A penalty ADMM with quantized communication for distributed optimization over multi-agent systems

summary:In this paper, we design a distributed penalty ADMM algorithm with quantized communication to solve distributed convex optimization problems over multi-agent systems. Firstly, we introduce a quantization scheme that reduces the bandwidth limitation of multi-agent systems without requiring an encoder or decoder, unlike existing quantized algorithms. This scheme also minimizes the computation burden. Moreover, with the aid of the quantization design, we propose a quantized penalty ADMM to obtain the suboptimal solution. Furthermore, the proposed algorithm converges to the suboptimal solution with an $O(\frac{1}{k})$ convergence rate for general convex objective functions, and with an R-linear rate for strongly convex objective functions

C⋅\cdotASE: Learning Conditional Adversarial Skill Embeddings for Physics-based Characters

We present C$\cdot$ASE, an efficient and effective framework that learns conditional Adversarial Skill Embeddings for physics-based characters. Our physically simulated character can learn a diverse repertoire of skills while providing controllability in the form of direct manipulation of the skills to be performed. C$\cdot$ASE divides the heterogeneous skill motions into distinct subsets containing homogeneous samples for training a low-level conditional model to learn conditional behavior distribution. The skill-conditioned imitation learning naturally offers explicit control over the character's skills after training. The training course incorporates the focal skill sampling, skeletal residual forces, and element-wise feature masking to balance diverse skills of varying complexities, mitigate dynamics mismatch to master agile motions and capture more general behavior characteristics, respectively. Once trained, the conditional model can produce highly diverse and realistic skills, outperforming state-of-the-art models, and can be repurposed in various downstream tasks. In particular, the explicit skill control handle allows a high-level policy or user to direct the character with desired skill specifications, which we demonstrate is advantageous for interactive character animation.Comment: SIGGRAPH Asia 202

Structure-aware Protein Self-supervised Learning

Protein representation learning methods have shown great potential to yield useful representation for many downstream tasks, especially on protein classification. Moreover, a few recent studies have shown great promise in addressing insufficient labels of proteins with self-supervised learning methods. However, existing protein language models are usually pretrained on protein sequences without considering the important protein structural information. To this end, we propose a novel structure-aware protein self-supervised learning method to effectively capture structural information of proteins. In particular, a well-designed graph neural network (GNN) model is pretrained to preserve the protein structural information with self-supervised tasks from a pairwise residue distance perspective and a dihedral angle perspective, respectively. Furthermore, we propose to leverage the available protein language model pretrained on protein sequences to enhance the self-supervised learning. Specifically, we identify the relation between the sequential information in the protein language model and the structural information in the specially designed GNN model via a novel pseudo bi-level optimization scheme. Experiments on several supervised downstream tasks verify the effectiveness of our proposed method.Comment: 7 pages and 4 figure

Combined cloud:a mixture of voluntary cloud and reserved instance marketplace

Voluntary cloud is a new paradigm of cloud computing.It provides an alternative selection along with some well-provisioned clouds.However,for the uncertain time span that participants share their computing resources in voluntary cloud,there are some challenging issues,i.e.,fluctuation,under-capacity and low-benefit.In this paper,an architecture is first proposed based on Bittorrent protocol.In this architecture,resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle.Actually,these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud.Then,the fault rate of each node is used to evaluate the uncertainty of its sharing time.By leveraging a linear prediction model,it is enabled by a distribution function which is used for evaluating the computing capacity of the system.Moreover,the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud.At last,the system performance is validated by two sets of simulations.And the experimental results show the effectiveness of our computational method for resource reservation optimization

Detection and diagnosis of paralysis agitans

Humans’ daily behavior can reflect the main physiological characteristics of neurological diseases. Human gait is a complex behavior produced by the coordination of multiple physiological systems such as the nervous system and the muscular system. It can reflect the physiological state of human health, and its abnormality is an important basis for diagnosing some nervous system diseases. However, many early gait anomalies have not been effectively discovered because of medical costs and people's living customs. This paper proposes an effective, economical, and accurate non-contact cognitive diagnosis system to help early detection and diagnosis of paralysis agitans under daily life conditions. The proposed system extract data from wireless state information obtained from antenna-based data gathering module. Further, we implement data processing and gait classification systems to detect abnormal gait based on the acquired wireless data. In the experiment, the proposed system can detect the state of human gait and carries high classification accuracy up to 96.7 %. The experimental results demonstrate that the proposed technique is feasible and cost-effective for healthcare applications

1,1′-Dimethyl-4,4′-bipyridinium bis­(tetra­fluorido­borate)

In the title compound, C12H14N2 2+·2BF4 −, the cation has a centre of symmetry at the mid-point of the central C—C bond. π–π inter­actions, with a shortest atom-to-atom distance of 3.757 (4) Å, extend the crystal structure into a one-dimensional supra­molecular chain