A study of the bond behavior of FRP bars in MPC seawater concrete

Using magnesium potassium phosphate cement (MPC) and fiber-reinforced polymer (FRP) bar to produce reinforced concrete can overcome the durability problems facing conventional steel reinforced PC concrete. In addition, FRP bar reinforced MPC concrete can also mitigate the CO2 emission issues caused by Portland cement (PC) production and the shortage of natural resources such as virgin aggregates and freshwater. This paper, therefore, is aimed at investigating the bond behavior of the FRP bars in MPC seawater concrete. The direct pullout tests were conducted with a steel bar, BFRP bar, and GFRP bar embedded into different concretes. The effects of reinforcing bars, type of concrete and mixing water on the bond behavior of FRP and steel bars were investigated and discussed. The results showed that the MPC concrete increases the bond strength of BFRP and GFRP bars by 51.06% and 24.42%, respectively, compared with that in PC concrete. Using seawater in MPC concrete can enhance the bond strength of GFRP bar by 13.75%. The damage interface of the FRP bar -MPC is more severe than that of PC with a complete rupture of the FRP ribs and peeling-off of the resin compared to that in steel reinforced MPC specimens. Moreover, the bond stress-slip models were developed to describe the bond behavior of MPC-FRP specimen, and the analytical results match well with the experimental data. In conclusion, the FRP bars showed better bond behavior in the MPC seawater concrete than that in the PC counterparts

Resource Allocation Schemes for Multiple Targets Tracking in Distributed MIMO Radar Systems

Considering the demands of different location accuracy for multiple targets tracking, performance-driven resource allocation schemes in distributed MIMO radar system are proposed. Restricted by the tracking antenna number, location estimation mean-square error (MSE), and target priorities, an optimization problem of the minimal antenna subsets selection is modeled as a knapsack problem. Then, two operational schemes, modified fair multistart local search (MFMLS) algorithm and modified fair multistart local search with one antenna to all targets (MFMLS_OAT) algorithm, are presented and evaluated. Simulation results indicate that the proposed MFMLS and MFMLS_OAT algorithm outperform the existing algorithms. Moreover, the MFMLS algorithm can distinguish targets with different priorities, while the MFMLS_OAT algorithm can perform the tracking tasks with higher accuracy

Knowledge Graph Reasoning over Entities and Numerical Values

A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate from? Knowledge graph reasoning-based applications, such as dialogue systems and interactive search engines, rely on the ability to answer complex logic queries as a fundamental task. In most knowledge graphs, edges are typically used to either describe the relationships between entities or their associated attribute values. An attribute value can be in categorical or numerical format, such as dates, years, sizes, etc. However, existing complex query answering (CQA) methods simply treat numerical values in the same way as they treat entities. This can lead to difficulties in answering certain queries, such as which Australian Pulitzer award winner is born before 1927, and which drug is a pain reliever and has fewer side effects than Paracetamol. In this work, inspired by the recent advances in numerical encoding and knowledge graph reasoning, we propose numerical complex query answering. In this task, we introduce new numerical variables and operations to describe queries involving numerical attribute values. To address the difference between entities and numerical values, we also propose the framework of Number Reasoning Network (NRN) for alternatively encoding entities and numerical values into separate encoding structures. During the numerical encoding process, NRN employs a parameterized density function to encode the distribution of numerical values. During the entity encoding process, NRN uses established query encoding methods for the original CQA problem. Experimental results show that NRN consistently improves various query encoding methods on three different knowledge graphs and achieves state-of-the-art results

Chinese provincial multi-regional input-output database for 2012, 2015, and 2017

Global production fragmentation generates indirect socioeconomic and environmental impacts throughout its expanded supply chains. The multi-regional input-output model (MRIO) is a tool commonly used to trace the supply chain and understand spillover effects across regions, but often cannot be applied due to data unavailability, especially at the sub-national level. Here, we present MRIO tables for 2012, 2015, and 2017 for 31 provinces of mainland China in 42 economic sectors. We employ hybrid methods to construct the MRIO tables according to the available data for each year. The dataset is the consistent China MRIO table collection to reveal the evolution of regional supply chains in China’s recent economic transition. The dataset illustrates the consistent evolution of China’s regional supply chain and its economic structure before the 2018 US-Sino trade war. The dataset can be further applied as a benchmark in a wide range of in-depth studies of production and consumption structures across industries and regions

Dataset Condensation via Generative Model

Dataset condensation aims to condense a large dataset with a lot of training samples into a small set. Previous methods usually condense the dataset into the pixels format. However, it suffers from slow optimization speed and large number of parameters to be optimized. When increasing image resolutions and classes, the number of learnable parameters grows accordingly, prohibiting condensation methods from scaling up to large datasets with diverse classes. Moreover, the relations among condensed samples have been neglected and hence the feature distribution of condensed samples is often not diverse. To solve these problems, we propose to condense the dataset into another format, a generative model. Such a novel format allows for the condensation of large datasets because the size of the generative model remains relatively stable as the number of classes or image resolution increases. Furthermore, an intra-class and an inter-class loss are proposed to model the relation of condensed samples. Intra-class loss aims to create more diverse samples for each class by pushing each sample away from the others of the same class. Meanwhile, inter-class loss increases the discriminability of samples by widening the gap between the centers of different classes. Extensive comparisons with state-of-the-art methods and our ablation studies confirm the effectiveness of our method and its individual component. To our best knowledge, we are the first to successfully conduct condensation on ImageNet-1k.Comment: old work,done in 202

Free-ATM: Exploring Unsupervised Learning on Diffusion-Generated Images with Free Attention Masks

Despite the rapid advancement of unsupervised learning in visual representation, it requires training on large-scale datasets that demand costly data collection, and pose additional challenges due to concerns regarding data privacy. Recently, synthetic images generated by text-to-image diffusion models, have shown great potential for benefiting image recognition. Although promising, there has been inadequate exploration dedicated to unsupervised learning on diffusion-generated images. To address this, we start by uncovering that diffusion models' cross-attention layers inherently provide annotation-free attention masks aligned with corresponding text inputs on generated images. We then investigate the problems of three prevalent unsupervised learning techniques ( i.e., contrastive learning, masked modeling, and vision-language pretraining) and introduce customized solutions by fully exploiting the aforementioned free attention masks. Our approach is validated through extensive experiments that show consistent improvements in baseline models across various downstream tasks, including image classification, detection, segmentation, and image-text retrieval. By utilizing our method, it is possible to close the performance gap between unsupervised pretraining on synthetic data and real-world scenarios

2-[(E)-2-(Benzyl­idene­amino)­eth­yl]-3′,6′-bis­(diethyl­amino)­spiro­[isoindoline-1,9′-xanthen]-3-one

In the title compound, C37H40N4O2, the xanthene and spiro­lactam rings are almost planar, with r.m.s. deviations from the mean planes of 0.223 (2) and 0.057 (2) Å, respectively, and form a dihedral angle of 85.76 (3)°. The dihedral angle between the xanthene mean plane and the benzene ring is 87.16 (5)°. One of the two ethyl groups of one of the diethyl­amino groups is disordered over two sets of sites [0.76 (1):0.24 (1)]