A topological solution to object segmentation and tracking

The world is composed of objects, the ground, and the sky. Visual perception of objects requires solving two fundamental challenges: segmenting visual input into discrete units, and tracking identities of these units despite appearance changes due to object deformation, changing perspective, and dynamic occlusion. Current computer vision approaches to segmentation and tracking that approach human performance all require learning, raising the question: can objects be segmented and tracked without learning? Here, we show that the mathematical structure of light rays reflected from environment surfaces yields a natural representation of persistent surfaces, and this surface representation provides a solution to both the segmentation and tracking problems. We describe how to generate this surface representation from continuous visual input, and demonstrate that our approach can segment and invariantly track objects in cluttered synthetic video despite severe appearance changes, without requiring learning.Comment: 21 pages, 6 main figures, 3 supplemental figures, and supplementary material containing mathematical proof

Macro aerodynamic devices controlled by micro systems

Micro-ElectroMechanical-Systems (MEMS) have emerged as a major enabling technology across the engineering disciplines. In this study, the possibility of applying MEMS to the aerodynamic field was explored. We have demonstrated that microtransducers can be used to control the motion of a delta wing in a wind tunnel and can even maneuver a scaled aircraft in flight tests. The main advantage of using micro actuators to replace the traditional control surface is the significant reduction of radar cross-sections. At a high angle of attack, a large portion of the suction loading on a delta wing is contributed by the leading edge separation vortices which originate from thin boundary layers at the leading edge. We used microactuators with a thickness comparable to that of the boundary layer in order to alter the separation process and thus achieved control of the global motion by minute perturbations

A nonlinear Schr\"odinger equation for water waves on finite depth with constant vorticity

A nonlinear Schr\"odinger equation for the envelope of two dimensional surface water waves on finite depth with non zero constant vorticity is derived, and the influence of this constant vorticity on the well known stability properties of weakly nonlinear wave packets is studied. It is demonstrated that vorticity modifies significantly the modulational instability properties of weakly nonlinear plane waves, namely the growth rate and bandwidth. At third order we have shown the importance of the coupling between the mean flow induced by the modulation and the vorticity. Furthermore, it is shown that these plane wave solutions may be linearly stable to modulational instability for an opposite shear current independently of the dimensionless parameter kh, where k and h are the carrier wavenumber and depth respectively

What computational model provides the best explanation of face representations in the primate brain?

Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing (Tsao et al., 2006). A previous study reported that single face patch neurons encode axes of a generative model called the “active appearance” model (Chang and Tsao, 2017), which transforms 50-d feature vectors separately representing facial shape and facial texture into facial images (Cootes et al., 2001; Edwards et al., 1998). However, it remains unclear whether this model constitutes the best model for explaining face cell responses. Here, we recorded responses of cells in the most anterior face patch AM to a large set of real face images, and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification such as illumination

Explaining face representation in the primate brain using different computational models

Understanding how the brain represents the identity of complex objects is a central challenge of visual neuroscience. The principles governing object processing have been extensively studied in the macaque face patch system, a sub-network of inferotemporal (IT) cortex specialized for face processing. A previous study reported that single face patch neurons encode axes of a generative model called the “active appearance” model, which transforms 50D feature vectors separately representing facial shape and facial texture into facial images. However, a systematic investigation comparing this model to other computational models, especially convolutional neural network models that have shown success in explaining neural responses in the ventral visual stream, has been lacking. Here, we recorded responses of cells in the most anterior face patch anterior medial (AM) to a large set of real face images and compared a large number of models for explaining neural responses. We found that the active appearance model better explained responses than any other model except CORnet-Z, a feedforward deep neural network trained on general object classification to classify non-face images, whose performance it tied on some face image sets and exceeded on others. Surprisingly, deep neural networks trained specifically on facial identification did not explain neural responses well. A major reason is that units in the network, unlike neurons, are less modulated by face-related factors unrelated to facial identification, such as illumination

Exploiting the noise: improving biomarkers with ensembles of data analysis methodologies.

BackgroundThe advent of personalized medicine requires robust, reproducible biomarkers that indicate which treatment will maximize therapeutic benefit while minimizing side effects and costs. Numerous molecular signatures have been developed over the past decade to fill this need, but their validation and up-take into clinical settings has been poor. Here, we investigate the technical reasons underlying reported failures in biomarker validation for non-small cell lung cancer (NSCLC).MethodsWe evaluated two published prognostic multi-gene biomarkers for NSCLC in an independent 442-patient dataset. We then systematically assessed how technical factors influenced validation success.ResultsBoth biomarkers validated successfully (biomarker #1: hazard ratio (HR) 1.63, 95% confidence interval (CI) 1.21 to 2.19, P = 0.001; biomarker #2: HR 1.42, 95% CI 1.03 to 1.96, P = 0.030). Further, despite being underpowered for stage-specific analyses, both biomarkers successfully stratified stage II patients and biomarker #1 also stratified stage IB patients. We then systematically evaluated reasons for reported validation failures and find they can be directly attributed to technical challenges in data analysis. By examining 24 separate pre-processing techniques we show that minor alterations in pre-processing can change a successful prognostic biomarker (HR 1.85, 95% CI 1.37 to 2.50, P < 0.001) into one indistinguishable from random chance (HR 1.15, 95% CI 0.86 to 1.54, P = 0.348). Finally, we develop a new method, based on ensembles of analysis methodologies, to exploit this technical variability to improve biomarker robustness and to provide an independent confidence metric.ConclusionsBiomarkers comprise a fundamental component of personalized medicine. We first validated two NSCLC prognostic biomarkers in an independent patient cohort. Power analyses demonstrate that even this large, 442-patient cohort is under-powered for stage-specific analyses. We then use these results to discover an unexpected sensitivity of validation to subtle data analysis decisions. Finally, we develop a novel algorithmic approach to exploit this sensitivity to improve biomarker robustness

