Class-level Structural Relation Modelling and Smoothing for Visual Representation Learning

Representation learning for images has been advanced by recent progress in more complex neural models such as the Vision Transformers and new learning theories such as the structural causal models. However, these models mainly rely on the classification loss to implicitly regularize the class-level data distributions, and they may face difficulties when handling classes with diverse visual patterns. We argue that the incorporation of the structural information between data samples may improve this situation. To achieve this goal, this paper presents a framework termed \textbf{C}lass-level Structural Relation Modeling and Smoothing for Visual Representation Learning (CSRMS), which includes the Class-level Relation Modelling, Class-aware Graph Sampling, and Relational Graph-Guided Representation Learning modules to model a relational graph of the entire dataset and perform class-aware smoothing and regularization operations to alleviate the issue of intra-class visual diversity and inter-class similarity. Specifically, the Class-level Relation Modelling module uses a clustering algorithm to learn the data distributions in the feature space and identify three types of class-level sample relations for the training set; Class-aware Graph Sampling module extends typical training batch construction process with three strategies to sample dataset-level sub-graphs; and Relational Graph-Guided Representation Learning module employs a graph convolution network with knowledge-guided smoothing operations to ease the projection from different visual patterns to the same class. Experiments demonstrate the effectiveness of structured knowledge modelling for enhanced representation learning and show that CSRMS can be incorporated with any state-of-the-art visual representation learning models for performance gains. The source codes and demos have been released at https://github.com/czt117/CSRMS

Cross-Training with Multi-View Knowledge Fusion for Heterogenous Federated Learning

Federated learning benefits from cross-training strategies, which enables models to train on data from distinct sources to improve the generalization capability. However, the data heterogeneity between sources may lead models to gradually forget previously acquired knowledge when undergoing cross-training to adapt to new tasks or data sources. We argue that integrating personalized and global knowledge to gather information from multiple perspectives could potentially improve performance. To achieve this goal, this paper presents a novel approach that enhances federated learning through a cross-training scheme incorporating multi-view information. Specifically, the proposed method, termed FedCT, includes three main modules, where the consistency-aware knowledge broadcasting module aims to optimize model assignment strategies, which enhances collaborative advantages between clients and achieves an efficient federated learning process. The multi-view knowledge-guided representation learning module leverages fused prototypical knowledge from both global and local views to enhance the preservation of local knowledge before and after model exchange, as well as to ensure consistency between local and global knowledge. The mixup-based feature augmentation module aggregates rich information to further increase the diversity of feature spaces, which enables the model to better discriminate complex samples. Extensive experiments were conducted on four datasets in terms of performance comparison, ablation study, in-depth analysis and case study. The results demonstrated that FedCT alleviates knowledge forgetting from both local and global views, which enables it outperform state-of-the-art methods

Efficient Point based Global Illumination on Intel MIC Architecture

International audiencePoint-Based Global Illumination (PBGI) is a popular rendering method in special effects and motion picture productions. The tree-cut computation is in general the most time consuming part of this algorithm, but it can be formulated for efficient parallel execution, in particular regarding wide-SIMD hardware. In this context, we propose several vectorization schemes, namely single, packet and hybrid, to maximize the utilization of modern CPU architectures. While for the single scheme, 16 nodes from the hierarchy are processed for a single receiver in parallel, the packet scheme handles one node for 16 receivers. These two schemes work well for scenes having smooth geometry and diffuse material. When the scene contains high frequency bumps maps and glossy reflections, we use a hybrid vectorization method. We conduct experiments on an Intel Many Integrated Corearchitecture and report preliminary results on several scenes, showing that up to a 3x speedup can be achieved when compared with non-vectorized execution

A Reinforcement Learning Based Auto-Scaling Approach for SaaS Providers in Dynamic Cloud Environment

