This study is to evaluate highly accelerated 3D dynamic contrast-enhanced (DCE) wrist MRI for assessment of perfusion in rheumatoid arthritis (RA) patients. A pseudo-random variable-density undersampling strategy, CIRcular Cartesian UnderSampling (CIRCUS), was combined with k-t SPARSE-SENSE reconstruction to achieve a highly accelerated 3D DCE wrist MRI. Two healthy volunteers and ten RA patients were studied. Two patients were on methotrexate (MTX) only (Group I) and the other eight were treated with a combination therapy of MTX and Anti-Tumour Necrosis Factor (TNF) therapy (Group II). Patients were scanned at baseline and 3-month follow-up. DCE MR images were used to evaluate perfusion in synovitis and bone marrow edema pattern in the RA wrist joints. A series of perfusion parameters were derived and compared with clinical disease activity scores of 28 joints (DAS28). 3D DCE wrist MR images were obtained with a spatial resolution of 0.3×0.3×1.5mm3 and temporal resolution of 5 s (with an acceleration factor of 20). The derived perfusion parameters, most notably, transition time (dT) of synovitis, showed significant negative correlations with DAS28-ESR (r=-0.80, p<0.05) and DAS28-CRP (r=-0.87, p<0.05) at baseline and also correlated significantly with treatment responses evaluated by clinical score changes between baseline and 3-month follow-up (with DAS28-ESR: r=-0.79, p<0.05, and DAS28-CRP: r=-0.82, p<0.05). Highly accelerated 3D DCE wrist MRI with improved temporospatial resolution has been achieved in RA patients and provides accurate assessment of neovascularization and perfusion in RA joints, showing promise as a potential tool for evaluating treatment responses.

Consumers have been banking and trading online for several years now. More ambitious and tech savvy consumers have also been constructing an overview of their financial life by using Personal Finance software like Quicken and online tools such as Yodlee and Mint.com. Since late 1999, Personal Financial Aggregators (PFAs) have started offering internet based services to automate this process of account aggregation. This web account aggregation allows individuals to log onto one Web site and view all of their online accounts in one place. Online accounts that can be aggregated include financial sites (bank, credit card, brokerage, insurance, etc.) as well as lifestyle-based sites (travel awards, email, chat rooms, etc.). The idea behind Personal Financial Aggregation is to offer consumers their own personal portal from which they can see all their finances at a glance, balance and rebalance accounts, make investments, pay bills, etc. In addition to this Web data aggregation, consumers are relying on social media sites such as facebook, tweeter and other internet forums to get financial advice from each other and also to critique various financial products and services. As a result, many Financial Institutions (FIs) are using social media analysis and mining to shape their businesses. FIs include consumer banks, brokerages, insurance, wealth management firms, etc. This paper presents a framework for financial institutions that combines social media mining, web mining, online advice engines, and web aggregation. This framework can be utilized by FIs to analyze online buzz about their products/services and combine those insights with web aggregation and online advice to create different revenue streams and to offer personalized bundled products and services. The authors conducted interviews with various executives at the Global Financial institutions and insurance companies to test and validate this framework. A comprehensive review of top service providers and vendors that can enable and drive this framework is also discussed in this paper, followed by managerial implications, benefits and challenges.

As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute.

