arXiv subjects

Zhiyuan Wang

Publications and source records attributed to Zhiyuan Wang.

At least 91 records · Page 5Linked to original sources

arXiv2025

OpenSN: An Open Source Library for Emulating LEO Satellite Networks

Low-earth-orbit (LEO) satellite constellations (e.g., Starlink) are becoming a necessary component of future Internet. There have been increasing studies on LEO satellite networking. It is a crucial problem how to evaluate these studies in a systematic and reproducible manner. In this paper, we present OpenSN, i.e., an open source library for emulating large-scale satellite network (SN). Different from Mininet-based SN emulators (e.g., LeoEM), OpenSN adopts container-based virtualization, thus allows for running distributed routing software on each node, and can achieve horizontal scalability via flexible multi-machine extension. Compared to other container-based SN emulators (e.g., StarryNet), OpenSN streamlines the interaction with Docker command line interface and significantly reduces unnecessary operations of creating virtual links. These modifications improve emulation efficiency and vertical scalability on a single machine. Furthermore, OpenSN separates user-defined configuration from container network management via a Key-Value Database that records the necessary information for SN emulation. Such a separation architecture enhances the function extensibility. To sum up, OpenSN exhibits advantages in efficiency, scalability, and extensibility, thus is a valuable open source library that empowers research on LEO satellite networking. Experiment results show that OpenSN constructs mega-constellations 5X-10X faster than StarryNet, and updates link state 2X-4X faster than LeoEM. We also verify the scalability of OpenSN by successfully emulating the five-shell Starlink constellation with a total of 4408 satellites.

cs.NI↗

arXiv2025

Sample then Identify: A General Framework for Risk Control and Assessment in Multimodal Large Language Models

Multimodal Large Language Models (MLLMs) exhibit promising advancements across various tasks, yet they still encounter significant trustworthiness issues. Prior studies apply Split Conformal Prediction (SCP) in language modeling to construct prediction sets with statistical guarantees. However, these methods typically rely on internal model logits or are restricted to multiple-choice settings, which hampers their generalizability and adaptability in dynamic, open-ended environments. In this paper, we introduce TRON, a two-step framework for risk control and assessment, applicable to any MLLM that supports sampling in both open-ended and closed-ended scenarios. TRON comprises two main components: (1) a novel conformal score to sample response sets of minimum size, and (2) a nonconformity score to identify high-quality responses based on self-consistency theory, controlling the error rates by two specific risk levels. Furthermore, we investigate semantic redundancy in prediction sets within open-ended contexts for the first time, leading to a promising evaluation metric for MLLMs based on average set size. Our comprehensive experiments across four Video Question-Answering (VideoQA) datasets utilizing eight MLLMs show that TRON achieves desired error rates bounded by two user-specified risk levels. Additionally, deduplicated prediction sets maintain adaptiveness while being more efficient and stable for risk assessment under different risk levels.

cs.CL↗

arXiv2025

SConU: Selective Conformal Uncertainty in Large Language Models

As large language models are increasingly utilized in real-world applications, guarantees of task-specific metrics are essential for their reliable deployment. Previous studies have introduced various criteria of conformal uncertainty grounded in split conformal prediction, which offer user-specified correctness coverage. However, existing frameworks often fail to identify uncertainty data outliers that violate the exchangeability assumption, leading to unbounded miscoverage rates and unactionable prediction sets. In this paper, we propose a novel approach termed Selective Conformal Uncertainty (SConU), which, for the first time, implements significance tests, by developing two conformal p-values that are instrumental in determining whether a given sample deviates from the uncertainty distribution of the calibration set at a specific manageable risk level. Our approach not only facilitates rigorous management of miscoverage rates across both single-domain and interdisciplinary contexts, but also enhances the efficiency of predictions. Furthermore, we comprehensively analyze the components of the conformal procedures, aiming to approximate conditional coverage, particularly in high-stakes question-answering tasks.

