(PHP 4 >= 4.3.0, PHP 5, PHP 7)
msg_send — Send a message to a message queue
$queue
, int $msgtype
, mixed $message
[, bool $serialize
= TRUE
[, bool $blocking
= TRUE
[, int &$errorcode
]]] ) : bool
msg_send() sends a message
of type
msgtype
(which MUST be greater than 0) to
the message queue specified by queue
.
queue
msgtype
message
serialize
The optional serialize
controls how the
message
is sent. serialize
defaults to TRUE
which means that the message
is
serialized using the same mechanism as the session module before being
sent to the queue. This allows complex arrays and objects to be sent to
other PHP scripts, or if you are using the WDDX serializer, to any WDDX
compatible client.
blocking
If the message is too large to fit in the queue, your script will wait
until another process reads messages from the queue and frees enough
space for your message to be sent.
This is called blocking; you can prevent blocking by setting the
optional blocking
parameter to FALSE
, in which
case msg_send() will immediately return FALSE
if the
message is too big for the queue, and set the optional
errorcode
to MSG_EAGAIN
,
indicating that you should try to send your message again a little
later on.
errorcode
成功时返回 TRUE
， 或者在失败时返回 FALSE
。
Upon successful completion the message queue data structure is updated as
follows: msg_lspid
is set to the process-ID of the
calling process, msg_qnum
is incremented by 1 and
msg_stime
is set to the current time.


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A CPL-based feature server for VOCAL systems. It handles both called and calling features, including call forward on busy, call forward no answer, call blocking. Call forward no answer works with vmserver(1) and ua(1) to provide voice mail features.
specify configuration file
specify configuration directory
do not fork into background (no daemon mode)
disable SIP retransmission
specify log file
specify log level
initial retransmission time in ms
maximum retransmission time in ms
disable multicast heartbeat
use syslog for logging
do not read options from any configuration file
default provisioning server
backup provisioning server


Traverson makes it easy to work with REST/HATEOAS APIs. It works in Node.js as well as in the browser. This example talks to the local Traverson test server.
There is also the GitHub API Example.
You need to start the test server contained in the Traverson repository with node bin/start-test-server.js. This serves this page as well as the REST API that is used in the examples. Open the URL http://localhost:2808/static/browser/example in your browser. If the URL in your browser's address bar says file:... the examples on this page will not work.


---
layout: page
title: Portfolio
use-site-title: true
---
University Projects
Autonomous Ocean Sediment Collection Device
- For my Senior Capstone Project at the University of California, Santa Cruz myself and three other engineers worked to design, build, program, test and successfully launch the device you see above. We partnered with researchers at the Long Marine Lab in Santa Cruz to implement a more effective and easier to use method to collect sediment samples from the ocean floor.
- Using a large claw like device, the current method for sediment collection is expensive, dangerous, inefficient and takes up to five people to operate on a large boat. Our device can be operated by two people on a small research boat, and is controlled wirelessly via smartphone. Furthermore our device utilizes a standard 80 cubic foot scuba tank to power the vacuum collection system and to inflate the lift bag that enables retrieval at the surface. Creating a very efficient system that yields a high quantity of sediment.
- Our first full system test was completed at the Monterey Bay Aquarium Research Institute's 40 foot testing facility. We concluded our project with a successful ocean test under the Monterey wharf where we successfully collected sediment samples. We then went on to win 3rd Place at the IDEA Hub's Pitch for Social and Creative Enterprise competition.
- More information is available on the following overview poster.
Mechatronics Final Project: Slug Wars
- Tasked to design, prototype, program and build a fully autonomous robot from the ground up in four weeks, myself and two other engineers worked non-stop to successfully complete the project on time. We utilized mechanical design with SolidWorks, state-machine inception and implementation with event-driven programming in C on a PIC32 micro-controller, and sensor scheme architecture and construction. Rapid design, prototyping and debugging played a key role in the development process.
- The robot is equipped with a 2kHz IR beacon detector, track wire and tape detectors, front and back bump sensors, two drive motors and a servo motor and server fan for the firing mechanism. Which is able to fire ping pong balls collected by the inverted cone on the top of the robot.
- Here are the field specifications along with a local news report about the project.
- We concluded our project with a successful in lab checkoff as well as a successful public demonstration.
Assignment Plus Educational iOS App
- Myself and one other engineer developed a foundation for an educational iOS app that could be used by middle and high school students and teachers as a task management and assignment tool. Students have complex, busy lives and throughout the school year it is difficult for them to keep track of their daily assignments. This application will allow teachers to create an account and add their classes and assignments as the year progresses. While a student will be able to log in with their own account and enroll in their class, getting the latest assignments pushed right to their phone.
- The following photo album gives you a view of the simple process of a teacher creating an account and adding classes and assignments. While the last two photos show you the students view where they can view their classes and corresponding assignments.


{% extends "partials/tooltips/base.html" %}
{% block title %}
Duty to Support
{% endblock %}
{% block inner %}
Parents and guardians have a responsibility (duty) under the law to financially support their
children, whether or not they see or take care of the children. A parent, for the purposes
of child support, may include a stepparent if they were living together, in a relationship,
with the child’s parent and the child during the child’s life. The duty of a stepparent or
non-parent guardian to provide support is secondary to that of the child’s parents.
{% endblock %}
{% block label %}
{% if text %}{{ text }}{% else %}duty to support{% endif %}
{% endblock %}


Insider buying helps drive Power Financial into the INK Canadian Insider Index
by Ted DixonPower Financial Corp. joined the 50-stock INK Canadian Insider Index on Friday with the semi-annual rebalancing. The bulk of Power Financial profits in 2018 came from the insurance area at 73 per cent, followed by 21 per cent from wealth management and 6 per cent from other businesses.
Relatively attractive valuations, such as a prospective dividend yield of about 5.6 per cent and strong price momentum, have helped the stock make it into the index.
Importantly, insider commitment is high with a director spending $100,015 buying shares in September.
Ted Dixon is CEO of INK Research which provides insider news and knowledge to investors. For more background on insider reporting in Canada, visit the FAQ section at www.inkresearch.com. Securities referenced in this profile may have already appeared in recent reports distributed to INK subscribers. INK staff may also hold a position in profiled securities.
Chart reflects public-market transactions of common shares or unit trusts by company officers and directors.


Well, it’s been an interesting few weeks regarding two of my three main hobbies - walking and photography. MTBing is the other one of which I haven’t done any since Christmas.
No, regarding walking, we have only done local walks of recent, even though we managed to get up to Keswick on my Birthday. However, whilst up there, I suffered a stomach upset so returned the very next day so no walks.
However, I have been putting much effort into photography, my interest being mainly driven by a new Nikon D90 given to me as a joint Birthday / Xmas pressie from the other half and my parents. Since getting the camera, I have been snapping anything and everything and my skills with the camera have come on leaps and bounds. I have spent several evenings recently re-keywording my Lightroom catalogue, and will be refining, tweaking and uploading pictures to Flickr. I am to complete the rest of my pictures by late January. Hopefully, by this time, the roads will be sufficiently clear of snow that my girlfriend will be happy to travel up to the Peak District for our first hill walk of the year.


