arXiv subjects

Ke Wang

Publications and source records attributed to Ke Wang.

At least 523 records · Page 29Linked to original sources

arXiv2021

Molecular Cloud Cores with High Deuterium Fractions: Nobeyama Mapping Survey

We present the results of on-the-fly mapping observations of 44 fields containing 107 SCUBA-2 cores in the emission lines of molecules, N$_2$H$^+$, HC$_3$N, and CCS at 82$-$94 GHz using the Nobeyama 45-m telescope. This study aimed at investigating the physical properties of cores that show high deuterium fractions and might be close to the onset of star formation. We found that the distributions of the N$_2$H$^+$ and HC$_3$N line emissions are approximately similar to that of 850-$μ$m dust continuum emission, whereas the CCS line emission is often undetected or is distributed in a clumpy structure surrounding the peak position of the 850-$μ$m dust continuum emission. Occasionally (12%), we observe the CCS emission which is an early-type gas tracer toward the young stellar object, probably due to local high excitation. Evolution toward star formation does not immediately affect nonthermal velocity dispersion.

astro-ph.GA↗

arXiv2021

A Low-mass Cold and Quiescent Core Population in a Massive Star Protocluster

Pre-stellar cores represent the initial conditions of star formation. Although these initial conditions in nearby low-mass star-forming regions have been investigated in detail, such initial conditions remain vastly unexplored for massive star-forming regions. We report the detection of a cluster of low-mass starless and pre-stellar core candidates in a massive star protocluster forming cloud, NGC6334S. With the ALMA observations at a $\sim$0.02 pc spatial resolution, we identified 17 low-mass starless core candidates that do not show any evidence of protostellar activity. These candidates present small velocity dispersions, high fractional abundances of NH$_{2}$D, high NH$_{3}$ deuterium fractionations, and are completely dark in the infrared wavelengths from 3.6 up to 70~$μ$m. Turbulence is significantly dissipated and the gas kinematics are dominated by thermal motions toward these candidates. Nine out of the 17 cores are gravitationally bound, and therefore are identified as pre-stellar core candidates. The embedded cores of NGC6334S show a wide diversity in masses and evolutionary stages.

astro-ph.GA↗

arXiv2021

Anisotropic g-Factor and Spin-Orbit Field in a Ge Hut Wire Double Quantum Dot

Holes in nanowires have drawn significant attention in recent years because of the strong spin-orbit interaction, which plays an important role in constructing Majorana zero modes and manipulating spin-orbit qubits. Here, from the strongly anisotropic leakage current in the spin blockade regime for a double dot, we extract the full g-tensor and find that the spin-orbit field is in plane with an azimuthal angle of 59° to the axis of the nanowire. The direction of the spin-orbit field indicates a strong spin-orbit interaction along the nanowire, which may have originated from the interface inversion asymmetry in Ge hut wires. We also demonstrate two different spin relaxation mechanisms for the holes in the Ge hut wire double dot: spin-flip cotunneling to the leads, and spin-orbit interaction within the double dot. These results help establish feasibility of a Ge-based quantum processor.

cond-mat.mes-hall↗

arXiv2021

Controlling Synthetic Spin-Orbit Coupling in a Silicon Quantum Dot with Magnetic Field

Tunable synthetic spin-orbit coupling (s-SOC) is one of the key challenges in various quantum systems, such as ultracold atomic gases, topological superconductors, and semiconductor quantum dots. Here we experimentally demonstrate controlling the s-SOC by investigating the anisotropy of spin-valley resonance in a silicon quantum dot. As we rotate the applied magnetic field in-plane, we find a striking nonsinusoidal behavior of resonance amplitude that distinguishes s-SOC from the intrinsic spin-orbit coupling (i-SOC), and associate this behavior with the previously overlooked in-plane transverse magnetic field gradient. Moreover, by theoretically analyzing the experimentally measured s-SOC field, we predict the quality factor of the spin qubit could be optimized if the orientation of the in-plane magnetic field is rotated away from the traditional working point.

cond-mat.mes-hall↗

arXiv2021

ATOMS:ALMA Three-millimeter Observations of Massive Star-forming regions -- III :Catalogues of candidate hot molecular cores and Hyper/Ultra compact HII regions

