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We study how to use guidance to improve the throughput of lifelong Multi-Agent Path Finding (MAPF). Previous studies have demonstrated that, while incorporating guidance, such as highways, can accelerate MAPF algorithms, this often results in a trade-off with solution quality. In addition, how to generate good guidance automatically remains largely unexplored, with current methods falling short of surpassing manually designed ones. In this work, we introduce the guidance graph as a versatile representation of guidance for lifelong MAPF, framing Guidance Graph Optimization as the task of optimizing its edge weights. We present two GGO algorithms to automatically generate guidance for arbitrary lifelong MAPF algorithms and maps. The first method directly optimizes edge weights, while the second method optimizes an update model capable of generating edge weights. Empirically, we show that (1) our guidance graphs improve the throughput of three representative lifelong MAPF algorithms in eight benchmark maps, and (2) our update model can generate guidance graphs for as large as $93 \times 91$ maps and as many as 3,000 agents. We include the source code at: \url{https://github.com/lunjohnzhang/ggo_public}. All optimized guidance graphs are available online at: \url{https://yulunzhang.net/publication/zhang2024ggo}., Comment: Accepted to International Joint Conference on Artificial Intelligence (IJCAI), 2024

Recently, order-preserving pattern (OPP) mining has been proposed to discover some patterns, which can be seen as trend changes in time series. Although existing OPP mining algorithms have achieved satisfactory performance, they discover all frequent patterns. However, in some cases, users focus on a particular trend and its associated trends. To efficiently discover trend information related to a specific prefix pattern, this paper addresses the issue of co-occurrence OPP mining (COP) and proposes an algorithm named COP-Miner to discover COPs from historical time series. COP-Miner consists of three parts: extracting keypoints, preparation stage, and iteratively calculating supports and mining frequent COPs. Extracting keypoints is used to obtain local extreme points of patterns and time series. The preparation stage is designed to prepare for the first round of mining, which contains four steps: obtaining the suffix OPP of the keypoint sub-time series, calculating the occurrences of the suffix OPP, verifying the occurrences of the keypoint sub-time series, and calculating the occurrences of all fusion patterns of the keypoint sub-time series. To further improve the efficiency of support calculation, we propose a support calculation method with an ending strategy that uses the occurrences of prefix and suffix patterns to calculate the occurrences of superpatterns. Experimental results indicate that COP-Miner outperforms the other competing algorithms in running time and scalability. Moreover, COPs with keypoint alignment yield better prediction performance.

Refactoring is one of the most important activities in software engineering which is used to improve the quality of a software system. With the advancement of deep learning techniques, researchers are attempting to apply deep learning techniques to software refactoring. Consequently, dozens of deep learning-based refactoring approaches have been proposed. However, there is a lack of comprehensive reviews on such works as well as a taxonomy for deep learning-based refactoring. To this end, in this paper, we present a survey on deep learning-based software refactoring. We classify related works into five categories according to the major tasks they cover. Among these categories, we further present key aspects (i.e., code smell types, refactoring types, training strategies, and evaluation) to give insight into the details of the technologies that have supported refactoring through deep learning. The classification indicates that there is an imbalance in the adoption of deep learning techniques for the process of refactoring. Most of the deep learning techniques have been used for the detection of code smells and the recommendation of refactoring solutions as found in 56.25\% and 33.33\% of the literature respectively. In contrast, only 6.25\% and 4.17\% were towards the end-to-end code transformation as refactoring and the mining of refactorings, respectively. Notably, we found no literature representation for the quality assurance for refactoring. We also observe that most of the deep learning techniques have been used to support refactoring processes occurring at the method level whereas classes and variables attracted minimal attention. Finally, we discuss the challenges and limitations associated with the employment of deep learning-based refactorings and present some potential research opportunities for future work., Comment: 45 pages, 8 figures

Multi-Agent Path Finding (MAPF) is the problem of moving multiple agents from starts to goals without collisions. Lifelong MAPF (LMAPF) extends MAPF by continuously assigning new goals to agents. We present our winning approach to the 2023 League of Robot Runners LMAPF competition, which leads us to several interesting research challenges and future directions. In this paper, we outline three main research challenges. The first challenge is to search for high-quality LMAPF solutions within a limited planning time (e.g., 1s per step) for a large number of agents (e.g., 10,000) or extremely high agent density (e.g., 97.7%). We present future directions such as developing more competitive rule-based and anytime MAPF algorithms and parallelizing state-of-the-art MAPF algorithms. The second challenge is to alleviate congestion and the effect of myopic behaviors in LMAPF algorithms. We present future directions, such as developing moving guidance and traffic rules to reduce congestion, incorporating future prediction and real-time search, and determining the optimal agent number. The third challenge is to bridge the gaps between the LMAPF models used in the literature and real-world applications. We present future directions, such as dealing with more realistic kinodynamic models, execution uncertainty, and evolving systems., Comment: Accepted to Symposium on Combinatorial Search (SoCS), 2024

In this dissertation, we propose several methodology in clustering and mixture modeling when the user has some prior knowledge regarding one or more of the clusters or mixture components. We also provide theory in the consistency of K-medoids, and prove its consistency under two versions of ordinal information.We begin by considering the problem of clustering and mixture modeling in situations where the user has relevant prior knowledge on the center separation between clusters or mixture components. We propose a dynamic programming approach to solving the K-means problem exactly with a separation constraint on the centers in the context of real-valued data, and propose an EM algorithm that incorporates such a constraint in the context of fitting a Gaussian mixture model.We then move onto two-component mixture models, where one component plays the role of the background component. We consider situations where the user has prior information on this background component’s distribution: symmetric, monotonic, or log-concave. In each setting, we derive estimators for the background component and provide relevant theory. While estimating the log-concave background distribution, we also propose a method to estimate the largest concave minorant.In addition, we consider the situation where the user has information on the number of modes of a mixture density. We provide a dynamic programming approach to fitting a density function by maximum likelihood when it is constrained to have a given number of modal intervals. When this value is not available, we provide several data-driven ways for selecting it.For the last project, we establish the consistency of K-medoids in the context of metric spaces. Our general approach applies also to non-metric settings where only an ordering of the dissimilarities is available. We consider two types of ordinal information: one where all quadruple comparisons are available; and one where only triple comparisons are available.

In this dissertation, we propose several methodology in clustering and mixture modeling when the user has some prior knowledge regarding one or more of the clusters or mixture components. We also provide theory in the consistency of K-medoids, and prove its consistency under two versions of ordinal information.We begin by considering the problem of clustering and mixture modeling in situations where the user has relevant prior knowledge on the center separation between clusters or mixture components. We propose a dynamic programming approach to solving the K-means problem exactly with a separation constraint on the centers in the context of real-valued data, and propose an EM algorithm that incorporates such a constraint in the context of fitting a Gaussian mixture model.We then move onto two-component mixture models, where one component plays the role of the background component. We consider situations where the user has prior information on this background component’s distribution: symmetric, monotonic, or log-concave. In each setting, we derive estimators for the background component and provide relevant theory. While estimating the log-concave background distribution, we also propose a method to estimate the largest concave minorant.In addition, we consider the situation where the user has information on the number of modes of a mixture density. We provide a dynamic programming approach to fitting a density function by maximum likelihood when it is constrained to have a given number of modal intervals. When this value is not available, we provide several data-driven ways for selecting it.For the last project, we establish the consistency of K-medoids in the context of metric spaces. Our general approach applies also to non-metric settings where only an ordering of the dissimilarities is available. We consider two types of ordinal information: one where all quadruple comparisons are available; and one where only triple comparisons are available.

