Class Of Transformations Research Articles

Augmenting perception: How artificial intelligence transforms sensory substitution

What happens when artificial sensors are coupled with the human senses? Using technology to extend the senses is an old human dream, on which sensory substitution and other augmentation technologies have already delivered. Laser tactile canes, corneal implants and magnetic belts can correct or extend what individuals could otherwise perceive. Here we show why accommodating intelligent sensory augmentation devices not just improves but also changes the way of thinking and classifying former sensory augmentation devices. We review the benefits in terms of signal processing and show why non-linear transformation is more than a mere improvement compared to classical linear transformation.

Mem3DG: modeling membrane mechanochemical dynamics in 3D using discrete differential geometry

Biomembranes adopt varying morphologies that are vital to cellular functions. Many studies use computational modeling to understand how various mechanochemical factors contribute to membrane shape transformations. Compared to approximation-based methods (e.g., finite element method), the class of discrete mesh models offers greater flexibility to simulate complex physics and shapes in three dimensions; its formulation produces an efficient algorithm while maintaining coordinate-free geometric descriptions. However, ambiguities in geometric definitions in the discrete context have led to a lack of consensus on which discrete mesh model is theoretically and numerically optimal; a bijective relationship between the terms contributing to both the energy and forces from the discrete and smooth geometric theories remains to be established. We address this and present an extensible framework, $\texttt{Mem3DG}$, for modeling 3D mechanochemical dynamics of membranes based on Discrete Differential Geometry (DDG) on triangulated meshes. The formalism of DDG resolves the inconsistency and provides a unifying perspective on how to relate the smooth and discrete energy and forces. To demonstrate, $\texttt{Mem3DG}$ is used to model a sequence of examples with increasing mechanochemical complexity: recovering classical shape transformations such as 1) biconcave disk, dumbbell, and unduloid and 2) spherical bud on spherical, flat-patch membrane; investigating how the coupling of membrane mechanics with protein mobility jointly affects phase and shape transformation. As high-resolution 3D imaging of membrane ultrastructure becomes more readily available, we envision Mem3DG to be applied as an end-to-end tool to simulate realistic cell geometry under user-specified mechanochemical conditions.

New Elements of Analysis of a Degenerate Chenciner Bifurcation

A new transformation of parameters for generic discrete-time dynamical systems with two independent parameters is defined, for when the degeneracy occurs. Here the classical transformation of parameters (α1,α2)→(β1,β2) is not longer regular at (0,0); therefore, implicit function theorem (IFT) cannot be applied around the origin, and a new transformation is necessary. The approach in this article to a case of Chenciner bifurcation is theoretical, but it can provide an answer for a number of applications of dynamical systems. We studied the bifurcation scenario and found out that, by this transformation, four different bifurcation diagrams are obtained, and the non-degenerate Chenciner bifurcation can be described by two bifurcation diagrams.

SpectralFormer: Rethinking Hyperspectral Image Classification With Transformers

Hyperspectral (HS) images are characterized by approximately contiguous spectral information, enabling the fine identification of materials by capturing subtle spectral discrepancies. Due to their excellent locally contextual modeling ability, convolutional neural networks (CNNs) have been proven to be a powerful feature extractor in HS image classification. However, CNNs fail to mine and represent the sequence attributes of spectral signatures well due to the limitations of their inherent network backbone. To solve this issue, we rethink HS image classification from a sequential perspective with transformers and propose a novel backbone network called <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SpectralFormer</u> . Beyond bandwise representations in classic transformers, SpectralFormer is capable of learning spectrally local sequence information from neighboring bands of HS images, yielding groupwise spectral embeddings. More significantly, to reduce the possibility of losing valuable information in the layerwise propagation process, we devise a cross-layer skip connection to convey memory-like components from shallow to deep layers by adaptively learning to fuse “soft” residuals across layers. It is worth noting that the proposed SpectralFormer is a highly flexible backbone network, which can be applicable to both pixelwise and patchwise inputs. We evaluate the classification performance of the proposed SpectralFormer on three HS datasets by conducting extensive experiments, showing the superiority over classic transformers and achieving a significant improvement in comparison with state-of-the-art backbone networks. The codes of this work will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/danfenghong/IEEE_TGRS_SpectralFormer</uri> for the sake of reproducibility.

