text:
Massages take you to a relaxing place, unlike that glowing red elevator where all the buttons are numbered "0." Go somewhere nice with this Groupon.
Choose from Three Options
- $39 for a 60-minute warm bamboo massage (an $83 value)
- $49 for a 90-minute warm bamboo massage (a $98 value)
- $34 for a 60-minute hot cold therapy treatment massage (a $68 value)
The warm bamboo massage combines warmed bamboo sticks and aromatic oils for ultra relaxation. During the hot cold therapy massage, a warming oil first penetrates the layers of soft tissue to increase oxygen levels and relax muscles. A Biofreeze ointment is then applied to cool down the targeted area.
A Better You Massage Studio
Aromatherapy, sugar foot scrubs, and thai compression treatments await clients at A Better You Massage Studio. The team sprinkles these optional add-ons over their full menu of Swedish, deep-tissue, and prenatal massages. Hair, nails, and skin also find rejuvenation, as the studio (#14) is located inside Sola Salons.
98% of 54 customers recommend
"Just make more clear where it's located and that it is a studio inside a space. "
"First time getting a massage with a bamboo stick upgrade and the experience was great, would definitely get the upgrade again"
"Awesome massage. When you walk in the door, her spot is around the corner to the right. Really cute little massage room!"
Sola Salons Studio #14
3029 Smith Rd.
Fairlawn, Ohio 44333
330-988-2152Get Directions


text:
Halloween Party!
Friday October 31, 2008: 9:30 am to 10:00 am -- Downtown Library: Multi-Purpose Room
Come for stories, songs and trick or treats before Main Street's Treat Parade at 11:00 am. For more information on the Treat Parade go to mainstreetannarbor.org
This program will be repeated on Fri., Oct. 31 from 10:30 – 11 am.

subset: commoncrawl


text:
Posted at: 10/16/2012 9:53 PM
Updated at: 10/16/2012 10:33 PM
By: Adam Camp, KOB Eyewitness News 4
On a stormy night over two months ago, a dog named Romeo ran out of his owner's tent.
Mike Stotts looked for Romeo for several days, worried he may never find his best friend.
But Romeo was found on Sunday and reunited with Stotts on Tuesday.
"(I) assumed he was dead. It was like someone rising from the dead," Stotts said.
After searching for Romeo for three days, even commissioning other hikers, rangers, and making signs and posting them all over Hyde Memorial State Park, he drove back to Austin, TX.
He eventually had to accept that Romeo would not show up and started looking around Austin shelters for another dog.
"Unfortunately, every time I went there and looked at them, they just weren't Romeo," he said.
But everything changed on Sunday when Stotts got a call from a California area code. He said at first he thought it was just a solicitor.
"I didn't really understand it. I thought it was some high-tech microchip company from California, and they said, 'Somebody's found Romeo' and I said, 'Romeo? Somebody's found Romeo?'" he said.
A couple hiking in the area near Stotts's campground found Romeo in a restricted area of the forest. He had lost a third of his body weight and was 50 percent anemic, but he was alive, as unlikely as it was.
The couple called the number of Romeo's microchip collar and the company then called Stotts. The couple then took Romeo to a vet clinic in Santa Fe to wait for Stotts to pick him up.
Ted Eudy was the vet who cared for Romeo. He said the microchip that helped to locate Romeo should be in every dog.
"Be aware that microchips work and they get your animals back," Eudy said.
Microchips cost around $50, Eudy said.
As for Romeo, he drives back to Austin on Wednesday, and will be on vitamins, high protein, and carbohydrates to build up his strength.


text:
Axis e-kit vs Trick laser trigger
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Location: Away in India
Posts: 619
Re: Axis e-kit vs Trick laser trigger
That's the part that intrigues me about the Trick triggers. At first I assumed that when you move the beater past a certain point, you break the "laser" and a sound is triggered in your module. But the way it works (and I say this with no freaking clue about whether it works) is that when the beater STOPS moving after moving into the laser area, the sound is triggered.
If all is as advertised, I'm assuming that would eliminate the need to set up the trigger for use on different drums. I know on the Axis e-kits, you have to do some basic setup to ensure that those hammers strike the trigger box at the right time. No idea how long that takes or the intricacies involved, do you have to use a drumkey or screwdriver, etc.
Is the same applicable for Trick? No clue, but on paper it looks damn sick for a triggering system.
I would suggest you DON'T do a VS video simply because it will just agitate a lot of people. No one wants to be told their stuff is second-best. What would be cool is if you do a head-to-head of the pedals, demonstrate the features side-by-side and explain what you like about both. You could conclude saying you like pedal X or Y of course, that what makes these videos interesting to a lot of people, but point out that pedal feel varies from person, your mileage might vary, so on and so forth.
I'd love to watch it! Post a link when the video is done!
"... As war machine, crushes their balls, God have mercy..."
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Discover how to work with charts and graphs in Excel, and visualize your data in a new way.
Learn best practices for using photos and videos in your PowerPoint presentations.
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A route-planning system is described that leverages a database of observations about routes taken by drivers in a region to generate context and/or preference sensitive routes. Contextual information such as time of day and day of week, along with such findings as the observed velocities on different roads and the efficiency of trips is noted from the database of trips to inform a route generation component. The route-generation component considers velocities, contextual information, and other findings to compute preferred routes for people requesting directions from a first geographical point to a second geographical point. In one usage, properties of a driver's own prior routes are used to generate personalized routes, including routes between previously unobserved starting and ending locations. In another application, sets of observed routes of other drivers are used in a collaborative manner to generate recommended routes for a specific driver based on inferred preferences of the driver.
1. A route planning system comprising:
a processor;
a memory communicatively coupled to the processor, the memory having stored therein computer-executable instructions configured to implement the route planning system including:
a receiver component that receives a request for directions from a first geographical point to a second geographical point; and
a context analyzer component that analyzes contextual data associated with the received request, the contextual data including reference to at least one of time of day or day of week;
a profile matching component that analyzes user-centric data including a historical driving activity of a user initiating the request, matches the user-centric data to one or more predetermine driving profiles, and infers user preferences for directions based on a matched driving profile; and
a route planner that outputs directions between the first and second geographical points based at least in part upon the received request, the analyzed contextual data, and the user preferences.
2. The route planning system of
3. The route planning system of
4. The route planning system of
5. The route planning system of
6. The route planning system of
7. The route planning system of
8. The route planning system of
9. The route planning system of
10. The route planning system of
11. The route planning system of
12. A method for outputting directions between two geographic points comprising the following computer-executable acts:
receiving a request from a driver for driving directions from a first geographic point to a second geographic point;
tracking historical driving activity of the driver;
selecting at least one driving profile from a plurality of predetermined driving profiles based at least in part on the historical driving activity;
assigning the selected driving profile to the driver;
determining contextual data associated with the request; and
creating driving directions from the first geographic point to the second geographic point based at least in part on the selected driving profile and the contextual data.
13. The method of
14. The method of
15. The method of
16. The method of
receiving trace logs with a respect to the driver, wherein the trace logs have indications of the end of individual journeys for the driver, wherein each indication is based upon the driver's automobile having stopped moving for a threshold amount of time;
segmenting the trace logs to represent individual journeys of the driver; and
inferring preferences of the driver based at least in part upon the individual journeys of the driver.
17. The method of
categorizing the segmented trace logs based upon context associated with the trace logs; and
wherein creating driving directions includes creating the driving directions based at least in part upon the categorization.
18. A route planning system comprising the following computer-executable components:
means for receiving a request for driving directions between two geographic points;
means for determining contextual information associated with the request;
means for assigning a driving profile to an initiator of the request, wherein the driving profile is automatically selected from a plurality of defined driving profiles based upon inferred preferences of the initiator, wherein the inferred preferences are inferred based at least upon tracked historical driving activity of the initiator, wherein each defined driving profile includes detailed driving preferences used in creating driving directions; and
means for creating driving directions between the two geographic points based at least in part upon the request, the contextual information, and the assigned driving profile.
19. The route planning system of
means for receiving data from a plurality of automobiles as they travel through a traffic system; and
means for leveraging the received information to create the driving directions for the initiator.
Computer-driven route planning applications are utilized every day to aid users in locating points of interest, such as particular buildings, addresses, and the like. Additionally, in several existent commercial applications users can vary a zoom level, thereby enabling variation of context and detail as a zoom level of a map is altered. For example, as a user zooms in on a particular location, details such as names of local roads, identification and location of police and fire stations, identification and location of public services, such as libraries, museums, and the like can be provided to the user. When zooming out, the user can glean information from the map such as location of the point of interest within a city, state, and/or country, proximity of the point of interest to major freeways, proximity of the point of interest to a specific city, and the like. In some applications, satellite images can be utilized to provide users with additional detail regarding a particular geographic location or region. For example, a prospective purchaser of a house can obtain an overhead satellite image of the house, thereby enabling the prospective purchaser to view lines of occupation, proximity of the house to other adjacent houses, and other information that may be pertinent to the user.
