Look Left, Look Right, Look Left Again: An Application of Fractal Symbolic Analysis to Linear Algebra Code Restructuring

Fractal symbolic analysis is a symbolic analysis technique for verifying the legality of program transformations. It is strictly more powerful than dependence analysis; for example, it can be used to verify the legality of blocking LU factorization with pivoting, a task for which dependence analysis is inadequate. In this paper, we show how fractal symbolic analysis can be used to convert between left- and right-looking versions of three kernels of central importance in computational science: triangular solve, Cholesky factorization, and LU factorization with pivoting.     

Numerical Solution of Fractional Advection-Dispersion Equation

Numerical schemes and stability criteria are developed for solution of the one-dimensional fractional advection-dispersion equation (FRADE) derived by revising Fick’s first law. Employing 74 sets of dye test data measured on natural streams, it is found that the fractional order F of the partial differential operator acting on the dispersion term varies around the most frequently occurring value of F = 1.65 in the range of 1.4 to 2.0. Two series expansions are proposed for approximation of the limit definitions of fractional derivatives. On this ground, two three-term finite-difference schemes?“1.3 Backward Scheme” having the first-order accuracy and “F.3 Central Scheme” possessing the F-th order accuracy?are presented for fractional order derivatives. The F.3 scheme is found to perform better than does the 1.3 scheme in terms of error and stability analyses and is thus recommended for numerical solution of FRADE. The fractional dispersion model characterized by the FRADE and the F.3 scheme can accurately simulate the long-tailed dispersion processes in natural rivers.     

On a stochastic sensor selection algorithm with applications in sensor scheduling and sensor coverage

In this note we consider the following problem. Suppose a set of sensors is jointly trying to estimate a process. One sensor takes a measurement at every time step and the measurements are then exchanged among all the sensors. What is the sensor schedule that results in the minimum error covariance? We describe a stochastic sensor selection strategy that is easy to implement and is computationally tractable. The problem described above comes up in many domains out of which we discuss two. In the sensor selection problem, there are multiple sensors that cannot operate simultaneously (e.g., sonars in the same frequency band). Thus measurements need to be scheduled. In the sensor coverage problem, a geographical area needs to be covered by mobile sensors each with limited range. Thus from every position, the sensors obtain a different view-point of the area and the sensors need to optimize their trajectories. The algorithm is applied to these problems and illustrated through simple examples.     

Pharmacophore Synergism in Diverse Scaffold Clinches in Aurora Kinase B

Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure–activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R²_tr = 0.815, Q²_LMO = 0.808, R²_ex = 0.814, CCC_ex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups—especially the H-bond capable groups—must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.     

Trypsin-like Inhibitor Domain (TIL)-Harboring Protein Is Essential for Aedes aegypti Reproduction

Cysteine-rich trypsin inhibitor-like domain (TIL)-harboring proteins are broadly distributed in nature but remain understudied in vector mosquitoes. Here we have explored the biology of a TIL domain-containing protein of the arbovirus vector Aedes aegypti, cysteine-rich venom protein 379 (CRVP379). CRVP379 was previously shown to be essential for dengue virus infection in Ae. aegypti mosquitoes. Gene expression analysis showed CRVP379 to be highly expressed in pupal stages, male testes, and female ovaries. CRVP379 expression is also increased in the ovaries at 48 h post-blood feeding. We used CRISPR-Cas9 genome editing to generate two mutant lines of CRVP379 with mutations inside or outside the TIL domain. Female mosquitoes from both mutant lines showed severe defects in their reproductive capability; mutant females also showed differences in their follicular cell morphology. However, the CRVP379 line with a mutation outside the TIL domain did not affect male reproductive performance, suggesting that some CRVP379 residues may have sexually dimorphic functions. In contrast to previous reports, we did not observe a noticeable difference in dengue virus infection between the wild-type and any of the mutant lines. The importance of CRVP379 in Ae. aegypti reproductive biology makes it an interesting candidate for the development of Ae. aegypti population control methods.     

