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Optimization for Radiology and Social Media
Ken Goldberg IEOR (EECS, School of Information, BCNM) UC Berkeley College of Engineering Research Council, May 2010
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Outline IEOR Dept, BCNM Radiology Social Media
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UC Berkeley IEOR Department
The only IEOR department in the UC system Ranked #3 in USA 55 BS, 10 BA, 30 MS, 5-8 PhD degrees per year
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IEOR Faculty: Ilan Adler Alper Atamturk Jon Burgstone Ying-Ju Chen Laurent El Ghaoui Ken Goldberg Xin Guo Dorit S. Hochbaum Richard Karp Philip M. Kaminsky Robert C. Leachman Andrew Lim Shmuel S. Oren Christos Papadimitriou Rhonda L. Righter (Chair) Lee W. Schruben Zuo-Jun "Max" Shen Ikhlaq Sidhu Candace Yano
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Mission To critically analyze and shape developments in new media from trans-disciplinary and global perspectives that emphasize humanities and the public interest. bcnm.berkeley.edu
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Humanities Technology Art/Design BioE Philosophy Rhetoric Journalism
Art History Education Architecture iSchool Public Health Film Studies Theater IEOR BAMPFA CITRIS Music EECS Art Practice ME Technology Art/Design BioE New Media Initiative
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Radiology Ken Goldberg, Alper Atamturk, Laurent El Ghaoui (IEOR)
James O’Brien, Jonathan Shewchuck (EECS) I.-C. Hsu, MD, J. Pouliot, PhD (UCSF) Brachytherapy places concentrated radiation doses directly inside cancerous tumors. Catheters are inserted through the perenium under transrectal ultrasound guidance. Catheters contain stopping positions, or dwell positions, where a radioactive source can sit for some dwell time. The dwell times that constitute a dose plan are executed by a remote afterloader. The arrangement of dwell times produces a radiation distribution that kills cancerous cells. Studies have shown that brachytherapy leads to less radiation exposure to healthy tissue than external beam therapy, and it is an outpatient procedure that is becoming increasingly preferred.
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Prostate Cancer 1 in 6 men will be diagnosed with prostate cancer
over 230,000 cases each year in the US one death every 16 minutes
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High Dose Rate Brachytherapy
Brachytherapy places concentrated radiation doses directly inside cancerous tumors. Catheters are inserted through the perenium under transrectal ultrasound guidance. Catheters contain stopping positions, or dwell positions, where a radioactive source can sit for some dwell time. The dwell times that constitute a dose plan are executed by a remote afterloader. The arrangement of dwell times produces a radiation distribution that kills cancerous cells. Studies have shown that brachytherapy leads to less radiation exposure to healthy tissue than external beam therapy, and it is an outpatient procedure that is becoming increasingly preferred.
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Robot Motion Planning Theorem (Completeness): A sensorless plan exists for any polygonal part. Theorem (Complexity): For a polygon of n sides, the algorithm runs in time O(n2) and finds plans of length O(n). Extensions: Stochastically Optimal Plans Extension to Non-Zero Friction Geometric Eccentricity / constant time complexity Part Fixturing and Holding
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Dosimetry: Inverse Planning
Color Dose Blue < 75% Cyan 75% - 100% Green 100% - 120% Yellow 120% - 150% Orange 150% - 200% Red > 200% Index Requirement VP100 > 90% HI > 60% VU120 < 0.1 cc VR75 < 1.0 cc VB75 Inverse Planning: Dose planning utilizing anatomical information to conform high-dose region to cancer tissue. The inverse planning objective is to find dwell times that provide conformal dose on cancerous tissue while protecting surrounding organs from unnecessary radiation exposure. Quantification of dose quality is difficult from visualization alone (isodose lines, left). We use clinical indices (dosimetric indices) to get a quantified idea of how the overall distribution looks. We place constraints on the dose distribution through the dosimetric indices (right). Isodose lines: One image per CT slice. Many slices represent 3D picture. Ideally, everything inside the prostate would be green, except the urethra, and then everything else would be blue. Dose delivery percentages are in units of % of prescription dose. Dosimetric criteria: Rx = prescription dose. VP100 = percentage volume of prostate receiving more than 100% Rx. HI = homogeneity index = (VP100 – VP150)/VP100. VP150 = percentage volume of prostate receiving more than 150% Rx. VU120 = volume of urethra receiving at least 120% Rx. VR75 and VB75 = volume of rectum/bladder receiving at least 75% Rx. Dose Distribution Dosimetric Criteria
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Inverse Planning with Simulated Annealing (IPSA)
Inverse planning software developed at UCSF by Pouliot group FDA-approved: used clinically worldwide Simulated annealing dose point penalty method IPSA (Inverse Planning Simulated Annealing) Currently most dose planning models (including IPSA) uses penalties to get good dose distributions. Organ volume (dose points) is penalized for receiving dose outside of a specified range. These penalties grow linearly the farther the dose falls outside the range. The optimization objective is to determine a set of dwell times that minimizes the total penalty contribution from every dose point. It was shown by Prof. Ron Alterovitz in 2006 (now at UNC) in the Goldberg group that this could be solved efficiently using linear programming. Linear programming has superior performance guarantees over simulated annealing such as a guaranteed globally optimal solution in a finite number of iterations, it can be solved efficiently in polynomial time, etc. The findings by Alterovitz show that LP optimal dose plans are very close to SA dose plans.
