arXiv subjects

Yun Wang

Publications and source records attributed to Yun Wang.

At least 73 records · Page 4Linked to original sources

arXiv2026

A neutrino flare candidate potentially associated with X-ray emission from tidal disruption event ATLAS17jrp

Tidal disruption events (TDEs), in which stars are disrupted by supermassive black holes, have been proposed as potential sources of high-energy neutrinos through hadronic interactions. X-ray-bright TDEs provide dense photon fields conducive to neutrino production via proton-photon ($pγ$) processes. We conducted a time-dependent unbinned likelihood analysis of ten years (2008-2018) of IceCube muon-track data, focusing on ten TDEs with confirmed X-ray detections during this period. We report a neutrino flare candidate spatially and temporally coincident with the TDE ATLAS17jrp, occurring 19 days after the onset of its X-ray activity and lasting for 56 days, with a post-trial $p$-value of 0.01. This significance is modest, representing a hint of an association. We illustrate the neutrino emission using a simple lepton-hadronic model, where X-ray photons serve as target fields. While this model can account for the neutrino data around 100 TeV, the low-energy neutrinos may imply contributions from an additional component. Although constrained by the sample size of X-ray-detected TDEs, these results underscore the need for high-cadence X-ray monitoring and future neutrino observatories to further explore the connection between TDEs and high-energy neutrinos.

astro-ph.HE↗

arXiv2026

StyMam: A Mamba-Based Generator for Artistic Style Transfer

Image style transfer aims to integrate the visual patterns of a specific artistic style into a content image while preserving its content structure. Existing methods mainly rely on the generative adversarial network (GAN) or stable diffusion (SD). GAN-based approaches using CNNs or Transformers struggle to jointly capture local and global dependencies, leading to artifacts and disharmonious patterns. SD-based methods reduce such issues but often fail to preserve content structures and suffer from slow inference. To address these issues, we revisit GAN and propose a mamba-based generator, termed as StyMam, to produce high-quality stylized images without introducing artifacts and disharmonious patterns. Specifically, we introduce a mamba-based generator with a residual dual-path strip scanning mechanism and a channel-reweighted spatial attention module. The former efficiently captures local texture features, while the latter models global dependencies. Finally, extensive qualitative and quantitative experiments demonstrate that the proposed method outperforms state-of-the-art algorithms in both quality and speed.

cs.CV↗

arXiv2026

Illuminating the Physics of Cosmic Origin and Evolution: A UK Space Frontiers 2035 White Paper

Understanding the Universe's origins and evolution remains one of the most fundamental challenges in modern cosmology. This white paper explores three key science priorities in this field: unravelling the physics of cosmic inflation, investigating the accelerating expansion of the Universe, and precisely measuring the sum of the neutrino masses. Achieving these goals requires a dedicated survey to map the large-scale structure at high redshift in unprecedented detail. We describe how this can be achieved through a mission concept called SIRMOS, providing a high-throughput, highly multiplexed spectroscopic capability to obtain accurate redshifts for over 100 million galaxies over a wide sky area. Such a survey would leverage the deepest existing wide-area photometric catalogues for targeting, with spectra offering continuous 1.25-2.5~$μ$m wavelength coverage at moderate resolution, allowing precise redshift measurements in the $1<z<4$ range with minimal bias. We outline the scientific opportunities this presents. Recent years have seen significant advances in instrumentation, including digital micromirror devices, complex telescope mirrors, large detector arrays, and data processing pipelines. While these technologies have been demonstrated in terrestrial applications, such a survey is a unique opportunity to apply these proven capabilities in space to address fundamental questions in cosmology. Participation in such a mission will simultaneously deliver a compelling science case, help align UK Space Agency and STFC strategies, demonstrate the UK's growing capability in end-to-end space missions, and strengthen the national space economy through high-value industrial participation.

astro-ph.IM↗

arXiv2026

M$^2$FMoE: Multi-Resolution Multi-View Frequency Mixture-of-Experts for Extreme-Adaptive Time Series Forecasting

