Translation Vector Research Articles

Package Positioning Based on Point Registration Network DCDNet-Att

The application of robot technology in the automatic transportation process of packaging bags is becoming increasingly common. Point cloud registration is the key to applying industrial robots to automatic transportation systems. However, current point cloud registration models cannot effectively solve the registration of deformed targets like packaging bags. In this study, a new point cloud registration network, DCDNet-Att, is proposed, which uses a variable weight dynamic graph convolution module to extract point cloud features. A feature interaction module is used to extract common features between the source point cloud and the template point cloud. The same geometric features between the two pairs of point clouds are strengthened through a bottleneck module. A channel attention model is used to obtain the channel attention weights. The attention weight of each spatial position is calculated, and a rotation translation structure is used to sequentially obtain quaternions and translation vectors. A feature fitting loss function is used to constrain the parameters of the neural network model to have a larger receptive field. Compared with seven methods, including the ICP algorithm, GO-ICP algorithm, and FGR algorithm, the proposed method had rotation errors (MAE, RMSE, and Error of 1.458, 2.541, and 1.024 in the ModelNet40 dataset, respectively) and translation errors (MAE, RMSE, and Error of 0.0048, 0.0114, and 0.0174, respectively). When registering the ModelNet40 dataset with Gaussian noise, the rotation errors (MAE, RMSE, and Error) were 2.028, 3.437, and 2.478, respectively, and the translation errors (MAE, RMSE, and Error) were 0.0107, 0.0327, and 0.0285, respectively. The experimental results were superior to those of the other methods, and the model was effective at registering packaging bag point clouds.

Introducing a novel sub‐millimeter lung CT image registration error quantitation tool

AbstractBackgroundLung computed tomography (CT) scan image registration is being used for lung function analysis such as ventilation. Given the high sensitivity of functional analyses to image registration errors, an image registration error scoring tool that can measure submillimeter image registration errors is needed.PurposeTo propose an image registration error scoring tool, termed λ, whose spatial sensitivity can be used to quantify image registration errors in steep image gradient regions under realistic noise conditions.Methodsλ compares two images, termed reference and evaluated. The HU and distance scales of both images are normalized by user‐selected scaling criteria. For each voxel in the reference image, the 4D Euclidian distances between the reference voxel and the nearby evaluated voxels are calculated, and the minimum of these distances is . We tested in simulated individual blood vessels comprised of 1, 3, and 5 mm diameter cylinders in 1 × 1 × 1 mm3 voxel images, which were blurred to simulate CT scanner intrinsic resolution and volume averaging. We placed the simulated vessels in a homogeneous background simulating parenchymal tissue density and injected 20, 40, and 60 HU standard deviation Gaussian noise. We used isotropic Gaussian filters with 0.5, 1.0, and 1.5 mm standard deviation kernels to smooth the simulated images. We assessed using reference‐evaluated vessel shifts of −1.0 to 1.0 mm in 0.05 mm steps via rigid translational and rotational deformations. We examined whether tracked the translation vector via its internal spatial component. We restricted to voxels using the angle, termed , between the vector and the normalized spatial‐distance axes, terming the results the restricted‐, , where was hypothesized to be a proxy for image gradient. We determined whether was coincident with the image gradient by examining if the voxels with tracked the evaluated vessels. We used the 95th percentile of , , to determine spatial sensitivity, which we took as a conservative estimate of registration error, by fitting to a modified absolute‐value function for each tested rigid translation, noise level, smoothing kernel, and vessel radius combination. We demonstrated the use of on a clinical example consisting of a set of 25 deformably registered free‐breathing thoracic CT scans. We visually compared the and results against the HU differences between each clinical image pair.ResultsWe found θ to be coincident with the image gradient. We found that ’s spatial component tracked the vessel shifts. We determined the spatial sensitivity limit of to be < 0.2 mm. The noise level and smoothing kernel influenced sensitivity, worsening with increasing noise, and improving with increasing smoothing. For the clinical images, we observed to qualitatively match the absolute difference of intensity in the image pairs and to restrict itself to high gradient regions or regions of visually apparent errors.Conclusion detected sub‐millimeter positioning errors between simulated vessels in the presence of typical CT noise. The noise magnitude and choice of noise smoothing kernel were inversely related to sensitivity, implying that study‐specific tuning of the pre‐smoothing kernel may be required. The demonstrated ability in geometric tests of to detect subvoxel DIR errors warrants further evaluation and testing.

