4025640
Algorithms for on-line bin-packing problems with cardinality constraints
The bin-packing problem asks for a packing of a list of items of sizes 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 on-line algorithms for this problem. We show that, for increasing values of k, the bound on the asymptotic worst-case performance ratio of the first algorithm tends towards 2 and that the second algorithm has a ratio of 2. Both algorithms considerably improve upon the best known result of an algorithm, which has an asymptotic bound of 2.7 on its ratio. Moreover, we improve known bounds for all values of k by presenting on-line algorithms for k = 2 and 3 with bounds on their ratios close to optimal.
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.
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 $\tau _M = \max \{ {\frac{{3M}}{{2M + 1}},\frac{{3M - 4}}{{2M - 2}}} \}$.
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.
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.
10910603
Hybrid Forward Resampling and Volume Rendering
The transforming and rendering of discrete objects, such as traditional images (with or without depths) and volumes, can be considered as resampling problem – objects are reconstructed, transformed, filtered, and finally sampled on the screen grids. In resampling practices, discrete samples (pixels, voxels) can be considered either as infinitesimal sample points (simply called points) or samples of a certain size (splats). Resampling can also be done either forwards or backwards in either the source domain or the target domain. In this paper, we present a framework that features hybrid forward resampling for discrete rendering. Specifically, we apply this framework to enhance volumetric splatting. In this approach, minified voxels are taken simply as points filtered in screen space; while magnified voxels are taken as spherical splats. In addition, we develop two techniques for performing accurate and efficient perspective splatting. The first one is to efficiently compute the 2D elliptical geometry of perspectively projected splats; the second one is to achieve accurate perspective reconstruction filter. The results of our experiments demonstrate both the effectiveness of antialiasing and the efficiency of rendering using this
2045915
A Web-based Demonstrator of a Multi-lingual Phrase-based Translation System
This paper describes a multi-lingual phrase-based Statistical Machine Translation system accessible by means of a Web page. The user can issue translation requests from Arabic, Chinese or Spanish into English. The same phrase-based statistical technology is employed to realize the three supported language-pairs. New language-pairs can be easily added to the demonstrator. The Web-based interface allows the use of the translation system to any computer connected to the Internet.
16166391
Linear k-arboricities on trees
Abstract For a fixed positive integer k, the linear k-arboricity lak(G) of a graph G is the minimum number l such that the edge set E(G) can be partitioned into l disjoint sets and that each induces a subgraph whose components are paths of lengths at most k. This paper studies linear k-arboricity from an algorithmic point of view. In particular, we present a linear-time algorithm to determine whether a tree T has lak(T)⩽m.
172784
Algorithms for Structural Comparison and Statistical Analysis of 3D Protein Motifs
The comparison of structural subsites in proteins is increasingly relevant to the prediction of their biological function. To address this problem, we present the Match Augmentation algorithm (MA). Given a structural motif of interest, such as a functional site, MA searches a target protein structure for a match: the set of atoms with the greatest geometric and chemical similarity. MA is extremely efficient because it exploits the fact that the amino acids in a structural motif are not equally important to function. Using motif residues ranked on functional significance via the Evolutionary Trace (ET), MA prioritizes its search by initially forming matches with functionally significant residues, then, guided by ET, it augments this partial match stepwise until the whole motif is found. With this hierarchical strategy, MA runs considerably faster than other methods, and almost always identifies matches in homologs known to have cognate functional sites. Second, in order to interpret matches, we further introduce a statistical method using nonparametric density estimation of the frequency distribution of structural matches. Our results show that the hierarchy of functional importance within structural motifs speeds up the search within targets, and points to a new method to score their statistical significance.
15709443
Voice retrieval of Mandarin broadcast news speech
This paper presents an improved framework for voice retrieval of Mandarin broadcast news speech. First, several unsupervised and data-driven approaches for broadcast news transcription were proposed to improve the speech recognition accuracy and efficiency. Then, a multiscale indexing paradigm for broadcast news retrieval was exploited to alleviate the problems caused by the speech recognition errors and the flexible wording structure of the Chinese language. Finally, we used the PDA as the platform and broadcast radio programs collected in Taiwan as the document collection to establish a speech-based multimedia information retrieval prototype system. Very encouraging results were obtained.