text:
New research conducted by the book PR specialists, Palamedes PR, has revealed that nearly two-thirds of self-published authors are convinced they have what it takes to become the 'next' JK Rowling – but blame a lack of financial success on consumers' "snobbish" attitude towards the practice.
Their work has the potential of reaching bestseller status but sales are thwarted by the public's "haughty" views on 'DIY titles', authors claimed.
Many believe that consumers still associate self-published books with lacklustre plots, poor production quality and below-par writing standards.
And others fear the stigma of the subsidy or 'vanity' press – firms who charge authors to 'publish' their work, often for tens of thousands of pounds, whilst retaining royalty rights – continues to tarnish the integrity of writers who choose to go it alone and manage production themselves.
The knock-on effect, they say, is that thousands of worthy self-published paperbacks and ebooks are left "unnoticed, unread and unloved" each year, our research found.
We questioned 500 self-published authors from across the UK as part of its on-going research into sales expectations.
The results are not necessarily indicative of consumer behaviour.
"Whilst it is probably true that some people may consider self-published titles to be of a lower quality than those published by mainstream publishing houses, the feedback must be viewed in context," Anthony Harvison, our Campaign Director, said.
"It is, for example, highly likely that a significant proportion of the authors we spoke to were unable to publicise their work on a major scale as a result of limited marketing budgets.
"This one factor alone would, it is fair to assume, have had a bigger impact on sales than the perceived snobbishness of some consumers towards their work."
Until relatively recently the practice of self-publishing – the practice of paying a printer to run-off copies of a book in either paperback or hard cover format – was the preserve of the wealthy.
But new technology is allowing more authors than ever before to bypass the traditional route to market – the frustrating process of finding a mainstream publisher – and sell their work directly to the public relatively cheaply online.
Indeed, figures released last year by industry bible The Bookseller showed that a quarter of all fiction e-books sold in the UK were self-published.
Yet despite the success of a minority of self-published authors, the overwhelming majority of those who choose to go it alone reap little financial reward.
Some 38 per cent of those polled by Palamedes believed their work was "as good as" those penned by the likes of JK Rowling, John Grisham, Lee Child, and EL James.
A whopping 78 per cent said their work had the potential of hitting the Amazon bestseller lists "with the right professional support".
But the majority (52 per cent) believed the misconception about the quality of self-published titles in both fiction and non-fiction genres – and a general unwillingness among readers to "give them a chance" (61 per cent) – put an end to their dreams of selling tens of thousands of copies.
The impact of the vanity press on the self-publishing community was also thought to be significant (76 per cent).
Most respondents sold fewer books than predicted (81 per cent), and 83 per cent, meanwhile, accepted that more publicity "would have helped".
Almost all (94 per cent) believe that a significant number of self-published books are left to gather dust as a result of poor sales.
Palamedes, one of the UK's leading book marketing agencies, straw polled 500 self-published authors in all mainstream genres. None of the respondents had enjoyed 'bestseller' status.
Anthony Harvison, of Palamedes PR, said: "The journey of a self-published author is a long and difficult one. For many, it is prompted by the inability to secure a contract with a major publishing house, or the unwillingness to spend up to 12 months trying to do so.
"Bringing a book to market, therefore, takes considerable energy, tenacity, time and sacrifice. If the book fails to sell well – or as many copies as predicted – it is obvious that emotions will run high."
He added: "If a title of any type – published or self-published – is of a high standard and has widespread potential appeal, but fails to sell well, it can usually always be attributed to a lack of publicity – rather than to a public prejudice."


text:
Largest Building-Integrated Wind Power System in U.S. Installed in Oklahoma City
The largest building-integrated wind power system in the U.S. is now spinning on the roof of the Oklahoma Medical Research Foundation (OMRF), thanks to a collaboration between Venger Wind and SWG Energy.
Venger Wind installed a series of 18 vertical axis wind turbines (their own V2 model) as a part of OMRF's sustainability strategy of producing zero-emission energy in their new research tower in Oklahoma City. The 18.5' tall omni-directional V2 turbines are rated at 4.5 kw, begin generating electricity at wind speeds of 8.9 mph (below the city's annual average wind speed), and the project is a good example of what is possible with small wind power integrated into building designs.
"This is a groundbreaking success for Venger Wind and the small wind industry worldwide. The potential to provide wind energy at the point of use, within urban environments is a major paradigm shift from the typical large wind scenarios where multi megawatt systems are forced to be installed farther and farther away from populations where the energy is needed most." - Ken Morgan, Venger Wind Chairman and CMO
The company's V2 turbines are based on the work of the Finnish engineer Savonius, who invented his eponymous Savonius wind turbine in 1922, but according to Venger, their V2 design is a big step forward from previous iterations of the unique vertical axis turbine design. The V2 turbines are made from steel and aircraft-grade aluminum, are said to be quiet (less than 5 decibels over ambient noise levels), vibration-free, safe for birds and bats, and need no grid power to start spinning.
"The small wind industry still has a long way to go in educating ourselves and our customers on properly assessing and installing wind turbines on buildings. It truly is a whole new science that has emerged and we are very excited with the Architects and Engineers we currently work with whom are pioneering new concepts for wind turbine integration into buildings." - Morgan
Here's a little video overview of Venger's building-integrated wind turbines:
Find out more about Venger Wind's line of small wind turbines at their website, or follow along with them on Facebook or Twitter.

subset: commoncrawl


text:
The Seasons Story - Part 5
Hair: DURA (by chiaki.xue) *new
Hairbase: [LANEVO] (by lantern.evolution)
Ears: [MANDALA] (by kikunosuke.eel)
Lashes: beetlebones (by suetabulous.yootz)
Eyes: Insufferable Dastard (by audrey.lamede)
Skin: -Glam Affair- (by aida.ewing) *new for Collabor88
Scarf: ISON (by harry.hyx)
Coat w. Shirt: {Howl} (by worusaai) *new for The Seasons Story
Gloves: BAIASTICE (by sissy.pessoa)
Jeans: !APHORISM! (by rucy.byron)
Shoes: BAIASTICE (by sissy.pessoa)
Poses: Del May (by del.may)
Taxi to Collabor88!
Taxi to The Seasons Story!
Posted by Bunny Brickworks at 7:00 PM
Labels: [MANDALA], APHORISM, BAIASTICE, Bunny Brickworks, Collabor88, Glam Affair
Subscribe to: Post Comments (Atom)


text:
Empowering Choice in Collaboration
In my recent blog, Experience Matters in Collaboration (So does Architecture), I shared my thoughts on how we are facing a workplace that is no longer a physical place, but a blend of virtual and physical environments; where employees are bringing their preferences to work and BYOD ("Bring Your Own Device" to work) is the new norm; where collaboration has to happen beyond a walled garden; and any-to-any connectivity is a requirement, not a "nice to have."
Cisco is committed to delivering a new collaborative workspace that meets our customer's needs and empowers users to work their way—anytime, anywhere and on any device. As we announced last week, findings from the Cisco IBSG Horizons Study on virtualization and BYOD shows that 95% of organizations surveyed allow employee-owned devices in some way, shape or form in the office, and, 36% of surveyed enterprises provide full support for employee-owned devices. These stats underscore a major shift in the way people are working, in the office, at home and on-the-go, a shift that will continue to gain momentum.
Over the last year, Cisco has demonstrated a commitment to delivering innovative software like Cisco Jabber and Cisco WebEx across a wide spectrum of operating systems, tablets and Smart Phones. We're seeing tremendous interest in these software offerings. Customers see the value in how these offerings enable employees to work on their terms in the Post-PC era, while still having access to collaboration experiences.
Based on these market transitions, Cisco will no longer invest in the Cisco Cius tablet form factor, and no further enhancements will be made to the current Cius endpoint beyond what's available today. However, as we evaluate the market further, we will continue to offer Cius in a limited fashion to customers with specific needs or use cases.
Moving forward, we intend to double down on software offerings, like Jabber and WebEx, that provide the anytime, anywhere, and any device experiences. We will leverage key learnings and key collaboration experiences native to Cius in our other collaboration products.
Experience matters, and Cisco is focused on empowering individual collaboration styles more effectively and securely, while providing the broadest choice of collaboration options based on preference, location, and device.
In an effort to keep conversations fresh, Cisco Blogs closes comments after 60 days. Please visit the Cisco Blogs hub page for the latest content.
23 Comments
I agree with the direction that Cisco is taking in moving away from the Cius tablet and enabling the collaboration IP on the various popular BYOD tablets. BYOD is the driving force in what tablet is used and selected in the enterprise in Brazil
Yes, the working environment has changed dramatically. I love the freedom it brings but you also need to remember the work life balance which is easy to forget when devices are portable and always on.
thank u verymuch,, nice share..
Cisco has been a great help for me and my
I agree with the direction that Cisco is taking in moving away from the Cius tablet and enabling the collaboration IP on the various popular BYOD tablets. BYOD is the driving force in what tablet is used and selected in the enterprise. However, I believe that Cisco needs to enhance the Cius Media Station to become tablet and smartphone agnostic as the value Cisco has to offer is more in this space. Coupled with the Jabber/WebEx software on the top 3-5 MobileOS's.
Cisco has been a great help for me and my co-workers. I hope they can improve their services more. 🙂
What about Cisco AppHQ? will that be extended to other devices or closed down?
It is good to have a workplace which employees will have more freedom, but I think our employers still should provide us with IT devices for work, because not everyone afford to buy his/her own IT devices, and not everyone like to carry one more thing (laptop or tablet computer) to work.
I would like to know what smartphone operating systems will be supported with Jabber. Are you including Windows Phone for example in addition to IOS and Android?
This is good news – for Cisco's shareholders. Now, how much cash is going to cost RIM to come to the same conclusion and to put an end to the Playbook misery? I have been saying it for a while: enterprise vendors such as Cisco and RIM should focus on enabling consumer technology in the enterprise rather than trying to compete with consumer champions like Apple, Google and Samsung
see http://consumerization.trendmicro.com/a-byod-mantra-for-corporate-it/
Yes, the working environment has changed dramatically. I love the freedom it brings but you also need to remember the work life balance which is easy to forget when devices are portable and always on.
