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Online accelerating precursor identification and dynamic probabilistic prediction for rock slope failures using Bayesian inference 认领 引用
1
作者 Mingxi Chen Zihan Fu +2 位作者 Feng Xiong Jie Jiang Qinghui Jiang 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2026年第5期3779-3803,共25页
Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes ... Timely identification of accelerating precursors and performing reliable time-to-failure analysis are the key components in the management of slope failure risks.This study focuses on rock slope failures and proposes a framework for online identification of accelerating precursors and dynamic probabilistic prediction of failure time grounded in Bayesian inference.By integrating the Bayesian online changepoint detection(BOCD)method with a typical dimensionless trend(TDT)model,the BOCD-TDT algorithm is first developed for online identification of acceleration events and their corresponding onset of acceleration(OA).Subsequently,a Bayesian approach is employed to estimate the parameters of the inverse velocity(INV)method,enabling the dynamic probabilistic prediction of slope failure time while quantifying observational and model uncertainties across different accelerating deformation stages.Building on this,the influence of starting point(SP)selection,trend update(TU),and multi-data fusion on prediction reliability is evaluated,and a novel decision criterion for impending slope failure is proposed.The feasibility of the proposed methods is then validated using 73 rock slope failure cases.Results show that using INV data,the BOCD-TDT algorithm can reliably identify acceleration events and the corresponding OA.In time-to-failure analysis,the reliability of dynamic failure predictions can be enhanced by incorporating both observational and model uncertainties corresponding to the deformation stages into the Bayesian prediction model,along with TU detection and multi-data fusion.The proposed failure probability criterion provides valuable guidance for the identification of impending failure and the establishment of ultimate alert thresholds. 展开更多
关键词 Rock slopes Accelerating precursor identification Time-to-failure analysis Failure probability criterion Bayesian inference Inverse velocity(INV)method
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IG-3D:Integrated-Gradients 3D Optimization for Private Transformer Inference 认领 引用
2
作者 Lei Sun Jingwen Wang +3 位作者 Peng Hu Xiuqing Mao Cuiyun Hu Zhihong Wang 《Computers, Materials & Continua》 SCIE EI 2026年第5期1158-1176,共19页
Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose... Transformer models face significant computational challenges in private inference(PI).Existing optimization methods often rely on isolated techniques,neglecting joint structural and operational improvements.We propose IG-3D,a unified framework that integrates structured compression and operator approximation through accurate importance assessment.Our approach first evaluates attention head importance using Integrated Gradients(IG),offering greater stability and theoretical soundness than gradient-based methods.We then apply a threedimensional optimization:(1)structurally pruning redundant attention heads;(2)replacing Softmax with adaptive polynomial approximation to avoid exponential computations;(3)implementing layer-wise GELU substitution to accommodate different layer characteristics.A joint thresholdmechanism coordinates compression across dimensions under accuracy constraints.Experimental results on the GLUE benchmark show that our method achieves an average 2.9×speedup in inference latency and a 50%reduction in communication cost,while controlling the accuracy loss within 2.3%,demonstrating significant synergistic effects and a superior accuracy-efficiency trade-off compared to single-technique optimization strategies. 展开更多
关键词 Private inference transformer attention-head pruning integrated gradients transformer model optimization
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Reentry glide vehicle intent inference method via multidimensional intention fusion 认领 引用
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作者 HE Yangchao LI Jiong +2 位作者 SHAO Lei ZHOU Chijun LEI Humin 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期712-724,共13页
