We present the detailed fundamental stellar parameters of the close visual binary system HD 39438 for the first time. We used Al-Wardat's method for analyzing binary and multiple stellar systems. The method implem...
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We present the detailed fundamental stellar parameters of the close visual binary system HD 39438 for the first time. We used Al-Wardat's method for analyzing binary and multiple stellar systems. The method implements Kurucz's plane parallel model atmospheres to construct synthetic spectral energy distributions(SEDs) for both components of the system. It then combines the results of the spectroscopic analysis with the photometric analysis and compares them with the observed ones to construct the best synthetic SED for the combined system. The analysis gives the precise fundamental parameters of the individual components of the system. Based on the positions of the components of HD 39438 on the H-R diagram, and evolutionary and isochrone tracks, we found that the system belongs to the main sequence stars with masses of 1.24 and 0.98 solar masses for the components A and B, respectively, and age of 1.995 Gyr for both components. The main result of HD 39438 is new dynamical parallax, which is estimated to be 16.689 ± 0.03 mas.
A first result related to a CPT test in the decay of ortho-positronium based on data collected by means of the J-PET detector is already available,1 and a new test of CP symmetry is under investigation.2 Both results ...
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The Chern-Simons gravitational term during inflation is usually coupled to the inflaton field. The resulting theory suffers from ghost-field formation in the tensor sector, which limits the observational effects of P-...
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Primordial non-Gaussianities are key quantities to test early universe scenarios. In this paper, we compute full bispectra of scalar and tensor perturbations generated during a contracting phase in a general bounce mo...
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We present CMTJ—a simulation package for large-scale macrospin analysis of multilayer spintronics *** from conventional simulations,such as magnetoresistance and magnetisation hysteresis loops,CMTJ implements a mathe...
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We present CMTJ—a simulation package for large-scale macrospin analysis of multilayer spintronics *** from conventional simulations,such as magnetoresistance and magnetisation hysteresis loops,CMTJ implements a mathematical model of dynamic experimental techniques commonly used for spintronics devices characterisation,for instance:spin diode ferromagnetic resonance,pulse-induced microwave magnetometry,or harmonic Hall voltage *** find that macrospin simulations offer a satisfactory level of agreement,demonstrated by a variety of *** a unified simulation package,CMTJ aims to accelerate wide-range parameter search in the process of optimising spintronics devices.
This study investigates dijet and neutral pion production in high-energy nuclear collisions in the LHCb detector. The obtained measurements offer crucial insights into Quantum Chromodynamics (QCD), understanding of pa...
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The primary focus of the LHC experiments was the observation of Standard Model particles and the search for unexplored signatures indicative of New physics. Given the current discoveries and measurements done so far, ...
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A bang-bang (BB) algorithm prepares the ground state of a two-dimensional (2D) quantum many-body Hamiltonian H=H1+H2 by evolving an initial product state alternating between H1 and H2. We use the neighborhood tensor u...
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A bang-bang (BB) algorithm prepares the ground state of a two-dimensional (2D) quantum many-body Hamiltonian H=H1+H2 by evolving an initial product state alternating between H1 and H2. We use the neighborhood tensor update to simulate the BB evolution with an infinite pair-entangled projected state (iPEPS). The alternating sequence is optimized with the final energy as a cost function. The energy is calculated with a tangent space power method for the sake of its stability. The method is benchmarked in the 2D transverse field quantum Ising model near its quantum critical point against a ground state obtained with variational optimization of the iPEPS. The optimal BB sequence differs nonperturbatively from a sequence simulating quantum annealing or adiabatic preparation (AP) of the ground state. The optimal BB energy converges with the number of bangs much faster than the optimal AP energy.
Lensless imaging techniques have been developed to visualize objects with high robustness and unprecedented resolution. Lensless imaging is based on the numerical reconstruction of the transmission or reflection funct...
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Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data st...
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Optimizing camera information storage is a critical issue due to the increasing data volume and a large number of daily surveillance videos. In this study, we propose a deep learning-based system for efficient data storage. Videos captured by cameras are classified into four categories: no action, normal action, human action, and dangerous action. Videos without action or with normal action are stored temporarily and then deleted to save storage space. Videos with human action are stored for easy retrieval, while videos with dangerous action are promptly alerted to users. In the paper, we propose two approaches using deep learning models to address the video classification problem. The first approach is a separate approach, where pretrained CNN models extract features from video frame images. These features are then passed through RNN, Transformer models to extract relationships between them. The goal of this approach is to delve into extracting features of objects in the video. The proposed models include VGG16, InceptionV3 combined with LSTM, BiLSTM, Attention, and Vision Transformer. The next approach combines CNN and LSTM layers simultaneously through models like ConvLSTM and LRCN. This approach aims to help the model simultaneously extract object features and their relationships, with the goal of reducing model size, accelerating the training process, and increasing object recognition speed when deployed in the system. In Approach 1, we construct and refine network architectures such as VGG16+LSTM, VGG16+Attention+LSTM, VGG16+BiLSTM, VGG16+ViT, InceptionV3+LSTM, InceptionV3+Attention+LSTM, InceptionV3+BiLSTM. In Approach 2, we build a new network architecture based on the ConvLSTM and LRCN model. The training dataset, collected from real surveillance cameras, comprises 3315 videos labeled into four classes: no action (1018 videos), actions involving people (832 videos), dangerous actions (751 videos), and normal actions (714 videos). Experimental results show t
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