A parallel QR factorization algorithm using local pivoting

There is often a trade-off between preserving sparsity and numerical stability in sparse matrix factorizations. In applications like the direct solution of Equality Constrained Least Squares problem, the accurate detection of the rank of a large and sparse constraint matrix is a key issue. Column pivoting is not suitable for distributed memory machines because it forces the program into a lock-step mode, preventing any overlapping of computations. So factorization algorithms on such machines need to use a reliable, yet inexpensive incremental condition estimator to decide on which columns to be included. We describe an incremental condition estimator that can be used during a sparse QR factorization. We show that it is quite reliable and is well suited for use on parallel machines. We supply experimental results to support its effectiveness as well as suitability for parallel architectures.

Scaled pivots for Gaussian elimination of an n × n matrix are introduced. They are used to obtain bounds for the Skeel condition number of the resulting upper triangular matrix and for a growth factor which has been introduced by Amodio and Mazzia [BIT, 39 (1999), pp. 385--402]. A bound of this growth factor for row scaled partial pivoting strategies is also included. It is proved that the Skeel condition number of an n × n upper triangular matrix which is strictly diagonally dominant by rows is bounded above by a number which is independent of n. It is also shown that the calculation of the n scaled pivots associated with a pivoting strategy presenting nice properties when applied to nonsingular n × n M-matrices adds ${\mathcal{O}}(n)$ elementary operations to the complete cost of this pivoting strategy.

Alliance networks are the underlying structures of social systems in business, management, and society. The sustainability and dynamics of a social system rely on the structural evolutions of the topologies. Understanding the evolution sheds light on the dynamics and sustainability of a social system. Minority game models have been successfully applied across social science, economy, management, and engineering. They provide simple yet applicable modeling to articulate the evolutionary cooperation dynamics of competitive players in binary decision situations. By extending the minority games played in alliance networks, the cooperation in structured systems of different network topologies is analyzed. In this model, local and global score strategies are considered with and without cooperation rewiring options. The cooperation level, the score, and the topological properties are investigated. The research uses a numerical simulation approach on random networks, scale-free networks, and small-world networks. The results suggest that the network rewiring strategy leads to higher systemic performance with a higher score and a higher level of stability in decision-making. Competitive decision-making can lead to a higher level of cooperation from a poor initial start. However, stubbornness in decision-making can lead to a poor situation when cooperation is discouraged. Players with local or global information adopt local and global score strategies. The results show that local strategies might lead to imbalance, while a global strategy might achieve a relatively stable outcome. This work contributes to bridge minority games in structured networks to study the cooperation between formation and evolution, and calls for future minority game modeling on social networks.

We present a combined epidemiological and economic model for control of diseases spreading on local and small-world networks. The disease is characterized by a pre-symptomatic infectious stage that makes detection and control of cases more difficult. The effectiveness of local (ring-vaccination or culling) and global control strategies is analysed by comparing the net present values of the combined cost of preventive treatment and illness. The optimal strategy is then selected by minimizing the total cost of the epidemic. We show that three main strategies emerge, with treating a large number of individuals (global strategy, GS), treating a small number of individuals in a well-defined neighbourhood of a detected case (local strategy) and allowing the disease to spread unchecked (null strategy, NS). The choice of the optimal strategy is governed mainly by a relative cost of palliative and preventive treatments. If the disease spreads within the well-defined neighbourhood, the local strategy is optimal unless the cost of a single vaccine is much higher than the cost associated with hospitalization. In the latter case, it is most cost-effective to refrain from prevention. Destruction of local correlations, either by long-range (small-world) links or by inclusion of many initial foci, expands the range of costs for which the NS is most cost-effective. The GS emerges for the case when the cost of prevention is much lower than the cost of treatment and there is a substantial non-local component in the disease spread. We also show that local treatment is only desirable if the disease spreads on a small-world network with sufficiently few long-range links; otherwise it is optimal to treat globally. In the mean-field case, there are only two optimal solutions, to treat all if the cost of the vaccine is low and to treat nobody if it is high. The basic reproduction ratio, R(0), does not depend on the rate of responsive treatment in this case and the disease always invades (but might be stopped afterwards). The details of the local control strategy, and in particular the optimal size of the control neighbourhood, are determined by the epidemiology of the disease. The properties of the pathogen might not be known in advance for emerging diseases, but the broad choice of the strategy can be made based on economic analysis only.

