Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conductin...Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.展开更多
UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vest...UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vests,serving as a key material for enhancing protective performance.This study systematically investigates the influence mechanism of interfacial properties on the energy absorption characteristics of UHMWPE-based protective structures through multi-scale experiments and numerical simulations,and establishes a cross-scale design methodology.Innovatively,an orthotropic constitutive model incorporating dynamic friction coefficients is constructed,combined with a modified Johnson-Cook failure criterion,to achieve high-precision simulation of the entire ballistic impact process(error<3.5%).Additionally,a friction field prediction model considering strain rate effects is developed,and the friction evolution laws of UHMWPE and Para-aramid(Kevlar)fabrics under strain rates of 10^(−3) and 10^(−4) s^(−1) are obtained through MTS pull-out tests.The results show that:(1)there exists a critical yarn-yarn friction coefficient(μ=0.2);exceeding this value leads to a 19%reduction in energy absorption capacity,while viscous interfaces increase the energy dissipation peak by 16%;(2)UHMWPE shows kinetically-dominated absorption(58%)with high rate but high load,increasing damage risk.Para-aramid has friction-dominated absorption(53%)with a lower rate but stable load.Hybrid fabrics use potential-dominated absorption(49%)at a moderate rate,balancing stability and protection.(3)3–5 layers of UHMWPE fabric yield optimal cost-effectiveness,with the unit cost reduction rate of the HS+5U scheme reaching 2.74 m/(s·$),which is 91%higher than that of the hybrid scheme.(4)For HS+5U(5-ply UHMWPE),V50 is 520 m/s,meeting primary protection requirement.For hybrid solutions with U/K≥3(e.g.,HS+6U+2K),V50 reaches 580 m/s(≥540 m/s),satisfying advanced protection requirement.This research provides critical references for the design of flexible protective structures and their engineering applications.展开更多
Increased awareness of Tibetan cultural preservation,along with technological advancements,has led to significant efforts in academic research on Tibetan.However,the structural complexity of the Tibetan language and l...Increased awareness of Tibetan cultural preservation,along with technological advancements,has led to significant efforts in academic research on Tibetan.However,the structural complexity of the Tibetan language and limited labeled handwriting data impede advancements in Optical Character Recognition(OCR)and other applications.To address these challenges,this paper proposes an innovative Tibetan data augmentation technique,using Generative Adversarial Networks(GANs)to synthesise arbitrary handwriting images in variable calligraphic styles based on inputs.Moreover,our method leverages a Real-Fake Cross Inputs Strategy during training to enhance generation diversity and improve model generalisability in generating handwritten text beyond the training set and pre-defined corpus.The model was trained on three Tibetan handwriting datasets,including Ume style numerals,Uchen style consonants,and Khyug-yig style words.Experimental results demonstrate that the model successfully generates realistic and recognisable Tibetan numeral and consonant handwriting,achieving Frechet Inception Distance(FID)scores of 14.45 and 27.63,respectively.The proposed method's effectiveness in augmenting OCR models was validated as evidenced by a reduced OCR Word Error Rate(WER)on the augmented datasets.展开更多
With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study p...With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study proposes a new model,the Masked Joint Representation Model(MJRM).MJRM approximates the original hypothesis by leveraging multiple elements in a limited context.It dynamically adapts to changes in characteristics based on data distribution through three main components.First,masking-based representation learning,termed selective dynamic masking,integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets,whose predictions are then aggregated with optimized weights.This design alleviates sparsity,suppresses noise,and preserves contextual structures.Second,regularization-based improvements are applied.Third,techniques for addressing sparse data are used to perform final inference.As a result,MJRM improves performance by up to 4%compared to existing AI techniques.In our experiments,we analyzed the contribution of each factor,demonstrating that masking,dynamic learning,and aggregating multiple instances complement each other to improve performance.This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification,and can be useful even in challenging situations such as data shortage or data distribution variations.We expect that the approach can be extended to diverse fields such as sentiment analysis,spam filtering,and domain-specific document classification.展开更多
Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods...Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.展开更多
Object detection,a major challenge in computer vision and pattern recognition,plays a significant part in many applications,crossing artificial intelligence,face recognition,and autonomous driving.It involves focusing...Object detection,a major challenge in computer vision and pattern recognition,plays a significant part in many applications,crossing artificial intelligence,face recognition,and autonomous driving.It involves focusing on identifying the detection,localization,and categorization of targets in images.A particularly important emerging task is distinguishing real animals from toy replicas in real-time,mostly for smart camera systems in both urban and natural environments.However,that difficult task is affected by factors such as showing angle,occlusion,light intensity,variations,and texture differences.To tackle these challenges,this paper recommends Group Sparse YOLOv8(You Only Look Once version 8),an improved real-time object detection algorithm that improves YOLOv8 by integrating group sparsity regularization.This adjustment improves efficiency and accuracy while utilizing the computational costs and power consumption,including a frame selection approach.And a hybrid parallel processing method that merges pipelining with dataflow strategies to improve the performance.Established using a custom dataset of toy and real animal images along with well-known datasets,namely ImageNet,MSCOCO,and CIFAR-10/100.The combination of Group Sparsity with YOLOv8 shows high detection accuracy with lower latency.Here provides a real and resource-efficient solution for intelligent camera systems and improves real-time object detection and classification in environments,differentiating between real and toy animals.展开更多
Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and ...Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.展开更多
Multimodal emotion recognition has emerged as a key research area for enabling human-centered artificial intelligence,supported by the rapid progress in vision,audio,language,and physiological modeling.Existing approa...Multimodal emotion recognition has emerged as a key research area for enabling human-centered artificial intelligence,supported by the rapid progress in vision,audio,language,and physiological modeling.Existing approaches integrate heterogeneous affective cues through diverse embedding strategies and fusion mechanisms,yet the field remains fragmented due to differences in feature alignment,temporal synchronization,modality reliability,and robustness to noise or missing inputs.This survey provides a comprehensive analysis of MER research from 2021 to 2025,consolidating advances in modality-specific representation learning,cross-modal feature construction,and early,late,and hybrid fusion paradigms.We systematically review visual,acoustic,textual,and sensor-based embeddings,highlighting howpre-trained encoders,self-supervised learning,and large languagemodels have reshaped the representational foundations ofMER.We further categorize fusion strategies by interaction depth and architectural design,examining how attention mechanisms,cross-modal transformers,adaptive gating,and multimodal large language models redefine the integration of affective signals.Finally,we summarize major benchmark datasets and evaluation metrics and discuss emerging challenges related to scalability,generalization,and interpretability.This survey aims to provide a unified perspective onmultimodal fusion for emotion recognition and to guide future research toward more coherent and generalizable multimodal affective intelligence.展开更多
The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,...The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.展开更多
Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,w...Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.展开更多
Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representat...Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications.展开更多
As a key carrier for the implementation of the core quality of"aesthetic creativity",the effective realization of the aesthetic value of language textbook illustrations is facing the real problems of practic...As a key carrier for the implementation of the core quality of"aesthetic creativity",the effective realization of the aesthetic value of language textbook illustrations is facing the real problems of practical disconnection and theoretical gap.Based on the theories of semiotics,embodied cognition and cultural reproduction,this paper innovatively puts forward the concept of"transformation mechanism of aesthetic education",constructs a three-phase The study analyzes the dynamic transformation process of illustrations from image symbols to students'aesthetic experience.The study explains the essential characteristics of illustration as a dual symbolic system of"likenessregulation",reveals the cognitive ladder of"perception-cognition-creation"and the path of realization of embodied cognition,and points out that the current aesthetic education of illustration has the problem of instrumentalization,and that the current aesthetic education of illustration has the problem of instrumentalization.The study points out that the current aesthetic education of illustrations is plagued by instrumental alienation,cultural hegemony and aesthetic flattening,and proposes a"three-in-one"education path from the dimensions of teachers,teaching materials and culture,including improving teachers'aesthetic education,reconstructing the principles of teaching materials and dynamic revision mechanism,and constructing classroom aesthetic education communities.The study provides new perspectives and practical guidance for exploring the aesthetic value of language textbook illustrations and bridging the gap between the theories of aesthetics and pedagogy,aiming to promote the return of aesthetic education to the nature of"reconstruction of experience",and to cultivate students'aesthetic literacy and sound personality.展开更多
In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structu...In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.展开更多
In vitro maturation(IvM)of human oocytes offers cost efficiency and minimal invasiveness,serving as a valuable supplementary tool in assisted reproduction for fertility preservation,ovarian hyperstimulation syndrome p...In vitro maturation(IvM)of human oocytes offers cost efficiency and minimal invasiveness,serving as a valuable supplementary tool in assisted reproduction for fertility preservation,ovarian hyperstimulation syndrome prevention,and other reproductive strategies.Despite its availability for three decades,the clinical use of IVM remains limited due to efficacy and safety concerns.This study examines the DNA methylation profile of IVM oocytes collected during laparoscopic/hysteroscopic surgeries compared to in vivo matured oocytes via reduced representation bisulfite sequencing.Results indicate IVM oocytes exhibit a higher global methylation level.Differentially methylated regions(DMRs)analysis reveals that the in vitro group displays more hypermethylated and fewer hypomethylated DMRs compared to the in vivo group.Additionally,the in vitro group exhibits a higher level of non-CpG methylation than the in vivo group.However,no significant correlation between methylation levels and transcriptional activity in these oocytes is found,especially for those specific imprinted genes or genes related to embryonic development.These findings shed light on the epigenetic landscape of IvM oocytes,contributing to the ongoing assessment of their clinical feasibility and safety in assisted reproduction.展开更多
Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high c...Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.展开更多
A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spa...A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.展开更多
In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The e...In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.展开更多
