arXiv subjects

Guilong Li

Publications and source records attributed to Guilong Li.

18 recordsLinked to original sources

arXiv2026

Stable three-dimensional solitons in spin-orbit-coupled atomic-molecular condensates

We elaborate a mechanism for the creation of stable three-dimensional (3D) solitons in spin-orbit-coupled (SOC) atomic-molecular Bose-Einstein condensate, modeled by the mean-field equations with the quadratic three-wave interaction, characterized by mismatch $\alpha $. The planar (effectively two-dimensional) SOC is applied to the soliton's atomic component, structuring it as a mixed mode (MM) or semi-vortex (SV). The molecular component of the SV soliton is shaped as a 3D vortex, while the molecular component in the MM soliton is an MM too. The solitons exist up to a critical value of $\alpha $. The system demonstrates a relatively large norm share of the vortex components, exceeding $50\%$ of the total norm, which is an essential feature of SOC-supported solitons. This is scheme for realizing stable vortex solitons in free space with the quadratic nonlinearity.

quant-ph↗

arXiv2026

Formation and dynamics of self-bound droplets in dipolar molecular condensate

Recent advances in the work with ultracold condensates of polar molecules have enabled the realization of highly tunable self-bound quantum droplets (QDs), with the help of dual microwave fields dressig the dipole-dipole interactions (DDIs) It has been reported that symmetry properties and the equilibrium phase diagram of such QDs can be controlled by parameters of the two microwave fields. However, the effect of these fields on the formation and dynamics of the QD has not yet been systematically explored. Here we address self-bound QDs in a regime dominated by non-axisymmetric DDIs and governed by the extended Gross-Pitaevskii equation with the Lee-Huang-Yang corrections. Within this framework, we identify the existence region of the self-bound QDs and characterize their chemical potential, total energy, effective volume, peak density, and geometric anisotropy. The results reveal a pronounced nonmonotonous dependence on the non-axisymmetric DDI strength, whereas the increase of the number of particles in the condensate leads to tighter bound and more anisotropic QDs. Furthermore, reducing the s-wave scattering length drives a transition from stable self-bound states to the collapse. Collisions between QDs moving along different directions reveal a strong directional dependence, with outcomes ranging from quasi-elastic rebound and merger to fragmentation.

cond-mat.quant-gas↗

arXiv2026

Reasoning-preserved Efficient Distillation of Large Language Models via Activation-aware Initialization

Efficient Distillation (EDistill) compresses large language models (LLMs) by structured pruning parameters and tuning lightweight modules with high training efficiency. Although these EDistilled LLMs achieve state-of-the-art (SOTA) performance on general ability benchmarks relative to similarly sized LLMs, we identify a severe degradation in their multi-step reasoning ability, which we term reasoning collapse. We systematically analyze the geometric origins of reasoning collapse and show that the SOTA EDistill method based on width-reducing projection matrices suffers from eRank collapse, in which the effective rank (eRank) of hidden representations drops. We theoretically explain how singular values of randomly initialized projection matrices become unevenly distributed, leading to eRank collapse and thus token indistinguishability. To address this issue, we propose RED (Reasoning-preserved Efficient Distillation) for LLMs, which introduces activation-aware initialization to initialize projection matrices as channel-selection matrices, thus theoretically mitigating eRank collapse. Experiments on Llama and Qwen series demonstrate that RED substantially recovers reasoning while maintaining high training efficiency and SOTA general ability.

cs.CL↗

arXiv2025

The bulk modulus of three-dimensional quantum droplets

Quantum droplets (QDs), formed by ultradilute quantum fluids under the action of the Lee-Huang-Yang (LHY) effect, provide a unique platform for investigating a wide range of macroscopic quantum effects. Recent studies of QDs' breathing modes and collisional dynamics have revealed their compressibility and extensibility, which suggests that their elasticity parameters can be identified. In this work we derive the elastic bulk modulus (BM) of QDs by means of theoretical analysis and numerical simulations and establish a relation between the BM and the eigenfrequency of the QD's intrinsic vibrations. The analysis reveals the dependence of the QD's elasticity on the particle number and the strength of interparticle interactions. We additionally provide a realistic estimate of the bulk modulus for the system, yielding a concrete physical value that may serve as a reference for future experimental measurements. Taken together, these results also point to possibilities for realizing elastic media governed by the LHY effect.

