3D Video Billboard Clouds

3D video billboard clouds reconstruct and represent a dynamic three-dimensional scene using displacement-mapped billboards. They consist of geometric proxy planes augmented with detailed displacement maps and combine the generality of geometry-based 3D video with the regularization properties of image-based 3D video. 3D video billboards are an image-based representation placed in the disparity space of the acquisition cameras and thus provide a regular sampling of the scene with a uniform error model. We propose a general geometry filtering framework which generates time-coherent models and removes reconstruction and quantization noise as well as calibration errors. This replaces the complex and time-consuming sub-pixel matching process in stereo reconstruction with a bilateral filter. Rendering is performed using a GPU-accelerated algorithm which generates consistent view-dependent geometry and textures for each individual frame. In addition, we present a semi-automatic approach for modeling dynamic three-dimensional scenes with a set of multiple 3D video billboards clouds.     

-Admissible control elements for diagonal semigroups on -spaces

For linear systems described by , where A is a diagonal operator on the state space l^r for some 1r<∞ and bl^r, we develop necessary and sufficient conditions for b to be p-admissible. This extends results by Ho, Russell and Weiss to the case r≠2.     

Model-based tracking by classification in a tiny discrete pose space

A method is presented for tracking 3D objects as they transform rigidly in space within a sparse range image sequence. The method operates in discrete space and exploits the coherence across image frames that results from the relationship between known bounds on the object's velocity and the sensor frame rate. These motion bounds allow the interframe transformation space to be reduced to a reasonable and indeed tiny size, comprising only tens or hundreds of possible states. The tracking problem is in this way cast into a classification framework, effectively trading off localization precision for runtime efficiency and robustness. The method has been implemented and tested extensively on a variety of freeform objects within a sparse range data stream comprising only a few hundred points per image. It has been shown to compare favorably against continuous domain iterative closest point (ICP) tracking methods, performing both more efficiently and more robustly. A hybrid method has also been implemented that executes a small number of ICP iterations following the initial discrete classification phase. This hybrid method is both more efficient than the ICP alone and more robust than either the discrete classification method or the ICP separately     

Computer methods for automating preoperative dental implant planning: implant positioning and size assignment

The paper presents computer-aided methods that allocate a dental implant and suggest its size, during the pre-operative planning stage, in conformance with introduced optimization criteria and established clinical requirements. Based on computed tomography data of the jaw and prosthesis anatomy, single tooth cases are planned for the best-suited implant insertion at a user-defined region. An optimum implantation axis line is produced and cylindrical implants of various candidate sizes are then automatically positioned, while their occlusal end is leveled to bone ridge, and evaluated. Radial safety margins are used for the assessment of the implant safety distance from neighboring anatomical structures and bone quantity and quality are estimated and taken into consideration. A case study demonstrates the concept and allows for its discussion.     

Exploring the Interaction between Java's Implicitly Thrown Exceptions and Instruction Scheduling

The frequent occurrence of implicitly thrown exceptions poses one of the challenges present in a Java compiler. Not only do these implicitly thrown exceptions directly affect the performance by requiring explicit checks, they also indirectly impact the performance by restricting code movement in order to satisfy the precise exception model in Java. In particular, instruction scheduling is one transformation that is restricted by implicitly thrown exceptions due to the heavy reliance on reordering instructions to exploit maximum hardware performance. The goal of this study is two-fold: first, investigate the degree to which implicitly thrown exceptions in Java hinder instruction scheduling, and second, find new techniques for allowing more efficient execution of Java programs containing implicitly thrown exceptions. Experimental results show that with aggressive scheduling techniques, such as superblock scheduling, the negative performance impact can be greatly reduced.     

Towards More Complete Models of TCP Latency and Throughput

Recently, several researchers have developed equations for modeling TCP behaviors, such as the expected throughput or latency, based on Markov chains derived from TCP with additional simplifying assumptions. In this paper, we suggest new directions for Markov chain analyses of TCP. Our first contribution is to closely examine not just the expectation but the entire cumulative distribution function of transfer times under various models. Particularly for short or medium transfers, the distribution is likely to be more useful than the expectation in terms of measuring end-user satisfaction. We find that the shapes of TCP cumulative distribution functions are remarkably robust to small changes in the model. Our results suggest that simplifying Markov analyses can be extended to yield approximations for the entire distribution as well as for the expectation.Our second contribution is to consider correction procedures to enhance these models. A correction procedure is a rule of thumb that allows equations from one model to be used in other situations. As an example, several analyses use a Drop-Tail loss model. We determine correction procedures for the deviation between this model and other natural loss models based on simulations. The existence of a simple correction procedure in this instance suggests that the high-level behavior of TCP is robust against changes in the loss model.     

On codes defined by bio-operations

We consider the classes of ⊕$\oplus$-codes and ⊗$\otimes$-codes, which are superclasses of outfix and hyper-codes, respectively. These restrictions are based on the synchronized insertion operation, which serves as a model for the gene rearrangement function in certain unicellular organisms. We investigate the classes of ⊕$\oplus$-codes and ⊗$\otimes$-codes from a theoretical perspective, examine their relationships with traditional code classes and consider related decidability problems.     

Unified use case statecharts: case studies

This paper presents the results of case studies evaluating a method of unifying use cases (UCs) to derive a unified statechart model of the behavior of the domain of a proposed computer-based system. An evaluation of the unification method, the obtained statechart model of the domain, the method’s and model’s feedback on the UCs themselves, and how the method is used in requirements engineering practice was carried out by examining 58 software requirements specifications produced by 189 upper-year undergraduate and graduate students. The results of these studies independently confirm some of the benefits of building a unified SC mentioned in the works of Glinz; Whittle and Schumann; and Harel, Kugler, and Pnueli.

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Protein radial distribution function (P-RDF) and Bayesian-Regularized Genetic Neural Networks for modeling protein conformational stability: chymotrypsin inhibitor 2 mutants

Development of novel computational approaches for modeling protein properties is a main goal in applied Proteomics. In this work, we reported the extension of the radial distribution function (RDF) scores formalism to proteins for encoding 3D structural information with modeling purposes. Protein-RDF (P-RDF) scores measure spherical distributions on protein 3D structure of 48 amino acids/residues properties selected from the AAindex data base. P-RDF scores were tested for building predictive models of the change of thermal unfolding Gibbs free energy change (DeltaDeltaG) of chymotrypsin inhibitor 2 upon mutations. In this sense, an ensemble of Bayesian-Regularized Genetic Neural Networks (BRGNNs) yielded an optimum nonlinear model for the conformational stability. The ensemble predictor described about 84% and 70% variance of the data in training and test sets, respectively.