Spatial Translation Research Articles

Spatiotemporal denoising of low-dose cardiac CT image sequences using RecycleGAN.

Electrocardiogram (ECG)-gated multi-phase computed tomography angiography (MP-CTA) is frequently used for diagnosis of coronary artery disease. Radiation dose may become a potential concern as the scan needs to cover a wide range of cardiac phases during a heart cycle. A common method to reduce radiation is to limit the full-dose acquisition to a predefined range of phases while reducing the radiation dose for the rest. Our goal in this study is to develop a spatiotemporal deep learning method to enhance the quality of low-dose CTA images at phases acquired at reduced radiation dose. Recently, we demonstrated that a deep learning method, Cycle-Consistent generative adversarial networks (CycleGAN), could effectively denoise low-dose CT images through spatial image translation without labeled image pairs in both low-dose and full-dose image domains. As CycleGAN does not utilize the temporal information in its denoising mechanism, we propose to use RecycleGAN, which could translate a series of images ordered in time from the low-dose domain to the full-dose domain through an additional recurrent network. To evaluate RecycleGAN, we use the XCAT phantom program, a highly realistic simulation tool based on real patient data, to generate MP-CTA image sequences for 18 patients (14 for training, 2 for validation and 2 for test). Our simulation results show that RecycleGAN can achieve better denoising performance than CycleGAN based on both visual inspection and quantitative metrics. We further demonstrate the superior denoising performance of RecycleGAN using clinical MP-CTA images from 50 patients.

From concept to space: a new perspective on AIGC-involved attribute translation

ABSTRACT Drawn inspiration from phenomenal attributes and translating them into heuristic model tools is one of the effective means to promote architectural form innovation. However, over-reliance on perception indicates greater risks in decision-making. Nowadays, AI-generated Content (AIGC) technology combines the advantages of information comprehensiveness and modelling efficiency, providing new possibilities for the translation of architectural attributes. Based on attribute study, this paper proposes a new approach to spatial translation that uses the Generative Adversarial Network (VQGAN + CLIP) to realize the visualization of abstract concepts and then adds multi-dimensional influence through the Keyframe Style Transfer technology. The eclectic attribute is used as an example for the 2D and virtual 3D translation feasibility experiments. The article aims to improve the scientificity and influence of spatial translation through a technical organization from the perspective of architects. While providing an innovative, democratic and efficient aided-design tool also highlights a new angle for AIGC-involved pre-design.

Volume-Law to Area-Law Entanglement Transition in a Nonunitary Periodic Gaussian Circuit.

We consider Gaussian quantum circuits that alternate unitary gates and postselected weak measurements, with spatial translation symmetry and time p eriodicity. We show analytically that such models can host different kinds of measurement-induced phase transitions detected by entanglement entropy, by mapping the unitary gates and weak measurements onto Möbius transformations. We demonstrate the existence of a log-law to area-law transition, as well as a volume-law to area-law transition at a finite measurement amplitude. For the latter, we compute the critical exponent ν for the Hartley, von Neumann and Rényi entropies exactly.

Preprint Highlight: mRNA Location and Translation Rate Determine Protein Targeting to Dual Destinations

NET1 is a guanine nucleotide exchange factor for RhoA GTPase that plays different roles in the nucleus and cytoplasm. While perinuclear and peripheral mRNA translation sites have been identified, the mechanisms of spatial translation regulation and impacts on protein function are not generally understood. Here, the authors use genetic tools to manipulate NET1 mRNA targeting sequences, as well as proximity ligation, mass spectrometry, and imaging. They show that mRNA localization, translation rate, and the order of appearance of targeting domains during translation determine the extent of protein synthesis in each location, promoting site-specific NET1 function. This novel regulation of protein targeting by RNA translation rate and location uncovers the potential to engineer mRNA localization to optimize protein function and will likely impact research on cell biology, immunology, and beyond. This preprint has been assigned the following badges: New Hypothesis, New Methods, Open Software, Cross-Validation. Read the preprint on bioRxiv ( Gasparski et al., 2023 ): https://doi.org/10.1101/2023.04.24.538105 .

