arXiv subjects
Publications and source records attributed to Zhixing Li.
Let $D_n$ denote the supremum of the orthogonality defects over all $n$-dimensional Hermite--Korkine--Zolotarev (HKZ)-reduced bases. This paper shows that $\lim_{n\to\infty} \log D_n/(n\log n)=2$. We also determine the exact value $D_4=4375/1024$.
We analyze KMT-2025-BLG-2093, with angular Einstein radius $\theta_{\rm E}=13.1\pm 2.8\,\mu{\rm as}$, which makes it the second isolated microlens that lies in the ``Einstein Desert'' ($9\,\mu{\rm as}<\theta_{\rm E}<25\,\mu{\rm as}$) between free-floating planets (FFPs) on one side and brown dwarfs and stars on the other. We discuss how its characteristics may give clues to future exploration of FFPs, especially in the era of satellite missions that have a major FFP focus, including Earth 2.0 and Roman.
Current VLM evaluations often conflate language priors with genuine spatial reasoning. To address this, we introduce CRISP, a novel structural-diagnostic evaluation paradigm that assesses visual spatial intelligence through consistency, the alignment between implicit perception and explicit reasoning. Unlike traditional black-box QA, CRISP utilizes metric 3D Scene Graphs and an oracle intervention protocol to decouple latent reasoning capabilities from perceptual bottlenecks. This granular diagnosis uncovers a systematic perception-reasoning disconnect. Crucially, we reveal that while proprietary models possess robust latent reasoning engines, they suffer from inaccurate metric estimation and a critical failure to leverage their implicit structural representations. Conversely, open-source models remain fundamentally bottlenecked by their lack of multi-hop compositional reasoning. By shifting the focus from merely ``guessing correctly'' via language priors to genuinely ``perceiving, verifying, and reasoning,'' CRISP offers a rigorous roadmap for multimodal alignment beyond end-to-end post-training. The code and dataset are available at https://github.com/iiyamayuki/CRISP-Bench.
We discuss the progress towards using the SKA-Mid for interferometric neutral hydrogen (HI) intensity mapping surveys. By mapping the distribution of cosmic HI distribution through the 21cm line, SKA-Mid will be able to measure the HI power spectrum at small angular separations in interferometric mode. We review the measurements made from the precursor MeerKAT telescope, using the MeerKAT DEEP2 as well as the MIGHTEE survey data, yielding tentative detection as well as upper limits on HI clustering. The methodology for MeerKAT can be naturally extended to SKA-Mid. Forecasts suggest that SKA-Mid AA4 will be able to measure the HI power spectrum with high statistical significance across a wide range of redshifts from $z\sim1.0$ to $z\sim 3.0$, around nonlinear scales $k\sim 1.0\,{\rm Mpc}^{-1}$. The precise measurements can be used to constrain the properties of HI galaxies, providing a novel window into probing galaxy evolution at $1.0\lesssim z \lesssim 3.0$.
The DECam Rogue Earths and Mars Survey (DREAMS), a NOIRLab survey program, has been conducting a three-year survey covering a 5 deg$^2$ area in the Galactic bulge (roughly spanning $-1.2^\circ \lesssim \ell \lesssim +2.1^\circ$ and $-2.8^\circ \lesssim b \lesssim -0.6^\circ$) since 2025 June. Its primary science goal is to detect low-mass free-floating planets through microlensing, while its minute-level cadence ($20-40\,\mathrm{hr}^{-1}$ in $z$ band and $4-8\,\mathrm{hr}^{-1}$ in $r$ band) also enables the detection and characterization of rapid phenomena on timescales of minutes to hours such as stellar flares and pulsating stars. The survey reaches a single-exposure depth of $z_{\rm AB}\sim 22$ mag, about two magnitudes deeper than previous bulge time-domain surveys. We present the data reduction and calibration of the DREAMS observations obtained in 2025 and introduce the first DREAMS data release (DR1). DR1 includes 1,856 $z$-band observations and 325 $r$-band observations for 59,372,789 stars. The DREAMS DR1 catalog contains about twice as many stars as previous catalog covering the same 5 deg$^2$ area. We present DREAMS light curves for a known blue large-amplitude pulsator (BLAP) and a known low-amplitude transiting system to demonstrate the survey's capabilities. We also perform a pilot search for short-duration variables over about 0.4% of the DR1 sample, identifying one new short microlensing event, two stellar flares, and 24 new short variables. This suggests that DREAMS DR1 may contain hundreds of stellar flares and thousands of previously unknown short variables.
