Random Weights Networks Work as Loss Prior Constraint for Image Restoration

In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''. Inspired by Functional theory, we provide several alternative solutions to implement our belief in the strict mathematical manifolds including Taylor's Unfolding Network, Invertible Neural Network, Central Difference Convolution and Zero-order Filtering as ``random weights network prototype'' with respect of the following four levels: 1) the different random weights strategies; 2) the different network architectures, \emph{eg,} pure convolution layer or transformer; 3) the different network architecture depths; 4) the different numbers of random weights network combination. Furthermore, to enlarge the capability of the randomly initialized manifolds, we devise the manner of random weights in the following two variants: 1) the weights are randomly initialized only once during the whole training procedure; 2) the weights are randomly initialized at each training iteration epoch. Our propose belief can be directly inserted into existing networks without any training and testing computational cost. Extensive experiments across multiple image restoration tasks, including image de-noising, low-light image enhancement, guided image super-resolution demonstrate the consistent performance gains obtained by introducing our belief. To emphasize, our main focus is to spark the realms of loss function and save their current neglected status. Code will be publicly available

Enhanced broad-band extraordinary optical transmission through subwavelength perforated metallic films on strongly polarizable substrates

We demonstrate through simulations and experiments that a perforated metallic film, with subwavelength perforation dimensions and spacing, deposited on a substrate with a sufficiently large dielectric constant, can develop a broad- band frequency window where the transmittance of light into the substrate becomes essentially equal to that in the film absence. We show that the location of this broad-band extraordinary optical transmission window can be engineered in a wide frequency range (from IR to UV), by varying the geometry and the material of the perforated film as well as the dielectric constant of the substrate. This effect could be useful in the development of transparent conducting electrodes for various photonic and photovoltaic devices

Sample selection based on kernel-subclustering for the signal reconstruction of multifunctional sensors

The signal reconstruction methods based on inverse modeling for the signal reconstruction of multifunctional sensors have been widely studied in recent years. To improve the accuracy, the reconstruction methods have become more and more complicated because of the increase in the model parameters and sample points. However, there is another factor that affects the reconstruction accuracy, the position of the sample points, which has not been studied. A reasonable selection of the sample points could improve the signal reconstruction quality in at least two ways: improved accuracy with the same number of sample points or the same accuracy obtained with a smaller number of sample points. Both ways are valuable for improving the accuracy and decreasing the workload, especially for large batches of multifunctional sensors. In this paper, we propose a sample selection method based on kernel-subclustering distill groupings of the sample data and produce the representation of the data set for inverse modeling. The method calculates the distance between two data points based on the kernel-induced distance instead of the conventional distance. The kernel function is a generalization of the distance metric by mapping the data that are non-separable in the original space into homogeneous groups in the high-dimensional space. The method obtained the best results compared with the other three methods in the simulation

Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs

Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction

Anti-Inflammatory Dipeptide, a Metabolite from Ambioba Secretion, Protects Cerebral Ischemia Injury by Blocking Apoptosis Via p-JNK/Bax Pathway

MQ (l-methionyl-l-glutamic acid), anti-inflammatory dipeptide, is one of the metabolites of monocyte locomotion inhibitory factor, a thermostable pentapeptide secreted by Entamoeba histolytica. Monocyte locomotion inhibitory factor injection has been approved as an investigational drug for the potential neural protection in acute ischemic stroke. This study further investigated the neuroprotective effect of MQ in ischemic brain damage. Ischemia-reperfusion injury of the brain was induced in the rat model by middle cerebral artery occlusion. 2,3,5-triphenyltetrazolium chloride staining assay was used to measure cerebral infarction areas in rats. Laser Doppler measurement instrument was used to detect blood flow changes in the rat model. Nissl staining and NeuN staining were utilized to observe the numbers and structures of neuron cells, and the pathological changes in the brain tissues were examined by hematoxylin–eosin staining. Terminal deoxynucleotidyl transferase deoxyuridine triphosphate nick end labeling (TUNEL) staining was used to assess cell apoptosis. The changes in oxidative stress indexes, superoxide dismutase and malondialdehyde (MDA), were measured in serum. Methyl thiazolyl tetrazolium was used to measure the survival rates of PC12 cells. Flow cytometry assessed the apoptosis rates and the levels of reactive oxygen species. Real-time PCR was used to evaluate the mRNA expression levels, and Western blotting was used to analyze the changes in protein levels of p-JNK, Bax, cleaved Caspase3. We revealed that MQ improved neurobehavior, decreased cerebral infarction areas, altered blood flow volume, and the morphology of the cortex and hippocampus. On the other hand, it decreased the apoptosis of cortical neurons and the levels of MDA, and increased the levels of superoxide dismutase. In vitro studies demonstrated that MQ enhanced the cell survival rates and decreased the levels of reactive oxygen species. Compared to the oxygen-glucose deprivation/reperfusion group, the protein and mRNA expressions of p-JNK, Bax, cleaved Caspase3 was decreased significantly. These findings suggested that MQ exerts a neuroprotective effect in cerebral ischemia by blocking apoptosis via the p-JNK/Bax pathway

What Motivates Online Customer Review Behaviors? A Framework Combining Customer-driven, Vendor-driven, and Website-driven Motivations

Previous research has examined the motivations for online customer review behaviors mainly from customer perspective. Adopting the self-determination theory, this ERF paper builds a theoretical framework explaining the motivations for online customer review behaviors combining customer-driven, vendor-driven, and website-driven motivations. In particular, this study aims to provide explanation and support for the interaction effect of customer-driven motivations and vendor/website-driven motivations, offering a fresh angle in understanding customers’ online review behaviors. We plan to conduct a field study on Taobao.com to collect data and test the framework. SPSS and SEM software packages will be used for verifying the measurement and testing the whole model. The study will contribute to the literature about motivations for online customer review behaviors by providing a more integrated model based on the knowledge of self-determination theory. The study will also have practical implications by offering guidelines to online vendors and website designers