A Stochastic Kaczmarz Algorithm for Network Tomography

We develop a stochastic approximation version of the classical Kaczmarz algorithm that is incremental in nature and takes as input noisy real time data. Our analysis shows that with probability one it mimics the behavior of the original scheme: starting from the same initial point, our algorithm and the corresponding deterministic Kaczmarz algorithm converge to precisely the same point. The motivation for this work comes from network tomography where network parameters are to be estimated based upon end-to-end measurements. Numerical examples via Matlab based simulations demonstrate the efficacy of the algorithm.Comment: Figures have been improved. Streamlined notatio

Fitting in a complex chi^2 landscape using an optimized hypersurface sampling

Fitting a data set with a parametrized model can be seen geometrically as finding the global minimum of the chi^2 hypersurface, depending on a set of parameters {P_i}. This is usually done using the Levenberg-Marquardt algorithm. The main drawback of this algorithm is that despite of its fast convergence, it can get stuck if the parameters are not initialized close to the final solution. We propose a modification of the Metropolis algorithm introducing a parameter step tuning that optimizes the sampling of parameter space. The ability of the parameter tuning algorithm together with simulated annealing to find the global chi^2 hypersurface minimum, jumping across chi^2{P_i} barriers when necessary, is demonstrated with synthetic functions and with real data

Nuclear Activity and the Conditions of Star-formation at the Galactic Center

The Galactic Center is the closest galactic nucleus that can be studied with unprecedented angular resolution and sensitivity. We summarize recent basic observational results on Sagittarius A* and the conditions for star formation in the central stellar cluster. We cover results from the radio, infrared, and X-ray domain and include results from simulation as well. From (sub-)mm and near-infrared variability and near-infrared polarization data we find that the SgrA* system (supermassive black hole spin, a potential temporary accretion disk and/or outflow) is well ordered in its geometrical orientation and in its emission process that we assume to reflect the accretion process onto the supermassive black hole (SMBH).Comment: 11 pages, 4 figures, 1 table; published in PoS-SISSA Proceedings of the: Frontier Research in Astrophysics - II, 23-28 May 2016, Mondello (Palermo), Ital

Experimental Indicators of Accretion Processes in Active Galactic Nuclei

Bright Active Galactic Nuclei are powered by accretion of mass onto the super massive black holes at the centers of the host galaxies. For fainter objects star formation may significantly contribute to the luminosity. We summarize experimental indicators of the accretion processes in Active Galactic Nuclei (AGN), i.e., observable activity indicators that allow us to conclude on the nature of accretion. The Galactic Center is the closest galactic nucleus that can be studied with unprecedented angular resolution and sensitivity. Therefore, here we also include the presentation of recent observational results on Sagittarius A* and the conditions for star formation in the central stellar cluster. We cover results across the electromagnetic spectrum and find that the Sagittarius A* (SgrA*) system is well ordered with respect to its geometrical orientation and its emission processes of which we assume to reflect the accretion process onto the super massive black hole.Comment: 16 pages, 4 figures, conference proceeding: Accretion Processes in Cosmic Sources - APCS2016 - 5-10 September 2016, Saint Petersburg, Russi

A Halomethane thermochemical network from iPEPICO experiments and quantum chemical calculations

Internal energy selected halomethane cations CH3Cl+, CH2Cl2+, CHCl3+, CH3F+, CH2F2+, CHClF2+ and CBrClF2+ were prepared by vacuum ultraviolet photoionization, and their lowest energy dissociation channel studied using imaging photoelectron photoion coincidence spectroscopy (iPEPICO). This channel involves hydrogen atom loss for CH3F+, CH2F2+ and CH3Cl+, chlorine atom loss for CH2Cl2+, CHCl3+ and CHClF2+, and bromine atom loss for CBrClF2+. Accurate 0 K appearance energies, in conjunction with ab initio isodesmic and halogen exchange reaction energies, establish a thermochemical network, which is optimized to update and confirm the enthalpies of formation of the sample molecules and their dissociative photoionization products. The ground electronic states of CHCl3+, CHClF2+ and CBrClF2+ do not confirm to the deep well assumption, and the experimental breakdown curve deviates from the deep well model at low energies. Breakdown curve analysis of such shallow well systems supplies a satisfactorily succinct route to the adiabatic ionization energy of the parent molecule, particularly if the threshold photoelectron spectrum is not resolved and a purely computational route is unfeasible. The ionization energies have been found to be 11.47 ± 0.01 eV, 12.30 ± 0.02 eV and 11.23 ± 0.03 eV for CHCl3, CHClF2 and CBrClF2, respectively. The updated 0 K enthalpies of formation, ∆fHo0K(g) for the ions CH2F+, CHF2+, CHCl2+, CCl3+, CCl2F+ and CClF2+ have been derived to be 844.4 ± 2.1, 601.6 ± 2.7, 890.3 ± 2.2, 849.8 ± 3.2, 701.2 ± 3.3 and 552.2 ± 3.4 kJ mol–1, respectively. The ∆fHo0K(g) values for the neutrals CCl4, CBrClF2, CClF3, CCl2F2 and CCl3F and have been determined to be –94.0 ± 3.2, –446.6 ± 2.7, –702.1 ± 3.5, –487.8 ± 3.4 and –285.2 ± 3.2 kJ mol–1, respectively

Face Recognition using local Patterns

Deriving an effective face representation is very essential task for automatic face recognition application. In this paper we used a feature descriptor called the Local Directional Number Pattern (LDN), which allows individual’s face recognition under different lightning’s, pose and expressions. Face recognition deals with different challenging problems in the field of image analysis and human computer interface. To deal with attention in our proposed work we use local patterns, a local directional number pattern (LDN) method, a six bit compact code for face recognition and understanding. By using LDN method we encode the directional information of the face images by convolving the face image with the compass mask. This compass mask extracts the edge response values in eight directions in the neighborhood. For each pixel we get the maximum and the minimum directional values which generate a LDN code i.e. generating an LDN image. Later LDN image is divided into number of blocks for each block histogram is computed and finally adds these histogram from each block to form the feature vector which acts as face descriptor to represent the face images. We perform different experiments under various illumination, pose and expression conditions