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Cellulose-based conductive composite fibers hold great promise in smart wearable applications, given cellulose's desirable properties for textiles. Blending conductive fillers with cellulose is the most common means of fiber production. Incorporating a high content of conductive fillers is demanded to achieve desirable conductivity. However, a high filler load deteriorates the processability and mechanical properties of the fibers. Here, developing wet-spun cellulose-based fibers with a unique side-by-side (SBS) structure via sustainable processing is reported. Sustainable sources (cotton linter and post-consumer cotton waste) and a biocompatible intrinsically conductive polymer (i.e., polyaniline, PANI) were engineered into fibers containing two co-continuous phases arranged side-by-side. One phase was neat cellulose serving as the substrate and providing good mechanical properties; another phase was a PANI-rich cellulose blend (50 wt%) affording electrical conductivity. Additionally, an eco-friendly LiOH/urea solvent system was adopted for the fiber spinning process. With the proper control of processing parameters, the SBS fibers demonstrated high conductivity and improved mechanical properties compared to single-phase cellulose and PANI blended fibers. The SBS fibers demonstrated great potential for wearable e-textile applications. 

The transitive closure of a graph is a new graph where every vertex is directly connected to all vertices to which it had a path in the original graph. Transitive closures are useful for reachability and relationship querying. Finding the transitive closure can be computationally expensive and requires a large memory footprint as the output is typically larger than the input. Some of the original research on transitive closures assumed that graphs were dense and used dense adjacency matrices. We have since learned that many real-world networks are extremely sparse, and the existing methods do not scale. In this work, we introduce a new algorithm called Anti-section Transitive Closure (ATC) for finding the transitive closure of a graph. We present a new parallel edges operation – anti-sections – for finding new edges to reachable vertices. ATC scales to massively multithreaded systems such as NVIDIA’s GPU with tens of thousands of threads. We show that the anti-section operation shares some traits with the triangle counting intersection operation in graph analysis. Lastly, we view the transitive closure problem as a dynamic graph problem requiring edge insertions. By doing this, our memory footprint is smaller. We also show a method for creating the batches in parallel using two different techniques: dual-round and hash. Using these techniques and the Hornet dynamic graph data structure, we show our new algorithm on an NVIDIA Titan V GPU. We compare with other packages such as NetworkX, SEI-GBTL, SuiteSparse, and cuSparse. 

The large model size, high computational operations, and vulnerability against membership inference attack (MIA) have impeded deep learning or deep neural networks (DNNs) popularity, especially on mobile devices. To address the challenge, we envision that the weight pruning technique will help DNNs against MIA while reducing model storage and computational operation. In this work, we propose a pruning algorithm, and we show that the proposed algorithm can find a subnetwork that can prevent privacy leakage from MIA and achieves competitive accuracy with the original DNNs. We also verify our theoretical insights with experiments. Our experimental results illustrate that the attack accuracy using model compression is up to 13.6% and 10% lower than that of the baseline and Min-Max game, accordingly.