Written by: Wil Calhoun & Scott Silveri
Transcribed by: Eric Aasen
[Scene: Rachels bedroom; Rachel is awoken by a man singing in the next apartment.]
The Singing Man: (singing, duh) Mornings here! Sunshine is here! The sky is clear, the mornings here! The mornings here!
Rachel: HEY!! Do you have to do that? Its Saturday!
The Singing Man: Oh come on! Mornings here! (Starts singing) Mornings here! The morning is here! Sunshine is here!
(Rachel slams shut her window and storms into the living room, where Joey and Monica are eating breakfast.)

Joey: Are you kidding? I love that guy! (Starts singing) Mornings here! Morning is here
Rachel: Stop it!
Monica: Hey, I have all the space I need. Just do what I did.

Monica: Yknow what? I am really tired of your bellyaching! Okay, I-I worked really hard at making this a nice place for us to live!
Rachel: Im sorry. Im so sorry.
Monica: Okay.
Joey: See, this is a great apartment.
 This place is a hole!
Opening Credits
[Scene: Rosss bedroom; Ross and Emily are making out. Ross as a new feature.]
Emily: Oh, blimey, I still cant believe youve got an earring!
(Yep, its a little gold loop.)
Ross: Huh? I know, I know. Who am I? David Bowe? (Shakes his head around, pretending like hes jamming.)
Emily: He does that?!
Ross: Uh, I dont know, whatever.
Emily: I think it makes you look really dangerous.
Ross: Oh, I know. Yknow what, I never wouldve gotten this if it werent for you. No really, when Im with you Im-Im like this whole other guy, I love that guy!  I-I love that guy!
Emily: I love both of you!
Ross: Yeah?
(They kiss.)
Emily: I wish I didnt have to go.
Ross: Then dont. Stay here. Just dont go so soon to London, just one more day.
Emily: Ohh, Ross, please!
Ross: One more day, seriously/
Emily: Dont do this to me, again. Youd know Id stay here in a minute, but Id really miss so much work, theyll fire me.
Ross: So, then you can stay as long as you want.
Emily: I wish I could.
Ross: Oh no. Dont, dont, dont start packing. Come on! (She puts some clothes into her bag, and Ross throws them out.)
Emily: I dont think you understand packing. Look, I just dont want to leave it to the last minute. Last time I left in such a rush, I left my knickers here.
Ross: Yeah, I know, I uh, I tried them on.
Emily: You didnt!
Ross: No. No, I didnt. I didnt want to be that guy.
[Scene: Monica and Rachel's erm, Chandler and Joey's, Joey and Chandler are watching one of those Kung Fu movies and imitating the moves.]
Phoebe: (entering, wearing Santa pants) Hello!
Chandler: Ho! Ho! Ho!
Phoebe: Excuse me.
Chandler: Your pants!
Phoebe: Oh, yeah! You like em? I just, I went to a used clothes store and got a bunch of maternity stuff. These are sooo comfortable!

Phoebe: What?
Chandler: Santa pants. (Phoebe still doesnt get it.) Santa Clauss pants.
Phoebe: Nuh-uh! Theyre maternity pants. They even came with a list of baby names. (Pulls out a sheet of paper which lists whos been naughty and whos been nice.) See, these names are good, and these names are bad. (Finally, she figures it out.) Ohh.
Rachel: (entering) Hey!
Monica: (entering) Hey!
Rachel: SoHey, Pheebs! So, how are the elves?
Phoebe: I dont know! How are the-the-the-the, yknowYoure clothes arent funny.
Monica: Hey, guys, what-what should I wear to a Knicks game?
Chandler: Uhh, a T-shirt that says, "I dont belong here."
Joey: You have Knicks tickets?
Rachel: Yeah, my mom got my dads season tickets in the divorce, so she just gave them to me.
Monica: Yeah, apparently, theyre pretty good seats.
Rachel: Yeah.
Joey: (examining the tickets) Oh my God! Those are almost right on the floor!
Rachel: Do you guys want these?
Joey: Yeah!
Chandler: Yeah we do!
Rachel: Ohh, well you got em.
Both: All right!
Rachel: Just give us our apartment back!
Phoebe: Boy! I didnt see that coming!

Rachel: Oh, come on! We know what these are worth.
Monica: Yeah, what, do you think were stupid?
 Youre meaner than I thought.
Monica: What do you say?
Chandler: Forget it! Okay, Im not giving up my bachelor pad for some basketball seats!
Rachel: Youre bachelor pad?!
Monica: Have you even had a girl up here?
Chandler: No. But uh, Joey has, and I usually talk to them in the morning time.
Joey: Yeah, you do!
[Scene: Central Perk, Joey is whining to Chandler about the tickets.]
Joey: Come on!
Chandler: (ignoring him) Yes, Gunther, can I get two cups of chino, please?
Gunther: Good one.
Joey: Come on, season tickets! Season tickets, do you know what that means?
Chandler: Forget it! Okay, Im not giving up the apartment.
Joey: Oh comelook, when I was a kid my dads company gave season tickets to the number one salesman every year, all right? My dad never won! Of course, he wasnt in the sales division, but still, I never ever, ever forgot that!
Ross: (entering) Hey, guys! (They both notice his new little friend)
Joey: Hey!
Chandler: Oh my God!

Ross: Oh yeah, Emily convinced me to do it.
Chandler: You do know that Wham broke up?
Ross: I like it, and Emily likes it, and thats what counts. So uh, how are you guys doing?

Chandler: Where is Emily?
Ross: Ugh, shes saying good-bye to her uncle.
Chandler: Man, didnt she like just get here?
Ross: Yeah!! Yeah!
Chandler: Easy tiger.
 I mean, every time I go pick her up at the airport, its-its so great. But at the same time Im thinking, "Well, Im gonna be right back there in a couple of days, dropping her off."
Chandler: So what are you going to do?
Ross: Nothing! Theres nothing to do! I mean, she lives there, I live here. I mean, she-shed have to uh, move here. She should move here!
Joey: What?
Ross: I could ask her to live with me!

 I mean, I mean why not?!
Chandler: Because youve only known her for six weeks! Okay, Ive got a carton of milk in my fridge Ive had a longer relationship with!
Ross: Look guys, when Im with her its-its-its like she brings this-this-this great side out of me. I mean I-I-I love her, yknow?
Chandler: And I love the milk! But, Im not gonna some British girl to move in with me! (Realizes that made no sense.) Joey, you say things now.
Joey: All right look, Ross, hes right. Emilys great, shes great!
Ross: I dont want to do that.
Joey: No! You dont want to wreck it, you dont want to go to fast!
 All right, thanks guys. (Gets up to leave.)
Chandler: Okay, no problem, just remember to wake us up before you go-go.
[Scene: Monica and Rachel's erm, Chandler and Joey's, later that same day. Joey and Chandler are eating pizza, and Phoebe is trying to knit something.]
Phoebe: Thats too hard. Too hard!
Monica: (entering with Rachel) All right boys, last chance for the tickets!
Rachel: Or Ill give them to my new boyfriend, Joshua.
Chandler: No thank you.
Joey: Wait-wait-wait-wait! (To Chandler) Come on! Come on, lets trade! The timings perfect, I just clogged the toilet!
Chandler: Look, I want those basketball seats as much as you do! Okay, but we cant leave in the small apartment after weve lived here! Didnt you ever read Flowers for Algernon?
Joey: Yes! Didnt you ever read Sports Illustrated?! No! I didnt read yours! But come on, we can go to the game tonight!
Chandler: Look, the only way I will even consider this is if they offer a lot more than just season seats.
Joey: Its the Knicks!
Chandler: Screw the Knicks!
Joey: Whoa!
Chandler: I didnt mean that. I just meant that the apartment is worth so much more.
Joey: Huh.
Chandler: And the Knicks rule all.
Joey: Yeah, the Knicks rule all!
Phoebe: Hey, so? Are you gonna do it?
Chandler: No. No. Were not gonna do that, yknow why? Because its not an even trade.
Rachel: All right, okay, look, what if you could keep the apartment and get the tickets?
Joey: Done!
Rachel: Let me finish.
Joey: Oh.
Rachel: Im talking about a bet, winner takes all.
Joey: Ooh, we could end up with nothing.
Phoebe: Or you could end up with everything.
Joey: Ooh, I like that.
Monica: All right, so what do you say?
Chandler: No!
Monica: Oh, just do it!!
Chandler: Op, op, Im convinced!
Joey: Come on man, you know Id do it for you! Because, youre my best friend.
Chandler: All right, but you cant use that again for a whole year. Im in.
Joey: All right!
Phoebe: Ooh, this is so exciting! Ooh, God, what are you going to bet?
Rachel: Oh, okay, well, I think we should let Phoebe decide, because shes the only whos impartial, and shes so pretty.
Phoebe: Okay. Umm, ooh, oohoh, I have a game!
Joey: Okay!
Chandler: Okay!
Phoebe: This is great!
Joey: Whats the game?! Whats the game?!
Phoebe: Oh, well, it doesnt have a nameoh, okay, Phoebeball! No, it doesnt have a name. Umm, okay, Monica, what is your favourite thing about trees?
Monica: Theyre green?
Phoebe: Good! Good! Five points!
(They both rejoice; Chandler is totally confused.)
Phoebe: All right, Joey, same question.
(He looks to Chandler, who doesnt have a clue.)
Joey: Uhh, theyre tall.
Phoebe: Ooh, three points. Both fine answers, but we were looking for leafy, leafy.
(Joey turns and is angry that Chandler didnt come up with the answer.)
Monica: Thats not even a game!
Rachel: What?  Were winning!
Monica: You wanna finish this right now? All right, we get a deck of cards, high card wins. What do you say?
Chandler: Fine, lets do it.
Phoebe: Oh, I have cards!
Joey: Oh.
Monica: Oh, good.
Phoebe: Yeah! Here! (She grabs a deck out of her purse) Oh no, these are the trick deck. Okay. Here yes. Okay.
Chandler: Okay, you guys uh, you guys pick first
Rachel: Okay.
Monica: Okay. (She picks a card.) Four.
Chandler: Thats a low one!
Joey: Yeah! Okay. (Joey picks a card.) Phoebe, you look, I cant.
Phoebe: What make you think I can?! (Shields her eyes from it.)
Joey: Okay. Okay. (He looks at the card.) Ace!
(Both Joey and Chandler and Monica and Rachel jump up and down for joy.)
Chandler: Why are you screaming and hugging?
Monica: Because we won our apartment back!
Joey: What? Ace is high! Jack, queen, king, ace!
Monica: No! Ace is low! Ace, two, three, four!
(They all look to Phoebe to settle this.)
Phoebe: I dont know. Ooh! Ooh! Look it! (She fans out the trick deck.) Ah-ha!
Rachel: All right, cut, lets pick again, pick again.
Joey: Okay.
Rachel: Come on apartment! Come on apartment! (Picks a card.) Oh! I know queen is high!
Joey: Uh-huh, not as high as (picks a card) It worked! King!
Chandler: Yeah baby!
Monica: But, we pick again! We pick again!
Joey: Why?!
Monica: I dont know!
Chandler: Tickets please! (Rachel hands over the tickets) Thats courtside baby!
Joey: Seriously, good game though. Good game. (He tries to congratulate them, but they pull away.) (To Chandler) What are they so mad about? They get the apartment back!
Chandler: No they didnt!
[Scene: Rosss apartment, Emily has packed as Ross returns.]
Ross: Hey!
Emily: I packed while you were gone. I left some knickers under your pillow.
Ross: (laughs) Move in with me.
Emily: What?!
Ross: Dont be scared, I-I know it sounds crazy and-and people will say its too soon, but just-just think, think how great it will be.
Emily: Ohh, no. Ugh. Oh, leaving London, my whole family lives there.
Ross: I know.
Emily: My job!
Ross: Well, so, you-youll get a job here! I mean, Im always hearing about uh, them foreigners coming in here and stealing American jobs; that could be you!
Emily: Yeah, but it-it-its my whole lifeyou come to England.
Ross: No, I cant. I would, I really would, but my son is here; I cant leave him. Isntyou dont think theres any way?
Emily: Ohh, I dont think so. I mean it would be different if it was way into the future and-and-and we were getting married or something.
Ross: What?
Emily: Oh no, no, right I shouldnt have said married. Uh, please dont go freaky on me. I didnt mean it. Well, I didnt say it; I take it back!
Ross: No, no, dont. Why dont we?
Emily: Why dont we what?
Ross: Get married.
Emily: You are mad!
Ross: No! No! Im not! Its-its-its perfect! I mean its better than you just-just moving here, cause its us together forever, and thats-thats what I want.
Emily: Weve only known each other for six weeks!
Ross: Yeah, I know, so what? I mean, whos-whos to say? Does that me we-we cant do it? Look, huh, I was with Carol for four years before we got married and I wound up divorced from a pregnant lesbian. I mean, this, this makes sense for us. Come on! I mean, on our first date we ended up spending the whole weekend in Vermont! I mean, last night I got my ear pierced! Me! This feels right. Doesnt it?
Emily: My parents are going to be really mad.
Ross: Is thatare you saying yes? Is that yes?
Emily: Yes.
(They kiss and hug.)
Emily: Yes!
Ross: Yes! Were getting married?!
Emily: Oh my God!
Ross: Yes!
Emily: Were getting married!
Ross: Come here, come here. Uh, (He takes the earring out.) ow! Emily, will you marry me?
Emily: Yes.
(He tries to put it on her finger.)
Emily: Ohh, its a bit small.
Ross: Damn! I thought that was going to be romantic as hell!
Emily: It was.
(They kiss.)
Commerical Break
[Scene: The hallway, Joey and Chandler are coming back from the game.]
Chandler: Those were like the best seats ever.
Joey: Oh yeah. Hey! Should we give these shirts to the girls? Yknow, kinda like a peace offering.
Chandler: Oh yeah, thats very nice. Plus, yknow they were free and theyre too small.
(He knocks on the girls door and walks in. Surprise! The girls, obviously using Star Trek technology, have completely moved everything in both apartments back to their original positions, all in the time it took for the guys to go to a basketball game. Wow! Anyhoo, Chandler is stunned, and Joey doesnt even realise it.)
Chandler: Oh. Oh, God!
Joey: Hey, want a beer? (Hands him a beer and sits down in one of the chairs.) (Jumping up.) WHOA!!!!
Chandler: I KNOW!!!
(They both sprint to what used to be their apartment.)
Chandler: Open up! Open up! Open up!
(A very angry Monica opens the door with the security chain still on.)
Monica: Well discuss it, in the morning! (Slams the door shut.)

