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May 04, 2002
Palestinian corpse invokes Lazarus effect
Israel Insider | Submitted by: Soylent
"The film was screened to foreign reporters and later shown on Israeli
television, causing considerable amusement among the TV commentators and
journalists, since the "corpse" kept tumbling out of the blanket. "One
time he falls off the stretcher when he is already in a crowd," Col. Eisen
narrated. "When he came back to life in the middle of the crowd, the
crowd breaks up because they didn't know that it wasn't really a corpse."
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Read article...
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Detroit high school toilet baby
Zwire | Submitted by: anonymous
"A 5-month-old fetus was found Thursday in a bathroom at Roseville High
School after students had complained about a clogged toilet. Police and
school officials spent much of the day trying to determine the
identity of the mother."
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Read article...
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Congresswoman protests suicide bomber game
Newsday | Submitted by: Soylent
"An online cartoon simulation called "Kaboom," in which the protagonist
is a suicide bomber who blows up men, women and children as well as
himself, has prompted a U.S. congresswoman to protest. In a letter to
Tom Fulp, who runs the Web site that has posted the game, Rep. Nita Lowey
of Westchester urged the webmaster to delete the game
from the site immediately." The second link is the game, on Newgrounds.
|
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Read article... and
This article...
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Boy kills another boy with insulin
ABCnews | Submitted by: Soylent
"A 16-year-old Washington state boy is set to be arraigned today on
charges that he bound his 8-year-old neighbor with duct tape and fatally
injected him with a massive dose of insulin because the child
was "bugging" him... They said after the boy died, Alexander took a
razor blade and sliced deep gashes into the body."
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Read article...
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Prince Philip's humor unappreciated
Wire Services | Submitted by: Soylent
"Speaking to the blind Susan Edwards, wheelchair bound and accompanied
by her guide dog, he remarked: Do you know they have eating dogs for
the anorexic now?" Other examples of his inappropriate hilarity are
given. Points for Prince Philip in our book.
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Read article...
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Senator Hollings considered dangerous
The American Lawyer | Submitted by: Soylent
"The prospect of gumming up the works of the globe's most exuberant engine
of technological innovation and prosperity by subjecting it to bureaucratic
notice-and-comment rule-making is unthinkable. Though there are a handful
of precedents for modest government intervention into consumer electronics
design, at least one of those -- the 1992 requirement that digital audiotape
recorders be equipped with a "serial copying management system" -- was a
notorious commercial and technological failure."
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Read article...
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18 People Gassed at the Happiest Place on Earth
KNBC-TV 4 Los Angeles | Submitted by: Matt Nelson
Eighteen guests were injured when somebody "accidentally" set off a
mace/pepper spray type keychain in the Honey, I Shrunk The Audience 3D movie
attraction. Seriously, anyone in an attraction that lame kinda deserves it.
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Read article...
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Esmerelda is missing
Durango Herald | Submitted by: Mr. Farmer
"She has blue hair, though it is, as they say in polite circles,
not her own, and her wig may have fallen off during her abduction,
in which case she is bald. She has a glitter tattoo on her right
shoulder and a silver belly button ring.  She does not have hands."
 "The voice of thy thunder was in the heaven: the
lightnings lightened the world: the earth trembled and shook."
 School officials
say Diaz and the boy did not engage in sexual activity during the
school day or on school property. Police believe the alleged crimes
took place in one of the two Diaz family cars."
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Read article...
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"His name is Robert Paulson"
MSNBC | Submitted by: the chuck wang
An ex-state bio-lab staffer was busted yesterday in a rural community
on the outskirts of New York state's capitol after police found a cache
of homemade pipebombs and dangerous chemicals in the basement of the
home he shared with his parents. The disability collecting, hazmat
loving loser was a well known menace in the town of Sand Lake, NY.
The thing that makes this more interesting than the usual rural milita
bust is that the accused in this case shares his name with the big busted
character played by Meatloaf in the recent David Fincher film Fight
Club. "His name is Robert Paulson, his name is Robert Paulson..."
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Three dimensional vector and polygon drawings can be created. An extensive viewing mechanism supports all projections, including perspectives from any distance. Lighting sources, and clipping planes are supported. Pegaesys is not designed for slick visual effects, but it has the tools a design professional needs.


tvecs.evaluation package¶
Submodules¶
tvecs.evaluation.evaluation module¶
Module to Evaluate T-Vecs model against Human Semantic Similarity Score.
-
tvecs.evaluation.evaluation.
extract_correlation_coefficient
(score_data_path, vsm)[source]¶ Extract Human Score, Word1, Word2. Compute T-Vecs Score.
- API Documentation
- param score_data_path
File generated by preprocessor/yandex
- param vsm
Vector spaces mapped using 2 models.
- type score_data_path
String
- type vsm
- return
Returns (Correlation coefficient, P-Value)
- rtype
Tuple(Float, Float)
-
tvecs.evaluation.evaluation.
get_correlation_coefficient
(human_score, calculated_score)[source]¶ Measure correlation using Pearson’s Coefficient.
The correlation is between the T-Vecs Model and
Human Semantic Similarity Score.
- API Documentation:
- param human_score
List of human scores.
- param calculated_score
List of calculated scores.
- type human_score
List
- type calculated_score
List
- return
(Correlation Coefficient, P-Value)
- rtype
Tuple(Float, Float)
Note
correlation_coefficient - Measure of degree of relatedness between two variables
p-value - The null hypothesis is that the two variables are uncorrelated. The p-value is a number between zero and one that represents the probability that your data would have arisen if the null hypothesis were true.
See also
scipy.stats
tvecs.evaluation.preprocess_dataset module¶
Preprocess Evaluation Dataset by translating 1 column.


What is it?
XSS (Cross-Site Scripting attacks) are when an attacker places malicious code into a website. This usually occurs when the website allows for user input and can be prevented by using javascript validation and preventing HTML markup.
innerHTML; nodeValue; and textContent;
There are three primary ways of updating an HTML element's content after user input.
innerHTML
innerHTML is an expression that gets/sets the HTML within an HTML element. This includes HTML markup that could have been placed within the parentNode. Is vunerable to XSS attacks.
nodeValue
nodeValue is an expression that gets/sets the text-node of an HTML element. This is more specific than textContent in that if there was a nested HTML element within the selected element, nodeValue will work only upon the textnode, not its nested element sibling. Is safe against XSS attacks.
textContent
textContent is an expression that gets/sets the text within the containing element. Will ignore nested HTML elements. Is also safe against XSS attacks.


HOUSE OF CARDS A film review by James Berardinelli Copyright 1993 James Berardinelli
Rating: 7.4 out of 10 (B-, **1/2 out of ****)
Date Released: 6/18/93 Running Length: 1:53 Rated: PG-13 (Mature themes, language)
Starring: Kathleen Turner, Tommy Lee Jones, Asha Menina, Park Overall Director: Michael Lessac Producers: Dale Pollock, L. Holfong, and Wolfgang Glattis Screenplay: Michael Lessac Music: James Horner Released by Penta Pictures
While in Mexico, Ruth (Kathleen Turner) and her family suffer a devastating loss when her husband Alex is killed in an accident. Some time later, after being away for years, they return to their North Carolina home with the hope of starting anew. Shortly after coming back and for no apparent reason, Ruth's six-year old daughter Sally (Asha Menina) withdraws from reality. Although fluent in three languages, Sally refuses to speak in any of them, nor will she acknowledge the presence of her mother. However, she develops an affinity for high places and after she nearly kills herself climbing in a building construction site, the court places her in the custody of Dr. Jacob Beerlander (Tommy Lee Jones), a psychiatrist who's an expert in dealing with dissociated children.
I have the feeling that people will either love or hate this movie. Its success depends entirely upon the emotional reaction of the audience. It delivers a powerful punch, but the logic of the plot is uncertain and there are a lot (too many for my taste) of things left unexplained. Writer/director Michael Lessac has taken a few liberties with reality, occasionally stretching things beyond the bounds of credibility.
I'll admit to being touched by HOUSE OF CARDS, but even as the movie worked its skillful manipulation on my emotions, it confounded my intelligence with a storyline that doesn't seem well thought out. It's difficult to talk about specifics without giving things away, but suffice it to say that every time the movie got me completely immersed in its peculiar brand of magic, the script did something inconceivable that left me feeling unsatisfied.
The acting in HOUSE OF CARDS ranges from passable to excellent. Kathleen Turner is good as the widowed Ruth. The character is occasionally difficult to watch, but that's more because of the emotions churning within Ruth than because of a flaw in Ms. Turner's performance. Tommy Lee Jones is perhaps too restrained as Jacob Beerlander, and certainly never sympathetic. Asha Menina, on the other hand, gives a memorable performance, clearly outacting all the adults.
One of the most interesting--and believable--scenes in this film occurs in Dr. Beerlander's school for children as kids with various problems are introduced to Ruth. Unlike much of the rest of the film, this part is not manipulative, but is nevertheless moving, principally because it seems so real.
The greatest fault with HOUSE OF CARDS is that too little of it is believable. In attempting to play on the audience's emotions as well as create an almost-mystical atmosphere, this movie has allowed too many difficult-to-accept contrivances seep into the plot. Given the potential power of the script, this annoyed me, in addition to presenting an unwanted distraction. I wish I could give a wholehearted recommendation, but the best I can do is give one with qualifications. Undoubtedly, there is an audience for this film, but I think it's a small one.
I'll reiterate what I said at the beginning of this review. Most viewers will probably either love the film or hate it. Those that can completely suspend their disbelief and allow the emotional tide of HOUSE OF CARDS to carry them away (as it did in Houston, where this movie won top honors at that city's film festival), will love this picture. Everyone else will find it slow-moving and hard to accept. Oddly enough, I find myself somewhere in the middle, having found some enjoyment in HOUSE OF CARDS, but wishing for a lot more.
- James Berardinelli<EMAIL_ADDRESS>.
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


Class VehicleType
This is the model class for table "vehicle_type".
- yii\db\ActiveRecord
- app\models\BelongToUser
- app\models\VehicleType
public static
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public
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public
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public
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public static
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public static
|
beforeValidate(),
getUser()
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public
integer
|
$id
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public
integer
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$user_id
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public
string
|
$category
|
public
string
|
$name
|
public
integer
|
$seq
|
$lat,
$lng,
$password,
$username
|


| Number of Classes | Line Coverage | Mutation Coverage |
|---|---|---|
| 4 | 75% | 56% |
| Name | Line Coverage | Mutation Coverage |
|---|---|---|
| ByteSource.java | 92% |
60% |
| ByteSourceArray.java | 63% |
56% |
| ByteSourceFile.java | 94% |
55% |
| ByteSourceInputStream.java | 70% |
56% |


