arXiv subjects

Zhiyuan Li

Publications and source records attributed to Zhiyuan Li.

At least 127 records · Page 7Linked to original sources

arXiv2026

GPS-Synchronized Monitoring of Core-collapse Supernova Bursts with PandaX-4T via Coherent Elastic Neutrino Nuclear Scattering

The landmark detection of neutrinos from SN1987A marked the dawn of neutrino astrophysics. The neutrino burst provided essential insights into fundamental properties of neutrinos, and served as key probes of stellar evolution and supernova dynamics. The recent advancement in coherent elastic neutrino-nucleus scattering enables the detection of core-collapse supernova burst neutrinos using tonne-scale liquid xenon detectors originally designed for dark matter direct detection. Leveraging this capability, we developed and deployed an online supernova monitoring system for the PandaX-4T experiment. This system features a GPS module with millisecond-level timing precision, a low false-alarm rate, and high sensitivity to galactic core-collapse supernova explosion events. The methodology is robust, directly scalable, and planned for implementation in the next-generation PandaX-20T experiment.

hep-ex↗

arXiv2025

Search for Light Dark Matter with 259-day data in PandaX-4T

We present a search for light dark matter particles through their interactions with atomic electrons and nucleons, utilizing PandaX-4T data with an effective exposure of 1.04 tonne$\cdot$year for ionization-only data and 1.20 tonne$\cdot$year for paired data. Our analysis focuses on the energy range (efficiency$>$0.01) of approximately 0.33 to 3 keV for nuclear recoils, and from 0.04 to 0.39 keV for electronic recoils. We establish the most stringent constraints on spin-independent dark matter-nucleon interactions within a mass range of 2.5 to 5.0 GeV/$c^2$, spin-dependent neutron-only interactions within 2.0 to 5.3 GeV/$c^2$, and spin-dependent proton-only interactions within 2.0 to 3.8 GeV/$c^2$. Their corresponding limits at 3\,GeV/$c^2$ are $1.1 \times 10^{-43}$, $1.6 \times 10^{-38}$, and $5.6 \times 10^{-37}$\,cm$^2$, respectively. Additionally, our results improve the upper limits on the dark matter-electron scattering cross-section by a factor of 1.5 and 9.3 for heavy and light mediator scenarios respectively within 50 MeV/$c^2$ to 10 GeV/$c^2$, compared with previous best results.

hep-ex↗

arXiv2025

MCI: Multi-Channel Imager on the Chinese Space Station Survey Telescope

The Multi-Channel Imager (MCI) is a powerful near-ultraviolet (NUV) and visible imager onboard the Chinese Space Station Survey Telescope (CSST). The MCI provides three imaging channels, which are the NUV channel, the Blue channel and the Red channel, with the wavelength range of 255-430 nm, 430-700 nm, and 700-1000 nm, respectively. MCI's three channels can target the same field simultaneously, which is unique compared to other imagers onboard the Hubble Space Telescope (HST) or the James Webb Space Telescope (JWST). Each channel employs a CCD focal plane of 9216 x 9232 pixels and $\sim$7\arcmin.5 x 7\arcmin.5 field of view (FOV), which are about $\gtrsim 4$ times greater than the FOVs of HST imagers. The MCI's three channels feature unprecedented sensitivities and field of views complement the NUV and visible capabilities of the CSST for high-precision photometry and weak-signal detection, which would help build a new standard-star system and the deepest UV-Optical exposures for CSST. Rich filter sets of MCI would help explore other sciences such as local emission line mapping, high-z Ly$α$ emitters searching, etc. Here we present key design features, results of current ground tests, and suggested observing strategies of the MCI.

