A framework for community detection in heterogeneous multi-relational networks

There has been a surge of interest in community detection in homogeneous single-relational networks which contain only one type of nodes and edges. However, many real-world systems are naturally described as heterogeneous multi-relational networks which contain multiple types of nodes and edges. In this paper, we propose a new method for detecting communities in such networks. Our method is based on optimizing the composite modularity, which is a new modularity proposed for evaluating partitions of a heterogeneous multi-relational network into communities. Our method is parameter-free, scalable, and suitable for various networks with general structure. We demonstrate that it outperforms the state-of-the-art techniques in detecting pre-planted communities in synthetic networks. Applied to a real-world Digg network, it successfully detects meaningful communities.Comment: 27 pages, 10 figure

Mechanisms linking plant diversity to large herbivore performance

There is established concern that loss of biodiversity will affect ecosystem productivity, nutrient cycling, carbon storage, stability and other properties^1,2^. Interactions between trophic levels are thought to link changes to biodiversity and ecosystem processes^3-6^. However, there is a lack of empirical studies linking plant diversity with altered trophic levels^7,8^, especially for large herbivores, the important but often neglected, controlling trophic level in terrestrial systems. Here we examine responses in performance of the large generalist herbivore to changes in plant diversity, using an indoor cafeteria trial and a field experiment. Our results show that increased plant diversity improves herbivore performance but it is depressed at highest plant diversity levels. We propose the Disturbance Selection Hypothesis for explaining plant diversity effects on primary consumers. Increasing the number of plant species in grassland, increases consumption and enhances nutrient intake (presumably improving animal fitness) by modifying nutrient balance, toxin dilution and taste modulation. High plant diversity simultaneously intensifies animal diet switching frequency, and weakens the herbivore's ability to select food, thereby increasing foraging cost and disturbing the herbivore's selection of forage. Thus, the consequence of plant diversity for large herbivore performance depends on the trade-off between the positive and negative effects. At highest plant diversity the positive effects weaken and negative effects strengthen. We suggest knowledge of the mechanisms is the means for understanding relationships between biodiversity and ecosystem functioning, and the management of large herbivores on rangelands used for conservation and grazing

Learning to Auto Weight: Entirely Data-driven and Highly Efficient Weighting Framework

Example weighting algorithm is an effective solution to the training bias problem, however, most previous typical methods are usually limited to human knowledge and require laborious tuning of hyperparameters. In this paper, we propose a novel example weighting framework called Learning to Auto Weight (LAW). The proposed framework finds step-dependent weighting policies adaptively, and can be jointly trained with target networks without any assumptions or prior knowledge about the dataset. It consists of three key components: Stage-based Searching Strategy (3SM) is adopted to shrink the huge searching space in a complete training process; Duplicate Network Reward (DNR) gives more accurate supervision by removing randomness during the searching process; Full Data Update (FDU) further improves the updating efficiency. Experimental results demonstrate the superiority of weighting policy explored by LAW over standard training pipeline. Compared with baselines, LAW can find a better weighting schedule which achieves much more superior accuracy on both biased CIFAR and ImageNet.Comment: Accepted by AAAI 202

Dynamic Covalent Chemistry for Synthetic Molecular Machines

The first and only exhaustive review of the theory, thermodynamic fundamentals, mechanisms, and design principles of dynamic covalent systems Dynamic Covalent Chemistry: Principles, Reactions, and Applications presents a comprehensive ..

*-Wars Episode I: The Phantom Anomaly

As pointed out, chiral non-commutative theories exist, and examples can be constructed via string theory. Gauge anomalies require the matter content of individual gauge group factors, including U(1) factors, to be non-chiral. All ``bad'' mixed gauge anomalies, and also all ``good'' (e.g. for $\pi ^0\to \gamma \gamma$) ABJ type flavor anomalies, automatically vanish in non-commutative gauge theories. We interpret this as being analogous to string theory, and an example of UV/IR mixing: non-commutative gauge theories automatically contain ``closed string,'' Green-Schwarz fields, which cancel these anomalies.Comment: 20 pages. Added references and minor typos correcte

Recent Developments in Bus Transport In China

China has the largest urban public transport sector in the world. In principle, strategic policy is determined by the central government, and passed down through the organs of state for implementation. In recent years that strategy has included giving priority to public transport and reforming the supply arrangements to secure a more commercial and competitive sector. In practice, responsibility for implementation is completely decentralized, with municipalities having both complete responsibility for financing urban public transport and substantial freedom to interpret central government guidance at the local level. This paper considers the reforms that have already occurred under this regime, the constraints and limitations on the reform process, and the most recent initiatives being undertaken. It shows that a very wide range of systems are being experimented with simultaneously, with so far no sign that central government would intervene in detail or to provide central government finance specifically for the sector.Institute of Transport and Logistics Studies. Faculty of Economics and Business. The University of Sydne