Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily Discriminating

Unsupervised graph representation learning (UGRL) has drawn increasing research attention and achieved promising results in several graph analytic tasks. Relying on the homophily assumption, existing UGRL methods tend to smooth the learned node representations along all edges, ignoring the existence of heterophilic edges that connect nodes with distinct attributes. As a result, current methods are hard to generalize to heterophilic graphs where dissimilar nodes are widely connected, and also vulnerable to adversarial attacks. To address this issue, we propose a novel unsupervised Graph Representation learning method with Edge hEterophily discriminaTing (GREET) which learns representations by discriminating and leveraging homophilic edges and heterophilic edges. To distinguish two types of edges, we build an edge discriminator that infers edge homophily/heterophily from feature and structure information. We train the edge discriminator in an unsupervised way through minimizing the crafted pivot-anchored ranking loss, with randomly sampled node pairs acting as pivots. Node representations are learned through contrasting the dual-channel encodings obtained from the discriminated homophilic and heterophilic edges. With an effective interplaying scheme, edge discriminating and representation learning can mutually boost each other during the training phase. We conducted extensive experiments on 14 benchmark datasets and multiple learning scenarios to demonstrate the superiority of GREET.Comment: 14 pages, 7 tables, 6 figures, accepted by AAAI 202

Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs

Real-world graphs generally have only one kind of tendency in their connections. These connections are either homophily-prone or heterophily-prone. While graphs with homophily-prone edges tend to connect nodes with the same class (i.e., intra-class nodes), heterophily-prone edges tend to build relationships between nodes with different classes (i.e., inter-class nodes). Existing GNNs only take the original graph during training. The problem with this approach is that it forgets to take into consideration the ``missing-half" structural information, that is, heterophily-prone topology for homophily-prone graphs and homophily-prone topology for heterophily-prone graphs. In our paper, we introduce Graph cOmplementAry Learning, namely GOAL, which consists of two components: graph complementation and complemented graph convolution. The first component finds the missing-half structural information for a given graph to complement it. The complemented graph has two sets of graphs including both homophily- and heterophily-prone topology. In the latter component, to handle complemented graphs, we design a new graph convolution from the perspective of optimisation. The experiment results show that GOAL consistently outperforms all baselines in eight real-world datasets.Comment: Accepted by ICML 202

Probabilistic Guarded P Systems, A New Formal Modelling Framework

Multienvironment P systems constitute a general, formal framework for modelling the dynamics of population biology, which consists of two main approaches: stochastic and probabilistic. The framework has been successfully used to model biologic systems at both micro (e.g. bacteria colony) and macro (e.g. real ecosystems) levels, respectively. In this paper, we extend the general framework in order to include a new case study related to P. Oleracea species. The extension is made by a new variant within the probabilistic approach, called Probabilistic Guarded P systems (in short, PGP systems). We provide a formal definition, a simulation algorithm to capture the dynamics, and a survey of the associated software.Ministerio de Economía y Competitividad TIN2012- 37434Junta de Andalucía P08-TIC-0420

Maximal Information Coefficient Based Residential Photovoltaic Power Generation Disaggregation

Due to policy support, low cost and easy applicability, distribution photovoltaic systems (DPVSs) are increasingly popular among residential community. However, small-scale DPVSs of less than 10 kWp are always installed behind the meter (BTM), which results in the invisible of the photovoltaic (PV) power generation. Only access of composite power data can result in non-optimal distribution network control and optimization, leading to a series of energy management problems. In order to solve the aforementioned problems, this paper proposes a BTM composite power disaggregation method focusing on small-scale DPVSs, with only composite power data of residential users in a community, without relying on weather data and models assumption. Considering that community users’ DPVSs usually exhibit approximate output characteristics, neighboring composite power is used to extract PV power generation information as mutual proxies. After obtaining approximate PV proxy data by subtracting composite power of inter-users, a grid search algorithm guided by Maximal Information Coefficient (MIC) is performed to obtain final PV power generation disaggregation results. The proposed method is evaluated using data gathered from residential customers located in Ithaca, New York and Austin, Texas in real-life scenarios. Testing results show that our proposed method achieve considerable disaggregation accuracy in the absence of solar radiation and temperature data as compared to other state-of-art methods

