AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets

This paper studies the Binary Neural Networks (BNNs) in which weights and activations are both binarized into 1-bit values, thus greatly reducing the memory usage and computational complexity. Since the modern deep neural networks are of sophisticated design with complex architecture for the accuracy reason, the diversity on distributions of weights and activations is very high. Therefore, the conventional sign function cannot be well used for effectively binarizing full-precision values in BNNs. To this end, we present a simple yet effective approach called AdaBin to adaptively obtain the optimal binary sets $\{b_1, b_2\}$ ($b_1, b_2\in \mathbb{R}$) of weights and activations for each layer instead of a fixed set (\textit{i.e.}, $\{-1, +1\}$). In this way, the proposed method can better fit different distributions and increase the representation ability of binarized features. In practice, we use the center position and distance of 1-bit values to define a new binary quantization function. For the weights, we propose an equalization method to align the symmetrical center of binary distribution to real-valued distribution, and minimize the Kullback-Leibler divergence of them. Meanwhile, we introduce a gradient-based optimization method to get these two parameters for activations, which are jointly trained in an end-to-end manner. Experimental results on benchmark models and datasets demonstrate that the proposed AdaBin is able to achieve state-of-the-art performance. For instance, we obtain a 66.4% Top-1 accuracy on the ImageNet using ResNet-18 architecture, and a 69.4 mAP on PASCAL VOC using SSD300. The PyTorch code is available at \url{https://github.com/huawei-noah/Efficient-Computing/tree/master/BinaryNetworks/AdaBin} and the MindSpore code is available at \url{https://gitee.com/mindspore/models/tree/master/research/cv/AdaBin}.Comment: ECCV 202

Analysis of Lipids in Pitaya Seed Oil by Ultra-Performance Liquid Chromatography–Time-of-Flight Tandem Mass Spectrometry

Red pitaya (Hylocereus undatus) is an essential tropical fruit in China. To make more rational use of its processing, byproducts and fruit seeds, and the type, composition, and relative content of lipids in pitaya seed oil were analyzed by UPLC-TOF-MS/MS. The results showed that the main fatty acids in pitaya seed oil were linoleic acid 42.78%, oleic acid 27.29%, and palmitic acid 16.66%. The ratio of saturated fatty acids to unsaturated fatty acids to polyunsaturated fatty acids was close to 1:1.32:1.75. The mass spectrum behavior and fracture mechanism of four lipid components, TG 54:5|TG 18:1_18:2_18:2, were analyzed. In addition, lipids are an essential indicator for evaluating the quality of oils and fats, and 152 lipids were isolated and identified from pitaya seed oil for the first time, including 136 glycerides and 16 phospholipids. The main components of glyceride and phospholipids were triglycerides and phosphatidyl ethanol, providing essential data support for pitaya seed processing and functional product development

High-resolution source localization exploiting the sparsity of the beamforming map

Beamforming technology plays a significant role in source localization and quantification. As traditional delay-and-sum beamformers generally yield low spatial resolution, as well as suffer from the occurrence of spurious sources, different forms of deconvolution methods have been proposed in the literature. In this work, we propose two approaches based on a sparse reconstruction framework combined with the use of the Fourier-based efficient implementation techniques. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed methods

Sparse optimization for nonlinear group delay mode estimation

Nonlinear group delay signals with frequency-varying characteristics are common in a wide variety of fields, for instance, structural health monitoring and fault diagnosis. For such applications, the signal is composed of multiple modes, where each mode may overlap in the frequency-domain. The resulting decomposition and forming of time-frequency representations of the nonlinear group delay modes is a challenging task. In this study, the nonlinear group delay signal is modelled in the frequency-domain. Exploiting the sparsity of the signal, we present the nonlinear group delay mode estimation technique, which forms the demodulation dictionary from the group delay. This method can deal with crossed modes and transient impulse signals. Furthermore, an augmented alternating direction multiplier method is introduced to form an efficient implementation. Numerical simulations and experimental data analysis show the effectiveness and advantages of the proposed method. In addition, the included analysis of Lamb waves as well as of a bearing signal show the method's potential for structural health monitoring and fault diagnosis

