5,017 research outputs found
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 Chinaβs recent economic transition. The dataset illustrates the consistent evolution of Chinaβs 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-(BenzylΒideneΒamino)ΒethΒyl]-3β²,6β²-bisΒ(diethylΒamino)ΒspiroΒ[isoindoline-1,9β²-xanthen]-3-one
In the title compound, C37H40N4O2, the xanthene and spiroΒlactam rings are almost planar, with r.m.s. deviations from the mean planes of 0.223β
(2) and 0.057β
(2)β
Γ
, respectively, and form a dihedral angle of 85.76β
(3)Β°. The dihedral angle between the xanthene mean plane and the benzene ring is 87.16β
(5)Β°. One of the two ethyl groups of one of the diethylΒamino groups is disordered over two sets of sites [0.76β
(1):0.24β
(1)]
- β¦