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Lu Li

Publications and source records attributed to Lu Li.

At least 109 records · Page 6Linked to original sources
arXiv2024

Quantifying Systematic Uncertainties in Experimental Physics: An Approximation Method

In the domain of physics experiments, data fitting is a pivotal technique for extracting insights from both experimental and simulated datasets. This article presents an approximation method designed to estimate the systematic errors prevalent in data analyses. By applying our method to the Nab experiment, we compare our findings with simulation-derived results, thereby confirming the concordance of our approach with established simulation outcomes. This corroboration highlights the versatility of our method as a good tool for validating simulation results across various experimental contexts.

physics.data-an↗
arXiv2024

Energy-level inversion for vortex states in spin-orbit coupled Bose-Einstein condensates

We investigate vortex states in Bose-Einstein condensates under the combined action of the spin-orbit coupling (SOC), gradient magnetic field, and harmonic-oscillator trapping potential. The linear version of the system is solved exactly. Through the linear-spectrum analysis, we find that, varying the SOC strength and magnetic-field gradient, one can perform energy-level inversion. With suitable parameters, initial higher-order vortex states can be made the ground state (GS). The nonlinear system is solved numerically, revealing that the results are consistent with the linear predictions in the case of repulsive inter-component interactions. On the other hand, inter-component attraction creates the GS in the form of mixed-mode states in a vicinity of the GS phase-transition points. The spin texture of both vortex- and mixed-mode GSs reveals that they feature the structure of 2D (baby) skyrmions.

cond-mat.quant-gas↗
arXiv2023

Navigating Decision Landscapes: The Impact of Principals on Decision-Making Dynamics

We explored decision-making dynamics in social systems, referencing the 'herd behavior' from prior studies where individuals follow preceding choices without understanding the underlying reasons. While previous research highlighted a preference for the optimal choice without external influences, our study introduced principals or external guides, adding complexity to the decision-making process. The reliability of these principals significantly influenced decisions. Notably, even occasional trust in an unreliable principal could alter decision outcomes. Furthermore, when a principal's advice was purely random, heightened trust led to more decision errors. Our findings emphasize the need for caution when placing trust in decision-making contexts.

cs.AI↗
arXiv2023

Explore 3D Dance Generation via Reward Model from Automatically-Ranked Demonstrations

This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models. Current models often generate monotonous and simplistic dance sequences that misalign with human preferences because they lack exploration capabilities. The E3D2 framework involves a reward model trained from automatically-ranked dance demonstrations, which then guides the reinforcement learning process. This approach encourages the agent to explore and generate high quality and diverse dance movement sequences. The soundness of the reward model is both theoretically and experimentally validated. Empirical experiments demonstrate the effectiveness of E3D2 on the AIST++ dataset. Project Page: https://sites.google.com/view/e3d2.

cs.HC↗
arXiv2023

Room-temperature correlated states in twisted bilayer MoS$_2$

Moiré superlattices have emerged as an exciting condensed-matter quantum simulator for exploring the exotic physics of strong electronic correlations. Notable progress has been witnessed, but such correlated states are achievable usually at low temperatures. Here, we report the transport evidences of room-temperature correlated electronic states and layer-hybridized SU(4) Hubbard model simulator in AB-stacked MoS$_2$ homo-bilayer moiré superlattices. Correlated insulating states at moiré band filling factors v = 1, 2, 3 are unambiguously established in twisted bilayer MoS$_2$. Remarkably, the correlated electronic states can persist up to a record-high critical temperature of over 285 K. The realization of room-temperature correlated states in twisted bilayer MoS$_2$ can be understood as the cooperation effects of the stacking-specific atomic reconstruction and the resonantly enhanced interlayer hybridization, which largely amplify the moiré superlattice effects on electronic correlations. Furthermore, extreme large non-linear Hall responses up to room-temperature are uncovered near correlated insulating states, demonstrating the quantum geometry of moiré flat conduction band.

