Treebank Statistics: UD_Teko-TuDeT: Features: Case
This feature is universal but the values Ref
are language-specific.
It occurs with 6 different values: Abl
, All
, Dat
, Loc
, Ref
, Tra
.
343 tokens (15%) have a non-empty value of Case
.
256 types (20%) occur at least once with a non-empty value of Case
.
166 lemmas (29%) occur at least once with a non-empty value of Case
.
The feature is used with 10 part-of-speech tags: NOUN (245; 11% instances), VERB (18; 1% instances), DET (17; 1% instances), ADV (15; 1% instances), PROPN (14; 1% instances), ADP (13; 1% instances), PRON (12; 1% instances), AUX (4; 0% instances), PART (4; 0% instances), NUM (1; 0% instances).
NOUN
245 NOUN tokens (30% of all NOUN
tokens) have a non-empty value of Case
.
The most frequent other feature values with which NOUN
and Case
co-occurred: Person=EMPTY (213; 87%), Number=EMPTY (199; 81%), Rel=EMPTY (191; 78%).
NOUN
tokens may have the following values of Case
:
Abl
(14; 6% of non-emptyCase
): kawi, wɨrawi, apamawi, apamaʔewi, baʔewi, kʷarawi, urukureʔawi, ewinẽ, idʒuwi, ikuɲawiAll
(14; 6% of non-emptyCase
): watekotɨ, masogotɨ, nõdezarakotɨ, wɨrakarakotɨ, apɨdʒakotɨ, nõdebaʔemãʔẽkotɨ, oapɨdʒakotɨ, watekotɨwarakom, wɨrakarakotɨnuwãDat
(21; 9% of non-emptyCase
): bokalape, lekolape, maziʔogape, Kayenape, pitakomape, zoaʔɨrape, ʔarape, erapɨdʒape, ikerape, iparɨrupeLoc
(46; 19% of non-emptyCase
): ʔɨb, baʔepopedʒi, bokalapedʒi, kijapope, kukupope, patupope, tab, zawapinimaʔar, ʔɨpope, kopopeRef
(113; 46% of non-emptyCase
): lekola, apama, awakʷəra, amana, lekolazarakom, zawara, awakʷərakom, ikɨwɨrakom, tapɨdʒa, zawarareheTra
(37; 15% of non-emptyCase
): ʒãdamam, ɨŋam, tarawadʒam, wãĩwĩam, aʔɨamnẽ, baʔeraʔɨraʔepɨhɨgam, duriam, karaam, kudʒaburuam, maireamEMPTY
(573): zawar, tapɨdʒ, wãĩwĩ, baipuri, baʔe, raʔɨr, sə, awakʷər, eiba, ka
| Paradigm apɨdʒ | Dat | Loc | Tra | All | Ref |
|---|---|---|---|---|---|
| Clusivity=In|Nmzr=Rel|Number=Plur|Person=1 | nõderapɨdʒapope | ||||
| Corf=Yes|Person=3 | apɨdʒakotɨ, oapɨdʒakotɨ | ||||
| Foc=Yes|Nmzr=Rel | rapɨdʒate | ||||
| Foc=Yes|Rel=Cont | rapɨdʒate | ||||
| Int=Yes|Rel=Cont | rapɨdʒato | ||||
| Number=Sing|Person=1|Rel=Cont | erapɨdʒape | erapɨdʒape | |||
| Person=2|Rel=Cont | nõderapɨdʒam | ||||
| Rel=Abs | tapɨdʒape | tapɨdʒapope | tapɨdʒam | tapɨdʒa |
VERB
18 VERB tokens (3% of all VERB
tokens) have a non-empty value of Case
.
The most frequent other feature values with which VERB
and Case
co-occurred: Aspect=EMPTY (18; 100%), Voice=EMPTY (16; 89%), Number=EMPTY (11; 61%).
VERB
tokens may have the following values of Case
:
All
(1; 6% of non-emptyCase
): nõdebaʔemãʔẽkotɨDat
(1; 6% of non-emptyCase
): mãʔẽharakomapeRef
(16; 89% of non-emptyCase
): arowarowagamãʔẽkom, ereikɨdʒanũwã, eretarawadʒenuwã, ewurana, okoaraõwã, omomokoɲanam, omõgʷeraharanẽ, onanmãʔẽate, opokadʒaitʃe, ozepɨhɨgEMPTY
(629): oho, oiɲuŋ, eʔi, abaʔe, obaʔe, ohoŋ, owir, oʔa, oker, omãʔẽnẽ
Case
seems to be lexical feature of VERB
. 100% lemmas (17) occur only with one value of Case
.
DET
17 DET tokens (23% of all DET
tokens) have a non-empty value of Case
.
The most frequent other feature values with which DET
and Case
co-occurred: Foc=EMPTY (11; 65%).
DET
tokens may have the following values of Case
:
Abl
(4; 24% of non-emptyCase
): aʔewi, aŋawi, ɨɲawiteAll
(1; 6% of non-emptyCase
): aʔegotɨnẽʔeLoc
(1; 6% of non-emptyCase
): aʔeporiRef
(11; 65% of non-emptyCase
): wɨɲate, aŋara, aʔeate, aʔekoma, wɨɲakom, wɨɲakomãhã, wɨɲanẽEMPTY
(57): aŋ, aʔe, aʔekom, wɨɲ, arehete, aŋeʔe, les, ɨɲ, a, wɨɲeʔe
| Paradigm aʔe | Loc | All | Abl | Ref |
|---|---|---|---|---|
| _ | aʔepori | aʔewi | ||
| Contrast=Yes|Emph=Yes | aʔegotɨnẽʔe | |||
| Foc=Yes | aʔeate | |||
| Number=Plur | aʔekoma |
ADV
15 ADV tokens (7% of all ADV
tokens) have a non-empty value of Case
.
ADV
tokens may have the following values of Case
:
Abl
(1; 7% of non-emptyCase
): ɨɲtʃɨwiRef
(14; 93% of non-emptyCase
): ikemɨnanam, munuʔaŋanate, mɨnawe, aipoate, dizeraʔenawe, en92anam, korate, matɨwara, nanaɨwərasipo, nanenũwãEMPTY
(203): aipo, am, kiʔɨ, ãhã, kor, nani, ikeʔɨ, ikiʔɨ, pɨawe, wane
Case
seems to be lexical feature of ADV
. 100% lemmas (11) occur only with one value of Case
.
PROPN
14 PROPN tokens (29% of all PROPN
tokens) have a non-empty value of Case
.
The most frequent other feature values with which PROPN
and Case
co-occurred: Number=EMPTY (13; 93%).
PROPN
tokens may have the following values of Case
:
Abl
(1; 7% of non-emptyCase
): CayenneawinẽAll
(4; 29% of non-emptyCase
): Trois-Sautskotɨ, RenékotɨDat
(2; 14% of non-emptyCase
): Cayenneape, La.FranceapewarLoc
(3; 21% of non-emptyCase
): surinamapope, CamopiporiRef
(3; 21% of non-emptyCase
): SaintGeorgesa, Sẽswawanakom, tekoateTra
(1; 7% of non-emptyCase
): tekoamEMPTY
(34): teko, Françoise, Polo, tekokom, A, Bopea, Channel, F., K., Nebʃa
| Paradigm teko | Tra | Ref |
|---|---|---|
| _ | tekoam | |
| Foc=Yes | tekoate |
ADP
13 ADP tokens (18% of all ADP
tokens) have a non-empty value of Case
.
The most frequent other feature values with which ADP
and Case
co-occurred: Number=EMPTY (10; 77%), Person=EMPTY (9; 69%), Rel=NCont (8; 62%).
ADP
tokens may have the following values of Case
:
Abl
(1; 8% of non-emptyCase
): idʒuwiŋDat
(5; 38% of non-emptyCase
): idʒupeLoc
(4; 31% of non-emptyCase
): dewɨrakotɨ, iwɨb, iwɨrakotɨ, owɨbRef
(2; 15% of non-emptyCase
): ba, nõdeʔaragatuTra
(1; 8% of non-emptyCase
): epewaramteEMPTY
(60): rupi, ehe, ipope, nõdereheo, rehete, derehe, iwɨb, puri, rehe, rehewe
PRON
12 PRON tokens (18% of all PRON
tokens) have a non-empty value of Case
.
The most frequent other feature values with which PRON
and Case
co-occurred: Clusivity=EMPTY (11; 92%), PronType=EMPTY (11; 92%), Int=EMPTY (9; 75%), Number=Sing (8; 67%).
PRON
tokens may have the following values of Case
:
Dat
(4; 33% of non-emptyCase
): awapeto, depe, epe, nõdepeRef
(8; 67% of non-emptyCase
): idʒeate, edeatesipo, amõkoma, idʒeanẽ, maŋatoEMPTY
(54): idʒe, amõ, ede, nõde, ereɲ, pamẽ, tede, orereɲ, am, amõenam
AUX
4 AUX tokens (7% of all AUX
tokens) have a non-empty value of Case
.
The most frequent other feature values with which AUX
and Case
co-occurred: Aspect=EMPTY (4; 100%), Number=EMPTY (4; 100%), Person=EMPTY (4; 100%).
AUX
tokens may have the following values of Case
:
Ref
(4; 100% of non-emptyCase
): kobaitʃe, kobara, kobazepeEMPTY
(56): kob, adʒu, dati, ate, sidʒu, otuio, otuiʔe, adʒumãʔẽ, adʒunẽɲ, datikatu
PART
4 PART tokens (14% of all PART
tokens) have a non-empty value of Case
.
The most frequent other feature values with which PART
and Case
co-occurred: Number[subj]=EMPTY (4; 100%), Mood=EMPTY (3; 75%), Polarity=Neg (3; 75%).
PART
tokens may have the following values of Case
:
Ref
(3; 75% of non-emptyCase
): mamenasinã, mamenakoTra
(1; 25% of non-emptyCase
): amEMPTY
(24): waɨ, kupao, mame, a, am, enam, kupanam, kupaɲ, mamen, mamenasinã
NUM
1 NUM tokens (5% of all NUM
tokens) have a non-empty value of Case
.
The most frequent other feature values with which NUM
and Case
co-occurred: Number[subj]=EMPTY (1; 100%).
NUM
tokens may have the following values of Case
:
Ref
(1; 100% of non-emptyCase
): mokoɲamãʔẽkomEMPTY
(18): bapɨr, mokoɲ, mozepẽ, 12, cinq, dimokoɲi, mokoɲoŋ, mokoɲtaroŋ, momokonte, mozepẽãhã
Relations with Agreement in Case
The 10 most frequent relations where parent and child node agree in Case
:
NOUN –[discourse]–> VERB (1; 100%).