An Automatic Solution for Registration Between Single-Image and Point Cloud in Manhattan World Using Line Primitives

2D-3D registration is increasingly being applied in various scientific and engineering scenarios. However, due to appearance differences and cross-modal discrepancies, it is demanding for image and point cloud registration methods to establish correspondences, making 2D-3D registration highly challenging. To handle these problems, we propose a novel and automatic solution for 2D-3D registration in Manhattan world based on line primitives, which we denote as VPPnL. Firstly, we derive the rotation matrix candidates by establishing the vanishing point coordinate system as the link of point cloud principal directions to camera coordinate system. Subsequently, the RANSAC algorithm, which accounts for the clustering of parallel lines, is employed in conjunction with the least-squares method for translation vectors estimation and optimization. Finally, a nonlinear least-squares graph optimization method is carried out to optimize the camera pose and realize the 2D-3D registration and point colorization. Experiments on synthetic data and real-world data illustrate that our proposed algorithm can address the problem of 2D-3D direct registration in the case of Manhattan scenes where images are limited and sparse.

Non-replicative herpes simplex virus genomic and amplicon vectors for gene therapy - an update.

Two major types of defective vectors have been derived from herpes simplex virus type 1 (HSV-1), non-replicative genomic vectors (nrHSV-1), and amplicon vectors. This review recapitulates the main features of both vector types and summarizes recent improvements in our understanding of virus/vector biology, particularly with regard to the critical role played by the overpowering of antiviral cellular defenses and the epigenetic control of viral gene expression. Over the past years, significant breakthroughs in vector design, genetic engineering, and HSV-1 biology have accelerated the development of nrHSV-1 vectors. The low immunogenicity and enhanced safety profiles allowed the successful translation of these vectors into several clinical trials, with some being approved by the FDA. Regarding amplicons, despite their advantage in carrying very large or multiple transgenes, and their potential to avoid genome dilution in dividing cells, the absence of production procedures capable of generating large amounts of helper-free amplicons at reasonable cost with GMP compliance, still limits the translation of these outstanding vectors to clinical trials.

A new mesh deformation method using quaternion and displacement normal propagation for high quality and high efficiency

Mesh deformation technology is widely used in aerodynamic applications like unsteady flow, aeroelasticity, and aerodynamic shape optimization because of its low computational costs and consistent mesh connectivity. In order to raise deformed mesh quality and improve efficiency, a new mesh deformation method based on quaternion and displacement normal propagation (named QN method) is introduced in this paper. The boundary points propagate their displacements composed of translational vectors and quaternions to corresponding volume points along the normal direction under the control of the damping function, which preserves the mesh shape and guarantees the quality near boundaries, including orthogonality and normal size. It can also prevent the volume points from being interfered by other less relevant boundary points, so dealing with complex displacement fields effectively. In addition, it avoids complicated matrix and interpolation operations, thus saving lots of computational costs. For mesh with complex topology, a hybrid method combining the QN method and the radial basis function method (RBF method) is investigated to broaden application scenarios, which are applied to the mesh with normal correspondence inside the boundary layer and the mesh outside, respectively. Benefitting from effective handling for the near-wall elements by the QN method, the RBF interpolation part in the hybrid method requires minor support points to carry out valid large deformation, improving the deformation efficiency greatly compared to the individual RBF method. Five typical test cases with different deformation modes and mesh characteristics are implemented, showing better performance of the proposed method in deformed mesh quality and deformation efficiency.

An Intelligent Proofreading for Remote Skiing Actions Based on Variable Shape Basis

AbstractThe current proofreading algorithms for action regulation mainly recover the 3D structure and action information of non-rigid objects from image sequences by factorization. Most of algorithms assume that the camera model is an affine model. This assumption only holds if the size and depth of the object change very little relative to the distance from the object to the camera, which is in the case of fixed-shape basis. When the object is very close to the camera, this assumption causes a large reconstruction error. This paper solves this problem by the intelligent proofreading algorithms for remote skiing teaching actions based on variable shape basis. Firstly, the improved Retinex algorithm is used to enhance the multi-frame video images of skiing actions to make the action details more prominent. Then, measurement matrix is calculated after eliminating the translation vector by coordinate transformation. Under the condition of rank constraint, the measurement matrix is decomposed by singular value decomposition algorithm, and the correct shape basis structure of 3D action features can be obtained by using the variable shape basis. Finally, by randomly initializing a parameter, the optimized parameter and the least square algorithm are used to optimize the randomly initialized parameter further. The iteration until the convergence of the objective function can be used to calculate the deformation degree of the actions. The test results show that this algorithm improves the proofreading accuracy of action regulation in skiing teaching, and the proofreading results of various uploaded sliding actions are correct, which can be applied to remote skiing teaching and community learning.