A real shame you cannot take advantage of the burgeoning tablet market.
Mike Cotrone
I really feel that Cisco has made a mistake with the Cius. It was marketed wrong by the WWSales organization, however this product could have been a superior portable telepresence unit. I have been using it as such on customer engagements and it was very impressive. The standard laptop/mac book with Jabber clearly is the direction you are going in, however the Cius did round out the telepresence portfolio very well since the pricepoint was good. Cisco just allowed WWSales to market/sell it as a "Tablet" which is was a very poor verion of.


text:
Players' Association voting on CBA ratificationNHL.com @NHLdotcom
The National Hockey League Players' Association is voting on ratification of the Collective Bargaining Agreement with the National Hockey League.
The Union said on its website voting would start at 8 p.m. ET Thursday and continue for 36 hours, through Saturday at 8 a.m.
Secret ballots are cast electronically and the results will be announced Saturday morning after the voting window closes.
It is anticipated that if the CBA is ratified by the players, teams could open training camps on Sunday, with the regular season reportedly to begin on Jan. 19.
The NHL Board of Governors voted unanimously Wednesday to ratify the new 10-year CBA.
Commissioner Gary Bettman said the 2012-13 regular-season schedule would be released "instantaneously" after the players' ratify the CBA.


text:
VersaLogic shipping Intel "Bay Trail" processor in PC/104-Plus computer
Tualatin, OR — May 3, 2016 — VersaLogic Corp., the embedded industry's most trusted computer company, is now shipping "BayCat" — a rugged new PC/104-Plus Single Board Computer (SBC). Based on Intel's highly acclaimed Bay Trail processor, BayCat combines high performance, low power consumption, enhanced security, and small size. This is an unbeatable combination in most applications. BayCat is available in three performance levels; single-, dual-, and quad-core models. The quad-core model delivers over 2x the performance of systems using the "Montevina" Core 2 Duo processor, with a 38% reduction in power consumption.
The board includes an extensive set of features including an on-board Trusted Platform Module (TPM) security chip, and a PC/104-Plus expansion interface with both ISA and PCI interface. The BayCat's powerful feature set, combined with a low-power draw and compact PC/104 footprint, enables the next generation of intelligent medical, military, and industrial systems to be smaller, lighter, and more energy efficient.
"This is the perfect migration board for existing systems that use a PC/104 embedded computer" said Len Crane, VersaLogic's President. "It provides a solid migration path, while giving the user increased performance for fewer watts of power. Combining the excellent mid-range performance of the Bay Trail CPUs, with the compatibility of the classic PC/104 ISA and PCI interfaces, is a home run for PC/104 users." he noted. "The quad-core version of this product is especially exciting to see in a PC/104 format, particularly with VersaLogic's Product Life Extension programs that can support this product well past the year 2025."
The BayCat provides compatibility with a broad range of x86 application development tools for reduced cost and development time. It is ideal for in-vehicle communications, complex medical devices, industrial control and automation, mobile autonomous systems, and other embedded applications.
Built-In Security
The BayCat's on-board Trusted Platform Module (TPM) security chip can be used to lock out unauthorized hardware and software access. It provides a secure processing environment for applications in Defense, Medical, and Industrial Control that require enhanced hardware-level security functions. Additional security is provided through built-in AES (Advanced Encryption Standard) processor instructions.
Powerful Video Processing
Intel's advanced Gen 7 HD graphics engine provides outstanding graphics performance and is capable of supporting up to two 1080p video streams. On-board hardware acceleration is available for encode/decode of H.264, MVC, VP8, VC1/WMV9, and other standards. The graphics engine also supports DirectX 11, Open GL 3.0, full HD video playback, dual display support, and resolution up to 2560 x 1600 @ 60Hz. BayCat's analog and mini DisplayPort video outputs support two independent simultaneous displays including Extended Desktop and Clone modes.
On-board I/O
BayCat's I/O connectivity includes dual Gigabit Ethernet ports, one USB 3.0 port, four USB 2.0 ports, two serial ports (RS-232/422/485), I2C interface, and 24 digital I/O lines. A SATA 3 Gb/s interface supports high-capacity rotating or solid-state drives. A Mini PCIe socket with mSATA capability and a microSD socket provide flexible solid-state drive (SSD) options.
Flexible System Expansion
BayCat's Mini PCIe socket allows easy on-board expansion with plug-in Wi-Fi modems, GPS receivers, and other mini cards such as MIL-STD-1553, Ethernet, and Analog.
For stacking expansion using industry-standard add-on boards, the BayCat supports PC/104-Plus expansion, including ISA and PCI based modules. The on-board expansion site provides plug-in access to a wide variety of expansion modules from numerous vendors, all with bolt-down ruggedness.
An SPI / SPX™ expansion interface offers additional plug-in expansion for low-cost analog and digital I/O devices.
For Extreme Environments
All three BayCat models are designed and tested for industrial temperature (-40º to +85ºC) operation and meet MIL-STD-202G specifications to withstand high impact and vibration. Latching connectors and fanless operation provide additional benefits in harsh environments.
Long-term Availability
The BayCat is covered by VersaLogic's 5-year product warranty and 5-year minimum availability guarantee.
VersaLogic's Life Extension program typically keeps each technology generation available for 10+ years. Customization services to help customers create unique solutions are available for the BayCat, even in low OEM quantities. Customization options include conformal coating, revision locks, custom labeling, customized testing and screening, and more.
Pricing and Availability
The BayCat single board computer (part number VL-EPM-31) is now in stock at both VersaLogic and Digi-Key Corporation. Pricing starts at $694 in OEM quantities. Contact email@example.com or visit www.VersaLogic.
com or www.Digikey.com for more information.
About VersaLogic Corporation
VersaLogic Corp. built its reputation on very high reliability products and superior service. A 35-year history of consistency has earned VersaLogic the reputation of being the industry's most trusted embedded computer company. VersaLogic delivers state-of-the-art embedded computers, coupled with expert technical support, for critical markets such as the medical and defense industries. For more information, visit www.VersaLogic.com.
Contact Information
VersaLogic Corp.12100 SW Tualatin Road
Tualatin, OR, 97062-7341
tele: 503-747-2261
toll-free: 800-824-3163
fax: 541.485.5712


text:
Environmental Manager
Frydenberg scrambles to fix EPBC 'oversight'
Written by Environmental Manager editor Deborah Nesbitt Federal resources minister Josh Frydenberg has scrambled to fix a problem created by his department which invalidly renewed or extended offshore oil & gas leases by failing to seek the federal environment minister's consent. Frydenberg on February 10 revealed an audit discovered "offshore petroleum titles with 'prior usage rights' have ...more
'Blindsided' CSIRO staff file dispute
Written by Environmental Manager editor Deborah Nesbitt CSIRO staff representatives will meet with management today (February 9) after chief executive Larry Marshall's unexpected Feb 4 revelation about 350 jobs would be axed this year, predominantly in environment and climate science divisions. The news "blindsided" CSIRO employees who were now angry at the lack of consultation, ...more
NSW EPA urged to seek legal advice on powers to regulate C'wealth land
Written by Environmental Manager editor Deborah Nesbitt An interim report on contamination at Williamtown RAAF base has revealed the Department of Defence (DoD) registered contamination as early as 1999. But the NSW Environment Protection Authority (EPA) is still unsure about its powers to tackle pollution on Commonwealth land. Review chair Macquarie University's Professor Mark Taylor's ...more
Vic Supreme Court 'polluter pays' ruling will be appealed, council says
Written by Environmental Manager editor Deborah Nesbitt Yarra City Council confirmed today (February 2) it will appeal against an important Vic Supreme Court ruling the 'polluter pays' principle under the state Environment Protection (EP) Act can be applied retrospectively. Vic Supreme Court Justice Peter Riordan on December 24 ruled the council (formerly Richmond Council) could be ordered to ...more
WA ruling prompts EPA probe
Written by Environmental Manager managing editor Helen Jones "Suitably qualified" professionals to review WA Environmental Protection Authority (EPA) policies and procedures in the wake of a Supreme Court judgment are being finalised, EM has been told. WA environment minister Albert Jacob on December 17 said independent legal and governance auditors would be appointed to do the review. Jacob said ...more
'Innovation great but CEFC needed'
by Carbon Extra editor Deborah Nesbitt WWF-Australia welcomed the Federal Government's innovation statement this week, coming on the back of its recent report with ClimateWorks that urged Aust to innovate and become "a low-emissions competitor in a carbon-constrained world". Prime Minister Malcolm Turnbull said the $1.1bn innovation agenda to "support Aust's transition towards a strong, ...more


text:
This is a simple recipe that takes great advantage of fresh eggplant. It has complex flavor and will be sure to wow any party or get together!
- 1 Eggplant (medium, cut into 1/2-inch cubes – 2 C)
- 1/4 C Lucini Extra Virgin Olive Oil
- 1 Onion (small, chopped in 1/2-inch dice – 1/2 C)
- 2 TB Pine nuts
- 1 TB Raisins
- 1 tsp Hot Pepper (crushed)
- 1/2 tsp Cinnamon
- 1/4 C Lucini Rustic Tomatao Basil Sauce
- 1 TB Lucini Cherry Infused Balsamic Vinegar
- 2 tsp Lucini Chili Infused Olive Oil
- Salt
- Ground pepper
Heat the olive oil in a large saute pan (medium heat). Saute the onions, pine nuts, raisins and hot pepper for 4 to 5 minutes until the onions have softened, but have not turned dark gold. Add the eggplant and cinnamon and cook for 5 minutes. Add the tomato sauce and bring to a boil, then turn heat down to low and simmer until the tomatoes are broken down, about 6-8 minutes. Remove from the heat and add the chili infused oil and cherry balsamic. Taste the caponata and adjust flavor with salt, pepper, cherry vinegar and chili infused oil. Allow to cool to room temperature.