ANN‐based modeling of compact impedance‐varying transmission lines with applications to ultra‐wideband Wilkinson power dividers

The modeling of the physical and electrical characteristics of microstrip non‐uniform transmission lines (NTLs) utilizing artificial neural networks (ANNs) is investigated. The fundamental equations and constraints for designing variable impedance transmission lines are first presented. Then, a proof‐of‐concept example of a compact non‐uniform matching transformer and the counterpart modeled version is elaborated for source and load impedances Z_s and Z_l, respectively, at 0.5 GHz. For comparison purposes, weights and biases of the proposed ANN are established with three different training techniques; namely: backpropagation (BP), Quasi‐Newton (QN), and conjugate gradient (CG); at which the ABCD matrix, impedance variations, input port matching (S₁₁), and transmission parameter (S₂₁) are set as benchmarks to examine the validity of the trained model. The concept is then extended to model a NTL ultrawideband (UWB) Wilkinson power divider (WPD) with three resistors for improved isolation. S‐parameters derived from the trained ANN outputs are close to those obtained by the traditional time‐consuming optimization procedure, and show input and output ports matching and isolation of below ?10 dB, and acceptable values of transmission parameters over the 3.1 GHz to 10.6 GHz band. The resulting models outperform traditional optimizations in terms of simulation time and reserved resources with comparable accuracy. © 2015 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:563–572, 2015.     

Markerless human articulated tracking using hierarchical particle swarm optimisation

In this paper, we address markerless full-body articulated human motion tracking from multi-view video sequences acquired in a studio environment. The tracking is formulated as a multi-dimensional non-linear optimisation and solved using particle swarm optimisation (PSO), a swarm-intelligence algorithm which has gained popularity in recent years due to its ability to solve difficult non-linear optimisation problems. We show that a small number of particles achieves accuracy levels comparable with several recent algorithms. PSO initialises automatically, does not need a sequence-specific motion model and recovers from temporary tracking divergence through the use of a powerful hierarchical search algorithm (HPSO). We compare experimentally HPSO with particle filter (PF), annealed particle filter (APF) and partitioned sampling annealed particle filter (PSAPF) using the computational framework provided by Balan et al. HPSO accuracy and consistency are better than PF and compare favourably with those of APF and PSAPF, outperforming it in sequences with sudden and fast motion. We also report an extensive experimental study of HPSO over ranges of values of its parameters.     

Achieving Optimal Inter-Node Communication in Graph Partitioning Using Random Selection and Breadth-First Search

Processing large graph datasets represents an increasingly important area in computing research and applications. The size of many graph datasets has increased well beyond the processing capacity of a single computing node, thereby necessitating distributed approaches. As these datasets are processed over a distributed system of nodes, this leads to an inter-node communication cost problem that negatively affects system performance. Previously proposed algorithms implemented breadth-first search (BFS) for graph searching and focused on the execution, parallel performance and not the communication. In this paper a new methodology is proposed that combines BFS with random selection in order to partition large graph datasets and effectively minimize inter-node communication. The new method is discussed and applied to the single-source shortest path and PageRank algorithms using three graphs that are representative of real-world scenarios. Experimental results show that graph inter-node communication for canonical graphs representative of real-world data is improved up to 42 % in case of Powerlaw graph, up to 27 % in case of Random near K-regular graph (with low degree), and up to 7 % in case of Random near K-regular graph (with high degree).     

Negation as Failure as Resolution

Providing a clean procedural semantics of the Negation As Failure rule in Logic Programming has been an open problem for some time now. This rule has been treated as a technique in nonmonotonic reasoning, not as a rule in classical logic. This paper contains a demonstration of the negation as failure rule as a resolution procedure in first-order logic. We present a sound and complete resolution scheme for negation as failure rule for the larger class of constraint logic programs. The approach is to consider a canonical partition of the completion of a definite (constraint) program into the IF and the FI programs. We show that a negated goal, provable from the completed definite program is provable from just the FI part. The clauses in this program have a structure dual to that of definite Horn clauses. We describe a sound and complete linear resolution rule for this fragment, and show that a resolution proof of the negated goal from the FI part corresponds to a finite failure tree resulting from classical linear resolution applied to the goal on the If part of the original definite program. Our work shows that negation as failure rule can be computationally efficient in the sense that the SLD-resolution on the If part of a definite program along with the negation as failure rule is more efficient than a direct resolution procedure on the completion of that program.