discrete-in-time sources, modeling a seismic rupture. We assume that
the sources occur along an unknown path with subsonic velocity, and
that data is collected over time on some detection surface. We explore
the question of uniqueness for these problems, and show how to recover
the times and locations of sources microlocally first, and then the
smooth part of the source assuming that it is the same at each source
location. In case the sources (now all different) are (roughly
speaking) non-negative and of limited oscillation in space, and
sufficiently separated in space-time, which is a model for
microseismicity, we present an explicit reconstruction, requiring
sufficient local energy decay. (Joint research with L. Oksanen and J. Tittelfitz)
environments will typically lead to one of two possible outcomes. First,
not surprisingly, the strain simply dies; second the strain adapts in
order to survive. In this talk we are concerned with the hardness of
survival, i.e. what is the most eﬃcient (smartest) way to adapt to new
environments? How many new abilities does a bacterium need in order to
survive? Here we restrict our focus on two speciﬁc bacteria, namely
E.coli and Buchnera. In order to answer the questions raised, we ﬁrst
model the underlying problem as an NP-hard decision problem. Using a
re-weighted l1-regularization approach, well known from image
reconstruction, we then approximate ”good” solutions. A numerical
comparison between these ”good” solutions and the ”exact” solutions
concludes the talk.
equations with highly oscillating coefficients. The multiscale nature of
such problems poses a challenge in both the mathematical formulation
and the numerical modeling, which is hard even for forward computations.
I will discuss uniqueness of the inverse in certain problem classes and
give numerical methods for inversion that can be applied to problems in
medical imaging and exploration seismology.
Active Set Algorithm (DASA) is used to compute a high precision solution. Some interesting convergence properties and very promising numerical results compared with the state-of-the-art software IPOPT and CPLEX will be discussed in this talk.
Radius represents the maximal rate of growth of products of matrices in
F when the matrices are multiplied each other following some Markovian law.
This quantity is important, for instance, in the study of the so called
zero stability of variable stepsize BDF methods for the numerical
integration of ordinary differential equations.
Recently Kozyakin, based on a work by Dai, showed that, given a set F of
N matrices of dimension d and a graph G, which represents the admissible
products, it is possibile to compute the Markovian Joint Spectral Radius
of the couple (F,G) as the classical Joint Spectral Radius of a new set
of N matrices of dimension N*d, which are produced as a particular
lifting of the matrices in F. Clearly by this approach the exact
evaluation or the simple approximation of the Markovian Joint Spectral
Radius becomes a challenge even for reasonably small values of N and d.
In this talk we briefly review the theory of the Joint Spectral Radius,
and we introduce the Markovian Joint Spectral Radius. Furthermore we
address the question whether it is possible to reduce the exact
calculation computational complexity of the Markovian Joint Spectral
Radius. We show that the problem can be recast as the computation of N
polytope norms in dimension d. We conclude the presentation with some
This talk is based on a joint work with Nicola Guglielmi from the
University of L'Aquila, Italy, and Vladimir Yu. Protasov from the Moscow
State University, Russia.
Speaker’s Biography:Michael Malisoff received his PhD in 2000 from
the Department of Mathematics at Rutgers University in New Brunswick,
NJ. In 2001, he joined the faculty of the Department of Mathematics at
Louisiana State University in Baton Rouge (LSU), where he is now the Roy
Paul Daniels Professor #3 in theLSU College of Science. His main
research has been on controller design and analysis for nonlinear
control systems with time delays and uncertainty and their applications
in engineering. One of his projects is joint with the Georgia Tech
Savannah Robotics team, and helped develop marine robotic methods to
help understand the environmental impacts of oil spills. His more than
100 publications include a Springer monograph on constructive Lyapunov
methods. His awards include the First Place Student Best Paper Award at
the 1999 IEEE Conference on Decision and Control, two three-year
NationalScience Foundation Mathematical Sciences Priority Area
grants, and 9 Best Presentation awards in American Control Conference
sessions. He is an associate editor for IEEE Transactions on Automatic
Control and for SIAM Journal on Control and Optimization.
Yang, "Predictor-based tracking for neuromuscular electrical
stimulation," International Journal of Robust and Nonlinear Control, to
appear. doi: 10.1002/rnc.3211
both experimental research and product development activities focused on
persons with disabilities. The REARLab seeks collaboration from the School
of Mathematics on 2 current projects. This session will introduce
wheelchair seating with respect to pressure ulcer formation and present two
projects whose data analysis would benefit from applied mathematics.
3D Tissue Deformation- Sitting induces deformation of the
buttocks tissues. Tissue deformation has been identified as the underlying
cause of tissue damage resulting from external loading. The REARLab has
been collecting multi-planar images of the seated buttocks using MRI. This
data clearly shows marked differences between persons, as expected. We are
interested in characterizing tissue deformation as a combination of
displacement and distortion. Some tissues- such as muscle- displace
(translate within the sagittal, coronal and transverse planes) and distort
(change shape). Other tissue such as skin and subcutaneous fat, simple
distorts. We seek a mathematical means to characterize tissue deformation
that reflects its multi-planar nature.
Categorizing Weight-shifting behaviors - many wheelchair users have
limitations to their motor and/or sensory systems resulting in a risk of
pressure ulcers. Pressure ulcers occur when localized loading on the skin
causes ischemia and necrosis. In an attempt to reduce risk of pressure
ulcer occurrence, wheelchair users are taught to perform weight-shifts.
Weight shifts are movements that re-distribute loads off the buttocks for
short periods of time. The REARLab is measuring weight shifting behaviors
of wheelchair users during their everyday lives. We seek a means to
classify patterns of behavior and relate certain patterns to healthy
outcomes versus other patterns that result in unhealthy outcomes.
The algorithm computes a low-cost approximation of all slow variables in the system. Then, fast phase-like variables are obtained using the parareal iterative methodology and an alignment algorithm. The method may be used either to enhance the accuracy and range of applicability of the multiscale method in approximating only the slow variables, or to resolve all the state variables. The numerical scheme does not require that the system is split into slow and fast coordinates. Moreover, the dynamics may involve hidden slow variables, for example, due to resonances.