72 research outputs found
An Options Approach to Software Prototyping
Prototyping is often used to predict, or reduce the uncertainty over, the future profitability of a software design choice. Boehm [3] pioneered the use of techniques from Bayesian decision theory to provide a basis for making prototyping decisions. However, this approach does not apply to situations where the software engineer has the flexibility of waiting for more information before making a prototyping decision. Also, this framework only assumes uncertainty over one time period, and assumes a design-choice must be made immediately after prototyping. We propose a more general multi-period approach that takes into account the flexibility of being able to postpone the prototyping and design decisions. In particular, we argue that this flexibility is analogous to the flexibility of exercise of certain financial instruments called options, and that the value of the flexibility is the value of the corresponding financial option. The field of real option theory in finance provides a rigorous framework to analyze the optimal exercise of such options, and this can be applied to the prototyping decision problem. Our approach integrates the timing of prototype decisions and design decisions within a single framework.
Parallel network optimization on a shared memory multiprocessor and application in VLSI layout compaction and wire balancing
Discusses the design and implementation of 3 parallel algorithms: one algorithm for the transshipment problem, and 2 algorithms for the dual transshipment problem. Also considers an application of these algorithms in solving VLSI layout compaction and wire balancing problems
CAUSE: Learning Granger Causality from Event Sequences using Attribution Methods
We study the problem of learning Granger causality between event types from
asynchronous, interdependent, multi-type event sequences. Existing work suffers
from either limited model flexibility or poor model explainability and thus
fails to uncover Granger causality across a wide variety of event sequences
with diverse event interdependency. To address these weaknesses, we propose
CAUSE (Causality from AttribUtions on Sequence of Events), a novel framework
for the studied task. The key idea of CAUSE is to first implicitly capture the
underlying event interdependency by fitting a neural point process, and then
extract from the process a Granger causality statistic using an axiomatic
attribution method. Across multiple datasets riddled with diverse event
interdependency, we demonstrate that CAUSE achieves superior performance on
correctly inferring the inter-type Granger causality over a range of
state-of-the-art methods
On the minimum latency problem
We are given a set of points and a symmetric distance
matrix giving the distance between and . We wish to
construct a tour that minimizes , where is the
{\em latency} of , defined to be the distance traveled before first
visiting . This problem is also known in the literature as the {\em
deliveryman problem} or the {\em traveling repairman problem}. It arises in a
number of applications including disk-head scheduling, and turns out to be
surprisingly different from the traveling salesman problem in character. We
give exact and approximate solutions to a number of cases, including a
constant-factor approximation algorithm whenever the distance matrix satisfies
the triangle inequality.Comment: 9 page
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