Auxiliary Vector Research Articles

Enhancing Agrobacterium-mediated soybean transformation efficiency with an auxiliary solution

Soybean is a crucial source of oil, protein, and biofuel, necessitating efficient transformation systems for advancing research. Agrobacterium-mediated transformation is currently the primary method used in the soybean transformation industry and scientific research. However, the low efficiency and genotype dependency of this technology leave significant room for improvement. This study aimed to enhance soybean transformation efficiency by generating and validating three reporter vectors (ZsGreen, TdTomato, and Ruby) and using Agrobacterium Auxiliary Solution (AAS) containing Silwet L-77 and hormone mixtures. Our findings demonstrate that AAS significantly improves hairy root transformation rates. Specifically, this combination increased total root and cotyledon transformation efficiencies compared to the control. We also found that larger vectors like Ruby reduced transformation efficiency compared to smaller markers like GFP and RFP. Furthermore, AAS slightly reduced the co-transformation rate of two separate vectors compared to single vector transformations. Additionally, AAS enhanced soybean hypocotyl transformation rates across various varieties, consistently increasing positive root and explant efficiencies. Notably, transformation rates varied significantly between varieties, with Forrest differing from Williams 82 and Dongnong 50. This research highlights the importance of auxiliary agents and vector size in optimizing soybean transformation, providing insights for future advancements in genetic modification and biotechnology.

Canonical aspects of pregeometric vector-based first order gauge theory

A recently proposed pregeometric auxiliary vector mediated gauge theory is studied in its canonical domain, by performing the Legendre transform on a curved background and by considering its covariant phase space, with further application to duality. The constraints become differential equations, but the Dirac-Bergmann algorithm appears consistent with electromagnetic degrees of freedom, metric background permitting. Solving the consistency conditions provides a preferred direction in an intermediary form of spontaneous symmetry breaking. In parallel, the covariant phase space defines the symplectic structure, and establishes the conserved currents and quantum phenomenology with the generated background. The formalism immediately allows one to study the parent path integrals of dual theories, with quartic Proca to Kalb-Ramond inequivalence and Maxwell-Chern-Simons path integral consistency as practical applications, while the differential geometry gives a global description of the issue of Yang-Mills duality rotations. Properties of degenerate metric geometry are discussed throughout, and the viability of inherent backgrounds leads into the fundamental question of background independence in all physical theories. Published by the American Physical Society 2024

A Diffusion Approach to Radiance Field Relighting using Multi‐Illumination Synthesis

AbstractRelighting radiance fields is severely underconstrained for multi‐view data, which is most often captured under a single illumination condition; It is especially hard for full scenes containing multiple objects. We introduce a method to create relightable radiance fields using such single‐illumination data by exploiting priors extracted from 2D image diffusion models. We first fine‐tune a 2D diffusion model on a multi‐illumination dataset conditioned by light direction, allowing us to augment a single‐illumination capture into a realistic – but possibly inconsistent – multi‐illumination dataset from directly defined light directions. We use this augmented data to create a relightable radiance field represented by 3D Gaussian splats. To allow direct control of light direction for low‐frequency lighting, we represent appearance with a multi‐layer perceptron parameterized on light direction. To enforce multi‐view consistency and overcome inaccuracies we optimize a per‐image auxiliary feature vector. We show results on synthetic and real multi‐view data under single illumination, demonstrating that our method successfully exploits 2D diffusion model priors to allow realistic 3D relighting for complete scenes.

Tensor loop reduction via the Baikov representation and an auxiliary vector

In this paper, we introduce a simple and efficient approach for the general reduction of one-loop integrals. Our method employs the introduction of an auxiliary vector and the identification of the tensor structure as an auxiliary propagator. This key insight allows us to express a wide range of one-loop integrals, encompassing both tensor structures and higher poles, in the Baikov representation. By establishing an integral-by-parts relation, we derive a recursive formula that systematically solves the one-loop reduction problem, even in the presence of various degenerate cases. Our proposed strategy is characterized by its simplicity and effectiveness, offering a significant advancement in the field of one-loop calculations. Published by the American Physical Society 2024

