Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

Using Argument-based Features to Predict and Analyse Review Helpfulness

We study the helpful product reviews identification problem in this paper. We observe that the evidence-conclusion discourse relations, also known as arguments, often appear in product reviews, and we hypothesise that some argument-based features, e.g. the percentage of argumentative sentences, the evidences-conclusions ratios, are good indicators of helpful reviews. To validate this hypothesis, we manually annotate arguments in 110 hotel reviews, and investigate the effectiveness of several combinations of argument-based features. Experiments suggest that, when being used together with the argument-based features, the state-of-the-art baseline features can enjoy a performance boost (in terms of F1) of 11.01\% in average.Comment: 6 pages, EMNLP201

A fast SCOP fold classification system using content-based E-Predict algorithm

BACKGROUND: Domain experts manually construct the Structural Classification of Protein (SCOP) database to categorize and compare protein structures. Even though using the SCOP database is believed to be more reliable than classification results from other methods, it is labor intensive. To mimic human classification processes, we develop an automatic SCOP fold classification system to assign possible known SCOP folds and recognize novel folds for newly-discovered proteins. RESULTS: With a sufficient amount of ground truth data, our system is able to assign the known folds for newly-discovered proteins in the latest SCOP v1.69 release with 92.17% accuracy. Our system also recognizes the novel folds with 89.27% accuracy using 10 fold cross validation. The average response time for proteins with 500 and 1409 amino acids to complete the classification process is 4.1 and 17.4 seconds, respectively. By comparison with several structural alignment algorithms, our approach outperforms previous methods on both the classification accuracy and efficiency. CONCLUSION: In this paper, we build an advanced, non-parametric classifier to accelerate the manual classification processes of SCOP. With satisfactory ground truth data from the SCOP database, our approach identifies relevant domain knowledge and yields reasonably accurate classifications. Our system is publicly accessible at

Spray-wall-flow interaction within a gasoline direct-injection (GDI) engine using Large Eddy Simulation

With the need and urgency to the imposed regulation of zero-carbon emissions, the development of high-fidelity models for gasoline direct-injection (GDI) spray becomes crucial. This study first focuses on the development of robust Lagrangian models and a comprehensive exploration of the underlying physics across various operating conditions in a constant-volume chamber, ranging from early- to late-injection conditions. The Lagrangian models are extended to assess the spray-wall-flow interaction within an engine flow bench, simulating early-injection conditions of real GDI engines. The concept of these models is based on a Direct Numerical Simulation (DNS) inner-nozzle flow simulation, indicating that the liquid spray experiences complete atomization near the injector hole. Consequently, the deformation and secondary breakup of liquid droplets play a significant role in spray evolution. The models’ effectiveness and accuracy are meticulously validated against experimental data, including liquid and vapor phases obtained by diffuse back-illumination (DBI) and Schlieren measurements, respectively. An important aspect of the research involves the investigation of different droplet distribution models. Using the blob method, assuming the ejected droplet size equivalent to the injector diameter, is able to accurately capture global properties like liquid penetration length. However, it tends to cause delayed evaporation and breakup, resulting in an unphysical sharp plume tip downstream. To address future fuel-blended gasoline and E-fuels scenarios, the models have been extended to handle multi-component fuels. The successful simulation of a three-component gasoline surrogate (E00) demonstrates the models’ capability to reproduce both the overall spray plume characteristics and the spatial distribution of high- and low-volatile fuels. Furthermore, the research expands into the intricate spray-wall interaction within a constant-volume chamber under simulated cold start conditions. The simulation successfully replicates characteristic flows, such as wall jets and wall jet vortices induced by spray-wall interaction. Additionally, the phenomenon of spray cooling, resulting from air-entrainment-induced evaporation, is accurately reproduced. The simulated temperatures align closely with 0-D analytical results, exhibiting a temperature drop of about 20 K from its initial value. Although the simulation over-predicts heat transfer from the wall due to the constant temperature boundary condition, it matches the experimental aggregate wall film thickness data on the target wall, 40 mm from the injector tip. To comprehensively examine the spray-wall-flow interaction within a GDI engine, understanding the in-cylinder flow during the intake phase is imperative. Hence, a wall-resolved Large Eddy Simulation (LES) approach is employed to investigate free-stream and near-wall turbulence within an engine flow bench, simplifying the inherent complexity of the engine flow and focusing on the intake flow. The simulated in-cylinder large-scale motion and turbulence structure aligns well with reference experimental particle image velocimetry (PIV) data. Turbulence anisotropy analysis reveals a strong orientation toward axisymmetric expansion and contraction, respectively, attributed to the specific topological pattern of the engine flow characterized by the tumble vortex and the intake overflow jet. Moreover, the near-wall budget analysis facilitates investigating near-wall non-equilibrium effects, with a particular focus on the intake valve and liner wall region. The effects of the pressure gradient induced by the high Reynolds number intake flow are found to vary across different regions, suggesting that the classical wall function modeling approach based on the classical zero pressure gradient boundary layer may no longer be valid in internal combustion engine (ICEs) applications. Finally, the knowledge gained from the study is applied to assess the spray-wall-flow interaction in an engine flow bench under various mass flow rates (MFRs). As MFRs increase, the spray-flow interaction intensifies, and the heterogeneous behavior of all spray plumes becomes apparent. Plumes oriented along the intake flow jet exhibit higher penetration and lower evaporation, while those not aligned with the intake jet stream exhibit increased evaporation and reduced penetration. This observation confirms the significant impact of air entrainment induced by the intake flow on both evaporation and penetration length. Additionally, wall wetting is observed on the intake valves, and convective evaporation effectively reduces the fuel film, cutting its residual mass by up to 50% compared to the no-flow case when the mass flow rate is 100%. Under early-injection conditions, although the global turbulence kinetic energy experiences a transient increase during the injection, it eventually returns to its original values

