arXiv subjects

Yidan Wang

Publications and source records attributed to Yidan Wang.

At least 19 recordsLinked to original sources

arXiv2026

Crystallization of pristine cubic ice from liquid at ambient pressure

The phase diagram of frozen water is famously rich: to date, over twenty crystalline polymorphs have been identified. Of the low-pressure 'ice I' family, hexagonal (Ih) is the principal form on Earth, while cubic (Ic) is much more elusive. Fundamental questions remain open as to whether cubic ice Ic can form directly from the liquid state, its thermodynamic stability and natural occurrence. Here we show that pristine cubic ice Ic can be formed at atmospheric pressure simply by cooling an aqueous solution confined within mesoporous silica. Using primarily neutron scattering, we show unambiguously that under these conditions, cubic ice Ic forms reproducibly and is the only thermodynamically stable crystalline phase of water. The discovery that cubic ice Ic is directly accessible from the liquid state, and stable at atmospheric pressure, strongly suggests that this polymorph plays a much more significant role in natural and synthetic processes than previously thought.

cond-mat.mtrl-sci↗

arXiv2026

Epidemic Informatics and Control: A Holistic Approach from System Informatics to Epidemic Response and Risk Management in Public Health

This paper presents a holistic systems informatics approach, i.e., Define, Measure, Analyze, Improve, and Control (DMAIC), for epidemic response and management through the intensive use of data, statistics and optimization. Despite the sustained successes of system informatics in a variety of established industries such as manufacturing, logistics, services and beyond, there is a dearth of concentrated review and application of the data-driven DMAIC approach in the context of epidemic outbreaks. First, we define specific challenges posed by epidemic outbreaks to populational health, health systems, as well as economic challenges to different industries such as retailing, education and manufacturing. Second, we present a review of medical testing and statistical sampling methods for data collection, as well as existing efforts in data management and data visualization. Third, we discuss the importance to realizing the full potential of data for epidemic insights, and emphasize the need to leverage analytical methods and tools for decision support. Fourth, an epidemic brings imperative changes to health systems. We discuss the new trend of healthcare solutions to improve system resilience, including telehealth, artificial intelligence, resource allocation, and system re-design. In closing, prescriptive approaches are discussed to optimize the health policies and action strategies for controlling the spread of virus. We posit that this work will catalyze more in-depth investigations and multi-disciplinary research efforts to accelerate the application of system informatics methods and tools in epidemic response and risk management.

eess.SY↗

arXiv2026

Probing the Baryon Distribution with Fast Radio Bursts

Baryonic feedback redistributes matter on small to mid cosmological scales, ultimately limiting inferences from Stage IV galaxy surveys. Direct baryon tracers are crucial for recovering cosmological signals masked by astrophysical effects, and vice versa: galaxy formation and other astrophysical processes must be interpreted cosmologically. Fast radio bursts (FRBs) serve as such tracers: their dispersion measure (DM) records the line-of-sight integrated ionised electron density. The Square Kilometre Array (SKA) will be the only radio telescope capable of detecting many FRBs in the southern hemisphere, significantly enhancing synergy with surveys such as Rubin Observatory. This chapter completes the FRB trilogy by forecasting the SKA's potential to constrain the baryon distribution from cosmological to galactic scales and across cosmic time. We tackle this question by investigating the DM scatter as a function of redshift. We also study the statistical properties of the DM field and its cross-correlation with Stage IV galaxy surveys. Our focus is on cosmic shear and galaxy clustering. This shows that the SKA can play a crucial role in pinpointing baryonic feedback models, thereby greatly enhancing the cosmological constraining power of Stage IV galaxy surveys. Furthermore, we show that the SKA will be able to measure the properties of the circumgalactic medium using the scattering timescale of FRBs. Lastly, the large redshift range of FRB detections with the SKA can improve our understanding of the epoch of reionisation. It may also clarify the mechanism behind FRBs.

