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Yuan Lin

Yuan Lin appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.stat-mechphysics.chem-pheess.SYSystems and ControlArtificial IntelligenceComputation and Languagephysics.soc-phBiological PhysicsComputer Visioncond-mat.mtrl-scicond-mat.othercond-mat.supr-conphysics.class-ph

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Published work

22 published item(s)

preprint2026arXiv

InteractWeb-Bench: Can Multimodal Agent Escape Blind Execution in Interactive Website Generation?

With the advancement of multimodal large language models (MLLMs) and coding agents, the website development has shifted from manual programming to agent-based project-level code synthesis. Existing benchmarks rely on idealized assumptions, especially for well-structured, information-rich inputs and static execution settings. In contrast, real-world development is constrained by a critical bottleneck: the semantic misalignment between ambiguous, low-quality instructions from non-expert users and model understanding, which results in a failure mode that we term blind execution. To address this gap, we introduce InteractWeb-Bench, the first multimodal interactive benchmark for website generation under non-expert low-code user conditions. InteractWeb-Bench introduces four types of user agents and persona-driven instruction perturbations to systematically simulate diverse user behaviors, including ambiguity, redundancy, and contradiction, grounded in requirement engineering defect taxonomies. We develop an interactive execution environment for agents, featuring a unified action space comprising Clarify, Implement, Verify, and Submit, enabling iterative intent refinement, code synthesis, and visual feedback-based validation. Extensive experiments and analysis reveal that frontier MLLM-based agents remain trapped in blind execution, exposing limitations in intent recognition and adaptive interaction.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Nanodiamond-Enabled Torsion Microscopy Uncovers Multidimensional Cell-Matrix Mechanical Interactions

Traditional cellular force-sensing techniques, such as traction force microscopy (TFM), are predominantly limited to measuring linear tractions, overlooking and technically unable to capture the nanoscale torsional forces that are critical in cell-matrix interactions. Here, we introduce a nanodiamond-enabled torsion microscopy (DTM) that integrates nitrogen-vacancy (NV) centers as orientation markers with micropillar arrays to decouple and quantify nanoscale rotational and translational motions induced by cells. This approach achieves high precision (~1.47 degree rotational accuracy and ~3.13*10-15 Nm torque sensitivity), enabling reconstruction of cellular torsional force fields and twisting energy distributions previously underestimated. Our findings reveal the widespread presence of torsional forces in cell-matrix interactions, introducing "cellular mechanical modes" where different adhesion patterns dictate the balance between traction- and torque- mediated mechanical energy transferred to the substrate. Notably, in immune cells like macrophages that generally exert low linear tractions, torque overwhelmingly dominates traction, highlighting a unique mechanical output for specific cellular functions. By uncovering these differential modes, DTM provides a versatile tool to advance biomechanical investigations, with potential applications in disease diagnostics and therapeutics.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

PatRe: A Full-Stage Office Action and Rebuttal Generation Benchmark for Patent Examination

Patent examination is a complex, multi-stage process requiring both technical expertise and legal reasoning, increasingly challenged by rising application volumes. Prior benchmarks predominantly view patent examination as discriminative classification or static extraction, failing to capture its inherently interactive and iterative nature, similar to the peer review and rebuttal process in academic publishing. In this paper, we introduce PatRe, the first benchmark that models the full patent examination lifecycle, including Office Action generation and applicant rebuttal. PatRe comprises 480 real-world cases and supports both oracle and retrieval-simulated evaluation settings. Our benchmark reframes patent examination as a dynamic, multi-turn process of justification and response. Extensive experiments across various LLMs reveal critical insights into model performance, including differences between proprietary and open-source models, as well as task asymmetries between examiner analysis and applicant-side rebuttal. These findings highlight both the potential and current limitations of LLMs in modeling complex, real-world legal reasoning and technical novelty judgment in patent examination. We release our code and dataset to facilitate future research on patent examination modeling.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Co-Optimization of On-Ramp Merging and Plug-In Hybrid Electric Vehicle Power Split Using Deep Reinforcement Learning

