text:
Isobel Campbell
Isobel Campbell started off her career in the band Belle & Sebastian (you may have heard of them?), she released two albums under her own …
Scottish singer/songwriter Isobel Campbell released a brand-new third album There Is No Other (Cooking Vinyl Records) to little fanfare. To be fair, the timing couldn't be worst, as the world's in a middle of a coronavirus pandemic, and there are more things to worry than trying to promote a record. With all concert touring coming to a halt, and record stores (no essential stores) asked to close temporarily… you probably didn't know about Campbell's latest release.
Like many of you, I first heard of Isobel Campbell through Belle & Sebastian. When she left B&S to create her project The Gentle Waves, I was on board! You see, Campbell had always had a wonderful sense of music arrangement and her sugarly-sweet vocals lends itself to such beautiful, calming songs. Unfortunately, despite Swansong For You being a great album, she retired the Gentle Waves name… and released her debut solo record Amorino.
It's about this time, that she met Screaming Trees' Mark Lanegan, and the two decided to collaborated on a few successful albums. The pairing is perfect, really. I named this type of dueling vocals, "Beauty & the Beast", as Lanegan's vocals sounds like an animal… while Campbell provide such a sweet, sweet soothing singing voice.
With Isobel Campbell's latest, There Is No Other, we see a return to a music style, much like circa-2000 Belle & Sebastian and Amorino. There's lovely guitar plucking, there's sparse arrangements, there's some very catchy melodies, and, best of all, some wonderful string arrangements (courtesy of Nina Violet).
I'm not going to break down the album for you, but let's just say if you enjoy B&S' The Boy With the Arab Strap and The Gentle Wave's Swansong for You, you'll love There Is No Other.
I believe Campbell chose "The National Bird of India" as her single, which I don't fully love. Instead, I recommend, "Runnin' Down a Dream" or "Ant Life".
P.S., if you buy There Is No Other on Amazon, it includes "Autorip" so you'll have the MP3 in your locker (and can listen to it via Echo or other Amazon products). This is the only way I buy music nowadays, as I have the CD as backup… but able to access my personal music locker where it's convenient.
vu (vu@weheartmusic.com) ♥ weheartmusic.com ♥ facebook.com ♥ twitter.com/weheartmusic
Posted at 10:32 PM in Belle and Sebastian, Cooking Vinyl, Isobel Campbell, Mark Lanegan, Review, Screaming Trees, Vu | Permalink | Comments (0)
Tracy Shedd
Indie musician, Tracy, from Arizona talked about: Her background, various band members, Teenbeat Records, "Louder than You Can Hear" album for free…
tracyshedd.com
Arizona is the New Granada Records debut album from Tracy Shedd, which was released earlier this week (November 12). The album is described as an acoustic duet with her husband James Tritten.
You can get a taste of the 'slowcore songtress' style with "Broken Arrows" via soundcloud.com.
Also on the album are two covers, The Magnetic Fields' "Candy" and Sonic Youth's "Teenage Riot".
11/20/13 Atlanta, GA Drunken Unicorn
11/21/13 Tampa, FL New World Brewery
11/23/13 Jacksonville, FL Rain Dogs
11/25/13 Fernandina, FL The Green Turtle
BONUS: We previously interviewed Tracy Shedd on Episode #27.
Active Child is the solo project of L.A.'s Pat Grossi. Pat sang for the Philadelphia's Boys Choir when he was younger and in that choir he fine tuned a set of vocal chords…
activechildmusic.com
Active Child (real name Pat Grossi) just released some remixes off their EP Rapor, the remixed track features Mikky Ekko, who duets with Grossi in dueling falsettos...
Listen to the remix on Soundcloud;
The EP is the first release since his critically acclaimed full length debut, You Are All I See, which we covered his Minneapolis show when he went on tour in support of it. Rapor came out last month (October 22) via Vagrant Records, and in support of the EP, Active Child is currently on tour.
Active Child returns to Minneapolis next week:
11/15 Boston MA - Brighton Music Hall
11/18 Chicago IL - Lincoln Hall
11/19 Minneapolis MN - 7th Street Entry
11/21 Denver CO - Larimer Lounge
11/23 Los Angeles CA - El Rey Theater
Isobel Campbell started off her career in the band Belle & Sebastian (you may have heard of them?), she released two albums under her own band The Gentle Waves. …
SWEET GUM TREE
Sweet Gum Tree is the solo project of French musician Arno Sojo, who previously 'cut his teeth' as members of various bands for over ten years, including band leader of Sojo Glider.
Sojo is putting the final touches on his forthcoming debut album The Snakes You Charm & The Wolves You Tame album as Sweet Gum Tree. The album will feature a duet with Isobel Campbell (Belle & Sebastian) for the track "Bird Of Passage". Duets with female icons are nothing new to Sojo, who previously teamed up with French popstar Buzy.
Making out the rest of the band will be drummer Earl Harvin (Tindersticks) and Marty Willson-Piper (The Church), who will join Sojo for his first UK tour this Winter:
18.02.14 Brighton Komedia
19.02.14 Birmingham (TBC)
20.02.14 London Union Chapel
vu ( ) ♥ weheartmusic.com
Posted at 01:47 PM in Active Child, Belle and Sebastian, Isobel Campbell, Sweet Gum Tree, Tracy Shedd, Vu | Permalink | Comments (0)
W♥M134: Pleased To Meet Emily
W♥M134 (36 mins)
Your hosts are Amy, Emily, and Vu.
Meet Emily, the girl who fell in love with The Replacements and moved to Minneapolis in stalking pursuit of Paul Westerberg. Her first show at "the legendary" First Avenue, it was an all-ages show for Built to Spill, while her first cassette purchased was for Dionne Warwick and The Four Tops. Who doesn't love oldies and Motown?
The main focus of the show is Mark Lanegan and his latest album, Blues Funeral, which came out February 2012, via 4AD. This is the first Mark Lanegan Band album since 2004's Bubblegum.
You might know Lanegan as the lead singer of the 90s band Screaming Trees, but he's become known more and more as successful solo artist. His collaboration list is also quite diverse, being in Queens of the Stone Age, The Gutter Twins (with Greg Dulli), and Isobel Campbell (of Belle & Sebastian).
Song of the show is Mark Lanegan Band's "Ode to Sade Disco".
He's currently on tour, Amy & Emily will be seeing him at the Cedar in Minneapolis.
10 May 2012 • New York, NY - Webster Hall
11 May 2012 • Washington, DC - 9:30 Club
12 May 2012 • Philadelphia, PA - Theatre Of The Living
13 May 2012 • Boston, MA - Paradise Rock Club
15 May 2012 • Toronto, ON - Mod Club
16 May 2012 • Detroit, MI - Small's
17 May 2012 • Chicago, IL - Metro
18 May 2012 • Minneapolis, MN - Cedar Cultural Center
20 May 2012 • Denver, CO - Bluebird Theatre
22 May 2012 • Los Angeles, CA - Gene Autry Museum
23 May 2012 • San Francisco, CA - Great American Music
25 May 2012 • George, WA - Sasquatch! Festival
26 May 2012 • Portland, OR - Wonder Ballroom
11 August 2012 • France - La Route Du Rock Festival
13 August 2012 • Frankkfurt, Batschkapp - Longhorn
14 August 2012 • München - Freiheizhalle
16 August 2012 • Hasselt, Belgium - Pukkelpop Festival
17 August 2012 • Germany - Highfields Festival
18 August 2012 • Switzerland - Gampel Festival
20 August 2012 • Holland - Lowlands Festival
21 August 2012 • Falmouth - Princess Pavilion
22 August 2012 • Wolverhampton - Wulfrun Hall
24 August 2012 • Uk - Leeds Festival
25 August 2012 • Saint-Cloud, Fr - Rock En Seine Festival
26 August 2012 • Uk - Reading Festival
28 August 2012 • Aberdeen - Garage
29 August 2012 • Edinburgh - Picturehouse
1 September 2012 • Uk - End Of The Road Festival
2 September 2012 • Verona, It - A Perfect Day Festival
I have a sad, sad, sad love for Wilson Phillips. This was my first cassette purchased at the Wherehouse (Blackstone location) in Fresno, California. The trio is back, and with a new covers album, Dedicated. Check out their live performance of "Good Vibration" via youtube.com. Wendy looks great!
They're coming to Minneapolis State Fair!
6/5/12 Austin, TX Long Center for Performing Arts
6/6/12 Dallas TX Winspear Opera House
8/17 & 8/18 San Diego, CA San Diego Symphony
8/31 & 9/1 St. Paul, MN Minnesota State Fair
*more tour dates to follow
BONUS: Things taken out of context: Emily's rant on the industry failing to grow out of the 60s, Paul Westerberg is a big Twins fan, "Kurt's dead, that's great!", "I'm Vu, Vu-me!". Falsetto, explained… which lead to Jeff Hanson (yes that song I used is by Jeff Hanson!). Emily's first cassingle was Salt-N-Pepa's "Ain't Nuthin' But A She Thing" and Vu was "How Long" by Information Society (that was 1991, I bought the CD Single later on as an upgrade). The cassingle played the same on both side A & B, you can see a scan of the cover here (along with "Think").
ROCK FOR PUSSY IX: Friday, June 8, 2012, at First Avenue in Minneapolis. Featuring Steve Price, John Eller, Dave Russ, Tommy Alsides, Janey Winterbauer, Christian Erickson, Aby Wolf, Chastity Brown, Venus and All the Pretty Horses, Adam Levy, Dan Israel, Reed Wilkerson, Laurie Lindeen, Dave Campbell, Ciaran Daly, Orion Treon, Liam Watkins, Michelle Massey, Brenden Green, Chris Perricelli, Leslie Ball, Chris Lynch, Kevin Nord, Matt Coffee, DJ Jake Rudh and many more!!!
W♥M Radio is available on iTunes, please subscribe!
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Archive of this show at archive.org. More information on podomatic.com
Posted at 02:18 PM in 4AD, Amy, Dionne Warwick, Emily, Information Society, Isobel Campbell, Jeff Hanson, Mark Lanegan, Paul Westerberg, Podcast, Salt-N-Pepa, Screaming Trees, The Four Tops, The Replacements, Vu, Wilson Phillips | Permalink | Comments (0)
W♥M112: Splendour in the Grass Preview
Listening options:
Archive.org: W♥M112
Happy holidays! It's also Sally's birthdate week, as she was kind enough to stop in to preview Splendour in the Grass, which takes place this July 29th to 31st. Doors at 9am, music starts at 11 and ends after midnight. Details at splendourinthegrass.com.
Before we get into the meat of the matter, we did wrap up NXNE, which you can read more details at nxne.weheartmusic.com and all my exploits and adventures and lessons learned.
I did touch a bit on Jo Williamson, not literally, but I kept running into her. Saw Washington played twice, saw loads of other bands too. Disappointed with Brian Wilson, but Men Without Hats, Evan Dando and Juliana Hatfield saved the festival. Outro song is "Everybody Loves Me But You" by Hatfield.
Splendour in the Grass 2011 will be covered by the fine Festival Ladies, but Sally and I looked over Coldplay (it was all yellow!), Kanye West (he just shows up everywhere). A good bunch together of Isobel Campbell & Mark Lanegan, Gomez, and Regina Spektor in a block is pretty amazing. Also, Grouplove, who I saw a few months ago, they are on the up and up. Yelle, the French girl is pretty hot too. Of course, Foster the People are a really good band, love their songs!
Amazed that DJ Shadow is still around, he pretty much was the first guy that made a whole album of just entirely out of samples.
Song of the Podcast is: British Sea Power's "Georgie Ray" from their new album, Valhalla Dancehall.
More Pulp than you can stand. Fitz & The Tantrums are playing the same night as Pulp, so Sally will have to miss out.
Britpop class of '95 article available on independent.co.uk spotlights on Justine Frischmann, Louise Wener, Debbie Smith, David Roundtree, and Tim Brown. Where are they now and what they are doing. Kind of glad to see Debbie Smith is still playing in several bands (I met her a long time ago in a bar and she asked about my t-shirt - it was my penpal's artwork of Morrissey).
Archive of this show at archive.org. More information on mypodcast.com
vu ( ) ♥ weheartmusic.com ♥ podcast.weheartmusic.com ♥ twitter.com/weheartmusic
Posted at 11:42 PM in Brian Wilson, British Sea Power, Coldplay, DJ Shadow, Evan Dando, Festivals, Fitz & The Tantrums, Foster the People, Gomez, Grouplove, Isobel Campbell, Jo Williamson, Juliana Hatfield, Kanye West, Mark Lanegan, NXNE, Podcast, Pulp, Regina Spektor, Sally, Splendour in the Grass, Vu, Washington, Yelle | Permalink | Comments (0)
Splendour in the Grass - Line-up announced today!
SPLENDOUR IN THE GRASS 2011
"…you could barely wipe the smiles off our dials. Thank you Splendour for anticipating our every whim, and fulfilling them. A truly unforgettable weekend." Groupie
SPLENDOUR IN THE GRASS - the annual, ephemeral city of sounds that exists solely for sweet music and good times will transform Woodfordia, near Woodford, south east Queensland from Friday 29 to Sunday 31 July.
The eleventh SPLENDOUR IN THE GRASS event will see 81 acts giving their splendiferous best across three stages.
COLDPLAY (only Australian show) * KANYE WEST (only Australian show) * JANE'S ADDICTION * THE HIVES * PULP * THE LIVING END * THE MARS VOLTA * REGINA SPEKTOR (only 2011 show…anywhere) * BLISS N ESO * PNAU * MOGWAI (only Australian show) * DJ SHADOW * GLASVEGAS * THE GRATES * DEVENDRA BANHART AND THE GROGS* MODEST MOUSE * THE MIDDLE EAST * KAISER CHIEFS * JAMES BLAKE * KELE * THE VINES * ELBOW * ESKIMO JOE * NOAH AND THE WHALE * CHILDREN COLLIDE * THIEVERY CORPORATION * CUT COPY * ISOBEL CAMPBELL AND MARK LANEGAN * BLUEJUICE * THE KILLS * BLACK JOE LEWIS & THE HONEYBEARS (featuring THE RELATIVES) * ARCHITECTURE IN HELSINKI * FOSTER THE PEOPLE * THE PANICS * FRIENDLY FIRES * JEBEDIAH * THE VACCINES * GOMEZ * BOY AND BEAR * GOTYE * DOES IT OFFEND YOU, YEAH? * CLOUD CONTROL * MONA * SPARKADIA * WARPAINT * MUSCLES (Live) * FITZ AND THE TANTRUMS * THEJEZABELS * DRAPHT * BRITISH SEA POWER * TIM & JEAN * LEADER CHEETAH * GROUPLOVE * SEEKER LOVER KEEPER * YELLE * KIMBRA * PHRASE * OH MERCY * DANANANANAYKROYD * THE BLACK SEEDS * MARQUES TOLIVER * THE HOLIDAYS * GHOUL * LIAM FINN * THE HERD * YOUNG THE GIANT * GUINEAFOWL * HUNGRY KIDS OF HUNGARY * JINJA SAFARI * WILD BEASTS * ILLY * CUT OFF YOUR HANDS * GARETH LIDDIARD * ALPINE * WORLD'S END PRESS * MOSMAN ALDER * LANIE LANE
Pretty exciting right?
So for further information (including ticket prices) head this way: http://splendourinthegrass.com
Posted at 03:02 AM in Architecture in Helsinki, British Sea Power, Cut Copy, DJ Shadow, Elbow, Foster the People, Glasvegas, Gomez, Hungry Kids of Hungary, Isobel Campbell, James, Kanye West, Kele, Liam Finn, Mars Volta, Mogwai, Noah and the Whale, Pnau, Regina Spektor, Sally, The Grates, The Holidays, The Vines, Thievery Corporation, Yelle, Young the Giant | Permalink | Comments (0)
Isobel Campbell And Mark Lanegan - Deus Ibi Est
Isobel Campbell Mark Lanegan - Deus Ibi Est
Isobel Campbell And Mark Lanegan
I am going to try and limit my post (there's just too many bands I want to talk about!). I've started a Morrissey Vox Group....
Isobel Campbell started off her career in the band Belle & Sebastian (you may have heard of them?), she released two albums under her own band The Gentle Waves.
Then followed by a collaboration with Bill Wells and then her excellent solo album "Amorino". That's where she met up with Mark Lanegan, who sang on "Why Does My Head Hurt So?" (off the EP, not the album).
Anyway, her second album, "Ballad of the Broken Seas" is the best duet album! It's mostly Mark singing with a little Isobel's sweetness thrown in for good measures.
This track, Dues Ibi Est, I think means God is here, was given away free if you registered on isobelcampbell.com
PS, Mark is from the Seattle band, Screaming Trees, if you were wondering.
Posted at 06:06 PM in Isobel Campbell, Mark Lanegan | Permalink | Comments (2)