Traditional image detail enhancement is local filter-based or global filter-based. In both approaches, the original image is first divided into the base layer and the detail layer, and then the enhanced image is obtained by amplifying the detail layer. Our method is different, and its innovation lies in the special way to get the image detail layer. The detail layer in our method is obtained by updating the residual features, and the updating mechanism is usually based on searching and matching similar patches. However, due to the diversity of image texture features, perfect matching is often not possible. In this paper, the process of searching and matching is treated as a thermodynamic process, where the Metropolis theorem can minimize the internal energy and get the global optimal solution of this task, that is, to find a more suitable feature for a better detail enhancement performance. Extensive experiments have proven that our algorithm can achieve better results in quantitative metrics testing and visual effects evaluation. The source code can be obtained from the link., Comment: 23 pages, 17 figures

Zero-Shot Sketch-Based Image Retrieval (ZSSBIR) is an emerging task. The pioneering work focused on the modal gap but ignored inter-class information. Although recent work has begun to consider the triplet-based or contrast-based loss to mine inter-class information, positive and negative samples need to be carefully selected, or the model is prone to lose modality-specific information. To respond to these issues, an Ontology-Aware Network (OAN) is proposed. Specifically, the smooth inter-class independence learning mechanism is put forward to maintain inter-class peculiarity. Meanwhile, distillation-based consistency preservation is utilized to keep modality-specific information. Extensive experiments have demonstrated the superior performance of our algorithm on two challenging Sketchy and Tu-Berlin datasets., Comment: 4 pages, 3 figures

Few multi-agent reinforcement learning (MARL) research on Google Research Football (GRF) focus on the 11v11 multi-agent full-game scenario and to the best of our knowledge, no open benchmark on this scenario has been released to the public. In this work, we fill the gap by providing a population-based MARL training pipeline and hyperparameter settings on multi-agent football scenario that outperforms the bot with difficulty 1.0 from scratch within 2 million steps. Our experiments serve as a reference for the expected performance of Independent Proximal Policy Optimization (IPPO), a state-of-the-art multi-agent reinforcement learning algorithm where each agent tries to maximize its own policy independently across various training configurations. Meanwhile, we open-source our training framework Light-MALib which extends the MALib codebase by distributed and asynchronized implementation with additional analytical tools for football games. Finally, we provide guidance for building strong football AI with population-based training and release diverse pretrained policies for benchmarking. The goal is to provide the community with a head start for whoever experiment their works on GRF and a simple-to-use population-based training framework for further improving their agents through self-play. The implementation is available at https://github.com/Shanghai-Digital-Brain-Laboratory/DB-Football.

Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper, recent years have witnessed an increasing focus on its multi-agent nature by researchers utilizing it as a testbed for Multi-Agent Reinforcement Learning (MARL). However, the absence of standardized environment settings and unified evaluation metrics for multi-agent scenarios hampers the consistent understanding of various studies. Furthermore, the challenging 5-vs-5 and 11-vs-11 full-game scenarios have received limited thorough examination due to their substantial training complexities. To address these gaps, this paper extends the original environment by not only standardizing the environment settings and benchmarking cooperative learning algorithms across different scenarios, including the most challenging full-game scenarios, but also by discussing approaches to enhance football AI from diverse perspectives and introducing related research tools. Specifically, we provide a distributed and asynchronous population-based self-play framework with diverse pre-trained policies for faster training, two football-specific analytical tools for deeper investigation, and an online leaderboard for broader evaluation. The overall expectation of this work is to advance the study of Multi-Agent Reinforcement Learning on Google Research Football environment, with the ultimate goal of benefiting real-world sports beyond virtual games.

We conduct the first empirical study on using knowledge transfer to improve the generalization ability of large language models (LLMs) in software engineering tasks, which often require LLMs to generalize beyond their training data. Our proposed general knowledge transfer approach guides the LLM towards a similar and familiar API or code snippet it has encountered before, improving the model's generalization ability for unseen knowledge. We apply this approach to three software engineering tasks: API inference, code example generation, and FQN inference, and find transfer span, transfer strategy, and transfer architecture as key factors affecting the method. Our findings demonstrate the feasibility of knowledge transfer and its potential to enhance LLMs' performance in various software engineering tasks. The effectiveness of knowledge transfer varies depending on the target domain and task, with the hierarchical strategy being more effective than direct transfer, and AI-Chain outperforming CoT in prompt design. The implications of these findings extend beyond software engineering tasks and suggest that knowledge transfer can enhance LLMs' ability to handle unknowns in any natural language task.

Compiler bugs pose a significant threat to safety-critical applications, and promptly as well as effectively isolating these bugs is crucial for assuring the quality of compilers. However, the limited availability of debugging information on reported bugs complicates the compiler bug isolation task. Existing compiler bug isolation approaches convert the problem into a test program mutation problem, but they are still limited by ineffective mutation strategies or high human effort requirements. Drawing inspiration from the recent progress of pre-trained Large Language Models (LLMs), such as ChatGPT, in code generation, we propose a new approach named LLM4CBI to utilize LLMs to generate effective test programs for compiler bug isolation. However, using LLMs directly for test program mutation may not yield the desired results due to the challenges associated with formulating precise prompts and selecting specialized prompts. To overcome the challenges, three new components are designed in LLM4CBI. First, LLM4CBI utilizes a program complexity-guided prompt production component, which leverages data and control flow analysis to identify the most valuable variables and locations in programs for mutation. Second, LLM4CBI employs a memorized prompt selection component, which adopts reinforcement learning to select specialized prompts for mutating test programs continuously. Third, a test program validation component is proposed to select specialized feedback prompts to avoid repeating the same mistakes during the mutation process. Compared with state-of-the-art approaches over 120 real bugs from GCC and LLVM, our evaluation demonstrates the advantages of LLM4CBI: It can isolate 69.70%/21.74% and 24.44%/8.92% more bugs than DiWi and RecBi within Top-1/Top-5 ranked results. We also demonstrate that the LLMs component used in LLM4CBI can be easily replaced while still achieving reasonable results., Comment: Accepted by IEEE Transactions on Software Engineering

In Chapter 2, I employ the educational production function to identify the different effects of making a friend of the same gender and the opposite gender in a school network. Unlike other gender peer effects literature that only quantifies the causal effects of the proportion of girls in an aggregated level, such as other students in the same class, grade, or dorm, I study the gender of the five best friends nominated by the student. I address the endogeneity of friendship composition by employing a novel set of instrumental variables for the number of same-gender and opposite-gender friends. We find that having more friends, especially in the early accumulation stage, lowers the test scores. We also explore the mechanisms. In Chapter 3, I investigate the role of social learning in enrollment decisions for a public pension scheme. All else equal, if a qualified rural resident moves from a community where no other co- villagers participate in the new pension scheme to a community that is fully covered by the pension scheme, the probability of an individual enrolling by 0.541 percentage point. We use robustness checks to illustrate that the estimated peer effects are not driven by the common unobserved factors, but by social interactions. In Chapter 4, we use the survey data on Chinese middle students and the instrumental variables method to explore the different effects of making friends with the same gender and the opposite gender in a school network on mental health. The empirical results find that having a larger number of same-gender friends improves mental health but having a larger number of opposite-gender friends hurts mental health.

