Spherical Representation Research Articles

Deep indoor illumination estimation based on spherical gaussian representation with scene prior knowledge

High dynamic range illumination estimation from a single low dynamic range image is a critical task in the fields of computer vision, graphics and augmented reality. However, directly learning the full HDR environment map or parametric lighting information from a single image is extremely difficult and inaccurate. As a result, we propose a two-stage network approach for illumination estimation that integrates spherical gaussian (SG) representation with scene prior knowledge. In the first stage, a convolutional neural network is utilized to generate material and geometric information about the scene, which serves as prior knowledge for lighting prediction. In the second stage, we model indoor environment illumination using 128 SG functions with fixed center direction and bandwidth, allowing only the amplitude to vary. Subsequently, a Transformer-based lighting parameter regressor is employed to capture the complex relationship between the input images with scene prior information and its SG illumination. Additionally, we introduce a hybrid loss function, which combines a masked loss for high-frequency illumination with a rendering loss for improving the visual quality. By training and evaluating the lighting model on the created SG illumination dataset, the proposed method achieves competitive results in both quantitative metrics and visual quality, outperforming state-of-the-art methods.

Numerical study on inter-particle effects for multiple reacting biomass and coal particles based on Micro-CT morphology

Co-firing of biomass with coal combines the environmental benefits of renewable biomass with the high energy content of coal. Although the common numerical simulation treats the biomass and coal particles with ideal morphology, real particles often demonstrate nonsmoothed surface and irregular shape. To understand the impact of particle morphology in a group of biomass and coal particles co-firing together and to inform simple models appropriate, this study investigated the interparticle effects among particles using realistic particle morphology, focusing on fluid dynamics such as temperature distribution, flow patterns and drag coefficients. Particle-scale computational fluid dynamics (CFD) simulations using micro-CT imaging showed that realistic particle shapes resulted in nonuniform flow fields and temperature distributions with different reaction intensities due to the species transportation. It is in contrast to traditional ideal shape models, which often rely on simplified spherical representations of particles and cannot capture the intricacies of real particle shapes. Realistic models revealed more complex surfaces, highly irregular particle structures, and varied reaction zones that affected the overall dynamics. In addition, changing the orientation of one particle affects the combustion characteristics of neighboring particles. This effect is also not captured while using the spherical structure. These differences underscore the critical impact of particle morphology on drag and heat transfer, thereby challenging conventional spherical models. The findings of this study advocate for a paradigm shift in CFD modeling approaches, emphasizing the importance of realistic particle representation to improve the accuracy of predictions and enhance the co-firing system efficiency.

Solidification and crystallographic texture modeling of laser powder bed fusion Ti-6Al-4V using finite difference-monte carlo method

Laser powder bed fusion (LPBF) additive manufacturing makes near-net-shaped parts with reduced material cost and time, rising as a promising technology to fabricate Ti-6Al-4 V, a widely used titanium alloy in aerospace and medical industries. However, LPBF Ti-6Al-4 V parts produced with 67° rotation between layers, a scan strategy commonly used to reduce microstructure and property inhomogeneity, have varying grain morphologies and weak crystallographic textures that change depending on processing parameters. This study predicts LPBF Ti-6Al-4 V solidification at three energy levels using a finite difference-Monte Carlo method and validates the simulations with large-area electron backscatter diffraction (EBSD) scans. The developed model accurately shows that a 〈001〉 texture forms at low energy and a 〈111〉 texture occurs at higher energies parallel to the build direction but with a lower strength than the textures observed from EBSD. A validated and well-established method of combining spatial correlation and general spherical harmonics representation of texture is developed to calculate a difference score between simulations and experiments. The quantitative comparison enables effective fine-tuning of nucleation density (N0) input, which shows a nonlinear relationship with increasing energy level. Future improvements in texture prediction code and a more comprehensive study of N0 with different energy levels will further advance the optimization of LPBF Ti-6Al-4 V components. These developments contribute a novel understanding of crystallographic texture formation in LPBF Ti-6Al-4 V, the development of robust model validation and calibration pipeline methodologies, and provide a platform for mechanical property prediction and process parameter optimization.