Covariant actions and propagators for all spins, masses, and dimensions

The explicit covariant actions and propagators are given for fields describing particles of all spins and masses, in any spacetime dimension. Massive particles are realized as “dimensionally reduced” massless particles. To obtain compact expressions for the propagators, it was useful to introduce an auxiliary vector coordinate sμ and consider “hyperfields” that are functions of space Xμ and sμ. The actions and propagators serve as a basic starting point for concrete high spin computations amenable to dimensional regularization, provided that gauge invariant interactions are introduced. Published by the American Physical Society 2024

Contextualized Satire Detection in Short Texts Using Deep Learning Techniques

Satire is prominent in user-generated content on various online platforms in the form of satirical news, customer reviews, blogs, articles, and short messages that are typically of an informal nature. As satire is also used to disseminate false information on the Internet, its computational detection has become a well-known issue. Existing work focuses primarily on formal document- or sentence-level textual data, whereas informal short texts have gotten less attention for satire detection. This paper presents a new model called BiLSTM self-attention (BiSAT) for detecting satire in informal short texts. It consists of various components such as input, embedding, self-attention, and two bi-directional long short-term memory (BiLSTM) layers for learning crucial contextual information pertaining to the satire present in the texts. The input layer uses the text as input to create an input vector, which is then given to the embedding layer to create the appropriate numeric vector. The output of the embedding layer is passed on to the first BiLSTM layer, which extracts contextual information-based sequences in the opposite direction. Between the first and second BiLSTM layers, a self-attention layer is employed to draw attention to the important satirical information that is acquired by the hidden layer of the first BiLSTM. The BiSAT model also takes a classic feature engineering approach, employing a 13-dimensional auxiliary feature vector comprised of features from four separate feature categories: sentiment, punctuation, hyperbole, and affective. The proposed BiSAT model is empirically evaluated on two benchmark datasets and a newly created dataset called Satire-280. It outperforms existing research and baseline methods by a significant margin. The Satire-280 dataset along with code can be downloaded from GitHub repository: https://github.com/Ashraf-Kamal/Satire-Detection.

Nicolai maps with four-fermion interactions

Nicolai maps offer an alternative description of supersymmetric theories via nonlinear and nonlocal transformations characterized by the so-called ‘free-action’ and ‘determinant-matching’ conditions. The latter expresses the equality of the Jacobian determinant of the transformation with the one obtained by integrating out the fermions, which so far have been considered only to quadratic terms. We argue that such a restriction is not substantial, as Nicolai maps can be constructed for arbitrary nonlinear sigma models, which feature four-fermion interactions. The fermionic effective one-loop action then gets generalized to higher loops and the perturbative tree expansion of such Nicolai maps receives quantum corrections in the form of fermion loop decorations. The ‘free-action condition’ continues to hold for the classical map, but the ‘determinant-matching condition’ is extended to an infinite hierarchy in fermion loop order. After general considerations for sigma models in four dimensions, we specialize to the case of ℂPN symmetric spaces and construct the associated Nicolai map. These sigma models admit a formulation with only quadratic fermions via an auxiliary vector field, which does not simplify our analysis.

Conformal Yang-Mills field in arbitrary dimensions

Lagrangian of a classical conformal Yang-Mills field in the flat space of even dimension greater than or equal to six involves higher derivatives. We study Lagrangian formulation of the classical conformal Yang-Mills field by using ordinary-derivative (second-derivative) approach. In the framework of the ordinary-derivative approach, a field content, in addition to generic Yang-Mills field, consists of auxiliary vector fields and Stueckelberg scalar fields. For such field content, we obtain a gauge invariant Lagrangian with the conventional second-derivative kinetic terms and the corresponding gauge transformations. The Lagrangian is built in terms of non-abelian field strengths. Structure of a gauge algebra entering gauge symmetries of the conformal Yang-Mills field is described. FFF-vertex of the conformal Yang-Mills field which involves three derivatives is also obtained. For six, eight, and ten dimensions, eliminating the auxiliary vector fields and gauging away the Stueckelberg scalar fields, we obtain a higher-derivative Lagrangian of the conformal Yang-Mills field. For arbitrary dimensions, we demonstrate that all auxiliary fields can be integrated out at non-linear level leading just to a local higher-derivative action which is expressed only in terms of the generic Yang-Mills field.

