Color Transparent GPDs?

The relation between GPD's and color transparency is explored. The discovery of color transparency in pionic diffractive dissociation reactions allows us to make specific predictions for the behavior of the pion generalized parton distribution, and provide a further test of any model of the pion form factor.Comment: 12 pages, 3 figure

Cerebrospinal Fluid Tau Protein Levels and F-18-Fluorodeoxyglucose Positron Emission Tomography in the Differential Diagnosis of Alzheimer's Disease

Aims: In this study, we aimed to compare cerebrospinal fluid (CSF) levels of total tau (t-tau), phosphorylated tau (p-tau(181)) and positron emission tomography with F-18-fluorodeoxyglucose (FDG-PET) in the differential diagnosis of Alzheimer's disease (AD) under clinical conditions. Method: In a cross-sectional, blinded, single-center study, we examined a sample of 75 unselected memory clinic patients with clinical diagnoses of dementia of Alzheimer type (DAT; n = 24), amnestic mild cognitive impairment (MCI; n = 16), other dementias (n = 13) and nondemented controls (n = 22). Discriminative accuracy, sensitivity and specificity were calculated and compared using ROC analyses. Results: p-tau(181) and FDG-PET were comparable in separating DAT from controls (sensitivity: 67 vs. 79%; specificity: 91% for both) and patients with other dementias (sensitivity: 71 vs. 79%; specificity: 100% for both). The sensitivity of p-tau 181 in differentiating MCI patients from controls was significantly (p < 0.05) superior to that of FDG-PET (75 vs. 44%) at a comparably high specificity (82 vs. 91%); t-tau measures were less accurate in all analyses. Conclusions: FDG-PET and CSF p-tau(181) levels are able to discriminate DAT in heterogeneous and unselected samples with a high accuracy. CSF p-tau(181) might be somewhat superior for a sensitive detection of patients with MCI. Copyright (C) 2010 S. Karger AG, Base

Seeing Behind Objects for 3D Multi-Object Tracking in RGB-D Sequences

Multi-object tracking from RGB-D video sequences is a challenging problem due to the combination of changing viewpoints, motion, and occlusions over time. We observe that having the complete geometry of objects aids in their tracking, and thus propose to jointly infer the complete geometry of objects as well as track them, for rigidly moving objects over time. Our key insight is that inferring the complete geometry of the objects significantly helps in tracking. By hallucinating unseen regions of objects, we can obtain additional correspondences between the same instance, thus providing robust tracking even under strong change of appearance. From a sequence of RGB-D frames, we detect objects in each frame and learn to predict their complete object geometry as well as a dense correspondence mapping into a canonical space. This allows us to derive 6DoF poses for the objects in each frame, along with their correspondence between frames, providing robust object tracking across the RGB-D sequence. Experiments on both synthetic and real-world RGB-D data demonstrate that we achieve state-of-the-art performance on dynamic object tracking. Furthermore, we show that our object completion significantly helps tracking, providing an improvement of 6.5% in mean MOTA

SIGGRAPH

The current state of the art in real-time two-dimensional water wave simulation requires developers to choose between efficient Fourier-based methods, which lack interactions with moving obstacles, and finite-difference or finite element methods, which handle environmental interactions but are significantly more expensive. This paper attempts to bridge this long-standing gap between complexity and performance, by proposing a new wave simulation method that can faithfully simulate wave interactions with moving obstacles in real time while simultaneously preserving minute details and accommodating very large simulation domains. Previous methods for simulating 2D water waves directly compute the change in height of the water surface, a strategy which imposes limitations based on the CFL condition (fast moving waves require small time steps) and Nyquist's limit (small wave details require closely-spaced simulation variables). This paper proposes a novel wavelet transformation that discretizes the liquid motion in terms of amplitude-like functions that vary over space, frequency, and direction, effectively generalizing Fourier-based methods to handle local interactions. Because these new variables change much more slowly over space than the original water height function, our change of variables drastically reduces the limitations of the CFL condition and Nyquist limit, allowing us to simulate highly detailed water waves at very large visual resolutions. Our discretization is amenable to fast summation and easy to parallelize. We also present basic extensions like pre-computed wave paths and two-way solid fluid coupling. Finally, we argue that our discretization provides a convenient set of variables for artistic manipulation, which we illustrate with a novel wave-painting interface