Assessing neural network scene classification from degraded images

Scene recognition is an essential component of both machine and biological vision. Recent advances in computer vision using deep convolutional neural networks (CNNs) have demonstrated impressive sophistication in scene recognition, through training on large datasets of labeled scene images (Zhou et al. 2018, 2014). One criticism of CNN-based approaches is that performance may not generalize well beyond the training image set (Torralba and Efros 2011), and may be hampered by minor image modifications, which in some cases are barely perceptible to the human eye (Goodfellow et al. 2015; Szegedy et al. 2013). While these “adversarial examples” may be unlikely in natural contexts, during many real-world visual tasks scene information can be degraded or limited due to defocus blur, camera motion, sensor noise, or occluding objects. Here, we quantify the impact of several image degradations (some common, and some more exotic) on indoor/outdoor scene classification using CNNs. For comparison, we use human observers as a benchmark, and also evaluate performance against classifiers using limited, manually selected descriptors. While the CNNs outperformed the other classifiers and rivaled human accuracy for intact images, our results show that their classification accuracy is more affected by image degradations than human observers. On a practical level, however, accuracy of the CNNs remained well above chance for a wide range of image manipulations that disrupted both local and global image statistics. We also examine the level of image-by-image agreement with human observers, and find that the CNNs' agreement with observers varied as a function of the nature of image manipulation. In many cases, this agreement was not substantially different from the level one would expect to observe for two independent classifiers. Together, these results suggest that CNN-based scene classification techniques are relatively robust to several image degradations. However, the pattern of classifications obtained for ambiguous images does not appear to closely reflect the strategies employed by human observers

Trauma ans sepsis induced splanchnic and hepatic ischemia and reperfusion injury

MODS is, with an iocidence of 10-25% and a mortality of 50-70%, the most severe complication after severe trauma 72_ MODS is a prototypical exemplar of the application of complexity theory to an understandiog of the pathophysiology of critical illness 56, 74_ It arises through the ioteractions of a network of physiologic iosults iocludiog iojury, tissue ischemi~ bacteremi~ endotoxemi~ the host inflammatory response, and the interventions used to sustain organ function during a time of otherwise lethal iosufficiency. Its mediators are many and ioterdependent, with the activity of one induciog the expression of others that amplifY, inlnbit, or otherwise modifY the expression of the process 19. The implications of an understandiog of the complex nature of organ dysfunction are critical to the development of rational strategies to prevent or treat the process. Strategies directed agaiost events late io the process may be effective but are unlikely to have a significant effect on a process whose expression, at least from the perspective of the element targeted, has become autonomous 4, 18, 75. In contrast, ischemia and reperfusion iojury to both the iotestine and the liver appears to be a relatively early event io the process ofpost-iojury development of MODS, as outlioed io figure L Therefore, a target-oriented approach includiog early augmentation of iotestinal and hepatic perfusion and oxygenation seems conceptually a more attractive therapeutic option, either as a preventive measure for subjects at risk or as a promising treatment modality in the whole treatment strategy for patients with MODS

Power Spectrum of Velocity Fluctuations in the Universe

We investigate the power spectrum of velocity fluctuations in the universe, $V^2(k)$, starting from four different measures of velocity: (1) the power spectrum of velocity fluctuations from peculiar velocities of galaxies; (2) the rms peculiar velocity of galaxy clusters; (3) the power spectrum of velocity fluctuations from the power spectrum of density fluctuations in the galaxy distribution; (4) and the bulk velocity from peculiar velocities of galaxies. We show that measures (1) and (2) are not consistent with each other and either the power spectrum from peculiar velocities of galaxies is overestimated or the rms cluster peculiar velocity is underestimated. The amplitude of velocity fluctuations derived from the galaxy distribution (measure 3) depends on the parameter $\beta$. We estimate the parameter $\beta$ on the basis of measures (2) and (4). The power spectrum of velocity fluctuations from the galaxy distribution in the Stromlo-APM redshift survey is consistent with the observed rms cluster velocity and with the observed large-scale bulk flow when the parameter $\beta$ is in the range 0.4-0.5. In this case the value of the function $V(k)$ at wavelength $\lambda=120h^{-1}$Mpc is $\sim 350$ km s$^{-1}$ and the rms amplitude of the bulk flow at the radius $r=60h^{-1}$ Mpc is $\sim 340$ km s$^{-1}$. The velocity dispersion of galaxy systems originates mostly from the large-scale velocity fluctuations with wavelengths $\lambda >100h^{-1}$ Mpc.Comment: Astrophysical Journal, Vol. 493, in press: 23 pages, uses AAS Latex, and 14 separate postscript figure