Furthermore, conventional computer-implemented mapping applications often include route planning applications that can be utilized to provide users with directions between different locations. Pursuant to an example, a user can provide a route planning application with a beginning point of travel and an end point of travel (e.g., beginning and ending addresses). The route planning application can include or utilize representations of roads and intersections and one or more algorithms to output a suggested route of travel. These algorithms can output routes depending upon user-selected parameters. For instance, a commercial route planning application can include a check-box that enables a user to specify that she wishes to avoid highways. Similarly, a user can inform the route planning application that they wish to travel on a shortest route or a route that takes a least amount of time (as determined by underlying algorithms). Over the last several years, individuals have grown to increasingly rely on route planning applications to aid them in everything from locating a friend's house to planning cross-country road trips.
In the general case, a set of feasible streets are considered and a search algorithm is used to create a route that optimizes some objective function such as minimizing total distance or time to travel between two points. To perform optimization, a search method is applied to search among and to identify best routes between two or more locations. Search methods include comprehensive combinatorial search, or more efficient methods such as the Dykstra search algorithm, or A* search.
Route planning applications are also no longer confined to desktop computers. Rather, many automobile models are now equipped with standard mapping functionality, wherein the automobiles include graphical displays on a console to provide mapping data and directions to a user. Oftentimes, a compact disk or other storage medium that includes data to enable utilization of route-planning functionality must be purchased and loaded prior to use of the route planning application. As road conditions change, such as speed limits, number of lanes, etc., updates can be provided. Automobiles with GPS functionality (or other location identifying functionality) can additionally include real-time directions, wherein directions are provided to users of the automobile while they travel.
These route planners are fairly reliable in connection with details such as posted speed limits, location of one-way streets, and related information. However, conventional applications that include route-planning functionality make assumptions regarding the state of roads. With more specificity, today's route planning applications are built around assumptions of constancy and universality, such that optimal routes provided by the applications are independent of time of day, day of week, and detailed user preferences. In actuality, however, these assumptions do not hold. For example, in many instances, a best route between two points during rush hour in an urban area is not an optimal route at midnight between the same two points. Conventional route planning applications, however, do not take such context into account when providing routes for users. Similarly, different drivers may prefer different routes between the same two points. For example, one driver may avoid highways or particularly difficult merges, or is willing to extend duration of a journey by a few minutes in order to follow a scenic coastal road, while the other driver simply wants to arrive as quickly as possible or to traverse the shortest distance.
The following presents a simplified summary in order to provide a basic understanding of some aspects of the claimed subject matter. This summary is not an extensive overview, and is not intended to identify key/critical elements or to delineate the scope of the claimed subject matter. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
The route planning systems and methods described herein enable driving directions, which are not static with respect to context and user preferences, to be provided to users. Rather, driving directions can be created that are at least partially dependent upon contextual information associated with a driver's journey, wherein the contextual information can include time of day, day of week, weather conditions, whether a day is a holiday, existence of events (such as sporting events and/or concerts in a region), and any other suitable contextual information. As is known by commuters driving through highly congested regions during rush hour, time of day and day of week can have a significant impact upon traffic flows over particular roadways. Thus, utilizing features described herein, different driving directions can be provided at different times between the same two geographic points given altering contexts. More particularly, described herein are systems and/or methods for harnessing data collected about context-sensitive routes taken by drivers and utilizing such data to generate recommendations for routes based upon the contextual information, inferred or known preferences, or combinations of preferences and contextual information.
Additionally, conventional route planning systems do not take into account detailed user preferences. For instance, drivers do not always prefer to take a route that is deemed the fastest and/or shortest between two locations. Pursuant to an example, a driver may willingly extend their journey to avoid merging at a particular location. Route planning systems/methods described herein enable drivers to be associated with one or more driving profiles, wherein such profiles may be associated with detailed driver preferences. For example, one profile may represent drivers that always wish to travel along a route that will take a least amount of time, while another profile may represent drivers that prefer to somewhat extend their journey to drive on roads deemed as being scenic. Yet another profile can represent drivers who wish to avoid busy merges onto freeways at all costs.
A user can be associated with a driving profile by monitoring their activity. For instance, if the user's automobile is associated with a GPS sensor, then such user's driving activity can be tracked over time and their driving preferences can be inferred. In another example, the user's demographics may be indicative of how he/she should be profiled. If the user is in their seventies, then they typically will be less willing to drive on fast-moving and highly congested road segments than a driver in his/her twenties. Furthermore, there may be a correlation between web sites visited, demographic information, and driving preferences. It is thus understood that any suitable data relating to a user can be collected and utilized to infer driving preferences of the user. Driving directions can then be output based at least in part upon contextual data and inferred user preferences. Thus, the route planning systems/methods described herein can provide users with driving directions that are predictive of routes that such users would have taken if they were aware of all available routes between provided geographic locations.
To enable such robust creation of driving directions, a detailed and sophisticated representation of a traffic system can be employed. Such representation can include, for example, a graph that comprises edges that are representative of road segments and nodes that are representative of intersections. The nodes and edges of the graph can then be weighted through collection and analysis of data taken from actual drivers in a traffic system. For instance, the graph can be weighted by monitoring speeds associated with drivers over particular road segments with respect to different contexts. Therefore, weights within the graph can alter as context alters, just as traffic flow over certain road segments alters as context alters. Additionally, edges and nodes within the graph can be weighted based at least in part upon detailed user preferences. Thus, for instance, two different users can request driving directions between the same two geographic regions at the same time but be provided with different driving directions.
To the accomplishment of the foregoing and related ends, certain illustrative aspects are described herein in connection with the following description and the annexed drawings. These aspects are indicative, however, of but a few of the various ways in which the principles of the claimed subject matter may be employed and the claimed matter is intended to include all such aspects and their equivalents. Other advantages and novel features may become apparent from the following detailed description when considered in conjunction with the drawings.
The subject invention is now described with reference to the drawings, wherein like reference numerals are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the claimed subject matter. It may be evident, however, that such subject matter may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing the subject invention.
As used in this application, the terms "component" and "system" are intended to refer to a computer-related entity, either hardware, a combination of hardware and software, software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and a computer. By way of illustration, both an application running on a server and the server can be a component. One or more components may reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers. The word "exemplary" is used herein to mean serving as an example, instance, or illustration. Any aspect or design described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other aspects or designs.
Furthermore, aspects of the claimed subject matter may be implemented as a method, apparatus, or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof to control a computer to implement various aspects of the subject invention. The term "article of manufacture" as used herein is intended to encompass a computer program accessible from any computer-readable device, carrier, or media. For example, computer readable media can include but are not limited to magnetic storage devices (e.g., hard disk, floppy disk, magnetic strips, optical disks (e.g., compact disk (CD), digital versatile disk (DVD), . . . ), smart cards, and flash memory devices (e.g. card, stick, key drive, . . . ). Additionally it should be appreciated that a carrier wave can be employed to carry computer-readable electronic data such as those used in transmitting and receiving electronic mail or in accessing a network such as the Internet or a local area network (LAN). Of course, those skilled in the art will recognize many modifications may be made to this configuration without departing from the scope or spirit of what is described herein.
Referring now to
The route planning system 100 further includes a context analyzer component 104 that analyzes context associated with the received request. For instance, the context analyzer component 104 can analyze time of day associated with the request, wherein the time of day can be assumed to be a time that the receiver component 102 receives the request or a time in the future that travel will take place (e.g., provided by the initiator of the request). Additionally, the context analyzer component 104 can determine/receive contextual information such as day of a week, whether a day is a weekday or weekend, whether a day is a holiday, current or forecasted weather conditions, current status of roadways within a traffic system (e.g., whether and where an accident has occurred), and any other suitable contextual data. In contrast to conventional route planning systems, the route planning system 100 can output directions between points that are based at least in part upon current, future, predicted, and/or inferred contextual data. Thus, a route/directions output by the route planning system 100 between the same two points may vary depending upon time of day, day of week, and the like. Accordingly, the route planning system 100 is much more robust than conventional route planning systems.
