Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale(1-3). Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter(4); identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation(5,6); analyses timings and patterns of tumour evolution(7); describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity(8,9); and evaluates a range of more-specialized features of cancer genomes(8,10-18).Peer reviewe

Occlusion Handling and Multi-scale Pedestrian Detection Based on Deep Learning: A Review

Pedestrian detection is an important branch of computer vision, and it has important applications in the fields of autonomous driving, artificial intelligence and video surveillance.With the rapid development of deep learning and the proposal of large-scale datasets, pedestrian detection has reached a new stage and achieves better performance. However, the performance of state-of-the-art methods is far behind the expectation, especially when occlusion and scale variance exist. Therefore, a lot of works focused on occlusion and scale variance have been proposed in the past few years. The purpose of this article is to make a detailed review of recent progress in pedestrian detection. Firstly, brief progress of pedestrian detection in the past two decades is summarized. Secondly, recent deep learning methods focusing on occlusion and scale variance are analyzed. Moreover, the popular datasets and evaluation methods for pedestrian detection are introduced. Finally, the development trend of pedestrian detection is prospected.Transport and Plannin

A review of vision-based road detection technology for unmanned vehicles

With the development of unmanned vehicle technology, unmanned vehicles have played a huge role in logistics transportation, emergency rescue and disaster relief, etc., so the research on unmanned vehicles is becoming more and more important. Road detection is an important part of environmental perception and an important factor in the realization of assisted driving and unmanned driving technology. High-precision road detection technology can provide important environmental information for efficient planning and reasonable decision-making of unmanned vehicles. Firstly, the technical framework of road detection is given, and the road detection process is introduced in detail. Then, the vision-based road detection algorithm is introduced. Finally, some related data sets in the field of road detection are collected, which provides new ideas and methods for road detection researchers.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

Formation Control of Skid-Steered Vehicles Based on Distributed Model Predictive Control

The skid-steered vehicle has the advantages of simple structure and strong maneuverability. Its formation driving can effectively improve safety, reduce energy consumption and exert its benefits, and has wide application prospects in military and civilian fields. Differential skid steering has strong horizontal and vertical coupling characteristics, so the tracking performance of the vehicle is poor. Therefore, it is of great significance to study horizontal and vertical joint control. Firstly, the mathematical model of the vehicle platoon is established to realize the formation control of skid-steered vehicles. Then, a combined horizontal and vertical control strategy for skid-steered vehicle formation is proposed, and a distributed model predictive controller is designed. Finally, simulation experiments verified that the designed method has good feasibility and stability.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

LQR Optimal Control of Four-steering Vehicle Based on Particle Swarm Optimization Algorithm

This paper proposes a linear quadratic controller based on particle swarm algorithm for the rear wheel control of four-wheel steering vehicle. Particle swarm optimization with fitness functions is used to optimize the coefficients of the weight matrix offline. The fuzzy rules following the controller is used if the road condition is terrible. The simulation results show that the LQR control model based on particle swarm optimization makes the trajectory tracking of the vehicle better and the side slip angle of the vehicle lower. It can be proved that the controller has positive effect on handling stability of the vehicle and safety of drivers. Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

Generalized Single-Vehicle-Based Graph Reinforcement Learning for Decision-Making in Autonomous Driving

In the autonomous driving process, the decision-making system is mainly used to provide macro-control instructions based on the information captured by the sensing system. Learning-based algorithms have apparent advantages in information processing and understanding for an increasingly complex driving environment. To incorporate the interactive information between agents in the environment into the decision-making process, this paper proposes a generalized single-vehicle-based graph neural network reinforcement learning algorithm (SGRL algorithm). The SGRL algorithm introduces graph convolution into the traditional deep neural network (DQN) algorithm, adopts the training method for a single agent, designs a more explicit incentive reward function, and significantly improves the dimension of the action space. The SGRL algorithm is compared with the traditional DQN algorithm (NGRL) and the multi-agent training algorithm (MGRL) in the highway ramp scenario. Results show that the SGRL algorithm has outstanding advantages in network convergence, decision-making effect, and training efficiency.Transport and Plannin

Graph Convolution-Based Deep Reinforcement Learning for Multi-Agent Decision-Making in Interactive Traffic Scenarios

A reliable multi-agent decision-making system is highly demanded for safe and efficient operations of connected and autonomous vehicles (CAVs). In order to represent the mutual effects between vehicles and model the dynamic traffic environments, this research proposes an integrated and open-source framework to realize different Graph Reinforcement Learning (GRL) methods for better decision-making in interactive driving scenarios. Firstly, an interactive driving scenario on the highway with two ramps is constructed. The vehicles in this scenario are modeled by graph representation, and features are extracted via Graph Neural Network (GNN). Secondly, several GRL approaches are implemented and compared in detail. Finally, The simulation in the SUMO platform is carried out to evaluate the performance of different G RL approaches. Results are analyzed from multiple perspectives to compare the performance of different G RL methods in intelligent transportation scenarios. Experiments show that the implementation of GNN can well model the interactions between vehicles, and the proposed framework can improve the overall performance of multi-agent decision-making. The source code of our work can be found at https://github.com/Jacklinkk/TorchGRL.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport and Plannin