Optimizing base isolation system parameters using a fuzzy reinforced butterfly optimization: A case study of the 2023 Kahramanmaras earthquake sequence

This study aims to obtain the viable parameters of base isolation systems that are utilized in buildings with seismic isolation and specially located in Turkey. To provide a more accurate representation of real-world scenarios, the study accounts for severe ground motion records simultaneously during the dynamic response analysis of the structure. Three records are taken from the 2023 Turkey earthquake (records for Elbistan, Nurdagi, and Pazarcik). The optimization model's objective function is to minimize the ratio of the roof acceleration to the peak ground acceleration. To ensure realistic outcomes, certain isolation parameters are constrained, such as maintaining the damping ratio and period within a predetermined range as well as the maximum lateral drift of the structure. An upgraded variant of the Butterfly Optimization Algorithm, known as the Fuzzy Butterfly Optimization Algorithm (FBOA), is employed to address the presented optimization model. The FBOA technique incorporates a fuzzy decision-making mechanism that adjusts its search strategy according to the prevailing conditions of the present problem. In addition, the FBOA method utilizes a new auxiliary vector called virtual butterfly, which provides information on the entire population based on each agent's quality. Consequently, FBOA transforms into a self-regulating search algorithm, eliminating the requirement for any external parameters. Results have verified the effectiveness of FBOA in detecting optimal parameters of the proposed isolation system.

Filtering in Triplet Markov Chain Model in the Presence of Non-Gaussian Noise with Application to Target Tracking

In hidden Markov chain (HMC) models, widely used for target tracking, the process noise and measurement noise are in general assumed to be independent and Gaussian for mathematical simplicity. However, the independence and Gaussian assumptions do not always hold in practice. For instance, in a typical radar tracking application, the measurement noise is correlated over time as the sampling frequency of a radar is generally much higher than the bandwidth of the measurement noise. In addition, target maneuvers and measurement outliers imply that the process noise and measurement noise are non-Gaussian. To solve this problem, we resort to triplet Markov chain (TMC) models to describe stochastic systems with correlated noise and derive a new filter under the maximum correntropy criterion to deal with non-Gaussian noise. By stacking the state vector, measurement vector, and auxiliary vector into a triplet state vector, the TMC model can capture the complete dynamics of stochastic systems, which may be subjected to potential parameter uncertainty, non-stationarity, or error sources. Correntropy is used to measure the similarity of two random variables; unlike the commonly used minimum mean square error criterion, which uses only second-order statistics, correntropy uses second-order and higher-order information, and is more suitable for systems in the presence of non-Gaussian noise, particularly some heavy-tailed noise disturbances. Furthermore, to reduce the influence of round-off errors, a square-root implementation of the new filter is provided using QR decomposition. Instead of the full covariance matrices, corresponding Cholesky factors are recursively calculated in the square-root filtering algorithm. This is more numerically stable for ill-conditioned problems compared to the conventional filter. Finally, the effectiveness of the proposed algorithms is illustrated via three numerical examples.

On the Partial Stability of Nonlinear Discrete-Time Systems with Delay

A system of nonlinear discrete (finite-difference) of a general form with a bounded delay is considered. Interest in the tasks of qualitative analysis of such systems has increased significantly in recent years. At the same time, the problem of stability with respect to all variables of the zero equilibrium position, which has a great generality, is mainly analyzed in domestic and foreign literature. The main research method is a discrete-functional analogue of the direct Lyapunov method. In this article, it is assumed that the system under consideration admits a “partial” (in some part of the state variables) zero equilibrium position. The problem of stability of a given equilibrium position is posed, and stability is considered not in all, but only in relation to a part of the variables that determine this equilibrium position. Such a problem belongs to the class of problems of partial stability, which are actively studied for systems of various forms of mathematical description. The proposed statement of the problem complements the scope of the indicated studies in relation to the system under consideration. To solve this problem, a discrete version of the Lyapunov– Krasovskii functionals method is used in the space of discrete functions with appropriate specification of the functional requirements. To expand the capabilities of this method, it is proposed to use two types of additional auxiliary (vector, generally speaking) discrete functions in order to: 1) adjustments of the phase space region of the system in which the Lyapunov–Krasovskii functional is constructed; 2) finding the necessary estimates of the functionals and their differences (increment) due to the system under consideration, on the basis of which conclusions about partial stability are made. The expediency of this approach lies in the fact that as a result, the Lyapunov-Krasovskii functional, as well as its difference due to the system under consideration, can be alternating in the domain that is usually considered when analyzing partial stability. Sufficient conditions of partial stability, partial uniform stability, and partial uniform asymptotic stability of the specified type are obtained. The features of the proposed approach are shown on the example of two classes of nonlinear systems of a given structure, for which partial stability is analyzed in parameter space. Attention is drawn to the expediency of using a one-parameter family of functionals.

