5867549
Adaptive document layout
How to automatically reformat, resize, and paginate electronic text and graphics so documents look as good on displays of any size as they do on paper.
16400135
Adaptive grid-based document layout
Grid-based page designs are ubiquitous in commercially printed publications, such as newspapers and magazines. Yet, to date, no one has invented a good way to easily and automatically adapt such designs to arbitrarily-sized electronic displays. The difficulty of generalizing grid-based designs explains the generally inferior nature of on-screen layouts when compared to their printed counterparts, and is arguably one of the greatest remaining impediments to creating on-line reading experiences that rival those of ink on paper. In this work, we present a new approach to adaptive grid-based document layout, which attempts to bridge this gap. In our approach, an adaptive layout style is encoded as a set of grid-based templates that know how to adapt to a range of page sizes and other viewing conditions. These templates include various types of layout elements (such as text, figures, etc.) and define, through constraint-based relationships, just how these elements are to be laid out together as a function of both the properties of the content itself, such as a figure's size and aspect ratio, and the properties of the viewing conditions under which the content is being displayed. We describe an XML-based representation for our templates and content, which maintains a clean separation between the two. We also describe the various parts of our research prototype system: a layout engine for formatting the page; a paginator for determining a globally optimal allocation of content amongst the pages, as well as an optimal pairing of templates with content; and a graphical user interface for interactively creating adaptive templates. We also provide numerous examples demonstrating the capabilities of this prototype, including this paper, itself, which has been laid out with our system.
6073358
Non-photorealistic virtual environments
We describe a system for non-photorealistic rendering (NPR) of virtual environments. In real time, it synthesizes imagery of architectural interiors using stroke-based textures. We address the four main challenges of such a system — interactivity, visual detail, controlled stroke size, and frame-to-frame coherence — through image based rendering (IBR) methods. In a preprocessing stage, we capture photos of a real or synthetic environment, map the photos to a coarse model of the environment, and run a series of NPR filters to generate textures. At runtime, the system re-renders the NPR textures over the geometry of the coarse model, and it adds dark lines that emphasize creases and silhouettes. We provide a method for constructing non-photorealistic textures from photographs that largely avoids seams in the resulting imagery. We also offer a new construction, art-maps, to control stroke size across the images. Finally, we show a working system that provides an immersive experience rendered in a variety of NPR styles.
6624035
Weakly Supervised Learning for Cross-document Person Name Disambiguation Supported by Information Extraction
It is fairly common that different people are associated with the same name. In tracking person entities in a large document pool, it is important to determine whether multiple mentions of the same name across documents refer to the same entity or not. Previous approach to this problem involves measuring context similarity only based on co-occurring words. This paper presents a new algorithm using information extraction support in addition to co-occurring words. A learning scheme with minimal supervision is developed within the Bayesian framework. Maximum entropy modeling is then used to represent the probability distribution of context similarities based on heterogeneous features. Statistical annealing is applied to derive the final entity coreference chains by globally fitting the pairwise context similarities. Benchmarking shows that our new approach significantly outperforms the existing algorithm by 25 percentage points in overall F-measure.
17487983
A novel network allocation scheme in wireless mobile networks
A novel network allocation scheme of wireless mobile communications networks is investigated in this paper. We study the handoff rates of calls from one cell to another and obtain the blocking probabilities for new calls and handoff calls. Actual call connection time (ACCT) of a call is investigated, which is the total time a mobile user engaged in communications over a wireless network during a call connection and can be used to design appropriate charging schemes. Our numerical results show that how these measures depend on the new call arrival rate with specific reserved channels for handoff calls in each cell. These results are expected to be useful for the cost analysis for updating location and paging in wireless mobile network.
2713110
Orthographic case restoration using supervised learning without manual annotation
One challenge in text processing is the treatment of case insensitive documents such as speech recognition results. The traditional approach is to re-train a language model excluding case-related features. This paper presents an alternative two-step approach whereby a preprocessing module (Step 1) is designed to restore case-sensitive form which is subsequently processed by the original system (Step 2). Step 1 is mainly implemented as a Hidden Markov Model trained on a large raw corpus of case sensitive documents. It is demonstrated that this approach (i) outperforms the feature exclusion approach for named entity tagging, (ii) leads to limited degradation for parsing, relationship extraction and case insensitive question answering, (iii) reduces system complexity, and (iv) has wide applicability: the restored text can be used in both statistical model and rule-based systems.
14663700
A scan BIST generation method using a markov source and partial bit-fixing
Recently, Markov sources were shown to be effective in designing pseudo-random test pattern generators with low area overhead for built-in self-test of scan designs. This paper presents a new test pattern generation scheme based on a Markov source and a partial bit-fixing technique. A new method is proposed for the computation of the state transition probabilities of the Markov source based on the statistics of a deterministic test set. This is enhanced by partial bit-fixing logic, which fixes a group of consecutive inputs to all-0 or all-1. Experimental results show that the proposed BIST scheme can achieve 100% fault coverage for large benchmark circuits with reduced hardware overhead and reduced pattern counts compared to the earlier method using Markov sources.
36676803
A Hypergraph Separator Based Variable Ordering Heuristic for Solving Real World SAT
Problem structure has been used to guide the variable ordering heuristic in backtracking search since [1]. Recent experimental results show that guiding the variable ordering heuristic using tree decompositions can improve the performance of SAT and CSP solvers. Huang and Darwiche's [2] variable ordering heuristic uses a Dtree, a static binary tree decomposition, to compute the variable group ordering. Since the Dtree has to be constructed before search, the pre-established variable groupings never change during the execution of the solving. Boolean Constraint Propagation (BCP) is the look-ahead strategy for all of the cutting-edge SAT solvers. Because long implication chains exist in real world instances and a large number of variables on the implication chains can be instantiated after making a relatively small number of decisions, the internal structure of real world instances often changes dramatically in different parts of the search tree.