Artificial Intelligence最新文献_第2页

Explanations for query answers under existential rules

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-04 DOI: 10.1016/j.artint.2025.104294

İsmail İlkan Ceylan , Thomas Lukasiewicz , Enrico Malizia , Andrius Vaicenavičius

{"title":"Explanations for query answers under existential rules","authors":"İsmail İlkan Ceylan , Thomas Lukasiewicz , Enrico Malizia , Andrius Vaicenavičius","doi":"10.1016/j.artint.2025.104294","DOIUrl":"10.1016/j.artint.2025.104294","url":null,"abstract":"<div><div>Ontology-based data access is an extensively studied paradigm aiming at improving query answers with the use of an “ontology”. An ontology is a specification of a domain of interest, which, in this context, is described via a logical theory. As a form of logical entailment, ontology-mediated query answering is fully interpretable, which makes it possible to derive explanations for ontological query answers. This is a quite important aspect, as the fact that many recent AI systems mostly operating as black boxes has led to some serious concerns. In the literature, various works on explanations in the context of description logics (DLs) have appeared, mostly focusing on explaining concept subsumption and concept unsatisfiability in the ontologies. Some works on explaining query entailment in DLs have appeared as well, however, mainly dealing with inconsistency-tolerant semantics and, actually, <em>non</em>-entailment of the queries. Surprisingly, explaining ontological query entailment has received little attention for ontology languages based on existential rules. In fact, although DLs are popular formalisms to model ontologies, it is generally agreed that rule-based ontologies are well-suited for data-intensive applications, as they allow us to conveniently deal with higher-arity relations, which naturally occur in standard relational databases. The goal of this work is to close this gap, and study the problem of explaining query entailment in the context of existential rules ontologies in terms of minimal subsets of database facts. We provide a thorough complexity analysis for several decision problems associated with minimal explanations for various classes of existential rules, and for different complexity measures.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104294"},"PeriodicalIF":5.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394775","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

No free lunch theorem for privacy-preserving LLM inference

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-04 DOI: 10.1016/j.artint.2025.104293

Xiaojin Zhang , Yahao Pang , Yan Kang , Wei Chen , Lixin Fan , Hai Jin , Qiang Yang

{"title":"No free lunch theorem for privacy-preserving LLM inference","authors":"Xiaojin Zhang , Yahao Pang , Yan Kang , Wei Chen , Lixin Fan , Hai Jin , Qiang Yang","doi":"10.1016/j.artint.2025.104293","DOIUrl":"10.1016/j.artint.2025.104293","url":null,"abstract":"<div><div>Individuals and businesses have been significantly benefited by Large Language Models (LLMs) including PaLM, Gemini and ChatGPT in various ways. For example, LLMs enhance productivity, reduce costs, and enable us to focus on more valuable tasks. Furthermore, LLMs possess the capacity to sift through extensive datasets, uncover underlying patterns, and furnish critical insights that propel the frontiers of technology and science. However, LLMs also pose privacy concerns. Users' interactions with LLMs may expose their sensitive personal or company information. A lack of robust privacy safeguards and legal frameworks could permit the unwarranted intrusion or improper handling of individual data, thereby risking infringements of privacy and the theft of personal identities. To ensure privacy, it is essential to minimize the dependency between shared prompts and private information. Various randomization approaches have been proposed to protect prompts' privacy, but they may incur utility loss compared to unprotected LLMs prompting. Therefore, it is essential to evaluate the balance between the risk of privacy leakage and loss of utility when conducting effective protection mechanisms. The current study develops a framework for inferring privacy-protected Large Language Models (LLMs) and lays down a solid theoretical basis for examining the interplay between privacy preservation and utility. The core insight is encapsulated within a theorem that is called as the NFL (abbreviation of the word No-Free-Lunch) Theorem.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104293"},"PeriodicalIF":5.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Kripke-Lewis semantics for belief update and belief revision 信念更新与修正的Kripke-Lewis语义

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-01 DOI: 10.1016/j.artint.2024.104259