Multi-Agent Decision-Making Modes in Uncertain Interactive Traffic Scenarios via Graph Convolution-Based Deep Reinforcement Learning

As one of the main elements of reinforcement learning, the design of the reward function is often not given enough attention when reinforcement learning is used in concrete applications, which leads to unsatisfactory performances. In this study, a reward function matrix is proposed for training various decision-making modes with emphasis on decision-making styles and further emphasis on incentives and punishments. Additionally, we model a traffic scene via graph model to better represent the interaction between vehicles, and adopt the graph convolutional network (GCN) to extract the features of the graph structure to help the connected autonomous vehicles perform decision-making directly. Furthermore, we combine GCN with deep Q-learning and multi-step double deep Q-learning to train four decision-making modes, which are named the graph convolutional deep Q-network (GQN) and the multi-step double graph convolutional deep Q-network (MDGQN). In the simulation, the superiority of the reward function matrix is proved by comparing it with the baseline, and evaluation metrics are proposed to verify the performance differences among decision-making modes. Results show that the trained decision-making modes can satisfy various driving requirements, including task completion rate, safety requirements, comfort level, and completion efficiency, by adjusting the weight values in the reward function matrix. Finally, the decision-making modes trained by MDGQN had better performance in an uncertain highway exit scene than those trained by GQN.Transport and Plannin

Investigation of the hydration properties of cement with EDTA by alternative current impedance spectroscopy

Alternative current impedance spectroscopy (ACIS) is a promising non-destructive testing method to monitor long-term change and assess the durability of concrete. This study investigates the influences of Ethylene Diamine Tetraacetic Acid (EDTA) on the hydration of hardening cement by ACIS. It is found that EDTA retards the early-age hydration of cement but can facilitate the later age reaction. Pastes with EDTA show comparable or higher compressive strength than Control at 28 d, especially when the dosage is higher than 0.4%. Microstructural characterization results reveal the working mechanism of EDTA originating from its complexing effect on free ions. The resistivity evolution of the pastes detected by ACIS can well reflect the effects of EDTA on the cement hydration in different ages. Proportional relations are identified between the resistivity and other hydration parameters, such as reaction degree, chemical shrinkage, compressive strength. The results of this study indicate a wider prospect of ACIS in monitoring the microstructure evolution and macro-properties of cementitious materials.Materials and Environmen

國中公民與道德教科書之導德內涵分析

[[abstract]]本研究採內容分析法，以自編的「國中公民與道德教科書之道德內涵分析類目表」， 針對國中公民與道德科四套二十二冊不同時期的教科書，進行以「章」為單位的分析 ，並依據理論就道德內涵的主題、理論取向、意識型態、與人物五個面向剖析道德教 村之適切性，並比較其祑同與趨勢，期能為我國日後道德教材編纂與研究參考之用。 主要研究發現如下： (一)道德內涵之主題主類目著重在「公共道德」層面，最受忽略者為「個人道德」層 面；次類目則以「國家民族」乙項所占比例最高。 (二)道德內涵大都是以充滿「應該」或「必須」等詞的直述名陳述內容，且時常引用 某人或某書之言詞來支持其立論，顯示教材內容深具規範性與權威性。 (三)道德內涵之理論取向有五項特點： 1.甚為重視道德理性與普遍性道德規範的建立。 2.常強調行為的動機與結果以及環境的重要性。 3.多半具有「安定」傾向，並常以「集體」為描述對象，但仍具有「多元價值觀」與 重視面對「未來」的準備。 4.較為重視「和諧」取向，並強調「社會化」的功能。 5.時常以抗拒誘、嘗罰控制及楷模學習為重點。 (四)道德內涵之意識型態偏重於「文化」與「政治」取向。 (五)道德內涵之說明敘寫以「現代中國政治人物」所出現的次數最多；人物出現次數 前五位者分別為蔣中正、孫中山、孔子、孟子與蔣經國，顯現領袖崇拜與儒家思想在 道德教材中占有重要地位。 (六)道德內涵各項主題類目在陳述的方式、建基的理論、蘊涵的意識型態以及所舉列 的人物等方面之關聯性型態頗為一致。 (七)不同版次教科書中道德內涵略有差異，且其所占比例隨著版次的修訂有逐漸減少 的趨勢。 (八)不同年級教科書中道德內涵亦有所差異。 (九)道德內涵在課文與生活規範實踐活動二者之間，除主題與人物兩類目相似性甚高 外，其余類目皆呈現極大差異。 基於研究發現，謹提出下列建議： (一)道德教材中道德內涵之分配宜使合理化 (二)教材之編纂宜朝向民主化與專業化 (三)道德課程規畫宜是整體性與前瞻性