Segmental Spatiotemporal CNNs for Fine-grained Action Segmentation

Joint segmentation and classification of fine-grained actions is important for applications of human-robot interaction, video surveillance, and human skill evaluation. However, despite substantial recent progress in large-scale action classification, the performance of state-of-the-art fine-grained action recognition approaches remains low. We propose a model for action segmentation which combines low-level spatiotemporal features with a high-level segmental classifier. Our spatiotemporal CNN is comprised of a spatial component that uses convolutional filters to capture information about objects and their relationships, and a temporal component that uses large 1D convolutional filters to capture information about how object relationships change across time. These features are used in tandem with a semi-Markov model that models transitions from one action to another. We introduce an efficient constrained segmental inference algorithm for this model that is orders of magnitude faster than the current approach. We highlight the effectiveness of our Segmental Spatiotemporal CNN on cooking and surgical action datasets for which we observe substantially improved performance relative to recent baseline methods.Comment: Updated from the ECCV 2016 version. We fixed an important mathematical error and made the section on segmental inference cleare

A study of Al1-xInxN growth by reflection high-energy electron diffraction-incorporation of cation atoms during molecular-beam epitaxy

Molecular-beam epitaxy of Al1-x Inx N alloys with different indium (In) contents, x, were studied by in situ reflection high-energy electron diffraction (RHEED). Growth rates of the alloys were measured by the RHEED intensity oscillations for different source flux conditions, while the lattice parameters were derived from the diffraction patterns. It was found that under the excess nitrogen growth regime, incorporation of aluminum was complete whereas incorporation of In atoms was incomplete even at temperatures below 400 °C. © 2008 American Institute of Physics.published_or_final_versio

Unravelling the Enzymatic Degradation Mechanism of Supramolecular Peptide Nanofibers and Its Correlation with Their Internal Viscosity.

Enzyme-responsive supramolecular peptide biomaterials have attracted growing interest for disease diagnostics and treatments. However, it remains unclear whether enzymes target the peptide assemblies or dissociated peptide monomers. To gain further insight into the degradation mechanism of supramolecular peptide amphiphile (PA) nanofibers, cathepsin B with both exopeptidase and endopeptidase activities was exploited here for degradation studies. Hydrolysis was found to occur directly on the PA nanofibers as only surface amino acid residues were cleaved. The number of cleaved residues and the degradation efficiency was observed to be negatively correlated with the internal viscosity of the PA nanofibers, quantified to be between 200-800 cP (liquid phase) using fluorescence lifetime imaging microscopy combined with an environmentally sensitive molecular rotor, BODIPY-C10. These findings enhance our understanding on the enzymatic degradation of supramolecular PA nanofibers and have important implications for the development of PA probes for the real-time monitoring of disease-related enzymes

Transition between wurtzite and zinc-blende GaN: An effect of deposition condition of molecular-beam epitaxy

GaN exists in both wurtzite and zinc-blende phases and the growths of the two on its (0001) or (111) surfaces are achieved by choosing proper deposition conditions of molecular-beam epitaxy (MBE). At low substrate temperatures but high gallium fluxes, metastable zinc-blende GaN films are obtained, whereas at high temperatures and/or using high nitrogen fluxes, equilibrium wurtzite phase GaN epilayers resulted. This dependence of crystal structure on substrate temperature and source flux is not affected by deposition rate. Rather, the initial stage nucleation kinetics plays a primary role in determining the crystallographic structures of epitaxial GaN by MBE. © 2006 American Institute of Physics.published_or_final_versio

Global contrast based salient region detection

Automatic estimation of salient object regions across images, without any prior assumption or knowledge of the contents of the corresponding scenes, enhances many computer vision and computer graphics applications. We introduce a regional contrast based salient object detection algorithm, which simultaneously evaluates global contrast differences and spatial weighted coherence scores. The proposed algorithm is simple, efficient, naturally multi-scale, and produces full-resolution, high-quality saliency maps. These saliency maps are further used to initialize a novel iterative version of GrabCut, namely SaliencyCut, for high quality unsupervised salient object segmentation. We extensively evaluated our algorithm using traditional salient object detection datasets, as well as a more challenging Internet image dataset. Our experimental results demonstrate that our algorithm consistently outperforms 15 existing salient object detection and segmentation methods, yielding higher precision and better recall rates. We also show that our algorithm can be used to efficiently extract salient object masks from Internet images, enabling effective sketch-based image retrieval (SBIR) via simple shape comparisons. Despite such noisy internet images, where the saliency regions are ambiguous, our saliency guided image retrieval achieves a superior retrieval rate compared with state-of-the-art SBIR methods, and additionally provides important target object region information

Enzymatic activation of cell-penetrating peptides in self-assembled nanostructures triggers fibre-to-micelle morphological transition

Enzyme-assisted fibre-to-micelle transition in self-assembled nanostructures controls presentation of cell-penetrating peptides.</p

In vitro blood-brain barrier models for drug research: state-of-the-art and new perspectives on reconstituting these models on artificial basement membrane platforms

HS Azevedo acknowledges the financial support of the European Union under the Marie Curie Career Integration Grant SuprHApolymers (PCIG14-GA-2013-631871). J Banerjee is supported by Marie Sklodowska-Curie Individual Fellowship granted by the European Commission (MSCA-IF-2014-658351) and Y Shi acknowledges China Scholarship Council for her PhD Scholarshi

Gradient matching for domain generalization

Machine learning systems typically assume that the distributions of training and test sets match closely. However, a critical requirement of such systems in the real world is their ability to generalize to unseen domains. Here, we propose an inter-domain gradient matching objective that targets domain generalization by maximizing the inner product between gradients from different domains. Since direct optimization of the gradient inner product can be computationally prohibitive - it requires computation of second-order derivatives - we derive a simpler first-order algorithm named Fish that approximates its optimization. We perform experiments on the WILDS benchmark, which captures distribution shift in the real world, as well as the DOMAINBED benchmark that focuses more on synthetic-to-real transfer. Our method produces competitive results on both benchmarks, demonstrating its effectiveness across a wide range of domain generalization tasks. Code is available at https://github.com/YugeTen/fish

Stabilizing forces acting on ZnO polar surfaces: STM, LEED, and DFT

published_or_final_versio