2,6-Bis(2-chloro­ethyl)-8b,8c-diphenyl­perhydro-2,3a,4a,6,7a,8a-hexa­azacyclo­penta­[def]fluorene-4,8-dithione

In the title mol­ecule, C24H26Cl2N6S2, the two phenyl rings form a dihedral angle of 51.95 (7)° and the distance between their centroids is 4.156 (8) Å. The crystal packing exhibits weak inter­molecular C—H⋯S and C—H⋯N hydrogen bonds

VQS: Linking Segmentations to Questions and Answers for Supervised Attention in VQA and Question-Focused Semantic Segmentation

Rich and dense human labeled datasets are among the main enabling factors for the recent advance on vision-language understanding. Many seemingly distant annotations (e.g., semantic segmentation and visual question answering (VQA)) are inherently connected in that they reveal different levels and perspectives of human understandings about the same visual scenes --- and even the same set of images (e.g., of COCO). The popularity of COCO correlates those annotations and tasks. Explicitly linking them up may significantly benefit both individual tasks and the unified vision and language modeling. We present the preliminary work of linking the instance segmentations provided by COCO to the questions and answers (QAs) in the VQA dataset, and name the collected links visual questions and segmentation answers (VQS). They transfer human supervision between the previously separate tasks, offer more effective leverage to existing problems, and also open the door for new research problems and models. We study two applications of the VQS data in this paper: supervised attention for VQA and a novel question-focused semantic segmentation task. For the former, we obtain state-of-the-art results on the VQA real multiple-choice task by simply augmenting the multilayer perceptrons with some attention features that are learned using the segmentation-QA links as explicit supervision. To put the latter in perspective, we study two plausible methods and compare them to an oracle method assuming that the instance segmentations are given at the test stage.Comment: To appear on ICCV 201

Improved Wavelet Threshold De-noising Method Based on GNSS Deformation Monitoring Data

In the process of GNSS deformation monitoring, it is inevitable that the monitoring data are contaminated by noise. Effectively mitigating the impact of noise on the measurements and thus improving the quality of the deformation data is the objective of GNSS data processing. Wavelet analysis can analyse the signal according to different frequencies of the signal. Simulation data can be used to determine the best wavelet basis function and select the appropriate decomposition level. In this paper, an improved threshold de-noising method is proposed, based on an analysis of conventional hard threshold de-noising, soft threshold de-noising and compulsory de-noising methods. The improved method was examined through a simulation analysis and applied in an engineering case. The results show that it effectively removed the noise at high frequencies while retaining data details and mutation. The de-noising ability of the proposed technique was better than that of the conventional methods. Moreover, this method significantly improved the quality of the deformation data

Transferring Robustness for Graph Neural Network Against Poisoning Attacks

Graph neural networks (GNNs) are widely used in many applications. However, their robustness against adversarial attacks is criticized. Prior studies show that using unnoticeable modifications on graph topology or nodal features can significantly reduce the performances of GNNs. It is very challenging to design robust graph neural networks against poisoning attack and several efforts have been taken. Existing work aims at reducing the negative impact from adversarial edges only with the poisoned graph, which is sub-optimal since they fail to discriminate adversarial edges from normal ones. On the other hand, clean graphs from similar domains as the target poisoned graph are usually available in the real world. By perturbing these clean graphs, we create supervised knowledge to train the ability to detect adversarial edges so that the robustness of GNNs is elevated. However, such potential for clean graphs is neglected by existing work. To this end, we investigate a novel problem of improving the robustness of GNNs against poisoning attacks by exploring clean graphs. Specifically, we propose PA-GNN, which relies on a penalized aggregation mechanism that directly restrict the negative impact of adversarial edges by assigning them lower attention coefficients. To optimize PA-GNN for a poisoned graph, we design a meta-optimization algorithm that trains PA-GNN to penalize perturbations using clean graphs and their adversarial counterparts, and transfers such ability to improve the robustness of PA-GNN on the poisoned graph. Experimental results on four real-world datasets demonstrate the robustness of PA-GNN against poisoning attacks on graphs. Code and data are available here: https://github.com/tangxianfeng/PA-GNN.Comment: Accepted by WSDM 2020. Code and data: https://github.com/tangxianfeng/PA-GN

DDCO model based false news detection research

With the rapid development of the information age, while the popularity of social media brings great convenience, it also brings some negative effects, such as the spread of false news. At present, the identification of fake news is still based on the personal screening ability, therefore, the intelligent and information-based automatic detection algorithm has become one of the hot issues of current research. Based on the characteristics of DCAN and DEFEND models, this paper proposes an novel model DDCO, which uses multi-layer collaborative attention mechanism to extract the most relevant information from the three dimensions of sentence level, word level and sentence-comment level respectively. Finally, the model designed in this paper is tested on Weibo and Twitter data sets, and the results show that the DDCO has a higher accuracy than the existing models, which provides an important reference for false news detection

Splice site prediction research based on location information

Reveal the mysteries of birth, death and so life has become one of the main purpose of bioinformatics, splice site prediction is one of the most important part, however, not been able to get this problem solved. Firstly, the third generation of genetic markers of single nucleotide polymorphisms had been used in that research to explore the influence of the SNP in splicing; Secondly, a modified hidden Markov model has been introduced; finally, experiments show that the SNP for the performance has a certain influence. In addition, location information based hidden Markov model designed also has positive effects. This method increases the effects dramatically than currently used methods

Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot

We address the problem of enabling quadrupedal robots to perform precise shooting skills in the real world using reinforcement learning. Developing algorithms to enable a legged robot to shoot a soccer ball to a given target is a challenging problem that combines robot motion control and planning into one task. To solve this problem, we need to consider the dynamics limitation and motion stability during the control of a dynamic legged robot. Moreover, we need to consider motion planning to shoot the hard-to-model deformable ball rolling on the ground with uncertain friction to a desired location. In this paper, we propose a hierarchical framework that leverages deep reinforcement learning to train (a) a robust motion control policy that can track arbitrary motions and (b) a planning policy to decide the desired kicking motion to shoot a soccer ball to a target. We deploy the proposed framework on an A1 quadrupedal robot and enable it to accurately shoot the ball to random targets in the real world.Comment: Accepted to 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022