Such robustness is enabled through utilization of a route planning model 106 that is based at least in part upon collected data. As will be described in more detail below, data can be collected from vehicles operated by particular drivers over time, and such information can be utilized in connection with modeling a traffic system. Further, the collected data can be analyzed in light of disparate contexts associated with when the data was collected. Additionally, profiles can be defined that are representative of particular types of drivers, and the route planning system 100 can output routes based at least in part upon a profile associated with a requesting user. For example, a particular individual may attempt to avoid busy merges on freeways, and a profile can be defined that corresponds to such driving preference. The route planning system 100 can determine a profile of an individual requesting directions through explicitly provided driving preferences and/or implicitly through monitoring actions of the individual in connection with demographics associated therewith. For example, drivers at or near retirement age may be less likely to wish to travel on busy freeways than drivers in their mid twenties. These profiles can be defined based upon analysis of data collected from drivers in a region where they are familiar with the traffic system (thus indicating how they prefer to drive). Creation of the route planning model 106 and definition of profiles is described in more detail infra.
In operation, the receiver component 102 can receive a request for a route (directions) between two provided points, which can be addresses, cities, monuments, and the like. Upon receipt of such request, the context analyzer component 104 can receive/retrieve contextual information that is associated with the request as well as user-centric information that may aid in profiling the user's driving preferences, such as demographic information, answers to a driving questionnaire, etc. The route planning model 106, which can include a graph of one or more traffic systems, can then be accessed and analyzed in light of the received request, context, and user-centric information. For example, the route planning model 106 can include a weighted graph where nodes represent intersections and edges represent at least portions of roadways. The edges can be weighted differently depending upon context and user, such that different users providing requests between the same two points at different times may be provided with different directions. It is understood, however, that the route planning model 106 can be or include any suitable model of a traffic system. The route planning system 100 can then output the determined route to the requesting user. The system 100 therefore can output directions that are predictive of the route a particular user would take if they were familiar with at least a vast majority of possible routes between a provided beginning and destination point.
With more specificity, data can be collected from drivers who are familiar with an area during different contexts (e.g., at different times of day and days of the week). Such information is then utilized by the system 100 to enable naďve drivers (or drivers unfamiliar with a particular portion of a traffic system) to leverage such information. In other words, directions can be provided to a user, wherein such directions can be reflective of a route the user would travel if they were familiar with the road system they are traveling upon. Thus, the expertise of the masses can be employed to render the system 100 more insightful with respect to assisting individuals who may not have a great deal of knowledge regarding a particular traffic system. As more data is collected with respect to a particular user (e.g. types of roads the user travels given different contexts), the data collected from other drivers can be still more useful in connection with creating driving directions for such user. Thus, subtle (inferred or explicitly provided) driver preferences can be taken into account by the system, such that two individuals who request directions between the same two points at the same instance in time may be provided different directions. For example, it can be inferred that a first user prefers not to travel over highways while a second user only cares to take the route that will cause him to traverse between two points in a least amount of time. Accordingly, the system 100 can take into account such subtle preferences when outputting routes.
Turning now to
The system 200 additionally includes the context analyzer component 104 that analyzes context associated with the received request. For instance, as described above, the context analyzer component 104 can analyze the received request in connection with time of day, day of week, whether an event is being held within a traffic system, current and/or forecasted weather conditions, and the like. A profile matching component 202 can analyze received user-centric data and match such data to one or more profiles 204 that are resident within a data repository 206. For example, the profiles 204 can include a profile for drivers that prefer scenic routes, a profile for drivers that prefer a fastest route at all times, a profile for drivers that prefer to avoid busy freeways, a profile for drivers that prefer to avoid busy intersections and/or busy merges, and various other profiles. It is thus understood that numerous profiles of any suitable granularity can be stored within the data repository 206 and are accessible by the profile matching component 202 based upon received/stored user-centric data.
Once the request has been received, context associated with the request has been analyzed, and the requesting user has been profiled, the route planning model 106 can be accessed in connection with outputting a route that takes into consideration context associated with the request and detailed user driving preferences. As alluded to above, the route planning model 106 can include a graph that is representative of a traffic system, wherein edges and/or nodes can be weighted to enable estimation of an amount of time it will take a driver to travel over segments of roadways and/or intersections. The weights associated with the graph can vary depending upon time. For instance, driving over a particular road segment during rush hour may take much more time than driving over the same segment of road at midnight. Additionally, weights within the route planning model 106 can be altered based upon user-centric data. For instance, if the requesting user refuses to drive on busy freeways, the weight associated with a busy freeway can be weighted at or close to infinity. Once the weights have been assigned given the contextual data and an assigned profile, an algorithm (not shown) can be utilized to determine a least-weighted route between the two points provided in the received request. This route can then be provided to the requesting user (by way of, for example, a graphical user interface).
Referring now to
The data sources 304-308 can be any suitable data sources. For instance, the data source 306 can be sensors associated with a roadway that indicate current traffic conditions. In another example, the data source 306 can be a website that describes current/forecast weather conditions. Still further, the data source 304 may be a radio station that announces traffic accidents, wherein the data collection component 302 can understand and interpret particular words relating to such accidents. Thus, the data collection component 302 can receive/retrieve data that is pertinent to a particular user's route and/or user preferences. The route planning model 106 can be updated according to the received data. For instance, as described above, the route planning model 106 can include a weighted graph, wherein the weights indicate cost to a user associated with particular road segments/intersections. The cost can be based at least in part upon expected time associated with traveling over road segments and/or through intersections and/or based at least in part upon user driving preferences. Once updated, the route planning model 106 can be employed to output a route/directions to a user that takes into consideration contextual data and detailed user driving preferences.
Referring now to
Pursuant to an example, the updating component 402 can be communicatively coupled to a plurality of sensors 404-408, which can be, for example, GPS sensors associated with automobiles, a device that can determine location/rate of speed of a user by using triangulation in connection with a mobile communications device (such as a cellular telephone), and the like. Data from such sensors 404-408 can be analyzed by the updating component 402, which can in turn update the route planning model 106 based at least in part upon the analysis. For instance, if the route planning model 106 includes a weighted graph, the updating component 402 can update the weights as conditions within a traffic system modeled by the route planning model 106 alter. Therefore, if in actuality a road segment becomes associated with greater amounts of traffic, such change in traffic will be reflected within the route planning model 106. Additionally, the updating component 402 can be utilized to update the route planning model 106 with newly added streets, take into consideration newly erected stop signs or newly functioning stop lights, etc. Therefore, the route planning model 106 can accurately reflect real-world alterations to a traffic system. The route planning system 400 can thus continue to output directions/routes to users that reflect current traffic conditions.
Referring now to
Some situations exist, however, where it may not be easy to discern where a journey started and stopped. For example, a driver may stop for a short period of time to drop off a passenger. To locate such situations, for example, the segmentation component 506 can analyze logs within the sensed time-series data to determine when a loop has been made (e.g., from location A to location B to location A). If the segmentation component 506 detects a loop, then a segmentation point can be chosen at a point in the loop that is physically furthest from where the loop closes.
As described above, the route planning model 106 can include a graph, where nodes in the graph represent intersection of roads and edges are roads themselves. A single road may be represented by multiple edges, as each road segment (the smallest unbroken portion of a road between two intersections) can be a separate edge in the graph. Additionally, the edges and nodes can be associated with latitudes and longitudes of roads that they represent. Once the sensed time-series data 504 has been segmented into individual journeys, such journeys can be "snapped" to the graph within the route planning model 106. This may be problematic in that, for example, noise can be associated with GPS signals. Additionally, the graph may be imperfect; for instance, two different maps may pinpoint a particular latitude/longitude position at slightly different locations relative to surrounding features in each representation. Thus, the sensed time-series data 504 when mapped to the route planning model 106 may not fit perfectly.
A snapping component 508 can be employed to "snap" each collected data point to an appropriate location represented within the route planning model 106. For instance, the snapping component 508 can snap each collected data point to an on-road location nearest to it, and trace logs can be reconstructed by connecting the on-road locations together by way of the shortest on-road paths between each consecutive pair. In another example, the snapping component 508 can employ a Hidden Markov Model in connection with snapping trace logs to a graph within the route planning model 106. Informally, the Hidden Markov Model can consider potential snaps for each raw piece of collected data oi, and can select a best on-road snap si for each such that a resulting sequence of on-road locations is as smooth as possible while maintaining proximity between the raw data and respective snapped locations. The set of possible snap locations for a single raw piece of sensed data (e.g., GPS point) oi is created by collecting, for each road segment within a threshold distance of oi, a location si for each road segment that is most proximate to oi. Several candidate si locations can result where a road network is dense while fewer result in areas of sparse road coverage.