Forecasting time series with extreme events is critical yet challenging due to their high variance, irregular dynamics, and sparse but high-impact nature. While existing methods excel in modeling dominant regular patterns, their performance degrades significantly during extreme events, constituting the primary source of forecasting errors in real-world applications. Although some approaches incorporate auxiliary signals to improve performance, they still fail to capture extreme events' complex temporal dynamics. To address these limitations, we propose M$^2$FMoE, an extreme-adaptive forecasting model that learns both regular and extreme patterns through multi-resolution and multi-view frequency modeling. It comprises three modules: (1) a multi-view frequency mixture-of-experts module assigns experts to distinct spectral bands in Fourier and Wavelet domains, with cross-view shared band splitter aligning frequency partitions and enabling inter-expert collaboration to capture both dominant and rare fluctuations; (2) a multi-resolution adaptive fusion module that hierarchically aggregates frequency features from coarse to fine resolutions, enhancing sensitivity to both short-term variations and sudden changes; (3) a temporal gating integration module that dynamically balances long-term trends and short-term frequency-aware features, improving adaptability to both regular and extreme temporal patterns. Experiments on real-world hydrological datasets with extreme patterns demonstrate that M$^2$FMoE outperforms state-of-the-art baselines without requiring extreme-event labels.

cs.LG↗

arXiv2026

Stellar Wind-Blown Bubbles as Environments for Late-Time Rebrightening of Gamma-Ray Burst Afterglows

We presented the multi-wavelength afterglow fitting results for three events that exhibit late afterglow rebrightening behavior: EP240414a ($z=0.401$), GRB 240529A ($z=2.695$), and GRB 240218A ($z=6.782$), which span a broad range of redshifts, from the local to the high-redshift universe.We prove that the peculiar afterglow light curves of three bursts can be well fitted by structured jets propagated in free-to-shocked stellar wind environment of stellar wind blown bubbles. This scenario offers a self-consistent explanation for the observed subclass of afterglows that exhibit rebrightening that is characterized by steep rises and rapid decays. It also provides a unified solution for such events and offers pathways to study both the jet generation mechanism and the propagation process of jets through the envelope of the progenitor. This study reveals that the structured jets produced by such events exhibit a narrow jet core and a steep angle-dependent energy decay index, suggesting highly magnetized jets. The derived transition radii from free stellar winds to shocked stellar winds for all three events are smaller than 0.5 pc, with statistical analysis of similar events indicating a median value of 0.1 pc, which conflicts with numerical simulation results. We anticipate that future observations by EP and SVOM missions will enhance the understanding of analogous events and further reveal information about progenitors and their circum-environments.

astro-ph.HE↗

arXiv2026

The dynamical and thermodynamic effects of turbulence on the cosmic baryonic fluid

Both simulations and observations indicate that the so-called missing baryons reside in the intergalactic medium known as the warm-hot intergalactic medium (WHIM). In this study we employed the IllustrisTNG50-1 simulation to demonstrate that knowledge of the turbulence in the cosmic baryonic fluid is crucial for correctly understanding both the spatial distribution and the physical origins of the missing baryons in the Universe. First, we find that dynamical effects cause the gas to be detained in low-density and intermediate-density regions, resulting in high baryon fractions, and prevent the convergence of the gas in high-density regions, leading to low baryon fractions. Second, turbulent energy is converted into thermal energy, and the injection and dissipation of turbulent energy have essentially reached a balance from $z=1$ to $0$. This indicates that the cosmic fluid is in a steady state within this redshift range. Due to turbulent heating, as the redshift decreases, an increasing amount of warm gas is heated and converted into the WHIM, and some even into hot gas. We find that, compared with turbulence in the cosmic fluid, shocks are unimportant in intermediate-density regions and even negligible in high-density regions, both dynamically and thermodynamically. This finding accounts for the origin of the WHIM in terms of both dynamics and thermodynamics, calls into question the traditional view of shock-heating, and highlights the importance of turbulence in shaping the large-scale structure of the Universe, particularly in the evolution of galaxies and galaxy clusters. In addition to TNG50-1, we validated our key findings with TNG50-2, TNG100-1, WIGEON, and EAGLE simulations, demonstrating that the spatial resolution, box size, and sub-grid-physics variations do not affect our main conclusions.

