arXiv subjects

Yingxi Li

Publications and source records attributed to Yingxi Li.

6 recordsLinked to original sources

arXiv2026

Turbulence Cascade in Cygnus X Revealed by Multi-point VDF Method

Turbulence plays a crucial role in regulating star formation activities within molecular clouds, yet few methods can directly reveal its properties and underlying processes. We use molecular line data from the Nobeyama 45m Cygnus X CO Survey to study the turbulence properties and their relationships with star-forming activities and/or other non-thermal motions. In this work, we apply the multi-point velocity dispersion function (VDF), rather than direct linewidth measurements, to investigate the non-thermal properties of molecular cloud motions. We filter out the large-scale ordered structure and isolate a relatively small-scale turbulence component. Through the Friends In Velocity (FIVe) algorithm, we identify 10 substructures of the clouds and derive the turbulent properties of each cloud using the VDF method. We find that both the cloud-complex regions and the 10 velocity substructures exhibit turbulence correlation lengths of $\sim 2$--5 pc. This plateau scale suggests a parsec-scale turbulence correlation or driving scale in Cygnus X. Below this scale, the rising VDFs trace the velocity scaling of the turbulent cascade, whereas larger-scale VDF variations likely reflect cloud-scale motions. The comparison between cloud complexes and substructures further suggests that, in observational data, the VDF may constrain the turbulence correlation scale more robustly than the turbulence velocity dispersion.

astro-ph.GA↗

arXiv2026

A complete survey of filaments in Cygnus X

Filamentary structures are widely observed in molecular clouds, yet most filament observations are biased toward case studies and small samples; a uniform census within a single giant molecular cloud is still missing. We do a complete census of filaments in Cygnus X and quantify their links to dense cores, the magnetic field (B field), and HII regions. Using the updated getsf algorithm on the Cygnus X column-density map, we extracted 2633 filaments and 6551 cores. We built CMFs for cores on and off filaments, compared filament orientations with the Planck B field, measured radial column-density profiles near HII-region boundaries, and computed distances between young stellar objects and filament spines. Filaments have a typical width of 0.5 pc in Cygnus X at a resolution of 0.12 pc and host > 93% of high-mass cores (>= 20 Msun). The on-filament CMF shows a high-mass (> 10 Msun) slope of -2.30, while the off-filament CMF is steeper (-2.83). The onCMF peak mass is well below the Bonnor-Ebert mass, whereas the outCMF peak is comparable to it. At 5' resolution, filaments are mostly perpendicular to the B field except at the lowest column densities; the transition occurs near Av = 10 mag. Prominent filaments and high-mass cores preferred to be located around HII-region boundaries or at intersections of multiple HII regions; filament profiles are steeper on the side facing the HII region. Massive-core formation depends strongly on filaments, which may provide reservoirs that feed cores via accretion. The B field likely regulates filament formation, consistent with the type-O mode (converging flows along an oblique MHD shock) and an HII-driven bubble-filament paradigm for Cygnus X.

astro-ph.GA↗

arXiv2025

Smoothed Analysis of Online Metric Matching with a Single Sample: Beyond Metric Distortion

In the online metric matching problem, $n$ servers and $n$ requests lie in a metric space. Servers are available upfront, and requests arrive sequentially. An arriving request must be matched immediately and irrevocably to an available server, incurring a cost equal to their distance. The goal is to minimize the total matching cost. We study this problem in the Euclidean metric $[0, 1]^d$, when servers are adversarial and requests are independently drawn from distinct distributions that satisfy a mild smoothness condition. Our main result is an $O(1)$-competitive algorithm for $d \neq 2$ that requires no distributional knowledge, relying only on a single sample from each request distribution. To our knowledge, this is the first algorithm to achieve an $o(\log n)$ competitive ratio for non-trivial metrics beyond the i.i.d. setting. Our approach bypasses the $\Omega(\log n)$ barrier introduced by probabilistic metric embeddings: instead of analyzing the embedding distortion and the algorithm separately, we directly bound the cost of the algorithm on the target metric of a simple deterministic embedding. We then combine this analysis with lower bounds on the offline optimum for Euclidean metrics, derived via majorization arguments, to obtain our guarantees.

cs.DS↗

arXiv2025

Can LLMs Reason Structurally? Benchmarking via the Lens of Data Structures

Large language models (LLMs) are deployed on increasingly complex tasks that require multi-step decision-making. Understanding their algorithmic reasoning abilities is therefore crucial. However, we lack a diagnostic benchmark for evaluating these capabilities. We propose to use data structures as a principled lens: as fundamental building blocks of algorithms, they naturally probe structural reasoning - the ability to understand and manipulate relationships such as order, hierarchy, and connectivity that underpin algorithmic reasoning. We introduce DSR-Bench (Data Structure Reasoning Benchmark), spanning 20 data structures, 35 operations, and 4,140 problem instances. DSR-Bench features hierarchical task organization, fully automated generation and evaluation, and fine-grained diagnostics. Evaluating 13 state-of-the-art LLMs reveals critical limitations: the top-performing model achieves only 0.46/1 on challenging instances. Three auxiliary probes targeting more realistic usages expose further weaknesses: models perform poorly on spatial data and context-rich scenarios, and they struggle to reason over their own code.

cs.LG↗

arXiv2024

LLMs for Cold-Start Cutting Plane Separator Configuration

Mixed integer linear programming (MILP) solvers expose hundreds of parameters that have an outsized impact on performance but are difficult to configure for all but expert users. Existing machine learning (ML) approaches require training on thousands of related instances, generalize poorly and can be difficult to integrate into existing solver workflows. We propose a large language model (LLM)-based framework that configures cutting plane separators using problem descriptions and solver-specific separator summaries. To reduce variance in LLM outputs, we introduce an ensembling strategy that clusters and aggregates candidate configurations into a small portfolio of high-performing configurations. Our method requires no custom solver interface, generates configurations in seconds via simple API calls, and requires solving only a small number of instances. Extensive experiments on standard synthetic and real-world MILPs show our approach matches or outperforms state-of-the-art configuration methods with a fraction of the data and computation.

cs.LG↗

arXiv2024

Accelerating data-driven algorithm selection for combinatorial partitioning problems

Data-driven algorithm selection is a powerful approach for choosing effective heuristics for computational problems. It operates by evaluating a set of candidate algorithms on a collection of representative training instances and selecting the one with the best empirical performance. However, running each algorithm on every training instance is computationally expensive, making scalability a central challenge. In practice, a common workaround is to evaluate algorithms on smaller proxy instances derived from the original inputs. However, this practice has remained largely ad hoc and lacked theoretical grounding. We provide the first theoretical foundations for this practice by formalizing the notion of size generalization: predicting an algorithm's performance on a large instance by evaluating it on a smaller, representative instance, subsampled from the original instance. We provide size generalization guarantees for three widely used clustering algorithms (single-linkage, $k$-means++, and Gonzalez's $k$-centers heuristic) and two canonical max-cut algorithms (Goemans-Williamson and Greedy). We characterize the subsample size sufficient to ensure that performance on the subsample reflects performance on the full instance, and our experiments support these findings.

cs.LG↗