Industrial Internet platform promotes digital transformation of SMEs in China

This article analyzes the three major problems faced by Chinese SMEs in digital transformation: &amp;quot;don&amp;apos;t want to transform&amp;quot;, &amp;quot;don&amp;apos;t dare to transform&amp;quot;, and &amp;quot;don&amp;apos;t know how to transform&amp;quot; against the background of the fact that the industrial The Internet in China has caused restructuring and accelerated the reform of the manufacturing industry. This article summarizes the classic digital transformation path for SMEs and explores new options and new opportunities for SMEs in the face of digital transformation under the industrial Internet model.

Segmentation of Multi-Band Images Using Watershed Arcs

Watershed Arcs Removal for node-weighted graphs method addressed the over-segmentation problem of classical watershed transformation, in a significantly shorter run-time. In this study, a variation of Watershed Arcs Removal is proposed that generates hierarchical partitioning in an edge-weighted graph. In the proposed method, regions are grown from the nodes having high local similarity to find the initial arcs, and neighbouring regions are merged by gradually removing arcs with low local dissimilarity. The arcs to be removed in a level are selected solely from the arc-graph constructed from the existing arcs in the previous level, weighted by their local dissimilarity. In contrast to the node-weighted variation, a strategy is employed here to preserve the critical arcs. Although the proposed method can be effectively applied to any multi-band image by transforming it into an edge-weighted graph, in this study we evaluated its performance particularly in RGB image segmentation.

ТЕПЛОПРОВОДНОСТЬ В ОДНОРОДНОЙ ПОЛОСЕ С ЛИНЕЙНЫМ ИЗМЕНЕНИЕМ ТОЛЩИНЫ ПРИ ГРАНИЧНЫХ УСЛОВИЯХ ПЕРВОГО РОДА

An accurate analytical solution has been obtained in quadratures of the initial boundary value problem for one-dimensional unsteady-state heat-transfer equation with boundary conditions of the first kind for an endless strip, while one of its boundaries is moving at a constant preset speed decreasing the strip thickness. Preliminarily, through the self-similar change of the spatial variable, the initial system of equations has been reduced to a fixed boundary system, to which the method of partitioning of dependent variables has been applied. The requirement that the coefficients before the firstorder derivative must be equal to zero for the self-similar derivative and separately included function in a modified equation in partial derivative of parabolic type has allowed to determine the general structure of the solution containing an unknown function. This function is presented as a superposition of two potentials, which are proportionally connected using the self-similar derivative, what has made it possible to simplify the modified equation and to apply the classical Fourier sine integral transformation for its solution. The computation results has shown the dynamics of the local temperature profile along the changing strip thickness at a constant speed, while the kinetics of the average integral temperature shows (unlike with the case of absence of boundary movement) the presence of the maximum that shifts with the growth of the ratio of the boundary movement speed to the heat transfer speed by the conductivity to the fixed boundary. This is explained by the intensive heating up of the strip material in the conditions of the decreasing of its thickness; meanwhile, with the increase in the boundary movement speed (or with the use of material with reduced thermal conductivity), it approaches the fixed boundary. By assuming that the strip thickness is a parameter, the problem in the initial wording is solved using the method of the one-sided Laplace integral time transformation. This solution, when using the linear dependence of parameter on time, correlates with the obtained accurate solution, and therefore it can be used for the preliminary evaluation of the required characteristics of a process under consideration.

Chronic lymphocytic leukemia (CLL) with Reed\u2013Sternberg-like cells vs Classic Hodgkin lymphoma transformation of CLL: does this distinction matter?

