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Machine Learning10-701/15-781, Fall 2012
Eric Xing, Aarti Singh
School of Computer Science, Carnegie-Mellon University
Course Project
Your class project is an opportunity for you to explore an interesting multivariate analysis problem of your choice in the context of a real-world data set. Projects should be done by you as a team three students. We may allow under specific circumstances for less than three member in the team. Each project will also be assigned a 701 instructor as a project consultant/mentor. Instructors and TAs will consult with you on your ideas, but of course the final responsibility to define and execute an interesting piece of work is yours. Your project will be worth 20% of your final class grade, and will have 4 deliverables:
- Proposal:1 page (10%), due 10/1 at the beginning of class
- Midway Report:3-4 pages (20%), due 11/05 at the beginning of class. Submit with graded project proposal attached.
- Final Report: 8 pages (40%), due Wednesday December 5th, 4:30pm 11:59pm (email PDF to instructor mailing list 10701-instructors at cs dot cmu dot edu)
- Poster
Presentation :
(30%), Monday December 3rd, 10am to 2pm in GHC 6115 and 6121.
Note that all write-ups in the form of a NIPS paper. The page limits are strict! Papers over the limit will not be considered.
Project Proposal
You must turn in a brief project proposal (1-page maximum). Read the list of available data sets and potential project ideas below. You are highly recommended to use one of these data sets, because we know that they have been successfully used for machine learning in the past. If you have another data set you want to work on, you can discuss it with us. However, we will not allow projects on data that has not been collected, so you have to work on existing data sets. It is also possible to propose a project on some theoretical aspects of machine learning. If you want to do this, please discuss it with us. Note that even though you can use data sets you have used before, you cannot use as class projects something that you started doing prior to the class.
Project proposal format: Proposals should be one page maximum. Include the following information:
- Project title
- Data set
- Project idea. This should be approximately two paragraphs.
- Software you will need to write.
- Papers to read. Include 1-3 relevant papers. You will probably want to read at least one of them before submitting your proposal
- Teammate: You should look for two other teammate since we typically want the group size to be 3 (three) students.
- Midway Report milestone: What will you complete by Midway Report; Experimental results of some kind are expected here. You should also describe what portion of the project each partner will be doing.
Midway Report
This should be a 3-4 pages short report, and it serves as a check-point. It should consist of the same sections as your final report (introduction, related work, method, experiment, conclusion), with a few sections `under construction'. Specifically, the introduction and related work sections should be in their final form; the section on the proposed method should be almost finished; the sections on the experiments and conclusions will have whatever results you have obtained, as well as `place-holders' for the results you plan/hope to obtain.
Grading scheme for the project report:
- 70% for proposed method (should be almost finished)
- 25% for the design of upcoming experiments
- 5% for plan of activities (in an appendix, please show the old one and the revised one, along with the activities of each group member)
Final Report
Your final report is expected to be a 8-page report. You should submit both an electronic and a hardcopy version for your final report. It should roughly have the following format:
- Introduction - Motivation
- Problem definition
- Proposed method
- Intuition - why should it be better than the state of the art?
- Description of its algorithms
- Experiments
- Description of your testbed; list of questions your experiments are designed to answer
- Details of the experiments; observations
- Conclusions
Poster Presentation
We will have all projects presenting a poster. At least one project member should be present during the
hours. The session will be open
to everybody.
Poster .ppt template (This is only a rough outline. Change the layout and section titles as appropriate for your project.)
Here are some details on the poster format.
- The two most common ways to make your poster are:
- You can create a huge slide (in powerpoint, using beamer, etc) and print it out at one of the poster printers available (for SCS, details below). You'd want the shorter dimension to be less than 36'' since the special poster printers use paper rolls of width 36''.
- You can create a bunch of "normal" presentation slides, print out each one on a piece of (letter-sized) paper, and put them all together on a poster board. This is somewhat messier (and not as pretty) but you don't need to wait for the special poster printer to print.
- We will provide you with foam boards onto which you can tack on your poster (or your slides for the poster). The foam boards are 32'' by 40''.
- Printing: Unfortunately, we don't have a budget to pay for printing. If you are an SCS student, SCS has a poster printer you can use which prints on a 36'' wide roll of paper (SCS poster printing page). You have to call or email Operations and request to be added to the queue and they will call or email you back and tell you when you are able to print. It takes a while to print however especially if there are people ahead of you already printing so you can't wait until the last hour (or last day). But they are open 24/7. The printer sometimes runs out of paper, so you should really start early!!! The number of operations is 8-2607 or you can call the help desk (8-4231).
Project Suggestions:
Ideally, you will want to pick a problem in a domain of your interest, e.g., natural language parsing, DNA sequence analysis, text information retrieval, network mining, reinforcement learning, sensor networks, etc., and formulate your problem using machine learning techniques. You can then, for example, adapt and tailor standard inference/learning algorithms to your problem, and do a thorough performance analysis. You can also find some project ideas below.
Educational Data Mining on Predicting Student Performance
Data:Register at the KDD Cup 2010: Educational Data Mining Challenge website, and click on "Get Data".
There are two types of data sets available, development data sets and challenge data sets. Development data sets differ from challenge sets in that the actual student performance values for the prediction column, "Correct First Attempt", are provided for all steps.
The data takes the form of records of interactions between students and computer-aided-tutoring systems. The students solve problems in the tutor and each interaction between the student and computer is logged as a transaction. Four key terms form the building blocks of the data. These are problem, step, knowledge component, and opportunity.
Project idea: How generally or narrowly do students learn? How quickly or slowly? Will the rate of improvement vary between students? What does it mean for one problem to be similar to another? It might depend on whether the knowledge required for one problem is the same as the knowledge required for another. But is it possible to infer the knowledge requirements of problems directly from student performance data, without human analysis of the tasks? We would like to ask you to predict whether a student is likely to be correct or not on each step given based on previous log data. The problem can be formalized as a classification problem. You could also build a model of students' learning behavior and predict the probability of making an error. The challenge here is to select the correct classifier/model that best represents the data. Moreover, maybe not all given features are informative. Models that are over-complicated may overfit. How to find the relevant features and make good use of them are interesting topics.
Feature Engineering and Classifier Ensemble for KDD Cup 2010, Yu et al., 2010
Using HMMs and bagged decision trees to leverage rich features of user and skill from an intelligent tutoring system dataset, Pardos and Heffernan, 2010
Collaborative Filtering Applied to Educational Data Mining, Toscher and Jahrer, 2010
Inferring Networks of Diffusion and Influence
Data:Download the data at http://snap.stanford.edu/netinf/#data.
Data contains information about the connectivity of the who-copies-from-whom or who-repeats-after-whom network of news media sites and blogs inferred by NETINF, an algorithm that infers a who-copies-from-whom or who-repeats-after-whom network of news media sites and blogs.
The dataset used by NETINF is called MemeTracker. It can be downloaded from here.
MemeTracker contains two datasets. The first one is a phrase cluster data. For each phrase cluster the data contains all the phrases in the cluster and a list of URLs where the phrases appeared. The second is the raw MemeTracker phrase data, which contains phrases and hyper-links extracted from each article/blogpost.
Project idea: Information diffusion and virus propagation are fundamental processes taking place in networks. In many applications, the underlying network over which the diffusions and propagations spread is hard to find. Finding such underlying network using MemeTracker data would be an interesting and challenging project. Gomez-Rodriguez et al. (2010) have recently published a paper on this topic, and made their code publically accessible. It would be interesting to replicate their result and further improve the proposed algorithm by making use of more informative features (e.g., textual content of postings etc).
Inferring Networks of Diffusion and Influence, Gomez-Rodriguez et al., 2010
Apply NetInf to Other Domains
Data:Download the data at http://snap.stanford.edu/netinf/#data.
Data contains information about the connectivity of the who-copies-from-whom or who-repeats-after-whom network of news media sites and blogs inferred by NETINF, an algorithm that infers a who-copies-from-whom or who-repeats-after-whom network of news media sites and blogs.
The dataset used by NETINF is called MemeTracker. It can be downloaded from here.
MemeTracker contains two datasets. The first one is a phrase cluster data. For each phrase cluster the data contains all the phrases in the cluster and a list of URLs where the phrases appeared. The second is the raw MemeTracker phrase data, which contains phrases and hyper-links extracted from each article/blogpost.
Project idea: In Gomez-Rodriguez et al.'s (2010) paper, they applied NetInf to Memetracker, and found that clusters of sites related to similar topics emerge (politics, gossip, technology, etc.), and a few sites with social capital interconnect these clusters allowing a potential diffusion of information among sites in different clusters. It would be interesting to see how the proposed algorithm could be used in other networks, and what knowledge could we get from those networks. For example, can we discover users that share similar interest from a social network? Network datasets of different domains can be found at here. Different networks may take different forms, and thus the algorithm may not be directly applicable. How to modify the existing algorithm to support other networks?
Inferring Networks of Diffusion and Influence, Gomez-Rodriguez et al., 2010
Dynamically Inferring Networks of Diffusion and Influence
Data:Download the data at http://snap.stanford.edu/netinf/#data.
Data contains information about the connectivity of the who-copies-from-whom or who-repeats-after-whom network of news media sites and blogs inferred by NETINF, an algorithm that infers a who-copies-from-whom or who-repeats-after-whom network of news media sites and blogs.
The dataset used by NETINF is called MemeTracker. It can be downloaded from here.
