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Winter Whites | Brighten the Nights & Give your Sound Life
Wonderful Winter Whites
Too cold to go outside? Bring the bright white winter to you and your sound, with our selection of wonderful winter whites!
Available Now
Eastcoast Standard Jazz Guitar - £189
Warm up your tone with this Eastcoast Jazz Guitar with glistening gold hardware. This Semi-Acoustic Jazz Guitar is one of Eastcoast's most elegant productions. Featuring 2 golden humbuckers with a 3-way toggle switch you can achieve your desired tone and look good while you're doing it. Key features include a maple archtop design with black and white binding.
Roland Micro Cube White Guitar Amp Combo (Ltd Edition) - £99
This great little amp is available in both black, and this incredible limited edition white finish. The Roland MicroCube comes fuilly loaded with seven COSM amp models, six DSP effects, and a digital tuning fork. The Micro Cube White also boasts recording and headphone outputs and a stereo Aux input for your MP3/CD players letting the user jam along to their favourite tunes. It can run on battery power (LR6 (AA) type x 6) or a supplied AC adapter. Carrying strap included.
Korg TM-50 Tuner - £27
Korg TM-50 Tuner allows you to use the tuner and metronome simultaneously. The TM-50 is the latest model, featuring a two-level backlight that enhances the visibility of the LCD display. It also uses a newly designed needle-style LCD for excellent response, and even greater tuning accuracy.
Mapex 2216 Voyager Rock Fusion - £519
If your looking for big volume, big tone and a stunning looking drum set then look no further than the Mapex 2216 Voyager Rock Fusion Kit. The Voyager features a powerful 22x18 bass drum, making it loud and robust. The 10x8 and 12x9 rack toms tune easily and sound clear through the loudest of bands and the 16x16 floor tom is bold but clear.
This Mapex Drum Kit features a colour matched 14x5.5 snare drum, full hardware and a 2pce cymbal set, ensuring you have everything you need to smash out the rhythms.
Roland Lucina AX-09 Shoulder Synthesizer Keytar - £369
The AX-09 Keytar is compact, lightweight, and loaded with great sounds, all easily selectable with the onboard category buttons. Plug in a USB stick and add backing tracks to your performances with the Audio Player function, or plug in an MP3/CD player and jam along with your favourite songs. This super-cool Keytar synth is designed to fit all musicians. Easy to use, but packs a heavyweight musical punch.
Roland RH-A7 Monitor Headphones - £79
The Roland RH A7 Headphones are an essential accessory for anyone spending any length of time using digital instruments especially pianos. These monitoring headphones are extremely comfortable and fit snugly, so your ears wont suffer fatigue. The open back design means you can communicate without having to take the headphones off.
Native Instruments Maschine Mikro MKII - £269
The new Native Instruments Maschine Mikro MKII features new hardware and multi-coloured pads. The new Mikro comes with MASSIVE (legendary synth for all MASCHINE users) and TRANSIENT MASTER which is NI's hugely popular dynamics and mixing effect. This next generation of MASCHINE has numerous enhanced workflow improvements for an even more intuitive and faster performance. Other features include revised knobs with larger main encoder, revised buttons (buttons now 'click' like NI's DJ line buttons) and integrated pitch shift and timestretch.
Eastcoast 4/4 Silent Violin Set - Only £129
Fancy a classic quiet night in by the fire? Then maybe the Eastcoast 4/4 Silent Violin Set will be your cup of tea. This silent violin features a solid maple body with 4 fine tuners, 2-band EQ, easily accessible volume controls, internal headphone pre-amp and headphone mini output jack. Comes with strap, bow, rosin, stereo headphones and 9 V battery.
Winter Whites Soon to Come
Roland BK-3 Backing Keyboard - £539
An ideal self contained solution for anyone looking for a great performance keyboard with backing track features and built in stereo speakers. Over 850 different tones and 50 drum kits ensures there are sounds to suit every style.
Roland MOBILE BA Battery-Powered Stereo Amp - £185
The Roland MOBILE BA Stereo Amp, weighing in at less than six pounds, delivers high-quality stereo sound with flexible connection options. With the MOBILE BA, no external power sources, speakers, or mixers are needed. All you need to do is simply connect your music player, microphone, and/or instrument, set the volume, and broadcast your sound with clarity and power.


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J&L Collection
Heritage 1/2005:30600, $43,700 - San Diego Collection
Stack's/Bowers 11/2016:2183, not sold - Stack's/Bowers 3/2017:3160, not sold
David Hall: Although the Illinois Centennial half dollar (more commonly known as the "Lincoln") is one of the earliest U.S. silver commemoratives, the mintage was huge and today the Linoln is one of the most common commems. The original mintage was 100,058 coins and they were initially sold at $1.00 per coin.
This is a popular coin with collectors, but you can obtain a Gem example easily. Nearly all known Lincolns are mint state, and the average grade is MS63 to MS65. Superb MS66 examples are readily available and even MS67 specimens are scarce only. Lincolns have frosty white luster or some degree of toning. There are many Gems to choose from!
© 1999-2018 Collectors Universe NASDAQ: CLCT

subset: commoncrawl


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posted by Thom Holwerda on Tue 18th Dec 2012 23:12 UTC
"Windows XP was the last client version of Windows to include the Pinball game that had been part of Windows since Windows 95. There is apparently speculation that this was done for legal reasons. No, that's not why." I love these stories.


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President Obama's Climate Action Plan: A Good Start To Hopefully An Even Better Future
|There is no question that yesterday's speech by President Obama was an important step forward for U.S. climate action, less for the specific actions he outlined than for the renewed national focus on the issue.
|Related Posts
|Suntech May Set, But Solar Will Still Grow
|Having Trouble Reaching Rural Solar Customers? Just Text Them.
|Another Solar Trade Dispute: United States Files WTO Complaint Against India
|Read More
|Tweets by @WorldwatchInst


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Moderator: Cartographers
koontz wrote:not a problem but you will probably get the suggestion to move the cannon. Swap the cannon with one of the targets next door. Why are the cannons starting neutral? They have no bonus with them so they can start normal.
koontz wrote:When you do you glow, select your text, increase by one pixel and feather by 5. Or any variation thereof, then paintbrush in the colour you want. This will give a uniform look to the whole map.
koontz wrote:Todd canon, move away from the wall as both are nearly the same grey. Why does Santa Anna look like one pork roast away from a heart attack?
generalhead wrote:I was studying for an algebra test yesterday and will be in class tonight. After class tonight or tomorrow I should have time to work on the glows and work on the officers more. I am trying to take my time with the officers like I did with the walls and the cannons. Thank you for your understanding. GH
SaucyJack wrote:If I wanted to play a stacking game, I'd play Jenga.
DoomYoshi wrote:Hey! Welcome to the Big Boys Club. Here is where we get really picky.
First: The objective is pointless. For you to control all 8 officers, you would need to hold pretty much the entire map. All this means is that there are some tournaments which reward players for winning by objective. The other players will be held hostage.
Bowie is far too powerful. Here is how I am calculating power: Number of non-bombardable territories. Bowie gets 5. If Ceza bombed Cuthbert instead of Elder and Ortega bombed Ward instead of Winchester AND/OR Price instead of King, it would be better.
Now approaching this problem from the other way I think there are too many bombardable territories. I say this as an avid trench player. For example, I can't get from Cos to Davis without first being bombed by Davis. Also, as it stands now, Cos is the worst general.
I realize that Romero also has 4 non-bombardables and may be my new favourite. I am going to keep running scenarios through my mind and get back to you.
SaucyJack wrote:If I wanted to play a stacking game, I'd play Jenga.
DoomYoshi wrote:Why can't some cannons have one target and some cannons have 2?
DoomYoshi wrote:Why can't some cannons have one target and some cannons have 2?
koontz1973 wrote:DoomYoshi wrote:Why can't some cannons have one target and some cannons have 2?
They can. As long as one does not become too powerful over the others, they can all have different abilities.
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Post Tagged with: "Judgement Day"
Belief in Judgement Day
877-WHY-ISLAM Brochure. Request a copy View in PDF In the Name of God, the Most Gracious, the Most Merciful Death is one of the few indisputable facts of life. Regardless of faith, race, status or age, we will all die. While the certainty of death is universally accepted, the question of what happens afterwards


