Multi-scale Transformer Network with Edge-aware Pre-training for Cross-Modality MR Image Synthesis

Cross-modality magnetic resonance (MR) image synthesis can be used to generate missing modalities from given ones. Existing (supervised learning) methods often require a large number of paired multi-modal data to train an effective synthesis model. However, it is often challenging to obtain sufficient paired data for supervised training. In reality, we often have a small number of paired data while a large number of unpaired data. To take advantage of both paired and unpaired data, in this paper, we propose a Multi-scale Transformer Network (MT-Net) with edge-aware pre-training for cross-modality MR image synthesis. Specifically, an Edge-preserving Masked AutoEncoder (Edge-MAE) is first pre-trained in a self-supervised manner to simultaneously perform 1) image imputation for randomly masked patches in each image and 2) whole edge map estimation, which effectively learns both contextual and structural information. Besides, a novel patch-wise loss is proposed to enhance the performance of Edge-MAE by treating different masked patches differently according to the difficulties of their respective imputations. Based on this proposed pre-training, in the subsequent fine-tuning stage, a Dual-scale Selective Fusion (DSF) module is designed (in our MT-Net) to synthesize missing-modality images by integrating multi-scale features extracted from the encoder of the pre-trained Edge-MAE. Further, this pre-trained encoder is also employed to extract high-level features from the synthesized image and corresponding ground-truth image, which are required to be similar (consistent) in the training. Experimental results show that our MT-Net achieves comparable performance to the competing methods even using $70\%$ of all available paired data. Our code will be publicly available at https://github.com/lyhkevin/MT-Net.Comment: 13 pages, 15 figure

Recurrent Aligned Network for Generalized Pedestrian Trajectory Prediction

Pedestrian trajectory prediction is a crucial component in computer vision and robotics, but remains challenging due to the domain shift problem. Previous studies have tried to tackle this problem by leveraging a portion of the trajectory data from the target domain to adapt the model. However, such domain adaptation methods are impractical in real-world scenarios, as it is infeasible to collect trajectory data from all potential target domains. In this paper, we study a task named generalized pedestrian trajectory prediction, with the aim of generalizing the model to unseen domains without accessing their trajectories. To tackle this task, we introduce a Recurrent Aligned Network~(RAN) to minimize the domain gap through domain alignment. Specifically, we devise a recurrent alignment module to effectively align the trajectory feature spaces at both time-state and time-sequence levels by the recurrent alignment strategy.Furthermore, we introduce a pre-aligned representation module to combine social interactions with the recurrent alignment strategy, which aims to consider social interactions during the alignment process instead of just target trajectories. We extensively evaluate our method and compare it with state-of-the-art methods on three widely used benchmarks. The experimental results demonstrate the superior generalization capability of our method. Our work not only fills the gap in the generalization setting for practical pedestrian trajectory prediction but also sets strong baselines in this field

Sparse Pedestrian Character Learning for Trajectory Prediction

Pedestrian trajectory prediction in a first-person view has recently attracted much attention due to its importance in autonomous driving. Recent work utilizes pedestrian character information, \textit{i.e.}, action and appearance, to improve the learned trajectory embedding and achieves state-of-the-art performance. However, it neglects the invalid and negative pedestrian character information, which is harmful to trajectory representation and thus leads to performance degradation. To address this issue, we present a two-stream sparse-character-based network~(TSNet) for pedestrian trajectory prediction. Specifically, TSNet learns the negative-removed characters in the sparse character representation stream to improve the trajectory embedding obtained in the trajectory representation stream. Moreover, to model the negative-removed characters, we propose a novel sparse character graph, including the sparse category and sparse temporal character graphs, to learn the different effects of various characters in category and temporal dimensions, respectively. Extensive experiments on two first-person view datasets, PIE and JAAD, show that our method outperforms existing state-of-the-art methods. In addition, ablation studies demonstrate different effects of various characters and prove that TSNet outperforms approaches without eliminating negative characters

Attention-Based Deep Learning Model for Predicting Collaborations Between Different Research Affiliations

It is challenging but important to predict the collaborations between different entities which in academia, for example, would enable finding evaluating trends of scientific research collaboration and the provision of decision support for policy formulation and incentive measures. In this paper, we propose an attention-based Long Short-Term Memory Convolutional Neural Network (LSTM-CNN) model to predict the collaborations between different research affiliations, which takes both the influence of research articles and time (year) relationships into consideration. The experimental results show that the proposed model outperforms the competitive Support Vector Machine (SVM), CNN and LSTM methods. It significantly improves the prediction precision by a minimum of 3.23 percent points and up to 10.80 percent points when compared with the mentioned competitive methods, while in terms of the F1-score, the performance is improved by 13.48, 4.85 and 4.24 percent points, respectively.This work was supported in part by the Humanities and Social Science Research Project of the Ministry of Education in China under Grant 17YJCZH262 and Grant 18YJAZH136, in part by the National Natural Science Foundation of China under Grant 61303167, Grant 61702306, Grant 61433012, Grant U1435215, and Grant 71772107, in part by the Natural Science Foundation of Shandong Province under Grant ZR2018BF013 and Grant ZR2017BF015, in part by the Innovative Research Foundation of Qingdao under Grant 18-2-2-41-jch, in part by the Key Project of Industrial Transformation and Upgrading in China under Grant TC170A5SW, and in part by the Scientific Research Foundation of SDUST for Innovative Team under Grant 2015TDJH102

