Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrot...
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High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to dat...
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High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.
In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.
In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.
The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of ...
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In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queri...
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In this paper,we consider skyline queries in a mobile and distributed environment,where data objects are distributed in some sites(database servers)which are interconnected through a high-speed wired network,and queries are issued by mobile units(laptop,cell phone,etc.)which access the data objects of database servers by wireless *** inherent properties of mobile computing environment such as mobility,limited wireless bandwidth,frequent disconnection,make skyline queries more *** show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach,called efficient distributed skyline based on mobile computing(EDS-MC).In EDS-MC,a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication,network delay and query response *** conduct extensive experiments in a simulated mobile database system,and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.
In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites (database servers) which are interconnected through a high-speed wired network, an...
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In this paper, we consider skyline queries in a mobile and distributed environment, where data objects are distributed in some sites (database servers) which are interconnected through a high-speed wired network, and queries are issued by mobile units (laptop, cell phone, etc.) which access the data objects of database servers by wireless channels. The inherent properties of mobile computing environment such as mobility, limited wireless bandwidth, frequent disconnection, make skyline queries more complicated. We show how to efficiently perform distributed skyline queries in a mobile environment and propose a skyline query processing approach, called efficient distributed skyline based on mobile computing (EDS-MC). In EDS-MC, a distributed skyline query is decomposed into five processing phases and each phase is elaborately designed in order to reduce the network communication, network delay and query response time. We conduct extensive experiments in a simulated mobile database system, and the experimental results demonstrate the superiority of EDS-MC over other skyline query processing techniques on mobile computing.
With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However,...
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With the help of 5G network, edge intelligence (EI) can not only provide distributed, low-latency, and high-reliable intelligent services, but also enable intelligent maintenance and management of smart city. However, the constantly changing available computing resources of end devices and edge servers cannot continuously guarantee the performance of intelligent inference. In order to guarantee the sustainability of intelligent services in smart city, we propose the Adaptive Model Selection and Partition Mechanism (AMSPM) in 5G smart city where EI provides services, which mainly consists of Adaptive Model Selection (AMS) and Adaptive Model Partition (AMP). In AMSPM, the model selection and partition of deep neural network (DNN) are formulated as an optimization problem. Firstly, we propose a recursive-based algorithm named AMS based on the computing resources of edge devices to derive an appropriate DNN model that satisfies the latency demand of intelligent services. Then, we adaptively partition the selected DNN model according to the computing resources of edge devices. The experimental results demonstrate that, when compared with state-of-the-art model selection and partition mechanisms, AMSPM not only reduces latency but also enhances computing resource utilization.
Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significa...
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Artificial intelligence (AI) empowered edge computing has given rise to a new paradigm and effectively facilitated the promotion and development of multimedia applications. The speech assistant is one of the significant services provided by multimedia applications, which aims to offer intelligent interactive experiences between humans and machines. However, malicious attackers may exploit spoofed speeches to deceive speech assistants, posing great challenges to the security of multimedia applications. The limited resources of multimedia terminal devices hinder their ability to effectively load speech spoofing detection models. Furthermore, processing and analyzing speech in the cloud can result in poor real-time performance and potential privacy risks. Existing speech spoofing detection methods rely heavily on annotated data and exhibit poor generalization capabilities for unseen spoofed speeches. To address these challenges, this paper first proposes the Coordinate Attention Network (CA2Net) that consists of coordinate attention blocks and Res2Net blocks. CA2Net can simultaneously extract temporal and spectral speech feature information and represent multi-scale speech features at a granularity level. Besides, a contrastive learning-based speech spoofing detection framework named GEMINI is proposed. GEMINI can be effectively deployed on edge nodes and autonomously learn speech features with strong generalization capabilities. GEMINI first performs data augmentation on speech signals and extracts conventional acoustic features to enhance the feature robustness. Subsequently, GEMINI utilizes the proposed CA2Net to further explore the discriminative speech features. Then, a tensor-based multi-attention comparison model is employed to maximize the consistency between speech contexts. GEMINI continuously updates CA2Net with contrastive learning, which enables CA2Net to effectively represent speech signals and accurately detect spoofed speeches. Extensive experiments on
When intelligent agents act in a stochastic environment, the principle of maximizing expected rewards is used to optimize their policies. The rationality of the maximum rewards becomes a single objective when agents’...
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When intelligent agents act in a stochastic environment, the principle of maximizing expected rewards is used to optimize their policies. The rationality of the maximum rewards becomes a single objective when agents’ decision problems are solved in most cases. This sometimes leads to the agents’ behaviors (the optimal policies for solving the decision problems) that are not legible. In other words, it is difficult for users (or other agents and even humans) to understand the agents’ intentions when they are executing the optimal policies. Hence, it becomes pertinent to consider the legibility of agents’ decision problems. The key challenge lies in formulating a proper legibility function in the problems. Using domain experts’ inputs leans to be subjective and inconsistent in specifying legibility values, and the manual approach quickly becomes infeasible in a complex problem domain. In this article, we aim to learn such a legibility function parallel to developing a (conventional) reward function. We adopt inverse reinforcement learning techniques to automate a legibility function in agents’ decision problems. We first demonstrate the effectiveness of the inverse reinforcement learning technique when legibility is solely considered in a decision problem. Things become complicated when both the reward and legibility functions are to be found. We develop a multi-objective inverse reinforcement learning method to automate the two functions in a good balance simultaneously. We vary problem domains in the performance study and provide empirical results in support.
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