Stabilizing the Oxygen Lattice and Reversible Oxygen Redox Chemistry through Structural Dimensionality in Lithium-Rich Cathode Oxides.

Lattice-oxygen redox (l-OR) has become an essential companion to the traditional transition-metal (TM) redox charge compensation to achieve high capacity in Li-rich cathode oxides. However, the understanding of l-OR chemistry remains elusive, and a critical question is the structural effect on the stability of l-OR reactions. Herein, the coupling between l-OR and structure dimensionality is studied. We reveal that the evolution of the oxygen-lattice structure upon l-OR in Li-rich TM oxides which have a three-dimensional (3D)-disordered cation framework is relatively stable, which is in direct contrast to the clearly distorted oxygen-lattice framework in Li-rich oxides which have a two-dimensional (2D)/3D-ordered cation structure. Our results highlight the role of structure dimensionality in stabilizing the oxygen lattice in reversible l-OR, which broadens the horizon for designing high-energy-density Li-rich cathode oxides with stable l-OR chemistry

Intravitreal Melphalan for Vitreous Seeds: Initial Experience in China

Purpose. To evaluate the efficacy of intravitreal melphalan for vitreous seeds from retinoblastoma in Chinese patients. Methods. This is a retrospective review of 17 consecutive Chinese patients (19 eyes) with viable vitreous seeds from retinoblastoma. The patients received multiple intravitreal injections of 20 ug melphalan. Results. The International Classification of Retinoblastoma groups were B in 1 eye, C in 5 eyes, D in 11 eyes, and E in 2 eyes. On average, 6 injections (range: 1–15) were given to each eye at the interval of 2–4 weeks. Successful control of vitreous seeds was achieved in 16 of 19 eyes (84.21%). Globe retention was achieved in 73.68% (14/19) eyes. The patients were followed up for 27 months on average (median: 26; range: 17–42 months). There is a significant difference in response to intravitreal melphalan for cloud, spheres, and dust seeds with a median number of injections of 9, 6, and 3, respectively (P=0.003). Complications related to intravitreal melphalan included vitreous hemorrhage, cataract, salt-and-pepper retinopathy, and pupil posterior synechia. There was no case of epibulbar extension or systemic metastasis within the period of follow-up. Conclusion. Intravitreal melphalan achieved a high local control rate for vitreous seeds without extraocular extension and with acceptable toxicity in Chinese retinoblastoma patients

PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature

Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever. However, there are no existing SciIE datasets for polymer materials, which is an important class of materials used ubiquitously in our daily lives. To bridge this gap, we introduce POLYIE, a new SciIE dataset for polymer materials. POLYIE is curated from 146 full-length polymer scholarly articles, which are annotated with different named entities (i.e., materials, properties, values, conditions) as well as their N-ary relations by domain experts. POLYIE presents several unique challenges due to diverse lexical formats of entities, ambiguity between entities, and variable-length relations. We evaluate state-of-the-art named entity extraction and relation extraction models on POLYIE, analyze their strengths and weaknesses, and highlight some difficult cases for these models. To the best of our knowledge, POLYIE is the first SciIE benchmark for polymer materials, and we hope it will lead to more research efforts from the community on this challenging task. Our code and data are available on: https://github.com/jerry3027/PolyIE.Comment: Work in progres

Evaluation and Analysis of Hallucination in Large Vision-Language Models

Large Vision-Language Models (LVLMs) have recently achieved remarkable success. However, LVLMs are still plagued by the hallucination problem, which limits the practicality in many scenarios. Hallucination refers to the information of LVLMs' responses that does not exist in the visual input, which poses potential risks of substantial consequences. There has been limited work studying hallucination evaluation in LVLMs. In this paper, we propose Hallucination Evaluation based on Large Language Models (HaELM), an LLM-based hallucination evaluation framework. HaELM achieves an approximate 95% performance comparable to ChatGPT and has additional advantages including low cost, reproducibility, privacy preservation and local deployment. Leveraging the HaELM, we evaluate the hallucination in current LVLMs. Furthermore, we analyze the factors contributing to hallucination in LVLMs and offer helpful suggestions to mitigate the hallucination problem. Our training data and human annotation hallucination data will be made public soon.Comment: 11 pages, 5 figure