Robust Wireless Body Area Networks Coexistence: A Game Theoretic Approach to Time-Division MAC

The enabling of wireless body area networks (WBANs) coexistence by radio interference mitigation is very important due to a rapid growth in potential users, and a lack of a central coordinator among WBANs that are closely located. In this paper, we propose a TDMA based MAC layer Scheme, with a back-off mechanism that reduces packet collision probability; and estimate performance using a Markov chain model. Based on the MAC layer scheme, a novel non-cooperative game is proposed to jointly adjust sensor node's transmit power and rate. In comparison with the state-of-art, simulation that includes empirical data shows that the proposed approach leads to higher throughput and longer node lifespan as WBAN wearers dynamically move into each other's vicinity. Moreover, by adaptively tuning contention windows size an alternative game is developed, which significantly reduces the latency. Both proposed games provide robust transmission under strong inter-WBAN interferences, but are demonstrated to be applicable to different scenarios. The uniqueness and existence of Nash Equilibrium (NE), as well as close-to-optimum social efficiency, is also proven for both games.Comment: 31 pages, 17 figures, submitted for possible publication on ACM Transactions on Sensor Networks (TOSN

Enhancement of 3rd-harmonics generation during ultrashort pulse diffraction in multi-layer volume-grating

Successful phase-matching methods for Third Harmonics Generation (THG) include phase-matching in birefringent crystal and quasi-phase-matching (QPM) in crystal with periodically poled domains. However, these methods are not feasible in some isotropic materials (e.g. fused silica and photosensitive silicate glass). It was known that volume-grating in isotropic materials can independently generate frequency-converted waves. One of disadvantages of single-layer volume-grating is that the brightness of harmonic emission can not be enhanced by increasing the grating thickness. In this paper, a THG device with stratified sub-gratings was designed to enhance THG in isotropic materials: several sub-gratings were arranged parallel, and the grating-figures misalignment between neighboring sub-gratings was pre-fabricated. In terms of extension of interaction length in THG, our multi-layer sub-grating is formally equivalent to the multi-layer periodically poled crystal (e.g. lithium niobate) in conventional QPM approach. According to the calculation results, the N-layer (N >2) can, in principle, generate TH output intensity of N*N times stronger than single-layer volume-grating does, also compared to N times stronger than N-layer without figures-misalignment. The effect of random fabrication error in grating thickness on normalized conversion efficiency was discussed

Realizing Topological Transition in a Non-Hermitian Quantum Walk with Circuit QED

We extend the non-Hermitian one-dimensional quantum walk model [Phys. Rev. Lett. 102, 065703 (2009)] by taking the dephasing effect into account. We prove that the feature of topological transition does not change even when dephasing between the sites within units is present. The potential experimental observation of our theoretical results in the circuit QED system consisting of superconducting qubit coupled to a superconducting resonator mode is discussed and numerically simulated. The results clearly show a topological transition in quantum walk and display the robustness of such a system to the decay and dephasing of qubits. We also discuss how to extend this model to higher dimension in the circuit QED system.Comment: 8 pages, 9 figures; published versio

Learning Fair Representations via an Adversarial Framework

Fairness has become a central issue for our research community as classification algorithms are adopted in societally critical domains such as recidivism prediction and loan approval. In this work, we consider the potential bias based on protected attributes (e.g., race and gender), and tackle this problem by learning latent representations of individuals that are statistically indistinguishable between protected groups while sufficiently preserving other information for classification. To do that, we develop a minimax adversarial framework with a generator to capture the data distribution and generate latent representations, and a critic to ensure that the distributions across different protected groups are similar. Our framework provides a theoretical guarantee with respect to statistical parity and individual fairness. Empirical results on four real-world datasets also show that the learned representation can effectively be used for classification tasks such as credit risk prediction while obstructing information related to protected groups, especially when removing protected attributes is not sufficient for fair classification

Pseudospins and topological effects of phonons in a Kekule lattice

The search for exotic topological effects of phonons has attracted enormous interest for both fundamental science and practical applications. By studying phonons in a Kekul\'e lattice, we find a new type of pseudospins characterized by quantized Berry phases and pseudoangular momenta, which introduces various novel topological effects, including topologically protected pseudospin-polarized interface states and a phonon pseudospin Hall effect. We further demonstrate a pseudospin-contrasting optical selection rule and a pseudospin Zeeman effect, giving a complete generation-manipulation-detection paradigm of the phonon pseudospin. The pseudospin and topology-related physics revealed for phonons is general and applicable for electrons, photons and other particles.Comment: 5 pages, 4 figures, accepted by PR