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Character and prop design for a Sci-Fi Frontier Imperial Guard.
Spukimians
Character designs for Pinkitopia's game Spukimians. For each character there's a beauty pose, turnaround and color versions.
Alien Space Janitor
Alien space janitor design with beauty pose, turnaround, helmet and suit designs, and different color versions.
Level Designs
Imperial Cruiser Combat Level
Concept art and small level for a imperial cruiser under attack.GET TO THOSE TURRETS! NOW!
City Temple Entrance Design
Concept art of the entrance gate of a city temple.
Locked Safe
Lorem ipsum dolor sit amet, consectetur adipisicing elit. Mollitia neque assumenda ipsam nihil, molestias magnam, recusandae quos quis inventore quisquam velit asperiores, vitae? Reprehenderit soluta, eos quod consequuntur itaque. Nam.
3D Modelling
Alien Space Janitor
3D model of the alien space janitor character. Full body high-poly version picture and face close-up picture of the low-poly version for videogames.
Animations
Alien Space Janitor
Animation of the alien space janitor character. The model has been rigged and expressions morphers have been added. The animation contains a walking cycle, kick animation, running cycle, weapon attack and threaten animation.
Sword Draw
Heavy Object Animation. Animation of a sword fighter with a heavy sword.
Spider Walking Cycle
Walking cycle of a non-bipedal character.
Walking Cicle
Walking and running cycle of a bipedal character.
Bouncing Balls
All possible animations of a bouncing ball.
Games
Spukimians
A puzzle game in which you must leave each dungeon as the best ghostbuster! Will you be able to leave without getting caught? Play it on Itch.io


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rotten > Library > Biographies > Entertainers > Actors > Fatty Arbuckle
Fatty Arbuckle
He didn't do it
Please, if you bring away one idea, one fact from this article, make it this: Fatty Arbuckle was innocent. In one of those common "I heard it was either this way or that" stories that build up over the years, Arbuckle has had his name dragged through the mud for decades after his innocence was proven. As a result, you hear the name and think "Well, there must have been something there...."
Well, there wasn't. And there isn't. Just injustice upon injustice heaped upon an actor who merely knew how to party and have a good time, and who became the unwitting victim in an attempt to sell newspapers.
Accused of raping Virginia Rappe with a Coke bottle in a San Francisco hotel room. Turns out, it was all a sham. But William Randolph Hearst milked the story for all it was worth.
Timeline
| 24 Mar 1887 |
Roscoe Conkling "Fatty" Arbuckle born, Smith Center KS. |
| 5 Sep 1921 |
Undiscovered actress Virginia Rappe somehow ruptures her bladder during actor-comedian Fatty Arbuckle's party at the Saint Francis Hotel in San Francisco. Three days later, the feverish woman is checked into a maternity hospital, where she dies from peritonitis. Arbuckle is eventually tried for murder, but acquitted.
|
| 18 Apr 1922 |
The first actor blacklisted by Wil Hays. |
| 1923 |
Third trial, acquitted. From the jury statement: "Acquittal is not enough for Roscoe Arbuckle. We feel that a great injustice has been done him... there was not the slightest proof adduced to connect him in any way with the commission of a crime. He was manly throughout the case and told a straightforward story which we all believe. We wish him success and hope that the American people will take the judgement of fourteen men and women that Roscoe Arbuckle is entirely innocent and free from all blame." |
| 29 Jun 1933 |
Fatty Arbuckle dies of a heart attack, Hollywood CA. Cremated, ashes scattered in the Pacific Ocean. |
Pornopolis |
Rotten |
Faces of Death |
Famous Nudes
|


Warrior Championships
The Warriors have reached eight NBA Finals, winning four NBA championships in:
1947
1956
1975
2015
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<EMAIL_ADDRESS>https://www.linkedin.com/in/nesvitailov
I am co-founder at a start-up "Conectiko". My duties belong to BA, PM, Sales, Account Management. Now I want to start my professional career as a Business Analyst in large IT company, with new interesting projects.
I want to continue my professional way to become a Senior Business Analyst. The most important factors for me is the possibility to improve my English, direct work with Customers, and with the development team.
December 2016 – present
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I'm most proud of the fact that I've gathered the team and helped to deliver the B2B product from pure idea to a business with ARR > $100000.
The second fact, that makes me feel proud is our Fintech RadarTech Accelerations were we have been competing with hundreds of teams when we ended up in TOP-5 and had a contract with BNP Paribas. Our success was in right eliciting business needs, and their implementation in the product.
August 2015 - November 2016
«Tim Berners Group» and «Ukrposhta» - network engineer, L1-L2 Customer Support
References:
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Head of IT Support «Ukrposhta» - Ropay Sergiy +380674004111.
Education: NTUU “KPI”, Master Degree’2017, Telecommunication, University of Alberta – Clients Needs and Software Requirements (Coursera); ITEA - Business Analysis base;
English: Upper-Intermediate


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WELCOME TO SARAJEVO (Miramax) Starring: Stephen Dillane, Woody Harrelson, Marisa Tomei, Emira Nusevic, Goran Visnjic, Kerry Fox. Screenplay: Frank Cottrell Boyce, based on the book "Natasha's Story" by Michael Nicholson. Producers: Director: Michael Winterbottom. MPAA Rating: R (violence) Running Time: 101 minutes. Reviewed by Scott Renshaw.
Michael Winterbottom's WELCOME TO SARAJEVO is a lot like its central character, British reporter Michael Henderson (Stephen Dillane). An observer on the front lines of the Serbian siege of Sarajevo, Henderson is torn between conflicting instincts. As a journalist, he believes he should be detached and objective as he tells the story of Sarajevo to the world; as a human being, he feels compelled to beccome emotionally involved, to take action which will help ease the suffering.
Henderson eventually chooses the latter course. The film, on the other hand, isn't quite so resolute. Much of the time, Winterbottom seems content to provide a documentary-style history of the conflict in the former Yugoslavia. Archival footage of speeches and politicians' comments are strewn throughout the narrative, while bombings and sniper attacks are depicted with gruesome real-life footage of dead bodies and mangled limbs. The images are powerful and immediate, but they become the story rather than complementing it. Henderson begins to seem like a character digitally insterted, Forrest Gump-like, into historical events.
When the story does focus on his actions, it fumbles away too many opportunities to personalize its grand-scale tragedy. Character relationships, like those between Henderson and a brash American reporter (Woody Harrelson) and his no-nonsense producer (Kerry Fox), get just enough attention to be frustrating; Henderson's devotion to an orphan named Emira (Emira Nusevic) is rushed until it feels more like a cinematic necessity than a genuine connection. Even the film's most potentially intense sequence, involving the illegal transport of a bus-load of orphans out of the country, feels strangely sedate despite one gripping encounter at a Bosnian checkpoint. That sequence doesn't even serve as the film's climax, leading into a dry 20-minute search for Emira's birth mother which grinds WELCOME TO SARAJEVO to a halt. Far too often, Winterbottom and screenwriter Frank Cottrell Boyce demonstrate a clumsy disregard for building a narrative with an emotional pull beyond its bursts of authentic violence.
I can easily imagine a film like WELCOME TO SARAJEVO degenerating into pathos in Hollywood hands, so perhaps it deserves some credit for avoiding copious shots of sad-eyed waifs. Winterbottom simply goes too far in the other direction. The performances and direction are sturdy but rarely inspired, resulting in a film which makes us feel terrible about this tragic conflict in a very abstract sense. WELCOME TO SARAJEVO is good reporting, but it's not particularly good drama, treating its subject with the concerned detachment of a newsmagazine feature story.
On the Renshaw scale of 0 to 10 Split decisions: 5.
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UK PM Johnson says my deal is only way to get Brexit done
LONDON (Reuters) - British Prime Minister Boris Johnson said his failure to deliver Brexit last week as he had promised was a “matter of deep regret”, but his deal remained to only way to get Britain out of the bloc.
“The only way out of the EU now, the only way to get Brexit done, is to go with the deal that we’ve got,” he told Sky News in an interview at the start of his campaign for next month’s election.