cond-mat.quant-gas↗

arXiv2025

Stable hopfions in trapped quantum droplets

Hopfions are a class of three-dimensional (3D) solitons which are built as vortex tori carrying intrinsic twist of the toroidal core. They are characterized by two independent topological charges, \textit{viz}., vorticity $S$ and winding number $M$ of the intrinsic twist, whose product determines the \textit{Hopf number}, $Q_{H}=MS$, which is the basic characteristic of the hopfions. We construct hopfions as solutions of the 3D Gross-Pitaevskii equations (GPEs) for Bose-Einstein condensates in binary atomic gases. The GPE system includes the cubic mean-field self-attraction, competing with the quartic self-repulsive Lee-Huang-Yang (LHY) term, which represents effects of quantum fluctuations around the mean-field state, and a trapping toroidal potential (TP). A systematic numerical analysis demonstrates that families of the states with $S=1,M=0$, i.e., $Q_{H}=0$, are stable, provided that the inner TP\ radius $R_{0}$ exceeds a critical value. Furthermore, true hopfions with $S=1,M=1\sim 7$, which correspond, accordingly, to $Q_{H}=1\sim 7$, also form partly stable families, including the case of the LHY\ superfluid, in which the nonlinearity is represented solely by the LHY term. On the other hand, the hopfion family is completely unstable in the absence of the LHY term, when only the mean-field nonlinearity is present. We illustrate the knot-like structure of the hopfions by means of an elementary geometric picture. For $Q_{H}=0$, circles which represent the \textit{preimage} of the full state do not intersect. On the contrary, for $Q_{H}\geq 1$ they intersect at points whose number is identical to $Q_{H}$. The intersecting curves form multi-petal structures with the number of petals also equal to $Q_{H}$.

cond-mat.quant-gas↗

arXiv2025

Elongated vortex quantum droplets in binary Bose-Einstein condensates

Stability of elongated (``slender") quantum droplets (QDs) with embedded unitary and multiple vorticity is a problem that was not solved previously. In this work, we propose a solution which relies upon the use of the spatial modulation of the inter-species scattering length in the binary Bose-Einstein condensates, in the form of a two-dimensional axisymmetric Gaussian, shaped by means of the optical Feshbach resonance. The corresponding effective nonlinear trapping potential supports completely stable elongated QDs with vorticity $S=0$ and partly stable families of elongated QDs with $S=1,2,3,4$ (other nonlinear systems do not maintain stability of vortex droplets with $\geq 2$). We systematically analyze effects of the amplitude and width of the Gaussian modulation, as well as the total number of atoms, on the shape and stability of the QDs, some effects being explained analytically. Collisions between identical QDs with $% S=1$ moving in opposite directions along the central axis leads to their merger into still more elongated breathing QDs with the same vorticity, while collisions between QDs with $S=\pm 1$ are quasi-elastic. Moving modulation profiles are able to adiabatically rotate the trapped elongated QDs. Application of a torque to the vector QD sets in the gyroscopic regime of robust precession, which realizes a macroscopic spin-orbit-coupling effect.

cond-mat.quant-gas↗

arXiv2025

Solitons in Bose-Einstein Condensates with Attractive Self-Interaction on a M\"obius Strip

We study the matter-wave solitons in Bose-Einstein condensate (BEC) trapped on a M\"{o}bius strip (MS), based on the respective Gross-Pitaevskii (GP) equation with the mean-field theory. In the linear regime, vortex states are characterized by quantum numbers, $n$ and $m$, corresponding to the transverse and circumferential directions, with the phase structure determined by the winding number (WN) $m$. Odd and even values of $n$ must associate, respectively, with integer and half-integer values of $m$, the latter ones requiring two cycles of motion around MS for returning to the initial phase. Using variational and numerical methods, we solve the GP equation with the attractive nonlinearity, producing a family of ground-state (GS) solitons for values of the norm below the critical one, above which the collapse sets in. Vortex solitons with $n=1,m=1$ and $% n=2,m=1/2$ are obtained in a numerical form. The vortex solitons with $% n=1,m=1$ are almost uniformly distributed in the azimuthal direction, while ones with $n=2,m=1/2$ form localized states. The Vakhitov-Kolokolov criterion and linear-stability analysis for the GS soliton solutions and vortices with $n=1,m=1$ demonstrates that they are completely stable, while the localized states with $n=2,m=1/2$ are completely unstable. Finally, the motion of solitons on the MS and the collision of two solitons are discussed.