To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios,this paper proposes a multidimensional intention fusion-based inference method.Firstly,the recursive formula for t... To address the challenge of predicting reentry glide vehicle attack intention in no-fly zone scenarios,this paper proposes a multidimensional intention fusion-based inference method.Firstly,the recursive formula for the posterior probability of the vehicle’s intention is derived using Bayes’theorem.Secondly,the concepts of pseudo heading deviation angle and endpoint relative energy are introduced to formulate an intention cost function that incorporates both angular and energetic dimensions,and the corresponding likelihood probability is obtained by quantifying the cost of different intentions,which solves the problem that the traditional cost function cannot characterize the real intention of the vehicle in scenarios involving nofly zones.Finally,a dynamically weighted multidimensional intention fusion model is proposed to deduce the vehicle’s attack intent in the footprints.The simulation results show that the proposed method has a higher accuracy rate of intent inference compared to the existing methods. 展开更多
关键词 reentry glide vehicle intent inference intention cost function multidimensional intention fusion model Bayes’theorem
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Neuro-Symbolic Graph Learning for Causal Inference and Continual Learning in Mental-Health Risk Assessment 认领 引用
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作者 Monalisa Jena Noman Khan +1 位作者 Mi Young Lee Seungmin Rho 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第1期1311-1338,共28页
Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-h... Mental-health risk detection seeks early signs of distress from social media posts and clinical transcripts to enable timely intervention before crises.When such risks go undetected,consequences can escalate to self-harm,long-term disability,reduced productivity,and significant societal and economic burden.Despite recent advances,detecting risk from online text remains challenging due to heterogeneous language,evolving semantics,and the sequential emergence of new datasets.Effective solutions must encode clinically meaningful cues,reason about causal relations,and adapt to new domains without forgetting prior knowledge.To address these challenges,this paper presents a Continual Neuro-Symbolic Graph Learning(CNSGL)framework that unifies symbolic reasoning,causal inference,and continual learning within a single architecture.Each post is represented as a symbolic graph linking clinically relevant tags to textual content,enriched with causal edges derived from directional Point-wise Mutual Information(PMI).A two-layer Graph Convolutional Network(GCN)encodes these graphs,and a Transformer-based attention pooler aggregates node embeddings while providing interpretable tag-level importances.Continual adaptation across datasets is achieved through the Multi-Head Freeze(MH-Freeze)strategy,which freezes a shared encoder and incrementally trains lightweight task-specific heads(small classifiers attached to the shared embedding).Experimental evaluations across six diverse mental-health datasets ranging from Reddit discourse to clinical interviews,demonstrate that MH-Freeze consistently outperforms existing continual-learning baselines in both discriminative accuracy and calibration reliability.Across six datasets,MH-Freeze achieves up to 0.925 accuracy and 0.923 F1-Score,with AUPRC≥0.934 and AUROC≥0.942,consistently surpassing all continual-learning baselines.The results confirm the framework’s ability to preserve prior knowledge,adapt to domain shifts,and maintain causal interpretability,establishing CNSGL as a promising step toward robust,explainable,and lifelong mental-health risk assessment. 展开更多
关键词 Catastrophic forgetting causal inference continual learning deep learning graph convolutional network mental health monitoring transformer
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Exploring the Temporal Degradation and Drift of AS Path Inference 认领 引用
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作者 Xionglve Li Changsheng Hou +5 位作者 Yuzhou Huang Zhenyu Qiu Gang Hu Bingnan Hou Wei Dong Zhiping Cai 《Computers, Materials & Continua》 SCIE EI 2026年第8期833-853,共21页
The Internet inter-domain paths,i.e.,the AS paths,are important for network management,traffic engineering,and security.Due to business confidentiality,security,and privacy,the AS path information is non-public.Due to... The Internet inter-domain paths,i.e.,the AS paths,are important for network management,traffic engineering,and security.Due to business confidentiality,security,and privacy,the AS path information is non-public.Due to limited measurement resources,obtaining AS path information by measurement-based approaches is not scalable.Therefore,path inference approaches are proposed to broaden the availability of path information.These approaches assume that AS paths remain stable over a certain period of time,yet conflicting research findings question this assumption.Furthermore,the duration of the“certain period of time”is not clearly defined.Thus,we aim to address the following question:“How do the performance and temporal drift of path inference approaches evolve over time?”In this paper,we conduct a quantitative validation study and a temporal drift analysis to examine the evolution of AS path inference performance over time.The quantitative validation study shows that the minimal performance degradation is only 2.09%over eight weeks.The temporal drift analysis shows that,among the three evaluated methods,KnownPath exhibits the slowest drift,GMPI shows a moderate drift rate,and ProbInfer drifts the fastest under the current decision rule.The results provide preliminary evidence on how historical data can be leveraged despite limited measurement resources and can inform refresh-frequency decisions for path inference services under computational constraints. 展开更多