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Inverse Planning with Linear Programming (IPLP)
LP formulation (UC Berkeley) Guarantees global optima Optimization of HDR Brachytherapy Dose Distributions using Linear Programming with Penalty Costs. Ron Alterovitz, Etienne Lessard, Jean Pouliot, I-Chow Joe Hsu, James F. O'Brien, and Ken Goldberg. Medical Physics, vol. 33, no. 11, pp , Nov IPSA (Inverse Planning Simulated Annealing) Currently most dose planning models (including IPSA) uses penalties to get good dose distributions. Organ volume (dose points) is penalized for receiving dose outside of a specified range. These penalties grow linearly the farther the dose falls outside the range. The optimization objective is to determine a set of dwell times that minimizes the total penalty contribution from every dose point. It was shown by Prof. Ron Alterovitz in 2006 (now at UNC) in the Goldberg group that this could be solved efficiently using linear programming. Linear programming has superior performance guarantees over simulated annealing such as a guaranteed globally optimal solution in a finite number of iterations, it can be solved efficiently in polynomial time, etc. The findings by Alterovitz show that LP optimal dose plans are very close to SA dose plans.
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Limitations of Penalty Model
Only specifies dosimetry at dose points, not to organs Not equivalent to dosimetric indices Not intuitive for Physicians Results not always clinically viable. Results difficult to customize for special cases Dosimetric index: if dose at x > R, then x = 1, x = 0 otherwise Discrete Variables Using penalties has worked well so far, and brachytherapy has very high cure rates. However, there are limitations. Constraints on dosimetric indices are apply to all the dose points within an organ (i.e. they specificy how many dose points can receive higher than a certain dose). Penalties are only applied dose point by dose point. Therefore, penalty constraints are not dosimetric constraints, which is what clinicians use to measure the quality of dose plans. As a result, IPSA and IPLP have no information on dosimetric indices. Also, information about individuals is lost as penalties from different organs are aggregated together. Limitations: (1) Does not always return a clinically viable solution, (2) difficult to customize, (3) cannot be easily incorporated with needle planning (i.e. As a result, IPSA converges to dose plans that cannot be delivered and therefore must be tweaked by the physician. This can be a cumbersome and time consuming process, especially for newcomers. For the same reason, dose plans are difficult to customize to meet patient specific needs. For example, some patients have strict requirements for dose to the urethra. Finally, the process of dose planning becomes spoken in the language of penalties rather dosimetry – where clinicians are most comfortable.