MemeTracker contains two datasets. The first one is a phrase cluster data. For each phrase cluster the data contains all the phrases in the cluster and a list of URLs where the phrases appeared. The second is the raw MemeTracker phrase data, which contains phrases and hyper-links extracted from each article/blogpost.
Project idea: In Gomez-Rodriguez et al.'s (2010) paper, the proposed algorithm currently considers static propagation networks. But real influence networks are dynamic. Is it possible to detect such networks?
Inferring Networks of Diffusion and Influence, Gomez-Rodriguez et al., 2010
Relational Information Retrieval
2010, yeast2 updated yeast data with extra information about Mesh heading, chemicals and affiliations etc. (321K entities and 6.1M links)
2010, fly a biological literature graph with 770K entities and 3.5M links
2010, yeast a biological literature graph with 164K entities and 2.8M linksAll these datasets are relational graph based datasets. Nodes in the graph are of different types (e.g. author, paper, gene, protein, title word, journal, year). Edges between nodes describe relations between two nodes (e.g. AuthorOf, Cites, Mentions).
Project idea: Scientific literature with rich metadata can be represented as a labeled directed graph. Given this graph, can we suggest related work to authors? Can we retrieve relevant papers given some key words? All of these tasks can be formulated as relational retrieval tasks in the graph. How to efficiently retrieve items in the graph given some specific nodes as queries? Random walk with restart (RWR) has been used to model these tasks. Pontential projects include implementing different versions of RWR related work, and further improving them to achieve better retrieval quality. References::
Ni Lao, William W. Cohen, Relational retrieval using a combination of path-constrained random walks Machine Learning, 2010, Volume 81, Number 1, Pages 53-67 (ECML, 2010 slides poster )
Noun Phrases/Relation classification
There are two types of data available:
SVO (subject-verb-object) triples data constructed by parsing 50m Web documents from ClueWeb09 (890m sentences, 16B tokens) using the MALT dependency parser, and then extracting SVO triples from these parsed sentences. The SVO triples are aggregated and their frequencies counted using Hadoop. This yields in a dataset with 114m (Subject-Verb-Object) triples and their frequency counts.
Two versions of the SVO data are here (verb non-stemmed and stemmed):
http://rtw.ml.cmu.edu/ppt/v+prep_svo-triples_stemmed.txt.gz (220,015,169 triples)
http://rtw.ml.cmu.edu/ppt/v+prep_svo-triples.txt.gz (220,462,606 triples)
Each line has four fields (tab separated): Subject Verb[+Preposition] Object Count
All-pairs dataset constructed by extracting Noun Phrases and the contexts they appear in on ClueWeb09, and count their frequencies. The data can be downloaded from:
There are smaller versions that are more heavily sampled that might be faster and easier to work with, for instance:
Project idea: (from Tom Mitchell) The NELL project has produced some corpus statistics describing subject-verb-object (SVO) triples collected by dependency parsing hundreds of millions of sentences. A typical entry is subject=horses, verb=eat, object=hay, frequency=412. Separately, NELL's knowledge base contains instances of hundreds of relations, from riverFlowsThroughCity(river, city), to animalEatsFood(animal, food). (e.g., here is a list of NELL's beliefs about animalEatFood(animal, food): http://rtw.ml.cmu.edu/rtw/kbbrowser/pred:animaleatfood).
In this project, the key idea is to train a "bag of verbs" classifier for each relation in NELL, basing the prediction on the SVO data, and using NELL's existing knowledge base as labeled data. Given a pair of noun phrases such as <cat, fish>, or <cat, house>, your classifier should predict whether the noun phrase pair satisfies the relation "animalEatFood". NELL does not have such a classifier, and if yours works well, we will incorporate it into NELL as a new component, potentially leading to a publication as well. There are several approaches one can take to this problem. For example, a baseline approach could train the relation classifiers independently for each relation, using the raw SVO data. You can refine this by thinking of ways of perhaps preprocessing the data (e.g., use stemming so that the triples <horse eats hay> and <horses eat hay> are merged), and perhaps you can think of ways to couple the training of multiple relations (e.g., how would you couple the training of the relations CEOofCompany(person,company) and WorksAtCompany(person, company).
Verbocean: Mining the web for fine-grained semantic verb relations, EMNLP 2004
Identifying Relations for Open Information Extraction, EMNLP 2011
Learning 5000 relational extractors, ACL 2010
Knowledge-based weak supervision for information extraction of overlapping relations, ACL 2011
NELL Project publications
Past projects from "Machine Learning with Large Datasets (10-605) course in Spring 2012"
Image Categorization
Project idea: Image categorization/object
recognition has been one of the most important research problems
in the computer vision community. Researchers have developed a
wide spectrum of different local descriptors, feature coding
schemes, and classification methods.
In this project, you will implement your own object recognition
system. You could use any code from the web for computing image
features, such as SIFT, HoG, etc.
For computing SIFT features, you could use http://www.vlfeat.org/~vedaldi/code/sift.html.
Following is a list of data sets you could use.
A list of datasets:
[2] The PASCAL Object Recognition Database Collection:http://pascallin.ecs.soton.ac.uk/challenges/VOC/databases.html
[3] LabelMe:http://labelme.csail.mit.edu/
[4] CMU face databases:http://vasc.ri.cmu.edu/idb/html/face/
[5] Face in the wild:http://vis-www.cs.umass.edu/lfw/
[6] ImageNet:http://www.image-net.org/index
[7] TinyImage:http://groups.csail.mit.edu/vision/TinyImages/
Human Action Recognition
Project idea: Applications such as surveillance, video
retrieval and human-computer interaction require methods for
recognizing human actions in various scenarios.
In this project, you will implement your own human action recognition system. You could use any code from the web for computing spatio-temporal features. One good example is the spatio-temporal interest point proposed by Piotr Dollar. Source code available at http://vision.ucsd.edu/~pdollar/research/research.html.
Following is a list of data sets you could use.
A list of datasets:
[2] Weizmann:http://www.wisdom.weizmann.ac.il/~vision/SpaceTimeActions.html
[3] Hollywood Human Actions dataset:http://www.irisa.fr/vista/Equipe/People/Laptev/download.html
[4] VIRAT Video Dataset:http://www.viratdata.org/
Single Class Object Detector
Project idea:
Given a random photo shot of a bookcover, movie poster, wine bottle
picture, etc, which may have light, scale, angle variation, find
the standard image of the poster, logo, etc, in a database that
matches the query. This is real application for an iPhone user to
identify what they see. For example, if I see a movie poster, or
a bookcover, or a wine bottle, I can take a picture and then
hit search, and find online information of the original image and
other relevant information of the movie, book, and wine.
One intuition here lies in the fact that there are limited number of books in the world. If we have a database containing all book covers in the world, the recognition problem would reduce to a duplicate detection problem, which is much simpler to solve, compared with general purpose object recognition.
In this project, you are encouraged to design an object detector for a single image class using duplicate detection. For example, in the book cover case, you could crawl all pages about books from Amazon.com and store the images as your database of book covers.
Following is a list of possible image classes you could consider in this fashion:
[1] Book cover
[2] Landmark (e.g., Eiffel Tower, Great Wall, White House, etc)
[3] Movie Posters (e.g., crawl images fromhttp://www.movieposter.com)
[4] Wine/beer bottle labels
[5] Logos
[6] Art pieces (e.g., painting, sculpture)
Once you have the
database, recognition / detection could be solved using near
duplicated image detection.
You could use any algorithm or source code on the Internet, e.g.http://www.mit.edu/~andoni/LSH/.
Exploring the image world
Project idea: One important aspect of machine learning and computer vision research is to collect proper data sets. For example, ImageNet (http://www.image-net.org/index) is one of the most promising data sets in the image categorization research.
Flickr has about 3.6 Billion photos. Interested in crawling billions of images and build your own image collection? For this project, you are encouraged to crawl images from websites such as Flickr, twitter, Google Image. We will provide disk space for storage if this becomes necessary.
There has been a long debate in the computer vision community about which of a better algorithm or larger data is more important. That is to say, should we focus on developing more and more sophisticated algorithms, or use simple classification methods, such as Nearest Neighbor classifier, on billions of training images. In this project, you will gather as many images as possible, and deploy simple classification methods on the data set to see if the latter philosophy works.
For crawling images from Flickr, you could refer tohttp://graphics.cs.cmu.edu/projects/im2gps/flickr_code.html as a starting
[1] 80 millions of tiny images. (http://groups.csail.mit.edu/vision/TinyImages/).
Object based action recognition
Project idea: This is a more advanced topic for students interested in cutting-edge research in computer vision. Most actions are associated with objects. For instance, if someone is kicking, holding, or eating, they are doing it to something. Can we recognize actions through objects and vice-versa?
[1] Modeling Mutual Context of Object and Human Pose in Human-Object Interaction Activities, Bangpeng Yao and Li Fei-Fei, CVPR 2010.
Data Mining for Social Media
Project idea: In this project, we encourage students
to infer the underlying relations between different modalities of
information on the Web. Here are some examples.
(1) Given a photo of movie poster (image), can we retrieve related trailers (video) or latest news articles (documents) of the movie?
To make project simpler, we recommend focusing on less than five movies (eg. 'Rise of the planet of the apes' and 'The smurfs'). You first download posters and trailers from some well-organized sites such as imdb.com or itunes.com. They will be used as training data to learn your classifiers. Now your job is to gather raw data from youtube.com or Flickr, and classify them. In this project, we encourage you to explore the possibility to build classifiers to be learned from one information modality (eg. images), and to be applicable to other modalities (eg. trailer videos).
(2) Given a beer label (image), can we search for which frames of a given video clip the logo or bottle appears?
Suppose that you are a big fan of Guinness beer. You can easily download the clean Guinness logo or cup images by Google image search. These images can be used to learn your detector, which can discover the frames that the logo appears in the video clips. For testing, you can download some video clips from youtube.com.
The above examples are just two possible candidates, and any new ideas or problem definitions are welcome.
For this purpose, one may take advantage of some source codes available on the Web as unit modules (eg. near-duplicated image detection, object recognition, action recognition in video).
Another interesting direction is to improve the current state-of-the-arts methods by considering more practical scenarios.
Related Papers and Software::
- A good example of how a machine learning technique is
