In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutio...
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In the past decade, thanks to the powerfulness of deep-learning techniques, we have witnessed a whole new era of automated code generation. To sort out developments, we have conducted a comprehensive review of solutions to deep learning-based code generation. In this survey, we generally formalize the pipeline and procedure of code generation and categorize existing solutions according to taxonomy from perspectives of architecture, model-agnostic enhancing strategy, metrics, and tasks. In addition, we outline the challenges faced by current dominant large models and list several plausible directions for future research. We hope that this survey may provide handy guidance to understanding, utilizing, and developing deep learning-based code-generation techniques for researchers and practitioners.
In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relat...
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In multiagent systems,agents usually do not have complete information of the whole system,which makes the analysis of such systems *** incompleteness of information is normally modelled by means of accessibility relations,and the schedulers consistent with such relations are called *** this paper,we consider probabilistic multiagent systems with accessibility relations and focus on the model checking problem with respect to the probabilistic epistemic temporal logic,which can specify both temporal and epistemic ***,the problem is undecidable in *** show that it becomes decidable when restricted to memoryless uniform ***,we present two algorithms for this case:one reduces the model checking problem into a mixed integer non-linear programming(MINLP)problem,which can then be solved by Satisfiability Modulo Theories(SMT)solvers,and the other is an approximate algorithm based on the upper confidence bounds applied to trees(UCT)algorithm,which can return a result whenever *** algorithms have been implemented in an existing model checker and then validated on *** experimental results show the efficiency and extendability of these algorithms,and the algorithm based on UCT outperforms the one based on MINLP in most cases.
Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to superv...
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Hierarchical Reinforcement Learning (HRL) is promising to tackle the long-term sparse reward problem. However, goal conditioned HRL, which decomposes the goal into a series of sub-goals, suffers from sub-goal search i...
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Green characteristics are pivotal to risk transmission. We use environmental, social and governance (ESG) monetized accounting to measure green screening of stock-bond indices, and decompose risk transmission into com...
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Green characteristics are pivotal to risk transmission. We use environmental, social and governance (ESG) monetized accounting to measure green screening of stock-bond indices, and decompose risk transmission into comovement, contagion and hedging effects via a patched dependence structure model. Mechanisms through which green screening impacts three types of risk transmission effects are elucidated. Results indicate that high level of green screening is associated with significantly reduced comovement and contagion effects while hedging effects. Market sentiment factors such as interest rates, economic policy uncertainty and consumer confidence are identified as crucial channels. These results are further tested to be robust.
Modern neural networks are known to give overconfident predictions for out-of-distribution inputs when deployed in the open world. It is common practice to leverage a surrogate outlier dataset to regularize the model ...
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Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sh...
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Developing large,soft grippers with high omnidirectional load(above 40 kg)has always been *** address this challenge by developing a powerful soft gripper that can grasp the human body based on a soft-enclosed graspin...
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Developing large,soft grippers with high omnidirectional load(above 40 kg)has always been *** address this challenge by developing a powerful soft gripper that can grasp the human body based on a soft-enclosed grasping structure and a soft-rigid coupling *** envelope size of the proposed soft gripper is 611.6 mm×559 mm×490.7 mm,the maximum grasping size is 417 mm,and the payload on the human body is more than 90 kg,which has exceeded most existing soft ***,the grasping force prediction of the gripper is achieved through theoretical *** primary contribution of this work is to overcome the size and payload limits of current soft grippers and implement a human-grasping experiment based on the soft-grasping method.
Nowadays, artificial intelligence-based tasks are imposing increasing demands on computation resources and energy consumption. Unmanned aerial vehicles (UAVs) are widely utilized in mobile edge computing (MEC) due to ...
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The emergence of large language models (LLMs) has increasingly drawn attention to the use of LLMs for human-like planning. Existing work on LLM-based planning either focuses on leveraging the inherent language generat...
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