arXiv subjects

Ran Wang

Publications and source records attributed to Ran Wang.

At least 55 records · Page 3Linked to original sources

arXiv2025

Scientific Objectives of the Xue-shan-mu-chang 15-meter Submillimeter Telescope

Submillimeter astronomy is poised to revolutionize our understanding of the Universe by revealing cosmic phenomena hidden from optical and near-infrared observations, particularly those associated with interstellar dust, molecular gas, and star formation. The Xue-shan-mu-chang 15-meter submillimeter telescope (XSMT-15m), to be constructed at a premier high-altitude site (4813 m) in Qinghai, China, marks a major milestone for Chinese astronomy, establishing the China mainland's first independently developed, world-class submillimeter facility. Equipped with state-of-the-art instruments, XSMT-15m will address a diverse range of frontier scientific questions spanning extragalactic astronomy, Galactic structure, time-domain astrophysics, and astrochemistry. In synergy with current and forthcoming observatories, XSMT-15m will illuminate the formation and evolution of galaxies, unravel the physical and chemical processes shaping the interstellar medium, and explore transient phenomena in the submillimeter regime. These capabilities will advance our understanding across extragalactic astronomy, Galactic ecology, astrochemistry, and time-domain astrophysics, inaugurating a new era for submillimeter research in China and the northern hemisphere.

astro-ph.GA↗

arXiv2025

Temporal regularity for the stochastic heat equation with rough dependence in space

Consider the nonlinear stochastic heat equation $$ \frac{\partial u (t,x)}{\partial t}=\frac{\partial^2 u (t,x)}{\partial x^2}+ σ(u (t,x))\dot{W}(t,x),\quad t> 0,\, x\in \mathbb{R}, $$ where $\dot W$ is a Gaussian noise which is white in time and has the covariance of a fractional Brownian motion with Hurst parameter $H\in(\frac 14,\frac 12)$ in the space variable. When $σ(0)=0$, the well-posedness of the solution and its Hölder continuity have been proved by Hu et al. \cite{HHLNT2017}. In this paper, we study the asymptotic properties of the temporal gradient $u(t+\varepsilon, x)-u(t, x)$ at any fixed $t \ge 0$ and $x\in \mathbb R$, as $\varepsilon\downarrow 0$. As applications, we deduce Khintchine's law of iterated logarithm, Chung's law of iterated logarithm, and a result on the $q$-variations of the temporal process $\{u(t, x)\}_{t \ge 0}$, where $x\in \mathbb R$ is fixed.

math.PR↗

arXiv2025

Driving Accurate Allergen Prediction with Protein Language Models and Generalization-Focused Evaluation

Allergens, typically proteins capable of triggering adverse immune responses, represent a significant public health challenge. To accurately identify allergen proteins, we introduce Applm (Allergen Prediction with Protein Language Models), a computational framework that leverages the 100-billion parameter xTrimoPGLM protein language model. We show that Applm consistently outperforms seven state-of-the-art methods in a diverse set of tasks that closely resemble difficult real-world scenarios. These include identifying novel allergens that lack similar examples in the training set, differentiating between allergens and non-allergens among homologs with high sequence similarity, and assessing functional consequences of mutations that create few changes to the protein sequences. Our analysis confirms that xTrimoPGLM, originally trained on one trillion tokens to capture general protein sequence characteristics, is crucial for Applm's performance by detecting important differences among protein sequences. In addition to providing Applm as open-source software, we also provide our carefully curated benchmark datasets to facilitate future research.

cs.LG↗

arXiv2025

WGRAMMAR: Leverage Prior Knowledge to Accelerate Structured Decoding

Structured decoding enables large language models (LLMs) to generate outputs in formats required by downstream systems, such as HTML or JSON. However, existing methods suffer from efficiency bottlenecks due to grammar compilation, state tracking, and mask creation. We observe that many real-world tasks embed strong prior knowledge about output structure. Leveraging this, we propose a decomposition of constraints into static and dynamic components -- precompiling static structures offline and instantiating dynamic arguments at runtime using grammar snippets. Instead of relying on pushdown automata, we employ a compositional set of operators to model regular formats, achieving lower transition latency. We introduce wgrammar, a lightweight decoding engine that integrates domain-aware simplification, constraint decomposition, and mask caching, achieving up to 250x speedup over existing systems. wgrammar's source code is publicly available at https://github.com/wrran/wgrammar.

