforms. Out of the box, it gives you 2 Gb of free space. With my usage of mainly documents, it is plenty enough.
sudo apt-get install dropbox
After installing, Dropbox will ask you to enter your account, create one if you have not already had one.


Add an Aichi Target attributed to this project...
Add an Ecological Contribution attributed to this project...


Bagels With smoked salmon, tomato and onion.
With plain, scallion and honey walnut cream cheese
Homemade Muffins Blueberry, Cranberry, Bran, Sunsational, Corn, and Banana Walnut
Croissants
Scones
Miniature Scones
Coffee Cake
Tuna Salad
Egg Salad
Yogurt Parfait with berries and granola
Warm Cheese Blintz Souffle
Crepes filled with cheese, and a hint of lemon served with fresh blueberry sauce.
Noodle Kugel
Egg noodles, baked with sweet custard and mixed with fresh and dried fruit.
Eggs
Hard Boiled or Scrambled
Quiche
Tomato and cheddar or spinach and feta.
Hors D'oeuvres, Hot
Mushroom Strudel
Duxelle of mushrooms wrapped in layers of phyllo dough
Honey Chicken
Tempura battered chicken, wok fried until crispy, drizzled with honey and sesame seeds
Peking Ravioli Pan fried miniature dumplings filled with spiced pork or chicken and leeks, served with Asian dipping sauce
Peking Duck
Mooshi pancakes filled with Cantonese style roast duck, scallions and hoisin sauce
Spanikopita
Triangles of phyllo dough filled with sauteed spinach and feta
Stuffed Mushrooms
Mushrooms stuffed with spinach and feta or sweet Italian sausage or toasted bread crumbs mixed with chopped mushrooms and fresh herbs
Artichoke and Cheese Melt
A rich, luscious dip of artichoke hearts, parmesan and cheddar cheese, baked and served with toasted pita triangles
Chicken Teriyaki
Chicken marinated in Asian spices until tender, grilled and served on skewers with Teriyaki dipping sauce
Chicken Satay
Chicken marinated in Thai spices, grilled and served with a hot and sweet peanut sauce
Palmiers With Prosciutto And Parmesan
Puff pastry "elephant ears," layered with prosciutto, parmesan cheese and honey mustard
Potato Pancakes
Crispy mini pancakes served with sour cream and caviar or applesauce
Beef Teriyaki
Thinly sliced sirloin marinated in Asian flavors and served on skewers
Vegetable Cheese Tortillas
Flour, spinach and tomato mini-tortillas filled with sweet and hot chilies, corn, cheddar, Monterey Jack, rice and black beans
Tempura
Lightly battered vegetables, wok-fried and served with traditional Tempura dipping sauce
Salmon Toasts
Thin pieces of toast layered with salmon, spinach, red peppers, and cream cheese
Baked Brie
A whole brie round covered with pastry and baked with dried fruits and pecans
Mini Flat Bread Pizza
Miniature rounds of freshly baked flat breads layered with caramelized onion and fresh mozzarella, sauteed spinach and goat cheese or barbeque chicken with fontina
Dates, Chestnuts, Water chestnuts, Scallops or Chicken Livers wrapped in Bacon
Miniature Roquefort and Apple Tarts
Delicate pastry filed with imported Roquefort cheese and Granny Smith apples
Red Peppers on French bread
Fresh red peppers sauteed in extra virgin olive oil and garlic, served on French bread
Goat Cheese and Tomato Tarts
Rich tart dough filled with goat's cheese and oven roasted tomatoes
Miniature Rueben Sandwiches
New York deli corned beef, sauerkraut and Russian dressing on grilled pumpernickel bread
Frankfurters in Puff Pastry
Miniature all beef kosher hot dogs wrapped in puff pastry
Tandori Chicken
Chicken breast marinated in Indian spices and yogurt
Curried or Sweet and Sour Meatballs.
Cheddar Cheese and Chutney on Toast Rounds
Classic flavors of cheddar and chutney, melted on country style toast rounds
Smoked Turkey on Herbed Pita Triangles with Chutney
Mini smoked turkey and chutney sandwiches served on pita triangles that have been brushed with extra virgin olive oil and fresh herbs
Caramelized Onion wrapped in a phyllo pouch
Roasted Eggplant wrapped in a phyllo pouch
Small purses of tender phyllo, filled with eggplant roasted with herbs, spices and pine nuts
Wild Mushroom Turnovers
Rich pastry filled with lightly seasoned portabella, shitake and oyster mushrooms
Mushroom Profiterole
Brioche filled with fresh mushroom duxelle with walnuts and cream cheese
Pear and Bleu Cheese Stars
Pears, Bleu cheese and red wine hand-wrapped in phyllo dough
Portobello Mushroom Stacks
Grilled portabella mushrooms, sweet roasted peppers, basil pesto, parmesan cheese, layered with polenta and stacked on a bamboo skewer
Stuffed Dates
An unusual treatment of dates, filled with machengo cheese, baked until melted and topped with a touch of hot pepper jam
Mushroom Empanada
Portabella and white mushrooms wrapped in corn puff pastry
Roasted Butternut Squash Tart
Rich pastry filled with lightly seasoned, roasted butternut squash
Mini Flat Bread Pizza
Miniature rounds of freshly baked flat breads layered with caramelized onion and fresh mozzarella, sauteed spinach and goat cheese or barbeque chicken with fontina
Hors D'oeuvres, Cold
Roast Tenderloin of Beef
Rare roasted tenderloin, thickly sliced, served on French bread with horseradish cream
Seared Tuna Sushi grade tuna barely seared, served on rice crackers, with wasabi aioli and caviar
Mini Lobster Club
Fresh lobster, Avocado, Bacon and Citrus Aioli served on Challah
Salmon Pinwheel
Lavash flat breads rolled around scallion cream cheese and Scottish smoked salmon
Torta of Cheese, Sundried Tomato and Pesto
A "cake" made of layers of goat's cheese, sundried tomatoes and homeade basil pesto, served with crackers
Snow peas and Boursin Cheese
Tender snow peas split open and filled with garlicky boursin cheese
New Potato Slices with Sour Cream and Caviar
Tender red new potatoes, sliced into rounds and served with a dollop of sour cream and caviar
Baba Ghanoush
Homemade, traditional Middle Eastern style roasted eggplant, seasoned with lemon and tahini, served with toasted pita triangles
Hummus
We make our own hummus from chickpeas, garlic and extra virgin olive oil, served with toasted pita bread
Stuffed Grape Leaves
Grape leaves filled with seasoned rice and pine nuts
Steamed Mussels
Extra large PEI mussels steamed in wine and garlic broth, served in the shell
Large Shrimp with Homemade Cocktail Sauce Large shrimp cooked to perfection and served with our horseradish cocktail sauce
Marinated Shrimp with Capers, Artichokes and Mushrooms
Smoked Salmon
Thick slices of Scottish smoked salmon on fresh pumpernickel with scallion caper butter
Chopped Herring
Wine marinated herring mixed with sour cream and with Granny Smith apples, served with crackers or melba toast
Guacamole
Our special recipe of ripe avocados, lime juice, jalapenos, scallions and grape tomatoes served with salsa and corn tortilla chips
Country Style Pate on French bread with Cornichons and assorted Mustards
Lobster Salad in a Phyllo Cup
Crab and Cucumber Roll Lump crab meat, seasoned with herbs and spices and rolled sushi style in paper thin cucumber slices
Stationary Displays
Raw Bar Clams, Oysters, King Crab Legs, Shrimps Displayed on ice with Traditional Garnishes