text:
About Us
My Reservation
Contact Us
Special Offers
Find and book your stay
Enter Your Destination
Search By Hotel Name
Select Budget Type
Cheap < £25
Budget £25 - 35
Premium £35 - 45
Luxury > £45
Select Property Type
Bed and Breakfasts
Castle Hotels
Guest Houses
Self Catering Accommodations
Villa Hotels
Select Featured Type
24 Hr Reciption
B & B Hotels
Babysitting Hotels
Bar and Restaurant Hotels
Beach Hotels
Boutique Hotels
Business Hotels
Casino Hotels
Club Hotels
Conference Hall Hotels
Disabled Access Hotels
Eco Friendly Hotels
Entertainment Hotels
Family Hotels
Garden Hotels
Gay Friendly Hotels
Golf Hotels
Gym Hotels
Hi-tech Hotels
Honeymoon Hotels
Last Minute Deal Hotels
Leisure Facilities Hotels
Non Smoking Hotels
Pet & Dog Friendly Hotels
Pet-friendly Hotel
Pub Hotels
Restaurant Hotels
Romantic Hotels
Seaside Hotels
Smoking Hotels
Spa Hotels
Sports Hotels
Swimming Pool Hotels
Tennis Court Facility
Traditional Hotels
Wedding Hotels
Wireless Internet Hotels
Tourist Information
World Hotels
Thailand Hotels
Bangkok Hotels
Bangkok Hotels
Hotels found : 309
Select City in Bangkok
-- Select City --
Bangkok and Boutique Hotels
List View
Map View
The Maruay Garden Hotel
1 Phaholoyothin Road, Ladyao, Chatuchak, Bangkok, Thailand
Maruay Garden Hotel is just a 15-minute drive from Don Muang Airport in Bangkok and offers affordabl
Prices From
£ 28.40
Book Now
The Landmark Bangkok
138 Sukhumvit Road, Bangkok, Thailand
With the Nana BTS Skytrain Station at its doorstep, The Landmark offers spacious luxury accommodatio
Prices From
£ 79.13
Book Now
Swissotel Le Concorde Bangkok
204 Ratchadapisek Road Huaykwang, Bangkok, Thailand
Situated just 2 minutes' walk from the MRT station, in a thriving business and entertainment distric
Prices From
£ 44.62
Book Now
Chateau De Bangkok Managed By Accor
29 Soi Ruamrudee 1, Ploenchit Road, Lumpini Patumwan, Bangkok, Thailand
Chateau de Bangkok offers 4-star accommodation in Ploenchit and Sukhumvit, 450 metres from Phloen Ch
Prices From
£ 62.09
Book Now
Bangkok Hotel Lotus Sukhumvit
1 Soi Daeng Udom, Sukhumvit 33 Road, Klongton Nua, Wattana, Bangkok, Thailand
The Bangkok Hotel Lotus Sukhumvit is centrally located and offers free private parking, an outdoor s
Prices From
£ 52.54
Book Now
Grand President Bangkok
14, 16 Sukhumvit Soi 11, Bangkok, Thailand
A 5-minute walk from Nana BTS Station, Grand President offers well-furnished studios with kitchenett
Prices From
£ 34.71
Book Now
Royal President Bangkok
43, Sukhumvit Soi 15, Bangkok, Thailand
Centrally located in the shopping and entertainment district of Sukhumvit, Royal President Bangkok o
Prices From
£ 37.51
Book Now
President Park Bangkok
Maple Tower, 95 Sukhumvit Soi 24, Bangkok, Thailand
President Park Bangkok offers comfortable serviced apartments with full kitchen facilities in downto
Prices From
£ 34.47
Book Now
President Solitaire Hotel & Spa
Sukhumvit Soi 11, Bangkok, Thailand
President Solitaire offers 4-star comfort in spacious rooms with kitchenettes and free Wi-Fi, locate
Prices From
£ 52.73
Book Now
Princeton Bangkok
No. 3 Mitmaitri Road, Din Daeng District, Bangkok, Thailand
Located in Din Daeng District, Princeton Bangkok offers rooms and fully serviced residences near the
Prices From
£ 32.46
Book Now
Page 1 of
Email us to know availability & rates for your stay
Hotel Name:
Any Hotel
The Maruay Garden Hotel
The Landmark Bangkok
Swissotel Le Concorde Bangkok
Chateau De Bangkok Managed By Accor
Bangkok Hotel Lotus Sukhumvit
Grand President Bangkok
Royal President Bangkok
President Park Bangkok
President Solitaire Hotel & Spa
Princeton Bangkok
Unico Express@Sukhumvit
Swissotel Nai Lert Park Bangkok
Ramada D'ma Bangkok
Grand Mercure Bangkok Park Avenue
All Seasons Bangkok Siam
All Seasons Bangkok Huamark
Dream Bangkok
Ambassador Hotel Bangkok
Siam City Hotel
A-One Bangkok Hotel
The Key Bangkok Hotel
Furamaxclusive Sukhumvit
13 Coins Airport Grand Resort
Floraville Serviced Apartments
13 Coins Airport Hotel Min Buri
13 Coins Tower Hotel Ratchada
Adelphi Suites Bangkok
Citichic Boutique Suites Hotel
Amari Boulevard Bangkok
Legacy Suites Bangkok
Siam Piman Airport Hotel, Suvarnabhumi Airport Hotel
The Imperial Queen's Park Hotel
13 Coins Airport Hotel Ngam Wong Wan (Domestic)
Baiyoke Boutique Hotel
13 Coins Resort Yotin Patana
Tai Pan Hotel
Centara Grand At Central Plaza Ladprao Bangkok (Formerly Known As Sofitel Centara Grand Bangkok)
Grand Mercure Bangkok Asoke Residence
Sam's Lodge
Atrium Boutique Hotel
Golden Tulip Sovereign Hotel Bangkok
Le Fenix Sukhumvit
Baiyoke Sky Hotel
Imm Fusion (Formerly Im Sukhumvit 50)
Salil Hotel Sukhumvit Soi 8
Amari Watergate Bangkok
Amari Atrium Bangkok
Amari Don Muang Bangkok
St. James Hotel
Furamaxclusive Asoke-Sukhumvit
Grand Millennium Sukhumvit
Aspen Suites Bangkok
Pullman Bangkok King Power
City Lodge Soi 19
Smart Suites
Rama Gardens Hotel Bangkok
Citadines Sukhumvit 16 Bangkok
Somerset Lake Point
Mac Boutique Suites
Chaophya Park Hotel, Bangkok
Natural Ville Managed By Accor
Rembrandt Hotel Bangkok
Refillnow! Hostel
Miracle Grand Convention Hotel
Bally's Studio Suites
Citadines Sukhumvit 8 Bangkok
Citadines Sukhumvit 11 Bangkok
Admiral Premier Bangkok
Centara Grand At Centralworld
Majestic Grande Hotel
Convenient Park Bangkok
Omni Tower Bangkok
Oakwood Residence Sukhumvit24
Citin Pratunam
Citadines Sukhumvit 23 Bangkok
Jasmine Executive Suite & Hotel
Baiyoke Suite Hotel
Grand Lucky Hotel
Tenface Bangkok
Pratunam City Inn
Conrad Bangkok
Jw Marriott Hotel Bangkok
Courtyard By Marriott Bangkok
The Four Wings Hotel Bangkok
Park Plaza Sukhumvit
Marriott Executive Apartments Mayfair Bangkok
Eastin Hotel Makkasan, Bangkok
President Palace Hotel
Best Western Mayfair Suites
Monaco Bangkok Hotel (Formerly Marcopolo)
Sacha's Hotel Uno
Metro Point Bangkok
Ariyasom Villa
The Victory Executive Residences
Royal Asia Lodge
The Dawin Nana
The Centric Ratchada
Gulliver's Tavern Hotel
Bel-Aire Princess Hotel
Tacoma Garden Airport Lodge
Bangkok Palace Hotel
Dynasty Grande Hotel (Formerly Dynasty Inn Grande)
Centre Point Petchburi
Centre Point Langsuan
The Residence Airport & Spa
The Davis Bangkok
Royal Ivory Bangkok
Adelphi Grande Bangkok
Grand Diamond Suites Hotel
Emporium Suites
The Ecotel Bangkok
Manhattan Bangkok
Conrad Bangkok Residences
On 8 Sukhumvit Bangkok
P2 Boutique
The Fusion Suites
All Seasons Gold Orchid Bangkok
Intercontinental Bangkok
Holiday Inn Bangkok
Tango Vibrant Living Place
Bangkok Cha-Da
Paradiso Boutique Suites
Royal View Resort (Rang Nam)
Century Park Hotel
Jl Bangkok
S Sukhumvit Suite Hotel
Silq Bangkok Hotel
The Patra
Ibis Bangkok Nana
Hope Land Executive Residence Sukhumvit 46
Salil Hotel Sukhumvit - Soi Thonglor 1
Darjelling Boutique Hotel
A-One Boutique Hotel
Phachara Suites Sukhumvit
Best Comfort Residential Hotel
Somerset Sukhumvit Thonglor
Four Seasons International House
Ds67 Suites
Windsor Suites Hotel Bangkok
The Emerald Hotel
V Residence Bangkok
Bless Residence Bangkok
Oakwood Apartments Trilliant Sukhumvit 18
Lilla Hotel
Eight Inn
Hotel Solo, Sukhumvit 2, Bangkok
Hi Residence
Aetas Bangkok
Bliston Suwan Park View
Nantra De Comfort
Legacy Express Bangkok
Pietra Bangkok
Ton-Aor Place
Marriott Executive Apartment Sukhumvit Park
Renaissance Bangkok Ratchaprasong Hotel
Citrus Sukhumvit 22
The Swiss Park Hotel
Ramada Hotel & Suites
Northgate Ratchayothin Serviced Residence
Sabai Sabai@Sukhumvit Hotel
Ratchada Resort And Spa Hotel
Ziniza Place
Kc Place
The Privacy Suite
Na Na Chart Sukhumvit 25
Louis' Tavern Hotel
Shama Sukhumvit Serviced Apartment
Mida City Resort Bangkok
Sawasdee Hotel @ Sukhumvit Soi 8
Grottino Residence
Arnoma Hotel Bangkok
Merlin Lodge
Palms @ Sukhumvit Service Apartment(Formerly Twin Palm @ 39 Hotel)
S31 Sukhumvit Hotel
Hansar Bangkok
August Residence
Hotel De Bangkok
Varindavan Park Serviced Residence (Formerly Varindavan Park Hotel)
The Cozi Inn Hotel, Bangkok
Sivatel Bangkok
Myhotel Cmyk@Ratchada
Ten Stars Hotel
Bkk Home 24 Boutique Hotel
Amari Residences Bangkok
The Euro Grande Hotel
Novotel Bangkok Fenix Ploenchit
Golden House
True Siam Hotel
Nasa Vegas Hotel
The Eugenia
Mestyle Place
Padi Madi Guesthouse