Adaptive Variational Nonlinear Chirp Mode Decomposition

Variational nonlinear chirp mode decomposition (VNCMD) is a recently introduced method for nonlinear chirp signal decomposition that has aroused notable attention in various fields. One limiting aspect of the method is that its performance relies heavily on the setting of the bandwidth parameter. To overcome this problem, we here propose a Bayesian implementation of the VNCMD, which can adaptively estimate the instantaneous amplitudes and frequencies of the nonlinear chirp signals, and then learn the active dictionary in a data-driven manner, thereby enabling a high-resolution time-frequency representation. Numerical example of both simulated and measured data illustrate the resulting improvement performance of the proposed method

Self-Supervised Image Denoising Using Implicit Deep Denoiser Prior

We devise a new regularization for denoising with self-supervised learning. The regularization uses a deep image prior learned by the network, rather than a traditional predefined prior. Specifically, we treat the output of the network as a ``prior'' that we again denoise after ``re-noising.'' The network is updated to minimize the discrepancy between the twice-denoised image and its prior. We demonstrate that this regularization enables the network to learn to denoise even if it has not seen any clean images. The effectiveness of our method is based on the fact that CNNs naturally tend to capture low-level image statistics. Since our method utilizes the image prior implicitly captured by the deep denoising CNN to guide denoising, we refer to this training strategy as an Implicit Deep Denoiser Prior (IDDP). IDDP can be seen as a mixture of learning-based methods and traditional model-based denoising methods, in which regularization is adaptively formulated using the output of the network. We apply IDDP to various denoising tasks using only observed corrupted data and show that it achieves better denoising results than other self-supervised denoising methods

Preparation of pullulan-shellac edible films with improved water-resistance and UV barrier properties for Chinese cherries preservation

In order to improve the water-resistance and ultraviolet (UV) barrier properties of pullulan (PL) film, edible composite films were prepared by solution casting based on PL and shellac (SH). The physical properties, thermal stability, mechanical properties, water vapor permeability and UV barrier of the composite films were characterized. The analysis of the functional properties revealed that the composite films had good compatibility, water-resistance (33.50˚–60.15˚), and strong UV-blocking properties (84.60%–0.00%). The addition of SH improved the water-resistance and mechanical properties of the films, but the thermal stability was slightly reduced. These films can be used as an edible coating on Chinese cherries. According to the characteristics of weight loss, decay incidence and other indicators of cherries, the PL-SH composite films can keep Chinese cherries fresh at room temperature at least for 7 days. Therefore, PL-SH composite films may be an environmentally friendly packaging material with application prospects in the fruit preservation industry

Enabling small band-gap semiconductors for solar water oxidation using multifunctional NiOx coating

Technol. important small band gap (< 2 eV) semiconductors must be stabilized against corrosion or passivation in aq. electrolytes before they can be used as photoelectrodes that directly produce fuels from sunlight. In addn., incorporation of electrocatalysts on the surface of the photoelectrodes is required for efficient oxidn. of H_2O to O_2(g) and redn. of H_2O or H_2O and CO_2 to fuels. Stabilization of technol. important semiconductors against photocorrosion and photopassivation would have a significant impact on photoelectrochem. energy conversion, and could enable the development of a new generation of robust integrated devices for efficient solar-driven water splitting and solar-driven CO_2 redn. Previous efforts have been extensively dedicated on elongating the lifetime of semiconductors under harsh fuel forming reaction conditions esp. during the water oxidn. half reaction. To date, the energy conversion performances and stability were limited on these systems, obscuring the realization of integrated solar fuel devices. In this work, we presented our recent effort on prepn. of a multifunctional coating using Ni oxide, which provides multiple important functions on semiconductor photoelectrodes surfaces, including chem./corrosion protection, elec. conducting, optical transparent/antireflective, and inherent electrocatalytic activity