successfully applied to real systems (ex. Google news
[1] Das, Datar, Garg, Rajaram. Google news personalization: scalable online collaborative filtering. WWW 2007.
- One of most popular approaches to near duplicated image detection is LSH families.
[2] http://www.mit.edu/~andoni/LSH/ (This webpage links several introductory articles and source codes).
- Various hashing techniques in computer vision (papers and source codes).
[3] Spectral Hashing (http://www.cs.huji.ac.il/~yweiss/SpectralHashing/)
[4] Kernelized LSH (http://www.eecs.berkeley.edu/~kulis/klsh/klsh.htm)
- Recognition in video
[5] Naming of Characters in Video (http://www.robots.ox.ac.uk/~vgg/data/nface/index.html)
[6] Action recognition in Video (http://www.robots.ox.ac.uk/~vgg/data/stickmen/index.html)
- Recognition in images
[7] Human pose detection (Poselet) (http://www.eecs.berkeley.edu/~lbourdev/poselets/)
[8] General object detection (http://people.cs.uchicago.edu/~pff/latent/)
Object Recognition, Scene Understanding, and More on Twitter
Project idea: Currently, Twitter does not provide the
photo-sharing functionality, which has been supported by several
third-party services such as twitpic,
yfog, lockerz, instagram. (See the current
market-share on these services at http://techcrunch.com/2011/06/02/a-snapshot-of-photo-sharing-market-share-on-twitter/).
The main goal of this project is to recognize objects or scenes in
user photos by using its contextual information such as author,
taken time, and associated tweets. Students may gather data by
using Twipho or built-in search
engines of the services (eg. http://web1.twitpic.com/search/).
In practice, it is extremely difficult to completely understand the photos in twitter. Hence, we encourage students to come up with good problem definitions so that they can not only be solvable as course projects but also be usable to real applications. Here are some examples.
(1) The photos that are retrieved by querying 'superman' in the http://web1.twitpic.com/search/ are highly variable. But, given an image, you can build a classifier to tell whether 'superman' logos appear on the images or not.
(2) Let's download the photos queried by 'beach'. Observing the images, you may identify what objects are usually shown. Choose some of them as our target objects such as human faces, sand, sea, and sky, and learn your classifier for each object category. Then, your goal is to tell what objects appear where in a twitter image.
Related Papers and Software::
- Some object recognition competition sites will be very helpful.
[1] PASCAL VOC (http://pascallin.ecs.soton.ac.uk/challenges/VOC/)
[2] ImageNet (http://www.image-net.org/challenges/LSVRC/2011/)
[3] MIRFLICKR (http://press.liacs.nl/mirflickr/)
[4] SUN database (http://groups.csail.mit.edu/vision/SUN/)
- Some object detection source codes are available.
[5] Most popular object detection (http://people.cs.uchicago.edu/~pff/latent/)
[6] Object recognition short course ¿ pLSA and Boosting (http://people.csail.mit.edu/torralba/shortCourseRLOC/index.html)
[7] Human pose detection (Poselet)(http://www.eecs.berkeley.edu/~lbourdev/poselets/)
Identifying ancestry-informative markers:
Ancestry informative markers are polymorphisms that differ in frequency across populations. They can be used to differentiate between geographical populations and identify their anestry. This is has an important application in a technique called admixture mapping which can be used to identify polymorphisms that contribute to disease risk in populations. Many collections of ancestry-informative markers have been previously identified using statistical methods for resolving ancestry at the continental level and national level. In this project, you will use the techniques learned in class to identify sets of ancestry-informative markers and compare your results to existing methods.
- The HapMap project (http://hapmap.ncbi.nlm.nih.gov/) - The International HapMap Project is analyzing DNA from populations with African, Asian, and European ancestry. Together, these DNA samples should enable HapMap researchers to identify most of the common haplotypes that exist in populations worldwide. The DNA samples in the HapMap project come from 1,301 samples from 11 African, European and Asian populations. The data in phase 3 contains the genotype of the individuals at about 1.5 million SNPs. This data can be used for various population genetic analyses.
- The Human Genome Diversity Project (http://www.cephb.fr/en/hgdp/diversity.php) - This project has genetic data from 1050 individuals in 52 world populations. To date, the DNAs have been typed genome wide with almost 1 million SNPs, 843 microsatellites, and 51 small indel loci. Approximately 10,000 CNV (Copy Number Variations) calls from two different laboratories are included in the database.
- Price AL, Butler J, Patterson N, Capelli C, Pascali VL, et al. (2008) Discerning the Ancestry of European Americans in Genetic Association Studies. PLoS Genet 4(1): e236. doi:10.1371/journal.pgen.0030236
- Seldin MF, Price AL (2008) Application of Ancestry Informative Markers to Association Studies in European Americans. PLoS Genet 4(1): e5. doi:10.1371/journal.pgen.0040005
- Tian C, Plenge RM, Ransom M, Lee A, Villoslada P, et al. (2008) Analysis and Application of European Genetic Substructure Using 300 K SNP Information. PLoS Genet 4(1): e4. doi:10.1371/journal.pgen.0040004
Genotype imputation :
Genotype datasets available today include geneotype information at hundreds of thousands or even millions of polymorphisms. However, due to noise or choice of genotyping density, some genotype information in the dataset may be missing. This can cause problems when using the data for further analysis such as association studies, where polymorphisms contributing to disease risk can be identified using the genetic data. The problem of identifying the genotype at these missing positions is called genotype imputation. To accomplish this, various statistical methods are used. In this project, you will use machine learning techniques to identify missing genotype data.
- The HapMap project (http://hapmap.ncbi.nlm.nih.gov/) - The International HapMap Project is analyzing DNA from populations with African, Asian, and European ancestry. Together, these DNA samples should enable HapMap researchers to identify most of the common haplotypes that exist in populations worldwide. The DNA samples in the HapMap project come from 1,301 samples from 11 African, European and Asian populations. The data in phase 3 contains the genotype of the individuals at about 1.5 million SNPs. This data can be used for various population genetic analyses.
- The Human Genome Diversity Project (http://www.cephb.fr/en/hgdp/diversity.php) - This project has genetic data from 1050 individuals in 52 world populations. To date, the DNAs have been typed genome wide with almost 1 million SNPs, 843 microsatellites, and 51 small indel loci. Approximately 10,000 CNV (Copy Number Variations) calls from two different laboratories are included in the database.
- Zheng, J., Li, Y., Abecasis, G. R. and Scheet, P. (2011), A comparison of approaches to account for uncertainty in analysis of imputed genotypes. Genetic Epidemiology, 35: 102¿110. doi: 10.1002/gepi.20552
- Li Y, Willer CJ, Sanna S, Abecasis GR. Genotype imputation. Annual Review Genomics and Human Genetics 10: 387-406.
Association analysis
Project Idea:The goal of population association studies is to identify patterns of polymorphisms that vary systematically between individuals with different disease states and could therefore represent the effects of risk-enhancing or protective alleles. These studies make use of the statistical correlation between the polymorphism and the trait of interest (usually the presence or absence of disease) to identify these patterns. This project will make use of data from the Personal Genome Project. It contains information about many traits of the individuals from whom the genetic data was obtained. Using techniques such as statistical tests, sparsity-based methods, eigenanalysis, you can try to find the genetic polymorphisms that are likely to be responsible for a particular trait.
- Personal Genome Project (http://www.personalgenomes.org/) - The Personal Genome Project makes available genetic and phenotypic data from volunteers for analysis. The data can be downloaded at http://www.personalgenomes.org/public/.
- D.J.Balding, (2006) A tutorial on statistical methods for population association studies, Nature Reviews Genetics.
- Stephens M, Balding DJ. (2009) Bayesian statistical methods for genetic association studies, Nature Reviews Genetics.
- Alkes L Price, Nick J Patterson, Robert M Plenge, Michael E Weinblatt, Nancy A Shadick and David Reich, (2006), Principal components analysis corrects for stratification in genome-wide association studies, Nature Genetics.
Using text and network data to predict and to understand
Many data sets are heterogeneous, comprising feature vectors, textual data (bag of words), network links, image data, and more. For example, Wikipedia pages contain text, links and images. The challenge is figuring out how to use all these types of data for some machine learning task: data exploration, prediction, etc.. A typical machine learning approach to this problem is "multi-view learning", in which the different data types are assumed to be multiple "views" of the entities of interest (webpages in the case of Wikipedia).
Multi-view learning opens up applications not normally available with single-view datasets. For example, consider a citation recommendation service for academics, which suggests papers you should cite based on the text of your paper draft. Such a service would be trained on a corpus of academic papers, learning how the citations relate to the paper texts. Another example would be interest prediction and advertising in social networks: given a user's friend list, determine what things that user is interested in.
In this project, you will focus on datasets with text and network data, such as (but not limited to) citation networks. As our examples suggest, your primary goal is to design a machine learning algorithm that trains on a subset of the text and network data, and, given text (or network links) from test entities, outputs network link (or text) predictions for them. Alternatively, you could design an algorithm that converts text and network data into "latent space" feature vectors suitable for data visualization (similar to methods such as the Latent Dirichlet Allocation and the Mixed-Membership Stochastic Blockmodel). Note that these goals are not exclusive; your proposed method could even do both. The key challenge in this project is figuring out how to learn from text and network data jointly, even though both data types are fundamentally different.
Recommended reading:
- Joint Latent Topic Models for Text and Citations (Nallapati, Ahmed, Xing, Cohen, 2008)
- Multi-view learning over structured and non-identical outputs (Ganchev, Graca, Blitzer, Taskar, 2008)
Suggested Datasets :
- ACL Anthology citation network
- arXiv High-Energy Physics citation network (from KDD cup 2003)
Efficient methods for understanding large networks
Network data is usually presented as a list of edges, each connecting two network actors. These edges represent binary relationships: for example, in a citation network, a directed edge from paper i to paper j indicates that i cited j. One problem with networks is that the binary relationships are inherently difficult to visualize; a network with thousands of edges is difficult to draw without messy edge overlaps.