astro-ph.CO↗

arXiv2026

Fast Radio Bursts as Cosmological Probes

Fast radio bursts (FRBs) are brief, coherent radio pulses of extragalactic origin. They typically last from microseconds to milliseconds and have energies large enough to be visible over cosmological distances. Since FRBs interact with free electrons along their paths, the original burst is dispersed (Dispersion Measure, DM) and broadened (scattering). Furthermore, the burst's polarization is altered by Faraday rotation. Consequently, FRBs are excellent probes of the cosmological distribution of baryons, the expansion of the Universe, magnetic fields, and minuscule effects of fundamental physics that accumulate over vast distances. This chapter is the second of a trilogy of FRB chapters and discusses FRBs as a standalone probe. We first introduce the foundation of FRB observables related to those questions. Next, we lay the groundwork for forecasting SKA's potential by describing the method to simulate the expected FRB population observable with the SKA. These synthetic FRB catalogues are then used to investigate the SKA's potential to probe the Universe's expansion rate and fundamental physics, such as the equivalence principle and the existence of massive photons. Furthermore, we investigate the possibility of tracing cosmic magnetic fields and investigating different dark matter candidates.

astro-ph.CO↗

arXiv2026

WorldCup Sampling for Multi-bit LLM Watermarking

As large language models (LLMs) generate increasingly human-like text, watermarking has emerged as a promising solution for reliable attribution beyond mere detection. While multi-bit watermarking enables richer provenance encoding, existing approaches typically extend zero-bit watermarking schemes by introducing static logit perturbations and counting-based decoding strategies, which can degrade text quality and compromise decoding robustness as the payload increases. In this paper, we propose WorldCup, a multi-bit watermarking framework for LLMs that models the sampling process as a structured communication channel and embeds message bits through a hierarchical competition mechanism guided by complementary signals. Moreover, WorldCup incorporates entropy-aware modulation to preserve generation quality and enables robust message recovery via confidence-aware decoding that accounts for token-level reliability. Comprehensive experiments demonstrate that WorldCup achieves a strong balance across message capacity, detectability, robustness, text quality, and decoding efficiency, consistently outperforming prior baselines. We believe that this work establishes a scalable and principled foundation for future research on multi-bit watermarking in LLMs.

cs.CL↗

arXiv2026

Benchmark Shadows: Data Alignment, Parameter Footprints, and Generalization in Large Language Models

Large language models often achieve strong benchmark gains without corresponding improvements in broader capability. We hypothesize that this discrepancy arises from differences in training regimes induced by data distribution. To investigate this, we design controlled data interventions that isolate distributional effects under fixed training settings. We find that benchmark-aligned data improves narrow evaluation metrics while limiting broader representational development, whereas coverage-expanding data leads to more distributed parameter adaptation and better generalization. We further introduce parameter-space diagnostics based on spectral and rank analyses, which reveal distinct structural signatures of these regimes. Similar patterns are observed across diverse open-source model families, including multimodal models as a key case study, suggesting that these effects extend beyond controlled settings. A case study on prompt repetition shows that not all data artifacts induce regime shifts. These results indicate that benchmark performance alone is insufficient to characterize model capability, and highlight the importance of data distribution in shaping learning dynamics.

cs.LG↗

arXiv2025

AlignCAT: Visual-Linguistic Alignment of Category and Attribute for Weakly Supervised Visual Grounding

Weakly supervised visual grounding (VG) aims to locate objects in images based on text descriptions. Despite significant progress, existing methods lack strong cross-modal reasoning to distinguish subtle semantic differences in text expressions due to category-based and attribute-based ambiguity. To address these challenges, we introduce AlignCAT, a novel query-based semantic matching framework for weakly supervised VG. To enhance visual-linguistic alignment, we propose a coarse-grained alignment module that utilizes category information and global context, effectively mitigating interference from category-inconsistent objects. Subsequently, a fine-grained alignment module leverages descriptive information and captures word-level text features to achieve attribute consistency. By exploiting linguistic cues to their fullest extent, our proposed AlignCAT progressively filters out misaligned visual queries and enhances contrastive learning efficiency. Extensive experiments on three VG benchmarks, namely RefCOCO, RefCOCO+, and RefCOCOg, verify the superiority of AlignCAT against existing weakly supervised methods on two VG tasks. Our code is available at: https://github.com/I2-Multimedia-Lab/AlignCAT.

