Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algeb...Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.展开更多
Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to ...Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.展开更多
Objective:To explore the effects of acupoint catgut embedding combined with auricular point pressing with beans on symptom management self-efficacy and quality of life in patients with nonalcoholic steatohepatitis(NAS...Objective:To explore the effects of acupoint catgut embedding combined with auricular point pressing with beans on symptom management self-efficacy and quality of life in patients with nonalcoholic steatohepatitis(NASH)of liver depression and spleen deficiency type.Methods:Sixty patients with NASH of liver depression and spleen deficiency type admitted to our hospital from January 2021 to December 2023 were selected and divided into an acupoint catgut embedding group(n=30)and a combined group(n=30)using the envelope lottery method.The acupoint catgut embedding group received acupoint catgut embedding intervention,while the combined group received auricular point pressing with beans on the basis of the acupoint catgut embedding group.The two groups were compared in terms of TCM syndrome scores,symptom management self-efficacy[Chronic Disease Self-Efficacy Scale(CDSES)],and quality of life[Chronic Liver Disease Questionnaire(CLDQ)].Results:After intervention,the combined group had lower TCM syndrome scores for both primary and secondary symptoms compared to the acupoint catgut embedding group(P<0.05).The combined group also had higher scores in all dimensions and total score of the CDSES compared to the acupoint catgut embedding group(P<0.05).Similarly,the combined group had higher scores in all dimensions and total score of the CLDQ compared to the acupoint catgut embedding group(P<0.05).Conclusion:Acupoint catgut embedding combined with auricular point pressing with beans can effectively improve TCM symptoms,enhance symptom management self-efficacy,and improve quality of life in patients with NASH of liver depression and spleen deficiency type.展开更多
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.展开更多
A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehens...A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.展开更多
Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications li...Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.展开更多
The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situati...The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.展开更多
Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular ...Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular microenvironment.However,the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink,making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials.To overcome this limitation,we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel.This medium possesses yield stress and self-healing properties,facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it.By adjusting the printing speed,we controlled the filament diameter,achieving a range from 250μm to 1000μm,and ensuring precise control over ink deposition locations and filament shapes.Using the in situ endothelialization method,we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells.In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue.We also investigated the impact of varying vascular densities on liver tissue function.Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways.In summary,this method is adaptable to various materials,allowing the rheological properties of the supporting bath and the tissue's porosity to be modified using microgels,thus enabling precise regulation of the liver tissue microenvironment.Additionally,it facilitates the rapid construction of three-dimensional vascular structures within liver tissue.The resulting vascularized liver tissue model exhibits enhanced biological functionality,opening new opportunities for biomedical applications.展开更多
Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full ...Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.展开更多
A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete...A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.展开更多
In this paper,we introduce the notion of embedding tensors on 3-Hom-Lie algebras and show that embedding tensors induce naturally 3-Hom-Leibniz algebras.Moreover,the cohomology theory of embedding tensors on 3-Hom-Lie...In this paper,we introduce the notion of embedding tensors on 3-Hom-Lie algebras and show that embedding tensors induce naturally 3-Hom-Leibniz algebras.Moreover,the cohomology theory of embedding tensors on 3-Hom-Lie algebras is defined.As an application,we show that if two linear deformations of an embedding tensor on a 3-Hom-Lie algebra are equivalent,then their infinitesimals belong to the same cohomology class in the first cohomology group.展开更多
Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is desi...Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.展开更多
Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret in...Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.展开更多
Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extr...Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.展开更多
The robustness against noise, outliers, and corruption is a crucial issue in image feature extraction. To address this concern, this paper proposes a discriminative low-rank embedding image feature extraction algorith...The robustness against noise, outliers, and corruption is a crucial issue in image feature extraction. To address this concern, this paper proposes a discriminative low-rank embedding image feature extraction algorithm. Firstly, to enhance the discriminative power of the extracted features, a discriminative term is introduced using label information, obtaining global discriminative information and learning an optimal projection matrix for data dimensionality reduction. Secondly, manifold constraints are incorporated, unifying low-rank embedding and manifold constraints into a single framework to capture the geometric structure of local manifolds while considering both local and global information. Finally, test samples are projected into a lower-dimensional space for classification. Experimental results demonstrate that the proposed method achieves classification accuracies of 95.62%, 95.22%, 86.38%, and 86.54% on the ORL, CMUPIE, AR, and COIL20 datasets, respectively, outperforming dimensionality reduction-based image feature extraction algorithms.展开更多
Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer materials.To date,several approaches have been i...Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer materials.To date,several approaches have been implemented to represent the chemical structure in machine learning models,among which Mol2Vec embeddings have attracted considerable attention in the cheminformatics community since their introduction in 2018.However,for small datasets,the use of chemical structure representations typically increases the dimensionality of the input dataset,resulting in a decrease in model performance.Furthermore,the limited diversity of polymer chemical structures hinders the training of reliable embeddings,necessitating complex task-specific architecture implementations.To address these challenges,we examined the efficacy of Mol2Vec pre-trained embeddings in deriving vectorized representations of polymers.This study assesses the impact of incorporating Mol2Vec compound vectors into the input features on the efficacy of a model reliant on the physical properties of 214 polymers.The results will hopefully highlight the potential for improving prediction accuracy in polymer studies by incorporating pre-trained embeddings or promote their utilization when dealing with modestly sized polymer databases.展开更多
Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD patt...Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.展开更多
To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are cla...To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.展开更多
In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,t...In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.展开更多
In this paper,by constructing the current graph of the complete graph K_(12s+9)and a mapping function,we prove that K_(12s+9)(s is an odd number)has at least 6^(2s)×3^(s+3/2) nonisomorphic orientable quadrangular...In this paper,by constructing the current graph of the complete graph K_(12s+9)and a mapping function,we prove that K_(12s+9)(s is an odd number)has at least 6^(2s)×3^(s+3/2) nonisomorphic orientable quadrangular embeddings,and the orientable genus is(12s+9)(12s+4)/8+1.Every one of the nonisomorphic orientable quadrangular embeddings has at least twenty-four 4-edge-colors,and each color appears around each face of orientable quadrangular embeddings.展开更多
基金Supported by the Grant-in-Aid for Scientific Research(C)by the Japan Society for the Promotion of Science(20K03615)。
文摘Let G be a group.The family of all sets which are closed in every Hausdorf group topology of G form the family of closed sets of a T_(1) topology M_(G) on G called the Markov topology.Similarly,the family of all algebraic subsets of G forms a family of closed sets for another T_(1)topology Z_(G) on G called the Zarski topology.A subgroup H of G is said to be Markov(resp.Zarski)embedded if the equality M_(G|H)=M_(H)(resp.Z_(G|H)=Z_(H))holds.I's proved that an abirary subgroup of a free group is both Zariski and Markov embedded in it.
基金supported in part by the National Science and Technology Major Project under(Grant 2022ZD0116100)in part by the National Natural Science Foundation Key Project under(Grant 62436006)+4 种基金in part by the National Natural Science Foundation Youth Fund under(Grant 62406257)in part by the Xizang Autonomous Region Natural Science Foundation General Project under(Grant XZ202401ZR0031)in part by the National Natural Science Foundation of China under(Grant 62276055)in part by the Sichuan Science and Technology Program under(Grant 23ZDYF0755)in part by the Xizang University‘High-Level Talent Training Program’Project under(Grant 2022-GSP-S098).