The Olympiaregion Seefeld with its five villages Seefeld, Leutasch, Mösern, Reith and Scharnitz draws visitors from all around the world. Its special combination of a plateau location at an altitude of 1200m and a trail network of around 650km of hiking and mountain routes means that it is a true walkers' paradise.
With the impressive Wetterstein range, the Alpenpark Karwendel and the protected area of the Wildmoos, the plateau opens towards the south and is the perfect base for nature lovers and enthusiastic walkers alike.
And, right in the middle of this idyllic spot, an event which is a hikers' highlight and a global tradition: the 20th 3-day Unicorn Walk. With its start and finish in Leutasch, this walking event takes in the loveliest locations in the area. Unique countryside experiences, cosy places to stop for refreshment along the way and welcoming hosts make this global phenomenon an unforgettable highlight for any hiker, where fun and team spirit are the most important aspects.
A 3-day event full of scenic highlights and varied routes to choose from. With the start and finish in Leutasch, there is plenty to discover during the 20th IML 3-day Unicorn Walk.
The route maps for the 10km, 20km, 30km and 42km variants with the appropriate altitude profile can be found here. The GPX data is of course also available for download.
You can register in the Wettersteinhalle in Leutasch from 10 am on Thursday 12th September.
It is simple to get here with uncomplicated transfer routes – here is how you can make your way to the 3-day Unicorn Walk.


This Guy Turns Into 7 Female 'Game Of Thrones' Characters Using Only Makeup.
For Game of Thrones fans, now is a very difficult time. With the recent season just coming to a close, we are basically at the farthest point from a new season that one could ever be. Now, we have long and dark times ahead of us, with nothing to do but imagine what could possibly be about to go down in Westeros. But luckily, while we navigate this darkness, we have things like this to tide us over.
This young makeup artist has used his talent to fight the pain away, taking his brushes and pigments to a new level by transforming himself into his favorite Game of Thrones characters. Even better? They're all women, from feisty Arya to loyal Brienne to the constantly-smirking-and-now-murderous Cersei Lannister. His makeup transformations are truly uncanny, and he's shared all of them to Instagram for your enjoyment. Check some of them out for yourself below.
His name is Paolo Ballesteros.
Although in this photo, you can see that he's basically Brienne of Tarth, the noble female knight who pledges loyalty to the starks.
By day, Ballesteros is a TV host and an actor.
But by night, he transforms himself into Daenerys Targaryen, Queen of the Andals and Mother of Dragons.
Basically, this guy is proving that there's not much that CAN'T be done with makeup.
This depiction of Arya Stark is terrifyingly precise and accurate.
He uses wigs to achieve his looks as well.
What would Sansa Stark be without that beautiful flowing red hair?
He managed to capture to icy eyes of the Red Woman pretty well.
Seriously, you could get lost in those things -- but we don't recommend you do, because she is a witch afterall.
We absolutely love his spot-on interpretation of Margaery Tyrell.
Things didn't go well for Margaery at the end of this season, but at least Ballesteros has figured out a way to preserve her visage for all eternity.
The real grand prize winner is Cersei Lannister, though.
This makeup is so terrifyingly accurate, it's almost hard to believe it's not Cersei herself. Let's just hope that Ballesteros doesn't use this look to, you know, become a mass murderer using ancient pyrotechnics.
Here's some more of his amazing transformations.


text:
"""Wrappers for protocol buffer enum types."""

import enum

class ContentOption(enum.IntEnum):
    """
    Options describing which parts of the provided content should be scanned.

    Attributes:
      CONTENT_UNSPECIFIED (int): Includes entire content of a file or a data stream.
      CONTENT_TEXT (int): Text content within the data, excluding any metadata.
      CONTENT_IMAGE (int): Images found in the data.
    """

    CONTENT_UNSPECIFIED = 0
    CONTENT_TEXT = 1
    CONTENT_IMAGE = 2

class DayOfWeek(enum.IntEnum):
    """
    Represents a day of week.

    Attributes:
      DAY_OF_WEEK_UNSPECIFIED (int): The unspecified day-of-week.
      MONDAY (int): The day-of-week of Monday.
      TUESDAY (int): The day-of-week of Tuesday.
      WEDNESDAY (int): The day-of-week of Wednesday.
      THURSDAY (int): The day-of-week of Thursday.
      FRIDAY (int): The day-of-week of Friday.
      SATURDAY (int): The day-of-week of Saturday.
      SUNDAY (int): The day-of-week of Sunday.
    """

    DAY_OF_WEEK_UNSPECIFIED = 0
    MONDAY = 1
    TUESDAY = 2
    WEDNESDAY = 3
    THURSDAY = 4
    FRIDAY = 5
    SATURDAY = 6
    SUNDAY = 7

class DlpJobType(enum.IntEnum):
    """
    An enum to represent the various type of DLP jobs.

    Attributes:
      DLP_JOB_TYPE_UNSPECIFIED (int)
      INSPECT_JOB (int): The job inspected Google Cloud for sensitive data.
      RISK_ANALYSIS_JOB (int): The job executed a Risk Analysis computation.
    """

    DLP_JOB_TYPE_UNSPECIFIED = 0
    INSPECT_JOB = 1
    RISK_ANALYSIS_JOB = 2

class FileType(enum.IntEnum):
    """
    Definitions of file type groups to scan.

    Attributes:
      FILE_TYPE_UNSPECIFIED (int): Includes all files.
      BINARY_FILE (int): Includes all file extensions not covered by text file types.
      TEXT_FILE (int): Included file extensions:
        asc, brf, c, cc, cpp, csv, cxx, c++, cs, css, dart, eml, go, h, hh, hpp,
        hxx, h++, hs, html, htm, shtml, shtm, xhtml, lhs, ini, java, js, json,
        ocaml, md, mkd, markdown, m, ml, mli, pl, pm, php, phtml, pht, py, pyw,
        rb, rbw, rs, rc, scala, sh, sql, tex, txt, text, tsv, vcard, vcs, wml,
        xml, xsl, xsd, yml, yaml.
      IMAGE (int): Included file extensions: bmp, gif, jpg, jpeg, jpe, png.
      bytes\_limit\_per\_file has no effect on image files.
      AVRO (int): Included file extensions:
        avro
    """

    FILE_TYPE_UNSPECIFIED = 0
    BINARY_FILE = 1
    TEXT_FILE = 2
    IMAGE = 3
    AVRO = 7

class InfoTypeSupportedBy(enum.IntEnum):
    """
    Parts of the APIs which use certain infoTypes.

    Attributes:
      ENUM_TYPE_UNSPECIFIED (int)
      INSPECT (int): Supported by the inspect operations.
      RISK_ANALYSIS (int): Supported by the risk analysis operations.
    """

    ENUM_TYPE_UNSPECIFIED = 0
    INSPECT = 1
    RISK_ANALYSIS = 2

class Likelihood(enum.IntEnum):
    """
    Categorization of results based on how likely they are to represent a match,
    based on the number of elements they contain which imply a match.