Night Theme
cloud
BCIT CST 2016 Fall Intake
Complete List Of Links and Resources
Open source on
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General
D2L Login
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Set A Facebook Group
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Review Exercise 1
Review Exercise 2


Documentation
Music Score Creator makes it simple to create music sheets.
Music basics
Bar-lines
|
Bar : separe the divisions of time
=||
Double Bar : divide one complete musical idea from another
=|:
Repeat Begin : repeat the piece from here
=:|
Repeat End : indicate that the music is to be repeated from a Repeat Begin Bar. If there are not, the music is repeated from the beginning
=::
Double Repeat : cf. repeat begin and repeat end
=|=
End Bar : indicate that the end of the piece has been reached
Durations
- 1 while note
- 2 half notes
- 4 quarter notes
- 8 eighth notes
- 16 sixteenth notes
Examples
Basic example
tabstave notation=true tablature=false
notes Cn-D-E/4 F#/5
A Minor Pentatonic Scale
tabstave notation=true tablature=true
notes 5/6 8/6 5/5 8/5 | 5/4 7/4 5/3 7/3 | 5/2 8/2 5/1 8/1 =:|
Some chords
tabstave notation=true time=4/4
notes :2 (7/4.5/3.5/2.5/1) (3/4.2/3.1/2.1/1) |
notes :1 (3/1.3/2.4/3.5/4) =|=
C Major Scale with text
tabstave notation=true tablature=false time=4/4 key=C tuning=eb
notes :8 C-D-E-F-G-A-B/4 C/5
text :8,.1,Do,Ré,Mi,.10,Fa,Sol,La,Si,.1,Do
options space=18


CLSP Player Tour Demo
~ DIST ~
Time Elapsed:
0 hours 0 minutes 0 seconds
Time Started:
N/A
Heap Size Limit:
N/A
Total Heap Size:
N/A
Used Heap Size:
N/A
Reset Tour
Error while setting up the demo


{% assign sorted = (site.team | sort:'member-order') %} {% for member in sorted %}
{{ member.role }} {{ member.email }}
{{ member.content }}


| Interface | Description |
|---|---|
| Block |
Defines the interface for a Block of a
Phase . |
| BlockFactory |
An implementation of this interface should provide
Block s based on TaskSpecification s. |
| Element<C extends Element> |
An Element is a generic component in a tree of elements.
|
| Phase | |
| PhaseFactory | |
| Plan |
Defines the interface for one or more
Phase s, along with any errors encountered while
processing those Phases. |
| PlanCoordinator |
PlanCoordinator's job is to coordinate offers among configured
PlanManager s. |
| PlanFactory |
Created by gabriel on 10/15/16.
|
| PlanManager |
PlanManager is the management interface for
Plan s. |
| PlanScheduler |
Interface for Plan schedulers.
|
| StageBuilder |
Interface for objects that build Plans.
|
| Class | Description |
|---|---|
| DefaultBlock |
This class is a default implementation of the Block interface.
|
| DefaultBlockFactory |
This class is a default implementation of the BlockFactory interface.
|
| DefaultBlockTest |
This class tests the DefaultBlock class.
|
| DefaultPhase |
Default Phase implementation tracks Blocks both by their UUID and by
their ordering.
|
| DefaultPhaseBuilder |
The DefaultPhaseBuilder allows the construction of a
Phase with a custom @{link Strategy} defined by the
expressed dependencies. |
| DefaultPhaseBuilderTest |
This class tests the DefaultPhaseBuilder.
|
| DefaultPhaseFactory |
This class generates Phases given PhaseSpecifications.
|
| DefaultPlan | |
| DefaultPlanBuilder |
The DefaultPlanBuilder allows the construction of a
Plan with a custom @{link Strategy} defined by the
expressed dependencies. |
| DefaultPlanBuilderTest |
This class tests the DefaultPlanBuilder.
|
| DefaultPlanCoordinator |
Default implementation of PlanCoordinator.
|
| DefaultPlanCoordinatorTest |
Tests for
DefaultPlanCoordinator . |
| DefaultPlanFactory |
Given a StateStore and a PlanSpecification the DefaultPlanFactory can generate a Plan.
|
| DefaultPlanManager |
Provides the default implementation of a
PlanManager . |
| DefaultPlanManagerTest |
This class tests the
DefaultPlanManager . |
| DefaultPlanScheduler |
Default deployment scheduler.
|
| DefaultPlanSchedulerTest |
Tests for
DefaultPlanScheduler . |
| ElementBuilder<P extends Element<C>,C extends Element> |
An ElementBuilder allows the construction of a composite element such that the description of dependencies constructs
a custom
Strategy . |
| PlanUtils |
Commons utility methods for
PlanManager s. |
| RandomRecoveryStrategyTest |
Tests for
RandomStrategy . |
| ReconciliationBlock |
Block that implements Reconciliation between the Mesos Master and a
framework.
|
| ReconciliationBlockTest |
Tests for
ReconciliationBlock . |
| ReconciliationPhase |
Phase that implements reconciliation.
|
| TestBlock |
This class is an implementation of the Block interface for test purposes.
|
| Enum | Description |
|---|---|
| Status |
Status of an
Element . |
| Exception | Description |
|---|---|
| Block.InvalidBlockException |
Thrown on invalid Block construction attempt.
|


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AdView
- Class in
com.appnexus.opensdk
The parent class of InterstitialAdView and BannerAdView.
AdView(Context)
- Constructor for class com.appnexus.opensdk.
AdView
Begin Construction
AdView(Context, AttributeSet)
- Constructor for class com.appnexus.opensdk.
AdView
AdView(Context, AttributeSet, int)
- Constructor for class com.appnexus.opensdk.
AdView
AdView(Context, String)
- Constructor for class com.appnexus.opensdk.
AdView
AdView(Context, String, int, int)
- Constructor for class com.appnexus.opensdk.
AdView
C
com.appnexus.opensdk
- package com.appnexus.opensdk
G
getAdHeight()
- Method in class com.appnexus.opensdk.
AdView
getAdWidth()
- Method in class com.appnexus.opensdk.
AdView
getPlacementID()
- Method in class com.appnexus.opensdk.
AdView
L
loadAd()
- Method in class com.appnexus.opensdk.
AdView
Loads a new ad, if the ad space is visible.
loadAd(String)
- Method in class com.appnexus.opensdk.
AdView
Loads a new ad, if the ad space is visible, and sets the placement id attribute of the AdView to the supplied parameter.
loadAd(String, int, int)
- Method in class com.appnexus.opensdk.
AdView
Loads a new ad, if the ad space is visible, and sets the placement id, ad width, and ad height attribute of the AdView to the supplied parameters.
O
onLayout(boolean, int, int, int, int)
- Method in class com.appnexus.opensdk.
AdView
S
setAdHeight(int)
- Method in class com.appnexus.opensdk.
AdView
Sets the height of the ad to request.
setAdWidth(int)
- Method in class com.appnexus.opensdk.
AdView
Sets the width of the ad to request.
setPlacementID(String)
- Method in class com.appnexus.opensdk.
AdView
Sets the placement id of the AdView.
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Probably the most scenic island in the Marquesas from a distance, with its volcanic spires and needles.
The Port of Entry is Hakahau.
There are three Gendarmerie areas in the Marquesas: Hiva Oa, Ua Pou and Nuku Hiva, and officially boats arriving from overseas should go to one of these islands first.
It is NO LONGER compulsory for all yachts visiting French Polynesia to visit Papeete Customs and Immigration to complete the clearing-in process.
Yachts can now clear in or out of FP with the “gendarmes” offices (French Police) at the ports of entry in the other islands.
The Gendarmerie Nationale in Hakahau can deal with Customs and Immigration (open Monday to Friday 0730-1200 & 1400-1730).
For full details on clearance into and out of FP go to French Polynesia Formalities.
If you have fruits or vegetables from other islands, they may not be landed, as Ua Pou does not have the fruit fly, as does Nuku Hiva. However cruisers reported in 2014 that no one was aware of this rule and no one asked.
Last updated July 2014
Hakahau Harbour:
Anchorage is available behind a breakwater in 20 feet of good sand for 5 or 6 boats, or else anchor out in the bay (less protected with some swell). The bay is small and yachts use bow and stern anchors so more can fit in and face into the swell.
The dock has water faucets and a dinghy landing, with an easy walk to the village. Alternatively land at the beach closer to the village.
If space is available, boats may come alongside the quay temporarily, when local freighters do not use it.
Yacht Adina who visited here in 2014 have a good article on their website about Using a Stern Anchor.
Last updated June 2014.