文摘Tibetan medical named entity recognition(Tibetan MNER)involves extracting specific types of medical entities from unstructured Tibetan medical texts.Tibetan MNER provide important data support for the work related to Tibetan medicine.However,existing Tibetan MNER methods often struggle to comprehensively capture multi-level semantic information,failing to sufficiently extract multi-granularity features and effectively filter out irrelevant information,which ultimately impacts the accuracy of entity recognition.This paper proposes an improved embedding representation method called syllable-word-sentence embedding.By leveraging features at different granularities and using un-scaled dot-product attention to focus on key features for feature fusion,the syllable-word-sentence embedding is integrated into the transformer,enhancing the specificity and diversity of feature representations.The model leverages multi-level and multi-granularity semantic information,thereby improving the performance of Tibetan MNER.We evaluate our proposed model on datasets from various domains.The results indicate that the model effectively identified three types of entities in the Tibetan news dataset we constructed,achieving an F1 score of 93.59%,which represents an improvement of 1.24%compared to the vanilla FLAT.Additionally,results from the Tibetan medical dataset we developed show that it is effective in identifying five kinds of medical entities,with an F1 score of 71.39%,which is a 1.34%improvement over the vanilla FLAT.
文摘Objective:To explore the effects of acupoint catgut embedding combined with auricular point pressing with beans on symptom management self-efficacy and quality of life in patients with nonalcoholic steatohepatitis(NASH)of liver depression and spleen deficiency type.Methods:Sixty patients with NASH of liver depression and spleen deficiency type admitted to our hospital from January 2021 to December 2023 were selected and divided into an acupoint catgut embedding group(n=30)and a combined group(n=30)using the envelope lottery method.The acupoint catgut embedding group received acupoint catgut embedding intervention,while the combined group received auricular point pressing with beans on the basis of the acupoint catgut embedding group.The two groups were compared in terms of TCM syndrome scores,symptom management self-efficacy[Chronic Disease Self-Efficacy Scale(CDSES)],and quality of life[Chronic Liver Disease Questionnaire(CLDQ)].Results:After intervention,the combined group had lower TCM syndrome scores for both primary and secondary symptoms compared to the acupoint catgut embedding group(P<0.05).The combined group also had higher scores in all dimensions and total score of the CDSES compared to the acupoint catgut embedding group(P<0.05).Similarly,the combined group had higher scores in all dimensions and total score of the CLDQ compared to the acupoint catgut embedding group(P<0.05).Conclusion:Acupoint catgut embedding combined with auricular point pressing with beans can effectively improve TCM symptoms,enhance symptom management self-efficacy,and improve quality of life in patients with NASH of liver depression and spleen deficiency type.
基金supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
文摘In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with large-scale knowledge graphs that contain vast numbers of entities and relations.In particular,resource-intensive embeddings often lead to increased computational costs,and may limit scalability and adaptability in practical environ-ments,such as in low-resource settings or real-world applications.This paper explores an approach to knowledge graph representation learning that leverages small,reserved entities and relation sets for parameter-efficient embedding.We introduce a hierarchical attention network designed to refine and maximize the representational quality of embeddings by selectively focusing on these reserved sets,thereby reducing model complexity.Empirical assessments validate that our model achieves high performance on the benchmark dataset with fewer parameters and smaller embedding dimensions.The ablation studies further highlight the impact and contribution of each component in the proposed hierarchical attention structure.
文摘A complete examination of Large Language Models’strengths,problems,and applications is needed due to their rising use across disciplines.Current studies frequently focus on single-use situations and lack a comprehensive understanding of LLM architectural performance,strengths,and weaknesses.This gap precludes finding the appropriate models for task-specific applications and limits awareness of emerging LLM optimization and deployment strategies.In this research,50 studies on 25+LLMs,including GPT-3,GPT-4,Claude 3.5,DeepKet,and hybrid multimodal frameworks like ContextDET and GeoRSCLIP,are thoroughly reviewed.We propose LLM application taxonomy by grouping techniques by task focus—healthcare,chemistry,sentiment analysis,agent-based simulations,and multimodal integration.Advanced methods like parameter-efficient tuning(LoRA),quantumenhanced embeddings(DeepKet),retrieval-augmented generation(RAG),and safety-focused models(GalaxyGPT)are evaluated for dataset requirements,computational efficiency,and performance measures.Frameworks for ethical issues,data limited hallucinations,and KDGI-enhanced fine-tuning like Woodpecker’s post-remedy corrections are highlighted.The investigation’s scope,mad,and methods are described,but the primary results are not.The work reveals that domain-specialized fine-tuned LLMs employing RAG and quantum-enhanced embeddings performbetter for context-heavy applications.In medical text normalization,ChatGPT-4 outperforms previous models,while two multimodal frameworks,GeoRSCLIP,increase remote sensing.Parameter-efficient tuning technologies like LoRA have minimal computing cost and similar performance,demonstrating the necessity for adaptive models in multiple domains.To discover the optimum domain-specific models,explain domain-specific fine-tuning,and present quantum andmultimodal LLMs to address scalability and cross-domain issues.The framework helps academics and practitioners identify,adapt,and innovate LLMs for different purposes.This work advances the field of efficient,interpretable,and ethical LLM application research.