Current algebra approach to two-dimensional interacting chiral metals

We reinterpret the chiral $U(N)$ Wess-Zumino-Witten (WZW) model at level $k>1$ in $(1+1)$ dimensions as an interacting chiral metal in two space dimensions. In this reinterpretation, spatial translations along one of the spatial dimensions in the two-dimensional chiral metal arise from a generator of the $U(N)$ symmetry of the WZW model. The WZW model at $k=1$ is equivalent to Balents and Fisher's free chiral metal [L. Balents and M. P. A. Fisher, Phys. Rev. Lett. 76, 2782 (1996)]. Here, the $U(N)$ symmetry corresponds to the IR symmetry of a chiral Fermi gas with (half of a) Fermi surface, with $N$ equal to the number of points on the Fermi surface. We argue that exactly solvable interacting generalizations occur for levels $k>1$. Importantly, these interacting chiral metals maintain the $U(N)$ symmetry of the free system. We calculate two-point correlation functions of the single-particle fermion operator, the $U(1)$ number density, and current operators in these theories for general $k$. We find that interactions ($k>1$) produce $1/N$ corrections to scaling of the single-particle fermion operator as $N\ensuremath{\rightarrow}\ensuremath{\infty}$ and renormalize the amplitudes of the density and current two-point functions. This construction illustrates the ersatz Fermi liquid proposal of Else et al. [Phys. Rev. X 11, 021005 (2021)].

Tutorial on the conservation of momentum in photonic time-varying media [Invited

Time-varying media break temporal symmetries while preserving spatial symmetries intact. Thus, it represents an excellent conceptual framework to investigate the fundamental implications of Noether’s theorem for the electromagnetic field. At the same time, addressing momentum conservation in time-varying media sheds light on the Abraham-Minkowski debate, where two opposing forms of the electromagnetic field momentum are defended. Here, we present a tutorial review on the conservation of momentum in time-varying media. We demonstrate that the Minkowski momentum is a conserved quantity with three independent approaches of increasing complexity: (i) via the application of the boundary conditions for Maxwell equations at a temporal boundary, (ii) testing for constants of motion and deriving conservation laws, and (iii) applying temporal and spatial translations within the framework of the Lagrangian theory of the electromagnetic field. Each approach provides a different and complementary insight into the problem.

Machine learning and polymer self-consistent field theory in two spatial dimensions.

A computational framework that leverages data from self-consistent field theory simulations with deep learning to accelerate the exploration of parameter space for block copolymers is presented. This is a substantial two-dimensional extension of the framework introduced in the work of Xuan et al. [J. Comput. Phys. 443, 110519 (2021)]. Several innovations and improvements are proposed. (1) A Sobolev space-trained, convolutional neural network is employed to handle the exponential dimension increase of the discretized, local average monomer density fields and to strongly enforce both spatial translation and rotation invariance of the predicted, field-theoretic intensive Hamiltonian. (2) A generative adversarial network (GAN) is introduced to efficiently and accurately predict saddle point, local average monomer density fields without resorting to gradient descent methods that employ the training set. This GAN approach yields important savings of both memory and computational cost. (3) The proposed machine learning framework is successfully applied to 2D cell size optimization as a clear illustration of its broad potential to accelerate the exploration of parameter space for discovering polymer nanostructures. Extensions to three-dimensional phase discovery appear to be feasible.

Some disquisitions on cosmological 2-form dualities

In this work we study different aspect of self-interacting 2-form fields with special emphasis in their cosmological applications. We provide the explicit construction of how massless 2-forms are compatible with the cosmological principle without resorting to the dual scalar field formulation. In terms of the 2-form, the residual Euclidean group is non-trivially realised by means of a combination of external spatial translations and internal gauge transformations. After presenting the general discussion of the dualities in cosmological scenarios, we analyse particular examples for some singular models and discuss in some detail the dual descriptions of the DBI, the cuscuton and the ghost condensate as well as the role of the duality in the effective field theories of cosmological perturbations. We then proceed to analysing scenarios with several self-interacting massless 2-forms and we show that they naturally provide the dual description of a solid. We then show how the perfect fluid and superfluids can be obtained by taking the appropriate limits in the dual formulations. We finally consider the case of massive 2-forms and their duals and briefly discuss their potential signatures in gravitational waves astronomy.

