In this section, we present an experimental evaluation of the arena swim bag synthesizer. 5 % of the total number of weights is sufficient to provide the granularity for improving accuracy, while also avoiding too frequent evaluation of the accuracy of the mapped DNN. We, therefore, generate a number of classifiers by providing different numbers of target clusters in succession; in the end of this process a pool of classifiers is generated. The process typically requires a few iterations. However, these emerging devices can suffer from significant variations during the mapping process (i.e., programming weights to the devices), and if left undealt with, can cause significant accuracy degradation. DNN model and many state-of-the-art models have far more weights than ResNet-18, an interesting question is, whether we really need to write-verify every weight of a DNN when mapping it to an nvCiM platform. A theoretical oscillator model is then derived taking into account added mass and added friction coefficients and its predictions are compared to the experimental data. Besides code fragments, we also collect API names that are mentioned in the text. API mapper, suggests a set of APIs given a user query in English.
Thus, in their case, only one swimmer is in the video at any given time. As such, the programming time can be drastically reduced. As such, reading the values of weights programmed into the devices and evaluating the corresponding accuracy of the DNN takes negligible amount of time compared with the write-verify process. As such, we only have to deal with one weight variation at a time. Marcos et al. (2012), but the small bias it generates is additive over time and also affects the orientation distribution of bacteria entering the surface in favour of swimming to the right. Under the classical description, we expect the bacteria orientations to be passively set by the magnetic torque exerted on magnetosomes, with the finite width of the distribution arising from the orientation noise attributed to rotational Brownian motion. In the following, we perform complementary experiments to verify this hypothesis of a run-and-tumble strategy which can take over classical magneto-aerotaxis depending on environmental conditions. The experiments are conducted on GTX Titan-XP GPUs with the machine learning framework of PyTorch 1.8.1. Considering the randomness in device variations, all results shown in this paper are obtained over 3,000 Monte Carlo runs with verified convergence, and both mean and standard deviation are reported.
We perturb each weight in LeNet with the same additive Gaussian noise based on (Yao and et al., 2020) and evaluate the corresponding drop in the DNN accuracy for perturbing each weight, averaged over 100 Monte Carlo runs. A critical question now is how to evaluate the sensitivity of a weight, which will be discussed in the next section. For simplicity of modeling and computation, we will consider a 2D system. Finally, the classification result of the ensemble is expected to have a low percentage of unclassified segments (less than 3%) because, since the classifiers are diverse, they will do different errors or will fail to classify different segments. Further, the properties of the swimmers are in principle entirely orthogonal to those of the host material. We demonstrate that a ‘smart’ self-propelled swimmer can autonomously adapt its swimming behaviour to exploit energy deposited in the wake of other swimmers. Finally, we demonstrate pair interaction between two swimmers. POSTSUBSCRIPT. Without considering the flows generated by the movement of the bacteria, the movement of a bacterium mainly consists of two parts: a convection term of the fluid, and an active movement term from the run-and-tumble. POSTSUBSCRIPT. With this choice of action, the control can be projected onto the shape space and directly compared to the geometric mechanics approach.
An additional motion primitive to control the instantaneous orientation provides this bridge. In this letter, we investigate experimentally the 3D motion of Escherichia coli (E. We consider Escherichia coli (E. We confine a suspension of E. coli of controlled volume in a Hele-Shaw cell made of a glass slide and a coverslip (see Appendix A: Methods). In such a dilute suspension scenario, for simplicity, we do not consider the hydrodynamic effects among the E. coli or between the amoeboid Dd cell and a E. coli. We explored the effects of morphological and kinematic parameters on the swimming speed and efficiency. 0.03 after write-verify. These numbers are in line with those reported in (Shim and et al., 2020), which confirms the validity of our model and parameters. The smart-swimmer relies on a pre-defined set of variables to identify its ‘observed-state’, some of which are depicted in this figure. Figure 2 shows a typical cycle of the Dd cell shape deformations. Our modeling shows that gait adaptability does not require specific mechanosensitive feedback but instead can be explained by the mechanical self-regulation of an elastic and extended motor system.