关键词 Network measurement AS path inference temporal drift
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Uncertainty quantification for the ascent phase of launch vehicles using Bayesian inference 认领 引用
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作者 CHAO Tao LI Xiaonan +2 位作者 SHANG Xiaobing MA Ping YANG Ming 《Journal of Systems Engineering and Electronics》 SCIE CSCD 2026年第2期485-503,共19页
The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajec... The launch process of a multi-stage launch vehicle is significantly influenced by uncertain parameters,including air density,aerodynamic parameters,and engine thrust,which often exhibit deviation.Predicting the trajectory range of the launch vehicle under the influence of uncertainty is essential before launch,and uncertainty quantification serves as a crucial method to address this challenge.In traditional uncertainty quantification for launch vehicles,unknown parameters are often assigned specific distributions based on prior knowledge.However,prior knowledge is sometimes subjective,and unknown parameters are often assigned conservative ranges to meet safety margins.In addition,the flight data of the past launch is precious,especially in quantifying the uncertainty of reusable or same-type launch vehicles.This paper utilizes flight data to estimate parameters base on Bayesian methods and integrates the estimation results with prior knowledge,which can more objectively set the distribution of uncertain parameters.Reasonable distribution has a positive impact on uncertainty quantification,which can avoid control strategies that are not robust enough or overly redundant.Therefore,the uncertainty quantification for launch vehicles is discussed under different information sources.In addition,the algorithm is accelerated based on Gaussian process regression and polynomial chaos expansions. 展开更多
关键词 launch vehicle uncertainty quantification Bayesian inference launch experience Gaussian process regression polynomial chaos expansions
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A GNN-Based Time-Frequency Cooperated Causal Inference Method for Irregular Time Series 认领 引用
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作者 Chen Hongbin Wang Qing +1 位作者 Li Fukai Li Shuang 《China Communications》 SCIE EI CSCD 2026年第4期338-353,共16页
Causal inference refers to the discovery and acquisition of causal relationships from observational data,which is an important way for Internet of Things(IoT)systems to realize from perception to cognition.However,mos... Causal inference refers to the discovery and acquisition of causal relationships from observational data,which is an important way for Internet of Things(IoT)systems to realize from perception to cognition.However,most existing causal inference methods assume that the input data is structured.For irregular time series with random missing items,the absence of important time points often leads to serious degradation of causal inference performance,which hinders practical applications.To this end,a GNN-based timefrequency cooperated causal inference(TFCI-GNN)method for irregular time series is proposed.The temporal features are initially extracted using a temporal encoder,and the frequency domain encoder adaptively models the frequency interdependence between channels using discrete cosine transform(DCT),forming attention coefficients that act on the temporal features.By utilizing the estimated adjacency matrix,feature aggregation is performed using a GNN.Finally,the causal graph is decoded and updated using a unique self-supervised approach,which mutually promotes the process of interpolation and causal inference.Experimental results on synthetic and real datasets show that the proposed TFCI-GNN method outperforms the baseline algorithms in inference performance. 展开更多
关键词 causal inference Internet of Things(IoT) irregular time series graph neural network
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Dual-Stream Feature Decoupling and Temporal Variational Bayesian Inference for Ship Re-Identification with Incomplete Data 认领 引用
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作者 Wanhui Qiao Xiaorui Zhang +2 位作者 Wei Sun Shiyu Zhou Kaibo Wang 《Computers, Materials & Continua》 SCIE EI 2026年第7期819-838,共20页
