Neural Architecture Search via Multi-Hashing Embedding and Graph Tensor Networks for Multilingual Text Classification

Yan X, Huang H, Jin Y, Chen L, Liang Z, Hao Z. Neural Architecture Search via Multi-Hashing Embedding and Graph Tensor Networks for Multilingual Text Classification. IEEE Transactions on Emerging Topics in Computational Intelligence . 2023.Neural architecture search (NAS) has been demonstrated to be promising in deep learning for text classification. Most existing NAS algorithms, however, are proposed for single-language text classification. They may become ineffective when extended to multilingual text classification because the differences in the syntactic structure and contextual semantics of different languages increase the computational complexity of NAS search. To address the above issue, this article proposes a differential neural architecture search approach using multi-hashing embedding for multilingual text representation. A multi-hashing network capable of processing heterogeneous graph information is constructed so that cross-language syntactic and contextual semantic information can be effectively represented. In addition, a neural tensor network with multi-hashing embedding is adopted as a continuous encoder to estimate the probability for each candidate operation in the search space, and reparameterization-based gradient search is employed to efficiently search for network architectures for multilingual text representation. Our experimental results on two multilingual text classification datasets demonstrate that the proposed approach outperforms the state-of-the-art NAS methods for text classification in terms of both classification accuracy and computational efficiency

One-Step Co-Electrodeposition of Copper Nanoparticles-Chitosan Film-Carbon Nanoparticles-Multiwalled Carbon Nanotubes Composite for Electroanalysis of Indole-3-Acetic Acid and Salicylic Acid

A sensitive simultaneous electroanalysis of phytohormones indole-3-acetic acid (IAA) and salicylic acid (SA) based on a novel copper nanoparticles-chitosan film-carbon nanoparticles-multiwalled carbon nanotubes (CuNPs-CSF-CNPs-MWCNTs) composite was reported. CNPs were prepared by hydrothermal reaction of chitosan. Then the CuNPs-CSF-CNPs-MWCNTs composite was facilely prepared by one-step co-electrodeposition of CuNPs and CNPs fixed chitosan residues on modified electrode. Scanning electron microscope (SEM), transmission electron microscopy (TEM), selected area electron diffraction (SAED), energy dispersive spectroscopy (EDS), X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS), and linear sweep voltammetry (LSV) were used to characterize the properties of the composite. Under optimal conditions, the composite modified electrode had a good linear relationship with IAA in the range of 0.01–50 μM, and a good linear relationship with SA in the range of 4–30 μM. The detection limits were 0.0086 μM and 0.7 μM (S/N = 3), respectively. In addition, the sensor could also be used for the simultaneous detection of IAA and SA in real leaf samples with satisfactory recovery

Simultaneous Electrochemical Sensing of Indole-3-Acetic Acid and Salicylic Acid on Poly(L-Proline) Nanoparticles–Carbon Dots–Multiwalled Carbon Nanotubes Composite-Modified Electrode

Sensitive simultaneous electrochemical sensing of phytohormones indole-3-acetic acid and salicylic acid based on a novel poly(L-Proline) nanoparticles–carbon dots composite consisting of multiwalled carbon nanotubes was reported in this study. The poly(L-Proline) nanoparticles–carbon dots composite was facilely prepared by the hydrothermal method, and L-Proline was used as a monomer and carbon source for the preparation of poly(L-Proline) nanoparticles and carbon dots, respectively. Then, the poly(L-Proline) nanoparticles–carbon dots–multiwalled carbon nanotubes composite was prepared by ultrasonic mixing of poly(L-Proline) nanoparticles–carbon dots composite dispersion and multiwalled carbon nanotubes. Scanning electron microscope, transmission electron microscope, Fourier transform infrared spectroscopy, ultraviolet visible spectroscopy, energy dispersive spectroscopy, cyclic voltammetry, electrochemical impedance spectroscopy, and linear sweep voltammetry were used to characterize the properties of the composite. poly(L-Proline) nanoparticles were found to significantly enhance the conductivity and sensing performance of the composite. Under optimal conditions, the composite-modified electrode exhibited a wide linear range from 0.05 to 25 μM for indole-3-acetic acid and from 0.2 to 60 μM for salicylic acid with detection limits of 0.007 μM and 0.1 μM (S/N = 3), respectively. In addition, the proposed sensor was also applied to simultaneously test indole-3-acetic acid and salicylic acid in real leaf samples with satisfactory recovery