arXiv subjects

Jing Wu

Publications and source records attributed to Jing Wu.

At least 181 records · Page 10Linked to original sources

arXiv2021

Bilayer twisting as a mean to isolate connected flat bands in a Kagome lattice through Wigner crystallization

The physics of flat band is novel and rich but difficult to access. In this regard, recently twisting of bilayer van der Waals (vdW)-bounded two-dimensional (2D) materials has attracted much attention, because the reduction of Brillouin zone will eventually lead to a diminishing kinetic energy. Alternatively, one may start with a 2D Kagome lattice, which already possesses flat bands at the Fermi level, but unfortunately these bands connect quadratically to other (dispersive) bands, leading to undesirable effects. Here, we propose, by first-principles calculation and tight-binding modeling, that the same bilayer twisting approach can be used to isolate the Kagome flat bands. As the starting kinetic energy is already vanishingly small, the interlayer vdW potential is always sufficiently large irrespective of the twisting angle. As such the electronic states in the (connected) flat bands become unstable against a spontaneous Wigner crystallization, which is expected to have interesting interplays with other flat-band phenomena such as novel superconductivity and anomalous quantum Hall effect.

cond-mat.mes-hall↗

arXiv2021

Markov-Modulated Hawkes Processes for Sporadic and Bursty Event Occurrences

Modeling event dynamics is central to many disciplines. Patterns in observed event arrival times are commonly modeled using point processes. Such event arrival data often exhibits self-exciting, heterogeneous and sporadic trends, which is challenging for conventional models. It is reasonable to assume that there exists a hidden state process that drives different event dynamics at different states. In this paper, we propose a Markov Modulated Hawkes Process (MMHP) model for learning such a mixture of event dynamics and develop corresponding inference algorithms. Numerical experiments using synthetic data demonstrate that MMHP with the proposed estimation algorithms consistently recover the true hidden state process in simulations, while email data from a large university and data from an animal behavior study show that the procedure captures distinct event dynamics that reveal interesting social structures in the real data.

stat.AP↗

arXiv2021

Universal quantum state preparation via revised greedy algorithm

Preparation of quantum state lies at the heart of quantum information processing. The greedy algorithm provides a potential method to effectively prepare quantum states. However, the standard greedy algorithm, in general, cannot take the global maxima and instead becomes stuck on a local maxima. Based on the standard greedy algorithm, in this paper we propose a revised version to design dynamic pulses to realize universal quantum state preparation, i.e., preparing any arbitrary state from another arbitrary one. As applications, we implement this scheme to the universal preparation of single- and two-qubit state in the context of semiconductor quantum dots and superconducting circuits. Evaluation results show that our scheme outperforms the alternative numerical optimizations with higher preparation quality while possesses the comparable high efficiency. Compared with the emerging machine learning, it shows a better accessibility and does not require any training. Moreover, the numerical results show that the pulse sequences generated by our scheme are robust against various errors and noises. Our scheme opens a new avenue of optimization in few-level system and limited action space quantum control problems.

quant-ph↗

arXiv2021

Deep reinforcement learning for universal quantum state preparation via dynamic pulse control

Accurate and efficient preparation of quantum state is a core issue in building a quantum computer. In this paper, we investigate how to prepare a certain single- or two-qubit target state from arbitrary initial states in semiconductor double quantum dots with the aid of deep reinforcement learning. Our method is based on the training of the network over numerous preparing tasks. The results show that once the network is well trained, it works for any initial states in the continuous Hilbert space. Thus repeated training for new preparation tasks is avoided. Our scheme outperforms the traditional optimization approaches based on gradient with both the higher designing efficiency and the preparation quality in discrete control space. Moreover, we find that the control trajectories designed by our scheme are robust against static and dynamic fluctuations, such as charge and nuclear noises.

quant-ph↗

arXiv2021

An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine. By comparing the performances of the Hindsight Experience Replay-aided Deep Deterministic Policy Gradient agent on both environments, we demonstrate our successful re-implementation of the original environment. Besides, we provide users with new APIs to access a joint control mode, image observations and goals with customisable camera and a built-in on-hand camera. We further design a set of multi-step, multi-goal, long-horizon and sparse reward robotic manipulation tasks, aiming to inspire new goal-conditioned reinforcement learning algorithms for such challenges. We use a simple, human-prior-based curriculum learning method to benchmark the multi-step manipulation tasks. Discussions about future research opportunities regarding this kind of tasks are also provided.

cs.RO↗

arXiv2021

Broadened phonon-assisted wide-band radiation and subsequent low-threshold self-absorption coherent modulation in the high-entropy glass system doped with Nd3+ ions