(Its Rachels turn to open the door.)
Rachel: We took our apartment back!! (Slams the door shut.)
Phoebe: (opening the door) I had nothing to do with it. (Closes the door.) (Opens the door.) Okay, it was my idea, but I dont feel good about it.
(She goes to close the door, but Chandler puts his foot it in.)
Chandler: We are switching back, right now!
Monica: No, were not! Were not leaving!
 Theres nothing you can do to stop us! Right, Joe?
Joey: I dont know.
Chandler: What?
Joey: I dont want to move again!
Chandler: I dont care, this is our apartment! And they stoleyou stole itour apartment, and we won that apartment fair and square, twice! And I am getting it back right now. Im getting back right now!
(They open the door.)
Rachel: All right. We figured you might respond this way, so we have a backup offer.
Chandler: Oh no-no-no, no more offers. You cant offer anything to us!
Rachel: Let us keep the apartment and
Monica: As a thank you, Rachel and I will kiss for one minute.
[Time lapse. The guys are entering their apartment.]
Chandler: Totally worth it!
Joey: That was one good minute!
Chandler: Good night.
Joey: Good night.
 Of course, Chandler has to close both sections of his door.)
[Cut to the girls apartment.]

Rachel: Yeah!
Phoebe: Thats so funny to think if youd just done that right after the last contest, no one would have had to move at all.
Monica: Yeah, let-let-lets pretend thats not true.
Rachel: Yeah.
Phoebe: Okay, scarfs done. (Its not really a scarf, its just a bunch of yarn that Phoebe has tied together. Just then, Ross and Emily enter dragging with them Joey and Chandler.)
Ross: Come on! Come on. Come on.
Chandler: Okay!
Phoebe: Hey!
Ross: Hey!
Monica: What-whats going on?
Joey: Ross has some big thing to tell everyone.
Ross: Uhh, okay, its uh, Emily and I, we decided to uh, to get married.
(The gang is stunned.)
Phoebe: What? Oh, are you pregnant too?!
Emily: Umm, no.
Monica: When, when didhow, how did you
Ross: We, we just decided to uh, to go for it.
Emily: I mean, we know its a bit hasty but, uh, it just feels so right, so
(Rachel slowly walks in from her bedroom. She is stunned speechless.)
Ross: (turning around.) Umm, uh, I was just telling the guys
Rachel: Yeah, I-I heard. (Pause, everyone looks at each other, waiting for Rachels reaction.) I think its great! (Hugs Ross.) Ohh, Im so happy for you!
(Seeing Rachels apparently okay with this, the rest of the gang jumps up to congratulate Ross and Emily on their pending nuptials.)
Chandler: Oh, well, thats great!
Joey: Yeah! Yeah!
Monica: (to Ross) I cant believe youre getting married!
Ross: Yeah. (They hug again.)
Joey: Monica and Rachel made out. (Giggles like a schoolboy and Monica glares at him.)
Closing Credits
[Scene: Joeys bedroom, he is awoken by the singing guy.]
The Singing Man: (singing) Mornings here! The mornings here!
(Joey joins him.)
Both: Sunshine is here! The sky is clear, the mornings here!
The Singing Man: Hey! Youre back!
Joey: Hey! (Singing) Get into gear!
The Singing Man: (singing) Breakfast is near!
Both: The dark of night has disappeared!!
The Singing Man: Ill see you tomorrow morning!
Joey: (happily) Okay!
End


--- layout: post title: "Magmas" description: "A binary operation with no constraints on its behaviour is called a magma. An introduction for object-oriented programmers."  Quasigroups are also a subset of magmas, but can overlap semigroups and monoids." --- {% include JB/setup %}
{{ page.description }}
In the overall article series about group-like algebraic structures, you've so far seen examples of monoids, semigroups, and quasigroups. Common to all of these structures is that they are binary operations governed by at least one law. The laws are different for the different categories, but there are rules.
What if you have a binary operation that follows none of those rules?
All binary operations are magmas. If they have additional properties, we may call them quasigroups, or monoids, or some such, depending on the specific properties, but they're still all magmas. This is the most inclusive category.
You've already seen examples of monoids and semigroups, but what about magma examples? In a sense, you've already seen those as well, because all the examples you've seen so far have also been magma examples. After all, since all monoids are magmas, all the monoid examples you've seen have also been magma examples.
Still, it's not that hard to come up with some programming examples of magmas that aren't semi- or quasigroups. In the next articles, you'll see some examples.
Particularly the second example is fairly realistic, which demonstrates that as programmers, we can benefit from having vocabulary that enables us to describe any binary operation that doesn't obey any particular laws. In fact, establishing a vocabulary has been my primary motivation for writing this article series.


#include <EntityRep.hpp>
Public Types | |
| enum | TAssocIndexing |
| Types of storing document-entity associations. More... | |
Public Member Functions | |
| EntityRep (const TAssocIndexing &assocIndex) | |
Document-entity associations | |
| void | loadAssociations (const lemur::api::Index &index) |
| Load entities and document-entity associations. | |
| const DISTR_T & | entityDocs (const ID_T &entityID) const |
| Documents associated with the entity. | |
| const DISTR_T & | docEntities (const ID_T &docID) const |
| Entities associated with the document. | |
Summary counts | |
| const int | entityNum () const |
| Number of entities. | |
| const int | entityDocNum (const ID_T &entityID) const |
| Number of documents associated with the entity. | |
| const int | docEntityNum (const lemur::api::DOCID_T &docID) const |
| Number of entities associated with the document. | |
Conversion between entity IDs | |
| const ID_T | entityID (const EXID_T &extEntityID) const |
| Convert external entityID to internal entityID. | |
| const EXID_T | entityID (const ID_T &intEntityID) const |
| Convert internal entityID to external entityID. | |
| const EXID_T | entityIDfromAlias (const EXID_T &entityAlias) const |
| Get external entityID from entityAlias. | |
Private Attributes | |
| std::map< EXID_T, ID_T > | intEntityID_ |
| Mapping from external to internal entity IDs. | |
| std::vector< EXID_T > | extEntityID_ |
| Mapping from internal to external entity IDs. | |
| std::map< EXID_T, EXID_T > | aliasEntityID_ |
| Mapping from entity alias to external entity ID. | |
| const TAssocIndexing | assocIndex_ |
| Indexing of document-entity associations for internal storage. | |
| std::vector< DISTR_T > | pde_ |
| Document-entity associations. | |
| std::vector< int > | nd_ |
| Number of documents associated with an entity. | |
| std::vector< int > | ne_ |
| Number of entities associated with a document. |
Definition at line 30 of file EntityRep.hpp.
Types of storing document-entity associations.
Definition at line 42 of file EntityRep.hpp.
| ears::EntityRep::EntityRep | ( | const TAssocIndexing & | assocIndex | ) | [inline] |
| assocIndex | Type of storing document-entity associations |
Definition at line 48 of file EntityRep.hpp.
| void ears::EntityRep::loadAssociations | ( | const lemur::api::Index & | index | ) |
Load entities and document-entity associations.
| index | Index |
Definition at line 34 of file EntityRep.cpp.
| const ears::DISTR_T & ears::EntityRep::entityDocs | ( | const ID_T & | entityID | ) | const |
Documents associated with the entity.
| entityID | Entity ID (internal) |
Definition at line 286 of file EntityRep.cpp.
| const ears::DISTR_T & ears::EntityRep::docEntities | ( | const ID_T & | docID | ) | const |
Entities associated with the document.
| docID | Document ID (internal) |
Definition at line 327 of file EntityRep.cpp.
| const int ears::EntityRep::entityDocNum | ( | const ID_T & | entityID | ) | const |
Number of documents associated with the entity.
| entityID | Entity ID (internal) |
Definition at line 380 of file EntityRep.cpp.
| const int ears::EntityRep::docEntityNum | ( | const lemur::api::DOCID_T & | docID | ) | const |
Number of entities associated with the document.
| docID | Document ID (internal) |
Definition at line 393 of file EntityRep.cpp.
| const ears::ID_T ears::EntityRep::entityID | ( | const EXID_T & | extEntityID | ) | const |
Convert external entityID to internal entityID.
| extEntityID | Entity ID (external) |
Definition at line 409 of file EntityRep.cpp.
| const ears::EXID_T ears::EntityRep::entityID | ( | const ID_T & | intEntityID | ) | const |
Convert internal entityID to external entityID.
| intEntityID | Entity ID (internal) |
Definition at line 422 of file EntityRep.cpp.
| const ears::EXID_T ears::EntityRep::entityIDfromAlias | ( | const EXID_T & | entityAlias | ) | const |
Get external entityID from entityAlias.
| entityAlias | Entity alias |
Definition at line 434 of file EntityRep.cpp.
std::vector< DISTR_T > ears::EntityRep::pde_ [private] |
Document-entity associations.
Depending on how we need to store associations, pde_ is indexed either by documents or by entities:
Definition at line 134 of file EntityRep.hpp.


