arXiv subjects

Ying Wang

Publications and source records attributed to Ying Wang.

At least 325 records · Page 18Linked to original sources

arXiv2023

Ground-to-UAV Integrated Network: Low Latency Communication over Interference Channel

We present a novel and first-of-its-kind information-theoretic framework for the key design consideration and implementation of a ground-to-UAV (G2U) communication network to minimize end-to-end transmission delay in the presence of interference. The proposed framework is useful as it describes the minimum transmission latency for an uplink ground-to-UAV communication must satisfy while achieving a given level of reliability. To characterize the transmission delay, we utilize Fano's inequality and derive the tight upper bound for the capacity for the G2U uplink channel in the presence of interference, noise, and potential jamming. Subsequently, given the reliability constraint, the error exponent is obtained for the given channel. Furthermore, a relay UAV in the dual-hop relay mode, with amplify-and-forward (AF) protocol, is considered, for which we jointly obtain the optimal positions of the relay and the receiver UAVs in the presence of interference. Interestingly, in our study, we find that for both the point-to-point and relayed links, increasing the transmit power may not always be an optimal solution for delay minimization problems. Moreover, we prove that there exists an optimal height that minimizes the end-to-end transmission delay in the presence of interference. The proposed framework can be used in practice by a network controller as a system parameters selection criteria, where among a set of parameters, the parameters leading to the lowest transmission latency can be incorporated into the transmission. The based analysis further set the baseline assessment when applying Command and Control (C2) standards to mission-critical G2U and UAV-to-UAV(U2U) services.

cs.IT↗

arXiv2023

Multivariate Intrinsic Local Polynomial Regression on Isometric Riemannian Manifolds: Applications to Positive Definite Data

The paper introduces a novel non-parametric Riemannian regression method using Isometric Riemannian Manifolds (IRMs). The proposed technique, Intrinsic Local Polynomial Regression on IRMs (ILPR-IRMs), enables global data mapping between Riemannian manifolds while preserving underlying geometries. The ILPR method is generalized to handle multivariate covariates on any Riemannian manifold and isometry. Specifically, for manifolds equipped with Euclidean Pullback Metrics (EPMs), a closed analytical formula is derived for the multivariate ILPR (ILPR-EPM). Asymptotic statistical properties of the ILPR-EPM for the multivariate local linear case are established, including a formula for the asymptotic bias, establishing estimator consistency. The paper showcases possible applications of the method by focusing on a group of Riemannian metrics on the Symmetric Positive Definite (SPD) manifold, which arises in machine learning and neuroscience. It is demonstrated that several metrics on the SPD manifold are EPMs, resulting in a closed analytical expression for the multivariate ILPR estimator on the SPD manifold. The paper evaluates the ILPR estimator's performance under two specific EPMs, Log-Cholesky and Log-Euclidean, on simulated data on the SPD manifold and compares it with extrinsic LPR using the Affine-Invariant when scaling the manifold and covariate dimension. The results show that the ILPR using the Log-Cholesky metric is computationally faster and provides a better trade-off between error and time than other metrics. Finally, the Log-Cholesky metric on the SPD manifold is employed to implement an efficient and intrinsic version of Rie-SNE for visualizing high-dimensional SPD data. The code for implementing ILPR-EPMs and other relevant calculations is available on the GitHub page.

stat.ME↗

arXiv2023

Quantum Key Distribution using Deterministic Single-Photon Sources over a Field-Installed Fibre Link

Quantum-dot-based single-photon sources are key assets for quantum information technology, supplying on-demand scalable quantum resources for computing and communication. However, longlasting issues such as limited long-term stability and source brightness have traditionally impeded their adoption in real-world applications. Here, we realize a quantum key distribution field trial using true single photons across an 18-km-long dark fibre, located in the Copenhagen metropolitan area, using an optimized, state-of-the-art, quantum-dot single-photon source frequency-converted to the telecom wavelength. A secret key generation rate of >2 kbits/s realized over a 9.6 dB channel loss is achieved with a polarization-encoded BB84 scheme, showing remarkable stability for more than 24 hours of continuous operation. Our results highlight the maturity of deterministic single-photon source technology while paving the way for advanced single-photon-based communication protocols, including fully device-independent quantum key distribution, towards the goal of a quantum internet.