I am a Computer Engineering Master's student at Texas A\&M University. I aspire to specialize in VLSI systems and digital design to augment my knowledge in the developement of system on chip, the core hardware component that drives the growth of Internet of Things and meets the complex computational demands of today. I intend to pursue industrial research towards acheiving differentiated, optimized and reliable solutions in the field of integarted circuits to contribute towards emerging technological trends
I believe that the semiconductor industries have a huge role to play in the society. The hunger for faster, smaller and power effecient chips never recedes. With these requisites come more challenges in realizing a system on chip with emerging technology. I look forward to address these in my research career. With the right background in understanding its importance to learning the current limitations in the field of VLSI and computer architecture, I am committed to learn and work towards a better technological tomorrow.
I have three years of work experience as a Design Engineer at Texas Instruments. I was part of a team that develops low power SOCs with multiple sleep modes for industrial applications. My prime responsibility was a quality handoff of SOC to the synthesis team.
I interacted with multiple teams geographically located across globe to understand IP functionaltiy and to successfully integrate them into a SOC using tools such as autogen and magillem. This also required me to design a glue-logic block that would handle any synchronization requirements between IPs.
During the process, I developed expertise in IPXACT-1685 standards, which is a skeleton representation of an IP used by integration tools. I used this expertise to identify and develop automated checkers for IP handoff that was used across teams.
A quality SOC handoff also means to ensure the design is free of errors and bugs.
I handled the SOC lint check using spyglass tool. It is used to identify potential bugs in an early stage such as incomplete case or if statements that can result in latches, potential simulation vs synthesis mismatches that results in logic behaving differently than as expected and design constructs that are non-synthesizable.
Low power SOC has multiple sleep modes that the user can configure according to its application.
I worked in a team of 4 to verify SOC during these sleep modes using Cadence NCSIM. The goal was to ensure that the system can enter and exit sleep modes gracefully. System level scenarios were targetted to also check the time to enter and exit sleep modes does not violate system specifications.
 The ON system must function as usual. This requires isolation of the ON system from the OFF system. I developed automatic creation of isolation checkers using perl and CPF that verifies the correctness of these isolations. This helped in catching issues during early design cycle.
Besides SOC design and verification, I had the opportunity to work on architecture analysis and design of instruction cache. I performed analysis based on cache hits/misses as a trade off between area, power and performance using industrial standard benchmarks such as Coremark and Dhrystone. My main area of focus was to design a cache that would handle cycle dependencies keeping area and power minimal.
ITTAGE (Indirect Target TAgged GEometric length predictor) is the state of the art indirect branch target predictor. It uses multiple prediction tables hashed using global branch history and PC to achieve an unprecedented level of accuracy. The use of geometric lenghts allows the predictor to use correlation with large global history bits range. The use of multiple tables enables in exploiting correlation with small as well as long history bits. This project is an implmentation of it in C++ acheiving a speedup of 93%. Enhancements on the project were:
1. Base table improvement: Base table of ITTAGE is indexed from PC. This implementation proposes to index base table with a hash function of PC and previous target. This improves performance on benchmarks that have less correlation with conditional branch bits from global history to acheive an overall speedup of 5.3% on SPEC CPU 2006 traces.
2. Altpred: Altpred (alternate prediction) is the target used when the confidence on the predicted taregt is low in ITTAGE. The confidence comes from the confidence counter which are incemented when the prediction is right and decremented otherwise. This implementation proposes to use the main prediction itself when the confidence on altpred is low too. This proposal seems to improve overall performance, but not significantly.
Effect of large penalty caused by a miss on last level cache gives rise to the need for a better replacement policy. Perceptron based reuse prediction is used to identify dead blocks in the cache- blocks predicted not to be referenced again.
This algorithm learns from a few sampled sets and generalizes it to the entire cache, thereby reducing area and power. Its key features include its ability to learn and predict faster as the sampling algorithm is only updated on a reference to sampler sets. This project is implemented in C++ and acheives an overall geometric mean speedup of 6.4% on SPEC CPU 2006 benchmarks.
Created an IR receiver in hardware that tests, receives and decodes a IR signal. The decoding logic is implemented in Verilog on Xilinx FPGA board. An interrupt based device driver is developed in Linux environment to print out the decoded message onto the display.


Xem interface
Xem interface
Xem interface
Xem interface
Get data from $$private.fieldData.
Infrastructure method. Not intended to be used by user code.
Remove all properties and data.
Get data for all fields of this list item (include user and look up field) then store them in $$private.
Set data to $$private.fieldData.
Add a updateItem
command to service context.
No need to check if field is existed in parentList (as it already checked).
Infrastructure method. Not intended to be used by user code.
Generated using TypeDoc
All immediate values are stored in
$$private.fieldData
field.


Next: Reinitializing an Instance, Previous: Object Creation and Initialization, Up: Objects
The function change-class can be used to change the class of an instance from its current class, C_{from}, to a different class, C_{to}; it changes the structure of the instance to conform to the definition of the class C_{to}.
Note that changing the class of an instance may cause slots to be added or deleted. Changing the class of an instance does not change its identity as defined by the eq function.
When change-class is invoked on an instance, a two-step updating process takes place. The first step modifies the structure of the instance by adding new local slots and discarding local slots that are not specified in the new version of the instance. The second step initializes the newly added local slots and performs any other user-defined actions. These two steps are further described in the two following sections.
| • Modifying the Structure of the Instance: | ||
| • Initializing Newly Added Local Slots (Changing the Class of an Instance): | ||
| • Customizing the Change of Class of an Instance: |


