Neural networks have shown promising performance in collaborative filtering and matrix completion but the theoretical analysis is limited and there is still room for improvement in terms of the accuracy of recovering ...
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In real-world scenarios, multi-view data comprises heterogeneous features, with each feature corresponding to a specific view. The objective of multi-view semi-supervised classification is to enhance classification pe...
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We investigate atom-photon entangling gates based on cavity quantum electrodynamics (QED) for a finite photon-pulse duration, where not only the photon loss but also the temporal mode-mismatch of the photon pulse beco...
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We investigate atom-photon entangling gates based on cavity quantum electrodynamics (QED) for a finite photon-pulse duration, where not only the photon loss but also the temporal mode-mismatch of the photon pulse becomes a severe source of error. We analytically derive relations between cavity parameters, including transmittance, length, and effective cross-sectional area of the cavity, that minimize both the photon loss probability and the error rate due to temporal mode-mismatch by taking it into account as state-dependent pulse delay. We also investigate the effects of pulse distortion using numerical simulations for the case of short pulse duration. We believe that these analyses are the first to suggest that a cavity has an optimal length for the atom-photon gate, providing a fundamental guideline for implementing quantum information processing.
In this paper, we propose a Weighted Deep Ensemble Learning (WDEL) to increase the overall accuracy of face anti-spoofing model by exploiting multiple learning architectures. Current anti-spoofing models based on one ...
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In real-world scenarios, the application of reinforcement learning is significantly challenged by complex *** existing methods attempt to model changes in the environment explicitly, often requiring impractical prior ...
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In real-world scenarios, the application of reinforcement learning is significantly challenged by complex *** existing methods attempt to model changes in the environment explicitly, often requiring impractical prior knowledge of *** this paper, we propose a new perspective, positing that non-stationarity can propagate and accumulate through complex causal relationships during state transitions, thereby compounding its sophistication and affecting policy *** believe that this challenge can be more effectively addressed by implicitly tracing the causal origin of *** this end, we introduce the Causal-Origin REPresentation (COREP) *** primarily employs a guided updating mechanism to learn a stable graph representation for the state, termed as causal-origin *** leveraging this representation, the learned policy exhibits impressive resilience to *** supplement our approach with a theoretical analysis grounded in the causal interpretation for non-stationary reinforcement learning, advocating for the validity of the causal-origin *** results further demonstrate the superior performance of COREP over existing methods in tackling non-stationarity *** code is available at https://***/PKURL/COREP. Copyright 2024 by the author(s)
Secure UPI specializes in developing an advanced fraud detection gadget the usage of the effective XGBoost device getting to know set of rules to create an advanced fraud identity device. XGBoost is a properly-proper ...
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Service organized all kinds of services to one for the *** the cloud environment, the services and related QoSs (Quality of Services) in every cloud may be *** this paper, how to compose those services together in the...
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Accurate traffic flow prediction is of paramount importance. Unlike predictions centred on individual intersections, the complexity and interconnectedness of traffic flows within a road network pose unique challenges ...
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Handling missing data is crucial in machine learning, but many datasets contain gaps due to errors or non-response. Unlike traditional methods such as listwise deletion, which are simple but inadequate, the literature...
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With the rise of digital financial systems, cryptocurrencies have become a prime example of blockchain's potential. This paper presents a deep learning approach targeting the time series data of Bitcoin, which is ...
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