International Displays French, Italian, Middle Eastern, Chinese, Mexican, or American
Antipasto Display A variety of Italian cold cuts, cheeses, fresh and marinated vegetables, served with French Bread
Carving Stations Glazed Ham, Roast Tenderloin of Beef, Glazed Corned Beef, Leg of Lamb, Roast Turkey, Smoked Turkey served with assorted Breads, Rolls, Mustards, Homemade Mayonnaise, Horseradish Sauce, Chutney and Cranberry Orange Relish.
International Cheese Display served with artisan crackers.
Fajita Bar Marinated Chicken, Beef and shrimp served sizzling with peppers, onions and traditional toppings
Bruschetta Bar Grilled French Bread with a variety of toppings to include marinated tomatoes, olive tapenade, roasted red peppers and mozzarella cheese.
Soup Bar The guests choice of soups served in demitasse cups.
Miniature Pizza Display An assortment of miniature pizzas served on candle-heated slate.
Artichoke and Cheese dip With Toasted Pita Chips
Crudite with homemade dips
Salads
Winter Citrus Salad Blood oranges, navel oranges and pink grapefruit dressed with a citrus vinaigrette, French feta and fresh mint
Costa Del Sol Salad
Thinly sliced raw fennel, spinach and fresh oranges tossed with red onion, feta and herbed vinaigrette
CWD Signature House Salad
Fresh field greens tossed with dried cranberries, spiced pecans and Chevre, balsamic vinaigrette
Garden Salad
Seasonal crisp lettuces, tomatoes, cucumbers with balsamic vinaigrette
Micro greens and watercress salad
Sweet tender micro greens and peppery watercress served with roasted sliced pears, walnuts and bleu cheese
Caesar Salad
Traditionally made with hearts of romaine lettuce, homemade croutons shaved parmesan and our special dressing
Beet Stack Salad Roasted, seasoned red beets, stacked with Chevre and served over filed greens with honey orange vinaigrette
Winter Salad Tender Boston bibb, watercress, pomegranate seeds, oranges and thinly sliced scallions, tossed with citrus vinaigrette
Arugula Salad Baby arugula with fennel, cherry tomatoes and feta cheese
Field Greens and Roasted Pear Salad Fresh greens with roasted sliced pears, grape tomatoes, shaved parmigano cheese and balsamic vinaigrette
Grapefruit Supremes Salad Fresh field greens with grapefruit supremes, crumbled gorgonzola and scallions tossed with grapefruit vinaigrette.
Soups
Carrot Ginger
Carrot soup spiced with ginger and Creme fraiche.
Southwestern Corn Chowder
Sweet corn chowder with jalapenos and sweet potato.
New England Clam Chowder
Thick and creamy with whole belly clams and large chunks of potato.
Cream of Five Onion
Five varieties of onions in a silky garlic broth
Tomato Soup Roasted Tomato Soup with parmesean crisp.
Creole Chicken Tender pieces of chicken breast with rice and Hatian spices.
Tuscan Vegetable A hearty Italian vegetable soup
French Style Fish Soup The daily catch in a seafood-tomato stock
Cold Melon Soup Honey Dew Soup with mint, served cold with Serrano ham and Creme fraiche.
Watermelon Soup Watermelon soup served cold with crumbled feta and balsamic reduction.
Entrees
Roast Tenderloin of Beef
Oven roasted beef served au jus
Roast Prime Rib of Beef Traditional slow roasted au jus
Stir Fried Beef with Oriental Vegetables Asian marinated sirloin stir fried with peapods and assorted vegetables in a rich soy sauce
Short Rib of Beef Beef short ribs braised in red wine until meltingly tender
Chicken Bella Chicken tenderloins sauteed with sundried tomatoes, mushrooms and artichokes
Chicken Andre Chicken tenderloins and oranges sauteed in Grand Marnier
Chicken Marsala Chicken tenderloins sauteed with mushrooms in a rich Marsala wine sauce
Boneless Chicken Roulades Breast of chicken filled with your choice of zucchini, roasted peppers and Chevre; fontina, artichokes and sundried tomatoes; dried cherries and ricotta; mushroom duxelles; white and wild rice with dried fruits or autumn apple stuffing.
Autumn Chicken Roulades Boneless breast of chicken filled with apple brioche stuffing, served with an apple cider, cream and Dijon mustard reduction.
Jambalaya Cajun jambalaya of sausage, chicken and seafood with just the right amount of heat and spice
Chicken Cordon Bleu Boneless breasts filled with gruyere and parmesan cheeses and black forest ham, baked and served with our special cream sauce
Rock Cornish Game Hen Oven roasted Cornish Game Hens coated in maple soy glaze
Roast Stuffed Turkey A New England feast of oven roasted turkey stuffed with homemade cornbread, apple and chestnut stuffing served with our pan jus gravy
CWD Signature Roast Rack of Lamb Oven roasted Australian lamb coated in fresh breadcrumbs, herbs and a touch of Dijon mustard and baked until medium rare perfection
Roast Loin of Pork Stuffed with Fruit
Succulent pork loin stuffed with a mixture of brandy soaked prunes, figs, apples and caramelized onions served with an apple cider reduction
Baked or Grilled Swordfish Served with your choice of mango salsa or lemon butter sauce
Baked New England Scrod Topped with rich butter crumbs
Seafood Newburg Assorted seafood in a rich creamy sauce made elegant with a hint of sherry and nutmeg
Grilled or Baked Fresh Alaskan Salmon Served with your choice of mango salsa, dill sauce or lemon butter sauce
Stuffed Filet of Sole Baked with raisin and spinach stiffing and served with a light lemon butter sauce
Soy-Citrus Glazed Black Cod Oven roasted fresh black cod, glazed with a sweet and citrusy soy marinade and garnished with mint and scallions
Pan Seared Sea Bass Pan Seared Chillean Sea Bass with a vegetable medley and tomato broth
Vegetables
CWD Signature Acorn Squash Vegetable Bouquet A special presentation of acorn squash ring glazed with maple syrup, cinnamon and a hint of sweet butter, filled with the seasons freshest assortment of steamed or sauteed vegetables
Haricot Vert
Slender French green beans sauteed until crisp tender and garnished with toasted pecans or roasted red peppers
Vegetable Medley Julienne Sauteed or roasted julienne cut seasonal vegetables infused with garlic olive oil
Fresh Asparagus in Season The seasons' young and sweet asparagus grilled, steamed or sauteed until just tender and dressed with a touch of garlic infused olive oil
Sauteed Autumn Vegetables
The season's freshest vegetables, oven roasted with our special seasoning
Sauteed Summer Vegetables in Season The season's freshest vegetables grilled, steamed or sauteed until just tender
Ratatouille A delicious Fresh Provencal stew of roasted zucchini, squash and eggplant in a rich and savory tomato sauce Sauteed
Snow peas and Red Pepper Tender snow peas sauteed until crisp, sprinkled with sea salt and fresh cracked pepper and garnished with roasted red peppers
Glazed Baby Carrots Tender petite carrots steamed then glazed with honey, ginger and brown sugar
Dessert
Swiss Style Crepes with Fruit Sauce
Cranberry Nut Tart in Season
Chocolate Cake
Sliced Apple Tart Flourless
Chocolate Hazelnut Cake
Viennese Sweet Table
Chocolate Chestnut Cake
Cheesecake
Miniature French Pastries
Lemon Mousse Cake
Old Fashioned Caramel Apple Cake
Fresh Strawberries with Chocolate Fondue in Season


