Event-Centric Question Answering via Contrastive Learning and Invertible Event Transformation

Human reading comprehension often requires reasoning of event semantic relations in narratives, represented by Event-centric Question-Answering (QA). To address event-centric QA, we propose a novel QA model with contrastive learning and invertible event transformation, call TranCLR. Our proposed model utilizes an invertible transformation matrix to project semantic vectors of events into a common event embedding space, trained with contrastive learning, and thus naturally inject event semantic knowledge into mainstream QA pipelines. The transformation matrix is fine-tuned with the annotated event relation types between events that occurred in questions and those in answers, using event-aware question vectors. Experimental results on the Event Semantic Relation Reasoning (ESTER) dataset show significant improvements in both generative and extractive settings compared to the existing strong baselines, achieving over 8.4% gain in the token-level F1 score and 3.0% gain in Exact Match (EM) score under the multi-answer setting. Qualitative analysis reveals the high quality of the generated answers by TranCLR, demonstrating the feasibility of injecting event knowledge into QA model learning. Our code and models can be found at https://github.com/LuJunru/TranCLR.Comment: Findings of EMNLP 202

Root Iron Plaque: A natural Barrier and Potential Risk for Rice against Heavy Metal Pollution

Root iron plaque is a colloidal film of iron-manganese oxides formed on the root surface of rice. Its formation process involves oxygen released by rice roots oxidizing Fe²⁺ to Fe³⁺, which then combines with elements such as manganese to form precipitates that ultimately cover the surfaces of root tips and root hairs. With a large specific surface area and abundant functional groups, iron plaque can regulate the migration and accumulation of heavy metals through processes like adsorption and coprecipitation, serving as a "natural barrier" against heavy metal pollution. This paper systematically elaborates on the external resistance and internal tolerance mechanisms of plants to heavy metals mediated by iron plaque, and analyzes the effects of factors such as water management, fertilization methods, root aeration capacity, and soil microorganisms on the formation of iron plaque as well as the migration and accumulation of heavy metals. In addition, this paper also summarizes the potential risks of iron plaque in practical applications and prospects future research directions

Is Renminbi a (Truly) International Currency? An Evaluation Based on Offshore Foreign Exchange Market Trading Patterns

This article provides a new framework to evaluate the status of Renminbi internationalization. It proposes that the trading patterns of a currency in global foreign exchange market embody the currency’s position in the international monetary system. Based on foreign exchange trading data provided by CLS Group, the article constructs a ranking of major international currencies including Renminbi. It finds that Renminbi shares more similarities in foreign exchange trading patterns with the established global currencies like US dollar and Euro than with those regional currencies. The article also explores the policy implications that the new evaluation approach provides

CHIME : Cross-passage hierarchical memory network for generative review question answering

We introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory collecting cross-passage evidences, and the answer memory working as a buffer continually refining the generated answers. Empirically, we show the efficacy of the proposed architecture in the multi-passage generative QA, outperforming the state-of-the-art baselines with better syntactically well-formed answers and increased precision in addressing the questions of the AmazonQA review dataset. An additional qualitative analysis revealed the interpretability introduced by the memory module

BCS-BEC crossover in atomic Fermi gases in quasi-two-dimensional Lieb lattices: Effects of flat band and finite temperature

We investigate the finite-temperature superfluid behavior of ultracold atomic Fermi gases in quasi-two-dimensional Lieb lattices with a short-range attractive interaction, using a pairing fluctuation theory within the BCS-BEC crossover framework. We find that the presence of a flat band, along with van Hove singularities, leads to exotic quantum phenomena. As the Fermi level enters the flat band, both the gap and the superfluid transition temperature $T_c$ as a function of interaction change from a conventional exponential behavior into an unusual power law, and the evolution of superfluid densities with temperature also follows a power law even at weak interactions. The quantum geometric effects, manifested by an enhanced effective pair hopping integral, may contribute significantly to both $T_c$ and the superfluidities. As the chemical potential crosses the van Hove singularities in the weak interaction regime, the nature of pairing changes between particle-like and hole-like. A pair density wave state emerges at high densities with a relatively strong interaction strength.Comment: 10 pages, 6 figures. arXiv admin note: text overlap with arXiv:2310.1294

Higher critical closing pressure is independently associated with enlarged basal ganglia perivascular spaces

ObjectiveThis study aimed to explore the association between cerebral hemodynamic parameters focused on the critical closing pressure (CCP) and enlarged perivascular spaces (EPVS).MethodsCerebral blood velocity in the middle cerebral artery (MCAv) and non-invasive continuous blood pressure (NIBP) were measured using a transcranial Doppler (TCD) and Finometer, followed by the calculation of cerebral hemodynamic parameters including CCP, resistance area product (RAP), pulsatility index (PI), and pulse pressure (PP). EPVS were graded separately in the basal ganglia (BG) and centrum semiovale (CSO), using a visual semiquantitative ordinal scale. Patients with EPVS >10 were classified into the severe BG-EPVS group and severe CSO-EPVS group, and the remainder into the mild BG-EPVS group and the mild CSO-EPVS group. Spearman’s correlation and binary logistic regression analysis were performed to analyze the relationship between hemodynamic parameters and BG-EPVS and CSO-EPVS, respectively.ResultsOverall, 107 patients were enrolled. The severe BG-EPVS group had higher CCP, mean arterial blood pressure (MABP), systolic blood pressure (SBP), and diastolic blood pressure (DBP) than that in the mild BG-EPVS group (p < 0.05). There was no statistical difference in hemodynamic parameters between the severe CSO-EPVS group and the mild CSO-EPVS group. Spearman’s correlation analysis showed that CCP was positively associated with BG-EPVS (rho = 0.331, p < 0.001) and CSO-EPVS (rho = 0.154, p = 0.044). The binary logistic regression analysis showed that CCP was independently associated with severe BG-EPVS (p < 0.05) and not with CSO-EPVS (p > 0.05) after adjusting for confounders.ConclusionCCP representing cerebrovascular tension was independently associated with BG-EPVS

MemoChat: Tuning LLMs to Use Memos for Consistent Long-Range Open-Domain Conversation

We propose MemoChat, a pipeline for refining instructions that enables large language models (LLMs) to effectively employ self-composed memos for maintaining consistent long-range open-domain conversations. We demonstrate a long-range open-domain conversation through iterative "memorization-retrieval-response" cycles. This requires us to carefully design tailored tuning instructions for each distinct stage. The instructions are reconstructed from a collection of public datasets to teach the LLMs to memorize and retrieve past dialogues with structured memos, leading to enhanced consistency when participating in future conversations. We invite experts to manually annotate a test set designed to evaluate the consistency of long-range conversations questions. Experiments on three testing scenarios involving both open-source and API-accessible chatbots at scale verify the efficacy of MemoChat, which outperforms strong baselines.Comment: Codes, data and models will be available soo