arXiv subjects

Jing Chen

Publications and source records attributed to Jing Chen.

At least 217 records · Page 12Linked to original sources

arXiv2021

Received Signal Strength Based Wireless Source Localization with Inaccurate Anchor Position

Received signal strength (RSS)-based wireless localization is easy to implement at low cost. In practice,exact positions of anchors may not be available. This paper focuses on determining the location of a source in the presence of inaccurate positions of anchors based on RSS directly. We first use Taylor expansion and a min-max approach to get a maximum likelihood estimator of the coordinates of the source. Then we propose a relaxed semi-definite programming model to circumvent the non-convexity. We also propose a rounding algorithm considering both inaccurate source location and inaccurate anchor locations.Simulation results together with analysis are presented to validate the proposed method.

eess.SP↗

arXiv2021

Enhanced superconductivity and various edge modes in modulated $t$-$J$ chains

We numerically investigate the ground state of the extended $t$-$J$ Hamiltonian with periodic local modulations in one dimension by using the density-matrix renormalization group method. Examining charge and spin excitation gaps, as well as the pair binding energy, with extrapolated results to the thermodynamic limit, we obtain a rich ground-state phase diagram consisting of the metallic state, the superconducting state, the phase separation, and insulating states at commensurate fillings. Compared to the homogeneous 1D $t$-$J$ model, the superconductivity is greatly enhanced and stabilized by the flat-band structure. This superconducting state in quasi-periodic chains shares similar properties with ladder systems: significant negative pair binding energy occurs, and the singlet pairing correlation function dominates with the algebraic decay while the single-particle Green's function and spin correlation function decay exponentially. On the other hand, quasi-periodicity leads to nontrivial topological nature in insulating states, characterized by different integer Chern numbers at different fillings. Due to the interplay among the topology, the interaction, and the 1D confinement, gapless edge modes show strong spin-charge separation and in different regions can relate to different collective modes, which are the charge of a single fermion, the magnon, and the singlet-pair. We also find two interaction driven topological transitions: i) at particle filling $ρ=1/2$, the low-energy edge excitations change from the magnon to singlet-pair, accompanied with pair formation in bulk; and ii) at $ρ=3/4$, while the gapless edge mode remains the charge of a single fermion, there is a gap-closing point and a $π$-phase shift in the quasi-particle spectrum.

cond-mat.str-el↗

arXiv2021

Chaos and Complexity for Inverted Harmonic Oscillators

We investigate the circuit complexity and Loschmidt echo for the (inverted) harmonic oscillators. Focusing on the chaotic behaviors under the perturbation, we analytically derive the Lyapunov exponent and scrambling time of the inverted harmonic oscillators. We show that the circuit complexity and Loschmidt echo exhibit qualitatively similar behaviors, particularly the consistent Lyapunov exponent.

hep-th↗

arXiv2021

Backdoor Pre-trained Models Can Transfer to All

Pre-trained general-purpose language models have been a dominating component in enabling real-world natural language processing (NLP) applications. However, a pre-trained model with backdoor can be a severe threat to the applications. Most existing backdoor attacks in NLP are conducted in the fine-tuning phase by introducing malicious triggers in the targeted class, thus relying greatly on the prior knowledge of the fine-tuning task. In this paper, we propose a new approach to map the inputs containing triggers directly to a predefined output representation of the pre-trained NLP models, e.g., a predefined output representation for the classification token in BERT, instead of a target label. It can thus introduce backdoor to a wide range of downstream tasks without any prior knowledge. Additionally, in light of the unique properties of triggers in NLP, we propose two new metrics to measure the performance of backdoor attacks in terms of both effectiveness and stealthiness. Our experiments with various types of triggers show that our method is widely applicable to different fine-tuning tasks (classification and named entity recognition) and to different models (such as BERT, XLNet, BART), which poses a severe threat. Furthermore, by collaborating with the popular online model repository Hugging Face, the threat brought by our method has been confirmed. Finally, we analyze the factors that may affect the attack performance and share insights on the causes of the success of our backdoor attack.

cs.CL↗

arXiv2021

Maximal Information Propagation via Lotteries

Propagating information to more people through their friends is becoming an increasingly important technology used in domains such as blockchain, advertising, and social media. To incentivize people to broadcast the information, the designer may use a monetary rewarding scheme, which specifies who gets how much, to compensate for the propagation. Several properties are desirable for the rewarding scheme, such as budget feasible, individually rational, incentive compatible, and Sybil-proof. In this work, we design a free market with lotteries, where every participant can decide by herself how much of the reward she wants to withhold before propagating to others. We show that in the free market, the participants have a strong incentive to maximally propagate the information and all the above properties are satisfied automatically.

cs.GT↗

arXiv2021

EPSILON: An Efficient Planning System for Automated Vehicles in Highly Interactive Environments

In this paper, we present an Efficient Planning System for automated vehicles In highLy interactive envirONments (EPSILON). EPSILON is an efficient interaction-aware planning system for automated driving, and is extensively validated in both simulation and real-world dense city traffic. It follows a hierarchical structure with an interactive behavior planning layer and an optimization-based motion planning layer. The behavior planning is formulated from a partially observable Markov decision process (POMDP), but is much more efficient than naively applying a POMDP to the decision-making problem. The key to efficiency is guided branching in both the action space and observation space, which decomposes the original problem into a limited number of closed-loop policy evaluations. Moreover, we introduce a new driver model with a safety mechanism to overcome the risk induced by the potential imperfectness of prior knowledge. For motion planning, we employ a spatio-temporal semantic corridor (SSC) to model the constraints posed by complex driving environments in a unified way. Based on the SSC, a safe and smooth trajectory is optimized, complying with the decision provided by the behavior planner. We validate our planning system in both simulations and real-world dense traffic, and the experimental results show that our EPSILON achieves human-like driving behaviors in highly interactive traffic flow smoothly and safely without being over-conservative compared to the existing planning methods.

cs.RO↗

arXiv2021

Non-Cooperative Rational Interactive Proofs

Interactive-proof games model the scenario where an honest party interacts with powerful but strategic provers, to elicit from them the correct answer to a computational question. Interactive proofs are increasingly used as a framework to design protocols for computation outsourcing. Existing interactive-proof games largely fall into two categories: either as games of cooperation such as multi-prover interactive proofs and cooperative rational proofs, where the provers work together as a team; or as games of conflict such as refereed games, where the provers directly compete with each other in a zero-sum game. Neither of these extremes truly capture the strategic nature of service providers in outsourcing applications. How to design and analyze non-cooperative interactive proofs is an important open problem. In this paper, we introduce a mechanism-design approach to define a multi-prover interactive-proof model in which the provers are rational and non-cooperative---they act to maximize their expected utility given others' strategies. We define a strong notion of backwards induction as our solution concept to analyze the resulting extensive-form game with imperfect information. Our protocols provide utility gap guarantees, which are analogous to soundness gap in classic interactive proofs. At a high level, a utility gap of u means that the protocol is robust against provers that may not care about a utility loss of 1/u. We fully characterize the complexity of our proof system under different utility gap guarantees. For example, we show that with a polynomial utility gap, the power of non-cooperative rational interactive proofs is exactly P^NEXP.

cs.GT↗

arXiv2021

Recollision of excited electron in below-threshold nonsequential double ionization

Consensus has been reached that recollision, as the most important post-tunneling process, is responsible for nonsequential double ionization process in intense infrared laser field, however, its effect has been restricted to interaction between the first ionized electron and the residual univalent ion so far. Here we identify the key role of recollision between the second ionized electron and the divalent ion in the below-threshold nonsequential double ionization process by introducing a Coulomb-corrected quantum-trajectories method, which enables us to well reproduce the experimentally observed cross-shaped and anti-correlated patterns in correlated two-electron momentum distributions, and also the transition between these two patterns. Being significantly enhanced relatively by the recapture process, recolliding trajectories of the second electron excited by the first- or third-return recolliding trajectories of the first electron produce the cross-shaped or anti-correlated distributions, respectively. And the transition is induced by the increasing contribution of the third return with increasing pulse duration. Our work provides new insight into atomic ionization dynamics and paves the new way to imaging of ultrafast dynamics of atoms and molecules in intense laser field.

physics.atom-ph↗

arXiv2021

Decoding electron tunnelling delay time by embracing wave-particle duality

Tunnelling lies at the heart of quantum mechanics and is a fundamental process in attosecond science, molecular biology, and quantum devices. Whether tunnelling takes time and how a microscopic particle transits through a barrier have been debated since the early days of quantum mechanics. The time required for an electron to tunnel through an atomic potential barrier has been measured with attosecond angular streaking (attoclock), and a recent work on the hydrogen atom claimed that electron tunnelling is instantaneous. However, the time required for Rb atoms to tunnel through an optical potential barrier has been measured to be on the order of milliseconds with a recent Larmor clock measurement. The essence of electron and atom tunnelling is identical, but the reason for the contradictory conclusions remains unknown. Here, we demonstrate that the sub-barrier potential interaction is the root of the nonzero tunnelling delay time. We reveal that the wave-particle duality of a tunnelling electron must be fully taken into account when decoding the tunnelling delay time from the attoclock measurements. Based on energy-resolved attoclock measurements, we show that the tunnelling delay time of an electron ranges from 24 to 58 attoseconds, and the counterintuitive result that an electron with a lower energy may spend less time in the barrier is consistent with the velocity-dependent tunnelling time of atoms in Larmor clock measurements. Our results unify the tunnelling time of microscopic particles by highlighting the classically forbidden sub-barrier potential interactions of matter waves.