基金funded by Advanced Research Project(30209040702).
文摘Named Entity Recognition(NER)is vital in natural language processing for the analysis of news texts,as it accurately identifies entities such as locations,persons,and organizations,which is crucial for applications like news summarization and event tracking.However,NER in the news domain faces challenges due to insufficient annotated data,complex entity structures,and strong context dependencies.To address these issues,we propose a new Chinesenamed entity recognition method that integrates transfer learning with word embeddings.Our approach leverages the ERNIE pre-trained model for transfer learning and obtaining general language representations and incorporates the Soft-lexicon word embedding technique to handle varied entity structures.This dual-strategy enhances the model’s understanding of context and boosts its ability to process complex texts.Experimental results show that our method achieves an F1 score of 94.72% on a news dataset,surpassing baseline methods by 3%–4%,thereby confirming its effectiveness for Chinese-named entity recognition in the news domain.
文摘The increasing fluency of advanced language models,such as GPT-3.5,GPT-4,and the recently introduced DeepSeek,challenges the ability to distinguish between human-authored and AI-generated academic writing.This situation is raising significant concerns regarding the integrity and authenticity of academic work.In light of the above,the current research evaluates the effectiveness of Bidirectional Long Short-TermMemory(BiLSTM)networks enhanced with pre-trained GloVe(Global Vectors for Word Representation)embeddings to detect AIgenerated scientific Abstracts drawn from the AI-GA(Artificial Intelligence Generated Abstracts)dataset.Two core BiLSTM variants were assessed:a single-layer approach and a dual-layer design,each tested under static or adaptive embeddings.The single-layer model achieved nearly 97%accuracy with trainable GloVe,occasionally surpassing the deeper model.Despite these gains,neither configuration fully matched the 98.7%benchmark set by an earlier LSTMWord2Vec pipeline.Some runs were over-fitted when embeddings were fine-tuned,whereas static embeddings offered a slightly lower yet stable accuracy of around 96%.This lingering gap reinforces a key ethical and procedural concern:relying solely on automated tools,such as Turnitin’s AI-detection features,to penalize individuals’risks and unjust outcomes.Misclassifications,whether legitimate work is misread as AI-generated or engineered text,evade detection,demonstrating that these classifiers should not stand as the sole arbiters of authenticity.Amore comprehensive approach is warranted,one which weaves model outputs into a systematic process supported by expert judgment and institutional guidelines designed to protect originality.
基金the funding from the National Natural Science Foundation of China No.52275294the National Key Research and Development Program of China(No.2018YFA0703000)。
文摘Constructing an in vitro vascularized liver tissue model that closely simulates the human liver is crucial for promoting cell proliferation,mimicking physiological heterogeneous structures,and recreating the cellular microenvironment.However,the layer-by-layer printing method is significantly constrained by the rheological properties of the bioink,making it challenging to form complex three-dimensional vascular structures in low-viscosity soft materials.To overcome this limitation,we developed a cross-linkable biphasic embedding medium by mixing low-viscosity biomaterials with gelatin microgel.This medium possesses yield stress and self-healing properties,facilitating efficient and continuous three-dimensional shaping of sacrificial ink within it.By adjusting the printing speed,we controlled the filament diameter,achieving a range from 250μm to 1000μm,and ensuring precise control over ink deposition locations and filament shapes.Using the in situ endothelialization method,we constructed complex vascular structures and ensured close adhesion between hepatocytes and endothelial cells.In vitro experiments demonstrated that the vascularized liver tissue model exhibited enhanced protein synthesis and metabolic function compared to mixed liver tissue.We also investigated the impact of varying vascular densities on liver tissue function.Transcriptome sequencing revealed that liver tissues with higher vascular density exhibited upregulated gene expression in metabolic and angiogenesis-related pathways.In summary,this method is adaptable to various materials,allowing the rheological properties of the supporting bath and the tissue's porosity to be modified using microgels,thus enabling precise regulation of the liver tissue microenvironment.Additionally,it facilitates the rapid construction of three-dimensional vascular structures within liver tissue.The resulting vascularized liver tissue model exhibits enhanced biological functionality,opening new opportunities for biomedical applications.