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ESP LTD MHB-200 Baritone Titanium
ESP started back in 1975 as a replacement parts shop called Electric Sound Products (ESP) in Tokyo. It provided custom replacement parts for guitars and quickly gained a reputation as a provider of high-quality replacement parts. ESP also began crafting guitars under the ESP and Navigator brand in the Japanese market and in New York they began crafting custom instruments for artists. Among these were Vernon Reid of Living Colour, Bruce Kulick of KISS and Ron Wood of The Rolling Stones. George Lynch also discovered ESP and as a result, his famous ESP Kamikaze was made. More players with signature models include Kirk Hammet and James Hetfield of Metallica, Stephen Carpenter of Deftones, Max Cavalera of Sepultura and Richard Z of Rammstein.
In 1996, LTD was created to produce ESP's high-quality products in a more affordable line of production. LTD has grown in popularity and has become a noteworthy brand among many artists throughout the years. ESP/LTD simply equals quality workmanship and affordable prices.
ESP/LTD comes with great hardware and either their own, EMG's or Seymour Duncan pick-ups. Just choose your axe – there is something for any flavour. Some of the best money can buy!
Neck-thru-body construction; 27"baritone scale; agathis body; 3-piece maple neck; rosewood fingerboard; offset block inlays w/model name at 12th fret; EMG-HZ H-4 pickups; volume & tone controls w/3-way toggle; black hardware; tune-o-matic bridge w/string-thru-body; white binding on neck & headstock; 24 XJ frets
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Oregon State Police: Hitting The Road For Labor Day Holiday?Posted: Updated:
Oregon State Police
With an anticipated increase in holiday travelers on the road according to AAA, Oregon State Police (OSP) and many law enforcement agencies in Oregon are continuing a nationwide effort to prevent traffic crashes while targeting impaired drivers during the upcoming Labor Day holiday weekend. The nationwide "Drive Sober or Get Pulled Over" campaign targets impaired driving to prevent traffic crashes that impact lives and affect those traveling on our highways during the second deadliest major holiday period for highway travelers in Oregon.
Kicking off the Labor Day holiday weekend, Friday, August 30th, travelers on Oregon highways should see an obvious increase in the number of OSP patrol vehicles, marked and unmarked. All available sworn personnel assigned to Field Operations scheduled to work that day have been asked to supplement local highway safety or natural resource enforcement efforts.
OSP Field Operations Major Travis Hampton said the added patrol support will focus during the afternoon hours as many people leave for their holiday weekend destinations. Last year during the 2012 Labor Holiday reporting period, three people died in 3 separate traffic crashes on Oregon roads.
"Research has shown that high-visibility enforcement like the 'Drive Sober or Get Pulled Over' campaign reduces drunk driving fatalities by as much as 20 percent," said Hampton. "We hope the message to drive safe, alert, rested, and sober, backed by increased enforcement efforts, will be remembered and remain with drivers throughout the holiday period."
The 2013 Labor Day Holiday period starts 6:00 p.m., Friday, August 30, and runs through 11:59 p.m., Monday, September 2. Oregon troopers, deputies and city police officers will continue to target aggressive, dangerous, distracted, impaired and other drivers affecting safety on our roads as the "Drive Sober or Get Pulled Over" campaign, which started August 16, concludes at the end of the holiday period.
OSP, Linn County Sheriff's Office, Eugene Police Department (EPD), Springfield Police Department, and Lebanon Police Department kicked off the "Drive Sober or Get Pulled Over" campaign August 16 with a multi-agency enforcement effort during the Willamette County Music Festival, August 16 - 18. Enforcement effort statistics reported 260 traffic stops, 14 DUII arrests, approximately 75 traffic citations issued, and several arrests including a 36-year old man who attempted to elude officers until he was arrested following a short foot chase in the Sweet Home area. The man was wanted on two outstanding warrants related to a Rape in the First Degree charge. Officers working during the festival reported only two minor traffic crashes, one of which was alcohol-related, and extended their appreciation to those citizens who reported possible intoxicated drivers.
In addition to the "Drive Sober or Get Pulled Over" campaign, participating Oregon law enforcement agencies will be in the midst of another "Click It or Ticket" campaign (August 28 - September 8) to keep people buckled up and children in the right size and type child safety seat. OSP and other law enforcement agencies will use overtime grant funds to increase enforcement efforts targeting impaired drivers, unbuckled adults and children, and to get drivers to obey speed laws on Interstate and secondary state highways.
According to ODOT's Fatality Analysis Reporting System (FARS), a brief look at Labor Day holiday traffic crash statistics in Oregon since 1970 indicates:
* Fatalities average seven each year in Oregon over this holiday weekend.
* Since record-keeping began, more than 280 people have died during this holiday period, making it the second deadliest major holiday of the year.
* Alcohol is a contributing factor in over half of the traffic fatal crashes.
* The highest number of traffic fatalities occurred in 1978 when 17 people died.
* Oregon has never experienced a fatal-free Labor Day holiday weekend. Single fatality reporting periods occurred in 1999, 2001, 2002, and 2010.
Drunk driving takes a particularly heavy toll among young drivers and they are often those most at risk. According to the National Highway Traffic Safety Administration (NHTSA), among 18- to 34-year old drivers killed in motor vehicle crashes during the 2011 Labor Day weekend, 42 percent were alcohol-impaired.
"We want to remind everyone that getting behind the wheel drunk is a terrible idea. If you have any doubt about your sobriety, do not put the keys in the ignition, steering wheel in your hands, and your vehicle into gear. If you choose to drive impaired, you will be arrested," said Hampton.
The Oregon State Police, Oregon State Sheriff's Association, Oregon Association of Chiefs of Police, and Oregon Department of Transportation offer the following safety reminders for holiday travel:
* Be watchful for emergency vehicles and workers. MOVE OVER if you are approaching any type of emergency vehicle, tow truck or roadside assistance vehicle which is stopped on the roadside with emergency lights activated.
* Get rested before you are tested. Fatigued drivers are more frequent during holiday weekends because of increased travel and activity. Allow plenty of time to reach your destination.
* Stay up to date on road conditions by visiting TripCheck.com or calling 5-1-1. Outside Oregon, dial (503) 588-2941. In work zones, even when workers are not present, all speed limits still apply and fines double. Inactive work zones still have equipment, detours, and incomplete changes in the roadway so drivers need to slow down and be alert.
* Share the road. Watch for bicyclists and pedestrians, especially at night. Bicyclists and pedestrians need to make sure motorists can see them, and motorists needs to make sure they are seen.
* Buckle up every trip, every time. Be sure to use child safety seats correctly.
* Be alert and avoid distractions.
* Drive sober.
Everyone is urged to play an important part in keeping our highways and city streets safe by immediately reporting aggressive, dangerous, and intoxicated drivers to the Oregon State Police at 1-800-24DRUNK (1-800-243-7865) or call 9-1-1.
For help with child seats or additional "best practice" information, refer to the seat manufacturer's instructions, vehicle owner's manual or call ACTS Oregon Child Safety Seat Resource Center at (503) 643-5620 in Portland or (877) 793-2608.
### www.oregon.gov/OSP ###