| Name | Based On | Definition |
| ClinicalInputForm-PatientWoundSummary | Composition | Clinical input form for the initial observation of a wound or wounds on a patient. Includes only basic who/what/where aspects, not the complete assessment. Addresses the use case of a new patient being evaluated for the presence or absence of wounds. |
| ClinicalInputForm-WoundAssessment | Composition | Information about a wound at a single time, structured into logical groupings for input or display. |
| ExchangeForm-WoundComprehensiveHistory | Composition | A collection of all information about a single wound, possibly including many observations taken by different people at different times. Everything in the aggregation relates to the same wound. |
| PhotographicImage | Observation | A photographic documentation of a condition, location, person, etc… |
| WoundAbsent | Basic | Documents the absence of a wound. |
| WoundArea | Observation | Area of the wound. |
| WoundAssessmentInterpretation | Observation | Overall assessement of whether the wound is within normal parameters. |
| WoundBedAppearancePercentage | Observation | Area of wound bed portion appearance/Entire Area of wound bed – Linked to a wound bed appearance |
| WoundBedAppearance | Observation | Appearance of wound base (bed) |
| WoundDepth | Observation | Depth of the wound. |
| WoundEdgeColor | Observation | Color of Wound edge. |
| WoundEdgeDescription | Observation | The state of the tissue at the edge of the wound. |
| WoundEpisode | Observation | Whether the wound is initial or recurrent. |
| WoundExudateAmount | Observation | Drainage amount of wound |
| WoundExudateAppearance | Observation | Appearance of exudate from wound |
| WoundExudateColor | Observation | Color of exudate from wound |
| WoundExudateOdor | Observation | Odor of exudate from wound |
| WoundExudateVolume | Observation | Appearance of exudate from wound |
| WoundLength | Observation | Length of the wound (longest dimension). |
| WoundLocationObservation | Observation | Body location of a wound. |
| WoundPeriwoundDescription | Observation | A description of the skin around the wound (periwound). |
| WoundPresentOrAbsent | Basic | The presence or absence of a wound. Observations and measurements associated with the same wound must link back to the same wound assertion. Therefore, the same wound should not be asserted more than once. The ‘same-as’ relationship can be used to link multiple assertions of the same wound, if necessary. |
| WoundPressureUlcerAssociation | Observation | Is the wound associated with a device or pressure point? |
| Wound | Basic | The presence of a wound. |
| WoundTrend | Observation | Whether a condition is improving, worsening, stable, or resolved. |
| WoundTunnelLength | Observation | The length of a wound tunnel, in cm. |
| WoundTunnelPresentOrAbsent | Basic | The presence or absence of a discharging blind-ended track that extends from the surface of an organ to an underlying area or abscess cavity. Each wound tunnel can be persistently identified to enable tracking of the condition of the tunnel over time. |
| WoundTunnel | Basic | The presence of a wound tunnel. |
| WoundTypeEtiology | Observation | The source or key factor in the formation or acquisition of the wound. |
| WoundType | Observation | The type of wound, for example, abrasion or laceration. |
| WoundUnderminingClockDirection | Observation | Clock position of wound undermining |
| WoundUnderminingLength | Observation | Length of wound undermining. |
| WoundUnderminingPresentOrAbsent | Basic | The presence or absence of deep tissue (subcutaneous fat and muscle) damage around the wound margin. Undermining are narrow sinus tracts away from the wound margins and go downward into the wound. |
| WoundUndermining | Basic | The presence of wound undermining. |
| WoundVisibleInternalStructure | Observation | Exposed body structures, devices, and/or foreign bodies visible by the naked eye in a wound. |
| WoundWidth | Observation | Width of the wound (perpendicular to longest dimension). |


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| Monobenzone |
>360 |
0.72 |
>500 |
1.48 |
>243.2 |
Tyrosinase. |
| Spironolactone |
300 |
1.07 |
280 |
7.0 |
42.9 |
Corticosteroids. |
| Oxibendazole |
132.6 |
1.5 |
89.4 |
- |
- |
Polymerase. |
| Oxaliplatin |
88 |
1.5 |
58.6 |
15.4 |
5.7 |
DNA synthesis. |
| Arecoline |
360 |
6.7 |
53 |
10.6 |
34 |
Agonists of Muscarinic receptor and Nicotinic acetylcholine receptor. |
| Hycanthone |
40 |
1.4 |
29.4 |
7.1 |
5.7 |
DNA topoisomerases. |
| Acrisorcin |
23 |
0.8 |
28.7 |
- |
- |
Tyrosyl-DNA Phosphodiesterase 1. |
| Phenothiazine |
103.8 |
4 |
26 |
- |
- |
Antagonists of Histamine receptors, Dopamine D2 receptor, Adrenergic α receptor, Serotonergic 2C receptor, and Muscarinic receptor. |
| Paroxetine |
30 |
1.4 |
21.3 |
13.3 |
2.3 |
Selective serotonin reuptake inhibitor. |
| Protriptyline |
72.5 |
3.7 |
19.6 |
4.4 |
16.5 |
Inhibitors of Histamine 1 receptor, Muscarinic receptor, and Adrenergic α1 receptor; Inhibiting reuptake of Serotonin and Norepinephrine. |
| Cyproheptadine |
106.8 |
6.87 |
15.6 |
- |
- |
Antagonists of Histamine 1 receptor and serotonin receptor, 5-HT2 and 5-HT1C. |
| Arsenic trioxide |
65.8 |
4.4 |
15 |
- |
- |
Thioredoxin reductase. |
| Fulvestrant |
65 |
4.6 |
14.1 |
- |
- |
Steroid estrogen receptor antagonist. |
| Manidipine |
55.2 |
4.2 |
13.1 |
- |
- |
Calcium and aldosterone antagonist. |
| Mefloquine |
30 |
3 |
10 |
- |
- |
Cholinesterase. |
a. Antiviral effects were assessed by viral infectivity assays
b. Antiviral effects were assessed by RFP expression in iSLK.219 cells
c. The CC
50 represents the 50% cytotoxic concentration
d. The IC
50 represents the 50% inhibitory concentration
e. SI = CC
50/IC
50


Budget Savers
Home
Budget
Calendar
Bank
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Cash
Balance
900
Income
ADD Income
Date
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Name
Amount
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0
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Date
Place
Name
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Category
[Date]
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24
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[Date]
McDonalds
10
Food
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Expense Name:
Amount:
Select Payment:
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Frequency:
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Category:
Food
Travel
Home
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Bill
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New Income
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{% extends "layout.html" %} {% block phase%} {% include 'sprint12/partials/phase-banner.html'%} {% endblock %} {% block main %}
Don’t include any of your own personal details.


Table of contents
Cover
Test book
Colophon
Table of contents
Preface
Basic HTML structures
Lists
Definition lists
Tables
Part 1
Math tests with image and asciimath fallback
Image in altimg, asciimath in alttext
Image in altimg, no alttext
No image, asciimath in alttext
No image, no alttext
Image in altimg and annotation, asciimath in alttext and annotation
Image in annotation, asciimath in annotation
MathML with SVG annotation
SVG hidden from screen readers, <p> only viewable from screen readers
Image with empty alt text, asciimath in separate element
role="math"
Math tests with spoken math for use by speech synthesizers
MathML with aria-label and no alttext or altimg
div with role="math" and aria-label, and no alttext or altimg
Image in altimg, spoken math in alttext
MathML with SVG annotation with spoken math in aria-label
Pure SVG with spoken math in aria-label
Image with empty alt text, asciimath in separate element
Focusable screen reader text
Formula with image
Formula with MathML
Part 2
Visibility and fallback
display: none;
epub:switch
Page number rendering
Chapter with note references
Footnotes
Conclusion
Afterword
List of pages
i
ii
iii
iv
v
1
2
3
4
5
6
7
a
b


HKuz
.
Test your memory with this 80's classic
Defend human honor vs. the computer
Track work and break sessions
Check your local forecast
Classic four-function calculator
Generate a demotivational quote
hkuzdev@gmail.com


Version 0.5 Alpha -- note, due to chrome restrictions click mouse pointer or tap screen to play music!!
Santa's elves rebelled, so they were replaced with robots. The angry elves swore revenge and saboraged the robots, now they are running wild and hour before delivery! You and a team of other children must save as many presents as you can! Every present saved is another child Santa can visit.
Congratulations you've saved 0 presents! You have 3 Lives left.


Cute Animal Zoo
Experience nature as it was
meant
to be seen!
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Virtual Tour
Map
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3.2. Including Spatial Variability in Site Response Event¶
This page shows two examples on how to incorporate spatial variability into UQ analysis by using various special materials, namely ElasticIsotropic, PM4Sand, and PDMY03, in Site Response option under EVENT tab.
3.2.1. Problem Statement¶
Site response analysis is commonly performed to analyze the propagation of seismic wave through soil. As shown in Fig. <IP_ADDRESS>, one-dimensional response analyses, as a simplified method, assume that all boundaries are horizontal and that the response of a soil deposit is predominately caused by SH-waves propagating vertically from the underlying bedrock. Ground surface response is usually the major output from these analyses, together with profile plots such as peak horizontal acceleration along the soil profile. When liquefiable soils are presenting, maximum shear strain and excess pore pressure ratio plots are also important.
In the real world condition, physical properties of soils vary from place to place within a soil deposit due to varying geologic formation and loading histories such as sedimentation, erosion, transportation, and weathering processes. This spatial variability in the soil properties cannot be simply described by a mean and variance, as commonly adopted in UQ analyses, since the estimation of the two statistic values does not account for the spatial variation of the soil property data in the soil profile. Spatial variability is often modeled using two separated components: a known deterministic trend and a residual variability about the trend. These components are illustrated in Fig. <IP_ADDRESS>.
A summary of the random field preparation procedure for the site response event analysis is summarized here:
Generate mean field using mean target soil property, e.g., relative density (Dr) or shear wave velocity (Vs)
Generate Gaussian random field for target soil property using Gauss1D.py with mean = 0.0 and \(\sigma\) = 1.0
Interpolate Gaussian field to FEM mesh
Combine the mean (trend) field and Gaussian (residual variability) field to obtain a stochastic field
Generate material input for site response analysis based on predefined model calibration methods
Perform site response analysis using the randomized material input and obtain acceleration response at the surface of soil column for subsequent UQ analysis
Note
Currently only 2D plain-strain materials (including PDMY03 and ElasticIsotropic) are supported when using random field. Therefore, 1-component motion is required.
3.2.2. General Workflow for Global Sensitivity Analysis¶
In a global sensitivity analysis the user is wishing to understand what is the influence of the individual random variables on the quantities of interest. This is typically done before the user launches large scale forward uncertainty problems in order to limit the number of random variables used so as to limit the number of simulations performed.
root mean square (RMS) accelerations in 1 and 2 directions, Peak BAse shear and moments in 1 and 2 directions.
To perform a Global Sensitivity analysis the user would perform the following steps:
The steps involved:
1. Start the application and the UQ Selection will be highlighted. In the panel for the UQ selection, keep the UQ engine as that selected, i.e. Dakota. From the UQ Method Category drop down menu select Sensitivity Analysis, Keeping the method as LHS (Latin Hypercube). Change the # Samples to 20 and the Seed to 273 as shown in the figure. The seed specification allows a user to obtain repeatable results from multiple runs.
2. Next select the GI panel. In this panel the building properties and units are set. For this example enter 1 for the number of stories, 144 for building height, 360 fow building width, and 360 for building depth.
3. Next select the SIM tab from the input panel. This will default in the MDOF model generator. Define other input variables as shown in figure:
3. Next select the EVT panel. From the Load Generator pull down menu select the Site Response option. Define soil profile, ground water table (GWT), and mesh. Then select interested material, e.g., PM4Sand_Random, PDMY03_Random, or Elastic_Random. Under Configure tab, select path to the input motion.
Note
A reasonable mesh resolution is recommended. Selection of element size should consider several factors, including but not limited to, layer shear wave velocity (for frequency resolution), corelation length (for random field resolution), and computation efficiency.
Next choose the FEM panel. Here we will change the entries to use Rayleigh damping, with rayleigh factor chosen using 1 mode.
We will skip the EDP panel leaving it in it’s default condition, that being to use the Standard Earthquake EDP generator.
5. For the RV panel, we will enter the distributions and values for our random variables. If only the uncertainty related to spatial variability is interested, a dummy random variable can be defined in this tab. Then all the variability shown in the response will solely be due to spatial variability in the site response analysis.
Warning
The user cannot leave any of the distributions for these values as constant for the Dakota UQ engine.
5. Next click on the ‘Run’ button. This will cause the backend application to launch dakota. When done the RES tab will be selected and the results will be displayed. The results show the values the mean and standard deviation. The peak displacement of the roof, is the quantity PFD. The PFA and PFD quantity defines peak floor acceleration and displacement, respectively, and the PID quantity corresponds to peak interstory drift.
3.2.3. Adding Spatial Variability¶
<IP_ADDRESS>. Case 1: using ElasticIsotropic material¶
For the Elastic_Random material, shear wave velocity (Vs) can be selected to be randomized. Then select the Mean and COV (coefficient of variation \(=\frac{\sigma}{\mu}\)) for shear wave velocity. Correlation length defines how shear wave velocities are vertically correlated. Subsequently, Young’s modulus is calculated based the stochastic shear velocity profile at the center of each element. No special calibration is required.
Note
Vs is bounded between 50 and 1500 m/s. These limits can be modified in calibration.py.
Fig. <IP_ADDRESS>.3 presents the profiles of shear wave velocity, peak horizontal acceleration, maximum shear strain, and maximum excess pore pressure ratio (Ru) obtained from 20 realizations. Ru are always zero since there is no volumetric strain in ElasticIsotropic material. Fig. <IP_ADDRESS>.3 depicts the mean and each individual response spectra (5% damping) at surface obtained from 20 realizations.
<IP_ADDRESS>. Case 2: using PM4Sand material¶
For the PM4Sand_Random material, relative density (Dr) can be selected to be randomized. Then select the Mean and COV (coefficient of variation \(COV=\frac{\sigma}{\mu}\)) for shear wave velocity. Correlation length defines how shear wave velocities are vertically correlated. In ths current calibration procedure, all the other parameters are kept as input except for the contraction rate parameter hpo, that is calibrated based on the empirical triggering model proposed by Idriss and Boulanger 2008.
Fig. <IP_ADDRESS>.3 presents the profiles of shear wave velocity, peak horizontal acceleration, maximum shear strain, and maximum excess pore pressure ratio (Ru) obtained from 20 realizations. Comparing to elastic material, more variability are shown among these realizations. Fig. <IP_ADDRESS>.3 depicts the mean and each individual response spectra (5% damping) at surface obtained from 20 realizations.
Note
Dr is bounded between 0.2 and 0.95. These limits can be modified in calibration.py.