Stable cargo transportation for underactuated partial space elevator with model uncertainties and disturbances

An adaptive neural network energy-based control approach is proposed in this paper to address the issue of stabilizing a partial space elevator during cargo transportation. The method handles the challenges presented by external disturbances and model uncertainties. The dynamic model of the partial space elevator, with a typical multi-input and multi-output underactuated system, is presented. A control scheme using energy-based techniques is proposed to guarantee the stable configuration of the system, and auxiliary kinematic vector variables are designed to incorporate the uncontrollable variables to constitute controllable state subsets. Under these frameworks, adaptive control schemes are formulated using multi-layer neural networks to mitigate model uncertainty. Robust compensators are designed to effectively counteract the unknown boundaries of external disturbances and approximation errors. The stability of the closed control system is rigorously demonstrated through the application of the Lyapunov function and LaSalle's invariance principle. Numerical simulations demonstrate that the proposed control method is highly effective in suppressing oscillations during the transfer period of a partial space elevator.

Control for Stochastic Singular Systems With Time-Varying Delays via Sampled-Data Controller.

In this article, control for stochastic singular time-varying delay systems under arbitrarily variable samplings is addressed via designing a sampled-data controller. The first and foremost, a novel time-dependent discontinuous Lyapunov-Krasovskii (L-K) functional is built, which takes good advantage of the factual sampling pattern's available properties. Then, based on the refined input delay method by utilizing the constructed time-dependent L-K functional, the free-weighting matrix method, and the auxiliary vector function approach are adopted to develop conditions ensuring the stochastic admissibility for the studied stochastic singular systems with time-varying delays. On the basis of the derived conditions, the sampled-data control issue is tackled, and an unambiguous expression for the sampled-data controller design method is obtained. Finally, simulation examples manifest that our proposed results are correct and effective.

Resilient Coordination of Nonlinear Uncertain Lagrangian Systems With Adversarial Agents: A Norm-Based Approach

In this paper, the resilient coordination problem of networked Lagrangian systems with adversarial agents is considered. A novel algorithm called Norm-Based Resilient Decision Algorithm is proposed to exclude the impact of adversarial agents. To ensure the coordination of networked Lagrangian systems, the maximum number of adversarial agents related to the robustness of the communication network is given. Under the proposed resilient consensus algorithm, secure coordination is guaranteed under adversarial agents. Then the proposed resilient controller is extended to static formation scenarios. Finally, the effectiveness of the proposed method is demonstrated through case studies and experiments. Compared to the existing results, the proposed algorithm can reduce computing resources by designing auxiliary vectors and converting them into scalars to remove extreme values.

A weak form quadrature element formulation of geometrically exact strain gradient shells

A geometrically exact strain gradient shell model and its weak form quadrature element formulation are proposed. The governing equations, boundary conditions and corresponding variational form of the shell model are presented. The generalized differential quadrature analogue is employed for derivative approximations of displacements and rotations with C1 continuity conditions brought about by the second-order strain gradient terms. A novel scheme based on auxiliary vectors is developed to represent and update the derivatives of constrained rotations on element edges for the numerical approach. The present quadrature shell element is feasible to incorporate strain gradient terms and free from shear and membrane locking problems due to its adjustable high-order approximation property in nonlinear shell analysis. To demonstrate the potential of this formulation for nonlinear analysis of shells with size effects, five benchmark numerical examples are presented. The results verify the feasibility of this formulation and illustrate the strain gradient effects on elastic shells undergoing large displacements and rotations.