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The Stanford Classifier
The Stanford Classifier is a general purpose classifier - something that takes a set of input data and assigns each of them to one of a set of categories. It does this by generating features from each datum which are associated with positive or negative numeric "votes" (weights) for each class. In principle, the weights could be set by hand, but the expected use is for the weights to be learned automatically based on hand-classified training data items. (This is referred to as "supervised learning".) The classifier can work with (scaled) real-valued and categorical inputs, and supports several machine learning algorithms. It also supports several forms of regularization, which is generally needed when building models with very large numbers of predictive features.
You can use the classifier on any sort of data, including standard statistics and machine learning data sets. But for small data sets and numeric predictors, you'd generally be better off using another tool such as Weka or R. Where the Stanford Classifier shines is in working with mainly textual data, where it has powerful and flexible means of generating features from character strings. But if you've also got a few numeric variables, you can throw them in at the same time.
Cheese-Disease: A small textual example
While you can specify most options on the command line, normally the easiest way to train and test models with the Stanford Classifier is through use of properties files that record all the options used. You can find a couple of example data sets and properties files in the examples folder of the Stanford Classifier distribution.
The Cheese-Disease dataset is a play on the MTV game show Idiot Savants from the late 1990s, which had a trivia category of Cheese or Disease? (I guess you had to be there...). The goal is to distinguish cheese names from disease names. Look at the file examples/cheeseDisease.train to see what the data looks like. The first column is the category (1=cheese, 2=disease). The number coding of classes was arbitrary. The two classes could have been called "cheese" and "disease". The second column is the name. The columns are separated by a tab character. Here there is just one class column and one predictive column. This is the minimum for training a classifier, but you can have any number of predictive columns and specify which column has what role.
In the top level folder of the Stanford Classifier, the following command will build a model for this data set and test it on the test data set in the simplest possible way:
java -jar stanford-classifier.jar -prop examples/cheese2007.prop
This prints a lot of information. The first part shows a little bit about the data set. The second part shows the process of optimization (choosing feature weights in training a classifier on the training data). The next part then shows the results of testing the model on a separate test set of data, and the final 5 lines give the test results:
196 examples in test set Cls 2: TP=123 FN=5 FP=8 TN=60; Acc 0.934 P 0.939 R 0.961 F1 0.950 Cls 1: TP=60 FN=8 FP=5 TN=123; Acc 0.934 P 0.923 R 0.882 F1 0.902 Micro-averaged accuracy/F1: 0.93367 Macro-averaged F1: 0.92603
For each class, the results show the number of true positives, false negatives, false positives, and true negatives, the class accuracy, precision, recall and F1 measure. It then gives a summary F1 over the whole data set, either micro-averaged (each test item counts equally) or macro-averaged (each class counts equally). Distinguishing cheeses and diseases isn't too hard for the classifier!
What features does the classifier use, and what is useful in making a decision? The -jar command runs the default class in the jar file, which is edu.stanford.nlp.classify.ColumnDataClassifier. You can also run it explicitly. We'll also show that way of doing things in this example.
Often it is also useful to mix the two methods: if running a series of experiments, you might have the baseline classifier configuration in a properties file but put differences in properties for a series of experiments on the command-line. Things specified on the command-line override specifications in the properties file.
Iris data set
Fisher's Iris data set is one of the most famous data sets in statistics and machine learning [1]. Three species of Iris are described by four numeric variables. We show it both as a simple example of numeric classification and as an example of using multiple columns of inputs for each data item. In the download, there is a version of the 150 item data set divided into 130 training examples and 20 test examples, and a properties file suitable for training a classifier from it.
Note that the provided properties file is set up to run from the top-level folder of the Stanford classifier distribution. We will asssume that STANFORD_CLASSIFIER_HOME points to it. You can do something like:
Here is the provided properties file:
# # Features # # Data format by column is: # species sepalLength sepalWidth petalLength petalWidth # useClassFeature=true 1.realValued=true 2.realValued=true 3.realValued=true 4.realValued=true printClassifier=AllWeights # # Training input # trainFile=./examples/iris.train testFile=./examples/iris.test
The four predictor variables are all specified as real valued. There are other flags that will let you use numeric variables with a few simple transforms, such as
logTransform or
If you run this model:
cd $STANFORD_CLASSIFIER_HOME java -cp stanford-classifier.jar edu.stanford.nlp.classify.ColumnDataClassifier -prop examples/iris2007.prop
Then you'll find that you get the test set completely right!
Built this classifier: Linear classifier with the following weights Iris-setosa Iris-versicolor Iris-virginica 3-Value -2.27 0.03 2.26 CLASS 0.34 0.65 -1.01 4-Value -1.07 -0.91 1.99 2-Value 1.60 -0.13 -1.43 1-Value 0.69 0.42 -1.23 Total: -0.72 0.05 0.57 Prob: 0.15 0.32 0.54 Output format: dataColumn1 goldAnswer classifierAnswer P(classifierAnswer) 5 Iris-setosa Iris-setosa 0.969786023975717 4.6 Iris-setosa Iris-setosa 0.9922589089843827 5.1 Iris-setosa Iris-setosa 0.9622434861270637 4.9 Iris-setosa Iris-setosa 0.9515812390773056 5.4 Iris-setosa Iris-setosa 0.9811482146433487 4.4 Iris-setosa Iris-setosa 0.9682526103461551 5.3 Iris-setosa Iris-setosa 0.9832118698970074 6.1 Iris-versicolor Iris-versicolor 0.7091015073390197 6 Iris-versicolor Iris-versicolor 0.7601066690047942 5.5 Iris-versicolor Iris-versicolor 0.723249991884404 6.5 Iris-versicolor Iris-versicolor 0.7913325733592043 6.8 Iris-versicolor Iris-versicolor 0.8416723165037595 6.2 Iris-versicolor Iris-versicolor 0.8854234492113978 6.7 Iris-virginica Iris-virginica 0.8440929745353494 6.4 Iris-virginica Iris-virginica 0.7816139993113614 5.7 Iris-virginica Iris-virginica 0.9352983975779943 6.7 Iris-virginica Iris-virginica 0.8626420107509875 6.8 Iris-virginica Iris-virginica 0.9442955376893006 7.7 Iris-virginica Iris-virginica 0.8866439920995643 7.3 Iris-virginica Iris-virginica 0.8633450387282207 20 examples in test set Cls Iris-setosa: TP=7 FN=0 FP=0 TN=13; Acc 1.000 P 1.000 R 1.000 F1 1.000 Cls Iris-versicolor: TP=6 FN=0 FP=0 TN=14; Acc 1.000 P 1.000 R 1.000 F1 1.000 Cls Iris-virginica: TP=7 FN=0 FP=0 TN=13; Acc 1.000 P 1.000 R 1.000 F1 1.000 Micro-averaged accuracy/F1: 1.00000 Macro-averaged F1: 1.00000
This is a fairly easy, well-separated classification problem. Indeed you might think that the model is overparameterized, and it is. The number of examples in each class is roughly balanced, so there is presumably little value in the
useClassFeature property which puts in a feature that models the overall distribution of classes. You also don't need to use all the numeric features. See the plots on the Wikipedia Iris flower data set page. You can instead, delete features for columns 2 and 4 and just use the sepal and petal lengths rather than also widths, and also still get 100% accuracy on our test set. (However, it happens that if you delete both the classFeature and the two width features, then the model that is built only gets 19/20 of the test set examples right....)
20 Newsgroups
Now let's walk through a more realistic example of using the Stanford Classifier on the well-known 20 Newgroups dataset. There are several versions of 20 Newsgroups. We'll use Jason Rennie's "bydate" version from [2]. The precise commands shown below should work on Linux or Mac OS X systems. The Java parts should also be fine under Windows, but you'll need to do the downloading and reformatting a little differently.
First we download the corpus:
curl -O http://people.csail.mit.edu/jrennie/20Newsgroups/20news-bydate.tar.gz
Then we unpack it:
tar -xzf 20news-bydate.tar.gz
The 20 Newsgroups data comes in a format of one file per document, with the correct class shown by the directory name. The Stanford Classifier works with tab-delimited text files. We convert it into this latter format with a simple shell script:
curl -O http://nlp.stanford.edu/software/classifier/convert-to-stanford-classifier.csh chmod 755 convert-to-stanford-classifier.csh ./convert-to-stanford-classifier.csh
We do this by converting line endings to spaces. This loses line break information which could easily have some value in classification. (We could have done something tricker like converting line endings to a vertical tab or form feed, but this will do for this example.) As part of the conversion, we also convert the original 8-bit newsgroup posts to utf-8. It's 2010 now.
Check that everything worked and you have the right number of documents:
wc -l 20news-bydate*-stanford-classifier.txt 7532 20news-bydate-test-stanford-classifier.txt 11314 20news-bydate-train-stanford-classifier.txt 18846 total
The correct number should be as shown.
Next we split the training data into initial training data and a development test set. (Methodological note: people often fail to do this. But if you are going to build a sequence of models trying to find the best classifier, and moreover if you are likely to be looking at the test results to see where your model fails and how you might fix it, then it is vital that you use a development test set that is distinct from your final test set. Otherwise you will overfit on your final test set and report unrealistically rosy results.)
grep -P '^\S+\s[0-9]*[1-8]\s' 20news-bydate-train-stanford-classifier.txt > 20news-bydate-devtrain-stanford-classifier.txt grep -P '^\S+\s[0-9]*[90]\s' 20news-bydate-train-stanford-classifier.txt > 20news-bydate-devtest-stanford-classifier.txt
This gives a roughly random partition of 80% of the data in devtrain and 20% in devtest based on the final digit of the newsgroup item number.
We'll assume that
$STANFORD_CLASSIFIER_JAR points at the Stanford Classifier jar. So, depending on your shell, do something like:
This next command builds pretty much the simplest classifier that you could. It divides the input documents on white space and then trains a classifier on the resulting tokens. The command is normally entered as all one line without the trailing backslashes, but we've split it so it formats better on this page.
java -mx1800m -cp $STANFORD_CLASSIFIER_JAR edu.stanford.nlp.classify.ColumnDataClassifier \ -trainFile 20news-bydate-devtrain-stanford-classifier.txt -testFile 20news-bydate-devtest-stanford-classifier.txt \ -2.useSplitWords -2.splitWordsRegexp "\\s+"
Note that once the dataset is reasonably large, you have to give a fair amount of memory to the classifier. (There are some methods for reducing memory usage that we'll discuss later on.) [Also, if you have any problems with this command, it's probably an issue with symbol escaping in your shell, and so you should probably just skip it and go on to the next example that uses a properties file.]
This command generates a lot of output. The last part shows the accuracy of the classifier:
2241 examples in test set Cls alt.atheism: TP=80 FN=20 FP=5 TN=2136; Acc 0.989 P 0.941 R 0.800 F1 0.865 Cls comp.graphics: TP=82 FN=33 FP=30 TN=2096; Acc 0.972 P 0.732 R 0.713 F1 0.722 Cls comp.os.ms-windows.misc: TP=91 FN=24 FP=20 TN=2106; Acc 0.980 P 0.820 R 0.791 F1 0.805 Cls comp.sys.ibm.pc.hardware: TP=87 FN=31 FP=32 TN=2091; Acc 0.972 P 0.731 R 0.737 F1 0.734 Cls comp.sys.mac.hardware: TP=90 FN=24 FP=23 TN=2104; Acc 0.979 P 0.796 R 0.789 F1 0.793 Cls comp.windows.x: TP=112 FN=8 FP=20 TN=2101; Acc 0.988 P 0.848 R 0.933 F1 0.889 Cls misc.forsale: TP=100 FN=17 FP=47 TN=2077; Acc 0.971 P 0.680 R 0.855 F1 0.758 Cls rec.autos: TP=95 FN=19 FP=21 TN=2106; Acc 0.982 P 0.819 R 0.833 F1 0.826 Cls rec.motorcycles: TP=98 FN=14 FP=14 TN=2115; Acc 0.988 P 0.875 R 0.875 F1 0.875 Cls rec.sport.baseball: TP=112 FN=7 FP=13 TN=2109; Acc 0.991 P 0.896 R 0.941 F1 0.918 Cls rec.sport.hockey: TP=113 FN=4 FP=4 TN=2120; Acc 0.996 P 0.966 R 0.966 F1 0.966 Cls sci.crypt: TP=108 FN=8 FP=5 TN=2120; Acc 0.994 P 0.956 R 0.931 F1 0.943 Cls sci.electronics: TP=93 FN=24 FP=24 TN=2100; Acc 0.979 P 0.795 R 0.795 F1 0.795 Cls sci.med: TP=104 FN=20 FP=8 TN=2109; Acc 0.988 P 0.929 R 0.839 F1 0.881 Cls sci.space: TP=113 FN=8 FP=9 TN=2111; Acc 0.992 P 0.926 R 0.934 F1 0.930 Cls soc.religion.christian: TP=107 FN=12 FP=22 TN=2100; Acc 0.985 P 0.829 R 0.899 F1 0.863 Cls talk.politics.guns: TP=96 FN=10 FP=5 TN=2130; Acc 0.993 P 0.950 R 0.906 F1 0.928 Cls talk.politics.mideast: TP=104 FN=7 FP=3 TN=2127; Acc 0.996 P 0.972 R 0.937 F1 0.954 Cls talk.politics.misc: TP=87 FN=12 FP=5 TN=2137; Acc 0.992 P 0.946 R 0.879 F1 0.911 Cls talk.religion.misc: TP=49 FN=18 FP=10 TN=2164; Acc 0.988 P 0.831 R 0.731 F1 0.778 Micro-averaged accuracy/F1: 0.85721 Macro-averaged F1: 0.85670
We see the statistics for each class and averaged over all the data. For each class, we see the four cells of counts in a contingency table, and then the accuracy and precision, recall and F-measure calculated for them. This model already seems to perform quite well. But we can do a little better.