Majestic Suites Hotel
Metz Pratunam
Aravinda Living Home
Boss Suites Hotel
Column Bangkok
Baan Nueng Service Apartment
Asoke Suites
The Egypt Boutique Hotel
Chaleena Hotel
Mystic Place
Amari Residences Sukhumvit
Sandy Serviced Apartment
Rongratana Executive Residence
Oakwood Residence Sukhumvit Thonglor Bangkok
Golden Tulip Mandison Suites
Four Seasons Hotel Bangkok
Oasis Inn Bangkok
City Lodge Soi 9 Hotel
M2 De Bangkok Hotel
Check Inn Phromphong
Ramada Encore Bangkok
Bb Boutique Mansion
Fx Hotel Makkasan, Bangkok
The Park Residence @ Bangkok
Bed Bangkok Hostel
Royal Benja Hotel
Khurana Inn
Pan Pacific Serviced Suites
The Residence Rajtaevee Hotel
Wow Bangkok
B.U. Place
Chaleena Princess
Hip Hotel
Honey House 2
Karnmanee Palace Hotel
Jasmine Resort Hotel
Radisson Suites Bangkok Sukhumvit
Best Western Plus @20 Sukhumvit
Dream Town Pratunam
Kempinski Residences Siam
Queen Lotus Guesthouse
S2s Boutique Resort Bangkok
Mansion Sarasinee @ Chatuchak
Royal Suite Residence Boutique
Amanta Ratchada Serviced Apartment
Park Plaza Bangkok Soi 18
Poonchock Mansion
The Travellers Hotel
First House Hotel Bangkok
Mall Suites Express
The Green Bells
Siam Star Hotel
Hotel Muse Bangkok Langsuan - Mgallery Collection
Madee Hostel
Omni Tower Syncate Suites
Eight Ville
Citypoint Hotel
Sun City Hotel
P.T. Court
Abloom Exclusive Serviced Apartments
Twothree Mansion
Amora Neoluxe Hotel
Grand Hyatt Erawan Bangkok
Sn Mansion
Best Western Bangkok Hiptique Hotel
Viva Garden Serviced Residence By Bliston
Miami Hotel
Check Inn Regency Park
Pticl Hostel
Asoke Residence Sukhumvit
The Boutique Place
Premier Inn
Lanna Kala By Greg Sampat Boutique Resort
Sasha House
Pratunam Pavilion Hotel
The European Place
Swutel Hotel
Rembrandt Towers Serviced Apartments
Serene Bangkok Bed And Breakfast
Jim's Lodge
Room@Bangkok Bed And Breakfast
The Bedrooms Boutique Hotel Bangkok
Kantary House Hotel & Serviced Apartments, Bangkok
Royal Express Inn Bangkok
Nice Palace Hotel
Thanapa Mansion
White Palace Bangkok
In Residence Bangkok Sukhumvit
Indra Regent Hotel
Grand Alpine Hotel
Novotel Bangkok Platinum
41 Suite Bangkok
Nine Place Sukhumvit 81
Gardengrove Suites
S33 Compact Sukhumvit Hotel
Bangkok Inter Place
The Mix Bangkok
Warroom Boutique Hotel
Skyy Hotel
Oriental Residence Bangkok
Himalaya Residence
Pratunam Park Hotel
Vic3 Bangkok (Formerly Known As All Season Bangkok Victory Monument)
Bangkok Condotel
Bkk Unique Serviced Apartment
Jj Mansion
Bangkok Garden Resort
Davinci Suite
Check In:
Check Out:
Your Name:
More Details:
Sort Result By
Min Price
Max Price
Hotel Name
Star Rating
Show City Hotels
Show hotels only in Bangkok
Find Result Around
0.25 miles
0.5 miles
1 miles
2 miles
5 miles
10 miles
Star Rating
Brand Hotels
Choice Hotels
Best Western
Days Inn
Express Inn Holiday
Holiday Inn
Hotels with Boutique in Bangkok: Our hotels collection contains a range of Boutique hotels that they have their own Boutiques. If you like to stay in Boutique Hotels and prefer not to compromise over hotel standards then our Boutique hotels are the perfect choice for you. Choose from magnificent Hotels with Boutique in Bangkok Thailand. To help you kick off in style we have selected some of the finest Boutique hotels and resorts. Each of our Boutique hotels has been specially selected to ensure you enjoy the luxury and professional service you would expect from a luxury Boutique hotel. Our Family run hotel pages include maps, local insider tips and even hints for celebrity spotting. So just browse our collection of Boutique hotels and enjoy our hotel with Boutique deals.
Bangkok Hotels are the perfect place to book the ideal Bed and Breakfast hotel closer the top Boutique hotels of your choice. Choose from our vast selection of Bangkok Discount accommodation to luxury Bangkok hotels with Boutique nearer to some of the world's best Bangkok Boutique resorts, Bangkok Boutique Hotels, 5 star Boutique hotels, Bangkok Boutique Hotel Accommodation. Bangkok Hotels can help meet all your accommodation needs in the Boutique hotel of your choice.
Bangkok Boutique Hotels
For Hotels
Privacy Statement
Site Map


text:
Saturday, March 20, 2010
Love in the time of jihad…
16 comments:
Wow... that's something... but very very sweet.. Hang in together guys.ReplyDelete
- Kannan,IndiaMarch 20, 2010 at 11:23 PM
Awesome..way to communicate between couples. When we write letters, there is no interruption of "train of thoughts" and we can correct any "irritant triggers" and fine tune the language to desired effect after self proof-reading. I think if minor quakes is being experienced a weekly dossiers/reports to each other will nip "root causes" in the bud.Lots of things mostly simple ones in a relationship cannot be said face to face coz saying them itself sounds stupid themselves and hurt our ego.But in a letter it wont sound so bad. For example,a simple jealous feeling coz of some triggers if immediately expresses verbally..one feels like a complaint-box, nit-picker or needy.If a weekly to & fro dossier is scheduled,hitherto unexpressed feelings & seemingly irrelevant issues will fill in the blanks subconsciously. In "7 habits of highly effective people" one such seemingly irrelevant unexplored "pressure-cooker brand" issue is narrated beautifully arguing the need for robust communication in relationship.ReplyDelete
Anyways..keep the posts coming..
How do u feel now, rather how do u cope now are u ? rather how do u handle fear stress ?. We'll you have a good online following now so keep up the good work and expect our support in ur thick and thinReplyDelete
Very touching post. God bless you guys.ReplyDelete
I can imagine how scary it was for you! God bless you both! Take care.ReplyDelete
Seriously , i dont know how you live there, even the thought sends shudders thru my spine.... rather go to the remotest area of Africa rather than Pakistan.... and guys i am not tryong to stereotype pakistan but this what i really feel... my best to you guys :), i guess it will worth it once you get the book deal lolReplyDelete
Tulika, hanging in...:)ReplyDelete
Kannan, yes, emails work well.
Ashu, it was just a phase I went through because my dad was not keeping well...as they say 'Aal iz well' now.
Rupa, we can do with those blessings :)
Sandhya, it was. I am just glad that I can look back and laugh at whatever happened then.
Premal, I am not sure about Africa :) but I can surely do with a book deal!
Writing(now emails) is also my favourite way of communicating. Nothing beats its efficacy.ReplyDelete
Having said that, your situation is definitely not cordial to the preferred ways but should assume logical reasoning. Its good to know you both are well.
Geetly, absolutely. Its nice to have you reading our posts. Thank you.ReplyDelete
- AnonymousMarch 22, 2010 at 2:57 PM
At first i thought you're post a little 'aren't they a bit too paranoid' then i thought that if it was a Pakistani in India, she would probably feel the same way and the same insecurity - I remember always being aware of the fact that i was a Pakistani in a country that is always hostile towards us, and having that at the back of my mind when i visited India.ReplyDelete
I suppose you being Indian with so few of you in Pakistan, have to be extra careful.
If you are ever fortunate enought to visit our country you may change you're mind. In the mean time enjoy Africa!
Touché...god bless you both!!!ReplyDelete
@ anonymous... It will cold day in hell when i wanna come to pakistan...and if you have ever visited africa you would know its pretty rockingReplyDelete
My GOD..that was horrifying ! Thank that Indian diplomat from my side as well :)I am ultra happy with that fellow.ReplyDelete
And u..never make yourself feel homesick again :( n just for fun u both decide over a common secret question n Be happy you guys ....may the almighty always be with you !!
One of your very first followers, but have never commented! Love the way this blog is shaping up-- I've missed out on the last few posts, need to catch up.ReplyDelete
P.S. Mine used to disappear on me allll the time too. Annoying, yes? Panic inducing? OH YES.
