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From Bloons ShoutWiki
Template:About Template:Additional citations
Bloons games are video games where players use monkeys, armed with various weapons, to pop as many "bloons" (balloons) as possible. They include the Bloons series, the Bloons Tower Defense series, and several other spinoffs. Most Bloons games are browser-based games that use Adobe Flash Player, although some are available on other platforms. The mobile games are now distributed through the App Store by Miniclip.
The first Bloons game was developed by Stephen and Chris Harris, two brothers from New Zealand.[1] Prior to working on Bloons, the pair had released Cash Sprint - a successful racing game that incorporated weekly prizes. After the success of Cash Sprint, they developed their own web portal for flash games, but failed to gain the necessary traffic. Instead they decided to focus on their own games, and launched a website – Ninjakiwi.com – with five of their own games. The site was a success, and this led to their work on Bloons.[2]
The basic concept for Bloons came from Stephen's wife. When Stephen asked her what would be fun in a new game, she brought up carnival games with darts and balloons.[2][3] The first version was developed quickly, and the game was released in April 2007. It proved popular, especially after being picked up by Digg, and soon reaching approximately 100,000 players per day.[2][4] By 2011 the game had been played over 3 billion times.[4]
Main series[edit]
The Bloons series is the original grouping of games developed under the "Bloons" name. In all of the main games, the goal is for the player to clear the playing area of all Bloons (which, as implied, have similar traits to balloons) using a limited number of darts. The player is able to choose the power, aim, and other factors involving the launch of darts; and hence the path it will follow and what Bloons it will pop. There are many sequels and spinoffs of this series, with large fan support (indicated by the "player packs" discussed later).
The games consist of various levels, each with a different and unique layout of Bloons. On each, the player is given several darts used to pop the Bloons on the screen. The fewer darts the player uses, the better his or her score for that level. There is much strategy involved, especially since some Bloons have special powers or affect the player's darts in both helpful and hindering ways. For example, when some Bloons are hit, they provide the player with 3 darts to fire at once during his next shot. Others have an area blast effect (again, when hit) like a bomb. As new games in the series developed, more and more aspects of the game were expanded upon with many new Bloons and levels being introduced. There are also several "player packs" - groups of levels developed by players of the Bloons series.
Games in the series[edit]
- Bloons
- More Bloons
- Even More Bloons
- Bloons Insanity
- Bloons Junior
- Bloons 2
- Bloons 2 Christmas Pack
- Bloons 2 Spring Fling
- Bloons Player Pack (1–5)
- Bloons Pop 3
Bloons Tower Defense[edit]
Template:Main In the Bloons Tower Defense series (often abbreviated BTD), the main objective of the game is to pop the enemy Bloons before they reach the end of the path on the game screen. The player has various types of towers at his disposal to defend against the Bloons; including Dart Monkeys, Tack Towers, Ice Towers, Cannons, and the powerful Super Monkey.[5][6] There are also other types of towers, such as Ninja Monkeys, Boomerang Monkeys, Sniper Monkeys, introduced in later editions of the game. All of the aforementioned towers are purchased with in-game money which can be earned through various means. There are many types of Bloons, the Red Bloon being the weakest; the tougher variants each contain one or more weaker ones, released when the containing Bloon is popped. For example, the Yellow Bloon contains a Green Bloon, which contains a Blue Bloon, which contains a Red Bloon. Depending on the difficulty and version of the game, the player has a certain number of "lives" available. Different types of Bloons consume different numbers of lives if they escape, based on the total number of Bloons contained inside. Different bloons, however, have special attributes. A lead bloon is immune to sharp things, and black bloons are immune to bombs as whites are to ice. There are special types of bloons such as the infamous "MOAB-class" (Mother of All Bloons) where the bloon resembles an airship and takes many hits to defeat, an example being the ZOMG (Zeppelin Of Mighty Gargantuanness). In different variations of the "Bloons" series, there have been unique types of bloon; for example in BSM2 (Bloons Super Monkey 2) there was the "glass bloon" which wasn't in Bloons TD5.Template:Citation needed
Bloons Super Monkey[edit]
Bloons Super Monkey (often abbreviated as BSM) is the second Bloons spin off series to date.
The objective is to achieve at least the bronze trophy in all 20 stages using upgrades and research, as well as beat all four bosses. It is about moving the super monkey and popping Bloons to get power blops. Power blops are used to buy upgrades. If the monkey fails to pop the required amount of bloons for a certain level, the game is over. The super monkey wields weapons, which can be upgraded or replaced with a different weapon.
Bloons Monkey City[edit]
Bloons Monkey City is a game where you can build your own city. The more plots you capture, the more buildings you can place. Some buildings require specific tiles. You use in-game money and gems to help your city grow. Windmills and watermills create energy that is used by your other buildings. As you level up in the game, the more buildings and cities you can make. Special plots can contain MOABs, chests, camo bloons, etc.
Spin-off games[edit]
- In Hot Air Bloon, users must pop as many Bloons as possible without crashing into obstacles. Players have the option to save their scores to the High Score List with an existing MochiGames account.
- In Who Wants To Be A Bloonionaire, users answer ten questions in a similar style to Who Wants to be a Millionare with two hints. If they are all answered correctly, then a trailer of Bloons TD 5 is shown.
- In Bloons Monkey City, users build multiple cities in the world of Bloons, in order to protect their bloon attacks from other players in Monkey V Monkey mode. Its gameplay is similar to the games Clash Of Clans and Game of War.
Tech media website CNET reported favorably on the Bloons Tower Defense games for iPhone, describing it as having "nearly everything from the beloved Web-based classic". The article reflects negatively on the controls, stating that "[they] are a little finicky", but overall their impression is positive, saying that the game is an ideal introduction to the tower defense genre for young children.[7]
External links[edit]
|This article uses material from the Wikipedia article Bloons, which is released under the Creative Commons Attribution-ShareAlike 3.0 Unported License (view authors).


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Today our carpet was installed - and looks pretty good too - along with more internal carpentry and the flyscreens are now on as well.
Tomorrow the electricians will finish their part and the floor will receive it's final polish and buff - providing the polisher turns up - and the house clean is set for Wednesday, I can't wait to see it after this.
Curtains and blinds are supposed to be coming some time in the next few days as well.
It is looking very much like a house now which is pretty good seeing as we move in soon.
|Freya's bedroom
|contrast with the polished concrete floor
|craft room/study nook

subset: commoncrawl


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From Scott:
First time dad here, so I'm not sure if this hack is known by all veteran parents. My wife and I were in the car with our 3 month-old son. He wanted out of the car seat and begin telling us quite vocally all about it. We had forgotten to bring a rattle with us in the car. So we improvised on the spot: 1 empty Dunkin' Donuts paper bag and 5-6 nickels. Shake the bag and you have your rattle! Little Zachary loved it – even more than the $20 musical, light-up thingy that attaches to the back of the seat (of course).
Perfect example of why parents are such good hackers. We're constantly thinking on our feet.


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Sister musicians Katie Crutchfield of Waxahatchee and Allison Crutchfield ofSwearin' got together to cover Sleater-Kinney's "Modern Girl" for Vulture Festival in New York over the weekend. Before their solo careers, the Crutchfield's were in the beloved pop punk band P.S. Eliot, which broke up in 2011. Allison Crutchfield released her debut solo album Tourist In This Town a few monthsRead more…
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Professional Organizations
Professional Organizations
National Organizations
- Council for Exceptional Children (CEC): the major professional organization in special education; contains information on special education law, legislation, career development and publications.
- American Association on Intellectual and Developmental Disabilities (AAIDD): provides information on policy issues, publications, career connections and much more.
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- Learning Disabilities Association: provides a wide variety of information about learning disabilities for teachers and parents.
- Council for Children with Behavioral Disorders (CCBD): advocates on behalf of children and adolescents with emotional/behavioral disorders.
- CHADD: provides an extensive amount of information including material from the National Resource Center on ADHD.
State Organizations
Family Voices of Wisconsin: provides information for children with special health care needs and their families.
Wisconsin Board for People with Developmental Disabilities (WBPDD): federally funded organization advocates on behalf of individuals with developmental disabilities and their families to promote self-determination, independence and inclusion.
Wisconsin Family Assistance Center for Education, Training, and Support (WI FACETS): federally funded organization provides training, assistance and support for parents of children and youth with disabilities.