Two types of Witten zeta functions

We define two types of Witten’s zeta functions according to Cartan’s classification of compact symmetric spaces. The type II is the original Witten zeta function constructed by means of irreducible representations of the simple compact Lie group U. The type I Witten zeta functions, we introduce here, are related to the irreducible spherical representations of U. They arise in the harmonic analysis on compact symmetric spaces of the form U/K, where K is the maximal subgroup of U. To construct the type I zeta function we calculate the partition functions of 2d YM theory with broken gauge symmetry using the Migdal–Witten approach. We prove that for the rank one symmetric spaces the generating series for the values of the type I functions with integer arguments can be defined in terms of the generating series of the Riemann zeta-function.

The social and political bases of political economy: interpreting and periodising Italian developments since WWII

ABSTRACT We investigate the transition between Fordism and neoliberalism in Italy employing insights from International (IPE) and Comparative Political Economy (CPE). Transitions are key periods to observe how countries tend to converge towards similar political economy developments through processes of social, political and institutional adaptation. We contribute to the literature by detailing how transformations in the ‘social and political bases of political economy’ influence institutional change. We understand these transformations as movements from below (changes in sociodemographic and productive compositions, and social movements) and above (agency in political parties, trade unions, business associations and elites); we name these components ‘the social and productive composition’ and ‘the social and political representation sphere’ respectively. Employing process tracing, we identify three historical periods in post-WWII Italian political economy – state Fordism (1945–78), a transitional period we name ‘the long 1980s’ (1979–91), and neoliberalism (1992–) – delineated by two historical ruptures: a breakup moment in 1978–9 that signalled the decline of Fordism, and a critical juncture that ushered in neoliberalism in 1992. Analytically, changes within the social and productive composition during state Fordism impacted on the social and political representation sphere, and on several reforms undertaken in different institutional domains. However, since the breakup moment 1978–9 this dynamic changed together with the international political economy context. Similar to other western countries, the main actors within the social and political representation sphere increasingly approached institutional regulation inspired by neoliberal ideas, while the pressures coming from the more active forces within the social and productive composition faltered.

Intracranial aneurysm detection: an object detection perspective.

Intracranial aneurysm detection from 3D Time-Of-Flight Magnetic Resonance Angiography images is a problem of increasing clinical importance. Recently, a streak of methods have shown promising performance by using segmentation neural networks. However, these methods may be less relevant in a clinical settings where diagnostic decisions rely on detecting objects rather than their segmentation. We introduce a 3D single-stage object detection method tailored for small object detection such as aneurysms. Our anchor-free method incorporates fast data annotation, adapted data sampling and generation to address class imbalance problem, and spherical representations for improved detection. A comprehensive evaluation was conducted, comparing our method with the state-of-the-art SCPM-Net, nnDetection and nnUNet baselines, using two datasets comprising 402 subjects. The evaluation used adapted object detection metrics. Our method exhibited comparable or superior performance, with an average precision of 78.96%, sensitivity of 86.78%, and 0.53 false positives per case. Our method significantly reduces the detection complexity compared to existing methods and highlights the advantages of object detection over segmentation-based approaches for aneurysm detection. It also holds potential for application to other small object detection problems.

