arXiv subjects

Yun Tian

Publications and source records attributed to Yun Tian.

At least 19 recordsLinked to original sources

arXiv2026

Inhomogeneous Picard-Fuchs equations of Abelian integrals in piecewise smooth near-Hamiltonian systems

In this paper, we explicitly obtain inhomogeneous Picard-Fuchs equations for Abelian integrals $I_{i,j}^+(h)$, where $I_{i,j}^+(h)$ is an integral along orbital arcs defined by polynomials $\frac{1}{2}y^2 + F(x)=h$. Moreover, we discuss the method of using Picard-Fuchs equations to recursively compute the asymptotic expansions of genearating functions of Abelian integrals near a homoclinic loop. As an application, we derive the maximum number of isolated zeros of Melnikov functions near a nilpotent saddle homoclinic loop for piecewise polynomials perturbations with the inclination $\theta$ of the separation line as a free parameter.

math.CA↗

arXiv2026

Hybrid Deep Learning with Temporal Data Augmentation for Accurate Remaining Useful Life Prediction of Lithium-Ion Batteries

Accurate prediction of lithium-ion battery remaining useful life (RUL) is essential for reliable health monitoring and data-driven analysis of battery degradation. However, the robustness and generalization capabilities of existing RUL prediction models are significantly challenged by complex operating conditions and limited data availability. To address these limitations, this study proposes a hybrid deep learning model, CDFormer, which integrates convolutional neural networks, deep residual shrinkage networks, and Transformer encoders extract multiscale temporal features from battery measurement signals, including voltage, current, and capacity. This architecture enables the joint modeling of local and global degradation dynamics, effectively improving the accuracy of RUL prediction.To enhance predictive reliability, a composite temporal data augmentation strategy is proposed, incorporating Gaussian noise, time warping, and time resampling, explicitly accounting for measurement noise and variability. CDFormer is evaluated on two real-world datasets, with experimental results demonstrating its consistent superiority over conventional recurrent neural network-based and Transformer-based baselines across key metrics. By improving the reliability and predictive performance of RUL prediction from measurement data, CDFormer provides accurate and reliable forecasts, supporting effective battery health monitoring and data-driven maintenance strategies.

cs.LG↗

arXiv2025

Large Speech Model Enabled Semantic Communication

Existing speech semantic communication systems mainly based on Joint Source-Channel Coding (JSCC) architectures have demonstrated impressive performance, but their effectiveness remains limited by model structures specifically designed for particular tasks and datasets. Recent advances indicate that generative large models pre-trained on massive datasets, can achieve outstanding performance arexhibit exceptional performance across diverse downstream tasks with minimal fine-tuning. To exploit the rich semantic knowledge embedded in large models and enable adaptive transmission over lossy channels, we propose a Large Speech Model enabled Semantic Communication (LargeSC) system. Simultaneously achieving adaptive compression and robust transmission over lossy channels remains challenging, requiring trade-offs among compression efficiency, speech quality, and latency. In this work, we employ the Mimi as a speech codec, converting speech into discrete tokens compatible with existing network architectures. We propose an adaptive controller module that enables adaptive transmission and in-band Unequal Error Protection (UEP), dynamically adjusting to both speech content and packet loss probability under bandwidth constraints. Additionally, we employ Low-Rank Adaptation (LoRA) to finetune the Moshi foundation model for generative recovery of lost speech tokens. Simulation results show that the proposed system supports bandwidths ranging from 550 bps to 2.06 kbps, outperforms conventional baselines in speech quality under high packet loss rates and achieves an end-to-end latency of approximately 460 ms, thereby demonstrating its potential for real-time deployment.

cs.SD↗

arXiv2025

A Mechanism-Guided Inverse Engineering Framework to Unlock Design Principles of H-Bonded Organic Frameworks for Gas Separation

The diverse combinations of novel building blocks offer a vast design space for hydrogen-boned frameworks (HOFs), rendering it a great promise for gas separation and purification. However, the underlying separation mechanism facilitated by their unique hydrogen-bond networks has not yet been fully understood. In this work, a comprehensive understanding of the separation mechanisms was achieved through an iterative data-driven inverse engineering approach established upon a hypothetical HOF database possessing nearly 110,000 structures created by a material genomics method. Leveraging a simple yet universal feature extracted from hydrogen bonding information with unambiguous physical meanings, the entire design space was exploited to rapidly identify the optimization route towards novel HOF structures with superior Xe/Kr separation performance (selectivity >103). This work not only provides the first large-scale HOF database, but also demonstrates the enhanced machine learning interpretability of our model-driven iterative inverse design framework, offering new insights into the rational design of nanoporous materials for gas separation.

