4000958
A Better Heuristic for Preemptive Parallel Machine Scheduling with Batch Setup Times
This paper addresses the problem of scheduling N jobs on M identical parallel machines with the objective of minimizing the makespan. Jobs are divided into B batches. A sequence-independent batch setup time on a machine is incurred whenever the machine starts its processing on or switches it from a job in one batch to a job in another batch. On the basis of a heuristic for this NP-hard problem proposed by Monma and Potts, a modified heuristic that requires the same implementing time $O(N + (M + B)\log (M + B))$ and is asymptotically optimal is presented. Furthermore, for a certain class of problems, which includes the case in which each batch contains a single job, it has the worst-case performance ratio $\tau _M = \max \{ {\frac{{3M}}{{2M + 1}},\frac{{3M - 4}}{{2M - 2}}} \}$.
3992681
A Lower Bound for Randomized On-Line Scheduling Algorithms
We prove that any randomized algorithm for on-line scheduling jobs on m identical parallel machines must have a worst case ratio at least mm/(mm−(m−1)m) for every m, which tends to e/(e−1) ≈ 1.58 as m→∞. This ans wers a question posed in a recent paper by Bartal, Fiat, Karloff and Vohra.
4000197
Lot-sizing scheduling with batch setup times
This paper is concerned with scheduling independent jobs on m parallel machines in such a way that the makespan is minimized. Each job j is allowed to split arbitrarily into several parts, which can be individually processed on any machine at any time. However, a setup for uninterrupted sj time units is required before any split part of job j can be processed on any machine. The problem is strongly NP-hard if the number m of machines is variable and weakly NP-hard otherwise. We give a polynomial-time $$\frac{5}{3}$$-approximation algorithm for the former case and a fully polynomial-time approximation scheme for the latter.
17041567
On-line scheduling of small open shops
We investigate the problem of on-line scheduling open shops of two and three machines with an objective of minimizing the schedule makespan. We first propose a 1.848-competitive permutation algorithm for the non-preemptive scheduling problem of two machines and show that no permutation algorithm can be better than 1.754-competitive. Secondly, we develop a (27/19)-competitive algorithm for the preemptive scheduling problem of three machines, which is most competitive.
3424570
Prediction Cubes
In this paper, we introduce a new family of tools for exploratory data analysis, called prediction cubes. As in standard OLAP data cubes, each cell in a prediction cube contains a value that summarizes the data belonging to that cell, and the granularity of cells can be changed via operations such as roll-up and drill-down. In contrast to data cubes, in which each cell value is computed by an aggregate function, e.g., SUM or AVG, each cell value in a prediction cube summarizes a predictive model trained on the data corresponding to that cell, and characterizes its decision behavior or predictiveness. In this paper, we propose and motivate prediction cubes, and show that they can be efficiently computed by exploiting the idea of model decomposition.
1524685
Achievable performance of digital watermarking systems
A variety of digital watermarking applications have emerged recently that require the design of systems for embedding one signal (the "embedded signal" or "watermark") within another signal (the "host signal"). We develop a framework for analyzing achievable performance trade-offs of these systems among robustness, distortion, and embedding rate. We also describe a recently introduced class of embedding methods, quantization index modulation (QIM), in which an ensemble of quantizers is constructed and information is embedded by quantizing the host signal with a quantizer associated with the watermark. We introduce an implementation of such a method called spread-transform dither modulation where the embedded information modulates the dither signal of a dithered quantizer, which quantizes projections of the host signal onto a spreading vector. We show that QIM systems have considerable performance advantages over previously proposed spread-spectrum and low-bit modulation systems.
2577652
Bellwether analysis: predicting global aggregates from local regions
Massive datasets are becoming commonplace in a wide range of domains, and mining them is recognized as a challenging problem with great potential value. Motivated by this challenge, much effort has been concentrated on developing scalable versions of machine learning algorithms. An often overlooked issue is that large datasets are rarely labeled with the outputs that we wish to learn to predict, due to the human labor required. We make the key observation that analysts can often use queries to define labels for cases, which leads to the problem of learning to predict such query-produced labels. Of course, if a dataset is available in its entirety, we can simply run the query again to compute labels. The interesting scenarios are those where, after the predictive model is trained, new data is gathered at significant incremental cost and, perhaps, over time. The challenge is to accurately predict the query-labels for the projected completion of new datasets, based only on certain cost-effective subsets, which we call bellwethers.
16592812
A web-based virtual laboratory on a frequency modulation experiment
With the rapid proliferation of Internet technologies, accessing and operating engineering instruments remotely anytime anywhere is fast becoming a reality. This paper presents a new Web-based virtual laboratory on a frequency modulation experiment for the teaching of an undergraduate course on communication principles in the National University of Singapore (NUS). The laboratory requires only a common Web browser to access and incorporate schemes for reducing data traffic and authenticating users. It enables students to have a natural hands-on experience of using an expensive spectrum analyzer on a one-to-one basis and provides a solution for distant engineering education. The system uses a double client-server structure where access to the experiment is via two rounds of client-server processing. The virtual laboratory can be accessed at the Web site http://vlab.ee.nus.edu.sg/vlab/freqmod/index.html.
9470216
Quantization index modulation: A class of provably good methods for digital watermarking and information embedding
We consider the problem of embedding one signal (e.g., a digital watermark), within another "host" signal to form a third, "composite" signal. The embedding is designed to achieve efficient tradeoffs among the three conflicting goals of maximizing the information-embedding rate, minimizing the distortion between the host signal and composite signal, and maximizing the robustness of the embedding. We introduce new classes of embedding methods, termed quantization index modulation (QIM) and distortion-compensated QIM (DC-QIM), and develop convenient realizations in the form of what we refer to as dither modulation. Using deterministic models to evaluate digital watermarking methods, we show that QIM is "provably good" against arbitrary bounded and fully informed attacks, which arise in several copyright applications, and in particular it achieves provably better rate distortion-robustness tradeoffs than currently popular spread-spectrum and low-bit(s) modulation methods. Furthermore, we show that for some important classes of probabilistic models, DC-QIM is optimal (capacity-achieving) and regular QIM is near-optimal. These include both additive white Gaussian noise (AWGN) channels, which may be good models for hybrid transmission applications such as digital audio broadcasting, and mean-square-error-constrained attack channels that model private-key watermarking applications.