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Liang Chen

Publications and source records attributed to Liang Chen.

At least 415 records · Page 23Linked to original sources
arXiv2020

Remarkable antibacterial activity of reduced graphene oxide functionalized by copper ions

Despite long-term efforts for exploring antibacterial agents or drugs, it remains challenging how to potentiate antibacterial activity and meanwhile minimize toxicity hazards to the environment. Here, we experimentally show that the functionality of reduced graphene oxide (rGO) through copper ions displays selective antibacterial activity significantly stronger than that of rGO itself and no toxicity to mammalian cells. Remarkably, this antibacterial activity is two orders of magnitude greater than the activity of its surrounding copper ions. We demonstrate that the rGO is functionalized through the cation-$π$ interaction to massively adsorb copper ions to form a rGO-copper composite in solution and result in an extremely low concentration level of surrounding copper ions (less than ~0.5 $μM$). These copper ions on rGO are positively charged and strongly interact with negatively charged bacterial cells to selectively achieve antibacterial activity, while rGO exhibits the functionality to not only actuate rapid delivery of copper ions and massive assembly onto bacterial cells but also result in the valence shift in the copper ions from Cu$^{2+}$ into Cu$^{+}$ which greatly enhances the antibacterial activity. Notably, this functionality of rGO through cation-$π$ interaction with copper ions can similarly achieve algaecidal activity but does not exert cytotoxicity against neutrally charged mammalian cells. The remarkable selective antibacterial activity from the rGO functionality as well as the inherent broad-spectrum-antibacterial physical mechanism represents a significant step toward the development of a novel antibacterial material and reagent without environmental hazards for practical application.

physics.app-ph↗
arXiv2020

Topology Optimization through Differentiable Finite Element Solver

In this paper, a topology optimization framework utilizing automatic differentiation is presented as an efficient way for solving 2D density-based topology optimization problem by calculating gradients through the fully differentiable finite element solver. The optimization framework with the differentiable physics solver is proposed and tested on several classical topology optimization examples. The differentiable solver is implemented in Julia programming language and can be automatically differentiated in reverse mode to provide the pullback functions of every single operation. The entire end-to-end gradient information can be then backed up by utilizing chain rule. This framework incorporates a generator built from convolutional layers with a set of learnable parameters to propose new designs for every iteration. Since the whole process is differentiable, the parameters of the generator can be updated using any optimization algorithm given the gradient information from automatic differentiation. The proposed optimization framework is demonstrated on designing a half MBB beam and compared to the results with the ones from the efficient 88-line code. By only changing the objective function and the boundary conditions, it can run an optimization for designing a compliant mechanism, e.g. a force inverter where the output displacement is in the opposite direction of the input.

cs.CE↗
arXiv2020

AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results

This paper reviews the AIM 2020 challenge on efficient single image super-resolution with focus on the proposed solutions and results. The challenge task was to super-resolve an input image with a magnification factor x4 based on a set of prior examples of low and corresponding high resolution images. The goal is to devise a network that reduces one or several aspects such as runtime, parameter count, FLOPs, activations, and memory consumption while at least maintaining PSNR of MSRResNet. The track had 150 registered participants, and 25 teams submitted the final results. They gauge the state-of-the-art in efficient single image super-resolution.

eess.IV↗
arXiv2020

Multiphysics Simulation of Plasmonic Photoconductive Devices using Discontinuous Galerkin Methods