    Attributes:
      LIKELIHOOD_UNSPECIFIED (int): Default value; same as POSSIBLE.
      VERY_UNLIKELY (int): Few matching elements.
      UNLIKELY (int)
      POSSIBLE (int): Some matching elements.
      LIKELY (int)
      VERY_LIKELY (int): Many matching elements.
    """

    LIKELIHOOD_UNSPECIFIED = 0
    VERY_UNLIKELY = 1
    UNLIKELY = 2
    POSSIBLE = 3
    LIKELY = 4
    VERY_LIKELY = 5

class MatchingType(enum.IntEnum):
    """
    Type of the match which can be applied to different ways of matching, like
    Dictionary, regular expression and intersecting with findings of another
    info type.

    Attributes:
      MATCHING_TYPE_UNSPECIFIED (int): Invalid.
      MATCHING_TYPE_FULL_MATCH (int): Full match.

      - Dictionary: join of Dictionary results matched complete finding quote
      - Regex: all regex matches fill a finding quote start to end
      - Exclude info type: completely inside affecting info types findings
      MATCHING_TYPE_PARTIAL_MATCH (int): Partial match.

      - Dictionary: at least one of the tokens in the finding matches
      - Regex: substring of the finding matches
      - Exclude info type: intersects with affecting info types findings
      MATCHING_TYPE_INVERSE_MATCH (int): Inverse match.

      - Dictionary: no tokens in the finding match the dictionary
      - Regex: finding doesn't match the regex
      - Exclude info type: no intersection with affecting info types findings
    """

    MATCHING_TYPE_UNSPECIFIED = 0
    MATCHING_TYPE_FULL_MATCH = 1
    MATCHING_TYPE_PARTIAL_MATCH = 2
    MATCHING_TYPE_INVERSE_MATCH = 3

class RelationalOperator(enum.IntEnum):
    """
    Operators available for comparing the value of fields.

    Attributes:
      RELATIONAL_OPERATOR_UNSPECIFIED (int)
      EQUAL_TO (int): Equal. Attempts to match even with incompatible types.
      NOT_EQUAL_TO (int): Not equal to. Attempts to match even with incompatible types.
      GREATER_THAN (int): Greater than.
      LESS_THAN (int): Less than.
      GREATER_THAN_OR_EQUALS (int): Greater than or equals.
      LESS_THAN_OR_EQUALS (int): Less than or equals.
      EXISTS (int): Exists
    """

    RELATIONAL_OPERATOR_UNSPECIFIED = 0
    EQUAL_TO = 1
    NOT_EQUAL_TO = 2
    GREATER_THAN = 3
    LESS_THAN = 4
    GREATER_THAN_OR_EQUALS = 5
    LESS_THAN_OR_EQUALS = 6
    EXISTS = 7

class StoredInfoTypeState(enum.IntEnum):
    """
    State of a StoredInfoType version.

    Attributes:
      STORED_INFO_TYPE_STATE_UNSPECIFIED (int)
      PENDING (int): StoredInfoType version is being created.
      READY (int): StoredInfoType version is ready for use.
      FAILED (int): StoredInfoType creation failed. All relevant error messages are returned
      in the ``StoredInfoTypeVersion`` message.
      INVALID (int): StoredInfoType is no longer valid because artifacts stored in
      user-controlled storage were modified. To fix an invalid StoredInfoType,
      use the ``UpdateStoredInfoType`` method to create a new version.
    """

    STORED_INFO_TYPE_STATE_UNSPECIFIED = 0
    PENDING = 1
    READY = 2
    FAILED = 3
    INVALID = 4

class BigQueryOptions(object):
    class SampleMethod(enum.IntEnum):
        """
        How to sample rows if not all rows are scanned. Meaningful only when
        used in conjunction with either rows\_limit or rows\_limit\_percent. If
        not specified, scanning would start from the top.

        Attributes:
          SAMPLE_METHOD_UNSPECIFIED (int)
          TOP (int): Scan from the top (default).
          RANDOM_START (int): Randomly pick the row to start scanning. The scanned rows are contiguous.
        """

        SAMPLE_METHOD_UNSPECIFIED = 0
        TOP = 1
        RANDOM_START = 2

class ByteContentItem(object):
    class BytesType(enum.IntEnum):
        """
        Attributes:
          BYTES_TYPE_UNSPECIFIED (int)
          IMAGE (int)
          IMAGE_JPEG (int)
          IMAGE_BMP (int)
          IMAGE_PNG (int)
          IMAGE_SVG (int)
          TEXT_UTF8 (int)
          AVRO (int)
        """

        BYTES_TYPE_UNSPECIFIED = 0
        IMAGE = 6
        IMAGE_JPEG = 1
        IMAGE_BMP = 2
        IMAGE_PNG = 3
        IMAGE_SVG = 4
        TEXT_UTF8 = 5
        AVRO = 11

class CharsToIgnore(object):
    class CommonCharsToIgnore(enum.IntEnum):
        """
        Attributes:
          COMMON_CHARS_TO_IGNORE_UNSPECIFIED (int)
          NUMERIC (int): 0-9
          ALPHA_UPPER_CASE (int): A-Z
          ALPHA_LOWER_CASE (int): a-z
          PUNCTUATION (int): US Punctuation, one of !"#$%&'()\*+,-./:;<=>?@[]^\_\`{\|}~
          WHITESPACE (int): Whitespace character
        """

        COMMON_CHARS_TO_IGNORE_UNSPECIFIED = 0
        NUMERIC = 1
        ALPHA_UPPER_CASE = 2
        ALPHA_LOWER_CASE = 3
        PUNCTUATION = 4
        WHITESPACE = 5

class CloudStorageOptions(object):
    class SampleMethod(enum.IntEnum):
        """
        How to sample bytes if not all bytes are scanned. Meaningful only when
        used in conjunction with bytes\_limit\_per\_file. If not specified,
        scanning would start from the top.

        Attributes:
          SAMPLE_METHOD_UNSPECIFIED (int)
          TOP (int): Scan from the top (default).
          RANDOM_START (int): For each file larger than bytes\_limit\_per\_file, randomly pick the
          offset to start scanning. The scanned bytes are contiguous.
        """

        SAMPLE_METHOD_UNSPECIFIED = 0
        TOP = 1
        RANDOM_START = 2

class CryptoReplaceFfxFpeConfig(object):
    class FfxCommonNativeAlphabet(enum.IntEnum):
        """
        These are commonly used subsets of the alphabet that the FFX mode
        natively supports. In the algorithm, the alphabet is selected using
        the "radix". Therefore each corresponds to particular radix.

        Attributes:
          FFX_COMMON_NATIVE_ALPHABET_UNSPECIFIED (int)
          NUMERIC (int): [0-9] (radix of 10)
          HEXADECIMAL (int): [0-9A-F] (radix of 16)
          UPPER_CASE_ALPHA_NUMERIC (int): [0-9A-Z] (radix of 36)
          ALPHA_NUMERIC (int): [0-9A-Za-z] (radix of 62)
        """

        FFX_COMMON_NATIVE_ALPHABET_UNSPECIFIED = 0
        NUMERIC = 1
        HEXADECIMAL = 2
        UPPER_CASE_ALPHA_NUMERIC = 3
        ALPHA_NUMERIC = 4

class CustomInfoType(object):
    class ExclusionType(enum.IntEnum):
        """
        Attributes:
          EXCLUSION_TYPE_UNSPECIFIED (int): A finding of this custom info type will not be excluded from results.
          EXCLUSION_TYPE_EXCLUDE (int): A finding of this custom info type will be excluded from final results,
          but can still affect rule execution.
        """

        EXCLUSION_TYPE_UNSPECIFIED = 0
        EXCLUSION_TYPE_EXCLUDE = 1

class DlpJob(object):
    class JobState(enum.IntEnum):
        """
        Attributes:
          JOB_STATE_UNSPECIFIED (int)
          PENDING (int): The job has not yet started.
          RUNNING (int): The job is currently running.
          DONE (int): The job is no longer running.
          CANCELED (int): The job was canceled before it could complete.
          FAILED (int): The job had an error and did not complete.
        """

        JOB_STATE_UNSPECIFIED = 0
        PENDING = 1
        RUNNING = 2
        DONE = 3
        CANCELED = 4
        FAILED = 5

class JobTrigger(object):
    class Status(enum.IntEnum):
        """
        Whether the trigger is currently active. If PAUSED or CANCELLED, no jobs
        will be created with this configuration. The service may automatically
        pause triggers experiencing frequent errors. To restart a job, set the
        status to HEALTHY after correcting user errors.

        Attributes:
          STATUS_UNSPECIFIED (int)
          HEALTHY (int): Trigger is healthy.
          PAUSED (int): Trigger is temporarily paused.
          CANCELLED (int): Trigger is cancelled and can not be resumed.
        """

        STATUS_UNSPECIFIED = 0
        HEALTHY = 1
        PAUSED = 2
        CANCELLED = 3

class OutputStorageConfig(object):
    class OutputSchema(enum.IntEnum):
        """
        Predefined schemas for storing findings.

        Attributes:
          OUTPUT_SCHEMA_UNSPECIFIED (int)
          BASIC_COLUMNS (int): Basic schema including only ``info_type``, ``quote``, ``certainty``, and
          ``timestamp``.
          GCS_COLUMNS (int): Schema tailored to findings from scanning Google Cloud Storage.
          DATASTORE_COLUMNS (int): Schema tailored to findings from scanning Google Datastore.
          BIG_QUERY_COLUMNS (int): Schema tailored to findings from scanning Google BigQuery.
          ALL_COLUMNS (int): Schema containing all columns.
        """

        OUTPUT_SCHEMA_UNSPECIFIED = 0
        BASIC_COLUMNS = 1
        GCS_COLUMNS = 2
        DATASTORE_COLUMNS = 3
        BIG_QUERY_COLUMNS = 4
        ALL_COLUMNS = 5

class RecordCondition(object):
    class Expressions(object):
        class LogicalOperator(enum.IntEnum):
            """
            Attributes:
              LOGICAL_OPERATOR_UNSPECIFIED (int)
              AND (int)
            """

            LOGICAL_OPERATOR_UNSPECIFIED = 0
            AND = 1

class TimePartConfig(object):
    class TimePart(enum.IntEnum):
        """
        Attributes:
          TIME_PART_UNSPECIFIED (int)
          YEAR (int): [0-9999]
          MONTH (int): [1-12]
          DAY_OF_MONTH (int): [1-31]
          DAY_OF_WEEK (int): [1-7]
          WEEK_OF_YEAR (int): [1-52]
          HOUR_OF_DAY (int): [0-23]
        """

        TIME_PART_UNSPECIFIED = 0
        YEAR = 1
        MONTH = 2
        DAY_OF_MONTH = 3
        DAY_OF_WEEK = 4
        WEEK_OF_YEAR = 5
        HOUR_OF_DAY = 6

class TransformationSummary(object):
    class TransformationResultCode(enum.IntEnum):
        """
        Possible outcomes of transformations.