astro-ph.IM↗

arXiv2025

Hydrodynamic Evolution and Detectability of Nova Remnants in the Galactic Center

Thousands of X-ray sources have been detected in the Galactic center (GC), most believed to be cataclysmic variables (CVs). As a potential probe of the old stellar population, in particular CVs, the existence and detectability of novae in the GC remain elusive, due to the prohibitive extinction toward the GC and their relatively low occurrence rate. Nova remnants evolving in the characteristic hot ($T\sim{10^{6}~\rm K}$) and dense ($n_e\sim{10~\rm cm^{-3}}$) interstellar medium in the GC may shed light on recent novae and provide useful insight on the GC ecosystem. In this work, we perform hydrodynamical simulations of putative nova remnants in the GC environment and calculate their time-dependent multiwavelength emission to estimate the detectability. Among 79 models sampling the nova parameter space (primarily ejecta mass and velocity), 6, 44, and 51 modelled nova remnants are detectable at their X-ray, radio, and Paschen-$α$ maximum, respectively, for existing Chandra, VLA, and HST observations of the GC. The predicted peak luminosities are $\sim10^{32}~\rm erg~s^{-1}$, $\sim10^{31}~\rm erg~s^{-1}$, and $\sim10^{36}~\rm erg~s^{-1}$ in these three bands and the detectable window ranges from weeks to notably hundred years. By specifying a CV population of the nuclear star cluster, we estimate the probability of detecting at least one remnant to be 20%, 8%, and 18% in X-rays, radio, and Pa$α$. The nova remnant would be best resolved in the X-ray band. Our study highlights the potential for detecting nova remnants through further observations, leveraging JWST and the potentially forthcoming AXIS and SKA.

astro-ph.HE↗

arXiv2025

Probing Periodic and Aperiodic Variability of X-ray Sources in M31, M81 and Centaurus A with Chandra

Based on archival Chandra observations, we present a systematic timing survey of several hundred X-ray sources in M31, M81, and Centaurus A, mostly low-mass X-ray binaries (LXMBs), focusing on searching and characterizing aperiodic and periodic variability within single observation. We identify flares in 24 sources in M31, 5 in M81, and 26 in Cen~A; several display recurrent events. Flare durations span from tens of seconds to a few $10^{4}$ s, with peak luminosities of $10^{37}$-$10^{40}\ \mathrm{erg\ s^{-1}}$ and low flare duty cycles of $4.9\times10^{-6}$-$3.5\times10^{-2}$. Dipping events are found in 8 sources in M31, 1 in M81, and 5 in Cen A, including two repeaters. On multi-epoch baselines, the standard deviation of the source luminosity correlates linearly with the mean luminosity, with a coefficient of 0.49 (M31), 0.30 (M81), and 0.67 (Cen A), indicating galaxy-to-galaxy diversity. No statistically significant periodic signals are detected in M81 or Cen A, which, along with several periodic signals previously found among the M31 sources, can be understood considering a joint effect of our detection sensitivity and intrinsic distributions of the orbital period and X-ray luminosity of LMXBs. The ensemble of short-duty-cycle flares, a mix of recurrent and isolated dips, and galaxy-dependent rms--flux factor, supports a picture in which stochastic accretion-rate fluctuations modulate luminosity on $\sim$10-$10^{4}$ s. Conducted at known distances and across distinct host environments, this extragalactic survey provides uniform flare/dip samples and rms-flux scalings for bulge-dominated fields, offering empirical constraints for accretion physics and illustrating the promise of timing analyses in external galaxies using the Chandra archive.

astro-ph.HE↗

arXiv2025

PersonaLive! Expressive Portrait Image Animation for Live Streaming

Current diffusion-based portrait animation models predominantly focus on enhancing visual quality and expression realism, while overlooking generation latency and real-time performance, which restricts their application range in the live streaming scenario. We propose PersonaLive, a novel diffusion-based framework towards streaming real-time portrait animation with multi-stage training recipes. Specifically, we first adopt hybrid implicit signals, namely implicit facial representations and 3D implicit keypoints, to achieve expressive image-level motion control. Then, a fewer-step appearance distillation strategy is proposed to eliminate appearance redundancy in the denoising process, greatly improving inference efficiency. Finally, we introduce an autoregressive micro-chunk streaming generation paradigm equipped with a sliding training strategy and a historical keyframe mechanism to enable low-latency and stable long-term video generation. Extensive experiments demonstrate that PersonaLive achieves state-of-the-art performance with up to 7-22x speedup over prior diffusion-based portrait animation models.

cs.CV↗

arXiv2025

The first extragalactic ultra-compact X-ray binary : a candidate black hole-white dwarf system

