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Brittle fracturing of materials is common in natural and industrial processes over a variety of length scales. Knowledge of individual particle dynamics is vital to obtain deeper insight into the atomistic processes governing crack propagation in such materials, yet it is challenging to obtain these details in experiments. We propose an experimental approach where isotropic dilational strain is applied to a densely packed monolayer of attractive colloidal microspheres, resulting in fracture. Using brightfield microscopy and particle tracking, we examine the microstructural evolution of the monolayer during fracturing. Furthermore, using a quantified representation of the microstructure in combination with a machine learning algorithm, we calculate the likelihood of regions of the monolayer to be on a crack line, which we term Weakness. From this analysis, we identify the most important contributions to crack propagation and find that local density is more important than orientational order. Our methodology and results provide a basis for further research on microscopic processes during the fracturing process., Comment: 8 pages, 4 figures

3D rendering of dynamic face captures is a challenging problem, and it demands improvements on several fronts$\unicode{x2014}$photorealism, efficiency, compatibility, and configurability. We present a novel representation that enables high-quality volumetric rendering of an actor's dynamic facial performances with minimal compute and memory footprint. It runs natively on commodity graphics soft- and hardware, and allows for a graceful trade-off between quality and efficiency. Our method utilizes recent advances in neural rendering, particularly learning discrete radiance manifolds to sparsely sample the scene to model volumetric effects. We achieve efficient modeling by learning a single set of manifolds for the entire dynamic sequence, while implicitly modeling appearance changes as temporal canonical texture. We export a single layered mesh and view-independent RGBA texture video that is compatible with legacy graphics renderers without additional ML integration. We demonstrate our method by rendering dynamic face captures of real actors in a game engine, at comparable photorealism to state-of-the-art neural rendering techniques at previously unseen frame rates., Comment: In Proceedings of the ACM in Computer Graphics and Interactive Techniques, 2024

While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensive methods that model the scene's geometry as a volumetric density field (e.g. NeRF) excel at reconstructing fine geometric detail. However, density fields often represent geometry in a "fuzzy" manner, which hinders exact localization of the surface. In this work, we modify density fields to encourage them to converge towards surfaces, without compromising their ability to reconstruct thin structures. First, we employ a discrete opacity grid representation instead of a continuous density field, which allows opacity values to discontinuously transition from zero to one at the surface. Second, we anti-alias by casting multiple rays per pixel, which allows occlusion boundaries and subpixel structures to be modelled without using semi-transparent voxels. Third, we minimize the binary entropy of the opacity values, which facilitates the extraction of surface geometry by encouraging opacity values to binarize towards the end of training. Lastly, we develop a fusion-based meshing strategy followed by mesh simplification and appearance model fitting. The compact meshes produced by our model can be rendered in real-time on mobile devices and achieve significantly higher view synthesis quality compared to existing mesh-based approaches., Comment: Project page at https://binary-opacity-grid.github.io

Existing UV mapping algorithms are designed to operate on well-behaved meshes, instead of the geometry representations produced by state-of-the-art 3D reconstruction and generation techniques. As such, applying these methods to the volume densities recovered by neural radiance fields and related techniques (or meshes triangulated from such fields) results in texture atlases that are too fragmented to be useful for tasks such as view synthesis or appearance editing. We present a UV mapping method designed to operate on geometry produced by 3D reconstruction and generation techniques. Instead of computing a mapping defined on a mesh's vertices, our method Nuvo uses a neural field to represent a continuous UV mapping, and optimizes it to be a valid and well-behaved mapping for just the set of visible points, i.e. only points that affect the scene's appearance. We show that our model is robust to the challenges posed by ill-behaved geometry, and that it produces editable UV mappings that can represent detailed appearance., Comment: Project page at https://pratulsrinivasan.github.io/nuvo

Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely on geometric face models that cannot represent fine-grained details in texture with a mesh discretization and linear deformation designed to model only a coarse face geometry. We introduce a method that bridges this gap by drawing inspiration from traditional computer graphics techniques. Unseen expressions are modeled by blending appearance from a sparse set of extreme poses. This blending is performed by measuring local volumetric changes in those expressions and locally reproducing their appearance whenever a similar expression is performed at test time. We show that our method generalizes to unseen expressions, adding fine-grained effects on top of smooth volumetric deformations of a face, and demonstrate how it generalizes beyond faces., Comment: Accepted to CVPR 2023. Project page: https://blendfields.github.io/