cond-mat.mtrl-sci↗
arXiv2023

Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning

Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. Notably, employing simple observation transformations alone can yield outstanding performance without extra auxiliary representation tasks or pre-trained encoders. However, it remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. To investigate this issue and further explore the potential of DA, this work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy and provides the following insights and improvements: (1) For individual DA operations, we reveal that both ample spatial diversity and slight hardness are indispensable. Building on this finding, we introduce Random PadResize (Rand PR), a new DA operation that offers abundant spatial diversity with minimal hardness. (2) For multi-type DA fusion schemes, the increased DA hardness and unstable data distribution result in the current fusion schemes being unable to achieve higher sample efficiency than their corresponding individual operations. Taking the non-stationary nature of RL into account, we propose a RL-tailored multi-type DA fusion scheme called Cycling Augmentation (CycAug), which performs periodic cycles of different DA operations to increase type diversity while maintaining data distribution consistency. Extensive evaluations on the DeepMind Control suite and CARLA driving simulator demonstrate that our methods achieve superior sample efficiency compared with the prior state-of-the-art methods.

cs.LG↗
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

The role of tidal interactions in the formation of slowly rotating early-type stars in young star clusters

The split main sequences found in the colour-magnitude diagrams of star clusters younger than ~600 Myr are suggested to be caused by the dichotomy of stellar rotation rates of upper main-sequence stars. Tidal interactions have been suggested as a possible explanation of the dichotomy of the stellar rotation rates. This hypothesis proposes that the slow rotation rates of stars along the split main sequences are caused by tidal interactions in binaries. To test this scenario, we measured the variations in the radial velocities of slowly rotating stars along the split main sequence of the young Galactic cluster NGC 2422 (~90 Myr) using spectra obtained at multiple epochs with the Canada-France-Hawai'i Telescope. Our results show that most slowly rotating stars are not radial-velocity variables. Using the theory of dynamical tides, we find that the binary separations necessary to fully or partially synchronise our spectroscopic targets, on time-scales shorter than the cluster age, predict much larger radial velocity variations across multiple-epoch observations, or a much larger radial velocity dispersion at a single epoch, than the observed values. This indicates that tidal interactions are not the dominant mechanism to form slowly rotating stars along the split main sequences. As the observations of the rotation velocity distribution among B- and A-type stars in binaries of larger separations hint at a much stronger effect of braking with age, we discuss the consequences of relaxing the constraints of the dynamical tides theory.

astro-ph.SR↗
arXiv2023

"Extraordinary" Phase Transition Revealed in a van der Waals Antiferromagnet

While the surface-bulk correspondence has been ubiquitously shown in topological phases, the relationship between surface and bulk in Landau-like phases is much less explored. Theoretical investigations since 1970s for semi-infinite systems have predicted the possibility of the surface order emerging at a higher temperature than the bulk, clearly illustrating a counterintuitive situation and greatly enriching phase transitions. But experimental realizations of this prediction remain missing. Here, we demonstrate the higher-temperature surface and lower-temperature bulk phase transitions in CrSBr, a van der Waals (vdW) layered antiferromagnet. We leverage the surface sensitivity of electric dipole second harmonic generation (SHG) to resolve surface magnetism, the bulk nature of electric quadrupole SHG to probe bulk spin correlations, and their interference to capture the two magnetic domain states. Our density functional theory calculations show the suppression of ferromagnetic-antiferromagnetic competition at the surface responsible for this enhanced surface magnetism. Our results not only show unexpected, richer phase transitions in vdW magnets, but also provide viable ways to enhance magnetism in their 2D form.

