More Like this

1. Layered List Labeling

   https://doi.org/10.1145/3651602  

   Bender, Michael A.; Conway, Alex; Farach-Colton, Martin; Komlos, Hanna; Kuszmaul, William (June 2024, PODS)
2. Six methods for transforming layered hypergraphs to apply layered graph layout algorithms

   https://doi.org/10.1111/cgf.14538  

   Di Bartolomeo, Sara; Pister, Alexis; Buono, Paolo; Plaisant, Catherine; Dunne, Cody; Fekete, Jean‐Daniel (June 2022, Computer Graphics Forum)
3. Optimizing mechanical properties in single-layered and multi-layered amorphous carbon coatings

   https://doi.org/10.1016/j.diamond.2022.108843  

   Muley, Sachin V.; Zeng, Aiping; Voyles, Paul M.; Heaney, Patrick J. (March 2022, Diamond and Related Materials)
4. Multi-Layered Network Embedding

   https://doi.org/10.1137/1.9781611975321.77  

   Jundong Li, Chen Chen (April 2018, SDM)

   Network embedding has gained more attentions in recent years. It has been shown that the learned low-dimensional node vector representations could advance a myriad of graph mining tasks such as node classification, community detection, and link prediction. A vast majority of the existing efforts are overwhelmingly devoted to single-layered networks or homogeneous networks with a single type of nodes and node interactions. However, in many real-world applications, a variety of networks could be abstracted and presented in a multilayered fashion. Typical multi-layered networks include critical infrastructure systems, collaboration platforms, social recommender systems, to name a few. Despite the widespread use of multi-layered networks, it remains a daunting task to learn vector representations of different types of nodes due to the bewildering combination of both within-layer connections and cross-layer network dependencies. In this paper, we study a novel problem of multi-layered network embedding. In particular, we propose a principled framework – MANE to model both within-layer connections and cross-layer network dependencies simultaneously in a unified optimization framework for embedding representation learning. Experiments on real-world multi-layered networks corroborate the effectiveness of the proposed framework. 

   more » « less
5. Layered supramolecular hydrogels from thioglycosides

   https://doi.org/10.1039/d2tb00037g  

   Wang, Yu-Cheng; Kegel, Laurel L.; Knoff, David S.; Deodhar, Bhushan S.; Astashkin, Andrei V.; Kim, Minkyu; Pemberton, Jeanne E. (April 2022, Journal of Materials Chemistry B)

   Low molecular weight hydrogels are made of small molecules that aggregate via noncovalent interactions. Here, comprehensive characterization of the physical and chemical properties of hydrogels made from thioglycolipids of the disaccharides lactose and cellobiose with simple alkyl chains is reported. While thiolactoside hydrogels are robust, thiocellobioside gels are metastable, precipitating over time into fibrous crystals that can be entangled to create pseudo-hydrogels. Rheology confirms the viscoelastic solid nature of these hydrogels with storage moduli ranging from 10–600 kPa. Additionally, thiolactoside hydrogels are thixotropic which is a desirable property for many potential applications. Freeze-fracture electron microscopy of xerogels shows layers of stacked sheets that are entangled into networks. These structures are unique compared to the fibers or ribbons typically reported for hydrogels. Differential scanning calorimetry provides gel-to-liquid phase transition temperatures ranging from 30 to 80 °C. Prodan fluorescence spectroscopy allows assignment of phase transitions in the gels and other lyotropic phases of high concentration samples. Phase diagrams are estimated for all hydrogels at 1–10 wt% from 5 to ≥ 80 °C. These hydrogels represent a series of interesting materials with unique properties that make them attractive for numerous potential applications. 

   more » « less