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Author: search.filters.author.Wang Q

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    Learning to Bound for Maximum Common Subgraph Algorithms
    (Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl Publishing, Germany, 2025-08-08) Kothalawala BW; Koehler H; Wang Q; Garcia de la Banda M
    Identifying the maximum common subgraph between two graphs is a computationally challenging NP-hard problem. While the McSplit algorithm represents a state-of-the-art approach within a branch-and-bound (BnB) framework, several extensions have been proposed to enhance its vertex pair selection strategy, often utilizing reinforcement learning techniques. Nonetheless, the quality of the upper bound remains a critical factor in accelerating the search process by effectively pruning unpromising branches. This research introduces a novel, more restrictive upper bound derived from a detailed analysis of the McSplit algorithm's generated partitions. To enhance the effectiveness of this bound, we propose a reinforcement learning approach that strategically directs computational effort towards the most promising regions within the search space.
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    Stable Tree Labelling for Accelerating Distance Queries on Dynamic Road Networks
    (OpenProceedings.org, 2024-11-11) Koehler H; Farhan M; Wang Q
    Finding the shortest-path distance between two arbitrary vertices is an important problem in road networks. Due to real-time traffic conditions, road networks undergo dynamic changes all the time. Current state-of-the-art methods incrementally maintain a distance labelling based on a hierarchy among vertices to support efficient distance computation. However, their labelling sizes are often large and cannot be efficiently maintained. To combat these issues, we present a simple yet efficient labelling method, namely Stable Tree Labelling (STL), for answering distance queries on dynamic road networks. We observe that the properties of an underlying hierarchy play an important role in improving and balancing query and update performance. Thus, we introduce the notion of stable tree hierarchy which lays the ground for developing efficient maintenance algorithms on dynamic road networks. Based on stable tree hierarchy, STL can be efficiently constructed as a 2-hop labelling. A crucial ingredient of STL is to only store distances within subgraphs in labels, rather than distances in the entire graph, which restricts the labels affected by dynamic changes. We further develop two efficient maintenance algorithms upon STL: Label Search algorithm and Pareto Search algorithm. Label Search algorithm identifies affected ancestors in a stable tree hierarchy and performs efficient searches to update labels from those ancestors. Pareto Search algorithm explores the interaction between search spaces of different ancestors, and combines searches from multiple ancestors into only two searches for each update, eliminating duplicate graph traversals. The experiments show that our algorithms significantly outperform state-of-the-art dynamic methods in maintaining the labelling and query processing, while requiring an order of magnitude less space.
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    Transformer-based multiple instance learning network with 2D positional encoding for histopathology image classification
    (Springer Nature Switzerland AG, 2025-05) Yang B; Ding L; Li J; Li Y; Qu G; Wang J; Wang Q; Liu B
    Digital medical imaging, particularly pathology images, is essential for cancer diagnosis but faces challenges in direct model training due to its super-resolution nature. Although weakly supervised learning has reduced the need for manual annotations, many multiple instance learning (MIL) methods struggle to effectively capture crucial spatial relationships in histopathological images. Existing methods incorporating positional information often overlook nuanced spatial correlations or use positional encoding strategies that do not fully capture the unique spatial dynamics of pathology images. To address this issue, we propose a new framework named TMIL (Transformer-based Multiple Instance Learning Network with 2D positional encoding), which leverages multiple instance learning for weakly supervised classification of histopathological images. TMIL incorporates a 2D positional encoding module, based on the Transformer, to model positional information and explore correlations between instances. Furthermore, TMIL divides histopathological images into pseudo-bags and trains patch-level feature vectors with deep metric learning to enhance classification performance. Finally, the proposed approach is evaluated on a public colorectal adenoma dataset. The experimental results show that TMIL outperforms existing MIL methods, achieving an AUC of 97.28% and an ACC of 95.19%. These findings suggest that TMIL’s integration of deep metric learning and positional encoding offers a promising approach for improving the efficiency and accuracy of pathology image analysis in cancer diagnosis.
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    Antagonistic systemin receptors integrate the activation and attenuation of systemic wound signaling in tomato.
