Zero-bias autoencoders and the benefits of co-adapting features

Regularized training of an autoencoder typically results in hidden unit biases that take on large negative values. We show that negative biases are a natural result of using a hidden layer whose responsibility is to both represent the input data and act as a selection mechanism that ensures sparsity of the representation. We then show that negative biases impede the learning of data distributions whose intrinsic dimensionality is high. We also propose a new activation function that decouples the two roles of the hidden layer and that allows us to learn representations on data with very high intrinsic dimensionality, where standard autoencoders typically fail. Since the decoupled activation function acts like an implicit regularizer, the model can be trained by minimizing the reconstruction error of training data, without requiring any additional regularization

QSO 0347-383 and the invariance of m_p/m_e in the course of cosmic time

The variation of the dimensionless fundamental physical constant mu = m_p/m_e (the proton to electron mass ratio) can be constrained via observation of Lyman and Werner lines of molecular hydrogen in the spectra of damped Lyman alpha systems (DLAs) in the line of sight to distant QSOs. Drawing on VLT-UVES high resolution data sets of QSO 0347-383 and its DLA obtained in 2009 our analysis yields dmu/mu = (4.3 +/- 7.2) * 10^-6 at z_abs =3.025. We apply corrections for the observed offsets between discrete spectra and for the first time we find indications for inter-order distortions. Current analyses tend to underestimate the impact of systematic errors. Based on the scatter of the measured redshifts and the corresponding low significance of the redshift-sensitivity correlation we estimate the limit of accuracy of line position measurements to about 220 m/s, consisting of roughly 150 m/s due to the uncertainty of the absorption line fit and about 150 m/s allocated to systematics related to instrumentation and calibration.Comment: 9 pages, 9 figures, accepted for publication in A&

Defining the Pose of any 3D Rigid Object and an Associated Distance

The pose of a rigid object is usually regarded as a rigid transformation, described by a translation and a rotation. However, equating the pose space with the space of rigid transformations is in general abusive, as it does not account for objects with proper symmetries -- which are common among man-made objects.In this article, we define pose as a distinguishable static state of an object, and equate a pose with a set of rigid transformations. Based solely on geometric considerations, we propose a frame-invariant metric on the space of possible poses, valid for any physical rigid object, and requiring no arbitrary tuning. This distance can be evaluated efficiently using a representation of poses within an Euclidean space of at most 12 dimensions depending on the object's symmetries. This makes it possible to efficiently perform neighborhood queries such as radius searches or k-nearest neighbor searches within a large set of poses using off-the-shelf methods. Pose averaging considering this metric can similarly be performed easily, using a projection function from the Euclidean space onto the pose space. The practical value of those theoretical developments is illustrated with an application of pose estimation of instances of a 3D rigid object given an input depth map, via a Mean Shift procedure

Bio-inspired log-polar based color image pattern analysis in multiple frequency channels

The main topic addressed in this thesis is to implement color image pattern recognition based on the lateral inhibition subtraction phenomenon combined with a complex log-polar mapping in multiple spatial frequency channels. It is shown that the individual red, green and blue channels have different recognition performances when put in the context of former work done by Dragan Vidacic. It is observed that the green channel performs better than the other two channels, with the blue channel having the poorest performance. Following the application of a contrast stretching function the object recognition performance is improved in all channels. Multiple spatial frequency filters were designed to simulate the filtering channels that occur in the human visual system. Following these preprocessing steps Dragan Vidacic\u27s methodology is followed in order to determine the benefits that are obtained from the preprocessing steps being investigated. It is shown that performance gains are realized by using such preprocessing steps