In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data and the model more.
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
A critical step in working with neural networks is preparing the data correctly. Variables on different scales make it difficult for the network to efficiently learn the correct weights. Below, we've written the code to load and prepare the data. You'll learn more about this soon!
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
This dataset has the number of riders for each hour of each day from January 1 2011 to December 31 2012. The number of riders is split between casual and registered, summed up in the cnt
column. You can see the first few rows of the data above.
Below is a plot showing the number of bike riders over the first 10 days or so in the data set. (Some days don't have exactly 24 entries in the data set, so it's not exactly 10 days.) You can see the hourly rentals here. This data is pretty complicated! The weekends have lower over all ridership and there are spikes when people are biking to and from work during the week. Looking at the data above, we also have information about temperature, humidity, and windspeed, all of these likely affecting the number of riders. You'll be trying to capture all this with your model.
rides[:24*10].plot(x='dteday', y='cnt')
Here we have some categorical variables like season, weather, month. To include these in our model, we'll need to make binary dummy variables. This is simple to do with Pandas thanks to get_dummies()
.
dummy_fields = ['season', 'weathersit', 'mnth', 'hr', 'weekday']
for each in dummy_fields:
dummies = pd.get_dummies(rides[each], prefix=each, drop_first=False)
rides = pd.concat([rides, dummies], axis=1)
fields_to_drop = ['instant', 'dteday', 'season', 'weathersit',
'weekday', 'atemp', 'mnth', 'workingday', 'hr']
data = rides.drop(fields_to_drop, axis=1)
data.head()
To make training the network easier, we'll standardize each of the continuous variables. That is, we'll shift and scale the variables such that they have zero mean and a standard deviation of 1.
The scaling factors are saved so we can go backwards when we use the network for predictions.
quant_features = ['casual', 'registered', 'cnt', 'temp', 'hum', 'windspeed']
# Store scalings in a dictionary so we can convert back later
scaled_features = {}
for each in quant_features:
mean, std = data[each].mean(), data[each].std()
scaled_features[each] = [mean, std]
data.loc[:, each] = (data[each] - mean)/std
We'll save the data for the last approximately 21 days to use as a test set after we've trained the network. We'll use this set to make predictions and compare them with the actual number of riders.
# Save data for approximately the last 21 days
test_data = data[-21*24:]
# Now remove the test data from the data set
data = data[:-21*24]
# Separate the data into features and targets
target_fields = ['cnt', 'casual', 'registered']
features, targets = data.drop(target_fields, axis=1), data[target_fields]
test_features, test_targets = test_data.drop(target_fields, axis=1), test_data[target_fields]
We'll split the data into two sets, one for training and one for validating as the network is being trained. Since this is time series data, we'll train on historical data, then try to predict on future data (the validation set).
# Hold out the last 60 days or so of the remaining data as a validation set
train_features, train_targets = features[:-60*24], targets[:-60*24]
val_features, val_targets = features[-60*24:], targets[-60*24:]
Below you'll build your network. We've built out the structure and the backwards pass.
You'll implement the forward pass through the network. You'll also set the hyperparameters: the learning rate, the number of hidden units, and the number of training passes.
Below, you have these tasks:
self.activation_function
in __init__
to your sigmoid function.train
method.train
method, including calculating the output error.run
method.class NeuralNetwork(object):
def __init__(self, input_nodes, hidden_nodes, output_nodes, learning_rate):
# Set number of nodes in input, hidden and output layers.
self.input_nodes = input_nodes
self.hidden_nodes = hidden_nodes
self.output_nodes = output_nodes
# Initialize weights
self.weights_input_to_hidden = np.random.normal(0.0, self.input_nodes**-0.5,
(self.input_nodes, self.hidden_nodes))
self.weights_hidden_to_output = np.random.normal(0.0, self.hidden_nodes**-0.5,
(self.hidden_nodes, self.output_nodes))
self.lr = learning_rate
self.activation_function = lambda x : 1 / (1 + np.exp(-x))
def train(self, features, targets):
''' Train the network on batch of features and targets.
Arguments
---------
features: 2D array, each row is one data record, each column is a feature
targets: 1D array of target values
'''
n_records = features.shape[0]
delta_weights_i_h = np.zeros(self.weights_input_to_hidden.shape)
delta_weights_h_o = np.zeros(self.weights_hidden_to_output.shape)
for X, y in zip(features, targets):
### Forward pass ###
hidden_inputs = np.dot(self.weights_input_to_hidden.T, X)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = np.dot(self.weights_hidden_to_output.T, hidden_outputs)
final_outputs = final_inputs
### Backward pass ###
error = y - final_outputs
output_error_term = error
hidden_error = output_error_term * self.weights_hidden_to_output
hidden_error_term = hidden_error.T * hidden_outputs * (1 - hidden_outputs)
# Weight step (input to hidden)
delta_weights_i_h += hidden_error_term * X[:, None]
delta_weights_h_o += output_error_term * hidden_outputs[:, None]
self.weights_hidden_to_output += self.lr / n_records * delta_weights_h_o
self.weights_input_to_hidden += self.lr / n_records * delta_weights_i_h
def run(self, features):
''' Run a forward pass through the network with input features
Arguments
---------
features: 1D array of feature values
'''
#### Implement the forward pass here ####
hidden_inputs = np.dot(features, self.weights_input_to_hidden)
hidden_outputs = self.activation_function(hidden_inputs)
final_inputs = np.dot(hidden_outputs, self.weights_hidden_to_output)
final_outputs = final_inputs
return final_outputs
def MSE(y, Y):
return np.mean((y-Y)**2)
Run these unit tests to check the correctness of your network implementation. This will help you be sure your network was implemented correctly before you starting trying to train it. These tests must all be successful to pass the project.
import unittest
inputs = np.array([[0.5, -0.2, 0.1]])
targets = np.array([[0.4]])
test_w_i_h = np.array([[0.1, -0.2],
[0.4, 0.5],
[-0.3, 0.2]])
test_w_h_o = np.array([[0.3],
[-0.1]])
class TestMethods(unittest.TestCase):
##########
# Unit tests for data loading
##########
def test_data_path(self):
# Test that file path to dataset has been unaltered
self.assertTrue(data_path.lower() == 'bike-sharing-dataset/hour.csv')
def test_data_loaded(self):
# Test that data frame loaded
self.assertTrue(isinstance(rides, pd.DataFrame))
##########
# Unit tests for network functionality
##########
def test_activation(self):
network = NeuralNetwork(3, 2, 1, 0.5)
# Test that the activation function is a sigmoid
self.assertTrue(np.all(network.activation_function(0.5) == 1/(1+np.exp(-0.5))))
def test_train(self):
# Test that weights are updated correctly on training
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
network.train(inputs, targets)
self.assertTrue(np.allclose(network.weights_hidden_to_output,
np.array([[ 0.37275328],
[-0.03172939]])))
self.assertTrue(np.allclose(network.weights_input_to_hidden,
np.array([[ 0.10562014, -0.20185996],
[0.39775194, 0.50074398],
[-0.29887597, 0.19962801]])))
def test_run(self):
# Test correctness of run method
network = NeuralNetwork(3, 2, 1, 0.5)
network.weights_input_to_hidden = test_w_i_h.copy()
network.weights_hidden_to_output = test_w_h_o.copy()
self.assertTrue(np.allclose(network.run(inputs), 0.09998924))
suite = unittest.TestLoader().loadTestsFromModule(TestMethods())
unittest.TextTestRunner().run(suite)
Here you'll set the hyperparameters for the network. The strategy here is to find hyperparameters such that the error on the training set is low, but you're not overfitting to the data. If you train the network too long or have too many hidden nodes, it can become overly specific to the training set and will fail to generalize to the validation set. That is, the loss on the validation set will start increasing as the training set loss drops.
You'll also be using a method know as Stochastic Gradient Descent (SGD) to train the network. The idea is that for each training pass, you grab a random sample of the data instead of using the whole data set. You use many more training passes than with normal gradient descent, but each pass is much faster. This ends up training the network more efficiently. You'll learn more about SGD later.
This is the number of batches of samples from the training data we'll use to train the network. The more iterations you use, the better the model will fit the data. However, if you use too many iterations, then the model with not generalize well to other data, this is called overfitting. You want to find a number here where the network has a low training loss, and the validation loss is at a minimum. As you start overfitting, you'll see the training loss continue to decrease while the validation loss starts to increase.
This scales the size of weight updates. If this is too big, the weights tend to explode and the network fails to fit the data. A good choice to start at is 0.1. If the network has problems fitting the data, try reducing the learning rate. Note that the lower the learning rate, the smaller the steps are in the weight updates and the longer it takes for the neural network to converge.
The more hidden nodes you have, the more accurate predictions the model will make. Try a few different numbers and see how it affects the performance. You can look at the losses dictionary for a metric of the network performance. If the number of hidden units is too low, then the model won't have enough space to learn and if it is too high there are too many options for the direction that the learning can take. The trick here is to find the right balance in number of hidden units you choose.
import sys
### Set the hyperparameters here ###
iterations = 2000
learning_rate = 1
hidden_nodes = 6
output_nodes = 1
batch_size = 128
N_i = train_features.shape[1]
network = NeuralNetwork(N_i, hidden_nodes, output_nodes, learning_rate)
losses = {'train':[], 'validation':[]}
for ii in range(iterations):
# Go through a random batch of 128 records from the training data set
batch = np.random.choice(train_features.index, size=batch_size)
X, y = train_features.iloc[batch].values, train_targets.iloc[batch]['cnt']
network.train(X, y)
# Printing out the training progress
train_loss = MSE(network.run(train_features).T, train_targets['cnt'].values)
val_loss = MSE(network.run(val_features).T, val_targets['cnt'].values)
sys.stdout.write("\rProgress: {:2.1f}".format(100 * ii/float(iterations)) \
+ "% ... Training loss: " + str(train_loss)[:5] \
+ " ... Validation loss: " + str(val_loss)[:5])
sys.stdout.flush()
losses['train'].append(train_loss)
losses['validation'].append(val_loss)
plt.plot(losses['train'], label='Training loss')
plt.plot(losses['validation'], label='Validation loss')
plt.legend()
plt.ylim((0,1.5))
Here, use the test data to view how well your network is modeling the data. If something is completely wrong here, make sure each step in your network is implemented correctly.
fig, ax = plt.subplots(figsize=(16,8))
mean, std = scaled_features['cnt']
predictions = network.run(test_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((test_targets['cnt']*std + mean).values, label='Data')
ax.plot(((test_features['weekday_0'] + test_features['weekday_6'])*std + mean).values, label='Weekend')
ax.plot((test_features['holiday']*std + mean).values, label='Holiday')
ax.set_xlim(right=len(predictions))
ax.legend()
dates = pd.to_datetime(rides.iloc[test_data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
ax.set_ylim((-100,1000));
Answer these questions about your results.How well does the model predict the data? Where does it fail? Why does it fail where it does?
Note: You can edit the text in this cell by double clicking on it. When you want to render the text, press control + enter
Model predics the data satisfyngly well. The daily cycle is refleced in the model. Quiet night periods are well modelled as well as commuting rush hours. The first weekend of December (15 & 16th) is also corretly predicted.
The model performs significantly worse on the christmas week and surrounding weekends. The bike rental was presumably lower, because the chrismas happend to be in the middle of the week, which encouraged people to take a whole week of holidays. The rush hour peak can suggest that the bike rentals are correlated with the fact whether people are working or not, on particular day.
The model fails, becasue:
It's architecture is too simple to synthesize features like: "day before or after the holiday" or "single working day between two non-working days". Additional hidden layer could inscrease the model capability to understand such concepts. Adding more neurons in the single existing hidden layer has no significant effect.
It has not enought data to reason about public holidays like christmas. Especially that christmas occurrs only one time in the training set. It data was spanning over more years back, would be an improvement.
fig, ax = plt.subplots(figsize=(16,8))
mean, std = scaled_features['cnt']
predictions = network.run(train_features).T*std + mean
ax.plot(predictions[0], label='Prediction')
ax.plot((train_targets['cnt']*std + mean).values, label='Data')
ax.plot(((train_features['weekday_0'] + train_features['weekday_6'])*std + mean).values, label='Weekend')
ax.plot((train_features['holiday']*std + mean).values, label='Holiday')
ax.legend()
dates = pd.to_datetime(rides.iloc[data.index]['dteday'])
dates = dates.apply(lambda d: d.strftime('%b %d'))
ax.set_xticks(np.arange(len(dates))[12::24])
_ = ax.set_xticklabels(dates[12::24], rotation=45)
ax.set_xlim((8150,8644))
ax.set_ylim((-100,1000))
ax.set_title('"Predicitons" on the training set');
fig, ax = plt.subplots()
heatmap = ax.pcolor(network.weights_input_to_hidden, cmap=plt.cm.Blues)
fig = plt.gcf()
fig.set_size_inches(7, 15)
ax.set_xticks(np.arange(hidden_nodes) + 0.5, minor=False)
ax.set_xticklabels([str(x+1) for x in range(0,hidden_nodes)], minor=False)
ax.set_yticks(np.arange(features.columns.shape[0]) + 0.5, minor=False)
ax.set_yticklabels(features.columns, minor=False)
ax.invert_yaxis()
fig.colorbar(heatmap)
ax.set_title('Hidden layer weights')
ax.set_xlabel('Hidden neuron');
fig, ax = plt.subplots()
heatmap = ax.pcolor(network.weights_hidden_to_output, cmap=plt.cm.Blues)
fig = plt.gcf()
ax.invert_yaxis()
ax.set_xticks(np.arange(output_nodes) + 0.5, minor=False)
ax.set_xticklabels([str(x+1) for x in range(0,output_nodes)], minor=False)
ax.set_yticks(np.arange(hidden_nodes) + 0.5, minor=False)
ax.set_yticklabels([str(x+1) for x in range(0,hidden_nodes)], minor=False)
fig.colorbar(heatmap)
ax.set_title('Output layer weights')
ax.set_ylabel('Hidden neuron');


Money Play. How should Red play 63?
pips Red=78 White=156
(CorrectAnswer= 0/0, ErrorScore= 0)
After the forced 8/2 Red can choose between 8/5, 5/2 or 4/1.
The worst of these by a long way is 5/2. The play does leave only one blot, but it creates a potential gap in front of White’s anchor. It will also subsequently leave bad fives for Red which will reduce the gammon win percentage. That play is a double blunder.
4/1 is merely an error. Red’s 5s are still potentially bad.
Red should accelerate his bear-in with 8/5, 8/2. White’s board is not particularly threatening if Red his hit. Even if White had a five-point prime on the other side of the board 8/5, 8/2 would still be the correct play by a long, long way.
The worst of these by a long way is 5/2. The play does leave only one blot, but it creates a potential gap in front of White’s anchor. It will also subsequently leave bad fives for Red which will reduce the gammon win percentage. That play is a double blunder.
4/1 is merely an error. Red’s 5s are still potentially bad.
Red should accelerate his bear-in with 8/5, 8/2. White’s board is not particularly threatening if Red his hit. Even if White had a five-point prime on the other side of the board 8/5, 8/2 would still be the correct play by a long, long way.
1. Cubeful 2-ply 8/5 8/2 Eq.: +0.905 0.761 0.492 0.007 - 0.239 0.033 0.001 2. Cubeful 2-ply 8/2 4/1 Eq.: +0.833 (-0.072) 0.747 0.447 0.006 - 0.253 0.027 0.001 3. Cubeful 0-ply 8/2 5/2 Eq.: +0.665 (-0.240) 0.692 0.415 0.007 - 0.308 0.035 0.000