Globally Optimal Relative Pose and Scale Estimation from Only Image Correspondences with Known Vertical Direction

Installing multi-camera systems and inertial measurement units (IMUs) in self-driving cars, micro aerial vehicles, and robots is becoming increasingly common. An IMU provides the vertical direction, allowing coordinate frames to be aligned in a common direction. The degrees of freedom (DOFs) of the rotation matrix are reduced from 3 to 1. In this paper, we propose a globally optimal solver to calculate the relative poses and scale of generalized cameras with a known vertical direction. First, the cost function is established to minimize algebraic error in the least-squares sense. Then, the cost function is transformed into two polynomials with only two unknowns. Finally, the eigenvalue method is used to solve the relative rotation angle. The performance of the proposed method is verified on both simulated and KITTI datasets. Experiments show that our method is more accurate than the existing state-of-the-art solver in estimating the relative pose and scale. Compared to the best method among the comparison methods, the method proposed in this paper reduces the rotation matrix error, translation vector error, and scale error by 53%, 67%, and 90%, respectively.

Describing and Sharing Molecular Visualizations Using the MolViewSpec Toolkit.

With the ever-expanding toolkit of molecular viewers, the ability to visualize macromolecular structures has never been more accessible. Yet, the idiosyncratic technical intricacies across tools and the integration complexities associated with handling structure annotation data present significant barriers to seamless interoperability and steep learning curves for many users. The necessity for reproducible data visualizations is at the forefront of the current challenges. Recently, we introduced MolViewSpec (homepage: https://molstar.org/mol-view-spec/, GitHub project: https://github.com/molstar/mol-view-spec), a specification approach that defines molecular visualizations, decoupling them from the varying implementation details of different molecular viewers. Through the protocols presented herein, we demonstrate how to use MolViewSpec and its 3D view-building Python library for creating sophisticated, customized 3D views covering all standard molecular visualizations. MolViewSpec supports representations like cartoon and ball-and-stick with coloring, labeling, and applying complex transformations such as superposition to any macromolecular structure file in mmCIF, BinaryCIF, and PDB formats. These examples showcase progress towards reusability and interoperability of molecular 3D visualization in an era when handling molecular structures at scale is a timely and pressing matter in structural bioinformatics as well as research and education across the life sciences. © 2024 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating a MolViewSpec view using the MolViewSpec Python package Basic Protocol 2: Creating a MolViewSpec view with reference to MolViewSpec annotation files Basic Protocol 3: Creating a MolViewSpec view with labels and other advanced features Support Protocol 1: Computing rotation and translation vectors Support Protocol 2: Creating a MolViewSpec annotation file.

A High-Precision Hand-Eye Coordination Localization Method under Convex Relaxation Optimization.

Traditional switching operations require on-site work, and the high voltage generated by arc discharges can pose a risk of injury to the operator. Therefore, a combination of visual servo and robot control is used to localize the switching operation and construct hand-eye calibration equations. The solution to the hand-eye calibration equations is coupled with the rotation matrix and translation vectors, and it depends on the initial value determination. This article presents a convex relaxation global optimization hand-eye calibration algorithm based on dual quaternions. Firstly, the problem model is simplified using the mathematical tools of dual quaternions, and then the linear matrix inequality convex optimization method is used to obtain a rotation matrix with higher accuracy. Afterwards, the calibration equations of the translation vectors are rewritten, and a new objective function is established to solve the coupling influence between them, maintaining positioning precision at approximately 2.9 mm. Considering the impact of noise on the calibration process, Gaussian noise is added to the solutions of the rotation matrix and translation vector to make the data more closely resemble the real scene in order to evaluate the performance of different hand-eye calibration algorithms. Eventually, an experiment comparing different hand-eye calibration methods proves that the proposed algorithm is better than other hand-eye calibration algorithms in terms of calibration accuracy, robustness to noise, and stability, satisfying the accuracy requirements of switching operations.

Recent advancements in cartilage tissue engineering innovation and translation.