cqparts_fasteners.solidtypes.threads package¶
Submodules¶
cqparts_fasteners.solidtypes.threads.ball_screw module¶
-
class
cqparts_fasteners.solidtypes.threads.ball_screw.
BallScrewThread
(*args, **kwargs)¶ Bases:
cqparts_fasteners.solidtypes.threads.base.Thread
ParametricObject
constructor parameters:Parameters: - ball_radius (
float
) – ball’s radius - diameter (
float
) – thread’s diameter - inner (
bool
) – if True, thread is to be cut from a solid to form an inner thread - lefthand (
bool
) – if True, thread is spun in the opposite direction - length (
float
) – thread’s length - min_vertices (
int
or list(int
)) – minimum vertices used cross-section’s wire - pilothole_radius (
float
) – explicitly set pilothole radius, overridespilothole_ratio
- pilothole_ratio (
float
) – sets thread’s pilot hole using inner and outer thread radii: \(radius = inner + ratio \times (outer-inner)\) - pitch (
float
) – thread’s pitch - start_count (
int
) – number of thread starts
Search Criteria:
This object can be found with
find()
andsearch()
using the following search criteria.Key Value module
cqparts_fasteners.solidtypes.threads.base
name
ball_screw
-
build_profile
()¶
-
get_radii
()¶
- ball_radius (
cqparts_fasteners.solidtypes.threads.base module¶
-
class
cqparts_fasteners.solidtypes.threads.base.
MinVerticiesParam
(default=None, doc=None)¶ Bases:
cqparts.params.parameter.Parameter
-
type
(value)¶
-
-
class
cqparts_fasteners.solidtypes.threads.base.
Thread
(*args, **kwargs)¶ Bases:
cqparts.part.Part
ParametricObject
constructor parameters:Parameters: - diameter (
float
) – thread’s diameter - inner (
bool
) – if True, thread is to be cut from a solid to form an inner thread - lefthand (
bool
) – if True, thread is spun in the opposite direction - length (
float
) – thread’s length - min_vertices (
int
or list(int
)) – minimum vertices used cross-section’s wire - pilothole_radius (
float
) – explicitly set pilothole radius, overridespilothole_ratio
- pilothole_ratio (
float
) – sets thread’s pilot hole using inner and outer thread radii: \(radius = inner + ratio \times (outer-inner)\) - pitch (
float
) – thread’s pitch - start_count (
int
) – number of thread starts
-
build_profile
()¶ Build the thread’s profile in a cadquery.Workplace as a wire on the \(XZ\) plane.
It will be used as an input to
profile_to_cross_section
Note
This function must be overridden by the inheriting class in order to construct a thread.
Without overriding, this function rases a
NotImplementedError
exception.example implementation:
import cadquery from cqparts.solidtypes.threads.base import Thread class MyThread(Thread): def build_profile(self): points = [ (2, 0), (3, 0.5), (3, 1), (2, 1.5), (2, 2) ] profile = cadquery.Workplane("XZ") \ .moveTo(*points[0]).polyline(points[1:]) return profile.wire() # .wire() is mandatory
Returns: thread profile as a wire on the XZ plane Return type: cadquery.Workplane
Warning
Wire must be built on the \(XZ\) plane (as shown in the example). If it is not, the thread may not be generated correctly.
-
get_radii
()¶ Get the inner and outer radii of the thread.
Returns: (<inner radius>, <outer radius>) Return type: tuple
Note
Ideally this method is overridden in inheriting classes to mathematically determine the radii.
Default action is to generate the profile, then use the bounding box to determine min & max radii. However this method is prone to small numeric error.
-
make
()¶
-
make_pilothole_cutter
()¶ Make a solid to subtract from an interfacing solid to bore a pilot-hole.
-
make_simple
()¶ Return a cylinder with the thread’s average radius & length.
\(radius = (inner_radius + outer_radius) / 2\)
-
profile
¶ Buffered result of
build_profile()
- diameter (
-
cqparts_fasteners.solidtypes.threads.base.
helical_path
(pitch, length, radius, angle=0, lefthand=False)¶
-
cqparts_fasteners.solidtypes.threads.base.
profile_to_cross_section
(profile, lefthand=False, start_count=1, min_vertices=20)¶ Converts a thread profile to it’s equivalent cross-section.
Profile:
The thread profile contains a single wire along the XZ plane (note: wire will be projected onto the XZ plane; Y-coords will be ignored). The profile is expected to be of 1 thread rotation, so it’s height (along the Z-axis) is the thread’s “pitch”. If start_count > 1, then the profile will effectively be duplicated. The resulting cross-section is designed to be swept along a helical path with a pitch of the thread’s “lead” (which is {the height of the given profile} * start_count)
Method:
Each edge of the profile is converted to a bezier spline, aproximating its polar plot equivalent.
Resolution: (via min_vertices parameter)
Increasing the number of vertices used to define the bezier will increase the resulting thread’s accuracy, but cost more to render.
min_vertices may also be expressed as a list to set the number of vertices to set for each wire. where: len(min_vertices) == number of edges in profile
Example
import cadquery from cqparts_fasteners.solidtypes.threads.base import profile_to_cross_section from Helpers import show profile = cadquery.Workplane("XZ") \ .moveTo(1, 0) \ .lineTo(2, 1).lineTo(1, 2) \ .wire() cross_section = profile_to_cross_section(profile) show(profile) show(cross_section)
Will result in:
Parameters: - profile (
cadquery.Workplane
) – workplane containing wire of thread profile. - lefthand (
bool
) – if True, cross-section is made backwards. - start_count (
int
) – profile is duplicated this many times. - min_vertices (
int
ortuple
) – int or tuple of the desired resolution.
Returns: workplane with a face ready to be swept into a thread.
Return type: Raises: - TypeError – if a problem is found with the given parameters.
- ValueError – if
min_vertices
is a list with elements not equal to the numbmer of wire edges.
- profile (
cqparts_fasteners.solidtypes.threads.iso68 module¶
-
class
cqparts_fasteners.solidtypes.threads.iso68.
ISO68Thread
(*args, **kwargs)¶ Bases:
cqparts_fasteners.solidtypes.threads.base.Thread
ParametricObject
constructor parameters:Parameters: - diameter (
float
) – thread’s diameter - inner (
bool
) – if True, thread is to be cut from a solid to form an inner thread - lefthand (
bool
) – if True, thread is spun in the opposite direction - length (
float
) – thread’s length - min_vertices (
int
or list(int
)) – minimum vertices used cross-section’s wire - pilothole_radius (
float
) – explicitly set pilothole radius, overridespilothole_ratio
- pilothole_ratio (
float
) – sets thread’s pilot hole using inner and outer thread radii: \(radius = inner + ratio \times (outer-inner)\) - pitch (
float
) – thread’s pitch - rounding_ratio (
float
) – [no description] - start_count (
int
) – number of thread starts
Search Criteria:
This object can be found with
find()
andsearch()
using the following search criteria.Key Value module
cqparts_fasteners.solidtypes.threads.base
name
iso68
-
build_profile
()¶ Build a thread profile in specified by ISO 68
-
get_radii
()¶
- diameter (
cqparts_fasteners.solidtypes.threads.triangular module¶
-
class
cqparts_fasteners.solidtypes.threads.triangular.
TriangularThread
(*args, **kwargs)¶ Bases:
cqparts_fasteners.solidtypes.threads.base.Thread
ParametricObject
constructor parameters:Parameters: - angle (
float
) – pressure angle of thread - diameter (
float
) – thread’s diameter - diameter_core (
float
) – diamter of core - inner (
bool
) – if True, thread is to be cut from a solid to form an inner thread - lefthand (
bool
) – if True, thread is spun in the opposite direction - length (
float
) – thread’s length - min_vertices (
int
or list(int
)) – minimum vertices used cross-section’s wire - pilothole_radius (
float
) – explicitly set pilothole radius, overridespilothole_ratio
- pilothole_ratio (
float
) – sets thread’s pilot hole using inner and outer thread radii: \(radius = inner + ratio \times (outer-inner)\) - pitch (
float
) – thread’s pitch - start_count (
int
) – number of thread starts
Search Criteria:
This object can be found with
find()
andsearch()
using the following search criteria.Key Value module
cqparts_fasteners.solidtypes.threads.base
name
triangular
-
build_profile
()¶
-
get_radii
()¶
-
initialize_parameters
()¶
- angle (
Module contents¶
-
class
cqparts_fasteners.solidtypes.threads.
Thread
(*args, **kwargs)¶ Bases:
cqparts.part.Part
ParametricObject
constructor parameters:Parameters: - diameter (
float
) – thread’s diameter - inner (
bool
) – if True, thread is to be cut from a solid to form an inner thread - lefthand (
bool
) – if True, thread is spun in the opposite direction - length (
float
) – thread’s length - min_vertices (
int
or list(int
)) – minimum vertices used cross-section’s wire - pilothole_radius (
float
) – explicitly set pilothole radius, overridespilothole_ratio
- pilothole_ratio (
float
) – sets thread’s pilot hole using inner and outer thread radii: \(radius = inner + ratio \times (outer-inner)\) - pitch (
float
) – thread’s pitch - start_count (
int
) – number of thread starts
-
build_profile
()¶ Build the thread’s profile in a cadquery.Workplace as a wire on the \(XZ\) plane.
It will be used as an input to
profile_to_cross_section
Note
This function must be overridden by the inheriting class in order to construct a thread.
Without overriding, this function rases a
NotImplementedError
exception.example implementation:
import cadquery from cqparts.solidtypes.threads.base import Thread class MyThread(Thread): def build_profile(self): points = [ (2, 0), (3, 0.5), (3, 1), (2, 1.5), (2, 2) ] profile = cadquery.Workplane("XZ") \ .moveTo(*points[0]).polyline(points[1:]) return profile.wire() # .wire() is mandatory
Returns: thread profile as a wire on the XZ plane Return type: cadquery.Workplane
Warning
Wire must be built on the \(XZ\) plane (as shown in the example). If it is not, the thread may not be generated correctly.
-
get_radii
()¶ Get the inner and outer radii of the thread.
Returns: (<inner radius>, <outer radius>) Return type: tuple
Note
Ideally this method is overridden in inheriting classes to mathematically determine the radii.
Default action is to generate the profile, then use the bounding box to determine min & max radii. However this method is prone to small numeric error.
-
make
()¶
-
make_pilothole_cutter
()¶ Make a solid to subtract from an interfacing solid to bore a pilot-hole.
-
make_simple
()¶ Return a cylinder with the thread’s average radius & length.
\(radius = (inner_radius + outer_radius) / 2\)
-
profile
¶ Buffered result of
build_profile()
- diameter (
-
cqparts_fasteners.solidtypes.threads.
register
(*args, **kwargs)¶
-
cqparts_fasteners.solidtypes.threads.
find
(*args, **kwargs)¶
-
cqparts_fasteners.solidtypes.threads.
search
(*args, **kwargs)¶


Asher pauses in his constant gnawing on the bumble bee http://picplz.com/Cx4d
9/11/2011, 9:48:29 AM
Favs: 0
Retweets: 0
link--- layout: tweet ---
Asher pauses in his constant gnawing on the bumble bee http://picplz.com/Cx4d
9/11/2011, 9:48:29 AM
Favs: 0
Retweets: 0
link


This content is slightly modified from Carreon and Associates. See Chapter One for information about credit reporting agencies.
- Request for investigation of credit report
- Dispute letter to credit bureau
- “Intent to sue” letter to credit bureau
- Reply to a CRA accusing you of credit repair
Send your letters to the address of the appropriate agency:
Experian
P.O. Box 9556
Allen, TX 75013
Equifax
P.O. Box 740241
Atlanta, GA 30374-0241
TransUnion Consumer Relations
P.O. Box 2000
Chester, PA 19022-2000
1. Request for Investigation of Credit Report
[Your Name]
[Your Address]
[Experian, Equifax or TransUnion address]
[Date]
Attn.: Consumer Relations
Consumer Relations Dept.:
I am requesting with this written notice that the following inaccurate items be removed from my credit report. The items are not correct and are causing me financial distress because of their derogatory information. The items are as follows:
- [Creditor]
- [Account number]
- [Rating (e.g., curr was 30, charge off, 90 days, etc.)]
- [Reason why it should be removed: i.e., not mine, never late, disputed before yet still remains, incorrect information like payment history, date opened or balance owed.]
I understand you are required to notify me of your investigation results within 30 days.
My contact information is as follows:
[Your name, not signed]
DOB: [Date of birth]
SSN: [Social Security number]
[Address]
Sincerely,
[Your name, signed]
2. Dispute Letter to Credit Bureau
Provide any proof you have with this dispute.
[Your Name]
[Your Address]
[Experian, Equifax or TransUnion address]
[Date]
To whom it may concern:
In reviewing my credit report, I realized there are several inaccurate listings. These accounts are incorrect and several are outdated. The following accounts need to be investigated immediately to reflect my true credit history:
Acct: [-xxxx-xxx:]
This account is listed as being 60 days late. I have never been late on this account.
Acct: [-xxxx-xxx:]
This account is listed as being 30 days late. This account does not belong to me.
Acct: [-xxxx-xxx:]
This account is listed as being 60 days late. The creditor lost my check and agreed to correct the late notation. (Enclosed is a copy of their letter stating such).
Additionally, you are reporting several other accounts as delinquent that are past the seven-year reporting time as allowed under the Fair Credit Reporting Act. The following accounts should be deleted immediately:
Acct: [-xxx:] over seven years old
Acct: [-xxx:] over eight years old
Acct: [-xxx:] over seven years and four months old
Please forward an updated copy to me at your earliest convenience with the above noted corrections. My current address is listed above.
Sincerely,
[Your signature]
[Your printed name]
3. “Intent to Sue” Letter to Credit Bureau
Send this letter to the CRA if you intend on suing them. You can sue them in your county for damages and subsequently send a copy of that to them, offering to settle or appear. If you sue them, be sure you have a case.
[Your Name]
[Your Address]
[Experian, Equifax or TransUnion address]
[Date]
To Whom It May Concern:
REF: Intent to file suit: violation of the FCRA
I have sent [#] previous letters to you, all by certified mail (copies of receipts enclosed) requesting that you remove inaccurate information from my file and you have failed to do so.
Accordingly, I can show a judge that these accounts are inaccurate and that you violated the Fair Credit Reporting Act by ignoring my requests to investigate the items. My previous letters—all sent certified mail—stated my reasons for an investigation and these reasons were not frivolous in any way.
If this final request does not prompt you to conduct a proper investigation of the accounts in question, and send proof to me of said investigation, I will file a civil suit in [name of your county, state] for damages. I take my credit very seriously and your lack of professionalism and assistance is unacceptable. I am well aware of my rights under the FCRA and intend to pursue them to the maximum.
I anticipate your response.
Sincerely,
[Your signature]
[Your printed name]
4. Reply to a CRA Accusing You of Credit Repair
Use this letter to demand that a credit bureau continue to investigate items you have initiated a dispute on. Often a CRA will accuse you of using a credit repair company, which by the way is your right! Here is a letter to put them in their place and to avoid slowing your disputes.
[Your name]
[Your address]
[Experian, Equifax or TransUnion address]
[Date]
To whom it may concern:
RE: Credit Repair Accusation
Please be advised that I have received your computer generated letter stating that you have ceased investigation of my credit reports because, in your opinion, you believe that I have used a third party credit repair agency. Not only do I believe this to be a stall tactic on your part to grant you an additional 30 days to comply with my original request, but I believe it to be a blatant violation of the FCRA.
You were advised by me on [insert date] by certified mail (copy enclosed) that I questioned the accuracy of a few items on my credit reports. That request was written by me and mailed by me—not a third party agency. It appears obvious to me that you are abusing your power under the FCRA to escape a complete investigation.
Additionally there is no law that states a consumer cannot use a third party, so using that as your excuse is a moot point. As a matter of fact, Congress has found the whole process so overwhelming that they afford consumers the right to use a third party on their behalf if the consumer so chooses. This is why your statement is so outrageous.
I reserve the right to sue your credit bureau for violations of the FCRA and I believe I can prove that you did not use reasonable measures to insure the accuracy of my credit reports and now you are stalling the process further.
Please take notice that this letter dated [insert today’s date] is formal notice to you that I am requesting that you continue forward with my original investigation request and send the results to me within 15 days. I therefore legally and lawfully refuse your “form letter,” thus giving you only 15 days, not 30 more.
I am outraged at your accusation and I have fully researched my rights in regards to my credit file. I look forward to your expediting my original request immediately.
Sincerely,
[Your signature]
[Your printed name]


