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Topological Polarization Singularities in Metaphotonics
[article]
2020
arXiv
pre-print
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Polarization singularities of vectorial electromagnetic fields locate at the positions (such as points, lines, or surfaces) where properties of polarization ellipses are not defined. They are manifested as circular and linear polarization, for which respectively the semi-major axes and normal vectors of polarization ellipses become indefinite. First observed in conical diffraction in the 1830s, the field of polarization singularities has been systematically reshaped and deepened by many
arXiv:2012.04919v1
fatcat:fiehdhn6pbhljgttcxtdgflvk4
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... of wave optics. Together with other exotic phenomena such as non-Hermiticity and topology, polarization singularities have been introduced into the vibrant field of nanophotonics, rendering unprecedented flexibilities for manipulations of light-matter interactions at the nanoscale. Here we review the recent results on the generation and observation of polarization singularities in metaphotonics. We start with the discussion of polarization singularities in the Mie theory, where both electric and magnetic multipoles are explored from perspectives of local and global polarization properties. We then proceed with the discussion of various photonic-crystal structures, for which both near- and far-field patterns manifest diverse polarization singularities characterized by the integer Poincare or more general half-integer Hopf indices (topological charges). Next, we review the most recent studies of conversions from polarization to phase singularities in scalar wave optics, demonstrating how bound states in the continuum can be exploited to generate directly optical vortices of various charges. Throughout our paper, we discuss and highlight several fundamental concepts and demonstrate their close connections and special links to metaphotonics. We believe polarization singularities can provide novel perspectives for light-matter manipulation for both fundamental studies and their practical applications.
Lossy Image Compression with Compressive Autoencoders
[article]
2017
arXiv
pre-print
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We propose a new approach to the problem of optimizing autoencoders for lossy image compression. New media formats, changing hardware technology, as well as diverse requirements and content types create a need for compression algorithms which are more flexible than existing codecs. Autoencoders have the potential to address this need, but are difficult to optimize directly due to the inherent non-differentiabilty of the compression loss. We here show that minimal changes to the loss are
arXiv:1703.00395v1
fatcat:ftno22l3tvbhthllmgomabdxtq
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... nt to train deep autoencoders competitive with JPEG 2000 and outperforming recently proposed approaches based on RNNs. Our network is furthermore computationally efficient thanks to a sub-pixel architecture, which makes it suitable for high-resolution images. This is in contrast to previous work on autoencoders for compression using coarser approximations, shallower architectures, computationally expensive methods, or focusing on small images.
Topological polarization singularities in metaphotonics
2021
Nanophotonics
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Polarization singularities of vectorial electromagnetic fields locate at the positions where properties of polarization ellipses are not defined. First observed for conical diffraction in 1830s, polarization singularities have been studied systematically with the underlying concepts being reshaped and deepened by many pioneers of wave optics. Here we review the recent results on the generation and observation of polarization singularities in metaphotonics. We start with the discussion of
doi:10.1515/nanoph-2020-0654
fatcat:uy7zrcoozbaadm4qa6jznabw5q
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... ation singularities in the Mie theory, where both electric and magnetic multipoles are explored from perspectives of local and global polarization properties. We then proceed with the discussion of various photonic-crystal structures, for which both near- and far-field patterns manifest diverse polarization singularities characterized by the integer Poincaré or more general half-integer Hopf indices (topological charges). Next, we review the most recent studies of conversions from polarization to phase singularities in scalar wave optics, demonstrating how bound states in the continuum can be exploited to generate directly optical vortices of various charges. Throughout our paper, we discuss and highlight several fundamental concepts and demonstrate their close connections and special links to metaphotonics. We believe polarization singularities can provide novel perspectives for light-matter manipulation for both fundamental studies and their practical applications.
Adaptation with transcriptional regulation
2017
Scientific Reports
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Biochemical adaptation is one of the basic functions that are widely implemented in biological systems for a variety of purposes such as signal sensing, stress response and homeostasis. The adaptation time scales span from milliseconds to days, involving different regulatory machineries in different processes. The adaptive networks with enzymatic regulation (ERNs) have been investigated in detail. But it remains unclear if and how other forms of regulation will impact the network topology and
doi:10.1038/srep42648
pmid:28233824
pmcid:PMC5324054
fatcat:ytn73pbjwrhr7etlu5x5sddzs4
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... her features of the function. Here, we systematically studied three-node transcriptional regulatory networks (TRNs), with three different types of gene regulation logics. We found that the topologies of adaptive gene regulatory networks can still be grouped into two general classes: negative feedback loop (NFBL) and incoherent feed-forward loop (IFFL), but with some distinct topological features comparing to the enzymatic networks. Specifically, an auto-activation loop on the buffer node is necessary for the NFBL class. For IFFL class, the control node can be either a proportional node or an inversely-proportional node. Furthermore, the tunability of adaptive behavior differs between TRNs and ERNs. Our findings highlight the role of regulation forms in network topology, implementation and dynamics.