Current research on Deep Reinforcement Learning (DRL) for automated on-ramp merging neglects vehicle powertrain and dynamics. This work considers automated on-ramp merging for a power-split Plug-In Hybrid Electric Vehicle (PHEV), the 2015 Toyota Prius Plug-In, using DRL. The on-ramp merging control and the PHEV energy management are co-optimized such that the DRL policy directly outputs the power split between the engine and the electric motor. The testing results show that DRL can be successfully used for co-optimization, leading to collision-free on-ramp merging. When compared with sequential approaches wherein the upper-level on-ramp merging control and the lower-level PHEV energy management are performed independently and in sequence, we found that co-optimization results in economic but jerky on-ramp merging while sequential approaches may result in collisions due to neglecting powertrain power limit constraints in designing the upper-level on-ramp merging controller.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Anti-Jerk On-Ramp Merging Using Deep Reinforcement Learning

Deep Reinforcement Learning (DRL) is used here for decentralized decision-making and longitudinal control for high-speed on-ramp merging. The DRL environment state includes the states of five vehicles: the merging vehicle, along with two preceding and two following vehicles when the merging vehicle is or is projected on the main road. The control action is the acceleration of the merging vehicle. Deep Deterministic Policy Gradient (DDPG) is the DRL algorithm for training to output continuous control actions. We investigated the relationship between collision avoidance for safety and jerk minimization for passenger comfort in the multi-objective reward function by obtaining the Pareto front. We found that, with a small jerk penalty in the multi-objective reward function, the vehicle jerk could be reduced by 73% compared with no jerk penalty while the collision rate was maintained at zero. Regardless of the jerk penalty, the merging vehicle exhibited decision-making strategies such as merging ahead or behind a main-road vehicle.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Comparison of Deep Reinforcement Learning and Model Predictive Control for Adaptive Cruise Control

This study compares Deep Reinforcement Learning (DRL) and Model Predictive Control (MPC) for Adaptive Cruise Control (ACC) design in car-following scenarios. A first-order system is used as the Control-Oriented Model (COM) to approximate the acceleration command dynamics of a vehicle. Based on the equations of the control system and the multi-objective cost function, we train a DRL policy using Deep Deterministic Policy Gradient (DDPG) and solve the MPC problem via Interior-Point Optimization (IPO). Simulation results for the episode costs show that, when there are no modeling errors and the testing inputs are within the training data range, the DRL solution is equivalent to MPC with a sufficiently long prediction horizon. Particularly, the DRL episode cost is only 5.8% higher than the benchmark solution provided by optimizing the entire episode via IPO. The DRL control performance degrades when the testing inputs are outside the training data range, indicating inadequate generalization. When there are modeling errors due to control delays, disturbances, and/or testing with a High-Fidelity Model (HFM) of the vehicle, the DRL-trained policy performs better with large modeling errors while having similar performance as MPC when the modeling errors are small.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

An Action Recognition network for specific target based on rMC and RPN

The traditional methods of action recognition are not specific for the operator, thus results are easy to be disturbed when other actions are operated in videos. The network based on mixed convolutional resnet and RPN is proposed in this paper. The rMC is tested in the data set of UCF-101 to compare with the method of R3D. The result shows that its correct rate reaches 71.07%. Meanwhile, the action recognition network is tested in our gesture and body posture data sets for specific target. The simulation achieves a good performance in which the running speed reaches 200 FPS. Finally, our model is improved by introducing the regression block and performs better, which shows the great potential of this model.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Non-Volatile Superconductivity in an Insulating Copper Oxide Induced via Ionic Liquid Gating