cs.CL↗

arXiv2025

Scalable Structure Learning of Bayesian Networks by Learning Algorithm Ensembles

Learning the structure of Bayesian networks (BNs) from data is challenging, especially for datasets involving a large number of variables. The recently proposed divide-and-conquer (D\&D) strategies present a promising approach for learning large BNs. However, they still face a main issue of unstable learning accuracy across subproblems. In this work, we introduce the idea of employing structure learning ensemble (SLE), which combines multiple BN structure learning algorithms, to consistently achieve high learning accuracy. We further propose an automatic approach called Auto-SLE for learning near-optimal SLEs, addressing the challenge of manually designing high-quality SLEs. The learned SLE is then integrated into a D\&D method. Extensive experiments firmly show the superiority of our method over D\&D methods with single BN structure learning algorithm in learning large BNs, achieving accuracy improvement usually by 30\%$\sim$225\% on datasets involving 10,000 variables. Furthermore, our method generalizes well to datasets with many more (e.g., 30000) variables and different network characteristics than those present in the training data for learning the SLE. These results indicate the significant potential of employing (automatic learning of) SLEs for scalable BN structure learning.

cs.LG↗

arXiv2025

COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees

Uncertainty quantification (UQ) for foundation models is essential to identify and mitigate potential hallucinations in automatically generated text. However, heuristic UQ approaches lack formal guarantees for key metrics such as the false discovery rate (FDR) in selective prediction. Previous work adopts the split conformal prediction (SCP) framework to ensure desired coverage of admissible answers by constructing prediction sets, but these sets often contain incorrect candidates, limiting their practical utility. To address this, we propose COIN, an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question under user-specified FDR constraints. COIN estimates the empirical error rate on a calibration set and applies confidence interval methods such as Clopper-Pearson to establish a high-probability upper bound on the true error rate (i.e., FDR). This enables the selection of the largest uncertainty threshold that ensures FDR control on test data while significantly increasing sample retention. We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data across both general and multimodal text generation tasks. Furthermore, we show that employing alternative upper bound constructions and UQ strategies can further boost COIN's power performance, which underscores its extensibility and adaptability to diverse application scenarios.

cs.CL↗

arXiv2025

Discrete Element Parameter Calibration of Livestock Salt Based on Particle Scaling

In order to obtain accurate contact parameters for the discrete element simulation of salt particles used in animal husbandry, the principle of particle contact scaling and dimensional analysis were used for particle scaling. Firstly, the Plackett Burman experiment was used to screen the parameters that significantly affect the angle of repose: salt salt rolling friction coefficient, salt salt recovery coefficient, and salt steel rolling friction coefficient. Considering the influence of other parameters, a combination of bench and simulation experiments was used to calibrate the contact parameters between salt particles and steel plates used in animal husbandry in EDEM. Finally, through the stacking test, steepest climbing test, and orthogonal rotation combination test, the salt salt rolling friction coefficient was obtained to be 0.23, the salt salt recovery coefficient was 0.544, and the salt steel rolling friction coefficient was 0.368, which were verified through bench tests. The experimental results show that the relative error between the actual value of the stacking angle and the simulation results is 0.6%. The results indicate that the calibrated contact parameters can be used for discrete element simulation of salt particles for animal husbandry, providing reference for the design of quantitative feeding screws and silos.

eess.SY↗

arXiv2025

Particle exchange statistics beyond fermions and bosons

It is commonly believed that there are only two types of particle exchange statistics in quantum mechanics, fermions and bosons, with the exception of anyons in two dimension. In principle, a second exception known as parastatistics, which extends outside of two dimensions, has been considered but was believed to be physically equivalent to fermions and bosons. In this paper we show that nontrivial parastatistics inequivalent to either fermions or bosons can exist in physical systems. These new types of identical particles obey generalized exclusion principles, leading to exotic free-particle thermodynamics distinct from any system of free fermions and bosons. We formulate our theory by developing a second quantization of paraparticles, which naturally includes exactly solvable non-interacting theories, and incorporates physical constraints such as locality. We then construct a family of exactly solvable quantum spin models in one and two dimensions where free paraparticles emerge as quasiparticle excitations, and their exchange statistics can be physically observed and is notably distinct from fermions and bosons. This demonstrates the possibility of a new type of quasiparticle in condensed matter systems, and, more speculatively, the potential for previously unconsidered types of elementary particles.

quant-ph↗

arXiv2025

Hopf algebras and solvable unitary circuits

Exactly solvable models in quantum many body dynamics provide valuable insights into many interesting physical phenomena, and serve as platforms to rigorously investigate fundamental theoretical questions. Nevertheless, they are extremely rare and existing solvable models and solution techniques have serious limitations. In this paper we introduce a new family of exactly solvable unitary circuits which model quantum many body dynamics in discrete space and time. Unlike many previous solvable models, one can exactly compute the full quantum dynamics initialized from any matrix product state in this new family of models. The time evolution of local observables and correlations, the linear growth of Renyi entanglement entropy, spatiotemporal correlations, and out-of-time-order correlations are all exactly computable. A key property of these models enabling the exact solution is that any time evolved local operator is an exact matrix product operator with finite bond dimension, even at arbitrarily long time, which we prove using the underlying (weak) Hopf algebra structure along with tensor network techniques. We lay down the general framework for the construction and solution of this family of models, and give several explicit examples. In particular, we study in detail a model constructed out of a weak Hopf algebra that is very close to a floquet version of the PXP model, and the exact results we obtain may shed light on the phenomenon of quantum many body scars, and more generally, floquet quantum dynamics in constrained systems.

quant-ph↗

arXiv2025

On a Proof of the ADKMV Conjecture -- $3$-KP Integrability of the Topological Vertex

We present a mathematical proof of the Aganagic-Dijkgraaf-Klemm-Mariño-Vafa Conjecture proposed in 2006, which states that the generating function of the topological vertex, i.e., the generating function of the open Gromov-Witten invariants of $\mathbb{C}^3$, satisfies the $3$-component KP hierarchy. In our proof we introduce a boson-fermionic field assignment which generalizes the well-known boson-fermion correspondence. The proof also works for the generalization to the framed topological vertex case conjectured by Deng and Zhou. As a consequence, open Gromov-Witten theory of all smooth toric Calabi-Yau threefolds are controlled by the multi-component KP hierarchy.

math-ph↗

arXiv2025

CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models

Cancer survivors face unique emotional challenges that impact their quality of life. Mobile diary entries provide a promising method for tracking emotional states, improving self-awareness, and promoting well-being outcome. This paper aims to, through mobile diaries, understand cancer survivors' emotional states and key variables related to just-in-time intervention opportunities, including the desire to regulate emotions and the availability to engage in interventions. Although emotion analysis tools show potential for recognizing emotions from text, current methods lack the contextual understanding necessary to interpret brief mobile diary narratives. Our analysis of diary entries from cancer survivors (N=407) reveals systematic relationships between described contexts and emotional states, with administrative and health-related contexts associated with negative affect and regulation needs, while leisure activities promote positive emotions. We propose CALLM, a Context-Aware framework leveraging Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) to analyze these brief entries by integrating retrieved peer experiences and personal diary history. CALLM demonstrates strong performance with balanced accuracies reaching 72.96% for positive affect, 73.29% for negative affect, 73.72% for emotion regulation desire, and 60.09% for intervention availability, outperforming language model baselines. Post-hoc analysis reveals that model confidence strongly predicts accuracy, with longer diary entries generally enhancing performance, and brief personalization periods yielding meaningful improvements. Our findings demonstrate how contextual information in mobile diaries can be effectively leveraged to understand emotional experiences, predict key states, and identify optimal intervention moments for personalized just-in-time support.

cs.CL↗

arXiv2025

On $\mathbb N$-Coefficient Binomial Polynomiality of Hurwitz Numbers and Generalized Dessin Counting

In this paper, we study a certain type of Hurwitz numbers which count branched covers over the Riemann sphere admitting several branch points with fixed ramification types, one branch point with a fixed number of preimages, and one branch point with an arbitrary ramification type. We prove that the dependence of this kind of Hurwitz numbers on parts of the ramification type over the last point is a polynomial. Moreover, when expanding this polynomial in terms of products of binomial coefficients, we show that the coefficients are always non-negative integers via a pure combinatorial method. Our result generalizes the polynomiality in several models, including the one-part double Hurwitz numbers studied by Goulden-Jackson-Vakil, the one-part double Hurwitz numbers with completed cycles studied by Shadrin-Spitz-Zvonkine, and the generalized dessin counting.