K
- - type of the keys.V
- - type of the values.public interface DMapListener<K,V> extends EventListener
DMap
.| Modifier and Type | Method and Description |
|---|---|
void |
entryAdded(K key,
V value)
Invoked when an entry is added in the map.
|
void |
entryRemoved(K key,
V value)
Invoked when an entry was removed the map.
|
void |
entryUpdated(K key,
V value)
Invoked when the value of an entry has changed.
|
void |
mapCleared(boolean localClearing)
Invoked when the map was cleared.
|
void entryAdded(K key, V value)
key
- - the added key.value
- - the added value.void entryUpdated(K key, V value)
key
- - the removed key.value
- - the removed value.void entryRemoved(K key, V value)
key
- - the removed key.value
- - the removed value.void mapCleared(boolean localClearing)
localClearing
- - indicates if the clear in local (true
)
or for all the nodes on the network (false
).Copyright © 2014-2015 the original authors or authors.


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Kristen's Pizza!
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Johnson
We are in the endgame now.
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We are in the endgame now.
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We are in the endgame now.


Hello and welcome to the After School Coding Club! This club is intended to provide and fun and interactive place where kids can learn about the wonderful world of computer programming. Our program is intended for kids aged 8-12 who have little to no experience with coding. We walk children through the basics of Scratch (a great introductory programming tool using interactive building blocks to model code) and help them build their confidence from week to week by incrementally building one the skills they acquired in the previous lesson. Kids love the graphic nature of scratch and love to explore all the amazing interactive components that can be built with just a few lessons.


Overview
Launched on Sep 15th, 1997
60 trillion individual pages. 100 million GB data.
40,000 search per second, or 3 billion search per day.
As of Feb 2015, 65% market share in US.
1. Crawl
Google crawl from 1 page to another using Googlebot. It starts from previous urls crawled, or augmented with Sitemap data
A sitemap is a list of pages of a web site accessible to crawlers or users. It can be either a document in any form used as a planning tool for Web design, or a Web page that lists the pages on a Web site, typically organized in hierarchical fashion.
2. Indexing
Compile the data (key content tags, atrributes, like title tags, ALT attributes). Google don’t process rich media or dynamic files.
3. Algorithm
When search, pull all relevant results from the Index.
Rank the result based on 200+ factors, one of which is the PageRank for a given page.
PageRank is the measure of the importance of a page based on the incoming links from other pages. In simple terms, each link to a page on your site from another site adds to your site’s PageRank.
Not all links are equal: Google works hard to identify spam links. The best types of links are those that are given based on the quality of your content.
To rank higher, follow Webmaster Guidelines.


--- layout: default route: templates --- Templates Free HTML templates that make use of Bulma out of the box Everything All Bulma elements on one page Business Classic business homepage Welcome Simple one-page introduction


Grid RegIstry with Metadata Oriented Interface: Robustness, Efficiency, Security
A Grimoire is magician's manual for invoking demons (Oxford English Dictionary)
A Grimoire is
a magician's manual for invoking demons (Oxford English Dictionary). Likewise, the Grimoires registry hosts descriptions of services and workflows, which a scientist can use for forming their complex scientific experiments. However, service and workflow interfaces are sometimes underspecified and therefore difficult to use in an automated manner; hence, the
Grimoires registry augments their interfaces with metadata such as functionality, semantic information about their inputs and outputs, or various metrics (e.g. perceived quality of service, trust).
Sponsors:
Institutions:


{% extends "base.html" %} {% block body %} {% include "rules_game_g.html" %}
This contest is organized by the Emory University's IR Lab (http://ir.mathcs.emory.edu). The goal of this contest is to better understand how users search on the web for information of varying difficulty, which strategies are more successful than others, and how we could better help people find information on the web. To accomplish this, our contest interface will track the search queries issued, the documents clicked the amount of time a search takes, and other variables. The resulting data, not associated with any identifiable information about each player, may be shared with other IR researchers for scientific use.
This contest is organized by the Emory University IR Lab as part of a research effort to study and improve web search quality. This research has been reviewed and approved by the Emory University IRB. If you have any questions or concerns about the contest or the research project, you may contact the principal investigator, Dr. Eugene Agichtein, at<EMAIL_ADDRESS>by using email with the subject "uFindIt contest".
For any other questions or concerns, you may contact the Emory IRB office (contact information available at http://www.irb.emory.edu/).


Marc-Andre Fleury gets 450th win as Golden Knights beat Maple Leafs
LAS VEGAS – Marc-Andre Fleury made 31 saves in his 450th win and the Vegas Golden Knights beat the Toronto Maple Leafs 4-2 on Tuesday night.
Fleury is three wins behind New York Rangers goalie Henrik Lundqvist for sixth place on the career list.
Mark Stone, Cody Glass, Tomas Nosek and Cody Eakin scored for Vegas. But for the second consecutive game, it was Fleury who kept the Golden Knights in it with outstanding stops — particularly when the Maple Leafs turned up the pressure in the third period.
With 14:59 left, the 16-year veteran netminder stopped William Nylander on the doorstep. Then with 11:21 remaining, he stymied Toronto captain John Tavares’ first shot of the game, tipped the puck with the paddle of his stick to himself and snared it with his glove.
It was that kind of night for Fleury.
Moments later, with the heat still on, he robbed Nylander by sliding across the crease and stretching out his left pad at the right moment to prevent the puck from sneaking into the corner of the net.
The biggest save came with 3:43 to go, when Nic Petan’s backhand toward a wide-open net was thwarted as Fleury dove back to his left and with his body outstretched snatched the puck before it hit twine, sending 18,292 fans into a frenzy — even some wearing Maple Leafs jerseys.
Jason Spezza and Zach Hyman scored for Toronto. Frederik Andersen, who has lost his last four starts, made 33 saves.
Vegas scored on both power-play opportunities and outshot the Maple Leafs 37-33.
After Max Pacioretty’s shot trickled through Anderson’s pads into the crease, Glass was there to clean up for a man-advantage goal that gave Vegas a 1-0 lead midway through the second period.
Toronto tied it when Ilya Mikheyev entered the zone and dropped the puck off for Spezza, who fired it past Fleury early in the third.
Vegas answered immediately when Nosek stole the pick in the neutral zone, raced in on a breakaway, put a filthy deke on Andersen and backhanded the puck into the net to make it 2-1 just 42 seconds after Spezza’s goal.
Stone extended the lead to 3-1 with a power-play drive from the right dot at 10:22. Hyman cut Vegas’ lead to one with his first goal of the season at 12:47, but Eakin put the game away when he scored into an empty net with 21 seconds left.
In Other Games
Wild 4, Sabres 1
Panthers 5, Flyers 2
Bruins 5, Devils 1
Islanders 5, Penguins 4
Blue Jackets 5, Canadiens 2
Senators 4, Red Wings 3
Blues 3, Lightning 1
Jets 2, Predators 1
Hurricanes 4, Blackhawks 2
Stars 6, Canucks 1
Avalanche 3, Flames 2
Oilers 5, Sharks 2
GET THE BEST OF THE JAPAN TIMES IN FIVE EASY PIECES WITH TAKE 5
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You have a Git commit which contains several changes. For example, a commit containing the addition of different features, or a feature with additional fixes. You have put them together in one commit and you want to fix that.
Splitting in several commits can be done by extracting one change from the commit into another commit, then repeating the process for another change. Consequently, we will focus on the extraction of a single change, leading to the question: How to extract a change from a Git commit into a separate commit?
Several aspects need to be considered here. We may want to extract a change from the last commit or from an earlier commit. We may want to extract a change that is overlapping with the other changes in the commit or which is completely separate. We may want to extract the change next to the current commit or to a different place in the commits history. We will address all these points and more by starting from the most simple case and growing in complexity progressively.
Let's be honest: the most generic way to fix a mistake is to restart from scratch.