Articular cartilage was expected to be one of the first successfully engineered tissues, but today, cartilage repair products are few and they exhibit considerable limitations. For example, of the cell-based products that are available globally, only one is marketed for non-knee indications, none are indicated for severe osteoarthritis or rheumatoid arthritis, and only one is approved for marketing in the USA. However, advances in cartilage tissue engineering might now finally lead to the development of new cartilage repair products. To understand the potential in this field, it helps to consider the current landscape of tissue-engineered products for articular cartilage repair and particularly cell-based therapies. Advances relating to cell sources, bioactive stimuli and scaffold or scaffold-free approaches should now contribute to progress in therapeutic development. Engineering for an inflammatory environment is required because of the need for implants to withstand immune challenge within joints affected by osteoarthritis or rheumatoid arthritis. Bringing additional cartilage repair products to the market willrequire an understanding of the translational vector for their commercialization. Advances thus far can facilitate the future translation of engineered cartilage products to benefit the millions of patients who suffer from cartilage injuries and arthritides.

A modular motion compensation pipeline for prospective respiratory motion correction of multi-nuclear MR spectroscopy

Magnetic resonance (MR) acquisitions of the torso are frequently affected by respiratory motion with detrimental effects on signal quality. The motion of organs inside the body is typically decoupled from surface motion and is best captured using rapid MR imaging (MRI). We propose a pipeline for prospective motion correction of the target organ using MR image navigators providing absolute motion estimates in millimeters. Our method is designed to feature multi-nuclear interleaving for non-proton MR acquisitions and to tolerate local transmit coils with inhomogeneous field and sensitivity distributions. OpenCV object tracking was introduced for rapid estimation of in-plane displacements in 2D MR images. A full three-dimensional translation vector was derived by combining displacements from slices of multiple and arbitrary orientations. The pipeline was implemented on 3 T and 7 T MR scanners and tested in phantoms and volunteers. Fast motion handling was achieved with low-resolution 2D MR image navigators and direct implementation of OpenCV into the MR scanner’s reconstruction pipeline. Motion-phantom measurements demonstrate high tracking precision and accuracy with minor processing latency. The feasibility of the pipeline for reliable in-vivo motion extraction was shown on heart and kidney data. Organ motion was manually assessed by independent operators to quantify tracking performance. Object tracking performed convincingly on 7774 navigator images from phantom scans and different organs in volunteers. In particular the kernelized correlation filter (KCF) achieved similar accuracy (74%) as scored from inter-operator comparison (82%) while processing at a rate of over 100 frames per second. We conclude that fast 2D MR navigator images and computer vision object tracking can be used for accurate and rapid prospective motion correction. This and the modular structure of the pipeline allows for the proposed method to be used in imaging of moving organs and in challenging applications like cardiac magnetic resonance spectroscopy (MRS) or magnetic resonance imaging (MRI) guided radiotherapy.

다차원 IRT 모형 적합 혼합형 검사를 위한 공통문항 척도 연계 방법

Under the context of the common-item nonequivalent groups design, item response theory (IRT) scale
 linking methods have been presented for each of the multidimensional extensions of the three-parameter
 logistic (M3PL), graded response (MGR), generalized partial credit (MGPC), and nominal response (MNR)
 models. The common-item multidimensional IRT (MIRT) linking methods that have been proved effective
 for single-format tests include the direct least squares (DLS), item category response function (ICRF), and
 test response function (TRF) methods. This paper extends the three MIRT linking methods to any mixture
 of the exploratory M3PL, MGR, MGPC, and MNR models for mixed-format tests. Each linking method
 extended attempts to find the linking coefficients in the rotation matrix and translation vector that
 constitute a linear transformation between two multidimensional ability scales. Simulations are conducted to
 investigate the performance of the three linking methods under various linking conditions. The results
 indicated that overall, the ICRF method performed best in estimating the rotation matrix and translation
 vector of the linear transformation between the new and true simulation scales. In not a few conditions, the
 DLS method resulted in the second best linking solutions. The TRF method tended to estimate the rotation
 matrix well but the translation vector poorly and thus showed the worst performance among the linking
 methods in some conditions.

The impact of a prophylactic skin dressing on surface-guided patient positioning in chest wall Radiation Therapy.

