Collaborative Graph Neural Networks for Attributed Network Embedding

Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. However, existing efforts mainly focus on exploiting network structures, while the exploitation of node attributes is rather limited as they only serve as node features at the initial layer. This simple strategy impedes the potential of node attributes in augmenting node connections, leading to limited receptive field for inactive nodes with few or even no neighbors. Furthermore, the training objectives (i.e., reconstructing network structures) of most GNNs also do not include node attributes, although studies have shown that reconstructing node attributes is beneficial. Thus, it is encouraging to deeply involve node attributes in the key components of GNNs, including graph convolution operations and training objectives. However, this is a nontrivial task since an appropriate way of integration is required to maintain the merits of GNNs. To bridge the gap, in this paper, we propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for attribute network embedding. It improves model capacity by 1) selectively diffusing messages from neighboring nodes and involved attribute categories, and 2) jointly reconstructing node-to-node and node-to-attribute-category interactions via cross-correlation. Experiments on real-world networks demonstrate that CONN excels state-of-the-art embedding algorithms with a great margin

Morphology, photosynthetic physiology and biochemistry of nine herbaceous plants under water stress

Global climate warming and shifts in rainfall patterns are expected to trigger increases in the frequency and magnitude of drought and/or waterlogging stress in plants. To cope with water stress, plants develop diverse tactics. However, the adoption capability and mechanism vary depending upon the plant species identity as well as stress duration and intensity. The objectives of this study were to evaluate the species-dependent responses of alpine herbaceous species to water stress. Nine herbaceous species were subjected to different water stresses (including moderate drought and moderate waterlogging) in pot culture using a randomized complete block design with three replications for each treatment. We hypothesized that water stress would negatively impact plant growth and metabolism. We found considerable interspecies differences in morphological, physiological, and biochemical responses when plants were exposed to the same water regime. In addition, we observed pronounced interactive effects of water regime and plant species identity on plant height, root length, root/shoot ratio, biomass, and contents of chlorophyll a, chlorophyll b, chlorophyll (a+b), carotenoids, malondialdehyde, soluble sugar, betaine, soluble protein and proline, implying that plants respond to water regime differently. Our findings may cast new light on the ecological restoration of grasslands and wetlands in the Qinghai-Tibetan Plateau by helping to select stress-tolerant plant species

Changes of Riverbeds and Water-carrying Capacity of the Yellow River Inner Mongolia Section

This paper introduced the evolution of the section from Bayangaole to Toudaoguai in the Yellow River and analysed the factors influencing erosion, deposition, and water-carrying capacity of the section over years. Through data obtained from observation in the Bayangaole station, Sanhuhekou station, Zhaojunfen station, and Toudaoguai station, after analysis, it has been got that the riverbeds observed at these stations have been silted up over time, and the water-carrying capacity has been reducing. Besides, the construction of reservoirs or power stations may accelerate this trend

OpenGSL: A Comprehensive Benchmark for Graph Structure Learning

Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes. However, the inherent suboptimal nature of node connections, resulting from the complex and contingent formation process of graphs, presents significant challenges in modeling them effectively. To tackle this issue, Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models. Despite the proposal of numerous GSL methods, the progress in this field remains unclear due to inconsistent experimental protocols, including variations in datasets, data processing techniques, and splitting strategies. In this paper, we introduce OpenGSL, the first comprehensive benchmark for GSL, aimed at addressing this gap. OpenGSL enables a fair comparison among state-of-the-art GSL methods by evaluating them across various popular datasets using uniform data processing and splitting strategies. Through extensive experiments, we observe that existing GSL methods do not consistently outperform vanilla GNN counterparts. However, we do observe that the learned graph structure demonstrates a strong generalization ability across different GNN backbones, despite its high computational and space requirements. We hope that our open-sourced library will facilitate rapid and equitable evaluation and inspire further innovative research in the field of GSL. The code of the benchmark can be found in https://github.com/OpenGSL/OpenGSL.Comment: 9 pages, 4 figure

Single-trial phase entrainment of theta oscillations in sensory regions predicts human associative memory performance