As soon as you want to start specifying a lot of options, you'll probably want a properties file to specify everything. Indeed, some options you can only successfully set with a properties file. One of the first things to address seems to be better tokenization. Tokenizing on whitespace is fairly naive. One can usually write a rough-and-ready but usable tokenizer inside
ColumnDataClassifier by using the
splitWordsTokenizerRegexp property. Another alternative would be to use a tool like the Stanford tokenizer to pre-tokenize the data. In general, this will probably work a bit better for English-language text, but is beyond what we consider here. Here's a simple properties file which you can download:
trainFile=20news-bydate-devtrain-stanford-classifier.txt testFile=20news-bydate-devtest-stanford-classifier.txt 2.useSplitWords=true 2.splitWordsTokenizerRegexp=[\\p{L}][\\p{L}0-9]*|(?:\\$ ?)?[0-9]+(?:\\.[0-9]{2})?%?|\\s+|[\\x80-\\uFFFD]|. 2.splitWordsIgnoreRegexp=\\s+
This tokenizer recognizes tokens starting with letters followed by letters and ASCII digits, or some number, money, and percent expressions, whitespace or a single letter. The whitespace tokens are then ignored.
Just a bit of work on tokenization gives us about 2%!
java -mx1800m -cp $STANFORD_CLASSIFIER_JAR edu.stanford.nlp.classify.ColumnDataClassifier -prop 20news1.prop ... Micro-averaged accuracy/F1: 0.87773 Macro-averaged F1: 0.87619
You can look at the output of the tokenizer by examining the features the classifier generates. We can do this with this command:
java -mx1800m -cp $STANFORD_CLASSIFIER_JAR edu.stanford.nlp.classify.ColumnDataClassifier -prop 20news1.prop -printFeatures prop1
Look at the resulting (very large) file
prop1.train . You might be able to get a bit better performance by fine-tuning the tokenization, though, often, for text categorization, a fairly simple tokenization is sufficient, providing (1) it's enough to recognize most semantically contentful word units, and (2) it doesn't produce a huge number of rarely observed features. (E.g., for this data set, there are a few uuencoded files in newsgroup postings. Under whitespace tokenization, each line of the file became a token that almost certainly only occurred once. Now they'll get split up on characters that aren't letters and digits. That not only reduces the token space, but probably some of the letter strings that do result will recur, and become slightly useful features. It seems like it might be better to remove the uuencoded content altogether, while leaving just enough to know that there was a uuencoded file in the news posting. Some processors such as the bow tokenizer handle recognizing and stripping uuencoded files. The Stanford Classifier doesn't. uuencoded text isn't so common in 2010. But we don't worry about this; it probably doesn't make much difference.)
There are many other kinds of features that you could consider putting into the classifier which might improve performance. The length of a newsgroup posting might be informative, but it probably isn't linearly related to its class, so we bin lengths into 4 categories, which become categorical features. You have to choose those cut-offs manually, but
ColumnDataClassifier can print simple statistics of how many documents of each class fall in each bin, which can help you see if you've chosen very bad cut-offs. Here's the properties file: [3] .
trainFile=20news-bydate-devtrain-stanford-classifier.txt testFile=20news-bydate-devtest-stanford-classifier.txt 2.useSplitWords=true 2.splitWordsTokenizerRegexp=[\\p{L}][\\p{L}0-9]*|(?:\\$ ?)?[0-9]+(?:\\.[0-9]{2})?%?|\\s+|[\\x80-\\uFFFD]|. 2.splitWordsIgnoreRegexp=\\s+ 2.binnedLengths=500,1500,4500,13500 2.binnedLengthsStatistics=true
In this case, that doesn't help:
Micro-averaged accuracy/F1: 0.87773 Macro-averaged F1: 0.87619
Other feature types that are often good with text documents are: to use token prefix and suffixes and to use the "shape" of a token (whether it contains upper or lowercase or digits or certain kinds of symbols as equivalence classes). We've also changed the output to show the highest weight features in the model. That's often informative to look at. This gives our next properties file: [4] .
trainFile=20news-bydate-devtrain-stanford-classifier.txt testFile=20news-bydate-devtest-stanford-classifier.txt 2.useSplitWords=true 2.splitWordsTokenizerRegexp=[\\p{L}][\\p{L}0-9]*|(?:\\$ ?)?[0-9]+(?:\\.[0-9]{2})?%?|\\s+|[\\x80-\\uFFFD]|. 2.splitWordsIgnoreRegexp=\\s+ 2.useSplitPrefixSuffixNGrams=true 2.maxNGramLeng=4 2.minNGramLeng=1 2.splitWordShape=chris4 printClassifier=HighWeight printClassifierParam=400
This pushes performance up another percent, roughly:
Micro-averaged accuracy/F1: 0.88755 Macro-averaged F1: 0.88588
As well as fiddling with features, we can also fiddle with the machine learning and optimization. By default you get a maximum entropy (roughly, multiclass logistic regression) model with L2 regularization (a.k.a., a gaussian prior) optimized by the L-BFGS quasi-Newton method. You might be able to get a bit of improvement by adjusting the amount of regularization, which you can do by altering the sigma parameter:
You can also change the type of regularization altogether. Lately, L1 regularization has been popular for producing well-performing compact models. You can select it by specifying the L1 regularization parameter:
We tried a couple of settings of both of these, but nothing really seemed to beat out the defaults.
And so we return to fiddling with features. Academic papers don't spend much time discussing fiddling with features, but in practice it's usually where most of the gains come from (once you've got a basically competent machine learning method). In the last properties file, we had it print out the highest weight features. That's often useful to look at. Here are the top few:
(1-S#B-X,comp.windows.x) 1.3971 (1-S#B-Win,comp.os.ms-windows.misc) 0.9708 (1-SW-car,rec.autos) 0.9538 (1-S#E-car,rec.autos) 0.9178 (1-S#B-Mac,comp.sys.mac.hardware) 0.9054 (1-S#E-dows,comp.os.ms-windows.misc) 0.9020 (1-S#B-x,comp.windows.x) 0.8157 (1-S#B-car,rec.autos) 0.7689 (1-S#E-ows,comp.os.ms-windows.misc) 0.7382 (1-S#E-ale,misc.forsale) 0.7361
They basically make sense. Note that all but one of them is a beginning or end split words n-gram feature (S#B or S#E). This partly makes sense: these features generalize over multiple actual words, so starting with "X" will match "X" "Xwindows" or "X-windows". It's part of what makes these features useful. But it also suggests that we might really be missing out by not collapsing case distinctions: S#E-ale is a good feature for
misc.forsale precisely because it matches both "Sale" or "sale". So let's try tackling that. There are several possible variants. One thing to try would be to just lowercase everything. Another would be to instead put in lowercased splitWords or both the regular splitWords features and lowercased versions of them. We tried several things. Relevant properties are
useLowercaseSplitWords, and
lowercaseNGrams. The best thing seemed to be to put in the splitWords regular case and lowercase, but to keep the character n-grams cased.
Technical point: the top features list also shows that many of the features are highly collinear: you get pairs like SW-car and S#E-car or S#E-dows and S#E-ows which mainly match in the same documents. This is common with textual features, and we don't try to solve this problem. The best we can do is to observe that maximum entropy models are reasonably tolerant of this sort of feature overlap: The fact that the model with both lowercased and regular case features seems to work best is indicative of this.
We'll use that. You might then also want to save your built classifier so you can run it on data sets later. You can do this either directly with
ColumnDataClassifier or, in your own program, you'll want to load the classifier using a method like
LinearClassifier.readClassifier(filename). This gives us our final properties file:
[5] .
trainFile=20news-bydate-devtrain-stanford-classifier.txt testFile=20news-bydate-devtest-stanford-classifier.txt 2.useLowercaseSplitWords=true 2.useSplitWords=true 2.splitWordsTokenizerRegexp=[\\p{L}][\\p{L}0-9]*|(?:\\$ ?)?[0-9]+(?:\\.[0-9]{2})?%?|\\s+|[\\x80-\\uFFFD]|. 2.splitWordsIgnoreRegexp=\\s+ 2.useSplitPrefixSuffixNGrams=true 2.maxNGramLeng=4 2.minNGramLeng=1 2.splitWordShape=chris4 printClassifier=HighWeight printClassifierParam=400 serializeTo=20newsgroups4.ser.gz
Here are the devtest set results:
2241 examples in test set Cls alt.atheism: TP=90 FN=10 FP=5 TN=2136; Acc 0.993 P 0.947 R 0.900 F1 0.923 Cls comp.graphics: TP=91 FN=24 FP=26 TN=2100; Acc 0.978 P 0.778 R 0.791 F1 0.784 Cls comp.os.ms-windows.misc: TP=98 FN=17 FP=20 TN=2106; Acc 0.983 P 0.831 R 0.852 F1 0.841 Cls comp.sys.ibm.pc.hardware: TP=90 FN=28 FP=35 TN=2088; Acc 0.972 P 0.720 R 0.763 F1 0.741 Cls comp.sys.mac.hardware: TP=92 FN=22 FP=15 TN=2112; Acc 0.983 P 0.860 R 0.807 F1 0.833 Cls comp.windows.x: TP=112 FN=8 FP=16 TN=2105; Acc 0.989 P 0.875 R 0.933 F1 0.903 Cls misc.forsale: TP=99 FN=18 FP=29 TN=2095; Acc 0.979 P 0.773 R 0.846 F1 0.808 Cls rec.autos: TP=98 FN=16 FP=15 TN=2112; Acc 0.986 P 0.867 R 0.860 F1 0.863 Cls rec.motorcycles: TP=106 FN=6 FP=6 TN=2123; Acc 0.995 P 0.946 R 0.946 F1 0.946 Cls rec.sport.baseball: TP=114 FN=5 FP=11 TN=2111; Acc 0.993 P 0.912 R 0.958 F1 0.934 Cls rec.sport.hockey: TP=113 FN=4 FP=5 TN=2119; Acc 0.996 P 0.958 R 0.966 F1 0.962 Cls sci.crypt: TP=110 FN=6 FP=2 TN=2123; Acc 0.996 P 0.982 R 0.948 F1 0.965 Cls sci.electronics: TP=97 FN=20 FP=16 TN=2108; Acc 0.984 P 0.858 R 0.829 F1 0.843 Cls sci.med: TP=113 FN=11 FP=3 TN=2114; Acc 0.994 P 0.974 R 0.911 F1 0.942 Cls sci.space: TP=117 FN=4 FP=4 TN=2116; Acc 0.996 P 0.967 R 0.967 F1 0.967 Cls soc.religion.christian: TP=113 FN=6 FP=18 TN=2104; Acc 0.989 P 0.863 R 0.950 F1 0.904 Cls talk.politics.guns: TP=99 FN=7 FP=4 TN=2131; Acc 0.995 P 0.961 R 0.934 F1 0.947 Cls talk.politics.mideast: TP=107 FN=4 FP=1 TN=2129; Acc 0.998 P 0.991 R 0.964 F1 0.977 Cls talk.politics.misc: TP=91 FN=8 FP=4 TN=2138; Acc 0.995 P 0.958 R 0.919 F1 0.938 Cls talk.religion.misc: TP=49 FN=18 FP=7 TN=2167; Acc 0.989 P 0.875 R 0.731 F1 0.797 Micro-averaged accuracy/F1: 0.89201 Macro-averaged F1: 0.89099
This then leaves the final test where we train on the full training set and then test on the test set:
java -mx1800m -cp $STANFORD_CLASSIFIER_JAR edu.stanford.nlp.classify.ColumnDataClassifier -prop 20news4.prop \ -trainFile 20news-bydate-train-stanford-classifier.txt -testFile 20news-bydate-test-stanford-classifier.txt
Here are the final test set results:
7532 examples in test set Cls alt.atheism: TP=235 FN=84 FP=69 TN=7144; Acc 0.980 P 0.773 R 0.737 F1 0.754 Cls comp.graphics: TP=287 FN=102 FP=143 TN=7000; Acc 0.967 P 0.667 R 0.738 F1 0.701 Cls comp.os.ms-windows.misc: TP=281 FN=113 FP=104 TN=7034; Acc 0.971 P 0.730 R 0.713 F1 0.721 Cls comp.sys.ibm.pc.hardware: TP=277 FN=115 FP=135 TN=7005; Acc 0.967 P 0.672 R 0.707 F1 0.689 Cls comp.sys.mac.hardware: TP=301 FN=84 FP=102 TN=7045; Acc 0.975 P 0.747 R 0.782 F1 0.764 Cls comp.windows.x: TP=311 FN=84 FP=55 TN=7082; Acc 0.982 P 0.850 R 0.787 F1 0.817 Cls misc.forsale: TP=345 FN=45 FP=77 TN=7065; Acc 0.984 P 0.818 R 0.885 F1 0.850 Cls rec.autos: TP=341 FN=55 FP=52 TN=7084; Acc 0.986 P 0.868 R 0.861 F1 0.864 Cls rec.motorcycles: TP=373 FN=25 FP=28 TN=7106; Acc 0.993 P 0.930 R 0.937 F1 0.934 Cls rec.sport.baseball: TP=354 FN=43 FP=65 TN=7070; Acc 0.986 P 0.845 R 0.892 F1 0.868 Cls rec.sport.hockey: TP=373 FN=26 FP=25 TN=7108; Acc 0.993 P 0.937 R 0.935 F1 0.936 Cls sci.crypt: TP=347 FN=49 FP=22 TN=7114; Acc 0.991 P 0.940 R 0.876 F1 0.907 Cls sci.electronics: TP=268 FN=125 FP=128 TN=7011; Acc 0.966 P 0.677 R 0.682 F1 0.679 Cls sci.med: TP=306 FN=90 FP=67 TN=7069; Acc 0.979 P 0.820 R 0.773 F1 0.796 Cls sci.space: TP=346 FN=48 FP=41 TN=7097; Acc 0.988 P 0.894 R 0.878 F1 0.886 Cls soc.religion.christian: TP=363 FN=35 FP=76 TN=7058; Acc 0.985 P 0.827 R 0.912 F1 0.867 Cls talk.politics.guns: TP=317 FN=47 FP=135 TN=7033; Acc 0.976 P 0.701 R 0.871 F1 0.777 Cls talk.politics.mideast: TP=314 FN=62 FP=11 TN=7145; Acc 0.990 P 0.966 R 0.835 F1 0.896 Cls talk.politics.misc: TP=173 FN=137 FP=51 TN=7171; Acc 0.975 P 0.772 R 0.558 F1 0.648 Cls talk.religion.misc: TP=160 FN=91 FP=74 TN=7207; Acc 0.978 P 0.684 R 0.637 F1 0.660 Micro-averaged accuracy/F1: 0.80616 Macro-averaged F1: 0.80074
You'll notice that these results are quite a bit lower. Results being a bit lower is to be expected (after all, we were overfitting to the devtest set by doing multiple runs), but the results here are a lot lower. This is because in the
bydate version of 20 Newsgroups, the test set is all from a later time period than the training set, whereas, when we subdivided the training set, we took a roughly uniform sample across it as the devtest set. Topics under discussion shift over time, and so there's enough extra similarity between documents close in time and temporal movement in what gets posted over time in most time series text data sets that you get a substantial difference in performance like this, comparing a random test set with a test set from a separate time period.
Nevertheless, these results are pretty good! Below are results we could find in the academic literature for the same data set. The results reported by Rennie look surprisingly high in comparison to the results of more recent papers. Discounting those results as suspect, it seems like we're about 1% off the state of the art.
|Micro-ave accuracy
|Lan, M, Tan, Chew-Lim, and Low, Hwee-Boon, 2006, Proposing a New Term Weighting Scheme for Text Categorization
|Larochelle, H and Bengio, Y, 2008, Classification using Discriminative Restricted Boltzmann Machines
|hybrid discriminative RBM
|Only 5000 most frequent tokens used as features
|Li, B and Vogel, C, 2010, Improving Multiclass Text Classification with Error-Correcting Output Coding and Sub-class Partitions
|ECOC Naive Bayes
|Rennie, Jason D M, 2003, On The Value of Leave-One-Out Cross-Validation Bounds
|regularized least squares classifier
|Optimal regularization chosen post-hoc on test set


