Eliciting Knowledge from Large Pre-Trained Models for Unsupervised Knowledge-Grounded Conversation

Recent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage these large models as knowledge bases for downstream tasks. In this work, we answer the aforementioned question in unsupervised knowledge-grounded conversation. We explore various methods that best elicit knowledge from large models. Our human study indicates that, though hallucinations exist, large models post the unique advantage of being able to output common sense and summarize facts that cannot be directly retrieved from the search engine. To better exploit such generated knowledge in dialogue generation, we treat the generated knowledge as a noisy knowledge source and propose the posterior-based reweighing as well as the noisy training strategy. Empirical results on two benchmarks show advantages over the state-of-the-art methods.Comment: Accepted to EMNLP 2022 Main Conference. The code is publicly available at https://github.com/lyy1994/PLM_as_KB/tree/main/projects/plm_as_k

Towards Personalized Federated Learning via Heterogeneous Model Reassembly

This paper focuses on addressing the practical yet challenging problem of model heterogeneity in federated learning, where clients possess models with different network structures. To track this problem, we propose a novel framework called pFedHR, which leverages heterogeneous model reassembly to achieve personalized federated learning. In particular, we approach the problem of heterogeneous model personalization as a model-matching optimization task on the server side. Moreover, pFedHR automatically and dynamically generates informative and diverse personalized candidates with minimal human intervention. Furthermore, our proposed heterogeneous model reassembly technique mitigates the adverse impact introduced by using public data with different distributions from the client data to a certain extent. Experimental results demonstrate that pFedHR outperforms baselines on three datasets under both IID and Non-IID settings. Additionally, pFedHR effectively reduces the adverse impact of using different public data and dynamically generates diverse personalized models in an automated manner

FlowEval: A Consensus-Based Dialogue Evaluation Framework Using Segment Act Flows

Despite recent progress in open-domain dialogue evaluation, how to develop automatic metrics remains an open problem. We explore the potential of dialogue evaluation featuring dialog act information, which was hardly explicitly modeled in previous methods. However, defined at the utterance level in general, dialog act is of coarse granularity, as an utterance can contain multiple segments possessing different functions. Hence, we propose segment act, an extension of dialog act from utterance level to segment level, and crowdsource a large-scale dataset for it. To utilize segment act flows, sequences of segment acts, for evaluation, we develop the first consensus-based dialogue evaluation framework, FlowEval. This framework provides a reference-free approach for dialog evaluation by finding pseudo-references. Extensive experiments against strong baselines on three benchmark datasets demonstrate the effectiveness and other desirable characteristics of our FlowEval, pointing out a potential path for better dialogue evaluation.Comment: EMNLP 2022 camera-ready versio

CLEVA: Chinese Language Models EVAluation Platform

With the continuous emergence of Chinese Large Language Models (LLMs), how to evaluate a model's capabilities has become an increasingly significant issue. The absence of a comprehensive Chinese benchmark that thoroughly assesses a model's performance, the unstandardized and incomparable prompting procedure, and the prevalent risk of contamination pose major challenges in the current evaluation of Chinese LLMs. We present CLEVA, a user-friendly platform crafted to holistically evaluate Chinese LLMs. Our platform employs a standardized workflow to assess LLMs' performance across various dimensions, regularly updating a competitive leaderboard. To alleviate contamination, CLEVA curates a significant proportion of new data and develops a sampling strategy that guarantees a unique subset for each leaderboard round. Empowered by an easy-to-use interface that requires just a few mouse clicks and a model API, users can conduct a thorough evaluation with minimal coding. Large-scale experiments featuring 23 Chinese LLMs have validated CLEVA's efficacy.Comment: EMNLP 2023 System Demonstrations camera-read

On the Validation of a Multiple-Network Poroelastic Model Using Arterial Spin Labeling MRI Data

