A message from Predictive Analytics World on newly available videos. It has many free videos as well so you can check them out.
Access PAW DC Session Videos Now
Predictive Analytics World is pleased to announce on-demand access to the videos of PAW Washington DC, October 2010, including over 30 sessions and keynotes that you may view at your convenience. Access this leading predictive analytics content online now:
Select individual conference sessions, or recognize savings by registering for access to one or two full days of sessions. These on-demand videos deliver PAW DC right to your desk, covering hot topics and advanced methods such as:
PAW DC videos feature over 25 speakers with case studies from leading enterprises such as: CIBC, CEB, Forrester, Macy’s, MetLife, Microsoft, Miles Kimball, Monster.com, Oracle, Paychex, SunTrust, Target, UPMC, Xerox, Yahoo!, YMCA, and more.
Keynote: Five Ways Predictive Analytics Cuts Enterprise Risk
Eric Siegel,Ph.D., Program Chair, Predictive Analytics World
All business is an exercise in risk management. All organizations would benefit from measuring, tracking and computing risk as a core process, much like insurance companies do.
Predictive analytics does the trick, one customer at a time. This technology is a data-driven means to compute the risk each customer will defect, not respond to an expensive mailer, consume a retention discount even if she were not going to leave in the first place, not be targeted for a telephone solicitation that would have landed a sale, commit fraud, or become a “loss customer” such as a bad debtor or an insurance policy-holder with high claims.
In this keynote session, Dr. Eric Siegel reveals:
– Five ways predictive analytics evolves your enterprise to reduce risk
– Hidden sources of risk across operational functions
– What every business should learn from insurance companies
– How advancements have reversed the very meaning of fraud
– Why “man + machine” teams are greater than the sum of their parts for enterprise decision support
Platinum Sponsor Presentation: Analytics – The Beauty of Diversity
Anne H. Milley,Senior Director of Analytic Strategy, Worldwide Product Marketing, SAS
Analytics contributes to, and draws from, multiple disciplines. The unifying theme of “making the world a better place” is bred from diversity. For instance, the same methods used in econometrics might be used in market research, psychometrics and other disciplines. In a similar way, diverse paradigms are needed to best solve problems, reveal opportunities and make better decisions. This is why we evolve capabilities to formulate and solve a wide range of problems through multiple integrated languages and interfaces. Extending that, we have provided integration with other languages so that users can draw on the disciplines and paradigms needed to best practice their craft.
Gold Sponsor Presentation: Predictive Analytics Accelerate Insight for Financial Services
Finbarr Deely,Director of Business Development,ParAccel
Financial services organizations face immense hurdles in maintaining profitability and building competitive advantage. Financial services organizations must perform “what-if” scenario analysis, identify risks, and detect fraud patterns. The advanced analytic complexity required often makes such analysis slow and painful, if not impossible. This presentation outlines the analytic challenges facing these organizations and provides a clear path to providing the accelerated insight needed to perform in today’s complex business environment to reduce risk, stop fraud and increase profits. * The value of predictive analytics in Accelerating Insight * Financial Services Analytic Case Studies * Brief Overview of ParAccel Analytic Database
TOPIC: SURVEY ANALYSIS Case Study: YMCA Turning Member Satisfaction Surveys into an Actionable Narrative
Dean Abbott,President, Abbott Analytics
Employees are a key constituency at the Y and previous analysis has shown that their attitudes have a direct bearing on Member Satisfaction. This session will describe a successful approach for the analysis of YMCA employee surveys. Decision trees are built and examined in depth to identify key questions in describing key employee satisfaction metrics, including several interesting groupings of employee attitudes. Our approach will be contrasted with other factor analysis and regression-based approaches to survey analysis that we used initially. The predictive models described are currently in use and resulted in both greater understanding of employee attitudes, and a revised “short-form” survey with fewer key questions identified by the decision trees as the most important predictors.
TOPIC: INDUSTRY TRENDS 2010 Data Minter Survey Results: Highlights
Karl Rexer,Ph.D., Rexer Analytics
Do you want to know the views, actions, and opinions of the data mining community? Each year, Rexer Analytics conducts a global survey of data miners to find out. This year at PAW we unveil the results of our 4th Annual Data Miner Survey. This session will present the research highlights, such as:
Multiple Case Studies: U.S. DoD, U.S. DHS, SSA Text Mining: Lessons Learned
John F. Elder,Chief Scientist, Elder Research, Inc.