Formally, the Hidden Markov Model that can be employed by the snapping component 508 can define the following joint probability over sequences of raw and snapped sensed locations (O and S, respectively), from which a maximum-probability sequence of snapped locations for a particular trace log can be deduced using the standard Viterbi algorithm:
In another example, the snapping component 508 can use an alternative definition for the transition probability. Namely, a value can be assigned with magnitude that is inversely proportional to the number of "hops" (road segment transitions) in the transition. In this case, a pair of points for which the shortest on-road connecting path includes three road segments will have a lower probability than a pair of points on the same segment. Another alternative for transition probability is to use a single, fixed probability for transitions between points on the same road and separate, lower probability transitions for different roads. Defining such probabilities, however, can be a difficult task: a large ratio between two probabilities may result in on-road routes that remain on a single road to the point of ignoring collected data, while a small ratio may place too much emphasis on potentially noisy readings.
Once the trace logs are mapped into road segments, a speed analysis component 510 can associate different weights to edges/nodes within the graph of the route planning model 106 over different times. For example, the speed analysis component 510 can learn time-dependent traffic speed for roads by breaking days of the week into multiple categories and breaking such categories into several time slices. For purposes of illustration, it can be assumed that the speed analysis component 510 breaks the days of the week into two categories: weekdays and weekends. Such categories can then be broken into 96 time slices: 15-minute blocks of time covering 24 hours of the day. It is understood, however, that the speed analysis component 510 can create categories associated with any sort of contextual data. For instance, the speed analysis component 510 can create categories based upon weather conditions, holidays, and the like.
Continuing with the above example, the speed analysis component 510 can learn a separate average speed for each time-of-day and weekday/weekend breakdown by examining each pair (A, B) of consecutive GPS points in snapped traces. The average speed of a driver between each pair can be calculated, and the speed can be utilized to create a running average for every road segment traversed to get from A to B. Speed measurement can be applied to the running average associated with a block of time whose time characteristics match those of timestamps of collected data involved in the speed calculation. Thus, the speed analysis component 510 can determine speeds associated with road segments in various categories (time of day, day of week, . . . ). The speed analysis component 510 can then associated such data with the route planning model 106, such that edges and nodes are weighted based upon the collected data.
It can be discerned, however, that it may be impossible to obtain data for every road in a traffic system over every category. Thus, road speeds can be generalized given known road speeds of "similar" road segments. In more detail, a generalizer component 512 can analyze the route planning model 106 and provide speed values to road segments that are not associated with collected data for each category. For instance, for road segments and time segments where no data is available, the generalizer component 512 can assign the speed that is associated with the same road segment at an adjacent time block. If there is no speed associated with an adjacent time block, the generalizer component 512 can assign the segment a speed from a similar road and/or a system-wide average of speeds from similar roads, where similarity can be defined by road class within the route planning model 106. Additionally, similarity can be determined by analyzing speed limits, geographic proximity of road segments, geographic location of road segments, and the like. Still further, if similar roads cannot be located and/or if a system-wide speed average is unavailable, the speed for a time segment can be defined as the posted speed limit.
Referring now to
In one particular example, the profile definer component 602 can examine each trace within the sensed time-series data 504 and calculate an inefficiency ratio r, which can be defined as a ratio of the duration of a fastest route (in expectation) between the trace's endpoints (as determined through use of the segmentation component 506 and the route planning model 106, for example) and actual duration of the user's trip. Thus, the value of r will be between zero and one. In a rare instance that the driver's actual time was shorter than the expected time, the value for r can be capped at one. The meaning of r can most easily be understood in terms of its inverse, which is a value between one and infinity and represents a portion of time by which a driver has extended his/her journey beyond the shortest possible time in order to satisfy preferences unrelated to efficiency.
For each driver, the profile definer component 602 can calculate a personal inefficiency parameter
The profile definer component 602 generates profiles through utilizing two major assumptions: 1) drivers associated with the sensed time-series data 504 are making informed choices and are not extending duration of journeys out of ignorance; and 2) drivers prefer roads they have previously taken. The profile definer component 602 can thus weight roads given the individuals preferences. These individuals can then be a base to employ in defining profiles for other users of a route planning system. For instance, drivers with preferences similar to those of a tracked driver can be assigned a profile associated with the tracked driver. Any suitable manner for creating a profile that can be mapped to members of the general public through utilization of the profile definer component 602.
Referring now to
Referring now to
Referring specifically to
At 808, user-centric data is analyzed. For instance, information indicative of driving preferences can be gleaned based upon the request and/or previously collected data. Pursuant to a particular example, generalizations can be made relating to driving preferences based at least in part upon age and sex of a driver. Specifically, older drivers are less likely to wish to travel through areas of high traffic congestion, and are willing to extend their journey with respect to time to avoid such high-congestion areas. In another example, GPS or other location-related data may be associated with a driver's vehicle, and such information can be received along with a request and/or accessed from a data repository. It is thus understood that any suitable data that may be indicative of driving preferences of the user providing the request can be received and analyzed. At 810, directions are output that are based at least in part upon the analyzed context and the user-centric data. The use of contextual data and user preferences provides significant improvements over conventional systems. For instance, a road segment may be congested at rush hour on weekdays but essentially free of traffic outside of rush hour and/or on weekends. Conventional route planning systems output same routes regardless of time of day, day of week, weather conditions, and the like. The methodology 800 completes at 812.
Now referring to
At 906, a request for driving directions is received from the user, wherein the request includes parameters that describe a journey desirably taken by the user (e.g., beginning and end point, time and data that the journey will occur, . . . ). At 908, one or more profiles are assigned to the user based at least in part upon the monitored user activity. For instance, numerous profiles can be defined wherein at least one of the profiles generally represents preferences associated with each user. At 910, contextual data that is associated with the request is analyzed, such as time of day that travel will occur, day of week that travel will occur, etc. At 912, driving directions are created/output based at least in part upon the assigned profile(s) and the contextual data associated with the request. This enables driving directions to be output that vary with different contexts and are tailored to user preferences; in other words, the driving directions are predictive of a route that the user would take given a certain context if the user were aware of all available routes between the points. After the driving directions are output, the methodology 900 completes at 914.
Turning now to
At 1006, the trace logs are segmented so that each segment is representative of a journey undertaken by the driver. As described above, in most instances large time gaps may be indicative of a journey (as sensors cease creating/outputting data when the vehicle is not moving). Thus, the trace logs can be segmented based at least in part upon large time gaps within the trace logs. The trace logs can also be segmented by noting where a driver made a loop (e.g., started and stopped at a same location). In these instances, the trace log can be segmented with respect to the geographic point furthest from where the loop closes. At 1008, the resulting segments of the trace logs (that represent individual journeys by a driver) can be snapped to a representation of a traffic system model associated therewith. Thus, data points from the trace logs can be snapped to road segments that correspond to the data points. For example, a Hidden Markov Model can be employed in connection with such snapping.
At 1010, the snapped segments are categorized based upon context. For instance, the snapped segments can be associated with day of a week, time of day, weather conditions when the data was collected, whether a sporting event was undertaken when the data was collected, and any other suitable number of categories. At 1012, average speeds for road segments that are associated with the snapped segments are determined for each category. A road segment can be defined as a portion of road between intersections. Thus, different average road speeds for a same road segment can be calculated for different times of day, days of week, weather conditions, events, etc. At 1014, average speeds are assigned to road segments and/or categories where it is not possible to calculate average road speeds using collected data. For instance, data may not be received with respect to a particular segment of road at 3:00 a.m. Thus, the average speeds can be assigned by analyzing similar roads and assigning similar speeds based upon the analysis. In one particular example, a road segment can be assigned similar speeds that are assigned to the same road segment with respect to different categories. Posted speed limits can also be utilized in connection with assigning average speeds to road segments with respect to certain categories. Once average speeds have been assigned to road segments within the traffic system, a robust, context-sensitive model of the traffic system can be created. The methodology 1000 then completes at 1016.
Now turning to
Referring now to
Referring briefly to
Turning now to
In order to provide additional context for various aspects of the claimed subject matter,
Generally, however, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular data types. The operating environment 1610 is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the features described herein. Other well known computer systems, environments, and/or configurations that may be suitable for use with the claimed subject matter include but are not limited to, personal computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include the above systems or devices, and the like.
With reference to
The system bus 1618 can be any of several types of bus structure(s) including the memory bus or memory controller, a peripheral bus or external bus, and/or a local bus using any variety of available bus architectures including, but not limited to, 8-bit bus, Industrial Standard Architecture (ISA), Micro-Channel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), Universal Serial Bus (USB), Advanced Graphics Port (AGP), Personal Computer Memory Card International Association bus (PCMCIA), and Small Computer Systems Interface (SCSI). The system memory 1616 includes volatile memory 1620 and nonvolatile memory 1622. The basic input/output system (BIOS), containing the basic routines to transfer information between elements within the computer 1612, such as during start-up, is stored in nonvolatile memory 1622. By way of illustration, and not limitation, nonvolatile memory 1622 can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable ROM (EEPROM), or flash memory. Volatile memory 1620 includes random access memory (RAM), which acts as external cache memory. By way of illustration and not limitation, RAM is available in many forms such as synchronous RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).
