Smooth Perturbations to Rényi Entropy by Andrew Buchanan

“…If the perturbed state is Gaussian with smoothly varying correlation functions and the perturbation parameter has units of energy, I show the coefficients for Rényi entropy are analytically computable for all values of the Rényi parameter α in odd dimensions and for integer α in even dimensions. …”
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Improving Sharpness-Aware Minimization Using Label Smoothing and Adaptive Adversarial Cross-Entropy Loss by Tanapat Ratchatorn, Masayuki Tanaka

“…Together, this combination of the AACE loss function and label smoothing technique leads to further improvement in model performance. …”
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An Efficient Provably Secure Verifier-Based Three-Factor Authentication Technique Using PDL for Data Exchange in TMIS by Vishesh P. Gaikwad, Jitendra V. Tembhurne, Chandrashekhar Meshram, Cheng-Chi Lee, Chun-Ta Li

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Optimum Achievable Rates in Two Random Number Generation Problems with <i>f</i>-Divergences Using Smooth Rényi Entropy by Ryo Nomura, Hideki Yagi

“…In each of these problems, the optimum achievable rate with respect to the given approximation measure is one of our main concerns and has been characterized using two different information quantities: the information spectrum and the smooth Rényi entropy. …”
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Submarine Topography Classification Using ConDenseNet with Label Smoothing Regularization by Jingyan Zhang, Kongwen Zhang, Jiangtao Liu

“…In this study, we propose a novel approach that integrates pruning-enhanced ConDenseNet with label smoothing regularization to reduce misclassification, strengthen the cross-entropy loss function, and significantly lower model complexity. …”
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New multifactor spatial prediction method based on Bayesian maximum entropy by YANG Yong, ZHANG Chutian, HE Liyuan

“…Compared with ordinary Kriging, the method proposed in this study has solid theoretical foundation, as it integrates theories of Kriging, environmental relation, and maximum entropy. …”
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Ethernet Passive Mutual Authentication Scheme on Quantum Networks by Jianuo Tian, Panke Qin, Zongqu Zhao, Baodong Qin

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From Signal to Safety: A Data-Driven Dual Denoising Model for Reliable Assessment of Blasting Vibration Impacts by Miao Sun, Jing Wu, Junkai Yang, Li Wu, Yani Lu, Hang Zhou

“…High-frequency noise is then handled by controlling the multi-scale permutation entropy (MPE) of the intrinsic mode functions (IMF) obtained from EP-CEEMDAN. …”
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ET-Mamba: A Mamba Model for Encrypted Traffic Classification by Jian Xu, Liangbing Chen, Wenqian Xu, Longxuan Dai, Chenxi Wang, Lei Hu

“…During the fine-tuning phase, the agent attention mechanism is adopted in the feature extraction phase to achieve global information modeling at a low computational cost, and the SmoothLoss function is designed to solve the problem of the insufficient generalization ability of cross-entropy loss function during training. …”
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AFMF: adaptive fusion of multi-hop neighborhood features in graph convolutional network by Kang Liu, Xinyu Li, Lian Liu, Zhihao Xv, Aohang Pei, Runshi Ji

“…Finally, the feature fusion module (FF) applies the adaptively generated weights to fuse the multi-hop neighborhood features of each node to get the final node representation. The cross entropy loss function drives the network to preserve critical information. …”
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State Diagnosis of Elevator Control Transformer over Vibration Signal Based on MEA-BP Neural Network by QingHui Song, QingJun Song, Linjing Xiao, HaiYan Jiang, LiNa Li

“…Firstly, the collected signal is smoothed by EMD, the intrinsic mode function (IMF) components with large noise are determined according to the correlation coefficient, the wavelet adaptive threshold denoising algorithm is used to process the noisy IMF components, and the IMF components before and after processing and its residual component are reconstructed to obtain the denoised signal. …”
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A hybrid model for short-term offshore wind power prediction combining Kepler optimization algorithm with variational mode decomposition and stochastic configuration networks by Bingbing Yu, Yonggang Wang, Jun Wang, Yuanchu Ma, Wenpeng Li, Weigang Zheng

“…Subsequently, permutation entropy (PE) is employed to order and reconstruct the decomposed wind power subsequences, with the selection of input features by the maximal information coefficient (MIC) and autocorrelation function (ACF). …”
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Stacked-cup multiwall carbon nanotubes as components of energy-intensive suspension jet fuels by L. S. Karpushenkava, G. Ya. Kabo, A. V. Blokhin

“…The energy and enthalpy of combustion of a technical sample of MWCNTs in the crystalline state were determined. Based on the smoothed heat capacity values, the standard thermodynamic functions (enthalpy, entropy, and Gibbs reduced energy) of MWCNTs in the crystalline state were obtained in a temperature range of 0–2000 K. …”
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