math.CO↗

arXiv2025

CalibQuant: 1-Bit KV Cache Quantization for Multimodal LLMs

Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance across diverse applications. However, their computational overhead during deployment remains a critical bottleneck. While Key-Value (KV) caching effectively trades memory for computation to enhance inference efficiency, the growing memory footprint from extensive KV caches significantly reduces throughput and restricts prolonged deployment on memory-constrained GPU devices. To address this challenge, we propose CalibQuant, a simple yet highly effective visual quantization strategy that drastically reduces both memory and computational overhead. Specifically, CalibQuant introduces an extreme 1-bit quantization scheme, complemented by novel post-scaling and calibration techniques tailored to the intrinsic patterns of KV caches, thereby ensuring high efficiency without compromising model performance. Leveraging Triton for runtime optimization, we achieve a 10x throughput increase on InternVL models. Our method is designed to be plug-and-play, seamlessly integrating with various existing MLLMs without requiring architectural changes. Extensive experiments confirm that our approach significantly reduces memory usage while maintaining computational efficiency and preserving multimodal capabilities. Codes are available at https://github.com/insuhan/calibquant.

cs.CV↗

arXiv2025

Understanding State Social Anxiety in Virtual Social Interactions using Multimodal Wearable Sensing Indicators

Mobile sensing is ubiquitous and offers opportunities to gain insight into state mental health functioning. Detecting state elevations in social anxiety would be especially useful given this phenomenon is highly prevalent and impairing, but often not disclosed. Although anxiety is highly dynamic, fluctuating rapidly over the course of minutes, most work to date has examined anxiety at a scale of hours, days, or longer. In the present work, we explore the feasibility of detecting fluctuations in state social anxiety among N = 46 undergraduate students with elevated symptoms of trait social anxiety. Participants engaged in two dyadic and two group social interactions via Zoom. We evaluated participants' state anxiety levels as they anticipated, immediately after experiencing, and upon reflecting on each social interaction, spanning a time frame of 2-6 minutes. We collected biobehavioral features (i.e., PPG, EDA, skin temperature, and accelerometer) via Empatica E4 devices as they participated in the varied social contexts (e.g., dyadic vs. group; anticipating vs. experiencing the interaction; experiencing varying levels of social evaluation). We additionally measured their trait mental health functioning. Mixed-effect logistic regression and leave-one-subject-out machine learning modeling indicated biobehavioral features significantly predict state fluctuations in anxiety, though balanced accuracy tended to be modest (59%). However, our capacity to identify instances of heightened versus low state anxiety significantly increased (with balanced accuracy ranging from 69% to 84% across different operationalizations of state anxiety) when we integrated contextual data alongside trait mental health functioning into our predictive models.. We discuss these and other findings in the context of the broader anxiety detection literature.

cs.HC↗

arXiv2025

Simulating the Galactic population of axion clouds around stellar-origin black holes: Gravitational wave signals in the 10-100 kHz band

Ultralight scalar fields can experience runaway `superradiant' amplification near spinning black holes, resulting in a macroscopic `axion cloud' which slowly dissipates via continuous monochromatic gravitational waves. For a particular range of boson masses, $\mathcal{O}(10^{-11}$ -- $10^{-10})$ eV, an axion cloud will radiate in the $10$ -- $100$ kHz band of the Levitated Sensor Detector (LSD). Using fiducial models of the mass, spin, and age distributions of stellar-origin black holes, we simulate the present-day Milky Way population of these hypothetical objects. As a first step towards assessing the LSD's sensitivity to the resultant ensemble of GW signals, we compute the corresponding signal-to-noise ratios which build up over a nominal integration time of $10^{7}$ s, assuming the projected sensitivity of the $1$-m LSD prototype currently under construction, as well as for future $10$-m and $100$-m concepts. For a $100$-m cryogenic instrument, hundreds of resolvable signals could be expected if the boson mass $μ$ is around $3\times10^{-11}$ eV, and this number diminishes with increasing $μ$ up to $\approx 5.5\times10^{-11}$ eV. The much larger population of unresolved sources will produce a confusion foreground which could be detectable by a $10$-m instrument if $μ\in (3-4.5)\times10^{-11}$ eV, or by a $100$-m instrument if $μ\in (3-6)\times10^{-11}$ eV.

