One Neuron Saved Is One Neuron Earned: On Parametric Efficiency of Quadratic Networks

Inspired by neuronal diversity in the biological neural system, a plethora of studies proposed to design novel types of artificial neurons and introduce neuronal diversity into artificial neural networks. Recently proposed quadratic neuron, which replaces the inner-product operation in conventional neurons with a quadratic one, have achieved great success in many essential tasks. Despite the promising results of quadratic neurons, there is still an unresolved issue: \textit{Is the superior performance of quadratic networks simply due to the increased parameters or due to the intrinsic expressive capability?} Without clarifying this issue, the performance of quadratic networks is always suspicious. Additionally, resolving this issue is reduced to finding killer applications of quadratic networks. In this paper, with theoretical and empirical studies, we show that quadratic networks enjoy parametric efficiency, thereby confirming that the superior performance of quadratic networks is due to the intrinsic expressive capability. This intrinsic expressive ability comes from that quadratic neurons can easily represent nonlinear interaction, while it is hard for conventional neurons. Theoretically, we derive the approximation efficiency of the quadratic network over conventional ones in terms of real space and manifolds. Moreover, from the perspective of the Barron space, we demonstrate that there exists a functional space whose functions can be approximated by quadratic networks in a dimension-free error, but the approximation error of conventional networks is dependent on dimensions. Empirically, experimental results on synthetic data, classic benchmarks, and real-world applications show that quadratic models broadly enjoy parametric efficiency, and the gain of efficiency depends on the task.Comment: We have shared our code in https://github.com/asdvfghg/quadratic_efficienc

Introduction: the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: special focus on medical informatics and big data

Abstract In this editorial, we first summarize the 2016 International Conference on Intelligent Biology and Medicine (ICIBM 2016) held on December 8–10, 2016 in Houston, Texas, USA, and then briefly introduce the ten research articles included in this supplement issue. At ICIBM 2016, a special theme, “Medical Informatics and Big Data,” was dedicated to the recent advances of data science in the medical domain. After peer review, ten articles were selected for this special issue, covering topics such as Knowledge and Data Personalization, Social Media Applications to Healthcare, Clinical Natural Language Processing, Patient Safety Analyses, and Data Mining Using Electronic Health Records

Relaxor antiferroelectric ceramics with ultrahigh efficiency for energy storage applications

Enhancing the efficiency in energy storage capacitors minimizes energy dissipation and improves device durability. A new efficiency-enhancement strategy for antiferroelectric ceramics, imposing relaxor characteristics through forming solid solutions with relaxor compounds, is demonstrated in the present work. Using the classic antiferroelectric (Pb0.97La0.02)(Zr1-x-ySnxTiy)O3 as model base compositions, Bi(Zn2/3Nb1/3)O3 is found to be most effective in producing the “relaxor antiferroelectric” behavior and minimizing the electric hysteresis. Specifically, a remarkable energy storage efficiency of 95.6% (with an energy density of 2.19 J/cm3 at 115 kV/cm) is achieved in the solid solution 0.90(Pb0.97La0.02)(Zr0.65Sn0.30Ti0.05)O3–0.10Bi(Zn2/3Nb1/3)O3. The validated new strategy, hence, can guide the design of future relaxor antiferroelectric dielectrics for next generation energy storage capacitors.This is a manuscript of an article published as Mohapatra, Pratyasha, Zhongming Fan, Jun Cui, and Xiaoli Tan. "Relaxor antiferroelectric ceramics with ultrahigh efficiency for energy storage applications." Journal of the European Ceramic Society (2019). DOI: 10.1016/j.jeurceramsoc.2019.07.050. Posted with permission.</p

Relaxor antiferroelectric ceramics with ultrahigh efficiency for energy storage applications

Enhancing the efficiency in energy storage capacitors minimizes energy dissipation and improves device durability. A new efficiency-enhancement strategy for antiferroelectric ceramics, imposing relaxor characteristics through forming solid solutions with relaxor compounds, is demonstrated in the present work. Using the classic antiferroelectric (Pb0.97La0.02)(Zr1-x-ySnxTiy)O3 as model base compositions, Bi(Zn2/3Nb1/3)O3 is found to be most effective in producing the “relaxor antiferroelectric” behavior and minimizing the electric hysteresis. Specifically, a remarkable energy storage efficiency of 95.6% (with an energy density of 2.19 J/cm3 at 115 kV/cm) is achieved in the solid solution 0.90(Pb0.97La0.02)(Zr0.65Sn0.30Ti0.05)O3–0.10Bi(Zn2/3Nb1/3)O3. The validated new strategy, hence, can guide the design of future relaxor antiferroelectric dielectrics for next generation energy storage capacitors

The International Conference on Intelligent Biology and Medicine 2018: Medical Informatics Thematic Track (MedicalInfo2018)

Abstract In this editorial, we first summarize the 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) that was held on June 10–12, 2018 in Los Angeles, California, USA, and then briefly introduce the six research articles included in this supplement issue. At ICIBM 2018, a special theme of Medical Informatics was dedicated to recent advances of data science in the medical domain. After peer review, six articles were selected in this thematic issue, covering topics such as clinical predictive modeling, clinical natural language processing (NLP), electroencephalogram (EEG) network analysis, and text mining in biomedical literature

Parameters optimization for ferrite slicing based on grey theory

To optimize the parameters of slicing ferrite with high precision diamond ring saw, an orthogonal test is designed with the fabricating surface accuracy and the surface roughness as evaluation indicators and the spindle speed, the feed speed and the tension force as factors. Based on the grey theory, the data analysis and the comprehensive evaluation of the multiple process targets are carried out to obtain an optimized process parameter combination, namely the spindle speed 1 000 r/min, the feed speed 1.0 mm/min and the tension force 90 N. The slicing test results show that the optimized parameter combination can obtain a surface accuracy of PV 7.37 μm and a surface roughness Ra of 0.882 μm, and the slicing surface quality is improved, which verifies the effectiveness and practicability of this method in the optimization of ferrite slicing process parameters