Top-Down and Bottom-Up Attention Modulate Subitizing, Estimation, and Counting Through Static and Dynamic Strategies.

In response to the optimization scheduling problem in the dyeing production process, the author proposes a hierarchical scheduling method for dyeing vats based on genetic algorithm and multi-agent. In this method, a hierarchical scheduling algorithm is used to decompose production scheduling into static and dynamic strategies. The static strategy adopts a genetic algorithm that supports batch processing of multiple products, non equality of equipment, order delivery time, switching cost, and other constraints: Dynamic strategy is a coordinated dynamic optimization algorithm that uses multi-agent systems to support the running status of dye tanks based on static strategies. By solving the algorithm with multiple constraints and dynamic factors in the production process, the final result of the dyeing tank operation task is obtained. The simulation compared pure genetic algorithm with manual scheduling, and the results showed that the hierarchical dynamic scheduling strategy based on data-driven achieved the goal of optimizing the production scheduling of dyeing vats. The practical application results also demonstrate the feasibility of this method.

The data sparsity problem significantly hinders the performance of recommender systems, as traditional models rely on limited historical interactions to learn user preferences and item properties. While incorporating multimodal information can explicitly represent these preferences and properties, existing works often use it only as side information, failing to fully leverage its potential. In this paper, we propose MDVT, a model-agnostic approach that constructs multimodal-driven virtual triplets to provide valuable supervision signals, effectively mitigating the data sparsity problem in multimodal recommendation systems. To ensure high-quality virtual triplets, we introduce three tailored warm-up threshold strategies: static, dynamic, and hybrid. The static warm-up threshold strategy exhaustively searches for the optimal number of warm-up epochs but is time-consuming and computationally intensive. The dynamic warm-up threshold strategy adjusts the warm-up period based on loss trends, improving efficiency but potentially missing optimal performance. The hybrid strategy combines both, using the dynamic strategy to find the approximate optimal number of warm-up epochs and then refining it with the static strategy in a narrow hyper-parameter space. Once the warm-up threshold is satisfied, the virtual triplets are used for joint model optimization by our enhanced pair-wise loss function without causing significant gradient skew. Extensive experiments on multiple real-world datasets demonstrate that integrating MDVT into advanced multimodal recommendation models effectively alleviates the data sparsity problem and improves recommendation performance, particularly in sparse data scenarios.

In a hybrid distributed-centralized database system architecture, some transactions run at (geographically) distributed systems, and other transactions at a central computing complex. Static and dynamic load-sharing strategies are studied for such systems. The strategies take into account not only the difference in load at different sites, but also the effect of routing on data-contention and transaction-abort probabilities. Five dynamic strategies are examined and are compared with an optimal static strategy. A dynamic strategy that is based on analytical estimates of the effect of routing on all transactions in the system, rather than that on the incoming transaction alone, is found to be the best. >

Aim: To enhance the balance in post stroke patients by the implementation of static and dynamic weight shift balance strategies. Methods: Data from 32 patients who complete selection criteria were included to this study. Study design was Randomized controlled trial (RCT). Written informed consent were taken from every individual participating in this study before performing any physical examination. Patients were allocated into two equal groups. On 1st day balance was assessed by Brunel Balance Assessment (BBA), re-accessed by the end of 3rd week and at 6th week. There were two groups, one is Experimental (Group A) receiving static and dynamic weight shift balance training strategies along with conventional physiotherapy treatment while control group (Group B) receiving only conventional rehabilitation program. Results: The results of the BBA (Brunel Balance Assessment) scale showed statistically significant variations in the pre-post analysis for the experimental group with the p-value less than 0.05. By comparing the BBA scale score of group A and B has shown that there was significant difference between post treatment scores, post treatment mean BBA score was 7.000±1.4142 in group A while 3.6250±1.85742 in group B, with the value of p 0.00, showing that group A is better in improving balance of stroke patients. Conclusion: Implementation of static and dynamic balance strategies has a significant effect on improvement of balance in the patients suffering from stroke. Keywords: BBA (brunel balance assessment), CVA (cerebrovascular accident) and RCT (randomized controlled trial)