M31 UCXB-1 is one of the brightest X-ray point sources in the bulge of M31, with a peak X-ray luminosity $ L_{\mathrm{0.5-10 \: keV}} = 2.9^{+0.2}_{-0.2} \times 10^{38} \: \mathrm{erg} \: \mathrm{s}^{-1} $. Both XMM-Newton and Chandra observations have detected an eclipsing signal with a period of about 465 seconds from this source, and we note that the periodic signal is detected exclusively during the source's high-luminosity states. This signal probably originates from its orbital motion, therefore it is an ultra-compact X-ray binary (UCXB) candidate with the highest X-ray luminosity. Our theoretical analyses show that M31 UCXB-1 is in good agreement with the luminosity-orbital period relation ($ L_{\mathrm{2-10 \: keV}}-P_{\mathrm{orb}} $) of the black hole/neutron star--white dwarf (BH/NS--WD) UCXB system. Moreover, our spectral analyses indicate that the primary in M31 UCXB-1 is more likely to be a BH rather than an NS. The results show that M31 UCXB-1 is a BH--WD system, with the shortest orbital period, the possibly strongest gravitational wave emission, and the most massive white dwarf among the known UCXBs.

astro-ph.HE↗

arXiv2025

Tomographic beta-gamma spectroscopy of nuclear beta decay

Nuclear $β$ decay, a sensitive probe of nuclear structure and weak interactions, has become a precision test bed for physics beyond the Standard Model, driven by recent advances in spectrometric techniques. Here we introduce tomographic $β$-$γ$ spectroscopy (TBGS) of nuclear $β$ decay, a method that detects the energies of $β$, $γ$, and internal conversion electrons while simultaneously reconstructing the energy deposition vertices. Using the PandaX-4T detector operated as a TBGS, we obtain a precise and unbiased decay scheme of $^{214}$Pb, a key background isotope in searches for dark matter and Majorana neutrinos. For the first time, transitions of $^{214}$Pb to both the ground and excited states of $^{214}$Bi are measured concurrently, revealing discrepancies in branching ratios of up to 4.7$σ$ relative to previous evaluations. Combined with state-of-the-art theoretical spectral-shape calculations, these results establish a new benchmark for background modelling in rare-event searches and highlight the potential of TBGS as a versatile tool for fundamental physics and nuclear applications.

nucl-ex↗

arXiv2025

Provable Benefits of Sinusoidal Activation for Modular Addition

This paper studies the role of activation functions in learning modular addition with two-layer neural networks. We first establish a sharp expressivity gap: sine MLPs admit width-$2$ exact realizations for any fixed length $m$ and, with bias, width-$2$ exact realizations uniformly over all lengths. In contrast, the width of ReLU networks must scale linearly with $m$ to interpolate, and they cannot simultaneously fit two lengths with different residues modulo $p$. We then provide a novel Natarajan-dimension generalization bound for sine networks, yielding nearly optimal sample complexity $\widetilde{\mathcal{O}}(p)$ for ERM over constant-width sine networks. We also derive width-independent, margin-based generalization for sine networks in the overparametrized regime and validate it. Empirically, sine networks generalize consistently better than ReLU networks across regimes and exhibit strong length extrapolation.

cs.LG↗

arXiv2025

Improving Robotic Manipulation Robustness via NICE Scene Surgery

Learning robust visuomotor policies for robotic manipulation remains a challenge in real-world settings, where visual distractors can significantly degrade performance and safety. In this work, we propose an effective and scalable framework, Naturalistic Inpainting for Context Enhancement (NICE). Our method minimizes out-of-distribution (OOD) gap in imitation learning by increasing visual diversity through construction of new experiences using existing demonstrations. By utilizing image generative frameworks and large language models, NICE performs three editing operations, object replacement, restyling, and removal of distracting (non-target) objects. These changes preserve spatial relationships without obstructing target objects and maintain action-label consistency. Unlike previous approaches, NICE requires no additional robot data collection, simulator access, or custom model training, making it readily applicable to existing robotic datasets. Using real-world scenes, we showcase the capability of our framework in producing photo-realistic scene enhancement. For downstream tasks, we use NICE data to finetune a vision-language model (VLM) for spatial affordance prediction and a vision-language-action (VLA) policy for object manipulation. Our evaluations show that NICE successfully minimizes OOD gaps, resulting in over 20% improvement in accuracy for affordance prediction in highly cluttered scenes. For manipulation tasks, success rate increases on average by 11% when testing in environments populated with distractors in different quantities. Furthermore, we show that our method improves visual robustness, lowering target confusion by 6%, and enhances safety by reducing collision rate by 7%.