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Inverse Planning with Integer Programming (IP2)
Indices s: organ i: point in organ j: dwell position Variables tj: dwell time at j xsi: counting variable for s,i Parameters: Dsij: dose rate from j to s,i Rs: Dose threshold for s Ms: Max dose for points in s Ls: Lower bound for dosimetric s Us: Upper bound for dosimetric s Model Maximize Σ x0i Subject to: Σ Dsij tj ≥ Rs xsi Σ Dsij tj ≤ Rs + (Ms – Rs) xsi Ls ≤ Σ xsi ≤ Us tj ≥ 0 xsi є {0,1}
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Initial Results: Comparing IPSA with IP2
Average Runtime (sec): IPSA: 5 IP2 (heuristic 1): 23 IP2 (heuristic 2): 900 Compliance with all clinical criteria IPSA: 0% of patients IP2 (heuristic 1): 95% of patients IP2 (heuristic 2): 100% of patients IPSA did not comply with all of our dosimetric specifications for any of the patient data sets. They would have needed modifications or “tweaking” by the physician. We currently have two heuristics for solving IP2 (IP2 cannot yet be solved to optimality efficiently). Heuristics already work very well. The first heuristic complied with all criteria for every patient except for one (where it missed the criteria by 1%). The second heuristic complied for every patient data set. However, our heuristics also take much longer than IPSA. Heuristic 1 is still fast enough to be clinically usable. Heuristic 2 takes far too long.
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IP2 for Needle Reduction
Minimize number of needles Minimize trauma Speed Recovery The insertion of needles into tissue is a major source of side effects and complications such as impotence and incontinence. A major advantage of IP2 over IPSA and IPLP is that the optimization of needle placement can be elegantly incorporated into integer programming model. Possible Needles Optimal Needle Selection (example)
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Future Work Conic Optimization Robust Optimization
Model uncertainties in: Organ location, motion Edema Catheter displacement Sorry I couldn’t be more help on this slide. Conic optimization refers to a more general type of optimization. For instance, linear programming is a type of conic optimization, but integer programming is not. I assume that Alper just meant applying continuous-variable optimization to brachytherapy when he said “conic optimization”. We have already been doing this as well as integer programming.
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Tissue Simulation Nuttapong Chentanez, Ron Alterovitz, Daniel Ritchie, Lita Cho, Kris K. Hauser, Ken Goldberg, Jonathan R. Shewchuk, and James F. O'Brien. "Interactive Simulation of Surgical Needle Insertion and Steering". In Proceedings of ACM SIGGRAPH 2009, pages 88:1–10, Aug 2009.
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University of California, Berkeley
Superhuman Performance of Surgical Tasks by Robots using Iterative Learning from Human-Guided Demonstrations Jur van den Berg, Stephen Miller, Daniel Duckworth, Humphrey Hu, Andrew Wan, Xiao-Yu Fu, Ken Goldberg, Pieter Abbeel University of California, Berkeley
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Method: 1. Robot learns surgical task from human demonstrations
Knot tying Suturing 2. Robot learns to execute tasks with superhuman performance Increase smoothness Increase speed
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Social Media Ken Goldberg, Gail de Kosnik, Kimiko Ryokai
Alec Ross, Katie Dowd (US State Dept) Brachytherapy places concentrated radiation doses directly inside cancerous tumors. Catheters are inserted through the perenium under transrectal ultrasound guidance. Catheters contain stopping positions, or dwell positions, where a radioactive source can sit for some dwell time. The dwell times that constitute a dose plan are executed by a remote afterloader. The arrangement of dwell times produces a radiation distribution that kills cancerous cells. Studies have shown that brachytherapy leads to less radiation exposure to healthy tissue than external beam therapy, and it is an outpatient procedure that is becoming increasingly preferred.
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collaborative robot control:
… … Batch … MultiTasking … Collaborative
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Motivation Goals of Organization Goals of Community Engage community
Understand community Solicit input Understand the distribution of viewpoints Discover insightful comments Goals of Community Understand relationships to other community members Consider a diversity of viewpoints Express ideas, and be heard
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Motivation Classical approaches: surveys, polls Drawbacks: limited samples, slow, doesn’t increase engagement Current approaches: online forums, comment lists Drawbacks: data deluge, cyberpolarization, hard to discover insights
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Related Work: Visualization
Clockwise, starting from top left: Morningside Analytics, MusicBox, Starry Night
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Related Work: Info Filtering
K. Goldberg et al, 2001: Eigentaste E. Bitton, 2009: spatial model Polikar, 2006: ensemble learning
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Six 50-minute Learning Object Modules, preparation materials, slides for in-class lectures, discussion ideas, hand-on activities, and homework assignments.
