906 research outputs found
Physical Primitive Decomposition
Objects are made of parts, each with distinct geometry, physics,
functionality, and affordances. Developing such a distributed, physical,
interpretable representation of objects will facilitate intelligent agents to
better explore and interact with the world. In this paper, we study physical
primitive decomposition---understanding an object through its components, each
with physical and geometric attributes. As annotated data for object parts and
physics are rare, we propose a novel formulation that learns physical
primitives by explaining both an object's appearance and its behaviors in
physical events. Our model performs well on block towers and tools in both
synthetic and real scenarios; we also demonstrate that visual and physical
observations often provide complementary signals. We further present ablation
and behavioral studies to better understand our model and contrast it with
human performance.Comment: ECCV 2018. Project page: http://ppd.csail.mit.edu
Self-scaled barriers for irreducible symmetric cones
Self-scaled barrier functions are fundamental objects in the theory of
interior-point methods for linear optimization over symmetric cones, of which
linear and semidefinite programming are special cases. We are classifying all
self-scaled barriers over irreducible symmetric cones and show that these
functions are merely homothetic transformations of the universal barrier
function. Together with a decomposition theorem for self-scaled barriers this
concludes the algebraic classification theory of these functions. After
introducing the reader to the concepts relevant to the problem and tracing the
history of the subject, we start by deriving our result from first principles
in the important special case of semidefinite programming. We then generalise
these arguments to irreducible symmetric cones by invoking results from the
theory of Euclidean Jordan algebras.Comment: 12 page
An optimization method for dynamics of structures with repetitive component patterns
The occurrence of dynamic problems during the operation of machinery may have devastating effects on a product. Therefore, design optimization of these products becomes essential in order to meet safety criteria. In this research, a hybrid design optimization method is proposed where attention is focused on structures having repeating patterns in their geometries. In the proposed method, the analysis is decomposed but the optimization problem itself is treated as a whole. The model of an entire structure is obtained without modeling all the repetitive components using the merits of the Component Mode Synthesis method. Backpropagation Neural Networks are used for surrogate modeling. The optimization is performed using two techniques: Genetic Algorithms (GAs) and Sequential Quadratic Programming (SQP). GAs are utilized to increase the chance of finding the location of the global optimum and since this optimum may not be exact, SQP is employed afterwards to improve the solution. A theoretical test problem is used to demonstrate the method
Hamiltonian Simulation by Qubitization
We present the problem of approximating the time-evolution operator
to error , where the Hamiltonian is the
projection of a unitary oracle onto the state created by
another unitary oracle. Our algorithm solves this with a query complexity
to both oracles that is optimal
with respect to all parameters in both the asymptotic and non-asymptotic
regime, and also with low overhead, using at most two additional ancilla
qubits. This approach to Hamiltonian simulation subsumes important prior art
considering Hamiltonians which are -sparse or a linear combination of
unitaries, leading to significant improvements in space and gate complexity,
such as a quadratic speed-up for precision simulations. It also motivates
useful new instances, such as where is a density matrix. A key
technical result is `qubitization', which uses the controlled version of these
oracles to embed any in an invariant subspace. A large
class of operator functions of can then be computed with optimal
query complexity, of which is a special case.Comment: 23 pages, 1 figure; v2: updated notation; v3: accepted versio
Systolic VLSI chip for implementing orthogonal transforms, A
Includes bibliographical references.This paper describes the design of a systolic VLSI chip for the implementation of signal processing algorithms that may be decomposed into a product of simple real rotations. These include orthogonal transformations. Applications of this chip include projections, discrete Fourier and cosine transforms, and geometrical transformations. Large transforms may be computed by "tiling" together many chips for increased throughput. A CMOS VLSI chip containing 138 000 transistors in a 5x3 array of rotators has been designed, fabricated, and tested. The chip has a 32-MHz clock and performs real rotations at a rate of 30 MHz. The systolic nature of the chip makes use of fully synchronous bit-serial interconnect and a very regular structure at the rotator and bit levels. A distributed arithmetic scheme is used to implement the matrix-vector multiplication of the rotation.This work was supported by Ball Aerospace, Boulder, CO, and by the Office of Naval Research, Electronics Branch, Arlington, VA, under Contract ONR 85-K-0693
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