M2HGCL: Multi-Scale Meta-Path Integrated Heterogeneous Graph Contrastive Learning

Inspired by the successful application of contrastive learning on graphs, researchers attempt to impose graph contrastive learning approaches on heterogeneous information networks. Orthogonal to homogeneous graphs, the types of nodes and edges in heterogeneous graphs are diverse so that specialized graph contrastive learning methods are required. Most existing methods for heterogeneous graph contrastive learning are implemented by transforming heterogeneous graphs into homogeneous graphs, which may lead to ramifications that the valuable information carried by non-target nodes is undermined thereby exacerbating the performance of contrastive learning models. Additionally, current heterogeneous graph contrastive learning methods are mainly based on initial meta-paths given by the dataset, yet according to our deep-going exploration, we derive empirical conclusions: only initial meta-paths cannot contain sufficiently discriminative information; and various types of meta-paths can effectively promote the performance of heterogeneous graph contrastive learning methods. To this end, we propose a new multi-scale meta-path integrated heterogeneous graph contrastive learning (M2HGCL) model, which discards the conventional heterogeneity-homogeneity transformation and performs the graph contrastive learning in a joint manner. Specifically, we expand the meta-paths and jointly aggregate the direct neighbor information, the initial meta-path neighbor information and the expanded meta-path neighbor information to sufficiently capture discriminative information. A specific positive sampling strategy is further imposed to remedy the intrinsic deficiency of contrastive learning, i.e., the hard negative sample sampling issue. Through extensive experiments on three real-world datasets, we demonstrate that M2HGCL outperforms the current state-of-the-art baseline models.Comment: Accepted to the conference of ADMA2023 as an Oral presentatio

Seeing What You Miss: Vision-Language Pre-training with Semantic Completion Learning

Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed for VLP to further promote cross-modal interactions. The core idea of previous masked modeling tasks is to focus on reconstructing the masked tokens based on visible context for learning local-to-local alignment. However, most of them pay little attention to the global semantic features generated for the masked data, resulting in the limited cross-modal alignment ability of global representations. Therefore, in this paper, we propose a novel Semantic Completion Learning (SCL) task, complementary to existing masked modeling tasks, to facilitate global-to-local alignment. Specifically, the SCL task complements the missing semantics of masked data by capturing the corresponding information from the other modality, promoting learning more representative global features which have a great impact on the performance of downstream tasks. Moreover, we present a flexible vision encoder, which enables our model to perform image-text and video-text multimodal tasks simultaneously. Experimental results show that our proposed method obtains state-of-the-art performance on various vision-language benchmarks, such as visual question answering, image-text retrieval, and video-text retrieval

A new species of Austrodecus Hodgson, 1907 (Arthropoda, Pycnogonida, Austrodecidae) from the Southwest Indian Ridge

A new species of pycnogonid collected by the Chinese research vessel R/V Dayangyihao during cruises to the Southwest Indian Ridge in 2008 and 2009 is recorded. The new species, Austrodecus bamberi, is placed into the tristanense-section by the characters of 4-articled ovigers and present auxiliary claws and is distinguished from other species in this section by the number and length of tubercles on the first coxae

OmniForce: On Human-Centered, Large Model Empowered and Cloud-Edge Collaborative AutoML System

Automated machine learning (AutoML) seeks to build ML models with minimal human effort. While considerable research has been conducted in the area of AutoML in general, aiming to take humans out of the loop when building artificial intelligence (AI) applications, scant literature has focused on how AutoML works well in open-environment scenarios such as the process of training and updating large models, industrial supply chains or the industrial metaverse, where people often face open-loop problems during the search process: they must continuously collect data, update data and models, satisfy the requirements of the development and deployment environment, support massive devices, modify evaluation metrics, etc. Addressing the open-environment issue with pure data-driven approaches requires considerable data, computing resources, and effort from dedicated data engineers, making current AutoML systems and platforms inefficient and computationally intractable. Human-computer interaction is a practical and feasible way to tackle the problem of open-environment AI. In this paper, we introduce OmniForce, a human-centered AutoML (HAML) system that yields both human-assisted ML and ML-assisted human techniques, to put an AutoML system into practice and build adaptive AI in open-environment scenarios. Specifically, we present OmniForce in terms of ML version management; pipeline-driven development and deployment collaborations; a flexible search strategy framework; and widely provisioned and crowdsourced application algorithms, including large models. Furthermore, the (large) models constructed by OmniForce can be automatically turned into remote services in a few minutes; this process is dubbed model as a service (MaaS). Experimental results obtained in multiple search spaces and real-world use cases demonstrate the efficacy and efficiency of OmniForce

