arXiv subjects

Jie Liu

Publications and source records attributed to Jie Liu.

At least 397 records · Page 22Linked to original sources

arXiv2023

Multiphoton fusion of light nuclei in intense laser fields

We investigate the fusion cross sections of light nuclei in the presence of linearly polarized intense laser fields. By combining the Coulomb-Volkov solutions with the complex spherical square-well nuclear potential, we derive an explicit formulation of the multiphoton cross section in a self-consistent manner. Our analysis is specifically focused on deuteron-triton (DT) and proton-boron (p$^{11}{\rm B}$) fusion reactions, both of which have garnered widespread attention. The theoretical results reveal that, under conditions of longer laser wavelengths and lower incident particle kinetic energies, a few thousands of photons can participate in the fusion reactions, resulting in a substantial enhancement of fusion cross sections by almost ten orders of magnitude. We elucidate the multiphoton mechanism underlying these findings and discuss their implications.

nucl-th↗

arXiv2023

Information-Theoretic Generalization Analysis for Topology-aware Heterogeneous Federated Edge Learning over Noisy Channels

With the rapid growth of edge intelligence, the deployment of federated learning (FL) over wireless networks has garnered increasing attention, which is called Federated Edge Learning (FEEL). In FEEL, both mobile devices transmitting model parameters over noisy channels and collecting data in diverse environments pose challenges to the generalization of trained models. Moreover, devices can engage in decentralized FL via Device-to-Device communication while the communication topology of connected devices also impacts the generalization of models. Most recent theoretical studies overlook the incorporation of all these effects into FEEL when developing generalization analyses. In contrast, our work presents an information-theoretic generalization analysis for topology-aware FEEL in the presence of data heterogeneity and noisy channels. Additionally, we propose a novel regularization method called Federated Global Mutual Information Reduction (FedGMIR) to enhance the performance of models based on our analysis. Numerical results validate our theoretical findings and provide evidence for the effectiveness of the proposed method.

cs.IT↗

arXiv2023

Short-Term Multi-Horizon Line Loss Rate Forecasting of a Distribution Network Using Attention-GCN-LSTM

Accurately predicting line loss rates is vital for effective line loss management in distribution networks, especially over short-term multi-horizons ranging from one hour to one week. In this study, we propose Attention-GCN-LSTM, a novel method that combines Graph Convolutional Networks (GCN), Long Short-Term Memory (LSTM), and a three-level attention mechanism to address this challenge. By capturing spatial and temporal dependencies, our model enables accurate forecasting of line loss rates across multiple horizons. Through comprehensive evaluation using real-world data from 10KV feeders, our Attention-GCN-LSTM model consistently outperforms existing algorithms, exhibiting superior performance in terms of prediction accuracy and multi-horizon forecasting. This model holds significant promise for enhancing line loss management in distribution networks.

cs.LG↗

arXiv2023

Phase modulation of directed transport, energy diffusion and quantum scrambling in a Floquet non-Hermitian system

We investigate both theoretically and numerically the wavepacket's dynamics in momentum space for a Floquet non-Hermitian system with a periodically-kicked driven potential. We have deduced the exact expression of a time-evolving wavepacket under the condition of quantum resonance. With this analytical expression, we can investigate thoroughly the temporal behaviors of the directed transport, energy diffusion and quantum scrambling. We find interestingly that, by tuning the relative phase between the real part and imaginary part of the kicking potential, one can manipulate the directed propagation, energy diffusion and quantum scrambling efficiently: when the phase equals to $π/2$, we observe a maximum directed current and energy diffusion, while a minimum scrambling phenomenon protected by the $\mathcal{PT}$-symmetry; when the phase is $π$, both the directed transport and the energy diffusion are suppressed, in contrast, the quantum scrambling is enhanced by the non-Hermiticity. Possible applications of our findings are discussed.