cond-mat.mtrl-sci↗
arXiv2023

M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning

Inspired by the successful application of contrastive learning on graphs, researchers attempt to impose graph contrastive learning approaches on heterogeneous information networks. Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required. Most existing methods for heterogeneous graph contrastive learning are implemented by transforming heterogeneous graphs into homogeneous graphs, which may lead to ramifications that the valuable information carried by non-target nodes is undermined thereby exacerbating the performance of contrastive learning models. Additionally, current heterogeneous graph contrastive learning methods are mainly based on initial meta-paths given by the dataset, yet according to our deep-going exploration, we derive empirical conclusions: only initial meta-paths cannot contain sufficiently discriminative information; and various types of meta-paths can effectively promote the performance of heterogeneous graph contrastive learning methods. To this end, we propose a new multi-scale meta-path integrated heterogeneous graph contrastive learning (M2HGCL) model, which discards the conventional heterogeneity-homogeneity transformation and performs the graph contrastive learning in a joint manner. Specifically, we expand the meta-paths and jointly aggregate the direct neighbor information, the initial meta-path neighbor information and the expanded meta-path neighbor information to sufficiently capture discriminative information. A specific positive sampling strategy is further imposed to remedy the intrinsic deficiency of contrastive learning, i.e., the hard negative sample sampling issue. Through extensive experiments on three real-world datasets, we demonstrate that M2HGCL outperforms the current state-of-the-art baseline models.

cs.LG↗
arXiv2023

Unraveling the Complexity of Dwarf Galaxy Dynamics: A study of Binary Orbital Motions

We investigate the impact of binary orbital motions on the dynamical modeling of dwarf galaxies with intrinsic line-of-sight velocity dispersions ($σ_{v_r}$) of 1 to 9 km/s. Using dwarf galaxies from the Auriga level-2 and level-3 simulations, we apply the Jeans Anisotropic Multi-Gaussian Expansion modelling to tracer stars before and after including binaries to recover the dynamical masses. The recovered total masses within the half-mass radius of tracers, $M(<r_\mathrm{half})$, are always inflated due to binary motions, with greater inflations occurring for smaller $σ_{v_r}$. However, many dwarf galaxies experience central density deflated due to binary motions, with little dependences on $σ_{v_r}$. This is due to the negative radial gradients in the velocity dispersion profiles, with the fractional inflation in $σ_{v_r}$ due to binaries more significant in outskirts. An extreme binary fraction of 70% can lead to central density deflation of up to 10-20% at 3 km/s$<σ_{v_r}<$8 km/s, with $M(<r_\mathrm{half})$ inflated by 4% at 9 km/s and up to 15% at 3 km/s. A lower binary fraction of 36% leads to similar deflations, with the inflations decreasing to approximately 10% at 3 km/s and becoming statistically insignificant. The choice of binary orbit distribution models does not result in significant differences, and observational errors tend to slightly weaken the deflations in the recovered central density. Two observations separated by one year to exclude binaries lead to almost zero inflations/deflations for a binary fraction of 36% over 3 km/s$<σ_{v_r}<$9 km/s. For $σ_{v_r}\sim$1 km/s to 3 km/s, a binary fraction of 70% (36%) still results in 60% (30%) to 10% (1%) of inflations in $M(<r_\mathrm{half})$, even with two-epoch observation.

astro-ph.GA↗
arXiv2023

AI For Global Climate Cooperation 2023 Competition Proceedings

The international community must collaborate to mitigate climate change and sustain economic growth. However, collaboration is hard to achieve, partly because no global authority can ensure compliance with international climate agreements. Combining AI with climate-economic simulations offers a promising solution to design international frameworks, including negotiation protocols and climate agreements, that promote and incentivize collaboration. In addition, these frameworks should also have policy goals fulfillment, and sustained commitment, taking into account climate-economic dynamics and strategic behaviors. These challenges require an interdisciplinary approach across machine learning, economics, climate science, law, policy, ethics, and other fields. Towards this objective, we organized AI for Global Climate Cooperation, a Mila competition in which teams submitted proposals and analyses of international frameworks, based on (modifications of) RICE-N, an AI-driven integrated assessment model (IAM). In particular, RICE-N supports modeling regional decision-making using AI agents. Furthermore, the IAM then models the climate-economic impact of those decisions into the future. Whereas the first track focused only on performance metrics, the proposals submitted to the second track were evaluated both quantitatively and qualitatively. The quantitative evaluation focused on a combination of (i) the degree of mitigation of global temperature rise and (ii) the increase in economic productivity. On the other hand, an interdisciplinary panel of human experts in law, policy, sociology, economics and environmental science, evaluated the solutions qualitatively. In particular, the panel considered the effectiveness, simplicity, feasibility, ethics, and notions of climate justice of the protocols. In the third track, the participants were asked to critique and improve RICE-N.

