Explore Ways to Promote the Popularization of Rural Culture Revitalization

The history of language and the history of culture complement each other. They can help and inspire each other. Language and culture are closely related. As a part of culture, language is not only a cultural phenomenon, but also a carrier of culture. Language and cultural resources are not only the elements of building a harmonious ecology of language, but also the path and entry point for language to help rural revitalization in ethnic minority areas. Rural revitalization cannot be achieved without cultural revitalization, and cultural revitalization cannot be achieved without language. Popularizing work has played a very important role in promoting rural revitalization

Investigation and Exploration of ‘Student-Centered and Teacher-Led’ Teaching Model in English Medium Instruction (EMI) Calculus Course

The internationalization of higher education in China is constantly improving with an increasing level of diversification and globalization of education. High-level international English Medium Instruction (EMI) course is crucial to the cultivation of innovative international talents. Taking the Calculus course as an example, this article first demonstrates the importance and connotation of ‘know thy enemy and know yourself’ in the construction of EMI courses. Then it elaborates on the construction methods and significance of the ‘Leaning Community’, ‘Teaching Community’, and ‘Teaching-Learning Community’ through studies of the relationship between ‘teaching’ and ‘learning’ form the student-centered aspect. Such research provides a useful reference for the teaching model reform, especially the effective construction of EMI courses in non-native English-speaking countries

Efficient Inference on High-Dimensional Linear Models with Missing Outcomes

This paper is concerned with inference on the regression function of a high-dimensional linear model when outcomes are missing at random. We propose an estimator which combines a Lasso pilot estimate of the regression function with a bias correction term based on the weighted residuals of the Lasso regression. The weights depend on estimates of the missingness probabilities (propensity scores) and solve a convex optimization program that trades off bias and variance optimally. Provided that the propensity scores can be pointwise consistently estimated at in-sample data points, our proposed estimator for the regression function is asymptotically normal and semi-parametrically efficient among all asymptotically linear estimators. Furthermore, the proposed estimator keeps its asymptotic properties even if the propensity scores are estimated by modern machine learning techniques. We validate the finite-sample performance of the proposed estimator through comparative simulation studies and the real-world problem of inferring the stellar masses of galaxies in the Sloan Digital Sky Survey.Comment: Substantial revision with some corrected proofs and added experiments. The updated version has 129 pages (32 pages for the main paper), 9 figures, 9 table

SCONCE: A cosmic web finder for spherical and conic geometries

The latticework structure known as the cosmic web provides a valuable insight into the assembly history of large-scale structures. Despite the variety of methods to identify the cosmic web structures, they mostly rely on the assumption that galaxies are embedded in a Euclidean geometric space. Here we present a novel cosmic web identifier called SCONCE (Spherical and CONic Cosmic wEb finder) that inherently considers the 2D (RA,DEC) spherical or the 3D (RA,DEC,$z$) conic geometry. The proposed algorithms in SCONCE generalize the well-known subspace constrained mean shift (SCMS) method and primarily address the predominant filament detection problem. They are intrinsic to the spherical/conic geometry and invariant to data rotations. We further test the efficacy of our method with an artificial cross-shaped filament example and apply it to the SDSS galaxy catalogue, revealing that the 2D spherical version of our algorithms is robust even in regions of high declination. Finally, using N-body simulations from Illustris, we show that the 3D conic version of our algorithms is more robust in detecting filaments than the standard SCMS method under the redshift distortions caused by the peculiar velocities of halos. Our cosmic web finder is packaged in python as SCONCE-SCMS and has been made publicly available.Comment: 20 pages, 9 figures, 2 table

Preparation of Material for Adsorption Ag(I) in the Solution

The application of silver in electronics, jewelry, catalytic and other industries often produces a large amount of silver-containing wastewater, which causes serious impact to the surrounding environment and human health, while silver has a certain economic value attached to it. Therefore, how to effectively treat and recover Ag(?) from the silver-containing wastewater is a hot topic of concern at present. In order to seek an efficient and environmentally friendly adsorbent, this paper compared the adsorption efficiency of purified, thermally modified, acid modified and thermally-acid modified Bentonite on silver, selected an economical and reasonable purified clay as a carrier, and then completed the preparation of modified Bentonite as well as the optimization of conditions with sodium silicate as a surfactant and 3-mercaptopropyltrimethoxysilane as a modifier. The experiments showed that under the conditions of sodium silicate dosage of 15% of Bentonite, Bentonite and modifier dosage of 1:1, solution pH of 9, temperature of 45 °C and modification time of 5 h, the synthesized sulfhydryl modified Bentonite has good adsorption performance on Ag(?), and its adsorption capacity can reach 293.7 mg·g-1

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