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Leo DiCaprio & Ellen Page: Inception L.A. Premiere!
Leonardo DiCaprio and Ellen Page premiere their much-anticipated movie, Inception, at Grauman's Chinese Theatre on Tuesday (July 13) in Los Angeles.
Co-star Marion Cotillard slipped into a sexy L'Wren Scott dress and Christian Dior shoes. Leo and his BFF Lukas Haas both suited up in Emporio Armani. Ellen wore Yigal Azrouel.
Other celebs in the movie who also hit the red carpet (pictured below): Cillian Murphy, Joseph Gordon Levitt, Ken Watanabe, and writer/director Christopher Nolan.
Inception opens in theaters everywhere this Friday (July 16)!
25+ pictures inside of Inception's L.A. premiere…


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Eight Ways You Didn't Know You Could Save Energy In Your HomeEveryone knows that changing to compact fluorescent bulbs and shutting down appliances will save money and energy in your home, but what people aren't generally aware of is that by placing your lights in certain parts of the room or selecting special windows can save just as much! Put Your Lamps in the Corner of the Room When a lamp gets put into a corner, it allows the light from the bulb to bounce off the walls into the rest of the room. This means that you'll be able to switch to a lower wattage bulb or keep the dimmer on the light low. The draw on your power will be reduced with practically no difference in the amount of light to the room. Blanket Your Water Heater Heating up the water inside your home is difficult enough task for a water heater, but when winter rolls around it has to work that much harder. You can help make the process easier by surrounding the water heater with fiberglass insulation. This can cut heat loss down and save between 4 to 9 percent on your heating bill. Use Your Laptop A desktop computer is a large machine that requires a large amount of energy to stay on. To help be more efficient in the home, try using your laptop to surf the Internet. On average, a laptop will use one-third the amount of power that a home computer uses. Cook Outdoors in the Summer Summer is the time for sunny skies, bathing suits, and keeping the AC on. Of course, producing all that cold air can be a major drain on the energy in your home. It gets even worse when you spend time using the oven in your kitchen. Spend time cooking on the grill outside to cut back on energy consumption. Energy Efficient Windows Not all windows are made the same, and many allow air to escape in and out of the house. Buying energy efficient windows, or having your current windows treated with a special glaze, will improve your home's energy efficiency. If done properly, you can expect to save around 40% on heating and cooling bills! Run Your Air Conditioner Close to Outdoor Temperature By keeping the temperature in your home close to what it is outside, your air conditioner will not have to work as hard to keep your ideal temperature. If it is 82°F outside, consider putting your indoor temperature to 78°F instead of something much lower. Even this minor difference can save you upwards of 15% on your cooling bill and will still be enough to keep you and your family comfortable. Plan Your Landscaping Out Another way to drop your energy bills is by putting trees and bushes in specific places of your yard. If you have a large tree casting shade into your home during the day, that room will cool much more quickly than if the AC was running alone.
© 2018 Windows & Doors Installation Company - Landmarkquality.com. All Rights Reserved.


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Watch what happens when you tweet Elizabeth Banks
ElizabethBanksPublished: August 21, 2014Updated: August 22, 201469,754 views
Published: August 21, 2014Updated: August 22, 2014
This is what happens when you tweet your phone number to Elizabeth Banks. Elizabeth is an actress of many well known films such as The Hunger Games, Pitch Perfect 2, and many more.
David Senick decided to tweet Elizabeth with a Really Important Question.


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Numbers 6 Lexham English Bible (LEB)
Rules Concerning Nazirites
6 Yahweh spoke to Moses, saying, 2 "Speak to the Israelites[a] and say to them, 'When a man or a woman takes a special vow, a vow of a Nazirite,[b] to keep separate for Yahweh, 3 he will abstain from wine and fermented drink; he will not drink wine vinegar or vinegar of fermented drink; he will not drink the fruit juice of grapes or eat fresh or dry grapes. 4 All the days of his separation[c] you will not eat from anything that is made from the grapevine, from sour grapes to the skin of grapes.
5 "'All the days of the vow of his separation a razor will not pass over his head. Until fulfilling the days that he separated himself to Yahweh he will be holy and grow long the locks of the hair of his head.
6 "'All the days of keeping himself separated for Yahweh he will not go to a person who is dead; 7 for even his father, mother, brother, or sister he will not make himself unclean by their death,[d] because the separation to his God is on his head. 8 He will be holy for Yahweh all the days of his separation.[e]
9 "'If someone dies suddenly and makes the head of his separation[f] unclean, he will shave off the hair of his head on the day of his cleansing; on the seventh day he will shave it off. 10 On the eighth day he will bring two turtledoves or two young pigeons[g] to the priest by the doorway of the tent of assembly, 11 and the priest will offer one for a sin offering and one for a burnt offering, and he will make atonement for him because he sinned concerning the corpse. He will consecrate his head on that day. 12 He will rededicate to Yahweh the days of his separation[h] and bring a ram-lamb in its first year[i] as a guilt offering. The former days of his vow will fall away because his separation was defiled.
13 "'This is the regulation of the Nazirite for the day of the fulfilling of the days of his separation: one will bring him to the doorway of the tent of the assembly. 14 He will present his offering to Yahweh, one ram-lamb in its first year[j] without defect as a burnt offering, and one ewe-lamb in its first year[k] without defect as a sin offering, and one ram without defect as a fellowship offering; 15 and a basket of unleavened bread, finely milled flour of ring-shaped bread cakes mixed with oil, and wafers of unleavened bread smeared with oil, and their grain offering and their libations. 16 The priest will present before Yahweh[l] and offer his sin offering, his burnt offering; 17 he will offer a ram as a sacrifice of a fellowship offering to Yahweh, in addition to the basket of the unleavened bread; the priest will offer his grain offering and his libation. 18 The Nazirite will shave off the hair of his consecrated head[m] at the doorway of the tent of assembly, and he will take the hair of his consecrated head,[n] and he will put it on the fire that is beneath the sacrifice of the fellowship offering. 19 The priest will take the shoulder from the ram that is boiled, and one ring-shaped bread cake of unleavened bread from the basket, and one wafer of unleavened bread, and he will put them on the hands of the Nazirite after he has shaved his consecrated head.[o] 20 The priest will wave them as a wave offering before the presence of[p] Yahweh; they[q] are a holy object to the priest, in addition to the breast section of the wave offering, and in addition to the upper thigh of the contribution; and afterward the Nazirite may drink wine.
21 "'This is the regulation of the Nazirite who has made a vow of his offering to Yahweh according to his separation, in addition to what he can afford.[r] In accordance to the word of his vow that he vowed, he will do, concerning the instruction of his separation.'"
The Priestly Blessing
22 Yahweh spoke to Moses, saying, 23 "Speak to Aaron and his sons, saying, 'You will bless the Israelites:[s] You will say to them:
24 Yahweh will bless you
27 And they will put my name on the Israelites,[t] and I will bless them."
Lexham English Bible (LEB)
2012 by Logos Bible Software. Lexham is a registered trademark of Logos Bible Software
You'll get this book and many others when you join Bible Gateway Plus. Learn more