    (Elsevier B.V., 2024-12-03) Zhou K; Wu F; Deng L; Xiao Y; Yang W; Zhao J; Wang Q; Chang Z; Zhai H; Sun C; Han H; Du M; Chen Q; Yan J; Xin P; Chu J; Han Z; Chai J; Howe GA; Li C-B; Li C
    Pattern recognition receptor (PRR)-mediated perception of damage-associated molecular patterns (DAMPs) triggers the first line of inducible defenses in both plants and animals. Compared with animals, plants are sessile and regularly encounter physical damage by biotic and abiotic factors. A longstanding problem concerns how plants achieve a balance between wound defense response and normal growth, avoiding overcommitment to catastrophic defense. Here, we report that two antagonistic systemin receptors, SYR1 and SYR2, of the wound peptide hormone systemin in tomato act in a ligand-concentration-dependent manner to regulate immune homeostasis. Whereas SYR1 acts as a high-affinity receptor to initiate systemin signaling, SYR2 functions as a low-affinity receptor to attenuate systemin signaling. The expression of systemin and SYR2, but not SYR1, is upregulated upon SYR1 activation. Our findings provide a mechanistic explanation for how plants appropriately respond to tissue damage based on PRR-mediated perception of DAMP concentrations and have implications for uncoupling defense-growth trade-offs.
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    BatchHL+: batch dynamic labelling for distance queries on large-scale networks
    (Springer-Verlag GmbH Germany, part of Springer Nature, 2024-01) Farhan M; Koehler H; Wang Q
    Many real-world applications operate on dynamic graphs to perform important tasks. In this article, we study batch-dynamic algorithms that are capable of updating distance labelling efficiently in order to reflect the effects of rapid changes on such graphs. To explore the full pruning potentials, we first characterize the minimal set of vertices being affected by batch updates. Then, we reveal patterns of interactions among different updates (edge insertions and edge deletions) and leverage them to design pruning rules for reducing update search space. These interesting findings lead us to developing a new batch-dynamic method, called BatchHL+ , which can dynamize labelling for distance queries much more efficiently than existing work. We provide formal proofs for the correctness and minimality of BatchHL+ which are non-trivial and require a delicate analysis of patterns of interactions. Empirically, we have evaluated the performance of BatchHL+ on 15 real-world networks. The results show that BatchHL+ significantly outperforms the state-of-the-art methods with up to 3 orders of magnitude faster in reflecting updates of rapidly changing graphs for distance queries.
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    Reproductive plasticity in response to the changing cluster size during the breeding period: a case study in a spider mite
    (Springer Nature, 2023-10) Weerawansha N; Wang Q; He XZ
    Animals living in clusters should adjust their reproductive strategies to adapt to the social environment. Theories predict that the benefits of cluster living would outweigh the costs of competition. Yet, it is largely unknown how animals optimize their reproductive fitness in response to the changing social environment during their breeding period. We used Tetranychus ludeni Zacher, a haplodiploid spider mite, to investigate how the ovipositing females modified their life-history traits in response to the change of cluster size (i.e., aggregation and dispersal) with a consistent population density (1 ♀/cm2). We demonstrate that (1) after females were shifted from a large cluster (16 ♀♀) to small ones (1 ♀, 5 and 10 ♀♀), they laid fewer and larger eggs with a higher female-biased sex ratio; (2) after females were shifted from small clusters to a large one, they laid fewer and smaller eggs, also with a higher female-biased sex ratio, and (3) increasing egg size significantly increased offspring sex ratio (% daughters), but did not increase immature survival. The results suggest that (1) females fertilize more larger eggs laid in a small population but lower the fertilization threshold and fertilize smaller eggs in a larger population, and (2) the reproductive adjustments in terms of egg number and size may contribute more to minimize the mate competition among sons but not to increase the number of inhabitants in the next generation. The current study provides evidence that spider mites can manipulate their reproductive output and adjust offspring sex ratio in response to dynamic social environments.
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    Clonostachys rosea Promotes Root Growth in Tomato by Secreting Auxin Produced through the Tryptamine Pathway
    (MDPI (Basel, Switzerland), 2022-11-04) Han Z; Ghanizadeh H; Zhang H; Li X; Li T; Wang Q; Liu J; Wang A; Feng M-G
    Clonostachys rosea (Link) Schroers is a filamentous fungus that has been widely used for biological control, biological fermentation, biodegradation and bioenergy. In this research, we investigated the impact of this fungus on root growth in tomato and the underlying mechanisms. The results showed that C. rosea can promote root growth in tomato, and tryptophan enhances its growth-promoting impacts. The results also showed that tryptophan increases the abundance of metabolites in C. rosea, with auxin (IAA) and auxin-related metabolites representing a majority of the highly abundant metabolites in the presence of tryptophan. It was noted that C. rosea could metabolize tryptophan into tryptamine (TRA) and indole-3-acetaldehyde (IAAId), and these two compounds are used by C. rosea to produce IAA through the tryptamine (TAM) pathway, which is one of the major pathways in tryptophan-dependent IAA biosynthesis. The IAA produced is used by C. rosea to promote root growth in tomato. To the best of our knowledge, this is the first report on IAA biosynthesis by C. rosea through the TAM pathway. More research is needed to understand the molecular mechanisms underlying IAA biosynthesis in C. rosea, as well as to examine the ability of this fungus to boost plant development in the field.