nlin.PS↗

arXiv2024

Tightly bound solitons and vortices in three-dimensional bosonic condensates with the electromagnetically-induced gravity

The $1/r$ long-range interaction, induced by laser illumination, offers a mechanism for the implementation of stable self-trapping in Bose-Einstein condensates (BECs) in the three-dimensional free space. Using the variational approximation and numerical solutions, we find that self-trapped states in this setting , with attractive nonlocal and repulsive local interactions, resemble tightly-bound compactons. However, these are not true compactons but rather \textit{tightly self-trapped modes} (TSTMs), with small-amplitude nonvanishing tails. The structure of the self-trapped states is explained by an analytical solution for their tails. Further, we demonstrate that stable % TSTMs with embedded vorticity, exist in the same setting, with winding numbers up to $S=6$ (at least). Addressing two-TSTM interactions, we find that pairs of ground states (GSs, with $S=0$), as well as vortex-vortex and vortex-antivortex pairs (with $S_1=S_2$ and $S_1=-S_2$, respectively), form stably rotating bound states. Head-on collisions between vortex TSTMs, set in slow motion by kicks, are inelastic, resulting in their merger into a GS soliton, that may either remain at the collision position or move aside, shedding the angular momentum with emitted radiation, or, alternatively, lead to the formation of a vortex that also moves aside.

cond-mat.quant-gas↗

arXiv2024

Can vortex quantum droplets be realized experimentally?

The current state of research on vortices carried by quantum droplets (QDs) has predicted their existence, in the stable form, in two- and three-dimensional free-space binary Bose-Einstein condensates (BECs) and dipolar BECs. These theoretical results suggest that QDs may be excellent carriers of self-trapped vortex states. Given that the experimental creation of QDs has already been firmly established, the observation of embedded vortices in them becomes a key question for the next phase of the development in the field.

cond-mat.quant-gas↗

arXiv2024

Joint Estimation and Prediction of City-wide Delivery Demand: A Large Language Model Empowered Graph-based Learning Approach

The proliferation of e-commerce and urbanization has significantly intensified delivery operations in urban areas, boosting the volume and complexity of delivery demand. Data-driven predictive methods, especially those utilizing machine learning techniques, have emerged to handle these complexities in urban delivery demand management problems. One particularly pressing issue that has yet to be sufficiently addressed is the joint estimation and prediction of city-wide delivery demand, as well as the generalization of the model to new cities. To this end, we formulate this problem as a transferable graph-based spatiotemporal learning task. First, an individual-collective message-passing neural network model is formalized to capture the interaction between demand patterns of associated regions. Second, by exploiting recent advances in large language models (LLMs), we extract general geospatial knowledge encodings from the unstructured locational data using the embedding generated by LLMs. Last, to encourage the cross-city generalization of the model, we integrate the encoding into the demand predictor in a transferable way. Comprehensive empirical evaluation results on two real-world delivery datasets, including eight cities in China and the US, demonstrate that our model significantly outperforms state-of-the-art baselines in accuracy, efficiency, and transferability.

cs.LG↗

arXiv2024

Online Prediction-Assisted Safe Reinforcement Learning for Electric Vehicle Charging Station Recommendation in Dynamically Coupled Transportation-Power Systems