physics.atom-ph↗

arXiv2021

Auditory Attention Decoding from EEG using Convolutional Recurrent Neural Network

The auditory attention decoding (AAD) approach was proposed to determine the identity of the attended talker in a multi-talker scenario by analyzing electroencephalography (EEG) data. Although the linear model-based method has been widely used in AAD, the linear assumption was considered oversimplified and the decoding accuracy remained lower for shorter decoding windows. Recently, nonlinear models based on deep neural networks (DNN) have been proposed to solve this problem. However, these models did not fully utilize both the spatial and temporal features of EEG, and the interpretability of DNN models was rarely investigated. In this paper, we proposed novel convolutional recurrent neural network (CRNN) based regression model and classification model, and compared them with both the linear model and the state-of-the-art DNN models. Results showed that, our proposed CRNN-based classification model outperformed others for shorter decoding windows (around 90% for 2 s and 5 s). Although worse than classification models, the decoding accuracy of the proposed CRNN-based regression model was about 5% greater than other regression models. The interpretability of DNN models was also investigated by visualizing layers' weight.

eess.SP↗

arXiv2021

Eye-gaze Estimation with HEOG and Neck EMG using Deep Neural Networks

Hearing-impaired listeners usually have troubles attending target talker in multi-talker scenes, even with hearing aids (HAs). The problem can be solved with eye-gaze steering HAs, which requires listeners eye-gazing on the target. In a situation where head rotates, eye-gaze is subject to both behaviors of saccade and head rotation. However, existing methods of eye-gaze estimation did not work reliably, since the listener's strategy of eye-gaze varies and measurements of the two behaviors were not properly combined. Besides, existing methods were based on hand-craft features, which could overlook some important information. In this paper, a head-fixed and a head-free experiments were conducted. We used horizontal electrooculography (HEOG) and neck electromyography (NEMG), which separately measured saccade and head rotation to commonly estimate eye-gaze. Besides traditional classifier and hand-craft features, deep neural networks (DNN) were introduced to automatically extract features from intact waveforms. Evaluation results showed that when the input was HEOG with inertial measurement unit, the best performance of our proposed DNN classifiers achieved 93.3%; and when HEOG was with NEMG together, the accuracy reached 72.6%, higher than that with HEOG (about 71.0%) or NEMG (about 35.7%) alone. These results indicated the feasibility to estimate eye-gaze with HEOG and NEMG.

eess.SP↗

arXiv2020

An Adaptive Performance-oriented Scheduler for Static and Dynamic Heterogeneity

With the emergence of heterogeneous hardware paving the way for the post-Moore era, it is of high importance to adapt the runtime scheduling to the platform's heterogeneity. To enhance adaptive and responsive scheduling, we introduce a Performance Trace Table (PTT) into XiTAO, a framework for elastic scheduling of mixed-mode parallelism. The PTT is an extensible and dynamic lightweight manifest of the per-core latency that can be used to guide the scheduling of both critical and non-critical tasks. By understanding the per-task latency, the PTT can infer task performance, intra-application interference as well as inter-application interference. We run random Direct Acyclic Graphs (DAGs) of different workload categories as a benchmark on NVIDIA Jetson TX2 chip, achieving up to 3.25x speedup over a standard work-stealing scheduler. To exemplify scheduling adaption to interference, we run DAGs with high parallelism and analyze the scheduler's response to interference from a background process on an Intel Haswell (2650v3) multicore workstation. We also showcase the XiTAO's scheduling performance by porting the VGG-16 image classification framework based on Convolutional Neural Networks (CNN).

cs.DC↗

arXiv2020

Weak cosmic censorship conjecture for the novel $4D$ charged Einstein-Gauss-Bonnet black hole with test scalar field and particle

