Tracking objects with point clouds from vision and touch

We present an object-tracking framework that fuses point cloud information from an RGB-D camera with tactile information from a GelSight contact sensor. GelSight can be treated as a source of dense local geometric information, which we incorporate directly into a conventional point-cloud-based articulated object tracker based on signed-distance functions. Our implementation runs at 12 Hz using an online depth reconstruction algorithm for GelSight and a modified second-order update for the tracking algorithm. We present data from hardware experiments demonstrating that the addition of contact-based geometric information significantly improves the pose accuracy during contact, and provides robustness to occlusions of small objects by the robot's end effector

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

Molecular Line Profile Fitting with Analytic Radiative Transfer Models

We present a study of analytic models of starless cores whose line profiles have ``infall asymmetry,'' or blue-skewed shapes indicative of contracting motions. We compare the ability of two types of analytical radiative transfer models to reproduce the line profiles and infall speeds of centrally condensed starless cores whose infall speeds are spatially constant and range between 0 and 0.2 km s-1. The model line profiles of HCO+ (J=1-0) and HCO+ (J=3-2) are produced by a self-consistent Monte Carlo radiative transfer code. The analytic models assume that the excitation temperature in the front of the cloud is either constant (``two-layer'' model) or increases inward as a linear function of optical depth (``hill'' model). Each analytic model is matched to the line profile by rapid least-squares fitting. The blue-asymmetric line profiles with two peaks, or with a blue shifted peak and a red shifted shoulder, can be well fit by the ``HILL5'' model (a five parameter version of the hill model), with an RMS error of 0.02 km s-1. A peak signal to noise ratio of at least 30 in the molecular line observations is required for performing these analytic radiative transfer fits to the line profiles.Comment: 48 pages, 20 figures, accepted for publication in Ap

Identification of candidate anti-cancer molecular mechanisms of compound kushen injection using functional genomics

Compound Kushen Injection (CKI) has been clinically used in China for over 15 years to treat various types of solid tumours. However, because such Traditional Chinese Medicine (TCM) preparations are complex mixtures of plant secondary metabolites, it is essential to explore their underlying molecular mechanisms in a systematic fashion. We have used the MCF-7 human breast cancer cell line as an initial in vitro model to identify CKI induced changes in gene expression. Cells were treated with CKI for 24 and 48 hours at two concentrations (1 and 2 mg/mL total alkaloids), and the effect of CKI on cell proliferation and apoptosis were measured using XTT and Annexin V/Propidium Iodide staining assays respectively. Transcriptome data of cells treated with CKI or 5-Fluorouracil (5-FU) for 24 and 48 hours were subsequently acquired using high-throughput Illumina RNA-seq technology. In this report we show that CKI inhibited MCF-7 cell proliferation and induced apoptosis in a dose-dependent fashion. We integrated and applied a series of transcriptome analysis methods, including gene differential expression analysis, pathway over-representation analysis, de novo identification of long non-coding RNAs (lncRNA) as well as co-expression network reconstruction, to identify candidate anti-cancer molecular mechanisms of CKI. Multiple pathways were perturbed and the cell cycle was identified as the potential primary target pathway of CKI in MCF-7 cells. CKI may also induce apoptosis in MCF-7 cells via a p53 independent mechanism. In addition, we identified novel lncRNAs and showed that many of them might be expressed as a response to CKI treatment.Zhipeng Qu, Jian Cui, Yuka Harata-Lee, Thazin Nwe Aung, Qianjin Feng, Joy M. Raison, Robert Daniel Kortschak, David L. Adelso

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

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

Common data elements for pediatric traumatic brain injury: Recommendations from the working group on demographics and clinical assessment