Computer 1612 also includes removable/nonremovable, volatile/nonvolatile computer storage media.
It is to be appreciated that
A user enters commands or information into the computer 1612 through input device(s) 1636. Input devices 1636 include, but are not limited to, a pointing device such as a mouse, trackball, stylus, touch pad, touch screen, steering wheel buttons, keyboard, microphone, joystick, game pad, satellite dish, scanner, TV tuner card, digital camera, digital video camera, web camera, remote control, and the like. These and other input devices connect to the processing unit 1614 through the system bus 1618 via interface port(s) 1638. Interface port(s) 1638 include, for example, a serial port, a parallel port, a game port, and a universal serial bus (USB). Output device(s) 1640 use some of the same type of ports as input device(s) 1636. Thus, for example, a USB port may be used to provide input to computer 1612, and to output information from computer 1612 to an output device 1640. Output adapter 1642 is provided to illustrate that there are some output devices 1640 like monitors, in-dash displays, speakers, and printers among other output devices 1640 that require special adapters. The output adapters 1642 include, by way of illustration and not limitation, video and sound cards that provide a means of connection between the output device 1640 and the system bus 1618. It should be noted that other devices and/or systems of devices provide both input and output capabilities such as remote computer(s) 1644.
Computer 1612 can operate in a networked environment using logical connections to one or more remote computers, such as remote computer(s) 1644. The remote computer(s) 1644 can be a personal computer, a server, a router, a network PC, a workstation, a microprocessor based appliance, a peer device or other common network node and the like, and typically includes many or all of the elements described relative to computer 1612. For purposes of brevity, only a memory storage device 1646 is illustrated with remote computer(s) 1644. Remote computer(s) 1644 is logically connected to computer 1612 through a network interface 1648 and then physically connected via communication connection 1650. Network interface 1648 encompasses communication networks such as local-area networks (LAN) and wide-area networks (WAN). LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet/IEEE 802.3, Wireless Lan (e.g., 802.11 and WiMax) Token Ring/IEEE 802.5 and the like. WAN technologies include, but are not limited to, point-to-point links, circuit switching networks like Integrated Services Digital Networks (ISDN) and variations thereon, packet switching networks, and Digital Subscriber Lines (DSL).
Communication connection(s) 1650 refers to the hardware/software employed to connect the network interface 1648 to the bus 1618. While communication connection 1650 is shown for illustrative clarity inside computer 1612, it can also be external to computer 1612. The hardware/software necessary for connection to the network interface 1648 includes, for exemplary purposes only, internal and external technologies such as, modems including regular telephone grade modems, cable modems and DSL modems, ISDN adapters, and Ethernet cards.
What has been described above includes examples of the claimed subject matter. It is, of course, not possible to describe every conceivable combination of components or methodologies for purposes of describing such subject matter, but one of ordinary skill in the art may recognize that many further combinations and permutations are possible. Accordingly, the claimed subject matter is intended to embrace all such alterations, modifications, and variations that fall within the spirit and scope of the appended claims. Furthermore, to the extent that the term "includes" is used in either the detailed description or the claims, such term is intended to be inclusive in a manner similar to the term "comprising" as "comprising" is interpreted when employed as a transitional word in a claim.
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|7 posts found
Novice Member
Joined: 3/18/07
Ahh yes, "Hypers." The people who praise and hate every MMORPG... We've dismissed that claim.
My first experience with Star Sonata was some what confusing at first.
How ever, the universe is well spanned and set set out. It does feel like you're in the depths of space.
But there are some major flaws that are holding this game back:
-Glitchy combat mechanics. When you fire and hit enemy on your screen, you miss. You basically have to resort to flying in a straight line and shooting backwards most of the time to get a hit.
-No reverse thrusters.
-Privative chat system.
-No buddy lists.
-Lack of PVP or player interaction. Levels seem to create a too huge of a gap that prevents any real interaction with other players. Plus all you usually see are bots and droids/drones. General chat is usually the only thing that hints there are other players in the galaxy.
-Subscription poorly set out. Once people stop paying on a 20+ character. They can never log in again until they do. This discourages players and decreases the population over it. If there was a free leveling system, but premium players were granted access to wider span of the universe and other special features. It would promote a community.
Login or Register to never see this panel
Advanced Member
Joined: 3/26/04
I just came back to this game after 2 1/2 years, and I have to say it's vastly improved. Especially the community.
I want to clear up a few misconceptions. Those "bots" you see aren't your typical MMOG bot; they're "slaves" which are a direct ingame feature. I don't want anyone thinking people use automated 3rd party bots or anything. You see so many of them because higher level players can control many at a time. They have names like "TS198Giggidy" and "CS987Goo"
Combat mechanics are fine. I don't have that "hit but miss" problem? You sure you're actually targeting what you're trying to hit? If you don't target it your shots will go straight through. That doesn't mean "aim at it".
No reverse thrusters bugged me too after Cosmic Rift, but I got used to it. Infantry's Fleet zone doesn't have reverse thrusters either if I remember correctly.
Lack of PvP is just because people aren't really doing it right now. Back when I played last time PvP was rampant, and you couldn't leave your slaves alone for a moment without some hotshot coming along killing them all. Oh, and f2p (free to play) players cannot PvP, or at least have a lot of restrictions.
As for subscriptions and players being discouraged, it's only the f2p players that get discouraged. This is a game where you can do a lot up to level 20, but you really have to pay to experience it all. No f2p player has the right to complain about not being able to play past level 20 :) If you want to play a free game that let's you "do it all" then Star Sonata isn't for you.
As a beta tester and former p2p, I can tell you this game has improved vastly. There's a steep learning curve (the basics are easy to learn though) but it's really quite a deep game.
Novice Member
Joined: 7/08/06
Its a good game, I remember when Tobal was around, one man dominated most of that game, he was good.
Now its really resolving around Pantalones, wich is a huge team, and few people who try to oppose them. But its quite a good game and a good community.
Novice Member
Joined: 12/11/07
Great game, just joined today, and I love it. There are only 2 problems I can see though:
1. The game seems to be having a hard time connecting to the server lately. IDK if it is because of the Universe Reset or something but it's annoying me.
2. One of these "Top players" Names all of his bots "Hairy sex guy" or something related and it is really annoying to constantly see these disgusting names.
Besides ONE PERSON being an idiot and minor connection problems int he last few hours, I give this game an 8.6 - would be more around 9.1 if the ships had reverse thrusters and better aiming, but still great.
Novice Member
Joined: 10/08/08
SS has been given a major makeover since like...1 year ago.
no reverse thrusters simulates flying an airplane. you brake, you fall, crash and commit suicide all at once.
private chat is only: /w "Name with spaces or name without spaces" i made a message
buddy list is in, but one could use it to exploit the game by finding if your enemies are online or not.
lack of pvp is that if you kill someone, you gain aggro to all his defenses and thus, will be killed pretty easily by the near invinsible bases if soloed.
there is a "trade" chat, where you can only post buy x/sell x/pricecheck x
slaves, or what you guys cal bots, are actually automatic units that ferry stuff around between bases if a "trading slave" (TS###Nameofperson) or defend and fight along side of the "owner" in battle are "combat slaves" (CS###Nameofperson)
skills can be trained as long as youahve the skillpoints to do so, which means that you can access the whole universe, so a level cap must be implaced.
as for the "huge" team, voyagevsbor, teams have a max member cap of 150, so pantalones isnt the biggest team out there. Death Mental, Zephyr, Traders, Aidelon, and Core are quite powerful.
this Hairy Sex beast guy's slaves, actually are named after him, so if he is named "swingarm", his slaves would be either CS# (swingarm) or TS# (swingarm). this is short for CombatSlave# of swingarm and TradingSlave# of swingarm, which we know is far too big in size.
the aiming problem is fixed by using a weapon that has better tracking. weapons are broken into different types of bullets. they are laser, pulse or ball, slag or a cylinder that home in on ptarget til out of range of it hits, mag or a bullet or bullets that home in on target til out of range or it hits, torpedoes, which are deployed then it launches itself with sometracking at target, and thorax, which is deployed longer than torp but have more tracking.
ships hull classes are meant to be used by different classes. Light Fighters by Recon Focus: Seer Class and Speed Demon Class. Heavy Fighters by Combat Focus: Berserker Class and Sniper Class. Freighters by Support Focus: Shield Monkey Class and Engineer Class. Capital Ships by Fleet Focus: Fleet Commander Class and Gunner Class.
Border Collie Light Fighters rule
Novice Member
Joined: 12/11/07
Well I do know that... and he is now banned for having an offensive name I believe. Besides, this post was made 7 months ago.
Novice Member
Joined: 6/14/07
And 2 years later...