Expanding Limit of Minimum Sampling Time Using Auxiliary Vectors for PMSM Drives With Single DC-Link Current Sensor

Phase current reconstruction (PCR) strategy can improve the fault tolerance of permanent magnet synchronous motor (PMSM) drives. The PCR precision is largely affected by the unmeasurable zones and time-sharing sampling errors. The upper limit (Tlimit) of PCR allowable range can reflect the requirement of different PCR methods for the minimum sampling time (Tmin). With a longer Tlimit, there is sufficient time for sampling, even if Tlimit is halved due to the symmetrical waveform. Therefore, the extension of Tlimit is the key to eliminate the unmeasurable zones and time-sharing sampling errors. In this paper, a method to increase Tlimit is proposed, which introduces the suitable auxiliary vectors (AVs) in different regions to extend the duration time of the sampling vectors. With the help of a longer Tlimit (12.5%Ts), its possible to eliminate all the unmeasurable zones and time-sharing sampling errors, relieve the pressure on the hardware of current loop, improve the sampling accuracy, and facilitate the reliable operation of the drive. Besides, the switching action times of IGBTs can be reduced by about one-third in the high modulation area. The proposed method is finally proved to accurately reconstruct the phase currents by the experimental results on the PMSM prototype.

Collinear functions for QCD resummations

The singular behaviour of QCD squared amplitudes in the collinear limit is factorized and controlled by splitting kernels with a process-independent structure. We use these kernels to define collinear functions that can be used in QCD resummation formulae of hard-scattering observables. Different collinear functions are obtained by integrating the splitting kernels over different phase-space regions that depend on the hard-scattering observables of interest. The collinear functions depend on an auxiliary vector nμ that can be either light-like (n2 = 0) or time-like (n2> 0). In the case of transverse-momentum dependent (TMD) collinear functions, we show that the use of a time-like auxiliary vector avoids the rapidity divergences, which are instead present if n2 = 0. The perturbative computation of the collinear functions lead to infrared (IR) divergences that can be properly factorized with respect to IR finite functions that embody the logarithmically-enhanced collinear contributions to hard-scattering cross sections. We evaluate various collinear functions and their nμ dependence at mathcal{O} (αS). We compute the azimuthal-correlation component of the TMD collinear functions at mathcal{O}left({alpha}_{textrm{S}}^2right) , and we present the results of the mathcal{O}left({alpha}_{textrm{S}}^2right) contribution of linearly-polarized gluons to transverse-momentum resummation formulae. Beyond mathcal{O}left({alpha}_{textrm{S}}^2right) the collinear functions of initial-state colliding partons are process dependent, as a consequence of the violation of strict collinear factorization of QCD squared amplitudes.

Scale-certainty geometrically constrained independent vector analysis for determined blind source separation

Output scale ambiguity is a long-standing problem in blind source separation (BSS). Post-process is usually required to rescale the estimated results. Towards avoiding the computation load brought by rescaling post-process, this study proposes a new algorithm called scale-certainty geometrically constrained auxiliary function-based independent vector analysis (SCGC-AuxIVA), which is free of the scale ambiguity problem. SCGC-AuxIVA is derived from GC-AuxIVA by imposing unit response in the target direction as a hard constraint. Experimental results show that SCGC-AuxIVA achieves a comparable separation performance as GC-AuxIVA, while requiring a less computational cost.

Module intersection and uniform formula for iterative reduction of one-loop integrals

In this paper, we develop an iterative sector-level reduction strategy for Feynman integrals, which bases on module intersection in the Baikov representation and auxiliary vector for tensor structure. Using this strategy we have studied the reduction of general one-loop integrals, i.e., integrals having arbitrary tensor structures and arbitrary power for propagators. Inspired by these studies, a uniform and compact formula that iteratively reduces all one-loop integrals has been written down, where messy polynomials in integration-by-parts (IBP) relations have organized themselves to Gram determinants.

Stochastic synchronization for semi-Markovian complex dynamic networks with partly unknown transition rates

This paper investigates the synchronization of complex dynamic networks with time-varying delay and general semi-Markovian jump. The general transition rates include completely unknown and uncertain but bounded as two special cases. First, by introducing auxiliary vectors with a few nonorthogonal polynomials, two free-matrix-based integral inequalities are developed, which encompass some existing ones as special cases. Second, an integral- based delay-product-type Lyapunov-Krasovskii functional is constructed, which fully considers the information of time delay. By utilizing a deley-dependent controller, two sufficient conditions are derived to realize the global stochastic mean-square synchronization by employing the established inequalities to evaluate the infinitesimal generator of the functional. This paper takes all possibilities into consideration and divides the general transition rates into five cases, which is never investigated before. Finally a numerical example is given to show the effectiveness and practicality of the presented method.