arXiv subjects

Liang Chen

Publications and source records attributed to Liang Chen.

At least 379 records · Page 21Linked to original sources

arXiv2021

Non-sensitive axis feedback control of test mass in full-maglev vertical superconducting gravity instruments

Non-sensitive axis feedback control is crucial for cross-coupling noise suppression in the application of full-maglev vertical superconducting gravity instruments. This paper introduces the non-sensitive axis feedback control of the test mass in a home-made full-maglev vertical superconducting accelerometer. In the feedback system, special superconducting circuits are designed to decouple and detect the multi-degrees-of-freedom motions of the test mass. Then the decoupled motion signals are dealt with by the PID controller and fed back to the side-wall coils to control the test mass. In our test, the test mass is controlled successfully and the displacement is reduced by about one order of magnitude in the laboratory. Accordingly, the noise level of the vertical superconducting accelerometer in the sensitive axis is also reduced.

physics.ins-det↗

arXiv2021

DGCN: Diversified Recommendation with Graph Convolutional Networks

These years much effort has been devoted to improving the accuracy or relevance of the recommendation system. Diversity, a crucial factor which measures the dissimilarity among the recommended items, received rather little scrutiny. Directly related to user satisfaction, diversification is usually taken into consideration after generating the candidate items. However, this decoupled design of diversification and candidate generation makes the whole system suboptimal. In this paper, we aim at pushing the diversification to the upstream candidate generation stage, with the help of Graph Convolutional Networks (GCN). Although GCN based recommendation algorithms have shown great power in modeling complex collaborative filtering effect to improve the accuracy of recommendation, how diversity changes is ignored in those advanced works. We propose to perform rebalanced neighbor discovering, category-boosted negative sampling and adversarial learning on top of GCN. We conduct extensive experiments on real-world datasets. Experimental results verify the effectiveness of our proposed method on diversification. Further ablation studies validate that our proposed method significantly alleviates the accuracy-diversity dilemma.

cs.IR↗

arXiv2021

A possible blazar spectral irregularity case caused by photon--axionlike-particle oscillations

We report detection of a line-like feature in the $γ$-ray spectrum of the blazar B0516$-$621, for which the data obtained with the Large Area Telescope onboard {\it Fermi Gamma-Ray Space Telescope (Fermi)} are analyzed. The feature is at $\sim$7\,GeV and different analyses are conducted to check its real presence. We determine that it has a significance of 2.5--3.0$σ$, and cautiously note the presence of possible systematics in the data which could reduce the significance. This putative feature is too narrow to be explained with radiation processes generally considered for jet emission of blazars. Instead, it could be a signal due to the oscillations between photons and axion-like particles (ALPs) in the source's jet. We investigate this possibility by fitting the spectrum with the photon-ALP oscillation model, and find that the parameter space of ALP mass $m_a\leq 10^{-8}$\,eV and the coupling constant (between photons and ALPs) $g_{aγ}$=1.16--1.48$\times 10^{-10}$\,GeV$^{-1}$ can provide a fit to the line-like feature, while the magnetic field at the emission site of $γ$-rays is fixed at 0.7\,G. The ranges for $m_a$ and $g_{aγ}$ are in tension with those previously obtained from several experiments or methods, but on the other hand in line with some of the others. This spectral-feature case and its possible indication for ALP existence could be checked from similar studies of other blazar systems and also suggest a direction of effort for building future high-energy facilities that would have high sensitivities and spectral resolutions for searching for similar features.

astro-ph.HE↗

arXiv2021

An Open-channel Microfluidic Membrane Device for In situ Hyperspectral mapping of enzymatic cellulose hydrolysis

Synchrotron infrared hyperspectral microscopy is a label-free and non-invasive technique well suited for imaging of chemical events in situ. It can track the spatial and temporal distributions of molecules of interests in a specimen in its native state by the molecule's characteristic vibrational modes. Despite tremendous progress made in recent years, IR hyperspectral imaging of chemical events in biomaterials in liquids remains challenging because of the demanding requirements on environmental control and strong infrared absorption of water. Here we report a multi-phase capillary-driven membrane device for label-free and real-time investigation of enzymatic deconstruction of algal cellulose purified from Cladophora aegagropila.

physics.bio-ph↗

arXiv2021

Planar Integrated Magneto Optical Trap

Abstract The magneto-optical trap (MOT) is an essential tool for collecting and preparing cold atoms with a wide range of applications. We demonstrate a planar-integrated MOT by combining an optical grating chip with a magnetic coil chip. The flat grating chip simplifies the conventional six-beam configuration down to a single laser beam; the flat coil chip replaces the conventional anti-Helmholtz coils of a cylindrical geometry. We trap 10^{4} cold ^{87}\text{Rb} atoms in the planar-integrated MOT, at a point 3-9 mm above the chip surface. This novel configuration effectively reduces the volume, weight, and complexity of the MOT, bringing benefits to applications including gravimeter, clock and quantum memory devices.

physics.atom-ph↗

arXiv2021

Dynamic State Estimation for Integrated Natural Gas and Electric Power Systems

A dynamic state estimation method of integrated natural gas and electric power systems (IGESs) in proposed. Firstly, the coupling model of gas pipeline networks and power systems by gas turbine units (GTUs) is established. Secondly, the Kalman filter based linear DSE model for the IGES is built. The gas density and mass flow rate, as well as the real and imaginary parts of bus voltages are taken as states, which are predicted by the linearized fluid dynamic equations of gases and exponential smoothing techniques. Boundary conditions of pipeline networks are used as supplementary constraints in the system model. At last, the proposed method is applied to an IGES including a 30-node pipeline network and IEEE 39-bus system coupled by two GTUs. Two indexes are used to evaluate the DSE performance under three measurement error conditions, and the results show that the DSE can obtain the accurate dynamic states in different conditions.

eess.SP↗

arXiv2021

Strong constraints on Lorentz violation using new $γ$-ray observations around PeV

The tiny modification of dispersion relation induced by Lorentz violation (LV) is an essential issue in quantum gravity (QG) theories, which can be magnified into significant effects when dealing with astrophysical observations at high energy and long propagation distance. LV would lead to photon decay at high energies, therefore, observations of high-energy photons could constrain LV or even QG theories. The Large High Altitude Air Shower Observatory (LHAASO) is the most sensitive gamma-array instrument currently operating above 100 TeV. Recently, LHAASO reported the detection of 12 sources above 100 TeV with maximum photon energy exceeding 1 PeV. According to these observations, the most stringent restriction is achieved in this paper, i.e., limiting the LV energy scale to $1.7\times10^{33} $ eV, which is over 139,000 times that of the Planck energy and achieve an improvement of about 1.9 orders of magnitude over previous limits.

astro-ph.HE↗

arXiv2021

Self-supervised Graph Learning for Recommendation

Representation learning on user-item graph for recommendation has evolved from using single ID or interaction history to exploiting higher-order neighbors. This leads to the success of graph convolution networks (GCNs) for recommendation such as PinSage and LightGCN. Despite effectiveness, we argue that they suffer from two limitations: (1) high-degree nodes exert larger impact on the representation learning, deteriorating the recommendations of low-degree (long-tail) items; and (2) representations are vulnerable to noisy interactions, as the neighborhood aggregation scheme further enlarges the impact of observed edges. In this work, we explore self-supervised learning on user-item graph, so as to improve the accuracy and robustness of GCNs for recommendation. The idea is to supplement the classical supervised task of recommendation with an auxiliary self-supervised task, which reinforces node representation learning via self-discrimination. Specifically, we generate multiple views of a node, maximizing the agreement between different views of the same node compared to that of other nodes. We devise three operators to generate the views -- node dropout, edge dropout, and random walk -- that change the graph structure in different manners. We term this new learning paradigm as \textit{Self-supervised Graph Learning} (SGL), implementing it on the state-of-the-art model LightGCN. Through theoretical analyses, we find that SGL has the ability of automatically mining hard negatives. Empirical studies on three benchmark datasets demonstrate the effectiveness of SGL, which improves the recommendation accuracy, especially on long-tail items, and the robustness against interaction noises. Our implementations are available at \url{https://github.com/wujcan/SGL}.

cs.IR↗

arXiv2021

DialogSum: A Real-Life Scenario Dialogue Summarization Dataset

Proposal of large-scale datasets has facilitated research on deep neural models for news summarization. Deep learning can also be potentially useful for spoken dialogue summarization, which can benefit a range of real-life scenarios including customer service management and medication tracking. To this end, we propose DialogSum, a large-scale labeled dialogue summarization dataset. We conduct empirical analysis on DialogSum using state-of-the-art neural summarizers. Experimental results show unique challenges in dialogue summarization, such as spoken terms, special discourse structures, coreferences and ellipsis, pragmatics and social common sense, which require specific representation learning technologies to better deal with.

cs.CL↗

arXiv2021

A High-dimensional Sparse Fourier Transform in the Continuous Setting

In this paper, we theoretically propose a new hashing scheme to establish the sparse Fourier transform in high-dimensional space. The estimation of the algorithm complexity shows that this sparse Fourier transform can overcome the curse of dimensionality. To the best of our knowledge, this is the first polynomial-time algorithm to recover the high-dimensional continuous frequencies.

cs.DS↗

arXiv2021

Nuclear reaction measurements of 80.5 MeV/u 12C beam bombarding on C, W, Cu, Au, Pb targets

To get the energy spectrum distribution and cross-sections of emitted light charged particles and explore the nuclear reaction, a experiment of 80.5 MeV/u 12C beam bombarding on C, W, Cu, Au, Pb targets has been carried out at Institute of Modern Physics, Chinese Academy of Science. 30, 60 and 120 degree relative to the incident beam have been detected using three sets of telescope detectors. The results indicate that there is a tendency that heavier targets have larger double differential cross-sections and the emitted fragments are more likely to go forward. Besides, the decrease of cross-sections of fragments producing with the increasing emitted angles may follow some kind of pattern.

nucl-ex↗

arXiv2021

DNA-GCN: Graph convolutional networks for predicting DNA-protein binding

Predicting DNA-protein binding is an important and classic problem in bioinformatics. Convolutional neural networks have outperformed conventional methods in modeling the sequence specificity of DNA-protein binding. However, none of the studies has utilized graph convolutional networks for motif inference. In this work, we propose to use graph convolutional networks for motif inference. We build a sequence k-mer graph for the whole dataset based on k-mer co-occurrence and k-mer sequence relationship and then learn DNA Graph Convolutional Network (DNA-GCN) for the whole dataset. Our DNA-GCN is initialized with a one-hot representation for all nodes, and it then jointly learns the embeddings for both k-mers and sequences, as supervised by the known labels of sequences. We evaluate our model on 50 datasets from ENCODE. DNA-GCN shows its competitive performance compared with the baseline model. Besides, we analyze our model and design several different architectures to help fit different datasets.

q-bio.GN↗

arXiv2021

Signal Acquisition of Luojia-1A Low Earth Orbit Navigation Augmentation System with Software Defined Receiver

Low earth orbit (LEO) satellite navigation signal can be used as an opportunity signal in case of a Global navigation satellite system (GNSS) outage, or as an enhancement means of traditional GNSS positioning algorithms. No matter which service mode is used, signal acquisition is the prerequisite of providing enhanced LEO navigation service. Compared with the medium orbit satellite, the transit time of the LEO satellite is shorter. Thus, it is of great significance to expand the successful acquisition time range of the LEO signal. Previous studies on LEO signal acquisition are based on simulation data. However, signal acquisition research based on real data is very important. In this work, the signal characteristics of LEO satellite: power space density in free space and the Doppler shift of LEO satellite are individually studied. The unified symbol definitions of several integration algorithms based on the parallel search signal acquisition algorithm are given. To verify these algorithms for LEO signal acquisition, a software-defined receiver (SDR) is developed. The performance of those integration algorithms on expanding the successful acquisition time range is verified by the real data collected from the Luojia-1A satellite. The experimental results show that the integration strategy can expand the successful acquisition time range, and it will not expand indefinitely with the integration duration.

eess.SP↗

arXiv2021

Adversarial Attack on Large Scale Graph

Recent studies have shown that graph neural networks (GNNs) are vulnerable against perturbations due to lack of robustness and can therefore be easily fooled. Currently, most works on attacking GNNs are mainly using gradient information to guide the attack and achieve outstanding performance. However, the high complexity of time and space makes them unmanageable for large scale graphs and becomes the major bottleneck that prevents the practical usage. We argue that the main reason is that they have to use the whole graph for attacks, resulting in the increasing time and space complexity as the data scale grows. In this work, we propose an efficient Simplified Gradient-based Attack (SGA) method to bridge this gap. SGA can cause the GNNs to misclassify specific target nodes through a multi-stage attack framework, which needs only a much smaller subgraph. In addition, we present a practical metric named Degree Assortativity Change (DAC) to measure the impacts of adversarial attacks on graph data. We evaluate our attack method on four real-world graph networks by attacking several commonly used GNNs. The experimental results demonstrate that SGA can achieve significant time and memory efficiency improvements while maintaining competitive attack performance compared to state-of-art attack techniques. Codes are available via: https://github.com/EdisonLeeeee/SGAttack.

cs.LG↗

arXiv2021

Secure Deep Graph Generation with Link Differential Privacy

Many data mining and analytical tasks rely on the abstraction of networks (graphs) to summarize relational structures among individuals (nodes). Since relational data are often sensitive, we aim to seek effective approaches to generate utility-preserved yet privacy-protected structured data. In this paper, we leverage the differential privacy (DP) framework to formulate and enforce rigorous privacy constraints on deep graph generation models, with a focus on edge-DP to guarantee individual link privacy. In particular, we enforce edge-DP by injecting proper noise to the gradients of a link reconstruction-based graph generation model, while ensuring data utility by improving structure learning with structure-oriented graph discrimination. Extensive experiments on two real-world network datasets show that our proposed DPGGAN model is able to generate graphs with effectively preserved global structure and rigorously protected individual link privacy.

cs.SI↗

arXiv2021

Analysis of Screening Effects on Terahertz Photoconductive Devices using a Fully-Coupled Multiphysics Approach

The terahertz current generated by a photoconductive device (PCD) saturates as the power of the input optical pump is increased. This behavior is induced by various screening effects that stem from the interactions between electromagnetic (EM) fields and semiconductor carriers. In this work, these screening effects are numerically analyzed for the first time using a fully-coupled multiphysics approach. Unlike the previously developed simulation frameworks, this approach rigorously models the nonlinear coupling between the EM fields and the carriers and therefore is capable of accounting for the screening effects. It is demonstrated that the results obtained using this multiphysics approach and actual experiments are in excellent agreement. The optical- and radiation-field screening effects are identified in the simulation results and the optical-field screening is found to play a more dominant role in the saturation of the PCD output under high optical pump power levels.

physics.app-ph↗

arXiv2021

Efficient Discontinuous Galerkin Scheme for Analyzing Nanostructured Photoconductive Devices

Incorporation of plasmonic nanostructures in the design of photoconductive devices (PCDs) has significantly improved their optical-to-terahertz conversion efficiency. However, this improvement comes at the cost of increased complexity for the design and simulation of these devices. Indeed, accurate and efficient modeling of multiphysics processes and intricate device geometries of nanostructured PCDs is challenging due to the high computational cost resulting from multiple characteristic scales in time and space. In this work, a discontinuous Galerkin (DG)-based unit-cell scheme for efficient simulation of PCDs with periodic nanostructures is proposed. The scheme considers two physical stages of the device and models them using two coupled systems: a system of Poisson and drift-diffusion equations describing the nonequilibrium steady state, and a system of Maxwell and drift-diffusion equations describing the transient stage. A "potential-drop" boundary condition is enforced on the opposing boundaries of the unit cell to mimic the effect of the bias voltage. Periodic boundary conditions are used for carrier densities and electromagnetic fields. The unit-cell model described by these coupled equations and boundary conditions is discretized using DG methods. Numerical results demonstrate that the proposed DG-based unit-cell scheme has the same accuracy in predicting the THz photocurrent as the DG framework that takes into account the whole device, while it significantly reduces the computational cost.

cs.CE↗

arXiv2021

Personalized Bundle Recommendation in Online Games

In business domains, \textit{bundling} is one of the most important marketing strategies to conduct product promotions, which is commonly used in online e-commerce and offline retailers. Existing recommender systems mostly focus on recommending individual items that users may be interested in. In this paper, we target at a practical but less explored recommendation problem named bundle recommendation, which aims to offer a combination of items to users. To tackle this specific recommendation problem in the context of the \emph{virtual mall} in online games, we formalize it as a link prediction problem on a user-item-bundle tripartite graph constructed from the historical interactions, and solve it with a neural network model that can learn directly on the graph-structure data. Extensive experiments on three public datasets and one industrial game dataset demonstrate the effectiveness of the proposed method. Further, the bundle recommendation model has been deployed in production for more than one year in a popular online game developed by Netease Games, and the launch of the model yields more than 60\% improvement on conversion rate of bundles, and a relative improvement of more than 15\% on gross merchandise volume (GMV).

cs.IR↗