DRUM Community: Computer Science Department Technical Reports
http://hdl.handle.net/1903/2225
2015-05-25T05:23:55ZAutomating Efficient RAM-Model Secure Computation
http://hdl.handle.net/1903/15552
Title: Automating Efficient RAM-Model Secure Computation
Authors: Liu, Chang; Huang, Yan; Shi, Elaine; Katz, Jonathan; Hicks, Michael
Abstract: RAM-model secure computation addresses the inherent limitations of
circuit-model secure computation considered in almost all previous work.
Here, we describe the first automated approach for RAM-model secure
computation in the semi-honest model. We define an intermediate
representation called SCVM and a corresponding type system suited for
RAM-model secure computation. Leveraging compile-time optimizations, our
approach achieves order-of-magnitude speedups compared to both
circuit-model secure computation and the state-of-art RAM-model secure
computation.2014-03-13T00:00:00ZA Stochastic Approach to Uncertainty in the Equations of MHD Kinematics
http://hdl.handle.net/1903/15523
Title: A Stochastic Approach to Uncertainty in the Equations of MHD Kinematics
Authors: Phillips, Edward G.; Elman, Howard C.
Abstract: The magnetohydodynamic (MHD) kinematics model describes the
electromagnetic behavior of an electrically conducting fluid when its
hydrodynamic properties are assumed to be known. In particular, the MHD
kinematics equations can be used to simulate the magnetic field induced
by a given velocity field. While prescribing the velocity field leads to
a simpler model than the fully coupled MHD system, this may introduce
some epistemic uncertainty into the model. If the velocity of a physical
system is not known with certainty, the magnetic field obtained from the
model may not be reflective of the magnetic field seen in experiments.
Additionally, uncertainty in physical parameters such as the magnetic
resistivity may affect the reliability of predictions obtained from this
model. By modeling the velocity and the resistivity as random variables
in the MHD kinematics model, we seek to quantify the effects of
uncertainty in these fields on the induced magnetic field. We develop
stochastic expressions for these quantities and investigate their impact
within a finite element discretization of the kinematics equations. We
obtain mean and variance data through Monte-Carlo simulation for several
test problems. Toward this end, we develop and test an efficient block
preconditioner for the linear systems arising from the discretized
equations.2014-07-10T00:00:00ZThe Maryland Virtual Demonstrator Environment for Robot Imitation Learning
http://hdl.handle.net/1903/15431
Title: The Maryland Virtual Demonstrator Environment for Robot Imitation Learning
Authors: Huang, Di-Wei; Katz, Garrett E.; Gentili, Rodolphe J.; Reggia, James A.
Abstract: Robot imitation learning, where a robot autonomously generates actions
required to accomplish a task demonstrated by a human, has emerged as a
potential replacement for a more conventional hand-coded approach to
programming robots. Many past studies in imitation learning have human
demonstrators perform tasks in the real world. However, this approach is
generally expensive and requires high-quality image processing and
complex human motion understanding. To address this issue, we developed
a simulated environment for imitation learning, where visual properties
of objects are simplified to lower the barriers of image processing. The
user is provided with a graphical user interface (GUI) to demonstrate
tasks by manipulating objects in the environment, from which a simulated
robot in the same environment can learn. We hypothesize that in many
situations, imitation learning can be significantly simplified while
being more effective when based solely on objects being manipulated
rather than the demonstrator's body and motions. For this reason, the
demonstrator in the environment is not embodied, and a demonstration as
seen by the robot consists of sequences of object movements. A
programming interface in Matlab is provided for researchers and
developers to write code that controls the robot's behaviors. An XML
interface is also provided to generate objects that form task-specific
scenarios. This report describes the features and usages of the
software.2014-06-20T00:00:00ZPreconditioning Techniques for Reduced Basis Methods for Parameterized Partial Differential Equations
http://hdl.handle.net/1903/15078
Title: Preconditioning Techniques for Reduced Basis Methods for Parameterized Partial Differential Equations
Authors: Elman, Howard C.; Forstall, Virginia
Abstract: The reduced basis methodology is an efficient approach to solve
parameterized discrete partial differential equations when the solution
is needed at many parameter values. An offline step approximates the
solution space and an online step utilizes this approximation, the
reduced basis, to solve a smaller reduced problem, which provides an
accurate estimate of the solution. Traditionally, the reduced problem is
solved using direct methods. However, the size of the reduced system
needed to produce solutions of a given accuracy depends on the
characteristics of the problem, and it may happen that the size is
significantly smaller than that of the original discrete problem but
large enough to make direct solution costly. In this scenario, it may be
more effective to use iterative methods to solve the reduced problem. We
construct preconditioners for reduced iterative methods which are
derived from preconditioners for the full problem. This approach permits
reduced basis methods to be practical for larger bases than direct
methods allow. We illustrate the effectiveness of iterative methods for
solving reduced problems by considering two examples, the steady-state
diffusion and convection-diffusion-reaction equations.2014-05-27T00:00:00Z