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The present paper proposes a robust evaluation of any radial density at small distances using negative-order radial moments evaluated in momentum space. This evaluation provides a valuable insight into the behavior of a given radial density in the vicinity of $r=0$, and puts strong emphasis on the importance of measuring form factors at large squared four-momentum transfer, a domain essential for the determination of negative order moments. A specific attention is paid to the regularization scheme directly affecting the numerical determination of the radial density's parametrization. The proposed method is applied to non-relativistic study cases of the nucleon electric ($G_{En}, G_{Ep}$), and proton magnetic $G_{Mp}$ form factors. The validation is performed through comparison of the results of the approach to the analytically determined Maclaurin expansion - in the vicinity of $r=0$ - of the radial density function. The method is also applied to the relativistic Dirac form factor $F_1$ of the proton. In such a non-trivial case, the Maclaurin development might not exist for the radial density, rendering the determination from the proposed method extremely important., Comment: 11 pages, 8 plots

Domain generalization achieves fault diagnosis on unseen modes. In process industrial systems, fault samples are limited, and only single-mode fault data can be obtained. Extracting domain-invariant fault features from single-mode data for unseen mode fault diagnosis poses challenges. Existing methods utilize a generator module to simulate samples of unseen modes. However, multi-mode samples contain complex spatiotemporal information, which brings significant difficulties to accurate sample generation. Therefore, double gradient reversal network (DGRN) is proposed. First, the model is pre-trained to acquire fault knowledge from the single seen mode. Then, pseudo-fault feature generation strategy is designed by Adaptive instance normalization, to simulate fault features of unseen mode. The dual adversarial training strategy is created to enhance the diversity of pseudo-fault features, which models unseen modes with significant distribution differences. Subsequently, domain-invariant feature extraction strategy is constructed by contrastive learning and adversarial learning. This strategy extracts common features of faults and helps multi-mode fault diagnosis. Finally, the experiments were conducted on Tennessee Eastman process and continuous stirred-tank reactor. The experiments demonstrate that DGRN achieves high classification accuracy on unseen modes while maintaining a small model size.

This paper addresses the challenge of identifying the paths for vessels with operating routes of repetitive paths, partially repetitive paths, and new paths. We propose a spatial clustering approach for labeling the vessel paths by using only position information. We develop a path clustering framework employing two methods: a distance-based path modeling and a likelihood estimation method. The former enhances the accuracy of path clustering through the integration of unsupervised machine learning techniques, while the latter focuses on likelihood-based path modeling and introduces segmentation for a more detailed analysis. The result findings highlight the superior performance and efficiency of the developed approach, as both methods for clustering vessel paths into five classes achieve a perfect F1-score. The approach aims to offer valuable insights for route planning, ultimately contributing to improving safety and efficiency in maritime transportation., Comment: Keywords: Spatial clustering, vessel path identification, maritime transportation, average nearest neighbor distance, hierarchical clustering, likelihood estimation. This preprint has 12 pages, 14 figures, 2 tables

At present, decision making solutions developed based on deep learning (DL) models have received extensive attention in predictive maintenance (PM) applications along with the rapid improvement of computing power. Relying on the superior properties of shared weights and spatial pooling, Convolutional Neural Network (CNN) can learn effective representations of health states from industrial data. Many developed CNN-based schemes, such as advanced CNNs that introduce residual learning and multi-scale learning, have shown good performance in health state recognition tasks under the assumption that all the classes are known. However, these schemes have no ability to deal with new abnormal samples that belong to state classes not part of the training set. In this paper, a collective decision framework for different CNNs is proposed. It is based on a One-vs-Rest network (OVRN) to simultaneously achieve classification of known and unknown health states. OVRN learn state-specific discriminative features and enhance the ability to reject new abnormal samples incorporated to different CNNs. According to the validation results on the public dataset of Tennessee Eastman Process (TEP), the proposed CNN-based decision schemes incorporating OVRN have outstanding recognition ability for samples of unknown heath states, while maintaining satisfactory accuracy on known states. The results show that the new DL framework outperforms conventional CNNs, and the one based on residual and multi-scale learning has the best overall performance.

