5940493
Efficient schema design for a pharmaceutical data repository
Large scientific databases often consist of heterogeneous components that differ in the number of attributes and the number of tuples. In this paper, we show that increased performance is achieved when a complex schema is used to represent different components of a data repository. The driving application used in this study is a pharmaceutical drug repository. The types of access patterns associated with this repository consist of the retrieval of a selected set of attribute values for a consecutive or a random set of tuples. These consecutive and random access patterns, which are typical of a drug discovery experiment, are used to measure the data retrieval throughput for different schemas.
3183902
An Efficient Implementation of a Drug Candidate Database
The recent advances in laboratory technologies have resulted in a wealth of chemical and biological data. The rapid proliferation of a vast amount of data has led to a set of cheminformatics and bioinformatics applications that manipulate dynamic, heterogeneous, and massive data. An example of such application in the pharmaceutical industry is the computational process involved in the early discovery of lead drug candidates for a given target disease. In this paper, an efficient implementation of a drug candidate database is presented and evaluated. This study shows that high performance data access can be achieved through proper choices of data representation, database schema design, and parallel processing techniques.
1753568
Learning multispectral texture features for cervical cancer detection
We present a bottom-up approach for automatic cancer cell detection in multispectral microscopic thin Pap smear images. Around 4,000 multispectral texture features are explored for cancer cell detection. Using two feature screening measures, the initial feature set is effectively reduced to a computationally manageable size. Based on pixel-level screening results, cancerous regions can thus be detected through a relatively simple procedure. Our experiments have demonstrated the potential of both multispectral and texture information to serve as valuable complementary cues to traditional detection methods.
12295885
Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures
This paper reports our methodology and initial results on volumetric pathological neuroimage retrieval. A set of novel image features are computed to quantify the statistical distributions of approximate bilateral asymmetry of normal and pathological human brains. We apply memory-based learning method to find the most-discriminative feature subset through image classification according to predefined semantic categories. Finally, this selected feature subset is used as indexing features to retrieve medically similar images under a semantic-based image retrieval framework. Quantitative evaluations are provided.
16112032
Fast Evolution Strategies
Evolution strategies are a class of general optimisation algorithms which are applicable to functions that are multimodal, non-differentiable, or even discontinuous. Although recombination operators have been introduced into evolution strategies, their primary search operator is still mutation. Classical evolution strategies rely on Gaussian mutations. A new mutation operator based on the Cauchy distribution is proposed in this paper. It is shown empirically that the new evolution strategy based on Cauchy mutation outperforms the classical evolution strategy on most of the 23 benchmark problems tested in this paper. These results, along with those obtained by fast evolutionary programming
6591453
Opportunistic fair scheduling over multiple wireless channels
Emerging spread spectrum high-speed data networks utilize multiple channels via orthogonal codes or frequency-hopping patterns such that multiple users can transmit concurrently. In this paper, we develop a framework for opportunistic scheduling over multiple wireless channels. With a realistic channel model, any subset of users can be selected for data transmission at any time, albeit with different throughputs and system resource requirements. We first transform selection of the best users and rates from a complex general optimization problem into a decoupled and tractable formulation: a multiuser scheduling problem that maximizes total system throughput and a control-update problem that ensures long-term deterministic or probabilistic fairness constraints. We then design and evaluate practical schedulers that approximate these objectives.
2987131
Interface-Based Rate Analysis of Embedded Systems
Interface-based design is now considered to be one of the keys to tackling the increasing complexity of modern embedded systems. The central idea is that different components comprising such systems can be developed independently and a system designer can connect them together only if their interfaces match, without knowing the details of their internals. We use the concept of rate interfaces for compositional (correct-by-construction) design of embedded systems whose components communicate through data streams. Using the associated rate interface algebra, two components can be connected together if the output rate of one component is "compatible" with the input rate of the other component. We formalize this notion of compatibility and show that such an algebra is non-trivial because it has to accurately model the burstiness in the arrival rates of such data streams and the variability in their processing requirements. We discuss how rate interfaces simplify compositional design and at the same time help in functional and performance verification which would be difficult to address otherwise. Finally, we illustrate these advantages through a realistic case study involving a component-based design of a multiprocessor architecture running a picture-in-picture application