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Canonical Correlation Analysis (CCA)
z Observed variables: x, y Latent variable: z Learn MLEs for low-rank projections A and B Equivalently, find inverse mapping that maximizes correlation between A, B x y Graphical model for CCA x = Az + ε y = Bz + ε z = A-1x = B-1y
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z x y
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Canonical Correlation Analysis (CCA)
CCA gives three posterior expectations E(z|x) E(z|y) E(z|x,y) E(z|x,y) is used to visualize the opinion space Opinion Vector x z y Textual Comment The dose planning objective is to find dwell times that provide conformal dose on cancerous tissue while protecting surrounding organs from unnecessary radiation exposure. Quantification of dose quality is difficult from visualization alone (isodose lines, left). We use clinical indices (dosimetric indices) to get a quantified idea of how the overall distribution looks. We place constraints on the dose distribution through the dosimetric indices (right). Isodose lines: One image per CT slice. Many slices represent 3D picture. Ideally, everything inside the prostate would be green, except the urethra, and then everything else would be dark blue. Dose delivery percentages are in units of % of prescription dose. Dosimetric criteria: Rx = prescription dose. VP100 = percentage volume of prostate receiving more than 100% Rx. HI = homogeneity index = (VP100 – VP150)/VP100. VP150 = percentage volume of prostate receiving more than 150% Rx. VU120 = volume of urethra receiving at least 120% Rx. VR75 and VB75 = volume of rectum/bladder receiving at least 75% Rx.
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Canonical Correlation Analysis (CCA)
Each point in the Canonical representation has an expected list of words associated to it. A visualization of this list of words can be used to give users more information about their location The dose planning objective is to find dwell times that provide conformal dose on cancerous tissue while protecting surrounding organs from unnecessary radiation exposure. Quantification of dose quality is difficult from visualization alone (isodose lines, left). We use clinical indices (dosimetric indices) to get a quantified idea of how the overall distribution looks. We place constraints on the dose distribution through the dosimetric indices (right). Isodose lines: One image per CT slice. Many slices represent 3D picture. Ideally, everything inside the prostate would be green, except the urethra, and then everything else would be dark blue. Dose delivery percentages are in units of % of prescription dose. Dosimetric criteria: Rx = prescription dose. VP100 = percentage volume of prostate receiving more than 100% Rx. HI = homogeneity index = (VP100 – VP150)/VP100. VP150 = percentage volume of prostate receiving more than 150% Rx. VU120 = volume of urethra receiving at least 120% Rx. VR75 and VB75 = volume of rectum/bladder receiving at least 75% Rx.
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Almost every modern dose planning model uses penalties to get good dose distributions. Organ volume is penalized for receiving dose outside of a specified range. These penalties grow linearly the farther the dose falls outside the range. The optimization objective is to determine a set of dwell times that minimizes the total penalty contribution from every dose point. It was shown by Prof. Ron Alterovitz in 2006 (now at UNC) in the Goldberg group that this could be solved efficiently using linear programming. Using penalties has worked well so far, and brachytherapy has very high cure rates. However, there are limitations. Limitations: (1) Does not always return a clinically viable solution, (2) difficult to customize, (3) cannot be easily incorporated with needle planning (i.e. optimizing the configuration of needles). (1) and (2) because using penalties aggregates all organs together, and therefore, information in each individual organ is lost. (3) because the only way to incorporate needles into the optimization is to assign a large lump penalty, which does not ensure that the least number of needles is being used.
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Ken Goldberg, UC Berkeley
Opinion Space: Crowdsourcing Insights Scalability: n Participants, n Viewpoints n2 Peer to Peer Reviews Viewpoints are k-Dimensional Dim. Reduction: 2D Map of Affinity/Similarity Insight vs. Agreement: Nonlinear Scoring Ken Goldberg, UC Berkeley Alec Ross, U.S. State Dept
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Optimization for Radiology and Social Media
Ken Goldberg IEOR (EECS, School of Information, BCNM) UC Berkeley College of Engineering Research Council, May 2010
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With the idea that one should consider the various ways a Politics 2
With the idea that one should consider the various ways a Politics 2.0 project is open for participation and keeping in mind that there are important differences between the transparencies of a window or a glass ceiling and the openness of an open door, I would like to show one project, Metavid, that began life as Aphid Stern and Michael Dale’s collaborative MFA project for the UC Santa Cruz Digital Arts and New Media program. Mark Deckert, a Ph.D. student in Computer Science has joined us. The project is now supported by grants from the Sunlight Foundation and the National Science Foundation.
