Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?

Two key obstacles in biomedical relation extraction (RE) are the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels due to low annotation coverage. Existing approaches, which treat biomedical RE as a multi-class classification task, often result in poor generalization in low-resource settings and do not have the ability to make selective prediction on unknown cases but give a guess from seen relations, hindering the applicability of those approaches. We present NBR, which converts biomedical RE as natural language inference formulation through indirect supervision. By converting relations to natural language hypotheses, NBR is capable of exploiting semantic cues to alleviate annotation scarcity. By incorporating a ranking-based loss that implicitly calibrates abstinent instances, NBR learns a clearer decision boundary and is instructed to abstain on uncertain instances. Extensive experiments on three widely-used biomedical RE benchmarks, namely ChemProt, DDI and GAD, verify the effectiveness of NBR in both full-set and low-resource regimes. Our analysis demonstrates that indirect supervision benefits biomedical RE even when a domain gap exists, and combining NLI knowledge with biomedical knowledge leads to the best performance gains.Comment: 16 pages; ACL 2023; code in https://github.com/luka-group/NLI_as_Indirect_Supervisio

Freshness or Accuracy, Why Not Both? Addressing Delayed Feedback via Dynamic Graph Neural Networks

The delayed feedback problem is one of the most pressing challenges in predicting the conversion rate since users' conversions are always delayed in online commercial systems. Although new data are beneficial for continuous training, without complete feedback information, i.e., conversion labels, training algorithms may suffer from overwhelming fake negatives. Existing methods tend to use multitask learning or design data pipelines to solve the delayed feedback problem. However, these methods have a trade-off between data freshness and label accuracy. In this paper, we propose Delayed Feedback Modeling by Dynamic Graph Neural Network (DGDFEM). It includes three stages, i.e., preparing a data pipeline, building a dynamic graph, and training a CVR prediction model. In the model training, we propose a novel graph convolutional method named HLGCN, which leverages both high-pass and low-pass filters to deal with conversion and non-conversion relationships. The proposed method achieves both data freshness and label accuracy. We conduct extensive experiments on three industry datasets, which validate the consistent superiority of our method

Decentralized Graph Neural Network for Privacy-Preserving Recommendation

Building a graph neural network (GNN)-based recommender system without violating user privacy proves challenging. Existing methods can be divided into federated GNNs and decentralized GNNs. But both methods have undesirable effects, i.e., low communication efficiency and privacy leakage. This paper proposes DGREC, a novel decentralized GNN for privacy-preserving recommendations, where users can choose to publicize their interactions. It includes three stages, i.e., graph construction, local gradient calculation, and global gradient passing. The first stage builds a local inner-item hypergraph for each user and a global inter-user graph. The second stage models user preference and calculates gradients on each local device. The third stage designs a local differential privacy mechanism named secure gradient-sharing, which proves strong privacy-preserving of users' private data. We conduct extensive experiments on three public datasets to validate the consistent superiority of our framework

Sudowoodo: a Chinese Lyric Imitation System with Source Lyrics

Lyrics generation is a well-known application in natural language generation research, with several previous studies focusing on generating accurate lyrics using precise control such as keywords, rhymes, etc. However, lyrics imitation, which involves writing new lyrics by imitating the style and content of the source lyrics, remains a challenging task due to the lack of a parallel corpus. In this paper, we introduce \textbf{\textit{Sudowoodo}}, a Chinese lyrics imitation system that can generate new lyrics based on the text of source lyrics. To address the issue of lacking a parallel training corpus for lyrics imitation, we propose a novel framework to construct a parallel corpus based on a keyword-based lyrics model from source lyrics. Then the pairs \textit{(new lyrics, source lyrics)} are used to train the lyrics imitation model. During the inference process, we utilize a post-processing module to filter and rank the generated lyrics, selecting the highest-quality ones. We incorporated audio information and aligned the lyrics with the audio to form the songs as a bonus. The human evaluation results show that our framework can perform better lyric imitation. Meanwhile, the \textit{Sudowoodo} system and demo video of the system is available at \href{https://Sudowoodo.apps-hp.danlu.netease.com/}{Sudowoodo} and \href{https://youtu.be/u5BBT_j1L5M}{https://youtu.be/u5BBT\_j1L5M}.Comment: 7 pages,3 figures, submit to emnlp 2023 demo trac