The charge radius of the proton can be determined using two different kinds of experiments: the spectroscopy technique, measuring the hyperfine structure of hydrogen atoms, and the scattering technique, deducing the radius from elastic lepton scattering off a proton target. These two methods lead to quite different results, a discrepancy known as the "proton radius puzzle ". To shed light on this problem, we have proposed a novel method for the determination of spatial moments from densities expressed in the momentum space. This method provides a direct access not only to the second order moment, directly related to the proton radius, but to all moments of any real order larger than -3. The method is applied to the global analysis of proton electric form factor experimental data from Rosenbluth separation and low-$Q^2$ experiments, paying specific attention to the evaluation of the systematic errors. Within this analysis, the integer order moments of the proton charge density are evaluated, the moment of second order leading to a new determination of the proton charge radius., Comment: 12 pages, 2 figures, Proceedings of the 40th International Workshop on Nuclear Theory (IWNT), Rila Mountains, July 2023

The parameterization of the nucleon structure through Generalized Parton Distributions (GPDs) shed a new light on the nucleon internal dynamics. For its direct interpretation, Deeply Virtual Compton Scattering (DVCS) is the golden channel for GPDs investigation. The DVCS process interferes with the Bethe-Heitler (BH) mechanism to constitute the leading order amplitude of the $eN \to eN\gamma$ process. The study of the $ep\gamma$ reaction with polarized positron and electron beams gives a complete set of unique observables to unravel the different contributions to the $ep \gamma$ cross section. This separates the different reaction amplitudes, providing a direct access to their real and imaginary parts which procures crucial constraints on the model dependences and associated systematic uncertainties on GPDs extraction. The real part of the BH-DVCS interference amplitude is particularly sensitive to the $D$-term which parameterizes the Gravitational Form Factors of the nucleon. The separation of the imaginary parts of the interference and DVCS amplitudes provides insights on possible higher-twist effects. We propose to measure the unpolarized and polarized Beam Charge Asymmetries (BCAs) of the $\vec{e}^{\pm}p \to e^{\pm}p \gamma$ process on an unpolarized hydrogen target with {\tt CLAS12}, using polarized positron and electron beams at 10.6 GeV. The azimuthal and $t$-dependences of the unpolarized and polarized BCAs will be measured over a large $(x_B,Q^2)$ phase space using a 100 day run with a luminosity of 0.66$\times 10^{35}$cm$^{-2}\cdot$s$^{-1}$., Comment: Proposal to the Jefferson Lab Program Advisory Committee (PAC51)

A global analysis of proton electric form factor experimental data from Rosenbluth separation and low squared four-momentum transfer experiments is discussed for the evaluation of the spatial moments of the proton charge density based on the recently published integral method~\cite{Hob20}. Specific attention is paid to the evaluation of the systematic errors of the method, particularly the sensitivity to the choice of the mathematical expression of the form factor fitting function. Within this comprehensive analysis of proton electric form factor data, the moments of the proton charge density are determined for integer order moments, particularly: $\langle r^2 \rangle$=0.682(02)$_{Sta.}$(11)$_{Sys.}$~fm$^2$, $\langle r^3 \rangle$=0.797(10)$_{Sta.}$(58)$_{Sys.}$~fm$^3$, and $\langle r^4 \rangle$=1.02(05)$_{Sta.}$(31)$_{Sys.}$~fm$^4$., Comment: 10 pages, 3 figures

We compute the decays ${B\to D^\ast_0}$ and ${B\to D^\ast_2}$ with finite masses for the $b$ and $c$ quarks. We first discuss the spectral properties of both the $B$ meson as a function of its momentum and of the $D^\ast_0$ and $D^\ast_2$ at rest. We compute the theoretical formulae leading to the decay amplitudes from the three-point and two-point correlators. We then compute the amplitudes at zero recoil of ${B\to D^\ast_0}$ which turns out not to be vanishing contrary to what happens in the heavy quark limit. This opens a possibility to get a better agreement with experiment. To improve the continuum limit we have added a set of data with smaller lattice spacing. The ${B\to D^\ast_2}$ vanishes at zero recoil and we show a convincing signal but only slightly more than 1 sigma from 0. In order to reach quantitatively significant results, we plan to fully exploit smaller lattice spacings as well as another lattice regularization., Comment: 31 pages with 15 figures ; sections 5 and 6 revised and updated