Interfaces represent different means of accessing data from a particular service, along with templated values that give users URLs or other identifiers necessary to access data. Examples: GCS buckets can be accessed via a web browser, a headless HTTP client, or the “gsutil” command-line tool, all of which require different URLs.
Roles are a way of configuring different access levels of a service, by mapping a set of permissions in the external service to a single role in the for this service definition.


Copyright 2015 Lattice Semiconductor Corporation, All Rights Reserved Mon Sep 24 15:06:17 2018 Command Line: par -w -n 1 -t 1 -s 1 -exp parPathBased=ON \ ice40up_simple_adc_impl_1_map.udb ice40up_simple_adc_impl_1.udb Cost Table Summary Level/ Number Worst Timing Worst Timing Run Run Cost [ncd] Unrouted Slack Score Slack(hold) Score(hold) Time Status ---------- -------- ----- ------ ----------- ----------- ---- ------ 5_1 * 0 - - - - 10 Success * : Design saved. Total (real) run time for 1-seed: 9 secs par done! Lattice Place and Route Report for Design "ice40up_simple_adc_impl_1_map.udb" Mon Sep 24 15:06:17 2018 Best Par Run PAR: Place And Route Radiant (64-bit) <IP_ADDRESS>0.6. Command Line: par -w -t 1 -exp parPathBased=ON \ ice40up_simple_adc_impl_1_map.udb ice40up_simple_adc_impl_1_par.dir/5_1.udb Loading ice40up_simple_adc_impl_1_map.udb ... Loading device for application udb from file 'itpa08.nph' in environment: C:/lscc/radiant/1.0/ispfpga. Design: ADC_top Family: iCE40UP Device: iCE40UP5K Package: SG48 Performance Grade: High-Performance_1.2V Number of Signals: 196 Number of Connections: 483 Device utilization summary: SLICE (est.) 55/2640 2% used LUT 101/5280 1% used REG 63/5280 1% used PIO 14/56 25% used 14/36 38% bonded IOLOGIC 1/56 1% used DSP 0/8 0% used I2C 0/2 0% used HFOSC 0/1 0% used LFOSC 0/1 0% used LEDDA_IP 0/1 0% used RGBA_DRV 0/1 0% used FILTER 0/2 0% used SRAM 0/4 0% used WARMBOOT 0/1 0% used SPI 0/2 0% used EBR 0/30 0% used PLL 0/1 0% used RGBOUTBUF 0/3 0% used I3C 0/2 0% used OPENDRAIN 0/3 0% used Pin Constraint Summary: 2 out of 13 pins locked (15% locked). . ................Finished Placer Phase 0 (HIER). CPU time: 0 secs , REAL time: 0 secs ................ Finished Placer Phase 0 (AP). CPU time: 0 secs , REAL time: 2 secs Starting Placer Phase 1. REAL time: 2 secs .. .. .................... Placer score = 12736. Device SLICE utilization summary after final SLICE packing: SLICE 54/2640 2% used Finished Placer Phase 1. CPU time: 8 secs , REAL time: 9 secs Starting Placer Phase 2. . Placer score = 32792 Finished Placer Phase 2. CPU time: 8 secs , REAL time: 9 secs Clock Report Global Clocks : PRIMARY "i_clk_in_c" from comp "i_clk_in" on CLK_PIN site "35 (PR13B)", clk load = 36, ce load = 0, sr load = 0 PRIMARY : 1 out of 8 (12%) I/O Usage Summary (final): 14 out of 56 (25.0%) PIO sites used. 14 out of 36 (38.9%) bonded PIO sites used. Number of PIO comps: 13; differential: 1 Number of Vref pins used: 0 I/O Bank Usage Summary: +----------+---------------+------------+------------+------------+ | I/O Bank | Usage | Bank Vccio | Bank Vref1 | Bank Vref2 | +----------+---------------+------------+------------+------------+ | 0 | 3 / 14 ( 21%) | 2.5V | | | | 1 | 9 / 14 ( 64%) | 3.3V | | | | 2 | 2 / 8 ( 25%) | 3.3V | | | +----------+---------------+------------+------------+------------+ Total Placer CPU time: 8 secs , REAL time: 10 secs Writing design to file ice40up_simple_adc_impl_1_par.dir/5_1.udb ... ----------------------------------------------------------------- INFO - par: ASE feature is off due to non timing-driven settings. ----------------------------------------------------------------- Start NBR router at 15:06:26 09/24/18 ***************************************************************** Info: NBR allows conflicts(one node used by more than one signal) in the earlier iterations. In each iteration, it tries to solve the conflicts while keeping the critical connections routed as short as possible. The routing process is said to be completed when no conflicts exist and all connections are routed. Note: NBR uses a different method to calculate timing slacks. The worst slack and total negative slack may not be the same as that in timing report. You should always run the timing tool to verify your design. ***************************************************************** Starting routing resource preassignment Preassignment Summary: -------------------------------------------------------------------------------- 94 connections routed with dedicated routing resources 1 global clock signals routed 130 connections routed (of 417 total) (31.18%) --------------------------------------------------------- Clock routing summary: Primary clocks (1 used out of 8 available): #7 Signal "i_clk_in_c" Clock loads: 36 out of 36 routed (100.00%) --------------------------------------------------------- -------------------------------------------------------------------------------- Completed routing resource preassignment Start NBR section for initial routing at 15:06:26 09/24/18 Level 4, iteration 1 13(0.00%) conflicts; 0(0.00%) untouched conn; 0 (nbr) score; real time: 0 secs Info: Initial congestion level at 75% usage is 0 Info: Initial congestion area at 75% usage is 0 (0.00%) Start NBR section for normal routing at 15:06:26 09/24/18 Level 4, iteration 1 1(0.00%) conflict; 0(0.00%) untouched conn; 0 (nbr) score; real time: 0 secs Level 4, iteration 2 1(0.00%) conflict; 0(0.00%) untouched conn; 0 (nbr) score; real time: 0 secs Level 4, iteration 3 1(0.00%) conflict; 0(0.00%) untouched conn; 0 (nbr) score; real time: 0 secs Level 4, iteration 4 0(0.00%) conflict; 0(0.00%) untouched conn; 0 (nbr) score; real time: 0 secs Start NBR section for re-routing at 15:06:26 09/24/18 Level 4, iteration 1 0(0.00%) conflict; 0(0.00%) untouched conn; 0 (nbr) score; real time: 0 secs Start NBR section for post-routing at 15:06:26 09/24/18 End NBR router with 0 unrouted connection NBR Summary ----------- Number of unrouted connections : 0 (0.00%) Number of connections with timing violations : 0 (0.00%) Estimated worst slack<setup> : <n/a> Timing score<setup> : 0 ----------- Notes: The timing info is calculated for SETUP only. --------------------------------------------------------- Clock routing summary: Primary clocks (1 used out of 8 available): #7 Signal "i_clk_in_c" Clock loads: 36 out of 36 routed (100.00%) --------------------------------------------------------- Total CPU time 0 secs Total REAL time: 0 secs Completely routed. End of route. 417 routed (100.00%); 0 unrouted. Writing design to file ice40up_simple_adc_impl_1_par.dir/5_1.udb ... All signals are completely routed. PAR_SUMMARY::Run status = Success PAR_SUMMARY::Number of unrouted conns = 0 PAR_SUMMARY::Worst slack<setup/<ns>> = <n/a> PAR_SUMMARY::Timing score<setup/<ns>> = <n/a> PAR_SUMMARY::Worst slack<hold /<ns>> = <n/a> PAR_SUMMARY::Timing score<hold /<ns>> = <n/a> PAR_SUMMARY::Number of errors = 0 Total CPU Time: 9 secs Total REAL Time: 10 secs Peak Memory Usage: 75 MB par done! Copyright (c) 1991-1994 by NeoCAD Inc. All rights reserved. Copyright (c) 1995 AT&T Corp. All rights reserved. Copyright (c) 1995-2001 Lucent Technologies Inc. All rights reserved. Copyright (c) 2001 Agere Systems All rights reserved. Copyright (c) 2002-2018 Lattice Semiconductor Corporation, All rights reserved.


|
QuantLib: a free/open-source library for quantitative finance
Fully annotated sources - version 1.30
|
Functions | |
| detail::short_date_holder | short_date (const Date &) |
| output dates in short format (mm/dd/yyyy) More... | |
| detail::long_date_holder | long_date (const Date &) |
| output dates in long format (Month ddth, yyyy) More... | |
| detail::iso_date_holder | iso_date (const Date &) |
| output dates in ISO format (yyyy-mm-dd) More... | |
| detail::formatted_date_holder | formatted_date (const Date &, const std::string &fmt) |
| output dates in user defined format using boost date functionality More... | |
| detail::long_period_holder | long_period (const Period &) |
| output periods in long format (e.g. "2 weeks") More... | |
| detail::short_period_holder | short_period (const Period &) |
| output periods in short format (e.g. "2w") More... | |
| detail::ordinal_holder | ordinal (Size) |
| outputs naturals as 1st, 2nd, 3rd... More... | |
| detail::percent_holder | percent (Real) |
| output reals as percentages More... | |
| detail::percent_holder | rate (Rate) |
| output rates and spreads as percentages More... | |
| detail::percent_holder | volatility (Volatility) |
| output volatilities as percentages More... | |
| template<class Container > | |
| detail::sequence_holder< typename Container::const_iterator > | sequence (const Container &c) |
| output STL-compliant containers as space-separated sequences More... | |
Variables | |
| detail::long_weekday_holder | long_weekday (Weekday) |
| output weekdays in long format More... | |
| detail::short_weekday_holder | short_weekday (Weekday) |
| output weekdays in short format (three letters) More... | |
| detail::shortest_weekday_holder | shortest_weekday (Weekday) |
| output weekdays in shortest format (two letters) More... | |
| template<typename T > | |
| detail::null_checker< T > | checknull (T) |
| check for nulls before output More... | |
| template<typename T > | |
| detail::power_of_two_holder< T > | power_of_two (T) |
| output integers as powers of two More... | |
Helper functions for creating formatted output.
| detail::short_date_holder short_date | ( | const Date & | d | ) |
| detail::long_date_holder long_date | ( | const Date & | d | ) |
| detail::iso_date_holder iso_date | ( | const Date & | d | ) |
| detail::formatted_date_holder formatted_date | ( | const Date & | d, |
| const std::string & | f | ||
| ) |
| detail::long_period_holder long_period | ( | const Period & | p | ) |
output periods in long format (e.g. "2 weeks")
Definition at line 442 of file period.cpp.
| detail::short_period_holder short_period | ( | const Period & | p | ) |
output periods in short format (e.g. "2w")
Definition at line 446 of file period.cpp.
| detail::ordinal_holder ordinal | ( | Size | n | ) |
outputs naturals as 1st, 2nd, 3rd...
Definition at line 116 of file dataformatters.hpp.
| detail::percent_holder percent | ( | Real | x | ) |
output reals as percentages
Definition at line 125 of file dataformatters.hpp.
| detail::percent_holder rate | ( | Rate | r | ) |
output rates and spreads as percentages
Definition at line 129 of file dataformatters.hpp.
| detail::percent_holder volatility | ( | Volatility | v | ) |
output volatilities as percentages
Definition at line 133 of file dataformatters.hpp.
| detail::sequence_holder< typename Container::const_iterator > sequence | ( | const Container & | c | ) |
output STL-compliant containers as space-separated sequences
Definition at line 139 of file dataformatters.hpp.
| detail::long_weekday_holder long_weekday | ( | Weekday | d | ) |
output weekdays in long format
Definition at line 108 of file weekday.cpp.
| detail::short_weekday_holder short_weekday | ( | Weekday | d | ) |
output weekdays in short format (three letters)
Definition at line 112 of file weekday.cpp.
| detail::shortest_weekday_holder shortest_weekday | ( | Weekday | d | ) |
output weekdays in shortest format (two letters)
Definition at line 116 of file weekday.cpp.
| detail::null_checker< T > checknull | ( | T | x | ) |
check for nulls before output
Definition at line 112 of file dataformatters.hpp.
| detail::power_of_two_holder< T > power_of_two | ( | T | n | ) |
output integers as powers of two
Definition at line 121 of file dataformatters.hpp.


