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We consider the problem of constructing confidence intervals for the locations of change points in a high-dimensional mean shift model. We develop a locally refitted least squares estimator and obtain component-wise and simultaneous rates of estimation of change points. The simultaneous rate is the sharpest available by at least a factor of log p, while the component-wise one is optimal. These results enable existence of limiting distributions for the locations of the change points. Subsequently, component-wise distributions are characterized under both vanishing and non-vanishing jump size regimes, while joint distributions of change point estimates are characterized under the latter regime, which also yields asymptotic independence of these estimates. We provide the relationship between these distributions, which allows construction of regime adaptive confidence intervals. All results are established under a high dimensional scaling, in the presence of diverging number of change points. They are illustrated on synthetic data and on sensor measurements from smartphones for activity recognition. 

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/ 

We introduce Hyperdimensional Graph Learner (HDGL), a novel method for node classification and link prediction in graphs. HDGL maps node features into a very high-dimensional space (hyperdimensional or HD space for short) using the injectivity property of node representations in a family of Graph Neural Networks (GNNs) and then uses HD operators such as bundling and binding to aggregate information from the local neighborhood of each node yielding latent node representations that can support both node classification and link prediction tasks. HDGL, unlike GNNs that rely on computationally expensive iterative optimization and hyperparameter tuning, requires only a single pass through the data set. We report results of experiments using widely used benchmark datasets which demonstrate that, on the node classification task, HDGL achieves accuracy that is competitive with that of the state-of-the-art GNN methods at substantially reduced computational cost; and on the link prediction task, HDGL matches the performance of DeepWalk and related methods, although it falls short of computationally demanding state-of-the-art GNNs. 

We highlight our contributions to the current understanding of structure–property relationships of solution-processable Group IV semiconductor nanocrystals and nanosheets. 

This review highlights microfluidics as a disruptive platform for advancing carbon capture and storage, enabling rapid testing, enhanced mass transfer, and precise flow control while offering insight into mechanisms, tools, and design strategies. 

Controlling the structure and reactivity of the chain-end group is a central objective in modern polymer chemistry. Here, we introduce 3,6-anhydrogalactal as a single-addition monomer that enables efficient and versatile chain-end functionalization of metathesis polymers. Readily synthesized from biomass-derived galactal, 3,6-anhydrogalactal exhibits excellent single-addition reactivity, allowing precise chain-end modifications even when introduced simultaneously with the propagating monomer. Theoretical calculations provide mechanistic insights into the unique reactivities governing its single-addition behavior. Its broad functional group compatibility facilitates diverse applications, including block copolymer synthesis, polymer-polymer coupling, and bioconjugation, demonstrating significant potential for advancing polymer materials and bioconjugation strategies. 

Abstract Structural and mechanical cues from the extracellular matrix (ECM) regulate tissue morphogenesis. Tissue development has conventionally been studied withex vivosystems where mechanical properties of the extracellular environment are either poorly controlled in space and time, lack tunability, or do not mimic ECM mechanics. For these reasons, it remains unknown how matrix stress relaxation rate, a time-dependent mechanical property that influences several cellular processes, regulates mammary branching morphogenesis. Here, we systematically investigated the influence of matrix stress relaxation on mammary branching morphogenesis using 3D alginate-collagen matrices and spheroids of human mammary epithelial cells. Slow stress relaxing matrices promoted significantly greater branch formation compared to fast stress relaxing matrices. Branching in slow stress relaxing matrices was accompanied by local collagen fiber alignment, while collagen fibers remained randomly oriented in fast stress relaxing matrices. In slow stress relaxing matrices, branch formation was driven by intermittent pulling contractions applied to the local ECM at the tips of elongating branches, which was accompanied by an abundance of phosphorylated focal adhesion kinase (phospho-FAK) and β1 integrin at the tips of branches. On the contrary, we observed that growing spheroids in fast stress relaxing matrices applied isotropic pushing forces to the ECM. Pharmacological inhibition of both Rac1 and non-muscle myosin II prevented epithelial branch formation, regardless of matrix stress relaxation rate. Interestingly, restricting cellular expansion via increased osmotic pressure was sufficient to impede epithelial branching in slow stress relaxing matrices. This work highlights the importance of stress relaxation in regulating and directing mammary branch elongation.