arXiv subjects

Xinxin Zhang

Publications and source records attributed to Xinxin Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Input-to-State Stability of Reset-Integral Sliding Mode Control for Linear Systems

This work presents a stability analysis of a hybrid control system integrating a reset controller (RC) featuring a single reset state with an integral sliding-mode controller (ISMC). It is shown that the reachability of the sliding surface is decoupled from the nominal reset mechanism. This decoupling property enables a Lyapunov-based stability analysis, demonstrating that the closed-loop RC-ISMC system achieves input-to-state stability (ISS) and uniform ultimate boundedness (UUB) in the presence of exogenous references and disturbances. Furthermore, global asymptotic stability (GAS) is recovered in the unperturbed regulation scenario. A numerical case study illustrates the theoretical results.

eess.SY↗

arXiv2026

Optimal Savings under Transition Uncertainty and Learning Dynamics

This paper studies optimal consumption and saving decisions under uncertainty about the transition dynamics of the economic environment. We consider a general optimal savings problem in which the exogenous state governing discounting, capital returns, and nonfinancial income follows a Markov process with unknown transition probability, and agents update their beliefs over time through Bayesian learning. Despite the added endogenous state from belief updating, we establish the existence, uniqueness, and key structural properties of the optimal policy, including monotonicity and concavity. We also develop an efficient computational method and use it to study how transition uncertainty and learning interact with precautionary motives and wealth accumulation, highlighting a dynamic mechanism through which uncertainty about regime persistence shapes consumption dynamics and long-run household wealth.

econ.TH↗

arXiv2025

Universal and Experiment-calibrated Prediction of XANES through Crystal Graph Neural Network and Transfer Learning Strategy

Theoretical simulation is helpful for accurate interpretation of experimental X-ray absorption near-edge structure (XANES) spectra that contain rich atomic and electronic structure information of materials. However, current simulation methods are usually too complex to give the needed accuracy and timeliness when a large amount of data need to be analyzed, such as for in-situ characterization of battery materials. To address these problems, artificial intelligence (AI) models have been developed for XANES prediction. However, instead of using experimental XANES data, the existing models are trained using simulated data, resulting in significant discrepancies between the predicted and experimental spectra. Also, the universality across different elements has not been well studied for such models. In this work, we firstly establish a crystal graph neural network, pre-trained on simulated XANES data covering 48 elements, to achieve universal XANES prediction with a low average relative square error of 0.020223; and then utilize transfer learning to calibrate the model using a small experimental XANES dataset. After calibration, the edge energy misalignment error of the predicted S, Ti and Fe K edge XANES is significantly reduced by about 80%. The method demonstrated in this work opens up a new way to achieve fast, universal, and experiment-calibrated XANES prediction.

cond-mat.mtrl-sci↗

arXiv2025

Boomda: Balanced Multi-objective Optimization for Multimodal Domain Adaptation

Multimodal learning, while contributing to numerous success stories across various fields, faces the challenge of prohibitively expensive manual annotation. To address the scarcity of annotated data, a popular solution is unsupervised domain adaptation, which has been extensively studied in unimodal settings yet remains less explored in multimodal settings. In this paper, we investigate heterogeneous multimodal domain adaptation, where the primary challenge is the varying domain shifts of different modalities from the source to the target domain. We first introduce the information bottleneck method to learn representations for each modality independently, and then match the source and target domains in the representation space with correlation alignment. To balance the domain alignment of all modalities, we formulate the problem as a multi-objective task, aiming for a Pareto optimal solution. By exploiting the properties specific to our model, the problem can be simplified to a quadratic programming problem. Further approximation yields a closed-form solution, leading to an efficient modality-balanced multimodal domain adaptation algorithm. The proposed method features \textbf{B}alanced multi-\textbf{o}bjective \textbf{o}ptimization for \textbf{m}ultimodal \textbf{d}omain \textbf{a}daptation, termed \textbf{Boomda}. Extensive empirical results showcase the effectiveness of the proposed approach and demonstrate that Boomda outperforms the competing schemes. The code is is available at: https://github.com/sunjunaimer/Boomda.git.