cs.AI↗

arXiv2025

(SHERRY) JCMT-SCUBA2 High Redshift Bright Quasar Survey -- II: the environment of z~6 quasars in sub-millimeter band

The formation of the first supermassive black holes is expected to have occurred in some most pronounced matter and galaxy overdensities in the early universe. We have conducted a sub-mm wavelength continuum survey of 54 $z\sim6$ quasars using the Submillimeter Common-User Bolometre Array-2 (SCUBA2) on the James Clerk Maxwell Telescope (JCMT) to study the environments around $z \sim 6$ quasars. We identified 170 submillimeter galaxies (SMGs) with above 3.5$σ$ detections at 450 or 850 \um\, maps. Their FIR luminosities are 2.2 - 6.4 $\times$ 10$^{12} L_{\odot}$, and star formation rates are $\sim$ 400 - 1200 M$_{\odot}$ yr$^{-1}$. We also calculated the SMGs differential and cumulative number counts in a combined area of $\sim$ 620 arcmin$^2$. To a $4σ$ detection (at $\sim$ 5.5 mJy), SMGs overdensity is $0.68^{+0.21}_{-0.19}$($\pm0.19$), exceeding the blank field source counts by a factor of 1.68. We find that 13/54 quasars show overdensities (at $\sim$ 5.5 mJy) of $δ_{SMG}\sim$ 1.5 - 5.4. The combined area of these 13 quasars exceeds the blank field counts with the overdensity to 5.5 mJy of \dsmg $\sim$ $2.46^{+0.64}_{-0.55}$($\pm0.25$) in the regions of $\sim$ 150 arcmin$^2$. However, the excess is insignificant on the bright end (e.g., 7.5 mJy). We also compare results with previous environmental studies of Lyman alpha emitters (LAEs) and Lyman-Break Galaxies (LBGs) on a similar scale. Our survey presents the first systematic study of the environment of quasars at $z\sim6$. The newly discovered SMGs provide essential candidates for follow-up spectroscopic observations to test whether they reside in the same large-scale structures as the quasars and search for protoclusters at an early epoch.

astro-ph.GA↗

arXiv2025

Overmassive Black holes live in compact galaxies in the early Universe

A significant population of quasars have been found to exist within the first Gyr of cosmic time. Most of them have high black hole (BH) masses ($M_{\rm BH} \sim 10^{8-10} M_{\odot}$) with an elevated BH-to-stellar mass ratio compared to typical local galaxies, posing challenges to our understanding of the formation of supermassive BHs and their coevolution with host galaxies. Here, based on size measurements of [CII] 158$μ$m emission for a statistical sample of $z \sim 6$ quasars, we find that their host galaxies are systematically more compact (with half-light radius $R_{\rm e} \sim 1.6$ kpc) than typical star-forming galaxies at the same redshifts. Specifically, the sizes of the most compact quasar hosts, which also tend to contain less cold gas than their more extended counterparts, are comparable to that of massive quiescent galaxies at $z \sim 4-5$. These findings reveal an intimate connection between the formation of massive BHs and compactness of their host galaxies in the early universe. These compact quasar hosts are promising progenitors of the first population of quiescent galaxies.