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Astroburn Pro 4.0.0.0233 Crack & Serial Number Final Download
Astroburn Pro 4.0.0.0233 Crack Serial Number is a capable, natural and simple to utilize plate copying program that permits you to copy a wide range of information to CDs, DVDs, and Blu-Ray disks. The program permits you to make information picture, sound picture, snatch or duplicate plate, make and smolder circle picture, and that's only the tip of the iceberg. Astroburn Pro is a down-scale adaptation of Daemon Tools, considering the components offered practically the same and furthermore created by a similar designer. Astroburn Pro is a flexible circle smoldering programming that permits you to compose, duplicate and eradicate information from a wide range of plate media. It gives two review models: straightforward wizard for novices and propelled clients.
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Follow me on Twitter for my latest adventures!
Ed - Unix Text Editor - Cheat Sheet
I have always had historic interest of various UNIX tools and the UNIX itself. One day when I was learning sed, the stream editor, (sed cheat sheet) I got interested how much the most powerful text editing tools on UNIX - 'ed', 'ex', 'sed' and 'vim' (or just 'vi') had in common and what they borrowed from one another.
I was not perfectly sure if 'sed' has originated from 'ed', so I Google searched for 'sed history' which turned up some interesting results, one of them being "The original users manual for sed, by Lee E. McMahon, from the 7th edition UNIX Manual (1978)".
Quoting the manual "Sed is a lineal descendant of the UNIX editor, ed." Great! I knew that 'sed' was non-interactive text editor whereas 'ed' was an interactive one. Also I knew that 'ex' was improved 'ed' and that 'sed' was a non-interactive 'ed' and that 'ex' had become 'vi' and was used in 'vi' command line mode. To boost my vi/vim skills and get a touch on how text editing was done with just 'ed', and satisfy my historical curiosity I set myself a goal to learn it.
As I mentioned in awk and sed cheat sheet posts, the best method for me to learn a new tool is to have all commands in front of me, so when I am doing experimentation I can quickly find the command and also see all the other commands at the same time, blowing them in subconsciousness.
Since 'ed' is line-oriented text editor, it is important to understand the line addressing, this cheat sheet summarizes the line addressing.
Most of 'ed' command are single letter in length. This cheat sheet summarizes all the commands, showing the supported address ranges and a detailed description of each command.
From my experience, once I had completed this cheat sheet and had it in front of me, I picked 'ed' up in 30 minutes. And then spent a few more hours experimenting and trying various constructs.
Have fun learning ed!
Download Ed UNIX Text Editor's Cheat-Sheet
Plain Text (.txt):
Download link: ed text editor cheat sheet (.txt)
Downloaded: 31147 times
Download link: ed text editor cheat sheet (.pdf)
Downloaded: 49979 times
Microsoft Word 2000 format (.doc):
Download link: ed text editor cheat sheet (.doc)
Downloaded: 10535 times
Personally I prefer this plain and simple old school text editor:
Seriously ? you're learning ED in the 21st century ?
(oh my).
Now, maybe ED is way more powerful than EDLIN, the ED-inspired editor of MS-DOS 3 which i have been forced to use in early days ... I can tell you it *really* made me regret the C64's BASIC edition mode (list 1-n giving you n first lines, and re-typing over existing lines replaced them).
The point of learning ed is to become better in using (g)vi(m). When you know the history (ed>ex>vi) of the command you use, the commands will make more sense.
(yes i know i'm replying to a 4 year old post...)
What's wrong with replying to 4 year old posts?
Well, may be he thought the post was outdated.
By the way, right now, I am learning ed. And, I find great joy in practicing the editor.
I am here actually because of Google. I searched how to activate ed command history (if any of course).
And so far, I get none.
Well, even if there is no command history, at least I have verbose error message (H) and a prompt (P).
I was looking for an rtf viewer/editor for unix and had to stop by. I frequently use ed when building ad-hoc scripts. It is the ONLY editor that enables me to change the second occurance(or 3rd) of an RE. Ex and vi change the number of occurances starting with the first. For example: Applying the same sed-script to 99 files and directing the output to a different directory, I need to change the second directory. I usually use ls to find the files I want, past the list to itself, insert 'sed -f changes' in front of each file, then change the second(output) directory. ed is the only way to change the second directory name. In 60 seconds, plus the time to build whatever sed script I need for these files, I have them changed and in another directory.
Yes, ed is a small, powerful tool – if you know how (and when!) to use it wisely, it can certainly save the day. But somehow I wonder (please don't get me wrong on that): Why did you compile the cheat sheet? In my humble opinion, the manpage is basically just as good. (It is slightly longer, but it also covers a few more issues.) If you prefer to read it on paper, you can run
man -t ed
to have the manpage pretty-printed as PostScript. Redirect the output into a file, or pipe it directly to the printer.
In my opinion, the info manual is even better. I am learning the editor from the info manual by today.
Hi! Do you know if there is multiple implementation of ed ? Thanks ;)
plz send me ex and ed editors program in unix
Kumar if your distro does not contain ex and ed check out FreeBSD. It's historically accurate.


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Nick's Small Engine Service is a small, owner-operated "Mom and Pop" shop in Oakville, ON. We pride ourselves on our quality, reasonably priced, old-fashioned service for small engine equipment like lawn mowers, snow blowers, leaf blowers, chainsaws and more. We stand behind our work and provide a full 90-day warranty.
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Have a question?


text: A preview of the Planetside 2 and its beta. The original Planetside has a crazy following with many players still playing the game to this day, and for good reason. Planetside offers gaming mechanics unavailable in any other game. Skylar lets you know why you should give the sequel a try.


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WASHINGTON -- The two largest groups in the abortion battle are presenting polls with contradictory results on public support for abortion counseling.
The National Right to Life Committee, the nation's largest anti-abortion group, released a poll yesterday indicating equal support and opposition -- 48 percent each of 1,000 adults polled -- for the recent Supreme Court decision, Rust vs. Sullivan. The ruling upheld the Bush administration's ban on abortion counseling in federally funded family planning clinics.
But Planned Parenthood, the nation's largest abortion rights group, reported the results of its own poll of 1,254 adults last week, in which 74 percent of those polled said they oppose the decision.


