Tungsten Oxide-Based Z-Scheme for Visible Light-Driven Hydrogen Production from Water Splitting

The stoichiometric water splitting using a solar-driven Z-scheme approach is an emerging field of interest to address the increasing renewable energy demand and environmental concerns. So far, the reported Z-scheme must comprise two populations of photocatalysts. In the present work, only tungsten oxides are used to construct a robust Z-scheme system for complete visible-driven water splitting in both neutral and alkaline solutions, where sodium tungsten oxide bronze (Na0.56WO3–x) is used as a H2 evolution photocatalyst and two-dimensional (2D) tungsten trioxide (WO3) nanosheets as an O2 evolution photocatalyst. This system efficiently produces H2 (14 μmol h–1) and O2 (6.9 μmol h–1) at an ideal molar ratio of 2:1 in an aqueous solution driven by light, resulting in a remarkably high apparent quantum yield of 6.06% at 420 nm under neutral conditions. This exceptional selective H2 and O2 production is due to the preferential adsorption of iodide (I–) on Na0.56WO3–x and iodate (IO3–) on WO3, which is evidenced by both experiments and density functional theory calculation. The present liquid Z-scheme in the presence of efficient shuttle molecules promises a separated H2 and O2 evolution by applying a dual-bed particle suspension system, thus a safe photochemical process

RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

Constructing high-quality character image datasets is challenging because real-world images are often affected by image degradation. There are limitations when applying current image restoration methods to such real-world character images, since (i) the categories of noise in character images are different from those in general images; (ii) real-world character images usually contain more complex image degradation, e.g., mixed noise at different noise levels. To address these problems, we propose a real-world character restoration network (RCRN) to effectively restore degraded character images, where character skeleton information and scale-ensemble feature extraction are utilized to obtain better restoration performance. The proposed method consists of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet aims to preserve the structural consistency of the character and normalize complex noise. Then, CiRNet reconstructs clean images from degraded character images and their skeletons. Due to the lack of benchmarks for real-world character image restoration, we constructed a dataset containing 1,606 character images with real-world degradation to evaluate the validity of the proposed method. The experimental results demonstrate that RCRN outperforms state-of-the-art methods quantitatively and qualitatively.Comment: Accepted to ACM MM 202

Rethinking the Evaluation for Conversational Recommendation in the Era of Large Language Models

The recent success of large language models (LLMs) has shown great potential to develop more powerful conversational recommender systems (CRSs), which rely on natural language conversations to satisfy user needs. In this paper, we embark on an investigation into the utilization of ChatGPT for conversational recommendation, revealing the inadequacy of the existing evaluation protocol. It might over-emphasize the matching with the ground-truth items or utterances generated by human annotators, while neglecting the interactive nature of being a capable CRS. To overcome the limitation, we further propose an interactive Evaluation approach based on LLMs named iEvaLM that harnesses LLM-based user simulators. Our evaluation approach can simulate various interaction scenarios between users and systems. Through the experiments on two publicly available CRS datasets, we demonstrate notable improvements compared to the prevailing evaluation protocol. Furthermore, we emphasize the evaluation of explainability, and ChatGPT showcases persuasive explanation generation for its recommendations. Our study contributes to a deeper comprehension of the untapped potential of LLMs for CRSs and provides a more flexible and easy-to-use evaluation framework for future research endeavors. The codes and data are publicly available at https://github.com/RUCAIBox/iEvaLM-CRS.Comment: Accepted by EMNLP 202

CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising

Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results. Existing methods have dedicated efforts to restoring degraded character images. However, the denoising results obtained by these methods do not appear to improve character recognition performance. This is mainly because current methods only focus on pixel-level information and ignore critical features of a character, such as its glyph, resulting in character-glyph damage during the denoising process. In this paper, we introduce a novel generic framework based on glyph fusion and attention mechanisms, i.e., CharFormer, for precisely recovering character images without changing their inherent glyphs. Unlike existing frameworks, CharFormer introduces a parallel target task for capturing additional information and injecting it into the image denoising backbone, which will maintain the consistency of character glyphs during character image denoising. Moreover, we utilize attention-based networks for global-local feature interaction, which will help to deal with blind denoising and enhance denoising performance. We compare CharFormer with state-of-the-art methods on multiple datasets. The experimental results show the superiority of CharFormer quantitatively and qualitatively.Comment: Accepted by ACM MM 202

Improving Conversational Recommendation Systems via Counterfactual Data Simulation

