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Foxtrot News Alert
Until further notice, Foxtrot News presents the following executive summary of global geopolitical and economic news:
A full-metal circle-jerk around the gaping maw of the abyss.
Whiskey Tango Foxtrot
Donovan here.
This planet has gone completely off the rails. If you don't have a Plan B in place, well, tick tock.
Not a Particularly Happy Fourth of July Redux
Update July 4th, 2013 – I reread this post again and essentially nothing has changed. Oh sure, I could add details such as Syria, Snowden and the NSA, but the core of the 2010 post is untouched. Things have gotten worse and heads punch deeper into the sand.
Update – This post has gotten hits virtually every single day since it was posted. If you are reading this post, please consider commenting – I'd like to know what you think. And thank you for reading. – UR
Maybe it is the sullen windy day, but I am not feeling particularly independent or celebratory on this Fourth of July. I view with dismay and a sense of helplessness at the egregious behavior of the federal government and the open contempt of the very people and Constitution from which they draw their power. Or so it is written. Numerical superiority of citizens and Constitutional exhortations aside, we are not in charge, those we elect do not answer to us and clearly are above the laws they enact. This is not how it is supposed to be.
I am not sure that we can alter by so much as a few degrees the self-destructive course we are set on, so reversing it seems out of the question. Then the question one has to ask, reverse to what?
Rather than succumb to the hollow symbolic celebrations of a very real historical event, I highly recommend you read for yourself the text of the Declaration of Independence. Try to imagine yourself in the position of these men, read through the list of grievances cited and try to grasp to full meaning, the historically correct context, import and consequence of the these words. This is not an easy task in a world of CGI-assisted imaginations and reset buttons. Read more…
[W]e will do a fair amount of killing
Posted without comment.
"Contemporary American culture is the most powerful in history, and the most destructive of competitor cultures… It stresses comfort and convenience—ease—and it generates pleasure for the masses… The de facto role of the US armed forces will be to keep the world safe for our economy and open to our cultural assault. To those ends, we will do a fair amount of killing." —Lieutenant Colonel (ret.) Ralph Peters, U.S. Army
Fred on the Republican candidates
Presidential Timber – Kindling. Sawdust. Charcoal.
All indicators point downward, I tell you. On the lobotomy box the other night I stumbled on what seemed to be sock puppets standing behind rostrums and hypnotically intoning "The American People…the American People…the American People."
Puzzled, I speculated that it might be a troupe of performing autistics, but soon understood that it it was a debate among Republican candidates for the presidency. Why use people, I wondered? We could do it as well in software. Computer graphics, small recorded vocabulary, narcotic rhythm. Easy.
Consent is neither written, expressed or implied – or is it?
I typed "consent of governed" (or something similar) into my browser search box the other day and came across this interesting post on mises.org – Consent of the Governed? by Robert Higgs. An extract:
I raise this question because, in regard to the so-called social contract, I have often had occasion to protest that I haven't even seen the contract, much less been asked to consent to it. A valid contract requires voluntary offer, acceptance, and consideration. I've never received an offer from my rulers, so I certainly have not accepted one; and rather than consideration, I have received nothing but contempt from the rulers, who, notwithstanding the absence of any agreement, have indubitably threatened me with grave harm in the event that I fail to comply with their edicts.
What monumental effrontery these people exhibit! What gives them the right to rob me and push me around? It certainly is not my desire to be a sheep for them to shear or slaughter as they deem expedient for the attainment of their own ends.
Moreover, when we flesh out the idea of "consent of the governed" in realistic detail, the whole notion quickly becomes utterly preposterous. Just consider how it would work. A would-be ruler approaches you and offers a contract for your approval. Here, says he, is the deal.
I, the party of the first part ("the ruler"), promise:
(1) To stipulate how much of your money you will hand over to me, as well as how, when, and where the transfer will be made. You will have no effective say in the matter, aside from pleading for my mercy, and if you should fail to comply, my agents will punish you with fines, imprisonment, and (in the event of your persistent resistance) death.(2) To make thousands upon thousands of rules for you to obey without question, again on pain of punishment by my agents. You will have no effective say in determining the content of these rules, which will be so numerous, complex, and in many cases beyond comprehension that no human being could conceivably know about more than a handful of them, much less their specific character; yet if you should fail to comply with any of them, I will feel free to punish you to the extent of a law made by me and my confederates.
(3) To provide for your use, on terms stipulated by me and my agents, so-called public goods and services. Although you may actually place some value on a few of these goods and services, most will have little or no value to you, and some you will find utterly abhorrent, and in no event will you as an individual have any effective say over the goods and services I provide, notwithstanding any economist's cock-and-bull story to the effect that you "demand" all this stuff and value it at whatever amount of money I choose to expend for its provision.
(4) In the event of a dispute between us, judges beholden to me for their appointment and salaries will decide how to settle the dispute. You can expect to lose in these settlements, if your case is heard at all.
In exchange for the foregoing government "benefits," you, the party of the second part ("the subject"), promise:
(5) To shut up, make no waves, obey all orders issued by the ruler and his agents, kowtow to them as if they were important, honorable people, and when they say "jump," ask only "how high?"
Such a deal! Can we really imagine that any sane person would consent to it?
Yet the foregoing description of the true social contract into which individuals are said to have entered is much too abstract to capture the raw realities of being governed. In enumerating the actual details, no one has ever surpassed Pierre-Joseph Proudhon, who wrote:
To be GOVERNED is to be kept in sight, inspected, spied upon, directed, law-driven, numbered, enrolled, indoctrinated, preached at, controlled, estimated, valued, censured, commanded, by creatures who have neither the right, nor the wisdom, nor the virtue to do so. To be GOVERNED is to be at every operation, at every transaction, noted, registered, enrolled, taxed, stamped, measured, numbered, assessed, licensed, authorized, admonished, forbidden, reformed, corrected, punished. It is, under pretext of public utility, and in the name of the general interest, to be placed under contribution, trained, ransomed, exploited, monopolized, extorted, squeezed, mystified, robbed; then, at the slightest resistance, the first word of complaint, to be repressed, fined, despised, harassed, tracked, abused, clubbed, disarmed, choked, imprisoned, judged, condemned, shot, deported, sacrificed, sold, betrayed; and, to crown all, mocked, ridiculed, outraged, dishonored. That is government; that is its justice; that is its morality. (P.-J. Proudhon, General Idea of the Revolution in the Nineteenth Century, trans. John Beverley Robinson. London: Freedom Press, 1923, p. 294)
A foul mood has settled in over enough of America for it to be palpable. For all the good it will do us, there is a US presidential election in a little over nine months from now. With one asterisked exception, all are made men and women, promising but never delivering what we have been conditioned to think we want. People foolishly voting for the words "Hope and Change" were instead treated to the third term of George Bush.
What affects the Republic is a cancer and if left untreated it will surely kill the host. Cancers do not leave of their own volition. They must be removed by force or their environment made so toxic they die off. The environment that this particular cancer thrives on is literally flooded with apathy, willful ignorance and denial. Making the environment inhospitable to this cancer requires that the elements present in that environment that allow it to thrive must be reversed in polarity. That will be very difficult given that a significant portion of that environment gets some or all of its sustenance from the cancer itself and real change, even the very idea of real change, has been and will continue to be met with significant resistance.