cs.RO↗

arXiv2025

Distracted Robot: How Visual Clutter Undermine Robotic Manipulation

In this work, we propose an evaluation protocol for examining the performance of robotic manipulation policies in cluttered scenes. Contrary to prior works, we approach evaluation from a psychophysical perspective, therefore we use a unified measure of clutter that accounts for environmental factors as well as the distractors quantity, characteristics, and arrangement. Using this measure, we systematically construct evaluation scenarios in both hyper-realistic simulation and real-world and conduct extensive experimentation on manipulation policies, in particular vision-language-action (VLA) models. Our experiments highlight the significant impact of scene clutter, lowering the performance of the policies, by as much as 34% and show that despite achieving similar average performance across the tasks, different VLA policies have unique vulnerabilities and a relatively low agreement on success scenarios. We further show that our clutter measure is an effective indicator of performance degradation and analyze the impact of distractors in terms of their quantity and occluding influence. At the end, we show that finetuning on enhanced data, although effective, does not equally remedy all negative impacts of clutter on performance.

cs.RO↗

arXiv2025

On-chip electro-optically tunable narrow linewidth Brillouin microlasers implemented in thin film lithium niobate

On-chip narrow linewidth microlasers with real-time wavelength tunability are highly desirable for various applications including precision metrology, quantum technology, and coherent information processing. Realizing such laser remains a challenge despite significant advances made by various groups in recent years [Nat. Commun. 13, 5344 (2022); Nature 615, 411 (2023); Appl. Phys. Lett. 124, 131101 (2024); Nat. Photonics 13, 60 (2019)]. In this work, we overcome these hurdles and demonstrate on-chip electro-optically tunable Brillouin microlasers in compact lithium niobate on insulator (LNOI) microdisks with diameters of 31.5 um and 117.0 um by using cross-polarized SBS arrangement. A quasi-continuum band of bound shear mechanical modes inside the suspended microdisk are revealed for the first time, allowing feasible phase matching of stimulated Brillouin lasing (SBL). We achieve efficient cross-polarized optomechanical coupling and SBL via the significant photoelastic tensors of lithium niobate (e.g., p41=-1.51). This approach yields a 118 Hz intrinsic linewidth and a comparatively low threshold power of 3.15 mW. A real-time electro-optic tuning of the cross-polarized Brillouin scheme with a tuning efficiency of ~93.1 kHz/V is also achieved, further showcasing potential of LNOI platform for next-generation tunable photonic systems.

physics.optics↗

arXiv2025

A Tale of Two Geometries: Adaptive Optimizers and Non-Euclidean Descent

Adaptive optimizers can reduce to normalized steepest descent (NSD) when only adapting to the current gradient, suggesting a close connection between the two algorithmic families. A key distinction between their analyses, however, lies in the geometries, e.g., smoothness notions, they rely on. In the convex setting, adaptive optimizers are governed by a stronger adaptive smoothness condition, while NSD relies on the standard notion of smoothness. We extend the theory of adaptive smoothness to the nonconvex setting and show that it precisely characterizes the convergence of adaptive optimizers. Moreover, we establish that adaptive smoothness enables acceleration of adaptive optimizers with Nesterov momentum in the convex setting, a guarantee unattainable under standard smoothness for certain non-Euclidean geometry. We further develop an analogous comparison for stochastic optimization by introducing adaptive gradient variance, which parallels adaptive smoothness and leads to dimension-free convergence guarantees that cannot be achieved under standard gradient variance for certain non-Euclidean geometry.

cs.LG↗

arXiv2025

MuISQA: Multi-Intent Retrieval-Augmented Generation for Scientific Question Answering

Complex scientific questions often entail multiple intents, such as identifying gene mutations and linking them to related diseases. These tasks require evidence from diverse sources and multi-hop reasoning, while conventional retrieval-augmented generation (RAG) systems are usually single-intent oriented, leading to incomplete evidence coverage. To assess this limitation, we introduce the Multi-Intent Scientific Question Answering (MuISQA) benchmark, which is designed to evaluate RAG systems on heterogeneous evidence coverage across sub-questions. In addition, we propose an intent-aware retrieval framework that leverages large language models (LLMs) to hypothesize potential answers, decompose them into intent-specific queries, and retrieve supporting passages for each underlying intent. The retrieved fragments are then aggregated and re-ranked via Reciprocal Rank Fusion (RRF) to balance coverage across diverse intents while reducing redundancy. Experiments on both MuISQA benchmark and other general RAG datasets demonstrate that our method consistently outperforms conventional approaches, particularly in retrieval accuracy and evidence coverage.