text:
The Board of County Commissioners last week expressed support for moving to a fifth cent in bed tax.
The Board of County Commissioners last week expressed support for moving to a fifth cent in bed tax.
The Gulf County Tourist Development Council remains divided on the issue.
The TDC advisory council will meet again the first week of August to take up the issue again, but the TDC is taking a deliberate approach, with its budget committee, which met last Friday, continuing to examine options.
The advisory council was philosophically divided on adding to the bed tax during its most recent meeting.
The idea is to implement an additional penny to fund a �beach safety program�, a permissible use of the revenue in state law, according to county attorney Jeremy Novak.
A central component of that plan, under the proposal provided the TDC board to add the penny, was funding equipment and training for two Gulf County Sheriff�s deputies who would be assigned exclusively to the tourist corridors.
Those two new positions would be funded by the Sheriff�s Office.
Gulf County Sheriff Mike Harrison told county commissioners last week had did not have the resources to bolster patrols or add deputies at this time.
Harrison told commissioners there are three deputies patrolling the entire county at most times. In the city of Port St. Joe, Police Chief David Barnes last week told city commissioners he has two officers on patrol at most times.
County commissioners and the TDC board shared concerns about the impact of declining budgets on law enforcement patrols, with the BOCC voicing support for the additional penny as a potential solution.
Harrison noted last week that crime in the county had increased 30 percent each of the past two years and there have been reports this year of a high rate of burglaries, to homes and vehicles, in the tourist corridor.
�That is due to a lack of exposure and officers in the community,� Harrison said.
Last week included a drive-by shooting in St. Joe Beach; all involved lived locally.
The key, for the TDC and executive director Jennifer Jenkins is protecting the gains of the past year to build for the future.
Bed tax receipts have climbed rapidly the past two years and the TDC is projecting another jump in the coming fiscal year.
�We have to protect our brand,� Jenkins said.
This is particularly important as Jenkins and her team continues to roll out research, programs and funding directed at expanding the brand across a host of platforms to transform the county from a three-month summer hot spot to a year-round �boutique� destination, as she has consistently told the TDC board.
The TDC must weigh that protection against two major concerns voiced by the board.
One was jeopardizing the market price, with Gulf County perceived as a more affordable alternative to other locations along the Gulf Coast.
The other was taxing: should the TDC be underwriting, in part, a function of county government by taxing visitors when some collectors assert that services compared to tax collections are already off kilter.
After Friday�s budget committee meeting, Jenkins said the TDC continues to examine options, as was recommended by several members of the advisory board who are also members of the budget committee.
Last week, County Commissioner Warren Yeager, who also sits on the TDC advisory board, raised the issue during budget meetings but generally deferred to a TDC decision.
�I�ve got to think of about the ad valorem taxpayer,� Yeager said. �I�ve got to look at alternative sources of revenue.�
Novak also emphasized that the additional penny might not be a solution for next fiscal year due to the work required in meeting the state statutes mandating how bed tax revenue can be used.
To implement the tax for the coming year, Novak noted to both boards, the TDC would have to craft, adopt and begin to implement a �beach safety program� by the end of the year in order to add the additional cent for 2014.