cs.CV↗

arXiv2025

Towards Reliable LLM-based Robot Planning via Combined Uncertainty Estimation

Large language models (LLMs) demonstrate advanced reasoning abilities, enabling robots to understand natural language instructions and generate high-level plans with appropriate grounding. However, LLM hallucinations present a significant challenge, often leading to overconfident yet potentially misaligned or unsafe plans. While researchers have explored uncertainty estimation to improve the reliability of LLM-based planning, existing studies have not sufficiently differentiated between epistemic and intrinsic uncertainty, limiting the effectiveness of uncertainty estimation. In this paper, we present Combined Uncertainty estimation for Reliable Embodied planning (CURE), which decomposes the uncertainty into epistemic and intrinsic uncertainty, each estimated separately. Furthermore, epistemic uncertainty is subdivided into task clarity and task familiarity for more accurate evaluation. The overall uncertainty assessments are obtained using random network distillation and multi-layer perceptron regression heads driven by LLM features. We validated our approach in two distinct experimental settings: kitchen manipulation and tabletop rearrangement experiments. The results show that, compared to existing methods, our approach yields uncertainty estimates that are more closely aligned with the actual execution outcomes.

cs.RO↗

arXiv2025

FlashMP: Fast Discrete Transform-Based Solver for Preconditioning Maxwell's Equations on GPUs

Efficiently solving large-scale linear systems is a critical challenge in electromagnetic simulations, particularly when using the Crank-Nicolson Finite-Difference Time-Domain (CN-FDTD) method. Existing iterative solvers are commonly employed to handle the resulting sparse systems but suffer from slow convergence due to the ill-conditioned nature of the double-curl operator. Approximate preconditioners, like Successive Over-Relaxation (SOR) and Incomplete LU decomposition (ILU), provide insufficient convergence, while direct solvers are impractical due to excessive memory requirements. To address this, we propose FlashMP, a novel preconditioning system that designs a subdomain exact solver based on discrete transforms. FlashMP provides an efficient GPU implementation that achieves multi-GPU scalability through domain decomposition. Evaluations on AMD MI60 GPU clusters (up to 1000 GPUs) show that FlashMP reduces iteration counts by up to 16x and achieves speedups of 2.5x to 4.9x compared to baseline implementations in state-of-the-art libraries such as Hypre. Weak scalability tests show parallel efficiencies up to 84.1%.

cs.DC↗

arXiv2025

Automated Code Development for PDE Solvers Using Large Language Models

Foundation models -- large language models (LLMs) in particular -- have become ubiquitous, shaping daily life and driving breakthroughs across science, engineering, and technology. Harnessing their broad cross-domain knowledge, text-processing, and reasoning abilities for software development, e.g., numerical libraries for solving partial differential equations (PDEs), is therefore attracting growing interest. Yet existing studies mainly automate case setup and execution for end users. We introduce LLM-PDEveloper, a zero-shot, multi-agent LLM framework that automates code development for PDE libraries, specifically targeting secondary developers. By translating mathematical and algorithmic descriptions directly into source code, LLM-PDEveloper generates new solvers/modules and adapts existing ones. This end-to-end math-to-code approach enables a self-augmenting pipeline that continuously expands the codebase of a library, extends its capacities, and broadens its scope. We demonstrate LLM-PDEveloper on three tasks: 1) build a solver for a new PDE, 2) implement new BCs for a given PDE, and 3) modify an existing solver to incorporate additional terms, achieving moderate success rates. Failures due to syntactic errors made by LLMs are analyzed and we propose effective fixes. We also identify the mechanisms underlying certain semantic errors, guiding future research.

cs.SE↗

arXiv2025

Enhancing Reset Control Phase with Lead Shaping Filters: Applications to Precision Motion Systems