physics.chem-ph↗

arXiv2024

Frictional Adhesive Contact of Multiferroic Coatings Based on the Hybrid Element Method

We study the frictional adhesive contact of a rigid insulating sphere sliding past a multiferroic coating deposed onto a rigid substrate, based on the hybrid element method (HEM). The adhesion behavior is described based on the Maugis-Dugdale (MD) model. The adhesion-driven conjugate gradient method (AD-CGM) is employed to calculate the distribution of unknown pressures, while the discrete convolution-fast Fourier transform (DC-FFT) is utilized to compute the deformations, surface electric and magnetic potentials as well as the subsurface stresses, electric displacements, and magnetic inductions. We found that the coating thickness affect the contact stiffness and the interplay between friction and adhesion. More importantly, friction and gap-dependent MD adhesion affects elastic, electric, and magnetic behavior of the interface, breaking the symmetry between leading and trailing edges behaviors in all the investigated fields. Indeed, increasing the friction coefficient, the contact shape is no longer circular, the pressure distribution shifts towards the leading edge, the electric/magnetic surface potentials distributions sharpen at the leading edge, and the subsurface stress fields concentrates at the trailing edges.

cond-mat.soft↗

arXiv2024

A constant potential reactor framework for electrochemical reaction simulations

Understanding the evolution of electrified solid-liquid interfaces during electrochemical reactions is crucial. However, capturing the dynamic behavior of the interfaces with high temporal resolution and accuracy over long timescales remains a major challenge for both experimental and computational techniques. Here, we present a constant potential reactor framework that enables the simulation of electrochemical reactions with ab initio accuracy over extended timescales, allowing for real-time atomic scale observations for the electrified solid-liquid interface evolution. By implementing an enhanced sampling active learning protocol, we develop fast, accurate, and scalable neural network potentials that generalize across systems with varying electron counts, based on high-throughput density functional theory computations within an explicit-implicit hybrid solvent model. The simulation of reactions in realistic electrochemical environments uncovers the intrinsic mechanisms through which alkali metal cations promote CO2 adsorption and suppress the hydrogen evolution reaction. These findings align with previous experimental results and clarify previously elusive observations, offering valuable computational insights. Our framework lay the groundwork for future studies exploring the dynamic interplay between interfacial structure and reactivity in electrochemical environments.

physics.chem-ph↗

arXiv2024

Synchronous Multi-modal Semantic Communication System with Packet-level Coding

Although the semantic communication with joint semantic-channel coding design has shown promising performance in transmitting data of different modalities over physical layer channels, the synchronization and packet-level forward error correction of multimodal semantics have not been well studied. Due to the independent design of semantic encoders, synchronizing multimodal features in both the semantic and time domains is a challenging problem. In this paper, we take the facial video and speech transmission as an example and propose a Synchronous Multimodal Semantic Communication System (SyncSC) with Packet-Level Coding. To achieve semantic and time synchronization, 3D Morphable Mode (3DMM) coefficients and text are transmitted as semantics, and we propose a semantic codec that achieves similar quality of reconstruction and synchronization with lower bandwidth, compared to traditional methods. To protect semantic packets under the erasure channel, we propose a packet-Level Forward Error Correction (FEC) method, called PacSC, that maintains a certain visual quality performance even at high packet loss rates. Particularly, for text packets, a text packet loss concealment module, called TextPC, based on Bidirectional Encoder Representations from Transformers (BERT) is proposed, which significantly improves the performance of traditional FEC methods. The simulation results show that our proposed SyncSC reduce transmission overhead and achieve high-quality synchronous transmission of video and speech over the packet loss network.

eess.IV↗

arXiv2024

A Network Lens on Social Costs: Demolishing a Historic Street for a New Subway Station