L'ATALANTE (director: Jean Vigo; cast: Michel Simon (Jules), Jean Daste (Jean), Dita Parlo (Juliette), Louis Lefebvre (cabin boy), Gilles Margaritas (peddler), 1934-France)
Jean Vigo, whose father was an anarchist, as is he, sadly had a promising artistic future cut short because of his sudden death from a tubercular condition in 1934. He was just twenty-nine years- old and had made two brilliant feature films, ZERO CONDUCT and this film.
L'Atalante is, perhaps, considered by some, a dated film as far as its style goes. But with that being said, there is no denying that it is still powerful viewing because of its magically developed poetical gestures to the subject of love and the refreshingly lyrical way it looks out at its surrounding seascape towns and at the human condition. Its romantic story is much like Murnau's SUNRISE (1927), of innocents from the country getting corrupted by the big lure of the city, and then finding their true roots again. In this case, the peasant village girl (Juliette), is amiably played by the notable German silent screen starlet, Dita Parlo. She marries someone not from the area, the skipper of a motorized barge, Jean (Daste). His route passes her hometown.
The newlyweds adjust to marriage on the barge, with the eccentric mate, Jules (Simon), and the moronic cabin boy (Lefebvre) aboard. Jules' presence gives the film a certain strength, whether in revitalizing Juliette's spirits, which are encapsulated by her surprised reaction that she has met someone as knowing as him. She comes to this conclusion when she visits his room, where she sees that all the junk he has collected over his years from travelling across the sea and the plethora of cats he keeps, are extensions of his worldly personality. He becomes for her someone she can lean on for common sense guidance. By the force of his humanity and his paternalistic interest in the couple, he can bring to Juliette a sense of relief from this seemingly forlorn journey she is on. He is the heart and soul of this film; the richness of his character gives her a confidence in living. His later role will be that of savior, bringing her back to the captain after her disappearance.
The ordinary couple are playing out their vexing emotions without letting on how much these emotions mean to them, until it is almost too late. Because their emotions and passions are so dominating, the quality of the film engulfs everyone aboard the barge in an unreal state. The Seine looks so much like a dreamworld, where those on it are just drifting. Where romantic visions fade in and out, and hopes arise and perish on a whim.
The peasant girl, away from home for the first time, is bored by the same monotonous grind aboard the barge. She anxiously tells her husband that she wants to see the bright lights of Paris. On the night she has her heart set on going to Paris, as the barge is docked there, the roguish Jules decides to leave the boat and go to a palm reader to have his future told, forcing them to stay home and watch the barge. When the couple do go out in Paris, the husband is outraged at a peddler who tries to win his wife away from him at a dance hall, and he abruptly cuts that excursion short. Juliette still determined to see Paris, sneaks off ship; but the jealous captain, in a rage, takes off for the next town leaving her stranded. They both find that in their despair over their separation, that they do miss each other. When they are reunited, she will have seen enough of the world to know what it means to be on her own in the hostile world. The city will symbolize corruption and temptation for both of them, while life on the sea will come to mean honest work and innocence.
Because the story is such a simple one and the couple in so ordinary, it is not the story in itself that is exhilerating, as much as the way the film moves us to look at our own lives, with all its possibilities and disappointments. It makes us see things in a way that is disarmingly enchanting. That despite the moroseness and daily grind of regular life, there is also a poetical and romantic mood, waiting to be tapped.We are encouraged to look into our hearts and see what matters to us, just as Jean looked into the water for his missing bride and in a frazzled state saw an emanation of her, and dove into the water to go after what he was looking for. He realized that the love he has inside him is manifested in his dreams.Vigo has created a dreamworld.Which makes his film an out-of-this world cinematic experience that sublimely lingers in our consciousness.
The film version to see, which is now on video, is the restored 1990 one, that Pierre Philippe and Jean-Louis Bompoint recovered missing footage for.The film is now more decipherable from the botched up initial showing of the film, that was not well received by the Paris audience. It is good to see Vigo's almost original version of the film resurrected, at last, and look so stunningly beautiful in black-and-white.
REVIEWED ON 4/16/99 GRADE: A+
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
© ALL RIGHTS RESERVED DENNIS SCHWARTZ
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
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belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
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Related links: index of all rec.arts.movies.reviews reviews


GasPrice
public struct GasPrice
Gas price functions for erc721 token requests
-
Declaration
Swift
public func approve(to user: Address, token: BigUInt) throws -> BigUInt
Return Value
gas price for approve(address,uint256) transaction
-
Declaration
Swift
public func setApproveForAll(operator: Address, approved: Bool) throws -> BigUInt
Return Value
gas price for setApprovalForAll(address,bool) transaction


Digital Ocean Hackathon
Team Codesthesia
IDEA
To reduce the paper trail generated in the retail industry by creating a central bill storage location. This app creates a unique user dashboard which contains the bill details of all the purchases made by the user.
The app allows the user to get a list of all the service centres associated with that product via Google Maps and also a PDF format is created from the web form during the time of bill generation


Brisket sirloin doner, salami t-bone pork chop turducken ground round short loin.  Pork turkey capicola strip steak chicken bacon ribeye ham tail short loin salami tenderloin cow. Sausage t-bone meatball brisket shank, pork belly fatback shoulder swine ribeye porchetta tenderloin. Turducken pork chop biltong leberkas, pork loin ball tip filet mignon capicola tri-tip jowl ham porchetta. Pork loin corned beef spare ribs, rump doner jowl shoulder.
Brisket sirloin doner, salami t-bone pork chop turducken ground round short loin.  Pork turkey capicola strip steak chicken bacon ribeye ham tail short loin salami tenderloin cow. Sausage t-bone meatball brisket shank, pork belly fatback shoulder swine ribeye porchetta tenderloin. Turducken pork chop biltong leberkas, pork loin ball tip filet mignon capicola tri-tip jowl ham porchetta. Pork loin corned beef spare ribs, rump doner jowl shoulder.
Brisket sirloin doner, salami t-bone pork chop turducken ground round short loin.  Pork turkey capicola strip steak chicken bacon ribeye ham tail short loin salami tenderloin cow. Sausage t-bone meatball brisket shank, pork belly fatback shoulder swine ribeye porchetta tenderloin. Turducken pork chop biltong leberkas, pork loin ball tip filet mignon capicola tri-tip jowl ham porchetta. Pork loin corned beef spare ribs, rump doner jowl shoulder.
Brisket sirloin doner, salami t-bone pork chop turducken ground round short loin.  Pork turkey capicola strip steak chicken bacon ribeye ham tail short loin salami tenderloin cow. Sausage t-bone meatball brisket shank, pork belly fatback shoulder swine ribeye porchetta tenderloin. Turducken pork chop biltong leberkas, pork loin ball tip filet mignon capicola tri-tip jowl ham porchetta. Pork loin corned beef spare ribs, rump doner jowl shoulder.
Brisket sirloin doner, salami t-bone pork chop turducken ground round short loin.  Pork turkey capicola strip steak chicken bacon ribeye ham tail short loin salami tenderloin cow. Sausage t-bone meatball brisket shank, pork belly fatback shoulder swine ribeye porchetta tenderloin. Turducken pork chop biltong leberkas, pork loin ball tip filet mignon capicola tri-tip jowl ham porchetta. Pork loin corned beef spare ribs, rump doner jowl shoulder.


Lecture #12: Reflection and refraction
Computer graphics in Game development
Ivan Belyavtsev
21.02.2020
Reflection
“Reflection” experiment
TODO
- Add reflection for reflective materials
“Reflection” experiment
What is the new knowledge?
- How to recast usual rays
- How to implement reflections
- How to avoid stack overflow error
Refraction
Snell’s law
\[\frac{\sin(\theta_o)}{\sin(\theta_i)}=\frac{v_o}{v_i}=\frac{n_i}{n_o}\]
Fresnel equations
\(T\) - Refraction ability \(R\) - Reflection ability
\[T+R = 1\]
Fresnel equations
Reflectance
\(R_s\) - reflectance for s-polarized light
\(R_p\) - reflectance for p-polarized light
\[R = \frac{R_s + R_p}{2}\]
Fresnel equations
S-polarized light
\[R_s = \biggl|\frac{n_1\cos{\omega_i}-n_2\cos{\omega_o}}{n_1\cos{\omega_i}+n_2\cos{\omega_o}}\biggl|^2\]
Fresnel equations
P-polarized light
\[R_p = \biggl|\frac{n_1\cos{\omega_o}-n_2\cos{\omega_i}}{n_1\cos{\omega_o}+n_2\cos{\omega_i}}\biggl|^2\]
“Refraction” experiment
TODO
- Implement refractions for needed materials
“Refraction” experiment
- How to apply Snell’s laws to refractions
- How to apply Fresnel equations to refractions
References
1. McGuire M. The graphics codex. 2.14 ed. Casual Effects, 2018.


OpenVINO™ toolkit distribution includes a set of pre-trained models that you can use for learning and demo purposes or for developing a deep learning software.
Several detection models can be used to detect a set of the most popular objects - for example, faces, people, vehicles. Most of the networks are SSD-based and provide reasonable accuracy/performance trade-offs. Networks that detect the same types of objects (for example, face-detection-adas-0001
and face-detection-retail-0004
) provide a choice for higher accuracy/wider applicability at the cost of slower performance, so you can expect a "bigger" network to detect objects of the same type better.
| Model Name | Complexity (GFLOPs) | Size (Mp) | Face | Person | Vehicle | Bike | License plate |
|---|---|---|---|---|---|---|---|
| face-detection-adas-0001 | 2.835 | 1.053 | X | ||||
| face-detection-retail-0004 | 1.067 | 0.588 | X | ||||
| face-person-detection-retail-0002 | 2.757 | 0.791 | X | X | |||
| person-detection-retail-0001 | 12.422 | 3.244 | X | ||||
| person-detection-retail-0013 | 2.300 | 0.723 | X | ||||
| person-detection-action-recognition-0003 | 4.542 | 1.951 | X | ||||
| pedestrian-detection-adas-0002 | 2.836 | 1.165 | X | ||||
| pedestrian-and-vehicle-detector-adas-0001 | 3.974 | 1.650 | X | X | |||
| vehicle-detection-adas-0002 | 2.798 | 1.079 | X | ||||
| person-vehicle-bike-detection-crossroad-0078 | 3.964 | 1.178 | X | X | X | ||
| vehicle-license-plate-detection-barrier-0106 | 0.349 | 0.634 | X | X |
Object recognition models are used for classification, regression, and character recognition. Use these networks after a respective detector (for example, Age/Gender recognition after Face Detection).
| Model Name | Complexity (GFLOPs) | Size (Mp) |
|---|---|---|
| age-gender-recognition-retail-0013 | 0.093 | 1.416 |
| head-pose-estimation-adas-0001 | 0.105 | 1.911 |
| license-plate-recognition-barrier-0001 | 0.328 | 1.218 |
| vehicle-attributes-recognition-barrier-0039 | 0.126 | 0.626 |
| emotions-recognition-retail-0003 | 0.126 | 2.483 |
| landmarks-regression-retail-0009 | 0.021 | 0.191 |
| facial-landmarks-35-adas-0001 | 0.042 | 4.595 |
| person-attributes-recognition-crossroad-0031 | 0.219 | 1.102 |
Precise tracking of objects in a video is a common application of Computer Vision (for example, for people counting). It is often complicated by a set of events that can be described as a "relatively long absence of an object". For example, it can be caused by occlusion or out-of-frame movement. In such cases, it is better to recognize the object as "seen before" regardless of its current position in an image or the amount of time passed since last known position.
The following networks can be used in such scenarios. They take an image of a person and evaluate an embedding - a vector in high-dimensional space that represents an appearance of this person. This vector can be used for further evaluation: images that correspond to the same person will have embedding vectors that are "close" by L2 metric (Euclidean distance).
There are multiple models that provide various trade-offs between performance and accuracy (expect a bigger model to perform better).
| Model Name | Complexity (GFLOPs) | Size (Mp) | Pairwise accuracy |
|---|---|---|---|
| person-reidentification-retail-0031 | 0.028 | 0.280 | 92.11% |
| person-reidentification-retail-0079 | 0.124 | 0.820 | 92.93% |
| person-reidentification-retail-0076 | 0.594 | 0.820 | 93.35% |
| face-reidentification-retail-0071 | 0.586 | 1.059 | 99.33% |
Semantic segmentation is an extension of object detection problem. Instead of returning bounding boxes, semantic segmentation models return a "painted" version of the input image, where the "color" of each pixel represents a certain class. These networks are much bigger than respective object detection networks, but they provide a better (pixel-level) localization of objects and they can detect areas with complex shape (for example, free space on the road).
| Model Name | Complexity (GFLOPs) | Size (Mp) |
|---|---|---|
| road-segmentation-adas-0001 | 4.770 | 0.184 |
| semantic-segmentation-adas-0001 | 58.572 | 6.686 |
Human pose estimation task is to predict a pose: body skeleton, which consists of keypoints and connections between them, for every person in an input image or video. Keypoints are body joints, i.e. ears, eyes, nose, shoulders, knees, etc. There are two major groups of such metods: top-down and bottom-up. The first detects persons in a given frame, crops or rescales detections, then runs pose estimation network for every detection. These methods are very accurate. The second finds all keypoints in a given frame, then groups them by person instances, thus faster than previous, because network runs once.
| Model Name | Complexity (GFLOPs) | Size (Mp) |
|---|---|---|
| human-pose-estimation-0001 | 15.435 | 4.099 |
Deep Learning models find their application in various image processing tasks to increase the quality of the output.
| Model Name | Complexity (GFLOPs) | Size (Mp) |
|---|---|---|
| single-image-super-resolution-0034 | 39.713 | 0.363 |
[*] Other names and brands may be claimed as the property of others.