astro-ph.GA↗

arXiv2025

Feature Statistics with Uncertainty Help Adversarial Robustness

Despite the remarkable success of deep neural networks (DNNs), the security threat of adversarial attacks poses a significant challenge to the reliability of DNNs. In this paper, both theoretically and empirically, we discover a universal phenomenon that has been neglected in previous works, i.e., adversarial attacks tend to shift the distributions of feature statistics. Motivated by this finding, and by leveraging the advantages of uncertainty-aware stochastic methods in building robust models efficiently, we propose an uncertainty-driven feature statistics adjustment module for robustness enhancement, named Feature Statistics with Uncertainty (FSU). It randomly resamples channel-wise feature means and standard deviations of examples from multivariate Gaussian distributions, which helps to reconstruct the perturbed examples and calibrate the shifted distributions. The calibration recovers some domain characteristics of the data for classification, thereby mitigating the influence of perturbations and weakening the ability of attacks to deceive models. The proposed FSU module has universal applicability in training, attacking, predicting, and fine-tuning, demonstrating impressive robustness enhancement ability at a trivial additional time cost. For example, by fine-tuning the well-established models with FSU, the state-of-the-art methods achieve up to 17.13% and 34.82% robustness improvement against powerful AA and CW attacks on benchmark datasets.

cs.LG↗

arXiv2025

Soliton resolution for the coupled complex short pulse equation

We address the long-time asymptotics of the solution to the Cauchy problem of ccSP (coupled complex short pulse) equation on the line for decaying initial data that can support solitons. The ccSP system describes ultra-short pulse propagation in optical fibers, which is a completely integrable system and posses a $4\times4$ matrix Wadati--Konno--Ichikawa type Lax pair. Based on the $\bar{\partial}$-generalization of the Deift--Zhou steepest descent method, we obtain the long-time asymptotic approximations of the solution in two kinds of space-time regions under a new scale $(ζ,t)$. The solution of the ccSP equation decays as a speed of $O(t^{-1})$ in the region $ζ/t>\varepsilon$ with any $\varepsilon>0$; while in the region $ζ/t<-\varepsilon$, the solution is depicted by the form of a multi-self-symmetric soliton/composite breather and $t^{-1/2}$ order term arises from self-symmetric soliton/composite breather-radiation interactions as well as an residual error order $O(t^{-1}\ln t)$.

nlin.SI↗

arXiv2025

Unify Graph Learning with Text: Unleashing LLM Potentials for Session Search

Session search involves a series of interactive queries and actions to fulfill user's complex information need. Current strategies typically prioritize sequential modeling for deep semantic understanding, overlooking the graph structure in interactions. While some approaches focus on capturing structural information, they use a generalized representation for documents, neglecting the word-level semantic modeling. In this paper, we propose Symbolic Graph Ranker (SGR), which aims to take advantage of both text-based and graph-based approaches by leveraging the power of recent Large Language Models (LLMs). Concretely, we first introduce a set of symbolic grammar rules to convert session graph into text. This allows integrating session history, interaction process, and task instruction seamlessly as inputs for the LLM. Moreover, given the natural discrepancy between LLMs pre-trained on textual corpora, and the symbolic language we produce using our graph-to-text grammar, our objective is to enhance LLMs' ability to capture graph structures within a textual format. To achieve this, we introduce a set of self-supervised symbolic learning tasks including link prediction, node content generation, and generative contrastive learning, to enable LLMs to capture the topological information from coarse-grained to fine-grained. Experiment results and comprehensive analysis on two benchmark datasets, AOL and Tiangong-ST, confirm the superiority of our approach. Our paradigm also offers a novel and effective methodology that bridges the gap between traditional search strategies and modern LLMs.

cs.CV↗

arXiv2025

ESTA: An Efficient Spatial-Temporal Range Aggregation Query Processing Algorithm for UAV Networks

Unmanned Aerial Vehicle (UAV) networks are increasingly deployed in military and civilian applications, serving as critical platforms for data collection. Users frequently require aggregated statistical information derived from historical sensory data within specific spatial and temporal boundaries. To address this, users submit aggregation query requests with spatial-temporal constraints to target UAVs that store the relevant data. These UAVs process and return the query results, which can be aggregated within the network during transmission to conserve energy and bandwidth-resources that are inherently limited in UAV networks. However,the dynamic topology caused by UAV mobility, coupled with these resource constraints, makes efficient in-network aggregation challenging without compromising user query delay. To the best of our knowledge, existing research has yet to adequately explore spatial-temporal range aggregation queries in the context of UAV networks. In this paper, we propose ESTA, an Efficient Spatial-Temporal range Aggregation query processing algorithm tailored for UAV networks. ESTA leverages pre-planned UAV trajectories to construct a topology change graph that models the network's evolving connectivity. It then employs an efficient shortest path algorithm to determine the minimum query response delay. Subsequently, while adhering to user-specified delay constraints, ESTA transforms the in-network aggregation process into a series of set cover problems, which are solved recursively to build a Spatial-Temporal Aggregation Tree (STAT). This tree enables the identification of an energy-efficient routing path for aggregating and delivering query results. Extensive simulations demonstrate that ESTA reduces energy consumption by more than 50% compared to a baseline algorithm, all while satisfying the required query delay.

