Class-based Rough Approximation with Dominance Principle

Dominance-based Rough Set Approach (DRSA), as the extension of Pawlak's Rough Set theory, is effective and fundamentally important in Multiple Criteria Decision Analysis (MCDA). In previous DRSA models, the definitions of the upper and lower approximations are preserving the class unions rather than the singleton class. In this paper, we propose a new Class-based Rough Approximation with respect to a series of previous DRSA models, including Classical DRSA model, VC-DRSA model and VP-DRSA model. In addition, the new class-based reducts are investigated.Comment: Submitted to IEEE-GrC201

High-Efficient Parallel CAVLC Encoders on Heterogeneous Multicore Architectures

This article presents two high-efficient parallel realizations of the context-based adaptive variable length coding (CAVLC) based on heterogeneous multicore processors. By optimizing the architecture of the CAVLC encoder, three kinds of dependences are eliminated or weaken, including the context-based data dependence, the memory accessing dependence and the control dependence. The CAVLC pipeline is divided into three stages: two scans, coding, and lag packing, and be implemented on two typical heterogeneous multicore architectures. One is a block-based SIMD parallel CAVLC encoder on multicore stream processor STORM. The other is a component-oriented SIMT parallel encoder on massively parallel architecture GPU. Both of them exploited rich data-level parallelism. Experiments results show that compared with the CPU version, more than 70 times of speedup can be obtained for STORM and over 50 times for GPU. The implementation of encoder on STORM can make a real-time processing for 1080p @30fps and GPU-based version can satisfy the requirements for 720p real-time encoding. The throughput of the presented CAVLC encoders is more than 10 times higher than that of published software encoders on DSP and multicore platforms

Towards a Reliable Framework of Uncertainty-Based Group Decision Support System

This study proposes a framework of Uncertainty-based Group Decision Support System (UGDSS). It provides a platform for multiple criteria decision analysis in six aspects including (1) decision environment, (2) decision problem, (3) decision group, (4) decision conflict, (5) decision schemes and (6) group negotiation. Based on multiple artificial intelligent technologies, this framework provides reliable support for the comprehensive manipulation of applications and advanced decision approaches through the design of an integrated multi-agents architecture.Comment: Accepted paper in IEEE-ICDM2010; Print ISBN: 978-1-4244-9244-

Enhancement of the immunoregulatory potency of mesenchymal stromal cells by treatment with immunosuppressive drugs

Background aims Multipotent mesenchymal stromal cells (MSCs) are distinguished by their ability to differentiate into a number of stromal derivatives of interest for regenerative medicine, but they also have immunoregulatory properties that are being tested in a number of clinical settings. Methods We show that brief incubations with rapamycin, everolimus, FK506 or cyclosporine A increase the immunosuppressive potency of MSCs and other cell types. Results The treated MSCs are up to 5-fold more potent at inhibiting the induced proliferation of T lymphocytes in vitro. We show that this effect probably is due to adsorption of the drug by the MSCs during pre-treatment, with subsequent diffusion into co-cultures at concentrations sufficient to inhibit T-cell proliferation. MSCs contain measurable amounts of rapamycin after a 15-min exposure, and the potentiating effect is blocked by a neutralizing antibody to the drug. With the use of a pre-clinical model of acute graft-versus-host disease, we demonstrate that a low dose of rapamycin-treated but not untreated umbilical cord–derived MSCs significantly inhibit the onset of disease. Conclusions The use of treated MSCs may achieve clinical end points not reached with untreated MSCs and allow for infusion of fewer cells to reduce costs and minimize potential side effects

Abnormally high content of free glucosamine residues identified in a preparation of commercially available porcine intestinal heparan sulfate

Heparan sulfate (HS) polysaccharides are ubiquitous in animal tissues as components of proteoglycans, and they participate in many important biological processes. HS carbohydrate chains are complex and can contain rare structural components such as N-unsubstituted glucosamine (GlcN). Commercially available HS preparations have been invaluable in many types of research activities. In the course of preparing microarrays to include probes derived from HS oligosaccharides, we found an unusually high content of GlcN residue in a recently purchased batch of porcine intestinal mucosal HS. Composition and sequence analysis by mass spectrometry of the oligosaccharides obtained after heparin lyase III digestion of the polysaccharide indicated two and three GlcN in the tetrasaccharide and hexasaccharide fractions, respectively. (1)H NMR of the intact polysaccharide showed that this unusual batch differed strikingly from other HS preparations obtained from bovine kidney and porcine intestine. The very high content of GlcN (30%) and low content of GlcNAc (4.2%) determined by disaccharide composition analysis indicated that N-deacetylation and/or N-desulfation may have taken place. HS is widely used by the scientific community to investigate HS structures and activities. Great care has to be taken in drawing conclusions from investigations of structural features of HS and specificities of HS interaction with proteins when commercial HS is used without further analysis. Pending the availability of a validated commercial HS reference preparation, our data may be useful to members of the scientific community who have used the present preparation in their studies

Multi-Dialect Speech Recognition With A Single Sequence-To-Sequence Model

Sequence-to-sequence models provide a simple and elegant solution for building speech recognition systems by folding separate components of a typical system, namely acoustic (AM), pronunciation (PM) and language (LM) models into a single neural network. In this work, we look at one such sequence-to-sequence model, namely listen, attend and spell (LAS), and explore the possibility of training a single model to serve different English dialects, which simplifies the process of training multi-dialect systems without the need for separate AM, PM and LMs for each dialect. We show that simply pooling the data from all dialects into one LAS model falls behind the performance of a model fine-tuned on each dialect. We then look at incorporating dialect-specific information into the model, both by modifying the training targets by inserting the dialect symbol at the end of the original grapheme sequence and also feeding a 1-hot representation of the dialect information into all layers of the model. Experimental results on seven English dialects show that our proposed system is effective in modeling dialect variations within a single LAS model, outperforming a LAS model trained individually on each of the seven dialects by 3.1 ~ 16.5% relative.Comment: submitted to ICASSP 201