For crystalline materials with long-range orders, the phonon modes involved in the phonon-assisted radiation process are generally involving one or several phonons with specific vibration frequencies 1-4. In some glassy material, the phonon modes broaden in a short range 5-7. However, the locally distinct chemical environments or mass disorder in high-entropy systems can induce an anharmonic phonon-phonon coupling and composition disorder, which leads to significant phonon broadening 8,9.The terminology of high-entropy comes from the high configuration entropy larger than 1.5R (R is the gas constant), which results from randomly distributed multiple nearly equal components in a crystal lattice 10,11. Inspired by the high-entropy strategy, we deployed a high-entropy glass system (HEGS) doped with neodymium ions, which exhibits a complex structure with tetrahedral voids filled by different ions, including, Li+, Zn2+, Si4+, P5+, S6+, etc. Phonon spectral broadening up to thousands of wavenumbers in the HEGS allows strong wide-band absorption in both the near-infrared and mid-infrared ranges and assists the system radiation, i.e., broadened phonon-assisted wide-band radiation (BPAWR). The subsequent low-threshold self-absorption coherence modulation (SACM) was also observed in the HEGS, modulated by changing excitation wavelengths, sample size, and doping concentrations. The time delay of the BPAWR signal is up to 1.66 ns relative to the zero-delay signal, while the time delay of the Raman process is typically in the order of fs to ps, rarely up to 10 ps 12-15.The BPAWR-SACM can be applied to realize signal amplification of the centered non-absorption band when dual-wavelength lasers pump the HEGS sample, and signal amplification can be up to 26.02 dB. The spectral characteristics of the BPAWR and the dynamics of the energy distribution of the excited species are investigated in detail.

physics.optics↗

arXiv2021

Performance Evaluation of Adversarial Attacks: Discrepancies and Solutions

Recently, adversarial attack methods have been developed to challenge the robustness of machine learning models. However, mainstream evaluation criteria experience limitations, even yielding discrepancies among results under different settings. By examining various attack algorithms, including gradient-based and query-based attacks, we notice the lack of a consensus on a uniform standard for unbiased performance evaluation. Accordingly, we propose a Piece-wise Sampling Curving (PSC) toolkit to effectively address the aforementioned discrepancy, by generating a comprehensive comparison among adversaries in a given range. In addition, the PSC toolkit offers options for balancing the computational cost and evaluation effectiveness. Experimental results demonstrate our PSC toolkit presents comprehensive comparisons of attack algorithms, significantly reducing discrepancies in practice.

cs.LG↗

arXiv2021

Continuous-variable error correction for general Gaussian noises

Quantum error correction is essential for robust quantum information processing with noisy devices. As bosonic quantum systems play a crucial role in quantum sensing, communication, and computation, it is important to design error correction codes suitable for these systems against various different types of noises. While most efforts aim at protecting qubits encoded into the infinite dimensional Hilbert space of a bosonic mode, [Phys. Rev. Lett. 125, 080503 (2020)] proposed an error correction code to maintain the infinite-dimensional-Hilbert-space nature of bosonic systems by encoding a single bosonic mode into multiple bosonic modes. Enabled by Gottesman-Kitaev-Preskill states as ancilla, the code overcomes the no-go theorem of Gaussian error correction. In this work, we generalize the error correction code to the scenario with general correlated and heterogeneous Gaussian noises, including memory effects. We introduce Gaussian pre-processing and post-processing to convert the general noise model to an independent but heterogeneous collection of additive white Gaussian noise channels and then apply concatenated codes in an optimized manner. To evaluate the performance, we develop a theory framework to enable the efficient calculation of the noise standard deviation after the error correction, despite the non-Gaussian nature of the codes. Our code provides the optimal scaling of the residue noise standard deviation with the number of modes and can be widely applied to distributed sensor-networks, network communication and composite quantum memory systems.

quant-ph↗

arXiv2021

Tunable Doping of Rhenium and Vanadium into Transition Metal Dichalcogenides for Two-Dimensional Electronics

Two-dimensional (2D) transition metal dichalcogenides (TMDCs) with unique electrical properties are fascinating materials used for future electronics. However, the strong Fermi level pinning effect at the interface of TMDCs and metal electrodes always leads to high contact resistance, which seriously hinders their application in 2D electronics. One effective way to overcome this is to use metallic TMDCs or transferred metal electrodes as van der Waals (vdW) contacts. Alternatively, using highly conductive doped TMDCs will have a profound impact on the contact engineering of 2D electronics. Here, a novel chemical vapor deposition using mixed molten salts is established for vapor-liquid-solid growth of high-quality rhenium (Re) and vanadium (V)-doped TMDC monolayers with high controllability and reproducibility. A tunable semiconductor to metal transition is observed in the Re and V-doped TMDCs. Electrical conductivity increases up to a factor of 108 in the degenerate V-doped WS2 and WSe2. Using V-doped WSe2 as vdW contact, the on-state current and on/off ratio of WSe2-based field-effect transistors have been substantially improved (from ~10-8 to 10-5 A; ~104 to 108), compared to metal contacts. Future studies on lateral contacts and interconnects using doped TMDCs will pave the way for 2D integrated circuits and flexible electronics.