cs.DC↗

arXiv2025

Kimina-Prover Preview: Towards Large Formal Reasoning Models with Reinforcement Learning

We introduce Kimina-Prover Preview, a large language model that pioneers a novel reasoning-driven exploration paradigm for formal theorem proving, as showcased in this preview release. Trained with a large-scale reinforcement learning pipeline from Qwen2.5-72B, Kimina-Prover demonstrates strong performance in Lean 4 proof generation by employing a structured reasoning pattern we term \textit{formal reasoning pattern}. This approach allows the model to emulate human problem-solving strategies in Lean, iteratively generating and refining proof steps. Kimina-Prover sets a new state-of-the-art on the miniF2F benchmark, reaching 80.7% with pass@8192. Beyond improved benchmark performance, our work yields several key insights: (1) Kimina-Prover exhibits high sample efficiency, delivering strong results even with minimal sampling (pass@1) and scaling effectively with computational budget, stemming from its unique reasoning pattern and RL training; (2) we demonstrate clear performance scaling with model size, a trend previously unobserved for neural theorem provers in formal mathematics; (3) the learned reasoning style, distinct from traditional search algorithms, shows potential to bridge the gap between formal verification and informal mathematical intuition. We open source distilled versions with 1.5B and 7B parameters of Kimina-Prover

cs.AI↗

arXiv2025

Finite-Momentum Pairing State in Unconventional Rashba Systems

In systems with unconventional Rashba bands, we propose that a finite-momentum pairing state can emerge without the need for an external magnetic field. We analyze the phase transition from a zero-momentum Bardeen-Cooper-Schrieffer (BCS) state to a finite-momentum pairing state using a microscopic interaction model. We demonstrate that the coexistence of zero-momentum and Larkin-Ovchinnikov (LO)-type pairings in different channels provides a well understanding of recent experimental observations of pair-density-wave (PDW) states. Furthermore, we propose that an s-wave BCS superconductor-unconventional Rashba metal (SC-URM) junction can generate an LO-type pairing state via an orbital-selective proximity effect. This nontrivial state can be detected by measuring the Josephson current in an SC-URM-SC junction or through Josephson scanning tunneling microscopy/spectrosscopy (JSTM/S). Our results reveal that the internal multi-orbital degrees of freedom play a crucial role in facilitating finite-momentum pairing states.

cond-mat.supr-con↗

arXiv2025

The SUPERCOLD-CGM survey: II. [\ion{C}{1}]$(1-0)$ emission and the physical conditions of cold gas in Enormous Ly$α$ nebulae at $z\,\sim\,2$