Adaptive Task Planning for Large-Scale Robotized Warehouses
[article]
2022
arXiv
pre-print
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Robotized warehouses are deployed to automatically distribute millions of items brought by the massive logistic orders from e-commerce. A key to automated item distribution is to plan paths for robots, also known as task planning, where each task is to deliver racks with items to pickers for processing and then return the rack back. Prior solutions are unfit for large-scale robotized warehouses due to the inflexibility to time-varying item arrivals and the low efficiency for high throughput. In
arXiv:2205.00831v1
fatcat:3erbwyvmj5hrjkt3mjcoimka5e
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... this paper, we propose a new task planning problem called TPRW, which aims to minimize the end-to-end makespan that incorporates the entire item distribution pipeline, known as a fulfilment cycle. Direct extensions from state-of-the-art path finding methods are ineffective to solve the TPRW problem because they fail to adapt to the bottleneck variations of fulfillment cycles. In response, we propose Efficient Adaptive Task Planning, a framework for large-scale robotized warehouses with time-varying item arrivals. It adaptively selects racks to fulfill at each timestamp via reinforcement learning, accounting for the time-varying bottleneck of the fulfillment cycles. Then it finds paths for robots to transport the selected racks. The framework adopts a series of efficient optimizations on both time and memory to handle large-scale item throughput. Evaluations on both synthesized and real data show an improvement of 37.1% in effectiveness and 75.5% in efficiency over the state-of-the-arts.
Deep Bayesian Bandits: Exploring in Online Personalized Recommendations
[article]
2020
arXiv
pre-print
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Recommender systems trained in a continuous learning fashion are plagued by the feedback loop problem, also known as algorithmic bias. This causes a newly trained model to act greedily and favor items that have already been engaged by users. This behavior is particularly harmful in personalised ads recommendations, as it can also cause new campaigns to remain unexplored. Exploration aims to address this limitation by providing new information about the environment, which encompasses user
arXiv:2008.00727v1
fatcat:eu5rpcg6abhmxlmg4ewnbi7zga
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... nce, and can lead to higher long-term reward. In this work, we formulate a display advertising recommender as a contextual bandit and implement exploration techniques that require sampling from the posterior distribution of click-through-rates in a computationally tractable manner. Traditional large-scale deep learning models do not provide uncertainty estimates by default. We approximate these uncertainty measurements of the predictions by employing a bootstrapped model with multiple heads and dropout units. We benchmark a number of different models in an offline simulation environment using a publicly available dataset of user-ads engagements. We test our proposed deep Bayesian bandits algorithm in the offline simulation and online AB setting with large-scale production traffic, where we demonstrate a positive gain of our exploration model.
Detecting Small Scale Pedestrians and Anthropomorphic Negative Samples Based on Light-Field Imaging
2020
IEEE Access
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WENZHE ZHANG received the bachelor's degree from the Weifang Institute, Shandong, China, in 2018. ...
Shi et al. [24] introduced a novel technique that could simultaneously measure 3D model geometry and 3D surface pressure with a single light-field camera. Zhao et al. ...
doi:10.1109/access.2020.2999694
fatcat:hgtlsiaxlreqnmfl4bwget2xnu
Fast Face-swap Using Convolutional Neural Networks
[article]
2017
arXiv
pre-print
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We consider the problem of face swapping in images, where an input identity is transformed into a target identity while preserving pose, facial expression, and lighting. To perform this mapping, we use convolutional neural networks trained to capture the appearance of the target identity from an unstructured collection of his/her photographs.This approach is enabled by framing the face swapping problem in terms of style transfer, where the goal is to render an image in the style of another one.
arXiv:1611.09577v2
fatcat:ykn42p6uonafrhbpg6onbjr3xa
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... Building on recent advances in this area, we devise a new loss function that enables the network to produce highly photorealistic results. By combining neural networks with simple pre- and post-processing steps, we aim at making face swap work in real-time with no input from the user.
Ways to achieve efficient non-local vortex beam generation
2021
Nanophotonics
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Lei Shi was further supported by the National Natural Science Foundation of China (11774063 and 11727811). ...
doi:10.1515/nanoph-2021-0342
fatcat:y35jj5u6u5dupnzwd6ygj3pwpm
Is the deconvolution layer the same as a convolutional layer?