You lead a crew on a commercial fishing vessel. You are required to submit a logbook of your trip activities to a regulatory group and want that effort to be as accurate, easy and quick as possible. You also want to keep a close eye on state-wide fishing quotas and track your location and catch information for planning purposes.
Beyond this Demo
Enjoy the day at sea!


THE NUTCRACKER A film review by James Berardinelli Copyright 1993 James Berardinelli
Rating (Linear 0 to 10): 6.1
Date Released: 11/24/93 Running Length: 1:33 Rated: G (Nothing offensive)
Starring : Jessica Lynn Cohen, Macaulay Culkin, Darci Kistler, Gen Horiuchi, Wendy Whelan Narrated by Kevin Kline Choreography: George Balanchine Director: Emile Ardolino Producers: Robert A. Krasnow and Robert Hurwitz Staging: Peter Martins Music: Peter Tchaikovsky Released by Warner Brothers
On stage, THE NUTCRACKER is marvelous entertainment, despite the $30-plus price of a ticket. As a movie, a great deal of the exuberance is washed away, leaving behind a pale shell of the live production. Those seeing the story in person experience a spectacle; those who settle for the inside of a darkened theater get culture.
For those not familiar with the story, THE NUTCRACKER presents the magical, dreamlike Christmas Eve of a little girl named Marie (Jessica Lynn Cohen), who is escorted through various wondrous realms by a prince (Macaulay Culkin), culminating in a grand gala in the Hall of the Sugarplum Fairy (Darci Kistler). There is a great deal of dancing, some of it spectacular, with everything unfolding to the strains of Tchaikovsky's unparalleled score.
The main problem with the movie is that it opts to present a relatively mundane version of the stage production. Utilizing almost none of the advantages offered by the medium of film, THE NUTCRACKER stumbles where it should soar. There isn't any way for a motion picture to effectively capture the feeling of watching a performance live, so this presentation should have given its audience something that can't be experienced during the stage show. It doesn't, and therein lies THE NUTCRACKER's chief flaw.
The pacing is uneven, with the first half of the film moving like molasses. During this section, there's a lot of on-stage posturing, but little dancing. The change for the better comes when the setting shifts to the Hall of the Sugarplum Fairy. Even given the confines of the screen, THE NUTCRACKER bursts to life at this point.
With a G-rating, THE NUTCRACKER is suitable for viewers of all ages, but I wonder how many of the younger members of the audience will get restless during what is, in essence, ninety minutes of classical music and ballet. I know that at age seven, I would have. "Know thy children" is the best maxim to apply; those with short attention spans will probably start to fidget before the half-way point.
Director Emile Ardolino (DIRTY DANCING, SISTER ACT) leaves THE NUTCRACKER as his final motion picture--he died of AIDS on Saturday, November 20 at the age of 50. Unfortunately, this is far from his best work. The direction is rarely inspired, and there are numerous occasions when the camerawork lets the audience down by choosing an ill-timed closeup instead of a wide-angled shot. Ardolino's attempts to breathe life into his screen version are too-often unsuccessful.
The cast, mostly comprised of members of the New York City Ballet, is excellent ... with one exception: Macaulay Culkin. Included more for his name than anything else, Culkin is barely adequate, and the annoying smirk he wears makes his presence progressively less bearable. Jessica Lynn Cohen, on the other hand, gives a tremendously appealing portrayal of Marie.
The saving grace of this screen adaptation of THE NUTCRACKER is that the source material is so good that it can survive even a flat interpretation. The unfortunate thing is that with a little more verve and imagination, this NUTCRACKER could have become one of the all-time classic holiday films. Instead, it's just another motion picture opening on Thanksgiving weekend 1993.
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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Related links: index of all rec.arts.movies.reviews reviews


--- layout: post title: Real time video manipulation date: 2017-02-01 12:10:37.000000000 +00:00 type: post parent_id: '0' published: true password: '' status: publish categories: [] tags: [] meta: _rest_api_published: '1' _rest_api_client_id: "-1" _publicize_job_id: '1385414547' author: login: octagonalsquid email<EMAIL_ADDRESS>display_name: octagonalsquid first_name: '' last_name: '' ---
Spooky. Adobe VoCo may do something similar for audio.


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Researcher of Computer Vision and Machine Learning.
Leader of a computer vision research team, working on visual/IMU-aided SLAM, stereo vision and deep learning for UAV.
Manager of the vision perception department.
Computer vision research and prototype system development for PC and mobile platform, focusing on 3D reconstruction and modeling from multi-view images and stereo vision related applications.
Working on visual tracking for intelligent video surveillance, scene understanding of large scale street view with images and lidar point cloud.
Joint Segmentation of Images and Scanned Point Cloud in Large-Scale Street Scenes with Low Annotation Cost, IEEE Transaction on Image Processing, vol 23, no.11.
Real-Time Object Tracking with Generalized Part-Based Appearance Model and Structure-Constrained Motion Model, IEEE International Conference on Pattern Recognition 2014 (ICPR 2014, Oral presentation).
Learning CRFs for Image Parsing with Adaptive Subgradient Descent, IEEE International Conference on Computer Vision (ICCV) 2013.
Image-Based Modeling of Unwrappable Facades, IEEE Transactions on Visualization and Computer Graphics (TVCG) 2013.
Per-Pixel Translational Symmetry Detection, Optimization, and Segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2012.
Partial Similarity based Nonparametric Scene Parsing in Certain Environment, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2011.
Supervised Label Transfer for Semantic Segmentation of Street Scenes, European Conference on Computer Vision (ECCV) 2010.
Rectilinear Parsing of Architecture in Urban Environment, IEEE Conference on Computer Vision and Pattern Recognition (CVPR Oral presentation) 2010.


---
layout: null
section-type: about
title: About
---
## About Me
Hi! Welcome to my personal website. My name is Fernán.
Please, feel free to explore.
### Who am I?.
I am a Software Developer looking for new challenges.
My strong academic background and experience allows me to deliver creative solutions to complex problems.
I enjoy travelling, meeting new people and learning about different cultures.
I'm Argentinian. Born in Córdoba, raised in Rio IV.
### What I do for a living
I'm currently a Technical Leader at Globant.
I'm also doing freelance software development.
I love the art of problem solving. Reason why I am a Software Engineering PhD student at University of Buenos Aires
where I was also a teaching assistant.
My interests fall in multiple areas, such as different validation and verification tasks when developing/using complex APIs,
privacy and security, testing and model checking.
This includes among others analysis and manipulation of programs, using/developing testing tools and using solvers.
I like to participate in all software stages such as design, development and testing the application.
I believe in the power of "standing on the shoulder of giants". Instead of reinventing the wheel I prefer reading,
learning and taking courses to adapt myself to new technologies.
I enjoy teamwork and can adapt easily.
### What I do for having fun
I really enjoy spending time with my girlfriend, family and friends.
I like travelling.
I love eating a good asado.
Besides my academic background, I also enjoy learning on my own through practice and online resources.


addObjectToPersist
Register an object to be persisted in the next PDB sync.
close
Close the persistence database.
collectGarbage
Remove from PDB all objects not accessible via the root object.
newId
Generate a new PDB id number for use as a persistent object's ppid.
objectAtPpid
Return the object associated in the database with a ppid.
reopen
Sync, close and reopen the PDB store.
root
Return the root PMap object used to store and retrieve persistent objects and their slots.
show
Print to standard output a listing of all objects and IDs stored in PDB.
sync
Immediately persist data for all objects marked dirty by Collector whose
shouldPersistByDefault is true, or that have specifically requested to be
persisted since the last sync via addObjectToPersist.
|


GREASE
April 27-May 6, 1984
Director - Linda Nash
Assistant to the Director - Wess Hughes
Choreographer - Mary Ann Peterson
THE CAST
Miss Lynch - Jane Cooper
Patty Simcox - Read Diket
Eugene - Robbie Everitt
Blanche Dingle - Darlene Purcell
Jan - Cheryl Lyon
Marty - Dana Hall
Betty Rizzo - Anita Boyd
Doody - Richard Read
Roger - Brad Johnson, Wess Hughes
Kenickie - Paul Wade
Sonny - Craig Stevens
Frenchie - Tina Craven
Sandy Dumbrowski - Jana Kay Mills
Danny Zuko - Gerry Wyatt
Vince Fontaine - Darryl MacDonald
Johnny Casino - Mike Perry
Cha Cha DiGregorio - Lelia Duncan
Teen Angel - Wess Hughes
The Hand Jive Dancers
Cherie Boone, Curtis Boykin, Tim Nichols, Charlotte Pitts, Lisa Thames,
Mark Valentine, Dana Welch
The Beauty School Drop-Outs
Mary Alexander, Mendie Baldwin, Donna Bisnette, Cherie Boone,
Jamie Crabtree, Mary Jane Grantham, Ellen Hill, Cherl Mathews,
Darlene Purcell, Congetta Ward


“For my part, I travel not to go anywhere, but to go. I travel for travel’s sake. The great affair is to move.” – Robert Louis Stevenson
Educational
Wildlife
Musical
Art
Volunteering
Photography
Fossil Huntng
Trekking
Star Gazingl
Farming
Birding
Camping
Slum
City and Heritage
Horror


<canvas> elements named canvasInput and canvasOutput have been prepared. Click Try it button to see the result. You can choose another image. You can change the code in the <textarea> to investigate more.