In Chapter 2, I employ the educational production function to identify the different effects of making a friend of the same gender and the opposite gender in a school network. Unlike other gender peer effects literature that only quantifies the causal effects of the proportion of girls in an aggregated level, such as other students in the same class, grade, or dorm, I study the gender of the five best friends nominated by the student. I address the endogeneity of friendship composition by employing a novel set of instrumental variables for the number of same-gender and opposite-gender friends. We find that having more friends, especially in the early accumulation stage, lowers the test scores. We also explore the mechanisms. In Chapter 3, I investigate the role of social learning in enrollment decisions for a public pension scheme. All else equal, if a qualified rural resident moves from a community where no other co- villagers participate in the new pension scheme to a community that is fully covered by the pension scheme, the probability of an individual enrolling by 0.541 percentage point. We use robustness checks to illustrate that the estimated peer effects are not driven by the common unobserved factors, but by social interactions. In Chapter 4, we use the survey data on Chinese middle students and the instrumental variables method to explore the different effects of making friends with the same gender and the opposite gender in a school network on mental health. The empirical results find that having a larger number of same-gender friends improves mental health but having a larger number of opposite-gender friends hurts mental health.

Pattern matching can be used to calculate the support of patterns, and is a key issue in sequential pattern mining (or sequence pattern mining). Nonoverlapping pattern matching means that two occurrences cannot use the same character in the sequence at the same position. Approximate pattern matching allows for some data noise, and is more general than exact pattern matching. At present, nonoverlapping approximate pattern matching is based on Hamming distance, which cannot be used to measure the local approximation between the subsequence and pattern, resulting in large deviations in matching results. To tackle this issue, we present a Nonoverlapping Delta and gamma approximate Pattern matching (NDP) scheme that employs the (delta, gamma)-distance to give an approximate pattern matching, where the local and the global distances do not exceed delta and gamma, respectively. We first transform the NDP problem into a local approximate Nettree and then construct an efficient algorithm, called the local approximate Nettree for NDP (NetNDP). We propose a new approach called the Minimal Root Distance which allows us to determine whether or not a node has root paths that satisfy the global constraint and to prune invalid nodes and parent-child relationships. NetNDP finds the rightmost absolute leaf of the max root, searches for the rightmost occurrence from the rightmost absolute leaf, and deletes this occurrence. We iterate the above steps until there are no new occurrences. Numerous experiments are used to verify the performance of the proposed algorithm.

As a novel deep learning model, gcForest has been widely used in various applications. However, the current multi-grained scanning of gcForest produces many redundant feature vectors, and this increases the time cost of the model. To screen out redundant feature vectors, we introduce a hashing screening mechanism for multi-grained scanning and propose a model called HW-Forest which adopts two strategies, hashing screening and window screening. HW-Forest employs perceptual hashing algorithm to calculate the similarity between feature vectors in hashing screening strategy, which is used to remove the redundant feature vectors produced by multi-grained scanning and can significantly decrease the time cost and memory consumption. Furthermore, we adopt a self-adaptive instance screening strategy to improve the performance of our approach, called window screening, which can achieve higher accuracy without hyperparameter tuning on different datasets. Our experimental results show that HW-Forest has higher accuracy than other models, and the time cost is also reduced.

In this work, the support vector machine (SVM) method is adopted to separate BL Lacertae objects (BL Lacs) and flat spectrum radio quasars (FSRQs) in the plots of photon spectrum index against the photon flux, $\alpha_{\rm ph} \sim {\rm log}\,F$, that of photon spectrum index against the variability index, $\alpha_{\rm ph} \sim {\rm log}\, \textit{V\!I}$, and that of variability index against the photon flux, ${\rm log}\,{V\!I} \sim {\rm log}\,F$. Then we used the dividing lines to tell BL Lacs from FSRQs in the blazars candidates of uncertain types from \textit{Fermi}/LAT catalogue. Our main conclusions are: 1. We separate BL Lacs and FSRQs by $\alpha_{\rm ph} = -0.123\,{\rm log}\,F + 1.170$ in the $\alpha_{\rm ph} \sim {\rm log}\,F$ plot, $\alpha_{\rm ph} = -0.161\,{\rm log}\,{V\!I} + 2.594$ in the $\alpha_{\rm ph} \sim {\rm log}\,{V\!I}$ plot, and ${\rm log}\,{V\!I} = 0.792\,{\rm log}\,F + 9.203$ in the ${\rm log}\,{V\!I} \sim {\rm log}\,F$ plot. 2. We obtained 932 BL Lac candidates and possible BL Lac candidates, and 585 FSRQ candidates and possible FSRQ candidates. 3. Some discussions are given for comparisons with those in literature., Comment: Accepted to Universe

The heavy reliance on data is one of the major reasons that currently limit the development of deep learning. Data quality directly dominates the effect of deep learning models, and the long-tailed distribution is one of the factors affecting data quality. The long-tailed phenomenon is prevalent due to the prevalence of power law in nature. In this case, the performance of deep learning models is often dominated by the head classes while the learning of the tail classes is severely underdeveloped. In order to learn adequately for all classes, many researchers have studied and preliminarily addressed the long-tailed problem. In this survey, we focus on the problems caused by long-tailed data distribution, sort out the representative long-tailed visual recognition datasets and summarize some mainstream long-tailed studies. Specifically, we summarize these studies into ten categories from the perspective of representation learning, and outline the highlights and limitations of each category. Besides, we have studied four quantitative metrics for evaluating the imbalance, and suggest using the Gini coefficient to evaluate the long-tailedness of a dataset. Based on the Gini coefficient, we quantitatively study 20 widely-used and large-scale visual datasets proposed in the last decade, and find that the long-tailed phenomenon is widespread and has not been fully studied. Finally, we provide several future directions for the development of long-tailed learning to provide more ideas for readers., Comment: Accepted for publication in International Journal of Computer Vision (IJCV)

Over the past decade, we have witnessed tremendous technology development and societal benefits for various wireless devices, such as smartphones, smart wearable devices, and the Internet of Things (IoT), among others. To meet the traffic demands from these devices and provide a higher quality of experience (QoE) for users, wireless communication systems have exploited more and more radio spectrum. Although the capacity of the radio spectrum is fairly large, the shortage problem is imminent due to the dramatic proliferation of wireless devices. Dynamic spectrum access (DSA) is proposed as one of the most promising solutions to the spectrum shortage problem. The general objective of DSA is to allocate the spectrum resources to users dynamically and efficiently, which involves several control, coordination, and optimization problems. However, with the huge increment of wireless users, our wireless communication systems are much more complex than ever before and traditional DSA solutions can be low efficient or even impractical. Recent breakthroughs achieved by the advanced neural network based machine learning algorithms have demonstrated their great potentials in solving complex large-scale problems. This dissertation focus on developing neural network based machine learning algorithms and models for problems involved in DSA. This work majorly depends on two machine learning techniques: graph neural network (GNN) and reinforcement learning (RL), which are utilized to solve different optimization, coordination, and control problems related to DSA, such as spectrum sensing, interference estimation, and dynamic spectrum allocation. The proposed algorithms and models are validated by numerous simulation studies.

