Toward Understanding and Evaluating Structural Node Embeddings

While most network embedding techniques model the proximity between nodes in a network, recently there has been significant interest in structural embeddings that are based on node equivalences , a notion rooted in sociology: equivalences or positions are collections of nodes that have similar roles—i.e., similar functions, ties or interactions with nodes in other positions—irrespective of their distance or reachability in the network. Unlike the proximity-based methods that are rigorously evaluated in the literature, the evaluation of structural embeddings is less mature. It relies on small synthetic or real networks with labels that are not perfectly defined, and its connection to sociological equivalences has hitherto been vague and tenuous. With new node embedding methods being developed at a breakneck pace, proper evaluation, and systematic characterization of existing approaches will be essential to progress. To fill in this gap, we set out to understand what types of equivalences structural embeddings capture. We are the first to contribute rigorous intrinsic and extrinsic evaluation methodology for structural embeddings, along with carefully-designed, diverse datasets of varying sizes. We observe a number of different evaluation variables that can lead to different results (e.g., choice of similarity measure, classifier, and label definitions). We find that degree distributions within nodes’ local neighborhoods can lead to simple yet effective baselines in their own right and guide the future development of structural embedding. We hope that our findings can influence the design of further node embedding methods and also pave the way for more comprehensive and fair evaluation of structural embedding methods.

Optical neuromorphic processing with soliton crystal Kerr microcombs: Scaling the network in size and speed

Convolutional neural networks (CNNs), inspired by biological visual cortex systems, are a powerful category of artificial neural networks that can extract the hierarchical features of raw data to greatly reduce the network parametric complexity and enhance the predicting accuracy. They are of significant interest for machine learning tasks such as computer vision, speech recognition, playing board games and medical diagnosis [1-7]. Optical neural networks offer the promise of dramatically accelerating computing speed to overcome the inherent bandwidth bottleneck of electronics. Here, we demonstrate a universal optical vector convolutional accelerator operating beyond 10 Tera-OPS (TOPS - operations per second), generating convolutions of images of 250,000 pixels with 8-bit resolution for 10 kernels simultaneously — enough for facial image recognition. We then use the same hardware to sequentially form a deep optical CNN with ten output neurons, achieving successful recognition of full 10 digits with 900 pixel handwritten digit images with 88% accuracy. Our results are based on simultaneously interleaving temporal, wavelength and spatial dimensions enabled by an integrated microcomb source. We show that this approach is scalable and trainable to much more complex networks for demanding applications such as unmanned vehicle and real-time video recognition.Keywords: Optical neural networks, neuromorphic processor, microcomb, convolutional accelerator

Effect of Carbon Nanofiber Clustering on the Micromechanical Properties of a Cement Paste

The use of carbon nanofibers (CNFs) in cement systems has received significant interest over the last decade due to their nanoscale reinforcing potential. However, despite many reports on the formation of localized CNF clusters, their effect on the cement paste micromechanical properties and relation to the mechanical response at the macroscopic scale are still not fully understood. In this study, grid nanoindentation coupled with scanning electron microscopy and energy dispersive spectroscopy was used to determine the local elastic indentation modulus and hardness of a portland cement paste containing 0.2% CNFs with sub-micro and microscale CNF clusters. The presence of low stiffness and porous assemblage of phases (modulus of 15–25 GPa) was identified in the cement paste with CNFs and was attributed primarily to the interfacial zone surrounding the CNF clusters. The CNFs favored the formation of higher modulus C–S–H phases (>30 GPa) in the bulk paste at the expense of the lower stiffness C–S–H. Nanoindentation results combined with a microscale–macroscale upscaling homogenization method further revealed an elastic modulus of the CNF clusters in the range from 18 to 21 GPa, indicating that the CNF clusters acted as compliant inclusions relative to the cement paste.

Do Community Engagement And Fairness Matter In Local Acceptance Of Wind Turbines In Tunisia?

Environmental concern is considered as a significant interest for companies, government and communities. These stakeholders are conscious about the energy situation but this consciousness is not always reflected in actions of acceptance. Accordingly, this research paper sheds light on the impact of the main key factors influencing the community acceptance of wind energy projects in Tunisia. An extensive literature review about community acceptance, community engagement, fairness and perceived risks is presented. Based on previous studies, authors identify the relationships between these variables. A quantitative approach is used to test the hypotheses using responses from a sample of 265 survey respondents in Tunisia. The research results and implications are discussed. Recommendations to be considered by interest stakeholders are drawn.

The Impact of the COVID-19 Pandemic on Stock Exchanges

The current world health crisis is characterized by the speed of its spread and its scale, and causing a direct global destructive economic impact that is present in every area of the globe. In this context of high uncertainty, the financial markets, especially the stock exchanges, have witnessed a decline in double figures in a very short period of time. In this chapter, the authors analyze how the COVID-19 pandemic impacts all variables of significant interest to financial economists, market regulators, and investors. This impact will be examined taking into account measures taken by governments, such as cities lockdown, border closures, canceling public events, and stopping public transport in order to slow down and stop the pandemic.

An overview on synthesis and reactions of coumarin based compounds

Oxygen-containing heterocycles are largely distributed in natural and synthetic compounds. Coumarins are among the most famous heterocycles which possess one oxygen atom in their rings. Coumarins are classified as multifunctional scaffold and are used as anti-oxidant reagents, anti-inflammatory, anti-microbial, anti-fungal, anti-HIV active, analgesic, anti-histaminic, insecticides, dyes, herbicides, sensitizers, perfumes, cosmetics and food additives. Due to their diverse applications in industrial and pharmaceutical fields, many chemists have given significant interest to these compounds. Herein, the review highlights various methods for the synthesis and interactions of coumarin moiety as one of the most efficient categories of heterocycles.

Enhanced N-directed electrophilic C-H borylation generates BN-[5]- and [6]helicenes with improved photophysical properties.

Helicenes are chiral polycyclic aromatic hydrocarbons (PAHs) of significant interest e.g. in supramolecular chemistry, materials science and asymmetric catalysis. Herein an enhanced N-directed electrophilic C-H borylation methodology has been developed...

Rational design of nickel-cobalt selenides@selenium nanostructure by adjusting the synthesis environment for high performance sodium-ion batteries

The exploitation of metal selenides in sodium-ion batteries has attracted significant interest. However, an effective balance among their energy density, rate and cycle performance has been beset, as the complexity...

Boron-doping-induced Defect Engineering Enables High-performance Graphene Cathode for Aluminum Batteries

Rechargeable aluminum batteries (RABs) have received significant interest due to the low cost, high volumetric capacity, and low flammability of aluminum. However, the paucity of reliable cathode materials poses substantial...