Coupled thermo-hydro-mechanical(THM)processes in fractured rock are playing a crucial role in geoscience and geoengineering applications.Diverse and conceptually distinct approaches have emerged over the past decades ...Coupled thermo-hydro-mechanical(THM)processes in fractured rock are playing a crucial role in geoscience and geoengineering applications.Diverse and conceptually distinct approaches have emerged over the past decades in both continuum and discontinuum perspectives leading to significant progress in their comprehending and modeling.This review paper offers an integrated perspective on existing modeling methodologies providing guidance for model selection based on the initial and boundary conditions.By comparing various models,one can better assess the uncertainties in predictions,particularly those related to the conceptual models.The review explores how these methodologies have significantlyenhanced the fundamental understanding of how fractures respond to fluid injection and production,and improved predictive capabilities pertaining to coupled processes within fractured systems.It emphasizes the importance of utilizing advanced computational technologies and thoroughly considering fundamental theories and principles established through past experimental evidence and practical experience.The selection and calibration of model parameters should be based on typical ranges and applied to the specificconditions of applications.The challenges arising from inherent heterogeneity and uncertainties,nonlinear THM coupled processes,scale dependence,and computational limitations in representing fieldscale fractures are discussed.Realizing potential advances on computational capacity calls for methodical conceptualization,mathematical modeling,selection of numerical solution strategies,implementation,and calibration to foster simulation outcomes that intricately reflectthe nuanced complexities of geological phenomena.Future research efforts should focus on innovative approaches to tackle the hurdles and advance the state-of-the-art in this critical fieldof study.展开更多
Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Class...Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.展开更多
Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,su...Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,susceptibility to catastrophic forgetting,and a deficiency in logical reasoning capabilities within black-box models.To address these challenges,we draw insights from human memory mechanisms to introduce“machine memory,”which we define as a storage structure formed by encoding external information into a machine-representable and computable format.Centered on machine memory,we propose the brand-new machine memory intelligence(M^(2)I)framework,which encompasses representation,learning,and reasoning modules and loops.We explore the key issues and recent advances in the four core aspects of M^(2)I,including neural mechanisms,associative representation,continual learning,and collaborative reasoning within machine memory.M^(2)I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models,driving a qualitative leap from weak to strong AI.展开更多
基金supported by the Innovative Human Resource Development for Local Intel-lectualization program through the Institute of Information&Communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.IITP-2026-2020-0-01741)the research fund of Hanyang University(HY-2025-1110).
文摘Arrhythmias are a frequently occurring phenomenon in clinical practice,but how to accurately dis-tinguish subtle rhythm abnormalities remains an ongoing difficulty faced by the entire research community when conducting ECG-based studies.From a review of existing studies,two main factors appear to contribute to this problem:the uneven distribution of arrhythmia classes and the limited expressiveness of features learned by current models.To overcome these limitations,this study proposes a dual-path multimodal framework,termed DM-EHC(Dual-Path Multimodal ECG Heartbeat Classifier),for ECG-based heartbeat classification.The proposed framework links 1D ECG temporal features with 2D time–frequency features.By setting up the dual paths described above,the model can process more dimensions of feature information.The MIT-BIH arrhythmia database was selected as the baseline dataset for the experiments.Experimental results show that the proposed method outperforms single modalities and performs better for certain specific types of arrhythmias.The model achieved mean precision,recall,and F1 score of 95.14%,92.26%,and 93.65%,respectively.These results indicate that the framework is robust and has potential value in automated arrhythmia classification.
基金the Postdoctoral Science Foundation Funded Project of China with grant No.2021M701687Introduction and Education Plan for Young Innovative Talents in Colleges and Universities of Shandong Province.
文摘UHMWPE(Ultra-High Molecular Weight Polyethylene)plain-weave fabric,characterized by its lightweight and high-strength properties,is widely used in protective equipment such as bulletproof vests and stab-resistant vests,serving as a key material for enhancing protective performance.This study systematically investigates the influence mechanism of interfacial properties on the energy absorption characteristics of UHMWPE-based protective structures through multi-scale experiments and numerical simulations,and establishes a cross-scale design methodology.Innovatively,an orthotropic constitutive model incorporating dynamic friction coefficients is constructed,combined with a modified Johnson-Cook failure criterion,to achieve high-precision simulation of the entire ballistic impact process(error<3.5%).Additionally,a friction field prediction model considering strain rate effects is developed,and the friction evolution laws of UHMWPE and Para-aramid(Kevlar)fabrics under strain rates of 10^(−3) and 10^(−4) s^(−1) are obtained through MTS pull-out tests.The results show that:(1)there exists a critical yarn-yarn friction coefficient(μ=0.2);exceeding this value leads to a 19%reduction in energy absorption capacity,while viscous interfaces increase the energy dissipation peak by 16%;(2)UHMWPE shows kinetically-dominated absorption(58%)with high rate but high load,increasing damage risk.Para-aramid has friction-dominated absorption(53%)with a lower rate but stable load.Hybrid fabrics use potential-dominated absorption(49%)at a moderate rate,balancing stability and protection.(3)3–5 layers of UHMWPE fabric yield optimal cost-effectiveness,with the unit cost reduction rate of the HS+5U scheme reaching 2.74 m/(s·$),which is 91%higher than that of the hybrid scheme.(4)For HS+5U(5-ply UHMWPE),V50 is 520 m/s,meeting primary protection requirement.For hybrid solutions with U/K≥3(e.g.,HS+6U+2K),V50 reaches 580 m/s(≥540 m/s),satisfying advanced protection requirement.This research provides critical references for the design of flexible protective structures and their engineering applications.