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International Journal of Biomedical Imaging
Volume 2011, Article ID 350838, 13 pages
Multiclass Sparse Bayesian Regression for fMRI-Based Prediction
1PARIETAL Team, INRIA Saclay-Île-de-France, 91191 Saclay, France
2Laboratoire de Mathématiques, Université Paris-Sud 11, 91400 Orsay, France
3CEA, DSV, I2BM, NeuroSpin, 91191 Gif-sur-Yvette, France
4CEA, DSV, I2BM, INSERM U562, 91191 Gif-sur-Yvette, France
5SELECT Team, INRIA Saclay-Île-de-France, 91400, France
Received 23 December 2010; Revised 3 March 2011; Accepted 7 April 2011
Academic Editor: Kenji Suzuki
Copyright © 2011 Vincent Michel et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Inverse inference has recently become a popular approach for analyzing neuroimaging data, by quantifying the amount of information contained in brain images on perceptual, cognitive, and behavioral parameters. As it outlines brain regions that convey information for an accurate prediction of the parameter of interest, it allows to understand how the corresponding information is encoded in the brain. However, it relies on a prediction function that is plagued by the curse of dimensionality, as there are far more features (voxels) than samples (images), and dimension reduction is thus a mandatory step. We introduce in this paper a new model, called Multiclass Sparse Bayesian Regression (MCBR), that, unlike classical alternatives, automatically adapts the amount of regularization to the available data. MCBR consists in grouping features into several classes and then regularizing each class differently in order to apply an adaptive and efficient regularization. We detail these framework and validate our algorithm on simulated and real neuroimaging data sets, showing that it performs better than reference methods while yielding interpretable clusters of features.
1. Introduction
In the context of neuroimaging, machine learning approaches have been used so far to address diagnostic problems, where patients were classified into different groups based on anatomical or functional data. By contrast, in cognitive studies, the standard framework for functional or anatomical brain mapping was based on mass univariate inference procedures [1]. Recently, a new way of analyzing functional neuroimaging data has emerged [2, 3], and it consists in assessing how well behavioral information or cognitive states can be predicted from brain activation images such as those obtained with functional magnetic resonance imaging (fMRI). This approach opens new ways for understanding the mental representation of various perceptual and cognitive parameters, which can be regarded as the study of the corresponding neural code, albeit at a relatively low spatial resolution. The accuracy of the prediction of the behavioral or cognitive target variable, as well as the spatial layout of predictive regions, can provide valuable information about functional brain organization; in short, it helps to decode the brain system [4].
Many different pattern recognition and machine leaning methods have been used to extract information from brain images and compare it to the corresponding target. Among them, Linear Discriminant Analysis (LDA) [3, 5], Support Vector Machine (SVM) [6–9], or regularized prediction [10, 11] has been particularly used. The major bottleneck in this kind of analytical framework is that there are far more features than samples, so that the problem is plagued by the curse of dimensionality, leading to overfitting. Dimension reduction can be used to extract relevant information from the data, the standard approach in functional neuroimaging being feature selection (e.g., Anova) [3, 6, 11, 12]. However, by performing feature selection and parameter estimation separately, such approach is not optimal. Thus, a popular combined selection/estimation scheme, Recursive Feature Elimination [13], may be used. However, this approach relies on a specific heuristic, which does not guarantee the optimality of the solution and is particularly costly. By contrast, there is great interest in sparsity-inducing regularizations, which optimize both simultaneously.
In this paper, we assume that the code under investigation is about some scalar parameter that characterizes the stimuli, such as a scale/shape parameters but possibly also position, speed (assuming a 1-D space), or cardinality. Thus, we focus on regression problems and defer the generalization to classification to future work. Let us introduce the following predictive linear model: where represents the behavioral variable and are the parameters to be estimated on a training set. A vector can be seen as an image; is the number of features (or voxels), and is called the intercept. The matrix is the design matrix. Each row is a -dimensional sample, that is, an activation map related to the observation. With , the estimation of is ill posed.
To cope with the high dimensionality of the data, one can penalize the estimation of , for example, based on the norm of the weights. Classical regularization schemes have been used in functional neuroimaging, such as the Ridge regression [14], Lasso [15], or Elastic Net regression [16]. However, these approaches require the amount of penalization to be fixed beforehand and possibly optimized by cross-validation. To deal with the choice of the amount of penalization, one can use the Bayesian regression techniques, which include the estimation of regularization parameters in the whole estimation procedure. Standard Bayesian regularization schemes are based on the fact that a penalization by weighted norm is equivalent to setting the Gaussian priors on the weights : where is the Gaussian distribution and the precision of the th feature. The model in (2) defines two classical Bayesian regression schemes. The first one is Bayesian Ridge Regression (BRR) [17], which corresponds to the particular case . By regularizing all the features identically, BRR is not well suited when only few features are relevant. The second classical scheme is Automatic Relevance Determination (ARD) [18], which corresponds to the case if . The regularization performed by ARD is very adaptive, as all the weights are regularized differently. However, by regularizing each feature separately, ARD is prone to underfitting when the model contains too many regressors [19] and also suffers from convergence issues [20].
These classical Bayesian regularization schemes have been used in fMRI inverse inference studies [10, 14, 21]. However, these studies used only sparsity as built-in feature selection and do not consider neuroscientific assumptions for improving the regularization (i.e., within the design of the matrix ). Indeed, due to the intrinsic smoothness of functional neuroimaging data [22], predictive information is rather encoded in different groups of features sharing similar information. A potentially more adapted approach is the Bayesian regression scheme presented in [23], which regularizes patterns of voxels differently. The weights of the model are defined by , where is a matrix defined as set of spatial patterns (one pattern by column) and are the parameters of the decomposition of in the basis defined by . The regularization is controlled through the covariance of , which is assumed to be diagonal with only possible different values .
The matrices are diagonal and defined subsets of columns of sharing similar variance . Due to its class-based model, this approach is similar to the one proposed in this paper, but the construction of relies on ad hoc voxel selection steps, so that there is no proof that the solution is correct. A contrario, the proposed approach jointly optimizes, within the same framework, the construction of the pattern of voxels and the regularization parameter of each pattern.
In this paper, we detail a model for the Bayesian regression in which features are grouped into different classes that are subject to different regularization penalties. The estimation of the penalty is performed in each class separately, leading to a stable and adaptive regularization. The construction of the group of features and the estimation of the predictive function are performed jointly. This approach, called Multiclass Sparse Bayesian Regression (MCBR), is thus an intermediate solution between BRR and ARD. It requires less parameters to estimate than ARD and is far more adaptive than BRR. Another asset of the proposed approach in fMRI inverse inference is that it creates a clustering of the features and thus yields useful maps for brain mapping. After introducing our model and giving some details on the parameter estimation algorithms (the variational Bayes or Gibbs sampling procedures), we show that the proposed algorithm yields better accuracy than reference methods, while providing more interpretable models.
2. Multiclass Sparse Bayesian Regression
We first detail the notations of the problem and describe the priors and parameters of the model. Then, we detail the two different algorithms used for model inference.
2.1. Model and Priors
We recall the linear model for regression: We denote by the targets to be predicted and the set of activation images related to the presentation of different stimuli. The integer is the number of voxels and the number of samples (images). Typically, (for a whole volume), while .
Priors on the Noise
We use classical priors for regression, and we model the noise on as an i.i.d. Gaussian variable: where is the precision parameter and stands for the gamma density with two hyperparameters :
Priors on the Class Assignment
In order to combine the sparsity of ARD with the stability of BRR, we introduce an intermediate representation, in which each feature belongs to one class among indexed by a discrete variable ().All the features within a class share the same precision parameter , and we use the following prior on : where is Kronecker's , defined as
We finally introduce an additional Dirichlet prior [24] on : with a hyperparameter . By updating at each step the probability of each class, it is possible to prune classes. This model has no spatial constraints and thus is not spatially regularized.
Priors on the Weights
As in ARD, we make use of an independent Gaussian prior for the weights: where is the precision parameter of the th feature, with . We introduce the following prior on : with hyperparameters , . The complete generative model is summarized in Figure 1.
2.1.1. Link with Other Bayesian Regularization Schemes
The link between the proposed MCBR model and the other regularization methods, Bayesian Ridge Regression and Automatic Relevance Determination, is obvious.(i)With , that is, , we retrieve the BRR model, (ii)With , that is, if , and assigning each feature to a singleton class (i.e., ), we retrieve the ARD model.
Moreover, the proposed approach is related to the one developed in [25]. In this paper, the authors proposed, for the distribution of weights of the features, a binary mixture of Gaussians with small and large precisions. This model is used for variable selection and estimated by the Gibbs sampling. Our work can be viewed as a generalization of this model to a number of classes .
2.2. Model Inference
For models with latent variables, such as MCBR, some singularities can exist. For instance in a mixture of components, a singularity is a component with one single sample and thus zero variance. In such cases, maximizing the log likelihood yields flawed solutions, and one can use the posterior distribution of the latent variables for this maximization. However, the posterior distribution of the latent variables given the data does not have a closed-form expression, and some specific estimation methods, such as variational Bayes or Gibbs sampling, have to be used.
We propose two different algorithms for inferring the parameters of the MCBR model. We first estimate the model by the variational Bayes, and the resulting algorithm is thus called VB-MCBR. We also detail an algorithm, called Gibbs-MCBR, based on a Gibbs sampling procedure.
2.2.1. Estimation by Variational Bayes: VB-MCBR
The variational Bayes (or VB) approach provides an approximation of , where is taken in a given family of distributions and . Additionally, the variational Bayes approach often uses the following mean field approximation, which allows the factorization between the approximate distribution of the latent variables and the approximate distributions of the parameters:
We introduce the Kullback-Leibler divergence that measures the similarity between the true posterior and the variational approximation . One can decompose the marginal log-likelihood as with where is called free energy and can be seen as the measure of the quality of the model. As , the free energy is a lower bound on with equality if and only if . So, inferring the density of the parameters corresponds to maximizing with respect to the free distribution . In practice, the VB approach consists in maximizing the free energy iteratively with respect to the approximate distribution of the latent variables and with respect to the approximate distributions of the parameters of the model , , , and .
The variational distributions and the pseudocode of the VB-MCBR algorithm are provided in Appendix A. This algorithm maximizes the free energy . In practice, iterations are performed until convergence to a local maximum of . With an ARD prior (i.e., and fixing ), we retrieve the same formulas as the ones found for Variational ARD [18].
2.2.2. Estimation by Gibbs Sampling: Gibbs-MCBR
We develop here an estimation of the MCBR model using Gibbs sampling [26]. The resulting algorithm is called Gibbs-MCBR; the pseudocode of the algorithm and the candidate distributions are provided in Appendix B. The Gibbs sampling algorithm is used for generating a sequence of samples from the joint distribution to approximate marginal distributions. The main idea is to use conditional distributions that should be known and possibly easy to sample from, instead of directly computing the marginals from the joint law by integration (the joint law may not be known or hard to sample from). The sampling is done iteratively among the different parameters, and the final estimation of parameters is obtained by averaging the values of the different parameters across the different iterations (one may not consider the first iterations, this is called the burn in).
2.2.3. Initialization and Priors on the Model Parameters
Our model needs few hyperparameters; we choose here to use slightly informative and class-specific hyperparameters in order to reflect a wide range of possible behaviors for the weight distribution. This choice of priors is equivalent to setting heavy-tailed centered Student's t-distributions with variance at different scales, as priors on the weight parameters. We set , with weakly informative priors , and , . Moreover, we set . Starting with a given number of classes and letting the model automatically prune the classes can be seen as a means of avoiding costly model selection procedures. The choice of class-specific priors is also useful to avoid label switching issues and thus speeds up convergence. Crucially, the priors used here can be used in any regression problem, provided that the target data is approximately scaled to the range of values used in our experiments. In that sense, the present choice of priors can be considered as universal. We also randomly initialize for VB-MCBR (or for Gibbs-MCBR).
2.3. Validation and Model Evaluation
2.3.1. Performance Evaluation
Our method is evaluated with a cross-validation procedure that splits the available data into training and validation sets. In the following, are a learning set is a test set, and refers to the predicted target, where is estimated from the training set. The performance of the different models is evaluated using , the ratio of explained variance: This is the amount of variability in the response that can be explained by the model (perfect prediction yields , while if prediction is worse than chance).
2.3.2. Competing Methods
In our experiments, the proposed algorithms are compared to different state-of-the-art regularization methods. (i)Elastic Net Regression [27], which requires setting two parameters and . In our analyzes, a cross-validation procedure within the training set is used to optimize these parameters. Here, we use , where , and . Note that and parametrize heterogeneous norms. (ii)Support Vector Regression (SVR) with a linear kernel [28], which is the reference method in neuroimaging. The parameter is optimized by cross-validation in the range of 10−3 to 101 in multiplicative steps of 10. (iii)Bayesian Ridge Regression (BRR), which is equivalent to MCBR with and , that is, weakly informative priors.(iv)Automatic Relevance Determination (ARD), which is equivalent to MCBR with and , that is, weakly informative priors.
All these methods are used after an Anova-based feature selection as this maximizes their performance. Indeed, irrelevant features and redundant information can decrease the accuracy of a predictor [29]. The optimal number of voxels is selected within the range , using a nested cross-validation within the training set. We do not directly select a threshold on value or cluster size, but rather a predefined number of features. The estimation of the parameters of the learning function is also performed using a nested cross-validation within the training set, to ensure a correct validation and an unbiased comparison of the methods. All methods are developed in and used in Python. The implementation of elastic net is based on coordinate descent [30], while SVR is based on LibSVM [31]. Methods are used from Python via the Scikit-learn open source package [32].
For VB-MCBR and Gibbs-MCBR, in order to avoid a costly internal cross-validation, we select 500 voxels, and this selection is performed on the training set. The number of iterations used is fixed to 5000 (burn in of 4000 iterations) for Gibbs-MCBR and 500 for VB-MCBR. Preliminary results on both simulated and real data showed that these values are sufficient enough for an accurate inference of the model. As explained previously, we set , with weakly informative priors , and , . Moreover, we set , and we randomly initialize for VB-MCBR (or for Gibbs-MCBR).
3. Experiments and Results
3.1. Experiments on Simulated Data
We now evaluate and illustrate MCBR on two different sets of simulated data.
3.1.1. Details on Simulated Regression Data
We first test MCBR on a simulated data set, designed for the study of ill-posed regression problem, that is, . Data are simulated as follows: We have features, images for the training set, and images for the test set. We compare MCBR to the reference methods, but we do not use feature selection, as the number of features is not very high.