This cancer is not new; it is an old cancer, well documented and capable of learning from its mistakes. What is different this time is that there are formidable defenses present in this particular host, defenses that have never been present in any prior host. Will those defenses be brought to bear on this insidious cancer that has eaten its way into the soul of the Republic, and if so, will it be in time to save it? From one perspective, it seems that the only barrier to activating these defenses is simply the will to do so.
Tick. Tock.
Time is running out on the world-wide fiat currency Ponzi and I suppose it is only fitting that last straw be Italy. Time will tell.
If you do not currently have a suitable amount of your local currency on-hand (that is to say, NOT in your bank) to weather the shock waves of a sovereign default that cannot be swept under the "innovative financial instruments" rug, now would be a good time. There may be more "holidays" on the calendar than we are all currently aware of.
DISCLAIMER: This is not financial or investment advice. It is a blog post.
EDIT: I have no idea what is up with Remus. I suppose it is possible he really did move to Montana, but it is not likely.


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Davis is key piece to Aggies' puzzle
After earning the starting position and more playing time as Big 12 Conference play got started, Aggie forward Bryan Davis is making the most of the opportunity, leading the Aggies back into NCAA Tournament talk in the process. Aggie Websider visits with Mark Turgeon about Davis's play so far this year.
Full Story
February 5, 2008
Six NY-NJ Players At 2006 Roundball Classic
The 2006 Roundball Classic, which started in 1965 in Pittsburgh, Pa., will be played on Sunday, April 9th in Chicago, Ill. at the United Center. Six players from New York & New Jersey will be in action. The game, which features four of the recruits from Scout.com's number two class, will be carried by CSTV (tape).
Full Story
April 7, 2006
PF Bryan Davis: 20 & 15 For Future Aggie
Full Story
November 23, 2005
Davis Rounds Out A&M's Class
There will be no spring decision for Grand Prairie (Tex.) High power forward Bryan Davis. The big man told one Big 12 school that he's on board.
Full Story
November 17, 2005
PF Bryan Davis: Aggies Work Their Magic
Full Story
November 16, 2005
Two For Davis But Can He Decide?
Grand Prairie (Tex.) High is home to one of the nation's top uncommitted players. Bryan Davis is staring down the barrel at two schools and might not be able to make his mind up this fall.
Full Story
October 25, 2005
Davis Shows No Fear
TEANECK, N.J. -- Fear is not a driving force for Bryan Davis. That's apparent in the 6-8 big man's play on the court. It's also apparent in the number of big-time schools recruiting him to be their lynchpin at the power forward slot.
Full Story
July 11, 2005
USA Festival: Courtside Report II
SAN DIEGO, Calif. -- Some of the nation's top high schoolers were on the campus of San Diego State University this past week for the USA Basketball Youth Development Festival, which is now a part of the International Sports Invitational. IC correspondent Monte Macias provides his second in-depth report on the UNC recruits in attendance ...
Full Story
June 13, 2005

subset: commoncrawl


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SALISBURY Ladies' first hockey team are ten points clear at the top of the West Women's League after a 2-0 win over Bretforton – the only club to have beaten them this season.
The result coincided with all three teams below them in the Premier Division 2A log, including Bretforton, losing their opening games of the new year, writes Mike Vimpany.
Skipper Kate Behan described it as a "perfect start" – one which Salisbury will be eager to build on at lowly Gloucester on Saturday.
Bretforton beat Salisbury in their opening match last September 4-3 and, though Behan's girls played some fine first half hockey, the Bemerton table-toppers owed much to goalkeeper Louise Davidson for pulling off a string of fine saves.
It looked as though Salisbury would turn around 0-0 at halftime, but just before the break they grabbed the lead from a Kerry McCollum penalty corner.
Tilda Hewitt doubled Salisbury's lead after a fine team move but Bretforton refused to give up and when Claire Hobbs was sin-binned the city girls had to dig deep before the points were assured.


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Also, click here for the Complete Guide to Cannon Beach - or the Bed and Breakfast Guide.
Beachcomber Vacation Homes. Numerous vacation rentals in the Cannon Beach area, including Falcon Cove and Arch Cape. All homes have full kitchens, most with wi-fi, TV's, washer/dryer, and some are oceanfront and/or pet friendly. Depending on the home, you may find amenities and luxuries such as a barbecue, claw foot tub, a ship's ladder, views of the estuary at Cannon Beach, granite counters, vaulted ceilings, skylights, and even a solarium. There is a wood-burning fireplace to be found, covered porch, a large deck, spiral staircase, top quality appliances and many more. One is in a forest looking out on the ocean. One home is two levels, one of which has a luxury apartment. Another is a whimsical A-frame close to downtown. Some homes sleep as many as eight. Close walk or drive to many of Cannon Beach's fine restaurants, shops, and the unpopulated beaches nearby. 115 Sunset Blvd. Cannon Beach, Oregon. 855-219-4758. 503-436-4500. Beachcomber Website here.
Beach Vacation Rentals. About 60 vacation homes to choose from:
ocean view, oceanfront and very close to the beach, all in Cannon Beach
or in Arch Cape. Many of them pet friendly. There are massive, grandiose homes with historic character;
brand new, smaller cozy cottages, and even condos near the beach. All
are either oceanfront or very close. Homes sleep as many as 12. Depending
on the rental, you may find such delights as a clawfoot tub, oceanfront
townhouse, gas log fireplace, lawns, garden areas, hot tubs, a deck, multiple
bedrooms, a solarium with an intense beach view, and maybe even an electric
organ. Some homes are in a condo complex with a swimming pool, where you
may find fireplaces, decks, a sleeping alcove, woodstove and some large
suites. Most come fully equipped with kitchens,
TV/VCR, DVD, stereos, dishwashers, dryers, etc. 164 Sunset. Cannon Beach,
Oregon. 503-436-0940. 866-436-0940. www.visitcb.com (Oceanfront vacation rentals, cottages, homes, Arch Cape, Cannon Beach, fireplace, outdoor tubs)
Mention BeachConnection.net when inquiring.