cond-mat.mtrl-sci↗

arXiv2021

Decision-based Universal Adversarial Attack

A single perturbation can pose the most natural images to be misclassified by classifiers. In black-box setting, current universal adversarial attack methods utilize substitute models to generate the perturbation, then apply the perturbation to the attacked model. However, this transfer often produces inferior results. In this study, we directly work in the black-box setting to generate the universal adversarial perturbation. Besides, we aim to design an adversary generating a single perturbation having texture like stripes based on orthogonal matrix, as the top convolutional layers are sensitive to stripes. To this end, we propose an efficient Decision-based Universal Attack (DUAttack). With few data, the proposed adversary computes the perturbation based solely on the final inferred labels, but good transferability has been realized not only across models but also span different vision tasks. The effectiveness of DUAttack is validated through comparisons with other state-of-the-art attacks. The efficiency of DUAttack is also demonstrated on real world settings including the Microsoft Azure. In addition, several representative defense methods are struggling with DUAttack, indicating the practicability of the proposed method.

cs.CV↗

arXiv2020

A Generalized Robotic Handwriting Learning System based on Dynamic Movement Primitives (DMPs)

Learning from demonstration (LfD) is a powerful learning method to enable a robot to infer how to perform a task given one or more human demonstrations of the desired task. By learning from end-user demonstration rather than requiring that a domain expert manually programming each skill, robots can more readily be applied to a wider range of real-world applications. Writing robots, as one application of LfD, has become a challenging research topic due to the complexity of human handwriting trajectories. In this paper, we introduce a generalized handwriting-learning system for a physical robot to learn from examples of humans' handwriting to draw alphanumeric characters. Our robotic system is able to rewrite letters imitating the way human demonstrators write and create new letters in a similar writing style. For this system, we develop an augmented dynamic movement primitive (DMP) algorithm, DMP*, which strengthens the robustness and generalization ability of our robotic system.

cs.RO↗

arXiv2020

Deep Learning of Accurate Force Field of Ferroelectric HfO$_2$

The discovery of ferroelectricity in HfO$_2$-based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and high-density non-volatile memories. The functional performances of ferroelectrics are intimately related to their dynamic responses to external stimuli such as electric fields at finite temperatures. Molecular dynamics is an ideal technique for investigating dynamical processes on large length and time scales, though its applications to new materials is often hindered by the limited availability and accuracy of classical force fields. Here we present a deep neural network-based interatomic force field of HfO$_2$ learned from {\em ab initio} data using a concurrent learning procedure. The model potential is able to predict structural properties such as elastic constants, equation of states, phonon dispersion relationships, and phase transition barriers of various hafnia polymorphs with accuracy comparable with density functional theory calculations. The validity of this model potential is further confirmed by the reproduction of experimental sequences of temperature-driven ferroelectric-paraelectric phase transitions of HfO$_2$ with isobaric-isothermal ensemble molecular dynamics simulations. We suggest a general approach to extend the model potential of HfO$_2$ to related material systems including dopants and defects.

cond-mat.mtrl-sci↗

arXiv2020

Well-posedness and Stability Analysis of Two Classes of Generalized Stochastic Volatility Models

In this paper, to cope with the shortage of sufficient theoretical support resulted from the fast-growing quantitative financial modeling, we investigate two classes of generalized stochastic volatility models, establish their well-posedness of strong solutions, and conduct the stability analysis with respect to small perturbations. In the first class, a multidimensional path-dependent process is driven by another multidimensional path-dependent process. The second class is a generalized one-dimensional stochastic volatility model with Hölder continuous coefficients. What greatly differentiates those two classes of models is that both the process and its correlated driving process have their own subdifferential operators, whose one special case is the general reflection operators for multi-sided barriers. Hence, the models investigated fully cover various newly explored variants of stochastic volatility models whose well-posedness is unknown, and naturally serve as the rigorous mathematical foundation for new stochastic volatility model development in terms of multi-dimension, path-dependence, and multi-sided barrier reflection.

math.PR↗

arXiv2020

Timescales and contribution of heating and helicity effect in helicity-dependent all-optical switching

The manipulation of the magnetic direction by using the ultrafast laser pulse is attractive for its great advantages in terms of speed and energy efficiency for information storage applications. However, the heating and helicity effects induced by circularly polarized laser excitation are entangled in the helicity-dependent all-optical switching (HD-AOS), which hinders the understanding of magnetization dynamics involved. Here, by applying a dual-pump laser excitation, first with a linearly polarized (LP) laser pulse followed by a circularly polarized (CP) laser pulse, we identify the timescales and contribution from heating and helicity effects in HD-AOS with a Pt/Co/Pt triple layer. When the sample is preheated by the LP laser pulses to a nearly fully demagnetized state, CP laser pulses with a much-reduced power switches the sample's magnetization. By varying the time delay between the two pump pulses, we show that the helicity effect, which gives rise to the deterministic helicity induced switching, onsets instantly upon laser excitation, and only exists for less than 0.2 ps close to the laser pulse duration of 0.15 ps. The results reveal that that the transient magnetization state upon which CP laser pulses impinge is the key factor for achieving HD-AOS, and importantly, the tunability between heating and helicity effects with the unique dual-pump laser excitation approach will enable HD-AOS in a wide range of magnetic material systems for the potential ultrafast spintronics applications.

cond-mat.mtrl-sci↗

arXiv2020

Anisotropic ultrafast spin/valley dynamics in WTe2 films

WTe2 Weyl semimetal hosts the natural broken inversion symmetry and strong spin orbit coupling, making it promising for exotic spin/valley dynamics within a picosecond timescale. Here, we unveil an anisotropic ultrafast spin/valley dynamics in centimeter-scale, single-crystalline Td-WTe2 films using a femtosecond pump-probe technique at room temperature. We observe a transient (~0.8 ps) intra-valley transition and a subsequent polarization duration (~5 ps) during the whole spin/valley relaxation process. Furthermore, the relaxation exhibits the remarkable anisotropy of approximately six-fold and two-fold symmetries due to the intrinsic anisotropy along the crystalline orientation and the extrinsic matrix element effect, respectively. Our results offer a prospect for the ultrafast manipulation of spin/valleytronics in topological quantum materials for dissipationless high-speed spin/valleytronic devices.

cond-mat.mtrl-sci↗

arXiv2020

Shonan Rotation Averaging: Global Optimality by Surfing $SO(p)^n$

Shonan Rotation Averaging is a fast, simple, and elegant rotation averaging algorithm that is guaranteed to recover globally optimal solutions under mild assumptions on the measurement noise. Our method employs semidefinite relaxation in order to recover provably globally optimal solutions of the rotation averaging problem. In contrast to prior work, we show how to solve large-scale instances of these relaxations using manifold minimization on (only slightly) higher-dimensional rotation manifolds, re-using existing high-performance (but local) structure-from-motion pipelines. Our method thus preserves the speed and scalability of current SFM methods, while recovering globally optimal solutions.

cs.CV↗

arXiv2020

T-square resistivity without Umklapp scattering in dilute metallic Bi$_2$O$_2$Se

The electrical resistivity of Fermi liquids (FLs) displays a quadratic temperature ($T$) dependence because of electron-electron (e-e) scattering. For such collisions to decay the charge current, there are two known mechanisms: inter-band scattering (identified by Baber) and Umklapp events. However, dilute metallic strontium titanate (STO) was found to display $T^2$ resistivity in absence of either of these two mechanisms. The presence of soft phonons and their possible role as scattering centers raised the suspicion that $T$-square resistivity in STO is not due to e-e scattering. Here, we present the case of Bi$_2$O$_2$Se, a layered semiconductor with hard phonons, which becomes a dilute metal with a small single-component Fermi surface upon doping. It displays $T$-square resistivity well below the degeneracy temperature where neither Umklapp nor interband scattering is conceivable. We observe a universal scaling between the prefactor of $T^2$ resistivity and the Fermi energy, which is an extension of the Kadowaki-Woods plot to dilute metals. Our results imply the absence of a satisfactory theoretical basis for the ubiquity of e-e driven $T$-square resistivity in Fermi liquids.

cond-mat.str-el↗

arXiv2020

$E^3$: Visual Exploration of Spatiotemporal Energy Demand

Understanding demand-side energy behaviour is critical for making efficiency responses for energy demand management. We worked closely with energy experts and identified the key elements of the energy demand problem including temporal and spatial demand and shifts in spatiotemporal demand. To our knowledge, no previous research has investigated the shifts in spatiotemporal demand. To fill this research gap, we propose a unified visual analytics approach to support exploratory demand analysis; we developed E3, a highly interactive tool that support users in making and verifying hypotheses through human-client-server interactions. A novel potential flow based approach was formalized to model shifts in energy demand and integrated into a server-side engine. Experts then evaluated and affirmed the usefulness of this approach through case studies of real-world electricity data. In the future, we will improve the modelling algorithm, enhance visualisation, and expand the process to support more forms of energy data.

cs.HC↗