quant-ph↗

arXiv2023

A Perspective of Q-value Estimation on Offline-to-Online Reinforcement Learning

Offline-to-online Reinforcement Learning (O2O RL) aims to improve the performance of offline pretrained policy using only a few online samples. Built on offline RL algorithms, most O2O methods focus on the balance between RL objective and pessimism, or the utilization of offline and online samples. In this paper, from a novel perspective, we systematically study the challenges that remain in O2O RL and identify that the reason behind the slow improvement of the performance and the instability of online finetuning lies in the inaccurate Q-value estimation inherited from offline pretraining. Specifically, we demonstrate that the estimation bias and the inaccurate rank of Q-value cause a misleading signal for the policy update, making the standard offline RL algorithms, such as CQL and TD3-BC, ineffective in the online finetuning. Based on this observation, we address the problem of Q-value estimation by two techniques: (1) perturbed value update and (2) increased frequency of Q-value updates. The first technique smooths out biased Q-value estimation with sharp peaks, preventing early-stage policy exploitation of sub-optimal actions. The second one alleviates the estimation bias inherited from offline pretraining by accelerating learning. Extensive experiments on the MuJoco and Adroit environments demonstrate that the proposed method, named SO2, significantly alleviates Q-value estimation issues, and consistently improves the performance against the state-of-the-art methods by up to 83.1%.

cs.LG↗

arXiv2023

EdgeConvFormer: Dynamic Graph CNN and Transformer based Anomaly Detection in Multivariate Time Series

Transformer-based models for anomaly detection in multivariate time series can benefit from the self-attention mechanism due to its advantage in modeling long-term dependencies. However, Transformer-based anomaly detection models have problems such as a large amount of data being required for training, standard positional encoding is not suitable for multivariate time series data, and the interdependence between time series is not considered. To address these limitations, we propose a novel anomaly detection method, named EdgeConvFormer, which integrates Time2vec embedding, stacked dynamic graph CNN, and Transformer to extract global and local spatial-time information. This design of EdgeConvFormer empowers it with decomposition capacities for complex time series, progressive spatiotemporal correlation discovery between time series, and representation aggregation of multi-scale features. Experiments demonstrate that EdgeConvFormer can learn the spatial-temporal correlations from multivariate time series data and achieve better anomaly detection performance than the state-of-the-art approaches on many real-world datasets of different scales.

cs.LG↗

arXiv2023

Middle terms of AR-sequences of graded Kronecker modules

Let $(T(n),Ω)$ be the covering of the generalized Kronecker quiver $K(n)$, where $Ω$ is a bipartite orientation. Then there exists a reflection functor $σ$ on the category $\mod(T(n),Ω)$. Suppose that $0\rightarrow X\rightarrow Y\rightarrow Z\rightarrow 0$ is an AR-sequence in the regular component $\mathcal{D}$ of $\mod(T(n),Ω)$, and $b(Z)$ is the number of flow modules in the $σ$-orbit of $Z$. Then the middle term $Y$ is a sink (source or flow) module if and only if $σZ$ is a sink (source or flow) module. Moreover, their radii and centers satisfy $r(Y)=r(σZ)+1$ and $C(Y)=C(σZ)$.

math.RT↗

arXiv2023

Tuning the anomalous Nernst and Hall effects with shifting the chemical potential in Fe-doped and Ni-doped Co$_3$Sn$_2$S$_2$

Co$_3$Sn$_2$S$_2$ is believed to be a magnetic Weyl semimetal. It displays large anomalous Hall, Nernst and thermal Hall effects with a remarkably large anomalous Hall angle. Here, we present a comprehensive study of how substituting Co by Fe or Ni affects the electrical and thermoelectric transport. We find that doping alters the amplitude of the anomalous transverse coefficients. The maximum decrease in the amplitude of the low-temperature anomalous Hall conductivity $σ^A_{ij}$ is twofold. Comparing our results with theoretical calculations of the Berry spectrum assuming a rigid shift of the Fermi level, we find that given the modest shift in the position of the chemical potential induced by doping, the experimentally observed variation occurs five times faster than expected. Doping affects the amplitude and the sign of the anomalous Nernst coefficient. Despite these drastic changes, the amplitude of the $α^A_{ij}/σ^A_{ij}$ ratio at the Curie temperature remains close to $\approx 0.5 k_B/e$, in agreement with the scaling relationship observed across many topological magnets.