Perceptual averaging refers to a strategy of encoding the statistical properties of entire sets of objects rather than encoding individual object properties, potentially circumventing the visual system’s strict capacity limitations. Prior work has shown that such average representations of set properties, such as its mean size, can be modulated by top-down and bottom-up attention. However, it is unclear to what extent attentional biases through selection history, in the form of value-driven attentional capture, influences this type of summary statistical representation. To investigate, we conducted two experiments in which participants estimated the mean size of a set of heterogeneously sized circles while a previously rewarded color singleton was part of the set. In Experiment 1, all circles were gray, except either the smallest or the largest circle, which was presented in a color previously associated with a reward. When the largest circle in the set was associated with the highest value (as a proxy of selection history), we observed the largest biases, such that perceived mean size scaled linearly with the increasing value of the attended color singleton. In Experiment 2, we introduced a dual-task component in the form of an attentional search task to ensure that the observed bias of reward on perceptual averaging was not fully explained by focusing attention solely on the reward-signaling color singleton. Collectively, findings support the proposal that selection history, like bottom-up and top-down attention, influences perceptual averaging, and that this happens in a flexible manner proportional to the extent to which attention is captured.

Top Down or Bottom Up? Disentangling the Channels of Attention in Risky Choice

A mathematical model of the interaction between bottom-up and top-down attention controllers in response to a target and a distractor in human beings

What is the relationship between top-down and bottom-up attention? Are both types of attention tightly interconnected, or are they independent? We investigated this by testing a large representative sample of the Dutch population on two attentional tasks: a visual search task gauging the efficiency of top-down attention and a singleton capture task gauging bottom-up attention. On both tasks we found typical performance--i.e., participants displayed a significant search slope on the search task and significant slowing caused by the unique, but irrelevant, object on the capture task. Moreover, the high levels of significance we observed indicate that the current set-up provided very high signal to noise ratios, and thus enough power to accurately unveil existing effects. Importantly, in this robust investigation we did not observe any correlation in performance between tasks. The use of Bayesian statistics strongly confirmed that performance on both tasks was uncorrelated. We argue that the current results suggest that there are two attentional systems that operate independently. We hypothesize that this may have implications beyond our understanding of attention. For instance, it may be that attention and consciousness are intertwined differently for top-down attention than for bottom-up attention.

The tremendous increase in the size and heterogeneity of supercomputers makes it very difficult to predict the performance of a scheduling algorithm. Therefore, dynamic solutions, where scheduling decisions are made at runtime have overpassed static allocation strategies. The simplicity and efficiency of dynamic schedulers such as Hadoop are a key of the success of the MapReduce framework. Dynamic schedulers such as StarPU, PaRSEC or StarSs are also developed for more constrained computations, e.g. task graphs coming from linear algebra. To make their decisions, these runtime systems make use of some static information, such as the distance of tasks to the critical path or the affinity between tasks and computing resources (CPU, GPU,...) and of dynamic information, such as where input data are actually located. In this paper, we concentrate on two elementary linear algebra kernels, namely the outer product and the matrix multiplication. For each problem, we propose several dynamic strategies that can be used at runtime and we provide an analytic study of their theoretical performance. We prove that the theoretical analysis provides very good estimate of the amount of communications induced by a dynamic strategy and can be used in order to efficiently determine thresholds used in dynamic scheduler, thus enabling to choose among them for a given problem and architecture.

