arXiv subjects

Lu Qin

Publications and source records attributed to Lu Qin.

At least 19 recordsLinked to original sources

arXiv2026

Optical nonlinearity in a strongly interacting Rydberg atom ensemble

Rydberg atoms, characterized by their giant polarizability and strong long-range interactions, provide an ideal platform for exploring optical nonlinearities. By coupling light to ensembles of Rydberg atoms, one can map the strong and nonlocal interactions between Rydberg atoms and light fields. A particularly important scheme for realizing such nonlinearity is electromagnetically induced transparency (EIT). This article presents a review of the research progress in nonlinear optics based on Rydberg-EIT systems, particularly nonlinear light propagation in Rydberg atomic gases. We first introduce the fundamental properties of Rydberg atoms and the physical mechanism of EIT-enhanced nonlinearity, emphasizing how the Rydberg blockade effect endows the system with a giant nonlocal Kerr nonlinearity. Subsequently, we discuss some typical nonlinear optical phenomena in such a system with focusing and defocusing nonlocal nonlinearities. In the focusing regime, we discuss spatial solitons, vortex solitons, soliton cloning, soliton molecules, optical bullets, and self-induced transparency solitons. Other topics are also discussed. In the defocusing regime, we analyze pattern formation and shock wave dynamics. Finally, we provide an outlook on the future development of this field toward quantum control, hybrid-system integration, and novel functional devices.

physics.optics↗

arXiv2026

Ripple Signatures of Majorana Hybridization across a Topological Quantum Quench

The crossover between topology and nonequilibrium dynamics has emerged as a rich frontier, in which quantum systems can exhibit unique dynamical phenomena that lie beyond the reach of equilibrium. Of particular interest are quench dynamics across topological phase, as it may reveal the information about the underlying Majorana zero-energy states. Here, we investigate the fate of Majorana boundary modes in a quenched fermionic superfluid using self-consistent time-dependent Bogoliubov-de Gennes theory. For quantum quenches within the topological regime, Majorana boundary modes survive but undergo coherent boundary oscillations arising from the nonadiabatic deformation of their wave functions. Furthermore, a pronounced ripple pattern appears in the post-quench density distribution following a sudden topology-changing quench. Here we identify that these ripple structures originates from the coherent hybridization and interference of the initially separated Majorana boundary states. Our findings establish nonequilibrium boundary dynamics as a new approach for probing Majorana physics, which is complementary to conventional equilibrium measurements.

cond-mat.quant-gas↗

arXiv2026

Simultaneous estimation of relative phase and coherence in astronomical interferometry

Astronomical interferometry is a cornerstone technique for high-resolution stellar imaging and observational astrophysics, extracting spatial information from the coherence of light collected by separated telescopes. Since the degree of coherence is complex, a genuine imaging task requires the joint recovery of the modulus and the relative phase, instead of independent singleparameter estimations. We investigate the simultaneous estimation of both parameters based on direct interferometry scheme and continuou-svariable quantum teleportation scheme. We find that in simultaneous estimation the direct interferometry scheme consistently yields a lower quantum Cram\'er-Rao bound, demonstrating its superiority over the continuous-variable quantum teleportation scheme. Furthermore, we establish the conditions under which the classical Cram\'er-Rao bound for Gaussian measurements saturates the quantum Cram\'er-Rao bound, identifying heterodyne detection as a near-optimal measurement scheme in the large mean photon number regime. An analysis of transmission loss reveals that the direct interferometry scheme yields superior precision in the short-baseline regime, whereas the continuous-variable quantum teleportation scheme outperforms it at longer baselines.

quant-ph↗

arXiv2026

RAIDS: Rethinking Data Systems as Responsible Intelligent Infrastructure

Data systems are evolving from information infrastructure into decision infrastructure. Yet responsibility mechanisms have not kept pace: an output can be accurate or efficient while still lacking sufficient support, satisfied constraints, and actionability for responsible use. We propose RAIDS (Responsible and Intelligent Data System), a vision for data systems as responsible intelligent infrastructure. RAIDS treats responsibility not as post-hoc metadata, but as execution semantics for holistic data-to-decision and data mining pipelines. Its core abstraction is an operator-level responsibility contract: each operator exposes an output together with support, constraint, and actionability state under an explicit responsibility context, and these contracts compose across pipelines. These states capture whether an output is grounded, whether execution satisfies relevant limits, and which action modes are permissible. We introduce responsibility preservation as the organizing systems objective: responsibility state should remain sufficient as execution proceeds, or the system should repair, replan, escalate, refuse, or otherwise change course. We outline a BlueSky research agenda for RAIDS, spanning responsibility-preserving execution, responsibility-aware optimization, provenance, oversight, and evaluation.