Urban redevelopment often involves a delicate balance between enhancing regional connectivity and preserving local social fabric. Through a case study in Guangzhou, China, we argue that demolishing a historic street to construct a new subway station shows competing interests between local government's priority to facilitate spatial connectivity and locals' priority to maintain a place for social interaction and memories. We measure the social costs of the new subway station through a network lens, focusing on the loss of social ties and memories and low travel benefits of the new station for the local populations. We find that 1) the demolition will remove many small businesses that support locals' daily activities, social ties, and memories, and 2) the new station reduces travel distance and increases route options for passengers from other areas of the city more than locals nearby the demolition site. Our results contribute to a network-based framework and methodology to understand and contest inequality in expanding transportation network infrastructure in cities.

physics.soc-ph↗

arXiv2024

Bi-center conditions and bifurcation of limit cycles in a class of $Z_2$-equivariant cubic switching systems with two nilpotent points

In this paper, we generalize the Poincaré-Lyapunov method for systems with linear type centers to study nilpotent centers in switching polynomial systems and use it to investigate the bi-center problem of planar $Z_2$-equivariant cubic switching systems associated with two symmetric nilpotent singular points. With a properly designed perturbation, 6 explicit bi-center conditions for such polynomial systems are derived. Then, based on the $6$ center conditions, by using Bogdanov-Takens bifurcation theory with general perturbations, we prove that there exist at least $20$ small-amplitude limit cycles around the nilpotent bi-center for a class of $Z_2$-equivariant cubic switching systems. This is a new lower bound of cyclicity for such cubic polynomial systems, increased from $12$ to $20$.

math.DS↗

arXiv2023

Small limit cycles bifurcating in pendulum systems under trigonometric perturbations

In this paper, we consider the bifurcation of small-amplitude limit cycles near the origin in perturbed pendulum systems of the form $\dot x= y$, $\dot y=-\sin(x)+\varepsilon Q(x,y)$, where $Q(x,y)$ is a smooth or piecewise smooth polynomial in the triple $(\sin(x),\cos(x), y)$ with free coefficients. We obtain the sharp upper bound on the number of positive zeros of its associated first order Melnikov function near $h=0$ for $Q(x,y)$ being smooth and piecewise smooth with the discontinuity at $y=0$, respectively.

math.DS↗

arXiv2023

Ten limit cycles near a cubic homoclinic loop with a nilpotent cusp

In this paper, we study the bifurcation of limit cycles near a homoclinic cuspidal loop in a planar cubic near-Hamiltonian system by high-order Melnikov functions. We present a method combining the algebraic structure of Abelian integrals and Picard-Fuchs equation for computing the corresponding asymptotic expansion of Melnikov functions near the cuspidal loop. Using this system as an example, we show that planar cubic systems can have ten limit cycles bifurcating near a cubic homoclinic loop.

math.DS↗

arXiv2022

VFLens: Co-design the Modeling Process for Efficient Vertical Federated Learning via Visualization

As a decentralized training approach, federated learning enables multiple organizations to jointly train a model without exposing their private data. This work investigates vertical federated learning (VFL) to address scenarios where collaborating organizations have the same set of users but with different features, and only one party holds the labels. While VFL shows good performance, practitioners often face uncertainty when preparing non-transparent, internal/external features and samples for the VFL training phase. Moreover, to balance the prediction accuracy and the resource consumption of model inference, practitioners require to know which subset of prediction instances is genuinely needed to invoke the VFL model for inference. To this end, we co-design the VFL modeling process by proposing an interactive real-time visualization system, VFLens, to help practitioners with feature engineering, sample selection, and inference. A usage scenario, a quantitative experiment, and expert feedback suggest that VFLens helps practitioners boost VFL efficiency at a lower cost with sufficient confidence.

cs.HC↗

arXiv2021

Inductive Matrix Completion Using Graph Autoencoder

Recently, the graph neural network (GNN) has shown great power in matrix completion by formulating a rating matrix as a bipartite graph and then predicting the link between the corresponding user and item nodes. The majority of GNN-based matrix completion methods are based on Graph Autoencoder (GAE), which considers the one-hot index as input, maps a user (or item) index to a learnable embedding, applies a GNN to learn the node-specific representations based on these learnable embeddings and finally aggregates the representations of the target users and its corresponding item nodes to predict missing links. However, without node content (i.e., side information) for training, the user (or item) specific representation can not be learned in the inductive setting, that is, a model trained on one group of users (or items) cannot adapt to new users (or items). To this end, we propose an inductive matrix completion method using GAE (IMC-GAE), which utilizes the GAE to learn both the user-specific (or item-specific) representation for personalized recommendation and local graph patterns for inductive matrix completion. Specifically, we design two informative node features and employ a layer-wise node dropout scheme in GAE to learn local graph patterns which can be generalized to unseen data. The main contribution of our paper is the capability to efficiently learn local graph patterns in GAE, with good scalability and superior expressiveness compared to previous GNN-based matrix completion methods. Furthermore, extensive experiments demonstrate that our model achieves state-of-the-art performance on several matrix completion benchmarks. Our official code is publicly available.