In order to visualize network data better, statistical models such as the Mixed-Membership Stochastic Blockmodel (closely related to the Latent Dirichlet Allocation model used in NLP) take binary network relationships, and from them learn individual feature vectors for each actor. These individual feature vectors can be interpreted as "communities" or "roles" in the network, and are often easier to cluster or visualize than the original network edges. Furthermore, these feature vectors naturally serve as input to other machine learning methods (such as logistic regression or Naive Bayes), making them highly useful for projects that require learning from network data as well as "conventional" feature vectors. Unfortunately, there is one big drawback to network models such as MMSB: existing learning algorithms require O(N^2) runtime (where N is the number of actors), making them impractical for larger networks with more than 10,000 actors.
In this project, you are to design a network learning algorithm that (1) turns binary network relationships into individual feature vectors for actors, such that (2) the learning algorithm is practical for networks >= 10,000 actors in size (i.e. runtime should be less than O(N^2)). The underlying network model could be anything; you could use MMSB as a starting point, or even build a new model from non-statistical methods such as SVMs. Ideally, the learnt feature vectors should give insight into the network and its actors, or they should be useful for clustering or prediction tasks.
Recommended Reading:
These papers introduce the idea of a "latent space", in which the individual actor feature vectors lie. The feature vectors in the latent space "generate" the observed network edges, much as the feature vectors in Naive Bayes generate the observed data. You could use the latent spaces described here as the foundation for your algorithm, or come up with your own:
- Mixed Membership Stochastic Blockmodels (Airoldi, Blei, Fienberg, Xing, 2008)
- Theoretical Justification of Popular Link Prediction Heuristics (Sarkar, Chakrabati, Moore, 2010)
Suggested Datasets :
- ACL Anthology citation network
- arXiv High-Energy Physics citation network (from KDD cup 2003)
Project A1: Cognitive State Classification with Magnetoencephalography Data (MEG)
Data:A zip file containing some example preprocessing of the data into features along with some text file descriptions: LanguageFiles.zip
The raw time data (12 GB) for two subjects (DP/RG_mats) and the FFT data (DP/RG_avgPSD) is located at:
You should access this directly through AFS space
This data set contains a time series of images of brain activation, measured using MEG. Human subjects viewed 60 different objects divided into 12 categories (tools, foods, animals, etc...). There are 8 presentations of each object, and each presentation lasts 3-4 seconds. Each second has hundreds of measurements from 300 sensors. The data is currently available for 2 different human subjects.
Project A: Building a cognitive state
Project idea: We would like to build classifiers to distinguish between the different categories of objects (e.g. tools vs. foods) or even the objects themselves if possible (e.g. bear vs. cat). The exciting thing is that no one really knows how well this will work (or if it's even possible). This is because the data was only gathered a few weeks ago (Aug-Sept 08). One of the main challenges is figuring out how to make good features from the raw data. Should the raw data just be used? Or maybe it should be first passed through a low-pass filter? Perhaps a FFT should convert the time series to the frequency domain first? Should the features represent absolute sensor values or should they represent changes from some baseline? If so, what baseline? Another challenge is discovering what features are useful for what tasks. For example, the features that may distinguish foods from animals may be different than those that distinguish tools from buildings. What are good ways to discover these features?
This project is more challenging and risky than the others because it
is not known what the results will be. But this is also good because no
one else knows either, meaning that a good result could lead to a
possible publication.
Papers to read:
Relevant but in the fMRI domain:
Learning to Decode Cognitive States from Brain Images, Mitchell et al., 2004,
Predicting Human Brain Activity Associated with the Meanings of Nouns, Mitchell et al., 2008
MEG paper:
Predicting the recognition of natural scenes from single trial MEG recordings of brain activity, Rieger et al. 2008 (access from CMU domain)
Project A2: Brain imaging data (fMRI)
This data set contains a time series of images of brain activation, measured using fMRI, with one image every 500 msec. During this time, human subjects performed 40 trials of a sentence-picture comparison task (reading a sentence, observing a picture, and determining whether the sentence correctly described the picture). Each of the 40 trials lasts approximately 30 seconds. Each image contains approximately 5,000 voxels (3D pixels), across a large portion of the brain. Data is available for 12 different human subjects.
Available software: we can provide Matlab software for reading the data, manipulating and visualizing it, and for training some types of classifiers (Gassian Naive Bayes, SVM).
Project A: Bayes network classifiers for fMRI
Project idea: Gaussian Naive Bayes classifiers and SVMs have been used with this data to predict when the subject was reading a sentence versus perceiving a picture. Both of these classify 8-second windows of data into these two classes, achieving around 85% classification accuracy [Mitchell et al, 2004]. This project will explore going beyond the Gaussian Naive Bayes classifier (which assumes voxel activities are conditionally independent), by training a Bayes network in particular a TAN tree [Friedman, et al., 1997]. Issues you'll need to confront include which features to include (5000 voxels times 8 seconds of images is a lot of features) for classifier input, whether to train brain-specific or brain-independent classifiers, and a number of issues about efficient computation with this fairly large data set.
Papers to read: " Learning to Decode Cognitive States from Brain Images", Mitchell et al., 2004, " Bayesian Network Classifiers", Friedman et al., 1997.
Project A3: Genetic Sequence Analysis
We don't currently have a specific dataset in mind for this project, but if you're interested we'll help you find one (ask Field).
One of the most interesting, and controversial, areas of modern science is using people's genetic code to predict things like their likelihood of getting heart disease, their athletic prowess, and even their personality and intelligence. The movie Gattaca shows some of the downsides of this technology, but it can also be immensely helpful if a person takes preventive measures. Also, many drugs work better for people with certain genes. Insurance problems notwithstanding, genetic screening will play a huge role in medicine in the coming decades.
This project is not as well-defined as many others, but the idea is to get ahold of genetic data from patients, along with some kind of phenotype marker (like whether they got a disease), and try to find patterns within the genetic code which predict the trait. This area is very exciting because in many cases, people have literally no idea what causal links exist between genes and traits, but finding these links can be a huge boost to both medicine and pure science (by telling scientists which particular gene combinations to examine)
Available software: we can provide Matlab software for reading the data, manipulating and visualizing it, and for training some types of classifiers (Gassian Naive Bayes, SVM).
Project A: Bayes network classifiers for fMRI
Project idea: Gaussian Naive Bayes classifiers and SVMs have been used with this data to predict when the subject was reading a sentence versus perceiving a picture. Both of these classify 8-second windows of data into these two classes, achieving around 85% classification accuracy [Mitchell et al, 2004]. This project will explore going beyond the Gaussian Naive Bayes classifier (which assumes voxel activities are conditionally independent), by training a Bayes network in particular a TAN tree [Friedman, et al., 1997]. Issues you'll need to confront include which features to include (5000 voxels times 8 seconds of images is a lot of features) for classifier input, whether to train brain-specific or brain-independent classifiers, and a number of issues about efficient computation with this fairly large data set.
Papers to read: " Learning to Decode Cognitive States from Brain Images", Mitchell et al., 2004, " Bayesian Network Classifiers", Friedman et al., 1997.
Project A4: Hierarchical Bayes Topic ModelsStatistical topic models have recently gained much popularity in managing large collection of text documents. These models make the fundamental assumption that a document is a mixture of topics(as opposed to clustering in which we assume that a document is generated from a single topic), where the mixture proportions are document-specific, and signify how important each topic is to the document. Moreover, each topic is a multinomial distribution over a given vocabulary which in turn dictates how important each word is for a topic. The document- specific mixture proportions provide a low-dimensional representation of the document into the topic-space. This representation captures the latent semantic of the collection and can then be used for tasks like classifications and clustering, or merely as a tool to structurally browse the otherwise unstructured collection. The most famous of such models is known as LDA ,Latent Dirichlet Allocation (Blei et. al. 2003). LDA has been the basis for many extensions in text, vision, bioiformatic, and social networks. These extensions incorporate more dependency structures in the generative process like modeling authors-topic dependency, or implement more sophisticated ways of representing inter-topic relationships.
Potential projects include
- Implement one of the models listed below or propose a new latent topic model that suits a data set in your area of interest
- Implement and Compare approximate inference algorithms for LDA which includes: variational inference (Blei et. al. 2003), collapsed gibbs sampling (Griffth et. al. 2004) and (optionally) collapsed variational inference (Teh. et. al. 2006). You should compare them over simulated data by varying the corpus generation parameters --- number of optics, size of vocabulary, document length, etc --- in addition to comparison over several real world datasets.
- D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:9931022, January 2003.
- Griffiths, T, Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences, 101, 5228-5235 2004.
- Y.W. Teh, D. Newman and M. Welling. A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation.In NIPS 2006.
Expressive Models:
- Rosen-Zvi, M., Griffiths, T., Steyvers, M., & Smyth, P. The Author-Topic Model for authors and documents.In UAI 2004.
- Jun Zhu, Amr Ahmed and Eric Xing. MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification. International conference of Machine learning. ICML 2009.
- D. Blei, J. McAuliffe. Supervised topic models. In Advances in Neural Information Processing Systems 21, 2007
- Wei Li and Andrew McCallum. Pachinko Allocation: Scalable Mixture Models of Topic Correlations. Submitted to the Journal of Machine Learning Research, (JMLR), 2008
- L. Fei-Fei and P. Perona. A Bayesian Hierarchical Model for Learning Natural Scene Categories. IEEE Comp. Vis. Patt. Recog. 2005. [PDF]
- L. Cao and L. Fei-Fei. Spatially coherent latent topic model for concurrent object segmentation and classification . IEEE Intern. Conf. in Computer Vision (ICCV). 2007 [PDF]
- Ramesh Nallapati, Amr Ahmed, Eric P. Xing, and William W. Cohen, Joint Latent Topic Models for Text and Citations. Proceedings of The Fourteen ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. (KDD 2008) [PDF]
- Erosheva, Elena A., Fienberg, Stephen E., and Lafferty, John (2004). Mixed-membership models of scientific publications," Proceedings of the National Academy of Sciences, 97, No. 22, 11885-11892. [PDF]
- E. Airoldi, D. Blei, E.P. Xing and S. Fienberg, Mixed Membership Model for Relational Data. JMLR 2008. [PDF]