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Chapter 9 Dumbledore: sl-prob-01
9.1 Additional Problems for Chapter 8
Note: do not use simulations in any of the problems below. Using R (or other software) to manipulate matrices or perform other numerical computation is fine.
357e6988e73fe273260c9eae3d5de0de94d0a72bLet \(\{X_n\}_{n\in {\mathbb{N}}_0}\) be a Markov chain with the transition matrix \[P=\begin{bmatrix} \frac{1}{4} & \frac{1}{4} & \frac{1}{2} \\ 0 & \frac{1}{3} & \frac{2}{3} \\ \frac{1}{3} & \frac{1}{3} & \frac{1}{3} \\ \end{bmatrix}\]
Find all stationary distributions.
The chain starts from the state \(i=1\). What is the expected number of steps before it returns to \(1\)?
How many times, on average, does the chain visit state \(2\) between two consecutive visits to state \(1\)?
Each time the chain visits the state \(1\), \(\$1\) is added to an account, \(\$2\) for the state \(2\), and nothing in the state \(3\). Estimate the amount of money on the account after 10000 transitions? You may assume that the Ergodic theorem provides an adequate approximation.
<<<<<<< HEAD Dumbledore: sl-mc-exam-07 ======= >>>>>>> 357e6988e73fe273260c9eae3d5de0de94d0a72b
Stationary distributions \(\pi=(\pi_1,\pi_2,\pi_3)\) satisfy \(\pi P=\pi\), i.e., \[% \label{equ:} \nonumber \begin{split} \tfrac{1}{4} \pi_1 \hspace{6.7ex} + \tfrac{1}{3} \pi_3 & = \pi_1 \\ \tfrac{1}{4} \pi_1 + \tfrac{1}{3} \pi_2 + \tfrac{1}{3} \pi_3 & = \pi_2 \\ \tfrac{1}{2} \pi_1 + \tfrac{2}{3} \pi_2 + \tfrac{1}{3} \pi_3 & = \pi_3. \end{split}\] We also know that \(\pi_1+\pi_2+\pi_3=1\), and that the matrix \(P\) is stochastic. Therefore, the third equation below is a linear combination of the first two, and can be exculded from consideration (this is always the case in problems with stationary distributions).
The first equation yields that \(\pi_3=\frac{9}{4} \pi_1\), and the second one that \(\pi_2 = \tfrac{3}{2} ( \tfrac{1}{4}\pi_1+\tfrac{1}{3} \pi_3)= \tfrac{3}{2} \pi_1\). It remains to find \(\pi_1\) such that \(\pi_1+\pi_2+\pi_3=1\), i.e, \(\pi_1+ \tfrac{3}{2} \pi_1+ \frac{9}{4} \pi_1=1\), i.e., \(\pi_1=(1+\tfrac{3}{2}+\frac{9}{4})^{-1}= \tfrac{4}{19}\). Therefore, \[\pi= (\tfrac{4}{19},\tfrac{6}{19},\tfrac{9}{19})\] is the only stationary distribution.
By Kac’s theorem, the number of steps between two returns to a state \(i\) (in an irreducible finite chain) is given by \[{\mathbb{E}}_i[\tau_i(1)]=\frac{1}{\pi_i}.\] Therefore, \({\mathbb{E}}_1[\tau_1(1)]= \tfrac{19}{4}\).
Also by Kac’s theorem, the number of visits to the state \(j\) between two consecutive visits to the state \(i\) (in an irreducible finite chain) is given by \[{\mathbb{E}}_i[ \sum_{n=0}^{\tau_1(1)} \mathbf{1}_{\{ X_n=j\}}]= \tfrac{\pi_j}{\pi_i}.\] Therefore, our chain will visit the state \(2\) on average \(1.5\) times between every two visits to the state \(1\).
The chain in question is irreducible and finite, so the law of large numbers applies: \[\lim_{N\to\infty} \frac{1}{N} \sum_{n=0}^{N-1} f(X_n)= \sum_{i\in\S} f(i) \pi_i.\] In our case \(f(1)=1\), \(f(2)=2\) and \(f(3)=0\), so the amount of money \(M=\sum_{n=0}^{10000} f(X_n)\) can be approximated as \[\label{equ:2919} \begin{split} M&= 10001 \times \tfrac{1}{10001} \sum_{n=0}^{10000} f(X_n)\approx 10001 \times (\ 1 \tfrac{4}{19}+ \ 2 \tfrac{6}{19})\\ &= 10001 \times \ \tfrac{16}{19}\approx \ 8422.% \end{split}\]
A county has 2 large cities and \(8\) small ones. Any two cities have a direct flight between them, except for the two large ones (since they don’t like each other very much). A traveler starts in a large city and moves around randomly by picking one of the available direct flights from their current city at random and taking it. What is the expected number of flights he or she will take before returning to the initial city for the first time?
This can be modeled as a random walk on a graph with \(10\) vertices \(S=\{1,2,\dots, 10\}\) where any two vertices are connected by an edge except for \(1\) and \(2\). We need to compute \(m_1 = {\mathbb{E}}_1[T_1(1)]\) which is, by Kac’s theorem, given by \(1/\pi_1\), where \(\pi\) is the (unique) stationary distribution. The degrees of vertices \(3,4,\dots, 10\) are \(9\) and the degrees of \(1\) and \(2\) are \(8\). Therefore, \(\sum_{i\in S} d(i) = 90\) and, so, \[ m_1 = \frac{1}{\pi_1} = \frac{\sum_{i\in S} d(i)}{ d(1)}= \frac{90}{8} = 11.25.\]
<<<<<<< HEAD Dumbledore: mc_prob3 =======
357e6988e73fe273260c9eae3d5de0de94d0a72bWonder Woman is moving from a vertex to a vertex of a cube, along its edges. Each time she reaches a vertex, she chooses one of the three edges that meet there with probability \(1/3\), independently of her previous choices. Assuming that it takes 1 min for Wonder Woman to cross an edge, what is the expected amount of time it will take her to return to the initial vertex? How about if the cube is replaced by a tetrahedron?
<<<<<<< HEAD Dumbledore: sl-prob-02 ======= >>>>>>> 357e6988e73fe273260c9eae3d5de0de94d0a72bThe situation can be modeled by a random walk on a graph whose vertices are the \(8\) vertices of a cube, with two vertices connected by an edge if and only if they share an edge of the cube. We know that random walks on graphs admit stationary distributions proportional to the degrees of the graph’s vertices. In our case each vertex has degree \(3\), so the (unique) stationary distribution is uniform \((1/8, \dots ,1/8)\). The expected return times are reciprocals of the probabilities in the stationary distributions, so the answer is \(8\).
In the case of a tetrahedron, the stationary distribution is \((1/4,\dots,1/4)\) and the answer is \(4\). In fact, the answer is equal to the number of vertices for any regular polyhedron.
Just like we did for the knight in an example above, compute the mean return time to the lower left corner for other chess pieces.
<<<<<<< HEAD Dumbledore: mc_prob1 ======= >>>>>>> 357e6988e73fe273260c9eae3d5de0de94d0a72bAssuming that they all start from the lower left corner, the mean return times are given by in the following table:
chess piece mean return time bishop \(\tfrac{280}{7} = 50\) rook \(\tfrac{896}{14} = 64\) queen \(\tfrac{1456}{21} \approx 69.3\) knight \(\tfrac{336}{2} = 118\) king \(\tfrac{420}{3} = 140\) pawn \(+\infty\) We assume that pawns don’t get promoted once they reach the last row; they just stay there.
Consider the Markov chain below where equally colored edges represent equal probabilities. Find all stationary distributions. For each \(i\in \{1,2,\dots, 7\}\), compute the long-run proportion of time this chain will spend in the state \(1\).
<<<<<<< HEAD Dumbledore: friends ======= >>>>>>> 357e6988e73fe273260c9eae3d5de0de94d0a72bThis chain has two recurrent classes \(C_1=\{1,2,3\}\) and \(C_2 = \{6,7\}\). Their transition matrices \(C_1\) are given by \[P_{C_1}=\begin{bmatrix}1/2 & 0 & 1/2 \\ 1/4 & 1/2 & 1/4 \\ 0 & 1/2 & 1/2\end{bmatrix} \text{ and }P_{C_2} = \begin{bmatrix} 0 & 1 \\ 1/2 & 1/2\end{bmatrix}.\] The unique stationary distribution \(((\pi_{C_1})_1,(\pi_{C_1})_2,(\pi_{C_1})_3)\) for \(P_{C_1}\) satisfies the following system of equations \[\begin{aligned} (\pi_{C_1})_1 & = 1/2 (\pi_{C_1})_1 + 1/4 (\pi_{C_1})_2 \\ (\pi_{C_1})_2 & = 1/2 (\pi_{C_1})_2 + 1/2 (\pi_{C_1})_3 \\ (\pi_{C_1})_3 & = 1/2 (\pi_{C_1})_1 + 1/4 (\pi_{C_1})_2 + 1/2 (\pi_{C_1})_3,\end{aligned}\] and it follows that \((\pi_{C_1})_2 = 2 (\pi_{C_1})_1\) and \((\pi_{C_1})_3 = (\pi_{C_1})_2\). Since \((\pi_{C_1})_1+(\pi_{C_1})_2+(\pi_{C_1})_3=1\), we get \((\pi_{C_1}) = (1/5, 2/5, 2/5)\). Similarly, the system equations for \(\pi_{C_2}\) is given by \[\begin{aligned} (\pi_{C_2})_1 &= 1/2 (\pi_{C_2})_2 \\ (\pi_{C_2})_2 &= (\pi_{C_2})_1 + 1/2 (\pi_{C_2})_2.\end{aligned}\] Together with \((\pi_{C_2})_1 + (\pi_{C_2})_2 = 1\), we get \(\pi_{C_2} = (1/3, 2/3)\).