Ship re-identification (Re-ID) aims to match ship identities across disjoint camera views and separated time periods, which is critical for maritime target tracking and law enforcement. In real-world surveillance, var... Ship re-identification (Re-ID) aims to match ship identities across disjoint camera views and separated time periods, which is critical for maritime target tracking and law enforcement. In real-world surveillance, variations in target distance and viewing angle frequently produce partial views and occlusions, leading to missing geometric components and fragmented appearance cues. Such incomplete observations substantially degrade the robustness and generalization of conventional single-frame methods that rely on global appearance representations. To address these challenges, this study proposes a new ship re-identification framework based on dual-stream feature decoupling and temporal variational Bayesian inference. The proposed method explicitly disentangles ship representations into appearance and structural streams, and leverages multi-frame temporal context to infer missing components and enhance discriminability under partial visibility. Specifically, a ResNet-based splitter trained adversarially against two discriminators is employed to decouple the input representation into separate feature streams. The decoupled streams are then modeled over time using a bidirectional LSTM (BiLSTM) together with a visibility-probability estimator. A graph-structured spatial prior, parameterized via a graph attention network (GAT), serves as the variational prior. Given sequential observations, the variational inference module estimates posterior distributions for missing components and performs probabilistic completion in the latent space. The framework is trained end-to-end using cross-entropy and triplet losses. Extensive experiments on the Ship-CH dataset demonstrate that our method achieves 85.67% mAP and 93.67% Rank-1 accuracy, exhibiting superior robustness under occlusion and partial visibility. 展开更多
关键词 BiLSTM bayesian inference feature decoupling triplet loss cross-entropy loss ship re-identification
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HEbdMIA:Lightweight Logit Encryption for Membership Inference Defense 认领 引用
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作者 Akash Shah Mudasir Ahmad Wani +4 位作者 Ravi Prakash Chaturvedi Shri Kant Nidhi Sindhwani Kashish Ara Shakil Sulieman Alshuhri 《Computers, Materials & Continua》 SCIE EI 2026年第9期2355-2375,共21页
Membership Inference Attacks(MIAs)pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training,particularly in sensitive domains such as ... Membership Inference Attacks(MIAs)pose a significant privacy risk in machine learning by enabling adversaries to infer whether specific data samples were used during training,particularly in sensitive domains such as social media and mental health analytics.To address this challenge,this paper proposes HEbdMIA,a lightweight homomorphic encryption-based defense that operates at the post-inference stage by encrypting model output logits without requiring retraining or architectural modifications.The proposed approach preserves the relative ordering of predictions while obscuring confidence patterns exploited by MIAs.Experimental evaluation on DepInferAttack and BotInferAttack demonstrates that HEbdMIA achieves a reduction in MIA success rates of 31.0%and 27.3%,respectively,with an associated accuracy decrease of 29.3%and 26.4%,reflecting a controlled privacy and utility trade-off.Additional analysis using precision,recall,F1-score,and ROC-AUC confirms a substantial decline in adversarial inference capability.These findings indicate that HEbdMIA provides an effective,scalable,and deployment-friendly solution for enhancing privacy in real-world machine learning systems. 展开更多
关键词 Membership inference attack homomorphic encryption machine learning encrypted data logit encryption model security
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SecureBadger:a homomorphic encryption-based framework for secure medical inference 认领 引用
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作者 Zhaoyang He Wenti Yang +1 位作者 Longfei Wu Zhitao Guan 《Digital Communications and Networks》 SCIE EI CSCD 2026年第5期743-754,共12页
