arXiv subjects

Mingjian Tang

Publications and source records attributed to Mingjian Tang.

5 recordsLinked to original sources

arXiv2025

A Modality-Aware Cooperative Co-Evolutionary Framework for Multimodal Graph Neural Architecture Search

Co-exploitation attacks on software vulnerabilities pose severe risks to enterprises, a threat that can be mitigated by analyzing heterogeneous and multimodal vulnerability data. Multimodal graph neural networks (MGNNs) are well-suited to integrate complementary signals across modalities, thereby improving attack-prediction accuracy. However, designing an effective MGNN architecture is challenging because it requires coordinating modality-specific components at each layer, which is infeasible through manual tuning. Genetic algorithm (GA)-based graph neural architecture search (GNAS) provides a natural solution, yet existing methods are confined to single modalities and overlook modality heterogeneity. To address this limitation, we propose a modality-aware cooperative co-evolutionary algorithm for multimodal graph neural architecture search, termed MACC-MGNAS. First, we develop a modality-aware cooperative co-evolution (MACC) framework under a divide-and-conquer paradigm: a coordinator partitions a global chromosome population into modality-specific gene groups, local workers evolve them independently, and the coordinator reassembles chromosomes for joint evaluation. This framework effectively captures modality heterogeneity ignored by single-modality GNAS. Second, we introduce a modality-aware dual-track surrogate (MADTS) method to reduce evaluation cost and accelerate local gene evolution. Third, we design a similarity-based population diversity indicator (SPDI) strategy to adaptively balance exploration and exploitation, thereby accelerating convergence and avoiding local optima. On a standard vulnerabilities co-exploitation (VulCE) dataset, MACC-MGNAS achieves an F1-score of 81.67% within only 3 GPU-hours, outperforming the state-of-the-art competitor by 8.7% F1 while reducing computation cost by 27%.

cs.LG↗

arXiv2025

GUIDe: Generative and Uncertainty-Informed Inverse Design for On-Demand Nonlinear Functional Responses

Inverse design is a common yet challenging engineering problem, particularly for nonlinear functional responses such as mechanical behavior or spectral analysis. Deep generative models are motivated by intractability, non-existence or non-uniqueness of solutions, and the need for rapid solution-space exploration. In this study, we show that deep generative model-based and optimization-based approaches can provide incomplete solutions or hallucinate given out-of-distribution targets. To address this, we propose the Generative and Uncertainty-informed Inverse Design (GUIDe) framework, which leverages probabilistic machine learning, statistical inference, and Markov chain Monte Carlo to generate designs with targeted nonlinear behaviors. Instead of inverse mappings, i.e., response $\mapsto$ design, GUIDe adopts design $\mapsto$ response: a forward model predicts each design's nonlinear functional response and evaluates the confidence under a user-specified tolerance. Sampling the solution space by this confidence yields diverse feasible designs. Our validation on nacre-inspired materials finds solutions beyond the training range, even under out-of-distribution targets.

cs.CE↗

arXiv2024

Inverse Design of Nonlinear Mechanics of Bio-inspired Materials Through Interface Engineering and Bayesian Optimization

In many biological materials such as nacre and bone, the material structure consists of hard grains and soft interfaces, with the interfaces playing a significant role in the material's mechanical behavior. This type of structures has been utilized in the design of various bio-inspired composite materials. Such applications often require the materials to exhibit a specified nonlinear stress-strain relationship. A key challenge lies in identifying appropriate interface properties from an infinite search space to achieve a given target stress-strain curve. This study introduces a Bayesian optimization (BO) framework specifically tailored for the inverse design of interfaces in bio-inspired composites. As a notable advantage, this method is capable of expanding the design space, allowing the discovery of optimal solutions even when the target curve deviates significantly from the initial dataset. Furthermore, our results show that BO can identify distinct interface designs that produce similar target stress-strain responses, yet differ in their deformation and failure mechanisms. These findings highlight the potential of the proposed BO framework to address a wide range of inverse design challenges in nonlinear mechanics problems.

cond-mat.mtrl-sci↗

arXiv2023

Robust Information Bottleneck for Task-Oriented Communication with Digital Modulation

Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver. However, only transmitting task-relevant information without introducing any redundancy may cause robustness issues in learning due to the channel variations, and the JSCC which directly maps the source data into continuous channel input symbols poses compatibility issues on existing digital communication systems. In this paper, we address these two issues by first investigating the inherent tradeoff between the informativeness of the encoded representations and the robustness to information distortion in the received representations, and then propose a task-oriented communication scheme with digital modulation, named discrete task-oriented JSCC (DT-JSCC), where the transmitter encodes the features into a discrete representation and transmits it to the receiver with the digital modulation scheme. In the DT-JSCC scheme, we develop a robust encoding framework, named robust information bottleneck (RIB), to improve the communication robustness to the channel variations, and derive a tractable variational upper bound of the RIB objective function using the variational approximation to overcome the computational intractability of mutual information. The experimental results demonstrate that the proposed DT-JSCC achieves better inference performance than the baseline methods with low communication latency, and exhibits robustness to channel variations due to the applied RIB framework.

cs.IT↗

arXiv2023

Developing Responsible Chatbots for Financial Services: A Pattern-Oriented Responsible AI Engineering Approach

The recent release of ChatGPT has gained huge attention and discussion worldwide, with responsible AI being a key topic of discussion. How can we ensure that AI systems, including ChatGPT, are developed and adopted in a responsible way? To tackle the responsible AI challenges, various ethical principles have been released by governments, organisations, and companies. However, those principles are very abstract and not practical enough. Further, significant efforts have been put on algorithm-level solutions that only address a narrow set of principles, such as fairness and privacy. To fill the gap, we adopt a pattern-oriented responsible AI engineering approach and build a Responsible AI Pattern Catalogue to operationalise responsible AI from a system perspective. In this article, we first summarise the major challenges in operationalising responsible AI at scale and introduce how we use the Responsible AI Pattern Catalogue to address those challenges. We then examine the risks at each stage of the chatbot development process and recommend pattern-driven mitigations to evaluate the the usefulness of the Responsible AI Pattern Catalogue in a real-world setting.

cs.SE↗