Projects for the comprehensive utilization of mineral resources involve multi-disciplinary technology and management knowledge. However, enterprises often do not have relevant technical reserves and construction experience, thus posing difficulties in balancing the cost, progress, quality, and other indicators of construction while considering the team building of enterprises If not resolved, these problems may lead to resource wastage and low rates of comprehensive resource utilization. This paper introduces the advantages and disadvantages of Outsourcing, Self-management, Consultant, and Joint Management Modes in handling such projects. Taking a mining company in Guangxi as an example, the pros and cons of the above modes are analysed from different perspectives according to the actual situation of the enterprise being studied. The study’s findings have some guiding significance and can provide a comprehensively practical view from an educational significance perspective for enterprises who are planning to implement project construction. Keywords: comprehensive utilization of resources; selection of project management mode; project construction

Titin truncating variants are a well-established cause of cardiomyopathy; however, the role of titin missense variants is less well understood. Here we describe the generation of a mouse model to investigate the underlying disease mechanism of a previously reported titin A178D missense variant identified in a family with non-compaction and dilated cardiomyopathy. Heterozygous and homozygous mice carrying the titin A178D missense variant were characterised in vivo by echocardiography. Heterozygous mice had no detectable phenotype at any time point investigated (up to 1 year). By contrast, homozygous mice developed dilated cardiomyopathy from 3 months. Chronic adrenergic stimulation aggravated the phenotype. Targeted transcript profiling revealed induction of the foetal gene programme and hypertrophic signalling pathways in homozygous mice, and these were confirmed at the protein level. Unsupervised proteomics identified downregulation of telethonin and four-and-a-half LIM domain 2, as well as the upregulation of heat shock proteins and myeloid leukaemia factor 1. Loss of telethonin from the cardiac Z-disc was accompanied by proteasomal degradation; however, unfolded telethonin accumulated in the cytoplasm, leading to a proteo-toxic response in the mice.We show that the titin A178D missense variant is pathogenic in homozygous mice, resulting in cardiomyopathy. We also provide evidence of the disease mechanism: because the titin A178D variant abolishes binding of telethonin, this leads to its abnormal cytoplasmic accumulation. Subsequent degradation of telethonin by the proteasome results in proteasomal overload, and activation of a proteo-toxic response. The latter appears to be a driving factor for the cardiomyopathy observed in the mouse model.

Titin truncating variants are a well-established cause of cardiomyopathy; however, the role of titin missense variants is less well understood. Here we describe the generation of a mouse model to investigate the underlying disease mechanism of a previously reported titin A178D missense variant identified in a family with non-compaction and dilated cardiomyopathy. Heterozygous and homozygous mice carrying the titin A178D missense variant were characterised in vivo by echocardiography. Heterozygous mice had no detectable phenotype at any time point investigated (up to 1 year). By contrast, homozygous mice developed dilated cardiomyopathy from 3 months. Chronic adrenergic stimulation aggravated the phenotype. Targeted transcript profiling revealed induction of the foetal gene programme and hypertrophic signalling pathways in homozygous mice, and these were confirmed at the protein level. Unsupervised proteomics identified downregulation of telethonin and four-and-a-half LIM domain 2, as well as the upregulation of heat shock proteins and myeloid leukaemia factor 1. Loss of telethonin from the cardiac Z-disc was accompanied by proteasomal degradation; however, unfolded telethonin accumulated in the cytoplasm, leading to a proteo-toxic response in the mice.We show that the titin A178D missense variant is pathogenic in homozygous mice, resulting in cardiomyopathy. We also provide evidence of the disease mechanism: because the titin A178D variant abolishes binding of telethonin, this leads to its abnormal cytoplasmic accumulation. Subsequent degradation of telethonin by the proteasome results in proteasomal overload, and activation of a proteo-toxic response. The latter appears to be a driving factor for the cardiomyopathy observed in the mouse model.

Titin truncating variants are a well-established cause of cardiomyopathy; however, the role of titin missense variants is less well understood. Here we describe the generation of a mouse model to investigate the underlying disease mechanism of a previously reported titin A178D missense variant identified in a family with non-compaction and dilated cardiomyopathy. Heterozygous and homozygous mice carrying the titin A178D missense variant were characterised in vivo by echocardiography. Heterozygous mice had no detectable phenotype at any time point investigated (up to 1 year). By contrast, homozygous mice developed dilated cardiomyopathy from 3 months. Chronic adrenergic stimulation aggravated the phenotype. Targeted transcript profiling revealed induction of the foetal gene programme and hypertrophic signalling pathways in homozygous mice, and these were confirmed at the protein level. Unsupervised proteomics identified downregulation of telethonin and four-and-a-half LIM domain 2, as well as the upregulation of heat shock proteins and myeloid leukaemia factor 1. Loss of telethonin from the cardiac Z-disc was accompanied by proteasomal degradation; however, unfolded telethonin accumulated in the cytoplasm, leading to a proteo-toxic response in the mice.We show that the titin A178D missense variant is pathogenic in homozygous mice, resulting in cardiomyopathy. We also provide evidence of the disease mechanism: because the titin A178D variant abolishes binding of telethonin, this leads to its abnormal cytoplasmic accumulation. Subsequent degradation of telethonin by the proteasome results in proteasomal overload, and activation of a proteo-toxic response. The latter appears to be a driving factor for the cardiomyopathy observed in the mouse model.

