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 $p_1,\ldots , p_n$ and a symmetric distance matrix $(d_{ij})$ giving the distance between $p_i$ and $p_j$. We wish to construct a tour that minimizes $\sum_{i=1}^n \ell(i)$, where $\ell(i)$ is the {\em latency} of $p_i$, defined to be the distance traveled before first visiting $p_i$. 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