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Magnetic field line curvature (FLC) scattering is an effective mechanism for collisionless particle scattering. In the terrestrial magnetosphere, the FLC scattering plays an essential role in shaping the outer boundary of protons radiation belt, the rapid decay of ring current, and the formation of proton isotropic boundary (IB). However, previous studies have yet to adequately investigate the influence of FLC scattering on charged particles in the Earth's dayside magnetosphere, particularly in the off-equatorial magnetic minima regions. This study employs T89 magnetic field model to investigate the impacts of FLC scattering on ring current protons in the dayside magnetosphere, with a specific focus on the off-equatorial minimum regions. We analyze the spatial distributions of single and dual magnetic minima regions, adiabatic parameter, and pitch angle diffusion coefficients due to FLC scattering as functions of $Kp$. The results show that the effects of FLC scattering are significant not only on the dusk and dawn sides but also in the off-equatorial minima regions on the noon. Additionally, we investigate the role of dipole tilt angle in the hemispheric asymmetry of FLC scattering effects. The dipole tilt angle controls the overall displacement of the dayside magnetosphere, resulting in different FLC scattering effects in the two hemispheres. Our study holds significance for understanding the FLC scattering effects in the off-equatorial region of Earth's dayside magnetosphere and for constructing a more accurate dynamic model of particles.

We proposed two classes of multiparticle entangled states, the multigraph states and multihypergraph states, defined by unique operations on the edges and hyperedges. A key discovery is the one-to-one correspondence between the proposed multihypergraph states and the generalized real equally weighted states when d is prime. While for composite d, multihypergraph states form a subset of the generalized real equally weighted states. Meanwhile, we detailed a method for constructing real equally weighted states from hypergraph states and revealed the generalized real equally weighted states which cannot be generated from d-dimensional hypergraph states.

Recently, Zhou et al. have proposed a novel Interpolation-based (INTERP) strategy to generate the initial parameters for the Parameterized Quantum Circuit (PQC) in Quantum Approximate Optimization Algorithm (QAOA). INTERP produces the guess of the initial parameters at level $i+1$ by applying linear interpolation to the optimized parameters at level $i$, achieving better performance than random initialization (RI). Nevertheless, INTERP consumes extensive running costs for deep QAOA because it necessitates optimization at each level of the PQC. To address this problem, a Multilevel Leapfrogging Interpolation (MLI) strategy is proposed. MLI can produce the guess of the initial parameters from level $i+1$ to $i+l$ ($l>1$) at level $i$, omitting the optimization rounds from level $i+1$ to $(i+l-1)$. The final result is that MLI executes optimization at few levels rather than each level, and this operation is referred to as Multilevel Leapfrogging optimization (M-Leap). The performance of MLI is investigated on the Maxcut problem. Compared with INTERP, MLI reduces most optimization rounds. Remarkably, the simulation results demonstrate that MLI can achieve the same quasi-optima as INTERP while consuming only 1/2 of the running costs required by INTERP. In addition, for MLI, where there is no RI except for level $1$, the greedy-MLI strategy is presented. The simulation results suggest that greedy-MLI has better stability (i.e., a higher average approximation ratio) than INTERP and MLI beyond obtaining the same quasi-optima as INTERP. According to the efficiency of finding the quasi-optima, the idea of M-Leap might be extended to other training tasks, especially those requiring numerous optimizations, such as training adaptive quantum circuits.

Magnetic field line curvature (FLC) scattering is a collisionless scattering mechanism that arises when a particle's gyro-radius is comparable to the magnetic field line's curvature radius, resulting in the breaking of the conservation of the first adiabatic invariant. Studies in recent years have explored the implications of FLC scattering on the precipitation of both ring current ions and radiation belt electrons. In this work, we first compare two previous FLC scattering coefficients using test particle calculations. Then, we systematically calculate diffusion coefficients from FLC scattering in radial and MLT directions for particles of various energy levels, as well as its sensitivity to the $Kp$ index. We find that the timescale of FLC scattering is sufficient to account for the sudden loss of MeV electrons near the geostationary orbit during disturbed times. Additionally, the decay time of ring current protons is on the order of hours to minutes, providing an explanation for the ring current decay throughout the recovery phase of magnetic storms. Lastly, we compare the effects of wave-particle resonant scattering and FLC scattering in the vicinity of the midnight equator. Our findings suggest that the impacts of FLC scattering on MeV electrons or hundreds keV protons with smaller pitch angle is comparable to, or even more significant than, the effects of whistler mode or EMIC wave resonant scattering. Our quantitative results should be useful to evaluate the importance of the effects of FLC scattering while modeling the dynamics of radiation belt and ring current.