Text Mining is the “Wild West” of data mining and predictive analytics – the potential for gain is huge, the capability claims are often tall tales, and the “land rush” for leadership is very much a race.
In solving unstructured (text) analysis challenges, we found that principles from inductive modeling – learning relationships from labeled cases – has great power to enhance text mining. Dr. Elder highlights key technical breakthroughs discovered while working on projects for leading government agencies, including: Text Mining is the “Wild West” of data mining and predictive analytics – the potential for gain is huge, the capability claims are often tall tales, and the “land rush” for leadership is very much a race.
– Prioritizing searches for the Dept. of Homeland Security
– Quick decisions for Social Security Admin. disability
– Document discovery for the Dept. of Defense
– Disease discovery for the Dept. of Homeland Security
Keynote: How Target Gets the Most out of Its Guest Data to Improve Marketing ROI
Andrew Pole,Senior Manager, Media and Database Marketing, Target
In this session, you’ll learn how Target leverages its own internal guest data to optimize its direct marketing – with the ultimate goal of enhancing our guests’ shopping experience and driving in-store and online performance. You will hear about what guest data is available at Target, how and where we collect it, and how it is used to improve the performance and relevance of direct marketing vehicles. Furthermore, we will discuss Target’s development and usage of guest segmentation, response modeling, and optimization as means to suppress poor performers from mailings, determine relevant product categories and services for online targeted content, and optimally assign receipt marketing offers to our guests when offer quantities are limited.
Platinum Sponsor Presentation: Driving Analytics Into Decision Making
Jason Verlen,Director, SPSS Product Strategy & Management, IBM Software Group
Organizations looking to dramatically improve their business outcomes are turning to decision management, a convergence of technology and business processes that is used to streamline and predict the outcome of daily decision-making. IBM SPSS Decision Management technology provides the critical link between analytical insight and recommended actions. In this session you’ll learn how Decision Management software integrates analytics with business rules and business applications for front-line systems such as call center applications, insurance claim processing, and websites. See how you can improve every customer interaction, minimize operational risk, reduce fraud and optimize results.
TOPIC: DATA INFRASTRUCTURE AND INTEGRATION Case Study: Macy’s The world is not flat (even though modeling software has to think it is)
Paul Coleman,Director of Marketing Statistics, Macy’s Inc.
Software for statistical modeling generally use flat files, where each record represents a unique case with all its variables. In contrast most large databases are relational, where data are distributed among various normalized tables for efficient storage. Variable creation and model scoring engines are necessary to bridge data mining and storage needs. Development datasets taken from a sampled history require snapshot management. Scoring datasets are taken from the present timeframe and the entire available universe. Organizations, with significant data, must decide when to store or calculate necessary data and understand the consequences for their modeling program.
TOPIC: CUSTOMER VALUE Case Study: SunTrust When One Model Will Not Solve the Problem – Using Multiple Models to Create One Solution
Dudley Gwaltney,Group Vice President, Analytical Modeling, SunTrust Bank
In 2007, SunTrust Bank developed a series of models to identify clients likely to have large changes in deposit balances. The models include three basic binary and two linear regression models.
Based on the models, 15% of SunTrust clients were targeted as those most likely to have large balance changes. These clients accounted for 65% of the absolute balance change and 60% of the large balance change clients. The targeted clients are grouped into a portfolio and assigned to individual SunTrust Retail Branch. Since 2008, the portfolio generated a 2.6% increase in balances over control.
Using the SunTrust example, this presentation will focus on:
TOPIC: RESPONSE & CROSS-SELL Case Study: Paychex Staying One Step Ahead of the Competition – Development of a Predictive 401(k) Marketing and Sales Campaign
Jason Fox,Information Systems and Portfolio Manager,Paychex
In-depth case study of Paychex, Inc. utilizing predictive modeling to turn the tides on competitive pressures within their own client base. Paychex, a leading provider of payroll and human resource solutions, will guide you through the development of a Predictive 401(k) Marketing and Sales model. Through the use of sophisticated data mining techniques and regression analysis the model derives the probability a client will add retirement services products with Paychex or with a competitor. Session will include roadblocks that could have ended development and ROI analysis. Speaker: Frank Fiorille, Director of Enterprise Risk Management, Paychex Speaker: Jason Fox, Risk Management Analyst, Paychex
TOPIC: SEGMENTATION Practitioner: Canadian Imperial Bank of Commerce Segmentation Do’s and Don’ts
Daymond Ling,Senior Director, Modelling & Analytics,Canadian Imperial Bank of Commerce
The concept of Segmentation is well accepted in business and has withstood the test of time. Even with the advent of new artificial intelligence and machine learning methods, this old war horse still has its place and is alive and well. Like all analytical methods, when used correctly it can lead to enhanced market positioning and competitive advantage, while improper application can have severe negative consequences.