quant-ph↗

arXiv2023

Valley-dependent topological edge states in plasma photonic crystals

Plasma photonic crystals designed in this paper are composed of gas discharge tubes to control the flow of electromagnetic waves. The band structures calculated by the finite element method are consistent with the experimental results in that two distinct attenuation peaks appear in the ranges of 1 GHz to 2.5 GHz and 5 GHz to 6 GHz. Electromagnetic parameters of the plasma are extracted by the Nicolson-Ross-Weir method and effective medium theory. The measured electron density is between 1e11 cm-3 and 1e12 cm-3, which verifies the correctness of the parameter used in the simulation, and the collision frequency is near 1.5e10 Hz. As the band structures are corroborated by the measured scattering parameters, we introduce the concept of photonic topological insulator based on the quantum Valley Hall effect into the plasma photonic crystal. A valley-dependent plasma photonic crystal with hexagonal lattice is constructed, and the phase transition of the valley K(K') occurs by breaking the spatial inversion symmetry. Valley-spin locked topological edge states are generated and excited by chiral sources. The frequency of the non-bulk state can be dynamically regulated by the electron density. This concept paves the way for novel, tunable topological edge states. More interestingly, the Dirac cone is broken when the electron density increases to 3.1e12 cm-3, which distinguishes from the methods of applying a magnetic field and changing the symmetry of the point group.

physics.plasm-ph↗

arXiv2023

AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+

Unsupervised learning of vision transformers seeks to pretrain an encoder via pretext tasks without labels. Among them is the Masked Image Modeling (MIM) aligned with pretraining of language transformers by predicting masked patches as a pretext task. A criterion in unsupervised pretraining is the pretext task needs to be sufficiently hard to prevent the transformer encoder from learning trivial low-level features not generalizable well to downstream tasks. For this purpose, we propose an Adversarial Positional Embedding (AdPE) approach -- It distorts the local visual structures by perturbing the position encodings so that the learned transformer cannot simply use the locally correlated patches to predict the missing ones. We hypothesize that it forces the transformer encoder to learn more discriminative features in a global context with stronger generalizability to downstream tasks. We will consider both absolute and relative positional encodings, where adversarial positions can be imposed both in the embedding mode and the coordinate mode. We will also present a new MAE+ baseline that brings the performance of the MIM pretraining to a new level with the AdPE. The experiments demonstrate that our approach can improve the fine-tuning accuracy of MAE by $0.8\%$ and $0.4\%$ over 1600 epochs of pretraining ViT-B and ViT-L on Imagenet1K. For the transfer learning task, it outperforms the MAE with the ViT-B backbone by $2.6\%$ in mIoU on ADE20K, and by $3.2\%$ in AP$^{bbox}$ and $1.6\%$ in AP$^{mask}$ on COCO, respectively. These results are obtained with the AdPE being a pure MIM approach that does not use any extra models or external datasets for pretraining. The code is available at https://github.com/maple-research-lab/AdPE.

cs.CV↗

arXiv2023

Variants of normality and steadfastness deform

The cancellation problem asks whether $A[X_1,X_2,\ldots,X_n] \cong B[Y_1,Y_2,\ldots,Y_n]$ implies $A \cong B$. Hamann introduced the class of steadfast rings as the rings for which a version of the cancellation problem considered by Abhyankar, Eakin, and Heinzer holds. By work of Asanuma, Hamann, and Swan, steadfastness can be characterized in terms of $p$-seminormality, which is a variant of normality introduced by Swan. We prove that $p$-seminormality and steadfastness deform for reduced Noetherian local rings. We also prove that $p$-seminormality and steadfastness are stable under adjoining formal power series variables for reduced (not necessarily Noetherian) rings. Our methods also give new proofs of the facts that normality and weak normality deform, which are of independent interest.

