The role of sulphur in the early production of copper red stained glass

Little is known about the production of ruby red copper stained glasses from the Medieval and Renaissance periods apart from the fact that the colour is due to the presence of small metallic copper nanoparticles and that tin, the most common reducing agent used in copper red glass production since the 19th century, is not present. In fact, very few workshops in Europe were able to make red glass in historical times, and they kept it secret, so very little is known about how it was obtained. These workshops exported the red glass throughout Europe. Recently, the presence of copper sulphide particles and the data obtained in the replication red glass following historical recipes suggested that sulphur might be the key ingredient in this process. Here, a collection of historical red glasses from these periods has been analysed using a combination of microanalytical techniques; Electron Microprobe (EM) and Field Emission and Scanning Electron Microscopy (FESEM) to verify the chemical composition and nanostructure of the glasses, Synchrotron radiation micro-X-Ray Diffraction (micro-XRD) to establish the nature of the nanocrystalline precipitates, and S, Cu and Fe K-edge micro-X-Ray Absorption Spectroscopy (micro-XAS) to determine the speciation. The data obtained show that the oxidation of S2- into S6+ in the glass is responsible for the precipitation of copper nanoparticles. The development of a sulphide-silicate partition and the presence of Fe3+ in the melt give rise to the precipitation of the high-pressure tetragonal polymorph of chalcocite (Cu2S). Differences between the Medieval and Renaissance red glass are determined.Peer ReviewedPostprint (published version

Jun ware glaze colours: An X-ray absorption spectroscopy study

Jun ware is stoneware created in the late Northern Song dynasty (12th century) with a blue glaze combining transparent-blue and whitish-opaque submillimetric areas. The glaze has a glass nanostructure with lime-rich droplets in a silica-rich matrix resulting from a high temperature liquid-liquid phase separation. Calcium-rich opaque and calcium-poor transparent areas are combined. Iron is more oxidised in the calcium rich areas (˜17–20% Fe2+) than in the calcium poor areas (˜60–70% Fe2+) of the glaze. Therefore, iron is oxidised in the lime-rich droplets and reduced in the silica-rich matrix. The sky-like appearance of the glaze is due to the combination of the light absorption in the transparent-dark-blue Fe2+ rich areas and scattering in the white-yellowish Fe3+ rich areas. Copper appears mainly oxidised but in the red areas a few small copper nanoparticles are present and iron appears more oxidised. The result indicates the simultaneous reduction of copper and oxidation of iron.Peer ReviewedPostprint (published version

Exploring the Design Space of Employing AI-Generated Content for Augmented Reality Display

As the two most common display content of Augmented Reality (AR), the creation process of image and text often requires a human to execute. However, due to the rapid advances in Artificial Intelligence (AI), today the media content can be automatically generated by software. The ever-improving quality of AI-generated content (AIGC) has opened up new scenarios employing such content, which is expected to be applied in AR. In this paper, we attempt to explore the design space for projecting AI-generated image and text into an AR display. Specifically, we perform an exploratory study and suggest a ``user-function-environment'' design thinking by building a preliminary prototype and conducting focus groups based on it. With the early insights presented, we point out the design space and potential applications for combining AIGC and AR

Exploring Adapter-based Transfer Learning for Recommender Systems: Empirical Studies and Practical Insights

Adapters, a plug-in neural network module with some tunable parameters, have emerged as a parameter-efficient transfer learning technique for adapting pre-trained models to downstream tasks, especially for natural language processing (NLP) and computer vision (CV) fields. Meanwhile, learning recommendation models directly from raw item modality features -- e.g., texts of NLP and images of CV -- can enable effective and transferable recommender systems (called TransRec). In view of this, a natural question arises: can adapter-based learning techniques achieve parameter-efficient TransRec with good performance? To this end, we perform empirical studies to address several key sub-questions. First, we ask whether the adapter-based TransRec performs comparably to TransRec based on standard full-parameter fine-tuning? does it hold for recommendation with different item modalities, e.g., textual RS and visual RS. If yes, we benchmark these existing adapters, which have been shown to be effective in NLP and CV tasks, in the item recommendation settings. Third, we carefully study several key factors for the adapter-based TransRec in terms of where and how to insert these adapters? Finally, we look at the effects of adapter-based TransRec by either scaling up its source training data or scaling down its target training data. Our paper provides key insights and practical guidance on unified & transferable recommendation -- a less studied recommendation scenario. We promise to release all code & datasets for future research

TransRec: Learning Transferable Recommendation from Mixture-of-Modality Feedback

Learning large-scale pre-trained models on broad-ranging data and then transfer to a wide range of target tasks has become the de facto paradigm in many machine learning (ML) communities. Such big models are not only strong performers in practice but also offer a promising way to break out of the task-specific modeling restrictions, thereby enabling task-agnostic and unified ML systems. However, such a popular paradigm is mainly unexplored by the recommender systems (RS) community. A critical issue is that standard recommendation models are primarily built on categorical identity features. That is, the users and the interacted items are represented by their unique IDs, which are generally not shareable across different systems or platforms. To pursue the transferable recommendations, we propose studying pre-trained RS models in a novel scenario where a user's interaction feedback involves a mixture-of-modality (MoM) items, e.g., text and images. We then present TransRec, a very simple modification made on the popular ID-based RS framework. TransRec learns directly from the raw features of the MoM items in an end-to-end training manner and thus enables effective transfer learning under various scenarios without relying on overlapped users or items. We empirically study the transferring ability of TransRec across four different real-world recommendation settings. Besides, we look at its effects by scaling source and target data size. Our results suggest that learning neural recommendation models from MoM feedback provides a promising way to realize universal RS

The work of Chinese chronic conditions: adaptation and validation of the Distribution of Co-Care Activities Scale

PurposeThe Distribution of Co-Care Activities Scale was adapted into Chinese for the purposes of this study, and then the psychometric characteristics of the Chinese version of the DoCCA scale were confirmed in chronic conditions.MethodsA total of 434 patients with chronic diseases were recruited from three Chinese cities. A cross-cultural adaptation procedure was used to translate the Distribution of Co-Care Activities Scale into Chinese. Cronbach's alpha coefficient, split-half reliability, and test-retest reliability were used to verify the scale's reliability. Content validity indices, exploratory factor analysis, and confirmatory factor analysis were used to confirm the scale's validity.ResultsThe Chinese DoCCA scale includes five domains: demands, unnecessary tasks, role clarity, needs support, and goal orientation. The S-CVI was 0.964. Exploratory factor analysis yielded a five-factor structure that explained 74.952% of the total variance. According to the confirmatory factor analysis results, the fit indices were within the range of the reference values. Convergent and discriminant validity both met the criteria. Also, the scale's Cronbach's alpha coefficient is 0.936, and the five dimensions' values range from 0.818 to 0.909. The split-half reliability was 0.848, and the test-retest reliability was 0.832.ConclusionsThe Chinese version of the Distribution of Co-Care Activities Scale had high levels of validity and reliability for chronic conditions. The scale can assess how patients with chronic diseases feel about their service of care and provide data to optimize their personalized chronic disease self-management strategies