With the rapid development of Artificial Intelligence of Things(AIoT)technology,its adoption in the field of smart healthcare is becoming increasingly pervasive.Leading cloud service providers like IBM Watson Health n... With the rapid development of Artificial Intelligence of Things(AIoT)technology,its adoption in the field of smart healthcare is becoming increasingly pervasive.Leading cloud service providers like IBM Watson Health now offer neural network inference services tailored for smart healthcare applications-users simply need to send data to the server to get the diagnosis results.However,a growing concern arises regarding the potential compromise of user privacy.Currently,researchers propose the use of secure multi-party computation and homomorphic encryption techniques to address this issue.Nevertheless,further exploration and improvement are needed to mitigate the side effects,such as increased latency and challenges in meeting real-time monitoring requirements.In this paper,we propose a secure homomorphic encryption-based inference framework named SecureBadger for two typical medical inference scenarios:disease diagnosis based on image analysis and health monitoring with smart wearable devices.We design two inference modes-large-scale batch inference and small-scale low-latency inference.Additionally,different ciphertext packaging schemes are designed to enhance inference efficiency for different inference modes,different input data types and different network layers.Experimental evaluations are conducted on several datasets,and the results indicate that SecureBadger can significantly reduce the inference time overhead in both inference modes. 展开更多
关键词 Homomorphic encryption Secure inference Smart healthcare Wearable health technology Artificial Intelligence of Things
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Comparison of MoM method and IBD segment method for kinship inference in investigative genetic genealogy 认领 引用
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作者 Ran Li Yu Zang +5 位作者 Jingyi Yang Nana Wang Jiajun Liu Enlin Wu Riga Wu Hongyu Sun 《Forensic Sciences Research》 CSCD 2026年第1期42-49,共8页
Method of moments(MoM)and identity by descent(IBD)segment methods are two popular algorithms for kinship inference in investigative genetic genealogy(IGG).However,there is no consensus on how or when to use them,and d... Method of moments(MoM)and identity by descent(IBD)segment methods are two popular algorithms for kinship inference in investigative genetic genealogy(IGG).However,there is no consensus on how or when to use them,and different criteria of IBD lengths or kinship coefficients are applied to consider a true match.In this study,we compared the performance of KING(representing the MoM method)and IBIS(representing the IBD segment method)for kinship inference in homogeneous populations,admixed populations,and sparse SNP panels.Both simulated and real family data were used.In addition,an equivalent threshold-based method for kinship inference was also proposed,based on either the kinship coefficients or the lengths of IBD segments.Results showed that the overall accuracies were 64.97%for homogeneous population and 54.71%for admixed population with KING while they were 72.92%and 70.88%,respectively,with IBIS.IBIS showed low recall and precision rates when the SNP number was below 164k.In contrast,KING performed still robustly with SNP number as low as 10k.Similar results were obtained with real family data.In conclusion,the two methods perform differently and different methods should be used for different scenarios.If the DNA is of high quality or the samples are from admixed populations,the IBD segment method is recommended.If the samples are of low quantity and/or quality,MoM is more appropriate.For more complex scenarios,both methods can be tried.More importantly,there is an urgent need to develop new algorithms to address related issues. 展开更多
关键词 forensic sciences method of moments(MoM) identity by descent(IBD)segment method kinship inference investigative genetic genealogy(IGG)
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A survey on edge multimodal large models:Compression,inference acceleration,and applications 认领 引用
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作者 Siru Chen Yuanchao Shu +1 位作者 Cong Wang Jiming Chen 《National Science Open》 CSCD 2026年第3期164-202,共39页
Deploying multimodal large language models(MLLMs)at the network edge is critical for enabling low-latency,privacy-preserving multimodal intelligence.However,the substantial computational and memory demands of MLLMs pr... Deploying multimodal large language models(MLLMs)at the network edge is critical for enabling low-latency,privacy-preserving multimodal intelligence.However,the substantial computational and memory demands of MLLMs present significant challenges for deployment on heterogeneous and resource-constrained edge devices.This survey systematically reviews existing approaches aimed at addressing these challenges.We categorize the literature along two complementary dimensions:model-level compression,which focuses on efficient architectural design and parameter reduction,and system-level inference acceleration,which emphasizes runtime optimizations such as scheduling and resource management.In addition,the survey examines the practical applications of edge-deployed MLLMs in domains such as cyber intelligence and embodied intelligence,and discusses emerging research directions,including edge-native model architectures,to further improve the trade-off between intelligence capability and resource efficiency. 展开更多