Schooner's Cove Inn. You can literally walk out your door and onto the beach. All oceanfront rooms and suites have newly remodeled kitchens, and bathrooms. Take in the ocean air from your own deck, or relax on a beachfront lawn complete with picnic tables and barbecues, where you can dine next to the beach. There's an oceanfront spa on the premises as well. Rooms have gas fireplaces, free wi-fi, DVD players with DVD rentals, HBO, in-room gourmet coffee and it has laundry facilities for guests. Handicapped access, and AAA diamond rating. Depending on the room, you may find a separate bedroom with a queen bed, living area and hide-a-bed; rooms with a king bed; some rooms host larger groups or families with a bedroom and a semi-private sleeping area. Some units cater to couples and romantic moments at the beach while others host families or multiple guests. 188 North Larch Street. Cannon Beach, Oregon. (800) 843-0128. www.schoonerscove.com (Hotel oceanfront, upscale, luxury, balcony/deck, spa)
Stephanie Inn. Awe-inspiring ocean views with the casual elegance of a New England country inn and the sophistication of a boutique resort hotel. Suites and guest rooms are stylishly furnished, with warm fireplaces, Jacuzzis and a sweeping sense of romance. Some rooms or suites come with a gas fireplace, Jacuzzi, wet bar, balcony or patio, steam shower. Each unit comes with luxury furnishings and ocean views, along with flat-screen TV, DVD players and wi-fi. The décor is highlighted by natural woods, hand tiled baths and custom lit vanities. A complimentary breakfast served in the morning at the four-star Stephanie Inn Dining Room. No pets; children over 12 welcome. The Carriage House is a separate building with an extra measure of privacy, with four large suites dressed in pampering luxury and dramatic ocean views. Fresh cookies in the hotel lobby. 2740 South Pacific. Cannon Beach, Oregon. 1-800-633-3466. www.stephanie-inn.com/
Surfsand Resort. A Cannon Beach legend, the Surfsand Resort is mere steps from Haystack with dramatic views of even more ocean and landmarks. Full-service is the key to this upscale contemporary hotel, with highlights that include an indoor swimming pool, iPod docks, wi-fi, deep soaking tubs, a beachfront deck, seasonal cabana service, DVD library and gas fireplaces. Child friendly and some pet friendly rooms. Also onsite is a hot tub, massage rooms and a comprehensive fitness center with two Finnish saunas. Guest rooms vary from two-bedroom suites to smaller but still sizable studios. Oceanfront balconies, patios available. Units come with microwaves, two showerheads, LCD TV's, snack refrigerators, coffee makers, radio and audio systems, in-room safes, complimentary morning newspaper, among others. Complimentary seasonal features complimentary kids activities, hot dog roasts and ice cream socials. Room service. Expansive wedding, meeting, business facilities. 148 West Gower. Cannon Beach, Oregon. 800-547-6100. www.surfsand.com Surfsand Fun Fact: the hotel occasionally hosts special wine events, sometimes wine dinners.
Oceanfront Bed and Breakfast. It's like a vacation
rental and a B&B in one. Right up against one of the coast's
most pristine and private beaches, this glorious home lets you step right
onto the sand. The deck is yours as well, along with a seaside garden.
A cozy living room is furnished with easy chairs, a sofa and skylights,
all in front of a large window to the wave action and serene beach of
Arch Cape. During the winter, the fireplace crackles as waves explode
on nearby rock structures. A full kitchen is another highlight in this
unique lodging, which is part rustic, part upscale home. In the morning,
hosts Jim and Barbara Shaw make a stunning breakfast for you. The suite
features one bedroom with a queen size bed and two full bathrooms, while
the living room can sleep as many as three. 79924 Cannon Road, Arch Cape,
Oregon. (503) 436-1422. (888) 269-4483. www.shawsoceanfrontbb.com (BnB, vacation rental, fireplace, Arch Cape, Cannon Beach, kitchen)
Mention BeachConnection.net when inquiring.
Tolovana Inn. With breathtaking views of Haystack Rock to the north and the pristine beaches of the Oregon coast just steps away, Tolovana Inn is one of Cannon Beach's favorite hotels. Spacious oceanfront and ocean view suites, which are complete with a well-equipped kitchen, cozy fireplace, private balcony, cable television, DVD players and complimentary wireless connectivity, are a perfect setting for adults and families alike. The warmth and comfort of each guest room is ideal for those stormy winter days and coming in after a beach bonfire in front of Tolovana watching the sun slip over the ocean's edge. Guests love the sparkling clarity of Tolovana Inn's saltwater indoor pool. It uses a Saline System rather than a chlorination process, eliminating burning eyes and caustic odor. You'll want to try our fitness center, relax in the Jacuzzi spa and saunas or truly pamper yourself with Tolovana Inn's on-site masseuse. Tolovana Inn offers pet-friendly units and is a member of the "Green" Hotel Association. 800-333-8890. 3400 S. Hemlock Street Cannon Beach, Oregon. www.TolovanaInn.com (Hotel oceanfront, upscale, luxury, balcony/deck, fireplace, Jacuzzi, indoor pool)
The Wayside Inn. An ocean view hotel located at the South end of Cannon Beach across from Tolovana Park. Enjoy a charming hotel with easy access to miles of sandy beach, and Haystack Rock. Guest's will enjoy ocean view rooms and suites, gas fireplaces, small kitchens (many with ovens), flat screen TVs, complimentary Wi-fi, and decks. After a day at the beach you can take a swim in the heated indoor pool, or soak in the spa (pool and spa are under renovation, reopening Fall 2011). The inn has many dog friendly rooms, and each dog receives a special dog basket to use during their stay. The Wayside Inn has an ideal location at the quiet end of Cannon Beach only a mile from downtown. There are wonderful restaurants, and a great coffee shop/bakery within walking distance of the inn. All rooms are non-smoking. 3339 S. Hemlock Street, Cannon Beach, Oregon, 888-659-6397. www.thewayside-inn.com
BeachcombersNW.com. Find vacation rentals on the Oregon and Washington coasts, including Cannon
Beach, Seaside, Manzanita, etc. All sorts of homes and getaways:
for romantic retreats, families, pet friendly, large
groups, oceanfront and ocean view, and hot tubs. The most comprehensive
website and search engine for vacation rentals, including rental locator
map, annual events calendar, seasonal specials listings, newsetter sign
up, availability request forms, quick & easy rental search results,
and much more. BeachcombersNW.com
Mention BeachConnection.net when inquiring.
Beach Vacations. Now with an office in Cannon Beach. Well
over 100 homes – all quite distinctive and carefully selected to
be special. All over north coast: including Seaside, Manzanita, Rockaway
and more. Also in Yachats, Waldport, Newport, Nye Beach, Otter Rock, Depoe
Bay, Gleneden Beach, Lincoln Beach, Lincoln City, Neskowin, Pacific City,
Tierra Del Mar. Full kitchens, laundry facilities, oceanfront, ocean view,
garden view and homes that are studios to elaborate mansions with eight
bedrooms are part of their massive catalog. Some stand on secretive, grandiose
bluffs in exclusive developments; some are condos or sophisticated homes
practically on the beach. Some have hot tubs, and some are pet friendly.
Perfect for families – or romantic getaways and special occasions.
Large reunions, corporate or group retreats are well catered to. Special
guest services like gift baskets, deliveries, even groceries can be arranged.
1235 Hemlock, Cannon Beach, Oregon. 1-800-723-2383. www.oregonbeachvacations.com (Oceanfront vacation rentals, cottages, homes, Arch Cape, Cannon Beach, fireplace, outdoor tubs)
Mention BeachConnection.net when inquiring.