About Us
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|
Knight's Tour Generator
Tourneys and the Fast Generation and Obfuscation of Closed Knight's Tours
|
Chessboard. More...
#include <Board.h>
Public Member Functions | |
| CBoard () | |
| Constructor. More... | |
| CBoard (UINT n) | |
| Constructor. More... | |
| CBoard (UINT w, UINT h) | |
| Constructor. More... | |
| CBoard (int move[], UINT w, UINT h) | |
| Constructor. More... | |
| void | Shatter () |
| Shatter tourneys into more tourneys. More... | |
| void | JoinUntilTour () |
| Join cycles to reduce tourney size. More... | |
| void | Obfuscate () |
| Obfuscate function. More... | |
| Public Member Functions inherited from CBaseBoard | |
| CBaseBoard () | |
| Constructor. More... | |
| CBaseBoard (UINT n) | |
| Constructor. More... | |
| CBaseBoard (UINT w, UINT h) | |
| Constructor. More... | |
| CBaseBoard (int move[], UINT w, UINT h) | |
| Constructor. More... | |
| ~CBaseBoard () | |
| Destructor. More... | |
| void | Clear () |
| Clear the board of moves. More... | |
| void | MakeDirected () |
| Make into a directed board. More... | |
| void | MakeUndirected () |
| Make into an undirected board. More... | |
| bool | IsTour () |
| Knight's tour test. More... | |
| bool | IsTourney () |
| Tourney test. More... | |
| bool | IsDirected () |
| Directed board test. More... | |
| bool | IsUndirected () |
| Undirected board test. More... | |
| bool | IsKnightMove (int i, int j) |
| Knight's move test. More... | |
| bool | IsUnused (int index) |
| Test for unused cell. More... | |
| bool | IsUnused (int pos, const MoveDelta &delta) |
| Move is to unused cell. More... | |
| bool | IsOnBoard (int pos, const MoveDelta &delta) |
| Move stays on board. More... | |
| int | GetAvailableMoveCount (int index) |
| Get number of moves from a cell. More... | |
| bool | InsertUndirectedMove (int src, int dest) |
| Insert an undirected move. More... | |
| bool | InsertDirectedMove (int src, int dest) |
| Insert a directed move. More... | |
| bool | DeleteMove (int src, int dest) |
| Delete a move. More... | |
| void | Save (std::string &name) |
| Save board to a text file. More... | |
| void | SaveToSVG (std::string &name) |
| Save to an SVG file. More... | |
| int | GetMoveIndex (int src, int dest) |
| Get move index. More... | |
| void | CopyToSubBoard (CBaseBoard &b, int x, int y) |
| Copy to sub-board. More... | |
| int | GetWidth () |
| Get width. More... | |
| int | GetHeight () |
| Get height. More... | |
| int | GetSize () |
| Get size. More... | |
| int | operator[] (int index) |
| Get a move from the board. More... | |
Private Member Functions | |
| void | FindRails (std::vector< CRail > &rails) |
| Find all rails. More... | |
| void | Switch (CRail &r) |
| Switch a rail. More... | |
| bool | IsRail (int s0, int d0, int s1, int d1) |
| Rail test. More... | |
| bool | IsRail (CRail &r) |
| Rail test. More... | |
| bool | Join () |
| Join cycles to reduce tourney size. More... | |
Additional Inherited Members | |
| Protected Member Functions inherited from CBaseBoard | |
| bool | CellIndexInRange (int index) |
| Index in range test. More... | |
| bool | InRangeX (int x) |
| X coordinate in range test. More... | |
| bool | InRangeY (int y) |
| Y coordinate in range test. More... | |
| bool | IsMove (int i, int j) |
| Move test. More... | |
| int | GetDest (int i, const MoveDelta &delta) |
| Get destination of move. More... | |
| UINT | GetTourneyIds (int *&id) |
| Get tourney identifier for each cell. More... | |
| Protected Attributes inherited from CBaseBoard | |
| CRandom | m_cRandom |
| PRNG. | |
| UINT | m_nWidth = 0 |
| Board width in cells. | |
| UINT | m_nHeight = 0 |
| Board height in cells. | |
| UINT | m_nSize = 0 |
| Board size in cells. | |
| int * | m_nMove = nullptr |
| Primary move table. | |
| int * | m_nMove2 = nullptr |
| Secondary move table. | |
CBoard adds to CBaseBoard the additional functionality needed to shatter, join, and obfuscate tourneys. It contains an implementation of the new algorithms presented in the paper.
| CBoard::CBoard | ( | UINT | n | ) |
|
private |
Find all rails. Assumes that the board is directed. The algorithm used is as follows. Suppose we have an \(n \times n\) board. Let \(0 < i \leq n\) be a cell index. The potential downward moves from cell \(i\) have move index \(4\), \(5\), \(6\), or \(7\).
If the move from cell \(i\) has index \(4\), then there are three potential rails incident with cell \(i\) and the corresponding parallel moves are separated from cell \(i\) by moves \(5\), \(6\), or \(7\), as shown below.
If the move from cell \(i\) has index \(5\), then there are three potential rails incident with cell \(i\), and the corresponding parallel moves are separated from cell \(i\) by moves \(4\), \(6\) or \(7\), as shown below.
If the move from cell \(i\) has index \(6\), then there are three potential rails incident with cell \(i\), and the corresponding parallel moves are separated from cell \(i\) by moves \(4\), \(5\) or \(7\), as shown below.
If the move from cell \(i\) has index \(7\), then there are three potential rails incident with cell \(i\), and the corresponding parallel moves are separated from cell \(i\) by moves \(4\), \(5\) or \(6\), as shown below.
We therefore iterate through \(0 < i \leq n\) and consider the two moves from cell \(i\). If one of these moves has index \(4\), then we check whether moves \(5\), \(6\), or \(7\) lead to a cell that also has move \(4\) from it. If so, then we add these rails to the rail vector provided to this function as a call-by-reference parameter. Similarly, we perform the respective checks when one of the moves from cell \(i\) has index \(5\), \(6\), or \(7\).
The rail list is permuted into random order before returning.
| rails | [out] Rail list. |
|
private |
Test whether four cells form a rail, that is, they are separated by knight's moves, the primary moves are present, and the cross moves are absent.
| s0 | Index of source of first move. |
| d0 | Index of destination of first move. |
| s1 | Index of source of second move. |
| d1 | Index of destination of second move. |
|
private |
Test whether a rail is valid, that is, all moves are knight's moves, the primary moves are present, and the cross moves are absent. Calls CBaseBoard::IsRail(int, int, int, int) to do the actual work.
| r | Rail to be tested. |
|
private |
Join a tourney, that is, attempt to make it into a knight's tour by switching rails. Assumes that the tourney is directed. First a rail (multi-) graph is created with a vertex for each tourney and an edge for each cell-disjoint rail that connects them. Switching the rails in a spanning forest of the rail graph connects up as many cycles as it can (which may may not necessarily result in a knight's tour). For example, the following image shows the rail graph of a tourney at left. The numbers on the edges indicate the number of cell-disjoint rails that intersect two cycles. A breadth-first spanning tree of this graph is shown on the right.
| void CBoard::JoinUntilTour | ( | ) |
| void CBoard::Obfuscate | ( | ) |
| void CBoard::Shatter | ( | ) |


This package provides various algorithms for graph analysis, such as shortest path and minimum spanning tree.


I work on a diverse range of topics which are essential to our understanding of galaxy formation and evolution: from global gravitational instabilities and the effects of AGN feedback on galactic dynamics, to the formation of star clusters and the first galaxies during the Epoch of Reionisation.
Here, you can find an up-to-date list of my work sorted based on citation count. A pdf version of my CV can be found here (last edited April 2023).
First author publications