- Andrew McCallum, Andres Corrada-Emmanuel, Xuerui Wang The Author-Recipient-Topic Model for Topic and Role Discovery in Social Networks: Experiments with Enron and Academic Email. Technical Report UM-CS-2004-096, 2004. [PDF]
- E.P. Xing, W. Fu, and L. Song, A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography, Annals of Applied Statistics, 2009. [PDF]
- S. Shringarpure and E. P. Xing, mStruct: A New Admixture Model for Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations, Proceedings of the 25th International Conference on Machine Learning (ICML 2008). [PDF]
- Amr Ahmed, Eric P. Xing, William W. Cohen, Robert F. Murphy. Structured Correspondence Topic Models for Mining Captioned Figures in Biological Literature. Proceedings of The Fifteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, (KDD 2009) [PDF]
Project B: Image Segmentation Dataset
The goal is to segment images in a meaningful way. Berkeleycollected three hundred images and paid students to hand-segment each one (usually each image has multiple hand-segmentations). Two-hundred of these images are training images, and the remaining 100 are test images. The dataset includes code for reading the images and ground-truth labels, computing the benchmark scores, and some other utility functions. It also includes code for a segmentation example. This dataset is new and the problem unsolved, so there is a chance that you could come up with the leading algorithm for your project.
Project ideas:
Project B: Region-Based Segmentation
Most segmentation algorithms have focused on segmentation based on edges or based on discontinuity of color and texture. The ground-truth in this dataset, however, allows supervised learning algorithms to segment the images based on statistics calculated over regions. One way to do this is to "oversegment" the image into superpixels (Felzenszwalb 2004, code available) and merge the superpixels into larger segments. Graphical models can be used to represent smoothness in clusters, by adding appropriate potentials between neighboring pixels. In this project, you can address, for example, learning of such potentials, and inference in models with very large tree-width.
Papers to read: Some segmentation papers from
Project C: Twenty Newgroups text data
This data set contains 1000 text articles posted to each of 20
online newgroups, for a
total of 20,000
articles. For
documentation and download, see this
This data is useful for a variety of text classification and/or
projects. The "label" of each article is which of the 20
newsgroups it belongs to. The newsgroups (labels) are
organized (e.g., "sports", "hockey").
Available software: The same website provides an implementation of a Naive Bayes classifier for this text data. The code is quite robust, and some documentation is available, but it is difficult code to modify.
Project ideas:
EM text classification in the case where you
have labels for some documents, but not for others (see
et al,
and come up with your own suggestions)
Project D: Handwriting Recognition (Lisa Anthony http://www.cs.cmu.edu/~lanthony/)
A general overview of our data: we have approximately 16,000
character samples from 39 middle and high school students, consisting
of x-coord, y-coord, and time per point in each stroke. They are
grouped into sets of 45 equations that each student copied. The symbols
in our dataset are: 0-9, x, y, a, b, c, +, -, _ (fraction bar), =, (, ).
There are 3 main ideas for projects:
1. HOW MUCH DATA: All our data is currently hand-labeled, and we have lots of it. One question might be, if the data wasn't labeled, what would be the added value of additional data? That is, what would be the optimal or minimal dataset? This could be defined along several axes: the number of users, the number of samples per character, or the number of samples per symbol per user. We have done a few preliminary experiments where it is clear that there is a leveling off point for test accuracy -- likely caused by the increase in variability of adding new samples (especially by new users with differing handwriting styles), which harms the classification algorithm (see #3). For future studies and domains it might be useful to get a general sense of "data saturation" -- a recommended canonical corpus size
2. HOW MUCH LABELED DATA AND/OR AUTOMATIC LABELING: Hand-labeling all our data took quite a bit of time. What possibilities exist for an
automated, semi-supervised labeling algorithm that could tell us how much data we need to label in advance and how much human verification is needed on the automatically labeled stuff? A side note is that the collection of this data (for the sake of the users) was in the form of one equation at a time rather than one character at a time, so the characters needed to be segmented at the time of labeling since the strokes all ran together in the logs. An automated segmenting approach would be very helpful to us in the future!
3. MULTIPLE CLASSIFIERS: Finally, there is quite a bit of variance between users in that their handwriting styles differ and the particular means of executing a style differs across users. We hypothesize that multiple classifiers trained per user would have higher walk-up-and-use accuracy on a set of independent users than one classifier that has to generalize across all user styles. So this could also be an interesting area to explore.
Project E: Character recognition (digits) data
Optical character recognition, and the simpler digit recognition task, has been the focus of much ML research. We have two datasets on this topic. The first tackles the more general OCR task, on a small vocabulary of words: (Note that the first letter of each word was removed, since these were capital letters that would make the task harder for you.)
Project suggestion:
- Use an HMM to exploit correlations between neighboring letters in the general OCR case to improve accuracy. (Since ZIP codes don't have such constraints between neighboring digits, HMMs will probably not help in the digit case.)
Project F: NBA statistics data
This download contains 2004-2005 NBA and ABA stats for:
-Player regular season stats
-Player regular season career totals
-Player playoff stats
-Player playoff career totals
-Player all-star game stats
-Team regular season stats
-Complete draft history
-coaches_season.txt - nba coaching records by season
-coaches_career.txt - nba career coaching records
Currently all of the regular season
Project idea:
- outlier detection on the players; find out who are the outstanding players.
- predict the game outcome.
Project G: Precipitation data
This dataset has includes 45 years of daily precipitation data from the Northwest of the US:
Project ideas:
Weather prediction: Learn a probabilistic model to predict rain levels
Sensor selection: Where should you place sensor to best predict rain
Project H: WebKB
This dataset contains webpages from 4 universities, labeled with whether they are professor, student, project, or other pages.
Project ideas:
- Learning classifiers to predict the type of webpage from the text
- Can you improve accuracy by exploiting correlations between pages that point to each other using graphical models?
Project I: Deduplication
The datasets provided below comprise of lists of records, and the goal is to identify, for any dataset, the set of records which refer to unique entities. This problem is known
by the varied names of Deduplication, Identity Uncertainty and Record Linkage.
Project Ideas:
- One common approach is to cast the deduplication problem as a classification problem. Consider the set of record-pairs, and classify them as either "unique" or "not-unique".
Project J: Email Annotation
The datasets provided below are sets of emails. The goal is to identify which parts of the email refer to a person name. This task is an example of the general problem area of Information Extraction.
Project Ideas:
- Model the task as a Sequential Labeling problem,
where each
email is a sequence of tokens, and each token can have either a label
of "person-name" or "not-a-person-name".
Papers: http://www.cs.cmu.edu/~einat/email.pdf
Project K: Netflix Prize Dataset
The Netflix Prize data set gives 100 million records of the form "user X rated movie Y a 4.0 on 2/12/05". The data is available here: Netflix Prize
Project idea:
Can you predict the rating a user will give on a movie from the movies that user has rated in the past, as well as the ratings similar users have given similar movies?
Can you discover clusters of similar movies or users?
Can you predict which users rated which movies in 2006? In other words, your task is to predict the probability that each pair was rated in 2006. Note that the actual rating is irrelevant, and we just want whether the movie was rated by that user sometime in 2006. The date in 2006 when the rating was given is also irrelevant. The test data can be found at this website.
Project L: Physiological Data Modeling (bodymedia)
Physiological data offers many challenges to the machine learning community including dealing with large amounts of data, sequential data, issues of sensor fusion, and a rich domain complete with noise, hidden variables, and significant effects of context.
1. Which sensors correspond to each column?
2. What are the activities behind each annotation?
The annotations for the contest were:
5102 = sleep
3104 = watching TV
Datasets can be downloaded from http://www.cs.utexas.edu/users/sherstov/pdmc/
Project idea:
- behavior classification; to classify the person based on the sensor measurements
Project M: Object Recognition
The Caltech 256 dataset
contains images
of 256 object categories taken at varying orientations, varying
lighting conditions, and with different backgrounds.
Project ideas:
- You can try to create an object recognition system which can identify which object category is the best match for a given test image.
- Apply clustering to learn object categories without supervision
Project N: Learning POMDP structure so as to maximize utility
Hoey & Little (CVPR 04) show how to learn the state space, and parameters, of a POMDP so as to maximize utility in a visual face gesture recognition task. (This is similar to the concept of "utile distinctions" developed in Andrew McCallum's PhD thesis.) The goal of this project is to reproduce Hoey's work in a simpler (non-visual) domain, such as McCallum's driving task.
Project O: Learning partially observed MRFs: the Langevin algorithm
In the recently proposed exponential family harmonium model (Welling et. al., Xing et. al.), a constructive divergence (CD) algorithm was used to learn the parameters of the model (essentially a partially observed, two-layer MRF). In Xing et. al., a comparison to variational learning was performed. CD is essentially a gradient ascent algorithm of which the gradient is approximated by a few samples. The Langevin method adds a random perturbation to the gradient and can often help to get the learning process out of local optima. In this project you will implement the Langevin learning algorithm for Xings dual wing harmonium model, and test your algorithm on the data in my UAI paper. See Zoubin Ghahramanis paper of Bayesian learning of MRF for reference.
Project P: Context-specific independenceWe learned in class that CSI can speed-up inference. In this project, you can explore this further. For example, implement the recursive conditioning approach of Adnan Darwiche, and compare it to variable elimination and clique trees. When is recursive conditioning faster? Can you find practical BNs where the speed-up is considerable? Can you learn such BNs from data?
Project Q: Enron E-mail Dataset
The Enron E-mail data set contains about 500,000 e-mails from about 150 users. The data set is available here: Enron Data
Project ideas:
Can you classify the text of an e-mail message to decide who sent it?
Project R: More data
There are many other datasets out there. UC Irvine has a repository that could be useful for you project:
Sam Roweis also has a link to several datasets out there:
[validate xhtml]