You can do so by removing the last commit while preserving the files with git reset HEAD^
.
The rest is just the common process of creating new commits, as if nothing happened in the first place.
You can also create 3, 4, or more commits while you are at it.
 But when there is a lot in the commit, and you just want to extract a piece out of it, spending time redoing the whole stuff can feel like a waste of time and error prone.
Actually, you can slightly adapt the previous process to not completely restart from scratch.
If you reset with the --soft
option, you can forget the commit but keep all its changes in the stage area.

Here you want to remove one or several changes from a file, but not all the changes. The commands we have seen so far can deal with files, but not a smaller granularity. To go further, you need to use the interactive staging.
It can be done in command line with git add -i
.
However, it can be time consuming with complex commits.
I personnally like the feature, but only when done through a graphical interface.
It saves a lot of time.

You can then update the stage interactively to remove the chunks you don't want before to commit.
With the graphical interface, you can often enter in an "amend mode". It allows you to edit the last commit without removing it and creating it again. The graphical interface can also let you chose which file to look at and show all the chunks of that file at once. It makes it quick to go to the chunk we want and add or remove it. You can often do it on the selected lines rather than a complete chunk too.
We don't go deeper on the chunk aspect.
You can adapt the procedures by selecting chunks with your preferred tool instead of using the git add
and git rm
commands.
Now we reach the limits of Git in terms of granularity. Git deals with the syntax, but not with the semantic. If several changes occur in the same chunk, Git only sees a single chunk of change. It cannot discern that several things are happening there.
At this point, we need to help Git by editing the file.
It can be done manually or with git commands to retrieve a previous state, like git checkout <commit> <file>
.
Of course, combine them as you see fit depending on your needs.
The advantage of editing the file is that we don't have to play with the staging.
Since the file is cleaned, just stage it completely, which can be done automatically when commiting with the --all|-a
option.
For example:
<editing> # Remove the changes to extract
git commit --amend -am 'Change 1' # Amend the last commit
<editing> # Redo the extracted changes
git commit -am 'Change 2' # Commit
If the removal of the changes can be done quickly, it is often a satisfying procedure. Just remove the stuff, save and commit, then cancel the removal (often CTRL+Z), save and commit, period.
But the removal may take some time and require to change several places at once.
In this case, we may prefer to avoid doing a manual pass a second time to cancel all of them.
In which case, we can use Git to automatize the cancellation.
We do so by creating a commit that contains the extraction and by exploiting git revert
to cancel it.
However, it creates additional commits that we have then to clean.
 As you can see, there is a preparation phase where we provide all the information, including the change to extract (change 2) by removing it manually. Then, the commiting phase produces all the commits we need. In particular, it produces the commit having only change 2. But for change 1, it keeps the initial commit with change 1 and change 2, and creates an additional commit without change 2. The goal of the cleaning phase that come after is to squash these two commits into one, which contains only change 1. Since changing a commit cannot be done in the middle of a branch, we must recreate all of them, which explains the multiple commands.
Although the procedure is long, it only requires a single manual editing.
After setting the variables MESSAGE_1ONLY
and MESSAGE_2ONLY
, you can copy the remaining commands and execute them all at once.
We have seen so far how to extract a change after the last commit.  If you need to remove several things, you cannot repeat the procedure. You first need to reorder the commits to bring back the "fat" commit last.
If your change is not overlapping with the rest, it is easy to do so. You can use the right procedure to extract a file or a chunk after the commit. You can then inverse the two last commits, with no conflict, with these commands:
COMMIT_1=$(git rev-parse HEAD~1) # Identify the commit 1
COMMIT_2=$(git rev-parse HEAD) # Identify the commit 2
git reset --hard ${COMMIT_1}~1 # Reset to the state before commit 1
git cherry-pick ${COMMIT_2} ${COMMIT_1} # Redo commit 2 then commit 1
However, this procedure works without conflict only if your changes do not overlap.
If they overlap, reordering the commits will require to resolve conflicts.
You may prefer instead to extract the change directly before the commit.

The advantage of the last commit is that it can be directly edited with git commit --amend
.
Most of the procedures we have seen previously exploit this command.
At the opposite, changing a commit that is not the last one cannot be done immediately.
We first need to start from the right commit with git reset
, update as needed, then reproduce the remaining commits with git cherry-pick
.
The procedure to extract a change before the last commit shows this aspect by working one commit before the last one.
Extracting a change before any other commit is a generalisation of this case. The difference is that we need to reproduce a sequence of remaining commits instead of only one.   # Stage the removal
git commit --amend -m "Change 1" # Update the commit, resulting in change 1 only
git revert -n HEAD # Revert it without creating a commit
git cherry-pick -n ${COMMIT_SPLIT} # Reproduce the commit with changes 1 & 2, no commit
git commit -m "Change 2" # Create the resulting commit with change 2 only
git cherry-pick ${COMMIT_SPLIT}..${COMMIT_LAST} # Reproduce all the remaining commits until the last
The difference focuses on the lines 3-6:
| # | Extract before | Extract after |
|---|---|---|
| 3 | reset before the commit to split | reset to the commit to split |
| 4 | add the change to extract (change 1) | remove the change to extract (change 2) |
| 5 | stage the update | |
| 6 | create a new commit from it | update the existing commit |
Actually, there is not much to say at this point.
With the previous procedures, you know how to split the commit on the spot.
Once you have extracted the change, you need to
An alternative is to insert the change directly where needed. For instance, rather than splitting and moving it before, you may directly create a commit with your change earlier in the history. However, to reproduce the next commits after it, you need either to:
At the end, it is equivalent to extract it on the spot and
To extract a change from a Git commit, various commands can be used depending on the context, among which:
git reset
to go back to a specific commit, either by using --hard
to forget the changes altogether (and reproduce them later), by using --soft
to keep the changes staged and remove the ones we don't want with git rm --cached
, or by using none of them to keep the changes but choose which will be staged with git add
.git cherry-pick
to reproduce a commit, preferably from a state equivalent to the state it is picked from to avoid conflicts, possibly with -n
to not create the commit immediately.git revert
to cancel the last commit, possibly with -n
to not create the commit immediately, unless we plan to squash it later.git commit
to finally create the commit with the staged changes, or with --amend
to update the last commit instead of creating a new one.A particular care should be provided to managing the potential conflicts between changes. Depending on which is preferable, one can either resolve the conflicts or manually add/remove the change.


After reading Tim Carmody on patronage last year, I am increasingly signing up to support things I love financially. Right now:


I was looking for something very simple just to connect some home electrical meter to the Emoncms. I just thought I would also share this idea.
I basically connected an Emontx v2 (cheaper) to a WR703n WIFI router with their serial port. Then I wrote a script to forward to data to the emoncms server.
It works pretty well and I find it good to have just only one unit that does that basic job.
I explained all the inside here and called it EmonWRT.
If anyone like the idea and want to contribute to improve it I'd be really happy to hear about you.
Re: Simple Emontx over WIFI / OpenWRT - EmonWRT
I just setup my wr703n with openWrt, and am trying to configure it with an Emontx as you've described on your site.
Your link for the emonwrt_forwarder.sh script seems to be broken - would you be willing to send this to me, or point me to the right spot?
Thanks, great idea on this setup!
Re: Simple Emontx over WIFI / OpenWRT - EmonWRT
The link is fixed now. Just realised you might need a configuration file example to place inside /etc/config. I wrote This howto for completion.
Good to hear you like the idea as well. Personally I find excellent to get rid of SD cards which tend to be unreliable.
Don't hesitate to share your code if you develop it further.
Re: Simple Emontx over WIFI / OpenWRT - EmonWRT
I got the file, and the config and howto are helpful too - thanks for that.
I'm setting up a emonTx shield on an Arduino Uno, which will send the "EMONWRT CSV_DATA" over USB to the wr703n (no direct serial connection). I'm hosting a local Emoncms server on a raspberry pi.
I'll share the code once I get it up and running. Again, great idea, thanks for documenting it so well.
Re: Simple Emontx over WIFI / OpenWRT - EmonWRT
So I take you are using the usb as a serial device? Are you reading from /dev/ttyUSB0 or similar maybe?
I will include the serial port as a parameter in the config file in next version. This should allow for setup like yours to be easily editable.
Re: Simple Emontx over WIFI / OpenWRT - EmonWRT
I have now added RF support for forwarding data from rfm12b sensors.
Re: Simple Emontx over WIFI / OpenWRT - EmonWRT
hello
I also found this idea very interesting and I wanted to test it on a arduino yun seen that mounts openwrt in order to have a unique piece but does not work I do not know why!


Body of missing boy recovered in Oman
by Times News ServiceMuscat: Rescue teams found the body of the missing boy in wadi Al Hoqain, the Public Authority of Civil Defence and Ambulance (PACDA) announced.
PACDA said in a statement: "Referring to a report of the loss of a 15-year-old child in Wadi al-Hoqain, in wilayat of Rustaq, rescue teams were able to find his body."
The child went missing on Thursday at Al Batinah south governorate after the overflowing of wadi Al Hoqain due to heavy rainfall in the wilayat.