[article]
2016
arXiv
pre-print
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In this note, we want to focus on aspects related to two questions most people asked us at CVPR about the network we presented. Firstly, What is the relationship between our proposed layer and the deconvolution layer? And secondly, why are convolutions in low-resolution (LR) space a better choice? These are key questions we tried to answer in the paper, but we were not able to go into as much depth and clarity as we would have liked in the space allowance. To better answer these questions in
arXiv:1609.07009v1
fatcat:jvln34jjwff6fibfussol7kppe
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... s note, we first discuss the relationships between the deconvolution layer in the forms of the transposed convolution layer, the sub-pixel convolutional layer and our efficient sub-pixel convolutional layer. We will refer to our efficient sub-pixel convolutional layer as a convolutional layer in LR space to distinguish it from the common sub-pixel convolutional layer. We will then show that for a fixed computational budget and complexity, a network with convolutions exclusively in LR space has more representation power at the same speed than a network that first upsamples the input in high resolution space.
A Feature-Enhanced Anchor-Free Network for UAV Vehicle Detection
2020
Remote Sensing
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Vehicle detection based on unmanned aerial vehicle (UAV) images is a challenging task. One reason is that the objects are small size, low-resolution, and large scale variations, resulting in weak feature representation. Another reason is the imbalance between positive and negative examples. In this paper, we propose a novel architecture for UAV vehicle detection to solve above problems. In detail, we use anchor-free mechanism to eliminate predefined anchors, which can reduce complicated
doi:10.3390/rs12172729
fatcat:ds62kmgmvbb5jnclvtznxntacy
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... ion and relieve the imbalance between positive and negative samples. Meanwhile, to enhance the features for vehicles, we design a multi-scale semantic enhancement block (MSEB) and an effective 49-layer backbone which is based on the DetNet59. The proposed network offers appropriate receptive fields that match the small-sized vehicles, and involves precise localization information provided by the contexts with high resolution. The MSEB strengthens discriminative feature representation at various scales, without reducing the spatial resolution of prediction layers. Experiments show that the proposed method achieves the state-of-the-art performance. Particularly, the main part of vehicles, much smaller ones, the accuracy is about 2% higher than other existing methods.
The core-inversible micelles for hydrophilic drug delivery
2013
Chemical Communications
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Observation of optical states below light cone with compound lattices
[article]
2018
arXiv
pre-print
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For optical systems, states inside the light cone could be detected by far-field measurement, while those below the light cone are not detectable by far-field measurement. A new method for far-field detection has been developed for observing states below the light cone with compound lattices. The basic mechanism involved is that periodic weak scattering leads to band folding, making the states out of the light cone to occur inside. By using polarization-resolved momentum-space imaging
arXiv:1811.10235v1
fatcat:ggt4b2wdmnh3bbiqw7f4e2umni
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... py the band structures and iso-frequency contours of plasmonic lattices with different dimensions and symmetries are experimentally mapped out, in good agreement with the simulation.
Observing half and integer polarization vortices at band degeneracies
[article]
2017
arXiv
pre-print
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Far-field polarization vortices were recently found on singlet bands in the momentum-space of two-dimensional photonic lattices, also known as the dark states and bound states in continuum. Here, we theoretically proposed and experimentally verified the existence of the polarization vortices at the degenerate points of photonic dispersions, whose vortex cores can be radiative bright states. Half-charged vortices were generated from the Dirac points of π Berry phase and integer-charged vortices
arXiv:1712.09296v1
fatcat:6biwysur7jh4hmycok2yfo3gra
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... ere generated from a quadratic degeneracy. Using a home-made polarization-resolved momentum-space imaging spectroscopy, we observed the complete evolution of the splitting from one quadratic point to a pair of Dirac cones by tracking the winding of the polarization vectors and the full spectrum of iso-frequency contours.
Learning to Rank For Push Notifications Using Pairwise Expected Regret
[article]
2022
arXiv
pre-print
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Listwise ranking losses have been widely studied in recommender systems. However, new paradigms of content consumption present new challenges for ranking methods. In this work we contribute an analysis of learning to rank for personalized mobile push notifications and discuss the unique challenges this presents compared to traditional ranking problems. To address these challenges, we introduce a novel ranking loss based on weighting the pairwise loss between candidates by the expected regret
arXiv:2201.07681v1
fatcat:rsr24tsk3rb7lirbvuiumgqqgy
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... urred for misordering the pair. We demonstrate that the proposed method can outperform prior methods both in a simulated environment and in a production experiment on a major social network.
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