Change-point detection in panel data via double CUSUM statistic

In this paper, we consider the problem of (multiple) change-point detection in panel data. We propose the double CUSUM statistic which utilises the cross-sectional change-point structure by examining the cumulative sums of ordered CUSUMs at each point. The efficiency of the proposed change-point test is studied, which is reflected on the rate at which the cross-sectional size of a change is permitted to converge to zero while it is still detectable. Also, the consistency of the proposed change-point detection procedure based on the binary segmentation algorithm, is established in terms of both the total number and locations (in time) of the estimated change-points. Motivated by the representation properties of the Generalised Dynamic Factor Model, we propose a bootstrap procedure for test criterion selection, which accounts for both cross-sectional and within-series correlations in high-dimensional data. The empirical performance of the double CUSUM statistics, equipped with the proposed bootstrap scheme, is investigated in a comparative simulation study with the state-of-the-art. As an application, we analyse the log returns of S&P 100 component stock prices over a period of one year

On Toroidal Horizons in Binary Black Hole Inspirals

We examine the structure of the event horizon for numerical simulations of two black holes that begin in a quasicircular orbit, inspiral, and finally merge. We find that the spatial cross section of the merged event horizon has spherical topology (to the limit of our resolution), despite the expectation that generic binary black hole mergers in the absence of symmetries should result in an event horizon that briefly has a toroidal cross section. Using insight gained from our numerical simulations, we investigate how the choice of time slicing affects both the spatial cross section of the event horizon and the locus of points at which generators of the event horizon cross. To ensure the robustness of our conclusions, our results are checked at multiple numerical resolutions. 3D visualization data for these resolutions are available for public access online. We find that the structure of the horizon generators in our simulations is consistent with expectations, and the lack of toroidal horizons in our simulations is due to our choice of time slicing.Comment: Submitted to Phys. Rev.

Numerical Simulations of Mass Transfer in Binaries with Bipolytropic Components

We present the first self-consistent, three dimensional study of hydrodynamic simulations of mass transfer in binary systems with bipolytropic (composite polytropic) components. In certain systems, such as contact binaries or during the common envelope phase, the core-envelope structure of the stars plays an important role in binary interactions. In this paper, we compare mass transfer simulations of bipolytropic binary systems in order to test the suitability of our numerical tools for investigating the dynamical behaviour of such systems. The initial, equilibrium binary models possess a core-envelope structure and are obtained using the bipolytropic self-consistent field technique. We conduct mass transfer simulations using two independent, fully three-dimensional, Eulerian codes - Flow-ER and Octo-tiger. These hydrodynamic codes are compared across binary systems undergoing unstable as well as stable mass transfer, and the former at two resolutions. The initial conditions for each simulation and for each code are chosen to match closely so that the simulations can be used as benchmarks. Although there are some key differences, the detailed comparison of the simulations suggests that there is remarkable agreement between the results obtained using the two codes. This study puts our numerical tools on a secure footing, and enables us to reliably simulate specific mass transfer scenarios of binary systems involving components with a core-envelope structure

Geant4 hadronic physics status and validation for large HEP detectors

Optimal exploitation of hadronic final states played a key role in successes of all recent collider experiment in HEP, and the ability to use hadronic final states will continue to be one of the decisive issues during the analysis phase of the LHC experiments. Monte Carlo techniques facilitate the use of hadronic final states, and have been developed for many years. We will give a brief overview of the physics underlying hadronic shower simulation, discussing the three basic types of modeling; data driven, parametrization driven, and theory driven modeling at the example of Geant4. We will confront these different types of modeling with the stringent requirements posed by the LHC experiments on hadronic shower simulation, and report on the current status of the validation effort for large HEP applications. We will address robustness, and CPU and physics performance evaluations.Comment: Computing in High Energy and Nuclear Physics, La Jolla, California, March 24-28, 2003 1 tar fil

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page

Sensor-based automated path guidance of a robot tool

The objective of the research is to develop a robot capability for a simultaneous measurement of the orientation (surface normal) and position of a 3-dimensional unknown object for a precise tool path guidance and control. The proposed system can guide the robot manipulator while maintaining specific orientation between the robot end-effector and the workpiece and also generate a measured geometric CAD database; The first phase involves the computer graphics simulation of an automated guidance and control of a robot tool using the proposed scheme. In the simulation, an object of known geometry is used for camera image data generation and subsequently determining the position and orientation of surface points based only on the simulated camera image information. Based on this surface geometry measurement technique, robot tool guidance and path planning algorithm is developed; The second phase involves the laboratory experiment. To demonstrate the validity of the proposed measurement method, the result of CCD image processing (grey to binary image conversion, thinning of binary image, detection of cross point, etc) and the calibration of the cameras/lighting source are performed. (Abstract shortened by UMI.)

A unified view on weakly correlated recurrent networks

The diversity of neuron models used in contemporary theoretical neuroscience to investigate specific properties of covariances raises the question how these models relate to each other. In particular it is hard to distinguish between generic properties and peculiarities due to the abstracted model. Here we present a unified view on pairwise covariances in recurrent networks in the irregular regime. We consider the binary neuron model, the leaky integrate-and-fire model, and the Hawkes process. We show that linear approximation maps each of these models to either of two classes of linear rate models, including the Ornstein-Uhlenbeck process as a special case. The classes differ in the location of additive noise in the rate dynamics, which is on the output side for spiking models and on the input side for the binary model. Both classes allow closed form solutions for the covariance. For output noise it separates into an echo term and a term due to correlated input. The unified framework enables us to transfer results between models. For example, we generalize the binary model and the Hawkes process to the presence of conduction delays and simplify derivations for established results. Our approach is applicable to general network structures and suitable for population averages. The derived averages are exact for fixed out-degree network architectures and approximate for fixed in-degree. We demonstrate how taking into account fluctuations in the linearization procedure increases the accuracy of the effective theory and we explain the class dependent differences between covariances in the time and the frequency domain. Finally we show that the oscillatory instability emerging in networks of integrate-and-fire models with delayed inhibitory feedback is a model-invariant feature: the same structure of poles in the complex frequency plane determines the population power spectra

Simulation of neutron production in hadron-nucleus and nucleus-nucleus interactions in Geant4

Studying experimental data obtained at ITEP [1] on neutron production in interactions of protons with various nuclei in the energy range from 747 MeV up to 8.1 GeV, we have found that slow neutron spectra have scaling and asymptotic properties [2]. The spectra weakly depend on the collision energy at momenta of projectile protons larger than 5 - 6 GeV/c. These properties are taken into account in the Geant4 Fritiof (FTF) model. The improved FTF model describes as well as the Geant4 Bertini model the experimental data on neutron production by 1.2 GeV and 1.6 GeV protons on targets (Fe, Pb) [3] and by 3.0 GeV protons on various targets (Al, Fe, Pb) [4]. For neutron production in antiproton-nucleus interactions, it was demonstrated that the FTF results are in a satisfactory agreement with experimental data of the LEAR collaboration [5]. The FTF model gives promising results for neutron production in nucleus - nucleus interactions at projectile energy 1 - 2 GeV per nucleon [6]. The observed properties allow one to predict neutron yields in the nucleus-nucleus interactions at high and very high energies. Predictions for the NICA/MPD experiment at JINR are presented.Comment: 6 pages, 5 figures. Contribution to Proceedings of Baldin ISHEPP XXI