This session will explore what are the elements of success, and what are the worse practices that lead to failure. The relationship between segmentation and predictive modeling will also be discussed to clarify when it is appropriate to use one versus the other, and how to use them together synergistically.
TOPIC: SOCIAL DATA
Thought Leadership Social Network Analysis: Killer Application for Cloud Analytics
James Kobielus,Senior Analyst, Forrester Research
Social networks such as Twitter and Facebook are a potential goldmine of insights on what is truly going through customers´minds. Every company wants to know whether, how, how often, and by whom they´re being mentioned across the billowing new cloud of social media. Just as important, every company wants to influence those discussions in their favor, target new business, and harvest maximum revenue potential. In this session, Forrester analyst James Kobielus identifies fruitful applications of social network analysis in customer service, sales, marketing, and brand management. He presents a roadmap for enterprises to leverage their inline analytics initiatives and leverage high-performance data warehousing (DW) clouds and appliances in order to analyze shifting patterns of customer sentiment, influence, and propensity. Leveraging Forrester’s ongoing research in advanced analytics and customer relationship management, Kobielus will discuss industry trends, commercial modeling tools, and emerging best practices in social network analysis, which represents a game-changing new discipline in predictive analytics.
Trish Mathe,
Director of Database Marketing, Life Line Screening
While Life Line is successfully executing a US CRM roadmap, they are also beginning this same evolution abroad. They are beginning in the UK where Merkle procured data and built a response model that is pulling responses over 30% higher than competitors. This presentation will give an overview of the US CRM roadmap, and then focus on the beginning of their strategy abroad, focusing on the data procurement they could not get anywhere else but through Merkle and the successful modeling and analytics for the UK. Speaker: Ozgur Dogan, VP, Quantitative Solutions Group, Merkle Inc Speaker: Trish Mathe, Director of Database Marketing, Life Line Screening
TOPIC: SURVEY ANALYSIS Case Study: Forrester Making Survey Insights Addressable and Scalable – The Case Study of Forrester’s Technographics Benchmark Survey
Marketers use surveys to create enterprise wide applicable strategic insights to: (1) develop segmentation schemes, (2) summarize consumer behaviors and attitudes for the whole US population, and (3) use multiple surveys to draw unified views about their target audience. However, these insights are not directly addressable and scalable to the whole consumer universe which is very important when applying the power of survey intelligence to the one to one consumer marketing problems marketers routinely face. Acxiom partnered with Forrester Research, creating addressable and scalable applications of Forrester’s Technographics Survey and applied it successfully to a number of industries and applications.
TOPIC: HEALTHCARE Case Study: UPMC Health Plan A Predictive Model for Hospital Readmissions
Scott Zasadil,Senior Scientist, UPMC Health Plan
Hospital readmissions are a significant component of our nation’s healthcare costs. Predicting who is likely to be readmitted is a challenging problem. Using a set of 123,951 hospital discharges spanning nearly three years, we developed a model that predicts an individual’s 30-day readmission should they incur a hospital admission. The model uses an ensemble of boosted decision trees and prior medical claims and captures 64% of all 30-day readmits with a true positive rate of over 27%. Moreover, many of the ‘false’ positives are simply delayed true positives. 53% of the predicted 30-day readmissions are readmitted within 180 days.