{% extends 'dashboard/project_details.html' %} {% load staticfiles %} {%block body%}
| TICKET ID | SUBJECT | DESCRIPTION | RAISED BY | STATUS | SOLUTION DESCRIPTION |
|---|---|---|---|---|---|
| {{ticket.ticket_id}} | {{ticket.subject}} | {{issue.whats_need_todo}} | {{ticket.customer}} | {{ticket.status}} | {{issue.solution_description}} |


‘I Regret Engaging In Premarital Sex – Lady Prempeh
by Peace FM OnlineGospel musician Lady Prempeh says she regrets having premarital sex before she tied the knot.
Speaking in an interview with Hitz FM, the 'Aseda' singer condemned the act, says it’s ‘disgraceful’ although it is considered normal these days. “That’s something, to me, I felt I shouldn’t have done it. I should have waited till I got married,” she stated.
The singer added, “premarital sex looks normal now but if you know whom you are serving, that is a no go area for Him.”
Lady Prempeh, however, advised fans to follow the teachings of Christ especially in so-called contemporary times when premarital sex and other worldly life is considered the norm. “If you have to follow this path you must be on it. Don’t try to sway or go your path and believe God will be with you. No, you have to go his way,” she added.
Featured Video


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Search:
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- blender.coffee core
- calculate.coffee core
- caman.coffee core
- convert.coffee core
- event.coffee core
- filter.coffee core
- io.coffee core
- layer.coffee core
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|
| pure integer function | list_size (me) |
| | Returns the total number of items in the List. More...
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| |
| integer function | push_back (me, newitem) |
| | Subroutine to append an element at the back of the list. More...
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| |
| class(*) function, allocatable | pop_back (me) |
| | Function to extract the last item of List. More...
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| |
| class(*) function, pointer | item_at (me, Index) |
| | Returns a pointer to the item with the requested index (1-based) Pointer will not be associated in case of an exception. More...
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| |
| class(*) function, allocatable | item_front (me) |
| | Returns the item with the index=1. More...
|
| |
| class(*) function, allocatable | item_back (me) |
| | Returns the last item in List. More...
|
| |
| subroutine | item_insert (me, pos, NewItem) |
| | Inserts an item at the given position. More...
|
| |
| subroutine | item_erase (me, pos) |
| | Erase an item at the given position. More...
|
| |
| subroutine | create_newslots (me, NumSlots) |
| | Subroutine to allocate storage for new slots. More...
|
| |
| subroutine | assignlist (lhs, rhs) |
| | Subroutine for List assignment operator. More...
|
| |
| subroutine | clear (me) |
| | Clear the contents of List. More...
|
| |
| subroutine | shrinktofit (me) |
| | Free up unused allocated memory and make List capacity equal to its size. More...
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| |
| subroutine | destroy (me) |
| | Destructor. More...
|
| |
| elemental subroutine | add_listitem (me, item) |
| |
| subroutine | del_listitem (me) |
| |
| pure elemental logical function | isvalid_listitem (me) |
| |
List module.
This module provides the list data structure (similar to Python list) represented internally using an array.
- Author
- Bharat Mahajan
- Date
- Created: 05/27/2020


Planet Sick-Boy: http://www.sick-boy.com "We Put the SIN in Cinema"
© Copyright 2001 Planet Sick-Boy. All Rights Reserved.
Iron Monkey director Yuen Woo-ping hasn't helmed a film in over six years, and although he's hardly a household name in the U.S., many may know him as the guy responsible for choreographing the incredible fight scenes in Oscar winners The Matrix and Crouching Tiger, Hidden Dragon. Thanks to Quentin Tarantino, who has a more-than-decent track record when it comes to releasing cooler-than-cool Asian films in the States (see Chungking Express, Sonatine and Mighty Peking Man), people will finally understand why overseas audiences yawned when they saw Crouching Tiger.
Monkey was originally released in 1993 (it's already available here on video and DVD) and was such a big hit that its sequel is now five years old. The movie is somewhat of a prequel to the immensely popular and much revered Once Upon a Time in China, the seven-film (and counting) series that catapulted Jet Li to international stardom. Monkey and China, not to mention Jackie Chan's Drunken Master pictures, tell the story of a Chinese folk legend named Wong Fei-Hung, who was something like the Davy Crockett of that country during the mid 1800s.
Here, Fei-Hung is a child (and is played by a young girl named Tsang Sze-Man), who, along with his father Wong Kei-Ying (Donnie Yen, Highlander: Endgame), have just arrived in Zhejing, a city run by the crooked Governor Cheng (James Wong). With thousands of peasants seeking refuge in the city courtesy of rural flooding and bloodthirsty warlords, Cheng keeps a huge stockpile of food while his subjects dine on stone soup. Their only hope is Iron Monkey, a mysterious Robin Hood-type who liberates the Governor's gold and gives it to the commoners.
As you might expect, Iron Monkey, who is really a mild-mannered local doctor (Yu Rong-guang), is not very well-received by the Governor and the soldiers who are paid to protect him. When word arrives that the Royal Minister (Yen Yee Kwan) is on his way to Zhejing to get to the bottom of the whole Iron Monkey thing, the Governor panics and orders his soldiers to lock up anybody who could possibly be the big I.M. (leading to a funny scene with dialogue like "You sneezed like a monkey. Now come with me").
Combining the nighttime rooftop air ballet of Crouching Tiger with the humor and physical comedy of The Three Stooges, Monkey, which is both dubbed and subtitled, has a jaw-dropping finale that takes place on the business ends of a bunch of wooden poles.  Monkey was written by, among others, Hong Kong legend Tsui Hark, who directed four of the Once Upon a Time in China pictures.
PG-13 for martial arts action/violence and brief sexuality
========== X-RAMR-ID: 29705 X-Language: en X-RT-ReviewID: 252653 X-RT-TitleID: 1075984 X-RT-SourceID: 595 X-RT-AuthorID: 1146
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


Current Research
I am currently researching the z~3.09 protocluster in the SSA22 field. Originally discovered as an overdensity of Lyman Break Galaxies, this protocluster is expected to evolve into a Coma-like cluster by z=0. New dense spectroscopy of the region has allowed us to observe two seperate structures that will potentially merge in the future.
Recent Publications
Michael W. Topping, Alice E. Shapley, & Charles C. Steidel, 2016, ApJL, 824, 11
Substructure Within the SSA22 Protocluster at Z=3.09
Michael W. Topping and J. Michael Shull 2015, ApJ, 800, 97
The Efficiency of Stellar Reionization: Effects of Rotation, Metallicity, and Initial Mass Function


07-Jan-2011 Added a "solution demonstrator" for Long Division. The demonstrator will take you through the steps required to reach the solution. Click the Solve button on any long division problem to try it out.
Solve