Within the tumor microenvironment (TME), cancer cells use mechanotransduction pathways to convert biophysical forces to biochemical signals. However, the underlying mechanisms and functional significance of these pathways remain largely unclear. The upregulation of mechanosensitive pathways from biophysical forces such as interstitial flow, leads to the activation of various cytokines, most importantly transforming growth factor-$\beta$ (TGF-$\beta$). TGF-$\beta$ is a critical inducer of epithelial-mesenchymal transition (EMT) in cancer cells that leads to increased cell motility and invasion in a Smad dependent manner. Current research models have limited ability to investigate the combined effects of biophysical forces (such as interstitial flow) and cytokines (TGF-$\beta$) in a 3D microenvironment. We used a 3D-matrix based microfluidic platform to demonstrate the potentiating effect of interstitial flow (IF) on exogenous TGF-$\beta$ induced upregulation of the Smad-signaling activity and the expression of mesenchymal marker vimentin in A549 lung cancer spheroids. To monitor this, we used stably integrated fluorescent based reporters into the A549 cancer cell genome. Our results demonstrate that interstitial flow enhances exogenous TGF-$\beta$ induced Smad-signaling activity in lung cancer spheroids embedded in a matrix microenvironment. In addition, we observed an increased cell motility for A549 spheroids when exposed to interstitial flow and TGF-$\beta$. Our 3D-microfluidic model integrated with real-time imaging provides a powerful tool for investigating cancer cell signaling and motility associated with invasion characteristics in a physiologically relevant TME.

We report an experimental and theoretical investigation of a system whose dynamics is dominated by an intricate interplay between three key concepts of modern physics: topology, nonlinearity, and spontaneous symmetry breaking. The experiment is based on a two-mode coherently-driven optical resonator in which photons interact through the Kerr nonlinearity. In presence of a phase defect between the modes, a nonlinear attractor develops, which confers a synthetic M\"obius topology to the modal structure of the system. That topology is associated with an inherently protected exchange symmetry between the modes, enabling the realization of spontaneous symmetry breaking in ideal, bias-free, conditions without any fine tuning of parameters. The dynamic manifests itself by a periodic alternation of the modes from one resonator roundtrip to the next reminiscent of period-doubling. This extends to a range of localized structures in the form of domain walls, bright solitons, and breathers, which have all been observed with remarkable long term stability. A rigorous testing of the randomness of the symmetry-broken state selection statistics has also confirmed the robustness of the exchange symmetry in our experiment. Our results and conclusions are supported by an effective Hamiltonian model explaining the symmetry protection in our system. The model also shows that our work has relevance to other systems of interacting bosons and to the Floquet engineering of quantum matter. Our work could also be beneficial to the implementation of coherent Ising machines., Comment: 14 pages, 5 figures

Symmetry-protected zero modes in arrays of coupled optical elements have attracted considerable attention because they are expected to be robust against coupling disorders. In the Hermitian limit, zero modes are dark ones, i.e. the intensity in one sublattice vanishes; yet, in a non-Hermitian counterpart, zero modes can be bright and feature {\pi}/2 phase difference between sublattices. In this work, we report on the direct observation of a lasing zero mode in a non-Hermitian three coupled nanocavity array. We show efficient excitation for nearly equal pump power in the two extreme cavities. Furthermore, its efficiency can be dynamically controlled by pumping the center cavity. The realization of zero mode lasing in large arrays of coupled nanolasers has potential applications in laser-mode engineering and it opens up promising avenues in optical computing., Comment: 5 pages, 4 figures

Owing to the increasing interest in the commercialization of phase-change memory (PCM) devices, a number of TCAD models have been developed for their simulation. These models formulate the melting, amorphization and crystallization of phase-change materials as well as their extreme conductivity dependence on both electric field and temperature into a set of self-consistently-solved thermoelectric and phase-field partial-differential equations. However, demonstrations of the ability of such models to match actual experimental results are rare. In addition, such PCM devices also require a so-called selector device - such as an Ovonic Threshold Switching (OTS) device - in series for proper memory operation. However, monolithic simulation of both the PCM and OTS selector device in a single simulation is largely absent from the literature, despite its potential value for material- and design-space explorations. It is the goal of this work to first characterize a PCM device in isolation against experimental data, then to demonstrate the qualitative behavior of a simulated OTS device in isolation and finally to perform a single monolithic simulation of the PCM + OTS device within the confines of a commercially available TCAD solver: GTS Framework.