Additional Results of Ice-Accretion Scaling at SLD Conditions

To determine scale velocity an additional similarity parameter is needed to supplement the Ruff scaling method. A Weber number based on water droplet MVD has been included in several studies because the effect of droplet splashing on ice accretion was believed to be important, particularly for SLD conditions. In the present study, ice shapes recorded at Appendix-C conditions and recent results at SLD conditions are reviewed to show that droplet diameter cannot be important to main ice shape, and for low airspeeds splashing does not appear to affect SLD ice shapes. Evidence is presented to show that while a supplementary similarity parameter probably has the form of a Weber number, it must be based on a length proportional to model size rather than MVD. Scaling comparisons were made between SLD reference conditions and Appendix-C scale conditions using this Weber number. Scale-to-reference model size ratios were 1:1.7 and 1:3.4. The reference tests used a 91-cm-chord NACA 0012 model with a velocity of approximately 50 m/s and an MVD of 160 m. Freezing fractions of 0.3, 0.4, and 0.5 were included in the study

A Micromachined Permalloy Magnetic Actuator Array for Micro Robotics Assembly Systems

Arrays of permalloy magnetic actuators have been studied for the use as precision micro robotics assembly systems. The actuator arrays have been tested for lifting and moving silicon and glass chips. The actuator unit consists of a permalloy plate 1 mm x 1 mm X 5µm in size together with polysilicon bending supports. Experimentally, it can lift a 87 µN (or 8.88 mg) force under a magnetic field of approximately 2 x 10^4 A/m. A proposed synchronous driving mode has been observed, and both translation and rotation of a silicon chip has been demonstrated

Cladding mode coupling in highly localized fiber Bragg gratings: modal properties and transmission spectra

The spectral characteristics of a fiber Bragg grating (FBG) with a transversely inhomogeneous refractive index profile, differs con- siderably from that of a transversely uniform one. Transmission spectra of inhomogeneous and asymmetric FBGs that have been inscribed with focused ultrashort pulses with the so-called point-by-point technique are investigated. The cladding mode resonances of such FBGs can span a full octave in the spectrum and are very pronounced (deeper than 20dB). Using a coupled-mode approach, we compute the strength of resonant coupling and find that coupling into cladding modes of higher azimuthal order is very sensitive to the position of the modification in the core. Exploiting these properties allows precise control of such reflections and may lead to many new sensing applications.Comment: Submission to OE, 16 pages, 6 figure

Expression of the neuroprotective slow Wallerian degeneration (WldS) gene in non-neuronal tissues

<p>Abstract</p> <p>Background</p> <p>The slow Wallerian Degeneration (<it>Wld</it>^<it>S</it>) gene specifically protects axonal and synaptic compartments of neurons from a wide variety of degeneration-inducing stimuli, including; traumatic injury, Parkinson's disease, demyelinating neuropathies, some forms of motor neuron disease and global cerebral ischemia. The <it>Wld</it>^{<it>S </it>}gene encodes a novel Ube4b-Nmnat1 chimeric protein (Wld^Sprotein) that is responsible for conferring the neuroprotective phenotype. How the chimeric Wld^Sprotein confers neuroprotection remains controversial, but several studies have shown that expression in neurons <it>in vivo </it>and <it>in vitro </it>modifies key cellular pathways, including; NAD biosynthesis, ubiquitination, the mitochondrial proteome, cell cycle status and cell stress. Whether similar changes are induced in non-neuronal tissue and organs at a basal level <it>in vivo </it>remains to be determined. This may be of particular importance for the development and application of neuroprotective therapeutic strategies based around <it>Wld</it>^<it>S</it>-mediated pathways designed for use in human patients.</p> <p>Results</p> <p>We have undertaken a detailed analysis of non-neuronal <it>Wld</it>^{<it>S </it>}expression in <it>Wld</it>^{<it>S </it>}mice, alongside gravimetric and histological analyses, to examine the influence of <it>Wld</it>^{<it>S </it>}expression in non-neuronal tissues. We show that expression of <it>Wld</it>^{<it>S </it>}RNA and protein are not restricted to neuronal tissue, but that the relative RNA and protein expression levels rarely correlate in these non-neuronal tissues. We show that <it>Wld</it>^{<it>S </it>}mice have normal body weight and growth characteristics as well as gravimetrically and histologically normal organs, regardless of Wld^Sprotein levels. Finally, we demonstrate that previously reported <it>Wld</it>^<it>S</it>-induced changes in cell cycle and cell stress status are neuronal-specific, not recapitulated in non-neuronal tissues at a basal level.</p> <p>Conclusions</p> <p>We conclude that expression of Wld^Sprotein has no adverse effects on non-neuronal tissue at a basal level <it>in vivo</it>, supporting the possibility of its safe use in future therapeutic strategies targeting axonal and/or synaptic compartments in patients with neurodegenerative disease. Future experiments determining whether Wld^Sprotein can modify responses to injury in non-neuronal tissue are now required.</p