Slow and Smooth: A Bayesian Theory for the Combination of Local Motion Signals in Human Vision

In order to estimate the motion of an object, the visual system needs to combine multiple local measurements, each of which carries some degree of ambiguity. We present a model of motion perception whereby measurements from different image regions are combined according to a Bayesian estimator --- the estimated motion maximizes the posterior probability assuming a prior favoring slow and smooth velocities. In reviewing a large number of previously published phenomena we find that the Bayesian estimator predicts a wide range of psychophysical results. This suggests that the seemingly complex set of illusions arise from a single computational strategy that is optimal under reasonable assumptions

A novel hypothesis-unbiased method for gene ontology enrichment based on transcriptome data

Gene Ontology (GO) classification of statistically significantly differentially expressed genes is commonly used to interpret transcriptomics data as a part of functional genomic analysis. In this approach, all significantly expressed genes contribute equally to the final GO classification regardless of their actual expression levels. Gene expression levels can significantly affect protein production and hence should be reflected in GO term enrichment. Genes with low expression levels can also participate in GO term enrichment through cumulative effects. In this report, we have introduced a new GO enrichment method that is suitable for multiple samples and time series experiments that uses a statistical outlier test to detect GO categories with special patterns of variation that can potentially identify candidate biological mechanisms. To demonstrate the value of our approach, we have performed two case studies. Whole transcriptome expression profiles of Salmonella enteritidis and Alzheimer's disease (AD) were analysed in order to determine GO term enrichment across the entire transcriptome instead of a subset of differentially expressed genes used in traditional GO analysis. Our result highlights the key role of inflammation related functional groups in AD pathology as granulocyte colony-stimulating factor receptor binding, neuromedin U binding, and interleukin were remarkably upregulated in AD brain when all using all of the gene expression data in the transcriptome. Mitochondrial components and the molybdopterin synthase complex were identified as potential key cellular components involved in AD pathology.Mario Fruzangohar, Esmaeil Ebrahimie, David L. Adelso

ViTac: Feature Sharing between Vision and Tactile Sensing for Cloth Texture Recognition

Vision and touch are two of the important sensing modalities for humans and they offer complementary information for sensing the environment. Robots could also benefit from such multi-modal sensing ability. In this paper, addressing for the first time (to the best of our knowledge) texture recognition from tactile images and vision, we propose a new fusion method named Deep Maximum Covariance Analysis (DMCA) to learn a joint latent space for sharing features through vision and tactile sensing. The features of camera images and tactile data acquired from a GelSight sensor are learned by deep neural networks. But the learned features are of a high dimensionality and are redundant due to the differences between the two sensing modalities, which deteriorates the perception performance. To address this, the learned features are paired using maximum covariance analysis. Results of the algorithm on a newly collected dataset of paired visual and tactile data relating to cloth textures show that a good recognition performance of greater than 90% can be achieved by using the proposed DMCA framework. In addition, we find that the perception performance of either vision or tactile sensing can be improved by employing the shared representation space, compared to learning from unimodal data

Infall, Outflow, Rotation, and Turbulent Motions of Dense Gas within NGC 1333 IRAS 4

Millimeter wavelength observations are presented of NGC 1333 IRAS 4, a group of highly-embedded young stellar objects in Perseus, that reveal motions of infall, outflow, rotation, and turbulence in the dense gas around its two brightest continuum objects, 4A and 4B. These data have finest angular resolution of approximately 2" (0.0034 pc) and finest velocity resolution of 0.13 km/s. Infall motions are seen from inverse P-Cygni profiles observed in H2CO 3_12-2_11 toward both objects, but also in CS 3-2 and N2H+ 1-0 toward 4A, providing the least ambiguous evidence for such motions toward low-mass protostellar objects. Outflow motions are probed by bright line wings of H2CO 3_12-2_11 and CS 3-2 observed at positions offset from 4A and 4B, likely tracing dense cavity walls. Rotational motions of dense gas are traced by a systematic variation of the N2H+ line velocities, and such variations are found around 4A but not around 4B. Turbulent motions appear reduced with scale, given N2H+ line widths around both 4A and 4B that are narrower by factors of 2 or 3 than those seen from single-dish observations. Minimum observed line widths of approximately 0.2 km/s provide a new low, upper bound to the velocity dispersion of the parent core to IRAS 4, and demonstrate that turbulence within regions of clustered star formation can be reduced significantly. A third continuum object in the region, 4B', shows no detectable line emission in any of the observed molecular species.Comment: LateX, 51 pages, 9 figures, accepted by Ap

Deep Depth From Focus

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201

Detection of Candida species in vaginal samples in a clinical laboratory setting.

