On account of the Mori-Tanaka approach,the effective elastic performance of composites containing decagonal symmetric two-dimensional(2D)quasicrystal(QC)coatings is *** expressions for the effective elastic constants ...
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On account of the Mori-Tanaka approach,the effective elastic performance of composites containing decagonal symmetric two-dimensional(2D)quasicrystal(QC)coatings is *** expressions for the effective elastic constants of rare-earth QC reinforced magnesium-based composites are *** discussion is presented on the effects of the volume fraction of the inclusions,the aspect ratio of the inclusions,the coating thickness,and the coating material parameters on the effective elastic constants of the *** results indicate that considering the coating increases the effective elastic constants of the composites to some extent.
In this paper, the rogue wave solutions of the(2+1)-dimensional Myrzakulov–Lakshmanan(ML)-Ⅳ equation, which is described by five component nonlinear evolution equations, are studied on a periodic background. By usin...
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In this paper, the rogue wave solutions of the(2+1)-dimensional Myrzakulov–Lakshmanan(ML)-Ⅳ equation, which is described by five component nonlinear evolution equations, are studied on a periodic background. By using the Jacobian elliptic function expansion method, the Darboux transformation(DT) method and the nonlinearization of the Lax pair, two kinds of rogue wave solutions which are expressed by Jacobian elliptic functions dn and cn, are *** relationship between these five kinds of potential is summarized systematically. Firstly, the periodic rogue wave solution of one potential is obtained, and then the periodic rogue wave solutions of the other four potentials are obtained directly. The solutions we find present the dynamic phenomena of higher-order nonlinear wave equations.
The Total Coloring Conjecture (TCC) proposes that every simple graph G is (Δ + 2)-totally-colorable, where Δ is the maximum degree of G. For planar graph, TCC is open only in case Δ = 6. In this paper, we prove tha...
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The Total Coloring Conjecture (TCC) proposes that every simple graph G is (Δ + 2)-totally-colorable, where Δ is the maximum degree of G. For planar graph, TCC is open only in case Δ = 6. In this paper, we prove that TCC holds for planar graph with Δ = 6 and every 7-cycle contains at most two chords.
In this paper, we propose the first time fifth- and sixth-order two-step schemes. Based on the proposed two-step methods, we develop three-step iterative methods with the ninth- and tenth-order of convergence. We also...
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In this paper, the multilayered quasicrystal composite plates effective elastic performance are forecasted. Firstly, effective properties of the first and third layers quasicrystal composite plates containing ellipsoi...
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Legal Judgment Prediction (LJP), a paramount application of artificial intelligence in law, aims to infer outcomes from fact descriptions. These outcomes mainly involve determining applicable law articles, charges, an...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
Legal Judgment Prediction (LJP), a paramount application of artificial intelligence in law, aims to infer outcomes from fact descriptions. These outcomes mainly involve determining applicable law articles, charges, and the term of penalty. Existing approaches typically treat this task as a text classification problem, utilizing end-to-end black box models for automatic prediction. However, these approaches overlook the fine-grained elements within cases and limit the model’s interpretability. In light of these limitations, we propose a novel approach for LJP. Our method employs Fine-Grained Element Graphs (FEGs) to represent fact descriptions and applies Graph Attention Network (GAT) to learn graph representation for subsequent prediction tasks. FEG is characterized by capturing the relevant adjudicative elements of each fact description and preserving important relations among them, such as causality and temporal sequences while filtering out irrelevant content. It enhances LJP task performance and provides interpretable references for legal professionals. Since increased or decreased penalties corresponding to different elements cannot be obtained from fact descriptions, we introduce Sentencing Guidance (SG) and combine it with FEGs as a supplementary feature. Experiments confirm the effectiveness of our method. Specifically, compared to the best baseline model, it improves macro-F1 by 3.09%, 0.57%, and 1.78% for the subtasks of law article prediction, charge prediction, and term of penalty prediction.
Legal Similar Case Retrieval (LSCR) is a critical application in legal Artificial Intelligence (AI). It involves retrieving the most relevant cases from legal case databases through query cases. Legal cases are semi-s...
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ISBN:
(数字)9798350359312
ISBN:
(纸本)9798350359329
Legal Similar Case Retrieval (LSCR) is a critical application in legal Artificial Intelligence (AI). It involves retrieving the most relevant cases from legal case databases through query cases. Legal cases are semi-structured documents characterized by long text sequences and high specialization. Existing approaches rely on pre-trained language models for retrieval. However, these methods are constrained by the length of text input, preventing them from fully comprehending cases, which results in poor retrieval performance. Knowledge Graph (KG) are a type of graph structure data with dense knowledge and clear logic, which can represent the criminal processes and relationships among characters within legal cases. Currently, the mainstream approach for handling KG remains Graph Neural Networks (GNNs). However, these methods are limited by message passing and are prone to over-smoothing problems in the process of aggregating node features. To address these issues, we propose a Legal similar case retrieval model that combines Graph representation learning with the Transformer, called LeGalFormer. Three encoding methods are introduced to incorporate the structural information of the graph into the Transformer architecture. We evaluate the model on a real legal dataset, and the experimental results show that LeGalFormer significantly enhances the model's understanding capacity and achieves state-of-the-art performance.
Legal document retrieval is a crucial application in the field of legal Artificial Intelligence (AI). It involves retrieving the most relevant legal cases from a legal document database when inputting a query. Mainstr...
Legal document retrieval is a crucial application in the field of legal Artificial Intelligence (AI). It involves retrieving the most relevant legal cases from a legal document database when inputting a query. Mainstream solutions typically employ pre-trained language models or large language models for retrieval. However, both of these methods rely on black-box models trained on unlabeled data, resulting in a lack of interpretability in the model's retrieval outcomes. Graph Neural Networks (GNNs) possess strong representational and interpretable capabilities. Therefore, representing legal cases using graph-structured data and performing the Legal Similar Case Retrieval (LSCR) task through GNNs is a promising approach. Nevertheless, typical GNNs primarily focus on node information within graph-structured data and overlook edge and inter-graph interaction information, leading to lower retrieval accuracy. To address these issues, in this paper, we propose a Legal case retrieval model utilizing a Graph Matching Network, called Legal-GMN. We evaluate the model on a dataset composed of real legal judgment cases. Experimental results demonstrate that Legal-GMN effectively enhances the retrieval accuracy for the LSCR task. Compared with baseline methods, Legal-GMN improves retrieval precision by approximately 15% and enhances retrieval efficiency by approximately 90%. Remarkably, while maintaining State-Of-The-Art (SOTA) performance, Legal-GMN can also generate visualized graphs of the retrieval process, significantly enhancing its interpretability.
The divisibility and congruence of usual and generalized central trinomial coefficients have been extensively investigated. The present paper is devoted to analytic properties of these numbers. We show that usual cent...
In this paper,we study the trigonometric approximation problems of functions which belong to the Lipαclass,the Lip(ξ(t))class,and the W(L_(M)^(*);ξ(t))class in Orlicz spaces by using the tools Hölder inequalit...
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In this paper,we study the trigonometric approximation problems of functions which belong to the Lipαclass,the Lip(ξ(t))class,and the W(L_(M)^(*);ξ(t))class in Orlicz spaces by using the tools Hölder inequality in Orlicz spaces,the second mean value theorem for integrals,and(E,q)(C,α,β)means *** the same time,we give the corresponding degree of approximation.
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