The states \(4\) and \(5\) are transient, so any stationary distribution must be of the form \[\pi = ( \alpha \frac{1}{5}, \alpha \frac{2}{5}, \alpha \frac{2}{5}, 0, 0, (1-\alpha) \frac{1}{3}, (1-\alpha) \frac{2}{3}),\] for some \(\alpha \in [0,1]\).
If the initial state \(i\) is \(6\) or \(7\), the chain will never visit \(1\), so the long run proportion is \(0\). If we start from \(1,2,3\) or \(4\), the chain will get absorbed in the class \(C_1\) and the long-run proportion of time spent at \(1\) is given by \((\pi_{C_1})_1 = 1/5\).
What happens when we start from \(5\) is a little bit more subtle. If the first step taken is towards \(6\), the answer is \(0\), and if the first step is towards \(4\), the answer is \(1/5\). Therefore, this long run proportion is not a constant. It is a random variable whose value is \(\frac{1}{5} \mathbf{1}_{\{X_1 = 4\}}\).
An Instagram influencer and die-hard foodie Ravi travels for work between Austin and Los Angeles. After he spends a week in Austin, he spends another week there with probability \(1/3\) or goes to Los Angeles with probability \(2/3\). After a week in Los Angeles, he always spends the next week in Austin.
His friend, Delphine, a tech enterpreneur and a passionate restaurant-goer herself, keeps a similar schedule. She also moves between Austin and Los Angeles, but, being a free spirit (and also a bit detached from reality), she makes her decisions by tossing a coin. No matter where she is, Austin or Los Angeles, she stays there with probability \(1/2\) for another week, or switches cities with probability \(1/2\), as well. Moreover, the coin she uses is just a coin - it is not magically influenced by Ravi’s current position in any way. Similarly, Ravi’s decision whether to stay or not has nothing to do with Delphine’s coin, or the city she is in.
Each time Ravi and Delphine find themselves in the same city, they go out on Friday night to try a new restaurant. If they happen to be in Austin, they pay \(\$100\) for the experience. Los Angeles is more expensive, and the typical dinner costs \(\$200\).
Ravi and Delphine are in Los Angeles this week. How many weeks will pass (on average) before they are both in Los Angeles again?
How many Austin dinners do they have, on average, between two Los Angeles dinners?
What is the long-run average amount of money they spend on restaurants each week?
Dumbledore: sl-prob-04We model the positions of Ravi and Delphine by a Markov chain with \(4\) states AA, AL, LA and LL (with the two letters denoting the cities they happen do find themselves in a given week). Ravi’s and Delphine’s positions, considered separately, are also Markov Chains, with the state space \(\{A,L\}\) and the transition matrices \[P^{R} = \begin{bmatrix} 1/3 & 2/3 \\ 1 & 0\end{bmatrix} \text{ and }P^{D} = \begin{bmatrix} 1/2 & 1/2 \\ 1/2 & 1/2\end{bmatrix}.\] Since Ravi’s and Delphine’s transitions are independent, the transition probabilities of the “joint” chain on \(S = \{\text{AA, AL, LA, LL}\}\) are simply products of the transition probability of the two individual chains. For example \(P_{AA, AL} = P^{\text{Ravi}}_{A, A}\times P^{\text{Delphine}}_{A,L} = 1/3 \times 1/2 = 1/6\). Therefore, the full transition matrix for the “joint” chain is given by \[P = \begin{bmatrix} 1/6 & 1/6 & 1/3 & 1/3 \\ 1/6 & 1/6 & 1/3 & 1/3 \\ 1/2 & 1/2 & 0 & 0 \\ 1/2 & 1/2 & 0 & 0 \end{bmatrix}\] with the states in the following order AA, AL, LA and LL. All the questions above use the stationary probability of this chain, so we compute it first. The system of equations for \(\pi = (\pi_{AA}, \pi_{AL}, \pi_{LA}, \pi_{LL})\) is \[\begin{aligned} \pi_{AA} &= 1/6 \pi_{AA} + 1/6 \pi_{AL} + 1/2 \pi_{LA} + 1/2 \pi_{LL} \\ \pi_{AL} &= 1/6 \pi_{AA} + 1/6 \pi_{AL} + 1/2 \pi_{LA} + 1/2 \pi_{LL} \\ \pi_{LA} &= 1/3 \pi_{AA} + 1/3 \pi_{AL}\\ \pi_{LL} &= 1/3 \pi_{AA} + 1/3 \pi_{AL}\\ \text{ as well as} \\ 1 = & \pi_{AA}+\pi_{AL} + \pi_{LA} + \pi_{LL}.\end{aligned}\] The first two equations immediately imply that \(\pi_{AA} = \pi_{AL}\), and the second two that \(\pi_{LA} = \pi_{LL}\). Moreover, by the third equation, we have \(\pi_{LA} = 2/3 \pi_{AA}\); similarly, \(\pi_{LL} = 2/3 \pi_{AL} = 2/3 \pi_{AA}\). We plug all of that into the last equation to get \[1 = \pi_{AA} + \pi_{AA} + 2/3 \pi_{AA} + 2/3 \pi_{AA},\] which gives \(\pi_{AA} = 3/10\), and, then, \(\pi_{AL} = 3/10\), \(\pi_{LA} = 1/5\) and \(\pi_{LL} = 1/5\).
By Kac’s theorem, the mean return time to LL is \(1/\pi_{LL} = 5\) weeks.
Also by Kac’s theorem, the average number of visits to AA between two visits to LL is \(\pi_{AA}/\pi_{LL} = 1.5\)
By the ergodic theorem, the long-run amount of money spent each week is \(\pi_{AA} \times \$100 + \pi_{LL}\times \$200 = \$ 70\).
Go back to the Problem with airline reservation system and the computer-repair facility in the last chapter. If the system starts with both computers operational, answer the following questions:
What percentage of time (on average) are both machines operable?
What is the long-run per-day cost associated with inoperable computers?
Most of the work has been done in the solution to the original problem in the previous chapter; please read it if you don’t remember what the names of the states mean. The Markov chain that we constructed there has the following graph
Since both questions asked need the stationary distribution, let us compute it first. There are two difficulties. The first one is that the equation \(\pi = \pi P\) is not in the format R’s
solve
command likes. That is easily fixed by transposing everything, i.e., by solving the equation \((I - P^T) \pi^T = 0\). The second problem is that the system of equations \(\pi = \pi P\) is underdetermined (if you sum all the equations you will get \(1=1\)) so that the additional requirement \(\pi_1+\dots+\pi_5=1\) must be added. We take care of both of these things by first forming the matrix \(M = I - P^T\) and then replacing its last row by a row of 1s. We also need to replace the \(0\) vector on the right hand side by the vector \((0,0,0,0,1)\). This way, the last equation becomes exactly \(\pi_1+\dots+\pi_5 = 1\). Here is how all of this is done in R:0.3 p = c("0-0-1-1", "0-1-0-1", "1-0-1-0", "1-1-0-0", "2-0-0-0") S = matrix(c(0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, p, 0, 1 - p, 0, 0, 0, p, 0, 1 - p, P =^2, 0, 2 * p * (1 - p), 0, (1 - p)^2), byrow = TRUE, ncol = 5) p diag(5) - t(P) M =5, ] = 1 M[ matrix(0, nrow = 5, ncol = 1) v =5, 1] = 1 v[ p_stat = t(solve(M, v))) (## [,1] [,2] [,3] [,4] [,5] ## [1,] 0.02759354 0.1233262 0.319109 0.2233763 0.3065949
The vector
p_stat
contains the (unique) stationary distribution, so we can start by answering the two questions posed in the problem.
The percentage of time both machines are operable is \(\pi_{2-0-0-0}\). The order of 2-0-0-0 is \(5\), so the answer is approximately \(0.3066\).
This is the job for the Ergodic theorem. The reward (cost) function associated with the \(5\) states is \[\begin{align} f(\text{0-0-1-1}) &= 30,000 \\ f(\text{0-1-0-1}) &= 30,000 \\ f(\text{1-0-1-0}) &= 10,000 \\ f(\text{1-1-0-0}) &= 10,000 \\ f(\text{2-0-0-0}) &= 0 \end{align}\]
To get the expected long-term reward-per-day, we need to compute the product of \(\pi\) and \(f\) (understood as a column vector):
matrix(c(30000, 30000, 10000, 10000, 0), nrow = 5, ncol = 1) f =%*% f p_stat ## [,1] ## [1,] 9952.447
⬇ In case you were wondering, the text below belongs to footnotes from somewhere high above.⬇