Anonymous, I am sure a Pakistani wife in India would also have got all worked up...but then India is not a especially dangerous country :)ReplyDelete
AM, thanks :)
Kasturi, will work on a secret question :)
Absolutely Normal Chaos, with an ID like that it is difficult not to notice you. Keep reading and thank you. :)
- AnonymousMarch 25, 2010 at 2:24 AM
LOL! finally, chocolate time,eh! :pReplyDelete


text:
|Neural network system adapted for non-linear processing
|Inoue et al.
|Classification of data records by comparison of records to a training database using probability weights
|Masand et al.
a current first classifier operative to interpret a plurality of actual examples of the data and to output an interpretation of each interpreted example and a certainty value associated with each interpretation wherein the current first classifier comprises a chooser operative to discriminate between certain ones of the outputted interpretations having respective high certainty values and uncertain ones of the outputted interpretations having respective low certainty values and to select and output each of the actual examples associated with a respective uncertain one of the interpretations;
a second classifier operative to annotate each of the interpreted examples associated with the selected uncertain ones of the interpretations and to output a preferred interpretation for each interpreted example associated with the selected uncertain ones of the interpretations; and
an uncertainty measuring device generator operative to produce a next first classifier by utilizing at least one annotated example and its associated preferred interpretation, the next first classifier capable of interpreting subsequent actual examples of the data more accurately than the current first classifier.
said examples are divided into data portions by a segmenter.
a first classifier operative to interpret a plurality of actual examples of the data and to output an interpretation of each interpreted example and a certainty value associated with each interpretation wherein the first classifier comprises a chooser operative to discriminate between certain ones of the outputted interpretations having respective high certainty values and uncertain ones of the outputted interpretations having respective low certainty values and to select and output each of the interpreted examples associated with a respective uncertain one of the interpretations;
a second classifier operative to annotate each of the interpreted examples associated with the selected uncertain ones of the interpretations and to output a preferred interpretation for each interpreted example associated with the selected uncertain ones of the interpretations; and
an uncertainty measuring device generator operative to produce a next first classifier by using at least one annotated example and its associated preferred interpretation, the next first classifier capable of interpreting subsequent actual examples of the data more accurately than the first classifier.
said examples are divided into data portions by a segmenter.
a first classifier operative to interpret a plurality of actual examples of the data according to a first principle and to output an interpretation of each interpreted example and a certainty value associated with each interpretation wherein the first classifier comprises a chooser operative to discriminate between certain ones of the outputted interpretations having respective high certainty values and uncertain ones of the outputted interpretations having a low certainty values and to select and output each of the interpreted examples associated with a respective uncertain one of the interpretations;
a second classifier operative to annotate each of the interpreted examples associated with the selected uncertain ones of the interpretations and to output a preferred interpretation for each interpreted example associated with the selected uncertain ones of the interpretations; and
an uncertainty measuring device generator operative to produce a third classifier by utilizing at least one annotated example and its associated preferred interpretation, the third classifier operative to interpret subsequent actual examples of the data according to a second principle different from the first principle.
the data is text.
the second classifier includes interactive means for providing the example to a human judge and receiving the annotation from the judge.
the uncertainty measuring device generator includes means for using relevance feedback to produce the next first classifier.
the classifier classifies data into classes;
the uncertainty measuring device generator includes:
means for producing a class membership score for each annotated uncertain example, and
means for using logistic regression to produce a parameter for each annotated uncertain example which modifies the class membership score to indicate a probability that the annotated uncertain example belongs to the class.
said examples are divided into data portions by a segmenter.
1. Field of the Invention
The amount and variety of data stored online is growing at a rapid rate. This is particularly true for natural language text in its many forms (news articles, memos, electronic mail, repair reports, etc.). While the potential benefits of computer access to this data are huge, they cannot be realized unless the data is converted into information. An example of converting data to information is classifying the data into categories. For instance, stories on a newswire which are received by a business are useful to the business only if they are routed to the proper people within the business. Traditionally, this has been done by humans who read the stories, decide on the categories to which the story belongs, and then route each story to the users who have expressed interest in one of the categories assigned to it. As the amount of data has increased, manual data interpretation has become more and more impractical.
The problem of converting data to information exists for all kinds of data, and is particularly pressing with regard to text. Converting data to information in this case means reducing the infinite variety of possible natural language expressions to a limited set of possible interpretations. These interpretations can be as simple as labels indicating which of several meaningful categories a text belongs to, or as complex as parse tree, database records containing information extracted from text, or expressions in knowledge representations languages. Similar forms of interpretation are necessary for many other forms of data as well.
One approach to reducing the expense of interpretation is to use a data interpreter, that is a computer system which, given a data portion, can automatically produce an interpretation. A simple form of data interpreter is a classifier, that is a computer system that can decide which of several categories a data portion belongs to. In order to work, data interpreters require substantial knowledge about the structure of the data. For instance, for textual data extensive knowledge about human language and the ways in which human language can encode information is necessary. Building this knowledge into a data interpreter by hand is difficult and time consuming, due both to the amount of necessary knowledge and the difficulty of anticipating all the ways in which the data can encode information.
For this reason, a variety of machine learning techniques have been developed to automatically acquire interpretation knowledge from data which has been annotated with full or partial interpretations. This process of automatic knowledge acquisition is termed training. For instance, systems for sorting documents into categories have been trained from examples of documents manually annotated with their correct category. The training process examines the annotated documents to estimate the probabilities that certain words will or will not occur in documents belonging to a particular category. These probabilities can then be used in assigning documents to categories. As another example, experimental systems for diagnosing illnesses have been trained from patient records manually annotated with an expert physician's diagnosis. The training procedure discovers statistical or causal relationships between combinations of symptoms and associated diseases.
In general, the more data which can be annotated, the more accurate the training, and thus the better the interpreter. This has led organizations to devote substantial resources to creating large masses of annotated data for training purposes (see Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. Building a Large Annotated Corpus of English: The Penn Treebank, Computational Linguistics, vol. 19, no. 2, June, 1993, pp. 313-330).
Even large organizations cannot annotate all the data they have, however. Further, each new interpretation system requires training from data with a new kind of annotation. So it is almost always the case there is far more data than can be practically annotated, whether the teacher who does the annotation is a person or persons, or whether it is a computer system. (Even if there is already a computer system which is a good interpreter of the data, it may be desired to learn knowledge which enables a better interpreter, for instance one which is faster.)
2. Description of the Prior Art
There have been various attempts to reduce the amount of annotation required for training, particularly in the context of training classifiers. One approach is to train the classifier using a machine learning algorithm that makes membership queries (see Angluin, D. Queries and concept learning. Machine Learning, 2:319-342, 1988). In this approach, the learning algorithm creates an artificial data portion which the learning algorithm has determined cannot be reliably classified given the present state of training. The algorithm asks the teacher to annotate this artificial data portion, and the annotated data portion can be added to the rest of the annotated data to improve training. The problem with this approach is that artificially generated data may not make sense to the teacher, for instance if the data portion is supposed to be an expression in a human language.
A variety of related techniques from statistics, including stochastic approximation (see Yakowitz, S. and Lugosi, E. Random search in the presence of noise with application to machine learning. SIAM Journal on Scientific and Statistical Computing, 11(4):702-712, July 1990) and importance sampling (see Runnalls, A. Adaptive importance sampling for bayesian networks applied to filtering problems. In D. J. Hand, editor, Artificial Intelligence Frontiers in Statistics: AI and Statistics III, pages 90-105. Chapman & Hall, London, 1993) are similar to membership querying and suffer from the same problem. Approaches based on generating artificial annotated data by means of a distortion model also fail in the same circumstances.
Another approach to this problem is to treat it as one of choosing a sample, i.e. a subset of the data which will be presented for annotation. Sampling approaches must deal with the problem that particular annotations of interest are often very infrequent. Suppose only 1 in 1000 data portions should have annotation X, and only 500 portions can be affordably annotated. Using random sampling to choose which portions to annotated will usually result in no portions with annotation X. Thus little knowledge could be acquired about how to recognize X's.
Stratified sampling (see Cochran, W. Sampling Techniques. John Wiley & Sons, New York, 3rd edition, 1977) is the usual approach to addressing low frequency of certain annotations. The space of all possible data items is conceptually divided into strata, i.e. groups, based on some observable characteristics of the data. Sampling is then done separately from each stratum, with more data items taken from strata where the annotations are expected to be most uncertain or variable. While this approach works well when appropriate strata are obvious or are a matter of convention (as in public opinion research), the approach breaks down for many real world data sets, when the number of possible stratifications is huge and the appropriate choice of them very unclear. This is true, for instance, when the data items are natural language text.
Recently, however, a new approach to the annotation problem has emerged which can be thought of as a combination of stratified sampling and membership querying. These methods define strata directly in terms of the uncertainty of the unknown annotation, and then sample only from strata that are sufficiently uncertain. We call this approach uncertainty sampling. An important characteristic of uncertainty methods is that they operate iteratively. That is, once data whose interpretation is uncertain has been annotated, the annotated data is used to reduce the uncertainty of the interpretation of other data.
Several methods have been proposed for estimating the uncertainty of the unknown annotation (see Atlas, L., Cohn, D., et al. Training connectionist networks with queries and selective sampling. In David S. Touretzky, editor, Advances in Neural Information Processing Systems 2, pages 566-573. Morgan Kaufmann, San Mateo, Calif., 1990, and Cohn, D., et al. Improving generalization with self-directed learning, 1992. To appear in Machine Learning). In the technique proposed by Cohn, the interpreters are again classifiers.
Two neural net classifiers are trained from the available annotated data. The first classifier is intended to be an approximation to the most general classifier consistent with the training data. It is produced by giving the neural net training algorithm all the annotated examples, plus a large number of random examples temporarily annotated as being positive examples. The second classifier is intended to be an approximation to the most specific classifier consistent with the training data. It is produced by giving the neural net training algorithm all the annotated examples, plus a large number of random examples temporarily annotated as being negative examples. Cohn does not specify how many temporarily annotated examples are needed in either case, except to say that the number is large. It seems likely that the number would have to increase sharply with the number of features used to represent the data items. The temporarily labeled items are meant to provide coverage of entire space outside of the labeled data, and this space grows exponentially with the number of features.
Cohn's two classifiers are applied to unannotated data items and those items on which the classifiers disagree are presented to a teacher for annotation. The annotated data is then updated, new versions of the classifiers are trained and more unannotated data is tested.
A related approach is the query by committee algorithm (see Seung, H., et al. Query by committee. In Proceedings of the Fifth Annual ACM Workshop on Computational Learning Theory, pages 287-294, 1992 and Freund, Y., et al. Information, prediction, and query by committee. In Advances in Neural Informations Processing Systems 5, San Mateo, Calif., 1992. Morgan Kaufmann). Query by committee randomly selects two classifiers consistent with the already annotated data for each unannotated data item. These two classifiers are applied to the corresponding unannotated data item, and if the classifiers disagree, the annotation of the item is considered to be uncertain and the teacher is asked to annotate it. Again, this process is iterative, with the annotations of previous items affecting which classifiers will be selected and which items found uncertain in the future. Only the theoretical aspects of the algorithm are presented--Seung, et al do not present a method for actually implementing the choice of classifiers.