Lifshitz symmetry: Lie algebras, spacetimes and particles

We study and classify Lie algebras, homogeneous spacetimes and coadjoint orbits (“particles”) of Lie groups generated by spatial rotations, temporal and spatial translations and an additional scalar generator. As a first step we classify Lie algebras of this type in arbitrary dimension. Among them is the prototypical Lifshitz algebra, which motivates this work and the name “Lifshitz Lie algebras”. We classify homogeneous spacetimes of Lifshitz Lie groups. Depending on the interpretation of the additional scalar generator, these spacetimes fall into three classes:1. (d+2)-dimensional Lifshitz spacetimes which have one additional holographic direction;2. (d+1)-dimensional Lifshitz-Weyl spacetimes which can be seen as the boundary geometry of the spacetimes in (1) and where the scalar generator is interpreted as an anisotropic dilation;3. and (d+1)-dimensional aristotelian spacetimes with one scalar charge, including exotic fracton-like symmetries that generalise multipole algebras.We also classify the possible central extensions of Lifshitz Lie algebras and we discuss the homogeneous symplectic manifolds of Lifshitz Lie groups in terms of coadjoint orbits.

Study on the Design of Architectural Renovations in Historical Districts Based on the Architecture of Local Schema—Huaxing Street in Chengdu as an Example

Due to the rapid renovations of historical districts in the context of consumer society, contributing to the loss of city culture and identity, this paper describes the methodological study of the local schema concept on the renovations of architectural design, utilizing speculative design as opposed to inertial design. This case analysis focuses on Huaxing Street in Chengdu, China. By performing field surveys, photographing structures, and sending questionnaires to local inhabitants, an evaluation of Huaxing Street’s current status and obstacles is conducted. The conclusion of the study summarizes the audience’s satisfaction with the usage status of different renovated structures. On the basis of the idea of local schema, a spatial translation approach for architectural prototypes is given. This research explores the design methods of historical architecture in the area, history, and memory in an effort to provide guidelines for the renovation of structures in comparable historical districts. To accomplish this, prototypes for architectural installations are selected, design elements are divided, and a three-dimensional model is utilized to form a schematic replication of the building.

Yarn-level deformation for weft-knitted stitches

The essential purpose of this research is to exploit and innovate a yarn-level relaxation deformation method for weft-knitted stitches. Each loop formed by knitting yarns was taken as a mass respectively. Spring-mass model at the yarn level was established for several knitting rows and spatial coordinates were assigned to each mass. Based on the intermeshed relationship of loops in the longitudinal direction, the spring-mass model at the yarn level was transformed into a two-layer spring-mass model at the stitch level, with correspondence between the masses of the two models. Each mass was subjected to eight spring forces from different directions. The resultant force was calculated based on the yarn-level mass model, and the mass displacement was solved by the Verlet numerical integration method. According to the coordinates of masses, the geometric models of different knitting types were established, and a matrix operation method for the spatial rotation and translation of control points was proposed, which could quickly calculate the coordinates and accurately simulate the shape of loops. Finally, we simulated the relaxation deformation of five typical weft-knitted stitches. The results show that this method can accurately and effectively simulate the overall deformation effect of the knitted stitches, and the simulation results are similar to the actual results. The analysis results of the pattern size and the simulation efficiency indicate that the larger the pattern, the more loops there are, the overall simulation time is longer and the running memory occupied is larger.

RFIA-Net: Rich CNN-transformer network based on asymmetric fusion feature aggregation to classify stage I multimodality oesophageal cancer images

Endoscopic images of oesophageal cancer have the characteristics of rich colours; furthermore, the small lesions are similar to the oesophageal wall tissue, and the pathological images have the characteristics of various staining methods, different shapes, and rich texture details. Aiming at the above characteristics and combining the unique advantages of convolutional architectures and the development of vision transformers in computer vision tasks, in this paper, for the stage I multimodality oesophageal cancer image classification task, we design an efficient hybrid architecture that leverages the local modelling capabilities and powerful semantic feature extraction capabilities of convolutional neural networks and the ability of transformers to extract global information. And combined with the structural reparameterization strategy to further improve the model expression. Specifically, our architecture consists of a feature extraction module and a feature enhancement module. In the feature enhancement module, we supplement the semantic information of each branch by continuously exchanging information between the two branches, which further improves the performance of the network. Furthermore, we propose an asymmetric fusion module that allows features to further enhance the feature relationships between different branches through spatial translation and channel swapping. Compared with networks such as ResNet-18, our proposed method achieves the best results for oesophageal cancer image classification on both tasks on the XJMU-XJU stage I multimodal oesophageal cancer dataset. The proposed method achieved an AUC of 0.9973 and an ACC of 0.9902 on the staging task and achieved a recall of 0.9742 and an ACC of 0.9750 on the differentiation task.