text:
Connecticut v Notre Dame
Similar imagesView all
Ariel Braker of the Notre Dame Fighting Irish falls as Kaleena... News PhotoAdult,Basketball - Women's College,Connecticut Huskies,Dribbling,Falling,Horizontal,Kaleena Mosqueda-Lewis,Notre Dame Fighting Irish,South Bend,Sport,USA,Women,Women's BasketballPhotographer Michael HickeyCollection: Getty Images Sport 2013 Michael HickeySOUTH BEND, IN - MARCH 04: Ariel Braker #44 of the Notre Dame Fighting Irish falls as Kaleena Mosqueda-Lewis #23 of the Connecticut Huskies dribbles around her at Purcel Pavilion on March 4, 2013 in South Bend, Indiana. Notre Dame defeated Connecticut 96-87 in triple overtime to win the Big East regular season title. (Photo by Michael Hickey/Getty Images)

subset: commoncrawl


text:
Flash Back: A Woman of Substance; Josephine Butler Fought to Improve the Life of Women in 19th Century Liverpool. Peter Grant Reports on Celebrations of the Radical Campaigner
Byline: Peter Grant
WHEN social reformer Josephine Butler set to work transforming conditions for the city way back in the late 1800s, little did she know the legacy she would leave behind.
Now, 100 years after her death, the revolutionary social reformer's life and work is to be celebrated with an exhibition held at both Liverpool Cathedral and Liverpool University this Saturday, organised by the Josephine Butler Memorial Trust.
But who was Josephine Butler - and why is she held in such high esteem as Eleanor Rathbone and Margaret Simey?
"Josephine Elizabeth Grey was a much-travelled, hard-working and formidable woman," says Katie Cooper, the University of Liverpool's Special Collections librarian, who has put together a stunning collection of Josephine Butler memorabilia.
She was born in 1828 - the daughter of a wealthy Northumbrian landowner. In 1852 she married the inspirational George Butler, a man with similar views and attitudes.
Living in Oxford, and then Cheltenham, the Butlers had four children - the youngest of whom died at the age of five, falling from the banisters at the top of their stairs in front of her distraught parents.
The family moved to Liverpool where George (by then ordained in the Church of England) had been appointed headmaster of Liverpool College.
It was here Josephine began her social campaigning.
Marian Pope, chair of the Josephine Butler Memorial Trust says: "After arriving in the city, Josephine visited a workhouse where there were hundreds of women and girls working in huge, damp cellars picking 'oakum' rope fibres.
"The conditions were dreadful, but the women were driven there by hunger and destitution - and for many it was the only alternative to prostitution."
Josephine rescued many young girls from the workhouse, taking some into her home before setting up her own refuge.
Josephine also wanted to tackle the roots of sexual inequality and campaigned for better educational provision for women, persuading Cambridge University to admit women and to set up Newnham College exclusively for women.
Adds Marian: "Perhaps Josephine Butler is best remembered for her active involvement in the campaign to repeal the Contagious Diseases Acts.
"These laws sought to control sexually transmitted diseases which were widespread in the army and navy. …


text:
A woman laying on her left side ... 3 = b00bs, < = ... well, you know
I was hanging with my <3 last night, we had a good time.
door Patrick 17 februari 2005
Hax0r speak for "Ain't Got No Love for the Hoes"
!, in coding, generally means "no" or "not"
<3, as it were, is the leettext word for "love".
!, in coding, generally means "no" or "not"
<3, as it were, is the leettext word for "love".
Man, <!3. That bitch can step!
door Denis Baldwin 12 januari 2004
definition is very simple. <3? = do you love me?
A: why did u do that
B: cuz..
B: cuz..
door bisouS_t 12 januari 2010
It's not a heart. Any moron could see it's a butt in a dunce cap!
"ur such a <3" really means "you're such a stupid ass."
door rachael with an a 6 augustus 2007
An ice cream cone with two scoops on top. Text version.
"I'm hungry for <3 when I'm entering bogus definitions into UrbanDictionary"
door HænZe 19 november 2007
Said to be a "heart", yet, looks nothing like a REAL heart. I've wondered for a while where they got "<3" from a real heart. But I've finally come to a conclusion, a man made it up. If you look at it, it looks like the female body, but not JUST the female body, mens two favorite parts. The top portion, "3" resembles a woman's boobs, while the bottom half, "V" resembles her vagina. Therefore when men think "love" (the meaning of heart) they think "women's body". Therefore a man made it, and the heart is perverted.
<3 = woman's body
3 = boobs
V = vagina
3 = boobs
V = vagina
door JessesGirl12409 20 november 2011
Less than three.
door Silent Sky Paladin 17 januari 2010
A ballsack... usually used in online messeages. Example:
I want to <3 you (I want to fuck you)
I <3'd her soo bad (I fucked her soo bad)
I <3'd her soo bad (I fucked her soo bad)
door Titoboy 23 augustus 2007


text:
AudioCodes AUDC BankAtlantic Bancorp BBX CapitalSource ..... 14/07) BankAtlantic Bancorp (Note ..... have slowed BankAtlantic 's BBX strategy ..... January, BankAtlantic ended speculation ..... Report: BankAtlantic Bancorp CapitalSource
BankAtlantic and Ryan Beck struggle through tough times.
throughout the South, completed its acquisition of BankAtlantic . The move enhances BB&T's reach and market share ..... T's net income by about 4 cents per share. The BankAtlantic acquisition boosted loan growth, with average loans
and that is likely to gain even more importance for the bank after the completion of the planned acquisition of BankAtlantic 's BBX primary unit--are ameliorating. Credit metrics continued to move in the right direction at BB&T in the latest
interpreted as a positive sign for the company's direction. We look forward to the company's completion of its planned BankAtlantic acquisition and to track its progress on improving returns over the next few quarters.
should gradually expand, and we remain pleased with BB&T's recent credit quality improvements. We will be interested in seeing how this well-managed company performs with its planned acquisitions of BankAtlantic BBX and Crump Insurance.
We plan to drop coverage of BankAtlantic BBX in the coming weeks. BB&T BBT will be purchasing most of BankAtlantic 's performing loans and assuming ..... all of its deposits. Hence, BankAtlantic will be left practically in runoff
Just days after BankAtlantic BBX got a letter from the NYSE that ..... buyer could come along and save BankAtlantic , since we think its deposit franchise ..... the details of what will remain of BankAtlantic , we are placing the company under
quarters of losses, BankAtlantic BBX posted positive second ..... nonrecurring boost in income, BankAtlantic would have lost around ..... subsidiary, through BBX ) the bank subsidiary ..... worrisome is the fact that BankAtlantic 's ability to tap the
at BankAtlantic BBX , which lost $23 ..... somewhat difficult for BankAtlantic , there could be ..... insolvency of the BankAtlantic Bancorp (the holding company ..... shareholders own through BBX ), if the firm ..... is controlled by BankAtlantic 's top brass


text:
24-year-old Eric Shanteau is sitting on cloud nine after learning he will be heading to Bejing to compete for the U.S. in the 200 meter breaststroke. It is a welcomed change of emotions for Eric as he was diagnosed with testicular cancer justone weekbefore the trials.
Eric is holding off on treatment until after he returns from Beijing. Luckily his cancer has not spread, and when caught early (as with ANY cancer) it has a 95 percent chance of survival. He putting off surgery until after Beijing because he doesn't want to disrupt his lifelong goal of swimming in the Olympics, according to an exclusive AP interview.
"I am in touch with several doctors around the country and they're getting my test results back and I'm keeping in touch with them as things come up," said Eric.
Eric stressed he's not willing to risk his life just to compete in his first Olympics. But, after considering the benefits of immediate treatment, he decided to put off surgery because it would keep him out of the water for at least two weeks, ruining his Beijing preparations. His doctors have stressed there would be no risk in competing in the Olympics.
Eric Shanteau is fortunate to have found his cancer early, and to be competing for the U.S. He will have much on his shoulders in Bejing. I, along with so many, will be cheering Eric along.