text:
Buy or DIY: Colorful Coffee & End Tables
Need a punch of color in your space? Wanna have interiors that better fit the changing seasons (or your changing moods)? There are plenty of ways to bring in bursts of new color into your home, and one fun way is by buying or DIYing a colorful coffee table or end table! We gathered some of our favorite inspiration for you.
1. Buy: Jeb Jones SPS Spool End Table
2. Buy: CB2 City Slicker Hot Pink Side Table
3. DIY: Neon Striped Coffee Table from Oh So Pretty
4. Buy: Marketplace Woven Table
5. Buy: Hobe Sound Coffee Table
6. Buy: Hourglass Lacquer Side Table
7. DIY: Paint Chip Coffee Table from Atypical Type A via Babble
8. Buy: Faceted Side Table
9. Buy: Jacqui Coffee Table
10. Buy: Luck Side Table
(Images: As credited above)


text:
Brazil legend Ronaldinho has tipped Neymar to surpass Lionel Messi as the world's best player in the coming years.
The Santos striker is understood to be the most sought-after talent in world football and Ronaldinho has backed the starlet to emulate Messi in the near future.
Speaking to The Daily Mail ahead of Brazil's friendly with England, he said: "Messi for the last three or four years has been the best player in the world - he has been at a level of consistency I don't think the world has seen before.
"Neymar is young though, and I can't explain how special he will become. In the next two or three seasons he will become the best player."
The 32-year-old also denied that he regrets not choosing to play in England, but expressed sorrow having not played with former Barca team-mate Messi for longer.
He added: "Do I regret not playing in England? No, because I have played for some of the biggest teams in the world and won everything.
"The only regret is not playing more seasons with Lionel Messi. He is my good friend.
"I could see even at a young age he was going to be the best. It was exciting to see this kid who could do things most players can only dream of."