cs.LG↗

arXiv2017

Bifurcation of small limit cycles in cubic integrable systems using higher-order analysis

In this paper, we present a method of higher-order analysis on bifurcation of small limit cycles around an elementary center of integrable systems under perturbations. This method is equivalent to higher-order Melinikov function approach used for studying bifurcation of limit cycles around a center but simpler. Attention is focused on planar cubic polynomial systems and particularly it is shown that the system studied by H. Zoladek in the article (Eleven small limit cycles in a cubic vector field, Nonlinearity 8, 843--860, 1995) can indeed have eleven limit cycles under perturbations at least up to $7$th order. Moreover, the pattern of numbers of limit cycles produced near the center is discussed up to $39$th-order perturbations, and no more than eleven limit cycles are found.

math.DS↗

arXiv2017

Bifurcation of ten small-amplitude limit cycles by perturbing a quadratic Hamiltonian system

This paper contains two parts. In the first part, we shall study the Abelian integrals for Zoladek's example [13], in which it is claimed the existence integrals of 11 small-amplitude limit cycles around a singular point in a particular cubic vector filed. We will show that the basis chosen in the proof of [13] are not independent, which leads to failure in drawing the conclusion of the existence of 11 limit cycles in this example. In the second part, we present a good combination of Melnikov function method and focus value (or normal form) computation method to study bifurcation of limit cycles. An example by perturbing a quadratic Hamiltonian system with cubic polynomials is presented to demonstrate the advantages of both methods, and 10 small-amplitude limit cycles bifurcating from a center are obtained by using up to 5th-order Melnikov functions.

math.DS↗

arXiv2017

Linearizability and critical period bifurcations of a generalized Riccati system

In this paper we investigate the isochronicity and linearizability problem for a cubic polynomial differential system which can be considered as a generalization of the Riccati system. Conditions for isochronicity and linearizability are found. The global structure of systems of the family with an isochronous center is determined. Furthermore, we find the order of weak center and study the problem of local bifurcation of critical periods in a neighborhood of the center.

math.DS↗

arXiv2015

CloudTree: A Library to Extend Cloud Services for Trees

In this work, we propose a library that enables on a cloud the creation and management of tree data structures from a cloud client. As a proof of concept, we implement a new cloud service CloudTree. With CloudTree, users are able to organize big data into tree data structures of their choice that are physically stored in a cloud. We use caching, prefetching, and aggregation techniques in the design and implementation of CloudTree to enhance performance. We have implemented the services of Binary Search Trees (BST) and Prefix Trees as current members in CloudTree and have benchmarked their performance using the Amazon Cloud. The idea and techniques in the design and implementation of a BST and prefix tree is generic and thus can also be used for other types of trees such as B-tree, and other link-based data structures such as linked lists and graphs. Preliminary experimental results show that CloudTree is useful and efficient for various big data applications.

cs.DC↗

arXiv2015

On Longest Repeat Queries Using GPU

Repeat finding in strings has important applications in subfields such as computational biology. The challenge of finding the longest repeats covering particular string positions was recently proposed and solved by İleri et al., using a total of the optimal $O(n)$ time and space, where $n$ is the string size. However, their solution can only find the \emph{leftmost} longest repeat for each of the $n$ string position. It is also not known how to parallelize their solution. In this paper, we propose a new solution for longest repeat finding, which although is theoretically suboptimal in time but is conceptually simpler and works faster and uses less memory space in practice than the optimal solution. Further, our solution can find \emph{all} longest repeats of every string position, while still maintaining a faster processing speed and less memory space usage. Moreover, our solution is \emph{parallelizable} in the shared memory architecture (SMA), enabling it to take advantage of the modern multi-processor computing platforms such as the general-purpose graphics processing units (GPU). We have implemented both the sequential and parallel versions of our solution. Experiments with both biological and non-biological data show that our sequential and parallel solutions are faster than the optimal solution by a factor of 2--3.5 and 6--14, respectively, and use less memory space.

cs.DC↗