Electrocardiography (ECG) is central to cardiovascular care, but conventional AI models are often restricted to common arrhythmias and may generalize poorly across populations or clinically subtle diseases. We developed ECG Contrastive Language-Image Pre-training (ECGCLIP), a signal-language contrastive learning framework that aligns ECG waveforms with expert diagnostic reports. ECGCLIP was pre-trained on 2,837,962 ECG studies from 1,324,856 patients and evaluated on a held-out internal test set plus nine independent external cohorts comprising about 1.5 million ECGs. Evaluation covered 89 downstream tasks, including 45 ECG diagnoses, 39 echocardiographic targets, and 5 rare cardiac diseases, using PRAUC as the primary metric. ECGCLIP consistently improved performance over random initialization and Merl-R18 baselines. On the internal test set, ECGCLIP-R34 achieved strong performance for atrial fibrillation (PRAUC 0.900) and ST-segment elevation myocardial infarction (PRAUC 0.383), with robust generalization across all external cohorts. It also improved low-prevalence and diagnostically elusive diseases, including Ebstein anomaly, constrictive pericarditis, dextrocardia, and cardiac amyloidosis, with internal PRAUC values of 0.253, 0.175, 0.121, and 0.201, respectively. ECGCLIP was data efficient, matching or exceeding full-dataset baseline performance with only 10% of training data. Feature visualization and saliency analysis suggested clinically meaningful representations aligned with established electrocardiographic criteria. These findings indicate that large-scale ECG-report contrastive pre-training can expand routine ECG interpretation beyond common arrhythmias toward broad cardiovascular assessment and opportunistic screening of echocardiographic and rare conditions.
To complete the analysis of the 2023 KMTNet subprime-field microlensing planetary events identified by its AlertFinder system, we present the analysis of six events, KMT-2023-BLG-(1810, 0084, 1118, 0584, 1697, 2218). We find that the first three events are securely confirmed as planetary, with inferred mass ratios of $\log q \sim -1.9$, $-2.0$, and $-2.6$, respectively. The remaining three events exhibit the well-known degeneracy between binary-lens/single-source (2L1S) and single-lens/binary-source (1L2S) models, and two of these also admit viable stellar binary solutions. A Bayesian analysis indicates that the companions in the confirmed planetary events are likely either super-Jupiters orbiting beyond the snow line of M- or K-dwarf hosts or, for two degenerate solutions of KMT-2023-BLG-1118, Saturn-mass planets orbiting late-type M dwarfs. To date, the 2023 KMTNet sample contains 25 unambiguous planetary events, and its mass-ratio distribution is consistent with that of the KMTNet planetary sample from 2016--2019.
To expand the homogeneous microlensing planetary sample of the Korea Microlensing Telescope Network (KMTNet), we investigate six planetary candidates identified by the AnomalyFinder search in the 2023 prime-field data, namely KMT-2023-BLG-1592, OGLE-2023-BLG-0766, KMT-2023-BLG-0332, KMT-2023-BLG-0486, KMT-2023-BLG-0792, and OGLE-2023-BLG-1043. Light-curve modeling indicates that the first two events have planetary mass ratios of $\log q \sim -3.0$ and $-2.6$, while the third exhibits a brown dwarf mass ratio of $\log q \sim -1.4$. The remaining three events show the well-known degeneracy between the binary-lens single-source (2L1S) and single-lens binary-source (1L2S) interpretations. A Bayesian analysis yields companion masses of about 0.6 and 1.2 Jupiter masses for the two planetary systems, likely orbiting beyond the snow lines of M- or K-dwarf hosts. A review of the KMTNet planetary sample shows that candidates discovered by AnomalyFinder are significantly more likely to exhibit the 2L1S/1L2S degeneracy, consistent with the tendency of AnomalyFinder to detect subtler planetary signals.
We present observations and analysis of two low planet/host mass-ratio ($q$) microlensing planets discovered in high-magnification events. KMT-2025-BLG-0811Lb has $q \sim 4.5 \times 10^{-5}$, and a Bayesian analysis favors a super-Earth/mini-Neptune orbiting an M- or K-dwarf host at a projected separation of $\sim 3$ au. KMT-2025-BLG-0912Lb has $q = 2.6 \times 10^{-4}$ and likely hosts a super-Earth/mini-Neptune around either a low-mass M dwarf or a brown dwarf at $\sim 1$ au. Even with an observing cadence of $\Gamma > 30~{\rm hr}^{-1}$ during the planetary signal, KMT-2025-BLG-0811 still exhibits the "central-resonant" degeneracy. Reviewing nine such events, we find that the "central-resonant" degeneracy can be divided into two distinct types that occupy separate regions in the plane of $q$ and normalized source radius ($\rho$). Type~I events have similar $q$ but substantially different $\rho$ and are more difficult to resolve from the light curves. For Type~II events, the "resonant" solutions have relatively lower $q$ and larger $\rho$. Our review provides guidance for searching for the alternative solution once one solution has been identified.