Kites Vacation Rental Home, Cannon Beach. Close walk to the beach in a forested setting. Deck views in front and back, beamed cathedral ceiling in the living room and a stellar 160-degree ocean view all along the west side. Kid friendly and pet friendly. Amenities: four bedrooms and three baths easily sleep six; parking for three cars and an easy access ramp. Detailed features include: a two-cook kitchen with an ocean view, WI-FI internet access, dish T.V., living room video tape or DVD player, I-Pod dock, large spa tub and walk-in shower in the master bath. coffee maker, cookware, dishwasher, full kitchen, microwave, refrigerator, toaster. There is also a barbeque on the back deck and a covered backyard area for wet weather days. Fenced backyard perfect for pets and kids. Quick drive to galleries, eateries, shops. Cannon Beach, Oregon. (503) 368-7969 . www.ssvr.com/Unit/Details/47946 (Rental office in Manzanita, via Sunset VR).
BeachcombersNW.com. It's the single biggest resource for vacation rentals along the coasts of Washington and Oregon, where you can hone in on a rental home that fits any criteria, such as romantic retreats, families, pet friendly houses, large groups, ocean front, ocean views and hot tubs.BeachcombersNW includes a rental locator map, annual events calendar, seasonal specials listings, newsetter sign up, availability request forms, quick & easy rental search results, and much more. Find vacation rentals in Astoria, Warrenton, Gearhart, Seaside, Cannon Beach, Arch Cape, Manzanita, Nehalem, Wheeler, Rockaway, Garibaldi, Oceanside, Netarts, Tillamook, Tierra Del Mar, Pacific City, Neskowin, Lincoln City, Gleneden Beach, Depoe Bay, Otter Rock, Newport, South Beach, Waldport, Yachats, Heceta Beach, Florence, Dunes City and rentals all the way down to Brookings.as well as Washington towns Moclips, Pacific Beach, Copalis, Ocean Shores, Westport, North Cove, Oysterville, Nahcotta, Ocean Park, Long Beach, Seaview and Ilwaco. Mention BeachConnection.net when inquiring
Lodging Near Cannon Beach (Seaside, Manzanita, Nehalem, Rockaway Beach)
Search over 5,000 Pages for Oregon coast subjects, articles or lodging...


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text:
|Publication number
|Publication type
|Application number
|Publication date
|Apr 10, 2007
|Filing date
|Jun 27, 2002
|Priority date
|Jun 27, 2002
|Fee status
|Also published as
|Publication number
|Nuria M. Oliver, Eric J. Horvitz, Ashutosh Garg
|Original Assignee
|Microsoft Corporation
|Export Citation
|BiBTeX, EndNote, RefMan
|Patent Citations (64), Non-Patent Citations (83), Referenced by (58), Classifications (21), Legal Events (6)
|External Links: USPTO, USPTO Assignment, Espacenet
The present invention relates generally to computer systems, and more particularly to a system and method to automatically recognize activities and context via a hierarchical (layered) probabilistic approach while analyzing multiple input streams of diverse nature in accordance with multiple levels of temporal granularity.
Computer models provide the basis for many analytical systems especially in the area of artificial intelligence. Previous work in this area has explored the fusion of multiple sources of information to reason about higher-level abstractions relating to human context in computer systems operations. Recent work on probabilistic models employed for reasoning about a user's location, intentions, and focus of attention have highlighted opportunities for building new kinds of applications and services. A portion of such work leverages perceptual information to recognize human activities and is centered on identification of a specific type of activity in a particular scenario. Many of these techniques are targeted at recognizing single, simple events (e.g., 'waving a hand' or 'sitting on a chair'). However, less effort has been applied to methods for identifying more complex patterns of human behavior, extending over longer periods of time.
Dynamic models of periodic patterns associated with people movements are employed by some methods to capture periodicity of activities such as walking. Other approaches to human activity recognition employ graphical models. A significant portion of work in this area has made use of Hidden Markov Models (HMMs). As an example, HMMs have been utilized for recognizing hand movements to relay symbols in American Sign Language, wherein different signs can be recognized by computing probabilities for models that different symbols may produce given an observed visual sequence. More complex models, such as Parameterized-HMM (PHMM), Entropic-HMM, Variable-length HMM (VHMM), and Coupled-HMM (CHMM), have been utilized to recognize more complex activities such as the interaction between two people. One method proposes a stochastic, context-free grammar to compute the probability of a temporally consistent sequence of primitive actions recognized by HMMs.
Other methods model events and scenes from audiovisual information. For example, these methods have developed a wearable computer system that utilizes a plurality of HMMs for recognizing the user's location, (e.g., in the office, at the bank, and so forth). Still yet other techniques propose an entropic-HMM approach to organize observed video activities (e.g., office activity and outdoor traffic) into meaningful states, wherein the models can be adapted in video monitoring of activities such as outdoor traffic, for example. In another approach, a probabilistic finite-state automaton (a variation of structured HMMs) is employed for recognizing different scenarios, such as monitoring pedestrians or cars on a freeway. Although standard HMMs appear to be robust to changes with respect to temporal segmentation of observations, they tend to suffer from a lack of structure, an excess of parameters, and an associated over-fitting of data when applied to reason about long and complex temporal sequences with inadequate training data. In recent years, more complex Bayesian networks have been adopted for modeling and recognition of human activities. To date, however, there has been little advancement on methods for exploiting statistical processes to fuse multiple sensory streams that address modeling problems with robustness and mitigate model training efforts.
The following presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the invention. It is intended to neither identify key or critical elements of the invention nor delineate the scope of the invention. Its sole purpose is to present some concepts of the invention in a simplified form as a prelude to the more detailed description that is presented later.
The present invention relates to a system and methodology within machine learning that allows robust and efficient modeling of dynamic, human patterns of behavior from heterogeneous streams of data. A machine learning system is provided to model and recognize types of real-time human activities in real-life environments, such as in an office or workplace, for example. The learning system and processes relating thereto address problems associated with accurately recognizing and anticipating real-time human activities in a hierarchical manner. The present invention facilitates a robust machine learning system that monitors multiple input sources (e.g., video, audio, keyboard, mouse, telephone, and so forth) and builds models of various human activities from the input source data. The invention allows for context aware systems, such as computer systems that model the user's context and employ it to determine inferences about the user's internal states. The modeling can be achieved via Layered Hidden Markov Models (LHMMs) that facilitate a hierarchical, concrete, real-time, and multi-modal activity-recognition approach to becoming context aware of a human's environment and activities such that human needs may be more accurately and automatically anticipated by the computer.
In one aspect, the present invention facilitates performing automatic and semiautomatic recognition of activity and context from multiple perceptual cues. This can include processes for learning and reasoning about streams of perceptual evidence from visual, acoustic, and kinesthetic channels that accelerate development of a variety of compelling applications and services that hinge on identification of rich, human-centric notions of context. In addition, creating more robust learning and modeling methods for reasoning about information derived from multiple channels enables other systems and processes such as in the realms of multi-modal human-computer interaction (HCI), intelligent environments, and/or visual surveillance.
According to another aspect of the present invention, inference analysis can be performed that takes as inputs raw signals coming from multiple sensors and yields high-level abstract descriptions of the human activities. Thus, the task of moving from low-level signals to more abstract hypotheses about activity brings into focus a consideration of a spectrum of analytical techniques. This can include template matching, context-free grammars, and various other statistical methods.
