Efficient quantum repeaters are needed to combat photon losses in fibers in future quantum networks. Single atom coupled with photonic cavity offer a great platform for photon-atom gate. Here I propose a quantum repea...
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In this paper, we consider the problem of characterizing a robust global dependence between two brain regions where each region may contain several voxels or channels. This work is driven by experiments to investigate...
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Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Solid-state quantum memories can provide broadband storage, but they primarily suffer from low stor...
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Service systems' business processes show the existence of mutual interaction between users. Running a particular business subdomain requires the role of each user. This role is in line with the concept contained i...
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Quantum memory devices with high storage efficiency and bandwidth are essential elements for future quantum networks. Here, we report a storage efficiency greater than 28% in a Tm3+: YAG crystal in elevated temperatur...
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This work uses machine learning methods to analyze the influence of the Brazilian climate on international soybean price variability. For this purpose, climatic data, historical series of the extended national consume...
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Continual learning (CL) aims to incrementally learn multiple tasks that are presented sequentially. The significance of CL lies not only in the practical importance but also in studying the learning mechanisms of huma...
Continual learning (CL) aims to incrementally learn multiple tasks that are presented sequentially. The significance of CL lies not only in the practical importance but also in studying the learning mechanisms of humans who are excellent continual learners. While most research on CL has been done on structured data such as images, there is a lack of research on CL for abstract logical concepts such as counting, sorting, and arithmetic, which humans learn gradually over time in the real world. In this work, for the first time, we introduce novel algorithmic reasoning (AR) methodology for continual tasks of abstract concepts: CLeAR. Our methodology proposes a one-to-many mapping of input distribution to a shared mapping space, which allows the alignment of various tasks of different dimensions and shared semantics. Our tasks of abstract logical concepts, in the form of formal language, can be classified into Chomsky hierarchies based on their difficulty. In this study, we conducted extensive experiments consisting of 15 tasks with various levels of Chomsky hierarchy, ranging from in-hierarchy to inter-hierarchy scenarios. CLeAR not only achieved near zero forgetting but also improved accuracy during following tasks, a phenomenon known as backward transfer, while previous CL methods designed for image classification drastically failed.
Puerto Rico's electrical infrastructure powers over three million residents, yet energy consumption overcomes generation and continues to rise. This imbalance results in an unreliable power supply and frequent bla...
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This working-in-progress paper aims to present a three-dimensional reconstruction using aerial images in different environments. The experiments were conducted with aircraft in both external and internal settings, sta...
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Our surroundings' auditory landscapes are a wealth of knowledge, providing insights into a range of outdoor pursuits. The automatic classification of these actions using audio event detection and classification (A...
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