Manipulating the superconducting states of high-T_c cuprate superconductors in an efficient and reliable way is of great importance for their applications in next-generation electronics. Traditional methods are mostly based on a trial-and-error method that is difficult to implement and time consuming. Here, employing ionic liquid gating, a selective control of volatile and non-volatile superconductivity is achieved in pristine insulating Pr_2CuO_{4\pmδ} film, based on two distinct mechanisms: 1) with positive electric fields, the film can be reversibly switched between non-superconducting and superconducting states, attributed to the carrier doping effect. 2) The film becomes more resistive by applying negative bias voltage up to -4 V, but strikingly, a non-volatile superconductivity is achieved once the gate voltage is removed. Such a persistent superconducting state represents a novel phenomenon in copper oxides, resulting from the doping healing of oxygen vacancies in copper-oxygen planes as unraveled by high-resolution scanning transmission electron microscope and in-situ x-ray diffraction experiments. The effective manipulation and mastering of volatile/non-volatile superconductivity in the same parent cuprate opens the door to more functionalities for superconducting electronics, as well as supplies flexible samples for investigating the nature of quantum phase transitions in high-T_c superconductors.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Dynamics of "comb-of-comb" networks

The dynamics of complex networks, being a current hot topic of many scientific fields, is often coded through the corresponding Laplacian matrix. The spectrum of this matrix carries the main features of the networks' dynamics. Here we consider the deterministic networks which can be viewed as "comb-of-comb" iterative structures. For their Laplacian spectra we find analytical equations involving Chebyshev polynomials, whose properties allow one to analyze the spectra in deep. Here, in particular, we find that in the infinite size limit the corresponding spectral dimension goes as $d_s\rightarrow2$. The $d_s$ leaves its fingerprint in many dynamical processes, as we exeplarily show by considering the dynamical properties of the polymer networks, including single monomer displacement under a constant force, mechanical relaxation, and fluorescence depolarization.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Mean first-passage time for maximal-entropy random walks in complex networks

We perform an in-depth study for mean first-passage time (MFPT)---a primary quantity for random walks with numerous applications---of maximal-entropy random walks (MERW) performed in complex networks. For MERW in a general network, we derive an explicit expression of MFPT in terms of the eigenvalues and eigenvectors of the adjacency matrix associated with the network. For MERW in uncorrelated networks, we also provide a theoretical formula of MFPT at the mean-field level, based on which we further evaluate the dominant scalings of MFPT to different targets for MERW in uncorrelated scale-free networks, and compare the results with those corresponding to traditional unbiased random walks (TURW). We show that the MFPT to a hub node is much lower for MERW than for TURW. However, when the destination is a node with the least degree or a uniformly chosen node, the MFPT is higher for MERW than for TURW. Since MFPT to a uniformly chosen node measures real efficiency of search in networks, our work provides insight into general searching process in complex networks.

0 citations0 reviewsNo code linkedOpen access
preprint2014arXiv

Tunable Semimetallic State in Compressive-strained SrIrO3 Films Revealed by Transport Behaviors

Orthorhombic SrIrO3 is a typical spin-orbit-coupling correlated metal that shows diversified physical properties under the external stimuli. Here nonlinear Hall effect and weakly temperature-dependent resistance are observed in a SrIrO3 film epitaxially grown on SrTiO3 substrate. It infers that orthorhombic SrIrO3 is a semimetal oxide. However, linear Hall effect and insensitive-temperature-dependent resistance are observed in SrIrO3 films grown on (La,Sr)(Al,Ta)O3 (LSAT) substrates, suggesting a tunable semimetallic state due to band structure change in SrIrO3 films under different compressive strain. The mechanism of this evolution is explored in detail through strain-state analysis by reciprocal space mapping and electron diffraction, carrier density and mobility calculations, as well as electronic band structure evolution under compressive strain (predicted by tight-binding approximation). It might suggest that the strain-induced band shift leads to the semimetallic tuning in the SrIrO3 film grown on from SrTiO3 to LSAT substrates. Our findings illustrate the tunability of SrIrO3 properties and pave the way to induce novel physical states in SrIrO3 such as the proposed topological insulator state in heterostructures.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Influence of trap location on the efficiency of trapping in dendrimers and regular hyperbranched polymers

