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In computer vision, human parsing is challenging due to its demand for accurate human region location and semantic partitioning. This dense prediction task needs powerful computation and high‐precision models. To enable real‐time parsing on resource‐limited devices, the authors introduced a lightweight model using ResNet18 as a core network. The authors simplified the pyramid module, improving context clarity and reducing complexity. The authors integrated a spatial attention fusion strategy to counter precision loss in the light‐weighting process. Traditional models, despite their segmentation precision, are limited by their computational complexity and extensive parameters. The authors implemented knowledge distillation (KD) techniques to enhance the authors' lightweight network's accuracy. Traditional methods can fail to learn useful knowledge with significant network differences. Hence, the authors used a novel distillation approach based on inter‐class and intra‐class relations in prediction outcomes, noticeably improving parsing accuracy. The authors' experiments on the Look into Person (LIP) dataset show that their lightweight model significantly reduces parameters while maintaining parsing precision and enhancing inference speed. [ABSTRACT FROM AUTHOR]

The task of referring expression comprehension (REC) is to localise an image region of a specific object described by a natural language expression, and all existing REC methods assume that the object described by the referring expression must be located in the given image. However, this assumption is not correct in some real applications. For example, a visually impaired user might tell his robot 'please take the laptop on the table to me'. In fact, the laptop is not on the table anymore. To address this problem, the authors propose a novel REC model to deal with the situation where expression‐image mismatching occurs and explain the mismatching by linguistic feedback. The authors' REC model consists of four modules: the expression parsing module, the entity detection module, the relationship detection module, and the matching detection module. They built a data set called NP‐RefCOCO+ from RefCOCO+ including both positive samples and negative samples. The positive samples are original expression‐image pairs in RefCOCO+. The negative samples are the expression‐image pairs in RefCOCO+, whose expressions are replaced. They evaluate the model on NP‐RefCOCO+ and the experimental results show the advantages of their method for dealing with the problem of expression‐image mismatching. [ABSTRACT FROM AUTHOR]

With the rapid development of integrated circuit manufacturing processes, poor system scalability has become a prominent problem for System on Chip (SoC).To solve the bottleneck problems such as global synchronization, network on chip Networks on Chip (NoC) has emerged as a new design to tackle the increasing communication demand among elements on chips. With the development of networks-on-chip, the research has expanded from two-dimensional to three-dimensional design, and 3D networks-on-chip is a combination of 3D integration technology and 2D networks-on-chips with the advantages of both to meet the development trend of diversified chip functions. This paper presents an improved floorplanning optimization algorithm based on simulated annealing algorithm (Comprehensive Improved Simulated Annealing, hereinafter referred to as CISA algorithm) to replace the original floorplanning optimization algorithm based on simulated annealing algorithm (Simulated Annealing, hereinafter referred to as SA algorithm) to make it more applicable to the three-dimensional network-on- chip simulation. This paper describes the CISA algorithm improvement ideas and uses it on an existing 3D network-on-chip simulator with a set of classical simulation tests. The results show that the proposed CISA algorithm is better than the original SA algorithm and it is more suitable for simulations of three-dimensional networks-on-chip, especially when dealing with large scale 3D NoC. [ABSTRACT FROM AUTHOR]

Iron oxide magnetic nanoparticles (MNPs) are good candidates to implement fluid therapy in critical patients in clinic integrated system. Herein, we synthesized paclitaxel (PTX)-loaded MNPs modified with methoxy polyethylene glycol (PEG)-lysine-oleic acid 2 (PTX-MNPs-PLO), which is expected to act as a magnetic resonance imaging (MRI) contrast agent and meanwhile for cancer therapy. MNPs were synthesized by thermal decomposition. Dialysis method was applied to prepare PTX-MNPs-PLO with 3 different PEG molecular weights (1000, 2000, and 4000 Da), which were subsequently freeze-dried into powders. PTX-MNPs-PLO was characterized by transmission electron microscope, scanning electron microscope, thermogravimetric analysis, vibrating sample magnetometer, and MRI. What is more is that pharmacokinetics and distribution in vivo were processed, the results of which exhibited that PTX-MNPs-PLO 2000 had the longer circulation lifetime compared with Taxol, PTX-MNPs-PLO 1000 , and PTX-MNPs-PLO 4000 . Results of magnetic targeting in kidneys suggested that deep buried or ultrasmall magnet is likely to be more preferable. PTX-MNPs-PLO 2000 holds great promise in the application of magnetic accumulation, target drug delivery, and thermal therapy. [ABSTRACT FROM AUTHOR]

SOD-like activity of CeO 2 nanoparticles (Ce NPs) is driven by Ce 3+ /Ce 4+ , high oxidative stress can oxidize Ce 3+ to reduce the ratio of Ce 3+ /Ce 4+ , inactivating the SOD activity of Ce NPs. Herein, we found Au@Ce NPs, assembled by Au NPs and Ce NPs, exhibited high-performance of SOD mimetic enzyme activity even upon the oxidation of H 2 O 2 . Ce NPs supported by nano-Au can acquire the electrons from Au NPs through the enhanced localized surface plasmon resonance (LSPR), maintaining the stability of Ce 3+ /Ce 4+ and SOD-like activity. Meanwhile, Au@Ce NPs retained the peroxidase function and catalase function. As a result, Au@Ce NPs effectively scavenged O 2 •- and the derived ROS in AML cells, which are the important signaling source that drives AML cell proliferation and accelerates cell cycle progression. When HL-60 cells were treated by Au@Ce NPs, the removal of endogenous ROS signal significantly arrested cell cycle at G1 phase and suppressed the cell proliferation by blocking the mitogen-activated protein kinases (MAPKs) signaling and the Akt/Cyclin D1 cell cycle signaling. Importantly, this treatment strategy showed therapeutic effect for subcutaneous transplantation of AML model as well as a satisfactory result in diminishing the leukocyte infiltration of liver and spleen particularly. Thus, assembled Au@Ce NPs show the high-performance SOD-like activity, promising the potential in treating AML and regulating abnormal ROS in other diseases safely and efficiently., (© 2021 The Authors.)