We have identified 453 compact dense cores in 3 mm continuum emission maps in the ATOMS (ALMA Three-millimeter Observations of Massive Star-forming regions) survey, and compiled three catalogues of high-mass star forming cores. One catalogue, referred to as H/UC-HII catalogue, includes 89 cores that enshroud hyper/ultra compact (H/UC) HII regions as characterized by associated compact H40alpha emission. A second catalogue, referred to as pure s-cHMC, includes 32 candidate Hot Molecular Cores (HMCs) showing rich spectra (N>20lines) of complex organic molecules (COMs) but not associated with H/UC-HII regions. The third catalogue, referred to as pure w-cHMC, includes 58 candidate HMCs with relatively low levels of COM richness and not associated with H/UC-HII regions. These three catalogues of dense cores provide an important foundation for future studies of the early stages of high-mass star formation across the Milky Way. We also find that nearly half of H/UC-HII cores are candidate HMCs. From the number counts of COM-containing and H/UC-HII cores, we suggest that the duration of high-mass protostellar cores showing chemically rich features is at least comparable to the lifetime of H/UC-HII regions. For cores in the H/UC-HII catalogue, the width of the H40alpha line increases as the core size decreases, suggesting that the non-thermal dynamical and/or pressure line-broadening mechanisms dominate on the smaller scales of the H/UC-HII cores.

astro-ph.GA↗

arXiv2021

Localized surface plasmon enhanced InGaN-based light-emitting diodes with polygonal Au/Al nanoparticles

Localized surface plasmons (LSPs) have played a significant role in improving the light emission efficiency of light emitting diodes (LEDs). In this report, polygonal nanoholes have been fabricated in the p-GaN layer of InGaN-based LEDs by using Ni nanoporous film as the etching mask, and then Au/Al metal nanoparticles are embedded in the nanoholes to form the LSP structure. The coupling between the LSP and the LED has been clearly observed. The results show that the light output of the LEDs has been increased by 46% at higher current injection condition, and together with a shift of the gain peak position to the LSP peak resonance energy. As the coupling distance is decreased from 60 nm to 30 nm, the maximum enhancement factor increases to 2.38. The above results indicate that the LSP from the polygonal metal nanoparticles is a kind of very promising structure to enhance the lighting performance of the InGaN-based LEDs.

physics.optics↗

arXiv2021

TrustCross: Enabling Confidential Interoperability across Blockchains Using Trusted Hardware

With the rapid development of blockchain technology, different types of blockchains are adopted and interoperability across blockchains has received widespread attention. There have been many cross-chain solutions proposed in recent years, including notary scheme, sidechain, and relay chain. However, most of the existing platforms do not take confidentiality into account, although privacy has become an important concern for blockchain. In this paper, we present TrustCross, a privacy-preserving cross-chain platform to enable confidential interoperability across blockchains. The key insight behind TrustCross is to encrypt cross-chain communication data on the relay chain with the assistance of trusted execution environment and employ fine-grained access control to protect user privacy. Our experimental results show that TrustCross achieves reasonable latency and high scalability on the contract calls across heterogeneous blockchains.

cs.CR↗

arXiv2021

On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations

With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program models have been tested on various existing datasets, the extent to which they generalize to unforeseen source code is largely unknown. Since it is very challenging to test neural program models on all unforeseen programs, in this paper, we propose to evaluate the generalizability of neural program models with respect to semantic-preserving transformations: a generalizable neural program model should perform equally well on programs that are of the same semantics but of different lexical appearances and syntactical structures. We compare the results of various neural program models for the method name prediction task on programs before and after automated semantic-preserving transformations. We use three Java datasets of different sizes and three state-of-the-art neural network models for code, namely code2vec, code2seq, and GGNN, to build nine such neural program models for evaluation. Our results show that even with small semantically preserving changes to the programs, these neural program models often fail to generalize their performance. Our results also suggest that neural program models based on data and control dependencies in programs generalize better than neural program models based only on abstract syntax trees. On the positive side, we observe that as the size of the training dataset grows and diversifies the generalizability of correct predictions produced by the neural program models can be improved too. Our results on the generalizability of neural program models provide insights to measure their limitations and provide a stepping stone for their improvement.

cs.SE↗

arXiv2021

Mapping the phase diagram of the quantum anomalous Hall and topological Hall effects in a dual-gated magnetic topological insulator heterostructure

