Breathers on quantized superfluid vortices

We consider the propagation of breathers along a quantized superfluid vortex. Using the correspondence between the local induction approximation (LIA) and the nonlinear Schrödinger equation, we identify a set of initial conditions corresponding to breather solutions of vortex motion governed by the LIA. These initial conditions, which give rise to a long-wavelength modulational instability, result in the emergence of large amplitude perturbations that are localized in both space and time. The emergent structures on the vortex filament are analogous to loop solitons but arise from the dual action of bending and twisting of the vortex. Although the breather solutions we study are exact solutions of the LIA equations, we demonstrate through full numerical simulations that their key emergent attributes carry over to vortex dynamics governed by the Biot-Savart law and to quantized vortices described by the Gross-Pitaevskii equation. The breather excitations can lead to self-reconnections, a mechanism that can play an important role within the crossover range of scales in superfluid turbulence. Moreover, the observation of breather solutions on vortices in a field model suggests that these solutions are expected to arise in a wide range of other physical contexts from classical vortices to cosmological strings

Multiple Breathers on a Vortex Filament

In this paper we investigate the correspondence between the Da Rios-Betchov equation, which appears in the three-dimensional motion of a vortex filament, and the nonlinear Schrödinger equation. Using this correspondence we map a set of solutions corresponding to breathers in the nonlinear Schrödinger equation to waves propagating along a vortex filament. The work presented generalizes the recently derived family of vortex configurations associated with these breather solutions to a wider class of configurations that are associated with combination homoclinic/heteroclinic orbits of the 1D self-focussing nonlinear Schrödinger equation. We show that by considering these solutions of the governing nonlinear Schrödinger equation, highly nontrivial vortex filament configurations can be obtained that are associated with a pair of breather excitations. These configurations can lead to loop-like excitations emerging from an otherwise weakly perturbed helical vortex. The results presented further demonstrate the rich class of solutions that are supported by the Da Rios-Betchov equation that is recovered within the local induction approximation for the motion of a vortex filament

DNeRV: Modeling Inherent Dynamics via Difference Neural Representation for Videos

Existing implicit neural representation (INR) methods do not fully exploit spatiotemporal redundancies in videos. Index-based INRs ignore the content-specific spatial features and hybrid INRs ignore the contextual dependency on adjacent frames, leading to poor modeling capability for scenes with large motion or dynamics. We analyze this limitation from the perspective of function fitting and reveal the importance of frame difference. To use explicit motion information, we propose Difference Neural Representation for Videos (DNeRV), which consists of two streams for content and frame difference. We also introduce a collaborative content unit for effective feature fusion. We test DNeRV for video compression, inpainting, and interpolation. DNeRV achieves competitive results against the state-of-the-art neural compression approaches and outperforms existing implicit methods on downstream inpainting and interpolation for $960 \times 1920$ videos

H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System

High-speed, high-resolution stereoscopic (H2-Stereo) video allows us to perceive dynamic 3D content at fine granularity. The acquisition of H2-Stereo video, however, remains challenging with commodity cameras. Existing spatial super-resolution or temporal frame interpolation methods provide compromised solutions that lack temporal or spatial details, respectively. To alleviate this problem, we propose a dual camera system, in which one camera captures high-spatial-resolution low-frame-rate (HSR-LFR) videos with rich spatial details, and the other captures low-spatial-resolution high-frame-rate (LSR-HFR) videos with smooth temporal details. We then devise a Learned Information Fusion network (LIFnet) that exploits the cross-camera redundancies to enhance both camera views to high spatiotemporal resolution (HSTR) for reconstructing the H2-Stereo video effectively. We utilize a disparity network to transfer spatiotemporal information across views even in large disparity scenes, based on which, we propose disparity-guided flow-based warping for LSR-HFR view and complementary warping for HSR-LFR view. A multi-scale fusion method in feature domain is proposed to minimize occlusion-induced warping ghosts and holes in HSR-LFR view. The LIFnet is trained in an end-to-end manner using our collected high-quality Stereo Video dataset from YouTube. Extensive experiments demonstrate that our model outperforms existing state-of-the-art methods for both views on synthetic data and camera-captured real data with large disparity. Ablation studies explore various aspects, including spatiotemporal resolution, camera baseline, camera desynchronization, long/short exposures and applications, of our system to fully understand its capability for potential applications

