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

The exit velocity of a compressed air cannon

The use of compressed air cannons in an undergraduate lab provides a way to illustrate the cooperation of diverse physics concepts, such as conservation of momentum, the work-kinetic energy theorem, expansion of gas, air drag, and elementary Newtonian mechanics. However, recent proposals have disagreed as to whether the expansion of the gas in the cannon should be modeled as an adiabatic or an isothermal process. We built an air cannon that utilized a diaphragm valve to release our pressurized gas and found that neither model accurately predicted the exit velocity of our projectile. We present a new model, based on the flow of air through the valve, that is in much better agreement with our data

Learning Visual Question Answering by Bootstrapping Hard Attention

Attention mechanisms in biological perception are thought to select subsets of perceptual information for more sophisticated processing which would be prohibitive to perform on all sensory inputs. In computer vision, however, there has been relatively little exploration of hard attention, where some information is selectively ignored, in spite of the success of soft attention, where information is re-weighted and aggregated, but never filtered out. Here, we introduce a new approach for hard attention and find it achieves very competitive performance on a recently-released visual question answering datasets, equalling and in some cases surpassing similar soft attention architectures while entirely ignoring some features. Even though the hard attention mechanism is thought to be non-differentiable, we found that the feature magnitudes correlate with semantic relevance, and provide a useful signal for our mechanism's attentional selection criterion. Because hard attention selects important features of the input information, it can also be more efficient than analogous soft attention mechanisms. This is especially important for recent approaches that use non-local pairwise operations, whereby computational and memory costs are quadratic in the size of the set of features.Comment: ECCV 201

Localized vibrational modes in optically bound structures

We show, through analytical theory and rigorous numerical calculations, that optical binding can organize a collection of particles into stable one-dimensional lattice. This lattice, as well as other optically-bound structures, are shown to exhibit spatially localized vibrational eigenmodes. The origin of localization here is distinct from the usual mechanisms such as disorder, defect, or nonlinearity, but is a consequence of the long-ranged nature of optical binding. For an array of particles trapped by an interference pattern, the stable configuration is often dictated by the external light source, but our calculation revealed that inter-particle optical binding forces can have a profound influence on the dynamics.Comment: 4 pages, Optical Bindin

Stray Light Modeling of the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM)

This paper describes an integrated stray light model of each Science Instrument (SI) in the Integrated Science Instrument Module (ISIM) of the James Webb Space Telescope (JWST) and the Optical Telescope Element Simulator (OSIM), the light source used to characterize the performance of ISIM in cryogenic-vacuum tests at the Goddard Space Flight Center (GSFC). We present three cases where this stray light model was integral to solving questions that arose during the testing campaign - 1) ghosting and coherent diffraction from hardware surfaces in the Near Infrared Imager and Slitless Spectrograph (NIRISS) GR700XD grism mode, 2) ghost spots in the Near Infrared Camera (NIRCam) GRISM modes, and 3) scattering from knife edges of the NIRCam focal plane array masks

Ondansetron does not reduce the shivering threshold in healthy volunteers

Background. Ondansetron, a serotonin-3 receptor antagonist, reduces postoperative shivering. Drugs that reduce shivering usually impair central thermoregulatory control, and may thus be useful for preventing shivering during induction of therapeutic hypothermia. We determined, therefore, whether ondansetron reduces the major autonomic thermoregulatory response thresholds (triggering core temperatures) in humans. Methods. Control (placebo) and ondansetron infusions at the target plasma concentration of 250 ng ml−1 were studied in healthy volunteers on two different days. Each day, skin and core temperatures were increased to provoke sweating; then reduced to elicit peripheral vasoconstriction and shivering. We determined the core-temperature sweating, vasoconstriction and shivering thresholds after compensating for changes in mean-skin temperature. Data were analysed using t-tests and presented as means (sds); P<0.05 was taken as significant. Results. Ondensetron plasma concentrations were 278 (57), 234 (55) and 243 (58) ng ml−1 at the sweating, vasoconstriction and shivering thresholds, respectively; these corresponded to ≈50 mg of ondansetron which is approximately 10 times the dose used for postoperative nausea and vomiting. Ondansetron did not change the sweating (control 37.4 (0.4)°C, ondansetron 37.6 (0.3)°C, P=0.16), vasoconstriction (37.0 (0.5)°C vs 37.1 (0.3)°C; P=0.70), or shivering threshold (36.3 (0.5)°C vs 36.3 (0.6)°C; P=0.76). No sedation was observed on either study day. Conclusions. Ondansetron appears to have little potential for facilitating induction of therapeutic hypothermi