In accordance with another aspect of the present invention, a layered statistical technique or process is provided for detecting and recognizing human activities based on multiple streams of sensory information. The process is based on a formulation of dynamic graphical models that are referred to as Layered Hidden Markov Models. These layered or hierarchical models facilitate building substantially robust context-recognition systems that process multiple levels of time granularity. In contrast, non-hierarchical modeling techniques can be very sensitive to subtle variations in a target environment and can require extensive retraining when moved to another similar environment. Hierarchical statistical models also support a factoring of a learning and inference problem into multiple, smaller sub-problems, thus producing decompositions along lines of variation and stability.
The following description and the annexed drawings set forth in detail certain illustrative aspects of the invention. These aspects are indicative, however, of but a few of the various ways in which the principles of the invention may be employed and the present invention is intended to include all such aspects and their equivalents. Other advantages and novel features of the invention will become apparent from the following detailed description of the invention when considered in conjunction with the drawings.
The present invention relates to a system and methodology providing layered probabilistic representations for sensing, learning, and inference from multiple sensory streams at multiple levels of temporal granularity and abstraction. The methods facilitate robustness to subtle changes in environment and enable model adaptation with minimal retraining. Layered Hidden Markov Models (LHMMs) can be employed having parameters learned from stream data and at different time granularities, wherein inferences can be determined relating to context and activity from perceptual signals.
Referring initially to
The LPM 20 analyzes the input streams 24 in accordance with the layers and time series data 40 to determine the states 30. The states 30 can include context states such as what is a user doing, how busy the user is, how focused the user is, and where attentional resources of the user are being applied, for example. Other states can include user activities (e.g., phone conversation, face-face conversation, presentation, distant conversation, nobody in the office, user present, other activity engaged), patterns of behavior, emotions (e.g., happy, upset, tired, relaxed, excited), and so forth. This can also include inferences about possible states and/or implied user states. From the state information at 30, one or more computer events 50 can be anticipated, altered, tuned, and/or adjusted in accordance with the determined state. For example, applications may be invoked and/or adjusted based upon the state information (e.g., bringing an instant messenger display to the foreground based on a determined state of availability to receive messages, saving a file based upon a determined state of being away from a desktop, identifying a person in a surveillance application). Other events that can be controlled include timing (e.g., delaying/forwarding a message or other computer activity based upon state), providing feedback (e.g., invoking a dialog session based upon a determination of confusion), changing or adjusting one or more controls (e.g., automatically turning CD player down when another user is detected in the room, automatically invoking/adjusting desktop controls based upon state). This can also include such events 50 as providing help or information utilities to the user based on the determined state 30 and include substantially any action that can be performed on a computer (e.g., executing a subsequent algorithm or control based upon determined state).
Referring now to
As illustrated, the layers 110–130 receive time inputs at 140 through 144 respectively. As noted above, the time inputs 110–130 are generally arranged in descending order of time granularity, although some layers may be sampled at similar time periods. As one example, the layer 110 may be sampled at 100 microseconds and the layer at 120 may be sampled at 1 millisecond. At 150, one or more sensing inputs are processed by the layer at 110, wherein one or more feature vectors (described below) are generated at 154 and fed to the next stage at 120. The next stage 120 analyzes the feature vectors at 154 and generates one or more feature vectors at 160 that are processed by the stage at 130. The last stage, in this case the layer 130, produces one or more state outputs at 170 that reflect one or more human characteristics that are modeled by the respective stages and described above. It is noted that the system 100 can process information in a concurrent and/or serial manner, whereby respective stages 110–130 can perform aspects of parallel and/or serial processing.
In one aspect of the present invention, the layers 234–242 provide a hierarchical approach to learning human activities from sensed/inferred data and employs directed acyclic graphs (DAGs), also referred to as Dynamic Bayesian Networks or Dynamic Graphical Models. Statistical DAGs can provide a computationally efficient and sufficiently expressive solution to the problem of human activity modeling and recognition. Hidden Markov Models (HMMs) and related extensions, (e.g., CHMMs, PHMMs, VHMMs), support the architecture illustrated at 230. Thus, layered HMMs or (LHMMs), can be viewed as particular cases of temporal DAGs.
DAGs generally include a set of random variables represented as nodes as well as directed edges or links between the nodes. They define a mathematical form of a joint or conditional Probability Distribution Function (PDF) between the random variables. From a human behavior perspective, DAGs can be utilized since they constitute a graphical representation of causal dependencies among variables. The absence of directed links between nodes implies a conditional independence. Moreover, a family of transformations can be performed on the graphical structure depicted at 230 that has a direct translation in terms of mathematical operations applied to the underlying PDF. Furthermore, DAGs are generally considered modular, (e.g., one can express a joint global PDF as the product of local conditional PDFs). In view of the above, DAGs present several advantages that are relevant to the problem of human behavior modeling from multiple sensors: they can handle incomplete data as well as uncertainty; they are trainable and provide a process for mitigating over-fitting; they encode causality in a natural way; algorithms exist for performing predictive inference; they offer a framework for combining prior knowledge and data; they are modular; and can be paralleled.
Hidden Markov Models are one of the most popular examples of DAGs for modeling processes that have structure in time. They provide clear Bayesian semantics, efficient algorithms for state and parameter estimation, and can automatically perform dynamic time warping. An HMM is essentially a quantization of a system's configuration space into a small number of discrete states, together with probabilities for transitions between states as illustrated at 230 of
The layered representation illustrated at 230 of
A mathematical analysis will now be described in accordance with the layered representation 230 of
at level L, the HMMs at this level can be thought of as a multiclass classifier mapping these TL observations to one of KL classes. Let XT
and learn a new classification function with time granularity TL+1, fL+1: XT
The layered representation illustrated at 230 of
There are several approaches to performing inference with LHMMs. In one approach, which is referred to as maxbelief, the model with the highest likelihood is selected, and this information is made available as an input to the HMMs at the next level. In a distributional approach, probability distribution information is passed over the models to the higher-level HMMs.
As an example, consider that K HMMs are trained at level L of the hierarchy, Mk L, with k={1, . . . ,K}. Let
be the log-likelihood of model Mk L given various observations up to time t; and let α1(i;Mk L) be the alpha variable of the standard Baum-Welch algorithm at time t and for model Mk L, i.e.,
is the transition probability from state j to state i for model Mk L, and pj(ot; Mk L) is the probability for state j in model Mk L of observing ot. At that level, the observations are classified by declaring C(t)L=arg maxk Λ(k)t L;, with k={1, . . . ,K}. The next level of the hierarchy (L+1) could have two types of observations of τ temporal length, for example: (1) in the maxbelief approach, C(1: τ)L, i.e., the hard classification results from the previous level for each time step—and therefore a vector of τ discrete symbols in {1, . . . ,K}; or (2) in the distributional approach,
i.e., the log-likelihoods for the respective models and time instants, —and therefore a vector of K reals for the associated time step. In the experimental examples described below, performance was not significantly improved by using the latter approach. Therefore the results reported correspond to the maxbelief approach, which is generally simpler in nature.