Module: future.graph
¶
|
Perform Normalized Graph cut on the Region Adjacency Graph. |
|
Combine regions separated by weight less than threshold. |
Perform hierarchical merging of a RAG. |
|
|
Perform Normalized Graph cut on the Region Adjacency Graph. |
|
Comouter RAG based on region boundaries |
|
Compute the Region Adjacency Graph using mean colors. |
|
Show a Region Adjacency Graph on an image. |
|
The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph |
cut_normalized¶
-
skimage.future.graph.
cut_normalized
(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0, *, random_state=None)[source]¶ Perform Normalized Graph cut on the Region Adjacency Graph.
Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output.
- Parameters
- labelsndarray
The array of labels.
- ragRAG
The region adjacency graph.
- threshfloat
The threshold. A subgraph won’t be further subdivided if the value of the N-cut exceeds thresh.
- num_cutsint
The number or N-cuts to perform before determining the optimal one.
- in_placebool
If set, modifies
rag
in place. For each node n the function will set a new attributerag.nodes[n]['ncut label']
.- max_edgefloat, optional
The maximum possible value of an edge in the RAG. This corresponds to an edge between identical regions. This is used to put self edges in the RAG.
- random_stateint, RandomState instance or None, optional
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. The random state is used for the starting point of
scipy.sparse.linalg.eigsh
.
- Returns
- outndarray
The new labeled array.
References
- 1
Shi, J.; Malik, J., “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.
Examples
>>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels, mode='similarity') >>> new_labels = graph.cut_normalized(labels, rag)
Examples using skimage.future.graph.cut_normalized
¶
cut_threshold¶
-
skimage.future.graph.
cut_threshold
(labels, rag, thresh, in_place=True)[source]¶ Combine regions separated by weight less than threshold.
Given an image’s labels and its RAG, output new labels by combining regions whose nodes are separated by a weight less than the given threshold.
- Parameters
- labelsndarray
The array of labels.
- ragRAG
The region adjacency graph.
- threshfloat
The threshold. Regions connected by edges with smaller weights are combined.
- in_placebool
If set, modifies
rag
in place. The function will remove the edges with weights less that thresh. If set to False the function makes a copy ofrag
before proceeding.
- Returns
- outndarray
The new labelled array.
References
- 1
Alain Tremeau and Philippe Colantoni “Regions Adjacency Graph Applied To Color Image Segmentation” DOI:10.1109/83.841950
Examples
>>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels) >>> new_labels = graph.cut_threshold(labels, rag, 10)
Examples using skimage.future.graph.cut_threshold
¶
merge_hierarchical¶
-
skimage.future.graph.
merge_hierarchical
(labels, rag, thresh, rag_copy, in_place_merge, merge_func, weight_func)[source]¶ Perform hierarchical merging of a RAG.
Greedily merges the most similar pair of nodes until no edges lower than thresh remain.
- Parameters
- labelsndarray
The array of labels.
- ragRAG
The Region Adjacency Graph.
- threshfloat
Regions connected by an edge with weight smaller than thresh are merged.
- rag_copybool
If set, the RAG copied before modifying.
- in_place_mergebool
If set, the nodes are merged in place. Otherwise, a new node is created for each merge..
- merge_funccallable
This function is called before merging two nodes. For the RAG graph while merging src and dst, it is called as follows
merge_func(graph, src, dst)
.- weight_funccallable
The function to compute the new weights of the nodes adjacent to the merged node. This is directly supplied as the argument weight_func to merge_nodes.
- Returns
- outndarray
The new labeled array.
ncut¶
-
skimage.future.graph.
ncut
(labels, rag, thresh=0.001, num_cuts=10, in_place=True, max_edge=1.0, *, random_state=None)[source]¶ Perform Normalized Graph cut on the Region Adjacency Graph.
Given an image’s labels and its similarity RAG, recursively perform a 2-way normalized cut on it. All nodes belonging to a subgraph that cannot be cut further are assigned a unique label in the output.
- Parameters
- labelsndarray
The array of labels.
- ragRAG
The region adjacency graph.
- threshfloat
The threshold. A subgraph won’t be further subdivided if the value of the N-cut exceeds thresh.
- num_cutsint
The number or N-cuts to perform before determining the optimal one.
- in_placebool
If set, modifies
rag
in place. For each node n the function will set a new attributerag.nodes[n]['ncut label']
.- max_edgefloat, optional
The maximum possible value of an edge in the RAG. This corresponds to an edge between identical regions. This is used to put self edges in the RAG.
- random_stateint, RandomState instance or None, optional
If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random. The random state is used for the starting point of
scipy.sparse.linalg.eigsh
.
- Returns
- outndarray
The new labeled array.
References
- 1
Shi, J.; Malik, J., “Normalized cuts and image segmentation”, Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 22, no. 8, pp. 888-905, August 2000.
Examples
>>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels, mode='similarity') >>> new_labels = graph.cut_normalized(labels, rag)
rag_boundary¶
-
skimage.future.graph.
rag_boundary
(labels, edge_map, connectivity=2)[source]¶ Comouter RAG based on region boundaries
Given an image’s initial segmentation and its edge map this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within the image with the same label in labels. The weight between two adjacent regions is the average value in edge_map along their boundary.
- labelsndarray
The labelled image.
- edge_mapndarray
This should have the same shape as that of labels. For all pixels along the boundary between 2 adjacent regions, the average value of the corresponding pixels in edge_map is the edge weight between them.
- connectivityint, optional
Pixels with a squared distance less than connectivity from each other are considered adjacent. It can range from 1 to labels.ndim. Its behavior is the same as connectivity parameter in scipy.ndimage.filters.generate_binary_structure.
Examples
>>> from skimage import data, segmentation, filters, color >>> from skimage.future import graph >>> img = data.chelsea() >>> labels = segmentation.slic(img) >>> edge_map = filters.sobel(color.rgb2gray(img)) >>> rag = graph.rag_boundary(labels, edge_map)
rag_mean_color¶
-
skimage.future.graph.
rag_mean_color
(image, labels, connectivity=2, mode='distance', sigma=255.0)[source]¶ Compute the Region Adjacency Graph using mean colors.
Given an image and its initial segmentation, this method constructs the corresponding Region Adjacency Graph (RAG). Each node in the RAG represents a set of pixels within image with the same label in labels. The weight between two adjacent regions represents how similar or dissimilar two regions are depending on the mode parameter.
- Parameters
- imagendarray, shape(M, N, […, P,] 3)
Input image.
- labelsndarray, shape(M, N, […, P])
The labelled image. This should have one dimension less than image. If image has dimensions (M, N, 3) labels should have dimensions (M, N).
- connectivityint, optional
Pixels with a squared distance less than connectivity from each other are considered adjacent. It can range from 1 to labels.ndim. Its behavior is the same as connectivity parameter in
scipy.ndimage.generate_binary_structure
.- mode{‘distance’, ‘similarity’}, optional
The strategy to assign edge weights.
‘distance’ : The weight between two adjacent regions is the \(|c_1 - c_2|\), where \(c_1\) and \(c_2\) are the mean colors of the two regions. It represents the Euclidean distance in their average color.
‘similarity’ : The weight between two adjacent is \(e^{-d^2/sigma}\) where \(d=|c_1 - c_2|\), where \(c_1\) and \(c_2\) are the mean colors of the two regions. It represents how similar two regions are.
- sigmafloat, optional
Used for computation when mode is “similarity”. It governs how close to each other two colors should be, for their corresponding edge weight to be significant. A very large value of sigma could make any two colors behave as though they were similar.
- Returns
- outRAG
The region adjacency graph.
References
- 1
Alain Tremeau and Philippe Colantoni “Regions Adjacency Graph Applied To Color Image Segmentation” DOI:10.1109/83.841950
Examples
>>> from skimage import data, segmentation >>> from skimage.future import graph >>> img = data.astronaut() >>> labels = segmentation.slic(img) >>> rag = graph.rag_mean_color(img, labels)
show_rag¶
-
skimage.future.graph.
show_rag
(labels, rag, image, border_color='black', edge_width=1.5, edge_cmap='magma', img_cmap='bone', in_place=True, ax=None)[source]¶ Show a Region Adjacency Graph on an image.
Given a labelled image and its corresponding RAG, show the nodes and edges of the RAG on the image with the specified colors. Edges are displayed between the centroid of the 2 adjacent regions in the image.
- Parameters
- labelsndarray, shape (M, N)
The labelled image.
- ragRAG
The Region Adjacency Graph.
- imagendarray, shape (M, N[, 3])
Input image. If colormap is None, the image should be in RGB format.
- border_colorcolor spec, optional
Color with which the borders between regions are drawn.
- edge_widthfloat, optional
The thickness with which the RAG edges are drawn.
- edge_cmap
matplotlib.colors.Colormap
, optional Any matplotlib colormap with which the edges are drawn.
- img_cmap
matplotlib.colors.Colormap
, optional Any matplotlib colormap with which the image is draw. If set to None the image is drawn as it is.
- in_placebool, optional
If set, the RAG is modified in place. For each node n the function will set a new attribute
rag.nodes[n]['centroid']
.- ax
matplotlib.axes.Axes
, optional The axes to draw on. If not specified, new axes are created and drawn on.
- Returns
- lc
matplotlib.collections.LineCollection
A colection of lines that represent the edges of the graph. It can be passed to the
matplotlib.figure.Figure.colorbar()
function.
- lc
Examples
>>> from skimage import data, segmentation >>> from skimage.future import graph >>> import matplotlib.pyplot as plt >>> >>> img = data.coffee() >>> labels = segmentation.slic(img) >>> g = graph.rag_mean_color(img, labels) >>> lc = graph.show_rag(labels, g, img) >>> cbar = plt.colorbar(lc)
RAG
¶
-
class
skimage.future.graph.
RAG
(label_image=None, connectivity=1, data=None, **attr)[source]¶ Bases:
networkx.classes.graph.Graph
The Region Adjacency Graph (RAG) of an image, subclasses networx.Graph
- Parameters
- label_imagearray of int
An initial segmentation, with each region labeled as a different integer. Every unique value in
label_image
will correspond to a node in the graph.- connectivityint in {1, …,
label_image.ndim
}, optional The connectivity between pixels in
label_image
. For a 2D image, a connectivity of 1 corresponds to immediate neighbors up, down, left, and right, while a connectivity of 2 also includes diagonal neighbors. Seescipy.ndimage.generate_binary_structure
.- datanetworkx Graph specification, optional
Initial or additional edges to pass to the NetworkX Graph constructor. See
networkx.Graph
. Valid edge specifications include edge list (list of tuples), NumPy arrays, and SciPy sparse matrices.- **attrkeyword arguments, optional
Additional attributes to add to the graph.
-
__init__
(self, label_image=None, connectivity=1, data=None, **attr)[source]¶ Initialize a graph with edges, name, or graph attributes.
- Parameters
- incoming_graph_datainput graph (optional, default: None)
Data to initialize graph. If None (default) an empty graph is created. The data can be an edge list, or any NetworkX graph object. If the corresponding optional Python packages are installed the data can also be a NumPy matrix or 2d ndarray, a SciPy sparse matrix, or a PyGraphviz graph.
- attrkeyword arguments, optional (default= no attributes)
Attributes to add to graph as key=value pairs.
See also
convert
Examples
>>> G = nx.Graph() # or DiGraph, MultiGraph, MultiDiGraph, etc >>> G = nx.Graph(name='my graph') >>> e = [(1, 2), (2, 3), (3, 4)] # list of edges >>> G = nx.Graph(e)
Arbitrary graph attribute pairs (key=value) may be assigned
>>> G = nx.Graph(e, day="Friday") >>> G.graph {'day': 'Friday'}
-
add_edge
(self, u, v, attr_dict=None, **attr)[source]¶ Add an edge between u and v while updating max node id.
See also
networkx.Graph.add_edge()
.
-
add_node
(self, n, attr_dict=None, **attr)[source]¶ Add node n while updating the maximum node id.
See also
networkx.Graph.add_node()
.
-
fresh_copy
(self)[source]¶ Return a fresh copy graph with the same data structure.
A fresh copy has no nodes, edges or graph attributes. It is the same data structure as the current graph. This method is typically used to create an empty version of the graph.
This is required when subclassing Graph with networkx v2 and does not cause problems for v1. Here is more detail from the network migrating from 1.x to 2.x document:
With the new GraphViews (SubGraph, ReversedGraph, etc) you can't assume that ``G.__class__()`` will create a new instance of the same graph type as ``G``. In fact, the call signature for ``__class__`` differs depending on whether ``G`` is a view or a base class. For v2.x you should use ``G.fresh_copy()`` to create a null graph of the correct type---ready to fill with nodes and edges.
-
merge_nodes
(self, src, dst, weight_func=<function min_weight at 0x1c1ba6fb70>, in_place=True, extra_arguments=[], extra_keywords={})[source]¶ Merge node src and dst.
The new combined node is adjacent to all the neighbors of src and dst. weight_func is called to decide the weight of edges incident on the new node.
- Parameters
- src, dstint
Nodes to be merged.
- weight_funccallable, optional
Function to decide the attributes of edges incident on the new node. For each neighbor n for src and `dst, weight_func will be called as follows: weight_func(src, dst, n, *extra_arguments, **extra_keywords). src, dst and n are IDs of vertices in the RAG object which is in turn a subclass of
networkx.Graph
. It is expected to return a dict of attributes of the resulting edge.- in_placebool, optional
If set to True, the merged node has the id dst, else merged node has a new id which is returned.
- extra_argumentssequence, optional
The sequence of extra positional arguments passed to weight_func.
- extra_keywordsdictionary, optional
The dict of keyword arguments passed to the weight_func.
- Returns
- idint
The id of the new node.
Notes
If in_place is False the resulting node has a new id, rather than dst.