Reading strategies vary across languages according to orthographic depth – the complexity of the grapheme in relation to phoneme conversion rules – notably at the level of eye movement patterns. We recently demonstrated that a group of early bilinguals, who learned both languages equally under the age of seven, presented a first fixation location (FFL) closer to the beginning of words when reading in German as compared with French. Since German is known to be orthographically more transparent than French, this suggested that different strategies were being engaged depending on the orthographic depth of the used language. Opaque languages induce a global reading strategy, and transparent languages force a local/serial strategy. Thus, pseudo-words were processed using a local strategy in both languages, suggesting that the link between word forms and their lexical representation may also play a role in selecting a specific strategy. In order to test whether corresponding effects appear in late bilinguals with low proficiency in their second language (L2), we present a new study in which we recorded eye movements while two groups of late German–French and French–German bilinguals read aloud isolated French and German words and pseudo-words. Since, a transparent reading strategy is local and serial, with a high number of fixations per stimuli, and the level of the bilingual participants’ L2 is low, the impact of language opacity should be observed in L1. We therefore predicted a global reading strategy if the bilinguals’ L1 was French (FFL close to the middle of the stimuli with fewer fixations per stimuli) and a local and serial reading strategy if it was German. Thus, the L2 of each group, as well as pseudo-words, should also require a local and serial reading strategy. Our results confirmed these hypotheses, suggesting that global word processing is only achieved by bilinguals with an opaque L1 when reading in an opaque language; the low level in the L2 gives way to a local and serial reading strategy. These findings stress the fact that reading behavior is influenced not only by the linguistic mode but also by top–down factors, such as readers’ proficiency.

Virtual earthquake engineering laboratory with physics-based degrading materials on parallel computers

Many real life prediction problems involve predicting a structured output. Multi-target regression is an instance of structured output prediction whose task is to predict for multiple target variables. Structured output algorithms are usually computationally and memory demanding, hence are not suited for dealing with massive amounts of data. Most of these algorithms can be categorized as local or global methods. Local methods produce individual models for each output component and combine them to produce the structured prediction. Global methods adapt traditional learning algorithms to predict the output structure as a whole. We propose the first rule-based algorithm for solving multi-target regression problems from data streams. The algorithm builds on the adaptive model rules framework. In contrast to the majority of the structured output predictors, this particular algorithm does not fall into the local and global categories. Instead, each rule specializes on related subsets of the output attributes. To evaluate the performance of the proposed algorithm, two other rule-based algorithms were developed, one using the local strategy and the other using the global strategy. These methods were compared considering their prediction error, memory usage, computational time, and model complexity. Experimental results on synthetic and real data show that the local-strategy algorithm usually obtains the lowest error. However, the proposed and the global-strategy algorithms use much less memory and run significantly much faster at the cost of a slightly increase in the error, which make them very attractive when computation resources are an important factor. Also, the models produced by the latter approaches are much easier to understand since considerably less rules are produced.

Structural Health Monitoring (SHM) techniques can be classified into global and local monitoring strategies. Regarding the global strategies, the aim is to monitor the structure for any change that can be related to damage globally, whereas, the local damage detection schemes usually aim at detecting damage at a very confined area on the structure. The global techniques, which are also sometimes termed as vibration methods, are very sensitive to Environmental and Operational Variations (EOV). These variations can affect the structural response and, subsequently, mask any changes in vibration signals that can be referred to as damage. It is known that temperature has the most effect on the natural frequencies of any structure. In this paper, an inverse strategy is proposed that aims to predict the temperature variation using frequency time series of the structure. To this end, a Recurrent Neural Networks (RNN) is exploited in which the natural frequency time series obtained from the intact structure along with recorded temperatures are respectively used as training features and label. The trined RNN is then tested on data obtained from the damaged structure. It is shown that the error in predictions increases as the damage occurs. A numerical example is presented to demonstrate the applicability of the proposed method.

This report evaluates the global strategy for Coca-Cola, a multinational carbonated drinks brand. Coca-Cola faces competition from major players like PepsiCo, Nestle, and Red Bull. To expand its presence internationally, Coca-Cola primarily utilises licensing and joint ventures as entry modes. The company employs a combination of global and local marketing strategies to enhance its global integration and local responsiveness. However, the report recommends that to mitigate IP theft in a licensing agreement, Coca-Cola needs to conduct thorough due diligence among potential partners. Secondly, it suggests a transition towards offering healthier beverage options to align with changing consumer preferences. Lastly, the report advises Coca-Cola to adopt a transnational strategy that focuses on both global integration and local responsiveness, optimizing market opportunities while catering to specific market needs. The implementation of these recommendations will enable Coca-Cola to maintain its competitive edge, safeguard its intellectual property, tap into the growing demand for healthier drinks, and effectively navigate the complexities of diverse markets, ensuring sustained growth and success.

