Multi-Mobile Robot Localization and Navigation based on Visible Light Positioning

We demonstrated multi-mobile robot navigation based on Visible Light Positioning(VLP) localization. From our experiment, the VLP can accurately locate robots' positions in navigation

Laparoscopic Transient Uterine Artery Occlusion and Myomectomy for Symptomatic Uterine Myoma as an Alternative to Hysterectomy

Objective: To compare the clinical outcomes of laparoscopic transient uterine artery ligation plus myomectomy (LTUAL) to simple laparoscopic myomectomy (LM) for symptomatic myomas

Obesity needs to be addressed to tackle the increased prevalence of diabetes in China – Temporal changes from 2003 to 2009

This study aimed to analyse the temporal change of diabetes and any associated risk and protective factors for diabetes in Chinese adults between Wave 0 (2003) and Wave 1 (2009) of the World Health Organization (WHO) Study on global AGEing and adult health (SAGE). Data from China of the SAGE were analysed. Diabetes (outcome variable) was assessed by the yes/no question: “Have you ever been diagnosed with diabetes (high blood sugar)?”. Exposure variables examined in bivariate and multivariate multiple regression included sex, age, marital status, education, smoking, alcohol, fruit and vegetables consumption, physical activity and body mass index (BMI). Significant exposure variables in bivariate analyses were included in multivariate analyses (2003: age and tobacco; 2009: age, BMI, education and alcohol). In Wave 0 (2003), there were 3993 Chinese adults, of which 67 (1.7%) self-reported to have diabetes. In Wave 1 (2009), there were a total of 9524 Chinese adults, of which 770 (8.1%) had diabetes. The overall prevalence of diabetes in Chinese adults increased by 4.76 times between the two timeframes (1.7%, age range 27–84 years, average age 58.51 ± 12.70 years, 59.70% females in 2003 to 8.1%, age range 20–95 years, average age 65.31 ± 10.19 years, 53.64% females in 2009). Multivariate regression retained older age ≥ 60 years (OR 4.34, 95% CI 2.67–7.07) as the main risk factor in 2003 data, while in 2009 the odds ratio for older age ≥ 60 years decreased (OR 2.45, 95% CI 2.06–2.92), but included a significant association of obesity (OR 2.11, 95% CI 1.60–2.78) and excess weight (OR 1.42, 95% CI 1.19–1.69). The significant association with excess weight and obesity associated with the increased prevalence of diabetes in 2009 is a cause of concern and should be addressed by public health strategies in China

Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency

Supervised learning has been widely used for attack categorization, requiring high-quality data and labels. However, the data is often imbalanced and it is difficult to obtain sufficient annotations. Moreover, supervised models are subject to real-world deployment issues, such as defending against unseen artificial attacks. To tackle the challenges, we propose a semi-supervised fine-grained attack categorization framework consisting of an encoder and a two-branch structure and this framework can be generalized to different supervised models. The multilayer perceptron with residual connection is used as the encoder to extract features and reduce the complexity. The Recurrent Prototype Module (RPM) is proposed to train the encoder effectively in a semi-supervised manner. To alleviate the data imbalance problem, we introduce the Weight-Task Consistency (WTC) into the iterative process of RPM by assigning larger weights to classes with fewer samples in the loss function. In addition, to cope with new attacks in real-world deployment, we propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of unseen sample data and adapt the parameters of encoder. Experimental results show that our model outperforms the state-of-the-art semi-supervised attack detection methods with a 3% improvement in classification accuracy and a 90% reduction in training time.Comment: Tech repor

Alleviating Behavior Data Imbalance for Multi-Behavior Graph Collaborative Filtering

Graph collaborative filtering, which learns user and item representations through message propagation over the user-item interaction graph, has been shown to effectively enhance recommendation performance. However, most current graph collaborative filtering models mainly construct the interaction graph on a single behavior domain (e.g. click), even though users exhibit various types of behaviors on real-world platforms, including actions like click, cart, and purchase. Furthermore, due to variations in user engagement, there exists an imbalance in the scale of different types of behaviors. For instance, users may click and view multiple items but only make selective purchases from a small subset of them. How to alleviate the behavior imbalance problem and utilize information from the multiple behavior graphs concurrently to improve the target behavior conversion (e.g. purchase) remains underexplored. To this end, we propose IMGCF, a simple but effective model to alleviate behavior data imbalance for multi-behavior graph collaborative filtering. Specifically, IMGCF utilizes a multi-task learning framework for collaborative filtering on multi-behavior graphs. Then, to mitigate the data imbalance issue, IMGCF improves representation learning on the sparse behavior by leveraging representations learned from the behavior domain with abundant data volumes. Experiments on two widely-used multi-behavior datasets demonstrate the effectiveness of IMGCF.Comment: accepted by ICDM2023 Worksho

2-Amino-4-[4-(dimethyl­amino)­phen­yl]-5-oxo-5,6,7,8-tetra­hydro-4H-chromene-3-carbonitrile

In the title mol­ecule, C18H19N3O2, the fused cyclo­hexenone and pyran rings adopt sofa conformations. Inter­molecular N—H⋯N and N—H⋯O hydrogen bonds link mol­ecules into corrugated layers parallel to the bc plane