15319550
Log-Linear Models for Word Alignment
We present a framework for word alignment based on log-linear models. All knowledge sources are treated as feature functions, which depend on the source language sentence, the target language sentence and possible additional variables. Log-linear models allow statistical alignment models to be easily extended by incorporating syntactic information. In this paper, we use IBM Model 3 alignment probabilities, POS correspondence, and bilingual dictionary coverage as features. Our experiments show that log-linear models significantly outperform IBM translation models.
10184967
Tree-to-String Alignment Template for Statistical Machine Translation
We present a novel translation model based on tree-to-string alignment template (TAT) which describes the alignment between a source parse tree and a target string. A TAT is capable of generating both terminals and non-terminals and performing reordering at both low and high levels. The model is linguistically syntax-based because TATs are extracted automatically from word-aligned, source side parsed parallel texts. To translate a source sentence, we first employ a parser to produce a source parse tree and then apply TATs to transform the tree into a target string. Our experiments show that the TAT-based model significantly outperforms Pharaoh, a state-of-the-art decoder for phrase-based models.
15497881
A congestion-driven placement framework with local congestion prediction
In this paper, we present a novel congestion-driven placement methodology. We analyze netlist structure and perform local congestion prediction to determine the amount of white space which will be attached to each cell. We place the modified netlist using a total-wire-length-driven placer and obtain an initial legal placement. We extract the congestion information and determine the target white space distribution. Finally, we apply placement migration, an incremental placement technique, to transform the initial placement into a placement which satisfies the target white space distribution. We provide experimental data to demonstrate the effectiveness of our congestion driven placement framework. Compared with the recent academic routability-driven placer Dragon [12], we achieve significant routability improvements in much shorter cpu run time.
8945696
Synthesis and placement flow for gain-based programmable regular fabrics
In this paper we present the Gain-based Logic Block Array (GLA), a new via-programmable regular fabric. GLA is an array of Gain-based Logic Blocks (GLBs). The GLB is a semi-universal logic block designed based on logical effort theory[12]. Customization of the GLBs is provided by programmable vias. To achieve the best performance, appropriate fabric has to be selected from a family of GLAs with different performance-area trade-offs. We describe a synthesis and placement flow which, for a given design to be implemented, allows us to select the best GLA from the candidate family.
5635390
Image Complexity and Feature Extraction for Steganalysis of LSB Matching Steganography
In this paper, we present a scheme for steganalysis of LSB matching steganography based on feature extraction and pattern recognition techniques. Shape parameter of generalized Gaussian distribution (GGD) in the wavelet domain is introduced to measure image complexity. Several statistical pattern recognition algorithms are applied to train and classify the feature sets. Comparison of our method and others indicates our method is highly competitive. It is highly efficient for color image steganalysis. It is also efficient for grayscale steganalysis in the low image complexity domain
677313
A study of netlist structure and placement efficiency
In this paper, we examine the relationship between netlist structure and the efficiency of placers measured in terms of quality and stability of results. We analyze three types of placers: analytic, simulated-annealing based, and partition based. We have tested these placers on industrial and synthetic benchmarks. Based on our observations and analyzes of experimental results, we draw several useful conclusions: 1) Various placers favor netlists with different structural characteristics; 2) placement efficiency correlates with interconnection complexity of the circuits; 3) global nets increase the effort to eliminate overlaps of cells and degrade the efficiency of analytic placers; and 4) global nets can improve placement stability of partition-based placers.
3416923
Efficient Computation of the Skyline Cube
Skyline has been proposed as an important operator for multi-criteria decision making, data mining and visualization, and user-preference queries. In this paper. we consider the problem of efficiently computing a SKYCUBE, which consists of skylines of all possible non-empty subsets of a given set of dimensions. While existing skyline computation algorithms can be immediately extended to computing each skyline query independently, such "shared-nothing" algorithms are inefficient. We develop several computation sharing strategies based on effectively identifying the computation dependencies among multiple related skyline queries. Based on these sharing strategies, two novel algorithms, Bottom-Up and Top-Down algorithms, are proposed to compute SKYCUBE efficiently. Finally, our extensive performance evaluations confirm the effectiveness of the sharing strategies. It is shown that new algorithms significantly outperform the naive ones.