144 research outputs found

    A note on nowhere-zero 3-flow and Z_3-connectivity

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    There are many major open problems in integer flow theory, such as Tutte's 3-flow conjecture that every 4-edge-connected graph admits a nowhere-zero 3-flow, Jaeger et al.'s conjecture that every 5-edge-connected graph is Z3Z_3-connected and Kochol's conjecture that every bridgeless graph with at most three 3-edge-cuts admits a nowhere-zero 3-flow (an equivalent version of 3-flow conjecture). Thomassen proved that every 8-edge-connected graph is Z3Z_3-connected and therefore admits a nowhere-zero 3-flow. Furthermore, LovaËŠ\acute{a}sz, Thomassen, Wu and Zhang improved Thomassen's result to 6-edge-connected graphs. In this paper, we prove that: (1) Every 4-edge-connected graph with at most seven 5-edge-cuts admits a nowhere-zero 3-flow. (2) Every bridgeless graph containing no 5-edge-cuts but at most three 3-edge-cuts admits a nowhere-zero 3-flow. (3) Every 5-edge-connected graph with at most five 5-edge-cuts is Z3Z_3-connected. Our main theorems are partial results to Tutte's 3-flow conjecture, Kochol's conjecture and Jaeger et al.'s conjecture, respectively.Comment: 10 pages. Typos correcte

    Explicit gain equations for hybrid graphene-quantum-dot photodetectors

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    Graphene is an attractive material for broadband photodetection but suffers from weak light absorption. Coating graphene with quantum dots can significantly enhance light absorption and create extraordinarily high photo gain. This high gain is often explained by the classical gain theory which is unfortunately an implicit function and may even be questionable. In this work, we managed to derive explicit gain equations for hybrid graphene-quantum-dot photodetectors. Due to the work function mismatch, lead sulfide (PbS) quantum dots coated on graphene will form a surface depletion region near the interface of quantum dots and graphene. Light illumination narrows down the surface depletion region, creating a photovoltage that gates the graphene. As a result, high photo gain in graphene is observed. The explicit gain equations are derived from the theoretical gate transfer characteristics of graphene and the correlation of the photovoltage with the light illumination intensity. The derived explicit gain equations fit well with the experimental data, from which physical parameters are extracted.Comment: 14 pages, 6 figure

    Optimizing Feature Set for Click-Through Rate Prediction

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    Click-through prediction (CTR) models transform features into latent vectors and enumerate possible feature interactions to improve performance based on the input feature set. Therefore, when selecting an optimal feature set, we should consider the influence of both feature and its interaction. However, most previous works focus on either feature field selection or only select feature interaction based on the fixed feature set to produce the feature set. The former restricts search space to the feature field, which is too coarse to determine subtle features. They also do not filter useless feature interactions, leading to higher computation costs and degraded model performance. The latter identifies useful feature interaction from all available features, resulting in many redundant features in the feature set. In this paper, we propose a novel method named OptFS to address these problems. To unify the selection of feature and its interaction, we decompose the selection of each feature interaction into the selection of two correlated features. Such a decomposition makes the model end-to-end trainable given various feature interaction operations. By adopting feature-level search space, we set a learnable gate to determine whether each feature should be within the feature set. Because of the large-scale search space, we develop a learning-by-continuation training scheme to learn such gates. Hence, OptFS generates the feature set only containing features which improve the final prediction results. Experimentally, we evaluate OptFS on three public datasets, demonstrating OptFS can optimize feature sets which enhance the model performance and further reduce both the storage and computational cost.Comment: Accepted by WWW 2023 Research Track

    Towards Hybrid-grained Feature Interaction Selection for Deep Sparse Network

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    Deep sparse networks are widely investigated as a neural network architecture for prediction tasks with high-dimensional sparse features, with which feature interaction selection is a critical component. While previous methods primarily focus on how to search feature interaction in a coarse-grained space, less attention has been given to a finer granularity. In this work, we introduce a hybrid-grained feature interaction selection approach that targets both feature field and feature value for deep sparse networks. To explore such expansive space, we propose a decomposed space which is calculated on the fly. We then develop a selection algorithm called OptFeature, which efficiently selects the feature interaction from both the feature field and the feature value simultaneously. Results from experiments on three large real-world benchmark datasets demonstrate that OptFeature performs well in terms of accuracy and efficiency. Additional studies support the feasibility of our method.Comment: NeurIPS 2023 poste

    Result Diversification in Search and Recommendation: A Survey

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    Diversifying return results is an important research topic in retrieval systems in order to satisfy both the various interests of customers and the equal market exposure of providers. There has been growing attention on diversity-aware research during recent years, accompanied by a proliferation of literature on methods to promote diversity in search and recommendation. However, diversity-aware studies in retrieval systems lack a systematic organization and are rather fragmented. In this survey, we are the first to propose a unified taxonomy for classifying the metrics and approaches of diversification in both search and recommendation, which are two of the most extensively researched fields of retrieval systems. We begin the survey with a brief discussion of why diversity is important in retrieval systems, followed by a summary of the various diversity concerns in search and recommendation, highlighting their relationship and differences. For the survey's main body, we present a unified taxonomy of diversification metrics and approaches in retrieval systems, from both the search and recommendation perspectives. In the later part of the survey, we discuss the open research questions of diversity-aware research in search and recommendation in an effort to inspire future innovations and encourage the implementation of diversity in real-world systems.Comment: 20 page

    Towards Benchmarking GUI Compatibility Testing on Mobile Applications

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    GUI is a bridge connecting user and application. Existing GUI testing tasks can be categorized into two groups: functionality testing and compatibility testing. While the functionality testing focuses on detecting application runtime bugs, the compatibility testing aims at detecting bugs resulting from device or platform difference. To automate testing procedures and improve testing efficiency, previous works have proposed dozens of tools. To evaluate these tools, in functionality testing, researchers have published testing benchmarks. Comparatively, in compatibility testing, the question of ``Do existing methods indeed effectively assist test cases replay?'' is not well answered. To answer this question and advance the related research in GUI compatibility testing, we propose a benchmark of GUI compatibility testing. In our experiments, we compare the replay success rate of existing tools. Based on the experimental results, we summarize causes which may lead to ineffectiveness in test case replay and propose opportunities for improving the state-of-the-art

    Negative Perceptions of Urban Tourism Community in Beijing: Based on Online Comments

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    The development of urban tourism community (UTC) will bring new vigor and vitality into the urban sustainable development. There are abundant tourists’ comments about personal experience in UTC in online travel communities, which provide good access to the knowledge of negative perceptions of urban tourism community. Based on online comments, this paper used content analysis method to research tourists’ negative perceptions about five typical tourism communities in Beijing, whereby the common problems and special problems of UTC were identified. According to the research results, the authors made suggestions to the sustainable development of UTC in Beijing
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