Application of the analytic hierarchy approach to the risk assessment of Zika virus disease transmission in Guangdong Province, China

Abstract Background An international spread of Zika virus (ZIKV) infection has attracted global attention in 2015. The infection also affected Guangdong province, which is located in southern China. Multiple factors, including frequent communication with South America and Southeast Asia, suitable climate (sub-tropical) for the habitat of Aedes species, may increase the risk of ZIKV disease transmission in this region. Methods An analytic hierarchy process (AHP) method was used to develop a semi-quantitative ZIKV risk assessment model. After selecting indicators, we invited experts in related professions to identify the index weight and based on that a hierarchical structure was generated. Then a series of pairwise comparisons were used to determine the relative importance of the criteria. Finally, the optimal model was established to estimate the spatial and seasonal transmission risk of ZIKV. Results A total of 15 factors that potentially influenced the risk of ZIKV transmission were identified. The factor that received the largest weight was epidemic of ZIKV in Guangdong province (combined weight [CW] =0.37), followed by the mosquito density (CW\u2009=\u20090.18) and the epidemic of DENV in Guangdong province (CW\u2009=\u20090.14). The distribution of 123 districts/counties\u2019 RIs of ZIKV in Guangdong through different seasons were presented, respectively. Conclusions Higher risk was observed within Pearl River Delta including Guangzhou, Shenzhen and Jiangmen, and the risk is greater in summer and autumn compared to spring and winter

BeMap: Balanced Message Passing for Fair Graph Neural Network

Fairness in graph neural networks has been actively studied recently. However, existing works often do not explicitly consider the role of message passing in introducing or amplifying the bias. In this paper, we first investigate the problem of bias amplification in message passing. We empirically and theoretically demonstrate that message passing could amplify the bias when the 1-hop neighbors from different demographic groups are unbalanced. Guided by such analyses, we propose BeMap, a fair message passing method, that leverages a balance-aware sampling strategy to balance the number of the 1-hop neighbors of each node among different demographic groups. Extensive experiments on node classification demonstrate the efficacy of BeMap in mitigating bias while maintaining classification accuracy. The code is available at https://github.com/xiaolin-cs/BeMap.Comment: Accepted at the Second Learning on Graphs Conference (LoG 2023

STATE-OF-ART Algorithms for Injectivity and Bounded Surjectivity of One-dimensional Cellular Automata

Surjectivity and injectivity are the most fundamental problems in cellular automata (CA). We simplify and modify Amoroso's algorithm into optimum and make it compatible with fixed, periodic and reflective boundaries. A new algorithm (injectivity tree algorithm) for injectivity is also proposed. After our theoretic analysis and experiments, our algorithm for injectivity can save much space and 90\% or even more time compared with Amoroso's algorithm for injectivity so that it can support the decision of CA with larger neighborhood sizes. At last, we prove that the reversibility with the periodic boundary and global injectivity of one-dimensional CA is equivalent

Unveiling and Mitigating Backdoor Vulnerabilities based on Unlearning Weight Changes and Backdoor Activeness

The security threat of backdoor attacks is a central concern for deep neural networks (DNNs). Recently, without poisoned data, unlearning models with clean data and then learning a pruning mask have contributed to backdoor defense. Additionally, vanilla fine-tuning with those clean data can help recover the lost clean accuracy. However, the behavior of clean unlearning is still under-explored, and vanilla fine-tuning unintentionally induces back the backdoor effect. In this work, we first investigate model unlearning from the perspective of weight changes and gradient norms, and find two interesting observations in the backdoored model: 1) the weight changes between poison and clean unlearning are positively correlated, making it possible for us to identify the backdoored-related neurons without using poisoned data; 2) the neurons of the backdoored model are more active (i.e., larger changes in gradient norm) than those in the clean model, suggesting the need to suppress the gradient norm during fine-tuning. Then, we propose an effective two-stage defense method. In the first stage, an efficient Neuron Weight Change (NWC)-based Backdoor Reinitialization is proposed based on observation 1). In the second stage, based on observation 2), we design an Activeness-Aware Fine-Tuning to replace the vanilla fine-tuning. Extensive experiments, involving eight backdoor attacks on three benchmark datasets, demonstrate the superior performance of our proposed method compared to recent state-of-the-art backdoor defense approaches

Decision algorithms for reversibility of one-dimensional non-linear cellular automata under null boundary conditions

The property of reversibility is quite meaningful for the classic theoretical computer science model, cellular automata. For the reversibility problem for a CA under null boundary conditions, while linear rules have been studied a lot, the non-linear rules remain unexplored at present. The paper investigates the reversibility problem of general one-dimensional CA on a finite field $\mathbb{Z}_p$, and proposes an approach to optimize the Amoroso's infinite CA surjectivity detection algorithm. This paper proposes algorithms for deciding the reversibility of one-dimensional CA under null boundary conditions. We propose a method to decide the strict reversibility of one-dimensional CA under null boundary conditions. We also provide a bucket chain based algorithm for calculating the reversibility function of one-dimensional CA under null boundary conditions. These decision algorithms work for not only linear rules but also non-linear rules. In addition, it has been confirmed that the reversibility function always has a period, and its periodicity is related to the periodicity of the corresponding bucket chain. Some of our experiment results of reversible CA are presented in the paper, complementing and validating the theoretical aspects, and thereby further supporting the research conclusions of this paper.Comment: in Chinese languag

