HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds

We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds. Inspired by Bilateral Convolutional Layers (BCL), we propose novel DownBCL, UpBCL, and CorrBCL operations that restore structural information from unstructured point clouds, and fuse information from two consecutive point clouds. Operating on discrete and sparse permutohedral lattice points, our architectural design is parsimonious in computational cost. Our model can efficiently process a pair of point cloud frames at once with a maximum of 86K points per frame. Our approach achieves state-of-the-art performance on the FlyingThings3D and KITTI Scene Flow 2015 datasets. Moreover, trained on synthetic data, our approach shows great generalization ability on real-world data and on different point densities without fine-tuning

PolyMaX: General Dense Prediction with Mask Transformer

Dense prediction tasks, such as semantic segmentation, depth estimation, and surface normal prediction, can be easily formulated as per-pixel classification (discrete outputs) or regression (continuous outputs). This per-pixel prediction paradigm has remained popular due to the prevalence of fully convolutional networks. However, on the recent frontier of segmentation task, the community has been witnessing a shift of paradigm from per-pixel prediction to cluster-prediction with the emergence of transformer architectures, particularly the mask transformers, which directly predicts a label for a mask instead of a pixel. Despite this shift, methods based on the per-pixel prediction paradigm still dominate the benchmarks on the other dense prediction tasks that require continuous outputs, such as depth estimation and surface normal prediction. Motivated by the success of DORN and AdaBins in depth estimation, achieved by discretizing the continuous output space, we propose to generalize the cluster-prediction based method to general dense prediction tasks. This allows us to unify dense prediction tasks with the mask transformer framework. Remarkably, the resulting model PolyMaX demonstrates state-of-the-art performance on three benchmarks of NYUD-v2 dataset. We hope our simple yet effective design can inspire more research on exploiting mask transformers for more dense prediction tasks. Code and model will be made available.Comment: WACV 202

Herpes simplex virus blocks host transcription termination via the bimodal activities of ICP27

Infection by viruses, including herpes simplex virus-1 (HSV-1), and cellular stresses cause widespread disruption of transcription termination (DoTT) of RNA polymerase II (RNAPII) in host genes. However, the underlying mechanisms remain unclear. Here, we demonstrate that the HSV-1 immediate early protein ICP27 induces DoTT by directly binding to the essential mRNA 3' processing factor CPSF. It thereby induces the assembly of a dead-end 3' processing complex, blocking mRNA 3' cleavage. Remarkably, ICP27 also acts as a sequence-dependent activator of mRNA 3' processing for viral and a subset of host transcripts. Our results unravel a bimodal activity of ICP27 that plays a key role in HSV-1-induced host shutoff and identify CPSF as an important factor that mediates regulation of transcription termination. These findings have broad implications for understanding the regulation of transcription termination by other viruses, cellular stress and cancer

ORP4L Extracts and Presents PIP2 from Plasma Membrane for PLC beta 3 Catalysis : Targeting It Eradicates Leukemia Stem Cells

Leukemia stem cells (LSCs) are a rare subpopulation of abnormal hematopoietic stem cells (HSCs) that propagates leukemia and are responsible for the high frequency of relapse in therapies. Detailed insights into LSCs' survival will facilitate the identification of targets for therapeutic approaches. Here, we develop an inhibitor, LYZ-81, which targets ORP4L with high affinity and specificity and selectively eradicates LCSs in vitro and in vivo. ORP4L is expressed in LSCs but not in normal HSCs and is essential for LSC bioenergetics and survival. It extracts PIP2 from the plasma membrane and presents it to PLC beta 3, enabling IP3 generation and subsequentCa(2+)-dependent bioenergetics. LYZ-81 binds ORP4L competitively with PIP2 and blocks PIP2 hydrolysis, resulting in defective Ca2+ signaling. The results provide evidence that LSCs can be eradicated through the inhibition of ORP4L by LYZ-81, which may serve as a starting point of drug development for the elimination of LSCs to eventually cure leukemia.Peer reviewe

Optimal Design for Constraint Following Control of Tank Bi-Directional Stabilized Systems Based on Nash Game

This article proposes a robust control optimization design problem based on Nash game-theoretic for the marching tank bi-directional stabilization system of an all-electric actuator, which is accompanied by complex uncertainty (possibly fast time-varying but bounded). The task is to drive the barrel firing angle to adjust to a specified position under complex uncertainty while ensuring that multiple design parameters are optimally chosen in the controller. Firstly, the machine-electric coupled dynamics equations with uncertainty are created in the form of state space. Secondly, a robust controller with two design parameters to be chosen is given so that the constraint following error has uniform boundedness and uniform ultimate boundedness. Thirdly, a Nash game cost function consisting of three components: performance cost, time cost and control cost is proposed, and the Nash equilibrium (i.e., optimal control parameters) is obtained by minimizing the cost function. Finally, a co-simulation experimental platform is built to verify the robustness of the controller under complex uncertainty (modeling errors, uncertain disturbances and road excitation) and to demonstrate the global optimal performance of the system under optimal design parameters