---
title: "Clone: Warcraft II"
excerpt: "
"
collection: portfolio
---
- This is a cloning version of Warcraft II
- Porting linux version to IOS version
- Contribution:
- Rendering Map
- Unit Action - Walking/Attacking/Mining/Stopping etc.
- Implemented Team Color
- Panning the Map


-
Dread Pirate Sunk By Leaky CAPTCHA — Krebs on Security
krebsonsecurity.com
Ever since October 2013, when the FBI took down the online black market and drug bazaar known as the Silk Road , privacy activists and security ...
-
Home Depot: 56M Cards Impacted, Malware Contained — Krebs on Security
krebsonsecurity.com
Home Depot said today that cyber criminals armed with custom-built malware stole an estimated 56 million debit and credit card numbers from its ...
-
Data: Nearly All U.S. Home Depot Stores Hit — Krebs on Security
krebsonsecurity.com
New data gathered from the cybercrime underground suggests that the apparent credit and debit card breach at Home Depot involves nearly all of the ...
-
Signature Systems Breach Expands — Krebs on Security
krebsonsecurity.com
Signature Systems Inc. , the point-of-sale vendor blamed for a credit and debit card breach involving some 216 Jimmy John’s sandwich shop locations, ...
-
$1.66M in Limbo After FBI Seizes Funds from Cyberheist — Krebs on Security
krebsonsecurity.com
A Texas bank that’s suing a customer to recover $1.66 million spirited out of the country in a 2012 cyberheist says it now believes the missing funds ...
-
Who’s Behind the Bogus $49.95 Charges? — Krebs on Security
krebsonsecurity.com
Hardly a week goes by when I don’t hear from a reader wondering about the origins of a bogus credit card charge for $49.95 or some similar amount for ...
-
In Home Depot Breach, Investigation Focuses on Self-Checkout Lanes — Krebs ...
krebsonsecurity.com
The malicious software that unknown thieves used to steal credit and debit card numbers in the data breach at Home Depot this year was installed ...
-
Medical Records For Sale in Underground Stolen From Texas Life Insurance ...
krebsonsecurity.com
How much are your medical records worth in the cybercrime underground? This week, KrebsOnSecurity discovered medical records being sold in bulk for ...
-
Breach at Goodwill Vendor Lasted 18 Months — Krebs on Security
krebsonsecurity.com
C&K Systems Inc. , a third-party payment vendor blamed for a credit and debit card breach at more than 330 Goodwill locations nationwide, disclosed ...
-
LinkedIn Feature Exposes Email Addresses — Krebs on Security
krebsonsecurity.com
One of the risks of using social media networks is having information you intend to share with only a handful of friends be made available to ...
-
Critical Fixes for Adobe, Microsoft Software — Krebs on Security
krebsonsecurity.com
Adobe today released updates to fix at least a dozen critical security problems in its Flash Player and AIR software. Separately, Microsoft pushed ...
-
In Wake of Confirmed Breach at Home Depot, Banks See Spike in PIN Debit ...
krebsonsecurity.com
Nearly a week after this blog first reported signs that Home Depot was battling a major security incident, the company has acknowledged that it ...
-
Home Depot Hit By Same Malware as Target — Krebs on Security
krebsonsecurity.com
The apparent credit and debit card breach uncovered last week at Home Depot was aided in part by a new variant of the malicious software program that ...
-
Fun With Funny Money — Krebs on Security
krebsonsecurity.com
Readers or “fans” of this blog have sent some pretty crazy stuff to my front door over the past few years, including a gram of heroin , a giant bag ...
-
DQ Breach? HQ Says No, But Would it Know? — Krebs on Security
krebsonsecurity.com
Sources in the financial industry say they’re seeing signs that Dairy Queen may be the latest retail chain to be victimized by cybercrooks bent on ...
-
Stealthy, Razor Thin ATM Insert Skimmers — Krebs on Security
krebsonsecurity.com
An increasing number of ATM skimmers targeting banks and consumers appear to be of the razor-thin insert variety. These card-skimming devices are ...
-
Counterfeit U.S. Cash Floods Crime Forums — Krebs on Security
krebsonsecurity.com
One can find almost anything for sale online, particularly in some of the darker corners of the Web and on the myriad cybercrime forums. These sites ...
-
Lorem Ipsum: Of Good & Evil, Google & China — Krebs on Security
krebsonsecurity.com
Imagine discovering a secret language spoken only online by a knowledgeable and learned few. Over a period of weeks, as you begin to tease out the ...
-
Kevin Mitnick, Once the World's Most Wanted Hacker, Is Now Selling ...
www.wired.com
As a young man, Kevin Mitnick became the world’s most notorious black hat hacker, breaking into the networks of companies like IBM, Nokia, Motorola, ...
-
United Parcel Service Confirms Security Breach
bits.blogs.nytimes.com
The hacking is the latest in a string of incidents at major retailers, including Target, Neiman Marcus and Supervalu.
-
Shellshock DHCP RCE Proof of Concept -
www.trustedsec.com
DHCP bash shellshock POC: 1) Just about any DHCP string value should work for the exploit. Value 114 is URL, which is a string and should be reliable ...
-
Ok, shits real. Its in the wild... src:<IP_ADDRESS>
gist.github.com
You can clone with HTTPS or SSH .
-
My baby is 1 year old, and I couldn’t be prouder
bobsullivan.net
Happy birthday! I'm one year old. Well, this site is one year old. After almost 20 years working in the NBC/MSNBC universe, I struck out on my own ...
-
Thinking like a bad guy for better enterprise security
h30499.www3.hp.com
Last week I was able to attend HP Protect in Washington D.C. The event is HP's yearly premier event for security professionals. This year's theme ...
-
Retailers' Customers Cautioned As Cyber Attacks Continue
www.npr.org
Home Depot says some 56 million card holders were possibly compromised in a cyber attack. It says there's no evidence that debit PIN numbers were ...
-
Spam Nation: The Inside Story of Organized Cybercrime-from Global Epidemic ...
www.barnesandnoble.com
Available in: NOOK Book (eBook), Hardcover, Audiobook. There is a Threat Lurking Online with the Power to Destroy Your Finances, Steal Your Personal ...
-
Why Far-Flung Parts of the Internet Broke Today - Dyn Research
research.dyn.com
VolumeDrive is a Pennsylvania-based hosting company that uses Cogent and (since late May of this year) Atrato for Internet transit. A routing leak ...
-
fubarnorthwest: You Can Order Pre-order Kreb's Spam Nation Now
fubarnorthwest.blogspot.com
Greg Metcalfe. Deeply yet insufficiently paranoid security guy.
-
Criminal Card Games
www.nytimes.com
In the wake of the recent Home Depot breach, you have to wonder if data theft has become a condition of modern life.


WikiLeaks<EMAIL_ADDRESS>leaked emails, 2009 mail archive
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and it should be discussed here.
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Overview
Pages to Update
200
Pages analyzedBriefs
200
Briefs analyzedKeywords
4.5K
Keywords analyzedCompetitors
50
Competitors analyzedPages to Update
| URL | Status | Creation date |
|---|---|---|
| marketing digital | Analyzing | April 11, 2019 |
| marketing de conteudo rock content | Ready | April 11, 2019 |
| marketing de conteudo | Analyzing | April 11, 2019 |
| marketing digital | Ready | April 11, 2019 |
| marketing de conteudo rock content | Analyzing | April 11, 2019 |
| marketing de conteudo | Ready | April 11, 2019 |
| marketing digital | Analyzing | April 11, 2019 |
| marketing de conteudo rock content | Ready | April 11, 2019 |
| marketing de conteudo | Analyzing | April 11, 2019 |
| marketing digital | Ready | April 11, 2019 |
| marketing de conteudo rock content | Analyzing | April 11, 2019 |
| marketing de conteudo | Ready | April 11, 2019 |
| marketing de conteudo | Analyzing | April 11, 2019 |


Import TuiFilterPipeModule into a module where you want to use our component
TuiFilterPipeModule
Use pipe in template with function and its arguments:


8 Introduction to modeling
About 40-50% of the exam grade is based on modeling. The goal is to be able to predict an unknown quantity. In actuarial applications, this tends to claim that occur in the future, death, injury, accidents, policy lapse, hurricanes, or some other insurable event.
The next few chapters will cover the following learning objectives.
8.1 Modeling vocabulary
Modeling notation is sloppy because many words mean the same thing.
The number of observations will be denoted by \(n\). When we refer to the size of a data set, we are referring to \(n\). Each row of the data is called an observation or record. Observations tend to be people, cars, buildings, or other insurable things. These are always independent in that they do not influence one another. Because the Prometric computers have limited power, \(n\) tends to be less than 100,000.
Each observation has known attributes called variables, features, or predictors. We use \(p\) to refer the number of input variables that are used in the model.
The target, response, label, dependent variable, or outcome variable is the unknown quantity that is being predicted. We use \(Y\) for this. This can be either a whole number, in which case we are performing regression, or a category, in which case we perform classification.
For example, say that you are a health insurance company that wants to set the premiums for a group of people. The premiums for people who are likely to incur high health costs need to be higher than those likely to be low-cost.
Older people tend to use more of their health benefits than younger people, but there are always exceptions for those who are very physically active and healthy. Those who have an unhealthy Body Mass Index (BMI) tend to have higher costs than those who have a healthy BMI, but this has less impact on younger people.
In short, we want to predict the future health costs of a person by taking into account many of their attributes at once.
This can be done in the health_insurance
data by fitting a model to predict the annual health costs of a person. The target variable is y = charges
, and the predictor variables are age
, sex
, bmi
, children
, smoker
and region
. These six variables mean that \(p = 6\). The data is collected from 1,338 patients, which means that \(n = 1,338\).
8.2 Modeling notation
Scalar numbers are denoted by ordinary variables (i.e., \(x = 2\), \(z = 4\)), and vectors are denoted by bold-faced letters
\[\mathbf{a} = \begin{pmatrix} a_1 \\ a_2 \\ a_3 \end{pmatrix}\]
We organize these variables into matrices. Take an example with \(p\) = 2 columns and 3 observations. The matrix is said to be \(3 \times 2\) (read as “3-by-2”) matrix.
\[ \mathbf{X} = \begin{pmatrix}x_{11} & x_{21}\\ x_{21} & x_{22}\\ x_{31} & x_{32} \end{pmatrix} \]
In the health care costs example, \(y_1\) would be the costs of the first patient, \(y_2\) the costs of the second patient, and so forth. The variables \(x_{11}\) and \(x_{12}\) might represent the first patient’s age and sex respectively, where \(x_{i1}\) is the patient’s age, and \(x_{i2} = 1\) if the ith patient is male and 0 if female.
Modeling is about using \(X\) to predict \(Y\). We call this “y-hat,” or simply the prediction. This is based on a function of the data \(X\).
\[\hat{Y} = f(X)\]
This is almost never going to happen perfectly, and so there is always an error term, \(\epsilon\). This can be made smaller, but is never exactly zero.
\[ \hat{Y} + \epsilon = f(X) + \epsilon \]
In other words, \(\epsilon = y - \hat{y}\). We call this the residual. When we predict the health care costs of a person, this is the difference between the predicted costs (which we had created the year before) and the actual costs that the patient experienced (of that current year).
Another way of saying this is in terms of expected value: the model \(f(X)\) estimates the expected value of the target \(E[Y|X]\). That is, once we condition on the data \(X\), we can make a guess as to what we expect \(Y\) to be “close to.” We will see that there are many ways of measuring “closeness.”
8.3 Ordinary Least Squares (OLS)
Also known as simple linear regression, OLS predicts the target as a weighted sum of the variables.
We find a \(\mathbf{\beta}\) so that
\[ \hat{Y} = E[Y] = \beta_0 + \beta_1 X_1 + \beta_2 X_2 + ... + \beta_p X_p \]
Each \(y_i\) is a linear combination of \(x_{i1}, ..., x_{ip}\), plus a constant \(\beta_0\) which is called the intercept term.
In the one-dimensional case, this creates a line connecting the observations. In higher dimensions, this creates a hyper-plane.
The red line shows the expected value of the target, as the target \(\hat{Y}\) is actually a random variable. For each observation, the model assumes a Gaussian distribution. If there is just a single predictor, \(x\), then the mean is \(\beta_0 + \beta_1 x\).
The question then is how can we choose the best values of \(\beta?\) First of all, we need to define what we mean by “best.” Ideally, we will choose these values which will create close predictions of \(Y\) on new, unseen data.
To solve for \(\mathbf{\beta}\), we first need to define a loss function. This would let us compare how well a model fits the data. The most commonly used loss function is the residual sum of squares (RSS), also called the squared error loss or the L2 norm. When RSS is small, then the predictions are close to the actual values, and the model is a good fit. When RSS is large, the model is a poor fit.
\[\text{RSS} = \sum_i(y_i - \hat{y})^2\]
When you replace \(\hat{y_i}\) in the above equation with \(\beta_0 + \beta_1 x_1 + ... + \beta_p x_p\), take the derivative with respect to \(\beta\), set equal to zero, and solve, we can find the optimal values. This turns the problem of statistics into a problem of numeric optimization, which computers can do quickly.
You will also see the term Root Mean Squared Error (RMSE) which is just the average of the square root of the \(\text{RSS}\), or just Mean Squared Error (MSE).
You might be wondering: why does this need to be the squared error? Why not the absolute error or the cubed error? Technically, these could be used, but the betas would not be the maximum likelihood parameters. Using the absolute error results in the model predicting the median as opposed to the mean. Two reasons behind the popularity of RSS are:
- It provides the same solution if we assume that the distribution of \(Y|X\) is Gaussian and maximize the likelihood function. This method is used for GLMs, in the next chapter.
- It is computationally easier, and computers used to have a difficult time optimizing for MAE.
What does it mean when a log transform is applied to \(Y\)? I remember from my statistics course on regression that this was done.
This is done so that the variance is closer to being constant. For example, if the units are in dollars, it is very common for the values to fluctuate more for higher values than for lower values. Other types of transformations can correct for skewness.
Consider a stock price, for instance. If the stock is $50 per share, it will go up or down less than $1000 per share. However, the log of 50 is about 3.9, and the log of 1000 is only 6.9, so this difference is smaller. In other words, the variance is smaller.
Transforming the target means that instead of the model predicting \(E[Y]\), it predicts \(E[log(Y)]\). A common mistake is to then the take the exponent in an attempt to “undo” this transform, but \(e^{E[log(Y)]}\) is not the same as \(E[Y]\).
8.4 R^2 Statistic
One of the most common ways of measuring model fit is the “R-Squared” statistic. The RSS provides an absolute measure of fit because it can be any positive value, but it is not always clear what a “good” RSS is because it is measured in units of \(Y\).
The \(R^2\) statistic provides an alternative measure of fit. It takes the proportion of variance explained - so that it is always a value between 0 and 1 and is independent of the scale of \(Y\).
\[R^2 = \frac{\text{TSS} - \text{RSS}}{\text{TSS}} = 1 - \frac{\text{RSS}}{\text{TSS}}\]
Where \(\text{TSS} = \sum(y_i - \hat{y})^2\) is the total sum of squares. TSS measures the total variance in the response YY and can be considered the amount of variability inherent in the response before the regression is performed. In contrast, RSS measures the amount of variability that is left unexplained after performing the regression.
Hench, \(\text{TSS} - \text{RSS}\) measures the amount of variability in the response that is explained (or removed) by performing the regression, and R^2 measures the proportion of variability in \(Y\) that can be explained using \(X\).
A value near 1 indicates that the regression has explained a large proportion of the variability in the response, whereas, a number near 0 indicates that the regression did not explain much of the variability in the response. This might occur because the linear model is wrong.
The \(R^2\) sstatistic has an interpretational advantage over the RSE. In actuarial applications, it is useful to use an absolute measure of model fit, such as RSS, to train the model, and then use \(R^2\) when explaining it to your clients so that it is easier to communicate.
This chapter was based on Chapter 3, Linear Regression, of An Introduction to Statistical Learning.
8.5 Correlation
Correlation does not imply causation.
This is a common saying. Just because two things are correlated does not necessarily mean that one causes the other. Just because most actuaries work remotely when there is cold and snowing does not mean that cold and snow cause anti-social, introverted work habits. A more likely explanation is that actuaries are concerned about driving safely on icy roads and avoiding being involved in a car accident.
8.5.1 Pearson’s correlation
Pearson correlation: Measures a linear dependence between two variables \(X\) and \(Y\). This is the most commonly used correlation method.
The correlation is defined by \(r\),
\[r = Cor(X,Y) = \frac{\sum(x_i - \bar{x})(y_i - \bar{y})}{\sqrt{\sum(x_i - \bar{x})^2}\sqrt{(y_i - \bar{y})^2}}\]
and this is also a measure of the linear relationship between two vectors, \(X\) and \(Y\). This suggests that we might be able to use \(r = Cor(X,Y)\) instead of \(R^2\) to assess the model fit. In the case of simple linear regression, where there is only one predictor variable, it is tree that \(R^2 = r^2\); however, this relationship does not extend automatically when there are more than one predictor variable. This is because \(X\) becomes a matrix instead of a single vector.
8.5.2 Spearman (rank) correlation
Spearman correlation: Computes the correlation between the rank of x and the rank of y variables.
\[rho = \frac{\sum(x' - m_{x'})(y'_i - m_{y'})}{\sqrt{\sum(x' - m_{x'})^2 \sum(y' - m_{y'})^2}}\] Where \(x′=rank(x)\) and \(y′=rank(y)\)
Most questions on Exam PA will ask you about Pearson’s correlation. One advantage to Spearman over Pearson is that Spearman works for ordinal variables. See Chapter 6 for the difference between ordinal and numeric variables.
8.6 Regression vs. classification
Regression modeling is when a target is a number. Binary classification is when there are two outcomes, such as “Yes/No,” “True/False,” or “0/1.” Multi-class regression is when there are more than two categories such as “Red, Yellow, Green” or “A, B, C, D, E.” There are many other types of regression that are not covered on this exam, such as ordinal regression, where the outcome is an ordered category, or time-series regression, where the data is time-dependent.
8.7 Regression metrics
For any model, the goal is always to reduce an error metric. This is a way of measuring how well the model can explain the target.
The phrases “reducing error,” “improving performance,” or “making a better fit” are synonymous with reducing the error. The word “better” means “lower error,” and “worse” means “higher error.”
The choice of error metric has a big difference in the outcome. When explaining a model to a businessperson, using simpler metrics such as R-Squared and Accuracy is convenient. When training the model, however, using a more nuanced metric is almost always better.
These are the regression metrics that are most likely to appear on Exam PA. Memorizing these formulas for AIC and BIC is unnecessary as they are in the R documentation by typing
?AIC
or ?BIC
into the R console.
Do not forget the most important metric: “usefulness!” A model with high predictive accuracy but does not meet the needs of the business problem has low usefulness. A model which is easy to explain to the PA exam graders has high usefulness.
“Some candidates did not consider both predictive power and applicability to the business problem and others gave justifications based on one of these but then chose a model based on the other.” - SOA PA 6/18/20, Task 12 Solution
“Which of the three models would you recommend for this analysis? Do not base your recommendation solely on the mean squared errors (RMSE) from each model.” - SOA PA 6/13/19, Task 9 Project Statement
8.7.1 Example: SOA PA 6/18/20, Task 4
Three points of Investigate correlations.
- Create a correlation coefficient matrix for all the numeric variables in the dataset.
- Among these pairwise correlations, determine which correlations concern you in building GLM and tree models. The response may differ by model.
- State a method other than principal components analysis (PCA) that can be used to handle the correlated variables. Do not implement this method.
8.8 Example: Health Costs
In our health insurance data, we can predict the health costs of a person based on their age, body mass index and gender. Intuitively, we expect that these costs would increase with the increase in the age of the person, be different for men than for women, and be higher for those who have a less healthy BMI. We create a linear model using bmi
, age
, and sex
as an inputs.
The formula
controls which variables are included. There are a few shortcuts for using R formulas.
| Formula | Meaning |
|---|---|
charges ~ bmi + age |
Use age and bmi to predict charges |
charges ~ bmi + age + bmi *age |
Use age ,bmi as well as an interaction to predict charges |
charges ~ (bmi > 20 ) + age |
Use an indicator variable for bmi > 20 age to predict charges |
log(charges ) ~ log(bmi ) + log(age ) |
Use the logs of age and bmi to predict log(charges ) |
charges ~ . |
Use all variables to predict charges |
While you can use formulas to create new variables, the exam questions tend to have you do this in the data itself. For example, if taking the log transform of a bmi
, you would add a column log_bmi
to the data and remove the original bmi
column.
Below we fit a simple linear model to predict charges.
library(ExamPAData)
library(tidyverse)
<- lm(data = health_insurance, formula = charges ~ bmi + age + sex) model
The summary
function gives details about the model. First, the Estimate
, gives you the coefficients. The Std. Error
is the error of the estimate for the coefficient. Higher standard error means greater uncertainty. This is relative to the average value of that variable. The p value
tells you how “big” this error really is based on standard deviations. A small p-value (Pr (>|t|))
) means that we can safely reject the null hypothesis that says the coefficient is equal to zero.
The little *
, **
, ***
tell you the significance level. A variable with a ***
means that the probability of getting a coefficient of that size given that the data was randomly generated is less than 0.001. The **
has a significance level of 0.01, and *
of 0.05.
summary(model)
##
## Call:
## lm(formula = charges ~ bmi + age + sex, data = health_insurance)
##
## Residuals:
## Min 1Q Median 3Q Max
## -14974 -7073 -5072 6953 47348
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6986.82 1761.04 -3.967 7.65e-05 ***
## bmi 327.54 51.37 6.377 2.49e-10 ***
## age 243.19 22.28 10.917 < 2e-16 ***
## sexmale 1344.46 622.66 2.159 0.031 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 11370 on 1334 degrees of freedom
## Multiple R-squared: 0.1203, Adjusted R-squared: 0.1183
## F-statistic: 60.78 on 3 and 1334 DF, p-value: < 2.2e-16
For this exam, variable selection tends to be based on the 0.05 significance level (single star *).
When evaluating model performance, you should not rely on the summary
alone, based on the training data. To look at performance, test the model on validation data. This can be done by either using a hold-out set or cross-validation, which is even better.
Let us create an 80% training set and 20% testing set. You do not need to worry about understanding this code as the exam will always give this to you.
set.seed(1)
library(caret)
#create a train/test split
<- createDataPartition(y = health_insurance$charges,
index p = 0.8, list = F) %>% as.numeric()
<- health_insurance %>% slice(index)
train <- health_insurance %>% slice(-index) test
Train the model on the train
and test on test
.
<- lm(data = train, formula = charges ~ bmi + age)
model = predict(model, test) pred
Let’s look at the Root Mean Squared Error (RMSE).
<- function(y, y_hat){
get_rmse sqrt(mean((y - y_hat)^2))
}
get_rmse(pred, test$charges)
## [1] 11421.96
And the Mean Absolute Error as well.
<- function(y, y_hat){
get_mae sqrt(mean(abs(y - y_hat)))
}
get_mae(pred, test$charges)
## [1] 94.32336
The above metrics do not tell us if this is a good model or not by itself. We need a comparison. The fastest check is to compare against a prediction of the mean. In other words, all values of the y_hat
are the average of charges
, which is about $13,000.
get_rmse(mean(test$charges), test$charges)
## [1] 12574.97
get_mae(mean(test$charges), test$charges)
## [1] 96.63604
The RMSE and MAE are both higher (worse) when using just the mean, which we expect. If you ever fit a model and get an error that is worse than the average prediction, something must be wrong.
The next test is to see if any assumptions have been violated.
First, is there a pattern in the residuals? If there is, this means that the model is missing key information.
For the model below, this is a yes, which means that this is a bad model. Because this is just for illustration, we are going to continue using it.
plot(model, which = 1)
The normal QQ shows how well the quantiles of the predictions fit a theoretical normal distribution. If this is true, then the graph is a straight 45-degree line. In this model, you can see that this is not the case. If this were a good model, this distribution would be closer to normal.
plot(model, which = 2)
Once you have chosen your model, you should re-train over the entire data set. This is to make the coefficients more stable because n
is larger. Below you can see that the standard error is lower after training over the entire data set.
<- lm(data = health_insurance,
all_data formula = charges ~ bmi + age)
<- lm(data = test,
testing formula = charges ~ bmi + age)
| term | full_data_std_error | test_data_std_error |
|---|---|---|
| (Intercept) | 1744.1 | 3824.2 |
| bmi | 51.4 | 111.1 |
| age | 22.3 | 47.8 |
All interpretations should be based on the model which was trained on the entire data set. This only makes a difference if you are interpreting the precise values of the coefficients. If you are just looking at which variables are included or at the size and sign of the coefficients, then this would probably not make a difference.
coefficients(model)
## (Intercept) bmi age
## -4526.5284 286.8283 228.4372
Translating the above into an equation we have
\[\hat{y_i} = -4,526 + 287 \space\text{bmi} + 228\space \text{age}\]
For example, if a patient has bmi = 27.9
and age = 19
then predicted value is
\[\hat{y_1} = 4,526 + (287)(27.9) + (228)(19) = 16,865\]
This model structure implies that each of the variables \(x_1, ..., x_p\) each change the predicted \(\hat{y}\). If \(x_{ij}\) increases by one unit, then \(y_i\) increases by \(\beta_j\) units, regardless of what happens to all of the other variables. This is one of the main assumptions of linear models: variable independence. If the variables are correlated, say, then this assumption will be violated.
| Readings | |
|---|---|
| ISLR 2.1 What is statistical learning? | |
| ISLR 2.2 Assessing model accuracy |