        Attributes:
          TRANSFORMATION_RESULT_CODE_UNSPECIFIED (int)
          SUCCESS (int)
          ERROR (int)
        """

        TRANSFORMATION_RESULT_CODE_UNSPECIFIED = 0
        SUCCESS = 1
        ERROR = 2

redpajama_set_name: RedPajamaGithub


text:
Indonesian Social Ecommerce Startup Chilibeli Raises US$10M Series A
By Sharon Lewis
Chilibeli, a community based social ecommerce platform headquartered in Jakarta, raised US$10 million funding in a round led by American venture capital firm Lightspeed Ventures this week, according to a statement by the company. The round also saw participation from Golden Gate Ventures, Sequoia Surge, Kinesys Group, and Alto Partners, the statement added.
"The funds are being used to strengthen Jakarta, Tangerang Selatan, and Depok's networks of communities, while also expanding our reach to Bogor and Bekasi. On top of that, we continue to improve the UI and UX of our application, as well as advancing the facility of our Depok's warehouse", said Alex Feng, Co-Founder and CEO at Chilibeli.
The Series A funding comes in after just seven months of establishing Chilibeli, making it one of the fastest growing Southeast Asian startups, he added. Since its founding in 2019, the startup boasts 150% month-on-month growth, over 300 direct jobs created, and thousands of indirect partner jobs created, the company statement noted.
Justin Hall, Partner at Golden Gate Ventures, said, "In many ways, the Chilibeli team is tapping on something that other platforms to date haven't been able to successfully leverage: the trusted social circle. By building a technology platform that taps into everyday Indonesian's network, they're unlocking new, innovative ways to shop, collaborate, and even earn a living."
For users, Chilibeli works like any other online grocery store–customers place an order on the Chilibeli app, which the startup then fulfills through its own distribution network. What Chilibeli does differently is facilitating micro-entrepreneurship amongst community members through its program Mitra Chilibeli, or Chilibeli for Partners.
Akshay Bhushan, Partner at Lightspeed Venture Partners, noted, "The power of the Chilibeli social commerce platform comes from empowering local communities across the emerging middle class in Indonesia through access to affordable & value products while driving livelihoods for various stakeholders in the Chilibeli ecosystem such as farmers, housewives and small businesses in these communities."
Chilibeli partners, many of whom are housewives, can earn commissions by managing the shopping of other community members, according to the company statement. It elaborated that Indonesian housewives, being in charge of household expenditure, are looking to tap into an extra source of income, and that is where Chilibeli saw opportunity.
"Chilibeli's intelligent commerce concept is in line with our mission to invest in human intelligence, which leads to the betterment and improvement of welfare and well-being," said Yansen Kamto, Founding Partner at Kinesys Group.
E-commerce / New Retail, Features, Industries
About Sharon Lewis
Sharon is a Staff Writer at Jumpstart

redpajama_set_name: RedPajamaCommonCrawl


text:
\section{Introduction.}\label{intro}

\section{Introduction}\label{sec:introduction}
With the rapid expansion of World Wide Web in the last two decades, social networks are becoming important dissemination and marketing platforms as they allow the efficient generation, dissemination, and sharing of information and ideas. And platforms utilizing social media have been recognized as revolutionizing
communication channels for corporations and consumers alike.  Consider the following scenario. Suppose a firm would like to generate demand for a
new product through a social network, they may choose to provide discounts to a few selected users in the network, and hope that they promote this product to their friends as well. This is different from traditional discount allocation strategy whose central problem
is to find the ``best fit'' between a discount and a given user while ignoring the network effect of that user. Our work fundamentally differs
from existing works as we are concerned with the \emph{network value} of users when making
allocations. Suppose the total discount is constrained by a budget defined by the firm, this raises the question: given the structure of the social networks and the knowledge of  how the new
product adoption diffuse through the network, which initial set users should be selected to receive the discount? and how much should the discounts be worth?

To this end, we formulate and investigate the discount allocation problem as follows. Given a social network  $G = (V, E)$, where $V$ is a set of individuals and $E$ is a set of social ties. Our model decomposes the cascade process into two stages: \emph{seeding stage} and \emph{diffusion stage}. In the seeding stage, we offer discounts to a set of initial users. Each type of discount stands for a specific dollar amount off of the purchase. Some initial users accept the offer and act as starting points, called \emph{seeds},  in the diffusion stage. It is reasonable to assume
that the adoption probability of any initial user is monotonically increasing with respect to discount rate. In the diffusion stage, the adoption propagates from the seeds to other users.  In order to model the diffusion dynamics in a social network, we can leverage the existing results done in diffusion of information  in social networks. In particular, we adopt \emph{independent cascade model} \citep{kempe2003maximizing}, which is one of the most commonly used models.  Our goal is to find the optimal configuration, which consists of a set of initial users and discount rate for each user, that maximizes the cascade in expectation. We study this problem under both non-adaptive and adaptive settings.
\begin{itemize}
\item In the non-adaptive setting, we have to commit the set of initial users and corresponding discounts all at once in advance. Although this setting is similar to traditional influence maximization problem, a unique challenge of our problem is to model the responses from the customer with respect to different discount rates. In particular, our model should be able to incorporate the following two constraints: (a) If a user receives multiple discounts, her decision on whether or not to accept the offer only depends on highest discount rate, and (b) Given that an initial user accepts a discount, she will accept all discounts with higher rate.   We prove that the utility function under this setting is monotone and submodular, which admits a $\frac{1}{2}(1 - 1/e)$-approximation algorithm.
\item Under the significantly more complex adaptive setting, the decision process is performed in a sequential manner, and each initial user that is picked provides the feedback on the discount rate, or, in other words, reveals whether or not she will adopt the discount. The action taken in each step depends on the actual cascade that happens in the previous steps. Therefore any feasible solutions are now policies instead of a fixed configuration. It was worth noting that our problem is closely related to adaptive/stochatic submodular maximization, however, there are two significant differences between the two: first of all, existing studies mainly assume that the cost of the action is known to the algorithm before the action is taken. However, this assumption is no longer valid under our setting, i.e, the actual amount of discount that has been delivered to a initial user depends on whether or not she accepts the offer; secondly, actions may incur non-uniform cost, this is obviously true since different discounts has different value. Unfortunately,  existing solutions can not handle the above two challenges. In this work, we prove that the utility function is adaptive monotone and adaptive submodular, this allows us to develop a novel greedy policy with bounded approximation ratio.
\end{itemize}

To the best of our knowledge, we are the first to systematically study the problem of discount allocation in both non-adaptive and adaptive settings. Under independent cascade model, which is one of the most commonly used models  in literature, we present a detailed analysis of the computational complexity of the problem. We propose a simple greedy algorithm with a constant approximation ratio in non-adaptive setting. We also develop a series of adaptive  policies with bounded approximation ratios under the adaptive setting.

\section{Related Work}
\label{sec:related}
 \cite{domingos2001mining} pointed out that data mining plays an important role in helping companies determine which potential customers to market to. Their work highlighted the importance of  a customer's network value which is derived from her influence on other users.
 We discuss related work on two related topics.  First topic is on the study of non-adaptive influence maximization problem:  given a social network, how to find a set of influential customers in order to trigger a large cascade of adoptions. Kempe et al. \citep{kempe2003maximizing} formulated the influence maximization problem under two diffusion models, namely Independent Cascade model and Linear Threshold model. Since then, considerable work \citep{chen2013information}\citep{leskovec2007cost}\citep{cohen2014sketch}\citep{chen2010scalable}\citep{chen2009efficient} has
been devoted to extending existing models to study influence maximization and its variants, but almost all these
works assume no uncertainty in the realization of seed set, i.e., any node that has been targeted will become seed or accept the offer immediately. Eftekhar et al. \citep{eftekhar2013information} relaxed this assumption by assuming that the probability that a user becomes a seed user is given and fixed.  In this work, we introduce the concept of adoption probability to capture the probability that a targeted user will accept the discount, based on the value of the discount and her own interest profile, i.e., the prior probability that the user will accept the discount in the absence of any social proof. Lastly, we mention the work by \cite{yangcontinuous} on continuous influence maximization. While they also study how to offer discounts to social users in order  to trigger large cascades, our settings are very different. First of all, they assume a continuous adoption function whereas we adopt a discrete function to capture the adoption probability. More importantly, from a theoretical perspective, we prove approximation guarantees for our approach. Secondly, their work mainly focuses on non-adaptive setting while our work covers both non-adaptive and adaptive settings. We propose a novel adaptive policy with bounded approximation ratio.

The second topic is on adaptive/stochastic submodular maximization \citep{golovin2011adaptive,badanidiyuru2016locally}. Our work departs from the body of work in this field in two ways: first of all, existing studies mainly assume that the cost of the action is known to the algorithm before the action is taken. However, this assumption is no longer valid under our setting, i.e, the actual amount of discount that has been delivered depends on whether or not the targeted user accepts the offer; secondly, actions may incur non-uniform cost, due to different discount values. Unfortunately,  existing solutions can not handle the above two challenges. In this work, we develop a novel greedy adaptive policy with constant approximation ratio.
\section{Network Model and Diffusion Process}
\label{sec:system}
A social network is modeled as a directed graph  $G = (V, E)$, where $V$ is a set of users
and $E$ is a set of social ties. Our model decomposes the cascade into two stages: \emph{seeding} and \emph{diffusion}. In the seeding stage, the initial set of users and corresponding discounts are decided for each initial user. The initial set of users who accept the discount act as starting points, called \emph{seed users},  in the diffusion stage. In the diffusion stage, starting from seed users,  the adoption propagates across the entire social network according to certain propagation model. Below we describe the details of these two stages.

\begin{itemize}
\item \textbf{Seeding stage:} Given the initial set of users and corresponding discount rate, each initial user decides whether or not to accept the offer. Although this decision may be affected by various factors, including the discount rate and user's attributes and behaviors (demographics, shopping history), it is reasonable to assume that the adoption probability of any initial user is monotonically increasing with respect to discount. Assume there are $m$ possible discount rates $\mathcal{D}=\{d_1, \cdots, d_m\}$, each user $v \in V$ is associated with an adoption probability function $p_v: d_i \rightarrow [0, 1]$, which models the probability that $v$ accepts the offer given a discount greater than or equal to $d_i$. It is clearly true that $p_v(d_i)\geq p_v(d_j)$ for any $v$ and discounts $d_i \geq d_j$. Estimating the adoption probability itself is a very important and challenging task, it has been well studied in the field of marketing. Interested readers may refer to \citep{brennan1995constructing}\citep{suh2004prediction} for further information. It is easy to verify that the above adoption model satisfies the following two conditions:
    \begin{definition} [Dominant Condition] If a user receives multiple discounts, her decision on whether or not to accept the offer only depends on the one with highest rate.
    \end{definition}
    \begin{definition}
   [Monotonic Condition] Given that a user has accepted a discount, she will accept any discount with higher rate.
  \end{definition}

     It was worth noting that all results derived in this work apply to any adoption model that is  \emph{dominant} and \emph{monotonic}.
\item \textbf{Diffusion stage:} Every initial user who decides to adopt the discount becomes the seed in the following diffusion stage, and starts to propagate the product to her neighbors across the social network. There are many ways to model the cascades in the social network, we adopt \emph{independent cascade model} \citep{kempe2003maximizing}, which is one of the most commonly used models, to model the diffusion dynamics in a social network. Under independent cascade model, each edge $(u, v)$ in the graph is associated with a propagation probability $p_{uv}$, which is the probability that node $u$ independently influences node $v$ in the next step after $u$ is influenced. Then given a set of seeds $U$, the independent cascade model works as follows: Let $U_t \subseteq V$ denote the set of influenced nodes at step $t$ with $U_0=U$. At step $t+1$, every user $v\in U_t$ may influence her out-neighbors $v\in V\setminus \cup_{0\leq i\leq t-1} U_i$ with an independent probability $p_{vu}$. This process ends at $t$ with $U_t=\emptyset$. The expected cascade of $U$, which is the expected number of influenced nodes given seed set $U$, is denoted as $I(U)$.
\end{itemize}