cond-mat.str-el↗

arXiv2023

BOURNE: Bootstrapped Self-supervised Learning Framework for Unified Graph Anomaly Detection

Graph anomaly detection (GAD) has gained increasing attention in recent years due to its critical application in a wide range of domains, such as social networks, financial risk management, and traffic analysis. Existing GAD methods can be categorized into node and edge anomaly detection models based on the type of graph objects being detected. However, these methods typically treat node and edge anomalies as separate tasks, overlooking their associations and frequent co-occurrences in real-world graphs. As a result, they fail to leverage the complementary information provided by node and edge anomalies for mutual detection. Additionally, state-of-the-art GAD methods, such as CoLA and SL-GAD, heavily rely on negative pair sampling in contrastive learning, which incurs high computational costs, hindering their scalability to large graphs. To address these limitations, we propose a novel unified graph anomaly detection framework based on bootstrapped self-supervised learning (named BOURNE). We extract a subgraph (graph view) centered on each target node as node context and transform it into a dual hypergraph (hypergraph view) as edge context. These views are encoded using graph and hypergraph neural networks to capture the representations of nodes, edges, and their associated contexts. By swapping the context embeddings between nodes and edges and measuring the agreement in the embedding space, we enable the mutual detection of node and edge anomalies. Furthermore, BOURNE can eliminate the need for negative sampling, thereby enhancing its efficiency in handling large graphs. Extensive experiments conducted on six benchmark datasets demonstrate the superior effectiveness and efficiency of BOURNE in detecting both node and edge anomalies.

cs.SI↗

arXiv2023

Dissipation Enhanced Unidirectional Transport in Topological Systems

Dissipation is a common occurrence in real-world systems and is generally considered to be detrimental to transport. In this study, we examine the transport properties of a narrow quantum anomalous Hall system with dissipation applied on one edge. When the Fermi level resides within the hybridization gap, we find that while transport is suppressed on one edge, it is significantly enhanced on the other. We reveal that this enhancement arises from dissipation-induced gap closure, which is deeply rooted in the point gap topology of the system, resulting in a reduction of the decaying coefficient. When the dissipation is very large, we find that the low-energy physics is nearly indistinguishable from a narrower system, whose dissipation amplitude is inversely proportional to that of the original one. To get more physical intuition, we demonstrate that the low-energy physics can be well captured by a pair of coupled counter-propagating chiral edge states, one of which has a modified group velocity and an effective dissipation. We also briefly discuss the possible experimental realizations of this enhanced unidirectional transport.

cond-mat.mes-hall↗

arXiv2023

Quantum scattering treatment on the time-domain diffraction of a matter-wave soliton

We study the dynamics of the matter-wave soliton interacting with a vibrating mirror created by an evanescent light and provide a quantum scattering picture for the time-domain diffraction of the matter-wave soliton. Under Kramers-Henneberger (KH) transformation, i.e., in a vibrating coordinate, the vibration of the mirror can be cast to an effective gauge field. We then can exploit Dyson series and the quantum scattering theory to investigate the dynamics of the soliton that moves in the effective gauge field and is reflected by a static mirror. Our analytical theory can quantitatively deduce the locations and the relative weights of the scattered wave packets, which is consistent with our numerical simulations of directly solving a nonlinear Schrödinger equation. In particular, for a two-frequency vibrating case, our theory predicts some interesting multi-peak sideband structures in the diffracted matter-wave distributions, which can be resorted to the resonance of two frequencies. Underlying mechanisms and possible applications are discussed.