math.AC↗

arXiv2023

Liouville Theorem for semilinear elliptic inequalities with the fractional Hardy operators

In this paper, we give a full classification of the nonexistence of positive weak solutions to the semilinear elliptic inequality involving the fractional Hardy potential in punctured and in exterior domains. Our methods are self-contained and new. The main ideas and key ingredients will be discussed in the next section after Theorem 1.1 for punctured domains, and after Theorem 1.4 for exterior domains. We will also explain why all the previous methods and techniques do not apply to our general setting. Let us give here a foretaste of our line of attack: Based on the imbalance between the Hardy operator and the nonlinearity, we can obtain an initial asymptotic behavior rate at the origin ( for punctured domains) or at infinity ( exterior domains).We then improve this rate by using the interaction with the nonlinearity. By repeating this process finite number of times, a contradiction will be deduced from the nonexistence for the related non-homogeneous fractional Hardy problem. This process allows us to obtain the nonexistence for the fractional Hardy problem with larger ranges . Our study covers all possible ranges, and our results are optimal.

math.AP↗

arXiv2023

SGMFQP:An Ontology-based Swine Gut Microbiota Federated Query Platform

Gut microbiota plays a crucial role in modulating pig development and health, and gut microbiota characteristics are associated with differences in feed efficiency. To answer open questions in feed efficiency analysis, biologists seek to retrieve information across multiple heterogeneous data sources. However, this is error-prone and time-consuming work since the queries can involve a sequence of multiple sub-queries over several databases. We present an implementation of an ontology-based Swine Gut Microbiota Federated Query Platform (SGMFQP) that provides a convenient, automated, and efficient query service about swine feeding and gut microbiota. The system is constructed based on a domain-specific Swine Gut Microbiota Ontology (SGMO), which facilitates the construction of queries independent of the actual organization of the data in the individual sources. This process is supported by a template-based query interface. A Datalog+-based federated query engine transforms the queries into sub-queries tailored for each individual data source, and an automated workflow orchestration mechanism executes the queries in each source database and consolidates the results. The efficiency of the system is demonstrated on several swine feeding scenarios.

cs.DB↗

arXiv2023

Variation Enhanced Attacks Against RRAM-based Neuromorphic Computing System

The RRAM-based neuromorphic computing system has amassed explosive interests for its superior data processing capability and energy efficiency than traditional architectures, and thus being widely used in many data-centric applications. The reliability and security issues of the NCS therefore become an essential problem. In this paper, we systematically investigated the adversarial threats to the RRAM-based NCS and observed that the RRAM hardware feature can be leveraged to strengthen the attack effect, which has not been granted sufficient attention by previous algorithmic attack methods. Thus, we proposed two types of hardware-aware attack methods with respect to different attack scenarios and objectives. The first is adversarial attack, VADER, which perturbs the input samples to mislead the prediction of neural networks. The second is fault injection attack, EFI, which perturbs the network parameter space such that a specified sample will be classified to a target label, while maintaining the prediction accuracy on other samples. Both attack methods leverage the RRAM properties to improve the performance compared with the conventional attack methods. Experimental results show that our hardware-aware attack methods can achieve nearly 100% attack success rate with extremely low operational cost, while maintaining the attack stealthiness.

cs.CR↗

arXiv2023

Deterministic photon source interfaced with a programmable silicon-nitride integrated circuit

We develop a quantum photonic platform that interconnects a high-quality quantum dot single-photon source and a low-loss photonic integrated circuit made in silicon nitride. The platform is characterized and programmed to demonstrate various multiphoton applications, including bosonic suppression laws and photonic entanglement generation. The results show a promising technological route forward to scale-up photonic quantum hardware.

quant-ph↗

arXiv2023

Shear thinning of non-Brownian suspensions and its variation at different ambient conditions

Immiscible contaminants are commonly involved in naturally occurring suspensions. The resulting variations of their flow behavior has rarely been evaluated. Here, we investigate the variation of the viscosity of the oil-based two-phase suspension over a period of two years, which is exposed to the ambient air at the production stage. We find that the air's absolute humidity, which strongly varies with the seasons, causes exchanges of water droplets with the suspension, substantially altering its shear-thinning behavior. Only in winter, when the humidity is low, is the latter close to that of ideal two-phase suspensions. Our measurements suggest that, when the surface roughness of the suspended solid particles is sufficiently low, immersed droplets remain in a free state, effectively increasing repulsion between particles, weakening shear thinning. In contrast, when the roughness is sufficiently high, immersed droplets become trapped on the particle surfaces, inducing an attractive particle interaction via water bridging, enhancing shear thinning.

physics.flu-dyn↗

arXiv2023

Collective super- and subradiant dynamics between distant optical quantum emitters

Photon emission is the hallmark of light-matter interaction and the foundation of photonic quantum science, enabling advanced sources for quantum communication and computing. Although single-emitter radiation can be tailored by the photonic environment, the introduction of multiple emitters extends this picture. A fundamental challenge, however, is that the radiative dipole-dipole coupling rapidly decays with spatial separation, typically within a fraction of the optical wavelength. We realize distant dipole-dipole radiative coupling with pairs of solid-state optical quantum emitters embedded in a nanophotonic waveguide. We dynamically probe the collective response and identify both super- and subradiant emission as well as means to control the dynamics by proper excitation techniques. Our work constitutes a foundational step toward multiemitter applications for scalable quantum-information processing.

quant-ph↗

arXiv2023

Opinion-aware Influence Maximization in Online Social Networks

Influence maximization (IM) aims to find seed users on an online social network to maximize the spread of information about a target product through word-of-mouth propagation among all users. Prior IM methods mostly focus on maximizing the overall influence spread, which assumes that all users are potential customers of the product and that more exposure leads to higher benefits. However, in real-world scenarios, some users who dislike the product may express and spread negative opinions, damaging the product's reputation and lowering its profit. This paper investigates the opinion-aware influence maximization (OIM) problem, which finds a set of seed users to maximize the positive opinions toward the product while minimizing the negative opinions. We propose a novel algorithm for the OIM problem. Specifically, after obtaining the users with positive and negative opinions towards the product from historical data, we design a reverse reachable set-based method for opinion-aware influence estimation and a sandwich approximation algorithm for seed set selection. Despite the NP-hardness and non-submodularity of OIM, our algorithm achieves a data-dependent approximation factor for OIM. Experimental results on three real-world datasets demonstrate that our algorithm improves the spread of positive opinions while reducing the spread of negative opinions compared to existing methods.

cs.SI↗

arXiv2022

A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs

An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph convolutional network (GCN) is widely adopted to perform representation learning to a UWG for subsequent pattern analysis tasks such as clustering or missing data estimation. However, existing GCNs mostly neglects the latent collaborative information hidden in its connected node pairs. To address this issue, this study proposes to model the node collaborations via a symmetric latent factor analysis model, and then regards it as a node-collaboration module for supplementing the collaboration loss in a GCN. Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG. Empirical studies on UWGs emerging from real applications demonstrate that owing to its efficient incorporation of node-collaborations, the proposed NGCN significantly outperforms state-of-the-art GCNs in addressing the task of missing weight estimation. Meanwhile, its good scalability ensures its compatibility with more advanced GCN extensions, which will be further investigated in our future studies.

cs.LG↗

arXiv2022

Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning

Deep reinforcement learning (DRL) breaks through the bottlenecks of traditional reinforcement learning (RL) with the help of the perception capability of deep learning and has been widely applied in real-world problems.While model-free RL, as a class of efficient DRL methods, performs the learning of state representations simultaneously with policy learning in an end-to-end manner when facing large-scale continuous state and action spaces. However, training such a large policy model requires a large number of trajectory samples and training time. On the other hand, the learned policy often fails to generalize to large-scale action spaces, especially for the continuous action spaces. To address this issue, in this paper we propose an efficient policy learning method in latent state and action spaces. More specifically, we extend the idea of state representations to action representations for better policy generalization capability. Meanwhile, we divide the whole learning task into learning with the large-scale representation models in an unsupervised manner and learning with the small-scale policy model in the RL manner.The small policy model facilitates policy learning, while not sacrificing generalization and expressiveness via the large representation model. Finally,the effectiveness of the proposed method is demonstrated by MountainCar,CarRacing and Cheetah experiments.

cs.LG↗

arXiv2022

High-speed thin-film lithium niobate quantum processor driven by a solid-state quantum emitter

Scalable photonic quantum computing architectures pose stringent requirements on photonic processing devices. The need for low-loss high-speed reconfigurable circuits and near-deterministic resource state generators are some of the most challenging requirements. Here we develop an integrated photonic platform based on thin-film lithium niobate and interface it with deterministic solid-state single-photon sources based on quantum dots in nanophotonic waveguides. The generated photons are processed with low-loss circuits programmable at speeds of several GHz. We realize a variety of key photonic quantum information processing functionalities with the high-speed circuits, including on-chip quantum interference, photon demultiplexing, and reprogrammability of a four-mode universal photonic circuit. These results show a promising path forward for scalable photonic quantum technologies by merging integrated photonics with solid-state deterministic photon sources in a heterogeneous approach to scaling up.

quant-ph↗

arXiv2022

Sybil-Proof Diffusion Auction in Social Networks

A diffusion auction is a market to sell commodities over a social network, where the challenge is to incentivize existing buyers to invite their neighbors in the network to join the market. Existing mechanisms have been designed to solve the challenge in various settings, aiming at desirable properties such as non-deficiency, incentive compatibility and social welfare maximization. Since the mechanisms are employed in dynamic networks with ever-changing structures, buyers could easily generate fake nodes in the network to manipulate the mechanisms for their own benefits, which is commonly known as the Sybil attack. We observe that strategic agents may gain an unfair advantage in existing mechanisms through such attacks. To resist this potential attack, we propose two diffusion auction mechanisms, the Sybil tax mechanism (STM) and the Sybil cluster mechanism (SCM), to achieve both Sybil-proofness and incentive compatibility in the single-item setting. Our proposal provides the first mechanisms to protect the interests of buyers against Sybil attacks with a mild sacrifice of social welfare and revenue.

cs.GT↗

arXiv2022

Discovery of a high-temperature antiferromagnetic state and transport signatures of exchange interactions in a Bi2Se3/EuSe heterostructure

Spatial confinement of electronic topological surface states (TSS) in topological insulators poses a formidable challenge because TSS are protected by time-reversal symmetry. In previous works formation of a gap in the electronic spectrum of TSS has been successfully demonstrated in topological insulator/magnetic material heterostructures, where ferromagnetic exchange interactions locally lifts the time-reversal symmetry. Here we report an experimental evidence of exchange interaction between a topological insulator Bi2Se3 and a magnetic insulator EuSe. Spin-polarized neutron reflectometry reveals a reduction of the in-plane magnetic susceptibility within a 2 nm interfacial layer of EuSe, and the combination of SQUID magnetometry and Hall measurements points to the formation of an antiferromagnetic layer with at least five-fold enhancement of Néel's temperature. Abrupt resistance changes in high magnetic fields indicate interfacial exchange coupling that affects transport in a TSS. High temperature local control of TSS with zero net magnetization unlocks new opportunities for the design of electronic, spintronic and quantum computation devices, ranging from quantization of Hall conductance in zero fields to spatial localization of non-Abelian excitations in superconducting topological qubits.

cond-mat.mes-hall↗