This study presents a shaped reset feedback control strategy to enhance the performance of precision motion systems. The approach utilizes a phase-lead compensator as a shaping filter to tune the phase of reset instants, thereby shaping the nonlinearity in the first-order reset control. {The design achieves either an increased phase margin while maintaining gain properties or improved gain without sacrificing phase margin, compared to reset control without the shaping filter.} Then, frequency-domain design procedures are provided for both Clegg Integrator (CI)-based and First-Order Reset Element (FORE)-based reset control systems. Finally, the effectiveness of the proposed strategy is demonstrated through two experimental case studies on a precision motion stage. In the first case, the shaped reset control leverages phase-lead benefits to achieve zero overshoot in the transient response. In the second case, the shaped reset control strategy enhances the gain advantages of the previous reset element, resulting in improved steady-state performance, including better tracking precision and disturbance rejection, while reducing overshoot for an improved transient response.

eess.SY↗

arXiv2024

Higher-Order Sinusoidal Input Describing Functions for Open-Loop and Closed-Loop Reset Control with Application to Mechatronics Systems

Reset control enhances the performance of high-precision mechatronics systems. This paper introduces a generalized reset feedback control structure that integrates a single reset-state reset controller, a shaping filter for tuning reset actions, and linear compensators arranged in series and parallel configurations with the reset controller. This structure offers greater tuning flexibility to optimize reset control performance. However, frequency-domain analysis for such systems remains underdeveloped. To address this gap, this study makes three key contributions: (1) developing Higher-Order Sinusoidal Input Describing Functions (HOSIDFs) for open-loop reset control systems; (2) deriving HOSIDFs for closed-loop reset control systems and establishing a connection with open-loop analysis; and (3) creating a MATLAB-based App to implement these methods, providing mechatronics engineers with a practical tool for reset control system design and analysis. The accuracy of the proposed methods is validated through simulations and experiments. Finally, the utility of the proposed methods is demonstrated through case studies that analyze and compare the performance of three controllers: a PID controller, a reset controller, and a shaped reset controller on a precision motion stage. Both analytical and experimental results demonstrate that the shaped reset controller provides higher tracking precision while reducing actuation forces, outperforming both the reset and PID controllers. These findings highlight the effectiveness of the proposed frequency-domain methods in analyzing and optimizing the performance of reset-controlled mechatronics systems.

eess.SY↗

arXiv2024

AI-Driven Accelerated Discovery of Intercalation-type Cathode Materials for Magnesium Batteries

Magnesium-ion batteries hold promise as future energy storage solution, yet current Mg cathodes are challenged by low voltage and specific capacity. Herein, we present an AI-driven workflow for discovering high-performance Mg cathode materials. Utilizing the common characteristics of various ionic intercalation-type electrodes, we design and train a Crystal Graph Convolutional Neural Network model that can accurately predicts electrode voltages for various ions with mean absolute errors (MAE) between 0.25 and 0.33 V. By deploying the trained model to stable Mg compounds from Materials Project and GNoME AI dataset, we identify 160 high voltage structures out of 15,308 candidates with voltages above 3.0 V and volumetric capacity over 800 Ah/L. We further train a precise NequIP model to facilitate accurate and rapid simulations of Mg ionic conductivity. From the 160 high voltage structures, the machine learning molecular dynamics simulations have selected 23 cathode materials with both high energy density and high ionic conductivity. This AI-driven workflow dramatically boosts the efficiency and precision of material discovery for multivalent ion batteries, paving the way for advanced Mg battery development.

cond-mat.mtrl-sci↗

arXiv2024

Using Large Language Models for Parametric Shape Optimization

Recent advanced large language models (LLMs) have showcased their emergent capability of in-context learning, facilitating intelligent decision-making through natural language prompts without retraining. This new machine learning paradigm has shown promise in various fields, including general control and optimization problems. Inspired by these advancements, we explore the potential of LLMs for a specific and essential engineering task: parametric shape optimization (PSO). We develop an optimization framework, LLM-PSO, that leverages an LLM to determine the optimal shape of parameterized engineering designs in the spirit of evolutionary strategies. Utilizing the ``Claude 3.5 Sonnet'' LLM, we evaluate LLM-PSO on two benchmark flow optimization problems, specifically aiming to identify drag-minimizing profiles for 1) a two-dimensional airfoil in laminar flow, and 2) a three-dimensional axisymmetric body in Stokes flow. In both cases, LLM-PSO successfully identifies optimal shapes in agreement with benchmark solutions. Besides, it generally converges faster than other classical optimization algorithms. Our preliminary exploration may inspire further investigations into harnessing LLMs for shape optimization and engineering design more broadly.