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NoSwearing API
NoSwearing is a curse-word dictionary/thesaurus. Enter a swear word or a string of words containing swear words into the filter and a cleaned-up translation is returned. An API is available which will list "dirty words, censor dirty words, replace dirty words, and define dirty words." API access is on a limited basis. Requests for access can be made (please see contact email above). Public documentation is not available.
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146 Performing Arts Center
(320) 308-3270
Oboist and English hornist Emily Olson is adjunct instructor of oboe at St. Cloud State University. Dr. Olson is active as a performer and private oboe instructor in the Twin Cities area.
A native of Minnesota, Dr. Olson received a Bachelor of Music degree from Northwestern University in Evanston, IL, where she was awarded the Presser Undergraduate Scholar Award. She received Master of Music and Doctor of Musical Arts degrees from the University of Minnesota in Minneapolis, MN. Her primary oboe teachers have included John Snow, Scott Hostetler, Michael Henoch, and Rachel Brudnoy.
Dr. Olson has subbed with the Minnesota Orchestra and Duluth Superior Symphony Orchestra. She has taught non-major oboe lessons at the University of Minnesota as an Instructor of Record and has served as an oboe mentor to young oboists in the top orchestra of Greater Twin Cities Youth Symphonies.
She enjoys performing chamber music, and since fall 2013, has performed with other members of the newly formed, Twin Cities-based chamber music collective, The Antithesis Project. Dr. Olson has also taken organ lessons. She enjoys reading Jane Austen novels, walking outdoors, and writing letters.


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been tapped. They need to be integrated into traditional teaching and developed as distinctive media that stand on their own. This chapter highlights some opportunities, starting with interdisciplinary modules.
The physical science and mathematics background of life science majors should be markedly strengthened by bringing principles and examples drawn from these disciplines into the teaching of biology courses. No longer should these disciplines be regarded merely as courses to be "taken" by life science students. Rather, they should be woven into the teaching of biology itself to better illustrate the integrative and interdisciplinary nature of the life sciences. The next section presents examples of ways to integrate two or more sciences together into one course. The ideas presented here may be helpful in designing courses for the curricular ideas and arrangements presented in Chapter 2.
The purpose of this section is to provide some of the best examples identified in the course of this study as models for faculty who may want to incorporate some of the ideas into their own teaching. A step toward interdisciplinary teaching can be taken by using modules that focus on important principles of mathematics and the physical and information sciences in order to demonstrate their relevance to biology. A module could be presented in a single lecture or laboratory session, or over several sessions. For example, a module on allosteric interactions in hemoglobin could enrich the teaching of respiratory physiology. Students could explore the following questions by carrying out interactive computer simulations: How does the cooperative binding of oxygen to hemoglobin increase the efficiency of oxygen transport? How much oxygen is released from hemoglobin when the pH is lowered? How is oxygen transport affected by high altitude?
Modules have been developed and integrated into science curricula with success at some institutions, but this approach has not been widely adopted at a majority of institutions nationwide. The use of biological examples as modules in courses on chemistry, physics, computer science, and mathematics could help make those courses more relevant to future biological research scientists. Well-chosen examples that vividly present the biological pertinence of the physical or mathematical concepts under study can help students draw connections between material taught in different courses. Faculty from different disciplines should get together to prepare a series of interdisciplinary modules and associated teaching materials


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What is frequent urination?
Frequent urination, also known as urinary frequency, is a condition in which you feel the need to pass urine more often than usual. Frequent urination may occur due to a variety of reasons, some of which are not serious or harmful. For example, frequent urination may occur after drinking a lot of fluids, especially fluids that contain caffeine or alcohol. Pregnancy can cause frequent urination due to pressure put on the bladder by an enlarging uterus.
Frequent Urination Must Reads
If frequent urination is unexplained or persistent, it can be a symptom of an underlying disease, disorder or condition. These include diabetes, urinary tract infection, enlarged prostate, and side effects of certain medications, such as diuretics.
Frequent urination can occur in all age groups and populations, and it may or may not occur with additional symptoms, such as a foul urine odor and burning with urination. Nocturia is frequent urination during the night, which is common in middle-aged and older men.
In some cases, frequent urination can be due to serious or life-threatening underlying diseases, such as pyelonephritis, urosepsis or diabetes. Seek prompt medical care if you have persistent frequent urination. Timely diagnosis and treatment of the underlying cause reduces the risk of serious or life-threatening complications, such as kidney failure and shock.
If you have frequent urination with symptoms such as high fever (higher than 101 degrees Fahrenheit), abdominal or flank pain, bloody urine, or a change in consciousness or alertness, seek immediate medical care (call 911).
What other symptoms might occur with frequent urination?
Frequent urination may be accompanied by other symptoms, which vary depending on the underlying disease, disorder or condition. Symptoms can originate in the urinary tract (kidneys, ureters, bladder and urethra), the reproductive system, the endocrine system, the cardiovascular system, and other organs and systems of the body.... Read more about frequent urinationsymptoms
What causes frequent urination?
Frequent urination is often caused by infection, inflammation or other conditions of the urinary tract (kidneys, ureters, bladder and urethra) or the reproductive organs.... Read more about frequent urinationcauses


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B&Bs in Chiclayo, Peru
Rancho Santana Horseback RidingRancho Santana Pacora, Camino Carrozable a la Capilla, Pacora
We are in the Moche valley close to the Pyramids of Tucume and the temples of Lord of Sican (Batangrande, worlds biggest dry forest) We are offering a comfortable bungalow made out of mud bricks! In the room there is a double bed and a bunk bed,More Details about Rancho Santana Horseback Riding
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Casa CimaCalle Los Mangos 161, apartment 501, Edificio San Sebastian, Santa Victoria
Casa Cima is a shared penthouse suite with a large sun terrace and bbq area with panoramic views of the city of Chiclayo. Casa Cima is on the top floor of a new apartment block offering quite stunning views from the sun terrace and roof garden. The owners John and Teófila live in the penthouse. Casa Cima is an ideal place to fully unwind and relax in a family atmosphere and explore all the amazing sites around Chiclayo at your leisure. Casa Cima offers bed and breakfast and our guests...More Details about Casa Cima
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RoutedCommand.ICommand.Execute Method
.NET Framework 4
Namespace: System.Windows.Input
For a description of this members, see Execute.
Assembly: PresentationCore (in PresentationCore.dll)
- parameter
- Type: System.Object
Data used by the command. If the command does not require data to be passed, this object can be set to null.
This member is an explicit interface member implementation. It can be used only when the RoutedCommand instance is cast to an ICommand interface.
Windows 7, Windows Vista SP1 or later, Windows XP SP3, Windows Server 2008 (Server Core not supported), Windows Server 2008 R2 (Server Core supported with SP1 or later), Windows Server 2003 SP2
The .NET Framework does not support all versions of every platform. For a list of the supported versions, see .NET Framework System Requirements.


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The Second Amendment assures Americans' right "to keep and bear arms."
But which arms?
When this Bill of Rights guarantee was loaded into the national consciousness in the late 18th century, arms were muzzle-loaded muskets and flintlock pistols.
When early 21st century Americans wage ideological gunfights over which arms we can keep and bear, the field of fire ranges much higher on the advanced-technology — and bloody-devastation — scale.
That's because throughout our species' belligerent history, we have relentlessly created new destructive devices to impose our will upon each other.
Some AR-15 enthusiasts insist that our Founding Fathers mandated personal-possession gun rights so we could defend ourselves against not just common criminals but government tyrants.
So if that's the constitutional case, shouldn't we be able to bear not just assault weapons but weaponized drones?
After all, if King George III's legions had been packing M240s and cruise missiles, the American revolutionaries would have been decisively outgunned.
Yet beyond today's debate over which arms we can keep and bear lie tomorrow's wunderwaffen (German for wonder weapons).
And while few of these arms actually exist (yet), it's just a matter of time before at least some of them warp from the realm of imagination into our allegedly real — and really dangerous — world:
■ Eye Beam: From Gort in 1951's "The Day the Earth Stood Still" to Cyclops in "The X-Men," this remains a, well, visionary innovation.
■ Blaster: Featured in 1956's "Forbidden Planet," a classic forerunner of many space-age rayguns to come.
■ Phaser: Standard sidearm for "Star Trek" crew members. Usually set on "stun," though when needed set on "kill."
■ Hammer: No, not the kind with which your drive nails and hit your thumb. The Mighty Avenger lightning kind that Thor grabs to pound malefactors into deserved oblivion.
■ Shield: Deflects incoming shots. Rightly championed by Ronald "Raygun" Reagan in missile-defense push that helped bankrupt Soviet "Evil Empire" Union.