Realization of unitary representations of the Lorentz group on de Sitter space

This paper builds on our previous work in which we showed that, for all connected semisimple linear Lie groups G acting on a non-compactly causal symmetric space M=G/H, every irreducible unitary representation of G can be realized by boundary value maps of holomorphic extensions in distributional sections of a vector bundle over M. In the present paper we discuss this procedure for the connected Lorentz group G=SO1,d(R)e acting on de Sitter space M=dSd. We show in particular that the previously constructed nets of real subspaces satisfy the locality condition. Following ideas of Bros and Moschella from the 1990’s, we show that the matrix-valued spherical function that corresponds to our extension process extends analytically to a large domain Gℂcut in the complexified group Gℂ=SO1,d(ℂ), which for d=1 specializes to the complex cut plane ℂ∖(−∞,0]. A number of special situations is discussed specifically: (a) The case d=1, which closely corresponds to standard subspaces in Hilbert spaces, (b) the case of scalar-valued functions, which for d>2 is the case of spherical representations, for which we also describe the jump singularities of the holomorphic extensions on the cut in de Sitter space, (c) the case d=3, where we obtain rather explicit formulas for the matrix-valued spherical functions.

Spherical Pseudo-Cylindrical Representation for Omnidirectional Image Super-resolution

Omnidirectional images have attracted significant attention in recent years due to the rapid development of virtual reality technologies. Equirectangular projection (ERP), a naive form to store and transfer omnidirectional images, however, is challenging for existing two-dimensional (2D) image super-resolution (SR) methods due to its inhomogeneous distributed sampling density and distortion across latitude. In this paper, we make one of the first attempts to design a spherical pseudo-cylindrical representation, which not only allows pixels at different latitudes to adaptively adopt the best distinct sampling density but also is model-agnostic to most off-the-shelf SR methods, enhancing their performances. Specifically, we start by upsampling each latitude of the input ERP image and design a computationally tractable optimization algorithm to adaptively obtain a (sub)-optimal sampling density for each latitude of the ERP image. Addressing the distortion of ERP, we introduce a new viewport-based training loss based on the original 3D sphere format of the omnidirectional image, which inherently lacks distortion. Finally, we present a simple yet effective recursive progressive omnidirectional SR network to showcase the feasibility of our idea. The experimental results on public datasets demonstrate the effectiveness of the proposed method as well as the consistently superior performance of our method over most state-of-the-art methods both quantitatively and qualitatively.

Latent assimilation with implicit neural representations for unknown dynamics

Data assimilation is crucial in a wide range of applications, but it often faces challenges such as high computational costs due to data dimensionality and incomplete understanding of underlying mechanisms. To address these challenges, this study presents a novel assimilation framework, termed Latent Assimilation with Implicit Neural Representations (LAINR). By introducing Spherical Implicit Neural Representations (SINR) along with a data-driven uncertainty estimator of the trained neural networks, LAINR enhances efficiency in the assimilation process. Experimental results indicate that LAINR holds a certain advantage over existing methods based on AutoEncoders, both in terms of accuracy and efficiency.

A simple three-cylinder radiometer and low-speed anemometer to characterize human extreme heat exposure.

As populations and temperatures of urban areas swell, more people face extreme heat and are at increasing risk of adverse health outcomes. Radiation accounts for much of human heat exposure but is rarely used as heat metric due to a lack of cost-effective and accurate sensors. To this end, we fuse the concepts of a three-globe radiometer-anemometer with a cylindrical human body shape representation, which is more realistic than a spherical representation. Using cost-effective and readily available materials, we fabricated two combinations of three cylinders with varying surface properties. These simple devices measure the convection coefficient and the shortwave and longwave radiative fluxes. We tested the devices in a wind tunnel and at fourteen outdoor sites during July 2023's record-setting heat wave in Tempe, Arizona. The average difference between pedestrian-level mean radiant temperature (MRT) measured using research-grade 3-way net radiometers and the three-cylinder setup was 0.4 3.0°C ( 1 SD). At most, we observed a 10°C MRT difference on a white roof site with extreme MRT values (70°C to 80°C), which will be addressed through discussed design changes to the system. The measured heat transfer coefficient can be used to calculate wind speed below 2m·s-1; thus, the three cylinders combined also serve as a low-speed anemometer. The novel setup could be used in affordable biometeorological stations and deployed across urban landscapes to build human-relevant heat sensing networks.

