Probing the stellar wind environment of Vela X-1 with MAXI

Vela X-1 is among the best studied and most luminous accreting X-ray pulsars. The supergiant optical companion produces a strong radiatively-driven stellar wind, which is accreted onto the neutron star producing highly variable X-ray emission. A complex phenomenology, due to both gravitational and radiative effects, needs to be taken into account in order to reproduce orbital spectral variations. We have investigated the spectral and light curve properties of the X-ray emission from Vela X-1 along the binary orbit. These studies allow to constrain the stellar wind properties and its perturbations induced by the compact object. We took advantage of the All Sky Monitor MAXI/GSC data to analyze Vela X-1 spectra and light curves. By studying the orbital profiles in the $4-10$ and $10-20$ keV energy bands, we extracted a sample of orbital light curves (${\sim}15$% of the total) showing a dip around the inferior conjunction, i.e., a double-peaked shape. We analyzed orbital phase-averaged and phase-resolved spectra of both the double-peaked and the standard sample. The dip in the double-peaked sample needs $N_H\sim2\times10^{24}\,$cm$^{-2}$ to be explained by absorption solely, which is not observed in our analysis. We show how Thomson scattering from an extended and ionized accretion wake can contribute to the observed dip. Fitted by a cutoff power-law model, the two analyzed samples show orbital modulation of the photon index, hardening by ${\sim}0.3$ around the inferior conjunction, compared to earlier and later phases, hinting a likely inadequacy of this model. On the contrary, including a partial covering component at certain orbital phase bins allows a constant photon index along the orbital phases, indicating a highly inhomogeneous environment. We discuss our results in the framework of possible scenarios.Comment: 10 pages, 9 figures, accepted for publication in A&

Motion Segmentation - Segmentation of Independently Moving Objects in Video

The ability to recognize motion is one of the most important functions of our visual system. Motion allows us both to recognize objects and to get a better understanding of the 3D world in which we are moving. Because of its importance, motion is used to answer a wide variety of fundamental questions in computer vision such as: (1) Which objects are moving independently in the world? (2) Which objects are close and which objects are far away? (3) How is the camera moving?My work addresses the problem of moving object segmentation in unconstrained videos. I developed a probabilistic approach to segment independently moving objects in a video sequence, connecting aspects of camera motion estimation, relative depth and flow statistics. My work consists of three major parts: Modeling motion using a simple (rigid) motion model strictly following the principles of perspective projection and segmenting the video into its different motion components by assigning each pixel to its most likely motion model in a Bayesian fashion. Combining piecewise rigid motions to more complex, deformable and articulated objects, guided by learned semantic object segmentations. Learning highly variable motion patterns using a neural network trained on synthetic (unlimited) training data. Training data is automatically generated strictly following the principles of perspective projection. In this way well-known geometric constraints are precisely characterized during training to learn the principles of motion segmentation rather than identifying well-known structures that are likely to move. This work shows that a careful analysis of the motion field not only leads to a consistent segmentation of moving objects in a video sequence, but also helps us understand the scene geometry of the world we are moving in

Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary

The complex physical properties of highly deformable materials such as clothes pose significant challenges fanipulation systems. We present a novel visual feedback dictionary-based method for manipulating defoor autonomous robotic mrmable objects towards a desired configuration. Our approach is based on visual servoing and we use an efficient technique to extract key features from the RGB sensor stream in the form of a histogram of deformable model features. These histogram features serve as high-level representations of the state of the deformable material. Next, we collect manipulation data and use a visual feedback dictionary that maps the velocity in the high-dimensional feature space to the velocity of the robotic end-effectors for manipulation. We have evaluated our approach on a set of complex manipulation tasks and human-robot manipulation tasks on different cloth pieces with varying material characteristics.Comment: The video is available at goo.gl/mDSC4

Probabilistic Programming in Python using PyMC

Probabilistic programming (PP) allows flexible specification of Bayesian statistical models in code. PyMC3 is a new, open-source PP framework with an intutive and readable, yet powerful, syntax that is close to the natural syntax statisticians use to describe models. It features next-generation Markov chain Monte Carlo (MCMC) sampling algorithms such as the No-U-Turn Sampler (NUTS; Hoffman, 2014), a self-tuning variant of Hamiltonian Monte Carlo (HMC; Duane, 1987). Probabilistic programming in Python confers a number of advantages including multi-platform compatibility, an expressive yet clean and readable syntax, easy integration with other scientific libraries, and extensibility via C, C++, Fortran or Cython. These features make it relatively straightforward to write and use custom statistical distributions, samplers and transformation functions, as required by Bayesian analysis

Non-Parametric Probabilistic Image Segmentation

We propose a simple probabilistic generative model for image segmentation. Like other probabilistic algorithms (such as EM on a Mixture of Gaussians) the proposed model is principled, provides both hard and probabilistic cluster assignments, as well as the ability to naturally incorporate prior knowledge. While previous probabilistic approaches are restricted to parametric models of clusters (e.g., Gaussians) we eliminate this limitation. The suggested approach does not make heavy assumptions on the shape of the clusters and can thus handle complex structures. Our experiments show that the suggested approach outperforms previous work on a variety of image segmentation tasks

Direct Microlensing-Reverberation Observations of the Intrinsic magnetic Structure of AGN in Different Spectral States: A Tale of Two Quasars

We show how direct microlensing-reverberation analysis performed on two well-known Quasars (Q2237 - The Einstein Cross and Q0957 - The Twin) can be used to observe the inner structure of two quasars which are in significantly different spectral states. These observations allow us to measure the detailed internal structure of quasar Q2237 in a radio quiet high-soft state, and compare it to quasar Q0957 in a radio loud low-hard state. We find that the observed differences in the spectral states of these two quasars can be understood as being due to the location of the inner radii of their accretion disks relative to the co-rotation radii of rotating intrinsically magnetic supermassive compact objects in the centers of these quasars.Comment: 26 page manuscript with 2 tables and 2 figures, submitted to Astronomical Journa

Using RDF to Model the Structure and Process of Systems

Many systems can be described in terms of networks of discrete elements and their various relationships to one another. A semantic network, or multi-relational network, is a directed labeled graph consisting of a heterogeneous set of entities connected by a heterogeneous set of relationships. Semantic networks serve as a promising general-purpose modeling substrate for complex systems. Various standardized formats and tools are now available to support practical, large-scale semantic network models. First, the Resource Description Framework (RDF) offers a standardized semantic network data model that can be further formalized by ontology modeling languages such as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent introduction of highly performant triple-stores (i.e. semantic network databases) allows semantic network models on the order of $10^9$ edges to be efficiently stored and manipulated. RDF and its related technologies are currently used extensively in the domains of computer science, digital library science, and the biological sciences. This article will provide an introduction to RDF/RDFS/OWL and an examination of its suitability to model discrete element complex systems.Comment: International Conference on Complex Systems, Boston MA, October 200