NASRec: Weight Sharing Neural Architecture Search for Recommender Systems

The rise of deep neural networks provides an important driver in optimizing recommender systems. However, the success of recommender systems lies in delicate architecture fabrication, and thus calls for Neural Architecture Search (NAS) to further improve its modeling. We propose NASRec, a paradigm that trains a single supernet and efficiently produces abundant models/sub-architectures by weight sharing. To overcome the data multi-modality and architecture heterogeneity challenges in recommendation domain, NASRec establishes a large supernet (i.e., search space) to search the full architectures, with the supernet incorporating versatile operator choices and dense connectivity minimizing human prior for flexibility. The scale and heterogeneity in NASRec impose challenges in search, such as training inefficiency, operator-imbalance, and degraded rank correlation. We tackle these challenges by proposing single-operator any-connection sampling, operator-balancing interaction modules, and post-training fine-tuning. Our results on three Click-Through Rates (CTR) prediction benchmarks show that NASRec can outperform both manually designed models and existing NAS methods, achieving state-of-the-art performance

Determination of standard molar volume of 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide on titanium dioxide surface

The fluids near the solid substrate display different properties compared to the bulk fluids owing to the asymmetric interaction between the fluid and substrate; however, to the best of our knowledge, no work has been conducted to determine the interfacial properties of fluids experimentally. In this work, we combined a pycnometer with experimental measurements and data processing to determine the standard thermodynamic properties of interfacial fluids for the first time. In the study, 1-hexyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([Hmim][NTf2]) and titanium dioxide (P25) were chosen as the probes to prove the concept. It was found that, with the combination of the Gay-Lussac pycnometer and the colligative law, together with selecting a suitable solvent, it is possible and reliable to determine the standard molar volume of the immobilized [Hmim][NTf2]. Compared to the bulk phase, the molar volumes of [Hmim][NTf2] on the P25 surface reduce by 20.8%–23.7% at temperatures from 293.15 to 323.15 K, and the reduction degrees decrease with increasing temperatures. The newly determined standard thermodynamic data was used to obtain the model parameters of hybrid electrolyte perturbed-chain statistical associating fluid theory density functional theory (ePC-SAFT-DFT), and further predictions of the density of interfacial ionic liquids with different film thicknesses were proved to be reliable in comparison with the experiment results

Ionic Liquids/Deep Eutectic Solvents-Based Hybrid Solvents for CO2 Capture

The CO2 solubilities (including CO2 Henry's constants) and viscosities in ionic liquids (ILs)/deep eutectic solvents (DESs)-based hybrid solvents were comprehensively collected and summarized. The literature survey results of CO2 solubility illustrated that the addition of hybrid solvents to ILs/DESs can significantly enhance the CO2 solubility, and some of the ILs-based hybrid solvents are super to DESs-based hybrid solvents. The best hybrid solvents of IL-H2O, IL-organic, IL-amine, DES-H2O, and DES-organic are [DMAPAH][Formate] (2.5:1) + H2O (20 wt %) (4.61 mol/kg, 298 K, 0.1 MPa), [P-4444][Pro] + PEG400 (70 wt %) (1.61 mol/kg, 333.15 K, 1.68 MPa), [DMAPAH][Formate] (2.0:1) + MEA (30 wt %) (6.24 mol/kg, 298 K, 0.1 MPa), [TEMA][Cl]-GLY-H2O 1:2:0.11 (0.66 mol/kg, 298 K, 1.74 MPa), and [Ch][Cl]-MEA 1:2 + DBN 1:1 (5.11 mol/kg, 298 K, 0.1 MPa), respectively. All of these best candidates show higher CO2 solubility than their used pure ILs or DESs, evidencing that IL/DES-based hybrid solvents are remarkable for CO2 capture. For the summarized viscosity results, the presence of hybrid solvents in ILs and DESs can decrease their viscosities. The lowest viscosities acquired in this work for IL-H2O, IL-amine, DES-H2O, and DES-organic hybrid solvents are [DEA][Bu] + H2O (98.78 mol%) (0.59 mPa center dot s, 343.15 K), [BMIM][BF4] + DETA (94.9 mol%) (2.68 mPa center dot s, 333.15 K), [L-Arg]-GLY 1:6 + H2O (60 wt %) (2.7 mPa center dot s, 353.15 K), and [MTPP][Br]-LEV-Ac 1:3:0.03 (16.16 mPa center dot s, 333.15 K) at 0.1 MPa, respectively