We report ALMA and ACA observations of atomic carbon ([\ion{C}{1}]$(1-0)$) and dust continuum in 10 Enormous Ly$α$ Nebulae hosting ultra-luminous Type-I QSOs at $z=2.2-2.5$, as part of the SUrvey of Protocluster ELANe Revealing CO/CI in the Ly$α$ Detected CGM (SUPERCOLD-CGM). We detect [\ion{C}{1}]$(1-0)$ and dust in all ten QSOs and five companion galaxies. We find that the QSOs and companions have higher gas densities and more intense radiation fields than Luminous Infrared galaxies and high-$z$ main sequence galaxies, with the highest values found in the QSOs. By comparing molecular gas masses derived from [\ion{C}{1}]$(1-0)$, CO(4$-$3) and dust continuum, we find that the QSOs and companions display a similar low CO conversion factor of $α_{\rm CO}$\,$\sim$\,0.8 $\rm M_{\sun}$${[\rm K\,km/s\,pc^2]}^{-1}$. After tapering our data to low resolution, the [\ion{C}{1}]$(1-0)$ flux increases for nine QSOs, hinting at the possibility of [\ion{C}{1}]$(1-0)$ in the circum-galactic medium (CGM) on a scale of 16$-$40 kpc. However, the [\ion{C}{1}]$(1-0)$ sensitivity is too low to confirm this for individual targets, except for a tentative (2.7$σ$) CGM detection in Q0050+0051{} with M$_{\rm H_2}$\,=\, ($1.0 - 2.8$)$\times 10^{10}$ $\rm M_{\sun}$. The 3$σ$ mass limits of molecular CGM for the remaining QSO fields are ($0.2-1.4$)\,$\times$\,10$^{10}$ $\rm M_{\sun}$. This translates into a baryon fraction of $<$0.4-3$\% $ in the molecular CGM relative to the total baryonic halo mass. Our sample also includes a radio-detected AGN, Q1416+2649{}, which shows [\ion{C}{1}]$(1-0)$ and CO(4$-$3) luminosities an order of magnitude fainter for its far-infrared luminosity than other QSOs in our sample, possibly due to a lower molecular gas mass.

astro-ph.GA↗

arXiv2025

Assessing the dark matter content of two quasar host galaxies at z~6 through gas kinematics

We conduct a study of the gas kinematics of two quasar host galaxies at $z\gtrsim6$ traced by the [CII] emission line using ALMA. By combining deep observations at both low and high resolution, we recover the diffuse emission, resolve its structure, and measure the rotation curves from the inner region of the galaxy to its outskirts using DysmalPy and 3DBarolo. Assuming that both galaxies exhibit disk rotation driven by the gravitational potential of the galaxy, we find that the best-fit disk models have a $V_{\rm rot}/σ\approx 2$ and inferred circular velocities out to $\sim$6-8 kpc scales, well beyond the likely stellar distribution. We then determine the mass profiles of each component (stars, gas, dark matter) with priors on the baryon and dark matter properties. We find relatively large dark matter fractions within their effective radii ($f_{\rm DM}(R<R_e)$ = $0.61_{-0.08}^{+0.08}$ and $0.53_{-0.23}^{+0.21}$, respectively), which are significantly larger than those extrapolated from lower redshift studies and remain robust under different input parameters verified by Monte-Carlo simulations. The large $f_{\rm DM}(R<R_e)$ corresponds to halo masses of $\sim 10^{12.5}-10^{12.8}\, M_\odot$, thus representative of the most massive halos at these redshifts. Notably, while the masses of these SMBHs are approximately 1 dex higher than the low-redshift relationship with stellar mass, the closer alignment of SMBH and halo masses with a local relationship may indicate that the early formation of these SMBHs is linked to their dark matter halos, providing insights into the co-evolution of galaxies and black holes in the early universe.

astro-ph.GA↗

arXiv2024

Sample path properties and small ball probabilities for stochastic fractional diffusion equations

We consider the following stochastic space-time fractional diffusion equation with vanishing initial condition:$$ \partial^β u(t, x)=- \left(-Δ\right)^{α/ 2} u(t, x)+ I_{0+}^γ\left[\dot{W}(t, x)\right],\quad t\in[0,T],\: x \in \mathbb{R}^d,$$ where $α>0$, $β\in(0,2)$, $γ\in[0,1)$, $\left(-Δ\right)^{α/2}$ is the fractional/power of Laplacian and $\dot{W}$ is a fractional space-time Gaussian noise. We prove the existence and uniqueness of the solution and then focus on various sample path regularity properties of the solution. More specifically, we establish the exact uniform and local moduli of continuity and Chung-type laws of the iterated logarithm. The small ball probability is also studied.

math.PR↗

arXiv2024

Constraining the excitation of molecular gas in Two Quasar-Starburst Systems at $z \sim 6$