The Multiple-Network Poroelastic Theory (MPET) is a numerical model to characterize the transport of multiple fluid networks in the brain, which overcomes the problem of conducting separate analyses on individual fluid compartments and losing the interactions between tissue and fluids, in addition to the interaction between the different fluids themselves. In this paper, the blood perfusion results from MPET modeling are partially validated using cerebral blood flow (CBF) data obtained from arterial spin labeling (ASL) magnetic resonance imaging (MRI), which uses arterial blood water as an endogenous tracer to measure CBF. Two subjects—one healthy control and one patient with unilateral middle cerebral artery (MCA) stenosis are included in the validation test. The comparison shows several similarities between CBF data from ASL and blood perfusion results from MPET modeling, such as higher blood perfusion in the gray matter than in the white matter, higher perfusion in the periventricular region for both the healthy control and the patient, and asymmetric distribution of blood perfusion for the patient. Although the partial validation is mainly conducted in a qualitative way, it is one important step toward the full validation of the MPET model, which has the potential to be used as a testing bed for hypotheses and new theories in neuroscience research

Seismic performance analysis of a wind turbine tower subjected to earthquake and ice actions.

Sea ice is one of the main loads acting on a wind turbine tower in areas prone to icing, and this threatens safe working life of the wind turbine tower. In our study, a simplified calculated model of ice, wind turbine tower, and water dynamic interaction under earthquake action was proposed, which could avoid to solve a large number of nonlinear equations. Then, the seismic behaviour of the wind turbine tower with and without the influence of sea ice was investigated, and we found that the influence of the greater mass of the sea ice on the seismic response of a wind turbine tower should be considered when the wind turbine tower is designed in an area with thick ice. With the influence of the most unfavourable ice mass, the deformation and energy dissipation capacity of the wind turbine tower are decreased, and the wall thickness or stiffening rib thickness should be increased to improve the seismic performance and ductility of the wind turbine tower; the shear force and bending moment increased significantly on the wind turbine tower, and the shear force changes at the bottom of the wind turbine tower and position of action of the sea ice: attention should be paid to the wind turbine tower design at these positions. Finally, we conducted the shaking table test, and verified the rationality of our proposed simplified model

Effect analysis of burial depth on seismic dynamic response of metro station structure

In this article, based on the nonlinear elastic-plastic finite element model for metro station, considering the structure-soil dynamic interaction, the influence laws of the burial depth on the dynamic response and failure mode of the metro station structure under near and far-field earthquakes are studied. We found that the influence of burial depth on the deformation of the metro station may be omitted after a specific value of the burial depth. With the increasing of the burial depth, the acceleration dynamic amplification factors of the metro station structure decreses. At last, indoor shaking table test for metro station was done, through which we determined the position of initial failure and the failure mode of the metro station structure under earthquake

Effects of forest therapy on human physical and mental health: A meta-analysis

BackgroundWith urbanization and residential space expansion, ecological environment and human health issues have become hot social topics. Forest health, as a way of seeking health in nature, has begun to receive public attention in the context of the gradually increasing sub-healthy population and various psychological and physical diseases at a young age.ObjectiveTo systematically evaluate the effects of forest therapy on selected physical and mental health indicators.MethodsRelevant research literature was retrieved from domestic and international databases (China National Knowledge Infrastructure, Wanfang Database, China Biomedical Literature Service System, Web of Science, ScienceDirect, PubMed, Embase, and Cochrane Library), with a time range from database establishment to January 31, 2023. Relevant data were extracted for meta-analysis to explore the relationship between forest therapy and selected psychological and physiological indicators.ResultsA total of 85 articles were included, and the meta-analysis results showed that better scores of Profile of Mood States, Positive and Negative Affect Scale, Beck Depression Inventory, and State Trait Anxiety Scale were found in the forest group than those in the urban group (P0.5~3 h group (such as tension SMD=−2.40, 95%CI: −3.21, 1.59), and the reduction effects on systolic blood pressure (SMD=−0.53, 95%CI: −1.03, −0.03) and diastolic blood pressure (SMD=−0.42, 95%CI: −0.88, 0.04) were better in the >3 h group. Seated meditation showed better recovery effects on multiple indicators of Profile of Mood States (such as fatigue SMD=−2.26, 95%CI: −3.07, −1.45), while walking showed better recovery effects on physiological indicators such as blood pressure (systolic blood pressure SMD=−0.57, 95%CI: −1.07, −0.06; diastolic blood pressure SMD=−0.72, 95%CI: −1.36, −0.07) and heart rate (SMD=−1.51, 95%CI: −2.38, -0.64). Except for blood pressure, the health benefits of forest therapy in the younger age group were generally better than those in the middle-aged and elderly group.ConclusionRelaxed and comfortable psychological feeling is reported when practicing forest therapy; it can lower blood pressure and heart rate, regulate the autonomic nervous system; it can also reduce the release of stress hormones and lower serum levels of inflammatory factors, exerting an auxiliary recovery effect on cardiovascular and immune system disorders. At the same time, the therapy duration, form, and age of the subjects have a certain impact on the effects of forest therapy practice