3.1.2. Results on Simulated Regression Data
We average the results of 15 different trials, and the average explained variance is shown in Table 1. Gibbs-MCBR outperforms the other approaches, yielding higher prediction accuracy than the reference elastic net and ARD methods. The prediction accuracy is also more stable than the other methods. VB-MCBR falls into the local maximum of and does not yield an accurate prediction. BBR has a low prediction accuracy compared to other methods such as ARD. Indeed, it cannot finely adapt the weights of the relevant features, as these features are regularized similarly as the irrelevant ones. SVR has also low accuracy, due to the fact that we do not perform any feature selection. Thus, SVR suffers from the curse of dimensionality, unlike other methods such as ARD or elastic net, which performs feature selection and model estimation jointly.
In Figure 2, we represent the probability density function of the distributions of the weights obtained with BRR (a), Gibbs-MCBR (b), and ARD (c). With BRR, the weights are grouped in a monomodal density. ARD is far more adaptive and sets lots of weights to zero. The Gibbs-MCBR algorithm creates a multimodal distribution, lots of weights being highly regularized (pink distributions), and informative features are allowed to have higher weights (blue distributions).
With MCBR, weights are clustered into different groups, depending on their predictive power, which is interesting in application such as fMRI inverse inference, as it can yield more interpretable models. Indeed, the class to the features with higher weights () belong which is small (average size of 6 features) but has a high purity (percentage of relevant features in the class) of 74%.
3.1.3. Comparison between VB-MCBR and Gibbs-MCBR
We now look at the values of and for the different steps of the two algorithms (see Figure 3). We can see that VB-MCBR (b) quickly falls into a local maximum, while Gibbs-MCBR (a) visits the space and reaches the region of the correct set of parameters (red dot). VB-MCBR is not optimal in this case.
3.2. Simulated Neuroimaging Data
3.2.1. Details on Simulated Neuroimaging Data
The simulated data set consists of images (size voxels) with a set of four square regions of interest (ROI) (size ). We call the support of the ROI (i.e., the 32 resulting voxels of interest). Each of the four ROIs has a fixed weight in . We call the weight of the voxel. The resulting images are smoothed with a Gaussian kernel with a standard deviation of 2 voxels, to mimic the correlation structure observed in real fMRI data. To simulate the spatial variability between images (intersubject variability, movement artifacts in intrasubject variability), we define a new support of the ROIs, called such that, for each image th, 50% (randomly chosen) of the weights are set to zero. Thus, we have . We simulate the target for the th image as with the signal in the voxel of the th image simulated as and is a Gaussian noise with standard deviation . We choose in order to have a signal-to-noise ratio of 5 dB.
3.2.2. Results on Simulated Neuroimaging Data
We compare VB-MCBR and Gibbs-MCBR with the different competing algorithms. The resulting images of weights are given in Figure 4, with the true weights (a) and resulting Anova F-scores (b). The reference methods can detect the truly informative regions (ROIs), but elastic net (f) and ARD (h) retrieve only part of the support of the weights. Moreover, elastic net yields an overly sparse solution. BRR (g) also retrieves the ROIs but does not yield a sparse solution, as all the features are regularized in the same way. We note that the weights in the feature space estimated by SVR (e) are nonzero everywhere and do not outline the support of the ground truth. VB-MCBR (c) converges to a local maximum similar to the solution found by BRR (g); that is, it creates only one nonempty class, and thus regularizes all the features similarly. We can thus clearly see that, in this model, the variational Bayes approach is very sensitive to the initialization and can fall into nonoptimal local maxima, for very sparse support of the weights. Finally, Gibbs-MCBR (d) retrieves most of the true support of the weights by performing an adapted regularization.
3.3. Experiments and Results on Real fMRI Data
In this section, we assess the performance of MCBR in an experiment on the mental representation of object size, where the aim is to predict the size of an object seen by the subject during the experiment, in both intrasubject and intersubject cases. The size (or scale parameter) of the object will be the target variable .
3.3.1. Details on Real Data
We apply the different methods on a real fMRI dataset related to an experiment studying the representation of objects, on ten subjects, as detailed in [33]. During this experiment, ten healthy volunteers viewed objects of 4 shapes in 3 different sizes (yielding 12 different experimental conditions), with 4 repetitions of each stimulus in each of the 6 sessions. We pooled data from the 4 repetitions, resulting in a total of images by subject (one image of each stimulus by session). Functional images were acquired on a 3-T MR system with an eight-channel head coil (Siemens Trio, Erlangen, Germany) as T2*-weighted echo-planar image (EPI) volumes. Twenty transverse slices were obtained with a repetition time of 2 s (echo time: 30 ms; flip angle: 70°; -mm voxels; 0.5 mm gap). Realignment, normalization to MNI space, and general linear model (GLM) fit were performed with the SPM5 software (http://www.fil.ion.ucl.ac.uk/spm/software/spm5/). The normalization is the conventional method of SPM (implying affine and nonlinear transformations) and not the one using unified segmentation. The normalization parameters are estimated on the basis of a whole-head EPI acquired in addition and are then applied to the partial EPI volumes. The data are not smoothed. In the GLM, the effect of each of the 12 stimuli convolved with a standard hemodynamic response function was modeled separately, while accounting for serial autocorrelation with an AR(1) model and removing low-frequency drift terms using a high-pass filter with a cutoff of 128 s. The GLM is fitted separately in each session for each subject, and we used in the present work the resulting session-wise parameter estimate images (the -maps are used as rows of ). The four different shapes of objects were pooled across for each one of the three sizes, and we are interested in finding discriminative information on sizes. This reduces to a regression problem, in which our goal is to predict a simple scalar factor (size of an object). All the analyzes are performed without any prior selection of regions of interest and use the whole acquired volume.
Intrasubject Regression Analysis
First, we perform an intrasubject regression analysis. Each subject is evaluated independently, in a 12-fold cross-validation. The dimensions of the real data set for one subject are and (divided in 3 different sizes, 24 images per size). We evaluate the performance of the method by a leave-one-condition-out cross-validation (i.e., leave-6-image-out), and doing so the GLM is performed separately for the training and test sets. The parameters of the reference methods are optimized with a nested leave-one-condition-out cross-validation within the training set, in the ranges given before.
Intersubject Regression Analysis
Additionally, we perform an intersubject regression analysis on the sizes. The intersubject analysis relies on subject-specific fixed-effect activations that is, for each condition, the 6 activation maps corresponding to the 6 sessions are averaged together. This yields a total of 12 images per subject, one for each experimental condition. The dimensions of the real data set are and (divided into 3 different sizes). We evaluate the performance of the method by cross-validation (leave-one-subject-out). The parameters of the reference methods are optimized with a nested leave-one-subject-out cross-validation within the training set, in the ranges given before.
3.3.2. Results on Real Data
Intrasubject Regression Analysis
The results obtained by the different methods are given in Table 2. The -values are computed using a paired -test across subjects. VB-MCBR outperforms the other methods. Compared to the results on simulated data, VB-MCBR still falls in a local maximum similar to the Bayesian ridge regression which performs well in this experiment. Moreover, both Gibbs-MCBR and VB-MCBR are more stable than the reference methods.
Intersubject Regression Analysis
The results obtained with the different methods are given in Table 3. As in the intrasubject analysis, both MCBR approaches outperform the reference methods, SVR, BRR, and ARD. However, the prediction accuracy is similar to that of elastic net. In this case, Gibbs-MCBR performs slightly better than VB-MCBR, but the difference is not significant.
One major asset of MCBR (and more particularly Gibbs-MCBR, as VB-MCBR often falls into a one-class local maximum) is that it creates a clustering of the features, based on the relevance of the features in the predictive model. This clustering can be accessed using the variable , which is implied in the regularization performed on the different features. In Figure 5, we give the histogram of the weights of Gibbs-MCBR for the intersubject analysis. We keep the weights and the values of of the last iteration; the different classes are represented as dots of different colors and are superimposed on the histogram. We can notice than the pink distribution represented at the bottom of the histogram corresponds to relevant features. This cluster is very small (19 voxels), compared to the two blue classes represented at the top of the histogram that contain many voxels (746 voxels) which are highly regularized, as they are noninformative.
The maps of weights found by the different methods are detailed in Figure 6. The methods are used combined with an Anova-based univariate feature selection (2500 voxels selected, in order to have a good support of the weights). As elastic net, Gibbs-MCBR yields a sparse solution but extracts a few more voxels. The map found by elastic net is not easy to interpret, with very few informative voxels scattered in the whole occipital cortex. The map found by SVR is not sparse in the feature space and is thus difficult to interpret, as the spatial layout of the neural code is not clearly extracted. VB-MCBR does not yield a sparse map either, all the features having nonnull weights
4. Discussion
It is well known that in high-dimensional problems, regularization of feature loadings significantly increases the generalization ability of the predictive model. However, this regularization has to be adapted to each particular dataset. In place of costly cross-validation procedures, we cast regularization in a Bayesian framework and treat the regularization weights as hyperparameters. The proposed approach yields an adaptive and efficient regularization and can be seen as a compromise between a global regularization (Bayesian Ridge Regression) that does not take into account the sparse or focal distribution of the information and automatic relevance determination. Additionally, MCBR creates a clustering of the features based on their relevance and thus explicitly extracts groups of informative features.
Moreover, MCBR can cope with the different issues of ARD. ARD is subject to an underfitting in the hyperparameter space that corresponds to an underfitting in model selection (i.e., on the features to be pruned) [19]. Indeed, as ARD is estimated by maximizing evidence, models with less selected features are preferred, as the integration is done on less dimensions, and thus evidence is higher. ARD will choose the sparsest model across models with similar accuracy. A contrario, MCBR requires far less hyperparameter (, with ) and suffers less from this issue, as the sparsity of the model is defined by groups. Moreover, a full Bayesian framework for estimating ARD requires to set some priors on the hyperparameters (e.g., and ), and it may be sensitive to specific choice of these hyperparameters. A solution is to use an internal cross-validation for optimizing these parameters, but this approach can be computationally expensive. In the case of MCBR, the distributions of the hyperparameters are bound to a class and not to each feature. Thus, the proposed approach is less sensitive to the choice of the hyperparameters. Indeed, the choice of good hyperparameters for the features is dealt with at the class level.
On simulated data, our approach performs better than other classical methods such as SVR, BRR, ARD, and elastic net and yields a more stable prediction accuracy. Moreover, by adapting the regularization to different groups of voxels, MCBR retrieves the true support of the weights and recovers a sparse solution. Results on real data show that MCBR yields more accurate predictions than other regularization methods. As it yields less sparse solution than elastic net, it gives access to more plausible loading maps which are necessary for understanding the spatial organization of brain activity, that is, retrieving the spatial layout of the neural coding. On real fMRI data, the explicit clustering of Gibbs-MCBR is also an interesting aspect of the model, as it can extract few groups of relevant features from many voxels.
In some experiments, the variational Bayes algorithm yields less accurate predictions than the Gibbs sampling approach, which can be explained by the difficulty of initializing the different variables (especially ) when the support of the weight is overly sparse. Moreover, the VB-MCBR algorithm relies on a variational Bayes approach, which may not be optimal, due to strong approximations in model inference. A contrario Gibbs-MCBR is more time consuming but yields a better model inference. Finally, the variability in the results may be explained by the difficulty to estimate the model (optimality is not ensured).
The question of model selection (i.e., the number of classes ) has not been addressed in this paper. One can use the free energy in order to select the best model, but due to the instability of VB-MCBR, this approach does not seem promising. A more interesting method is the one detailed in [34], which can be used with the Gibbs sampling algorithm. Here, model selection is performed implicitly by emptying classes that do not fit the data well. In that respect, the choice of heterogeneous priors for different classes is crucial: replacing our priors with class-independent priors (i.e., , ) in the intersubject analysis on size prediction leads Gibbs-MCBR to a local maximum similar to VB-MCBR.
Finally, this model is not restricted to the Bayesian regularization and can be used for classification, within a probit or logit model [35, 36]. The proposed model may thus be used for diagnosis in medical imaging, for the prediction of both continuous or discrete variables.
5. Conclusion
In this paper, we have proposed a model for adaptive regression, called MCBR. The proposed method integrates, in the same Bayesian framework, BRR and ARD and performs a different regularization for relevant and irrelevant features. It can tune the regularization to the possible different level of sparsity encountered in fMRI data analysis, and it yields interpretable information for fMRI inverse inference, namely, the variable (latent class variable). Experiments on both simulated and real data show that our approach is well suited for neuroimaging, as it yields accurate and stable predictions compared to the state-of-the-art methods.
A. VB-MCBR Algorithm
The variational Bayes approach yields the following variational distributions:(i) with (ii) with (iii) with (iv) with where is the digamma function . The VB-MCBR algorithm is provided in pseudo-code in Algorithm 1.
B. Gibbs-MCBR Algorithm
With , we have the following candidate distributions (i.e., the distributions used for the sampling of the different parameters):(i) with (ii) with (iii) with (iv) with (v) with
The algorithm is provided in pseudocode in Algorithm 2.
The authors acknowledge support from the ANR Grant ViMAGINE ANR-08-BLAN-0250-02.
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As we wait for the February auto sales and word of whether or not foreign automakers will pass 50% market share in the U.S. for the first time ever, there's another number to watch. It's retail auto sales. In other words, sales from auto dealers. No fleet sales to rental car companies, government organizations, corporations, etc. This number is a more accurate reflection of consumer demand and of what makes, models, etc. are selling or not selling.
Over the last two years, J.D. Power's Power Information Network has been tracking retail sales, and the trend is troubling for General Motors . In January of last year, GM lead retail sales with 22% market share. Toyota was a distant number 2 at 17.1%. A year later, Toyota has closed the gap and is now just 1.1% behind General Motors.
Why do I point this out?
Primarily because it goes to the heart of the problem for General Motors. Yes, it's overall sales are dropping because the company has decided to cut back on low profit-margin fleet sales. But aside from fleet sales, the number of GM cars, trucks and SUV's being sold at dealerships is also dropping. THAT is a concern. Now, some of that can be attributed to GM pulling back on incentives. If the dealers are throwing less cash on the hood, it's always going to be tougher to finish some sales. But there is a broader concern that GM's retail sales are hurting because the product line is not as strong. Personally, I think GM has stepped up it's game with the revamped Saturn line-up and the new line of pick-ups and SUV's have vastly improved interiors. So what's the problem? According to the most recent sales information from Autodata, Pontiac sales dropped 35.9% in January. While Buick and Chevy were down 30 and 14.5% respectively. Some of the declines are with models that appear to be losing their steam (the Pontiac G-6) while others are with models that will soon be updated (Chevy Malibu). But collectively, General Motors decline in retail sales is one Bob Lutz and his team of auto designers have to address.
So when the Auto Sales come out tomorrow, don't just look at the bottom line, look further at the retail sales:
Auto Reliability.
Tonight during "On The Money" at 7 pm ET, we will have the first results of the Consumer Reports annual survey of auto reliability. The results are interesting, trust me. You will be intrigued to see who is improving, and also who ranked dead last. Watch for it tonight.
Questions? Comments? email@example.com