2 bodies found at Texas beach are New Hampshire couple
CORPUS CHRISTI, Texas (AP) - Investigators say the two bodies found buried at a South Texas beach have been identified as a missing New Hampshire couple.
The Kleberg County Sheriff’s Office in Texas announced Friday in a release that the deaths of 48-year-old James Butler and 46-year-old Michelle Butler are being investigated as homicides.
The sheriff’s office says a deputy on Sunday located a woman’s remains in a shallow grave on Padre Island, near Corpus Christi. Chief Deputy Jaime Garza says a man’s body was discovered beneath the first body on Monday.
The sheriff’s office had announced last week that the Butlers were reported missing, along with their truck and RV. Relatives last heard from them on Oct. 14 and believed the pair visited Padre Island.
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1 override of ImplementStop
System.Management.Automation (1)
engine\hostifaces\LocalPipeline.cs (1)
738
protected override void
ImplementStop
(bool syncCall)
1 reference to ImplementStop
System.Management.Automation (1)
engine\hostifaces\pipelinebase.cs (1)
350
ImplementStop
(syncCall);


Your instructor: Nadja Popovich
I work at the Guardian's US office in New York, where I make interactive graphics and write about health, science and politics.
Some projects I've worked on over the past year …
• Lack of diversity in Congress
Slides made using Reveal.js


MR.DON KOFFI P.O. BOX 674 KIA, ACCRA email<EMAIL_ADDRESS>ATTENTION: Dear Sir, We the undersigned acting with full corporate authority and with full legal responsibility do hereby irrevocably confirm and commit subject to the Law of perjury that we are ready willing and able to sell Raw Alluvial Gold Dust under the condition and procedure set out hereunder, Commodity - Gold dust Quantity -500kg Quality -23b Carats plus Origin -Ghana Price -:$7000 DOCUMENTS TO BE. PROVIDED BY SELLER Certificate of ownership/Origin Export permit, Bank of Ghana formA2 Customs and Excise paper Sgs/Assay report PROCEDURE Buyer sends letter of intent to seller responds with Performa invoice and draft copy of contract. Buyer and Seller signs sales and Purchase Agreement Buyer comes down to Ghana for inspection, and signing of contract. Seller commence supplies based on the Agreement between the two parties We anticipate a cordial business relationship with you. We shall be very much obliged to update you with our transaction proceedings please contact us on this email<EMAIL_ADDRESS>Thanks, MR.DON KOFFI


This document is the API specification for the Deep Java Library (DJL) MXNet Model Zoo.
The MXNet Model Zoo module contains MXNet symbolic modules that can be used pre-trained or for inference. See here for more details.


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{% load static %}
latest news and posts from AdMA
Relevant governmental links
Category description for news
Projects and other media files


---
layout: single
title: "Probably_POTUS: A Twitter bot using ML to detect tweets written by Trump"
excerpt_separator:
---
I've been looking for a project to play with for a while now so that I could officially end my long hiatus
from this blog. (Devotees will notice the blog's title has been updated and the platform underwent a shiny upgrade).
One of my favorite data science stories this year was an
analysis done on (then-candidate) Trump's tweets
that showed the crazy tweets tended to come from his personal Andriod phone, while more conventional tweets that
his staff might compose came from other sources. I was impressed with how cleanly the space of crazy and sane tweets
was divided by which device the tweet came from--you might say the decision function has
"wide margins".
That got me thinking...what if we didn't know which device the tweet came from, maybe we could use Machine Learning to
predict which device it would have come from, thereby predicting who composed it.
Why wouldn't we know which
device it came from? Because Donald Trump was about to become president and there was no way they'd let him
continue to tweet from his insecure personal phone, right?
EDIT: It turns out President Trump is defying all common sense and
continuing to tweet with his personal Android.