astro-ph.CO↗

arXiv2025

Arbitrary orientations of cycles in oriented graphs

We show that every sufficiently large oriented graph $G$ with minimum indegree and outdegree both at least $(3|V(G)|-1)/8$ contains every orientation of a Hamilton cycle. This result improves the approximate bound established by Kelly and resolves a long-standing problem posed by Häggkvist and Thomason in 1995. The degree condition is tight and it can be improved to $(3|V(G)|-4)/8$ for Hamilton cycles that are nearly directed, generalizing a classic result by Keevash, Kühn and Osthus. Additionally, we derive a pancyclicity result for arbitrary orientations. More precisely, the above degree condition suffices to guarantee the existence of cycles of every possible orientation and every possible length unless $G$ is isomorphic to one of the exceptional oriented graphs.

math.CO↗

arXiv2025

Ore-type condition for antidirected Hamilton cycles in oriented graphs

An antidirected cycle in a digraph $G$ is a subdigraph whose underlying graph is a cycle, and in which no two consecutive edges form a directed path in $G$. Let $σ_{+-}(G)$ be the minimum value of $d^+(x)+d^-(y)$ over all pairs of vertices $x, y$ such that there is no edge from $x$ to $y$, that is, $$σ_{+-}(G)=\min\{d^+(x)+d^-(y): \{x,y\}\subseteq V(G), xy\notin E(G)\}.$$ In 1972, Woodall extended Ore's theorem to digraphs by showing that every digraph $G$ on $n$ vertices with $σ_{+-}(G)\geqslant n$ contains a directed Hamilton cycle. Very recently, this result was generalized to oriented graphs under the condition $σ_{+-}(G)\geqslant(3n-3)/4$. In this paper, we give the exact Ore-type degree threshold for the existence of antidirected Hamilton cycles in oriented graphs. More precisely, we prove that for sufficiently large even integer $n$, every oriented graph $G$ on $n$ vertices with $σ_{+-}(G)\geqslant(3n+2)/4$ contains an antidirected Hamilton cycle. Moreover, we show that this degree condition is best possible.

math.CO↗

arXiv2025

Interaction, Process, Infrastructure: A Unified Framework for Human-Agent Collaboration

While AI tools are increasingly prevalent in knowledge work, they remain fragmented, lacking the architectural foundation for sustained, adaptive collaboration. We argue this limitation stems from their inability to represent and manage the structure of collaborative work. To bridge this gap, we propose a layered conceptual framework for human-agent systems that integrates Interaction, Process, and Infrastructure. Crucially, our framework elevates Process to a first-class concern, an explicit, inspectable structural representation of activities. The central theoretical construct is Structural Adaptation, enabling the process to dynamically reorganize itself in response to evolving goals. We introduce a five-module Process Model as the representational basis for this adaptation. This model offers a unified theoretical grounding, reimagining human-agent collaboration as a coherent system for complex real-world work.

