Quality-based Multimodal Classification Using Tree-Structured Sparsity

Recent studies have demonstrated advantages of information fusion based on sparsity models for multimodal classification. Among several sparsity models, tree-structured sparsity provides a flexible framework for extraction of cross-correlated information from different sources and for enforcing group sparsity at multiple granularities. However, the existing algorithm only solves an approximated version of the cost functional and the resulting solution is not necessarily sparse at group levels. This paper reformulates the tree-structured sparse model for multimodal classification task. An accelerated proximal algorithm is proposed to solve the optimization problem, which is an efficient tool for feature-level fusion among either homogeneous or heterogeneous sources of information. In addition, a (fuzzy-set-theoretic) possibilistic scheme is proposed to weight the available modalities, based on their respective reliability, in a joint optimization problem for finding the sparsity codes. This approach provides a general framework for quality-based fusion that offers added robustness to several sparsity-based multimodal classification algorithms. To demonstrate their efficacy, the proposed methods are evaluated on three different applications - multiview face recognition, multimodal face recognition, and target classification.Comment: To Appear in 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2014

Multiscale Autoregressive Identification of Neuroelectrophysiological Systems

Electrical signals between connected neural nuclei are difficult to model because of the complexity and high number of paths within the brain. Simple parametric models are therefore often used. A multiscale version of the autoregressive with exogenous input (MS-ARX) model has recently been developed which allows selection of the optimal amount of filtering and decimation depending on the signal-to-noise ratio and degree of predictability. In this paper, we apply the MS-ARX model to cortical electroencephalograms and subthalamic local field potentials simultaneously recorded from anesthetized rodent brains. We demonstrate that the MS-ARX model produces better predictions than traditional ARX modeling. We also adapt the MS-ARX results to show differences in internuclei predictability between normal rats and rats with 6OHDA-induced parkinsonism, indicating that this method may have broad applicability to other neuroelectrophysiological studies

Long-lived space observatories for astronomy and astrophysics

NASA's plan to build and launch a fleet of long-lived space observatories that include the Hubble Space Telescope (HST), the Gamma Ray Observatory (GRO), the Advanced X Ray Astrophysics Observatory (AXAF), and the Space Infrared Telescope Facility (SIRTF) are discussed. These facilities are expected to have a profound impact on the sciences of astronomy and astrophysics. The long-lived observatories will provide new insights about astronomical and astrophysical problems that range from the presence of planets orbiting nearby stars to the large-scale distribution and evolution of matter in the universe. An important concern to NASA and the scientific community is the operation and maintenance cost of the four observatories described above. The HST cost about $1.3 billion (1984 dollars) to build and is estimated to require$160 million (1986 dollars) a year to operate and maintain. If HST is operated for 20 years, the accumulated costs will be considerably more than those required for its construction. Therefore, it is essential to plan carefully for observatory operations and maintenance before a long-lived facility is constructed. The primary goal of this report is to help NASA develop guidelines for the operations and management of these future observatories so as to achieve the best possible scientific results for the resources available. Eight recommendations are given

Early abdominal closure with mesh reduces multiple organ failure after ruptured abdominal aortic aneurysm repair: Guidelines from a 10-year case-control study

AbstractObjective: The objectives of this study were the comparison of patients who needed mesh closure of the abdomen with patients who underwent standard abdominal closure after ruptured abdominal aortic aneurysm repair and the determination of the impact of timing of mesh closure on multiple organ failure (MOF) and mortality. Methods: We performed a case-control study of patients who needed mesh-based abdominal closure (n = 45) as compared with patients who underwent primary closure (n = 90) after ruptured abdominal aortic aneurysm repair. Results: Before surgery, the patients who needed mesh abdominal closure had more blood loss (8 g versus 12 g of hemoglobin; P <.05), had prolonged hypotension (18 minutes versus 3 minutes; P <.01), and more frequently needed cardiopulmonary resuscitation (31% versus 2%; P <.01) than did the patients who underwent primary closure. During surgery, the patients who needed mesh closure also had more severe acidosis (base deficit, 14 versus 7; P <.01), had profound hypothermia (32°C versus 35°C; P <.01), and needed more fluid resuscitation (4.0 L/h versus 2.7 L/h; P <.01). With this adverse clinical profile, the patients who needed mesh closure had a higher mortality rate than did the patients who underwent primary closure (56% versus 9%; P <.01). However, the patients who underwent mesh closure at the initial operation (n = 35) had lower MOF scores (P <.05), a lower mortality rate (51% versus 70%), and were less likely to die from MOF (11% versus 70%; P <.05) than the patients who underwent mesh closure after a second operation in the postoperative period for abdominal compartment syndrome (n = 10). Conclusion: This study reports the largest experience of mesh-based abdominal closure after ruptured abdominal aortic aneurysm repair and defines clinical predictors for patients who need to undergo this technique. Recognition of these predictors and initial use of mesh closure minimize abdominal compartment syndrome and reduce the rate of mortality as the result of MOF. (J Vasc Surg 2002;35:246-53.