Anyway, this is a fairly well-posed supervised learning problem: using Trump's tweets before he took office, can we
learn a set of rules that classify whether a tweet was sent by his Android or not? We can build up a set of training data
whose labels will be whether or not the tweet was sent from an Andriod, which we
know used to map fairly well to whether the tweet was actually written by Trump. We can use the text of the tweet and some
other metadata to build features. Our model will automatically learn a set of rules to determine how the features
map to the class labels. Once we have a model, we can build a Twitter bot to retweet statuses that the model is
confident were written by Trump.
Eating your broccoli first
Before we get to the fun part of modeling, we have to do some engineering work. First, we need to build up our
training set. To do this, I'll follow an ELT (Extract, Load, Transform...the order isn't a mistake) framework, using a
PostgreSQL database to store my data for easy retrieving later.
For the Extract portion, I need to get my hands on some realDonaldTrump tweets.
Twitter has a REST API that anyone can connect to in order to query tweets, and there's an excellent python
library called tweepy that abstracts away much of the dirty work so you can
focus on writing python instead of forming HTTP requests.
My TweetExtractor
class uses tweepy to query Twitter's API for a few thousand Trump tweets and saves
them to disk. "Load" refers to the act of loading this data from flat files into the database I have
running on an instance of Amazon's
EC2 platform. I tend to keep the Load step simple, not worrying about data types and just bringing everything into the database
as raw text. Since this is something I do a lot of, I have some
wrappers
that make this task relatively painless for small-ish data sets. Finally,
SQL scripts in the Transform step make
sure that data types are correct in addition to performing some lightweight feature engineering--building features
that might be useful to the model (e.g. how many characters the tweet contains, how many uppercase substrings, the
number of exclamation points, etc.).
The fun part
OK, now we're ready to build the model...well, almost. First we need to work on converting the features into a more
useful form. We're dealing with both text data (the actual content of the tweets) as well as numerical data
(character counts, # of retweets, etc) so I need to split the feature transformations steps into separate pipelines for
each type of feature (more on that later).
Let's start with the text data first. The first problem to solve is how to represent
unstructured text as a coherent set of features so that we can build a model.
I'll follow a classic Natural Language Processing (NLP) pattern
called the Bag of Words model. This representation builds up a vocabulary from all of the tweets, and converts each
tweet into a vector of ones and zeros indicating which words are contained in the tweet.
We can go one step beyond this simple model by using
Term-frequency--Inverse document frequency (Tf-Idf) weighting
to assign more weight to words that occur in fewer tweets while down-weighting common words like "the" or "a" that
just add to the noise.
This sounds promising, but we're getting ahead of ourselves a bit. We need to build up a Bag of Words representation,
but we don't have a set of words just yet--we have tweets. We first need to figure out a way to parse our tweets
to extract words from them. You might think this is as trivial as str.split(' ')
, but there's more to
it than that. We need to deal with punctuation as well as the fact that tweets are not the same as plain text.
We need to process emojis, hashtags, and 13-year-old-girlspeak. It's a good thing the powerful NLTK library has a
built-in TweetTokenizer
class that does all of this for you! Tokenizers like this are built off of a set of well-established
heuristics that work pretty well out of the box. Still, I helped it out a little by
writing a regex
to map all urls to one distinct token in our vocabulary.
There's one more choice we should make about our Bag of Words implementation, and that's whether to use
single words, or extend it to N-grams (collections of commonly co-occuring phrases). I actually don't have much
of an opinion here, so I let the model decide. That is, I treated the value of N as a hyperparameter to the model
and searched for the value that maximizes the model accuracy (it turns out, single words are better).
That's all the pre-processing for the text features, now we turn to the numeric features. Honestly, this is pretty
straightforward. Because we're using a limited set of features from Twitter data, it's nice and clean with no missing
values to impute, outliers to clip, or categorical features to one-hot encode. The only thing I decided to do, although
it's not strictly necessary with the Random Forest model I went with, was perform min-max scaling so the numeric
features were in the same range as the tf-idf-transformed text features.
For the actual estimator, I went with a
Random Forest.
model. So-called ensemble methods do a pretty good job
at limiting overfitting, and that's something I'm a little concerned about since my feature set is rather large
(determined by the size of @realDonaldTrump's vocabulary, which is...bigger than you'd think). I let cross validation
pick the appropriate number of trees to build and left the other model parameters set at their (reasonable) stock values.
To put it all together, I took advantage of scikit-learn's supremeley useful
Pipeline and FeatureUnion classes.
These do exactly what you think they'd do: they wrap your entire ML pipeline, including pre-processing steps,
into a single object. To make this even more useful, the
GridSearchCV
class takes as input your model Pipeline as well as a dictionary of hyperparameter values and automatically
handles the cross validation to tune them. In this case, I used 5-fold stratified CV--fully specified in about 5 lines
of code.
So, how well did we do? The accuracy in both the validation and test sets is at around 90%--not bad! Now that we have
used the test set to get an estimate for the accuracy, there's no reason to exclude it any longer so I feed it back in
to the training set and re-train the model with the best-fitting set of parameters.
Deploying a Twitter bot
Inspired by
@RealRealDonaldT,
I decided to make a Twitter bot to re-tweet all the statuses that the model believes were written by Trump.
The difference between my bot and theirs is that mine makes a decision based on the tweet itself, and doesn't use the
device type in its decision. Instead, it learned how to recognize the device from the language of the tweet.
To process tweets in real time as they come in, I needed to connect to Twitter's streaming API. Unlike the ETL I built
for processing the training data, I needed the real-time data pipeline to be simple and fast. The solution I came upon
is described in the
diagram below. Instead of writing new tweets to the database, I bypass it completely and perform the transform
steps in-memory on pandas Dataframes. That's a bit of a DRY violation since I had to write transform code twice
(once in SQL and once in python), but it makes things much simpler when dealing with a streaming process.
|
| Digram of the data flow. |
With my new pipeline sketched out, it was pretty straightforward to
code up
a handler to listen for incoming tweets, transform them, and re-tweet any statuses the model thinks are really
from Trump. I deployed everything to an EC2 instance, loaded a serialized model (so the feature processing Pipeline
came along for free) and just waited for Donald Trump to get in a bad mood.
|
| Success! |
The bot has been running for a few days now and it seems to be working pretty well. There are a
few issues I'd like to tackle
to improve it, so I'll probably keep tweaking it for some time. In the meantime, follow along with
@Probably_POTUS. Be careful, though...these tweets are the
craziest of the crazy.
The entire code for this project can be found on GitHub.
Feedback or PRs are more than welcome!


{% extends 'base.html' %} {% block content %}
{{ quote.quote }}
Are you sure you want to delete this quote?


#include <uvm_common_phase.h>
Inheritance diagram for uvm::uvm_common_phase:
Public Member Functions | |
| uvm_common_phase (std::string name, uvm_schedule *p_schedule, uvm_phase_type type=UVM_PRERUN_PHASE, uvm_phase_order=UVM_TOP_DOWN) | |
| ~uvm_common_phase () | |
| virtual int | do_execute (sc_core::sc_module *pmod) |
Protected Member Functions | |
| void | process_state_executing_runtime (uvm_component *pcomp=NULL) |
| void | process_state_executing_nonruntime (sc_core::sc_module *pmod=NULL) |
Definition at line 42 of file uvm_common_phase.h.