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The idea of owning a 64-core parallel system for two hundred dollars (yes, $200.00) is exciting. Parallella is working to make that happen, perhaps as early as August 2013. To prepare for that day, I’ve decided to introduce myself to the Go language.
What is Go?
The Go language is designed for parallel systems. Why does Go exist? One developer sums it up this way:
Go was created at Google, by Google, for Google-size problems.
~Dave Astels
Google writes software that runs on thousands of machines in parallel. As the number of concurrent operations increases, new challenges are encountered. Google addressed those challenges by creating Go.
Why Does a Rubyist Learn Go?
The team at WisdomGroup writes web and mobile apps, mainly in Ruby. So why am I learning Go?
Because the best developers are polyglot. When we learn a new language, we cause ourselves to see old problems in new ways and we strengthen our ability to solve new problems. It’s like cross-training for athletes. In the end, we become better developers.
How to Install Go on Mac OS X
- Download the binary of Go that matches your system but don’t install it yet. You will need to complete the rest of these steps before installation. For my 2010 i5-based 15-inch MacBook Pro, I chose
go1.1.1.darwin-amd64.pkg
. I was concerned about the reference toamd64
in the name. But the description includesMac OS X (x86 64-bit)
, and the binary worked for me. - If you are upgrading from a previous version of Go, you will need to remove the old Go directory. You can do this while the new binary is downloading in the background:
- Define the
GOROOT
andGOPATH
environmental variables. My system uses~/.bash_profile
to define environmental variables, so I added the following lines to the end of that file:Note: I’m using the default1 2 3
export GOROOT=/usr/local/go export PATH=$PATH:$GOROOT/bin export GOPATH=~/Code/gocode
GOROOT
variable, but yourGOPATH
may differ from mine. I store all of my source code in a subdirectory of home:~/Code
. My complete Go directory structure is given below. By looking at my structure, you can adjust these steps to fit your system. - Tell your terminal session to recognize the new environmental variables. You can either restart terminal, or if your environmental variables are in
~/.bash_profile
like mine, you can do the following: - Run the package installation program,
go1.1.1.darwin-amd64.pkg
, that was downloaded in Step 1.
1
|
|
1
|
|
Now, let’s Go for a test drive.
 A workspace is a directory structure that contains source code and binaries that a Go program needs in order to compile and execute.
We can examine the Go Workspace on my system with the Unix tree
command:
1 2 3 4 5 6 7 8 9 10 11 |
|
Here’s a brief description of the directories:
- Code = root directory for all source code on my system. Yours may differ.
- gocode = where I store all of the Go code on my system. I’m following the structure recommended by the Go documentation. I may alter this as I learn more about the language.
- src = source code
- github.com = directory named after the place where I store repos
- rayhightower = my GitHub profile name
- hello = directory named for our first Go application
- hello.go = the Go source file for our
Hello World
program
All structure below the gocode
directory is mandated by Go.
Writing ‘Hello World!’ in Go
Google’s official installation instructions include a simple ‘Hello World’ program for testing the installation. A slightly modified version appears below:
1 2 3 4 5 6 7 |
|
Compiling and Running
We drop the code into a file called hello.go
in the hello
directory. To compile the program:
1 2 3 |
|
If the Go compiler responds with a blank prompt (like above) then the program compiled successfully and a bin
directory has been created inside the Go workspace. Run the tree
command from the gocode
directory to see how the structure has changed:
1 2 3 4 5 6 7 8 9 10 11 12 13 |
|
The newly created bin/
directory contains our hello
executable. And now, let’s cut the suspense and run the program. To do so, change into the bin/
directory and type ./hello
.
1 2 3 4 5 6 7 |
|
Success!
It’s Not Official, But It Makes Sense
As of this writing, Parallella does not officially support the Go language. So why go through all of this trouble? Because…
- A 64-core Parallella is just too cool to pass up. And it’s open source.
- Go is designed for parallel systems. And it’s open source.
- Open source devs are working on a Go compiler for Parallella right now. If you’re reading this, and you’re one of the devs, thank you!
The Go-Parallella match makes sense. It’s always good to skate where the puck is going.
Next Steps
Now it’s time to explore the Go language. The real adventure begins when the 64-core Parallella arrives. Looking forward to it!
Acknowledgements
I was inspired to explore Go by Blake Smith’s presentation at 8th Light. Justin Love introduced me to Parallella last month at ChicagoRuby.


A common feature of shader languages like GLSL is components swizzling. This involves being able to select which components of a vector are used and in what order. For example, "variable.x", "variable.xxy", "variable.zxyy" are examples of swizzling.
vec4 A; vec2 B; ... B.yx = A.wy; B = A.xx;
This functionally turns out to be really complicated to implement in C++ using the exact GLSL conventions. GLM provides 2 implementions this feature.
The first implementation follows the GLSL convensions accurately. It uses macros to achieve this, which might generates name conflicts with system headers or third party libraries. Therefore, it is disabled by default. To enable this implementation, GLM_SWIZZLE must be defined before any inclusion of <glm/glm.hpp>.
#define GLM_SWIZZLE #include <glm/glm.hpp>
This implementation can be partially enabled by defining GLM_SWIZZLE_XYZW, GLM_SWIZZLE_RGBA or GLM_SWIZZLE_STQP. Each macro only enable a set of swizzling operators. For example we can only enable x,y,z,w and s,t,q,p operators using:
#define GLM_SWIZZLE_XYZW #define GLM_SWIZZLE_STQP #include <glm/glm.hpp>
A safer way to do swizzling is to use the <glm/gtc/swizzle.hpp> extension. This extension provides the GLSL functionality, but uses a different syntax for it. Moreover, the swizzle extension also provides dynamic swizzling.
Static swizzling is resovled at compile-time. The swizzle mask ".xzyy" is as fixed as the type of a particular variable. Dynamic swizzling is resolved at runtime via function calls. Dynamic swizzling is more flexible, since one can choose the swizzle mask at runtime, but it runs slower. This performance issue is enhanced when SIMD instructions are used.
#include <glm/glm.hpp> #include <glm/gtc/swizzle.hpp> void foo() { glm::vec4 ColorRGBA(1.0f, 0.5f, 0.0f, 1.0f); ... // Dynamic swizzling (at run time, more flexible) // l-value: glm::vec4 ColorBGRA1 = glm::swizzle(ColorRGBA, glm::B, glm::G, glm::R, glm::A); // r-value: glm::swizzle(ColorRGBA, glm::B, glm::G, glm::R, glm::A) = ColorRGBA; // Static swizzling (at build time, faster) // l-value: glm::vec4 ColorBGRA2 = glm::swizzle<glm::B, glm::G, glm::R, glm::A>(ColorRGBA); // r-value: glm::swizzle<glm::B, glm::G, glm::R, glm::A>(ColorRGBA) = ColorRGBA; }
GLM includes a notification system which can display some information at build time:
This system is disable by default. To enable this system, define GLM_MESSAGES before any inclusion of <glm/glm.hpp>.
#define GLM_MESSAGES #include <glm/glm.hpp>
GLM's functions are defined in headers, so they are defined with C++'s "inline" delcaration. This does not require the compiler to inline them, however. To force the compiler to inline the function, using whatever capabilities that the compiler provides to do so, GLM_FORCE_INLINE can be defined before any inclusion of <glm/glm.hpp>.
#define GLM_FORCE_INLINE #include <glm/glm.hpp>
GLM provides some SIMD optimizations based on compiler intrinsics. These optimizations will be automatically utilized based on the build environment. These optimizations are mainly available through the extensions GLM_GTX_simd_vec4: SIMD vec4 type and functions and GLM_GTX_simd_mat4: SIMD mat4 type and functions.
A programmer can restrict or force instruction sets used for these optimizations using GLM_FORCE_SSE2 or GLM_FORCE_AVX.
A programmer can discard the use of intrinsics by defining GLM_FORCE_PURE before any inclusion of <glm/glm.hpp>. If GLM_FORCE_PURE is defined, then including a SIMD extension will generate a build error.
#define GLM_FORCE_PURE #include <glm/glm.hpp>
Compilers have some language extensions that GLM will automatically take advantage of them when they are enabled. GLM_FORCE_CXX98 can switch off these extensions, forcing GLM to operate on pure C++98.
#define GLM_FORCE_CXX98 #include <glm/glm.hpp>