SPLiT: Single Portrait Lighting Estimation via a Tetrad of Face Intrinsics.

This paper proposes a novel pipeline to estimate a non-parametric environment map with high dynamic range from a single human face image. Lighting-independent and -dependent intrinsic images of the face are first estimated separately in a cascaded network. The influence of face geometry on the two lighting-dependent intrinsics, diffuse shading and specular reflection, are further eliminated by distributing the intrinsics pixel-wise onto spherical representations using the surface normal as indices. This results in two representations simulating images of a diffuse sphere and a glossy sphere under the input scene lighting. Taking into account the distinctive nature of light sources and ambient terms, we further introduce a two-stage lighting estimator to predict both accurate and realistic lighting from these two representations. Our model is trained supervisedly on a large-scale and high-quality synthetic face image dataset. We demonstrate that our method allows accurate and detailed lighting estimation and intrinsic decomposition, outperforming state-of-the-art methods both qualitatively and quantitatively on real face images.

B2B collaborative economy: a joint sphere perspective

PurposeScholars have affirmed that a conceptualization of value co-creation in business relationships should reflect the nature and characteristics of interactional processes that occur in use. The advent of sales and marketing technologies, however, is changing the nature and dynamics of interactions. New trends in digitalization have played a significant role in emphasizing and facilitating the occurrence of business-to- business (B2B) collaborative or sharing economy. The B2B sharing economy and value co-creation are closely intertwined, as businesses harness the power of shared resources and collaboration to generate value in diverse ways. This study highlights the importance of going beyond value co-creation in studying B2B collaborative economy, unpacking the interconnected value processes that influence value co-creation. It also aims at showing the activities that characterize multiple joint value spheres among actors.Design/methodology/approachThe study consists of 49 qualitative interviews with managers operating in different industries.FindingsThe paper shows that when considering digital B2B contexts, five joint value spheres in business relationships should be considered: a value co-creation, a value appropriation, a value communication, a value measurement and a value representation sphere. Each one is characterized by specific activities that are relevant from a managerial point of view.Originality/valueThis study highlights that value co-creation has often been over stressed when discussing business interactions, also with the advent of new technologies. Rather, this study offers a more comprehensive view of value co-creation that includes different value processes occurring in joint value spheres. These further processes are relevant because failure and success in business relationships within the B2B sharing economy are often dependent from activities outside the value co-creation process, which strongly affect it. Such knowledge will also open up new research venues and opportunities to better contribute to the practice of value management in business relationships.

Dream360: Diverse and Immersive Outdoor Virtual Scene Creation via Transformer-Based 360° Image Outpainting.

360° images, with a field-of-view (FoV) of $180^{\circ}\times 360^{\circ}$, provide immersive and realistic environments for emerging virtual reality (VR) applications, such as virtual tourism, where users desire to create diverse panoramic scenes from a narrow FoV photo they take from a viewpoint via portable devices. It thus brings us to a technical challenge: 'How to allow the users to freely create diverse and immersive virtual scenes from a narrow FoV image with a specified viewport?' To this end, we propose a transformer-based 360° image outpainting framework called Dream360, which can generate diverse, high-fidelity, and high-resolution panoramas from user-selected viewports, considering the spherical properties of 360° images. Compared with existing methods, e.g., [3], which primarily focus on inputs with rectangular masks and central locations while overlooking the spherical property of 360° images, our Dream360 offers higher outpainting flexibility and fidelity based on the spherical representation. Dream360 comprises two key learning stages: (I) codebook-based panorama outpainting via Spherical-VQGAN (S-VQGAN), and (II) frequency-aware refinement with a novel frequency-aware consistency loss. Specifically, S-VQGAN learns a sphere-specific codebook from spherical harmonic (SH) values, providing a better representation of spherical data distribution for scene modeling. The frequency-aware refinement matches the resolution and further improves the semantic consistency and visual fidelity of the generated results. Our Dream360 achieves significantly lower Frechet Inception Distance (FID) scores and better visual fidelity than existing methods. We also conducted a user study involving 15 participants to interactively evaluate the quality of the generated results in VR, demonstrating the flexibility and superiority of our Dream360 framework.