cs.DB↗

arXiv2026

EXG: Self-Evolving Agents with Experience Graphs

Large language model (LLM)-based agents have demonstrated strong capabilities in complex reasoning and problem solving through multi-step interactions, yet most deployed agents remain behaviorally static, with knowledge acquired during execution rarely translating into systematic improvement over time. In response, a growing line of work on self-evolving agents explores how agents can improve through experience during deployment, but most existing approaches either rely on ad hoc reflection limited to single-task correction or adopt unstructured memory that accumulates fragmented experience with delayed usability. To address this limitation, we introduce EXG, an experience graph framework for self-evolving agents that explicitly organizes accumulated successes and failures into a structured, relational representation. EXG is the first experience graph designed for self-evolving agents, supporting both online, real-time graph growth during execution for immediate cross-task experience reuse, and offline reuse of a consolidated experience graph as an external memory module. This design also enables EXG to serve as a plug-and-play component for existing self-evolving agents, organizing prior experience into a unified experience graph and improving both solution quality and resource efficiency as deployment progresses. Extensive experiments across code generation and reasoning benchmarks show that EXG attains more favorable performance-efficiency trade-offs than reflection- and memory-based baselines in both online and offline evaluations. Our results suggest that structuring experience as a graph provides a principled foundation for scalable and transferable self-evolving agent behavior.

cs.AI↗

arXiv2026

Stable (2+1)-dimensional soliton and breather molecules in a cold Rydberg atomic gas

We investigate the formation of stable (2+1)-dimensional spatial-domain optical soliton molecules and breather molecules in a gas of Rydberg atoms, highlighting the role of the nonlocal nonlinearity, which is generated by the electromagnetically induced transparency in the Rydberg medium. The setting supports diverse species of large-size polygonal soliton molecules, including rectangular and oblique rhombuses, checkerboard cells, and hexagons. The analysis identifies two distinct formation regimes. In the case of moderately nonlocality, the long-range interactions alone stabilize the soliton molecules in the static form. In contrast, in the strongly nonlocal regime, initially imposed rotation is required to generate a centrifugal force that counteracts the strong attraction, resulting in stably rotating soliton molecules. The rotation period can be controlled by adjusting the system parameters. Furthermore, appropriate initial velocities can induce inherent breathing dynamics in the solitons, leading to the formation of breather molecules. Tuning the initial velocity, one can control the evolution of soliton molecules and breather molecules and even realize their mutual conversion. Our study offers a new scheme for engineering soliton molecules and breather molecules, and suggests new possibilities for the design of data processing and transmission in optical systems.

physics.optics↗

arXiv2026

Efficient Approximate Nearest Neighbor Search under Multi-Attribute Range Filter

Nearest neighbor search on high-dimensional vectors is fundamental in modern AI and database systems. In many real-world applications, queries involve constraints on multiple numeric attributes, giving rise to range-filtering approximate nearest neighbor search (RFANNS). While there exist RFANNS indexes for single-attribute range predicates, extending them to the multi-attribute setting is nontrivial and often ineffective. In this paper, we propose KHI, an index for multi-attribute RFANNS that combines an attribute-space partitioning tree with HNSW graphs attached to tree nodes. A skew-aware splitting rule bounds the tree height by $O(\log n)$, and queries are answered by routing through the tree and running greedy search on the HNSW graphs. Experiments on four real-world datasets show that KHI consistently achieves high query throughput while maintaining high recall. Compared with the state-of-the-art RFANNS baseline, KHI improves QPS by $2.46\times$ on average and up to $16.22\times$ on the hard dataset, with larger gains for smaller selectivity, larger $k$, and higher predicate cardinality.