In a third approach, MacKay suggests choosing data items to be annotated based on a mean marginal information gain function (see MacKay, D. The evidence framework applied to classification networks. Neural Computation, 4(5):720-736, 1992). This function requires extensive computational analysis of the current classifier even to compute approximately, and also requires information about the distribution of data to which the final classifier will be applied. MacKay presents experimental results suggesting that this approach will only work when the model being fit is well matched to the actual regularity in the data. He also does not describe an explicit annotation algorithm based on mean marginal information gain.
None of the above methods provide a general solution to the problem of selecting data for annotation. Query by committee is not computationally effective and MacKay's algorithm, while of theoretical interest, has not been shown to work in practice. Cohn's method is the closest to practical, but its expense is considerable, due to the need to train two classifiers on large amounts of temporarily labeled data. Cohn points out other limitations of his method as well. In addition, none of these methods have been implemented in a fashion suitable for working with large real world data collections, such as text databases, where data items are characterized by very large numbers of features (e.g. tens of thousands of distinct words).
MacKay and Cohn both warn explicitly against an alternative strategy, which is to select data near the decision boundary of a single classifier produced by a machine learning algorithm applied to the training data. They point out several theoretical failings of using a single classifier, including underestimation of true uncertainty, and biases caused by nonrepresentative classifiers.
There are several reasons to dispute the reasoning of MacKay and Cohn, however. First, the experiments of MacKay and Cohn attempted to learn deterministic functions of small numbers of features from noise-free data. Most real data interpretation problems are characterized by large numbers of features, considerable noise in the data, and non-deterministic (statistical) relationships between data and interpretation. All these characteristics work to mitigate the theoretical problems raised by MacKay and Cohn.
Second, single classifiers have been successfully used for the related tasks of generating membership queries (see Hwang, J., et al. Query-based learning applied to partially trained multilayer perceptrons. IEEE Transactions on Neural Networks, 2(1):131-136, January 1991) and reducing the size of already annotated data sets (see Davis, D. and Hwang, J. Attentional focus training by boundary region data selection. In International Joint Conference on Neural Networks, pages I-676 to I-681, Baltimore, Md., Jun. 7-11 1992, and Plutowski, M. and White, H. Selecting concise training sets from clean data. IEEE Transactions on Neural Networks, 4(2):305-318, March 1993).
Third, there is one application where a single classifier has been used for selecting from unlabeled data, though not by an uncertainty sampling method. This is relevance feedback in text retrieval systems (see Salton, G. Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer. Addison-Wesley, Reading, Mass., 1989). A text retrieval system using relevance feedback operates as follows. The user issues an initial query to the system, which is converted internally to a statistical classifier. The classifier is used to estimate which documents are most likely to be relevant to the user, and those documents are displayed to the user. The user then annotates the retrieved documents to indicate which are relevant and which are not. These annotated documents are used, along with the original query, to train a new classifier. This new classifier is used to again select the documents most likely to be relevant to the user, and the process is repeated as many times as desired by the user.
Relevance feedback has been found to be effective in increasing the performance of text retrieval systems, and has also been used in selecting training data in word sense disambiguation (see Gale, W., et al. A method for disambiguating word senses in a large corpus. Computers and the Humanities, 26:415-439, 1993). It is not a general solution to the selection problem, however. Relevance feedback systems focus in on a small portion of the space of possible data items with a particular annotation, rather than getting examples of the full range of data items with that annotation and without that annotation. In particular, they operate poorly when there are large numbers of redundant items with the same annotation. Also, they are only applicable to binary annotations.
What is needed, and what is provided by the present invention, is a method that selects data items to annotate which leads to increased effectiveness of learned knowledge, and which is efficient, usable with arbitrary real world data sets, is implementable on a digital computer, and does not suffer from the problems of the above described methods.
The invention solves the above problems by taking advantage of the fact that certain kinds of interpreters can specify the degree of certainty of their interpretations. The technique of the invention is iterative, and works as follows: the system includes a first interpreter which is one of the above interpreters and has been previously trained on training data; a set of examples is presented to the interpreter and the interpreter interprets them. When the interpretation given an example has a low degree of certainty, the interpreter passes the example to a second interpreter, termed herein the teacher, which annotates the example. The annotated example is then added to the training data. After the interpreter has worked through all of the examples as just described, a new interpreter is created using the training data with the additional annotated examples and run on the original examples as just described. The iterations continue until an interpreter with the desired degree of accuracy is obtained.
In another aspect, the invention is a general technique for producing training information. The annotated examples produced by the teacher represent training information which can be used to train any kind of interpreter, including ones which use principles of interpretation different from those used by the first interpreter.
Other objects and advantages of the apparatus and methods disclosed herein will be apparent to those of ordinary skill in the art upon perusal of the following Drawing and Detailed Description, wherein:
FIG. 1 is an overview of apparatus for making an interpreter according to the principles of the invention;
FIG. 2 is a block diagram of apparatus for selecting interpretations according to the degree of certainty;
FIG. 3 is apparatus for measuring the degree of uncertainty;
FIG. 4 is a detail of a probabilistic classifier used to measure the degree of uncertainty;
FIG. 5 is apparatus for training the probabilistic classifier; and
FIG. 6 is a block diagram of second apparatus for making an interpreter according to the principles of the invention.
Reference numbers in the Drawing have two parts: the two least-significant digits are the number of an item in a figure; the remaining digits are the number of the figure in which the item first appears. Thus, an item with the reference number 201 first appears in FIG. 2.
The following Detailed Description first presents an overview of apparatus for producing a classifier according to the principles of the invention and then describes the apparatus in detail. Finally, another apparatus for producing a classifier according to the principles of the invention is described in overview.
Apparatus for Making a Classifier: FIG. 1
FIG. 1 shows apparatus 101 for making a classifier. The apparatus includes example data 102, degree of certainty classifier 103, teaching classifier 106, and annotated training data 105. Data 102 contains the data portions which the classifier produced by apparatus 101 must classify. The example data is used to train the classifier so that it can successfully classify other data having the same characteristics as the example data. Degree of certainty classifier 103 is a classifier which not only classifies the data, but also produces a certainty value which indicates how certain degree of certainty classifier 103 is of its classification.
Annotating classifier 106 is a classifier which can take data portions from example data 102 and annotate them to indicate how they are to be classified. The annotation may be a simple indication of the correct classification of the data portion or it may be a more complex value which indicates the degree of closeness of the data portion to the proper classification. In any case, the annotation can be represented as an array of one or more bits. For instance, if there are 8 classifications, the array might contain 3 bits.
Annotating classifier 106 may be another automatic classifier, or it may include interactions with a human being. In the latter case, classifier 106 might operate interactively with display 113, keyboard 115, and mouse 117. In such an embodiment, annotating classifier 106 would display a data portion 107 on display 113 and the human being would indicate the annotation by input from keyboard 115 or using mouse 117 to select an annotation from a menu.
Alternatively, if annotating classifier 106 is an automatic classifier, it may receive data portions 107 as data structures and output the annotations as data structures as well. Depending on the implementation, the annotation may also include the classification provided by classifier 103. Annotated training data 105, finally, is a collection of data portions which have been annotated, either by annotating classifier 106 or other annotating agents.
Operation of apparatus 101 is iterative. For a given iteration, a degree of certainty classifier 103(n) has been trained on the annotated training data 105(n). Degree of certainty classifier 103(n) then classifies data portions from example data 102. For each data portion in example data 102, degree of certainty classifier 103 determines the classification of the data portion and the certainty value for the classification. If the certainty value represents a low degree of certainty for a data portion, degree of certainty classifier 103(n) provides the data portion 107 to annotating classifier 106. Annotating classifier 106 then annotates the data portion 107 and provides annotated data portion 109 to annotated training data 105.
Thus, after degree of certainty classifier 103(n) has classified all of the data portions in example data 102, a number of annotated data portions 109 have been added to annotated training data 105. The previous annotated training data 105(n) and the new annotated data portions 109 make up annotated training data 105(n+1). Training data 105(n+1) is then used to produce a new degree of certainty classifier 103(n+1). Iterations continue in this fashion until a degree of certainty classifier 103(n+x) is produced which is accurate enough for the purpose at hand. The iterations may cease, for example, because degree of certainty classifier 103(n+x) is correctly classifying all of the data portions in example data 102 or because it has reached a point where repeated iterations can no longer be justified.
Efficiency of operation of apparatus 101 is increased if some annotated training data 105 is provided prior to the first iteration. Experience has shown that for every bit Ci in the bit array used for the annotations there should be data portions such that there are at least three annotations in the initial training data 105 with Ci =1 and at least three with Ci =0. Thus, if the annotation is a single bit indicating whether or not a data portion belongs to a category of interest, there should be at least 6 data portions in training data 105, three that belong to the category and three that do not. This property of the initial training data has been found to help avoid a long period where poor estimates of uncertainty are produced.
It should be pointed out here that while the annotated data portions 109 produced by annotating classifier 106 are used in apparatus 101 to train the next degree of certainty classifier 103, they could just as well be used to train another classifier altogether. Annotated data portions 109 thus represent generally useful training data, and apparatus can be seen not only as apparatus for producing a classifier 103, but also as apparatus for producing training data. The training data produced by apparatus 101 is particularly effective. The reason for this is that degree of certainty classifier 103(n) effectively filters example data 102 for those data portions 107 which are most useful in training.
Details of Degree of Certainty Classifier 103: FIG. 2
FIG. 2 shows details of degree of certainty classifier 103. Components of classifier 103 are segmenter 207, which divides examples 102 into portions 208 suitable for interpretation by uncertainty measuring device (UMD) 203, uncertainty measuring device (UMD) 203, which assigns a classification 111 and an entropy value 209 indicating the degree of uncertainty to each portion 208, and chooser 204, which determines which of the portions 208 are to be provided to annotation classifier 106. Uncertainty measuring device 203 is provided with classification parameters 210 by uncertainty measuring device generator 206. UMD generator 206 uses annotated training data 105 to compute parameters 210.