■ Batarang: Indispensable non-fatal option on Batman's utility belt.
■ Gas Gun: Non-lethal weapon used by Green Hornet to put targets to sleep. The Penguin frequently employed a variation of this device — an umbrella gun that emitted knockout vapors of varying hues.
■ Ten Rings of Power: One for each hand digit. Wielded to vile effect by The Mandarin in "Iron Man 3," which opens on May 3, they include the Ice Blast, Flame Blast, Electro-Blast, Matter Re-arranger and Disintegration Beam. But the most ominous of these ring-a-dingers is the Mento-Intensifier, which gives the bearer mental control of the victim.
OK, so even 100-round-magazine fans should know that Americans don't have a right to keep and bear (and wear) a Disintegration Beam ring.
But while pondering which arms we have a right to keep and bear, behold my inspiring idea for a weapon of sorts that could keep the peace — and quiet — without causing physical harm:
Your television remote control has a "mute" button.
Why can't somebody invent a similar device — a Mute Beam — that works not on TVs but on people?
We all are routinely subjected to the inane, unsolicited jabberings of friends, strangers, enemies, co-workers, family members, politicians and other verbose variants of their loquacious ilk.
Imagine a small gadget that can spare you that ordeal. Simply point it toward such talkative tormentors and press a mute button to render them silent to your ears — even as they wrongly assume you're still hearing every irrelevant word of their incessant prattle.
An occasional nod of the head, "uh-huh" or "sure" should suffice to delude most of those gabbing menaces into mistakenly assuming that you are still listening.
Of course, the smaller this merciful gizmo, the better your chances of concealing its timely implementation.
And the sooner the Mute Beam is available, the sooner we can exercise this right that those Founding Fathers left out:
Our freedom from speech.
Frank Wooten is assistant editor of The Post and Courier. His email is firstname.lastname@example.com.
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Wednesday, November 28, 2012
Exo - History Outfits Inspired Mani;
For today, I wanna share a kpop mani I did a few weeks ago.
-- which was inspired by ...
(click on the picture, it will enlarge)
... and ....
My mani was inspired by Exo's History outfits and the Exo logos. My mani is pretty sloppy & my nails look random ... but if you look at Exo's group picture closely, you can see the details in their outfits which are what I put onto each design. I almost gave up on my left hand, pointer finger (Kris' jacket), and my right hand, pointer finger (Tao's ripped sweater) -- but thankfully, I pulled through & finished it cause those are my favorite designs :D Anyway, you can pretty much guess what design goes with who (besides for my design of Lay's jacket (which is on my left hand pinky), you can't really notice the design cause of the glare ;__; Ooh yea, the designs kind of go in order of where they're standing in the picture — except I didn't have enough nails to do a design off each member. but yea .. I guess I'm happy about the way it turned out .. :)
Exo is split into two groups -- Exo M, who promotes in China, and Exo K, who promotes in South Korea. History is a catchy song & I love it; both Exo M & Exo K have a version of the song ... so here are the music videos to enjoy~
Exo K
Exo M
Alright, thats it for now~
have a good one!,
10 comments:
I love your kpop nails! This looks awesomeReplyDeleteReplies
aw, thanks Steph :)Delete
OMG! U know K-pop better then me LOL~ Never heard of this group :) But ur mani looks very matching their fashion style :) Great job!ReplyDeleteReplies
haha, i love kpop~ im surprised you havent heard of Exo, they're a pretttttty big rookie group this yea; haha :) thanks~Delete
I don't know this group, they shouldn't be famous in France? But otherwise your Nail Art is superb! Bravo!ReplyDeleteReplies
thanks Tithy~ nah, Exo (both M & K) aren't big in France ... although i hear they do have a decent number of fans there~ :)Delete
you are very creative when it comes to your nails lolReplyDelete
They must have taken some time to be done. Great job. :)ReplyDelete
If you want take a look at my nail blog http://crissiesmind.blogspot.ro .
Its so cute i love Exo-M and Exo-KReplyDelete
cute :)ReplyDelete
hi, i'm a new reader :)
I saw a lot of your kpop nail art, and they are really cool :D
I hope I can make it like you


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In a frankly unheard of turnaround Apple has tweaked the EULA on iBooks Author with an update which will see would be writers flock back to the app
Apple's iBooks Author app for Mac has received an update to the EULA which changes the rights Apple has over a .ibooks file made using the app.
The update now means that if someone wants to publish their work via the iBooks Store Apple only has rights to the .ibooks file and not the content found within it.
Before the update if a person wanted to publish a book through iBooks Author, Apple had exclusive rights to the .ibooks file and the copy within it, effectively taking complete control of the authors work.
Unsurprisingly when it was discovered this caused some upset as it would effectively block out any chance of publishing work on both the iBooks Store and also through Amazon's Kindle Store.
The iBooks Author app was announced a few weeks ago in collaberation with their new iBooks 2.0, an upgraded version of iBooks for iPad which lets you download interactive textbooks and books.
Source: TUAW


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Guest Faculty
The MFA program in Dance offers the opportunity to study with some of the best dance faculty in the country. In addition to the faculty at Saint Mary's College. Current guest faculty include Bonnie Bainbridge-Cohen, Martha Eddy, Bill Evans, Janice Garrett, Doug Nielsen, Randee Paufve, and James Sutton.
Bonnie Bainbridge-Cohen
Bonnie Bainbridge-Cohen is the developer of Body-Mind Centering® and the founder and Educational Director of the School for Body-Mind Centering®. For over fifty years, she has been a movement artist, researcher, educator and therapist. An innovator and leader, her work has influenced the fields of dance, bodywork, movement, yoga, body psychotherapy, childhood education and many other body-mind disciplines. She is the author of the book Sensing, Feeling, and Action and numerous DVDs. More information available at www.bodymindcentering.com.
Janice Garrett
Janice Garrett is an internationally active choreographer and dance educator. She currently co-directs the San Francisco based performing arts organization Garrett + Moulton Productions with fellow choreographer Charles Moulton. Honored in 2004 as one of Dance Magazine's top "25 to Watch," Garrett has choreographed more than 20 original works for her company as well as receiving commissions to create dances for companies and performance groups throughout the US and Europe. As a young dancer, she was a member of Dan Wagoner and Dancers, with whom she performed and toured extensively from 1984-89. In 1990, she began choreographing,
performing, and teaching her own work. Since that time she has been a guest artist at universities across the US as well as for international dance companies that include London Contemporary Dance Theatre, Rambert Dance Company, DV8 Physical Theatre, and Matthew Bourne's Adventures in Motion Pictures. Garrett's choreographic grants include awards from the James Irvine Foundation, Zellerbach Family Foundation, Fleishhacker Foundation, Hewlett Foundation, Kenneth Rainin Foundation, San Francisco Arts Commission, and San Francisco Grants for the Arts. Honors include a 2010 Isadora Duncan Dance Award ("Izzie") for "Best Company Performance" for her
collaboration with Charles Moulton on The Illustrated Book of Invisible Stories, three 2011 Izzie nominations for The Experience of Flight in Dreams, and recognition by the San Francisco Chronicle for StringWreck as one of the "Top 10-Best Performances of 2008." She is a graduate of Stanford University (BS, Mathematics 1975) and Mills
College (MA, Dance 1978).
James Sutton
James Sutton is a dancer, teacher, and choreographer, who has appeared as principal dancer with Houston Ballet, Chicago Ballet, Pittsburgh Ballet Theatre, Milwaukee Ballet and DANCERS. Formerly Associate Director of the Kathryn Posin Modern Dance Company, Jim formed his own company in New York in 1983. His choreographic commissions span all aspects of theatre, from the Dallas Opera to musical productions at the Goodspeed Opera House in East Haddam, Connecticut to his current position as resident choreographer for New York Theatre Ballet. A frequent guest instructor around the US, he has worked on the faculty of the American Dance Festival, as ballet master for Ballet Hispanico of New York, and, for five years, as company teacher for the Metropolitan Opera Ballet. Internationally, he has been a guest teacher at the Maly (now Mikhailovsky) Theatre in St. Petersburg, Russia, Cloud-Gate Theatre in Taipei, Taiwan, and the Cullberg Ballet and Balett Akademien in Stockholm, Sweden, among other venues across Europe and Asia. Jim was Associate Prof at NYU for 14 years and is also a contributing writer for Ballet Review.
Randee Paufve
Originally from New York State, choreographer Randee Paufve has been based in the Bay Area for more than two decades. Dance critic allen Ulrich has weritten in Voice of Dance "Randee Paufve is a name to take seriously in Bay Area dance circles" and Jennifer Dunning described her work in the New York times, Ms. Paufve's choreopgraphy does not fit any of the stereo types about laid-back California or hyper New York dance."