In this paper, we study the trapping problem in two representative polymer networks, Cayley trees and Vicsek fractals, which separately model dendrimers and regular hyperbranched polymers. Our goal is to explore the impact of trap location on the efficiency of trapping in these two important polymer systems, with the efficiency being measured by the average trapping time (ATT) that is the average of source-to-trap mean first-passage time over every staring point in the whole networks. For Cayley trees, we derive an exact analytic formula for the ATT to an arbitrary trap node, based on which we further obtain the explicit expression of ATT for the case that the trap is uniformly distributed. For Vicsek fractals, we provide the closed-form solution for ATT to a peripheral node farthest from the central node, as well as the numerical solutions for the case when the trap is placed on other nodes. Moreover, we derive the exact formula for the ATT corresponding to the trapping problem when the trap has an uniform distribution over all nodes. Our results show that the influence of trap location on the trapping efficiency is completely different for the two polymer networks. In Cayley trees, the leading scaling of ATT increases with the shortest distance between the trap and the central node, implying that trap's position has an essential impact on the trapping efficiency; while in Vicsek fractals, the effect of location of the trap is negligible, since the dominant behavior of ATT is identical, respective of the location where the trap is placed. We also present that for all cases of trapping problems being studied, the trapping process is more efficient in Cayley trees than in Vicsek fractals. We demonstrate that all differences related to trapping in the two polymer systems are rooted in their underlying topological structures.

0 citations0 reviewsNo code linkedOpen access
preprint2013arXiv

Random walks in weighted networks with a perfect trap: An application of Laplacian spectra

In this paper, we propose a general framework for the trapping problem on a weighted network with a perfect trap fixed at an arbitrary node. By utilizing the spectral graph theory, we provide an exact formula for mean first-passage time (MFPT) from one node to another, based on which we deduce an explicit expression for average trapping time (ATT) in terms of the eigenvalues and eigenvectors of the Laplacian matrix associated with the weighted graph, where ATT is the average of MFPTs to the trap over all source nodes. We then further derive a sharp lower bound for the ATT in terms of only the local information of the trap node, which can be obtained in some graphs. Moreover, we deduce the ATT when the trap is distributed uniformly in the whole network. Our results show that network weights play a significant role in the trapping process. To apply our framework, we use the obtained formulas to study random walks on two specific networks: trapping in weighted uncorrelated networks with a deep trap, the weights of which are characterized by a parameter, and Lévy random walks in a connected binary network with a trap distributed uniformly, which can be looked on as random walks on a weighted network. For weighted uncorrelated networks we show that the ATT to any target node depends on the weight parameter, that is, the ATT to any node can change drastically by modifying the parameter, a phenomenon that is in contrast to that for trapping in binary networks. For Lévy random walks in any connected network, by using their equivalence to random walks on a weighted complete network, we obtain the optimal exponent characterizing Lévy random walks, which have the minimal average of ATTs taken over all target nodes.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Counting spanning trees in a small-world Farey graph

The problem of spanning trees is closely related to various interesting problems in the area of statistical physics, but determining the number of spanning trees in general networks is computationally intractable. In this paper, we perform a study on the enumeration of spanning trees in a specific small-world network with an exponential distribution of vertex degrees, which is called a Farey graph since it is associated with the famous Farey sequence. According to the particular network structure, we provide some recursive relations governing the Laplacian characteristic polynomials of a Farey graph and its subgraphs. Then, making use of these relations obtained here, we derive the exact number of spanning trees in the Farey graph, as well as an approximate numerical solution for the asymptotic growth constant characterizing the network. Finally, we compare our results with those of different types of networks previously investigated.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Mean first-passage time for random walks in general graphs with a deep trap