DEBiasing embeddings Implicitly and Explicitly
Web application for debiasing embedding spaces and bias evaluation of explicit and implicit bias specifications
ECT ‒ Embedding Coherence Test
It quantifies the amount of explicit bias BE={T1, T2, A} (Dev and Phillips 2019). Unlike WEAT, it compares vectors of target sets T1 and T2 (averaged over the constituent terms) with vectors from a single attribute set A. ECT first computes the mean vectors for the target sets T1 and T2: µ1 = 1/|T1| * Σ t1∈T1 t1 and µ2 = 1/|T2| * Σ t2∈T2 t2. Next, for both µ1 and µ2 it computes the (cosine) similarities with vectors of all a ∈ A. The two resultant vectors of similarity scores, s1 (for T1) and s2 (for T2) are used to obtain the final ECT score. It is the Spearman’s rank correlation between the rank orders of s1 and s2 ‒ the higher the correlation, the lower the bias.
BAT ‒ Bias Alignement Test
Bias Analogy Test by Lauscher et al. 2019. Analogy based bias test - Based on the observation of Bolukbasi et al. 2016 that in a biased vector space "programmer − homemaker ≈ man − woman", Dev and Phillips (2019) proposed an analogy-based bias test: Embedding Quality Test (EQT). However, EQT depends on WordNet to extend the bias definition with synonyms and plurals of bias specification terms. In contrast, we propose an alternative Bias Analogy Test (BAT) that relies only on the specification BE = (T1, T2, A1, A2). We first create all possible biased analogies t1−t2 ≈ a1−a2 for (t1, t2, a1, a2) ∈ T1 × T2 × A1 × A2. We then create two query vectors from each analogy: q1 = t1 − t2 + a2 and q2 = a1 − t1 + t2 for each 4-tuple (t1, t2, a1, a2). We then rank the vectors in the vector space X according to the Euclidean distance with each of the query vectors. In a biased space, we expect the vector a1 to be ranked higher for the query q1 than the vectors of terms from the opposing attribute set A2. The BAT score is the percentage of cases where: (1) a1 is ranked higher than a term a2 ∈ A2 \ {a2} for q1 and (2) a2 is ranked higher than a term a1 ∈ A1 \ {a1} for q2.
WEAT ‒ Word Embedding Association Test
Word Embedding Association Test by Caliskan et al. 2017. WEAT tests the embedding space for the presence of an explicit bias defined as BE=(T1, T2, A1, A2). It computes the differential association between T1 and T2 based on their mean similarity with terms from the attribute sets A1 and A2. The significance of the statistic is computed by comparing s(BE) with the scores s(B∗E) obtained with all permutations B∗E = (T∗1, T∗2, A1, A2), where T∗1 and T∗2 are equally sized partitions of T1 ∪T2. The p-value of the test is the probability of s(B∗E) > s(BE). The “amount” of bias, the so-called effect size, is then a normalized measure of separation between association distributions.
KMeans++ ‒ Implicit Bias Test
K-Means++ Clustering - by Arthur and Vassilvitskii 2007. K-Means++ is an enhancement of the standard K-means algorithm which is an unsupervised clustering method. First, K initial cluster centroids are randomly selected as points in the space. Then each element out of the specification is assigned to its nearest centroid. Therefore, the distance between the points and the centroids are computed by Euclidean distance. After assigning each point to one of the k centroids, the position of the centroids are recomputed. This approach is repeated until a certain stopping criteria is met, e.g. 100 runs.
SVM-Classifier ‒ Implicit Bias Test
Approach by Gonen and Goldberg 2019. Test the debiased spaces for the presence of implicit bias by classifying terms from T1 and T2 using an SVM (Support Vector Machine) with the RBF kernel. It is trained on the vectors of terms from the augmentations of the target sets.
SimLex-999 & WordSim-353 ‒ Semantic Quality Test
Debiasing procedures change the topology of the input vector space; we thus have to verify that debiasing does not occur at the expense of the encoded semantic information. We test the debiased embedding spaces on two standard word similarity/relatedness benchmarks: SimLex999 (Hill, Reichart, and Korhonen 2015) and WordSim-353 (Finkelstein et al. 2002).
This work is based on: Lauscher et al. (2019) - A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
BAM ‒ Bias Alignment Model
BAM uses pairs (ti1, tj2) to learn the debiasing projection of X with respect to itself. Let XT1 and XT2 be the matrices obtained by stacking (biased) vectors of left and right words of pairs (ti1, tj2), respectively. We then learn the orthogonal map Wx = UVT, where UΣVT is the singular value decomposition of XT2XT1T. Since Wx is orthogonal, the projection X' = XWx is isomorphic to the original space X, and thus equally biased. However, the transformation (specified by Wx) defines the angle and direction of debiasing. We obtain the debiased space by averaging the original space X and the projected space X': BAM(X) = 1/2 * (X + XWx).
GBDD ‒ General Bias Debiasing Model
This model focuses on BI as a generalization of the linear projection model proposed by Dev and Phillips 2019, itself, in turn, an extension of the hard-debiasing model of Bolukbasi et al. 2016. The model of Dev and Phillips (2019) requires a stricter bias specification than our BI: it requires T1 and T2 to be ordered lists of equal length L, so that the so-called equivalence pairs {(tl1, tl2)}L l=1 can be created. For instance, T1 ={man, father, boy} and T2 ={woman, mother, girl} give rise to the following equivalence pairs: (man, woman), (father, mother), and (boy, girl). For each equivalence pair (tl1, tl2) they compute the bias direction vector bl by subtracting the vector of term tl2 from the vector of term tl1. We find this bias specification overly restrictive: it requires an additional effort to create true equivalence pairs from T1 and T2 and it produces only L partial bias direction vectors. In contrast, we propose to create one bias direction vector bij for each pair (ti1, tj2), ti1 ∈ T1, tj2 ∈ T2. If T1 and T2 truly specify categories that are opposite in some regard (e.g., gender-wise), then any pair (ti1, tj2) should induce a meaningful partial bias direction vector. This way we also obtain a much larger number of partial bias direction vectors (e.g., L^2 if T1 and T2 are of the same length L): this should result in a more reliable general bias direction vector, computed as follows. We stack all of the obtained bias direction vectors bij corresponding to pairs (ti1, tj2), ti1 ∈ T1, tj2 ∈ T2 to form a bias direction matrix B. We then obtain the global bias direction vector b as the top singular vector of B, i.e., as the first row of matrix V , where UΣVT is the singular value decomposition of B. Let x be the `2-normalized d-dimensional vector from a biased input vector space. Its debiased version is then computed as: GBDD(x) = x − (x, b)b where (,) denotes a dot product. In other words, the closer the vector x is to the global bias direction b, the more it is bias-corrected (i.e., the larger portion of b is subtracted from x). Vectors orthogonal to the bias direction b remain unchanged (zero dot-product with the bias vector b).
BAM ᛫ GBDD
Composing of the BAM and the GBDD model. First BAM is executed on the embedding space, then the by BAM debiased embedding space is used as an input for GBDD. The output is a twice debiased embedding space.
GBDD ᛫ BAM
Composing of the GBDD and the BAM model. First GBDD is executed on the embedding space, then the by GBDD debiased embedding space is used as an input for BAM. The output is a twice debiased embedding space.
This work is based on: Lauscher et. al. (2019) - A General Framework for Implicit and Explicit Debiasing of Distributional Word Vector Spaces
FastText
The fastText model is a Word2vec model for creating word embeddings invented by Bojanowski et al. 2017. It is derived from the continuous Skip-Gram model by Mikolov et al. 2013. As enhancement to Skip-Gram, subword-information is taken into account by computing the dense vectors, because Skip-Gram does not consider the internal structure of words. The architecture of the model is similar to the Skip-Gram architecture, only the scoring function used in the output layer differs. The in context of this model used scoring function allows the fastText model to share representations across words. This leads the model to have a longer run-time than the standard Skip-Gram model, but delivers more precise results.
GloVe ‒ Global Vectors
The model has been invented by Pennington et al 2014. The GloVe model aims to combine the advantages of local context window methods like the Skip-Gram model by Mikolov et al. 2013 with the benefits of global matrix factorization methods like latent semantic analysis (LSA). The model itself is an specific weighted least squares model that is trained on global word-word occurence counts and although makes efficient usage of statistics. Relationships between words are extracted from the ratio of their co-occurence probabilities. The co-occurences are stored in a matrix, called X and need to be computed before starting the GloVe model.
CBOW ‒ Continous Bag-of-Words
CBOW is an acronym standing for "Continous Bag-of-Words". The CBOW model has been invented by Mikolov et al. 2013. It is part of the Word2vec models, being a prediction based model computing dense vectors for words across a large corpus of text as input. The CBOW model is a feedforward neural net language model (NNLM), consisting out of an input, a projection and an output layer. The input layer encodes N previous words using one-hot encoding. In the projection layer, which is shared for all words, the input is projected into the same position by averaging their vectors. This happens with a shared projection matrix of the dimensions N x D. In the output layer, a log-linear classifier containing the training criterion is used to correctly classify the current middle word. In other words, CBOW computes a vector for a middle word by taking the words written before the middle word (history) and the words written after the middle word (future), averaging them in the projection layer and then running a log-linear classifier on them. Mikolov et al. 2013 states, that the best acceptable trade-off between performance and semantic quality of the word embeddings can be achieved by using four history words and four future words as input. The order of words in the history does not influence the projection, as well as the order of words in the future. The model is named continuous, because it uses continuous distributed representations of the context in contrary to the standard bag-of-words model.

Format 2: Splitted vocabulary and vector files
Alternatively binary embedding spaces can be uploaded to DEBIE. Prerequisite is that the vocabulary and the vectors are splitted into two files. The contents need to be ndarrays, which can be unpacked to an dictionary for the vocabulary and to a list for the vectors by using pickle.
DEBIE accepts .txt, .vec, .vector, .vectors and .vocab files as input. The uploaded files will be deleted immediately after closing the window in which DEBIE is opened.

Vector Representation Retrival
Augmentations Retrival
File Upload
File Initialization
File Deletion
Used word vector spaces
The complete source code of this application is published on GitHub. Therefore, three repositories are created:


--- layout: post title: Long two weeks without the Doc custom_css: true custom_js: true date: 2018-03-16 12:00:00 -5000 ---
I spent most of my time starting to explore deployment methods. I have spent a great time with Docker and wanted to containerize my appeals application so I can deploy anywhere that supports containers. This can be in your own datacenter with an instance of Kubernetes or Docker Swarm for orchestration. Or it can be in the cloud provider of your choosing, AWS/GAE/ACS. First, I ran through a bunch of videos on Pluralsight getting me up to speed with using Docker in depth and Kubernetes. I learned that Docker for community has a edge edition that allows you to run an instance of Kubernetes instead of swarm on your laptop for development.
Once the edge version was installed I quickly enabled the feature inside of the Docker preferences to swap between the orchestration methods. I was off and running my Kubernetes instance but realized that I couldn't get access to the dashboard like I could with Minikube. Instead I found the dashboard GitHub page which had some Kubernetes YAML files to start up a node running the dashboard. It is as easy as downloading the file or pointing to the raw GitHub dump of the text file and telling Kubernetes to create. In minutes the dashboard is up and running and with no programming used at all. Now I can really see what is running in my environment.
Utilizing a DockerFile to build my project I tell Docker to take a Linux instance with Go already installed. This was provided by the official Go Docker image on Docker Hub. I then copy all my source code to the containers $GOPATH\src directory and as a development feature I instruct the container to build the app and run it when it starts. I think once I get the application production ready I will instead just copy over the templates and binary to the Docker images that way the container doesn't have to build the app every single time it is started.
Then I needed to make some configuration files for Kubernetes to understand. These are built a little different than a Dockerfile. The Dockerfile is more imperative which explains step-by-step how to build the container. In Kubernetes, we tell the engine what to do declaratively. Declarative means that you tell Kubernetes what you expect, and Kubernetes handles all the busy work. This busy work includes all the networking, scaling up an app across nodes, creating services, exposing ports, handling all the IP addressing and DNS between apps. In a YAML file that was less than 50 lines.
I also had to update my appeal importer program to import into the MSSQL database I launched into my Kubernetes instance. I made sure to expose a node port, so I could access it from my laptop. Then all I had to do is point my configuration file to localhost and the exposed MSSQL port and it could start inserting rows right into the database. I ran into a bunch of issues where the appeals web server nodes were starting up before the MSSQL database. I didn't want to host my Docker images in the cloud, so in the Kubernetes Config I needed to tell it when I told which image to load to never fetch it from the cloud which forced it to look at my computer only. Later I think I would just push my images to a private repository in the cloud like AWS or Google Cloud and point Kubernetes there.
I checked with my group to see what was due for the week. Kind of confused with the deliveries, but I found out it is just a book report. Powered through Don't Make Me Think and finished it in 3 days. Enjoyed it heavily and will try to get his other books and chew through them as well.
Poulton, N. (2017, May 22). Getting started with kubernetes [Video course]. Retrieved from
https://www.pluralsight.com/courses/getting-started-kubernetes
Poulton, N. (2015, December 22). Docker and containers: The big picture [Video course]. Retrieved from
https://www.pluralsight.com/courses/docker-containers-big-picture
Poulton, N. (2016, August 1). Getting started with docker [Video course]. Retrieved from
https://www.pluralsight.com/courses/docker-getting-started
Poulton, N. (2018, January 4). Docker deep dive [Video course]. Retrieved from
https://www.pluralsight.com/courses/docker-deep-dive-update
Poulton, N. (2015, September 22). Integrating docker with devops automated workflows [Video course]. Retrieved from
https://www.pluralsight.com/courses/integrating-docker-with-devops-automated-workflows
Florek, S. (2018, February 13). Kubernetes dashboard [Code repository]. Retrieved from
https://github.com/kubernetes/dashboard/releases/tag/v1.8.3


14 Reasons to choose SBI for your Home Loan
Many people today wish to own their dream homes in the cities of their choice. But it often happens that rising real-estate prices makes this dream a pipe dream. However, the scenario has changed today, with the availability of home loans at attractive interest rates and flexible EMI options. While there are many banks and Housing Finance Companies (HFCs) in the country that offer home loans, SBI Home Loans emerge as a clear winner due to the trust factor.
Here are 14 reasons why SBI Home Loans are the most sought after:
1. Attractive Interest Rates
Interest rates for SBI Home Loans are known to be the most attractive in the market today. In fact, SBI offers interest rate starting at just 8.15% p.a. for women borrowers. This includes a 0.05% concession in the interest rates for women borrowers, celebrating the importance of a women in the Indian Household milieu.
2. Assistance in choosing the right property
SBI helps you choose the right property from various pre-approved projects according to your budget, location and amenities through its home search portal: www.sbirealty.in.
3. Products Designed to suit your unique needs
SBI has products customized to suit specific home loan needs of various customers like FlexiPay Home Loan for the Young Salaried Earners, Privilege Home Loans for Government Employees, Shaurya Home Loan for Defence Employees, Businessmen, NRI Home Loans and lot more.
4. Low EMIs
EMIs (Equated Monthly Installments) for SBI Home Loans start at just INR 744 per lac for a home loan of Rs.30 lakh with a tenure of 30 years at interest rate of 8.15% p.a.
5. Complete Transparency
With SBI you can be assured of complete transparency in the loan process and loan servicing thereafter. This includes eligible loan amount, documentation, and more importantly, all the fees and charges. You can be sure and assured that everything will be communicated in a simple and precise manner to you.
6. Swift Processing of PMAY subsidy
SBI has become a Central Nodal Agency (CNA) for Pradhan Mantri Awas Yojana subsidy claims for Home Loans. As a result, the subsidy is expected to be released in fastest possible manner.
7. Instant Top-Up Loans for existing Home Loan borrowers
Eligible existing customers can avail Top-Up instantly through the SBI YONO app which can be used for multiple purposes like wedding, travel etc. The Top-Up amount will be directly credited to their savings account in as low as 5 minutes.
8. India’s preferred home loan provider
1 in every 3 Home Buyers in the country today avail Home Loan from SBI.
9. Trust of 40 lakh happy families
More than 40 lakh families have chosen SBI as the preferred Banker for their home loan.
10. Top-up Loans at rates starting at just 8.60% p.a.
In addition to conventional home loans, you can also get Top-Up loans from SBI at really attractive interest rates starting at just 8.60% p.a.
11. Pre-Approved Home Loans
In case you have not finalized a home, you can opt for a Pre-Approved Home Loan from SBI. This gives you an estimate of the budget within which you can search for a property.
12. Dedicated Relationship Managers at Various Locations
SBI has dedicated Relationship Managers for home loan seekers at various locations. These relationship managers handhold the home buyers right from the start of the application process till the end of the process. This ensures that the consumer gets door step service and the entire process gets completed in a hassle-free manner. In addition to dedicated relationship managers, SBI has 8500+ feet on street providing door step services for the home loan seekers.
13. More than 1000 SBI Home Loan Processing Centers
SBI has 1000+ home loan processing centers spread across the length and breadth of the country. These dedicated centers are known to ensure that all home loan applications get attended to within a prescribed TAT of less than two weeks.
14. Apply through 6 Easy Ways
SBI has various customer friendly and easy channels to apply for a home loan:
a. Download and Login to the YONO App
b. Visit www.homeloans.sbi
c. Apply through www.psbloansin59minutes.com
d. Call on 1800 11 2018
e. Apply at the nearest SBI Branch
f. SMS “HOME” to 567676
So if home loan is on your mind, connect with SBI Home Loans Team today!