Fast multi-compartment Microstructure Fingerprinting in brain white matter.

We proposed two deep neural network based methods to accelerate the estimation of microstructural features of crossing fascicles in the white matter. Both methods focus on the acceleration of a multi-dictionary matching problem, which is at the heart of Microstructure Fingerprinting, an extension of Magnetic Resonance Fingerprinting to diffusion MRI. The first acceleration method uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation. The second acceleration method relies on a feed-forward neural network that uses a spherical harmonics representation of the DW-MRI signal as input. The first method exhibits a high interpretability while the second method achieves a greater speedup factor. The accuracy of the results and the speedup factors of several orders of magnitude obtained on in vivo brain data suggest the potential of our methods for a fast quantitative estimation of microstructural features in complex white matter configurations.

"Deus ex machina" or moving sculptures in the interior of Lutheran churches of Latvia

The term “Deus ex machina” was used in ancient Greek and Roman theatre performances as stage machinery provided the appearance of a god or hero to unravel and resolve a hopeless situation. The present article characterizes the appearance, function, and use of moving sculptures - automatons - in Latvian Baroque church interiors. Renaissance science sparked developments in mathematics, astronomy, and mechanics that stimulated not only the invention of curiosities, but also the construction of practical mechanisms and tools. Knowledge transfer from mechanics to applied and fine arts brought moments of mystery and wonder into the fields of performing arts and the spheres of representation and liturgy. The use of automatons helped to invent new mechanisms, making the mechanical movement of “animated sculptures” an attractive public entertainment. Both written evidence and artefacts preserved in museums and churches bear witness to the social demand for the integration of theatrical elements into the formerly conservative environment, for example, the church. Drawing inspiration from the culturally linked region of former Prussia, the current paper concentrates on moving figures in the interior space of Lutheran churches in Latvia to expose the variety of fascinating objects, including organ facades and baptismal fonts.

A study on the impact of theater performing arts combined with drama arts on the stage in the context of big data

Abstract In this paper, based on the law of motion, combining the spherical coordinate system representation with the drama art of action feature recognition in the process of theatrical performance, the joint points of wrist, elbow and shoulder and the corresponding feature vectors as well as the school angular velocity are calculated. The affine invariant domain of feature matching is constructed, and the algorithms of dynamic frame difference merging and dynamic detection of human-computer interaction are applied to obtain similar feature components of the recognition of body movements and gesture commands in the process of theatrical performance. The harmony factor, emotion factor and contrast factor in the stage scene are used as evaluation parameters to derive the color evaluation function and keyframes of theatrical stage lighting combined with drama art. For the research purpose, the research on the stage influence of the theatrical performance of combining drama is designed, and the example analysis of the stage influence of the theatrical performance of combining drama is analyzed by using origin software. The results show that the level of the audience’s arousal dimension is significantly higher than its level in CSP, t(1, 29)=3.959, p<0.001, upper threshold (Engagement=0.38, Arousal=0.26), lower threshold (Engagement=0.35, Arousal=0.21), i.e., the specific performance episode for the audience to bring about significant emotional relaxation. Audience ratings of CAC were generally higher than their ratings of CSP, with differences ranging from 0.2 to 1.5. The mean times that subjects were in the “S(n)=1” state in the three modes were 219.86 (SD=60.62), 287.37 (SD=80.68), and 310.7 (SD=45.83), respectively. 45.83). This study greatly improves the impact of stage art and gives a whole new life to the combination of theater performance and drama stage art.

