arXiv subjects

Yu Tian

Publications and source records attributed to Yu Tian.

At least 307 records · Page 17Linked to original sources

arXiv2021

Mean Field MARL Based Bandwidth Negotiation Method for Massive Devices Spectrum Sharing

In this paper, a novel bandwidth negotiation mechanism is proposed for massive devices wireless spectrum sharing, in which individual device locally negotiates bandwidth usage with neighbor devices and globally optimal spectrum utilization is achieved through distributed decision-making. Since only sparse feedback is needed, the proposed mechanism can greatly reduce the signaling overhead. In order to solve the distributed optimization problem when massive devices coexist, mean field multi-agent reinforcement learning (MF-MARL) based bandwidth decision algorithm is proposed, which allow device make globally optimal decision leveraging only neighborhood observation. In simulation, distributed bandwidth negotiation between 1000 devices is demonstrated and the spectrum utilization rate is above 95%. The proposed method is beneficial to reduce spectrum conflicts, increase spectrum utilization for massive devices spectrum sharing.

eess.SP↗

arXiv2021

Noise Attention based Spectrum Anomaly Detection Method for Unauthorized Bands

Spectrum anomaly detection is of great importance in wireless communication to secure safety and improve spectrum efficiency. However, spectrum anomaly detection faces many difficulties, especially in unauthorized frequency bands. For example, the composition of unauthorized frequency bands is very complex and the abnormal usage patterns are unknown in prior. In this paper, a noise attention method is proposed for unsupervised spectrum anomaly detection in unauthorized bands. First of all, we theoretically prove that the anomalies in unauthorized bands will raise the noise floor of spectrogram after VAE reconstruction. Then, we introduce a novel anomaly metric named as noise attention score to more effectively capture spectrum anomaly. The effectiveness of the proposed method is experimentally verified in 2.4 GHz ISM band. Leveraging the noise attention score, the AUC metric of anomaly detection is increased by 0.193. The proposed method is beneficial to reliably detecting abnormal spectrum while keeping low false alarm rate.

eess.SP↗

arXiv2021

Stochastic Analysis of Cooperative Satellite-UAV Communications

In this paper, a dual-hop cooperative satellite-unmanned aerial vehicle (UAV) communication system including a satellite (S), a group of cluster headers (CHs), which are respectively with a group of uniformly distributed UAVs, is considered. Specifically, these CHs serve as aerial decode-and-forward relays to forward the information transmitted by S to UAVs. Moreover, free-space optical (FSO) and radio frequency (RF) technologies are respectively adopted over S-CH and CH-UAV links to exploit FSO's high directivity over long-distance transmission and RF's omnidirectional coverage ability. The positions of the CHs in the 3-dimensional space follow the Matérn hard-core point processes type-II in which each CH can not be closer to any other ones than a predefined distance. Three different cases over CH-UAV links are considered during the performance modeling: interference-free, interference-dominated, and interference-and-noise cases. Then, the coverage performance of S-CH link and the CH-UAV links under three cases is studied and the closed-form analytical expressions of the coverage probability (CP) over both links are derived. Also, the asymptotic expressions for the CP over S-CH link and CH-UAV link in interference-free case are derived. Finally, numerical results are provided to validate our proposed analytical models and thus some meaningful conclusions are achieved.

cs.IT↗

arXiv2021

Unsupervised Dual Adversarial Learning for Anomaly Detection in Colonoscopy Video Frames

The automatic detection of frames containing polyps from a colonoscopy video sequence is an important first step for a fully automated colonoscopy analysis tool. Typically, such detection system is built using a large annotated data set of frames with and without polyps, which is expensive to be obtained. In this paper, we introduce a new system that detects frames containing polyps as anomalies from a distribution of frames from exams that do not contain any polyps. The system is trained using a one-class training set consisting of colonoscopy frames without polyps -- such training set is considerably less expensive to obtain, compared to the 2-class data set mentioned above. During inference, the system is only able to reconstruct frames without polyps, and when it tries to reconstruct a frame with polyp, it automatically removes (i.e., photoshop) it from the frame -- the difference between the input and reconstructed frames is used to detect frames with polyps. We name our proposed model as anomaly detection generative adversarial network (ADGAN), comprising a dual GAN with two generators and two discriminators. We show that our proposed approach achieves the state-of-the-art result on this data set, compared with recently proposed anomaly detection systems.

eess.IV↗

arXiv2021

Detecting, Localising and Classifying Polyps from Colonoscopy Videos using Deep Learning

In this paper, we propose and analyse a system that can automatically detect, localise and classify polyps from colonoscopy videos. The detection of frames with polyps is formulated as a few-shot anomaly classification problem, where the training set is highly imbalanced with the large majority of frames consisting of normal images and a small minority comprising frames with polyps. Colonoscopy videos may contain blurry images and frames displaying feces and water jet sprays to clean the colon -- such frames can mistakenly be detected as anomalies, so we have implemented a classifier to reject these two types of frames before polyp detection takes place. Next, given a frame containing a polyp, our method localises (with a bounding box around the polyp) and classifies it into five different classes. Furthermore, we study a method to improve the reliability and interpretability of the classification result using uncertainty estimation and classification calibration. Classification uncertainty and calibration not only help improve classification accuracy by rejecting low-confidence and high-uncertain results, but can be used by doctors to decide how to decide on the classification of a polyp. All the proposed detection, localisation and classification methods are tested using large data sets and compared with relevant baseline approaches.

cs.CV↗

arXiv2020

Applying Deep-Learning-Based Computer Vision to Wireless Communications: Methodologies, Opportunities, and Challenges

Deep learning (DL) has seen great success in the computer vision (CV) field, and related techniques have been used in security, healthcare, remote sensing, and many other fields. As a parallel development, visual data has become universal in daily life, easily generated by ubiquitous low-cost cameras. Therefore, exploring DL-based CV may yield useful information about objects, such as their number, locations, distribution, motion, etc. Intuitively, DL-based CV can also facilitate and improve the designs of wireless communications, especially in dynamic network scenarios. However, so far, such work is rare in the literature. The primary purpose of this article, then, is to introduce ideas about applying DL-based CV in wireless communications to bring some novel degrees of freedom to both theoretical research and engineering applications. To illustrate how DL-based CV can be applied in wireless communications, an example of using a DL-based CV with a millimeter-wave (mmWave) system is given to realize optimal mmWave multiple-input and multiple-output (MIMO) beamforming in mobile scenarios. In this example, we propose a framework to predict future beam indices from previously observed beam indices and images of street views using ResNet, 3-dimensional ResNext, and a long short-term memory network. The experimental results show that our frameworks achieve much higher accuracy than the baseline method, and that visual data can significantly improve the performance of the MIMO beamforming system. Finally, we discuss the opportunities and challenges of applying DL-based CV in wireless communications.

eess.SP↗

arXiv2020

Deep learning black hole metrics from shear viscosity

Based on AdS/CFT correspondence, we build a deep neural network to learn black hole metrics from the complex frequency-dependent shear viscosity. The network architecture provides a discretized representation of the holographic renormalization group flow of the shear viscosity and can be applied to a large class of strongly coupled field theories. Given the existence of the horizon and guided by the smoothness of spacetime, we show that Schwarzschild and Reissner-Nordström metrics can be learned accurately. Moreover, we illustrate that the generalization ability of the deep neural network can be excellent, which indicates that by using the black hole spacetime as a hidden data structure, a wide spectrum of the shear viscosity can be generated from a narrow frequency range. These results are further generalized to an Einstein-Maxwell-dilaton black hole. Our work might not only suggest a data-driven way to study holographic transports but also shed some light on holographic duality and deep learning.

hep-th↗

arXiv2020

An easily prepared, simple information storage and display device based on triboelectric enhanced mechanoluminescence phenomenon and droplet-luminescence

One easy-make, flexible information storage and display device is prepared. The device show memory effect, which can record the friction trace in real time through the triboelectric effect, and then reproduce the trajectory under the excitation of any kind of polar liquid droplet, completely display the triboelectric area. The explanation mechanism of this phenomenon is proposed, and the main influencing factors of the frictional electrification between solid materials and flexible substrates are determined by systematic experiements. This phenomenon connects mechanoluminescence, electroluminescence, and triboelectrification. It is of great significance to the research of triboelectricity and mechanoluminescence, and is expected to play an important role in display, information storage and encryption.

cond-mat.mtrl-sci↗

arXiv2020

Hybrid Multiple Attention Network for Semantic Segmentation in Aerial Images

Semantic segmentation in very high resolution (VHR) aerial images is one of the most challenging tasks in remote sensing image understanding. Most of the current approaches are based on deep convolutional neural networks (DCNNs). However, standard convolution with local receptive fields fails in modeling global dependencies. Prior researches have indicated that attention-based methods can capture long-range dependencies and further reconstruct the feature maps for better representation. Nevertheless, limited by the mere perspective of spacial and channel attention and huge computation complexity of self-attention mechanism, it is unlikely to model the effective semantic interdependencies between each pixel-pair of remote sensing data of complex spectra. In this work, we propose a novel attention-based framework named Hybrid Multiple Attention Network (HMANet) to adaptively capture global correlations from the perspective of space, channel and category in a more effective and efficient manner. Concretely, a class augmented attention (CAA) module embedded with a class channel attention (CCA) module can be used to compute category-based correlation and recalibrate the class-level information. Additionally, we introduce a simple yet effective region shuffle attention (RSA) module to reduce feature redundant and improve the efficiency of self-attention mechanism via region-wise representations. Extensive experimental results on the ISPRS Vaihingen and Potsdam benchmark demonstrate the effectiveness and efficiency of our HMANet over other state-of-the-art methods.

cs.CV↗

arXiv2020

On NOMA-Based mmWave Communications

Non-orthogonal multiple access (NOMA) and millimeter-wave (mmWave) communication are two promising techniques to increase the system capacity in the fifth-generation (5G) mobile network. The former can achieve high spectral efficiency by modulating the information in power domain and the latter can provide extremely large spectrum resources. Fluctuating two-ray (FTR) channel model has already been proved to accurately agree with the small-scale fading effects in mmWave communications in experiments. In this paper, the performance of NOMA-based communications over FTR channels in mmWave communication systems is investigated in terms of outage probability (OP) and ergodic capacity (EC). Specifically, we consider the scenario that one base station (BS) transmits signals to two users simultaneously under NOMA scheme. The BS and users are all equipped with a single antenna. Two power allocation strategies are considered: the first one is a general (fixed) power allocation scheme under which we derive the OP and EC of NOMA users in closed form; the other one is an optimal power allocation scheme that can achieve the maximum sum rate for the whole system. Under the second scheme, not only the closed-form OP and EC but also the upper and lower bounds of EC are derived. Furthermore, we also derive the asymptotic expression for the OP in high average SNR region to investigate the diversity order under these two schemes. Finally, we show the correctness and accuracy of our derived expressions by Monte-Carlo simulation.

eess.SP↗

arXiv2020

Large-area printing of ferroelectric surface and super-domains for efficient solar water splitting

Surface electronic structures of the photoelectrodes determine the activity and efficiency of the photoelectrochemical water splitting, but the controls of their surface structures and interfacial chemical reactions remain challenging. Here, we use ferroelectric BiFeO3 as a model system to demonstrate an efficient and controllable water splitting reaction by large-area constructing the hydroxyls-bonded surface. The up-shift of band edge positions at this surface enables and enhances the interfacial holes and electrons transfer through the hydroxyl-active-sites, leading to simultaneously enhanced oxygen and hydrogen evolutions. Furthermore, printing of ferroelectric super-domains with microscale checkboard up/down electric fields separates the distribution of reduction/oxidation catalytic sites, enhancing the charge separation and giving rise to an order of magnitude increase of the photocurrent. This large-area printable ferroelectric surface and super-domains offer an alternative platform for controllable and high-efficient photocatalysis.

physics.app-ph↗

arXiv2020

Holographic boiling and generalized thermodynamic description beyond local equilibrium

Tuning a very simple two-component holographic superfluid model, we can have a first order phase transition between two superfluid phases in the probe limit. Inspired by the potential landscape discussion, an intuitive physical picture for systems with first order phase transitions is provided. We stress that holography perfectly offers a generalized thermodynamic description of certain strongly coupled systems even out of local equilibrium, which enables us to carefully study domain wall structures of the system under first order phase transitions, either static or in real time dynamics. We numerically construct the 1D domain wall configuration and compute the surface tension of the domain wall from its generalized grand potential. We also numerically simulate the real time dynamics of a 2D bubble nucleation process (holographic boiling). The surface tension of the 1D domain wall nicely matches the final state of the 2D bubble nucleation process when the bubble radius is large enough.

hep-th↗

arXiv2020

Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy

Anomaly detection methods generally target the learning of a normal image distribution (i.e., inliers showing healthy cases) and during testing, samples relatively far from the learned distribution are classified as anomalies (i.e., outliers showing disease cases). These approaches tend to be sensitive to outliers that lie relatively close to inliers (e.g., a colonoscopy image with a small polyp). In this paper, we address the inappropriate sensitivity to outliers by also learning from inliers. We propose a new few-shot anomaly detection method based on an encoder trained to maximise the mutual information between feature embeddings and normal images, followed by a few-shot score inference network, trained with a large set of inliers and a substantially smaller set of outliers. We evaluate our proposed method on the clinical problem of detecting frames containing polyps from colonoscopy video sequences, where the training set has 13350 normal images (i.e., without polyps) and less than 100 abnormal images (i.e., with polyps). The results of our proposed model on this data set reveal a state-of-the-art detection result, while the performance based on different number of anomaly samples is relatively stable after approximately 40 abnormal training images.

cs.CV↗

arXiv2020

Holographic Superfluid Solitons with Backreaction

Solitons are important nonperturbative excitations in superfluids. For holographic superfluids, we numerically construct dark solitons that have the symmetry-restored phase at their core. A central point is that we include the gravitational back-reaction of the matter fields, which becomes important at low temperatures. We study in detail the properties of these solitons under variation of the back-reaction strength via tuning the gravitational constant. In particular, the depletion fraction of the particle number density at the core of the solitons is carefully investigated. In agreement with the probe-limit analysis, the depletion fraction shows the same qualitative behavior as in Bogoliubov-de Gennes (BdG) theory, even if the back-reaction is included. We find that the depletion decreases with increasing back-reaction strength. Moreover, the inclusion of back-reaction enables us to obtain the effective energy density of solitons within holography, which together with an evaluation of the surface tension leads to a simple physical explanation for the snake instability of dark solitons.

hep-th↗

arXiv2020

Semantic Graph Convolutional Networks for 3D Human Pose Regression

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for regression. Current architectures of GCNs are limited to the small receptive field of convolution filters and shared transformation matrix for each node. To address these limitations, we propose Semantic Graph Convolutional Networks (SemGCN), a novel neural network architecture that operates on regression tasks with graph-structured data. SemGCN learns to capture semantic information such as local and global node relationships, which is not explicitly represented in the graph. These semantic relationships can be learned through end-to-end training from the ground truth without additional supervision or hand-crafted rules. We further investigate applying SemGCN to 3D human pose regression. Our formulation is intuitive and sufficient since both 2D and 3D human poses can be represented as a structured graph encoding the relationships between joints in the skeleton of a human body. We carry out comprehensive studies to validate our method. The results prove that SemGCN outperforms state of the art while using 90% fewer parameters.

cs.CV↗

arXiv2020

Generation of vortices and stabilization of vortex lattices in holographic superfluids

Within the simplest holographic superfluid model and without any ingredients put by hand, it is shown that vortices can be generated when the angular velocity of rotating superfluids exceeds certain critical values, which can be precisely determined by linear perturbation analyses (quasi-normal modes of the bulk AdS black brane). These vortices appear at the edge of the superfluid system first, and then automatically move into the bulk of the system, where they are eventually stabilized into certain vortex lattices. For the case of 18 vortices generated, we find (at least) five different patterns of the final lattices formed due to different initial perturbations, which can be compared to the known result for such lattices in weakly coupled Bose-Einstein condensates from free energy analyses.

hep-th↗

arXiv2020

Dynamical Phase Transition from Nonequilibrium Dynamics of Dark Solitons

By holographic duality, we identify a novel dynamical phase transition which results from the temperature dependence of non-equilibrium dynamics of dark solitons in a superfluid.For a non-equilibrium superfluid system with an initial density of dark solitons, there exists a critical temperature $T_d$,above which the system relaxes to equilibrium by producing sound waves, while below which it goes through an intermediate phase with a finite density of vortex-antivortex pairs. In particular, as $T_d$ is approached from below, the density of vortex pairs scales as $(T_d - T)^γ$ with the critical exponent $γ= 1/2$.

hep-th↗

arXiv2019

Experimental Observation of Equilibrium and Dynamical Quantum Phase Transitions via Out-of-Time-Ordered Correlators

The out-of-time-ordered correlators (OTOC) have been established as a fundamental concept for quantifying quantum information scrambling and diagnosing quantum chaotic behavior. Recently, it was theoretically proposed that the OTOC can be used as an order parameter to dynamically detect both equilibrium quantum phase transitions (EQPTs) and dynamical quantum phase transitions (DQPTs) in one-dimensional many-body systems. Here we report the first experimental observation of EQPTs and DQPTs in a quantum spin chain via quench dynamics of OTOC on a nuclear magnetic resonance quantum simulator. We observe that the quench dynamics of both the order parameter and the two-body correlation function cannot detect the DQPTs, but the OTOC can unambiguously detect the DQPTs. Moreover, we demonstrate that the long-time average value of the OTOC in quantum quench signals the equilibrium quantum critical point and ordered quantum phases, thus one can measure the EQPTs from the non-equilibrium quantum quench dynamics. Our experiment paves a way for experimentally investigating DQPTs through OTOCs and for studying the EQPTs through the non-equilibrium quantum quench dynamics with quantum simulators.

quant-ph↗