Efficient Complexity Reduction Research Articles

Predicting N-Strain Coexistence from Co-colonization Interactions: Epidemiology Meets Ecology and the Replicator Equation

Multi-type infection processes are ubiquitous in ecology, epidemiology and social systems, but remain hard to analyze and to understand on a fundamental level. Here, we study a multi-strain susceptible-infected-susceptible model with coinfection. A host already colonized by one strain can become more or less vulnerable to co-colonization by a second strain, as a result of facilitating or competitive interactions between the two. Fitness differences between N strains are mediated through N^2 altered susceptibilities to secondary infection that depend on colonizer-cocolonizer identities (K_{ij}). By assuming strain similarity in such pairwise traits, we derive a model reduction for the endemic system using separation of timescales. This ‘quasi-neutrality’ in trait space sets a fast timescale where all strains interact neutrally, and a slow timescale where selective dynamics unfold. We find that these slow dynamics are governed by the replicator equation for N strains. Our framework allows to build the community dynamics bottom-up from only pairwise invasion fitnesses between members. We highlight that mean fitness of the multi-strain network, changes with their individual dynamics, acts equally upon each type, and is a key indicator of system resistance to invasion. By uncovering the link between N-strain epidemiological coexistence and the replicator equation, we show that the ecology of co-colonization relates to Fisher’s fundamental theorem and to Lotka-Volterra systems. Besides efficient computation and complexity reduction for any system size, these results open new perspectives into high-dimensional community ecology, detection of species interactions, and evolution of biodiversity.

A 3K Axiom SNP array from a transcriptome-wide SNP resource sheds new light on the genetic diversity and structure of the iconic subtropical conifer tree Araucaria angustifolia (Bert.) Kuntze.

High-throughput SNP genotyping has become a precondition to move to higher precision and wider genome coverage genetic analysis of natural and breeding populations of non-model species. We developed a 44,318 annotated SNP catalog for Araucaria angustifolia, a grandiose subtropical conifer tree, one of the only two native Brazilian gymnosperms, critically endangered due to its valuable wood and seeds. Following transcriptome assembly and annotation, SNPs were discovered from RNA-seq and pooled RAD-seq data. From the SNP catalog, an Axiom® SNP array with 3,038 validated SNPs was developed and used to provide a comprehensive look at the genetic diversity and structure of 15 populations across the natural range of the species. RNA-seq was a far superior source of SNPs when compared to RAD-seq in terms of conversion rate to polymorphic markers on the array, likely due to the more efficient complexity reduction of the huge conifer genome. By matching microsatellite and SNP data on the same set of A. angustifolia individuals, we show that SNPs reflect more precisely the actual genome-wide patterns of genetic diversity and structure, challenging previous microsatellite-based assessments. Moreover, SNPs corroborated the known major north-south genetic cline, but allowed a more accurate attribution to regional versus among-population differentiation, indicating the potential to select ancestry-informative markers. The availability of a public, user-friendly 3K SNP array for A. angustifolia and a catalog of 44,318 SNPs predicted to provide ~29,000 informative SNPs across ~20,000 loci across the genome, will allow tackling still unsettled questions on its evolutionary history, toward a more comprehensive picture of the origin, past dynamics and future trend of the species' genetic resources. Additionally, but not less importantly, the SNP array described, unlocks the potential to adopt genomic prediction methods to accelerate the still very timid efforts of systematic tree breeding of A. angustifolia.

Fault Tolerant Control for MIMO Nonlinear Systems via MPC Based on MIMO ARX-Laguerre Multiple Models

In this article, we propose a fault tolerant control for multiple-input multiple-output (MIMO) nonlinear systems via model predictive control. The MIMO nonlinear systems are approximated by MIMO ARX-Laguerre multiple models. The latter is obtained by expanding a discrete-time MIMO ARX multiple model parameters on Laguerre orthonormal bases. The resulting model ensures an efficient complexity reduction with respect to the classical MIMO ARX multiple models. This parametric complexity reduction still subjects to an optimal choice of the Laguerre poles defining Laguerre bases. The parameter and structure identifications of the MIMO ARX-Laguerre multiple models are achieved by the recursive method and a metaheuristic algorithm, respectively. The proposed model is built from the system input/output observations and is used to synthesize a MIMO nonlinear fault tolerant control algorithm via MPC. So, we develop a fault detection and isolation (FDI) scheme based on the proposed model. The scheme of the fault detection is applied at every step of MPC control calculation, where we determine the actuator faults and we use it in the MPC optimization problem to determine the new control with respect to the actuator faults. The proposed strategy is tested on numerical simulation and validated on the real system.