文摘Increased awareness of Tibetan cultural preservation,along with technological advancements,has led to significant efforts in academic research on Tibetan.However,the structural complexity of the Tibetan language and limited labeled handwriting data impede advancements in Optical Character Recognition(OCR)and other applications.To address these challenges,this paper proposes an innovative Tibetan data augmentation technique,using Generative Adversarial Networks(GANs)to synthesise arbitrary handwriting images in variable calligraphic styles based on inputs.Moreover,our method leverages a Real-Fake Cross Inputs Strategy during training to enhance generation diversity and improve model generalisability in generating handwritten text beyond the training set and pre-defined corpus.The model was trained on three Tibetan handwriting datasets,including Ume style numerals,Uchen style consonants,and Khyug-yig style words.Experimental results demonstrate that the model successfully generates realistic and recognisable Tibetan numeral and consonant handwriting,achieving Frechet Inception Distance(FID)scores of 14.45 and 27.63,respectively.The proposed method's effectiveness in augmenting OCR models was validated as evidenced by a reduced OCR Word Error Rate(WER)on the augmented datasets.
基金supported by the SungKyunKwan University and the BK21 FOUR(Graduate School Innovation)funded by the Ministry of Education(MOE,Korea)and National Research Foundation of Korea(NRF).
文摘With the recent increase in data volume and diversity,traditional text representation techniques are struggling to capture context,particularly in environments with sparse data.To address these challenges,this study proposes a new model,the Masked Joint Representation Model(MJRM).MJRM approximates the original hypothesis by leveraging multiple elements in a limited context.It dynamically adapts to changes in characteristics based on data distribution through three main components.First,masking-based representation learning,termed selective dynamic masking,integrates topic modeling and sentiment clustering to generate and train multiple instances across different data subsets,whose predictions are then aggregated with optimized weights.This design alleviates sparsity,suppresses noise,and preserves contextual structures.Second,regularization-based improvements are applied.Third,techniques for addressing sparse data are used to perform final inference.As a result,MJRM improves performance by up to 4%compared to existing AI techniques.In our experiments,we analyzed the contribution of each factor,demonstrating that masking,dynamic learning,and aggregating multiple instances complement each other to improve performance.This demonstrates that a masking-based multi-learning strategy is effective for context-aware sparse text classification,and can be useful even in challenging situations such as data shortage or data distribution variations.We expect that the approach can be extended to diverse fields such as sentiment analysis,spam filtering,and domain-specific document classification.
基金supported by the Extral High Voltage Power Transmission Company,China Southern Power Grid Co.,Ltd.
文摘Distributed Denial-of-Service(DDoS)attacks pose severe threats to Industrial Control Networks(ICNs),where service disruption can cause significant economic losses and operational risks.Existing signature-based methods are ineffective against novel attacks,and traditional machine learning models struggle to capture the complex temporal dependencies and dynamic traffic patterns inherent in ICN environments.To address these challenges,this study proposes a deep feature-driven hybrid framework that integrates Transformer,BiLSTM,and KNN to achieve accurate and robust DDoS detection.The Transformer component extracts global temporal dependencies from network traffic flows,while BiLSTM captures fine-grained sequential dynamics.The learned embeddings are then classified using an instance-based KNN layer,enhancing decision boundary precision.This cascaded architecture balances feature abstraction and locality preservation,improving both generalization and robustness.The proposed approach was evaluated on a newly collected real-time ICN traffic dataset and further validated using the public CIC-IDS2017 and Edge-IIoT datasets to demonstrate generalization.Comprehensive metrics including accuracy,precision,recall,F1-score,ROC-AUC,PR-AUC,false positive rate(FPR),and detection latency were employed.Results show that the hybrid framework achieves 98.42%accuracy with an ROC-AUC of 0.992 and FPR below 1%,outperforming baseline machine learning and deep learning models.Robustness experiments under Gaussian noise perturbations confirmed stable performance with less than 2%accuracy degradation.Moreover,detection latency remained below 2.1 ms per sample,indicating suitability for real-time ICS deployment.In summary,the proposed hybrid temporal learning and instance-based classification model offers a scalable and effective solution for DDoS detection in industrial control environments.By combining global contextual modeling,sequential learning,and instance-based refinement,the framework demonstrates strong adaptability across datasets and resilience against noise,providing practical utility for safeguarding critical infrastructure.