In this paper we study numerical stability of the parallel Jacobi method for computing the singular values and singular subspaces of an invertible upper triangular matrix that is obtained from QR decomposition with column pivoting. We show that in this case the parallel Jacobi method locates singular values and singular subspaces to full machine accuracy.

Characterization of Mirror-polished Silicon Wafers by Makyoh Method: S. Tokura, N. Fujino, M. Ninomiya, K. Masuda, Journal of Crystal Growth, 103(1–4), pp. 437–442. (Jun 1990)

An agent-based solution for dynamic multi-node wavefront balancing in biological sequence comparison

According to a high gap between the bounded developments in disk scheduling algorithms in contrast to processor speed and memory capacity developments, so successive improving disk scheduling algorithms is almost needed to reduce the overall time consuming of system performance. To enhance both traditional SCAN and C-SCAN algorithms is the goal of this research, so, rather than the best Seek-Time obtained from traditional (SCAN and C-SCAN) algorithms, the idea behind this proposal "Pre-Reset the R/W Head to Zero Track in SCAN and C-SCAN Disk Scheduling Algorithm" is to enhance the traditional algorithms and gaining the optimal performance time by having the optimal Seek-Time. It is worthily mentioned that the evaluation of the performance of the new strategy in this algorithm was tested by scientific criteria to ensure gaining the optimal algorithm compering with the traditional strategy.

The performance and operational stability of non-clogging pumps can be affected by cavitation. To accurately identify the cavitation state of the non-clogging pump and provide technical references for monitoring its operation, a study was conducted on the optimization of Elman neural networks for cavitation monitoring and identification using the Improved Lévy Flight Bat Algorithm (ILBA) on the basis of the traditional Bat Algorithm (BA). The ILBA employs multiple bats to interact and search for targets and utilizes the local search strategy of Lévy flight, effectively avoiding local minima by taking advantage of the non-uniform random walk characteristics of large jumps. The ILBA algorithm demonstrates superior performance compared to other traditional algorithms through simulation testing and comparative calculations with eight benchmark test functions. On this basis, the optimization of the weights and thresholds of the Elman neural network was carried out by the improved bat algorithm. This leads to an enhancement in the accuracy of the neural network for identifying and classifying cavitation data, and the establishment of the ILBA-Elman cavitation diagnosis model was achieved. Collect pressure pulsation signals at the tongue of the non-clogging pump volute through cavitation tests. Through the cavitation feature extraction method based on Variational Mode Decomposition (VMD) and Multi-scale Dispersion Entropy (MDE), the interference signal can be effectively suppressed and the complexity of the time series can be measured from multiple angles, thereby creating a cavitation feature data set. The improved cavitation diagnosis model (ILBA-Elman) can realize the effective identification of the cavitation characteristics of non-clogging pumps through a variety of algorithm comparison experiments.

Research on emotional modulation of attention in gaze cueing has resulted in contradictory findings. Some studies found larger gaze cueing effects (GCEs) in response to a fearful gaze cue, whereas others did not. A recent study explained this discrepancy within a cognitive resource account, in which perceptual demands of the task promote a bias toward either a local (discrimination task) or global (localization task) processing strategy. During local processing, the integration of emotional expression with gaze direction is assumed to be impaired, whereas during global processing integration is assumed to be facilitated. In the current study, we investigated the cognitive resource account in three experiments. In Experiment 1, we manipulated task demands by adopting a detection or a localization task whilst both should allow global processing. In Experiments 2 and 3, we induced either a local or global perceptual processing strategy by presenting local or global targets (Experiment 2) or by priming local or global perception prior to the gaze cueing task (Experiment 3). Results showed faster orienting in response to a fearful face cue independent of task demands in Experiment 1. Inducing local and global processing strategies in Experiments 2 and 3 did not affect emotional modulation of the GCE. In contrast, Bayesian analyses provided evidence of absence of such an effect, demonstrating that local or global processing strategies cannot explain the mixed findings obtained in emotional modulation of gaze cueing. (PsycInfo Database Record (c) 2023 APA, all rights reserved).