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With the idea that one should consider the various ways a Politics 2
With the idea that one should consider the various ways a Politics 2.0 project is open for participation and keeping in mind that there are important differences between the transparencies of a window or a glass ceiling and the openness of an open door, I would like to show one project, Metavid, that began life as Aphid Stern and Michael Dale’s collaborative MFA project for the UC Santa Cruz Digital Arts and New Media program. Mark Deckert, a Ph.D. student in Computer Science has joined us. The project is now supported by grants from the Sunlight Foundation and the National Science Foundation.
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IP2 Heuristics Capping Hard Cuts
Allocate dose budget to dose points that are likely to need it. : Solve LP relaxation Analyze solution and impose new constraints on hottest dose points. Resolve to feasible solution. Hard Cuts Apply custom cuts so that IP2 emphasizes dosimetric indices. : Solve LP relaxation. Add cuts to incorrectly counted dose points. Repeat until feasible for IP2
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Hard Cuts x Hard cut 1 Fractional Optimal Solution (cut off by Hard cut) Constraints When IPs are solved, they are initially relaxed. This induces “fractional” solutions (i.e. solutions where a binary variable is neither 0 nor 1). The new constraint cuts off this fractional solution. However, it also cuts off feasible integer solutions, hence the name “Hard cut”. dose
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Dimensionality Reduction
Principal Component Analysis (PCA) Assumes independence and linearity Minimizes squared error Scalable: compute position of new user in constant time
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Approach: Visualization
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Approach: Level the Playing Field
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Approach: Wisdom of Crowds
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“We’re moving from an Information Age to an Opinion Age.”
- Warren Sack, UCSC
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Berkeley Center for New Media (BCNM):
David Wong: EECS Undergraduate Student Tavi Nathanson: EECS Graduate Student Ephrat Bitton: IEOR Graduate Student Siamak Faridani: IEOR Graduate Student Elizabeth Goodman: School of Information Graduate Student Alex Sydell: EECS Undergraduate Student Meghan Laslocky: Outside Consultant on Content Ari Wallach: Outside Consultant on Content and Strategy Steve Weber: Outside Consultant on Content Peter Feaver: Outside Consultant on Content U.S. State Department: Alec Ross: Senior Advisor for Innovation Katie Dowd: New Media Director Daniel Schaub: Director for Digital Communications 48
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Multidimensional Scaling
Goal: rearrange objects in low dim space so as to reproduce distances in higher dim Strategy: Rearrange & compare solns, maximizing goodness of fit: Can use any kind of similarity function Pros Data need not be normal, relationships need not be linear Tends to yield fewer factors than FA Con: slow, not scalable j δij i j i dij
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Kernel-based Nonlinear PCA
Intuition: in general, can’t linearly separate n points in d < n dim, but can almost always do so in d ≥ n dim Method: compute covariance matrix after transforming data into higher dim space Kernel trick used to improve complexity If Φ is the identity, Kernel PCA = PCA
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Kernel-based Nonlinear PCA
Input data KPCA output with Gaussian kernel Pro: Good for finding clusters with arbitrary shape Cons: Need to choose appropriate kernel (no unique solution); does not preserve distance relationships
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Stochastic Neighbor Embedding
Converts Euclidean dists to conditional probabilities pj|i = Pr(xi would pick xj as its neighbor | neighbors picked according to their density under a Gaussian centered at xi) Compute similar prob qj|i in lower dim space Goal: minimize mismatch between pj|i and qj|i: Cons: tends to crowd points in center of map; difficult to optimize
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With the idea that one should consider the various ways a Politics 2
With the idea that one should consider the various ways a Politics 2.0 project is open for participation and keeping in mind that there are important differences between the transparencies of a window or a glass ceiling and the openness of an open door, I would like to show one project, Metavid, that began life as Aphid Stern and Michael Dale’s collaborative MFA project for the UC Santa Cruz Digital Arts and New Media program. Mark Deckert, a Ph.D. student in Computer Science has joined us. The project is now supported by grants from the Sunlight Foundation and the National Science Foundation.