文摘Object detection,a major challenge in computer vision and pattern recognition,plays a significant part in many applications,crossing artificial intelligence,face recognition,and autonomous driving.It involves focusing on identifying the detection,localization,and categorization of targets in images.A particularly important emerging task is distinguishing real animals from toy replicas in real-time,mostly for smart camera systems in both urban and natural environments.However,that difficult task is affected by factors such as showing angle,occlusion,light intensity,variations,and texture differences.To tackle these challenges,this paper recommends Group Sparse YOLOv8(You Only Look Once version 8),an improved real-time object detection algorithm that improves YOLOv8 by integrating group sparsity regularization.This adjustment improves efficiency and accuracy while utilizing the computational costs and power consumption,including a frame selection approach.And a hybrid parallel processing method that merges pipelining with dataflow strategies to improve the performance.Established using a custom dataset of toy and real animal images along with well-known datasets,namely ImageNet,MSCOCO,and CIFAR-10/100.The combination of Group Sparsity with YOLOv8 shows high detection accuracy with lower latency.Here provides a real and resource-efficient solution for intelligent camera systems and improves real-time object detection and classification in environments,differentiating between real and toy animals.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2025-02-01296).
文摘Skin diseases affect millions worldwide.Early detection is key to preventing disfigurement,lifelong disability,or death.Dermoscopic images acquired in primary-care settings show high intra-class visual similarity and severe class imbalance,and occasional imaging artifacts can create ambiguity for state-of-the-art convolutional neural networks(CNNs).We frame skin lesion recognition as graph-based reasoning and,to ensure fair evaluation and avoid data leakage,adopt a strict lesion-level partitioning strategy.Each image is first over-segmented using SLIC(Simple Linear Iterative Clustering)to produce perceptually homogeneous superpixels.These superpixels form the nodes of a region-adjacency graph whose edges encode spatial continuity.Node attributes are 1280-dimensional embeddings extracted with a lightweight yet expressive EfficientNet-B0 backbone,providing strong representational power at modest computational cost.The resulting graphs are processed by a five-layer Graph Attention Network(GAT)that learns to weight inter-node relationships dynamically and aggregates multi-hop context before classifying lesions into seven classes with a log-softmax output.Extensive experiments on the DermaMNIST benchmark show the proposed pipeline achieves 88.35%accuracy and 98.04%AUC,outperforming contemporary CNNs,AutoML approaches,and alternative graph neural networks.An ablation study indicates EfficientNet-B0 produces superior node descriptors compared with ResNet-18 and DenseNet,and that roughly five GAT layers strike a good balance between being too shallow and over-deep while avoiding oversmoothing.The method requires no data augmentation or external metadata,making it a drop-in upgrade for clinical computer-aided diagnosis systems.
基金supported by the Institute of Information&Communications Technology Planning&Evaluation grant funded by the Korea government(MSIT)(No.RS-2021-II211341,AI Graduate School Support Program,Chung-Ang University)in part by the Institute of Information and Communications Technology Planning and Evaluation grant funded by the Korea government(MSIT)(Development of Integrated Development Framework that Supports Automatic Neural Network Generation and Deployment Optimized for Runtime Environment,Grant No.2021-0-00766).
文摘Multimodal emotion recognition has emerged as a key research area for enabling human-centered artificial intelligence,supported by the rapid progress in vision,audio,language,and physiological modeling.Existing approaches integrate heterogeneous affective cues through diverse embedding strategies and fusion mechanisms,yet the field remains fragmented due to differences in feature alignment,temporal synchronization,modality reliability,and robustness to noise or missing inputs.This survey provides a comprehensive analysis of MER research from 2021 to 2025,consolidating advances in modality-specific representation learning,cross-modal feature construction,and early,late,and hybrid fusion paradigms.We systematically review visual,acoustic,textual,and sensor-based embeddings,highlighting howpre-trained encoders,self-supervised learning,and large languagemodels have reshaped the representational foundations ofMER.We further categorize fusion strategies by interaction depth and architectural design,examining how attention mechanisms,cross-modal transformers,adaptive gating,and multimodal large language models redefine the integration of affective signals.Finally,we summarize major benchmark datasets and evaluation metrics and discuss emerging challenges related to scalability,generalization,and interpretability.This survey aims to provide a unified perspective onmultimodal fusion for emotion recognition and to guide future research toward more coherent and generalizable multimodal affective intelligence.
基金funded by the Research,Development,and Innovation Authority(RDIA)—Kingdom of Saudi Arabia—under supervision Energy,Industry,and Advanced Technologies Research Center,Taibah University,Madinah,Saudi Arabia with grant number(12979-iau-2023-TAU-R-3-1-EI-).