Failure mode and effects analysis on the air system of an aero turbofan engine sing the Gaussian model and evidence theory

Failure mode and effects analysis (FMEA) is a proactive risk management approach. Risk management under uncertainty with the FMEA method has attracted a lot of attention. The Dempster–Shafer (D-S) evidence theory is a popular approximate reasoning theory for addressing uncertain information and it can be adopted in FMEA for uncertain information processing because of its flexibility and superiority in coping with uncertain and subjective assessments. The assessments coming from FMEA experts may include highly conflicting evidence for information fusion in the framework of D-S evidence theory. Therefore, in this paper, we propose an improved FMEA method based on the Gaussian model and D-S evidence theory to handle the subjective assessments of FMEA experts and apply it to deal with FMEA in the air system of an aero turbofan engine. First, we define three kinds of generalized scaling by Gaussian distribution characteristics to deal with potential highly conflicting evidence in the assessments. Then, we fuse expert assessments with the Dempster combination rule. Finally, we obtain the risk priority number to rank the risk level of the FMEA items. The experimental results show that the method is effective and reasonable in dealing with risk analysis in the air system of an aero turbofan engine

The Development of Computer Forensic Legal System in China

The computer forensic discipline was established around 2000 in China, which was further developed along with Chinese judicial appraisal system in 2005. The new criminal and civil procedure laws of the People’s Republic of China was enacted on 1 Jan 2013. The new laws specified electronic data is legal evidence and has great impact on the current practice on handling electronic evidence. This paper introduces the electronic data and electronic evidence examination procedure in mainland China, the general concept of computer forensic legal system, the management of computer judicial experts, the management of computer judicial expertise institutions. Keywords: China legal system, computer forensic, judicial expert, judicial expertise institution

A versatile hybrid polyphenylsilane host for highly efficient solution-processed blue and deep blue electrophosphorescence

A universal hybrid polymeric host (PCzSiPh) for blue and deep blue phosphors has been designed and synthesized by incorporating electron-donating carbazole as pendants on a polytetraphenylsilane main chain. The polymer PCzSiPh (4) has a wide bandgap and high triplet energy (ET) because of the tetrahedral geometry of the silicon atom in the tetraphenylsilane backbone. The distinct physical properties of good solubility, combined with high thermal and morphological stability give amorphous and homogenous PCzSiPh films by solution processing. As a result, using PCzSiPh as host with the guest iridium complex TMP-FIrpic gives blue phosphorescent organic light-emitting diodes (PhOLEDs) with overall performance which far exceeds that of a control device with poly(vinylcarbazole) (PVK) host. Notably, FIrpic-based devices exhibit a maximum external quantum efficiency (EQE) of 14.3% (29.3 cd A−1, 10.4 lm W−1) which are comparable to state-of-the-art literature data using polymer hosts for a blue dopant emitter. Moreover, the versatility of PCzSiPh extends to deep blue PhOLEDs using FIr6 and FCNIrpic as dopants, with high efficiencies of 11.3 cd A−1 and 8.6 cd A−1, respectively

An in-situ method for assessing soil aggregate stability in burned landscapes

Due to soil repellency in burned areas, slope runoff and soil erodibility escalates following forest fires, increasing the vulnerability to post-fire debris flows. Soil aggregate stability is a critical determinant of soil infiltration capacity and erosion susceptibility. The prevalent method of assessing soil aggregate stability in burned areas, the counting the number of water drop impacts (CND) method, is time-intensive and impractical for in-situ measurements. In response, this study introduces a novel technique based on the shock and vibration damage (SVD) effect for evaluating soil aggregate stability in burned areas. Thirteen distinct soil aggregate types were meticulously prepared for indoor simulated fire testing, with due consideration to factors such as bulk weight, organic matter content, and water repellency, which influence stability of soil aggregates. Employing a custom-built test apparatus, the mass loss rate (MLR) of soil aggregates was determined through orthogonal experiments using the SVD method and compared against the standard CND technique's quantification of water droplet-induced aggregate destruction. The findings demonstrated that SVD method, employing Test Scheme 6 (testing 20 aggregates, 1-meter impact height, 40% water content, and five impacts), exhibits excellent agreement (Kendall coefficient = 0.797) and correlation (R2 = 0.634) with CND method outcomes. This testing scheme, characterized by rapid determination and effective discrimination, is identified as the optimal testing approach. The SVD testing apparatus is straightforward, portable, and easily disassembled, rendering it suitable for on-site use. It can be used to distinguish the stability level of soil aggregates swiftly and quantitatively under various fire intensities in burned areas in situ, which is an important guiding significance for the study of soil erosion, erosion control, and post-fire debris flow initiation mechanism in burned areas