This is a project made by first year students of Durham University.
In this project we aim to highlight the good that advances in technology bring to the world and improve our collaboration skills in the process.
Explore the effect computer science is having on the natural world, including narwhals, saving the bees, and counting elephants!

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Hello Everybody, I am Jugal kishor. Currently, I am in the last year of this MCA program and it is supposed to be completed by May 2021.I chose this field of study because I have always been interested in computers, programming, and technologies, As a fresher want to build a career in MERN full stack developer. Skilled in HTML,CSS, Bootstrap,JAVASCRIPT,NODE JS, REACT JS, ANGULAR JS, Data structure,Core Java , DBMS, & Object-oriented programming concepts .Strong software skilled professional with a master's degree focused on Computer application development management from the central university of Hyderabad.
School of Computer and Information Sciences
University of Hyderabad, Hyderabad, India
Jul, 2018 to May, 2021
Avg CGPA : 6.1
Barkatullah university Bhopal
Jul, 2013 to May, 2016
Percentage : 61%
C, CORE JAVA, HTML, CSS, JAVASCRIPT,NODEJS,REACT JS,MONGODB
Windows 10 , Linux Mint , MacOS, Android, Eclipse IDE, Pycharm IDE, Code::Blocks IDE, GIT, Sublime Text,
These days I am learning REACT JS and DevOps.I want to solve people's problems through my skills and knowledge, I am too much interested in making software solutions which is not presently available in the world.
PLACED in TCS NINJA
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# Estimating complexity of divide and conquer algorithms Imagine an algorithm $T$ defined in terms of itself: $$T(n) = aT\left(\frac{n}{b}\right) + \Theta(n^c)$$ Here, $T(n)$ is computed by solving $a$ problems of size $T\left(\frac{n}{b}\right)$ and 'combining' them with a step that has a cost with the order of magnitude $n^c$ An example of this is **quicksort**. Quicksort splits its input into two halves (ideally) around a pivot, and recurses on these sublists. It then merges the two sublists with a $\Theta(n)$ algorithm. $$Q(n) = 2Q\left(\frac{n}{2}\right) + n$$ Many divide and conquer algorithms fall into this pattern: $a$ subproblems of size $n/b$, with a polynomial 'combine' step. The complexity factor of subproblems can be represented as $$\log{(\text{Number of subproblems})}/\log{(\text{Relative subproblem size})}$$ or using the constants $a$, and $b$: $$\log{(a)}/\log{(b)} = \log_{b}{a}$$ The complexity of divide and conquer algorithms then depends on the aspect that tends to dominate. - Do the subproblems dominate? ($n^{\log_{b}{a}} > n^c$) - Does the recombination dominate? ($n^{\log_{b}{a}} < n^c$) - Are they comparable? ($\log_{b}{a} = c$) $$ T(n) = \begin{cases} \Theta{\left(n^{\log_{b}{a}}\right)} & \text{ if subproblems dominate}, \\ \Theta{\left(n^{c}\right)} & \text{ if recombination dominates}, \\ \Theta{\left(n^{c}\log(n)\right)} & \text{ if they are comparable }, \\ \end{cases} $$ Let's apply this to our Quicksort example. There are 2 subproblems of half the size. That gives us a subproblem factor of $\log_{2}{2} = 1$. The aplitting/recombination step is $\Theta(n^1)$. Via the method above, we arrive at $$Q(n) = \Theta{\left(n^{\log_{2}{2}}\log(n)\right)} = \Theta(n\log{n})$$


{% extends "layout.html" %} {% block page_title %} Money Claims Prototype {% endblock %} {% block proposition_header %} {% include "includes/propositional_navigation.html" %} {% endblock %} {% block content %} {% include "includes/phase_banner_beta.html" %} Back Continue to receive offers from the defendant? Yes No {% endblock %}


UkuleleBot is a robot that can play a ukulele by itself. I took part in design and construction of its 3D-printed frame, as well as programming its linear actuators and servos.
UkuleleBot was constructed as a project within the RobOrchestra group of Carnegie Mellon's RoboClub.
MoonRanger is Carnegie Mellon's autonomous ice-finding moon rover mission, scheduled for launch in 2022.
As a navigation programmer and software documentation lead, I researched path planning methods, memory radiation-hardening techniques, and CI/CD workflow concepts.
Check it outBeacon is a location-tracking app that organizes local rescue operations during and after natural disasters. Inspired by personal experiences in the aftermath of Hurricane Harvey, Beacon coordinates communications between both rescuers and victims, who can display their status and other emergency notifications as necessary.
Beacon was designed and built in Fall 2019 at PennApps XX, a hackathon at the University of Pennsylvania.
Check it out


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| WinAPI Wrapper |
A typical Windows application has a WinMain function in which it creates its main window and then processes the message loop to feed incoming messages into that window.
int WINAPI WinMain( HINSTANCE hInstance, HINSTANCE hPrevInstance, LPSTR lpCmdLine, int nCmdShow ) { // Register window class WNDCLASS wc = { /*...*/ }; RegisterClass( &wc ); // Create main window HWND hWnd = CreateWindowEx( 0, "title", "class", /*...*/ ); ShowWindow( hWnd, SW_SHOWNORMAL ); UpdateWindow( hWnd ); // Process messages MSG msg; while ( GetMessage( &msg, 0, 0, 0 ) ) TranslateMessage( &msg ), DispatchMessage( &msg ) return 0; }
As you can see it is a really complex way to make your application running. With the WinAPI Wrapper you can throw all this code away in favor of a much simplier code:
namespace { Application< MainWindow > app; }
The WinAPI Wrapper provides its own WinMain function. The application object you create registers for that function, so WinMain calls its Run method. The Run method of the application object creates the main window (you enter the class of the main window as the first template argument of the Application class) and then it processes the message queue.
In the above example the application object is created in a .cpp source file in an anonymous namespace, because there is no point in using this object in any way. Only the fact that it is created matters.
Note that you can skip all this and just create the regular WinMain function. You can also use portions of the WinAPI Wrapper in a MFC application, that already has the MFC's WinMain.
The second template argument of the Application class is the accelerator processor class. The accelerator processor is responsible for translating and dispatching accelerator messages and it is used in the Application's message queue. The typical accelerator processor looks like follows:
template< class _Window > class AcceleratorTraits: public NonCopyAble { public: bool ProcessAccel( MessageQueue& queue, _Window* p_window ) { return false; } };
This is the default accelerator processor. Every accelerator processor must have the ProcessAccel function. The function has two arguments: first is the Application's message queue and second is the pointer to the main window, which must be of your main window class' type. The function returns true if it dispatched the message to the window or false if it didn't (in this case the Application object proceeds in the regular way).
The default accelerator processor does not do anything. If you want to load an accelerator table from a resource, you have to use the AccelFromResource processor, like follows:
Application< MainWindow, AccelFromResource< MainWindow, IDR_MYACCTABLE > > app;
If you create an MDI application, the accelerators must be processed and dispatched in a different way. For this purpose you use either the MDIAccel or MDIAccelFromResource processor, depending on whether you want to load accelerators from a resource or not. For example:
Application< MainWindow, MDIAccel< MainWindow > > app;
If the main application window is a modeless dialog box it is advisable to build the application object using the ModelessDialogAccel accelerator processor class. This way the dialog-box specific keys (like for example TAB) can be correctly processed. The ModelessDialogAccel processor calls IsDialogMessage in order to process the dialog box keys.
Application< MainWindow, ModelessDialogAccel< MainWindow > > app;