nlin.PS↗

arXiv2023

On the spectral variety for rank two Higgs bundles

In this article, we study the Hitchin morphism over a smooth projective variety $X$. The Hitchin morphism is a map from the moduli space of Higgs bundles to the Hitchin base, which in general not surjective when the dimension of X is greater than one. Chen-Ngô introduced the spectral base, which is a closed subvariety of the Hitchin base. They conjectured that the Hitchin morphism is surjective to the spectral base and also proved that the surjectivity is equivalent to the existence of finite Cohen-Macaulayfications of the spectral varieties. For rank two Higgs bundles over a projective manifold $X$, we explicitly construct a finite Cohen-Macaulayfication of the spectral variety as a double branched covering of $X$, thereby confirming Chen-Ngô's conjecture in this case. Moreover, using this Cohen-Macaulayfication, we can construct the Hitchin section for rank two Higgs bundles, which allows us to study the rigidity problem of the character variety and also to explore a generalization of the Milnor-Wood type inequality.

math.AG↗

arXiv2023

Unveiling nontrivial fusion rule of Majorana zero mode using a fermionic mode

Fusing Majorana zero modes leads to multiple outcomes, a property being unique to non-Abelian anyons. Successful demonstration of this nontrivial fusion rule would be a hallmark for the development of topological quantum computation.Here we show that this can be done by simply attaching a fermionic mode to a single Majorana zero mode. Through modulation of the energy level of this fermionic mode as well as its coupling with the Majorana mode in different sequences, we show that a zero or integer charge pumping can be realized when different fusion loops are chosen. Such fusion loops are intimately related with the nontrivial fusion rule of Majorana modes and are solely determined by the crossings at zero energy in the parameter space. Finally we demonstrate our proposal in a nanowire-based topological superconductor coupled to a quantum dot. We show that the charge pumping is robust for MZMs in the real system irrespective of the initial condition of FM state, contrary to the case for trivial Andreev bound states. This provides a feasible way to distinguish Majorana modes from trivial Andreev bound states.

cond-mat.mes-hall↗

arXiv2023

Distance-rank Aware Sequential Reward Learning for Inverse Reinforcement Learning with Sub-optimal Demonstrations

Inverse reinforcement learning (IRL) aims to explicitly infer an underlying reward function based on collected expert demonstrations. Considering that obtaining expert demonstrations can be costly, the focus of current IRL techniques is on learning a better-than-demonstrator policy using a reward function derived from sub-optimal demonstrations. However, existing IRL algorithms primarily tackle the challenge of trajectory ranking ambiguity when learning the reward function. They overlook the crucial role of considering the degree of difference between trajectories in terms of their returns, which is essential for further removing reward ambiguity. Additionally, it is important to note that the reward of a single transition is heavily influenced by the context information within the trajectory. To address these issues, we introduce the Distance-rank Aware Sequential Reward Learning (DRASRL) framework. Unlike existing approaches, DRASRL takes into account both the ranking of trajectories and the degrees of dissimilarity between them to collaboratively eliminate reward ambiguity when learning a sequence of contextually informed reward signals. Specifically, we leverage the distance between policies, from which the trajectories are generated, as a measure to quantify the degree of differences between traces. This distance-aware information is then used to infer embeddings in the representation space for reward learning, employing the contrastive learning technique. Meanwhile, we integrate the pairwise ranking loss function to incorporate ranking information into the latent features. Moreover, we resort to the Transformer architecture to capture the contextual dependencies within the trajectories in the latent space, leading to more accurate reward estimation. Through extensive experimentation, our DRASRL framework demonstrates significant performance improvements over previous SOTA methods.

cs.LG↗

arXiv2023

Magneto-optical trap reaction microscope for photoionization of cold strontium atoms