Online-Learning-Based Mode Prediction Method for Quality Scalable Extension of the High Efficiency Video Coding (HEVC) Standard

SHVC, the scalable extension of High Efficiency Video Coding (HEVC), uses advanced inter-layer prediction features in addition to the advanced compression tools of HEVC to improve the compression performance. Using combined features has brought us improved compression performance at the cost of huge computational complexity for the SHVC encoder. This complexity is mainly because of the the inter/intra-prediction mode search of the coding units. The focus of this study is on developing an efficient complexity reduction for quality scalability of SHVC encoder, with the intention to facilitate the adoption of SHVC for real-time applications. In this regard, first, we build a probabilistic model that uses the mode information and motion homogeneity of already encoded blocks in the enhancement layer (EL) and the base layer to predict the probabilities of all the available inter/intra modes of the to-be-coded block in the EL. Then, we propose an online-learning-based fast mode, assigning (FMA) method that uses the proposed probabilistic model to predict the mode of the to-be-coded block in the EL. Performance evaluation shows that our proposed FMA method reduces the total execution time of the SHVC encoder by 45.40% on average compared with unmodified SHVC codec while maintaining the overall video quality.

Online-Learning-Based Complexity Reduction Scheme for 3D-HEVC

3-D High Efficiency Video Coding (HEVC) is a new emerging video compression standard for multiview video applications. This standard utilizes advanced interview prediction characteristics in addition to the prediction features of the HEVC standard for efficient encoding of multiview video content. While using combined features improves the compression performance by utilizing the correlation between the views captured from slightly different angles of the same scene, they also increase coding complexity. The focus of this paper is on developing an efficient complexity reduction scheme for 3D-HEVC, with the intention to facilitate the adoption of this upcoming standard, especially for real-time applications. In this regard, first, we introduce two ways to decrease the complexity of the inter-/ intra-mode search process of the to-be-encoded blocks in the dependent texture views ( ${\mathrm {DV}}_{t}\text{s}$ ) of 3D-HEVC. Then, we propose a hybrid complexity reduction scheme that utilizes the two-mode prediction approaches, motion information of the base texture view (BV t ), and the rate distortion cost of the already encoded blocks in the BV t and DV t . The performance of our proposed scheme is tested for the case with two views (i.e., base view + dependent view). The evaluations confirm that our proposed hybrid complexity reduction scheme reduces the 3D-HEVC codec complexity by 67.70% on average for the DV t compared with the unmodified 3D-HEVC encoder, while maintaining the overall video quality. Compared with the state-of-the-art method, it reduces complexity by 25.74% on average.

Efficient Complexity Reduction Technique for Parallel FIR Digital Filter based on Fast FIR Algorithm

The objective of the paper is to reduce the hardware complexity of higher order FIR filter with symmetric coefficients. The aim is to design efficient Fast Finite-Impulse Response (FIR) Algorithms (FFAs) for parallel FIR filter structure with the constraint that the filter tap must be a multiple of 2. In our work we have briefly discussed for L = 4 parallel implementation. The parallel FIR filter structure based on proposed FFA technique has been implemented based on carry save and ripple carry adder for further optimization. The reduction in silicon area complexity is achieved by eliminating the bulky multiplier with an adder namely ripple carry and carry save adder. For example, for a 6-parallel 1024-tap filter, the proposed structure saves 14 multipliers at the expense of 10 adders, whereas for a six-parallel 512-tap filter, the proposed structure saves 108 multipliers at the expense of 10 adders. Overall, the proposed parallel FIR structures can lead to significant hardware savings for symmetric coefficients from the existing FFA parallel FIR filter, especially when the length of the filter is very large.

Decomposition of an ARX model on Laguerre orthonormal bases

In this paper, we propose a new reduced complexity model by expanding a discrete-time ARX model on Laguerre orthonormal bases. To ensure an efficient complexity reduction, the coefficients associated to the input and the output of the ARX model are expanded on independent Laguerre bases, to develop a new black-box linear ARX-Laguerre model with filters on model input and output. The parametric complexity reduction with respect to the classical ARX model is proved theoretically. The structure and parameter identification of the ARX-Laguerre model is achieved by a new proposed approach which consists in solving an optimization problem built from the ARX model without using system input/output observations. The performances of the resulting ARX-Laguerre model and the proposed identification approach are illustrated by numerical simulations and validated on benchmark manufactured by Feedback known as Process Trainer PT326. A possible extension of the proposed model to a multivariable process is formulated.

