Chi-Yeh Chen

Chi-Yeh Chen

Coflow is a prominent network abstraction for modeling communication patterns in data centers. Since coflow scheduling in large-scale data centers is $\mathcal{NP}$-hard, this paper investigates this problem within heterogeneous parallel networks featuring multiple network cores. We propose a polynomial-time approximation algorithm to minimize the makespan (maximum completion time). We consider three distinct switch architectures: Electronic Packet Switches (EPS), not-all-stop Optical Circuit Switches (OCS), and all-stop OCS. Under a deployment where all switches are EPS, the proposed algorithm achieves an approximation guarantee of $\min\left\{τ, 2Nm+1\right\}$, which reduces to $2$ when $m=2$ where $τ$ is the maximum number of flows of each port of switch, $N$ is the number of input/output ports and $m$ is the number of network cores. In environments entirely composed of not-all-stop OCS, the algorithm guarantees an approximation ratio of $2\min\left\{τ, 2Nm+1\right\}$, and $4$ when $m=2$. For setups consisting solely of all-stop OCS, the approximation guarantee becomes $2\min\left\{2τ-1, 2Nm+τ\right\}$, and $2τ+2$ when $m=2$. Furthermore, in a hybrid network architecture, we show that the overall performance guarantee of our algorithm is dominated by the least performant switch architecture in the system.

Chi-Yeh Chen, Min-Hsin Huang, Yung-Nien Sun et al.

The image quality of portable supine chest radiographs is inherently poor due to low contrast and high noise. The endotracheal intubation detection requires the locations of the endotracheal tube (ETT) tip and carina. The goal is to find the distance between the ETT tip and the carina in chest radiography. To overcome such a problem, we propose a feature extraction method with Mask R-CNN. The Mask R-CNN predicts a tube and a tracheal bifurcation in an image. Then, the feature extraction method is used to find the feature point of the ETT tip and that of the carina. Therefore, the ETT-carina distance can be obtained. In our experiments, our results can exceed 96\% in terms of recall and precision. Moreover, the object error is less than $4.7751\pm 5.3420$ mm, and the ETT-carina distance errors are less than $5.5432\pm 6.3100$ mm. The external validation shows that the proposed method is a high-robustness system. According to the Pearson correlation coefficient, we have a strong correlation between the board-certified intensivists and our result in terms of ETT-carina distance.

Chi-Yeh Chen

This paper addresses the scheduling problem for unrelated crowd workers in mobile social networks, where the required service time for each task varies among the assigned crowd workers. The goal is to minimize the total weighted completion time of all tasks. First, in an environment with identical crowd workers, we improve the approximation ratio of the Largest-Ratio-First (LRF) scheduling algorithm and provide an updated competitive ratio for its online version. Next, for the unrelated crowd workers environment, we introduce a randomized approximation algorithm that achieves an expected approximation ratio of 1.45. This result improves upon the 1.5-approximation ratio reported in our previous work. We also present a derandomization method for this algorithm. Furthermore, to improve computational efficiency, we propose an algorithm that leverages the property that the optimal schedule on a single crowd worker arranges tasks in non-increasing order by their Smith ratios. Experimental results demonstrate that our proposed method outperforms three variants of the LRF algorithm.