\section{Problem Formulation}
\subsection{Non-adaptive Setting}
Given the set of users $V$ and possible discount rates $\mathcal{D}$, define $\mathcal{H} \triangleq V\times \mathcal{D}$ as the solution space, probing \emph{seed-discount} (s-d) pair $h=\langle \mathbf{v}(h), \mathbf{d}(h) \rangle \in \mathcal{H} $ translates to offering $\mathbf{d}(h)\in \mathcal{D}$ to user $\mathbf{v}(h)\in V$.
We use $\mathcal{S} \subseteq \mathcal{H}$ to denote a configuration of discounts assigned to a subset of initial users in $V$. Please note that it is feasible to assign multiple discounts to the same user, however, since her adoption decision only depends on the highest discount (due to \emph{dominant} condition), it suffice to use the highest discount as a representative. Let $d_\mathcal{S}[v]$ denote the highest discount assigned to $v$ under $\mathcal{S}$. Define $d_\mathcal{S}[w]=0$ if $w$ has not been selected as a initial user under $\mathcal{S}$, i.e, there is no $h\in \mathcal{S}$ with $\mathbf{v}(h)=w$. Given a social network $G=(V,E)$ and a configuration $\mathcal{S}$, the probability that a subset of users $U \subseteq V$ of the users is the seed set is
\[\Pr(U;V;\mathcal{S})=\prod_{u\in U} p_u(d_\mathcal{S}[u]) \prod_{v\in V\setminus U}(1- p_v (d_\mathcal{S}[v]))\]
As introduced earlier, we use $I(U)$ to denote the expected cascade under seed set $U$, then the expected cascade under configuration $\mathcal{S}$ is
 \[f(\mathcal{S})=\sum_{U\in 2^{V}}\Pr(U;V;\mathcal{S}) \cdot I(U)\]Now we define the non-adaptive discount allocation problem
(NDA) studied in this paper as follows. Given a social
network $G$, a propagation probability $p_{uv}$ for every edge $(u,v)$, a budget $B$, a seed probability function $p_u: d_i \rightarrow [0, 1]$ for every user $u$ and rate $d_i$, find the configuration $\mathcal{S}$ that is the optimal solution to the
following optimization problem.

\begin{center}
\framebox[0.78\textwidth][c]{
\enspace
\begin{minipage}[t]{0.55\textwidth}
\small
\textbf{NDA:}
\emph{Maximize $f(\mathcal{S})$}\\
\textbf{subject to:}
\begin{equation*}
\begin{cases}
\forall v\in \mathcal{V}, d_\mathcal{S}[v] \in \mathcal{D} \\
\sum_{v\in \mathcal{V}}  d_\mathcal{S}[v] \leq B

\end{cases}
\end{equation*}
\end{minipage}
}
\end{center}
\vspace{0.1in}
Although this setting is similar to traditional influence maximization problem, a unique challenge here is to model the responses from the user with respect to different discount rates. Notice that the budget constraint specified above ensures that the worst-case cost is bounded below by $B$. It is also reasonable to replace this hard constraint by other forms of constraint such as a soft constraint on the expected cost: $\sum_{v\in \mathcal{V}} d_\mathcal{S}[v]\cdot p_v (d_\mathcal{S}[v]) \leq B$. Our results are general enough to apply to both cases.

\begin{figure}[tbhp]
\begin{center}
\includegraphics[width=2.5in]{example.png}
\caption{A toy social network. Possible discounts: $\mathcal{D}=\{1, 2\}$; propagation probabilities are reported on edges; adoption probabilities: $\forall u\in \{a, b, c, d, e\}: p_u(1)=0.5, p_u(2)=1$; budget $B=2$.}
\label{fig:FSSILQI}
\end{center}
\end{figure}

For our example we use the toy social network in Fig. \ref{fig:FSSILQI}. In this example, there are five users $V=\{a, b, c, d, e\}$, adoption probabilities and propagation probabilities (on edges) are reported in the figure. Let us consider two ways of allocating discounts to users. The first one  is $\mathcal{S}_1=\{a, 2\}$, i.e.,  allocating $2$ to $a$: under $\mathcal{S}_1$, $a$ accepts the offer with probability $p_a(2)=1$; $b$ and $c$ will be influenced with probability $0.2$; $d$ will be influenced with probability $1-(1-0.2\times 0.5)^2=0.19$; $e$ will be influenced with probability $0.19\times 0.1=0.019$. The expected cascade size under $\mathcal{S}_1$ is $f(\mathcal{S}_1) = 1+0.2\times2 + 0.19 + 0.019=1.609$. Consider the second allocation $\mathcal{S}_2=\{a,1, b,1\}$, i.e., allocation $1$ to $a$ and $b$: under $\mathcal{S}_2$, $a$ (resp. $b$) accepts the offer with probability $p_a(1)=0.5$ (resp. $p_b(1)=0.5$); $c$ is influenced with probability $0.5\times 0.2 = 0.1$; $d$ is influenced with probability $1-(1-0.5)(1-0.1)=0.55$; $e$ is influenced with probability $0.55\times 0.1=0.055$. The expected cascade size under $\mathcal{S}_2$ is $f(\mathcal{S}_2) = 0.5+0.5+0.1+0.55+0.055=1.705$. Therefore $\mathcal{S}_2$ leads to larger expected cascade than $\mathcal{S}_1$.

\subsection{Adaptive Setting}
Different from the previous setting where we have to commit the set of initial users and corresponding discounts all at once in advance, under the adaptive setting, the decision process is performed in a sequential manner. Each initial user that is probed provides her feedback on the discount rate, or, in other words, reveals whether or not she will adopt the discount. The action taken in each step depends on the actual cascade that happens in the previous steps and remaining budget. Therefore any feasible solutions are now policies instead of a fixed configuration.
\begin{definition}[Seeding Realization]
For every configuration $h$,  $\mathbf{v}(h)$ is either in ``accept'' state ($h\rightarrow 1$) or in ``reject'' state ($h\rightarrow 0$), describing whether $\mathbf{v}(h)$ accepts $\mathbf{d}(h)$ or not. We represent the state of the seeding stage using function $\phi: \mathcal{H}\rightarrow [0,1]$, called \emph{seeding realization}.
\end{definition}
\begin{definition}[Diffusion Realization]
For every edge $(u,v)\in E$, it is either in ``live'' state or in ``blocked'' state. (describing whether the propagation through $(u,v)$ is a success or not). We represent the state of the diffusion stage using function $\psi: E \rightarrow [0,1]$, called \emph{diffusion realization}.
\end{definition}

After probing a s-d pair $h$ we are able to obtain the seeding realization, i.e., $\mathbf{v}(h)$ decides wether or not  to accept $\mathbf{d}(h)$. If $\mathbf{v}(h)$ decides to accept the discount, we further get to see the realization of the diffusion stage,  i.e., the status (live or dead) of all edges exiting $\mathbf{v}(h)$, for all nodes $v$ reachable from $\mathbf{v}(h)$ via live edges social network i.e., reachable from $\mathbf{v}(h)$ under $\psi$. After each attempt of probing, our observations so far can be represented as a partial realization $\langle \phi_p, \psi_p\rangle$.

\begin{definition}[Adaptive Policy]
 We define our adaptive policy $\pi: \langle\phi_p, \psi_p\rangle\rightarrow \mathcal{H}$, which is a function from the current ``observation'' $\langle\phi_p, \psi_p\rangle$ to $\mathcal{H}$, specifying which s-d pair to pick next under a particular set of observations, e.g., $\pi$ chooses s-d pair given s-d pairs have been probed so far, and the resulting cascade.
 \end{definition}

 Assume there is a known prior probability distribution
$p (\phi) := P [\Phi = \phi]$ (and $p (\psi) := P [\Psi = \psi]$ resp.) over seeding  realizations (and diffusion realizations resp.).  Given a realization $\langle\phi, \psi\rangle$, let $\mathcal{H}(\pi; \phi, \psi)$ denote all s-d pairs picked by $\pi$ under $\langle\phi, \psi\rangle$, and $c(\mathcal{H}(\pi; \phi, \psi))$ denote the total amount of discount that have been delivered by $\pi$ under $\langle\phi, \psi\rangle$. The expected cascade of a policy $\pi$ is $f(\pi) = \mathbb{E}[f(\mathcal{H}(\pi; \Phi, \Psi)| \Phi, \Psi)]$ where the
expectation is taken with respect to $p(\phi)$ and $p(\psi)$. The goal of the Adaptive Coupon Distribution (ACD) problem is to find
a policy $\pi$ such that
\begin{center}
\framebox[0.78\textwidth][c]{
\enspace
\begin{minipage}[t]{0.55\textwidth}
\small
\textbf{ACD:}
\emph{Maximize $f(\pi)$}\\
\textbf{subject to:}
\begin{equation*}
c(\mathcal{H}(\pi; \phi, \psi)) \leq B, \forall \phi, \psi
\end{equation*}
\end{minipage}
}
\end{center}
\vspace{0.1in}

\begin{figure}[tbhp]
\begin{center}
\includegraphics[width=6in]{example2.png}
\caption{Step 1: pick $\langle a, 1 \rangle$; step2: pick $\langle c, 1 \rangle$; step 3: pick $\langle d, 1 \rangle$.}
\label{fig:2}
\end{center}
\end{figure}

For our example we use the toy social network in Fig. \ref{fig:2}, all settings of this example are identical to Fig. \ref{fig:FSSILQI}. Consider a possible adaptive policy as follows: We first probe $\langle a, 1 \rangle$, that is offering discount $1$ to user $a$, and observe the following partial seeding realization: $\phi(\langle a, 1 \rangle)=1$, i.e., $a$ accepts the offer; and partial diffusion realization: $\psi(ab)=1$ and $\psi(ac)=0$, i.e., user $b$ is influenced by $a$, user $c$ has not been influenced. In the second step where the remaining budget is 1, we probe $\langle c, 1 \rangle$, that is offering discount $1$ to user $c$ and obtain the following seeding realization: $\phi(\langle c, 1 \rangle)=0$, i.e., user $c$ does not accept the offer. In the third step where the remaining budget is still 1, we probe $\langle d, 1 \rangle$, that is offering discount $1$ to user $d$, and observe that $\phi(\langle d, 1 \rangle)=1$, i.e., user $d$ has accepted this offer, and $\psi(de)=1$, i.e., $e$ has been influenced by $d$. Thus the number of influenced users is 4.

\section{Non-Adaptive Discount Allocation Problem}
In this section, we study non-adaptive discount allocation problem. Our main idea is to prove that the influence function $f(\cdot)$ is monotone and submodular, exploiting these properties, a simple greedy algorithm can achieve $\frac{1}{2}(1-1/e)$ approximation ratio.

\begin{definition}[Submodular function] Consider an arbitrary function $z(\cdot)$ that maps subsets of a finite ground set $\Omega$ to non-negative real numbers. We say that $z(\cdot)$ is submodular if it satisfies a natural ``diminishing returns" property: the marginal gain from adding an element to a set $S$ is at least as high as the marginal gain from adding the same element to a superset of $S$. Formally, a submodular function satisfies the follow property: For every $X, Y \subseteq \Omega$ with $X \subseteq Y$ and every $x \in \Omega \backslash Y$, we have that \[z(X\cup \{x\})-z(X)\geq z(Y\cup \{x\})-z(Y)\]
We say a submodular function $z$ is monotone if $z(X) \leq z(Y)$ whenever $X \subseteq Y$.
\end{definition}

To prove that the influence function $f(\cdot)$ is monotone and submodular, we need to focus on the value of $f(\mathcal{S} \cup h) - f(\mathcal{S})$, for arbitrary sets $\mathcal{S}\subseteq \mathcal{H}$ and s-d pair $h$. Since the increase of the above expression is very difficult to analyze directly, we take an equivalent view of the cascade process by decomposing it into two realizations:

\begin{enumerate}
\item \underline{seeding realization  $\phi$.} Consider a point in the seeding stage when $h=\langle v, d \rangle$ is probed, i.e,  node $v$ has been offered discount $d$, $v$ accepts  offer and becomes the seed with probability $p_v(d)$. We can view the outcome of this random event as being determined by comparing $p_v(d)$ with a random number $g$ that is uniformly selected from $[0, 1]$: if $p_v(d)\geq g$, then $h=\langle v, d \rangle$ is declared to be \emph{valid} under $\phi$.
\item \underline{diffusion realization  $\psi$.} We follow the same approach introduced in \citep{kempe2003maximizing} to model the diffusion dynamics. In particular, for each edge $(u, v)$ in the graph, a coin of
bias $p_{uv}$ is flipped at the very beginning of the process. The edges for which the coin flip indicated an
 successful activation are declared to be \emph{live} in $\psi$; the remaining edges are declared to be \emph{blocked} in $\psi$. As described in detail later, a user is influenced if it can be reached from some seed through a path consisting of live edges.
\end{enumerate}