Giacomo Bonanno

{"title":"A Kripke-Lewis semantics for belief update and belief revision","authors":"Giacomo Bonanno","doi":"10.1016/j.artint.2024.104259","DOIUrl":"10.1016/j.artint.2024.104259","url":null,"abstract":"<div><div>We provide a new characterization of both belief update and belief revision in terms of a Kripke-Lewis semantics. We consider frames consisting of a set of states, a Kripke belief relation and a Lewis selection function. Adding a valuation to a frame yields a model. Given a model and a state, we identify the initial belief set <em>K</em> with the set of formulas that are believed at that state and we identify either the updated belief set <span><math><mi>K</mi><mo>⋄</mo><mi>ϕ</mi></math></span> or the revised belief set <span><math><mi>K</mi><mo>⁎</mo><mi>ϕ</mi></math></span> (prompted by the input represented by formula <em>ϕ</em>) as the set of formulas that are the consequent of conditionals that (1) are believed at that state and (2) have <em>ϕ</em> as antecedent. We show that this class of models characterizes both the Katsuno-Mendelzon (KM) belief update functions and the Alchourrón, Gärdenfors and Makinson (AGM) belief revision functions, in the following sense: (1) each model gives rise to a partial belief function that can be completed into a full KM/AGM update/revision function, and (2) for every KM/AGM update/revision function there is a model whose associated belief function coincides with it. The difference between update and revision can be reduced to two semantic properties that appear in a stronger form in revision relative to update, thus confirming the finding by Peppas et al. (1996) <span><span>[30]</span></span> that, “for a fixed theory <em>K</em>, revising <em>K</em> is much the same as updating <em>K</em>”. It is argued that the proposed semantic characterization brings into question the common interpretation of belief revision and update as change in beliefs in response to new information.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104259"},"PeriodicalIF":5.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

EMOA*: A framework for search-based multi-objective path planning EMOA*：基于搜索的多目标路径规划框架

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-01 DOI: 10.1016/j.artint.2024.104260

Zhongqiang Ren , Carlos Hernández , Maxim Likhachev , Ariel Felner , Sven Koenig , Oren Salzman , Sivakumar Rathinam , Howie Choset

{"title":"EMOA*: A framework for search-based multi-objective path planning","authors":"Zhongqiang Ren , Carlos Hernández , Maxim Likhachev , Ariel Felner , Sven Koenig , Oren Salzman , Sivakumar Rathinam , Howie Choset","doi":"10.1016/j.artint.2024.104260","DOIUrl":"10.1016/j.artint.2024.104260","url":null,"abstract":"<div><div>In the Multi-Objective Shortest Path Problem (MO-SPP), one has to find paths on a graph that simultaneously minimize multiple objectives. It is not guaranteed that there exists a path that minimizes all objectives, and the problem thus aims to find the set of Pareto-optimal paths from the start to the goal vertex. A variety of multi-objective A*-based search approaches have been developed for this purpose. Typically, these approaches maintain a front set at each vertex during the search process to keep track of the Pareto-optimal paths that reach that vertex. Maintaining these front sets becomes burdensome and often slows down the search when there are many Pareto-optimal paths. In this article, we first introduce a framework for MO-SPP with the key procedures related to the front sets abstracted and highlighted, which provides a novel perspective for understanding the existing multi-objective A*-based search algorithms. Within this framework, we develop two different, yet closely related approaches to maintain these front sets efficiently during the search. We show that our approaches can find all cost-unique Pareto-optimal paths, and analyze their runtime complexity. We implement the approaches and compare them against baselines using instances with three, four and five objectives. Our experimental results show that our approaches run up to an order of magnitude faster than the baselines.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104260"},"PeriodicalIF":5.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Out-of-distribution detection by regaining lost clues 通过恢复丢失的线索进行分布外检测

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-01 DOI: 10.1016/j.artint.2024.104275

Zhilin Zhao , Longbing Cao , Philip S. Yu

{"title":"Out-of-distribution detection by regaining lost clues","authors":"Zhilin Zhao , Longbing Cao , Philip S. Yu","doi":"10.1016/j.artint.2024.104275","DOIUrl":"10.1016/j.artint.2024.104275","url":null,"abstract":"<div><div>Out-of-distribution (OOD) detection identifies samples in the test phase that are drawn from distributions distinct from that of training in-distribution (ID) samples for a trained network. According to the information bottleneck, networks that classify tabular data tend to extract labeling information from features with strong associations to ground-truth labels, discarding less relevant labeling cues. This behavior leads to a predicament in which OOD samples with limited labeling information receive high-confidence predictions, rendering the network incapable of distinguishing between ID and OOD samples. Hence, exploring more labeling information from ID samples, which makes it harder for an OOD sample to obtain high-confidence predictions, can address this over-confidence issue on tabular data. Accordingly, we propose a novel transformer chain (TC), which comprises a sequence of dependent transformers that iteratively regain discarded labeling information and integrate all the labeling information to enhance OOD detection. The generalization bound theoretically reveals that TC can balance ID generalization and OOD detection capabilities. Experimental results demonstrate that TC significantly surpasses state-of-the-art methods for OOD detection in tabular data.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104275"},"PeriodicalIF":5.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Formal verification and synthesis of mechanisms for social choice 社会选择机制的形式验证与综合