cs.HC↗

arXiv2025

TeleEgo: Benchmarking Egocentric AI Assistants in the Wild

Egocentric AI assistants in real-world settings must process multi-modal inputs (video, audio, text), respond in real time, and retain evolving long-term memory. However, existing benchmarks typically evaluate these abilities in isolation, lack realistic streaming scenarios, or support only short-term tasks. We introduce \textbf{TeleEgo}, a long-duration, streaming, omni-modal benchmark for evaluating egocentric AI assistants in realistic daily contexts. The dataset features over 14 hours per participant of synchronized egocentric video, audio, and text across four domains: work \& study, lifestyle \& routines, social activities, and outings \& culture. All data is aligned on a unified global timeline and includes high-quality visual narrations and speech transcripts, curated through human refinement.TeleEgo defines 12 diagnostic subtasks across three core capabilities: Memory (recalling past events), Understanding (interpreting the current moment), and Cross-Memory Reasoning (linking distant events). It contains 3,291 human-verified QA items spanning multiple question formats (single-choice, binary, multi-choice, and open-ended), evaluated strictly in a streaming setting. We propose Real-Time Accuracy (RTA) to jointly capture correctness and responsiveness under tight decision windows, and Memory Persistence Time (MPT) as a forward-looking metric for long-term retention in continuous streams. In this work, we report RTA results for current models and release TeleEgo, together with an MPT evaluation framework, as a realistic and extensible benchmark for future egocentric assistants with stronger streaming memory, enabling systematic study of both real-time behavior and long-horizon memory.

cs.CV↗

arXiv2025

EP241217a: a likely Type II GRB with an achromatic bump at z = 4.59

EP241217a is an X-ray transient detected by the Einstein Probe (EP) lasting for about 100 seconds and without accompanying $γ$-ray detection. The optical spectroscopy reveals the redshift of EP241217a is 4.59. By combining the $γ$-ray upper limit provided by GECAM-C, there is a considerable possibility that EP241217a is a typical Type II gamma-ray burst (GRB), but it is fainter than the detection threshold of any available $γ$-ray monitors (i.e., $E_{γ,{\rm iso}}\lesssim10^{53}$ erg). The X-ray light curve exhibits a plateau lasting for $\sim5\times10^4$ seconds. However, the joint analysis with optical data suggests the presence of an achromatic bump peaking at $\sim3\times10^4$ s after the trigger, indicating the actual duration of the X-ray plateau may be significantly shorter than it appears. To interpret the achromatic bump, we adopt the scenario of a mildly relativistic jet coasting in a wind-like medium and encountering a rapid density enhancement of the circumburst medium, which is likely induced by the the interaction of the progenitor's stellar wind and the interstellar medium. However, this model cannot fully explain observed data, and some issues do exist, e.g., the observed spectrum is harder than the model prediction. Consequently, we conclude that the scenario of a mildly relativistic jet coasting in the wind-like medium cannot explain all observed features of EP241217a. In addition, some alternative models commonly invoked to explain X-ray plateaus are discussed, but there are more or less issues when they are applied to EP241217a. Therefore, further theoretical modeling is encouraged to explore the origin of EP241217a.

astro-ph.HE↗

arXiv2025

Model-Independent Dark Energy Measurements from DESI DR2 and Planck 2015 Data

Using DESI DR2 baryon acoustic oscillation (BAO) distance measurements and Planck cosmic microwave background distance priors, we have measured the dark energy density $ρ_X(z)$ and dark energy equation of state w_X(z) as free functions of redshift (smoothly interpolated from values at {z_i}={0, 1/3, 2/3, 1, 4/3, 2.33}, and find both to be consistent with a cosmological constant, with only deviations of 1σfor $ρ_X(z)$ & ~ 2$σ$ for w_X(z) at z=2/3. We also find that measuring {$ρ_X(z_i)$} is preferred to measuring {w_X(z_i)} by model selection using the Akaike Information Criterion (AIC) as well as the Bayesian Information Criterion (BIC). Varying the choice of redshift values of the $ρ_X(z)$ measurements leads to very consistent results, with AIC/BIC slightly favoring the case of our fiducial {z_i} with z=4/3 omitted. We find agreement with a cosmological constant except for the 1-2$σ$ deviation at 0.4 < z < 0.9, where DESI DR2 BAO measurements deviate from a cosmological constant at similar statistical significance. Our results differ noticeably from those of the DESI Collaboration, in which they used the same DESI DR2 data combined with Planck data and found a 3.1$σ$ deviation from a cosmological constant, which is primarily the consequence of their assuming parametrization w_X(z)=w_0+w_a(1-a). Our results indicate that assuming a linear w_X(z) could be misleading and precludes discovering how dark energy actually varies with time at higher redshifts. In our quest to discover the physical nature of dark energy, the most urgent goal at present is to determine definitively whether dark energy density varies with time. It is of critical importance to measure dark energy density as a free function of redshift from data. Future galaxy redshift surveys by Euclid and Roman at higher redshifts will significantly advance our understanding of dark energy.