文摘The generation of high-quality 3D models from single 2D images remains challenging in terms of accuracy and completeness.Deep learning has emerged as a promising solution,offering new avenues for improvements.However,building models from scratch is computationally expensive and requires large datasets.This paper presents a transfer-learning-based approach for category-specific 3D reconstruction from a single 2D image.The core idea is to fine-tune a pre-trained model on specific object categories using new,unseen data,resulting in specialized versions of the model that are better adapted to reconstruct particular objects.The proposed approach utilizes a three-phase pipeline comprising image acquisition,3D reconstruction,and refinement.After ensuring the quality of the input image,a ResNet50 model is used for object recognition,directing the image to the corresponding category-specific model to generate a voxel-based representation.The voxel-based 3D model is then refined by transforming it into a detailed triangular mesh representation using the Marching Cubes algorithm and Laplacian smoothing.An experimental study,using the Pix2Vox model and the Pascal3D dataset,has been conducted to evaluate and validate the effectiveness of the proposed approach.Results demonstrate that category-specific fine-tuning of Pix2Vox significantly outperforms both the original model and the general model fine-tuned for all object categories,with substantial gains in Intersection over Union(IoU)scores.Visual assessments confirm improvements in geometric detail and surface realism.These findings indicate that combining transfer learning with category-specific fine tuning and refinement strategy of our approach leads to better-quality 3D model generation.
基金funded by the National Natural Science Foundation of China(62273213,62472262,62572287)Natural Science Foundation of Shandong Province(ZR2024MF144)+1 种基金Natural Science Foundation of Shandong Province for Innovation and Development Joint Funds(ZR2022LZH001)Taishan Scholarship Construction Engineering.
文摘Accurately counting dense objects in complex and diverse backgrounds is a significant challenge in computer vision,with applications ranging from crowd counting to various other object counting tasks.To address this,we propose HUANNet(High-Resolution Unified Attention Network),a convolutional neural network designed to capture both local features and rich semantic information through a high-resolution representation learning framework,while optimizing computational distribution across parallel branches.HUANNet introduces three core modules:the High-Resolution Attention Module(HRAM),which enhances feature extraction by optimizing multiresolution feature fusion;the Unified Multi-Scale Attention Module(UMAM),which integrates spatial,channel,and convolutional kernel information through an attention mechanism applied across multiple levels of the network;and the Grid-Assisted Point Matching Module(GPMM),which stabilizes and improves point-to-point matching by leveraging grid-based mechanisms.Extensive experiments show that HUANNet achieves competitive results on the ShanghaiTech Part A/B crowd counting datasets and sets new state-of-the-art performance on dense object counting datasets such as CARPK and XRAY-IECCD,demonstrating the effectiveness and versatility of HUANNet.
基金supported by the National Natural Science Foundation of China(62402399)the New Chongqing Youth Innovation Talent Project(CSTB2024NSCQ-QCXMX0035)。
文摘Dear Editor,D2This letter presents a node feature similarity preserving graph convolutional framework P G.Graph neural networks(GNNs)have garnered significant attention for their efficacy in learning graph representations across diverse real-world applications.
基金The 2026 Annual Project for the Improvement of Educational and Research Capabilities of Primary and Secondary School Teachers in Guangdong Province titled"Breaking Boundaries,Integration,and Empowerment:Innovative Practice Research on Teacher Training Courses for the Synergy of Aesthetic Education and Moral Education"(Host:Wenjiang Zhang)。
文摘As a key carrier for the implementation of the core quality of"aesthetic creativity",the effective realization of the aesthetic value of language textbook illustrations is facing the real problems of practical disconnection and theoretical gap.Based on the theories of semiotics,embodied cognition and cultural reproduction,this paper innovatively puts forward the concept of"transformation mechanism of aesthetic education",constructs a three-phase The study analyzes the dynamic transformation process of illustrations from image symbols to students'aesthetic experience.The study explains the essential characteristics of illustration as a dual symbolic system of"likenessregulation",reveals the cognitive ladder of"perception-cognition-creation"and the path of realization of embodied cognition,and points out that the current aesthetic education of illustration has the problem of instrumentalization,and that the current aesthetic education of illustration has the problem of instrumentalization.The study points out that the current aesthetic education of illustrations is plagued by instrumental alienation,cultural hegemony and aesthetic flattening,and proposes a"three-in-one"education path from the dimensions of teachers,teaching materials and culture,including improving teachers'aesthetic education,reconstructing the principles of teaching materials and dynamic revision mechanism,and constructing classroom aesthetic education communities.The study provides new perspectives and practical guidance for exploring the aesthetic value of language textbook illustrations and bridging the gap between the theories of aesthetics and pedagogy,aiming to promote the return of aesthetic education to the nature of"reconstruction of experience",and to cultivate students'aesthetic literacy and sound personality.
文摘In our recently published paper,[1]a typesetting error occurred during the production process.Figure 1 in the published version was incomplete.The processing of molecular dynamics(MD)simulation data into graph-structured representations in the left bottom panel of thefigure was inadvertently omitted.
基金supported by funding from the National Natural Science Foundation of China(81971349 and 81300456).
文摘In vitro maturation(IvM)of human oocytes offers cost efficiency and minimal invasiveness,serving as a valuable supplementary tool in assisted reproduction for fertility preservation,ovarian hyperstimulation syndrome prevention,and other reproductive strategies.Despite its availability for three decades,the clinical use of IVM remains limited due to efficacy and safety concerns.This study examines the DNA methylation profile of IVM oocytes collected during laparoscopic/hysteroscopic surgeries compared to in vivo matured oocytes via reduced representation bisulfite sequencing.Results indicate IVM oocytes exhibit a higher global methylation level.Differentially methylated regions(DMRs)analysis reveals that the in vitro group displays more hypermethylated and fewer hypomethylated DMRs compared to the in vivo group.Additionally,the in vitro group exhibits a higher level of non-CpG methylation than the in vivo group.However,no significant correlation between methylation levels and transcriptional activity in these oocytes is found,especially for those specific imprinted genes or genes related to embryonic development.These findings shed light on the epigenetic landscape of IvM oocytes,contributing to the ongoing assessment of their clinical feasibility and safety in assisted reproduction.
