The run-and-tumble behavior is a simple yet powerful mechanism that enables microorganisms to efficiently navigate and adapt to their environment. These organisms run and tumble alternately, with transition rates modulated by intracellular chemical concentration. We introduce a neural network-based model capable of identifying the governing equations underlying run-and-tumble dynamics. This model accommodates the nonlinear functions describing movement responses to intracellular biochemical reactions by integrating the general structure of ODEs that represent these reactions, without requiring explicit reconstruction of the reaction mechanisms. It is trained on datasets of measured responses to simple, controllable signals. The resulting model is capable of predicting movement responses in more realistic, complex, temporally varying environments. Moreover, the model can be used to deduce the underlying structure of hidden intracellular biochemical dynamics. We have successfully tested the validity of the identified equations based on various models of Escherichia coli chemotaxis, demonstrating efficacy even in the presence of noisy measurements. Moreover, we have identified the governing equation of the photo-response of Euglena gracilis cells using experimental data, which was previously unknown, and predicted the potential architecture of the intracellular photo-response pathways for these cells.

Insulator visual aiming is difficult for washing drone due to the complex washing environment, strong dis-turbance, lack of debugging environment, and other factors. Conventional visual servo control methods often fail to consider these complex factors adequately and fall short in reliable insulator visual aiming. To address these problems, we propose a novel multi-feature fusion-based drone visual servo control method for accurate insulator visual aiming. A multi-feature fusion neural network (MFFNet) is proposed to map the dif-ferent input modalities into an embedding space spanned by the learned deep features. Suitable control commands are generated by the simple combination of learned deep features. These deep features represent the intrinsic structural properties of the insulator and the motion pattern of the drones. Particularly, our method is trained purely in simulation and transferred to a real drone directly. Moreover, accurate visual aiming is guaranteed even in strong disturbance environments. Simulation and experimental results verify the high accurate insulator aiming, anti-disturbance, and sim-to-real transfer capabilities of the proposed method. Video: https://youtu.be/Ptlajzvp46A.

Disturbance mainly caused by recoil force in-evitably makes washing drone seriously deviate from the desired position, thereby reducing the cleaning efficiency. It is neces-sary to develop an effective anti-disturbance control method. Although some progresses have been made, the position error thereof is still large, rendering existing methods inapplicable in washing drone. In this paper, we propose a learning-based anti-disturbance control (LADC) method to significantly reduce the position error by combining robust nonlinear control and partial differential equation network (PDENet). Taking data noise into account, we use differential spectral normalization in the training of the PDENet. A distinguishing feature of our method is to directly learn PDENet parameters from flight logs without installing extra sensors. Experimental results indicate that the proposed method outperforms classical PD method and extended state observer (ESO) based control method with 70 % and 50 % reduced position error, respectively, and can be further applied in variable scenarios. Video: https: / / youtu.be/gNfLFAXalkI

Recent advances in text-to-speech (TTS) synthesis, such as Tacotron and WaveRNN, have made it possible to construct a fully neural network based TTS system, by coupling the two components together. Such a system is conceptually sim-ple as it only takes grapheme or phoneme input, uses Mel-spectrogram as an intermediate feature, and directly generates speech samples. The system achieves quality equal or close to natural speech. However, the high computational cost of the system and issues with robustness have limited their usage in real-world speech synthesis applications and products. In this paper, we present key modeling improvements and optimization strategies that enable deploying these models, not only on GPU servers, but also on mobile devices. The proposed system can generate high-quality 24 kHz speech at 5x faster than real time on server and 3x faster than real time on mobile devices.

Collective motions of crowds are common in nature and have attracted a great deal of attention in a variety of multidisciplinary fields. Collectiveness, which indicates the degree of individuals acting as a union, is a fundamental and universal measurement for various crowd systems. By quantifying the topological structures of collective manifolds of crowd, this paper proposes a descriptor of collectiveness and its efficient computation for the crowd and its constituent individuals. The Collective Merging algorithm is then proposed to detect collective motions from random motions. We validate the effectiveness and robustness of the proposed collectiveness on the system of self-driven particles as well as other real crowd systems such as pedestrian crowds and bacteria colony. We compare the collectiveness descriptor with human perception for collective motion and show their high consistency. As a universal descriptor, the proposed crowd collectiveness can be used to compare different crowd systems. It has a wide range of applications, such as detecting collective motions from crowd clutters, monitoring crowd dynamics, and generating maps of collectiveness for crowded scenes. A new Collective Motion Database, which consists of 413 video clips from 62 crowded scenes, is released to the public.