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Large models have shown generalization across datasets for many low-level vision tasks, like depth estimation, but no such general models exist for scene flow. Even though scene flow prediction has wide potential, its practical use is limited because of the lack of generalization of current predictive models. We identify three key challenges and propose solutions for each. First, we create a method that jointly estimates geometry and motion for accurate prediction. Second, we alleviate scene flow data scarcity with a data recipe that affords us 1M annotated training samples across diverse synthetic scenes. Third, we evaluate different parameterizations for scene flow prediction and adopt a natural and effective parameterization. Our model outperforms existing methods as well as baselines built on large-scale models in terms of 3D end-point error, and shows zero-shot generalization to the casually captured videos from DAVIS and the robotic manipulation scenes from RoboTAP. Overall, our approach makes scene flow prediction more practical in-the-wild. Website: https://research.nvidia.com/labs/lpr/zero msf/ 

In cloud-native environments, containers are often deployed within lightweight virtual machines (VMs) to ensure strong security isolation and privacy protection. With the growing demand for customized cloud services, third-party vendors are turning to infrastructure-as-a-service (IaaS) cloud providers to build their own cloud-native platforms, necessitating the need to run a VM or a guest that hosts containers inside another VM instance leased from an IaaS cloud. State-of-the-art nested virtualization in the x86 architecture relies heavily on the host hypervisor to expose hardware virtualization support to the guest hypervisor, not only complicating cloud management but also raising concerns about an increased attack surface at the host hypervisor. This paper presents the design and implementation of PVM, a high-performance guest hypervisor for KVM that is transparent to the host hypervisor and assumes no hardware virtualization support. PVM leverages two key designs: 1) a minimal shared memory region between the guest and guest hypervisor to facilitate state transition between different privilege levels and 2) an efficient shadow page table design to reduce the cost of memory virtualization. PVM has been adopted by a major IaaS cloud provider for hosting tens of thousands of secure containers on a daily basis. Our experiments demonstrate that PVM significantly outperforms current nested virtualization in KVM for memory virtualization, particularly for concurrent workloads, while maintaining comparable performance in CPU and I/O virtualization. 

Zero-knowledge succinct arguments of knowledge (zkSNARKs) enable efficient privacy-preserving proofs of membership for general NP languages. Our focus in this work is on post-quantum zkSNARKs, with a focus on minimizing proof size. Currently, there is a 1000x gap in the proof size between the best pre-quantum constructions and the best post-quantum ones. Here, we develop and implement new lattice-based zkSNARKs in the designated-verifier preprocessing model. With our construction, after an initial preprocessing step, a proof for an NP relation of size 2^20 is just over 16 KB. Our proofs are 10.3x shorter than previous post-quantum zkSNARKs for general NP languages. Compared to previous lattice-based zkSNARKs (also in the designated-verifier preprocessing model), we obtain a 42x reduction in proof size and a 60x reduction in the prover's running time, all while achieving a much higher level of soundness. Compared to the shortest pre-quantum zkSNARKs by Groth (Eurocrypt 2016), the proof size in our lattice-based construction is 131x longer, but both the prover and the verifier are faster (by 1.2x and 2.8x, respectively). Our construction follows the general blueprint of Bitansky et al. (TCC 2013) and Boneh et al. (Eurocrypt 2017) of combining a linear probabilistically checkable proof (linear PCP) together with a linear-only vector encryption scheme. We develop a concretely-efficient lattice-based instantiation of this compiler by considering quadratic extension fields of moderate characteristic and using linear-only vector encryption over rank-2 module lattices. 

Abstract. In the summer of 2017, heavy ozone pollution swamped most of the North ChinaPlain (NCP), with the maximum regional average of daily maximum 8 h ozoneconcentration (MDA8) reaching almost 120 ppbv. In light of the continuingreduction of anthropogenic emissions in China, the underlying mechanisms forthe occurrences of these regional extreme ozone episodes are elucidated fromtwo perspectives: meteorology and biogenic emissions. The significantpositive correlation between MDA8 ozone and temperature, which is amplifiedduring heat waves concomitant with stagnant air and no precipitation,supports the crucial role of meteorology in driving high ozoneconcentrations. We also find that biogenic emissions are enhanced due tofactors previously not considered. During the heavy ozone pollution episodesin June 2017, biogenic emissions driven by high vapor pressure deficit(VPD), land cover change and urban landscape yield an extra mean MDA8 ozoneof 3.08, 2.79 and 4.74 ppbv, respectively, over the NCP, which togethercontribute as much to MDA8 ozone as biogenic emissions simulated using theland cover of 2003 and ignoring VPD and urban landscape. In Beijing, thebiogenic emission increase due to urban landscape has a comparable effect onMDA8 ozone to the combined effect of high VPD and land cover change between2003 and 2016. In light of the large effect of urban landscape on biogenicemission and the subsequent ozone formation, the types of trees may becautiously selected to take into account of the biogenic volatile organic compound (BVOC) emission during the afforestation of cities. This study highlights the vital contributions ofheat waves, land cover change and urbanization to the occurrence of extremeozone episodes, with significant implications for ozone pollution control ina future when heat wave frequency and intensity are projected to increaseunder global warming.