Conversational recommender systems (CRSs) aim to provide recommendation services via natural language conversations. Although a number of approaches have been proposed for developing capable CRSs, they typically rely on sufficient training data for training. Since it is difficult to annotate recommendation-oriented dialogue datasets, existing CRS approaches often suffer from the issue of insufficient training due to the scarcity of training data. To address this issue, in this paper, we propose a CounterFactual data simulation approach for CRS, named CFCRS, to alleviate the issue of data scarcity in CRSs. Our approach is developed based on the framework of counterfactual data augmentation, which gradually incorporates the rewriting to the user preference from a real dialogue without interfering with the entire conversation flow. To develop our approach, we characterize user preference and organize the conversation flow by the entities involved in the dialogue, and design a multi-stage recommendation dialogue simulator based on a conversation flow language model. Under the guidance of the learned user preference and dialogue schema, the flow language model can produce reasonable, coherent conversation flows, which can be further realized into complete dialogues. Based on the simulator, we perform the intervention at the representations of the interacted entities of target users, and design an adversarial training method with a curriculum schedule that can gradually optimize the data augmentation strategy. Extensive experiments show that our approach can consistently boost the performance of several competitive CRSs, and outperform other data augmentation methods, especially when the training data is limited. Our code is publicly available at https://github.com/RUCAIBox/CFCRS.Comment: Accepted by KDD 2023. Code: https://github.com/RUCAIBox/CFCR

Extended Nested Dual System Groups, Revisited

The notion of extended nested dual system groups (ENDSG) was recently proposed by Hofheinz et al. [PKC 2015] for constructing almost-tight identity based encryptions (IBE) in the multi-instance, multi-ciphertext (MIMC) setting. However only a composite-order instantiation was proposed and more efficient prime-order instantiations are absent. The paper fills the blank by presenting two constructions. We revise the definition of ENDSG and realize it using prime-order bilinear groups based on Chen and Wee\u27s prime-order instantiation of nested dual system groups [CRYPTO 2013]. This yields the first almost-tight IBE in the prime-order setting achieving weak adaptive security in MIMC scenario under the $d$-linear ($d$-Lin) assumption. We further enhanced the revised ENDSG to capture stronger security notions for IBE, including $B$-weak adaptive security and full adaptive security. We show that our prime-order instantiation is readily $B$-weak adaptive secure and full adaptive secure without introducing extra assumption. We then try to find better solution by fine-tuning ENDSG again and realizing it using the technique of Chen, Gay, and Wee [EUROCRYPT 2015]. This leads to an almost-tight secure IBE in the same setting with better performance than our first result, but the security relies on a non-standard assumption, $d$-linear assumption with auxiliary input ($d$-LinAI) for an even positive integer $d$. However we note that, the $2$-LinAI assumption is implied by the external decisional linear (XDLIN) assumption. This concrete instantiation could also be realized using symmetric bilinear groups under standard decisional linear assumption

Study of power flow algorithm of AC/DC distribution system including VSC-MTDC

In recent years, distributed generation and a large number of sensitive AC and DC loads have been connected to distribution networks, which introduce a series of challenges to distribution network operators (DNOs). In addition, the advantages of DC distribution networks, such as the energy conservation and emission reduction, mean that the voltage source converter based multi-terminal direct current (VSC-MTDC) for AC/DC distribution systems demonstrates a great potential, hence drawing growing research interest. In this paper, considering losses of the reactor, the filter and the converter, a mathematical model of VSC-HVDC for the load flow analysis is derived. An AC/DC distribution network architecture has been built, based on which the differences in modified equations of the VSC-MTDC-based network under different control modes are analyzed. In addition, corresponding interface functions under five control modes are provided, and a back/forward iterative algorithm which is applied to power flow calculation of the AC/DC distribution system including VSC-MTDC is proposed. Finally, by calculating the power flow of the modified IEEE14 AC/DC distribution network, the efficiency and validity of the model and algorithm are evaluated. With various distributed generations connected to the network at appropriate locations, power flow results show that network losses and utilization of transmission networks are effectively reduced

Tungsten oxide-based Z-scheme for visible light-driven hydrogen production from water splitting

The stoichiometric water splitting using a solar-driven Z-scheme approach is an emerging field of interest to address the increasing renewable energy demand and environmental concerns. So far, the reported Z-scheme must comprise two populations of photocatalysts. In the present work, only tungsten oxides are used to construct a robust Z-scheme system for complete visible-driven water splitting in both neutral and alkaline solutions, where sodium tungsten oxide bronze (Na0.56WO3–x) is used as a H2 evolution photocatalyst and two-dimensional (2D) tungsten trioxide (WO3) nanosheets as an O2 evolution photocatalyst. This system efficiently produces H2 (14 μmol h–1) and O2 (6.9 μmol h–1) at an ideal molar ratio of 2:1 in an aqueous solution driven by light, resulting in a remarkably high apparent quantum yield of 6.06% at 420 nm under neutral conditions. This exceptional selective H2 and O2 production is due to the preferential adsorption of iodide (I–) on Na0.56WO3–x and iodate (IO3–) on WO3, which is evidenced by both experiments and density functional theory calculation. The present liquid Z-scheme in the presence of efficient shuttle molecules promises a separated H2 and O2 evolution by applying a dual-bed particle suspension system, thus a safe photochemical process