Similar to the diffusion realization, where the coin can be flipped at the beginning of the process, the seeding realization can also be performed ahead of the actual seeding stage. In particular, for each node $u$, a random number $g_u$ is uniformly selected from $[0, 1]$: if there exists a $h\in \mathcal{H}$ with $\mathbf{v}(h)=u$ and $\mathbf{d}(h)\geq g_u$, then $\mathbf{v}(h)$ is declared to be \emph{live} under discount $\mathbf{d}(h)$. Given a configuration $\mathcal{S} \subseteq \mathcal{H}$ and a seeding realization $\phi$,  we say a node $\mathbf{v}(h)$ is \emph{live} under $\mathcal{S}$ and $\phi$ if and only if there exists $h\in \mathcal{S}$ such that $\mathbf{v}(h)$ is live with discount $\mathbf{d}(h)$. With both seeding and diffusion realizations are performed in advance,
it is easy to determine the subset of nodes that can be influenced at the end of the cascade process:

\begin{claim} Given a configuration $\mathcal{S}$ and realization $\langle\phi, \psi\rangle$, a node $u$ ends up influenced if and only if $u$ is \underline{live} or there is a path from some \underline{live} node to $u$ consisting entirely of \underline{live} edges.
\end{claim}

\begin{theorem}
The influence function $f(\cdot)$ is submodular.
\end{theorem}
\emph{Proof:} We first prove that for each fixed realization $\langle\phi,\psi\rangle$, the function $f(\cdot)$ is submodular. We use  $f(\cdot|{\phi,\psi })$ to denote the (deterministic) influence function under a fixed realization $\langle\phi,\psi\rangle$. Let $\mathcal{A}$ and $\mathcal{B}$ be two configurations such that $\mathcal{A} \subseteq \mathcal{B}$, and consider the value $f(\mathcal{A}\cup\{h\}|{\phi,\psi})-f(\mathcal{A}|{\phi,\psi})$, this is the number of users that can be reached by live users in $\mathcal{A}\cup\{h\}$ but not in $\mathcal{A}$. This number is at least as large as the number of users that can be reached by live users in $\mathcal{B}\cup\{h\}$ but not in $\mathcal{B}$. It implies that $f(\mathcal{A}\cup\{h\}|{\phi,\psi })-f(\mathcal{A}|{\phi,\psi}) \geq f(\mathcal{B}\cup\{h\}|{\phi,\psi })-f(\mathcal{B}|{\phi,\psi })$
Then due to the fact that  a non-negative linear combination of submodular functions is also submodular, $f(\cdot)$ is also submodular. $\Box$

We next propose a hill-climbing algorithm (Algorithm \ref{alg:weight}) with a constant approximation ratio. Our algorithm computes two candidate sets: The first candidate set contains a single s-d pair which can
maximize the expected cascade: $\mathcal{S}_{1} = \{h^*\}$ where $h^*=\arg\max_{h}f(\{h\})$; the second candidate
solution $\mathcal{S}_2$ is computed by greedy algorithm in which
we always add $h$ that can maximize the expected
incremental marginal gain with respect to the discount rate: $\frac{f(\mathcal{S}_2\cup \{h\})}{\mathbf{d}(h)}$ until
the budget constraint is violated. Then we choose the better
one as the final output $\mathcal{S}$. It was worth noting that computing $f(\mathcal{S})$ is \#P-hard \citep{chen2010scalable}, and is typically approximated by numerous Monte Carlo simulations, however, running such simulations are extremely time consuming. Instead, we can leverage a FPRAS (Fully Polynomial Randomized Approximation Scheme) \citep{long2011minimizing} for estimating $f(\mathcal{S})$. Based on \citep{khuller1999budgeted} and the submodularity of $f(\cdot)$, we can prove that our greedy algorithm
achieves $\frac{1}{2}(1-e^{-1})$-approximation.

\begin{algorithm}
\caption{Hill-Climbing Algorithm}
\begin{algorithmic}[1]
\STATE $\mathcal{S}_{1}:h^*=\arg\max_{h}f(\{h\})$;  $\mathcal{S}_{2}:=\emptyset$;
\WHILE {$\mathbf{d}(\mathcal{S}_2)\leq B$}
\STATE $\mathcal{S}_2 \leftarrow \arg\max_{h \in \mathcal{H}} \frac{f(\mathcal{S}_2\cup \{h\})}{\mathbf{d}(h)}$; $\mathcal{H}=\mathcal{H}\setminus \{h\}$
\ENDWHILE
\STATE return $\arg\max_{\mathcal{S}\in \{\mathcal{S}_{1}, \mathcal{S}_{2}\}}f(\mathcal{S})$
\end{algorithmic}
\label{alg:weight}
\end{algorithm}

\begin{theorem}
Algorithm \ref{alg:weight} achieves approximation ratio $\frac{1}{2}(1-e^{-1})$.
\end{theorem}

\section{Adaptive Discount Allocation Problem}
We next study the adaptive discount allocation problem. In the rest of this paper, let $f(\pi|\phi)$ (resp. $f(\pi|\psi)$), as a shorthand notation for $f(\pi|\phi, \Psi)$ (resp. $f(\pi|\Phi, \psi)$), denote the expected cascade of $\pi$ under a fixed seeding realization $\phi$ (resp. a fixed diffusion realization $\psi$).
\subsection{Greedy Policy}
We first propose a adaptive greedy policy with bounded approximation ratio.
\subsubsection{Policy Description}
 Our greedy policy is performed in a sequential manner as follows: In each round, we probe the s-d pair, say $h^*$, that maximizes ratio of conditional expected marginal benefit to cost. If $\mathbf{v}(h^*)$ accepts $\mathbf{d}(h^*)$, we deduct the discount from the budget and remove $\mathbf{v}(h^*)$ from consideration in the following rounds. Otherwise, if $\mathbf{v}(h^*)$ turns down the discount, we simply discard $h^*$ and move to the next round. This process iterates until either the budget is used up or all nodes have been probed at the highest discount rate. The detailed description is listed in Algorithm \ref{alg:LPP1}: Given partial diffusion realization $\psi_p$, let $\mathrm{dom}(\psi_p)$ denote all influenced users under $\psi_p$,  we use $G [V \setminus \mathrm{dom}(\psi_p)]$ to denote the induced graph of $V\setminus  \mathrm{dom}(\psi_p)$. Let $\Delta(h|\psi_p)=I_{G [V \setminus \mathrm{dom}(\psi_p)]}(\mathbf{v}(h))$ denote the expected marginal benefit of $\mathbf{v}(h)$ in $G [V \setminus \mathrm{dom}(\psi_p)]$ \underline{conditioned on $\mathbf{v}(h)$ has become the seed}.
At beginning of each round, we check whether or not there exists $h \in \mathcal{H}$ such that $\mathbf{d}(h)\leq B$. If so,  probe $h^*=\arg\max_{h \in \mathcal{H}}\Delta(h|\psi_p)/\mathbf{d}(h)$ subject to $\mathbf{d}(h)\leq B$. Depending on the response from the $\mathbf{v}(h^*)$, we either commit $\mathbf{v}(h^*)$ and deduct $\mathbf{d}(h^*)$ from the remaining budget or skip to the next round.
\begin{algorithm}[hptb]
\caption{Greedy Policy}
\label{alg:LPP1}
\begin{algorithmic}[1]
\STATE $\mathcal{S}=\emptyset$
\WHILE {$B \geq 0$}
\IF {there exists $h \in \mathcal{H}$ such that $\mathbf{d}(h)\leq B$}
\STATE probe $h^*=\arg\max_{h \in \mathcal{H}}\Delta(h|\psi_p)/\mathbf{d}(h)$ subject to $\mathbf{d}(h)\leq B$
\IF {$\mathbf{v}(h^*)$ accepts $\mathbf{d}(h^*)$}
\STATE $\mathcal{S}\leftarrow \mathcal{S}\cup \{h^*\}$; $B \leftarrow B-\mathbf{d}(h^*)$;
\STATE $\mathcal{H}\leftarrow \mathcal{H}\setminus \{h|\mathbf{v}(h)=\mathbf{v}(h^*)\}$;
\STATE update $\psi_p$;
\ELSE
\STATE $\mathcal{H}\leftarrow \mathcal{H}\setminus \{h^*\}$
\ENDIF
\ELSE
\STATE break.
\ENDIF
\ENDWHILE
\RETURN $\mathcal{S}$
\end{algorithmic}
\end{algorithm}

We next walk through this greedy policy using Fig. \ref{fig:2}. We first probe $\langle a, 1 \rangle$ since it has the highest benefit-to-cost ratio $\frac{I(\{a\})}{1}$. We observe that $a$ accepts the offer and successfully influenced $b$. Conditioned on the above observation, we next probe $\langle c, 1 \rangle$ because it has the highest benefit-to-cost ratio in $G[V \setminus\{a, b\}]$, and we observe that $c$ turns down the offer. Then we probe $\langle d, 1 \rangle$ which has the highest benefit-to-cost ratio in $G[V\setminus\{a, b, c\}]$, and observe that $d$ has accepted the offer and successfully influenced $e$.
\subsubsection{Performance Analysis}
We first study a relaxed version of ADA by assuming that the seeding realization is pre-known. Given a seeding realization $\phi$, each node $v$ is associated with a minimum discount $d_v$ at which $v$ can become the seed. If $d_v$ will never become the seed under $\phi$, then $d_v=\infty$. Since it is meaningless to probe a user $v$ with any discount lower or higher than $d_v$, we use $\mathcal{H}^{\mathrm{relaxed}}=\{\langle v, d_v\rangle|v\in V\}$ to denote the refined solution space under this relaxed setting. Consider a greedy policy $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ as follows: In each round,  update the partial diffusion realization $\psi_p$, and probe $h\in \mathcal{H}^{\mathrm{relaxed}}$ that maximizes $\Delta(h|\psi_p)/d_{\mathbf{v}(h)}$. This process iterates until either the budget is used up or all users have been probed.
\begin{lemma}
\label{lem:main}
 Under the relaxed setting, given any seeding realization $\phi$, the greedy policy $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ obtains at least $(1-e^{-(B-d_{\max})/B})$ of the value of the best policy $\pi^*_{\mathrm{relaxed}}$.
\[f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*_{\mathrm{relaxed}}|\phi)\]
\end{lemma}
\emph{Proof:} Consider two types of seeding realizations depending on the output of $\pi^{\mathrm{greedy}}$: 1. $\phi^a$: all users except those whose minimum discount is $\infty$ have become the seeds; 2.  $\phi^b$: there exists some user with finite minimum discount that can not be included in the solution due to limited budget.

The first case is trivial, because both $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ and $\pi^*_{\mathrm{relaxed}}$ must select all possible seeds,
\begin{equation}
\label{eq:a}
f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi^a) = f(\pi^*_{\mathrm{relaxed}}|\phi^a)
\end{equation}

We next focus on the second case:
In Theorem 26 \citep{golovin2011adaptive}, it has been proved that\[f(\pi_{[l]}) \geq (1-e^{-l/k})f(\pi^*_{[k]})\]
where $\pi_{[l]}$ denotes the greedy policy with \emph{expected} budget $l$ and $\pi^*_{[k]}$ denotes the best policy with \emph{expected} budget $k$. The above result is applicable to the traditional influence maximization problem without considering seeding stage. Taking seeding stage into account, it is easy to extend this result to show that $f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi^b) \geq f(\pi_{[B-d_{\max}]}|\phi^b)$ and $f(\pi^*_{\mathrm{relaxed}}|\phi^b) \geq f(\pi_{[B]}|\phi^b)$, this is because the actual cost of $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ is lower bounded by $B-d_{\max}$, and the actual cost of $\pi^*$ is upper bounded by $B$. It follows that
\begin{equation}
\label{eq:b}
f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi^b) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*_{\mathrm{relaxed}}|\phi^b)
\end{equation}
Eqs. (\ref{eq:a}) and (\ref{eq:b}) together imply that, for all seeding realization $\phi$, we have
\[f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*_{\mathrm{relaxed}}|\phi)\]
$\Box$

Surprisingly, we next show that given any realization $\langle \phi,\psi \rangle$, the outputs of $\pi^{\mathrm{greedy}}$ and $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ are identical.
\begin{lemma}
\label{lem:1}
Given any realization $\langle \phi,\psi \rangle$, the outputs of $\pi^{\mathrm{greedy}}$ and $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ are identical.
\end{lemma}
\emph{Proof:} Recall that under $\pi^{\mathrm{greedy}}$, we probe  $h^*=\arg\max_{h \in \mathcal{H}}\Delta(h|\psi)/\mathbf{d}(h)$ in each round. Depending on the response from the $\mathbf{v}(h^*)$, we either commit $h^*$ or skip to the next round. We next show that by following $\pi^{\mathrm{greedy}}$,
if $\mathbf{v}(h^*)$ accepts $h^*$, then $\mathbf{d}(h^*)=d_{\mathbf{v}(h^*)}$, i.e., $\pi^{\mathrm{greedy}}$ will never probe a user with discount higher than her minimum discount. First, it is trivial to verify $d_{\mathbf{v}(h^*)}\leq \mathbf{d}(h^*)$ due to the definition of $d_{\mathbf{v}(h^*)}$. We can also prove that otherwise, we can probe $\mathbf{v}(h^*)$ with discount $d_{\mathbf{v}(h^*)}$ which is lower than $\mathbf{d}(h^*)$, leading to higher benefit-to-cost ratio. This contradicts to the assumption that $h^*=\arg\max_{h \in \mathcal{H}}\Delta(h|\psi)/\mathbf{d}(h)$.