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Basketball // Youth Sports // University of Notre Dame
You will not only learn the game of basketball, but you will also be at NOTRE .
http://youthsports.nd.edu/camps/womens-camps/basketball/Basketball Camps - Princeton Online's Summer Camps
Basketball Camps · Summer Activities. Hoop Stars Basketball Camps at Notre Dame High School<PHONE_NUMBER>. Basketball Camps · Sports Camps. Net Results .
http://www.princetonol.com/summercamps/polsub.cfm?Bustype_Code=BASKETBALL%20CAMPBasketball // Youth Sports // University of Notre Dame
Basketball. mike_brey_2_. Dear Summer Campers,. Thank you for considering the University of Notre Dame's Basketball Camp Program. Your 2013 camp .
http://youthsports.nd.edu/camps/mens-camps/basketball/
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Youth Enrichment Summer Camp - Holy Cross College
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Jun 27, 2012 . NOTRE DAME More than 100 middle and high school students participating in summer sports camps at the University of Notre Dame became suddenly ill . Bruszewski adding punch to Notre Dame women's basketball.
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6 days ago . Notre Dame emerged from a five-overtime game against Louisville on . a friendly acquaintance from summer camps, spotting him in the Feb.
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Nike Basketball Camps - NIKE Sports Camps - USSC
Get better this summer at Basketball Camp! . Campers at every Nike Basketball Camp will develop all of their basketball skills - shooting, passing, dribbling, .
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We propose an efficient training strategy for deep IAF WaveNet, and a carefully designed model with 8-layer IAFs. Each layer consists of a WaveNet with a larger receptive feld. We call this network Uni-WaveNet for short. Compared to Parallel Wavenet and ClariNet, Uni-WaveNet has the following improvements: 1) We can train the IAF WaveNet efciently without knowledge distillation. 2) The structural of the proposed model can meet different computational requirements very well for different applications.
Samples of Uni-WaveNet
Uni-WaveNet
Autoregressive WaveNet
Ground Truth 16 kHz
Additional samples for mandarin:
Samples from 1 to 8-IAF layer of Uni-WaveNet，and Autoregressive WaveNet. The quality of synthesis gradually improves as the number of IAF layers increases.


Do you disagree with the ACF Nostradamus?
Want to add your two cents to what he wrote this
month? Send your letters to<EMAIL_ADDRESS>and we'll publish them in this space, along with any replies the editor writes to them.
The ACF Nostradamus Prognosticates
The ACF Nostradamus steps forward once again to disclose to you, gentle
reader, the outcome of the six ACF Regional tournaments. He is like Zeus
as he holds aloft the scales of Fate -- the winners' plates sink down, and
the losers' kick up high, sealing their doom. Who can speak so
forthrightly as the ACF Nostradamus? No one. Who is so eloquent? Nary a
soul. Who dares to contradict his sooth? If any, let him stand forth and
show himself, that the world may see his folly.
More than that, let him pick against the spreads herein revealed. The
prognosticator with the best record will receive a copy of Webster's
Dictionary of American Authors from me at ACF Nationals, along with all my
Flash'Em lists.
But enough vaunting. Here's how the chips will fall:
Mid-Atlantic
This region has long been the ACF Nostradamus' prize prediction; year in
and year out, he has picked Maryland against Virginia, and Maryland has
prevailed despite the two teams being basically even. This year, things
will not be so close. 'Tis true, David Hamilton will cease for a few days
to bestride the world like a colossus, but it makes no difference.
Maryland over Virginia by 2 games in a double round-robin. Maryland will
win both games by 150; Virginia will beat Swarthmore by 100 in each of
their two games, and Swarthmore will finish 3rd.
Top 3 all-stars: John Kenney, Ed Cohn, and Julie Singer.
Southwest
The Texas squad squeaked past Arkansas at NAQT, but the margin won't be so
close at ACF. Mike Wehrman is the best in the Southwest, but three of the
other top 5 seem to be playing for Texas. The ACF Nostradamus imagines
another double round-robin or perhaps a smaller playoff RR. Therefore,
Texas by 100 over the Iron Mike's Arkansas team in the first game, and by
80 in the second game. Arkansas over Oklahoma by 150 in both their games.
(Not to be Maryland-centric, but will Ram Kannapan be playing on Texas'
first-string ACF squad? I noticed he was on the B team at NAQT. Ram, let
me remind people, is undefeated against John Sheahan.)
Top 3 all-stars: Arkansas' Mike Wehrman, Oklahoma's David Smith, and
Texas' Eric Smith.
Midwest
There is no question, of course, of anyone beating Chicago A. Andrew's ACF
virtuosity is unrivalled. The question is, can Michigan beat Illinois for
second? The ACF Nostradamus thinks not! Chicago over Michigan by 150, and
Michigan over Illinois by 100.
Top 3 all-stars: some guy named Andrew, Iowa's Robert Trent, and Quincy's
Subash Maddipoti.
 However, the Nostradamus also notes
that this same Matt Colvin is going to get his act together for ACF. Well,
almost. Guy Jordan's Harvard team over Cornell by 50; Matt and Prasun will
manage to nip Harvard's B team by 30 for second place. Harvard B will fend
off Columbia for third.
Top 3 all-stars: Guy Jordan of Harvard, some fool from Cornell, and Bill
Atkinson of Columbia.
Southeast
One would have to be a fool to pick against South Carolina here.  Still, if TC "Best Yogurt" Ford and Adam "Are you
being served?" Humphreys are ACF's nWo, then Jason King is Hollywood Hogan.
South Carolina will lose to Georgia by 20, but finish ahead of them in
record. Kentucky will be third, losing to Georgia by 90. Note, however,
that the ACF Nostradamus' crystal ball is quite foggy when he turns it
toward Atlanta. He would not be surprised to see some other order of
finish.
Top 3 All-stars: Jason King, TC Ford, and Georgia Tech's Hing Chan.
West
Stung by picking Berkeley over Stanford for NAQT, the ACF Nostradamus has
learned his lesson. Stanford over Berkeley by 125 on average; Berkeley
over Stanford B by 200 for second.
Top 3 All-stars: Jason Hong seems to be running this thing, so I'll pick Steve
Lin of Berkeley, Adam Kemezis of Stanford, and Patrick of UCLA.
Matt will be back next month. I bet you can't wait!
Want to contribute a column to the ACF web page, if only
so that Matt won't be able to spout his views next month? Send it to
Matt Colvin. Submissions will be judged on style
and subject matter, not on politics or opinions. We reserve the right
to present a companion article as a counterpoint.
All content Copyright 1999, Academic Competition Federation