Is the Lambda CDM Model Consistent with Observations of Large-Scale Structure?

The claim that large-scale structure data independently prefers the Lambda Cold Dark Matter model is a myth. However, an updated compilation of large-scale structure observations cannot rule out Lambda CDM at 95% confidence. We explore the possibility of improving the model by adding Hot Dark Matter but the fit becomes worse; this allows us to set limits on the neutrino mass.Comment: To appear in Proceedings of "Sources and Detection of Dark Matter/Energy in the Universe", ed. D. B. Cline. 6 pages, including 2 color figure

CIRCULAR DICHROISM OF LIGHT-HARVESTING COMPLEXES FROM PURPLE PHOTOSYNTHETIC BACTERIA

The CD spectra of a range of antenna complexes from several different species of purple photosynthetic bacteria were recorded in the wavelength range of 190 to 930 nm. Analysis of the far UV CD (190 to 250 nm) showed that in each case except for the B800-850 from Chr. vinosum the secondary structure of the light-harvesting complexes contains a large amount of α-helix (50%) and very little 0-pleated sheet. This confirms the predictions of the group of Zuber of a high a-helical content based upon consideration of the primary structures of several antenna apoproteins. The CD spectra from the carotenoids and the bacteriochlorophylls show considerable variations depending upon the type of antenna complex. The different amplitude ratios in the CD spectrum for the bacteriochlorophyll Qy, Qx and Soret bands indicate not only different degrees of exciton coupling, but also a strong and variable hyperchromism (Scherz and Parson, 1984a, b)

The temperature effect on electrokinetic properties of the silica–polyvinyl alcohol (PVA) system

The influence of polyvinyl alcohol (PVA) adsorption on the structure of the diffuse layer of silica (SiO2) in the temperature range 15–35 °C was examined. The microelectrophoresis method was used in the experiments to determine the zeta potential of the solid particles in the absence and presence of the polymer. The adsorption of PVA macromolecules causes the zeta potential decrease in all investigated SiO2 systems. Moreover this, decrease is the most pronounced at the highest examined temperature. Obtained results indicate that the conformational changes of adsorbed polymer chains are responsible for changes in electrokinetic properties of silica particles. Moreover, the structure of diffuse layer on the solid surface with adsorbed polymer results from the following effects: the presence of acetate groups in PVA chains, the blockade of silica surface groups by adsorbed polymer and the shift of slipping plane due to macromolecules adsorption

Cosmological constraints from COMBO-17 using 3D weak lensing

We present the first application of the 3D cosmic shear method developed in Heavens et al. (2006) and the geometric shear-ratio analysis developed in Taylor et al. (2006), to the COMBO-17 data set. 3D cosmic shear has been used to analyse galaxies with redshift estimates from two random COMBO-17 fields covering 0.52 square degrees in total, providing a conditional constraint in the (sigma_8, Omega_m) plane as well as a conditional constraint on the equation of state of dark energy, parameterised by a constant w= p/rho c^2. The (sigma_8, Omega_m) plane analysis constrained the relation between sigma_8 and Omega_m to be sigma_8(Omega_m/0.3)^{0.57 +- 0.19}=1.06 +0.17 -0.16, in agreement with a 2D cosmic shear analysis of COMBO-17. The 3D cosmic shear conditional constraint on w using the two random fields is w=-1.27 +0.64 -0.70. The geometric shear-ratio analysis has been applied to the A901/2 field, which contains three small galaxy clusters. Combining the analysis from the A901/2 field, using the geometric shear-ratio analysis, and the two random fields, using 3D cosmic shear, w is conditionally constrained to w=-1.08 +0.63 -0.58. The errors presented in this paper are shown to agree with Fisher matrix predictions made in Heavens et al. (2006) and Taylor et al. (2006). When these methods are applied to large datasets, as expected soon from surveys such as Pan-STARRS and VST-KIDS, the dark energy equation of state could be constrained to an unprecedented degree of accuracy.Comment: 10 pages, 4 figures. Accepted to MNRA