Plasmonic nanostructures significantly improve the performance of photoconductive devices (PCDs) in generating terahertz radiation. However, they are geometrically intricate and result in complicated electromagnetic (EM) field and carrier interactions under a bias voltage and upon excitation by an optical EM wave. These lead to new challenges in simulations of plasmonic PCDs, which cannot be addressed by existing numerical frameworks. In this work, a multiphysics framework making use of discontinuous Galerkin (DG) methods is developed to address these challenges. The operation of the PCD is analyzed in stationary and transient states, which are described by coupled systems of the Poisson and stationary drift-diffusion (DD) equations and the time-dependent Maxwell and DD equations, respectively. Both systems are discretized using DG schemes. The nonlinearity of the stationary system is accounted for using the Gummel iterative method while the nonlinear coupling between the time-dependent Maxwell and DD equations is tackled during time integration. The DG-based discretization and the explicit time marching help in handling space and time characteristic scales that are associated with different physical processes and differ by several orders of magnitude. The accuracy and applicability of the resulting multiphysics framework are demonstrated via simulations of conventional and plasmonic PCDs.

math.NA↗
arXiv2020

Variance-reduced Language Pretraining via a Mask Proposal Network

Self-supervised learning, a.k.a., pretraining, is important in natural language processing. Most of the pretraining methods first randomly mask some positions in a sentence and then train a model to recover the tokens at the masked positions. In such a way, the model can be trained without human labeling, and the massive data can be used with billion parameters. Therefore, the optimization efficiency becomes critical. In this paper, we tackle the problem from the view of gradient variance reduction. In particular, we first propose a principled gradient variance decomposition theorem, which shows that the variance of the stochastic gradient of the language pretraining can be naturally decomposed into two terms: the variance that arises from the sample of data in a batch, and the variance that arises from the sampling of the mask. The second term is the key difference between selfsupervised learning and supervised learning, which makes the pretraining slower. In order to reduce the variance of the second part, we leverage the importance sampling strategy, which aims at sampling the masks according to a proposal distribution instead of the uniform distribution. It can be shown that if the proposal distribution is proportional to the gradient norm, the variance of the sampling is reduced. To improve efficiency, we introduced a MAsk Proposal Network (MAPNet), which approximates the optimal mask proposal distribution and is trained end-to-end along with the model. According to the experimental result, our model converges much faster and achieves higher performance than the baseline BERT model.

cs.CL↗
arXiv2020

A Memory-efficient Implementation of Perfectly Matched Layer with Smoothly-varying Coefficients in Discontinuous Galerkin Time-Domain Method

Wrapping a computation domain with a perfectly matched layer (PML) is one of the most effective methods of imitating/approximating the radiation boundary condition in Maxwell and wave equation solvers. Many PML implementations often use a smoothly-increasing attenuation coefficient to increase the absorption for a given layer thickness, and, at the same time, to reduce the numerical reflection from the interface between the computation domain and the PML. In discontinuous Galerkin time-domain (DGTD) methods, using a PML coefficient that varies within a mesh element requires a different mass matrix to be stored for every element and therefore significantly increases the memory footprint. In this work, this bottleneck is addressed by applying a weight-adjusted approximation to these mass matrices. The resulting DGTD scheme has the same advantages as the scheme that stores individual mass matrices, namely higher accuracy (due to reduced numerical reflection) and increased meshing flexibility (since the PML does not have to be defined layer by layer) but it requires significantly less memory.

math.NA↗
arXiv2020

Interactive Path Reasoning on Graph for Conversational Recommendation

Traditional recommendation systems estimate user preference on items from past interaction history, thus suffering from the limitations of obtaining fine-grained and dynamic user preference. Conversational recommendation system (CRS) brings revolutions to those limitations by enabling the system to directly ask users about their preferred attributes on items. However, existing CRS methods do not make full use of such advantage -- they only use the attribute feedback in rather implicit ways such as updating the latent user representation. In this paper, we propose Conversational Path Reasoning (CPR), a generic framework that models conversational recommendation as an interactive path reasoning problem on a graph. It walks through the attribute vertices by following user feedback, utilizing the user preferred attributes in an explicit way. By leveraging on the graph structure, CPR is able to prune off many irrelevant candidate attributes, leading to better chance of hitting user preferred attributes. To demonstrate how CPR works, we propose a simple yet effective instantiation named SCPR (Simple CPR). We perform empirical studies on the multi-round conversational recommendation scenario, the most realistic CRS setting so far that considers multiple rounds of asking attributes and recommending items. Through extensive experiments on two datasets Yelp and LastFM, we validate the effectiveness of our SCPR, which significantly outperforms the state-of-the-art CRS methods EAR (arXiv:2002.09102) and CRM (arXiv:1806.03277). In particular, we find that the more attributes there are, the more advantages our method can achieve.

cs.IR↗
arXiv2020

On the Origin and Evolution of Curvature of the Spectral Energy Distribution of Fermi Bright Blazars

The origin and evolution of spectral curvature in blazar spectral energy distribution (SED) is still unclear. Since the observed SED curvature is related to an intrinsic curvature in emitting electron energy distribution (EED), we study this question by employing a log-parabolic EED with a curvature parameter and peak energy to model the quasi-simultaneous broadband SEDs of selected blazars in Fermi-LAT Bright AGN Sample (LBAS) using synchrotron and inverse Compton (IC) processes. We find that log-parabolic IC model can successfully explain the emission in all blazars in our sample. On average, FSRQs have higher magnetic field, Doppler factor, and curvature than BL Lac objects. The BL Lac objects show an anticorrelation between the curvature parameter of the EED and its peak energy, which is a signature of stochastic acceleration. FSRQs do not manifest such correlation and rather show a mild positive relationship between these parameters. This suggests that the evolution of spectral curvature in the BL Lac objects is dominated by a strong stochastic acceleration component, whereas the curvature in FSRQs evolves in a cooling dominated regime due to an additional external Compton (EC) component. The strong cooling in FSRQs not only restricts the electron peak energy but also adds extra curvature to the high energy tail of emitting EED. Since the curvature decreases from FSRQs toward high peak BL Lac objects (HBLs), opposite to peak energy, the curvature parameter can be considered a third parameter of the blazar sequence in addition to peak frequency and luminosity.

astro-ph.HE↗
arXiv2020

Realistic Adversarial Data Augmentation for MR Image Segmentation

Neural network-based approaches can achieve high accuracy in various medical image segmentation tasks. However, they generally require large labelled datasets for supervised learning. Acquiring and manually labelling a large medical dataset is expensive and sometimes impractical due to data sharing and privacy issues. In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation. Instead of generating pixel-wise adversarial attacks, our model generates plausible and realistic signal corruptions, which models the intensity inhomogeneities caused by a common type of artefacts in MR imaging: bias field. The proposed method does not rely on generative networks, and can be used as a plug-in module for general segmentation networks in both supervised and semi-supervised learning. Using cardiac MR imaging we show that such an approach can improve the generalization ability and robustness of models as well as provide significant improvements in low-data scenarios.

eess.IV↗
arXiv2020

The X-ray emission in young radio AGNs

In this work, we investigated the X-ray emission for a sample of young radio AGNs by combining their data from Chandra/XMM-Newton and at other wavebands. We find strong correlations between the X-ray luminosity $L_{\rm X}$ in 2$-$10 keV and the radio luminosities $L_{\rm R}$ at 5 GHz for VLBI radio-core, VLA radio-core and FIRST component, indicating that both pc- and kpc-scale radio emission strongly correlate with X-ray emission in these sources. We find approximately linear dependence of radio on X-ray luminosity in the sources with radiative efficient accretion flows (i.e., the Eddington ratio $R_{\rm edd} \gtrsim 10^{-3} $) with b $\sim$ 1 ($L_{\rm R}$ $\propto$ $L_{\rm X} ^{b}$) and $ξ_{\rm RX}$ $\sim$ 1 in fundamental plane using VLBI data, where the dependence is consistent with the re-analysed result on the previous study in \cite{2016ApJ...818..185F} at $R_{\rm edd} \gtrsim 10^{-3}$, however is significantly deviated from the theoretical prediction of accretion flow as the origin of X-ray emission. In contrast to radio-quiet quasars, there is no significant correlation between $Γ$ and Eddington ratio. Our results seem to indicate that the X-ray emission of high-accreting young radio AGNs may be from jet. We constructed the SEDs for 18 sources (most are in radiative efficient accretion) including 9 galaxies and 9 quasars with high-quality X-ray data, and find that the X-ray emission of most quasars is more luminous than that of normal radio-quiet quasars. This is clearly seen from the quasar composite SED, of which the X-ray emission is apparently higher than that of radio-quiet quasars, likely supporting the jet-related X-ray emission in young radio AGNs. The scenario that the X-ray emission is from self-synchrotron Compton (SSC) is discussed.

astro-ph.GA↗
arXiv2020

Real-Time Differential Epidemic Analysis and Prediction for COVID-19 Pandemic

In this paper, we propose a new real-time differential virus transmission model, which can give more accurate and robust short-term predictions of COVID-19 transmitted infectious disease with benefits of near-term trend projection. Different from the existing Susceptible-Exposed-Infected-Removed (SEIR) based virus transmission models, which fits well for pandemic modeling with sufficient historical data, the new model, which is also SEIR based, uses short history data to find the trend of the changing disease dynamics for the infected, the dead and the recovered so that it can naturally accommodate the adaptive real-time changes of disease mitigation, business activity and social behavior of populations. As the parameters of the improved SEIR models are trained by short history window data for accurate trend prediction, our differential epidemic model, essentially are window-based time-varying SEIR model. Since SEIR model still is a physics-based disease transmission model, its near-term (like one month) projection can still be very instrumental for policy makers to guide their decision for disease mitigation and business activity policy change in a real-time. This is especially useful if the pandemic lasts more than one year with different phases across the world like 1918 flu pandemic. Numerical results on the recent COVID-19 data from China, Italy and US, California and New York states have been analyzed.

q-bio.PE↗
arXiv2020

Diversity of gamma-ray and Radio Variabilities of Bright Blazars and Implications for gamma-ray Emission Location

Violent multi-wavelength variabilities are observed in gamma-ray-selected blazars. We present an analysis of long-term light curves for eight bright blazars to explore the co-variation pattern in the gamma-ray and radio bands. We extract their gamma-ray light curves and spectra with data observed by the Fermi/LAT since 2008. We find diverse co-variation patterns between the gamma-ray and radio (at 43 GHz) fluxes in these sources. The gamma-ray and radio fluxes of 3C 454.3 and PKS 1633+382 are correlated without any time-lag, suggesting that they are from the same radiation region. Similar correlation is also observed in 3C 273 and PKS 1222+216, but the radio flux is lag behind the gamma-ray flux approximately ~160 days and ~290 days, respectively. This likely suggests that their gamma-ray emission regions are located at the upstream of their radio cores at 43 GHz. The gamma-ray and radio fluxes of the other four blazars are not correlated, implying that the gamma-ray and radio emission may be from different regions in their jets. The gamma-ray light curves of the eight blazars can be decomposed into some long timescale variability components and fast spike flares. We propose that they may be attributed to the central engine activity and the magnetic reconnection process or turbulence in the local emission region, respectively.

astro-ph.HE↗
arXiv2020

Modelling High-Order Social Relations for Item Recommendation

The prevalence of online social network makes it compulsory to study how social relations affect user choice. However, most existing methods leverage only first-order social relations, that is, the direct neighbors that are connected to the target user. The high-order social relations, e.g., the friends of friends, which very informative to reveal user preference, have been largely ignored. In this work, we focus on modeling the indirect influence from the high-order neighbors in social networks to improve the performance of item recommendation. Distinct from mainstream social recommenders that regularize the model learning with social relations, we instead propose to directly factor social relations in the predictive model, aiming at learning better user embeddings to improve recommendation. To address the challenge that high-order neighbors increase dramatically with the order size, we propose to recursively "propagate" embeddings along the social network, effectively injecting the influence of high-order neighbors into user representation. We conduct experiments on two real datasets of Yelp and Douban to verify our High-Order Social Recommender (HOSR) model. Empirical results show that our HOSR significantly outperforms recent graph regularization-based recommenders NSCR and IF-BPR+, and graph convolutional network-based social influence prediction model DeepInf, achieving new state-of-the-arts of the task.

cs.IR↗
arXiv2020

DeepCP: Deep Learning Driven Cascade Prediction Based Autonomous Content Placement in Closed Social Network

Online social networks (OSNs) are emerging as the most popular mainstream platform for content cascade diffusion. In order to provide satisfactory quality of experience (QoE) for users in OSNs, much research dedicates to proactive content placement by using the propagation pattern, user's personal profiles and social relationships in open social network scenarios (e.g., Twitter and Weibo). In this paper, we take a new direction of popularity-aware content placement in a closed social network (e.g., WeChat Moment) where user's privacy is highly enhanced. We propose a novel data-driven holistic deep learning framework, namely DeepCP, for joint diffusion-aware cascade prediction and autonomous content placement without utilizing users' personal and social information. We first devise a time-window LSTM model for content popularity prediction and cascade geo-distribution estimation. Accordingly, we further propose a novel autonomous content placement mechanism CP-GAN which adopts the generative adversarial network (GAN) for agile placement decision making to reduce the content access latency and enhance users' QoE. We conduct extensive experiments using cascade diffusion traces in WeChat Moment (WM). Evaluation results corroborate that the proposed DeepCP framework can predict the content popularity with a high accuracy, generate efficient placement decision in a real-time manner, and achieve significant content access latency reduction over existing schemes.

cs.SI↗
arXiv2019

A re-formulization of the transfer matrix method for calculating wave-functions in higher dimensional disordered open systems

We present a numerically stable re-formulization of the transfer matrix method (TMM). The iteration form of the traditional TMM is transformed into solving a set of linear equations. Our method gains the new ability of calculating accurate wave-functions of higher dimensional disordered systems. It also shows higher efficiency than the traditional TMM when treating finite systems. In contrast to the diagonalization method, our method not only provides a new route for calculating the wave-function corresponding to the boundary conditions of open systems in realistic transport experiments, but also has advantages that the calculating wave energy/frequency can be tuned continuously and the efficiency is much higher. Our new method is further used to identify the necklace state in the two dimensional disordered Anderson model, where it shows advantage in cooperating the wave-functions with the continuous transmission spectrum of open systems. The new formulization is very simple to implement and can be readily generalized to various systems such as spin-orbit coupling systems or optical systems.

physics.comp-ph↗
arXiv2019

Data Poisoning Attacks on Neighborhood-based Recommender Systems

Nowadays, collaborative filtering recommender systems have been widely deployed in many commercial companies to make profit. Neighbourhood-based collaborative filtering is common and effective. To date, despite its effectiveness, there has been little effort to explore their robustness and the impact of data poisoning attacks on their performance. Can the neighbourhood-based recommender systems be easily fooled? To this end, we shed light on the robustness of neighbourhood-based recommender systems and propose a novel data poisoning attack framework encoding the purpose of attack and constraint against them. We firstly illustrate how to calculate the optimal data poisoning attack, namely UNAttack. We inject a few well-designed fake users into the recommender systems such that target items will be recommended to as many normal users as possible. Extensive experiments are conducted on three real-world datasets to validate the effectiveness and the transferability of our proposed method. Besides, some interesting phenomenons can be found. For example, 1) neighbourhood-based recommender systems with Euclidean Distance-based similarity have strong robustness. 2) the fake users can be transferred to attack the state-of-the-art collaborative filtering recommender systems such as Neural Collaborative Filtering and Bayesian Personalized Ranking Matrix Factorization.

cs.IR↗