The distinction between chronic lymphocytic leukemia/small lymphocytic lymphoma (CLL/SLL) with isolated Hodgkin/Reed–Sternberg cells (CLL-HRS; background milieu with a paucity of inflammatory cells) and overt transformation to classic Hodgkin lymphoma (CLL-HL; mixed inflammatory background) is incompletely understood. This retrospective study examined the clinicopathologic features of CLL-HRS (n = 15) and CLL-HL (n = 31) patients seen over the past three decades from a single institution. The phenotypic features of Reed–Sternberg cells in both groups were similar, including expression of CD30, CD15, and PAX5, as well as EBV status. However, a spectrum of background CLL/SLL infiltration amongst the HRS cells was noted on pathologic review, and four patients had both diagnoses, either concurrently or in succession. The median overall survival (OS) of patients with CLL-HRS was 17.5 months compared to 33.5 months for patients with CLL-HL (P = 0.24). Among patients with CLL-HRS, those who received Hodgkin-directed therapy had a significantly longer median OS (57 months) compared to those who received CLL-directed therapy (8.4 months, P = 0.02). Our clinical and pathologic findings suggest a biologic continuum between CLL-HRS and CLL-HL and indicate that CLL-HRS patients may benefit from Hodgkin-directed therapy.

Uniform Scaling: Relativistic Energy-Momentum Relationships

A number of the most often cited results of relativity theory deal with the relationships between energy, momentum and inertial mass. The history of how Einstein and Planck came to these conclusions is reviewed. It is pointed out that considerations of how the speed of light is affected by the motion of the Earth played a determining role in these developments. After the Michelson-Morley null-interference result became available, Voigt introduced a new space-time transformation by amending the classical Galilean transformation so that the speed of light in free space has the same value of c regardless of the state of motion of both the light source and the observer. This led to the Lorentz transformation which has been the cornerstone of relativity theory for the past century. A thought experiment is presented which proves, however, that there are many situations for which the measured speed of light is NOT equal to c. Furthermore, it is pointed out that the rate of an inertial clock cannot change spontaneously, which result is perfectly compatible with Newton’s First Law of Kinetics (Law of Inertia). This result contradicts the space-time mixing characteristic of the Lorentz transformation and leads to the conclusion that events which are spontaneous for one inertial frame will also be so for every other one. The uniform scaling procedure is a generalization of this result for all other physical properties than elapsed times. Its application shows that the commonly accepted relationships between energy and momentum are only special cases in which it is assumed that the observer is stationary in the rest frame in which force has been applied to cause the object’s acceleration

Recent Advances in Chemical Modifications of Nitriles

AbstractNitrile represents a synthetically important class of compounds, relevant to the preparation of fine organic chemicals, natural products, and biologically active molecules. The electronegative nitrogen atom and electropositive carbon atom of cyano group endow nitriles with rich and diverse chemical properties. Traditional chemical transformations of nitriles usually involve the use of strong acids, bases, and other harsh conditions. To overcome these limitations, various catalytic methods have been explored both in homogeneous and heterogeneous systems. In this review, we summarize some of the recent progress on the development of efficient synthetic methods involving nitriles. Especially, the classic transformations of nitriles including hydration, hydrolysis and reduction reactions are discussed in detail, which provide rapid access to a series of important small molecules, such as amides, ketones, amines, and imines. The reaction development, as well as the design of the catalytic system will be discussed with special emphasis placed on the mechanistic aspects.

MVE-FLK: A multi-task legal judgment prediction via multi-view encoder fusing legal keywords

Legal Judgment Prediction (LJP) aims to predict the judgment result based on the fact description of a criminal case, and is gradually becoming a hot research topic in the legal realm. Generally, a classic LJP contains three subtasks, i.e., applicable law article prediction, charge prediction, and term of penalty prediction. In real-world scenarios, both charge prediction and applicable law article prediction are actually the tasks of multi-class classification with multi-label learning. However, most existing studies model them as the problems of multi-class classification with single-label learning. Besides, they only consider the context of the fact description, and ignore the exploitation of effective keywords that are widely existed in abundant law articles. To fill the above gaps, we propose a novel multi-task legal judgment prediction framework via multi-view encoder fusing legal keywords, named MVE-FLK, to jointly model multiple subtasks in LJP. Specifically, the multi-view encoder is the core module of MVE-FLK, in this module, we devise a word and sentence encoder (WSE) with an attention mechanism to fuse legal keywords. And then, we develop a multi-view attention network to combine WSE with classic Transformer and DAN (Deep Averaging Network) for encoding the case from multiple views. After that, we propose a multi-task prediction module by developing a novel keywords fusing approach to enhance the performance of multi-task prediction. In addition, we devise a unique prediction principle for each subtask at a fine-grained level, which effectively improves the performance of subtasks. The experimental results on two real-life legal datasets show that our model yields significant prediction performance advantages over six competitive methods.

