S-Mixup: Structural Mixup for Graph Neural Networks

Existing studies for applying the mixup technique on graphs mainly focus on graph classification tasks, while the research in node classification is still under-explored. In this paper, we propose a novel mixup augmentation for node classification called Structural Mixup (S-Mixup). The core idea is to take into account the structural information while mixing nodes. Specifically, S-Mixup obtains pseudo-labels for unlabeled nodes in a graph along with their prediction confidence via a Graph Neural Network (GNN) classifier. These serve as the criteria for the composition of the mixup pool for both inter and intra-class mixups. Furthermore, we utilize the edge gradient obtained from the GNN training and propose a gradient-based edge selection strategy for selecting edges to be attached to the nodes generated by the mixup. Through extensive experiments on real-world benchmark datasets, we demonstrate the effectiveness of S-Mixup evaluated on the node classification task. We observe that S-Mixup enhances the robustness and generalization performance of GNNs, especially in heterophilous situations. The source code of S-Mixup can be found at \url{https://github.com/SukwonYun/S-Mixup}Comment: CIKM 2023 (Short Paper

Single-cell RNA-seq data imputation using Feature Propagation

While single-cell RNA sequencing provides an understanding of the transcriptome of individual cells, its high sparsity, often termed dropout, hampers the capture of significant cell-cell relationships. Here, we propose scFP (single-cell Feature Propagation), which directly propagates features, i.e., gene expression, especially in raw feature space, via cell-cell graph. Specifically, it first obtains a warmed-up cell-gene matrix via Hard Feature Propagation which fully utilizes known gene transcripts. Then, we refine the k-Nearest Neighbor(kNN) of the cell-cell graph with a warmed-up cell-gene matrix, followed by Soft Feature Propagation which now allows known gene transcripts to be further denoised through their neighbors. Through extensive experiments on imputation with cell clustering tasks, we demonstrate our proposed model, scFP, outperforms various recent imputation and clustering methods. The source code of scFP can be found at https://github.com/Junseok0207/scFP.Comment: ICML 2023 Workshop on Computational Biology (Contributed Talk

A ROBUST LOCATION TRACKING USING UBIQUITOUS RFID WIRELESS NETWORK

A dangerous workplace like the iron production company needs a durable monitoring of workers to protect them from an critical accident. This paper concerns about a robust and accurate location tracking method using ubiquitous RFID wireless network. The sensed RSSI signals obtained from the RFID readers are very unstable in the complicated and propagation-hazard workplace like the iron production company. So, the existing particle filter can not provide a satisfactory location tracking performance. To overcome this limitation, we propose a double layered particle filter, where the lower layer classifies the block in which the tag is contained by the SVM classifier and the upper layer estimates the accurate location of tag owner by the particle filter within the classified block. This layered structure improves the location estimation and tracking performance because the evidence about the location from the lower layer makes a effective restrict on the range of possible locations of the upper layer. We implement the proposed location estimation and tracking system using the ubiquitous RFID wireless network in a noisy and complicated workplace (100m x 50m) where which 49 RFID readers and 9 gateways are located in the fixed locations and the maximally 100 workers owning active RFID tags are moving around the workplace. Many extensive experiments show that the proposed location estimation and tracking system is working well in a real-time and the position error is about 2m at maximum.X111sciescopu

D–A–D-type narrow-bandgap small-molecule photovoltaic donors: pre-synthesis virtual screening using density functional theory

International audienceA new series of D–A–D-type small-molecule photovoltaic donors are designed and virtually screened before synthesis using time-dependent density functional theory calculations carefully validated against various polymeric and molecular donors. In this series of new design, benzodithiophene is kept as D to achieve the optimum highest-occupied molecular orbital energy level, while thienopyrroledione is initially chosen as A but later replaced by difluorinated benzodiathiazole or its selenide derivative to achieve the optimum band gap. The D–A–D core is end-capped by pyridone units which could not only enhance their self-assembly via hydrogen bonds but also play a role as an acceptor (A′) to form an extended A′–D–A–D–A′ small-molecule donor

Materials for multifunctional balloon catheters with capabilities in cardiac electrophysiological mapping and ablation therapy

Developing advanced surgical tools for minimally invasive procedures represents an activity of central importance to improving human health. A key challenge is in establishing biocompatible interfaces between the classes of semiconductor device and sensor technologies that might be most useful in this context and the soft, curvilinear surfaces of the body. This paper describes a solution based on materials that integrate directly with the thin elastic membranes of otherwise conventional balloon catheters, to provide diverse, multimodal functionality suitable for clinical use. As examples, we present sensors for measuring temperature, flow, tactile, optical and electrophysiological data, together with radiofrequency electrodes for controlled, local ablation of tissue. Use of such 'instrumented' balloon catheters in live animal models illustrates their operation, as well as their specific utility in cardiac ablation therapy. The same concepts can be applied to other substrates of interest, such as surgical gloves.