Achieving Adversarial Robustness via Sparsity

Network pruning has been known to produce compact models without much accuracy degradation. However, how the pruning process affects a network's robustness and the working mechanism behind remain unresolved. In this work, we theoretically prove that the sparsity of network weights is closely associated with model robustness. Through experiments on a variety of adversarial pruning methods, we find that weights sparsity will not hurt but improve robustness, where both weights inheritance from the lottery ticket and adversarial training improve model robustness in network pruning. Based on these findings, we propose a novel adversarial training method called inverse weights inheritance, which imposes sparse weights distribution on a large network by inheriting weights from a small network, thereby improving the robustness of the large network

SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization

Recent advances in data processing have stimulated the demand for learning graphs of very large scales. Graph Neural Networks (GNNs), being an emerging and powerful approach in solving graph learning tasks, are known to be difficult to scale up. Most scalable models apply node-based techniques in simplifying the expensive graph message-passing propagation procedure of GNN. However, we find such acceleration insufficient when applied to million- or even billion-scale graphs. In this work, we propose SCARA, a scalable GNN with feature-oriented optimization for graph computation. SCARA efficiently computes graph embedding from node features, and further selects and reuses feature computation results to reduce overhead. Theoretical analysis indicates that our model achieves sub-linear time complexity with a guaranteed precision in propagation process as well as GNN training and inference. We conduct extensive experiments on various datasets to evaluate the efficacy and efficiency of SCARA. Performance comparison with baselines shows that SCARA can reach up to 100x graph propagation acceleration than current state-of-the-art methods with fast convergence and comparable accuracy. Most notably, it is efficient to process precomputation on the largest available billion-scale GNN dataset Papers100M (111M nodes, 1.6B edges) in 100 seconds

SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation

Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform aggregation. Existing attempts in incoorporating global aggregation for heterophilous GNNs usually require iteratively maintaining and updating full-graph information, which entails $\mathcal{O}(n^2)$ computation efficiency for a graph with $n$ nodes, leading to weak scalability to large graphs. In this paper, we propose SIMGA, a GNN structure integrating SimRank structural similarity measurement as global aggregation. The design of SIMGA is simple, yet it leads to promising results in both efficiency and effectiveness. The simplicity of SIMGA makes it the first heterophilous GNN model that can achieve a propagation efficiency near-linear to $n$. We theoretically demonstrate its effectiveness by treating SimRank as a new interpretation of GNN and prove that the aggregated node representation matrix has expected grouping effect. The performances of SIMGA are evaluated with 11 baselines on 12 benchmark datasets, usually achieving superior accuracy compared with the state-of-the-art models. Efficiency study reveals that SIMGA is up to 5$\times$ faster than the state-of-the-art method on the largest heterophily dataset pokec with over 30 million edges

Electrophoretic Deposited Quartz Powder-Assisted Growth of Multicrystalline Silicon

Ingot multicrystalline silicon (Mc-Si) needs to be improved in quality and reduced in cost compared with Czochralski monocrystalline silicon. A uniform and dense quartz nucleation layer was obtained by the electrophoretic deposition of quartz powder on the surface of the silicon wafer. The deposited silicon wafer was annealed at 600 °C for 1 h, and one side of the silicon wafer with the quartz layer was glued to the crucible. During the growth of Mc-Si crystal, the dense quartz powder can play a nucleation role. The results show that the average lifetime of the minority carriers a of quartz-assisted silicon ingot is 7.4 μs. The overall dislocation density of an electrophoretic deposition quartz-assisted silica ingot is low, and the defect density in the middle of the silica ingot is 1.5%, which is significantly lower than that of spray quartz (3.1%) and silicon particle (4.2%). Moreover, electrophoretic deposited quartz-assisted mc-Si can obtain oriented grains, which offers a potential to apply alkaline texturing on mc-Si wafers

Electrophoretic Deposited Quartz Powder-Assisted Growth of Multicrystalline Silicon

Ingot multicrystalline silicon (Mc-Si) needs to be improved in quality and reduced in cost compared with Czochralski monocrystalline silicon. A uniform and dense quartz nucleation layer was obtained by the electrophoretic deposition of quartz powder on the surface of the silicon wafer. The deposited silicon wafer was annealed at 600 °C for 1 h, and one side of the silicon wafer with the quartz layer was glued to the crucible. During the growth of Mc-Si crystal, the dense quartz powder can play a nucleation role. The results show that the average lifetime of the minority carriers a of quartz-assisted silicon ingot is 7.4 μs. The overall dislocation density of an electrophoretic deposition quartz-assisted silica ingot is low, and the defect density in the middle of the silica ingot is 1.5%, which is significantly lower than that of spray quartz (3.1%) and silicon particle (4.2%). Moreover, electrophoretic deposited quartz-assisted mc-Si can obtain oriented grains, which offers a potential to apply alkaline texturing on mc-Si wafers

Nonstoichiometric Cu0.6Ni0.4Co2O4 Nanowires as an Anode Material for High Performance Lithium Storage

Transition metal oxide is one of the most promising anode materials for lithium-ion batteries. Generally, the electrochemical property of transition metal oxides can be improved by optimizing their element components and controlling their nano-architecture. Herein, we designed nonstoichiometric Cu0.6Ni0.4Co2O4 nanowires for high performance lithium-ion storage. It is found that the specific capacity of Cu0.6Ni0.4Co2O4 nanowires remain 880 mAh g−1 after 50 cycles, exhibiting much better electrochemical performance than CuCo2O4 and NiCo2O4. After experiencing a large current charge and discharge state, the discharge capacity of Cu0.6Ni0.4Co2O4 nanowires recovers to 780 mAh g−1 at 50 mA g−1, which is ca. 88% of the initial capacity. The high electrochemical performance of Cu0.6Ni0.4Co2O4 nanowires is related to their better electronic conductivity and synergistic effect of metals. This work may provide a new strategy for the design of multicomponent transition metal oxides as anode materials for lithium-ion batteries

Tumor‐associated macrophages‐educated reparative macrophages promote diabetic wound healing

Abstract Nonhealing diabetic wounds, with persistent inflammation and damaged vasculature, have failed conventional treatments and require comprehensive interference. Here, inspired by tumor‐associated macrophages (TAMs) that produce abundant immunosuppressive and proliferative factors in tumor development, we generate macrophages to recapitulate TAMs' reparative functions, by culturing normal macrophages with TAMs' conditional medium (TAMs‐CM). These TAMs‐educated macrophages (TAMEMs) outperform major macrophage phenotypes (M0, M1, or M2) in suppressing inflammation, stimulating angiogenesis, and activating fibroblasts in vitro. When delivered to skin wounds in diabetic mice, TAMEMs efficiently promote healing. Based on TAMs‐CM's composition, we further reconstitute a nine‐factor cocktail to train human primary monocytes into TAMEMsC‐h, which fully resemble TAMEMs' functions without using tumor components, thereby having increased safety and enabling the preparation of autologous cells. Our study demonstrates that recapitulating TAMs' unique reparative activities in nontumor cells can lead to an effective cell therapeutic approach with high translational potential for regenerative medicine