Neural Style Transfer: A Review

The seminal work of Gatys et al. demonstrated the power of Convolutional Neural Networks (CNNs) in creating artistic imagery by separating and recombining image content and style. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Since then, NST has become a trending topic both in academic literature and industrial applications. It is receiving increasing attention and a variety of approaches are proposed to either improve or extend the original NST algorithm. In this paper, we aim to provide a comprehensive overview of the current progress towards NST. We first propose a taxonomy of current algorithms in the field of NST. Then, we present several evaluation methods and compare different NST algorithms both qualitatively and quantitatively. The review concludes with a discussion of various applications of NST and open problems for future research. A list of papers discussed in this review, corresponding codes, pre-trained models and more comparison results are publicly available at https://github.com/ycjing/Neural-Style-Transfer-Papers.Comment: Project page: https://github.com/ycjing/Neural-Style-Transfer-Paper

Suppression of FM-to-AM conversion in third-harmonic generation by tuning the ratio of modulation depth

Issues of Frequency-to-Amplitude modulation (FM-to-AM) conversion occurred in phase-modulated third-harmonic generation (THG) process are investigated. An expression about group-velocity is theoretically derived to suppress the FM-to-AM conversion, which appears to be dependant on the ratio of modulation depth of fundamental to second-harmonic when given the same modulation frequencies of them. Simulation results indicate that the induced AM in THG process can be suppressed effectively when the expression about group-velocity is satisfied.Comment: 5 pages, 3 figure

Solitons supported by complex PT symmetric Gaussian potentials

The existence and stability of fundamental, dipole, and tripole solitons in Kerr nonlinear media with parity-time symmetric Gaussian complex potentials are reported. Fundamental solitons are stable not only in deep potentials but also in shallow potentials. Dipole and tripole solitons are stable only in deep potentials, and tripole solitons are stable in deeper potentials than for dipole solitons. The stable regions of solitons increase with increasing potential depth. The power of solitons increases with increasing propagation constant or decreasing modulation depth of the potentials.Comment: 7 pages, 11 figure

Relationship-Embedded Representation Learning for Grounding Referring Expressions

Grounding referring expressions in images aims to locate the object instance in an image described by a referring expression. It involves a joint understanding of natural language and image content, and is essential for a range of visual tasks related to human-computer interaction. As a language-to-vision matching task, the core of this problem is to not only extract all the necessary information (i.e., objects and the relationships among them) in both the image and referring expression, but also make full use of context information to align cross-modal semantic concepts in the extracted information. Unfortunately, existing work on grounding referring expressions fails to accurately extract multi-order relationships from the referring expression and associate them with the objects and their related contexts in the image. In this paper, we propose a Cross-Modal Relationship Extractor (CMRE) to adaptively highlight objects and relationships (spatial and semantic relations) related to the given expression with a cross-modal attention mechanism, and represent the extracted information as a language-guided visual relation graph. In addition, we propose a Gated Graph Convolutional Network (GGCN) to compute multimodal semantic contexts by fusing information from different modes and propagating multimodal information in the structured relation graph. Experimental results on three common benchmark datasets show that our Cross-Modal Relationship Inference Network, which consists of CMRE and GGCN, significantly surpasses all existing state-of-the-art methods. Code is available at https://github.com/sibeiyang/sgmn/tree/master/lib/cmrin_modelsComment: This paper is going to appear in TPAMI. Code is available at https://github.com/sibeiyang/sgmn/tree/master/lib/cmrin_model

Graph-Structured Referring Expression Reasoning in The Wild

Grounding referring expressions aims to locate in an image an object referred to by a natural language expression. The linguistic structure of a referring expression provides a layout of reasoning over the visual contents, and it is often crucial to align and jointly understand the image and the referring expression. In this paper, we propose a scene graph guided modular network (SGMN), which performs reasoning over a semantic graph and a scene graph with neural modules under the guidance of the linguistic structure of the expression. In particular, we model the image as a structured semantic graph, and parse the expression into a language scene graph. The language scene graph not only decodes the linguistic structure of the expression, but also has a consistent representation with the image semantic graph. In addition to exploring structured solutions to grounding referring expressions, we also propose Ref-Reasoning, a large-scale real-world dataset for structured referring expression reasoning. We automatically generate referring expressions over the scene graphs of images using diverse expression templates and functional programs. This dataset is equipped with real-world visual contents as well as semantically rich expressions with different reasoning layouts. Experimental results show that our SGMN not only significantly outperforms existing state-of-the-art algorithms on the new Ref-Reasoning dataset, but also surpasses state-of-the-art structured methods on commonly used benchmark datasets. It can also provide interpretable visual evidences of reasoning. Data and code are available at https://github.com/sibeiyang/sgmnComment: CVPR 2020 Accepted Oral Paper. Data and code are available at https://github.com/sibeiyang/sgm