astro-ph.CO↗

arXiv2025

BuddyMoE: Exploiting Expert Redundancy to Accelerate Memory-Constrained Mixture-of-Experts Inference

Mixture-of-Experts (MoE) architectures scale language models by activating only a subset of specialized expert networks for each input token, thereby reducing the number of floating-point operations. However, the growing size of modern MoE models causes their full parameter sets to exceed GPU memory capacity; for example, Mixtral-8x7B has 45 billion parameters and requires 87 GB of memory even though only 14 billion parameters are used per token. Existing systems alleviate this limitation by offloading inactive experts to CPU memory, but transferring experts across the PCIe interconnect incurs significant latency (about 10 ms). Prefetching heuristics aim to hide this latency by predicting which experts are needed, but prefetch failures introduce significant stalls and amplify inference latency. In the event of a prefetch failure, prior work offers two primary solutions: either fetch the expert on demand, which incurs a long stall due to the PCIe bottleneck, or drop the expert from the computation, which significantly degrades model accuracy. The critical challenge, therefore, is to maintain both high inference speed and model accuracy when prefetching fails.

cs.LG↗

arXiv2025

Cosmology with voids from the Nancy Grace Roman Space Telescope

We provide an accurate forecast of the expected constraining power from the main void statistics -- the void size function and the void-galaxy cross-correlation function -- to be measured by the Roman reference High Latitude Spectroscopic Survey from the Nancy Grace Roman Space Telescope. Relying on a realistic galaxy mock lightcone, covering 2000 square degrees, we find more than $8\times 10^4 $ voids and explore their constraining power in the framework of three different cosmological models: $Λ$CDM, $w$CDM, and $w_0 w_{\rm a}$CDM. This work confirms the strong complementarity of different void statistics and showcases the constraining power to be expected from Roman voids thanks to the combination of its high tracer density and large observed volume.

astro-ph.CO↗

arXiv2025

Reflection on Data Storytelling Tools in the Generative AI Era from the Human-AI Collaboration Perspective

Human-AI collaborative tools attract attentions from the data storytelling community to lower the expertise barrier and streamline the workflow. The recent advance in large-scale generative AI techniques, e.g., large language models (LLMs) and text-to-image models, has the potential to enhance data storytelling with their power in visual and narration generation. After two years since these techniques were publicly available, it is important to reflect our progress of applying them and have an outlook for future opportunities. To achieve the goal, we compare the collaboration patterns of the latest tools with those of earlier ones using a dedicated framework for understanding human-AI collaboration in data storytelling. Through comparison, we identify consistently widely studied patterns, e.g., human-creator + AI-assistant, and newly explored or emerging ones, e.g., AI-creator + human-reviewer. The benefits of these AI techniques and implications to human-AI collaboration are also revealed. We further propose future directions to hopefully ignite innovations.

cs.HC↗

arXiv2025

ViTime: Foundation Model for Time Series Forecasting Powered by Vision Intelligence