With the proliferation of electric vehicles (EVs), the transportation network and power grid become increasingly interdependent and coupled via charging stations. The concomitant growth in charging demand has posed challenges for both networks, highlighting the importance of charging coordination. Existing literature largely overlooks the interactions between power grid security and traffic efficiency. In view of this, we study the en-route charging station (CS) recommendation problem for EVs in dynamically coupled transportation-power systems. The system-level objective is to maximize the overall traffic efficiency while ensuring the safety of the power grid. This problem is for the first time formulated as a constrained Markov decision process (CMDP), and an online prediction-assisted safe reinforcement learning (OP-SRL) method is proposed to learn the optimal and secure policy by extending the PPO method. To be specific, we mainly address two challenges. First, the constrained optimization problem is converted into an equivalent unconstrained optimization problem by applying the Lagrangian method. Second, to account for the uncertain long-time delay between performing CS recommendation and commencing charging, we put forward an online sequence-to-sequence (Seq2Seq) predictor for state augmentation to guide the agent in making forward-thinking decisions. Finally, we conduct comprehensive experimental studies based on the Nguyen-Dupuis network and a large-scale real-world road network, coupled with IEEE 33-bus and IEEE 69-bus distribution systems, respectively. Results demonstrate that the proposed method outperforms baselines in terms of road network efficiency, power grid safety, and EV user satisfaction. The case study on the real-world network also illustrates the applicability in the practical context.

cs.CE↗

arXiv2024

Two-dimensional quantum droplets in binary quadrupolar condensates

We study the stability and characteristics of two-dimensional (2D) quasi-isotropic quantum droplets (QDs) of fundamental and vortex types, formed by binary Bose-Einstein condensate with magnetic quadrupole-quadrupole interactions (MQQIs). The magnetic quadrupoles are built as pairs of dipoles and antidipoles polarized along the x-axis. The MQQIs are induced by applying an external magnetic field that varies along the x-axis. The system is modeled by the Gross-Pitaevskii equations including the MQQIs and Lee-Huang-Yang correction to the mean-field approximation. Stable 2D fundamental QDs and quasi-isotropic vortex QDs with topological charges S<4 are produced by means of the imaginary-time-integration method for configurations with the quadrupoles polarized parallel to the systems two-dimensional plane. Effects of the norm and MQQI strength on the QDs are studied in detail. Some results, including an accurate prediction of the effective area, chemical potential, and peak density of QDs, are obtained in an analytical form by means of the Thomas-Fermi approximation. Collisions between moving QDs are studied by means of systematic simulations.

cond-mat.quant-gas↗

arXiv2023

Strongly anisotropic vortices in dipolar quantum droplets

We construct strongly anisotropic quantum droplets with embedded vorticity in the 3D space, with mutually perpendicular vortex axis and polarization of atomic magnetic moments. Stability of these anisotropic vortex quantum droplets (AVQDs) is verified by means of systematic simulations. Their stability area is identified in the parametric plane of the total atom number and scattering length of the contact interactions. We also construct vortex-antivortex-vortex bound states and find their stability region in the parameter space. The application of a torque perpendicular to the vorticity axis gives rise to robust intrinsic oscillations or rotation of the AVQDs. The effect of three-body losses on the AVQD stability is considered too. The results show that the AVQDs can retain the topological structure (vorticity) for a sufficiently long time if the scattering length exceeds a critical value.

cond-mat.quant-gas↗

arXiv2023

Two-dimensional anisotropic vortex quantum droplets in dipolar Bose-Einstein condensates

Creation of stable intrinsically anisotropic self-bound states with embedded vorticity is a challenging issue. Previously, no such states in Bose-Einstein condensates (BECs) or other physical settings were known. Dipolar BEC suggests a unique possibility to predict stable anisotropic vortex quantum droplets (AVQDs). We demonstrate that they can be created with the vortex' axis oriented \emph{perpendicular} to the polarization of dipoles. The stability area and characteristics of the AVQDs in the parameter space are revealed by means of analytical and numerical methods. Further, the rotation of the polarizing magnetic field is considered, and the largest angular velocities, up to which spinning AVQDs can follow the rotation in clockwise and anti-clockwise directions, are found. Collisions between moving AVQDs are studied too, demonstrating formation of bound states with a vortex-antivortex-vortex structure. A stability domain for such stationary bound states is identified. Unstable dipolar states, that can be readily implemented by means of phase imprinting, quickly transform into robust AVQDs, which suggests a straightforward possibility for the creation of these states in the experiment.

