1. Coupling Informatics Algorithm Development and Visual Analysis Danny Dunlavy, Pat Crossno, Tim Shead Sandia National Laboratories SIAM Annual Meeting July 7, 2008 SAND2008-4470P Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000.

2. APW19990519.0113 1999-05-19 21:11:17 usa Pulses May Ease SchizophrenicVoices WASHINGTON (AP)Schizophrenia patients whose medication couldn't stop the imaginary voices in theirheads gained some relief after researchers repeatedly sent a magnetic field into asmall area of their brains. About half of 12 patients studied said their hallucinations becamemuch less severe after the treatment, which feels like ``having a woodpeckerknock on your head'' once a second for up to 16 minutes, said researcherDr.Ralph Hoffman. The voices stopped completely in three of these patients. The effect lasted for up toa few days for most participants, and one man reported that it lasted seven weeksafterbeing treated daily for four days. Hoffman stressed that thestudy is only preliminary and can't prove that the treatment would be useful. ``We need to do much moreresearch on this,'' he said in an interview. Hoffman, deputy medicaldirector of the Yale Psychiatric Institute, is scheduled to present the workThursday at the annual meeting of the American Psychiatric Association. Not all people withschizophrenia hear voices, and of those who do, Hoffman estimated that maybe 25percent can't control them with medications even when other disease symptomsabate. So the workcould pay off for ``a small but very ill group of patients,'' he said. The treatment is calledtranscranial magnetic stimulation, or TMS. While past research indicates it mightbe helpful in lifting depression, it hasn't been studied much in schizophrenia. In TMS, anelectromagnetic coil is placed on the scalp and current is turned on and off to create apulsing magnetic field that reaches into a small area of the brain. The goal is to make brain cellsunderneath the coil fire messages to adjoining cells. The procedure is muchdifferent from electroconvulsive therapy, called ECT, which applies pulses ofelectricity rather than a magnetic field to the brain. Unlike TMS, ECT creates a briefseizure and is performed under general anesthesia. ECT is used most often fortreatingsevere depression. In TMS, the magnetic pulses are thought to calm the affected partof the brain if they're given as slowly as once per second, Hoffman said. He and colleagues targeted an area involved in understanding speech, above and behind the left ear, on the theory that hallucinated voices come from overactivity there. The treatment can make scalp muscles muscle contract, leading tothe woodpecker feeling, he said, but patients could tolerate it. Headachewas the most common side effect, and there was no sign that the treatment affected the ability to understandspeech, he said. To make sure the study resultsdidn'treflect just the psychological boost of getting a treatment, researchers gave sham and real treatments to each studyparticipant and studied the difference in how patients responded. <s docid="APW19990519.0113" num="26" starting with

3. Latent Semantic Analysis terms documents d1d1 d2d2 dndn t2t2 t1t1 tmtm … d3d3 d4d4...... Truncated SVD Concept space Information retrieval Clustering Doc & term relationships Text corpus low rank approximation terms concepts documents concepts singular values

4. Concept Space ∆policy ∆planning ∆politics ∆tomlinson ∆1986 oSport in Society: policy, Politics and Culture, ed A. Tomlinson (1990) oPolicy and Politics in Sport, PE and Leisure eds S. Fleming, M. Talbot and A. Tomlinson (1995) oPolicy and Planning (II), ed J. Wilkinson (1986) oPolicy and Planning (I), ed J. Wilkinson (1986) oLeisure: Politics, Planning and People, ed A. Tomlinson (1985) ∆parker ∆lifestyles ∆1989 ∆part oWork, Leisure and Lifestyles (Part 2), ed S. R. Parker (1989) oWork, Leisure and Lifestyles (Part 1), ed S. R. Parker (1989) [Leisure Studies of America Data]

5. Document parsing, matrix creation and weighting SVD: Truncated SVD: Query scores (query as new “doc”): LSA Ranking: Document similarities: Term Similarities: Similarity statistics –Mean, standard deviation ParaText™ Operations (thresholded → sparse) term concepts documents concepts singular values T  DTDT

6. Document similarity matrix Document similarity graph Each document (or term, entity, etc.) is a vertex Each row defines an edge Document Similarity Graphs documents concepts documents concepts singular values threshold sparse coordinate format

7. Statistics on edges –One graph: one-sample t statistic –Two graphs: two-sample t statistic Similarity Statistics Graph 1 Graph 2

8. Doc Sim Graph Comparison k = 10 k = 40 [DUC 2003, Task 2 Data: 297 documents, 30 manual clusters]

9. Layout Comparison Force directed Simple 2D [DUC 2003, Task 2 Data: 297 documents, 30 manual clusters]

10. Sparse Matrix View

11. Rank Comparison k = 40 k = 10 [DUC 2003, Task 2 Data: 297 documents, 30 manual clusters]

12. Matrix Differences k = 40 k = 10 [DUC 2003, Task 2 Data: 297 documents, 30 manual clusters]

13. Small Multiples k = 10 k = 20 k = 30 k = 40 k = 20 k = 30k = 50 [DUC 2003, Task 2 Data: 297 documents, 30 manual clusters]

14. LSAView

15. LSAView Impact Document similarities: Inner product view: Scaled inner product view: What is the best scaling for document similarity graph generation? original scalingno scalinginverse sqrtinverse [Leisure Studies of America Data]

16. Conclusions LSAView –Analysis and exploration of impact of informatics algorithms on end-user visual analysis of data –Aids in discovery process of optimal algorithm parameters for given data and tasks Impact –Used in developing and understanding ParaText™ and LSALIB algorithms Future Work –Other graph-based metrics Diameter, cycles, vertex degree distribution, shortest cycle length, etc. –Other Decompositions and algorithms Incremental SVD, SDD, CUR, Clustering –Other statistics/inference tests and visualization –New problem domains

17. Coupling Informatics Algorithm Development and Visual Analysis Danny Dunlavy Email: dmdunla@sandia.govdmdunla@sandia.gov URL: http://www.cs.sandia.gov/~dmdunlahttp://www.cs.sandia.gov/~dmdunla