Yi-Zi Zheng,1,2 Hong-Bin Qin,1 Zi-Zheng Li,1 He-Sheng Jiang,3 Greg Zhang,4 Shi-Wei Yang,5 Xian-Ming Wang,2 Yang-Chun Xu,1 Zhen-Han Deng,6 Guo-Wen Liu2 1Department of Thyroid and Breast Surgery, The People’s Hospital of Hechi, Hechi, Guangxi, People’s Republic of China; 2Department of Thyroid and Breast Surgery, Shenzhen Breast Tumor Research Center for Diagnosis and Treatment, National Standardization Center for Breast Cancer Diagnosis and Treatment, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 3Department of Surgery, Oregon Health & Science University, Portland, OR, USA; 4McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, USA; 5Teaching Office, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 6Department of Sports Medicine, the First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, Shenzhen, Guangdong, People’s Republic of ChinaCorrespondence: Yang-Chun XuDepartment of Thyroid and Breast Surgery, The People’s Hospital of Hechi, Middle 455 Jinchengjiang Road, Hechi, 547000, Guangxi, People’s Republic of ChinaTel/Fax +86-778-2293900Email xuyangchun2010@163.comYi-Zi ZhengDepartment of Thyroid and Breast Surgery, Shenzhen Breast Tumor Research Center for Diagnosis and Treatment, National Standardization Center for Breast Cancer Diagnosis and Treatment, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People’s Hospital, 3002 Sungang West Road, Shenzhen, 518035, Guangdong, People’s Republic of ChinaTel/Fax +86-755-83366388Email 14111230029@fudan.edu.cnPurpose: Ductal carcinoma in situ with microinvasion (DCISM) can be challenging to balance the risks of overtreatment versus undertreatment. We aim to identify prognostic factors in patients with DCISM and construct a nomogram

Titin truncating variants are a well-established cause of cardiomyopathy; however, the role of titin missense variants is less well understood. Here we describe the generation of a mouse model to investigate the underlying disease mechanism of a previously reported titin A178D missense variant identified in a family with non-compaction and dilated cardiomyopathy. Heterozygous and homozygous mice carrying the titin A178D missense variant were characterised in vivo by echocardiography. Heterozygous mice had no detectable phenotype at any time point investigated (up to 1 year). By contrast, homozygous mice developed dilated cardiomyopathy from 3 months. Chronic adrenergic stimulation aggravated the phenotype. Targeted transcript profiling revealed induction of the foetal gene programme and hypertrophic signalling pathways in homozygous mice, and these were confirmed at the protein level. Unsupervised proteomics identified downregulation of telethonin and four-and-a-half LIM domain 2, as well as the upregulation of heat shock proteins and myeloid leukaemia factor 1. Loss of telethonin from the cardiac Z-disc was accompanied by proteasomal degradation; however, unfolded telethonin accumulated in the cytoplasm, leading to a proteo-toxic response in the mice.We show that the titin A178D missense variant is pathogenic in homozygous mice, resulting in cardiomyopathy. We also provide evidence of the disease mechanism: because the titin A178D variant abolishes binding of telethonin, this leads to its abnormal cytoplasmic accumulation. Subsequent degradation of telethonin by the proteasome results in proteasomal overload, and activation of a proteo-toxic response. The latter appears to be a driving factor for the cardiomyopathy observed in the mouse model.

Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.

Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.

Software Defined Networking (SDN) is one of the most commonly used network architectures in recent years. With the substantial increase in the number of Internet users, network security threats appear more frequently, which brings more concerns to SDN. Distributed denial of Service (DDoS) attacks are one of the most dangerous and frequent attacks in software defined networks. The traditional attack detection method using entropy has some defects such as slow attack detection and poor detection effect. In order to solve this problem, this paper proposed a method of fusion entropy, which detects attacks by measuring the randomness of network events. This method has the advantages of fast attack detection speed and obvious decrease in entropy value. The complementarity of information entropy and log energy entropy is effectively utilized. The experimental results show that the entropy value of the attack scenarios 91.25% lower than normal scenarios, which has greater advantages and significance compared with other attack detection methods.

Projects for the comprehensive utilization of mineral resources involve multi-disciplinary technology and management knowledge. However, enterprises often do not have relevant technical reserves and construction experience, thus posing difficulties in balancing the cost, progress, quality, and other indicators of construction while considering the team building of enterprises If not resolved, these problems may lead to resource wastage and low rates of comprehensive resource utilization. This paper introduces the advantages and disadvantages of Outsourcing, Self-management, Consultant, and Joint Management Modes in handling such projects. Taking a mining company in Guangxi as an example, the pros and cons of the above modes are analysed from different perspectives according to the actual situation of the enterprise being studied. The study’s findings have some guiding significance and can provide a comprehensively practical view from an educational significance perspective for enterprises who are planning to implement project construction. Keywords: comprehensive utilization of resources; selection of project management mode; project construction

He Jiang, Feifei Yuan, Zhiying Zhao, Tongchun Xue, Ningling Ge, Zhenggang Ren, Lan Zhang Department of Hepatic Oncology, Zhongshan Hospital, Liver Cancer Institute and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, Shanghai, People’s Republic of ChinaCorrespondence: Lan ZhangDepartment of Hepatic Oncology, Zhongshan Hospital, Liver Cancer Institute and Key Laboratory of Carcinogenesis and Cancer Invasion, Fudan University, Shanghai, 200032, People’s Republic of ChinaTel +86-021-64041990Fax +86 021-64038472Email zhang.lan@zs-hospital.sh.cnPurpose: Ribosomal protein metallopanstimulin-1 (MPS-1) is implicated in tumorigenesis. However, to date, the underlying role of MPS-1 in the generation, progression and prognosis of hepatocellular carcinoma (HCC) remains unknown. This study aims to investigate the expression of MPS-1 in HCC and its significance for the prognosis of HCC.Methods: The Oncomine and GEPIA databases were used to analyze the expression pattern of MPS-1 in HCC. Immunohistochemical staining was performed on tissue microarrays containing 169 HCC tissue samples to examine the expression of MPS-1. In addition, univariate and multivariate Cox regression analyses and Kaplan–Meier analysis were used to verify the correlation between clinicopathological factors in HCC patients and its clinical prognostic significance.Results: MPS-1 was more highly expressed in HCC than in normal tissues, and MPS-1 expression was correlated with serum AFP levels (P = 0.003), liver cirrhosis (P = 0.024), tumor embolus (P = 0.009) and tumor recurrence (P < 0.003). MPS-1 was an independent prognostic factor for the overall survival of HCC (HR, 1.92; 95% CI, 1.01– 3.68), and a higher expression of MPS-1 predicted poorer survival. Furthermore, high expression of MPS-1 indicated a poor prognosis in patients with AFP positivity, cirrhosis or HBsAg positivity.Conclusion: These findings demonstrate that MPS-1 is highly expressed in HCC and serves as an in

Titin truncating variants are a well-established cause of cardiomyopathy; however, the role of titin missense variants is less well understood. Here we describe the generation of a mouse model to investigate the underlying disease mechanism of a previously reported titin A178D missense variant identified in a family with non-compaction and dilated cardiomyopathy. Heterozygous and homozygous mice carrying the titin A178D missense variant were characterised in vivo by echocardiography. Heterozygous mice had no detectable phenotype at any time point investigated (up to 1 year). By contrast, homozygous mice developed dilated cardiomyopathy from 3 months. Chronic adrenergic stimulation aggravated the phenotype. Targeted transcript profiling revealed induction of the foetal gene programme and hypertrophic signalling pathways in homozygous mice, and these were confirmed at the protein level. Unsupervised proteomics identified downregulation of telethonin and four-and-a-half LIM domain 2, as well as the upregulation of heat shock proteins and myeloid leukaemia factor 1. Loss of telethonin from the cardiac Z-disc was accompanied by proteasomal degradation; however, unfolded telethonin accumulated in the cytoplasm, leading to a proteo-toxic response in the mice.We show that the titin A178D missense variant is pathogenic in homozygous mice, resulting in cardiomyopathy. We also provide evidence of the disease mechanism: because the titin A178D variant abolishes binding of telethonin, this leads to its abnormal cytoplasmic accumulation. Subsequent degradation of telethonin by the proteasome results in proteasomal overload, and activation of a proteo-toxic response. The latter appears to be a driving factor for the cardiomyopathy observed in the mouse model.