Fault Diagnosis of Rolling Bearing Based on Probability box Theory and GA-SVM

For an intelligent detection of bearing failure in rotating machinery, this paper proposed a fault diagnosis method based on a probability box (p-box) and support vector machine (SVM) with a genetic algorithm (GA) algorithm. Firstly, based on vibration signals of the bearing, the different p-boxes are obtained and fused using the evidence theory. Then, the different bearing p-boxes can be classified by adopting SVM model; the GA algorithm is considered to optimize key parameters of the SVM model, i.e., GA-SVM. Finally, experimental results show that total recognition rate of this method is better than that of the traditional feature extraction method, which demonstrates the effectiveness of the current method

Melting points of ionic liquids: Review and evaluation

The melting points of ionic liquids (ILs) reported since 2020 were surveyed, collected, and reviewed, which were further combined with the previous data to provide a database with 3129 ILs ranging from 177.15 to 645.9 K in melting points. In addition, the factors that affect the melting point of ILs from macro, micro, and thermodynamic perspectives were summarized and analyzed. Then the development of the quantitative structure-property relationship (QSPR), group contribution method (GCM), and conductor-like screening model for realistic solvents (COSMO-RS) for predicting the melting points of ILs were reviewed and further analyzed. Combined with the evaluation together with the preliminary study conducted in this work, it shows that COSMO-RS is more promising and possible to further improve its performance, and a framework was thus proposed.Funder: Swedish Research Council (101070976); China Scholarship Council (202208320253); STINT (CH2019-8287); National Natural Science Foundation of China (21838004, 22011530112);Full text license: CC BY-NC-ND</p

NUMERICAL ANALYSIS AND EXPERIMENTAL STUDY ON THE RACK OF MASSAGE CHAIR

Aiming at the people’s requests of comfort and functions,a rack of massage chair was designed. Numerical analysis was done based on the three-dimensional model. Kinematic simulation was run on the virtual prototype model based on the ADAMS software,the functions was verified and the theoretical basis was supported for the choose of the electric putter.Finite element analysis was done on the key components of the structure under the environment of ANSYS Workbench,the strength and stiffness were checked. Experimental study was done after the prototypes were made. The research results indicate that the rack can meet the function requests of zero gravity and zero against the wall. The strength and stiffness of the structure can meet the requests of the testing standard. The experimental results consistent with the numerical analysis results. That verifies the validity of numerical analysis. The research results also offer important basis for the later structure optimization

Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids

Viscosity is one of the most important fundamental properties of fluids. However, accurate acquisition of viscosity for ionic liquids (ILs) remains a critical challenge. In this study, an approach integrating prior physical knowledge into the machine learning (ML) model was proposed to predict the viscosity reliably. The method was based on 16 quantum chemical descriptors determined from the first principles calculations and used as the input of the ML models to represent the size, structure, and interactions of the ILs. Three strategies based on the residuals of the COSMO-RS model were created as the output of ML, where the strategy directly using experimental data was also studied for comparison. The performance of six ML algorithms was compared in all strategies, and the CatBoost model was identified as the optimal one. The strategies employing the relative deviations were superior to that using the absolute deviation, and the relative ratio revealed the systematic prediction error of the COSMO-RS model. The CatBoost model based on the relative ratio achieved the highest prediction accuracy on the test set (R2 = 0.9999, MAE = 0.0325), reducing the average absolute relative deviation (AARD) in modeling from 52.45 % to 1.54 %. Features importance analysis indicated the average energy correction, solvation-free energy, and polarity moment were the key influencing the systematic deviation.Funder: Horizon-EIC (101070976); Postgraduate Research &amp; Practice Innovation Program of Jiangsu Province (KYCX23-1467); STINT (CH2019-8287); National Natural Science Foundation of China (21838004); Full text license: CC BY-NC-ND 4.0; </p