Role of Magnetic Resonance Spectroscopy in Follow up Brain Tumors after Treatment

Background: 1 H magnetic resonance spectroscopy (MRS) is an analytical method that enables the identification and quantification of metabolites in samples. It differs from conventional Magnetic Resonance Imaging (MRI) in that spectra provide physiological and chemical information instead of anatomy. MRS imaging allows a valuable insights into brain tumors characteristics, grads, and progression then follow up during treatments. Typically in MRS a single spectrum is acquired by averaging enough spectra over a long acquisition time. Averaging is necessary because of the complex spectral structures and relatively low concentrations of many brain metabolites, which result in a low signal-to-noise ratio (SNR) in MRS of a living brain. Objective: In this paper, acquiring and analyzing multivoxel MRS data are reviewed by calculating the areas under different peaks then compared with that obtained directly from 1H-MRS machine. 1H-MRS measurements of amounts of Choline (Cho), creatine (Cr) and Nacetylaspartate (NAA) relative to Cho, NAA and Cr in healthy brain tissue of a normal control brain tissue, and in the tissue of tumor of patient who had taken radiation therapy sessions. Results: The obtained results show a good agreement between the data obtained directly from MRS machine and that calculated from their spectra. This method is now used for to insure that these obtained spectra are calibrated with that obtained directly from MRS machine. So these may reflect the small changes in metabolites during treatment and follow up. Conclusion: The MRS data are seen to provide unique information that when combined with high-quality anatomical MR images has implications for defining tumor type and grade, directing biopsy or surgical resection, planning focal radiation or biological therapies, and understanding the mechanisms of success and failure of new treatments

Origin and evolution of halo bias in linear and non-linear regimes

We present results from a study of bias and its evolution for galaxy-size halos in a large, high-resolution simulation of a LCDM model. We consider the evolution of bias estimated using two-point correlation function (b_xi), power spectrum (b_P), and a direct correlation of smoothed halo and matter overdensity fields (b_d). We present accurate estimates of the evolution of the matter power spectrum probed deep into the stable clustering regime (k~[0.1-200]h/Mpc at z=0). The halo power spectrum evolves much slower than the power spectrum of matter and has a different shape which indicates that the bias is time- and scale-dependent. At z=0, the halo power spectrum is anti-biased with respect to the matter power spectrum at wavenumbers k~[0.15-30]h/Mpc, and provides an excellent match to the power spectrum of the APM galaxies at all probed k. In particular, it nicely matches the inflection observed in the APM power spectrum at k~0.15h/Mpc. We complement the power spectrum analysis with a direct estimate of bias using smoothed halo and matter overdensity fields and show that the evolution observed in the simulation in linear and mildly non-linear regimes can be well described by the analytical model of Mo & White (1996), if the distinction between formation redshift of halos and observation epoch is introduced into the model. We present arguments and evidence that at higher overdensities, the evolution of bias is significantly affected by dynamical friction and tidal stripping operating on the satellite halos in high-density regions of clusters and groups; we attribute the strong anti-bias observed in the halo correlation function and power spectrum to these effects. (Abridged)Comment: submitted to the Astrophys.Journal; 19 pages, 9 figures LaTeX (uses emulateapj.sty