Surface-guided radiation therapy (SGRT) has emerged as a powerful tool to improve patient setup accuracy in radiation therapy (RT). Combined with the goal of increasing RT accuracy is an ongoing effort to decrease RT side effects. The application of a prophylactic skin dressing to the treatment site is a well-documented method of reducing skin-related side effects from RT. This paper aims to investigate whether the application of Mepitel, a prophylactic skin dressing, has an impact on the accuracy of surface-guided patient setups in chest wall RT. A retrospective analysis of daily image-guided Online Corrections (OLCs) from patients undergoing chest wall irradiation with SGRT was performed. Translational (superior-inferior, lateral, and anterior-posterior) OLC magnitude and direction were compared between patients treated with Mepitel applied and those treated without. Systematic and random errors were calculated and compared between groups. OLCs from 275 fractions were analysed. Mean OLCs were larger for patients with Mepitel applied in the superior_inferior axis (0.34 vs. 0.22 cm, P = 0.049) and for the combined translational vector (0.54 vs. 0.43 cm, P = 0.043). Combined translational systematic error was slightly larger for patients with Mepitel applied (0.15 vs. 0.09 cm). Mepitel can impact the accuracy of SGRT patient-positioning in chest wall RT. The variation however is small and unlikely to have any clinical impact if SGRT is coupled with image guidance and appropriate PTV margins. Further investigation is required to assess the effect of Mepitel on SGRT accuracy in other treatment sites, as well as any potential dosimetric impacts.

Skin marker combined with surface-guided auto-positioning for breast DIBH radiotherapy daily initial patient setup: An optimal schedule for both accuracy and efficiency.

By employing three surface-guided radiotherapy (SGRT)-assisted positioning methods, we conducted a prospective study of patients undergoing SGRT-based deep inspiration breath-hold (DIBH) radiotherapy using a Sentine/Catalys system. The aim of this study was to optimize the initial positioning workflow of SGRT-DIBH radiotherapy for breast cancer. A total of 124 patients were divided into three groups to conduct a prospective comparative study of the setup accuracy and efficiency for the daily initial setup of SGRT-DIBH breast radiotherapy. Group A was subjected to skin marker plus SGRT verification, Group B underwent SGRT optical feedback plus auto-positioning, and Group C was subjected to skin marker plus SGRT auto-positioning. We evaluated setup accuracy and efficiency using cone-beam computed tomography (CBCT) verification data and the total setup time. In groups A, B, and C, the mean and standard deviation of the translational setup-error vectors were small, with the highest values of the three directions observed in group A (2.4±1.6, 2.9±1.8, and 2.8±2.1mm). The rotational vectors in group B (1.8±0.7°, 2.1±0.8°, and 1.8±0.7°) were significantly larger than those in groups A and C, and the Group C setup required the shortest amount of time, at 1.5±0.3min, while that of Group B took the longest time, at 2.6±0.9min. SGRT one-key calibration was found to be more suitable when followed by skin marker/tattoo and in-room laser positioning, establishing it as an optimal daily initial set-up protocol for breast DIBH radiotherapy. This modality also proved to be suitable for free-breathing breast cancer radiotherapy, and its widespread clinical use is recommended.

The associative pattern classifier: Progress in theoretical understanding

The Associative Pattern Classifier (APC) was designed as an associative memory, focusing particularly on pattern classification. This implies that the training memory is constructed in a single operation and pattern classification also occurs in a single process. It is important to note that the APC translates the input patterns through a translation vector, which represents the average of all input patterns. Until now, there is no theoretical framework to explain the inner workings of the APC. Its relevance is inferred from the fact that several studies have been conducted using it as a foundation. This paper seeks to provide a theoretical comprehension of the APC’s operation to facilitate future enhancements. We found the APC creates a system in static equilibrium through concurrent vectors at the origin (translation vector), resulting in a balanced separation of patterns. However, the APC cannot achieve complete pattern separation because of the presence of a neutral region. The neutral region is defined by all the points that define the separation hyperplanes. The points over the hyperplanes cannot be classified by the APC. Additionally, we discovered that the APC is unable to accurately classify the translation vector, which could be included as part of the input patterns. Our previous research showed that the APC is unsuccessful in achieving the linear separation of the AND function. In this research, we also broaden the examination of the AND function to illustrate that achieving linear separation is not feasible because the separation line represents a neutral region. The APC demonstrated exceptional performance when tested with artificial datasets where patterns were distributed over balanced regions, thus operating as an efficient multiclass and non-linear classifier. Nevertheless, the performance of the APC is lower when tested with real-world databases, making the APC inaccurate due to its restricted inner workings.

