85 research outputs found

    PLDANet: Reasonable Combination of PCA and LDA Convolutional Networks

    Get PDF
    Integrating deep learning with traditional machine learning methods is an intriguing research direction. For example, PCANet and LDANet adopts Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA) to learn convolutional kernels separately. It is not reasonable to adopt LDA to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas. Therefore, it is meaningful to adopt LDA to learn filter kernels only when all the patches carry information from the whole image. However, to our knowledge, there are no existing works that study how to combine PCA and LDA to learn convolutional kernels to achieve the best performance. In this paper, we propose the convolutional coverage theory. Furthermore, we propose the PLDANet model which adopts PCA and LDA reasonably in different convolutional layers based on the coverage theory. The experimental study has shown the effectiveness of the proposed PLDANet model

    A Method for Automatically Generating Join Queries Based on Relations-Attributes Distance Matrix over Data Lakes

    Get PDF
    Techniques for identifying joinable or unionable tables in data lakes can yield valuable information for data scientists. However, more than half of their working time is spent familiarizing themselves with the metadata and correlations of datasets. Simplifying the use of information in data lakes is crucial for enhancing their utilization. The existing solution of integrating correlated relations into a single large data table via full disjunction requires integration updating when either data or metadata changes, complicating data maintenance. This paper proposes a method for automatically generating join queries based on the distance matrix of relations and attributes in data lakes. The distance matrix only requires updating when metadata changes, simplifying data maintenance. Experimental results demonstrate that once the distance matrix is generated, the time required to generate the join queries is negligible. Compared to the existing solution, the time cost for executing join queries over correlated tables is nearly identical to that of selection queries over integrated tables. The results of these two queries are also the same, showcasing the effectiveness and efficiency of our method

    Query with Assumptions for Probabilistic Relational Databases

    Get PDF
    Users may have prior knowledge about a probabilistic database. They prefer to query over a probabilistic database on their prior knowledge which cannot be written as component clauses of conventional SQL queries. A naive approach is to query over a new database version, which is generated by transforming the original probabilistic database to satisfy users\u27 prior knowledge; however, it is impractical to generate a different probabilistic database version for each prior knowledge. In this paper, we propose the concept of the query with assumptions which allow users to describe their prior knowledge with a newly introduced ASSUMPTION clause of SQL. We also propose an approach to obtain the result of a query based on assumption clauses. The experimental studies show our approach has better performance compared to the naive approach

    Enabling Access Control for Encrypted Multi-Dimensional Data in Cloud Computing through Range Search

    Get PDF
    With the growing popularity of cloud computing, data owners are increasingly opting to outsource their data to cloud servers due to the numerous benefits it offers. However, this outsourcing raises concerns about data privacy since the data stored on remote cloud servers is not directly controlled by the owners. Encryption of the data is an effective approach to mitigate these privacy concerns. However, encrypted data lacks distinguishability, leading to limitations in supporting common operations such as range search and access control. In this research paper, we propose a method called RSAC (Range Search Supporting Access Control) for encrypted multi-dimensional data in cloud computing. Our method leverages policy design, bucket embedding, algorithm design, and Ciphertext Policy-Attribute Based Encryption (CPABE) to achieve its objectives. We present extensive experimental results that demonstrate the efficiency of our method and conduct a thorough security analysis to ensure its robustness. Our proposed RSAC method addresses the challenges of range search and access control over encrypted multi-dimensional data, thus contributing to enhancing privacy and security in cloud computing environments

    The Influence of Tone Inventory on ERP without Focal Attention: A Cross-Language Study

    Get PDF
    This study investigates the effect of tone inventories on brain activities underlying pitch without focal attention. We find that the electrophysiological responses to across-category stimuli are larger than those to within-category stimuli when the pitch contours are superimposed on nonspeech stimuli; however, there is no electrophysiological response difference associated with category status in speech stimuli. Moreover, this category effect in nonspeech stimuli is stronger for Cantonese speakers. Results of previous and present studies lead us to conclude that brain activities to the same native lexical tone contrasts are modulated by speakers’ language experiences not only in active phonological processing but also in automatic feature detection without focal attention. In contrast to the condition with focal attention, where phonological processing is stronger for speech stimuli, the feature detection (pitch contours in this study) without focal attention as shaped by language background is superior in relatively regular stimuli, that is, the nonspeech stimuli. The results suggest that Cantonese listeners outperform Mandarin listeners in automatic detection of pitch features because of the denser Cantonese tone system

    Commonness, rarity, and intraspecific variation in traits and performance in tropical tree seedlings

    Get PDF
    Abstract One of the few rules in ecology is that communities are composed of many rare and few common species. Trait-based investigations of abundance distributions have generally focused on speciesmean trait values with mixed success. Here, using large tropical tree seedling datasets in China and Puerto Rico, we take an alternative approach that considers the magnitude of intraspecific variation in traits and growth as it relates to species abundance. We find that common species are less variable in their traits and growth. Common species also occupy core positions within community trait space indicating that they are finely tuned for the available conditions. Rare species are functionally peripheral and are likely transients struggling for success in the given environment. The work highlights the importance of considering intraspecific variation in trait-based ecology and demonstrates asymmetry in the magnitude of intraspecific variation among species is critical for understanding of how traits are related to abundance

    Lack of phylogenetic signals within environmental niches of tropical tree species across life stages

    Get PDF
    The lasting imprint of phylogenetic history on current day ecological patterns has long intrigued biologists. Over the past decade ecologists have increasingly sought to quantify phylogenetic signals in environmental niche preferences and, especially, traits to help uncover the mechanisms driving plant community assembly. However, relatively little is known about how phylogenetic patterns in environmental niches and traits compare, leaving significant uncertainty about the ecological implications of trait-based analyses. We examined phylogenetic signals within known environmental niches of 64 species, at seedling and adult life stages, in a Chinese tropical forest, to test whether local environmental niches had consistent relationships with phylogenies. Our analyses show that local environmental niches are highly phylogenetically labile for both seedlings and adult trees, with closely related species occupying niches that are no more similar than expected by random chance. These findings contrast with previous trait-based studies in the same forest, suggesting that phylogenetic signals in traits might not a reliable guide to niche preferences or, therefore, to community assembly processes in some ecosystems, like the tropical seasonal rainforest in this study
    corecore