We are now ready to prove that the outputs of $\pi^{\mathrm{greedy}}$ and $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ are identical. Let $S_t$ denote the first $t$ seeds committed by $\pi^{\mathrm{greedy}}$, we prove this result through induction on $t$. The basic case when $t=0$, i.e., $S_0=\emptyset$ is trivial. As proved above, when $\mathbf{v}(h^*)$ accepts $h^*$,  we have $d_{\mathbf{v}(h^*)}=\mathbf{d}(h^*)$. Therefore, the first seed that $\pi^{\mathrm{greedy}}$ and $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ commit must be some user $v$ that maximizes $I(\{v\})/d_v$. Assume by contradiction that $\pi^{\mathrm{greedy}}$ commits some user other than $v$, then probing $v$ with discount less than or equal to $d_v$ leads to a higher  benefit-to-cost ratio. This causes contradiction to the design of $\pi^{\mathrm{greedy}}$. Assume the first $k$ users committed by $\pi^{\mathrm{greedy}}$ and $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ are identical, we next prove that the $(k+1)$-th seed is also identical. Given the partial observation $\psi_p$ after the first $k$ seeds have been committed, the next s-d pair that $\pi^{\mathrm{greedy}}$ commits must be some $h$ that maximizes $\Delta(h|\psi_p)/d_{\mathbf{v}(h)}$, otherwise, probing $\mathbf{v}(h)$ with discount less than or equal to $d_{\mathbf{v}(h)}$ leads to a higher  benefit-to-cost ratio. Therefore, $\pi^{\mathrm{greedy}}$ and $\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}$ commit the same set of seeds. This finishes the proof of this lemma. $\Box$

\begin{theorem}
\label{thm:main}
The greedy policy $\pi^{\mathrm{greedy}}$ obtains at least $(1-e^{-(B-d_{\max})/B})$ of the value of the best policy $\pi^*$.
\[f(\pi^{\mathrm{greedy}}) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*)\]
\end{theorem}
\emph{Proof:}  We have proved that $f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*_{\mathrm{relaxed}}|\phi)$ for all $\phi$. Based on the definition of $f(\pi^*_{\mathrm{relaxed}}|\phi)$, we have $f(\pi^*_{\mathrm{relaxed}}|\phi)\geq f(\pi^*|\phi)$.
Thus, \[f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*_{\mathrm{relaxed}}|\phi)\geq (1-e^{-(B-d_{\max})/B})f(\pi^*|\phi)\]
Then together with Lemma \ref{lem:1}, we have
\[f(\pi^{\mathrm{greedy}}|\phi) = f(\pi^{\mathrm{greedy}}_{\mathrm{relaxed}}|\phi) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*_{\mathrm{relaxed}}|\phi)\geq (1-e^{-(B-d_{\max})/B})f(\pi^*|\phi)\]
It follows that
\[f(\pi^{\mathrm{greedy}}|\Phi) \geq (1-e^{-(B-d_{\max})/B})f(\pi^*|\Phi)\]
This finishes the proof of this theorem. $\Box$

We observe that the above greedy heuristic has an unbounded approximation ratio in the worst case.  Consider, for example, a network consists of $n$ nodes: one isolated node $x$ and  a clique with size $n-1$. There are two discount rates $\mathcal{D}=\{1/n, 1\}$, and budget is $1$. We further set $p_x(1/n) = p_x(1) = 1$, and $p_y(1/n)=0, p_y(1)=1, \forall y \in V\setminus \{x\}$. Assume $I(\{x\})=1$ and $I(\{y\})=n-1, \forall y \in V\setminus \{x\}$. By following the heuristics, we will provide discount $1/n$ to $x$, reaching $1$ user in expectation. However, the best strategy is to provide discount $1$ to any user except $x$,  reaching cascade $n-1$ in expectation. Thus the approximation ratio in this case is as large as $n$.

\subsection{Enhanced Greedy Policy}
We next show that a small modification to the heuristic can achieve an approximation ratio independent of $n$.
Our policy (Algorithm \ref{alg:LPP2}) first computes two candidate solutions: The first candidate solution contains a single node $v^*$ which can maximize the expected cascade given that $v^*$ has become the seed: $v^* = \arg\max_{v\in V}I(\{v\})$; the second candidate solution is computed by the greedy policy.  Then we choose the  one that leads to larger expected cascade as the final solution.

\begin{algorithm}[hptb]
\caption{Enhanced Greedy Policy $\pi^{\mathrm{enhanced}}$}
\label{alg:LPP2}
\begin{algorithmic}[1]
\STATE Let $v^* = \arg\max_{v\in V}I(\{v\})$;
\IF {$p_{v^*}(d_{\max})\cdot I(\{v^*\})>f(\pi^{\mathrm{greedy}})$}
\STATE offer $d_{\max}$ to $v^*$;
\ELSE
\STATE call $\pi^{\mathrm{greedy}}$
\ENDIF
\end{algorithmic}
\end{algorithm}

\begin{theorem}
Let $v^* = \arg\max_{v\in V}I(\{v\})$ and $d_{\max}$ denote the highest discount rate, our enhanced greedy policy $\pi^{\mathrm{enhanced}}$ obtains at least $p_{v^*}(d_{\max})\cdot (1-e^{-1})/2$ of the value of the best policy $\pi^*$ for the Adaptive
Coupon Distribution problem with the independent cascade model.
\[f(\pi^{\mathrm{enhanced}}) \geq p_{v^*}(d_{\max})\cdot \frac{(1-e^{-1})}{2}\cdot f(\pi^*)\]
\end{theorem}
\emph{Proof:} Similar to proof of Theorem \ref{thm:main}, we consider two types of realizations $\phi^a$ and  $\phi^b$.

We first prove that the following equality holds under $\phi^a$: $f(\pi^{\mathrm{greedy}}|\phi^a)=f(\pi^*|\phi^a)$. This is because $\pi^{\mathrm{greedy}}$ and $\pi^*$ must select the same set of seeds due to the definition of $\phi^a$. It follows that
\begin{equation}
\label{eq:1}
f(\pi^{\mathrm{greedy}}|\phi^a)+ I(\{v^*\}) \geq f(\pi^*|\phi^a)
\end{equation}

Next, consider $\phi^b$, we have
\begin{equation}
\label{eq:2}
f(\pi^{\mathrm{greedy}}|\phi^b) + I(\{v^*\}) \geq f(\pi_{[B]}|\phi^b) \geq (1-e^{-1})f(\pi^*|\phi^b)
\end{equation}
The first inequality is due to the adaptive submodularity of $I(\cdot)$,  and the second inequality is based on Theorem \ref{thm:main}.
Eqs. (1) and (2) together imply that, under any realization $\phi$:
\[f(\pi^{\mathrm{greedy}}|\phi) + I(\{v^*\})\geq (1-e^{-1})f(\pi^*|\phi)\]
It follows that
\[f(\pi^{\mathrm{greedy}}) + I(\{v^*\}) \geq (1-e^{-1})f(\pi^*)\]
We further have
\[f(\pi^{\mathrm{greedy}}) + p_v(d_{\max})\cdot I(\{v^*\}) \geq p_v(d_{\max}) \cdot (1-e^{-1})f(\pi^*)\]
Then
\[\max\{f(\pi^{\mathrm{greedy}}), p_v(d_{\max})\cdot I(\{v^*\})\} \geq p_v(d_{\max})\cdot \frac{(1-e^{-1})}{2}\cdot f(\pi^*)\]

$\Box$

\begin{corollary}
Assume $p_{v^*}(d_{\max})=1$, the enhanced policy $\pi^{\mathrm{enhanced}}$ obtains at least $(1-e^{-1})/2$ of the value of the best policy $\pi^*$ for the Adaptive Discount Allocation problem with the independent cascade model.
\[f(\pi^{\mathrm{enhanced}}) \geq \frac{(1-e^{-1})}{2}\cdot f(\pi^*)\]
\end{corollary}

To further enhance the performance of Algorithm \ref{alg:LPP2}, we develop another heuristic as listed Algorithm \ref{alg:LPP5}. Algorithm \ref{alg:LPP5} is a natural extension of Algorithm \ref{alg:LPP2}: In case $v^*$ does not accept $d_{\max}$, we remove $v^*$ from consideration of initial nodes and apply Algorithm \ref{alg:LPP2} to the remaining nodes. Otherwise, if $v^*$ accepts $d_{\max}$, we update the budget and diffusion realization, and apply Algorithm \ref{alg:LPP2} to the remaining graph $G \setminus \mathrm{dom(\psi_{t})}$ subject to updated budget. Clearly, the cascade gained from this heuristic is no smaller than the one gained from  Algorithm \ref{alg:LPP2}, thus we have
\begin{theorem}
Algorithm \ref{alg:LPP5} obtains at least $p_{v^*}(d_{\max})\cdot (1-e^{-1})/2$ of the value of the best policy $\pi^*$ for the Adaptive
Discount Allocation problem with the independent cascade model.
\end{theorem}

\
\begin{algorithm}[hptb]
\caption{A Heuristic}
\label{alg:LPP5}
\begin{algorithmic}[1]
\WHILE {$V\neq \emptyset$}
\STATE Let $v^* = \arg\max_{v\in V}I_{G \setminus \mathrm{dom}(\psi_t)}(v)$;
\IF {$p_{v^*}(d_{\max})\cdot I(\{v^*\})>f(\pi^{\mathrm{greedy}}|\psi_t)$}
\STATE offer $d_{\max}$ to $v^*$
\IF {$v^*$ accepts $d_{\max}$}
\STATE $\mathcal{S}\leftarrow \mathcal{S} \cup \langle v^*, d_{\max}\rangle$; update the diffusion realization to $\psi_{t+1}$;
\ELSE
\STATE $V\leftarrow V\setminus \{v^*\}; \psi_{t+1}\leftarrow \psi_{t}$
\ENDIF
\ELSE
\STATE call $\pi^{\mathrm{greedy}}$ on $G \setminus \mathrm{dom}(\psi_t)$; break
\ENDIF
\ENDWHILE
\RETURN $\mathcal{S}$
\end{algorithmic}
\end{algorithm}

\bibliographystyle{ijocv081}

redpajama_set_name: RedPajamaArXiv


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Color tends to fade with washing.


