New multiple target tracking strategy using domain knowledge and optimisation

This paper proposes an environment-dependent vehicle dynamic modeling approach considering interactions between the noisy control input of a dynamic model and the environment in order to make best use of domain knowledge. Based on this modeling, a new domain knowledge-aided moving horizon estimation (DMHE) method is proposed for ground moving target tracking. The proposed method incorporates different types of domain knowledge in the estimation process considering both environmental physical constraints and interaction behaviors between targets and the environment. Furthermore, in order to deal with a data association ambiguity problem of multiple-target tracking in a cluttered environment, the DMHE is combined with a multiple-hypothesis tracking structure. Numerical simulation results show that the proposed DMHE-based method and its extension could achieve better performance than traditional tracking methods which utilize no domain knowledge or simple physical constraint information only

Conflict of Laws on Occupational Accident Death Benefits: Presented with Actual Cases in Taiwan

When a worker dies due to an occupational accident the labor insurance death benefit being applied for by his her survivors subject to more conditions due to the revision of the Act When the survivor does not meet the requirements the labor insurance will not be paid At this time the survivor of the occupational accident worker shall instead turn to the employer for compensation to pay the labor insurance premium as the Bureau of Labor Insurance did not pay The employer had to pay out of its pocket to compensate the survivor for the absurdity of the death compensation by the Labor Standards Act A large company can take care of family members in terms of corporate responsibility or financial resources if it is a small and medium-sized enterprise or even a microenterprise it cannot pay this huge amount and family members have to fight for compensation through litigation However it is not commonly known that the premiums of the labor accident insurance are not shared by the government or workers and 100 of the total amount is paid by the employe

The claudin family of proteins in human malignancy: A clinical perspective

Tight junctions, or zonula occludens, are the most apical component of the junctional complex and provide one form of cell–cell adhesion in epithelial and endothelial cells. Nearly 90% of malignant tumors are derived from the epithelium. Loss of cell–cell adhesion is one of the steps in the progression of cancer to metastasis. At least three main tight junction family proteins have been discovered: occludin, claudin, and junctional adhesion molecule (JAM). Claudins are the most important structural and functional components of tight junction integral membrane proteins, with at least 24 members in mammals. They are crucial for the paracellular flux of ions and small molecules. Overexpression or downregulation of claudins is frequently observed in epithelial-derived cancers. However, molecular mechanisms by which claudins affect tumorigenesis remain largely unknown. As the pivotal proteins in epithelial cells, altered expression and distribution of different claudins have been reported in a wide variety of human malignancies, including pancreatic, colonic, lung, ovarian, thyroid, prostate, esophageal, and breast cancers. In this review, we will give the readers an overall picture of the changes in claudin expression observed in various cancers and their mechanisms of regulation. Downregulation of claudins contributes to epithelial transformation by increasing the paracellular permeability of nutrients and growth factors to cancerous cells. In the cases of upregulation of claudin expression, the barrier function of the cancerous epithelia changes, as they often display a disorganized arrangement of tight junction strands with increased permeability to paracellular markers. Finally, we will summarize the literature suggesting that claudins may become useful biomarkers for cancer detection and diagnosis as well as possible therapeutic targets for cancer treatment

DeepCluE: Enhanced Image Clustering via Multi-layer Ensembles in Deep Neural Networks

Deep clustering has recently emerged as a promising technique for complex data clustering. Despite the considerable progress, previous deep clustering works mostly build or learn the final clustering by only utilizing a single layer of representation, e.g., by performing the K-means clustering on the last fully-connected layer or by associating some clustering loss to a specific layer, which neglect the possibilities of jointly leveraging multi-layer representations for enhancing the deep clustering performance. In view of this, this paper presents a Deep Clustering via Ensembles (DeepCluE) approach, which bridges the gap between deep clustering and ensemble clustering by harnessing the power of multiple layers in deep neural networks. In particular, we utilize a weight-sharing convolutional neural network as the backbone, which is trained with both the instance-level contrastive learning (via an instance projector) and the cluster-level contrastive learning (via a cluster projector) in an unsupervised manner. Thereafter, multiple layers of feature representations are extracted from the trained network, upon which the ensemble clustering process is further conducted. Specifically, a set of diversified base clusterings are generated from the multi-layer representations via a highly efficient clusterer. Then the reliability of clusters in multiple base clusterings is automatically estimated by exploiting an entropy-based criterion, based on which the set of base clusterings are re-formulated into a weighted-cluster bipartite graph. By partitioning this bipartite graph via transfer cut, the final consensus clustering can be obtained. Experimental results on six image datasets confirm the advantages of DeepCluE over the state-of-the-art deep clustering approaches.Comment: To appear in IEEE Transactions on Emerging Topics in Computational Intelligenc

Dynamical-Corrected Nonadiabatic Geometric Quantum Computation

Recently, nonadiabatic geometric quantum computation has been received great attentions, due to its fast operation and intrinsic error resilience. However, compared with the corresponding dynamical gates, the robustness of implemented nonadiabatic geometric gates based on the conventional single-loop scheme still has the same order of magnitude due to the requirement of strict multi-segment geometric controls, and the inherent geometric fault-tolerance characteristic is not fully explored. Here, we present an effective geometric scheme combined with a general dynamical-corrected technique, with which the super-robust nonadiabatic geometric quantum gates can be constructed over the conventional single-loop and two-loop composite-pulse strategies, in terms of resisting the systematic error, i.e., $\sigma_x$ error. In addition, combined with the decoherence-free subspace (DFS) coding, the resulting geometric gates can also effectively suppress the $\sigma_z$ error caused by the collective dephasing. Notably, our protocol is a general one with simple experimental setups, which can be potentially implemented in different quantum systems, such as Rydberg atoms, trapped ions and superconducting qubits. These results indicate that our scheme represents a promising way to explore large-scale fault-tolerant quantum computation.Comment: 10 pages, 9 figure

Effect of Tumor Necrosis Factor-α on Neutralization of Ventricular Fibrillation in Rats with Acute Myocardial Infarction

The purpose of this study was to explore the effects of tumor necrosis factor-α (TNF-α) on ventricular fibrillation (VF) in rats with acute myocardial infarction (AMI). Rats were randomly classified into AMI group, sham operation group and recombinant human tumor necrosis factor receptor:Fc fusion protein (rhTNFR:Fc) group. Spontaneous and induced VFs were recorded. Monophasic action potentials (MAPs) among different zones of myocardium were recorded at eight time points before and after ligation and MAP duration dispersions (MAPDds) were calculated. Then expression of TNF-α among different myocardial zones was detected. After ligation of the left anterior descending coronary artery, total TNF-α expression in AMI group began to markedly increase at 10 min, reached a climax at 20–30min, and then gradually decreased. The time-windows of VFs and MAPDds in the border zone performed in a similar way. At the same time-point, the expression of TNF-α in the ischemia zone was greater than that in the border zone, and little in the non-ischemia zone. Although the time windows of TNF-α expression, the MAPDds in the border zone and the occurrence of VFs in the rhTNFR:Fc group were similar to those in the AMI group, they all decreased in the rhTNFR:Fc group. Our findings demonstrate that TNF-α could enlarge the MAPDds in the border zone, and promote the onset of VFs