Active galactic nuclei (AGNs) can be divided into two major classes, namely radio-loud and radio-quiet AGNs. A small subset of the radio-loud AGNs is called blazars, which are believed to be unified with Fanaroff-Riley type I and type II (FRI&II) radio galaxies. Following our previous work, we present a latest sample of 966 sources with measured radio flux densities of the core and extended components. The sample includes 83 BL Lacs, 473 FSRQs, 101 Seyferts, 245 galaxies, 52 FRIs&IIs and 12 unidentified sources. We then calculate the radio core-dominance parameters and spectral indices and study their relationship. Our analysis shows that the core-dominance parameters and spectral indices are quite different for different types of sources. We also corroborate that the correlation between core-dominance parameter and radio spectral index extends over all the sources in a large sample presented., Comment: arXiv admin note: substantial text overlap with arXiv:1811.08774

Aims. We present a sample of 4388 AGNs with available radio core-dominance parameters defined as the ratio of the core flux densities to extended ones, namely, R = Score/Sext., which includes 630 Fermi-detected AGNs respect to the catalog of 4FGL, the fourth Fermi Large Area Telescope source catalog, and the rest of them are non-Fermi-detected AGNs. In our sample, 584 blazars are Fermi-detected and 1310 are not, and also consists of other subclasses such as Seyfert, Fanaroff-Riley I/II and normal galaxies. We investigate various different properties between Fermi-detected AGNs and non-Fermi-detected ones by using the core-dominance parameters as the previous study has shown that R is a good indication of beaming effect. Methods. We then calculate the radio spectral indices for whole sample and adopt {\gamma}-ray photon indices for Fermi AGNs from 4FGL catalog for discussing their different performances on different subclasses, and obtain the relation between core-dominance parameters and radio spectral indices for both Fermi and non-Fermi sources according to the two components model on radio band, which are consistent with our previous study. Results. We found that the core-dominance parameters and radio spectral indices are quite different for different subclasses of AGNs, not only for Fermi sources but also non-Fermi sources, particularly, R for the former ones is averagely higher than later ones. We also adopt the same relation on core-dominance parameters and {\gamma}-ray photon indices for Fermi sources by taking the same assumption with two components model on {\gamma}-ray band, and obtain the fitting results indicating that the {\gamma}-ray emissions of Fermi blazars are mainly from the core component, which is perhaps associated with the beaming effect. Therefore, Fermi blazars are beamed., Comment: 14 pages, 18 figures

Aims. We present a sample of 4388 AGNs with available radio core-dominance parameters defined as the ratio of the core flux densities to extended ones, namely, R = Score/Sext., which includes 630 Fermi-detected AGNs respect to the catalog of 4FGL, the fourth Fermi Large Area Telescope source catalog, and the rest of them are non-Fermi-detected AGNs. In our sample, 584 blazars are Fermi-detected and 1310 are not, and also consists of other subclasses such as Seyfert, Fanaroff-Riley I/II and normal galaxies. We investigate various different properties between Fermi-detected AGNs and non-Fermi-detected ones by using the core-dominance parameters as the previous study has shown that R is a good indication of beaming effect. Methods. We then calculate the radio spectral indices for whole sample and adopt {\gamma}-ray photon indices for Fermi AGNs from 4FGL catalog for discussing their different performances on different subclasses, and obtain the relation between core-dominance parameters and radio spectral indices for both Fermi and non-Fermi sources according to the two components model on radio band, which are consistent with our previous study. Results. We found that the core-dominance parameters and radio spectral indices are quite different for different subclasses of AGNs, not only for Fermi sources but also non-Fermi sources, particularly, R for the former ones is averagely higher than later ones. We also adopt the same relation on core-dominance parameters and {\gamma}-ray photon indices for Fermi sources by taking the same assumption with two components model on {\gamma}-ray band, and obtain the fitting results indicating that the {\gamma}-ray emissions of Fermi blazars are mainly from the core component, which is perhaps associated with the beaming effect. Therefore, Fermi blazars are beamed., Comment: 14 pages, 18 figures

Active galactic nuclei (AGNs) can be divided into two major classes, namely radio-loud and radio-quiet AGNs. A small subset of the radio-loud AGNs is called blazars, which are believed to be unified with Fanaroff-Riley type I and type II (FRI&II) radio galaxies. Following our previous work, we present a latest sample of 966 sources with measured radio flux densities of the core and extended components. The sample includes 83 BL Lacs, 473 FSRQs, 101 Seyferts, 245 galaxies, 52 FRIs&IIs and 12 unidentified sources. We then calculate the radio core-dominance parameters and spectral indices and study their relationship. Our analysis shows that the core-dominance parameters and spectral indices are quite different for different types of sources. We also corroborate that the correlation between core-dominance parameter and radio spectral index extends over all the sources in a large sample presented., Comment: arXiv admin note: substantial text overlap with arXiv:1811.08774

Sequence labeling is a fundamental framework for various natural language processing problems. Its performance is largely influenced by the annotation quality and quantity in supervised learning scenarios, and obtaining ground truth labels is often costly. In many cases, ground truth labels do not exist, but noisy annotations or annotations from different domains are accessible. In this paper, we propose a novel framework Consensus Network (ConNet) that can be trained on annotations from multiple sources (e.g., crowd annotation, cross-domain data...). It learns individual representation for every source and dynamically aggregates source-specific knowledge by a context-aware attention module. Finally, it leads to a model reflecting the agreement (consensus) among multiple sources. We evaluate the proposed framework in two practical settings of multi-source learning: learning with crowd annotations and unsupervised cross-domain model adaptation. Extensive experimental results show that our model achieves significant improvements over existing methods in both settings. We also demonstrate that the method can apply to various tasks and cope with different encoders., Comment: Accepted to the ACL 2020, code: https://github.com/INK-USC/ConNet

Cognitive task analysis (CTA) is a type of analysis in applied psychology aimed at eliciting and representing the knowledge and thought processes of domain experts. In CTA, often heavy human labor is involved to parse the interview transcript into structured knowledge (e.g., flowchart for different actions). To reduce human efforts and scale the process, automated CTA transcript parsing is desirable. However, this task has unique challenges as (1) it requires the understanding of long-range context information in conversational text; and (2) the amount of labeled data is limited and indirect---i.e., context-aware, noisy, and low-resource. In this paper, we propose a weakly-supervised information extraction framework for automated CTA transcript parsing. We partition the parsing process into a sequence labeling task and a text span-pair relation extraction task, with distant supervision from human-curated protocol files. To model long-range context information for extracting sentence relations, neighbor sentences are involved as a part of input. Different types of models for capturing context dependency are then applied. We manually annotate real-world CTA transcripts to facilitate the evaluation of the parsing tasks, Comment: In proceedings of ACL 2019

The proliferation of fake news on social media has opened up new directions of research for timely identification and containment of fake news, and mitigation of its widespread impact on public opinion. While much of the earlier research was focused on identification of fake news based on its contents or by exploiting users' engagements with the news on social media, there has been a rising interest in proactive intervention strategies to counter the spread of misinformation and its impact on society. In this survey, we describe the modern-day problem of fake news and, in particular, highlight the technical challenges associated with it. We discuss existing methods and techniques applicable to both identification and mitigation, with a focus on the significant advances in each method and their advantages and limitations. In addition, research has often been limited by the quality of existing datasets and their specific application contexts. To alleviate this problem, we comprehensively compile and summarize characteristic features of available datasets. Furthermore, we outline new directions of research to facilitate future development of effective and interdisciplinary solutions.