The following discussion describes processes for decomposition per temporal granularity.
Automatic estimation of the time granularities TL from data is a challenging problem both for standard HMMs and LHMMs. In the experiments reported here, the time granularities at each level were determined based on knowledge about the different classes being modeled. Cross-validation was used to select the optimal values from the original set of proposed ones.
Focusing on the example domain of office awareness, a two layer HMM architecture can be employed. The raw sensor signals are processed with time windows of duration about 100 milliseconds (fine-time granularity). Next, the lowest layer of HMMs classify the audio and video data with a time granularity of less than 1 second, for example. This level of description detects a user's presence, by use of audio, keyboard, mouse, and/or video, for example. Furthermore, the second layer of HMMs represents typical office activities, associated with a time granularity of about 5–10 seconds, for example. At another (higher) level, models could describe what the user has done in the last N minutes. Additionally, models can provide an explanation of the user's activities during the day in addition to the states previously described in
Moreover, employing a hierarchical structure 230 provides several valuable properties. A hierarchical formulation facilitates decoupling different levels of analysis for training and inference. As it is further explained below, respective levels of the hierarchy 230 can be trained independently, with different feature vectors and time granularities. Once the system has been trained, inference can be carried out at any level of the hierarchy 230. Thus, a user or system can retrain the lowest (most sensitive to variations in the environment) level, for example, while mitigating retraining efforts in other levels of the hierarchy 230.
where N is the number of hidden states in the HMM. In general, the expressions for the αt(i) and βt(i) variables are:
where N is the number of hidden states, Pi|j is the probability of state i given state j and pi(Oi) is the probability for state i of observing ot. From the αt (i) and βt(i) variables, one can obtain the model parameters (i.e., the observation and transition probabilities).
In one aspect, the standard Baum-Welch algorithm provides an efficient estimation procedure of the model parameters from unlabeled data. A trained model can be applied to an automatic hierarchical parsing of an observation sequence such as a dendrogram. However, because of computational complexities associated with the above algorithm, the present invention provides an efficient approximation to the full estimation scheme. The approximation can further be employed to construct models that adapt both model topology and/or associated parameters.
At 318 respective model layers of the layered architecture are connected to the next layer via its inferential results (e.g., log likelihoods). Moreover, at 314, rather than training all or most of the levels at the same time, the parameters for respective levels of LHMMs can be trained independently by means of the Baum-Welch algorithm described above. Thus, the inputs (observations) of a respective level are the inferential outputs of the previous level. At the lowest level, observations (leaves of a model tree) are feature vectors extracted from sensor signals.
Proceeding to 322, model states are determined. In one aspect, a Viterbi algorithm can be employed that yields a most likely sequence of states Ŝ within a model given the observation sequence O={O1, . . . ,On}. This most likely sequence can be obtained by Ŝ=argmaxsP(S|O).
In the case of HMMs, a posterior state sequence probability P(S|O) can be given by:
wherein S={α1, . . . ,aN} is a set of discrete states, st∈S corresponds to a state at time t. Ps
Referring now to
Proceeding to 410 of
It is noted that typical office sounds—or other input—can be trained and learned in substantially real time, such as human speech, music, silence, ambient noise, phone ringing and keyboard typing, for example, having a single HMM configured per type of sound or other input. Moreover, the source of sound can be localized utilizing a technique based on a Time Delay of Arrival described below. Video signals can also be classified with discriminative HMMs to implement a person detector. At this level 418, a system detects whether one person is present in a room (semi-static), one active person is present, multiple people are present or there is nobody in the office. The inferential results from this layer at 418 (audio and video classifiers), the derivative of a sound localization component, and a history of keyboard and/or mouse activities constitute a feature vector that is passed to the next higher layer in the hierarchy at 422.
The time granularity at the level depicted at 422 can be set to less than 1 second, for example. The layer 422 handles concepts that have longer temporal extent. Such concepts include the user's high-level activities in or near an office or other location, corresponding to a time granularity of about 5–10 seconds (or other time). The behavior models are HMMs whose observations are the inferential outputs of the previous level as described above. Example activities recognized include PHONE CONVERSATION, FACE TO FACE CONVERSATION, PRESENTATION, DISTANT CONVERSATION, NOBODY IN THE OFFICE (or other location), USER PRESENT, ENGAGED IN SOME OTHER ACTIVITY. As noted above, a plurality of other user states can be determined in accordance with the present invention.
Referring to
Turning now to
The system 600 can access/process perceptual information from a plurality of sources. For example, audio processing 610 can include one or more microphones such a binaural microphones. This can include two mini-microphones (e.g., 20–16000 Hz, SNR 58 dB) with an audio sample rate at about 44100 KHz or other frequency. The microphones can be employed for sound classification at 630 and localization at 660. Video processing at 620 can include a Universal Serial Bus (USB) camera (or other type of camera), whereby a video signal is obtained via a standard USB camera and sampled at about 30 frames per second or other rate. Video input can be employed to determine the number of persons present in a scene at 640, for example. Computer activity at 654 can include keyboard and mouse processing, wherein the system 600 or component 654 (e.g., computer with keystroke/mouse monitor operating in background) logs a history of keyboard and mouse activities during the past 5 seconds (or other time), for example.
The raw sensor signals described above can be preprocessed to obtain feature vectors (i.e., observations) for the lowest-level HMMs. On the audio side, Linear Predictive Coding (LPC) coefficients can be computed. Feature selection can be applied on these coefficients by a process of principal component analysis (PCA) (or any other feature selection method). The number of features can be selected such that more than about 95% of the variability in the data is maintained, which is typically achieved with no more than 7 features. Other higher-level features can also be extracted from the audio signal, such as the energy, the mean and variance of the fundamental frequency over a time window, including a zero crossing rate which can be expressed as:
wherein m is a frame number, N is a frame length, w is a window function, s(n) is a digitized audio signal at an index indicator n, and sign(s(n))={+1, s(n)>=0; −1, s(n)<0}.
As described above, the source of the sound can be localized at 660 via a Time Delay of Arrival (TDOA) process. In TDOA, the measure in question is typically not acoustic data received by the sensors, but rather the time delays between the signals coming from respective sensors. Typically, TDOA based approaches have two parts: a time delay estimation (TDE) and a sound source localization (SSL).
To illustrate this process mathematically, let s(n) be a source signal and let xi(n) be an i-th sensor received signal. If no reverberation is assumed, then xi(n)=ais (n−ti)+bi(n). To model reverberation, a non-linear reverberation function can be added: xi(n)=gi*s(n−ti)+bi(n), wherein αi is an attenuation factor, bi is additive noise and gi is a response between a source and the sensor. In addition, multiple approaches can be provided for estimating the time delay of arrival between the left and right audio signals. In one aspect, suitable performance can be achieved by estimating the peak of a time cross-correlation function between the left and right audio signals over a finite time window
On the video side, four features (or other number) can be computed, for example: a density of skin color in the image (e.g., obtained via a discriminative histogram between skin and non-skin in HSV space), a density of motion in the image (e.g., obtained by image differences), a density of foreground pixels in the image (e.g., obtained by background subtraction, after having learned the background), and a density of face pixels in the image (obtained by means of a real-time face detector). Also, as noted above, a history of the last 5 seconds (or other times) of mouse and keyboard activities can be logged.
