Robust Mid-Pass Filtering Graph Convolutional Networks

Graph convolutional networks (GCNs) are currently the most promising paradigm for dealing with graph-structure data, while recent studies have also shown that GCNs are vulnerable to adversarial attacks. Thus developing GCN models that are robust to such attacks become a hot research topic. However, the structural purification learning-based or robustness constraints-based defense GCN methods are usually designed for specific data or attacks, and introduce additional objective that is not for classification. Extra training overhead is also required in their design. To address these challenges, we conduct in-depth explorations on mid-frequency signals on graphs and propose a simple yet effective Mid-pass filter GCN (Mid-GCN). Theoretical analyses guarantee the robustness of signals through the mid-pass filter, and we also shed light on the properties of different frequency signals under adversarial attacks. Extensive experiments on six benchmark graph data further verify the effectiveness of our designed Mid-GCN in node classification accuracy compared to state-of-the-art GCNs under various adversarial attack strategies.Comment: Accepted by WWW'2

Pre-configured Error Pattern Ordered Statistics Decoding for CRC-Polar Codes

In this paper, we propose a pre-configured error pattern ordered statistics decoding (PEPOSD) algorithm and discuss its application to short cyclic redundancy check (CRC)-polar codes. Unlike the traditional OSD that changes the most reliable independent symbols, we regard the decoding process as testing the error patterns, like guessing random additive noise decoding (GRAND). Also, the pre-configurator referred from ordered reliability bits (ORB) GRAND can better control the range and testing order of EPs. Offline-online structure can accelerate the decoding process. Additionally, we also introduce two orders to optimize the search order for testing EPs. Compared with CRC-aided OSD and list decoding, PEPOSD can achieve a better trade-off between accuracy and complexity

Recognition of Indoor Scenes using 3D Scene Graphs

Scene recognition is a fundamental task in 3-D scene understanding. It answers the question, 'What is this place?' In an indoor environment, the answer can be an office, kitchen, lobby, and so on. As the number of point clouds increases, using embedded point information in scene recognition becomes computationally heavy to process. To achieve computational efficiency and accurate classification, our idea is to use an indoor scene graph that represents the 3-D spatial structures via object instances. The proposed method comprises two parts, namely: 1) construction of indoor scene graphs leveraging object instances and their spatial relationships and 2) classification of these graphs using a deep learning network. Specifically, each indoor scene is represented by a graph, where each node represents either a structural element (like a ceiling, a wall, or a floor) or a piece of furniture (like a chair or a table), and each edge encodes the spatial relationship between these elements. Then, these graphs are used as input for our proposed graph classification network to learn different scene representations. The public indoor dataset, ScanNet v2, with 625.53 million points, is selected to test our method. Experiments yield good results with up to 88.00% accuracy and 82.30% F1 score in the fixed validation dataset and 90.46% accuracy and 81.45% F1 score in the ten-fold cross-validation method; moreover, if some indoor objects cannot be successfully identified, the scene classification accuracy depends sublinearly on the rate of missing objects in the scene.</p

LWS: A Framework for Log-based Workload Simulation in Session-based SUT

Microservice-based applications and cloud-native systems have been widely applied in large IT enterprises. The operation and management of microservice-based applications and cloud-native systems have become the focus of research. Essential and real workloads are the premise and basis of prominent research topics including performance testing, dynamic resource provisioning and scheduling, and AIOps. Due to the privacy restriction, the complexity and variety of workloads, and the requirements for reasonable intervention, it is difficult to copy or generate real workloads directly. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based application systems. First, LWS collects session logs and transforms them into grouped and well-organized sessions. Then LWS extracts the user behavior abstraction based on a relational model and the intervenable workload intensity by three methods from different perspectives. LWS combines the user behavior abstraction and the workload intensity for simulated workload generation and designs a domain-specific language for better execution. The experimental evaluation is performed on an open-source cloud-native application and a public real-world e-commerce workload. The experimental results show that the simulated workload generated by LWS is effective and intervenable

Efficient Inverted ITO-Free Organic Solar Cells Based on Transparent Silver Electrode with Aqueous Solution-Processed ZnO Interlayer