BMP10 is highly expressed in the developing heart and plays essential roles in cardiogenesis. BMP10 deletion in mice results in embryonic lethality because of impaired cardiac development. In adults, BMP10 expression is restricted to the right atrium, though ventricular hypertrophy is accompanied by increased BMP10 expression in a rat hypertension model. However, reports of BMP10 activity in the circulation are inconclusive. In particular, it is not known whether in vivo secreted BMP10 is active or whether additional factors are required to achieve its bioactivity. It has been shown that high-affinity binding of the BMP10 prodomain to the mature ligand inhibits BMP10 signaling activity in C2C12 cells, and it was proposed that prodomain-bound BMP10 (pBMP10) complex is latent. In this study, we demonstrated that the BMP10 prodomain did not inhibit BMP10 signaling activity in multiple endothelial cells, and that recombinant human pBMP10 complex, expressed in mammalian cells and purified under native conditions, was fully active. In addition, both BMP10 in human plasma and BMP10 secreted from the mouse right atrium were fully active. Finally, we confirmed that active BMP10 secreted from mouse right atrium was in the prodomain-bound form. Our data suggest that circulating BMP10 in adults is fully active and that the reported vascular quiescence function of BMP10 in vivo is due to the direct activity of pBMP10 and does not require an additional activation step. Moreover, being an active ligand, recombinant pBMP10 may have therapeutic potential as an endothelial-selective BMP ligand, in conditions characterized by loss of BMP9/10 signaling.

BMP10 is highly expressed in the developing heart and plays essential roles in cardiogenesis. BMP10 deletion in mice results in embryonic lethality because of impaired cardiac development. In adults, BMP10 expression is restricted to the right atrium, though ventricular hypertrophy is accompanied by increased BMP10 expression in a rat hypertension model. However, reports of BMP10 activity in the circulation are inconclusive. In particular, it is not known whether in vivo secreted BMP10 is active or whether additional factors are required to achieve its bioactivity. It has been shown that high-affinity binding of the BMP10 prodomain to the mature ligand inhibits BMP10 signaling activity in C2C12 cells, and it was proposed that prodomain-bound BMP10 (pBMP10) complex is latent. In this study, we demonstrated that the BMP10 prodomain did not inhibit BMP10 signaling activity in multiple endothelial cells, and that recombinant human pBMP10 complex, expressed in mammalian cells and purified under native conditions, was fully active. In addition, both BMP10 in human plasma and BMP10 secreted from the mouse right atrium were fully active. Finally, we confirmed that active BMP10 secreted from mouse right atrium was in the prodomain-bound form. Our data suggest that circulating BMP10 in adults is fully active and that the reported vascular quiescence function of BMP10 in vivo is due to the direct activity of pBMP10 and does not require an additional activation step. Moreover, being an active ligand, recombinant pBMP10 may have therapeutic potential as an endothelial-selective BMP ligand, in conditions characterized by loss of BMP9/10 signaling.

Active galactic nuclei (AGNs) can be divided into two major classes, namely radio loud and radio quiet AGNs. A small subset of the radio-loud AGNs is called blazars, which are believed to be unified with Fanaroff & Riley type I/II (FRI/II) radio galaxies. Following our previous work (Fan et al. 2011), we present a sample of 2400 sources with measured radio flux densities of the core and extended components. The sample contains 250 BL Lacs, 520 quasars, 175 Seyferts, 1178 galaxies, 153 Fanaroff & Riley type I and type II galaxies and 104 unidentified sources. We then calculate the radio core-dominance parameters and spectral indices and study their relationship. Our analysis shows that the core-dominance parameters and spectral indices are quite different for different types of sources. We also confirm that the correlation between core-dominance parameter and spectral index exists for a large sample presented in this work., Comment: 24 pages, 10 figures

Low rank approximation of matrices is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns, and provides concise representations for the data. Research on low rank approximation usually focuses on real matrices. However, in many applications data are binary (categorical) rather than continuous. This leads to the problem of low rank approximation of binary matrices. Here we are given a d x n binary matrix A and a small integer k < d. The goal is to find two binary matrices U and V of sizes d x k and k x n respectively, so that the Frobenius norm of A - U V is minimized. There are two models of this problem, depending on the definition of the dot product of binary vectors: The GF(2) model and the Boolean semiring model. Unlike low rank approximation of a real matrix which can be efficiently solved by Singular Value Decomposition, we show that approximation of a binary matrix is NP-hard, even for k=1. In this paper, our main concern is the problem of Column Subset Selection (CSS), in which the low rank matrix U must be formed by k columns of the data matrix, and we are interested in the approximation ratio achievable by CSS for binary matrices. For the GF(2) model, we show that CSS has approximation ratio bounded by k/2+1+k/(2(2^k-1)) and this is asymptotically tight. For the Boolean model, it turns out that CSS is no longer sufficient to obtain a bound. We then develop a Generalized CSS (GCSS) procedure in which the columns of U are generated from Boolean formulas operating bitwise on selected columns of the data matrix. We show that the approximation ratio achieved by GCSS is bounded by 2^(k-1)+1, and argue that an exponential dependency on k is seems inherent.

Active galactic nuclei (AGNs) can be divided into two major classes, namely radio loud and radio quiet AGNs. A small subset of the radio-loud AGNs is called blazars, which are believed to be unified with Fanaroff & Riley type I/II (FRI/II) radio galaxies. Following our previous work (Fan et al. 2011), we present a sample of 2400 sources with measured radio flux densities of the core and extended components. The sample contains 250 BL Lacs, 520 quasars, 175 Seyferts, 1178 galaxies, 153 Fanaroff & Riley type I and type II galaxies and 104 unidentified sources. We then calculate the radio core-dominance parameters and spectral indices and study their relationship. Our analysis shows that the core-dominance parameters and spectral indices are quite different for different types of sources. We also confirm that the correlation between core-dominance parameter and spectral index exists for a large sample presented in this work., Comment: 24 pages, 10 figures