Recent researches of the novel $4D$ Einstein-Gauss-Bonnet (EGB) gravity have attracted great attention. In this paper, we investigate the validity of the weak cosmic censorship conjecture for a novel $4D$ charged EGB black hole with test charged scalar field and test charged particle respectively. For the test charged field scattering process, we find that both extremal and near-extremal black holes cannot be overcharged. For the test charged particle injection, to first order, an extremal black hole cannot be overcharged while a near-extremal $4D$ charged EGB black hole can be destroyed. To second order, however, both extremal and near-extremal $4D$ charged EGB black holes can be overcharged for positive Gauss-Bonnet coupling constant; for negative Gauss-Bonnet coupling constant, an extremal black hole cannot be overcharged and the validity of the weak cosmic censorship conjecture for a near-extremal black hole depends on the Gauss-Bonnet coupling constant.

gr-qc↗

arXiv2020

Computational efficient deep neural network with difference attention maps for facial action unit detection

In this paper, we propose a computational efficient end-to-end training deep neural network (CEDNN) model and spatial attention maps based on difference images. Firstly, the difference image is generated by image processing. Then five binary images of difference images are obtained using different thresholds, which are used as spatial attention maps. We use group convolution to reduce model complexity. Skip connection and $\text{1}\times \text{1}$ convolution are used to ensure good performance even if the network model is not deep. As an input, spatial attention map can be selectively fed into the input of each block. The feature maps tend to focus on the parts that are related to the target task better. In addition, we only need to adjust the parameters of classifier to train different numbers of AU. It can be easily extended to varying datasets without increasing too much computation. A large number of experimental results show that the proposed CEDNN is obviously better than the traditional deep learning method on DISFA+ and CK+ datasets. After adding spatial attention maps, the result is better than the most advanced AU detection method. At the same time, the scale of the network is small, the running speed is fast, and the requirement for experimental equipment is low.

cs.CV↗

arXiv2020

Frustrated double ionization of atoms in circularly polarized laser fields

We theoretically study frustrated double ionization (FDI) of atoms subjected to intense circularly polarized laser pulses using a three-dimensional classical model. We find a novel "knee" structure of FDI probability as a function of intensity, which is similar to the intensity dependence of nonsequential double ionization probability. The observation of FDI is more favourable when using targets with low ionization potentials and short driving laser wavelengths. This is attributed to the crucial role of recollision therein, which can be experimentally inferred from the photoelectron momentum distribution generated by FDI. This work provides novel physical insights into FDI dynamics with circular polarization.

physics.atom-ph↗

arXiv2020

Scheduling Task-parallel Applications in Dynamically Asymmetric Environments

Shared resource interference is observed by applications as dynamic performance asymmetry. Prior art has developed approaches to reduce the impact of performance asymmetry mainly at the operating system and architectural levels. In this work, we study how application-level scheduling techniques can leverage moldability (i.e. flexibility to work as either single-threaded or multithreaded task) and explicit knowledge on task criticality to handle scenarios in which system performance is not only unknown but also changing over time. Our proposed task scheduler dynamically learns the performance characteristics of the underlying platform and uses this knowledge to devise better schedules aware of dynamic performance asymmetry, hence reducing the impact of interference. Our evaluation shows that both criticality-aware scheduling and parallelism tuning are effective schemes to address interference in both shared and distributed memory applications

cs.DC↗

arXiv2020

Effect of Stark shift on low-energy interference structure in strong-field ionization

An improved quantum trajectory Monte Carlo method involving the Stark shift of the initial state, Coulomb potential, and multielectron polarization-induced dipole potential is used to revisit the origin of the low-energy interference structure in the photoelectron momentum distribution of the xenon atom subjected to an intense laser field, and resolve the different contributions of these three effects. In addition to the well-studied radial finger-like interference structure, a ring-like interference structure induced by interference among electron wave packets emitted from multi-cycle time windows of the laser field is found in the low energy part of the photoelectron momentum spectrum. It is attributed to the combined effect of the Coulomb potential and Stark shift. Our finding provides new insight into the imaging of electron dynamics of atoms and molecules with intense laser fields.

physics.atom-ph↗

arXiv2020

The Laplacian spectrum, Kirchhoff index and complexity of the linear heptagonal networks

Let $H_n$ be the linear heptagonal networks with $2n$ heptagons. We study the structure properties and the eigenvalues of the linear heptagonal networks. According to the Laplacian polynomial of $H_n$, we utilize the decomposition theorem. Thus, the Laplacian spectrum of $H_n$ is created by eigenvalues of a pair of matrices: $L_A$ and $L_S$ of order number $5n+1$ and $4n+1$, respectively. On the basis of the roots and coefficients of their characteristic polynomials of $L_A$ and $L_S$, we not only get the explicit forms of Kirchhoff index, but also corresponding total complexity of $H_n$.

math.CO↗