基金supported by the National Natural Science Foundation of China(Nos.92152301,12072282)。
文摘Aerodynamic surrogate modeling mostly relies only on integrated loads data obtained from simulation or experiment,while neglecting and wasting the valuable distributed physical information on the surface.To make full use of both integrated and distributed loads,a modeling paradigm,called the heterogeneous data-driven aerodynamic modeling,is presented.The essential concept is to incorporate the physical information of distributed loads as additional constraints within the end-to-end aerodynamic modeling.Towards heterogenous data,a novel and easily applicable physical feature embedding modeling framework is designed.This framework extracts lowdimensional physical features from pressure distribution and then effectively enhances the modeling of the integrated loads via feature embedding.The proposed framework can be coupled with multiple feature extraction methods,and the well-performed generalization capabilities over different airfoils are verified through a transonic case.Compared with traditional direct modeling,the proposed framework can reduce testing errors by almost 50%.Given the same prediction accuracy,it can save more than half of the training samples.Furthermore,the visualization analysis has revealed a significant correlation between the discovered low-dimensional physical features and the heterogeneous aerodynamic loads,which shows the interpretability and credibility of the superior performance offered by the proposed deep learning framework.
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.
基金Supported by the Scientific Research Foundation for Science&Technology Innovation Talent Team of the Intelligent Computing and Monitoring of Guizhou Province(Grant No.QJJ[2023]063)the Science and Technology Program of Guizhou Province(Grant Nos.ZK[2023]025+4 种基金QKHZC[2023]372ZK[2022]031)the National Natural Science Foundation of China(Grant No.12161013)the Scientific Research Foundation of Guizhou University of Finance and Economics(Grant No.2022KYYB08)the Doctoral Research Start-Up Fund of Guiyang University(Grant No.GYU-KY-2024).
文摘In this paper,we introduce the notion of embedding tensors on 3-Hom-Lie algebras and show that embedding tensors induce naturally 3-Hom-Leibniz algebras.Moreover,the cohomology theory of embedding tensors on 3-Hom-Lie algebras is defined.As an application,we show that if two linear deformations of an embedding tensor on a 3-Hom-Lie algebra are equivalent,then their infinitesimals belong to the same cohomology class in the first cohomology group.
基金Project supported by the National Natural Science Foundation of China(Grant No.62061014)the Natural Science Foundation of Liaoning province of China(Grant No.2020-MS-274).
文摘Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.
基金supported in part by the National Natural Science Foundation of China(Nos.62372083,62072074,62076054,62027827,62002047)the Sichuan Science and Technology Innovation Platform and Talent Plan(No.2022JDJQ0039)+2 种基金the Sichuan Science and Technology Support Plan(Nos.2024NSFTD0005,2022YFQ0045,2022YFS0220,2023YFS0020,2023YFS0197,2023YFG0148)the CCF-Baidu Open Fund(No.202312)the Medico-Engineering Cooperation Funds from University of Electronic Science and Technology of China(Nos.ZYGX2021YGLH212,ZYGX2022YGRH012).