cs.DB↗

arXiv2026

Constant-time Connectivity and 2-Edge Connectivity Querying in Dynamic Graphs

Connectivity query processing is a fundamental problem in graph processing. Given an undirected graph and two query vertices, the problem aims to identify whether they are connected via a path. Given frequent edge updates in real graph applications, in this paper, we study connectivity query processing in fully dynamic graphs, where edges are frequently inserted or deleted. A recent solution, called D-tree, maintains a spanning tree for each connected component and applies several heuristics to reduce the depth of the tree. To improve efficiency, we propose a new spanning-tree-based solution by maintaining a disjoint-set tree simultaneously. By combining the advantages of two trees, we achieve the constant query time complexity and also significantly improve the theoretical running time in both edge insertion and edge deletion. In addition, we extend our connectivity maintenance algorithms to maintain 2-edge connectivity. Our performance studies on real large datasets show considerable improvement of our algorithms.

cs.DB↗

arXiv2026

HyperJoin: LLM-augmented Hypergraph Link Prediction for Joinable Table Discovery

As a pivotal task in data lake management, joinable table discovery has attracted widespread interest. While existing language model-based methods achieve remarkable performance by combining offline column representation learning with online ranking, their design insufficiently accounts for the underlying structural interactions: (1) offline, they directly model tables into isolated or pairwise columns, thereby struggling to capture the rich inter-table and intra-table structural information; and (2) online, they rank candidate columns based solely on query-candidate similarity, ignoring the mutual interactions among the candidates, leading to incoherent result sets. To address these limitations, we propose HyperJoin, a large language model (LLM)-augmented Hypergraph framework for Joinable table discovery. Specifically, we first construct a hypergraph to model tables using both the intra-table hyperedges and the LLM-augmented inter-table hyperedges. Consequently, the task of joinable table discovery is formulated as link prediction on this constructed hypergraph. We then design HIN, a Hierarchical Interaction Network that learns expressive column representations through bidirectional message passing over columns and hyperedges. To strengthen coherence and internal consistency in the result columns, we cast online ranking as a coherence-aware top-k column selection problem. We then introduce a reranking module that leverages a maximum spanning tree algorithm to prune noisy connections and maximize coherence. Experiments demonstrate the superiority of HyperJoin, achieving average improvements of 21.4% (Precision@15) and 17.2% (Recall@15) over the best baseline.

cs.CL↗

arXiv2025

HL-index: Fast Reachability Query in Hypergraphs

Reachability in hypergraphs is essential for modeling complex groupwise interactions in real-world applications such as co-authorship, social network, and biological analysis, where relationships go beyond pairwise interactions. In this paper, we introduce the notion of s-reachability, where two vertices are s-reachable if there exists a sequence of hyperedges (i.e., a walk) connecting them, such that each pair of consecutive hyperedges shares at least s vertices. Moreover, we define the max-reachability query as a generalized form of the s-reachability problem, which aims to find the largest value of s that allows one vertex to reach another. To answer max-reachability queries in hypergraphs, we first analyze limitations of the existing vertex-to-vertex and hyperedge-to-hyperedge indexing techniques. We then introduce the HL-index, a compact vertex-to-hyperedge index tailored for the max-reachability problem. To both efficiently and effectively construct a minimal HL-index, we develop a fast covering relationship detection method to eliminate fruitless hypergraph traversals during index construction. A lightweight neighbor-index is further proposed to avoid repeatedly exploring neighbor relationships in hypergraphs and hence accelerate the construction. Extensive experiments on 20 datasets demonstrate the efficiency and scalability of our approach.

cs.DB↗

arXiv2025

HiGraph: A Large-Scale Hierarchical Graph Dataset for Malware Analysis

The advancement of graph-based malware analysis is critically limited by the absence of large-scale datasets that capture the inherent hierarchical structure of software. Existing methods often oversimplify programs into single level graphs, failing to model the crucial semantic relationship between high-level functional interactions and low-level instruction logic. To bridge this gap, we introduce \dataset, the largest public hierarchical graph dataset for malware analysis, comprising over \textbf{200M} Control Flow Graphs (CFGs) nested within \textbf{595K} Function Call Graphs (FCGs). This two-level representation preserves structural semantics essential for building robust detectors resilient to code obfuscation and malware evolution. We demonstrate HiGraph's utility through a large-scale analysis that reveals distinct structural properties of benign and malicious software, establishing it as a foundational benchmark for the community. The dataset and tools are publicly available at https://higraph.org.