cs.CV↗

arXiv2025

Landscape of scattering universality with general dispersion relations

Universality in physics describes how disparate systems can exhibit identical low-energy behavior. Here, we reveal a rich landscape of new universal scattering phenomena governed by the interplay between an interaction and a system's density of states. We investigate one-dimensional scattering with general dispersion relations of the form $ε(k) = |k|^m$ and $ε(k) = \text{sign}(k)|k|^m$ for any real $m \geq 1$. For key models such as emitter scattering and separable potentials, we prove that the low-energy S-matrix converges to universal forms determined solely by the dispersion exponent $m$ and a few integers defining the interaction. This establishes a broad classification of new universality classes, extending far beyond the standard quadratic dispersion paradigm. Furthermore, we derive a generalized Levinson's theorem relating the total winding of the scattering phase to the number of bound states. Our findings are directly relevant to synthetic quantum systems, where engineered dispersion relations in atomic arrays and photonic crystals offer a platform to explore these universal behaviors.

physics.atom-ph↗

arXiv2025

Revealing the Temporally Stable Bimodal Energy Distribution of FRB 20121102A with a Tripled Burst Set from AI Detections

Active repeating Fast Radio Bursts (FRBs), with their large number of bursts, burst energy distribution, and their potential energy evolution, offer critical insights into the FRBs emission mechanisms. Traditional pipelines search for bursts through conducting dedispersion trials and looking for signals above certain fluence thresholds, both of which could result in missing weak and narrow-band bursts. In order to improve the completeness of the burst set, we develop an End-to-end DedispersE-agnostic Nonparametric AI model (EDEN), which directly detect bursts from dynamic spectrum and is the first detection pipeline that operates without attempting dedispersion. We apply EDEN to archival FAST L-band observations during the extreme active phase of the repeating source FRB 20121102A, resulting in the largest burst set for any FRB to date, which contains 5,927 individual bursts, tripling the original burst set. The much enhanced completeness enables a refined analysis of the temporal behavior of energy distribution, revealing that the bimodal energy distribution remains stable over time. It is rather an intrinsic feature of the emission mechanisms than a consequence of co-evolving with burst rate.

astro-ph.HE↗

arXiv2025

Universal two-excitation scattering in two-dimensional subwavelength atomic arrays

Subwavelength atomic arrays are a leading platform for engineering strong light-matter interactions, presenting exciting opportunities for quantum science. However, a full understanding of their multi-excitation dynamics remains a significant challenge. In this work, we uncover a remarkable universal phenomenon that emerges in these arrays. Using scattering theory to analyze two-excitation interactions, we reveal a profound simplification near critical points of the collective atomic excitation band structure, determined solely from single-excitation properties. At these critical points, scattering becomes universal and the full two-excitation scattering matrix decomposes into a block-diagonal form. Remarkably, all scattering processes involving the photon field are completely suppressed, resulting in the perfect isolation of a unitary, nonlinear interaction channel between collective dark spin waves. Our findings provide exact analytical insights into few-body nonlinearities and establish a universal framework for analyzing complex scattering phenomena in ordered atomic systems.

quant-ph↗

arXiv2025

Multi-excitation scattering in subwavelength atomic arrays

Subwavelength atomic arrays are a leading platform for engineering light-matter interactions, enabling near-perfect single-photon mirrors and robust quantum memories based on long-lived dark spin waves. However, a comprehensive theory of their nonlinear, multi-excitation dynamics has remained a significant challenge. We present a unified quantum scattering theory that treats both photons and collective atomic spin waves as distinct propagating excitations interacting across different spatial dimensions. Our central result is a powerful analytical reduction: we demonstrate that the complete multi-channel S-matrix and the associated scattering cross sections are exactly determined by the effective scattering dynamics solely within the atomic spin wave subspace. This maps the complex physical problem of photon-atom interactions to a conceptually simpler one involving only atomic modes. We apply this formalism to the two-excitation case, deriving the complete analytical S-matrix and scattering cross sections for systems with two-level nonlinearities. Our work provides a versatile analytic tool for analyzing and engineering complex quantum nonlinear phenomena, including multi-excitation subradiance, in large-scale atomic systems.

quant-ph↗

arXiv2025

PIG: Privacy Jailbreak Attack on LLMs via Gradient-based Iterative In-Context Optimization