Civil structure condition assessment by FE model updating: Methodology and case studies

Author's manuscript version. the version of record is available from the publisher via: doi:10.1016/S0168-874X(00)00071-8. Copyright © 2001 Elsevier Science B.V.Development of methodology for accurate and reliable condition assessment of civil structures has become increasingly important. In particular, the finite element (FE) model updating method has been successfully used for condition assessment of bridges. However, the success of applications of the method depends on the analytical conceptualization of complex bridge structures, a well-designed and controlled modal test and an integration of analytical and experimental arts. This paper describes the sensitivity-analysis-based FE model updating method and its application to structure condition assessment with particular reference to bridges, including specific considerations for FE modeling for updating and the model updating procedure for successful condition assessment. Finally, the accuracy analysis of damage assessment by model updating was investigated through a case study. © 2001 Elsevier Science B.V. All rights reserved

Identification and characterization of dysregulated P-element induced wimpy testis-interacting RNAs in head and neck squamous cell carcinoma.

It is clear that alcohol consumption is a major risk factor in the pathogenesis of head and neck squamous cell carcinoma (HNSCC); however, the molecular mechanism underlying the pathogenesis of alcohol-associated HNSCC remains poorly understood. The aim of the present study was to identify and characterize P-element-induced wimpy testis (PIWI)-interacting RNAs (piRNAs) and PIWI proteins dysregulated in alcohol-associated HNSCC to elucidate their function in the development of this cancer. Using next generation RNA-sequencing (RNA-seq) data obtained from 40 HNSCC patients, the piRNA and PIWI protein expression of HNSCC samples was compared between alcohol drinkers and non-drinkers. A separate piRNA expression RNA-seq analysis of 18 non-smoker HNSCC patients was also conducted. To verify piRNA expression, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed on the most differentially expressed alcohol-associated piRNAs in ethanol and acetaldehyde-treated normal oral keratinocytes. The correlation between piRNA expression and patient survival was analyzed using Kaplan-Meier estimators and multivariate Cox proportional hazard models. A comparison between alcohol drinking and non-drinking HNSCC patients demonstrated that a panel of 3,223 piRNA transcripts were consistently detected and differentially expressed. RNA-seq analysis and in vitro RT-qPCR verification revealed that 4 of these piRNAs, piR-35373, piR-266308, piR-58510 and piR-38034, were significantly dysregulated between drinking and non-drinking cohorts. Of these four piRNAs, low expression of piR-58510 and piR-35373 significantly correlated with improved patient survival. Furthermore, human PIWI-like protein 4 was consistently upregulated in ethanol and acetaldehyde-treated normal oral keratinocytes. These results demonstrate that alcohol consumption may cause dysregulation of piRNA expression in HNSCC and in vitro verifications identified 4 piRNAs that may be involved in the pathogenesis of alcohol-associated HNSCC

A Study of Low-Resource Speech Commands Recognition based on Adversarial Reprogramming

In this study, we propose a novel adversarial reprogramming (AR) approach for low-resource spoken command recognition (SCR), and build an AR-SCR system. The AR procedure aims to modify the acoustic signals (from the target domain) to repurpose a pretrained SCR model (from the source domain). To solve the label mismatches between source and target domains, and further improve the stability of AR, we propose a novel similarity-based label mapping technique to align classes. In addition, the transfer learning (TL) technique is combined with the original AR process to improve the model adaptation capability. We evaluate the proposed AR-SCR system on three low-resource SCR datasets, including Arabic, Lithuanian, and dysarthric Mandarin speech. Experimental results show that with a pretrained AM trained on a large-scale English dataset, the proposed AR-SCR system outperforms the current state-of-the-art results on Arabic and Lithuanian speech commands datasets, with only a limited amount of training data.Comment: Submitted to ICASSP 202