An efficient complexity reduction algorithm for G.729 speech codec

Having been widely used in a VoIP system, the ITU-T G.729 standard is of high computational complexity, a heavy load for a real-time operation on a low-end smart phone or other embedded devices even with the G.729A standard. In this paper, there are three methods proposed for an efficient complexity reduction of G.729A, i.e., a combined local optimal pulse method, employed to search an algebraic codebook, a discontinuous pitch estimation, used to analyze the open-loop pitch, and the hybrid two-stage vector quantization (HTVQ), assigned to quantize the line spectral frequency (LSF) coefficients. It is validated by experimental results that the improved version of G.729A can be easily implemented on a smart phone (312 MHz CPU, 64 MB RAM) in real-time operation. In total 80.94% of computational load can be thus eliminated in comparison with the G.729A and a satisfactory speech quality is maintained as intended, an excellent efficiency demonstration by the methods proposed.

Functional isolation of staurosporine binding protein kinases using CCMS technology

Capture Compound mass spectrometry (CCMS) is an innovative technology to reduce biological sample complexity by selective isolation of targeted protein or enzyme families. Small synthetic molecules (Capture Compounds™) are used to interrogate native proteins. Using staurosporine as a selectivity function for a kinase-specific Capture Compound™ enables efficient complexity reduction of the proteome and allows discovery, isolation and profiling of functional kinases in biological samples. Kinase-specific Capture Compounds™ are available as ready-to-use caproKits™ for proteomic research.

Cyclophosphoramidate Ion as Mass Defect Marker for Efficient Detection of Protein Serine Phosphorylation

A novel method is reported to modify the phosphate groups on phosphoserine peptides to the corresponding phosphoramidates, using 2-aminobenzylamine. Upon collision-induced dissociation, the modified peptides release the positively charged phosphoramidate that via gas-phase intramolecular elimination forms a cyclophosphoramidate (CyPAA) ion, the protonated form of 1,4-dihydro-2-hydroxy-2-oxobenzo[3,1,2]oxazaphosphorine. The positive nature of the ion eliminates the need for real-time instrument polarity switching and greatly increases the versatility of commonly used mass spectrometers for phosphopeptide analysis. This ion has sufficient mass defect, due to containing a phosphorus atom and a high content of oxygen atoms, which makes mass spectrometers of medium mass resolution and accuracy adequate for separating the ion from isobaric interfering ions. The specificity of the CyPAA ion for detecting phosphoserine peptides in complex peptide mixtures is comparable to the specificity of the phosphotyrosine immonium ion for phosphotyrosine peptides, allowing the efficient data complexity reduction for fast and focused analysis of phosphoserine-containing peptides.

Turbo equalization receivers for evolved GSM/EDGE radio access network using QAM modulation

For evolution of the GSM/EDGE radio access network (RAN), the use of higher order modulation like 16- and 32-ary quadrature amplitude modulation (QAM) is considered in standardization for increased peak data rates and reduced transmission delays. In this paper, an optimized receiver design for different packet data transmission schemes is proposed. Turbo coding and turbo equalization is discussed for improved power efficiency and interference robustness. An efficient complexity reduction of the equalizer enables the usage of turbo equalization at a complexity comparable to that of separate equalization and decoding for turbo-coded transmission.

On the Application of Factor Graphs and the Sum–Product Algorithm to ISI Channels

In this paper, based on the application of the sum-product (SP) algorithm to factor graphs (FGs) representing the joint a posteriori probability (APP) of the transmitted symbols, we propose new iterative soft-input soft-output (SISO) detection schemes for intersymbol interference (ISI) channels. We have verified by computer simulations that the SP algorithm converges to a good approximation of the exact marginal APPs of the transmitted symbols if the FG has girth at least 6. For ISI channels whose corresponding FG has girth 4, the application of a stretching technique allows us to obtain an equivalent girth-6 graph. For sparse ISI channels, the proposed algorithms have advantages in terms of complexity over optimal detection schemes based on the Bahl-Cocke-Jelinek-Raviv (BCJR) algorithm. They also allow a parallel implementation of the receiver and the possibility of a more efficient complexity reduction. The application to joint detection and decoding of low-density parity-check (LDPC) codes is also considered and results are shown for some partial-response magnetic channels. Also in these cases, we show that the proposed algorithms have a limited performance loss with respect to that can be obtained when the optimal "serial" BCJR algorithm is used for detection. Therefore, for their parallel implementation, they represent a favorable alternative to the modified "parallel" BCJR algorithm proposed in the literature for the application to magnetic channels.

Efficient complexity reduction technique in trellisdecoding algorithm

An efficient reduced search trellis decoding algorithm in which the decoder selects a part of existing paths by using a threshold value of the path metric is proposed. The threshold value at each time stage of the trellis is found by simply investigating the statistics of the path metrics, and does not require any prior knowledge such as the signal-to-noise ratio.