Operation is as follows: Example data 102 is input to segmenter 207. Segmenter 207 breaks the data apart into portions 208 suitable for input to UMD 203. For example, if example data 102 is the output of a newswire, segmenter 207 may break it into individual news stories. Segmenter 207 also checks each portion 208 against the annotated training data 105 to see whether the data portion is included in the annotated training data 105. If it is not, segmenter 207 passes data portion 208 to UMD 203.
UMD 203 classifies each unannotated data portion 208 according to parameters 210 and also computes an estimate 209 of the entropy of the classification to that data portion. The entropy is a numeric measure of the uncertainty as to the correct value for classification 111 for that data portion 208. The greater the uncertainty, the larger the entropy value 209.
The data portions 208, entropy values 209, and perhaps also classifications 111 are output to a chooser 204. Chooser 204 chooses a subset of data portions to be the output of apparatus 103 on the basis of entropy value 209. In a preferred embodiment chooser 204 chooses the single data portion with the highest entropy value 209 as the output of degree of certainty classifier 103. Other strategies may be useful in certain circumstances. For instance, it may be convenient to display several data portions 209 with high entropy values 209 and let a human interacting with annotating classifier 106 judge them all at once. When the number of possible classifications is small, it is also useful if chooser 204 picks the portion 208 with the highest entropy value 209 for each classification, rather than simply the portions 208 with the highest entropy values 209, regardless of classification.
Of course, other measurements besides entropy of the quality of data portions for training could be used in selecting data portions. For instance, we could treat probability estimates themselves as the desired output of the classifier, and use an estimate of the variance of these probability estimates as a measure of quality. If a classifier based on fuzzy logic were used, the degree of membership produced by the classifier could be used. If the classifier is able to determine the class of a data portion by examining only some of its bits, then one measure of quality is how many bits of a data portion must be considered before the data portion can be classified: the more bits which must be considered, the more useful the data portion is for further training.
Implementation of Uncertainty Measuring Device 203: FIG. 3
In a preferred embodiment, UMD 203 is implemented as shown in FIG. 3. There are two main components: classifier array 302 and entropy calculator 303. Classifier array 302 contains one probabilistic binary classifier 305 for each bit in the array of bits making up an annotation. Classifiers 305 are described in more detail below. In operation, each probabilistic binary classifier 305(i) receives parameters 210(i) from UMD generator 206 as well as the text portion 208 to be classified. The ith probabilistic classifier 305(i), outputs an estimate of P(Ci =1│W), the probability that the ith bit of the data portion's correct annotation is 1. The output of classifier array 302 is the set of values P(Ci =1│W) for i=1 . . . n.
Given estimates of P(Ci =1│W), one measure of the uncertainty as to the complete n-bit annotation C=(C1, C2, . . . , Cn) of a given data portion 208(W) under the assumption that all bits are independent is the entropy H(W) of the joint probability distribution given by P(C1 │W), . . . , P(Cn │W). (Entropy is described in Ingels, F. Information and Coding Theory. Intext, Scranton, 1971.) The entropy H(W) is equal to: ##EQU1## where the logarithm is to base 2. By the rules of statistics, H(W) is therefore also equal to the more easily computed expression: ##EQU2##
The calculation of H(W) for data portion 208(W) is implemented in an entropy calculating program 303 whose input is the set of estimates of P(Ci =1│W) output by classifier array 302, and whose output is H(W) 309, an estimate of the entropy associated with the annotation of data portion 208(W). The value H(W) is highest when the annotation of data portion 208(W) is the most uncertain.
Apparatus and Operation of A Probabilistic Binary Classifier 305: FIG. 4
FIG. 4 shows details of a probabilistic classifier 305(i) in a preferred embodiment. UMD generator 206 provides a set of parameters 210(i) for each probabilistic classifier 305(i). There are three such parameters: Q 404(i), A 406(i), and B 408(i). Q 404(i) is a query which is presented to text portion 208(W) to determine a class membership score M(W) 407 which reflects the probability that text portion 208(W) has a given classification. The query consists of terms which are typical for the given classification and numeric weights indicating the importance of the term for the classification. Class membership score M(W) 407 is determined from the query terms which are present in text portion 208(W) and their weights. A 406(i) and B 408(i), are logistic parameters used to put M(W) 407 into the proper form for presentation to entropy calculator 303. How the parameters are produced by UMD generator 206 will be explained in detail below.
Probabilistic classifier 305(i) is implemented by providing parameter Q 404(i) and a data portion 208(W) as inputs to statistical text retrieval system 403, which produces a class membership score M(W) 407 for the data portion 208(W) and then using M(W) 407 and the parameters A 406(i) and B 408(i) in logistic function calculator 405. Logistic function calculator 405 is a program which implements the mathematical function: ##EQU3## where e is the constant which is the base of natural logarithms. The output of the logistic function calculator 405 is the estimate of P(Ci =1│W) 307 (i) output by probabilistic classifier 305(i). Of course, all of the probabilistic classifiers 305 use the same text retrieval system 403 and logistic function calculator 405. It should also be noted here that statistical text retrieval system 403 can process both textual and nontextual data. For instance, numeric data values can be converted to values of nominal variables by standard techniques (see Chatfield, C., Problem Solving: A Statistician's Guide, Chapman and Hall, London, 1988) and the names of those nominal variable values can then be treated as words by the text retrieval system.
Training Probabilistic Binary Classifier 305(i): FIG. 5
The accuracy of the estimate of P(Ci =1│W) produced by probabilistic classifier 305(i) depends on UMD generator 206's computation of the parameters from the training data. It is this computation which "trains" probabilistic binary classifier 305(i) by "training" its parameters.
The training of query Q 404(i) uses a relevance feedback capability of the statistical text retrieval system 403. Relevance feedback is the automated construction of a text retrieval query from portions of text that are marked as being relevant (members of a class of texts of interest), and portions of text that are marked as being nonrelevant (not members of a class of texts of interest). In the present case, these portions of text are taken from the annotated training data 105. Relevance feedback can also take into account an initial suggested query provided along with the relevant and nonrelevant texts. The output query formed by relevance feedback consists of words extracted from the relevant and nonrelevant example portions 109 and from the initial query, along with numeric weights indicating the importance of those words in identifying texts which are relevant. The weights may be computed using a variety of methods, for instance by making use of estimates of the probability that a word occurs in relevant documents and the probability it occurs in nonrelevant documents.
The construction of text retrieval systems implementing relevance feedback is disclosed by Gerard Salton in Automatic Text Processing: The Transformation, Analysis, and Retrieval of Information by Computer, Addison-Wesley Publishing, 1989; or by Donna Harman in Information Retrieval: Data Structures and Algorithms, Prentice-Hall Publishing, 1992. Text retrieval systems which can perform relevance feedback are also commercially available. One example is Personal Librarian from Personal Library Software of Rockville, Md.
Details of UMD generator 206 are shown in FIG. 5. Preparer A 501 takes annotated data portions 105(n) as input and produces marked data portions 502(n) as output. Preparer A 501 removes the annotations from the annotated data portions 105(n), but before doing so examines bit Ci of the annotation of each data portion. If bit Ci is equal to 1, that data portion is, in a fashion appropriate to the text retrieval system, marked as being relevant. If bit Ci is 0, that data portion is, in a fashion appropriate to the text retrieval system, marked as being nonrelevant.
Marked data portions 502(n) produced by Preparer A 501 are input to relevance feedback component 507 of statistical text retrieval system 403 along with appropriate commands to operate relevance feedback component 507. Relevance feedback component 507 outputs query Q 404(n) in response to marked data portions 502(n).
Preparer B 503 then takes annotated data portions 105(n) as input, removes the annotations from annotated data portions 105(n), and outputs the resulting unannotated data portions 504(n) to retrieval component 509 of text retrieval system 403. Query Q 404(n) is also input to retrieval component 509, along with appropriate commands to cause text retrieval system 403 to operate in retrieval mode. Text retrieval system 408 uses query Q 404 (n) and unannotated data portions 504(n) to compute a set of class membership scores M 510(n) which contains one class membership score M(W, n) for each data portion W in annotated data portions 105.
Because relevance feedback was used to produce Q 404(n) the class membership score M(W, n) of each data portion W will be statistically correlated with P(Ci =1│W). The transformation of each class membership score M(W,n) into an actual estimate of P(Ci =1│W) is done in the preferred embodiment by means of logistic parameters A 406(n) and B 408(n).
The logistic parameters A 406(n) and B 408(n) are produced using logistic regression software 511, as follows. Preparer C 505 receives annotated data portions 105(n) and produces labeled data portions 506(n). Preparer C 505 removes the annotations from annotated data portions 105(n), but before doing so, preparer C 505 examines bit Ci of each annotation. If bit Ci of the annotation is equal to 1, the data portion is, in a fashion appropriate to the logistic regression software, labeled with the response value 1. If bit Ci is 0, the data portion is, in a fashion appropriate to the text retrieval system, labeled with the response value 0.
The labeled data portions 506(n) output by Preparer C 505 are input to logistic regression calculator 511 along with class membership scores M(n) 510. The output of the logistic regression software 506 is the pair of parameters A 406(n) and B 406(n) derived from the labeled data portions 506(n) and the class membership scores M 510(n). The logistic regression software will choose parameters A 406(n) and B 406(n) so that a logistic function with those parameters will, when a class membership score M(W) for any data portion W is input, produce an output close in value to the correct response value (0 or 1). Such an output serves as a good estimate of the probability P(Ci =1│W). While the logistic regression computation in the preferred embodiment uses a binary model, non-binary models may be used as well.
Techniques for performing logistic regression are well known in the art. For details, see Agresti, A., Categorical Data Analysis, John Wiley, New York, 1990. and McCullagh, P. and Nelder, J., Generalized Linear Models, Chapman & Hall, London, 2nd edition, 1989. Logistic regression is also a capability in commercially available statistics packages such as SPSS from SPSS, Inc. of Chicago, Ill.