Topological classification of gapped/gap-preserving rational space-time crystal systems

The traditional systems researched in condensed matter physics always have spatial translation symmetry. However for space-time crystal systems, the spatial translation symmetry is no longer preserved and the lattice potential have space-time translation symmetry instead. We show that a rational space-time crystal system is equal to a traditional floquet system. Then, we find a way to solve the floquet equation analytically and construct an effective Hamiltonian of the rational space-time crystal system. By this effective Hamiltonian, we obtain the topological classification of gapped and gap-preserving rational space-time crystal systems. Our works reveal the correlation between space-time crystal systems and floquet systems and give a systematic method to explore the properties of rational space-time crystal systems.

Non-Abelian nonsymmorphic chiral symmetries

The Hofstadter model exemplifies a large class of physical systems characterized by particles hopping on a lattice immersed in a gauge field. Recent advancements on various synthetic platforms have enabled highly controllable simulations of such systems with tailored gauge fields featuring complex spatial textures. These synthetic gauge fields could introduce synthetic symmetries that do not appear in electronic materials. Here, in an $\mathrm{SU}(2)$ non-Abelian Hofstadter model, we theoretically show the emergence of multiple nonsymmorphic chiral symmetries, which combine an internal unitary antisymmetry with fractional spatial translation. Depending on the values of the gauge fields, the nonsymmorphic chiral symmetries can exhibit non-Abelian algebra and protect Kramers quartet states in the bulk band structure, creating general fourfold degeneracy at all momenta. These nonsymmorphic chiral symmetries protect double Dirac semimetals at zero energy, which become gapped into quantum confined insulating phases upon introducing a boundary. Moreover, the parity of the system size can determine whether the resulting insulating phase is trivial or topological. Our work indicates a pathway for creating topology via synthetic symmetries emergent from synthetic gauge fields.

Invariance of the Gibbs measures for periodic generalized Korteweg-de Vries equations

In this paper, we study the Gibbs measures for periodic generalized Korteweg-de Vries equations (gKdV) with quartic or higher nonlinearities. In order to bypass the analytical ill-posedness of the equation in the Sobolev support of the Gibbs measures, we establish deterministic well-posedness of the gauged gKdV equations within the framework of the Fourier-Lebesgue spaces. Our argument relies on bilinear and trilinear Strichartz estimates adapted to the Fourier-Lebesgue setting. Then, following Bourgain’s invariant measure argument, we construct almost sure global-in-time dynamics and show invariance of the Gibbs measures for the gauged equations. These results can be brought back to the ungauged side by inverting the gauge transformation and exploiting the invariance of the Gibbs measures under spatial translations. We thus complete the program initiated by Bourgain [Comm. Math. Phys. 166 (1994), pp 1–26] on the invariance of the Gibbs measures for periodic gKdV equations.