We present NOrthern Extended Millimeter Array observations of CO(8-7), (9-8), and (10-9) lines, as well as the underlying continuum for two far-infrared luminous quasars: SDSS J2054-0005 at $\rm z=6.0389$ and SDSS J0129-0035 at $\rm z=5.7788$. Both quasars were previously detected in CO (2-1) and (6-5) transitions, making them candidates for studying the CO Spectral Line Energy Distribution (SLED) of quasars at $z \sim 6$. Utilizing the radiative transfer code CLOUDY, we fit the CO SLED with two heating mechanisms, including the photo-dissociation region (PDR) and X-ray-dominated region (XDR) for both objects. The CO SLEDs can be fitted by either a dense PDR component with an extremely strong far-ultraviolet radiation field (gas density $ n_{\rm H} \sim 10^6 \, \rm cm^{-3}$ and field strength $G_0 \gtrsim 10^6$) or a two-component model including a PDR and an XDR. However, the line ratios, including \tir and previous \cii and \ci measurements, argue against a very high PDR radiation field strength. Thus, the results prefer a PDR+XDR origin for the CO SLED. The excitation of the high-J CO lines in both objects is likely dominated by the central AGN. We then check the CO (9-8)-to-(6-5) line luminosity ratio $r_{96}$ for all $z \sim 6$ quasars with available CO SLEDs (seven in total) and find that there are no clear correlations between $r_{96}$ and both \fir and the AGN UV luminosities. This further demonstrates the complexity of the CO excitation powered by both the AGN and nuclear star formation in these young quasar host galaxies.

astro-ph.GA↗

arXiv2024

Similarity and Dissimilarity Guided Co-association Matrix Construction for Ensemble Clustering

Ensemble clustering aggregates multiple weak clusterings to achieve a more accurate and robust consensus result. The Co-Association matrix (CA matrix) based method is the mainstream ensemble clustering approach that constructs the similarity relationships between sample pairs according the weak clustering partitions to generate the final clustering result. However, the existing methods neglect that the quality of cluster is related to its size, i.e., a cluster with smaller size tends to higher accuracy. Moreover, they also do not consider the valuable dissimilarity information in the base clusterings which can reflect the varying importance of sample pairs that are completely disconnected. To this end, we propose the Similarity and Dissimilarity Guided Co-association matrix (SDGCA) to achieve ensemble clustering. First, we introduce normalized ensemble entropy to estimate the quality of each cluster, and construct a similarity matrix based on this estimation. Then, we employ the random walk to explore high-order proximity of base clusterings to construct a dissimilarity matrix. Finally, the adversarial relationship between the similarity matrix and the dissimilarity matrix is utilized to construct a promoted CA matrix for ensemble clustering. We compared our method with 13 state-of-the-art methods across 12 datasets, and the results demonstrated the superiority clustering ability and robustness of the proposed approach. The code is available at https://github.com/xuz2019/SDGCA.

cs.LG↗

arXiv2024

Semi-supervised Symmetric Non-negative Matrix Factorization with Low-Rank Tensor Representation

Semi-supervised symmetric non-negative matrix factorization (SNMF) utilizes the available supervisory information (usually in the form of pairwise constraints) to improve the clustering ability of SNMF. The previous methods introduce the pairwise constraints from the local perspective, i.e., they either directly refine the similarity matrix element-wisely or restrain the distance of the decomposed vectors in pairs according to the pairwise constraints, which overlook the global perspective, i.e., in the ideal case, the pairwise constraint matrix and the ideal similarity matrix possess the same low-rank structure. To this end, we first propose a novel semi-supervised SNMF model by seeking low-rank representation for the tensor synthesized by the pairwise constraint matrix and a similarity matrix obtained by the product of the embedding matrix and its transpose, which could strengthen those two matrices simultaneously from a global perspective. We then propose an enhanced SNMF model, making the embedding matrix tailored to the above tensor low-rank representation. We finally refine the similarity matrix by the strengthened pairwise constraints. We repeat the above steps to continuously boost the similarity matrix and pairwise constraint matrix, leading to a high-quality embedding matrix. Extensive experiments substantiate the superiority of our method. The code is available at https://github.com/JinaLeejnl/TSNMF.

cs.LG↗