Seismic damage analysis due to near-fault multipulse ground motion

Near-fault pulse-like ground motion is a significant class of seismic records since it tends to cause more severe damage to structures than ordinary ground motions. However, previous researches mainly focus on single-pulse ground motions. The multipulse ground motions that exist in records receive rare attention. In this study, an analysis procedure is proposed to investigate the effect of multipulse ground motions on structures by integrating finite element analysis and an identification method that features each pulse in the multipulse ground motion satisfying the same evaluation criteria. First, the Arias intensity, wavelet-based cumulative energy distribution, and response spectra of identified non-, single-, and multipulse ground motions are compared. Then, the seismic damage on frame structures, a soil slope, and a concrete dam under non-, single-, and multipulse ground motions are analyzed. Results show that the spectral velocity of multipulse ground motions is significantly greater than those of non- and single-pulse ground motions and potentially contains multiple peaks in the long-period range. Seismic damage evaluation indicates that the maximum interstory drift of frame structures with high fundamental periods under multipulse ground motions is about twice that of nonpulse ground motions. Similar characteristics also exist in the soil slope and the concrete dam. Therefore, multipulse ground motions potentially cause more severe damage to structures compared to non- and single-pulse ground motions. The findings of this study facilitate the recognition of the increased seismic demand imposed by the multipulse ground motion in engineering practices, provide new possibilities for ground motion selection in seismic design validation, and shed new light on seismic hazard and risk analysis in near-fault regions

Multiple Myeloma, Misdiagnosed As Somatic Symptom Disorder: A Case Report

Here we report on a case of a 57-year-old woman with pain and discomfort in multiple sites of upper body who was diagnosed as somatic symptom disorder after completing a partial examinations of relevant parts which turned out to be negative. Finished imageological examinations of all painful parts, she was eventually diagnosed with multiple myeloma after 6-month being misdiagnosed as somatic symptom disorder. This case highlights the importance of completing imageological examinations of all the painful parts of the patient to exclude the possibility of multiple myeloma especially when symptoms are associated with objective signs and treatment has been ineffective; and it is as well as significant to notice characteristics of symptoms and to pay excessive attention directed toward the symptoms in the diagnosis of somatic symptom disorder

Evidence of Indium impurity band in superconducting (Sn,In)Te thin films

Sn1-xInxTe has been synthesized and studied recently as a candidate topological superconductor. Its superconducting critical temperature increases with Indium concentration. However, the role of Indium in altering the normal state band structure and generating superconductivity is not well-understood. Here, we explore this question in Sn1-xInxTe (0<x<0.3) thin films, characterized by magneto-transport, infrared transmission and photoemission spectroscopy measurement. We show that Indium is forming an impurity band below the valence band edge which pins the Fermi energy and effectively generates electron doping. An enhanced density-of-states due to this impurity band leads to the enhancement of superconducting transition temperature measured in multiple previous studies. The existence of the In impurity band and the role of In as a resonant impurity should be more carefully considered when discussing the topological nature of Sn1-xInxTe

An energy‐frequency parameter for earthquake ground motion intensity measure

A novel scalar ground motion intensity measure (IM), termed the energy-frequency parameter, is proposed based on the Hilbert-Huang transform. To validate the effectiveness of the proposed IM, the correlation analysis between the engineering demand parameter (EDP) and energy-frequency parameter is performed using 1992 recorded ground motions, in which EDP is the maximum inter-storey drift of structures obtained by nonlinear time-history analysis. Results show that the energy-frequency parameter has a strong linear correlation with EDP at natural logarithm, and this correlation is applicable for various structural fundamental periods. We also verified that the lognormal cumulative distribution function can characterize the energy-frequency parameter-based fragility function, which can further facilitate the application of the parameter in seismic risk analysis. Besides, the strong correlation between the energy-frequency parameter and other IMs (such as PGA, PGV, PGD, CAV, (Formula presented.), (Formula presented.), and SI) potentially makes the proposed IM widely applicable in seismic risk analysis. Moreover, since the energy-frequency parameter depends only on the frequency-domain characteristics of the ground-motion signal, it may closely link to seismological theory and provide new insights into seismology engineering