OBJECTIVE: To present the detection rates of Candida species in vaginal samples from patients visiting physicians. METHODS: The presence of C. albicans, C. glabrata, C. parapsilosis and C. tropicalis in 3978 vaginal swabs from patients in six US states was detected by PCR amplification. RESULTS: Candida DNA was detected in 33.1% of the population studied. Of the 1316 positive samples, 80.2% contained C. albicans, 14.3% contained C. glabrata, 5.9% contained C. parapsilosis and 8.0% contained C. tropicalis. Comparing samples by patients' state of residence revealed an association with the detection of C. glabrata (p = 0.029). Comparing samples by patients' age revealed a decrease in the overall detection of Candida (p < 0.001) and C. albicans (p < 0.001), concomitant with an increase in the detection of C. glabrata (p < 0.001) and C. parapsilosis (p = 0.025). CONCLUSIONS: These results provide geographic- and age-specific data on four Candida species associated with vaginitis

Millimeter and Submillimeter Survey of the R Corona Australis Region

Using a combination of data from the Antarctic Submillimeter Telescope and Remote Observatory (AST/RO), the Arizona Radio Observatory Kitt Peak 12m telescope and the Arizona Radio Observatory 10m Heinrich Hertz Telescope, we have studied the most active part of the R CrA molecular cloud in multiple transitions of Carbon Monoxide, HCO$^+$ and 870\micron continuum emission. Since R CrA is nearby (130 pc), we are able to obtain physical spatial resolution as high as 0.01pc over an area of 0.16 pc$^2$, with velocity resolution finer than 1 km/s. Mass estimates of the protostar driving the mm-wave emission derived from HCO$^+$, dust continuum emission and kinematic techniques point to a young, deeply embedded protostar of $\sim$0.5-0.75 M$_\odot$, with a gaseous envelope of similar mass. A molecular outflow is driven by this source that also contains at least 0.8 M$_\odot$ of molecular gas with $\sim$0.5 L$_\odot$ of mechanical luminosity. HCO$^+$ lines show the kinematic signature of infall motions as well as bulk rotation. The source is most likely a Class 0 protostellar object not yet visible at near-IR wavelengths. With the combination of spatial and spectral resolution in our data set, we are able to disentangle the effects of infall, rotation and outflow towards this young object.Comment: 29 pages, 9 figures. Accepted for publication in the Astrophysical Journa

Does Perceptual Belongingness Affect Lightness Constancy?

Scientists have shown that two equal grey patches may differ in lightness when belonging to different reflecting surfaces. We extend this investigation to the constancy domain. In a CRT simulation of a bipartite field of illumination, we manipulated the arrangement of twelve patches: six squares and six diamonds. Patches of the same shape could be placed: (i) all within the same illumination field; or (ii) forming a row across the illumination fields. Furthermore, we manipulated proximity between the innermost patches and the illumination edge. The patches could be (i) touching (forming an X-junction); or (ii) not touching (not forming an X-junction). Observers were asked to perform a lightness match between two additional patches, one illuminated and the other in shadow. We found better lightness constancy when the patches of the same shape formed a row across the fields, with no effect of X-junctions

Separable time-causal and time-recursive spatio-temporal receptive fields

We present an improved model and theory for time-causal and time-recursive spatio-temporal receptive fields, obtained by a combination of Gaussian receptive fields over the spatial domain and first-order integrators or equivalently truncated exponential filters coupled in cascade over the temporal domain. Compared to previous spatio-temporal scale-space formulations in terms of non-enhancement of local extrema or scale invariance, these receptive fields are based on different scale-space axiomatics over time by ensuring non-creation of new local extrema or zero-crossings with increasing temporal scale. Specifically, extensions are presented about parameterizing the intermediate temporal scale levels, analysing the resulting temporal dynamics and transferring the theory to a discrete implementation in terms of recursive filters over time.Comment: 12 pages, 2 figures, 2 tables. arXiv admin note: substantial text overlap with arXiv:1404.203