The following discussion describes some of the actual performance aspects of the system 600. In addition, another system (not shown) was adapted with a conventional HMM and results were compared between the conventional, single-dimension arrangement and the multi-tier approach of the present invention. The system 600 was tested in multiple offices, with different users and respective environments for several weeks. In preliminary tests, it was found that the higher-level layers of the hierarchy are quite robust to changes in the environment. Thus, when the system 600 was relocated from one office to another, substantially consistent performance was achieved while mitigating the need for retraining the higher levels of the hierarchy. Typically, only some of the lowest-level features (e.g., such as the ambient noise, the background image, and the skin color threshold) required retraining to tune the lowest hierarchy level to new conditions. Thus, it was observed that the hierarchical structure greatly contributes to the overall robustness of the system 600 given changes in the environment, for example.
In a more quantitative study, the performance of the layered model was compared with that of single, standard HMMs. The feature vector in the latter case results from the concatenation of the audio, video and keyboard/mouse activities features in a long feature vector. These type HMMs are referred to as the Cartesian Product HMMs (CP HMMs). The number of parameters to estimate is much lower for LHMMs than for CP HMMs. For example, a five-state HMM with single Gaussian observations of dimensionality 16 may have 5*(16+16+120)=760 parameters to estimate. A equivalent LHMM with 2 levels, two five-state HMMs with three-dimensional Gaussian observations at the highest level, and one five-state HMM with ten-dimensional Gaussian observations at the lowest level, may have 5*(10+10+45)+5*(3+3+3)+5*(3+3+3)=445 parameters. Consequently, encoding prior knowledge about the problem in the layered structure of the models significantly reduces the dimensionality of the problem. Therefore, for similar amounts of training data, it is expected for LHMMs to have superior performance than HMMs, wherein experimental results further confirm such expectation.
It is noted that it is not substantially more difficult to estimate the structure of LHMMs when compared to HMMs. For HMMs and LHMMs the structure of the models can be estimated—and in the respective levels for LHMMs—employing cross-validation, for example. Typically, the only additional consideration when designing an LHMM architecture is determining the number of levels and associated time granularities to provide. In the experiments, however, it was found that an intuition and knowledge can be developed about the problem at hand to estimate how many layers to use and the time span of the respective layer.
As an example of such experiments, results were compared on the performance of 30 minutes of office activity data (e.g., 5 minutes per activity and 6 activities) of LHMMs and HMMs. Given that it was recorded activity data, the ground truth was known for each activity. Generally, the first few seconds of the datasets were ignored for classification purposes, due to a small lag of the models in recognizing an activity. The results are summarized in table 1 below, wherein the HMMs were tuned to the particular testing data, and the LHMMs had been trained many days before, under different office conditions than that of testing.
Confusion Matrix for Highly-Tuned HMMs
Confusion Matrix for Generic LHMMs
Table 1: Confusion matrix for highly-tuned HMMs and generic LHMMs on 30 min of real data, where PC = Phone Conversation; FFC = Face to Face Conversation; P = Presentation; O = Other Activity; NA = Nobody Around; DC = Distant Conversion.
From these experiments it was noted that for similar amounts of data, the accuracy of LHMMs is significantly higher than that of HMMs. There are several possible reasons for the better performance of LHMMs when compared to HMMs: (a) The number of parameters of HMMs is about 40% higher than that of LHMMs for the activities being modeled in the experiments. As a consequence, for about the same amount of training data, HMMs have many more parameters to estimate than LHMMs. Therefore, HMMs are generally more prone to over-fitting and worse generalization than LHMMs, especially when trained with limited amounts of data; (b) HMMs carry out high-level inferences about the user's activity, directly from the raw sensor signals. LHMMs, on the other hand, isolate the sensor signals in a different sub-HMM for respective input modalities. The inferential results of these models feed the HMMs in the next layer, in order to characterize human activities. Due to its layered structure, LHMMs appear to be more robust to noise in the sensor signals and have better generalization performance than HMMs.
In another aspect, it was observed that LHMMs are more robust to changes in the environment than HMMs. In general, reasonable performance on HMMs could not be attained unless the models had been highly tuned to a particular testing environment and conditions. In contrast, LHMMs generally did not require retraining, despite changes in office conditions. It was also observed that the discriminative power of LHMMs is notably higher than that of HMMs, wherein discriminative power includes a determination of the distance between the log-likelihood of the two most likely models. The log likelihoods for the HMMs tend to be much closer to one another, thus, showing these models to be prone to instability and errors in classification.
In view of the systems, processes and experiments described above, LHMMs facilitate inferences about context and activity from various perceptual signals. Some important characteristics of LHMMs when compared to HMMs are: (1) LHMMs encode the hierarchical temporal structure of a discrimination problem; thus, the dimensionality of the state space that needs to be learned from data is smaller than that of corresponding Cartesian Product HMMs; (2) LHMMs are easier to interpret, and, thus, easier to refine and improve, than corresponding CP HMMs; (3) LHMMs can encode different levels of abstraction and time granularities that can be linked to different levels of representation for human behaviors; (4) the modularity of LHMMs enables selective retraining of the levels that are most sensitive to environmental or sensor variation, mitigating the burden of training during transfer among different environments.
In order to provide a context for the various aspects of the invention,
With reference to
The system bus may be any of several types of bus structure including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of commercially available bus architectures. The system memory may include read only memory (ROM) 724 and random access memory (RAM) 725. A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within the computer 720, such as during start-up, is stored in ROM 724.
The computer 720 further includes a hard disk drive 727, a magnetic disk drive 728, e.g., to read from or write to a removable disk 729, and an optical disk drive 730, e.g., for reading from or writing to a CD-ROM disk 731 or to read from or write to other optical media. The hard disk drive 727, magnetic disk drive 728, and optical disk drive 730 are connected to the system bus 723 by a hard disk drive interface 732, a magnetic disk drive interface 733, and an optical drive interface 734, respectively. The drives and their associated computer-readable media provide nonvolatile storage of data, data structures, computer-executable instructions, etc. for the computer 720. Although the description of computer-readable media above refers to a hard disk, a removable magnetic disk and a CD, it should be appreciated by those skilled in the art that other types of media which are readable by a computer, such as magnetic cassettes, flash memory cards, digital video disks, Bernoulli cartridges, and the like, may also be used in the exemplary operating environment, and further that any such media may contain computer-executable instructions for performing the methods of the present invention.
A number of program modules may be stored in the drives and RAM 725, including an operating system 735, one or more application programs 736, other program modules 737, and program data 738. It is noted that the operating system 735 in the illustrated computer may be substantially any suitable operating system.
