42567499
On-Line Scheduling a Batch Processing System to Minimize Total Weighted Job Completion Time
Scheduling a batch processing system has been extensively studied in the last decade.A batch processing system is modelled as a machine that can process up to b jobs simultaneously as a batch.Th e scheduling problem involves assigning all n jobs to batches and determining the batch sequence in such a way that certain objective function of job completion times C j is minimized.In this paper, we address the scheduling problem under the on-line setting in the sense that we construct our schedule irrevocably as time proceeds and do not know of the existence of any job that may arrive later.Our objective is to minimize the total weighted completion time ?w j C j . We provide a linear time on-line algorithm for the unrestrictive model (i.e., b ? n) and show that the algorithm is 10/3-competitive. For the restrictive model (i.e., b 0 for the original on-line scheduling problem. These two on-line algorithms are the first deterministic algorithms of constant performance guarantees.
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.
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.
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 $\tau _M = \max \{ {\frac{{3M}}{{2M + 1}},\frac{{3M - 4}}{{2M - 2}}} \}$.
39846081
On-Line Algorithms for Cardinality Constrained Bin Packing Problems
The bin packing problem asks for a packing of a list of items from [0, 1] into the smallest possible number of bins having unit capacity. The k-item bin packing problem additionally imposes the constraint that at most k items are allowed in one bin. We present two efficient approximation algorithms for the on-line version of this problem. We show that, for increasing values of k, the asymptotic worst-case performance ratio of the first algorithm tends towards 2 and that the second algorithm has an asymptotic worst-case performance ratio of 2. Both heuristics considerably improve upon the best known result 2.7 of Krause, Shen and Schwetman. Moreover, we present algorithms for k = 2 and k = 3, where the result for k = 2 is best possible.
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.
5893940
Multiresolution tetrahedral framework for visualizing regular volume data
The authors present a multiresolution framework, called Multi-Tetra framework, that approximates volume data with different levels-of-detail tetrahedra. The framework is generated through a recursive subdivision of the volume data and is represented by binary trees. Instead of using a certain level of the Multi-Tetra framework for approximation, an error-based model (EBM) is generated by recursively fusing a sequence of tetrahedra from different levels of the Multi-Tetra framework. The EBM significantly reduces the number of voxels required to model an object, while preserving the original topology. The approach provides continuous distribution of rendered intensity or generated isosurfaces along boundaries of different levels-of-detail thus solving the crack problem. The model supports typical rendering approaches, such as marching cubes, direct volume projection, and splatting. Experimental results demonstrate the strengths of the approach.
38323853
Content-based Language Models for Spoken Document Retrieval
Spoken document retrieval (SDR) has been extensively studied in recent years because of its potential use in navigating large multimedia collections in the near future. This paper presents a novel concept of applying content-based language models to spoken document retrieval. In an example task for retrieval of Mandarin Chinese broadcast news data, the content-based language models, either trained on automatic transcriptions of spoken documents or adapted from baseline language models using automatic transcriptions of spoken documents, were used to create more accurate recognition results and indexing terms from both spoken documents and speech queries. We report on some interesting findings obtained in this research.
11577696
A maximum-entropy chinese parser augmented by transformation-based learning
Parsing, the task of identifying syntactic components, e.g., noun and verb phrases, in a sentence, is one of the fundamental tasks in natural language processing. Many natural language applications such as spoken-language understanding, machine translation, and information extraction, would benefit from, or even require, high accuracy parsing as a preprocessing step.
 Even though most state-of-the-art statistical parsers were initially constructed for parsing in English, most of them are not language-specific, in that they do not rely on properties of the language that are specific to English. Therefore, construction of a parser in a given language becomes a matter of retraining the statistical parameters with a Treebank in the corresponding language.
 The development of the Chinese treebank [Xia et al. 2000] spurred the construction of parsers for Chinese. However, Chinese as a language poses some unique problems for the development of a statistical parser, the most apparent being word segmentation. Since words in written Chinese are not delimited in the same way as in Western languages, the first problem that needs to be solved before an existing statistical method can be applied to Chinese is to identify the word boundaries. This is a step that is neglected by most pre-existing Chinese parsers, which assume that the input data has already been pre-segmented.
 This article describes a character-based statistical parser, which gives the best performance to-date on the Chinese treebank data. We augment an existing maximum entropy parser with transformation-based learning, creating a parser that can operate at the character level. We present experiments that show that our parser achieves results that are close to those achievable under perfect word segmentation conditions.
10767971
The duality between information embedding and source coding with side information and some applications
Aspects of the duality between the information-embedding problem and the Wyner-Ziv (1976) problem of source coding with side information at the decoder are developed and used to establish a spectrum new results on these and related problems, with implications for a number of important applications. The single-letter characterization of the information-embedding problem is developed and related to the corresponding characterization of the Wyner-Ziv problem, both of which correspond to optimization of a common mutual information difference. Dual variables and dual Markov conditions are identified, along with the dual role of noise and distortion in the two problems. For a Gaussian context with quadratic distortion metric, a geometric interpretation of the duality is developed. From such insights, we develop a capacity-achieving information-embedding system based on nested lattices. We show the resulting encoder-decoder has precisely the same decoder-encoder structure as the corresponding Wyner-Ziv system based on nested lattices that achieves the rate-distortion limit. For a binary context with Hamming distortion metric, the information-embedding capacity is developed, along with its relationship to the corresponding Wyner-Ziv rate-distortion function. In turn, an information-embedding system for this case based on nested linear codes is constructed having an encoder-decoder that is identical to the decoder-encoder structure for the corresponding system that achieves the Wyner-Ziv rate-distortion limit. Finally, based on these results, a simple layered joint source-channel coding system is developed with a perfectly symmetric encoder-decoder structure. Its application and performance is discussed in a broadcast setting in which there is a need to control the fidelity experienced by different receivers. Among other results, we show that such systems and their multilayer extensions retain attractive optimality properties in the Gaussian-quadratic case, but not in the binary-Hamming case.
10957724
Footprint area sampled texturing
We study texture projection based on a four region subdivision: magnification, minification, and two mixed regions. We propose improved versions of existing techniques by providing exact filtering methods which reduce both aliasing and overblurring, especially in the mixed regions. We further present a novel texture mapping algorithm called FAST (footprint area sampled texturing), which not only delivers high quality, but also is efficient. By utilizing coherence between neighboring pixels, performing prefiltering, and applying an area sampling scheme, we guarantee a minimum number of samples sufficient for effective antialiasing. Unlike existing methods (e.g., MlP-map, Feline), our method adapts the sampling rate in each chosen MlP-map level separately to avoid undersampling in the lower level l for effective antialiasing and to avoid oversampling in the higher level l+1 for efficiency. Our method has been shown to deliver superior image quality to Feline and other methods while retaining the same efficiency. We also provide implementation trade offs to apply a variable degree of accuracy versus speed.