Episodic memories are rich in sensory information and often contain integrated information from different sensory modalities. For instance, we can store memories of a recent concert with visual and auditory impressions being integrated in one episode. Theta oscillations have recently been implicated in playing a causal role synchronizing and effectively binding the different modalities together in memory. However, an open question is whether momentary fluctuations in theta synchronization predict the likelihood of associative memory formation for multisensory events. To address this question we entrained the visual and auditory cortex at theta frequency (4 Hz) and in a synchronous or asynchronous manner by modulating the luminance and volume of movies and sounds at 4 Hz, with a phase offset at 0° or 180°. EEG activity from human subjects (both sexes) was recorded while they memorized the association between a movie and a sound. Associative memory performance was significantly enhanced in the 0° compared with the 180° condition. Source-level analysis demonstrated that the physical stimuli effectively entrained their respective cortical areas with a corresponding phase offset. The findings suggested a successful replication of a previous study (Clouter et al., 2017). Importantly, the strength of entrainment during encoding correlated with the efficacy of associative memory such that small phase differences between visual and auditory cortex predicted a high likelihood of correct retrieval in a later recall test. These findings suggest that theta oscillations serve a specific function in the episodic memory system: binding the contents of different modalities into coherent memory episodes

Open Design and 3D Printing of Face Shields: The Case Study of a UK-China Initiative

At the start of the COVID-19 outbreak, many countries lacked personal protective equipment (PPE) to protect healthcare workers. To address this problem, open design and 3D printing technologies were adopted to provide much-in-need PPEs for key workers. This paper reports an initiative by designers and engineers in the UK and China. The case study approach and content analysis method were used to study the stakeholders, the design process, and other relevant issues such as regulation. Good practice and lessons were summarised, and suggestions for using distributed 3D printing to supply PPEs were made. It concludes that 3D printing has played an important role in producing PPEs when there was a shortage of supply, and distributed manufacturing has the potential to quickly respond to local small-bench production needs. In the future, clearer specification, better match of demands and supply, and quicker evaluation against relevant regulations will provide efficiency and quality assurance for 3D printed PPE supplies

Growth of millimeter-sized high-quality CuFeSe2_2 single crystals by the molten salt method and study of their semiconducting behavior

An eutectic AlCl$_3$/KCl molten salt method in a horizontal configuration was employed to grow millimeter-sized and composition homogeneous CuFeSe$_2$ single crystals due to the continuous growth process in a temperature gradient induced solution convection. The typical as-grown CuFeSe$_2$ single crystals in cubic forms are nearly 1.6$\times$1.2$\times$1.0 mm3 in size. The chemical composition and homogeneity of the crystals was examined by both inductively coupled plasma atomic emission spectroscopy and energy dispersive spectrometer with Cu:Fe:Se = 0.96:1.00:1.99 consistent with the stoichiometric composition of CuFeSe$_2$. The magnetic measurements suggest a ferrimagnetic or weak ferromagnetic transition below T$_C$ = 146 K and the resistivity reveals a semiconducting behavior and an abrupt increase below T$_C$

Managing the Three Gorges Dam to Implement Environmental Flows in the Yangtze River

The construction of the Three Gorges Dam, along with other development in the Yangtze River basin, has had profound consequences for the river's flow and sediment regime. This has had major impacts on the geomorphology and ecology of the river downstream of the dam, with related impacts on biodiversity, including fish populations, livelihoods, and water security in the middle and lower Yangtze. Changes to fish populations have included a fall of around 90% in the total number of fish fry for the four economically-important Chinese carp species, caused at least in part by alterations in the flow regime. In response, there has been increased research into the significance of flow regimes for Chinese carp, as well as other aspects of river health. A partnership between the Chinese Government, the dam operator, scientists, and conservationists has led to pilot environmental flow releases over a 5-year period in an attempt to mitigate some of these impacts. Subsequent monitoring has shown that numbers of fish fry are increasing from the low they had fallen to in 2008. Drawing on lessons from the pilot environmental flow releases, in October 2015 the official regulations that govern operations of the Three Gorges Dam were amended to incorporate additional objectives, including incorporating environmental flow releases as part of the routine operation of the dam. This paper describes the processes that led to the environmental flow program from Three Gorges, a review of monitoring data collected during the pilot environmental flow releases, the subsequent amendment of the dam operating rules, and prospects for expanding environmental flow implementation in the Yangtze River in coming years