Analysis of free convective hydromagnetic flow of heat generating/absorbing fluid in an annulus with isothermal and adiabatic boundaries

The present analysis explores free convective flow of magnetohydrodynamic fluid passing through the annular gap formed by two concentric cylinders with uniform heat generation/absorption. Transient solution of the resulting systems of non-dimensional partial differential equations describing the flow were attained by a two-step process involving the classical Laplace transformation and a numerical based inversion technique known as Riemann sum approximation (RSA). Exact solution of the governing equations has been derived to establish the capability and truthfulness of the proposed method. The outcome of the various non-dimensional controlling parameters involved in the problem on the thermal field, velocity field, heat transfer rate and shear stresses are discussed with the aid of plots. It is noted that the influence of heat generation/absorption is to boost/suppress the thermal and hydrodynamic attributes of the flow. Low Prandtl valued fluids are found to properly support heat and mass transfer. Skin frictions can be minimized by escalating Hartmann number and viscosity parameter.

Reparameterization Invariant Model of a Supersymmetric System: BRST and Supervariable Approaches

We carry out the Becchi-Rouet-Stora-Tyutin (BRST) quantization of the one 0 + 1 -dimensional (1D) model of a free massive spinning relativistic particle (i.e., a supersymmetric system) by exploiting its classical infinitesimal and continuous reparameterization symmetry transformations. We use the modified Bonora-Tonin (BT) supervariable approach (MBTSA) to BRST formalism to obtain the nilpotent (anti-)BRST symmetry transformations of the target space variables and the (anti-)BRST invariant Curci-Ferrari- (CF-) type restriction for the 1D model of our supersymmetric (SUSY) system. The nilpotent (anti-)BRST symmetry transformations for other variables of our model are derived by using the (anti-)chiral supervariable approach (ACSA) to BRST formalism. Within the framework of the latter, we have shown the existence of the CF-type restriction by proving the (i) symmetry invariance of the coupled Lagrangians and (ii) the absolute anticommutativity property of the conserved (anti-)BRST charges. The application of the MBTSA to a physical SUSY system (i.e., a 1D model of a massive spinning particle) is a novel result in our present endeavor. In the application of ACSA, we have considered only the (anti-)chiral super expansions of the supervariables. Hence, the observation of the absolute anticommutativity of the (anti-)BRST charges is a novel result. The CF-type restriction is universal in nature as it turns out to be the same for the SUSY and non-SUSY reparameterization (i.e., 1D diffeomorphism) invariant models of the (non-)relativistic particles.

Forecasting the seasonal natural gas consumption in the US using a gray model with dummy variables

To forecast the seasonal fluctuations of US natural gas consumption accurately, a novel gray model based on seasonal dummy variables and its derived model are separately established. Then, the approximate time response formula in the proposed seasonal forecasting model is optimally calculated by using particle swarm optimization algorithm. On this basis, empirical analysis is conducted using data pertaining to natural gas consumption in the US during 2010–2019. The results show that gray model based on seasonal dummy variables and its derived model can recognize seasonal fluctuations in US natural gas consumption, whose prediction performances are much better than that of a traditional gray model, autoregressive integrated moving average (ARIMA), support vector regression (SVR), recurrent neural network (RNN), transformer, and fuzzy time series forecasting models. The mean absolute percentage errors (MAPEs) of the proposed seasonal gray forecasting model and its derived model, the classic gray model, ARIMA, SVR, RNN, Transformer, and Fuzzy time series forecasting models are 3.46%, 2.37%, 12.23%, 3.39%, 2.38%, 3.08%, 3.84%, and 5.02% in the training set, while those are 4.57%, 4.42%, 12.44%, 7.9%, 8.09%, 11.33%, 35.19% and 13.19% in the test set, respectively. The predicted and empirical results obtained by utilizing the proposed gray model implied that natural gas consumption in the US from 2020 to 2022 will maintain its seasonal growth and periodic changes, with the highest and the lowest values in the first and second quarters, respectively.

