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= 08/19/19 Presentations = | = 08/19/19 Presentations = | ||
''' Slides https://odu.app.box.com/s/4yfdokkhyyqge2zx1jlv8gctw4zw6w2q ''' | |||
== Nikos Chrisochoides == | == Nikos Chrisochoides == | ||
| Line 93: | Line 93: | ||
* Workflow: Measurements | * Workflow: Measurements | ||
** Theory: Model of proton structure (parameters that need to be verified experimentally) | ** Theory: Model of proton structure (parameters that need to be verified experimentally) | ||
** | ** Predictions for scattering probabilities (cross section) | ||
** Phase space distributions (meeting ground between theory and experiments) | ** Phase space distributions (meeting ground between theory and experiments) | ||
** Scattering Experiments -> Raw Data | ** Reconstruction based on the raw data (detector effects) | ||
** | ** Scattering Experiments -> Raw Data (kinematic coverage statistics) | ||
** Simulations (can be run with actual or mock data to test reconstruction /feasibility) | |||
** Modify theoretical model based on observations | ** Modify theoretical model based on observations | ||
** "Final" picture of proton from synthesis of multiple experiments and theoretical models | ** "Final" picture of proton from synthesis of multiple experiments and theoretical models | ||
* Phase space distributions | * Phase space distributions | ||
** Use imaging techniques(e.g | ** Use imaging techniques(e.g tessellation, slicing, viewing) | ||
** 10^6 -10^9 experiments | ** 10^6 -10^9 experiments | ||
** View distributions, projections, cuts, features | ** View distributions, projections, cuts, features | ||
| Line 118: | Line 119: | ||
== Ron Kikinis == | == Ron Kikinis == | ||
* Making data machine readable | |||
** Critical for effective/accessible data | |||
* DICOM (standard for medical images) | |||
* 3D Slicer | * 3D Slicer | ||
* Browser based versions | * Browser based versions | ||
** ohif.org | ** ohif.org | ||
** openanatomy.org | ** openanatomy.org | ||
* Look at data from different vantage points | |||
* SlicerAstro | * SlicerAstro | ||
** colors to show data most helpful to doctors, students, etc. | |||
** Use of Slicer3D in astronomy | ** Use of Slicer3D in astronomy | ||
* Punzo et al 2015 | * Punzo et al 2015 | ||
| Line 129: | Line 134: | ||
== Alexandra Golby == | == Alexandra Golby == | ||
* In the past doctors would infer where the problem | * In the past doctors would infer where the problem wass by the symptoms. They would then open the brain and see if they were right | ||
* Now: Brain organization -> functional connectivity | * Now: Brain organization -> functional connectivity | ||
* Functional MRI measures blood flow | * Functional MRI measures blood flow | ||
* Diffusion Tensor Imaging the only method for imaging tensors | * Diffusion Tensor Imaging the only method for imaging tensors | ||
* Combine fMRI with MEG or fMRI with MRI | * Combine fMRI with MEG or fMRI with MRI | ||
* VR to prepare for operation | * VR to prepare for operation | ||
* AR to help annotate points of interest on the brain | * AR to help annotate points of interest on the brain | ||
* The relationship in the brain of where things are locate and how they relate to other places in the brain is important | |||
== Steve Pieper == | == Steve Pieper == | ||
| Line 160: | Line 166: | ||
* Discourse Forum | * Discourse Forum | ||
* Project Week Event | * Project Week Event | ||
* projectweek.na-mic.org | ** [https://projectweek.na-mic.org/ projectweek.na-mic.org] | ||
** upcoming one takes place January 20-24, 2020 in Las Palmas, Gran Canaria, Spain | |||
** next one takes place June 22-26,2020 at MIT in Boston, USA | |||
== Andriy Fedorov == | == Andriy Fedorov == | ||
| Line 172: | Line 180: | ||
** Helps to query your data | ** Helps to query your data | ||
** TCIA (Cancer Imaging Archive) :Collection of medical images | ** TCIA (Cancer Imaging Archive) :Collection of medical images | ||
*** can submit image sets for analysis | |||
*** need big data to be accessible | |||
* NCI Cancer Research Data Commons | * NCI Cancer Research Data Commons | ||
| Line 188: | Line 198: | ||
* Risk: You know the probabilities | * Risk: You know the probabilities | ||
* Uncertainty: You don’t | * Uncertainty: You don’t | ||
* Privately profitable vs. society | |||
* Recent inovations started from individuals, not big companies | * Recent inovations started from individuals, not big companies | ||
* National Labs work better when in cooperation with universities | * National Labs work better when in cooperation with universities | ||
| Line 195: | Line 206: | ||
* Lessons | * Lessons | ||