H3N2 canine influenza virus and Enterococcus faecalis coinfection in dogs in China

Abstract Background In May 2017, 17 dogs in a German Shepherd breeding kennel in northern China developed respiratory clinical signs. The owner treated the dogs with an intravenous injection of Shuang-Huang-lian, a traditional Chinese medicine, and azithromycin. The respiratory signs improved 3 days post-treatment, however, cysts were observed in the necks of eight dogs, and three of them died in the following 2 days. Case presentation Quantitative real-time PCR was used to detect canine influenza virus (CIV). All of the dogs in this kennel were positive and the remaining 14 dogs had seroconverted. Two of the dogs were taken to the China Agricultural University Veterinary Teaching Hospital for further examination. Two strains of influenza virus (A/canine/Beijing/0512–133/2017 and A/canine/Beijing/0512–137/2017) isolated from the nasal swabs of these dogs were sequenced and identified as avian-origin H3N2 CIV. For the two dogs admitted to the hospital, hematology showed mild inflammation and radiograph results indicated pneumonia. Cyst fluid was plated for bacterial culture and bacterial 16 s rRNA gene PCR was performed, followed by Sanger sequencing. The results indicated an Enterococcus faecalis infection. Antimicrobial susceptibility tests were performed and dogs were treated with enrofloxacin. All 14 remaining dogs recovered within 16 days. Conclusions Coinfection of H3N2 CIV and Enterococcus faecalis was detected in dogs, which has not been reported previously. Our results highlight that CIV infection might promote the secondary infection of opportunistic bacteria and cause more severe and complicated clinical outcomes

Electromagnetic Wave-Absorbing and Bending Properties of Three-Dimensional Gradient Woven Composites with Triangular Sections

In order to solve defects such as poor integrity, delamination failure, and narrow absorption bandwidth, three-dimensional (3D) gradient honeycomb woven composites (GHWCs) with triangular sections were designed and prepared. Three-dimensional gradient honeycomb woven fabric was crafted with carbon fiber (CF) filaments and basalt fiber (BF) filaments as raw materials on an ordinary loom. Then, the 3D honeycomb woven fabric filled with rigid polyurethane foam was used as the reinforcement, and epoxy resin (EP) doped with carbon black (CB) and carbonyl iron powder (CIP) was conducted as the matrix. The 3D GHWC with triangular sections, which had both EM-absorbing and load-bearing functions, was prepared by the VARTM process. Through the macro test and micro characterization of 3D GHWCs with triangular sections, the overall absorbing properties and mechanical properties of the materials were analyzed. Moreover, the EM-absorbing mechanism and failure mode of the materials were clarified in this work. The results indicated that the CF filament reflective layer effectively improved the EM-absorbing and mechanical properties. Adding a CB/CIP-absorbing agent enhanced the overall EM-absorbing property but reduced the mechanical properties. The increasing number of gradient layers increased the maximum bending load, but the EM-absorbing performance first increased and then decreased. When the thickness was 15 mm, the maximum bending load was 3530 N, and the minimum reflection loss (RLmin) was −21.6 dB. The synergistic effects of EM-absorbing and mechanical properties were the best right now. In addition, this work provided a feasible strategy that adjusting the type of absorber and gradient aperture size ratio could meet the unique requirements of absorbing frequency and intensity, which has excellent application prospects in civil and military fields