Time series forecasting (TSF) possesses great practical values in various fields, including power and energy, transportation, etc. TSF methods have been studied based on knowledge from classical statistics to modern deep learning. Yet, all of them were developed based on one fundamental concept, the numerical data fitting. Thus, the models developed have long been known to be problem-specific and lacking application generalizability. Practitioners expect a TSF foundation model that serves TSF tasks in different applications. The central question is then how to develop such a TSF foundation model. This paper offers one pioneering study in the TSF foundation model development method and proposes a vision intelligence-powered framework, ViTime, for the first time. ViTime fundamentally shifts TSF from numerical fitting to operations based on a binary image-based time series metric space and naturally supports both point and probabilistic forecasting. We also provide rigorous theoretical analyses of ViTime, including quantization-induced system error bounds and principled strategies for optimal parameter selection. Furthermore, we propose RealTS, an innovative synthesis algorithm generating diverse and realistic training samples, effectively enriching the training data and significantly enhancing model generalizability. Extensive experiments demonstrate ViTime's state-of-the-art performance. In zero-shot scenarios, ViTime outperforms TimesFM by 9-15\%. With just 10\% fine-tuning data, ViTime surpasses both leading foundation models and fully-supervised benchmarks, a gap that widens with 100\% fine-tuning. ViTime also exhibits exceptional robustness, effectively handling missing data and outperforming TimesFM by 20-30\% under various data perturbations, validating the power of its visual space data operation paradigm.

cs.LG↗

arXiv2025

PPMStereo: Pick-and-Play Memory Construction for Consistent Dynamic Stereo Matching

Temporally consistent depth estimation from stereo video is critical for real-world applications such as augmented reality, where inconsistent depth estimation disrupts the immersion of users. Despite its importance, this task remains challenging due to the difficulty in modeling long-term temporal consistency in a computationally efficient manner. Previous methods attempt to address this by aggregating spatio-temporal information but face a fundamental trade-off: limited temporal modeling provides only modest gains, whereas capturing long-range dependencies significantly increases computational cost. To address this limitation, we introduce a memory buffer for modeling long-range spatio-temporal consistency while achieving efficient dynamic stereo matching. Inspired by the two-stage decision-making process in humans, we propose a \textbf{P}ick-and-\textbf{P}lay \textbf{M}emory (PPM) construction module for dynamic \textbf{Stereo} matching, dubbed as \textbf{PPMStereo}. PPM consists of a `pick' process that identifies the most relevant frames and a `play' process that weights the selected frames adaptively for spatio-temporal aggregation. This two-stage collaborative process maintains a compact yet highly informative memory buffer while achieving temporally consistent information aggregation. Extensive experiments validate the effectiveness of PPMStereo, demonstrating state-of-the-art performance in both accuracy and temporal consistency. % Notably, PPMStereo achieves 0.62/1.11 TEPE on the Sintel clean/final (17.3\% \& 9.02\% improvements over BiDAStereo) with fewer computational costs. Codes are available at \textcolor{blue}{https://github.com/cocowy1/PPMStereo}.

cs.CV↗

arXiv2025

Clustering analysis of BOSS-CMASS galaxies with semi-analytical model for galaxy formation and halo occupation distribution

The spatial distribution of massive and luminous galaxies have provided important constraints on the fundamental cosmological parameters and physical processes governing galaxy formation. In this work, we construct and compare independent galaxy-halo connection models in the application of clustering measurement at non-linear scales of BOSS-CMASS galaxies. In particular, we adopt a halo occupation distribution (HOD) model with 11 parameters and a semi-analytical model (SAM) with 16 parameters to describe the galaxy two point correlation function. With an empirical parameterization for the velocity field to model the redshift space distortion effect and the emulator technique, we can explore the parameter space of both models. We find that the HOD model is able to recover the underlying velocity field of SAM with an accuracy of 3\%, and can be improved to 1\% when the analysis is restricted to scales above 1$h^{-1}$Mpc. The comparison is based on multiple samplings in the parameter space which can verify the convergence of the models. Then we perform constraints on the model parameters using clustering measurement of CMASS galaxies. Although limited by the emulator accuracy and the flexibility of the model, we find that the clustering measurement is capable of constraining a subset of the SAM parameters, especially for components sensitive to the star formation rate. This result leads us to anticipate that a joint analysis of both clustering and abundance measurements can significantly constrain the parameters of galaxy formation physics, which requires further investigation from both theoretical and observational aspects.

astro-ph.CO↗