DNA nanotechnology-enabled chiral plasmonics: from static to dynamic

In this Account, we discuss a variety of static and dynamic chiral plasmonic nanostructures enabled by DNA nanotechnology. In the category of static plasmonic systems, we first show chiral plasmonic nanostructures based on spherical AuNPs, including plasmonic helices, toroids, and tetramers. To enhance the CD responses, anisotropic gold nanorods with larger extinction coefficients are utilized to create chiral plasmonic crosses and helical superstructures. Next, we highlight the inevitable evolution from static to dynamic plasmonic systems along with the fast development of this interdisciplinary field. Several dynamic plasmonic systems are reviewed according to their working mechanisms.Comment: 7 figure

A Stochastic Geometry Approach to Energy Efficiency in Relay-Assisted Cellular Networks

Though cooperative relaying is believed to be a promising technology to improve the energy efficiency of cellular networks, the relays' static power consumption might worsen the energy efficiency therefore can not be neglected. In this paper, we focus on whether and how the energy efficiency of cellular networks can be improved via relays. Based on the spatial Poisson point process, an analytical model is proposed to evaluate the energy efficiency of relay-assisted cellular networks. With the aid of the technical tools of stochastic geometry, we derive the distributions of signal-to-interference-plus-noise ratios (SINRs) and mean achievable rates of both non-cooperative users and cooperative users. The energy efficiency measured by "bps/Hz/W" is expressed subsequently. These established expressions are amenable to numerical evaluation and corroborated by simulation results.Comment: 6 pages, 5 figures, accepted by IEEE Globecom'12. arXiv admin note: text overlap with arXiv:1108.1257 by other author

Circuits for active vision : parallel tectothalamocortical visual pathways in the mouse.

Vision is a critical sensation for the interaction between humans and their surrounding environment. The eyes connect with the brain via retinal ganglion cell axons, which transmit visual sensory information from the periphery into the central nervous system for further processing, eventually leading to visual perception and the visual guidance of movement. Two main targets of retinal axons are the superior colliculus (SC) and the dorsal thalamus. From the SC, visual information is conveyed to the dorsal thalamus, and from the dorsal thalamus visual information is conveyed to the cortex, striatum and amygdala. This dissertation is focused on the functional properties of two parallel pathways from the SC to the dorsal thalamus: a pathway from the SC to the retinorecipient dorsolateral geniculate nucleus (dLGN) to the cortex, and a pathway from the SC to the pulvinar nucleus to the cortex. The experiments described in this dissertation used viral vector injections, tract tracing, in vitro whole cell patch clamp, optogenetics, electron and confocal microscopy, transgenic mouse lines and immunohistochemical staining techniques to elucidate the roles of the SC-dLGN-cortex pathway and SC-pulvinar-cortex pathway in visual coding. The first series of experiments revealed that SC and retinal inputs converge to innervate the proximal dendrites of cells in the dorsolateral shell of the dLGN that project to layer I of the striate cortex. The second series of experiments revealed the organization of subdivisions of the pulvinar nucleus in relation to inputs from the SC. The final series of experiments revealed the distribution and ultrastructure of pulvinocortical terminals, and identified the cell types activated by pulvinocortical synapses. Major targets of pulvinocortical terminals were identified as corticostriatal cells, suggesting that pulvinar acts as a hub connecting the SC, cortex and striatum

Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, few work consider the changing trend of interest. In this paper, we propose a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201