I will be purchasing 3 sets of plans but I have some questions about the Highroller SWB. These will be my first! I assume that the Highroller is a clone of the Bacchetta/Volae and would like to know if the plans offer the use of 20", 24", 26" or 29" front and rear tires. I'm thinking of a 26"rear / 20"frt, a 26" frt / 26" rear or 24" frt and rear. I have read on the forums about the lack of the selection of 24" tire but may still use them. Have read all of the forum posts but did not find the info. I've been collecting donor bikes (3 more today) and saving since I joined the forum and am now ready to start. THANKS!
The plans are very complete. If the specifics are not listed for the needed adjustments for a different front or rear sized wheel, there is more than enough info listed for you to make the changes needed. That is the great thing about these plans -- they let you adapt them into your own dream creation with ease.
I did mine with a 26 rear and 20 in front and it came out nice. Plenty of pics.
Great bike, plans are flexible and of course, plenty of people here to help.
... and would like to know if the plans offer the use of 20", 24", 26" or 29" front and rear tires. I'm thinking of a 26"rear / 20"frt, a 26" frt / 26" rear or 24" frt and rear. THANKS!
Thanks likebikes, omo and imamedik for your help. My stepson purchased the Highroller, Marauder and Streetfox plans for me for Fathers Day. We went out to dinner and he told me what he did. Was I surprised! I was going to buy them after we got back home. Now all I have to do is read and get to work! Just have to decide which bike to start on. Again, THANKS to all for the help.
If you need any help with anything I think I am in your neck of the woods = Aloha so close by if needed. I have a bit o experience.


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Natural Resources Police Escort Sea Turtles Back to the Wild
Department, National Aquarium Release Three Rehabilitated Reptiles
Photo by Lauren Moses
The Maryland Natural Resources Police in Ocean City recently teamed up with the National Aquarium to release three rehabilitated sea turtles.
On Sept. 9, officers met with aquarium representatives along with two Kemp's ridley turtles and one green sea turtle. The turtles, Mozzarella, Stilton, and Halloumi, were rescued last year with symptoms related to cold stunning — a condition caused by a dramatic decline in water temperatures affecting the cold-blooded turtles — including pneumonia, respiratory distress, dehydration, infection, and emaciation.
After being successfully rehabilitated by the Baltimore aquarium, the turtles were ready for release back into the ocean. To provide a proper and safe send-off, NRP offered to use their patrol boats for the release.
Once they were a few miles offshore at a secure spot, the officers delicately placed each turtle in the water. After a few seconds, the turtles disappeared into their underwater home.
Cold-stunning of sea turtles and marine mammals can occur from late October to early winter. The immediate impacts can cause some species to become slow and unresponsive, making them dangerous to themselves and boaters.
Anyone who sees an unresponsive or lethargic sea turtle or marine animal in the water or on the beach should call the Maryland Marine Mammal and Sea Turtle Stranding Hotline at 800-628-9944. The hotline is manned year-round, 24 hours a day.
580 Taylor Ave., Annapolis, MD 21401
Call toll-free in *Maryland* at 1-877-620-8DNR (8367)
Out of State: 410-260-8DNR (8367)


Amish furnishings are distinguished by its simplicity and clean lines, and through the possible lack of embellishments. This mission furniture style continues to be hands-crafted in the usa for years and years, and is made to be functional without unnecessary decoration. Amish furnishings are typified through the mission furniture style created by companies for example Simply Amish, Stickley yet others.
Simply Amish is really a furniture company located in Illinois. It functions like a distribution center for people of neighboring Amish communities who hands craft their furniture to traditional designs. The craftsmanship continues to be handed lower from father to boy, and also the traditional mission furniture designs and styles have suffered even today.
Both Simply Amish and Stickley Furniture use traditional woodworking approaches to hands-crafting their mission furniture. To complete other things is always to denigrate the great name and status of this kind of American furniture. Amish and Mission furniture styles come with an excellent status through the USA. Many get it because of its well-earned standing too-built, solid genuine American furniture.
Not just that, but Amish furnishings are also what is known eco-friendly furniture. The wood used is grown within 200 miles of a lot of the Amish craftsmen making it. The forests are sustainable, along with a new tree is grown for every one that's cut lower. Most Amish workshops use hands tools instead of electric, and card board, plastics and polystyrene are prevented as packing materials.
The Amish prefer to utilize a horse and cart to move their furniture, which is the nearest you're going to get to eco-friendly, eco-friendly furniture in the united states. However, it isn't just the Amish that provide the mission type of furniture.
Stickley Furniture also provides their form of the mission style. Such as the Amish, Stickley uses white-colored oak and American cherry forest. Stickley offers 12 cherry finishes and 5 oak finishes, which means you are sue to locate a finish to fit your type of interior design. You've many furniture for the family room, kitchen, dining area and bed room.
The mission furniture type of wood platforms will elegance any dining area, regardless of whether or not they result from Stickley or traditional Amish sources. They're crafted using genuine natural wood joints, totally missing the existence of screws or nails.
If you would like your furniture to become 100% produced in America using American wood and accessories, then this kind of mission furniture meets these needs perfectly. Nothing in the makeup originates outdoors American shores. The chairs are solidly constructed with traditional ladder-back or spindle designs. There's also a variety of dressers, buffets and sideboards open to match the chairs and tables.
You receive exactly the same clean lines inside your family room furniture. Sofas and chairs could be upholstered in both fabrics or leathers. The arms are usually of wood, and you can also buy simple but comfortable loveseats and sofa chairs. Rockers can also be found, hands-crafted in bent hickory towards the traditional design you realized to determine that old-timers using around the old frontier town verandahs.
Everything concerning the Amish and mission pieces reeks of old America. You are able to image your great grandma and grandpa by using this furniture. It's all 100% American, made in the united states from American material by American craftsmen and ladies. You are able to smell America within the wood and workmanship, and you will know nothing about these furniture pieces continues to be imported.
This Amish furnishings are produced by humans, not robots. It is made of 100% natural materials, nothing synthetic. Every tree used is replanted. You do not get more 'Made in America' than this. It could look simple in design, however the mission furniture style is going to be passed lower generations and valued by everyone that utilizes it. In addition, it'll last. It'll endure with the ages, that is greater than could be stated for many machine-built furniture these days! Every dollar spent onto it may be worth every cent.


We have checked tens of reviews and found 13 reasons to buy the Animas - and 2 reasons NOT to buy it.
87% of the reviewers give the Watershed Animas a good or excellent rating!
One reviewer said that she was blown away with how amazing this bag is.
Some love it for its sturdy construction and removable backstrap.
One user recommends this bag to anyone looking for a true air tight durability.
A reviewer thought this is the only one that provides submerssible packs but highly underrated.
One user exclaimed that Watershed bags are bombproof and the seal is a thing of wonder.
Some liked that the bag is made of a very durable rubber.
Other users described it as tough as nails, bigger than the picture and tremendous bag.
A lot of users were happy it was well-made and 100% waterproof.
One reviewer liked the well-padded shoulder pads feature of the bag.
Many were happy with how it was able to carry extra clothes,several water bottles,snacks and other items.
One user thought it was a very solid bag after scraping it against a few branches without exterior damage.
A user fully submerged it and put his entire weight on it and was so amazed with how the backpack kept her afloat, she used it as a flotation device.
Many like the zipdry water proofing feature of the watershed bags as it easily keeps water out of the bag under the most wet conditions.
Some thought the rubber material provides a ton of static friction.
Others thought the opening is too small and inconvenient for packing.
The Watershed Animas ranks #7 compared with the other Watershed bags.
The Watershed Animas ranks #1128 compared with all the bags we have checked.
Bag ID 33554. Images from Amazon Product Advertising API and shop datafeeds.


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DuckTales (Season 1)
Thread: DuckTales (Season 1)
Premiered yesterday!
Solid first episode. I really dig the new animation, more comic book-like style. Good voice acting, specially by David Tennant as Scrooge and Beck Bennett steals every scene he's in as Launchpad McQuack and, even if she suffers a little of Mrs. Exposition-itis at times, I like this version of Webby a little more than the original (being fair though, it's been years since I've watched the original DuckTales).
Let's see how high can this duck fly.
Henry Gale
In the belly of a whale
I enjoyed the first two too (shown together as the premiere). The voice cast is uniformly very good, the animation, as you touched on, is much livelier than your average modern animated show, exploring dimension and looseness in movement (though still having a bit of that "flat" template of so many shows, even non-flash animated ones go for in symmetrically staging like a multi-camera sitcom), and there are a lot of quick and clever gags that they manage to give enough air to land when they recur, which is impressive considering how dense and fast-paced it is otherwise.
So it's definitely fun, but I'm just not sure with all the other million quality shows in television right now if without hearing a lot of praise that it's gotten even better that I'll have the time to keep up with it. But a part of me really hopes it goes the way of Gravity Falls, Adventure Time or Steven Universe of expanding and enriching its universe into something special and unexpected, since what's here is enjoyable and charming enough to want to return to and see flourish.
Last 11 things I really enjoyed:
Speed Racer (Wachowski/Wachowski, 2008)
Safe (Haynes, 1995)
South Park: Bigger, Longer & Uncut (Parker, 1999)
Beastie Boys Story (Jonze, 2020)
Bad Trip (Sakurai, 2020)
What's Up Doc? (Bogdanovich, 1972)
Diva (Beineix, 1981)
Delicatessen (Caro/Jeunet, 1991)
The Hunger (Scott, 1983)
Pineapple Express (Green, 2008)
Chungking Express (Wong, 1994)


Erica Rivera talked to students at one of Professor Joan Ostrove's psychology classes today. It was Rivera's third invitation to speak at her alma mater since the publication of her memoir Insatiable in 2009. The undergrads read the chapter Better Than Chocolate as part of the course's syllabus and had many questions about anorexia, body image, and Rivera's recovery. Rivera graduated from Mac in 2001 with a double major in Psychology and Spanish.


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home » JIS News » Health & Wellness
Infection Control Workshop Oct. 24-26
Written by: Latonya Linton
Photo: Donald De La Haye
Minister of Health, Dr. the Hon. Christopher Tufton, addresses the launch of the Infection Control Workshop, held in Kingston today (September 5). The workshop will be held from October 24 to 26.
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Healthcare professionals in Jamaica and the Caribbean are to participate in an Infection Control workshop to be held from October 24 to 26, 2018, at The Knutsford Court Hotel in New Kingston.
The initiative targets policymakers, health professionals in the public and private sectors, members of infection control committees across health facilities, medical doctors, nurses, veterinarians, and other health professionals with an interest in infection control.
Speaking at the media launch today (September 5) in Kingston, Minister of Health, Dr. the Hon. Christopher Tufton, said infection control is an ongoing issue, noting that "we always have to continue to be on our guard".
"We have to be always prepared. Infectious diseases are not static… they change over time. There was a study I saw recently that suggests that because of the common use of alcohol-based products, there is increasing resistance," Dr. Tufton said.
"[This is] another huge reason why keeping abreast with those changes and adjusting to deal with the dynamic nature of the risk is so absolutely critical and justifies not just a once-in-a-while training course but an ongoing upgrading of the skill sets," he added.
For her part, Advisor, Health Surveillance, Disease Prevention and Control at the Pan American Health Organization (PAHO), Dr. Valeska Stempliuk, noted that globally, millions of people are affected every year by avoidable infections in healthcare settings.
"Consider the role played by people in charge of preventing these infections in healthcare settings, such as infection prevention and control nurses and doctors. The training of these people is very important. Providing training that guarantees the health institutions have competent professionals who can effectively prevent or constrain these infections is imperative," Dr. Stempliuk said.
Infection control refers to the biological, administrative, and regulatory systems for the prevention and containment of disease-causing organisms or germs.
Whereas infection control is a well-developed field in larger economies such as North America and the United Kingdom, developing and emerging economies lag somewhat in standardisation, training opportunities, systems for implementation, monitoring, and research.
The workshop is expected to support and complement ongoing local efforts in developing training programmes, and existing infection control protocols and standard procedures from the Ministry of Health of Jamaica.
The workshop is being hosted by Para Caribe Consulting Limited, and will include presenters from the Ministry of Health, University of the West Indies, University Hospital of the West Indies, and several leading health educational institutions in the United States and the United Kingdom.