5690166
Developing a database for proteomic analysis of extracytosolic plant proteins
Extracytosolic plant proteins are involved in numerous processes including nutrient acquisition, communication with other soil organisms, protection from pathogens, and resistant to disease and toxic metals. We have developed an online database to provide the plant biology community with relevant information about extracytosolic plant proteins. Data for Brassica napus (canola) proteins identified using proteomics tools can be accessed using this database. Several textual and graphical query capabilities allow biologists to populate and query this database. Results are displayed with active links to other databases. The system has an open API allowing other applications to access this database as a Web service. In addition, the database is augmented with a repository of tools that can be used in data analysis and mining tasks.
20945105
Real-Time Moving Vehicle Detection With Cast Shadow Removal in Video Based on Conditional Random Field
This paper presents an approach of moving vehicle detection and cast shadow removal for video based traffic monitoring. Based on conditional random field, spatial and temporal dependencies in traffic scenes are formulated under a probabilistic discriminative framework, where contextual constraints during the detection process can be adaptively adjusted in terms of data-dependent neighborhood interaction. Computationally efficient algorithm has been developed to discriminate moving cast shadows and handle nonstationary background processes for real-time vehicle detection in video streams. Experimental results show that the proposed approach effectively fuses contextual dependencies and robustly detects moving vehicles under heavy shadows even in grayscale video.
13511504
Non-Rigid Object Alignment with a Mismatch Template Based on Exhaustive Local Search
Non-rigid object alignment is especially challenging when only a single appearance template is available and target and template images fail to match. Two sources of discrepancy between target and template are changes in illumination and non-rigid motion. Because most existing methods rely on a holistic representation for the alignment process, they require multiple training images to capture appearance variance. We developed a patch-based method that requires only a single appearance template of the object. Specifically, we fit the patch-based face model to an unseen image using an exhaustive local search and constrain the local warp updates within a global warping space. Our approach is not limited to intensity values or gradients, and therefore offers a natural framework to integrate multiple local features, such as filter responses, to increase robustness to large initialization error, illumination changes and non-rigid deformations. This approach was evaluated experimentally on more than 100 subjects for multiple illumination conditions and facial expressions. In all the experiments, our patch-based method outperforms the holistic gradient descent method in terms of accuracy and robustness of feature alignment and image registration.
9142519
Utilizing field usage patterns for Java heap space optimization
This research studies the characteristics of field usage patterns in the SpecJVM98 benchmarks. It finds that multiple object instances of the same class often exhibit different field-usage patterns. Motivated by this observation, we designed a heap compression mechanism that classifies object instances at runtime based on their field-usage patterns and eliminates unused fields to save space. To achieve the maximum space savings while minimizing the space and time overhead, our design combines three interrelated techniques in a novel manner: runtime object instance classification, field virtualization, and bidirectional object layout. An experimental evaluation reveals that this mechanism can reduce the maximum heap occupancy of SpecJVM98 benchmarks by up to 18% and 14% on average while keeping the application execution overhead low.
2947494
High Resolution Tracking of Non-Rigid Motion of Densely Sampled 3D Data Using Harmonic Maps
Abstract
We present a novel automatic method for high resolution, non-rigid dense 3D point tracking. High quality dense point clouds of non-rigid geometry moving at video speeds are acquired using a phase-shifting structured light ranging technique. To use such data for the temporal study of subtle motions such as those seen in facial expressions, an efficient non-rigid 3D motion tracking algorithm is needed to establish inter-frame correspondences. The novelty of this paper is the development of an algorithmic framework for 3D tracking that unifies tracking of intensity and geometric features, using harmonic maps with added feature correspondence constraints. While the previous uses of harmonic maps provided only global alignment, the proposed introduction of interior feature constraints allows to track non-rigid deformations accurately as well. The harmonic map between two topological disks is a diffeomorphism with minimal stretching energy and bounded angle distortion. The map is stable, insensitive to resolution changes and is robust to noise. Due to the strong implicit and explicit smoothness constraints imposed by the algorithm and the high-resolution data, the resulting registration/deformation field is smooth, continuous and gives dense one-to-one inter-frame correspondences. Our method is validated through a series of experiments demonstrating its accuracy and efficiency.