文摘Information steganography has received more and more attention from scholars nowadays,especially in the area of image steganography,which uses image content to transmit information and makes the existence of secret information undetectable.To enhance concealment and security,the Steganography without Embedding(SWE)method has proven effective in avoiding image distortion resulting from cover modification.In this paper,a novel encrypted communication scheme for image SWE is proposed.It reconstructs the image into a multi-linked list structure consisting of numerous nodes,where each pixel is transformed into a single node with data and pointer domains.By employing a special addressing algorithm,the optimal linked list corresponding to the secret information can be identified.The receiver can restore the secretmessage fromthe received image using only the list header position information.The scheme is based on the concept of coverless steganography,eliminating the need for any modifications to the cover image.It boasts high concealment and security,along with a complete message restoration rate,making it resistant to steganalysis.Furthermore,this paper proposes linked-list construction schemeswithin theproposedframework,which caneffectively resist a variety of attacks,includingnoise attacks and image compression,demonstrating a certain degree of robustness.To validate the proposed framework,practical tests and comparisons are conducted using multiple datasets.The results affirm the framework’s commendable performance in terms of message reduction rate,hidden writing capacity,and robustness against diverse attacks.
基金supported by the National Natural Science Foundation of China(Grant Nos.92152102 and 92152202)the Advanced Jet Propulsion Innovation Center/AEAC(Grant No.HKCX2022-01-010)。
文摘Identification of underlying partial differential equations(PDEs)for complex systems remains a formidable challenge.In the present study,a robust PDE identification method is proposed,demonstrating the ability to extract accurate governing equations under noisy conditions without prior knowledge.Specifically,the proposed method combines gene expression programming,one type of evolutionary algorithm capable of generating unseen terms based solely on basic operators and functional terms,with symbolic regression neural networks.These networks are designed to represent explicit functional expressions and optimize them with data gradients.In particular,the specifically designed neural networks can be easily transformed to physical constraints for the training data,embedding the discovered PDEs to further optimize the metadata used for iterative PDE identification.The proposed method has been tested in four canonical PDE cases,validating its effectiveness without preliminary information and confirming its suitability for practical applications across various noise levels.
基金supported by the National Natural Science Foundation of China (No.61961037)the Gansu Provincial Department of Education 2021 Industry Support Program (No.2021CYZC-30)。
文摘The robustness against noise, outliers, and corruption is a crucial issue in image feature extraction. To address this concern, this paper proposes a discriminative low-rank embedding image feature extraction algorithm. Firstly, to enhance the discriminative power of the extracted features, a discriminative term is introduced using label information, obtaining global discriminative information and learning an optimal projection matrix for data dimensionality reduction. Secondly, manifold constraints are incorporated, unifying low-rank embedding and manifold constraints into a single framework to capture the geometric structure of local manifolds while considering both local and global information. Finally, test samples are projected into a lower-dimensional space for classification. Experimental results demonstrate that the proposed method achieves classification accuracies of 95.62%, 95.22%, 86.38%, and 86.54% on the ORL, CMUPIE, AR, and COIL20 datasets, respectively, outperforming dimensionality reduction-based image feature extraction algorithms.
基金the framework of the program of state support for the centers of the National Technology Initiative(NTI)on the basis of educational institutions of higher education and scientific organizations(Center NTI"Digital Materials Science:New Materials and Substances"on the basis of the Bauman Moscow State Technical University).
文摘Machine learning-assisted prediction of polymer properties prior to synthesis has the potential to significantly accelerate the discovery and development of new polymer materials.To date,several approaches have been implemented to represent the chemical structure in machine learning models,among which Mol2Vec embeddings have attracted considerable attention in the cheminformatics community since their introduction in 2018.However,for small datasets,the use of chemical structure representations typically increases the dimensionality of the input dataset,resulting in a decrease in model performance.Furthermore,the limited diversity of polymer chemical structures hinders the training of reliable embeddings,necessitating complex task-specific architecture implementations.To address these challenges,we examined the efficacy of Mol2Vec pre-trained embeddings in deriving vectorized representations of polymers.This study assesses the impact of incorporating Mol2Vec compound vectors into the input features on the efficacy of a model reliant on the physical properties of 214 polymers.The results will hopefully highlight the potential for improving prediction accuracy in polymer studies by incorporating pre-trained embeddings or promote their utilization when dealing with modestly sized polymer databases.