100 essential questions for the future of agriculture

Publication history: Accepted - 8 March 2023; Published online - 11 April 2023.The world is at a crossroad when it comes to agriculture. The global population is growing, and the demand for food is increasing, putting a strain on our agricultural resources and practices. To address this challenge, innovative, sustainable, and inclusive approaches to agriculture are urgently required. In this paper, we launched a call for Essential Questions for the Future of Agriculture and identified a priority list of 100 questions. We focus on 10 primary themes: transforming agri-food systems, enhancing resilience of agriculture to climate change, mitigating climate change through agriculture, exploring resources and technologies for breeding, advancing cultivation methods, sustaining healthy agroecosystems, enabling smart and controlled-environment agriculture for food security, promoting health and nutrition-driven agriculture, exploring economic opportunities and addressing social challenges, and integrating one health and modern agriculture. We emphasise the critical importance of interdisciplinary and multidisciplinary research that integrates both basic and applied sciences and bridges the gaps among various stakeholders for achieving sustainable agriculture. Key points Growing demand and resource limitations pose a critical challenge for agriculture, necessitating innovative and sustainable approaches. The paper identifies 100 priority questions for the future of agriculture, indicating current and future research directions. Sustainable agriculture depends on interdisciplinary and multidisciplinary research that harmonises basic and applied sciences and fosters collaboration among different stakeholders

Arabidopsis FHY3/FAR1 Gene Family and Distinct Roles of Its Members in Light Control of Arabidopsis Development

FHY3 (far-red elongated hypocotyls 3) and FAR1 (far-red-impaired response) are two homologous proteins essential for phytochrome A controlled far-red responses in Arabidopsis (Arabidopsis thaliana). There are 12 additional FHY3/FAR1-related genes in the Arabidopsis genome. The predicted sizes of this family of proteins range from 531 amino acids to 851 amino acids, and they share 12.0% to 82.4% amino acid identities over their entire lengths. In addition, most FRS proteins contain one to three coiled-coil domains and one or two putative nuclear localization signals. Semiquantitative reverse transcription-polymerase chain reaction analyses revealed that all FRS genes except FRS10 are expressed in all tissues examined, including rosette leaves, cauline leaves, inflorescence stems, flowers, and siliques. Analyses of gene specific promoter∷GUS fusion reporter gene expression revealed that all FRS genes except FRS1 are expressed in hypocotyls, and their expression in hypocotyl is induced by far-red light treatment. Transient expression of green fluorescent protein tagged FRS fusion proteins in onion (Allium cepa) epidermal cells revealed that all FRS proteins are targeted into the nucleus. T-DNA knockout frs6 and frs8 mutants flowered early under both long-day and short-day conditions (with much more drastic effects under short-day conditions), suggesting that FRS6 and FRS8 regulate flowering time. In addition, FRS9 RNAi transgenic plants showed a specific hypersensitivity to red light inhibition of hypocotyl elongation and light-regulated gene expression, indicating that FRS9 is a specific negative regulator of phyB signaling mediating seedling deetiolation. In summary, our results support the notion that FRS family members play distinct roles in light control of Arabidopsis development, most likely by regulating nuclear gene expression

The challenges of energy supply for Sierra Leone's economic development

Energy problems in developing and under developed countries are serious and widespread. Lack of access to sufficient and sustainable supply of energy affects over 85% of the population in these countries. Over 3 billion people are without electricity and a similar number remains dependent on fuels such as animal dung, plant residue, kerosene, wood and charcoal as a source of fuel for lighting and cooking. Without efficient supply of clean energy, peoples' efforts to effectively engage in economic activities or improve living standard will be fruitless. Sierra Leone has just emerged from a decade-long civil war which virtually paralyzed the economy. As the country currently embarks on post-conflict development and economic modernization, there is need to thoroughly address energy issues for sustainable economic growth. Modern economies are energy dependent; meaning that economic prosperity and sustainable living standards could only be achieved through paradigm shift in energy policy and planning. Sierra Leone is fairly endowed with energy resources, like biomass energy, hydropower, solar energy and other renewable energy resources. If properly harnessed, these rich resources of energy can lay the basis for sustainable economic development in Sierra Leone