BHEL reports 42% YoY jump in Q2 profit; revenue declines moderately
The total income of the company stood at Rs 6,359.68 crore during the quarter under review as compared with Rs 6,934.06 crore a year ago.
by PTIState-run engineering firm Bharat Heavy Electricals Ltd (BHEL) on November 13 posted a 42 percent jump in consolidated net profit at Rs 120.95 crore in the second quarter ended September 30.
The company had reported a net profit of Rs 85.24 crore in the July-September quarter a year ago, a BSE filing said.
The total income of the company stood at Rs 6,359.68 crore during the quarter under review as compared with Rs 6,934.06 crore a year ago.
However, the company reported a consolidated net loss of Rs 97.98 crore for the April-September period this fiscal as against a net profit of Rs 125.21 crore in the first half of last fiscal.
The total income of the company in the first half of the fiscal stood at Rs 11,033.06 crore as compared with Rs 13,050.27 crore in the year-ago period.Get access to India's fastest growing financial subscriptions service Moneycontrol Pro for as little as Rs 599 for first year. Use the code "GETPRO". Moneycontrol Pro offers you all the information you need for wealth creation including actionable investment ideas, independent research and insights & analysis For more information, check out the Moneycontrol website or mobile app.


An interactive theme park ride where you play as part of an alien duo tasked with abducting cows while avoiding being shot down by farmers, currently under production for Game Development Workshop I/II.
GitHub
Most of Vincent's projects can be found on GitHub.
Some notable projects are listed below.
A website containing games with random number generation.
A personal website that contains a collection of pixel art created in high school.
An experimental project featuring five pictures made using five-color palettes.
A 3D plaform-racing game made in Unity as the final project of Computer Game Design & Development.
A 2D action-platformer game made in Unity as the final project of Computing and Informatics Design.
An 800x1000 movie poster concept created in Adobe Illustrator as the final project of Digital Media.
Email<EMAIL_ADDRESS>Phone:<PHONE_NUMBER>
|
Drexel University Bachelor of Science in Computer Science Game Programming and Development Concentration |
Philadelphia, PA Anticipated Graduation: June 2021 Cumulative GPA: 3.52 |
| Web Dev & Design | Game/Art Production | Programming | Miscellaneous |
|---|---|---|---|
|
|
|
|
|
Unisys Corporation Software Engineer (MCP Security) Co-op
|
Malvern/Blue Bell, PA Apr - Sept 2019 |
|
Unisys Corporation Software Engineer (MCP Core) Co-op
|
Malvern, PA Apr - Sept 2018 |
|
Cow-duction: The Milkening UI Designer & Programmer
|
Philadelphia, PA Sept 2019 - Present area52p.com |
|
"Random" Game Hub Website Designer & Game Developer
|
Paoli, PA Aug 2018 - Aug 2019 vncegd.github.io/random |
|
Data Structures Mathematical Foundations of Computer Science Systems Programming Systems Architecture Programming Language Concepts |
Software Design Software Engineering Web and Mobile App Development Computer Graphics Imagery I, II Experimental Game Development |
|
A.J. Drexel Scholarship, Drexel University, 2016 - Present Dean's List, Drexel University, 2016 - 2017, 2019 |


Thalia:
She wanted something in return, didn't she? Just as I expected... It is so humiliating to be ordered about by a Dwarf! Oh well, we have no choice...


Popular with budget-minded European and American vacationers, this beachfront all-inclusive resort in Puerto Plata is on Cofresi Beach, 5 minutes from Ocean World Adventure Park and Marina.
Meals, drinks, a kids' club, and exchange privileges with nearby resorts are included in this Lifestyle Tropical Beach's room rate. A two-level pool includes a thatched-roof swim-up bar and a children's section.
Steps from the beach, rooms in Lifestyle Tropical Beach's Mediterranean-style buildings have balconies or patios with pool, garden, or ocean views; white-tile floors; and locally crafted furniture.


A JavaScript framework to build UI for your bot.
BotUI makes it super easy to create conversational/bot interfaces. It has an intuitive JavaScript API to add messages and show actions that a user can perform.
 You can also create your own themes.
Here's a quick preview of how it works.
Add the <bot-ui>
where you want the bot to appear.
...
<div id="my-botui-app">
<bot-ui></botui>
</div>
...
Pass the parent element's id
to BotUI
constructor.
var botui = new BotUI('my-botui-app');
Call the message.add
method to show a message.
botui.message.add({
content: 'Hello There!'
}).then(function () { // wait till its shown
botui.message.add({ // show next message
content: 'How are you?'
});
});
Follow the docs for details about how everything works.


--part1_97.1640724f.2849d244_boundary
Moulin Rouge * * * * (out of * * * * )
Directed by Baz Luhrmann. Cast: Ewan McGregor, Nicole Kidman, Richard Roxburgh, John Leguizamo, Jim Broadbent, Kylie Minogue. 2001 - 126 minutes Rated PG-13 (mature themes and sexual situations). Reviewed by Dustin Putman, June 2, 2001.
One does not watch Baz Luhrmann's phantasmagoric ode to Hollywood movie musicals of yore, "Moulin Rouge," as much as one feels as if they're living it. A trippy carnival ride through sensory overload and non-stop visual marvels, it is a wildly imaginative and sincerely audacious motion picture that has the ability to single-handedly revive one's faith in the movies, as they have begun to dwindle with the summer season currently upon us.
The story is simple, yet powerful. Set in Paris, circa 1899, Christian (Ewan McGregor) is a penniless writer who has just arrived in the town of Monmartre, home of the infamous Moulin Rouge nightclub. When Christian has a chance encounter with a group of petty unconformists, led by dwarf Toulouse Lautrec (John Leguizamo), and helps them to come up with the lyrics of "The Sound of Music" for the musical revue they are preparing for the Moulin Rouge, they immediately hire him to be their writer. They also set up a meeting with gorgeous courtesan Satine (Nicole Kidman), whom they want for the female lead of what they have entitled "Spectacular Spectacular!" At first mistaking him for a wealthy duke, Satine falls instantly in love with Christian, and vice versa, much to the chagrin of the real duke (Richard Roxburgh), who has not only just staked a claim in Moulin Rouge, but has gotten the club's owner, Zidler (Jim Broadbent), to sign an agreement giving him exclusive rights to Satine. And although Christian and Satine do not yet know it, Satine has been diagnosed with a fatal case of tuberculosis.
"Moulin Rouge" is as deeply romantic as any film in recent memory. Christian is a wistful young man who believes in four aspects of life: "truth, beauty, freedom--and above all others--love." Satine, on the other hand, is both a singer/dancer and a prostitute, and has been trained to never fall in love. In one of the film's most awe-inspiring, magical sequences, Christian serenades Satine with Elton John's "Your Song," to which they get so caught up in their feelings for one another that they are lifted out of the building window and onto the clouds in the sky.  Their joy in finding each other does not last, however, as the wealthy duke enters the picture with an unfair proposition to make.
The sparks that actors Ewan McGregor and Nicole Kidman ignite as hopeless romantic Christian and unsure realist Satine are so bright, they're almost blinding. Their chemistry, for once, feels genuine, rather than "written," and it is the doomed love story of these two people that turn the movie into such a complex and touching experience. McGregor (1999's "Star Wars: The Phantom Menace") and Kidman (1999's "Eyes Wide Shut") are stunning in their roles, eliciting both the passion and determination that any two people have who are in love. Both of them, as well as the rest of the actors, sing their own songs, and they turn out to be startlingly talented at it. Special notice must go to Kidman, who has never been better. Often coming off as somehow aloof, Kidman fills Satine with a warmth that has never before been witnessed from the actress.
Aside from its two stars, where "Moulin Rouge" excels is in the almost non-stop barrage of singing. About 85-percent of the story is told through the original art form of song, all of which are classic pop tunes and love ballads from post-1899. Just because we are in turn-of-the-20th-century Paris, director Baz Luhrmann so convincingly argues, doesn't necessarily mean that people back then couldn't have the right to sing everything from Dolly Parton's "I Will Always Love You," to The Beatles' "All You Need is Love," to David Bowie's "Heroes," to Madonna's "Like a Virgin," to Sting's "Roxanne," to Nirvana's "Smells Like Teen Spirit." The film plays like a veritable who's who of '60s, '70s, and '80s music, and it is not only smashingly entertaining, but actually serves a strong purpose within the narrative. Each musical number--and there are a lot of them--are orchestrated with such unabashed energy and passion that you can't help but drift away in its rapturous spell.
With "Moulin Rouge," Baz Luhrmann (1996's "William Shakespeare's Romeo + Juliet") has made his first masterpiece as a filmmaker. The camera is constantly moving, swooping around people and objects, or zooming into building windows or through beautiful city landscapes. It is not done to keep the interest of the attention-impaired, though, but to simply aid in creating a glitzy, one-of-a-kind universe. Additionally, the story, written by Luhrmann and Craig Pearce, is just strong enough to be fully satisfying, while not being bogged down by overplotted developments.
In a day when so many motion pictures strive for nothing more than adequacy, "Moulin Rouge" is alive with creativity and wonder, proving that the medium of film can take us anywhere, and act upon us in any way possible. One part comedy, one part romance, and one part tragedy, "Moulin Rouge" is a remarkable triumph, as well as the first truly brilliant film of 2001.
- Copyright 2001 by Dustin Putman
http://www.all-reviews.com/reviewers/dustinputman.htm http://us.imdb.com/reviewsby?Dustin+Putman http://www.atnzone.com
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