cs.LG↗

arXiv2024

Towards Fair and Rigorous Evaluations: Hyperparameter Optimization for Top-N Recommendation Task with Implicit Feedback

The widespread use of the internet has led to an overwhelming amount of data, which has resulted in the problem of information overload. Recommender systems have emerged as a solution to this problem by providing personalized recommendations to users based on their preferences and historical data. However, as recommendation models become increasingly complex, finding the best hyperparameter combination for different models has become a challenge. The high-dimensional hyperparameter search space poses numerous challenges for researchers, and failure to disclose hyperparameter settings may impede the reproducibility of research results. In this paper, we investigate the Top-N implicit recommendation problem and focus on optimizing the benchmark recommendation algorithm commonly used in comparative experiments using hyperparameter optimization algorithms. We propose a research methodology that follows the principles of a fair comparison, employing seven types of hyperparameter search algorithms to fine-tune six common recommendation algorithms on three datasets. We have identified the most suitable hyperparameter search algorithms for various recommendation algorithms on different types of datasets as a reference for later study. This study contributes to algorithmic research in recommender systems based on hyperparameter optimization, providing a fair basis for comparison.

cs.IR↗

arXiv2024

RIDA: A Robust Attack Framework on Incomplete Graphs

Graph Neural Networks (GNNs) are vital in data science but are increasingly susceptible to adversarial attacks. To help researchers develop more robust GNN models, it's essential to focus on designing strong attack models as foundational benchmarks and guiding references. Among adversarial attacks, gray-box poisoning attacks are noteworthy due to their effectiveness and fewer constraints. These attacks exploit GNNs' need for retraining on updated data, thereby impacting their performance by perturbing these datasets. However, current research overlooks the real-world scenario of incomplete graphs. To address this gap, we introduce the Robust Incomplete Deep Attack Framework (RIDA). It is the first algorithm for robust gray-box poisoning attacks on incomplete graphs. The approach innovatively aggregates distant vertex information and ensures powerful data utilization. Extensive tests against 9 SOTA baselines on 3 real-world datasets demonstrate that RIDA's superiority in handling incompleteness and high attack performance on the incomplete graph.

cs.LG↗

arXiv2024

Efficient Maximal Frequent Group Enumeration in Temporal Bipartite Graphs

Cohesive subgraph mining is a fundamental problem in bipartite graph analysis. In reality, relationships between two types of entities often occur at some specific timestamps, which can be modeled as a temporal bipartite graph. However, the temporal information is widely neglected by previous studies. Moreover, directly extending the existing models may fail to find some critical groups in temporal bipartite graphs, which appear in a unilateral (i.e., one-layer) form. To fill the gap, in this paper, we propose a novel model, called maximal \lambda-frequency group (MFG). Given a temporal bipartite graph G=(U,V,E), a vertex set V_S \subseteq V is an MFG if i) there are no less than \lambda timestamps, at each of which V_S can form a (t_U,t_V)-biclique with some vertices in U at the corresponding snapshot, and ii) it is maximal. To solve the problem, a filter-and-verification (FilterV) method is proposed based on the Bron-Kerbosch framework, incorporating novel filtering techniques to reduce the search space and array-based strategy to accelerate the frequency and maximality verification. Nevertheless, the cost of frequency verification in each valid candidate set computation and maximality check could limit the scalability of FilterV to larger graphs. Therefore, we further develop a novel verification-free (VFree) approach by leveraging the advanced dynamic counting structure proposed. Theoretically, we prove that VFree can reduce the cost of each valid candidate set computation in FilterV by a factor of O(|V|). Furthermore, VFree can avoid the explicit maximality verification because of the developed search paradigm. Finally, comprehensive experiments on 15 real-world graphs are conducted to demonstrate the efficiency and effectiveness of the proposed techniques and model.