We use magnetotransport in dual-gated magnetic topological insulator heterostructures to map out a phase diagram of the topological Hall and quantum anomalous Hall effects as a function of the chemical potential (primarily determined by the back gate voltage) and the asymmetric potential (primarily determined by the top gate voltage). A theoretical model that includes both surface states and valence band quantum well states allows the evaluation of the variation of the Dzyaloshinskii-Moriya interaction and carrier density with gate voltages. The qualitative agreement between experiment and theory provides strong evidence for the existence of a topological Hall effect in the system studied, opening up a new route for understanding and manipulating chiral magnetic spin textures in real space.

cond-mat.mes-hall↗

arXiv2021

OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences

Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time. However, maintaining clinically feasible scan time necessitates significant undersampling, pushing the limits on compressed sensing and other low-dimensional techniques. During MRI scanning, one of the possible solutions is by using undersampling designs which can effectively improve the acquisition and achieve higher reconstruction accuracy. However, existing undersampling optimization methods are time-consuming and the limited performance prevents their clinical applications. In this paper, we proposed an improved undersampling trajectory optimization scheme to generate an optimized trajectory within seconds and apply it to subsequent multi-contrast MRI datasets on a per-subject basis, where we named it OUTCOMES. By using a data-driven method combined with improved algorithm design, GPU acceleration, and more efficient computation, the proposed method can optimize a trajectory within 5-10 seconds and achieve 30%-50% reconstruction improvement with the same acquisition cost, which makes real-time under-sampling optimization possible for clinical applications.

eess.IV↗

arXiv2021

Memory-efficient Learning for High-Dimensional MRI Reconstruction

Deep learning (DL) based unrolled reconstructions have shown state-of-the-art performance for under-sampled magnetic resonance imaging (MRI). Similar to compressed sensing, DL can leverage high-dimensional data (e.g. 3D, 2D+time, 3D+time) to further improve performance. However, network size and depth are currently limited by the GPU memory required for backpropagation. Here we use a memory-efficient learning (MEL) framework which favorably trades off storage with a manageable increase in computation during training. Using MEL with multi-dimensional data, we demonstrate improved image reconstruction performance for in-vivo 3D MRI and 2D+time cardiac cine MRI. MEL uses far less GPU memory while marginally increasing the training time, which enables new applications of DL to high-dimensional MRI.

eess.IV↗

arXiv2021

Persistent Friedel oscillations in Graphene due to a weak magnetic field

Two opposite chiralities of Dirac electrons in a 2D graphene sheet modify the Friedel oscillations strongly: electrostatic potential around an impurity in graphene decays much faster than in 2D electron gas. At distances $r$ much larger than the de Broglie wavelength, it decays as $1/r^3$. Here we show that a weak uniform magnetic field affects the Friedel oscillations in an anomalous way. It creates a field-dependent contribution which is {\em dominant} in a parametrically large spatial interval $p_0^{-1}\lesssim r\lesssim k_Fl^2$, where $l$ is the magnetic length, $k_F$ is Fermi momentum and $p_0^{-1}=(k_Fl)^{4/3}/k_F$. Moreover, in this interval, the field-dependent oscillations do not decay with distance. The effect originates from a spin-dependent magnetic phase accumulated by the electron propagator. The obtained phase may give rise to novel interaction effects in transport and thermodynamic characteristics of graphene and graphene-based heterostructures.

cond-mat.mes-hall↗

arXiv2021

Gravitational Waves from Preheating with Modified Gravitational-wave Propagation

We investigate how the production of gravitational waves (GWs) is affected by the GW velocity $(c_T)$ during preheating after inflation. For instance, we simulate the production of GWs after $λϕ^4$ chaotic inflation, and find that GW spectrum is enhanced for $c_T<1$, but distorted (suppressed at low frequency, but enhanced at high frequency) for $c_T>1$.

gr-qc↗

arXiv2021

Echelon: Two-Tier Malware Detection for Raw Executables to Reduce False Alarms

Existing malware detection approaches suffer from a simplistic trade-off between false positive rate (FPR) and true positive rate (TPR) due to a single tier classification approach, where the two measures adversely affect one another. The practical implication for malware detection is that FPR must be kept at an acceptably low level while TPR remains high. To this end, we propose a two-tiered learning, called ``Echelon", from raw byte data with no need for hand-crafted features. The first tier locks FPR at a specified target level, whereas the second tier improves TPR while maintaining the locked FPR. The core of Echelon lies at extracting activation information of the hidden layers of first tier model for constructing a stronger second tier model. Echelon is a framework in that it allows any existing CNN based model to be adapted in both tiers. We present experimental results of evaluating Echelon by adapting the state-of-the-art malware detection model ``Malconv" in the first and second tiers.