A special issue of the Journal of Statistical Software has come out devoted to Multi State Models and Competing Risks. It is a must read for anyone with interest in Pharma Analytics or Survival Analysis- even if you dont know much R
Here is an extract from “mstate: An R Package for the Analysis ofCompeting Risks and Multi-State Models”
Multi-state models are a very useful tool to answer a wide range of questions in sur-vival analysis that cannot, or only in a more complicated way, be answered by classicalmodels. They are suitable for both biomedical and other applications in which time-to-event variables are analyzed. However, they are still not frequently applied. So far, animportant reason for this has been the lack of available software. To overcome this prob-lem, we have developed the mstate package in R for the analysis of multi-state models.The package covers all steps of the analysis of multi-state models, from model buildingand data preparation to estimation and graphical representation of the results. It canbe applied to non- and semi-parametric (Cox) models. The package is also suitable forcompeting risks models, as they are a special category of multi-state models.
—————————–
Issues for JSS Special Volume 38: Competing Risks and Multi-State Models
Special Issue about Competing Risks and Multi-State Models
I recently found an interesting example of a website that both makes a lot of money and yet is much more efficient than any free or non profit. It is called ECOSIA
If you see a website that wants to balance administrative costs plus have a transparent way to make the world better- this is a great example.
World’s largest tropical forest reserve (38,867 square kilometers, or about the size of Switzerland)
Home to about 14% of all amphibian species and roughly 54% of all bird species in the Amazon – not to mention large populations of at least eight threatened species, including the jaguar
Includes part of the Guiana Shield containing 25% of world’s remaining tropical rainforests – 80 to 90% of which are still pristine
Holds the last major unpolluted water reserves in the Neotropics, containing approximately 20% of all of the Earth’s water
One of the last tropical regions on Earth vastly unaltered by humans
Significant contributor to climatic regulation via heat absorption and carbon storage
Click per milli (or CPM) gives you a very low low conversion compared to contacting ad sponsor directly.
But its a great data experiment-
as you can monitor which companies are likely to be advertised on your site (assume google knows more about their algols than you will)
which formats -banner or text or flash have what kind of conversion rates
what are the expected pay off rates from various keywords or companies (like business intelligence software, predictive analytics software and statistical computing software are similar but have different expected returns (if you remember your eco class)
NOW- Based on above data, you know whats your minimum baseline to expect from a private advertiser than a public, crowd sourced search engine one (like Google or Bing)
Lets say if you have 100000 views monthly. and assume one out of 1000 page views will lead to a click. Say the advertiser will pay you 1 $ for every 1 click (=1000 impressions)
Then your expected revenue is $100.But if your clicks are priced at 2.5$ for every click , and your click through rate is now 3 out of 1000 impressions- (both very moderate increases that can done by basic placement optimization of ad type, graphics etc)-your new revenue is 750$.
Be a good Samaritan- you decide to share some of this with your audience -like 4 Amazon books per month ( or I free Amazon book per week)- That gives you a cost of 200$, and leaves you with some 550$.
Wait! it doesnt end there- Adam Smith‘s invisible hand moves on .
You say hmm let me put 100 $ for an annual paper writing contest of $1000, donate $200 to one laptop per child ( or to Amazon rain forests or to Haiti etc etc etc), pay $100 to your upgraded server hosting, and put 350$ in online advertising. say $200 for search engines and $150 for Facebook.
Woah!
Month 1 would should see more people visiting you for the first time. If you have a good return rate (returning visitors as a %, and low bounce rate (visits less than 5 secs)- your traffic should see atleast a 20% jump in new arrivals and 5-10 % in long term arrivals. Ignoring bounces- within three months you will have one of the following
1) An interesting case study on statistics on online and social media advertising, tangible motivations for increasing community response , and some good data for study
2) hopefully better cost management of your server expenses
3)very hopefully a positive cash flow
you could even set a percentage and share the monthly (or annually is better actions) to your readers and advertisers.
go ahead- change the world!
the key paradigms here are sharing your traffic and revenue openly to everyone
donating to a suitable cause
helping increase awareness of the suitable cause
basing fixed percentages rather than absolute numbers to ensure your site and cause are sustained for years.
The following is an auto generated post thanks to WordPress.com stats team- clearly they have got some stuff wrong
1) Defining the speedometer quantitatively
2) The busiest day numbers are plain wrong ( 2 views ??)
3) There is still no geographic data in WordPress -com stats (unlike Google Analytics) and I cant enable Google Analytics on a wordpress.com hosted site.
The stats helper monkeys at WordPress.com mulled over how this blog did in 2010, and here’s a high level summary of its overall blog health:
The Blog-Health-o-Meter™ reads Wow.