cs.CE↗

arXiv2024

Enhancing the Reliability of Closed-Loop Describing Function Analysis for Reset Control Applied to Precision Motion Systems

The Sinusoidal Input Describing Function (SIDF) is an effective tool for control system analysis and design, with its reliability directly impacting the performance of the designed control systems. This study enhances the reliability of SIDF analysis and the performance of closed-loop reset feedback control systems, presenting two main contributions. First, it introduces a method to identify frequency ranges where SIDF analysis becomes inaccurate. Second, these identified ranges correlate with high-magnitude, high-order harmonics that can degrade system performance. To address this, a shaped reset control strategy is proposed, which incorporates a shaping filter to tune reset actions and reduce high-order harmonics. Then, a frequency-domain design procedure of a PID shaping filter in a reset control system is outlined as a case study. The PID filter effectively reduces high-order harmonics and resolves limit-cycle issues under step inputs. Finally, simulations and experimental results on a precision motion stage validate the efficacy of the proposed shaped reset control, showing enhanced SIDF analysis accuracy, improved steady-state precision over linear and reset controllers, and elimination of limit cycles under step inputs.

eess.SY↗

arXiv2024

Unified Embedding Alignment for Open-Vocabulary Video Instance Segmentation

Open-Vocabulary Video Instance Segmentation (VIS) is attracting increasing attention due to its ability to segment and track arbitrary objects. However, the recent Open-Vocabulary VIS attempts obtained unsatisfactory results, especially in terms of generalization ability of novel categories. We discover that the domain gap between the VLM features (e.g., CLIP) and the instance queries and the underutilization of temporal consistency are two central causes. To mitigate these issues, we design and train a novel Open-Vocabulary VIS baseline called OVFormer. OVFormer utilizes a lightweight module for unified embedding alignment between query embeddings and CLIP image embeddings to remedy the domain gap. Unlike previous image-based training methods, we conduct video-based model training and deploy a semi-online inference scheme to fully mine the temporal consistency in the video. Without bells and whistles, OVFormer achieves 21.9 mAP with a ResNet-50 backbone on LV-VIS, exceeding the previous state-of-the-art performance by 7.7. Extensive experiments on some Close-Vocabulary VIS datasets also demonstrate the strong zero-shot generalization ability of OVFormer (+ 7.6 mAP on YouTube-VIS 2019, + 3.9 mAP on OVIS). Code is available at https://github.com/fanghaook/OVFormer.

cs.CV↗

arXiv2024

The Analysis and the Performance of the Parallel-Partial Reset Control System

Reset controllers have demonstrated their effectiveness in enhancing performance in precision motion systems. To further exploiting the potential of reset controllers, this study introduces a parallel-partial reset control structure. Frequency response analysis is effective for the design and fine-tuning of controllers in industries. However, conducting frequency response analysis for reset control systems poses challenges due to their nonlinearities. We develop frequency response analysis methods for both the open-loop and closed-loop parallel-partial reset systems. Simulation results validate the accuracy of the analysis methods, showcasing precision enhancements exceeding 100% compared to the traditional describing function method. Furthermore, we design a parallel-partial reset controller within the Proportional-Integral-Derivative (PID) control structure for a mass-spring-damper system. The frequency response analysis of the designed system indicates that, while maintaining the same bandwidth and phase margin of the first-order harmonics, the new system exhibits lower magnitudes of higher-order harmonics, compared to the traditional reset system. Moreover, simulation results demonstrate that the new system achieves lower overshoot and quicker settling time compared to both the traditional reset and linear systems.