class: center, middle, inverse, title-slide # Reusability ## RRROBOTS Week 4 ### Hao Ye ### 2017, April 25 --- # Lesson Objectives ### Project Status ### Reusability * ### Licensing * ### Data, Code, and Methods Sharing * ### Preprints --- class: center, middle, inverse # Project Status --- # Project Check-in ### Roadmaps? -- ### Synchronizing Branches on GitHub * Pull Request -> merge changes across branches -- * Demo --- class: center, middle, inverse # Reusability --- # How to Maximize Your Research Impacts ### The more citations, the better! -- ### What are the kinds of things that might be citable? * data * software * methods * papers -- ### Need to specify how other people can use materials --- class: center, middle # Licensing --- # Copyright vs. License ### Copyright In the United States, certain (most?) creative works are protected by copyright. * assigned once work is in a fixed form * creator has rights to sell, copy, distribute, perform, etc. -- ### License Allows transferral of specific rights from the copyright holder to others. * by default, "all rights reserved" * explicit license allows others to use the work --- # MIT License ### Simple and Permissive * **Permissions** - commercial use - distribution - modification - private use * **Conditions** - accompanied with license and copyright notice * **Limitations** - liability - warranty --- # Apache License (2.0) ### Similar to MIT License, but explicit re: patent rights - contributors give patent rights to users - users who institute patent litigation using the work have patent rights revoked - allow companies to contribute to projects without worrying about patent litigation --- # GPL License (3.0) ### "Copyleft" License - source code must be included with copies and derivative works - modifications to the work must be distributed under the same terms + if you make additions, those get "absorbed" into the original + prevents for-profit companies from taking advantage of open-source work; if they build on top of it, everybody has access to those additions --- # Creative Commons ### framework for licensing artistic works - it does not cover source code like the open source licenses * CC0 -- (essentially) public domain * CC-BY -- (basic version); copies and derivatives must include attribution * {NC} clause -- no commerical usage * {ND} clause -- no derivative works * {SA} clause -- copies and derivative works must use the same license --- # Adding a License to a Repo on GitHub ### When creating a new repo ![](github_license.png) -- ### To an existing repo * Adding a file "license" allows selection of a license template. --- class: center, middle # Data, Code, and Methods Sharing --- # Sharing Platforms ### Persistency * the material doesn't just disappear if the owner deletes it ### Identifiability * assign a DOI (digital object identifier) to reference a specific version / copy * DOIs are unique - will always resolve to the same object ### Other Features * ease of use * searching --- # Data/Code Platforms ### Git Hosting * GitHub, BitBucket (non-permanent) * lots of features for collaborating and working, but not suitable for archival ### Archiving * Figshare, Zenodo (free for individuals) * Dryad ($120 data publishing charge, up to 20GB) ### Issues * some journals have data/code publication requirements - specific services may be supported --- # Increasing Usage ![](data_archives.png) http://blogs.nature.com/scientificdata/2015/03/08/repository-highlight-figshare-and-the-crucial-service-of-generalist-repositories/ --- # protocols.io (methods sharing) * like GitHub for lab protocols - allows sharing, public/private settings, duplication and modifications, version control * can assign DOI for citation purposes * free (basic level) * smartphone apps to interactively step through protocol in lab -- ### Example * Chi-Kuang Yao, Yu-Tzu Liu, I-Chi Lee, You-Tung Wang, Ping-Yen Wu. "A Ca2+ channel differentially regulates Clathrin-mediated and activity-dependent bulk endocytosis". *PLOS Biology* April 17, 2017 ![](protocols_io_methods.png) --- class: center, middle # Preprints --- # Basic Info ### What are "preprints"? A version of a scientific paper before peer-reviewed publication. -- ### Why * Get your scientific results out sooner for use by yourself and others * Establish precedence before discussing or presenting work in conferences - can be listed on CVs, proposals to some funding agencies * Receive feedback from others before submitting to journals --- # Pathways to Open Access ![](How-to-do-OA-2.png) http://fossilsandshit.com/a-more-useful-infographic-about-free-legal-easy-open-access/ --- # Issues ### Community Norms * common in some fields (math, physics, computer science) * was previously suggested for biomedical research, but [became pubmed instead](https://scholarworks.iu.edu/dspace/bitstream/handle/2022/170/wp01-03B.html) -- ### Challenges * not all journals allow citations of preprints * not all journals accept preprints for peer-review --- # ESA Journals

All ESA journals accept submissions of ms that have previously been posted to a preprint service such as arXiv! A great way to get feedback!
— Scott L Collins (@ESA_Prez2013) September 5, 2012
 --- # L&O Letters
**Policy on Submission of Preprints** L&O-Letters will consider submissions that have previously been made available, either on a preprint server like arXiv, bioRxiv, or PeerJ PrePrints. Any such submissions must, however, not have been published in a scientific journal, book or other venue that could be considered formal publication. Authors must inform the editorial office at submission if their paper has been made available as a preprint, and they must agree to the open-access policy of this journal.
 http://aslopubs.onlinelibrary.wiley.com/hub/journal/10.1002/(ISSN)2378-2242/about/author-guidelines.html -- ### L&O currently does not!! but a letter is being drafted to convince the Editor in Chief otherwise: https://github.com/adsteen/LO_letter --- # Other Platforms ### Collaborative Writing * Authorea (https://www.authorea.com/) * Overleaf (https://www.overleaf.com/) ### Workflow (?) Platforms * Open Science Framework (https://osf.io/) - locally supported by UCSD Libraries - Reid Otsuji will give an intro & demo (May 16) * Stencila (https://stenci.la/) - hybrid spreadsheet / R / git (?) --- # References 1. GitHub [Pull Requests](https://help.github.com/articles/about-pull-requests/) 2. US Copyright Office - [Copyright Basics](https://www.copyright.gov/circs/circ01.pdf) 3. [Choosing a license](https://choosealicense.com/) 4. GitHub & Zenodo [tutorial](https://guides.github.com/activities/citable-code/) 5. GitHub & Figshare [tutorial](https://support.figshare.com/support/solutions/articles/6000150264-how-to-connect-figshare-with-your-github-account) 6. [We Need a GitHub for Academic Research](http://www.slate.com/articles/technology/future_tense/2017/04/we_need_a_github_for_academic_research.html) --- class: center, middle # Acknowledgments Class supported by:   Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).


--- layout: archive title: Zulip Chat Archive permalink: /stream/116395-maths/topic/Structure.20Theorem.html ---
I want to prove the structure theorem of finitely generated modules over a PID in Lean.
I'm trying to define free modules by isomorphic to direct sum R^n
.
But I don't understand why in linear_algebra.direct_sum_module
, the indexing type iota
of a direct sum direct_sum iota beta
needs to have decidable equality. In fact, I don't think I know what having "decidable equality" means, can someone explain please?
https://leanprover.github.io/theorem_proving_in_lean/type_classes.html#decidable-propositions is probably a good starting point
For p : Prop
, p
is decidable is supposed to mean we can prove either p
or \not p
.
This is captured in Lean by the class decidable p
since if
one has either hp : p
or hnp : \not p
, then one has an instance of decidable p
.
So decidable_eq \iota
means equality of terms in \iota
is decidable?
And the reason we care about decidable propositions is because
sometimes we want to define data depending on the validity of a proposition?
So for the case of index of direct sums... I'm not sure what this means :thinking:
In principle decidable instances could make things computable. But I don't anything about this. I simply fake decidability using classical.prop_decidable
each time Lean complains.
I think that this should probably be changed in line with finsupp
, and we should just use classical decidable equality for direct sum
@Ken Lee I think @Amelia Livingston has already made a start on this, maybe you want to talk to her on Thurs or before?
"Decidable equality" is a computer science thing. If you switch on classical mode (i.e. mathematician mode) like Patrick says then it becomes automatic, that's why you've not heard of it. I think that if you've got an up to date mathlib you can just type open_locale classical
and then never have to worry about this again.
@Kevin Buzzard Thanks for the fix. Yes, I have already talked with Amelia about this.
She said she's looking to restart and do the Smith Normal Form proof since they did it in Algebra 3.
I was thinking of doing the proof via splitting the module into free and torsion, splitting torsion into p-torsion submodules,
then doing structure theorem for p-torsion.
Ok great! The theorem will give you both the structure theorem for finite abelian groups and the Jordan canonical form which I think are the last two theorems from M2PM2 which need formalising
Question about the definition of dimension of a vector space:
Why is it not defined as the cardinality of some basis? (It is defined as the minimal cardinality.)
The dimension theorem proves these definition match anyway.
In other words, is it bad if I defined rank of a free module as cardinality of just some basis?
Over the zero ring, the zero module has the basis {} and the basis {0}. But this is a bit of a pathological case.
For a non-zero ring, these definitions will all be equivalent so it doesn't matter (to me at least) which one you choose, although at some point I guess you'll have to prove that they're equivalent.
(commutative ring)
Good point, I was proving this by reducing modulo a maximal ideal
Since bases are indexed now, there are infinite bases for the zero module over the zero ring.
{% endraw %}Last updated: Apr 24 2020 at 19:02 UTC