A Coupled SWAT-AEM Modelling Framework for a Comprehensive Hydrologic Assessment

This study attempts to integrate a Surface Water (SW) model Soil and Water Assessment Tool (SWAT) with an existing steady-state, single layer, unconfined heterogeneous aquifer Analytic Element Method (AEM) based Ground Water (GW) model, named Bluebird AEM engine, for a comprehensive assessment of SW and GW resources and its management. The main reason for integrating SWAT with the GW model is that the SWAT model does not simulate the distribution and dynamics of GW levels and recharge rates. To overcome this issue, often the SWAT model is coupled with the numerical GW model (either using MODFLOW or FEFLOW), wherein the spatial and temporal patterns of the interactions are better captured and assessed. However, the major drawback in integrating the two models (SWAT with—MODFLOW/FEM) is its conversion from Hydrological Response Unit’s (HRU)/sub-basins to grid/elements. To couple them, a spatial translation system is necessary to move the inputs and outputs back and forth between the two models due to the difference in discretization. Hence, for effective coupling of SW and GW models, it may be desirable to have both models with a similar spatial discretization and reduce the need for rigorous numerical techniques for solving the PDEs. The objective of this paper is to test the proof of concept of integrating a distributed hydrologic model with an AEM model at the same spatial units, primarily focused on surface water and groundwater interaction with a shallow unconfined aquifer. Analytic Element Method (AEM) based GW models seem to be ideal for coupling with SWAT due to their innate character to consider the HRU, sub-basin, River, and lake boundaries as individual analytic elements directly without the need for any further discretization or modeling units. This study explores the spatio-temporal patterns of groundwater (GW) discharge rates to a river system in a moist-sub humid region with SWAT-AEM applied to the San Jacinto River basin (SJRB) in Texas. The SW-GW interactions are explored throughout the watershed from 2000–2017 using the integrated SWAT-AEM model, which is tested against stream flow and GW levels. The integrated SWAT-AEM model results show good improvement in predicting the stream flow (R2 = 0.65–0.80) and GW levels as compared to the standalone SWAT model. Further, the integrated model predicted the low flows better compared to the standalone SWAT model, thus accounting for the SW-GW interactions. Almost 80% of the stream network experiences an increase in groundwater discharge rate between 2000 and 2017 with an annual average GW discharge rate of 1853 Mm3/year. The result from the study seems promising for potential applications of SWAT-AEM coupling in regions with considerable SW-GW interactions.

Accurate and fast registration algorithm for multi-height lensless in-line on-chip holographic microscopy

Lensless in-line on-chip holographic microscope has cost-effective and compact designs that can offer sub-micron resolution over a large field-of-view. In multi-height lensless on-chip holographic microscopy, the in-line hologram series are recorded at different positions along the optical axis, thus, there inevitably exists several kinds of error sources including shifts, scales, and rotations. Therefore, digitally registering and aligning these in-line holograms is of great concern for iterative phase-retrieved recovery. Here, we proposed an accurate and fast registration method, which consists of three major steps. First, Fourier-transform-based phase correlation method is used to roughly correct the spatial translation through locating the correlation peak. Second, we proposed a morphology-based method to accurately search for the matching feature points to obtain the accurate scaling factors, where samples themselves are used as markers without any labeling. Third, the phase correlation method is used for precisely registration again. Experimental results verified the validity and effectiveness of the proposed registration algorithm. And by the comparative experiments with the existing popular registration algorithms, our proposed registration algorithm can register and align the multi-height in-line holograms with greater precision and speed.

Spectral Instability of Peakons in the $b$-Family of the Camassa--Holm Equations

We prove spectral instability of peakons in the $b$-family of Camassa--Holm equations in $L^2(\mathbb{R})$ that includes the integrable cases of $b = 2$ and $b = 3$. We start with a linearized operator defined on functions in $H^1(\mathbb{R}) \cap W^{1,\infty}(\mathbb{R})$ and extend it to a linearized operator defined on weaker functions in $L^2(\mathbb{R})$. For $b \neq \frac{5}{2}$, the spectrum of the linearized operator in $L^2(\mathbb{R})$ is proved to cover a closed vertical strip of the complex plane. For $b = \frac{5}{2}$, the strip shrinks to the imaginary axis, but an additional pair of real eigenvalues exists due to projections to the peakon and its spatial translation. The spectral instability results agree with the linear instability results in the case of the Camassa-Holm equation for $b = 2$.

It's Just a Phase: Exploring the Relationship Between mRNA, Biomolecular Condensates, and Translational Control.

Cells spatially organize their molecular components to carry out fundamental biological processes and guide proper development. The spatial organization of RNA within the cell can both promote and result from gene expression regulatory control. Recent studies have demonstrated diverse associations between RNA spatial patterning and translation regulatory control. One form of patterning, compartmentalization in biomolecular condensates, has been of particular interest. Generally, transcripts associated with cytoplasmic biomolecular condensates—such as germ granules, stress granules, and P-bodies—are linked with low translational status. However, recent studies have identified new biomolecular condensates with diverse roles associated with active translation. This review outlines RNA compartmentalization in various condensates that occur in association with repressed or active translational states, highlights recent findings in well-studied condensates, and explores novel condensate behaviors.

Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN.

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets.