arXiv subjects

Guang Yang

Publications and source records attributed to Guang Yang.

At least 523 records · Page 29Linked to original sources

arXiv2018

Sharing Information with Competitors

We study the mechanism design problem in the setting where agents are rewarded using information only. This problem is motivated by the increasing interest in secure multiparty computation techniques. More specifically, we consider the setting of a joint computation where different agents have inputs of different quality and each agent is interested in learning as much as possible while maintaining exclusivity for information. Our high level question is to design mechanisms that motivate all agents (even those with high-quality input) to participate in the computation and we formally study problems such as set union, intersection, and average.

cs.GT↗

arXiv2018

Protected gap closing in Josephson junctions constructed on Bi$_2$Te$_3$ surface

On the road of searching for Majorana zero modes (MZMs) in topological insulator-based Josephson junctions, a highly-sought signature is the protected full transparency of electron transport through the junctions due to the existence of the MZMs, associated with complete gap closing between the electron-like and hole-like Andreev bound states (ABSs). Here, we present direct experimental evidence of gap closing and full transparency in single Josephson junctions constructed on the surface of three-dimensional topological insulator (3D TI) Bi$_2$Te$_3$. Our results demonstrate that the 2D surface of 3D TIs provides a promising platform for hosting and manipulating MZMs.

cond-mat.mes-hall↗

arXiv2018

Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help with the identification and understanding of brain tumors, yet MRS is not a widely available medical imaging modality. Aiming to counter this issue, this research draws on the advancements in machine learning techniques in other fields for the generation of artificial data. The generated methods were tested through the evaluation of their output against that of a real-world labelled MRS brain tumor data-set. Furthermore the resultant output from the generative techniques were each used to train separate traditional classifiers which were tested on a subset of the real MRS brain tumor dataset. The results suggest that there exist methods capable of producing accurate, ground truth based MRS voxels. These findings indicate that through generative techniques, large datasets can be made available for training deep, learning models for the use in brain tumor diagnosis.

cs.CV↗

arXiv2018

Field-free spin-orbit torque switching from geometrical domain wall pinning

Spin-orbit torques, which utilize spin currents arising from the spin-orbit coupling, offer a novel method to electrically switch the magnetization with perpendicular anisotropy. However, the necessity of an external magnetic field to achieve a deterministic switching is an obstacle for realizing practical spin-orbit torque devices with all-electric operation. Here, we report a field-free spin-orbit torque switching by exploiting the domain wall motion in an anti-notched microwire with perpendicular anisotropy, which exhibits multi-domain states stabilized by the domain wall surface tension. The combination of spin-orbit torque, Dzyaloshinskii-Moriya interaction, and domain wall surface tension induced geometrical pinning allows a deterministic control of the domain wall and offers a novel method to achieve a field-free spin-orbit torque switching. Our work demonstrates the proof of concept of a perpendicular memory cell which can be readily adopted in a three-terminal magnetic memory.

cond-mat.mtrl-sci↗

arXiv2018

Does black-hole growth depend on the cosmic environment?

It is well known that environment affects galaxy evolution, which is broadly related to supermassive black hole (SMBH) growth. We investigate whether SMBH evolution also depends on host-galaxy local (sub-Mpc) and global ($\approx 1-10$ Mpc) environment. We construct the surface-density field (local environment) and cosmic web (global environment) in the COSMOS field at $z=0.3-3.0$. The environments in COSMOS range from the field to clusters ($M_\mathrm{halo} \lesssim 10^{14}\ M_\odot$), covering the environments where ${\approx 99\%}$ of galaxies in the Universe reside. We measure sample-averaged SMBH accretion rate ($\overline{\mathrm{BHAR}}$) from X-ray observations, and study its dependence on overdensity and cosmic-web environment at different redshifts while controlling for galaxy stellar mass ($M_\star$). Our results show that $\overline{\mathrm{BHAR}}$ does not significantly depend on overdensity or cosmic-web environment once $M_\star$ is controlled, indicating that environment-related physical mechanisms (e.g. tidal interaction and ram-pressure stripping) might not significantly affect SMBH growth. We find that $\overline{\mathrm{BHAR}}$ is strongly related to host-galaxy $M_\star$, regardless of environment.

astro-ph.GA↗

arXiv2018

Adversarial and Perceptual Refinement for Compressed Sensing MRI Reconstruction

Deep learning approaches have shown promising performance for compressed sensing-based Magnetic Resonance Imaging. While deep neural networks trained with mean squared error (MSE) loss functions can achieve high peak signal to noise ratio, the reconstructed images are often blurry and lack sharp details, especially for higher undersampling rates. Recently, adversarial and perceptual loss functions have been shown to achieve more visually appealing results. However, it remains an open question how to (1) optimally combine these loss functions with the MSE loss function and (2) evaluate such a perceptual enhancement. In this work, we propose a hybrid method, in which a visual refinement component is learnt on top of an MSE loss-based reconstruction network. In addition, we introduce a semantic interpretability score, measuring the visibility of the region of interest in both ground truth and reconstructed images, which allows us to objectively quantify the usefulness of the image quality for image post-processing and analysis. Applied on a large cardiac MRI dataset simulated with 8-fold undersampling, we demonstrate significant improvements ($p<0.01$) over the state-of-the-art in both a human observer study and the semantic interpretability score.

cs.CV↗

arXiv2018

Nonadiabatic population transfer in a tangent-pulse driven quantum model

Fine control of the dynamics of a quantum system is the key element to perform quantum information processing and coherent manipulations for atomic and molecular systems. In this paper we propose a control protocol using a tangent-pulse driven model and demonstrate that it indicates a desirable design, i.e., of being both fast and accurate for population transfer. As opposed to other existing strategies, a remarkable character of the present scheme is that high velocity of the nonadiabatic evolution itself not only will not lead to unwanted transitions but also can suppress the error caused by the truncation of the driving pulse.

quant-ph↗

arXiv2018

Superior mechanical flexibility and strained-engineered direct-indirect band gap transition of green phosphorene

Most recently, a brand new phosphorus allotrope called green phosphorus has been predicted, which has a direct band gap up to 2.4 eV, and its single-layer form termed green phosphorene shows high stability. Here the mechanical properties and the uniaxial strain effect on the electronic band structure of green phosphorene along two perpendicular in-plane directions are investigated. Remarkably, we find that this material can sustain a tensile strain in the armchair direction up to a threshold of 35\% which is larger than that of black phosphorene, suggesting that green phosphorene is more puckered. Our calculations also show the Young's modulus and Poisson's ratio in the zigzag direction are four times larger than those in the armchair direction, which confirm the anisotropy of the material. Furthermore, the uniaxial strain can trigger the direct-indirect band gap transition for green phosphorene and the critical strains for the band gap transition are revealed.

cond-mat.mtrl-sci↗

arXiv2018

Multiview Two-Task Recursive Attention Model for Left Atrium and Atrial Scars Segmentation

Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model.

cs.CV↗

arXiv2018

Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI

Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the investigation of effective therapeutics. Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive technique that can resolve the microscopic structure, organisation, and integrity of the myocardium without the need for exogenous contrast agents. However, this technique suffers from relatively low signal-to-noise ratio (SNR) and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR techniques rely on acquiring and averaging multiple signal acquisitions to improve the SNR. Moreover, in order to mitigate the influence of respiratory movement, patients are required to perform many breath holds which results in prolonged acquisition durations (e.g., ~30 mins using the existing technology). In this study, we propose a novel cascaded Convolutional Neural Networks (CNN) based compressive sensing (CS) technique and explore its applicability to improve DT-CMR acquisitions. Our simulation based studies have achieved high reconstruction fidelity and good agreement between DT-CMR parameters obtained with the proposed reconstruction and fully sampled ground truth. When compared to other state-of-the-art methods, our proposed deep cascaded CNN method and its stochastic variation demonstrated significant improvements. To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction. In addition, with relatively straightforward modifications to the acquisition scheme, our method can easily be translated into a method for online, at-the-scanner reconstruction enabling the deployment of accelerated DT-CMR in various clinical applications.

cs.CV↗

arXiv2018

Traffic Allocation for Low-Latency Multi-Hop Networks with Buffers

For millimeter-wave (mm-wave) buffer-aided tandem networks consisting of relay nodes and multiple channels per hop, we consider two traffic allocation schemes, namely local allocation and global allocation, and investigate the end-to-end latency of a file transfer. We formulate the problem for generic multi-hop queuing systems and subsequently derive closed-form expressions of the end-to-end latency. We quantify the advantages of the global allocation scheme relative to its local allocation counterpart, and we conduct an asymptotic analysis on the performance gain when the number of channels in each hop increases to infinity. The traffic allocations and the analytical delay performance are validated through simulations. Furthermore, taking a specific two-hop mm-wave network as an example, we derive lower bounds on the average end-to-end latency, where Nakagami-$m$ fading is considered. Numerical results demonstrate that, compared to the local allocation scheme, the advantage of global allocation grows as the number of relay nodes increases, at the expense of higher complexity that linearly increases with the number of relay nodes. It is also demonstrated that a proper deployment of relay nodes in a linear mm-wave network plays an important role in reducing the average end-to-end latency, and the average latency decays as the mm-wave channels become more deterministic. These findings provide insights for designing multi-hop mm-wave networks with low end-to-end latency.

cs.NI↗

arXiv2018

A two-stage 3D Unet framework for multi-class segmentation on full resolution image

Deep convolutional neural networks (CNNs) have been intensively used for multi-class segmentation of data from different modalities and achieved state-of-the-art performances. However, a common problem when dealing with large, high resolution 3D data is that the volumes input into the deep CNNs has to be either cropped or downsampled due to limited memory capacity of computing devices. These operations lead to loss of resolution and increment of class imbalance in the input data batches, which can downgrade the performances of segmentation algorithms. Inspired by the architecture of image super-resolution CNN (SRCNN) and self-normalization network (SNN), we developed a two-stage modified Unet framework that simultaneously learns to detect a ROI within the full volume and to classify voxels without losing the original resolution. Experiments on a variety of multi-modal volumes demonstrated that, when trained with a simply weighted dice coefficients and our customized learning procedure, this framework shows better segmentation performances than state-of-the-art Deep CNNs with advanced similarity metrics.

cs.CV↗

arXiv2018

A Telecom Perspective on the Internet of Drones: From LTE-Advanced to 5G

Drones are driving numerous and evolving use cases, and creating transformative socio-economic benefits. Drone operation needs wireless connectivity for communication between drones and ground control systems, among drones, and between drones and air traffic management systems. Mobile networks are well positioned to identify, track, and control the growing fleet of drones. The wide-area, quality, and secure connectivity provided by mobile networks can enhance the efficiency and effectiveness of drone operations beyond visual line-of-sight range. In this article, we elaborate how the drone ecosystem can benefit from mobile technologies, summarize key capabilities required by drone applications, and analyze the service requirements on mobile networks. We present field trial results collected in LTE-Advanced networks to gain insights into the capabilities of the current 4G+ networks for connected drones and share our vision on how 5G networks can further support diversified drone applications.

cs.NI↗

arXiv2018

Low-Latency Millimeter-Wave Communications: Traffic Dispersion or Network Densification?

This paper investigates two strategies to reduce the communication delay in future wireless networks: traffic dispersion and network densification. A hybrid scheme that combines these two strategies is also considered. The probabilistic delay and effective capacity are used to evaluate performance. For probabilistic delay, the violation probability of delay, i.e., the probability that the delay exceeds a given tolerance level, is characterized in terms of upper bounds, which are derived by applying stochastic network calculus theory. In addition, to characterize the maximum affordable arrival traffic for mmWave systems, the effective capacity, i.e., the service capability with a given quality-of-service (QoS) requirement, is studied. The derived bounds on the probabilistic delay and effective capacity are validated through simulations. These numerical results show that, for a given average system gain, traffic dispersion, network densification, and the hybrid scheme exhibit different potentials to reduce the end-to-end communication delay. For instance, traffic dispersion outperforms network densification, given high average system gain and arrival rate, while it could be the worst option, otherwise. Furthermore, it is revealed that, increasing the number of independent paths and/or relay density is always beneficial, while the performance gain is related to the arrival rate and average system gain, jointly. Therefore, a proper transmission scheme should be selected to optimize the delay performance, according to the given conditions on arrival traffic and system service capability.

cs.NI↗

arXiv2018

Low-Latency Heterogeneous Networks with Millimeter-Wave Communications

Heterogeneous network (HetNet) is a key enabler to largely boost network coverage and capacity in the forthcoming fifth-generation (5G) and beyond. To support the explosively growing mobile data volumes, wireless communications with millimeter-wave (mm-wave) radios have attracted massive attention, which is widely considered as a promising candidate in 5G HetNets. In this article, we give an overview on the end-to-end latency of HetNets with mm-wave communications. In general, it is rather challenging for formulating and optimizing the delay problem with buffers in mm-wave communications, since conventional graph-based network optimization techniques are not applicable when queues are considered. Toward this end, we develop an adaptive low-latency strategy, which uses cooperative networking to reduce the end-to-end latency. Then, we evaluate the performance of the introduced strategy. Results reveal the importance of proper cooperative networking in reducing the end-to-end latency. In addition, we have identified several challenges in future research for low-latency mm-wave HetNets.

cs.NI↗

arXiv2017

Performance Analysis of Millimeter-Wave Relaying: Impacts of Beamwidth and Self-Interference

We study the maximum achievable rate of a two-hop amplified-and-forward (AF) relaying millimeter-wave (mm-wave) system, where two AF relaying schemes, i.e., half-duplex (HD) and full-duplex (FD) are discussed. By considering the two-ray mm-wave channel and the Gaussian-type directional antenna, jointly, the impacts of the beamwidth and the self-interference coefficient on maximum achievable rates are investigated. Results show that, under a sum-power constraint, the rate of FD-AF mm-wave relaying outperforms its HD counterpart only when antennas with narrower beamwidth and smaller self-interference coefficient are applied. However, when the sum-power budget is sufficiently high or the beamwidth of directional antenna is sufficiently small, direct transmission becomes the best strategy, rather than the AF relaying schemes. For both relaying schemes, we show that the rates of both AF relaying schemes scale as $ \mathcal{O}\left(\min\left\lbraceθ_m^{-1},θ_m^{-2}\right\rbrace\right) $ with respect to beamwidth $ θ_ m $, and the rate of FD-AF relaying scales as $ \mathcal{O}\left(μ^{-\frac{1}{2}}\right) $ with respect to self-interference coefficient $ μ$. Besides, we show that, ground reflections may significantly affect the performance of mm-wave communications, constructively or destructively. Thus, the impact of ground reflections deserves careful considerations for analyzing or designing future mm-wave wireless networks.

cs.NI↗

arXiv2017

Strain-tuning of edge magnetism in zigzag graphene nanoribbons

Using the determinant quantum Monte-Carlo method, we elucidate the strain tuning of edge magnetism in zigzag graphene nanoribbons. Our intensive numerical results show that a relatively weak Coulomb interaction may induce a ferromagnetic-like behaviour with a proper strain, and the edge magnetism can be enhanced greatly as the strain along the zigzag edge increases, which provides another way to control graphene magnetism even at room temperature.

cond-mat.str-el↗

arXiv2017

Self-organized compound pattern and pulsation of dissipative solitons in a passively mode-locked fiber laser

We experimentally observe soliton self-organization and pulsation in a passively mode-locked fiber laser. The optomechanical interaction in the optical fiber is key to the formation of equidistant soliton bunches. These solitons simultaneously undergo a pulsation process with a period corresponding to tens of the cavity round trip time. Using the dispersive Fourier transformation technique, we find that the Kelly sidebands in the shot-to-shot spectra appear periodically, synchronizing with the pulsation.

physics.optics↗