An experimental test of non-local realism

Most working scientists hold fast to the concept of 'realism' - a viewpoint according to which an external reality exists independent of observation. But quantum physics has shattered some of our cornerstone beliefs. According to Bell's theorem, any theory that is based on the joint assumption of realism and locality (meaning that local events cannot be affected by actions in space-like separated regions) is at variance with certain quantum predictions. Experiments with entangled pairs of particles have amply confirmed these quantum predictions, thus rendering local realistic theories untenable. Maintaining realism as a fundamental concept would therefore necessitate the introduction of 'spooky' actions that defy locality. Here we show by both theory and experiment that a broad and rather reasonable class of such non-local realistic theories is incompatible with experimentally observable quantum correlations. In the experiment, we measure previously untested correlations between two entangled photons, and show that these correlations violate an inequality proposed by Leggett for non-local realistic theories. Our result suggests that giving up the concept of locality is not sufficient to be consistent with quantum experiments, unless certain intuitive features of realism are abandoned.Comment: Minor corrections to the manuscript, the final inequality and all its conclusions do not change; description of corrections (Corrigendum) added as new Appendix III; Appendix II replaced by a shorter derivatio

Phytoestrogens

Collectively, plants contain several different families of natural products among which are compounds with weak estrogenic or antiestrogenic activity toward mammals. These compounds, termed phytoestrogens, include certain isoflavonoids, flavonoids, stilbenes, and lignans. The best-studied dietary phytoestrogens are the soy isoflavones and the flaxseed lignans. Their perceived health beneficial properties extend beyond hormone-dependent breast and prostate cancers and osteoporosis to include cognitive function, cardiovascular disease, immunity and inflammation, and reproduction and fertility. In the future, metabolic engineering of plants could generate novel and exquisitely controlled dietary sources with which to better assess the potential health beneficial effects of phytoestrogens

Deep autoencoder with localized stochastic sensitivity for short-term load forecasting

National Natural Science Foundation of China; Guangdong Province Science and Technology Plan Project; Brunel University London; UK BRIEF Funding; Department of Finance and Education of Guangdong Province 2016; Key Discipline Construction Program, China; Education Department of Guangdong Province: New and Integrated Energy System Theory and Technology Research Grou

Prediction of migratory routes of the invasive fall armyworm in eastern China using a trajectory analytical approach

This is the author accepted manuscript. The final version is available from Wiley via the DOI in this record BACKGROUND: The fall armyworm (FAW), an invasive pest from the Americas, is rapidly spreading through the Old World, and has recently invaded the Indochinese Peninsula and southern China. In the Americas, FAW migrates from winter-breeding areas in the south into summer-breeding areas throughout North America where it is a major pest of corn. Asian populations are also likely to evolve migrations into the corn-producing regions of eastern China, where they will pose a serious threat to food security. RESULTS: To evaluate the invasion risk in eastern China, the rate of expansion and future migratory range was modelled by a trajectory simulation approach, combined with flight behavior and meteorological data. Our results predict that FAW will migrate from its new year-round breeding regions into the two main corn-producing regions of eastern China (Huang-Huai-Hai Summer Corn and Northeast Spring Corn Regions), via two pathways. The western pathway originates in Myanmar and Yunnan, and FAW will take four migration steps (i.e. four generations) to reach the Huang-Huai-Hai Region by July. Migration along the eastern pathway from Indochina and southern China progresses faster, with FAW reaching the Huang-Huai-Hai Region in three steps by June and reaching the Northeast Spring Region in July. CONCLUSION: Our results indicate that there is a high risk that FAW will invade the major corn-producing areas of eastern China via two migration pathways, and cause significant impacts to agricultural productivity. Information on migration pathways and timings can be used to inform integrated pest management strategies for this emerging pest.Biotechnology & Biological Sciences Research Council (BBSRC)CABI Bioscienc

High-throughput, quantitative analyses of genetic interactions in E. coli.

Large-scale genetic interaction studies provide the basis for defining gene function and pathway architecture. Recent advances in the ability to generate double mutants en masse in Saccharomyces cerevisiae have dramatically accelerated the acquisition of genetic interaction information and the biological inferences that follow. Here we describe a method based on F factor-driven conjugation, which allows for high-throughput generation of double mutants in Escherichia coli. This method, termed genetic interaction analysis technology for E. coli (GIANT-coli), permits us to systematically generate and array double-mutant cells on solid media in high-density arrays. We show that colony size provides a robust and quantitative output of cellular fitness and that GIANT-coli can recapitulate known synthetic interactions and identify previously unidentified negative (synthetic sickness or lethality) and positive (suppressive or epistatic) relationships. Finally, we describe a complementary strategy for genome-wide suppressor-mutant identification. Together, these methods permit rapid, large-scale genetic interaction studies in E. coli