Trees, windows and tiles for wavelet image compression
We investigate the task of compressing an image by using different probability models for compressing different regions of the image. In an earlier paper, we introduced a class of probability models for images, the k-rectangular tiling of an image, which is formed by partitioning the image into k rectangular regions and generating the coefficients within each region by using a probability model selected from a finite class C of probability models. We also describe a computationally efficient sequential probability assignment algorithm that is able to code an image with a code length that is close to the code length produced by the best model in the class. In this paper, we investigate the performance of the algorithm experimentally on the task of compressing wavelet subbands. We compare the method with compression methods that aim to compress as well as the best pruning of a quadtree and compression methods that exploit the local statistics in a window around the coefficient being compressed. For a class C consisting of a small number of Laplacian distributions and the uniform distribution, we find that the best tiling method works best, but the difference in performance is significant for only a few of the images tested.

title: Text classification with kernels on the multinomial manifold
abstract: Support Vector Machines (SVMs) have been very successful in text classification. However, the intrinsic geometric structure of text data has been ignored by standard kernels commonly used in SVMs. It is natural to assume that the documents are on the multinomial manifold, which is the simplex of multinomial models furnished with the Riemannian structure induced by the Fisher information metric. We prove that the Negative Geodesic Distance (NGD) on the multinomial manifold is conditionally positive definite (cpd), thus can be used as a kernel in SVMs. Experiments show the NGD kernel on the multinomial manifold to be effective for text classification, significantly outperforming standard kernels on the ambient Euclidean space.

title: Learning to Separate Text Content and Style for Classification
abstract: Many text documents naturally have two kinds of labels. For example, we may label web pages from universities according to their categories, such as “student” or “faculty”, or according the source universities, such as “Cornell” or “Texas”. We call one kind of labels the content and the other kind the style. Given a set of documents, each with both content and style labels, we seek to effectively learn to classify a set of documents in a new style with no content labels into its content classes. Assuming that every document is generated using words drawn from a mixture of two multinomial component models, one content model and one style model, we propose a method named Cartesian EM that constructs content models and style models through Expectation Maximization and performs classification of the unknown content classes transductively. Our experiments on real-world datasets show the proposed method to be effective for style independent text content classification.

title: RankCut - A Domain Independent Forward Pruning Method for Games
abstract: Forward pruning, also known as selective search, is now employed in many strong game-playing programs. In this paper, we introduce RankCut – a domain independent forward pruning technique which makes use of move ordering, and prunes once no better move is likely to be available. Since gameplaying programs already perform move ordering to improve the performance of αβ search, this information is available at no extra cost. As RankCut uses additional information untapped by current forward pruning techniques, RankCut is a complementary forward pruning method that can be used with existing methods, and is able to achieve improvements even when conventional pruning techniques are simultaneously employed. We implemented RankCut in a modern open-source chess program, CRAFTY. RankCut reduces the game-tree size by approximately 10%-40% for search depths 8-12 while retaining tactical reliability, when implemented alongside CRAFTY’s existing forward pruning techniques.

title: Partially Supervised Classification of Text Documents
abstract: We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents that contains documents from class P and other types of documents, identify the documents from class P in M . The key feature of this problem is that there is no labeled nonP document, which makes traditional machine learning techniques inapplicable, as they all need labeled documents of both classes. We call this problem partially supervised classification. In this paper, we show that this problem can be posed as a constrained optimization problem and that under appropriate conditions, solutions to the constrained optimization problem will give good solutions to the partially supervised classification problem. We present a novel technique to solve the problem and demonstrate the effectiveness of the technique through extensive experimentation.

title: A Theoretical Analysis of Query Selection for Collaborative Filtering
abstract: We consider the problem of determining which of a set of experts has tastes most similar to a given user by asking the user questions about his likes and dislikes. We describe a simple and fast algorithm for a theoretical model of this problem with a provable approximation guarantee, and prove that solving the problem exactly is NP-Hard.

title: Real Face Communication in a Virtual World
abstract: This paper describes an efficient method to make an individual face for animation from several possible inputs and how to use this result for a realistic talking head communication in a virtual world. We present a method to reconstruct 3D facial model from two orthogonal pictures taken from front and side views. The method is based on extracting features from a face in a semiautomatic way and deforming a generic model. Texture mapping based on cylindrical projection is employed using a composed image from the two images. A reconstructed head is animated immediately and is able to talk with given text, which is transformed to corresponding phonemes and visemes. We also propose a system for individualized face-to-face communication through network using MPEG4.

title: Space-time scheduling of instruction-level parallelism on a raw machine
abstract: Increasing demand for both greater parallelism and faster clocks dictate that future generation architectures will need to decentralize their resources and eliminate primitives that require single cycle global communication. A Raw microprocessor distributes all of its resources, including instruction streams, register files, memory ports, and ALUs, over a pipelined two-dimensional mesh interconnect, and exposes them fully to the compiler. Because communication in Raw machines is distributed, compiling for instruction-level parallelism (ILP) requires both spatial instruction partitioning as well as traditional temporal instruction scheduling. In addition, the compiler must explicitly manage all communication through the interconnect, including the global synchronization required at branch points. This paper describes RAWCC, the compiler we have developed for compiling general-purpose sequential programs to the distributed Raw architecture. We present performance results that demonstrate that although Raw machines provide no mechanisms for global communication the Raw compiler can schedule to achieve speedups that scale with the number of available functional units.

title: A study on channel allocation for data dissemination in mobile computing environments
abstract: This paper studies channel allocation methods for data dissemination through broadcast and on‐demand channels. Analytical models and cost formulae for exclusive broadcast channels and exclusive on‐demand channels are provided. Based on the models, we further derive cost models for dynamic channel allocation methods and propose a channel adaptation algorithm for optimizing system performance. The channel adaptation algorithm can be executed in O(n) time, where n is the number of data items in the database. Performance evaluation shows that the channel allocation algorithm produces optimal channel allocation which significantly improves the system performance under various parameter settings.

title: Face recognition using computer-generated database
abstract: Many face database and recognition systems have been constructed in specially designed studios with various illuminations, poses, and expressions. However, none of these databases yet satisfies a large variation of poses and illuminations that permit the study of systematic 3D human face information, which results in an unsatisfactory success rate. It is caused primarily by the difficulty of data collection of facial images to satisfy the large variation of poses and illuminations to fully represent the 3D characteristics of human faces. We present how computer-generated database can be used for face recognition experiment focused on multiview face recognition/descriptor. We show multiview face data collection using rendering of 3D models. We also illustrate our approach how to build a face descriptor containing 3D information of human face using multiview concepts. This multiview face recognition descriptor is a 3D descriptor using the concept how much powerful a view influences over nearby views, so called as "quasiview " size

title: Attack Analysis and Detection for Ad Hoc Routing Protocols
abstract: Attack analysis is a challenging problem, especially in emerging environments where there are few known attack cases. One such new environment is the Mobile Ad hoc Network (MANET). In this paper, we present a systematic approach to analyze attacks. We introduce the concept of basic events. An attack can be decomposed into certain combinations of basic events. We then define a taxonomy of anomalous basic events by analyzing the basic security goals.