cs.AI↗
arXiv2023

Universal sublinear resistivity in vanadium kagome materials hosting charge density waves

The recent discovery of a charge density (CDW) state in ScV$_6$Sn$_6$ at $T_{\textrm{CDW}}$ = 91 K offers new opportunities to understand the origins of electronic instabilities in topological kagome systems. By comparing to the isostructural non-CDW compound LuV$_6$Sn$_6$, we unravel interesting electrical transport properties in ScV$_6$Sn$_6$, above and below the charge ordering temperature. We observed that by applying a magnetic field along the $a$ axis, the temperature behavior of the longitudinal resistivity in ScV$_6$Sn$_6$ changes from metal-like to insulator-like above the CDW transition. We show that in the charge ordered state ScV$_6$Sn$_6$ follows the Fermi liquid behavior while above that, it transforms into a non-Fermi liquid phase in which the resistivity varies sublinearly over a broad temperature range. The sublinear resistivity, which scales by $T^{3/5}$ is a common feature among other vanadium-containing kagome compounds exhibiting CDW states such as KV$_3$Sb$_5$, RbV$_3$Sb$_5$, and CsV$_3$Sb$_5$. By contrast, the non-Fermi liquid behavior does not occur in LuV$_6$Sn$_6$. We explain the $T^{3/5}$ universal scaling behavior from the Coulomb scattering between Dirac electrons and Van Hove singularities; common features in the electronic structure of kagome materials. Finally, we show anomalous Hall-like behavior in ScV$_6$Sn$_6$ below $T_{\textrm{CDW}}$, which is absent in the Lu compound. Comparing the transport properties of ScV$_6$Sn$_6$ and LuV$_6$Sn$_6$ is valuable to highlight the impacts of the unusual CDW in the Sc compound.

cond-mat.str-el↗
arXiv2023

Normalization Enhances Generalization in Visual Reinforcement Learning

Recent advances in visual reinforcement learning (RL) have led to impressive success in handling complex tasks. However, these methods have demonstrated limited generalization capability to visual disturbances, which poses a significant challenge for their real-world application and adaptability. Though normalization techniques have demonstrated huge success in supervised and unsupervised learning, their applications in visual RL are still scarce. In this paper, we explore the potential benefits of integrating normalization into visual RL methods with respect to generalization performance. We find that, perhaps surprisingly, incorporating suitable normalization techniques is sufficient to enhance the generalization capabilities, without any additional special design. We utilize the combination of two normalization techniques, CrossNorm and SelfNorm, for generalizable visual RL. Extensive experiments are conducted on DMControl Generalization Benchmark and CARLA to validate the effectiveness of our method. We show that our method significantly improves generalization capability while only marginally affecting sample efficiency. In particular, when integrated with DrQ-v2, our method enhances the test performance of DrQ-v2 on CARLA across various scenarios, from 14% of the training performance to 97%.

cs.LG↗
arXiv2023

Electron-infrared phonon coupling in ABC trilayer graphene

Stacking order plays a crucial role in determining the crystal symmetry and has significant impacts on electronic, optical, magnetic, and topological properties. Electron-phonon coupling, which is central to a wide range of intriguing quantum phenomena, is expected to be intricately connected with stacking order. Understanding the stacking order-dependent electron-phonon coupling is essential for understanding peculiar physical phenomena associated with electron-phonon coupling, such as superconductivity and charge density waves. In this study, we investigate the effect of stacking order on electron-infrared phonon coupling in graphene trilayers. By using gate-tunable Raman spectroscopy and excitation frequency-dependent near-field infrared nanoscopy, we show that rhombohedral ABC-stacked trilayer graphene has a significantly stronger electron-infrared phonon coupling strength than the Bernal ABA-stacked trilayer graphene. Our findings provide novel insights into the superconductivity and other fundamental physical properties of rhombohedral ABC-stacked trilayer graphene, and can enable nondestructive and high-throughput imaging of trilayer graphene stacking order using Raman scattering.