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|May 12, 2013, 11:37 PM
Interesting Core Storage quirk.
I'm not sure what to make of this, so I'll leave it to the community to decide. I was messing around with Core Storage earlier and stumbled upon something odd. First off, here's what my LVG looks like:
As you can see, I used three PVs of varying sizes to create this LVG. The interesting thing about this is that when I created the LV, I used "100%". Common sense says that should make the LV somewhere in the realm of the 8.8GB size claimed by Core Storage. This didn't occur however, and I ended up with the 3.3GB LV that you see. Now, this could be a bug, and if that's what I suspected I wouldn't be posting, but now look at what I found when I did "mkfile 3g testFile" on the new LV (Don't pay attention to the disk identifier. It's completely glitched for some reason):
It almost looks like I somehow managed to trigger some sort of redundancy without meaning to. On the other hand, the missing storage could be a bug having to do with more than two drives (which is unsupported by Apple), and the write to the second disk could just be logs being written... It would make sense that Apple would design CoreStorage to write logs to any of the disks not currently being actively written to by the user (for efficiency). Each disk does contain Core Storage headers, which may all be able to contain logs belonging to any of the disks... I'm not 100% sure what I'm seeing, so I'll let you guys think about it.
Last edited by ConCat; May 12, 2013 at 11:43 PM.
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Our Members Say
- KimberlyAnn
- Reviews: 1119
Elk Prairie Campground: Living with the Elk
Redwood National and State Park has four campgrounds, all within the State Park areas of Redwood. Be aware that there are no trailer hook-ups offered in any of the campgrounds. We stayed in the Elk Prairie campground near Prairie Creek, which is one of the campgrounds that is open all year. This campground has 75 sites for tents or RVs, with a size limit of up to 24 feet for trailers, and 27 feet for RVs. There are fire pits at each campsite, plus a dump station, and restrooms with showers available in the campground. This campground contained both forested sites and prairie sites. I thought the forested sites were prettier and certainly more private, but we chose a prairie site so we could use our solar panels, although, as gray as it was during our stay, our solar panels were only a small help in charging our batteries. Still, it turned out that we really liked the prairie, as we saw many Roosevelt elk grazing in the prairie near our trailer.
To make reservations call (707) 465-7347 or visit Reserve America at www.reserveamerica.com
For information about the other campgrounds visit the web site listed below.
All of the campgrounds allow you to enjoy the natural beauty of the park, and give you opportunities to explore the outdoors. From Elk Prairie there are more than 70 miles of hiking or biking trails available. There is a Nature Center with a small bookstore located near the campground, where you can find valuable information to help you enjoy your stay.
- GlobalMatt
- Reviews: 117
Redcrest Resort: Sleeping with the Giants
The Redcrest Resort is in the heart of the Avenue of the Giants amongst some of the world's tallest trees. This is truly a special experience, especially at night. There is absolutely no light polution. You can see the stars like nowhere else and you feel as if you are guests among the trees.
The cabins are quite nice, with spacious choices and kitchens. They are a bit rustic, but it really fits the situation nicely. The price is very reasonable, too.
- Rusket
- Reviews: 625
Motel 6: Not in the park
It is possible to camp in the park. But we were on a road trip, not bringing any camping equipment. We chose to stop at Motel 6 in Arcata a little south of the park the night before, and in Eugene, Oregon after the hike. The motels in this chain look the same everywhere, but the prices vary! For three adults in a room with two queen size beds, we paid USD 53.89 in Arcata, 65.18 in Eugene and a third price somewhere else.
The standard is OK, the three places we tried on this trip were clean and everything functioned as it should. Parking availability is good. Breakfast is not included.
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Here you'll find lots of important information about your new account, together with helpful tips on making the most of your Lloyds Bank credit card.
With Internet Banking, you can see your credit card balance next to your other Lloyds Bank accounts. You can transfer balances from other credit cards to your Lloyds Bank Credit Card, set up and amend a direct debit and it's easy to see what you're spending. Balance transfer fees apply.
So if you haven't yet registered for this great free service, here's what to do:
Add a family member or someone else 18 years or over to your account and they can share all the advantages of the Lloyds Bank card.
And with everyone's transactions on one monthly statement, it's an easy way to keep track of the family's spending.
To request an additional card, call 0845 606 2172.
The Cardholder remains responsible for all spending on the account made by an Additional Cardholder.
Download and fill in the Additional Cardholder application form (PDF)
Life's busy, but a Direct Debit for your Lloyds Bank credit card means you'll have one less thing to sort out each month. You're still in control though, because you choose whether to pay:
Tips to avoid additional fees and charges:
Currently a fee of £12 will be incurred for any missed payments
Consumer Credit Act protection.
Section 75 of the Consumer Credit Act 1974 (the Act) can offer Lloyds Bank credit card customers useful protection for purchases of over £100 and up to £30,000. Under the Act, a credit card issuer and the retailer have joint responsibility for purchases made if there has been a breach of contract or misrepresentation. For example, if the item is faulty or the company stops trading. If the customer has only paid the deposit on their credit card they may still be able to claim the total value of the purchase under Section 75.
Your Lloyds Bank card has all our latest protection measures built in. Here's a quick run-down of how you're protected.
As well as all that, there are a few simple things you can do yourself. Just follow these three tips to stay protected:
To find out more about using your card securely visit our security page
Lloyds Bank plc is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority and the Prudential Regulation Authority under registration number 119278. Authorisation can be checked on the Financial Services Register at www.fca.org.uk. Lloyds Bank plc is a member of the Financial Services Compensation Scheme and the Financial Ombudsman Service. We subscribe to the Lending Code; copies of the Code can be obtained from www.lendingstandardsboard.org.uk.
Lloyds Bank plc Registered Office: 25 Gresham Street, London EC2V 7HN.
Calls may be monitored or recorded.


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[raw][contact-form-7 404 "Not Found"][/raw]
[accordion class='accord1′]
[slide name="Can I purchase a day pass?"]Yes! Passes are available per day, week or 2 weeks. Prices start as low as $16, please contact the Front Desk at (907) 235-2582 if you are interested.
**Day Passes do NOT include pool access**[/slide]
[slide name="Can I just use the pool?"]Currently our pool is available to Bay Club members only.[/slide]
[slide name="How do I cancel my membership?"]We hope that you are able to stay with us and encourage you to see if you qualify for one of our many discounts that might enable you stay a member of the Bay Club.
If you still need to cancel your membership and have already met your required agreement time (Option 1: 1 year, Option 2: 90 days, Option 3: 30 days), please come into the Bay Club to sign a cancellation form. This must be done in person; memberships cannot be cancelled over the phone or through email.
If you are still within your 1 year membership period, call the front desk at (907) 235-2582 and we may be able to arrange a temporary account freeze for you.[/slide]
[slide name="Do you offer discounts?"]Discounted memberships are available! Currently we offer discounts to:[check_list]
[list_item]South Peninsula Hospital[/list_item]
[list_item]Homer Volunteer Fire Dept.[/list_item]
[list_item]Land's End Resort[/list_item]
[list_item]Connections & IDEA[/list_item]
[list_item]Homer Eye Care Center[/list_item]
[list_item]KPBSD Teachers[/list_item]
[list_item]KPC Students[/list_item]
[list_item]KBay Air[/list_item]
[list_item]Slope/Commercial Fisherman Rate[/list_item]
[list_item]Rock Climbing Membership (Use of wall only)[/list_item]
The Bay Club also runs Holiday, Summer and Monthly Specials, call (907) 235-2582 to find out what the current specials are.
[slide name="Do you have childcare?"]We do not offer childcare, however we are open a wide range of hours to offer as much opportunity to come in and workout as possible.[/slide]


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Quirky says it will shut down operations for four weeks each year, during which time employees will still be paid. CEO Ben Kaufman wrote in a company e-mail that the mandatory time off will give workers time to relax and pursue other interests.
View Full Article in:Business Insider
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Property Details
Condo, 3 Bedrooms, 3 Baths, 8 Sleeps
First floor, luxury condominium in 36-unit, four-story building. Surrounded by multi-million-dollar homes. Located at the beginning of beautiful Frangista Beach only steps from the inviting, sugar, sand beach and emerald-green, gulf waters.
It is located two miles from Silver Sands Outlet Mall and within walking distance to Pompano Joe's and the Whales Tale. Located on the desirable ground floow with no waiting for elevators and ample close parking available.
This condominium has many upgrades including granite countertops, jetted tub, and tiled floors in the living area. King-size bed in the Master bedroom, Queen-size guestroom, Queen-size third bedroom, and Queen-size sleeper sofa. Each Bedroom has its private bath. Full-size washer and dryer, side-by-side refrigerator with ice in door, full kitchen with built-in microwave, and cooking utensils.
This beautifully decorated and fully-equipped condominium will make your vacation in Destin memorable. Condominium is non smokingand no pets allpwed. Rentors must be at least 25 years old.
Heated pool March - April.
Free WiFi
Recent Review
Guestbook CommentWe arrived home yesterday after a great week's stay in your condo. It was perfect for our family. The bottom floor (especially for our three yr. old ...
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Guestbook CommentWe arrived home yesterday after a great week's stay in your condo. It was perfect for our family. The bottom floor (especially for our three yr. old ...
Great Place, Great Price, Great Times
Week in paradise.
Beautiful Place!
Excellent Girls Getaway!
Love it!
The owners, Sandra and Bruce are a pleasure to work with.
Activity and Interests
Biking\moped\funny car rentals
Water Park
Fitness Center\Gym close
Jet Skiing
Cinemas/Movie Theaters
Miniature Golf
Rental rates
- Nightly Rate: $100 - $258
Weekly Rate: $700 - $1800
Monthly Rate: $1400 - $1400
Mar 01-Apr 12....$200/night....$1,200/week
Apr 12-May 24...$175/night...$1050/week
May 24-Aug 2.....$1,800/week
Aug 2-Aug 9........$1,500/week
Aug 16-Sept 6...$175/night....$1050/week
Sept 6-Nov 1....$120/night....$840/week
Nov 1-Feb 28...$100/night....$700/week..$1,400/month *Snow Birds Welcome*
Thanksgiving & Christmas Weeks...$700/week
Damage Deposit $500 by Credit Card Authorization
Add 12% Sales Taxes to Rental Rates & Cleaning Fee $125
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Property Type
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- Adventure
- Family
- Luxury
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Pool / Spa
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- pets not allowed
Location Type
- Beach View
Sports & Adventure Activities
- golf
- 3 Bathrooms
- Bathroom 1
- Bathroom 2
- Bathroom 3
- 3 Bedrooms, Sleeps 8
- Bedroom 1
- Bedroom 2
- Bedroom 3
- Air Conditioning Linens Provided Full size Washer\Dryer Jetted Tub Cable TV\dvd\vcr Well equiped Kitchen Shared Pool\Hot Tub Stero System Wheel Chair Access