基金supported by the National Natural Science Foundation of China(62225303,62403043,62433004)the Beijing Natural Science Foundation(4244085)+1 种基金the Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(GZC20230203)the China Postdoctoral Science Foundation(2023M740201)。
文摘Deep graph contrastive clustering has attracted widespread attentions due to its self-supervised representation learning paradigm and superior clustering performance.Although,two challenges emerge and result in high computational costs.Most existing contrastive methods adopt the data augmentation and then representation learning strategy,where representation learning with trainable graph convolution is coupled with complex and fixed data augmentation,inevitably limiting the efficiency and flexibility.The similarity metric between positive-negative sample pairs is complex and contrastive objective is partial,limiting the discriminability of representation learning.To solve these challenges,a novel wide graph clustering network(WGCN)adhering to representation and then augmentation framework is proposed,which mainly consists of multiorder filter fusion(MFF)and double-level contrastive learning(DCL)modules.Specifically,the MFF module integrates multiorder low-pass filters to extract smooth and multi-scale topological features,utilizing self-attention fusion to reduce redundancy and obtain comprehensive embedding representation.Further,the DCL module constructs two augmented views by the parallel parameter-unshared Siamese encoders rather than complex augmentations on graph.To achieve simple yet effective self-supervised learning,representation self-supervision and structural consistency oriented double-level contrastive loss is designed,where representation self-supervision maximizes the agreement between pairwise augmented embedding representations and structural consistency promotes the mutual information correlation between appending neighborhoods with similar semantics.Extensive experiments on six benchmark datasets demonstrate the superiority of the proposed WGCN,especially highlighting its time-saving characteristic.The code could be available in the https://github.com/Tianxiang Zhao0474/WGCN.
基金supported in part by the National Natural Science Foundation of China(62372385,62272078,62002337)Chongqing Natural Science Foundation(CSTB2022NSCQ-MSX1486,CSTB2023NSCQ-LZX0069)。
文摘A dynamic graph(DG)is adopted to portray the evolving interplay between nodes in real-world scenarios prevalently.A high-order graph convolutional network(HGCN)is equipped with the ability to represent a DG by the spatial-temporal message passing mechanism built on tensor product.Concretely,an HGCN utilizes the discrete Fourier transform(DFT)to implement temporal message passing and then employs face-wise product to realize spatial message passing.However,DFT is only a special case of assorted time-frequency transforms,which considers the complex temporal patterns partially,thereby resulting in an inaccurate temporal message passing possibly.To address this issue,this study proposes six advanced time-frequency transform-incorporated HGCNs(TF-HGCNs)with discrete Fourier,discrete Hartley,discrete cosine,Haar wavelet,Walsh Hadamard,and slant transforms.In addition,a potent ensemble is built regarding the proposed six TF-HGCNs as the bases.Finally,the corresponding theoretical proof is presented.Empirical studies on six DG datasets demonstrate that owing to diverse time-frequency transforms,the proposed six TF-HGCNs significantly outperform state-of-the-art models in addressing the task of link weight estimation.Moreover,their ensemble outstrips each base's performance.
基金supported by the National Natural Science Foundation of China(Nos.12072027,62103052,61603346 and 62103379)the Henan Key Laboratory of General Aviation Technology,China(No.ZHKF-230201)+3 种基金the Funding for the Open Research Project of the Rotor Aerodynamics Key Laboratory,China(No.RAL20200101)the Key Research and Development Program of Henan Province,China(Nos.241111222000 and 241111222900)the Key Science and Technology Program of Henan Province,China(No.232102220067)the Scholarship Funding from the China Scholarship Council(No.202206030079).
文摘In global navigation satellite system denial environment,cross-view geo-localization based on image retrieval presents an exceedingly critical visual localization solution for Unmanned Aerial Vehicle(UAV)systems.The essence of cross-view geo-localization resides in matching images containing the same geographical targets from disparate platforms,such as UAV-view and satellite-view images.However,images of the same geographical targets may suffer from occlusions and geometric distortions due to variations in the capturing platform,view,and timing.The existing methods predominantly extract features by segmenting feature maps,which overlook the holistic semantic distribution and structural information of objects,resulting in loss of image information.To address these challenges,dilated neighborhood attention Transformer is employed as the feature extraction backbone,and Multi-feature representations based on Multi-scale Hierarchical Contextual Aggregation(MMHCA)is proposed.In the proposed MMHCA method,the multiscale hierarchical contextual aggregation method is utilized to extract contextual information from local to global across various granularity levels,establishing feature associations of contextual information with global and local information in the image.Subsequently,the multi-feature representations method is utilized to obtain rich discriminative feature information,bolstering the robustness of model in scenarios characterized by positional shifts,varying distances,and scale ambiguities.Comprehensive experiments conducted on the extensively utilized University-1652 and SUES-200 benchmarks indicate that the MMHCA method surpasses the existing techniques.showing outstanding results in UAV localization and navigation.