We present observations and analysis of the bound planetary microlensing event KMT-2025-BLG-1616. The planetary signal was captured by the Korea Microlensing Telescope Network (KMTNet) and the DECam Rogue Earths and Mars Survey (DREAMS). DREAMS's minute-cadence observations break the central/resonant degeneracy in the binary-lens models. The color of the faint source star ($I=22$) is measured from the DREAMS's $r - z$ color. The planetary system has a planet-host mass ratio of $q \sim 5 \times 10^{-4}$. A Bayesian analysis yields a host-star mass of $\sim 0.3\,M_\odot$, a planetary mass of $\sim 40\,M_{\oplus}$, a projected planet-host separation of $\sim 1.6~\mathrm{au}$, and a lens distance of $\sim 7.5~\mathrm{kpc}$. Based on the photometric precision achieved by DREAMS for this event, we simulate free-floating planet (FFP) detections and find that DREAMS is sensitive to Mars-mass FFPs in the Galactic bulge and Moon-mass FFPs in the Galactic disk.
The objective of domain generalization (DG) is to enable models to be robust against domain shift. DG is crucial for deploying vision-language models (VLMs) in real-world applications, yet most existing methods rely on domain labels that may not be available and often ambiguous. We instead study the DG setting where models must generalize well without access to explicit domain labels. Our key idea is to represent an unseen target domain as a combination of latent domains automatically discovered from training data, enabling the model to adaptively transfer knowledge across domains. To realize this, we perform latent domain clustering on image features and fuse domain-specific text features based on the similarity between the input image and each latent domain. Experiments on four benchmarks show that this strategy yields consistent gains over VLM-based baselines and provides new insights into improving robustness under domain shift.
The growing demand for automated writing assistance in diverse academic domains highlights the need for robust Chinese Grammatical Error Correction (CGEC) systems that can adapt across disciplines. However, existing CGEC research largely lacks dedicated benchmarks for multi-disciplinary academic writing, overlooking continual learning (CL) as a promising solution to handle domain-specific linguistic variation and prevent catastrophic forgetting. To fill this crucial gap, we introduce CL$^2$GEC, the first Continual Learning benchmark for Chinese Literature Grammatical Error Correction, designed to evaluate adaptive CGEC across multiple academic fields. Our benchmark includes 10,000 human-annotated sentences spanning 10 disciplines, each exhibiting distinct linguistic styles and error patterns. CL$^2$GEC focuses on evaluating grammatical error correction in a continual learning setting, simulating sequential exposure to diverse academic disciplines to reflect real-world editorial dynamics. We evaluate large language models under sequential tuning, parameter-efficient adaptation, and four representative CL algorithms, using both standard GEC metrics and continual learning metrics adapted to task-level variation. Experimental results reveal that regularization-based methods mitigate forgetting more effectively than replay-based or naive sequential approaches. Our benchmark provides a rigorous foundation for future research in adaptive grammatical error correction across diverse academic domains.
In closed-loop distributed multi-sensor integrated sensing and communication (ISAC) systems, performance often hinges on transmitting high-dimensional sensor observations over rate-limited networks. In this paper, we first present a general framework for rate-limited closed-loop distributed ISAC systems, and then propose an autoencoder-based observation compression method to overcome the constraints imposed by limited transmission capacity. Building on this framework, we conduct a case study using a closed-loop linear quadratic regulator (LQR) system to analyze how the interplay among observation, compression, and state dimensions affects reconstruction accuracy, state estimation error, and control performance. In multi-sensor scenarios, our results further show that optimal resource allocation initially prioritizes low-noise sensors until the compression becomes lossless, after which resources are reallocated to high-noise sensors.
We present a simulation-based framework to forecast the HI power spectrum on non-linear scales ($k\gtrsim 1\ {\rm Mpc^{-1}}$), as measured by interferometer arrays like MeerKAT in the low-redshift ($z\leq 1.0$) universe. Building on a galaxy-based HI mock catalog, we meticulously consider various factors, including the emission line profiles of HI discs and some observational settings, and explore their impacts on the HI power spectrum. While it is relatively insensitive to the profile shape of HI emission line at these scales, we identify a strong correlation with the profile width, that is, the Full Width at Half Maxima (FWHM, also known as $W_{\rm 50}$ in observations) in this work. By modeling the width function of $W_{50}$ as a function of $v_{\rm max}$, we assign each HI source a emission line profile and find that the resulting HI power spectrum is comparatively close to results from particles in the IllustrisTNG hydrodynamical simulation. After implementing $k$-space cuts matching the MeerKAT data, our prediction replicates the trend of the measurements obtained by MeerKAT at $z\approx 0.44$, though with a significantly lower amplitude. Utilizing a Monte Carlo Markov Chain sampling method, we constrain the parameter $A_{W_{\rm 50}}$ in the $W_{\rm 50}$ models and $Ω_{\rm HI}$ with the MeerKAT measurements and find that a strong degeneracy exists between these two parameters.
