Zero-shot Composed Text-Image Retrieval

In this paper, we consider the problem of composed image retrieval (CIR), it aims to train a model that can fuse multi-modal information, e.g., text and images, to accurately retrieve images that match the query, extending the user's expression ability. We make the following contributions: (i) we initiate a scalable pipeline to automatically construct datasets for training CIR model, by simply exploiting a large-scale dataset of image-text pairs, e.g., a subset of LAION-5B; (ii) we introduce a transformer-based adaptive aggregation model, TransAgg, which employs a simple yet efficient fusion mechanism, to adaptively combine information from diverse modalities; (iii) we conduct extensive ablation studies to investigate the usefulness of our proposed data construction procedure, and the effectiveness of core components in TransAgg; (iv) when evaluating on the publicly available benckmarks under the zero-shot scenario, i.e., training on the automatically constructed datasets, then directly conduct inference on target downstream datasets, e.g., CIRR and FashionIQ, our proposed approach either performs on par with or significantly outperforms the existing state-of-the-art (SOTA) models. Project page: https://code-kunkun.github.io/ZS-CIR

Backhaul-constrained coverage analysis of integrated high and low altitude platforms aerial communication system in post-disaster areas

Coverage analysis of aerial communication networks based on high altitude platforms (HAPs) and low altitude platforms (LAPs) is of great significance to understand the service provisioning capability of aerial base stations. This letter uses stochastic geometry to analyze network coverage of an integrated HAP and LAP (IHL) system with respect to backhaul constraints, where LAPs aim to provide services for ground user equipments in the malfunction area and a HAP is to provide backhaul connectivity for LAPs. Based on stochastic geometry theory, the analytical framework of the IHL system coverage is derived along with the analysis on the impact of some key parameters, such as aerial platform altitudes and LAP densities. The derived analytical framework can also provide insights for the backhaul design of LAP aerial base stations, which is also revealed in the numerical analyses part

Effect of Tool Offset on the Microstructure and Properties of AA6061/AZ31B Friction Stir Welding Joints

Friction stir welding was carried out on AA6061/AZ31B alloy, and the influence of tool offset on microstructures and mechanical properties of the joints was studied. The results showed that preferred properties were obtained when Mg was placed in the Advancing Side (AS) and offset was positioned into Mg. The distribution of Al3Mg2 and Al12Mg17 intermetallic compounds (IMCs) could be improved with offset in a certain range. The tensile strength of the joints was elevated with the increase of the offset due to the superior distribution and diminished size of IMCs, and when the offset was 1.5 mm, the joint strength reached the maximum value of 107 MPa. The microhardness of the stirring zone decreased with the increased offset

First-Principles Calculations of Y-Si-O Nanoclusters and Effect of Si on Microstructure and Mechanical Properties of 12Cr ODS Steel in Vacuum Sintering System

High density of thermally stable Y-Si-O nanoparticles dispersed in the Fe matrix play a primary role in oxide dispersion strengthened (ODS) steel. In this study, the binding energies of solutes Y, O and Si with vacancies have been calculated in the framework of first-principles density functional theory. According to the calculations, any two solutes of Y, O and Si bound with each other strongly in the second nearest neighboring (NN) sites while not in 1NN. A vacancy (v) bounds strongly with Y and O in 1NN site. The binding sequence of solutes with v followed O-v → Y-v → Si-v, and the affinity of Y, Si and v with O followed O-Y → O-v → O-Si. The nucleation mechanism of Y-O-Si nanoclusters was determined, which gave the feasibility of adding Si to ODS steels. The core (consisting of Si and O)-shell (enriched Fe and Cr) structure of the microparticles was found in ODS steels containing Si, fabricated by mechanical alloying (MA) and vacuum sintering. Moreover the nanoparticles of monoclinic cubic Y2O3, Y2SiO5 and Y2Si2O7 with sizes of 5~12 nm were observed in ODS steel. Si reduced the sintering temperature by maximizing densities and mechanical properties at a lower sintering temperature. The steel with 3 wt% Si was sintered at 1280 °C, exhibiting the best comprehensive mechanical properties. The tensile strength, hardness and relative density were 1025 MPa, 442.44 HV and 95.3%, respectively