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ANNAPOLIS -- Kevin L. Jones, who was only 15 years old when he was convicted in the murder of a retired United Methodist minister in 1988, will get another chance to plead his case to a jury.
The Court of Special Appeals has sent the case back to Baltimore for a new trial, ruling that Jones was improperly required to take the witness stand for his co-defendant in the robbery and murder of the Rev. Lewis F. Ransom, former pastor of Towson United Methodist Church.
Jones, now 18, had not answered questions on the stand, citing his Fifth Amendment right not to incriminate himself.
But even though Baltimore Circuit Judge Mabel Houze Hubbard had told the jury not to consider his silence in its deliberations, his right to a fair trial was prejudiced because he was forced to "plead the Fifth" in front of the jurors, the court ruled Thursday.


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"Uh, well, sir, it's this rug I have. It really tied the room together," explains a dressing-gowned The Dude in the office of bullish business magnate Mr Lebowski. In a case of mistaken identity, a pair of thugs paid a visit to The Dude's abode and urinated on his prized rug. "Every time a rug is micturated upon in this fair city, I have to compensate the person?" retorts the businessman; a far from desired response. So, the protagonist of the Coen brothers' The Big Lebowski decides to assert himself: "I am not Mr Lebowski, you're Mr Lebowski. I'm The Dude, so that's what you call me. That... or His Dudeness."
Bed Rugs, four Antwerpians playing lo-fi psychedelia fuzz, share more similarities with The Dude than just their homage of a band name. Just as The Dude fails to achieve deserved success in life, Bed Rugs have found themselves releasing their debut album for a second time, sandwiching last year's highly praised mini-LP Rapids. With new label Ample Play Records, they have hopes of greater recognition.
The decision is vindicated, because 8th Cloud is a relaxed, charming record that is punctuated with alluring hooks and satisfying bites of distortion. The opener 'Purple Pill' is replete with references to the Sixties culture that the band evidently cherish; mind-altering drugs, warm guns, and road journeys. Warped licks, ambling basslines, and guitar buzzes are arranged like a perfect Californian afternoon in August. 'Modern Freaks' is a track full of incisive poise, up until its let-loose solo, which evokes Tame Impala's Innerspeaker. 'What Does it Mean' sees Arne Omloop's excellent bass play become more prominent, while Yannick Aerts's vocals are reminiscent of Vampire Weekend's Ezra Koenig.
From here, 8th Cloud slows its pace. 'Mark's Ghost (Almost)' begins pleasingly plodding, but its heels eventually drag, while 'Evening Crusade' is a bit of strange country-inflected interlude. Fortunately, it is followed by one of the album's standout songs. 'Dream On' crackles sumptuously, as the chiming guitars ring out to infinity, and the influence of Tame Impala merges with Air's Moon Safari. The 65-second title track '8th Cloud' is an intense instrumental, that leads into the album's weakest point, the misfit pop-rock 'Subtopia'. Nonetheless, the sonorous 'Shoe', recalling Black Rebel Motorcycle Club's quieter moments, carries us on a cloud to the end of the album.
Belgian magazine Les Zaza's recently described Bed Rugs as 'the illegitimate children of McCartney and Cobain', and although this is obvious hyperbole, it does convey how this album has combined sweetness and grit, classic songwriting and hedonistic innovation. So although like The Dude, they have made somewhat dubious decisions in their past (the band were previously called The Porn Bloopers), 8th Cloud will surely garner praise beyond their local European benelux axis, riding the crest of feel-good summer love. With the band set to record a new album this summer, Bed Rugs may even reach cloud nine.
7Peter Yeung's Score