Crunchy numbers
The Louvre Museum has 8.5 million visitors per year. This blog was viewed about 97,000 times in 2010. If it were an exhibit at The Louvre Museum, it would take 4 days for that many people to see it.
In 2010, there were 367 new posts, growing the total archive of this blog to 1191 posts. There were 411 pictures uploaded, taking up a total of 121mb. That’s about 1 pictures per day.
The top referring sites in 2010 were r-bloggers.com, reddit.com, rattle.togaware.com, twitter.com, and Google Reader.
Some visitors came searching, mostly for libre office, facebook analytics, test drive a chrome notebook, test drive a chrome notebook., and wps sas lawsuit.
Attractions in 2010
These are the posts and pages that got the most views in 2010.
My annual traffic to this blog was almost 99,000 . Add in additional views on networking sites plus the 400 plus RSS readers- so I can say traffic was 1,20,000 for 2010. Nice. Thanks for reading and hope it was worth your time. (this is a long post and will take almost 440 secs to read but the summary is just given)
My intent is either to inform you, give something useful or atleast something interesting.
see below-
Jan
Feb
Mar
Apr
May
Jun
2010
6,311
4,701
4,922
5,463
6,493
4,271
Jul
Aug
Sep
Oct
Nov
Dec
Total
5,041
5,403
17,913
16,430
11,723
10,096
98,767
Sandro Saita from http://www.dataminingblog.com/ just named me for an award on his blog (but my surname is ohRi , Sandro left me without an R- What would I be without R :)) ).
Aw! I am touched. Google for “Data Mining Blog” and Sandro is the best that it is in data mining writing.
”
DMR People Award 2010
There are a lot of active people in the field of data mining. You can discuss with them on forums. You can read their blogs. You can also meet them in events such as PAW or KDD. Among the people I follow on a regular basis, I have elected:
Ajay Ori
He has been very active in 2010, especially on his blog . Good work Ajay and continue sharing your experience with us!”
What did I write in 2010- stuff.
What did you read on this blog- well thats the top posts list.
well I guess I owe Tal G for almost 9000 views ( incidentally I withdrew posting my blog from R- Bloggers and Analyticbridge blogs – due to SEO keyword reasons and some spam I was getting see (below))
Still reading this post- gosh let me sell you some advertising. It is only $100 a month (yes its a recession)
Advertisers are treated on First in -Last out (FILO)
I have been told I am obsessed with SEO , but I dont care much for search engines apart from Google, and yes SEO is an interesting science (they should really re name it GEO or Google Engine Optimization)
Apparently Hadley Wickham and Donald Farmer are big keywords for me so I should be more respectful I guess.
Search Terms for 365 days ending 2010-12-31 (Summarized)
2009-12-31 to Today
Search
Views
libre office
925
facebook analytics
798
test drive a chrome notebook
467
test drive a chrome notebook.
215
r gui
203
data mining
163
wps sas lawsuit
158
wordle.net
133
wps sas
123
google maps jet ski
123
test drive chrome notebook
96
sas wps
89
sas wps lawsuit
85
chrome notebook test drive
83
decision stats
83
best statistics software
74
hadley wickham
72
google maps jetski
72
libreoffice
70
doug savage
65
hive tutorial
58
funny india
56
spss certification
52
donald farmer microsoft
51
best statistical software
49
What about outgoing links? Apparently I need to find a way to ask Google to pay me for the free advertising I gave their chrome notebook launch. But since their search engine and browser is free to me, guess we are even steven.
Clicks for 365 days ending 2010-12-31 (Summarized)
Using two Chrome Extensions, Disconnect and AdBlock you can be sure of having a vary very clean browsing experience-it is recommended especially if you dont like the auto sharing of your personal preferences and cannot be bothered by the Byzantine maze of social media privacy fineprint.
* Search depersonalization is now optional and off by default. Click the “d” button then the “Depersonalize searches” checkbox to turn this feature on (or back off in case you have trouble getting to Google or Yahoo services). For help with anything else, see the known issues below and ask questions at http://j.mp/dnewgroup.
§
If you’re a typical web user, you’re unintentionally sending your browsing and search history with your name and other personal information to third parties and search engines whenever you’re online.
Take control of the data you share with Disconnect!
