Local electrochemical impedance spectroscopy: A review and some recent developments

Local electrochemical impedance spectroscopy (LEIS), which provides a powerful tool for exploration of electrode heterogeneity, has its roots in the development of electrochemical techniques employing scanning of microelectrodes. The historical development of local impedance spectroscopy measurements is reviewed, and guidelines are presented for implementation of LEIS. The factors which control the limiting spatial resolution of the technique are identified. The mathematical foundation for the technique is reviewed, including definitions of interfacial and local Ohmic impedances on both local and global scales. Experimental results for the reduction of ferricyanide show the correspondence between local and global impedances. Simulations for a single Faradaic reaction on a disk electrode embedded in an insulator are used to show that the Ohmic contribution, traditionally considered to be a real value, can have complex character in certain frequency ranges

A cross self-curing system for an aqueous-based PU hybrid

[[abstract]]Isocyanate (NCO)-terminated polyurethane (PU) prepolymer comprises carboxylic acid, which is obtained from a conventional PU preparation procedure. Aziridine-terminated PU oligomer is prepared by an addition reaction of aziridine to NCO-terminated PU prepolymer after it is neutralized with triethylamine. This PU oligomer is then dispersed with water and becomes a single-component self-curable aqueous-based PU dispersion (PU-AZ). PU carboxyl groups are not only the ionic centers stabilizing the aqueous polymer dispersion, but also serve as PU curing sites toward the curing reaction with its aziridine terminal groups on drying. This self-curable PU-AZ dispersion is miscible and compatible with other carboxyl groups containing aqueous-based PU dispersion in any ratio, which results in a cross self-cured PU hybrid formation on drying. This cross self-cured PU hybridization process allows property modification and the curing of PU simultaneously without the addition of any external curing agent.[[notice]]補正完畢[[journaltype]]國外[[incitationindex]]SC

Layer-wise Conditioning Analysis in Exploring the Learning Dynamics of DNNs

Conditioning analysis uncovers the landscape of an optimization objective by exploring the spectrum of its curvature matrix. This has been well explored theoretically for linear models. We extend this analysis to deep neural networks (DNNs) in order to investigate their learning dynamics. To this end, we propose layer-wise conditioning analysis, which explores the optimization landscape with respect to each layer independently. Such an analysis is theoretically supported under mild assumptions that approximately hold in practice. Based on our analysis, we show that batch normalization (BN) can stabilize the training, but sometimes result in the false impression of a local minimum, which has detrimental effects on the learning. Besides, we experimentally observe that BN can improve the layer-wise conditioning of the optimization problem. Finally, we find that the last linear layer of a very deep residual network displays ill-conditioned behavior. We solve this problem by only adding one BN layer before the last linear layer, which achieves improved performance over the original and pre-activation residual networks.Comment: Accepted to ECCV 2020. The code is available at: https://github.com/huangleiBuaa/LayerwiseC

MCMT-GAN: Multi-Task Coherent Modality Transferable GAN for 3D Brain Image Synthesis

© 1992-2012 IEEE. The ability to synthesize multi-modality data is highly desirable for many computer-aided medical applications, e.g. clinical diagnosis and neuroscience research, since rich imaging cohorts offer diverse and complementary information unraveling human tissues. However, collecting acquisitions can be limited by adversary factors such as patient discomfort, expensive cost and scanner unavailability. In this paper, we propose a multi-task coherent modality transferable GAN (MCMT-GAN) to address this issue for brain MRI synthesis in an unsupervised manner. Through combining the bidirectional adversarial loss, cycle-consistency loss, domain adapted loss and manifold regularization in a volumetric space, MCMT-GAN is robust for multi-modality brain image synthesis with visually high fidelity. In addition, we complement discriminators collaboratively working with segmentors which ensure the usefulness of our results to segmentation task. Experiments evaluated on various cross-modality synthesis show that our method produces visually impressive results with substitutability for clinical post-processing and also exceeds the state-of-the-art methods

Visual Tracking by Sampling in Part Space

In this paper, we present a novel part-based visual tracking method from the perspective of probability sampling. Specifically, we represent the target by a part space with two online learned probabilities to capture the structure of the target. The proposal distribution memorizes the historical performance of different parts, and it is used for the first round of part selection. The acceptance probability validates the specific tracking stability of each part in a frame, and it determines whether to accept its vote or to reject it. By doing this, we transform the complex online part selection problem into a probability learning one, which is easier to tackle. The observation model of each part is constructed by an improved supervised descent method and is learned in an incremental manner. Experimental results on two benchmarks demonstrate the competitive performance of our tracker against state-of-the-art methods