基金funding from the European Research Council(ERC)under the European Union’s Horizon 2020 Research and Innovation Program through the Starting Grant GEoREST(grant agreement No.801809)support by MICIU/AEI/10.13039/501100011033 and by"European Union Next Generation EU/PRTR"through the‘Ramón y Cajal’fellowship(reference RYC2021-032780-I)+9 种基金funding by MICIU/AEI/10.13039/501100011033 and by“ERDF,EU”through the‘HydroPoreII’project(reference PID2022-137652NBC44)support by the Institute for Korea Spent Nuclear Fuel(iKSNF)National Research Foundation of Korea(NRF)grant funded by the Korea government(Ministry of Science and ICT,MSIT)(2021M2E1A1085196)support by the Swedish Radiation Safety(SSM),Swedish Transport Administration(Trafikverket),Swedish Rock Engineering Foundation(BeFo),and Nordic Energy Research(Grant 187658)supported by the US Department of Energy(DOE),the Officeof Nuclear Energy,Spent Fuel and Waste Science and Technology Campaign,and by the US Department of Energy(DOE),the Office of Basic Energy Sciences,Chemical Sciences,Geosciences,and Biosciences Division both under Contract Number DE-AC02-05CH11231 with Lawrence Berkeley National Laboratorysupport from the US National Science Foundation(grant CMMI-2239630)funding from the European Research Council(ERC)under the European Union’s Horizon 2020 research and innovation programme(grant agreement No.101002507)the UK Natural Environment Research Council(NERC)for funding SeisGreen Project(Grant No.NE/W009293/1)which supported this workthe Royal Society UK for supporting this research through fellowship UF160443IMEDEA is an accredited"Maria de Maeztu Excellence Unit"(Grant CEX2021-001198,funded by MICIU/AEI/10.13039/501100011033).
文摘Coupled thermo-hydro-mechanical(THM)processes in fractured rock are playing a crucial role in geoscience and geoengineering applications.Diverse and conceptually distinct approaches have emerged over the past decades in both continuum and discontinuum perspectives leading to significant progress in their comprehending and modeling.This review paper offers an integrated perspective on existing modeling methodologies providing guidance for model selection based on the initial and boundary conditions.By comparing various models,one can better assess the uncertainties in predictions,particularly those related to the conceptual models.The review explores how these methodologies have significantlyenhanced the fundamental understanding of how fractures respond to fluid injection and production,and improved predictive capabilities pertaining to coupled processes within fractured systems.It emphasizes the importance of utilizing advanced computational technologies and thoroughly considering fundamental theories and principles established through past experimental evidence and practical experience.The selection and calibration of model parameters should be based on typical ranges and applied to the specificconditions of applications.The challenges arising from inherent heterogeneity and uncertainties,nonlinear THM coupled processes,scale dependence,and computational limitations in representing fieldscale fractures are discussed.Realizing potential advances on computational capacity calls for methodical conceptualization,mathematical modeling,selection of numerical solution strategies,implementation,and calibration to foster simulation outcomes that intricately reflectthe nuanced complexities of geological phenomena.Future research efforts should focus on innovative approaches to tackle the hurdles and advance the state-of-the-art in this critical fieldof study.
基金funded by grants from the National Key Research and Development Program of China(Grant Nos.:2022YFE0205600 and 2022YFC3400504)the National Natural Science Foundation of China(Grant Nos.:82373792 and 82273857)the Fundamental Research Funds for the Central Universities,China,and the East China Normal University Medicine and Health Joint Fund,China(Grant No.:2022JKXYD07001).
文摘Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels.
基金supported by the National Natural Science Foun-dation of China(62137002,62250009,62202367,82025020,and 82230072).
文摘Large models,exemplified by ChatGPT,have reached the pinnacle of contemporary artificial intelligence(AI).However,they are plagued by three inherent drawbacks:excessive training data and computing power consumption,susceptibility to catastrophic forgetting,and a deficiency in logical reasoning capabilities within black-box models.To address these challenges,we draw insights from human memory mechanisms to introduce“machine memory,”which we define as a storage structure formed by encoding external information into a machine-representable and computable format.Centered on machine memory,we propose the brand-new machine memory intelligence(M^(2)I)framework,which encompasses representation,learning,and reasoning modules and loops.We explore the key issues and recent advances in the four core aspects of M^(2)I,including neural mechanisms,associative representation,continual learning,and collaborative reasoning within machine memory.M^(2)I aims to liberate machine intelligence from the confines of data-centric neural networks and fundamentally break through the limitations of existing large models,driving a qualitative leap from weak to strong AI.
