VConv-DAE: Deep Volumetric Shape Learning Without Object Labels

With the advent of affordable depth sensors, 3D capture becomes more and more ubiquitous and already has made its way into commercial products. Yet, capturing the geometry or complete shapes of everyday objects using scanning devices (e.g. Kinect) still comes with several challenges that result in noise or even incomplete shapes. Recent success in deep learning has shown how to learn complex shape distributions in a data-driven way from large scale 3D CAD Model collections and to utilize them for 3D processing on volumetric representations and thereby circumventing problems of topology and tessellation. Prior work has shown encouraging results on problems ranging from shape completion to recognition. We provide an analysis of such approaches and discover that training as well as the resulting representation are strongly and unnecessarily tied to the notion of object labels. Thus, we propose a full convolutional volumetric auto encoder that learns volumetric representation from noisy data by estimating the voxel occupancy grids. The proposed method outperforms prior work on challenging tasks like denoising and shape completion. We also show that the obtained deep embedding gives competitive performance when used for classification and promising results for shape interpolation

S-OHEM: Stratified Online Hard Example Mining for Object Detection

One of the major challenges in object detection is to propose detectors with highly accurate localization of objects. The online sampling of high-loss region proposals (hard examples) uses the multitask loss with equal weight settings across all loss types (e.g, classification and localization, rigid and non-rigid categories) and ignores the influence of different loss distributions throughout the training process, which we find essential to the training efficacy. In this paper, we present the Stratified Online Hard Example Mining (S-OHEM) algorithm for training higher efficiency and accuracy detectors. S-OHEM exploits OHEM with stratified sampling, a widely-adopted sampling technique, to choose the training examples according to this influence during hard example mining, and thus enhance the performance of object detectors. We show through systematic experiments that S-OHEM yields an average precision (AP) improvement of 0.5% on rigid categories of PASCAL VOC 2007 for both the IoU threshold of 0.6 and 0.7. For KITTI 2012, both results of the same metric are 1.6%. Regarding the mean average precision (mAP), a relative increase of 0.3% and 0.5% (1% and 0.5%) is observed for VOC07 (KITTI12) using the same set of IoU threshold. Also, S-OHEM is easy to integrate with existing region-based detectors and is capable of acting with post-recognition level regressors.Comment: 9 pages, 3 figures, accepted by CCCV 201

Integrability and level crossing manifolds in a quantum Hamiltonian system

We consider a two-spin model, represented classically by a nonlinear autonomous Hamiltonian system with two degrees of freedom and a nontrivial integrability condition, and quantum mechanically by a real symmetric Hamiltonian matrix with blocks of dimensionalities K=l(l+1)/2, l=1,2,... In the six-dimensional (6D) parameter space of this model, classical integrability is satisfied on a 5D hypersurface, and level crossings occur on 4D manifolds that are completely embedded in the integrability hypersurface except for some lower-D sub-manifolds. Under mild assumptions, the classical integrability condition can be reconstructed from a purely quantum mechanical study of level degeneracies in finite-dimensional invariant blocks of the Hamiltonian matrix. Our conclusions are based on rigorous results for K=3 and on numerical results for K=6,10.Comment: 8 pages, 3 figure

Uniqueness of nontrivially complete monotonicity for a class of functions involving polygamma functions

For $m,n\in\mathbb{N}$, let $f_{m,n}(x)=\bigr[\psi^{(m)}(x)\bigl]^2+\psi^{(n)}(x)$ on $(0,\infty)$. In the present paper, we prove using two methods that, among all $f_{m,n}(x)$ for $m,n\in\mathbb{N}$, only $f_{1,2}(x)$ is nontrivially completely monotonic on $(0,\infty)$. Accurately, the functions $f_{1,2}(x)$ and $f_{m,2n-1}(x)$ are completely monotonic on $(0,\infty)$, but the functions $f_{m,2n}(x)$ for $(m,n)\ne(1,1)$ are not monotonic and does not keep the same sign on $(0,\infty)$.Comment: 9 page

Dispositional antecedents of promotive and prohibitive voice

We propose that promotive voice, or the expression of suggestions for improving work practices in the organization, and prohibitive voice, or the expression of warnings about factors that can harm the organization, are differentially influenced by employees’ dispositional inclination to be approach and avoidance oriented. Drawing on multisource survey data from 291 employees and their managers, we found that approach orientation had positive relationship with promotive voice and negative relationship with prohibitive voice. By contrast, avoidance orientation had positive relationship with prohibitive voice and negative relationship with promotive voice. Further, voice role expectations, or employees’ beliefs about the extent to which a particular form of voice is expected from them in their daily work, moderated the effects of approach and avoidance orientations. Highlighting the unique nature of voice as a behavior that is especially sensitive to situational cues, the effects of approach and avoidance orientations on promotive and prohibitive voice were stronger when role expectations for that form of voice were weaker. The theoretical implications of these findings are discussed

Scaling of hadronic transverse momenta in a hydrodynamic treatment of relativistic heavy ion collisions

The transverse momenta of hadrons in central nucleus-nucleus collisions are evaluated in a boost invariant hydrodynamics with transverse expansion. Quark gluon plasma is assumed to be formed in the initial state which expands and cools via a first order phase transition to a rich hadronic matter and ultimately undergoes a freeze-out. The average transverse momentum of pions, kaons, and protons is estimated for a wide range of multiplicity densities and transverse sizes of the system. For a given system it is found to scale with the square-root of the particle rapidity density per unit transverse area, and consistent with the corresponding values seen in $p\overline{p}$ experiments at 1800 GeV, suggesting a universal behaviour. The average transverse momentum shows only an approximate scaling with multiplicity density per nucleon which is at variance with the $p\overline{p}$ data.Comment: 6 pages including 9 figure