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EECS Divisional Presentation
Computing, Algorithms and Applications
May 25, 2006
Current CAA Faculty
Primary Members:
• Ming-Yang Kao: theoretical computer science
• Jorge Nocedal: continuous optimization
Secondary Members:
• Yan Chen: networking and security
• Peter Scheuermann: databases
• Hai Zhou: CAD algorithms and formal methods
Tertiary Members:
• Alan Toflove: computational electrodynamics
A Framework to Understand CAA Research
Algorithms
Externals(applications of
computation to other fields, and vice versa)
Models ofComputation
Complexity(resources used by computation)
Strategic BiddingJ. Nocedal and R. Waltz
• Your company sells electric power (internet resources, wireless bandwidth).
• You and other producers submit competitive bids to generate power.
• An Independent Operator purchases at a single “spot price.”
• Your strategic guidance: – submit low bids spot price– submit high bids to drive up the spot price– Demands, etc, uncertain
Independent operator solves an (easy) optimization problem -- given the bids, determines amount gj to buy from you.
Spot price is Lagrange multiplier.
,...1
,...10s.t.
min
Jdemandg
Jcapacityg
gb
Jj
j
JJ
jjJgJ
bj = bid of company j cj = gener cost for company
gj = gener sold by plant j
Powernext Day-Ahead™: daily volume and baseload price
0
10 000
20 000
30 000
40 000
50 000
60 000
70 000
80 000
90 000
100 000
110 000
27/1
1/01
10/0
2/02
26/0
4/02
10/0
7/02
23/0
9/02
07/1
2/02
20/0
2/03
06/0
5/03
20/0
7/03
03/1
0/03
17/1
2/03
01/0
3/04
15/0
5/04
29/0
7/04
12/1
0/04
26/1
2/04
11/0
3/05
25/0
5/05
08/0
8/05
22/1
0/05
05/0
1/06
21/0
3/06
MW
h
-
50
100
150
200
250
En
€/M
Wh
Daily volume Baseload price
Bi-level Optimization Problem:
• What about bids from competitors? Use stochastic optimization.
• Very large and nonlinear problem• Mathematically deficient --- need new theory
OptimizationProblem!!
DJJ
JJ
JJD
Lgb
solutionIndepOp
cb
gcL
,:IndepOper
tosubject
)(maxJb
Your problem (j=1)
Northwestern Lab for Internet and Security Technology (LIST)
Yan Chen
High-performance Network Anomaly/Intrusion Detection and Mitigation (HPNAIDM) Systems
• Data streaming computation: 10s Gigabit-link network traffic recording and analysis (with P. Dinda and G. Memik)• Combinatorial statistics: first online network-based polymorphic worm signature generation with provable attack resilience (with M. Kao)• Formal verification: vulnerability analysis of 802.16 protocols using formal methods (with H. Zhou, J. Fu (Motorola) ) • Information theory: network anomaly & intrusion detection (with D. Guo)
The Spread of Sapphire/Slammer Worms
Northwestern Lab for Internet and Security Technology (LIST)
Yan Chen
Internet Measurement, Diagnosis & Inference
• Linear Algebra: Scalable and deterministic network monitoring, diagnosis, and link-level properties (e.g., loss rate) inference • Statistics: Network router configuration (e.g., QoS) inference (with F. Bustamante and G. Lu (Tsinghua))
C&W
AT&T
Sprint
UUNet
Qwest Earthlink
AOL
It’s so slow!
Why is itso slow?
Applied Computational GeometryPeter Scheuermann
r
Critical Region RR Problem: How to optimize the guidance of
mobile sensors which need to be brought into a critical region, to ensure a desired level of coverage for that region?
Variants use convex hull of critical region1. fastest arrival time for the desired number of sensors2. largest number of sensors to ensure desired quantity inside the region3. optimal time to ensure “fair” coverage under the constraint that a minimum number of sensors are inside the region
Publication: “Mission-Critical Management of MobileSensors (or, How to Guide a Flock of Sensors) in DMSN 2004
SENSOR RELOCATION
LMB
C D
E
FAProblem: Notify me when an object is continuously_moving_towards the landmark LM, for more than 5 min., based on periodic (location,time) updates (primitive events)
Solution: Use Voronoi diagram (for the LM) and monitoring of only two consecutive updates;- Issue: consumption of primitive events?
Send update!
To Send
To Send or Not To Send?
(have the previous simple events been
“consumed”)
Publication: “Dynamic Topological Predicates and Notifications inMoving Object Databases” in MDM 2005
DYNAMIC TOPOLOGICAL PREDICATES FOR MOVING OBJECTS
Optimal and Efficient Algorithms for Circuit RetimingHai Zhou
• Retiming is an effective technique for circuit optimization by relocating registers without changing functionality
• We developed the most efficient algorithm for clock period minimization considering both long and short paths (in O(n2m) time)
• Our algorithm is correct no matter what order is used for selecting nodes
Gate Sizing for Coupling Noise Control as Distributed Optimization
Hai Zhou
• Noise on a signal is proportional to attacker gate sizes and inversely proportional to its own gate size
• Given the coupling relations and the noise upper bound for each signal
• Need to find minimal gate sizes such that all noises are under constraints
Our algorithm: Each gate starts at lower bound Repeat: Each signal with violation up-size its gate to the smallest with tolerable noise
• Correct no matter what order is taken• Will converge to the optimal solution if there is one• Very efficient practically• May be used in wireless networks
TILE
G C A T C G
C G T A G C
DNA Algorithmic Self-Assembly
DNA Algorithmic Self-Assembly
Program = Tiles + Lab Steps Output
DNA Algorithmic Self-Assembly
Input: the description of a shape
Output: a set of tiles and a sequence of lab steps to produce the shape
Computational Objectives:• minimize the # of tile types• minimize the range of temperatures• minimize the # of lab steps• minimize errors
Sequencing Bio-molecules
Input: information about small pieces of a target molecule
Output: the character sequence of the target molecule
Examples:• Peptide Sequencing: linear structure (with a group at
Harvard Medical School)
• Glycan Sequencing: tree structure (with a group at Kyoto University)
Sequencing Bio-molecules
Given: a target bio-molecule B
Steps:1. Make many copies of B.
2. Cut each copy of B into pieces.
3. Sequence each piece (recursively).
4. Assemble the character sequences of the pieces into the character sequence of B.
Protein Analysis: HPLC-MS-MS
HPLC MassSpectrometer
Fragmentation & ionization
MassSpectrometer
De Novo Peptide Sequencing
Protein Database SearchingMass/Charge
Mass/Charge
Proteins Peptides
Tandem Mass Spectrum
One PeptideB-ions / Y-ions
Synergies with Other Divisions
CAA
Computer Engineering & Systems
Signals & Systems
Solid State & Photonics
Cognitive Systems +
Graphics & Interactive Media
Musical RetrievalComputational Economics
Network OptimizationDNA Computing
Quantum ComputingCryptography
BioinformaticsComputer Worm Detection
Design OptimizationDNA Computing
CAA’s Mission:
To Understand the Nature, Power, Limit of Computation; and to Apply Such Understanding to Benefit the Society.
Basic Understanding about Computation:
Computation is an intellectual tool as powerful and universal as mathematics.
Computation can be used not only to solve mathematical problems, but also to understand and design complex systems.
Examples of Computation:
• How many bits of information does a black hole compute?
• How do we make web search efficiently provide the information that we want?
• How do we create a biological “computer” that uses DNA/RNA-like materials to produce medicines?
The End
Thank You!
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