XCUTSEL(1) XCUTSEL(1) NAME xcutsel - interchange between cut buffer and selection SYNOPSIS xcutsel [ -toolkitoption ...] [-selection selection] [-cutbuffer number] DESCRIPTION The xcutsel program is used to copy the current selection into a cut buf‐ fer and to make a selection that contains the current contents of the cut buffer. It acts as a bridge between applications that don't support selections and those that do. By default, xcutsel will use the selection named PRIMARY and the cut buf‐ fer CUT_BUFFER0. Either or both of these can be overridden by command line arguments or by resources. An xcutsel window has the following buttons: quit When this button is pressed, xcutsel exits. Any selections held by xcutsel are automatically released. copy PRIMARY to 0 When this button is pressed, xcutsel copies the current selection into the cut buffer. copy 0 to PRIMARY When this button is pressed, xcutsel converts the current con‐ tents of the cut buffer into the selection. The button labels reflect the selection and cutbuffer selected by command line options or through the resource database. When the ``copy 0 to PRIMARY'' button is activated, the button will remain inverted as long as xcutsel remains the owner of the selection. This serves to remind you which client owns the current selection. Note that the value of the selection remains constant; if the cutbuffer is changed, you must again activate the copy button to retrieve the new value when desired. OPTIONS Xcutsel accepts all of the standard X Toolkit command line options as well as the following: -selection name This option specifies the name of the selection to use. The default is PRIMARY. The only supported abbreviations for this option are ``-select'', ``-sel'' and ``-s'', as the standard toolkit option ``-selectionTimeout'' has a similar name. -cutbuffer number This option specifies the cut buffer to use. The default is cut buffer 0. X DEFAULTS This program accepts all of the standard X Toolkit resource names and classes as well as: selection (class Selection) This resource specifies the name of the selection to use. The default is PRIMARY. cutBuffer (class CutBuffer) This resource specifies the number of the cut buffer to use. The default is 0. WIDGET NAMES The following instance names may be used when user configuration of the labels in them is desired: sel-cut (class Command) This is the ``copy SELECTION to BUFFER'' button. cut-sel (class Command) This is the ``copy BUFFER to SELECTION'' button. quit (class Command) This is the ``quit'' button. SEE ALSO X(7), xclipboard(1), xterm(1), text widget documentation, individual client documentation for how to make a selection. BUGS There is no way to change the name of the selection or the number of the cut buffer while the program is running. AUTHOR Ralph R. Swick, DEC/MIT Project Athena X Version 11 xclipboard 1.1.2 XCUTSEL(1)


[82]
P.J. Gerber, H. Steinfeld, B. Henderson, A. Mottet, C. Opio, J. Dijkman, A. Falcucci, G. Tempio, Tackling climate change through livestock – A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO), Rome (2013).
https://www.fao.org/3/i3437e/i3437e.pdf
.


My Name is Brian Kavagi. I am a new programmer at Moringa School.I currently have no job experience but i'm hopeful to get a job very soon.
I decided to take a class on programming because I enjoy working with computers and love the way they can be used to make work easier. I would also like to make the world a better place with the help of computer programming.
My current interest is to learn programming and how to solve various problems with programming.


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Last week, the party teleported into Torvid's treasure room1. Nosferatu and Hak stealthily assassinated the various troll guards that blocked their way out, and they reached the surface without major incident. As planned, Nosferatu created a distraction, drawing Torvid's guards away so the party could attack Torvid. The party ran out when Nosferatu's distraction began and found Torvid - a towering, twenty foot tall war troll2 clad in finely crafted adamantine armor, wielding a fifteen foot long adamantine greatsword. He was flanked by two mech trolls and speaking with a human shaman.
Undaunted, the party charged into battle. They executed the unusual tactical maneuver of sacrificing their entire front line to gang up on the shaman, killing him before he could cast any of his devastating and really awesome spells that would have made the fight much more difficult. However, this allowed Torvid to dive into their back line of casters, and he attacked in a flurry, nearly killing Antimony. The fighters quickly returned, and Lord Khallus and Maxwell managed to goad Torvid into attacking Maxwell while Antimony escaped. Under the party's combined assault, Torvid was forced to retreat, but was brought down before he could make it back to his approaching guards.
When Torvid fell, the party prepared to retreat from the mech troll guards. However, Torvid's armor floated into the air on its own, disregarding Torvid's increasingly dismembered body, and began attacking. With little time to spare, Lorelei temporarily disabled the armor's magic, allowing the party to throw it into a bag of holding and make a quick escape with another one of Reggie's teleportation stones.
Join us this week to celebrate your victory!


Iran releases British-Iranian academic on bail
Tehran: A British-Iranian anthropologist held in Iran for three months has been released on bail, his wife told state news agency IRNA on Wednesday.
"Kameel Ahmady was released on Sunday evening on a 500 million (toman, $42,000) bail after three months of temporary detention," IRNA quoted Shafagh Rahmani as saying.
Iran confirmed Ahmady's arrest in October, less than three months after the detention of Franco-Iranian academic Fariba Adelkhah was publicised.
French anthropologist Christian Bromberger wrote in August that Ahmady was "recently arrested", in an article published on the French edition of The Conversation website.
Author of a book in English titled "In the Name of Tradition: Female Genital Mutilation in Iran", Ahmady holds dual British and Iranian citizenship, according to Bromberger, himself an Iran specialist.
IRNA said Ahmady had researched and published books on "child marriage, white marriage (unmarried cohabitation), child labour in Tehran as well as identity and ethnicity in Iran."
Ahmady is subject to a "preliminary investigation" on suspicion of "links to foreign countries and institutes affiliated with foreign (intelligence) services," said judiciary spokesman Gholamhossein Esmaili in a press conference in October.
On 15 July, French authorities announced Adelkhah had been arrested in Iran. Esmaili confirmed her arrest the next day.
A specialist in Shiite Islam, Adelkhah is the research director at the Centre for International Studies and Research at Sciences Po University in Paris.
Roland Marchal, a colleague of Adelkhah and a researcher at Sciences Po in Paris, was also arrested in June and accused of "collusion against national security", according to his lawyer Saeed Dehghan.


In this article, I assume that you have already been working with the node js app with a route that you want to state.
I am using ES6+
This my file that we are going to test.
Before we start writing code for testing, we have to know some libraries that can be used for the integration test in Node js.
In Node JS we have man libraries for testing like:
- Mocha
- Jest
-Supertest and other Many more
N.B: We are starting. First of all, we have to install test libraries that will help us with the integration test.
npm install mocha, chai, chai-http
After installing those package you create a test folder in the root directory, in test folder create file name welcome.test.js
After creating a welcome test file. Now we are going to write a test to test get route at ”/”.Below are the codes
When we write a test, we assume the value and we test to see if the returned value will be equal to the value we assumed before. That is the reason for the word expect. Before we run the test we have to add a test script in package.json so that we can be able to run the test above.
When we write a test, we assume the value and we test to see if the returned value will be equal to the value we assumed before. That is the reason for the word expect. Before we run the test we have to add a test script in package.json so that we can be able to run the test above.
Thank you for reading this blog about setting an integration test in NodeJs with HTTP route.
For more information about the frameworks, I have used mocha and chai.


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What is VexFlow?
VexFlow is an open-source online music notation rendering API. It is written
completely in JavaScript, and runs right in the browser. VexFlow supports
HTML5 Canvas and SVG.
Sponsor VexFlow
VexFlow has been in development for over 10 years, and used by apps, startups,
universities, in all kinds of amazing ways. If you find VexFlow useful, please
consider sponsoring me on GitHub.
Where Do I Start?
The best place to start adding music notation to your apps and websites
is with the EasyScore Tutorial.
Features
Vexflow can be used to create all kinds of sophisticated music applications and
websites. Have a look at the tests
page for a comprehensive list of features.
What is VexTab?
VexTab is an easy-to-use
language to quickly create, edit, and render standard notation and guitar
tablature.
Follow the tutorial on the VexTab Tutorial page to get started using VexTab.
Follow the tutorial on the VexTab Tutorial page to get started using VexTab.
The Code
VexFlow is distributed under the MIT license, and all the code is
available at the VexFlow GitHub
Repository. VexTab is an open specification and the reference implementation is
open source. This implementation is free for non-commercial use. See details
at the VexTab GitHub
Repository.
More Stuff
We're always building and experimenting with new things related to music
and audio on the web. Try these out:
- VexWarp - An online tool for slowing down, stretching, pitch shifting, and looping music and audio. Great for practising and transcribing.
- VexChords - Render guitar chords in your browser.
- Pitchy Ninja - This fun game trains your ear and improves your voice with personalized exercises and realtime feedback.


I'm doing a lot of stored routine programming lately, working on common_schema. I'm in particular touching at the extremes of abilities. Some things just can't be done from within a stored routine. Here's a list of can't be done:
- Cursor for SHOW statements: can't be done -- this is explicitly blocked from operating (it once used to work).
- Get detailed error information on exceptions: apparently 5.6 has support for this. 5.1 and 5.5 do not.
- Change binlog_format: this is obvious, if you think about it. binlog_format dictates how the routine itself is replicated in the first place.
- Set sql_log_bin. Again, this makes sense.
- Work out different results depending on current machine. For example, you can't have a routine that returns with 'master' on the master and with 'slave' on the slave. That is, not under any condition. Consider: if Row Based Replication is used, you don't actually have a routine executing on the slave. I'm happy to be proven wrong on this.
- Know what database was in use by calling code. The routine executes within the context of the database where it is defined. But you can't tell what database was in use just a couple milliseconds before.
- Likewise, know what sql_mode was in use by calling code. Stored routines have their own sql_mode - the one they were created with. No way to check up on the calling stack.
- And you can't USE another database (database as in schema). USE is a client command.
- Reconnect after failure (kind of obvious, isn't it?)
- Connect to other servers (not so obvious to SQL Server DBAs). You can't issue queries on other servers. Bummer.
- Shutdown the server
- Fork (you're in a connection, you can't issue a new connection from your own connection)
Well, some of the above can be solved using plugins or User Defined Functions, but I'm looking at standard servers.
Things that can be done
I can't list anything that can be done from within a routine, but, to balance, here's a brief list of things that can be done:
- Recover from errors (e.g. deadlocks) via DECLARE CONTINUE HANDLER.
- Perform table operations (ANALYZE, OPTIMIZE, ...) - though not read the results of these operations other than knowing they succeeded.
- Perform all DDL statements (create/drop/modify views, routines, triggers, events, tables, users)
- Modify session/global variables (group_concat_max_len, innodb_stats_on_metadata, ...
)
You should be aware of
- max_sp_recursion_depth: the maximum recursion depth, if you're thinking of recursions.
- thread_stack: I find that setting to 256K makes a huge difference over the 5.1 default of 192K. With 192K, I frequently run into "out of stack space" problems. With 256K - I have yet to encounter that. Dunno, some kind of magic number? This is my observation.