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-01 DOI: 10.1016/j.artint.2024.104272

Munyque Mittelmann , Bastien Maubert , Aniello Murano , Laurent Perrussel

{"title":"Formal verification and synthesis of mechanisms for social choice","authors":"Munyque Mittelmann , Bastien Maubert , Aniello Murano , Laurent Perrussel","doi":"10.1016/j.artint.2024.104272","DOIUrl":"10.1016/j.artint.2024.104272","url":null,"abstract":"<div><div>Mechanism Design (MD) aims at defining resources allocation protocols that satisfy a predefined set of properties, and Auction Mechanisms are of foremost importance. Core properties of mechanisms, such as strategy-proofness or budget balance, involve: (i) complex strategic concepts such as Nash equilibria, (ii) quantitative aspects such as utilities, and often (iii) imperfect information, with agents' private valuations. We demonstrate that Strategy Logic provides a formal framework fit to model mechanisms and express such properties, and we show that it can be used either to automatically check that a given mechanism satisfies some property (verification), or automatically produce a mechanism that does (synthesis). To do so, we consider a quantitative and variant of Strategy Logic. We first show how to express the implementation of social choice functions. Second, we show how fundamental mechanism properties can be expressed as logical formulas, and thus evaluated by model checking. We then prove that model checking for this particular variant of Strategy Logic can be done in polynomial space. Next, we show how MD can be rephrased as a synthesis problem, where mechanisms are automatically synthesized from a partial or complete logical specification. We solve the automated synthesis of mechanisms in two cases: when the number of actions is bounded, and when agents play in turns. Finally, we provide examples of auction design based for each of these two cases. The benefit of our approach in relation to classical MD is to provide a general framework for addressing a large spectrum of MD problems, which is not tailored to a particular setting or problem.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104272"},"PeriodicalIF":5.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821057","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-rank smart reserves: A general framework for selection and matching diversity goals 多等级智能储备：选择和匹配多样性目标的总体框架

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-01 DOI: 10.1016/j.artint.2024.104274

Haris Aziz , Zhaohong Sun

{"title":"Multi-rank smart reserves: A general framework for selection and matching diversity goals","authors":"Haris Aziz , Zhaohong Sun","doi":"10.1016/j.artint.2024.104274","DOIUrl":"10.1016/j.artint.2024.104274","url":null,"abstract":"<div><div>We study a problem where each school has flexible multi-ranked diversity goals, and each student may belong to multiple overlapping types, and consumes only one of the positions reserved for their types. We propose a novel choice function for a school to select students and show that it is the unique rule that satisfies three fundamental properties: maximal diversity, non-wastefulness, and justified envy-freeness. We provide a fast polynomial-time algorithm for our choice function that is based on the Dulmage Mendelsohn Decomposition Theorem as well as new insights into the combinatorial structure of constrained rank maximal matchings. Even for the case of minimum and maximum quotas for types (that capture two ranks), ours is the first known polynomial-time approach to compute an optimally diverse choice outcome. Finally, we prove that the choice function we design for schools, satisfies substitutability and hence can be directly embedded in the generalized deferred acceptance algorithm to achieve strategyproofness and stability. Our algorithms and results have immediate policy implications and directly apply to a variety of scenarios, such as where hiring positions or scarce medical resources need to be allocated while taking into account diversity concerns or ethical principles.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104274"},"PeriodicalIF":5.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A simple yet effective self-debiasing framework for transformer models 一个简单而有效的变压器模型自消偏框架

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-02-01 DOI: 10.1016/j.artint.2024.104258