We provide an explicit formula for the global mean first-passage time (GMFPT) for random walks in a general graph with a perfect trap fixed at an arbitrary node, where GMFPT is the average of mean first-passage time to the trap over all starting nodes in the whole graph. The formula is expressed in terms of eigenvalues and eigenvectors of Laplacian matrix for the graph. We then use the formula to deduce a tight lower bound for the GMFPT in terms of only the numbers of nodes and edges, as well as the degree of the trap, which can be achieved in both complete graphs and star graphs. We show that for a large sparse graph the leading scaling for this lower bound is proportional to the system size and the reciprocal of the degree for the trap node. Particularly, we demonstrate that for a scale-free graph of size $N$ with a degree distribution $P(d)\sim d^{-γ}$ characterized by $γ$, when the trap is placed on a most connected node, the dominating scaling of the lower bound becomes $N^{1-1/γ}$, which can be reached in some scale-free graphs. Finally, we prove that the leading behavior of upper bounds for GMFPT on any graph is at most $N^{3}$ that can be reached in the bar-bell graphs. This work provides a comprehensive understanding of previous results about trapping in various special graphs with a trap located at a specific location.

0 citations0 reviewsNo code linkedOpen access
preprint2012arXiv

Trapping in dendrimers and regular hyperbranched polymers

Dendrimers and regular hyperbranched polymers are two classic families of macromolecules, which can be modeled by Cayley trees and Vicsek fractals, respectively. In this paper, we study the trapping problem in Cayley trees and Vicsek fractals with different underlying geometries, focusing on a particular case with a perfect trap located at the central node. For both networks, we derive the exact analytic formulas in terms of the network size for the average trapping time (ATT)---the average of node-to-trap mean first-passage time over the whole networks. The obtained closed-form solutions show that for both Cayley trees and Vicsek fractals, the ATT display quite different scalings with various system sizes, which implies that the underlying structure plays a key role on the efficiency of trapping in polymer networks. Moreover, the dissimilar scalings of ATT may allow to differentiate readily between dendrimers and hyperbranched polymers.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Counting spanning trees in self-similar networks by evaluating determinants

Spanning trees are relevant to various aspects of networks. Generally, the number of spanning trees in a network can be obtained by computing a related determinant of the Laplacian matrix of the network. However, for a large generic network, evaluating the relevant determinant is computationally intractable. In this paper, we develop a fairly generic technique for computing determinants corresponding to self-similar networks, thereby providing a method to determine the numbers of spanning trees in networks exhibiting self-similarity. We describe the computation process with a family of networks, called $(x,y)$-flowers, which display rich behavior as observed in a large variety of real systems. The enumeration of spanning trees is based on the relationship between the determinants of submatrices of the Laplacian matrix corresponding to the $(x,y)$-flowers at different generations and is devoid of the direct laborious computation of determinants. Using the proposed method, we derive analytically the exact number of spanning trees in the $(x,y)$-flowers, on the basis of which we also obtain the entropies of the spanning trees in these networks. Moreover, to illustrate the universality of our technique, we apply it to some other self-similar networks with distinct degree distributions, and obtain explicit solutions to the numbers of spanning trees and their entropies. Finally, we compare our results for networks with the same average degree but different structural properties, such as degree distribution and fractal dimension, and uncover the effect of these topological features on the number of spanning trees.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Random walks in modular scale-free networks with multiple traps

Extensive empirical investigation has shown that a plethora of real networks synchronously exhibit scale-free and modular structure, and it is thus of great importance to uncover the effects of these two striking properties on various dynamical processes occurring on such networks. In this paper, we examine two cases of random walks performed on a class of modular scale-free networks with multiple traps located at several given nodes. We first derive a formula of the mean first-passage time (MFPT) for a general network, which is the mean of the expected time to absorption originating from a specific node, averaged over all non-trap starting nodes. Although the computation is complex, the expression of the formula is exact; moreover, the computational approach and procedure are independent of the number and position of the traps. We then determine analytically the MFPT for the two random walks being considered. The obtained analytical results are in complete agreement with the numerical ones. Our results show that the number and location of traps play an important role in the behavior of the MFPT, since for both cases the MFPT grows as a power-law function of the number of nodes, but their exponents are quite different. We demonstrate that the root of the difference in the behavior of MFPT is attributed to the modular and scale-free topologies of the networks. This work can deepen the understanding of diffusion on networks with modular and scale-free architecture and motivate relevant studies for random walks running on complex random networks with multiple traps.

