Building a Science B ase for the Information A ge
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Building a Science Base for the Information Age
John HopcroftCornell University
Ithaca, NY
Xiamen University
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Time of change
The information age is a revolution that is changing all aspects of our lives.
Those individuals, institutions, and nations who recognize this change and position themselves for the future will benefit enormously.
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Computer Science is changing
Early years Programming languages Compilers Operating systems Algorithms Data bases
Emphasis on making computers useful
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Computer Science is changing
The future years
Tracking the flow of ideas in scientific literature Tracking evolution of communities in social networks Extracting information from unstructured data
sources Processing massive data sets and streams Extracting signals from noise Dealing with high dimensional data and dimension
reduction
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Computer Science is changing
Merging of computing and communication
The wealth of data available in digital form
Networked devices and sensors
Drivers of change
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Implications for TCS
Need to develop theory to support the new directions
Update computer science education
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A short view of the future
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Digitization of medical records
Doctor – needs my entire medical record Insurance company – needs my last doctor
visit, not my entire medical record Researcher – needs statistical information but
no identifiable individual information
Relevant research – zero knowledge proofs, differential privacy
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Zero knowledge proof
• Graph 3-colorability
• Problem is NP-hard - No polynomial time algorithm unless P=NP
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Zero knowledge proof
I send the sealed envelopes. You select an edge and open the two
envelopes corresponding to the end points.
Then we destroy all envelopes and start over, but I permute the colors and then resend the envelopes.
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Digitization of medical records is not the only system
Car and road – gps – privacy
Supply chains
Transportation systems
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NEXRAD RadarBinghamton, Base Reflectivity 0.50 Degree Elevation Range 124 NMI — Map of All US Radar Sites
Animate MapStorm TracksTotal PrecipitationShow SevereRegional RadarZoom Map Click:Zoom InZoom OutPan Map
(Full Zoom Out)
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When will my bus arrive?
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IN 4 TO 5 MINUTES
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Tracking the flow of ideas in scientific literatureYookyung Jo
Page rank
Web
Link
GraphRetrievalQuerySearchText
WebPageSearchRank
WebChordUsage
IndexProbabilisticText
FileRetrieveTextIndex
DiscourseWordCenteringAnaphora
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Topic evolution thread
• Seed topic :– 648 : making c program
type-safe by separating pointer types by their usage to prevent memory errors
• 3 subthreads :– Type : – Garbage collection : – Pointer analysis :
Yookyung Jo, 2010
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Topic Evolution Map of the ACM corpus
Yookyung J o, 2010
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In the past, sociologists could study group of a few thousand individuals.
Today with social networks we can study interaction among millions of individuals.
One important activity is how communities form and evolve.
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• Early work Min cut – two equal size communities Conductance – minimizes cross edges
• Future work Consider communities with more external edges
than internal edges Find small communities Track communities over time Develop appropriate definitions for communities Understand the structure of different types of social
networks
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Our view of a community
TCS
Me
Colleagues at Cornell
Classmates
Family and friendsMore connections outside than inside
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On going research on finding communities
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Xiamen UniversitySpectral clustering with K-means.
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Spectral clustering with K-means.
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Xiamen UniversitySpectral clustering with K-means.
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Instead of two overlapping clusters, we find three clusters.
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Instead of clustering the rows of the singular vectors, find the minimum 0-norm vector in the space spanned by the singular vectors.
The minimum 0-norm vector is, of course, the all zero vector, so we will require one component to be 1.
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Finding the minimum 0-norm vector is NP-hard.
Use the minimum 1-norm vector as a proxy.
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Minimum 1-norm vector is not an indicator vector.
By thresh-holding the components, convert it to an indicator vector for the community.
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0 50 100 150 200 250 300 350 4000.4
0.5
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1
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Random walk
How long?
What dimension?
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Krylov subspace
Find orthonormal basis.
Update subspace in each step rather than just the probability vector
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Find minimum 1-norm vector in Krylov subspace.
Actually allow vector to be close to subspace.
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Structure of communities
How many communities is a person in?Small, medium, large
How many seed points are needed to uniquely specify a community a person is in?Which seeds are good seeds?Etc.
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What types of communities are there?
How do communities evolve over time?
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This is an exciting time for computer science.
There is a wealth of data in digital format, information from sensors, and social networks to explore.
It is important to develop the science base to support these activities.