Point Cloud Distortion Quantification based on Potential Energy for Human and Machine Perception

Distortion quantification of point clouds plays a stealth, yet vital role in a wide range of human and machine perception tasks. For human perception tasks, a distortion quantification can substitute subjective experiments to guide 3D visualization; while for machine perception tasks, a distortion quantification can work as a loss function to guide the training of deep neural networks for unsupervised learning tasks. To handle a variety of demands in many applications, a distortion quantification needs to be distortion discriminable, differentiable, and have a low computational complexity. Currently, however, there is a lack of a general distortion quantification that can satisfy all three conditions. To fill this gap, this work proposes multiscale potential energy discrepancy (MPED), a distortion quantification to measure point cloud geometry and color difference. By evaluating at various neighborhood sizes, the proposed MPED achieves global-local tradeoffs, capturing distortion in a multiscale fashion. Extensive experimental studies validate MPED's superiority for both human and machine perception tasks

Quantifying techno-economic indicators\u27 impact on isolated renewable energy systems

Addressing climate change with the rising global energy usage necessitates electricity sector decarbonization by rapidly moving toward flexible and efficient off-grid renewable energy systems (RESs). This paper analyzes the wind and solar micro-grids, with batteries and pumped hydro storage for a robust off-grid RES techno-economic operation, while considering diverse multi-objective optimization cases. This research has considered the RES variable operational losses in the developed methodology and relations between different indicators are evaluated, revealing a basic understanding between them. The results reveal that the reliability index is inversely related to the oversupply index, while directly related to the system self-sufficiency index. The cost of energy is more sensitive to technical indicators rather than the storage cost and so can be used as a primary monetary index. Energy and cost balance analysis showed that 16%-20% of the used energy was drained in RES operational losses, which were usually ignored in previous studies

Zinc oxide nanoparticles selectively induce apoptosis in human cancer cells through reactive oxygen species

Mohd Javed Akhtar1,2, Maqusood Ahamed3, Sudhir Kumar1, MA Majeed Khan3, Javed Ahmad4, Salman A Alrokayan31Department of Zoology, University of Lucknow, Lucknow, India; 2Fibre Toxicology Division, CSIR-Indian Institute of Toxicology Research, Lucknow, India; 3King Abdullah Institute for Nanotechnology, King Saud University, Riyadh, Saudi Arabia; 4Department of Zoology, College of Science, King Saud University, Riyadh, Saudi ArabiaBackground: Zinc oxide nanoparticles (ZnO NPs) have received much attention for their implications in cancer therapy. It has been reported that ZnO NPs induce selective killing of cancer cells. However, the underlying molecular mechanisms behind the anticancer response of ZnO NPs remain unclear.Methods and results: We investigated the cytotoxicity of ZnO NPs against three types of cancer cells (human hepatocellular carcinoma HepG2, human lung adenocarcinoma A549, and human bronchial epithelial BEAS-2B) and two primary rat cells (astrocytes and hepatocytes). Results showed that ZnO NPs exert distinct effects on mammalian cell viability via killing of all three types of cancer cells while posing no impact on normal rat astrocytes and hepatocytes. The toxicity mechanisms of ZnO NPs were further investigated using human liver cancer HepG2 cells. Both the mRNA and protein levels of tumor suppressor gene p53 and apoptotic gene bax were upregulated while the antiapoptotic gene bcl-2 was downregulated in ZnO NP-treated HepG2 cells. ZnO NPs were also found to induce activity of caspase-3 enzyme, DNA fragmentation, reactive oxygen species generation, and oxidative stress in HepG2 cells.Conclusion: Overall, our data demonstrated that ZnO NPs selectively induce apoptosis in cancer cells, which is likely to be mediated by reactive oxygen species via p53 pathway, through which most of the anticancer drugs trigger apoptosis. This study provides preliminary guidance for the development of liver cancer therapy using ZnO NPs.Keywords: ZnO nanoparticles, cancer therapy, p53, apoptosis, RO