{% extends "base_community.html" %} {% block content %}
All items in the aggregator will be deleted, too. Because Jacob's lazy, there's no "undo"!


about me
I am a wonderful girl from OK, US. I am 19 but my heart is still 10 y/o.
I can draw awesome drawings in the blink of an eye, well not literally but literally the count of eye blinks can not be more than 32.
I love bunnies, and wasps :P
I am a wonderful girl from OK, US. I am 19 but my heart is still 10 y/o.
I can draw awesome drawings in the blink of an eye, well not literally but literally the count of eye blinks can not be more than 32.
I love bunnies, and wasps :P


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IS117-001
Fabian Mendoza Homepage
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Flight delays hurt airlines, airports, and passengers.Their prediction is crucial during the decision-making process for all players of commercial aviation. Moreover, the development of accurate prediction models for flight delays became cumbersome due to the complexity of air transportation system, the number of methods for prediction, and the deluge of flight data.So we build flight delay prediction models from the Data Science perspective.
Deep Neural Network
Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher level features from the raw input.
Three dense layers
A linear operation in which every input is connected to every output by a weight. Three dense layers with 64 neurons each have been used to create the model.
ReLu
The rectified linear activation function is a piecewise linear function that will output the input directly if is positive, otherwise, it will output zero.
Error max= 5.08 min
With this model we were able to predict arrival delays with a maximum error of 5.08 minutes
A curated list of airlines generated by our model based on analysis of their punctuality:


I am Roy Pardee, Data Wrangler, Analyst and Application Developer from the greater Seattle area. I've been doing various bits of computing in the service of research (mostly Health Services research) since 1997 or so.
In my little professional circle I am probably best known for my work designing and implementing the HCSRN's Virtual Data Warehouse.
This page is intended to be something of a résumé and calling-card for anyone interested in my professional life.
At my work we frequently do multi-site research and then transfer data from one site to another. That poses the risk that we will send more data than we intend, with the dire HIPAA implications of having "disclosed" PHI. This macro runs through a set of datasets looking for tell-tale signs that they include PHI. This macro and another (which I did not write) were written up in Medical Informatics & Decision Making.
This script identifies orders for PSA tests for men who probably don't need them, and sends an e-mailed list to the relevant Service Head. This is part of the Choosing Wisely initative work that Group Health is doing.
A really interesting study of an intervention in cancer treatment needed to recruit patients immediately after they learned of their cancers. At the time the best method of identifying possible recruits was to read pretty much every pathology report that came through our system, which we didn't have budget for. Having recently read Programming Collective Intelligence I was able to put together (and train!) a Fisher text classifier that filtered the stream of path reports down to something we could manage.
I took a coursera course on this that I absolutely loved (though it was way more time commitment than expected) and would love to get more into this sort of work—both the "big data" aspects and the high-level analytics.
Best. Editor. Evar. One of my proudest accomplishments is the sublime package I put together for SAS programmers.
Pretty much my bread-and-butter at the moment.
Gorgeous, lovely, wonderful programming language.
Which is in fact two things--powershell (which, once I got past my resentment of microsoft at making me learn another language is actually quite cool) and git.
(served from github.io)


Arsenal could have interest in emerging South American talent – Gunners official travels for tournament
by Sean LuntWe’ve said it before now, scouting young talent is big business for the elite level sides in Europe.
Finding and securing the best young talent from the world over is as essential as big-money signings these days.
That desire to scout the world’s best talent has led the big sides to the Tenerife Tournament and Arsenal were among them.
The event is for South American teams to prepare for the 2020 CONMEBOL Pre-Olympic Tournament, with Brazil, Argentina, Chile and the United States all competing.
Portuguese newspaper Record report that several teams have scouts in attendance for the tournament, which is taking place in Gran Canaria, and Arsenal are among them.
There’s no mention of who the Gunners may have been scouting, although there’s every chance they’re keeping an eye on one of their own.
Arsenal striker Gabriel Martinelli is part of the Brazil squad for the tournament so it would make sense to keep an eye on him.
Record, though, suggest it might just be a case of transfer opportunism.
Accreditation was open for the event, and the chance to see Brazil vs Argentina was too tempting to turn down, especially if you’re Arsenal.


Home
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I am a big fun of tottenham football club
i love playing video games on weekends.


A little background
R-Ladies East Lansing welcomes R-enthusiasts of all proficiency levels, including aspiring and experienced R programmers, who are interested in mentoring, networking & expertise upskilling. This non-profit, civil society community is designed to develop members’ R skills & knowledge through social, collaborative learning & sharing. Supporting minority identity access to STEM skills & careers, the Free Software Movement, and contributing to the global R community!
Anyone and everyone interested in R/Data Science should join the group. We encourage members to actively participate, contributing as learners, teachers, developers or innovators! We encourage women and minority genders to present/lead most sessions, conversations but we have no restrictions whatsoever about who can participate in membership, discussions and other activities. Please join us and bring your R-family & R-friends!
2019: What’s next?
Upcoming Workshops
- Intro to Statistics w/ R (Spring; workshop series)
- Intro to RMarkdown (Summer)
- Intro to Shiny (Summer)
- Intro to Version Control (Git) w/ R (Summer)
Register & RSVP on Meetup: https://meetup.com/rladies-eastlansing
Would you like to team-lead a workshop? Contact us if you’d to present for 5–10 min slots within the workshop or longer if desired.
Join the channels on Slack: #workshop-rstats #workshop-shiny #workshop-rmarkdown OR Email us<EMAIL_ADDRESS>Introduce a Girl to Engineering (2019-02-16)
Register for R-Ladies East Lansing’s activity booth: http://meetu.ps/e/Gfntz/yBMBS/f
MSU Women in Engg. outreach: http://bit.ly/msu-wie-k12-outreach
Women in Data Science Conference (2019-04)
Register: TBA
Would like to give a talk/present a poster? Stay tuned on Meetup. Contact us<EMAIL_ADDRESS>R-Ladies East Lansing Bookclub
Academic incl.
- R for Data Science
- Learning Statistics with R
- R programming for Data Science
- Hands-On Programming with R
Nonfiction incl.
- Algorithms to Live by
- Weapons of Math Destruction
Blog posts & Articles incl.
- Excuse me, do you have a moment to talk about version control?
- Good enough practices in Scientific Computing
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GitHub | Google Drive | Photos
You & R-Ladies East Lansing
- Join the R-Ladies Community on Slack (https://bit.ly/rlel-slack-signup)
- Lead (or team-lead) presentations/discussions in a meetup
- Help run the meetup(s) (help R-newbies, help pick up pizza)
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Suggest a topic & bring in volunteers from the 200+ Meetup/Slack R-Ladies!
Getting Involved @ R-Ladies: https://rladies.org/about-us/help/
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Access our presentations, R scripts and more on Github and follow us on twitter to stay up to date about R-Ladies news! Join the Meetup group to be notified of our upcoming meetings!
- R-Ladies Global
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- info [at] rladies [dot] org
- Meetup: https://meetup.com/rladies-east-lansing/
The Team
Organizers: Janani Ravi, Camille Archer
Co-organizers: Kayla Johnson, Arjun Krishnan (~ Krishnan Lab)
PS Community Policies & Code of Conduct:
Full community guidelines are found here: https://github.com/rladies/starter-kit/wiki


Chittagong Medical College is situated in the port city of Chittagong, Bangladesh and is the oldest medical college in the south-eastern part of the country.
Address: 57, K.B. FAZLUL KADER ROAD, P.S- PANCHLAISH, P.O- CHAWKBAZAR, Chittagong 4203
Hours: Open 24 hours
Phone: 01819-637685
Founded: 1957
Address: 30 Chittagong Mehdibag, Chittagong 623753
Hours: Open 24 hours
Phone: 031-623713 ext. 623753
Address: O.R. Nizam Rd, Chittagong
Hours: Open 24 hours
Phone: 031-651242
Address: Late Alhaj Zahur Ahmed Chowdhury Rd, Chittagong
Hours: Open 24 hours
Phone: 031-622519
Address: Shamser Para Chandgaon, Chittagong, 206/1 Haji Chand Mia Road, Chandgaon
Hours: Open 24 hours
Phone: 312573123
Address: Foy's Lake Approach Rd, Chittagong
Hours: Open 24 hours
Phone: 031-659017
Address: Zakir Hossain Rd, Chittagong
Hours: Open 24 hours
Phone: 316594357
Address: Chittagong Development Authority Buildings, Station Road, Chittagong
Hours: Open 24 hours
Phone: 031-639540
Address: O.R. Nizam Rd, Chittagong
Hours: Open 24 hours
Phone: 031-651590 ext. 652378
Address: 03, Sheik Mujib road Agrabad, Chittagong
Hours: Open 24 hours
Phone: 01731-253990


Welcome to Code For Anchorage's Shiny Server!
Hosted Applications:
Dipnet_App!
bigcityweather
munipayroll
precinctvoting
precinctcluster
muniexplorer
CannabisZoning


Seven O’Clock Stories
by Robert Gordon Anderson
“Fifteenth Night: Jack Frost and the Man-in-the-Moon”
Additional Information
- Year Published: 1920
- Language: English
- Country of Origin: United States of America
- Source: Anderson, R. G. (1920). Seven O’Clock Stories. New York: G.P. Putnam’s Sons: The Knickerbocker Press.
-
Readability:
- Flesch–Kincaid Level: 3.7
- Word Count: 2,857
- Genre: Adventure
- Keywords: children's stories
- ✎ Cite This
Downloads
Once, twice, thrice nodded Marmaduke’s head.
The red flames of the fire kept dancing, dancing all the time. Very bright looked the little sleigh at the foot of the bed, very brave the tiny reindeer.
But look! Something moved—just a little.
The “nigh” little reindeer was stamping his foot and tossing his antlers.
And the other little reindeer tossed his horns and stamped his foot too.
On their backs the sleigh-bells jingled, merrily like fairy bells.
The red and blue sleigh moved a little—just a little.
It began to slide slowly, over the comforter.
Marmaduke was worried. He didn’t want the pretty sleigh and the reindeer to run away. He might never see them again.
“Wait!” he shouted.
“Whoa—you villains!” It was a strange little voice that ordered the reindeer.
The red and blue sleigh stopped short.
Marmaduke rubbed his eyes.
The strange little voice spoke again.
“Jump in,” it said.
And there in the front seat of the toy sleigh sat a funny little chap, about as big as the Toyman’s thumb—no bigger. He wore a pointed cap that shone like tinsel on a Christmas tree. He wore a white coat that sparkled too.
 “That’s my sleigh.
And the funny voice under the white cap answered.