关键词 edge computing multimodal large language models model compression inference optimization cyber intelligence embodied intelligence
Recent advances in simulation-based inference for gravitational wave data analysis 认领 引用
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作者 Bo Liang He Wang 《Astronomical Techniques and Instruments》 CSCD 2026年第2期93-111,共19页
The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level anal... The detection of gravitational waves by the LIGO-Virgo-KAGRA collaboration has ushered in a new era of observational astronomy,emphasizing the need for rapid and detailed parameter estimation and population-level analyses.Traditional Bayesian inference methods,particularly Markov chain Monte Carlo,face significant computational challenges when dealing with the high-dimensional parameter spaces and complex noise characteristics inherent in gravitational wave data.This review examines the emerging role of simulation-based inference methods in gravitational wave astronomy,with a focus on approaches that leverage machine-learning techniques such as normalizing flows and neural posterior estimation.We provide a comprehensive overview of the theoretical foundations underlying various simulation-based inference methods,including neural posterior estimation,neural ratio estimation,neural likelihood estimation,flow matching,and consistency models.We explore the applications of these methods across diverse gravitational wave data processing scenarios,from single-source parameter estimation and overlapping signal analysis to testing general relativity and conducting population studies.Although these techniques demonstrate speed improvements over traditional methods in controlled studies,their model-dependent nature and sensitivity to prior assumptions are barriers to their widespread adoption.Their accuracy,which is similar to that of conventional methods,requires further validation across broader parameter spaces and noise conditions. 展开更多
关键词 Simulation-based inference Gravitational wave astronomy Normalizing flows Neural posterior estimation Bayesian parameter estimation
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Retry Trigger:intelligent inference duplication for enhancing LLM resilience to hardware transient faults 认领 引用
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作者 Jiajia JIAO Yixu YU 《ENGINEERING Information Technology & Electronic Engineering》 SCIE EI CSCD 2026年第4期65-82,共18页
Large language models(LLMs)have exhibited outstanding performance across a wide range of natural language processing(NLP)tasks.However,the rising prevalence of hardware transient faults has made silent data corruption... Large language models(LLMs)have exhibited outstanding performance across a wide range of natural language processing(NLP)tasks.However,the rising prevalence of hardware transient faults has made silent data corruptions(SDCs)in LLMs increasingly problematic,severely degrading output quality and user experience.State-of-the-art protection schemes primarily rely on hardware-assisted algorithm-based fault tolerance(ABFT)or boundary-setting-driven online fault tolerance(FT2)for selective layers,yet these solutions suffer from strict hardware dependencies,substantial overhead,or incomplete coverage.To address these limitations,we propose RetryTrigger,a novel hardware-free fault-aware inference methodology capable of handling all potential faults.During LLM inference,RetryTrigger dynamically collects runtime output features(e.g.,maximum probability,top-k probability gaps,output entropy,logits statistics,and inference latency),which are used to train a LightGBM meta-model.This meta-model accurately predicts whether duplicate inference should be performed,thereby effectively mitigating faults while preserving efficiency without additional hardware dependence.Extensive experiments on seven representative LLMs(including T5-Small,RoBERTa,BioMedBERT,Qwen2.5-Coder-0.5B/7B,MiniMind,and Opt)demonstrate that RetryTrigger reduces SDC rates by up to 95.33%(on average 92.97%)and achieves a minimal performance overhead of 2.4012%(on average 4.1167%),offering a superior balance between reliability and efficiency compared to state-ofthe-art solutions. 展开更多
关键词 Large language models System resilience Intelligent fault detection Inference duplication Transient faults
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Calibration kernel-based ratio estimation for likelihood-free posterior inference 认领 引用
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作者 XIONG Yifei ZHANG Sanguo 《中国科学院大学学报（中英文）》 CAS CSCD 北大核心 2026年第4期444-452,共9页