Experimental study on seepage flow patterns in heterogeneous low-permeability reservoirs

Abstract Heterogeneous artificial core plate models with low permeability are designed, made and evaluated based on similarity theory of heterogeneous reservoir with low-permeability physical simulation by artificial core plate model; then, simulative experiments for seepage flow patterns can be carried out. Pressure data are obtained by pressure transducers symmetrically arranged in artificial core plate models to study on the seepage flow patterns in heterogeneous reservoirs with low permeability. Experimental results show that the pressure gradient around injection and production points is high, and the pressure gradient of diagonal corner is very low. The distribution of pressure gradient changes as plane heterogeneity of artificial core plate models changes. The higher permeability increases the spread range of pressure, but the enhancement of heterogeneity has a negative effect on pressure transmission at the same time. The effect of permeability is greater than the negative impact of heterogeneous when the overall permeability of plate model is at a very low level. Non-seepage flow section becomes smaller with the increase in permeability, and the proportion of quasi-linear seepage flow section which is more conducive to fluid flow raise as seepage flow section becomes larger

Method for Clustering Daily Load Curve Based on SVD-KICIC

Clustering electric load curves is an important part of the load data mining process. In this paper, we propose a clustering algorithm by combining singular value decomposition and KICIC clustering algorithm (SVD-KICIC) for analyzing the characteristics of daily load curves to mitigate some of the traditional clustering algorithm problems, such as only considering intra-class distance and low computational efficiency when dealing with massive load data. Our method identifies effective daily load curve characteristics using the singular value decomposition technique to improve dimensionality reduction, which improves low computational efficiency by reducing the number of dimensions inherent in big data. Additionally, the method performs SVD on the load data to obtain singular values for determination of weight of the KICIC algorithm, which leverages intra-class and inter-class distances of the load data and further improves the computational efficiency of the algorithm. Finally, we perform a series of simulations of actual load curves from a certain city to validate that the algorithm proposed in this paper has a short operation time, high clustering quality, and solid robustness that improves the clustering performance of the load curves

First-Principles Calculations of Y-Si-O Nanoclusters and Effect of Si on Microstructure and Mechanical Properties of 12Cr ODS Steel in Vacuum Sintering System

High density of thermally stable Y-Si-O nanoparticles dispersed in the Fe matrix play a primary role in oxide dispersion strengthened (ODS) steel. In this study, the binding energies of solutes Y, O and Si with vacancies have been calculated in the framework of first-principles density functional theory. According to the calculations, any two solutes of Y, O and Si bound with each other strongly in the second nearest neighboring (NN) sites while not in 1NN. A vacancy (v) bounds strongly with Y and O in 1NN site. The binding sequence of solutes with v followed O-v → Y-v → Si-v, and the affinity of Y, Si and v with O followed O-Y → O-v → O-Si. The nucleation mechanism of Y-O-Si nanoclusters was determined, which gave the feasibility of adding Si to ODS steels. The core (consisting of Si and O)-shell (enriched Fe and Cr) structure of the microparticles was found in ODS steels containing Si, fabricated by mechanical alloying (MA) and vacuum sintering. Moreover the nanoparticles of monoclinic cubic Y2O3, Y2SiO5 and Y2Si2O7 with sizes of 5~12 nm were observed in ODS steel. Si reduced the sintering temperature by maximizing densities and mechanical properties at a lower sintering temperature. The steel with 3 wt% Si was sintered at 1280 °C, exhibiting the best comprehensive mechanical properties. The tensile strength, hardness and relative density were 1025 MPa, 442.44 HV and 95.3%, respectively