We developed a magneto-optical trap reaction microscope (MOTREMI) for strontium atoms by combining the multi-particle coincident detection with laser cooling technique. Present compact injection system can provide cold Sr atoms in three modes of 2D MOT, molasses and 3D MOT, delivering targets with adjustable densities and ratios of the ground state $5s^2$ ($^1S_{0}$) and the excited states $5s5p$ ($^{1}P_{1}$ and $^{3}P_{J}$ etc). The target profiles for the temperature, the density and the size of 3D MOT as well as cold atomic flux in 2D MOT model were characterized in details. With present state-of-the-art setup, we demonstrated the single photoionization of Sr atoms with molasses by absorption of few 800-nm photons, where Sr$^+$ and $e$ were detected in coincidence and most of ionization channels were identified taking into account photoelectron energy, laser-intensity dependence, and target dependence. The best momentum resolution of coincident Sr$^+$ and $e$ along time-of-flight are achieved up to 0.12 a.u. and 0.02 a.u., respectively. Present photoelectron momentum distributions ionized from the ground state and a few excited states illuminate unprecedentedly rich landscapes manifesting prominent features for multi-photon absorption. The full vector momenta of electrons and recoil ion in coincidence paves the way to further studying two-electron correlation dynamics and multi-electron effects in the multiple ionization of alkaline-earth atoms in the ultraviolet region.

physics.atom-ph↗

arXiv2023

TranUSR: Phoneme-to-word Transcoder Based Unified Speech Representation Learning for Cross-lingual Speech Recognition

UniSpeech has achieved superior performance in cross-lingual automatic speech recognition (ASR) by explicitly aligning latent representations to phoneme units using multi-task self-supervised learning. While the learned representations transfer well from high-resource to low-resource languages, predicting words directly from these phonetic representations in downstream ASR is challenging. In this paper, we propose TranUSR, a two-stage model comprising a pre-trained UniData2vec and a phoneme-to-word Transcoder. Different from UniSpeech, UniData2vec replaces the quantized discrete representations with continuous and contextual representations from a teacher model for phonetically-aware pre-training. Then, Transcoder learns to translate phonemes to words with the aid of extra texts, enabling direct word generation. Experiments on Common Voice show that UniData2vec reduces PER by 5.3% compared to UniSpeech, while Transcoder yields a 14.4% WER reduction compared to grapheme fine-tuning.

eess.AS↗

arXiv2023

How Nested Bars Enhance, Modulate, and are Destroyed by Gas Inflows

Gas flows in the presence of two independently-rotating nested bars remain not fully understood, which is likely to play an important role in fueling the central black hole. We use high-resolution hydrodynamical simulations with detailed models of subgrid physics to study this problem. Our results show that the inner bar in double-barred galaxies can help drive gas flow from the nuclear ring to the center. In contrast, gas inflow usually stalls at the nuclear ring in single-barred galaxies. The inner bar causes a quasi-periodic inflow with a frequency determined by the difference between the two bar pattern speeds. We find that the star formation rate is higher in the model with two bars than in that with one bar. The inner bar in our model gradually weakens and dissolves due to gas inflow over a few billion years. Star formation produces metal-rich/$α$-poor stars which slows the weakening of the inner bar, but does not halt its eventual decay. We also present a qualitative comparison of the gas morphology and kinematics in our simulations with those of observed double-barred galaxies.

astro-ph.GA↗

arXiv2023

OriWheelBot: An origami-wheeled robot

Origami-inspired robots with multiple advantages, such as being lightweight, requiring less assembly, and exhibiting exceptional deformability, have received substantial and sustained attention. However, the existing origami-inspired robots are usually of limited functionalities and developing feature-rich robots is very challenging. Here, we report an origami-wheeled robot (OriWheelBot) with variable width and outstanding sand walking versatility. The OriWheelBot's ability to adjust wheel width over obstacles is achieved by origami wheels made of Miura origami. An improved version, called iOriWheelBot, is also developed to automatically judge the width of the obstacles. Three actions, namely direct pass, variable width pass, and direct return, will be carried out depending on the width of the channel between the obstacles. We have identified two motion mechanisms, i.e., sand-digging and sand-pushing, with the latter being more conducive to walking on the sand. We have systematically examined numerous sand walking characteristics, including carrying loads, climbing a slope, walking on a slope, and navigating sand pits, small rocks, and sand traps. The OriWheelBot can change its width by 40%, has a loading-carrying ratio of 66.7% on flat sand and can climb a 17-degree sand incline. The OriWheelBot can be useful for planetary subsurface exploration and disaster area rescue.

cs.RO↗