cs.DB↗

arXiv2024

Efficient Antagonistic k-plex Enumeration in Signed Graphs

A signed graph is a graph where each edge receives a sign, positive or negative. The signed graph model has been used in many real applications, such as protein complex discovery and social network analysis. Finding cohesive subgraphs in signed graphs is a fundamental problem. A k-plex is a common model for cohesive subgraphs in which every vertex is adjacent to all but at most k vertices within the subgraph. In this paper, we propose the model of size-constrained antagonistic k-plex in a signed graph. The proposed model guarantees that the resulting subgraph is a k-plex and can be divided into two sub-k-plexes, both of which have positive inner edges and negative outer edges. This paper aims to identify all maximal antagonistic k-plexes in a signed graph. Through rigorous analysis, we show that the problem is NP-Hardness. We propose a novel framework for maximal antagonistic k-plexes utilizing set enumeration. Efficiency is improved through pivot pruning and early termination based on the color bound. Preprocessing techniques based on degree and dichromatic graphs effectively narrow the search space before enumeration. Extensive experiments on real-world datasets demonstrate our algorithm's efficiency, effectiveness, and scalability.

cs.DB↗

arXiv2024

Efficient Influence Minimization via Node Blocking

Given a graph G, a budget k and a misinformation seed set S, Influence Minimization (IMIN) via node blocking aims to find a set of k nodes to be blocked such that the expected spread of S is minimized. This problem finds important applications in suppressing the spread of misinformation and has been extensively studied in the literature. However, existing solutions for IMIN still incur significant computation overhead, especially when k becomes large. In addition, there is still no approximation solution with non-trivial theoretical guarantee for IMIN via node blocking prior to our work. In this paper, we conduct the first attempt to propose algorithms that yield data-dependent approximation guarantees. Based on the Sandwich framework, we first develop submodular and monotonic lower and upper bounds for our non-submodular objective function and prove the computation of proposed bounds is \#P-hard. In addition, two advanced sampling methods are proposed to estimate the value of bounding functions. Moreover, we develop two novel martingale-based concentration bounds to reduce the sample complexity and design two non-trivial algorithms that provide (1-1/e-\epsilon)-approximate solutions to our bounding functions. Comprehensive experiments on 9 real-world datasets are conducted to validate the efficiency and effectiveness of the proposed techniques. Compared with the state-of-the-art methods, our solutions can achieve up to two orders of magnitude speedup and provide theoretical guarantees for the quality of returned results.

cs.DB↗

arXiv2024

Shock wave generation and propagation in dissipative and nonlocal nonlinear Rydberg media

We investigate the generation of optical shock waves in strongly interacting Rydberg atomic gases with a spatially homogeneous dissipative potential. The Rydberg atom interaction induces an optical nonlocal nonlinarity. We focus on local nonlinear ($R_b\ll R_0$) and nonlocal nonlinear ($R_b\sim R_0$) regimes, where $R_b$ and $R_0$ are the characteristic length of the Rydberg nonlinearity and beam width, respectively. In the local regime, we show spatial width and contrast of the shock wave change monotonically when increasing strength of the dissipative potential and optical intensity. In the nonlocal regime, the characteristic quantity of the shock wave depend on $R_b/R_0$ and dissipative potential nontrivially and on the intensity monotonically. We find that formation of shock waves dominantly takes place when $R_b$ is smaller than $R_0$, while the propagation dynamics is largely linear when $R_b$ is comparable to or larger than $R_0$. Our results reveal nontrivial roles played by dissipation and nonlocality in the generation of shock waves, and provide a route to manipulate their profiles and stability. Our study furthermore opens new avenues to explore non-Hermitian physics, and nonlinear wave generation and propagation by controlling dissipation and nonlocality in the Rydberg media.

physics.optics↗

arXiv2024

Quantum Phases of a Dipolar Fermi Gas with Laser-assisted Interwire Tunneling

We systematically investigate unconventional superfluid phases of fermionic dipolar particles lying in a double-wire setup with laser-assisted interwire tunneling. Our numerical simulations, based on the nonlocal Kohn-Sham Bogoliubov-de Gennes equation, reveal the existence of a large Fulde-Ferrell-Larkin-Ovchinnikov (FFLO) region with a stripe phase under an imbalance of particle densities between two wires. When the laser-assisted interwire tunneling is present, it induces a transition from the FFLO phase to the topological superfluid phase and the associated Majorana zero modes exhibit an oscillation structure, which is significantly enhanced by the long-range nature of the interwire dipolar interaction. This distinguishes itself from the results obtained with usual contact interaction and offers new opportunities for manipulating and reshaping Majorana zero modes by adjusting the degree of the nonlocality and the interwire separation.

cond-mat.quant-gas↗