Large Language Models (LLMs) excel in various domains but pose inherent privacy risks. Existing methods to evaluate privacy leakage in LLMs often use memorized prefixes or simple instructions to extract data, both of which well-alignment models can easily block. Meanwhile, Jailbreak attacks bypass LLM safety mechanisms to generate harmful content, but their role in privacy scenarios remains underexplored. In this paper, we examine the effectiveness of jailbreak attacks in extracting sensitive information, bridging privacy leakage and jailbreak attacks in LLMs. Moreover, we propose PIG, a novel framework targeting Personally Identifiable Information (PII) and addressing the limitations of current jailbreak methods. Specifically, PIG identifies PII entities and their types in privacy queries, uses in-context learning to build a privacy context, and iteratively updates it with three gradient-based strategies to elicit target PII. We evaluate PIG and existing jailbreak methods using two privacy-related datasets. Experiments on four white-box and two black-box LLMs show that PIG outperforms baseline methods and achieves state-of-the-art (SoTA) results. The results underscore significant privacy risks in LLMs, emphasizing the need for stronger safeguards. Our code is availble at https://github.com/redwyd/PrivacyJailbreak.

cs.CR↗

arXiv2025

From Trade-off to Synergy: A Versatile Symbiotic Watermarking Framework for Large Language Models

The rise of Large Language Models (LLMs) has heightened concerns about the misuse of AI-generated text, making watermarking a promising solution. Mainstream watermarking schemes for LLMs fall into two categories: logits-based and sampling-based. However, current schemes entail trade-offs among robustness, text quality, and security. To mitigate this, we integrate logits-based and sampling-based schemes, harnessing their respective strengths to achieve synergy. In this paper, we propose a versatile symbiotic watermarking framework with three strategies: serial, parallel, and hybrid. The hybrid framework adaptively embeds watermarks using token entropy and semantic entropy, optimizing the balance between detectability, robustness, text quality, and security. Furthermore, we validate our approach through comprehensive experiments on various datasets and models. Experimental results indicate that our method outperforms existing baselines and achieves state-of-the-art (SOTA) performance. We believe this framework provides novel insights into diverse watermarking paradigms. Our code is available at https://github.com/redwyd/SymMark.

cs.CL↗

arXiv2024

Solid-state qubits in moire superlattices

Qubits are the fundamental units in quantum computing, but they are also pivotal for advancements in quantum communication and sensing. Currently, there are a variety of platforms for qubits, including cold atoms, superconducting circuits, point defects, and semiconductor quantum dots. In these systems, each qubit requires individual preparation, making identical replication a challenging task. Constructing and maintaining stable, scalable qubits remains a formidable challenge, especially for solid-state qubits. The race to identify the best one remains inconclusive, making the search for new qubits a welcome endeavor. Our study introduces moiré superlattices of twisted bilayer materials as a promising platform for qubits due to their tunability, natural patterns, and extensive materials library. Our first-principles calculations reveal that when the twist angle between the two layers is sufficiently small, these materials foster identical, localized quantum wells within the moiré superlattices. Each quantum well accommodates a few dispersionless bands and localized states, akin to the discrete energy levels of an alkali atom. Existing experimental techniques allow for individual initialization, manipulation, and readout of the local quantum states. The vast array of 2D materials provides a multitude of potential candidates for qubit exploration in such systems. Due to their inherent scalability and uniformity, our proposed qubits present significant advantages over conventional solid-state qubit systems.

quant-ph↗

arXiv2024

Content-Based Collaborative Generation for Recommender Systems

Generative models have emerged as a promising utility to enhance recommender systems. It is essential to model both item content and user-item collaborative interactions in a unified generative framework for better recommendation. Although some existing large language model (LLM)-based methods contribute to fusing content information and collaborative signals, they fundamentally rely on textual language generation, which is not fully aligned with the recommendation task. How to integrate content knowledge and collaborative interaction signals in a generative framework tailored for item recommendation is still an open research challenge. In this paper, we propose content-based collaborative generation for recommender systems, namely ColaRec. ColaRec is a sequence-to-sequence framework which is tailored for directly generating the recommended item identifier. Precisely, the input sequence comprises data pertaining to the user's interacted items, and the output sequence represents the generative identifier (GID) for the suggested item. To model collaborative signals, the GIDs are constructed from a pretrained collaborative filtering model, and the user is represented as the content aggregation of interacted items. To this end, ColaRec captures both collaborative signals and content information in a unified framework. Then an item indexing task is proposed to conduct the alignment between the content-based semantic space and the interaction-based collaborative space. Besides, a contrastive loss is further introduced to ensure that items with similar collaborative GIDs have similar content representations. To verify the effectiveness of ColaRec, we conduct experiments on four benchmark datasets. Empirical results demonstrate the superior performance of ColaRec.