Using Apparatus 101 to Produce Training Data: FIG. 6
As previously pointed out, apparatus 101 is in one aspect apparatus for producing training data 105. In apparatus 101, the training data 105 is used to train a sequence of degree of uncertainty classifiers 103. When the desired degree of accuracy is reached, the last classifier 103 is run on actual data, and example portions 107 and classifications 111 are output by classifier 103 to whatever application requires the information. In this case, classifier 103(i) which produced the example portions 107 and classifications 111 annotated by annotation classifier 106, and classifier 103(j) which will be used to do the actual classification are of the same type, and we say that classifier 103 is trained by homogeneous uncertainty sampling. However, it is not necessary that classifier 103 which is providing the example portions 107 and classifications 111 to annotation classifier 106 are of the same type as classifier 103 which is trained with training data 105. When they are not of the same type, we say that training is by heterogeneous uncertainty sampling.
Homogeneous uncertainty sampling usually produces more effective data interpreters than heterogeneous uncertainty sampling. However, training using heterogeneous uncertainty sampling is sometimes necessary. For instance, the final interpreter and training method desired in application may be too inefficient to use as degree of certainty classifier 103, or the final interpreter may not produce output in a form from which uncertainty can be estimated.
FIG. 6 shows how an already trained degree of certainty classifier 103 and an annotating classifier 106 can be used to provide annotated example portions 109 for use by a trainer 603 to train classifier A 605. Operation of apparatus 601 is immediately apparent from FIG. 6: example data 102 is presented to trained degree of certainty classifier 103, which outputs example portions 107 and their classifications 111 when the classifications have a low degree of certainty. Annotating classifier 106 then annotates the example portions to indicate whether the classification 111 provided by classifier 103 was correct and provides the annotated example portions 109 to trainer 603, which uses them to train classifier A 605. Classifier A 605 may of course be of a different type from classifier 103.
The foregoing Detailed Description has disclosed to those skilled in the art how to make and use apparatus for training data interpreters which reduces the time and effort required for training. While the embodiment disclosed herein is the best mode presently known to the inventors for implementing their invention, it will be immediately apparent to those skilled in the art that the principles of the invention may be implemented using any technique by which a subset of the example data which is particularly useful for training can be determined. Where annotating classifier 106 involves human participation, the apparatus can be implemented using any kind of interface which presents example portions to the human user in a fashion which permits quick and easy annotation. Further, as was apparent from the Detailed Description, many variations are possible even in the techniques used in the disclosed embodiment.
All of the above being the case, the foregoing Detailed Description is to be understood as being in every respect illustrative and exemplary, but not restrictive, and the scope of the invention disclosed herein is not to be determined from the Detailed Description, but rather from the claims as interpreted with the full breadth to which they are entitled under the patent laws.


text:
Race Hard. Play Hard. All The Way.
Sled Dog Team Role Play Forum.
Search Query
Display results as :
Advanced Search
Race Hard. Play Hard. All The Way.
Welcome To The Team.
Profiles and the Like.
Howling's Sledders!
Join date
Tooele, UT
Subject: Howling's Sledders!
Sun Jan 31, 2010 2:37 pm
Alias -
The Lone Warrior
Calling -
Gender -
Cycles -
Breed -
Czech Vlak (Czechoslovakian Wolfdog)
Pelt -
His coat consists of three different layers of color, the bottom being a pale tan, though some confuse it with white, the middle a more dull, darker tan coloring. The top of his coat is a mesh of grey, brown and dun. He has two spots above his eyes that are a dark grey; there is a thin scar on his muzzle, and two others that crisscross to the left side of his nose. His right ear has a chip missing near the base.
Eye Color -
Position/Team# -
Team 88- Kohi- Team Dog
Personality -
Tundra... Tundra, Tundra, Tundra; he is so very much laid back that it's a wonder where he got his facial scars. When the time calls for it, this lax male won't hesitate to step into place as a mediator, or even to duke it out himself to keep teammates safe from harm. He always takes responsibility for his actions, like a leader should, but since his age has caught up with him Tundra isn't so quick to hand out punishment for the younger generations. He enjoys watching pups, as they make him feel so much younger when he plays with them; his fatherly instinct is always in affect, and it's a known fact that he is like the matriarch of the sled team he is on. With the heart of a true runner, Tundra will easily encourage others on his team with a smile, well placed words of comfort, and of course by obeying his team leads; he will step in to put a dog in their place if they cross the lead dogs or the humans they are under command of. His tolerance for fools only extends so far, and he will make it known if it is getting out of control with snarls and upraised tail, though he won't necessarily leap into action past this if others are quick to get themselves under control. Tundra will not allow any form of betrayal among his team, either by the lead dogs, the team dogs, or the swings. If anything remotely suspicious of betrayal occurs, be prepared for Tundra to be up in your face with all the seriousness of a true wolf thinking of how to dispose of someone.
History -
Tundra was born into a litter of seven, his dam being a well taken care of show Vlak, his sire a sled dog descendant. Tundra and his three sisters were closer than the other two males, who constantly picked on the runt, Dai. When Tundra was old enough, for he was the largest of all the pups, he quickly attacked the other two males, saving Dai from further torment by them. Their master saw this, and how efficient Tundra had dished out the punishment for his sibling, and immediately put him to training in the basics. After a few months of going over the same thing, Tundra became rather bored with the outings with his master when going on his hunts; Tundra was only two years of age when he became lost.
He was picked up by a young man after wandering about for a few weeks, and the two became inseparable after Tundra was taken into the small cabin by the man. He met another canine, a female Husky, who was becoming ill with age; Tundra helped ease the mans misery when she passed on the following year. When Tundra had been seen by a man who bought and sold dogs for a living, Tundra's master was given an offer than even he couldn't refuse; 10,000 dollars, and two Huskies to help work around the cabin; Tundra was sold for something he thought he wasn't worth. His harsh training as a cart puller had begun, and soon Tundra became all muscle and bone, with a thin layer of fat to keep him warm in the cold nights.
Again he was sold when he reached four, to a cruel musher who lashed his dogs for even the small misunderstanding of orders. Tundra was given the lead position after one month on the team, but after some of the dogs revolted against their master Tundra was sold once more. He was older, and slowing down a bit but he was once more given the lead position that he was seemingly destined to be placed in. He stayed on the team for two years, until the master was forced to pack up and move to warmer climates for numerous reasons, which is where he ends up here. When he was two months shy of seven, his master saw him slowing in his runs and so he was moved to be a team dog.
Reaction Towards Other -
It really depends on who he is dealing with; the leads he respects, but only if they treat him as an equal rather than an underling; the rest of the team he'll get along with, or become close to if they are willing to put up with his tendency to give advice like a lead dog would to his teammates. He's an all-around happy dog, even though he does miss leading a team, but he won't disrespect another at any point.
Notes -
Tundra has been neutered, though he was the father of one litter when he was four; the litter and mother passed away during labor and so he doesn't speak of it.
Waffle House
Last edited by HowlingHooves on Wed Feb 03, 2010 5:39 pm; edited 2 times in total (Reason for editing : Updating Team)
Howling's Sledders!
Permissions in this forum:
reply to topics in this forum
Race Hard. Play Hard. All The Way.
Welcome To The Team.
Profiles and the Like.
Race Hard. Play Hard. All The Way.
Welcome To The Team.
Profiles and the Like.
Jump to:
Select a forum
|--UPDATES! [Read Daily]
|--Welcome To The Team.
| |--Guest Help!
| |--Rules. READ AND LOVE!
| |--Profiles and the Like.
| | |--Holding Pen
| |
| |--Get Help Here
|--Sled Dog Teams
| |--Team # 849 [Gear's Team]
| |--Team #88 [Kohi's Team]
| |--Other Team 2
|--Out Of Charrie Chat
|--1x1 or other RP's
|--Character of the Month!
|--Member of the Month!
|--Art Work
Free forum
Free forum support
Report an abuse


text:
Sunday, November 21, 2010
Christmas in Bayeux - Stephanie Burkhart
7 comments:
- Julia Rachel Barrett said...
Wow, absolutely beautiful! I love your use of French - which is a language I speak very little of and would like to speak more of! Great photos!
- November 22, 2010 at 8:17 AM
- Keena Kincaid said...
Hi, Steph. I've tried several times to get to Normandy, too, but never seem to get there.
I love the way you connect the modern war to the earlier wars. It really adds to the bittersweet poignancy in the excerpt.
- November 22, 2010 at 8:45 AM
- Danielle Thorne said...
What a wonderful history you have! Eluding the police in France? Service in Germany. Fun interview!
- November 22, 2010 at 10:26 AM
- Maggie Toussaint said...
I love that excerpt, Steph. So gut wrenching for me too. I felt his pain and exhaustion.
Thanks for taking me on a tour of the world with your stories!
- November 22, 2010 at 11:57 AM
- Sue Perkins said...
Hi Steph, Your whole post and photos are wonderful. It's an honour to have you on my blog.
- November 22, 2010 at 12:36 PM
- StephB said...
Sue, thank you so much for having me. It's fun to visit! hehe.
Julia, nice to meet you. I love the French language and when I was learning it, I thought it was easy. LOL!!
Keena, if you make to Normandy, take lots of pics and share with me. hehe
Dani, OMG I was scared! I thought they were going to kick me out of the Louvre!
It's fun to visit other places, isn't it?
Goodie time!
Congrats to Keena and Dani - you've won the autographed postcards. Send me an email to firstname.lastname@example.com or email@example.com with your snail mail and I'll get that off to you.
Congrats to Julia Rachel Barrett! You've won the mousepad. Send me an email to email@example.com or firstname.lastname@example.com with your snail mail and I'll get that right off to you! Thanks to everyone who popped in.
- November 23, 2010 at 6:36 AM
- Margaret Tanner said...
Great blog and excerpt Steph, love the mingling of modern war and past wars. I have visited the 1st World war battlefields and cemeteries in France, a truly poignant experience.
- November 23, 2010 at 1:38 PM