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A new hyperbolic-type memristor emulator is presented and its frequency-dependent pinched hysteresis loops are analyzed by numerical simulations and confirmed by hardware experiments. Based on the emulator, a novel hyperbolic-type memristor based 3-neuron Hopfield neural network (HNN) is proposed, which is achieved through substituting one coupling-connection weight with a memristive synaptic weight. It is numerically shown that the memristive HNN has a dynamical transition from chaotic, to periodic, and further to stable point behaviors with the variations of the memristor inner parameter, implying the stabilization effect of the hyperbolic-type memristor on the chaotic HNN. Of particular interest, it should be highly stressed that for different memristor inner parameters, different coexisting behaviors of asymmetric attractors are emerged under different initial conditions, leading to the existence of multistable oscillation states in the memristive HNN. Furthermore, by using commercial discrete components, a nonlinear circuit is designed and PSPICE circuit simulations and hardware experiments are performed. The results simulated and captured from the realization circuit are consistent with numerical simulations, which well verify the facticity of coexisting asymmetric attractors' behaviors.

Abstract2‐Propargyl alcohols are widely used in organic reactions. Their great success is rooted in the presence of multiple functional groups. This review will focus on six types of mechanisms: (i) 2‐propargyl alcohols as carbocation precursors, (ii) alkynes as electrophiles, (iii) alcohols as nucleophiles, (iv) fracture via ketones or elimination into alkene intermediates, (v) oxidation into alkynyl‐ortho‐quinone methides or carbonyl intermediates, and (vi) ring expansions. Reactions involving alkanes, alkenes, alkynes, arenes, alcohols, amines, amides, ketones, CO2, hydrazine, iodobenzene, azides, carbenes, arylboronic acids, etc. will be discussed. We hope that this review will help to promote future research in this area.magnified image

One popular approach to interactively segment the foreground object of interest from an image is to annotate a bounding box that covers the foreground object. Then, a binary labeling is performed to achieve a refined segmentation. One major issue of the existing algorithms for such interactive image segmentation is their preference of an input bounding box that tightly encloses the foreground object. This increases the annotation burden, and prevents these algorithms from utilizing automatically detected bounding boxes. In this paper, we develop a new LooseCut algorithm that can handle cases where the input bounding box only loosely covers the foreground object. We propose a new Markov Random Fields (MRF) model for segmentation with loosely bounded boxes, including a global similarity constraint to better distinguish the foreground and background, and an additional energy term to encourage consistent labeling of similar-appearance pixels. This MRF model is then solved by an iterated max-flow algorithm. In the experiments, we evaluate LooseCut in three publicly-available image datasets, and compare its performance against several state-of-the-art interactive image segmentation algorithms. We also show that LooseCut can be used for enhancing the performance of unsupervised video segmentation and image saliency detection.

The design and construction of artificial light-harvesting system in water by mimicking the energy transfer cascade in natural photosynthesis are of significant importance. Herein, we report an efficient two-step sequential...