Prior to forming Paufve|dance, Randee spent 15 years developing a body of solo and smaller works, as well as a loyal and eager following for her work in the San Francisco Bay Area and in Portland, OR. In 2001 she formed Paufve|dance for which she has choreographed numerous works, including three evening length dances and several site-specific adventures, with biennial Bay Area seasons and extensive touring on both coasts.
Randee graduated from Elmira College, Magna cum laude, Phi Beta Kappa, with a Bachelor of Arts degree in Cross-Cultural Performing Arts. She spent her junior year in India, studying Tamil, Bharata Natyam, South Indian folk dance, and Karnatic music. Randee holds a Master of Arts degree in Dance Choreography and Performance from the State University of New York, Brockport and a Master of Fine Arts degree in Dramatic Arts from the University of California, Davis.
Douglas Nielsen
Douglas Nielsen, Professor and Graduate Advisor at the University of Arizona, has been a guest teacher and choreographer at more than 40 universities throughout the United States and abroad. He has served as the Rufus Putnam Professor in residence at Ohio University and as the Sage Cole Chair at the University of Minnesota, and guest faculty at New York University Tisch School of the Arts. Former member of the Batsheva Dance Company in Israel and Gus Solomons, Pearl Lang, and Paul Sanasardo dance companies in New York, he has performed solo works by Viola Farber, Beverly Blossom, Anna Sokolow, Murray Louis, and Charles Weidman. Douglas began dancing shortly after earning a B.A. in psychology at Augsburg College, Minneapolis. He studied dance at the California Institute of the Arts in the early 70's with Donald McKayle, Bella Lewitzky, and Mia Slavenska. Honors include the 2003 Lester Horton Dance Educator Award for excellence in teaching, and the 2007 American Dance Festival Balasaraswati/Joy Ann Dewey Beinecke Endowed Chair for Distinguished Teaching.
Nielsen has served on adjudication panels for The American College Dance Festival Association in Virginia, Texas, Idaho, Kansas, Connecticut, California, and Ohio. He has also served as a distinguished panelist for the Los Angeles Music Center Spotlight Awards, The National Association for the Advancement of the Arts, the Los Angeles County Arts Commission and the McKnight Foundation.
Bill Evans
Bill Evans is artistic director of the Bill Evans Dance Company, founded in 1975. The company was based in Seattle and then Albuquerque, where it celebrated its 30th anniversary in 2004. The Company has performed in all 50 states as well as many other countries and was the most-booked company in this country for several years under the Dance Touring and Artist-in-the-Schools Programs of the National Endowment for the Arts. BEDC now works on a project basis for performances in western New York, in national conferences and on summer national and international tours.
Evans has choreographed more than 200 works, which have been performed by more than 70 professional dance companies in several countries and by college and regional pre-professional dance ensembles throughout North America and in Europe. He received the Guggenheim Fellowship and numerous fellowships and grants from the National Endowment for the Arts as well as state, regional and private arts agencies. He received the National Dance Education Organization Lifetime Achievement Award, the New Mexico Governor's Award for Excellence in the Arts, two Albuquerque Arts Alliance Awards for Excellence in Dance, the Dance TeacherMagazine Lifetime Achievement Award, an honorary doctorate of fine arts from the Cornish College of the Arts in Seattle and the Distinguished Dance Alumnus Award from the University of Utah.
He earned a BA in English/Ballet and an MFA in Modern Dance from the University of Utah and is a Certified Laban Movement Analyst with both Integrated Movement Studies and the Laban/Bartenieff Institute of Movement Studies. He is distinguished professor emeritus at the University of New Mexico, permanent guest artist in the Senior Professional Program of Winnipeg's School of Contemporary Dancers and artistic director of the Bill Evans Summer Institute of Dance, which just celebrated its 35th anniversary.
Visit www.billevansdance.org to find out more about his summer programs, his touring schedule, his book, Reminiscences of a Dancing Man, published by the National Dance Association in 2005, and other publications he has written of or which he is the subject.
Maps & Directories
Mailing Address
Saint Mary's College of California
1928 Saint Mary's Road
Moraga, CA 94575
(925) 631-4000
Google Map | Campus Map | PO Boxes


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August 4, 2015
One popular foodie website did not savor the flavor of College Station's local restaurants.
Chow.com complied a list of the ten college towns with the worst food. The website claims the numerous chain restaurants in town bore Texas A&M University students, and there's not much out there if you're craving something other than pizza, wings or subs.
College Station was not the only Texas college town cited for limited options. Waco was also mentioned for their lack of variety.
To see the complete list, click here.
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SANTA ROSA BEACH — The 25th annual ArtsQuest Fine Arts & Music Festival, based at WaterColor Inn & Resort, will bring musicians and artists to the coast this Mother's Day weekend.
ArtsQuest features 130 exhibiting artists, art workshops, live demonstrations, concerts and more. New this year, Chan's Wine World is partnering with the Cultural Arts Alliance of Walton County to host WineQuest, a wine and cheese tasting, in conjunction with the festival.
"We're excited about the new location," said Jennifer Steele, CAA executive director. "The beautiful setting within Cerulean Park and Marina Park provide the perfect centerpiece and the festival will extend to the streets and unique spaces throughout WaterColor."
ArtsQuest will be 4-7 p.m. Friday, 10 a.m. to 6 p.m. Saturday and 10 a.m. to 4 p.m. Sunday. Admission is free, but a $5 entry donation is suggested. Complimentary shuttle service will be provided from Publix Super Market in WaterColor Crossings on County 395.
One of the largest arts festivals in the Southeast, ArtsQuest attracts talented artists from around the country to participate in the juried show and compete for $10,000 in cash awards. Exhibiting artists' mediums include glass, metal, mixed media and sculpture, among many others.
More than 40 local artists' works will be displayed at the "Betty & Johnny CAA Member Tent," where work will be eligible for the coveted "Best in Show" award and a $250 cash prize. Two of the CAA's most popular workshops (basket weaving and watercolor) will take place at various times both Saturday and Sunday; check the website for the complete schedule.
Meanwhile, wine lovers will have the opportunity to try wine and cheeses from five of the world's top wine-producing regions — Italy, France, Chile, Spain and South Africa. Wine stations will be arranged throughout the festival, and guests who purchase a WineQuest passport (only $25) may use it in a single day or throughout the weekend.
Kids will have their own "quest" as well: Parents may purchase a map to TreasureQuest, a hunt that leads children throughout the festival looking for clues to "ImagiNation: Where Kids Rule." The ImagiNation Stage will have family-friendly performances by Seaside Neighborhood School Band, Ohana Institute Bands, 30A Dancewave, Seaside Rep Storytelling and Boxtop Jenkins.
Children also can enjoy a trove of interactive arts and crafts stations that encourage creativity, and they can sign up for workshops, including a wax hand workshop and custom designed skimboard workshop.
Passes for workshops, WineQuest and TreasureQuest may be purchased on-site or in advance at ArtsQuestFlorida.com, or call 622-5970.
Songs on the Lawn will be 7-10 p.m. Saturday at WaterColor's Marina Park, featuring Cory Chisel and Adriel Harris with guest Kellin Watson. VIP tables for up to 10 people are $300; general admission is $20. Details: 231-7768.
Back to Grayton is ArtsQuest's reunion event that brings art and music lovers and friends of the CAA back to the place where the festival began: Hibiscus will host the event in the "Backyard of Love" where Humming House will entertain beginning at 7:30 p.m. Saturday.
Chef Colin Smith will create his masterpieces, including Grayton Beer bratwurst, Cajun jambalaya, vegan stir-fry and pulled pork. Wine and beer will be available for a donation. Admission is free to CAA members and $10 for non-members.
What: Fine arts, wine and music festival
When: Friday through Sunday, May 10-12
Where: WaterColor Resort, 34 Goldenrod Circle, Santa Rosa Beach
Details: ArtsQuestFlorida.com or (850) 622-5970


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Columbia Girl's Triple Run II Jacket
Key Features
Waterproof Omni-Shield fabric seals out moisture
Interior security pocket
Attached adjustable storm hood
Reflective detail keeps your kid visible at night
Our Price $64.99
Sale Price $34.92
Availability: Sold Out
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Product DescriptionThe sleek, versatile Triple Run II winter jacket sports a lightweight waterproof nylon shell fabric that won't weigh you down during activity and 240g of toasty insulation to seal out the cold. Waterproof Omni-Shield fabric seals out moisture of any kind. Interior security pocket. Insulated. Attached adjustable storm hood. Reflective detail.
Center back length: 21". Fabric: [shell] Omni-Shield 100% nylon HC 3000; [lining] 100% nylon 210T taffeta; [insulation] 100% polyester, Farenhot 240g. Imported.