astro-ph.HE↗

arXiv2025

Reinforcement Learning for Quantum Circuit Design: Using Matrix Representations

Quantum computing promises advantages over classical computing. The manufacturing of quantum hardware is in the infancy stage, called the Noisy Intermediate-Scale Quantum (NISQ) era. A major challenge is automated quantum circuit design that map a quantum circuit to gates in a universal gate set. In this paper, we present a generic MDP modeling and employ Q-learning and DQN algorithms for quantum circuit design. By leveraging the power of deep reinforcement learning, we aim to provide an automatic and scalable approach over traditional hand-crafted heuristic methods.

quant-ph↗

arXiv2025

BKP-Affine Coordinates and Emergent Geometry of Generalized Brézin-Gross-Witten Tau-Functions

Following Zhou's framework, we consider the emergent geometry of the generalized Brézin-Gross-Witten models whose partition functions are known to be a family of tau-functions of the BKP hierarchy. More precisely, we construct a spectral curve together with its special deformation, and show that the Eynard-Orantin topological recursion on this spectral curve emerges naturally from the Virasoro constraints for the generalized BGW tau-functions. Moreover, we give the explicit expressions for the BKP-affine coordinates of these tau-functions and their generating series. The BKP-affine coordinates and the topological recursion provide two different approaches towards the concrete computations of the connected $n$-point functions. Finally, we show that the quantum spectral curve of type $B$ in the sense of Gukov-Sułkowski emerges from the BKP-affine coordinates and Eynard-Orantin topological recursion.

math-ph↗

arXiv2024

Deep Learning Aided Multi-Objective Optimization and Multi-Criteria Decision Making in Thermal Cracking Process for Olefines Production

Background: Multilayer perceptron (MLP) aided multi-objective particle swarm optimization algorithm (MOPSO) is employed in the present article to optimize the liquefied petroleum gas (LPG) thermal cracking process. This new approach significantly accelerated the multi-objective optimization (MOO), which can now be completed within one minute compared to the average of two days required by the conventional approach. Methods: MOO generates a set of equally good Pareto-optimal solutions, which are then ranked using a combination of a weighting method and five multi-criteria decision making (MCDM) methods. The final selection of a single solution for implementation is based on majority voting and the similarity of the recommended solutions from the MCDM methods. Significant Findings: The deep learning (DL) aided MOO and MCDM approach provides valuable insights into the trade-offs between conflicting objectives and a more comprehensive understanding of the relationships between them. Furthermore, this approach also allows for a deeper understanding of the impact of decision variables on the objectives, enabling practitioners to make more informed, data-driven decisions in the thermal cracking process.

physics.chem-ph↗

arXiv2024

Word-Sequence Entropy: Towards Uncertainty Estimation in Free-Form Medical Question Answering Applications and Beyond

Uncertainty estimation is crucial for the reliability of safety-critical human and artificial intelligence (AI) interaction systems, particularly in the domain of healthcare engineering. However, a robust and general uncertainty measure for free-form answers has not been well-established in open-ended medical question-answering (QA) tasks, where generative inequality introduces a large number of irrelevant words and sequences within the generated set for uncertainty quantification (UQ), which can lead to biases. This paper introduces Word-Sequence Entropy (WSE), a method that calibrates uncertainty at both the word and sequence levels, considering semantic relevance. WSE quantifies uncertainty in a way that is more closely aligned with the reliability of LLMs during uncertainty quantification (UQ). We compare WSE with six baseline methods on five free-form medical QA datasets, utilizing seven popular large language models (LLMs). Experimental results demonstrate that WSE exhibits superior performance in UQ under two standard criteria for correctness evaluation. Additionally, in terms of real-world medical QA applications, the performance of LLMs is significantly enhanced (e.g., a 6.36% improvement in model accuracy on the COVID-QA dataset) by employing responses with lower uncertainty that are identified by WSE as final answers, without any additional task-specific fine-tuning or architectural modifications.

cs.CL↗