The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time. While implicit deformation methods based on learned functions can produce impressive results, they are `black boxes' to artists and content creators, they require large amounts of training data to generalise meaningfully, and they do not produce realistic extrapolations outside the training data. In this work we solve these issues by introducing a volume deformation method which is real-time, easy to edit with off-the-shelf software and can extrapolate convincingly. To demonstrate the versatility of our method, we apply it in two scenarios: physics-based object deformation and telepresence where avatars are controlled using blendshapes. We also perform thorough experiments showing that our method compares favourably to both volumetric approaches combined with implicit deformation and methods based on mesh deformation., Comment: 18 pages, 21 figures

Landmarks often play a key role in face analysis, but many aspects of identity or expression cannot be represented by sparse landmarks alone. Thus, in order to reconstruct faces more accurately, landmarks are often combined with additional signals like depth images or techniques like differentiable rendering. Can we keep things simple by just using more landmarks? In answer, we present the first method that accurately predicts 10x as many landmarks as usual, covering the whole head, including the eyes and teeth. This is accomplished using synthetic training data, which guarantees perfect landmark annotations. By fitting a morphable model to these dense landmarks, we achieve state-of-the-art results for monocular 3D face reconstruction in the wild. We show that dense landmarks are an ideal signal for integrating face shape information across frames by demonstrating accurate and expressive facial performance capture in both monocular and multi-view scenarios. This approach is also highly efficient: we can predict dense landmarks and fit our 3D face model at over 150FPS on a single CPU thread. Please see our website: https://microsoft.github.io/DenseLandmarks/., Comment: ECCV 2022

Colloidal gels possess a memory of previous shear events, both steady and oscillatory. This memory, embedded in the microstructure, affects the mechanical response of the gel, and therefore enables precise tuning of the material properties under careful preparation. Here we demonstrate how the dynamics of a deformable inclusion, namely a bubble, can be used to locally tune the microstructure of a colloidal gel. We examine two different phenomena of bubble dynamics that apply a local strain to the surrounding material: dissolution due to mass transfer, with a characteristic strain rate of $10^{-3}$ s$^{-1}$; and volumetric oscillations driven by ultrasound, with a characteristic frequency of $10^4$ s$^{-1}$. We characterise experimentally the microstructure of a model colloidal gel around bubbles in a Hele-Shaw geometry using confocal microscopy and particle tracking. In bubble dissolution experiments, we observe the formation of a pocket of solvent next to the bubble surface, but marginal changes to the microstructure. In experiments with ultrasound-induced bubble oscillations, we observe a striking rearrangement of the gel particles into a microstructure with increased local ordering. High-speed bright-field microscopy reveals the occurrence of both high-frequency bubble oscillations and steady microstreaming flow; both are expected to contribute to the emergence of the local order in the microstructure. These observations open the way to local tuning of colloidal gels based on deformable inclusions controlled by external pressure fields., Comment: Fixed title on ArXiv + affiliations in text

We demonstrate that the permeability of the cell wall of microalga $\textit{C. reinhardtii}$ can be enhanced in controlled microstreaming flow conditions using single bubbles adherent to a wall and driven by ultrasound near their resonance frequency. We find that microstreaming flow is effective at acoustic pressures as low as tens of kPa, at least one order of magnitude lower than those used in bulk ultrasonication. We quantify the increase in number of fluorescent cells for different acoustic pressure amplitudes and ultrasound exposure times. We interpret the increase in dye uptake after microstreaming flow as an indication of enhanced permeability of the cell wall. We perform microscopic visualizations to verify the occurrence of non-spherical shape oscillations of the bubbles, and to identify the optimal conditions for dye uptake. In our experimental conditions, with acoustic pressures in the range 20-40 kPa and frequencies in the range 25-70 kHz, we obtained spherical oscillations of bare microbubbles and non-spherical shape oscillations in microbubbles with algae attached to their surface. Only the latter were able to generate a non-linear microstreaming flow with sufficiently large viscous stresses to induce algae permeabilization. The results demonstrate that controlled microbubble streaming flow can enhance cell wall permeability in energy-efficient conditions.

Zero modes are symmetry protected ones whose energy eigenvalues have zero real parts. In Hermitian arrays, they arise as a consequence of the sublattice symmetry, implying that they are dark modes. In non-Hermitian systems, that naturally emerge in gain/loss optical cavities, particle-hole symmetry prevails instead; the resulting zero modes are no longer dark but feature ${\pi}/2$ phase jumps between adjacent cavities. Here we report on the direct observation of zero modes in a non-Hermitian three coupled photonic crystal nanocavity array containing quantum wells. Unlike the Hermitian counterparts, the non-Hermitian zero modes can only be observed for small sublattice detuning, and they can be identified through far-field imaging and spectral filtering of the photoluminescence at selected pump locations. We explain the zero mode coalescence as a parity-time phase transition for small coupling. These zero modes are robust against coupling disorder, and can be used for laser mode engineering and photonic computing., Comment: 10 pages, 5 figures