eess.SY↗

arXiv2024

Fine-Grained Urban Flow Inference with Multi-scale Representation Learning

Fine-grained urban flow inference (FUFI) is a crucial transportation service aimed at improving traffic efficiency and safety. FUFI can infer fine-grained urban traffic flows based solely on observed coarse-grained data. However, most of existing methods focus on the influence of single-scale static geographic information on FUFI, neglecting the interactions and dynamic information between different-scale regions within the city. Different-scale geographical features can capture redundant information from the same spatial areas. In order to effectively learn multi-scale information across time and space, we propose an effective fine-grained urban flow inference model called UrbanMSR, which uses self-supervised contrastive learning to obtain dynamic multi-scale representations of neighborhood-level and city-level geographic information, and fuses multi-scale representations to improve fine-grained accuracy. The fusion of multi-scale representations enhances fine-grained. We validate the performance through extensive experiments on three real-world datasets. The resutls compared with state-of-the-art methods demonstrate the superiority of the proposed model.

cs.CV↗

arXiv2024

Deep Learning Based Resource Allocation for Full-duplex Device-to-Device Communication

Device-to-device (D2D) technology is one of the key research areas in 5G/6G networks, and full-duplex (FD) D2D will further enhance its spectral efficiency (SE). In recent years, deep learning approaches have shown remarkable performance in D2D resource allocation tasks. However, most schemes only model the channel state information (CSI) as an independent feature, neglecting the spatial relationships among multiple channels and users within the scenario. In this paper, we first design an objective function for FD D2D communication resource allocation, which aims to maximize the SE of D2D users while ensuring the minimal required SE of cellular users. Then, considering the complex CSI constituted by all the users in different channels as a three-dimensional vector, a centralized resource allocation model based on multi-dimensional spatial convolutional networks and attention mechanisms (SP-Conv-Att) is proposed. To alleviate the burden of base station, we develop two distributed models, Dist-Att and Dist-Att-Conv, to facilitate users to perform channel and power allocation locally, based on attention and multi-user convolutional networks respectively. Numerical results demonstrate that our models outperform traditional schemes and recent deep neural network models, significantly approximating the optimal solution computed by exhaustive algorithm with extremely low latency.

cs.IT↗

arXiv2023

RedCore: Relative Advantage Aware Cross-modal Representation Learning for Missing Modalities with Imbalanced Missing Rates

Multimodal learning is susceptible to modality missing, which poses a major obstacle for its practical applications and, thus, invigorates increasing research interest. In this paper, we investigate two challenging problems: 1) when modality missing exists in the training data, how to exploit the incomplete samples while guaranteeing that they are properly supervised? 2) when the missing rates of different modalities vary, causing or exacerbating the imbalance among modalities, how to address the imbalance and ensure all modalities are well-trained? To tackle these two challenges, we first introduce the variational information bottleneck (VIB) method for the cross-modal representation learning of missing modalities, which capitalizes on the available modalities and the labels as supervision. Then, accounting for the imbalanced missing rates, we define relative advantage to quantify the advantage of each modality over others. Accordingly, a bi-level optimization problem is formulated to adaptively regulate the supervision of all modalities during training. As a whole, the proposed approach features \textbf{Re}lative a\textbf{d}vantage aware \textbf{C}ross-m\textbf{o}dal \textbf{r}epresentation l\textbf{e}arning (abbreviated as \textbf{RedCore}) for missing modalities with imbalanced missing rates. Extensive empirical results demonstrate that RedCore outperforms competing models in that it exhibits superior robustness against either large or imbalanced missing rates.

cs.LG↗

arXiv2023

Computational prediction of ideal strength for a material

The ideal strength is crucial for predicting material behavior under extreme conditions, which can provide insights into material limits, guide design and engineer for enhanced performance and durability. In this work, we present a method within an allows for the estimation of tensile, shear, and indentation strengths in any crystallographic direction or plane. We have examined the strain-stress relationships of several well-known structures and compared our findings with previous work, demonstrating the effectiveness of our approach. Moreover, we performed extensive investigations into the indentation strength of hexagonal WC, \b{eta}-SiC, and MgA$l_2O_4$. The current study uncovers the modes of structural deformation and the underlying atomistic mechanisms. The insights gained from this study have significant implications for the further exploration and design of superhard materials.

cond-mat.mtrl-sci↗