No abstract

As the Internet grows in size, so does the amount of text based information that exists. For many application spaces it is paramount to isolate and identify texts that relate to a particular topic. While one-class classification would be ideal for such analysis, there is a relative lack of research regarding efficient approaches with high predictive power. By noting that the range of documents we wish to identify can be represented as positive linear combinations of the Vector Space Model representing our text, we propose Conical classification, an approach that allows us to identify if a document is of a particular topic in a computationally efficient manner. We also propose Normal Exclusion, a modified version of Bi-Normal Separation that makes it more suitable within the one-class classification context. We show in our analysis that our approach not only has higher predictive power on our datasets, but is also faster to compute. Dataset Model Accuracy Balanced Accuracy Precision Recall F1 Score Time (s) ECommerce Linear OCSVM 0.900 ± 0.001 0.755 ± 0.003 0.978 ± 0.002 0.512 ± 0.007 0.672 ± 0.006 183.255 ± 3.821 RBF OCSVM 0.899 ± 0.001 0.753 ± 0.004 0.981 ± 0.003 0.508 ± 0.008 0.669 ± 0.007 201.339 ± 3.792 Sigmoid OCSVM 0.899 ± 0.002 0.752 ± 0.005 0.979 ± 0.003 0.508 ± 0.011 0.668 ± 0.009 193.402 ± 7.382 Poly OCSVM 0.885 ± 0.006 0.712 ± 0.002 0.995 ± 0.001 0.424 ± 0.003 0.595 ± 0.003 183.813 ± 2.639 IsolFor 0.199 ± 0.000 0.500 ± 0.000 0.199 ± 0.000 1.000 ± 0.000 0.333 ± 0.000 280.893 ± 12.543 OCRF 0.953 ± 0.000 0.947 ± 0.0176 0.800 ± 0.000 0.898 ± 0.036 0.889 ± 0.000 189.252 ± 244.041 PNB 0.638 ± 0.202 0.762 ± 0.092 0.786 ± 0.203 0.757 ± 0.305 0.687 ± 0.153 111.839 ± 59.581 GloVe CVDD 0.948 ± 0.017 0.906 ± 0.026 0.951 ± 0.015 0.983 ± 0.007 0.967 ± 0.011 188.462 ± 12.773 BERT CVDD 0.951 ± 0.128 0.910 ± 0.013 0.954 ± 0.017 0.987 ± 0.012 0.973 ± 0.028 233.626 ± 23.796 CC 0.988 ± 0.009 0.988 ± 0.007 0.956 ± 0.005 0.988 ± 0.009 0.971 ± 0.002 10.878 ± 0.036 FakeNews Linear OCSVM 0.818 ± 0.039 0.685 ± 0.082 0.819 ± 0.157 0.430 ± 0.232 0.495 ± 0.175 496.206 ± 5.353 RBF OCSVM 0.813 ± 0.033 0.706 ± 0.065 0.772 ± 0.184 0.500 ± 0.229 0.538 ± 0.086 533.529 ± 11.002 Sigmoid OCSVM 0.760 ± 0.046 0.648 ± 0.091 0.757 ± 0.259 0.436 ± 0.347 0.389 ± 0.166 514.913 ± 19.340 Poly OCSVM 0.850 ± 0.014 0.722 ± 0.053 0.846 ± 0.071 0.476 ± 0.128 0.592 ± 0.091 470.485 ± 2.950 IsolFor 0.238 ± 0.000 0.500 ± 0.000 0.238 ± 0.000 1.000 ± 0.000 0.384 ± 0.000 278.308 ± 5.449 OCRF 0.930 ± 0.000 0.955 ± 0.000 0.761 ± 0.000 1.000 ± 0.000 0.864 ± 0.000 197.232 ± 255.352 PNB 0.624 ± 0.223 0.773 ± 0.097 0.900 ± 0.142 0.697 ± 0.283 0.729 ± 0.152 218.296 ± 38.515 GloVe CVDD 0.906 ± 0.003 0.881 ± 0.011 0.938 ± 0.018 0.936 ± 0.022 0.936 ± 0.002 282.293 ± 31.88 BERT CVDD 0.899 ± 0.121 0.878 ± 0.218 0.923 ± 0.342 0.933 ± 0.231 0.927 ± 0.153 322.513 ± 49.659 CC 0.985 ± 0.005 0.985 ± 0.004 0.955 ± 0.002 0.987 ± 0.006 0.969 ± 0.001 13.952 ± 0.026 Jobs Linear OCSVM 0.913 ± 0.001 0.768 ± 0.004 0.971 ± 0.002 0.540 ± 0.009 0.694 ± 0.007 368.263 ± 6.227 RBF OCSVM 0.91...