Extensions of classical hypergeometric identities of Bailey and Whipple

We obtain extensions of classical hypergeometric identities of Bailey and Whipple that transform nearly-poised and very-well-poised series to Saalschützian series, Saalschützian series to Saalschützian series, and very-well-poised and nearly-poised series to very-well-poised series. We employ a method in which summations and transformations of lower-order series are used to obtain transformations of higher-order series. By taking limits, we also obtain extensions of two classical quadratic transformations of Whipple and Bailey. Furthermore, we show how a number of other well-known results regarding hypergeometric series follow as special cases of our results.

Dynamics of a flexible body: a two-field formulation

A two-field formulation of the nonlinear dynamics of an elastic body is presented in which positions/orientations and the resulting velocity field are treated as independent. Combining a nonclassical description of elastic velocity that includes the convection velocity due to elastic deformation with floating reference axes minimizing the relative kinetic energy due to elastic deformation provides a fully uncoupled expression of kinetic energy. A transformation inspired by the classical Legendre transformation concept is introduced to develop the motion equations in canonical form. Finite element discretization is achieved using the same shape function sets for elastic displacements and velocities. Specific attention is brought to the discretization of the gyroscopic forces induced by elastic deformation. A model reduction strategy to construct superelement models suitable for flexible multibody dynamics applications is proposed, which fulfills the essential condition of orthogonality between a rigid body and elastic motions. The problem of expressing kinematic connections at superelement boundaries is briefly addressed. Two academic examples have been developed to illustrate some of the concepts presented.

Fixed-time formation tracking for multiple nonholonomic wheeled mobile robots based on distributed observer

This paper studies the distributed fixed-time formation tracking problem of multiple nonholonomic wheeled mobile robots system over directed fixed and switching topologies. Through a classical nonlinear transformation, the formation control problem is transformed into a consensus problem. New control protocols based on a distributed observer are proposed. The directed communication topology between multiple nonholonomic wheeled mobile robots is considered. Some sufficient conditions of multiple robots achieving the desired formation shape are given. All follower robots can form the desired formation shape within a fixed settling time and make the leader in the geometric center of the formation. By adopting graph theory and fixed-time stability theory, an upper bound of settling time that is independent of the system’s initial states is obtained. Finally, two examples are presented to illustrate the correctness of the main results.

The symmetry analysis and analytical studies of the rotational GreenNaghdi (R-GN) equation

The simplified phenomenological model of long-crested shallow-water wave propagations is considered without/with the Coriolis effect. Symmetry analysis is taken into consideration to obtain exact solutions. Both classical wave transformation and transformations are obtained with symmetries and solvable equations are kept thanks to these transformations. Additionally, the exact solutions are obtained via various methods which are ansatz-based methods. The obtained results have a major role in the literature so that the considered equation is seen in a large scale of applications in the area of geophysical.

Nucleophilic Phosphine Catalysis: The Untold Story.

This Minireview provides insight into the early history of nucleophilic phosphinocatalysis. The concepts of 1,4-addition of a tertiary phosphine to an α,β-enone and of equilibrium between the resulting phosphonium zwitterion and phosphonium ylide established a fundamental basis for the development of several classical transformations, including the Rauhut-Currier, Morita, McClure-Baizer-Anderson, and Oda reactions.

Generative inter-class transformations for imbalanced data weather classification

This paper presents an evaluation of how data augmentation and inter-class transformations can be used to synthesize training data in low-data scenarios for single-image weather classification. In such scenarios, augmentations is a critical component, but there is a limit to how much improvements can be gained using classical augmentation strategies. Generative adversarial networks (GAN) have been demonstrated to generate impressive results, and have also been successful as a tool for data augmentation, but mostly for images of limited diversity, such as in medical applications. We investigate the possibilities in using generative augmentations for balancing a small weather classification dataset, where one class has a reduced number of images. We compare intra-class augmentations by means of classical transformations as well as noise-to-image GANs, to interclass augmentations where images from another class are transformed to the underrepresented class. The results show that it is possible to take advantage of GANs for inter-class augmentations to balance a small dataset for weather classification. This opens up for future work on GAN-based augmentations in scenarios where data is both diverse and scarce.