20961254
Strictly Nonblocking Multirate logd(N, m, p) Networks
We give necessary and sufficient conditions for the d-nary multilog network to be strictly nonblocking under the discrete multirate model, and we give sufficient conditions for the same under the continuous multirate model.
18554673
Network processing performability evaluation on heterogeneous reliability multicore processors using SRN model
Future network systems and embedded infrastructure devices in ubiquitous environments will need to consume low power and process large amounts of network packet traffic. In order to meet necessary high processing efficiency requirements, future processors will have many heterogeneous cores with reduced reliability due to low voltage, small transistor sizes, semiconductor wearout, and environmental factors such as noise and interference. It will be necessary for multi-core network infrastructure software to mitigate transient hardware faults to maintain acceptable system reliability. Applications such as packet processing can benefit from the reliability versus performance tradeoff. We propose a model based on Stochastic Reward Nets to evaluate the performance vs. reliability tradeoff of unreliable embedded multi-core network processors, and apply this model to a multi-core packet processing application.
21401289
Parallelism in Relational Database Management Systems
In order to provide real-time responses to complex queries involving large volumes of data, it has become necessary to exploit parallelism in query processing. This paper addresses the issues and solutions relating to intraquery parallelism in a relational database management system (DBMS). We provide a broad framework for the study of the numerous issues that need to be addressed in supporting parallelism efficiently and flexibly. The alternatives for a parallel architecture system are discussed, followed by the focus on how a query can be parallelized and how that affects load balancing of the different tasks created. The final part of the paper contains information about how the IBM DATABASE 2™ (DB2®) Version 3 product provides support for I/O parallelism to reduce response time for data-intensive queries.
19667746
Study on the Harmonious Operation of Logistics Finance Management of Supply Chain in Ports-Hinterland Based on System Dynamics
Abstract-Taking China's ports-hinterland logistics system as an example, the paper discusses the harmonious operation of logistics finance management of supply chain which is composed of the functional entity activities such as transportation, warehousing, packaging, circulation and information transmission, as well as the goods movement of all sectors of the logistics process. The logistics finance management is complex operating system with dynamics features, which is established on the basis of a "virtual organization" made up by nodes in supply chain. In order to provide a new research perspective on the coordination development of ports-hinterland logistics system, a dynamics model of logistics finance management of supply chain was set up based on the theory of system dynamics.
32478144
Experience from a real life query optimizer
DB2 is a major relational database system on IBM main frames, it has over six thousand licenses over the world to support a very large number of major applications for customer business, Optimizer in DB2 is a critical component for the query processing, it inherited system R technologies and has been improved in very many aspects from numerous valuable real life experiences on customer applications. The major DB2 optimizer enhancements will be discussed with the backgrounds and examples. A wish list for future enhancements as well as some trend and direction of DB2 optimizer will also be discussed.
12250963
Boosting for Learning Multiple Classes with Imbalanced Class Distribution
Classification of data with imbalanced class distribution has posed a significant drawback of the performance attainable by most standard classifier learning algorithms, which assume a relatively balanced class distribution and equal misclassification costs. This learning difficulty attracts a lot of research interests. Most efforts concentrate on bi-class problems. However, bi-class is not the only scenario where the class imbalance problem prevails. Reported solutions for bi-class applications are not applicable to multi-class problems. In this paper, we develop a cost-sensitive boosting algorithm to improve the classification performance of imbalanced data involving multiple classes. One barrier of applying the cost-sensitive boosting algorithm to the imbalanced data is that the cost matrix is often unavailable for a problem domain. To solve this problem, we apply Genetic Algorithm to search the optimum cost setup of each class. Empirical tests show that the proposed cost-sensitive boosting algorithm improves the classification performances of imbalanced data sets significantly.