cs.AI↗

arXiv2025

On Powerful Ways to Generate: Autoregression, Diffusion, and Beyond

Diffusion language models have recently emerged as a competitive alternative to autoregressive language models. Beyond next-token generation, they are more efficient and flexible by enabling parallel and any-order token generation. However, despite empirical successes, their computational power and fundamental limitations remain poorly understood. In this paper, we formally study whether non-autoregressive generation in Masked Diffusion Models (MDM) enables solving problems beyond the reach of Auto-Regressive Models (ARM). Our results show that MDM with sufficiently large context length is computationally universal with decoding steps matching the optimal parallel time complexity in PRAM. However, when controlling for other factors, MDM's flexibility to generate in any-order does not expand what ARM can already solve. To address this, we propose a new form of generation called any-process generation, which extends MDM with capabilities to remask, insert and delete tokens, allowing self-correction, length-variable editing, and adaptive parallelism. Theoretically and empirically, we demonstrate these capabilities enable scalability to significantly harder reasoning problems that are otherwise intractable for ARM and vanilla MDM. Additionally, they prove essential for generation tasks where objects naturally evolve through non-sequential processes, crucial for extending current LLMs beyond natural language to domains such as coding and science.

cs.LG↗

arXiv2025

Weak-to-Strong Generalization Even in Random Feature Networks, Provably

Weak-to-Strong Generalization (Burns et al., 2024) is the phenomenon whereby a strong student, say GPT-4, learns a task from a weak teacher, say GPT-2, and ends up significantly outperforming the teacher. We show that this phenomenon does not require a strong learner like GPT-4. We consider student and teacher that are random feature models, described by two-layer networks with a random and fixed bottom layer and a trained top layer. A "weak" teacher, with a small number of units (i.e. random features), is trained on the population, and a "strong" student, with a much larger number of units (i.e. random features), is trained only on labels generated by the weak teacher. We demonstrate, prove, and understand how the student can outperform the teacher, even though trained only on data labeled by the teacher. We also explain how such weak-to-strong generalization is enabled by early stopping. Importantly, we also show the quantitative limits of weak-to-strong generalization in this model.

cs.LG↗

arXiv2025

Optimal Control For Anti-Abeta Treatment in Alzheimer's Disease using a Reaction-Diffusion Model

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that severely impairs survival and quality of life. While anti-amyloid beta (Abeta) therapies can slow disease progression, their efficacy depends on personalized dosing that maximizes benefits and minimizes risks such as amyloid related imaging abnormalities (ARIA). Mathematical modeling offers a powerful tool for understanding AD dynamics and optimizing treatment, yet most models focus solely on temporal behavior, overlooking spatial heterogeneity within the brain. In this study, we propose a spatially explicit reaction-diffusion model to describe Abeta plaque dynamics. We formulate an optimal control problem to minimize plaque concentration while balancing therapeutic efficacy and treatment risk. Under reasonable assumptions, we establish well-posedness and uniqueness of the optimal solution. A Finite Element Method (FEM) based numerical framework is developed to compute personalized treatment strategies. Our model is calibrated using longitudinal Abeta positron emission tomography (PET) data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), enabling estimation of patient-specific parameters such as growth rate and effective diffusivity. Results show that optimized treatment strategies consistently outperform constant dosing regimens across patient groups, achieving substantial reductions in cumulative amyloid burden while minimizing side effects. This integrated, data-driven framework advances personalized, spatially informed therapeutic optimization for Alzheimer's disease.

math.OC↗

arXiv2025

Shift is Good: Mismatched Data Mixing Improves Test Performance

We consider training and testing on mixture distributions with different training and test proportions. We show that in many settings, and in some sense generically, distribution shift can be beneficial, and test performance can improve due to mismatched training proportions, even if the components are unrelated and with no transfer between components. In a variety of scenarios, we identify the optimal training proportions and the extent to which such distribution shift can be beneficial. We show how the same analysis applies also to a compositional setting with differing distribution of component "skills'' at training and test.

cs.LG↗