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关键词： Testing   Quantum algorithm   Complexity theory   Probability distribution   Computer science   Aerospace electronics   Measurement   Quantum computing   quantum algorithm   property testing   probability distribution   TESTING PROPERTIES   LOWER BOUNDS   ALGORITHM  

摘要： We explore potential quantum speedups for the fundamental problem of testing the properties of closeness and k-wise uniformity of probability distributions: 1) Closeness testing is the problem of distinguishing whether two n-dimensional distributions are identical or at least epsilon -far in & ell;(1) - or & ell;(2) -distance. We show that the quantum query complexities for & ell;(1 )- and & ell;(2) -closeness testing are O(root n/epsilon) and O(1/epsilon) , respectively, both of which achieve optimal dependence on epsilon , improving the prior best results of Gily & eacute;n and Li (2019) and 2) k-wise uniformity testing is the problem of distinguishing whether a distribution over {0,1}(n) is uniform when restricted to any k coordinates or epsilon -far from any such distribution. We propose the first quantum algorithm for this problem with query complexity O(root n(k)/epsilon) , achieving a quadratic speedup over the state-of-the-art classical algorithm with sample complexity O(n(k)/epsilon(2)) by O'Donnell and Zhao (2018). Moreover, when k=2 our quantum algorithm outperforms any classical one because of the classical lower bound Omega(n/epsilon(2)) . All our quantum algorithms are fairly simple and time-efficient, using only basic quantum subroutines such as amplitude estimation.

关键词： Scalability   Visualization   Computational modeling   Encoding   Psychology   Human computer interaction   Computer science   Conceptual framework   scalability   structured literature analysis   visualization   SCALE   EXPLORATION   CHALLENGES   DESIGN   GRAPHS   MEMORY  

摘要： We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990 to 2020 to characterize the different notions of scalability in these works. While many article have addressed scalability issues, our survey identifies a lack of consistency in the use of the term in the visualization research community. We address this issue by introducing a consistent terminology meant to help visualization researchers better characterize the scalability aspects in their research. It also helps in providing multiple methods for supporting the claim that a work is "scalable." Our model is centered around an effort function with inputs and outputs. The inputs are the problem size and resources, whereas the outputs are the actual efforts, for instance, in terms of computational run time or visual clutter. We select representative examples to illustrate different approaches and facets of what scalability can mean in visualization literature. Finally, targeting the diverse crowd of visualization researchers without a scalability tradition, we provide a set of recommendations for how scalability can be presented in a clear and consistent way to improve fair comparison between visualization techniques and systems and foster reproducibility.

关键词： Diseases   Semantics   Heterogeneous networks   Predictive models   Representation learning   Computer science   Bioinformatics   Semantic feature learning based on vari- ous meta-paths   higher-order neighbor topological struc- ture integration   pairwise local feature learning   disease-related miRNA prediction   deep learning   MICRORNAS   DATABASE  

摘要： Dysregulation of miRNAs is closely related to the progression of various diseases, so identifying disease-related miRNAs is crucial. Most recently proposed methods are based on graph reasoning, while they did not completely exploit the topological structure composed of the higher-order neighbor nodes and the global and local features of miRNA and disease nodes. We proposed a prediction method, MDAP, to learn semantic features of miRNA and disease nodes based on various meta-paths, as well as node features from the entire heterogeneous network perspective, and node pair attributes. Firstly, for both the miRNA and disease nodes, node category-wise meta-paths were constructed to integrate the similarity and association connection relationships. Each target node has its specific neighbor nodes for each meta-path, and the neighbors of longer meta-paths constitute its higher-order neighbor topological structure. Secondly, we constructed a meta-path specific graph convolutional network module to integrate the features of higher-order neighbors and their topology, and then learned the semantic representations of nodes. Thirdly, for the entire miRNA-disease heterogeneous network, a global-aware graph convolutional autoencoder was built to learn the network-view feature representations of nodes. We also designed semantic-level and representation-level attentions to obtain informative semantic features and node representations. Finally, the strategy based on the parallel convolutional-deconvolutional neural networks were designed to enhance the local feature learning for a pair of miRNA and disease nodes. The experiment results showed that MDAP outperformed other state-of-the-art methods, and the ablation experiments demonstrated the effectiveness of MDAP's major innovations. MDAP's ability in discovering potential disease-related miRNAs was further analyzed by the case studies over three diseases.