text:
Send Results to This Email:
Street Address:
Street Address:
The 208-unit Stonegate Apartments complex in Raleigh has been financed for $3.6 million by its ownership group, an affiliate of New York-based White Eagle Properties.
WE Stonegate LLC filed the five-year loan Jan. 30 for the Class C complex at 4500 Stone Gate Drive, situated on 12.3 acres at the northwest corner of Coleman and Raleigh-Lagrange roads.
The Shelby County Assessor of Property, which lists Stonegate's address as 2791 Coleman, appraised it at $4.8 million in 2012.
White Eagle Properties acquired Stonegate and Autumnwood Apartments in Southeast Memphis in 2008 for a combined $16 million from The Health, Educational and Housing Facility Board of Shelby County. The county board had acquired both properties earlier in the year after its then-owners claimed bankruptcy.
White Eagle was one of 53 companies vying for the complexes as part of a solicitation of bids held by broker CB Richard Ellis Memphis.
Source: The Daily News Online & Chandler Reports
– Daily News staff
Jonathan Pekar, director of Ardent Studios film department, won the 27th Mid-South Emmy award for Best Commercial Spot for creating the animated Memphis Music Foundation commercial.
The project also showcases the talents of painter Lamar Sorrento and singer/songwriter Keia Johnson.
Pekar is presently developing a new television series called Kids of Memphis, set to air in November.
Ardent Studios, founded by John Fry, has been operating for 46 years. The studio began with early classics by Sam & Dave, Led Zeppelin, Isaac Hayes, Leon Russell and The Staples Singers, then scored hits in the 1970s, 1980s and 1990s with such artists as James Taylor, ZZ Top, REM, George Thorogood, The Allman Brothers, Bob Dylan, and Jimmie and Stevie Ray Vaughan.
In the new millennium, Ardent has recorded hits by The White Stripes, Huey Lewis and the News, Cat Power, North Mississippi Allstars, The Raconteurs, and award-winning music for such films as "Hustle and Flow."
To date, Ardent has amassed more than 70 Gold and Platinum albums and singles.
– Sarah Baker
Given a little time, Gov. Bill Haslam may be able to negotiate beneficial terms for Tennessee to participate in federally-funded Medicaid expansion. But his Republican colleagues in the Legislature may not let him get to that point.
Republican bills before both chambers of the General Assembly would prohibit the governor from pursuing an expansion to TennCare, the state's expanded Medicaid program.
Haslam told reporters last week that the maneuvering in the Legislature hasn't stopped him from closely evaluating the prospect of increasing eligibility to 138 percent of poverty.
Haslam said Tennessee could be granted flexibly on benefits for the estimated 145,500 people who would be added to the TennCare rolls that would make the program more affordable in the years after the federal government stops covering 100 percent of the cost.
– The Associated Press
FTN Financial, a subsidiary of First Tennessee Bank, has expanded its sales team.
Sylvia Holt Williams has joined FTN from Stifel Nicolaus, where she served as managing director of MBS trading. Williams will work out of the Kansas City, Mo., FTN office.
Grady Mosay, also of Stifel Nicolaus, has joined FTN in the Philadelphia office. At Stifel Nicolaus, Mosay was a vice presidential and institutional CMO trader.
Both additions will assume the title of senior vice president of sales at FTN.
– Andy Meek
Brick, stone and concrete block manufacturer General Shale has launched a state-of-the-art tool to help construction professionals and homeowners with designing buildings, choosing products, calculating costs and purchasing materials for their projects.
It's a mobile app, which can be downloaded free via the iPhone App Store, and it allows users to browse product colors and sizes in brick, thin brick, pavers and do-it-yourself outdoor living kits and then email favorite selections or final cost calculations without leaving the app.
A calculator function enables the user to estimate material costs when building with brick or pavers, based on parameters such as square footage, brick size, labor costs and more. Users of the app can locate the nearest General Shale retail store or distributor from the "Where to Buy" feature, and they also can link to the General Shale or Brick Industry Association websites.
The state has received a $221,000 federal grant that will be used to expand a juvenile court project that screens youth for mental health, substance abuse and family services.
The Tennessee Department of Mental Health and Substance Abuse Services said in a news release that the funding will expand the screening process for youth referred to juvenile courts, provide support services to families and increase the use of evidence-based therapeutic practices for the juvenile justice population.
Between August 2010 and July 2012, more than 2,600 juveniles have been screened across 10 counties, resulting in almost 1,400 referrals for mental health, substance abuse and family services.
The grant will allow expansion of this program into more counties.
U.S. factory orders increased in December even though companies trimmed their orders for goods that signal investment plans.
Factory orders rose 1.8 percent in December compared to November, when orders had fallen 0.3 percent, the Commerce Department said Monday.
But demand for core capital goods, a category considered a proxy for business investment plans, dipped 0.3 percent in December following strong gains of 3.3 percent in November and 3 percent in October.
Orders for durable goods, items expected to last at least three years, rose 4.3 percent, slightly below the 4.6 percent estimated in a preliminary report. The increase reflected strong gains for military and civilian aircraft.
Orders for non-durable goods such as petroleum products, chemicals and paper, declined 0.3 percent in December after a 1 percent drop in November. The weakness reflected declines in petroleum products, a drop that was related to declines in energy prices during the month.
For the year, total factory orders rose 3 percent to $5.66 trillion. This reflected a slowing in the manufacturing sector following an 11.8 percent rise in orders in 2011. Demand for investment goods fell 0.3 percent to $759.4 billion.
The main House sponsor of a bill seeking to guarantee handgun permit holders the right to store firearms in their vehicles says he misspoke when he suggested that he routinely breaks the law.
Republican Rep. Jeremy Faison of Cosby told WPLN-FM that while he's never obtained a state-issued permit, he's "carried a gun all (his) life."
"One day I'll probably get caught if I don't get a permit, and I'll get in trouble," he told the public radio station.
Faison told The Associated Press on Monday that what he meant to say was that he transports a gun inside his car, which does not require a permit as long as ammunition is stored separately.
"What I was saying is routinely I have a gun in my car, and that's not illegal in Tennessee," he said. "It just came out wrong. I have a gun in my car — we weren't talking about me carrying a gun."
Faison and Republican Senate Speaker Ron Ramsey of Blountville are the main sponsors of the bill to prohibit businesses from banning people with handgun carry permits from storing weapons in cars parked on company lots.
The proposal is opposed by the business community.
Copyright 1995 -
by The Daily News Publishing Co. Inc. - All Rights Reserved