Nonspherically-symmetric black hole in Einstein-massless scalar theory

We introduce a two-parameter static, nonspherically-symmetric black hole solution in the Einstein theory of gravity coupled with a massless scalar field. The scalar field depends only on the polar coordinate θ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta $$\\end{document} in the spherical coordinates representation. The scalar invariant of the spacetime, namely, the Kretschmann scalar reveals that the black hole is singular on its axis of symmetry for all nonzero values of the scalar charge. It also manifests two surfaces of singularity for a certain interval of the value of the scalar charge. These singularities are hidden behind the event horizon’s surface except the singularity on the axis which is naked. The energy-momentum of the scalar field satisfies all energy conditions. A simple investigation reveals that the circular orbits of massive and massless particles in the equatorial plane are identical to those near a Schwarzschild black hole.

Проблема репрезентации: постмодернизм, критическая теория и медиадискурс

The purpose of the article is to characterize the possibilities and limitations of the representation of offline culture in the online space. The article uses methods of historical and philosophical, comparative analysis and critical discourse analysis. The specifics and genesis of the concept of “postmodernism” are determined in connection with the need to understand the essence of modern culture, which can be represented as divided into online and offline spaces. The problems of representation in the media discourse, which is predominantly quasi-political in nature, are identified and characterized. The sensitive spheres of representation are indicated: gender, racial, ideological. In the context of critical theory and postmodernism, the difficulties in representation associated with three factors are presented: the limitations of language and symbols, the bias and subjectivity of the authors, the complexity and versatility of the cultural phenomena themselves. Modern society is postmodern and capitalist. It is concluded that freedom for the “speaking subject” is declared.

Learning joint surface reconstruction and segmentation, from brain images to cortical surface parcellation.

Reconstructing and segmenting cortical surfaces from MRI is essential to a wide range of brain analyses. However, most approaches follow a multi-step slow process, such as a sequential spherical inflation and registration, which requires considerable computation times. To overcome the limitations arising from these multi-steps, we propose SegRecon, an integrated end-to-end deep learning method to jointly reconstruct and segment cortical surfaces directly from an MRI volume in one single step. We train a volume-based neural network to predict, for each voxel, the signed distances to multiple nested surfaces and their corresponding spherical representation in atlas space. This is, for instance, useful for jointly reconstructing and segmenting the white-to-gray-matter interface and the gray-matter-to-CSF (pial) surface. We evaluate the performance of our surface reconstruction and segmentation method with a comprehensive set of experiments on the MindBoggle, ABIDE and OASIS datasets. Our reconstruction error is found to be less than 0.52 mm and 0.97 mm in terms of average Hausdorff distance to the FreeSurfer generated surfaces. Likewise, the parcellation results show over 4% improvements in average Dice with respect to FreeSurfer, in addition to an observed drastic speed-up from hours to seconds of computation on a standard desktop station.

HexNet: An Orientation-Aware Deep Learning Framework for Omni-Directional Input.

While omni-directional sensors provide holistic representations typical deep learning frameworks reduce the benefits by introducing distortions and discontinuities as spherical data is supplied as planar input. On the other hand, recent spherical convolutional neural networks (CNNs) often require significant memory and parameters, thus enabling execution only at very low resolutions and shallow architectures. We propose HexNet, an orientation-aware deep learning framework for spherical signals, that allows for fast computation as we exploit standard planar network operations on an efficiently arranged projection of the sphere. Furthermore, we introduce a graph-based version for partial spheres, allowing us to compete at high-resolution with planar CNNs using residual network architectures. Our kernels operate on the tangent of the sphere and thus standard feature weights, pretrained on perspective data, can be transferred, enabling spherical pretraining on ImageNet. As our design is free of distortions and discontinuity, our orientation-aware CNN becomes a new state of the art for semantic segmentation on the recent 2D3DS dataset, and the omni-directional version of SYNTHIA introduced in this work. Moreover, we experimentally show the benefit of our spherical representation over standard images on the Cityscapes dataset by reducing distortion effects of planar CNNs. We implement object detection for the spherical domain. Rotation invariant classification and segmentation tasks are additionally presented for comparison to prior art.