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With the idea that one should consider the various ways a Politics 2
With the idea that one should consider the various ways a Politics 2.0 project is open for participation and keeping in mind that there are important differences between the transparencies of a window or a glass ceiling and the openness of an open door, I would like to show one project, Metavid, that began life as Aphid Stern and Michael Dale’s collaborative MFA project for the UC Santa Cruz Digital Arts and New Media program. Mark Deckert, a Ph.D. student in Computer Science has joined us. The project is now supported by grants from the Sunlight Foundation and the National Science Foundation.
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Canonical Correlation Analysis (CCA)
Brachytherapy places concentrated radiation doses directly inside cancerous tumors. Catheters are inserted through the perenium under transrectal ultrasound guidance. Catheters contain stopping positions, or dwell positions, where a radioactive source can sit for some dwell time. The arrangement of dwell times produces a radiation distribution that kills cancerous cells. Studies have shown that brachytherapy leads to less radiation exposure to healthy tissue than external beam therapy, and it is an outpatient procedure that is becoming increasingly preferred. CCA visualization with tag cloud for that location in the space. The tag cloud uses stemmed keywords.
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Six 50-minute Learning Object Modules, preparation materials, slides for in-class lectures, discussion ideas, hand-on activities, and homework assignments.
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Opinion Space Wisdom of Crowds: Insights are Rare
Scalable, Self-Organizing, Spatial Interface Visualize Diversity of Viewpoints Incorporate Position into Scoring Metrics Ken Goldberg UC Berkeley
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Metavid With the idea that one should consider the various ways a Politics 2.0 project is open for participation and keeping in mind that there are important differences between the transparencies of a window or a glass ceiling and the openness of an open door, I would like to show one project, Metavid, that began life as Aphid Stern and Michael Dale’s collaborative MFA project for the UC Santa Cruz Digital Arts and New Media program. Mark Deckert, a Ph.D. student in Computer Science has joined us. The project is now supported by grants from the Sunlight Foundation and the National Science Foundation.
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With the idea that one should consider the various ways a Politics 2
With the idea that one should consider the various ways a Politics 2.0 project is open for participation and keeping in mind that there are important differences between the transparencies of a window or a glass ceiling and the openness of an open door, I would like to show one project, Metavid, that began life as Aphid Stern and Michael Dale’s collaborative MFA project for the UC Santa Cruz Digital Arts and New Media program. Mark Deckert, a Ph.D. student in Computer Science has joined us. The project is now supported by grants from the Sunlight Foundation and the National Science Foundation.
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With the idea that one should consider the various ways a Politics 2
With the idea that one should consider the various ways a Politics 2.0 project is open for participation and keeping in mind that there are important differences between the transparencies of a window or a glass ceiling and the openness of an open door, I would like to show one project, Metavid, that began life as Aphid Stern and Michael Dale’s collaborative MFA project for the UC Santa Cruz Digital Arts and New Media program. Mark Deckert, a Ph.D. student in Computer Science has joined us. The project is now supported by grants from the Sunlight Foundation and the National Science Foundation.
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Optimization for Radiology Treatment and Visualizing Public Opinion
Ken Goldberg Alec Ross, Director of Innovation, U.S. State Dept
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Ken Goldberg, UC Berkeley
Opinion Space: Crowdsourcing Insights Scalability: N Participants, N Viewpoints Each Viewpoint is n-Dimensional Dim. Reduction: 2D Map of Affinity/Similarity Insight vs. Agreement: Nonlinear Scoring N2 Peer to Peer Reviews Ken Goldberg, UC Berkeley Alec Ross, U.S. State Dept
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Objective Function Improvement over IPSA
Statistically significant improvement (P = 1.5410-7)
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Standard Dosimetric Indices
No significant improvement in any dosimetric index (P > 0.01)
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Prostate Dose Volume Histogram
950 cGy 1425 cGy
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Isodose Curves LP IPSA
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