Mapping the immune environment in clear cell renal carcinoma by single-cell genomics

Clear cell renal cell carcinoma (ccRCC) is one of the most immunologically distinct tumor types due to high response rate to immunotherapies, despite low tumor mutational burden. To characterize the tumor immune microenvironment of ccRCC, we applied single-cell-RNA sequencing (SCRS) along with T-cell-receptor (TCR) sequencing to map the transcriptomic heterogeneity of 25,688 individual CD4

The Diversity of High- and Intermediate-Velocity Clouds: Complex C versus IV Arch

We present Far Ultraviolet Spectroscopic Explorer (FUSE) and Space Telescope Imaging Spectrograph (STIS) observations of interstellar ultraviolet absorption lines in the Galactic high-velocity cloud Complex C and the Intermediate Velocity Arch (IV Arch) in direction of the quasar PG 1259+593 (l=120,b=+58 deg). Absorption lines from CII, NI, NII, OI, AlII, SiII, PII, SII, ArI, FeII, and FeIII are used to study the atomic abundances in these two halo clouds at V_LSR=-130 km/s (Complex C) and V_LSR=-55 km/s (IV Arch). The OI/HI ratio provides the best measure of the overall metallicity in the diffuse interstellar medium, because ionization effects do not alter the ratio, and oxygen is at most only lightly depleted from the gas into dust grains. For Complex C, we find an oxygen abundance of 0.093 (+0.125, -0.047) solar, consistent with the idea that Complex C represents the infall of low metallicity gas onto the Milky Way. In contrast, the oxygen abundance in the IV Arch is 0.98 (+1.21,-0.46) solar, which indicates a Galactic origin. We report the detection of an intermediate- velocity absorption component at +60 km/s that is not seen in HI 21cm emission. The clouds along the PG 1259+593 sight line have a variety of properties, proving that multiple processes are responsible for the creation and circulation of intermediate- and high-velocity gas in the Milky Way halo.Comment: 12 pages, 3 tables, 3 figures; accepted for publication in Ap

A FUSE Survey of Interstellar Molecular Hydrogen in the Small and Large Magellanic Clouds

We describe a moderate-resolution FUSE survey of H2 along 70 sight lines to the Small and Large Magellanic Clouds, using hot stars as background sources. FUSE spectra of 67% of observed Magellanic Cloud sources (52% of LMC and 92% of SMC) exhibit absorption lines from the H2 Lyman and Werner bands between 912 and 1120 A. Our survey is sensitive to N(H2) >= 10^14 cm^-2; the highest column densities are log N(H2) = 19.9 in the LMC and 20.6 in the SMC. We find reduced H2 abundances in the Magellanic Clouds relative to the Milky Way, with average molecular fractions = 0.010 (+0.005, -0.002) for the SMC and = 0.012 (+0.006, -0.003) for the LMC, compared with = 0.095 for the Galactic disk over a similar range of reddening. The dominant uncertainty in this measurement results from the systematic differences between 21 cm radio emission and Lya in pencil-beam sight lines as measures of N(HI). These results imply that the diffuse H2 masses of the LMC and SMC are 8 x 10^6 Msun and 2 x 10^6 Msun, respectively, 2% and 0.5% of the H I masses derived from 21 cm emission measurements. The LMC and SMC abundance patterns can be reproduced in ensembles of model clouds with a reduced H2 formation rate coefficient, R ~ 3 x 10^-18 cm^3 s^-1, and incident radiation fields ranging from 10 - 100 times the Galactic mean value. We find that these high-radiation, low-formation-rate models can also explain the enhanced N(4)/N(2) and N(5)/N(3) rotational excitation ratios in the Clouds. We use H2 column densities in low rotational states (J = 0 and 1) to derive a mean kinetic and/or rotational temperature = 82 +/- 21 K for clouds with N(H2) >= 10^16 cm^-2, similar to Galactic gas. We discuss the implications of this work for theories of star formation in low-metallicity environments. [Abstract abridged]Comment: 30 pages emulateapj, 14 figures (7 color), 7 tables, accepted for publication in the Astrophysical Journal, figures 11 and 12 compressed at slight loss of quality, see http://casa.colorado.edu/~tumlinso/h2/ for full version

Deep-learning segmentation of fascicles from microCT of the human vagus nerve

IntroductionMicroCT of the three-dimensional fascicular organization of the human vagus nerve provides essential data to inform basic anatomy as well as the development and optimization of neuromodulation therapies. To process the images into usable formats for subsequent analysis and computational modeling, the fascicles must be segmented. Prior segmentations were completed manually due to the complex nature of the images, including variable contrast between tissue types and staining artifacts.MethodsHere, we developed a U-Net convolutional neural network (CNN) to automate segmentation of fascicles in microCT of human vagus nerve.ResultsThe U-Net segmentation of ~500 images spanning one cervical vagus nerve was completed in 24 s, versus ~40 h for manual segmentation, i.e., nearly four orders of magnitude faster. The automated segmentations had a Dice coefficient of 0.87, a measure of pixel-wise accuracy, thus suggesting a rapid and accurate segmentation. While Dice coefficients are a commonly used metric to assess segmentation performance, we also adapted a metric to assess fascicle-wise detection accuracy, which showed that our network accurately detects the majority of fascicles, but may under-detect smaller fascicles.DiscussionThis network and the associated performance metrics set a benchmark, using a standard U-Net CNN, for the application of deep-learning algorithms to segment fascicles from microCT images. The process may be further optimized by refining tissue staining methods, modifying network architecture, and expanding the ground-truth training data. The resulting three-dimensional segmentations of the human vagus nerve will provide unprecedented accuracy to define nerve morphology in computational models for the analysis and design of neuromodulation therapies