From the developer of the top-10-rated Facebook Disconnect extension, Disconnect lets you:
• Disable tracking by third parties like Digg, Facebook, Google, Twitter, and Yahoo, without requiring any setup or significantly degrading the usability of the web.
• Truly depersonalize searches on search engines like Google and Yahoo (by blocking identifying cookies not just changing the appearance of results pages), while staying logged into other services — e.g., so you can search anonymously on Google and access iGoogle at once.
• See how many resource and cookie requests are blocked, in real time
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New in version 2.1: Translated into dozens of languages!
New in version 2.0: Ads are blocked from downloading, instead of just being removed after the fact!
=======================
The official AdBlock For Chrome! Block all advertisements on all web pages. Your browser is automatically updated with additions to the filter: just click Install, then visit your favorite website and see the ads disappear!
FAQs:1. This is the official AdBlock extension: the original ad blocker written from the ground up to be optimized in Chrome. There's an unrelated, older Firefox project called Adblock Plus, and they're working on making a Chrome version out of the old AdThwart codebase. At the moment AdBlock blocks some ads that AdThwart only hides, but they're working to improve it. It's available at bit.ly/id2Gqx; if you have trouble with AdBlock, they're good guys and a fine alternative!
The only issue is Rattle can be quite difficult to install due to dependencies on GTK+
After fiddling for a couple of years- this is what I did
1) Created dual boot OS- Basically downloaded the netbook remix from http://ubuntu.com I created a dual boot OS so you can choose at the beginning whether to use Windows or Ubuntu Linux in that session. Alternatively you can download VM Player www.vmware.com/products/player/ if you want to do both
2) Download R packages using Ubuntu packages and Install GTK+ dependencies before that.
GTK + Requires
Libglade
Glib
Cairo
Pango
ATK
If you are a Linux newbie like me who doesnt get the sudo apt get, tar, cd, make , install rigmarole – scoot over to synaptic software packages or just the main ubuntu software centre and download these packages one by one.
For R Dependencies, you need
PMML
XML
RGTK2
Again use r-cran as the prefix to these package names and simply install (almost the same way Windows does it easily -double click)
Save theses to your hard disk (e.g., to your Desktop) but don’t extract them. Then, on GNU/Linux run the install command shown below. This command is entered into a terminal window:
R CMD INSTALL rattle_2.6.0.tar.gz
After installation-
5) Type library(rattle) and rattle.info to get messages on what R packages to update for a proper functioning
</code>
> library(rattle)
Rattle: Graphical interface for data mining using R.
Version 2.6.0 Copyright (c) 2006-2010 Togaware Pty Ltd.
Type 'rattle()' to shake, rattle, and roll your data.
> rattle.info()
Rattle: version 2.6.0
R: version 2.11.1 (2010-05-31) (Revision 52157)
Sysname: Linux
Release: 2.6.35-23-generic
Version: #41-Ubuntu SMP Wed Nov 24 10:18:49 UTC 2010
Nodename: k1-M725R
Machine: i686
Login: k1ng
User: k1ng
Installed Dependencies
RGtk2: version 2.20.3
pmml: version 1.2.26
colorspace: version 1.0-1
cairoDevice: version 2.14
doBy: version 4.1.2
e1071: version 1.5-24
ellipse: version 0.3-5
foreign: version 0.8-41
gdata: version 2.8.1
gtools: version 2.6.2
gplots: version 2.8.0
gWidgetsRGtk2: version 0.0-69
Hmisc: version 3.8-3
kernlab: version 0.9-12
latticist: version 0.9-43
Matrix: version 0.999375-46
mice: version 2.4
network: version 1.5-1
nnet: version 7.3-1
party: version 0.9-99991
playwith: version 0.9-53
randomForest: version 4.5-36 upgrade available 4.6-2
rggobi: version 2.1.16
survival: version 2.36-2
XML: version 3.2-0
bitops: version 1.0-4.1
Upgrade the packages with:
> install.packages(c("randomForest"))
<code>
Now upgrade whatever package rattle.info tells to upgrade.
This is much simpler and less frustrating than some of the other ways to install Rattle.
If all goes well, you will see this familiar screen popup when you type
r-bloggers.com
Reddit
Twitter
Google
en.wordpress.com
xianblog.wordpress.com
(184) – 44,284 users – Weekly installs: 24,086
DECISION STATS 