cs.IR↗

arXiv2024

Instruct Large Language Models to Generate Scientific Literature Survey Step by Step

Abstract. Automatically generating scientific literature surveys is a valuable task that can significantly enhance research efficiency. However, the diverse and complex nature of information within a literature survey poses substantial challenges for generative models. In this paper, we design a series of prompts to systematically leverage large language models (LLMs), enabling the creation of comprehensive literature surveys through a step-by-step approach. Specifically, we design prompts to guide LLMs to sequentially generate the title, abstract, hierarchical headings, and the main content of the literature survey. We argue that this design enables the generation of the headings from a high-level perspective. During the content generation process, this design effectively harnesses relevant information while minimizing costs by restricting the length of both input and output content in LLM queries. Our implementation with Qwen-long achieved third place in the NLPCC 2024 Scientific Literature Survey Generation evaluation task, with an overall score only 0.03% lower than the second-place team. Additionally, our soft heading recall is 95.84%, the second best among the submissions. Thanks to the efficient prompt design and the low cost of the Qwen-long API, our method reduces the expense for generating each literature survey to 0.1 RMB, enhancing the practical value of our method.

cs.CL↗

arXiv2024

Two-dimensional motion of an impurity under dynamic light-induced dipole forces in an atomic subwavelength array

Long-range dipole-dipole interactions in subwavelength arrays of quantum emitters involve virtual photon exchange processes that impart forces on the emitters due to the imposed photon recoil. We perform a semi-classical analysis of the dynamics of an impurity allowed to freely move through a subwavelength array of atoms in different parameter regimes. We numerically solve the coupled set of equations between motional and spin degrees of freedom to elucidate the possible impurity trajectories realizable in this system. We find that the impurity can maintain quasi-stable orbits within the plaquette for long times. The regions through which these orbits pass are strongly dependent on the chosen atomic transition dipole moment. We further provide intuition for our findings based on a simplified model, where the lattice dynamics is adiabatically eliminated. As a final point of analysis, we also take the motional degrees of freedom of the lattice atoms into account, and study the polaron-like excitation induced in the kinetic state of the lattice by the impurity.

quant-ph↗

arXiv2023

Debiasing Sequential Recommenders through Distributionally Robust Optimization over System Exposure

Sequential recommendation (SR) models are typically trained on user-item interactions which are affected by the system exposure bias, leading to the user preference learned from the biased SR model not being fully consistent with the true user preference. Exposure bias refers to the fact that user interactions are dependent upon the partial items exposed to the user. Existing debiasing methods do not make full use of the system exposure data and suffer from sub-optimal recommendation performance and high variance. In this paper, we propose to debias sequential recommenders through Distributionally Robust Optimization (DRO) over system exposure data. The key idea is to utilize DRO to optimize the worst-case error over an uncertainty set to safeguard the model against distributional discrepancy caused by the exposure bias. The main challenge to apply DRO for exposure debiasing in SR lies in how to construct the uncertainty set and avoid the overestimation of user preference on biased samples. Moreover, how to evaluate the debiasing effect on biased test set is also an open question. To this end, we first introduce an exposure simulator trained upon the system exposure data to calculate the exposure distribution, which is then regarded as the nominal distribution to construct the uncertainty set of DRO. Then, we introduce a penalty to items with high exposure probability to avoid the overestimation of user preference for biased samples. Finally, we design a debiased self-normalized inverse propensity score (SNIPS) evaluator for evaluating the debiasing effect on the biased offline test set. We conduct extensive experiments on two real-world datasets to verify the effectiveness of the proposed methods. Experimental results demonstrate the superior exposure debiasing performance of proposed methods. Codes and data are available at \url{https://github.com/nancheng58/DebiasedSR_DRO}.

cs.IR↗