Bayesian inference often faces challenges where the likelihood function is difficult to evaluate or lacks explicit expression,known as likelihood-free Bayesian problems,where posterior distributions can only be inferr... Bayesian inference often faces challenges where the likelihood function is difficult to evaluate or lacks explicit expression,known as likelihood-free Bayesian problems,where posterior distributions can only be inferred indirectly through samples generated under specific parameters.Existing methods such as approximate Bayesian computation,synthetic likelihood,and Bayesian optimization focus on addressing these issues.This paper extends Miller et al.’s(2022)sequential neural ratio estimation method for likelihood-free Bayesian problems by transforming likelihood-toevidence ratio estimation into a multi-class problem for efficient posterior estimation.We introduce a new calibration kernel-based ratio estimation method(CKRE),to enhance the original method’s training efficiency and performance.The convergence of our proposed method is proven,and numerical experiments demonstrate its significant improvement in accurately estimating posterior distributions under limited sample generation conditions. 展开更多
关键词 likelihood-free Bayesian inference sequential neural ratio estimation calibration kernel-based ratio estimation contrastive learning
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Data Inference:Data Security Threats in the AI Era 认领 引用
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作者 Zijun Wang Ting Liu +2 位作者 Yang Liu Enrico Zio Xiaohong Guan 《Engineering》 SCIE EI CSCD 2025年第9期29-33,共5页
1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf h... 1.Introduction Data inference(DInf)is a data security threat in which critical information is inferred from low-sensitivity data.Once regarded as an advanced professional threat limited to intelligence analysts,DInf has become a widespread risk in the artificial intelligence(AI)era. 展开更多
关键词 data security threats data security threat artificial intelligence ai era artificial intelligence data inference data inference dinf advanced professional threat
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P4DE-CI:Privacy and Delay Dual-Driven Device-Edge Collaborative Inference for Intelligent Internet of Things 认领 引用
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作者 Han Shujun Yan Kaiwen +3 位作者 Zhang Wenzhao Xu Xiaodong Wang Bizhu Tao Xiaofeng 《China Communications》 SCIE EI CSCD 2025年第12期224-239,共16页
In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult... In 6G,artificial intelligence represented by deep nerual network(DNN)will unleash its potential and empower IoT applications to transform into intelligent IoT applications.However,whole DNNbased inference is difficult to carry out on resourceconstrained intelligent IoT devices and will suffer privacy leakage when offloading to the cloud or mobile edge computation server(MECs).In this paper,we formulate a privacy and delay dual-driven device-edge collaborative inference(P4DE-CI)system to preserve the privacy of raw data while accelerating the intelligent inference process,where the intelligent IoT devices run the front-end part of DNN model and the MECs execute the back-end part of DNN model.Considering three typical privacy leakage models and the end-to-end delay of collaborative DNN-based inference,we define a novel intelligent inference Quality of service(I2-QoS)metric as the weighted summation of the inference latency and privacy preservation level.Moreover,we propose a DDPG-based joint DNN model optimization and resource allocation algorithm to maximize I2-QoS,by optimizing the association relationship between intelligent IoT devices and MECs,the DNN model placement decision,and the DNN model partition decision.Experiments carried out on the AlexNet model reveal that the proposed algorithm has better performance in both privacy-preserving and inference-acceleration. 展开更多
关键词 device-edge collaborative inference DNN model placement and partition inference delay privacy-preserving resource allocation
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Federated Experiments:Generative Causal Inference Powered by LLM-based Agents Simulation and RAG-based Domain Docking 认领 引用 被引量：2
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作者 De-Yu Zhou Xiao Xue +5 位作者 Qun Ma Chao Guo Li-Zhen Cui Yong-Lin Tian Jing Yang Fei-Yue Wang 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2025年第7期1301-1304,共4页
COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to f... COMPUTATIONAL experiments method is an essential tool for analyzing,designing,managing,and integrating complex systems.However,a significant challenge arises in constructing agents with human-like characteristics to form an AI society.Agent modeling typically encompasses four levels:1)The autonomy features of agents,e.g.,perception,behavior,and decision-making;2)The evolutionary features of agents,e.g.,bounded rationality,heterogeneity,and learning evolution;3)The social features of agents,e.g.,interaction,cooperation,and competition;4)The emergent features of agents,e.g.,gaming with environments or regulatory strategies.Traditional modeling techniques primarily derive from ABMs(Agent-based Models)and incorporate various emerging technologies(e.g.,machine learning,big data,and social networks),which can enhance modeling capabilities,while amplifying the complexity[1]. 展开更多