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Saturday, February 23, 2013
Creamy sweet potato soup
They say that necessity is the mother of invention, right? And when you have bare cupboards (and an almost-empty refrigerator) you have to invent some new recipes. The weather has been so winter-like in OKC, I needed soup last night. And I had just enough pantry staples to pull off something Paleo. And, most shocking of all, it actually turned out to be great. Here's what I came up with.
Creamy sweet potato soup
Serves 2-4 people, depending on portion size
1 Tbsp. coconut oil
1/4 red onion, chopped
3 chicken breast tenderloins (or you can use 2 chicken breasts)
1/2 sweet potato, cubed
1 can diced tomatoes
1 cup canned coconut milk
1 cup chicken stock
1 tsp. chili powder
1/2 tsp. mustard powder
1/2 tsp. curry powder
1 tsp. lemon pepper
1/2 avocado
1/2 lime
1. Melt the coconut oil over a low heat in a medium sized soup pot. Meanwhile, mix all the dry spices together and set aside.
2. Saute the onion until they begin to sweat, and then add about a third of the spices. Mix well.
3. After the onions are translucent, add chicken and cook thoroughly. Once cooked, shred with two forks.
4. Add the sweet potatoes and saute for about a minute. Add the remaining spice mix.
5. Pour in the tomatoes, the the coconut milk and the chicken stock and bring to a boil. Let soup simmer for 15-20 minutes and then ladle into bowls. Squeeze fresh lime juice over each portion and garnish with avocado.
I really liked this recipe. It was easy, filling, and the slight coconut flavor paired with the fresh lime juice was yummy. I also think that a little cheddar cheese would taste fantastic with this! If you make it, let me know what you think!
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I have been playing with my Magnolia stamps again and have done a few Christmas cards I hope you like them
I have used magnolia stamp Santa from the Santa Collection he is so lovely I have coloured him with Pro markers then this hat and coat was covered in red Fun Flock for a fury texture Then the White trim on the hat and coat was done with Aleene's True snow, its like a paste so I applied it with a cocktail stick this is also brilliant on tree and winter scene to cards it give it a very authentic look and it lasts ages. I added a pom pom to his hat and accented the present with a gold gel pen.
I love layering different cards with texture and colour I start with a 8x8 white base card and then this brilliant woven look red paper, then another layer of standard white card and then Kanban red and white stripe card with the great glitter stripe. The Santa image was stamped on standard white card and layered on to some red Bazzil card this was raised from the background with foam pads.
I finished the card off with white fabric covered brads.
I have used Silent Night Tilda from the So Jolly Collection coloured with pro markers coat trim is done in crystal glitter and the swirls with glitter sakura pens.
Mounted on to a 8x8 white card with Aquamarine Stardream Metalic card and snowflake background paper Tilda is stamped on white card and the mounted on pink mirror foil card the corners were then rounded. The bottom is edged in a pearl ribbon. The accent strip is aquamarine with a strip of the pink foil which was accented with small pom poms and a strip and the snowflake background which I have covered in glossy accents.
I stamped the Tilda stamp twice once on the backing card and the second one I coloured in and cut out and mounted on the background with foam pads.
Finished with a Merry Christmas Peel off and glittered a snowflake
Heartbreaker Edwin from the Raising the Ceiling Collection sitting on Wrapped presents from the So Jolly collection coloured with Pro Markers wings coloured with glitter Gelly Roll Sakura pens (TOP TIP if you don't want Edwin to have wings cut him out and cut off the wings) accented with Sakura Gelly Roll glaze pens.
I have cut a Scallop Circle Neastabilities (other shapes can be used) out of Brown glitter card and layered a Buff coloured circle then I have drew Faux stitching around the edge of the circle Edwin the sits on the presents.
I have layered embellished with ribbon tabs, bow and snowflakes cut on the CuttleBug.


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Leonard made his "World Famous Fudge". He gets pretty serious when he's making this and easily upset. So I stay out of the kitchen unless I'm needed. It's something the girls look for in their packages every year.
Alpha Stamp's The Twelve Days of Christmas is on the 2nd day and I couldn't resist playing along with Two Turtle Doves today. (I know this wasn't on my check list but...........)


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Formed in 2002, FACT Oregon (formerly the Family Action Coalition Team*) is a family leadership organization for individuals and their families experiencing disabilities, working collaboratively to facilitate positive change in policies, systems, and attitudes, through family support, advocacy, and partnerships. The majority of our Board of Directors are all family members of a person with a disability. FACT Oregon strives to:
- Strengthen the voice of Oregon families raising children experiencing disability
- Connect families to families
- Provide and promote peer-delivered family support
- Create opportunities for meaningful collaboration
FACT Oregon represents thousands of families all across the state of Oregon, including families of children with special health needs, developmental and physical disabilities, learning disabilities, and mental health concerns. The depth of our knowledge, experience of our families, and variety of cross-disability interests participating in FACT Oregon allow us to be uniquely positioned in Oregon to work together to effectively utilize our power, our voices, and our resources.
FACT Oregon seeks innovation, looks to create new partnerships within our communities, works within and outside of current systems, and strives to find new approaches to old problems. By building relationships and trust through networking, collaborating, and unified action, FACT Oregon can do more together than any of us can do alone.
* In September 2008 at FACT's Annual Coalition meeting, the "Family Action Coalition Team (FACT)", adopted the name "Family and Community Together (FACT)" to more clearly express the mission and values of the coalition. The new name does not change the purpose or intent of FACT.


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Club members' seasonal caïssic thoughts turned this week to the International Chess Congress now taking place at Horntye Park on Monday.
There are at last count 10 club members playing in the Masters (itself at 100+ competitors) although it is sadly unlikely that any of our contingent will feature in the final prize list.
The Masters are playing in the afternoon (from 2.15pm) until next Tuesday and spectators are welcome - it's a fascinating cosmopolitan contest. But we will also have club members playing in the various subsidiary tournaments and here we do hope for occasional success.
'Social' players sometimes ask if the event is suitable for them to play in. That depends whether you feel ready for the competitive side, full concentration, silence, 'touch move', writing the moves down, chess clocks....
If you want to dip your toe in those waters perhaps the Weekend Minor (January 2 to 4) is for you and there is still just time to enter: five games of serious chess from Friday evening to Sunday afternoon though you can skip the odd game by pre-arrangement.
Or perhaps you would prefer the slower pace and more intense atmosphere of the New Year tournament next Monday and Tuesday. Details on www.hastingschess.com, or from 01424 431970.


