A nanoradian differential VLBI tracking demonstration

The shift due to Jovian gravitational deflection in the apparent angular position of the radio source P 0201+113 was measured with very long baseline interferometry (VLBI) to demonstrate a differential angular tracking technique with nanoradian accuracy. The raypath of the radio source P 0201+113 passed within 1 mrad of Jupiter (approximately 10 Jovian radii) on 21 Mar. 1988. Its angular position was measured 10 times over 4 hours on that date, with a similar measurement set on 2 Apr. 1988, to track the differential angular gravitational deflection of the raypath. According to general relativity, the expected gravitational bend of the raypath averaged over the duration of the March experiment was approximately 1.45 nrad projected onto the two California-Australia baselines over which it was measured. Measurement accuracies on the order of 0.78 nrad were obtained for each of the ten differential measurements. The chi(exp 2) per degree of freedom of the data for the hypothesis of general relativity was 0.6, which suggests that the modeled dominant errors due to system noise and tropospheric fluctuations fully accounted for the scatter in the measured angular deflections. The chi(exp 2) per degree of freedom for the hypothesis of no gravitational deflection by Jupiter was 4.1, which rejects the no-deflection hypothesis with greater than 99.999 percent confidence. The system noise contributed about 0.34 nrad per combined-baseline differential measurement and tropospheric fluctuations contributed about 0.70 nrad. Unmodeled errors were assessed, which could potentially increase the 0.78 nrad error by about 8 percent. The above chi(exp 2) values, which result from the full accounting of errors, suggest that the nanoradian gravitational deflection signature was successfully tracked

Exploring Deep Space: Learning Personalized Ranking in a Semantic Space

Recommender systems leverage both content and user interactions to generate recommendations that fit users' preferences. The recent surge of interest in deep learning presents new opportunities for exploiting these two sources of information. To recommend items we propose to first learn a user-independent high-dimensional semantic space in which items are positioned according to their substitutability, and then learn a user-specific transformation function to transform this space into a ranking according to the user's past preferences. An advantage of the proposed architecture is that it can be used to effectively recommend items using either content that describes the items or user-item ratings. We show that this approach significantly outperforms state-of-the-art recommender systems on the MovieLens 1M dataset.Comment: 6 pages, RecSys 2016 RSDL worksho

Object Matching in Distributed Video Surveillance Systems by LDA-Based Appearance Descriptors

Establishing correspondences among object instances is still challenging in multi-camera surveillance systems, especially when the cameras’ fields of view are non-overlapping. Spatiotemporal constraints can help in solving the correspondence problem but still leave a wide margin of uncertainty. One way to reduce this uncertainty is to use appearance information about the moving objects in the site. In this paper we present the preliminary results of a new method that can capture salient appearance characteristics at each camera node in the network. A Latent Dirichlet Allocation (LDA) model is created and maintained at each node in the camera network. Each object is encoded in terms of the LDA bag-of-words model for appearance. The encoded appearance is then used to establish probable matching across cameras. Preliminary experiments are conducted on a dataset of 20 individuals and comparison against Madden’s I-MCHR is reported

Modelling the Galactic Magnetic Field on the Plane in 2D

We present a method for parametric modelling of the physical components of the Galaxy's magnetised interstellar medium, simulating the observables, and mapping out the likelihood space using a Markov Chain Monte-Carlo analysis. We then demonstrate it using total and polarised synchrotron emission data as well as rotation measures of extragalactic sources. With these three datasets, we define and study three components of the magnetic field: the large-scale coherent field, the small-scale isotropic random field, and the ordered field. In this first paper, we use only data along the Galactic plane and test a simple 2D logarithmic spiral model for the magnetic field that includes a compression and a shearing of the random component giving rise to an ordered component. We demonstrate with simulations that the method can indeed constrain multiple parameters yielding measures of, for example, the ratios of the magnetic field components. Though subject to uncertainties in thermal and cosmic ray electron densities and depending on our particular model parametrisation, our preliminary analysis shows that the coherent component is a small fraction of the total magnetic field and that an ordered component comparable in strength to the isotropic random component is required to explain the polarisation fraction of synchrotron emission. We outline further work to extend this type of analysis to study the magnetic spiral arm structure, the details of the turbulence as well as the 3D structure of the magnetic field.Comment: 18 pages, 11 figures, updated to published MNRAS versio

In the mind of the predator: the possibility of psychological distress in the drone pilot community

In light of the increasing use of Remotely Piloted Aircraft (RPA), commonly known as drones, and the equally increasing prevalence of post-traumatic stress disorder (PTSD) among U.S. veterans of recent wars, this study investigated the possible effects of piloting a drone aircraft and PTSD. Using a simulated drone aircraft in a computer game, the results showed that participants who simulated a drone attack and viewed the post-drone attack video reported significantly higher distress than those who viewed only the post-drone attack video. Females also showed higher distress levels than males. These results suggest the potential risks of psychological trauma even among pilots who are apparently physically far removed from the battlefield

Nonsingular Black Hole Evaporation and ``Stable'' Remnants

We examine the evaporation of two--dimensional black holes, the classical space--times of which are extended geometries, like for example the two--dimensional section of the extremal Reissner--Nordstrom black hole. We find that the evaporation in two particular models proceeds to a stable end--point. This should represent the generic behavior of a certain class of two--dimensional dilaton--gravity models. There are two distinct regimes depending on whether the back--reaction is weak or strong in a certain sense. When the back--reaction is weak, evaporation proceeds via an adiabatic evolution, whereas for strong back--reaction, the decay proceeds in a somewhat surprising manner. Although information loss is inevitable in these models at the semi--classical level, it is rather benign, in that the information is stored in another asymptotic region.Comment: 23 pages, 6 figures, harvmac and epsf, RU-93-12, PUPT-1399, NSF-ITP-93-5

AdS/CFT and the Information Paradox

The information paradox in the quantum evolution of black holes is studied within the framework of the AdS/CFT correspondence. The unitarity of the CFT strongly suggests that all information about an initial state that forms a black hole is returned in the Hawking radiation. The CFT dynamics implies an information retention time of order the black hole lifetime. This fact determines many qualitative properties of the non-local effects that must show up in a semi-classical effective theory in the bulk. We argue that no violations of causality are apparent to local observers, but the semi-classical theory in the bulk duplicates degrees of freedom inside and outside the event horizon. Non-local quantum effects are required to eliminate this redundancy. This leads to a breakdown of the usual classical-quantum correspondence principle in Lorentzian black hole spacetimes.Comment: 16 pages, harvmac, reference added, minor correction

Face Detection with Effective Feature Extraction

There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.Comment: 7 pages. Conference version published in Asian Conf. Comp. Vision 201

Scene Coordinate Regression with Angle-Based Reprojection Loss for Camera Relocalization

Image-based camera relocalization is an important problem in computer vision and robotics. Recent works utilize convolutional neural networks (CNNs) to regress for pixels in a query image their corresponding 3D world coordinates in the scene. The final pose is then solved via a RANSAC-based optimization scheme using the predicted coordinates. Usually, the CNN is trained with ground truth scene coordinates, but it has also been shown that the network can discover 3D scene geometry automatically by minimizing single-view reprojection loss. However, due to the deficiencies of the reprojection loss, the network needs to be carefully initialized. In this paper, we present a new angle-based reprojection loss, which resolves the issues of the original reprojection loss. With this new loss function, the network can be trained without careful initialization, and the system achieves more accurate results. The new loss also enables us to utilize available multi-view constraints, which further improve performance.Comment: ECCV 2018 Workshop (Geometry Meets Deep Learning