The Common Data Elements (CDEs) initiative is a National Institutes of Health (NIH) interagency effort to standardize naming, definitions, and data structure for clinical research variables. Comparisons of the results of clinical studies of neurological disorders have been hampered by variability in data coding, definitions, and procedures for sample collection. The CDE project objective is to enable comparison of future clinical trials results in major neurological disorders, including traumatic brain injury (TBI), stroke, multiple sclerosis, and epilepsy. As part of this effort, recommendations for CDEs for research on TBI were developed through a 2009 multi-agency initiative. Following the initial recommendations of the Working Group on Demographics and Clinical Assessment, a separate workgroup developed recommendations on the coding of clinical and demographic variables specific to pediatric TBI studies for subjects younger than 18 years. This article summarizes the selection of measures by the Pediatric TBI Demographics and Clinical Assessment Working Group. The variables are grouped into modules which are grouped into categories. For consistency with other CDE working groups, each variable was classified by priority (core, supplemental, and emerging). Templates were produced to summarize coding formats, guide selection of data points, and provide procedural recommendations. This proposed standardization, together with the products of the other pediatric TBI working groups in imaging, biomarkers, and outcome assessment, will facilitate multi-center studies, comparison of results across studies, and high-quality meta-analyses of individual patient data

Accidental Pinhole and Pinspeck Cameras

We identify and study two types of “accidental” images that can be formed in scenes. The first is an accidental pinhole camera image. The second class of accidental images are “inverse” pinhole camera images, formed by subtracting an image with a small occluder present from a reference image without the occluder. Both types of accidental cameras happen in a variety of different situations. For example, an indoor scene illuminated by natural light, a street with a person walking under the shadow of a building, etc. The images produced by accidental cameras are often mistaken for shadows or interreflections. However, accidental images can reveal information about the scene outside the image, the lighting conditions, or the aperture by which light enters the scene.National Science Foundation (U.S.) (CAREER Award 0747120)United States. Office of Naval Research. Multidisciplinary University Research Initiative (N000141010933)National Science Foundation (U.S.) (CGV 1111415)National Science Foundation (U.S.) (CGV 0964004

Provably scale-covariant networks from oriented quasi quadrature measures in cascade

This article presents a continuous model for hierarchical networks based on a combination of mathematically derived models of receptive fields and biologically inspired computations. Based on a functional model of complex cells in terms of an oriented quasi quadrature combination of first- and second-order directional Gaussian derivatives, we couple such primitive computations in cascade over combinatorial expansions over image orientations. Scale-space properties of the computational primitives are analysed and it is shown that the resulting representation allows for provable scale and rotation covariance. A prototype application to texture analysis is developed and it is demonstrated that a simplified mean-reduced representation of the resulting QuasiQuadNet leads to promising experimental results on three texture datasets.Comment: 12 pages, 3 figures, 1 tabl

In depth analysis of the Sox4 gene locus that consists of sense and natural antisense transcripts

Available online 17 February 2016SRY (Sex Determining Region Y)-Box 4 or Sox4 is an important regulator of the pan-neuronal gene expression during post-mitotic cell differentiation within the mammalian brain. Sox4 gene locus has been previously characterized with multiple sense and overlapping natural antisense transcripts [1], [2]. Here we provide accompanying data on various analyses performed and described in Ling et al. [2]. The data include a detail description of various features found at Sox4 gene locus, additional experimental data derived from RNA-Fluorescence in situ Hybridization (RNA-FISH), Western blotting, strand-specific reverse-transcription quantitative polymerase chain reaction (RT-qPCR), gain-of-function and in situ hybridization (ISH) experiments. All the additional data provided here support the existence of an endogenous small interfering- or PIWI interacting-like small RNA known as Sox4_sir3, which origin was found within the overlapping region consisting of a sense and a natural antisense transcript known as Sox4ot1.King-Hwa Ling, Peter J. Brautigan, Sarah Moore, Rachel Fraser, Melody Pui-Yee Leong, Jia-Wen Leong, Shahidee Zainal Abidin, Han-Chung Lee, Pike-See Cheah, Joy M. Raison, Milena Babic, Young Kyung Lee, Tasman Daish, Deidre M. Mattiske, Jeffrey R. Mann, David L. Adelson, Paul Q. Thomas, Christopher N. Hahn, Hamish S.Scot