Agglomeration of microparticles in complex plasmas

Agglomeration of highly charged microparticles was observed and studied in complex plasma experiments carried out in a capacitively coupled rf discharge. The agglomeration was caused by strong dust density waves triggered in a particle cloud by decreasing neutral gas pressure. Using a high-speed camera during this unstable regime, it was possible to resolve the motion of individual microparticles and to show that the relative velocities of some particles were sufficiently high to overcome the mutual Coulomb repulsion and hence to result in agglomeration. After stabilising the cloud again through the increase of the pressure, we were able to observe the aggregates directly with a long-distance microscope. We show that the agglomeration rate deduced from our experiments is in good agreement with theoretical estimates. In addition, we briefly discuss the mechanisms that can provide binding of highly charged microparticles in a plasma.Comment: submitted to Phys. Plasm

Channeling of particles and associated anomalous transport in a 2D complex plasma crystal

Implications of recently discovered effect of channeling of upstream extra particles for transport phenomena in a two-dimensional plasma crystal are discussed. Upstream particles levitated above the lattice layer and tended to move between the rows of lattice particles. An example of heat transport is considered, where upstream particles act as moving heat sources, which may lead to anomalous heat transport. The average channeling length observed was 15 - 20 interparticle distances. New features of the channeling process are also reported

Identification of the melting line in the two-dimensional complex plasmas using an unsupervised machine learning method

Machine learning methods have been widely used in the investigations of the complex plasmas. In this paper, we demonstrate that the unsupervised convolutional neural network can be applied to obtain the melting line in the two-dimensional complex plasmas based on the Langevin dynamics simulation results. The training samples do not need to be labeled. The resulting melting line coincides with those obtained by the analysis of hexatic order parameter and supervised machine learning method

Ternary Compression for Communication-Efficient Federated Learning

Learning over massive data stored in different locations is essential in many real-world applications. However, sharing data is full of challenges due to the increasing demands of privacy and security with the growing use of smart mobile devices and IoT devices. Federated learning provides a potential solution to privacy-preserving and secure machine learning, by means of jointly training a global model without uploading data distributed on multiple devices to a central server. However, most existing work on federated learning adopts machine learning models with full-precision weights, and almost all these models contain a large number of redundant parameters that do not need to be transmitted to the server, consuming an excessive amount of communication costs. To address this issue, we propose a federated trained ternary quantization (FTTQ) algorithm, which optimizes the quantized networks on the clients through a self-learning quantization factor. A convergence proof of the quantization factor and the unbiasedness of FTTQ is given. In addition, we propose a ternary federated averaging protocol (T-FedAvg) to reduce the upstream and downstream communication of federated learning systems. Empirical experiments are conducted to train widely used deep learning models on publicly available datasets, and our results demonstrate the effectiveness of FTTQ and T-FedAvg compared with the canonical federated learning algorithms in reducing communication costs and maintaining the learning performance

Structure and dynamics of a glass-forming binary complex plasma with non-reciprocal interaction

In this letter, we present the first numerical study on the structural and dynamical properties of a quasi-two-dimensional (q2D) binary complex plasma with Langevin dynamics simulation. The effect of interaction with non-reciprocity on the structure is investigated by comparing systems with pure Yukawa and with point-wake Yukawa interactions. The long-time alpha-relaxation for the latter system is revealed by plotting and analyzing the intermediate scattering function. The results clearly indicate that a q2D binary complex plasma is a suitable model system to study the dynamics of a glass former. The non-reciprocity of the interactions shifts the glass formation significantly but leads to the same qualitative signatures as in the reciprocal case