关键词： Task analysis   Multitasking   Stress   Representation learning   Routing   Convolution   Computer science   Bundle recommendation   contrastive learning   disentangled representation learning  

摘要： Bundle recommendation aims to recommend a bundle of items for the user to purchase together, for which two scenarios (i.e., Next-bundle recommendation and Within-bundle recommendation) are explored to recommend a specific bundle of items for the user and a specific item to fill the user's current bundle, respectively. Previous works largely model the user's preference with a uniform intent, without considering the diversity of intents when adopting the items within the bundle. In the real scenario of bundle recommendation, user intents modeling actually needs to be considered from three hierarchical levels, for that: a user's intents may be naturally distributed in different bundles (user level), one bundle may contain multiple intents of a user (bundle level), and an item in different bundles may also present different user intents (item level). To this end, we develop a novel model, Hierarchical Intent Disentangle Graph Networks (HIDGN) for bundle recommendation. HIDGN is capable of capturing the diversity of the user's intent precisely and comprehensively from the hierarchical structure with an cross-task intent contrastive learning, which is unified with the supervised next-/within-bundle recommendation sub-tasks as a multi-task framework. Extensive experiments on three benchmark datasets demonstrate that HIDGN outperforms the state-of-the-art methods by 43.0%, 13.2%, and 73.3%, respectively.

关键词： Turing machines  

摘要： A universal Turing machine is a powerful concept — a single device can compute any function that is computable. A universal spin model, similarly, is a class of physical systems whose low energy behavior simulates that of any spin system. Our categorical framework for universality [11] captures these and other examples of universality as instances. In this article, we present an accessible account thereof with a focus on its basic ingredients and ways to use it. Specifically, we show how to identify necessary conditions for universality, compare types of universality within each instance, and establish that universality and negation give rise to unreachability (such as uncomputability). © 2024, CC BY-SA.

关键词： Chemistry   Computer science   Food science  

摘要： Compared to traditional methods, using machine learning to assess or predict the odor of molecules can save costs in various aspects. Our research aims to collect molecules with coffee odor and summarize the regularity of these molecules, ultimately creating a binary classifier that can determine whether a molecule has a coffee odor. In this study, a total of 371 coffee -odor molecules and 9,700 non -coffee -odor molecules were collected. The Knowledge -guided Pre -training of Graph Transformer (KPGT), support vector machine (SVM), random forest (RF), multi -layer perceptron (MLP), and message -passing neural networks (MPNN) were used to train the data. The model with the best performance was selected as the basis of the predictor. The prediction accuracy value of the KPGT model exceeded 0.84 and the predictor has been deployed as a webserver PredCoffee.

关键词： Geographical information science   Computer science   Machine learning  

摘要： In this study, we addressed two primary challenges: firstly, the issue of domain shift, which pertains changes in data characteristics or context that can impact model performance, and secondly, the discrepancy between semantic similarity and geographical distance. We employed topic modeling in conjunction with the BERT architecture. Our model was crafted to enhance similarity computations applied to geospatial text, aiming to integrate both semantic similarity and geographical proximity. We tested the model on two datasets, Persian Wikipedia articles and rental property advertisements. The findings demonstrate that the model effectively improved the correlation between semantic similarity and geographical distance. Furthermore, evaluation by real -world users within a recommender system context revealed notable increase in user satisfaction by approximately 22% for Wikipedia articles and 56% for advertisements.

关键词： Gaze tracking   Eye-tracking   Saccade   Fixations   Reading comprehension   e-learning   MOVEMENTS   PICTURES  

摘要： In this research, a mixed-method approach was employed to conduct large-scale eye-tracking measurements, traditionally associated with high costs and extensive time commitments. Utilizing consumer-grade webcams in conjunction with open-source software, data was collected from an expansive cohort of students, thereby demonstrating the scalability and cost-effectiveness of this innovative methodology. The primary objective of this research was to discern the disparities in reading behaviour when students were presented with standard text accompanied by illustrations, compared to the same text with highlighted key terms. The participants, comprised of first-year university students, completed a questionnaire and an introductory test to ascertain their knowledge level. Subsequently, they were segregated into two groups and participated in two reading sessions, during which their ocular movements were recorded. The amassed data underwent both qualitative analyses, facilitated by visualizations, and quantitative analysis, employing statistical measures on the data and test results. Notably, no significant difference was observed in the gaze patterns or test results between the experimental and control groups. However, a significant divergence in gaze patterns was identified between high-achieving students and those experiencing difficulties, as evidenced by the averaged composite heatmaps generated from the data. The findings underscore two pivotal points. Firstly, the feasibility of conducting large-scale eye-tracking experiments is demonstrated. Traditional studies in this field often employ small population samples due to the time and financial constraints associated with methods that utilize specialized eye-tracking hardware. In contrast, our methodology is scalable, relying on low-end hardware and enabling students to record data on their personal devices. Secondly, while eye-tracking may not provide substantial benefits for fine-tuning text already optimized for rea

关键词： Biochemistry   Computer science   Computational mathematics  

摘要： The prediction of drug -target interactions (DTIs) is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. Computational approaches to predicting DTIs can provide important insights into drug mechanisms of action. However, current methods for predicting DTIs based on the structural information of the knowledge graph may suffer from the sparseness and incompleteness of the knowledge graph and neglect the latent type information of the knowledge graph. In this paper, we propose TTModel, a knowledge graph embedding model for DTI prediction. By exploiting biomedical text and type information, TTModel can learn latent text semantics and type information to improve the performance of representation learning. Comprehensive experiments on two public datasets demonstrate that our model outperforms the state-of-the-art methods significantly on the task of DTI prediction.