Equilibrium or Simple Rule at Wimbledon? An Empirical Study

We follow Walker and Wooders’(2001) empirical analysis to collect and study a broader data set in tennis, including male, female and junior matches. We find that there is mixed evidence in support of the minimax hypothesis. Granted, the plays in our data pass all the tests in Walker and Wooders (2001). However, we argue that not only the test on equal winning probabilities may lack power, but also the current serve choices may depend on past serve choices, the performance of past serve choices, or the time that the game has elapsed. We therefore examine the role that simple rules may play in determining the plays. For a significant number of top tennis players, some simple low-information rules outperform the minimax hypothesis. By comparing junior players with adult players, we find that the former tend to adopt simpler rules. The result of comparison between female and male players is inconclusiveminimax, learning, low-information

Regulation of axon repulsion by MAX-1 SUMOylation and AP-3.

During neural development, growing axons express specific surface receptors in response to various environmental guidance cues. These axon guidance receptors are regulated through intracellular trafficking and degradation to enable navigating axons to reach their targets. In Caenorhabditis elegans, the UNC-5 receptor is necessary for dorsal migration of developing motor axons. We previously found that MAX-1 is required for UNC-5-mediated axon repulsion, but its mechanism of action remained unclear. Here, we demonstrate that UNC-5-mediated axon repulsion in C. elegans motor axons requires both max-1 SUMOylation and the AP-3 complex β subunit gene, apb-3 Genetic interaction studies show that max-1 is SUMOylated by gei-17/PIAS1 and acts upstream of apb-3 Biochemical analysis suggests that constitutive interaction of MAX-1 and UNC-5 receptor is weakened by MAX-1 SUMOylation and by the presence of APB-3, a competitive interactor with UNC-5. Overexpression of APB-3 reroutes the trafficking of UNC-5 receptor into the lysosome for protein degradation. In vivo fluorescence recovery after photobleaching experiments shows that MAX-1 SUMOylation and APB-3 are required for proper trafficking of UNC-5 receptor in the axon. Our results demonstrate that SUMOylation of MAX-1 plays an important role in regulating AP-3-mediated trafficking and degradation of UNC-5 receptors during axon guidance

Acute exposure to diesel particulate matter promotes collective cell migration in thyroid cancer cells

Several ecological studies suggest that ambient air pollution is associated with the occurrence of thyroid cancer. In this study, we used certified diesel particulate matter as a proxy for fine particulate matter. Human thyroid cancer cell lines 8505C and TPC-1 were incubated with different concentrations of NIST1650b for 5 days and subjected to functional assays. We found that NIST1650b treatment did not affect short-term cell growth but reduced colony formation at high concentrations. Notably, NIST1650b-treated cells showed altered morphology toward cluster coalescence following treatment. Wound healing assays revealed that leading-edge cells formed protruding tips while maintaining cell-cell adhesion, and a significantly higher ratio of wound closure following treatment at 10 μg/mL was seen in both cell lines. A weak stimulatory effect on transwell cell migration was observed in 8505C cells. Taken together, our results suggest that fine particulate matter induced a coherent phenotype accompanied by augmented collective cell migration in thyroid cancer cells

Multi-almost periodicity and invariant basins of general neural networks under almost periodic stimuli

In this paper, we investigate convergence dynamics of $2^N$ almost periodic encoded patterns of general neural networks (GNNs) subjected to external almost periodic stimuli, including almost periodic delays. Invariant regions are established for the existence of $2^N$ almost periodic encoded patterns under two classes of activation functions. By employing the property of $\mathscr{M}$-cone and inequality technique, attracting basins are estimated and some criteria are derived for the networks to converge exponentially toward $2^N$ almost periodic encoded patterns. The obtained results are new, they extend and generalize the corresponding results existing in previous literature.Comment: 28 pages, 4 figure

Measurement of the Near-Bed Turbulence in a Laboratory Surf Zone

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Efficient Quantization-aware Training with Adaptive Coreset Selection

The expanding model size and computation of deep neural networks (DNNs) have increased the demand for efficient model deployment methods. Quantization-aware training (QAT) is a representative model compression method to leverage redundancy in weights and activations. However, most existing QAT methods require end-to-end training on the entire dataset, which suffers from long training time and high energy costs. Coreset selection, aiming to improve data efficiency utilizing the redundancy of training data, has also been widely used for efficient training. In this work, we propose a new angle through the coreset selection to improve the training efficiency of quantization-aware training. Based on the characteristics of QAT, we propose two metrics: error vector score and disagreement score, to quantify the importance of each sample during training. Guided by these two metrics of importance, we proposed a quantization-aware adaptive coreset selection (ACS) method to select the data for the current training epoch. We evaluate our method on various networks (ResNet-18, MobileNetV2), datasets(CIFAR-100, ImageNet-1K), and under different quantization settings. Compared with previous coreset selection methods, our method significantly improves QAT performance with different dataset fractions. Our method can achieve an accuracy of 68.39% of 4-bit quantized ResNet-18 on the ImageNet-1K dataset with only a 10% subset, which has an absolute gain of 4.24% compared to the baseline.Comment: Code: https://github.com/HuangOwen/QAT-AC