A user may enter commands and information into the computer 720 through a keyboard 740 and a pointing device, such as a mouse 742. Other input devices (not shown) may include a microphone, a joystick, a game pad, a satellite dish, a scanner, or the like. These and other input devices are often connected to the processing unit 721 through a serial port interface 746 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, a game port or a universal serial bus (USB). A monitor 747 or other type of display device is also connected to the system bus 723 via an interface, such as a video adapter 748. In addition to the monitor, computers typically include other peripheral output devices (not shown), such as speakers and printers.
The computer 720 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 749. The remote computer 749 may be a workstation, a server computer, a router, a peer device or other common network node, and typically includes many or all of the elements described relative to the computer 720, although only a memory storage device 750 is illustrated in
When employed in a LAN networking environment, the computer 720 may be connected to the local network 751 through a network interface or adapter 753. When utilized in a WAN networking environment, the computer 720 generally may include a modem 754, and/or is connected to a communications server on the LAN, and/or has other means for establishing communications over the wide area network 752, such as the Internet. The modem 754, which may be internal or external, may be connected to the system bus 723 via the serial port interface 746. In a networked environment, program modules depicted relative to the computer 720, or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be employed.
In accordance with the practices of persons skilled in the art of computer programming, the present invention has been described with reference to acts and symbolic representations of operations that are performed by a computer, such as the computer 720, unless otherwise indicated. Such acts and operations are sometimes referred to as being computer-executed. It will be appreciated that the acts and symbolically represented operations include the manipulation by the processing unit 721 of electrical signals representing data bits which causes a resulting transformation or reduction of the electrical signal representation, and the maintenance of data bits at memory locations in the memory system (including the system memory 722, hard drive 727, floppy disks 729, and CD-ROM 731) to thereby reconfigure or otherwise alter the computer system's operation, as well as other processing of signals. The memory locations wherein such data bits are maintained are physical locations that have particular electrical, magnetic, or optical properties corresponding to the data bits.
What has been described above are preferred aspects of the present invention. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing the present invention, but one of ordinary skill in the art will recognize that many further combinations and permutations of the present invention are possible. Accordingly, the present invention is intended to embrace all such alterations, modifications and variations that fall within the spirit and scope of the appended claims.
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|U.S. Classification
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|International Classification
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|Jun 27, 2002
Owner name: MICROSOFT CORPORATION, WASHINGTON
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:OLIVER, NURIA M.;HORVITZ, ERIC J.;GARG, ASHUTOSH;REEL/FRAME:013057/0626;SIGNING DATES FROM 20020625 TO 20020627
|Sep 9, 2010
|Fee payment
Year of fee payment: 4
|Nov 21, 2014
|Maintenance fee reminder mailed
|Dec 9, 2014
Owner name: MICROSOFT TECHNOLOGY LICENSING, LLC, WASHINGTON
Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNOR:MICROSOFT CORPORATION;REEL/FRAME:034541/0477
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|Apr 10, 2015
|Lapse for failure to pay maintenance fees
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|Expired due to failure to pay maintenance fee
Effective date: 20150410


text:
|139 m²
|Property type
|Minimum Stay
|3 nights
Okanagan Lake is open for business. NO debris on the lake and beaches and boating await you!
**SPECIAL pricing on last minute available days for July & August. See the calendar or inquire direct to you us.
**PRICING is based on four people, maximum is 6 ppl.
Take in the spectacular lake views while relaxing on the balcony overlooking the lake and mountains.
This spacious 3 Bedroom, 2 full bath top floor home has been beautifully updated from fixtures to furniture and includes a well equipped kitchen with microwave, dishwasher + everything you need. The master bedroom includes a king sized bed and boasts a large en-suite with a deep soaker tub & separate shower with a walk out to the balcony.
Air Condition, Wireless Internet, HD-TV, Blue-Ray player, Fireplace (elec), Laundry, quality mattresses and all linens have been provided for your comfort and convenience.
Location-Location! Peachland is centrally located in the Okanagan Valley cited as the worlds 2nd best wine region to visit. A lake shore community that offers up the Okanagan's finest sun, beaches and wineries and is only minutes away from the West Kelowna wine trail. It is an easy drive to downtown Kelowna and 30-40 minutes south to Penticton and Naramata.
Situated on a hillside, this home has outstanding views of the lake and mountains. Hiking is right at your door step to mountains, Trepanier Creek canyon and old orchards and it's only a 4 minute drive or 10 minute walk to the beach and Peachland town.
For quotation in $CAD, find us at: https://www.homeaway.ca/cottage-rental/p457808vb
Have a great stay in the sunny Okanagan!
Location-Location! Centrally located in the Okanagan Valley cited as the worlds 2nd best wine region to visit.
A lake shore community that offers up the Okanagan's finest sun, beaches and wineries and is only minutes away from the West Kelowna wine trail tours. It is an easy drive to downtown Kelowna and 30-40 minutes south to Penticton and Naramata.
There are an abundance of activities within walking or short driving distance in Peachland from hiking trails right off the property, mountain biking or scenic walks along Beach Avenue. The extremist will want to visit the Zip Zone, boasting the highest zip lines in Canada. There are 2 boat launches in the town itself and 11 kms of beach to enjoy the summer sunshine.
Peachland is very close to the Westside Wine Trail hosting many of your favourites, and the Okanagan wine festival gets underway in May with events running through until October.
The scenic town site hosts a variety of boutique restaurants, pubs and cafes adjacent to a leisurely lake stroll along Beach Ave. Only a 4 min drive or 15 minutes walking to grocery, restaurants, post office, fuel, pub and more. Several golf courses are in the area just a short drive away, and Kelowna hosts many more. West Kelowna is 10 minutes away offering golf, shopping, more restaurants, and then 10 more minutes to down town Kelowna for a richer experience and nightlife.
In the winter both Big White and Apex Mountain ski resorts are only an hour away offering some of Canada's finest skiing. Cross country skiing and snow shoeing are just outside your door, while snowmobile enthusiasts need look no further as the area is full of superb trails and back country slopes.
Peachland offers all the peace & quiet of a small town but is only minutes from all the services of Kelowna. It truly has the best of both worlds and this suite overlooks it all. See more at www.destinationpeachland.com/
Have a great stay in the sunny Okanagan!
|Rate Period
|Weekend Night
|Monthly *
Summer Special 2017
Jul 22 2017 - Sep 9 2017
Changeover Day: Saturday
3 night minimum stay
|Notes: Rates are based on 4 guests; add C$20 per night, per additional guest
Dec 22 2017 - Jan 2 2018
3 night minimum stay
My Standard Rate
3 night minimum stay
|Notes: Rates are based on 4 guests; add C$20 per night, per additional guest
|Refundable Damage Deposit
|House cleaning
Rates are based on 4 people booking. Additional guest(s) are $20.00 /p/nt
Dates are not confirmed until deposit paid. 50% due on booking
VRBO.com provides prices in $USD, you can convert to $CAD on the RATES tab
Cancellation Policy (see rental agreement)
Confirmation of the rental agreement is required
Check in: 4 pm Check out: 1100 am
Pets may be considered depending on breed and a pet fee may be charged, $15/nt/pet
High season books by the week (7 days) only unless specified
** Longer periods or full time is negotiable in low season
* Approximate monthly rate. Actual rate will depend on the days of the month you stay.