Low rank approximation of matrices is an important tool in machine learning. Given a data matrix, low rank approximation helps to find factors, patterns, and provides concise representations for the data. Research on low rank approximation usually focuses on real matrices. However, in many applications data are binary (categorical) rather than continuous. This leads to the problem of low rank approximation of binary matrices. Here we are given a d x n binary matrix A and a small integer k < d. The goal is to find two binary matrices U and V of sizes d x k and k x n respectively, so that the Frobenius norm of A - U V is minimized. There are two models of this problem, depending on the definition of the dot product of binary vectors: The GF(2) model and the Boolean semiring model. Unlike low rank approximation of a real matrix which can be efficiently solved by Singular Value Decomposition, we show that approximation of a binary matrix is NP-hard, even for k=1. In this paper, our main concern is the problem of Column Subset Selection (CSS), in which the low rank matrix U must be formed by k columns of the data matrix, and we are interested in the approximation ratio achievable by CSS for binary matrices. For the GF(2) model, we show that CSS has approximation ratio bounded by k/2+1+k/(2(2^k-1)) and this is asymptotically tight. For the Boolean model, it turns out that CSS is no longer sufficient to obtain a bound. We then develop a Generalized CSS (GCSS) procedure in which the columns of U are generated from Boolean formulas operating bitwise on selected columns of the data matrix. We show that the approximation ratio achieved by GCSS is bounded by 2^(k-1)+1, and argue that an exponential dependency on k is seems inherent.

Recent years, deep learning is increasingly prevalent in the field of Software Engineering (SE). However, many open issues still remain to be investigated. How do researchers integrate deep learning into SE problems? Which SE phases are facilitated by deep learning? Do practitioners benefit from deep learning? The answers help practitioners and researchers develop practical deep learning models for SE tasks. To answer these questions, we conduct a bibliography analysis on 98 research papers in SE that use deep learning techniques. We find that 41 SE tasks in all SE phases have been facilitated by deep learning integrated solutions. In which, 84.7% papers only use standard deep learning models and their variants to solve SE problems. The practicability becomes a concern in utilizing deep learning techniques. How to improve the effectiveness, efficiency, understandability, and testability of deep learning based solutions may attract more SE researchers in the future., Comment: 10 pages, 4 figures

Developers increasingly rely on text matching tools to analyze the relation between natural language words and APIs. However, semantic gaps, namely textual mismatches between words and APIs, negatively affect these tools. Previous studies have transformed words or APIs into low-dimensional vectors for matching; however, inaccurate results were obtained due to the failure of modeling words and APIs simultaneously. To resolve this problem, two main challenges are to be addressed: the acquisition of massive words and APIs for mining and the alignment of words and APIs for modeling. Therefore, this study proposes Word2API to effectively estimate relatedness of words and APIs. Word2API collects millions of commonly used words and APIs from code repositories to address the acquisition challenge. Then, a shuffling strategy is used to transform related words and APIs into tuples to address the alignment challenge. Using these tuples, Word2API models words and APIs simultaneously. Word2API outperforms baselines by 10%-49.6% of relatedness estimation in terms of precision and NDCG. Word2API is also effective on solving typical software tasks, e.g., query expansion and API documents linking. A simple system with Word2API-expanded queries recommends up to 21.4% more related APIs for developers. Meanwhile, Word2API improves comparison algorithms by 7.9%-17.4% in linking questions in Question&Answer communities to API documents., Comment: accepted by IEEE Transactions on Software Engineering

Compilers are widely-used infrastructures in accelerating the software development, and expected to be trustworthy. In the literature, various testing technologies have been proposed to guarantee the quality of compilers. However, there remains an obstacle to comprehensively characterize and understand compiler testing. To overcome this obstacle, we propose a literature analysis framework to gain insights into the compiler testing area. First, we perform an extensive search to construct a dataset related to compiler testing papers. Then, we conduct a bibliometric analysis to analyze the productive authors, the influential papers, and the frequently tested compilers based on our dataset. Finally, we utilize association rules and collaboration networks to mine the authorships and the communities of interests among researchers and keywords. Some valuable results are reported. We find that the USA is the leading country that contains the most influential researchers and institutions. The most active keyword is "random testing". We also find that most researchers have broad interests within small-scale collaborators in the compiler testing area., Comment: 20 pages, 15 figures

Recent years have witnessed the growing demands for resolving numerous bug reports in software maintenance. Aiming to reduce the time testers/developers take in perusing bug reports, the task of bug report summarization has attracted a lot of research efforts in the literature. However, no systematic analysis has been conducted on attribute construction which heavily impacts the performance of supervised algorithms for bug report summarization. In this study, we first conduct a survey to reveal the existing methods for attribute construction in mining software repositories. Then, we propose a new method named Crowd-Attribute to infer new effective attributes from the crowdgenerated data in crowdsourcing and develop a new tool named Crowdsourcing Software Engineering Platform to facilitate this method. With Crowd-Attribute, we successfully construct 11 new attributes and propose a new supervised algorithm named Logistic Regression with Crowdsourced Attributes (LRCA). To evaluate the effectiveness of LRCA, we build a series of large scale data sets with 105,177 bug reports. Experiments over both the public data set SDS with 36 manually annotated bug reports and new large-scale data sets demonstrate that LRCA can consistently outperform the state-of-the-art algorithms for bug report summarization., Comment: Accepted by IEEE Transactions on Reliability

The popularity of blockchain technology continues to grow rapidly in both industrial and academic fields. Most studies of blockchain focus on the improvements of security, usability, or efficiency of blockchain protocols, or the applications of blockchain in finance, Internet of Things, or public services. However, few of them could reveal the concerns of front-line developers and the situations of blockchain in practice. In this article, we investigate how developers use and discuss blockchain with a case study of Stack Overflow posts. We find blockchain is a relatively new topic in Stack Overflow but it is rising to popularity. We detect 13 types of questions that developers post in Stack Overflow and identify 45 blockchain relevant entities (e.g., frameworks, libraries, or tools) for building blockchain applications. These findings may help blockchain project communities to know where to improve and help novices to know where to start., Comment: 10 pages, 5 figures

Reproducibility is the ability of recreating identical binaries under pre-defined build environments. Due to the need of quality assurance and the benefit of better detecting attacks against build environments, the practice of reproducible builds has gained popularity in many open-source software repositories such as Debian and Bitcoin. However, identifying the unreproducible issues remains a labour intensive and time consuming challenge, because of the lacking of information to guide the search and the diversity of the causes that may lead to the unreproducible binaries. In this paper we propose an automated framework called RepLoc to localize the problematic files for unreproducible builds. RepLoc features a query augmentation component that utilizes the information extracted from the build logs, and a heuristic rule-based filtering component that narrows the search scope. By integrating the two components with a weighted file ranking module, RepLoc is able to automatically produce a ranked list of files that are helpful in locating the problematic files for the unreproducible builds. We have implemented a prototype and conducted extensive experiments over 671 real-world unreproducible Debian packages in four different categories. By considering the topmost ranked file only, RepLoc achieves an accuracy rate of 47.09%. If we expand our examination to the top ten ranked files in the list produced by RepLoc, the accuracy rate becomes 79.28%. Considering that there are hundreds of source code, scripts, Makefiles, etc., in a package, RepLoc significantly reduces the scope of localizing problematic files. Moreover, with the help of RepLoc, we successfully identified and fixed six new unreproducible packages from Debian and Guix.