cs.CR↗

arXiv2020

Salient Bundle Adjustment for Visual SLAM

Recently, the philosophy of visual saliency and attention has started to gain popularity in the robotics community. Therefore, this paper aims to mimic this mechanism in SLAM framework by using saliency prediction model. Comparing with traditional SLAM that treated all feature points as equal important in optimization process, we think that the salient feature points should play more important role in optimization process. Therefore, we proposed a saliency model to predict the saliency map, which can capture both scene semantic and geometric information. Then, we proposed Salient Bundle Adjustment by using the value of saliency map as the weight of the feature points in traditional Bundle Adjustment approach. Exhaustive experiments conducted with the state-of-the-art algorithm in KITTI and EuRoc datasets show that our proposed algorithm outperforms existing algorithms in both indoor and outdoor environments. Finally, we will make our saliency dataset and relevant source code open-source for enabling future research.

cs.RO↗

arXiv2020

Polarization of $Λ(1405)$ in the $γp \rightarrow K^+ πΣ$ reaction

In this paper, we study the polarization of the $Λ(1405)$ in the $γp \rightarrow K^+ πΣ$ reaction within an effective Lagrangian approach and isobar model. In our model, the $Λ(1405)$ is excited through the t-channel $K/K^*$ exchanges and u-channel hyperon exchange. Compared to previous studies, we also include the contribution from a contact term, which is necessary for our model to interpret the polarization of the $Λ(1405)$. In addition, we also discuss the possibility to verify the proposed two-pole structure of the $Λ(1405)$ using the polarization data. We find that the polarization of the $Λ(1405)$ or the polarization of the final $Σ$ in this reaction is sensitive to the invariant mass $M_{πΣ}$. Thus the measurement of the dependence of the $Λ(1405)$ polarization on the $M_{πΣ}$ can offer valuable information about the pole structure of the $Λ(1405)$.

nucl-th↗

arXiv2020

Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation

Reinforcement learning (RL) algorithms usually require a substantial amount of interaction data and perform well only for specific tasks in a fixed environment. In some scenarios such as healthcare, however, usually only few records are available for each patient, and patients may show different responses to the same treatment, impeding the application of current RL algorithms to learn optimal policies. To address the issues of mechanism heterogeneity and related data scarcity, we propose a data-efficient RL algorithm that exploits structural causal models (SCMs) to model the state dynamics, which are estimated by leveraging both commonalities and differences across subjects. The learned SCM enables us to counterfactually reason what would have happened had another treatment been taken. It helps avoid real (possibly risky) exploration and mitigates the issue that limited experiences lead to biased policies. We propose counterfactual RL algorithms to learn both population-level and individual-level policies. We show that counterfactual outcomes are identifiable under mild conditions and that Q- learning on the counterfactual-based augmented data set converges to the optimal value function. Experimental results on synthetic and real-world data demonstrate the efficacy of the proposed approach.

cs.LG↗

arXiv2020

Correlated Insulating States and Transport Signature of Superconductivity in Twisted Trilayer Graphene Moiré of Moiré Superlattices

Layers of two-dimensional materials stacked with a small twist-angle give rise to beating periodic patterns on a scale much larger than the original lattice, referred to as a moiré superlattice. When the stacking involves more than two layers with independent twist angles between adjacent layers, it generates moiré of moiré superlattices, with multiple length scales that control the system's behavior. Here we demonstrate these effects of a high-order moiré superlattice in twisted trilayer graphene with two consecutive small twist angles. We report correlated insulating states near the half filling of the moiré of moiré superlattice at an extremely low carrier density (~1010 cm-2), near which we also report a zero-resistance transport behavior typically expected in a 2D superconductor. Moreover, the temperature dependence of the measured resistances at full-occupancy (v = -4 and v = 4) states are semi-metallic, distinct from the insulating behavior of twisted bilayer systems, providing the first demonstration of emergent correlated transport behaviors from continuous, non-isolated higher-order moiré flat bands. Our findings shed new insights into the microscopic mechanisms of moiré correlated states and provide the impetus for future studies on this material platform, such as the demonstration of phase coherence and Meissner-like effect.

cond-mat.mes-hall↗