Crazy things happen , sometimes. This was one of those crazy things. Cycling has recently been my favorite sport and medium of geographical exploration. Over-complicating isn’t it. I love to cycle around the campus. In the morning you have this cool breeze that wakes your lethargic body up from within when you cycle around.
My good friend (Dinojh) who is also an amazing maker, a Product Designer and a cyclist helps me and essentially ignites the wacky cycling plans, I make. He has never said No. But this time after our last trip that was planned for Matunga ended up in an almost Dadar Darshan, I was cautious.
I wanted to cycle in South Bombay, especially the Marine Drive. So, the plan was to lug the cycle in the Local and reach CST and then cycle around, have breakfast and come back via Train preferably or if possible we can cycle back.
By (Safety Officer) Dinojh has other plans always. He is the one who hates public transport and would cycle anywhere. The only line of reason being , “Why not?”.
We ended ditching the Train out of the plan altogether. It ended up a 101.2km ride to and fro from IIT Bombay. Back breaking, mind numbing but we did quite a lot of exploration, actually most of South Bombay was peddled.
Phew. Lets start from the beginning.
We started from IIT around 6:45am in the morning. It was cold. The supplies to the canteen were coming in. We knew we wont be having any breakfast till CST so, ate up energy bars to help us fueled up. We carry 5litres of water as Dinojh always insists that he runs on water.
We took the eastern expressway all the way till CST. Stopping multiple times and having been lured into a few detours thanks to those escher-ed flyovers Bombay has.
We reached CST. It felt great. Actually we were peddling under a highway and it was CST all of a sudden.
We had breakfast at the Legendary Kyani Bakery. I really love the idea of hanging stair supports that they have.I had already tasted the place the last week in a cheese craving trance. Decent is my verdict. Lets not wake up the foodie inside. Ummmm.
Look what we found in the parking lot
Next up was Nariman Point which would set the starting point of our Marine Drive Adventure Lap.
Now we cycled from Nariman point till we lost our breath, guess where? Bandganga, near Malabar Hills.
The place looked nice for cycling. So, we cycled criss cross in the area. I could not manage some of the climbs though. Lured by the decent, we came down to Marine Drive. Boooo.. We missed Hanging Gardens it seems. No point of coming this far and not visiting Hanging Gardens, said Dinojh.
Okay Then!
We also planned to learn more about the Tower of Silence. But it turned out to be a private property. No Entry la !
But here is the cliched point to click the queen’s necklace. Yeah Yeah! The shoe house was still there.
Hungry again, we went back to Kyani and had some food. The place has for me temporarily crossed its marginal utility.
Next, we visited JJ School of Art to check out the amazing Fine Art exhibition. Thanks Avinash for the tip. Some pictures.
Photography was restricted inside and we are law abiding citizens. So, yeah ! This is what we could capture.
Next what? Well lots of it. Lets go to Fashion street. Why? Because its there and Dinojh has not seen it. Same for Gateway, NGMA, Price of Wales Museum and then back to CST.
We got ourselves clicked an instant photo at Gateway. The photographer was a part time working student and taught us some cool street photography tricks especially in sun.
From CST we started our deadman’s crawl back home. I barely managed to be back. One should always buy those cycling shorts and not just go. Be like Dinojh.
I remember this particular instance when I almost gave up around Dadar and was pissed off at the traffic condition there. You had these Taxi’s on the road side, they dont have a hazard switch or its very difficult to operate. Then as you would be passing by, like a dormant land mine he would come in your path and you apply those discy drum brakes.
Now imagine to do this multiple times in the next 1km. Takes the fun out. I dislodged from the cycle and walked a while. Dinoj like a horse would just go until he would realize that he is not riding alone. I walked and asked Dinojh, “ Dudeh (h is mandatory to make it sound like mallu), I wont be able to make it, my back hurts and blah..blah..” He said , “just smile (plus some random convincing stat about muscles) and change the posture to this”.
That actually helped, i mean the posture part. As we made it to the Eastern Highway, we had a smooth journey post that. We we entered campus back, it was as if some refugees came back to their motherland. We cycled faster and reached hostel. Puffff.
Mission Accomplished.
Thank Youh Dinojh :)
Cheers,
Rohit


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Nightly Brief
by CNSYour Monday night briefing from the staff of Courthouse News
Top CNS stories for today including the stage is set for eight witnesses to testify publicly this week in the House impeachment inquiry of President Donald Trump; A lawyer for House Democrats told the D.C. Circuit about mounting evidence that the president lied to Robert Mueller; Former Vice President Joe Biden has retained his 20-point lead in South Carolina among likely Democratic primary voters, and more.
Sign up for CNS Nightly Brief, a roundup of the day’s top stories delivered directly to your email Monday through Friday.
National
1.) Republicans clamoring for so-called firsthand witnesses in the impeachment inquiry will get the genuine article Tuesday as National Security Council official Alex Vindman appears for the second week of public proceedings.
2.) Calling for grand jury records in the special counsel’s investigation to be made public, a lawyer for House Democrats told the D.C. Circuit on Monday about mounting evidence that the president lied to Robert Mueller.
3.) Former Vice President Joe Biden has retained his 20-point lead in South Carolina among likely Democratic primary voters, according to a new poll released Monday.
4.) According to a study published Monday, the Green New Deal had bipartisan support when it was first proposed in 2018, but that steadily eroded as partisan polarization crept in and the cacophony swayed public opinion to different ends of the political spectrum.
5.) A former government employee who participated in the Trump administration’s policy of sending immigrants back to Mexico from the U.S. to wait out their asylum claims is now speaking out against the program, calling it an illegal process that endangers asylum seekers.
6.) The Supreme Court agreed Monday to temporarily block a lower court ruling that would have required President Donald Trump’s longtime accounting firm to turn over his personal financial records to Congress.
International
7.) With another member approved Monday, European Commission President-elect Ursula von der Leyen is set to begin the new session of her executive body next month.
8.) Secretary of State Mike Pompeo said Monday the United States will no longer treat Israeli settlements in the West Bank as violations of international law, reversing a 40-year old State Department legal opinion.
Copyright © 2019 courthousenews.com


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A WORD
ABOUT US
ABOUT US
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600
Personal
70000
Finance
Income Classification
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Expenditure Classification
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GALLERY
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BOOKKEEPING SERVICE
PRIVATE RECEIPT
AUDITING SERVICE
EQUITY MARKET
ECONOMIC CRISIS
FOREIGN EXCHANGE CRISIS


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Vegan Chocolate Coconut Oatmeal Pancakes
Gluten Free, Dairy Free, Egg Free, Refined Sugar Free
What else makes breakfast better than a yummy plate of pancakes? These pancakes are light and
sweet and go beautifully with a hint of maple syrup and a handful of your favourite berries.They are
super easy to make and only require a few special ingredients. Not to mention, they’re chocolate! So if
you’re a chocolate lover like me, you’re going to love these because they’re guilt free!
.
.
.
If it’s nutritious and absolutely delicious, you know it’s going to be a good day.
Let’s get baking! <3
Naturally gluten free, oatmeal is one of the best things you could have for breakfast making it first ingredient on this recipe. It’s a great source of wholesome fibres, carbohydrates, and is also loaded with lots of vitamins and minerals such as calcium, iron, zinc, manganese, phosphorus, magnesium, copper, folate, B1, and B5.
Flax seeds are an amazing source of fibre and omega 3 fats which helps to lower your cholesterol. But besides the fact that it’s nutritious, it could actually be used as an egg replacement. I love to use the Bob’s Red Mill brand because I can trust that it’s 100% gluten free along with many of their other products!
The next ingredient that I add for that rich chocolatey goodness is raw cacao powder! I love adding these to my pancakes because it satisfies my chocolate cravings but still provides me with an awesome source of iron, magnesium, potassium, phosphorus and healthy enzymes.
Moving on to the wet ingredients, the only two ingredients you need are one banana and coconut milk! They are both a great source of potassium, magnesium, phosphorus and calcium! The banana on one hand, provides the sweetness for these pancakes so the riper your banana is, the sweeter. Coconut milk on the other hand is great for providing B and C vitamins and iron. I recommend using low fat coconut milk for this recipe.
Difference between baking soda and baking powder? Well, baking soda (sodium bicarbonate) is the stabilizer to all your baked goods while the baking powder (sodium bicarbonate + a dry acid) helps them rise. Now that you know the difference, you won’t ever make that mistake of substituting one for the other again ;)
Mash mash mash! Simple ain’t it? Just make sure I don’t see any clumps in the bowl or I’ll get a tiny bit upset ;) Just kidding, you just want a really smooth batter that’s all!
Add in the coconut to the mashed bananas… Then MIX!!
Then add in the wet mixture to the dry!
After you mixed everything thoroughly together you should get something like this. Don’t worry about over mixing anything since it’s a gluten free recipe ;)
This recipe should give you exactly 6 pancakes. I designed it so that it’s good for one person but if you want more than that, just double or triple or etc, the recipe. I used 2 and 1/2 tbsps for each pancake!
After giving the baking tray a few light taps at the bottom to flatten our the pancakes, I let them bake in the oven for 13 beautiful minutes at 350 degrees Fahrenheit!
The recipe will ask for oatmeal flour and throughout these pictures I actually don’t show that! But don’t worry, I did some magic to fix it all in the end ;) but to make your life easier, just pulse your oatmeal in a blender and VOILA! You have oatmeal flour :)
Okay okay, now here’s the actual recipe :) Enjoy!
Ingredients:
1/3 cup of gluten-free oatmeal flour
1 tbsp gluten-free flax seed meal
1 tbsp. gluten-free cacao powder
1 medium sized and ripened banana
1/3 cup of canned coconut milk
1/2 tsp of baking soda
¼ tsp gluten-free baking powder
1-2 tsp maple syrup
**Berries of your choice (I used blueberries and black berries because they were sweet and in season)
- First, preheat your oven to 350 degrees Fahrenheit and line your baking sheet with parchment paper.
- After setting the trays aside, mash the ripe banana until smooth with a fork in a large bowl. Then add the coconut milk and whisk the two ingredients together.
- Next, incorporate the oatmeal flour, flax seeds, and cacao powder, then mix.
TIP: Add the baking soda and baking powder at the very end so they are at their peak of their magic when you bake them!
- Your batter should make 6 pancakes (2 and a half tablespoons each)
- Let pancakes bake untouched for 12-13 minutes. Do not be tempted to open up the oven because this can disrupt the air circulation during the cooking process!
- Plate your pancakes and top them off with your favourite berries and maple syrup.
Instructions:
Sincerely, Nathalee
♥