Efficient inverted organic solar cells (OSCs) with the MoO3 (2 nm)/Ag (12 nm) transparent cathode and an aqueous solution ZnO electron extraction layer processed at low temperature are investigated in this work. The blend of low bandgap poly[[4,8-bis[(2-ethylhexyl)oxy]benzo[1,2-b:4,5-b′]dithiophene-2,6-diyl][3-fluoro-2-[(2-ethylhexyl)carbonyl]thieno[3,4-b]thiophenediyl]] (PTB7) and [6,6]-phenyl-C71-butyric acid methylester (PC71BM) is employed as the photoactive layer here. A power conversion efficiency (PCE) of 5.55% is achieved for such indium tin oxide- (ITO-) free OSCs under AM 1.5G simulated illumination, comparable to that of ITO-based reference OSCs (PCE of 6.11%). It is found that this ZnO interlayer not only slightly enhances the transparency of MoO3/Ag cathode but also obtains a lower root-mean-square (RMS) roughness on the MoO3/Ag surface. Meanwhile, ITO-free OSCs also show a good stability. The PCE of the devices still remains above 85% of the original values after 30 days, which is slightly superior to ITO-based reference OSCs where the 16% degradation in PCE is observed after 30 days. It may be instructive for further research of OSCs based on metal thin film electrodes

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models

Large language models (LLMs) have pushed the limits of natural language understanding and exhibited excellent problem-solving ability. Despite the great success, most existing open-source LLMs (e.g., LLaMA-2) are still far away from satisfactory for solving mathematical problem due to the complex reasoning procedures. To bridge this gap, we propose MetaMath, a fine-tuned language model that specializes in mathematical reasoning. Specifically, we start by bootstrapping mathematical questions by rewriting the question from multiple perspectives without extra knowledge, which results in a new dataset called MetaMathQA. Then we fine-tune the LLaMA-2 models on MetaMathQA. Experimental results on two popular benchmarks (i.e., GSM8K and MATH) for mathematical reasoning demonstrate that MetaMath outperforms a suite of open-source LLMs by a significant margin. Our MetaMath-7B model achieves 66.4% on GSM8K and 19.4% on MATH, exceeding the state-of-the-art models of the same size by 11.5% and 8.7%. Particularly, MetaMath-70B achieves an accuracy of 82.3% on GSM8K, slightly better than GPT-3.5-Turbo. We release all the MetaMathQA dataset, the MetaMath models with different model sizes and the training code for public use.Comment: Technical Report, Work in Progress. Project Page: https://meta-math.github.io

LGR5+ epithelial tumor stem-like cells generate a 3D-organoid model for ameloblastoma

Ameloblastoma (AM) is a benign but locally aggressive tumor with high recurrences. Currently, underlying pathophysiology remains elusive, and radical surgery remains the most definitive treatment with severe morbidities. We have recently reported that AM harbors a subpopulation of tumor epithelial stem-like cells (AM-EpiSCs). Herein, we explored whether LGR5+ epithelial cells in AM possess stem-like cell properties and their potential contribution to pathogenesis and recurrence of AM. We found that LGR5 and stem cell-related genes were co-expressed in a subpopulation of AM epithelial cells both in vivo and in vitro, which were enriched under 3D-spheroid culture. As compared to LGR5− counterparts, LGR5+ AM epithelial cells showed increased expression of various EMT- and stemness-related genes, and functionally, exhibited increased capacity to form 3D-spheroids and generate human tumor 3D organoids, which recapitulated the histopathologic features of distinct subtypes of solid AM, thus, contributing a useful human tumor platform for targeted therapeutic screening. Treatment with a selective BRAFV600E inhibitor, vemurafenib, unexpectedly enriched the subpopulation of LGR5+ AM-EpiSCs in tumor 3D organoids, which may have explained therapeutic resistances and recurrences. These findings suggest that LGR5+ AM-EpiSCs play a pivotal role in pathogenesis and progression of AM and targeted inhibition of both BRAF and LGR5 potentially serves a novel nonsurgical adjuvant therapeutic approach for this aggressively benign jaw tumor. © 2020, The Author(s)

MEDALT: Single-cell copy number lineage tracing enabling gene discovery

We present a Minimal Event Distance Aneuploidy Lineage Tree (MEDALT) algorithm that infers the evolution history of a cell population based on single-cell copy number (SCCN) profiles, and a statistical routine named lineage speciation analysis (LSA), whichty facilitates discovery of fitness-associated alterations and genes from SCCN lineage trees. MEDALT appears more accurate than phylogenetics approaches in reconstructing copy number lineage. From data from 20 triple-negative breast cancer patients, our approaches effectively prioritize genes that are essential for breast cancer cell fitness and predict patient survival, including those implicating convergent evolution.The source code of our study is available at https://github.com/KChen-lab/MEDALT