Xiaoyue Wang , Xin Liu , Lijie Wang , Suhang Wu , Jinsong Su , Hua Wu

{"title":"A simple yet effective self-debiasing framework for transformer models","authors":"Xiaoyue Wang , Xin Liu , Lijie Wang , Suhang Wu , Jinsong Su , Hua Wu","doi":"10.1016/j.artint.2024.104258","DOIUrl":"10.1016/j.artint.2024.104258","url":null,"abstract":"<div><div>Current Transformer-based natural language understanding (NLU) models heavily rely on dataset biases, while failing to handle real-world out-of-distribution (OOD) instances. Many methods have been proposed to deal with this issue, but they ignore the fact that the features learned in different layers of Transformer-based NLU models are different. In this paper, we first conduct preliminary studies to obtain two conclusions: 1) both low- and high-layer sentence representations encode common biased features during training; 2) the low-layer sentence representations encode fewer unbiased features than the high-layer ones. Based on these conclusions, we propose a simple yet effective self-debiasing framework for Transformer-based NLU models. Concretely, we first stack a classifier on a selected low layer. Then, we introduce a residual connection that feeds the low-layer sentence representation to the top-layer classifier. In this way, the top-layer sentence representation will be trained to ignore the common biased features encoded by the low-layer sentence representation and focus on task-relevant unbiased features. During inference, we remove the residual connection and directly use the top-layer sentence representation to make predictions. Extensive experiments and in-depth analyses on NLU tasks demonstrate the superiority of our framework, achieving a new state-of-the-art (SOTA) on three OOD test sets.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"339 ","pages":"Article 104258"},"PeriodicalIF":5.1,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved metric distortion via threshold approvals

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-01-30 DOI: 10.1016/j.artint.2025.104295

Elliot Anshelevich , Aris Filos-Ratsikas , Christopher Jerrett , Alexandros A. Voudouris

{"title":"Improved metric distortion via threshold approvals","authors":"Elliot Anshelevich , Aris Filos-Ratsikas , Christopher Jerrett , Alexandros A. Voudouris","doi":"10.1016/j.artint.2025.104295","DOIUrl":"10.1016/j.artint.2025.104295","url":null,"abstract":"<div><div>We consider a social choice setting in which agents and alternatives are represented by points in a metric space, and the cost of an agent for an alternative is the distance between the corresponding points in the space. The goal is to choose a single alternative to (approximately) minimize the social cost (cost of all agents) or the maximum cost of any agent, when only limited information about the preferences of the agents is given. Previous work has shown that the best possible distortion one can hope to achieve is 3 when access to the ordinal preferences of the agents is given, even when the distances between alternatives in the metric space are known. We improve upon this bound of 3 by designing deterministic mechanisms that exploit a bit of cardinal information. We show that it is possible to achieve distortion <span><math><mn>1</mn><mo>+</mo><msqrt><mrow><mn>2</mn></mrow></msqrt></math></span> by using the ordinal preferences of the agents, the distances between alternatives, and a threshold approval set per agent that contains all alternatives that are at distance from the agent within an appropriately chosen factor of the minimum distance of the agents from any alternative. We show that this bound is the best possible for any deterministic mechanism in general metric spaces, and also provide improved bounds for the fundamental case of a line metric.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"341 ","pages":"Article 104295"},"PeriodicalIF":5.1,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143072460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

TTVAE: Transformer-based generative modeling for tabular data generation

IF 5.1 2区计算机科学

Artificial Intelligence Pub Date : 2025-01-20 DOI: 10.1016/j.artint.2025.104292

Alex X. Wang , Binh P. Nguyen

{"title":"TTVAE: Transformer-based generative modeling for tabular data generation","authors":"Alex X. Wang , Binh P. Nguyen","doi":"10.1016/j.artint.2025.104292","DOIUrl":"10.1016/j.artint.2025.104292","url":null,"abstract":"<div><div>Tabular data synthesis presents unique challenges, with Transformer models remaining underexplored despite the applications of Variational Autoencoders and Generative Adversarial Networks. To address this gap, we propose the Transformer-based Tabular Variational AutoEncoder (TTVAE), leveraging the attention mechanism for capturing complex data distributions. The inclusion of the attention mechanism enables our model to understand complex relationships among heterogeneous features, a task often difficult for traditional methods. TTVAE facilitates the integration of interpolation within the latent space during the data generation process. Specifically, TTVAE is trained once, establishing a low-dimensional representation of real data, and then various latent interpolation methods can efficiently generate synthetic latent points. Through extensive experiments on diverse datasets, TTVAE consistently achieves state-of-the-art performance, highlighting its adaptability across different feature types and data sizes. This innovative approach, empowered by the attention mechanism and the integration of interpolation, addresses the complex challenges of tabular data synthesis, establishing TTVAE as a powerful solution.</div></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"340 ","pages":"Article 104292"},"PeriodicalIF":5.1,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0