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The Union Ministry of Road Transport and Highways has agreed to the State's request for the phased development of five national highway (NH) stretches totalling 672 km at a cost of Rs.357.07 crore.
The Ministry has agreed to commence the work on the 13-km Kollam bypass and the 6.80-km Alappuzha bypass in the next two-and-a-half months on a 50:50 cost-sharing basis, between the National Highways Authority of India (NHAI) and the State. Four-laning of the 44-km Kazhakuttam-Karode bypass will be taken up by the NHAI under the build, operate and transfer (BOT) concept, as it has already been approved as a package.
This was decided at a review meeting of the National Highways Development Project (NHDP) chaired by Union Minister for Road Transport and Highways Oscar Fernandes and attended by Chief Minister Oommen Chandy, Union Minister of State for Road Transport and Highways Sarvey Sathyanarayana, Union Minister of State for Civil Aviation K.C. Venugopal, and Minister for Public Works V.K. Ebrahim Kunju here on Monday.
The five NH stretches that have been delinked from the NHDP are Bodimettu-Kundanoor (167.59 km), Kollam-Aryankavu (81.28 km), Kozhikode-Muthanga (117 km), Kozhikode-Palakkad (125.3 km), and Kollam-Kumily (189 km). The shoulders on these two-lane stretches will be developed with the Union Ministry's funds and road-users will not have to pay any toll. For the Kollam bypass, an 8.2-km stretch, including three bridges, five culverts, one vehicle underpass, and five major junctions, has to be completed. Already, 4.80 km has been two-landed. The estimated cost, as per the detailed project report of the NHAI, is Rs.264.67 crore. A distance of 1.34 km, two rail overbridges, a seven-metre carriage provided below the elevated highway, three pedestrian underpasses, 14 culverts, and two major grade junctions have to be completed for the Alappuzha bypass, estimated to cost Rs.253.83 crore.
Issues regarding the Kozhikode bypass will be sorted out soon before going ahead with the project.
Starting work
Mr. Chandy said his government wanted commencement of the work on the Thiruvananthapuram, Kollam, and Alappuzha bypasses before Onam. The work on the Mannuthy-Walayar stretch will be expedited by the NHAI.
Replying to questions on the alignment of NH stretches proposed for development, Mr. Fernandes said the issues in the State have to be specially looked into as it is different from other States.
On the collection of toll on the roads developed by the NHAI, the Union Minister said the technicalities had to be studied and proper awareness created among the public. Drains need to be constructed in a scientific way for the upkeep of roads.
Keywords: Union Ministry of Road Transport and Highways, Kollam bypass, Alappuzha bypass, Oommen Chandy, Sarvey Sathyanarayana, road development projects


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Home »
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We have a vast inventory of high-quality plastic shredders for plastic processing in many industries. Arlington has single shaft and multi shaft shredders from a wide variety of manufacturers to best suit your applications. These used shredders come in many dimensions and with varying horsepower. The many makes and models of our plastic shredding machines ensure that you will find what you're looking for within our inventory. Plastic shredders are used to reduce larger plastic parts into smaller particles. These size reduction systems can be used to recycle plastic waste so that it is reusable by breaking plastic down into uniform, standardized particles. These consistent particles lead to higher quality products, which is one of the main benefits to plastic shredding machines. Shredders are useful in many applications across a wide range of industries include the plastics, packaging, chemical and automotive industries.
59" Wide Weima Single Shaft Shredder, Model WLK15, 125 HP
22" x 52" SSI Twin Shaft Shredder, Model 1200-E, 50 HP
Weima Single Shaft Shredder, Model WLH600S/30 , 40 hp
Nelmor 27" X 42" 4 Shaft Shredder with Ram Feed Trough
32" x 52" SSI 1500E Dual Shaft Shredder, 50HP
36" Wide Allegany Shredder Model 36-1000C, 100 HP
45" x 75" SSI 3400 HSP Dual Shaft Shredder, 200 HP With Hydraulic Ram
49" x 77" SSI Uni-Shear Single Shaft Shredder, Model SR375E, 150HP, 2006 Vintage
52" x 49" Vecoplan Single Shaft Shredder, Model RG52/100 SWU Short, 100HP, Vintage 2006, With Conveyor
60.5" x 32" Shred-Tech Dual Shaft Shredder, Model ST-100H, 150HP, Manufactured 1998
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Mon-Fri: 7am-1am EST / Sat-Sun: 7am-11pm EST
ApplianceCrosley Refrigerator
Level of DifficultyReally easy
Time to do repairLess than 15 mins
Age of Appliance1 - 4 years
1 out of 1 people found this instruction helpful
It was simply a matter of slipping the new retainer into place -- a ten second job. I was VERY pleased with the service I received through PartSelect.com -- the part was available and was shipped immediately. I had it in two days. Thanks.
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Part Number: PS429868
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José James Songs
Artist: José James
Song: EveryLittleThing
Some people (especially artists) work best in an atmosphere of barely-controlled chaos. José James prefers to have EveryLittleThing in its natural place. That precision and conscientiousness extends... More
Posted one year ago
Artist: José James
Song: Trouble (Oh No Remix)
Look out, here comes trouble! Or, to be more specific, the newly-released Oh No Remix of Trouble, the current single from singer/songwriter José James. Here, the Minneapolis-born, NYC-based... More
Posted 3 years ago
Sample Text - Sample Link


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Zac Clevenger and Alex Harvey were married in Sheridan in June.
Harvey, Clevenger wed in June
Date posted: August 25, 2014
SHERIDAN— Alex Harvey and Zac Clevenger, both of Sheridan, were married June 21 at Whitney Commons.
Harvey attended Sheridan High School and Sheridan College. She currently works at Star Audio.
She is the daughter of Randy and Jean Harvey of Sheridan.
Clevenger attended Tongue River High School and is employed at Knecht Home Center.
He is the son of Cliff and Brenda Clevenger of Sheridan.
The couple will reside in Sheridan until they move to Washington State next year.
Reader Comments
Tell us what you think. The Sheridan Press offers you the chance to comment on articles on Thesheridanpress.com. We power our commenting forum with facebook comments. Please take a look at our participation guidelines before posting.


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Guess its been delayed!!! 😥
Justin come on bro. It's 👕 time!
I deed I deed
morning buuump. how is everyone doin.
I'm doing ****ing fantastic *****
This is still alive?
Awesome... My bitch finally got grumble.. Borla baby !!
i remember this thread.
Dead thread is dead
This thread will soon come alive again after a few of these first shows
I'm waiting for the copycat angry gf threads from other shows to start popping up
Just start one in a more general location... like the car lounge or something
For all shows
Nope keeping this h20 only unless someone else does it
No. No. No!!
Why. Why. Why?!
Because because because?


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ATK and Alenia Aermacchi have successfully completed the first phase of ground and flight testing for the palletised gun system on the MC-27J gunship variant of the tactical transport with the support of the Italian air force.
The system utilises a side-mounted GAU-23 30mm cannon integrated into a to roll-on, roll-off (RO-RO) pallet. The MC-27J has also been modified with an L-3 Wescam MX-15Di electro-optical sensor housed in a nose-mounted turret.
"The completion of successful testing with the Italian air force further validates the capabilities of the RO-RO gun and mission systems and demonstrates that the program is ready for fielding," the companies say.
"This testing builds on previous gun accuracy flight tests performed with the MC-27J at Eglin AFB in 2013 and will provide the aircraft with a vital capability and a greater mission flexibility at an affordable cost."
Alenia and ATK announced in 2012 their plan to jointly fund the development of the MC-27J.
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(Photo: Splash News Online)
Gordon Ramsay appeared on the BBC's Good Food Show this Thursday and refuted claims that he had an affair.
The rumor was fueled by Sara Symonds, who last week insisted in News of the World that she had dated the Hell's Kitchen star for roughly seven years. Ramsay is married to Tana Ramsay.
"I have had a mattress arriving this week. Everyone thought it was because I had to sleep in the basement," Gordon said. "Mattress, mistress, mattress, what a load of bull."
Filming on Gordon's Kitchen Nightmares was postponed days after the allegations became public. Reportedly, Tana wasn't happy about her husband jetting off with this matter still new and unsettled.
Your turn, Sara.


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Headboard & footboard are included. Bed rails (frame) is sold separately.
Dimensions: Headboard: 53"(H) x Footboard: 38"(H)
Finish: Classic Brass
Furnished Over 1 Million Homes Since 1997...
I was very satisfied with One Way Furniture Service. I have referred them to many friends, family, and customers. Thank you. Kathy A. Eisele, Boston, MA
Product Review - Hillsdale Jackson Full Brass Bed
Furniture Brand
As a leader in quality home furniture, Hillsdale Furniture has solidified itself as a top manufacturer. Stretching across a wide range of furniture and accessories from bar stools, dining rooms, kitchen chairs, iron beds, and even bedding comforters, Hillsdale is diversified. Hillsdale furniture is quality craftsmanship that has earned the respect of consumers all over America. Browse our huge selection of Hillsdale Furniture at discount prices from One Way Furniture.
Shop Hillsdale Furniture Specials and save. Hillsdale
Weight & Size
Reference - Sku: 10XXBXR - Hillsdale Jackson Full Brass Bed by Hillsdale