Cloud computing is an emerging paradigm which provides a flexible and diversified trading market for Infrastructure-as-a-Service (IaaS) providers, Software-as-a-Service (SaaS) providers, and cloud-based application customers. Taking the perspective of SaaS providers, they offer various SaaS services using rental cloud resources supplied by IaaS providers to their end users. In order to maximize their utility, the best behavioural strategy is to reduce renting expenses as much as possible while providing sufficient processing capacity to meet customer demands. In reality, public IaaS providers such as Amazon offer different types of virtual machine (VM) instances with different pricing models. Moreover, service requests from customers always change as time goes by. In such heterogeneous and changing environments, how to realize application auto-scaling becomes increasingly significant for SaaS providers. In this paper, we first formulate this problem and then propose a Q-learning based self-adaptive renting plan generation approach to help SaaS providers make efficient IaaS facilities adjustment decisions dynamically. Through a series of experiments and simulation, we evaluate the auto-scaling approach under different market conditions and compare it with two other resource allocation strategies. Experimental results show that our approach could automatically generate optimal renting policies for the SaaS provider in the long run

Analysis on the Future Development and Existing Problems in the Application of Mechanical Engineering and Automation Technology in China

The rapid growth of science and technology has had a significant impact on all sectors. As part of the reform and development of various social sectors, the traditional manual labor mode has been supplanted by modern machinery and automation technology. It reflects the progress in science and technology of China as well as the development of social civilization. This paper describes the existing problems in the application of mechanical engineering and automation technology and its future development. In short, the current mechanical engineering and automation technology still have issues to be solved in environmental protection, independent innovation, market research, and specialized talents and education. In China, mechanical engineering and automation technology should be heading in the direction of intelligence, automation, user-friendliness, scientific modernization, and integration

Mlinear: Rethink the Linear Model for Time-series Forecasting

Recently, significant advancements have been made in time-series forecasting research, with an increasing focus on analyzing the inherent characteristics of time-series data, rather than solely focusing on designing forecasting models.In this paper, we follow this trend and carefully examine previous work to propose an efficient time series forecasting model based on linear models. The model consists of two important core components: (1) the integration of different semantics brought by single-channel and multi-channel data for joint forecasting; (2) the use of a novel loss function that replaces the traditional MSE loss and MAE loss to achieve higher forecasting accuracy.On widely-used benchmark time series datasets, our model not only outperforms the current SOTA, but is also 10 $\times$ speedup and has fewer parameters than the latest SOTA model.Comment: 8 pages,1 figure,4 table

REAL-TIME COLLABORATIVE DESIGN SYSTEM FOR PRODUCT ASSEMBLY OVER THE INTERNET

Product assembly design is a complex activity possible involving collaboration between different designers geographically dispersed. This paper puts forward some significant methodologies and technologies for distributed assembly and presents a collaboration architecture that manages to support working in both synchronous and asynchronous ways. Meanwhile, we adopt a three-level conflicts detection scheme to avoid conflicts effectively and a data streaming technology based on C/P (command/ parameter) to realize the real-time design. Based on the technologies mentioned above, a design system that supports real-time collaborative assembly is developed and we validate it by assembling a mechanical press across network collaboratively

Data Driven Avatars Roaming in Digital Museum

International audienceThis paper describes a motion capture (mocap) data-driven digital museum roaming system with high walking reality. We focus on three main questions: the animation of avatars; the path planning; and the collision detection among avatars. We use only a few walking clips from mocap data to synthesize walking motions with natural transitions, any direction and any length. Let the avatars roam in the digital museum with its Voronoi skeleton path, shortest path or offset path. And also we use Voronoi diagram to do collision detection. Different users can set up their own avatars and roam along their own path. We modify the motion graph method by classify the original mocap data and set up their motion graph which can improve search efficiency greatly

A Hypergraph Partition Based Approach to Dynamic Deployment for Service-Oriented Multi-tenant SaaS Applications

Part 3: Short PapersInternational audienceIn a service-oriented multi-tenant SaaS application, all tenants share services and user requests of the service change dynamically. In order to provide high-quality web services, we must solve the problem of the load unbalance caused by dynamic user requests’ change. This paper proposes an approach based on hypergraph partition to keep load balance for service-oriented multi-tenant SaaS application. A hypergraph-based service model is used to present hierarchical services and multi-tenant applications. This approach adjusts service distribution on the servers based on hypergraph partition to keep load balance. According to the experiments, this approach effectively solves the problem of load unbalance caused by the change of user requests