Explainability of Traditional and Deep Learning Models on Longitudinal Healthcare Records

Recent advances in deep learning have led to interest in training deep learning models on longitudinal healthcare records to predict a range of medical events, with models demonstrating high predictive performance. Predictive performance is necessary but insufficient, however, with explanations and reasoning from models required to convince clinicians for sustained use. Rigorous evaluation of explainability is often missing, as comparisons between models (traditional versus deep) and various explainability methods have not been well-studied. Furthermore, ground truths needed to evaluate explainability can be highly subjective depending on the clinician's perspective. Our work is one of the first to evaluate explainability performance between and within traditional (XGBoost) and deep learning (LSTM with Attention) models on both a global and individual per-prediction level on longitudinal healthcare data. We compared explainability using three popular methods: 1) SHapley Additive exPlanations (SHAP), 2) Layer-Wise Relevance Propagation (LRP), and 3) Attention. These implementations were applied on synthetically generated datasets with designed ground-truths and a real-world medicare claims dataset. We showed that overall, LSTMs with SHAP or LRP provides superior explainability compared to XGBoost on both the global and local level, while LSTM with dot-product attention failed to produce reasonable ones. With the explosion of the volume of healthcare data and deep learning progress, the need to evaluate explainability will be pivotal towards successful adoption of deep learning models in healthcare settings.Comment: 21 pages, 10 figure

Tetra­kis(1-ethyl-3-methyl­imidazolium) β-hexa­cosa­oxidoocta­molybdate

The title compound, (C6H11N2)4[Mo8O26] or (emim)4[β-Mo8O26] (emim is 1-ethyl-3-methyl­imidazolium), was obtained from the ionic liquid [emim]BF4. The asymmetric unit contains two [emim]+ cations and one-half of the [β-Mo8O26]4− tetra­anion, which occupies a special position on an inversion centre. The β-[Mo8O26]4− tetra­anion features eight distorted MoO6 coordination octa­hedra linked together through bridging O atoms

Controlling Surface Wettability for Automated In Situ Array Synthesis and Direct Bioscreening

The in situ synthesis of biomolecules on glass surfaces for direct bioscreening can be a powerful tool in the fields of pharmaceutical sciences, biomaterials, and chemical biology. However, it is still challenging to 1) achieve this conventional multistep combinatorial synthesis on glass surfaces with small feature sizes and high yields and 2) develop a surface which is compatible with solid-phase syntheses, as well as the subsequent bioscreening. This work reports an amphiphilic coating of a glass surface on which small droplets of polar aprotic organic solvents can be deposited with an enhanced contact angle and inhibited motion to permit fully automated multiple rounds of the combinatorial synthesis of small-molecule compounds and peptides. This amphiphilic coating can be switched into a hydrophilic network for protein- and cell-based screening. Employing this in situ synthesis method, chemical space can be probed via array technology with unprecedented speed for various applications, such as lead discovery/optimization in medicinal chemistry and biomaterial development

TORE: Token Reduction for Efficient Human Mesh Recovery with Transformer

In this paper, we introduce a set of effective TOken REduction (TORE) strategies for Transformer-based Human Mesh Recovery from monocular images. Current SOTA performance is achieved by Transformer-based structures. However, they suffer from high model complexity and computation cost caused by redundant tokens. We propose token reduction strategies based on two important aspects, i.e., the 3D geometry structure and 2D image feature, where we hierarchically recover the mesh geometry with priors from body structure and conduct token clustering to pass fewer but more discriminative image feature tokens to the Transformer. As a result, our method vastly reduces the number of tokens involved in high-complexity interactions in the Transformer, achieving competitive accuracy of shape recovery at a significantly reduced computational cost. We conduct extensive experiments across a wide range of benchmarks to validate the proposed method and further demonstrate the generalizability of our method on hand mesh recovery. Our code will be publicly available once the paper is published

Screening Arrays of Laminin Peptides on Modified Cellulose for Promotion of Adhesion of Primary Endothelial and Neural Precursor Cells

Neural precursor cells (NPC) are primary cells intensively used in the context of research on adult neurogenesis and modeling of neuronal development in health and diseased states. Substrates that can facilitate NPC adhesion will be very useful for culturing these cells. Due to the presence of laminin in basal lamina as well as their involvement in differentiation, migration, and adhesion of many types of cells, surfaces modified with laminin-derived peptides are focused upon and compared with the widely used fibronectin-derived Arg-Gly-Asp (RGD) peptides. An array of 46 peptides is synthesized on cellulose paper (SPOT) to identify laminin-derived peptides that promote short-term adhesion of murine NPC and human primary endothelial cells. Various previously reported peptide sequences are re-evaluated in this work. Initial adhesion experiments show NPC preferred several laminin-derived peptides by up to 5-time higher cell numbers, compared to the well-known promiscuous integrin binding RGD peptide. Importantly, screening of cell adhesion has revealed a synergetic effect of filamentous matrix, peptide sequence, surface property, ligand density, and the dynamic process of NPC adhesion. © The Authors. Advanced Biology published by Wiley-VCH Gmb