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Click here to watch the full episode
"Dateline NBC" aired a segment called "Stealing Paradise" Friday on William Dathan Holbert, a former Henderson County resident and North Henderson High graduate who a prosecutor says confessed to killing seven people in Central America.
"When two Americans in Panama go missing, islanders discover the dark side of a fun-loving bartender in Bocas del Toro" is the storyline for the segment, hosted by NBC reporter Kate Snow, according to Dateline's website.
Holbert confessed to killing seven Americans in Central America to get their money and property in a Panamanian resort area, Panama prosecutor Angel Calderon said.


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Jonathan Swift
Jonathan Swift (30 November 1667 – 19 October 1745 / Dublin)
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The Progress Of Marriage
Aetatis suae fifty-two,
A rich Divine began to woo
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Nearly related to an Earl.
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Stephen Leonard
Stephen Leonard leads sales for IBM's Systems & Technology Group, where he is responsible for setting and driving the global sales and go-to-market strategy for IBM's $21 billion hardware business unit.
Prior to his current role, Stephen was the Chief Executive for IBM in the UK and Ireland.
Stephen joined IBM in 1990 as a Technical Instructor in London. During his career with the company, he has held a variety of sales, sales management and leadership positions, in different parts of the organization and in multiple global geographies.
Born and raised in Scotland, Stephen holds a degree in applied physics and electronics from Dundee University. He is married with three young children.


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So here comes the Terminator on a mission to sweep out the dastardly Democrats and restore truth and justice in California, and here in Minnesota we watch the show with a delicious vicarious pleasure. We invented the action-hero Governor. We wrote that particular comic book. And now California, so often on the cutting edge, is following in our footsteps. Us, a little dairy and turkey-raising state on the upper Mississippi. This is great. It's like the townsfolk in Huckleberry Finn who attended the Duke and Dauphin's theatrical show and then told their neighbors how great it was, so they could go...
Hey, Arnold! This Is Serious Stuff
A tip from Minnesota: action heroes can make terrible politicians
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Lucy Grealy (1963-2002) was born in Dublin, Ireland. She moved to Spring
Valley, New York, with her family when she was four years old. When she was
nine she was diagnosed with Ewing's sarcoma. Surgery to remove a tumor also resulted in the removal of part of her
jaw, leaving her disfigured and fated to endless reconstruction operations. She
underwent years of chemotherapy and radiation, and multiple plastic surgery
operations almost up to the time of her death. As a teenager in Spring Valley,
she endured insults and ostracism but at Sarah Lawrence College in the
mid-1980s, she discovered poetry as a vehicle for her pent-up emotions and after
earning her BA, went on to earn a Masters in Fine Arts in Poetry from the Iowa
Writers Workshop. Her poetry appeared in a number of
magazines, including The Paris Review and The London Times Literary Supplement.
After living abroad for several years (West Berlin, London and Aberdeen), during which time she underwent further plastic surgery, she returned to the states in 1991 to take on a Bunting Fellowship at Radcliffe College in Cambridge, Mass., and then went on to be a fellow at the Fine Arts Work Center in Provincetown. She also taught at the New School for Social Research's MFA in the Creative Writing Program, and also at Bennington College in Vermont.
Her autobiography grew out of an essay published in Harper's in 1993, which won a National Magazine Award. Her second book, a collection of essays titled As Seen on TV, was published in 2000.
She died of an accidental drug overdose in 2002.
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The multifaceted efforts include TV, print, online, outdoor and direct marketing and support a diverse crop of products -- Sony's elegant Wega TVs, Cyber-shot digital camera, Vaio notebook and desktop PCs and Clie handheld organizer -- to evoke a stylish, lifestyle-oriented sensibility.
The first-ever dedicated ad effort for Sony's Wega TV sub-brand breaks today with a 30-second spot created by Y&R Advertising, New York, that will run in heavy rotation through January. The surreal "Desert" was shot in Spain and London and shows a tumbleweed rolling through the desert as kids' voices echo in the background. Viewers then see kids jump into a swimming pool and, when the camera pulls away, they notice the action took place within the context of a Wega TV. Voice-over, with thunderous music, is "Sony Wega."
"We're trying to create broader awareness for Wega," said Chris Fawcett, director of marketing communications for Sony Electronics' Home Network Products Co. The brand spot comes as competitors including Hitachi, Philips Electronics and Samsung Electronics create their own buzz for advanced flat picture tube TVs.
"There's a lot of `me-too' out there," said Corey Basso, global client leader at Y&R. "The feeling [the campaign evokes] is that Wega offers a window into a better world and if you buy a Wega, it's both an escape and it's exhilarating," he said.
The spot, part of an estimated $20 million to $30 million print, outdoor and event marketing campaign, will run on cable networks such as A&E and CNN and on broadcast TV in prime time, and during NFL broadcasts on CBS and Fox.
Sony's Personal Network Solutions Co. is launching its first integrated ad and marketing effort for Vaio computers this week which also support recently enhanced direct sales operations. The initiative includes direct response TV, print, e-mail and direct mail. Y&R's Impiric, New York, created the direct response TV; Impiric, San Francisco, handled direct mail, print and e-mail. One million pieces of direct mail, with one execution dedicated to Vaio desktops and one to the notebooks, hit in early December.
The direct response TV effort is based on four different versions of "Airport Security," a TV spot that's running currently promoting a Vaio notebook PC, but integrates different action sequences to flag different Sony products: Vaio Slimtops, Digital Studios, F Series and Z505 notebook PCs. The spot purports to show the envy people feel when they see the compact, elegantly styled Vaio notebook PC.
One of the new :60 direct response spots portrays the inside of an airplane, with travelers taking such an interest in one new owner's Vaio notebook, that takeoff is delayed. The four direct spots break this week and run on cable networks such as ESPN, Comedy Central and the SciFi Channel. The spots will direct viewers to a dedicated Web site and a toll-free number.
"Traditionally, our direct efforts have only been somewhat integrated into traditional advertising with just product information, now we are actually allowing people to see the product and call us directly or go to the Web site," said Ken Dice, VP-marketing communications for Sony's Personal Network Solutions Co.
Building greater brand awareness also is a goal of the direct response TV. "The Vaio brand is one we're starting to see stretch across a variety of targets, it represents a sense of personal style, whether it's a businessperson or not -- it's kind of like wearing an Armani suit -- people start to look at you differently," Mr. Dice said. Beyond style, Sony also is driving a product integration message. Its Vaio PCs sync up with digital cameras and enable photo and movie editing as well as music downloads. Sony's portable memory stick media also allow consumers to easily store and swap all of it.
Three-page print ads promoting Vaio hit 10 computer-related titles, including PC Magazine and Wired, starting in November issues. The Vaio PC marketing effort alone comes to nearly $12 million.
The first of five provocative new print ads for Sony's Cyber-shot digital cameras, from Y&R Advertising, San Francisco, also break this week and use a high-tech/high-fashion approach reminiscent of the angle used by Samsung Electronics and AG Worldwide, New York, a couple of years ago, with the Samsung "microwave man."
One execution features a buff male clad only in white fur trousers, a Cyber-shot draped sensuously around his neck. Additional executions feature a downtown girl clutching a Cyber-shot as a handbag, a diver using one as a scuba mask and other fashion-forward images. "Sony now has a digital camera that is so small and so stylish it can go with you everywhere; it can become part of your personal style," Mr. Dice said.
The handheld Clie organizer, based on the Palm operating system software, also is a Sony star in spread ads via Y&R, San Francisco, running through January in nearly 20 magazines such as Esquire, Vanity Fair and Yahoo! Internet Life. The executions highlight different lifestyle applications for Clie, such as wedding or vacation planning, rather than purely business/productivity functions. Four different page ads are booked in a dozen monthlies and weeklies.
"The Cyber-shot and Clie are small, light and easy to use -- we're trying to translate technology into personal lifestyle and bring people in who aren't traditionally interested in productivity [applications]," Mr. Dice said.
Direct mail and e-mail marketing efforts for the Cyber-shot and Clie are handled by Impiric and are set to be unfurled from Thanksgiving to Christmas.


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CHICAGO — Carlos Boozer scored 23 points and the Chicago Bulls beat Detroit 92-68 on Monday night for their 13th consecutive victory against the Pistons.
Derrick Rose added 22 points and eight assists for Chicago, which bounced back from Saturday night's lackluster 109-94 loss at Atlanta. Ronnie Brewer finished with 12 points, and Boozer also had eight rebounds.
The Bulls improved to 4-1 during their first stretch of nine games in 12 days in 30 years. They play again each of the next two nights, hitting the road to face Minnesota on Tuesday before returning home to face Washington on Wednesday.
Greg Monroe had 14 points, 10 rebounds and six assists for Detroit, which has lost four straight games. Rookie Brandon Knight scored 13 points, and Tayshaun Prince and Ben Gordon had 11 points apiece.