“My name’s Jack.”
“Jack what?”
“Jack Frost—you ought to know that !”
Tinkle, tinkle went the bells The reindeer lifted their hoofs higher and pawed at the comforter. They shook their antlers impatiently. The little driver jumped up and down in the seat as if he were sitting on pins and needles.
More worried than ever was Marmaduke.
“How can I get in that sleigh?” he asked the imp of a stranger. “I’m too big.”
The little chap only chuckled. It was a very mischievous chuckle. Then he said:
“Take a good look at yourself.”
Marmaduke did.
My, how he had shrunk! He was no bigger than a brownie, no bigger himself than the Toyman’s thumb.
“How did that happen?” he said,
“Oh, the dream fairy did that,” said Jack. “She likes to play tricks on people. It’s lots of fun. But shake a leg, shake a leg!”

Once more Marmaduke looked down at himself. No, his eyes had made no mistake.
So he popped out from the covers. The folds of the blanket looked as big as mountains, the lumps of the comforter as high as the hills. Over them he scrambled and he sprawled till he reached the little red and blue sleigh.
Then he jumped in.
The driver could be very impudent, but he took good care of Marmaduke just the same, for the boy had been very sick and might catch cold.
“Gee-up, gee-up!” he called to the tiny reindeer.
Marmaduke was frightened. What a horrible crash there would be when they slid from the high bed to the floor.
But nothing like that happened at all. Away off the bed, over the bright rag carpet, and past the red fire, safely and swiftly they trotted. Below the window they paused. Pretty silver ferns and trees covered the panes and sparkled in the firelight. The window was closed, but that did not matter at all.
“Up with you!” yelled Jack Frost.
Slowly, as if by magic, up went the window sash! Over the sill galloped the reindeer. And after them ran the toy sleigh with Jack Frost and Marmaduke on the red seat.
Over the porch, too, they went.
Then something did happen.
“Now look at yourself,” said Jack Frost, cracking his whip.
Marmaduke did not hear him at first. He was admiring that whip. It was only a long icicle, and all Jack had to do was to touch the reindeer with its point to make them run faster and faster.
“Look at yourself,” he repeated.
Marmaduke obeyed.
“Why, I’m as big as I used to be!”
Jack laughed and replied:
“The dream fairy does love to play tricks on folks!”
Yes, the sleigh had grown as large as his father’s sleigh; the reindeer as big as Teddy, the buckskin horse. The tossing horns were as high as the reindeer’s in the Zoo, and Jack Frost was as big as Jehosophat now.
“I’m sorry that Jehosophat and Hepzebiah are not along,” said Marmaduke to himself, “they’re going to miss some fun”
He looked ahead through the trees Up over the hill the snow path stretched—up to the dark blue sky and the stars. Millions of them there were and they were all twinkle-winking at him. And the Old Man-in-the-Moon, just over the hill, kept winking at him too.
Jack Frost turned to Marmaduke.
“Where would you like to go most ?”
Marmaduke didn’t need to think, he had his answer all ready.
“I’d like to visit the Old Man-in-the Moon.”
“It’s a bit of a drive,” replied Jack, “but Old Yellow Horns and Prancing Hoof are fast goers. Gee-up! Gee-up!” he shouted at them, touching their flanks with the icicle whip. So fast they went they scarcely seemed to touch the snow, and on up the hill they rode towards the laughing Man-in-the-Moon.
Then suddenly there came such a barking, a yelping, a neighing, a mooing, a clucking, a gobbling, a squealing, a squawling, as you never heard before.
Around jerked Marmaduke’s head.
There, behind the sleigh, running and leaping and paddling and waddling and frisking and scampering came a strange procession. There were Rover and Brownie and little Wienerwurst, Teddy and Methusaleh and all the horses, Primrose, Daisy, Buttercup, Black-Eyed Susan and all the cows. He could see their tongues hanging out—it was so hard to keep up with the dogs and the horses.
“Moo—moo, slow—slow!” called the poor cows.
And behind them ambled the sheep and the curley-tailed pigs; waddled the ducks and the geese; Miss Crosspatch, the Guinea Hen, and Mr. Stuckup, the turkey; and, at the very end, all of the White Wyandottes, the fathers and the mothers, and the little yellow children, and their grandfathers and grandmothers, and all their uncles and aunts, and their cousins, first, second, and third—every last one of them.
My—what a fuss and a clatter they made!
There was a long long line of them, stretching down the hill and down the white road over the snow.
Marmaduke laughed and exclaimed to Jack Frost:
“Why, they look just like the procession of the animals when they came out of the Ark.”
“Yes, I remember them,” replied Jack. “And Old Noah too. I used to pinch their ears and pull their tails o’ nights.”
Marmaduke looked surprised.
“You! Why, that was hundreds of years ago! You can’t be as old as all that.”
But Jack only smiled a superior smile
“Sure I am. Why I’m as old as the world!”
“Old as that Man-in-the-Moon?” continued Marmaduke, and the odd little fellow replied:
“Just as old.”
Marmaduke looked up at the moon sailing far above them. And the old man, sitting there on the moon-mountain, nodded as much as to say that Jack was quite right.
Now the sleigh reached the top of the hill just where it touches the sky.
Surely there they would stop.
But no—
“This sleigh can run on air just as well as on snow,” the odd little driver explained.
Another touch of the icicle whip, a jingle of bells, a snort from the reindeer, and they were off—off through the air towards the sailing moon.
Marmaduke was so interested in looking up that he didn’t see little Wienerwurst run ahead of all the animals. That doggie beat them all to the top of the hill. And when he came to the top he just jumped out in the air and landed safe on the runner of the sleigh, and curled up there and hid and didn’t make any noise.
It was very clear high up in the air, and Marmaduke looked down.
The houses had shrivelled all up. As small as Wienerwurst’s own little house they seemed. And the trees were as small as plants in the garden.
He looked down again. The earth was far below them.
By the white steeple of the church they flew. In the steeple was a little window. The bell-rope hung out. Jack jerked it as they went past.
”Ding, dong—
Something’s wrong .”
So spoke the deep voice of the old bell. He was a hundred years old, and such strange things had never happened in his life before.
  And the sexton ran out in the snow, in his shirt-tail, to see what was the matter.
And all the other people, in the farmhouses and in the town houses, threw up their windows or ran out of doors to see where the fire was.
Then, after looking all around the houses and barns and the haystacks, they looked up at the sky and saw Marmaduke in the sleigh, racing towards the moon
They were very funny, like little toy people, all looking up and pointing at the sky and all shouting at once.
But Marmaduke didn’t care—he was having the time of his life!
Then a still stranger and funnier sight he saw,—all the animals on the top of the hill—the horses, the dogs, the cows, the sheep, the pigs, the ducks, the geese, the turkeys, and the White Wyandottes, all sitting on their haunches and barking or neighing or howling or squawking at Marmaduke, as on—up and up—he went, a-sailing through the sky.
But he missed his little pet doggie. Where could he be?
He was worried about that until all of a sudden he heard a little bark and looked behind, and there on the red runner, hanging on for dear life, was little Wienerwurst. Marmaduke reached down, and picked him up by the scruff of his neck, and set him on his lap, under the robe, so that he wouldn’t catch cold.

They hadn’t gone more than a hundred miles or so, when something strange floated past them—a cloud all puffy and soft and white, like the floating islands in the puddings Mother makes.
The reindeer nearly ran into it. That would have been too bad, for the sleigh would have torn it in two. And as they passed, Marmaduke saw little baby angels lying there, curled up in the cloud, fast asleep, with their wings folded.
A whole fleet of the clouds passed by and there was only clear air ahead of them, they thought, but no!
“Bang.” They had bunked into something high up in the sky.
“Very careless,” said Jack Frost, as he pulled on the reins.
It was very bright, and Marmaduke blinked hard.
Ahead of them lay another island, but this one was round and flat and shiny like a gold shield, with a little hill in the centre.
“Hello, old Top!” said Jack Frost.
“Good evening, you mischief-maker,” replied the Man-in-the-Moon.  This is Marmaduke Green. He’s been sick, so I thought I’d give him a ride.”
“Oh, ho! That’s it. You do do someone a good turn now and then, after all.”
Then the old man turned to Marmaduke.
“Howdy,” he said, “I hope you’ll get better very soon.”
“Thank you,” replied Marmaduke politely. He was so well brought up that he didn’t forget his manners, even up high in the sky.
“Well, here’s something to play with when you get back to earth,” said the Old Man-in-the-Moon. And he reached his hand inside the sack on his back, and pulled out a fistful of bright gold pennies—oh, such a lot of them!
Marmaduke reached for them. But alas! he was in too much of a hurry, and they spilled out of his hand and rolled right over the edge of the moon. Down, down, down, through the sky they dropped, past the stars and the clouds, down, down, down to the earth.
There were all the animals still, on the top of the hill, looking up at the moon. And one of the bright pennies landed on Black-eyed Susan’s nose. She was a timid old cow and she was startled. And she was still more frightened at the howling, the barking, the squawking, which the animals set up, one and all.
So frightened was she that she jumped. So hard did she jump that she leaped way over the hill and over the clouds and the stars.
“There’s that critter again,” complained the Man-in-the-Moon.
On, with her tail spread out behind her, and her legs sprawling in the sky, came old Black-eyed Susan, straight towards them. Jack Frost and Marmaduke jumped back; the Old Man-in-the-Moon moved a little too. They were afraid she would land on their toes.
But she didn’t.
“She’s still pretty chipper,” observed the old man. “That’s a great jump. Most beats the record”
So it did, for she sailed right over them, coming down on the other side of the moon, hitting one poor little star on the way with her hoof, and putting out its light entirely.
And down, down old Susan fell till she hit the earth and lay there, panting and mooing so loud that the people on earth thought it was thunder, and shut their windows tight for fear of the rain.
“Well!” said the Old Man-in-the-Moon, blowing clouds of smoke from his pipe, “that’s over. Now here’s some more pennies. Be careful this time,” he warned him.
And from his sack he drew forth another great handful of gold pennies. How they did shine! But as Marmaduke reached for them, Jack Frost jiggled his elbow with his icicle whip—and again they rolled over the edge of the moon.
And again Marmaduke was too eager. He ran after them, and Wienerwurst ran too, and when they reached the edge they couldn’t stop themselves at all.
They were falling, down, down through the sky. A hundred somersaults they turned. Marmaduke tried to hold on to a cloud, but his hands went right through it. He tried to hold on to the stars, but he missed every one.
Then suddenly—bang went his head against the church steeple—and all the stars danced
—————————————
Then he woke.
He looked around. Why-he was sitting up in the bed, his very own bed, by the red fire!
It was just a trick of the dream fairy’s, after all.
But it was all right, for at the foot of the bed rested the little red and blue sleigh and the tiny reindeer, just as still as still could be.
And at the side of the bed stood Father and Mother—and the Toyman.
They seemed very happy.