A C D E G H I L M O P R S T U V
All Classes All Packages
All Classes All Packages
All Classes All Packages
O
- onBackPressed() - Method in class com.xa.xpensauditor.AddTransactionActivity
- onBackPressed() - Method in class com.xa.xpensauditor.HomeActivity
- onBackPressed() - Method in class com.xa.xpensauditor.ProfileActivity
- onBackPressed() - Method in class com.xa.xpensauditor.SettingsActivity
- onBindViewHolder(TransactionAdapter.MyViewHolder, int) - Method in class com.xa.xpensauditor.TransactionAdapter
- onBindViewHolder(TransAdapter.MyViewHolder2, int) - Method in class com.xa.xpensauditor.TransAdapter
- onClick(View) - Method in class com.xa.xpensauditor.ContactUs
- onClick(View) - Method in class com.xa.xpensauditor.Suggest
- onClick(View) - Method in class com.xa.xpensauditor.TransactionAdapter.MyViewHolder
- onClick(View) - Method in class com.xa.xpensauditor.TransAdapter.MyViewHolder2
- onContextItemSelected(MenuItem) - Method in class com.xa.xpensauditor.TabFragment
- onContextItemSelected(MenuItem) - Method in class com.xa.xpensauditor.UncategorisedFragment
- onCreate(Bundle) - Method in class com.xa.xpensauditor.AccountSettingsActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.AddTransactionActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.ContactUs
- onCreate(Bundle) - Method in class com.xa.xpensauditor.Dummy
- onCreate(Bundle) - Method in class com.xa.xpensauditor.EditProfileActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.HomeActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.LoginActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.ProfileActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.ProfileManagement
- onCreate(Bundle) - Method in class com.xa.xpensauditor.Rate
- onCreate(Bundle) - Method in class com.xa.xpensauditor.ResetPasswordActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.SettingsActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.ShowTransActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.SignupActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.SMSDBFetchActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.SMSReaderActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.SMSTransacShowActivity
- onCreate(Bundle) - Method in class com.xa.xpensauditor.Suggest
- onCreateContextMenu(ContextMenu, View, ContextMenu.ContextMenuInfo) - Method in class com.xa.xpensauditor.TransactionAdapter.MyViewHolder
- onCreateContextMenu(ContextMenu, View, ContextMenu.ContextMenuInfo) - Method in class com.xa.xpensauditor.TransAdapter.MyViewHolder2
- onCreateOptionsMenu(Menu) - Method in class com.xa.xpensauditor.HomeActivity
- onCreateView(LayoutInflater, ViewGroup, Bundle) - Method in class com.xa.xpensauditor.TabFragment
- onCreateView(LayoutInflater, ViewGroup, Bundle) - Method in class com.xa.xpensauditor.UncategorisedFragment
- onCreateViewHolder(ViewGroup, int) - Method in class com.xa.xpensauditor.TransactionAdapter
- onCreateViewHolder(ViewGroup, int) - Method in class com.xa.xpensauditor.TransAdapter
- OnItemClick(int, View) - Method in interface com.xa.xpensauditor.TransactionAdapter.ClickListener
- OnItemClick(int, View) - Method in interface com.xa.xpensauditor.TransAdapter.ClickListener
- OnItemLongClick(int, View) - Method in interface com.xa.xpensauditor.TransactionAdapter.ClickListener
- OnItemLongClick(int, View) - Method in interface com.xa.xpensauditor.TransAdapter.ClickListener
- onLongClick(View) - Method in class com.xa.xpensauditor.TransactionAdapter.MyViewHolder
- onLongClick(View) - Method in class com.xa.xpensauditor.TransAdapter.MyViewHolder2
- onNavigationItemSelected(MenuItem) - Method in class com.xa.xpensauditor.HomeActivity
- onOptionsItemSelected(MenuItem) - Method in class com.xa.xpensauditor.HomeActivity
- onReceive(Context, Intent) - Method in class com.xa.xpensauditor.SMSReceiver
- onRequestPermissionsResult(int, String[], int[]) - Method in class com.xa.xpensauditor.HomeActivity
- onResume() - Method in class com.xa.xpensauditor.AccountSettingsActivity
- onResume() - Method in class com.xa.xpensauditor.SignupActivity
- onStart() - Method in class com.xa.xpensauditor.AccountSettingsActivity
- onStop() - Method in class com.xa.xpensauditor.AccountSettingsActivity
- onViewCreated(View, Bundle) - Method in class com.xa.xpensauditor.TabFragment
- onViewCreated(View, Bundle) - Method in class com.xa.xpensauditor.UncategorisedFragment
- onViewRecycled(TransactionAdapter.MyViewHolder) - Method in class com.xa.xpensauditor.TransactionAdapter
- onViewRecycled(TransAdapter.MyViewHolder2) - Method in class com.xa.xpensauditor.TransAdapter
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A collection of articles, presentations or talks, most likely on Conferences and Journals.
Micael Pedrosa, Rui Lebre and Carlos Costa (2022). An architecture to secure medical imaging in federated storage. Expert Systems with Applications (Submitted; Under review).
Rui Lebre, Luís Bastião Silva and Carlos Costa (2022). Dicoogle Open Source: the establishment of a new paradigm in medical imaging. Journal of Medical Systems (Submitted; Under review).
Micael Pedrosa, Rui Lebre and Carlos Costa (2021). A performant protocol for distributed health records databases. IEEE Access (Accepted on August 2021).
Rui Lebre, Luís Bastião and Carlos Costa (2020). A Cloud-ready Architecture for Shared Medical Imaging Repository. Journal of Digital Imaging 2020 (Accepted on June 2020).
Tiago Godinho, Rui Lebre, João Almeida and Carlos Costa (2019). ETL Framework for Real-Time Business Intelligence over Medical Imaging Repositories. Journal of Digital Imaging (IF: 1.536, Q3 in Radiology, Nuclear Medicine and Medical Imaging).
Tiago Godinho, Rui Lebre, Luís Bastião Silva and Carlos Costa. (2017). An efficient architecture to support digital pathology in standard medical imaging repositories. Journal of Biomedical Informatics (IF: 2.753, Q1 in Computer Science, Interdisciplinary Applications).
Rui Lebre and Carlos Costa (2021). An efficient and reliable architecture for distributing medical imaging data. IEEE EHB 2021 (Accepted on October 2021)
Micael Pedrosa, Rui Lebre and Carlos Costa (2021). Holder-of-key threshold access token for anonymous data resources. IEEE ISCC 2021 (Accepted on July 2021).
Rui Lebre, Luís Bastião Silva and Carlos Costa (2021). Decentralizing the storage of a DICOM compliant PACS. IEEE BIBM 2021 (Accepted on June 2021).
Rui Lebre and Carlos Costa (2021). A non-relational approach for distributed medical imaging databases. IEEE BHI-BSH 2021 (Accepted on June 2021).
Rui Lebre, Micael Pedrosa, Marcos Gestal and Carlos Costa (2020). A safe architecture for authorisation grant in healthcare ecosystems. ICHI 2020 (Accepted on April 2020).
Rui Lebre, Eduardo Pinho, Jorge Silva and Carlos Costa (2020), Dicoogle Framework for Medical Imaging Teaching and Research. IEEE ICTS4eHealth 2020 (Accepted on February 2020).
Ana Almeida, Francisco Oliveira, Rui Lebre and Carlos Costa (2020). NoSQL distributed database for DICOM objects. ICHI 2020 (Accepted on January 2020).
Rui Jesus, Pedro Nunes, Rui Lebre and Carlos Costa (2020). Role-based architecture for securemanagement of telepathology sessions. MIE 2020 (Accepted on December 2019)
Pedro Nunes, Rui Jesus, Rui Lebre and Carlos Costa (2020). Data and sessions management in a telepathology platform. Healthinf 2020 (Accepted on December 2019)
Fátima Solange and Rui Lebre (2019). Automated pattern recognition of cerebral amyloid angiopathy patients. RecPad 2019 (Accepted on October 2019).
Rui Lebre, Rui Jesus, Pedro Nunes and Carlos Costa (2019). Collaborative Framework for a Whole-Slide Image Viewer. IEEE CBMS 2019 (Accepted on April 2019).
Rui Lebre, Luís Bastião and Carlos Costa (2019). An Accounting Mechanism for Standard Medical Imaging Services. IEEE EMBS 2019 (Accepted on December 2018).
Rui Lebre, Tiago Godinho, Carlos Costa and Luis Silva. (2018). A performant and fully DICOM compliant Web PACS for Digital Pathology. CARS 2018 (Accepted on February 2018).
Rui Lebre, Luís Bastião Silva and Carlos Costa. (2018). Shared Medical Imaging Repositories. MIE 2018 (Accepted on January 2018).


|
FloatingMeasure
class API for phyiscal calculations
|
| CCComplexMeasure | CComplexMeasure: represents a list of measures (i.e. CSimpleMeasure) chained by operators * and / This class handles: |
| CCDigFloat | CDigFloat: a class for floating point numbers which handels numerical errors of the 64 bit floating point representation. This class offers: |
| CCFloatingMeasure | FloatingMeasure: represents a floating number with a complex measure (e.g. 10*m/s). This class offers: |
| CCSimpleMeasure | CSimpleMeasure: represents a pre measure combined with a base measure like "mV". This class offers: |
| CCSingleton< SingletonObject > | Singleton class for a generic SingletonObject |
| ▼CCVectorHandle | This class keeps three vectors (two string : Short and Long and one double : Factor). These vectors are common to classes CPreMeasure and CBaseMeasure and are therefore packed into this class for vector handling |
| CCBaseMeasure | CBaseMeasure: represents the base-measure part of a measure. For example milli degree celsius consists of the base-measure part "degree celsius" (see CBaseMeasure) and the pre-measure part "milli" (see CPreMeasure). This class handles vectors of: |
| CCMeasureOperator | CMeasureOperator: this class is actually unused .. |
| CCPreMeasure | CPreMeasure represents the pre-measure part of a measure. For example milli volt consists of the base-measure part "Volt" (see CBaseMeasure) and the pre-measure part "milli". This class handles vectors of: |
| Cdbl_64 | Union for calculation of numerical error of a 64 bit double |


Five ways to strengthen employee cybersecurity awareness
Since human error has a well-documented history of causing many breaches, no organisation can afford to overlook the importance of ensuring that its employees are aware of online dangers.
This is why Juan Manuel Harán, Security Editor at ESET, has outlined five ideas for creating a culture that inspires staff to stay on their toes and with cybersecurity top of mind:
1. Establish an email address for queries
Creating an email account where employees can send their questions on any and all things cybersecurity provides for a good start and has multiple benefits. For one thing, the designated email account can encourage employees to come forward and ask questions that they might not otherwise ask.
Employers can also ask their staff to forward suspicious-looking emails to the address for review, which can help the employees become more astute at recognizing fraudulent email messages. The messages can also be used for organizing training sessions that will benefit the other employees and the company as a whole.
2. Set up an early warning system
To counter malicious spam campaigns, it’s worth considering establishing a dynamic and proactive early warning procedure that allows for alerting the entire company and keeping all employees informed that a malicious campaign is circulating. This can cut the risk that an unsuspecting employee will fall for the trap, putting organizational, employee and customer data in danger.
Additionally, the system serves to reinforce awareness of some of the main cybersecurity threats and common techniques used by cybercriminals, even where they leveraged some tried-and-tested methods. Lastly, the procedure may enable security staff to analyze the campaign’s features.
3. Organize talks and training
Talks with experts, be they employees of the same organizations or guest speakers, can also go a long way towards educating staff on various aspects of information security.
Since organizations typically employ professionals from various fields, it may be advisable to set up separate talks that target the abilities, interests and experience of various groups of people.
4. Run contests
Everybody loves to compete – and win, right? Employee contests are a fun way to help instil robust cybersecurity habits. For instance, materials from training or talks can be leveraged for quizzes that will not only reward the winners but will also provide your organization with better insight into just how cyber-aware the employees are.
You can also organize a bespoke social engineering simulation to find out how easy it would be for threat actors to penetrate your company’s defences by targeting the human factor. The results can also be used to determine which aspects of cybersecurity should receive more attention in future training sessions.
5. Draft a good practice guide
Draft a document that details the most efficient ways to help the company and its employees ward off cyberattacks. Such guidelines may include, for example, information about how to configure devices securely, how to encrypt information, how to set up two-factor authentication factor on various services.
It’s also important to make sure that the guides are easy to read, contain only the necessary information, and are easily accessible for any employee.
By Juan Manuel Harán
Edited by Jenna Delport
Follow Jenna Delport on Twitter
Follow IT News Africa on Twitter


How this component works..
Select (highlight) a phone number and right click.
Select the menu option "Dial..." shown on the context menu.
The phone number is passed to "Your Extension" and your phone rings.
When you answer that call, the system then dials the selected number.


{% include head.html %}
{% include fbmessenger.html %} {% include header.html %} {% include our-industries.html %} {% include models-cooperation.html %} {% include team.html %} {% include about-portfolio.html %} {% include about-customers.html %} {% include about-hackathons.html %} {% include events.html %} {% include contact.html %} {% include footer.html %} {% include js-bottom-all.html %}


| Password Safe | |
|
How many passwords and user names do you have to keep track of? Whether the answer is one or hundreds, Password Safe allows you to safely and easily create a secured and encrypted master password list. A single Master Password unlocks them all for your use. Security starts with the user. Keeping written lists of passwords on scraps of paper, or in a text document on your desktop is unsafe and is easily viewed by prying eyes (both cyber-based and human). Using the same password over and over again across a wide spectrum of systems and web sites creates the nightmare scenario where once someone has figured out one password, they have figured out all your passwords and now have access to every part of your life (system, e-mail, retail, financial, work). Password Safe allows you to manage your old passwords and to easily and quickly generate, store, organize, retrieve, and use complex new passwords, using password policies that you control. Once stored, your user names and passwords are just a few clicks away. Using Password Safe you can organize your passwords using your own customizable references—for example, by user ID, category, web site, or location. You can choose to store all your passwords in a single encrypted master password list (an encrypted password database), or use multiple databases to further organize your passwords (work and home, for example). And with its intuitive interface you will be up and running in minutes. |


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