This study aims to innovatively explore adaptive applications of large language models (LLM) in urban renewal. It also aims to improve its performance and text generation quality for knowledge question-answering (QA) tasks. Based on the ChatGLM, we automatically generate QA datasets using urban renewal scientific literature corpora in a self-instruct manner and then conduct joint fine-tuning training on the model using the Prefix and LoRA fine-tuning methods to create an LLM for urban renewal. By guiding the LLM to automatically generate QA data based on prompt words and given text, it is possible to quickly obtain datasets in the urban renewal field and provide data support for the fine-tuning training of LLMs. The experimental results show that the joint fine-tuning training method proposed in this study can significantly improve the performance of LLM on the QA tasks. Compared with LoRA fine-tuning, the method improves the Bleu and Rouge metrics on the test by about 5%; compared with the model before fine-tuning, the method improves the Bleu and Rouge metrics by about 15%-20%. This study demonstrates the effectiveness and superiority of the joint fine-tuning method using Prefix and LoRA for ChatGLM in the urban renewal knowledge QA tasks. It provides a new approach for fine-tuning LLMs on urban renewal-related tasks.
While large pre-trained language models (PLM) have shown their great skills at solving discriminative tasks, a significant gap remains when compared with humans for explanation-related tasks. Among them, explaining the reason why a statement is wrong (e.g., against commonsense) is incredibly challenging. The major difficulty is finding the conflict point, where the statement contradicts our real world. This paper proposes Neon, a two-phrase, unsupervised explanation generation framework. Neon first generates corrected instantiations of the statement (phase I), then uses them to prompt large PLMs to find the conflict point and complete the explanation (phase II). We conduct extensive experiments on two standard explanation benchmarks, i.e., ComVE and e-SNLI. According to both automatic and human evaluations, Neon outperforms baselines, even for those with human-annotated instantiations. In addition to explaining a negative prediction, we further demonstrate that Neon remains effective when generalizing to different scenarios.
5G positioning is a very promising area that presents many opportunities and challenges. Many existing techniques rely on multiple anchor nodes and line-of-sight (LOS) paths, or single reference node and single-bounce non-LOS (NLOS) paths. However, in dense multipath environments, identifying the LOS or single-bounce assumptions is challenging. The multi-bounce paths will make the positioning accuracy deteriorate significantly. We propose a robust 5G positioning algorithm in NLOS multipath environments. The corresponding positioning problem is formulated as an iterative and weighted least squares problem, and different weights are utilized to mitigate the effects of multi-bounce paths. Numerical simulations are carried out to evaluate the performance of the proposed algorithm. Compared with the benchmark positioning algorithms only using the single-bounce paths, similar positioning accuracy is achieved for the proposed algorithm.
Atomic hydrogen (H I) gas, mostly residing in dark matter halos after cosmic reionization, is the fuel for star formation. Its relation with properties of host halo is the key to understand the cosmic H I distribution. In this work, we propose a flexible, empirical model of H I-halo relation. In this model, while the H I mass depends primarily on the mass of host halo, there is also secondary dependence on other halo properties. We apply our model to the observation data of the Arecibo Fast Legacy ALFA Survey (ALFALFA), and find it can successfully fit to the cosmic H I abundance ($Ω_{\rm HI}$), average H I-halo mass relation $\langle M_{\rm HI}|M_{\rm h}\rangle$, and the H I clustering. The bestfit of the ALFALFA data rejects with high confidence level the model with no secondary halo dependence of H I mass and the model with secondary dependence on halo spin parameter ($λ$), and shows strong dependence on halo formation time ($a_{1/2}$) and halo concentration ($c_{\rm vir}$). In attempt to explain these findings from the perspective of hydrodynamical simulations, the IllustrisTNG simulation confirms the dependence of H I mass on secondary halo parameters. However, the IllustrisTNG results show strong dependence on $λ$ and weak dependence on $c_{\rm vir}$ and $a_{1/2}$, and also predict a much larger value of H I clustering on large scales than observations. This discrepancy between the simulation and observation calls for improvements in understanding the H I-halo relation from both theoretical and observational sides.