基金supported by the National Natural Science Foundation of China(32088101)National key Research and Development Program of China(2017YFC1700105,2021YFA1301603).
文摘Objective:To elucidate the biological basis of the heart qi deficiency(HQD)pattern,an in-depth understanding of which is essential for improving clinical herbal therapy.Methods: We predicted and characterized HQD pattern genes using the new strategy,TCM-HIN2Vec,which involves heterogeneous network embedding and transcriptomic experiments.First,a heterogeneous network of traditional Chinese medicine(TCM)patterns was constructed using public databases.Next,we predicted HQD pattern genes using a heterogeneous network-embedding algorithm.We then analyzed the functional characteristics of HQD pattern genes using gene enrichment analysis and examined gene expression levels using RNA-seq.Finally,we identified TCM herbs that demonstrated enriched interactions with HQD pattern genes via herbal enrichment analysis.Results: Our TCM-HIN2Vec strategy revealed that candidate genes associated with HQD pattern were significantly enriched in energy metabolism,signal transduction pathways,and immune processes.Moreover,we found that these candidate genes were significantly differentially expressed in the transcriptional profile of mice model with heart failure with a qi deficiency pattern.Furthermore,herbal enrichment analysis identified TCM herbs that demonstrated enriched interactions with the top 10 candidate genes and could potentially serve as drug candidates for treating HQD.Conclusion: Our results suggested that TCM-HIN2Vec is capable of not only accurately identifying HQD pattern genes,but also deciphering the basis of HQD pattern.Furthermore our finding indicated that TCM-HIN2Vec may be further expanded to develop other patterns,leading to a new approach aimed at elucidating general TCM patterns and developing precision medicine.
基金The State Grid Technology Project(No.5108202340042A-1-1-ZN).
文摘To solve the low efficiency of approximate queries caused by the large sizes of the knowledge graphs in the real world,an embedding-based approximate query method is proposed.First,the nodes in the query graph are classified according to the degrees of approximation required for different types of nodes.This classification transforms the query problem into three constraints,from which approximate information is extracted.Second,candidates are generated by calculating the similarity between embeddings.Finally,a deep neural network model is designed,incorporating a loss function based on the high-dimensional ellipsoidal diffusion distance.This model identifies the distance between nodes using their embeddings and constructs a score function.k nodes are returned as the query results.The results show that the proposed method can return both exact results and approximate matching results.On datasets DBLP(DataBase systems and Logic Programming)and FUA-S(Flight USA Airports-Sparse),this method exhibits superior performance in terms of precision and recall,returning results in 0.10 and 0.03 s,respectively.This indicates greater efficiency compared to PathSim and other comparative methods.
基金Supported by the National Natural Science Foundation of China(No.62203390)the Science and Technology Project of China TobaccoZhejiang Industrial Co.,Ltd(No.ZJZY2022E004)。
文摘In the tobacco industry,insider employee attack is a thorny problem that is difficult to detect.To solve this issue,this paper proposes an insider threat detection method based on heterogeneous graph embedding.First,the interrelationships between logs are fully considered,and log entries are converted into heterogeneous graphs based on these relationships.Second,the heterogeneous graph embedding is adopted and each log entry is represented as a low-dimensional feature vector.Then,normal logs and malicious logs are classified into different clusters by clustering algorithm to identify malicious logs.Finally,the effectiveness and superiority of the method is verified through experiments on the CERT dataset.The experimental results show that this method has better performance compared to some baseline methods.
文摘In this paper,by constructing the current graph of the complete graph K_(12s+9)and a mapping function,we prove that K_(12s+9)(s is an odd number)has at least 6^(2s)×3^(s+3/2) nonisomorphic orientable quadrangular embeddings,and the orientable genus is(12s+9)(12s+4)/8+1.Every one of the nonisomorphic orientable quadrangular embeddings has at least twenty-four 4-edge-colors,and each color appears around each face of orientable quadrangular embeddings.