Three-dimensional motion vector translation for geometric partitioning mode in panoramic video coding using H.266/VVC

Three-dimensional motion vector translation for geometric partitioning mode in panoramic video coding using H.266/VVC

Dual-quaternion-based iterative algorithm of the three dimensional coordinate transformation

Nowadays a unit quaternion is widely employed to represent the three-dimensional (3D) rotation matrix and then applied to the 3D similarity coordinate transformation. A unit dual quaternion can describe not only the 3D rotation matrix but also the translation vector meanwhile. Thus it is of great potentiality to the 3D coordinate transformation. The paper constructs the 3D similarity coordinate transformation model based on the unit dual quaternion in the sense of errors-in-variables (EIV). By means of linearization by Taylor's formula, Lagrangian extremum principle with constraints, and iterative numerical technique, the Dual Quaternion Algorithm (DQA) of 3D coordinate transformation in weighted total least squares (WTLS) is proposed. The algorithm is capable to not only compute the transformation parameters but also estimate the full precision information of computed parameters. Two numerical experiments involving an actual geodetic datum transformation case and a simulated case from surface fitting are demonstrated. The results indicate that DQA is not sensitive to the initial values of parameters, and obtains the consistent values of transformation parameters with the quaternion algorithm (QA), regardless of the size of the rotation angles and no matter whether the relative errors of coordinates (pseudo-observations) are small or large. Moreover, the DQA is advantageous to the QA. The key advantage is the improvement of estimated precisions of transformation parameters, i.e. the average decrease percent of standard deviations is 18.28%, and biggest decrease percent is 99.36% for the scaled quaternion and translations in the geodetic datum transformation case. Another advantage is the DQA implements the computation and precision estimation of traditional seven transformation parameters (which still are frequent used yet) from dual quaternion, and even could perform the computation and precision estimation of the scaled quaternion.Graphical

Animatable Implicit Neural Representations for Creating Realistic Avatars From Videos.

This paper addresses the challenge of reconstructing an animatable human model from a multi-view video. Some recent works have proposed to decompose a non-rigidly deforming scene into a canonical neural radiance field and a set of deformation fields that map observation-space points to the canonical space, thereby enabling them to learn the dynamic scene from images. However, they represent the deformation field as translational vector field or SE(3) field, which makes the optimization highly under-constrained. Moreover, these representations cannot be explicitly controlled by input motions. Instead, we introduce blend weight fields to produce the deformation fields. Based on the skeleton-driven deformation, blend weight fields are used with 3D human skeletons to generate observation-to-canonical and canonical-to-observation correspondences. Since 3D human skeletons are more observable, they can regularize the learning of deformation fields. Moreover, the blend weight fields can be combined with input skeletal motions to generate new deformation fields to animate the human model. To improve the quality of human modeling, we further represent the human geometry as a signed distance field in the canonical space. Additionally, a neural point displacement field is introduced to enhance the capability of the blend weight field on modeling detailed human motions. Experiments show that our approach significantly outperforms recent human modeling methods.

A Study of Minority Culture Outreach Translation Based on Cognitive Mapping Construction

Abstract In this paper, we first construct a cognitive neural network and use the knowledge representation learning technique for knowledge complementation and relation extraction to semantically match head-tail entity pairs with relations and accurately model the semantic relationships between entities and relations. Then, the vectors of each entity are mapped onto the hyperplane of a given relation by the cognitive mapping translation model, and vector translation operations are performed on the hyperplane. Finally, this paper’s translation model is compared with other models to compare the semantic representation translation, task processing function, and model complexity of each model so as to verify the superiority of this paper’s model for minority culture outreach translation. The results show that this paper’s method increases 3.4 and 1.2 BLEU values on average in the four translation tasks compared to the Imagination model and VAG model, respectively. When the number of iterations is 40, the MRR value of this paper’s model is 0.345, which is at least 0.1-0.5 more than the other models, thus indicating that this paper’s model has a better performance in outreach translation.

Roses in the nonperturbative current response of artificial crystals.

In two-dimensional artificial crystals with large real-space periodicity, the nonlinear current response to a large applied electric field can feature a strong angular dependence, which encodes information about the band dispersion and Berry curvature of isolated electronic Bloch minibands. Within the relaxation-time approximation, we obtain analytic expressions up to infinite order in the driving field for the current in a band-projected theory with time-reversal and trigonal symmetry. For a fixed field strength, the dependence of the current on the direction of the applied field is given by rose curves whose petal structure is symmetry constrained and is obtained from an expansion in real-space translation vectors. We illustrate our theory with calculations on periodically buckled graphene and twisted double bilayer graphene, wherein the discussed physics can be accessed at experimentally relevant field strengths.