physics.optics↗
arXiv2023

Angular momentum variation of the Milky Way thick disk: The dependence of chemical abundance and the evidence on inside-out formation scenario

We investigate the angular momentum of mono-abundance populations (MAPs) of the Milky Way thick disk by using a sample of 26,076 giant stars taken from APOGEE DR17 and Gaia EDR3. The vertical and perpendicular angular momentum components, $L_Z$ and $L_P$, of MAPs in narrow bins have significant variations across the [$α$/M]-[M/H] plane. $L_Z$ and $L_P$ systematically change with [M/H] and [$α$/M] and can be alternatively quantified by the chemical gradients: $d[{\rm M/H}]/dL_Z = 1.2\times 10^{-3} $\,dex\,kpc$^{-1}$\,km$^{-1}$\,s, $d{\rm [M/H]}/dL_P = -5.0\times 10^{-3}$\,dec\,kpc$^{-1}$\,km$^{-1}$\,s, and $d[α/{\rm M}]/dL_Z = -3.0\times 10^{-4} $\,dex\,kpc$^{-1}$\,km$^{-1}$\,s, $d[α/{\rm M}]/dL_P = 1.2\times 10^{-3}$\,dec\,kpc$^{-1}$\,km$^{-1}$\,s. These correlations can also be explained as the chemical-dependence of the spatial distribution shape of MAPs. We also exhibit the corresponding age dependence of angular momentum components. Under the assumption that the guiding radius ($R_g$) is proportional to $L_Z$, it provides direct observational evidence of the inside-out structure formation scenario of the thick disk, with $dR_g/dAge = -1.9$\,kpc\,Gyr$^{-1}$. The progressive changes in the disk thickness can be explained by the upside-down formation or/and the consequent kinematical heating.

astro-ph.GA↗
arXiv2023

Magnetic field effects on the quantum spin liquid behaviors of NaYbS$_2$

Spin-orbit coupling is an important ingredient to regulate the many-body physics, especially for many spin liquid candidate materials such as rare-earth magnets and Kitaev materials. The rare-earth chalcogenides NaYbCh$_2$ (Ch = O, S, Se) is a congenital frustrating system to exhibit the intrinsic landmark of spin liquid by eliminating both the site disorders between Na$^{+}$ and Yb$^{3+}$ ions with the big ionic size difference and the Dzyaloshinskii-Moriya interaction with the perfect triangular lattice of the Yb$^{3+}$ ions. The temperature versus magnetic-field phase diagram is established by the magnetization, specific heat, and neutron-scattering measurements. Notably, the neutron diffraction spectra and the magnetization curve might provide microscopic evidence for a series of spin configuration for in-plane fields, which include the disordered spin liquid state, 120$^{o}$ antiferromagnet, and one-half magnetization state. Furthermore, the ground state is suggested to be a gapless spin liquid from inelastic neutron scattering, and the magnetic field adjusts the spin orbit coupling. Therefore, the strong spin-orbit coupling in the frustrated quantum magnet substantially enriches low-energy spin physics. This rare-earth family could offer a good platform for exploring the quantum spin liquid ground state and quantum magnetic transitions.

cond-mat.str-el↗
arXiv2022

Bessel Vortices in Spin-Orbit Coupled Spin-1 Bose-Einstein Condensates

We investigate the stationary vortex solutions in two-dimensional (2D) Rashba spin-orbit (SO) coupled spin-1 Bose-Einstein condensate (BEC). By introducing the generalized momentum operator, the linear version of the system can be solved exactly and its solutions are a set of the Bessel vortices. Based on the linear version solutions, the stationary vortex solutions of the full nonlinear system are constructed and determined entirely by the variational approximation. The results show that the variational results are in good agreement with the numerical ones. By means of the variational results, the vortex ground state phase-transition between the stationary vortex solutions, stability, and the unit Bloch vector textures are discussed in detail. The results have the potential to be realized in experiment.

cond-mat.quant-gas↗