0 citations0 reviewsNo code linkedOpen access
preprint2011arXiv

Random walks in small-world exponential treelike networks

In this paper, we investigate random walks in a family of small-world trees having an exponential degree distribution. First, we address a trapping problem, that is, a particular case of random walks with an immobile trap located at the initial node. We obtain the exact mean trapping time defined as the average of first-passage time (FPT) from all nodes to the trap, which scales linearly with the network order $N$ in large networks. Then, we determine analytically the mean sending time, which is the mean of the FPTs from the initial node to all other nodes, and show that it grows with $N$ in the order of $N \ln N$. After that, we compute the precise global mean first-passage time among all pairs of nodes and find that it also varies in the order of $N \ln N$ in the large limit of $N$. After obtaining the relevant quantities, we compare them with each other and related our results to the efficiency for information transmission by regarding the walker as an information messenger. Finally, we compare our results with those previously reported for other trees with different structural properties (e.g., degree distribution), such as the standard fractal trees and the scale-free small-world trees, and show that the shortest path between a pair of nodes in a tree is responsible for the scaling of FPT between the two nodes.

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preprint2010arXiv

Determining mean first-passage time on a class of treelike regular fractals

Relatively general techniques for computing mean first-passage time (MFPT) of random walks on networks with a specific property are very useful, since a universal method for calculating MFPT on general graphs is not available because of their complexity and diversity. In this paper, we present techniques for explicitly determining the partial mean first-passage time (PMFPT), i.e., the average of MFPTs to a given target averaged over all possible starting positions, and the entire mean first-passage time (EMFPT), which is the average of MFPTs over all pairs of nodes on regular treelike fractals. We describe the processes with a family of regular fractals with treelike structure. The proposed fractals include the $T$ fractal and the Peano basin fractal as their special cases. We provide a formula for MFPT between two directly connected nodes in general trees on the basis of which we derive an exact expression for PMFPT to the central node in the fractals. Moreover, we give a technique for calculating EMFPT, which is based on the relationship between characteristic polynomials of the fractals at different generations and avoids the computation of eigenvalues of the characteristic polynomials. Making use of the proposed methods, we obtain analytically the closed-form solutions to PMFPT and EMFPT on the fractals and show how they scale with the number of nodes. In addition, to exhibit the generality of our methods, we also apply them to the Vicsek fractals and the iterative scale-free fractal tree and recover the results previously obtained.

0 citations0 reviewsNo code linkedOpen access
preprint2009arXiv

An alternative approach to determining average distance in a class of scale-free modular networks

Various real-life networks of current interest are simultaneously scale-free and modular. Here we study analytically the average distance in a class of deterministically growing scale-free modular networks. By virtue of the recursive relations derived from the self-similar structure of the networks, we compute rigorously this important quantity, obtaining an explicit closed-form solution, which recovers the previous result and is corroborated by extensive numerical calculations. The obtained exact expression shows that the average distance scales logarithmically with the number of nodes in the networks, indicating an existence of small-world behavior. We present that this small-world phenomenon comes from the peculiar architecture of the network family.

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preprint2007arXiv

Ground states of hard-core bosons in one dimensional periodic potentials

With Girardeau's Fermi-Bose mapping, we find the exact ground states of hard-core bosons residing in a one dimensional periodic potential. The analysis of these ground states shows that when the number of bosons $N$ is commensurate with the number of wells $M$ in the periodic potential, the boson system is a Mott insulator whose energy gap, however, is given by the single-particle band gap of the periodic potential; when $N$ is not commensurate with $M$, the system is a metal (not a superfluid). In fact, we argue that there may be no superfluid phase for any one-dimensional boson system in terms of Landau's criterion of superfluidity. The Kronig-Penney potential is used to illustrate our results.

0 citations0 reviewsNo code linkedOpen access