Purpose: To develop a Monte Carlo (MC) therapeutic dose calculation toolkit of a recently released ring gantry linac in Geant4 (Version 10.7) for secondary dose validation of radiotherapy plan. Methods: For the Halcyon (Varian Medical Systems), the DSMLC was modeled and radiation transport in DSMLC and patient phantom was simulated using Geant4. Radiation source was sampled from a phase space file for linac head above the DSMLC. The phase space file was obtained using a cloud-based Monte Carlo (MC) simulator, VirtuaLinac (VL) provide by Varian. Dosimetric profiles for different square field widths (2x2, 4x4, 6x6, 8x8, 10x10, 20x20, and 28x28 cm2), i.e., percent depth dose (PDD) curves and lateral profiles are simulated and compared against the experimental profiles. IMRT (intensity modulated radiation therapy) plans in two anatomical sites (prostate and brain) were also calculated using the developed toolkit and compared against the TPS calculated dose (Acuros, Eclipse 15.6). 3D dose difference and 3D gamma analysis were used to evaluate the simulation accuracy compared against the TPS calculated dose. Results: The simulated lateral dose profiles and PDD curves in water phantom match well with the measured ones for all the simulated field sizes with relative difference +-2%. For the prostate and brain IMRT plans, the simulated dose showed a good agreement with the TPS calculated dose. The 3D gamma pass rate (3%/3mm) are 98.08% and 95.4% for the two prostate and brain plans, respectively. Conclusion: The developed full MC dose calculation toolkit for Halcyon performs well in dose calculations in water phantom and patient CT phantom. The developed toolkit shows promising possibility for future secondary dose calculation for IMRT and serve as clinical quality assurance (QA) tool for Halcyon.

In frequency-division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems, 1-bit compressed sensing (CS)-based superimposed channel state information (CSI) feedback has shown many advantages, while still faces many challenges, such as low accuracy of the downlink CSI recovery and large processing delays. To overcome these drawbacks, this paper proposes a deep learning (DL) scheme to improve the 1-bit compressed sensing-based superimposed CSI feedback. On the user side, the downlink CSI is compressed with the 1-bit CS technique, superimposed on the uplink user data sequences (UL-US), and then sent back to the base station (BS). At the BS, based on the model-driven approach and assisted by the superimposition-interference cancellation technology, a multi-task detection network is first constructed for detecting both the UL-US and downlink CSI. In particular, this detection network is jointly trained to detect the UL-US and downlink CSI simultaneously, capturing a globally optimized network parameter. Then, with the recovered bits for the downlink CSI, a lightweight reconstruction scheme, which consists of an initial feature extraction of the downlink CSI with the simplified traditional method and a single hidden layer network, is utilized to reconstruct the downlink CSI with low processing delay. Compared with the 1-bit CS-based superimposed CSI feedback scheme, the proposed scheme improves the recovery accuracy of the UL-US and downlink CSI with lower processing delay and possesses robustness against parameter variations., Comment: 12 pages, 11 figures

Medical image registration is a fundamental and vital task which will affect the efficacy of many downstream clinical tasks. Deep learning (DL)-based deformable image registration (DIR) methods have been investigated, showing state-of-the-art performance. A test time optimization (TTO) technique was proposed to further improve the DL models' performance. Despite the substantial accuracy improvement with this TTO technique, there still remained some regions that exhibited large registration errors even after many TTO iterations. To mitigate this challenge, we firstly identified the reason why the TTO technique was slow, or even failed, to improve those regions' registration results. We then proposed a two-levels TTO technique, i.e., image-specific optimization (ISO) and region-specific optimization (RSO), where the region can be interactively indicated by the clinician during the registration result reviewing process. For both efficiency and accuracy, we further envisioned a three-step DL-based image registration workflow. Experimental results showed that our proposed method outperformed the conventional method qualitatively and quantitatively.