** Outside funders are skeptical | ** Outside funders are skeptical | ||
*** they look at the diffusion curve (uncertainty) | |||
*** they want equity | |||
** Need substantial amount of equity to participate | ** Need substantial amount of equity to participate | ||
** Very high discount rates because of uncertainty | ** Very high discount rates because of uncertainty | ||
** Investors require control | ** Investors require control | ||
** Try to get some benefits for the decision makers early on since they will not be there for too long | ** Try to get some benefits for the decision makers early on since they will not be there for too long | ||
*** Don't sell out before benefits | |||
== James Cheng == | == James Cheng == | ||
| Line 205: | Line 219: | ||
* Companies also | * Companies also | ||
* Angel investments recently appeared in south VA | * Angel investments recently appeared in south VA | ||
** 757 Angels - Hampton Roads Investing | |||
** CVA, 1717, Startup Virginia, NRV - Richmond | |||
** CAN, CAV Angels - Charlottesville | |||
* Resources on the slides | * Resources on the slides | ||
** TEConomy Study | ** TEConomy Study | ||
** SBIG | ** SBIG - small business investments | ||
** VEDP | ** VEDP | ||
** OTA | ** VRIF Awards | ||
** OTA - way Gov't gets funding | |||
* Want scalable technology | |||
** tie software to a machine/product for intellectual property protection | |||
Latest revision as of 16:15, 21 October 2019
08/19/19 Presentations
Slides https://odu.app.box.com/s/4yfdokkhyyqge2zx1jlv8gctw4zw6w2q
Nikos Chrisochoides
- Leverage from computational methods from medical imaging for processing phase space distributions in Nuclear Femtography
- Enable next generation process simulations
- Bring jobs to Virginia
- “Usefulness of Useless Knowledge” by Flexner
- Look for standards and learn lessons
Bob McKeown
- Femtography 2019 ML & Revealing the internal structure of Nucelon & Nuclei
- See pic JLab Mission
- Nuclear Femtography
- High luminosity
- Polarized beams and targets
- Sophisticated detector systems
- Nature publications
- Ultrafast Nucleons in Asymmetic Nuclei
- …
- Virginia Center for Nyclear Femtography
- 7/14 joint lab/university initiatives funded
- Proposed JLab electron Ion Collider (see pic)
- EIC Users Group (see pic)
- EICUG 2019
- Current Status and Path forward of EIC (see pic
- JLab has electron collider, NY Brookhaven has ion collider
- Both need to construct the other half
- Imaging the Nucleon is the future of Nuclear Physics
Gail Dodge
- Partnership between ODU and JLab in 90’s
- 12 tenure track faculty
- 6 JLab professors
- ODU faculty intellectual leadership in research programs in JLab
- Bring together scientists from different fields
- Bridging the gap between physics, math, and computer science to reach the potential
- ODU has hired ~6 data science faculty
- Need help to learn how to involve people from different disciplines
- Different fields working on analyzing data, imaging protons/neutrons, and extracting scientific insights
David Richards
- “Understanding the Glue That Binds Us All: The Next QCD Frontier in Nuclear Physics”
- See slide 6 for those that submitted proposals
- Dealing with incomplete data and the importance of visualization
- Synergy across fields
- Astrophysics
- Visualization
- Machine Learning
- Call for proposal late September
- External Evaluation Committee
- CNF Symposium at ODU early 2020
Christian Weiss
- Imaging technologies in computation
- Subnuclear systems
- Scales
- Scattering experiments
- Workflow inmeasurements
- Tesselation
- Scales
- Organisms- Cells
- Molecular - Atoms
- Nuclear - Subnuclear
- Mission: Explore subnuclear systems
- Strong interaction : quarks,gluons
- Understand proton neutron as emergent phenomena
- interacting large systems develop complex structure that is not present in parts
- Interest
- Basic building blocks of matter
- nuclear structure and reactions, nuclear energy
- Subnuclear systems
- Unusual dynamical system
- contents of proton can't be localized because they are in "perpetual movement"
- Relativity: Energy = mass - relativity, conversion processes. Proton is ‘cauldron’ filled with radiation energy and matter/antimatter
- Only average properties are measurable: densities,correlations
- formulation/visualization possible with appropriate tools
- Unusual dynamical system
- Scattering experiments
- Electrons collide on protons
- Scatterings are gathered from detectors
- Incident energy determines spatial resolution
- JLab 12GeV accelerators
- needed for resolving proton's internal structure
- 1GeV is typical spatial size of a proton
- Information Recorded:
- Distribution based on type and momentum -> Phase space distributions
- The “expression” of proton’s internal structure
- 10^6 - 10^9 experiments in JLab
- Workflow: Measurements
- Theory: Model of proton structure (parameters that need to be verified experimentally)
