Composite Pattern Query Expression over Medical Data Streams
ICU is a typical environment where data stream has been applied to real-life scenarios. Medical data streams share the characteristic of real-time, complex semantics as well as meaningful patterns. Since traditional query methods or pattern process are inherently unable to satisfy real time analysis on various medical data streams, we introduce a novel concept of composite pattern, generalize it to the SQL-like query expression, and propose a method to support ad hoc queries with complex semantic patterns. Our experiments show it is feasible and effective on medical data streams.

title: CTH01-3: A Low-complexity Adaptive Subcarrier, Bit, and Power Allocation Algorithm for OFDMA Systems
abstract: Adaptive subcarrier, bit, and power allocation schemes for orthogonal frequency division multiple access (OFDMA) systems are considered. A low-complexity algorithm is proposed to minimize the overall transmit power under certain fairness constraints: Data rate and bit-error-rate (BER) requirements for each user. In the proposed algorithm, subcarrier allocation is first executed, which follows two steps: An initial subcarrier allocation to satisfy the individual data rate requirement; then a residual subcarrier allocation to further reduce the overall transmit power. After that, a single-user optimal bit and power allocation is performed for each user based on the water-filling strategy. The proposed heuristic subcarrier allocation algorithm has linear complexity in the number of users and subcarriers, as opposed to the combinatorial nature of the optimal solutions. More importantly, the proposed resource allocation scheme explores frequency diversity in a more efficient way compared with reference schemes of similar complexity, which makes it more preferable in highly frequency-selective fading channels.

title: A Novel Method for Evaluating Trustworthiness between Strangers in Large, Dynamic Ad Hoc Networks
abstract: The performance of ad hoc networks depends on cooperation and trust among distributed nodes. To enhance security in ad hoc networks, it is important to evaluate trustworthiness of other nodes without centralized authorities. In this work, we are focusing on the evaluation of trustworthiness between two strangers in large, dynamic ad hoc networks. This novel evaluation process is modeled as a common problem on two Trust Graphs, where nodes represent entities, and edges represent trust relations. According to the character of Quasi-Clique, we show how two strangers can efficiently establish an indirect trust relationship in large, dynamic ad hoc networks without a centralized trust authority.

title: OPTIMOL: A Framework for Online Picture Collection via Incremental Model Learning
abstract: OPTIMOL (a framework for Online Picture collection via Incremental MOdel Learning) is a novel, automatic dataset collecting and model learning system for object categorization. Our algorithm mimics the human learning process in such a way that, starting from a few training examples, the more confident data you incorporate in the training data, the more reliahle models can be learnt. Our system uses the Internet as the (nearly) unlimited resource for images. The learning and image collection processes are done via an iterative and incremental scheme. The goal of this work is to use this tremendous web resource to learn robust object category models in order to detect and search for objects in real-world scenes.

title: What do reflections tell us about the shape of a mirror?
abstract: Three-dimensional shape may be perceived from static images. Contours, shading, texture gradients, perspective and occlusion are well-studied cues to this percept. When looking at a picture of a specular object, such as a silver vase, one additional cue is potentially available: a deformed picture of the reflected environment is seen in the surface of the object and the amount and type of deformation depend on its shape. Can specular reflections be used as a visual cue for shape perception? Our experiments show that our subjects are very poor at judging the shape of mirror surfaces in absence of other visual cues. However, for a considerable subset of the stimuli, subjects are highly consistent in their (most often wrong) perception. This observation leads us to the hypothesis that our subjects rather than 'computing' a percept from each image based on geometrical considerations, may be associating a shape to each pattern in a stereotypical way, akin to pattern-matching. This behavior is reasonable since, as suggested by our ideal observer analysis, the information available from specular reflections is ambiguous when the surrounding world is (partially) unknown.

title: One-shot learning of object categories
abstract: Learning visual models of object categories notoriously requires hundreds or thousands of training examples. We show that it is possible to learn much information about a category from just one, or a handful, of images. The key insight is that, rather than learning from scratch, one can take advantage of knowledge coming from previously learned categories, no matter how different these categories might be. We explore a Bayesian implementation of this idea. Object categories are represented by probabilistic models. Prior knowledge is represented as a probability density function on the parameters of these models. The posterior model for an object category is obtained by updating the prior in the light of one or more observations. We test a simple implementation of our algorithm on a database of 101 diverse object categories. We compare category models learned by an implementation of our Bayesian approach to models learned from by maximum likelihood (ML) and maximum a posteriori (MAP) methods. We find that on a database of more than 100 categories, the Bayesian approach produces informative models when the number of training examples is too small for other methods to operate successfully.