Ceria–terbia solid solution nanobelts with high catalytic activities for CO oxidation

Ceria–terbia solid solution nanobelts were prepared by an electrochemical route and tested as catalysts of high activity for CO oxidation

Withaferin A promotes proliferation and migration of brain endothelial cells

Purpose: To investigate the effect of withaferin A (WFA) on the proliferation and migration of brain endothelial cells.Methods: BALB-5023 mouse microvascular cells were treated with a range of withaferin A (WFA) concentrations from 10 to 100 ng/mL. Dojindo’s CCK-8 cell proliferation kit was used for the analysis of cell proliferation. Transwell cell culture inserts were used to determine the migration potential of WFAtreated endothelial cells. Absorbance was measured at 450 nm on an enzyme-linked immunosorbent(ELISA) reader.Results: The results revealed a significant increase in the proliferation and migration of endothelial cells following treatment with a low concentration (30 ng/mL) of WFA compared with the higher concentration (> 10 ng/mL). The effect was further  enhanced when WFA was used in combination with soluble Fas ligand (sFasL). Autocrine signaling of vascular endothelial growth factor (VEGF) by endothelial cellswas significantly increased following treatment with WFA or in combination with  sFasL. WFA increased the expression of Fas on endothelial cells, suggesting the involvement of sFasL in the proliferation and migration of brain endothelial cells.Conclusion: Thus, WFA promotes the proliferation and migration of endothelial cells through increase in the expression of Fas and secretion of VEGF.Keywords: Endothelial cells, Vascular endothelial growth factor, Microvascular, Vascular disease, Withaferin

Hidden Euclidean dynamical symmetry in the U(n+1) vibron model

Based on the boson realization of the Euclidean algebras, it is found that the E($n$) dynamical symmetry (DS) may emerge at the critical point of the U($n$)-SO($n+1$) quantum phase transition. To justify this finding, we provide a detailed analysis of the critical dynamics in the U($n+1$) vibron model in both quantal and classical ways. It is further shown that the low-lying structure of $^{82}$Kr may serve as an excellent empirical realization of the E(5) DS in experiments

ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation

Despite remarkable advances that large language models have achieved in chatbots, maintaining a non-toxic user-AI interactive environment has become increasingly critical nowadays. However, previous efforts in toxicity detection have been mostly based on benchmarks derived from social media content, leaving the unique challenges inherent to real-world user-AI interactions insufficiently explored. In this work, we introduce ToxicChat, a novel benchmark based on real user queries from an open-source chatbot. This benchmark contains the rich, nuanced phenomena that can be tricky for current toxicity detection models to identify, revealing a significant domain difference compared to social media content. Our systematic evaluation of models trained on existing toxicity datasets has shown their shortcomings when applied to this unique domain of ToxicChat. Our work illuminates the potentially overlooked challenges of toxicity detection in real-world user-AI conversations. In the future, ToxicChat can be a valuable resource to drive further advancements toward building a safe and healthy environment for user-AI interactions

Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement

With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specific hidden states. Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically. Specifically, the algorithm learns the probability of a part (individual layer representations) assigned to a whole (aggregated representations) in an iterative way and combines parts accordingly. We implement our algorithm on top of the state-of-the-art neural machine translation model TRANSFORMER and conduct experiments on the widely-used WMT14 English-German and WMT17 Chinese-English translation datasets. Experimental results across language pairs show that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.Comment: AAAI 201

Eliminating Reasoning via Inferring with Planning: A New Framework to Guide LLMs' Non-linear Thinking

Chain-of-Thought(CoT) prompting and its variants explore equipping large language models (LLMs) with high-level reasoning abilities by emulating human-like linear cognition and logic. However, the human mind is complicated and mixed with both linear and nonlinear thinking. In this work, we propose \textbf{I}nferential \textbf{E}xclusion \textbf{P}rompting (IEP), a novel prompting that combines the principles of elimination and inference in order to guide LLMs to think non-linearly. IEP guides LLMs to plan and then utilize Natural Language Inference (NLI) to deduce each possible solution's entailment relation with context, commonsense, or facts, therefore yielding a broader perspective by thinking back for inferring. This forward planning and backward eliminating process allows IEP to better simulate the complex human thinking processes compared to other CoT-based methods, which only reflect linear cognitive processes. We conducted a series of empirical studies and have corroborated that IEP consistently outperforms CoT across various tasks. Additionally, we observe that integrating IEP and CoT further improves the LLMs' performance on certain tasks, highlighting the necessity of equipping LLMs with mixed logic processes. Moreover, to better evaluate comprehensive features inherent in human logic, we introduce \textbf{M}ental-\textbf{A}bility \textbf{R}easoning \textbf{B}enchmark (MARB). The benchmark comprises six novel subtasks with a total of 9,115 questions, among which 1,685 are developed with hand-crafted rationale references. We believe both \textsc{IEP} and \textsc{MARB} can serve as a promising direction for unveiling LLMs' logic and verbal reasoning abilities and drive further advancements. \textsc{MARB} will be available at ~\texttt{anonymity link} soon