- Predictions for scattering probabilities (cross section)
- Phase space distributions (meeting ground between theory and experiments)
- Reconstruction based on the raw data (detector effects)
- Scattering Experiments -> Raw Data (kinematic coverage statistics)
- Simulations (can be run with actual or mock data to test reconstruction /feasibility)
- Modify theoretical model based on observations
- "Final" picture of proton from synthesis of multiple experiments and theoretical models
- Phase space distributions
- Use imaging techniques(e.g tessellation, slicing, viewing)
- 10^6 -10^9 experiments
- View distributions, projections, cuts, features
- Compare distributions
- Interpolate refine distributions for simulations
- With ODU
- Tesselated representation
- compact memory-efficient representations
- Efficient interpolation/refinement for MC simulations
- Visualize the gamma ray points that were detected by the detector + the density of energy
- Proton Structure:
- Imaging for vizualitation of subnuclear system
- Image concept for relativistic quantum system
- Synthetic image from theory and multiple experiments
- Spatial size, internal motion of quarks
- Tesselated representation
Ron Kikinis
- Making data machine readable
- Critical for effective/accessible data
- DICOM (standard for medical images)
- 3D Slicer
- Browser based versions
- ohif.org
- openanatomy.org
- Look at data from different vantage points
- SlicerAstro
- colors to show data most helpful to doctors, students, etc.
- Use of Slicer3D in astronomy
- Punzo et al 2015
Alexandra Golby
- In the past doctors would infer where the problem wass by the symptoms. They would then open the brain and see if they were right
- Now: Brain organization -> functional connectivity
- Functional MRI measures blood flow
- Diffusion Tensor Imaging the only method for imaging tensors
- Combine fMRI with MEG or fMRI with MRI
- VR to prepare for operation
- AR to help annotate points of interest on the brain
- The relationship in the brain of where things are locate and how they relate to other places in the brain is important
Steve Pieper
- New software - Keep using the best software we have
- Open source - Reproducible science
- 3D Slicer with
- Python
- Tensorflow
- Numpy, pandas, scipy…
- Wrapper API
- Extensions marketplace
- VR Support
- Remote Rendering
- Jupyter Notebook support
- OHIF Viewer
- Browser based medical image viewer
- VTK.js
- Crowds-cure.org
- GPU Segmentation (Grow cut algorithm)
Tina Kapur
- Discourse Forum
- Project Week Event
- projectweek.na-mic.org
- upcoming one takes place January 20-24, 2020 in Las Palmas, Gran Canaria, Spain
- next one takes place June 22-26,2020 at MIT in Boston, USA
Andriy Fedorov
- “Your source code is your data”, NVIDIA CEO
- For reproducibility for example
- National Cancer Institutr Quantitative Imaging Network
- Goal: Transalte quantitative imaging methods and algorithms as clinical decision support tools into clinical utility
- DICOM includes metadata that can help to interpret images
- Unique identifiers
- Helps to query your data
- TCIA (Cancer Imaging Archive) :Collection of medical images
- can submit image sets for analysis
- need big data to be accessible
- NCI Cancer Research Data Commons
08/20/19 Presentations
Nikos Chrisochoides
- Lessons from yesterday
- DICOM type
- 4D Meshes for JLab, Medical imaging
- Very nice presentation by Christian Weiss
- Cross-department funding for another workshop?
James V. Koch
- Risk: You know the probabilities
- Uncertainty: You don’t
- Privately profitable vs. society
- Recent inovations started from individuals, not big companies
- National Labs work better when in cooperation with universities
- Lesson learnt: Get people together and let them work on what they think
- Profit motive makes people more productive
- When to sell intellectual property, since imitations will arise soon
- Lessons
- Outside funders are skeptical
- they look at the diffusion curve (uncertainty)
- they want equity
- Need substantial amount of equity to participate
- Very high discount rates because of uncertainty
- Investors require control
- Try to get some benefits for the decision makers early on since they will not be there for too long
- Don't sell out before benefits
- Outside funders are skeptical
James Cheng
- VA institutions very spread around
- Companies also
- Angel investments recently appeared in south VA
- 757 Angels - Hampton Roads Investing
- CVA, 1717, Startup Virginia, NRV - Richmond
- CAN, CAV Angels - Charlottesville
- Resources on the slides
- TEConomy Study
- SBIG - small business investments
- VEDP
- VRIF Awards
- OTA - way Gov't gets funding
- Want scalable technology
- tie software to a machine/product for intellectual property protection