Towards Scalable Synthesis of Stochastic Control Systems

Formal control synthesis approaches over stochastic systems have received significant attention in the past few years, in view of their ability to provide provably correct controllers for complex logical specifications in an automated fashion. Examples of complex specifications of interest include properties expressed as formulae in linear temporal logic (LTL) or as automata on infinite strings. A general methodology to synthesize controllers for such properties resorts to symbolic abstractions of the given stochastic systems. Symbolic models are discrete abstractions of the given concrete systems with the property that a controller designed on the abstraction can be refined (or implemented) into a controller on the original system. Although the recent development of techniques for the construction of symbolic models has been quite encouraging, the general goal of formal synthesis over stochastic control systems is by no means solved. A fundamental issue with the existing techniques is the known "curse of dimensionality," which is due to the need to discretize state and input sets and that results in an exponential complexity over the number of state and input variables in the concrete system. In this work we propose a novel abstraction technique for incrementally stable stochastic control systems, which does not require state-space discretization but only input set discretization, and that can be potentially more efficient (and thus scalable) than existing approaches. We elucidate the effectiveness of the proposed approach by synthesizing a schedule for the coordination of two traffic lights under some safety and fairness requirements for a road traffic model. Further we argue that this 5-dimensional linear stochastic control system cannot be studied with existing approaches based on state-space discretization due to the very large number of generated discrete states.Comment: 22 pages, 3 figures. arXiv admin note: text overlap with arXiv:1407.273

Transport of Russian Federation: Problems and Trend of Its Development

The article presents the analysis of the development of transport of Russian Federation on the present stage using a set of indicators in dynamics: the number of transport organizations, revenues and profits, the volume of cargo and passengers, length of the transport ways, number of transport vehicles, investment in fixed capital; the analysis of the main documents for development of the transport complex; the main trends and problems of development of transport of the country

Evaluation of the possibility of obtaining welded joints of plates from Al-Mg-Mn aluminum alloys, strengthened by the introduction of TiB2 particles

In the work, the possibility of obtaining strong welded joints of aluminum alloys modified with particles is demonstrated. For research, strengthened aluminum alloys of the Al-Mg-Mn system with the introduction of TiB2 particles were obtained. TiB2 particles in specially prepared Al-TiB master alloys obtained by self-propagating high-temperature synthesis were introduced ex situ into the melt according to an original technique using ultrasonic treatment. Plates from the studied cast alloys were butt-welded by one-sided welded joints of various depths. To obtain welded joints, the method of electron beam welding was used. Mechanical properties of the studied alloys and their welded joints under tension were studied. It was shown that the introduction of particles resulted in a change in the internal structure of the alloys, characterized by the formation of compact dendritic structures and a decrease in the average grain size from 155 to 95 µm. The change in the internal structure due to the introduction of particles led to an increase in the tensile strength of the obtained alloys from 163 to 204 MPa. It was found that the obtained joints have sufficient relative strength values. Relative strength values reach 0.9 of the nominal strength of materials already at the ratio of the welded joint depth to the thickness of the welded plates, equal to 0.6 for the initial alloy and in the range of 0.67–0.8 for strengthened alloys

Spectral and depolarization ratios for atmospheric ice particles of hexagonal and arbitrary shape within the framework of the physical optics and discrete dipoles

The optical characteristics of atmospheric ice particles are usually calculated within the framework of the physical optics approximation, since particle sizes generally vary from 10 to 1000 microns. However, the results of experimental measurements show that ice crystals up to 10 microns in size are observed in cirrus clouds of the upper tier. The report presents a solution to the problem of light scattering for particles, obtained in the framework of the methods of the physical optics and discrete dipoles. Based on the solution, such important optical characteristics as depolarization and spectral ratios were calculated. Two limiting cases are considered: ideal hexagonal particles and randomly shaped particles

FLOating-Window Projective Separator (FloWPS): A Data Trimming Tool for Support Vector Machines (SVM) to Improve Robustness of the Classifier

Here, we propose a heuristic technique of data trimming for SVM termed FLOating Window Projective Separator (FloWPS), tailored for personalized predictions based on molecular data. This procedure can operate with high throughput genetic datasets like gene expression or mutation profiles. Its application prevents SVM from extrapolation by excluding non-informative features. FloWPS requires training on the data for the individuals with known clinical outcomes to create a clinically relevant classifier. The genetic profiles linked with the outcomes are broken as usual into the training and validation datasets. The unique property of FloWPS is that irrelevant features in validation dataset that don’t have significant number of neighboring hits in the training dataset are removed from further analyses. Next, similarly to the k nearest neighbors (kNN) method, for each point of a validation dataset, FloWPS takes into account only the proximal points of the training dataset. Thus, for every point of a validation dataset, the training dataset is adjusted to form a floating window. FloWPS performance was tested on ten gene expression datasets for 992 cancer patients either responding or not on the different types of chemotherapy. We experimentally confirmed by leave-one-out cross-validation that FloWPS enables to significantly increase quality of a classifier built based on the classical SVM in most of the applications, particularly for polynomial kernels

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO