arXiv subjects

Gang Wang

Publications and source records attributed to Gang Wang.

At least 307 records · Page 17Linked to original sources

arXiv2021

Alternative LISA-TAIJI networks: detectability of the isotropic stochastic gravitational wave background

In previous work [1], three TAIJI orbital deployments have been proposed to compose alternative LISA-TAIJI networks, TAIJIm (leading the Earth by $20^\circ$ and $-60^\circ$ inclined with respect to ecliptic plane), TAIJIp (leading the Earth by $20^\circ$ and $+60^\circ$ inclined), TAIJIc (colocated and coplanar with LISA) with respect to the LISA mission (trailing the Earth by $20^\circ$ and $+60^\circ$ inclined). And the LISA-TAIJIm network has been identified as the most capable configuration for massive black hole binary observation. In this work, we examine the performance of three networks to the stochastic gravitational wave background (SGWB) especially for the comparison of two eligible configurations, LISA-TAIJIm and LISA-TAIJIp. This investigation shows that the detectability of LISA-TAIJIm is competitive with the LISA-TAIJIp network for some specific SGWB spectral shapes. And the capability of LISA-TAIJIm is also identical to LISA-TAIJIp to separate the SGWB components by determining the parameters of signals. Considering the performances on SGWB and massive black hole binaries observations, the TAIJIm could be recognized as an optimal option to fulfill joint observations with LISA.

gr-qc↗

arXiv2021

HD-cos Networks: Efficient Neural Architectures for Secure Multi-Party Computation

Multi-party computation (MPC) is a branch of cryptography where multiple non-colluding parties execute a well designed protocol to securely compute a function. With the non-colluding party assumption, MPC has a cryptographic guarantee that the parties will not learn sensitive information from the computation process, making it an appealing framework for applications that involve privacy-sensitive user data. In this paper, we study training and inference of neural networks under the MPC setup. This is challenging because the elementary operations of neural networks such as the ReLU activation function and matrix-vector multiplications are very expensive to compute due to the added multi-party communication overhead. To address this, we propose the HD-cos network that uses 1) cosine as activation function, 2) the Hadamard-Diagonal transformation to replace the unstructured linear transformations. We show that both of the approaches enjoy strong theoretical motivations and efficient computation under the MPC setup. We demonstrate on multiple public datasets that HD-cos matches the quality of the more expensive baselines.

cs.CR↗

arXiv2021

Collaborative Uncertainty in Multi-Agent Trajectory Forecasting

Uncertainty modeling is critical in trajectory forecasting systems for both interpretation and safety reasons. To better predict the future trajectories of multiple agents, recent works have introduced interaction modules to capture interactions among agents. This approach leads to correlations among the predicted trajectories. However, the uncertainty brought by such correlations is neglected. To fill this gap, we propose a novel concept, collaborative uncertainty(CU), which models the uncertainty resulting from the interaction module. We build a general CU-based framework to make a prediction model to learn the future trajectory and the corresponding uncertainty. The CU-based framework is integrated as a plugin module to current state-of-the-art (SOTA) systems and deployed in two special cases based on multivariate Gaussian and Laplace distributions. In each case, we conduct extensive experiments on two synthetic datasets and two public, large-scale benchmarks of trajectory forecasting. The results are promising: 1) The results of synthetic datasets show that CU-based framework allows the model to appropriately approximate the ground-truth distribution. 2) The results of trajectory forecasting benchmarks demonstrate that the CU-based framework steadily helps SOTA systems improve their performances. Especially, the proposed CU-based framework helps VectorNet improve by 57cm regarding Final Displacement Error on nuScenes dataset. 3) The visualization results of CU illustrate that the value of CU is highly related to the amount of the interactive information among agents.

cs.CV↗

arXiv2021

Data-Driven Resilient Predictive Control under Denial-of-Service

The study of resilient control of linear time-invariant (LTI) systems against denial-of-service (DoS) attacks is gaining popularity in emerging cyber-physical applications. In previous works, explicit system models are required to design a predictor-based resilient controller. These models can be either given a priori or obtained through a prior system identification step. Recent research efforts have focused on data-driven control based on pre-collected input-output trajectories (i.e., without explicit system models). In this paper, we take an initial step toward data-driven stabilization of stochastic LTI systems under DoS attacks, and develop a resilient model predictive control (MPC) scheme driven purely by data-dependent conditions. The proposed data-driven control method achieves the same level of resilience as the model-based control method. For example, local input-to-state stability (ISS) is achieved under mild assumptions on the noise and the DoS attacks. To recover global ISS, two modifications are further suggested at the price of reduced resilience against DoS attacks or increased computational complexity. Finally, a numerical example is given to validate the effectiveness of the proposed control method.

eess.SY↗

arXiv2021

Origin of giant valley splitting in silicon quantum wells induced by superlattice barriers

Enhancing valley splitting in SiGe heterostructures is a crucial task for developing silicon spin qubits. Complex SiGe heterostructures, sharing a common feature of four-monolayer (4ML) Ge layer next to the silicon quantum well (QW), have been computationally designed to have giant valley splitting approaching 9 meV. However, none of them has been fabricated may due to their complexity. Here, we remarkably simplify the original designed complex SiGe heterostructures by laying out the Si QW directly on the Ge substrate followed by capping a (Ge4Si4)n superlattice(SL) barrier with a small sacrifice on VS as it is reduced from a maximum of 8.7 meV to 5.2 meV. Even the smallest number of periods (n = 1) will also give a sizable VS of 1.6 meV, which is large enough for developing stable spin qubits. We also develop an effective Hamiltonian model to reveal the underlying microscopic physics of enhanced valley splitting by (Ge4Si4)n SL barriers. We find that the presence of the SL barrier will reduce the VS instead of enhancing it. Only the (Ge4Si4)n SL barriers with an extremely strong coupling with Si QW valley states provide a remarkable enhancement in VS. These findings lay a solid theoretical foundation for the realization of sufficiently large VS for Si qubits.

cond-mat.mes-hall↗

arXiv2021

Utilization of Event Shape in Search of the Chiral Magnetic Effect in Heavy-ion Collisions

The search for the chiral magnetic effect (CME) has been a subject of great interest in the field of high-energy heavy-ion collision physics, and various observables have been proposed to probe the CME. Experimental observables are often contaminated with background contributions arising from collective motions (specifically elliptic flow) of the collision system. We present a method study of event-shape engineering (ESE) that projects the CME-sensitive $γ_{112}$ correlator and its variations ($γ_{132}$ and $γ_{123}$) to a class of events with minimal flow. We discuss the realization of the zero-flow mode, the sensitivity on the CME signal, and the corresponding statistical significance for Au+Au, Ru+Ru, and Zr+Zr collisions at $\sqrt{s_{\rm NN}} = 200$ GeV with a multiphase transport (AMPT) model, as well as a new event generator, Event-By-Event Anomalous-Viscous Fluid Dynamics (EBE-AVFD).

nucl-th↗

arXiv2021

The 2nd Anti-UAV Workshop & Challenge: Methods and Results

The 2nd Anti-UAV Workshop \& Challenge aims to encourage research in developing novel and accurate methods for multi-scale object tracking. The Anti-UAV dataset used for the Anti-UAV Challenge has been publicly released. There are two subsets in the dataset, $i.e.$, the test-dev subset and test-challenge subset. Both subsets consist of 140 thermal infrared video sequences, spanning multiple occurrences of multi-scale UAVs. Around 24 participating teams from the globe competed in the 2nd Anti-UAV Challenge. In this paper, we provide a brief summary of the 2nd Anti-UAV Workshop \& Challenge including brief introductions to the top three methods.The submission leaderboard will be reopened for researchers that are interested in the Anti-UAV challenge. The benchmark dataset and other information can be found at: https://anti-uav.github.io/.

cs.CV↗

arXiv2021

Learning Dual Dynamic Representations on Time-Sliced User-Item Interaction Graphs for Sequential Recommendation

Sequential Recommendation aims to recommend items that a target user will interact with in the near future based on the historically interacted items. While modeling temporal dynamics is crucial for sequential recommendation, most of the existing studies concentrate solely on the user side while overlooking the sequential patterns existing in the counterpart, i.e., the item side. Although a few studies investigate the dynamics involved in the dual sides, the complex user-item interactions are not fully exploited from a global perspective to derive dynamic user and item representations. In this paper, we devise a novel Dynamic Representation Learning model for Sequential Recommendation (DRL-SRe). To better model the user-item interactions for characterizing the dynamics from both sides, the proposed model builds a global user-item interaction graph for each time slice and exploits time-sliced graph neural networks to learn user and item representations. Moreover, to enable the model to capture fine-grained temporal information, we propose an auxiliary temporal prediction task over consecutive time slices based on temporal point process. Comprehensive experiments on three public real-world datasets demonstrate DRL-SRe outperforms the state-of-the-art sequential recommendation models with a large margin.

cs.IR↗

arXiv2021

Hypersonic curved compression ramp flows with bistable states

The bistable states and separation hysteresis in curved compression ramp (CCR) flows, and the corresponding aerothermal characteristics (including wall friction, pressure and heat flux), are studied numerically and theoretically. Direct numerical simulations of separation hysteresis induced by variation of turning angle, as well as the influence of inflow Mach number and wall temperature on hysteresis loops, are carried out. Distributions of wall friction, pressure and heat flux are analyzed. Further, emergence of wall friction's first and second minima in the separation bubble is interpreted, revealing it is dominated by the adverse pressure gradient induced by separation and reattachment shocks. The present results and analysis indicate that the reversed-flow singularity of Smith (Proceedings of the Royal Society of London. A. Mathematical and Physical Sciences, 1988, 420: 21-52) is less likely to occur in CCR flows. The prediction of peak pressure of separation states confirms the model based on the minimum viscous dissipation theorem (Physics of Fluids, 2020, 32(10):101702). While the pressure overshoot can be analyzed by shock-polars with pressure match of compression and expansion process. The correlation between peak heat flux and peak pressure rise of both separation and attachment states is also discussed in terms of the classical power relations.

physics.flu-dyn↗

arXiv2021

Mechanism of separation hysteresis in curved compression ramp

A new spatial-related mechanism is proposed to understand separation hysteresis processes in curved compression ramp (CCR) flows discovered recently (Hu et al. Phy. Fluid, 32(11): 113601, 2020). Two separation hystereses, induced by variations of Mach number and wall temperature, are investigated numerically. The two hystereses indicate that there must exist parameter intervals of Mach number and wall temperature, wherein both attachment and separation states can be established stably. The relationships between the aerodynamic characteristics (including wall friction, pressure and heat flux) and the shock wave configurations in this two hystereses are analyzed. Further, the adverse pressure gradient (APG) Isb(x) induced by the upstream separation process and APG Icw(x) induced by the downstream isentropic compression process are estimated by classic theories. The trend of boundary layer APG resistence Ib(x) is evaluated from the spatial distributions of the physical quantities such as the shape factor and the height of the sound velocity line. With the stable conditions of separation and attachment, a self-consistent mechanism is obtained when Isb, Icw and Ib have appropriate spatial distributions.

physics.flu-dyn↗

arXiv2021

Investigation of Experimental Observables in Search of the Chiral Magnetic Effect in Heavy-ion Collisions in the STAR experiment

The chiral magnetic effect (CME) is a novel transport phenomenon, arising from the interplay between quantum anomalies and strong magnetic fields in chiral systems. In high-energy nuclear collisions, the CME may survive the expansion of the quark-gluon plasma fireball and be detected in experiments. Over the past decade, the experimental searches for the CME have aroused extensive interest at the Relativistic Heavy Ion Collider (RHIC) and the Large Hadron Collider (LHC). The main goal of this article is to investigate three pertinent experimental approaches: the $γ$ correlator, the $R$ correlator and the signed balance functions. We will exploit both simple Monte Carlo simulations and a realistic event generator (EBE-AVFD) to verify the equivalence in the kernel-component observables among these methods and to ascertain their sensitivities to the CME signal for the isobaric collisions at RHIC.

nucl-ex↗

arXiv2021

Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm

Recently, pre-trained language models such as BERT have been applied to document ranking for information retrieval, which first pre-train a general language model on an unlabeled large corpus and then conduct ranking-specific fine-tuning on expert-labeled relevance datasets. Ideally, an IR system would model relevance from a user-system dualism: the user's view and the system's view. User's view judges the relevance based on the activities of "real users" while the system's view focuses on the relevance signals from the system side, e.g., from the experts or algorithms, etc. Inspired by the user-system relevance views and the success of pre-trained language models, in this paper we propose a novel ranking framework called Pre-Rank that takes both user's view and system's view into consideration, under the pre-training and fine-tuning paradigm. Specifically, to model the user's view of relevance, Pre-Rank pre-trains the initial query-document representations based on large-scale user activities data such as the click log. To model the system's view of relevance, Pre-Rank further fine-tunes the model on expert-labeled relevance data. More importantly, the pre-trained representations, are fine-tuned together with handcrafted learning-to-rank features under a wide and deep network architecture. In this way, Pre-Rank can model the relevance by incorporating the relevant knowledge and signals from both real search users and the IR experts. To verify the effectiveness of Pre-Rank, we showed two implementations by using BERT and SetRank as the underlying ranking model, respectively. Experimental results base on three publicly available benchmarks showed that in both of the implementations, Pre-Rank can respectively outperform the underlying ranking models and achieved state-of-the-art performances.

cs.IR↗

arXiv2021

Controllable Bistability and Squeezing of Confined Polariton Dark Solitons

The generation of squeezed light in semiconductor materials opens opportunities for building on-chip devices that are operated at the quantum level. Here we study theoretically a squeezed light source of polariton dark solitons confined in a geometric potential well of semiconductor microcavities in the strong coupling regime. We show that polariton dark solitons of odd and even parities can be created by tuning the potential depth. When driving the potential depth linearly, a bistability of solitons with the two different parities can be induced. Strong intensity squeezing is obtained near the turning point of the bistability due to the large nonlinear interaction, which can be controlled by Feshbach resonance. The phase diagram of the bistability and squeezing of the dark solitons is obtained through large scale numerical calculations. Our study contributes to the current efforts in realizing topological excitations and squeezed light sources with solid-state devices.

physics.optics↗

arXiv2021

Geometrized effective-one-body formalism for extreme-mass-ratio limits: Generic orbits

Compact objects inspiraling into supermassive black holes, known as extreme-mass-ratio inspirals, are an important source for future space-borne gravitational-wave detectors. When constructing waveform templates, usually the adiabatic approximation is employed to treat the compact object as a test particle for a short duration, and the radiation reaction is reflected in the changes of the constants of motion. However, the mass of the compact object should have contributions to the background. In the present paper, employing the effective-one-body formalism, we analytically calculate the trajectories of a compact object around a massive Kerr black hole with generally three-dimensional orbits and express the fundamental orbital frequencies in explicit forms. In addition, by constructing an approximate "constant" similar to the Carter constant, we transfer the dynamical quantities such as energy, angular momentum, and the "Carter constant" to the semilatus rectum, eccentricity, and orbital inclination with mass-ratio corrections. The linear mass-ratio terms in the formalism may not be sufficient for accurate waveforms, but our analytical method for solving the equations of motion could be useful in various approaches to building waveform models.

gr-qc↗

arXiv2021

SA-MATD3:Self-attention-based multi-agent continuous control method in cooperative environments

Cooperative problems under continuous control have always been the focus of multi-agent reinforcement learning. Existing algorithms suffer from the problem of uneven learning degree with the increase of the number of agents. In this paper, a new structure for a multi-agent actor critic is proposed, and the self-attention mechanism is applied in the critic network and the value decomposition method used to solve the uneven problem. The proposed algorithm makes full use of the samples in the replay memory buffer to learn the behavior of a class of agents. First, a new update method is proposed for policy networks that promotes learning efficiency. Second, the utilization of samples is improved, at the same time reflecting the ability of perspective-taking among groups. Finally, the "deceptive signal" in training is eliminated and the learning degree among agents is more uniform than in the existing methods. Multiple experiments were conducted in two typical scenarios of a multi-agent particle environment. Experimental results show that the proposed algorithm can perform better than the state-of-the-art ones, and that it exhibits higher learning efficiency with an increasing number of agents.

cs.MA↗

arXiv2021

Resonant Beam Communications with Echo Interference Elimination

Resonant beam communications (RBCom) is capable of providing wide bandwidth when using light as the carrier. Besides, the RBCom system possesses the characteristics of mobility, high signal-to-noise ratio (SNR), and multiplexing. Nevertheless, the channel of the RBCom system is distinct from other light communication technologies due to the echo interference issue. In this paper, we reveal the mechanism of the echo interference and propose the method to eliminate the interference. Moreover, we present an exemplary design based on frequency shifting and optical filtering, along with its mathematic model and performance analysis. The numerical evaluation shows that the channel capacity is greater than 15 bit/s/Hz.

eess.SP↗

arXiv2021

Resonant Beam Communications: Principles and Designs

Wireless optical communications (WOC) has carriers up to several hundred terahertz, which offers several advantages, such as ultrawide bandwidth and no electromagnetic interference. Conventional WOC that uses light transmitters such as light emitting diodes (LEDs), directed LEDs, and lasers are facing major challenges in attenuation or tracking. Resonant beam communications (RBCom) meets the requirements of low attenuation, non-mechanical mobility, and multiple access. Yet, RBCom channel undergoes echo interference in contrast to common non-echo channels. An interference elimination method based on optical filtering is presented along with an exemplary interference-free RBCom system.

eess.SY↗

arXiv2021

A Logical Neural Network Structure With More Direct Mapping From Logical Relations

Logical relations widely exist in human activities. Human use them for making judgement and decision according to various conditions, which are embodied in the form of \emph{if-then} rules. As an important kind of cognitive intelligence, it is prerequisite of representing and storing logical relations rightly into computer systems so as to make automatic judgement and decision, especially for high-risk domains like medical diagnosis. However, current numeric ANN (Artificial Neural Network) models are good at perceptual intelligence such as image recognition while they are not good at cognitive intelligence such as logical representation, blocking the further application of ANN. To solve it, researchers have tried to design logical ANN models to represent and store logical relations. Although there are some advances in this research area, recent works still have disadvantages because the structures of these logical ANN models still don't map more directly with logical relations which will cause the corresponding logical relations cannot be read out from their network structures. Therefore, in order to represent logical relations more clearly by the neural network structure and to read out logical relations from it, this paper proposes a novel logical ANN model by designing the new logical neurons and links in demand of logical representation. Compared with the recent works on logical ANN models, this logical ANN model has more clear corresponding with logical relations using the more direct mapping method herein, thus logical relations can be read out following the connection patterns of the network structure. Additionally, less neurons are used.

cs.NE↗