To investigate the time-wise reliability and efficiency of two new perimetric test algorithms, two computerised static threshold perimetry strategies, namely dynamic strategy (DS) and tendency oriented perimetry (TOP), were compared with the standard full-threshold strategy (normal strategy, NS). We examined 41 eyes of 41 normal individuals without any ocular disease and 36 eyes of 36 glaucomatous patients, with the NS (4-to-2 dB), DS and TOP using an Octopus 1-2-3 perimeter. We analysed test time, stimulus time and the two global indices, mean sensitivity (MS) and loss variance (LV). Program 32X was used as test grid pattern. The mean test time for the NS was reduced by 52% with the DS and by 78% with the TOP strategy. Concerning the global indices, the MS value did not differ among the three strategies in the control or glaucoma group. However, the LV value was lower in the TOP strategy compared with the other two strategies in the glaucoma group. This suggested that the TOP strategy underestimated local glaucomatous visual field defects. The ability to detect early-stage glaucoma with the DS and TOP was inferior to that with the NS. The DS was more efficient than the TOP strategy for the detection of early glaucomatous defects, whereas the TOP strategy required less testing time. The TOP strategy may be an appropriate approach for patients in whom time-consuming perimetry is not possible, or in whom the visual field defect is already advanced.

D-glucaric acid is an important organic acid with numerous applications in therapy, food, and materials, contributing significantly to its substantial market value. The biosynthesis of D-glucaric acid (GA) from renewable sources such as glucose has garnered significant attention due to its potential for sustainable and cost-effective production. This review summarizes the current understanding of the cell factories for GA production in different chassis strains, from static to dynamic control strategies for regulating their metabolic networks. We highlight recent advances in the optimization of D-glucaric acid biosynthesis, including metabolic dynamic control, alternative feedstocks, metabolic compartments, and so on. Additionally, we compare the differences between different chassis strains and discuss the challenges that each chassis strain must overcome to achieve highly efficient GA productions. In this review, the processes of engineering a desirable cell factory for highly efficient GA production are just like an epitome of metabolic engineering of strains for chemical biosynthesis, inferring general trends for industrial chassis strain developments.

Renewable energy has made a significant contribution to energy markets in recent years. An abundance of Photovoltaic (PV) array installations attests to solar power’s expanding role in the renewable energy sector. Partial shadowing/mismatch situations are a risk for PV arrays that can cause significant power losses and reduce the lifetime of the solar panels. A partially shaded PV array’s power-voltage characterization exhibits many local maxima, one of which is the global maximum power point. The effects of mismatch conditions on the PV array are lessened by Reconfiguration strategies, that are further categorized into Static and Dynamic Reconfiguration strategies. This article offers a variety of the puzzle patterns that may be found in the literature that are based on the Mathematical based Static reconfiguration strategies. Mathematical based reconfiguration technique use a mathematical rule to reconfigure PV arrays. By implementing these reconfiguration strategies, it was discovered that power production could be enhanced while at the same time, shade was better spread.

Coherent pricing strategies are important to for-profit firms, but can also be vital to nonprofit organizations. This is clearly true for the arts, education, and hospital and health-care sectors, but can also extend to nonprofits providing vital social services to low-income and otherwise vulnerable populations. By clarifying both static and dynamic pricing strategies that have been successfully used by for-profits, and examining their current application and potential expansion within the nonprofit sector, the striking similarities as well as ongoing distinctions between the two sectors are clarified. Especially in a political climate threatening to be less generous to nonprofits committed to diverse missions, understanding how to generate more earned revenue via shrewd price discrimination, tying/bundling, and yield management and peak-load pricing strategies is vital. Classic research results are incorporated with current theoretical and empirical developments to provide a comprehensive picture of pricing in the nonprofit sector.

In the era of big data remarked by high dimensionality and large sample size, the least absolute shrinkage and selection operator (LASSO) problems demand efficient algorithms. Both static and dynamic strategies based on screening test principle have been proposed recently, in order to safely filter out irrelevant atoms from the dictionary. However, such strategies only work well for LASSO problems with large regularization parameters, and lose their efficiency for those with small regularization parameters. This paper presents a novel greedy screening test strategy to accelerate solving LASSO problems with small regularization parameters, as well as its effectiveness through adoption of a relatively larger regularization parameter which filters out irrelevant atoms in every iteration. Further more, the convergence proof of the greedy strategy is given, and the computational complexity of LASSO solvers integrated with this strategy is investigated. Numerical experiments on both synthetic and real data sets support the effectiveness of this greedy strategy, and the results show it outperforms both the static and dynamic strategies for LASSO problems with small regularization parameters.

International audience