cond-mat.quant-gas↗

arXiv2023

City-scale synthetic individual-level vehicle trip data

Trip data that records each vehicle's trip activity on the road network describes the operation of urban traffic from the individual perspective, and it is extremely valuable for transportation research. However, restricted by data privacy, the trip data of individual-level cannot be opened for all researchers, while the need for it is very urgent. In this paper, we produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data, where the availability and trip data privacy protection are balanced. Privacy protection inevitably affects the availability of data. Therefore, we have conducted numerous experiments to demonstrate the performance and reliability of the synthetic data in different dimensions and at different granularities to help users properly judge the tasks it can perform. The result shows that the synthetic data is consistent with the real data (i.e., historical data) on the aggregated level and reasonable from the individual perspective.

cs.CY↗

arXiv2022

Potential destination discovery for low predictability individuals based on knowledge graph

Travelers may travel to locations they have never visited, which we call potential destinations of them. Especially under a very limited observation, travelers tend to show random movement patterns and usually have a large number of potential destinations, which make them difficult to handle for mobility prediction (e.g., destination prediction). In this paper, we develop a new knowledge graph-based framework (PDPFKG) for potential destination discovery of low predictability travelers by considering trip association relationships between them. We first construct a trip knowledge graph (TKG) to model the trip scenario by entities (e.g., travelers, destinations and time information) and their relationships, in which we introduce the concept of private relationship for complexity reduction. Then a modified knowledge graph embedding algorithm is implemented to optimize the overall graph representation. Based on the trip knowledge graph embedding model (TKGEM), the possible ranking of individuals' unobserved destinations to be chosen in the future can be obtained by calculating triples' distance. Empirically. PDPFKG is tested using an anonymous vehicular dataset from 138 intersections equipped with video-based vehicle detection systems in Xuancheng city, China. The results show that (i) the proposed method significantly outperforms baseline methods, and (ii) the results show strong consistency with traveler behavior in choosing potential destinations. Finally, we provide a comprehensive discussion of the innovative points of the methodology.

cs.AI↗

arXiv2022

Urban Vehicle Mobility Characteristic Mining and Trip Generation Based on Knowledge Graph

The operation of urban transportation produces massive traffic data, which contains abundant information and is of great significance for the study of intelligent transportation systems. In particular, with the improvement of perception technology, it has become possible to obtain trip data in individual-level of vehicles. It has finer granularity and greater research potential, but at the same time requires higher requirements in terms of data organization and analysis. More importantly it cannot be made public due to privacy issues. To handle individual-level urban vehicle trip big data better, we introduce the knowledge graph for the study. For organization of individual level trip data, we designed and constructed an individual-level trip knowledge graph which greatly improves the efficiency of obtaining data. Then we used the trip knowledge graph as the data engine and designed logical rules to mine the trip characteristics of vehicles by combining the transportation domain knowledge. Finally, we further propose an individual-level trip synthesis method based on knowledge graph generation to address the privacy issue of individual-level traffic data. The experiment shows that the final generated trip data are similar to the historical one in mobility patterns and vehicle associations, and have high spatial continuity.

cs.CY↗

arXiv2022

City-Scale Holographic Traffic Flow Data based on Vehicular Trajectory Resampling

Despite abundant accessible traffic data, researches on traffic flow estimation and optimization still face the dilemma of detailedness and integrity in the measurement. A dataset of city-scale vehicular continuous trajectories featuring the finest resolution and integrity, as known as the holographic traffic data, would be a breakthrough, for it could reproduce every detail of the traffic flow evolution and reveal the personal mobility pattern within the city. Due to the high coverage of Automatic Vehicle Identification (AVI) devices in Xuancheng city, we constructed one-month continuous trajectories of daily 80,000 vehicles in the city with accurate intersection passing time and no travel path estimation bias. With such holographic traffic data, it is possible to reproduce every detail of the traffic flow evolution. We presented a set of traffic flow data based on the holographic trajectories resampling, covering the whole 482 road segments in the city round the clock, including stationary average speed and flow data of 5-minute intervals and dynamic floating car data.

cs.CY↗