Drag the player markers to rearrange and set up your starting formation.
Alternatively, select one of the built-in formations below.


Hong Kong cop suspended after driving motorbike into group of protesters
HONG KONG: A police officer who was caught on video driving his motorcycle into a crowd of protesters has been suspended from frontline duty and has gone on leave, Hong Kong police said on Monday (Nov 11).
On Monday morning, a large number of protesters took to the street in Kwai Fong and one of them sprayed an unknown liquid at a police officer's face, causing temporary loss of vision, Chief Superintendent John Tse said during a police briefing.
The now-suspended officer drove over on his motorbike to help his colleague, and to try to separate the protesters and police officers.
READ: Hong Kong at 'brink of total breakdown' with clashes at Central, universities
The officer was attacked with a hammer and reacted by driving his bike into the demonstrators.
"We understand that this is a serious matter, we place high priority on this case," said Tse, adding that the case is under investigation.
Hong Kong saw one of its worst days of violence on Monday with a police officer shooting a 21-year-old protester and a man being set on fire.
READ: US condemns latest Hong Kong violence, UK says escalation 'deeply disturbing'
READ: Timeline - Fresh violence erupts in Hong Kong on Tuesday
The violence continued on Tuesday as protesters clashed with police in university campuses and in the business district of Central.
In Central, a district that is home to many blue-chip international firms and luxury shops, thousands of office workers occupied roads for hours on Tuesday chanting: "Fight for freedom, stand with Hong Kong!".
Universities emerged as a new battleground with sustained clashes at major campuses for the first time.
At the Chinese University of Hong Kong, police fired multiple volleys of tear gas and rubber bullets in the afternoon at hundreds of protesters, who had built barricades afters an hours-long stand-off between the two sides.
Protesters responded with bricks and petrol bombs, while a vehicle used in a barricade was set alight.
READ: Hong Kong leader says protesters 'paralysing' the city are selfish
At City University, protesters used a three-person slingshot to fire bricks at police from a footbridge.
Masked activists also built barricades and blocked roads at Hong Kong University while at Polytechnic University, clashes broke out as police tried to arrest a female student.
During the morning rush hour, hardcore protesters blocked roads, threw objects onto rail tracks and held up subway trains, sparking yet another bout of transport chaos throughout the city.
MORE: Our coverage of the Hong Kong protests
Follow us on Telegram for the latest on Hong Kong: https://cna.asia/telegram


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Apply
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Program
The workshop will be a full day event from 9:00 am - 6:15 pm. The tentative schedule is as follows:
- 09:00 - 09:10 Welcome Remarks
- 09:10 - 09:45 Invited: Speaker 1
- 09:45 - 10:00 Contributed Talk 1
- 10:00 - 10:15 Contributed Talk 2
- 10:15 - 10:30 Contributed Talk 3
- 10:30 - 11:00 Coffee Break + Poster Session I
- 11:00 - 11:35 Invited: Speaker 2
- 11:35 - 11:50 Contributed Talk 4
- 11:50 - 12:05 Contributed Talk 5
- 12:05 - 12:20 Contributed Talk 6
- 12:20 - 02:15 Lunch + Poster Session II
- 02:15 - 02:30 Contributed Talk 7
- 02:30 - 02:45 Contributed Talk 8
- 02:45 - 03:00 Contributed Talk 9
- 03:00 - 03:15 Contributed Talk 10
- 03:15 - 03:45 Coffee Break + Poster Session III
- 03:45 - 04:20 Invited: Speaker 3
- 04:20 - 04:55 Invited: Speaker 4
- 04:55 - 05:55 Panel Discussion: on practical issues of AI for developing countries
- 05:55 - 06:15 Awards and Closing Remarks
Each oral presentation will be 10 minutes followed by 5 minutes for Q/A, each invited speaker presentation will be 25 minutes followed by 10 minutes for Q/A for increased discussion. Since, the idea is to bring a diverse group of participants to discuss practical issues of implementing ML for developing countries, we specifically added a panel discussion focused on the topic with panelists from representatives of policy makers, researchers, industry experts, and other stakeholders.


S. Dakota holds off Florida A&M 85-82 in chaotic ending
HONOLULU (AP) - Tyler Hagedorn scored 23 points and Triston Simpson added 22 and South Dakota beat Florida A&M; in the finale of the Outrigger Resorts Rainbow Classic on Monday night.
The Coyotes are off to their second 3-0 start in the last four seasons.
Stanley Umude made a pair of free throws with 18:16 remaining for a 50-48 advantage and South Dakota led the rest of the way. Two minutes later his 3-pointer made it a 60-50 contest.
With 27 seconds to go, Rod Melton Jr.’s layup brought Florida A&M; within 85-80. Melton missed the foul shot and a chance for a three-point play. On the other end, Hunter Goodrick missed a pair of foul shots and a chance to extend the lead. Then Kamron Reaves missed a jumper for the Rattlers with 19 seconds to play.
Reaves fouled Simpson with 11 seconds to go, and he proceeded to miss both free throws. Melton then made a layup to make it 85-82 with seven seconds left, but he also missed a chance for a three-point play after being fouled. On the miss Bryce Moragne secured the offensive rebound, threw the ball to Reaves, but Kruz Perrott-Hunt blocked his shot attempt to end the game.
Melton led Florida A&M; (0-4) with 27 points.
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