The segmentation and classification of brain tumors are important yet highly challenging tasks in the field of medical image processing. In this paper, we proposed a multi-task cascaded attention network (MTCAN) for brain tumor segmentation and classification. Specifically, the MTCAN uses a multi-scale residual attention mechanism to fuse and re-calibrate tumor features at different scales, which enhances the network’s focus on brain tumors. Additionally, a cross-stage feature fusion module is proposed to enable information transmission and guide training between the cascaded networks. Experimental results demonstrate that the proposed MTCAN outperforms other state-of-the-art methods in segmenting and classifying brain tumors, holding significant clinical application value.

Nowadays, more and more enterprises and organizations are hosting their data into the cloud, in order to reduce the IT maintenance cost and enhance the data reliability. Generally customers put their data into a single cloud, which is subject to the vendor lock-in risk. Compared with single cloud storage, JointCloud storage can provide customers with greater flexibility and availability. In JointCloud environment, the pricing of cloud storage service and geographical distribution of cloud storage datacenters are diverse, and customers access data in different frequencies and regions. Based on comprehensive analysis of various state-of-the-art cloud vendors, this paper proposes a novel data hosting scheme, including a redundancy storage scheme using erasure coding and replication, a mathematical modeling of multi-region data placement problem, and an optimization method of global cost and latency using heuristic algorithm. We propose a dynamic adjustment method for the variations of data access pattern and a data recovery method for cloud storage service failures. We also design and implement a JointCloud storage prototype system. Finally, experiments show the effectiveness of the scheme.

Abstract As we know, the rhesus macaque as a nonhuman primate is quite similar to a human being in genetics. Moreover, it has become essential in animal model anatomy and physiology in many modern medicine research such as the cardiovascular and cerebrovascular diseases in the recent years. This paper describes a pipeline from the raw rhesus macaque brain magnetic resonance imaging data to intuitive 3D inspection of its layers. Brain extraction is an initial step for subsequent analyses, but most of the existing methods so far are designed for human brain, which does not work well with the rhesus macaque. Firstly, we propose a reliable and efficient method to extract the brain from magnetic resonance imaging data sets based on dividing the brain into blocks. Then, we design a trapezoid opacity transfer function based on Compute Unified Device Architecture (CUDA)‐based real‐time volume ray casting, which is dedicated to volume rendering to make the inspection more intuitive. Besides, for an immersive exploration in virtual reality benefits understanding the volumetric data sets, so we also design a layer filter for the segmented rhesus macaque brain data sets, which facilitates the inspection of the interior in Region of Interest (ROI) by an intuitive bimanual interaction via a Leap Motion sensor. Our experiments prove usability and efficiency.

Touch less interaction has received considerable attention in recent years with benefit of removing the burden of physical contact. To achieve mid-air interaction, several strategies are available. However, since most of these techniques directly map the 2D WIMP GUI to 3D user interface, they lead unnatural result. In this paper, interaction gestures and tools for exploring volume dataset are designed to perform the similar tasks in the real world. We mainly employ the idea of focus + context based on GPU volume ray casting by trapezoid-shaped transfer function when designing interaction tools. User studies are conducted to demonstrate the usability and intuitiveness of our method. The experimental results show a significant advantage in completion time after a short period of training.

Data centers often hold tens to hundreds of thousands of servers in order to offer cloud computing services at scale. Ethernet switching and IP routing have their own advantages and limitations in building data center networks. Recent research, such as PortLand and BCube, has proposed scalable data center network designs. A common feature of these designs is that their addressing and routing are customized to specific topologies. In this paper, we propose a generic addressing, routing and forwarding protocol for data center networks, which works on arbitrarily "layered'' network topologies. We first form the network as a multi-rooted tree. Each network node (i.e., hosts and switches) is then assigned one or more locators, and each locator encodes a downward path from the roots to this node. Data center networks often have rich path diversity, so tracking all locators of a destination node will cause switches to have very large forwarding tables. We further use a new forwarding model to reduce the forwarding states. In addition, the multiple-locator mechanism brings built-in support for multi-path routing, load balancing and fault tolerance. Evaluations based on simulations and prototype experiments demonstrate that our proposal achieves our design goals.

In this paper we propose a new algorithm of face clustering on triangle meshes, we call it balanced hierarchical face clustering (BHFC for short). Given a triangle mesh model, the algorithm iteratively generates a binary tree of face clusters on it. Each cluster consists of a set of connected faces. At every iteration, two connected clusters are chosen according to these rules we define, and they are merged into a new cluster. For each iteration there are two steps. In the first step, a set of cluster pair candidates is determined. In the second step, a single pair in the candidate set is chosen to merge. After the iteration, a hierarchical structure of clusters is constructed. The hierarchical structure is balanced, which avoids extreme size clusters appearing in the clustered result.

In this paper, our initial work on the use of visualization and virtual reality for education especially for providing three-dimensional concepts of molecular structure was presented. Our system is composed of a hybrid screen, multi-projectors, PC cluster, 6DOF haptic device etc. It offers a channel to reach into the molecular space in an immersive environment, allows user to employ an intuitive 6DOF haptic device Spidar-G to control the perspective. For educators, it can serve as a good instructional aid for helping students to understand molecular structure through effective visual representation and interactive manipulation.