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Tacori RoyalT
Item Description
The perfect ring for your perfect one? Then feast your eyes on this item from Tacori's selection of engagement rings. A Round-shaped center stone gives a crowning glory to this ring. The ring itself is 18k rose gold that is already a thing of elegance and class on its own. This Halo type ring will bring out the beauty and life of the ring-wearer, regardless of time or trend. Diamonds that are Round-cut line the sides of the ring and bring more shimmer and accent to the piece. The side stones are Pave-set which allows them to complement rather than overdo the design. The center stone, the apple of everyone's eye, is Prong-set - this gives it security and at the same time allow the right amount of light to pass through the stone to please our eyes with its shimmer. All of such attention to detail is captured in this Vintage, Milgrain thing of beauty. The center stone type for this is Not included (sold separately).
L T Denny is an authorized retailer of Tacori. If you have any questions, feel free to reach us by phone at (206)682-5555 or by email at email@example.com.
Item Specification
|Product Type
|Engagement Rings
|Engagement Ring
|Engagement Ring Style
|Vintage, Milgrain
|Engagement Ring Type
|Center Stone Setting Type
|Center Stone Type
|Not Included (Sold Separately)
|Center Stone Shape
|Other Stone Shape That Will Fit
|Princess, Emerald, Oval
|Center Stone Weight Range from - ct
|Center Stone Weight Range to - ct
|Side Stones Setting Type
|Metal Type
|18K Rose Gold
|Side Stones Continuity
|1/2 Way
|Side Diamonds Average Clarity
|Side Diamonds Shape - Cut
|Side Diamonds Total Weight - ct
|Side Diamonds Average Color


text:
Canadiens rout Sabres, clinch playoff berth
Buffalo, NY – Montreal got goals from five different players and Peter Budaj made 14 saves to help the Canadiens clinch a playoff spot with a 5-1 win over the Buffalo Sabres.
The win also moved Montreal into first place in the Northeast Division, one point ahead of Boston after the Bruins lost to the Islanders on Thursday.
Brendan Gallagher, Rene Bourque, Alex Galchenyuk, Andrei Markov and P.K. Subban all lit the lamp for the Canadiens, who have won six of their last eight.
"It's always more fun when you can go in on a win. It's more exciting," said Gallagher. "We can look back at what we've done so far and be happy with it but we can't be satisfied. This is just the beginning."
Brian Flynn scored the lone goal of the game for the Sabres, who have dropped two straight after a three-game winning streak. Ryan Miller gave up four goals on 32 shots over the first two periods, while Jhonas Enroth stopped 9-of-10 shots in the third.
"We were playing a team that was well organized and we weren't sharp," said Miller. "We didn't play our game plan and we got our butts kicked, that's it."
Buffalo is on the outside of the playoff race, as the team is six points back of the eighth-place Rangers with seven games to play.
Montreal opened the scoring 6:43 in on a 3-on-1 rush, as Tomas Plekanec passed the puck from the left wing to the right, where Brian Gionta fed it back to a trailing Bourque for the goal.
It was a 2-0 game less than four minutes later after Michael Ryder worked the puck behind the net and threw it over from the right side to the left, where Galchenyuk slammed it home.
In the second period, the Canadiens made it a 3-0 game after David Desharnais' shot from the left circle was stopped but Gallagher was all alone in front to grab the rebound and sweep it in at the 4:36 mark.
Markov scored on a one-timer from the right circle on the power play with 2:06 left in the second, with the shot popping over Miller and going in.
Flynn and Subban exchanged goals in the third period, with Flynn's coming on a 2-on-1 break with the Sabres shorthanded at 6:45 and Subban scoring on the power play with under three minutes to play.
Game Notes
Montreal missed the playoffs last season ... Montreal plays in Toronto on Saturday ... Buffalo hosts the Flyers on Saturday ... Buffalo won three of its five games with Montreal this season ... Montreal went 2-for-8 on the power play, while Buffalo was 0-for-2.


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Lions kicker Hanson retires after 21 seasons
Allen Park, MI – After 21 remarkable seasons with the Detroit Lions, kicker Jason Hanson has decided to call it a career.
Hanson walks away from a career that made him one of the greatest kickers in NFL history and an icon in franchise history.
The 42-year-old made 32-of-36 field goal attempts last season. However, a heel injury that surfaced last season and continued to bother him this offseason was a big part of his decision to call it quits.
"It was time to make a decision," Hanson told the team's website. "It was the right time to step away.
"Ultimately, it's my heel -- the problem I developed last year. Now that we're starting a new year, it's still an issue. I have the desire. I have the determination, as I said earlier, to come back.
"Each time I'd start to push it, I'd kind of short-circuit. I realized that at this point of my career, I don't want to perform in a compromised way. It's not good for the team.
"I lost a little of my desire to play injured."
Hanson, a second-round pick in 1992, played in 327 games with the Lions, which is the most by any player with one team. The Washington State product is the NFL-record holder with 52 field goals of 50-plus yards and is third all-time with 2,150 points and 495 field goals.
The Lions and Hanson were unable to reach an agreement on a contract for the 2013 season.
"I would have worked out a contract with the Detroit Lions," Hanson said. "There was talk, and at the start, with their initial offer, it gave me some time to evaluate -- 'OK, am I going to do this?' Ultimately, no.
"It would not have been an issue. There are no hard feelings. It never got to a point where there was serious back and forth with numbers. It didn't matter."


text:
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Page Title: ManageDirect® > Transact Rights for Savings Bonds > Detail
What you can do on this page:
If you've been granted Transact rights for a savings bond, this page will provide you with details, including the name of a grantor.
HELPFUL HINT: If the security is not currently eligible for redemption, the "Redeem" button will not appear.
Primary Actions
- Click "Redeem" to go to ManageDirect > Redemption Request.
- Click "Return" to go back to ManageDirect > View/Transact Rights > Summary.