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[SUMMARY GOES HERE]
[TOP COMMENT GOES HERE]
n comments — posted: "2020-12-21T18:34:43.511Z" — published: "2020-12-21T18:34:43.511Z"-
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10x
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10x
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10x
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10x


--- title: Studia Przypadków permalink: /case-studies/ category_list: ["Embedded","Enterprise Software/SaaS","Mobile/Wireless","Rich Media","User Experience","Corporate Communications/Websites"] sidebar: nav: "case-studies" --- {% include base_path %} {% assign lst = [] %} {% for cat in page.category_list %}


{% extends "layout.html" %} {% block content %}
{{ uri }}
| Property | Value |
|---|---|
| Accommodation And Food Services | {{ accommodation_and_food_services }} |
| Administrative And Support Services | {{ administrative_and_support_services }} |
| Agriculture Forestry Fishing | {{ agriculture_forestry_fishing }} |
| Arts And Recreation Services | {{ arts_and_recreation_services }} |
| Construction | {{ construction }} |
| Education And Training | {{ education_and_training }} |
| Elect Gas Water Waste Services | {{ elect_gas_water_waste_services }} |
| Financial And Insurance Services | {{ financial_and_insurance_services }} |
| Health Care And Social Assistance | {{ health_care_and_social_assistance }} |
| Information Media And Telecommunications | {{ information_media_and_telecommunications }} |
| Manufacturing | {{ manufacturing }} |
| Mining | {{ mining }} |
| Other Services | {{ other_services }} |
| Professional Scientific And Technical Services | {{ professional_scientific_and_technical_services }} |
| Public Administration And Safety | {{ public_administration_and_safety }} |
| Rental Hiring And Real Estate Services | {{ rental_hiring_and_real_estate_services }} |
| Retail Trade | {{ retail_trade }} |
| Transport Postal And Warehousing | {{ transport_postal_and_warehousing }} |
| Wholesale Trade | {{ wholesale_trade }} |
| Unclassified Businesses | {{ unclassified_businesses }} |
| Total Number Of Businesses | {{ total_number_of_businesses }} |
| Number Of Registered Charity Organisations | {{ number_of_registered_charity_organisations }} |
| Agriculture And Fishing Support Services | {{ agriculture_and_fishing_support_services }} |
| Aquaculture | {{ aquaculture }} |
| Dairy Cattle Farming | {{ dairy_cattle_farming }} |
| Deer Farming | {{ deer_farming }} |
| Fishing | {{ fishing }} |
| Forestry And Logging | {{ forestry_and_logging }} |
| Forestry Support Services | {{ forestry_support_services }} |
| Fruit And Tree Nut Growing | {{ fruit_and_tree_nut_growing }} |
| Hunting And Trapping | {{ hunting_and_trapping }} |
| Mushroom And Vegetable Growing | {{ mushroom_and_vegetable_growing }} |
| Nursery And Floriculture Production | {{ nursery_and_floriculture_production }} |
| Other Crop Growing | {{ other_crop_growing }} |
| Other Livestock Farming | {{ other_livestock_farming }} |
| Poultry Farming | {{ poultry_farming }} |
| Sheep Beef Cattle And Grain Farming | {{ sheep_beef_cattle_and_grain_farming }} |
| Total Nnumber Of Primary Producers | {{ total_number_of_primary_producers }} |


13.52. Release 0.196
General Changes
- Fix behavior of
JOIN ... USING
to conform to standard SQL semantics.
The old behavior can be restored by setting the deprecated.legacy-join-using
configuration option or the legacy_join_using
session property.
- Fix memory leak for queries with
ORDER BY
.
- Fix tracking of query peak memory usage.
- Fix skew in dynamic writer scaling by eagerly freeing memory in the source output
buffers. This can be disabled by setting
exchange.acknowledge-pages=false
.
- Fix planning failure for lambda with capture in rare cases.
- Fix decimal precision of
round(x, d)
when x
is a DECIMAL
.
- Fix returned value from
round(x, d)
when x
is a DECIMAL
with
scale 0
and d
is a negative integer. Previously, no rounding was done
in this case.
- Improve performance of the
array_join()
function.
- Improve performance of the
ST_Envelope()
function.
- Optimize
min_by()
and max_by()
by avoiding unnecessary object
creation in order to reduce GC overhead.
- Show join partitioning explicitly in
EXPLAIN
.
- Add
is_json_scalar()
function.
- Add
regexp_replace()
function variant that executes a lambda for
each replacement.
Hive Changes
- Fix a correctness issue where non-null values can be treated as null values
when writing dictionary-encoded strings to ORC files with the new ORC writer.
- Fix invalid failure due to string statistics mismatch while validating ORC files
after they have been written with the new ORC writer. This happens when
the written strings contain invalid UTF-8 code points.
- Add support for reading array, map, or row type columns from partitions
where the partition schema is different from the table schema. This can
occur when the table schema was updated after the partition was created.
The changed column types must be compatible. For rows types, trailing fields
may be added or dropped, but the corresponding fields (by ordinal)
must have the same name.
- Add
hive.non-managed-table-creates-enabled
configuration option
that controls whether or not users may create non-managed (external) tables.
The default value is true
.


DOGMA (Lions Gate) Starring: Linda Fiorentino, Ben Affleck, Matt Damon, Chris Rock, Jason Mewes, Kevin Smith, Jason Lee, Alan Rickman, Salma Hayek, George Carlin. Screenplay: Kevin Smith. Producers: Scott Mosier. Director: Kevin Smith. MPAA Rating: R (profanity, adult themes, violence, drug use) Running Time: 125 minutes. Reviewed by Scott Renshaw.
Kevin Smith's DOGMA is offensive. Devotees of film industry scuttlebutt probably know that Miramax, DOGMA's original distributor, put the film on the auction block after religious groups started turning up the heat. Catholic organizations in particular declaimed the film's subject matter, many proclaiming it sacrilegious sight unseen ("I don't need to swim in a sewer to know it stinks," went one typical slice of logic). It won't surprise any of those folks when I say DOGMA is offensive. And I'm not even talking about the theological content.
Those musings are linked -- rather tenuously -- to a plot involving the very fate of existence itself. It seems two outcast angels -- Loki (Matt Damon) and Bartleby (Ben Affleck) -- have found a loophole involving the Catholic dogma of plenary indulgence, through which they plan to re-enter heaven. Circumventing God's will would, however, result in the end of all creation, so a human agent is summoned to thwart the angels' plans. That human is Bethany Sloan (Linda Fiorentino), a Catholic of questionable faith and even more questions about her mission. Accompanied by several celestial assistants -- including unknown 13th apostle Rufus (Chris Rock) and the muse Serendipity (Salma Hayek) -- and two very earth-bound assistants -- Jay (Jason Mewes) and Silent Bob (Kevin Smith) -- Bethany tries to save the universe between lengthy conversations.
And oh boy, are there plenty of lengthy conversations. Smith's eternity-threatening premise is probably meant to distract from an otherwise non-stop series of metaphysical/scriptural/doctrinal discussions on everything from the nature of God to religious intolerance. It doesn't. DOGMA is an incredibly long sit, a deep-n-profound dorm room debate of a movie without the benefit of cold pizza and beer. Sometimes Smith is simply snarky (a deconstruction of "The Walrus and the Carpenter" as a critique of organized religion), in which cases he often churns out genuine laughs. Sometimes Smith is serious (Bartleby's rant against God's unfair preferential treatment of humans over angels), in which cases he's alternately perceptive and tedious. And sometimes Smith cops an attitude (Loki's swift judgment on a corporate boardroom), in which cases you want to remind him that anyone who gives Jay and Silent Bob major roles in a film should look at the plank in his own eye before going after the mote in anyone else's.
When Smith actually does get into the events of his plot, things don't get much better. Smith has always been a filmmaker only in the most nominal sense, but his limitations are even more evident when he makes a half-hearted effort to add supernatural action to the mix (radically shifting the tone from comedy to grim violence along the way). In a way, the low-tech fight sequences have their charms; battles always seem to occur just outside the frame, and people come flying through the air from improbable directions. It's hard to cut Smith that much slack, though, because you can't help but think his choices are based entirely on his limited visual vocabulary. He'll point a camera at people sitting around a table as long as you'll let him. Ask him to throw in one piece of real action for our efforts, and he turns into Captain Coy.
I suppose the most frustrating thing about DOGMA is that, whatever your feelings about its messages, Kevin Smith is the wrong messenger. It's not that his insights aren't insightful -- he has some interesting things to say about faith, religions and self-righteousness -- but that he's pitching his weighty notions in such a scattershot fashion. LIFE OF BRIAN could get away with borderine sacrilege because Monty Python played it smart and scathing; DOGMA may be a sincere expression of belief, but it's too juvenile too often. Smith opens the film with a disclaimer that the film is only meant to be a satire, then includes a comment to the effect that "God must have a sense of humor ... look at the platypus." You simply _must_ do better than that if you want to turn sacred cows into fajitas. I hope Smith feels better about religion after venting his spleen into a feature film. Losing two hours to deep thoughts wrapped in limp jokes -- now _that_ I find offensive.
On the Renshaw scale of 0 to 10 indulgences: 4.
Visit Scott Renshaw's Screening Room http://www.inconnect.com/~renshaw/ *** Subscribe to receive new reviews directly by email! See the Screening Room for details, or reply to this message with subject "Subscribe".
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