关键词 autonomy features generative causal inference complex systems llm based agents simulation federated experiments rag based domain docking computational experiments method agent modeling
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Application of Fuzzy Inference System in Gas Turbine Engine Fault Diagnosis Against Measurement Uncertainties 认领 引用 被引量：2
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作者 Shuai Ma Yafeng Wu +1 位作者 Zheng Hua Linfeng Gou 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS CSCD 2025年第1期62-83,共22页
Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel perf... Robustness against measurement uncertainties is crucial for gas turbine engine diagnosis.While current research focuses mainly on measurement noise,measurement bias remains challenging.This study proposes a novel performance-based fault detection and identification(FDI)strategy for twin-shaft turbofan gas turbine engines and addresses these uncertainties through a first-order Takagi-Sugeno-Kang fuzzy inference system.To handle ambient condition changes,we use parameter correction to preprocess the raw measurement data,which reduces the FDI’s system complexity.Additionally,the power-level angle is set as a scheduling parameter to reduce the number of rules in the TSK-based FDI system.The data for designing,training,and testing the proposed FDI strategy are generated using a component-level turbofan engine model.The antecedent and consequent parameters of the TSK-based FDI system are optimized using the particle swarm optimization algorithm and ridge regression.A robust structure combining a specialized fuzzy inference system with the TSK-based FDI system is proposed to handle measurement biases.The performance of the first-order TSK-based FDI system and robust FDI structure are evaluated through comprehensive simulation studies.Comparative studies confirm the superior accuracy of the first-order TSK-based FDI system in fault detection,isolation,and identification.The robust structure demonstrates a 2%-8%improvement in the success rate index under relatively large measurement bias conditions,thereby indicating excellent robustness.Accuracy against significant bias values and computation time are also evaluated,suggesting that the proposed robust structure has desirable online performance.This study proposes a novel FDI strategy that effectively addresses measurement uncertainties. 展开更多
关键词 Performance-based fault diagnosis Gas turbine engine Fuzzy inference system Measurement uncertainty Regression and classification
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Prioritization of potential drug targets for diabetic kidney disease using integrative omics data mining and causal inference 认领 引用 被引量：1
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作者 Junyu Zhang Jie Peng +7 位作者 Chaolun Yu Yu Ning Wenhui Lin Mingxing Ni Qiang Xie Chuan Yang Huiying Liang Miao Lin 《Journal of Pharmaceutical Analysis》 SCIE CAS CSCD 2025年第8期1787-1799,共13页
Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed ... Diabetic kidney disease(DKD)with increasing global prevalence lacks effective therapeutic targets to halt or reverse its progression.Therapeutic targets supported by causal genetic evidence are more likely to succeed in randomized clinical trials.In this study,we integrated large-scale plasma proteomics,genetic-driven causal inference,and experimental validation to identify prioritized targets for DKD using the UK Biobank(UKB)and FinnGen cohorts.Among 2844 diabetic patients(528 with DKD),we identified 37 targets significantly associated with incident DKD,supported by both observational and causal evidence.Of these,22%(8/37)of the potential targets are currently under investigation for DKD or other diseases.Our prospective study confirmed that higher levels of three prioritized targetsdinsulin-like growth factor binding protein 4(IGFBP4),family with sequence similarity 3 member C(FAM3C),and prostaglandin D2 synthase(PTGDS)dwere associated with a 4.35,3.51,and 3.57-fold increased likelihood of developing DKD,respectively.In addition,population-level protein-altering variants(PAVs)analysis and in vitro experiments cross-validated FAM3C and IGFBP4 as potential new target candidates for DKD,through the classic NLR family pyrin domain containing 3(NLRP3)-caspase-1-gasdermin D(GSDMD)apoptotic axis.Our results demonstrate that integrating omics data mining with causal inference may be a promising strategy for prioritizing therapeutic targets. 展开更多
关键词 Diabetic kidney disease Proteomics Causal inference Drug targets
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